data/*
models/*
*.caffemodel
+*.caffemodel.h5
*.solverstate
+*.solverstate.h5
*.binaryproto
*leveldb
*lmdb
# one using CMake, and one using make.
env:
matrix:
- - WITH_CUDA=false WITH_CMAKE=false
- - WITH_CUDA=false WITH_CMAKE=true
- - WITH_CUDA=true WITH_CMAKE=false
- - WITH_CUDA=true WITH_CMAKE=true
+ - WITH_CUDA=false WITH_CMAKE=false WITH_IO=true
+ - WITH_CUDA=false WITH_CMAKE=true WITH_IO=true PYTHON_VERSION=3
+ - WITH_CUDA=true WITH_CMAKE=false WITH_IO=true
+ - WITH_CUDA=true WITH_CMAKE=true WITH_IO=true
+ - WITH_CUDA=false WITH_CMAKE=false WITH_IO=false
+ - WITH_CUDA=false WITH_CMAKE=true WITH_IO=false PYTHON_VERSION=3
language: cpp
# Cache Ubuntu apt packages.
-cache: apt
+cache:
+ apt: true
+ directories:
+ - /home/travis/miniconda
+ - /home/travis/miniconda2
+ - /home/travis/miniconda3
compiler: gcc
before_install:
- export NUM_THREADS=4
- export SCRIPTS=./scripts/travis
+ - export CONDA_DIR="/home/travis/miniconda$PYTHON_VERSION"
install:
- sudo -E $SCRIPTS/travis_install.sh
before_script:
- - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib:/usr/local/cuda/lib64
- - export PATH=/home/travis/miniconda/bin:$PATH
+ - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib:/usr/local/cuda/lib64:$CONDA_DIR/lib
+ - export PATH=$CONDA_DIR/bin:$PATH
- if ! $WITH_CMAKE; then $SCRIPTS/travis_setup_makefile_config.sh; fi
script: $SCRIPTS/travis_build_and_test.sh
cmake_minimum_required(VERSION 2.8.7)
+if(POLICY CMP0046)
+ cmake_policy(SET CMP0046 NEW)
+endif()
+if(POLICY CMP0054)
+ cmake_policy(SET CMP0054 NEW)
+endif()
# ---[ Caffe project
project(Caffe C CXX)
+# ---[ Caffe version
+set(CAFFE_TARGET_VERSION "1.0.0-rc3")
+set(CAFFE_TARGET_SOVERSION "1.0.0-rc3")
+add_definitions(-DCAFFE_VERSION=${CAFFE_TARGET_VERSION})
+
# ---[ Using cmake scripts and modules
list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake/Modules)
+include(ExternalProject)
+
include(cmake/Utils.cmake)
include(cmake/Targets.cmake)
include(cmake/Misc.cmake)
include(cmake/ConfigGen.cmake)
# ---[ Options
-caffe_option(CPU_ONLY "Build Caffe wihtout CUDA support" OFF) # TODO: rename to USE_CUDA
-caffe_option(USE_CUDNN "Build Caffe with cuDNN libary support" ON IF NOT CPU_ONLY)
+caffe_option(CPU_ONLY "Build Caffe without CUDA support" OFF) # TODO: rename to USE_CUDA
+caffe_option(USE_CUDNN "Build Caffe with cuDNN library support" ON IF NOT CPU_ONLY)
caffe_option(BUILD_SHARED_LIBS "Build shared libraries" ON)
caffe_option(BUILD_python "Build Python wrapper" ON)
-set(python_version "2" CACHE STRING "Specify which python version to use")
+set(python_version "2" CACHE STRING "Specify which Python version to use")
caffe_option(BUILD_matlab "Build Matlab wrapper" OFF IF UNIX OR APPLE)
caffe_option(BUILD_docs "Build documentation" ON IF UNIX OR APPLE)
-caffe_option(BUILD_python_layer "Build the caffe python layer" ON)
+caffe_option(BUILD_python_layer "Build the Caffe Python layer" ON)
+caffe_option(USE_OPENCV "Build with OpenCV support" ON)
+caffe_option(USE_LEVELDB "Build with levelDB" ON)
+caffe_option(USE_LMDB "Build with lmdb" ON)
+caffe_option(ALLOW_LMDB_NOLOCK "Allow MDB_NOLOCK when reading LMDB files (only if necessary)" OFF)
# ---[ Dependencies
include(cmake/Dependencies.cmake)
# ---[ Linter target
add_custom_target(lint COMMAND ${CMAKE_COMMAND} -P ${PROJECT_SOURCE_DIR}/cmake/lint.cmake)
+# ---[ pytest target
+if(BUILD_python)
+ add_custom_target(pytest COMMAND python${python_version} -m unittest discover -s caffe/test WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/python )
+ add_dependencies(pytest pycaffe)
+endif()
+
# ---[ Configuration summary
caffe_print_configuration_summary()
--- /dev/null
+# Contributing
+
+## Issues
+
+Specific Caffe design and development issues, bugs, and feature requests are maintained by GitHub Issues.
+
+_Please do not post usage, installation, or modeling questions, or other requests for help to Issues._
+Use the [caffe-users list](https://groups.google.com/forum/#!forum/caffe-users) instead. This helps developers maintain a clear, uncluttered, and efficient view of the state of Caffe.
+
+When reporting a bug, it's most helpful to provide the following information, where applicable:
+
+* What steps reproduce the bug?
+* Can you reproduce the bug using the latest [master](https://github.com/BVLC/caffe/tree/master), compiled with the `DEBUG` make option?
+* What hardware and operating system/distribution are you running?
+* If the bug is a crash, provide the backtrace (usually printed by Caffe; always obtainable with `gdb`).
+
+Try to give your issue a title that is succinct and specific. The devs will rename issues as needed to keep track of them.
+
+## Pull Requests
+
+Caffe welcomes all contributions.
+
+See the [contributing guide](http://caffe.berkeleyvision.org/development.html) for details.
+
+Briefly: read commit by commit, a PR should tell a clean, compelling story of _one_ improvement to Caffe. In particular:
+
+* A PR should do one clear thing that obviously improves Caffe, and nothing more. Making many smaller PRs is better than making one large PR; review effort is superlinear in the amount of code involved.
+* Similarly, each commit should be a small, atomic change representing one step in development. PRs should be made of many commits where appropriate.
+* Please do rewrite PR history to be clean rather than chronological. Within-PR bugfixes, style cleanups, reversions, etc. should be squashed and should not appear in merged PR history.
+* Anything nonobvious from the code should be explained in comments, commit messages, or the PR description, as appropriate.
See http://caffe.berkeleyvision.org/installation.html for the latest
installation instructions.
-Check the issue tracker in case you need help:
-https://github.com/BVLC/caffe/issues
+Check the users group in case you need help:
+https://groups.google.com/forum/#!forum/caffe-users
COPYRIGHT
All contributions by the University of California:
-Copyright (c) 2014, The Regents of the University of California (Regents)
+Copyright (c) 2014, 2015, The Regents of the University of California (Regents)
All rights reserved.
All other contributions:
-Copyright (c) 2014, the respective contributors
+Copyright (c) 2014, 2015, the respective contributors
All rights reserved.
Caffe uses a shared copyright model: each contributor holds copyright over
PROJECT := caffe
CONFIG_FILE := Makefile.config
+# Explicitly check for the config file, otherwise make -k will proceed anyway.
+ifeq ($(wildcard $(CONFIG_FILE)),)
+$(error $(CONFIG_FILE) not found. See $(CONFIG_FILE).example.)
+endif
include $(CONFIG_FILE)
BUILD_DIR_LINK := $(BUILD_DIR)
-RELEASE_BUILD_DIR ?= .$(BUILD_DIR)_release
-DEBUG_BUILD_DIR ?= .$(BUILD_DIR)_debug
+ifeq ($(RELEASE_BUILD_DIR),)
+ RELEASE_BUILD_DIR := .$(BUILD_DIR)_release
+endif
+ifeq ($(DEBUG_BUILD_DIR),)
+ DEBUG_BUILD_DIR := .$(BUILD_DIR)_debug
+endif
DEBUG ?= 0
ifeq ($(DEBUG), 1)
\( -name '*.cpp' -o -name '*.proto' \) | grep -q ." \; -print)
# The target shared library name
+LIBRARY_NAME := $(PROJECT)
LIB_BUILD_DIR := $(BUILD_DIR)/lib
-STATIC_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).a
-DYNAMIC_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).so
+STATIC_NAME := $(LIB_BUILD_DIR)/lib$(LIBRARY_NAME).a
+DYNAMIC_VERSION_MAJOR := 1
+DYNAMIC_VERSION_MINOR := 0
+DYNAMIC_VERSION_REVISION := 0-rc3
+DYNAMIC_NAME_SHORT := lib$(LIBRARY_NAME).so
+#DYNAMIC_SONAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR)
+DYNAMIC_VERSIONED_NAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR).$(DYNAMIC_VERSION_MINOR).$(DYNAMIC_VERSION_REVISION)
+DYNAMIC_NAME := $(LIB_BUILD_DIR)/$(DYNAMIC_VERSIONED_NAME_SHORT)
+COMMON_FLAGS += -DCAFFE_VERSION=$(DYNAMIC_VERSION_MAJOR).$(DYNAMIC_VERSION_MINOR).$(DYNAMIC_VERSION_REVISION)
##############################
# Get all source files
src/$(PROJECT) \
include/$(PROJECT) \
python/$(PROJECT) \
- matlab/$(PROJECT) \
+ matlab/+$(PROJECT)/private \
examples \
tools \
-name "*.cpp" -or -name "*.hpp" -or -name "*.cu" -or -name "*.cuh")
# PY$(PROJECT)_SRC is the python wrapper for $(PROJECT)
PY$(PROJECT)_SRC := python/$(PROJECT)/_$(PROJECT).cpp
PY$(PROJECT)_SO := python/$(PROJECT)/_$(PROJECT).so
-PY$(PROJECT)_HXX := include/$(PROJECT)/python_layer.hpp
-# MAT$(PROJECT)_SRC is the matlab wrapper for $(PROJECT)
-MAT$(PROJECT)_SRC := matlab/$(PROJECT)/mat$(PROJECT).cpp
+PY$(PROJECT)_HXX := include/$(PROJECT)/layers/python_layer.hpp
+# MAT$(PROJECT)_SRC is the mex entrance point of matlab package for $(PROJECT)
+MAT$(PROJECT)_SRC := matlab/+$(PROJECT)/private/$(PROJECT)_.cpp
ifneq ($(MATLAB_DIR),)
MAT_SO_EXT := $(shell $(MATLAB_DIR)/bin/mexext)
endif
-MAT$(PROJECT)_SO := matlab/$(PROJECT)/$(PROJECT).$(MAT_SO_EXT)
+MAT$(PROJECT)_SO := matlab/+$(PROJECT)/private/$(PROJECT)_.$(MAT_SO_EXT)
##############################
# Derive generated files
EXAMPLE_OBJS := $(addprefix $(BUILD_DIR)/, ${EXAMPLE_SRCS:.cpp=.o})
# Output files for automatic dependency generation
DEPS := ${CXX_OBJS:.o=.d} ${CU_OBJS:.o=.d} ${TEST_CXX_OBJS:.o=.d} \
- ${TEST_CU_OBJS:.o=.d}
+ ${TEST_CU_OBJS:.o=.d} $(BUILD_DIR)/${MAT$(PROJECT)_SO:.$(MAT_SO_EXT)=.d}
# tool, example, and test bins
TOOL_BINS := ${TOOL_OBJS:.o=.bin}
EXAMPLE_BINS := ${EXAMPLE_OBJS:.o=.bin}
LIBRARY_DIRS += $(CUDA_LIB_DIR)
LIBRARIES := cudart cublas curand
endif
-LIBRARIES += glog gflags protobuf leveldb snappy \
- lmdb boost_system hdf5_hl hdf5 m \
- opencv_core opencv_highgui opencv_imgproc
-PYTHON_LIBRARIES := boost_python python2.7
+
+LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_hl hdf5
+
+# handle IO dependencies
+USE_LEVELDB ?= 1
+USE_LMDB ?= 1
+USE_OPENCV ?= 1
+
+ifeq ($(USE_LEVELDB), 1)
+ LIBRARIES += leveldb snappy
+endif
+ifeq ($(USE_LMDB), 1)
+ LIBRARIES += lmdb
+endif
+ifeq ($(USE_OPENCV), 1)
+ LIBRARIES += opencv_core opencv_highgui opencv_imgproc
+
+ ifeq ($(OPENCV_VERSION), 3)
+ LIBRARIES += opencv_imgcodecs
+ endif
+
+endif
+PYTHON_LIBRARIES ?= boost_python python2.7
WARNINGS := -Wall -Wno-sign-compare
##############################
# Set build directories
##############################
+DISTRIBUTE_DIR ?= distribute
DISTRIBUTE_SUBDIRS := $(DISTRIBUTE_DIR)/bin $(DISTRIBUTE_DIR)/lib
DIST_ALIASES := dist
ifneq ($(strip $(DISTRIBUTE_DIR)),distribute)
CXX ?= /usr/bin/g++
GCCVERSION := $(shell $(CXX) -dumpversion | cut -f1,2 -d.)
# older versions of gcc are too dumb to build boost with -Wuninitalized
- ifeq ($(shell echo $(GCCVERSION) \< 4.6 | bc), 1)
+ ifeq ($(shell echo | awk '{exit $(GCCVERSION) < 4.6;}'), 1)
WARNINGS += -Wno-uninitialized
endif
# boost::thread is reasonably called boost_thread (compare OS X)
# We will also explicitly add stdc++ to the link target.
LIBRARIES += boost_thread stdc++
+ VERSIONFLAGS += -Wl,-soname,$(DYNAMIC_VERSIONED_NAME_SHORT) -Wl,-rpath,$(ORIGIN)/../lib
endif
# OS X:
# libstdc++ for NVCC compatibility on OS X >= 10.9 with CUDA < 7.0
ifeq ($(OSX), 1)
CXX := /usr/bin/clang++
- CUDA_VERSION := $(shell $(CUDA_DIR)/bin/nvcc -V | grep -o 'release \d' | grep -o '\d')
- ifeq ($(shell echo $(CUDA_VERSION) \< 7.0 | bc), 1)
- CXXFLAGS += -stdlib=libstdc++
- LINKFLAGS += -stdlib=libstdc++
+ ifneq ($(CPU_ONLY), 1)
+ CUDA_VERSION := $(shell $(CUDA_DIR)/bin/nvcc -V | grep -o 'release \d' | grep -o '\d')
+ ifeq ($(shell echo | awk '{exit $(CUDA_VERSION) < 7.0;}'), 1)
+ CXXFLAGS += -stdlib=libstdc++
+ LINKFLAGS += -stdlib=libstdc++
+ endif
+ # clang throws this warning for cuda headers
+ WARNINGS += -Wno-unneeded-internal-declaration
endif
- # clang throws this warning for cuda headers
- WARNINGS += -Wno-unneeded-internal-declaration
# gtest needs to use its own tuple to not conflict with clang
COMMON_FLAGS += -DGTEST_USE_OWN_TR1_TUPLE=1
# boost::thread is called boost_thread-mt to mark multithreading on OS X
# we need to explicitly ask for the rpath to be obeyed
DYNAMIC_FLAGS := -install_name @rpath/libcaffe.so
ORIGIN := @loader_path
+ VERSIONFLAGS += -Wl,-install_name,$(DYNAMIC_VERSIONED_NAME_SHORT) -Wl,-rpath,$(ORIGIN)/../../build/lib
else
ORIGIN := \$$ORIGIN
endif
COMMON_FLAGS += -DUSE_CUDNN
endif
+# configure IO libraries
+ifeq ($(USE_OPENCV), 1)
+ COMMON_FLAGS += -DUSE_OPENCV
+endif
+ifeq ($(USE_LEVELDB), 1)
+ COMMON_FLAGS += -DUSE_LEVELDB
+endif
+ifeq ($(USE_LMDB), 1)
+ COMMON_FLAGS += -DUSE_LMDB
+ifeq ($(ALLOW_LMDB_NOLOCK), 1)
+ COMMON_FLAGS += -DALLOW_LMDB_NOLOCK
+endif
+endif
+
# CPU-only configuration
ifeq ($(CPU_ONLY), 1)
OBJS := $(PROTO_OBJS) $(CXX_OBJS)
# OS X packages atlas as the vecLib framework
LIBRARIES += cblas
# 10.10 has accelerate while 10.9 has veclib
- XCODE_CLT_VER := $(shell pkgutil --pkg-info=com.apple.pkg.CLTools_Executables | grep -o 'version: 6')
- ifneq (,$(findstring version: 6,$(XCODE_CLT_VER)))
+ XCODE_CLT_VER := $(shell pkgutil --pkg-info=com.apple.pkg.CLTools_Executables | grep 'version' | sed 's/[^0-9]*\([0-9]\).*/\1/')
+ XCODE_CLT_GEQ_6 := $(shell [ $(XCODE_CLT_VER) -gt 5 ] && echo 1)
+ ifeq ($(XCODE_CLT_GEQ_6), 1)
BLAS_INCLUDE ?= /System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/
LDFLAGS += -framework Accelerate
else
##############################
# Define build targets
##############################
-.PHONY: all test clean docs linecount lint lintclean tools examples $(DIST_ALIASES) \
+.PHONY: all lib test clean docs linecount lint lintclean tools examples $(DIST_ALIASES) \
py mat py$(PROJECT) mat$(PROJECT) proto runtest \
superclean supercleanlist supercleanfiles warn everything
-all: $(STATIC_NAME) $(DYNAMIC_NAME) tools examples
+all: lib tools examples
+
+lib: $(STATIC_NAME) $(DYNAMIC_NAME)
everything: $(EVERYTHING_TARGETS)
$(PY$(PROJECT)_SO): $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX) | $(DYNAMIC_NAME)
@ echo CXX/LD -o $@ $<
$(Q)$(CXX) -shared -o $@ $(PY$(PROJECT)_SRC) \
- -o $@ $(LINKFLAGS) -l$(PROJECT) $(PYTHON_LDFLAGS) \
+ -o $@ $(LINKFLAGS) -l$(LIBRARY_NAME) $(PYTHON_LDFLAGS) \
-Wl,-rpath,$(ORIGIN)/../../build/lib
mat$(PROJECT): mat
CXX="$(CXX)" \
CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \
CXXLIBS="\$$CXXLIBS $(STATIC_LINK_COMMAND) $(LDFLAGS)" -output $@
+ @ if [ -f "$(PROJECT)_.d" ]; then \
+ mv -f $(PROJECT)_.d $(BUILD_DIR)/${MAT$(PROJECT)_SO:.$(MAT_SO_EXT)=.d}; \
+ fi
runtest: $(TEST_ALL_BIN)
$(TOOL_BUILD_DIR)/caffe
pytest: py
cd python; python -m unittest discover -s caffe/test
+mattest: mat
+ cd matlab; $(MATLAB_DIR)/bin/matlab -nodisplay -r 'caffe.run_tests(), exit()'
+
warn: $(EMPTY_WARN_REPORT)
$(EMPTY_WARN_REPORT): $(ALL_WARNS) | $(BUILD_DIR)
$(DYNAMIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
@ echo LD -o $@
- $(Q)$(CXX) -shared -o $@ $(OBJS) $(LINKFLAGS) $(LDFLAGS) $(DYNAMIC_FLAGS)
+ $(Q)$(CXX) -shared -o $@ $(OBJS) $(VERSIONFLAGS) $(LINKFLAGS) $(LDFLAGS) $(DYNAMIC_FLAGS)
+ @ cd $(BUILD_DIR)/lib; rm -f $(DYNAMIC_NAME_SHORT); ln -s $(DYNAMIC_VERSIONED_NAME_SHORT) $(DYNAMIC_NAME_SHORT)
$(STATIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
@ echo AR -o $@
| $(DYNAMIC_NAME) $(TEST_BIN_DIR)
@ echo CXX/LD -o $@ $<
$(Q)$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \
- -o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(ORIGIN)/../lib
+ -o $@ $(LINKFLAGS) $(LDFLAGS) -l$(LIBRARY_NAME) -Wl,-rpath,$(ORIGIN)/../lib
$(TEST_CU_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CU_BUILD_DIR)/%.o \
$(GTEST_OBJ) | $(DYNAMIC_NAME) $(TEST_BIN_DIR)
@ echo LD $<
$(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) \
- -o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(ORIGIN)/../lib
+ -o $@ $(LINKFLAGS) $(LDFLAGS) -l$(LIBRARY_NAME) -Wl,-rpath,$(ORIGIN)/../lib
$(TEST_CXX_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CXX_BUILD_DIR)/%.o \
$(GTEST_OBJ) | $(DYNAMIC_NAME) $(TEST_BIN_DIR)
@ echo LD $<
$(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) \
- -o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(ORIGIN)/../lib
+ -o $@ $(LINKFLAGS) $(LDFLAGS) -l$(LIBRARY_NAME) -Wl,-rpath,$(ORIGIN)/../lib
# Target for extension-less symlinks to tool binaries with extension '*.bin'.
$(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR)
$(TOOL_BINS): %.bin : %.o | $(DYNAMIC_NAME)
@ echo CXX/LD -o $@
- $(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(PROJECT) $(LDFLAGS) \
+ $(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(LIBRARY_NAME) $(LDFLAGS) \
-Wl,-rpath,$(ORIGIN)/../lib
$(EXAMPLE_BINS): %.bin : %.o | $(DYNAMIC_NAME)
@ echo CXX/LD -o $@
- $(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(PROJECT) $(LDFLAGS) \
+ $(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(LIBRARY_NAME) $(LDFLAGS) \
-Wl,-rpath,$(ORIGIN)/../../lib
proto: $(PROTO_GEN_CC) $(PROTO_GEN_HEADER)
cp $(EXAMPLE_BINS) $(DISTRIBUTE_DIR)/bin
# add libraries
cp $(STATIC_NAME) $(DISTRIBUTE_DIR)/lib
- cp $(DYNAMIC_NAME) $(DISTRIBUTE_DIR)/lib
+ install -m 644 $(DYNAMIC_NAME) $(DISTRIBUTE_DIR)/lib
+ cd $(DISTRIBUTE_DIR)/lib; rm -f $(DYNAMIC_NAME_SHORT); ln -s $(DYNAMIC_VERSIONED_NAME_SHORT) $(DYNAMIC_NAME_SHORT)
# add python - it's not the standard way, indeed...
cp -r python $(DISTRIBUTE_DIR)/python
# CPU-only switch (uncomment to build without GPU support).
# CPU_ONLY := 1
+# uncomment to disable IO dependencies and corresponding data layers
+# USE_OPENCV := 0
+# USE_LEVELDB := 0
+# USE_LMDB := 0
+
+# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
+# You should not set this flag if you will be reading LMDBs with any
+# possibility of simultaneous read and write
+# ALLOW_LMDB_NOLOCK := 1
+
+# Uncomment if you're using OpenCV 3
+# OPENCV_VERSION := 3
+
# To customize your choice of compiler, uncomment and set the following.
# N.B. the default for Linux is g++ and the default for OSX is clang++
# CUSTOM_CXX := g++
# BLAS_INCLUDE := /path/to/your/blas
# BLAS_LIB := /path/to/your/blas
+# Homebrew puts openblas in a directory that is not on the standard search path
+# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
+# BLAS_LIB := $(shell brew --prefix openblas)/lib
+
# This is required only if you will compile the matlab interface.
# MATLAB directory should contain the mex binary in /bin.
# MATLAB_DIR := /usr/local
# $(ANACONDA_HOME)/include/python2.7 \
# $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \
+# Uncomment to use Python 3 (default is Python 2)
+# PYTHON_LIBRARIES := boost_python3 python3.5m
+# PYTHON_INCLUDE := /usr/include/python3.5m \
+# /usr/lib/python3.5/dist-packages/numpy/core/include
+
# We need to be able to find libpythonX.X.so or .dylib.
PYTHON_LIB := /usr/lib
# PYTHON_LIB := $(ANACONDA_HOME)/lib
+# Homebrew installs numpy in a non standard path (keg only)
+# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
+# PYTHON_LIB += $(shell brew --prefix numpy)/lib
+
# Uncomment to support layers written in Python (will link against Python libs)
# WITH_PYTHON_LAYER := 1
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
+# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
+# INCLUDE_DIRS += $(shell brew --prefix)/include
+# LIBRARY_DIRS += $(shell brew --prefix)/lib
+
# Uncomment to use `pkg-config` to specify OpenCV library paths.
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1
# Caffe
+[![Build Status](https://travis-ci.org/BVLC/caffe.svg?branch=master)](https://travis-ci.org/BVLC/caffe)
+[![License](https://img.shields.io/badge/license-BSD-blue.svg)](LICENSE)
+
Caffe is a deep learning framework made with expression, speed, and modularity in mind.
It is developed by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and community contributors.
list(FIND current_includes ${PROJECT_BINARY_DIR} __index)
list(REMOVE_AT current_includes ${__index})
+ # removing numpy includes (since not required for client libs)
+ set(__toremove "")
+ foreach(__i ${current_includes})
+ if(${__i} MATCHES "python")
+ list(APPEND __toremove ${__i})
+ endif()
+ endforeach()
+ if(__toremove)
+ list(REMOVE_ITEM current_includes ${__toremove})
+ endif()
+
caffe_list_unique(current_includes)
set(${includes_variable} ${current_includes} PARENT_SCOPE)
endfunction()
list(APPEND Caffe_DEFINITIONS -DCPU_ONLY)
endif()
+ if(USE_OPENCV)
+ list(APPEND Caffe_DEFINITIONS -DUSE_OPENCV)
+ endif()
+
+ if(USE_LMDB)
+ list(APPEND Caffe_DEFINITIONS -DUSE_LMDB)
+ if (ALLOW_LMDB_NOLOCK)
+ list(APPEND Caffe_DEFINITIONS -DALLOW_LMDB_NOLOCK)
+ endif()
+ endif()
+
+ if(USE_LEVELDB)
+ list(APPEND Caffe_DEFINITIONS -DUSE_LEVELDB)
+ endif()
+
if(NOT HAVE_CUDNN)
set(HAVE_CUDNN FALSE)
else()
configure_file("cmake/Templates/CaffeConfig.cmake.in" "${PROJECT_BINARY_DIR}/cmake/CaffeConfig.cmake" @ONLY)
- # Install the CaffeConfig.cmake and export set to use wuth install-tree
+ # Install the CaffeConfig.cmake and export set to use with install-tree
install(FILES "${PROJECT_BINARY_DIR}/cmake/CaffeConfig.cmake" DESTINATION ${install_cmake_suffix})
install(EXPORT CaffeTargets DESTINATION ${install_cmake_suffix})
endfunction()
################################################################################################
-# Short command for cuda comnpilation
+# Short command for cuda compilation
# Usage:
# caffe_cuda_compile(<objlist_variable> <cuda_files>)
macro(caffe_cuda_compile objlist_variable)
set(HAVE_CUDNN TRUE PARENT_SCOPE)
set(CUDNN_FOUND TRUE PARENT_SCOPE)
+ file(READ ${CUDNN_INCLUDE}/cudnn.h CUDNN_VERSION_FILE_CONTENTS)
+
+ # cuDNN v3 and beyond
+ string(REGEX MATCH "define CUDNN_MAJOR * +([0-9]+)"
+ CUDNN_VERSION_MAJOR "${CUDNN_VERSION_FILE_CONTENTS}")
+ string(REGEX REPLACE "define CUDNN_MAJOR * +([0-9]+)" "\\1"
+ CUDNN_VERSION_MAJOR "${CUDNN_VERSION_MAJOR}")
+ string(REGEX MATCH "define CUDNN_MINOR * +([0-9]+)"
+ CUDNN_VERSION_MINOR "${CUDNN_VERSION_FILE_CONTENTS}")
+ string(REGEX REPLACE "define CUDNN_MINOR * +([0-9]+)" "\\1"
+ CUDNN_VERSION_MINOR "${CUDNN_VERSION_MINOR}")
+ string(REGEX MATCH "define CUDNN_PATCHLEVEL * +([0-9]+)"
+ CUDNN_VERSION_PATCH "${CUDNN_VERSION_FILE_CONTENTS}")
+ string(REGEX REPLACE "define CUDNN_PATCHLEVEL * +([0-9]+)" "\\1"
+ CUDNN_VERSION_PATCH "${CUDNN_VERSION_PATCH}")
+
+ if(NOT CUDNN_VERSION_MAJOR)
+ set(CUDNN_VERSION "???")
+ else()
+ set(CUDNN_VERSION "${CUDNN_VERSION_MAJOR}.${CUDNN_VERSION_MINOR}.${CUDNN_VERSION_PATCH}")
+ endif()
+
+ message(STATUS "Found cuDNN: ver. ${CUDNN_VERSION} found (include: ${CUDNN_INCLUDE}, library: ${CUDNN_LIBRARY})")
+
+ string(COMPARE LESS "${CUDNN_VERSION_MAJOR}" 3 cuDNNVersionIncompatible)
+ if(cuDNNVersionIncompatible)
+ message(FATAL_ERROR "cuDNN version >3 is required.")
+ endif()
+
+ set(CUDNN_VERSION "${CUDNN_VERSION}" PARENT_SCOPE)
mark_as_advanced(CUDNN_INCLUDE CUDNN_LIBRARY CUDNN_ROOT)
- message(STATUS "Found cuDNN (include: ${CUDNN_INCLUDE}, library: ${CUDNN_LIBRARY})")
+
endif()
endfunction()
-
################################################################################################
### Non macro section
################################################################################################
set(Caffe_LINKER_LIBS "")
# ---[ Boost
-find_package(Boost 1.46 REQUIRED COMPONENTS system thread)
+find_package(Boost 1.46 REQUIRED COMPONENTS system thread filesystem)
include_directories(SYSTEM ${Boost_INCLUDE_DIR})
list(APPEND Caffe_LINKER_LIBS ${Boost_LIBRARIES})
list(APPEND Caffe_LINKER_LIBS ${CMAKE_THREAD_LIBS_INIT})
# ---[ Google-glog
-find_package(Glog REQUIRED)
+include("cmake/External/glog.cmake")
include_directories(SYSTEM ${GLOG_INCLUDE_DIRS})
list(APPEND Caffe_LINKER_LIBS ${GLOG_LIBRARIES})
# ---[ Google-gflags
-find_package(GFlags REQUIRED)
+include("cmake/External/gflags.cmake")
include_directories(SYSTEM ${GFLAGS_INCLUDE_DIRS})
list(APPEND Caffe_LINKER_LIBS ${GFLAGS_LIBRARIES})
list(APPEND Caffe_LINKER_LIBS ${HDF5_LIBRARIES})
# ---[ LMDB
-find_package(LMDB REQUIRED)
-include_directories(SYSTEM ${LMDB_INCLUDE_DIR})
-list(APPEND Caffe_LINKER_LIBS ${LMDB_LIBRARIES})
+if(USE_LMDB)
+ find_package(LMDB REQUIRED)
+ include_directories(SYSTEM ${LMDB_INCLUDE_DIR})
+ list(APPEND Caffe_LINKER_LIBS ${LMDB_LIBRARIES})
+ add_definitions(-DUSE_LMDB)
+ if(ALLOW_LMDB_NOLOCK)
+ add_definitions(-DALLOW_LMDB_NOLOCK)
+ endif()
+endif()
# ---[ LevelDB
-find_package(LevelDB REQUIRED)
-include_directories(SYSTEM ${LevelDB_INCLUDE})
-list(APPEND Caffe_LINKER_LIBS ${LevelDB_LIBRARIES})
+if(USE_LEVELDB)
+ find_package(LevelDB REQUIRED)
+ include_directories(SYSTEM ${LevelDB_INCLUDE})
+ list(APPEND Caffe_LINKER_LIBS ${LevelDB_LIBRARIES})
+ add_definitions(-DUSE_LEVELDB)
+endif()
# ---[ Snappy
-find_package(Snappy REQUIRED)
-include_directories(SYSTEM ${Snappy_INCLUDE_DIR})
-list(APPEND Caffe_LINKER_LIBS ${Snappy_LIBRARIES})
+if(USE_LEVELDB)
+ find_package(Snappy REQUIRED)
+ include_directories(SYSTEM ${Snappy_INCLUDE_DIR})
+ list(APPEND Caffe_LINKER_LIBS ${Snappy_LIBRARIES})
+endif()
# ---[ CUDA
include(cmake/Cuda.cmake)
if(NOT HAVE_CUDA)
if(CPU_ONLY)
- message("-- CUDA is disabled. Building without it...")
+ message(STATUS "-- CUDA is disabled. Building without it...")
else()
- message("-- CUDA is not detected by cmake. Building without it...")
+ message(WARNING "-- CUDA is not detected by cmake. Building without it...")
endif()
# TODO: remove this not cross platform define in future. Use caffe_config.h instead.
endif()
# ---[ OpenCV
-find_package(OpenCV QUIET COMPONENTS core highgui imgproc imgcodecs)
-if(NOT OpenCV_FOUND) # if not OpenCV 3.x, then imgcodecs are not found
- find_package(OpenCV REQUIRED COMPONENTS core highgui imgproc)
+if(USE_OPENCV)
+ find_package(OpenCV QUIET COMPONENTS core highgui imgproc imgcodecs)
+ if(NOT OpenCV_FOUND) # if not OpenCV 3.x, then imgcodecs are not found
+ find_package(OpenCV REQUIRED COMPONENTS core highgui imgproc)
+ endif()
+ include_directories(SYSTEM ${OpenCV_INCLUDE_DIRS})
+ list(APPEND Caffe_LINKER_LIBS ${OpenCV_LIBS})
+ message(STATUS "OpenCV found (${OpenCV_CONFIG_PATH})")
+ add_definitions(-DUSE_OPENCV)
endif()
-include_directories(SYSTEM ${OpenCV_INCLUDE_DIRS})
-list(APPEND Caffe_LINKER_LIBS ${OpenCV_LIBS})
-message(STATUS "OpenCV found (${OpenCV_CONFIG_PATH})")
# ---[ BLAS
if(NOT APPLE)
# Find the matching boost python implementation
set(version ${PYTHONLIBS_VERSION_STRING})
- STRING( REPLACE "." "" boost_py_version ${version} )
+ STRING( REGEX REPLACE "[^0-9]" "" boost_py_version ${version} )
find_package(Boost 1.46 COMPONENTS "python-py${boost_py_version}")
set(Boost_PYTHON_FOUND ${Boost_PYTHON-PY${boost_py_version}_FOUND})
while(NOT "${version}" STREQUAL "" AND NOT Boost_PYTHON_FOUND)
STRING( REGEX REPLACE "([0-9.]+).[0-9]+" "\\1" version ${version} )
+
+ STRING( REGEX REPLACE "[^0-9]" "" boost_py_version ${version} )
+ find_package(Boost 1.46 COMPONENTS "python-py${boost_py_version}")
+ set(Boost_PYTHON_FOUND ${Boost_PYTHON-PY${boost_py_version}_FOUND})
+
STRING( REGEX MATCHALL "([0-9.]+).[0-9]+" has_more_version ${version} )
if("${has_more_version}" STREQUAL "")
break()
endif()
-
- STRING( REPLACE "." "" boost_py_version ${version} )
- find_package(Boost 1.46 COMPONENTS "python-py${boost_py_version}")
- set(Boost_PYTHON_FOUND ${Boost_PYTHON-PY${boost_py_version}_FOUND})
endwhile()
if(NOT Boost_PYTHON_FOUND)
find_package(Boost 1.46 COMPONENTS python)
--- /dev/null
+if (NOT __GFLAGS_INCLUDED) # guard against multiple includes
+ set(__GFLAGS_INCLUDED TRUE)
+
+ # use the system-wide gflags if present
+ find_package(GFlags)
+ if (GFLAGS_FOUND)
+ set(GFLAGS_EXTERNAL FALSE)
+ else()
+ # gflags will use pthreads if it's available in the system, so we must link with it
+ find_package(Threads)
+
+ # build directory
+ set(gflags_PREFIX ${CMAKE_BINARY_DIR}/external/gflags-prefix)
+ # install directory
+ set(gflags_INSTALL ${CMAKE_BINARY_DIR}/external/gflags-install)
+
+ # we build gflags statically, but want to link it into the caffe shared library
+ # this requires position-independent code
+ if (UNIX)
+ set(GFLAGS_EXTRA_COMPILER_FLAGS "-fPIC")
+ endif()
+
+ set(GFLAGS_CXX_FLAGS ${CMAKE_CXX_FLAGS} ${GFLAGS_EXTRA_COMPILER_FLAGS})
+ set(GFLAGS_C_FLAGS ${CMAKE_C_FLAGS} ${GFLAGS_EXTRA_COMPILER_FLAGS})
+
+ ExternalProject_Add(gflags
+ PREFIX ${gflags_PREFIX}
+ GIT_REPOSITORY "https://github.com/gflags/gflags.git"
+ GIT_TAG "v2.1.2"
+ UPDATE_COMMAND ""
+ INSTALL_DIR ${gflags_INSTALL}
+ CMAKE_ARGS -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}
+ -DCMAKE_INSTALL_PREFIX=${gflags_INSTALL}
+ -DBUILD_SHARED_LIBS=OFF
+ -DBUILD_STATIC_LIBS=ON
+ -DBUILD_PACKAGING=OFF
+ -DBUILD_TESTING=OFF
+ -DBUILD_NC_TESTS=OFF
+ -BUILD_CONFIG_TESTS=OFF
+ -DINSTALL_HEADERS=ON
+ -DCMAKE_C_FLAGS=${GFLAGS_C_FLAGS}
+ -DCMAKE_CXX_FLAGS=${GFLAGS_CXX_FLAGS}
+ LOG_DOWNLOAD 1
+ LOG_INSTALL 1
+ )
+
+ set(GFLAGS_FOUND TRUE)
+ set(GFLAGS_INCLUDE_DIRS ${gflags_INSTALL}/include)
+ set(GFLAGS_LIBRARIES ${gflags_INSTALL}/lib/libgflags.a ${CMAKE_THREAD_LIBS_INIT})
+ set(GFLAGS_LIBRARY_DIRS ${gflags_INSTALL}/lib)
+ set(GFLAGS_EXTERNAL TRUE)
+
+ list(APPEND external_project_dependencies gflags)
+ endif()
+
+endif()
--- /dev/null
+# glog depends on gflags
+include("cmake/External/gflags.cmake")
+
+if (NOT __GLOG_INCLUDED)
+ set(__GLOG_INCLUDED TRUE)
+
+ # try the system-wide glog first
+ find_package(Glog)
+ if (GLOG_FOUND)
+ set(GLOG_EXTERNAL FALSE)
+ else()
+ # fetch and build glog from github
+
+ # build directory
+ set(glog_PREFIX ${CMAKE_BINARY_DIR}/external/glog-prefix)
+ # install directory
+ set(glog_INSTALL ${CMAKE_BINARY_DIR}/external/glog-install)
+
+ # we build glog statically, but want to link it into the caffe shared library
+ # this requires position-independent code
+ if (UNIX)
+ set(GLOG_EXTRA_COMPILER_FLAGS "-fPIC")
+ endif()
+
+ set(GLOG_CXX_FLAGS ${CMAKE_CXX_FLAGS} ${GLOG_EXTRA_COMPILER_FLAGS})
+ set(GLOG_C_FLAGS ${CMAKE_C_FLAGS} ${GLOG_EXTRA_COMPILER_FLAGS})
+
+ # depend on gflags if we're also building it
+ if (GFLAGS_EXTERNAL)
+ set(GLOG_DEPENDS gflags)
+ endif()
+
+ ExternalProject_Add(glog
+ DEPENDS ${GLOG_DEPENDS}
+ PREFIX ${glog_PREFIX}
+ GIT_REPOSITORY "https://github.com/google/glog"
+ GIT_TAG "v0.3.4"
+ UPDATE_COMMAND ""
+ INSTALL_DIR ${gflags_INSTALL}
+ CONFIGURE_COMMAND env "CFLAGS=${GLOG_C_FLAGS}" "CXXFLAGS=${GLOG_CXX_FLAGS}" ${glog_PREFIX}/src/glog/configure --prefix=${glog_INSTALL} --enable-shared=no --enable-static=yes --with-gflags=${GFLAGS_LIBRARY_DIRS}/..
+ LOG_DOWNLOAD 1
+ LOG_CONFIGURE 1
+ LOG_INSTALL 1
+ )
+
+ set(GLOG_FOUND TRUE)
+ set(GLOG_INCLUDE_DIRS ${glog_INSTALL}/include)
+ set(GLOG_LIBRARIES ${GFLAGS_LIBRARIES} ${glog_INSTALL}/lib/libglog.a)
+ set(GLOG_LIBRARY_DIRS ${glog_INSTALL}/lib)
+ set(GLOG_EXTERNAL TRUE)
+
+ list(APPEND external_project_dependencies glog)
+ endif()
+
+endif()
+
-# ---[ Configurations types
+# ---[ Configuration types
set(CMAKE_CONFIGURATION_TYPES "Debug;Release" CACHE STRING "Possible configurations" FORCE)
mark_as_advanced(CMAKE_CONFIGURATION_TYPES)
# ---[ Set debug postfix
set(Caffe_DEBUG_POSTFIX "-d")
-set(CAffe_POSTFIX "")
+set(Caffe_POSTFIX "")
if(CMAKE_BUILD_TYPE MATCHES "Debug")
- set(CAffe_POSTFIX ${Caffe_DEBUG_POSTFIX})
+ set(Caffe_POSTFIX ${Caffe_DEBUG_POSTFIX})
endif()
find_library(GFLAGS_LIBRARY gflags)
endif()
-find_package_handle_standard_args(GFLAGS DEFAULT_MSG GFLAGS_INCLUDE_DIR GFLAGS_LIBRARY)
+find_package_handle_standard_args(GFlags DEFAULT_MSG GFLAGS_INCLUDE_DIR GFLAGS_LIBRARY)
if(GFLAGS_FOUND)
PATH_SUFFIXES lib lib64)
endif()
-find_package_handle_standard_args(GLOG DEFAULT_MSG GLOG_INCLUDE_DIR GLOG_LIBRARY)
+find_package_handle_standard_args(Glog DEFAULT_MSG GLOG_INCLUDE_DIR GLOG_LIBRARY)
if(GLOG_FOUND)
set(GLOG_INCLUDE_DIRS ${GLOG_INCLUDE_DIR})
SET(Open_BLAS_INCLUDE_SEARCH_PATHS
/usr/include
+ /usr/include/openblas
/usr/include/openblas-base
/usr/local/include
+ /usr/local/include/openblas
/usr/local/include/openblas-base
/opt/OpenBLAS/include
$ENV{OpenBLAS_HOME}
# Finds Google Protocol Buffers library and compilers and extends
-# the standart cmake script with version and python generation support
+# the standard cmake script with version and python generation support
find_package( Protobuf REQUIRED )
include_directories(SYSTEM ${PROTOBUF_INCLUDE_DIR})
list(APPEND Caffe_LINKER_LIBS ${PROTOBUF_LIBRARIES})
# As of Ubuntu 14.04 protoc is no longer a part of libprotobuf-dev package
-# and should be installed separately as in: sudo apt-get install protobuf-compiler
+# and should be installed separately as in: sudo apt-get install protobuf-compiler
if(EXISTS ${PROTOBUF_PROTOC_EXECUTABLE})
message(STATUS "Found PROTOBUF Compiler: ${PROTOBUF_PROTOC_EXECUTABLE}")
else()
################################################################################################
-# Prints accumulatd caffe configuration summary
+# Prints accumulated caffe configuration summary
# Usage:
# caffe_print_configuration_summary()
caffe_status("")
caffe_status("******************* Caffe Configuration Summary *******************")
caffe_status("General:")
- caffe_status(" Version : ${Caffe_VERSION}")
+ caffe_status(" Version : ${CAFFE_TARGET_VERSION}")
caffe_status(" Git : ${Caffe_GIT_VERSION}")
caffe_status(" System : ${CMAKE_SYSTEM_NAME}")
caffe_status(" C++ compiler : ${CMAKE_CXX_COMPILER}")
caffe_status(" BUILD_matlab : ${BUILD_matlab}")
caffe_status(" BUILD_docs : ${BUILD_docs}")
caffe_status(" CPU_ONLY : ${CPU_ONLY}")
+ caffe_status(" USE_OPENCV : ${USE_OPENCV}")
+ caffe_status(" USE_LEVELDB : ${USE_LEVELDB}")
+ caffe_status(" USE_LMDB : ${USE_LMDB}")
+ caffe_status(" ALLOW_LMDB_NOLOCK : ${ALLOW_LMDB_NOLOCK}")
caffe_status("")
caffe_status("Dependencies:")
caffe_status(" BLAS : " APPLE THEN "Yes (vecLib)" ELSE "Yes (${BLAS})")
caffe_status(" Boost : Yes (ver. ${Boost_MAJOR_VERSION}.${Boost_MINOR_VERSION})")
caffe_status(" glog : Yes")
- caffe_status(" gflags : Yes")
+ caffe_status(" gflags : Yes")
caffe_status(" protobuf : " PROTOBUF_FOUND THEN "Yes (ver. ${PROTOBUF_VERSION})" ELSE "No" )
- caffe_status(" lmdb : " LMDB_FOUND THEN "Yes (ver. ${LMDB_VERSION})" ELSE "No")
- caffe_status(" Snappy : " SNAPPY_FOUND THEN "Yes (ver. ${Snappy_VERSION})" ELSE "No" )
- caffe_status(" LevelDB : " LEVELDB_FOUND THEN "Yes (ver. ${LEVELDB_VERSION})" ELSE "No")
- caffe_status(" OpenCV : Yes (ver. ${OpenCV_VERSION})")
+ if(USE_LMDB)
+ caffe_status(" lmdb : " LMDB_FOUND THEN "Yes (ver. ${LMDB_VERSION})" ELSE "No")
+ endif()
+ if(USE_LEVELDB)
+ caffe_status(" LevelDB : " LEVELDB_FOUND THEN "Yes (ver. ${LEVELDB_VERSION})" ELSE "No")
+ caffe_status(" Snappy : " SNAPPY_FOUND THEN "Yes (ver. ${Snappy_VERSION})" ELSE "No" )
+ endif()
+ if(USE_OPENCV)
+ caffe_status(" OpenCV : Yes (ver. ${OpenCV_VERSION})")
+ endif()
caffe_status(" CUDA : " HAVE_CUDA THEN "Yes (ver. ${CUDA_VERSION})" ELSE "No" )
caffe_status("")
if(HAVE_CUDA)
caffe_status(" Target GPU(s) : ${CUDA_ARCH_NAME}" )
caffe_status(" GPU arch(s) : ${NVCC_FLAGS_EXTRA_readable}")
if(USE_CUDNN)
- caffe_status(" cuDNN : " HAVE_CUDNN THEN "Yes" ELSE "Not found")
+ caffe_status(" cuDNN : " HAVE_CUDNN THEN "Yes (ver. ${CUDNN_VERSION})" ELSE "Not found")
else()
caffe_status(" cuDNN : Disabled")
endif()
caffe_status(" Install path : ${CMAKE_INSTALL_PREFIX}")
caffe_status("")
endfunction()
-
################################################################################################
# Collecting sources from globbing and appending to output list variable
# Usage:
-# caffe_source_group(<output_variable> GLOB[_RECURSE] <globbing_expression>)
+# caffe_collect_sources(<output_variable> GLOB[_RECURSE] <globbing_expression>)
function(caffe_collect_sources variable)
cmake_parse_arguments(CAFFE_COLLECT_SOURCES "" "" "GLOB;GLOB_RECURSE" ${ARGN})
if(CAFFE_COLLECT_SOURCES_GLOB)
ARCHIVE_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/lib"
LIBRARY_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/lib"
RUNTIME_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/bin")
+ # make sure we build all external depepdencies first
+ if (DEFINED external_project_dependencies)
+ add_dependencies(${target} ${external_project_dependencies})
+ endif()
endfunction()
################################################################################################
set(result "")
foreach(line ${__lines})
set(result "${result}${PROJECT_SOURCE_DIR}/${line}\n")
- endforeach()
+ endforeach()
file(WRITE ${file}.gen.cmake ${result})
endfunction()
################################################################################################
-# Filter outs all files that are not inlcuded in selected list
+# Filter out all files that are not included in selected list
# Usage:
# caffe_leave_only_selected_tests(<filelist_variable> <selected_list>)
function(caffe_leave_only_selected_tests file_list)
# Caffe and this config file depends on opencv,
# so put `find_package(OpenCV)` before searching Caffe
# via `find_package(Caffe)`. All other lib/includes
-# dependencies are hard coded int the file
+# dependencies are hard coded in the file
#
# After successful configuration the following variables
# will be defined:
#
# Caffe_HAVE_CUDA - signals about CUDA support
# Caffe_HAVE_CUDNN - signals about cuDNN support
-
-# OpenCV dependency
-if(NOT OpenCV_FOUND)
- set(Caffe_OpenCV_CONFIG_PATH "@OpenCV_CONFIG_PATH@")
- if(Caffe_OpenCV_CONFIG_PATH)
- get_filename_component(Caffe_OpenCV_CONFIG_PATH ${Caffe_OpenCV_CONFIG_PATH} ABSOLUTE)
+# OpenCV dependency (optional)
- if(EXISTS ${Caffe_OpenCV_CONFIG_PATH} AND NOT TARGET opencv_core)
- message(STATUS "Caffe: using OpenCV config from ${Caffe_OpenCV_CONFIG_PATH}")
- include(${Caffe_OpenCV_CONFIG_PATH}/OpenCVModules.cmake)
- endif()
+if(@USE_OPENCV@)
+ if(NOT OpenCV_FOUND)
+ set(Caffe_OpenCV_CONFIG_PATH "@OpenCV_CONFIG_PATH@")
+ if(Caffe_OpenCV_CONFIG_PATH)
+ get_filename_component(Caffe_OpenCV_CONFIG_PATH ${Caffe_OpenCV_CONFIG_PATH} ABSOLUTE)
+
+ if(EXISTS ${Caffe_OpenCV_CONFIG_PATH} AND NOT TARGET opencv_core)
+ message(STATUS "Caffe: using OpenCV config from ${Caffe_OpenCV_CONFIG_PATH}")
+ include(${Caffe_OpenCV_CONFIG_PATH}/OpenCVModules.cmake)
+ endif()
- else()
- find_package(OpenCV REQUIRED)
+ else()
+ find_package(OpenCV REQUIRED)
+ endif()
+ unset(Caffe_OpenCV_CONFIG_PATH)
endif()
- unset(Caffe_OpenCV_CONFIG_PATH)
endif()
# Compute paths
set(Caffe_INCLUDE_DIRS "@Caffe_INCLUDE_DIRS@")
@Caffe_INSTALL_INCLUDE_DIR_APPEND_COMMAND@
-
+
# Our library dependencies
if(NOT TARGET caffe AND NOT caffe_BINARY_DIR)
include("${Caffe_CMAKE_DIR}/CaffeTargets.cmake")
set(PACKAGE_VERSION "@Caffe_VERSION@")
-
+
# Check whether the requested PACKAGE_FIND_VERSION is compatible
if("${PACKAGE_VERSION}" VERSION_LESS "${PACKAGE_FIND_VERSION}")
set(PACKAGE_VERSION_COMPATIBLE FALSE)
/* Matlab */
#cmakedefine HAVE_MATLAB
+
+/* IO libraries */
+#cmakedefine USE_OPENCV
+#cmakedefine USE_LEVELDB
+#cmakedefine USE_LMDB
+#cmakedefine ALLOW_LMDB_NOLOCK
################################################################################################
# Command alias for debugging messages
# Usage:
-# dmgs(<message>)
+# dmsg(<message>)
function(dmsg)
message(STATUS ${ARGN})
endfunction()
endmacro()
################################################################################################
-# Clears variables from lsit
+# Clears variables from list
# Usage:
-# caffe_list_unique(<variables_list>)
+# caffe_clear_vars(<variables_list>)
macro(caffe_clear_vars)
foreach(_var ${ARGN})
unset(${_var})
if(__add_cache)
set(${name} ${${name}} CACHE INTERNAL "${name} parsed from ${FILENAME}" FORCE)
elseif(__parnet_scope)
- set(${name} "${${name}}" PARENT_SCOPE)
+ set(${name} "${${name}}" PARENT_SCOPE)
endif()
else()
unset(${name} CACHE)
endfunction()
################################################################################################
-# Helper function to parse current linker libs into link directoris, libflags and osx frameworks
+# Helper function to parse current linker libs into link directories, libflags and osx frameworks
# Usage:
# caffe_parse_linker_libs(<Caffe_LINKER_LIBS_var> <directories_var> <libflags_var> <frameworks_var>)
function(caffe_parse_linker_libs Caffe_LINKER_LIBS_variable folders_var flags_var frameworks_var)
set(EXCLUDE_FILE_EXTENSTIONS pb.h pb.cc)
set(LINT_DIRS include src/caffe examples tools python matlab)
-cmake_policy(SET CMP0009 NEW) # supress cmake warning
+cmake_policy(SET CMP0009 NEW) # suppress cmake warning
# find all files of interest
foreach(ext ${SRC_FILE_EXTENSIONS})
execute_process(
COMMAND ${LINT_COMMAND} ${LINT_SOURCES}
- ERROR_VARIABLE LINT_OUTPUT
+ ERROR_VARIABLE LINT_OUTPUT
ERROR_STRIP_TRAILING_WHITESPACE
)
echo "Downloading..."
-wget http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz
+wget -c http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz
echo "Unzipping..."
Other docs, such as installation guides, are written in the `docs` directory and manually linked to from the `index.md` page.
-We strive to provide provide lots of usage examples, and to document all code in docstrings.
+We strive to provide lots of usage examples, and to document all code in docstrings.
We absolutely appreciate any contribution to this effort!
### Versioning
#### [Shelhamer's](https://github.com/shelhamer) “life of a branch in four acts”
Make the `feature` branch off of the latest `bvlc/master`
-```
-git checkout master
-git pull upstream master
-git checkout -b feature
-# do your work, make commits
-```
+
+ git checkout master
+ git pull upstream master
+ git checkout -b feature
+ # do your work, make commits
Prepare to merge by rebasing your branch on the latest `bvlc/master`
-```
-# make sure master is fresh
-git checkout master
-git pull upstream master
-# rebase your branch on the tip of master
-git checkout feature
-git rebase master
-```
+
+ # make sure master is fresh
+ git checkout master
+ git pull upstream master
+ # rebase your branch on the tip of master
+ git checkout feature
+ git rebase master
Push your branch to pull request it into `BVLC/caffe:master`
-```
-git push origin feature
-# ...make pull request to master...
-```
+
+ git push origin feature
+ # ...make pull request to master...
Now make a pull request! You can do this from the command line (`git pull-request -b master`) if you install [hub](https://github.com/github/hub). Hub has many other magical uses.
**General dependencies**
- sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev
+ sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
+ sudo apt-get install --no-install-recommends libboost-all-dev
+
+**CUDA**: Install via the NVIDIA package instead of `apt-get` to be certain of the library and driver versions.
+Install the library and latest driver separately; the driver bundled with the library is usually out-of-date.
+This can be skipped for CPU-only installation.
+
+**BLAS**: install ATLAS by `sudo apt-get install libatlas-base-dev` or install OpenBLAS or MKL for better CPU performance.
+
+**Python** (optional): if you use the default Python you will need to `sudo apt-get install` the `python-dev` package to have the Python headers for building the pycaffe interface.
**Remaining dependencies, 14.04**
- sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler
+Everything is packaged in 14.04.
+
+ sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
**Remaining dependencies, 12.04**
+These dependencies need manual installation in 12.04.
+
# glog
wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
tar zxvf glog-0.3.3.tar.gz
export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
make && make install
# lmdb
- git clone git://gitorious.org/mdb/mdb.git
- cd mdb/libraries/liblmdb
+ git clone https://github.com/LMDB/lmdb
+ cd lmdb/libraries/liblmdb
make && make install
Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.
-**CUDA**: Install via the NVIDIA package instead of `apt-get` to be certain of the library and driver versions.
-Install the library and latest driver separately; the driver bundled with the library is usually out-of-date.
-
-**BLAS**: install ATLAS by `sudo apt-get install libatlas-base-dev` or install OpenBLAS or MKL for better CPU performance.
-
-**Python** (optional): if you use the default Python you will need to `sudo apt-get install` the `python-dev` package to have the Python headers for building the pycaffe interface.
-
Continue with [compilation](installation.html#compilation).
**CUDA**: Install via the NVIDIA package that includes both CUDA and the bundled driver. **CUDA 7 is strongly suggested.** Older CUDA require `libstdc++` while clang++ is the default compiler and `libc++` the default standard library on OS X 10.9+. This disagreement makes it necessary to change the compilation settings for each of the dependencies. This is prone to error.
-**Library Path**: We find that everything compiles successfully if `$LD_LIBRARY_PATH` is not set at all, and `$DYLD_FALLBACK_LIBRARY_PATH` is set to to provide CUDA, Python, and other relevant libraries (e.g. `/usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib`).
+**Library Path**: We find that everything compiles successfully if `$LD_LIBRARY_PATH` is not set at all, and `$DYLD_FALLBACK_LIBRARY_PATH` is set to provide CUDA, Python, and other relevant libraries (e.g. `/usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib`).
In other `ENV` settings, things may not work as expected.
**General dependencies**
- brew install --fresh -vd snappy leveldb gflags glog szip lmdb
+ brew install -vd snappy leveldb gflags glog szip lmdb
# need the homebrew science source for OpenCV and hdf5
brew tap homebrew/science
- hdf5 opencv
+ brew install hdf5 opencv
If using Anaconda Python, a modification to the OpenCV formula might be needed
Do `brew edit opencv` and change the lines that look like the two lines below to exactly the two lines below.
**Remaining dependencies, with / without Python**
# with Python pycaffe needs dependencies built from source
- brew install --build-from-source --with-python --fresh -vd protobuf
- brew install --build-from-source --fresh -vd boost boost-python
+ brew install --build-from-source --with-python -vd protobuf
+ brew install --build-from-source -vd boost boost-python
# without Python the usual installation suffices
brew install protobuf boost
After this, run
- for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done
- brew uninstall protobuf; brew install --build-from-source --with-python --fresh -vd protobuf
- brew install --build-from-source --fresh -vd boost boost-python
+ for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source -vd $x; done
+ brew uninstall protobuf; brew install --build-from-source --with-python -vd protobuf
+ brew install --build-from-source -vd boost boost-python
If this is not done exactly right then linking errors will trouble you.
# Update homebrew; hopefully this works without errors!
brew update
- # Switch back to the caffe branches with the forumlae that you modified earlier
+ # Switch back to the caffe branches with the formulae that you modified earlier
cd /usr/local
git rebase master caffe
# Fix any merge conflicts and commit to caffe branch
export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
make && make install
# lmdb
- git clone git://gitorious.org/mdb/mdb.git
- cd mdb/libraries/liblmdb
+ git clone https://github.com/LMDB/lmdb
+ cd lmdb/libraries/liblmdb
make && make install
Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.
## Prerequisites
-Caffe has several dependencies.
+Caffe has several dependencies:
* [CUDA](https://developer.nvidia.com/cuda-zone) is required for GPU mode.
* library version 7.0 and the latest driver version are recommended, but 6.* is fine too
* 5.5, and 5.0 are compatible but considered legacy
* [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) via ATLAS, MKL, or OpenBLAS.
* [Boost](http://www.boost.org/) >= 1.55
+* `protobuf`, `glog`, `gflags`, `hdf5`
+
+Optional dependencies:
+
* [OpenCV](http://opencv.org/) >= 2.4 including 3.0
-* `protobuf`, `glog`, `gflags`
-* IO libraries `hdf5`, `leveldb`, `snappy`, `lmdb`
+* IO libraries: `lmdb`, `leveldb` (note: leveldb requires `snappy`)
+* cuDNN for GPU acceleration (v3)
Pycaffe and Matcaffe interfaces have their own natural needs.
* For Python Caffe: `Python 2.7` or `Python 3.3+`, `numpy (>= 1.7)`, boost-provided `boost.python`
* For MATLAB Caffe: MATLAB with the `mex` compiler.
-**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. For now cuDNN v1 is integrated but see [PR #1731](https://github.com/BVLC/caffe/pull/1731) for v2.
+**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. The current version is cuDNN v3; older versions are supported in older Caffe.
**CPU-only Caffe**: for cold-brewed CPU-only Caffe uncomment the `CPU_ONLY := 1` flag in `Makefile.config` to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.
Install MATLAB, and make sure that its `mex` is in your `$PATH`.
-*Caffe's MATLAB interface works with versions 2014a/b, 2013a/b, and 2012b.*
+*Caffe's MATLAB interface works with versions 2015a, 2014a/b, 2013a/b, and 2012b.*
#### Windows
--- /dev/null
+---
+title: Multi-GPU Usage, Hardware Configuration Assumptions, and Performance
+---
+
+# Multi-GPU Usage
+
+Currently Multi-GPU is only supported via the C/C++ paths and only for training.
+
+The GPUs to be used for training can be set with the "-gpu" flag on the command line to the 'caffe' tool. e.g. "build/tools/caffe train --solver=models/bvlc_alexnet/solver.prototxt --gpu=0,1" will train on GPUs 0 and 1.
+
+**NOTE**: each GPU runs the batchsize specified in your train_val.prototxt. So if you go from 1 GPU to 2 GPU, your effective batchsize will double. e.g. if your train_val.prototxt specified a batchsize of 256, if you run 2 GPUs your effective batch size is now 512. So you need to adjust the batchsize when running multiple GPUs and/or adjust your solver params, specifically learning rate.
+
+# Hardware Configuration Assumptions
+
+The current implementation uses a tree reduction strategy. e.g. if there are 4 GPUs in the system, 0:1, 2:3 will exchange gradients, then 0:2 (top of the tree) will exchange gradients, 0 will calculate
+updated model, 0\-\>2, and then 0\-\>1, 2\-\>3.
+
+For best performance, P2P DMA access between devices is needed. Without P2P access, for example crossing PCIe root complex, data is copied through host and effective exchange bandwidth is greatly reduced.
+
+Current implementation has a "soft" assumption that the devices being used are homogeneous. In practice, any devices of the same general class should work together, but performance and total size is limited by the smallest device being used. e.g. if you combine a TitanX and a GTX980, peformance will be limited by the 980. Mixing vastly different levels of boards, e.g. Kepler and Fermi, is not supported.
+
+"nvidia-smi topo -m" will show you the connectivity matrix. You can do P2P through PCIe bridges, but not across socket level links at this time, e.g. across CPU sockets on a multi-socket motherboard.
+
+# Scaling Performance
+
+Performance is **heavily** dependent on the PCIe topology of the system, the configuration of the neural network you are training, and the speed of each of the layers. Systems like the DIGITS DevBox have an optimized PCIe topology (X99-E WS chipset). In general, scaling on 2 GPUs tends to be ~1.8X on average for networks like AlexNet, CaffeNet, VGG, GoogleNet. 4 GPUs begins to have falloff in scaling. Generally with "weak scaling" where the batchsize increases with the number of GPUs you will see 3.5x scaling or so. With "strong scaling", the system can become communication bound, especially with layer performance optimizations like those in [cuDNNv3](http://nvidia.com/cudnn), and you will likely see closer to mid 2.x scaling in performance. Networks that have heavy computation compared to the number of parameters tend to have the best scaling performance.
\ No newline at end of file
sudo nvidia-smi -i 0 -ac 3004,875 # repeat with -i x for each GPU ID
-but note that this configuration resets across driver reloading / rebooting. Include these commands in a boot script to intialize these settings. For a simple fix, add these commands to `/etc/rc.local` (on Ubuntu).
+but note that this configuration resets across driver reloading / rebooting. Include these commands in a boot script to initialize these settings. For a simple fix, add these commands to `/etc/rc.local` (on Ubuntu).
## NVIDIA Titan
This data layer definition
- layers {
+ layer {
name: "mnist"
- # DATA layer loads leveldb or lmdb storage DBs for high-throughput.
- type: DATA
+ # Data layer loads leveldb or lmdb storage DBs for high-throughput.
+ type: "Data"
# the 1st top is the data itself: the name is only convention
top: "data"
# the 2nd top is the ground truth: the name is only convention
top: "label"
- # the DATA layer configuration
+ # the Data layer configuration
data_param {
# path to the DB
source: "examples/mnist/mnist_train_lmdb"
**Transformations**: data preprocessing is parametrized by transformation messages within the data layer definition.
- layers {
+ layer {
name: "data"
- type: DATA
+ type: "Data"
[...]
transform_param {
scale: 0.1
These computations follow immediately from defining the model: Caffe plans and carries out the forward and backward passes for you.
- The `Net::Forward()` and `Net::Backward()` methods carry out the respective passes while `Layer::Forward()` and `Layer::Backward()` compute each step.
-- Every layer type has `forward_{cpu,gpu}()` and `backward_{cpu,gpu}` methods to compute its steps according to the mode of computation. A layer may only implement CPU or GPU mode due to constraints or convenience.
+- Every layer type has `forward_{cpu,gpu}()` and `backward_{cpu,gpu}()` methods to compute its steps according to the mode of computation. A layer may only implement CPU or GPU mode due to constraints or convenience.
The [Solver](solver.html) optimizes a model by first calling forward to yield the output and loss, then calling backward to generate the gradient of the model, and then incorporating the gradient into a weight update that attempts to minimize the loss. Division of labor between the Solver, Net, and Layer keep Caffe modular and open to development.
**Training**: `caffe train` learns models from scratch, resumes learning from saved snapshots, and fine-tunes models to new data and tasks:
-* All training requires a solver configuration through the `-solver solver.prototxt` argument.
-* Resuming requires the `-snapshot model_iter_1000.solverstate` argument to load the solver snapshot.
+* All training requires a solver configuration through the `-solver solver.prototxt` argument.
+* Resuming requires the `-snapshot model_iter_1000.solverstate` argument to load the solver snapshot.
* Fine-tuning requires the `-weights model.caffemodel` argument for the model initialization.
For example, you can run:
**Testing**: `caffe test` scores models by running them in the test phase and reports the net output as its score. The net architecture must be properly defined to output an accuracy measure or loss as its output. The per-batch score is reported and then the grand average is reported last.
- #
- # score the learned LeNet model on the validation set as defined in the
+ # score the learned LeNet model on the validation set as defined in the
# model architeture lenet_train_test.prototxt
caffe test -model examples/mnist/lenet_train_test.prototxt -weights examples/mnist/lenet_iter_10000.caffemodel -gpu 0 -iterations 100
# query the first device
caffe device_query -gpu 0
+**Parallelism**: the `-gpu` flag to the `caffe` tool can take a comma separated list of IDs to run on multiple GPUs. A solver and net will be instantiated for each GPU so the batch size is effectively multiplied by the number of GPUs. To reproduce single GPU training, reduce the batch size in the network definition accordingly.
+
+ # train on GPUs 0 & 1 (doubling the batch size)
+ caffe train -solver examples/mnist/lenet_solver.prototxt -gpu 0,1
+ # train on all GPUs (multiplying batch size by number of devices)
+ caffe train -solver examples/mnist/lenet_solver.prototxt -gpu all
+
## Python
The Python interface -- pycaffe -- is the `caffe` module and its scripts in caffe/python. `import caffe` to load models, do forward and backward, handle IO, visualize networks, and even instrument model solving. All model data, derivatives, and parameters are exposed for reading and writing.
-- `caffe.Net` is the central interface for loading, configuring, and running models. `caffe.Classsifier` and `caffe.Detector` provide convenience interfaces for common tasks.
+- `caffe.Net` is the central interface for loading, configuring, and running models. `caffe.Classifier` and `caffe.Detector` provide convenience interfaces for common tasks.
- `caffe.SGDSolver` exposes the solving interface.
- `caffe.io` handles input / output with preprocessing and protocol buffers.
- `caffe.draw` visualizes network architectures.
Tutorial IPython notebooks are found in caffe/examples: do `ipython notebook caffe/examples` to try them. For developer reference docstrings can be found throughout the code.
-Compile pycaffe by `make pycaffe`. The module dir caffe/python/caffe should be installed in your PYTHONPATH for `import caffe`.
+Compile pycaffe by `make pycaffe`.
+Add the module directory to your `$PYTHONPATH` by `export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH` or the like for `import caffe`.
## MATLAB
-The MATLAB interface -- matcaffe -- is the `caffe` mex and its helper m-files in caffe/matlab. Load models, do forward and backward, extract output and read-only model weights, and load the binaryproto format mean as a matrix.
+The MATLAB interface -- matcaffe -- is the `caffe` package in caffe/matlab in which you can integrate Caffe in your Matlab code.
+
+In MatCaffe, you can
+
+* Creating multiple Nets in Matlab
+* Do forward and backward computation
+* Access any layer within a network, and any parameter blob in a layer
+* Get and set data or diff to any blob within a network, not restricting to input blobs or output blobs
+* Save a network's parameters to file, and load parameters from file
+* Reshape a blob and reshape a network
+* Edit network parameter and do network surgery
+* Create multiple Solvers in Matlab for training
+* Resume training from solver snapshots
+* Access train net and test nets in a solver
+* Run for a certain number of iterations and give back control to Matlab
+* Intermingle arbitrary Matlab code with gradient steps
+
+An ILSVRC image classification demo is in caffe/matlab/demo/classification_demo.m (you need to download BVLC CaffeNet from [Model Zoo](http://caffe.berkeleyvision.org/model_zoo.html) to run it).
+
+### Build MatCaffe
+
+Build MatCaffe with `make all matcaffe`. After that, you may test it using `make mattest`.
+
+Common issue: if you run into error messages like `libstdc++.so.6:version 'GLIBCXX_3.4.15' not found` during `make mattest`, then it usually means that your Matlab's runtime libraries do not match your compile-time libraries. You may need to do the following before you start Matlab:
+
+ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
+ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
+
+Or the equivalent based on where things are installed on your system, and do `make mattest` again to see if the issue is fixed. Note: this issue is sometimes more complicated since during its startup Matlab may overwrite your `LD_LIBRARY_PATH` environment variable. You can run `!ldd ./matlab/+caffe/private/caffe_.mexa64` (the mex extension may differ on your system) in Matlab to see its runtime libraries, and preload your compile-time libraries by exporting them to your `LD_PRELOAD` environment variable.
+
+After successful building and testing, add this package to Matlab search PATH by starting `matlab` from caffe root folder and running the following commands in Matlab command window.
+
+ addpath ./matlab
+
+You can save your Matlab search PATH by running `savepath` so that you don't have to run the command above again every time you use MatCaffe.
+
+### Use MatCaffe
+
+MatCaffe is very similar to PyCaffe in usage.
+
+Examples below shows detailed usages and assumes you have downloaded BVLC CaffeNet from [Model Zoo](http://caffe.berkeleyvision.org/model_zoo.html) and started `matlab` from caffe root folder.
+
+ model = './models/bvlc_reference_caffenet/deploy.prototxt';
+ weights = './models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel';
+
+#### Set mode and device
+
+**Mode and device should always be set BEFORE you create a net or a solver.**
+
+Use CPU:
+
+ caffe.set_mode_cpu();
+
+Use GPU and specify its gpu_id:
+
+ caffe.set_mode_gpu();
+ caffe.set_device(gpu_id);
+
+#### Create a network and access its layers and blobs
+
+Create a network:
+
+ net = caffe.Net(model, weights, 'test'); % create net and load weights
+
+Or
+
+ net = caffe.Net(model, 'test'); % create net but not load weights
+ net.copy_from(weights); % load weights
+
+which creates `net` object as
+
+ Net with properties:
+
+ layer_vec: [1x23 caffe.Layer]
+ blob_vec: [1x15 caffe.Blob]
+ inputs: {'data'}
+ outputs: {'prob'}
+ name2layer_index: [23x1 containers.Map]
+ name2blob_index: [15x1 containers.Map]
+ layer_names: {23x1 cell}
+ blob_names: {15x1 cell}
+
+The two `containers.Map` objects are useful to find the index of a layer or a blob by its name.
+
+You have access to every blob in this network. To fill blob 'data' with all ones:
+
+ net.blobs('data').set_data(ones(net.blobs('data').shape));
+
+To multiply all values in blob 'data' by 10:
+
+ net.blobs('data').set_data(net.blobs('data').get_data() * 10);
+
+**Be aware that since Matlab is 1-indexed and column-major, the usual 4 blob dimensions in Matlab are `[width, height, channels, num]`, and `width` is the fastest dimension. Also be aware that images are in BGR channels.** Also, Caffe uses single-precision float data. If your data is not single, `set_data` will automatically convert it to single.
+
+You also have access to every layer, so you can do network surgery. For example, to multiply conv1 parameters by 10:
+
+ net.params('conv1', 1).set_data(net.params('conv1', 1).get_data() * 10); % set weights
+ net.params('conv1', 2).set_data(net.params('conv1', 2).get_data() * 10); % set bias
+
+Alternatively, you can use
+
+ net.layers('conv1').params(1).set_data(net.layers('conv1').params(1).get_data() * 10);
+ net.layers('conv1').params(2).set_data(net.layers('conv1').params(2).get_data() * 10);
+
+To save the network you just modified:
+
+ net.save('my_net.caffemodel');
+
+To get a layer's type (string):
+
+ layer_type = net.layers('conv1').type;
+
+#### Forward and backward
+
+Forward pass can be done using `net.forward` or `net.forward_prefilled`. Function `net.forward` takes in a cell array of N-D arrays containing data of input blob(s) and outputs a cell array containing data from output blob(s). Function `net.forward_prefilled` uses existing data in input blob(s) during forward pass, takes no input and produces no output. After creating some data for input blobs like `data = rand(net.blobs('data').shape);` you can run
+
+ res = net.forward({data});
+ prob = res{1};
+
+Or
+
+ net.blobs('data').set_data(data);
+ net.forward_prefilled();
+ prob = net.blobs('prob').get_data();
+
+Backward is similar using `net.backward` or `net.backward_prefilled` and replacing `get_data` and `set_data` with `get_diff` and `set_diff`. After creating some gradients for output blobs like `prob_diff = rand(net.blobs('prob').shape);` you can run
+
+ res = net.backward({prob_diff});
+ data_diff = res{1};
+
+Or
+
+ net.blobs('prob').set_diff(prob_diff);
+ net.backward_prefilled();
+ data_diff = net.blobs('data').get_diff();
+
+**However, the backward computation above doesn't get correct results, because Caffe decides that the network does not need backward computation. To get correct backward results, you need to set `'force_backward: true'` in your network prototxt.**
+
+After performing forward or backward pass, you can also get the data or diff in internal blobs. For example, to extract pool5 features after forward pass:
+
+ pool5_feat = net.blobs('pool5').get_data();
+
+#### Reshape
+
+Assume you want to run 1 image at a time instead of 10:
+
+ net.blobs('data').reshape([227 227 3 1]); % reshape blob 'data'
+ net.reshape();
+
+Then the whole network is reshaped, and now `net.blobs('prob').shape` should be `[1000 1]`;
+
+#### Training
+
+Assume you have created training and validation lmdbs following our [ImageNET Tutorial](http://caffe.berkeleyvision.org/gathered/examples/imagenet.html), to create a solver and train on ILSVRC 2012 classification dataset:
+
+ solver = caffe.Solver('./models/bvlc_reference_caffenet/solver.prototxt');
+
+which creates `solver` object as
+
+ Solver with properties:
+
+ net: [1x1 caffe.Net]
+ test_nets: [1x1 caffe.Net]
+
+To train:
+
+ solver.solve();
+
+Or train for only 1000 iterations (so that you can do something to its net before training more iterations)
+
+ solver.step(1000);
+
+To get iteration number:
+
+ iter = solver.iter();
+
+To get its network:
+
+ train_net = solver.net;
+ test_net = solver.test_nets(1);
+
+To resume from a snapshot "your_snapshot.solverstate":
+
+ solver.restore('your_snapshot.solverstate');
+
+#### Input and output
+
+`caffe.io` class provides basic input functions `load_image` and `read_mean`. For example, to read ILSVRC 2012 mean file (assume you have downloaded imagenet example auxiliary files by running `./data/ilsvrc12/get_ilsvrc_aux.sh`):
+
+ mean_data = caffe.io.read_mean('./data/ilsvrc12/imagenet_mean.binaryproto');
+
+To read Caffe's example image and resize to `[width, height]` and suppose we want `width = 256; height = 256;`
+
+ im_data = caffe.io.load_image('./examples/images/cat.jpg');
+ im_data = imresize(im_data, [width, height]); % resize using Matlab's imresize
+
+**Keep in mind that `width` is the fastest dimension and channels are BGR, which is different from the usual way that Matlab stores an image.** If you don't want to use `caffe.io.load_image` and prefer to load an image by yourself, you can do
+
+ im_data = imread('./examples/images/cat.jpg'); % read image
+ im_data = im_data(:, :, [3, 2, 1]); % convert from RGB to BGR
+ im_data = permute(im_data, [2, 1, 3]); % permute width and height
+ im_data = single(im_data); % convert to single precision
+
+Also, you may take a look at caffe/matlab/demo/classification_demo.m to see how to prepare input by taking crops from an image.
-A MATLAB demo is in caffe/matlab/caffe/matcaffe_demo.m
+We show in caffe/matlab/hdf5creation how to read and write HDF5 data with Matlab. We do not provide extra functions for data output as Matlab itself is already quite powerful in output.
-Note that MATLAB matrices and memory are in column-major layout counter to Caffe's row-major layout! Double-check your work accordingly.
+#### Clear nets and solvers
-Compile matcaffe by `make matcaffe`.
+Call `caffe.reset_all()` to clear all solvers and stand-alone nets you have created.
To create a Caffe model you need to define the model architecture in a protocol buffer definition file (prototxt).
-Caffe layers and their parameters are defined in the protocol buffer definitions for the project in [caffe.proto](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto). The latest definitions are in the [dev caffe.proto](https://github.com/BVLC/caffe/blob/dev/src/caffe/proto/caffe.proto).
-
-TODO complete list of layers linking to headings
+Caffe layers and their parameters are defined in the protocol buffer definitions for the project in [caffe.proto](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto).
### Vision Layers
#### Convolution
-* LayerType: `CONVOLUTION`
+* Layer type: `Convolution`
* CPU implementation: `./src/caffe/layers/convolution_layer.cpp`
* CUDA GPU implementation: `./src/caffe/layers/convolution_layer.cu`
* Parameters (`ConvolutionParameter convolution_param`)
- `n * c_i * h_i * w_i`
* Output
- `n * c_o * h_o * w_o`, where `h_o = (h_i + 2 * pad_h - kernel_h) / stride_h + 1` and `w_o` likewise.
-* Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
+* Sample (as seen in `./models/bvlc_reference_caffenet/train_val.prototxt`)
- layers {
+ layer {
name: "conv1"
- type: CONVOLUTION
+ type: "Convolution"
bottom: "data"
top: "conv1"
- blobs_lr: 1 # learning rate multiplier for the filters
- blobs_lr: 2 # learning rate multiplier for the biases
- weight_decay: 1 # weight decay multiplier for the filters
- weight_decay: 0 # weight decay multiplier for the biases
+ # learning rate and decay multipliers for the filters
+ param { lr_mult: 1 decay_mult: 1 }
+ # learning rate and decay multipliers for the biases
+ param { lr_mult: 2 decay_mult: 0 }
convolution_param {
num_output: 96 # learn 96 filters
kernel_size: 11 # each filter is 11x11
}
}
-The `CONVOLUTION` layer convolves the input image with a set of learnable filters, each producing one feature map in the output image.
+The `Convolution` layer convolves the input image with a set of learnable filters, each producing one feature map in the output image.
#### Pooling
-* LayerType: `POOLING`
+* Layer type: `Pooling`
* CPU implementation: `./src/caffe/layers/pooling_layer.cpp`
* CUDA GPU implementation: `./src/caffe/layers/pooling_layer.cu`
* Parameters (`PoolingParameter pooling_param`)
- `n * c * h_i * w_i`
* Output
- `n * c * h_o * w_o`, where h_o and w_o are computed in the same way as convolution.
-* Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
+* Sample (as seen in `./models/bvlc_reference_caffenet/train_val.prototxt`)
- layers {
+ layer {
name: "pool1"
- type: POOLING
+ type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
#### Local Response Normalization (LRN)
-* LayerType: `LRN`
+* Layer type: `LRN`
* CPU Implementation: `./src/caffe/layers/lrn_layer.cpp`
* CUDA GPU Implementation: `./src/caffe/layers/lrn_layer.cu`
* Parameters (`LRNParameter lrn_param`)
#### im2col
-`IM2COL` is a helper for doing the image-to-column transformation that you most likely do not need to know about. This is used in Caffe's original convolution to do matrix multiplication by laying out all patches into a matrix.
+`Im2col` is a helper for doing the image-to-column transformation that you most likely do not need to know about. This is used in Caffe's original convolution to do matrix multiplication by laying out all patches into a matrix.
### Loss Layers
#### Softmax
-* LayerType: `SOFTMAX_LOSS`
+* Layer type: `SoftmaxWithLoss`
The softmax loss layer computes the multinomial logistic loss of the softmax of its inputs. It's conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient.
#### Sum-of-Squares / Euclidean
-* LayerType: `EUCLIDEAN_LOSS`
+* Layer type: `EuclideanLoss`
The Euclidean loss layer computes the sum of squares of differences of its two inputs, $$\frac 1 {2N} \sum_{i=1}^N \| x^1_i - x^2_i \|_2^2$$.
#### Hinge / Margin
-* LayerType: `HINGE_LOSS`
+* Layer type: `HingeLoss`
* CPU implementation: `./src/caffe/layers/hinge_loss_layer.cpp`
* CUDA GPU implementation: none yet
* Parameters (`HingeLossParameter hinge_loss_param`)
* Samples
# L1 Norm
- layers {
+ layer {
name: "loss"
- type: HINGE_LOSS
+ type: "HingeLoss"
bottom: "pred"
bottom: "label"
}
# L2 Norm
- layers {
+ layer {
name: "loss"
- type: HINGE_LOSS
+ type: "HingeLoss"
bottom: "pred"
bottom: "label"
top: "loss"
#### Sigmoid Cross-Entropy
-`SIGMOID_CROSS_ENTROPY_LOSS`
+`SigmoidCrossEntropyLoss`
#### Infogain
-`INFOGAIN_LOSS`
+`InfogainLoss`
#### Accuracy and Top-k
-`ACCURACY` scores the output as the accuracy of output with respect to target -- it is not actually a loss and has no backward step.
+`Accuracy` scores the output as the accuracy of output with respect to target -- it is not actually a loss and has no backward step.
### Activation / Neuron Layers
#### ReLU / Rectified-Linear and Leaky-ReLU
-* LayerType: `RELU`
+* Layer type: `ReLU`
* CPU implementation: `./src/caffe/layers/relu_layer.cpp`
* CUDA GPU implementation: `./src/caffe/layers/relu_layer.cu`
* Parameters (`ReLUParameter relu_param`)
- Optional
- `negative_slope` [default 0]: specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0.
-* Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
+* Sample (as seen in `./models/bvlc_reference_caffenet/train_val.prototxt`)
- layers {
+ layer {
name: "relu1"
- type: RELU
+ type: "ReLU"
bottom: "conv1"
top: "conv1"
}
-Given an input value x, The `RELU` layer computes the output as x if x > 0 and negative_slope * x if x <= 0. When the negative slope parameter is not set, it is equivalent to the standard ReLU function of taking max(x, 0). It also supports in-place computation, meaning that the bottom and the top blob could be the same to preserve memory consumption.
+Given an input value x, The `ReLU` layer computes the output as x if x > 0 and negative_slope * x if x <= 0. When the negative slope parameter is not set, it is equivalent to the standard ReLU function of taking max(x, 0). It also supports in-place computation, meaning that the bottom and the top blob could be the same to preserve memory consumption.
#### Sigmoid
-* LayerType: `SIGMOID`
+* Layer type: `Sigmoid`
* CPU implementation: `./src/caffe/layers/sigmoid_layer.cpp`
* CUDA GPU implementation: `./src/caffe/layers/sigmoid_layer.cu`
-* Sample (as seen in `./examples/imagenet/mnist_autoencoder.prototxt`)
+* Sample (as seen in `./examples/mnist/mnist_autoencoder.prototxt`)
- layers {
+ layer {
name: "encode1neuron"
bottom: "encode1"
top: "encode1neuron"
- type: SIGMOID
+ type: "Sigmoid"
}
-The `SIGMOID` layer computes the output as sigmoid(x) for each input element x.
+The `Sigmoid` layer computes the output as sigmoid(x) for each input element x.
#### TanH / Hyperbolic Tangent
-* LayerType: `TANH`
+* Layer type: `TanH`
* CPU implementation: `./src/caffe/layers/tanh_layer.cpp`
* CUDA GPU implementation: `./src/caffe/layers/tanh_layer.cu`
* Sample
- layers {
+ layer {
name: "layer"
bottom: "in"
top: "out"
- type: TANH
+ type: "TanH"
}
-The `TANH` layer computes the output as tanh(x) for each input element x.
+The `TanH` layer computes the output as tanh(x) for each input element x.
#### Absolute Value
-* LayerType: `ABSVAL`
+* Layer type: `AbsVal`
* CPU implementation: `./src/caffe/layers/absval_layer.cpp`
* CUDA GPU implementation: `./src/caffe/layers/absval_layer.cu`
* Sample
- layers {
+ layer {
name: "layer"
bottom: "in"
top: "out"
- type: ABSVAL
+ type: "AbsVal"
}
-The `ABSVAL` layer computes the output as abs(x) for each input element x.
+The `AbsVal` layer computes the output as abs(x) for each input element x.
#### Power
-* LayerType: `POWER`
+* Layer type: `Power`
* CPU implementation: `./src/caffe/layers/power_layer.cpp`
* CUDA GPU implementation: `./src/caffe/layers/power_layer.cu`
* Parameters (`PowerParameter power_param`)
- `shift` [default 0]
* Sample
- layers {
+ layer {
name: "layer"
bottom: "in"
top: "out"
- type: POWER
+ type: "Power"
power_param {
power: 1
scale: 1
}
}
-The `POWER` layer computes the output as (shift + scale * x) ^ power for each input element x.
+The `Power` layer computes the output as (shift + scale * x) ^ power for each input element x.
#### BNLL
-* LayerType: `BNLL`
+* Layer type: `BNLL`
* CPU implementation: `./src/caffe/layers/bnll_layer.cpp`
* CUDA GPU implementation: `./src/caffe/layers/bnll_layer.cu`
* Sample
- layers {
+ layer {
name: "layer"
bottom: "in"
top: "out"
#### Database
-* LayerType: `DATA`
+* Layer type: `Data`
* Parameters
- Required
- `source`: the name of the directory containing the database
#### In-Memory
-* LayerType: `MEMORY_DATA`
+* Layer type: `MemoryData`
* Parameters
- Required
- `batch_size`, `channels`, `height`, `width`: specify the size of input chunks to read from memory
#### HDF5 Input
-* LayerType: `HDF5_DATA`
+* Layer type: `HDF5Data`
* Parameters
- Required
- `source`: the name of the file to read from
#### HDF5 Output
-* LayerType: `HDF5_OUTPUT`
+* Layer type: `HDF5Output`
* Parameters
- Required
- `file_name`: name of file to write to
#### Images
-* LayerType: `IMAGE_DATA`
+* Layer type: `ImageData`
* Parameters
- Required
- `source`: name of a text file, with each line giving an image filename and label
#### Windows
-`WINDOW_DATA`
+`WindowData`
#### Dummy
-`DUMMY_DATA` is for development and debugging. See `DummyDataParameter`.
+`DummyData` is for development and debugging. See `DummyDataParameter`.
### Common Layers
#### Inner Product
-* LayerType: `INNER_PRODUCT`
+* Layer type: `InnerProduct`
* CPU implementation: `./src/caffe/layers/inner_product_layer.cpp`
* CUDA GPU implementation: `./src/caffe/layers/inner_product_layer.cu`
* Parameters (`InnerProductParameter inner_product_param`)
- `n * c_o * 1 * 1`
* Sample
- layers {
+ layer {
name: "fc8"
- type: INNER_PRODUCT
- blobs_lr: 1 # learning rate multiplier for the filters
- blobs_lr: 2 # learning rate multiplier for the biases
- weight_decay: 1 # weight decay multiplier for the filters
- weight_decay: 0 # weight decay multiplier for the biases
+ type: "InnerProduct"
+ # learning rate and decay multipliers for the weights
+ param { lr_mult: 1 decay_mult: 1 }
+ # learning rate and decay multipliers for the biases
+ param { lr_mult: 2 decay_mult: 0 }
inner_product_param {
num_output: 1000
weight_filler {
top: "fc8"
}
-The `INNER_PRODUCT` layer (also usually referred to as the fully connected layer) treats the input as a simple vector and produces an output in the form of a single vector (with the blob's height and width set to 1).
+The `InnerProduct` layer (also usually referred to as the fully connected layer) treats the input as a simple vector and produces an output in the form of a single vector (with the blob's height and width set to 1).
#### Splitting
-The `SPLIT` layer is a utility layer that splits an input blob to multiple output blobs. This is used when a blob is fed into multiple output layers.
+The `Split` layer is a utility layer that splits an input blob to multiple output blobs. This is used when a blob is fed into multiple output layers.
#### Flattening
-The `FLATTEN` layer is a utility layer that flattens an input of shape `n * c * h * w` to a simple vector output of shape `n * (c*h*w) * 1 * 1`.
+The `Flatten` layer is a utility layer that flattens an input of shape `n * c * h * w` to a simple vector output of shape `n * (c*h*w)`
+
+#### Reshape
+
+* Layer type: `Reshape`
+* Implementation: `./src/caffe/layers/reshape_layer.cpp`
+* Parameters (`ReshapeParameter reshape_param`)
+ - Optional: (also see detailed description below)
+ - `shape`
+
+* Input
+ - a single blob with arbitrary dimensions
+* Output
+ - the same blob, with modified dimensions, as specified by `reshape_param`
+
+* Sample
+
+ layer {
+ name: "reshape"
+ type: "Reshape"
+ bottom: "input"
+ top: "output"
+ reshape_param {
+ shape {
+ dim: 0 # copy the dimension from below
+ dim: 2
+ dim: 3
+ dim: -1 # infer it from the other dimensions
+ }
+ }
+ }
+
+The `Reshape` layer can be used to change the dimensions of its input, without changing its data. Just like the `Flatten` layer, only the dimensions are changed; no data is copied in the process.
+
+Output dimensions are specified by the `ReshapeParam` proto. Positive numbers are used directly, setting the corresponding dimension of the output blob. In addition, two special values are accepted for any of the target dimension values:
+
+* **0** means "copy the respective dimension of the bottom layer". That is, if the bottom has 2 as its 1st dimension, the top will have 2 as its 1st dimension as well, given `dim: 0` as the 1st target dimension.
+* **-1** stands for "infer this from the other dimensions". This behavior is similar to that of -1 in *numpy*'s or `[]` for *MATLAB*'s reshape: this dimension is calculated to keep the overall element count the same as in the bottom layer. At most one -1 can be used in a reshape operation.
+
+As another example, specifying `reshape_param { shape { dim: 0 dim: -1 } }` makes the layer behave in exactly the same way as the `Flatten` layer.
#### Concatenation
-* LayerType: `CONCAT`
+* Layer type: `Concat`
* CPU implementation: `./src/caffe/layers/concat_layer.cpp`
* CUDA GPU implementation: `./src/caffe/layers/concat_layer.cu`
* Parameters (`ConcatParameter concat_param`)
- Optional
- - `concat_dim` [default 1]: 0 for concatenation along num and 1 for channels.
+ - `axis` [default 1]: 0 for concatenation along num and 1 for channels.
* Input
- `n_i * c_i * h * w` for each input blob i from 1 to K.
* Output
- - if `concat_dim = 0`: `(n_1 + n_2 + ... + n_K) * c_1 * h * w`, and all input `c_i` should be the same.
- - if `concat_dim = 1`: `n_1 * (c_1 + c_2 + ... + c_K) * h * w`, and all input `n_i` should be the same.
+ - if `axis = 0`: `(n_1 + n_2 + ... + n_K) * c_1 * h * w`, and all input `c_i` should be the same.
+ - if `axis = 1`: `n_1 * (c_1 + c_2 + ... + c_K) * h * w`, and all input `n_i` should be the same.
* Sample
- layers {
+ layer {
name: "concat"
bottom: "in1"
bottom: "in2"
top: "out"
- type: CONCAT
+ type: "Concat"
concat_param {
- concat_dim: 1
+ axis: 1
}
}
-The `CONCAT` layer is a utility layer that concatenates its multiple input blobs to one single output blob. Currently, the layer supports concatenation along num or channels only.
+The `Concat` layer is a utility layer that concatenates its multiple input blobs to one single output blob.
#### Slicing
-The `SLICE` layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices.
+The `Slice` layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices.
* Sample
- layers {
+ layer {
name: "slicer_label"
- type: SLICE
+ type: "Slice"
bottom: "label"
## Example of label with a shape N x 3 x 1 x 1
top: "label1"
top: "label2"
top: "label3"
slice_param {
- slice_dim: 1
- slice_point: 1
- slice_point: 2
+ axis: 1
+ slice_point: 1
+ slice_point: 2
}
}
-`slice_dim` indicates the target dimension and can assume only two values: 0 for num or 1 for channel; `slice_point` indicates indexes in the selected dimension (the number of indexes must be equal to the number of top blobs minus one).
+`axis` indicates the target axis; `slice_point` indicates indexes in the selected dimension (the number of indices must be equal to the number of top blobs minus one).
#### Elementwise Operations
-`ELTWISE`
+`Eltwise`
#### Argmax
-`ARGMAX`
+`ArgMax`
#### Softmax
-`SOFTMAX`
+`Softmax`
#### Mean-Variance Normalization
The loss in Caffe is computed by the Forward pass of the network.
Each layer takes a set of input (`bottom`) blobs and produces a set of output (`top`) blobs.
Some of these layers' outputs may be used in the loss function.
-A typical choice of loss function for one-versus-all classification tasks is the `SOFTMAX_LOSS` function, used in a network definition as follows, for example:
+A typical choice of loss function for one-versus-all classification tasks is the `SoftmaxWithLoss` function, used in a network definition as follows, for example:
- layers {
+ layer {
name: "loss"
- type: SOFTMAX_LOSS
+ type: "SoftmaxWithLoss"
bottom: "pred"
bottom: "label"
top: "loss"
}
-In a `SOFTMAX_LOSS` function, the `top` blob is a scalar (dimensions $$1 \times 1 \times 1 \times 1$$) which averages the loss (computed from predicted labels `pred` and actuals labels `label`) over the entire mini-batch.
+In a `SoftmaxWithLoss` function, the `top` blob is a scalar (empty shape) which averages the loss (computed from predicted labels `pred` and actuals labels `label`) over the entire mini-batch.
### Loss weights
-For nets with multiple layers producing a loss (e.g., a network that both classifies the input using a `SOFTMAX_LOSS` layer and reconstructs it using a `EUCLIDEAN_LOSS` layer), *loss weights* can be used to specify their relative importance.
+For nets with multiple layers producing a loss (e.g., a network that both classifies the input using a `SoftmaxWithLoss` layer and reconstructs it using a `EuclideanLoss` layer), *loss weights* can be used to specify their relative importance.
-By convention, Caffe layer types with the suffix `_LOSS` contribute to the loss function, but other layers are assumed to be purely used for intermediate computations.
+By convention, Caffe layer types with the suffix `Loss` contribute to the loss function, but other layers are assumed to be purely used for intermediate computations.
However, any layer can be used as a loss by adding a field `loss_weight: <float>` to a layer definition for each `top` blob produced by the layer.
-Layers with the suffix `_LOSS` have an implicit `loss_weight: 1` for the first `top` blob (and `loss_weight: 0` for any additional `top`s); other layers have an implicit `loss_weight: 0` for all `top`s.
-So, the above `SOFTMAX_LOSS` layer could be equivalently written as:
+Layers with the suffix `Loss` have an implicit `loss_weight: 1` for the first `top` blob (and `loss_weight: 0` for any additional `top`s); other layers have an implicit `loss_weight: 0` for all `top`s.
+So, the above `SoftmaxWithLoss` layer could be equivalently written as:
- layers {
+ layer {
name: "loss"
- type: SOFTMAX_LOSS
+ type: "SoftmaxWithLoss"
bottom: "pred"
bottom: "label"
top: "loss"
## Blob storage and communication
-A Blob is a wrapper over the actual data being processed and passed along by Caffe, and also under the hood provides synchronization capability between the CPU and the GPU. Mathematically, a blob is a 4-dimensional array that stores things in the order of (Num, Channels, Height and Width), from major to minor, and stored in a C-contiguous fashion. The main reason for putting Num (the name is due to legacy reasons, and is equivalent to the notation of "batch" as in minibatch SGD).
+A Blob is a wrapper over the actual data being processed and passed along by Caffe, and also under the hood provides synchronization capability between the CPU and the GPU. Mathematically, a blob is an N-dimensional array stored in a C-contiguous fashion.
-Caffe stores and communicates data in 4-dimensional arrays called blobs. Blobs provide a unified memory interface, holding data e.g. batches of images, model parameters, and derivatives for optimization.
+Caffe stores and communicates data using blobs. Blobs provide a unified memory interface holding data; e.g., batches of images, model parameters, and derivatives for optimization.
Blobs conceal the computational and mental overhead of mixed CPU/GPU operation by synchronizing from the CPU host to the GPU device as needed. Memory on the host and device is allocated on demand (lazily) for efficient memory usage.
-The conventional blob dimensions for data are number N x channel K x height H x width W. Blob memory is row-major in layout so the last / rightmost dimension changes fastest. For example, the value at index (n, k, h, w) is physically located at index ((n * K + k) * H + h) * W + w.
+The conventional blob dimensions for batches of image data are number N x channel K x height H x width W. Blob memory is row-major in layout, so the last / rightmost dimension changes fastest. For example, in a 4D blob, the value at index (n, k, h, w) is physically located at index ((n * K + k) * H + h) * W + w.
-- Number / N is the batch size of the data. Batch processing achieves better throughput for communication and device processing. For an ImageNet training batch of 256 images B = 256.
+- Number / N is the batch size of the data. Batch processing achieves better throughput for communication and device processing. For an ImageNet training batch of 256 images N = 256.
- Channel / K is the feature dimension e.g. for RGB images K = 3.
-Note that although we have designed blobs with its dimensions corresponding to image applications, they are named purely for notational purpose and it is totally valid for you to do non-image applications. For example, if you simply need fully-connected networks like the conventional multi-layer perceptron, use blobs of dimensions (Num, Channels, 1, 1) and call the InnerProductLayer (which we will cover soon).
+Note that although many blobs in Caffe examples are 4D with axes for image applications, it is totally valid to use blobs for non-image applications. For example, if you simply need fully-connected networks like the conventional multi-layer perceptron, use 2D blobs (shape (N, D)) and call the InnerProductLayer (which we will cover soon).
-Caffe operations are general with respect to the channel dimension / K. Grayscale and hyperspectral imagery are fine. Caffe can likewise model and process arbitrary vectors in blobs with singleton. That is, the shape of blob holding 1000 vectors of 16 feature dimensions is 1000 x 16 x 1 x 1.
-
-Parameter blob dimensions vary according to the type and configuration of the layer. For a convolution layer with 96 filters of 11 x 11 spatial dimension and 3 inputs the blob is 96 x 3 x 11 x 11. For an inner product / fully-connected layer with 1000 output channels and 1024 input channels the parameter blob is 1 x 1 x 1000 x 1024.
+Parameter blob dimensions vary according to the type and configuration of the layer. For a convolution layer with 96 filters of 11 x 11 spatial dimension and 3 inputs the blob is 96 x 3 x 11 x 11. For an inner product / fully-connected layer with 1000 output channels and 1024 input channels the parameter blob is 1000 x 1024.
For custom data it may be necessary to hack your own input preparation tool or data layer. However once your data is in your job is done. The modularity of layers accomplishes the rest of the work for you.
is defined by
name: "LogReg"
- layers {
+ layer {
name: "mnist"
- type: DATA
+ type: "Data"
top: "data"
top: "label"
data_param {
batch_size: 64
}
}
- layers {
+ layer {
name: "ip"
- type: INNER_PRODUCT
+ type: "InnerProduct"
bottom: "data"
top: "ip"
inner_product_param {
num_output: 2
}
}
- layers {
+ layer {
name: "loss"
- type: SOFTMAX_LOSS
+ type: "SoftmaxWithLoss"
bottom: "ip"
bottom: "label"
top: "loss"
I0902 22:52:17.935807 2079114000 data_layer.cpp:135] Opening leveldb input_leveldb
I0902 22:52:17.937155 2079114000 data_layer.cpp:195] output data size: 64,1,28,28
I0902 22:52:17.938570 2079114000 net.cpp:103] Top shape: 64 1 28 28 (50176)
- I0902 22:52:17.938593 2079114000 net.cpp:103] Top shape: 64 1 1 1 (64)
+ I0902 22:52:17.938593 2079114000 net.cpp:103] Top shape: 64 (64)
I0902 22:52:17.938611 2079114000 net.cpp:67] Creating Layer ip
I0902 22:52:17.938617 2079114000 net.cpp:394] ip <- data
I0902 22:52:17.939177 2079114000 net.cpp:356] ip -> ip
I0902 22:52:17.939196 2079114000 net.cpp:96] Setting up ip
- I0902 22:52:17.940289 2079114000 net.cpp:103] Top shape: 64 2 1 1 (128)
+ I0902 22:52:17.940289 2079114000 net.cpp:103] Top shape: 64 2 (128)
I0902 22:52:17.941270 2079114000 net.cpp:67] Creating Layer loss
I0902 22:52:17.941305 2079114000 net.cpp:394] loss <- ip
I0902 22:52:17.941314 2079114000 net.cpp:394] loss <- label
I0902 22:52:17.941323 2079114000 net.cpp:356] loss -> loss
# set up the loss and configure the backward pass
I0902 22:52:17.941328 2079114000 net.cpp:96] Setting up loss
- I0902 22:52:17.941328 2079114000 net.cpp:103] Top shape: 1 1 1 1 (1)
+ I0902 22:52:17.941328 2079114000 net.cpp:103] Top shape: (1)
I0902 22:52:17.941329 2079114000 net.cpp:109] with loss weight 1
I0902 22:52:17.941779 2079114000 net.cpp:170] loss needs backward computation.
I0902 22:52:17.941787 2079114000 net.cpp:170] ip needs backward computation.
The solver orchestrates model optimization by coordinating the network's forward inference and backward gradients to form parameter updates that attempt to improve the loss.
The responsibilities of learning are divided between the Solver for overseeing the optimization and generating parameter updates and the Net for yielding loss and gradients.
-The Caffe solvers are Stochastic Gradient Descent (SGD), Adaptive Gradient (ADAGRAD), and Nesterov's Accelerated Gradient (NESTEROV).
+The Caffe solvers are:
+
+- Stochastic Gradient Descent (`type: "SGD"`),
+- AdaDelta (`type: "AdaDelta"`),
+- Adaptive Gradient (`type: "AdaGrad"`),
+- Adam (`type: "Adam"`),
+- Nesterov's Accelerated Gradient (`type: "Nesterov"`) and
+- RMSprop (`type: "RMSProp"`)
The solver
### SGD
-**Stochastic gradient descent** (`solver_type: SGD`) updates the weights $$ W $$ by a linear combination of the negative gradient $$ \nabla L(W) $$ and the previous weight update $$ V_t $$.
+**Stochastic gradient descent** (`type: "SGD"`) updates the weights $$ W $$ by a linear combination of the negative gradient $$ \nabla L(W) $$ and the previous weight update $$ V_t $$.
The **learning rate** $$ \alpha $$ is the weight of the negative gradient.
The **momentum** $$ \mu $$ is the weight of the previous update.
[ImageNet Classification with Deep Convolutional Neural Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf).
*Advances in Neural Information Processing Systems*, 2012.
+### AdaDelta
+
+The **AdaDelta** (`type: "AdaDelta"`) method (M. Zeiler [1]) is a "robust learning rate method". It is a gradient-based optimization method (like SGD). The update formulas are
+
+$$
+\begin{align}
+(v_t)_i &= \frac{\operatorname{RMS}((v_{t-1})_i)}{\operatorname{RMS}\left( \nabla L(W_t) \right)_{i}} \left( \nabla L(W_{t'}) \right)_i
+\\
+\operatorname{RMS}\left( \nabla L(W_t) \right)_{i} &= \sqrt{E[g^2] + \varepsilon}
+\\
+E[g^2]_t &= \delta{E[g^2]_{t-1} } + (1-\delta)g_{t}^2
+\end{align}
+$$
+
+and
+
+$$
+(W_{t+1})_i =
+(W_t)_i - \alpha
+(v_t)_i.
+$$
+
+[1] M. Zeiler
+ [ADADELTA: AN ADAPTIVE LEARNING RATE METHOD](http://arxiv.org/pdf/1212.5701.pdf).
+ *arXiv preprint*, 2012.
+
### AdaGrad
-The **adaptive gradient** (`solver_type: ADAGRAD`) method (Duchi et al. [1]) is a gradient-based optimization method (like SGD) that attempts to "find needles in haystacks in the form of very predictive but rarely seen features," in Duchi et al.'s words.
+The **adaptive gradient** (`type: "AdaGrad"`) method (Duchi et al. [1]) is a gradient-based optimization method (like SGD) that attempts to "find needles in haystacks in the form of very predictive but rarely seen features," in Duchi et al.'s words.
Given the update information from all previous iterations $$ \left( \nabla L(W) \right)_{t'} $$ for $$ t' \in \{1, 2, ..., t\} $$,
the update formulas proposed by [1] are as follows, specified for each component $$i$$ of the weights $$W$$:
[Adaptive Subgradient Methods for Online Learning and Stochastic Optimization](http://www.magicbroom.info/Papers/DuchiHaSi10.pdf).
*The Journal of Machine Learning Research*, 2011.
+### Adam
+
+The **Adam** (`type: "Adam"`), proposed in Kingma et al. [1], is a gradient-based optimization method (like SGD). This includes an "adaptive moment estimation" ($$m_t, v_t$$) and can be regarded as a generalization of AdaGrad. The update formulas are
+
+$$
+(m_t)_i = \beta_1 (m_{t-1})_i + (1-\beta_1)(\nabla L(W_t))_i,\\
+(v_t)_i = \beta_2 (v_{t-1})_i + (1-\beta_2)(\nabla L(W_t))_i^2
+$$
+
+and
+
+$$
+(W_{t+1})_i =
+(W_t)_i - \alpha \frac{\sqrt{1-(\beta_2)_i^t}}{1-(\beta_1)_i^t}\frac{(m_t)_i}{\sqrt{(v_t)_i}+\varepsilon}.
+$$
+
+Kingma et al. [1] proposed to use $$\beta_1 = 0.9, \beta_2 = 0.999, \varepsilon = 10^{-8}$$ as default values. Caffe uses the values of `momemtum, momentum2, delta` for $$\beta_1, \beta_2, \varepsilon$$, respectively.
+
+[1] D. Kingma, J. Ba.
+ [Adam: A Method for Stochastic Optimization](http://arxiv.org/abs/1412.6980).
+ *International Conference for Learning Representations*, 2015.
+
### NAG
-**Nesterov's accelerated gradient** (`solver_type: NESTEROV`) was proposed by Nesterov [1] as an "optimal" method of convex optimization, achieving a convergence rate of $$ \mathcal{O}(1/t^2) $$ rather than the $$ \mathcal{O}(1/t) $$.
+**Nesterov's accelerated gradient** (`type: "Nesterov"`) was proposed by Nesterov [1] as an "optimal" method of convex optimization, achieving a convergence rate of $$ \mathcal{O}(1/t^2) $$ rather than the $$ \mathcal{O}(1/t) $$.
Though the required assumptions to achieve the $$ \mathcal{O}(1/t^2) $$ convergence typically will not hold for deep networks trained with Caffe (e.g., due to non-smoothness and non-convexity), in practice NAG can be a very effective method for optimizing certain types of deep learning architectures, as demonstrated for deep MNIST autoencoders by Sutskever et al. [2].
The weight update formulas look very similar to the SGD updates given above:
[On the Importance of Initialization and Momentum in Deep Learning](http://www.cs.toronto.edu/~fritz/absps/momentum.pdf).
*Proceedings of the 30th International Conference on Machine Learning*, 2013.
+### RMSprop
+
+The **RMSprop** (`type: "RMSProp"`), suggested by Tieleman in a Coursera course lecture, is a gradient-based optimization method (like SGD). The update formulas are
+
+$$
+(v_t)_i =
+\begin{cases}
+(v_{t-1})_i + \delta, &(\nabla L(W_t))_i(\nabla L(W_{t-1}))_i > 0\\
+(v_{t-1})_i \cdot (1-\delta), & \text{else}
+\end{cases}
+$$
+
+$$
+(W_{t+1})_i =(W_t)_i - \alpha (v_t)_i,
+$$
+
+If the gradient updates results in oscillations the gradient is reduced by times $$1-\delta$$. Otherwise it will be increased by $$\delta$$. The default value of $$\delta$$ (`rms_decay`) is set to $$\delta = 0.02$$.
+
+[1] T. Tieleman, and G. Hinton.
+ [RMSProp: Divide the gradient by a running average of its recent magnitude](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf).
+ *COURSERA: Neural Networks for Machine Learning.Technical report*, 2012.
+
## Scaffolding
The solver scaffolding prepares the optimization method and initializes the model to be learned in `Solver::Presolve()`.
--- /dev/null
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Instant Recognition with Caffe\n",
+ "\n",
+ "In this example we'll classify an image with the bundled CaffeNet model based on the network architecture of Krizhevsky et al. for ImageNet. We'll compare CPU and GPU operation then reach into the model to inspect features and the output.\n",
+ "\n",
+ "(These feature visualizations follow the DeCAF visualizations originally by Yangqing Jia.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "First, import required modules, set plotting parameters, and run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the pretrained CaffeNet model if it hasn't already been fetched."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "# Make sure that caffe is on the python path:\n",
+ "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n",
+ "import sys\n",
+ "sys.path.insert(0, caffe_root + 'python')\n",
+ "\n",
+ "import caffe\n",
+ "\n",
+ "plt.rcParams['figure.figsize'] = (10, 10)\n",
+ "plt.rcParams['image.interpolation'] = 'nearest'\n",
+ "plt.rcParams['image.cmap'] = 'gray'\n",
+ "\n",
+ "import os\n",
+ "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
+ " print(\"Downloading pre-trained CaffeNet model...\")\n",
+ " !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Set Caffe to CPU mode, load the net in the test phase for inference, and configure input preprocessing."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "caffe.set_mode_cpu()\n",
+ "net = caffe.Net(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n",
+ " caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n",
+ " caffe.TEST)\n",
+ "\n",
+ "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n",
+ "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n",
+ "transformer.set_transpose('data', (2,0,1))\n",
+ "transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1)) # mean pixel\n",
+ "transformer.set_raw_scale('data', 255) # the reference model operates on images in [0,255] range instead of [0,1]\n",
+ "transformer.set_channel_swap('data', (2,1,0)) # the reference model has channels in BGR order instead of RGB"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's start with a simple classification. We'll set a batch of 50 to demonstrate batch processing, even though we'll only be classifying one image. (Note that the batch size can also be changed on-the-fly.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# set net to batch size of 50\n",
+ "net.blobs['data'].reshape(50,3,227,227)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Feed in the image (with some preprocessing) and classify with a forward pass."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Predicted class is #281.\n"
+ ]
+ }
+ ],
+ "source": [
+ "net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(caffe_root + 'examples/images/cat.jpg'))\n",
+ "out = net.forward()\n",
+ "print(\"Predicted class is #{}.\".format(out['prob'][0].argmax()))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "What did the input look like?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x7f665b02ae90>"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
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+ "IBAI7BP/B0EEnTIvM42+AAAAAElFTkSuQmCC\n"
+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f66a46f8a10>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.imshow(transformer.deprocess('data', net.blobs['data'].data[0]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Adorable, but was our classification correct?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n",
+ " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n",
+ " 'n02127052 lynx, catamount']\n"
+ ]
+ }
+ ],
+ "source": [
+ "# load labels\n",
+ "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
+ "try:\n",
+ " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
+ "except:\n",
+ " !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
+ " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
+ "\n",
+ "# sort top k predictions from softmax output\n",
+ "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n",
+ "print labels[top_k]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Indeed! But how long did it take?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 loops, best of 3: 7.14 s per loop\n"
+ ]
+ }
+ ],
+ "source": [
+ "# CPU mode\n",
+ "net.forward() # call once for allocation\n",
+ "%timeit net.forward()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "That's a while, even for a batch size of 50 images. Let's switch to GPU mode."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "10 loops, best of 3: 90.9 ms per loop\n"
+ ]
+ }
+ ],
+ "source": [
+ "# GPU mode\n",
+ "caffe.set_device(0)\n",
+ "caffe.set_mode_gpu()\n",
+ "net.forward() # call once for allocation\n",
+ "%timeit net.forward()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Much better. Now let's look at the net in more detail.\n",
+ "\n",
+ "First, the layer features and their shapes (1 is the batch size, corresponding to the single input image in this example)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[('data', (50, 3, 227, 227)),\n",
+ " ('conv1', (50, 96, 55, 55)),\n",
+ " ('pool1', (50, 96, 27, 27)),\n",
+ " ('norm1', (50, 96, 27, 27)),\n",
+ " ('conv2', (50, 256, 27, 27)),\n",
+ " ('pool2', (50, 256, 13, 13)),\n",
+ " ('norm2', (50, 256, 13, 13)),\n",
+ " ('conv3', (50, 384, 13, 13)),\n",
+ " ('conv4', (50, 384, 13, 13)),\n",
+ " ('conv5', (50, 256, 13, 13)),\n",
+ " ('pool5', (50, 256, 6, 6)),\n",
+ " ('fc6', (50, 4096)),\n",
+ " ('fc7', (50, 4096)),\n",
+ " ('fc8', (50, 1000)),\n",
+ " ('prob', (50, 1000))]"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "[(k, v.data.shape) for k, v in net.blobs.items()]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The parameters and their shapes. The parameters are `net.params['name'][0]` while biases are `net.params['name'][1]`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[('conv1', (96, 3, 11, 11)),\n",
+ " ('conv2', (256, 48, 5, 5)),\n",
+ " ('conv3', (384, 256, 3, 3)),\n",
+ " ('conv4', (384, 192, 3, 3)),\n",
+ " ('conv5', (256, 192, 3, 3)),\n",
+ " ('fc6', (4096, 9216)),\n",
+ " ('fc7', (4096, 4096)),\n",
+ " ('fc8', (1000, 4096))]"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "[(k, v[0].data.shape) for k, v in net.params.items()]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Helper functions for visualization"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# take an array of shape (n, height, width) or (n, height, width, channels)\n",
+ "# and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\n",
+ "def vis_square(data, padsize=1, padval=0):\n",
+ " data -= data.min()\n",
+ " data /= data.max()\n",
+ " \n",
+ " # force the number of filters to be square\n",
+ " n = int(np.ceil(np.sqrt(data.shape[0])))\n",
+ " padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)\n",
+ " data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))\n",
+ " \n",
+ " # tile the filters into an image\n",
+ " data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n",
+ " data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
+ " \n",
+ " plt.imshow(data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The input image"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The first layer filters, `conv1`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
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+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7ffaf3877710>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# the parameters are a list of [weights, biases]\n",
+ "filters = net.params['conv1'][0].data\n",
+ "vis_square(filters.transpose(0, 2, 3, 1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The first layer output, `conv1` (rectified responses of the filters above, first 36 only)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
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+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7ffb01f952d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "feat = net.blobs['conv1'].data[0, :36]\n",
+ "vis_square(feat, padval=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The second layer filters, `conv2`\n",
+ "\n",
+ "There are 256 filters, each of which has dimension 5 x 5 x 48. We show only the first 48 filters, with each channel shown separately, so that each filter is a row."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
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+ "RK5CYII=\n"
+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7ffb01f42290>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "filters = net.params['conv2'][0].data\n",
+ "vis_square(filters[:48].reshape(48**2, 5, 5))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The second layer output, `conv2` (rectified, only the first 36 of 256 channels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
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+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7ffb01f42310>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "feat = net.blobs['conv2'].data[0, :36]\n",
+ "vis_square(feat, padval=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The third layer output, `conv3` (rectified, all 384 channels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
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+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7ffb01dfb3d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "feat = net.blobs['conv3'].data[0]\n",
+ "vis_square(feat, padval=0.5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The fourth layer output, `conv4` (rectified, all 384 channels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
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+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7ffb01cf9110>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "feat = net.blobs['conv4'].data[0]\n",
+ "vis_square(feat, padval=0.5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The fifth layer output, `conv5` (rectified, all 256 channels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
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+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7ffb01c2f0d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "feat = net.blobs['conv5'].data[0]\n",
+ "vis_square(feat, padval=0.5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The fifth layer after pooling, `pool5`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
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+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7ffb01bbe590>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "feat = net.blobs['pool5'].data[0]\n",
+ "vis_square(feat, padval=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The first fully connected layer, `fc6` (rectified)\n",
+ "\n",
+ "We show the output values and the histogram of the positive values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
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+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7ffb01ae5a90>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "feat = net.blobs['fc6'].data[0]\n",
+ "plt.subplot(2, 1, 1)\n",
+ "plt.plot(feat.flat)\n",
+ "plt.subplot(2, 1, 2)\n",
+ "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The second fully connected layer, `fc7` (rectified)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
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+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7ffb01953f90>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "feat = net.blobs['fc7'].data[0]\n",
+ "plt.subplot(2, 1, 1)\n",
+ "plt.plot(feat.flat)\n",
+ "plt.subplot(2, 1, 2)\n",
+ "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The final probability output, `prob`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x7ffb01f12a50>]"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
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+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7ffb019b9dd0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "feat = net.blobs['prob'].data[0]\n",
+ "plt.plot(feat.flat)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's see the top 5 predicted labels."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n",
+ " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n",
+ " 'n02127052 lynx, catamount']\n"
+ ]
+ }
+ ],
+ "source": [
+ "# load labels\n",
+ "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
+ "try:\n",
+ " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
+ "except:\n",
+ " !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
+ " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
+ "\n",
+ "# sort top k predictions from softmax output\n",
+ "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n",
+ "print labels[top_k]"
+ ]
+ }
+ ],
+ "metadata": {
+ "description": "Instant recognition with a pre-trained model and a tour of the net interface for visualizing features and parameters layer-by-layer.",
+ "example_name": "Image Classification and Filter Visualization",
+ "include_in_docs": true,
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.9"
+ },
+ "priority": 1
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
--- /dev/null
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Python solving with LeNet\n",
+ "\n",
+ "In this example, we'll explore learning with Caffe in Python, using the fully-exposed `Solver` interface."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "os.chdir('..')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "sys.path.insert(0, './python')\n",
+ "import caffe\n",
+ "\n",
+ "from pylab import *\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We'll be running the provided LeNet example (make sure you've downloaded the data and created the databases, as below)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Downloading...\n",
+ "--2015-06-30 14:41:56-- http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
+ "Resolving yann.lecun.com... 128.122.47.89\n",
+ "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 9912422 (9.5M) [application/x-gzip]\n",
+ "Saving to: 'train-images-idx3-ubyte.gz'\n",
+ "\n",
+ "train-images-idx3-u 100%[=====================>] 9.45M 146KB/s in 57s \n",
+ "\n",
+ "2015-06-30 14:42:53 (171 KB/s) - 'train-images-idx3-ubyte.gz' saved [9912422/9912422]\n",
+ "\n",
+ "--2015-06-30 14:42:53-- http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
+ "Resolving yann.lecun.com... 128.122.47.89\n",
+ "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 28881 (28K) [application/x-gzip]\n",
+ "Saving to: 'train-labels-idx1-ubyte.gz'\n",
+ "\n",
+ "train-labels-idx1-u 100%[=====================>] 28.20K 107KB/s in 0.3s \n",
+ "\n",
+ "2015-06-30 14:42:53 (107 KB/s) - 'train-labels-idx1-ubyte.gz' saved [28881/28881]\n",
+ "\n",
+ "--2015-06-30 14:42:53-- http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
+ "Resolving yann.lecun.com... 128.122.47.89\n",
+ "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 1648877 (1.6M) [application/x-gzip]\n",
+ "Saving to: 't10k-images-idx3-ubyte.gz'\n",
+ "\n",
+ "t10k-images-idx3-ub 100%[=====================>] 1.57M 205KB/s in 8.2s \n",
+ "\n",
+ "2015-06-30 14:43:02 (197 KB/s) - 't10k-images-idx3-ubyte.gz' saved [1648877/1648877]\n",
+ "\n",
+ "--2015-06-30 14:43:02-- http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
+ "Resolving yann.lecun.com... 128.122.47.89\n",
+ "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 4542 (4.4K) [application/x-gzip]\n",
+ "Saving to: 't10k-labels-idx1-ubyte.gz'\n",
+ "\n",
+ "t10k-labels-idx1-ub 100%[=====================>] 4.44K 26.9KB/s in 0.2s \n",
+ "\n",
+ "2015-06-30 14:43:02 (26.9 KB/s) - 't10k-labels-idx1-ubyte.gz' saved [4542/4542]\n",
+ "\n",
+ "Unzipping...\n",
+ "Done.\n",
+ "Creating lmdb...\n",
+ "Done.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Download and prepare data\n",
+ "!data/mnist/get_mnist.sh\n",
+ "!examples/mnist/create_mnist.sh"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We need two external files to help out:\n",
+ "* the net prototxt, defining the architecture and pointing to the train/test data\n",
+ "* the solver prototxt, defining the learning parameters\n",
+ "\n",
+ "We start with the net. We'll write the net in a succinct and natural way as Python code that serializes to Caffe's protobuf model format.\n",
+ "\n",
+ "This network expects to read from pregenerated LMDBs, but reading directly from `ndarray`s is also possible using `MemoryDataLayer`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from caffe import layers as L\n",
+ "from caffe import params as P\n",
+ "\n",
+ "def lenet(lmdb, batch_size):\n",
+ " # our version of LeNet: a series of linear and simple nonlinear transformations\n",
+ " n = caffe.NetSpec()\n",
+ " n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,\n",
+ " transform_param=dict(scale=1./255), ntop=2)\n",
+ " n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))\n",
+ " n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
+ " n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))\n",
+ " n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
+ " n.ip1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n",
+ " n.relu1 = L.ReLU(n.ip1, in_place=True)\n",
+ " n.ip2 = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))\n",
+ " n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n",
+ " return n.to_proto()\n",
+ " \n",
+ "with open('examples/mnist/lenet_auto_train.prototxt', 'w') as f:\n",
+ " f.write(str(lenet('examples/mnist/mnist_train_lmdb', 64)))\n",
+ " \n",
+ "with open('examples/mnist/lenet_auto_test.prototxt', 'w') as f:\n",
+ " f.write(str(lenet('examples/mnist/mnist_test_lmdb', 100)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The net has been written to disk in more verbose but human-readable serialization format using Google's protobuf library. You can read, write, and modify this description directly. Let's take a look at the train net."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "layer {\r\n",
+ " name: \"data\"\r\n",
+ " type: \"Data\"\r\n",
+ " top: \"data\"\r\n",
+ " top: \"label\"\r\n",
+ " transform_param {\r\n",
+ " scale: 0.00392156862745\r\n",
+ " }\r\n",
+ " data_param {\r\n",
+ " source: \"examples/mnist/mnist_train_lmdb\"\r\n",
+ " batch_size: 64\r\n",
+ " backend: LMDB\r\n",
+ " }\r\n",
+ "}\r\n",
+ "layer {\r\n",
+ " name: \"conv1\"\r\n",
+ " type: \"Convolution\"\r\n",
+ " bottom: \"data\"\r\n",
+ " top: \"conv1\"\r\n",
+ " convolution_param {\r\n",
+ " num_output: 20\r\n",
+ " kernel_size: 5\r\n",
+ " weight_filler {\r\n",
+ " type: \"xavier\"\r\n",
+ " }\r\n",
+ " }\r\n",
+ "}\r\n",
+ "layer {\r\n",
+ " name: \"pool1\"\r\n",
+ " type: \"Pooling\"\r\n",
+ " bottom: \"conv1\"\r\n",
+ " top: \"pool1\"\r\n",
+ " pooling_param {\r\n",
+ " pool: MAX\r\n",
+ " kernel_size: 2\r\n",
+ " stride: 2\r\n",
+ " }\r\n",
+ "}\r\n",
+ "layer {\r\n",
+ " name: \"conv2\"\r\n",
+ " type: \"Convolution\"\r\n",
+ " bottom: \"pool1\"\r\n",
+ " top: \"conv2\"\r\n",
+ " convolution_param {\r\n",
+ " num_output: 50\r\n",
+ " kernel_size: 5\r\n",
+ " weight_filler {\r\n",
+ " type: \"xavier\"\r\n",
+ " }\r\n",
+ " }\r\n",
+ "}\r\n",
+ "layer {\r\n",
+ " name: \"pool2\"\r\n",
+ " type: \"Pooling\"\r\n",
+ " bottom: \"conv2\"\r\n",
+ " top: \"pool2\"\r\n",
+ " pooling_param {\r\n",
+ " pool: MAX\r\n",
+ " kernel_size: 2\r\n",
+ " stride: 2\r\n",
+ " }\r\n",
+ "}\r\n",
+ "layer {\r\n",
+ " name: \"ip1\"\r\n",
+ " type: \"InnerProduct\"\r\n",
+ " bottom: \"pool2\"\r\n",
+ " top: \"ip1\"\r\n",
+ " inner_product_param {\r\n",
+ " num_output: 500\r\n",
+ " weight_filler {\r\n",
+ " type: \"xavier\"\r\n",
+ " }\r\n",
+ " }\r\n",
+ "}\r\n",
+ "layer {\r\n",
+ " name: \"relu1\"\r\n",
+ " type: \"ReLU\"\r\n",
+ " bottom: \"ip1\"\r\n",
+ " top: \"ip1\"\r\n",
+ "}\r\n",
+ "layer {\r\n",
+ " name: \"ip2\"\r\n",
+ " type: \"InnerProduct\"\r\n",
+ " bottom: \"ip1\"\r\n",
+ " top: \"ip2\"\r\n",
+ " inner_product_param {\r\n",
+ " num_output: 10\r\n",
+ " weight_filler {\r\n",
+ " type: \"xavier\"\r\n",
+ " }\r\n",
+ " }\r\n",
+ "}\r\n",
+ "layer {\r\n",
+ " name: \"loss\"\r\n",
+ " type: \"SoftmaxWithLoss\"\r\n",
+ " bottom: \"ip2\"\r\n",
+ " bottom: \"label\"\r\n",
+ " top: \"loss\"\r\n",
+ "}\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "!cat examples/mnist/lenet_auto_train.prototxt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now let's see the learning parameters, which are also written as a `prototxt` file. We're using SGD with momentum, weight decay, and a specific learning rate schedule."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "# The train/test net protocol buffer definition\r\n",
+ "train_net: \"examples/mnist/lenet_auto_train.prototxt\"\r\n",
+ "test_net: \"examples/mnist/lenet_auto_test.prototxt\"\r\n",
+ "# test_iter specifies how many forward passes the test should carry out.\r\n",
+ "# In the case of MNIST, we have test batch size 100 and 100 test iterations,\r\n",
+ "# covering the full 10,000 testing images.\r\n",
+ "test_iter: 100\r\n",
+ "# Carry out testing every 500 training iterations.\r\n",
+ "test_interval: 500\r\n",
+ "# The base learning rate, momentum and the weight decay of the network.\r\n",
+ "base_lr: 0.01\r\n",
+ "momentum: 0.9\r\n",
+ "weight_decay: 0.0005\r\n",
+ "# The learning rate policy\r\n",
+ "lr_policy: \"inv\"\r\n",
+ "gamma: 0.0001\r\n",
+ "power: 0.75\r\n",
+ "# Display every 100 iterations\r\n",
+ "display: 100\r\n",
+ "# The maximum number of iterations\r\n",
+ "max_iter: 10000\r\n",
+ "# snapshot intermediate results\r\n",
+ "snapshot: 5000\r\n",
+ "snapshot_prefix: \"examples/mnist/lenet\"\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "!cat examples/mnist/lenet_auto_solver.prototxt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's pick a device and load the solver. We'll use SGD (with momentum), but Adagrad and Nesterov's accelerated gradient are also available."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "caffe.set_device(0)\n",
+ "caffe.set_mode_gpu()\n",
+ "solver = caffe.SGDSolver('examples/mnist/lenet_auto_solver.prototxt')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To get an idea of the architecture of our net, we can check the dimensions of the intermediate features (blobs) and parameters (these will also be useful to refer to when manipulating data later)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[('data', (64, 1, 28, 28)),\n",
+ " ('label', (64,)),\n",
+ " ('conv1', (64, 20, 24, 24)),\n",
+ " ('pool1', (64, 20, 12, 12)),\n",
+ " ('conv2', (64, 50, 8, 8)),\n",
+ " ('pool2', (64, 50, 4, 4)),\n",
+ " ('ip1', (64, 500)),\n",
+ " ('ip2', (64, 10)),\n",
+ " ('loss', ())]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# each output is (batch size, feature dim, spatial dim)\n",
+ "[(k, v.data.shape) for k, v in solver.net.blobs.items()]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[('conv1', (20, 1, 5, 5)),\n",
+ " ('conv2', (50, 20, 5, 5)),\n",
+ " ('ip1', (500, 800)),\n",
+ " ('ip2', (10, 500))]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# just print the weight sizes (not biases)\n",
+ "[(k, v[0].data.shape) for k, v in solver.net.params.items()]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Before taking off, let's check that everything is loaded as we expect. We'll run a forward pass on the train and test nets and check that they contain our data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'loss': array(2.301163673400879, dtype=float32)}"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "solver.net.forward() # train net\n",
+ "solver.test_nets[0].forward() # test net (there can be more than one)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 5. 0. 4. 1. 9. 2. 1. 3.]\n"
+ ]
+ },
+ {
+ "data": {
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+ "eU5QDbaKiorKc8L/DzAr6bE92WeRAAAAAElFTkSuQmCC\n"
+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f7939901710>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# we use a little trick to tile the first eight images\n",
+ "imshow(solver.net.blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray')\n",
+ "print solver.net.blobs['label'].data[:8]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 7. 2. 1. 0. 4. 1. 4. 9.]\n"
+ ]
+ },
+ {
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+ "AAAASUVORK5CYII=\n"
+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f7939901490>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "imshow(solver.test_nets[0].blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray')\n",
+ "print solver.test_nets[0].blobs['label'].data[:8]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Both train and test nets seem to be loading data, and to have correct labels.\n",
+ "\n",
+ "Let's take one step of (minibatch) SGD and see what happens."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "solver.step(1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Do we have gradients propagating through our filters? Let's see the updates to the first layer, shown here as a $4 \\times 5$ grid of $5 \\times 5$ filters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x7f79383819d0>"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
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+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f7939901850>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "imshow(solver.net.params['conv1'][0].diff[:, 0].reshape(4, 5, 5, 5)\n",
+ " .transpose(0, 2, 1, 3).reshape(4*5, 5*5), cmap='gray')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Something is happening. Let's run the net for a while, keeping track of a few things as it goes.\n",
+ "Note that this process will be the same as if training through the `caffe` binary. In particular:\n",
+ "* logging will continue to happen as normal\n",
+ "* snapshots will be taken at the interval specified in the solver prototxt (here, every 5000 iterations)\n",
+ "* testing will happen at the interval specified (here, every 500 iterations)\n",
+ "\n",
+ "Since we have control of the loop in Python, we're free to compute additional things as we go, as we show below. We can do many other things as well, for example:\n",
+ "* write a custom stopping criterion\n",
+ "* change the solving process by updating the net in the loop"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Iteration 0 testing...\n",
+ "Iteration 25 testing...\n",
+ "Iteration 50 testing...\n",
+ "Iteration 75 testing...\n",
+ "Iteration 100 testing...\n",
+ "Iteration 125 testing...\n",
+ "Iteration 150 testing...\n",
+ "Iteration 175 testing...\n",
+ "CPU times: user 12.3 s, sys: 3.96 s, total: 16.2 s\n",
+ "Wall time: 15.7 s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "niter = 200\n",
+ "test_interval = 25\n",
+ "# losses will also be stored in the log\n",
+ "train_loss = zeros(niter)\n",
+ "test_acc = zeros(int(np.ceil(niter / test_interval)))\n",
+ "output = zeros((niter, 8, 10))\n",
+ "\n",
+ "# the main solver loop\n",
+ "for it in range(niter):\n",
+ " solver.step(1) # SGD by Caffe\n",
+ " \n",
+ " # store the train loss\n",
+ " train_loss[it] = solver.net.blobs['loss'].data\n",
+ " \n",
+ " # store the output on the first test batch\n",
+ " # (start the forward pass at conv1 to avoid loading new data)\n",
+ " solver.test_nets[0].forward(start='conv1')\n",
+ " output[it] = solver.test_nets[0].blobs['ip2'].data[:8]\n",
+ " \n",
+ " # run a full test every so often\n",
+ " # (Caffe can also do this for us and write to a log, but we show here\n",
+ " # how to do it directly in Python, where more complicated things are easier.)\n",
+ " if it % test_interval == 0:\n",
+ " print 'Iteration', it, 'testing...'\n",
+ " correct = 0\n",
+ " for test_it in range(100):\n",
+ " solver.test_nets[0].forward()\n",
+ " correct += sum(solver.test_nets[0].blobs['ip2'].data.argmax(1)\n",
+ " == solver.test_nets[0].blobs['label'].data)\n",
+ " test_acc[it // test_interval] = correct / 1e4"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's plot the train loss and test accuracy."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.text.Text at 0x7f793878f490>"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
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+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f79387025d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "_, ax1 = subplots()\n",
+ "ax2 = ax1.twinx()\n",
+ "ax1.plot(arange(niter), train_loss)\n",
+ "ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')\n",
+ "ax1.set_xlabel('iteration')\n",
+ "ax1.set_ylabel('train loss')\n",
+ "ax2.set_ylabel('test accuracy')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The loss seems to have dropped quickly and coverged (except for stochasticity), while the accuracy rose correspondingly. Hooray!\n",
+ "\n",
+ "Since we saved the results on the first test batch, we can watch how our prediction scores evolved. We'll plot time on the $x$ axis and each possible label on the $y$, with lightness indicating confidence."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
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+ "text/plain": [
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+ "text/plain": [
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "for i in range(8):\n",
+ " figure(figsize=(2, 2))\n",
+ " imshow(solver.test_nets[0].blobs['data'].data[i, 0], cmap='gray')\n",
+ " figure(figsize=(10, 2))\n",
+ " imshow(output[:50, i].T, interpolation='nearest', cmap='gray')\n",
+ " xlabel('iteration')\n",
+ " ylabel('label')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We started with little idea about any of these digits, and ended up with correct classifications for each. If you've been following along, you'll see the last digit is the most difficult, a slanted \"9\" that's (understandably) most confused with \"4\".\n",
+ "\n",
+ "Note that these are the \"raw\" output scores rather than the softmax-computed probability vectors. The latter, shown below, make it easier to see the confidence of our net (but harder to see the scores for less likely digits)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
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+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7d792baffb50>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "for i in range(8):\n",
+ " figure(figsize=(2, 2))\n",
+ " imshow(solver.test_nets[0].blobs['data'].data[i, 0], cmap='gray')\n",
+ " figure(figsize=(10, 2))\n",
+ " imshow(exp(output[:50, i].T) / exp(output[:50, i].T).sum(0), interpolation='nearest', cmap='gray')\n",
+ " xlabel('iteration')\n",
+ " ylabel('label')"
+ ]
+ }
+ ],
+ "metadata": {
+ "description": "Define, train, and test the classic LeNet with the Python interface.",
+ "example_name": "Learning LeNet",
+ "include_in_docs": true,
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.9"
+ },
+ "priority": 2
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
--- /dev/null
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Brewing Logistic Regression then Going Deeper\n",
+ "\n",
+ "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "import os\n",
+ "os.chdir('..')\n",
+ "\n",
+ "import sys\n",
+ "sys.path.insert(0, './python')\n",
+ "import caffe\n",
+ "\n",
+ "\n",
+ "import os\n",
+ "import h5py\n",
+ "import shutil\n",
+ "import tempfile\n",
+ "\n",
+ "import sklearn\n",
+ "import sklearn.datasets\n",
+ "import sklearn.linear_model\n",
+ "\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
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+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x11dbd0090>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "X, y = sklearn.datasets.make_classification(\n",
+ " n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \n",
+ " n_clusters_per_class=2, hypercube=False, random_state=0\n",
+ ")\n",
+ "\n",
+ "# Split into train and test\n",
+ "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n",
+ "\n",
+ "# Visualize sample of the data\n",
+ "ind = np.random.permutation(X.shape[0])[:1000]\n",
+ "df = pd.DataFrame(X[ind])\n",
+ "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 0.783\n",
+ "Accuracy: 0.783\n",
+ "Accuracy: 0.783\n",
+ "Accuracy: 0.783\n",
+ "1 loops, best of 3: 508 ms per loop\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%timeit\n",
+ "# Train and test the scikit-learn SGD logistic regression.\n",
+ "clf = sklearn.linear_model.SGDClassifier(\n",
+ " loss='log', n_iter=1000, penalty='l2', alpha=1e-3, class_weight='auto')\n",
+ "\n",
+ "clf.fit(X, y)\n",
+ "yt_pred = clf.predict(Xt)\n",
+ "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Save the dataset to HDF5 for loading in Caffe."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Write out the data to HDF5 files in a temp directory.\n",
+ "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n",
+ "dirname = os.path.abspath('./examples/hdf5_classification/data')\n",
+ "if not os.path.exists(dirname):\n",
+ " os.makedirs(dirname)\n",
+ "\n",
+ "train_filename = os.path.join(dirname, 'train.h5')\n",
+ "test_filename = os.path.join(dirname, 'test.h5')\n",
+ "\n",
+ "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n",
+ "# To show this off, we'll list the same data file twice.\n",
+ "with h5py.File(train_filename, 'w') as f:\n",
+ " f['data'] = X\n",
+ " f['label'] = y.astype(np.float32)\n",
+ "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n",
+ " f.write(train_filename + '\\n')\n",
+ " f.write(train_filename + '\\n')\n",
+ " \n",
+ "# HDF5 is pretty efficient, but can be further compressed.\n",
+ "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n",
+ "with h5py.File(test_filename, 'w') as f:\n",
+ " f.create_dataset('data', data=Xt, **comp_kwargs)\n",
+ " f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n",
+ "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n",
+ " f.write(test_filename + '\\n')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's define logistic regression in Caffe through Python net specification. This is a quick and natural way to define nets that sidesteps manually editing the protobuf model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from caffe import layers as L\n",
+ "from caffe import params as P\n",
+ "\n",
+ "def logreg(hdf5, batch_size):\n",
+ " # logistic regression: data, matrix multiplication, and 2-class softmax loss\n",
+ " n = caffe.NetSpec()\n",
+ " n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n",
+ " n.ip1 = L.InnerProduct(n.data, num_output=2, weight_filler=dict(type='xavier'))\n",
+ " n.accuracy = L.Accuracy(n.ip1, n.label)\n",
+ " n.loss = L.SoftmaxWithLoss(n.ip1, n.label)\n",
+ " return n.to_proto()\n",
+ " \n",
+ "with open('examples/hdf5_classification/logreg_auto_train.prototxt', 'w') as f:\n",
+ " f.write(str(logreg('examples/hdf5_classification/data/train.txt', 10)))\n",
+ " \n",
+ "with open('examples/hdf5_classification/logreg_auto_test.prototxt', 'w') as f:\n",
+ " f.write(str(logreg('examples/hdf5_classification/data/test.txt', 10)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Time to learn and evaluate our Caffeinated logistic regression in Python."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 0.782\n",
+ "Accuracy: 0.782\n",
+ "Accuracy: 0.782\n",
+ "Accuracy: 0.782\n",
+ "1 loops, best of 3: 287 ms per loop\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%timeit\n",
+ "caffe.set_mode_cpu()\n",
+ "solver = caffe.get_solver('examples/hdf5_classification/solver.prototxt')\n",
+ "solver.solve()\n",
+ "\n",
+ "accuracy = 0\n",
+ "batch_size = solver.test_nets[0].blobs['data'].num\n",
+ "test_iters = int(len(Xt) / batch_size)\n",
+ "for i in range(test_iters):\n",
+ " solver.test_nets[0].forward()\n",
+ " accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+ "accuracy /= test_iters\n",
+ "\n",
+ "print(\"Accuracy: {:.3f}\".format(accuracy))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Do the same through the command line interface for detailed output on the model and solving."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "I0318 00:58:32.322571 2013098752 caffe.cpp:117] Use CPU.\n",
+ "I0318 00:58:32.643163 2013098752 caffe.cpp:121] Starting Optimization\n",
+ "I0318 00:58:32.643229 2013098752 solver.cpp:32] Initializing solver from parameters: \n",
+ "train_net: \"examples/hdf5_classification/logreg_auto_train.prototxt\"\n",
+ "test_net: \"examples/hdf5_classification/logreg_auto_test.prototxt\"\n",
+ "test_iter: 250\n",
+ "test_interval: 1000\n",
+ "base_lr: 0.01\n",
+ "display: 1000\n",
+ "max_iter: 10000\n",
+ "lr_policy: \"step\"\n",
+ "gamma: 0.1\n",
+ "momentum: 0.9\n",
+ "weight_decay: 0.0005\n",
+ "stepsize: 5000\n",
+ "snapshot: 10000\n",
+ "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n",
+ "solver_mode: CPU\n",
+ "I0318 00:58:32.643333 2013098752 solver.cpp:61] Creating training net from train_net file: examples/hdf5_classification/logreg_auto_train.prototxt\n",
+ "I0318 00:58:32.643465 2013098752 net.cpp:42] Initializing net from parameters: \n",
+ "state {\n",
+ " phase: TRAIN\n",
+ "}\n",
+ "layer {\n",
+ " name: \"data\"\n",
+ " type: \"HDF5Data\"\n",
+ " top: \"data\"\n",
+ " top: \"label\"\n",
+ " hdf5_data_param {\n",
+ " source: \"examples/hdf5_classification/data/train.txt\"\n",
+ " batch_size: 10\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"ip1\"\n",
+ " type: \"InnerProduct\"\n",
+ " bottom: \"data\"\n",
+ " top: \"ip1\"\n",
+ " inner_product_param {\n",
+ " num_output: 2\n",
+ " weight_filler {\n",
+ " type: \"xavier\"\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"accuracy\"\n",
+ " type: \"Accuracy\"\n",
+ " bottom: \"ip1\"\n",
+ " bottom: \"label\"\n",
+ " top: \"accuracy\"\n",
+ "}\n",
+ "layer {\n",
+ " name: \"loss\"\n",
+ " type: \"SoftmaxWithLoss\"\n",
+ " bottom: \"ip1\"\n",
+ " bottom: \"label\"\n",
+ " top: \"loss\"\n",
+ "}\n",
+ "I0318 00:58:32.644197 2013098752 layer_factory.hpp:74] Creating layer data\n",
+ "I0318 00:58:32.644219 2013098752 net.cpp:84] Creating Layer data\n",
+ "I0318 00:58:32.644230 2013098752 net.cpp:338] data -> data\n",
+ "I0318 00:58:32.644256 2013098752 net.cpp:338] data -> label\n",
+ "I0318 00:58:32.644269 2013098752 net.cpp:113] Setting up data\n",
+ "I0318 00:58:32.644278 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n",
+ "I0318 00:58:32.644327 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n",
+ "I0318 00:58:32.646458 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
+ "I0318 00:58:32.646502 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+ "I0318 00:58:32.646518 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
+ "I0318 00:58:32.646538 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
+ "I0318 00:58:32.646546 2013098752 net.cpp:380] label_data_1_split <- label\n",
+ "I0318 00:58:32.646556 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
+ "I0318 00:58:32.646569 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
+ "I0318 00:58:32.646579 2013098752 net.cpp:113] Setting up label_data_1_split\n",
+ "I0318 00:58:32.646586 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+ "I0318 00:58:32.646595 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+ "I0318 00:58:32.646601 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
+ "I0318 00:58:32.646615 2013098752 net.cpp:84] Creating Layer ip1\n",
+ "I0318 00:58:32.646622 2013098752 net.cpp:380] ip1 <- data\n",
+ "I0318 00:58:32.646664 2013098752 net.cpp:338] ip1 -> ip1\n",
+ "I0318 00:58:32.646689 2013098752 net.cpp:113] Setting up ip1\n",
+ "I0318 00:58:32.652330 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+ "I0318 00:58:32.652371 2013098752 layer_factory.hpp:74] Creating layer ip1_ip1_0_split\n",
+ "I0318 00:58:32.652393 2013098752 net.cpp:84] Creating Layer ip1_ip1_0_split\n",
+ "I0318 00:58:32.652407 2013098752 net.cpp:380] ip1_ip1_0_split <- ip1\n",
+ "I0318 00:58:32.652421 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_0\n",
+ "I0318 00:58:32.652467 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_1\n",
+ "I0318 00:58:32.652480 2013098752 net.cpp:113] Setting up ip1_ip1_0_split\n",
+ "I0318 00:58:32.652489 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+ "I0318 00:58:32.652498 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+ "I0318 00:58:32.652505 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
+ "I0318 00:58:32.652521 2013098752 net.cpp:84] Creating Layer accuracy\n",
+ "I0318 00:58:32.652534 2013098752 net.cpp:380] accuracy <- ip1_ip1_0_split_0\n",
+ "I0318 00:58:32.652545 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
+ "I0318 00:58:32.652562 2013098752 net.cpp:338] accuracy -> accuracy\n",
+ "I0318 00:58:32.652577 2013098752 net.cpp:113] Setting up accuracy\n",
+ "I0318 00:58:32.652590 2013098752 net.cpp:120] Top shape: (1)\n",
+ "I0318 00:58:32.652642 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+ "I0318 00:58:32.652655 2013098752 net.cpp:84] Creating Layer loss\n",
+ "I0318 00:58:32.652663 2013098752 net.cpp:380] loss <- ip1_ip1_0_split_1\n",
+ "I0318 00:58:32.652672 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
+ "I0318 00:58:32.652679 2013098752 net.cpp:338] loss -> loss\n",
+ "I0318 00:58:32.652689 2013098752 net.cpp:113] Setting up loss\n",
+ "I0318 00:58:32.652701 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+ "I0318 00:58:32.652716 2013098752 net.cpp:120] Top shape: (1)\n",
+ "I0318 00:58:32.652724 2013098752 net.cpp:122] with loss weight 1\n",
+ "I0318 00:58:32.652740 2013098752 net.cpp:167] loss needs backward computation.\n",
+ "I0318 00:58:32.652746 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
+ "I0318 00:58:32.652753 2013098752 net.cpp:167] ip1_ip1_0_split needs backward computation.\n",
+ "I0318 00:58:32.652760 2013098752 net.cpp:167] ip1 needs backward computation.\n",
+ "I0318 00:58:32.652786 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
+ "I0318 00:58:32.652801 2013098752 net.cpp:169] data does not need backward computation.\n",
+ "I0318 00:58:32.652808 2013098752 net.cpp:205] This network produces output accuracy\n",
+ "I0318 00:58:32.652815 2013098752 net.cpp:205] This network produces output loss\n",
+ "I0318 00:58:32.652825 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
+ "I0318 00:58:32.652833 2013098752 net.cpp:217] Network initialization done.\n",
+ "I0318 00:58:32.652839 2013098752 net.cpp:218] Memory required for data: 528\n",
+ "I0318 00:58:32.652964 2013098752 solver.cpp:154] Creating test net (#0) specified by test_net file: examples/hdf5_classification/logreg_auto_test.prototxt\n",
+ "I0318 00:58:32.652986 2013098752 net.cpp:42] Initializing net from parameters: \n",
+ "state {\n",
+ " phase: TEST\n",
+ "}\n",
+ "layer {\n",
+ " name: \"data\"\n",
+ " type: \"HDF5Data\"\n",
+ " top: \"data\"\n",
+ " top: \"label\"\n",
+ " hdf5_data_param {\n",
+ " source: \"examples/hdf5_classification/data/test.txt\"\n",
+ " batch_size: 10\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"ip1\"\n",
+ " type: \"InnerProduct\"\n",
+ " bottom: \"data\"\n",
+ " top: \"ip1\"\n",
+ " inner_product_param {\n",
+ " num_output: 2\n",
+ " weight_filler {\n",
+ " type: \"xavier\"\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"accuracy\"\n",
+ " type: \"Accuracy\"\n",
+ " bottom: \"ip1\"\n",
+ " bottom: \"label\"\n",
+ " top: \"accuracy\"\n",
+ "}\n",
+ "layer {\n",
+ " name: \"loss\"\n",
+ " type: \"SoftmaxWithLoss\"\n",
+ " bottom: \"ip1\"\n",
+ " bottom: \"label\"\n",
+ " top: \"loss\"\n",
+ "}\n",
+ "I0318 00:58:32.653069 2013098752 layer_factory.hpp:74] Creating layer data\n",
+ "I0318 00:58:32.653080 2013098752 net.cpp:84] Creating Layer data\n",
+ "I0318 00:58:32.653090 2013098752 net.cpp:338] data -> data\n",
+ "I0318 00:58:32.653128 2013098752 net.cpp:338] data -> label\n",
+ "I0318 00:58:32.653146 2013098752 net.cpp:113] Setting up data\n",
+ "I0318 00:58:32.653154 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n",
+ "I0318 00:58:32.653192 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n",
+ "I0318 00:58:32.654850 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
+ "I0318 00:58:32.654897 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+ "I0318 00:58:32.654914 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
+ "I0318 00:58:32.654933 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
+ "I0318 00:58:32.654943 2013098752 net.cpp:380] label_data_1_split <- label\n",
+ "I0318 00:58:32.654953 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
+ "I0318 00:58:32.654966 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
+ "I0318 00:58:32.654976 2013098752 net.cpp:113] Setting up label_data_1_split\n",
+ "I0318 00:58:32.654985 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+ "I0318 00:58:32.654992 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+ "I0318 00:58:32.655000 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
+ "I0318 00:58:32.655010 2013098752 net.cpp:84] Creating Layer ip1\n",
+ "I0318 00:58:32.655017 2013098752 net.cpp:380] ip1 <- data\n",
+ "I0318 00:58:32.655030 2013098752 net.cpp:338] ip1 -> ip1\n",
+ "I0318 00:58:32.655041 2013098752 net.cpp:113] Setting up ip1\n",
+ "I0318 00:58:32.655061 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+ "I0318 00:58:32.655072 2013098752 layer_factory.hpp:74] Creating layer ip1_ip1_0_split\n",
+ "I0318 00:58:32.655148 2013098752 net.cpp:84] Creating Layer ip1_ip1_0_split\n",
+ "I0318 00:58:32.655159 2013098752 net.cpp:380] ip1_ip1_0_split <- ip1\n",
+ "I0318 00:58:32.655170 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_0\n",
+ "I0318 00:58:32.655180 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_1\n",
+ "I0318 00:58:32.655190 2013098752 net.cpp:113] Setting up ip1_ip1_0_split\n",
+ "I0318 00:58:32.655199 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+ "I0318 00:58:32.655206 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+ "I0318 00:58:32.655213 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
+ "I0318 00:58:32.655223 2013098752 net.cpp:84] Creating Layer accuracy\n",
+ "I0318 00:58:32.655230 2013098752 net.cpp:380] accuracy <- ip1_ip1_0_split_0\n",
+ "I0318 00:58:32.655237 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
+ "I0318 00:58:32.655251 2013098752 net.cpp:338] accuracy -> accuracy\n",
+ "I0318 00:58:32.655259 2013098752 net.cpp:113] Setting up accuracy\n",
+ "I0318 00:58:32.655267 2013098752 net.cpp:120] Top shape: (1)\n",
+ "I0318 00:58:32.655340 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+ "I0318 00:58:32.655354 2013098752 net.cpp:84] Creating Layer loss\n",
+ "I0318 00:58:32.655361 2013098752 net.cpp:380] loss <- ip1_ip1_0_split_1\n",
+ "I0318 00:58:32.655369 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
+ "I0318 00:58:32.655378 2013098752 net.cpp:338] loss -> loss\n",
+ "I0318 00:58:32.655388 2013098752 net.cpp:113] Setting up loss\n",
+ "I0318 00:58:32.655397 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+ "I0318 00:58:32.655414 2013098752 net.cpp:120] Top shape: (1)\n",
+ "I0318 00:58:32.655422 2013098752 net.cpp:122] with loss weight 1\n",
+ "I0318 00:58:32.655438 2013098752 net.cpp:167] loss needs backward computation.\n",
+ "I0318 00:58:32.655446 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
+ "I0318 00:58:32.655455 2013098752 net.cpp:167] ip1_ip1_0_split needs backward computation.\n",
+ "I0318 00:58:32.655462 2013098752 net.cpp:167] ip1 needs backward computation.\n",
+ "I0318 00:58:32.655469 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
+ "I0318 00:58:32.655477 2013098752 net.cpp:169] data does not need backward computation.\n",
+ "I0318 00:58:32.655483 2013098752 net.cpp:205] This network produces output accuracy\n",
+ "I0318 00:58:32.655489 2013098752 net.cpp:205] This network produces output loss\n",
+ "I0318 00:58:32.655503 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
+ "I0318 00:58:32.655511 2013098752 net.cpp:217] Network initialization done.\n",
+ "I0318 00:58:32.655517 2013098752 net.cpp:218] Memory required for data: 528\n",
+ "I0318 00:58:32.655547 2013098752 solver.cpp:42] Solver scaffolding done.\n",
+ "I0318 00:58:32.655567 2013098752 solver.cpp:222] Solving \n",
+ "I0318 00:58:32.655575 2013098752 solver.cpp:223] Learning Rate Policy: step\n",
+ "I0318 00:58:32.655583 2013098752 solver.cpp:266] Iteration 0, Testing net (#0)\n",
+ "I0318 00:58:32.683643 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.3736\n",
+ "I0318 00:58:32.683686 2013098752 solver.cpp:315] Test net output #1: loss = 1.00555 (* 1 = 1.00555 loss)\n",
+ "I0318 00:58:32.683846 2013098752 solver.cpp:189] Iteration 0, loss = 0.869394\n",
+ "I0318 00:58:32.683861 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.3\n",
+ "I0318 00:58:32.683871 2013098752 solver.cpp:204] Train net output #1: loss = 0.869394 (* 1 = 0.869394 loss)\n",
+ "I0318 00:58:32.683883 2013098752 solver.cpp:464] Iteration 0, lr = 0.01\n",
+ "I0318 00:58:32.698721 2013098752 solver.cpp:266] Iteration 1000, Testing net (#0)\n",
+ "I0318 00:58:32.701917 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7848\n",
+ "I0318 00:58:32.701961 2013098752 solver.cpp:315] Test net output #1: loss = 0.590972 (* 1 = 0.590972 loss)\n",
+ "I0318 00:58:32.702014 2013098752 solver.cpp:189] Iteration 1000, loss = 0.54742\n",
+ "I0318 00:58:32.702029 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n",
+ "I0318 00:58:32.702041 2013098752 solver.cpp:204] Train net output #1: loss = 0.54742 (* 1 = 0.54742 loss)\n",
+ "I0318 00:58:32.702051 2013098752 solver.cpp:464] Iteration 1000, lr = 0.01\n",
+ "I0318 00:58:32.718360 2013098752 solver.cpp:266] Iteration 2000, Testing net (#0)\n",
+ "I0318 00:58:32.721529 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7696\n",
+ "I0318 00:58:32.721562 2013098752 solver.cpp:315] Test net output #1: loss = 0.593946 (* 1 = 0.593946 loss)\n",
+ "I0318 00:58:32.721593 2013098752 solver.cpp:189] Iteration 2000, loss = 0.729569\n",
+ "I0318 00:58:32.721603 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.5\n",
+ "I0318 00:58:32.721613 2013098752 solver.cpp:204] Train net output #1: loss = 0.729569 (* 1 = 0.729569 loss)\n",
+ "I0318 00:58:32.721622 2013098752 solver.cpp:464] Iteration 2000, lr = 0.01\n",
+ "I0318 00:58:32.740182 2013098752 solver.cpp:266] Iteration 3000, Testing net (#0)\n",
+ "I0318 00:58:32.743494 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.77\n",
+ "I0318 00:58:32.743544 2013098752 solver.cpp:315] Test net output #1: loss = 0.591229 (* 1 = 0.591229 loss)\n",
+ "I0318 00:58:32.744209 2013098752 solver.cpp:189] Iteration 3000, loss = 0.406097\n",
+ "I0318 00:58:32.744231 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.8\n",
+ "I0318 00:58:32.744249 2013098752 solver.cpp:204] Train net output #1: loss = 0.406096 (* 1 = 0.406096 loss)\n",
+ "I0318 00:58:32.744266 2013098752 solver.cpp:464] Iteration 3000, lr = 0.01\n",
+ "I0318 00:58:32.764135 2013098752 solver.cpp:266] Iteration 4000, Testing net (#0)\n",
+ "I0318 00:58:32.769110 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7848\n",
+ "I0318 00:58:32.769170 2013098752 solver.cpp:315] Test net output #1: loss = 0.590972 (* 1 = 0.590972 loss)\n",
+ "I0318 00:58:32.769223 2013098752 solver.cpp:189] Iteration 4000, loss = 0.54742\n",
+ "I0318 00:58:32.769242 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n",
+ "I0318 00:58:32.769255 2013098752 solver.cpp:204] Train net output #1: loss = 0.54742 (* 1 = 0.54742 loss)\n",
+ "I0318 00:58:32.769265 2013098752 solver.cpp:464] Iteration 4000, lr = 0.01\n",
+ "I0318 00:58:32.785846 2013098752 solver.cpp:266] Iteration 5000, Testing net (#0)\n",
+ "I0318 00:58:32.788722 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7696\n",
+ "I0318 00:58:32.788751 2013098752 solver.cpp:315] Test net output #1: loss = 0.593946 (* 1 = 0.593946 loss)\n",
+ "I0318 00:58:32.788811 2013098752 solver.cpp:189] Iteration 5000, loss = 0.72957\n",
+ "I0318 00:58:32.788833 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.5\n",
+ "I0318 00:58:32.788846 2013098752 solver.cpp:204] Train net output #1: loss = 0.729569 (* 1 = 0.729569 loss)\n",
+ "I0318 00:58:32.788856 2013098752 solver.cpp:464] Iteration 5000, lr = 0.001\n",
+ "I0318 00:58:32.804762 2013098752 solver.cpp:266] Iteration 6000, Testing net (#0)\n",
+ "I0318 00:58:32.808061 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7856\n",
+ "I0318 00:58:32.808112 2013098752 solver.cpp:315] Test net output #1: loss = 0.59028 (* 1 = 0.59028 loss)\n",
+ "I0318 00:58:32.808732 2013098752 solver.cpp:189] Iteration 6000, loss = 0.415444\n",
+ "I0318 00:58:32.808753 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n",
+ "I0318 00:58:32.808773 2013098752 solver.cpp:204] Train net output #1: loss = 0.415444 (* 1 = 0.415444 loss)\n",
+ "I0318 00:58:32.808786 2013098752 solver.cpp:464] Iteration 6000, lr = 0.001\n",
+ "I0318 00:58:32.827118 2013098752 solver.cpp:266] Iteration 7000, Testing net (#0)\n",
+ "I0318 00:58:32.831614 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7848\n",
+ "I0318 00:58:32.831657 2013098752 solver.cpp:315] Test net output #1: loss = 0.589454 (* 1 = 0.589454 loss)\n",
+ "I0318 00:58:32.831707 2013098752 solver.cpp:189] Iteration 7000, loss = 0.538038\n",
+ "I0318 00:58:32.831728 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.8\n",
+ "I0318 00:58:32.831745 2013098752 solver.cpp:204] Train net output #1: loss = 0.538037 (* 1 = 0.538037 loss)\n",
+ "I0318 00:58:32.831759 2013098752 solver.cpp:464] Iteration 7000, lr = 0.001\n",
+ "I0318 00:58:32.849634 2013098752 solver.cpp:266] Iteration 8000, Testing net (#0)\n",
+ "I0318 00:58:32.852712 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7796\n",
+ "I0318 00:58:32.852748 2013098752 solver.cpp:315] Test net output #1: loss = 0.589365 (* 1 = 0.589365 loss)\n",
+ "I0318 00:58:32.852792 2013098752 solver.cpp:189] Iteration 8000, loss = 0.684219\n",
+ "I0318 00:58:32.852840 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.5\n",
+ "I0318 00:58:32.852852 2013098752 solver.cpp:204] Train net output #1: loss = 0.684219 (* 1 = 0.684219 loss)\n",
+ "I0318 00:58:32.852861 2013098752 solver.cpp:464] Iteration 8000, lr = 0.001\n",
+ "I0318 00:58:32.868440 2013098752 solver.cpp:266] Iteration 9000, Testing net (#0)\n",
+ "I0318 00:58:32.871438 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7816\n",
+ "I0318 00:58:32.871461 2013098752 solver.cpp:315] Test net output #1: loss = 0.589656 (* 1 = 0.589656 loss)\n",
+ "I0318 00:58:32.872109 2013098752 solver.cpp:189] Iteration 9000, loss = 0.421879\n",
+ "I0318 00:58:32.872131 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n",
+ "I0318 00:58:32.872143 2013098752 solver.cpp:204] Train net output #1: loss = 0.421879 (* 1 = 0.421879 loss)\n",
+ "I0318 00:58:32.872153 2013098752 solver.cpp:464] Iteration 9000, lr = 0.001\n",
+ "I0318 00:58:32.889981 2013098752 solver.cpp:334] Snapshotting to examples/hdf5_classification/data/train_iter_10000.caffemodel\n",
+ "I0318 00:58:32.890224 2013098752 solver.cpp:342] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\n",
+ "I0318 00:58:32.890362 2013098752 solver.cpp:248] Iteration 10000, loss = 0.538933\n",
+ "I0318 00:58:32.890380 2013098752 solver.cpp:266] Iteration 10000, Testing net (#0)\n",
+ "I0318 00:58:32.893728 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.782\n",
+ "I0318 00:58:32.893757 2013098752 solver.cpp:315] Test net output #1: loss = 0.589366 (* 1 = 0.589366 loss)\n",
+ "I0318 00:58:32.893775 2013098752 solver.cpp:253] Optimization Done.\n",
+ "I0318 00:58:32.893786 2013098752 caffe.cpp:134] Optimization Done.\n"
+ ]
+ }
+ ],
+ "source": [
+ "!./build/tools/caffe train -solver examples/hdf5_classification/solver.prototxt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you look at output or the `logreg_auto_train.prototxt`, you'll see that the model is simple logistic regression.\n",
+ "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n",
+ "That network is given in `nonlinear_auto_train.prototxt`, and that's the only change made in `nonlinear_solver.prototxt` which we will now use.\n",
+ "\n",
+ "The final accuracy of the new network should be higher than logistic regression!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from caffe import layers as L\n",
+ "from caffe import params as P\n",
+ "\n",
+ "def nonlinear_net(hdf5, batch_size):\n",
+ " # one small nonlinearity, one leap for model kind\n",
+ " n = caffe.NetSpec()\n",
+ " n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n",
+ " # define a hidden layer of dimension 40\n",
+ " n.ip1 = L.InnerProduct(n.data, num_output=40, weight_filler=dict(type='xavier'))\n",
+ " # transform the output through the ReLU (rectified linear) non-linearity\n",
+ " n.relu1 = L.ReLU(n.ip1, in_place=True)\n",
+ " # score the (now non-linear) features\n",
+ " n.ip2 = L.InnerProduct(n.ip1, num_output=2, weight_filler=dict(type='xavier'))\n",
+ " # same accuracy and loss as before\n",
+ " n.accuracy = L.Accuracy(n.ip2, n.label)\n",
+ " n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n",
+ " return n.to_proto()\n",
+ " \n",
+ "with open('examples/hdf5_classification/nonlinear_auto_train.prototxt', 'w') as f:\n",
+ " f.write(str(nonlinear_net('examples/hdf5_classification/data/train.txt', 10)))\n",
+ " \n",
+ "with open('examples/hdf5_classification/nonlinear_auto_test.prototxt', 'w') as f:\n",
+ " f.write(str(nonlinear_net('examples/hdf5_classification/data/test.txt', 10)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 0.832\n",
+ "Accuracy: 0.832\n",
+ "Accuracy: 0.832\n",
+ "Accuracy: 0.831\n",
+ "1 loops, best of 3: 386 ms per loop\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%timeit\n",
+ "caffe.set_mode_cpu()\n",
+ "solver = caffe.get_solver('examples/hdf5_classification/nonlinear_solver.prototxt')\n",
+ "solver.solve()\n",
+ "\n",
+ "accuracy = 0\n",
+ "batch_size = solver.test_nets[0].blobs['data'].num\n",
+ "test_iters = int(len(Xt) / batch_size)\n",
+ "for i in range(test_iters):\n",
+ " solver.test_nets[0].forward()\n",
+ " accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+ "accuracy /= test_iters\n",
+ "\n",
+ "print(\"Accuracy: {:.3f}\".format(accuracy))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Do the same through the command line interface for detailed output on the model and solving."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "I0318 00:58:43.336922 2013098752 caffe.cpp:117] Use CPU.\n",
+ "I0318 00:58:43.654698 2013098752 caffe.cpp:121] Starting Optimization\n",
+ "I0318 00:58:43.654747 2013098752 solver.cpp:32] Initializing solver from parameters: \n",
+ "train_net: \"examples/hdf5_classification/nonlinear_auto_train.prototxt\"\n",
+ "test_net: \"examples/hdf5_classification/nonlinear_auto_test.prototxt\"\n",
+ "test_iter: 250\n",
+ "test_interval: 1000\n",
+ "base_lr: 0.01\n",
+ "display: 1000\n",
+ "max_iter: 10000\n",
+ "lr_policy: \"step\"\n",
+ "gamma: 0.1\n",
+ "momentum: 0.9\n",
+ "weight_decay: 0.0005\n",
+ "stepsize: 5000\n",
+ "snapshot: 10000\n",
+ "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n",
+ "solver_mode: CPU\n",
+ "I0318 00:58:43.654855 2013098752 solver.cpp:61] Creating training net from train_net file: examples/hdf5_classification/nonlinear_auto_train.prototxt\n",
+ "I0318 00:58:43.655004 2013098752 net.cpp:42] Initializing net from parameters: \n",
+ "state {\n",
+ " phase: TRAIN\n",
+ "}\n",
+ "layer {\n",
+ " name: \"data\"\n",
+ " type: \"HDF5Data\"\n",
+ " top: \"data\"\n",
+ " top: \"label\"\n",
+ " hdf5_data_param {\n",
+ " source: \"examples/hdf5_classification/data/train.txt\"\n",
+ " batch_size: 10\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"ip1\"\n",
+ " type: \"InnerProduct\"\n",
+ " bottom: \"data\"\n",
+ " top: \"ip1\"\n",
+ " inner_product_param {\n",
+ " num_output: 40\n",
+ " weight_filler {\n",
+ " type: \"xavier\"\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"relu1\"\n",
+ " type: \"ReLU\"\n",
+ " bottom: \"ip1\"\n",
+ " top: \"ip1\"\n",
+ "}\n",
+ "layer {\n",
+ " name: \"ip2\"\n",
+ " type: \"InnerProduct\"\n",
+ " bottom: \"ip1\"\n",
+ " top: \"ip2\"\n",
+ " inner_product_param {\n",
+ " num_output: 2\n",
+ " weight_filler {\n",
+ " type: \"xavier\"\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"accuracy\"\n",
+ " type: \"Accuracy\"\n",
+ " bottom: \"ip2\"\n",
+ " bottom: \"label\"\n",
+ " top: \"accuracy\"\n",
+ "}\n",
+ "layer {\n",
+ " name: \"loss\"\n",
+ " type: \"SoftmaxWithLoss\"\n",
+ " bottom: \"ip2\"\n",
+ " bottom: \"label\"\n",
+ " top: \"loss\"\n",
+ "}\n",
+ "I0318 00:58:43.655120 2013098752 layer_factory.hpp:74] Creating layer data\n",
+ "I0318 00:58:43.655139 2013098752 net.cpp:84] Creating Layer data\n",
+ "I0318 00:58:43.655264 2013098752 net.cpp:338] data -> data\n",
+ "I0318 00:58:43.655297 2013098752 net.cpp:338] data -> label\n",
+ "I0318 00:58:43.655310 2013098752 net.cpp:113] Setting up data\n",
+ "I0318 00:58:43.655318 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n",
+ "I0318 00:58:43.655365 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n",
+ "I0318 00:58:43.657317 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
+ "I0318 00:58:43.657342 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+ "I0318 00:58:43.657356 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
+ "I0318 00:58:43.657373 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
+ "I0318 00:58:43.657384 2013098752 net.cpp:380] label_data_1_split <- label\n",
+ "I0318 00:58:43.657395 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
+ "I0318 00:58:43.657407 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
+ "I0318 00:58:43.657418 2013098752 net.cpp:113] Setting up label_data_1_split\n",
+ "I0318 00:58:43.657426 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+ "I0318 00:58:43.657433 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+ "I0318 00:58:43.657441 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
+ "I0318 00:58:43.657451 2013098752 net.cpp:84] Creating Layer ip1\n",
+ "I0318 00:58:43.657459 2013098752 net.cpp:380] ip1 <- data\n",
+ "I0318 00:58:43.657467 2013098752 net.cpp:338] ip1 -> ip1\n",
+ "I0318 00:58:43.657479 2013098752 net.cpp:113] Setting up ip1\n",
+ "I0318 00:58:43.662454 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
+ "I0318 00:58:43.662477 2013098752 layer_factory.hpp:74] Creating layer relu1\n",
+ "I0318 00:58:43.662497 2013098752 net.cpp:84] Creating Layer relu1\n",
+ "I0318 00:58:43.662508 2013098752 net.cpp:380] relu1 <- ip1\n",
+ "I0318 00:58:43.662520 2013098752 net.cpp:327] relu1 -> ip1 (in-place)\n",
+ "I0318 00:58:43.662530 2013098752 net.cpp:113] Setting up relu1\n",
+ "I0318 00:58:43.662539 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
+ "I0318 00:58:43.662546 2013098752 layer_factory.hpp:74] Creating layer ip2\n",
+ "I0318 00:58:43.662555 2013098752 net.cpp:84] Creating Layer ip2\n",
+ "I0318 00:58:43.662562 2013098752 net.cpp:380] ip2 <- ip1\n",
+ "I0318 00:58:43.662571 2013098752 net.cpp:338] ip2 -> ip2\n",
+ "I0318 00:58:43.662580 2013098752 net.cpp:113] Setting up ip2\n",
+ "I0318 00:58:43.662595 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+ "I0318 00:58:43.662606 2013098752 layer_factory.hpp:74] Creating layer ip2_ip2_0_split\n",
+ "I0318 00:58:43.662654 2013098752 net.cpp:84] Creating Layer ip2_ip2_0_split\n",
+ "I0318 00:58:43.662665 2013098752 net.cpp:380] ip2_ip2_0_split <- ip2\n",
+ "I0318 00:58:43.662678 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_0\n",
+ "I0318 00:58:43.662689 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_1\n",
+ "I0318 00:58:43.662698 2013098752 net.cpp:113] Setting up ip2_ip2_0_split\n",
+ "I0318 00:58:43.662706 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+ "I0318 00:58:43.662714 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+ "I0318 00:58:43.662722 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
+ "I0318 00:58:43.662734 2013098752 net.cpp:84] Creating Layer accuracy\n",
+ "I0318 00:58:43.662740 2013098752 net.cpp:380] accuracy <- ip2_ip2_0_split_0\n",
+ "I0318 00:58:43.662749 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
+ "I0318 00:58:43.662756 2013098752 net.cpp:338] accuracy -> accuracy\n",
+ "I0318 00:58:43.662766 2013098752 net.cpp:113] Setting up accuracy\n",
+ "I0318 00:58:43.662818 2013098752 net.cpp:120] Top shape: (1)\n",
+ "I0318 00:58:43.662827 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+ "I0318 00:58:43.662839 2013098752 net.cpp:84] Creating Layer loss\n",
+ "I0318 00:58:43.662847 2013098752 net.cpp:380] loss <- ip2_ip2_0_split_1\n",
+ "I0318 00:58:43.662854 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
+ "I0318 00:58:43.662863 2013098752 net.cpp:338] loss -> loss\n",
+ "I0318 00:58:43.662873 2013098752 net.cpp:113] Setting up loss\n",
+ "I0318 00:58:43.662883 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+ "I0318 00:58:43.662901 2013098752 net.cpp:120] Top shape: (1)\n",
+ "I0318 00:58:43.662909 2013098752 net.cpp:122] with loss weight 1\n",
+ "I0318 00:58:43.662922 2013098752 net.cpp:167] loss needs backward computation.\n",
+ "I0318 00:58:43.662930 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
+ "I0318 00:58:43.662936 2013098752 net.cpp:167] ip2_ip2_0_split needs backward computation.\n",
+ "I0318 00:58:43.662942 2013098752 net.cpp:167] ip2 needs backward computation.\n",
+ "I0318 00:58:43.662976 2013098752 net.cpp:167] relu1 needs backward computation.\n",
+ "I0318 00:58:43.662988 2013098752 net.cpp:167] ip1 needs backward computation.\n",
+ "I0318 00:58:43.662997 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
+ "I0318 00:58:43.663003 2013098752 net.cpp:169] data does not need backward computation.\n",
+ "I0318 00:58:43.663009 2013098752 net.cpp:205] This network produces output accuracy\n",
+ "I0318 00:58:43.663017 2013098752 net.cpp:205] This network produces output loss\n",
+ "I0318 00:58:43.663028 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
+ "I0318 00:58:43.663035 2013098752 net.cpp:217] Network initialization done.\n",
+ "I0318 00:58:43.663041 2013098752 net.cpp:218] Memory required for data: 3728\n",
+ "I0318 00:58:43.663158 2013098752 solver.cpp:154] Creating test net (#0) specified by test_net file: examples/hdf5_classification/nonlinear_auto_test.prototxt\n",
+ "I0318 00:58:43.663179 2013098752 net.cpp:42] Initializing net from parameters: \n",
+ "state {\n",
+ " phase: TEST\n",
+ "}\n",
+ "layer {\n",
+ " name: \"data\"\n",
+ " type: \"HDF5Data\"\n",
+ " top: \"data\"\n",
+ " top: \"label\"\n",
+ " hdf5_data_param {\n",
+ " source: \"examples/hdf5_classification/data/test.txt\"\n",
+ " batch_size: 10\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"ip1\"\n",
+ " type: \"InnerProduct\"\n",
+ " bottom: \"data\"\n",
+ " top: \"ip1\"\n",
+ " inner_product_param {\n",
+ " num_output: 40\n",
+ " weight_filler {\n",
+ " type: \"xavier\"\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"relu1\"\n",
+ " type: \"ReLU\"\n",
+ " bottom: \"ip1\"\n",
+ " top: \"ip1\"\n",
+ "}\n",
+ "layer {\n",
+ " name: \"ip2\"\n",
+ " type: \"InnerProduct\"\n",
+ " bottom: \"ip1\"\n",
+ " top: \"ip2\"\n",
+ " inner_product_param {\n",
+ " num_output: 2\n",
+ " weight_filler {\n",
+ " type: \"xavier\"\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"accuracy\"\n",
+ " type: \"Accuracy\"\n",
+ " bottom: \"ip2\"\n",
+ " bottom: \"label\"\n",
+ " top: \"accuracy\"\n",
+ "}\n",
+ "layer {\n",
+ " name: \"loss\"\n",
+ " type: \"SoftmaxWithLoss\"\n",
+ " bottom: \"ip2\"\n",
+ " bottom: \"label\"\n",
+ " top: \"loss\"\n",
+ "}\n",
+ "I0318 00:58:43.663349 2013098752 layer_factory.hpp:74] Creating layer data\n",
+ "I0318 00:58:43.663365 2013098752 net.cpp:84] Creating Layer data\n",
+ "I0318 00:58:43.663373 2013098752 net.cpp:338] data -> data\n",
+ "I0318 00:58:43.663385 2013098752 net.cpp:338] data -> label\n",
+ "I0318 00:58:43.663396 2013098752 net.cpp:113] Setting up data\n",
+ "I0318 00:58:43.663422 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n",
+ "I0318 00:58:43.663457 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n",
+ "I0318 00:58:43.664719 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
+ "I0318 00:58:43.664739 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+ "I0318 00:58:43.664754 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
+ "I0318 00:58:43.664772 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
+ "I0318 00:58:43.664783 2013098752 net.cpp:380] label_data_1_split <- label\n",
+ "I0318 00:58:43.664791 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
+ "I0318 00:58:43.664803 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
+ "I0318 00:58:43.664813 2013098752 net.cpp:113] Setting up label_data_1_split\n",
+ "I0318 00:58:43.664822 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+ "I0318 00:58:43.664829 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+ "I0318 00:58:43.664837 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
+ "I0318 00:58:43.664846 2013098752 net.cpp:84] Creating Layer ip1\n",
+ "I0318 00:58:43.664854 2013098752 net.cpp:380] ip1 <- data\n",
+ "I0318 00:58:43.664862 2013098752 net.cpp:338] ip1 -> ip1\n",
+ "I0318 00:58:43.664875 2013098752 net.cpp:113] Setting up ip1\n",
+ "I0318 00:58:43.664901 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
+ "I0318 00:58:43.664924 2013098752 layer_factory.hpp:74] Creating layer relu1\n",
+ "I0318 00:58:43.664945 2013098752 net.cpp:84] Creating Layer relu1\n",
+ "I0318 00:58:43.664958 2013098752 net.cpp:380] relu1 <- ip1\n",
+ "I0318 00:58:43.664966 2013098752 net.cpp:327] relu1 -> ip1 (in-place)\n",
+ "I0318 00:58:43.664975 2013098752 net.cpp:113] Setting up relu1\n",
+ "I0318 00:58:43.664983 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
+ "I0318 00:58:43.664990 2013098752 layer_factory.hpp:74] Creating layer ip2\n",
+ "I0318 00:58:43.665000 2013098752 net.cpp:84] Creating Layer ip2\n",
+ "I0318 00:58:43.665006 2013098752 net.cpp:380] ip2 <- ip1\n",
+ "I0318 00:58:43.665015 2013098752 net.cpp:338] ip2 -> ip2\n",
+ "I0318 00:58:43.665030 2013098752 net.cpp:113] Setting up ip2\n",
+ "I0318 00:58:43.665052 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+ "I0318 00:58:43.665066 2013098752 layer_factory.hpp:74] Creating layer ip2_ip2_0_split\n",
+ "I0318 00:58:43.665077 2013098752 net.cpp:84] Creating Layer ip2_ip2_0_split\n",
+ "I0318 00:58:43.665086 2013098752 net.cpp:380] ip2_ip2_0_split <- ip2\n",
+ "I0318 00:58:43.665093 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_0\n",
+ "I0318 00:58:43.665103 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_1\n",
+ "I0318 00:58:43.665113 2013098752 net.cpp:113] Setting up ip2_ip2_0_split\n",
+ "I0318 00:58:43.665122 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+ "I0318 00:58:43.665128 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+ "I0318 00:58:43.665137 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
+ "I0318 00:58:43.665144 2013098752 net.cpp:84] Creating Layer accuracy\n",
+ "I0318 00:58:43.665153 2013098752 net.cpp:380] accuracy <- ip2_ip2_0_split_0\n",
+ "I0318 00:58:43.665168 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
+ "I0318 00:58:43.665180 2013098752 net.cpp:338] accuracy -> accuracy\n",
+ "I0318 00:58:43.665192 2013098752 net.cpp:113] Setting up accuracy\n",
+ "I0318 00:58:43.665200 2013098752 net.cpp:120] Top shape: (1)\n",
+ "I0318 00:58:43.665207 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+ "I0318 00:58:43.665216 2013098752 net.cpp:84] Creating Layer loss\n",
+ "I0318 00:58:43.665223 2013098752 net.cpp:380] loss <- ip2_ip2_0_split_1\n",
+ "I0318 00:58:43.665230 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
+ "I0318 00:58:43.665241 2013098752 net.cpp:338] loss -> loss\n",
+ "I0318 00:58:43.665251 2013098752 net.cpp:113] Setting up loss\n",
+ "I0318 00:58:43.665259 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+ "I0318 00:58:43.665273 2013098752 net.cpp:120] Top shape: (1)\n",
+ "I0318 00:58:43.665282 2013098752 net.cpp:122] with loss weight 1\n",
+ "I0318 00:58:43.665290 2013098752 net.cpp:167] loss needs backward computation.\n",
+ "I0318 00:58:43.665338 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
+ "I0318 00:58:43.665351 2013098752 net.cpp:167] ip2_ip2_0_split needs backward computation.\n",
+ "I0318 00:58:43.665380 2013098752 net.cpp:167] ip2 needs backward computation.\n",
+ "I0318 00:58:43.665387 2013098752 net.cpp:167] relu1 needs backward computation.\n",
+ "I0318 00:58:43.665393 2013098752 net.cpp:167] ip1 needs backward computation.\n",
+ "I0318 00:58:43.665400 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
+ "I0318 00:58:43.665407 2013098752 net.cpp:169] data does not need backward computation.\n",
+ "I0318 00:58:43.665415 2013098752 net.cpp:205] This network produces output accuracy\n",
+ "I0318 00:58:43.665421 2013098752 net.cpp:205] This network produces output loss\n",
+ "I0318 00:58:43.665431 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
+ "I0318 00:58:43.665441 2013098752 net.cpp:217] Network initialization done.\n",
+ "I0318 00:58:43.665446 2013098752 net.cpp:218] Memory required for data: 3728\n",
+ "I0318 00:58:43.665534 2013098752 solver.cpp:42] Solver scaffolding done.\n",
+ "I0318 00:58:43.665568 2013098752 solver.cpp:222] Solving \n",
+ "I0318 00:58:43.665577 2013098752 solver.cpp:223] Learning Rate Policy: step\n",
+ "I0318 00:58:43.665586 2013098752 solver.cpp:266] Iteration 0, Testing net (#0)\n",
+ "I0318 00:58:43.683938 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.5184\n",
+ "I0318 00:58:43.683981 2013098752 solver.cpp:315] Test net output #1: loss = 0.716141 (* 1 = 0.716141 loss)\n",
+ "I0318 00:58:43.684236 2013098752 solver.cpp:189] Iteration 0, loss = 0.764954\n",
+ "I0318 00:58:43.684267 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.5\n",
+ "I0318 00:58:43.684285 2013098752 solver.cpp:204] Train net output #1: loss = 0.764954 (* 1 = 0.764954 loss)\n",
+ "I0318 00:58:43.684305 2013098752 solver.cpp:464] Iteration 0, lr = 0.01\n",
+ "I0318 00:58:43.714700 2013098752 solver.cpp:266] Iteration 1000, Testing net (#0)\n",
+ "I0318 00:58:43.721762 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8168\n",
+ "I0318 00:58:43.721818 2013098752 solver.cpp:315] Test net output #1: loss = 0.434918 (* 1 = 0.434918 loss)\n",
+ "I0318 00:58:43.721899 2013098752 solver.cpp:189] Iteration 1000, loss = 0.282425\n",
+ "I0318 00:58:43.721917 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n",
+ "I0318 00:58:43.721932 2013098752 solver.cpp:204] Train net output #1: loss = 0.282426 (* 1 = 0.282426 loss)\n",
+ "I0318 00:58:43.721942 2013098752 solver.cpp:464] Iteration 1000, lr = 0.01\n",
+ "I0318 00:58:43.750509 2013098752 solver.cpp:266] Iteration 2000, Testing net (#0)\n",
+ "I0318 00:58:43.754590 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8224\n",
+ "I0318 00:58:43.754621 2013098752 solver.cpp:315] Test net output #1: loss = 0.416874 (* 1 = 0.416874 loss)\n",
+ "I0318 00:58:43.754660 2013098752 solver.cpp:189] Iteration 2000, loss = 0.51988\n",
+ "I0318 00:58:43.754672 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n",
+ "I0318 00:58:43.754683 2013098752 solver.cpp:204] Train net output #1: loss = 0.51988 (* 1 = 0.51988 loss)\n",
+ "I0318 00:58:43.754690 2013098752 solver.cpp:464] Iteration 2000, lr = 0.01\n",
+ "I0318 00:58:43.782609 2013098752 solver.cpp:266] Iteration 3000, Testing net (#0)\n",
+ "I0318 00:58:43.789728 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8176\n",
+ "I0318 00:58:43.789777 2013098752 solver.cpp:315] Test net output #1: loss = 0.415907 (* 1 = 0.415907 loss)\n",
+ "I0318 00:58:43.790487 2013098752 solver.cpp:189] Iteration 3000, loss = 0.5093\n",
+ "I0318 00:58:43.790510 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n",
+ "I0318 00:58:43.790530 2013098752 solver.cpp:204] Train net output #1: loss = 0.509301 (* 1 = 0.509301 loss)\n",
+ "I0318 00:58:43.790544 2013098752 solver.cpp:464] Iteration 3000, lr = 0.01\n",
+ "I0318 00:58:43.817451 2013098752 solver.cpp:266] Iteration 4000, Testing net (#0)\n",
+ "I0318 00:58:43.821740 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8252\n",
+ "I0318 00:58:43.821770 2013098752 solver.cpp:315] Test net output #1: loss = 0.409124 (* 1 = 0.409124 loss)\n",
+ "I0318 00:58:43.821822 2013098752 solver.cpp:189] Iteration 4000, loss = 0.284815\n",
+ "I0318 00:58:43.821835 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n",
+ "I0318 00:58:43.821846 2013098752 solver.cpp:204] Train net output #1: loss = 0.284815 (* 1 = 0.284815 loss)\n",
+ "I0318 00:58:43.821890 2013098752 solver.cpp:464] Iteration 4000, lr = 0.01\n",
+ "I0318 00:58:43.847015 2013098752 solver.cpp:266] Iteration 5000, Testing net (#0)\n",
+ "I0318 00:58:43.852102 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8256\n",
+ "I0318 00:58:43.852145 2013098752 solver.cpp:315] Test net output #1: loss = 0.404445 (* 1 = 0.404445 loss)\n",
+ "I0318 00:58:43.852188 2013098752 solver.cpp:189] Iteration 5000, loss = 0.511566\n",
+ "I0318 00:58:43.852200 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n",
+ "I0318 00:58:43.852210 2013098752 solver.cpp:204] Train net output #1: loss = 0.511566 (* 1 = 0.511566 loss)\n",
+ "I0318 00:58:43.852219 2013098752 solver.cpp:464] Iteration 5000, lr = 0.001\n",
+ "I0318 00:58:43.876060 2013098752 solver.cpp:266] Iteration 6000, Testing net (#0)\n",
+ "I0318 00:58:43.880080 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8328\n",
+ "I0318 00:58:43.880105 2013098752 solver.cpp:315] Test net output #1: loss = 0.396847 (* 1 = 0.396847 loss)\n",
+ "I0318 00:58:43.880700 2013098752 solver.cpp:189] Iteration 6000, loss = 0.397858\n",
+ "I0318 00:58:43.880718 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n",
+ "I0318 00:58:43.880729 2013098752 solver.cpp:204] Train net output #1: loss = 0.397858 (* 1 = 0.397858 loss)\n",
+ "I0318 00:58:43.880738 2013098752 solver.cpp:464] Iteration 6000, lr = 0.001\n",
+ "I0318 00:58:43.913795 2013098752 solver.cpp:266] Iteration 7000, Testing net (#0)\n",
+ "I0318 00:58:43.917851 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8316\n",
+ "I0318 00:58:43.917876 2013098752 solver.cpp:315] Test net output #1: loss = 0.398135 (* 1 = 0.398135 loss)\n",
+ "I0318 00:58:43.917956 2013098752 solver.cpp:189] Iteration 7000, loss = 0.243849\n",
+ "I0318 00:58:43.917971 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n",
+ "I0318 00:58:43.917989 2013098752 solver.cpp:204] Train net output #1: loss = 0.243849 (* 1 = 0.243849 loss)\n",
+ "I0318 00:58:43.918002 2013098752 solver.cpp:464] Iteration 7000, lr = 0.001\n",
+ "I0318 00:58:43.943681 2013098752 solver.cpp:266] Iteration 8000, Testing net (#0)\n",
+ "I0318 00:58:43.947589 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8312\n",
+ "I0318 00:58:43.947615 2013098752 solver.cpp:315] Test net output #1: loss = 0.394763 (* 1 = 0.394763 loss)\n",
+ "I0318 00:58:43.947651 2013098752 solver.cpp:189] Iteration 8000, loss = 0.513399\n",
+ "I0318 00:58:43.947664 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n",
+ "I0318 00:58:43.947674 2013098752 solver.cpp:204] Train net output #1: loss = 0.513399 (* 1 = 0.513399 loss)\n",
+ "I0318 00:58:43.947682 2013098752 solver.cpp:464] Iteration 8000, lr = 0.001\n",
+ "I0318 00:58:43.973080 2013098752 solver.cpp:266] Iteration 9000, Testing net (#0)\n",
+ "I0318 00:58:43.977033 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.834\n",
+ "I0318 00:58:43.977056 2013098752 solver.cpp:315] Test net output #1: loss = 0.395663 (* 1 = 0.395663 loss)\n",
+ "I0318 00:58:43.977710 2013098752 solver.cpp:189] Iteration 9000, loss = 0.399341\n",
+ "I0318 00:58:43.977735 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n",
+ "I0318 00:58:43.977746 2013098752 solver.cpp:204] Train net output #1: loss = 0.399342 (* 1 = 0.399342 loss)\n",
+ "I0318 00:58:43.977756 2013098752 solver.cpp:464] Iteration 9000, lr = 0.001\n",
+ "I0318 00:58:44.003437 2013098752 solver.cpp:334] Snapshotting to examples/hdf5_classification/data/train_iter_10000.caffemodel\n",
+ "I0318 00:58:44.003702 2013098752 solver.cpp:342] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\n",
+ "I0318 00:58:44.003850 2013098752 solver.cpp:248] Iteration 10000, loss = 0.244639\n",
+ "I0318 00:58:44.003871 2013098752 solver.cpp:266] Iteration 10000, Testing net (#0)\n",
+ "I0318 00:58:44.008216 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8308\n",
+ "I0318 00:58:44.008252 2013098752 solver.cpp:315] Test net output #1: loss = 0.397291 (* 1 = 0.397291 loss)\n",
+ "I0318 00:58:44.008262 2013098752 solver.cpp:253] Optimization Done.\n",
+ "I0318 00:58:44.008270 2013098752 caffe.cpp:134] Optimization Done.\n"
+ ]
+ }
+ ],
+ "source": [
+ "!./build/tools/caffe train -solver examples/hdf5_classification/nonlinear_solver.prototxt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n",
+ "shutil.rmtree(dirname)"
+ ]
+ }
+ ],
+ "metadata": {
+ "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.",
+ "example_name": "Off-the-shelf SGD for classification",
+ "include_in_docs": true,
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.9"
+ },
+ "priority": 3
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
--- /dev/null
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Fine-tuning a Pretrained Network for Style Recognition\n",
+ "\n",
+ "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network and fine-tune the parameters on your custom data.\n",
+ "\n",
+ "The upside of such approach is that, since pre-trained networks are learned on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful feature that you can treat as a black box. On top of that, only a few layers will be needed to obtain a very good performance of the data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "First, we will need to prepare the data. This involves the following parts:\n",
+ "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n",
+ "(2) Download a subset of the overall Flickr style dataset for this demo.\n",
+ "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "os.chdir('..')\n",
+ "import sys\n",
+ "sys.path.insert(0, './python')\n",
+ "\n",
+ "import caffe\n",
+ "import numpy as np\n",
+ "from pylab import *\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# This downloads the ilsvrc auxiliary data (mean file, etc),\n",
+ "# and a subset of 2000 images for the style recognition task.\n",
+ "!data/ilsvrc12/get_ilsvrc_aux.sh\n",
+ "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n",
+ "!python examples/finetune_flickr_style/assemble_data.py \\\n",
+ " --workers=-1 --images=2000 --seed=1701 --label=5"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's show what is the difference between the fine-tuning network and the original caffe model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1c1\r\n",
+ "< name: \"CaffeNet\"\r\n",
+ "---\r\n",
+ "> name: \"FlickrStyleCaffeNet\"\r\n",
+ "4c4\r\n",
+ "< type: \"Data\"\r\n",
+ "---\r\n",
+ "> type: \"ImageData\"\r\n",
+ "15,26c15,19\r\n",
+ "< # mean pixel / channel-wise mean instead of mean image\r\n",
+ "< # transform_param {\r\n",
+ "< # crop_size: 227\r\n",
+ "< # mean_value: 104\r\n",
+ "< # mean_value: 117\r\n",
+ "< # mean_value: 123\r\n",
+ "< # mirror: true\r\n",
+ "< # }\r\n",
+ "< data_param {\r\n",
+ "< source: \"examples/imagenet/ilsvrc12_train_lmdb\"\r\n",
+ "< batch_size: 256\r\n",
+ "< backend: LMDB\r\n",
+ "---\r\n",
+ "> image_data_param {\r\n",
+ "> source: \"data/flickr_style/train.txt\"\r\n",
+ "> batch_size: 50\r\n",
+ "> new_height: 256\r\n",
+ "> new_width: 256\r\n",
+ "31c24\r\n",
+ "< type: \"Data\"\r\n",
+ "---\r\n",
+ "> type: \"ImageData\"\r\n",
+ "42,51c35,36\r\n",
+ "< # mean pixel / channel-wise mean instead of mean image\r\n",
+ "< # transform_param {\r\n",
+ "< # crop_size: 227\r\n",
+ "< # mean_value: 104\r\n",
+ "< # mean_value: 117\r\n",
+ "< # mean_value: 123\r\n",
+ "< # mirror: true\r\n",
+ "< # }\r\n",
+ "< data_param {\r\n",
+ "< source: \"examples/imagenet/ilsvrc12_val_lmdb\"\r\n",
+ "---\r\n",
+ "> image_data_param {\r\n",
+ "> source: \"data/flickr_style/test.txt\"\r\n",
+ "53c38,39\r\n",
+ "< backend: LMDB\r\n",
+ "---\r\n",
+ "> new_height: 256\r\n",
+ "> new_width: 256\r\n",
+ "323a310\r\n",
+ "> # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer\r\n",
+ "360c347\r\n",
+ "< name: \"fc8\"\r\n",
+ "---\r\n",
+ "> name: \"fc8_flickr\"\r\n",
+ "363c350,351\r\n",
+ "< top: \"fc8\"\r\n",
+ "---\r\n",
+ "> top: \"fc8_flickr\"\r\n",
+ "> # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained\r\n",
+ "365c353\r\n",
+ "< lr_mult: 1\r\n",
+ "---\r\n",
+ "> lr_mult: 10\r\n",
+ "369c357\r\n",
+ "< lr_mult: 2\r\n",
+ "---\r\n",
+ "> lr_mult: 20\r\n",
+ "373c361\r\n",
+ "< num_output: 1000\r\n",
+ "---\r\n",
+ "> num_output: 20\r\n",
+ "384a373,379\r\n",
+ "> name: \"loss\"\r\n",
+ "> type: \"SoftmaxWithLoss\"\r\n",
+ "> bottom: \"fc8_flickr\"\r\n",
+ "> bottom: \"label\"\r\n",
+ "> top: \"loss\"\r\n",
+ "> }\r\n",
+ "> layer {\r\n",
+ "387c382\r\n",
+ "< bottom: \"fc8\"\r\n",
+ "---\r\n",
+ "> bottom: \"fc8_flickr\"\r\n",
+ "393,399d387\r\n",
+ "< }\r\n",
+ "< layer {\r\n",
+ "< name: \"loss\"\r\n",
+ "< type: \"SoftmaxWithLoss\"\r\n",
+ "< bottom: \"fc8\"\r\n",
+ "< bottom: \"label\"\r\n",
+ "< top: \"loss\"\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "!diff models/bvlc_reference_caffenet/train_val.prototxt models/finetune_flickr_style/train_val.prototxt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For your record, if you want to train the network in pure C++ tools, here is the command:\n",
+ "\n",
+ "<code>\n",
+ "build/tools/caffe train \\\n",
+ " -solver models/finetune_flickr_style/solver.prototxt \\\n",
+ " -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n",
+ " -gpu 0\n",
+ "</code>\n",
+ "\n",
+ "However, we will train using Python in this example."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "iter 0, finetune_loss=3.360094, scratch_loss=3.136188\n",
+ "iter 10, finetune_loss=2.672608, scratch_loss=9.736364\n",
+ "iter 20, finetune_loss=2.071996, scratch_loss=2.250404\n",
+ "iter 30, finetune_loss=1.758295, scratch_loss=2.049553\n",
+ "iter 40, finetune_loss=1.533391, scratch_loss=1.941318\n",
+ "iter 50, finetune_loss=1.561658, scratch_loss=1.839706\n",
+ "iter 60, finetune_loss=1.461696, scratch_loss=1.880035\n",
+ "iter 70, finetune_loss=1.267941, scratch_loss=1.719161\n",
+ "iter 80, finetune_loss=1.192778, scratch_loss=1.627453\n",
+ "iter 90, finetune_loss=1.541176, scratch_loss=1.822061\n",
+ "iter 100, finetune_loss=1.029039, scratch_loss=1.654087\n",
+ "iter 110, finetune_loss=1.138547, scratch_loss=1.735837\n",
+ "iter 120, finetune_loss=0.917412, scratch_loss=1.851918\n",
+ "iter 130, finetune_loss=0.971519, scratch_loss=1.801927\n",
+ "iter 140, finetune_loss=0.868252, scratch_loss=1.745545\n",
+ "iter 150, finetune_loss=0.790020, scratch_loss=1.844925\n",
+ "iter 160, finetune_loss=1.092668, scratch_loss=1.695591\n",
+ "iter 170, finetune_loss=1.055344, scratch_loss=1.661715\n",
+ "iter 180, finetune_loss=0.969769, scratch_loss=1.823639\n",
+ "iter 190, finetune_loss=0.780566, scratch_loss=1.820862\n",
+ "done\n"
+ ]
+ }
+ ],
+ "source": [
+ "niter = 200\n",
+ "# losses will also be stored in the log\n",
+ "train_loss = np.zeros(niter)\n",
+ "scratch_train_loss = np.zeros(niter)\n",
+ "\n",
+ "caffe.set_device(0)\n",
+ "caffe.set_mode_gpu()\n",
+ "# We create a solver that fine-tunes from a previously trained network.\n",
+ "solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
+ "solver.net.copy_from('models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
+ "# For reference, we also create a solver that does no finetuning.\n",
+ "scratch_solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
+ "\n",
+ "# We run the solver for niter times, and record the training loss.\n",
+ "for it in range(niter):\n",
+ " solver.step(1) # SGD by Caffe\n",
+ " scratch_solver.step(1)\n",
+ " # store the train loss\n",
+ " train_loss[it] = solver.net.blobs['loss'].data\n",
+ " scratch_train_loss[it] = scratch_solver.net.blobs['loss'].data\n",
+ " if it % 10 == 0:\n",
+ " print 'iter %d, finetune_loss=%f, scratch_loss=%f' % (it, train_loss[it], scratch_train_loss[it])\n",
+ "print 'done'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's look at the training loss produced by the two training procedures respectively."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x7fbb36f0ad50>,\n",
+ " <matplotlib.lines.Line2D at 0x7fbb36f0afd0>]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
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+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fbb37f20990>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot(np.vstack([train_loss, scratch_train_loss]).T)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Notice how the fine-tuning procedure produces a more smooth loss function change, and ends up at a better loss. A closer look at small values, clipping to avoid showing too large loss during training:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x7fbb347a8310>,\n",
+ " <matplotlib.lines.Line2D at 0x7fbb347a8590>]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
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+ "h9mydYjlFuDFNtc6lr3juRhYkfcXkuacHymrfw7iDr5ooZR11/qS+n5gR97fATw42ObMHRHxHfBn\n",
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+ "ZUSMAmuBZyStaj4Z6Xc3x3maCsTPse3uLWApsIK0JurrXa51LNuQtBD4BHghIv5uPjeT/jmIBP8b\n",
+ "MNz09TAVr7gy10TEmfznJGnx4DHgnKTFAJJup/iK75Z0il9rf70rH7MOIuJ8ZMA7XHlk4FgWIGke\n",
+ "Kbm/HxG78+FS+ucgEvz/hVKS5pMKpfYM4HNrQdICSbfk/ZuB1cARUgw35Ms2kKaHsOI6xW8PsE7S\n",
+ "fElLgWXAvgraN2fkBNTwEKl/gmPZkyQB24GjEfFG06lS+mffl+yLDoVS/f7cGhkCPk39gJuADyJi\n",
+ "r6T9wC5JTwATwKPVNXF2k7QTuA+4TdIp4GVgG23iFxFHJe0CjgKXgafznanRNpZbgHFJK0iPCk4C\n",
+ "jSJIx7K3lcDjwGFJB/KxTZTUP13oZGZWU16T1cysppzgzcxqygnezKymnODNzGrKCd7MrKac4M3M\n",
+ "asoJ3sysppzgzcxq6j+vUsbacqJa4gAAAABJRU5ErkJggg==\n"
+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fbb37f207d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot(np.vstack([train_loss, scratch_train_loss]).clip(0, 4).T)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's take a look at the testing accuracy after running 200 iterations. Note that we are running a classification task of 5 classes, thus a chance accuracy is 20%. As we will reasonably expect, the finetuning result will be much better than the one from training from scratch. Let's see."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy for fine-tuning: 0.570000001788\n",
+ "Accuracy for training from scratch: 0.224000000954\n"
+ ]
+ }
+ ],
+ "source": [
+ "test_iters = 10\n",
+ "accuracy = 0\n",
+ "scratch_accuracy = 0\n",
+ "for it in arange(test_iters):\n",
+ " solver.test_nets[0].forward()\n",
+ " accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+ " scratch_solver.test_nets[0].forward()\n",
+ " scratch_accuracy += scratch_solver.test_nets[0].blobs['accuracy'].data\n",
+ "accuracy /= test_iters\n",
+ "scratch_accuracy /= test_iters\n",
+ "print 'Accuracy for fine-tuning:', accuracy\n",
+ "print 'Accuracy for training from scratch:', scratch_accuracy"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Huzzah! So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n",
+ "\n",
+ "http://demo.vislab.berkeleyvision.org/"
+ ]
+ }
+ ],
+ "metadata": {
+ "description": "Fine-tune the ImageNet-trained CaffeNet on new data.",
+ "example_name": "Fine-tuning for Style Recognition",
+ "include_in_docs": true,
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.9"
+ },
+ "priority": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
if(UNIX OR APPLE)
# Funny command to make tutorials work
# TODO: remove in future as soon as naming is standartaized everywhere
- set(__outname ${PROJECT_BINARY_DIR}/examples/${folder}/${name}${CAffe_POSTFIX})
+ set(__outname ${PROJECT_BINARY_DIR}/examples/${folder}/${name}${Caffe_POSTFIX})
add_custom_command(TARGET ${name} POST_BUILD
COMMAND ln -sf "${__outname}" "${__outname}.bin")
endif()
# N.B. input image must be in CIFAR-10 format
# as described at http://www.cs.toronto.edu/~kriz/cifar.html
input: "data"
-input_dim: 1
-input_dim: 3
-input_dim: 32
-input_dim: 32
+input_shape {
+ dim: 1
+ dim: 3
+ dim: 32
+ dim: 32
+}
layer {
name: "conv1"
type: "Convolution"
--- /dev/null
+# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10
+# then another factor of 10 after 10 more epochs (5000 iters)
+
+# The train/test net protocol buffer definition
+net: "examples/cifar10/cifar10_full_sigmoid_train_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 10
+# Carry out testing every 1000 training iterations.
+test_interval: 1000
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.001
+momentum: 0.9
+#weight_decay: 0.004
+# The learning rate policy
+lr_policy: "step"
+gamma: 1
+stepsize: 5000
+# Display every 200 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 60000
+# snapshot intermediate results
+snapshot: 10000
+snapshot_prefix: "examples/cifar10_full_sigmoid"
+# solver mode: CPU or GPU
+solver_mode: GPU
--- /dev/null
+# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10
+# then another factor of 10 after 10 more epochs (5000 iters)
+
+# The train/test net protocol buffer definition
+net: "examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 10
+# Carry out testing every 1000 training iterations.
+test_interval: 1000
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.001
+momentum: 0.9
+#weight_decay: 0.004
+# The learning rate policy
+lr_policy: "step"
+gamma: 1
+stepsize: 5000
+# Display every 200 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 60000
+# snapshot intermediate results
+snapshot: 10000
+snapshot_prefix: "examples/cifar10_full_sigmoid_bn"
+# solver mode: CPU or GPU
+solver_mode: GPU
--- /dev/null
+name: "CIFAR10_full"
+layer {
+ name: "cifar"
+ type: "Data"
+ top: "data"
+ top: "label"
+ include {
+ phase: TRAIN
+ }
+ transform_param {
+ mean_file: "examples/cifar10/mean.binaryproto"
+ }
+ data_param {
+ source: "examples/cifar10/cifar10_train_lmdb"
+ batch_size: 111
+ backend: LMDB
+ }
+}
+layer {
+ name: "cifar"
+ type: "Data"
+ top: "data"
+ top: "label"
+ include {
+ phase: TEST
+ }
+ transform_param {
+ mean_file: "examples/cifar10/mean.binaryproto"
+ }
+ data_param {
+ source: "examples/cifar10/cifar10_test_lmdb"
+ batch_size: 1000
+ backend: LMDB
+ }
+}
+layer {
+ name: "conv1"
+ type: "Convolution"
+ bottom: "data"
+ top: "conv1"
+ param {
+ lr_mult: 1
+ }
+ param {
+ lr_mult: 2
+ }
+ convolution_param {
+ num_output: 32
+ pad: 2
+ kernel_size: 5
+ stride: 1
+ weight_filler {
+ type: "gaussian"
+ std: 0.0001
+ }
+ bias_filler {
+ type: "constant"
+ }
+ }
+}
+layer {
+ name: "pool1"
+ type: "Pooling"
+ bottom: "conv1"
+ top: "pool1"
+ pooling_param {
+ pool: MAX
+ kernel_size: 3
+ stride: 2
+ }
+}
+
+
+
+layer {
+ name: "Sigmoid1"
+ type: "Sigmoid"
+ bottom: "pool1"
+ top: "Sigmoid1"
+}
+
+layer {
+ name: "conv2"
+ type: "Convolution"
+ bottom: "Sigmoid1"
+ top: "conv2"
+ param {
+ lr_mult: 1
+ }
+ param {
+ lr_mult: 2
+ }
+ convolution_param {
+ num_output: 32
+ pad: 2
+ kernel_size: 5
+ stride: 1
+ weight_filler {
+ type: "gaussian"
+ std: 0.01
+ }
+ bias_filler {
+ type: "constant"
+ }
+ }
+}
+
+
+layer {
+ name: "Sigmoid2"
+ type: "Sigmoid"
+ bottom: "conv2"
+ top: "Sigmoid2"
+}
+layer {
+ name: "pool2"
+ type: "Pooling"
+ bottom: "Sigmoid2"
+ top: "pool2"
+ pooling_param {
+ pool: AVE
+ kernel_size: 3
+ stride: 2
+ }
+}
+layer {
+ name: "conv3"
+ type: "Convolution"
+ bottom: "pool2"
+ top: "conv3"
+ convolution_param {
+ num_output: 64
+ pad: 2
+ kernel_size: 5
+ stride: 1
+ weight_filler {
+ type: "gaussian"
+ std: 0.01
+ }
+ bias_filler {
+ type: "constant"
+ }
+ }
+ param {
+ lr_mult: 1
+ }
+ param {
+ lr_mult: 1
+ }
+
+}
+
+layer {
+ name: "Sigmoid3"
+ type: "Sigmoid"
+ bottom: "conv3"
+ top: "Sigmoid3"
+}
+
+layer {
+ name: "pool3"
+ type: "Pooling"
+ bottom: "Sigmoid3"
+ top: "pool3"
+ pooling_param {
+ pool: AVE
+ kernel_size: 3
+ stride: 2
+ }
+}
+
+layer {
+ name: "ip1"
+ type: "InnerProduct"
+ bottom: "pool3"
+ top: "ip1"
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ param {
+ lr_mult: 2
+ decay_mult: 0
+ }
+ inner_product_param {
+ num_output: 10
+ weight_filler {
+ type: "gaussian"
+ std: 0.01
+ }
+ bias_filler {
+ type: "constant"
+ }
+ }
+}
+layer {
+ name: "accuracy"
+ type: "Accuracy"
+ bottom: "ip1"
+ bottom: "label"
+ top: "accuracy"
+ include {
+ phase: TEST
+ }
+}
+layer {
+ name: "loss"
+ type: "SoftmaxWithLoss"
+ bottom: "ip1"
+ bottom: "label"
+ top: "loss"
+}
--- /dev/null
+name: "CIFAR10_full"
+layer {
+ name: "cifar"
+ type: "Data"
+ top: "data"
+ top: "label"
+ include {
+ phase: TRAIN
+ }
+ transform_param {
+ mean_file: "examples/cifar10/mean.binaryproto"
+ }
+ data_param {
+ source: "examples/cifar10/cifar10_train_lmdb"
+ batch_size: 100
+ backend: LMDB
+ }
+}
+layer {
+ name: "cifar"
+ type: "Data"
+ top: "data"
+ top: "label"
+ include {
+ phase: TEST
+ }
+ transform_param {
+ mean_file: "examples/cifar10/mean.binaryproto"
+ }
+ data_param {
+ source: "examples/cifar10/cifar10_test_lmdb"
+ batch_size: 1000
+ backend: LMDB
+ }
+}
+layer {
+ name: "conv1"
+ type: "Convolution"
+ bottom: "data"
+ top: "conv1"
+ param {
+ lr_mult: 1
+ }
+ convolution_param {
+ num_output: 32
+ pad: 2
+ kernel_size: 5
+ stride: 1
+ bias_term: false
+ weight_filler {
+ type: "gaussian"
+ std: 0.0001
+ }
+ }
+}
+layer {
+ name: "pool1"
+ type: "Pooling"
+ bottom: "conv1"
+ top: "pool1"
+ pooling_param {
+ pool: MAX
+ kernel_size: 3
+ stride: 2
+ }
+}
+
+layer {
+ name: "bn1"
+ type: "BatchNorm"
+ bottom: "pool1"
+ top: "bn1"
+ param {
+ lr_mult: 0
+ }
+ param {
+ lr_mult: 0
+ }
+ param {
+ lr_mult: 0
+ }
+}
+
+layer {
+ name: "Sigmoid1"
+ type: "Sigmoid"
+ bottom: "bn1"
+ top: "Sigmoid1"
+}
+
+layer {
+ name: "conv2"
+ type: "Convolution"
+ bottom: "Sigmoid1"
+ top: "conv2"
+ param {
+ lr_mult: 1
+ }
+ convolution_param {
+ num_output: 32
+ pad: 2
+ kernel_size: 5
+ stride: 1
+ bias_term: false
+ weight_filler {
+ type: "gaussian"
+ std: 0.01
+ }
+ }
+}
+
+layer {
+ name: "bn2"
+ type: "BatchNorm"
+ bottom: "conv2"
+ top: "bn2"
+ param {
+ lr_mult: 0
+ }
+ param {
+ lr_mult: 0
+ }
+ param {
+ lr_mult: 0
+ }
+}
+
+layer {
+ name: "Sigmoid2"
+ type: "Sigmoid"
+ bottom: "bn2"
+ top: "Sigmoid2"
+}
+layer {
+ name: "pool2"
+ type: "Pooling"
+ bottom: "Sigmoid2"
+ top: "pool2"
+ pooling_param {
+ pool: AVE
+ kernel_size: 3
+ stride: 2
+ }
+}
+layer {
+ name: "conv3"
+ type: "Convolution"
+ bottom: "pool2"
+ top: "conv3"
+ param {
+ lr_mult: 1
+ }
+ convolution_param {
+ num_output: 64
+ pad: 2
+ kernel_size: 5
+ stride: 1
+ bias_term: false
+ weight_filler {
+ type: "gaussian"
+ std: 0.01
+ }
+ }
+}
+
+layer {
+ name: "bn3"
+ type: "BatchNorm"
+ bottom: "conv3"
+ top: "bn3"
+ param {
+ lr_mult: 0
+ }
+ param {
+ lr_mult: 0
+ }
+ param {
+ lr_mult: 0
+ }
+}
+
+layer {
+ name: "Sigmoid3"
+ type: "Sigmoid"
+ bottom: "bn3"
+ top: "Sigmoid3"
+}
+layer {
+ name: "pool3"
+ type: "Pooling"
+ bottom: "Sigmoid3"
+ top: "pool3"
+ pooling_param {
+ pool: AVE
+ kernel_size: 3
+ stride: 2
+ }
+}
+
+layer {
+ name: "ip1"
+ type: "InnerProduct"
+ bottom: "pool3"
+ top: "ip1"
+ param {
+ lr_mult: 1
+ decay_mult: 1
+ }
+ param {
+ lr_mult: 1
+ decay_mult: 0
+ }
+ inner_product_param {
+ num_output: 10
+ weight_filler {
+ type: "gaussian"
+ std: 0.01
+ }
+ bias_filler {
+ type: "constant"
+ }
+ }
+}
+layer {
+ name: "accuracy"
+ type: "Accuracy"
+ bottom: "ip1"
+ bottom: "label"
+ top: "accuracy"
+ include {
+ phase: TEST
+ }
+}
+layer {
+ name: "loss"
+ type: "SoftmaxWithLoss"
+ bottom: "ip1"
+ bottom: "label"
+ top: "loss"
+}
max_iter: 60000
# snapshot intermediate results
snapshot: 10000
+snapshot_format: HDF5
snapshot_prefix: "examples/cifar10/cifar10_full"
# solver mode: CPU or GPU
solver_mode: GPU
max_iter: 65000
# snapshot intermediate results
snapshot: 5000
+snapshot_format: HDF5
snapshot_prefix: "examples/cifar10/cifar10_full"
# solver mode: CPU or GPU
solver_mode: GPU
max_iter: 70000
# snapshot intermediate results
snapshot: 5000
+snapshot_format: HDF5
snapshot_prefix: "examples/cifar10/cifar10_full"
# solver mode: CPU or GPU
solver_mode: GPU
name: "CIFAR10_quick_test"
input: "data"
-input_dim: 1
-input_dim: 3
-input_dim: 32
-input_dim: 32
+input_shape {
+ dim: 1
+ dim: 3
+ dim: 32
+ dim: 32
+}
layer {
name: "conv1"
type: "Convolution"
max_iter: 4000
# snapshot intermediate results
snapshot: 4000
+snapshot_format: HDF5
snapshot_prefix: "examples/cifar10/cifar10_quick"
# solver mode: CPU or GPU
solver_mode: GPU
max_iter: 5000
# snapshot intermediate results
snapshot: 5000
+snapshot_format: HDF5
snapshot_prefix: "examples/cifar10/cifar10_quick"
# solver mode: CPU or GPU
solver_mode: GPU
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/db.hpp"
+#include "caffe/util/format.hpp"
using caffe::Datum;
using boost::scoped_ptr;
for (int fileid = 0; fileid < kCIFARTrainBatches; ++fileid) {
// Open files
LOG(INFO) << "Training Batch " << fileid + 1;
- snprintf(str_buffer, kCIFARImageNBytes, "/data_batch_%d.bin", fileid + 1);
- std::ifstream data_file((input_folder + str_buffer).c_str(),
+ string batchFileName = input_folder + "/data_batch_"
+ + caffe::format_int(fileid+1) + ".bin";
+ std::ifstream data_file(batchFileName.c_str(),
std::ios::in | std::ios::binary);
CHECK(data_file) << "Unable to open train file #" << fileid + 1;
for (int itemid = 0; itemid < kCIFARBatchSize; ++itemid) {
read_image(&data_file, &label, str_buffer);
datum.set_label(label);
datum.set_data(str_buffer, kCIFARImageNBytes);
- int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d",
- fileid * kCIFARBatchSize + itemid);
string out;
CHECK(datum.SerializeToString(&out));
- txn->Put(string(str_buffer, length), out);
+ txn->Put(caffe::format_int(fileid * kCIFARBatchSize + itemid, 5), out);
}
}
txn->Commit();
read_image(&data_file, &label, str_buffer);
datum.set_label(label);
datum.set_data(str_buffer, kCIFARImageNBytes);
- int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d", itemid);
string out;
CHECK(datum.SerializeToString(&out));
- txn->Put(string(str_buffer, length), out);
+ txn->Put(caffe::format_int(itemid, 5), out);
}
txn->Commit();
test_db->Close();
You will first need to download and convert the data format from the [CIFAR-10 website](http://www.cs.toronto.edu/~kriz/cifar.html). To do this, simply run the following commands:
- cd $CAFFE_ROOT/data/cifar10
- ./get_cifar10.sh
cd $CAFFE_ROOT
+ ./data/cifar10/get_cifar10.sh
./examples/cifar10/create_cifar10.sh
If it complains that `wget` or `gunzip` are not installed, you need to install them respectively. After running the script there should be the dataset, `./cifar10-leveldb`, and the data set image mean `./mean.binaryproto`.
# reduce learning rate by factor of 10
$TOOLS/caffe train \
--solver=examples/cifar10/cifar10_full_solver_lr1.prototxt \
- --snapshot=examples/cifar10/cifar10_full_iter_60000.solverstate
+ --snapshot=examples/cifar10/cifar10_full_iter_60000.solverstate.h5
# reduce learning rate by factor of 10
$TOOLS/caffe train \
--solver=examples/cifar10/cifar10_full_solver_lr2.prototxt \
- --snapshot=examples/cifar10/cifar10_full_iter_65000.solverstate
+ --snapshot=examples/cifar10/cifar10_full_iter_65000.solverstate.h5
--- /dev/null
+#!/usr/bin/env sh
+
+TOOLS=./build/tools
+
+$TOOLS/caffe train \
+ --solver=examples/cifar10/cifar10_full_sigmoid_solver.prototxt
+
--- /dev/null
+#!/usr/bin/env sh
+
+TOOLS=./build/tools
+
+$TOOLS/caffe train \
+ --solver=examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt
+
# reduce learning rate by factor of 10 after 8 epochs
$TOOLS/caffe train \
--solver=examples/cifar10/cifar10_quick_solver_lr1.prototxt \
- --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate
+ --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate.h5
+++ /dev/null
-{
- "metadata": {
- "description": "Use the pre-trained ImageNet model to classify images with the Python interface.",
- "example_name": "ImageNet classification",
- "include_in_docs": true,
- "priority": 1,
- "signature": "sha256:a2b12abaa1eb252f436d59833c08ab97948c8a7a0513197f31afad0a0690e318"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Classifying ImageNet: the instant Caffe way\n",
- "===========================================\n",
- "\n",
- "Caffe has a Python interface, pycaffe, with a `caffe.Net` interface for models. There are both Python and MATLAB interfaces. While this example uses the off-the-shelf Python `caffe.Classifier` interface there is also a MATLAB example at `matlab/caffe/matcaffe_demo.m`.\n",
- "\n",
- "Before we begin, you must compile Caffe. You should add the Caffe module to your `PYTHONPATH` although this example includes it automatically. If you haven't yet done so, please refer to the [installation instructions](http://caffe.berkeleyvision.org/installation.html). This example uses our pre-trained CaffeNet model, an ILSVRC12 image classifier. You can download it by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet` or let the first step of this example download it for you.\n",
- "\n",
- "Ready? Let's start."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n",
- "\n",
- "# Make sure that caffe is on the python path:\n",
- "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n",
- "import sys\n",
- "sys.path.insert(0, caffe_root + 'python')\n",
- "\n",
- "import caffe\n",
- "\n",
- "# Set the right path to your model definition file, pretrained model weights,\n",
- "# and the image you would like to classify.\n",
- "MODEL_FILE = '../models/bvlc_reference_caffenet/deploy.prototxt'\n",
- "PRETRAINED = '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n",
- "IMAGE_FILE = 'images/cat.jpg'\n",
- "\n",
- "import os\n",
- "if not os.path.isfile(PRETRAINED):\n",
- " print(\"Downloading pre-trained CaffeNet model...\")\n",
- " !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 1
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Loading a network is easy. `caffe.Classifier` takes care of everything. Note the arguments for configuring input preprocessing: mean subtraction switched on by giving a mean array, input channel swapping takes care of mapping RGB into the reference ImageNet model's BGR order, and raw scaling multiplies the feature scale from the input [0,1] to the ImageNet model's [0,255].\n",
- "\n",
- "We will set the phase to test since we are doing testing, and will first use CPU for the computation."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "caffe.set_mode_cpu()\n",
- "net = caffe.Classifier(MODEL_FILE, PRETRAINED,\n",
- " mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1),\n",
- " channel_swap=(2,1,0),\n",
- " raw_scale=255,\n",
- " image_dims=(256, 256))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 2
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's take a look at our example image with Caffe's image loading helper."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "input_image = caffe.io.load_image(IMAGE_FILE)\n",
- "plt.imshow(input_image)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 3,
- "text": [
- "<matplotlib.image.AxesImage at 0x7fda204c0e10>"
- ]
- },
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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IKqyrUEsKkeG59FMkhNGdvjnnh86+C5dLZ+zbU4KSKciGT6otrpz5YIggA9gbUTM5aVFq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71Uxl70yVqVOiQqaGuxTtFYg28yxgW8BAhgTal9SbknQjWuaCXT+5ypVklRfIAMcUVHm9FWdNZt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\nxSrNEpvXKuWZlFekVvjYGkGf3L7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lN7sbnh6+QNTCZnvNZrtlXRe0s5TcFBBGyubsLhm/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\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- "text": [
- "<matplotlib.figure.Figure at 0x7fda517da210>"
- ]
- }
- ],
- "prompt_number": 3
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Time to classify. The default is to actually do 10 predictions, cropping the center and corners of the image as well as their mirrored versions, and average over the predictions:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "prediction = net.predict([input_image]) # predict takes any number of images, and formats them for the Caffe net automatically\n",
- "print 'prediction shape:', prediction[0].shape\n",
- "plt.plot(prediction[0])\n",
- "print 'predicted class:', prediction[0].argmax()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "prediction shape: (1000,)\n",
- "predicted class: 281\n"
- ]
- },
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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- "text": [
- "<matplotlib.figure.Figure at 0x7fda20473950>"
- ]
- }
- ],
- "prompt_number": 4
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can see that the prediction is 1000-dimensional, and is pretty sparse.\n",
- "\n",
- "The predicted class 281 is \"Tabby cat.\" Our pretrained model uses the synset ID ordering of the classes, as listed in `../data/ilsvrc12/synset_words.txt` if you fetch the auxiliary imagenet data by `../data/ilsvrc12/get_ilsvrc_aux.sh`. If you look at the top indices that maximize the prediction score, they are cats, foxes, and other cute mammals. Not unreasonable predictions, right?\n",
- "\n",
- "Now let's classify by the center crop alone by turning off oversampling. Note that this makes a single input, although if you inspect the model definition prototxt you'll see the network has a batch size of 10. The python wrapper handles batching and padding for you!"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "prediction = net.predict([input_image], oversample=False)\n",
- "print 'prediction shape:', prediction[0].shape\n",
- "plt.plot(prediction[0])\n",
- "print 'predicted class:', prediction[0].argmax()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "prediction shape: (1000,)\n",
- "predicted class: 281\n"
- ]
- },
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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- "text": [
- "<matplotlib.figure.Figure at 0x7fda1abd8e10>"
- ]
- }
- ],
- "prompt_number": 5
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "Now, why don't we see how long it takes to perform the classification end to end? This result is run from an Intel i5 CPU, so you may observe some performance differences."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%timeit net.predict([input_image])"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "1 loops, best of 3: 355 ms per loop\n"
- ]
- }
- ],
- "prompt_number": 6
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "It may look a little slow, but note that time is spent on cropping, python interfacing, and running 10 images. For performance, if you really want to make prediction fast, you can optionally code in C++ and pipeline operations better. For experimenting and prototyping the current speed is fine.\n",
- "\n",
- "Let's time classifying a single image with input preprocessed:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# Resize the image to the standard (256, 256) and oversample net input sized crops.\n",
- "input_oversampled = caffe.io.oversample([caffe.io.resize_image(input_image, net.image_dims)], net.crop_dims)\n",
- "# 'data' is the input blob name in the model definition, so we preprocess for that input.\n",
- "caffe_input = np.asarray([net.transformer.preprocess('data', in_) for in_ in input_oversampled])\n",
- "# forward() takes keyword args for the input blobs with preprocessed input arrays.\n",
- "%timeit net.forward(data=caffe_input)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "1 loops, best of 3: 210 ms per loop\n"
- ]
- }
- ],
- "prompt_number": 7
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "OK, so how about GPU? it is actually pretty easy:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "caffe.set_mode_gpu()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 8
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Voila! Now we are in GPU mode. Let's see if the code gives the same result:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "prediction = net.predict([input_image])\n",
- "print 'prediction shape:', prediction[0].shape\n",
- "plt.plot(prediction[0])"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "prediction shape: (1000,)\n"
- ]
- },
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 9,
- "text": [
- "[<matplotlib.lines.Line2D at 0x7fda1ac309d0>]"
- ]
- },
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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- "text": [
- "<matplotlib.figure.Figure at 0x7fda1ab53650>"
- ]
- }
- ],
- "prompt_number": 9
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Good, everything is the same. And how about time consumption? The following benchmark is obtained on the same machine with a GTX 770 GPU:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# Full pipeline timing.\n",
- "%timeit net.predict([input_image])"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "10 loops, best of 3: 174 ms per loop\n"
- ]
- }
- ],
- "prompt_number": 10
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# Forward pass timing.\n",
- "%timeit net.forward(data=caffe_input)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "10 loops, best of 3: 34.2 ms per loop\n"
- ]
- }
- ],
- "prompt_number": 11
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Pretty fast right? Not as fast as you expected? Indeed, in this python demo you are seeing only 4 times speedup. But remember - the GPU code is actually very fast, and the data loading, transformation and interfacing actually start to take **more** time than the actual conv. net computation itself!\n",
- "\n",
- "To fully utilize the power of GPUs, you really want to:\n",
- "\n",
- "* Use larger batches, and minimize python call and data transfer overheads.\n",
- "* Pipeline data load operations, like using a subprocess.\n",
- "* Code in C++. A little inconvenient, but maybe worth it if your dataset is really, really large."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Parting Words\n",
- "-------------\n",
- "\n",
- "So this is python! We hope the interface is easy enough for one to use. The python wrapper is interfaced with boost::python, and source code can be found at `python/caffe` with the main interface in `pycaffe.py` and the classification wrapper in `classifier.py`. If you have customizations to make, start there! Do let us know if you make improvements by sending a pull request!"
- ]
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
--- /dev/null
+#include <caffe/caffe.hpp>
+#ifdef USE_OPENCV
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+#endif // USE_OPENCV
+#include <algorithm>
+#include <iosfwd>
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#ifdef USE_OPENCV
+using namespace caffe; // NOLINT(build/namespaces)
+using std::string;
+
+/* Pair (label, confidence) representing a prediction. */
+typedef std::pair<string, float> Prediction;
+
+class Classifier {
+ public:
+ Classifier(const string& model_file,
+ const string& trained_file,
+ const string& mean_file,
+ const string& label_file);
+
+ std::vector<Prediction> Classify(const cv::Mat& img, int N = 5);
+
+ private:
+ void SetMean(const string& mean_file);
+
+ std::vector<float> Predict(const cv::Mat& img);
+
+ void WrapInputLayer(std::vector<cv::Mat>* input_channels);
+
+ void Preprocess(const cv::Mat& img,
+ std::vector<cv::Mat>* input_channels);
+
+ private:
+ shared_ptr<Net<float> > net_;
+ cv::Size input_geometry_;
+ int num_channels_;
+ cv::Mat mean_;
+ std::vector<string> labels_;
+};
+
+Classifier::Classifier(const string& model_file,
+ const string& trained_file,
+ const string& mean_file,
+ const string& label_file) {
+#ifdef CPU_ONLY
+ Caffe::set_mode(Caffe::CPU);
+#else
+ Caffe::set_mode(Caffe::GPU);
+#endif
+
+ /* Load the network. */
+ net_.reset(new Net<float>(model_file, TEST));
+ net_->CopyTrainedLayersFrom(trained_file);
+
+ CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input.";
+ CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output.";
+
+ Blob<float>* input_layer = net_->input_blobs()[0];
+ num_channels_ = input_layer->channels();
+ CHECK(num_channels_ == 3 || num_channels_ == 1)
+ << "Input layer should have 1 or 3 channels.";
+ input_geometry_ = cv::Size(input_layer->width(), input_layer->height());
+
+ /* Load the binaryproto mean file. */
+ SetMean(mean_file);
+
+ /* Load labels. */
+ std::ifstream labels(label_file.c_str());
+ CHECK(labels) << "Unable to open labels file " << label_file;
+ string line;
+ while (std::getline(labels, line))
+ labels_.push_back(string(line));
+
+ Blob<float>* output_layer = net_->output_blobs()[0];
+ CHECK_EQ(labels_.size(), output_layer->channels())
+ << "Number of labels is different from the output layer dimension.";
+}
+
+static bool PairCompare(const std::pair<float, int>& lhs,
+ const std::pair<float, int>& rhs) {
+ return lhs.first > rhs.first;
+}
+
+/* Return the indices of the top N values of vector v. */
+static std::vector<int> Argmax(const std::vector<float>& v, int N) {
+ std::vector<std::pair<float, int> > pairs;
+ for (size_t i = 0; i < v.size(); ++i)
+ pairs.push_back(std::make_pair(v[i], i));
+ std::partial_sort(pairs.begin(), pairs.begin() + N, pairs.end(), PairCompare);
+
+ std::vector<int> result;
+ for (int i = 0; i < N; ++i)
+ result.push_back(pairs[i].second);
+ return result;
+}
+
+/* Return the top N predictions. */
+std::vector<Prediction> Classifier::Classify(const cv::Mat& img, int N) {
+ std::vector<float> output = Predict(img);
+
+ N = std::min<int>(labels_.size(), N);
+ std::vector<int> maxN = Argmax(output, N);
+ std::vector<Prediction> predictions;
+ for (int i = 0; i < N; ++i) {
+ int idx = maxN[i];
+ predictions.push_back(std::make_pair(labels_[idx], output[idx]));
+ }
+
+ return predictions;
+}
+
+/* Load the mean file in binaryproto format. */
+void Classifier::SetMean(const string& mean_file) {
+ BlobProto blob_proto;
+ ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
+
+ /* Convert from BlobProto to Blob<float> */
+ Blob<float> mean_blob;
+ mean_blob.FromProto(blob_proto);
+ CHECK_EQ(mean_blob.channels(), num_channels_)
+ << "Number of channels of mean file doesn't match input layer.";
+
+ /* The format of the mean file is planar 32-bit float BGR or grayscale. */
+ std::vector<cv::Mat> channels;
+ float* data = mean_blob.mutable_cpu_data();
+ for (int i = 0; i < num_channels_; ++i) {
+ /* Extract an individual channel. */
+ cv::Mat channel(mean_blob.height(), mean_blob.width(), CV_32FC1, data);
+ channels.push_back(channel);
+ data += mean_blob.height() * mean_blob.width();
+ }
+
+ /* Merge the separate channels into a single image. */
+ cv::Mat mean;
+ cv::merge(channels, mean);
+
+ /* Compute the global mean pixel value and create a mean image
+ * filled with this value. */
+ cv::Scalar channel_mean = cv::mean(mean);
+ mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean);
+}
+
+std::vector<float> Classifier::Predict(const cv::Mat& img) {
+ Blob<float>* input_layer = net_->input_blobs()[0];
+ input_layer->Reshape(1, num_channels_,
+ input_geometry_.height, input_geometry_.width);
+ /* Forward dimension change to all layers. */
+ net_->Reshape();
+
+ std::vector<cv::Mat> input_channels;
+ WrapInputLayer(&input_channels);
+
+ Preprocess(img, &input_channels);
+
+ net_->ForwardPrefilled();
+
+ /* Copy the output layer to a std::vector */
+ Blob<float>* output_layer = net_->output_blobs()[0];
+ const float* begin = output_layer->cpu_data();
+ const float* end = begin + output_layer->channels();
+ return std::vector<float>(begin, end);
+}
+
+/* Wrap the input layer of the network in separate cv::Mat objects
+ * (one per channel). This way we save one memcpy operation and we
+ * don't need to rely on cudaMemcpy2D. The last preprocessing
+ * operation will write the separate channels directly to the input
+ * layer. */
+void Classifier::WrapInputLayer(std::vector<cv::Mat>* input_channels) {
+ Blob<float>* input_layer = net_->input_blobs()[0];
+
+ int width = input_layer->width();
+ int height = input_layer->height();
+ float* input_data = input_layer->mutable_cpu_data();
+ for (int i = 0; i < input_layer->channels(); ++i) {
+ cv::Mat channel(height, width, CV_32FC1, input_data);
+ input_channels->push_back(channel);
+ input_data += width * height;
+ }
+}
+
+void Classifier::Preprocess(const cv::Mat& img,
+ std::vector<cv::Mat>* input_channels) {
+ /* Convert the input image to the input image format of the network. */
+ cv::Mat sample;
+ if (img.channels() == 3 && num_channels_ == 1)
+ cv::cvtColor(img, sample, cv::COLOR_BGR2GRAY);
+ else if (img.channels() == 4 && num_channels_ == 1)
+ cv::cvtColor(img, sample, cv::COLOR_BGRA2GRAY);
+ else if (img.channels() == 4 && num_channels_ == 3)
+ cv::cvtColor(img, sample, cv::COLOR_BGRA2BGR);
+ else if (img.channels() == 1 && num_channels_ == 3)
+ cv::cvtColor(img, sample, cv::COLOR_GRAY2BGR);
+ else
+ sample = img;
+
+ cv::Mat sample_resized;
+ if (sample.size() != input_geometry_)
+ cv::resize(sample, sample_resized, input_geometry_);
+ else
+ sample_resized = sample;
+
+ cv::Mat sample_float;
+ if (num_channels_ == 3)
+ sample_resized.convertTo(sample_float, CV_32FC3);
+ else
+ sample_resized.convertTo(sample_float, CV_32FC1);
+
+ cv::Mat sample_normalized;
+ cv::subtract(sample_float, mean_, sample_normalized);
+
+ /* This operation will write the separate BGR planes directly to the
+ * input layer of the network because it is wrapped by the cv::Mat
+ * objects in input_channels. */
+ cv::split(sample_normalized, *input_channels);
+
+ CHECK(reinterpret_cast<float*>(input_channels->at(0).data)
+ == net_->input_blobs()[0]->cpu_data())
+ << "Input channels are not wrapping the input layer of the network.";
+}
+
+int main(int argc, char** argv) {
+ if (argc != 6) {
+ std::cerr << "Usage: " << argv[0]
+ << " deploy.prototxt network.caffemodel"
+ << " mean.binaryproto labels.txt img.jpg" << std::endl;
+ return 1;
+ }
+
+ ::google::InitGoogleLogging(argv[0]);
+
+ string model_file = argv[1];
+ string trained_file = argv[2];
+ string mean_file = argv[3];
+ string label_file = argv[4];
+ Classifier classifier(model_file, trained_file, mean_file, label_file);
+
+ string file = argv[5];
+
+ std::cout << "---------- Prediction for "
+ << file << " ----------" << std::endl;
+
+ cv::Mat img = cv::imread(file, -1);
+ CHECK(!img.empty()) << "Unable to decode image " << file;
+ std::vector<Prediction> predictions = classifier.Classify(img);
+
+ /* Print the top N predictions. */
+ for (size_t i = 0; i < predictions.size(); ++i) {
+ Prediction p = predictions[i];
+ std::cout << std::fixed << std::setprecision(4) << p.second << " - \""
+ << p.first << "\"" << std::endl;
+ }
+}
+#else
+int main(int argc, char** argv) {
+ LOG(FATAL) << "This example requires OpenCV; compile with USE_OPENCV.";
+}
+#endif // USE_OPENCV
--- /dev/null
+---
+title: CaffeNet C++ Classification example
+description: A simple example performing image classification using the low-level C++ API.
+category: example
+include_in_docs: true
+priority: 10
+---
+
+# Classifying ImageNet: using the C++ API
+
+Caffe, at its core, is written in C++. It is possible to use the C++
+API of Caffe to implement an image classification application similar
+to the Python code presented in one of the Notebook example. To look
+at a more general-purpose example of the Caffe C++ API, you should
+study the source code of the command line tool `caffe` in `tools/caffe.cpp`.
+
+## Presentation
+
+A simple C++ code is proposed in
+`examples/cpp_classification/classification.cpp`. For the sake of
+simplicity, this example does not support oversampling of a single
+sample nor batching of multiple independant samples. This example is
+not trying to reach the maximum possible classification throughput on
+a system, but special care was given to avoid unnecessary
+pessimization while keeping the code readable.
+
+## Compiling
+
+The C++ example is built automatically when compiling Caffe. To
+compile Caffe you should follow the documented instructions. The
+classification example will be built as `examples/classification.bin`
+in your build directory.
+
+## Usage
+
+To use the pre-trained CaffeNet model with the classification example,
+you need to download it from the "Model Zoo" using the following
+script:
+```
+./scripts/download_model_binary.py models/bvlc_reference_caffenet
+```
+The ImageNet labels file (also called the *synset file*) is also
+required in order to map a prediction to the name of the class:
+```
+./data/ilsvrc12/get_ilsvrc_aux.sh.
+```
+Using the files that were downloaded, we can classify the provided cat
+image (`examples/images/cat.jpg`) using this command:
+```
+./build/examples/cpp_classification/classification.bin \
+ models/bvlc_reference_caffenet/deploy.prototxt \
+ models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \
+ data/ilsvrc12/imagenet_mean.binaryproto \
+ data/ilsvrc12/synset_words.txt \
+ examples/images/cat.jpg
+```
+The output should look like this:
+```
+---------- Prediction for examples/images/cat.jpg ----------
+0.3134 - "n02123045 tabby, tabby cat"
+0.2380 - "n02123159 tiger cat"
+0.1235 - "n02124075 Egyptian cat"
+0.1003 - "n02119022 red fox, Vulpes vulpes"
+0.0715 - "n02127052 lynx, catamount"
+```
+
+## Improving Performance
+
+To further improve performance, you will need to leverage the GPU
+more, here are some guidelines:
+
+* Move the data on the GPU early and perform all preprocessing
+operations there.
+* If you have many images to classify simultaneously, you should use
+batching (independent images are classified in a single forward pass).
+* Use multiple classification threads to ensure the GPU is always fully
+utilized and not waiting for an I/O blocked CPU thread.
{
- "metadata": {
- "description": "Run a pretrained model as a detector in Python.",
- "example_name": "R-CNN detection",
- "include_in_docs": true,
- "priority": 3,
- "signature": "sha256:5d53dc49c9b6b93c1a2714c99043a763029ec98aebfb44acfa8d9e61781c9499"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[R-CNN](https://github.com/rbgirshick/rcnn) is a state-of-the-art detector that classifies region proposals by a finetuned Caffe model. For the full details of the R-CNN system and model, refer to its project site and the paper:\n",
- "\n",
- "> *Rich feature hierarchies for accurate object detection and semantic segmentation*. Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. CVPR 2014. [Arxiv 2013](http://arxiv.org/abs/1311.2524).\n",
- "\n",
- "In this example, we do detection by a pure Caffe edition of the R-CNN model for ImageNet. The R-CNN detector outputs class scores for the 200 detection classes of ILSVRC13. Keep in mind that these are raw one vs. all SVM scores, so they are not probabilistically calibrated or exactly comparable across classes. Note that this off-the-shelf model is simply for convenience, and is not the full R-CNN model.\n",
- "\n",
- "Let's run detection on an image of a bicyclist riding a fish bike in the desert (from the ImageNet challenge\u2014no joke).\n",
- "\n",
- "First, we'll need region proposals and the Caffe R-CNN ImageNet model:\n",
- "\n",
- "- [Selective Search](http://koen.me/research/selectivesearch/) is the region proposer used by R-CNN. The [selective_search_ijcv_with_python](https://github.com/sergeyk/selective_search_ijcv_with_python) Python module takes care of extracting proposals through the selective search MATLAB implementation. To install it, download the module and name its directory `selective_search_ijcv_with_python`, run the demo in MATLAB to compile the necessary functions, then add it to your `PYTHONPATH` for importing. (If you have your own region proposals prepared, or would rather not bother with this step, [detect.py](https://github.com/BVLC/caffe/blob/master/python/detect.py) accepts a list of images and bounding boxes as CSV.)\n",
- "\n",
- "-Run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the Caffe R-CNN ImageNet model.\n",
- "\n",
- "With that done, we'll call the bundled `detect.py` to generate the region proposals and run the network. For an explanation of the arguments, do `./detect.py --help`."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "!mkdir -p _temp\n",
- "!echo `pwd`/images/fish-bike.jpg > _temp/det_input.txt\n",
- "!../python/detect.py --crop_mode=selective_search --pretrained_model=../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel --model_def=../models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt --gpu --raw_scale=255 _temp/det_input.txt _temp/det_output.h5"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "WARNING: Logging before InitGoogleLogging() is written to STDERR\r\n",
- "I0218 20:43:25.383932 2099749632 net.cpp:42] Initializing net from parameters: \r\n",
- "name: \"R-CNN-ilsvrc13\"\r\n",
- "input: \"data\"\r\n",
- "input_dim: 10\r\n",
- "input_dim: 3\r\n",
- "input_dim: 227\r\n",
- "input_dim: 227\r\n",
- "state {\r\n",
- " phase: TEST\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"conv1\"\r\n",
- " type: \"Convolution\"\r\n",
- " bottom: \"data\"\r\n",
- " top: \"conv1\"\r\n",
- " convolution_param {\r\n",
- " num_output: 96\r\n",
- " kernel_size: 11\r\n",
- " stride: 4\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"relu1\"\r\n",
- " type: \"ReLU\"\r\n",
- " bottom: \"conv1\"\r\n",
- " top: \"conv1\"\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"pool1\"\r\n",
- " type: \"Pooling\"\r\n",
- " bottom: \"conv1\"\r\n",
- " top: \"pool1\"\r\n",
- " pooling_param {\r\n",
- " pool: MAX\r\n",
- " kernel_size: 3\r\n",
- " stride: 2\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"norm1\"\r\n",
- " type: \"LRN\"\r\n",
- " bottom: \"pool1\"\r\n",
- " top: \"norm1\"\r\n",
- " lrn_param {\r\n",
- " local_size: 5\r\n",
- " alpha: 0.0001\r\n",
- " beta: 0.75\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"conv2\"\r\n",
- " type: \"Convolution\"\r\n",
- " bottom: \"norm1\"\r\n",
- " top: \"conv2\"\r\n",
- " convolution_param {\r\n",
- " num_output: 256\r\n",
- " pad: 2\r\n",
- " kernel_size: 5\r\n",
- " group: 2\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r",
- "\r\n",
- " name: \"relu2\"\r\n",
- " type: \"ReLU\"\r\n",
- " bottom: \"conv2\"\r\n",
- " top: \"conv2\"\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"pool2\"\r\n",
- " type: \"Pooling\"\r\n",
- " bottom: \"conv2\"\r\n",
- " top: \"pool2\"\r\n",
- " pooling_param {\r\n",
- " pool: MAX\r\n",
- " kernel_size: 3\r\n",
- " stride: 2\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"norm2\"\r\n",
- " type: \"LRN\"\r\n",
- " bottom: \"pool2\"\r\n",
- " top: \"norm2\"\r\n",
- " lrn_param {\r\n",
- " local_size: 5\r\n",
- " alpha: 0.0001\r\n",
- " beta: 0.75\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"conv3\"\r\n",
- " type: \"Convolution\"\r\n",
- " bottom: \"norm2\"\r\n",
- " top: \"conv3\"\r\n",
- " convolution_param {\r\n",
- " num_output: 384\r\n",
- " pad: 1\r\n",
- " kernel_size: 3\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"relu3\"\r\n",
- " type: \"ReLU\"\r\n",
- " bottom: \"conv3\"\r\n",
- " top: \"conv3\"\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"conv4\"\r\n",
- " type: \"Convolution\"\r\n",
- " bottom: \"conv3\"\r\n",
- " top: \"conv4\"\r\n",
- " convolution_param {\r\n",
- " num_output: 384\r\n",
- " pad: 1\r\n",
- " kernel_size: 3\r\n",
- " group: 2\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"relu4\"\r\n",
- " type: \"ReLU\"\r\n",
- " bottom: \"conv4\"\r\n",
- " top: \"conv4\"\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"conv5\"\r\n",
- " type: \"Convolution\"\r\n",
- " bottom: \"conv4\"\r\n",
- " top: \"conv5\"\r\n",
- " convolution_param {\r\n",
- " num_output: 256\r\n",
- " pad: 1\r\n",
- " kernel_size: 3\r\n",
- " group: 2\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"relu5\"\r\n",
- " type: \"ReLU\"\r\n",
- " bottom: \"conv5\"\r\n",
- " top: \"conv5\"\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"pool5\"\r\n",
- " type: \"Pooling\"\r\n",
- " bottom: \"conv5\"\r\n",
- " top: \"pool5\"\r\n",
- " pooling_param {\r\n",
- " pool: MAX\r\n",
- " kernel_size: 3\r\n",
- " stride: 2\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"fc6\"\r\n",
- " type: \"InnerProduct\"\r\n",
- " bottom: \"pool5\"\r\n",
- " top: \"fc6\"\r\n",
- " inner_product_param {\r\n",
- " num_output: 4096\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"relu6\"\r\n",
- " type: \"ReLU\"\r\n",
- " bottom: \"fc6\"\r\n",
- " top: \"fc6\"\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"drop6\"\r\n",
- " type: \"Dropout\"\r\n",
- " bottom: \"fc6\"\r\n",
- " top: \"fc6\"\r\n",
- " dropout_param {\r\n",
- " dropout_ratio: 0.5\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"fc7\"\r\n",
- " type: \"InnerProduct\"\r\n",
- " bottom: \"fc6\"\r\n",
- " top: \"fc7\"\r\n",
- " inner_product_param {\r\n",
- " num_output: 4096\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"relu7\"\r\n",
- " type: \"ReLU\"\r\n",
- " bottom: \"fc7\"\r\n",
- " top: \"fc7\"\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"drop7\"\r\n",
- " type: \"Dropout\"\r\n",
- " bottom: \"fc7\"\r\n",
- " top: \"fc7\"\r\n",
- " dropout_param {\r\n",
- " dropout_ratio: 0.5\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"fc-rcnn\"\r\n",
- " type: \"InnerProduct\"\r\n",
- " bottom: \"fc7\"\r\n",
- " top: \"fc-rcnn\"\r\n",
- " inner_product_param {\r\n",
- " num_output: 200\r\n",
- " }\r\n",
- "}\r\n",
- "I0218 20:43:25.385720 2099749632 net.cpp:336] Input 0 -> data\r\n",
- "I0218 20:43:25.385769 2099749632 layer_factory.hpp:74] Creating layer conv1\r\n",
- "I0218 20:43:25.385783 2099749632 net.cpp:76] Creating Layer conv1\r\n",
- "I0218 20:43:25.385790 2099749632 net.cpp:372] conv1 <- data\r\n",
- "I0218 20:43:25.385802 2099749632 net.cpp:334] conv1 -> conv1\r\n",
- "I0218 20:43:25.385815 2099749632 net.cpp:105] Setting up conv1\r\n",
- "I0218 20:43:25.386574 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\r\n",
- "I0218 20:43:25.386610 2099749632 layer_factory.hpp:74] Creating layer relu1\r\n",
- "I0218 20:43:25.386625 2099749632 net.cpp:76] Creating Layer relu1\r\n",
- "I0218 20:43:25.386631 2099749632 net.cpp:372] relu1 <- conv1\r\n",
- "I0218 20:43:25.386641 2099749632 net.cpp:323] relu1 -> conv1 (in-place)\r\n",
- "I0218 20:43:25.386649 2099749632 net.cpp:105] Setting up relu1\r\n",
- "I0218 20:43:25.386656 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\r\n",
- "I0218 20:43:25.386663 2099749632 layer_factory.hpp:74] Creating layer pool1\r\n",
- "I0218 20:43:25.386675 2099749632 net.cpp:76] Creating Layer pool1\r\n",
- "I0218 20:43:25.386682 2099749632 net.cpp:372] pool1 <- conv1\r\n",
- "I0218 20:43:25.386690 2099749632 net.cpp:334] pool1 -> pool1\r\n",
- "I0218 20:43:25.386699 2099749632 net.cpp:105] Setting up pool1\r\n",
- "I0218 20:43:25.386716 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\r\n",
- "I0218 20:43:25.386725 2099749632 layer_factory.hpp:74] Creating layer norm1\r\n",
- "I0218 20:43:25.386736 2099749632 net.cpp:76] Creating Layer norm1\r\n",
- "I0218 20:43:25.386744 2099749632 net.cpp:372] norm1 <- pool1\r\n",
- "I0218 20:43:25.386803 2099749632 net.cpp:334] norm1 -> norm1\r\n",
- "I0218 20:43:25.386819 2099749632 net.cpp:105] Setting up norm1\r\n",
- "I0218 20:43:25.386832 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\r\n",
- "I0218 20:43:25.386842 2099749632 layer_factory.hpp:74] Creating layer conv2\r\n",
- "I0218 20:43:25.386852 2099749632 net.cpp:76] Creating Layer conv2\r\n",
- "I0218 20:43:25.386865 2099749632 net.cpp:372] conv2 <- norm1\r\n",
- "I0218 20:43:25.386878 2099749632 net.cpp:334] conv2 -> conv2\r\n",
- "I0218 20:43:25.386899 2099749632 net.cpp:105] Setting up conv2\r\n",
- "I0218 20:43:25.387024 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\r\n",
- "I0218 20:43:25.387042 2099749632 layer_factory.hpp:74] Creating layer relu2\r\n",
- "I0218 20:43:25.387050 2099749632 net.cpp:76] Creating Layer relu2\r\n",
- "I0218 20:43:25.387058 2099749632 net.cpp:372] relu2 <- conv2\r\n",
- "I0218 20:43:25.387066 2099749632 net.cpp:323] relu2 -> conv2 (in-place)\r\n",
- "I0218 20:43:25.387075 2099749632 net.cpp:105] Setting up relu2\r\n",
- "I0218 20:43:25.387081 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\r\n",
- "I0218 20:43:25.387089 2099749632 layer_factory.hpp:74] Creating layer pool2\r\n",
- "I0218 20:43:25.387097 2099749632 net.cpp:76] Creating Layer pool2\r\n",
- "I0218 20:43:25.387104 2099749632 net.cpp:372] pool2 <- conv2\r\n",
- "I0218 20:43:25.387112 2099749632 net.cpp:334] pool2 -> pool2\r\n",
- "I0218 20:43:25.387121 2099749632 net.cpp:105] Setting up pool2\r\n",
- "I0218 20:43:25.387130 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n",
- "I0218 20:43:25.387137 2099749632 layer_factory.hpp:74] Creating layer norm2\r\n",
- "I0218 20:43:25.387145 2099749632 net.cpp:76] Creating Layer norm2\r\n",
- "I0218 20:43:25.387152 2099749632 net.cpp:372] norm2 <- pool2\r\n",
- "I0218 20:43:25.387161 2099749632 net.cpp:334] norm2 -> norm2\r\n",
- "I0218 20:43:25.387168 2099749632 net.cpp:105] Setting up norm2\r\n",
- "I0218 20:43:25.387176 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n",
- "I0218 20:43:25.387228 2099749632 layer_factory.hpp:74] Creating layer conv3\r\n",
- "I0218 20:43:25.387249 2099749632 net.cpp:76] Creating Layer conv3\r\n",
- "I0218 20:43:25.387258 2099749632 net.cpp:372] conv3 <- norm2\r\n",
- "I0218 20:43:25.387266 2099749632 net.cpp:334] conv3 -> conv3\r\n",
- "I0218 20:43:25.387276 2099749632 net.cpp:105] Setting up conv3\r\n",
- "I0218 20:43:25.389375 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n",
- "I0218 20:43:25.389408 2099749632 layer_factory.hpp:74] Creating layer relu3\r\n",
- "I0218 20:43:25.389421 2099749632 net.cpp:76] Creating Layer relu3\r\n",
- "I0218 20:43:25.389430 2099749632 net.cpp:372] relu3 <- conv3\r\n",
- "I0218 20:43:25.389438 2099749632 net.cpp:323] relu3 -> conv3 (in-place)\r\n",
- "I0218 20:43:25.389447 2099749632 net.cpp:105] Setting up relu3\r\n",
- "I0218 20:43:25.389456 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n",
- "I0218 20:43:25.389462 2099749632 layer_factory.hpp:74] Creating layer conv4\r\n",
- "I0218 20:43:25.389472 2099749632 net.cpp:76] Creating Layer conv4\r\n",
- "I0218 20:43:25.389478 2099749632 net.cpp:372] conv4 <- conv3\r\n",
- "I0218 20:43:25.389487 2099749632 net.cpp:334] conv4 -> conv4\r\n",
- "I0218 20:43:25.389497 2099749632 net.cpp:105] Setting up conv4\r\n",
- "I0218 20:43:25.391810 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n",
- "I0218 20:43:25.391856 2099749632 layer_factory.hpp:74] Creating layer relu4\r\n",
- "I0218 20:43:25.391871 2099749632 net.cpp:76] Creating Layer relu4\r\n",
- "I0218 20:43:25.391880 2099749632 net.cpp:372] relu4 <- conv4\r\n",
- "I0218 20:43:25.391888 2099749632 net.cpp:323] relu4 -> conv4 (in-place)\r\n",
- "I0218 20:43:25.391898 2099749632 net.cpp:105] Setting up relu4\r\n",
- "I0218 20:43:25.391906 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n",
- "I0218 20:43:25.391913 2099749632 layer_factory.hpp:74] Creating layer conv5\r\n",
- "I0218 20:43:25.391923 2099749632 net.cpp:76] Creating Layer conv5\r\n",
- "I0218 20:43:25.391929 2099749632 net.cpp:372] conv5 <- conv4\r\n",
- "I0218 20:43:25.391937 2099749632 net.cpp:334] conv5 -> conv5\r\n",
- "I0218 20:43:25.391947 2099749632 net.cpp:105] Setting up conv5\r\n",
- "I0218 20:43:25.393072 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n",
- "I0218 20:43:25.393108 2099749632 layer_factory.hpp:74] Creating layer relu5\r\n",
- "I0218 20:43:25.393122 2099749632 net.cpp:76] Creating Layer relu5\r\n",
- "I0218 20:43:25.393129 2099749632 net.cpp:372] relu5 <- conv5\r\n",
- "I0218 20:43:25.393138 2099749632 net.cpp:323] relu5 -> conv5 (in-place)\r\n",
- "I0218 20:43:25.393148 2099749632 net.cpp:105] Setting up relu5\r\n",
- "I0218 20:43:25.393157 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n",
- "I0218 20:43:25.393167 2099749632 layer_factory.hpp:74] Creating layer pool5\r\n",
- "I0218 20:43:25.393175 2099749632 net.cpp:76] Creating Layer pool5\r\n",
- "I0218 20:43:25.393182 2099749632 net.cpp:372] pool5 <- conv5\r\n",
- "I0218 20:43:25.393190 2099749632 net.cpp:334] pool5 -> pool5\r\n",
- "I0218 20:43:25.393199 2099749632 net.cpp:105] Setting up pool5\r\n",
- "I0218 20:43:25.393209 2099749632 net.cpp:112] Top shape: 10 256 6 6 (92160)\r\n",
- "I0218 20:43:25.393218 2099749632 layer_factory.hpp:74] Creating layer fc6\r\n",
- "I0218 20:43:25.393226 2099749632 net.cpp:76] Creating Layer fc6\r\n",
- "I0218 20:43:25.393232 2099749632 net.cpp:372] fc6 <- pool5\r\n",
- "I0218 20:43:25.393240 2099749632 net.cpp:334] fc6 -> fc6\r\n",
- "I0218 20:43:25.393249 2099749632 net.cpp:105] Setting up fc6\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "I0218 20:43:25.516396 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
- "I0218 20:43:25.516445 2099749632 layer_factory.hpp:74] Creating layer relu6\r\n",
- "I0218 20:43:25.516463 2099749632 net.cpp:76] Creating Layer relu6\r\n",
- "I0218 20:43:25.516470 2099749632 net.cpp:372] relu6 <- fc6\r\n",
- "I0218 20:43:25.516480 2099749632 net.cpp:323] relu6 -> fc6 (in-place)\r\n",
- "I0218 20:43:25.516490 2099749632 net.cpp:105] Setting up relu6\r\n",
- "I0218 20:43:25.516497 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
- "I0218 20:43:25.516505 2099749632 layer_factory.hpp:74] Creating layer drop6\r\n",
- "I0218 20:43:25.516515 2099749632 net.cpp:76] Creating Layer drop6\r\n",
- "I0218 20:43:25.516521 2099749632 net.cpp:372] drop6 <- fc6\r\n",
- "I0218 20:43:25.516530 2099749632 net.cpp:323] drop6 -> fc6 (in-place)\r\n",
- "I0218 20:43:25.516538 2099749632 net.cpp:105] Setting up drop6\r\n",
- "I0218 20:43:25.516557 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
- "I0218 20:43:25.516566 2099749632 layer_factory.hpp:74] Creating layer fc7\r\n",
- "I0218 20:43:25.516576 2099749632 net.cpp:76] Creating Layer fc7\r\n",
- "I0218 20:43:25.516582 2099749632 net.cpp:372] fc7 <- fc6\r\n",
- "I0218 20:43:25.516589 2099749632 net.cpp:334] fc7 -> fc7\r\n",
- "I0218 20:43:25.516599 2099749632 net.cpp:105] Setting up fc7\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "I0218 20:43:25.604786 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
- "I0218 20:43:25.604838 2099749632 layer_factory.hpp:74] Creating layer relu7\r\n",
- "I0218 20:43:25.604852 2099749632 net.cpp:76] Creating Layer relu7\r\n",
- "I0218 20:43:25.604859 2099749632 net.cpp:372] relu7 <- fc7\r\n",
- "I0218 20:43:25.604868 2099749632 net.cpp:323] relu7 -> fc7 (in-place)\r\n",
- "I0218 20:43:25.604878 2099749632 net.cpp:105] Setting up relu7\r\n",
- "I0218 20:43:25.604885 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
- "I0218 20:43:25.604893 2099749632 layer_factory.hpp:74] Creating layer drop7\r\n",
- "I0218 20:43:25.604902 2099749632 net.cpp:76] Creating Layer drop7\r\n",
- "I0218 20:43:25.604908 2099749632 net.cpp:372] drop7 <- fc7\r\n",
- "I0218 20:43:25.604917 2099749632 net.cpp:323] drop7 -> fc7 (in-place)\r\n",
- "I0218 20:43:25.604924 2099749632 net.cpp:105] Setting up drop7\r\n",
- "I0218 20:43:25.604933 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
- "I0218 20:43:25.604939 2099749632 layer_factory.hpp:74] Creating layer fc-rcnn\r\n",
- "I0218 20:43:25.604948 2099749632 net.cpp:76] Creating Layer fc-rcnn\r\n",
- "I0218 20:43:25.604954 2099749632 net.cpp:372] fc-rcnn <- fc7\r\n",
- "I0218 20:43:25.604962 2099749632 net.cpp:334] fc-rcnn -> fc-rcnn\r\n",
- "I0218 20:43:25.604971 2099749632 net.cpp:105] Setting up fc-rcnn\r\n",
- "I0218 20:43:25.606878 2099749632 net.cpp:112] Top shape: 10 200 1 1 (2000)\r\n",
- "I0218 20:43:25.606904 2099749632 net.cpp:165] fc-rcnn does not need backward computation.\r\n",
- "I0218 20:43:25.606909 2099749632 net.cpp:165] drop7 does not need backward computation.\r\n",
- "I0218 20:43:25.606916 2099749632 net.cpp:165] relu7 does not need backward computation.\r\n",
- "I0218 20:43:25.606922 2099749632 net.cpp:165] fc7 does not need backward computation.\r\n",
- "I0218 20:43:25.606928 2099749632 net.cpp:165] drop6 does not need backward computation.\r\n",
- "I0218 20:43:25.606935 2099749632 net.cpp:165] relu6 does not need backward computation.\r\n",
- "I0218 20:43:25.606940 2099749632 net.cpp:165] fc6 does not need backward computation.\r\n",
- "I0218 20:43:25.606946 2099749632 net.cpp:165] pool5 does not need backward computation.\r\n",
- "I0218 20:43:25.606952 2099749632 net.cpp:165] relu5 does not need backward computation.\r\n",
- "I0218 20:43:25.606958 2099749632 net.cpp:165] conv5 does not need backward computation.\r\n",
- "I0218 20:43:25.606964 2099749632 net.cpp:165] relu4 does not need backward computation.\r\n",
- "I0218 20:43:25.606971 2099749632 net.cpp:165] conv4 does not need backward computation.\r\n",
- "I0218 20:43:25.606976 2099749632 net.cpp:165] relu3 does not need backward computation.\r\n",
- "I0218 20:43:25.606982 2099749632 net.cpp:165] conv3 does not need backward computation.\r\n",
- "I0218 20:43:25.606988 2099749632 net.cpp:165] norm2 does not need backward computation.\r\n",
- "I0218 20:43:25.606995 2099749632 net.cpp:165] pool2 does not need backward computation.\r\n",
- "I0218 20:43:25.607002 2099749632 net.cpp:165] relu2 does not need backward computation.\r\n",
- "I0218 20:43:25.607007 2099749632 net.cpp:165] conv2 does not need backward computation.\r\n",
- "I0218 20:43:25.607013 2099749632 net.cpp:165] norm1 does not need backward computation.\r\n",
- "I0218 20:43:25.607199 2099749632 net.cpp:165] pool1 does not need backward computation.\r\n",
- "I0218 20:43:25.607213 2099749632 net.cpp:165] relu1 does not need backward computation.\r\n",
- "I0218 20:43:25.607219 2099749632 net.cpp:165] conv1 does not need backward computation.\r\n",
- "I0218 20:43:25.607225 2099749632 net.cpp:201] This network produces output fc-rcnn\r\n",
- "I0218 20:43:25.607239 2099749632 net.cpp:446] Collecting Learning Rate and Weight Decay.\r\n",
- "I0218 20:43:25.607255 2099749632 net.cpp:213] Network initialization done.\r\n",
- "I0218 20:43:25.607262 2099749632 net.cpp:214] Memory required for data: 62425920\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "E0218 20:43:26.388214 2099749632 upgrade_proto.cpp:618] Attempting to upgrade input file specified using deprecated V1LayerParameter: ../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "I0218 20:43:27.089423 2099749632 upgrade_proto.cpp:626] Successfully upgraded file specified using deprecated V1LayerParameter\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "GPU mode\r\n",
- "Loading input...\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "selective_search_rcnn({'/Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg'}, '/var/folders/bk/dtkn5qjd11bd17b2j36zplyw0000gp/T/tmpakaRLL.mat')\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "Processed 1570 windows in 102.895 s.\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "/Users/shelhamer/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py:2453: PerformanceWarning: \r\n",
- "your performance may suffer as PyTables will pickle object types that it cannot\r\n",
- "map directly to c-types [inferred_type->mixed,key->block1_values] [items->['prediction']]\r\n",
- "\r\n",
- " warnings.warn(ws, PerformanceWarning)\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "Saved to _temp/det_output.h5 in 0.298 s.\r\n"
- ]
- }
- ],
- "prompt_number": 1
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This run was in GPU mode. For CPU mode detection, call `detect.py` without the `--gpu` argument.\n",
- "\n",
- "Running this outputs a DataFrame with the filenames, selected windows, and their detection scores to an HDF5 file.\n",
- "(We only ran on one image, so the filenames will all be the same.)"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import numpy as np\n",
- "import pandas as pd\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n",
- "\n",
- "df = pd.read_hdf('_temp/det_output.h5', 'df')\n",
- "print(df.shape)\n",
- "print(df.iloc[0])"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "(1570, 5)\n",
- "prediction [-2.62247, -2.84579, -2.85122, -3.20838, -1.94...\n",
- "ymin 79.846\n",
- "xmin 9.62\n",
- "ymax 246.31\n",
- "xmax 339.624\n",
- "Name: /Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg, dtype: object\n"
- ]
- }
- ],
- "prompt_number": 2
- },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[R-CNN](https://github.com/rbgirshick/rcnn) is a state-of-the-art detector that classifies region proposals by a finetuned Caffe model. For the full details of the R-CNN system and model, refer to its project site and the paper:\n",
+ "\n",
+ "> *Rich feature hierarchies for accurate object detection and semantic segmentation*. Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. CVPR 2014. [Arxiv 2013](http://arxiv.org/abs/1311.2524).\n",
+ "\n",
+ "In this example, we do detection by a pure Caffe edition of the R-CNN model for ImageNet. The R-CNN detector outputs class scores for the 200 detection classes of ILSVRC13. Keep in mind that these are raw one vs. all SVM scores, so they are not probabilistically calibrated or exactly comparable across classes. Note that this off-the-shelf model is simply for convenience, and is not the full R-CNN model.\n",
+ "\n",
+ "Let's run detection on an image of a bicyclist riding a fish bike in the desert (from the ImageNet challenge—no joke).\n",
+ "\n",
+ "First, we'll need region proposals and the Caffe R-CNN ImageNet model:\n",
+ "\n",
+ "- [Selective Search](http://koen.me/research/selectivesearch/) is the region proposer used by R-CNN. The [selective_search_ijcv_with_python](https://github.com/sergeyk/selective_search_ijcv_with_python) Python module takes care of extracting proposals through the selective search MATLAB implementation. To install it, download the module and name its directory `selective_search_ijcv_with_python`, run the demo in MATLAB to compile the necessary functions, then add it to your `PYTHONPATH` for importing. (If you have your own region proposals prepared, or would rather not bother with this step, [detect.py](https://github.com/BVLC/caffe/blob/master/python/detect.py) accepts a list of images and bounding boxes as CSV.)\n",
+ "\n",
+ "-Run `./scripts/download_model_binary.py models/bvlc_reference_rcnn_ilsvrc13` to get the Caffe R-CNN ImageNet model.\n",
+ "\n",
+ "With that done, we'll call the bundled `detect.py` to generate the region proposals and run the network. For an explanation of the arguments, do `./detect.py --help`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "1570 regions were proposed with the R-CNN configuration of selective search. The number of proposals will vary from image to image based on its contents and size -- selective search isn't scale invariant.\n",
- "\n",
- "In general, `detect.py` is most efficient when running on a lot of images: it first extracts window proposals for all of them, batches the windows for efficient GPU processing, and then outputs the results.\n",
- "Simply list an image per line in the `images_file`, and it will process all of them.\n",
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING: Logging before InitGoogleLogging() is written to STDERR\n",
+ "I0218 20:43:25.383932 2099749632 net.cpp:42] Initializing net from parameters: \n",
+ "name: \"R-CNN-ilsvrc13\"\n",
+ "input: \"data\"\n",
+ "input_dim: 10\n",
+ "input_dim: 3\n",
+ "input_dim: 227\n",
+ "input_dim: 227\n",
+ "state {\n",
+ " phase: TEST\n",
+ "}\n",
+ "layer {\n",
+ " name: \"conv1\"\n",
+ " type: \"Convolution\"\n",
+ " bottom: \"data\"\n",
+ " top: \"conv1\"\n",
+ " convolution_param {\n",
+ " num_output: 96\n",
+ " kernel_size: 11\n",
+ " stride: 4\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"relu1\"\n",
+ " type: \"ReLU\"\n",
+ " bottom: \"conv1\"\n",
+ " top: \"conv1\"\n",
+ "}\n",
+ "layer {\n",
+ " name: \"pool1\"\n",
+ " type: \"Pooling\"\n",
+ " bottom: \"conv1\"\n",
+ " top: \"pool1\"\n",
+ " pooling_param {\n",
+ " pool: MAX\n",
+ " kernel_size: 3\n",
+ " stride: 2\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"norm1\"\n",
+ " type: \"LRN\"\n",
+ " bottom: \"pool1\"\n",
+ " top: \"norm1\"\n",
+ " lrn_param {\n",
+ " local_size: 5\n",
+ " alpha: 0.0001\n",
+ " beta: 0.75\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"conv2\"\n",
+ " type: \"Convolution\"\n",
+ " bottom: \"norm1\"\n",
+ " top: \"conv2\"\n",
+ " convolution_param {\n",
+ " num_output: 256\n",
+ " pad: 2\n",
+ " kernel_size: 5\n",
+ " group: 2\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"relu2\"\n",
+ " type: \"ReLU\"\n",
+ " bottom: \"conv2\"\n",
+ " top: \"conv2\"\n",
+ "}\n",
+ "layer {\n",
+ " name: \"pool2\"\n",
+ " type: \"Pooling\"\n",
+ " bottom: \"conv2\"\n",
+ " top: \"pool2\"\n",
+ " pooling_param {\n",
+ " pool: MAX\n",
+ " kernel_size: 3\n",
+ " stride: 2\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"norm2\"\n",
+ " type: \"LRN\"\n",
+ " bottom: \"pool2\"\n",
+ " top: \"norm2\"\n",
+ " lrn_param {\n",
+ " local_size: 5\n",
+ " alpha: 0.0001\n",
+ " beta: 0.75\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"conv3\"\n",
+ " type: \"Convolution\"\n",
+ " bottom: \"norm2\"\n",
+ " top: \"conv3\"\n",
+ " convolution_param {\n",
+ " num_output: 384\n",
+ " pad: 1\n",
+ " kernel_size: 3\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"relu3\"\n",
+ " type: \"ReLU\"\n",
+ " bottom: \"conv3\"\n",
+ " top: \"conv3\"\n",
+ "}\n",
+ "layer {\n",
+ " name: \"conv4\"\n",
+ " type: \"Convolution\"\n",
+ " bottom: \"conv3\"\n",
+ " top: \"conv4\"\n",
+ " convolution_param {\n",
+ " num_output: 384\n",
+ " pad: 1\n",
+ " kernel_size: 3\n",
+ " group: 2\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"relu4\"\n",
+ " type: \"ReLU\"\n",
+ " bottom: \"conv4\"\n",
+ " top: \"conv4\"\n",
+ "}\n",
+ "layer {\n",
+ " name: \"conv5\"\n",
+ " type: \"Convolution\"\n",
+ " bottom: \"conv4\"\n",
+ " top: \"conv5\"\n",
+ " convolution_param {\n",
+ " num_output: 256\n",
+ " pad: 1\n",
+ " kernel_size: 3\n",
+ " group: 2\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"relu5\"\n",
+ " type: \"ReLU\"\n",
+ " bottom: \"conv5\"\n",
+ " top: \"conv5\"\n",
+ "}\n",
+ "layer {\n",
+ " name: \"pool5\"\n",
+ " type: \"Pooling\"\n",
+ " bottom: \"conv5\"\n",
+ " top: \"pool5\"\n",
+ " pooling_param {\n",
+ " pool: MAX\n",
+ " kernel_size: 3\n",
+ " stride: 2\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"fc6\"\n",
+ " type: \"InnerProduct\"\n",
+ " bottom: \"pool5\"\n",
+ " top: \"fc6\"\n",
+ " inner_product_param {\n",
+ " num_output: 4096\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"relu6\"\n",
+ " type: \"ReLU\"\n",
+ " bottom: \"fc6\"\n",
+ " top: \"fc6\"\n",
+ "}\n",
+ "layer {\n",
+ " name: \"drop6\"\n",
+ " type: \"Dropout\"\n",
+ " bottom: \"fc6\"\n",
+ " top: \"fc6\"\n",
+ " dropout_param {\n",
+ " dropout_ratio: 0.5\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"fc7\"\n",
+ " type: \"InnerProduct\"\n",
+ " bottom: \"fc6\"\n",
+ " top: \"fc7\"\n",
+ " inner_product_param {\n",
+ " num_output: 4096\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"relu7\"\n",
+ " type: \"ReLU\"\n",
+ " bottom: \"fc7\"\n",
+ " top: \"fc7\"\n",
+ "}\n",
+ "layer {\n",
+ " name: \"drop7\"\n",
+ " type: \"Dropout\"\n",
+ " bottom: \"fc7\"\n",
+ " top: \"fc7\"\n",
+ " dropout_param {\n",
+ " dropout_ratio: 0.5\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"fc-rcnn\"\n",
+ " type: \"InnerProduct\"\n",
+ " bottom: \"fc7\"\n",
+ " top: \"fc-rcnn\"\n",
+ " inner_product_param {\n",
+ " num_output: 200\n",
+ " }\n",
+ "}\n",
+ "I0218 20:43:25.385720 2099749632 net.cpp:336] Input 0 -> data\n",
+ "I0218 20:43:25.385769 2099749632 layer_factory.hpp:74] Creating layer conv1\n",
+ "I0218 20:43:25.385783 2099749632 net.cpp:76] Creating Layer conv1\n",
+ "I0218 20:43:25.385790 2099749632 net.cpp:372] conv1 <- data\n",
+ "I0218 20:43:25.385802 2099749632 net.cpp:334] conv1 -> conv1\n",
+ "I0218 20:43:25.385815 2099749632 net.cpp:105] Setting up conv1\n",
+ "I0218 20:43:25.386574 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\n",
+ "I0218 20:43:25.386610 2099749632 layer_factory.hpp:74] Creating layer relu1\n",
+ "I0218 20:43:25.386625 2099749632 net.cpp:76] Creating Layer relu1\n",
+ "I0218 20:43:25.386631 2099749632 net.cpp:372] relu1 <- conv1\n",
+ "I0218 20:43:25.386641 2099749632 net.cpp:323] relu1 -> conv1 (in-place)\n",
+ "I0218 20:43:25.386649 2099749632 net.cpp:105] Setting up relu1\n",
+ "I0218 20:43:25.386656 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\n",
+ "I0218 20:43:25.386663 2099749632 layer_factory.hpp:74] Creating layer pool1\n",
+ "I0218 20:43:25.386675 2099749632 net.cpp:76] Creating Layer pool1\n",
+ "I0218 20:43:25.386682 2099749632 net.cpp:372] pool1 <- conv1\n",
+ "I0218 20:43:25.386690 2099749632 net.cpp:334] pool1 -> pool1\n",
+ "I0218 20:43:25.386699 2099749632 net.cpp:105] Setting up pool1\n",
+ "I0218 20:43:25.386716 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\n",
+ "I0218 20:43:25.386725 2099749632 layer_factory.hpp:74] Creating layer norm1\n",
+ "I0218 20:43:25.386736 2099749632 net.cpp:76] Creating Layer norm1\n",
+ "I0218 20:43:25.386744 2099749632 net.cpp:372] norm1 <- pool1\n",
+ "I0218 20:43:25.386803 2099749632 net.cpp:334] norm1 -> norm1\n",
+ "I0218 20:43:25.386819 2099749632 net.cpp:105] Setting up norm1\n",
+ "I0218 20:43:25.386832 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\n",
+ "I0218 20:43:25.386842 2099749632 layer_factory.hpp:74] Creating layer conv2\n",
+ "I0218 20:43:25.386852 2099749632 net.cpp:76] Creating Layer conv2\n",
+ "I0218 20:43:25.386865 2099749632 net.cpp:372] conv2 <- norm1\n",
+ "I0218 20:43:25.386878 2099749632 net.cpp:334] conv2 -> conv2\n",
+ "I0218 20:43:25.386899 2099749632 net.cpp:105] Setting up conv2\n",
+ "I0218 20:43:25.387024 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\n",
+ "I0218 20:43:25.387042 2099749632 layer_factory.hpp:74] Creating layer relu2\n",
+ "I0218 20:43:25.387050 2099749632 net.cpp:76] Creating Layer relu2\n",
+ "I0218 20:43:25.387058 2099749632 net.cpp:372] relu2 <- conv2\n",
+ "I0218 20:43:25.387066 2099749632 net.cpp:323] relu2 -> conv2 (in-place)\n",
+ "I0218 20:43:25.387075 2099749632 net.cpp:105] Setting up relu2\n",
+ "I0218 20:43:25.387081 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\n",
+ "I0218 20:43:25.387089 2099749632 layer_factory.hpp:74] Creating layer pool2\n",
+ "I0218 20:43:25.387097 2099749632 net.cpp:76] Creating Layer pool2\n",
+ "I0218 20:43:25.387104 2099749632 net.cpp:372] pool2 <- conv2\n",
+ "I0218 20:43:25.387112 2099749632 net.cpp:334] pool2 -> pool2\n",
+ "I0218 20:43:25.387121 2099749632 net.cpp:105] Setting up pool2\n",
+ "I0218 20:43:25.387130 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n",
+ "I0218 20:43:25.387137 2099749632 layer_factory.hpp:74] Creating layer norm2\n",
+ "I0218 20:43:25.387145 2099749632 net.cpp:76] Creating Layer norm2\n",
+ "I0218 20:43:25.387152 2099749632 net.cpp:372] norm2 <- pool2\n",
+ "I0218 20:43:25.387161 2099749632 net.cpp:334] norm2 -> norm2\n",
+ "I0218 20:43:25.387168 2099749632 net.cpp:105] Setting up norm2\n",
+ "I0218 20:43:25.387176 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n",
+ "I0218 20:43:25.387228 2099749632 layer_factory.hpp:74] Creating layer conv3\n",
+ "I0218 20:43:25.387249 2099749632 net.cpp:76] Creating Layer conv3\n",
+ "I0218 20:43:25.387258 2099749632 net.cpp:372] conv3 <- norm2\n",
+ "I0218 20:43:25.387266 2099749632 net.cpp:334] conv3 -> conv3\n",
+ "I0218 20:43:25.387276 2099749632 net.cpp:105] Setting up conv3\n",
+ "I0218 20:43:25.389375 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n",
+ "I0218 20:43:25.389408 2099749632 layer_factory.hpp:74] Creating layer relu3\n",
+ "I0218 20:43:25.389421 2099749632 net.cpp:76] Creating Layer relu3\n",
+ "I0218 20:43:25.389430 2099749632 net.cpp:372] relu3 <- conv3\n",
+ "I0218 20:43:25.389438 2099749632 net.cpp:323] relu3 -> conv3 (in-place)\n",
+ "I0218 20:43:25.389447 2099749632 net.cpp:105] Setting up relu3\n",
+ "I0218 20:43:25.389456 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n",
+ "I0218 20:43:25.389462 2099749632 layer_factory.hpp:74] Creating layer conv4\n",
+ "I0218 20:43:25.389472 2099749632 net.cpp:76] Creating Layer conv4\n",
+ "I0218 20:43:25.389478 2099749632 net.cpp:372] conv4 <- conv3\n",
+ "I0218 20:43:25.389487 2099749632 net.cpp:334] conv4 -> conv4\n",
+ "I0218 20:43:25.389497 2099749632 net.cpp:105] Setting up conv4\n",
+ "I0218 20:43:25.391810 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n",
+ "I0218 20:43:25.391856 2099749632 layer_factory.hpp:74] Creating layer relu4\n",
+ "I0218 20:43:25.391871 2099749632 net.cpp:76] Creating Layer relu4\n",
+ "I0218 20:43:25.391880 2099749632 net.cpp:372] relu4 <- conv4\n",
+ "I0218 20:43:25.391888 2099749632 net.cpp:323] relu4 -> conv4 (in-place)\n",
+ "I0218 20:43:25.391898 2099749632 net.cpp:105] Setting up relu4\n",
+ "I0218 20:43:25.391906 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n",
+ "I0218 20:43:25.391913 2099749632 layer_factory.hpp:74] Creating layer conv5\n",
+ "I0218 20:43:25.391923 2099749632 net.cpp:76] Creating Layer conv5\n",
+ "I0218 20:43:25.391929 2099749632 net.cpp:372] conv5 <- conv4\n",
+ "I0218 20:43:25.391937 2099749632 net.cpp:334] conv5 -> conv5\n",
+ "I0218 20:43:25.391947 2099749632 net.cpp:105] Setting up conv5\n",
+ "I0218 20:43:25.393072 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n",
+ "I0218 20:43:25.393108 2099749632 layer_factory.hpp:74] Creating layer relu5\n",
+ "I0218 20:43:25.393122 2099749632 net.cpp:76] Creating Layer relu5\n",
+ "I0218 20:43:25.393129 2099749632 net.cpp:372] relu5 <- conv5\n",
+ "I0218 20:43:25.393138 2099749632 net.cpp:323] relu5 -> conv5 (in-place)\n",
+ "I0218 20:43:25.393148 2099749632 net.cpp:105] Setting up relu5\n",
+ "I0218 20:43:25.393157 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n",
+ "I0218 20:43:25.393167 2099749632 layer_factory.hpp:74] Creating layer pool5\n",
+ "I0218 20:43:25.393175 2099749632 net.cpp:76] Creating Layer pool5\n",
+ "I0218 20:43:25.393182 2099749632 net.cpp:372] pool5 <- conv5\n",
+ "I0218 20:43:25.393190 2099749632 net.cpp:334] pool5 -> pool5\n",
+ "I0218 20:43:25.393199 2099749632 net.cpp:105] Setting up pool5\n",
+ "I0218 20:43:25.393209 2099749632 net.cpp:112] Top shape: 10 256 6 6 (92160)\n",
+ "I0218 20:43:25.393218 2099749632 layer_factory.hpp:74] Creating layer fc6\n",
+ "I0218 20:43:25.393226 2099749632 net.cpp:76] Creating Layer fc6\n",
+ "I0218 20:43:25.393232 2099749632 net.cpp:372] fc6 <- pool5\n",
+ "I0218 20:43:25.393240 2099749632 net.cpp:334] fc6 -> fc6\n",
+ "I0218 20:43:25.393249 2099749632 net.cpp:105] Setting up fc6\n",
+ "I0218 20:43:25.516396 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+ "I0218 20:43:25.516445 2099749632 layer_factory.hpp:74] Creating layer relu6\n",
+ "I0218 20:43:25.516463 2099749632 net.cpp:76] Creating Layer relu6\n",
+ "I0218 20:43:25.516470 2099749632 net.cpp:372] relu6 <- fc6\n",
+ "I0218 20:43:25.516480 2099749632 net.cpp:323] relu6 -> fc6 (in-place)\n",
+ "I0218 20:43:25.516490 2099749632 net.cpp:105] Setting up relu6\n",
+ "I0218 20:43:25.516497 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+ "I0218 20:43:25.516505 2099749632 layer_factory.hpp:74] Creating layer drop6\n",
+ "I0218 20:43:25.516515 2099749632 net.cpp:76] Creating Layer drop6\n",
+ "I0218 20:43:25.516521 2099749632 net.cpp:372] drop6 <- fc6\n",
+ "I0218 20:43:25.516530 2099749632 net.cpp:323] drop6 -> fc6 (in-place)\n",
+ "I0218 20:43:25.516538 2099749632 net.cpp:105] Setting up drop6\n",
+ "I0218 20:43:25.516557 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+ "I0218 20:43:25.516566 2099749632 layer_factory.hpp:74] Creating layer fc7\n",
+ "I0218 20:43:25.516576 2099749632 net.cpp:76] Creating Layer fc7\n",
+ "I0218 20:43:25.516582 2099749632 net.cpp:372] fc7 <- fc6\n",
+ "I0218 20:43:25.516589 2099749632 net.cpp:334] fc7 -> fc7\n",
+ "I0218 20:43:25.516599 2099749632 net.cpp:105] Setting up fc7\n",
+ "I0218 20:43:25.604786 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+ "I0218 20:43:25.604838 2099749632 layer_factory.hpp:74] Creating layer relu7\n",
+ "I0218 20:43:25.604852 2099749632 net.cpp:76] Creating Layer relu7\n",
+ "I0218 20:43:25.604859 2099749632 net.cpp:372] relu7 <- fc7\n",
+ "I0218 20:43:25.604868 2099749632 net.cpp:323] relu7 -> fc7 (in-place)\n",
+ "I0218 20:43:25.604878 2099749632 net.cpp:105] Setting up relu7\n",
+ "I0218 20:43:25.604885 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+ "I0218 20:43:25.604893 2099749632 layer_factory.hpp:74] Creating layer drop7\n",
+ "I0218 20:43:25.604902 2099749632 net.cpp:76] Creating Layer drop7\n",
+ "I0218 20:43:25.604908 2099749632 net.cpp:372] drop7 <- fc7\n",
+ "I0218 20:43:25.604917 2099749632 net.cpp:323] drop7 -> fc7 (in-place)\n",
+ "I0218 20:43:25.604924 2099749632 net.cpp:105] Setting up drop7\n",
+ "I0218 20:43:25.604933 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+ "I0218 20:43:25.604939 2099749632 layer_factory.hpp:74] Creating layer fc-rcnn\n",
+ "I0218 20:43:25.604948 2099749632 net.cpp:76] Creating Layer fc-rcnn\n",
+ "I0218 20:43:25.604954 2099749632 net.cpp:372] fc-rcnn <- fc7\n",
+ "I0218 20:43:25.604962 2099749632 net.cpp:334] fc-rcnn -> fc-rcnn\n",
+ "I0218 20:43:25.604971 2099749632 net.cpp:105] Setting up fc-rcnn\n",
+ "I0218 20:43:25.606878 2099749632 net.cpp:112] Top shape: 10 200 1 1 (2000)\n",
+ "I0218 20:43:25.606904 2099749632 net.cpp:165] fc-rcnn does not need backward computation.\n",
+ "I0218 20:43:25.606909 2099749632 net.cpp:165] drop7 does not need backward computation.\n",
+ "I0218 20:43:25.606916 2099749632 net.cpp:165] relu7 does not need backward computation.\n",
+ "I0218 20:43:25.606922 2099749632 net.cpp:165] fc7 does not need backward computation.\n",
+ "I0218 20:43:25.606928 2099749632 net.cpp:165] drop6 does not need backward computation.\n",
+ "I0218 20:43:25.606935 2099749632 net.cpp:165] relu6 does not need backward computation.\n",
+ "I0218 20:43:25.606940 2099749632 net.cpp:165] fc6 does not need backward computation.\n",
+ "I0218 20:43:25.606946 2099749632 net.cpp:165] pool5 does not need backward computation.\n",
+ "I0218 20:43:25.606952 2099749632 net.cpp:165] relu5 does not need backward computation.\n",
+ "I0218 20:43:25.606958 2099749632 net.cpp:165] conv5 does not need backward computation.\n",
+ "I0218 20:43:25.606964 2099749632 net.cpp:165] relu4 does not need backward computation.\n",
+ "I0218 20:43:25.606971 2099749632 net.cpp:165] conv4 does not need backward computation.\n",
+ "I0218 20:43:25.606976 2099749632 net.cpp:165] relu3 does not need backward computation.\n",
+ "I0218 20:43:25.606982 2099749632 net.cpp:165] conv3 does not need backward computation.\n",
+ "I0218 20:43:25.606988 2099749632 net.cpp:165] norm2 does not need backward computation.\n",
+ "I0218 20:43:25.606995 2099749632 net.cpp:165] pool2 does not need backward computation.\n",
+ "I0218 20:43:25.607002 2099749632 net.cpp:165] relu2 does not need backward computation.\n",
+ "I0218 20:43:25.607007 2099749632 net.cpp:165] conv2 does not need backward computation.\n",
+ "I0218 20:43:25.607013 2099749632 net.cpp:165] norm1 does not need backward computation.\n",
+ "I0218 20:43:25.607199 2099749632 net.cpp:165] pool1 does not need backward computation.\n",
+ "I0218 20:43:25.607213 2099749632 net.cpp:165] relu1 does not need backward computation.\n",
+ "I0218 20:43:25.607219 2099749632 net.cpp:165] conv1 does not need backward computation.\n",
+ "I0218 20:43:25.607225 2099749632 net.cpp:201] This network produces output fc-rcnn\n",
+ "I0218 20:43:25.607239 2099749632 net.cpp:446] Collecting Learning Rate and Weight Decay.\n",
+ "I0218 20:43:25.607255 2099749632 net.cpp:213] Network initialization done.\n",
+ "I0218 20:43:25.607262 2099749632 net.cpp:214] Memory required for data: 62425920\n",
+ "E0218 20:43:26.388214 2099749632 upgrade_proto.cpp:618] Attempting to upgrade input file specified using deprecated V1LayerParameter: ../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel\n",
+ "I0218 20:43:27.089423 2099749632 upgrade_proto.cpp:626] Successfully upgraded file specified using deprecated V1LayerParameter\n",
+ "GPU mode\n",
+ "Loading input...\n",
+ "selective_search_rcnn({'/Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg'}, '/var/folders/bk/dtkn5qjd11bd17b2j36zplyw0000gp/T/tmpakaRLL.mat')\n",
+ "Processed 1570 windows in 102.895 s.\n",
+ "/Users/shelhamer/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py:2453: PerformanceWarning: \n",
+ "your performance may suffer as PyTables will pickle object types that it cannot\n",
+ "map directly to c-types [inferred_type->mixed,key->block1_values] [items->['prediction']]\n",
"\n",
- "Although this guide gives an example of R-CNN ImageNet detection, `detect.py` is clever enough to adapt to different Caffe models\u2019 input dimensions, batch size, and output categories. You can switch the model definition and pretrained model as desired. Refer to `python detect.py --help` for the parameters to describe your data set. There's no need for hardcoding.\n",
- "\n",
- "Anyway, let's now load the ILSVRC13 detection class names and make a DataFrame of the predictions. Note you'll need the auxiliary ilsvrc2012 data fetched by `data/ilsvrc12/get_ilsvrc12_aux.sh`."
+ " warnings.warn(ws, PerformanceWarning)\n",
+ "Saved to _temp/det_output.h5 in 0.298 s.\n"
]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "with open('../data/ilsvrc12/det_synset_words.txt') as f:\n",
- " labels_df = pd.DataFrame([\n",
- " {\n",
- " 'synset_id': l.strip().split(' ')[0],\n",
- " 'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]\n",
- " }\n",
- " for l in f.readlines()\n",
- " ])\n",
- "labels_df.sort('synset_id')\n",
- "predictions_df = pd.DataFrame(np.vstack(df.prediction.values), columns=labels_df['name'])\n",
- "print(predictions_df.iloc[0])"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "name\n",
- "accordion -2.622471\n",
- "airplane -2.845788\n",
- "ant -2.851219\n",
- "antelope -3.208377\n",
- "apple -1.949950\n",
- "armadillo -2.472935\n",
- "artichoke -2.201684\n",
- "axe -2.327404\n",
- "baby bed -2.737925\n",
- "backpack -2.176763\n",
- "bagel -2.681061\n",
- "balance beam -2.722538\n",
- "banana -2.390628\n",
- "band aid -1.598909\n",
- "banjo -2.298197\n",
- "...\n",
- "trombone -2.582361\n",
- "trumpet -2.352853\n",
- "turtle -2.360859\n",
- "tv or monitor -2.761043\n",
- "unicycle -2.218467\n",
- "vacuum -1.907717\n",
- "violin -2.757079\n",
- "volleyball -2.723689\n",
- "waffle iron -2.418540\n",
- "washer -2.408994\n",
- "water bottle -2.174899\n",
- "watercraft -2.837425\n",
- "whale -3.120338\n",
- "wine bottle -2.772960\n",
- "zebra -2.742913\n",
- "Name: 0, Length: 200, dtype: float32\n"
- ]
- }
- ],
- "prompt_number": 3
- },
+ }
+ ],
+ "source": [
+ "!mkdir -p _temp\n",
+ "!echo `pwd`/images/fish-bike.jpg > _temp/det_input.txt\n",
+ "!../python/detect.py --crop_mode=selective_search --pretrained_model=../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel --model_def=../models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt --gpu --raw_scale=255 _temp/det_input.txt _temp/det_output.h5"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This run was in GPU mode. For CPU mode detection, call `detect.py` without the `--gpu` argument.\n",
+ "\n",
+ "Running this outputs a DataFrame with the filenames, selected windows, and their detection scores to an HDF5 file.\n",
+ "(We only ran on one image, so the filenames will all be the same.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's look at the activations."
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(1570, 5)\n",
+ "prediction [-2.62247, -2.84579, -2.85122, -3.20838, -1.94...\n",
+ "ymin 79.846\n",
+ "xmin 9.62\n",
+ "ymax 246.31\n",
+ "xmax 339.624\n",
+ "Name: /Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg, dtype: object\n"
]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "plt.gray()\n",
- "plt.matshow(predictions_df.values)\n",
- "plt.xlabel('Classes')\n",
- "plt.ylabel('Windows')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 4,
- "text": [
- "<matplotlib.text.Text at 0x114f15f90>"
- ]
- },
- {
- "metadata": {},
- "output_type": "display_data",
- "text": [
- "<matplotlib.figure.Figure at 0x114254b50>"
- ]
- },
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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TkxP4/X40Gg1MJhO43W7BnK1Wq9wo/X4fTqdTXt/x8TGcTicSiQSazabsvIFAAIqi4OXL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V1fX1cul9PNzY22trZ0dnZmhgGqG77++muzMaH7WFm5c5Dxc6RSKYMbh8OhhTs6c6pJ0/+t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\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zj9o3yJlgigksRhlDLSvJO+twOLRhIT8b2RcRx/A0JJmOiWK90+mYlonMC3Sm0+lYiAAa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\n",
- "text": [
- "<matplotlib.figure.Figure at 0x114254ad0>"
- ]
- }
- ],
- "prompt_number": 4
- },
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "df = pd.read_hdf('_temp/det_output.h5', 'df')\n",
+ "print(df.shape)\n",
+ "print(df.iloc[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1570 regions were proposed with the R-CNN configuration of selective search. The number of proposals will vary from image to image based on its contents and size -- selective search isn't scale invariant.\n",
+ "\n",
+ "In general, `detect.py` is most efficient when running on a lot of images: it first extracts window proposals for all of them, batches the windows for efficient GPU processing, and then outputs the results.\n",
+ "Simply list an image per line in the `images_file`, and it will process all of them.\n",
+ "\n",
+ "Although this guide gives an example of R-CNN ImageNet detection, `detect.py` is clever enough to adapt to different Caffe models’ input dimensions, batch size, and output categories. You can switch the model definition and pretrained model as desired. Refer to `python detect.py --help` for the parameters to describe your data set. There's no need for hardcoding.\n",
+ "\n",
+ "Anyway, let's now load the ILSVRC13 detection class names and make a DataFrame of the predictions. Note you'll need the auxiliary ilsvrc2012 data fetched by `data/ilsvrc12/get_ilsvrc12_aux.sh`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now let's take max across all windows and plot the top classes."
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "name\n",
+ "accordion -2.622471\n",
+ "airplane -2.845788\n",
+ "ant -2.851219\n",
+ "antelope -3.208377\n",
+ "apple -1.949950\n",
+ "armadillo -2.472935\n",
+ "artichoke -2.201684\n",
+ "axe -2.327404\n",
+ "baby bed -2.737925\n",
+ "backpack -2.176763\n",
+ "bagel -2.681061\n",
+ "balance beam -2.722538\n",
+ "banana -2.390628\n",
+ "band aid -1.598909\n",
+ "banjo -2.298197\n",
+ "...\n",
+ "trombone -2.582361\n",
+ "trumpet -2.352853\n",
+ "turtle -2.360859\n",
+ "tv or monitor -2.761043\n",
+ "unicycle -2.218467\n",
+ "vacuum -1.907717\n",
+ "violin -2.757079\n",
+ "volleyball -2.723689\n",
+ "waffle iron -2.418540\n",
+ "washer -2.408994\n",
+ "water bottle -2.174899\n",
+ "watercraft -2.837425\n",
+ "whale -3.120338\n",
+ "wine bottle -2.772960\n",
+ "zebra -2.742913\n",
+ "Name: 0, Length: 200, dtype: float32\n"
]
- },
+ }
+ ],
+ "source": [
+ "with open('../data/ilsvrc12/det_synset_words.txt') as f:\n",
+ " labels_df = pd.DataFrame([\n",
+ " {\n",
+ " 'synset_id': l.strip().split(' ')[0],\n",
+ " 'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]\n",
+ " }\n",
+ " for l in f.readlines()\n",
+ " ])\n",
+ "labels_df.sort('synset_id')\n",
+ "predictions_df = pd.DataFrame(np.vstack(df.prediction.values), columns=labels_df['name'])\n",
+ "print(predictions_df.iloc[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's look at the activations."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
{
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "max_s = predictions_df.max(0)\n",
- "max_s.sort(ascending=False)\n",
- "print(max_s[:10])"
- ],
- "language": "python",
+ "data": {
+ "text/plain": [
+ "<matplotlib.text.Text at 0x114f15f90>"
+ ]
+ },
+ "execution_count": 4,
"metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "name\n",
- "person 1.835771\n",
- "bicycle 0.866110\n",
- "unicycle 0.057080\n",
- "motorcycle -0.006122\n",
- "banjo -0.028209\n",
- "turtle -0.189831\n",
- "electric fan -0.206788\n",
- "cart -0.214235\n",
- "lizard -0.393519\n",
- "helmet -0.477942\n",
- "dtype: float32\n"
- ]
- }
- ],
- "prompt_number": 5
+ "output_type": "execute_result"
},
{
- "cell_type": "markdown",
+ "data": {
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x114254b50>"
+ ]
+ },
"metadata": {},
- "source": [
- "The top detections are in fact a person and bicycle.\n",
- "Picking good localizations is a work in progress; we pick the top-scoring person and bicycle detections."
- ]
+ "output_type": "display_data"
},
{
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# Find, print, and display the top detections: person and bicycle.\n",
- "i = predictions_df['person'].argmax()\n",
- "j = predictions_df['bicycle'].argmax()\n",
- "\n",
- "# Show top predictions for top detection.\n",
- "f = pd.Series(df['prediction'].iloc[i], index=labels_df['name'])\n",
- "print('Top detection:')\n",
- "print(f.order(ascending=False)[:5])\n",
- "print('')\n",
- "\n",
- "# Show top predictions for second-best detection.\n",
- "f = pd.Series(df['prediction'].iloc[j], index=labels_df['name'])\n",
- "print('Second-best detection:')\n",
- "print(f.order(ascending=False)[:5])\n",
- "\n",
- "# Show top detection in red, second-best top detection in blue.\n",
- "im = plt.imread('images/fish-bike.jpg')\n",
- "plt.imshow(im)\n",
- "currentAxis = plt.gca()\n",
- "\n",
- "det = df.iloc[i]\n",
- "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
- "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))\n",
- "\n",
- "det = df.iloc[j]\n",
- "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
- "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))"
- ],
- "language": "python",
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+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x114254ad0>"
+ ]
+ },
"metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "Top detection:\n",
- "name\n",
- "person 1.835771\n",
- "swimming trunks -1.150371\n",
- "rubber eraser -1.231106\n",
- "turtle -1.266037\n",
- "plastic bag -1.303265\n",
- "dtype: float32\n",
- "\n",
- "Second-best detection:\n",
- "name\n",
- "bicycle 0.866110\n",
- "unicycle -0.359139\n",
- "scorpion -0.811621\n",
- "lobster -0.982891\n",
- "lamp -1.096808\n",
- "dtype: float32\n"
- ]
- },
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 6,
- "text": [
- "<matplotlib.patches.Rectangle at 0x118576a90>"
- ]
- },
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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CKjRVWzPaXKeal4TgOZuNMVKh8oy93V3Qmv76CKUUa6M1nh8dMlpfp5cXjCdj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1y8chlzqcXWZgcjAykhSALe9oY38ofPPMdxWxDtdBmtbnH+sXv5zPZnqM+7nDtxD8P5Rf78S1/k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b9V2kEn/LVRlfCHm4HigqWxpN25ZTPfBB6aa2Shz9LGXRqAbLnFg+itBzKDyLY53z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fXVV/mVf/dLHB0d8a//1b9iZWWFH/6hH6Lb7dLr9Th//jz9bo9WENJtdxCVvm/nHnqExx57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\nwNe2trWt7QVq/w/Uvjt8hhUJzgAAAABJRU5ErkJggg==\n",
- "text": [
- "<matplotlib.figure.Figure at 0x116bcb210>"
- ]
- }
- ],
- "prompt_number": 6
- },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.gray()\n",
+ "plt.matshow(predictions_df.values)\n",
+ "plt.xlabel('Classes')\n",
+ "plt.ylabel('Windows')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now let's take max across all windows and plot the top classes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "That's cool. Let's take all 'bicycle' detections and NMS them to get rid of overlapping windows."
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "name\n",
+ "person 1.835771\n",
+ "bicycle 0.866110\n",
+ "unicycle 0.057080\n",
+ "motorcycle -0.006122\n",
+ "banjo -0.028209\n",
+ "turtle -0.189831\n",
+ "electric fan -0.206788\n",
+ "cart -0.214235\n",
+ "lizard -0.393519\n",
+ "helmet -0.477942\n",
+ "dtype: float32\n"
]
- },
+ }
+ ],
+ "source": [
+ "max_s = predictions_df.max(0)\n",
+ "max_s.sort(ascending=False)\n",
+ "print(max_s[:10])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The top detections are in fact a person and bicycle.\n",
+ "Picking good localizations is a work in progress; we pick the top-scoring person and bicycle detections."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
{
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def nms_detections(dets, overlap=0.3):\n",
- " \"\"\"\n",
- " Non-maximum suppression: Greedily select high-scoring detections and\n",
- " skip detections that are significantly covered by a previously\n",
- " selected detection.\n",
- "\n",
- " This version is translated from Matlab code by Tomasz Malisiewicz,\n",
- " who sped up Pedro Felzenszwalb's code.\n",
- "\n",
- " Parameters\n",
- " ----------\n",
- " dets: ndarray\n",
- " each row is ['xmin', 'ymin', 'xmax', 'ymax', 'score']\n",
- " overlap: float\n",
- " minimum overlap ratio (0.3 default)\n",
- "\n",
- " Output\n",
- " ------\n",
- " dets: ndarray\n",
- " remaining after suppression.\n",
- " \"\"\"\n",
- " x1 = dets[:, 0]\n",
- " y1 = dets[:, 1]\n",
- " x2 = dets[:, 2]\n",
- " y2 = dets[:, 3]\n",
- " ind = np.argsort(dets[:, 4])\n",
- "\n",
- " w = x2 - x1\n",
- " h = y2 - y1\n",
- " area = (w * h).astype(float)\n",
- "\n",
- " pick = []\n",
- " while len(ind) > 0:\n",
- " i = ind[-1]\n",
- " pick.append(i)\n",
- " ind = ind[:-1]\n",
- "\n",
- " xx1 = np.maximum(x1[i], x1[ind])\n",
- " yy1 = np.maximum(y1[i], y1[ind])\n",
- " xx2 = np.minimum(x2[i], x2[ind])\n",
- " yy2 = np.minimum(y2[i], y2[ind])\n",
- "\n",
- " w = np.maximum(0., xx2 - xx1)\n",
- " h = np.maximum(0., yy2 - yy1)\n",
- "\n",
- " wh = w * h\n",
- " o = wh / (area[i] + area[ind] - wh)\n",
- "\n",
- " ind = ind[np.nonzero(o <= overlap)[0]]\n",
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Top detection:\n",
+ "name\n",
+ "person 1.835771\n",
+ "swimming trunks -1.150371\n",
+ "rubber eraser -1.231106\n",
+ "turtle -1.266037\n",
+ "plastic bag -1.303265\n",
+ "dtype: float32\n",
"\n",
- " return dets[pick, :]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 7
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "scores = predictions_df['bicycle']\n",
- "windows = df[['xmin', 'ymin', 'xmax', 'ymax']].values\n",
- "dets = np.hstack((windows, scores[:, np.newaxis]))\n",
- "nms_dets = nms_detections(dets)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 8
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Show top 3 NMS'd detections for 'bicycle' in the image and note the gap between the top scoring box (red) and the remaining boxes."
+ "Second-best detection:\n",
+ "name\n",
+ "bicycle 0.866110\n",
+ "unicycle -0.359139\n",
+ "scorpion -0.811621\n",
+ "lobster -0.982891\n",
+ "lamp -1.096808\n",
+ "dtype: float32\n"
]
},
{
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "plt.imshow(im)\n",
- "currentAxis = plt.gca()\n",
- "colors = ['r', 'b', 'y']\n",
- "for c, det in zip(colors, nms_dets[:3]):\n",
- " currentAxis.add_patch(\n",
- " plt.Rectangle((det[0], det[1]), det[2]-det[0], det[3]-det[1],\n",
- " fill=False, edgecolor=c, linewidth=5)\n",
- " )\n",
- "print 'scores:', nms_dets[:3, 4]"
- ],
- "language": "python",
+ "data": {
+ "text/plain": [
+ "<matplotlib.patches.Rectangle at 0x118576a90>"
+ ]
+ },
+ "execution_count": 6,
"metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "scores: [ 0.86610985 -0.70051557 -1.34796357]\n"
- ]
- },
- {
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w7PUhxMaYuqqRUnLj+nUCMByOGK6N2NjaYDQa8elnn6G1xnrPlhCMJ2eoPPKE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W6Ho01Mx7k+XnVkV+t3SCYqsljKn0N1IirrMAUn5rlqgjX9XHPiMIMFTBk9Smb176Zcuv4ryPPp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wenNsjbnpcteZYHjsvsSnvQ2N63I73aBJkm6Ge5gS0tZwG2//+gtcCoL8R7bNYSxS5SGPqx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\nEqcl9b0u1vyhKNZYn87kIrmBtXVFXTbkaUqRO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- "text": [
- "<matplotlib.figure.Figure at 0x114f15d10>"
- ]
- }
- ],
- "prompt_number": 9
+ "output_type": "execute_result"
},
{
- "cell_type": "markdown",
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+ "wNe2trWt7QVq/w/Uvjt8hhUJzgAAAABJRU5ErkJggg==\n"
+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x116bcb210>"
+ ]
+ },
"metadata": {},
- "source": [
- "This was an easy instance for bicycle as it was in the class's training set. However, the person result is a true detection since this was not in the set for that class.\n",
- "\n",
- "You should try out detection on an image of your own next!"
- ]
- },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Find, print, and display the top detections: person and bicycle.\n",
+ "i = predictions_df['person'].argmax()\n",
+ "j = predictions_df['bicycle'].argmax()\n",
+ "\n",
+ "# Show top predictions for top detection.\n",
+ "f = pd.Series(df['prediction'].iloc[i], index=labels_df['name'])\n",
+ "print('Top detection:')\n",
+ "print(f.order(ascending=False)[:5])\n",
+ "print('')\n",
+ "\n",
+ "# Show top predictions for second-best detection.\n",
+ "f = pd.Series(df['prediction'].iloc[j], index=labels_df['name'])\n",
+ "print('Second-best detection:')\n",
+ "print(f.order(ascending=False)[:5])\n",
+ "\n",
+ "# Show top detection in red, second-best top detection in blue.\n",
+ "im = plt.imread('images/fish-bike.jpg')\n",
+ "plt.imshow(im)\n",
+ "currentAxis = plt.gca()\n",
+ "\n",
+ "det = df.iloc[i]\n",
+ "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
+ "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))\n",
+ "\n",
+ "det = df.iloc[j]\n",
+ "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
+ "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "That's cool. Let's take all 'bicycle' detections and NMS them to get rid of overlapping windows."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def nms_detections(dets, overlap=0.3):\n",
+ " \"\"\"\n",
+ " Non-maximum suppression: Greedily select high-scoring detections and\n",
+ " skip detections that are significantly covered by a previously\n",
+ " selected detection.\n",
+ "\n",
+ " This version is translated from Matlab code by Tomasz Malisiewicz,\n",
+ " who sped up Pedro Felzenszwalb's code.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " dets: ndarray\n",
+ " each row is ['xmin', 'ymin', 'xmax', 'ymax', 'score']\n",
+ " overlap: float\n",
+ " minimum overlap ratio (0.3 default)\n",
+ "\n",
+ " Output\n",
+ " ------\n",
+ " dets: ndarray\n",
+ " remaining after suppression.\n",
+ " \"\"\"\n",
+ " x1 = dets[:, 0]\n",
+ " y1 = dets[:, 1]\n",
+ " x2 = dets[:, 2]\n",
+ " y2 = dets[:, 3]\n",
+ " ind = np.argsort(dets[:, 4])\n",
+ "\n",
+ " w = x2 - x1\n",
+ " h = y2 - y1\n",
+ " area = (w * h).astype(float)\n",
+ "\n",
+ " pick = []\n",
+ " while len(ind) > 0:\n",
+ " i = ind[-1]\n",
+ " pick.append(i)\n",
+ " ind = ind[:-1]\n",
+ "\n",
+ " xx1 = np.maximum(x1[i], x1[ind])\n",
+ " yy1 = np.maximum(y1[i], y1[ind])\n",
+ " xx2 = np.minimum(x2[i], x2[ind])\n",
+ " yy2 = np.minimum(y2[i], y2[ind])\n",
+ "\n",
+ " w = np.maximum(0., xx2 - xx1)\n",
+ " h = np.maximum(0., yy2 - yy1)\n",
+ "\n",
+ " wh = w * h\n",
+ " o = wh / (area[i] + area[ind] - wh)\n",
+ "\n",
+ " ind = ind[np.nonzero(o <= overlap)[0]]\n",
+ "\n",
+ " return dets[pick, :]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "scores = predictions_df['bicycle']\n",
+ "windows = df[['xmin', 'ymin', 'xmax', 'ymax']].values\n",
+ "dets = np.hstack((windows, scores[:, np.newaxis]))\n",
+ "nms_dets = nms_detections(dets)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Show top 3 NMS'd detections for 'bicycle' in the image and note the gap between the top scoring box (red) and the remaining boxes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "(Remove the temp directory to clean up, and we're done.)"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "scores: [ 0.86610985 -0.70051557 -1.34796357]\n"
]
},
{
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "!rm -rf _temp"
- ],
- "language": "python",
+ "data": {
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+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x114f15d10>"
+ ]
+ },
"metadata": {},
- "outputs": [],
- "prompt_number": 10
+ "output_type": "display_data"
}
],
- "metadata": {}
+ "source": [
+ "plt.imshow(im)\n",
+ "currentAxis = plt.gca()\n",
+ "colors = ['r', 'b', 'y']\n",
+ "for c, det in zip(colors, nms_dets[:3]):\n",
+ " currentAxis.add_patch(\n",
+ " plt.Rectangle((det[0], det[1]), det[2]-det[0], det[3]-det[1],\n",
+ " fill=False, edgecolor=c, linewidth=5)\n",
+ " )\n",
+ "print 'scores:', nms_dets[:3, 4]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This was an easy instance for bicycle as it was in the class's training set. However, the person result is a true detection since this was not in the set for that class.\n",
+ "\n",
+ "You should try out detection on an image of your own next!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "(Remove the temp directory to clean up, and we're done.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "!rm -rf _temp"
+ ]
}
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+ "description": "Run a pretrained model as a detector in Python.",
+ "example_name": "R-CNN detection",
+ "include_in_docs": true,
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.9"
+ },
+ "priority": 6
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
===================
In this tutorial, we will extract features using a pre-trained model with the included C++ utility.
-Note that we recommend using the Python interface for this task, as for example in the [filter visualization example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/filter_visualization.ipynb).
+Note that we recommend using the Python interface for this task, as for example in the [filter visualization example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb).
Follow instructions for [installing Caffe](../../installation.html) and run `scripts/download_model_binary.py models/bvlc_reference_caffenet` from caffe root directory.
If you need detailed information about the tools below, please consult their source code, in which additional documentation is usually provided.
rm -rf examples/_temp/features/
-If you'd like to use the Python wrapper for extracting features, check out the [layer visualization notebook](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/filter_visualization.ipynb).
+If you'd like to use the Python wrapper for extracting features, check out the [filter visualization notebook](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb).
Clean Up
--------
+++ /dev/null
-{
- "metadata": {
- "description": "Extracting features and visualizing trained filters with an example image, viewed layer-by-layer.",
- "example_name": "Filter visualization",
- "include_in_docs": true,
- "priority": 2,
- "signature": "sha256:64c88129e2eeaa956e4c8a26467ff6119f24ea3d7ef15f8217326249973bea8f"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Here we visualize filters and outputs using the network architecture proposed by Krizhevsky et al. for ImageNet and implemented in `caffe`.\n",
- "\n",
- "(This page follows DeCAF visualizations originally by Yangqing Jia.)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "First, import required modules, set plotting parameters, and run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the pretrained CaffeNet model if it hasn't already been fetched."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n",
- "\n",
- "# Make sure that caffe is on the python path:\n",
- "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n",
- "import sys\n",
- "sys.path.insert(0, caffe_root + 'python')\n",
- "\n",
- "import caffe\n",
- "\n",
- "plt.rcParams['figure.figsize'] = (10, 10)\n",
- "plt.rcParams['image.interpolation'] = 'nearest'\n",
- "plt.rcParams['image.cmap'] = 'gray'\n",
- "\n",
- "import os\n",
- "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
- " print(\"Downloading pre-trained CaffeNet model...\")\n",
- " !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 1
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Set Caffe to CPU mode, load the net in the test phase for inference, and configure input preprocessing."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "caffe.set_mode_cpu()\n",
- "net = caffe.Net(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n",
- " caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n",
- " caffe.TEST)\n",
- "\n",
- "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n",
- "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n",
- "transformer.set_transpose('data', (2,0,1))\n",
- "transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1)) # mean pixel\n",
- "transformer.set_raw_scale('data', 255) # the reference model operates on images in [0,255] range instead of [0,1]\n",
- "transformer.set_channel_swap('data', (2,1,0)) # the reference model has channels in BGR order instead of RGB"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 2
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Classify the image by reshaping the net for the single input then doing the forward pass."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "net.blobs['data'].reshape(1,3,227,227)\n",
- "net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(caffe_root + 'examples/images/cat.jpg'))\n",
- "out = net.forward()\n",
- "print(\"Predicted class is #{}.\".format(out['prob'].argmax()))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "Predicted class is #281.\n"
- ]
- }
- ],
- "prompt_number": 3
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The layer features and their shapes (1 is the batch size, corresponding to the single input image in this example)."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "[(k, v.data.shape) for k, v in net.blobs.items()]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 4,
- "text": [
- "[('data', (1, 3, 227, 227)),\n",
- " ('conv1', (1, 96, 55, 55)),\n",
- " ('pool1', (1, 96, 27, 27)),\n",
- " ('norm1', (1, 96, 27, 27)),\n",
- " ('conv2', (1, 256, 27, 27)),\n",
- " ('pool2', (1, 256, 13, 13)),\n",
- " ('norm2', (1, 256, 13, 13)),\n",
- " ('conv3', (1, 384, 13, 13)),\n",
- " ('conv4', (1, 384, 13, 13)),\n",
- " ('conv5', (1, 256, 13, 13)),\n",
- " ('pool5', (1, 256, 6, 6)),\n",
- " ('fc6', (1, 4096)),\n",
- " ('fc7', (1, 4096)),\n",
- " ('fc8', (1, 1000)),\n",
- " ('prob', (1, 1000))]"
- ]
- }
- ],
- "prompt_number": 4
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The parameters and their shapes. The parameters are `net.params['name'][0]` while biases are `net.params['name'][1]`."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "[(k, v[0].data.shape) for k, v in net.params.items()]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 5,
- "text": [
- "[('conv1', (96, 3, 11, 11)),\n",
- " ('conv2', (256, 48, 5, 5)),\n",
- " ('conv3', (384, 256, 3, 3)),\n",
- " ('conv4', (384, 192, 3, 3)),\n",
- " ('conv5', (256, 192, 3, 3)),\n",
- " ('fc6', (4096, 9216)),\n",
- " ('fc7', (4096, 4096)),\n",
- " ('fc8', (1000, 4096))]"
- ]
- }
- ],
- "prompt_number": 5
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Helper functions for visualization"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# take an array of shape (n, height, width) or (n, height, width, channels)\n",
- "# and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\n",
- "def vis_square(data, padsize=1, padval=0):\n",
- " data -= data.min()\n",
- " data /= data.max()\n",
- " \n",
- " # force the number of filters to be square\n",
- " n = int(np.ceil(np.sqrt(data.shape[0])))\n",
- " padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)\n",
- " data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))\n",
- " \n",
- " # tile the filters into an image\n",
- " data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n",
- " data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
- " \n",
- " plt.imshow(data)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 6
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The input image"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "plt.imshow(transformer.deprocess('data', net.blobs['data'].data[0]))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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677sZausTsgLUkjKCiPQkQVnziTnp+Sw1UCw93swJ5bPOU/SdS6jIeRmAIGsn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zz12uoZORjd9bOyd5k6RErREhFTry5yfn93K41a5SKoDVfwWBxnO8/9yV1DbQsi0Wr+nhCR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\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7N24EL67Fm0KZgwKrrCQUce72vsMi6EOZi5ncQd+jpy5fKz47PAptc3ri44pzx2Dg/Y+U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\n5xXWJqP41sysdxbu2UzQz+kC40OgjiU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- "text": [
- "<matplotlib.figure.Figure at 0x7fd6306e1cd0>"
- ]
- }
- ],
- "prompt_number": 7
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The first layer filters, `conv1`"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# the parameters are a list of [weights, biases]\n",
- "filters = net.params['conv1'][0].data\n",
- "vis_square(filters.transpose(0, 2, 3, 1))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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HsnuPT6BhpP/ARhMFuOtruPHh1MmwX3/7sWOQp1rDDRtjzpC3VCAbERyjo0ToTZ5v1Jly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\nRK5CYII=\n",
- "text": [
- "<matplotlib.figure.Figure at 0x7fd63064b6d0>"
- ]
- }
- ],
- "prompt_number": 8
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The first layer output, `conv1` (rectified responses of the filters above, first 36 only)"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "feat = net.blobs['conv1'].data[0, :36]\n",
- "vis_square(feat, padval=1)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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ms2YoOpgSpqZhCsFvW62WPg9sBU7MjEwmo30ERsrKK5fP5z2z0cjIiMlIueZ5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wGNgv//Iv22uvvWb/83/+T/u1X/s1+6M/+iP77Gc/az/6oz9qX/va1+xHfuRH7LOf/ayZmb3+\n+uv2m7/5m/b666/bq6++aj/3cz93KhJORUVFRUVFRcUHEimNlMeP/diPTf/Lf/kv0xdffHH67rvv\nTqfT6fTWrVvTF198MWijPvvZz4b7/9bf+lvTz3/+81IjFZMw/EkV9y0uLs6cjjudTuMUq07LSgLi\nE7CSdiAtDofD6fLy8oxUwf/hxN9GWlL1g0aKf3/hhRemL7zwwsyJGnVtIxl46cX3V7/fn3Y6nVAO\na36uXbs2vXbtWrh/MBg0pByuN9oRk4a4PFzz46okUP8M6qLayxrB2D1cHv5jCQ1lKQkppjH07eA+\nn0cqPs3/eNxUXVK/xeqdktpPQ3Jt01+oS04L2Fb7lFvXqo9ykrXab8bj8YymWNVPadZK29FGI9VW\nw6i0I7E17OcEl9tWI5HqA+7LeeaYqkupVgzPpvaQ3FpBWdwOfg+Ab0+3201+p1jbqcpT3zF8A/v9\nfrAaseZKadTUXE/tBak9ejweJ60Qajzw/VlfX29cV+X0+3051uqscSKNFONb3/qWffGLX7RPfvKT\ndvv2bbt69aqZmV29ejXwkN555x179tlnwzPPPvusvf3226VFVFRUVFRUVFR8R6GIbP706VP7iZ/4\nCfuVX/mVQPYFmMSlELuGzNae9MZBJkHcXVpasnv37s3cNxUB4BQBlYmvitjM9Zz+fyIbPwNiHciI\n0+m0QSBMkeJz4LJAHNzY2LBvfOMbM/cNh8NQbirwGL9Hkb+Hw2Gj/fwOkBIvXbpk7777buN5Xx4T\nD1UIC+5z1JvDB4CUCbDDADCZTGYI+2ZxQjqTeGPwZfr70UaeT5iL9+7da4SF6HQ6kqTq34d7+d9Y\nXjB/v7oWcy/3z8T66iSBJ5VLfGodptqhrufK5+dU/VN55mKu656kW0pL4Lqqd6f6xcwazh/qPi4D\nf6f23dyzJfU3i2dCSCEWdBEAmRf9s7W11djL+dug1g9+U9+QnZ2dIueF2DVF6seegb5Q5eJ3s+O9\nIZf1QM1P7C87OzvJEDQc7gXOSQrb29uNkAO9Xq/xdbyGBgAAIABJREFUG/cHjzmCFjN8qBgGOzup\nTBipfQnv3d3dnZkLXE8ut9/vzzxjdtRXmGMoQ+X9m0wm4T2oc7/fT+7lMWQPUvv7+/YTP/ET9tM/\n/dP24z/+42Z2pIV699137dq1a3br1i27cuWKmZk988wz9uabb4Zn33rrLXvmmWfkeweDgZxYKysr\nwSMLHc6HqI2NDTMze/DgQfgN93W73XCIwLvZi40PED6cfafTkRsYns11ro92zhFt+ePpPxQcmRUT\n4datW6FcvENFSuaJj/ZsbW0lNz7uA35fylPu0qVLZmZ29+7dpOegqhf3Pf5Gny8sLNijR4/Ce8xm\nJzcQiwWmUPLx29raarRDRW3nAyG/12+WDCzqGPz455Lgpg5XsQMBf2RwzXsVKs8Wxjwx3Eo+WrHD\nf9tDZK5M1J8Pm+pe5elZ6hGYa5PyvPP9qTxvDw4OGv2vUqZMpzr5tkKqL+fhsfp5zvOYy0plR+D1\n5r3ccod/zGPez9ShOJciKFUG+mVxcTHsWWp9cxv9oYTbq1LJcF3wG3vgpfa9nFckyptOp41DU+kc\nHwwGoV4Yk/X19YZiA/fyfQqpa1yvDnmuKoGB40T55OvT6bQRKX00GjXm1nR6nEEAv3G52Eun06l9\n5jOfSdY7adqbTqf2sz/7s/bSSy/ZP/yH/zD8/qlPfcp+/dd/3czMfv3Xfz0csD71qU/Zb/zGb9je\n3p5985vftK9//ev2fd/3ffLdo9Eo+9GpqKioqKioqHi/0aHAnrmDVJJs/nu/93vTTqczffnll6ev\nvPLK9JVXXpn+x//4H6f37t2b/siP/IgMf/BP/+k/nX73d3/39MUXX5y++uqr8r0mSGNM8FLktStX\nrkyvXLkiyXeerOnfj79z5HCQ33CfIsktLi6G8pj8ze/G+1MERSbheUKeIsjH/kuRNNlVluuC8nIk\nU9WvnhjPBEdFsGQ3bz/mTIJMkWdV6IfYfyXurnxdkTTRP2ruqPFS5fl5Pu8YqnnMdTrt/it5n7qu\nSKfzEOzbluv73LvMx4iuar6ftgt2ai6q/lJOJ3zPSUjQbUMIpOYd14X3dO/Ak1uH2PO5TaoPuC6K\n3K/23tP8L0ea5/+w36UcGzyJPDbnuB94TgPLy8uN/sC+FesH3o/V/Lxw4cL0woUL2TmEfROEcDXW\nykEi9m2bd+3hnaPRKLtWUg5m3Bdqj8EcjyFp2vvBH/zBqNr3v/7X/yp///SnP22f/vSnU6+tqKio\nqKioqPhTgXPNtcccFGXrZ2I7eFO5qOfKZuzt0WwzBiltb28vlF1i62XEeC6eIB3jUShyo7o3xZFi\ngOC9v7/fIH3v7OxI/osiNXK0eQ/1Dh4bNQ5+jBWRldueGusY0TrlUKCeZf6aJwXz/GSeGPoF13Z3\ndxtzhudYiscQmzv+mdycyM13Bf9sjq/FmIdDhTJL24H5gn95bqq5MZ1OZ+Z5qrxUubn6c3kxxPqS\nia5mOsJ97N2+z3Nty6FkP+EyVF/xNbWHM1cN1zwvxUzv/34sudxcvkvflhiHy9dTOX8oHmgpb5P3\nmhyfTbVJrTM/HrH7GKm9HFBjrXi9OQcE/pbMu09gjpiluXRmx21nXjFzC08KkPpT6+3cDlKrq6v2\n9OnTBiHz8uXLdvfuXTObnejKU8qTbnlygwDI74599AE/6OpjzQtSpUJQmwSXmfPIiSE2iL5fbty4\nYe+8807j3XxwxCJQH2nuA38oKa2Dmdnq6qqZHXt8sNdhKn0LI+V5FeszVRf/DB+QAE6jgHKHw2Fw\nMuD3rq2tmZnZw4cPw/MXLlwws+MDP8+Jth9rBj9b8p7YARMCA4996sOX24BK7uP5ospS7VGJrxmp\n98yTKJo9RFPJedVHK3XojB1A/JxW4xXzCCs5XPP7+ECdc1AogSJL817IDii4LyUMpZKcs9MEtx97\nEtalam+bj6evn+pT3i9Sa5C/P6lDkxrzjY2NsFfG9kVFjE4J61BEsMc0Dhvdbre1N6bqX3iz7+zs\nyH5XTj2evK4wHA6lsAFwu9XcBkr3XjVP19fXzexoT0daudh7atLiioqKioqKioo5ca6mvdXV1Uao\nAwa0GuxWyupZ/8zS0lJ4N6sw/amUEx6revHJf15tAkuVpUk8Uwl5FVQySga77aLc9fX1mdARXF+z\n4xO5SnTLcUtS8U2URH3hwoUQ6oAlUa+14XAVKr4JoKRElna4XiVSEUuJ7M6MMcF86pAnh+orIGba\nS5nTUvfFNE2lSMUW47H38WGm02lRouXYb0BbbUEbsxVrR7xGKmeuTGlAfH081P056kFJ+JCTaKRU\nXbrdblK6LwWX77XK3D8ot9PpNLQObcY1pdVRe2Vq/ikNoVlTu6j2Pda28R7i1y3vjwCbwXhuQJOD\nslKWETzrk+2yOZW17bie0/hjT+D3KU09WyT4/hjUuKHtvV5P0nnmnZedTieYLdGXvMcpjbOax8CU\nQnYwPQR7ctVIVVRUVFRUVFScMs5NI+Wl+Hk0PyDnKS0UtBqdTmcm4raZts2WSvw5iSqlqTGbldbM\njk7leB8TlVOkP2UTZo0D/+2fee6552aCppodaYhUADMvSU2nzWjy0+lU9qvnVyleEpfLZEilQVJS\nkNeysLYoJTWtr68HaS2l/RuNRqFtmFus0WPJFpINB1ct4euoeXcSZ4MYudVLiTmybEqDFFsrag2X\ncFAYpdpbBdbkASyht43CrNDtdpOSeWr98xrIOWaUagFL0MaJYJ4+x3NewzGZTIo4izyf1P2soU7x\nYZQmpC0PsJRYHtMaAtA4KctHbP2Uam2ZBI+/FfcNdWCNj+ILA8vLy+FZjmaOvRn7rNIgzeP4wGtV\njSssF7jGjlI8njleZUn5ykLA45HTSJ2rac/suBNUJHJW0YHgCzx69KjRqOFw2DiUmB13iA8bz3+r\nhcGbZsprQplxTkLqjCHlbceHML7fH3xUaoAYlJnCq0JjGzSi3b/33nvJ+qe8SdRET21QMbNl6X3+\nYMbmSG6DH0f1gdzf30+mUWCUmMlyh6u2ZrCTmOliKImyHSNhnwbUQUqp9s2aQhU7Q+TI/N60a5b2\nMOS9waek8F6HgPLgKj1wlXxYTktwZJOS309yxG2upz9AxQ76fo2yMJYyX+eg9hpAHUQXFhakZzDa\nhL4dj8fhvpgDEu4v3Se8iWoe5MYfptO9vT3ZnyUepG0O8KUo2TOU8xQ/Mw9dopr2KioqKioqKirO\nCEVJi88CMMMoqcnnJhoMBjOu5mY6lgVLMNCI3L9/P7xPaTGUFJNzXfUnW5aKvLt37D2QXFhCU32R\nyo2WI5vH1N8pF12Y5A4ODqRE6FXJq6urQQ3MUrSqlyfx9Xq9oIliU2BK6lBaNJT74MGDhqSkJGCu\nWyrHn8Lh4WFD4t/f37fLly+bmdmdO3fCvSWaFyXlKGeI0vAHOVI0a8nU+/ycaEOGT8XDUnXJadty\nxO0UclpgH3YlRzbn93GMG8CXo2KVKVM8O7QwsEa8eaEEsbhrXIfcu9pqC5V2TplfeE/hZ/xew//P\n+wprosyO1qCyYKTMh6nYTApqf2RtFGvJ/R4eM58rrWbbfYKfUXGXeN75+3gNKy0qUx7g9MW/qfmk\ntFT+fr6eattwOJQ5cttadVIUBLUeJ5PJXFq0qpGqqKioqKioqJgT58aRQnDKEmkcz5hpIitwcHBg\nFy9eNLNj7RO/i6PsQtPD1z2nZTQaNSQKlYW9pL2+7ilwpmpVBiSg3d3dxunZc728rTim2Shxy/bB\nPv2zrKkr4SsoXgK79CrJmoNhqnHw5XI7XnjhBTM70hopkrmPwqzIsqyhYelUSWglHAYljXU6zWCz\npc8yV6Wt1qENfym1BpR78TwR0/2zJXwdL2Uroj1fZ9f1WCgPBoeISAUXzfEvUlw/BeWW38bZwCM3\nx9oSn/l9irjLdblx44aZHfMn2e2etZCeJ6YcVvwzvtxUm0oJ0jkHAwa0KHgvBy9V/c1OKqWEdhXG\ngYHfee8q5S/5PlpdXU1yvDBGOzs7kqTtMU/Ef/Usxnx9fT3s9bBuqP6NaXRzzmHAB5ZsjgWPiNCe\n1MsYjUaNOFKDwUCq0wEedFZn8jvMjlXE7InA11VHqw2eI8bi/lRUdPbe815FiiyXIspyO7xq2S+6\npaWl0BZ+nsProzw1NqlNnFPT+A1gZWWlcXhRZlKz9EdG1YnHPbXhfeITnzAzsy984QuNODR8AMl5\n/vkNXm14fIhIbdzqt1gKhpKPm+rT0yKqp57NEUtPy+EiZypUH5ecB6LZUZ978vBJ+jJGXvZ7R+xj\n6ddzKXE3Fg9N1fM0xkS110fb9vd78xgTt7nOqn6pOiuzW2qO59LGlEKZrdoKEGZlWRn478FgILNx\neKeJbrc7Q5Mx06l1GKkUPP1+P9QrFZ281+s1+uHg4ECOoeovNrf5aynEaDX+wL20tBTWVy7jQiWb\nV1RUVFRUVFScEc49/IFCadLglPkIp/vRaBROqCCsd7vdcOJWJh48y6bHtiYWszKybCyGSqmElNMM\nsPSCuqTyfSmpbmNjw8yOiPvcN/iXXXjxr9IqpRIK50yBkGJQPkdFZ9Mi6sB1Rl1QT46RArBmQM0/\nlbcKYLMLwjk8efIkOc9z41uamy6loeF3qcTY6pmSeaeIu2bNNVIaoTt3jccjlX+RSbXqnUDMJOa1\nT6wZxDtGo5F0CikNV+GxsbFh9+/fb/w+r0YK9zJiz51G+AluL68R1CNl2lehT1LhUJaXl8Pv2C8O\nDw/ld8JH/D5JPK7hcBieV/OZ12qJyZvDYAArKyuhbUqDydHVfUYHj1QdsC/2+/0wtzhvIe/rbbC8\nvBzGve2zpXMb+wCewb9ohyK+K4uH6h/1bWoTR6pqpCoqKioqKioq5sS5a6RSHCAm7AHQLuzv74eT\nOUIdMLGUA0EC165dMzOzd999t3EtxtdR9yluFksbqIsi7voI2Dmtm4Ii0LE2iwmynsQZsxmnAjWi\nf2/fvm2XLl0yM7O7d++G+1TeK3Wfh4qortp5cHCQJJEDrBliCeOv//W/bmZmv/Vbv2VmZlevXrXb\nt29Hn83VRV3z48l8nVzYgFKOyllFu55X+4n3mOUJzbzm8Xcuv1iJZpqlxFKCP4PL9e1TvD4VpJXb\nlBpXJdHG3lca3V+hVJup0HY+cXtLnuE1z2MFCwFrotAHHN7g5s2bZmb2rW99K1r34XAY9olU3rfS\nqPIxYv5JwnOU4EMf+pB9+9vfbvzO1odUcOCU40YbsHYfWF9fN7Nj7b7KRMB7NIdfyK1ntM3XNaZt\n9yE91LNtAtCqsf7Aks2VeQnXfCesrq6GhVEatZbf7zfkfr8frvOiThG8FZk7RdyMDaaPH8J9gfvZ\nW5AjZZcc9Lxniz/MKc8XRW7mwxgI3vfu3QsHGvTHo0eP5ELzfc79q9T36qOJxb+4uNj4oKlQ/v1+\nXx6GfByUWP+pjbHkYx6LGeTVy3y9dNmphMLzeMDh79JEoanDXxt40qrygFLkUDVPecx5PbKX52ke\npNS64LhpKVNrzGzpTe0cAT9FjPV1Ve+Olauu51KclCLlQcbR4nkfA1RUb2W+SQlbnAIotz+WzOOc\nGa+UoA9BcmdnJ7SZ6Q7KDK4O3qrtucNr6oCXMkMuLCw0PH5ZyIZA/fDhQ5lSDM+kMgRwWhvl1IUx\nUt/5nKdpKfiw5mNW8j7LwnE17VVUVFRUVFRUnBHO3bR31lhcXAwEMiUVtUXOBKSgJKEU8ZrBmjoV\nlTinKUtJjDkNjCeMcyR1nNpHo1G4rrQ3XBfl3ot+gAQSi5uFMjgxKurN7/XarrW1teBkkNLoKA2I\nUk1Pp8dJmlW4DCBmZio17aXGhjVxSur0Gk6uM9oYi8njTXGlcdNipsJU0uKURmcwGCRzreHadDqd\nIfirmD25uDZmR32VCruh8iYq6bhEa+SfLXWuOa0QEnhXqanDfyK8OdXsaFyUNsGPMc87brfPg1oa\neoTRVovH4EjzqXnC5HW1nz377LNmdrwf3717VzrKqLqrvYaBvRLzs9PpJPuN2465Dezu7oY2Yf2w\nRYEdeZSjCt6nQunMY0b01I3RaDTjvIR/1btZy2o2GxqJ90BvwWBLUiocCb69VSNVUVFRUVFRUXEG\nODeNVKfTsUuXLs3kJjOL50Ty0sb6+nrQNOA0a2YNImPO7fEk4JOtD/oZIzJ6KYbrznmk0M4Uj0Gd\nnmORzZV2hG3BzM8y09G6n3/+eXvjjTei9WF4jRvzJVLETeZLgNv0+PHjGe6E2awElIpOyxopRspd\nXWn5lLu10iTgPg7PoMB2eh/cTpHwmUunNBeqzopHxJJcShOiuBQpDkosjIcqw8+rHB+K36Gke1zf\n29uTvLQUZ4Trir8Vx0MRlFXg1hwPR9XFa7tOsiXHeFgn2ftSWtSS/cVMR5Wfh/isophjzbEmUQUY\nVih1aPBjpLQjinvLOeNYi4b9Ee1Q5XtNPMaQwxWoPeEk/VsKH/KGSeQ5zqAKIuo5pu/3sSTnOPCB\nJZsDHN/IbNazjVNx+CSe/PFXGxrewQTk3KHKx1/hd7Yd4FinK2I2P5MqI5Uygz8C/JuPg8MHi1Kv\nk9RBJXbw9W3p9XrhYPTgwYPwPLw/+DeA1b18qPLgQ5tSdasPFRPU0TYfNX04HDb6JmfG4UOi/+Dy\nfdynpYec1PxQZaRiLpXO41ITUIy87OeOiorMBG42r6m6qphlqs+BXq8nYwnxdSC11tXc5o9YKm5R\nKkUNv48TKadMYoA6cPM45BKBl3qBpg5h6iBVOheBnFmdvbj98wsLC2FuqQNtjpwcywih6sj1V/HE\nmHCN8i5evBj2E9zPsQ1VCjM8G0uXljq8cqytnPemf159e08CZcbn+iu03feYpM8ONf6b3+l0GueE\nNp6wlWxeUVFRUVFRUXFGOFfT3srKitQwKJS4dLIrLJPNVM6z0pxIXkpQpjiun3LtBJiozu+FtgVa\nNyV5sdReKkVxfZDMWWnCYiiNRA6tg3d/jYEJxSl3V2BlZSW8G9IWE99ZioGLLscR8wRVjg+kXGHV\nb0AsSrhfRjGyuUJp/jVFIo+9IwY2fXltJmscuU9LtFjz5ILLQfWLbzuXq/q8tDzWipSGMEhpzGKm\nfa6X2SwZnq+pMfaIRUX34xkzu6Y0ZTHSsgf3hQppwxpfs3hSbU9e5gwMrOXhcTc76u+SkDixNZVy\nMOD2pL4/THLGnswOKeoZH9NQhdpYWFgI72P6CM9T7G34TVkNcnP2tFBCAWAg/iCHiMgBfYBv4NOn\nT+X+he8T7t/c3JTaLqXhxvs4DEbVSFVUVFRUVFRUnBH6+VvOBtPptFgbpbQFfDLM5Qfyv7NdVQXB\nY5url8ZYwuC8czHbv5nZ5cuXzcxmomlDonrmmWfs7bffnimDeSRo5+PHj2eIzLjPBxRjqZzrpCSV\nnJaPc2ahnSqYZurdrCHwZMQbN24kIxSzZIPyVPksYfgo0YrnxME92TkAUg7mhAoBobggDE+KV+3h\nZ1nKBpQmTIW/YC0K6qzCcyiJNMbXAnIchZSWQkl+KQ1XTLsYCyvBdWIttEJMgvR1VBoY9d4YqRbg\nZzB3lKu+Gn8mc5doDNiFPdUHw+FwRrMBpDRMjBTXj6E0tX4uqnnV7/elRgjzEtcUn21paSmpkYJW\nfWFhIWjveI2mnuVvTkq7zI5BKT4pQzlXAIpPxN8ihppbav/07+v1eqHMXF39+2IhgEo0v6wd5X3Y\nz2O+D9+SpaWlsL+r7w/v1XgPZ8JAn/M3xJ8r+H2+/SmcO9kc4Eja7JFhFt8k/EdOqZIVyYyBRbWy\nstLK7OXhyY39fj+oLnFgzHnPKKItBp3Vn7yg8AxPLBVHCiYv7yWJ+/00UKRF3vRT6QdiJkxApY9B\nnRcXFxubw9LSUnieD3dqPF966SUzM3v99dfDb95EyRsjH5phYgUZP5bCJuUwAMRMe7GI/lwXM30Y\nScXc4jFX3melKVj8Zt3pdGZMa6i7Mrv5D5+KYm6WjgXEY+X7iuekMjvnzKlc53ljDqmPSCwdDA7m\nXEeUC6EoFg/JE/JjKI1BpaC8D0sPzX5P5TWV+qSwB5wS4NBn/A5O91JiVu/3+w2vPR4z0Bzu3bsX\nfmMPZvSlipXHccp8Wqj19XXpNOPLYHMk5kG3221QKLy5D+sLv62trYX1EEvRZBYfDx+7CX3Pv7EA\nVwqek17wVYfSXDw8vuZ/6/f7YS/AwWxhYSGMScqRiueOGn9GNe1VVFRUVFRUVJwRzjXXXqfTaRAP\n1akvFhtlXvBJP/c+zi9kNnt6ZkKoiricAqSLxcVFGedIoYTMx4Rcs1mCvVk8onVJriN2x2WzZswE\nYqYlfnbz9poNljqh0ev3+0HSS5FDzY4SEpsdm1FZg6BCLUA7sr293Vo1zeTvErK50lwokinqbTYr\nSSsNrdfM+PFHuUqrVBpbKJVHkqXa1FjyNa+RWlpaCvcxWdfvDdxXrBVUufYUqT7nDu4ldAVuU2qM\nmETOfQCpH9d8DkkgFe6D4bUOrEVt64CgwkfE9jFPfL548aLU0KZCj/C88/NYaZ9WVlbCdaZXeA3h\nxYsXZ7RNvi4qxxtbMNT3Z21tzcz03oF5rDTYPOasfeT9GOWjfqjTZDKZCRGCtnNdAdaUpcJ9cHvV\nfpwC2rG7u9voo1wsOIVUCCJ2Eos5bqHOmIv4XvA8ZOJ4SW7JhYWFMCa89qpGqqKioqKioqLijHCu\nHKnBYNA44bFUydoiLwGp0+54PG5omJg0zZocH0JgNBo1cqj1+/1iQvxJwBIIyvfEdyaMMo+FpQSz\nZrgHr5Eya0qJTG5WQfzYNq+CgirOg5fuWXJUQeHUNHzllVfMzOxLX/pS4xpLQKxl8ZI8E3dT4QI4\n6Cvz3JiMjt9UxnBF8PTSPYdd4P5RWoBUv7A2RgU+BVLSYEq6MpudB34slSs5a4uU1hDvG4/HQapX\nbVRaHn7fCy+8YGZm3/jGNxrXNzc3wztR3s7OTqOdsZAIpURhIKfN9tc5OrXiubGEnuLxAIrrleMn\nxojMuM8/E5snnkivuGmlITFiZfi2ra2thXWdqx+AtfLgwQOpRUG9S8c853gDpHiAvV6vwa8ymw02\nbXbURqy9yWTSyInX7XaTfLNYvVE/zEHPSeb7Ynyj0qwIvm1q3cUClGJs4Kz19OnT8A1sy9uKzZPS\ncBA5jdS5HqRisT1S4M2uRBVvpiPGerOBmtAcFTnllafiPq2vrwcPA/7I4W82jfj6YxGa6YWYApP5\n2HQK1fSjR49kv+U8rYBUTC6F1IedN31efCmzBk98f4hU0YZjdfJ1iSVTVeX6g4Iyp/DhqhTcB6mP\nei71i8fS0lKoK+YfH+qA4XAYPl4sfPj5wn2lxkqZPDn+Wsr8DfX8kydPGuOwsbER/kbdOVI/H0C5\n//xmWfrhVve1OYT5D63K0KAOL7H3YazZKzfVtpQZnO9TfQXEPg/IAoC+Z1OsQmqexszbvl94v+Cx\n4jmD96kPc0ldFhcXZ8xtZkdrAWOJvrp//76kL6BfONq6P9guLS2FuqqYgBi3w8PDGQ9t1AGCCB/I\nlPdZ6SHrtODX6/LyciOhPdcnd8j2czuXSqaUqoD5sr29nUyxxg5QT58+raa9ioqKioqKioqzwLlp\npM6h2IqKioqKioqK1qgaqYqKioqKioqKM8C5RTZvyx85a/xp1ZJ5QqnqdyaMl5IuU+CAeCpPX1tw\ncEMmh3r79s2bN8N9t27dMrMjjoQPfslctZTLL8J0mB3b+DlAJfpsOp2G93AQTM/X4fvaotPphDqA\n73b//v0i/kOv1wukWw7ICn4T+kJFGlaR1weDgb344otmZvbss8+amdmrr76arAPGZW9vL9QfoUX+\n7//9v/L+v/SX/pKZHUer/8pXvtK4bzgchnc/evQoSno2O+ZkMMcr54KtrpdyM08Tyr2c+RzKeYHH\nLZVnTHHCSvkmzLNKub8zXyuVlxTtWFpaCtwy7ElcBvhCT548CX+jHTs7O8HZAHPtzp07jbUSc1gB\nbty4Ecr1GRguXboUSN+4786dO2FOoP+uXLkS8ukBCwsLwZkA/Xv37t1GmBZ2lOp2u2F9Mh/OB4Jm\nxyH1Pcs5o6DfwCPinKXzoITMrbjSy8vLYY9BH3z9619P7p+lc5bz8KU4yFgzscj7M21IXv0OQVtv\nGwVOjBuL7XIaKI3tclbgDzwmXo70zwcC1JvvBzkeH+bDw0MZx6UtOM0MNij2ikNfYrO8d++eHDsf\nL4XniYqvxWX4RLYqvVDucMTphRRSHz7ExXrrrbdCvUE2HY1Gjai+Cs8++2w4bPBBitMxeKhsABxv\nBtf/+I//OFougz1SOYJyDAsLC+Hjm5pL+/v79vzzzyffxR6mqEPuAAWoMeOoz2ZxL0t8YN955x0z\nK0+grIQ6FfuK35WK/m0268WMuqTmbQkZv+R+1AVjzoINDkAXL160b3/72zPtuHDhwozAYKaT0vJB\nCs/u7OzYM888M1MGz3tey74P2CEA6wz14Gc5KwPatri4GOqAvVA5v3C8Lp+FwOz44DAej0P/Xb58\nWa5x763b6XRC2Zjjm5ubM+R3tJOv+7LRb88995y9+eabjXJLoeaFX3sqOvnTp0/DuoGn7vXr1+2N\nN96IloX3Mck9tX4XFxfD2CJNGyOVCqrRpuwdFRUVFRUVFRUVEn8qNFJ8sp3X1ZOl7NPQcMUAKfss\ntV45eA0J9xnarrQGnU4nSFjQQnW73SC55UI1QJ2NMm7fvj0jqeJ9XvLNaXLQl7HxwvPqOpsXcupn\nsyNppq02MaeRggQJCYjNH5Cst7a2gmTOZkT0KcNH2X/ppZfsT/7kTxr3Ybxg9mOoNmLMO51OkAw5\nKj9McZBg2SzA2k8gpS7v9Xoz0mkM4/E45G4sxcHBQSM0gArF0ev1GhqL2BxR5k9I1D/0Qz9kZma/\n8zu/U1Q/VYaaO7HI4NBm+phV/EwsunuqDhwziESDAAAgAElEQVRvyM8Pte9yuagfP4u+vXr1atBI\nAY8ePQrxgz70oQ+Zmc3MYX73tWvXzMzsy1/+cvgNmtKf/MmfNLMjjQO0Upzfztefo6fzv9izPvrR\nj5rZrJkZzz7zzDNhL0T9YCJjcDYI1iTiPdw2jAM0RAz+3qUS1D/33HNh30YfbG9v28c+9jEzO9J2\nm80mfMd9GxsbyZhY2LsuXbo0E9stBT+XOYwLz2OYREs1/8DTp09Dv6Mv1d7/4MGDMBd8CA1GyZmi\naqQqKioqKioqKubEnwqN1EkCjjFh2AeyOwuN1ElyBJ4GcvZetJnbDmmMg6oBKtt5v99vaJUmk4nk\nukAjwIRRz4dS4PxxqczhV65cCWXgPuZDQRqPaT04VxPfHwPuN2tybpi8zvOAo1KbHfUJ584zM/vI\nRz5i/+t//a9GeZ7wPB6PG5rVXq+X5OaoOQkJjaVU5pMxRwTl/sW/+BfNzCSPAe0YjUbhWcXNAra3\nt+2b3/ymmaU1UpcvX57Rcnkw34iJ0V4bxnXBePksAR4qIjvu4z4t1USp4IwMNZ9UwGDFh+IsBrg/\nFZ06lSszppFN8T8h6V++fDloGjB37927Zy+99JKZmb3++uvhmg8Oa3a8Tyi+lCoXWqqPfvSjQcsC\nrQeTjaF16fV6MpApygMBmjVSeMe1a9fCPECdmV8FTCaTmSCyqBP6CP28s7MT6qXmxGAwaATdVH3w\n5ptvBq0t+IQPHz4Me/f3f//3m5nZf/tv/62xl6r8iQwE4t3c3AyaQ69dLAHax5YQ9A002xsbG42c\njDGUWnx8JoR5LUXfMQep0lDubaGSjJYednJ18h80jlR7lkip6tn7iz9A3uuMoQ5XTLiFeQmLcGtr\na8aLxOyoD/A3+mAymTS85/hggA2SzS4qtQYDfc3pFHAvFstoNJLphVAHbF4LCwthM8XHPFYuDh7q\nsMkRjdEHbBJLmdhwkMEm5eEPGd1uN/QB+u/u3bshibNS0/PmgTkBkjsfpPiAizJgfnny5EmoCxN7\n/bM3btyQh29/QN3c3JSmBPQRri0vLze8ohhqbfI8xgeGD4acXsYT1flvPlh4oYvnLObE8vJyo+18\nGMK6UIl4VVt4fvq0Rfwb15k99PxHk6Pd8/rIJQoHMN98Si6z4w8uTEFmx1HAt7e3g6caAx9V9pjD\n+mPTjZpPADxCf/qnf9p+//d/38yO++j5558PcxXzc2lpaUagMTs6IGGOxcjjZkeHqx/4gR8wM7Pf\n+q3fMrMj72GsEV4Xvi+XlpbCPEG/3L17N4wDr0OAD8OYs7u7u/KQ4QUfbjMOrxsbG2H81TvQLxsb\nG2E/YWoE2pf6Lg6HwxlHAQDtxLxfXFxs9NH9+/cb5t6vfe1r4fpf+At/wczM/uAP/iD8prwZGWhH\nW3qARzXtVVRUVFRUVFTMie8YjZRXG5+WZorf47UNq6uryaTFKp4Hv8O/r9PpnCmR3dcrdk2ZxDiv\nkNlsbjRoGvb392dMV3gfJEKOQaPaB+lJxa2BdLS8vNwwu02n02TMHtR1bW0tSLGQdliCRJ2UdMfA\n9dh9KA9SzNLSUng3a5p8vKnBYNAIxXBwcBCkSWiLWJ2O+pfOl8PDwyD1w6xw9+7dMIaK/MpAHTAP\nGKjLzs5OIPh+/OMfNzOz3/7t37b//t//e/S96IvV1dWG2QjtN0ur7MfjcXCFhrvy06dPky7ROcDc\nzKE9UAfOAae0uyoHHMB58DCfY5oTH5dqb28v9D8nafXm5a2tLbt+/bqZHcdN4zWdMs91u92GhP70\n6VNpOikN2YB3oz1Ke8MaM+wHKysrUlvgf2OtMb/Hk5x7vV7YB6C5/JM/+ZNQns9ZyGXt7u6GdY91\n9PLLLweN1Oc///mZcsyOye6vvfZa0JgweH4Dnty8ublpN2/eNLPZdZta/zzHWOvpwwscHh7K/RO/\nYe50u90wdtD87u/vhzJQlydPnjS+MZ1OJ2iQVMJtYG9vL5gXWWMO8N7/4Q9/2MxmQ6xA64V/r127\nFrRKwM2bN4MWU5m+OQemd7745Cc/GUyTGHPOfRtD1UhVVFRUVFRUVMyJ7xiN1FllsGZpy2sLSqOv\nM1GdwWRfvO8kka098TVWbg6K9+HBrt+s8UHZqSCJ3KeQ6gaDQZAOU4HOdnd3G1GnmfcD7s7CwkKQ\n8CGdeMkEKOXXld4HSU4FcVNgDaAn7vf7/RC4MRWosrSs4XDYiE5869atBlE4FhwSv0GDdfHixfA3\nS7VYj9CcHBwcRPuf0e12Ax8G47a0tCTJ30qjC6kYayEXckOBtSc8NqxhMotHLvfzhLUFnEEA70Gd\nVXgW1rayBglrAHVi7TjPT8V98Xwofoa16ABL6F4juLS01Ojj2NxBnVM8QdYkod2TyaTRL6urqw3+\n3+HhYYO/dPny5QYnr9vtNur8uc99zj75yU+a2XF4jjfffLNB8FcEc9bOs7YYcwfr97XXXpNkZUUA\n5wjjHhgDDhURc3ZAHTGu/X6/oeEcDochi4DSXEIDtrOz0yiHg81CM7O0tBQ0VtDajMfjMF4pztpg\nMAh9CI0TRw7HHN/b2wuaqJRTxO3bt0PbwYcbj8eNbzi3Vzmv4Hu2t7cXHB8wN9jKEMN3zEHqrMAb\njN9s9vf3ZzaZNhiPxw1SKh+G5qmnJ3Cf9HCpzBWY0IpYOplMisnyXh0cM5F6UwIvFixqrgsWbq/X\na9TlwoULYSPjDUEdjNShiU1wZkfjpj7YODygzqXjsLe315gTvV4vbHzqcIpNKTf/8N7xeNw4APT7\n/bAp4N/9/X1pLkA7UZcrV66Efub3+ojAMYK0go/GzKpzlKE+Ptvb2yGWEOJrzbMG2Lyt5oGKfJ+K\nsRT74Pm1vrKyEuY3j6dfzyrq+OPHjxv0Br4vlR5DUQ8ODg4a5iXen1CXzc3Nxoc5ZupTcel8nfmw\njXLX1tYaB5DpdNowrR8cHIT5i/2EvfG4vcBzzz1nZkeHJqQzgil4Z2encZDa398PcxsfUOUty2C6\ng/rYY01xP+LdPL+wn+Cgcf369RDjKQeeV/hmoW3379+fyRIBsPMN6oA+54OZ32en06n9uT/350Jb\nzI7G0sev4yjxXE+0Dwel8XgcDi+o5//4H/+j0TYFXovoZyVc3bhxI8R1S+HJkydhvP0+n0I17VVU\nVFRUVFRUzIk/8xopSHJ8smVpFa6okKx2d3eTkbKhzeBccCwFzKuRMjs+mXvCdxtwXUrNgmhTm9AN\nyuzhESOlp6DCNHD9UuZKjhXkNWG9Xm8m+jLKgHTDIRR8NOF+vz8zP2Jg8xHquba2FuqlzDSlOQuZ\nnAxp/c//+T9vZkdmEk78apaPAo9/33nnnYaE94M/+IPhb5A6S6O9TyaTUDZL/pBmYbqNSY/etX7e\ncCIq8bDXnqgI3rE8eMr84PuYTR4cdsHnPGSND2tMvCYo5djCYG07a0D8uE6n0xlHEMAn4uVQMaoM\nbje0jfgtli/OjyPfh3V5cHDQ0A6ovTDWF//5P//nmf+/fv16cJ/n+QAHit/93d81szhlwNe10+lI\nzTG0T2jH9evXg4kNfTYYDELft8nx5sHfJ2gcp9Op3EfQl6jf9vZ2Q6uo5vve3p599atfDdd9uVgL\nV65cScaUYjMp1nsJsTsHpR0/PDwMWjSVJB3odrtz7S1VI1VRUVFRUVFRMSeqRur/S1Z7e3szOfvM\njiRSH/BuOByGZzjKrnLZB5gc7kmX83A8cNrm03MpmGsF9Hq9GekF9YKk3FbqjwUF9JwYzgQPrKys\nBII6RzbmvG0AXL9Rv0ePHklCvu9rHkPUZTQahbFml3Mf+XZ7e7vR591uN6mJAtiFHfXkHFqpSN8x\ngq/ndTGfDITQr3zlKw1SqpkFd2sOeAhAWmXJDlqvZ5991r7whS/MXC+dI6x9Qnvv3bsXfvuu7/ou\nMzviOajwE8zxwvtKwRonxbvw4So4snkq7IrKycfA2AwGgzA+0D4uLi6GflDcDvRrjHyvNE0eKro7\nR/dHG/v9fiPcA0v3OQ2YL4OBEAFqrm1ubiY19Zh3m5ubRfPswoULQQPDdYHmCFYGXm8Yo93d3WIn\nIwAaq+eff74xH9nBAGvqYx/7WNBIAd1utxGewbdVBez16HQ6je/Yyy+/HCKxK81xymEj9n3xhPKN\njY0wTmjbgwcPwringuaW1mU8HifHH98wDvqK9XZ4eBj2FkBppnLc2hj+zB6kFPnOdxybYrza0mw2\nsjHex2p6v7nxJD4NL8SS+C4KyjThyeC5tCJKzQ81O3sn4gM5Ho/DpMZi4f7A5sZxXNjUho8+PqSj\n0SgcrnhR+zovLCzMmOoAb2KIpQbAs2xS8oer0nHgZ9k7Dgs/lZpoNBpJ0iPex95niLWEcYGXktmx\nSYk3Gz4sYB6rOsDz53Of+1z4YGDMSzcdThTKfe49165fvy4PUrieiu8WA/pIpQgya65TZdpTODw8\nTApGylwGKJPd2tpag4zMf/PhKUV85/v9sxyDiFPiqJhWCr4tqi5mZUTdra2tRtwy9hbkvRcfUh+T\njvGRj3wkHKSwR/CBBu9l8zFMchcuXLA/+qM/mnkfR9lmUyL6BoeE9fX1xrxS9bt//35Yo4iBtbu7\n20gz5PtOHTbVnMJ4goR/48YN+57v+R4zO05QfP/+/eI1672J+/1+w4R5//79YKrH2Dx+/LiRmubR\no0fhujrsptDr9cJhUqWuYeEKfckx3HCYfOWVV8zsaB7w3sjlMEr2gGraq6ioqKioqKiYE39mNVIp\nNbRXeZvNuiYr6Y6TH/v3qoSoHwRwO7yJgLU37FoPaYk1UmxGM5tVSTMJ20vAo9EomNZ8dHQz7fLP\nrrqchBj1hJTIdVa5AkuRigGD982jXVTxuLgdbFrBNSXdc0RmsyMtGeLaqPaCzP3ee+8F8xLa2O/3\ng5YN489jifufPn06o0EEVB4/j93d3TB3lDkUkqaKrM7Rk1OJinPguc3zza9rdc3/jv/HOLBGR4Ur\n8Dg8PGwQ1dn8yaY2HzpDmSEODg4aDgVsFlTP8rgpYrmH0j4xbYGfQZugHWFtG7C3t9fItcfjj71h\nbW1txgxpprU0MPlzHzAwVkqrORgMghbLm6DRTrQL2hb02be+9S372Mc+NnOfwu3bt0P4DobPxsDx\nq8z0fsjZJGLXvva1r9lHPvIRMzt28OBwC7yv+P7lfZtpKdhHmIiPdY/I5ltbW6Evsa/s7e2FdkIr\nzqFseP75LBWbm5uhDn/tr/01MzvKq+ctHJwjk8cf2ieUsbq62nA66nQ6jX6v4Q8qKioqKioqKs4Q\nf2Y1Up5YGoNy8/USpiJ9MgGVr5Wcbt8vKK6FkqRY0zQPud3sSDJQbrQlJO3BYBD6XN3PweO89KSi\n7HY6nSDtKCItwBo4lmzm5bcpDcfu7m7gWEDLs7q6GjQvIKMPh8MkLwjvW15eDnVWfQXJ7+HDh0Ei\nhKSbC+OAPuj3+6EM1j6VkHS571hLgr+huVBu7aw5ZR7QPNqpkkCbqWuoN7fDbJbr5Z/Z2dlpSMCK\n9H1wcCCjnat8nkDKGUJx8xgYQ3ZeyYVTUGPtf+NxRRkbGxuSwO+1AHfu3Ananddee83MjrRATAo3\nO9awMNjhQoX78IFIGTyfVZ5F7pcf+IEfMDOzL3/5y+F9WF/QqG1tbTXqsLW11Qh10u12G3uVH4OU\nRp3Xkg+qurW1FbQxnGsT8xf5Ae/cuRPGBFrK3d1d+c0CZ5WBfkf53W63kRXh6tWrMzn7zGbDh7DG\nD3WAhv2dd94Jz4DHdv369RDYk4Mcp0jp0Iju7+83LA7T6VQGo87hz+xBCuCkj5h4PBHYRGR2NOg+\nUSR7uOGaUn8Ph8MP1EEKKD0ccWwsZfLynnBms2Rt/3EuLVd5RPHG6+OvxOrH5fpNklMroB2x6N8n\ngVLF+wjuy8vLoQ99ShkPzDfc1+/3wxxTh0iORI6DG6dHUMRob8pcX18PhzrewFVfeXLoZDJppGLi\n6MkqThgOk3xIYEFonoNUydzL3cMemH6+8YGRY0Gxuc3sqJ2KqK7Iyt48Z2bBJIYxZAGO36vMjN6D\nNLZm1KEzZqbMYXNzM8xtFgzUx8vPbfaiTAkLHA1cHaRia8nsaI75bBZ7e3vB+wxr6uDgIBzi2AyJ\nyPuI1P366683HDim02k4EDCR23/8d3d3Z8aw1PHB7zGHh4cNcjY7BLE3M+qIsel2uw0z6vb2dvib\nD6Uxhx2z4/V8+/btxnybTCYzacjMjvoZBx41XhC4ePzxN6cNwns5vRSX29ZDM4Zq2quoqKioqKio\nmBN/5jVSQKfTacTxODw8DKd1SA6K9Nnr9RqSvMrndZKI5B8U+DaxJgdSx/b2dpBivKbBTEvAkKz3\n9/dnYn/EUGISRD19Pi3WrKl3lpruWFuE9paGQlDSJRPGfXyomDTqzUss7XL8Ik8ev3HjRkMCZscB\nSNkc+whQGpirV6/KWF9Km+gJrTE1PJs18RwkZmjd+v1+Mv5WDKUJqn3mAxVKQIUN4Hqzy74i2vv5\nppIb43mz4z7tdrtBE8WOHEqb5dctz1M1t1KOKLFYVSlCO8Bu/tweFdUfmiVoTh89ehTejfmpknm/\n9dZbYT9hrSznnjM7Cueh5izezeRuhP7AuO3u7ja0NzxuKpYR0O/3wxh6U68H6ry5udnKcsD/MtD2\nCxcuhDnLoR8QcoQTUGO9og8mk0mgI+TWnp8z+/v7yXyEnCAZ9UrFaFM5KLe3txvhMZaXl8O70dcp\nDVpbVI1URUVFRUVFRcWc+FOlkSoNoMfAiZVP74rrwTnSIElzlGCACZT4Haf20wjCeVLEAucppCTL\nnITh2zoYDBpRqS9f/n/sfVmMZPdV/qm9uqr3np5uz4xn2p6xPR5P7PGS2CJWzBA7JgRCpCwiUQQP\nIQ+8RUECEQkwL8RIIERYJBQQL0H8o0hkIZKJIRhHTnCM7dhWbMbjbezMjKdn66V6qb3+D6Xv9HfP\n79xb1W3DxNH9Xnqmqu69v/3+znfO7zuzeoQYluPZs2dd5fDtio+i7OVyOZJPT6TfRyxnEVdHBqzo\nYrEY8bvjHhgTgyw0L2bDlqHVagXxK3FWmR3vGxsbGmeAv9VqVeMNEJ+ytramljQs8Hw+r58hJuS1\n114Lnlmv1wMrcHZ2NlBr5npyvcHQePOB289j5Wz7Mmu8HSTNAWaQkw43eMHhPCZs37RaLRUDfOaZ\nZ4LnWeZnUJm73W5ifNOggHo77nieceygt0ZaRpe/44BwzAv0/8bGhn4PRiCOGbIHLlj+AsHOcYci\n7JiYnp4OGIipqSn3uYAn58H1RHwQ5szY2JiuY97BEI41shIMY2NjOsc5Rgv/Xl9fDwLy3wpWVlaU\nNcNzPVaQ2xeq9Pv379dyoT9YtBTf3XzzzVrnp59+WuuUBHzPQfFe9gIW1LZr99raWsD0eeK+Hlhi\nA+8mLkscfqY2Uvl8ftsbKf59UkJXDiLHAoSB02q1Ii8jkWhqkmFOwvxfYVhXRiaTCdoyl8sF7gWv\nvUulUqBHk81mA6Xic+fODZ06AAsnFqClpSWdJNyudgIxrb7dF26xWNT6oi/X19cHLgZJ92M9FQAb\nTB5P9mSoVRcGsNijP9bX1+WGG24Qka024EUdixzrvyAwljdXSYrla2trwcvec1tnMpmIZgvqbdMu\niUQPeKC+aAPUrVarBXOI1djjkBQsnWQkdDqdIPCYjTX85SBdL4sB+iiTyegGioN07cas1WqpFhI2\np54rml22nmtxUH29JMi231utVuDeLBQK7hppx2qj0dBycVooO5aLxaLbR0jpgVNZlUolOM3oGS6T\nk5PabjAmrr/+enn88ccjv+M+uv7660WkHyyOzRX0kNbX1/UzfqmjrfDZwsKCbqT4JKzdiDYajUjC\nbpH+eMBGj+uEeXPu3LmhwxmGBZ4H1fE33nhjqOveeOMNLRfWRzZYUffXX39dXazWjcg4evSoqo7b\nssWB1w4+jAAkHeriTSDWY7yb6vV6oIA/TFqi1LWXIkWKFClSpEixQ/xMMVJvZcfOUgee9cYWOB9d\nts+2AesiW9bTICZjWDfT24Wk4FFO2AuUy+UgpyC7ujjHkg0o5aTQ2y3fzMyMWsCcH4utobgyiyQf\n745TbkaZtyt/EMcciURzcQFe4ulMJqNlwJgZHx8PpARKpZLeD+XM5XIRnRSRKNMAC4xx+PBhEelT\n99CU8SzCQ4cOiUifIQADizb1XCTsRka7rK6uBswgs5/M3mJOYXx5fTU2Nubqc3FQtXXBs+vUyxkJ\ntNttbX/Oq2mf1Ww2Azbby+02Ozur9/Pal+tnmVqv7q1WKzhI4bV5vV4P9Jds3QEeRyJRto0TvFtw\nMK8n48B9ZNnLS5cu6fcsGwDWjo+rA1aLiHHVVVcpIwUm23OLMVN79dVXi0jfpf29730vUs9cLhdh\ncLncjOnpaXV/4fmVSkWfwwH/ds7zuOJyoa12Et4wCKzxJyJy1113BaxdHFBGLxsD/8bLaQngXbJr\n1y65//77RUTk4YcfjpRtGGx3jeb3sH0nMPu0nfZOGakUKVKkSJEiRYod4meKkRoWcQHX2D3D2u71\nerrb5ViEpN0y+8FhEXKgMnbAcUraIn3LK2mXPezRbQYfB4W1yXEp+DfiVzwL0suh1mq1gmu57DZ4\nVWQrXoJzIjHwPFiRKysrbnsw44JneO1qn+H1fyaTCeKXvJgqjsOLC7CNw+joqBvQ6cV5wFoD05PL\n5ZQtAiOVy+WCo9XValW/Z2sdMTdsjWGc43ftdlvjQk6cOBGU5d3vfreI9BkpfIZ7eOKf3BcY2+Vy\nOYg54HZEn/OBgCTZkEqlEomHSArYZqFKOw/Z4se1HOvniW+y8KEdn71eL2CLLl26JAcOHBCRfvwI\nyoL7cH94Ae025rLX6+m9PQV0ZsltMDzLGniMLceB2bbkY/JApVLR4NwXXnhBLLyYFhZ4BDiw14pI\nNhoNjW/BdzyWMH95Trz66qsiInLttdcGcWTMoh48eFBEojFQ+D1ndMDf66+/XvsQ4PbDWNu7d28Q\neH7ttdeqcCcQt5azcOd2FbeHfU+A/Ww2m0EM7LDIZrMaZ3by5EkRiYp04vAKzx+MiVdffVXHAPpw\nenpa62vHwduJpPgnux4k4R2zkbJ6OrwQbBfeiRovzYN3CrDT6ehvETBYr9cjAZZcTjxPpL/Y2Xqw\nqjNTv0nuqDgdl6TfscsDA3jY4Gt+SXguO6u/lMvlguBhTn7Jgbl4cYNab7fbusjgfnGbSpsEeRDw\nshkZGQkU63u9nk5Y3G98fDxImeKl4CiVSkOXxasLXqDYqLZareAZXtoDb2HN5/O6WeLf48XH4wRl\n+e///m8R6S9e3gKKDRwDfbNv3z4R8U/3MTBn9uzZoy83DxgPi4uLOnb4pW1fDpcuXUpU2eZNM282\n+FAF/54/s8rSIr66NrtOeWyj/VGnS5cu6UbaUzPHJuKGG26QJ598MvIM3tRxOe192BVnQxX433wC\n0guaZ9hNBOs5AZcuXdIXKQPjCRpES0tL6p7HS3NtbU3d1jw2PDVuPNcLk0B9WVsKm5hnnnlG7rzz\nThER+eEPfxhciw0UzzuUr9vtBhvHZrMZGJYXL15UVyJcfJubm8GGet++fcFGiscB2ur8+fP6XFYT\nHxasuTWMZhK3N6u1J23gOBMB2vq2224Tka2TeiJb74vdu3frHMC1XpB7vV7X92vSITCRrX5nVXQP\n2OjDJbtdXa4kpK69FClSpEiRIkWKHeIdw0jZxJ5xgWDD0JmDWAN247ElivtyYmKRKKsEsPXAFp1V\nwM5ms0EQZy6X2zbb5lnWbDXybh6Uta2byBZDk8/n1SphNV/c03PzAZ1OR60IlIeD0lnDCdYBB25a\nVVqRLevw1ltvFZG+pYkjs0l9PjIyos9ltyXXCWW26s8bGxtaBquHIxI9cj6MG2pzc9PV18IRYVif\ncUHunlsQAJuRy+Xco7+wuMGO3HTTTWoJMkMQFzQuspWcVWRrjqDs1sIWibIoCNa99tprY+sgsmW1\n8/xhloyZUJF+vyXJlfR6vUDHLU5vzmOJkgLjWRbAMsLdblfLCkufj7hjPPP90Q8bGxtBWZiN4vXH\nlq/ZbAZH6/k3zELZ+eJJcjQajSB3WzabDZ7b6XRcKRMvPyDYJLjpRLZyMkLqgMuPecaJu71Aec4u\nsXfvXhGJslNgejz88z//s4iI3HffffoZAsaPHj0a0f0S6Y9ne7DkzJkz8tGPflREthippaWl4F11\n5syZSFJjkSgbxP2Audzr9QI9o0G6iey+ZlY0DiyngXKVy+WAEapWq0GC4unpaR0n+K5arep7AvW4\nfPmyylp4awar2GMtGBRMjufi2lqt5npbsH5ibFy6dGkoaYNhkDJSKVKkSJEiRYoUO8Q7hpECBrFJ\n22VyvOA6WBq5XE6tJlhHnU5HrQkOlrRWWy6XSzwybUU9GTtRavYYOu+zUqnkBoBbpsyLfRKRIFDd\ni1XjZ3jZuYetC98X/YNARmZn+Li3tTD4aDpYAGY72OLzWAXuT1s+DiK1TAmDpQxsDBJnJYdFhczl\njMuXLyfGxqF8XH+PqUMfjI2NKZsEluSVV15RyxVSB2fPnlWmhKUn7Hj3LDseN6j3oMBR9OvExEQg\nyJnJZIL29eIdGax8zHFsXgwiM1b2O/7Msr+5XC4SV4nf2bWlVqtpvBn3Mdoc/bC8vKxj1ZMcGKSA\njrWKWY9BCvl4FuqE/uS4KY4Js3IC+XzeVbYHOCcgys9xTla9mtkMDv5HG3lzHv1VrVbduYLyMTtj\n++iNN94YKhNFp9PRYHOWBbGsMSvvY419+eWX9fAHrz/4ntvCk93hMgyD9fV1ZbMQJ1Sr1YL1uNVq\nBYLG9XpdxxGvhVbqgplsTyoCv9+/f38sey0SfR/i30kxUocPH9aDMZxDEdd6quhgKcfGxlRYlPPv\noa1wj2Ha+R23kXq7kBSJz5osXsAmOrgay6EAACAASURBVBQDjE9U4LtSqeS68YBhE61yOXcaXM+I\n28x4wdIchCoSv0GywbyD6FK02/T0tG5a4dZaWloKFrDZ2Vn9nhcqq2gdd2Jv2PQAANqiUChoG3jt\nxpuYpBcUn+S0aDabugh5WjcYJ5ubm5EAUHtvvJQymYz2F2s3AVgkxsfHXVfMjTfeKCJbfeglha1U\nKkGKCA/eAuRtEj2srq5q/3qpNfACjJsTfCLNO2Vpy+alRxEJlfJZb45PtnmLPdqG3XToJ2h3nThx\nQjcZ6Jtms6nP4/lvN3g85tjVZU/R8jWcpikpkTHQbDaD5+bz+WAst9ttPY0LsGuX1b1tm05PT+u8\nZq00ezjl8uXLQdqWQqEQbKTiDk0ggP+zn/2siIh8+ctfDsZBuVzWzS42BJ67m09WY36Vy2V59NFH\ntVwi/bXTO9zDSbfxnTdf2IDbaUYFka2TeXv27BGR/lhDW7POFcrF4xnjiDcd3rzDmoB6Tk9Pq/GF\neR83XzFO5ubmRCTq2ktaW0+cOKGbQ4yhyclJXUs9dz3GFZ8qxNrKm3VPMT0OqWsvRYoUKVKkSJFi\nh7jijNRONJHeDnjHy+3Os9lsBlYbH4nGd7lczmWf8AwOruaAzbjnJpUzDt7Om8Fshy1Dt9vVcns6\nV7C8ms2mqxyNf7PlgM9gffZ6vSD/HVvMHHgKSwCurtXV1YDW9Y5qVyoVrb9nXSdJSoiE7TKsK7Ld\nbgcJqr378v3YsobllZQgVyRZzwv1ZlaA2w9gDR2PObQHArzxtLGxIdddd52IJAfADwKswGKxGFDw\nrAmGscQq0UChUHBdz8wW2Vxxnkub9Zfw3EKhEATu87jia71E257SN8oFd8T4+LjWifsVfcdjA8/m\n53pMmA2G5+Bw7nN7LJ/BLi97P5aP8K4BZmZmlJHgdkvKg4ZAeXZlsz6UF0Jhsbm5mXjog/vDrhOL\ni4v6DHbZAZ5rFowV54xj2QebpYCfy+8Nuy5Vq9VIWyUFXVuNMRFfSR39kcvldIzBxV+tVuXFF1+M\nPGtkZCTI2Tk6Oqp9zWs46gdJlHK5LLfffruIRMc7B4WL+G7BiYkJHZe8tg4KIRDpu2dRJ/zl/vLe\nqcwec6LoYZHISP3kJz+R48ePy0033SRHjx6VL33pSyIi8sADD8i+ffvk1ltvlVtvvVUeeughveaL\nX/yiXHfddXL48GGVe0+RIkWKFClSpPhZRCIjVSgU5M///M/l2LFjsra2Jrfffrvcd999kslk5POf\n/7x8/vOfj/z+hRdekK9+9avywgsvyJkzZ+Tee++VkydPJsYDYSe9XSXVtwuehelZsxynYWNzstms\nfobdcz6fjwRnikRjrlg6YdigwSQMClBntsNjz6xlFqf+7ln19nf5fN4NprdHlguFgraD5/9Piqfx\nrDO22j2wZW3rwWJ/+MsWNsdN4RruN3zG49iKkjJ7Z8cQ3yPuMxuXxHIUnP+RJSfwF+0Fi+7ChQtB\njFS1WpXTp0+LyJYS+vz8vPzkJz8JygXm4Nlnn9XPkhgOD3ys3YLXDC/mJ44BtHF9Xo5HHtteH3oM\nKzM0NhiZj40n5ctkBXQrCMqoVqsa14f4j0ajoe3Ac93ej5/Nf+1zisViIDXBB0e4PjYmLC6fJdhJ\nlIXlAWDdT09Pa1CwJ6XivSs4+NcGvk9OTgZs5rlz57TdPHzjG98QkT5bYcUbT58+rXE6NuZLJPlA\nyNraWhCv0263dd7yGsd5SUWiTAh+Nzc3pzIKHlhhHmOiUqmozAPmcrvdDuLSOp2O9gP+Tk1N6bxH\nYL4nKLq+vq5K5ZjD3I547uzsrLYT+vjVV1/VeiZ5WziulQ91eED/o0znzp3T8mCdmp6e1vJ76wjq\nsba2pkw5SzEMQuJGan5+Xgs3OjoqN954owafeo3wzW9+Uz75yU9KoVCQhYUFOXTokDzxxBNy1113\nxT7j7dhEvB3g+ngvPg5eRYeymwsdwpsmb3Nj0zJ4gZv/m2BXCCeIBfjFnBSQm4S4TZ2dEKz+zOWz\n7hl2sXA/gbbFwL9w4cJQ48mrb6VScXVLbFAzuxcGwepDvVV4CbF5UyoSfeGiLUqlUsQ9KxINIkf7\njY2NBfX1xubs7Ky7wOOFN+xGCuWr1WrBBqRSqegLhTWjAEv7A6g796F1e/MJTXYVWVeil+aFy+i5\n1ZKSYHP5UJeVlZUgGTVvLPjfKD+fOrPGnzc2vXnLmQaSTvHGrWF2MzU3N6duIwQ0eyemPAOMxxi7\n+AHrzmV4bsJut6vuIj65aNsZ89MCfcSGBq/XcVhaWtLy80bJu8Z+1m63Iy9zkX7f43ee4cgn6jhc\nApsgbCIuXrwYUcgX8ed1nCK4l9IJz8Bms1KpBAdPLl26pBs4BKqvrq5uO2ieQ2OSgD6fnJzUsrAB\nhLrzCUG0Aydzx33Y5TkIQwebnzp1Sn70ox/ppugv//Iv5ZZbbpHPfOYzOmHOnj2r/lGRvq/UO/WT\nIkWKFClSpEjxs4Chgs3X1tbkYx/7mPzFX/yFjI6Oym/91m/JH/zBH4iIyO///u/Lb//2b8vf//3f\nu9cOSvg3DMPxdsM76tztdoOAPU7mC3AyVXYFWLdAHGthg7WHSYj4diBJi4PL7+UcA1jrJCnAnF1n\nHGxuaXmPPWLXFKhVzlHI+jG4HtbRoLHEVqVNeBxnJSW5nFllP4ml4ntY9V92CwHsEmFr0DIz3rXF\nYlHbCrQ6SyJ4bQQrenR0VJ+NoE/P4m+1WkHCVpHQJTrIXY82t6ySSJR1g9XIgdksw8HWvQ2M9nR8\nODjcSwrMLIyXsQDP8OYSjwnPFWfZrHa7rQwJrx1emwDMHHiMkaeH5v3GO3BjD8h4v8vn88FzvWDy\n5eVl1y0EwA3GbjIe+5ZtGR0dDdp8cXExYFl4XnB7o8yeO4rhaSOBUUkKoajX6zru4F5bXFzUunM5\nPTeVlT9ZXl7W+7GuFtBoNPQ5zAbZrA38b87QYNvSc5MOAspUKpWCvu52u/o96jE7O6vf8yGQJNYR\n4NAYb71FXy8vL+v8YnV8lAUMU6lU0rnsBZbHjQ8PAxmpVqslH/3oR+XTn/60fOQjHxGRrZMImUxG\nfvM3f1OeeOIJEelLr3M8xenTp1WOPUWKFClSpEiR4p2GBx54IPH7REaq1+vJZz7zGTly5Ih87nOf\n08/ffPNNDUr7+te/Lu9617tEROTDH/6wfOpTn5LPf/7zcubMGXnppZfkPe95z1usws6QxMCwYrXH\nDHnCmTZIXCTKZllLmL/3dtHe/f434cX9cIAygDKWSqXAOmXrntsLlhTHjsHygaW0k8MESSrY3lFi\nhtfmfIjAg8ekeHIPsJS9uC1mR5KOz9rg5LiyMCOCfyMWqdfrqZgj2mBkZMRVjuZDECL9PrWKxa1W\nS5+Ba722jWPvuAxcxzgwK2xjvbz8j+Vy2bVcOa7GshPMnnhsAgdQW2Vz/h514v5g1ssbO7ZvWVDU\nW5d4vcB98JcZBCvnwP/2WErvcAVf68mCeEHznpwDwPFEYAG63a6WmdkK3AfMADNSzALYmJtdu3Zp\nIDMHZts1iduAWSqMHcS+xMWmep9j3iRJNzQaDc0jB0aXJSr4vjbnHYv/8v1w7eTkpLuegDXzAtoB\nL/5vYmIiCJhfWVkJBE9FtvoO606r1QqYq2azmcii4vmXLl3S+3DMLNhJfm/Yuc5eI29t4bLb+cWH\nxNBG5XJZ48gwB5aXl93+f+CBB+SP/uiPYuuXuJH6/ve/L1/5ylfk5ptv1mSxf/zHfyz/9E//JM88\n84xkMhm55ppr5G//9m9FROTIkSPyiU98Qo4cOSL5fF7+5m/+5v/MdWUxbJoV3ijZl2Y+nw9OXnmn\nWFiHib+zJ7Q4USjroNiFzKNd3yq4LMNsarzBVCqVAp0hdkNgkXmrJzCTEsoOq56+E10yz03mbb7s\nAp/L5XSx8V5GScjlcm4gMBYbaK2IbC1uBw8eFJFo0k+UvVAoBMrmm5ubwVgsFou6+OI7T2/KG4ec\nzNuWG98nwW46+BnY3PFCjs+4LQBOAcNl8F4snuvOM3y8wwj8ArUuJ64Lb0A4Cbl9rgdeB2z5vXHl\n3a/VagVuXO93/HLlQwlJCv48p7yXJn6Hcbe+vu6ms8HY9soFNz2f+GJY99za2lqwXnj9x2NskPFq\njederxdkrvCCq0X8uYn2wLV8chHYtWuXm2IH13gnW6+66iq9BuPE60NvM7y+vq5uQWzGer2ejjvO\nioC1Fn9x2IHBoRtJ6Ha7eviG1ySris5ufIADxj14G01+H2PcYS5vbm5qnVDf8fHxYNwhxCQJiRup\nu+++27VIP/jBD8Ze84UvfEG+8IUvDHxwihQpUqRIkSLFOx1XXNl8GHjBjYOwXSYsk8kEsgb5fD5g\nqfjob1LQNLsPAc+1x/pFnA/L0yV6KxiUtNQGCnuWaaPRcD/36G5rOfBxa3ZNWJdOLpdzNYLQ/4PU\nZllaQSQ+cTOXC9d5yvbe/e3vOp2Oa53aMcjX4rtisRj0CVt3Xi4u1InbnettZRI4XxratFQqBS7K\nTqejz01yycbNLfQNLL84QKcHbhqG51ICE9VqtQKmptfrRVgnT5fMMkLz8/PqiuLneO45227tdjvo\na09egPua5zUsYO7XJO0pgNucx0ZSsLmnc+XdD3VkXTLPbcgJy725YdlEri+PVbArniwA2paZfzwf\nAd8i0Xyoln32+s9LHM9gFsqTg7AJlHft2uXOebA1YKZOnDihEkJJ7nwvUTFL0Fy+fDmQhmAGCeN9\nkJQOu4fBRHEOPWanAOvuW19fj8gZ7BTo/4mJiUDnrl6vu9IjSfI2HgvN71GrX8bgDAJWC2wYOZc0\n116KFClSpEiRIsUO8Y5gpOJySiWBg7+TLD3eeVvBSI59SvKrs4XjSSLw/a21w0wYLJbV1VUNgoM1\nPqxwaVx92XK1wmRcBi/A2xMoZQvOE0xLCthkkTkbBO0JmQ5SLGfYmAyvHxqNhrIiaFcO/uSDALYs\ncUr0XkyWZc/m5+dVVw1tHxdHBSvIi0vBmGBGgi1vjnnBX8u2ViqVQBCx1Wqple0xf16sD89LlmXA\nX48JscrLDDAXLG/BAcNJx55Rfy5roVDQtkTA+Llz5wLrlWMHef7b/ucYlCSxTl4T+OCAjdPiIGOO\nn7TP9cZJHMNtGZdWqxUwCJ1OZyhBUY5VwfO8wOJMJqMHkJCbbt++ffpvjiuDpe+pjzPrZRkuZqTw\n2ejoaBBgzfMT5eQ6egwDr7d2fnOfow2q1aobw4XP7r33XhHpM1KWXeb+wb83NzdVgxHzgiU5NjY2\ngtikCxcuaO4/jO0LFy4oI4wyT09PB1kMeDwxq4RAfHx/6dKloD3q9XpwCKPb7SayYbwW4d5g4dbX\n13Xeo8/j3h+Yc15QPebMxz/+cfna174WW5YkCZi1tTUdn8PKuIi8QzZSmUxmx8HXcUq/9vRKp9MJ\nTgx5C3ev1wt0pOy9udwi0VQs1vUUdx8Mbj4tlLSZss+KA7srk2jZyclJLRf/zkutYelzbgNvovFL\nwgaPDwoSH5SCY9gNF07VMDCZWO8KtLbVfxLZepFms1mX5rcHC1g9l9vCujLZxeKlMEH5ZmdnNdgU\nn42NjWn/okzsrkDgZLPZdE/3JbnQ7QZNJGpg2ID2XC7nui2T1KExDnbt2hUkRPZemrYMtt06nY6W\nl/VrcB+8QNm1khR83Wg0IsriItHNhhfQjvvxxtc7Mctj22rtcKgAu8PtJoyzBfDmBeWDunen0wkU\n9/m0LbC6uuoefLDzbGRkJDA2eY55ekjssuMNHu5v14Jnn31WX/Q43eeNB0794p1C816MnE7HzgHP\nDRq3Tj3//PMiInLgwAH9zGYBmJiYkKuvvlpEJCIXhHKxawlrRrPZDMrFfYjTgoVCQdsE97t8+XKw\nKdjY2NC2RL+22229H8aJiLjjyep0VatVXT8xLzzNtVwup/OR72fXLA/tdluf6ym0o5w/+clP5J57\n7hERkUcffTS4j5d4Gm22e/dudW/iWcOECaWuvRQpUqRIkSJFih3iHcFIDdI8GfZagFWHmUVhut3+\nji0WTxHYUu8eFe9JLHhUPD8PO/RByY09BsNDu912WQfUGW6XpaUlN18RgHYZHR3Vz3HfYrGobgMv\naJn1XmxZPBcq18k7ypsUWD4IcBcwe8IMIihn/sxT8EW5vWPKuJZdCqylA5bojTfe0O/xXA7+Rflg\nQYJNYYyNjalFCKttc3NTLXM+ip2UT80DuyM9GQpm3FBmD3BdeEfFAa8vW62WO755/tjrPFap1+tp\nWZmJQhuh7ZkZ5PuifWGBx7E2tkysg8NzxZMpsM8dGRkJAp6ZpffcdJh7uVxOrXB28Vi2hscYM1O2\nTl6QO7MUfB3YFYQMcDu++OKLItI/xg9GCnVbWlpS5g/1qNVq6v4C4+SVZWJiQr/3xiCPWdQTbcCe\nCYDrhX6OY/PRbw899JCIiBw/flweeeSRyG8uXLgg1113nYhEGSkweOzyRNuPjIy47xSM1RdeeEFE\nonpT6POVlZUg/+Hy8rKr3I05jjY4ePCg5lBEP8zMzASs19ramr478LfT6ehcQdnr9XokmTruO6xc\nzTB5aR9//HH57Gc/KyJb7ff000/r9/jMS2R88eJF90DLIKSMVIoUKVKkSJEixQ7xjmCkGIPihIbd\n2dpj6L1eL1H9Ny6g3F7Lv7G/42BtvhZWAFs+NvZhEGswLENXKpXUEoDlwkHwnGSag4bxDFyLnbyN\nsxAZfCAAdfesi2Hr4TFXzBZ6IqgePMuSrXHLcGQyGdePj3InCTeyT57lD2y8FseRWDE/vh8zoSyJ\ngLKA6VpdXdU2QH9NTEwEMVxxCvE2Tozb1DsmbWO5LGDhIk7k6quvjljmIn1rEOMO7eiNNRGfyeEx\nZtnnQqEQSHawJAKL76JNWIjWSiyMjY2plcvsLVgvfpadx81mMzjAwfMR4P+Dqbl48aI7Dzx2zNYt\nl8vpGPNYfk8qgg8beOwI2oMD2zF+ef183/veJyIi3/ve90SkL4eBWD+sL61WSxkVPghi5/Lhw4fl\nxz/+ceQzZhkH5UtDW/E1llnjdsE48JhHka3YIjAgMzMzQRB5o9EIDjZ0Oh39N2IDefx1u91gzE5M\nTARzguvBYxd9iJRty8vLWlY+YIRnYOyurKzomMZ60m63lXXCvM3lctomPFeS8lcyi4+1IimXHjOF\ngwLAv/zlL4tIXwtTpN8PGEcssYDycVxaXOaGJLzjNlJJ2ImadZJLjN1unvYR/86e1uHfcICpl3LE\nlpv1Q3ZSN+uO5PKza8877cSwaSpY0ZoDs7d7EGBQYuQkeHpedlMskrzxzGazEfetSH9Bta4zkWhy\nWfzeJreO2/xZ9yZvHPmUJP7NitAAFjTv5KJV9eb68bWlUilIEDw5OakLtjdegLGxMb3G02dh4HnD\npj3CInzttdcG37G71HMLx40TWwdepPEi4JcPFlDvJJ9IcjA66lur1dzgV/tS4hehVw9eQ2wqDL4v\n+m1kZCSiAYbP7EveC1SPc4PzeEO78ObGlpWBIF0EMYtsvaSxyV5dXQ1cuWxIJB1EEAkV1b3fr6+v\n6wlCTy0cqFarrmGbFCIwSKXeGmYnT55UHSkGJ9MV6a8RdlzxeCmXy8GmaWVlJTDWeLzbwxoiIq++\n+qqIiOzZs0cNGi4fNlze6W2uG1yFfJjIriOcHJxhk9fzePf0vLwTycPiscceE5H+BhLzm0NWMM7x\nmbc5HQapay9FihQpUqRIkWKH+JlipHYC1p2xGjT8PWuAJCUjtteJRAOVAb6HtRK3s+v2jvd62il8\nbJe1UESiOl1gRRqNRpC4lq1ittqtBcX1ZPrWHnu2/8bvbf1Z5yqJBRqWtet2u0GeuV6v59L1Htvm\nWaNsmQGeZcPSALg/WA9PywptVigU9N4oE5cNv2fmhPN0oe/Qz6yRlaR6z+woJ4dN0mIBhu2P119/\nPfgsn88HVio/A/WwyV8tut2uWv3oD9aygdXuSSuMjY0FyVkzmUwkiB+w44QPUnjK/0lzwAsL8PTE\nvHHIgdSezhXDBqgXCoWALeBgeJSBE5ozrCL05OSktt+hQ4dEpM9qIHwArBG7vPm++B3Xw7rVz549\nGxyGGcRqAaVSKVjzOfzCq+MgPUOwrEePHhURkR//+MfuvEI9OBQB/cmJjzG+Z2Zm3PUE1zMDZ5nV\n+fl5Xduwnpw9e1brCRaqUCgEDCy/79grA3YKbtzFxUUdJzxvmXED7PuQxx3AEis87rCOJamNHzp0\nSJk3sKPnzp0LxjGvK0myC8MgZaRSpEiRIkWKFCl2iCvKSA0KDt+u1MGwiBNutP5vjpHi4G+bj4rj\ndfheSXFOnuSBjYsYBp4Ctc3nFXdPvhY7fY+VSVI273Q6anHjeeVyOWCz4tgOK4LKbelZ7W/XWBjE\nZAAoC+II2K+OupXLZb0fs3be2Ia1iO82Nze1zdnatQH5vV4vovosEm1TtrJwLazjq666KhCy46D0\nOHV1kSjr4al2M2CdbjdYs1wuB+OYcy6iXVZXVzXIFeWK60cOckW5vXgnVkq2zEatVguuYcFLgAUl\nOVbKrhO8nvD6Y9c5jnNBmUZGRvQZ3O9oG09CgfPR2d83m80gBoXvC0t+dXVVy4W2RyyUBVgnMFK8\nloBN4XUR8Uuzs7PKSnmyC8xOWObg/Pnz+lyoqMflvrTwFNq5DB7rBBY/DpivCJQXCZnDbDYbrLO3\n3367PPXUUyKyFQC/vr6u45vlShhYe5kNtvPv3LlzOo5Z0NSuT5lMJmBgS6VSwOjityJbTHw2mw3W\nGH6/e7n5mLmymRfW19f1M/wuLrOBRa1W09+h/by9Ri6Xk8OHD4tIX4Ee2L9/v4hE5WgG4YpupAY1\nytvx0oxL1eI9i0+04Hd28HKSYS+5JW+8LD3vqUD3ej2d9DvRQUoCT2Dv1CFrQXEaDpHoIsjtYTc0\nxWJRr8HLcG1tLQigbjQawYJSLpd1sUoKDu/1eon6QcO6kNCXvDDj+a1WK3Alzc/Pa7t4Qat8msye\nOhEJXR25XC7QKuIy4CXnuc44sJgXf9su7CLAPWZmZvSlit+vrq5u+2WTdDCDF9ztbqS4Puyq5g0U\ngPb1lOlFtl4UvOijHfCS4GS6rCCOdmNXEq7BC6NarQZBvLzB402GFyyLNuRn2PHLc8HLLgCwO5LT\nL7FGFeCNT4APa6CeuC+f1EXdSqWSu8nwTkAB2CjxCU30O58+RHtzPfhZmAOsTo3Tn9hIeSrgHtbW\n1iJK74BXN2vwxQEvYdRxeno66LtcLhfMb14P0I7s8j59+rQelgC4/9G+XtYOEXE3SPbUdLPZ1BOh\nWMvr9brOEdStVqvpaUisgRykjQ3V2NiYPsM7oe0dXkDd2Zjg0ALvIIs1gBYXF3UMcpkwjrHGLC0t\n6TjBhqper2tf3HzzzSIi8txzzwXPtEhdeylSpEiRIkWKFDvEOy7Y/O1QNo+DDfaOUye3LhZ2H7AG\njQ3c9HbTce7N7bItg8BMCKxhlIutd3Zb2ABvDtLlgGdr7eZyOb2GXTb2ucMqajNYtsCOgWKxqM/g\nHE+cv8uWiWH7h6ldT2uJlc0B7i+wFLDMWR+ImRAbRO65drit8JnHKDWbzWDcMqsAq41dO2izuHyB\nNjDWQ6VSSXQRemAXhlWY5n9zni5YwnHBvlY3iJkyMAMe8+v16+TkpFr8sFI3NzdVroEDWsHWeBIL\n3lrF84zXDJFoX+PfzEhinPI9+OAA8rwxo2EDkJmBYx07OwaYveNDJZ6bCW3PDCOSg4NB5HbGWOQg\nZzArrE4O1ogT6N56660iIvLwww+749b2sZdAWyScwzz3GCjfoLCLd7/73SIi8u1vf1tE+mMDUgLc\nB1aeAcrkqCfgjSdgfn5eWTiAc1nieUtLS4FMQqPR0HcC/p4/f16fDXmDsbExVaDHenjs2DFluFC3\nyclJZbMwrhqNhjJRXpuy3hzWAlYTxzhHG3hudZGtPuH8hZY1ZvcxK75jjmB95DAI1A1jOAkpI5Ui\nRYoUKVKkSLFDvOMYqWGZKC8/3LBgBsmzwj2L38YOcdAvx5hYSynOyn+7mCiA41Y4gFmkb3XCYuGg\nb8RJDauuPuzvOKv2sHFhNk6H+5XlHoYVB0XfwJfebreD/FGMQSyGB1sWPjYMcA41T7bCUwaGVclB\nmsy6wcLEZ5cvXw5YOY6HwXM9dtRrC89qr1QqQf97By64TmBYvNgHZkKATqejbcVB8xzzxuUR6beV\njf/jtvSENIG1tTX97fXXXy8ifYFFe7SaWTTOGWdjyrg9eH1KEsnEPTiGhqUzLDqdTkROgOsistUu\nXF+v7pwbzZaBpTMYp06dEpFozkXb/ysrK6quDXmDqampIPiay8TH5HntAMCYcHydlX7gXJrMLts2\n3LdvnyqQe/IxnpQFAyw0+pd/j7WGY7M49hLzAs8X2WKiKpVKUNZarRaJFRPptyXqjDYYHR3V9mIm\nDOMWfVmpVLSNINbJdQLr+swzz+i9AR7b3PbsGUgCGCEwf0tLS5H1C/Wwh3V4jQBLduDAgaAe3A9g\n3brdbpDl4/Tp09o3uBZsWRLecRspVKrZbA6VwHAnQCe1Wi03fYOnEmwl/wuFgpug2F7LJwy2izi6\n2gM/1y6CnU4nUZcDGB8fD6haTnHjuWfYbWCfwfQtn3DyTjtxWUX6E48Xe4C1sUSip6fYdYJ7ey+d\nYcEqvN6GISl9jp3AIlFNGCywvABgcWaXK/qDXyL2hXzx4kVdHDBeqtVqkL7D2/x7L45qterqJuHe\n/PLy2sWmP7L/FumPG29M2vqKRF/c1oXFGzKeKzY5a6PR0Pvg9+12W9ebkydP6nPRZ3ySz75sWEMJ\n4EBg7zSkF7bAIQM2PZOIr2tkle25D3kTbsMHuK3wXG+NiTNmbcqZSqUSuFg6nU5w8u38+fOBW2hj\nYyNwfx06dEhdWXzYgJPQohzoV84iiAAAIABJREFUN+5LgA/P2LVw9+7duiZwmw67zmIziXHKp+l4\ns+idNsV8RH9NT09Hkj3b+bC4uBhot9VqNR0TaLf5+Xnd4HuHZvg96iVd905p2hOhfLoTrjA+XMHZ\nPbx5bdNBZbNZnT+479raWvDOr1arej+0b61W01OCcAsykcDPt2XxDpgMo3SeuvZSpEiRIkWKFCl2\niHccIxWXLNKCLQjrihvWncQWPyucW8qeWRT+zqrmsg4GdvLbzVPH2I7bki1MTwsKFgPKNzMzo8F7\nwCuvvBJYBFwnrzysqI3nern+gHK5HLgemdlIchsyNc1WsxfEC8Dq6PV6QRsUCgW1aPg4cFL5OQjX\nO7LOR4P5viJbtDsncWXYZMTZbFatRU8TzKsvPmu1WloGPi4PizTJhc4BrfyXA7JF4sf2MO7yQqHg\nuudxLefL4vtZ6QW2OD03Hn5/yy23yLPPPisiW5ph586dC8a0F2Sdy+W0H5iZsgmUmeFOYoFwT5Go\ny9Zaz3yEnTMIePITSbn2UKZ6vR4cCGk2my5T5h0sADuCcb9nzx7tJ9YC8rSbcD/W0kNQP+YC9zMf\nBOG1DUBbeQmck9bNcrnsfo+6J117/PhxLRd+t7S0pOwIz0eoiXMwvm17ZhK9+ciadmj7RqMR5Ow8\nd+6cuqHf8573iEifOUMZmEHGs22iZQsv5ABlxHN37dqlcw6fLS0tRSRxUE8wbxgvY2NjWmbOWYpn\noD9brZaum2CSNjc3dQzC9Tk7O6vjB9fm83l3jUYZ8G6I081jpIxUihQpUqRIkSLFDvGOY6R2giRG\nwvudxzTgWhaKY2bCi4fiIF78tWzWWxUdtWWOU223x1/jAOvz0qVLierfrKjsHceGFYayxIk02nbw\nLL5hBTnj6uZZFF6OQq9sNhYgLp7NSmKI+OMIFlpSHi8va7tIOI6ZFeDyJTFR+Lu6uqptAOZobGxM\nrUUICnp15LKyhIIV1Yurg9cuVsVcZMsiTAr+Z1FX1IHr5AVVe7m9nn322SD3GN8HiFN3xtjjeBL0\nNc8L2+9xSvhWRZ4D6nE/vhbzY3Nz02Wc7f04YBzPrVQqAQNfqVSCeRUX+wag/zlgGc+qVqvu2LJs\nZrFYlJdeeinyG7AWItH1BGsbMzhgHcCqD+vJqNfrQbxrp9MJMg14uPPOO+VLX/qSiESZMBbaxX29\n9R/zHvNyY2ND477iBFnxOQsLY91mUUpISTz99NMisnVQQsQX2sX9OCaQwdk/8HubbaDVagWxSrw2\nYK1pt9tB/lCvvl68XiaTUcYS44BjyxDvVigUIrF7ItH284DxZAPrPbyjN1JJGx+G1UuJmww2maEX\nWM5S/Uy7cwCjiK83xfcedPpjWOB5LMufdNJLZGtg8IuKlYxxH5ygYO0RLw2IdTkUCgWtH5+y4cTP\neFbSBinp5CW7dNjtikUoKaAwn88nbqA8DKvCz/WJK7eIH8CIzxqNhrYVFlJ+QbPrE32JhdFzCYpI\n8HLd3NzUxRX3KJfLrioygAWIg/XhUuDAYu8UHcDZAnh8WlcRnxbjcWDno33J4//cD/ZAA/cLp4NA\nG/IL227C2ZDCS5r1mlD3bDYbJPHmwxUcFmDHOW82PSOF54fVcOMk0577zgt29xLL4sXHAcODkqpj\nTWB3lQ14LxaLOkbhktnY2NCxj3Qvr732mo55e7pUZKvtX3nllYhrUiS6+cNf3rwkGVEXL14Mxkkm\nkwlOuHlYWlrS58HFW6vV9GWNzRXPLcwZ1gnz3k/efFxdXQ1c8XxQBWVutVo6x1GWq6++Wt2QuPeh\nQ4fUlYe2X1hY0OfhfpwSiceTPSm5urqqv8NGdmZmJtj8lUqlYH0qFovBJifOyLYGpve7VqsVHIbJ\nZDJaPxwS8DDMQazUtZciRYoUKVKkSLFDZHpvt2DRMA8dInjr/6oMXvWZQbLWrGcZ8lFiDnxkyhy/\nt8F3mUwm0DfhgEwupw1U5wS/Xn2y2ay6KWAt8PHTJEX18fHxoY59MlD+arUaMG5e3j9+vsdSDAvP\nXcHfXYEh7iayFvGPpAOcIBdWM6z2l19+WT8DC7S4uKgWFQIt7ZF7ANYw+qhWq+lYxHetVkv7PMkt\nWSgU9BrUp1KpqBXLFqlFNpvVa5gRgGXNeRvxPAR/r62tKVvk5bdkFzueUSwWtU6YA6zqDuzevVvL\nj7E4Pj6uz+Mxa3WQbr/9dnn++ecjdWfwtTaoml2HSWsSB6V7uftQn7GxsSAJNjO/ljVg5HK5YB6O\njIxEpCRE+uMEaxr6eH5+XiUJvABvgNsZ+OAHPygPPfSQiIjcddddItJnpHA/jPGLFy8GrNf58+cj\nmRdE+qwHuxVForngOKgb7c/sPCeyRn2sq5jlLRCYXS6Xdb318qeCzd/Y2AjWqfn5+YhLGWCpExtE\nXi6Xta2ZDcb6wKEZ+IxZxRtvvFFE+rpQuJ93WAcsIcocx95YTSsP+/btU5Yd7Nfq6qqb5xLq6mDO\n3y4vzk6A9TzuXZIyUilSpEiRIkWKFDvEOzpGapiAYZHQ0uN4DFjAvV5Pd+t87N7G9bCvnQPncA1/\nBouQn2fz+LGgJft6YTHy870jzDamJS7fmed3hzWTy+UCP/MgNgp147gaWAyDAjs9NsHLb5ik9Ozl\nt2Mrz2O40M4TExNqoXGMkY1HaTQaem/Ez3Q6HbVEOV8erM2k+CCRLf89ypfL5bRvmE2C1cbB12AB\n+Bk2SNdeI9JnPdAnsOS9nIqe8F6pVNI24KPWrIYsEu3TOCZKpN+OlkG4fPlyEA+VzWYDUVDOD8b1\n5TlnVdM3NzcDFjCXy2kboR7nz58PYig5VornNyxuMBFPPfWU/g7PajQaroCmzavI8SY8ti37XK/X\nXfFNO0dqtZqymGApOp2OG2tl2alsNqt1R7+ura3p2Odcep5qP7OdIv1xYgUxvTHG6zfa9uqrr1ZG\nyjJEIluCnBwPhbJ7TDCPEZTpjjvukCeffFJEouPJBlKvra0FivBcFoyDp59+OvAQMEPIc9/KUGxs\nbARrlg3qt/Oa13SsP6z0Dybp7NmzWl5mxcBEcbsgTyPGfj6fD/L5xcFjomxM4OnTpyNi1FxflAEA\nq/jT4MEahHfcRsrbvGACc9AaD1T7GVN0XnA4axbZjRQHcwKc4oInEv7N98BkYnee3QzxwGENGvsC\n4sS9fCIpKXksB7IOe5LFgz1lYWFPbrESOX+HCc5t5FG4NkUMLyIc1I/y8GKEa/ESzmQySnvjPl4S\nWQYfJrCHDdjFMiixrw2O5sWRU5hgw8PJTPE9U/YYR9we2CR67egdHMBLwqvvgQMHVNXbaqWJbLVp\n3AlPq3q/uroaHMJgdzPGZrVa1fHJLy3WS0L9ue7WZZLL5YKgb3Z1cV1wH97QsDsQ94cBAtfE2NiY\n9kPcBs+CFaG9QzD2s9HRUffkq90MTU9PR05N4TcYOzzn0dZWDV5ka5xUKpXgUALr6wGXLl1SdXK8\nUBuNxsDQAxGRH//4x/oZ2uy1117Tzzx3tXWvMvjUq103GMViMUhAXSqVgtOMnMYJ4HGG5/FY84Dv\nRkZGgg1wu90O3iv5fN49ScfwMk1gA4I5sn//ft0MJZ1SE4kenHirKJVKwea/0Who+bDutFqt4MAF\nbyJ5zGD+e2mIvLbnvQHanMcuxiyf9rVr6jB6jalrL0WKFClSpEiRYof4qWOkmMlhi9Vq5zA9zwrC\nlqVi1W5mn6yFxDtWpnaTGIYkBVxWQOe62eSXHnOVzWYTpR2YwsZOHn/5956C8LCq7iJbFiiOQnN+\nK3xWLpcDpqLRaAT91Ww23bxXKGPSEdNqtartxhaVtWI6nU6Qj65erwduV9bVwe8mJia0H7hvbH8x\nC8gYRg/Mc69ymXGke2lpydXG8tTd0R6sFmzZkUwmo98zI5GUUNTLm4g+7/V6ykTBkltcXHSZA9QN\nDAu7rfFduVzWenIyYg92Plran1XJUUfLSDPw2dTUVOAi4gBl1rlCG/Ixedu3lUolVjvN1sPLH4Z2\n5/lj16xisRjonG1sbESOlaN8dn5xcLOnw4X25/FntcgYnU5HrwUj1Wq1dI1Gma+++mrVkULbg/ES\n8VlXjCt2g3KZwayiHbk94SL3NOYuXbqk1wL5fD5Y1ycnJ93xiGDoJKyvrwcuO9ZDYuacMyrwd0CS\nBqD3nkIbDeuaGwTWNMN4Qvt5TBe/e731EX3DCu18CMNT1LdM8vT0dESyR6TPrGLMsEo8WCw+1INx\nlqSZOAxSRipFihQpUqRIkWKH+KlhpFhQ0opz8b/tTh7XiESZBo5ZsSwVW7HMpthd/aD4JX7+oPgm\nkb4FiR0353izO2qWU+AdOCwMVlr1FIs94bykY/fValW/Rz9wQDGshWuvvTbi1xbpW1woFwebD8PQ\nsAgdwIKHDGvxeFIH1WpVrTlmRwapq8chn88Hlnwul4uwK7g/LG+OebLxAVNTUxrEySKDtl8LhYKK\n5XF7g6XgtvVYAstwlEoltf44pgRt5fUV7sF5ujB2e72extWApVpdXXWZLVs+jvuAxcmWtpUCsWBp\nEpF+G3P5wbKw1cmxLiL9cWqPaq+urkby34lExwsr71sG2ZNE8dhHjk9kxtmOE+9+zWYz+B23N1Ss\nmfGzMV8M7zDJ1NRUhB0SicZmoQ08hWkRP+YSc8XGLvJ3zFyg/zhGhmUfrPimyFY/gY1k6QOMU1sv\nkX7fQ5AV92UG1ovr4tgsPOcXf/EXRaTP/Fjph263m3hoBuB1kNfvJFFiVvAeFmjza665Rucd5y3E\nvED7cp+iHbjtk2KuvBhjryztdjsYq+VyWdsE68/o6KiymXguZ2jA79rtts511If3Aaj33NycvieS\nFOuHwU/NRooXVxu466UI4cBt/swGN3on0jhwG2g0GpG0EyLRRRoLgbcosQuI3XOWmqzX64FKb1yK\nCC9Q3abl8E648YkfnsA8URHIzAlCUUYsSisrK9veeHjAIBeJbpZFfDrV20R5pzK9l7+3CPLpL4Dr\nmwTvFCBvrrFoxpUbwAI/OTkZbLg7nY5ObA4sxwIA6txLoBsHu6GZmJhwNzkYT0m0di6XUxcGNmGj\no6O6UcFCe/nyZf0dv8jsRnRmZsYNfLa/Hx0dDU7ysZsW9WGXiEh0AyUS1V/CHBgdHdUNFPpwbW3N\nfVFgo+htPAYlHOcxIxIds2wA2bHI//fSxjDQNtgotNvtwN3iqclvbm7KsWPHRCSqI2TLzmuAVw+g\nWq26p7ZsH7PyPso+OTmpYQMcuG3B85vnAtqFP8O9Uf5msxkE5l++fFnd6Qg6f+WVV4L6ch8g6e/X\nv/51/YzTrWC8eGObtbc4WJrrYJ/H2l3eOmbH+yDgfXPixAk91QedrkuXLrkJtIfB+Pi4XsvzAp/h\nPcB9BD2qSqXiZsewrt+9e/fq+OD3GSeUF4mOT+/d4WUuwYY7m81qGRGIPsypwdS1lyJFihQpUqRI\nsUNccUbKHmf2FKGZBfDkBfhYvQ1u63Q6usO02h14nkh0F82qyNjB4zPPCuVjnrDkCoVCxPqzz0O9\ny+VyQCtyQDMrvlpGgo/2c7t4LkwGB42/nYBlVqlU1BKAJc+sohcEzzoowzIvANo8n89HAphF+tbM\nMLmSmClB2fkAAlv8wCClXfT1TTfdJCL9RMAYPyhznAQF2ARm8ZKU6JMORbTb7YB1Onz4sFp3SbT2\n7OysloHlEvA8SCOIbLFBVlWawZIASXo/rH3E7I09mm7ZEbRnUo46DgDm3+NeLAfgMVG2rflatIHn\nSmIcPnxYRPrMgA2g9YLIvQB0DtJlBgdjh12BNt/o2NiYMlFevjGPkUC7eG1y8OBBee655xLrDKD8\nYGI9lt9TbWdgjeAsCmDE2JXNDBjKz2MC6zr3l/Uk8JrPuecATq5sXVk8VsB+bmxsBEH9XEcOuPZk\nXoC3Il8jMnwQOh/wEumvAyw/ItJfP7H+Y1xxOyd5Fbx68DuA1wzoXJ04cSK4Bm09MTGhDBjnFsW4\nhe5XrVbT9kf5OGQE8ibDIGWkUqRIkSJFihQpdogrzkhZwTRmpDyrk4N5+Wgw/nrWixc3ZRkuZho4\nOJzzMvF3FlYVnZkhji3wBEOtwKMXZF8oFCLBmXiWZeA4SI+tIbAJ3lFSDgqE1eFZEMViUS09tMvG\nxobGIcCqi7PGYaF4FiH6kNuX46tgmaEeo6OjAWu3tLSk1k1SjEKr1QrkGbw4Ie8eIyMjQT+Njo4G\nSukiWz52T43bkzIAWP6CjxnDguc2SmKivFg0BMvefPPN8h//8R+x1wLz8/PKsvAYh0I1sxioU1LG\ngXq97s45q57d7Xa1r3l+W/FIFuHlMjCsACgzNAwbgM4CqhwEa4+hMyPFeek8JhTBzWxRW/HNer0e\nxCOtr68Ha0ej0QiCkVmyAe02NzenLLQnEcAClgD6kGPHkoLN40Rd7cGCTqcTUU0X6TNoSTkAPUV3\nvq9d8ycmJjQOlBkpjGPUt1gsKjP4+OOP6+/snOL1zKsn8iyKSMCYMlgqhMU5RfzMBDzetxtUvhPk\n8/ngYBa/Y9DO3lp58eJFLSvHSiblnsRadPbs2eDQFLcfxzajDaHez/2Le6ysrOgcwP3YQ+TlCuTx\nh3HiHVyKwxXfSFlZf95ssIvKLiJ8egbwdKR4knkn2zhwG/9G5/OmzXPTAVxmTjJpF0OmKHmz5KWX\nwb/55WUHtJf0l9uFn4FNwdTUVIQWF+n3gUfXY9DihdZqtXRRSXIHFYtFfUbSi35mZkbvg3Ytl8v6\nQuMNGuqEcvJnwwZG8gsw6TQMnxJBG+G5PHGBer3uvoywkcKYaLVa7ibXolqtRvRPRKJBkMPCewbK\ntLGxEQS8ejh06JC6itjN7J3WsSdS4zZSduyUy2UdYxhfnEEA/cYLvb2nBcpQr9cjbn6ReL05u2Dy\neLFrA8MzwjiND14wzWZTT0h5Rh3D6pzxIRIgm80Gp9JarVZEwV+k/+JGv+MlyCdmOaAcGwU2IryA\ne6va7r1s+IQmtz0nbBfpj1MvZMIeEmJg/nrK8PV6PWLQAPbwzPT0tAabM5LWE0/LCe1YKBQS3doM\njBMvzIHXbR5HduwdO3ZM64R1qdFoaB96p0CBfD6v7zfUqVqt6jX2JPkwQFngfltYWEh0waFurAXF\nxIBt6xdffHHosthnxR0MQRlYMxFGIt5DfJggDqlrL0WKFClSpEiRYoe44oyUpY2ZGWL3i0f5WY0n\n1hkB+Bil9zvW0rG78Fqt5gZCWrar2WxGcgnhM9ZkwvMtS+VJNuB6C8vAcPAh7uclMhbZYpWWl5cT\nc+3BSuF8UF4+qyQMOhYOMEXMrhM8jy1DtCH+VqvV4Ej3oOBJ7mvLHExNTem/0S7QLIm7D7uAPXcV\nmAFmP5NkEqx7izFMwDzgKQIDYES63a5arkkB681mU90KPBeSZCo8qx1lajQaQb+Vy+Xg6Hy73Q7G\nZ7FYDFguVm3nujCT56mhs+q3SH8ceElv8Tu0P+ddY80g637igHYOWrY6bB7LyIHlQK/X0zaEe/3C\nhQvBWOn1esG9Obdk0nF5j30oFosuW23736uHx1qPjIzo+ODn2TWt0+m4Ol0Az0HrPlxdXdW1gJk4\naFQxOM8fYBkufidxYLlFHGuM8Yl8flwnnqM24Tb/jssAPPPMM3L99deLiETkSNA3LJeDazmUBvOZ\nXfFJ69OwgKvznnvukYMHD4rIVu7E8+fPK3uPz44fPx6EqGxubuqY9Vxx20UulwsC/BuNRuDqLhQK\nOkfBTIGRTULKSKVIkSJFihQpUuwQmV5SsMb/1kPJEsGumVkZT/3XskDM5ABsMXsxCHytlSHg2CJm\neiwLxMdyPXBgK3bXsHBYXZUDxq0sQLfbda1Day00Gg29N8dmcfvZoDsGi35uN/4mCdlsNoihaDab\nWj/Er6ytrQ0V3xSnpJwEjkux/crB9YME6NDXYIuazeZQx457vZ4KHl5zzTUiIvLII49oHAGex+3E\nrGJSWfhaOyY4jshjpHAPWLAifiArrNS5ublAAkRkK2CXmQ0wJWBJvKWFmR/Ak3bgz7xgY6BYLEaO\nOHt5ytDvzBraIF8ODmcFd0/cFuDYIXs/DlQHdu3aFaiSM9sCSzhO3NKqZjOSpAJEtvoGfc1zCoHZ\nLIuCsnAdPMYU7TM6Opoofuix5ACXhZ/rlQHwxgQzzmiHpHvs379f64J+a7fbAePMYwMxVcxWe8/g\neySNtUGAYj3HYKINr7rqqogSPOpuxwLHXLLyvhdre6WBOTU6OqrtluRpKJVKbvYRb61AG6FvWEQ0\nSaEd907KvXvFNlI/TZ2XIkWKFClSpEgRh6R9S+raS5EiRYoUKVKk2CGuWLC5dTXxcf8kdw8o3ZmZ\nGfcoOgL7oA8ClVX7bNDZcE3U63WXtrVuIS/HH8PmrxLZyrV27ty5xEBgT38Hzy+XyxGXg8jgJLyT\nk5NKXXuuvSTXqXcN78a9PH+sxWGp90Eup0FIcl14x7OZTrdUPVsWNrCUn5XNZtVFtHv3bhHZCkAc\nBD5YwO3mJTf2+hGuSZQ5LnCXc8WJRFXxd+KuZRexSL8NOJkyvrPBytPT01oWjF8+2MD38/qf743/\no414rHmuWIwxdolzjjLcJ0mJPp/P64EMz9WJvGSbm5tDHb7Yv39/JBksAFcN6rG2thbM6263q+3B\nLopf//VfF5Gt9emRRx5xn21dGN1uV6677joR2eobL4D3pptukrvvvltERL7yla9Efs+Ym5vT8Qm3\niydN4R0W2dzcTNQJhAuSE3yzKxF9hN+dPn3aDZC+/fbbRUTkqaee0s8GuYhF+q49lJsD1dGW0AHj\nnHxYG1ZWVoL1nedtklaWyFa/YU0qlUr63M3NTXXpWg3EYTCMi5XvN6zHiPUH0a5ezti34oHisg9z\nHw4twe/faujKoOemjFSKFClSpEiRIsUOccXlDwAvONTL7TQolxV2xcwI2czTIyMjykjxkVjLcHFg\nLAeHA5wbC5YDP/fo0aMiIpHAZu/Ysa0vW4EcDA9LxTuOzLCCfIxBOdsYVrSQP0N92eLylIf5/2CO\nhs1UvhNrBkxUnPhp0v3ssXyPCeHP2eJOYhoZNth4ZGREg3jRLr1eT9kTqASvrq4G/c7WPf7ycXCM\ngxtuuEEDYVkgz2tfW/5utxswZp7Y4eXLl4dSX+50OtpH3L6W+Wi32wGDtJ3YSsz1TqcTMBC5XC4i\nIYDngW245ZZbRCTK+IB5ueGGGwJxS5RNZIvF+PCHPyx/9Vd/FZTLY2G8HKAYi5yf7d///d9FZCso\nfHJyUuvBR+utCGa5XNYxlrR2cFshgNpjpEZGRoIj4cxqc242MD0e62oD4Pnfs7OzgRDi0tKSPhes\nXNx8Qz/Ay3Dx4kUdT1hHmcH08pOifxcWFuS+++4TEZEvf/nLwbOwFs7MzOj7hO/DuUAB7+AIyoU2\nZ+X6YYPTWdaA36nDCLyKhGtjJpPRa/i+VqKIn8XB98NkXoiTwWBGVaTfRigL1jPODMKC1Z4gtM0Z\nyOBgfCuD5P3e4optpKwuBg8sTBbvpAVcK/l83n0RWNcPJ11FEsJutxsk7uU0Ct4mgelU+9xutxss\nOCMjI9qZWDB4A4FnVCoVHQBeMmE8Y3Z21j0NhZcl63Cg/Qap7HJbeakc7O9GR0cjKTBEfF0ge2+U\n2dus2bQcIv5GJelUF5+4sBsQ1vjx3GOeLhk/A+DNlU2mKyKu+2iYjeDm5qYukkh7kMvl1H2Ev1df\nfbVutDEHWq2WO0cAjMmnn35aP3vPe94jIiLPPfecPpfd1sPoyMQpJWOceydheHH1XGw2eXmn0wmU\n/D3XA6d+4Pt4qZD4/9g0YUysrq7q+vChD31IRPrtZjceJ0+eDF7wfOoML/B8Pi933HGHiIg8+eST\n+luUldvQc1ejXHAFZjKZYD4fPnxYjh8/LiIiX/ziF4N78EaFN1pxWF5eVvXopHEwPz8fjDse48O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dxu119/vW6kvD73MssD3mkyjxIf9gQk3w+IS9I8DIrForYvK3Czmw/PQNtw0l+0KasFY8OD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\nvIt12YdecMMjOfeE5Vhs0dqw5NbW1mK5U0Q6LIAbDs7h6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- "text": [
- "<matplotlib.figure.Figure at 0x7fd621d91150>"
- ]
- }
- ],
- "prompt_number": 9
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The second layer filters, `conv2`\n",
- "\n",
- "There are 256 filters, each of which has dimension 5 x 5 x 48. We show only the first 48 filters, with each channel shown separately, so that each filter is a row."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "filters = net.params['conv2'][0].data\n",
- "vis_square(filters[:48].reshape(48**2, 5, 5))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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0vzuvmqNWAfPO3EFL6ws5BltgaNfyy/LKGnOdQ8BnrH9o3PTtiEPGRv98b9eu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\nZsxo8/gdQWq+jUUreVymxRalLOrMsuefbf+Ots2sAAB+YKnms61ChvM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NAAAg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\n1k/7BsAeRj8v6JfrgWVZePb8OqPQWYG7du0ayyKM6HsB22sjRnPK9dhaT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dZbb+3kdrwB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\nxcV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c3/Gb9+s973lPRIy/53ekP5xi5p9yhg+A9wtOIb9/t9fMvG9TU1O92CV+gyy07dgO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- "text": [
- "<matplotlib.figure.Figure at 0x7fd630658d90>"
- ]
- }
- ],
- "prompt_number": 10
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The second layer output, `conv2` (rectified, only the first 36 of 256 channels)"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "feat = net.blobs['conv2'].data[0, :36]\n",
- "vis_square(feat, padval=1)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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44sWLqd6jk/OahTXn6zYWLlwYeU2rRmu6rL29PbLeq6++6uJDhw4Nux//99rc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\n",
- "text": [
- "<matplotlib.figure.Figure at 0x7fd621d73990>"
- ]
- }
- ],
- "prompt_number": 11
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The third layer output, `conv3` (rectified, all 384 channels)"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "feat = net.blobs['conv3'].data[0]\n",
- "vis_square(feat, padval=0.5)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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Okec+WKymeN9jDGirKHE81xm3v7CvTg1LbQRziVSkkiRJkiRJOrJDKFLOM57x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\nbZbwMQj4m+t2jYZrq6B4Xi+i19yv0P0ugbY/LCiZbdsu9UM7od5ro+1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tmZrYJHWYfAy0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C/MCPAMUmVb+sXzmRIFU/rJTol8H1sc5YD/w/vo4ptQh6HecjifX9cv6R+FZQLQDlAn83LFp8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\nVOIHKWOMMcaYSv4PwNFhvU7XHUcAAAAASUVORK5CYII=\n",
- "text": [
- "<matplotlib.figure.Figure at 0x7fd63064ffd0>"
- ]
- }
- ],
- "prompt_number": 12
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The fourth layer output, `conv4` (rectified, all 384 channels)"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "feat = net.blobs['conv4'].data[0]\n",
- "vis_square(feat, padval=0.5)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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3h8fOeeE1eY0XPP+LLrpop+0BXo57YShweMucH/93qLvk/U8WFooX3rJnfXn2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\n",
- "text": [
- "<matplotlib.figure.Figure at 0x7fd621d25190>"
- ]
- }
- ],
- "prompt_number": 13
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The fifth layer output, `conv5` (rectified, all 256 channels)"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "feat = net.blobs['conv5'].data[0]\n",
- "vis_square(feat, padval=0.5)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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QkQIAAIjEhRQAAEAkUntoq8MtKb3izJYeyDJE3m1/c+sivW7dujC2d+/eoqaT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\nxEaKiIiISKf/AMt+wa3UEUvkAAAAAElFTkSuQmCC\n",
- "text": [
- "<matplotlib.figure.Figure at 0x7fd621d1b3d0>"
- ]
- }
- ],
- "prompt_number": 14
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The fifth layer after pooling, `pool5`"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "feat = net.blobs['pool5'].data[0]\n",
- "vis_square(feat, padval=1)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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GTMu3v7/fiGn7PS8vz2q72udN3M9bTUFBgRGzPW9tc9Gm9Gu0c1nT3t5uxOrq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\nIqDoAAAAAElFTkSuQmCC\n",
- "text": [
- "<matplotlib.figure.Figure at 0x7fd621d7cd90>"
- ]
- }
- ],
- "prompt_number": 15
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The first fully connected layer, `fc6` (rectified)\n",
- "\n",
- "We show the output values and the histogram of the positive values"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "feat = net.blobs['fc6'].data[0]\n",
- "plt.subplot(2, 1, 1)\n",
- "plt.plot(feat.flat)\n",
- "plt.subplot(2, 1, 2)\n",
- "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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SpqSP+cc+lmx+eVKtbu+Xeh+HP9Nzm7kk7cAZ5jhz+tx/v7RsWdqlSMfWrft+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\ni4iIiEgxDlhEREREinHAIiIiIlLsO5hP7FaK+KtUAAAAAElFTkSuQmCC\n",
- "text": [
- "<matplotlib.figure.Figure at 0x7fd63064b890>"
- ]
- }
- ],
- "prompt_number": 16
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The second fully connected layer, `fc7` (rectified)"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "feat = net.blobs['fc7'].data[0]\n",
- "plt.subplot(2, 1, 1)\n",
- "plt.plot(feat.flat)\n",
- "plt.subplot(2, 1, 2)\n",
- "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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oo7xIJvWCXOztt6XLL+/92bBhuT4cTjIZ6c//XFq/PtzYomC6nm64IdzOy6tX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\nGERyBQAAYND/B6hFJTD2mK/NAAAAAElFTkSuQmCC\n",
- "text": [
- "<matplotlib.figure.Figure at 0x7fd6306fb450>"
- ]
- }
- ],
- "prompt_number": 17
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The final probability output, `prob`"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "feat = net.blobs['prob'].data[0]\n",
- "plt.plot(feat.flat)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 18,
- "text": [
- "[<matplotlib.lines.Line2D at 0x7fd63060d990>]"
- ]
- },
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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+acS6dRH/+q8RU6akPhoAJpqx7Ja6I2ENDQ2xcuXKWLRoUVSr1Vi2bFl0dHTEqlWrIiJi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\n",
- "text": [
- "<matplotlib.figure.Figure at 0x7fd630627290>"
- ]
- }
- ],
- "prompt_number": 18
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's see the top 5 predicted labels."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# load labels\n",
- "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
- "try:\n",
- " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
- "except:\n",
- " !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
- " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
- "\n",
- "# sort top k predictions from softmax output\n",
- "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n",
- "print labels[top_k]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n",
- " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n",
- " 'n02127052 lynx, catamount']\n"
- ]
- }
- ],
- "prompt_number": 19
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
import argparse
import numpy as np
import pandas as pd
+from skimage import io
import multiprocessing
# Flickr returns a special image if the request is unavailable.
urllib.urlretrieve(url, filename)
with open(filename) as f:
assert hashlib.sha1(f.read()).hexdigest() != MISSING_IMAGE_SHA1
+ test_read_image = io.imread(filename)
return True
except KeyboardInterrupt:
raise Exception() # multiprocessing doesn't catch keyboard exceptions
'-w', '--workers', type=int, default=-1,
help="num workers used to download images. -x uses (all - x) cores [-1 default]."
)
+ parser.add_argument(
+ '-l', '--labels', type=int, default=0,
+ help="if set to a positive value, only sample images from the first number of labels."
+ )
args = parser.parse_args()
np.random.seed(args.seed)
csv_filename = os.path.join(example_dirname, 'flickr_style.csv.gz')
df = pd.read_csv(csv_filename, index_col=0, compression='gzip')
df = df.iloc[np.random.permutation(df.shape[0])]
+ if args.labels > 0:
+ df = df.loc[df['label'] < args.labels]
if args.images > 0 and args.images < df.shape[0]:
df = df.iloc[:args.images]
Therefore, we change the name of the last layer from `fc8` to `fc8_flickr` in our prototxt.
Since there is no layer named that in the `bvlc_reference_caffenet`, that layer will begin training with random weights.
-We will also decrease the overall learning rate `base_lr` in the solver prototxt, but boost the `blobs_lr` on the newly introduced layer.
+We will also decrease the overall learning rate `base_lr` in the solver prototxt, but boost the `lr_mult` on the newly introduced layer.
The idea is to have the rest of the model change very slowly with new data, but let the new layer learn fast.
Additionally, we set `stepsize` in the solver to a lower value than if we were training from scratch, since we're virtually far along in training and therefore want the learning rate to go down faster.
-Note that we could also entirely prevent fine-tuning of all layers other than `fc8_flickr` by setting their `blobs_lr` to 0.
+Note that we could also entirely prevent fine-tuning of all layers other than `fc8_flickr` by setting their `lr_mult` to 0.
## Procedure
+++ /dev/null
-{
- "metadata": {
- "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.",
- "example_name": "Off-the-shelf SGD for classification",
- "include_in_docs": true,
- "priority": 4,
- "signature": "sha256:741422697d76b1667287180dc7c6360cf105ee774b1e2def800dc8fe80f78f67"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Caffeinated Logistic Regression of HDF5 Data\n",
- "\n",
- "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n",
- "\n",
- "# Make sure that caffe is on the python path:\n",
- "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n",
- "import sys\n",
- "sys.path.insert(0, caffe_root + 'python')\n",
- "\n",
- "import caffe\n",
- "\n",
- "import os\n",
- "import h5py\n",
- "import shutil\n",
- "import tempfile\n",
- "\n",
- "import sklearn\n",
- "import sklearn.datasets\n",
- "import sklearn.linear_model"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 1
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "X, y = sklearn.datasets.make_classification(\n",
- " n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \n",
- " n_clusters_per_class=2, hypercube=False, random_state=0\n",
- ")\n",
- "\n",
- "# Split into train and test\n",
- "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n",
- "\n",
- "# Visualize sample of the data\n",
- "ind = np.random.permutation(X.shape[0])[:1000]\n",
- "df = pd.DataFrame(X[ind])\n",
- "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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ikQzBoItAYBQYobj4OhIRLz7VxtP7R7lm7Uz+n3vvPZ0qvJ2dO1vRNCeQRdNe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dJjiJgJ+tTFAgoOoKGjIvuDGOqs00PHJUVAGxWLFQ7SYAmbgJCYNjl0OY5wWzQHGcM+EygI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\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YTGe2SZKELx7HaDQyNjaGMhpFrVQiSVKi/ocoYk5JwWuxEAqFWLV4AT1HjjA82E+KR06f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\nIAIgiiLDqRQXTpx4ywUjvu9z7OmnOTg6elXKv6VpJGMxyqur6Lt2XdXKMG0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16fV9Hnvo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JUuMre6yvC11774GuX1dQpISFK8m4+EoNWp0OM5ZGWDjAiIgoj1loMLXDs8zOT4OIePHmXJ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xf38zLpcas7kCtVpDc3M7IyN7MBj0ZGYWnCKzTpdCT8/AhDECsGTJYtLSUvnyy4M4nTYW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\n",
- "text": [
- "<matplotlib.figure.Figure at 0x11dade4d0>"
- ]
- }
- ],
- "prompt_number": 2
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# Train and test the scikit-learn SGD logistic regression.\n",
- "clf = sklearn.linear_model.SGDClassifier(\n",
- " loss='log', n_iter=1000, penalty='l2', alpha=1e-3, class_weight='auto')\n",
- "\n",
- "%timeit clf.fit(X, y)\n",
- "yt_pred = clf.predict(Xt)\n",
- "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "1 loops, best of 3: 499 ms per loop\n",
- "Accuracy: 0.756\n"
- ]
- }
- ],
- "prompt_number": 3
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Save the dataset to HDF5 for loading in Caffe."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# Write out the data to HDF5 files in a temp directory.\n",
- "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n",
- "dirname = os.path.abspath('./hdf5_classification/data')\n",
- "if not os.path.exists(dirname):\n",
- " os.makedirs(dirname)\n",
- "\n",
- "train_filename = os.path.join(dirname, 'train.h5')\n",
- "test_filename = os.path.join(dirname, 'test.h5')\n",
- "\n",
- "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n",
- "# To show this off, we'll list the same data file twice.\n",
- "with h5py.File(train_filename, 'w') as f:\n",
- " f['data'] = X\n",
- " f['label'] = y.astype(np.float32)\n",
- "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n",
- " f.write(train_filename + '\\n')\n",
- " f.write(train_filename + '\\n')\n",
- " \n",
- "# HDF5 is pretty efficient, but can be further compressed.\n",
- "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n",
- "with h5py.File(test_filename, 'w') as f:\n",
- " f.create_dataset('data', data=Xt, **comp_kwargs)\n",
- " f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n",
- "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n",
- " f.write(test_filename + '\\n')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 4
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Learn and evaluate logistic regression in Caffe."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def learn_and_test(solver_file):\n",
- " caffe.set_mode_cpu()\n",
- " solver = caffe.get_solver(solver_file)\n",
- " solver.solve()\n",
- "\n",
- " accuracy = 0\n",
- " test_iters = int(len(Xt) / solver.test_nets[0].blobs['data'].num)\n",
- " for i in range(test_iters):\n",
- " solver.test_nets[0].forward()\n",
- " accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
- " accuracy /= test_iters\n",
- " return accuracy\n",
- "\n",
- "%timeit learn_and_test('hdf5_classification/solver.prototxt')\n",
- "acc = learn_and_test('hdf5_classification/solver.prototxt')\n",
- "print(\"Accuracy: {:.3f}\".format(acc))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "1 loops, best of 3: 240 ms per loop\n",
- "Accuracy: 0.752"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 5
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Do the same through the command line interface for detailed output on the model and solving."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "!../build/tools/caffe train -solver hdf5_classification/solver.prototxt"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "I0307 01:34:29.141863 2099749632 caffe.cpp:103] Use CPU.\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "I0307 01:34:29.418283 2099749632 caffe.cpp:107] Starting Optimization\r\n",
- "I0307 01:34:29.418323 2099749632 solver.cpp:32] Initializing solver from parameters: \r\n",
- "test_iter: 250\r\n",
- "test_interval: 1000\r\n",
- "base_lr: 0.01\r\n",
- "display: 1000\r\n",
- "max_iter: 10000\r\n",
- "lr_policy: \"step\"\r\n",
- "gamma: 0.1\r\n",
- "momentum: 0.9\r\n",
- "weight_decay: 0.0005\r\n",
- "stepsize: 5000\r\n",
- "snapshot: 10000\r\n",
- "snapshot_prefix: \"hdf5_classification/data/train\"\r\n",
- "solver_mode: CPU\r\n",
- "net: \"hdf5_classification/train_val.prototxt\"\r\n",
- "I0307 01:34:29.418416 2099749632 solver.cpp:70] Creating training net from net file: hdf5_classification/train_val.prototxt\r\n",
- "I0307 01:34:29.418583 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\r\n",
- "I0307 01:34:29.418598 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\r\n",
- "I0307 01:34:29.418608 2099749632 net.cpp:42] Initializing net from parameters: \r\n",
- "name: \"LogisticRegressionNet\"\r\n",
- "state {\r\n",
- " phase: TRAIN\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"data\"\r\n",
- " type: \"HDF5Data\"\r\n",
- " top: \"data\"\r\n",
- " top: \"label\"\r\n",
- " include {\r\n",
- " phase: TRAIN\r\n",
- " }\r\n",
- " hdf5_data_param {\r\n",
- " source: \"hdf5_classification/data/train.txt\"\r\n",
- " batch_size: 10\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"fc1\"\r\n",
- " type: \"InnerProduct\"\r\n",
- " bottom: \"data\"\r\n",
- " top: \"fc1\"\r\n",
- " param {\r\n",
- " lr_mult: 1\r\n",
- " decay_mult: 1\r\n",
- " }\r\n",
- " param {\r\n",
- " lr_mult: 2\r\n",
- " decay_mult: 0\r\n",
- " }\r\n",
- " inner_product_param {\r\n",
- " num_output: 2\r\n",
- " weight_filler {\r\n",
- " type: \"gaussian\"\r\n",
- " std: 0.01\r\n",
- " }\r\n",
- " bias_filler {\r\n",
- " type: \"constant\"\r\n",
- " value: 0\r\n",
- " }\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"loss\"\r\n",
- " type: \"SoftmaxWithLoss\"\r\n",
- " bottom: \"fc1\"\r\n",
- " bottom: \"label\"\r\n",
- " top: \"loss\"\r\n",
- "}\r\n",
- "I0307 01:34:29.418692 2099749632 layer_factory.hpp:74] Creating layer data\r\n",
- "I0307 01:34:29.418853 2099749632 net.cpp:84] Creating Layer data\r\n",
- "I0307 01:34:29.418879 2099749632 net.cpp:338] data -> data\r\n",
- "I0307 01:34:29.418905 2099749632 net.cpp:338] data -> label\r\n",
- "I0307 01:34:29.418918 2099749632 net.cpp:113] Setting up data\r\n",
- "I0307 01:34:29.418926 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/train.txt\r\n",
- "I0307 01:34:29.418992 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\r\n",
- "I0307 01:34:29.420812 2099749632 net.cpp:120] Top shape: 10 4 (40)\r\n",
- "I0307 01:34:29.420841 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
- "I0307 01:34:29.420852 2099749632 layer_factory.hpp:74] Creating layer fc1\r\n",
- "I0307 01:34:29.420866 2099749632 net.cpp:84] Creating Layer fc1\r\n",
- "I0307 01:34:29.420872 2099749632 net.cpp:380] fc1 <- data\r\n",
- "I0307 01:34:29.420882 2099749632 net.cpp:338] fc1 -> fc1\r\n",
- "I0307 01:34:29.420894 2099749632 net.cpp:113] Setting up fc1\r\n",
- "I0307 01:34:29.425689 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
- "I0307 01:34:29.425709 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
- "I0307 01:34:29.425724 2099749632 net.cpp:84] Creating Layer loss\r\n",
- "I0307 01:34:29.425731 2099749632 net.cpp:380] loss <- fc1\r\n",
- "I0307 01:34:29.425739 2099749632 net.cpp:380] loss <- label\r\n",
- "I0307 01:34:29.425747 2099749632 net.cpp:338] loss -> loss\r\n",
- "I0307 01:34:29.425756 2099749632 net.cpp:113] Setting up loss\r\n",
- "I0307 01:34:29.425767 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
- "I0307 01:34:29.425781 2099749632 net.cpp:120] Top shape: (1)\r\n",
- "I0307 01:34:29.425789 2099749632 net.cpp:122] with loss weight 1\r\n",
- "I0307 01:34:29.425801 2099749632 net.cpp:167] loss needs backward computation.\r\n",
- "I0307 01:34:29.425808 2099749632 net.cpp:167] fc1 needs backward computation.\r\n",
- "I0307 01:34:29.425815 2099749632 net.cpp:169] data does not need backward computation.\r\n",
- "I0307 01:34:29.425822 2099749632 net.cpp:205] This network produces output loss\r\n",
- "I0307 01:34:29.425829 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\r\n",
- "I0307 01:34:29.425837 2099749632 net.cpp:217] Network initialization done.\r\n",
- "I0307 01:34:29.425843 2099749632 net.cpp:218] Memory required for data: 284\r\n",
- "I0307 01:34:29.425961 2099749632 solver.cpp:154] Creating test net (#0) specified by net file: hdf5_classification/train_val.prototxt\r\n",
- "I0307 01:34:29.425984 2099749632 net.cpp:257] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\r\n",
- "I0307 01:34:29.425997 2099749632 net.cpp:42] Initializing net from parameters: \r\n",
- "name: \"LogisticRegressionNet\"\r\n",
- "state {\r\n",
- " phase: TEST\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"data\"\r\n",
- " type: \"HDF5Data\"\r\n",
- " top: \"data\"\r\n",
- " top: \"label\"\r\n",
- " include {\r\n",
- " phase: TEST\r\n",
- " }\r\n",
- " hdf5_data_param {\r\n",
- " source: \"hdf5_classification/data/test.txt\"\r\n",
- " batch_size: 10\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"fc1\"\r\n",
- " type: \"InnerProduct\"\r\n",
- " bottom: \"data\"\r\n",
- " top: \"fc1\"\r\n",
- " param {\r\n",
- " lr_mult: 1\r\n",
- " decay_mult: 1\r\n",
- " }\r\n",
- " param {\r\n",
- " lr_mult: 2\r\n",
- " decay_mult: 0\r\n",
- " }\r\n",
- " inner_product_param {\r\n",
- " num_output: 2\r\n",
- " weight_filler {\r\n",
- " type: \"gaussian\"\r\n",
- " std: 0.01\r\n",
- " }\r\n",
- " bias_filler {\r\n",
- " type: \"constant\"\r\n",
- " value: 0\r\n",
- " }\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"loss\"\r\n",
- " type: \"SoftmaxWithLoss\"\r\n",
- " bottom: \"fc1\"\r\n",
- " bottom: \"label\"\r\n",
- " top: \"loss\"\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"accuracy\"\r\n",
- " type: \"Accuracy\"\r\n",
- " bottom: \"fc1\"\r\n",
- " bottom: \"label\"\r\n",
- " top: \"accuracy\"\r\n",
- " include {\r\n",
- " phase: TEST\r\n",
- " }\r\n",
- "}\r\n",
- "I0307 01:34:29.426126 2099749632 layer_factory.hpp:74] Creating layer data\r\n",
- "I0307 01:34:29.426311 2099749632 net.cpp:84] Creating Layer data\r\n",
- "I0307 01:34:29.426331 2099749632 net.cpp:338] data -> data\r\n",
- "I0307 01:34:29.426343 2099749632 net.cpp:338] data -> label\r\n",
- "I0307 01:34:29.426354 2099749632 net.cpp:113] Setting up data\r\n",
- "I0307 01:34:29.426362 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/test.txt\r\n",
- "I0307 01:34:29.426484 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\r\n",
- "I0307 01:34:29.427692 2099749632 net.cpp:120] Top shape: 10 4 (40)\r\n",
- "I0307 01:34:29.427711 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
- "I0307 01:34:29.427721 2099749632 layer_factory.hpp:74] Creating layer label_data_1_split\r\n",
- "I0307 01:34:29.427731 2099749632 net.cpp:84] Creating Layer label_data_1_split\r\n",
- "I0307 01:34:29.427738 2099749632 net.cpp:380] label_data_1_split <- label\r\n",
- "I0307 01:34:29.427747 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_0\r\n",
- "I0307 01:34:29.427759 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_1\r\n",
- "I0307 01:34:29.427768 2099749632 net.cpp:113] Setting up label_data_1_split\r\n",
- "I0307 01:34:29.427777 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
- "I0307 01:34:29.427784 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
- "I0307 01:34:29.427791 2099749632 layer_factory.hpp:74] Creating layer fc1\r\n",
- "I0307 01:34:29.427804 2099749632 net.cpp:84] Creating Layer fc1\r\n",
- "I0307 01:34:29.427813 2099749632 net.cpp:380] fc1 <- data\r\n",
- "I0307 01:34:29.427821 2099749632 net.cpp:338] fc1 -> fc1\r\n",
- "I0307 01:34:29.427831 2099749632 net.cpp:113] Setting up fc1\r\n",
- "I0307 01:34:29.427845 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
- "I0307 01:34:29.427857 2099749632 layer_factory.hpp:74] Creating layer fc1_fc1_0_split\r\n",
- "I0307 01:34:29.427866 2099749632 net.cpp:84] Creating Layer fc1_fc1_0_split\r\n",
- "I0307 01:34:29.427872 2099749632 net.cpp:380] fc1_fc1_0_split <- fc1\r\n",
- "I0307 01:34:29.427881 2099749632 net.cpp:338] fc1_fc1_0_split -> fc1_fc1_0_split_0\r\n",
- "I0307 01:34:29.427891 2099749632 net.cpp:338] fc1_fc1_0_split -> fc1_fc1_0_split_1\r\n",
- "I0307 01:34:29.427942 2099749632 net.cpp:113] Setting up fc1_fc1_0_split\r\n",
- "I0307 01:34:29.427955 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
- "I0307 01:34:29.427965 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
- "I0307 01:34:29.427976 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
- "I0307 01:34:29.427991 2099749632 net.cpp:84] Creating Layer loss\r\n",
- "I0307 01:34:29.428001 2099749632 net.cpp:380] loss <- fc1_fc1_0_split_0\r\n",
- "I0307 01:34:29.428009 2099749632 net.cpp:380] loss <- label_data_1_split_0\r\n",
- "I0307 01:34:29.428017 2099749632 net.cpp:338] loss -> loss\r\n",
- "I0307 01:34:29.428026 2099749632 net.cpp:113] Setting up loss\r\n",
- "I0307 01:34:29.428035 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
- "I0307 01:34:29.428048 2099749632 net.cpp:120] Top shape: (1)\r\n",
- "I0307 01:34:29.428056 2099749632 net.cpp:122] with loss weight 1\r\n",
- "I0307 01:34:29.428064 2099749632 layer_factory.hpp:74] Creating layer accuracy\r\n",
- "I0307 01:34:29.428076 2099749632 net.cpp:84] Creating Layer accuracy\r\n",
- "I0307 01:34:29.428084 2099749632 net.cpp:380] accuracy <- fc1_fc1_0_split_1\r\n",
- "I0307 01:34:29.428092 2099749632 net.cpp:380] accuracy <- label_data_1_split_1\r\n",
- "I0307 01:34:29.428102 2099749632 net.cpp:338] accuracy -> accuracy\r\n",
- "I0307 01:34:29.428131 2099749632 net.cpp:113] Setting up accuracy\r\n",
- "I0307 01:34:29.428140 2099749632 net.cpp:120] Top shape: (1)\r\n",
- "I0307 01:34:29.428148 2099749632 net.cpp:169] accuracy does not need backward computation.\r\n",
- "I0307 01:34:29.428154 2099749632 net.cpp:167] loss needs backward computation.\r\n",
- "I0307 01:34:29.428161 2099749632 net.cpp:167] fc1_fc1_0_split needs backward computation.\r\n",
- "I0307 01:34:29.428167 2099749632 net.cpp:167] fc1 needs backward computation.\r\n",
- "I0307 01:34:29.428174 2099749632 net.cpp:169] label_data_1_split does not need backward computation.\r\n",
- "I0307 01:34:29.428181 2099749632 net.cpp:169] data does not need backward computation.\r\n",
- "I0307 01:34:29.428189 2099749632 net.cpp:205] This network produces output accuracy\r\n",
- "I0307 01:34:29.428324 2099749632 net.cpp:205] This network produces output loss\r\n",
- "I0307 01:34:29.428342 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\r\n",
- "I0307 01:34:29.428350 2099749632 net.cpp:217] Network initialization done.\r\n",
- "I0307 01:34:29.428357 2099749632 net.cpp:218] Memory required for data: 528\r\n",
- "I0307 01:34:29.428388 2099749632 solver.cpp:42] Solver scaffolding done.\r\n",
- "I0307 01:34:29.428412 2099749632 solver.cpp:222] Solving LogisticRegressionNet\r\n",
- "I0307 01:34:29.428421 2099749632 solver.cpp:223] Learning Rate Policy: step\r\n",
- "I0307 01:34:29.428431 2099749632 solver.cpp:266] Iteration 0, Testing net (#0)\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "I0307 01:34:29.471674 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.4532\r\n",
- "I0307 01:34:29.471724 2099749632 solver.cpp:315] Test net output #1: loss = 0.694067 (* 1 = 0.694067 loss)\r\n",
- "I0307 01:34:29.471853 2099749632 solver.cpp:189] Iteration 0, loss = 0.692695\r\n",
- "I0307 01:34:29.471878 2099749632 solver.cpp:204] Train net output #0: loss = 0.692695 (* 1 = 0.692695 loss)\r\n",
- "I0307 01:34:29.471890 2099749632 solver.cpp:464] Iteration 0, lr = 0.01\r\n",
- "I0307 01:34:29.483834 2099749632 solver.cpp:266] Iteration 1000, Testing net (#0)\r\n",
- "I0307 01:34:29.486868 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7424\r\n",
- "I0307 01:34:29.486896 2099749632 solver.cpp:315] Test net output #1: loss = 0.601764 (* 1 = 0.601764 loss)\r\n",
- "I0307 01:34:29.486922 2099749632 solver.cpp:189] Iteration 1000, loss = 0.472665\r\n",
- "I0307 01:34:29.486934 2099749632 solver.cpp:204] Train net output #0: loss = 0.472665 (* 1 = 0.472665 loss)\r\n",
- "I0307 01:34:29.486944 2099749632 solver.cpp:464] Iteration 1000, lr = 0.01\r\n",
- "I0307 01:34:29.498821 2099749632 solver.cpp:266] Iteration 2000, Testing net (#0)\r\n",
- "I0307 01:34:29.501900 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7364\r\n",
- "I0307 01:34:29.501941 2099749632 solver.cpp:315] Test net output #1: loss = 0.60818 (* 1 = 0.60818 loss)\r\n",
- "I0307 01:34:29.501988 2099749632 solver.cpp:189] Iteration 2000, loss = 0.6863\r\n",
- "I0307 01:34:29.502003 2099749632 solver.cpp:204] Train net output #0: loss = 0.6863 (* 1 = 0.6863 loss)\r\n",
- "I0307 01:34:29.502013 2099749632 solver.cpp:464] Iteration 2000, lr = 0.01\r\n",
- "I0307 01:34:29.513921 2099749632 solver.cpp:266] Iteration 3000, Testing net (#0)\r\n",
- "I0307 01:34:29.517227 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.6964\r\n",
- "I0307 01:34:29.517300 2099749632 solver.cpp:315] Test net output #1: loss = 0.604707 (* 1 = 0.604707 loss)\r\n",
- "I0307 01:34:29.518105 2099749632 solver.cpp:189] Iteration 3000, loss = 0.617542\r\n",
- "I0307 01:34:29.518154 2099749632 solver.cpp:204] Train net output #0: loss = 0.617542 (* 1 = 0.617542 loss)\r\n",
- "I0307 01:34:29.518170 2099749632 solver.cpp:464] Iteration 3000, lr = 0.01\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "I0307 01:34:29.531672 2099749632 solver.cpp:266] Iteration 4000, Testing net (#0)\r\n",
- "I0307 01:34:29.534873 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7424\r\n",
- "I0307 01:34:29.534920 2099749632 solver.cpp:315] Test net output #1: loss = 0.601764 (* 1 = 0.601764 loss)\r\n",
- "I0307 01:34:29.534950 2099749632 solver.cpp:189] Iteration 4000, loss = 0.472666\r\n",
- "I0307 01:34:29.534962 2099749632 solver.cpp:204] Train net output #0: loss = 0.472665 (* 1 = 0.472665 loss)\r\n",
- "I0307 01:34:29.534973 2099749632 solver.cpp:464] Iteration 4000, lr = 0.01\r\n",
- "I0307 01:34:29.546567 2099749632 solver.cpp:266] Iteration 5000, Testing net (#0)\r\n",
- "I0307 01:34:29.549762 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7364\r\n",
- "I0307 01:34:29.549789 2099749632 solver.cpp:315] Test net output #1: loss = 0.60818 (* 1 = 0.60818 loss)\r\n",
- "I0307 01:34:29.549815 2099749632 solver.cpp:189] Iteration 5000, loss = 0.686301\r\n",
- "I0307 01:34:29.549828 2099749632 solver.cpp:204] Train net output #0: loss = 0.6863 (* 1 = 0.6863 loss)\r\n",
- "I0307 01:34:29.549837 2099749632 solver.cpp:464] Iteration 5000, lr = 0.001\r\n",
- "I0307 01:34:29.562142 2099749632 solver.cpp:266] Iteration 6000, Testing net (#0)\r\n",
- "I0307 01:34:29.565335 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7476\r\n",
- "I0307 01:34:29.565373 2099749632 solver.cpp:315] Test net output #1: loss = 0.59775 (* 1 = 0.59775 loss)\r\n",
- "I0307 01:34:29.566051 2099749632 solver.cpp:189] Iteration 6000, loss = 0.664614\r\n",
- "I0307 01:34:29.566086 2099749632 solver.cpp:204] Train net output #0: loss = 0.664614 (* 1 = 0.664614 loss)\r\n",
- "I0307 01:34:29.566097 2099749632 solver.cpp:464] Iteration 6000, lr = 0.001\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "I0307 01:34:29.577900 2099749632 solver.cpp:266] Iteration 7000, Testing net (#0)\r\n",
- "I0307 01:34:29.580993 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7524\r\n",
- "I0307 01:34:29.581015 2099749632 solver.cpp:315] Test net output #1: loss = 0.597349 (* 1 = 0.597349 loss)\r\n",
- "I0307 01:34:29.581038 2099749632 solver.cpp:189] Iteration 7000, loss = 0.456775\r\n",
- "I0307 01:34:29.581050 2099749632 solver.cpp:204] Train net output #0: loss = 0.456774 (* 1 = 0.456774 loss)\r\n",
- "I0307 01:34:29.581059 2099749632 solver.cpp:464] Iteration 7000, lr = 0.001\r\n",
- "I0307 01:34:29.592854 2099749632 solver.cpp:266] Iteration 8000, Testing net (#0)\r\n",
- "I0307 01:34:29.595973 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7568\r\n",
- "I0307 01:34:29.596002 2099749632 solver.cpp:315] Test net output #1: loss = 0.597265 (* 1 = 0.597265 loss)\r\n",
- "I0307 01:34:29.596027 2099749632 solver.cpp:189] Iteration 8000, loss = 0.673885\r\n",
- "I0307 01:34:29.596040 2099749632 solver.cpp:204] Train net output #0: loss = 0.673885 (* 1 = 0.673885 loss)\r\n",
- "I0307 01:34:29.596048 2099749632 solver.cpp:464] Iteration 8000, lr = 0.001\r\n",
- "I0307 01:34:29.607822 2099749632 solver.cpp:266] Iteration 9000, Testing net (#0)\r\n",
- "I0307 01:34:29.610930 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7432\r\n",
- "I0307 01:34:29.610960 2099749632 solver.cpp:315] Test net output #1: loss = 0.597777 (* 1 = 0.597777 loss)\r\n",
- "I0307 01:34:29.611558 2099749632 solver.cpp:189] Iteration 9000, loss = 0.66526\r\n",
- "I0307 01:34:29.611583 2099749632 solver.cpp:204] Train net output #0: loss = 0.66526 (* 1 = 0.66526 loss)\r\n",
- "I0307 01:34:29.611593 2099749632 solver.cpp:464] Iteration 9000, lr = 0.001\r\n",
- "I0307 01:34:29.623009 2099749632 solver.cpp:334] Snapshotting to hdf5_classification/data/train_iter_10000.caffemodel\r\n",
- "I0307 01:34:29.623209 2099749632 solver.cpp:342] Snapshotting solver state to hdf5_classification/data/train_iter_10000.solverstate\r\n",
- "I0307 01:34:29.623319 2099749632 solver.cpp:248] Iteration 10000, loss = 0.457922\r\n",
- "I0307 01:34:29.623333 2099749632 solver.cpp:266] Iteration 10000, Testing net (#0)\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "I0307 01:34:29.626454 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.752\r\n",
- "I0307 01:34:29.626484 2099749632 solver.cpp:315] Test net output #1: loss = 0.597362 (* 1 = 0.597362 loss)\r\n",
- "I0307 01:34:29.626493 2099749632 solver.cpp:253] Optimization Done.\r\n",
- "I0307 01:34:29.626502 2099749632 caffe.cpp:121] Optimization Done.\r\n"
- ]
- }
- ],
- "prompt_number": 6
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If you look at output or the `train_val.prototxt`, you'll see that the model is simple logistic regression.\n",
- "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n",
- "That network is given in `train_val2.prototxt`, and that's the only change made in `solver2.prototxt` which we will now use.\n",
- "\n",
- "The final accuracy of the new network be higher than logistic regression!"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def learn_and_test(solver_file):\n",
- " caffe.set_mode_cpu()\n",
- " solver = caffe.get_solver(solver_file)\n",
- " solver.solve()\n",
- "\n",
- " accuracy = 0\n",
- " test_iters = int(len(Xt) / solver.test_nets[0].blobs['data'].num)\n",
- " for i in range(test_iters):\n",
- " solver.test_nets[0].forward()\n",
- " accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
- " accuracy /= test_iters\n",
- " return accuracy\n",
- "\n",
- "%timeit learn_and_test('hdf5_classification/solver2.prototxt')\n",
- "acc = learn_and_test('hdf5_classification/solver2.prototxt')\n",
- "print(\"Accuracy: {:.3f}\".format(acc))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "1 loops, best of 3: 333 ms per loop\n",
- "Accuracy: 0.818"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 7
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Do the same through the command line interface for detailed output on the model and solving."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "!../build/tools/caffe train -solver hdf5_classification/solver2.prototxt"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "I0307 01:34:31.589234 2099749632 caffe.cpp:103] Use CPU.\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "I0307 01:34:31.872560 2099749632 caffe.cpp:107] Starting Optimization\r\n",
- "I0307 01:34:31.872596 2099749632 solver.cpp:32] Initializing solver from parameters: \r\n",
- "test_iter: 250\r\n",
- "test_interval: 1000\r\n",
- "base_lr: 0.01\r\n",
- "display: 1000\r\n",
- "max_iter: 10000\r\n",
- "lr_policy: \"step\"\r\n",
- "gamma: 0.1\r\n",
- "momentum: 0.9\r\n",
- "weight_decay: 0.0005\r\n",
- "stepsize: 5000\r\n",
- "snapshot: 10000\r\n",
- "snapshot_prefix: \"hdf5_classification/data/train\"\r\n",
- "solver_mode: CPU\r\n",
- "net: \"hdf5_classification/train_val2.prototxt\"\r\n",
- "I0307 01:34:31.872687 2099749632 solver.cpp:70] Creating training net from net file: hdf5_classification/train_val2.prototxt\r\n",
- "I0307 01:34:31.872865 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\r\n",
- "I0307 01:34:31.872882 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\r\n",
- "I0307 01:34:31.872891 2099749632 net.cpp:42] Initializing net from parameters: \r\n",
- "name: \"LogisticRegressionNet\"\r\n",
- "state {\r\n",
- " phase: TRAIN\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"data\"\r\n",
- " type: \"HDF5Data\"\r\n",
- " top: \"data\"\r\n",
- " top: \"label\"\r\n",
- " include {\r\n",
- " phase: TRAIN\r\n",
- " }\r\n",
- " hdf5_data_param {\r\n",
- " source: \"hdf5_classification/data/train.txt\"\r\n",
- " batch_size: 10\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"fc1\"\r\n",
- " type: \"InnerProduct\"\r\n",
- " bottom: \"data\"\r\n",
- " top: \"fc1\"\r\n",
- " param {\r\n",
- " lr_mult: 1\r\n",
- " decay_mult: 1\r\n",
- " }\r\n",
- " param {\r\n",
- " lr_mult: 2\r\n",
- " decay_mult: 0\r\n",
- " }\r\n",
- " inner_product_param {\r\n",
- " num_output: 40\r\n",
- " weight_filler {\r\n",
- " type: \"gaussian\"\r\n",
- " std: 0.01\r\n",
- " }\r\n",
- " bias_filler {\r\n",
- " type: \"constant\"\r\n",
- " value: 0\r\n",
- " }\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"relu1\"\r\n",
- " type: \"ReLU\"\r\n",
- " bottom: \"fc1\"\r\n",
- " top: \"fc1\"\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"fc2\"\r\n",
- " type: \"InnerProduct\"\r\n",
- " bottom: \"fc1\"\r\n",
- " top: \"fc2\"\r\n",
- " param {\r\n",
- " lr_mult: 1\r\n",
- " decay_mult: 1\r\n",
- " }\r\n",
- " param {\r\n",
- " lr_mult: 2\r\n",
- " decay_mult: 0\r\n",
- " }\r\n",
- " inner_product_param {\r\n",
- " num_output: 2\r\n",
- " weight_filler {\r\n",
- " type: \"gaussian\"\r\n",
- " std: 0.01\r\n",
- " }\r\n",
- " bias_filler {\r\n",
- " type: \"constant\"\r\n",
- " value: 0\r\n",
- " }\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"loss\"\r\n",
- " type: \"SoftmaxWithLoss\"\r\n",
- " bottom: \"fc2\"\r\n",
- " bottom: \"label\"\r\n",
- " top: \"loss\"\r\n",
- "}\r\n",
- "I0307 01:34:31.873246 2099749632 layer_factory.hpp:74] Creating layer data\r\n",
- "I0307 01:34:31.873276 2099749632 net.cpp:84] Creating Layer data\r\n",
- "I0307 01:34:31.873292 2099749632 net.cpp:338] data -> data\r\n",
- "I0307 01:34:31.873332 2099749632 net.cpp:338] data -> label\r\n",
- "I0307 01:34:31.873352 2099749632 net.cpp:113] Setting up data\r\n",
- "I0307 01:34:31.873361 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/train.txt\r\n",
- "I0307 01:34:31.873443 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\r\n",
- "I0307 01:34:31.875783 2099749632 net.cpp:120] Top shape: 10 4 (40)\r\n",
- "I0307 01:34:31.875816 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
- "I0307 01:34:31.875829 2099749632 layer_factory.hpp:74] Creating layer fc1\r\n",
- "I0307 01:34:31.875846 2099749632 net.cpp:84] Creating Layer fc1\r\n",
- "I0307 01:34:31.875857 2099749632 net.cpp:380] fc1 <- data\r\n",
- "I0307 01:34:31.875875 2099749632 net.cpp:338] fc1 -> fc1\r\n",
- "I0307 01:34:31.875892 2099749632 net.cpp:113] Setting up fc1\r\n",
- "I0307 01:34:31.882478 2099749632 net.cpp:120] Top shape: 10 40 (400)\r\n",
- "I0307 01:34:31.882505 2099749632 layer_factory.hpp:74] Creating layer relu1\r\n",
- "I0307 01:34:31.882524 2099749632 net.cpp:84] Creating Layer relu1\r\n",
- "I0307 01:34:31.882532 2099749632 net.cpp:380] relu1 <- fc1\r\n",
- "I0307 01:34:31.882544 2099749632 net.cpp:327] relu1 -> fc1 (in-place)\r\n",
- "I0307 01:34:31.882555 2099749632 net.cpp:113] Setting up relu1\r\n",
- "I0307 01:34:31.882565 2099749632 net.cpp:120] Top shape: 10 40 (400)\r\n",
- "I0307 01:34:31.882583 2099749632 layer_factory.hpp:74] Creating layer fc2\r\n",
- "I0307 01:34:31.882609 2099749632 net.cpp:84] Creating Layer fc2\r\n",
- "I0307 01:34:31.882619 2099749632 net.cpp:380] fc2 <- fc1\r\n",
- "I0307 01:34:31.882632 2099749632 net.cpp:338] fc2 -> fc2\r\n",
- "I0307 01:34:31.882644 2099749632 net.cpp:113] Setting up fc2\r\n",
- "I0307 01:34:31.882663 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
- "I0307 01:34:31.882678 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
- "I0307 01:34:31.882694 2099749632 net.cpp:84] Creating Layer loss\r\n",
- "I0307 01:34:31.882704 2099749632 net.cpp:380] loss <- fc2\r\n",
- "I0307 01:34:31.882712 2099749632 net.cpp:380] loss <- label\r\n",
- "I0307 01:34:31.882779 2099749632 net.cpp:338] loss -> loss\r\n",
- "I0307 01:34:31.882796 2099749632 net.cpp:113] Setting up loss\r\n",
- "I0307 01:34:31.882810 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
- "I0307 01:34:31.882833 2099749632 net.cpp:120] Top shape: (1)\r\n",
- "I0307 01:34:31.882844 2099749632 net.cpp:122] with loss weight 1\r\n",
- "I0307 01:34:31.882860 2099749632 net.cpp:167] loss needs backward computation.\r\n",
- "I0307 01:34:31.882869 2099749632 net.cpp:167] fc2 needs backward computation.\r\n",
- "I0307 01:34:31.882877 2099749632 net.cpp:167] relu1 needs backward computation.\r\n",
- "I0307 01:34:31.882886 2099749632 net.cpp:167] fc1 needs backward computation.\r\n",
- "I0307 01:34:31.882894 2099749632 net.cpp:169] data does not need backward computation.\r\n",
- "I0307 01:34:31.882904 2099749632 net.cpp:205] This network produces output loss\r\n",
- "I0307 01:34:31.882931 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\r\n",
- "I0307 01:34:31.882942 2099749632 net.cpp:217] Network initialization done.\r\n",
- "I0307 01:34:31.882951 2099749632 net.cpp:218] Memory required for data: 3484\r\n",
- "I0307 01:34:31.883157 2099749632 solver.cpp:154] Creating test net (#0) specified by net file: hdf5_classification/train_val2.prototxt\r\n",
- "I0307 01:34:31.883189 2099749632 net.cpp:257] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\r\n",
- "I0307 01:34:31.883203 2099749632 net.cpp:42] Initializing net from parameters: \r\n",
- "name: \"LogisticRegressionNet\"\r\n",
- "state {\r\n",
- " phase: TEST\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"data\"\r\n",
- " type: \"HDF5Data\"\r\n",
- " top: \"data\"\r\n",
- " top: \"label\"\r\n",
- " include {\r\n",
- " phase: TEST\r\n",
- " }\r\n",
- " hdf5_data_param {\r\n",
- " source: \"hdf5_classification/data/test.txt\"\r\n",
- " batch_size: 10\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"fc1\"\r\n",
- " type: \"InnerProduct\"\r\n",
- " bottom: \"data\"\r\n",
- " top: \"fc1\"\r\n",
- " param {\r\n",
- " lr_mult: 1\r\n",
- " decay_mult: 1\r\n",
- " }\r\n",
- " param {\r\n",
- " lr_mult: 2\r\n",
- " decay_mult: 0\r\n",
- " }\r\n",
- " inner_product_param {\r\n",
- " num_output: 40\r\n",
- " weight_filler {\r\n",
- " type: \"gaussian\"\r\n",
- " std: 0.01\r\n",
- " }\r\n",
- " bias_filler {\r\n",
- " type: \"constant\"\r\n",
- " value: 0\r\n",
- " }\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"relu1\"\r\n",
- " type: \"ReLU\"\r\n",
- " bottom: \"fc1\"\r\n",
- " top: \"fc1\"\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"fc2\"\r\n",
- " type: \"InnerProduct\"\r\n",
- " bottom: \"fc1\"\r\n",
- " top: \"fc2\"\r\n",
- " param {\r\n",
- " lr_mult: 1\r\n",
- " decay_mult: 1\r\n",
- " }\r\n",
- " param {\r\n",
- " lr_mult: 2\r\n",
- " decay_mult: 0\r\n",
- " }\r\n",
- " inner_product_param {\r\n",
- " num_output: 2\r\n",
- " weight_filler {\r\n",
- " type: \"gaussian\"\r\n",
- " std: 0.01\r\n",
- " }\r\n",
- " bias_filler {\r\n",
- " type: \"constant\"\r\n",
- " value: 0\r\n",
- " }\r\n",
- " }\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"loss\"\r\n",
- " type: \"SoftmaxWithLoss\"\r\n",
- " bottom: \"fc2\"\r\n",
- " bottom: \"label\"\r\n",
- " top: \"loss\"\r\n",
- "}\r\n",
- "layer {\r\n",
- " name: \"accuracy\"\r\n",
- " type: \"Accuracy\"\r\n",
- " bottom: \"fc2\"\r\n",
- " bottom: \"label\"\r\n",
- " top: \"accuracy\"\r\n",
- " include {\r\n",
- " phase: TEST\r\n",
- " }\r\n",
- "}\r\n",
- "I0307 01:34:31.883535 2099749632 layer_factory.hpp:74] Creating layer data\r\n",
- "I0307 01:34:31.883548 2099749632 net.cpp:84] Creating Layer data\r\n",
- "I0307 01:34:31.883556 2099749632 net.cpp:338] data -> data\r\n",
- "I0307 01:34:31.883569 2099749632 net.cpp:338] data -> label\r\n",
- "I0307 01:34:31.883579 2099749632 net.cpp:113] Setting up data\r\n",
- "I0307 01:34:31.883585 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/test.txt\r\n",
- "I0307 01:34:31.883664 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\r\n",
- "I0307 01:34:31.884842 2099749632 net.cpp:120] Top shape: 10 4 (40)\r\n",
- "I0307 01:34:31.884860 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
- "I0307 01:34:31.884870 2099749632 layer_factory.hpp:74] Creating layer label_data_1_split\r\n",
- "I0307 01:34:31.884879 2099749632 net.cpp:84] Creating Layer label_data_1_split\r\n",
- "I0307 01:34:31.884886 2099749632 net.cpp:380] label_data_1_split <- label\r\n",
- "I0307 01:34:31.884896 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_0\r\n",
- "I0307 01:34:31.884909 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_1\r\n",
- "I0307 01:34:31.884919 2099749632 net.cpp:113] Setting up label_data_1_split\r\n",
- "I0307 01:34:31.884927 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
- "I0307 01:34:31.884934 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
- "I0307 01:34:31.884941 2099749632 layer_factory.hpp:74] Creating layer fc1\r\n",
- "I0307 01:34:31.884951 2099749632 net.cpp:84] Creating Layer fc1\r\n",
- "I0307 01:34:31.884958 2099749632 net.cpp:380] fc1 <- data\r\n",
- "I0307 01:34:31.884989 2099749632 net.cpp:338] fc1 -> fc1\r\n",
- "I0307 01:34:31.885000 2099749632 net.cpp:113] Setting up fc1\r\n",
- "I0307 01:34:31.885017 2099749632 net.cpp:120] Top shape: 10 40 (400)\r\n",
- "I0307 01:34:31.885030 2099749632 layer_factory.hpp:74] Creating layer relu1\r\n",
- "I0307 01:34:31.885041 2099749632 net.cpp:84] Creating Layer relu1\r\n",
- "I0307 01:34:31.885048 2099749632 net.cpp:380] relu1 <- fc1\r\n",
- "I0307 01:34:31.885056 2099749632 net.cpp:327] relu1 -> fc1 (in-place)\r\n",
- "I0307 01:34:31.885064 2099749632 net.cpp:113] Setting up relu1\r\n",
- "I0307 01:34:31.885071 2099749632 net.cpp:120] Top shape: 10 40 (400)\r\n",
- "I0307 01:34:31.885079 2099749632 layer_factory.hpp:74] Creating layer fc2\r\n",
- "I0307 01:34:31.885088 2099749632 net.cpp:84] Creating Layer fc2\r\n",
- "I0307 01:34:31.885094 2099749632 net.cpp:380] fc2 <- fc1\r\n",
- "I0307 01:34:31.885103 2099749632 net.cpp:338] fc2 -> fc2\r\n",
- "I0307 01:34:31.885113 2099749632 net.cpp:113] Setting up fc2\r\n",
- "I0307 01:34:31.885126 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
- "I0307 01:34:31.885138 2099749632 layer_factory.hpp:74] Creating layer fc2_fc2_0_split\r\n",
- "I0307 01:34:31.885149 2099749632 net.cpp:84] Creating Layer fc2_fc2_0_split\r\n",
- "I0307 01:34:31.885155 2099749632 net.cpp:380] fc2_fc2_0_split <- fc2\r\n",
- "I0307 01:34:31.885164 2099749632 net.cpp:338] fc2_fc2_0_split -> fc2_fc2_0_split_0\r\n",
- "I0307 01:34:31.885174 2099749632 net.cpp:338] fc2_fc2_0_split -> fc2_fc2_0_split_1\r\n",
- "I0307 01:34:31.885182 2099749632 net.cpp:113] Setting up fc2_fc2_0_split\r\n",
- "I0307 01:34:31.885190 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
- "I0307 01:34:31.885242 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
- "I0307 01:34:31.885256 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
- "I0307 01:34:31.885267 2099749632 net.cpp:84] Creating Layer loss\r\n",
- "I0307 01:34:31.885275 2099749632 net.cpp:380] loss <- fc2_fc2_0_split_0\r\n",
- "I0307 01:34:31.885285 2099749632 net.cpp:380] loss <- label_data_1_split_0\r\n",
- "I0307 01:34:31.885296 2099749632 net.cpp:338] loss -> loss\r\n",
- "I0307 01:34:31.885308 2099749632 net.cpp:113] Setting up loss\r\n",
- "I0307 01:34:31.885316 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
- "I0307 01:34:31.885330 2099749632 net.cpp:120] Top shape: (1)\r\n",
- "I0307 01:34:31.885337 2099749632 net.cpp:122] with loss weight 1\r\n",
- "I0307 01:34:31.885346 2099749632 layer_factory.hpp:74] Creating layer accuracy\r\n",
- "I0307 01:34:31.885360 2099749632 net.cpp:84] Creating Layer accuracy\r\n",
- "I0307 01:34:31.885368 2099749632 net.cpp:380] accuracy <- fc2_fc2_0_split_1\r\n",
- "I0307 01:34:31.885375 2099749632 net.cpp:380] accuracy <- label_data_1_split_1\r\n",
- "I0307 01:34:31.885383 2099749632 net.cpp:338] accuracy -> accuracy\r\n",
- "I0307 01:34:31.885392 2099749632 net.cpp:113] Setting up accuracy\r\n",
- "I0307 01:34:31.885401 2099749632 net.cpp:120] Top shape: (1)\r\n",
- "I0307 01:34:31.885407 2099749632 net.cpp:169] accuracy does not need backward computation.\r\n",
- "I0307 01:34:31.885413 2099749632 net.cpp:167] loss needs backward computation.\r\n",
- "I0307 01:34:31.885419 2099749632 net.cpp:167] fc2_fc2_0_split needs backward computation.\r\n",
- "I0307 01:34:31.885426 2099749632 net.cpp:167] fc2 needs backward computation.\r\n",
- "I0307 01:34:31.885432 2099749632 net.cpp:167] relu1 needs backward computation.\r\n",
- "I0307 01:34:31.885438 2099749632 net.cpp:167] fc1 needs backward computation.\r\n",
- "I0307 01:34:31.885444 2099749632 net.cpp:169] label_data_1_split does not need backward computation.\r\n",
- "I0307 01:34:31.885452 2099749632 net.cpp:169] data does not need backward computation.\r\n",
- "I0307 01:34:31.885457 2099749632 net.cpp:205] This network produces output accuracy\r\n",
- "I0307 01:34:31.885613 2099749632 net.cpp:205] This network produces output loss\r\n",
- "I0307 01:34:31.885632 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\r\n",
- "I0307 01:34:31.885639 2099749632 net.cpp:217] Network initialization done.\r\n",
- "I0307 01:34:31.885645 2099749632 net.cpp:218] Memory required for data: 3728\r\n",
- "I0307 01:34:31.885685 2099749632 solver.cpp:42] Solver scaffolding done.\r\n",
- "I0307 01:34:31.885711 2099749632 solver.cpp:222] Solving LogisticRegressionNet\r\n",
- "I0307 01:34:31.885721 2099749632 solver.cpp:223] Learning Rate Policy: step\r\n",
- "I0307 01:34:31.885730 2099749632 solver.cpp:266] Iteration 0, Testing net (#0)\r\n",
- "I0307 01:34:31.901005 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.5944\r\n",
- "I0307 01:34:31.901049 2099749632 solver.cpp:315] Test net output #1: loss = 0.693021 (* 1 = 0.693021 loss)\r\n",
- "I0307 01:34:31.901177 2099749632 solver.cpp:189] Iteration 0, loss = 0.693163\r\n",
- "I0307 01:34:31.901192 2099749632 solver.cpp:204] Train net output #0: loss = 0.693163 (* 1 = 0.693163 loss)\r\n",
- "I0307 01:34:31.901203 2099749632 solver.cpp:464] Iteration 0, lr = 0.01\r\n",
- "I0307 01:34:31.920586 2099749632 solver.cpp:266] Iteration 1000, Testing net (#0)\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "I0307 01:34:31.924612 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7556\r\n",
- "I0307 01:34:31.924646 2099749632 solver.cpp:315] Test net output #1: loss = 0.511002 (* 1 = 0.511002 loss)\r\n",
- "I0307 01:34:31.924684 2099749632 solver.cpp:189] Iteration 1000, loss = 0.38536\r\n",
- "I0307 01:34:31.924696 2099749632 solver.cpp:204] Train net output #0: loss = 0.38536 (* 1 = 0.38536 loss)\r\n",
- "I0307 01:34:31.924706 2099749632 solver.cpp:464] Iteration 1000, lr = 0.01\r\n",
- "I0307 01:34:31.944727 2099749632 solver.cpp:266] Iteration 2000, Testing net (#0)\r\n",
- "I0307 01:34:31.948729 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7824\r\n",
- "I0307 01:34:31.948763 2099749632 solver.cpp:315] Test net output #1: loss = 0.489214 (* 1 = 0.489214 loss)\r\n",
- "I0307 01:34:31.948799 2099749632 solver.cpp:189] Iteration 2000, loss = 0.532582\r\n",
- "I0307 01:34:31.948812 2099749632 solver.cpp:204] Train net output #0: loss = 0.532582 (* 1 = 0.532582 loss)\r\n",
- "I0307 01:34:31.948823 2099749632 solver.cpp:464] Iteration 2000, lr = 0.01\r\n",
- "I0307 01:34:31.968670 2099749632 solver.cpp:266] Iteration 3000, Testing net (#0)\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "I0307 01:34:31.972393 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7956\r\n",
- "I0307 01:34:31.972411 2099749632 solver.cpp:315] Test net output #1: loss = 0.454184 (* 1 = 0.454184 loss)\r\n",
- "I0307 01:34:31.973024 2099749632 solver.cpp:189] Iteration 3000, loss = 0.541374\r\n",
- "I0307 01:34:31.973057 2099749632 solver.cpp:204] Train net output #0: loss = 0.541374 (* 1 = 0.541374 loss)\r\n",
- "I0307 01:34:31.973067 2099749632 solver.cpp:464] Iteration 3000, lr = 0.01\r\n",
- "I0307 01:34:31.994829 2099749632 solver.cpp:266] Iteration 4000, Testing net (#0)\r\n",
- "I0307 01:34:31.998638 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.798\r\n",
- "I0307 01:34:31.998663 2099749632 solver.cpp:315] Test net output #1: loss = 0.456348 (* 1 = 0.456348 loss)\r\n",
- "I0307 01:34:31.998705 2099749632 solver.cpp:189] Iteration 4000, loss = 0.490437\r\n",
- "I0307 01:34:31.998718 2099749632 solver.cpp:204] Train net output #0: loss = 0.490437 (* 1 = 0.490437 loss)\r\n",
- "I0307 01:34:31.998725 2099749632 solver.cpp:464] Iteration 4000, lr = 0.01\r\n",
- "I0307 01:34:32.021085 2099749632 solver.cpp:266] Iteration 5000, Testing net (#0)\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "I0307 01:34:32.024950 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.804\r\n",
- "I0307 01:34:32.024981 2099749632 solver.cpp:315] Test net output #1: loss = 0.46184 (* 1 = 0.46184 loss)\r\n",
- "I0307 01:34:32.025017 2099749632 solver.cpp:189] Iteration 5000, loss = 0.467703\r\n",
- "I0307 01:34:32.025028 2099749632 solver.cpp:204] Train net output #0: loss = 0.467704 (* 1 = 0.467704 loss)\r\n",
- "I0307 01:34:32.025038 2099749632 solver.cpp:464] Iteration 5000, lr = 0.001\r\n",
- "I0307 01:34:32.044390 2099749632 solver.cpp:266] Iteration 6000, Testing net (#0)\r\n",
- "I0307 01:34:32.048216 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.8208\r\n",
- "I0307 01:34:32.048239 2099749632 solver.cpp:315] Test net output #1: loss = 0.423084 (* 1 = 0.423084 loss)\r\n",
- "I0307 01:34:32.048790 2099749632 solver.cpp:189] Iteration 6000, loss = 0.480104\r\n",
- "I0307 01:34:32.048809 2099749632 solver.cpp:204] Train net output #0: loss = 0.480105 (* 1 = 0.480105 loss)\r\n",
- "I0307 01:34:32.048827 2099749632 solver.cpp:464] Iteration 6000, lr = 0.001\r\n",
- "I0307 01:34:32.067795 2099749632 solver.cpp:266] Iteration 7000, Testing net (#0)\r\n",
- "I0307 01:34:32.071524 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.8124\r\n",
- "I0307 01:34:32.071542 2099749632 solver.cpp:315] Test net output #1: loss = 0.423947 (* 1 = 0.423947 loss)\r\n",
- "I0307 01:34:32.071570 2099749632 solver.cpp:189] Iteration 7000, loss = 0.447471\r\n",
- "I0307 01:34:32.071617 2099749632 solver.cpp:204] Train net output #0: loss = 0.447472 (* 1 = 0.447472 loss)\r\n",
- "I0307 01:34:32.071626 2099749632 solver.cpp:464] Iteration 7000, lr = 0.001\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "I0307 01:34:32.091625 2099749632 solver.cpp:266] Iteration 8000, Testing net (#0)\r\n",
- "I0307 01:34:32.095410 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.814\r\n",
- "I0307 01:34:32.095432 2099749632 solver.cpp:315] Test net output #1: loss = 0.423586 (* 1 = 0.423586 loss)\r\n",
- "I0307 01:34:32.095461 2099749632 solver.cpp:189] Iteration 8000, loss = 0.386258\r\n",
- "I0307 01:34:32.095474 2099749632 solver.cpp:204] Train net output #0: loss = 0.386259 (* 1 = 0.386259 loss)\r\n",
- "I0307 01:34:32.095481 2099749632 solver.cpp:464] Iteration 8000, lr = 0.001\r\n",
- "I0307 01:34:32.117184 2099749632 solver.cpp:266] Iteration 9000, Testing net (#0)\r\n",
- "I0307 01:34:32.121587 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.8208\r\n",
- "I0307 01:34:32.121608 2099749632 solver.cpp:315] Test net output #1: loss = 0.419969 (* 1 = 0.419969 loss)\r\n",
- "I0307 01:34:32.122161 2099749632 solver.cpp:189] Iteration 9000, loss = 0.468262\r\n",
- "I0307 01:34:32.122181 2099749632 solver.cpp:204] Train net output #0: loss = 0.468262 (* 1 = 0.468262 loss)\r\n",
- "I0307 01:34:32.122191 2099749632 solver.cpp:464] Iteration 9000, lr = 0.001\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "I0307 01:34:32.141635 2099749632 solver.cpp:334] Snapshotting to hdf5_classification/data/train_iter_10000.caffemodel\r\n",
- "I0307 01:34:32.141860 2099749632 solver.cpp:342] Snapshotting solver state to hdf5_classification/data/train_iter_10000.solverstate\r\n",
- "I0307 01:34:32.141978 2099749632 solver.cpp:248] Iteration 10000, loss = 0.441529\r\n",
- "I0307 01:34:32.141995 2099749632 solver.cpp:266] Iteration 10000, Testing net (#0)\r\n",
- "I0307 01:34:32.145747 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.8148\r\n",
- "I0307 01:34:32.145771 2099749632 solver.cpp:315] Test net output #1: loss = 0.4216 (* 1 = 0.4216 loss)\r\n",
- "I0307 01:34:32.145779 2099749632 solver.cpp:253] Optimization Done.\r\n",
- "I0307 01:34:32.145786 2099749632 caffe.cpp:121] Optimization Done.\r\n"
- ]
- }
- ],
- "prompt_number": 8
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n",
- "shutil.rmtree(dirname)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 9
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
--- /dev/null
+layer {
+ name: "data"
+ type: "HDF5Data"
+ top: "data"
+ top: "label"
+ hdf5_data_param {
+ source: "examples/hdf5_classification/data/test.txt"
+ batch_size: 10
+ }
+}
+layer {
+ name: "ip1"
+ type: "InnerProduct"
+ bottom: "data"
+ top: "ip1"
+ inner_product_param {
+ num_output: 40
+ weight_filler {
+ type: "xavier"
+ }
+ }
+}
+layer {
+ name: "relu1"
+ type: "ReLU"
+ bottom: "ip1"
+ top: "ip1"
+}
+layer {
+ name: "ip2"
+ type: "InnerProduct"
+ bottom: "ip1"
+ top: "ip2"
+ inner_product_param {
+ num_output: 2
+ weight_filler {
+ type: "xavier"
+ }
+ }
+}
+layer {
+ name: "accuracy"
+ type: "Accuracy"
+ bottom: "ip2"
+ bottom: "label"
+ top: "accuracy"
+}
+layer {
+ name: "loss"
+ type: "SoftmaxWithLoss"
+ bottom: "ip2"
+ bottom: "label"
+ top: "loss"
+}
--- /dev/null
+layer {
+ name: "data"
+ type: "HDF5Data"
+ top: "data"
+ top: "label"
+ hdf5_data_param {
+ source: "examples/hdf5_classification/data/train.txt"
+ batch_size: 10
+ }
+}
+layer {
+ name: "ip1"
+ type: "InnerProduct"
+ bottom: "data"
+ top: "ip1"
+ inner_product_param {
+ num_output: 40
+ weight_filler {
+ type: "xavier"
+ }
+ }
+}
+layer {
+ name: "relu1"
+ type: "ReLU"
+ bottom: "ip1"
+ top: "ip1"
+}
+layer {
+ name: "ip2"
+ type: "InnerProduct"
+ bottom: "ip1"
+ top: "ip2"
+ inner_product_param {
+ num_output: 2
+ weight_filler {
+ type: "xavier"
+ }
+ }
+}
+layer {
+ name: "accuracy"
+ type: "Accuracy"
+ bottom: "ip2"
+ bottom: "label"
+ top: "accuracy"
+}
+layer {
+ name: "loss"
+ type: "SoftmaxWithLoss"
+ bottom: "ip2"
+ bottom: "label"
+ top: "loss"
+}
--- /dev/null
+train_net: "examples/hdf5_classification/nonlinear_auto_train.prototxt"
+test_net: "examples/hdf5_classification/nonlinear_auto_test.prototxt"
+test_iter: 250
+test_interval: 1000
+base_lr: 0.01
+lr_policy: "step"
+gamma: 0.1
+stepsize: 5000
+display: 1000
+max_iter: 10000
+momentum: 0.9
+weight_decay: 0.0005
+snapshot: 10000
+snapshot_prefix: "examples/hdf5_classification/data/train"
+solver_mode: CPU
phase: TRAIN
}
hdf5_data_param {
- source: "hdf5_classification/data/train.txt"
+ source: "examples/hdf5_classification/data/train.txt"
batch_size: 10
}
}
phase: TEST
}
hdf5_data_param {
- source: "hdf5_classification/data/test.txt"
+ source: "examples/hdf5_classification/data/test.txt"
batch_size: 10
}
}
inner_product_param {
num_output: 40
weight_filler {
- type: "gaussian"
- std: 0.01
+ type: "xavier"
}
bias_filler {
type: "constant"
inner_product_param {
num_output: 2
weight_filler {
- type: "gaussian"
- std: 0.01
+ type: "xavier"
}
bias_filler {
type: "constant"
-net: "hdf5_classification/train_val.prototxt"
+train_net: "examples/hdf5_classification/logreg_auto_train.prototxt"
+test_net: "examples/hdf5_classification/logreg_auto_test.prototxt"
test_iter: 250
test_interval: 1000
base_lr: 0.01
momentum: 0.9
weight_decay: 0.0005
snapshot: 10000
-snapshot_prefix: "hdf5_classification/data/train"
+snapshot_prefix: "examples/hdf5_classification/data/train"
solver_mode: CPU
+++ /dev/null
-net: "hdf5_classification/train_val2.prototxt"
-test_iter: 250
-test_interval: 1000
-base_lr: 0.01
-lr_policy: "step"
-gamma: 0.1
-stepsize: 5000
-display: 1000
-max_iter: 10000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 10000
-snapshot_prefix: "hdf5_classification/data/train"
-solver_mode: CPU
phase: TRAIN
}
hdf5_data_param {
- source: "hdf5_classification/data/train.txt"
+ source: "examples/hdf5_classification/data/train.txt"
batch_size: 10
}
}
phase: TEST
}
hdf5_data_param {
- source: "hdf5_classification/data/test.txt"
+ source: "examples/hdf5_classification/data/test.txt"
batch_size: 10
}
}
inner_product_param {
num_output: 2
weight_filler {
- type: "gaussian"
- std: 0.01
+ type: "xavier"
}
bias_filler {
type: "constant"
We all experience times when the power goes out, or we feel like rewarding ourself a little by playing Battlefield (does anyone still remember Quake?). Since we are snapshotting intermediate results during training, we will be able to resume from snapshots. This can be done as easy as:
- ./build/tools/caffe train --solver=models/bvlc_reference_caffenet/solver.prototxt --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate
+ ./build/tools/caffe train --solver=models/bvlc_reference_caffenet/solver.prototxt --snapshot=models/bvlc_reference_caffenet/caffenet_train_iter_10000.solverstate
-where in the script `caffenet_train_10000.solverstate` is the solver state snapshot that stores all necessary information to recover the exact solver state (including the parameters, momentum history, etc).
+where in the script `caffenet_train_iter_10000.solverstate` is the solver state snapshot that stores all necessary information to recover the exact solver state (including the parameters, momentum history, etc).
Parting Words
-------------
Many researchers have gone further since the ILSVRC 2012 challenge, changing the network architecture and/or fine-tuning the various parameters in the network to address new data and tasks.
**Caffe lets you explore different network choices more easily by simply writing different prototxt files** - isn't that exciting?
-And since now you have a trained network, check out how to use it with the Python interface for [classifying ImageNet](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/classification.ipynb).
+And since now you have a trained network, check out how to use it with the Python interface for [classifying ImageNet](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb).
./build/tools/caffe train \
--solver=models/bvlc_reference_caffenet/solver.prototxt \
- --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate
+ --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate.h5
#include <gflags/gflags.h>
#include <glog/logging.h>
#include <google/protobuf/text_format.h>
+
+#if defined(USE_LEVELDB) && defined(USE_LMDB)
#include <leveldb/db.h>
#include <leveldb/write_batch.h>
#include <lmdb.h>
+#endif
+
#include <stdint.h>
#include <sys/stat.h>
#include <string>
#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/format.hpp"
+
+#if defined(USE_LEVELDB) && defined(USE_LMDB)
using namespace caffe; // NOLINT(build/namespaces)
using std::string;
char label;
char* pixels = new char[rows * cols];
int count = 0;
- const int kMaxKeyLength = 10;
- char key_cstr[kMaxKeyLength];
string value;
Datum datum;
label_file.read(&label, 1);
datum.set_data(pixels, rows*cols);
datum.set_label(label);
- snprintf(key_cstr, kMaxKeyLength, "%08d", item_id);
+ string key_str = caffe::format_int(item_id, 8);
datum.SerializeToString(&value);
- string keystr(key_cstr);
// Put in db
if (db_backend == "leveldb") { // leveldb
- batch->Put(keystr, value);
+ batch->Put(key_str, value);
} else if (db_backend == "lmdb") { // lmdb
mdb_data.mv_size = value.size();
mdb_data.mv_data = reinterpret_cast<void*>(&value[0]);
- mdb_key.mv_size = keystr.size();
- mdb_key.mv_data = reinterpret_cast<void*>(&keystr[0]);
+ mdb_key.mv_size = key_str.size();
+ mdb_key.mv_data = reinterpret_cast<void*>(&key_str[0]);
CHECK_EQ(mdb_put(mdb_txn, mdb_dbi, &mdb_key, &mdb_data, 0), MDB_SUCCESS)
<< "mdb_put failed";
} else {
}
LOG(ERROR) << "Processed " << count << " files.";
}
- delete pixels;
+ delete[] pixels;
}
int main(int argc, char** argv) {
}
return 0;
}
+#else
+int main(int argc, char** argv) {
+ LOG(FATAL) << "This example requires LevelDB and LMDB; " <<
+ "compile with USE_LEVELDB and USE_LMDB.";
+}
+#endif // USE_LEVELDB and USE_LMDB
name: "LeNet"
input: "data"
-input_dim: 64
-input_dim: 1
-input_dim: 28
-input_dim: 28
+input_shape {
+ dim: 64
+ dim: 1
+ dim: 28
+ dim: 28
+}
layer {
name: "conv1"
type: "Convolution"
--- /dev/null
+# The train/test net protocol buffer definition
+net: "examples/mnist/lenet_train_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 1.0
+lr_policy: "fixed"
+momentum: 0.95
+weight_decay: 0.0005
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "examples/mnist/lenet_adadelta"
+# solver mode: CPU or GPU
+solver_mode: GPU
+type: "AdaDelta"
+delta: 1e-6
-# The training protocol buffer definition
-train_net: "lenet_train.prototxt"
-# The testing protocol buffer definition
-test_net: "lenet_test.prototxt"
+# The train/test net protocol buffer definition
+train_net: "examples/mnist/lenet_auto_train.prototxt"
+test_net: "examples/mnist/lenet_auto_test.prototxt"
# test_iter specifies how many forward passes the test should carry out.
# In the case of MNIST, we have test batch size 100 and 100 test iterations,
# covering the full 10,000 testing images.
momentum: 0.9
weight_decay: 0.0005
# The learning rate policy
-lr_policy: "stepearly"
-gamma: 0.9
-stepearly: 1
+lr_policy: "inv"
+gamma: 0.0001
+power: 0.75
# Display every 100 iterations
display: 100
# The maximum number of iterations
max_iter: 10000
# snapshot intermediate results
snapshot: 5000
-snapshot_prefix: "lenet"
-# solver mode: 0 for CPU and 1 for GPU
-solver_mode: 1
-device_id: 1
+snapshot_prefix: "examples/mnist/lenet"
--- /dev/null
+# The train/test net protocol buffer definition
+# this follows "ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION"
+net: "examples/mnist/lenet_train_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# All parameters are from the cited paper above
+base_lr: 0.001
+momentum: 0.9
+momentum2: 0.999
+# since Adam dynamically changes the learning rate, we set the base learning
+# rate to a fixed value
+lr_policy: "fixed"
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "examples/mnist/lenet"
+# solver mode: CPU or GPU
+type: "Adam"
+solver_mode: GPU
--- /dev/null
+# The train/test net protocol buffer definition
+net: "examples/mnist/lenet_train_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.01
+momentum: 0.0
+weight_decay: 0.0005
+# The learning rate policy
+lr_policy: "inv"
+gamma: 0.0001
+power: 0.75
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "examples/mnist/lenet_rmsprop"
+# solver mode: CPU or GPU
+solver_mode: GPU
+type: "RMSProp"
+rms_decay: 0.98
--- /dev/null
+net: "examples/mnist/mnist_autoencoder.prototxt"
+test_state: { stage: 'test-on-train' }
+test_iter: 500
+test_state: { stage: 'test-on-test' }
+test_iter: 100
+test_interval: 500
+test_compute_loss: true
+base_lr: 1.0
+lr_policy: "fixed"
+momentum: 0.95
+delta: 1e-8
+display: 100
+max_iter: 65000
+weight_decay: 0.0005
+snapshot: 10000
+snapshot_prefix: "examples/mnist/mnist_autoencoder_adadelta_train"
+# solver mode: CPU or GPU
+solver_mode: GPU
+type: "AdaDelta"
snapshot_prefix: "examples/mnist/mnist_autoencoder_adagrad_train"
# solver mode: CPU or GPU
solver_mode: GPU
-solver_type: ADAGRAD
+type: "AdaGrad"
momentum: 0.95
# solver mode: CPU or GPU
solver_mode: GPU
-solver_type: NESTEROV
+type: "Nesterov"
Currently, we will read the MNIST data from the lmdb we created earlier in the demo. This is defined by a data layer:
- layers {
+ layer {
name: "mnist"
- type: DATA
+ type: "Data"
+ transform_param {
+ scale: 0.00390625
+ }
data_param {
source: "mnist_train_lmdb"
backend: LMDB
batch_size: 64
- scale: 0.00390625
}
top: "data"
top: "label"
Let's define the first convolution layer:
- layers {
+ layer {
name: "conv1"
- type: CONVOLUTION
- blobs_lr: 1.
- blobs_lr: 2.
+ type: "Convolution"
+ param { lr_mult: 1 }
+ param { lr_mult: 2 }
convolution_param {
num_output: 20
- kernelsize: 5
+ kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
The fillers allow us to randomly initialize the value of the weights and bias. For the weight filler, we will use the `xavier` algorithm that automatically determines the scale of initialization based on the number of input and output neurons. For the bias filler, we will simply initialize it as constant, with the default filling value 0.
-`blobs_lr` are the learning rate adjustments for the layer's learnable parameters. In this case, we will set the weight learning rate to be the same as the learning rate given by the solver during runtime, and the bias learning rate to be twice as large as that - this usually leads to better convergence rates.
+`lr_mult`s are the learning rate adjustments for the layer's learnable parameters. In this case, we will set the weight learning rate to be the same as the learning rate given by the solver during runtime, and the bias learning rate to be twice as large as that - this usually leads to better convergence rates.
### Writing the Pooling Layer
Phew. Pooling layers are actually much easier to define:
- layers {
+ layer {
name: "pool1"
- type: POOLING
+ type: "Pooling"
pooling_param {
kernel_size: 2
stride: 2
Writing a fully connected layer is also simple:
- layers {
+ layer {
name: "ip1"
- type: INNER_PRODUCT
- blobs_lr: 1.
- blobs_lr: 2.
+ type: "InnerProduct"
+ param { lr_mult: 1 }
+ param { lr_mult: 2 }
inner_product_param {
num_output: 500
weight_filler {
top: "ip1"
}
-This defines a fully connected layer (for some legacy reason, Caffe calls it an `innerproduct` layer) with 500 outputs. All other lines look familiar, right?
+This defines a fully connected layer (known in Caffe as an `InnerProduct` layer) with 500 outputs. All other lines look familiar, right?
### Writing the ReLU Layer
A ReLU Layer is also simple:
- layers {
+ layer {
name: "relu1"
- type: RELU
+ type: "ReLU"
bottom: "ip1"
top: "ip1"
}
After the ReLU layer, we will write another innerproduct layer:
- layers {
+ layer {
name: "ip2"
- type: INNER_PRODUCT
- blobs_lr: 1.
- blobs_lr: 2.
+ type: "InnerProduct"
+ param { lr_mult: 1 }
+ param { lr_mult: 2 }
inner_product_param {
num_output: 10
weight_filler {
Finally, we will write the loss!
- layers {
+ layer {
name: "loss"
- type: SOFTMAX_LOSS
+ type: "SoftmaxWithLoss"
bottom: "ip2"
bottom: "label"
}
Layer definitions can include rules for whether and when they are included in the network definition, like the one below:
- layers {
+ layer {
// ...layer definition...
include: { phase: TRAIN }
}
If we change `TRAIN` with `TEST`, then this layer will be used only in test phase.
By default, that is without layer rules, a layer is always included in the network.
Thus, `lenet_train_test.prototxt` has two `DATA` layers defined (with different `batch_size`), one for the training phase and one for the testing phase.
-Also, there is an `ACCURACY` layer which is included only in `TEST` phase for reporting the model accuracy every 100 iteration, as defined in `lenet_solver.prototxt`.
+Also, there is an `Accuracy` layer which is included only in `TEST` phase for reporting the model accuracy every 100 iteration, as defined in `lenet_solver.prototxt`.
## Define the MNIST Solver
MNIST is a small dataset, so training with GPU does not really introduce too much benefit due to communication overheads. On larger datasets with more complex models, such as ImageNet, the computation speed difference will be more significant.
-### How to reduce the learning rate a fixed steps?
+### How to reduce the learning rate at fixed steps?
Look at lenet_multistep_solver.prototxt
--- /dev/null
+#!/usr/bin/env sh
+
+./build/tools/caffe train --solver=examples/mnist/lenet_solver_adam.prototxt
--- /dev/null
+#!/usr/bin/env sh
+
+./build/tools/caffe train --solver=examples/mnist/lenet_solver_rmsprop.prototxt
--- /dev/null
+#!/bin/bash
+
+./build/tools/caffe train \
+ --solver=examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
{
- "metadata": {
- "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
- "example_name": "Editing model parameters",
- "include_in_docs": true,
- "priority": 5,
- "signature": "sha256:f21c804f76329e70847ccb87e28a91e5d8a375f5da0ba6dd85d3b87a05bebd72"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Net Surgery\n",
- "\n",
- "Caffe networks can be transformed to your particular needs by editing the model parameters. The data, diffs, and parameters of a net are all exposed in pycaffe.\n",
- "\n",
- "Roll up your sleeves for net surgery with pycaffe!"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n",
- "import Image\n",
- "\n",
- "# Make sure that caffe is on the python path:\n",
- "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n",
- "import sys\n",
- "sys.path.insert(0, caffe_root + 'python')\n",
- "\n",
- "import caffe\n",
- "\n",
- "# configure plotting\n",
- "plt.rcParams['figure.figsize'] = (10, 10)\n",
- "plt.rcParams['image.interpolation'] = 'nearest'\n",
- "plt.rcParams['image.cmap'] = 'gray'"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 1
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Designer Filters\n",
- "\n",
- "To show how to load, manipulate, and save parameters we'll design our own filters into a simple network that's only a single convolution layer. This net has two blobs, `data` for the input and `conv` for the convolution output and one parameter `conv` for the convolution filter weights and biases."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# Load the net, list its data and params, and filter an example image.\n",
- "caffe.set_mode_cpu()\n",
- "net = caffe.Net('net_surgery/conv.prototxt', caffe.TEST)\n",
- "print(\"blobs {}\\nparams {}\".format(net.blobs.keys(), net.params.keys()))\n",
- "\n",
- "# load image and prepare as a single input batch for Caffe\n",
- "im = np.array(Image.open('images/cat_gray.jpg'))\n",
- "plt.title(\"original image\")\n",
- "plt.imshow(im)\n",
- "plt.axis('off')\n",
- "\n",
- "im_input = im[np.newaxis, np.newaxis, :, :]\n",
- "net.blobs['data'].reshape(*im_input.shape)\n",
- "net.blobs['data'].data[...] = im_input"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "blobs ['data', 'conv']\n",
- "params ['conv']\n"
- ]
- },
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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wOD+7rZYez/FQzmvBLddIBpl0ZLwxxp9nDRd1dKIXWRtjJ2NlZaU4fDbAGQCw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lUint2qvVaolWPu/c52sIw9DlCoYViATzjiKAfhyYLJ0bNr1eLxnQrVYr0ebu3buSzsOR8CRz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\nfKhKpaJarZb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OPk8YQq7YY0jd++IoDzLQHWE3XjC24BXQQ2rJeVjbK5s7qkdfHWFx9Gw2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\nJ30z36Xk8TmIYK3hpNqRMuWiTHmxFng2mzV4XrPZjHfv3iWV4uLiI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eYRUa6eoFaRyd8m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\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CizFwuhBPlujJi4SBtEIz5MjGQeAsrL1erxRH3CeEjNFRgo2yo7WIeg2WvLkRCMZ5f38f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\nl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j4jBfpSej2/+81P/g8v8pauFrLQIAAm7bnizYtvZ8ZRe8QuakOoysi7JJv6+Y7WbUB4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\no5hg26exMZZ+TpELSPFu2uPx8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- "text": [
- "<matplotlib.figure.Figure at 0x112797f50>"
- ]
- }
- ],
- "prompt_number": 2
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The convolution weights are initialized from Gaussian noise while the biases are initialized to zero. These random filters give output somewhat like edge detections."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# helper show filter outputs\n",
- "def show_filters(net):\n",
- " net.forward()\n",
- " plt.figure()\n",
- " filt_min, filt_max = net.blobs['conv'].data.min(), net.blobs['conv'].data.max()\n",
- " for i in range(3):\n",
- " plt.subplot(1,4,i+2)\n",
- " plt.title(\"filter #{} output\".format(i))\n",
- " plt.imshow(net.blobs['conv'].data[0, i], vmin=filt_min, vmax=filt_max)\n",
- " plt.tight_layout()\n",
- " plt.axis('off')\n",
- "\n",
- "# filter the image with initial \n",
- "show_filters(net)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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4URgkf3g6BJoVFgCwUMY2oJwMJk7RC2ERFMyLr/xYR7A+Ed9++2392I/9WFJo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\n",
- "text": [
- "<matplotlib.figure.Figure at 0x113e04090>"
- ]
- }
- ],
- "prompt_number": 3
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Raising the bias of a filter will correspondingly raise its output:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# pick first filter output\n",
- "conv0 = net.blobs['conv'].data[0, 0]\n",
- "print(\"pre-surgery output mean {:.2f}\".format(conv0.mean()))\n",
- "# set first filter bias to 10\n",
- "net.params['conv'][1].data[0] = 1.\n",
- "net.forward()\n",
- "print(\"post-surgery output mean {:.2f}\".format(conv0.mean()))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "pre-surgery output mean -12.93\n",
- "post-surgery output mean -11.93\n"
- ]
- }
- ],
- "prompt_number": 4
- },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Net Surgery\n",
+ "\n",
+ "Caffe networks can be transformed to your particular needs by editing the model parameters. The data, diffs, and parameters of a net are all exposed in pycaffe.\n",
+ "\n",
+ "Roll up your sleeves for net surgery with pycaffe!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "import Image\n",
+ "\n",
+ "# Make sure that caffe is on the python path:\n",
+ "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n",
+ "import sys\n",
+ "sys.path.insert(0, caffe_root + 'python')\n",
+ "\n",
+ "import caffe\n",
+ "\n",
+ "# configure plotting\n",
+ "plt.rcParams['figure.figsize'] = (10, 10)\n",
+ "plt.rcParams['image.interpolation'] = 'nearest'\n",
+ "plt.rcParams['image.cmap'] = 'gray'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Designer Filters\n",
+ "\n",
+ "To show how to load, manipulate, and save parameters we'll design our own filters into a simple network that's only a single convolution layer. This net has two blobs, `data` for the input and `conv` for the convolution output and one parameter `conv` for the convolution filter weights and biases."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Altering the filter weights is more exciting since we can assign any kernel like Gaussian blur, the Sobel operator for edges, and so on. The following surgery turns the 0th filter into a Gaussian blur and the 1st and 2nd filters into the horizontal and vertical gradient parts of the Sobel operator.\n",
- "\n",
- "See how the 0th output is blurred, the 1st picks up horizontal edges, and the 2nd picks up vertical edges."
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "blobs ['data', 'conv']\n",
+ "params ['conv']\n"
]
},
{
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "ksize = net.params['conv'][0].data.shape[2:]\n",
- "# make Gaussian blur\n",
- "sigma = 1.\n",
- "y, x = np.mgrid[-ksize[0]//2 + 1:ksize[0]//2 + 1, -ksize[1]//2 + 1:ksize[1]//2 + 1]\n",
- "g = np.exp(-((x**2 + y**2)/(2.0*sigma**2)))\n",
- "gaussian = (g / g.sum()).astype(np.float32)\n",
- "net.params['conv'][0].data[0] = gaussian\n",
- "# make Sobel operator for edge detection\n",
- "net.params['conv'][0].data[1:] = 0.\n",
- "sobel = np.array((-1, -2, -1, 0, 0, 0, 1, 2, 1), dtype=np.float32).reshape((3,3))\n",
- "net.params['conv'][0].data[1, 0, 1:-1, 1:-1] = sobel # horizontal\n",
- "net.params['conv'][0].data[2, 0, 1:-1, 1:-1] = sobel.T # vertical\n",
- "show_filters(net)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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6rjiupf4Cj5aQOy8AIySOjNHfLJs/HKpUOi3GzfPTY+lRaO6ds8MGg0PE5N69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\nXi/AHOsJhfHw8BCsD5/n83m1Wi1Jq+A8gMTh4WFcy8fRgwJhcxgzZ46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lxGYlBsKBKwod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\neJp22rYCTlKa3xVsVpDoJqXjI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+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x112797f50>"
+ ]
+ },
"metadata": {},
- "source": [
- "With net surgery, parameters can be transplanted across nets, regularized by custom per-parameter operations, and transformed according to your schemes."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Casting a Classifier into a Fully Convolutional Network\n",
- "\n",
- "Let's take the standard Caffe Reference ImageNet model \"CaffeNet\" and transform it into a fully convolutional net for efficient, dense inference on large inputs. This model generates a classification map that covers a given input size instead of a single classification. In particular a 8 $\\times$ 8 classification map on a 451 $\\times$ 451 input gives 64x the output in only 3x the time. The computation exploits a natural efficiency of convolutional network (convnet) structure by amortizing the computation of overlapping receptive fields.\n",
- "\n",
- "To do so we translate the `InnerProduct` matrix multiplication layers of CaffeNet into `Convolutional` layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 \\times 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding."
- ]
- },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load the net, list its data and params, and filter an example image.\n",
+ "caffe.set_mode_cpu()\n",
+ "net = caffe.Net('net_surgery/conv.prototxt', caffe.TEST)\n",
+ "print(\"blobs {}\\nparams {}\".format(net.blobs.keys(), net.params.keys()))\n",
+ "\n",
+ "# load image and prepare as a single input batch for Caffe\n",
+ "im = np.array(Image.open('images/cat_gray.jpg'))\n",
+ "plt.title(\"original image\")\n",
+ "plt.imshow(im)\n",
+ "plt.axis('off')\n",
+ "\n",
+ "im_input = im[np.newaxis, np.newaxis, :, :]\n",
+ "net.blobs['data'].reshape(*im_input.shape)\n",
+ "net.blobs['data'].data[...] = im_input"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The convolution weights are initialized from Gaussian noise while the biases are initialized to zero. These random filters give output somewhat like edge detections."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
{
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "!diff imagenet/bvlc_caffenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt"
- ],
- "language": "python",
+ "data": {
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+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x113e04090>"
+ ]
+ },
"metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "diff: imagenet/bvlc_caffenet_full_conv.prototxt: No such file or directory\r\n"
- ]
- }
- ],
- "prompt_number": 6
- },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# helper show filter outputs\n",
+ "def show_filters(net):\n",
+ " net.forward()\n",
+ " plt.figure()\n",
+ " filt_min, filt_max = net.blobs['conv'].data.min(), net.blobs['conv'].data.max()\n",
+ " for i in range(3):\n",
+ " plt.subplot(1,4,i+2)\n",
+ " plt.title(\"filter #{} output\".format(i))\n",
+ " plt.imshow(net.blobs['conv'].data[0, i], vmin=filt_min, vmax=filt_max)\n",
+ " plt.tight_layout()\n",
+ " plt.axis('off')\n",
+ "\n",
+ "# filter the image with initial \n",
+ "show_filters(net)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Raising the bias of a filter will correspondingly raise its output:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The only differences needed in the architecture are to change the fully connected classifier inner product layers into convolutional layers with the right filter size -- 6 x 6, since the reference model classifiers take the 36 elements of `pool5` as input -- and stride 1 for dense classification. Note that the layers are renamed so that Caffe does not try to blindly load the old parameters when it maps layer names to the pretrained model."
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "pre-surgery output mean -12.93\n",
+ "post-surgery output mean -11.93\n"
]
- },
+ }
+ ],
+ "source": [
+ "# pick first filter output\n",
+ "conv0 = net.blobs['conv'].data[0, 0]\n",
+ "print(\"pre-surgery output mean {:.2f}\".format(conv0.mean()))\n",
+ "# set first filter bias to 10\n",
+ "net.params['conv'][1].data[0] = 1.\n",
+ "net.forward()\n",
+ "print(\"post-surgery output mean {:.2f}\".format(conv0.mean()))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Altering the filter weights is more exciting since we can assign any kernel like Gaussian blur, the Sobel operator for edges, and so on. The following surgery turns the 0th filter into a Gaussian blur and the 1st and 2nd filters into the horizontal and vertical gradient parts of the Sobel operator.\n",
+ "\n",
+ "See how the 0th output is blurred, the 1st picks up horizontal edges, and the 2nd picks up vertical edges."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
{
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# Make sure that caffe is on the python path:\n",
- "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n",
- "import sys\n",
- "sys.path.insert(0, caffe_root + 'python')\n",
- "\n",
- "import caffe\n",
- "\n",
- "# Load the original network and extract the fully connected layers' parameters.\n",
- "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', \n",
- " '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel', \n",
- " caffe.TEST)\n",
- "params = ['fc6', 'fc7', 'fc8']\n",
- "# fc_params = {name: (weights, biases)}\n",
- "fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}\n",
- "\n",
- "for fc in params:\n",
- " print '{} weights are {} dimensional and biases are {} dimensional'.format(fc, fc_params[fc][0].shape, fc_params[fc][1].shape)"
- ],
- "language": "python",
+ "data": {
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+ "IiIi4pXfPRZNtgyLF3IAAAAASUVORK5CYII=\n"
+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x112bc2190>"
+ ]
+ },
"metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "fc6 weights are (4096, 9216) dimensional and biases are (4096,) dimensional\n",
- "fc7 weights are (4096, 4096) dimensional and biases are (4096,) dimensional\n",
- "fc8 weights are (1000, 4096) dimensional and biases are (1000,) dimensional\n"
- ]
- }
- ],
- "prompt_number": 7
- },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ksize = net.params['conv'][0].data.shape[2:]\n",
+ "# make Gaussian blur\n",
+ "sigma = 1.\n",
+ "y, x = np.mgrid[-ksize[0]//2 + 1:ksize[0]//2 + 1, -ksize[1]//2 + 1:ksize[1]//2 + 1]\n",
+ "g = np.exp(-((x**2 + y**2)/(2.0*sigma**2)))\n",
+ "gaussian = (g / g.sum()).astype(np.float32)\n",
+ "net.params['conv'][0].data[0] = gaussian\n",
+ "# make Sobel operator for edge detection\n",
+ "net.params['conv'][0].data[1:] = 0.\n",
+ "sobel = np.array((-1, -2, -1, 0, 0, 0, 1, 2, 1), dtype=np.float32).reshape((3,3))\n",
+ "net.params['conv'][0].data[1, 0, 1:-1, 1:-1] = sobel # horizontal\n",
+ "net.params['conv'][0].data[2, 0, 1:-1, 1:-1] = sobel.T # vertical\n",
+ "show_filters(net)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With net surgery, parameters can be transplanted across nets, regularized by custom per-parameter operations, and transformed according to your schemes."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Casting a Classifier into a Fully Convolutional Network\n",
+ "\n",
+ "Let's take the standard Caffe Reference ImageNet model \"CaffeNet\" and transform it into a fully convolutional net for efficient, dense inference on large inputs. This model generates a classification map that covers a given input size instead of a single classification. In particular a 8 $\\times$ 8 classification map on a 451 $\\times$ 451 input gives 64x the output in only 3x the time. The computation exploits a natural efficiency of convolutional network (convnet) structure by amortizing the computation of overlapping receptive fields.\n",
+ "\n",
+ "To do so we translate the `InnerProduct` matrix multiplication layers of CaffeNet into `Convolutional` layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 \\times 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Consider the shapes of the inner product parameters. The weight dimensions are the output and input sizes while the bias dimension is the output size."
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1,2c1\r\n",
+ "< # Fully convolutional network version of CaffeNet.\r\n",
+ "< name: \"CaffeNetConv\"\r\n",
+ "---\r\n",
+ "> name: \"CaffeNet\"\r\n",
+ "4c3\r\n",
+ "< input_dim: 1\r\n",
+ "---\r\n",
+ "> input_dim: 10\r\n",
+ "6,7c5,6\r\n",
+ "< input_dim: 451\r\n",
+ "< input_dim: 451\r\n",
+ "---\r\n",
+ "> input_dim: 227\r\n",
+ "> input_dim: 227\r\n",
+ "152,153c151,152\r\n",
+ "< name: \"fc6-conv\"\r\n",
+ "< type: \"Convolution\"\r\n",
+ "---\r\n",
+ "> name: \"fc6\"\r\n",
+ "> type: \"InnerProduct\"\r\n",
+ "155,156c154,155\r\n",
+ "< top: \"fc6-conv\"\r\n",
+ "< convolution_param {\r\n",
+ "---\r\n",
+ "> top: \"fc6\"\r\n",
+ "> inner_product_param {\r\n",
+ "158d156\r\n",
+ "< kernel_size: 6\r\n",
+ "164,165c162,163\r\n",
+ "< bottom: \"fc6-conv\"\r\n",
+ "< top: \"fc6-conv\"\r\n",
+ "---\r\n",
+ "> bottom: \"fc6\"\r\n",
+ "> top: \"fc6\"\r\n",
+ "170,171c168,169\r\n",
+ "< bottom: \"fc6-conv\"\r\n",
+ "< top: \"fc6-conv\"\r\n",
+ "---\r\n",
+ "> bottom: \"fc6\"\r\n",
+ "> top: \"fc6\"\r\n",
+ "177,181c175,179\r\n",
+ "< name: \"fc7-conv\"\r\n",
+ "< type: \"Convolution\"\r\n",
+ "< bottom: \"fc6-conv\"\r\n",
+ "< top: \"fc7-conv\"\r\n",
+ "< convolution_param {\r\n",
+ "---\r\n",
+ "> name: \"fc7\"\r\n",
+ "> type: \"InnerProduct\"\r\n",
+ "> bottom: \"fc6\"\r\n",
+ "> top: \"fc7\"\r\n",
+ "> inner_product_param {\r\n",
+ "183d180\r\n",
+ "< kernel_size: 1\r\n",
+ "189,190c186,187\r\n",
+ "< bottom: \"fc7-conv\"\r\n",
+ "< top: \"fc7-conv\"\r\n",
+ "---\r\n",
+ "> bottom: \"fc7\"\r\n",
+ "> top: \"fc7\"\r\n",
+ "195,196c192,193\r\n",
+ "< bottom: \"fc7-conv\"\r\n",
+ "< top: \"fc7-conv\"\r\n",
+ "---\r\n",
+ "> bottom: \"fc7\"\r\n",
+ "> top: \"fc7\"\r\n",
+ "202,206c199,203\r\n",
+ "< name: \"fc8-conv\"\r\n",
+ "< type: \"Convolution\"\r\n",
+ "< bottom: \"fc7-conv\"\r\n",
+ "< top: \"fc8-conv\"\r\n",
+ "< convolution_param {\r\n",
+ "---\r\n",
+ "> name: \"fc8\"\r\n",
+ "> type: \"InnerProduct\"\r\n",
+ "> bottom: \"fc7\"\r\n",
+ "> top: \"fc8\"\r\n",
+ "> inner_product_param {\r\n",
+ "208d204\r\n",
+ "< kernel_size: 1\r\n",
+ "214c210\r\n",
+ "< bottom: \"fc8-conv\"\r\n",
+ "---\r\n",
+ "> bottom: \"fc8\"\r\n"
]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# Load the fully convolutional network to transplant the parameters.\n",
- "net_full_conv = caffe.Net('net_surgery/bvlc_caffenet_full_conv.prototxt', \n",
- " '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n",
- " caffe.TEST)\n",
- "params_full_conv = ['fc6-conv', 'fc7-conv', 'fc8-conv']\n",
- "# conv_params = {name: (weights, biases)}\n",
- "conv_params = {pr: (net_full_conv.params[pr][0].data, net_full_conv.params[pr][1].data) for pr in params_full_conv}\n",
- "\n",
- "for conv in params_full_conv:\n",
- " print '{} weights are {} dimensional and biases are {} dimensional'.format(conv, conv_params[conv][0].shape, conv_params[conv][1].shape)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "fc6-conv weights are (4096, 256, 6, 6) dimensional and biases are (4096,) dimensional\n",
- "fc7-conv weights are (4096, 4096, 1, 1) dimensional and biases are (4096,) dimensional\n",
- "fc8-conv weights are (1000, 4096, 1, 1) dimensional and biases are (1000,) dimensional\n"
- ]
- }
- ],
- "prompt_number": 8
- },
+ }
+ ],
+ "source": [
+ "!diff net_surgery/bvlc_caffenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The only differences needed in the architecture are to change the fully connected classifier inner product layers into convolutional layers with the right filter size -- 6 x 6, since the reference model classifiers take the 36 elements of `pool5` as input -- and stride 1 for dense classification. Note that the layers are renamed so that Caffe does not try to blindly load the old parameters when it maps layer names to the pretrained model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The convolution weights are arranged in output $\\times$ input $\\times$ height $\\times$ width dimensions. To map the inner product weights to convolution filters, we could roll the flat inner product vectors into channel $\\times$ height $\\times$ width filter matrices, but actually these are identical in memory (as row major arrays) so we can assign them directly.\n",
- "\n",
- "The biases are identical to those of the inner product.\n",
- "\n",
- "Let's transplant!"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "fc6 weights are (4096, 9216) dimensional and biases are (4096,) dimensional\n",
+ "fc7 weights are (4096, 4096) dimensional and biases are (4096,) dimensional\n",
+ "fc8 weights are (1000, 4096) dimensional and biases are (1000,) dimensional\n"
]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "for pr, pr_conv in zip(params, params_full_conv):\n",
- " conv_params[pr_conv][0].flat = fc_params[pr][0].flat # flat unrolls the arrays\n",
- " conv_params[pr_conv][1][...] = fc_params[pr][1]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 9
- },
+ }
+ ],
+ "source": [
+ "# Make sure that caffe is on the python path:\n",
+ "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n",
+ "import sys\n",
+ "sys.path.insert(0, caffe_root + 'python')\n",
+ "\n",
+ "import caffe\n",
+ "\n",
+ "# Load the original network and extract the fully connected layers' parameters.\n",
+ "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', \n",
+ " '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel', \n",
+ " caffe.TEST)\n",
+ "params = ['fc6', 'fc7', 'fc8']\n",
+ "# fc_params = {name: (weights, biases)}\n",
+ "fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}\n",
+ "\n",
+ "for fc in params:\n",
+ " print '{} weights are {} dimensional and biases are {} dimensional'.format(fc, fc_params[fc][0].shape, fc_params[fc][1].shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Consider the shapes of the inner product parameters. The weight dimensions are the output and input sizes while the bias dimension is the output size."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Next, save the new model weights."
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "fc6-conv weights are (4096, 256, 6, 6) dimensional and biases are (4096,) dimensional\n",
+ "fc7-conv weights are (4096, 4096, 1, 1) dimensional and biases are (4096,) dimensional\n",
+ "fc8-conv weights are (1000, 4096, 1, 1) dimensional and biases are (1000,) dimensional\n"
]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "net_full_conv.save('net_surgery/bvlc_caffenet_full_conv.caffemodel')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 10
- },
+ }
+ ],
+ "source": [
+ "# Load the fully convolutional network to transplant the parameters.\n",
+ "net_full_conv = caffe.Net('net_surgery/bvlc_caffenet_full_conv.prototxt', \n",
+ " '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n",
+ " caffe.TEST)\n",
+ "params_full_conv = ['fc6-conv', 'fc7-conv', 'fc8-conv']\n",
+ "# conv_params = {name: (weights, biases)}\n",
+ "conv_params = {pr: (net_full_conv.params[pr][0].data, net_full_conv.params[pr][1].data) for pr in params_full_conv}\n",
+ "\n",
+ "for conv in params_full_conv:\n",
+ " print '{} weights are {} dimensional and biases are {} dimensional'.format(conv, conv_params[conv][0].shape, conv_params[conv][1].shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The convolution weights are arranged in output $\\times$ input $\\times$ height $\\times$ width dimensions. To map the inner product weights to convolution filters, we could roll the flat inner product vectors into channel $\\times$ height $\\times$ width filter matrices, but actually these are identical in memory (as row major arrays) so we can assign them directly.\n",
+ "\n",
+ "The biases are identical to those of the inner product.\n",
+ "\n",
+ "Let's transplant!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "for pr, pr_conv in zip(params, params_full_conv):\n",
+ " conv_params[pr_conv][0].flat = fc_params[pr][0].flat # flat unrolls the arrays\n",
+ " conv_params[pr_conv][1][...] = fc_params[pr][1]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next, save the new model weights."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "net_full_conv.save('net_surgery/bvlc_caffenet_full_conv.caffemodel')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To conclude, let's make a classification map from the example cat image and visualize the confidence of \"tiger cat\" as a probability heatmap. This gives an 8-by-8 prediction on overlapping regions of the 451 $\\times$ 451 input."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To conclude, let's make a classification map from the example cat image and visualize the confidence of \"tiger cat\" as a probability heatmap. This gives an 8-by-8 prediction on overlapping regions of the 451 $\\times$ 451 input."
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[282 282 281 281 281 281 277 282]\n",
+ " [281 283 283 281 281 281 281 282]\n",
+ " [283 283 283 283 283 283 287 282]\n",
+ " [283 283 283 281 283 283 283 259]\n",
+ " [283 283 283 283 283 283 283 259]\n",
+ " [283 283 283 283 283 283 259 259]\n",
+ " [283 283 283 283 259 259 259 277]\n",
+ " [335 335 283 259 263 263 263 277]]\n"
]
},
{
- "cell_type": "code",
- "collapsed": true,
- "input": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n",
- "\n",
- "# load input and configure preprocessing\n",
- "im = caffe.io.load_image('images/cat.jpg')\n",
- "transformer = caffe.io.Transformer({'data': net_full_conv.blobs['data'].data.shape})\n",
- "transformer.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1))\n",
- "transformer.set_transpose('data', (2,0,1))\n",
- "transformer.set_channel_swap('data', (2,1,0))\n",
- "transformer.set_raw_scale('data', 255.0)\n",
- "# make classification map by forward and print prediction indices at each location\n",
- "out = net_full_conv.forward_all(data=np.asarray([transformer.preprocess('data', im)]))\n",
- "print out['prob'][0].argmax(axis=0)\n",
- "# show net input and confidence map (probability of the top prediction at each location)\n",
- "plt.subplot(1, 2, 1)\n",
- "plt.imshow(transformer.deprocess('data', net_full_conv.blobs['data'].data[0]))\n",
- "plt.subplot(1, 2, 2)\n",
- "plt.imshow(out['prob'][0,281])"
- ],
- "language": "python",
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x12379a690>"
+ ]
+ },
+ "execution_count": 11,
"metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "[[282 282 281 281 281 281 277 282]\n",
- " [281 283 283 281 281 281 281 282]\n",
- " [283 283 283 283 283 283 287 282]\n",
- " [283 283 283 281 283 283 283 259]\n",
- " [283 283 283 283 283 283 283 259]\n",
- " [283 283 283 283 283 283 259 259]\n",
- " [283 283 283 283 259 259 259 277]\n",
- " [335 335 283 259 263 263 263 277]]\n"
- ]
- },
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 11,
- "text": [
- "<matplotlib.image.AxesImage at 0x12379a690>"
- ]
- },
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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m4NvfEI5zItkeCZ5geiiRuN9wtdtQUsGXQtKCLRZNlegjxRowrkn05kqYWYy2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\nc6pqy/2Hj9ibL+m6Yafy8Q6ld3cDhN87A3BY70hGy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- "text": [
- "<matplotlib.figure.Figure at 0x12666be50>"
- ]
- }
- ],
- "prompt_number": 11
+ "output_type": "execute_result"
},
{
- "cell_type": "markdown",
+ "data": {
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+ "A43ph1xlbAoPAAAAAElFTkSuQmCC\n"
+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x12666be50>"
+ ]
+ },
"metadata": {},
- "source": [
- "The classifications include various cats -- 282 = tiger cat, 281 = tabby, 283 = persian -- and foxes and other mammals.\n",
- "\n",
- "In this way the fully connected layers can be extracted as dense features across an image (see `net_full_conv.blobs['fc6'].data` for instance), which is perhaps more useful than the classification map itself.\n",
- "\n",
- "Note that this model isn't totally appropriate for sliding-window detection since it was trained for whole-image classification. Nevertheless it can work just fine. Sliding-window training and finetuning can be done by defining a sliding-window ground truth and loss such that a loss map is made for every location and solving as usual. (This is an exercise for the reader.)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "*A thank you to Rowland Depp for first suggesting this trick.*"
- ]
+ "output_type": "display_data"
}
],
- "metadata": {}
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "# load input and configure preprocessing\n",
+ "im = caffe.io.load_image('images/cat.jpg')\n",
+ "transformer = caffe.io.Transformer({'data': net_full_conv.blobs['data'].data.shape})\n",
+ "transformer.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1))\n",
+ "transformer.set_transpose('data', (2,0,1))\n",
+ "transformer.set_channel_swap('data', (2,1,0))\n",
+ "transformer.set_raw_scale('data', 255.0)\n",
+ "# make classification map by forward and print prediction indices at each location\n",
+ "out = net_full_conv.forward_all(data=np.asarray([transformer.preprocess('data', im)]))\n",
+ "print out['prob'][0].argmax(axis=0)\n",
+ "# show net input and confidence map (probability of the top prediction at each location)\n",
+ "plt.subplot(1, 2, 1)\n",
+ "plt.imshow(transformer.deprocess('data', net_full_conv.blobs['data'].data[0]))\n",
+ "plt.subplot(1, 2, 2)\n",
+ "plt.imshow(out['prob'][0,281])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The classifications include various cats -- 282 = tiger cat, 281 = tabby, 283 = persian -- and foxes and other mammals.\n",
+ "\n",
+ "In this way the fully connected layers can be extracted as dense features across an image (see `net_full_conv.blobs['fc6'].data` for instance), which is perhaps more useful than the classification map itself.\n",
+ "\n",
+ "Note that this model isn't totally appropriate for sliding-window detection since it was trained for whole-image classification. Nevertheless it can work just fine. Sliding-window training and finetuning can be done by defining a sliding-window ground truth and loss such that a loss map is made for every location and solving as usual. (This is an exercise for the reader.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "*A thank you to Rowland Depp for first suggesting this trick.*"
+ ]
}
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+ "description": "How to do net surgery and manually change model parameters for custom use.",
+ "example_name": "Editing model parameters",
+ "include_in_docs": true,
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.9"
+ },
+ "priority": 5
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
# Fully convolutional network version of CaffeNet.
name: "CaffeNetConv"
input: "data"
-input_dim: 1
-input_dim: 3
-input_dim: 451
-input_dim: 451
+input_shape {
+ dim: 1
+ dim: 3
+ dim: 451
+ dim: 451
+}
layer {
name: "conv1"
type: "Convolution"
# Simple single-layer network to showcase editing model parameters.
name: "convolution"
input: "data"
-input_dim: 1
-input_dim: 1
-input_dim: 100
-input_dim: 100
+input_shape {
+ dim: 1
+ dim: 1
+ dim: 100
+ dim: 100
+}
layer {
name: "conv"
type: "Convolution"
--- /dev/null
+from __future__ import print_function
+from caffe import layers as L, params as P, to_proto
+from caffe.proto import caffe_pb2
+
+# helper function for common structures
+
+def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):
+ conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
+ num_output=nout, pad=pad, group=group)
+ return conv, L.ReLU(conv, in_place=True)
+
+def fc_relu(bottom, nout):
+ fc = L.InnerProduct(bottom, num_output=nout)
+ return fc, L.ReLU(fc, in_place=True)
+
+def max_pool(bottom, ks, stride=1):
+ return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)
+
+def caffenet(lmdb, batch_size=256, include_acc=False):
+ data, label = L.Data(source=lmdb, backend=P.Data.LMDB, batch_size=batch_size, ntop=2,
+ transform_param=dict(crop_size=227, mean_value=[104, 117, 123], mirror=True))
+
+ # the net itself
+ conv1, relu1 = conv_relu(data, 11, 96, stride=4)
+ pool1 = max_pool(relu1, 3, stride=2)
+ norm1 = L.LRN(pool1, local_size=5, alpha=1e-4, beta=0.75)
+ conv2, relu2 = conv_relu(norm1, 5, 256, pad=2, group=2)
+ pool2 = max_pool(relu2, 3, stride=2)
+ norm2 = L.LRN(pool2, local_size=5, alpha=1e-4, beta=0.75)
+ conv3, relu3 = conv_relu(norm2, 3, 384, pad=1)
+ conv4, relu4 = conv_relu(relu3, 3, 384, pad=1, group=2)
+ conv5, relu5 = conv_relu(relu4, 3, 256, pad=1, group=2)
+ pool5 = max_pool(relu5, 3, stride=2)
+ fc6, relu6 = fc_relu(pool5, 4096)
+ drop6 = L.Dropout(relu6, in_place=True)
+ fc7, relu7 = fc_relu(drop6, 4096)
+ drop7 = L.Dropout(relu7, in_place=True)
+ fc8 = L.InnerProduct(drop7, num_output=1000)
+ loss = L.SoftmaxWithLoss(fc8, label)
+
+ if include_acc:
+ acc = L.Accuracy(fc8, label)
+ return to_proto(loss, acc)
+ else:
+ return to_proto(loss)
+
+def make_net():
+ with open('train.prototxt', 'w') as f:
+ print(caffenet('/path/to/caffe-train-lmdb'), file=f)
+
+ with open('test.prototxt', 'w') as f:
+ print(caffenet('/path/to/caffe-val-lmdb', batch_size=50, include_acc=True), file=f)
+
+if __name__ == '__main__':
+ make_net()
--- /dev/null
+import caffe
+import numpy as np
+
+
+class EuclideanLossLayer(caffe.Layer):
+ """
+ Compute the Euclidean Loss in the same manner as the C++ EuclideanLossLayer
+ to demonstrate the class interface for developing layers in Python.
+ """
+
+ def setup(self, bottom, top):
+ # check input pair
+ if len(bottom) != 2:
+ raise Exception("Need two inputs to compute distance.")
+
+ def reshape(self, bottom, top):
+ # check input dimensions match
+ if bottom[0].count != bottom[1].count:
+ raise Exception("Inputs must have the same dimension.")
+ # difference is shape of inputs
+ self.diff = np.zeros_like(bottom[0].data, dtype=np.float32)
+ # loss output is scalar
+ top[0].reshape(1)
+
+ def forward(self, bottom, top):
+ self.diff[...] = bottom[0].data - bottom[1].data
+ top[0].data[...] = np.sum(self.diff**2) / bottom[0].num / 2.
+
+ def backward(self, top, propagate_down, bottom):
+ for i in range(2):
+ if not propagate_down[i]:
+ continue
+ if i == 0:
+ sign = 1
+ else:
+ sign = -1
+ bottom[i].diff[...] = sign * self.diff / bottom[i].num
--- /dev/null
+name: 'LinearRegressionExample'
+# define a simple network for linear regression on dummy data
+# that computes the loss by a PythonLayer.
+layer {
+ type: 'DummyData'
+ name: 'x'
+ top: 'x'
+ dummy_data_param {
+ shape: { dim: 10 dim: 3 dim: 2 }
+ data_filler: { type: 'gaussian' }
+ }
+}
+layer {
+ type: 'DummyData'
+ name: 'y'
+ top: 'y'
+ dummy_data_param {
+ shape: { dim: 10 dim: 3 dim: 2 }
+ data_filler: { type: 'gaussian' }
+ }
+}
+# include InnerProduct layers for parameters
+# so the net will need backward
+layer {
+ type: 'InnerProduct'
+ name: 'ipx'
+ top: 'ipx'
+ bottom: 'x'
+ inner_product_param {
+ num_output: 10
+ weight_filler { type: 'xavier' }
+ }
+}
+layer {
+ type: 'InnerProduct'
+ name: 'ipy'
+ top: 'ipy'
+ bottom: 'y'
+ inner_product_param {
+ num_output: 10
+ weight_filler { type: 'xavier' }
+ }
+}
+layer {
+ type: 'Python'
+ name: 'loss'
+ top: 'loss'
+ bottom: 'ipx'
+ bottom: 'ipy'
+ python_param {
+ # the module name -- usually the filename -- that needs to be in $PYTHONPATH
+ module: 'pyloss'
+ # the layer name -- the class name in the module
+ layer: 'EuclideanLossLayer'
+ }
+ # set loss weight so Caffe knows this is a loss layer.
+ # since PythonLayer inherits directly from Layer, this isn't automatically
+ # known to Caffe
+ loss_weight: 1
+}
#include "glog/logging.h"
#include "google/protobuf/text_format.h"
-#include "leveldb/db.h"
#include "stdint.h"
#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/format.hpp"
#include "caffe/util/math_functions.hpp"
+#ifdef USE_LEVELDB
+#include "leveldb/db.h"
+
uint32_t swap_endian(uint32_t val) {
val = ((val << 8) & 0xFF00FF00) | ((val >> 8) & 0xFF00FF);
return (val << 16) | (val >> 16);
char label_i;
char label_j;
char* pixels = new char[2 * rows * cols];
- const int kMaxKeyLength = 10;
- char key[kMaxKeyLength];
std::string value;
caffe::Datum datum;
datum.set_label(0);
}
datum.SerializeToString(&value);
- snprintf(key, kMaxKeyLength, "%08d", itemid);
- db->Put(leveldb::WriteOptions(), std::string(key), value);
+ std::string key_str = caffe::format_int(itemid, 8);
+ db->Put(leveldb::WriteOptions(), key_str, value);
}
delete db;
- delete pixels;
+ delete [] pixels;
}
int main(int argc, char** argv) {
}
return 0;
}
+#else
+int main(int argc, char** argv) {
+ LOG(FATAL) << "This example requires LevelDB; compile with USE_LEVELDB.";
+}
+#endif // USE_LEVELDB
{
- "metadata": {
- "description": "Extracting features and plotting the Siamese network embedding.",
- "example_name": "Siamese network embedding",
- "include_in_docs": true,
- "priority": 6,
- "signature": "sha256:845bb18929f96543ba2611eb5eca744fd98939cbef876df6bc319c29f616fc64"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Setup\n",
- "\n",
- "Import Caffe and the usual modules."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n",
- "\n",
- "# Make sure that caffe is on the python path:\n",
- "caffe_root = '../../' # this file is expected to be in {caffe_root}/examples/siamese\n",
- "import sys\n",
- "sys.path.insert(0, caffe_root + 'python')\n",
- "\n",
- "import caffe"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 1
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Load the trained net\n",
- "\n",
- "Load the model definition and weights and set to CPU mode TEST phase computation with input scaling."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "MODEL_FILE = 'mnist_siamese.prototxt'\n",
- "# decrease if you want to preview during training\n",
- "PRETRAINED_FILE = 'mnist_siamese_iter_50000.caffemodel' \n",
- "caffe.set_mode_cpu()\n",
- "net = caffe.Net(MODEL_FILE, PRETRAINED_FILE, caffe.TEST)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 2
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Load some MNIST test data"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "TEST_DATA_FILE = '../../data/mnist/t10k-images-idx3-ubyte'\n",
- "TEST_LABEL_FILE = '../../data/mnist/t10k-labels-idx1-ubyte'\n",
- "n = 10000\n",
- "\n",
- "with open(TEST_DATA_FILE, 'rb') as f:\n",
- " f.read(16) # skip the header\n",
- " raw_data = np.fromstring(f.read(n * 28*28), dtype=np.uint8)\n",
- "\n",
- "with open(TEST_LABEL_FILE, 'rb') as f:\n",
- " f.read(8) # skip the header\n",
- " labels = np.fromstring(f.read(n), dtype=np.uint8)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 3
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Generate the Siamese features"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# reshape and preprocess\n",
- "caffe_in = raw_data.reshape(n, 1, 28, 28) * 0.00390625 # manually scale data instead of using `caffe.io.Transformer`\n",
- "out = net.forward_all(data=caffe_in)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 4
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Visualize the learned Siamese embedding"
- ]
- },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Setup\n",
+ "\n",
+ "Import Caffe and the usual modules."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "# Make sure that caffe is on the python path:\n",
+ "caffe_root = '../../' # this file is expected to be in {caffe_root}/examples/siamese\n",
+ "import sys\n",
+ "sys.path.insert(0, caffe_root + 'python')\n",
+ "\n",
+ "import caffe"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load the trained net\n",
+ "\n",
+ "Load the model definition and weights and set to CPU mode TEST phase computation with input scaling."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "MODEL_FILE = 'mnist_siamese.prototxt'\n",
+ "# decrease if you want to preview during training\n",
+ "PRETRAINED_FILE = 'mnist_siamese_iter_50000.caffemodel' \n",
+ "caffe.set_mode_cpu()\n",
+ "net = caffe.Net(MODEL_FILE, PRETRAINED_FILE, caffe.TEST)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load some MNIST test data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "TEST_DATA_FILE = '../../data/mnist/t10k-images-idx3-ubyte'\n",
+ "TEST_LABEL_FILE = '../../data/mnist/t10k-labels-idx1-ubyte'\n",
+ "n = 10000\n",
+ "\n",
+ "with open(TEST_DATA_FILE, 'rb') as f:\n",
+ " f.read(16) # skip the header\n",
+ " raw_data = np.fromstring(f.read(n * 28*28), dtype=np.uint8)\n",
+ "\n",
+ "with open(TEST_LABEL_FILE, 'rb') as f:\n",
+ " f.read(8) # skip the header\n",
+ " labels = np.fromstring(f.read(n), dtype=np.uint8)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Generate the Siamese features"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# reshape and preprocess\n",
+ "caffe_in = raw_data.reshape(n, 1, 28, 28) * 0.00390625 # manually scale data instead of using `caffe.io.Transformer`\n",
+ "out = net.forward_all(data=caffe_in)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Visualize the learned Siamese embedding"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
{
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "feat = out['feat']\n",
- "f = plt.figure(figsize=(16,9))\n",
- "c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff', \n",
- " '#ff00ff', '#990000', '#999900', '#009900', '#009999']\n",
- "for i in range(10):\n",
- " plt.plot(feat[labels==i,0].flatten(), feat[labels==i,1].flatten(), '.', c=c[i])\n",
- "plt.legend(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])\n",
- "plt.grid()\n",
- "plt.show()"
- ],
- "language": "python",
+ "data": {
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+ ],
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fadf4552a90>"
+ ]
+ },
"metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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V//ELwP+gpqYC2J4j95vbj2+//fZ54/E8evQob775JgMDamXzjz76iF/96ldJ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\nmQpmVsCdLcyKeM1mXGC6H+dTJdHH1XTny+FQP2S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DVVVKG7x6aYNEwD6xZw/6T5/Gjbt3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w6+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\nAABAVv1/lzHCzGUnjVoAAAAASUVORK5CYII=\n",
- "text": [
- "<matplotlib.figure.Figure at 0x7fadf4552a90>"
- ]
- }
- ],
- "prompt_number": 5
+ "output_type": "display_data"
}
],
- "metadata": {}
+ "source": [
+ "feat = out['feat']\n",
+ "f = plt.figure(figsize=(16,9))\n",
+ "c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff', \n",
+ " '#ff00ff', '#990000', '#999900', '#009900', '#009999']\n",
+ "for i in range(10):\n",
+ " plt.plot(feat[labels==i,0].flatten(), feat[labels==i,1].flatten(), '.', c=c[i])\n",
+ "plt.legend(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])\n",
+ "plt.grid()\n",
+ "plt.show()"
+ ]
}
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+ "description": "Extracting features and plotting the Siamese network embedding.",
+ "example_name": "Siamese network embedding",
+ "include_in_docs": true,
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.9"
+ },
+ "priority": 7
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
name: "mnist_siamese"
input: "data"
-input_dim: 10000
-input_dim: 1
-input_dim: 28
-input_dim: 28
+input_shape {
+ dim: 10000
+ dim: 1
+ dim: 28
+ dim: 28
+}
layer {
name: "conv1"
type: "Convolution"
we have replaced the top layers that produced probabilities over the 10 digit
classes with a linear "feature" layer that produces a 2 dimensional vector.
- layers {
+ layer {
name: "feat"
- type: INNER_PRODUCT
+ type: "InnerProduct"
bottom: "ip2"
top: "feat"
- blobs_lr: 1
- blobs_lr: 2
+ param {
+ name: "feat_w"
+ lr_mult: 1
+ }
+ param {
+ name: "feat_b"
+ lr_mult: 2
+ }
inner_product_param {
num_output: 2
}
images (`pair_data`) and a binary label saying if they belong to the same class
or different classes (`sim`).
- layers {
+ layer {
name: "pair_data"
- type: DATA
+ type: "Data"
top: "pair_data"
top: "sim"
- data_param {
- source: "examples/siamese/mnist-siamese-train-leveldb"
+ include { phase: TRAIN }
+ transform_param {
scale: 0.00390625
+ }
+ data_param {
+ source: "examples/siamese/mnist_siamese_train_leveldb"
batch_size: 64
}
- include: { phase: TRAIN }
}
In order to pack a pair of images into the same blob in the database we pack one
slices it along the channel dimension so that we have a single image in `data`
and its paired image in `data_p.`
- layers {
- name: "slice_pair"
- type: SLICE
- bottom: "pair_data"
- top: "data"
- top: "data_p"
- slice_param {
- slice_dim: 1
- slice_point: 1
- }
+ layer {
+ name: "slice_pair"
+ type: "Slice"
+ bottom: "pair_data"
+ top: "data"
+ top: "data_p"
+ slice_param {
+ slice_dim: 1
+ slice_point: 1
+ }
}
### Building the First Side of the Siamese Net
the siamese net. In the definition this looks like:
...
- param: "conv1_w"
- param: "conv1_b"
+ param { name: "conv1_w" ... }
+ param { name: "conv1_b" ... }
...
- param: "conv2_w"
- param: "conv2_b"
+ param { name: "conv2_w" ... }
+ param { name: "conv2_b" ... }
...
- param: "ip1_w"
- param: "ip1_b"
+ param { name: "ip1_w" ... }
+ param { name: "ip1_b" ... }
...
- param: "ip2_w"
- param: "ip2_b"
+ param { name: "ip2_w" ... }
+ param { name: "ip2_b" ... }
...
### Building the Second Side of the Siamese Net
together in feature space while pushing non-matching pairs apart. This cost
function is implemented with the `CONTRASTIVE_LOSS` layer:
- layers {
+ layer {
name: "loss"
- type: CONTRASTIVE_LOSS
+ type: "ContrastiveLoss"
contrastive_loss_param {
margin: 1.0
}
import tornado.httpserver
import numpy as np
import pandas as pd
-import Image
+from PIL import Image
import cStringIO as StringIO
import urllib
import exifutil
import caffe
-REPO_DIRNAME = os.path.abspath(os.path.dirname(__file__) + '/../..')
+REPO_DIRNAME = os.path.abspath(os.path.dirname(os.path.abspath(__file__)) + '/../..')
UPLOAD_FOLDER = '/tmp/caffe_demos_uploads'
ALLOWED_IMAGE_EXTENSIONS = set(['png', 'bmp', 'jpg', 'jpe', 'jpeg', 'gif'])
numpy
pandas
pillow
+pyyaml
#include "caffe/common.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/syncedmem.hpp"
-#include "caffe/util/math_functions.hpp"
-const int kMaxBlobAxes = INT_MAX;
+const int kMaxBlobAxes = 32;
namespace caffe {
* @brief Returns the 'canonical' version of a (usually) user-specified axis,
* allowing for negative indexing (e.g., -1 for the last axis).
*
- * @param index the axis index.
+ * @param axis_index the axis index.
* If 0 <= index < num_axes(), return index.
* If -num_axes <= index <= -1, return (num_axes() - (-index)),
* e.g., the last axis index (num_axes() - 1) if index == -1,
const Dtype* cpu_data() const;
void set_cpu_data(Dtype* data);
+ const int* gpu_shape() const;
const Dtype* gpu_data() const;
const Dtype* cpu_diff() const;
const Dtype* gpu_diff() const;
protected:
shared_ptr<SyncedMemory> data_;
shared_ptr<SyncedMemory> diff_;
+ shared_ptr<SyncedMemory> shape_data_;
vector<int> shape_;
int count_;
int capacity_;
#include "caffe/layer.hpp"
#include "caffe/layer_factory.hpp"
#include "caffe/net.hpp"
+#include "caffe/parallel.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/solver.hpp"
+#include "caffe/solver_factory.hpp"
#include "caffe/util/benchmark.hpp"
#include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/util/upgrade_proto.hpp"
#endif // CAFFE_CAFFE_HPP_
#include "caffe/util/device_alternate.hpp"
+// Convert macro to string
+#define STRINGIFY(m) #m
+#define AS_STRING(m) STRINGIFY(m)
+
// gflags 2.1 issue: namespace google was changed to gflags without warning.
-// Luckily we will be able to use GFLAGS_GFAGS_H_ to detect if it is version
+// Luckily we will be able to use GFLAGS_GFLAGS_H_ to detect if it is version
// 2.1. If yes, we will add a temporary solution to redirect the namespace.
// TODO(Yangqing): Once gflags solves the problem in a more elegant way, let's
// remove the following hack.
class Caffe {
public:
~Caffe();
- inline static Caffe& Get() {
- if (!singleton_.get()) {
- singleton_.reset(new Caffe());
- }
- return *singleton_;
- }
+
+ // Thread local context for Caffe. Moved to common.cpp instead of
+ // including boost/thread.hpp to avoid a boost/NVCC issues (#1009, #1010)
+ // on OSX. Also fails on Linux with CUDA 7.0.18.
+ static Caffe& Get();
+
enum Brew { CPU, GPU };
// This random number generator facade hides boost and CUDA rng
static void SetDevice(const int device_id);
// Prints the current GPU status.
static void DeviceQuery();
+ // Parallel training info
+ inline static int solver_count() { return Get().solver_count_; }
+ inline static void set_solver_count(int val) { Get().solver_count_ = val; }
+ inline static bool root_solver() { return Get().root_solver_; }
+ inline static void set_root_solver(bool val) { Get().root_solver_ = val; }
protected:
#ifndef CPU_ONLY
shared_ptr<RNG> random_generator_;
Brew mode_;
- static shared_ptr<Caffe> singleton_;
+ int solver_count_;
+ bool root_solver_;
private:
// The private constructor to avoid duplicate instantiation.
+++ /dev/null
-#ifndef CAFFE_COMMON_LAYERS_HPP_
-#define CAFFE_COMMON_LAYERS_HPP_
-
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/loss_layers.hpp"
-#include "caffe/neuron_layers.hpp"
-#include "caffe/proto/caffe.pb.h"
-
-namespace caffe {
-
-/**
- * @brief Compute the index of the @f$ K @f$ max values for each datum across
- * all dimensions @f$ (C \times H \times W) @f$.
- *
- * Intended for use after a classification layer to produce a prediction.
- * If parameter out_max_val is set to true, output is a vector of pairs
- * (max_ind, max_val) for each image.
- *
- * NOTE: does not implement Backwards operation.
- */
-template <typename Dtype>
-class ArgMaxLayer : public Layer<Dtype> {
- public:
- /**
- * @param param provides ArgMaxParameter argmax_param,
- * with ArgMaxLayer options:
- * - top_k (\b optional uint, default 1).
- * the number @f$ K @f$ of maximal items to output.
- * - out_max_val (\b optional bool, default false).
- * if set, output a vector of pairs (max_ind, max_val) for each image.
- */
- explicit ArgMaxLayer(const LayerParameter& param)
- : Layer<Dtype>(param) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "ArgMax"; }
- virtual inline int ExactNumBottomBlobs() const { return 1; }
- virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
- /**
- * @param bottom input Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the inputs @f$ x @f$
- * @param top output Blob vector (length 1)
- * -# @f$ (N \times 1 \times K \times 1) @f$ or, if out_max_val
- * @f$ (N \times 2 \times K \times 1) @f$
- * the computed outputs @f$
- * y_n = \arg\max\limits_i x_{ni}
- * @f$ (for @f$ K = 1 @f$).
- */
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- /// @brief Not implemented (non-differentiable function)
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
- NOT_IMPLEMENTED;
- }
- bool out_max_val_;
- size_t top_k_;
-};
-
-/**
- * @brief Takes at least two Blob%s and concatenates them along either the num
- * or channel dimension, outputting the result.
- */
-template <typename Dtype>
-class ConcatLayer : public Layer<Dtype> {
- public:
- explicit ConcatLayer(const LayerParameter& param)
- : Layer<Dtype>(param) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "Concat"; }
- virtual inline int MinBottomBlobs() const { return 2; }
- virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
- /**
- * @param bottom input Blob vector (length 2+)
- * -# @f$ (N \times C \times H \times W) @f$
- * the inputs @f$ x_1 @f$
- * -# @f$ (N \times C \times H \times W) @f$
- * the inputs @f$ x_2 @f$
- * -# ...
- * - K @f$ (N \times C \times H \times W) @f$
- * the inputs @f$ x_K @f$
- * @param top output Blob vector (length 1)
- * -# @f$ (KN \times C \times H \times W) @f$ if axis == 0, or
- * @f$ (N \times KC \times H \times W) @f$ if axis == 1:
- * the concatenated output @f$
- * y = [\begin{array}{cccc} x_1 & x_2 & ... & x_K \end{array}]
- * @f$
- */
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- /**
- * @brief Computes the error gradient w.r.t. the concatenate inputs.
- *
- * @param top output Blob vector (length 1), providing the error gradient with
- * respect to the outputs
- * -# @f$ (KN \times C \times H \times W) @f$ if axis == 0, or
- * @f$ (N \times KC \times H \times W) @f$ if axis == 1:
- * containing error gradients @f$ \frac{\partial E}{\partial y} @f$
- * with respect to concatenated outputs @f$ y @f$
- * @param propagate_down see Layer::Backward.
- * @param bottom input Blob vector (length K), into which the top gradient
- * @f$ \frac{\partial E}{\partial y} @f$ is deconcatenated back to the
- * inputs @f$
- * \left[ \begin{array}{cccc}
- * \frac{\partial E}{\partial x_1} &
- * \frac{\partial E}{\partial x_2} &
- * ... &
- * \frac{\partial E}{\partial x_K}
- * \end{array} \right] =
- * \frac{\partial E}{\partial y}
- * @f$
- */
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- int count_;
- int num_concats_;
- int concat_input_size_;
- int concat_axis_;
-};
-
-/**
- * @brief Compute elementwise operations, such as product and sum,
- * along multiple input Blobs.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class EltwiseLayer : public Layer<Dtype> {
- public:
- explicit EltwiseLayer(const LayerParameter& param)
- : Layer<Dtype>(param) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "Eltwise"; }
- virtual inline int MinBottomBlobs() const { return 2; }
- virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- EltwiseParameter_EltwiseOp op_;
- vector<Dtype> coeffs_;
- Blob<int> max_idx_;
-
- bool stable_prod_grad_;
-};
-
-/**
- * @brief Reshapes the input Blob into flat vectors.
- *
- * Note: because this layer does not change the input values -- merely the
- * dimensions -- it can simply copy the input. The copy happens "virtually"
- * (thus taking effectively 0 real time) by setting, in Forward, the data
- * pointer of the top Blob to that of the bottom Blob (see Blob::ShareData),
- * and in Backward, the diff pointer of the bottom Blob to that of the top Blob
- * (see Blob::ShareDiff).
- */
-template <typename Dtype>
-class FlattenLayer : public Layer<Dtype> {
- public:
- explicit FlattenLayer(const LayerParameter& param)
- : Layer<Dtype>(param) {}
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "Flatten"; }
- virtual inline int ExactNumBottomBlobs() const { return 1; }
- virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
- /**
- * @param bottom input Blob vector (length 2+)
- * -# @f$ (N \times C \times H \times W) @f$
- * the inputs
- * @param top output Blob vector (length 1)
- * -# @f$ (N \times CHW \times 1 \times 1) @f$
- * the outputs -- i.e., the (virtually) copied, flattened inputs
- */
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- /**
- * @brief Computes the error gradient w.r.t. the concatenate inputs.
- *
- * @param top output Blob vector (length 1), providing the error gradient with
- * respect to the outputs
- * @param propagate_down see Layer::Backward.
- * @param bottom input Blob vector (length K), into which the top error
- * gradient is (virtually) copied
- */
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-/**
- * @brief Also known as a "fully-connected" layer, computes an inner product
- * with a set of learned weights, and (optionally) adds biases.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class InnerProductLayer : public Layer<Dtype> {
- public:
- explicit InnerProductLayer(const LayerParameter& param)
- : Layer<Dtype>(param) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "InnerProduct"; }
- virtual inline int ExactNumBottomBlobs() const { return 1; }
- virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- int M_;
- int K_;
- int N_;
- bool bias_term_;
- Blob<Dtype> bias_multiplier_;
-};
-
-/**
- * @brief Normalizes the input to have 0-mean and/or unit (1) variance.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class MVNLayer : public Layer<Dtype> {
- public:
- explicit MVNLayer(const LayerParameter& param)
- : Layer<Dtype>(param) {}
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "MVN"; }
- virtual inline int ExactNumBottomBlobs() const { return 1; }
- virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- Blob<Dtype> mean_, variance_, temp_;
-
- /// sum_multiplier is used to carry out sum using BLAS
- Blob<Dtype> sum_multiplier_;
-};
-
-/**
- * @brief Ignores bottom blobs while producing no top blobs. (This is useful
- * to suppress outputs during testing.)
- */
-template <typename Dtype>
-class SilenceLayer : public Layer<Dtype> {
- public:
- explicit SilenceLayer(const LayerParameter& param)
- : Layer<Dtype>(param) {}
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top) {}
-
- virtual inline const char* type() const { return "Silence"; }
- virtual inline int MinBottomBlobs() const { return 1; }
- virtual inline int ExactNumTopBlobs() const { return 0; }
-
- protected:
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top) {}
- // We can't define Forward_gpu here, since STUB_GPU will provide
- // its own definition for CPU_ONLY mode.
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-/**
- * @brief Computes the softmax function.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class SoftmaxLayer : public Layer<Dtype> {
- public:
- explicit SoftmaxLayer(const LayerParameter& param)
- : Layer<Dtype>(param) {}
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "Softmax"; }
- virtual inline int ExactNumBottomBlobs() const { return 1; }
- virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- int outer_num_;
- int inner_num_;
- int softmax_axis_;
- /// sum_multiplier is used to carry out sum using BLAS
- Blob<Dtype> sum_multiplier_;
- /// scale is an intermediate Blob to hold temporary results.
- Blob<Dtype> scale_;
-};
-
-#ifdef USE_CUDNN
-/**
- * @brief cuDNN implementation of SoftmaxLayer.
- * Fallback to SoftmaxLayer for CPU mode.
- */
-template <typename Dtype>
-class CuDNNSoftmaxLayer : public SoftmaxLayer<Dtype> {
- public:
- explicit CuDNNSoftmaxLayer(const LayerParameter& param)
- : SoftmaxLayer<Dtype>(param), handles_setup_(false) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual ~CuDNNSoftmaxLayer();
-
- protected:
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- bool handles_setup_;
- cudnnHandle_t handle_;
- cudnnTensor4dDescriptor_t bottom_desc_;
- cudnnTensor4dDescriptor_t top_desc_;
-};
-#endif
-
-/**
- * @brief Creates a "split" path in the network by copying the bottom Blob
- * into multiple top Blob%s to be used by multiple consuming layers.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class SplitLayer : public Layer<Dtype> {
- public:
- explicit SplitLayer(const LayerParameter& param)
- : Layer<Dtype>(param) {}
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "Split"; }
- virtual inline int ExactNumBottomBlobs() const { return 1; }
- virtual inline int MinTopBlobs() const { return 1; }
-
- protected:
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- int count_;
-};
-
-/**
- * @brief Takes a Blob and slices it along either the num or channel dimension,
- * outputting multiple sliced Blob results.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class SliceLayer : public Layer<Dtype> {
- public:
- explicit SliceLayer(const LayerParameter& param)
- : Layer<Dtype>(param) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "Slice"; }
- virtual inline int ExactNumBottomBlobs() const { return 1; }
- virtual inline int MinTopBlobs() const { return 2; }
-
- protected:
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- int count_;
- int num_slices_;
- int slice_size_;
- int slice_axis_;
- vector<int> slice_point_;
-};
-
-} // namespace caffe
-
-#endif // CAFFE_COMMON_LAYERS_HPP_
+++ /dev/null
-#ifndef CAFFE_DATA_LAYERS_HPP_
-#define CAFFE_DATA_LAYERS_HPP_
-
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "boost/scoped_ptr.hpp"
-#include "hdf5.h"
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/data_transformer.hpp"
-#include "caffe/filler.hpp"
-#include "caffe/internal_thread.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/net.hpp"
-#include "caffe/proto/caffe.pb.h"
-#include "caffe/util/db.hpp"
-
-namespace caffe {
-
-/**
- * @brief Provides base for data layers that feed blobs to the Net.
- *
- * TODO(dox): thorough documentation for Forward and proto params.
- */
-template <typename Dtype>
-class BaseDataLayer : public Layer<Dtype> {
- public:
- explicit BaseDataLayer(const LayerParameter& param);
- virtual ~BaseDataLayer() {}
- // LayerSetUp: implements common data layer setup functionality, and calls
- // DataLayerSetUp to do special data layer setup for individual layer types.
- // This method may not be overridden except by the BasePrefetchingDataLayer.
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top) {}
- // Data layers have no bottoms, so reshaping is trivial.
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top) {}
-
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
-
- protected:
- TransformationParameter transform_param_;
- shared_ptr<DataTransformer<Dtype> > data_transformer_;
- bool output_labels_;
-};
-
-template <typename Dtype>
-class BasePrefetchingDataLayer :
- public BaseDataLayer<Dtype>, public InternalThread {
- public:
- explicit BasePrefetchingDataLayer(const LayerParameter& param)
- : BaseDataLayer<Dtype>(param) {}
- virtual ~BasePrefetchingDataLayer() {}
- // LayerSetUp: implements common data layer setup functionality, and calls
- // DataLayerSetUp to do special data layer setup for individual layer types.
- // This method may not be overridden.
- void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual void CreatePrefetchThread();
- virtual void JoinPrefetchThread();
- // The thread's function
- virtual void InternalThreadEntry() {}
-
- protected:
- Blob<Dtype> prefetch_data_;
- Blob<Dtype> prefetch_label_;
- Blob<Dtype> transformed_data_;
-};
-
-template <typename Dtype>
-class DataLayer : public BasePrefetchingDataLayer<Dtype> {
- public:
- explicit DataLayer(const LayerParameter& param)
- : BasePrefetchingDataLayer<Dtype>(param) {}
- virtual ~DataLayer();
- virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "Data"; }
- virtual inline int ExactNumBottomBlobs() const { return 0; }
- virtual inline int MinTopBlobs() const { return 1; }
- virtual inline int MaxTopBlobs() const { return 2; }
-
- protected:
- virtual void InternalThreadEntry();
-
- shared_ptr<db::DB> db_;
- shared_ptr<db::Cursor> cursor_;
-};
-
-/**
- * @brief Provides data to the Net generated by a Filler.
- *
- * TODO(dox): thorough documentation for Forward and proto params.
- */
-template <typename Dtype>
-class DummyDataLayer : public Layer<Dtype> {
- public:
- explicit DummyDataLayer(const LayerParameter& param)
- : Layer<Dtype>(param) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- // Data layers have no bottoms, so reshaping is trivial.
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top) {}
-
- virtual inline const char* type() const { return "DummyData"; }
- virtual inline int ExactNumBottomBlobs() const { return 0; }
- virtual inline int MinTopBlobs() const { return 1; }
-
- protected:
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
-
- vector<shared_ptr<Filler<Dtype> > > fillers_;
- vector<bool> refill_;
-};
-
-/**
- * @brief Provides data to the Net from HDF5 files.
- *
- * TODO(dox): thorough documentation for Forward and proto params.
- */
-template <typename Dtype>
-class HDF5DataLayer : public Layer<Dtype> {
- public:
- explicit HDF5DataLayer(const LayerParameter& param)
- : Layer<Dtype>(param) {}
- virtual ~HDF5DataLayer();
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- // Data layers have no bottoms, so reshaping is trivial.
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top) {}
-
- virtual inline const char* type() const { return "HDF5Data"; }
- virtual inline int ExactNumBottomBlobs() const { return 0; }
- virtual inline int MinTopBlobs() const { return 1; }
-
- protected:
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
- virtual void LoadHDF5FileData(const char* filename);
-
- std::vector<std::string> hdf_filenames_;
- unsigned int num_files_;
- unsigned int current_file_;
- hsize_t current_row_;
- std::vector<shared_ptr<Blob<Dtype> > > hdf_blobs_;
- std::vector<unsigned int> data_permutation_;
- std::vector<unsigned int> file_permutation_;
-};
-
-/**
- * @brief Write blobs to disk as HDF5 files.
- *
- * TODO(dox): thorough documentation for Forward and proto params.
- */
-template <typename Dtype>
-class HDF5OutputLayer : public Layer<Dtype> {
- public:
- explicit HDF5OutputLayer(const LayerParameter& param)
- : Layer<Dtype>(param), file_opened_(false) {}
- virtual ~HDF5OutputLayer();
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- // Data layers have no bottoms, so reshaping is trivial.
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top) {}
-
- virtual inline const char* type() const { return "HDF5Output"; }
- // TODO: no limit on the number of blobs
- virtual inline int ExactNumBottomBlobs() const { return 2; }
- virtual inline int ExactNumTopBlobs() const { return 0; }
-
- inline std::string file_name() const { return file_name_; }
-
- protected:
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void SaveBlobs();
-
- bool file_opened_;
- std::string file_name_;
- hid_t file_id_;
- Blob<Dtype> data_blob_;
- Blob<Dtype> label_blob_;
-};
-
-/**
- * @brief Provides data to the Net from image files.
- *
- * TODO(dox): thorough documentation for Forward and proto params.
- */
-template <typename Dtype>
-class ImageDataLayer : public BasePrefetchingDataLayer<Dtype> {
- public:
- explicit ImageDataLayer(const LayerParameter& param)
- : BasePrefetchingDataLayer<Dtype>(param) {}
- virtual ~ImageDataLayer();
- virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "ImageData"; }
- virtual inline int ExactNumBottomBlobs() const { return 0; }
- virtual inline int ExactNumTopBlobs() const { return 2; }
-
- protected:
- shared_ptr<Caffe::RNG> prefetch_rng_;
- virtual void ShuffleImages();
- virtual void InternalThreadEntry();
-
- vector<std::pair<std::string, int> > lines_;
- int lines_id_;
-};
-
-/**
- * @brief Provides data to the Net from memory.
- *
- * TODO(dox): thorough documentation for Forward and proto params.
- */
-template <typename Dtype>
-class MemoryDataLayer : public BaseDataLayer<Dtype> {
- public:
- explicit MemoryDataLayer(const LayerParameter& param)
- : BaseDataLayer<Dtype>(param), has_new_data_(false) {}
- virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "MemoryData"; }
- virtual inline int ExactNumBottomBlobs() const { return 0; }
- virtual inline int ExactNumTopBlobs() const { return 2; }
-
- virtual void AddDatumVector(const vector<Datum>& datum_vector);
- virtual void AddMatVector(const vector<cv::Mat>& mat_vector,
- const vector<int>& labels);
-
- // Reset should accept const pointers, but can't, because the memory
- // will be given to Blob, which is mutable
- void Reset(Dtype* data, Dtype* label, int n);
- void set_batch_size(int new_size);
-
- int batch_size() { return batch_size_; }
- int channels() { return channels_; }
- int height() { return height_; }
- int width() { return width_; }
-
- protected:
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- int batch_size_, channels_, height_, width_, size_;
- Dtype* data_;
- Dtype* labels_;
- int n_;
- size_t pos_;
- Blob<Dtype> added_data_;
- Blob<Dtype> added_label_;
- bool has_new_data_;
-};
-
-/**
- * @brief Provides data to the Net from windows of images files, specified
- * by a window data file.
- *
- * TODO(dox): thorough documentation for Forward and proto params.
- */
-template <typename Dtype>
-class WindowDataLayer : public BasePrefetchingDataLayer<Dtype> {
- public:
- explicit WindowDataLayer(const LayerParameter& param)
- : BasePrefetchingDataLayer<Dtype>(param) {}
- virtual ~WindowDataLayer();
- virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "WindowData"; }
- virtual inline int ExactNumBottomBlobs() const { return 0; }
- virtual inline int ExactNumTopBlobs() const { return 2; }
-
- protected:
- virtual unsigned int PrefetchRand();
- virtual void InternalThreadEntry();
-
- shared_ptr<Caffe::RNG> prefetch_rng_;
- vector<std::pair<std::string, vector<int> > > image_database_;
- enum WindowField { IMAGE_INDEX, LABEL, OVERLAP, X1, Y1, X2, Y2, NUM };
- vector<vector<float> > fg_windows_;
- vector<vector<float> > bg_windows_;
- Blob<Dtype> data_mean_;
- vector<Dtype> mean_values_;
- bool has_mean_file_;
- bool has_mean_values_;
- bool cache_images_;
- vector<std::pair<std::string, Datum > > image_database_cache_;
-};
-
-} // namespace caffe
-
-#endif // CAFFE_DATA_LAYERS_HPP_
--- /dev/null
+#ifndef CAFFE_DATA_READER_HPP_
+#define CAFFE_DATA_READER_HPP_
+
+#include <map>
+#include <string>
+#include <vector>
+
+#include "caffe/common.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/util/blocking_queue.hpp"
+#include "caffe/util/db.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Reads data from a source to queues available to data layers.
+ * A single reading thread is created per source, even if multiple solvers
+ * are running in parallel, e.g. for multi-GPU training. This makes sure
+ * databases are read sequentially, and that each solver accesses a different
+ * subset of the database. Data is distributed to solvers in a round-robin
+ * way to keep parallel training deterministic.
+ */
+class DataReader {
+ public:
+ explicit DataReader(const LayerParameter& param);
+ ~DataReader();
+
+ inline BlockingQueue<Datum*>& free() const {
+ return queue_pair_->free_;
+ }
+ inline BlockingQueue<Datum*>& full() const {
+ return queue_pair_->full_;
+ }
+
+ protected:
+ // Queue pairs are shared between a body and its readers
+ class QueuePair {
+ public:
+ explicit QueuePair(int size);
+ ~QueuePair();
+
+ BlockingQueue<Datum*> free_;
+ BlockingQueue<Datum*> full_;
+
+ DISABLE_COPY_AND_ASSIGN(QueuePair);
+ };
+
+ // A single body is created per source
+ class Body : public InternalThread {
+ public:
+ explicit Body(const LayerParameter& param);
+ virtual ~Body();
+
+ protected:
+ void InternalThreadEntry();
+ void read_one(db::Cursor* cursor, QueuePair* qp);
+
+ const LayerParameter param_;
+ BlockingQueue<shared_ptr<QueuePair> > new_queue_pairs_;
+
+ friend class DataReader;
+
+ DISABLE_COPY_AND_ASSIGN(Body);
+ };
+
+ // A source is uniquely identified by its layer name + path, in case
+ // the same database is read from two different locations in the net.
+ static inline string source_key(const LayerParameter& param) {
+ return param.name() + ":" + param.data_param().source();
+ }
+
+ const shared_ptr<QueuePair> queue_pair_;
+ shared_ptr<Body> body_;
+
+ static map<const string, boost::weak_ptr<DataReader::Body> > bodies_;
+
+DISABLE_COPY_AND_ASSIGN(DataReader);
+};
+
+} // namespace caffe
+
+#endif // CAFFE_DATA_READER_HPP_
void Transform(const vector<Datum> & datum_vector,
Blob<Dtype>* transformed_blob);
+#ifdef USE_OPENCV
/**
* @brief Applies the transformation defined in the data layer's
* transform_param block to a vector of Mat.
*/
void Transform(const vector<cv::Mat> & mat_vector,
Blob<Dtype>* transformed_blob);
+
/**
* @brief Applies the transformation defined in the data layer's
* transform_param block to a cv::Mat
* set_cpu_data() is used. See image_data_layer.cpp for an example.
*/
void Transform(const cv::Mat& cv_img, Blob<Dtype>* transformed_blob);
+#endif // USE_OPENCV
/**
* @brief Applies the same transformation defined in the data layer's
*/
void Transform(Blob<Dtype>* input_blob, Blob<Dtype>* transformed_blob);
+ /**
+ * @brief Infers the shape of transformed_blob will have when
+ * the transformation is applied to the data.
+ *
+ * @param datum
+ * Datum containing the data to be transformed.
+ */
+ vector<int> InferBlobShape(const Datum& datum);
+ /**
+ * @brief Infers the shape of transformed_blob will have when
+ * the transformation is applied to the data.
+ * It uses the first element to infer the shape of the blob.
+ *
+ * @param datum_vector
+ * A vector of Datum containing the data to be transformed.
+ */
+ vector<int> InferBlobShape(const vector<Datum> & datum_vector);
+ /**
+ * @brief Infers the shape of transformed_blob will have when
+ * the transformation is applied to the data.
+ * It uses the first element to infer the shape of the blob.
+ *
+ * @param mat_vector
+ * A vector of Mat containing the data to be transformed.
+ */
+#ifdef USE_OPENCV
+ vector<int> InferBlobShape(const vector<cv::Mat> & mat_vector);
+ /**
+ * @brief Infers the shape of transformed_blob will have when
+ * the transformation is applied to the data.
+ *
+ * @param cv_img
+ * cv::Mat containing the data to be transformed.
+ */
+ vector<int> InferBlobShape(const cv::Mat& cv_img);
+#endif // USE_OPENCV
+
protected:
/**
* @brief Generates a random integer from Uniform({0, 1, ..., n-1}).
} // namespace caffe
#endif // CAFFE_DATA_TRANSFORMER_HPP_
-
#include <string>
#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/syncedmem.hpp"
#include "caffe/util/math_functions.hpp"
};
/**
- * @brief Fills a Blob with values @f$ x \sim U(-a, +a) @f$ where @f$ a @f$
- * is set inversely proportional to the number of incoming nodes.
+ * @brief Fills a Blob with values @f$ x \sim U(-a, +a) @f$ where @f$ a @f$ is
+ * set inversely proportional to number of incoming nodes, outgoing
+ * nodes, or their average.
*
* A Filler based on the paper [Bengio and Glorot 2010]: Understanding
- * the difficulty of training deep feedforward neuralnetworks, but does not
- * use the fan_out value.
+ * the difficulty of training deep feedforward neuralnetworks.
*
- * It fills the incoming matrix by randomly sampling uniform data from
- * [-scale, scale] where scale = sqrt(3 / fan_in) where fan_in is the number
- * of input nodes. You should make sure the input blob has shape (num, a, b, c)
- * where a * b * c = fan_in.
+ * It fills the incoming matrix by randomly sampling uniform data from [-scale,
+ * scale] where scale = sqrt(3 / n) where n is the fan_in, fan_out, or their
+ * average, depending on the variance_norm option. You should make sure the
+ * input blob has shape (num, a, b, c) where a * b * c = fan_in and num * b * c
+ * = fan_out. Note that this is currently not the case for inner product layers.
*
* TODO(dox): make notation in above comment consistent with rest & use LaTeX.
*/
virtual void Fill(Blob<Dtype>* blob) {
CHECK(blob->count());
int fan_in = blob->count() / blob->num();
- Dtype scale = sqrt(Dtype(3) / fan_in);
+ int fan_out = blob->count() / blob->channels();
+ Dtype n = fan_in; // default to fan_in
+ if (this->filler_param_.variance_norm() ==
+ FillerParameter_VarianceNorm_AVERAGE) {
+ n = (fan_in + fan_out) / Dtype(2);
+ } else if (this->filler_param_.variance_norm() ==
+ FillerParameter_VarianceNorm_FAN_OUT) {
+ n = fan_out;
+ }
+ Dtype scale = sqrt(Dtype(3) / n);
caffe_rng_uniform<Dtype>(blob->count(), -scale, scale,
blob->mutable_cpu_data());
CHECK_EQ(this->filler_param_.sparse(), -1)
}
};
+/**
+ * @brief Fills a Blob with values @f$ x \sim N(0, \sigma^2) @f$ where
+ * @f$ \sigma^2 @f$ is set inversely proportional to number of incoming
+ * nodes, outgoing nodes, or their average.
+ *
+ * A Filler based on the paper [He, Zhang, Ren and Sun 2015]: Specifically
+ * accounts for ReLU nonlinearities.
+ *
+ * Aside: for another perspective on the scaling factor, see the derivation of
+ * [Saxe, McClelland, and Ganguli 2013 (v3)].
+ *
+ * It fills the incoming matrix by randomly sampling Gaussian data with std =
+ * sqrt(2 / n) where n is the fan_in, fan_out, or their average, depending on
+ * the variance_norm option. You should make sure the input blob has shape (num,
+ * a, b, c) where a * b * c = fan_in and num * b * c = fan_out. Note that this
+ * is currently not the case for inner product layers.
+ */
+template <typename Dtype>
+class MSRAFiller : public Filler<Dtype> {
+ public:
+ explicit MSRAFiller(const FillerParameter& param)
+ : Filler<Dtype>(param) {}
+ virtual void Fill(Blob<Dtype>* blob) {
+ CHECK(blob->count());
+ int fan_in = blob->count() / blob->num();
+ int fan_out = blob->count() / blob->channels();
+ Dtype n = fan_in; // default to fan_in
+ if (this->filler_param_.variance_norm() ==
+ FillerParameter_VarianceNorm_AVERAGE) {
+ n = (fan_in + fan_out) / Dtype(2);
+ } else if (this->filler_param_.variance_norm() ==
+ FillerParameter_VarianceNorm_FAN_OUT) {
+ n = fan_out;
+ }
+ Dtype std = sqrt(Dtype(2) / n);
+ caffe_rng_gaussian<Dtype>(blob->count(), Dtype(0), std,
+ blob->mutable_cpu_data());
+ CHECK_EQ(this->filler_param_.sparse(), -1)
+ << "Sparsity not supported by this Filler.";
+ }
+};
+
+/*!
+@brief Fills a Blob with coefficients for bilinear interpolation.
+
+A common use case is with the DeconvolutionLayer acting as upsampling.
+You can upsample a feature map with shape of (B, C, H, W) by any integer factor
+using the following proto.
+\code
+layer {
+ name: "upsample", type: "Deconvolution"
+ bottom: "{{bottom_name}}" top: "{{top_name}}"
+ convolution_param {
+ kernel_size: {{2 * factor - factor % 2}} stride: {{factor}}
+ num_output: {{C}} group: {{C}}
+ pad: {{ceil((factor - 1) / 2.)}}
+ weight_filler: { type: "bilinear" } bias_term: false
+ }
+ param { lr_mult: 0 decay_mult: 0 }
+}
+\endcode
+Please use this by replacing `{{}}` with your values. By specifying
+`num_output: {{C}} group: {{C}}`, it behaves as
+channel-wise convolution. The filter shape of this deconvolution layer will be
+(C, 1, K, K) where K is `kernel_size`, and this filler will set a (K, K)
+interpolation kernel for every channel of the filter identically. The resulting
+shape of the top feature map will be (B, C, factor * H, factor * W).
+Note that the learning rate and the
+weight decay are set to 0 in order to keep coefficient values of bilinear
+interpolation unchanged during training. If you apply this to an image, this
+operation is equivalent to the following call in Python with Scikit.Image.
+\code{.py}
+out = skimage.transform.rescale(img, factor, mode='constant', cval=0)
+\endcode
+ */
+template <typename Dtype>
+class BilinearFiller : public Filler<Dtype> {
+ public:
+ explicit BilinearFiller(const FillerParameter& param)
+ : Filler<Dtype>(param) {}
+ virtual void Fill(Blob<Dtype>* blob) {
+ CHECK_EQ(blob->num_axes(), 4) << "Blob must be 4 dim.";
+ CHECK_EQ(blob->width(), blob->height()) << "Filter must be square";
+ Dtype* data = blob->mutable_cpu_data();
+ int f = ceil(blob->width() / 2.);
+ float c = (2 * f - 1 - f % 2) / (2. * f);
+ for (int i = 0; i < blob->count(); ++i) {
+ float x = i % blob->width();
+ float y = (i / blob->width()) % blob->height();
+ data[i] = (1 - fabs(x / f - c)) * (1 - fabs(y / f - c));
+ }
+ CHECK_EQ(this->filler_param_.sparse(), -1)
+ << "Sparsity not supported by this Filler.";
+ }
+};
/**
* @brief Get a specific filler from the specification given in FillerParameter.
return new UniformFiller<Dtype>(param);
} else if (type == "xavier") {
return new XavierFiller<Dtype>(param);
+ } else if (type == "msra") {
+ return new MSRAFiller<Dtype>(param);
+ } else if (type == "bilinear") {
+ return new BilinearFiller<Dtype>(param);
} else {
CHECK(false) << "Unknown filler name: " << param.type();
}
/**
* Virtual class encapsulate boost::thread for use in base class
* The child class will acquire the ability to run a single thread,
- * by reimplementing the virutal function InternalThreadEntry.
+ * by reimplementing the virtual function InternalThreadEntry.
*/
class InternalThread {
public:
InternalThread() : thread_() {}
virtual ~InternalThread();
- /** Returns true if the thread was successfully started. **/
- bool StartInternalThread();
+ /**
+ * Caffe's thread local state will be initialized using the current
+ * thread values, e.g. device id, solver index etc. The random seed
+ * is initialized using caffe_rng_rand.
+ */
+ void StartInternalThread();
/** Will not return until the internal thread has exited. */
- bool WaitForInternalThreadToExit();
+ void StopInternalThread();
bool is_started() const;
with the code you want your thread to run. */
virtual void InternalThreadEntry() {}
+ /* Should be tested when running loops to exit when requested. */
+ bool must_stop();
+
+ private:
+ void entry(int device, Caffe::Brew mode, int rand_seed, int solver_count,
+ bool root_solver);
+
shared_ptr<boost::thread> thread_;
};
#include "caffe/common.hpp"
#include "caffe/layer_factory.hpp"
#include "caffe/proto/caffe.pb.h"
-#include "caffe/util/device_alternate.hpp"
+#include "caffe/util/math_functions.hpp"
+
+/**
+ Forward declare boost::thread instead of including boost/thread.hpp
+ to avoid a boost/NVCC issues (#1009, #1010) on OSX.
+ */
+namespace boost { class mutex; }
namespace caffe {
* layer.
*/
explicit Layer(const LayerParameter& param)
- : layer_param_(param) {
+ : layer_param_(param), is_shared_(false) {
// Set phase and copy blobs (if there are any).
phase_ = param.phase();
if (layer_param_.blobs_size() > 0) {
*/
void SetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
+ InitMutex();
CheckBlobCounts(bottom, top);
LayerSetUp(bottom, top);
Reshape(bottom, top);
const vector<Blob<Dtype>*>& top) {}
/**
- * @brief Adjust the shapes of top blobs and internal buffers to accomodate
+ * @brief Whether a layer should be shared by multiple nets during data
+ * parallelism. By default, all layers except for data layers should
+ * not be shared. data layers should be shared to ensure each worker
+ * solver access data sequentially during data parallelism.
+ */
+ virtual inline bool ShareInParallel() const { return false; }
+
+ /** @brief Return whether this layer is actually shared by other nets.
+ * If ShareInParallel() is true and using more than one GPU and the
+ * net has TRAIN phase, then this function is expected return true.
+ */
+ inline bool IsShared() const { return is_shared_; }
+
+ /** @brief Set whether this layer is actually shared by other nets
+ * If ShareInParallel() is true and using more than one GPU and the
+ * net has TRAIN phase, then is_shared should be set true.
+ */
+ inline void SetShared(bool is_shared) {
+ CHECK(ShareInParallel() || !is_shared)
+ << type() << "Layer does not support sharing.";
+ is_shared_ = is_shared;
+ }
+
+ /**
+ * @brief Adjust the shapes of top blobs and internal buffers to accommodate
* the shapes of the bottom blobs.
*
* @param bottom the input blobs, with the requested input shapes
* This method should reshape top blobs as needed according to the shapes
* of the bottom (input) blobs, as well as reshaping any internal buffers
* and making any other necessary adjustments so that the layer can
- * accomodate the bottom blobs.
+ * accommodate the bottom blobs.
*/
virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) = 0;
* (Backward_cpu or Backward_gpu) to compute the bottom blob diffs given the
* top blob diffs.
*
- * Your layer should implement Forward_cpu and (optionally) Forward_gpu.
+ * Your layer should implement Backward_cpu and (optionally) Backward_gpu.
*/
inline void Backward(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down,
}
}
+ private:
+ /** Whether this layer is actually shared by other nets*/
+ bool is_shared_;
+
+ /** The mutex for sequential forward if this layer is shared */
+ shared_ptr<boost::mutex> forward_mutex_;
+
+ /** Initialize forward_mutex_ */
+ void InitMutex();
+ /** Lock forward_mutex_ if this layer is shared */
+ void Lock();
+ /** Unlock forward_mutex_ if this layer is shared */
+ void Unlock();
+
DISABLE_COPY_AND_ASSIGN(Layer);
}; // class Layer
template <typename Dtype>
inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
+ // Lock during forward to ensure sequential forward
+ Lock();
Dtype loss = 0;
+ Reshape(bottom, top);
switch (Caffe::mode()) {
case Caffe::CPU:
Forward_cpu(bottom, top);
default:
LOG(FATAL) << "Unknown caffe mode.";
}
+ Unlock();
return loss;
}
#include <map>
#include <string>
+#include <vector>
#include "caffe/common.hpp"
+#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"
namespace caffe {
// Get a layer using a LayerParameter.
static shared_ptr<Layer<Dtype> > CreateLayer(const LayerParameter& param) {
- LOG(INFO) << "Creating layer " << param.name();
+ if (Caffe::root_solver()) {
+ LOG(INFO) << "Creating layer " << param.name();
+ }
const string& type = param.type();
CreatorRegistry& registry = Registry();
CHECK_EQ(registry.count(type), 1) << "Unknown layer type: " << type
- << " (known types: " << LayerTypeList() << ")";
+ << " (known types: " << LayerTypeListString() << ")";
return registry[type](param);
}
+ static vector<string> LayerTypeList() {
+ CreatorRegistry& registry = Registry();
+ vector<string> layer_types;
+ for (typename CreatorRegistry::iterator iter = registry.begin();
+ iter != registry.end(); ++iter) {
+ layer_types.push_back(iter->first);
+ }
+ return layer_types;
+ }
+
private:
// Layer registry should never be instantiated - everything is done with its
// static variables.
LayerRegistry() {}
- static string LayerTypeList() {
- CreatorRegistry& registry = Registry();
- string layer_types;
- for (typename CreatorRegistry::iterator iter = registry.begin();
- iter != registry.end(); ++iter) {
- if (iter != registry.begin()) {
- layer_types += ", ";
+ static string LayerTypeListString() {
+ vector<string> layer_types = LayerTypeList();
+ string layer_types_str;
+ for (vector<string>::iterator iter = layer_types.begin();
+ iter != layer_types.end(); ++iter) {
+ if (iter != layer_types.begin()) {
+ layer_types_str += ", ";
}
- layer_types += iter->first;
+ layer_types_str += *iter;
}
- return layer_types;
+ return layer_types_str;
}
};
--- /dev/null
+#ifndef CAFFE_ABSVAL_LAYER_HPP_
+#define CAFFE_ABSVAL_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes @f$ y = |x| @f$
+ *
+ * @param bottom input Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the computed outputs @f$ y = |x| @f$
+ */
+template <typename Dtype>
+class AbsValLayer : public NeuronLayer<Dtype> {
+ public:
+ explicit AbsValLayer(const LayerParameter& param)
+ : NeuronLayer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "AbsVal"; }
+ virtual inline int ExactNumBottomBlobs() const { return 1; }
+ virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+ /// @copydoc AbsValLayer
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ /**
+ * @brief Computes the error gradient w.r.t. the absolute value inputs.
+ *
+ * @param top output Blob vector (length 1), providing the error gradient with
+ * respect to the outputs
+ * -# @f$ (N \times C \times H \times W) @f$
+ * containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+ * with respect to computed outputs @f$ y @f$
+ * @param propagate_down see Layer::Backward.
+ * @param bottom input Blob vector (length 2)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x @f$; Backward fills their diff with
+ * gradients @f$
+ * \frac{\partial E}{\partial x} =
+ * \mathrm{sign}(x) \frac{\partial E}{\partial y}
+ * @f$ if propagate_down[0]
+ */
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+} // namespace caffe
+
+#endif // CAFFE_ABSVAL_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_ACCURACY_LAYER_HPP_
+#define CAFFE_ACCURACY_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes the classification accuracy for a one-of-many
+ * classification task.
+ */
+template <typename Dtype>
+class AccuracyLayer : public Layer<Dtype> {
+ public:
+ /**
+ * @param param provides AccuracyParameter accuracy_param,
+ * with AccuracyLayer options:
+ * - top_k (\b optional, default 1).
+ * Sets the maximum rank @f$ k @f$ at which a prediction is considered
+ * correct. For example, if @f$ k = 5 @f$, a prediction is counted
+ * correct if the correct label is among the top 5 predicted labels.
+ */
+ explicit AccuracyLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "Accuracy"; }
+ virtual inline int ExactNumBottomBlobs() const { return 2; }
+
+ // If there are two top blobs, then the second blob will contain
+ // accuracies per class.
+ virtual inline int MinTopBlobs() const { return 1; }
+ virtual inline int MaxTopBlos() const { return 2; }
+
+ protected:
+ /**
+ * @param bottom input Blob vector (length 2)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the predictions @f$ x @f$, a Blob with values in
+ * @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
+ * the @f$ K = CHW @f$ classes. Each @f$ x_n @f$ is mapped to a predicted
+ * label @f$ \hat{l}_n @f$ given by its maximal index:
+ * @f$ \hat{l}_n = \arg\max\limits_k x_{nk} @f$
+ * -# @f$ (N \times 1 \times 1 \times 1) @f$
+ * the labels @f$ l @f$, an integer-valued Blob with values
+ * @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
+ * indicating the correct class label among the @f$ K @f$ classes
+ * @param top output Blob vector (length 1)
+ * -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ * the computed accuracy: @f$
+ * \frac{1}{N} \sum\limits_{n=1}^N \delta\{ \hat{l}_n = l_n \}
+ * @f$, where @f$
+ * \delta\{\mathrm{condition}\} = \left\{
+ * \begin{array}{lr}
+ * 1 & \mbox{if condition} \\
+ * 0 & \mbox{otherwise}
+ * \end{array} \right.
+ * @f$
+ */
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+
+ /// @brief Not implemented -- AccuracyLayer cannot be used as a loss.
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+ for (int i = 0; i < propagate_down.size(); ++i) {
+ if (propagate_down[i]) { NOT_IMPLEMENTED; }
+ }
+ }
+
+ int label_axis_, outer_num_, inner_num_;
+
+ int top_k_;
+
+ /// Whether to ignore instances with a certain label.
+ bool has_ignore_label_;
+ /// The label indicating that an instance should be ignored.
+ int ignore_label_;
+ /// Keeps counts of the number of samples per class.
+ Blob<Dtype> nums_buffer_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_ACCURACY_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_ARGMAX_LAYER_HPP_
+#define CAFFE_ARGMAX_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Compute the index of the @f$ K @f$ max values for each datum across
+ * all dimensions @f$ (C \times H \times W) @f$.
+ *
+ * Intended for use after a classification layer to produce a prediction.
+ * If parameter out_max_val is set to true, output is a vector of pairs
+ * (max_ind, max_val) for each image. The axis parameter specifies an axis
+ * along which to maximise.
+ *
+ * NOTE: does not implement Backwards operation.
+ */
+template <typename Dtype>
+class ArgMaxLayer : public Layer<Dtype> {
+ public:
+ /**
+ * @param param provides ArgMaxParameter argmax_param,
+ * with ArgMaxLayer options:
+ * - top_k (\b optional uint, default 1).
+ * the number @f$ K @f$ of maximal items to output.
+ * - out_max_val (\b optional bool, default false).
+ * if set, output a vector of pairs (max_ind, max_val) unless axis is set then
+ * output max_val along the specified axis.
+ * - axis (\b optional int).
+ * if set, maximise along the specified axis else maximise the flattened
+ * trailing dimensions for each index of the first / num dimension.
+ */
+ explicit ArgMaxLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "ArgMax"; }
+ virtual inline int ExactNumBottomBlobs() const { return 1; }
+ virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+ /**
+ * @param bottom input Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ * -# @f$ (N \times 1 \times K) @f$ or, if out_max_val
+ * @f$ (N \times 2 \times K) @f$ unless axis set than e.g.
+ * @f$ (N \times K \times H \times W) @f$ if axis == 1
+ * the computed outputs @f$
+ * y_n = \arg\max\limits_i x_{ni}
+ * @f$ (for @f$ K = 1 @f$).
+ */
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ /// @brief Not implemented (non-differentiable function)
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+ NOT_IMPLEMENTED;
+ }
+ bool out_max_val_;
+ size_t top_k_;
+ bool has_axis_;
+ int axis_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_ARGMAX_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_BASE_CONVOLUTION_LAYER_HPP_
+#define CAFFE_BASE_CONVOLUTION_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/im2col.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Abstract base class that factors out the BLAS code common to
+ * ConvolutionLayer and DeconvolutionLayer.
+ */
+template <typename Dtype>
+class BaseConvolutionLayer : public Layer<Dtype> {
+ public:
+ explicit BaseConvolutionLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline int MinBottomBlobs() const { return 1; }
+ virtual inline int MinTopBlobs() const { return 1; }
+ virtual inline bool EqualNumBottomTopBlobs() const { return true; }
+
+ protected:
+ // Helper functions that abstract away the column buffer and gemm arguments.
+ // The last argument in forward_cpu_gemm is so that we can skip the im2col if
+ // we just called weight_cpu_gemm with the same input.
+ void forward_cpu_gemm(const Dtype* input, const Dtype* weights,
+ Dtype* output, bool skip_im2col = false);
+ void forward_cpu_bias(Dtype* output, const Dtype* bias);
+ void backward_cpu_gemm(const Dtype* input, const Dtype* weights,
+ Dtype* output);
+ void weight_cpu_gemm(const Dtype* input, const Dtype* output, Dtype*
+ weights);
+ void backward_cpu_bias(Dtype* bias, const Dtype* input);
+
+#ifndef CPU_ONLY
+ void forward_gpu_gemm(const Dtype* col_input, const Dtype* weights,
+ Dtype* output, bool skip_im2col = false);
+ void forward_gpu_bias(Dtype* output, const Dtype* bias);
+ void backward_gpu_gemm(const Dtype* input, const Dtype* weights,
+ Dtype* col_output);
+ void weight_gpu_gemm(const Dtype* col_input, const Dtype* output, Dtype*
+ weights);
+ void backward_gpu_bias(Dtype* bias, const Dtype* input);
+#endif
+
+ /// @brief The spatial dimensions of the input.
+ inline int input_shape(int i) {
+ return (*bottom_shape_)[channel_axis_ + i];
+ }
+ // reverse_dimensions should return true iff we are implementing deconv, so
+ // that conv helpers know which dimensions are which.
+ virtual bool reverse_dimensions() = 0;
+ // Compute height_out_ and width_out_ from other parameters.
+ virtual void compute_output_shape() = 0;
+
+ /// @brief The spatial dimensions of a filter kernel.
+ Blob<int> kernel_shape_;
+ /// @brief The spatial dimensions of the stride.
+ Blob<int> stride_;
+ /// @brief The spatial dimensions of the padding.
+ Blob<int> pad_;
+ /// @brief The spatial dimensions of the dilation.
+ Blob<int> dilation_;
+ /// @brief The spatial dimensions of the convolution input.
+ Blob<int> conv_input_shape_;
+ /// @brief The spatial dimensions of the col_buffer.
+ vector<int> col_buffer_shape_;
+ /// @brief The spatial dimensions of the output.
+ vector<int> output_shape_;
+ const vector<int>* bottom_shape_;
+
+ int num_spatial_axes_;
+ int bottom_dim_;
+ int top_dim_;
+
+ int channel_axis_;
+ int num_;
+ int channels_;
+ int group_;
+ int out_spatial_dim_;
+ int weight_offset_;
+ int num_output_;
+ bool bias_term_;
+ bool is_1x1_;
+ bool force_nd_im2col_;
+
+ private:
+ // wrap im2col/col2im so we don't have to remember the (long) argument lists
+ inline void conv_im2col_cpu(const Dtype* data, Dtype* col_buff) {
+ if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+ im2col_cpu(data, conv_in_channels_,
+ conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
+ kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+ pad_.cpu_data()[0], pad_.cpu_data()[1],
+ stride_.cpu_data()[0], stride_.cpu_data()[1],
+ dilation_.cpu_data()[0], dilation_.cpu_data()[1], col_buff);
+ } else {
+ im2col_nd_cpu(data, num_spatial_axes_, conv_input_shape_.cpu_data(),
+ col_buffer_shape_.data(), kernel_shape_.cpu_data(),
+ pad_.cpu_data(), stride_.cpu_data(), dilation_.cpu_data(), col_buff);
+ }
+ }
+ inline void conv_col2im_cpu(const Dtype* col_buff, Dtype* data) {
+ if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+ col2im_cpu(col_buff, conv_in_channels_,
+ conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
+ kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+ pad_.cpu_data()[0], pad_.cpu_data()[1],
+ stride_.cpu_data()[0], stride_.cpu_data()[1],
+ dilation_.cpu_data()[0], dilation_.cpu_data()[1], data);
+ } else {
+ col2im_nd_cpu(col_buff, num_spatial_axes_, conv_input_shape_.cpu_data(),
+ col_buffer_shape_.data(), kernel_shape_.cpu_data(),
+ pad_.cpu_data(), stride_.cpu_data(), dilation_.cpu_data(), data);
+ }
+ }
+#ifndef CPU_ONLY
+ inline void conv_im2col_gpu(const Dtype* data, Dtype* col_buff) {
+ if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+ im2col_gpu(data, conv_in_channels_,
+ conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
+ kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+ pad_.cpu_data()[0], pad_.cpu_data()[1],
+ stride_.cpu_data()[0], stride_.cpu_data()[1],
+ dilation_.cpu_data()[0], dilation_.cpu_data()[1], col_buff);
+ } else {
+ im2col_nd_gpu(data, num_spatial_axes_, num_kernels_im2col_,
+ conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
+ kernel_shape_.gpu_data(), pad_.gpu_data(),
+ stride_.gpu_data(), dilation_.gpu_data(), col_buff);
+ }
+ }
+ inline void conv_col2im_gpu(const Dtype* col_buff, Dtype* data) {
+ if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+ col2im_gpu(col_buff, conv_in_channels_,
+ conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
+ kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+ pad_.cpu_data()[0], pad_.cpu_data()[1],
+ stride_.cpu_data()[0], stride_.cpu_data()[1],
+ dilation_.cpu_data()[0], dilation_.cpu_data()[1], data);
+ } else {
+ col2im_nd_gpu(col_buff, num_spatial_axes_, num_kernels_col2im_,
+ conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
+ kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(),
+ dilation_.gpu_data(), data);
+ }
+ }
+#endif
+
+ int num_kernels_im2col_;
+ int num_kernels_col2im_;
+ int conv_out_channels_;
+ int conv_in_channels_;
+ int conv_out_spatial_dim_;
+ int kernel_dim_;
+ int col_offset_;
+ int output_offset_;
+
+ Blob<Dtype> col_buffer_;
+ Blob<Dtype> bias_multiplier_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_BASE_CONVOLUTION_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_DATA_LAYERS_HPP_
+#define CAFFE_DATA_LAYERS_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/blocking_queue.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Provides base for data layers that feed blobs to the Net.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class BaseDataLayer : public Layer<Dtype> {
+ public:
+ explicit BaseDataLayer(const LayerParameter& param);
+ // LayerSetUp: implements common data layer setup functionality, and calls
+ // DataLayerSetUp to do special data layer setup for individual layer types.
+ // This method may not be overridden except by the BasePrefetchingDataLayer.
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ // Data layers should be shared by multiple solvers in parallel
+ virtual inline bool ShareInParallel() const { return true; }
+ virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {}
+ // Data layers have no bottoms, so reshaping is trivial.
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {}
+
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+
+ protected:
+ TransformationParameter transform_param_;
+ shared_ptr<DataTransformer<Dtype> > data_transformer_;
+ bool output_labels_;
+};
+
+template <typename Dtype>
+class Batch {
+ public:
+ Blob<Dtype> data_, label_;
+};
+
+template <typename Dtype>
+class BasePrefetchingDataLayer :
+ public BaseDataLayer<Dtype>, public InternalThread {
+ public:
+ explicit BasePrefetchingDataLayer(const LayerParameter& param);
+ // LayerSetUp: implements common data layer setup functionality, and calls
+ // DataLayerSetUp to do special data layer setup for individual layer types.
+ // This method may not be overridden.
+ void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ // Prefetches batches (asynchronously if to GPU memory)
+ static const int PREFETCH_COUNT = 3;
+
+ protected:
+ virtual void InternalThreadEntry();
+ virtual void load_batch(Batch<Dtype>* batch) = 0;
+
+ Batch<Dtype> prefetch_[PREFETCH_COUNT];
+ BlockingQueue<Batch<Dtype>*> prefetch_free_;
+ BlockingQueue<Batch<Dtype>*> prefetch_full_;
+
+ Blob<Dtype> transformed_data_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_DATA_LAYERS_HPP_
--- /dev/null
+#ifndef CAFFE_BATCHNORM_LAYER_HPP_
+#define CAFFE_BATCHNORM_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Normalizes the input to have 0-mean and/or unit (1) variance across
+ * the batch.
+ *
+ * This layer computes Batch Normalization described in [1]. For
+ * each channel in the data (i.e. axis 1), it subtracts the mean and divides
+ * by the variance, where both statistics are computed across both spatial
+ * dimensions and across the different examples in the batch.
+ *
+ * By default, during training time, the network is computing global mean/
+ * variance statistics via a running average, which is then used at test
+ * time to allow deterministic outputs for each input. You can manually
+ * toggle whether the network is accumulating or using the statistics via the
+ * use_global_stats option. IMPORTANT: for this feature to work, you MUST
+ * set the learning rate to zero for all three parameter blobs, i.e.,
+ * param {lr_mult: 0} three times in the layer definition.
+ *
+ * Note that the original paper also included a per-channel learned bias and
+ * scaling factor. It is possible (though a bit cumbersome) to implement
+ * this in caffe using a single-channel DummyDataLayer filled with zeros,
+ * followed by a Convolution layer with output the same size as the current.
+ * This produces a channel-specific value that can be added or multiplied by
+ * the BatchNorm layer's output.
+ *
+ * [1] S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network
+ * Training by Reducing Internal Covariate Shift." arXiv preprint
+ * arXiv:1502.03167 (2015).
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class BatchNormLayer : public Layer<Dtype> {
+ public:
+ explicit BatchNormLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "BatchNorm"; }
+ virtual inline int ExactNumBottomBlobs() const { return 1; }
+ virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ Blob<Dtype> mean_, variance_, temp_, x_norm_;
+ bool use_global_stats_;
+ Dtype moving_average_fraction_;
+ int channels_;
+ Dtype eps_;
+
+ // extra temporarary variables is used to carry out sums/broadcasting
+ // using BLAS
+ Blob<Dtype> batch_sum_multiplier_;
+ Blob<Dtype> num_by_chans_;
+ Blob<Dtype> spatial_sum_multiplier_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_BATCHNORM_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_BATCHREINDEX_LAYER_HPP_
+#define CAFFE_BATCHREINDEX_LAYER_HPP_
+
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Index into the input blob along its first axis.
+ *
+ * This layer can be used to select, reorder, and even replicate examples in a
+ * batch. The second blob is cast to int and treated as an index into the
+ * first axis of the first blob.
+ */
+template <typename Dtype>
+class BatchReindexLayer : public Layer<Dtype> {
+ public:
+ explicit BatchReindexLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "BatchReindex"; }
+ virtual inline int ExactNumBottomBlobs() const { return 2; }
+ virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+ /**
+ * @param bottom input Blob vector (length 2+)
+ * -# @f$ (N \times ...) @f$
+ * the inputs @f$ x_1 @f$
+ * -# @f$ (M) @f$
+ * the inputs @f$ x_2 @f$
+ * @param top output Blob vector (length 1)
+ * -# @f$ (M \times ...) @f$:
+ * the reindexed array @f$
+ * y = x_1[x_2]
+ * @f$
+ */
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ /**
+ * @brief Computes the error gradient w.r.t. the reordered input.
+ *
+ * @param top output Blob vector (length 1), providing the error gradient
+ * with respect to the outputs
+ * -# @f$ (M \times ...) @f$:
+ * containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+ * with respect to concatenated outputs @f$ y @f$
+ * @param propagate_down see Layer::Backward.
+ * @param bottom input Blob vector (length 2):
+ * - @f$ \frac{\partial E}{\partial y} @f$ is de-indexed (summing where
+ * required) back to the input x_1
+ * - This layer cannot backprop to x_2, i.e. propagate_down[1] must be
+ * false.
+ */
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ private:
+ struct pair_sort_first {
+ bool operator()(const std::pair<int, int> &left,
+ const std::pair<int, int> &right) {
+ return left.first < right.first;
+ }
+ };
+ void check_batch_reindex(int initial_num, int final_num,
+ const Dtype* ridx_data);
+};
+
+} // namespace caffe
+
+#endif // CAFFE_BATCHREINDEX_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_BNLL_LAYER_HPP_
+#define CAFFE_BNLL_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes @f$ y = x + \log(1 + \exp(-x)) @f$ if @f$ x > 0 @f$;
+ * @f$ y = \log(1 + \exp(x)) @f$ otherwise.
+ *
+ * @param bottom input Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the computed outputs @f$
+ * y = \left\{
+ * \begin{array}{ll}
+ * x + \log(1 + \exp(-x)) & \mbox{if } x > 0 \\
+ * \log(1 + \exp(x)) & \mbox{otherwise}
+ * \end{array} \right.
+ * @f$
+ */
+template <typename Dtype>
+class BNLLLayer : public NeuronLayer<Dtype> {
+ public:
+ explicit BNLLLayer(const LayerParameter& param)
+ : NeuronLayer<Dtype>(param) {}
+
+ virtual inline const char* type() const { return "BNLL"; }
+
+ protected:
+ /// @copydoc BNLLLayer
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ /**
+ * @brief Computes the error gradient w.r.t. the BNLL inputs.
+ *
+ * @param top output Blob vector (length 1), providing the error gradient with
+ * respect to the outputs
+ * -# @f$ (N \times C \times H \times W) @f$
+ * containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+ * with respect to computed outputs @f$ y @f$
+ * @param propagate_down see Layer::Backward.
+ * @param bottom input Blob vector (length 2)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x @f$; Backward fills their diff with
+ * gradients @f$
+ * \frac{\partial E}{\partial x}
+ * @f$ if propagate_down[0]
+ */
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+} // namespace caffe
+
+#endif // CAFFE_BNLL_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_CONCAT_LAYER_HPP_
+#define CAFFE_CONCAT_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Takes at least two Blob%s and concatenates them along either the num
+ * or channel dimension, outputting the result.
+ */
+template <typename Dtype>
+class ConcatLayer : public Layer<Dtype> {
+ public:
+ explicit ConcatLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "Concat"; }
+ virtual inline int MinBottomBlobs() const { return 1; }
+ virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+ /**
+ * @param bottom input Blob vector (length 2+)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x_1 @f$
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x_2 @f$
+ * -# ...
+ * - K @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x_K @f$
+ * @param top output Blob vector (length 1)
+ * -# @f$ (KN \times C \times H \times W) @f$ if axis == 0, or
+ * @f$ (N \times KC \times H \times W) @f$ if axis == 1:
+ * the concatenated output @f$
+ * y = [\begin{array}{cccc} x_1 & x_2 & ... & x_K \end{array}]
+ * @f$
+ */
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ /**
+ * @brief Computes the error gradient w.r.t. the concatenate inputs.
+ *
+ * @param top output Blob vector (length 1), providing the error gradient with
+ * respect to the outputs
+ * -# @f$ (KN \times C \times H \times W) @f$ if axis == 0, or
+ * @f$ (N \times KC \times H \times W) @f$ if axis == 1:
+ * containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+ * with respect to concatenated outputs @f$ y @f$
+ * @param propagate_down see Layer::Backward.
+ * @param bottom input Blob vector (length K), into which the top gradient
+ * @f$ \frac{\partial E}{\partial y} @f$ is deconcatenated back to the
+ * inputs @f$
+ * \left[ \begin{array}{cccc}
+ * \frac{\partial E}{\partial x_1} &
+ * \frac{\partial E}{\partial x_2} &
+ * ... &
+ * \frac{\partial E}{\partial x_K}
+ * \end{array} \right] =
+ * \frac{\partial E}{\partial y}
+ * @f$
+ */
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ int count_;
+ int num_concats_;
+ int concat_input_size_;
+ int concat_axis_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_CONCAT_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_CONTRASTIVE_LOSS_LAYER_HPP_
+#define CAFFE_CONTRASTIVE_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes the contrastive loss @f$
+ * E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 +
+ * \left(1-y\right) \max \left(margin-d, 0\right)^2
+ * @f$ where @f$
+ * d = \left| \left| a_n - b_n \right| \right|_2 @f$. This can be
+ * used to train siamese networks.
+ *
+ * @param bottom input Blob vector (length 3)
+ * -# @f$ (N \times C \times 1 \times 1) @f$
+ * the features @f$ a \in [-\infty, +\infty]@f$
+ * -# @f$ (N \times C \times 1 \times 1) @f$
+ * the features @f$ b \in [-\infty, +\infty]@f$
+ * -# @f$ (N \times 1 \times 1 \times 1) @f$
+ * the binary similarity @f$ s \in [0, 1]@f$
+ * @param top output Blob vector (length 1)
+ * -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ * the computed contrastive loss: @f$ E =
+ * \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 +
+ * \left(1-y\right) \max \left(margin-d, 0\right)^2
+ * @f$ where @f$
+ * d = \left| \left| a_n - b_n \right| \right|_2 @f$.
+ * This can be used to train siamese networks.
+ */
+template <typename Dtype>
+class ContrastiveLossLayer : public LossLayer<Dtype> {
+ public:
+ explicit ContrastiveLossLayer(const LayerParameter& param)
+ : LossLayer<Dtype>(param), diff_() {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline int ExactNumBottomBlobs() const { return 3; }
+ virtual inline const char* type() const { return "ContrastiveLoss"; }
+ /**
+ * Unlike most loss layers, in the ContrastiveLossLayer we can backpropagate
+ * to the first two inputs.
+ */
+ virtual inline bool AllowForceBackward(const int bottom_index) const {
+ return bottom_index != 2;
+ }
+
+ protected:
+ /// @copydoc ContrastiveLossLayer
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ /**
+ * @brief Computes the Contrastive error gradient w.r.t. the inputs.
+ *
+ * Computes the gradients with respect to the two input vectors (bottom[0] and
+ * bottom[1]), but not the similarity label (bottom[2]).
+ *
+ * @param top output Blob vector (length 1), providing the error gradient with
+ * respect to the outputs
+ * -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+ * as @f$ \lambda @f$ is the coefficient of this layer's output
+ * @f$\ell_i@f$ in the overall Net loss
+ * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+ * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+ * (*Assuming that this top Blob is not used as a bottom (input) by any
+ * other layer of the Net.)
+ * @param propagate_down see Layer::Backward.
+ * @param bottom input Blob vector (length 2)
+ * -# @f$ (N \times C \times 1 \times 1) @f$
+ * the features @f$a@f$; Backward fills their diff with
+ * gradients if propagate_down[0]
+ * -# @f$ (N \times C \times 1 \times 1) @f$
+ * the features @f$b@f$; Backward fills their diff with gradients if
+ * propagate_down[1]
+ */
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ Blob<Dtype> diff_; // cached for backward pass
+ Blob<Dtype> dist_sq_; // cached for backward pass
+ Blob<Dtype> diff_sq_; // tmp storage for gpu forward pass
+ Blob<Dtype> summer_vec_; // tmp storage for gpu forward pass
+};
+
+} // namespace caffe
+
+#endif // CAFFE_CONTRASTIVE_LOSS_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_CONV_LAYER_HPP_
+#define CAFFE_CONV_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/base_conv_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Convolves the input image with a bank of learned filters,
+ * and (optionally) adds biases.
+ *
+ * Caffe convolves by reduction to matrix multiplication. This achieves
+ * high-throughput and generality of input and filter dimensions but comes at
+ * the cost of memory for matrices. This makes use of efficiency in BLAS.
+ *
+ * The input is "im2col" transformed to a channel K' x H x W data matrix
+ * for multiplication with the N x K' x H x W filter matrix to yield a
+ * N' x H x W output matrix that is then "col2im" restored. K' is the
+ * input channel * kernel height * kernel width dimension of the unrolled
+ * inputs so that the im2col matrix has a column for each input region to
+ * be filtered. col2im restores the output spatial structure by rolling up
+ * the output channel N' columns of the output matrix.
+ */
+template <typename Dtype>
+class ConvolutionLayer : public BaseConvolutionLayer<Dtype> {
+ public:
+ /**
+ * @param param provides ConvolutionParameter convolution_param,
+ * with ConvolutionLayer options:
+ * - num_output. The number of filters.
+ * - kernel_size / kernel_h / kernel_w. The filter dimensions, given by
+ * kernel_size for square filters or kernel_h and kernel_w for rectangular
+ * filters.
+ * - stride / stride_h / stride_w (\b optional, default 1). The filter
+ * stride, given by stride_size for equal dimensions or stride_h and stride_w
+ * for different strides. By default the convolution is dense with stride 1.
+ * - pad / pad_h / pad_w (\b optional, default 0). The zero-padding for
+ * convolution, given by pad for equal dimensions or pad_h and pad_w for
+ * different padding. Input padding is computed implicitly instead of
+ * actually padding.
+ * - dilation (\b optional, default 1). The filter
+ * dilation, given by dilation_size for equal dimensions for different
+ * dilation. By default the convolution has dilation 1.
+ * - group (\b optional, default 1). The number of filter groups. Group
+ * convolution is a method for reducing parameterization by selectively
+ * connecting input and output channels. The input and output channel dimensions must be divisible
+ * by the number of groups. For group @f$ \geq 1 @f$, the
+ * convolutional filters' input and output channels are separated s.t. each
+ * group takes 1 / group of the input channels and makes 1 / group of the
+ * output channels. Concretely 4 input channels, 8 output channels, and
+ * 2 groups separate input channels 1-2 and output channels 1-4 into the
+ * first group and input channels 3-4 and output channels 5-8 into the second
+ * group.
+ * - bias_term (\b optional, default true). Whether to have a bias.
+ * - engine: convolution has CAFFE (matrix multiplication) and CUDNN (library
+ * kernels + stream parallelism) engines.
+ */
+ explicit ConvolutionLayer(const LayerParameter& param)
+ : BaseConvolutionLayer<Dtype>(param) {}
+
+ virtual inline const char* type() const { return "Convolution"; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual inline bool reverse_dimensions() { return false; }
+ virtual void compute_output_shape();
+};
+
+} // namespace caffe
+
+#endif // CAFFE_CONV_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_CUDNN_CONV_LAYER_HPP_
+#define CAFFE_CUDNN_CONV_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/conv_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+/*
+ * @brief cuDNN implementation of ConvolutionLayer.
+ * Fallback to ConvolutionLayer for CPU mode.
+ *
+ * cuDNN accelerates convolution through forward kernels for filtering and bias
+ * plus backward kernels for the gradient w.r.t. the filters, biases, and
+ * inputs. Caffe + cuDNN further speeds up the computation through forward
+ * parallelism across groups and backward parallelism across gradients.
+ *
+ * The CUDNN engine does not have memory overhead for matrix buffers. For many
+ * input and filter regimes the CUDNN engine is faster than the CAFFE engine,
+ * but for fully-convolutional models and large inputs the CAFFE engine can be
+ * faster as long as it fits in memory.
+*/
+template <typename Dtype>
+class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
+ public:
+ explicit CuDNNConvolutionLayer(const LayerParameter& param)
+ : ConvolutionLayer<Dtype>(param), handles_setup_(false) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual ~CuDNNConvolutionLayer();
+
+ protected:
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ bool handles_setup_;
+ cudnnHandle_t* handle_;
+ cudaStream_t* stream_;
+
+ // algorithms for forward and backwards convolutions
+ cudnnConvolutionFwdAlgo_t *fwd_algo_;
+ cudnnConvolutionBwdFilterAlgo_t *bwd_filter_algo_;
+ cudnnConvolutionBwdDataAlgo_t *bwd_data_algo_;
+
+ vector<cudnnTensorDescriptor_t> bottom_descs_, top_descs_;
+ cudnnTensorDescriptor_t bias_desc_;
+ cudnnFilterDescriptor_t filter_desc_;
+ vector<cudnnConvolutionDescriptor_t> conv_descs_;
+ int bottom_offset_, top_offset_, bias_offset_;
+
+ size_t *workspace_fwd_sizes_;
+ size_t *workspace_bwd_data_sizes_;
+ size_t *workspace_bwd_filter_sizes_;
+ size_t workspaceSizeInBytes; // size of underlying storage
+ void *workspaceData; // underlying storage
+ void **workspace; // aliases into workspaceData
+};
+#endif
+
+} // namespace caffe
+
+#endif // CAFFE_CUDNN_CONV_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_CUDNN_LCN_LAYER_HPP_
+#define CAFFE_CUDNN_LCN_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/lrn_layer.hpp"
+#include "caffe/layers/power_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+template <typename Dtype>
+class CuDNNLCNLayer : public LRNLayer<Dtype> {
+ public:
+ explicit CuDNNLCNLayer(const LayerParameter& param)
+ : LRNLayer<Dtype>(param), handles_setup_(false), tempDataSize(0),
+ tempData1(NULL), tempData2(NULL) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual ~CuDNNLCNLayer();
+
+ protected:
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ bool handles_setup_;
+ cudnnHandle_t handle_;
+ cudnnLRNDescriptor_t norm_desc_;
+ cudnnTensorDescriptor_t bottom_desc_, top_desc_;
+
+ int size_, pre_pad_;
+ Dtype alpha_, beta_, k_;
+
+ size_t tempDataSize;
+ void *tempData1, *tempData2;
+};
+#endif
+
+} // namespace caffe
+
+#endif // CAFFE_CUDNN_LCN_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_CUDNN_LRN_LAYER_HPP_
+#define CAFFE_CUDNN_LRN_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/lrn_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+template <typename Dtype>
+class CuDNNLRNLayer : public LRNLayer<Dtype> {
+ public:
+ explicit CuDNNLRNLayer(const LayerParameter& param)
+ : LRNLayer<Dtype>(param), handles_setup_(false) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual ~CuDNNLRNLayer();
+
+ protected:
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ bool handles_setup_;
+ cudnnHandle_t handle_;
+ cudnnLRNDescriptor_t norm_desc_;
+ cudnnTensorDescriptor_t bottom_desc_, top_desc_;
+
+ int size_;
+ Dtype alpha_, beta_, k_;
+};
+#endif
+
+} // namespace caffe
+
+#endif // CAFFE_CUDNN_LRN_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_CUDNN_POOLING_LAYER_HPP_
+#define CAFFE_CUDNN_POOLING_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/pooling_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+/*
+ * @brief cuDNN implementation of PoolingLayer.
+ * Fallback to PoolingLayer for CPU mode.
+*/
+template <typename Dtype>
+class CuDNNPoolingLayer : public PoolingLayer<Dtype> {
+ public:
+ explicit CuDNNPoolingLayer(const LayerParameter& param)
+ : PoolingLayer<Dtype>(param), handles_setup_(false) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual ~CuDNNPoolingLayer();
+ // Currently, cuDNN does not support the extra top blob.
+ virtual inline int MinTopBlobs() const { return -1; }
+ virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ bool handles_setup_;
+ cudnnHandle_t handle_;
+ cudnnTensorDescriptor_t bottom_desc_, top_desc_;
+ cudnnPoolingDescriptor_t pooling_desc_;
+ cudnnPoolingMode_t mode_;
+};
+#endif
+
+} // namespace caffe
+
+#endif // CAFFE_CUDNN_POOLING_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_CUDNN_RELU_LAYER_HPP_
+#define CAFFE_CUDNN_RELU_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+#include "caffe/layers/relu_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+/**
+ * @brief CuDNN acceleration of ReLULayer.
+ */
+template <typename Dtype>
+class CuDNNReLULayer : public ReLULayer<Dtype> {
+ public:
+ explicit CuDNNReLULayer(const LayerParameter& param)
+ : ReLULayer<Dtype>(param), handles_setup_(false) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual ~CuDNNReLULayer();
+
+ protected:
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ bool handles_setup_;
+ cudnnHandle_t handle_;
+ cudnnTensorDescriptor_t bottom_desc_;
+ cudnnTensorDescriptor_t top_desc_;
+};
+#endif
+
+} // namespace caffe
+
+#endif // CAFFE_CUDNN_RELU_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_CUDNN_SIGMOID_LAYER_HPP_
+#define CAFFE_CUDNN_SIGMOID_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+#include "caffe/layers/sigmoid_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+/**
+ * @brief CuDNN acceleration of SigmoidLayer.
+ */
+template <typename Dtype>
+class CuDNNSigmoidLayer : public SigmoidLayer<Dtype> {
+ public:
+ explicit CuDNNSigmoidLayer(const LayerParameter& param)
+ : SigmoidLayer<Dtype>(param), handles_setup_(false) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual ~CuDNNSigmoidLayer();
+
+ protected:
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ bool handles_setup_;
+ cudnnHandle_t handle_;
+ cudnnTensorDescriptor_t bottom_desc_;
+ cudnnTensorDescriptor_t top_desc_;
+};
+#endif
+
+} // namespace caffe
+
+#endif // CAFFE_CUDNN_SIGMOID_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_CUDNN_SOFTMAX_LAYER_HPP_
+#define CAFFE_CUDNN_SOFTMAX_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/softmax_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+/**
+ * @brief cuDNN implementation of SoftmaxLayer.
+ * Fallback to SoftmaxLayer for CPU mode.
+ */
+template <typename Dtype>
+class CuDNNSoftmaxLayer : public SoftmaxLayer<Dtype> {
+ public:
+ explicit CuDNNSoftmaxLayer(const LayerParameter& param)
+ : SoftmaxLayer<Dtype>(param), handles_setup_(false) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual ~CuDNNSoftmaxLayer();
+
+ protected:
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ bool handles_setup_;
+ cudnnHandle_t handle_;
+ cudnnTensorDescriptor_t bottom_desc_;
+ cudnnTensorDescriptor_t top_desc_;
+};
+#endif
+
+} // namespace caffe
+
+#endif // CAFFE_CUDNN_SOFTMAX_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_CUDNN_TANH_LAYER_HPP_
+#define CAFFE_CUDNN_TANH_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+#include "caffe/layers/tanh_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+/**
+ * @brief CuDNN acceleration of TanHLayer.
+ */
+template <typename Dtype>
+class CuDNNTanHLayer : public TanHLayer<Dtype> {
+ public:
+ explicit CuDNNTanHLayer(const LayerParameter& param)
+ : TanHLayer<Dtype>(param), handles_setup_(false) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual ~CuDNNTanHLayer();
+
+ protected:
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ bool handles_setup_;
+ cudnnHandle_t handle_;
+ cudnnTensorDescriptor_t bottom_desc_;
+ cudnnTensorDescriptor_t top_desc_;
+};
+#endif
+
+} // namespace caffe
+
+#endif // CAFFE_CUDNN_TANH_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_DATA_LAYER_HPP_
+#define CAFFE_DATA_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/data_reader.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/base_data_layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/db.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+class DataLayer : public BasePrefetchingDataLayer<Dtype> {
+ public:
+ explicit DataLayer(const LayerParameter& param);
+ virtual ~DataLayer();
+ virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ // DataLayer uses DataReader instead for sharing for parallelism
+ virtual inline bool ShareInParallel() const { return false; }
+ virtual inline const char* type() const { return "Data"; }
+ virtual inline int ExactNumBottomBlobs() const { return 0; }
+ virtual inline int MinTopBlobs() const { return 1; }
+ virtual inline int MaxTopBlobs() const { return 2; }
+
+ protected:
+ virtual void load_batch(Batch<Dtype>* batch);
+
+ DataReader reader_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_DATA_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_DECONV_LAYER_HPP_
+#define CAFFE_DECONV_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/base_conv_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Convolve the input with a bank of learned filters, and (optionally)
+ * add biases, treating filters and convolution parameters in the
+ * opposite sense as ConvolutionLayer.
+ *
+ * ConvolutionLayer computes each output value by dotting an input window with
+ * a filter; DeconvolutionLayer multiplies each input value by a filter
+ * elementwise, and sums over the resulting output windows. In other words,
+ * DeconvolutionLayer is ConvolutionLayer with the forward and backward passes
+ * reversed. DeconvolutionLayer reuses ConvolutionParameter for its
+ * parameters, but they take the opposite sense as in ConvolutionLayer (so
+ * padding is removed from the output rather than added to the input, and
+ * stride results in upsampling rather than downsampling).
+ */
+template <typename Dtype>
+class DeconvolutionLayer : public BaseConvolutionLayer<Dtype> {
+ public:
+ explicit DeconvolutionLayer(const LayerParameter& param)
+ : BaseConvolutionLayer<Dtype>(param) {}
+
+ virtual inline const char* type() const { return "Deconvolution"; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual inline bool reverse_dimensions() { return true; }
+ virtual void compute_output_shape();
+};
+
+} // namespace caffe
+
+#endif // CAFFE_DECONV_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_DROPOUT_LAYER_HPP_
+#define CAFFE_DROPOUT_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief During training only, sets a random portion of @f$x@f$ to 0, adjusting
+ * the rest of the vector magnitude accordingly.
+ *
+ * @param bottom input Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the computed outputs @f$ y = |x| @f$
+ */
+template <typename Dtype>
+class DropoutLayer : public NeuronLayer<Dtype> {
+ public:
+ /**
+ * @param param provides DropoutParameter dropout_param,
+ * with DropoutLayer options:
+ * - dropout_ratio (\b optional, default 0.5).
+ * Sets the probability @f$ p @f$ that any given unit is dropped.
+ */
+ explicit DropoutLayer(const LayerParameter& param)
+ : NeuronLayer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "Dropout"; }
+
+ protected:
+ /**
+ * @param bottom input Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the computed outputs. At training time, we have @f$
+ * y_{\mbox{train}} = \left\{
+ * \begin{array}{ll}
+ * \frac{x}{1 - p} & \mbox{if } u > p \\
+ * 0 & \mbox{otherwise}
+ * \end{array} \right.
+ * @f$, where @f$ u \sim U(0, 1)@f$ is generated independently for each
+ * input at each iteration. At test time, we simply have
+ * @f$ y_{\mbox{test}} = \mathbb{E}[y_{\mbox{train}}] = x @f$.
+ */
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ /// when divided by UINT_MAX, the randomly generated values @f$u\sim U(0,1)@f$
+ Blob<unsigned int> rand_vec_;
+ /// the probability @f$ p @f$ of dropping any input
+ Dtype threshold_;
+ /// the scale for undropped inputs at train time @f$ 1 / (1 - p) @f$
+ Dtype scale_;
+ unsigned int uint_thres_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_DROPOUT_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_DUMMY_DATA_LAYER_HPP_
+#define CAFFE_DUMMY_DATA_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Provides data to the Net generated by a Filler.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class DummyDataLayer : public Layer<Dtype> {
+ public:
+ explicit DummyDataLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ // Data layers should be shared by multiple solvers in parallel
+ virtual inline bool ShareInParallel() const { return true; }
+ // Data layers have no bottoms, so reshaping is trivial.
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {}
+
+ virtual inline const char* type() const { return "DummyData"; }
+ virtual inline int ExactNumBottomBlobs() const { return 0; }
+ virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+
+ vector<shared_ptr<Filler<Dtype> > > fillers_;
+ vector<bool> refill_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_DUMMY_DATA_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_ELTWISE_LAYER_HPP_
+#define CAFFE_ELTWISE_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Compute elementwise operations, such as product and sum,
+ * along multiple input Blobs.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class EltwiseLayer : public Layer<Dtype> {
+ public:
+ explicit EltwiseLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "Eltwise"; }
+ virtual inline int MinBottomBlobs() const { return 2; }
+ virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ EltwiseParameter_EltwiseOp op_;
+ vector<Dtype> coeffs_;
+ Blob<int> max_idx_;
+
+ bool stable_prod_grad_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_ELTWISE_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_ELU_LAYER_HPP_
+#define CAFFE_ELU_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Exponential Linear Unit non-linearity @f$
+ * y = \left\{
+ * \begin{array}{lr}
+ * x & \mathrm{if} \; x > 0 \\
+ * \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0
+ * \end{array} \right.
+ * @f$.
+ */
+template <typename Dtype>
+class ELULayer : public NeuronLayer<Dtype> {
+ public:
+ /**
+ * @param param provides ELUParameter elu_param,
+ * with ELULayer options:
+ * - alpha (\b optional, default 1).
+ * the value @f$ \alpha @f$ by which controls saturation for negative inputs.
+ */
+ explicit ELULayer(const LayerParameter& param)
+ : NeuronLayer<Dtype>(param) {}
+
+ virtual inline const char* type() const { return "ELU"; }
+
+ protected:
+ /**
+ * @param bottom input Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the computed outputs @f$
+ * y = \left\{
+ * \begin{array}{lr}
+ * x & \mathrm{if} \; x > 0 \\
+ * \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0
+ * \end{array} \right.
+ * @f$.
+ */
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ /**
+ * @brief Computes the error gradient w.r.t. the ELU inputs.
+ *
+ * @param top output Blob vector (length 1), providing the error gradient with
+ * respect to the outputs
+ * -# @f$ (N \times C \times H \times W) @f$
+ * containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+ * with respect to computed outputs @f$ y @f$
+ * @param propagate_down see Layer::Backward.
+ * @param bottom input Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x @f$; Backward fills their diff with
+ * gradients @f$
+ * \frac{\partial E}{\partial x} = \left\{
+ * \begin{array}{lr}
+ * 1 & \mathrm{if} \; x > 0 \\
+ * y + \alpha & \mathrm{if} \; x \le 0
+ * \end{array} \right.
+ * @f$ if propagate_down[0].
+ */
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+
+} // namespace caffe
+
+#endif // CAFFE_ELU_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_EMBED_LAYER_HPP_
+#define CAFFE_EMBED_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief A layer for learning "embeddings" of one-hot vector input.
+ * Equivalent to an InnerProductLayer with one-hot vectors as input, but
+ * for efficiency the input is the "hot" index of each column itself.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class EmbedLayer : public Layer<Dtype> {
+ public:
+ explicit EmbedLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "Embed"; }
+ virtual inline int ExactNumBottomBlobs() const { return 1; }
+ virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ int M_;
+ int K_;
+ int N_;
+ bool bias_term_;
+ Blob<Dtype> bias_multiplier_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_EMBED_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_EUCLIDEAN_LOSS_LAYER_HPP_
+#define CAFFE_EUCLIDEAN_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes the Euclidean (L2) loss @f$
+ * E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n
+ * \right| \right|_2^2 @f$ for real-valued regression tasks.
+ *
+ * @param bottom input Blob vector (length 2)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the predictions @f$ \hat{y} \in [-\infty, +\infty]@f$
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the targets @f$ y \in [-\infty, +\infty]@f$
+ * @param top output Blob vector (length 1)
+ * -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ * the computed Euclidean loss: @f$ E =
+ * \frac{1}{2n} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n
+ * \right| \right|_2^2 @f$
+ *
+ * This can be used for least-squares regression tasks. An InnerProductLayer
+ * input to a EuclideanLossLayer exactly formulates a linear least squares
+ * regression problem. With non-zero weight decay the problem becomes one of
+ * ridge regression -- see src/caffe/test/test_sgd_solver.cpp for a concrete
+ * example wherein we check that the gradients computed for a Net with exactly
+ * this structure match hand-computed gradient formulas for ridge regression.
+ *
+ * (Note: Caffe, and SGD in general, is certainly \b not the best way to solve
+ * linear least squares problems! We use it only as an instructive example.)
+ */
+template <typename Dtype>
+class EuclideanLossLayer : public LossLayer<Dtype> {
+ public:
+ explicit EuclideanLossLayer(const LayerParameter& param)
+ : LossLayer<Dtype>(param), diff_() {}
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "EuclideanLoss"; }
+ /**
+ * Unlike most loss layers, in the EuclideanLossLayer we can backpropagate
+ * to both inputs -- override to return true and always allow force_backward.
+ */
+ virtual inline bool AllowForceBackward(const int bottom_index) const {
+ return true;
+ }
+
+ protected:
+ /// @copydoc EuclideanLossLayer
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ /**
+ * @brief Computes the Euclidean error gradient w.r.t. the inputs.
+ *
+ * Unlike other children of LossLayer, EuclideanLossLayer \b can compute
+ * gradients with respect to the label inputs bottom[1] (but still only will
+ * if propagate_down[1] is set, due to being produced by learnable parameters
+ * or if force_backward is set). In fact, this layer is "commutative" -- the
+ * result is the same regardless of the order of the two bottoms.
+ *
+ * @param top output Blob vector (length 1), providing the error gradient with
+ * respect to the outputs
+ * -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+ * as @f$ \lambda @f$ is the coefficient of this layer's output
+ * @f$\ell_i@f$ in the overall Net loss
+ * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+ * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+ * (*Assuming that this top Blob is not used as a bottom (input) by any
+ * other layer of the Net.)
+ * @param propagate_down see Layer::Backward.
+ * @param bottom input Blob vector (length 2)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the predictions @f$\hat{y}@f$; Backward fills their diff with
+ * gradients @f$
+ * \frac{\partial E}{\partial \hat{y}} =
+ * \frac{1}{n} \sum\limits_{n=1}^N (\hat{y}_n - y_n)
+ * @f$ if propagate_down[0]
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the targets @f$y@f$; Backward fills their diff with gradients
+ * @f$ \frac{\partial E}{\partial y} =
+ * \frac{1}{n} \sum\limits_{n=1}^N (y_n - \hat{y}_n)
+ * @f$ if propagate_down[1]
+ */
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ Blob<Dtype> diff_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_EUCLIDEAN_LOSS_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_EXP_LAYER_HPP_
+#define CAFFE_EXP_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes @f$ y = \gamma ^ {\alpha x + \beta} @f$,
+ * as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
+ * and base @f$ \gamma @f$.
+ */
+template <typename Dtype>
+class ExpLayer : public NeuronLayer<Dtype> {
+ public:
+ /**
+ * @param param provides ExpParameter exp_param,
+ * with ExpLayer options:
+ * - scale (\b optional, default 1) the scale @f$ \alpha @f$
+ * - shift (\b optional, default 0) the shift @f$ \beta @f$
+ * - base (\b optional, default -1 for a value of @f$ e \approx 2.718 @f$)
+ * the base @f$ \gamma @f$
+ */
+ explicit ExpLayer(const LayerParameter& param)
+ : NeuronLayer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "Exp"; }
+
+ protected:
+ /**
+ * @param bottom input Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the computed outputs @f$
+ * y = \gamma ^ {\alpha x + \beta}
+ * @f$
+ */
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ /**
+ * @brief Computes the error gradient w.r.t. the exp inputs.
+ *
+ * @param top output Blob vector (length 1), providing the error gradient with
+ * respect to the outputs
+ * -# @f$ (N \times C \times H \times W) @f$
+ * containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+ * with respect to computed outputs @f$ y @f$
+ * @param propagate_down see Layer::Backward.
+ * @param bottom input Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x @f$; Backward fills their diff with
+ * gradients @f$
+ * \frac{\partial E}{\partial x} =
+ * \frac{\partial E}{\partial y} y \alpha \log_e(gamma)
+ * @f$ if propagate_down[0]
+ */
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ Dtype inner_scale_, outer_scale_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_EXP_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_FILTER_LAYER_HPP_
+#define CAFFE_FILTER_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Takes two+ Blobs, interprets last Blob as a selector and
+ * filter remaining Blobs accordingly with selector data (0 means that
+ * the corresponding item has to be filtered, non-zero means that corresponding
+ * item needs to stay).
+ */
+template <typename Dtype>
+class FilterLayer : public Layer<Dtype> {
+ public:
+ explicit FilterLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "Filter"; }
+ virtual inline int MinBottomBlobs() const { return 2; }
+ virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+ /**
+ * @param bottom input Blob vector (length 2+)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs to be filtered @f$ x_1 @f$
+ * -# ...
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs to be filtered @f$ x_K @f$
+ * -# @f$ (N \times 1 \times 1 \times 1) @f$
+ * the selector blob
+ * @param top output Blob vector (length 1+)
+ * -# @f$ (S \times C \times H \times W) @f$ ()
+ * the filtered output @f$ x_1 @f$
+ * where S is the number of items
+ * that haven't been filtered
+ * @f$ (S \times C \times H \times W) @f$
+ * the filtered output @f$ x_K @f$
+ * where S is the number of items
+ * that haven't been filtered
+ */
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ /**
+ * @brief Computes the error gradient w.r.t. the forwarded inputs.
+ *
+ * @param top output Blob vector (length 1+), providing the error gradient with
+ * respect to the outputs
+ * @param propagate_down see Layer::Backward.
+ * @param bottom input Blob vector (length 2+), into which the top error
+ * gradient is copied
+ */
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ bool first_reshape_;
+ vector<int> indices_to_forward_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_FILTER_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_FLATTEN_LAYER_HPP_
+#define CAFFE_FLATTEN_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Reshapes the input Blob into flat vectors.
+ *
+ * Note: because this layer does not change the input values -- merely the
+ * dimensions -- it can simply copy the input. The copy happens "virtually"
+ * (thus taking effectively 0 real time) by setting, in Forward, the data
+ * pointer of the top Blob to that of the bottom Blob (see Blob::ShareData),
+ * and in Backward, the diff pointer of the bottom Blob to that of the top Blob
+ * (see Blob::ShareDiff).
+ */
+template <typename Dtype>
+class FlattenLayer : public Layer<Dtype> {
+ public:
+ explicit FlattenLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "Flatten"; }
+ virtual inline int ExactNumBottomBlobs() const { return 1; }
+ virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+ /**
+ * @param bottom input Blob vector (length 2+)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs
+ * @param top output Blob vector (length 1)
+ * -# @f$ (N \times CHW \times 1 \times 1) @f$
+ * the outputs -- i.e., the (virtually) copied, flattened inputs
+ */
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ /**
+ * @brief Computes the error gradient w.r.t. the concatenate inputs.
+ *
+ * @param top output Blob vector (length 1), providing the error gradient with
+ * respect to the outputs
+ * @param propagate_down see Layer::Backward.
+ * @param bottom input Blob vector (length K), into which the top error
+ * gradient is (virtually) copied
+ */
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+} // namespace caffe
+
+#endif // CAFFE_FLATTEN_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_HDF5_DATA_LAYER_HPP_
+#define CAFFE_HDF5_DATA_LAYER_HPP_
+
+#include "hdf5.h"
+
+#include <string>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/base_data_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Provides data to the Net from HDF5 files.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class HDF5DataLayer : public Layer<Dtype> {
+ public:
+ explicit HDF5DataLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual ~HDF5DataLayer();
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ // Data layers should be shared by multiple solvers in parallel
+ virtual inline bool ShareInParallel() const { return true; }
+ // Data layers have no bottoms, so reshaping is trivial.
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {}
+
+ virtual inline const char* type() const { return "HDF5Data"; }
+ virtual inline int ExactNumBottomBlobs() const { return 0; }
+ virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+ virtual void LoadHDF5FileData(const char* filename);
+
+ std::vector<std::string> hdf_filenames_;
+ unsigned int num_files_;
+ unsigned int current_file_;
+ hsize_t current_row_;
+ std::vector<shared_ptr<Blob<Dtype> > > hdf_blobs_;
+ std::vector<unsigned int> data_permutation_;
+ std::vector<unsigned int> file_permutation_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_HDF5_DATA_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_HDF5_OUTPUT_LAYER_HPP_
+#define CAFFE_HDF5_OUTPUT_LAYER_HPP_
+
+#include "hdf5.h"
+
+#include <string>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+#define HDF5_DATA_DATASET_NAME "data"
+#define HDF5_DATA_LABEL_NAME "label"
+
+/**
+ * @brief Write blobs to disk as HDF5 files.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class HDF5OutputLayer : public Layer<Dtype> {
+ public:
+ explicit HDF5OutputLayer(const LayerParameter& param)
+ : Layer<Dtype>(param), file_opened_(false) {}
+ virtual ~HDF5OutputLayer();
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ // Data layers should be shared by multiple solvers in parallel
+ virtual inline bool ShareInParallel() const { return true; }
+ // Data layers have no bottoms, so reshaping is trivial.
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {}
+
+ virtual inline const char* type() const { return "HDF5Output"; }
+ // TODO: no limit on the number of blobs
+ virtual inline int ExactNumBottomBlobs() const { return 2; }
+ virtual inline int ExactNumTopBlobs() const { return 0; }
+
+ inline std::string file_name() const { return file_name_; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void SaveBlobs();
+
+ bool file_opened_;
+ std::string file_name_;
+ hid_t file_id_;
+ Blob<Dtype> data_blob_;
+ Blob<Dtype> label_blob_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_HDF5_OUTPUT_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_HINGE_LOSS_LAYER_HPP_
+#define CAFFE_HINGE_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes the hinge loss for a one-of-many classification task.
+ *
+ * @param bottom input Blob vector (length 2)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the predictions @f$ t @f$, a Blob with values in
+ * @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
+ * the @f$ K = CHW @f$ classes. In an SVM, @f$ t @f$ is the result of
+ * taking the inner product @f$ X^T W @f$ of the D-dimensional features
+ * @f$ X \in \mathcal{R}^{D \times N} @f$ and the learned hyperplane
+ * parameters @f$ W \in \mathcal{R}^{D \times K} @f$, so a Net with just
+ * an InnerProductLayer (with num_output = D) providing predictions to a
+ * HingeLossLayer and no other learnable parameters or losses is
+ * equivalent to an SVM.
+ * -# @f$ (N \times 1 \times 1 \times 1) @f$
+ * the labels @f$ l @f$, an integer-valued Blob with values
+ * @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
+ * indicating the correct class label among the @f$ K @f$ classes
+ * @param top output Blob vector (length 1)
+ * -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ * the computed hinge loss: @f$ E =
+ * \frac{1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^K
+ * [\max(0, 1 - \delta\{l_n = k\} t_{nk})] ^ p
+ * @f$, for the @f$ L^p @f$ norm
+ * (defaults to @f$ p = 1 @f$, the L1 norm; L2 norm, as in L2-SVM,
+ * is also available), and @f$
+ * \delta\{\mathrm{condition}\} = \left\{
+ * \begin{array}{lr}
+ * 1 & \mbox{if condition} \\
+ * -1 & \mbox{otherwise}
+ * \end{array} \right.
+ * @f$
+ *
+ * In an SVM, @f$ t \in \mathcal{R}^{N \times K} @f$ is the result of taking
+ * the inner product @f$ X^T W @f$ of the features
+ * @f$ X \in \mathcal{R}^{D \times N} @f$
+ * and the learned hyperplane parameters
+ * @f$ W \in \mathcal{R}^{D \times K} @f$. So, a Net with just an
+ * InnerProductLayer (with num_output = @f$k@f$) providing predictions to a
+ * HingeLossLayer is equivalent to an SVM (assuming it has no other learned
+ * outside the InnerProductLayer and no other losses outside the
+ * HingeLossLayer).
+ */
+template <typename Dtype>
+class HingeLossLayer : public LossLayer<Dtype> {
+ public:
+ explicit HingeLossLayer(const LayerParameter& param)
+ : LossLayer<Dtype>(param) {}
+
+ virtual inline const char* type() const { return "HingeLoss"; }
+
+ protected:
+ /// @copydoc HingeLossLayer
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ /**
+ * @brief Computes the hinge loss error gradient w.r.t. the predictions.
+ *
+ * Gradients cannot be computed with respect to the label inputs (bottom[1]),
+ * so this method ignores bottom[1] and requires !propagate_down[1], crashing
+ * if propagate_down[1] is set.
+ *
+ * @param top output Blob vector (length 1), providing the error gradient with
+ * respect to the outputs
+ * -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+ * as @f$ \lambda @f$ is the coefficient of this layer's output
+ * @f$\ell_i@f$ in the overall Net loss
+ * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+ * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+ * (*Assuming that this top Blob is not used as a bottom (input) by any
+ * other layer of the Net.)
+ * @param propagate_down see Layer::Backward.
+ * propagate_down[1] must be false as we can't compute gradients with
+ * respect to the labels.
+ * @param bottom input Blob vector (length 2)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the predictions @f$t@f$; Backward computes diff
+ * @f$ \frac{\partial E}{\partial t} @f$
+ * -# @f$ (N \times 1 \times 1 \times 1) @f$
+ * the labels -- ignored as we can't compute their error gradients
+ */
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+
+} // namespace caffe
+
+#endif // CAFFE_HINGE_LOSS_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_IM2COL_LAYER_HPP_
+#define CAFFE_IM2COL_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief A helper for image operations that rearranges image regions into
+ * column vectors. Used by ConvolutionLayer to perform convolution
+ * by matrix multiplication.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class Im2colLayer : public Layer<Dtype> {
+ public:
+ explicit Im2colLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "Im2col"; }
+ virtual inline int ExactNumBottomBlobs() const { return 1; }
+ virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ /// @brief The spatial dimensions of a filter kernel.
+ Blob<int> kernel_shape_;
+ /// @brief The spatial dimensions of the stride.
+ Blob<int> stride_;
+ /// @brief The spatial dimensions of the padding.
+ Blob<int> pad_;
+ /// @brief The spatial dimensions of the dilation.
+ Blob<int> dilation_;
+
+ int num_spatial_axes_;
+ int bottom_dim_;
+ int top_dim_;
+
+ int channel_axis_;
+ int num_;
+ int channels_;
+
+ bool force_nd_im2col_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_IM2COL_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_IMAGE_DATA_LAYER_HPP_
+#define CAFFE_IMAGE_DATA_LAYER_HPP_
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/base_data_layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Provides data to the Net from image files.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class ImageDataLayer : public BasePrefetchingDataLayer<Dtype> {
+ public:
+ explicit ImageDataLayer(const LayerParameter& param)
+ : BasePrefetchingDataLayer<Dtype>(param) {}
+ virtual ~ImageDataLayer();
+ virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "ImageData"; }
+ virtual inline int ExactNumBottomBlobs() const { return 0; }
+ virtual inline int ExactNumTopBlobs() const { return 2; }
+
+ protected:
+ shared_ptr<Caffe::RNG> prefetch_rng_;
+ virtual void ShuffleImages();
+ virtual void load_batch(Batch<Dtype>* batch);
+
+ vector<std::pair<std::string, int> > lines_;
+ int lines_id_;
+};
+
+
+} // namespace caffe
+
+#endif // CAFFE_IMAGE_DATA_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_INFOGAIN_LOSS_LAYER_HPP_
+#define CAFFE_INFOGAIN_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief A generalization of MultinomialLogisticLossLayer that takes an
+ * "information gain" (infogain) matrix specifying the "value" of all label
+ * pairs.
+ *
+ * Equivalent to the MultinomialLogisticLossLayer if the infogain matrix is the
+ * identity.
+ *
+ * @param bottom input Blob vector (length 2-3)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the predictions @f$ \hat{p} @f$, a Blob with values in
+ * @f$ [0, 1] @f$ indicating the predicted probability of each of the
+ * @f$ K = CHW @f$ classes. Each prediction vector @f$ \hat{p}_n @f$
+ * should sum to 1 as in a probability distribution: @f$
+ * \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$.
+ * -# @f$ (N \times 1 \times 1 \times 1) @f$
+ * the labels @f$ l @f$, an integer-valued Blob with values
+ * @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
+ * indicating the correct class label among the @f$ K @f$ classes
+ * -# @f$ (1 \times 1 \times K \times K) @f$
+ * (\b optional) the infogain matrix @f$ H @f$. This must be provided as
+ * the third bottom blob input if not provided as the infogain_mat in the
+ * InfogainLossParameter. If @f$ H = I @f$, this layer is equivalent to the
+ * MultinomialLogisticLossLayer.
+ * @param top output Blob vector (length 1)
+ * -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ * the computed infogain multinomial logistic loss: @f$ E =
+ * \frac{-1}{N} \sum\limits_{n=1}^N H_{l_n} \log(\hat{p}_n) =
+ * \frac{-1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^{K} H_{l_n,k}
+ * \log(\hat{p}_{n,k})
+ * @f$, where @f$ H_{l_n} @f$ denotes row @f$l_n@f$ of @f$H@f$.
+ */
+template <typename Dtype>
+class InfogainLossLayer : public LossLayer<Dtype> {
+ public:
+ explicit InfogainLossLayer(const LayerParameter& param)
+ : LossLayer<Dtype>(param), infogain_() {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ // InfogainLossLayer takes 2-3 bottom Blobs; if there are 3 the third should
+ // be the infogain matrix. (Otherwise the infogain matrix is loaded from a
+ // file specified by LayerParameter.)
+ virtual inline int ExactNumBottomBlobs() const { return -1; }
+ virtual inline int MinBottomBlobs() const { return 2; }
+ virtual inline int MaxBottomBlobs() const { return 3; }
+
+ virtual inline const char* type() const { return "InfogainLoss"; }
+
+ protected:
+ /// @copydoc InfogainLossLayer
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ /**
+ * @brief Computes the infogain loss error gradient w.r.t. the predictions.
+ *
+ * Gradients cannot be computed with respect to the label inputs (bottom[1]),
+ * so this method ignores bottom[1] and requires !propagate_down[1], crashing
+ * if propagate_down[1] is set. (The same applies to the infogain matrix, if
+ * provided as bottom[2] rather than in the layer_param.)
+ *
+ * @param top output Blob vector (length 1), providing the error gradient
+ * with respect to the outputs
+ * -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+ * as @f$ \lambda @f$ is the coefficient of this layer's output
+ * @f$\ell_i@f$ in the overall Net loss
+ * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+ * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+ * (*Assuming that this top Blob is not used as a bottom (input) by any
+ * other layer of the Net.)
+ * @param propagate_down see Layer::Backward.
+ * propagate_down[1] must be false as we can't compute gradients with
+ * respect to the labels (similarly for propagate_down[2] and the
+ * infogain matrix, if provided as bottom[2])
+ * @param bottom input Blob vector (length 2-3)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the predictions @f$ \hat{p} @f$; Backward computes diff
+ * @f$ \frac{\partial E}{\partial \hat{p}} @f$
+ * -# @f$ (N \times 1 \times 1 \times 1) @f$
+ * the labels -- ignored as we can't compute their error gradients
+ * -# @f$ (1 \times 1 \times K \times K) @f$
+ * (\b optional) the information gain matrix -- ignored as its error
+ * gradient computation is not implemented.
+ */
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ Blob<Dtype> infogain_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_INFOGAIN_LOSS_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_INNER_PRODUCT_LAYER_HPP_
+#define CAFFE_INNER_PRODUCT_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Also known as a "fully-connected" layer, computes an inner product
+ * with a set of learned weights, and (optionally) adds biases.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class InnerProductLayer : public Layer<Dtype> {
+ public:
+ explicit InnerProductLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "InnerProduct"; }
+ virtual inline int ExactNumBottomBlobs() const { return 1; }
+ virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ int M_;
+ int K_;
+ int N_;
+ bool bias_term_;
+ Blob<Dtype> bias_multiplier_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_INNER_PRODUCT_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_LOG_LAYER_HPP_
+#define CAFFE_LOG_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes @f$ y = log_{\gamma}(\alpha x + \beta) @f$,
+ * as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
+ * and base @f$ \gamma @f$.
+ */
+template <typename Dtype>
+class LogLayer : public NeuronLayer<Dtype> {
+ public:
+ /**
+ * @param param provides LogParameter log_param,
+ * with LogLayer options:
+ * - scale (\b optional, default 1) the scale @f$ \alpha @f$
+ * - shift (\b optional, default 0) the shift @f$ \beta @f$
+ * - base (\b optional, default -1 for a value of @f$ e \approx 2.718 @f$)
+ * the base @f$ \gamma @f$
+ */
+ explicit LogLayer(const LayerParameter& param)
+ : NeuronLayer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "Log"; }
+
+ protected:
+ /**
+ * @param bottom input Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the computed outputs @f$
+ * y = log_{\gamma}(\alpha x + \beta)
+ * @f$
+ */
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ /**
+ * @brief Computes the error gradient w.r.t. the exp inputs.
+ *
+ * @param top output Blob vector (length 1), providing the error gradient with
+ * respect to the outputs
+ * -# @f$ (N \times C \times H \times W) @f$
+ * containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+ * with respect to computed outputs @f$ y @f$
+ * @param propagate_down see Layer::Backward.
+ * @param bottom input Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x @f$; Backward fills their diff with
+ * gradients @f$
+ * \frac{\partial E}{\partial x} =
+ * \frac{\partial E}{\partial y} y \alpha \log_e(gamma)
+ * @f$ if propagate_down[0]
+ */
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ Dtype base_scale_;
+ Dtype input_scale_, input_shift_;
+ Dtype backward_num_scale_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_LOG_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_LOSS_LAYER_HPP_
+#define CAFFE_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+const float kLOG_THRESHOLD = 1e-20;
+
+/**
+ * @brief An interface for Layer%s that take two Blob%s as input -- usually
+ * (1) predictions and (2) ground-truth labels -- and output a
+ * singleton Blob representing the loss.
+ *
+ * LossLayers are typically only capable of backpropagating to their first input
+ * -- the predictions.
+ */
+template <typename Dtype>
+class LossLayer : public Layer<Dtype> {
+ public:
+ explicit LossLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void LayerSetUp(
+ const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(
+ const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
+
+ virtual inline int ExactNumBottomBlobs() const { return 2; }
+
+ /**
+ * @brief For convenience and backwards compatibility, instruct the Net to
+ * automatically allocate a single top Blob for LossLayers, into which
+ * they output their singleton loss, (even if the user didn't specify
+ * one in the prototxt, etc.).
+ */
+ virtual inline bool AutoTopBlobs() const { return true; }
+ virtual inline int ExactNumTopBlobs() const { return 1; }
+ /**
+ * We usually cannot backpropagate to the labels; ignore force_backward for
+ * these inputs.
+ */
+ virtual inline bool AllowForceBackward(const int bottom_index) const {
+ return bottom_index != 1;
+ }
+};
+
+} // namespace caffe
+
+#endif // CAFFE_LOSS_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_LRN_LAYER_HPP_
+#define CAFFE_LRN_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/eltwise_layer.hpp"
+#include "caffe/layers/pooling_layer.hpp"
+#include "caffe/layers/power_layer.hpp"
+#include "caffe/layers/split_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Normalize the input in a local region across or within feature maps.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class LRNLayer : public Layer<Dtype> {
+ public:
+ explicit LRNLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "LRN"; }
+ virtual inline int ExactNumBottomBlobs() const { return 1; }
+ virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ virtual void CrossChannelForward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void CrossChannelForward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void WithinChannelForward(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void CrossChannelBackward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void CrossChannelBackward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void WithinChannelBackward(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ int size_;
+ int pre_pad_;
+ Dtype alpha_;
+ Dtype beta_;
+ Dtype k_;
+ int num_;
+ int channels_;
+ int height_;
+ int width_;
+
+ // Fields used for normalization ACROSS_CHANNELS
+ // scale_ stores the intermediate summing results
+ Blob<Dtype> scale_;
+
+ // Fields used for normalization WITHIN_CHANNEL
+ shared_ptr<SplitLayer<Dtype> > split_layer_;
+ vector<Blob<Dtype>*> split_top_vec_;
+ shared_ptr<PowerLayer<Dtype> > square_layer_;
+ Blob<Dtype> square_input_;
+ Blob<Dtype> square_output_;
+ vector<Blob<Dtype>*> square_bottom_vec_;
+ vector<Blob<Dtype>*> square_top_vec_;
+ shared_ptr<PoolingLayer<Dtype> > pool_layer_;
+ Blob<Dtype> pool_output_;
+ vector<Blob<Dtype>*> pool_top_vec_;
+ shared_ptr<PowerLayer<Dtype> > power_layer_;
+ Blob<Dtype> power_output_;
+ vector<Blob<Dtype>*> power_top_vec_;
+ shared_ptr<EltwiseLayer<Dtype> > product_layer_;
+ Blob<Dtype> product_input_;
+ vector<Blob<Dtype>*> product_bottom_vec_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_LRN_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_MEMORY_DATA_LAYER_HPP_
+#define CAFFE_MEMORY_DATA_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/base_data_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Provides data to the Net from memory.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class MemoryDataLayer : public BaseDataLayer<Dtype> {
+ public:
+ explicit MemoryDataLayer(const LayerParameter& param)
+ : BaseDataLayer<Dtype>(param), has_new_data_(false) {}
+ virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "MemoryData"; }
+ virtual inline int ExactNumBottomBlobs() const { return 0; }
+ virtual inline int ExactNumTopBlobs() const { return 2; }
+
+ virtual void AddDatumVector(const vector<Datum>& datum_vector);
+#ifdef USE_OPENCV
+ virtual void AddMatVector(const vector<cv::Mat>& mat_vector,
+ const vector<int>& labels);
+#endif // USE_OPENCV
+
+ // Reset should accept const pointers, but can't, because the memory
+ // will be given to Blob, which is mutable
+ void Reset(Dtype* data, Dtype* label, int n);
+ void set_batch_size(int new_size);
+
+ int batch_size() { return batch_size_; }
+ int channels() { return channels_; }
+ int height() { return height_; }
+ int width() { return width_; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ int batch_size_, channels_, height_, width_, size_;
+ Dtype* data_;
+ Dtype* labels_;
+ int n_;
+ size_t pos_;
+ Blob<Dtype> added_data_;
+ Blob<Dtype> added_label_;
+ bool has_new_data_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_MEMORY_DATA_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_MULTINOMIAL_LOGISTIC_LOSS_LAYER_HPP_
+#define CAFFE_MULTINOMIAL_LOGISTIC_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes the multinomial logistic loss for a one-of-many
+ * classification task, directly taking a predicted probability
+ * distribution as input.
+ *
+ * When predictions are not already a probability distribution, you should
+ * instead use the SoftmaxWithLossLayer, which maps predictions to a
+ * distribution using the SoftmaxLayer, before computing the multinomial
+ * logistic loss. The SoftmaxWithLossLayer should be preferred over separate
+ * SoftmaxLayer + MultinomialLogisticLossLayer
+ * as its gradient computation is more numerically stable.
+ *
+ * @param bottom input Blob vector (length 2)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the predictions @f$ \hat{p} @f$, a Blob with values in
+ * @f$ [0, 1] @f$ indicating the predicted probability of each of the
+ * @f$ K = CHW @f$ classes. Each prediction vector @f$ \hat{p}_n @f$
+ * should sum to 1 as in a probability distribution: @f$
+ * \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$.
+ * -# @f$ (N \times 1 \times 1 \times 1) @f$
+ * the labels @f$ l @f$, an integer-valued Blob with values
+ * @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
+ * indicating the correct class label among the @f$ K @f$ classes
+ * @param top output Blob vector (length 1)
+ * -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ * the computed multinomial logistic loss: @f$ E =
+ * \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n})
+ * @f$
+ */
+template <typename Dtype>
+class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
+ public:
+ explicit MultinomialLogisticLossLayer(const LayerParameter& param)
+ : LossLayer<Dtype>(param) {}
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "MultinomialLogisticLoss"; }
+
+ protected:
+ /// @copydoc MultinomialLogisticLossLayer
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ /**
+ * @brief Computes the multinomial logistic loss error gradient w.r.t. the
+ * predictions.
+ *
+ * Gradients cannot be computed with respect to the label inputs (bottom[1]),
+ * so this method ignores bottom[1] and requires !propagate_down[1], crashing
+ * if propagate_down[1] is set.
+ *
+ * @param top output Blob vector (length 1), providing the error gradient with
+ * respect to the outputs
+ * -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+ * as @f$ \lambda @f$ is the coefficient of this layer's output
+ * @f$\ell_i@f$ in the overall Net loss
+ * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+ * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+ * (*Assuming that this top Blob is not used as a bottom (input) by any
+ * other layer of the Net.)
+ * @param propagate_down see Layer::Backward.
+ * propagate_down[1] must be false as we can't compute gradients with
+ * respect to the labels.
+ * @param bottom input Blob vector (length 2)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the predictions @f$ \hat{p} @f$; Backward computes diff
+ * @f$ \frac{\partial E}{\partial \hat{p}} @f$
+ * -# @f$ (N \times 1 \times 1 \times 1) @f$
+ * the labels -- ignored as we can't compute their error gradients
+ */
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+} // namespace caffe
+
+#endif // CAFFE_MULTINOMIAL_LOGISTIC_LOSS_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_MVN_LAYER_HPP_
+#define CAFFE_MVN_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Normalizes the input to have 0-mean and/or unit (1) variance.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class MVNLayer : public Layer<Dtype> {
+ public:
+ explicit MVNLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "MVN"; }
+ virtual inline int ExactNumBottomBlobs() const { return 1; }
+ virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ Blob<Dtype> mean_, variance_, temp_;
+
+ /// sum_multiplier is used to carry out sum using BLAS
+ Blob<Dtype> sum_multiplier_;
+ Dtype eps_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_MVN_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_NEURON_LAYER_HPP_
+#define CAFFE_NEURON_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief An interface for layers that take one blob as input (@f$ x @f$)
+ * and produce one equally-sized blob as output (@f$ y @f$), where
+ * each element of the output depends only on the corresponding input
+ * element.
+ */
+template <typename Dtype>
+class NeuronLayer : public Layer<Dtype> {
+ public:
+ explicit NeuronLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline int ExactNumBottomBlobs() const { return 1; }
+ virtual inline int ExactNumTopBlobs() const { return 1; }
+};
+
+} // namespace caffe
+
+#endif // CAFFE_NEURON_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_POOLING_LAYER_HPP_
+#define CAFFE_POOLING_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Pools the input image by taking the max, average, etc. within regions.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class PoolingLayer : public Layer<Dtype> {
+ public:
+ explicit PoolingLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "Pooling"; }
+ virtual inline int ExactNumBottomBlobs() const { return 1; }
+ virtual inline int MinTopBlobs() const { return 1; }
+ // MAX POOL layers can output an extra top blob for the mask;
+ // others can only output the pooled inputs.
+ virtual inline int MaxTopBlobs() const {
+ return (this->layer_param_.pooling_param().pool() ==
+ PoolingParameter_PoolMethod_MAX) ? 2 : 1;
+ }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ int kernel_h_, kernel_w_;
+ int stride_h_, stride_w_;
+ int pad_h_, pad_w_;
+ int channels_;
+ int height_, width_;
+ int pooled_height_, pooled_width_;
+ bool global_pooling_;
+ Blob<Dtype> rand_idx_;
+ Blob<int> max_idx_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_POOLING_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_POWER_LAYER_HPP_
+#define CAFFE_POWER_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes @f$ y = (\alpha x + \beta) ^ \gamma @f$,
+ * as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
+ * and power @f$ \gamma @f$.
+ */
+template <typename Dtype>
+class PowerLayer : public NeuronLayer<Dtype> {
+ public:
+ /**
+ * @param param provides PowerParameter power_param,
+ * with PowerLayer options:
+ * - scale (\b optional, default 1) the scale @f$ \alpha @f$
+ * - shift (\b optional, default 0) the shift @f$ \beta @f$
+ * - power (\b optional, default 1) the power @f$ \gamma @f$
+ */
+ explicit PowerLayer(const LayerParameter& param)
+ : NeuronLayer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "Power"; }
+
+ protected:
+ /**
+ * @param bottom input Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the computed outputs @f$
+ * y = (\alpha x + \beta) ^ \gamma
+ * @f$
+ */
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ /**
+ * @brief Computes the error gradient w.r.t. the power inputs.
+ *
+ * @param top output Blob vector (length 1), providing the error gradient with
+ * respect to the outputs
+ * -# @f$ (N \times C \times H \times W) @f$
+ * containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+ * with respect to computed outputs @f$ y @f$
+ * @param propagate_down see Layer::Backward.
+ * @param bottom input Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x @f$; Backward fills their diff with
+ * gradients @f$
+ * \frac{\partial E}{\partial x} =
+ * \frac{\partial E}{\partial y}
+ * \alpha \gamma (\alpha x + \beta) ^ {\gamma - 1} =
+ * \frac{\partial E}{\partial y}
+ * \frac{\alpha \gamma y}{\alpha x + \beta}
+ * @f$ if propagate_down[0]
+ */
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ /// @brief @f$ \gamma @f$ from layer_param_.power_param()
+ Dtype power_;
+ /// @brief @f$ \alpha @f$ from layer_param_.power_param()
+ Dtype scale_;
+ /// @brief @f$ \beta @f$ from layer_param_.power_param()
+ Dtype shift_;
+ /// @brief Result of @f$ \alpha \gamma @f$
+ Dtype diff_scale_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_POWER_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_PRELU_LAYER_HPP_
+#define CAFFE_PRELU_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Parameterized Rectified Linear Unit non-linearity @f$
+ * y_i = \max(0, x_i) + a_i \min(0, x_i)
+ * @f$. The differences from ReLULayer are 1) negative slopes are
+ * learnable though backprop and 2) negative slopes can vary across
+ * channels. The number of axes of input blob should be greater than or
+ * equal to 2. The 1st axis (0-based) is seen as channels.
+ */
+template <typename Dtype>
+class PReLULayer : public NeuronLayer<Dtype> {
+ public:
+ /**
+ * @param param provides PReLUParameter prelu_param,
+ * with PReLULayer options:
+ * - filler (\b optional, FillerParameter,
+ * default {'type': constant 'value':0.25}).
+ * - channel_shared (\b optional, default false).
+ * negative slopes are shared across channels.
+ */
+ explicit PReLULayer(const LayerParameter& param)
+ : NeuronLayer<Dtype>(param) {}
+
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "PReLU"; }
+
+ protected:
+ /**
+ * @param bottom input Blob vector (length 1)
+ * -# @f$ (N \times C \times ...) @f$
+ * the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ * -# @f$ (N \times C \times ...) @f$
+ * the computed outputs for each channel @f$i@f$ @f$
+ * y_i = \max(0, x_i) + a_i \min(0, x_i)
+ * @f$.
+ */
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ /**
+ * @brief Computes the error gradient w.r.t. the PReLU inputs.
+ *
+ * @param top output Blob vector (length 1), providing the error gradient with
+ * respect to the outputs
+ * -# @f$ (N \times C \times ...) @f$
+ * containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+ * with respect to computed outputs @f$ y @f$
+ * @param propagate_down see Layer::Backward.
+ * @param bottom input Blob vector (length 1)
+ * -# @f$ (N \times C \times ...) @f$
+ * the inputs @f$ x @f$; For each channel @f$i@f$, backward fills their
+ * diff with gradients @f$
+ * \frac{\partial E}{\partial x_i} = \left\{
+ * \begin{array}{lr}
+ * a_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\
+ * \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i > 0
+ * \end{array} \right.
+ * @f$.
+ * If param_propagate_down_[0] is true, it fills the diff with gradients
+ * @f$
+ * \frac{\partial E}{\partial a_i} = \left\{
+ * \begin{array}{lr}
+ * \sum_{x_i} x_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\
+ * 0 & \mathrm{if} \; x_i > 0
+ * \end{array} \right.
+ * @f$.
+ */
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ bool channel_shared_;
+ Blob<Dtype> multiplier_; // dot multiplier for backward computation of params
+ Blob<Dtype> backward_buff_; // temporary buffer for backward computation
+ Blob<Dtype> bottom_memory_; // memory for in-place computation
+};
+
+} // namespace caffe
+
+#endif // CAFFE_PRELU_LAYER_HPP_
class PythonLayer : public Layer<Dtype> {
public:
PythonLayer(PyObject* self, const LayerParameter& param)
- : Layer<Dtype>(param), self_(self) { }
+ : Layer<Dtype>(param), self_(bp::handle<>(bp::borrowed(self))) { }
virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
- try {
- bp::call_method<bp::object>(self_, "setup", bottom, top);
- } catch (bp::error_already_set) {
- PyErr_Print();
- throw;
+ // Disallow PythonLayer in MultiGPU training stage, due to GIL issues
+ // Details: https://github.com/BVLC/caffe/issues/2936
+ if (this->phase_ == TRAIN && Caffe::solver_count() > 1
+ && !ShareInParallel()) {
+ LOG(FATAL) << "PythonLayer is not implemented in Multi-GPU training";
}
+ self_.attr("param_str") = bp::str(
+ this->layer_param_.python_param().param_str());
+ self_.attr("setup")(bottom, top);
}
-
virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
- try {
- bp::call_method<bp::object>(self_, "reshape", bottom, top);
- } catch (bp::error_already_set) {
- PyErr_Print();
- throw;
- }
+ self_.attr("reshape")(bottom, top);
+ }
+
+ virtual inline bool ShareInParallel() const {
+ return this->layer_param_.python_param().share_in_parallel();
}
virtual inline const char* type() const { return "Python"; }
protected:
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
- try {
- bp::call_method<bp::object>(self_, "forward", bottom, top);
- } catch (bp::error_already_set) {
- PyErr_Print();
- throw;
- }
+ self_.attr("forward")(bottom, top);
}
virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
- try {
- bp::call_method<bp::object>(self_, "backward", top, propagate_down,
- bottom);
- } catch (bp::error_already_set) {
- PyErr_Print();
- throw;
- }
+ self_.attr("backward")(top, propagate_down, bottom);
}
private:
- PyObject* self_;
+ bp::object self_;
};
} // namespace caffe
--- /dev/null
+#ifndef CAFFE_REDUCTION_LAYER_HPP_
+#define CAFFE_REDUCTION_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Compute "reductions" -- operations that return a scalar output Blob
+ * for an input Blob of arbitrary size, such as the sum, absolute sum,
+ * and sum of squares.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class ReductionLayer : public Layer<Dtype> {
+ public:
+ explicit ReductionLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "Reduction"; }
+ virtual inline int ExactNumBottomBlobs() const { return 1; }
+ virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ /// @brief the reduction operation performed by the layer
+ ReductionParameter_ReductionOp op_;
+ /// @brief a scalar coefficient applied to all outputs
+ Dtype coeff_;
+ /// @brief the index of the first input axis to reduce
+ int axis_;
+ /// @brief the number of reductions performed
+ int num_;
+ /// @brief the input size of each reduction
+ int dim_;
+ /// @brief a helper Blob used for summation (op_ == SUM)
+ Blob<Dtype> sum_multiplier_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_REDUCTION_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_RELU_LAYER_HPP_
+#define CAFFE_RELU_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Rectified Linear Unit non-linearity @f$ y = \max(0, x) @f$.
+ * The simple max is fast to compute, and the function does not saturate.
+ */
+template <typename Dtype>
+class ReLULayer : public NeuronLayer<Dtype> {
+ public:
+ /**
+ * @param param provides ReLUParameter relu_param,
+ * with ReLULayer options:
+ * - negative_slope (\b optional, default 0).
+ * the value @f$ \nu @f$ by which negative values are multiplied.
+ */
+ explicit ReLULayer(const LayerParameter& param)
+ : NeuronLayer<Dtype>(param) {}
+
+ virtual inline const char* type() const { return "ReLU"; }
+
+ protected:
+ /**
+ * @param bottom input Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the computed outputs @f$
+ * y = \max(0, x)
+ * @f$ by default. If a non-zero negative_slope @f$ \nu @f$ is provided,
+ * the computed outputs are @f$ y = \max(0, x) + \nu \min(0, x) @f$.
+ */
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ /**
+ * @brief Computes the error gradient w.r.t. the ReLU inputs.
+ *
+ * @param top output Blob vector (length 1), providing the error gradient with
+ * respect to the outputs
+ * -# @f$ (N \times C \times H \times W) @f$
+ * containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+ * with respect to computed outputs @f$ y @f$
+ * @param propagate_down see Layer::Backward.
+ * @param bottom input Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x @f$; Backward fills their diff with
+ * gradients @f$
+ * \frac{\partial E}{\partial x} = \left\{
+ * \begin{array}{lr}
+ * 0 & \mathrm{if} \; x \le 0 \\
+ * \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0
+ * \end{array} \right.
+ * @f$ if propagate_down[0], by default.
+ * If a non-zero negative_slope @f$ \nu @f$ is provided,
+ * the computed gradients are @f$
+ * \frac{\partial E}{\partial x} = \left\{
+ * \begin{array}{lr}
+ * \nu \frac{\partial E}{\partial y} & \mathrm{if} \; x \le 0 \\
+ * \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0
+ * \end{array} \right.
+ * @f$.
+ */
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+} // namespace caffe
+
+#endif // CAFFE_RELU_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_XXX_LAYER_HPP_
+#define CAFFE_XXX_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/*
+ * @brief Reshapes the input Blob into an arbitrary-sized output Blob.
+ *
+ * Note: similarly to FlattenLayer, this layer does not change the input values
+ * (see FlattenLayer, Blob::ShareData and Blob::ShareDiff).
+ */
+template <typename Dtype>
+class ReshapeLayer : public Layer<Dtype> {
+ public:
+ explicit ReshapeLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "Reshape"; }
+ virtual inline int ExactNumBottomBlobs() const { return 1; }
+ virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {}
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {}
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+
+ /// @brief vector of axes indices whose dimensions we'll copy from the bottom
+ vector<int> copy_axes_;
+ /// @brief the index of the axis whose dimension we infer, or -1 if none
+ int inferred_axis_;
+ /// @brief the product of the "constant" output dimensions
+ int constant_count_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_XXX_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_SIGMOID_CROSS_ENTROPY_LOSS_LAYER_HPP_
+#define CAFFE_SIGMOID_CROSS_ENTROPY_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+#include "caffe/layers/sigmoid_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes the cross-entropy (logistic) loss @f$
+ * E = \frac{-1}{n} \sum\limits_{n=1}^N \left[
+ * p_n \log \hat{p}_n +
+ * (1 - p_n) \log(1 - \hat{p}_n)
+ * \right]
+ * @f$, often used for predicting targets interpreted as probabilities.
+ *
+ * This layer is implemented rather than separate
+ * SigmoidLayer + CrossEntropyLayer
+ * as its gradient computation is more numerically stable.
+ * At test time, this layer can be replaced simply by a SigmoidLayer.
+ *
+ * @param bottom input Blob vector (length 2)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the scores @f$ x \in [-\infty, +\infty]@f$,
+ * which this layer maps to probability predictions
+ * @f$ \hat{p}_n = \sigma(x_n) \in [0, 1] @f$
+ * using the sigmoid function @f$ \sigma(.) @f$ (see SigmoidLayer).
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the targets @f$ y \in [0, 1] @f$
+ * @param top output Blob vector (length 1)
+ * -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ * the computed cross-entropy loss: @f$
+ * E = \frac{-1}{n} \sum\limits_{n=1}^N \left[
+ * p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n)
+ * \right]
+ * @f$
+ */
+template <typename Dtype>
+class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
+ public:
+ explicit SigmoidCrossEntropyLossLayer(const LayerParameter& param)
+ : LossLayer<Dtype>(param),
+ sigmoid_layer_(new SigmoidLayer<Dtype>(param)),
+ sigmoid_output_(new Blob<Dtype>()) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "SigmoidCrossEntropyLoss"; }
+
+ protected:
+ /// @copydoc SigmoidCrossEntropyLossLayer
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ /**
+ * @brief Computes the sigmoid cross-entropy loss error gradient w.r.t. the
+ * predictions.
+ *
+ * Gradients cannot be computed with respect to the target inputs (bottom[1]),
+ * so this method ignores bottom[1] and requires !propagate_down[1], crashing
+ * if propagate_down[1] is set.
+ *
+ * @param top output Blob vector (length 1), providing the error gradient with
+ * respect to the outputs
+ * -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+ * as @f$ \lambda @f$ is the coefficient of this layer's output
+ * @f$\ell_i@f$ in the overall Net loss
+ * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+ * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+ * (*Assuming that this top Blob is not used as a bottom (input) by any
+ * other layer of the Net.)
+ * @param propagate_down see Layer::Backward.
+ * propagate_down[1] must be false as gradient computation with respect
+ * to the targets is not implemented.
+ * @param bottom input Blob vector (length 2)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the predictions @f$x@f$; Backward computes diff
+ * @f$ \frac{\partial E}{\partial x} =
+ * \frac{1}{n} \sum\limits_{n=1}^N (\hat{p}_n - p_n)
+ * @f$
+ * -# @f$ (N \times 1 \times 1 \times 1) @f$
+ * the labels -- ignored as we can't compute their error gradients
+ */
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ /// The internal SigmoidLayer used to map predictions to probabilities.
+ shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
+ /// sigmoid_output stores the output of the SigmoidLayer.
+ shared_ptr<Blob<Dtype> > sigmoid_output_;
+ /// bottom vector holder to call the underlying SigmoidLayer::Forward
+ vector<Blob<Dtype>*> sigmoid_bottom_vec_;
+ /// top vector holder to call the underlying SigmoidLayer::Forward
+ vector<Blob<Dtype>*> sigmoid_top_vec_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_SIGMOID_CROSS_ENTROPY_LOSS_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_SIGMOID_LAYER_HPP_
+#define CAFFE_SIGMOID_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Sigmoid function non-linearity @f$
+ * y = (1 + \exp(-x))^{-1}
+ * @f$, a classic choice in neural networks.
+ *
+ * Note that the gradient vanishes as the values move away from 0.
+ * The ReLULayer is often a better choice for this reason.
+ */
+template <typename Dtype>
+class SigmoidLayer : public NeuronLayer<Dtype> {
+ public:
+ explicit SigmoidLayer(const LayerParameter& param)
+ : NeuronLayer<Dtype>(param) {}
+
+ virtual inline const char* type() const { return "Sigmoid"; }
+
+ protected:
+ /**
+ * @param bottom input Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the computed outputs @f$
+ * y = (1 + \exp(-x))^{-1}
+ * @f$
+ */
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ /**
+ * @brief Computes the error gradient w.r.t. the sigmoid inputs.
+ *
+ * @param top output Blob vector (length 1), providing the error gradient with
+ * respect to the outputs
+ * -# @f$ (N \times C \times H \times W) @f$
+ * containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+ * with respect to computed outputs @f$ y @f$
+ * @param propagate_down see Layer::Backward.
+ * @param bottom input Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x @f$; Backward fills their diff with
+ * gradients @f$
+ * \frac{\partial E}{\partial x}
+ * = \frac{\partial E}{\partial y} y (1 - y)
+ * @f$ if propagate_down[0]
+ */
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+} // namespace caffe
+
+#endif // CAFFE_SIGMOID_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_SILENCE_LAYER_HPP_
+#define CAFFE_SILENCE_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Ignores bottom blobs while producing no top blobs. (This is useful
+ * to suppress outputs during testing.)
+ */
+template <typename Dtype>
+class SilenceLayer : public Layer<Dtype> {
+ public:
+ explicit SilenceLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {}
+
+ virtual inline const char* type() const { return "Silence"; }
+ virtual inline int MinBottomBlobs() const { return 1; }
+ virtual inline int ExactNumTopBlobs() const { return 0; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {}
+ // We can't define Forward_gpu here, since STUB_GPU will provide
+ // its own definition for CPU_ONLY mode.
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+} // namespace caffe
+
+#endif // CAFFE_SILENCE_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_SLICE_LAYER_HPP_
+#define CAFFE_SLICE_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Takes a Blob and slices it along either the num or channel dimension,
+ * outputting multiple sliced Blob results.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class SliceLayer : public Layer<Dtype> {
+ public:
+ explicit SliceLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "Slice"; }
+ virtual inline int ExactNumBottomBlobs() const { return 1; }
+ virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ int count_;
+ int num_slices_;
+ int slice_size_;
+ int slice_axis_;
+ vector<int> slice_point_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_SLICE_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_SOFTMAX_LAYER_HPP_
+#define CAFFE_SOFTMAX_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Computes the softmax function.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class SoftmaxLayer : public Layer<Dtype> {
+ public:
+ explicit SoftmaxLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "Softmax"; }
+ virtual inline int ExactNumBottomBlobs() const { return 1; }
+ virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ int outer_num_;
+ int inner_num_;
+ int softmax_axis_;
+ /// sum_multiplier is used to carry out sum using BLAS
+ Blob<Dtype> sum_multiplier_;
+ /// scale is an intermediate Blob to hold temporary results.
+ Blob<Dtype> scale_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_SOFTMAX_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_SOFTMAX_WITH_LOSS_LAYER_HPP_
+#define CAFFE_SOFTMAX_WITH_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+#include "caffe/layers/softmax_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes the multinomial logistic loss for a one-of-many
+ * classification task, passing real-valued predictions through a
+ * softmax to get a probability distribution over classes.
+ *
+ * This layer should be preferred over separate
+ * SoftmaxLayer + MultinomialLogisticLossLayer
+ * as its gradient computation is more numerically stable.
+ * At test time, this layer can be replaced simply by a SoftmaxLayer.
+ *
+ * @param bottom input Blob vector (length 2)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the predictions @f$ x @f$, a Blob with values in
+ * @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
+ * the @f$ K = CHW @f$ classes. This layer maps these scores to a
+ * probability distribution over classes using the softmax function
+ * @f$ \hat{p}_{nk} = \exp(x_{nk}) /
+ * \left[\sum_{k'} \exp(x_{nk'})\right] @f$ (see SoftmaxLayer).
+ * -# @f$ (N \times 1 \times 1 \times 1) @f$
+ * the labels @f$ l @f$, an integer-valued Blob with values
+ * @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
+ * indicating the correct class label among the @f$ K @f$ classes
+ * @param top output Blob vector (length 1)
+ * -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ * the computed cross-entropy classification loss: @f$ E =
+ * \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n})
+ * @f$, for softmax output class probabilites @f$ \hat{p} @f$
+ */
+template <typename Dtype>
+class SoftmaxWithLossLayer : public LossLayer<Dtype> {
+ public:
+ /**
+ * @param param provides LossParameter loss_param, with options:
+ * - ignore_label (optional)
+ * Specify a label value that should be ignored when computing the loss.
+ * - normalize (optional, default true)
+ * If true, the loss is normalized by the number of (nonignored) labels
+ * present; otherwise the loss is simply summed over spatial locations.
+ */
+ explicit SoftmaxWithLossLayer(const LayerParameter& param)
+ : LossLayer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "SoftmaxWithLoss"; }
+ virtual inline int ExactNumTopBlobs() const { return -1; }
+ virtual inline int MinTopBlobs() const { return 1; }
+ virtual inline int MaxTopBlobs() const { return 2; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ /**
+ * @brief Computes the softmax loss error gradient w.r.t. the predictions.
+ *
+ * Gradients cannot be computed with respect to the label inputs (bottom[1]),
+ * so this method ignores bottom[1] and requires !propagate_down[1], crashing
+ * if propagate_down[1] is set.
+ *
+ * @param top output Blob vector (length 1), providing the error gradient with
+ * respect to the outputs
+ * -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+ * as @f$ \lambda @f$ is the coefficient of this layer's output
+ * @f$\ell_i@f$ in the overall Net loss
+ * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+ * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+ * (*Assuming that this top Blob is not used as a bottom (input) by any
+ * other layer of the Net.)
+ * @param propagate_down see Layer::Backward.
+ * propagate_down[1] must be false as we can't compute gradients with
+ * respect to the labels.
+ * @param bottom input Blob vector (length 2)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the predictions @f$ x @f$; Backward computes diff
+ * @f$ \frac{\partial E}{\partial x} @f$
+ * -# @f$ (N \times 1 \times 1 \times 1) @f$
+ * the labels -- ignored as we can't compute their error gradients
+ */
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ /// Read the normalization mode parameter and compute the normalizer based
+ /// on the blob size. If normalization_mode is VALID, the count of valid
+ /// outputs will be read from valid_count, unless it is -1 in which case
+ /// all outputs are assumed to be valid.
+ virtual Dtype get_normalizer(
+ LossParameter_NormalizationMode normalization_mode, int valid_count);
+
+ /// The internal SoftmaxLayer used to map predictions to a distribution.
+ shared_ptr<Layer<Dtype> > softmax_layer_;
+ /// prob stores the output probability predictions from the SoftmaxLayer.
+ Blob<Dtype> prob_;
+ /// bottom vector holder used in call to the underlying SoftmaxLayer::Forward
+ vector<Blob<Dtype>*> softmax_bottom_vec_;
+ /// top vector holder used in call to the underlying SoftmaxLayer::Forward
+ vector<Blob<Dtype>*> softmax_top_vec_;
+ /// Whether to ignore instances with a certain label.
+ bool has_ignore_label_;
+ /// The label indicating that an instance should be ignored.
+ int ignore_label_;
+ /// How to normalize the output loss.
+ LossParameter_NormalizationMode normalization_;
+
+ int softmax_axis_, outer_num_, inner_num_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_SOFTMAX_WITH_LOSS_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_SPLIT_LAYER_HPP_
+#define CAFFE_SPLIT_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Creates a "split" path in the network by copying the bottom Blob
+ * into multiple top Blob%s to be used by multiple consuming layers.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class SplitLayer : public Layer<Dtype> {
+ public:
+ explicit SplitLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "Split"; }
+ virtual inline int ExactNumBottomBlobs() const { return 1; }
+ virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ int count_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_SPLIT_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_SPP_LAYER_HPP_
+#define CAFFE_SPP_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Does spatial pyramid pooling on the input image
+ * by taking the max, average, etc. within regions
+ * so that the result vector of different sized
+ * images are of the same size.
+ */
+template <typename Dtype>
+class SPPLayer : public Layer<Dtype> {
+ public:
+ explicit SPPLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "SPP"; }
+ virtual inline int ExactNumBottomBlobs() const { return 1; }
+ virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ // calculates the kernel and stride dimensions for the pooling layer,
+ // returns a correctly configured LayerParameter for a PoolingLayer
+ virtual LayerParameter GetPoolingParam(const int pyramid_level,
+ const int bottom_h, const int bottom_w, const SPPParameter spp_param);
+
+ int pyramid_height_;
+ int bottom_h_, bottom_w_;
+ int num_;
+ int channels_;
+ int kernel_h_, kernel_w_;
+ int pad_h_, pad_w_;
+ bool reshaped_first_time_;
+
+ /// the internal Split layer that feeds the pooling layers
+ shared_ptr<SplitLayer<Dtype> > split_layer_;
+ /// top vector holder used in call to the underlying SplitLayer::Forward
+ vector<Blob<Dtype>*> split_top_vec_;
+ /// bottom vector holder used in call to the underlying PoolingLayer::Forward
+ vector<vector<Blob<Dtype>*>*> pooling_bottom_vecs_;
+ /// the internal Pooling layers of different kernel sizes
+ vector<shared_ptr<PoolingLayer<Dtype> > > pooling_layers_;
+ /// top vector holders used in call to the underlying PoolingLayer::Forward
+ vector<vector<Blob<Dtype>*>*> pooling_top_vecs_;
+ /// pooling_outputs stores the outputs of the PoolingLayers
+ vector<Blob<Dtype>*> pooling_outputs_;
+ /// the internal Flatten layers that the Pooling layers feed into
+ vector<FlattenLayer<Dtype>*> flatten_layers_;
+ /// top vector holders used in call to the underlying FlattenLayer::Forward
+ vector<vector<Blob<Dtype>*>*> flatten_top_vecs_;
+ /// flatten_outputs stores the outputs of the FlattenLayers
+ vector<Blob<Dtype>*> flatten_outputs_;
+ /// bottom vector holder used in call to the underlying ConcatLayer::Forward
+ vector<Blob<Dtype>*> concat_bottom_vec_;
+ /// the internal Concat layers that the Flatten layers feed into
+ shared_ptr<ConcatLayer<Dtype> > concat_layer_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_SPP_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_TANH_LAYER_HPP_
+#define CAFFE_TANH_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief TanH hyperbolic tangent non-linearity @f$
+ * y = \frac{\exp(2x) - 1}{\exp(2x) + 1}
+ * @f$, popular in auto-encoders.
+ *
+ * Note that the gradient vanishes as the values move away from 0.
+ * The ReLULayer is often a better choice for this reason.
+ */
+template <typename Dtype>
+class TanHLayer : public NeuronLayer<Dtype> {
+ public:
+ explicit TanHLayer(const LayerParameter& param)
+ : NeuronLayer<Dtype>(param) {}
+
+ virtual inline const char* type() const { return "TanH"; }
+
+ protected:
+ /**
+ * @param bottom input Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the computed outputs @f$
+ * y = \frac{\exp(2x) - 1}{\exp(2x) + 1}
+ * @f$
+ */
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ /**
+ * @brief Computes the error gradient w.r.t. the sigmoid inputs.
+ *
+ * @param top output Blob vector (length 1), providing the error gradient with
+ * respect to the outputs
+ * -# @f$ (N \times C \times H \times W) @f$
+ * containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+ * with respect to computed outputs @f$ y @f$
+ * @param propagate_down see Layer::Backward.
+ * @param bottom input Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x @f$; Backward fills their diff with
+ * gradients @f$
+ * \frac{\partial E}{\partial x}
+ * = \frac{\partial E}{\partial y}
+ * \left(1 - \left[\frac{\exp(2x) - 1}{exp(2x) + 1} \right]^2 \right)
+ * = \frac{\partial E}{\partial y} (1 - y^2)
+ * @f$ if propagate_down[0]
+ */
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+} // namespace caffe
+
+#endif // CAFFE_TANH_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_THRESHOLD_LAYER_HPP_
+#define CAFFE_THRESHOLD_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Tests whether the input exceeds a threshold: outputs 1 for inputs
+ * above threshold; 0 otherwise.
+ */
+template <typename Dtype>
+class ThresholdLayer : public NeuronLayer<Dtype> {
+ public:
+ /**
+ * @param param provides ThresholdParameter threshold_param,
+ * with ThresholdLayer options:
+ * - threshold (\b optional, default 0).
+ * the threshold value @f$ t @f$ to which the input values are compared.
+ */
+ explicit ThresholdLayer(const LayerParameter& param)
+ : NeuronLayer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "Threshold"; }
+
+ protected:
+ /**
+ * @param bottom input Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ * -# @f$ (N \times C \times H \times W) @f$
+ * the computed outputs @f$
+ * y = \left\{
+ * \begin{array}{lr}
+ * 0 & \mathrm{if} \; x \le t \\
+ * 1 & \mathrm{if} \; x > t
+ * \end{array} \right.
+ * @f$
+ */
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ /// @brief Not implemented (non-differentiable function)
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+ NOT_IMPLEMENTED;
+ }
+
+ Dtype threshold_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_THRESHOLD_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_TILE_LAYER_HPP_
+#define CAFFE_TILE_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Copy a Blob along specified dimensions.
+ */
+template <typename Dtype>
+class TileLayer : public Layer<Dtype> {
+ public:
+ explicit TileLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "Tile"; }
+ virtual inline int ExactNumBottomBlobs() const { return 1; }
+ virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ unsigned int axis_, tiles_, outer_dim_, inner_dim_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_TILE_LAYER_HPP_
--- /dev/null
+#ifndef CAFFE_WINDOW_DATA_LAYER_HPP_
+#define CAFFE_WINDOW_DATA_LAYER_HPP_
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/base_data_layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Provides data to the Net from windows of images files, specified
+ * by a window data file.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class WindowDataLayer : public BasePrefetchingDataLayer<Dtype> {
+ public:
+ explicit WindowDataLayer(const LayerParameter& param)
+ : BasePrefetchingDataLayer<Dtype>(param) {}
+ virtual ~WindowDataLayer();
+ virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "WindowData"; }
+ virtual inline int ExactNumBottomBlobs() const { return 0; }
+ virtual inline int ExactNumTopBlobs() const { return 2; }
+
+ protected:
+ virtual unsigned int PrefetchRand();
+ virtual void load_batch(Batch<Dtype>* batch);
+
+ shared_ptr<Caffe::RNG> prefetch_rng_;
+ vector<std::pair<std::string, vector<int> > > image_database_;
+ enum WindowField { IMAGE_INDEX, LABEL, OVERLAP, X1, Y1, X2, Y2, NUM };
+ vector<vector<float> > fg_windows_;
+ vector<vector<float> > bg_windows_;
+ Blob<Dtype> data_mean_;
+ vector<Dtype> mean_values_;
+ bool has_mean_file_;
+ bool has_mean_values_;
+ bool cache_images_;
+ vector<std::pair<std::string, Datum > > image_database_cache_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_WINDOW_DATA_LAYER_HPP_
+++ /dev/null
-#ifndef CAFFE_LOSS_LAYERS_HPP_
-#define CAFFE_LOSS_LAYERS_HPP_
-
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/neuron_layers.hpp"
-#include "caffe/proto/caffe.pb.h"
-
-namespace caffe {
-
-const float kLOG_THRESHOLD = 1e-20;
-
-/**
- * @brief Computes the classification accuracy for a one-of-many
- * classification task.
- */
-template <typename Dtype>
-class AccuracyLayer : public Layer<Dtype> {
- public:
- /**
- * @param param provides AccuracyParameter accuracy_param,
- * with AccuracyLayer options:
- * - top_k (\b optional, default 1).
- * Sets the maximum rank @f$ k @f$ at which a prediction is considered
- * correct. For example, if @f$ k = 5 @f$, a prediction is counted
- * correct if the correct label is among the top 5 predicted labels.
- */
- explicit AccuracyLayer(const LayerParameter& param)
- : Layer<Dtype>(param) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "Accuracy"; }
- virtual inline int ExactNumBottomBlobs() const { return 2; }
- virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
- /**
- * @param bottom input Blob vector (length 2)
- * -# @f$ (N \times C \times H \times W) @f$
- * the predictions @f$ x @f$, a Blob with values in
- * @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
- * the @f$ K = CHW @f$ classes. Each @f$ x_n @f$ is mapped to a predicted
- * label @f$ \hat{l}_n @f$ given by its maximal index:
- * @f$ \hat{l}_n = \arg\max\limits_k x_{nk} @f$
- * -# @f$ (N \times 1 \times 1 \times 1) @f$
- * the labels @f$ l @f$, an integer-valued Blob with values
- * @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
- * indicating the correct class label among the @f$ K @f$ classes
- * @param top output Blob vector (length 1)
- * -# @f$ (1 \times 1 \times 1 \times 1) @f$
- * the computed accuracy: @f$
- * \frac{1}{N} \sum\limits_{n=1}^N \delta\{ \hat{l}_n = l_n \}
- * @f$, where @f$
- * \delta\{\mathrm{condition}\} = \left\{
- * \begin{array}{lr}
- * 1 & \mbox{if condition} \\
- * 0 & \mbox{otherwise}
- * \end{array} \right.
- * @f$
- */
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
-
- /// @brief Not implemented -- AccuracyLayer cannot be used as a loss.
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
- for (int i = 0; i < propagate_down.size(); ++i) {
- if (propagate_down[i]) { NOT_IMPLEMENTED; }
- }
- }
-
- int label_axis_, outer_num_, inner_num_;
-
- int top_k_;
-
- /// Whether to ignore instances with a certain label.
- bool has_ignore_label_;
- /// The label indicating that an instance should be ignored.
- int ignore_label_;
-};
-
-/**
- * @brief An interface for Layer%s that take two Blob%s as input -- usually
- * (1) predictions and (2) ground-truth labels -- and output a
- * singleton Blob representing the loss.
- *
- * LossLayers are typically only capable of backpropagating to their first input
- * -- the predictions.
- */
-template <typename Dtype>
-class LossLayer : public Layer<Dtype> {
- public:
- explicit LossLayer(const LayerParameter& param)
- : Layer<Dtype>(param) {}
- virtual void LayerSetUp(
- const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
- virtual void Reshape(
- const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
-
- virtual inline int ExactNumBottomBlobs() const { return 2; }
-
- /**
- * @brief For convenience and backwards compatibility, instruct the Net to
- * automatically allocate a single top Blob for LossLayers, into which
- * they output their singleton loss, (even if the user didn't specify
- * one in the prototxt, etc.).
- */
- virtual inline bool AutoTopBlobs() const { return true; }
- virtual inline int ExactNumTopBlobs() const { return 1; }
- /**
- * We usually cannot backpropagate to the labels; ignore force_backward for
- * these inputs.
- */
- virtual inline bool AllowForceBackward(const int bottom_index) const {
- return bottom_index != 1;
- }
-};
-
-/**
- * @brief Computes the contrastive loss @f$
- * E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d +
- * \left(1-y\right) \max \left(margin-d, 0\right)
- * @f$ where @f$
- * d = \left| \left| a_n - b_n \right| \right|_2^2 @f$. This can be
- * used to train siamese networks.
- *
- * @param bottom input Blob vector (length 3)
- * -# @f$ (N \times C \times 1 \times 1) @f$
- * the features @f$ a \in [-\infty, +\infty]@f$
- * -# @f$ (N \times C \times 1 \times 1) @f$
- * the features @f$ b \in [-\infty, +\infty]@f$
- * -# @f$ (N \times 1 \times 1 \times 1) @f$
- * the binary similarity @f$ s \in [0, 1]@f$
- * @param top output Blob vector (length 1)
- * -# @f$ (1 \times 1 \times 1 \times 1) @f$
- * the computed contrastive loss: @f$ E =
- * \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d +
- * \left(1-y\right) \max \left(margin-d, 0\right)
- * @f$ where @f$
- * d = \left| \left| a_n - b_n \right| \right|_2^2 @f$.
- * This can be used to train siamese networks.
- */
-template <typename Dtype>
-class ContrastiveLossLayer : public LossLayer<Dtype> {
- public:
- explicit ContrastiveLossLayer(const LayerParameter& param)
- : LossLayer<Dtype>(param), diff_() {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline int ExactNumBottomBlobs() const { return 3; }
- virtual inline const char* type() const { return "ContrastiveLoss"; }
- /**
- * Unlike most loss layers, in the ContrastiveLossLayer we can backpropagate
- * to the first two inputs.
- */
- virtual inline bool AllowForceBackward(const int bottom_index) const {
- return bottom_index != 2;
- }
-
- protected:
- /// @copydoc ContrastiveLossLayer
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- /**
- * @brief Computes the Contrastive error gradient w.r.t. the inputs.
- *
- * Computes the gradients with respect to the two input vectors (bottom[0] and
- * bottom[1]), but not the similarity label (bottom[2]).
- *
- * @param top output Blob vector (length 1), providing the error gradient with
- * respect to the outputs
- * -# @f$ (1 \times 1 \times 1 \times 1) @f$
- * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
- * as @f$ \lambda @f$ is the coefficient of this layer's output
- * @f$\ell_i@f$ in the overall Net loss
- * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
- * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
- * (*Assuming that this top Blob is not used as a bottom (input) by any
- * other layer of the Net.)
- * @param propagate_down see Layer::Backward.
- * @param bottom input Blob vector (length 2)
- * -# @f$ (N \times C \times 1 \times 1) @f$
- * the features @f$a@f$; Backward fills their diff with
- * gradients if propagate_down[0]
- * -# @f$ (N \times C \times 1 \times 1) @f$
- * the features @f$b@f$; Backward fills their diff with gradients if
- * propagate_down[1]
- */
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- Blob<Dtype> diff_; // cached for backward pass
- Blob<Dtype> dist_sq_; // cached for backward pass
- Blob<Dtype> diff_sq_; // tmp storage for gpu forward pass
- Blob<Dtype> summer_vec_; // tmp storage for gpu forward pass
-};
-
-/**
- * @brief Computes the Euclidean (L2) loss @f$
- * E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n
- * \right| \right|_2^2 @f$ for real-valued regression tasks.
- *
- * @param bottom input Blob vector (length 2)
- * -# @f$ (N \times C \times H \times W) @f$
- * the predictions @f$ \hat{y} \in [-\infty, +\infty]@f$
- * -# @f$ (N \times C \times H \times W) @f$
- * the targets @f$ y \in [-\infty, +\infty]@f$
- * @param top output Blob vector (length 1)
- * -# @f$ (1 \times 1 \times 1 \times 1) @f$
- * the computed Euclidean loss: @f$ E =
- * \frac{1}{2n} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n
- * \right| \right|_2^2 @f$
- *
- * This can be used for least-squares regression tasks. An InnerProductLayer
- * input to a EuclideanLossLayer exactly formulates a linear least squares
- * regression problem. With non-zero weight decay the problem becomes one of
- * ridge regression -- see src/caffe/test/test_sgd_solver.cpp for a concrete
- * example wherein we check that the gradients computed for a Net with exactly
- * this structure match hand-computed gradient formulas for ridge regression.
- *
- * (Note: Caffe, and SGD in general, is certainly \b not the best way to solve
- * linear least squares problems! We use it only as an instructive example.)
- */
-template <typename Dtype>
-class EuclideanLossLayer : public LossLayer<Dtype> {
- public:
- explicit EuclideanLossLayer(const LayerParameter& param)
- : LossLayer<Dtype>(param), diff_() {}
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "EuclideanLoss"; }
- /**
- * Unlike most loss layers, in the EuclideanLossLayer we can backpropagate
- * to both inputs -- override to return true and always allow force_backward.
- */
- virtual inline bool AllowForceBackward(const int bottom_index) const {
- return true;
- }
-
- protected:
- /// @copydoc EuclideanLossLayer
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- /**
- * @brief Computes the Euclidean error gradient w.r.t. the inputs.
- *
- * Unlike other children of LossLayer, EuclideanLossLayer \b can compute
- * gradients with respect to the label inputs bottom[1] (but still only will
- * if propagate_down[1] is set, due to being produced by learnable parameters
- * or if force_backward is set). In fact, this layer is "commutative" -- the
- * result is the same regardless of the order of the two bottoms.
- *
- * @param top output Blob vector (length 1), providing the error gradient with
- * respect to the outputs
- * -# @f$ (1 \times 1 \times 1 \times 1) @f$
- * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
- * as @f$ \lambda @f$ is the coefficient of this layer's output
- * @f$\ell_i@f$ in the overall Net loss
- * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
- * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
- * (*Assuming that this top Blob is not used as a bottom (input) by any
- * other layer of the Net.)
- * @param propagate_down see Layer::Backward.
- * @param bottom input Blob vector (length 2)
- * -# @f$ (N \times C \times H \times W) @f$
- * the predictions @f$\hat{y}@f$; Backward fills their diff with
- * gradients @f$
- * \frac{\partial E}{\partial \hat{y}} =
- * \frac{1}{n} \sum\limits_{n=1}^N (\hat{y}_n - y_n)
- * @f$ if propagate_down[0]
- * -# @f$ (N \times C \times H \times W) @f$
- * the targets @f$y@f$; Backward fills their diff with gradients
- * @f$ \frac{\partial E}{\partial y} =
- * \frac{1}{n} \sum\limits_{n=1}^N (y_n - \hat{y}_n)
- * @f$ if propagate_down[1]
- */
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- Blob<Dtype> diff_;
-};
-
-/**
- * @brief Computes the hinge loss for a one-of-many classification task.
- *
- * @param bottom input Blob vector (length 2)
- * -# @f$ (N \times C \times H \times W) @f$
- * the predictions @f$ t @f$, a Blob with values in
- * @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
- * the @f$ K = CHW @f$ classes. In an SVM, @f$ t @f$ is the result of
- * taking the inner product @f$ X^T W @f$ of the D-dimensional features
- * @f$ X \in \mathcal{R}^{D \times N} @f$ and the learned hyperplane
- * parameters @f$ W \in \mathcal{R}^{D \times K} @f$, so a Net with just
- * an InnerProductLayer (with num_output = D) providing predictions to a
- * HingeLossLayer and no other learnable parameters or losses is
- * equivalent to an SVM.
- * -# @f$ (N \times 1 \times 1 \times 1) @f$
- * the labels @f$ l @f$, an integer-valued Blob with values
- * @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
- * indicating the correct class label among the @f$ K @f$ classes
- * @param top output Blob vector (length 1)
- * -# @f$ (1 \times 1 \times 1 \times 1) @f$
- * the computed hinge loss: @f$ E =
- * \frac{1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^K
- * [\max(0, 1 - \delta\{l_n = k\} t_{nk})] ^ p
- * @f$, for the @f$ L^p @f$ norm
- * (defaults to @f$ p = 1 @f$, the L1 norm; L2 norm, as in L2-SVM,
- * is also available), and @f$
- * \delta\{\mathrm{condition}\} = \left\{
- * \begin{array}{lr}
- * 1 & \mbox{if condition} \\
- * -1 & \mbox{otherwise}
- * \end{array} \right.
- * @f$
- *
- * In an SVM, @f$ t \in \mathcal{R}^{N \times K} @f$ is the result of taking
- * the inner product @f$ X^T W @f$ of the features
- * @f$ X \in \mathcal{R}^{D \times N} @f$
- * and the learned hyperplane parameters
- * @f$ W \in \mathcal{R}^{D \times K} @f$. So, a Net with just an
- * InnerProductLayer (with num_output = @f$k@f$) providing predictions to a
- * HingeLossLayer is equivalent to an SVM (assuming it has no other learned
- * outside the InnerProductLayer and no other losses outside the
- * HingeLossLayer).
- */
-template <typename Dtype>
-class HingeLossLayer : public LossLayer<Dtype> {
- public:
- explicit HingeLossLayer(const LayerParameter& param)
- : LossLayer<Dtype>(param) {}
-
- virtual inline const char* type() const { return "HingeLoss"; }
-
- protected:
- /// @copydoc HingeLossLayer
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- /**
- * @brief Computes the hinge loss error gradient w.r.t. the predictions.
- *
- * Gradients cannot be computed with respect to the label inputs (bottom[1]),
- * so this method ignores bottom[1] and requires !propagate_down[1], crashing
- * if propagate_down[1] is set.
- *
- * @param top output Blob vector (length 1), providing the error gradient with
- * respect to the outputs
- * -# @f$ (1 \times 1 \times 1 \times 1) @f$
- * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
- * as @f$ \lambda @f$ is the coefficient of this layer's output
- * @f$\ell_i@f$ in the overall Net loss
- * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
- * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
- * (*Assuming that this top Blob is not used as a bottom (input) by any
- * other layer of the Net.)
- * @param propagate_down see Layer::Backward.
- * propagate_down[1] must be false as we can't compute gradients with
- * respect to the labels.
- * @param bottom input Blob vector (length 2)
- * -# @f$ (N \times C \times H \times W) @f$
- * the predictions @f$t@f$; Backward computes diff
- * @f$ \frac{\partial E}{\partial t} @f$
- * -# @f$ (N \times 1 \times 1 \times 1) @f$
- * the labels -- ignored as we can't compute their error gradients
- */
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-/**
- * @brief A generalization of MultinomialLogisticLossLayer that takes an
- * "information gain" (infogain) matrix specifying the "value" of all label
- * pairs.
- *
- * Equivalent to the MultinomialLogisticLossLayer if the infogain matrix is the
- * identity.
- *
- * @param bottom input Blob vector (length 2-3)
- * -# @f$ (N \times C \times H \times W) @f$
- * the predictions @f$ \hat{p} @f$, a Blob with values in
- * @f$ [0, 1] @f$ indicating the predicted probability of each of the
- * @f$ K = CHW @f$ classes. Each prediction vector @f$ \hat{p}_n @f$
- * should sum to 1 as in a probability distribution: @f$
- * \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$.
- * -# @f$ (N \times 1 \times 1 \times 1) @f$
- * the labels @f$ l @f$, an integer-valued Blob with values
- * @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
- * indicating the correct class label among the @f$ K @f$ classes
- * -# @f$ (1 \times 1 \times K \times K) @f$
- * (\b optional) the infogain matrix @f$ H @f$. This must be provided as
- * the third bottom blob input if not provided as the infogain_mat in the
- * InfogainLossParameter. If @f$ H = I @f$, this layer is equivalent to the
- * MultinomialLogisticLossLayer.
- * @param top output Blob vector (length 1)
- * -# @f$ (1 \times 1 \times 1 \times 1) @f$
- * the computed infogain multinomial logistic loss: @f$ E =
- * \frac{-1}{N} \sum\limits_{n=1}^N H_{l_n} \log(\hat{p}_n) =
- * \frac{-1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^{K} H_{l_n,k}
- * \log(\hat{p}_{n,k})
- * @f$, where @f$ H_{l_n} @f$ denotes row @f$l_n@f$ of @f$H@f$.
- */
-template <typename Dtype>
-class InfogainLossLayer : public LossLayer<Dtype> {
- public:
- explicit InfogainLossLayer(const LayerParameter& param)
- : LossLayer<Dtype>(param), infogain_() {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- // InfogainLossLayer takes 2-3 bottom Blobs; if there are 3 the third should
- // be the infogain matrix. (Otherwise the infogain matrix is loaded from a
- // file specified by LayerParameter.)
- virtual inline int ExactNumBottomBlobs() const { return -1; }
- virtual inline int MinBottomBlobs() const { return 2; }
- virtual inline int MaxBottomBlobs() const { return 3; }
-
- virtual inline const char* type() const { return "InfogainLoss"; }
-
- protected:
- /// @copydoc InfogainLossLayer
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- /**
- * @brief Computes the infogain loss error gradient w.r.t. the predictions.
- *
- * Gradients cannot be computed with respect to the label inputs (bottom[1]),
- * so this method ignores bottom[1] and requires !propagate_down[1], crashing
- * if propagate_down[1] is set. (The same applies to the infogain matrix, if
- * provided as bottom[2] rather than in the layer_param.)
- *
- * @param top output Blob vector (length 1), providing the error gradient
- * with respect to the outputs
- * -# @f$ (1 \times 1 \times 1 \times 1) @f$
- * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
- * as @f$ \lambda @f$ is the coefficient of this layer's output
- * @f$\ell_i@f$ in the overall Net loss
- * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
- * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
- * (*Assuming that this top Blob is not used as a bottom (input) by any
- * other layer of the Net.)
- * @param propagate_down see Layer::Backward.
- * propagate_down[1] must be false as we can't compute gradients with
- * respect to the labels (similarly for propagate_down[2] and the
- * infogain matrix, if provided as bottom[2])
- * @param bottom input Blob vector (length 2-3)
- * -# @f$ (N \times C \times H \times W) @f$
- * the predictions @f$ \hat{p} @f$; Backward computes diff
- * @f$ \frac{\partial E}{\partial \hat{p}} @f$
- * -# @f$ (N \times 1 \times 1 \times 1) @f$
- * the labels -- ignored as we can't compute their error gradients
- * -# @f$ (1 \times 1 \times K \times K) @f$
- * (\b optional) the information gain matrix -- ignored as its error
- * gradient computation is not implemented.
- */
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- Blob<Dtype> infogain_;
-};
-
-/**
- * @brief Computes the multinomial logistic loss for a one-of-many
- * classification task, directly taking a predicted probability
- * distribution as input.
- *
- * When predictions are not already a probability distribution, you should
- * instead use the SoftmaxWithLossLayer, which maps predictions to a
- * distribution using the SoftmaxLayer, before computing the multinomial
- * logistic loss. The SoftmaxWithLossLayer should be preferred over separate
- * SoftmaxLayer + MultinomialLogisticLossLayer
- * as its gradient computation is more numerically stable.
- *
- * @param bottom input Blob vector (length 2)
- * -# @f$ (N \times C \times H \times W) @f$
- * the predictions @f$ \hat{p} @f$, a Blob with values in
- * @f$ [0, 1] @f$ indicating the predicted probability of each of the
- * @f$ K = CHW @f$ classes. Each prediction vector @f$ \hat{p}_n @f$
- * should sum to 1 as in a probability distribution: @f$
- * \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$.
- * -# @f$ (N \times 1 \times 1 \times 1) @f$
- * the labels @f$ l @f$, an integer-valued Blob with values
- * @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
- * indicating the correct class label among the @f$ K @f$ classes
- * @param top output Blob vector (length 1)
- * -# @f$ (1 \times 1 \times 1 \times 1) @f$
- * the computed multinomial logistic loss: @f$ E =
- * \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n})
- * @f$
- */
-template <typename Dtype>
-class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
- public:
- explicit MultinomialLogisticLossLayer(const LayerParameter& param)
- : LossLayer<Dtype>(param) {}
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "MultinomialLogisticLoss"; }
-
- protected:
- /// @copydoc MultinomialLogisticLossLayer
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- /**
- * @brief Computes the multinomial logistic loss error gradient w.r.t. the
- * predictions.
- *
- * Gradients cannot be computed with respect to the label inputs (bottom[1]),
- * so this method ignores bottom[1] and requires !propagate_down[1], crashing
- * if propagate_down[1] is set.
- *
- * @param top output Blob vector (length 1), providing the error gradient with
- * respect to the outputs
- * -# @f$ (1 \times 1 \times 1 \times 1) @f$
- * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
- * as @f$ \lambda @f$ is the coefficient of this layer's output
- * @f$\ell_i@f$ in the overall Net loss
- * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
- * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
- * (*Assuming that this top Blob is not used as a bottom (input) by any
- * other layer of the Net.)
- * @param propagate_down see Layer::Backward.
- * propagate_down[1] must be false as we can't compute gradients with
- * respect to the labels.
- * @param bottom input Blob vector (length 2)
- * -# @f$ (N \times C \times H \times W) @f$
- * the predictions @f$ \hat{p} @f$; Backward computes diff
- * @f$ \frac{\partial E}{\partial \hat{p}} @f$
- * -# @f$ (N \times 1 \times 1 \times 1) @f$
- * the labels -- ignored as we can't compute their error gradients
- */
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-/**
- * @brief Computes the cross-entropy (logistic) loss @f$
- * E = \frac{-1}{n} \sum\limits_{n=1}^N \left[
- * p_n \log \hat{p}_n +
- * (1 - p_n) \log(1 - \hat{p}_n)
- * \right]
- * @f$, often used for predicting targets interpreted as probabilities.
- *
- * This layer is implemented rather than separate
- * SigmoidLayer + CrossEntropyLayer
- * as its gradient computation is more numerically stable.
- * At test time, this layer can be replaced simply by a SigmoidLayer.
- *
- * @param bottom input Blob vector (length 2)
- * -# @f$ (N \times C \times H \times W) @f$
- * the scores @f$ x \in [-\infty, +\infty]@f$,
- * which this layer maps to probability predictions
- * @f$ \hat{p}_n = \sigma(x_n) \in [0, 1] @f$
- * using the sigmoid function @f$ \sigma(.) @f$ (see SigmoidLayer).
- * -# @f$ (N \times C \times H \times W) @f$
- * the targets @f$ y \in [0, 1] @f$
- * @param top output Blob vector (length 1)
- * -# @f$ (1 \times 1 \times 1 \times 1) @f$
- * the computed cross-entropy loss: @f$
- * E = \frac{-1}{n} \sum\limits_{n=1}^N \left[
- * p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n)
- * \right]
- * @f$
- */
-template <typename Dtype>
-class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
- public:
- explicit SigmoidCrossEntropyLossLayer(const LayerParameter& param)
- : LossLayer<Dtype>(param),
- sigmoid_layer_(new SigmoidLayer<Dtype>(param)),
- sigmoid_output_(new Blob<Dtype>()) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "SigmoidCrossEntropyLoss"; }
-
- protected:
- /// @copydoc SigmoidCrossEntropyLossLayer
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- /**
- * @brief Computes the sigmoid cross-entropy loss error gradient w.r.t. the
- * predictions.
- *
- * Gradients cannot be computed with respect to the target inputs (bottom[1]),
- * so this method ignores bottom[1] and requires !propagate_down[1], crashing
- * if propagate_down[1] is set.
- *
- * @param top output Blob vector (length 1), providing the error gradient with
- * respect to the outputs
- * -# @f$ (1 \times 1 \times 1 \times 1) @f$
- * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
- * as @f$ \lambda @f$ is the coefficient of this layer's output
- * @f$\ell_i@f$ in the overall Net loss
- * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
- * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
- * (*Assuming that this top Blob is not used as a bottom (input) by any
- * other layer of the Net.)
- * @param propagate_down see Layer::Backward.
- * propagate_down[1] must be false as gradient computation with respect
- * to the targets is not implemented.
- * @param bottom input Blob vector (length 2)
- * -# @f$ (N \times C \times H \times W) @f$
- * the predictions @f$x@f$; Backward computes diff
- * @f$ \frac{\partial E}{\partial x} =
- * \frac{1}{n} \sum\limits_{n=1}^N (\hat{p}_n - p_n)
- * @f$
- * -# @f$ (N \times 1 \times 1 \times 1) @f$
- * the labels -- ignored as we can't compute their error gradients
- */
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- /// The internal SigmoidLayer used to map predictions to probabilities.
- shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
- /// sigmoid_output stores the output of the SigmoidLayer.
- shared_ptr<Blob<Dtype> > sigmoid_output_;
- /// bottom vector holder to call the underlying SigmoidLayer::Forward
- vector<Blob<Dtype>*> sigmoid_bottom_vec_;
- /// top vector holder to call the underlying SigmoidLayer::Forward
- vector<Blob<Dtype>*> sigmoid_top_vec_;
-};
-
-// Forward declare SoftmaxLayer for use in SoftmaxWithLossLayer.
-template <typename Dtype> class SoftmaxLayer;
-
-/**
- * @brief Computes the multinomial logistic loss for a one-of-many
- * classification task, passing real-valued predictions through a
- * softmax to get a probability distribution over classes.
- *
- * This layer should be preferred over separate
- * SoftmaxLayer + MultinomialLogisticLossLayer
- * as its gradient computation is more numerically stable.
- * At test time, this layer can be replaced simply by a SoftmaxLayer.
- *
- * @param bottom input Blob vector (length 2)
- * -# @f$ (N \times C \times H \times W) @f$
- * the predictions @f$ x @f$, a Blob with values in
- * @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
- * the @f$ K = CHW @f$ classes. This layer maps these scores to a
- * probability distribution over classes using the softmax function
- * @f$ \hat{p}_{nk} = \exp(x_{nk}) /
- * \left[\sum_{k'} \exp(x_{nk'})\right] @f$ (see SoftmaxLayer).
- * -# @f$ (N \times 1 \times 1 \times 1) @f$
- * the labels @f$ l @f$, an integer-valued Blob with values
- * @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
- * indicating the correct class label among the @f$ K @f$ classes
- * @param top output Blob vector (length 1)
- * -# @f$ (1 \times 1 \times 1 \times 1) @f$
- * the computed cross-entropy classification loss: @f$ E =
- * \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n})
- * @f$, for softmax output class probabilites @f$ \hat{p} @f$
- */
-template <typename Dtype>
-class SoftmaxWithLossLayer : public LossLayer<Dtype> {
- public:
- /**
- * @param param provides LossParameter loss_param, with options:
- * - ignore_label (optional)
- * Specify a label value that should be ignored when computing the loss.
- * - normalize (optional, default true)
- * If true, the loss is normalized by the number of (nonignored) labels
- * present; otherwise the loss is simply summed over spatial locations.
- */
- explicit SoftmaxWithLossLayer(const LayerParameter& param)
- : LossLayer<Dtype>(param) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "SoftmaxWithLoss"; }
- virtual inline int ExactNumTopBlobs() const { return -1; }
- virtual inline int MinTopBlobs() const { return 1; }
- virtual inline int MaxTopBlobs() const { return 2; }
-
- protected:
- /// @copydoc SoftmaxWithLossLayer
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- /**
- * @brief Computes the softmax loss error gradient w.r.t. the predictions.
- *
- * Gradients cannot be computed with respect to the label inputs (bottom[1]),
- * so this method ignores bottom[1] and requires !propagate_down[1], crashing
- * if propagate_down[1] is set.
- *
- * @param top output Blob vector (length 1), providing the error gradient with
- * respect to the outputs
- * -# @f$ (1 \times 1 \times 1 \times 1) @f$
- * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
- * as @f$ \lambda @f$ is the coefficient of this layer's output
- * @f$\ell_i@f$ in the overall Net loss
- * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
- * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
- * (*Assuming that this top Blob is not used as a bottom (input) by any
- * other layer of the Net.)
- * @param propagate_down see Layer::Backward.
- * propagate_down[1] must be false as we can't compute gradients with
- * respect to the labels.
- * @param bottom input Blob vector (length 2)
- * -# @f$ (N \times C \times H \times W) @f$
- * the predictions @f$ x @f$; Backward computes diff
- * @f$ \frac{\partial E}{\partial x} @f$
- * -# @f$ (N \times 1 \times 1 \times 1) @f$
- * the labels -- ignored as we can't compute their error gradients
- */
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-
- /// The internal SoftmaxLayer used to map predictions to a distribution.
- shared_ptr<Layer<Dtype> > softmax_layer_;
- /// prob stores the output probability predictions from the SoftmaxLayer.
- Blob<Dtype> prob_;
- /// bottom vector holder used in call to the underlying SoftmaxLayer::Forward
- vector<Blob<Dtype>*> softmax_bottom_vec_;
- /// top vector holder used in call to the underlying SoftmaxLayer::Forward
- vector<Blob<Dtype>*> softmax_top_vec_;
- /// Whether to ignore instances with a certain label.
- bool has_ignore_label_;
- /// The label indicating that an instance should be ignored.
- int ignore_label_;
- /// Whether to normalize the loss by the total number of values present
- /// (otherwise just by the batch size).
- bool normalize_;
-
- int softmax_axis_, outer_num_, inner_num_;
-};
-
-} // namespace caffe
-
-#endif // CAFFE_LOSS_LAYERS_HPP_
template <typename Dtype>
class Net {
public:
- explicit Net(const NetParameter& param);
- explicit Net(const string& param_file, Phase phase);
+ explicit Net(const NetParameter& param, const Net* root_net = NULL);
+ explicit Net(const string& param_file, Phase phase,
+ const Net* root_net = NULL);
virtual ~Net() {}
/// @brief Initialize a network with a NetParameter.
string Forward(const string& input_blob_protos, Dtype* loss = NULL);
/**
+ * @brief Zeroes out the diffs of all net parameters.
+ * Should be run before Backward.
+ */
+ void ClearParamDiffs();
+
+ /**
* The network backward should take no input and output, since it solely
* computes the gradient w.r.t the parameters, and the data has already been
* provided during the forward pass.
/// @brief Updates the network weights based on the diff values computed.
void Update();
+ /**
+ * @brief Shares weight data of owner blobs with shared blobs.
+ *
+ * Note: this is called by Net::Init, and thus should normally not be
+ * called manually.
+ */
+ void ShareWeights();
/**
* @brief For an already initialized net, implicitly copies (i.e., using no
*/
void CopyTrainedLayersFrom(const NetParameter& param);
void CopyTrainedLayersFrom(const string trained_filename);
+ void CopyTrainedLayersFromBinaryProto(const string trained_filename);
+ void CopyTrainedLayersFromHDF5(const string trained_filename);
/// @brief Writes the net to a proto.
void ToProto(NetParameter* param, bool write_diff = false) const;
+ /// @brief Writes the net to an HDF5 file.
+ void ToHDF5(const string& filename, bool write_diff = false) const;
/// @brief returns the network name.
inline const string& name() const { return name_; }
inline const vector<vector<Blob<Dtype>*> >& top_vecs() const {
return top_vecs_;
}
+ /// @brief returns the ids of the top blobs of layer i
+ inline const vector<int> & top_ids(int i) const {
+ CHECK_GE(i, 0) << "Invalid layer id";
+ CHECK_LT(i, top_id_vecs_.size()) << "Invalid layer id";
+ return top_id_vecs_[i];
+ }
+ /// @brief returns the ids of the bottom blobs of layer i
+ inline const vector<int> & bottom_ids(int i) const {
+ CHECK_GE(i, 0) << "Invalid layer id";
+ CHECK_LT(i, bottom_id_vecs_.size()) << "Invalid layer id";
+ return bottom_id_vecs_[i];
+ }
inline const vector<vector<bool> >& bottom_need_backward() const {
return bottom_need_backward_;
}
inline const vector<Dtype>& blob_loss_weights() const {
return blob_loss_weights_;
}
+ inline const vector<bool>& layer_need_backward() const {
+ return layer_need_backward_;
+ }
/// @brief returns the parameters
inline const vector<shared_ptr<Blob<Dtype> > >& params() const {
return params_;
}
- /// @brief returns the parameter learning rate multipliers
+ inline const vector<Blob<Dtype>*>& learnable_params() const {
+ return learnable_params_;
+ }
+ /// @brief returns the learnable parameter learning rate multipliers
inline const vector<float>& params_lr() const { return params_lr_; }
+ inline const vector<bool>& has_params_lr() const { return has_params_lr_; }
+ /// @brief returns the learnable parameter decay multipliers
inline const vector<float>& params_weight_decay() const {
return params_weight_decay_;
}
+ inline const vector<bool>& has_params_decay() const {
+ return has_params_decay_;
+ }
const map<string, int>& param_names_index() const {
return param_names_index_;
}
/// @brief Helper for displaying debug info in Update.
void UpdateDebugInfo(const int param_id);
- /// @brief Get misc parameters, e.g. the LR multiplier and weight decay.
- void GetLearningRateAndWeightDecay();
-
/// @brief The network name
string name_;
/// @brief The phase: TRAIN or TEST
vector<Blob<Dtype>*> net_output_blobs_;
/// The parameters in the network.
vector<shared_ptr<Blob<Dtype> > > params_;
- /// the learning rate multipliers
+ vector<Blob<Dtype>*> learnable_params_;
+ /**
+ * The mapping from params_ -> learnable_params_: we have
+ * learnable_param_ids_.size() == params_.size(),
+ * and learnable_params_[learnable_param_ids_[i]] == params_[i].get()
+ * if and only if params_[i] is an "owner"; otherwise, params_[i] is a sharer
+ * and learnable_params_[learnable_param_ids_[i]] gives its owner.
+ */
+ vector<int> learnable_param_ids_;
+ /// the learning rate multipliers for learnable_params_
vector<float> params_lr_;
- /// the weight decay multipliers
+ vector<bool> has_params_lr_;
+ /// the weight decay multipliers for learnable_params_
vector<float> params_weight_decay_;
+ vector<bool> has_params_decay_;
/// The bytes of memory used by this net
size_t memory_used_;
/// Whether to compute and display debug info for the net.
bool debug_info_;
-
+ /// The root net that actually holds the shared layers in data parallelism
+ const Net* const root_net_;
DISABLE_COPY_AND_ASSIGN(Net);
};
+++ /dev/null
-#ifndef CAFFE_NEURON_LAYERS_HPP_
-#define CAFFE_NEURON_LAYERS_HPP_
-
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/net.hpp"
-#include "caffe/proto/caffe.pb.h"
-
-#define HDF5_DATA_DATASET_NAME "data"
-#define HDF5_DATA_LABEL_NAME "label"
-
-namespace caffe {
-
-/**
- * @brief An interface for layers that take one blob as input (@f$ x @f$)
- * and produce one equally-sized blob as output (@f$ y @f$), where
- * each element of the output depends only on the corresponding input
- * element.
- */
-template <typename Dtype>
-class NeuronLayer : public Layer<Dtype> {
- public:
- explicit NeuronLayer(const LayerParameter& param)
- : Layer<Dtype>(param) {}
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline int ExactNumBottomBlobs() const { return 1; }
- virtual inline int ExactNumTopBlobs() const { return 1; }
-};
-
-/**
- * @brief Computes @f$ y = |x| @f$
- *
- * @param bottom input Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the inputs @f$ x @f$
- * @param top output Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the computed outputs @f$ y = |x| @f$
- */
-template <typename Dtype>
-class AbsValLayer : public NeuronLayer<Dtype> {
- public:
- explicit AbsValLayer(const LayerParameter& param)
- : NeuronLayer<Dtype>(param) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "AbsVal"; }
- virtual inline int ExactNumBottomBlobs() const { return 1; }
- virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
- /// @copydoc AbsValLayer
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- /**
- * @brief Computes the error gradient w.r.t. the absolute value inputs.
- *
- * @param top output Blob vector (length 1), providing the error gradient with
- * respect to the outputs
- * -# @f$ (N \times C \times H \times W) @f$
- * containing error gradients @f$ \frac{\partial E}{\partial y} @f$
- * with respect to computed outputs @f$ y @f$
- * @param propagate_down see Layer::Backward.
- * @param bottom input Blob vector (length 2)
- * -# @f$ (N \times C \times H \times W) @f$
- * the inputs @f$ x @f$; Backward fills their diff with
- * gradients @f$
- * \frac{\partial E}{\partial x} =
- * \mathrm{sign}(x) \frac{\partial E}{\partial y}
- * @f$ if propagate_down[0]
- */
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-/**
- * @brief Computes @f$ y = x + \log(1 + \exp(-x)) @f$ if @f$ x > 0 @f$;
- * @f$ y = \log(1 + \exp(x)) @f$ otherwise.
- *
- * @param bottom input Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the inputs @f$ x @f$
- * @param top output Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the computed outputs @f$
- * y = \left\{
- * \begin{array}{ll}
- * x + \log(1 + \exp(-x)) & \mbox{if } x > 0 \\
- * \log(1 + \exp(x)) & \mbox{otherwise}
- * \end{array} \right.
- * @f$
- */
-template <typename Dtype>
-class BNLLLayer : public NeuronLayer<Dtype> {
- public:
- explicit BNLLLayer(const LayerParameter& param)
- : NeuronLayer<Dtype>(param) {}
-
- virtual inline const char* type() const { return "BNLL"; }
-
- protected:
- /// @copydoc BNLLLayer
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- /**
- * @brief Computes the error gradient w.r.t. the BNLL inputs.
- *
- * @param top output Blob vector (length 1), providing the error gradient with
- * respect to the outputs
- * -# @f$ (N \times C \times H \times W) @f$
- * containing error gradients @f$ \frac{\partial E}{\partial y} @f$
- * with respect to computed outputs @f$ y @f$
- * @param propagate_down see Layer::Backward.
- * @param bottom input Blob vector (length 2)
- * -# @f$ (N \times C \times H \times W) @f$
- * the inputs @f$ x @f$; Backward fills their diff with
- * gradients @f$
- * \frac{\partial E}{\partial x}
- * @f$ if propagate_down[0]
- */
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-/**
- * @brief During training only, sets a random portion of @f$x@f$ to 0, adjusting
- * the rest of the vector magnitude accordingly.
- *
- * @param bottom input Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the inputs @f$ x @f$
- * @param top output Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the computed outputs @f$ y = |x| @f$
- */
-template <typename Dtype>
-class DropoutLayer : public NeuronLayer<Dtype> {
- public:
- /**
- * @param param provides DropoutParameter dropout_param,
- * with DropoutLayer options:
- * - dropout_ratio (\b optional, default 0.5).
- * Sets the probability @f$ p @f$ that any given unit is dropped.
- */
- explicit DropoutLayer(const LayerParameter& param)
- : NeuronLayer<Dtype>(param) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "Dropout"; }
-
- protected:
- /**
- * @param bottom input Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the inputs @f$ x @f$
- * @param top output Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the computed outputs. At training time, we have @f$
- * y_{\mbox{train}} = \left\{
- * \begin{array}{ll}
- * \frac{x}{1 - p} & \mbox{if } u > p \\
- * 0 & \mbox{otherwise}
- * \end{array} \right.
- * @f$, where @f$ u \sim U(0, 1)@f$ is generated independently for each
- * input at each iteration. At test time, we simply have
- * @f$ y_{\mbox{test}} = \mathbb{E}[y_{\mbox{train}}] = x @f$.
- */
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- /// when divided by UINT_MAX, the randomly generated values @f$u\sim U(0,1)@f$
- Blob<unsigned int> rand_vec_;
- /// the probability @f$ p @f$ of dropping any input
- Dtype threshold_;
- /// the scale for undropped inputs at train time @f$ 1 / (1 - p) @f$
- Dtype scale_;
- unsigned int uint_thres_;
-};
-
-/**
- * @brief Computes @f$ y = \gamma ^ {\alpha x + \beta} @f$,
- * as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
- * and base @f$ \gamma @f$.
- */
-template <typename Dtype>
-class ExpLayer : public NeuronLayer<Dtype> {
- public:
- /**
- * @param param provides ExpParameter exp_param,
- * with ExpLayer options:
- * - scale (\b optional, default 1) the scale @f$ \alpha @f$
- * - shift (\b optional, default 0) the shift @f$ \beta @f$
- * - base (\b optional, default -1 for a value of @f$ e \approx 2.718 @f$)
- * the base @f$ \gamma @f$
- */
- explicit ExpLayer(const LayerParameter& param)
- : NeuronLayer<Dtype>(param) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "Exp"; }
-
- protected:
- /**
- * @param bottom input Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the inputs @f$ x @f$
- * @param top output Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the computed outputs @f$
- * y = \gamma ^ {\alpha x + \beta}
- * @f$
- */
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- /**
- * @brief Computes the error gradient w.r.t. the exp inputs.
- *
- * @param top output Blob vector (length 1), providing the error gradient with
- * respect to the outputs
- * -# @f$ (N \times C \times H \times W) @f$
- * containing error gradients @f$ \frac{\partial E}{\partial y} @f$
- * with respect to computed outputs @f$ y @f$
- * @param propagate_down see Layer::Backward.
- * @param bottom input Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the inputs @f$ x @f$; Backward fills their diff with
- * gradients @f$
- * \frac{\partial E}{\partial x} =
- * \frac{\partial E}{\partial y} y \alpha \log_e(gamma)
- * @f$ if propagate_down[0]
- */
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- Dtype inner_scale_, outer_scale_;
-};
-
-/**
- * @brief Computes @f$ y = (\alpha x + \beta) ^ \gamma @f$,
- * as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
- * and power @f$ \gamma @f$.
- */
-template <typename Dtype>
-class PowerLayer : public NeuronLayer<Dtype> {
- public:
- /**
- * @param param provides PowerParameter power_param,
- * with PowerLayer options:
- * - scale (\b optional, default 1) the scale @f$ \alpha @f$
- * - shift (\b optional, default 0) the shift @f$ \beta @f$
- * - power (\b optional, default 1) the power @f$ \gamma @f$
- */
- explicit PowerLayer(const LayerParameter& param)
- : NeuronLayer<Dtype>(param) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "Power"; }
-
- protected:
- /**
- * @param bottom input Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the inputs @f$ x @f$
- * @param top output Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the computed outputs @f$
- * y = (\alpha x + \beta) ^ \gamma
- * @f$
- */
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- /**
- * @brief Computes the error gradient w.r.t. the power inputs.
- *
- * @param top output Blob vector (length 1), providing the error gradient with
- * respect to the outputs
- * -# @f$ (N \times C \times H \times W) @f$
- * containing error gradients @f$ \frac{\partial E}{\partial y} @f$
- * with respect to computed outputs @f$ y @f$
- * @param propagate_down see Layer::Backward.
- * @param bottom input Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the inputs @f$ x @f$; Backward fills their diff with
- * gradients @f$
- * \frac{\partial E}{\partial x} =
- * \frac{\partial E}{\partial y}
- * \alpha \gamma (\alpha x + \beta) ^ {\gamma - 1} =
- * \frac{\partial E}{\partial y}
- * \frac{\alpha \gamma y}{\alpha x + \beta}
- * @f$ if propagate_down[0]
- */
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- /// @brief @f$ \gamma @f$ from layer_param_.power_param()
- Dtype power_;
- /// @brief @f$ \alpha @f$ from layer_param_.power_param()
- Dtype scale_;
- /// @brief @f$ \beta @f$ from layer_param_.power_param()
- Dtype shift_;
- /// @brief Result of @f$ \alpha \gamma @f$
- Dtype diff_scale_;
-};
-
-/**
- * @brief Rectified Linear Unit non-linearity @f$ y = \max(0, x) @f$.
- * The simple max is fast to compute, and the function does not saturate.
- */
-template <typename Dtype>
-class ReLULayer : public NeuronLayer<Dtype> {
- public:
- /**
- * @param param provides ReLUParameter relu_param,
- * with ReLULayer options:
- * - negative_slope (\b optional, default 0).
- * the value @f$ \nu @f$ by which negative values are multiplied.
- */
- explicit ReLULayer(const LayerParameter& param)
- : NeuronLayer<Dtype>(param) {}
-
- virtual inline const char* type() const { return "ReLU"; }
-
- protected:
- /**
- * @param bottom input Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the inputs @f$ x @f$
- * @param top output Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the computed outputs @f$
- * y = \max(0, x)
- * @f$ by default. If a non-zero negative_slope @f$ \nu @f$ is provided,
- * the computed outputs are @f$ y = \max(0, x) + \nu \min(0, x) @f$.
- */
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- /**
- * @brief Computes the error gradient w.r.t. the ReLU inputs.
- *
- * @param top output Blob vector (length 1), providing the error gradient with
- * respect to the outputs
- * -# @f$ (N \times C \times H \times W) @f$
- * containing error gradients @f$ \frac{\partial E}{\partial y} @f$
- * with respect to computed outputs @f$ y @f$
- * @param propagate_down see Layer::Backward.
- * @param bottom input Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the inputs @f$ x @f$; Backward fills their diff with
- * gradients @f$
- * \frac{\partial E}{\partial x} = \left\{
- * \begin{array}{lr}
- * 0 & \mathrm{if} \; x \le 0 \\
- * \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0
- * \end{array} \right.
- * @f$ if propagate_down[0], by default.
- * If a non-zero negative_slope @f$ \nu @f$ is provided,
- * the computed gradients are @f$
- * \frac{\partial E}{\partial x} = \left\{
- * \begin{array}{lr}
- * \nu \frac{\partial E}{\partial y} & \mathrm{if} \; x \le 0 \\
- * \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0
- * \end{array} \right.
- * @f$.
- */
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-#ifdef USE_CUDNN
-/**
- * @brief CuDNN acceleration of ReLULayer.
- */
-template <typename Dtype>
-class CuDNNReLULayer : public ReLULayer<Dtype> {
- public:
- explicit CuDNNReLULayer(const LayerParameter& param)
- : ReLULayer<Dtype>(param), handles_setup_(false) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual ~CuDNNReLULayer();
-
- protected:
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- bool handles_setup_;
- cudnnHandle_t handle_;
- cudnnTensor4dDescriptor_t bottom_desc_;
- cudnnTensor4dDescriptor_t top_desc_;
-};
-#endif
-
-/**
- * @brief Sigmoid function non-linearity @f$
- * y = (1 + \exp(-x))^{-1}
- * @f$, a classic choice in neural networks.
- *
- * Note that the gradient vanishes as the values move away from 0.
- * The ReLULayer is often a better choice for this reason.
- */
-template <typename Dtype>
-class SigmoidLayer : public NeuronLayer<Dtype> {
- public:
- explicit SigmoidLayer(const LayerParameter& param)
- : NeuronLayer<Dtype>(param) {}
-
- virtual inline const char* type() const { return "Sigmoid"; }
-
- protected:
- /**
- * @param bottom input Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the inputs @f$ x @f$
- * @param top output Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the computed outputs @f$
- * y = (1 + \exp(-x))^{-1}
- * @f$
- */
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- /**
- * @brief Computes the error gradient w.r.t. the sigmoid inputs.
- *
- * @param top output Blob vector (length 1), providing the error gradient with
- * respect to the outputs
- * -# @f$ (N \times C \times H \times W) @f$
- * containing error gradients @f$ \frac{\partial E}{\partial y} @f$
- * with respect to computed outputs @f$ y @f$
- * @param propagate_down see Layer::Backward.
- * @param bottom input Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the inputs @f$ x @f$; Backward fills their diff with
- * gradients @f$
- * \frac{\partial E}{\partial x}
- * = \frac{\partial E}{\partial y} y (1 - y)
- * @f$ if propagate_down[0]
- */
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-#ifdef USE_CUDNN
-/**
- * @brief CuDNN acceleration of SigmoidLayer.
- */
-template <typename Dtype>
-class CuDNNSigmoidLayer : public SigmoidLayer<Dtype> {
- public:
- explicit CuDNNSigmoidLayer(const LayerParameter& param)
- : SigmoidLayer<Dtype>(param), handles_setup_(false) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual ~CuDNNSigmoidLayer();
-
- protected:
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- bool handles_setup_;
- cudnnHandle_t handle_;
- cudnnTensor4dDescriptor_t bottom_desc_;
- cudnnTensor4dDescriptor_t top_desc_;
-};
-#endif
-
-/**
- * @brief TanH hyperbolic tangent non-linearity @f$
- * y = \frac{\exp(2x) - 1}{\exp(2x) + 1}
- * @f$, popular in auto-encoders.
- *
- * Note that the gradient vanishes as the values move away from 0.
- * The ReLULayer is often a better choice for this reason.
- */
-template <typename Dtype>
-class TanHLayer : public NeuronLayer<Dtype> {
- public:
- explicit TanHLayer(const LayerParameter& param)
- : NeuronLayer<Dtype>(param) {}
-
- virtual inline const char* type() const { return "TanH"; }
-
- protected:
- /**
- * @param bottom input Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the inputs @f$ x @f$
- * @param top output Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the computed outputs @f$
- * y = \frac{\exp(2x) - 1}{\exp(2x) + 1}
- * @f$
- */
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- /**
- * @brief Computes the error gradient w.r.t. the sigmoid inputs.
- *
- * @param top output Blob vector (length 1), providing the error gradient with
- * respect to the outputs
- * -# @f$ (N \times C \times H \times W) @f$
- * containing error gradients @f$ \frac{\partial E}{\partial y} @f$
- * with respect to computed outputs @f$ y @f$
- * @param propagate_down see Layer::Backward.
- * @param bottom input Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the inputs @f$ x @f$; Backward fills their diff with
- * gradients @f$
- * \frac{\partial E}{\partial x}
- * = \frac{\partial E}{\partial y}
- * \left(1 - \left[\frac{\exp(2x) - 1}{exp(2x) + 1} \right]^2 \right)
- * = \frac{\partial E}{\partial y} (1 - y^2)
- * @f$ if propagate_down[0]
- */
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-#ifdef USE_CUDNN
-/**
- * @brief CuDNN acceleration of TanHLayer.
- */
-template <typename Dtype>
-class CuDNNTanHLayer : public TanHLayer<Dtype> {
- public:
- explicit CuDNNTanHLayer(const LayerParameter& param)
- : TanHLayer<Dtype>(param), handles_setup_(false) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual ~CuDNNTanHLayer();
-
- protected:
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- bool handles_setup_;
- cudnnHandle_t handle_;
- cudnnTensor4dDescriptor_t bottom_desc_;
- cudnnTensor4dDescriptor_t top_desc_;
-};
-#endif
-
-/**
- * @brief Tests whether the input exceeds a threshold: outputs 1 for inputs
- * above threshold; 0 otherwise.
- */
-template <typename Dtype>
-class ThresholdLayer : public NeuronLayer<Dtype> {
- public:
- /**
- * @param param provides ThresholdParameter threshold_param,
- * with ThresholdLayer options:
- * - threshold (\b optional, default 0).
- * the threshold value @f$ t @f$ to which the input values are compared.
- */
- explicit ThresholdLayer(const LayerParameter& param)
- : NeuronLayer<Dtype>(param) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "Threshold"; }
-
- protected:
- /**
- * @param bottom input Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the inputs @f$ x @f$
- * @param top output Blob vector (length 1)
- * -# @f$ (N \times C \times H \times W) @f$
- * the computed outputs @f$
- * y = \left\{
- * \begin{array}{lr}
- * 0 & \mathrm{if} \; x \le t \\
- * 1 & \mathrm{if} \; x > t
- * \end{array} \right.
- * @f$
- */
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- /// @brief Not implemented (non-differentiable function)
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
- NOT_IMPLEMENTED;
- }
-
- Dtype threshold_;
-};
-
-/**
- * @brief Parameterized Rectified Linear Unit non-linearity @f$
- * y_i = \max(0, x_i) + a_i \min(0, x_i)
- * @f$. The differences from ReLULayer are 1) negative slopes are
- * learnable though backprop and 2) negative slopes can vary across
- * channels. The number of axes of input blob should be greater than or
- * equal to 2. The 1st axis (0-based) is seen as channels.
- */
-template <typename Dtype>
-class PReLULayer : public NeuronLayer<Dtype> {
- public:
- /**
- * @param param provides PReLUParameter prelu_param,
- * with PReLULayer options:
- * - filler (\b optional, FillerParameter,
- * default {'type': constant 'value':0.25}).
- * - channel_shared (\b optional, default false).
- * negative slopes are shared across channels.
- */
- explicit PReLULayer(const LayerParameter& param)
- : NeuronLayer<Dtype>(param) {}
-
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "PReLU"; }
-
- protected:
- /**
- * @param bottom input Blob vector (length 1)
- * -# @f$ (N \times C \times ...) @f$
- * the inputs @f$ x @f$
- * @param top output Blob vector (length 1)
- * -# @f$ (N \times C \times ...) @f$
- * the computed outputs for each channel @f$i@f$ @f$
- * y_i = \max(0, x_i) + a_i \min(0, x_i)
- * @f$.
- */
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- /**
- * @brief Computes the error gradient w.r.t. the PReLU inputs.
- *
- * @param top output Blob vector (length 1), providing the error gradient with
- * respect to the outputs
- * -# @f$ (N \times C \times ...) @f$
- * containing error gradients @f$ \frac{\partial E}{\partial y} @f$
- * with respect to computed outputs @f$ y @f$
- * @param propagate_down see Layer::Backward.
- * @param bottom input Blob vector (length 1)
- * -# @f$ (N \times C \times ...) @f$
- * the inputs @f$ x @f$; For each channel @f$i@f$, backward fills their
- * diff with gradients @f$
- * \frac{\partial E}{\partial x_i} = \left\{
- * \begin{array}{lr}
- * a_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\
- * \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i > 0
- * \end{array} \right.
- * @f$.
- * If param_propagate_down_[0] is true, it fills the diff with gradients
- * @f$
- * \frac{\partial E}{\partial a_i} = \left\{
- * \begin{array}{lr}
- * \sum_{x_i} x_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\
- * 0 & \mathrm{if} \; x_i > 0
- * \end{array} \right.
- * @f$.
- */
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- bool channel_shared_;
- Blob<Dtype> multiplier_; // dot multipler for backward computation of params
- Blob<Dtype> bottom_memory_; // memory for in-place computation
-};
-
-} // namespace caffe
-
-#endif // CAFFE_NEURON_LAYERS_HPP_
--- /dev/null
+#ifndef CAFFE_PARALLEL_HPP_
+#define CAFFE_PARALLEL_HPP_
+
+#include <boost/date_time/posix_time/posix_time.hpp>
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/solver.hpp"
+#include "caffe/syncedmem.hpp"
+#include "caffe/util/blocking_queue.hpp"
+
+namespace caffe {
+
+// Represents a net parameters. Once a net is created, its parameter buffers can
+// be replaced by ones from Params, to allow parallelization. Params ensures
+// parameters are allocated in one consecutive array.
+template<typename Dtype>
+class Params {
+ public:
+ explicit Params(shared_ptr<Solver<Dtype> > root_solver);
+ virtual ~Params() {
+ }
+
+ inline size_t size() const {
+ return size_;
+ }
+ inline Dtype* data() const {
+ return data_;
+ }
+ inline Dtype* diff() const {
+ return diff_;
+ }
+
+ protected:
+ const size_t size_; // Size of buffers
+ Dtype* data_; // Network parameters
+ Dtype* diff_; // Gradient
+
+DISABLE_COPY_AND_ASSIGN(Params);
+};
+
+// Params stored in GPU memory.
+template<typename Dtype>
+class GPUParams : public Params<Dtype> {
+ public:
+ GPUParams(shared_ptr<Solver<Dtype> > root_solver, int device);
+ virtual ~GPUParams();
+
+ void configure(Solver<Dtype>* solver) const;
+
+ protected:
+ using Params<Dtype>::size_;
+ using Params<Dtype>::data_;
+ using Params<Dtype>::diff_;
+};
+
+class DevicePair {
+ public:
+ DevicePair(int parent, int device)
+ : parent_(parent),
+ device_(device) {
+ }
+ inline int parent() {
+ return parent_;
+ }
+ inline int device() {
+ return device_;
+ }
+
+ // Group GPUs in pairs, by proximity depending on machine's topology
+ static void compute(const vector<int> devices, vector<DevicePair>* pairs);
+
+ protected:
+ int parent_;
+ int device_;
+};
+
+// Synchronous data parallelism using map-reduce between local GPUs.
+template<typename Dtype>
+class P2PSync : public GPUParams<Dtype>, public Solver<Dtype>::Callback,
+ public InternalThread {
+ public:
+ explicit P2PSync(shared_ptr<Solver<Dtype> > root_solver,
+ P2PSync<Dtype>* parent, const SolverParameter& param);
+ virtual ~P2PSync();
+
+ inline const shared_ptr<Solver<Dtype> >& solver() const {
+ return solver_;
+ }
+
+ void run(const vector<int>& gpus);
+
+ protected:
+ void on_start();
+ void on_gradients_ready();
+
+ void InternalThreadEntry();
+
+ P2PSync<Dtype>* parent_;
+ vector<P2PSync<Dtype>*> children_;
+ BlockingQueue<P2PSync<Dtype>*> queue_;
+ const int initial_iter_;
+ Dtype* parent_grads_;
+ shared_ptr<Solver<Dtype> > solver_;
+
+ using Params<Dtype>::size_;
+ using Params<Dtype>::data_;
+ using Params<Dtype>::diff_;
+};
+
+} // namespace caffe
+
+#endif
--- /dev/null
+#ifndef CAFFE_SGD_SOLVERS_HPP_
+#define CAFFE_SGD_SOLVERS_HPP_
+
+#include <string>
+#include <vector>
+
+#include "caffe/solver.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Optimizes the parameters of a Net using
+ * stochastic gradient descent (SGD) with momentum.
+ */
+template <typename Dtype>
+class SGDSolver : public Solver<Dtype> {
+ public:
+ explicit SGDSolver(const SolverParameter& param)
+ : Solver<Dtype>(param) { PreSolve(); }
+ explicit SGDSolver(const string& param_file)
+ : Solver<Dtype>(param_file) { PreSolve(); }
+ virtual inline const char* type() const { return "SGD"; }
+
+ const vector<shared_ptr<Blob<Dtype> > >& history() { return history_; }
+
+ protected:
+ void PreSolve();
+ Dtype GetLearningRate();
+ virtual void ApplyUpdate();
+ virtual void Normalize(int param_id);
+ virtual void Regularize(int param_id);
+ virtual void ComputeUpdateValue(int param_id, Dtype rate);
+ virtual void ClipGradients();
+ virtual void SnapshotSolverState(const string& model_filename);
+ virtual void SnapshotSolverStateToBinaryProto(const string& model_filename);
+ virtual void SnapshotSolverStateToHDF5(const string& model_filename);
+ virtual void RestoreSolverStateFromHDF5(const string& state_file);
+ virtual void RestoreSolverStateFromBinaryProto(const string& state_file);
+ // history maintains the historical momentum data.
+ // update maintains update related data and is not needed in snapshots.
+ // temp maintains other information that might be needed in computation
+ // of gradients/updates and is not needed in snapshots
+ vector<shared_ptr<Blob<Dtype> > > history_, update_, temp_;
+
+ DISABLE_COPY_AND_ASSIGN(SGDSolver);
+};
+
+template <typename Dtype>
+class NesterovSolver : public SGDSolver<Dtype> {
+ public:
+ explicit NesterovSolver(const SolverParameter& param)
+ : SGDSolver<Dtype>(param) {}
+ explicit NesterovSolver(const string& param_file)
+ : SGDSolver<Dtype>(param_file) {}
+ virtual inline const char* type() const { return "Nesterov"; }
+
+ protected:
+ virtual void ComputeUpdateValue(int param_id, Dtype rate);
+
+ DISABLE_COPY_AND_ASSIGN(NesterovSolver);
+};
+
+template <typename Dtype>
+class AdaGradSolver : public SGDSolver<Dtype> {
+ public:
+ explicit AdaGradSolver(const SolverParameter& param)
+ : SGDSolver<Dtype>(param) { constructor_sanity_check(); }
+ explicit AdaGradSolver(const string& param_file)
+ : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); }
+ virtual inline const char* type() const { return "AdaGrad"; }
+
+ protected:
+ virtual void ComputeUpdateValue(int param_id, Dtype rate);
+ void constructor_sanity_check() {
+ CHECK_EQ(0, this->param_.momentum())
+ << "Momentum cannot be used with AdaGrad.";
+ }
+
+ DISABLE_COPY_AND_ASSIGN(AdaGradSolver);
+};
+
+
+template <typename Dtype>
+class RMSPropSolver : public SGDSolver<Dtype> {
+ public:
+ explicit RMSPropSolver(const SolverParameter& param)
+ : SGDSolver<Dtype>(param) { constructor_sanity_check(); }
+ explicit RMSPropSolver(const string& param_file)
+ : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); }
+ virtual inline const char* type() const { return "RMSProp"; }
+
+ protected:
+ virtual void ComputeUpdateValue(int param_id, Dtype rate);
+ void constructor_sanity_check() {
+ CHECK_EQ(0, this->param_.momentum())
+ << "Momentum cannot be used with RMSProp.";
+ CHECK_GE(this->param_.rms_decay(), 0)
+ << "rms_decay should lie between 0 and 1.";
+ CHECK_LT(this->param_.rms_decay(), 1)
+ << "rms_decay should lie between 0 and 1.";
+ }
+
+ DISABLE_COPY_AND_ASSIGN(RMSPropSolver);
+};
+
+template <typename Dtype>
+class AdaDeltaSolver : public SGDSolver<Dtype> {
+ public:
+ explicit AdaDeltaSolver(const SolverParameter& param)
+ : SGDSolver<Dtype>(param) { AdaDeltaPreSolve(); }
+ explicit AdaDeltaSolver(const string& param_file)
+ : SGDSolver<Dtype>(param_file) { AdaDeltaPreSolve(); }
+ virtual inline const char* type() const { return "AdaDelta"; }
+
+ protected:
+ void AdaDeltaPreSolve();
+ virtual void ComputeUpdateValue(int param_id, Dtype rate);
+
+ DISABLE_COPY_AND_ASSIGN(AdaDeltaSolver);
+};
+
+/**
+ * @brief AdamSolver, an algorithm for first-order gradient-based optimization
+ * of stochastic objective functions, based on adaptive estimates of
+ * lower-order moments. Described in [1].
+ *
+ * [1] D. P. Kingma and J. L. Ba, "ADAM: A Method for Stochastic Optimization."
+ * arXiv preprint arXiv:1412.6980v8 (2014).
+ */
+template <typename Dtype>
+class AdamSolver : public SGDSolver<Dtype> {
+ public:
+ explicit AdamSolver(const SolverParameter& param)
+ : SGDSolver<Dtype>(param) { AdamPreSolve();}
+ explicit AdamSolver(const string& param_file)
+ : SGDSolver<Dtype>(param_file) { AdamPreSolve(); }
+ virtual inline const char* type() const { return "Adam"; }
+
+ protected:
+ void AdamPreSolve();
+ virtual void ComputeUpdateValue(int param_id, Dtype rate);
+
+ DISABLE_COPY_AND_ASSIGN(AdamSolver);
+};
+
+} // namespace caffe
+
+#endif // CAFFE_SGD_SOLVERS_HPP_
-#ifndef CAFFE_OPTIMIZATION_SOLVER_HPP_
-#define CAFFE_OPTIMIZATION_SOLVER_HPP_
-
+#ifndef CAFFE_SOLVER_HPP_
+#define CAFFE_SOLVER_HPP_
+#include <boost/function.hpp>
#include <string>
#include <vector>
#include "caffe/net.hpp"
+#include "caffe/solver_factory.hpp"
namespace caffe {
/**
+ * @brief Enumeration of actions that a client of the Solver may request by
+ * implementing the Solver's action request function, which a
+ * a client may optionally provide in order to request early termination
+ * or saving a snapshot without exiting. In the executable caffe, this
+ * mechanism is used to allow the snapshot to be saved when stopping
+ * execution with a SIGINT (Ctrl-C).
+ */
+ namespace SolverAction {
+ enum Enum {
+ NONE = 0, // Take no special action.
+ STOP = 1, // Stop training. snapshot_after_train controls whether a
+ // snapshot is created.
+ SNAPSHOT = 2 // Take a snapshot, and keep training.
+ };
+ }
+
+/**
+ * @brief Type of a function that returns a Solver Action enumeration.
+ */
+typedef boost::function<SolverAction::Enum()> ActionCallback;
+
+/**
* @brief An interface for classes that perform optimization on Net%s.
*
- * Requires implementation of ComputeUpdateValue to compute a parameter update
+ * Requires implementation of ApplyUpdate to compute a parameter update
* given the current state of the Net parameters.
*/
template <typename Dtype>
class Solver {
public:
- explicit Solver(const SolverParameter& param);
- explicit Solver(const string& param_file);
+ explicit Solver(const SolverParameter& param,
+ const Solver* root_solver = NULL);
+ explicit Solver(const string& param_file, const Solver* root_solver = NULL);
void Init(const SolverParameter& param);
void InitTrainNet();
void InitTestNets();
+
+ // Client of the Solver optionally may call this in order to set the function
+ // that the solver uses to see what action it should take (e.g. snapshot or
+ // exit training early).
+ void SetActionFunction(ActionCallback func);
+ SolverAction::Enum GetRequestedAction();
// The main entry of the solver function. In default, iter will be zero. Pass
// in a non-zero iter number to resume training for a pre-trained net.
virtual void Solve(const char* resume_file = NULL);
inline void Solve(const string resume_file) { Solve(resume_file.c_str()); }
void Step(int iters);
- // The Restore function implements how one should restore the solver to a
- // previously snapshotted state. You should implement the RestoreSolverState()
- // function that restores the state from a SolverState protocol buffer.
+ // The Restore method simply dispatches to one of the
+ // RestoreSolverStateFrom___ protected methods. You should implement these
+ // methods to restore the state from the appropriate snapshot type.
void Restore(const char* resume_file);
+ // The Solver::Snapshot function implements the basic snapshotting utility
+ // that stores the learned net. You should implement the SnapshotSolverState()
+ // function that produces a SolverState protocol buffer that needs to be
+ // written to disk together with the learned net.
+ void Snapshot();
virtual ~Solver() {}
+ inline const SolverParameter& param() const { return param_; }
inline shared_ptr<Net<Dtype> > net() { return net_; }
inline const vector<shared_ptr<Net<Dtype> > >& test_nets() {
return test_nets_;
}
int iter() { return iter_; }
+ // Invoked at specific points during an iteration
+ class Callback {
+ protected:
+ virtual void on_start() = 0;
+ virtual void on_gradients_ready() = 0;
+
+ template <typename T>
+ friend class Solver;
+ };
+ const vector<Callback*>& callbacks() const { return callbacks_; }
+ void add_callback(Callback* value) {
+ callbacks_.push_back(value);
+ }
+
+ void CheckSnapshotWritePermissions();
+ /**
+ * @brief Returns the solver type.
+ */
+ virtual inline const char* type() const { return ""; }
+
protected:
- // Get the update value for the current iteration.
- virtual void ComputeUpdateValue() = 0;
- // The Solver::Snapshot function implements the basic snapshotting utility
- // that stores the learned net. You should implement the SnapshotSolverState()
- // function that produces a SolverState protocol buffer that needs to be
- // written to disk together with the learned net.
- void Snapshot();
+ // Make and apply the update value for the current iteration.
+ virtual void ApplyUpdate() = 0;
+ string SnapshotFilename(const string extension);
+ string SnapshotToBinaryProto();
+ string SnapshotToHDF5();
// The test routine
void TestAll();
void Test(const int test_net_id = 0);
- virtual void SnapshotSolverState(SolverState* state) = 0;
- virtual void RestoreSolverState(const SolverState& state) = 0;
+ virtual void SnapshotSolverState(const string& model_filename) = 0;
+ virtual void RestoreSolverStateFromHDF5(const string& state_file) = 0;
+ virtual void RestoreSolverStateFromBinaryProto(const string& state_file) = 0;
void DisplayOutputBlobs(const int net_id);
+ void UpdateSmoothedLoss(Dtype loss, int start_iter, int average_loss);
SolverParameter param_;
int iter_;
int current_step_;
shared_ptr<Net<Dtype> > net_;
vector<shared_ptr<Net<Dtype> > > test_nets_;
+ vector<Callback*> callbacks_;
+ vector<Dtype> losses_;
+ Dtype smoothed_loss_;
- DISABLE_COPY_AND_ASSIGN(Solver);
-};
+ // The root solver that holds root nets (actually containing shared layers)
+ // in data parallelism
+ const Solver* const root_solver_;
+ // A function that can be set by a client of the Solver to provide indication
+ // that it wants a snapshot saved and/or to exit early.
+ ActionCallback action_request_function_;
-/**
- * @brief Optimizes the parameters of a Net using
- * stochastic gradient descent (SGD) with momentum.
- */
-template <typename Dtype>
-class SGDSolver : public Solver<Dtype> {
- public:
- explicit SGDSolver(const SolverParameter& param)
- : Solver<Dtype>(param) { PreSolve(); }
- explicit SGDSolver(const string& param_file)
- : Solver<Dtype>(param_file) { PreSolve(); }
-
- const vector<shared_ptr<Blob<Dtype> > >& history() { return history_; }
-
- protected:
- void PreSolve();
- Dtype GetLearningRate();
- virtual void ComputeUpdateValue();
- virtual void ClipGradients();
- virtual void SnapshotSolverState(SolverState * state);
- virtual void RestoreSolverState(const SolverState& state);
- // history maintains the historical momentum data.
- // update maintains update related data and is not needed in snapshots.
- // temp maintains other information that might be needed in computation
- // of gradients/updates and is not needed in snapshots
- vector<shared_ptr<Blob<Dtype> > > history_, update_, temp_;
-
- DISABLE_COPY_AND_ASSIGN(SGDSolver);
-};
-
-template <typename Dtype>
-class NesterovSolver : public SGDSolver<Dtype> {
- public:
- explicit NesterovSolver(const SolverParameter& param)
- : SGDSolver<Dtype>(param) {}
- explicit NesterovSolver(const string& param_file)
- : SGDSolver<Dtype>(param_file) {}
+ // True iff a request to stop early was received.
+ bool requested_early_exit_;
- protected:
- virtual void ComputeUpdateValue();
-
- DISABLE_COPY_AND_ASSIGN(NesterovSolver);
+ DISABLE_COPY_AND_ASSIGN(Solver);
};
+/**
+ * @brief Solver that only computes gradients, used as worker
+ * for multi-GPU training.
+ */
template <typename Dtype>
-class AdaGradSolver : public SGDSolver<Dtype> {
+class WorkerSolver : public Solver<Dtype> {
public:
- explicit AdaGradSolver(const SolverParameter& param)
- : SGDSolver<Dtype>(param) { constructor_sanity_check(); }
- explicit AdaGradSolver(const string& param_file)
- : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); }
+ explicit WorkerSolver(const SolverParameter& param,
+ const Solver<Dtype>* root_solver = NULL)
+ : Solver<Dtype>(param, root_solver) {}
protected:
- virtual void ComputeUpdateValue();
- void constructor_sanity_check() {
- CHECK_EQ(0, this->param_.momentum())
- << "Momentum cannot be used with AdaGrad.";
+ void ApplyUpdate() {}
+ void SnapshotSolverState(const string& model_filename) {
+ LOG(FATAL) << "Should not be called on worker solver.";
}
-
- DISABLE_COPY_AND_ASSIGN(AdaGradSolver);
-};
-
-template <typename Dtype>
-Solver<Dtype>* GetSolver(const SolverParameter& param) {
- SolverParameter_SolverType type = param.solver_type();
-
- switch (type) {
- case SolverParameter_SolverType_SGD:
- return new SGDSolver<Dtype>(param);
- case SolverParameter_SolverType_NESTEROV:
- return new NesterovSolver<Dtype>(param);
- case SolverParameter_SolverType_ADAGRAD:
- return new AdaGradSolver<Dtype>(param);
- default:
- LOG(FATAL) << "Unknown SolverType: " << type;
+ void RestoreSolverStateFromBinaryProto(const string& state_file) {
+ LOG(FATAL) << "Should not be called on worker solver.";
}
- return (Solver<Dtype>*) NULL;
-}
+ void RestoreSolverStateFromHDF5(const string& state_file) {
+ LOG(FATAL) << "Should not be called on worker solver.";
+ }
+};
} // namespace caffe
-#endif // CAFFE_OPTIMIZATION_SOLVER_HPP_
+#endif // CAFFE_SOLVER_HPP_
--- /dev/null
+/**
+ * @brief A solver factory that allows one to register solvers, similar to
+ * layer factory. During runtime, registered solvers could be called by passing
+ * a SolverParameter protobuffer to the CreateSolver function:
+ *
+ * SolverRegistry<Dtype>::CreateSolver(param);
+ *
+ * There are two ways to register a solver. Assuming that we have a solver like:
+ *
+ * template <typename Dtype>
+ * class MyAwesomeSolver : public Solver<Dtype> {
+ * // your implementations
+ * };
+ *
+ * and its type is its C++ class name, but without the "Solver" at the end
+ * ("MyAwesomeSolver" -> "MyAwesome").
+ *
+ * If the solver is going to be created simply by its constructor, in your c++
+ * file, add the following line:
+ *
+ * REGISTER_SOLVER_CLASS(MyAwesome);
+ *
+ * Or, if the solver is going to be created by another creator function, in the
+ * format of:
+ *
+ * template <typename Dtype>
+ * Solver<Dtype*> GetMyAwesomeSolver(const SolverParameter& param) {
+ * // your implementation
+ * }
+ *
+ * then you can register the creator function instead, like
+ *
+ * REGISTER_SOLVER_CREATOR(MyAwesome, GetMyAwesomeSolver)
+ *
+ * Note that each solver type should only be registered once.
+ */
+
+#ifndef CAFFE_SOLVER_FACTORY_H_
+#define CAFFE_SOLVER_FACTORY_H_
+
+#include <map>
+#include <string>
+#include <vector>
+
+#include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+template <typename Dtype>
+class Solver;
+
+template <typename Dtype>
+class SolverRegistry {
+ public:
+ typedef Solver<Dtype>* (*Creator)(const SolverParameter&);
+ typedef std::map<string, Creator> CreatorRegistry;
+
+ static CreatorRegistry& Registry() {
+ static CreatorRegistry* g_registry_ = new CreatorRegistry();
+ return *g_registry_;
+ }
+
+ // Adds a creator.
+ static void AddCreator(const string& type, Creator creator) {
+ CreatorRegistry& registry = Registry();
+ CHECK_EQ(registry.count(type), 0)
+ << "Solver type " << type << " already registered.";
+ registry[type] = creator;
+ }
+
+ // Get a solver using a SolverParameter.
+ static Solver<Dtype>* CreateSolver(const SolverParameter& param) {
+ const string& type = param.type();
+ CreatorRegistry& registry = Registry();
+ CHECK_EQ(registry.count(type), 1) << "Unknown solver type: " << type
+ << " (known types: " << SolverTypeListString() << ")";
+ return registry[type](param);
+ }
+
+ static vector<string> SolverTypeList() {
+ CreatorRegistry& registry = Registry();
+ vector<string> solver_types;
+ for (typename CreatorRegistry::iterator iter = registry.begin();
+ iter != registry.end(); ++iter) {
+ solver_types.push_back(iter->first);
+ }
+ return solver_types;
+ }
+
+ private:
+ // Solver registry should never be instantiated - everything is done with its
+ // static variables.
+ SolverRegistry() {}
+
+ static string SolverTypeListString() {
+ vector<string> solver_types = SolverTypeList();
+ string solver_types_str;
+ for (vector<string>::iterator iter = solver_types.begin();
+ iter != solver_types.end(); ++iter) {
+ if (iter != solver_types.begin()) {
+ solver_types_str += ", ";
+ }
+ solver_types_str += *iter;
+ }
+ return solver_types_str;
+ }
+};
+
+
+template <typename Dtype>
+class SolverRegisterer {
+ public:
+ SolverRegisterer(const string& type,
+ Solver<Dtype>* (*creator)(const SolverParameter&)) {
+ // LOG(INFO) << "Registering solver type: " << type;
+ SolverRegistry<Dtype>::AddCreator(type, creator);
+ }
+};
+
+
+#define REGISTER_SOLVER_CREATOR(type, creator) \
+ static SolverRegisterer<float> g_creator_f_##type(#type, creator<float>); \
+ static SolverRegisterer<double> g_creator_d_##type(#type, creator<double>) \
+
+#define REGISTER_SOLVER_CLASS(type) \
+ template <typename Dtype> \
+ Solver<Dtype>* Creator_##type##Solver( \
+ const SolverParameter& param) \
+ { \
+ return new type##Solver<Dtype>(param); \
+ } \
+ REGISTER_SOLVER_CREATOR(type, Creator_##type##Solver)
+
+} // namespace caffe
+
+#endif // CAFFE_SOLVER_FACTORY_H_
#include <cstdlib>
#include "caffe/common.hpp"
-#include "caffe/util/math_functions.hpp"
namespace caffe {
-// Theoretically, CaffeMallocHost and CaffeFreeHost should simply call the
-// cudaMallocHost and cudaFree functions in order to create pinned memory.
-// However, those codes rely on the existence of a cuda GPU (I don't know
-// why that is a must since allocating memory should not be accessing the
-// GPU resorce, but it just creates an error as of Cuda 5.0) and will cause
-// problem when running on a machine without GPU. Thus, we simply define
-// these two functions for safety and possible future change if the problem
-// of calling cuda functions disappears in a future version.
-//
-// In practice, although we are creating unpinned memory here, as long as we
-// are constantly accessing them the memory pages almost always stays in
-// the physical memory (assuming we have large enough memory installed), and
-// does not seem to create a memory bottleneck here.
-
-inline void CaffeMallocHost(void** ptr, size_t size) {
+// If CUDA is available and in GPU mode, host memory will be allocated pinned,
+// using cudaMallocHost. It avoids dynamic pinning for transfers (DMA).
+// The improvement in performance seems negligible in the single GPU case,
+// but might be more significant for parallel training. Most importantly,
+// it improved stability for large models on many GPUs.
+inline void CaffeMallocHost(void** ptr, size_t size, bool* use_cuda) {
+#ifndef CPU_ONLY
+ if (Caffe::mode() == Caffe::GPU) {
+ CUDA_CHECK(cudaMallocHost(ptr, size));
+ *use_cuda = true;
+ return;
+ }
+#endif
*ptr = malloc(size);
+ *use_cuda = false;
CHECK(*ptr) << "host allocation of size " << size << " failed";
}
-inline void CaffeFreeHost(void* ptr) {
+inline void CaffeFreeHost(void* ptr, bool use_cuda) {
+#ifndef CPU_ONLY
+ if (use_cuda) {
+ CUDA_CHECK(cudaFreeHost(ptr));
+ return;
+ }
+#endif
free(ptr);
}
public:
SyncedMemory()
: cpu_ptr_(NULL), gpu_ptr_(NULL), size_(0), head_(UNINITIALIZED),
- own_cpu_data_(false) {}
+ own_cpu_data_(false), cpu_malloc_use_cuda_(false), own_gpu_data_(false),
+ gpu_device_(-1) {}
explicit SyncedMemory(size_t size)
: cpu_ptr_(NULL), gpu_ptr_(NULL), size_(size), head_(UNINITIALIZED),
- own_cpu_data_(false) {}
+ own_cpu_data_(false), cpu_malloc_use_cuda_(false), own_gpu_data_(false),
+ gpu_device_(-1) {}
~SyncedMemory();
const void* cpu_data();
void set_cpu_data(void* data);
const void* gpu_data();
+ void set_gpu_data(void* data);
void* mutable_cpu_data();
void* mutable_gpu_data();
enum SyncedHead { UNINITIALIZED, HEAD_AT_CPU, HEAD_AT_GPU, SYNCED };
SyncedHead head() { return head_; }
size_t size() { return size_; }
+#ifndef CPU_ONLY
+ void async_gpu_push(const cudaStream_t& stream);
+#endif
+
private:
void to_cpu();
void to_gpu();
size_t size_;
SyncedHead head_;
bool own_cpu_data_;
+ bool cpu_malloc_use_cuda_;
+ bool own_gpu_data_;
+ int gpu_device_;
DISABLE_COPY_AND_ASSIGN(SyncedMemory);
}; // class SyncedMemory
typedef ::testing::Types<float, double> TestDtypes;
-struct FloatCPU {
- typedef float Dtype;
+template <typename TypeParam>
+struct CPUDevice {
+ typedef TypeParam Dtype;
static const Caffe::Brew device = Caffe::CPU;
};
-struct DoubleCPU {
- typedef double Dtype;
- static const Caffe::Brew device = Caffe::CPU;
+template <typename Dtype>
+class CPUDeviceTest : public MultiDeviceTest<CPUDevice<Dtype> > {
};
#ifdef CPU_ONLY
-typedef ::testing::Types<FloatCPU, DoubleCPU> TestDtypesAndDevices;
+typedef ::testing::Types<CPUDevice<float>,
+ CPUDevice<double> > TestDtypesAndDevices;
#else
-struct FloatGPU {
- typedef float Dtype;
+template <typename TypeParam>
+struct GPUDevice {
+ typedef TypeParam Dtype;
static const Caffe::Brew device = Caffe::GPU;
};
-struct DoubleGPU {
- typedef double Dtype;
- static const Caffe::Brew device = Caffe::GPU;
+template <typename Dtype>
+class GPUDeviceTest : public MultiDeviceTest<GPUDevice<Dtype> > {
};
-typedef ::testing::Types<FloatCPU, DoubleCPU, FloatGPU, DoubleGPU>
- TestDtypesAndDevices;
+typedef ::testing::Types<CPUDevice<float>, CPUDevice<double>,
+ GPUDevice<float>, GPUDevice<double> >
+ TestDtypesAndDevices;
#endif
void CheckGradientEltwise(Layer<Dtype>* layer,
const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
+ // Checks the gradient of a single output with respect to particular input
+ // blob(s). If check_bottom = i >= 0, check only the ith bottom Blob.
+ // If check_bottom == -1, check everything -- all bottom Blobs and all
+ // param Blobs. Otherwise (if check_bottom < -1), check only param Blobs.
void CheckGradientSingle(Layer<Dtype>* layer,
const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top,
int check_bottom, int top_id, int top_data_id, bool element_wise = false);
CHECK_EQ(top_count, bottom[blob_id]->count());
}
}
- // First, figure out what blobs we need to check against.
+ // First, figure out what blobs we need to check against, and zero init
+ // parameter blobs.
vector<Blob<Dtype>*> blobs_to_check;
- vector<bool> propagate_down(bottom.size(), check_bottom < 0);
+ vector<bool> propagate_down(bottom.size(), check_bottom == -1);
for (int i = 0; i < layer->blobs().size(); ++i) {
- blobs_to_check.push_back(layer->blobs()[i].get());
+ Blob<Dtype>* blob = layer->blobs()[i].get();
+ caffe_set(blob->count(), static_cast<Dtype>(0), blob->mutable_cpu_diff());
+ blobs_to_check.push_back(blob);
}
- if (check_bottom < 0) {
+ if (check_bottom == -1) {
for (int i = 0; i < bottom.size(); ++i) {
blobs_to_check.push_back(bottom[i]);
}
- } else {
+ } else if (check_bottom >= 0) {
CHECK_LT(check_bottom, bottom.size());
blobs_to_check.push_back(bottom[check_bottom]);
propagate_down[check_bottom] = true;
}
+ CHECK_GT(blobs_to_check.size(), 0) << "No blobs to check.";
// Compute the gradient analytically using Backward
Caffe::set_random_seed(seed_);
// Ignore the loss from the layer (it's just the weighted sum of the losses
|| fabs(feature) > kink_ + kink_range_) {
// We check relative accuracy, but for too small values, we threshold
// the scale factor by 1.
- Dtype scale = std::max(
- std::max(fabs(computed_gradient), fabs(estimated_gradient)), 1.);
+ Dtype scale = std::max<Dtype>(
+ std::max(fabs(computed_gradient), fabs(estimated_gradient)),
+ Dtype(1.));
EXPECT_NEAR(computed_gradient, estimated_gradient, threshold_ * scale)
<< "debug: (top_id, top_data_id, blob_id, feat_id)="
<< top_id << "," << top_data_id << "," << blob_id << "," << feat_id
--- /dev/null
+#ifndef CAFFE_UTIL_BLOCKING_QUEUE_HPP_
+#define CAFFE_UTIL_BLOCKING_QUEUE_HPP_
+
+#include <queue>
+#include <string>
+
+namespace caffe {
+
+template<typename T>
+class BlockingQueue {
+ public:
+ explicit BlockingQueue();
+
+ void push(const T& t);
+
+ bool try_pop(T* t);
+
+ // This logs a message if the threads needs to be blocked
+ // useful for detecting e.g. when data feeding is too slow
+ T pop(const string& log_on_wait = "");
+
+ bool try_peek(T* t);
+
+ // Return element without removing it
+ T peek();
+
+ size_t size() const;
+
+ protected:
+ /**
+ Move synchronization fields out instead of including boost/thread.hpp
+ to avoid a boost/NVCC issues (#1009, #1010) on OSX. Also fails on
+ Linux CUDA 7.0.18.
+ */
+ class sync;
+
+ std::queue<T> queue_;
+ shared_ptr<sync> sync_;
+
+DISABLE_COPY_AND_ASSIGN(BlockingQueue);
+};
+
+} // namespace caffe
+
+#endif
#include "caffe/common.hpp"
#include "caffe/proto/caffe.pb.h"
+#define CUDNN_VERSION_MIN(major, minor, patch) \
+ (CUDNN_VERSION >= (major * 1000 + minor * 100 + patch))
+
#define CUDNN_CHECK(condition) \
do { \
cudnnStatus_t status = condition; \
template<> class dataType<float> {
public:
static const cudnnDataType_t type = CUDNN_DATA_FLOAT;
+ static float oneval, zeroval;
+ static const void *one, *zero;
};
template<> class dataType<double> {
public:
static const cudnnDataType_t type = CUDNN_DATA_DOUBLE;
+ static double oneval, zeroval;
+ static const void *one, *zero;
};
template <typename Dtype>
-inline void createTensor4dDesc(cudnnTensor4dDescriptor_t* desc) {
- CUDNN_CHECK(cudnnCreateTensor4dDescriptor(desc));
+inline void createTensor4dDesc(cudnnTensorDescriptor_t* desc) {
+ CUDNN_CHECK(cudnnCreateTensorDescriptor(desc));
}
template <typename Dtype>
-inline void setTensor4dDesc(cudnnTensor4dDescriptor_t* desc,
+inline void setTensor4dDesc(cudnnTensorDescriptor_t* desc,
int n, int c, int h, int w,
int stride_n, int stride_c, int stride_h, int stride_w) {
CUDNN_CHECK(cudnnSetTensor4dDescriptorEx(*desc, dataType<Dtype>::type,
- n, c, h, w, stride_n, stride_c, stride_h, stride_w));
+ n, c, h, w, stride_n, stride_c, stride_h, stride_w));
}
template <typename Dtype>
-inline void setTensor4dDesc(cudnnTensor4dDescriptor_t* desc,
+inline void setTensor4dDesc(cudnnTensorDescriptor_t* desc,
int n, int c, int h, int w) {
const int stride_w = 1;
const int stride_h = w * stride_w;
const int stride_c = h * stride_h;
const int stride_n = c * stride_c;
setTensor4dDesc<Dtype>(desc, n, c, h, w,
- stride_n, stride_c, stride_h, stride_w);
+ stride_n, stride_c, stride_h, stride_w);
}
template <typename Dtype>
inline void createFilterDesc(cudnnFilterDescriptor_t* desc,
int n, int c, int h, int w) {
CUDNN_CHECK(cudnnCreateFilterDescriptor(desc));
- CUDNN_CHECK(cudnnSetFilterDescriptor(*desc, dataType<Dtype>::type,
+ CUDNN_CHECK(cudnnSetFilter4dDescriptor(*desc, dataType<Dtype>::type,
n, c, h, w));
}
template <typename Dtype>
inline void setConvolutionDesc(cudnnConvolutionDescriptor_t* conv,
- cudnnTensor4dDescriptor_t bottom, cudnnFilterDescriptor_t filter,
+ cudnnTensorDescriptor_t bottom, cudnnFilterDescriptor_t filter,
int pad_h, int pad_w, int stride_h, int stride_w) {
- CUDNN_CHECK(cudnnSetConvolutionDescriptor(*conv, bottom, filter,
+ CUDNN_CHECK(cudnnSetConvolution2dDescriptor(*conv,
pad_h, pad_w, stride_h, stride_w, 1, 1, CUDNN_CROSS_CORRELATION));
}
template <typename Dtype>
-inline void createPoolingDesc(cudnnPoolingDescriptor_t* conv,
+inline void createPoolingDesc(cudnnPoolingDescriptor_t* pool_desc,
PoolingParameter_PoolMethod poolmethod, cudnnPoolingMode_t* mode,
- int h, int w, int stride_h, int stride_w) {
+ int h, int w, int pad_h, int pad_w, int stride_h, int stride_w) {
switch (poolmethod) {
case PoolingParameter_PoolMethod_MAX:
*mode = CUDNN_POOLING_MAX;
break;
case PoolingParameter_PoolMethod_AVE:
- *mode = CUDNN_POOLING_AVERAGE;
+ *mode = CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
break;
default:
LOG(FATAL) << "Unknown pooling method.";
}
- CUDNN_CHECK(cudnnCreatePoolingDescriptor(conv));
- CUDNN_CHECK(cudnnSetPoolingDescriptor(*conv, *mode, h, w,
- stride_h, stride_w));
+ CUDNN_CHECK(cudnnCreatePoolingDescriptor(pool_desc));
+ CUDNN_CHECK(cudnnSetPooling2dDescriptor(*pool_desc, *mode, h, w,
+ pad_h, pad_w, stride_h, stride_w));
}
} // namespace cudnn
#include <string>
-#include "leveldb/db.h"
-#include "leveldb/write_batch.h"
-#include "lmdb.h"
-
#include "caffe/common.hpp"
#include "caffe/proto/caffe.pb.h"
DISABLE_COPY_AND_ASSIGN(DB);
};
-class LevelDBCursor : public Cursor {
- public:
- explicit LevelDBCursor(leveldb::Iterator* iter)
- : iter_(iter) { SeekToFirst(); }
- ~LevelDBCursor() { delete iter_; }
- virtual void SeekToFirst() { iter_->SeekToFirst(); }
- virtual void Next() { iter_->Next(); }
- virtual string key() { return iter_->key().ToString(); }
- virtual string value() { return iter_->value().ToString(); }
- virtual bool valid() { return iter_->Valid(); }
-
- private:
- leveldb::Iterator* iter_;
-};
-
-class LevelDBTransaction : public Transaction {
- public:
- explicit LevelDBTransaction(leveldb::DB* db) : db_(db) { CHECK_NOTNULL(db_); }
- virtual void Put(const string& key, const string& value) {
- batch_.Put(key, value);
- }
- virtual void Commit() {
- leveldb::Status status = db_->Write(leveldb::WriteOptions(), &batch_);
- CHECK(status.ok()) << "Failed to write batch to leveldb "
- << std::endl << status.ToString();
- }
-
- private:
- leveldb::DB* db_;
- leveldb::WriteBatch batch_;
-
- DISABLE_COPY_AND_ASSIGN(LevelDBTransaction);
-};
-
-class LevelDB : public DB {
- public:
- LevelDB() : db_(NULL) { }
- virtual ~LevelDB() { Close(); }
- virtual void Open(const string& source, Mode mode);
- virtual void Close() {
- if (db_ != NULL) {
- delete db_;
- db_ = NULL;
- }
- }
- virtual LevelDBCursor* NewCursor() {
- return new LevelDBCursor(db_->NewIterator(leveldb::ReadOptions()));
- }
- virtual LevelDBTransaction* NewTransaction() {
- return new LevelDBTransaction(db_);
- }
-
- private:
- leveldb::DB* db_;
-};
-
-inline void MDB_CHECK(int mdb_status) {
- CHECK_EQ(mdb_status, MDB_SUCCESS) << mdb_strerror(mdb_status);
-}
-
-class LMDBCursor : public Cursor {
- public:
- explicit LMDBCursor(MDB_txn* mdb_txn, MDB_cursor* mdb_cursor)
- : mdb_txn_(mdb_txn), mdb_cursor_(mdb_cursor), valid_(false) {
- SeekToFirst();
- }
- virtual ~LMDBCursor() {
- mdb_cursor_close(mdb_cursor_);
- mdb_txn_abort(mdb_txn_);
- }
- virtual void SeekToFirst() { Seek(MDB_FIRST); }
- virtual void Next() { Seek(MDB_NEXT); }
- virtual string key() {
- return string(static_cast<const char*>(mdb_key_.mv_data), mdb_key_.mv_size);
- }
- virtual string value() {
- return string(static_cast<const char*>(mdb_value_.mv_data),
- mdb_value_.mv_size);
- }
- virtual bool valid() { return valid_; }
-
- private:
- void Seek(MDB_cursor_op op) {
- int mdb_status = mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_, op);
- if (mdb_status == MDB_NOTFOUND) {
- valid_ = false;
- } else {
- MDB_CHECK(mdb_status);
- valid_ = true;
- }
- }
-
- MDB_txn* mdb_txn_;
- MDB_cursor* mdb_cursor_;
- MDB_val mdb_key_, mdb_value_;
- bool valid_;
-};
-
-class LMDBTransaction : public Transaction {
- public:
- explicit LMDBTransaction(MDB_dbi* mdb_dbi, MDB_txn* mdb_txn)
- : mdb_dbi_(mdb_dbi), mdb_txn_(mdb_txn) { }
- virtual void Put(const string& key, const string& value);
- virtual void Commit() { MDB_CHECK(mdb_txn_commit(mdb_txn_)); }
-
- private:
- MDB_dbi* mdb_dbi_;
- MDB_txn* mdb_txn_;
-
- DISABLE_COPY_AND_ASSIGN(LMDBTransaction);
-};
-
-class LMDB : public DB {
- public:
- LMDB() : mdb_env_(NULL) { }
- virtual ~LMDB() { Close(); }
- virtual void Open(const string& source, Mode mode);
- virtual void Close() {
- if (mdb_env_ != NULL) {
- mdb_dbi_close(mdb_env_, mdb_dbi_);
- mdb_env_close(mdb_env_);
- mdb_env_ = NULL;
- }
- }
- virtual LMDBCursor* NewCursor();
- virtual LMDBTransaction* NewTransaction();
-
- private:
- MDB_env* mdb_env_;
- MDB_dbi mdb_dbi_;
-};
-
DB* GetDB(DataParameter::DB backend);
DB* GetDB(const string& backend);
--- /dev/null
+#ifdef USE_LEVELDB
+#ifndef CAFFE_UTIL_DB_LEVELDB_HPP
+#define CAFFE_UTIL_DB_LEVELDB_HPP
+
+#include <string>
+
+#include "leveldb/db.h"
+#include "leveldb/write_batch.h"
+
+#include "caffe/util/db.hpp"
+
+namespace caffe { namespace db {
+
+class LevelDBCursor : public Cursor {
+ public:
+ explicit LevelDBCursor(leveldb::Iterator* iter)
+ : iter_(iter) { SeekToFirst(); }
+ ~LevelDBCursor() { delete iter_; }
+ virtual void SeekToFirst() { iter_->SeekToFirst(); }
+ virtual void Next() { iter_->Next(); }
+ virtual string key() { return iter_->key().ToString(); }
+ virtual string value() { return iter_->value().ToString(); }
+ virtual bool valid() { return iter_->Valid(); }
+
+ private:
+ leveldb::Iterator* iter_;
+};
+
+class LevelDBTransaction : public Transaction {
+ public:
+ explicit LevelDBTransaction(leveldb::DB* db) : db_(db) { CHECK_NOTNULL(db_); }
+ virtual void Put(const string& key, const string& value) {
+ batch_.Put(key, value);
+ }
+ virtual void Commit() {
+ leveldb::Status status = db_->Write(leveldb::WriteOptions(), &batch_);
+ CHECK(status.ok()) << "Failed to write batch to leveldb "
+ << std::endl << status.ToString();
+ }
+
+ private:
+ leveldb::DB* db_;
+ leveldb::WriteBatch batch_;
+
+ DISABLE_COPY_AND_ASSIGN(LevelDBTransaction);
+};
+
+class LevelDB : public DB {
+ public:
+ LevelDB() : db_(NULL) { }
+ virtual ~LevelDB() { Close(); }
+ virtual void Open(const string& source, Mode mode);
+ virtual void Close() {
+ if (db_ != NULL) {
+ delete db_;
+ db_ = NULL;
+ }
+ }
+ virtual LevelDBCursor* NewCursor() {
+ return new LevelDBCursor(db_->NewIterator(leveldb::ReadOptions()));
+ }
+ virtual LevelDBTransaction* NewTransaction() {
+ return new LevelDBTransaction(db_);
+ }
+
+ private:
+ leveldb::DB* db_;
+};
+
+
+} // namespace db
+} // namespace caffe
+
+#endif // CAFFE_UTIL_DB_LEVELDB_HPP
+#endif // USE_LEVELDB
--- /dev/null
+#ifdef USE_LMDB
+#ifndef CAFFE_UTIL_DB_LMDB_HPP
+#define CAFFE_UTIL_DB_LMDB_HPP
+
+#include <string>
+
+#include "lmdb.h"
+
+#include "caffe/util/db.hpp"
+
+namespace caffe { namespace db {
+
+inline void MDB_CHECK(int mdb_status) {
+ CHECK_EQ(mdb_status, MDB_SUCCESS) << mdb_strerror(mdb_status);
+}
+
+class LMDBCursor : public Cursor {
+ public:
+ explicit LMDBCursor(MDB_txn* mdb_txn, MDB_cursor* mdb_cursor)
+ : mdb_txn_(mdb_txn), mdb_cursor_(mdb_cursor), valid_(false) {
+ SeekToFirst();
+ }
+ virtual ~LMDBCursor() {
+ mdb_cursor_close(mdb_cursor_);
+ mdb_txn_abort(mdb_txn_);
+ }
+ virtual void SeekToFirst() { Seek(MDB_FIRST); }
+ virtual void Next() { Seek(MDB_NEXT); }
+ virtual string key() {
+ return string(static_cast<const char*>(mdb_key_.mv_data), mdb_key_.mv_size);
+ }
+ virtual string value() {
+ return string(static_cast<const char*>(mdb_value_.mv_data),
+ mdb_value_.mv_size);
+ }
+ virtual bool valid() { return valid_; }
+
+ private:
+ void Seek(MDB_cursor_op op) {
+ int mdb_status = mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_, op);
+ if (mdb_status == MDB_NOTFOUND) {
+ valid_ = false;
+ } else {
+ MDB_CHECK(mdb_status);
+ valid_ = true;
+ }
+ }
+
+ MDB_txn* mdb_txn_;
+ MDB_cursor* mdb_cursor_;
+ MDB_val mdb_key_, mdb_value_;
+ bool valid_;
+};
+
+class LMDBTransaction : public Transaction {
+ public:
+ explicit LMDBTransaction(MDB_dbi* mdb_dbi, MDB_txn* mdb_txn)
+ : mdb_dbi_(mdb_dbi), mdb_txn_(mdb_txn) { }
+ virtual void Put(const string& key, const string& value);
+ virtual void Commit() { MDB_CHECK(mdb_txn_commit(mdb_txn_)); }
+
+ private:
+ MDB_dbi* mdb_dbi_;
+ MDB_txn* mdb_txn_;
+
+ DISABLE_COPY_AND_ASSIGN(LMDBTransaction);
+};
+
+class LMDB : public DB {
+ public:
+ LMDB() : mdb_env_(NULL) { }
+ virtual ~LMDB() { Close(); }
+ virtual void Open(const string& source, Mode mode);
+ virtual void Close() {
+ if (mdb_env_ != NULL) {
+ mdb_dbi_close(mdb_env_, mdb_dbi_);
+ mdb_env_close(mdb_env_);
+ mdb_env_ = NULL;
+ }
+ }
+ virtual LMDBCursor* NewCursor();
+ virtual LMDBTransaction* NewTransaction();
+
+ private:
+ MDB_env* mdb_env_;
+ MDB_dbi mdb_dbi_;
+};
+
+} // namespace db
+} // namespace caffe
+
+#endif // CAFFE_UTIL_DB_LMDB_HPP
+#endif // USE_LMDB
const char* cublasGetErrorString(cublasStatus_t error);
const char* curandGetErrorString(curandStatus_t error);
-// CUDA: thread number configuration.
-// Use 1024 threads per block, which requires cuda sm_2x or above,
-// or fall back to attempt compatibility (best of luck to you).
-#if __CUDA_ARCH__ >= 200
- const int CAFFE_CUDA_NUM_THREADS = 1024;
-#else
- const int CAFFE_CUDA_NUM_THREADS = 512;
-#endif
+// CUDA: use 512 threads per block
+const int CAFFE_CUDA_NUM_THREADS = 512;
// CUDA: number of blocks for threads.
inline int CAFFE_GET_BLOCKS(const int N) {
--- /dev/null
+#ifndef CAFFE_UTIL_FORMAT_H_
+#define CAFFE_UTIL_FORMAT_H_
+
+#include <iomanip> // NOLINT(readability/streams)
+#include <sstream> // NOLINT(readability/streams)
+#include <string>
+
+namespace caffe {
+
+inline std::string format_int(int n, int numberOfLeadingZeros = 0 ) {
+ std::ostringstream s;
+ s << std::setw(numberOfLeadingZeros) << std::setfill('0') << n;
+ return s.str();
+}
+
+}
+
+#endif // CAFFE_UTIL_FORMAT_H_
--- /dev/null
+#ifndef CAFFE_UTIL_GPU_UTIL_H_
+#define CAFFE_UTIL_GPU_UTIL_H_
+
+namespace caffe {
+
+template <typename Dtype>
+inline __device__ Dtype caffe_gpu_atomic_add(const Dtype val, Dtype* address);
+
+template <>
+inline __device__
+float caffe_gpu_atomic_add(const float val, float* address) {
+ return atomicAdd(address, val);
+}
+
+// double atomicAdd implementation taken from:
+// http://docs.nvidia.com/cuda/cuda-c-programming-guide/#axzz3PVCpVsEG
+template <>
+inline __device__
+double caffe_gpu_atomic_add(const double val, double* address) {
+ unsigned long long int* address_as_ull = // NOLINT(runtime/int)
+ // NOLINT_NEXT_LINE(runtime/int)
+ reinterpret_cast<unsigned long long int*>(address);
+ unsigned long long int old = *address_as_ull; // NOLINT(runtime/int)
+ unsigned long long int assumed; // NOLINT(runtime/int)
+ do {
+ assumed = old;
+ old = atomicCAS(address_as_ull, assumed,
+ __double_as_longlong(val + __longlong_as_double(assumed)));
+ } while (assumed != old);
+ return __longlong_as_double(old);
+}
+
+} // namespace caffe
+
+#endif // CAFFE_UTIL_GPU_UTIL_H_
--- /dev/null
+#ifndef CAFFE_UTIL_HDF5_H_
+#define CAFFE_UTIL_HDF5_H_
+
+#include <string>
+
+#include "hdf5.h"
+#include "hdf5_hl.h"
+
+#include "caffe/blob.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void hdf5_load_nd_dataset_helper(
+ hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
+ Blob<Dtype>* blob);
+
+template <typename Dtype>
+void hdf5_load_nd_dataset(
+ hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
+ Blob<Dtype>* blob);
+
+template <typename Dtype>
+void hdf5_save_nd_dataset(
+ const hid_t file_id, const string& dataset_name, const Blob<Dtype>& blob,
+ bool write_diff = false);
+
+int hdf5_load_int(hid_t loc_id, const string& dataset_name);
+void hdf5_save_int(hid_t loc_id, const string& dataset_name, int i);
+string hdf5_load_string(hid_t loc_id, const string& dataset_name);
+void hdf5_save_string(hid_t loc_id, const string& dataset_name,
+ const string& s);
+
+int hdf5_get_num_links(hid_t loc_id);
+string hdf5_get_name_by_idx(hid_t loc_id, int idx);
+
+} // namespace caffe
+
+#endif // CAFFE_UTIL_HDF5_H_
namespace caffe {
template <typename Dtype>
+void im2col_nd_cpu(const Dtype* data_im, const int num_spatial_axes,
+ const int* im_shape, const int* col_shape,
+ const int* kernel_shape, const int* pad, const int* stride,
+ const int* dilation, Dtype* data_col);
+
+template <typename Dtype>
void im2col_cpu(const Dtype* data_im, const int channels,
const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w, const int stride_h,
- const int stride_w, Dtype* data_col);
+ const int stride_w, const int dilation_h, const int dilation_w,
+ Dtype* data_col);
+
+template <typename Dtype>
+void col2im_nd_cpu(const Dtype* data_col, const int num_spatial_axes,
+ const int* im_shape, const int* col_shape,
+ const int* kernel_shape, const int* pad, const int* stride,
+ const int* dilation, Dtype* data_im);
template <typename Dtype>
void col2im_cpu(const Dtype* data_col, const int channels,
- const int height, const int width, const int patch_h, const int patch_w,
+ const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w, const int stride_h,
- const int stride_w, Dtype* data_im);
+ const int stride_w, const int dilation_h, const int dilation_w,
+ Dtype* data_im);
+
+template <typename Dtype>
+void im2col_nd_gpu(const Dtype* data_im, const int num_spatial_axes,
+ const int col_size, const int* im_shape, const int* col_shape,
+ const int* kernel_shape, const int* pad, const int* stride,
+ const int* dilation, Dtype* data_col);
template <typename Dtype>
void im2col_gpu(const Dtype* data_im, const int channels,
const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w, const int stride_h,
- const int stride_w, Dtype* data_col);
+ const int stride_w, const int dilation_h, const int dilation_w,
+ Dtype* data_col);
+
+template <typename Dtype>
+void col2im_nd_gpu(const Dtype* data_col, const int num_spatial_axes,
+ const int im_size, const int* im_shape, const int* col_shape,
+ const int* kernel_shape, const int* pad, const int* stride,
+ const int* dilation, Dtype* data_im);
template <typename Dtype>
void col2im_gpu(const Dtype* data_col, const int channels,
- const int height, const int width, const int patch_h, const int patch_w,
+ const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w, const int stride_h,
- const int stride_w, Dtype* data_im);
+ const int stride_w, const int dilation_h, const int dilation_w,
+ Dtype* data_im);
} // namespace caffe
#ifndef CAFFE_UTIL_IO_H_
#define CAFFE_UTIL_IO_H_
-#include <unistd.h>
+#include <boost/filesystem.hpp>
+#include <iomanip>
+#include <iostream> // NOLINT(readability/streams)
#include <string>
#include "google/protobuf/message.h"
-#include "hdf5.h"
-#include "hdf5_hl.h"
-#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/format.hpp"
-#define HDF5_NUM_DIMS 4
+#ifndef CAFFE_TMP_DIR_RETRIES
+#define CAFFE_TMP_DIR_RETRIES 100
+#endif
namespace caffe {
using ::google::protobuf::Message;
-
-inline void MakeTempFilename(string* temp_filename) {
- temp_filename->clear();
- *temp_filename = "/tmp/caffe_test.XXXXXX";
- char* temp_filename_cstr = new char[temp_filename->size() + 1];
- // NOLINT_NEXT_LINE(runtime/printf)
- strcpy(temp_filename_cstr, temp_filename->c_str());
- int fd = mkstemp(temp_filename_cstr);
- CHECK_GE(fd, 0) << "Failed to open a temporary file at: " << *temp_filename;
- close(fd);
- *temp_filename = temp_filename_cstr;
- delete[] temp_filename_cstr;
-}
+using ::boost::filesystem::path;
inline void MakeTempDir(string* temp_dirname) {
temp_dirname->clear();
- *temp_dirname = "/tmp/caffe_test.XXXXXX";
- char* temp_dirname_cstr = new char[temp_dirname->size() + 1];
- // NOLINT_NEXT_LINE(runtime/printf)
- strcpy(temp_dirname_cstr, temp_dirname->c_str());
- char* mkdtemp_result = mkdtemp(temp_dirname_cstr);
- CHECK(mkdtemp_result != NULL)
- << "Failed to create a temporary directory at: " << *temp_dirname;
- *temp_dirname = temp_dirname_cstr;
- delete[] temp_dirname_cstr;
+ const path& model =
+ boost::filesystem::temp_directory_path()/"caffe_test.%%%%-%%%%";
+ for ( int i = 0; i < CAFFE_TMP_DIR_RETRIES; i++ ) {
+ const path& dir = boost::filesystem::unique_path(model).string();
+ bool done = boost::filesystem::create_directory(dir);
+ if ( done ) {
+ *temp_dirname = dir.string();
+ return;
+ }
+ }
+ LOG(FATAL) << "Failed to create a temporary directory.";
+}
+
+inline void MakeTempFilename(string* temp_filename) {
+ static path temp_files_subpath;
+ static uint64_t next_temp_file = 0;
+ temp_filename->clear();
+ if ( temp_files_subpath.empty() ) {
+ string path_string="";
+ MakeTempDir(&path_string);
+ temp_files_subpath = path_string;
+ }
+ *temp_filename =
+ (temp_files_subpath/caffe::format_int(next_temp_file++, 9)).string();
}
bool ReadProtoFromTextFile(const char* filename, Message* proto);
bool DecodeDatumNative(Datum* datum);
bool DecodeDatum(Datum* datum, bool is_color);
+#ifdef USE_OPENCV
cv::Mat ReadImageToCVMat(const string& filename,
const int height, const int width, const bool is_color);
cv::Mat DecodeDatumToCVMat(const Datum& datum, bool is_color);
void CVMatToDatum(const cv::Mat& cv_img, Datum* datum);
-
-template <typename Dtype>
-void hdf5_load_nd_dataset_helper(
- hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
- Blob<Dtype>* blob);
-
-template <typename Dtype>
-void hdf5_load_nd_dataset(
- hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
- Blob<Dtype>* blob);
-
-template <typename Dtype>
-void hdf5_save_nd_dataset(
- const hid_t file_id, const string& dataset_name, const Blob<Dtype>& blob);
+#endif // USE_OPENCV
} // namespace caffe
void caffe_exp(const int n, const Dtype* a, Dtype* y);
template <typename Dtype>
+void caffe_log(const int n, const Dtype* a, Dtype* y);
+
+template <typename Dtype>
void caffe_abs(const int n, const Dtype* a, Dtype* y);
template <typename Dtype>
Dtype caffe_cpu_strided_dot(const int n, const Dtype* x, const int incx,
const Dtype* y, const int incy);
-template <typename Dtype>
-int caffe_cpu_hamming_distance(const int n, const Dtype* x, const Dtype* y);
-
// Returns the sum of the absolute values of the elements of vector x
template <typename Dtype>
Dtype caffe_cpu_asum(const int n, const Dtype* x);
void caffe_gpu_exp(const int n, const Dtype* a, Dtype* y);
template <typename Dtype>
+void caffe_gpu_log(const int n, const Dtype* a, Dtype* y);
+
+template <typename Dtype>
void caffe_gpu_powx(const int n, const Dtype* a, const Dtype b, Dtype* y);
// caffe_gpu_rng_uniform with two arguments generates integers in the range
void caffe_gpu_dot(const int n, const Dtype* x, const Dtype* y, Dtype* out);
template <typename Dtype>
-uint32_t caffe_gpu_hamming_distance(const int n, const Dtype* x,
- const Dtype* y);
-
-template <typename Dtype>
void caffe_gpu_asum(const int n, const Dtype* x, Dtype* y);
template<typename Dtype>
DEFINE_VSL_UNARY_FUNC(Sqr, y[i] = a[i] * a[i]);
DEFINE_VSL_UNARY_FUNC(Exp, y[i] = exp(a[i]));
+DEFINE_VSL_UNARY_FUNC(Ln, y[i] = log(a[i]));
DEFINE_VSL_UNARY_FUNC(Abs, y[i] = fabs(a[i]));
// A simple way to define the vsl unary functions with singular parameter b.
--- /dev/null
+#ifndef INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_
+#define INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_
+
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/solver.hpp"
+
+namespace caffe {
+
+class SignalHandler {
+ public:
+ // Contructor. Specify what action to take when a signal is received.
+ SignalHandler(SolverAction::Enum SIGINT_action,
+ SolverAction::Enum SIGHUP_action);
+ ~SignalHandler();
+ ActionCallback GetActionFunction();
+ private:
+ SolverAction::Enum CheckForSignals() const;
+ SolverAction::Enum SIGINT_action_;
+ SolverAction::Enum SIGHUP_action_;
+};
+
+} // namespace caffe
+
+#endif // INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_
// Return true iff the net is not the current version.
bool NetNeedsUpgrade(const NetParameter& net_param);
+// Check for deprecations and upgrade the NetParameter as needed.
+bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param);
+
+// Read parameters from a file into a NetParameter proto message.
+void ReadNetParamsFromTextFileOrDie(const string& param_file,
+ NetParameter* param);
+void ReadNetParamsFromBinaryFileOrDie(const string& param_file,
+ NetParameter* param);
+
// Return true iff any layer contains parameters specified using
// deprecated V0LayerParameter.
bool NetNeedsV0ToV1Upgrade(const NetParameter& net_param);
const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type);
-// Check for deprecations and upgrade the NetParameter as needed.
-bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param);
+// Return true iff the solver contains any old solver_type specified as enums
+bool SolverNeedsTypeUpgrade(const SolverParameter& solver_param);
-// Read parameters from a file into a NetParameter proto message.
-void ReadNetParamsFromTextFileOrDie(const string& param_file,
- NetParameter* param);
-void ReadNetParamsFromBinaryFileOrDie(const string& param_file,
- NetParameter* param);
+bool UpgradeSolverType(SolverParameter* solver_param);
+
+// Check for deprecations and upgrade the SolverParameter as needed.
+bool UpgradeSolverAsNeeded(const string& param_file, SolverParameter* param);
+
+// Read parameters from a file into a SolverParameter proto message.
+void ReadSolverParamsFromTextFileOrDie(const string& param_file,
+ SolverParameter* param);
} // namespace caffe
+++ /dev/null
-#ifndef CAFFE_VISION_LAYERS_HPP_
-#define CAFFE_VISION_LAYERS_HPP_
-
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
-#include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/loss_layers.hpp"
-#include "caffe/neuron_layers.hpp"
-#include "caffe/proto/caffe.pb.h"
-
-namespace caffe {
-
-/**
- * @brief Abstract base class that factors out the BLAS code common to
- * ConvolutionLayer and DeconvolutionLayer.
- */
-template <typename Dtype>
-class BaseConvolutionLayer : public Layer<Dtype> {
- public:
- explicit BaseConvolutionLayer(const LayerParameter& param)
- : Layer<Dtype>(param) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline int MinBottomBlobs() const { return 1; }
- virtual inline int MinTopBlobs() const { return 1; }
- virtual inline bool EqualNumBottomTopBlobs() const { return true; }
-
- protected:
- // Helper functions that abstract away the column buffer and gemm arguments.
- // The last argument in forward_cpu_gemm is so that we can skip the im2col if
- // we just called weight_cpu_gemm with the same input.
- void forward_cpu_gemm(const Dtype* input, const Dtype* weights,
- Dtype* output, bool skip_im2col = false);
- void forward_cpu_bias(Dtype* output, const Dtype* bias);
- void backward_cpu_gemm(const Dtype* input, const Dtype* weights,
- Dtype* output);
- void weight_cpu_gemm(const Dtype* input, const Dtype* output, Dtype*
- weights);
- void backward_cpu_bias(Dtype* bias, const Dtype* input);
-
-#ifndef CPU_ONLY
- void forward_gpu_gemm(const Dtype* col_input, const Dtype* weights,
- Dtype* output, bool skip_im2col = false);
- void forward_gpu_bias(Dtype* output, const Dtype* bias);
- void backward_gpu_gemm(const Dtype* input, const Dtype* weights,
- Dtype* col_output);
- void weight_gpu_gemm(const Dtype* col_input, const Dtype* output, Dtype*
- weights);
- void backward_gpu_bias(Dtype* bias, const Dtype* input);
-#endif
-
- // reverse_dimensions should return true iff we are implementing deconv, so
- // that conv helpers know which dimensions are which.
- virtual bool reverse_dimensions() = 0;
- // Compute height_out_ and width_out_ from other parameters.
- virtual void compute_output_shape() = 0;
-
- int kernel_h_, kernel_w_;
- int stride_h_, stride_w_;
- int num_;
- int channels_;
- int pad_h_, pad_w_;
- int height_, width_;
- int group_;
- int num_output_;
- int height_out_, width_out_;
- bool bias_term_;
- bool is_1x1_;
-
- private:
- // wrap im2col/col2im so we don't have to remember the (long) argument lists
- inline void conv_im2col_cpu(const Dtype* data, Dtype* col_buff) {
- im2col_cpu(data, conv_in_channels_, conv_in_height_, conv_in_width_,
- kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, col_buff);
- }
- inline void conv_col2im_cpu(const Dtype* col_buff, Dtype* data) {
- col2im_cpu(col_buff, conv_in_channels_, conv_in_height_, conv_in_width_,
- kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, data);
- }
-#ifndef CPU_ONLY
- inline void conv_im2col_gpu(const Dtype* data, Dtype* col_buff) {
- im2col_gpu(data, conv_in_channels_, conv_in_height_, conv_in_width_,
- kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, col_buff);
- }
- inline void conv_col2im_gpu(const Dtype* col_buff, Dtype* data) {
- col2im_gpu(col_buff, conv_in_channels_, conv_in_height_, conv_in_width_,
- kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, data);
- }
-#endif
-
- int conv_out_channels_;
- int conv_in_channels_;
- int conv_out_spatial_dim_;
- int conv_in_height_;
- int conv_in_width_;
- int kernel_dim_;
- int weight_offset_;
- int col_offset_;
- int output_offset_;
-
- Blob<Dtype> col_buffer_;
- Blob<Dtype> bias_multiplier_;
-};
-
-/**
- * @brief Convolves the input image with a bank of learned filters,
- * and (optionally) adds biases.
- *
- * Caffe convolves by reduction to matrix multiplication. This achieves
- * high-throughput and generality of input and filter dimensions but comes at
- * the cost of memory for matrices. This makes use of efficiency in BLAS.
- *
- * The input is "im2col" transformed to a channel K' x H x W data matrix
- * for multiplication with the N x K' x H x W filter matrix to yield a
- * N' x H x W output matrix that is then "col2im" restored. K' is the
- * input channel * kernel height * kernel width dimension of the unrolled
- * inputs so that the im2col matrix has a column for each input region to
- * be filtered. col2im restores the output spatial structure by rolling up
- * the output channel N' columns of the output matrix.
- */
-template <typename Dtype>
-class ConvolutionLayer : public BaseConvolutionLayer<Dtype> {
- public:
- /**
- * @param param provides ConvolutionParameter convolution_param,
- * with ConvolutionLayer options:
- * - num_output. The number of filters.
- * - kernel_size / kernel_h / kernel_w. The filter dimensions, given by
- * kernel_size for square filters or kernel_h and kernel_w for rectangular
- * filters.
- * - stride / stride_h / stride_w (\b optional, default 1). The filter
- * stride, given by stride_size for equal dimensions or stride_h and stride_w
- * for different strides. By default the convolution is dense with stride 1.
- * - pad / pad_h / pad_w (\b optional, default 0). The zero-padding for
- * convolution, given by pad for equal dimensions or pad_h and pad_w for
- * different padding. Input padding is computed implicitly instead of
- * actually padding.
- * - group (\b optional, default 1). The number of filter groups. Group
- * convolution is a method for reducing parameterization by selectively
- * connecting input and output channels. The input and output channel dimensions must be divisible
- * by the number of groups. For group @f$ \geq 1 @f$, the
- * convolutional filters' input and output channels are separated s.t. each
- * group takes 1 / group of the input channels and makes 1 / group of the
- * output channels. Concretely 4 input channels, 8 output channels, and
- * 2 groups separate input channels 1-2 and output channels 1-4 into the
- * first group and input channels 3-4 and output channels 5-8 into the second
- * group.
- * - bias_term (\b optional, default true). Whether to have a bias.
- * - engine: convolution has CAFFE (matrix multiplication) and CUDNN (library
- * kernels + stream parallelism) engines.
- */
- explicit ConvolutionLayer(const LayerParameter& param)
- : BaseConvolutionLayer<Dtype>(param) {}
-
- virtual inline const char* type() const { return "Convolution"; }
-
- protected:
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual inline bool reverse_dimensions() { return false; }
- virtual void compute_output_shape();
-};
-
-/**
- * @brief Convolve the input with a bank of learned filters, and (optionally)
- * add biases, treating filters and convolution parameters in the
- * opposite sense as ConvolutionLayer.
- *
- * ConvolutionLayer computes each output value by dotting an input window with
- * a filter; DeconvolutionLayer multiplies each input value by a filter
- * elementwise, and sums over the resulting output windows. In other words,
- * DeconvolutionLayer is ConvolutionLayer with the forward and backward passes
- * reversed. DeconvolutionLayer reuses ConvolutionParameter for its
- * parameters, but they take the opposite sense as in ConvolutionLayer (so
- * padding is removed from the output rather than added to the input, and
- * stride results in upsampling rather than downsampling).
- */
-template <typename Dtype>
-class DeconvolutionLayer : public BaseConvolutionLayer<Dtype> {
- public:
- explicit DeconvolutionLayer(const LayerParameter& param)
- : BaseConvolutionLayer<Dtype>(param) {}
-
- virtual inline const char* type() const { return "Deconvolution"; }
-
- protected:
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual inline bool reverse_dimensions() { return true; }
- virtual void compute_output_shape();
-};
-
-#ifdef USE_CUDNN
-/*
- * @brief cuDNN implementation of ConvolutionLayer.
- * Fallback to ConvolutionLayer for CPU mode.
- *
- * cuDNN accelerates convolution through forward kernels for filtering and bias
- * plus backward kernels for the gradient w.r.t. the filters, biases, and
- * inputs. Caffe + cuDNN further speeds up the computation through forward
- * parallelism across groups and backward parallelism across gradients.
- *
- * The CUDNN engine does not have memory overhead for matrix buffers. For many
- * input and filter regimes the CUDNN engine is faster than the CAFFE engine,
- * but for fully-convolutional models and large inputs the CAFFE engine can be
- * faster as long as it fits in memory.
-*/
-template <typename Dtype>
-class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
- public:
- explicit CuDNNConvolutionLayer(const LayerParameter& param)
- : ConvolutionLayer<Dtype>(param), handles_setup_(false) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual ~CuDNNConvolutionLayer();
-
- protected:
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- bool handles_setup_;
- cudnnHandle_t* handle_;
- cudaStream_t* stream_;
- vector<cudnnTensor4dDescriptor_t> bottom_descs_, top_descs_;
- cudnnTensor4dDescriptor_t bias_desc_;
- cudnnFilterDescriptor_t filter_desc_;
- vector<cudnnConvolutionDescriptor_t> conv_descs_;
- int bottom_offset_, top_offset_, weight_offset_, bias_offset_;
-};
-#endif
-
-/**
- * @brief A helper for image operations that rearranges image regions into
- * column vectors. Used by ConvolutionLayer to perform convolution
- * by matrix multiplication.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class Im2colLayer : public Layer<Dtype> {
- public:
- explicit Im2colLayer(const LayerParameter& param)
- : Layer<Dtype>(param) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "Im2col"; }
- virtual inline int ExactNumBottomBlobs() const { return 1; }
- virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- int kernel_h_, kernel_w_;
- int stride_h_, stride_w_;
- int channels_;
- int height_, width_;
- int pad_h_, pad_w_;
-};
-
-// Forward declare PoolingLayer and SplitLayer for use in LRNLayer.
-template <typename Dtype> class PoolingLayer;
-template <typename Dtype> class SplitLayer;
-
-/**
- * @brief Normalize the input in a local region across or within feature maps.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class LRNLayer : public Layer<Dtype> {
- public:
- explicit LRNLayer(const LayerParameter& param)
- : Layer<Dtype>(param) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "LRN"; }
- virtual inline int ExactNumBottomBlobs() const { return 1; }
- virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- virtual void CrossChannelForward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void CrossChannelForward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void WithinChannelForward(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void CrossChannelBackward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void CrossChannelBackward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void WithinChannelBackward(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- int size_;
- int pre_pad_;
- Dtype alpha_;
- Dtype beta_;
- Dtype k_;
- int num_;
- int channels_;
- int height_;
- int width_;
-
- // Fields used for normalization ACROSS_CHANNELS
- // scale_ stores the intermediate summing results
- Blob<Dtype> scale_;
-
- // Fields used for normalization WITHIN_CHANNEL
- shared_ptr<SplitLayer<Dtype> > split_layer_;
- vector<Blob<Dtype>*> split_top_vec_;
- shared_ptr<PowerLayer<Dtype> > square_layer_;
- Blob<Dtype> square_input_;
- Blob<Dtype> square_output_;
- vector<Blob<Dtype>*> square_bottom_vec_;
- vector<Blob<Dtype>*> square_top_vec_;
- shared_ptr<PoolingLayer<Dtype> > pool_layer_;
- Blob<Dtype> pool_output_;
- vector<Blob<Dtype>*> pool_top_vec_;
- shared_ptr<PowerLayer<Dtype> > power_layer_;
- Blob<Dtype> power_output_;
- vector<Blob<Dtype>*> power_top_vec_;
- shared_ptr<EltwiseLayer<Dtype> > product_layer_;
- Blob<Dtype> product_input_;
- vector<Blob<Dtype>*> product_bottom_vec_;
-};
-
-
-/**
- * @brief Pools the input image by taking the max, average, etc. within regions.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class PoolingLayer : public Layer<Dtype> {
- public:
- explicit PoolingLayer(const LayerParameter& param)
- : Layer<Dtype>(param) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
-
- virtual inline const char* type() const { return "Pooling"; }
- virtual inline int ExactNumBottomBlobs() const { return 1; }
- virtual inline int MinTopBlobs() const { return 1; }
- // MAX POOL layers can output an extra top blob for the mask;
- // others can only output the pooled inputs.
- virtual inline int MaxTopBlobs() const {
- return (this->layer_param_.pooling_param().pool() ==
- PoolingParameter_PoolMethod_MAX) ? 2 : 1;
- }
-
- protected:
- virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- int kernel_h_, kernel_w_;
- int stride_h_, stride_w_;
- int pad_h_, pad_w_;
- int channels_;
- int height_, width_;
- int pooled_height_, pooled_width_;
- bool global_pooling_;
- Blob<Dtype> rand_idx_;
- Blob<int> max_idx_;
-};
-
-#ifdef USE_CUDNN
-/*
- * @brief cuDNN implementation of PoolingLayer.
- * Fallback to PoolingLayer for CPU mode.
-*/
-template <typename Dtype>
-class CuDNNPoolingLayer : public PoolingLayer<Dtype> {
- public:
- explicit CuDNNPoolingLayer(const LayerParameter& param)
- : PoolingLayer<Dtype>(param), handles_setup_(false) {}
- virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual ~CuDNNPoolingLayer();
- // Currently, cuDNN does not support the extra top blob.
- virtual inline int MinTopBlobs() const { return -1; }
- virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
- virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
- const vector<Blob<Dtype>*>& top);
- virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
- const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- bool handles_setup_;
- cudnnHandle_t handle_;
- cudnnTensor4dDescriptor_t bottom_desc_, top_desc_;
- cudnnPoolingDescriptor_t pooling_desc_;
- cudnnPoolingMode_t mode_;
-};
-#endif
-
-} // namespace caffe
-
-#endif // CAFFE_VISION_LAYERS_HPP_
--- /dev/null
+classdef test_io < matlab.unittest.TestCase
+ methods (Test)
+ function test_read_write_mean(self)
+ % randomly generate mean data
+ width = 200;
+ height = 300;
+ channels = 3;
+ mean_data_write = 255 * rand(width, height, channels, 'single');
+ % write mean data to binary proto
+ mean_proto_file = tempname();
+ caffe.io.write_mean(mean_data_write, mean_proto_file);
+ % read mean data from saved binary proto and test whether they are equal
+ mean_data_read = caffe.io.read_mean(mean_proto_file);
+ self.verifyEqual(mean_data_write, mean_data_read)
+ delete(mean_proto_file);
+ end
+ end
+end
--- /dev/null
+classdef test_net < matlab.unittest.TestCase
+
+ properties
+ num_output
+ model_file
+ net
+ end
+
+ methods (Static)
+ function model_file = simple_net_file(num_output)
+ model_file = tempname();
+ fid = fopen(model_file, 'w');
+ fprintf(fid, [ ...
+ 'name: "testnet" force_backward: true\n' ...
+ 'layer { type: "DummyData" name: "data" top: "data" top: "label"\n' ...
+ 'dummy_data_param { num: 5 channels: 2 height: 3 width: 4\n' ...
+ ' num: 5 channels: 1 height: 1 width: 1\n' ...
+ ' data_filler { type: "gaussian" std: 1 }\n' ...
+ ' data_filler { type: "constant" } } }\n' ...
+ 'layer { type: "Convolution" name: "conv" bottom: "data" top: "conv"\n' ...
+ ' convolution_param { num_output: 11 kernel_size: 2 pad: 3\n' ...
+ ' weight_filler { type: "gaussian" std: 1 }\n' ...
+ ' bias_filler { type: "constant" value: 2 } }\n' ...
+ ' param { decay_mult: 1 } param { decay_mult: 0 }\n' ...
+ ' }\n' ...
+ 'layer { type: "InnerProduct" name: "ip" bottom: "conv" top: "ip"\n' ...
+ ' inner_product_param { num_output: ' num2str(num_output) ...
+ ' weight_filler { type: "gaussian" std: 2.5 }\n' ...
+ ' bias_filler { type: "constant" value: -3 } } }\n' ...
+ 'layer { type: "SoftmaxWithLoss" name: "loss" bottom: "ip" bottom: "label"\n' ...
+ ' top: "loss" }' ]);
+ fclose(fid);
+ end
+ end
+ methods
+ function self = test_net()
+ self.num_output = 13;
+ self.model_file = caffe.test.test_net.simple_net_file(self.num_output);
+ self.net = caffe.Net(self.model_file, 'train');
+ % also make sure get_solver runs
+ caffe.get_net(self.model_file, 'train');
+
+ % fill in valid labels
+ self.net.blobs('label').set_data(randi( ...
+ self.num_output - 1, self.net.blobs('label').shape));
+
+ delete(self.model_file);
+ end
+ end
+ methods (Test)
+ function self = test_blob(self)
+ self.net.blobs('data').set_data(10 * ones(self.net.blobs('data').shape));
+ self.verifyEqual(self.net.blobs('data').get_data(), ...
+ 10 * ones(self.net.blobs('data').shape, 'single'));
+ self.net.blobs('data').set_diff(-2 * ones(self.net.blobs('data').shape));
+ self.verifyEqual(self.net.blobs('data').get_diff(), ...
+ -2 * ones(self.net.blobs('data').shape, 'single'));
+ original_shape = self.net.blobs('data').shape;
+ self.net.blobs('data').reshape([6 5 4 3 2 1]);
+ self.verifyEqual(self.net.blobs('data').shape, [6 5 4 3 2 1]);
+ self.net.blobs('data').reshape(original_shape);
+ self.net.reshape();
+ end
+ function self = test_layer(self)
+ self.verifyEqual(self.net.params('conv', 1).shape, [2 2 2 11]);
+ self.verifyEqual(self.net.layers('conv').params(2).shape, 11);
+ self.verifyEqual(self.net.layers('conv').type(), 'Convolution');
+ end
+ function test_forward_backward(self)
+ self.net.forward_prefilled();
+ self.net.backward_prefilled();
+ end
+ function test_inputs_outputs(self)
+ self.verifyEqual(self.net.inputs, cell(0, 1))
+ self.verifyEqual(self.net.outputs, {'loss'});
+ end
+ function test_save_and_read(self)
+ weights_file = tempname();
+ self.net.save(weights_file);
+ model_file2 = caffe.test.test_net.simple_net_file(self.num_output);
+ net2 = caffe.Net(model_file2, 'train');
+ net2.copy_from(weights_file);
+ net3 = caffe.Net(model_file2, weights_file, 'train');
+ delete(model_file2);
+ delete(weights_file);
+ for l = 1:length(self.net.layer_vec)
+ for i = 1:length(self.net.layer_vec(l).params)
+ self.verifyEqual(self.net.layer_vec(l).params(i).get_data(), ...
+ net2.layer_vec(l).params(i).get_data());
+ self.verifyEqual(self.net.layer_vec(l).params(i).get_data(), ...
+ net3.layer_vec(l).params(i).get_data());
+ end
+ end
+ end
+ end
+end
--- /dev/null
+classdef test_solver < matlab.unittest.TestCase
+
+ properties
+ num_output
+ solver
+ end
+
+ methods
+ function self = test_solver()
+ self.num_output = 13;
+ model_file = caffe.test.test_net.simple_net_file(self.num_output);
+ solver_file = tempname();
+
+ fid = fopen(solver_file, 'w');
+ fprintf(fid, [ ...
+ 'net: "' model_file '"\n' ...
+ 'test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9\n' ...
+ 'weight_decay: 0.0005 lr_policy: "inv" gamma: 0.0001 power: 0.75\n' ...
+ 'display: 100 max_iter: 100 snapshot_after_train: false\n' ]);
+ fclose(fid);
+
+ self.solver = caffe.Solver(solver_file);
+ % also make sure get_solver runs
+ caffe.get_solver(solver_file);
+ caffe.set_mode_cpu();
+ % fill in valid labels
+ self.solver.net.blobs('label').set_data(randi( ...
+ self.num_output - 1, self.solver.net.blobs('label').shape));
+ self.solver.test_nets(1).blobs('label').set_data(randi( ...
+ self.num_output - 1, self.solver.test_nets(1).blobs('label').shape));
+
+ delete(solver_file);
+ delete(model_file);
+ end
+ end
+ methods (Test)
+ function test_solve(self)
+ self.verifyEqual(self.solver.iter(), 0)
+ self.solver.step(30);
+ self.verifyEqual(self.solver.iter(), 30)
+ self.solver.solve()
+ self.verifyEqual(self.solver.iter(), 100)
+ end
+ end
+end
--- /dev/null
+classdef Blob < handle
+ % Wrapper class of caffe::Blob in matlab
+
+ properties (Access = private)
+ hBlob_self
+ end
+
+ methods
+ function self = Blob(hBlob_blob)
+ CHECK(is_valid_handle(hBlob_blob), 'invalid Blob handle');
+
+ % setup self handle
+ self.hBlob_self = hBlob_blob;
+ end
+ function shape = shape(self)
+ shape = caffe_('blob_get_shape', self.hBlob_self);
+ end
+ function reshape(self, shape)
+ shape = self.check_and_preprocess_shape(shape);
+ caffe_('blob_reshape', self.hBlob_self, shape);
+ end
+ function data = get_data(self)
+ data = caffe_('blob_get_data', self.hBlob_self);
+ end
+ function set_data(self, data)
+ data = self.check_and_preprocess_data(data);
+ caffe_('blob_set_data', self.hBlob_self, data);
+ end
+ function diff = get_diff(self)
+ diff = caffe_('blob_get_diff', self.hBlob_self);
+ end
+ function set_diff(self, diff)
+ diff = self.check_and_preprocess_data(diff);
+ caffe_('blob_set_diff', self.hBlob_self, diff);
+ end
+ end
+
+ methods (Access = private)
+ function shape = check_and_preprocess_shape(~, shape)
+ CHECK(isempty(shape) || (isnumeric(shape) && isrow(shape)), ...
+ 'shape must be a integer row vector');
+ shape = double(shape);
+ end
+ function data = check_and_preprocess_data(self, data)
+ CHECK(isnumeric(data), 'data or diff must be numeric types');
+ self.check_data_size_matches(data);
+ if ~isa(data, 'single')
+ data = single(data);
+ end
+ end
+ function check_data_size_matches(self, data)
+ % check whether size of data matches shape of this blob
+ % note: matlab arrays always have at least 2 dimensions. To compare
+ % shape between size of data and shape of this blob, extend shape of
+ % this blob to have at least 2 dimensions
+ self_shape_extended = self.shape;
+ if isempty(self_shape_extended)
+ % target blob is a scalar (0 dim)
+ self_shape_extended = [1, 1];
+ elseif isscalar(self_shape_extended)
+ % target blob is a vector (1 dim)
+ self_shape_extended = [self_shape_extended, 1];
+ end
+ % Also, matlab cannot have tailing dimension 1 for ndim > 2, so you
+ % cannot create 20 x 10 x 1 x 1 array in matlab as it becomes 20 x 10
+ % Extend matlab arrays to have tailing dimension 1 during shape match
+ data_size_extended = ...
+ [size(data), ones(1, length(self_shape_extended) - ndims(data))];
+ is_matched = ...
+ (length(self_shape_extended) == length(data_size_extended)) ...
+ && all(self_shape_extended == data_size_extended);
+ CHECK(is_matched, ...
+ sprintf('%s, input data/diff size: [ %s] vs target blob shape: [ %s]', ...
+ 'input data/diff size does not match target blob shape', ...
+ sprintf('%d ', data_size_extended), sprintf('%d ', self_shape_extended)));
+ end
+ end
+end
--- /dev/null
+classdef Layer < handle
+ % Wrapper class of caffe::Layer in matlab
+
+ properties (Access = private)
+ hLayer_self
+ attributes
+ % attributes fields:
+ % hBlob_blobs
+ end
+ properties (SetAccess = private)
+ params
+ end
+
+ methods
+ function self = Layer(hLayer_layer)
+ CHECK(is_valid_handle(hLayer_layer), 'invalid Layer handle');
+
+ % setup self handle and attributes
+ self.hLayer_self = hLayer_layer;
+ self.attributes = caffe_('layer_get_attr', self.hLayer_self);
+
+ % setup weights
+ self.params = caffe.Blob.empty();
+ for n = 1:length(self.attributes.hBlob_blobs)
+ self.params(n) = caffe.Blob(self.attributes.hBlob_blobs(n));
+ end
+ end
+ function layer_type = type(self)
+ layer_type = caffe_('layer_get_type', self.hLayer_self);
+ end
+ end
+end
--- /dev/null
+classdef Net < handle
+ % Wrapper class of caffe::Net in matlab
+
+ properties (Access = private)
+ hNet_self
+ attributes
+ % attribute fields
+ % hLayer_layers
+ % hBlob_blobs
+ % input_blob_indices
+ % output_blob_indices
+ % layer_names
+ % blob_names
+ end
+ properties (SetAccess = private)
+ layer_vec
+ blob_vec
+ inputs
+ outputs
+ name2layer_index
+ name2blob_index
+ layer_names
+ blob_names
+ end
+
+ methods
+ function self = Net(varargin)
+ % decide whether to construct a net from model_file or handle
+ if ~(nargin == 1 && isstruct(varargin{1}))
+ % construct a net from model_file
+ self = caffe.get_net(varargin{:});
+ return
+ end
+ % construct a net from handle
+ hNet_net = varargin{1};
+ CHECK(is_valid_handle(hNet_net), 'invalid Net handle');
+
+ % setup self handle and attributes
+ self.hNet_self = hNet_net;
+ self.attributes = caffe_('net_get_attr', self.hNet_self);
+
+ % setup layer_vec
+ self.layer_vec = caffe.Layer.empty();
+ for n = 1:length(self.attributes.hLayer_layers)
+ self.layer_vec(n) = caffe.Layer(self.attributes.hLayer_layers(n));
+ end
+
+ % setup blob_vec
+ self.blob_vec = caffe.Blob.empty();
+ for n = 1:length(self.attributes.hBlob_blobs);
+ self.blob_vec(n) = caffe.Blob(self.attributes.hBlob_blobs(n));
+ end
+
+ % setup input and output blob and their names
+ % note: add 1 to indices as matlab is 1-indexed while C++ is 0-indexed
+ self.inputs = ...
+ self.attributes.blob_names(self.attributes.input_blob_indices + 1);
+ self.outputs = ...
+ self.attributes.blob_names(self.attributes.output_blob_indices + 1);
+
+ % create map objects to map from name to layers and blobs
+ self.name2layer_index = containers.Map(self.attributes.layer_names, ...
+ 1:length(self.attributes.layer_names));
+ self.name2blob_index = containers.Map(self.attributes.blob_names, ...
+ 1:length(self.attributes.blob_names));
+
+ % expose layer_names and blob_names for public read access
+ self.layer_names = self.attributes.layer_names;
+ self.blob_names = self.attributes.blob_names;
+ end
+ function layer = layers(self, layer_name)
+ CHECK(ischar(layer_name), 'layer_name must be a string');
+ layer = self.layer_vec(self.name2layer_index(layer_name));
+ end
+ function blob = blobs(self, blob_name)
+ CHECK(ischar(blob_name), 'blob_name must be a string');
+ blob = self.blob_vec(self.name2blob_index(blob_name));
+ end
+ function blob = params(self, layer_name, blob_index)
+ CHECK(ischar(layer_name), 'layer_name must be a string');
+ CHECK(isscalar(blob_index), 'blob_index must be a scalar');
+ blob = self.layer_vec(self.name2layer_index(layer_name)).params(blob_index);
+ end
+ function forward_prefilled(self)
+ caffe_('net_forward', self.hNet_self);
+ end
+ function backward_prefilled(self)
+ caffe_('net_backward', self.hNet_self);
+ end
+ function res = forward(self, input_data)
+ CHECK(iscell(input_data), 'input_data must be a cell array');
+ CHECK(length(input_data) == length(self.inputs), ...
+ 'input data cell length must match input blob number');
+ % copy data to input blobs
+ for n = 1:length(self.inputs)
+ self.blobs(self.inputs{n}).set_data(input_data{n});
+ end
+ self.forward_prefilled();
+ % retrieve data from output blobs
+ res = cell(length(self.outputs), 1);
+ for n = 1:length(self.outputs)
+ res{n} = self.blobs(self.outputs{n}).get_data();
+ end
+ end
+ function res = backward(self, output_diff)
+ CHECK(iscell(output_diff), 'output_diff must be a cell array');
+ CHECK(length(output_diff) == length(self.outputs), ...
+ 'output diff cell length must match output blob number');
+ % copy diff to output blobs
+ for n = 1:length(self.outputs)
+ self.blobs(self.outputs{n}).set_diff(output_diff{n});
+ end
+ self.backward_prefilled();
+ % retrieve diff from input blobs
+ res = cell(length(self.inputs), 1);
+ for n = 1:length(self.inputs)
+ res{n} = self.blobs(self.inputs{n}).get_diff();
+ end
+ end
+ function copy_from(self, weights_file)
+ CHECK(ischar(weights_file), 'weights_file must be a string');
+ CHECK_FILE_EXIST(weights_file);
+ caffe_('net_copy_from', self.hNet_self, weights_file);
+ end
+ function reshape(self)
+ caffe_('net_reshape', self.hNet_self);
+ end
+ function save(self, weights_file)
+ CHECK(ischar(weights_file), 'weights_file must be a string');
+ caffe_('net_save', self.hNet_self, weights_file);
+ end
+ end
+end
--- /dev/null
+classdef Solver < handle
+ % Wrapper class of caffe::SGDSolver in matlab
+
+ properties (Access = private)
+ hSolver_self
+ attributes
+ % attribute fields
+ % hNet_net
+ % hNet_test_nets
+ end
+ properties (SetAccess = private)
+ net
+ test_nets
+ end
+
+ methods
+ function self = Solver(varargin)
+ % decide whether to construct a solver from solver_file or handle
+ if ~(nargin == 1 && isstruct(varargin{1}))
+ % construct a solver from solver_file
+ self = caffe.get_solver(varargin{:});
+ return
+ end
+ % construct a solver from handle
+ hSolver_solver = varargin{1};
+ CHECK(is_valid_handle(hSolver_solver), 'invalid Solver handle');
+
+ % setup self handle and attributes
+ self.hSolver_self = hSolver_solver;
+ self.attributes = caffe_('solver_get_attr', self.hSolver_self);
+
+ % setup net and test_nets
+ self.net = caffe.Net(self.attributes.hNet_net);
+ self.test_nets = caffe.Net.empty();
+ for n = 1:length(self.attributes.hNet_test_nets)
+ self.test_nets(n) = caffe.Net(self.attributes.hNet_test_nets(n));
+ end
+ end
+ function iter = iter(self)
+ iter = caffe_('solver_get_iter', self.hSolver_self);
+ end
+ function restore(self, snapshot_filename)
+ CHECK(ischar(snapshot_filename), 'snapshot_filename must be a string');
+ CHECK_FILE_EXIST(snapshot_filename);
+ caffe_('solver_restore', self.hSolver_self, snapshot_filename);
+ end
+ function solve(self)
+ caffe_('solver_solve', self.hSolver_self);
+ end
+ function step(self, iters)
+ CHECK(isscalar(iters) && iters > 0, 'iters must be positive integer');
+ iters = double(iters);
+ caffe_('solver_step', self.hSolver_self, iters);
+ end
+ end
+end
--- /dev/null
+function net = get_net(varargin)
+% net = get_net(model_file, phase_name) or
+% net = get_net(model_file, weights_file, phase_name)
+% Construct a net from model_file, and load weights from weights_file
+% phase_name can only be 'train' or 'test'
+
+CHECK(nargin == 2 || nargin == 3, ['usage: ' ...
+ 'net = get_net(model_file, phase_name) or ' ...
+ 'net = get_net(model_file, weights_file, phase_name)']);
+if nargin == 3
+ model_file = varargin{1};
+ weights_file = varargin{2};
+ phase_name = varargin{3};
+elseif nargin == 2
+ model_file = varargin{1};
+ phase_name = varargin{2};
+end
+
+CHECK(ischar(model_file), 'model_file must be a string');
+CHECK(ischar(phase_name), 'phase_name must be a string');
+CHECK_FILE_EXIST(model_file);
+CHECK(strcmp(phase_name, 'train') || strcmp(phase_name, 'test'), ...
+ sprintf('phase_name can only be %strain%s or %stest%s', ...
+ char(39), char(39), char(39), char(39)));
+
+% construct caffe net from model_file
+hNet = caffe_('get_net', model_file, phase_name);
+net = caffe.Net(hNet);
+
+% load weights from weights_file
+if nargin == 3
+ CHECK(ischar(weights_file), 'weights_file must be a string');
+ CHECK_FILE_EXIST(weights_file);
+ net.copy_from(weights_file);
+end
+
+end
--- /dev/null
+function solver = get_solver(solver_file)
+% solver = get_solver(solver_file)
+% Construct a Solver object from solver_file
+
+CHECK(ischar(solver_file), 'solver_file must be a string');
+CHECK_FILE_EXIST(solver_file);
+pSolver = caffe_('get_solver', solver_file);
+solver = caffe.Solver(pSolver);
+
+end
--- /dev/null
+classdef io
+ % a class for input and output functions
+
+ methods (Static)
+ function im_data = load_image(im_file)
+ % im_data = load_image(im_file)
+ % load an image from disk into Caffe-supported data format
+ % switch channels from RGB to BGR, make width the fastest dimension
+ % and convert to single
+ % returns im_data in W x H x C. For colored images, C = 3 in BGR
+ % channels, and for grayscale images, C = 1
+ CHECK(ischar(im_file), 'im_file must be a string');
+ CHECK_FILE_EXIST(im_file);
+ im_data = imread(im_file);
+ % permute channels from RGB to BGR for colored images
+ if size(im_data, 3) == 3
+ im_data = im_data(:, :, [3, 2, 1]);
+ end
+ % flip width and height to make width the fastest dimension
+ im_data = permute(im_data, [2, 1, 3]);
+ % convert from uint8 to single
+ im_data = single(im_data);
+ end
+ function mean_data = read_mean(mean_proto_file)
+ % mean_data = read_mean(mean_proto_file)
+ % read image mean data from binaryproto file
+ % returns mean_data in W x H x C with BGR channels
+ CHECK(ischar(mean_proto_file), 'mean_proto_file must be a string');
+ CHECK_FILE_EXIST(mean_proto_file);
+ mean_data = caffe_('read_mean', mean_proto_file);
+ end
+ function write_mean(mean_data, mean_proto_file)
+ % write_mean(mean_data, mean_proto_file)
+ % write image mean data to binaryproto file
+ % mean_data should be W x H x C with BGR channels
+ CHECK(ischar(mean_proto_file), 'mean_proto_file must be a string');
+ CHECK(isa(mean_data, 'single'), 'mean_data must be a SINGLE matrix');
+ caffe_('write_mean', mean_data, mean_proto_file);
+ end
+ end
+end
--- /dev/null
+function CHECK(expr, error_msg)
+
+if ~expr
+ error(error_msg);
+end
+
+end
--- /dev/null
+function CHECK_FILE_EXIST(filename)
+
+if exist(filename, 'file') == 0
+ error('%s does not exist', filename);
+end
+
+end
--- /dev/null
+//
+// caffe_.cpp provides wrappers of the caffe::Solver class, caffe::Net class,
+// caffe::Layer class and caffe::Blob class and some caffe::Caffe functions,
+// so that one could easily use Caffe from matlab.
+// Note that for matlab, we will simply use float as the data type.
+
+// Internally, data is stored with dimensions reversed from Caffe's:
+// e.g., if the Caffe blob axes are (num, channels, height, width),
+// the matcaffe data is stored as (width, height, channels, num)
+// where width is the fastest dimension.
+
+#include <sstream>
+#include <string>
+#include <vector>
+
+#include "mex.h"
+
+#include "caffe/caffe.hpp"
+
+#define MEX_ARGS int nlhs, mxArray **plhs, int nrhs, const mxArray **prhs
+
+using namespace caffe; // NOLINT(build/namespaces)
+
+// Do CHECK and throw a Mex error if check fails
+inline void mxCHECK(bool expr, const char* msg) {
+ if (!expr) {
+ mexErrMsgTxt(msg);
+ }
+}
+inline void mxERROR(const char* msg) { mexErrMsgTxt(msg); }
+
+// Check if a file exists and can be opened
+void mxCHECK_FILE_EXIST(const char* file) {
+ std::ifstream f(file);
+ if (!f.good()) {
+ f.close();
+ std::string msg("Could not open file ");
+ msg += file;
+ mxERROR(msg.c_str());
+ }
+ f.close();
+}
+
+// The pointers to caffe::Solver and caffe::Net instances
+static vector<shared_ptr<Solver<float> > > solvers_;
+static vector<shared_ptr<Net<float> > > nets_;
+// init_key is generated at the beginning and everytime you call reset
+static double init_key = static_cast<double>(caffe_rng_rand());
+
+/** -----------------------------------------------------------------
+ ** data conversion functions
+ **/
+// Enum indicates which blob memory to use
+enum WhichMemory { DATA, DIFF };
+
+// Copy matlab array to Blob data or diff
+static void mx_mat_to_blob(const mxArray* mx_mat, Blob<float>* blob,
+ WhichMemory data_or_diff) {
+ mxCHECK(blob->count() == mxGetNumberOfElements(mx_mat),
+ "number of elements in target blob doesn't match that in input mxArray");
+ const float* mat_mem_ptr = reinterpret_cast<const float*>(mxGetData(mx_mat));
+ float* blob_mem_ptr = NULL;
+ switch (Caffe::mode()) {
+ case Caffe::CPU:
+ blob_mem_ptr = (data_or_diff == DATA ?
+ blob->mutable_cpu_data() : blob->mutable_cpu_diff());
+ break;
+ case Caffe::GPU:
+ blob_mem_ptr = (data_or_diff == DATA ?
+ blob->mutable_gpu_data() : blob->mutable_gpu_diff());
+ break;
+ default:
+ mxERROR("Unknown Caffe mode");
+ }
+ caffe_copy(blob->count(), mat_mem_ptr, blob_mem_ptr);
+}
+
+// Copy Blob data or diff to matlab array
+static mxArray* blob_to_mx_mat(const Blob<float>* blob,
+ WhichMemory data_or_diff) {
+ const int num_axes = blob->num_axes();
+ vector<mwSize> dims(num_axes);
+ for (int blob_axis = 0, mat_axis = num_axes - 1; blob_axis < num_axes;
+ ++blob_axis, --mat_axis) {
+ dims[mat_axis] = static_cast<mwSize>(blob->shape(blob_axis));
+ }
+ // matlab array needs to have at least one dimension, convert scalar to 1-dim
+ if (num_axes == 0) {
+ dims.push_back(1);
+ }
+ mxArray* mx_mat =
+ mxCreateNumericArray(dims.size(), dims.data(), mxSINGLE_CLASS, mxREAL);
+ float* mat_mem_ptr = reinterpret_cast<float*>(mxGetData(mx_mat));
+ const float* blob_mem_ptr = NULL;
+ switch (Caffe::mode()) {
+ case Caffe::CPU:
+ blob_mem_ptr = (data_or_diff == DATA ? blob->cpu_data() : blob->cpu_diff());
+ break;
+ case Caffe::GPU:
+ blob_mem_ptr = (data_or_diff == DATA ? blob->gpu_data() : blob->gpu_diff());
+ break;
+ default:
+ mxERROR("Unknown Caffe mode");
+ }
+ caffe_copy(blob->count(), blob_mem_ptr, mat_mem_ptr);
+ return mx_mat;
+}
+
+// Convert vector<int> to matlab row vector
+static mxArray* int_vec_to_mx_vec(const vector<int>& int_vec) {
+ mxArray* mx_vec = mxCreateDoubleMatrix(int_vec.size(), 1, mxREAL);
+ double* vec_mem_ptr = mxGetPr(mx_vec);
+ for (int i = 0; i < int_vec.size(); i++) {
+ vec_mem_ptr[i] = static_cast<double>(int_vec[i]);
+ }
+ return mx_vec;
+}
+
+// Convert vector<string> to matlab cell vector of strings
+static mxArray* str_vec_to_mx_strcell(const vector<std::string>& str_vec) {
+ mxArray* mx_strcell = mxCreateCellMatrix(str_vec.size(), 1);
+ for (int i = 0; i < str_vec.size(); i++) {
+ mxSetCell(mx_strcell, i, mxCreateString(str_vec[i].c_str()));
+ }
+ return mx_strcell;
+}
+
+/** -----------------------------------------------------------------
+ ** handle and pointer conversion functions
+ ** a handle is a struct array with the following fields
+ ** (uint64) ptr : the pointer to the C++ object
+ ** (double) init_key : caffe initialization key
+ **/
+// Convert a handle in matlab to a pointer in C++. Check if init_key matches
+template <typename T>
+static T* handle_to_ptr(const mxArray* mx_handle) {
+ mxArray* mx_ptr = mxGetField(mx_handle, 0, "ptr");
+ mxArray* mx_init_key = mxGetField(mx_handle, 0, "init_key");
+ mxCHECK(mxIsUint64(mx_ptr), "pointer type must be uint64");
+ mxCHECK(mxGetScalar(mx_init_key) == init_key,
+ "Could not convert handle to pointer due to invalid init_key. "
+ "The object might have been cleared.");
+ return reinterpret_cast<T*>(*reinterpret_cast<uint64_t*>(mxGetData(mx_ptr)));
+}
+
+// Create a handle struct vector, without setting up each handle in it
+template <typename T>
+static mxArray* create_handle_vec(int ptr_num) {
+ const int handle_field_num = 2;
+ const char* handle_fields[handle_field_num] = { "ptr", "init_key" };
+ return mxCreateStructMatrix(ptr_num, 1, handle_field_num, handle_fields);
+}
+
+// Set up a handle in a handle struct vector by its index
+template <typename T>
+static void setup_handle(const T* ptr, int index, mxArray* mx_handle_vec) {
+ mxArray* mx_ptr = mxCreateNumericMatrix(1, 1, mxUINT64_CLASS, mxREAL);
+ *reinterpret_cast<uint64_t*>(mxGetData(mx_ptr)) =
+ reinterpret_cast<uint64_t>(ptr);
+ mxSetField(mx_handle_vec, index, "ptr", mx_ptr);
+ mxSetField(mx_handle_vec, index, "init_key", mxCreateDoubleScalar(init_key));
+}
+
+// Convert a pointer in C++ to a handle in matlab
+template <typename T>
+static mxArray* ptr_to_handle(const T* ptr) {
+ mxArray* mx_handle = create_handle_vec<T>(1);
+ setup_handle(ptr, 0, mx_handle);
+ return mx_handle;
+}
+
+// Convert a vector of shared_ptr in C++ to handle struct vector
+template <typename T>
+static mxArray* ptr_vec_to_handle_vec(const vector<shared_ptr<T> >& ptr_vec) {
+ mxArray* mx_handle_vec = create_handle_vec<T>(ptr_vec.size());
+ for (int i = 0; i < ptr_vec.size(); i++) {
+ setup_handle(ptr_vec[i].get(), i, mx_handle_vec);
+ }
+ return mx_handle_vec;
+}
+
+/** -----------------------------------------------------------------
+ ** matlab command functions: caffe_(api_command, arg1, arg2, ...)
+ **/
+// Usage: caffe_('get_solver', solver_file);
+static void get_solver(MEX_ARGS) {
+ mxCHECK(nrhs == 1 && mxIsChar(prhs[0]),
+ "Usage: caffe_('get_solver', solver_file)");
+ char* solver_file = mxArrayToString(prhs[0]);
+ mxCHECK_FILE_EXIST(solver_file);
+ SolverParameter solver_param;
+ ReadSolverParamsFromTextFileOrDie(solver_file, &solver_param);
+ shared_ptr<Solver<float> > solver(
+ SolverRegistry<float>::CreateSolver(solver_param));
+ solvers_.push_back(solver);
+ plhs[0] = ptr_to_handle<Solver<float> >(solver.get());
+ mxFree(solver_file);
+}
+
+// Usage: caffe_('solver_get_attr', hSolver)
+static void solver_get_attr(MEX_ARGS) {
+ mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+ "Usage: caffe_('solver_get_attr', hSolver)");
+ Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
+ const int solver_attr_num = 2;
+ const char* solver_attrs[solver_attr_num] = { "hNet_net", "hNet_test_nets" };
+ mxArray* mx_solver_attr = mxCreateStructMatrix(1, 1, solver_attr_num,
+ solver_attrs);
+ mxSetField(mx_solver_attr, 0, "hNet_net",
+ ptr_to_handle<Net<float> >(solver->net().get()));
+ mxSetField(mx_solver_attr, 0, "hNet_test_nets",
+ ptr_vec_to_handle_vec<Net<float> >(solver->test_nets()));
+ plhs[0] = mx_solver_attr;
+}
+
+// Usage: caffe_('solver_get_iter', hSolver)
+static void solver_get_iter(MEX_ARGS) {
+ mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+ "Usage: caffe_('solver_get_iter', hSolver)");
+ Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
+ plhs[0] = mxCreateDoubleScalar(solver->iter());
+}
+
+// Usage: caffe_('solver_restore', hSolver, snapshot_file)
+static void solver_restore(MEX_ARGS) {
+ mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsChar(prhs[1]),
+ "Usage: caffe_('solver_restore', hSolver, snapshot_file)");
+ Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
+ char* snapshot_file = mxArrayToString(prhs[1]);
+ mxCHECK_FILE_EXIST(snapshot_file);
+ solver->Restore(snapshot_file);
+ mxFree(snapshot_file);
+}
+
+// Usage: caffe_('solver_solve', hSolver)
+static void solver_solve(MEX_ARGS) {
+ mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+ "Usage: caffe_('solver_solve', hSolver)");
+ Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
+ solver->Solve();
+}
+
+// Usage: caffe_('solver_step', hSolver, iters)
+static void solver_step(MEX_ARGS) {
+ mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsDouble(prhs[1]),
+ "Usage: caffe_('solver_step', hSolver, iters)");
+ Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
+ int iters = mxGetScalar(prhs[1]);
+ solver->Step(iters);
+}
+
+// Usage: caffe_('get_net', model_file, phase_name)
+static void get_net(MEX_ARGS) {
+ mxCHECK(nrhs == 2 && mxIsChar(prhs[0]) && mxIsChar(prhs[1]),
+ "Usage: caffe_('get_net', model_file, phase_name)");
+ char* model_file = mxArrayToString(prhs[0]);
+ char* phase_name = mxArrayToString(prhs[1]);
+ mxCHECK_FILE_EXIST(model_file);
+ Phase phase;
+ if (strcmp(phase_name, "train") == 0) {
+ phase = TRAIN;
+ } else if (strcmp(phase_name, "test") == 0) {
+ phase = TEST;
+ } else {
+ mxERROR("Unknown phase");
+ }
+ shared_ptr<Net<float> > net(new caffe::Net<float>(model_file, phase));
+ nets_.push_back(net);
+ plhs[0] = ptr_to_handle<Net<float> >(net.get());
+ mxFree(model_file);
+ mxFree(phase_name);
+}
+
+// Usage: caffe_('net_get_attr', hNet)
+static void net_get_attr(MEX_ARGS) {
+ mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+ "Usage: caffe_('net_get_attr', hNet)");
+ Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+ const int net_attr_num = 6;
+ const char* net_attrs[net_attr_num] = { "hLayer_layers", "hBlob_blobs",
+ "input_blob_indices", "output_blob_indices", "layer_names", "blob_names"};
+ mxArray* mx_net_attr = mxCreateStructMatrix(1, 1, net_attr_num,
+ net_attrs);
+ mxSetField(mx_net_attr, 0, "hLayer_layers",
+ ptr_vec_to_handle_vec<Layer<float> >(net->layers()));
+ mxSetField(mx_net_attr, 0, "hBlob_blobs",
+ ptr_vec_to_handle_vec<Blob<float> >(net->blobs()));
+ mxSetField(mx_net_attr, 0, "input_blob_indices",
+ int_vec_to_mx_vec(net->input_blob_indices()));
+ mxSetField(mx_net_attr, 0, "output_blob_indices",
+ int_vec_to_mx_vec(net->output_blob_indices()));
+ mxSetField(mx_net_attr, 0, "layer_names",
+ str_vec_to_mx_strcell(net->layer_names()));
+ mxSetField(mx_net_attr, 0, "blob_names",
+ str_vec_to_mx_strcell(net->blob_names()));
+ plhs[0] = mx_net_attr;
+}
+
+// Usage: caffe_('net_forward', hNet)
+static void net_forward(MEX_ARGS) {
+ mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+ "Usage: caffe_('net_forward', hNet)");
+ Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+ net->ForwardPrefilled();
+}
+
+// Usage: caffe_('net_backward', hNet)
+static void net_backward(MEX_ARGS) {
+ mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+ "Usage: caffe_('net_backward', hNet)");
+ Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+ net->Backward();
+}
+
+// Usage: caffe_('net_copy_from', hNet, weights_file)
+static void net_copy_from(MEX_ARGS) {
+ mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsChar(prhs[1]),
+ "Usage: caffe_('net_copy_from', hNet, weights_file)");
+ Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+ char* weights_file = mxArrayToString(prhs[1]);
+ mxCHECK_FILE_EXIST(weights_file);
+ net->CopyTrainedLayersFrom(weights_file);
+ mxFree(weights_file);
+}
+
+// Usage: caffe_('net_reshape', hNet)
+static void net_reshape(MEX_ARGS) {
+ mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+ "Usage: caffe_('net_reshape', hNet)");
+ Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+ net->Reshape();
+}
+
+// Usage: caffe_('net_save', hNet, save_file)
+static void net_save(MEX_ARGS) {
+ mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsChar(prhs[1]),
+ "Usage: caffe_('net_save', hNet, save_file)");
+ Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+ char* weights_file = mxArrayToString(prhs[1]);
+ NetParameter net_param;
+ net->ToProto(&net_param, false);
+ WriteProtoToBinaryFile(net_param, weights_file);
+ mxFree(weights_file);
+}
+
+// Usage: caffe_('layer_get_attr', hLayer)
+static void layer_get_attr(MEX_ARGS) {
+ mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+ "Usage: caffe_('layer_get_attr', hLayer)");
+ Layer<float>* layer = handle_to_ptr<Layer<float> >(prhs[0]);
+ const int layer_attr_num = 1;
+ const char* layer_attrs[layer_attr_num] = { "hBlob_blobs" };
+ mxArray* mx_layer_attr = mxCreateStructMatrix(1, 1, layer_attr_num,
+ layer_attrs);
+ mxSetField(mx_layer_attr, 0, "hBlob_blobs",
+ ptr_vec_to_handle_vec<Blob<float> >(layer->blobs()));
+ plhs[0] = mx_layer_attr;
+}
+
+// Usage: caffe_('layer_get_type', hLayer)
+static void layer_get_type(MEX_ARGS) {
+ mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+ "Usage: caffe_('layer_get_type', hLayer)");
+ Layer<float>* layer = handle_to_ptr<Layer<float> >(prhs[0]);
+ plhs[0] = mxCreateString(layer->type());
+}
+
+// Usage: caffe_('blob_get_shape', hBlob)
+static void blob_get_shape(MEX_ARGS) {
+ mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+ "Usage: caffe_('blob_get_shape', hBlob)");
+ Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+ const int num_axes = blob->num_axes();
+ mxArray* mx_shape = mxCreateDoubleMatrix(1, num_axes, mxREAL);
+ double* shape_mem_mtr = mxGetPr(mx_shape);
+ for (int blob_axis = 0, mat_axis = num_axes - 1; blob_axis < num_axes;
+ ++blob_axis, --mat_axis) {
+ shape_mem_mtr[mat_axis] = static_cast<double>(blob->shape(blob_axis));
+ }
+ plhs[0] = mx_shape;
+}
+
+// Usage: caffe_('blob_reshape', hBlob, new_shape)
+static void blob_reshape(MEX_ARGS) {
+ mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsDouble(prhs[1]),
+ "Usage: caffe_('blob_reshape', hBlob, new_shape)");
+ Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+ const mxArray* mx_shape = prhs[1];
+ double* shape_mem_mtr = mxGetPr(mx_shape);
+ const int num_axes = mxGetNumberOfElements(mx_shape);
+ vector<int> blob_shape(num_axes);
+ for (int blob_axis = 0, mat_axis = num_axes - 1; blob_axis < num_axes;
+ ++blob_axis, --mat_axis) {
+ blob_shape[blob_axis] = static_cast<int>(shape_mem_mtr[mat_axis]);
+ }
+ blob->Reshape(blob_shape);
+}
+
+// Usage: caffe_('blob_get_data', hBlob)
+static void blob_get_data(MEX_ARGS) {
+ mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+ "Usage: caffe_('blob_get_data', hBlob)");
+ Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+ plhs[0] = blob_to_mx_mat(blob, DATA);
+}
+
+// Usage: caffe_('blob_set_data', hBlob, new_data)
+static void blob_set_data(MEX_ARGS) {
+ mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsSingle(prhs[1]),
+ "Usage: caffe_('blob_set_data', hBlob, new_data)");
+ Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+ mx_mat_to_blob(prhs[1], blob, DATA);
+}
+
+// Usage: caffe_('blob_get_diff', hBlob)
+static void blob_get_diff(MEX_ARGS) {
+ mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+ "Usage: caffe_('blob_get_diff', hBlob)");
+ Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+ plhs[0] = blob_to_mx_mat(blob, DIFF);
+}
+
+// Usage: caffe_('blob_set_diff', hBlob, new_diff)
+static void blob_set_diff(MEX_ARGS) {
+ mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsSingle(prhs[1]),
+ "Usage: caffe_('blob_set_diff', hBlob, new_diff)");
+ Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+ mx_mat_to_blob(prhs[1], blob, DIFF);
+}
+
+// Usage: caffe_('set_mode_cpu')
+static void set_mode_cpu(MEX_ARGS) {
+ mxCHECK(nrhs == 0, "Usage: caffe_('set_mode_cpu')");
+ Caffe::set_mode(Caffe::CPU);
+}
+
+// Usage: caffe_('set_mode_gpu')
+static void set_mode_gpu(MEX_ARGS) {
+ mxCHECK(nrhs == 0, "Usage: caffe_('set_mode_gpu')");
+ Caffe::set_mode(Caffe::GPU);
+}
+
+// Usage: caffe_('set_device', device_id)
+static void set_device(MEX_ARGS) {
+ mxCHECK(nrhs == 1 && mxIsDouble(prhs[0]),
+ "Usage: caffe_('set_device', device_id)");
+ int device_id = static_cast<int>(mxGetScalar(prhs[0]));
+ Caffe::SetDevice(device_id);
+}
+
+// Usage: caffe_('get_init_key')
+static void get_init_key(MEX_ARGS) {
+ mxCHECK(nrhs == 0, "Usage: caffe_('get_init_key')");
+ plhs[0] = mxCreateDoubleScalar(init_key);
+}
+
+// Usage: caffe_('reset')
+static void reset(MEX_ARGS) {
+ mxCHECK(nrhs == 0, "Usage: caffe_('reset')");
+ // Clear solvers and stand-alone nets
+ mexPrintf("Cleared %d solvers and %d stand-alone nets\n",
+ solvers_.size(), nets_.size());
+ solvers_.clear();
+ nets_.clear();
+ // Generate new init_key, so that handles created before becomes invalid
+ init_key = static_cast<double>(caffe_rng_rand());
+}
+
+// Usage: caffe_('read_mean', mean_proto_file)
+static void read_mean(MEX_ARGS) {
+ mxCHECK(nrhs == 1 && mxIsChar(prhs[0]),
+ "Usage: caffe_('read_mean', mean_proto_file)");
+ char* mean_proto_file = mxArrayToString(prhs[0]);
+ mxCHECK_FILE_EXIST(mean_proto_file);
+ Blob<float> data_mean;
+ BlobProto blob_proto;
+ bool result = ReadProtoFromBinaryFile(mean_proto_file, &blob_proto);
+ mxCHECK(result, "Could not read your mean file");
+ data_mean.FromProto(blob_proto);
+ plhs[0] = blob_to_mx_mat(&data_mean, DATA);
+ mxFree(mean_proto_file);
+}
+
+// Usage: caffe_('write_mean', mean_data, mean_proto_file)
+static void write_mean(MEX_ARGS) {
+ mxCHECK(nrhs == 2 && mxIsSingle(prhs[0]) && mxIsChar(prhs[1]),
+ "Usage: caffe_('write_mean', mean_data, mean_proto_file)");
+ char* mean_proto_file = mxArrayToString(prhs[1]);
+ int ndims = mxGetNumberOfDimensions(prhs[0]);
+ mxCHECK(ndims >= 2 && ndims <= 3, "mean_data must have at 2 or 3 dimensions");
+ const mwSize *dims = mxGetDimensions(prhs[0]);
+ int width = dims[0];
+ int height = dims[1];
+ int channels;
+ if (ndims == 3)
+ channels = dims[2];
+ else
+ channels = 1;
+ Blob<float> data_mean(1, channels, height, width);
+ mx_mat_to_blob(prhs[0], &data_mean, DATA);
+ BlobProto blob_proto;
+ data_mean.ToProto(&blob_proto, false);
+ WriteProtoToBinaryFile(blob_proto, mean_proto_file);
+ mxFree(mean_proto_file);
+}
+
+// Usage: caffe_('version')
+static void version(MEX_ARGS) {
+ mxCHECK(nrhs == 0, "Usage: caffe_('version')");
+ // Return version string
+ plhs[0] = mxCreateString(AS_STRING(CAFFE_VERSION));
+}
+
+/** -----------------------------------------------------------------
+ ** Available commands.
+ **/
+struct handler_registry {
+ string cmd;
+ void (*func)(MEX_ARGS);
+};
+
+static handler_registry handlers[] = {
+ // Public API functions
+ { "get_solver", get_solver },
+ { "solver_get_attr", solver_get_attr },
+ { "solver_get_iter", solver_get_iter },
+ { "solver_restore", solver_restore },
+ { "solver_solve", solver_solve },
+ { "solver_step", solver_step },
+ { "get_net", get_net },
+ { "net_get_attr", net_get_attr },
+ { "net_forward", net_forward },
+ { "net_backward", net_backward },
+ { "net_copy_from", net_copy_from },
+ { "net_reshape", net_reshape },
+ { "net_save", net_save },
+ { "layer_get_attr", layer_get_attr },
+ { "layer_get_type", layer_get_type },
+ { "blob_get_shape", blob_get_shape },
+ { "blob_reshape", blob_reshape },
+ { "blob_get_data", blob_get_data },
+ { "blob_set_data", blob_set_data },
+ { "blob_get_diff", blob_get_diff },
+ { "blob_set_diff", blob_set_diff },
+ { "set_mode_cpu", set_mode_cpu },
+ { "set_mode_gpu", set_mode_gpu },
+ { "set_device", set_device },
+ { "get_init_key", get_init_key },
+ { "reset", reset },
+ { "read_mean", read_mean },
+ { "write_mean", write_mean },
+ { "version", version },
+ // The end.
+ { "END", NULL },
+};
+
+/** -----------------------------------------------------------------
+ ** matlab entry point.
+ **/
+// Usage: caffe_(api_command, arg1, arg2, ...)
+void mexFunction(MEX_ARGS) {
+ mexLock(); // Avoid clearing the mex file.
+ mxCHECK(nrhs > 0, "Usage: caffe_(api_command, arg1, arg2, ...)");
+ // Handle input command
+ char* cmd = mxArrayToString(prhs[0]);
+ bool dispatched = false;
+ // Dispatch to cmd handler
+ for (int i = 0; handlers[i].func != NULL; i++) {
+ if (handlers[i].cmd.compare(cmd) == 0) {
+ handlers[i].func(nlhs, plhs, nrhs-1, prhs+1);
+ dispatched = true;
+ break;
+ }
+ }
+ if (!dispatched) {
+ ostringstream error_msg;
+ error_msg << "Unknown command '" << cmd << "'";
+ mxERROR(error_msg.str().c_str());
+ }
+ mxFree(cmd);
+}
--- /dev/null
+function valid = is_valid_handle(hObj)
+% valid = is_valid_handle(hObj) or is_valid_handle('get_new_init_key')
+% Check if a handle is valid (has the right data type and init_key matches)
+% Use is_valid_handle('get_new_init_key') to get new init_key from C++;
+
+% a handle is a struct array with the following fields
+% (uint64) ptr : the pointer to the C++ object
+% (double) init_key : caffe initialization key
+
+persistent init_key;
+if isempty(init_key)
+ init_key = caffe_('get_init_key');
+end
+
+% is_valid_handle('get_new_init_key') to get new init_key from C++;
+if ischar(hObj) && strcmp(hObj, 'get_new_init_key')
+ init_key = caffe_('get_init_key');
+ return
+else
+ % check whether data types are correct and init_key matches
+ valid = isstruct(hObj) ...
+ && isscalar(hObj.ptr) && isa(hObj.ptr, 'uint64') ...
+ && isscalar(hObj.init_key) && isa(hObj.init_key, 'double') ...
+ && hObj.init_key == init_key;
+end
+
+end
--- /dev/null
+function reset_all()
+% reset_all()
+% clear all solvers and stand-alone nets and reset Caffe to initial status
+
+caffe_('reset');
+is_valid_handle('get_new_init_key');
+
+end
--- /dev/null
+function results = run_tests()
+% results = run_tests()
+% run all tests in this caffe matlab wrapper package
+
+% use CPU for testing
+caffe.set_mode_cpu();
+
+% reset caffe before testing
+caffe.reset_all();
+
+% put all test cases here
+results = [...
+ run(caffe.test.test_net) ...
+ run(caffe.test.test_solver) ...
+ run(caffe.test.test_io) ];
+
+% reset caffe after testing
+caffe.reset_all();
+
+end
--- /dev/null
+function set_device(device_id)
+% set_device(device_id)
+% set Caffe's GPU device ID
+
+CHECK(isscalar(device_id) && device_id >= 0, ...
+ 'device_id must be non-negative integer');
+device_id = double(device_id);
+
+caffe_('set_device', device_id);
+
+end
--- /dev/null
+function set_mode_cpu()
+% set_mode_cpu()
+% set Caffe to CPU mode
+
+caffe_('set_mode_cpu');
+
+end
--- /dev/null
+function set_mode_gpu()
+% set_mode_gpu()
+% set Caffe to GPU mode
+
+caffe_('set_mode_gpu');
+
+end
--- /dev/null
+function version_str = version()
+% version()
+% show Caffe's version.
+
+version_str = caffe_('version');
+
+end
endfunction()
# global settings
-file(GLOB Matlab_srcs caffe/matcaffe.cpp)
-set(Matlab_caffe_mex ${PROJECT_SOURCE_DIR}/matlab/caffe/caffe.mex)
+file(GLOB Matlab_srcs +caffe/private/caffe_.cpp)
+set(Matlab_caffe_mex ${PROJECT_SOURCE_DIR}/matlab/+caffe/private/caffe_.mex)
caffe_get_current_cflags(cflags)
caffe_parse_linker_libs(Caffe_LINKER_LIBS folders libflags macos_frameworks)
string(REPLACE ";" ":" rpath_folders "${folders}")
if(build_using MATCHES "Matlab")
- set(libflags -lcaffe${CAffe_POSTFIX} ${libflags}) # Matlab R2014a complans for -Wl,--whole-archive
+ set(libflags -lcaffe${Caffe_POSTFIX} ${libflags}) # Matlab R2014a complans for -Wl,--whole-archive
caffe_fetch_and_set_proper_mexext(Matlab_caffe_mex)
add_custom_command(OUTPUT ${Matlab_caffe_mex} COMMAND ${Matlab_mex}
if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
set(libflags -Wl,-force_load,$<TARGET_LINKER_FILE:caffe> ${libflags})
elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
- set(libflags -Wl,--whole-archive -lcaffe${CAffe_POSTFIX} -Wl,--no-whole-archive ${libflags})
+ set(libflags -Wl,--whole-archive -lcaffe${Caffe_POSTFIX} -Wl,--no-whole-archive ${libflags})
endif()
add_custom_command(OUTPUT ${Matlab_caffe_mex} COMMAND ${Octave_compiler}
+++ /dev/null
-//
-// matcaffe.cpp provides a wrapper of the caffe::Net class as well as some
-// caffe::Caffe functions so that one could easily call it from matlab.
-// Note that for matlab, we will simply use float as the data type.
-
-#include <sstream>
-#include <string>
-#include <vector>
-
-#include "mex.h"
-
-#include "caffe/caffe.hpp"
-
-#define MEX_ARGS int nlhs, mxArray **plhs, int nrhs, const mxArray **prhs
-
-// Log and throw a Mex error
-inline void mex_error(const std::string &msg) {
- LOG(ERROR) << msg;
- mexErrMsgTxt(msg.c_str());
-}
-
-using namespace caffe; // NOLINT(build/namespaces)
-
-// The pointer to the internal caffe::Net instance
-static shared_ptr<Net<float> > net_;
-static int init_key = -2;
-
-// Five things to be aware of:
-// caffe uses row-major order
-// matlab uses column-major order
-// caffe uses BGR color channel order
-// matlab uses RGB color channel order
-// images need to have the data mean subtracted
-//
-// Data coming in from matlab needs to be in the order
-// [width, height, channels, images]
-// where width is the fastest dimension.
-// Here is the rough matlab for putting image data into the correct
-// format:
-// % convert from uint8 to single
-// im = single(im);
-// % reshape to a fixed size (e.g., 227x227)
-// im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
-// % permute from RGB to BGR and subtract the data mean (already in BGR)
-// im = im(:,:,[3 2 1]) - data_mean;
-// % flip width and height to make width the fastest dimension
-// im = permute(im, [2 1 3]);
-//
-// If you have multiple images, cat them with cat(4, ...)
-//
-// The actual forward function. It takes in a cell array of 4-D arrays as
-// input and outputs a cell array.
-
-static mxArray* do_forward(const mxArray* const bottom) {
- const vector<Blob<float>*>& input_blobs = net_->input_blobs();
- if (static_cast<unsigned int>(mxGetDimensions(bottom)[0]) !=
- input_blobs.size()) {
- mex_error("Invalid input size");
- }
- for (unsigned int i = 0; i < input_blobs.size(); ++i) {
- const mxArray* const elem = mxGetCell(bottom, i);
- if (!mxIsSingle(elem)) {
- mex_error("MatCaffe require single-precision float point data");
- }
- if (mxGetNumberOfElements(elem) != input_blobs[i]->count()) {
- std::string error_msg;
- error_msg += "MatCaffe input size does not match the input size ";
- error_msg += "of the network";
- mex_error(error_msg);
- }
-
- const float* const data_ptr =
- reinterpret_cast<const float* const>(mxGetPr(elem));
- switch (Caffe::mode()) {
- case Caffe::CPU:
- caffe_copy(input_blobs[i]->count(), data_ptr,
- input_blobs[i]->mutable_cpu_data());
- break;
- case Caffe::GPU:
- caffe_copy(input_blobs[i]->count(), data_ptr,
- input_blobs[i]->mutable_gpu_data());
- break;
- default:
- mex_error("Unknown Caffe mode");
- } // switch (Caffe::mode())
- }
- const vector<Blob<float>*>& output_blobs = net_->ForwardPrefilled();
- mxArray* mx_out = mxCreateCellMatrix(output_blobs.size(), 1);
- for (unsigned int i = 0; i < output_blobs.size(); ++i) {
- // internally data is stored as (width, height, channels, num)
- // where width is the fastest dimension
- mwSize dims[4] = {output_blobs[i]->width(), output_blobs[i]->height(),
- output_blobs[i]->channels(), output_blobs[i]->num()};
- mxArray* mx_blob = mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL);
- mxSetCell(mx_out, i, mx_blob);
- float* data_ptr = reinterpret_cast<float*>(mxGetPr(mx_blob));
- switch (Caffe::mode()) {
- case Caffe::CPU:
- caffe_copy(output_blobs[i]->count(), output_blobs[i]->cpu_data(),
- data_ptr);
- break;
- case Caffe::GPU:
- caffe_copy(output_blobs[i]->count(), output_blobs[i]->gpu_data(),
- data_ptr);
- break;
- default:
- mex_error("Unknown Caffe mode");
- } // switch (Caffe::mode())
- }
-
- return mx_out;
-}
-
-static mxArray* do_backward(const mxArray* const top_diff) {
- const vector<Blob<float>*>& output_blobs = net_->output_blobs();
- const vector<Blob<float>*>& input_blobs = net_->input_blobs();
- if (static_cast<unsigned int>(mxGetDimensions(top_diff)[0]) !=
- output_blobs.size()) {
- mex_error("Invalid input size");
- }
- // First, copy the output diff
- for (unsigned int i = 0; i < output_blobs.size(); ++i) {
- const mxArray* const elem = mxGetCell(top_diff, i);
- const float* const data_ptr =
- reinterpret_cast<const float* const>(mxGetPr(elem));
- switch (Caffe::mode()) {
- case Caffe::CPU:
- caffe_copy(output_blobs[i]->count(), data_ptr,
- output_blobs[i]->mutable_cpu_diff());
- break;
- case Caffe::GPU:
- caffe_copy(output_blobs[i]->count(), data_ptr,
- output_blobs[i]->mutable_gpu_diff());
- break;
- default:
- mex_error("Unknown Caffe mode");
- } // switch (Caffe::mode())
- }
- // LOG(INFO) << "Start";
- net_->Backward();
- // LOG(INFO) << "End";
- mxArray* mx_out = mxCreateCellMatrix(input_blobs.size(), 1);
- for (unsigned int i = 0; i < input_blobs.size(); ++i) {
- // internally data is stored as (width, height, channels, num)
- // where width is the fastest dimension
- mwSize dims[4] = {input_blobs[i]->width(), input_blobs[i]->height(),
- input_blobs[i]->channels(), input_blobs[i]->num()};
- mxArray* mx_blob = mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL);
- mxSetCell(mx_out, i, mx_blob);
- float* data_ptr = reinterpret_cast<float*>(mxGetPr(mx_blob));
- switch (Caffe::mode()) {
- case Caffe::CPU:
- caffe_copy(input_blobs[i]->count(), input_blobs[i]->cpu_diff(), data_ptr);
- break;
- case Caffe::GPU:
- caffe_copy(input_blobs[i]->count(), input_blobs[i]->gpu_diff(), data_ptr);
- break;
- default:
- mex_error("Unknown Caffe mode");
- } // switch (Caffe::mode())
- }
-
- return mx_out;
-}
-
-static mxArray* do_get_weights() {
- const vector<shared_ptr<Layer<float> > >& layers = net_->layers();
- const vector<string>& layer_names = net_->layer_names();
-
- // Step 1: count the number of layers with weights
- int num_layers = 0;
- {
- string prev_layer_name = "";
- for (unsigned int i = 0; i < layers.size(); ++i) {
- vector<shared_ptr<Blob<float> > >& layer_blobs = layers[i]->blobs();
- if (layer_blobs.size() == 0) {
- continue;
- }
- if (layer_names[i] != prev_layer_name) {
- prev_layer_name = layer_names[i];
- num_layers++;
- }
- }
- }
-
- // Step 2: prepare output array of structures
- mxArray* mx_layers;
- {
- const mwSize dims[2] = {num_layers, 1};
- const char* fnames[2] = {"weights", "layer_names"};
- mx_layers = mxCreateStructArray(2, dims, 2, fnames);
- }
-
- // Step 3: copy weights into output
- {
- string prev_layer_name = "";
- int mx_layer_index = 0;
- for (unsigned int i = 0; i < layers.size(); ++i) {
- vector<shared_ptr<Blob<float> > >& layer_blobs = layers[i]->blobs();
- if (layer_blobs.size() == 0) {
- continue;
- }
-
- mxArray* mx_layer_cells = NULL;
- if (layer_names[i] != prev_layer_name) {
- prev_layer_name = layer_names[i];
- const mwSize dims[2] = {static_cast<mwSize>(layer_blobs.size()), 1};
- mx_layer_cells = mxCreateCellArray(2, dims);
- mxSetField(mx_layers, mx_layer_index, "weights", mx_layer_cells);
- mxSetField(mx_layers, mx_layer_index, "layer_names",
- mxCreateString(layer_names[i].c_str()));
- mx_layer_index++;
- }
-
- for (unsigned int j = 0; j < layer_blobs.size(); ++j) {
- // internally data is stored as (width, height, channels, num)
- // where width is the fastest dimension
- mwSize dims[4] = {layer_blobs[j]->width(), layer_blobs[j]->height(),
- layer_blobs[j]->channels(), layer_blobs[j]->num()};
-
- mxArray* mx_weights =
- mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL);
- mxSetCell(mx_layer_cells, j, mx_weights);
- float* weights_ptr = reinterpret_cast<float*>(mxGetPr(mx_weights));
-
- switch (Caffe::mode()) {
- case Caffe::CPU:
- caffe_copy(layer_blobs[j]->count(), layer_blobs[j]->cpu_data(),
- weights_ptr);
- break;
- case Caffe::GPU:
- caffe_copy(layer_blobs[j]->count(), layer_blobs[j]->gpu_data(),
- weights_ptr);
- break;
- default:
- mex_error("Unknown Caffe mode");
- }
- }
- }
- }
-
- return mx_layers;
-}
-
-static void get_weights(MEX_ARGS) {
- plhs[0] = do_get_weights();
-}
-
-static void set_mode_cpu(MEX_ARGS) {
- Caffe::set_mode(Caffe::CPU);
-}
-
-static void set_mode_gpu(MEX_ARGS) {
- Caffe::set_mode(Caffe::GPU);
-}
-
-static void set_device(MEX_ARGS) {
- if (nrhs != 1) {
- ostringstream error_msg;
- error_msg << "Expected 1 argument, got " << nrhs;
- mex_error(error_msg.str());
- }
-
- int device_id = static_cast<int>(mxGetScalar(prhs[0]));
- Caffe::SetDevice(device_id);
-}
-
-static void get_init_key(MEX_ARGS) {
- plhs[0] = mxCreateDoubleScalar(init_key);
-}
-
-static void init(MEX_ARGS) {
- if (nrhs != 3) {
- ostringstream error_msg;
- error_msg << "Expected 3 arguments, got " << nrhs;
- mex_error(error_msg.str());
- }
-
- char* param_file = mxArrayToString(prhs[0]);
- char* model_file = mxArrayToString(prhs[1]);
- char* phase_name = mxArrayToString(prhs[2]);
-
- Phase phase;
- if (strcmp(phase_name, "train") == 0) {
- phase = TRAIN;
- } else if (strcmp(phase_name, "test") == 0) {
- phase = TEST;
- } else {
- mex_error("Unknown phase.");
- }
-
- net_.reset(new Net<float>(string(param_file), phase));
- net_->CopyTrainedLayersFrom(string(model_file));
-
- mxFree(param_file);
- mxFree(model_file);
- mxFree(phase_name);
-
- init_key = random(); // NOLINT(caffe/random_fn)
-
- if (nlhs == 1) {
- plhs[0] = mxCreateDoubleScalar(init_key);
- }
-}
-
-static void reset(MEX_ARGS) {
- if (net_) {
- net_.reset();
- init_key = -2;
- LOG(INFO) << "Network reset, call init before use it again";
- }
-}
-
-static void forward(MEX_ARGS) {
- if (nrhs != 1) {
- ostringstream error_msg;
- error_msg << "Expected 1 argument, got " << nrhs;
- mex_error(error_msg.str());
- }
-
- plhs[0] = do_forward(prhs[0]);
-}
-
-static void backward(MEX_ARGS) {
- if (nrhs != 1) {
- ostringstream error_msg;
- error_msg << "Expected 1 argument, got " << nrhs;
- mex_error(error_msg.str());
- }
-
- plhs[0] = do_backward(prhs[0]);
-}
-
-static void is_initialized(MEX_ARGS) {
- if (!net_) {
- plhs[0] = mxCreateDoubleScalar(0);
- } else {
- plhs[0] = mxCreateDoubleScalar(1);
- }
-}
-
-static void read_mean(MEX_ARGS) {
- if (nrhs != 1) {
- mexErrMsgTxt("Usage: caffe('read_mean', 'path_to_binary_mean_file'");
- return;
- }
- const string& mean_file = mxArrayToString(prhs[0]);
- Blob<float> data_mean;
- LOG(INFO) << "Loading mean file from: " << mean_file;
- BlobProto blob_proto;
- bool result = ReadProtoFromBinaryFile(mean_file.c_str(), &blob_proto);
- if (!result) {
- mexErrMsgTxt("Couldn't read the file");
- return;
- }
- data_mean.FromProto(blob_proto);
- mwSize dims[4] = {data_mean.width(), data_mean.height(),
- data_mean.channels(), data_mean.num() };
- mxArray* mx_blob = mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL);
- float* data_ptr = reinterpret_cast<float*>(mxGetPr(mx_blob));
- caffe_copy(data_mean.count(), data_mean.cpu_data(), data_ptr);
- mexWarnMsgTxt("Remember that Caffe saves in [width, height, channels]"
- " format and channels are also BGR!");
- plhs[0] = mx_blob;
-}
-
-/** -----------------------------------------------------------------
- ** Available commands.
- **/
-struct handler_registry {
- string cmd;
- void (*func)(MEX_ARGS);
-};
-
-static handler_registry handlers[] = {
- // Public API functions
- { "forward", forward },
- { "backward", backward },
- { "init", init },
- { "is_initialized", is_initialized },
- { "set_mode_cpu", set_mode_cpu },
- { "set_mode_gpu", set_mode_gpu },
- { "set_device", set_device },
- { "get_weights", get_weights },
- { "get_init_key", get_init_key },
- { "reset", reset },
- { "read_mean", read_mean },
- // The end.
- { "END", NULL },
-};
-
-
-/** -----------------------------------------------------------------
- ** matlab entry point: caffe(api_command, arg1, arg2, ...)
- **/
-void mexFunction(MEX_ARGS) {
- mexLock(); // Avoid clearing the mex file.
- if (nrhs == 0) {
- mex_error("No API command given");
- return;
- }
-
- { // Handle input command
- char *cmd = mxArrayToString(prhs[0]);
- bool dispatched = false;
- // Dispatch to cmd handler
- for (int i = 0; handlers[i].func != NULL; i++) {
- if (handlers[i].cmd.compare(cmd) == 0) {
- handlers[i].func(nlhs, plhs, nrhs-1, prhs+1);
- dispatched = true;
- break;
- }
- }
- if (!dispatched) {
- ostringstream error_msg;
- error_msg << "Unknown command '" << cmd << "'";
- mex_error(error_msg.str());
- }
- mxFree(cmd);
- }
-}
+++ /dev/null
-function [scores,list_im] = matcaffe_batch(list_im, use_gpu)
-% scores = matcaffe_batch(list_im, use_gpu)
-%
-% Demo of the matlab wrapper using the ILSVRC network.
-%
-% input
-% list_im list of images files
-% use_gpu 1 to use the GPU, 0 to use the CPU
-%
-% output
-% scores 1000 x num_images ILSVRC output vector
-%
-% You may need to do the following before you start matlab:
-% $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
-% $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
-% Or the equivalent based on where things are installed on your system
-%
-% Usage:
-% scores = matcaffe_batch({'peppers.png','onion.png'});
-% scores = matcaffe_batch('list_images.txt', 1);
-if nargin < 1
- % For test purposes
- list_im = {'peppers.png','onions.png'};
-end
-if ischar(list_im)
- %Assume it is a file contaning the list of images
- filename = list_im;
- list_im = read_cell(filename);
-end
-% Adjust the batch size and dim to match with models/bvlc_reference_caffenet/deploy.prototxt
-batch_size = 10;
-dim = 1000;
-disp(list_im)
-if mod(length(list_im),batch_size)
- warning(['Assuming batches of ' num2str(batch_size) ' images rest will be filled with zeros'])
-end
-
-% init caffe network (spews logging info)
-if exist('use_gpu', 'var')
- matcaffe_init(use_gpu);
-else
- matcaffe_init();
-end
-
-d = load('ilsvrc_2012_mean');
-IMAGE_MEAN = d.image_mean;
-
-% prepare input
-
-num_images = length(list_im);
-scores = zeros(dim,num_images,'single');
-num_batches = ceil(length(list_im)/batch_size)
-initic=tic;
-for bb = 1 : num_batches
- batchtic = tic;
- range = 1+batch_size*(bb-1):min(num_images,batch_size * bb);
- tic
- input_data = prepare_batch(list_im(range),IMAGE_MEAN,batch_size);
- toc, tic
- fprintf('Batch %d out of %d %.2f%% Complete ETA %.2f seconds\n',...
- bb,num_batches,bb/num_batches*100,toc(initic)/bb*(num_batches-bb));
- output_data = caffe('forward', {input_data});
- toc
- output_data = squeeze(output_data{1});
- scores(:,range) = output_data(:,mod(range-1,batch_size)+1);
- toc(batchtic)
-end
-toc(initic);
-
-if exist('filename', 'var')
- save([filename '.probs.mat'],'list_im','scores','-v7.3');
-end
-
-
-
+++ /dev/null
-function [scores, maxlabel] = matcaffe_demo(im, use_gpu)
-% scores = matcaffe_demo(im, use_gpu)
-%
-% Demo of the matlab wrapper using the ILSVRC network.
-%
-% input
-% im color image as uint8 HxWx3
-% use_gpu 1 to use the GPU, 0 to use the CPU
-%
-% output
-% scores 1000-dimensional ILSVRC score vector
-%
-% You may need to do the following before you start matlab:
-% $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64
-% $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
-% Or the equivalent based on where things are installed on your system
-%
-% Usage:
-% im = imread('../../examples/images/cat.jpg');
-% scores = matcaffe_demo(im, 1);
-% [score, class] = max(scores);
-% Five things to be aware of:
-% caffe uses row-major order
-% matlab uses column-major order
-% caffe uses BGR color channel order
-% matlab uses RGB color channel order
-% images need to have the data mean subtracted
-
-% Data coming in from matlab needs to be in the order
-% [width, height, channels, images]
-% where width is the fastest dimension.
-% Here is the rough matlab for putting image data into the correct
-% format:
-% % convert from uint8 to single
-% im = single(im);
-% % reshape to a fixed size (e.g., 227x227)
-% im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
-% % permute from RGB to BGR and subtract the data mean (already in BGR)
-% im = im(:,:,[3 2 1]) - data_mean;
-% % flip width and height to make width the fastest dimension
-% im = permute(im, [2 1 3]);
-
-% If you have multiple images, cat them with cat(4, ...)
-
-% The actual forward function. It takes in a cell array of 4-D arrays as
-% input and outputs a cell array.
-
-
-% init caffe network (spews logging info)
-if exist('use_gpu', 'var')
- matcaffe_init(use_gpu);
-else
- matcaffe_init();
-end
-
-if nargin < 1
- % For demo purposes we will use the peppers image
- im = imread('peppers.png');
-end
-
-% prepare oversampled input
-% input_data is Height x Width x Channel x Num
-tic;
-input_data = {prepare_image(im)};
-toc;
-
-% do forward pass to get scores
-% scores are now Width x Height x Channels x Num
-tic;
-scores = caffe('forward', input_data);
-toc;
-
-scores = scores{1};
-size(scores)
-scores = squeeze(scores);
-scores = mean(scores,2);
-
-[~,maxlabel] = max(scores);
-
-% ------------------------------------------------------------------------
-function images = prepare_image(im)
-% ------------------------------------------------------------------------
-d = load('ilsvrc_2012_mean');
-IMAGE_MEAN = d.image_mean;
-IMAGE_DIM = 256;
-CROPPED_DIM = 227;
-
-% resize to fixed input size
-im = single(im);
-im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
-% permute from RGB to BGR (IMAGE_MEAN is already BGR)
-im = im(:,:,[3 2 1]) - IMAGE_MEAN;
-
-% oversample (4 corners, center, and their x-axis flips)
-images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
-indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;
-curr = 1;
-for i = indices
- for j = indices
- images(:, :, :, curr) = ...
- permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :), [2 1 3]);
- images(:, :, :, curr+5) = images(end:-1:1, :, :, curr);
- curr = curr + 1;
- end
-end
-center = floor(indices(2) / 2)+1;
-images(:,:,:,5) = ...
- permute(im(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:), ...
- [2 1 3]);
-images(:,:,:,10) = images(end:-1:1, :, :, curr);
+++ /dev/null
-function scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
-% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
-%
-% Demo of the matlab wrapper using the networks described in the BMVC-2014 paper "Return of the Devil in the Details: Delving Deep into Convolutional Nets"
-%
-% INPUT
-% im - color image as uint8 HxWx3
-% use_gpu - 1 to use the GPU, 0 to use the CPU
-% model_def_file - network configuration (.prototxt file)
-% model_file - network weights (.caffemodel file)
-% mean_file - mean BGR image as uint8 HxWx3 (.mat file)
-%
-% OUTPUT
-% scores 1000-dimensional ILSVRC score vector
-%
-% EXAMPLE USAGE
-% model_def_file = 'zoo/VGG_CNN_F_deploy.prototxt';
-% model_file = 'zoo/VGG_CNN_F.caffemodel';
-% mean_file = 'zoo/VGG_mean.mat';
-% use_gpu = true;
-% im = imread('../../examples/images/cat.jpg');
-% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file);
-%
-% NOTES
-% the image crops are prepared as described in the paper (the aspect ratio is preserved)
-%
-% PREREQUISITES
-% You may need to do the following before you start matlab:
-% $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
-% $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
-% Or the equivalent based on where things are installed on your system
-
-% init caffe network (spews logging info)
-matcaffe_init(use_gpu, model_def_file, model_file);
-
-% prepare oversampled input
-% input_data is Height x Width x Channel x Num
-tic;
-input_data = {prepare_image(im, mean_file)};
-toc;
-
-% do forward pass to get scores
-% scores are now Width x Height x Channels x Num
-tic;
-scores = caffe('forward', input_data);
-toc;
-
-scores = scores{1};
-% size(scores)
-scores = squeeze(scores);
-% scores = mean(scores,2);
-
-% [~,maxlabel] = max(scores);
-
-% ------------------------------------------------------------------------
-function images = prepare_image(im, mean_file)
-% ------------------------------------------------------------------------
-IMAGE_DIM = 256;
-CROPPED_DIM = 224;
-
-d = load(mean_file);
-IMAGE_MEAN = d.image_mean;
-
-% resize to fixed input size
-im = single(im);
-
-if size(im, 1) < size(im, 2)
- im = imresize(im, [IMAGE_DIM NaN]);
-else
- im = imresize(im, [NaN IMAGE_DIM]);
-end
-
-% RGB -> BGR
-im = im(:, :, [3 2 1]);
-
-% oversample (4 corners, center, and their x-axis flips)
-images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
-
-indices_y = [0 size(im,1)-CROPPED_DIM] + 1;
-indices_x = [0 size(im,2)-CROPPED_DIM] + 1;
-center_y = floor(indices_y(2) / 2)+1;
-center_x = floor(indices_x(2) / 2)+1;
-
-curr = 1;
-for i = indices_y
- for j = indices_x
- images(:, :, :, curr) = ...
- permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :)-IMAGE_MEAN, [2 1 3]);
- images(:, :, :, curr+5) = images(end:-1:1, :, :, curr);
- curr = curr + 1;
- end
-end
-images(:,:,:,5) = ...
- permute(im(center_y:center_y+CROPPED_DIM-1,center_x:center_x+CROPPED_DIM-1,:)-IMAGE_MEAN, ...
- [2 1 3]);
-images(:,:,:,10) = images(end:-1:1, :, :, curr);
+++ /dev/null
-function scores = matcaffe_demo_vgg_mean_pix(im, use_gpu, model_def_file, model_file)
-% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file)
-%
-% Demo of the matlab wrapper based on the networks used for the "VGG" entry
-% in the ILSVRC-2014 competition and described in the tech. report
-% "Very Deep Convolutional Networks for Large-Scale Image Recognition"
-% http://arxiv.org/abs/1409.1556/
-%
-% INPUT
-% im - color image as uint8 HxWx3
-% use_gpu - 1 to use the GPU, 0 to use the CPU
-% model_def_file - network configuration (.prototxt file)
-% model_file - network weights (.caffemodel file)
-%
-% OUTPUT
-% scores 1000-dimensional ILSVRC score vector
-%
-% EXAMPLE USAGE
-% model_def_file = 'zoo/deploy.prototxt';
-% model_file = 'zoo/model.caffemodel';
-% use_gpu = true;
-% im = imread('../../examples/images/cat.jpg');
-% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file);
-%
-% NOTES
-% mean pixel subtraction is used instead of the mean image subtraction
-%
-% PREREQUISITES
-% You may need to do the following before you start matlab:
-% $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
-% $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
-% Or the equivalent based on where things are installed on your system
-
-% init caffe network (spews logging info)
-matcaffe_init(use_gpu, model_def_file, model_file);
-
-% mean BGR pixel
-mean_pix = [103.939, 116.779, 123.68];
-
-% prepare oversampled input
-% input_data is Height x Width x Channel x Num
-tic;
-input_data = {prepare_image(im, mean_pix)};
-toc;
-
-% do forward pass to get scores
-% scores are now Width x Height x Channels x Num
-tic;
-scores = caffe('forward', input_data);
-toc;
-
-scores = scores{1};
-% size(scores)
-scores = squeeze(scores);
-% scores = mean(scores,2);
-
-% [~,maxlabel] = max(scores);
-
-% ------------------------------------------------------------------------
-function images = prepare_image(im, mean_pix)
-% ------------------------------------------------------------------------
-IMAGE_DIM = 256;
-CROPPED_DIM = 224;
-
-% resize to fixed input size
-im = single(im);
-
-if size(im, 1) < size(im, 2)
- im = imresize(im, [IMAGE_DIM NaN]);
-else
- im = imresize(im, [NaN IMAGE_DIM]);
-end
-
-% RGB -> BGR
-im = im(:, :, [3 2 1]);
-
-% oversample (4 corners, center, and their x-axis flips)
-images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
-
-indices_y = [0 size(im,1)-CROPPED_DIM] + 1;
-indices_x = [0 size(im,2)-CROPPED_DIM] + 1;
-center_y = floor(indices_y(2) / 2)+1;
-center_x = floor(indices_x(2) / 2)+1;
-
-curr = 1;
-for i = indices_y
- for j = indices_x
- images(:, :, :, curr) = ...
- permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :), [2 1 3]);
- images(:, :, :, curr+5) = images(end:-1:1, :, :, curr);
- curr = curr + 1;
- end
-end
-images(:,:,:,5) = ...
- permute(im(center_y:center_y+CROPPED_DIM-1,center_x:center_x+CROPPED_DIM-1,:), ...
- [2 1 3]);
-images(:,:,:,10) = images(end:-1:1, :, :, curr);
-
-% mean BGR pixel subtraction
-for c = 1:3
- images(:, :, c, :) = images(:, :, c, :) - mean_pix(c);
-end
+++ /dev/null
-function matcaffe_init(use_gpu, model_def_file, model_file)
-% matcaffe_init(model_def_file, model_file, use_gpu)
-% Initilize matcaffe wrapper
-
-if nargin < 1
- % By default use CPU
- use_gpu = 0;
-end
-if nargin < 2 || isempty(model_def_file)
- % By default use imagenet_deploy
- model_def_file = '../../models/bvlc_reference_caffenet/deploy.prototxt';
-end
-if nargin < 3 || isempty(model_file)
- % By default use caffe reference model
- model_file = '../../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel';
-end
-
-
-if caffe('is_initialized') == 0
- if exist(model_file, 'file') == 0
- % NOTE: you'll have to get the pre-trained ILSVRC network
- error('You need a network model file');
- end
- if ~exist(model_def_file,'file')
- % NOTE: you'll have to get network definition
- error('You need the network prototxt definition');
- end
- % load network in TEST phase
- caffe('init', model_def_file, model_file, 'test')
-end
-fprintf('Done with init\n');
-
-% set to use GPU or CPU
-if use_gpu
- fprintf('Using GPU Mode\n');
- caffe('set_mode_gpu');
-else
- fprintf('Using CPU Mode\n');
- caffe('set_mode_cpu');
-end
-fprintf('Done with set_mode\n');
+++ /dev/null
-% ------------------------------------------------------------------------
-function images = prepare_batch(image_files,IMAGE_MEAN,batch_size)
-% ------------------------------------------------------------------------
-if nargin < 2
- d = load('ilsvrc_2012_mean');
- IMAGE_MEAN = d.image_mean;
-end
-num_images = length(image_files);
-if nargin < 3
- batch_size = num_images;
-end
-
-IMAGE_DIM = 256;
-CROPPED_DIM = 227;
-indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;
-center = floor(indices(2) / 2)+1;
-
-num_images = length(image_files);
-images = zeros(CROPPED_DIM,CROPPED_DIM,3,batch_size,'single');
-
-parfor i=1:num_images
- % read file
- fprintf('%c Preparing %s\n',13,image_files{i});
- try
- im = imread(image_files{i});
- % resize to fixed input size
- im = single(im);
- im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
- % Transform GRAY to RGB
- if size(im,3) == 1
- im = cat(3,im,im,im);
- end
- % permute from RGB to BGR (IMAGE_MEAN is already BGR)
- im = im(:,:,[3 2 1]) - IMAGE_MEAN;
- % Crop the center of the image
- images(:,:,:,i) = permute(im(center:center+CROPPED_DIM-1,...
- center:center+CROPPED_DIM-1,:),[2 1 3]);
- catch
- warning('Problems with file',image_files{i});
- end
-end
\ No newline at end of file
+++ /dev/null
-function res=print_cell(input,file,linesep,cellsep)
-assert(iscell(input),'The input should be a cell')
-if nargin < 4
- cellsep = '\t';
-end
-if nargin < 3
- linesep = '\n';
-end
-if exist('file','var') && ~isempty(file)
- %%
- fid = fopen(file,'w');
- for l=1:length(input)
- if iscell(input{l})
- for i=1:length(input{l})
- fprintf(fid,['%s' cellsep],input{l}{i});
- end
- fprintf(fid,linesep);
- else
- if size(input,2) > 1
- for i=1:size(input,2)
- fprintf(fid,'%s ',input{l,i});
- end
- fprintf(fid,linesep);
- else
- fprintf(fid,['%s' linesep],input{l});
- end
- end
- end
- fclose(fid);
-else
- res = '';
- for l=1:length(input)
- if iscell(input{l})
- for i=1:length(input{l})
- res = [res sprintf([cellsep{1} '%s' cellsep{2}],input{l}{i})];
- end
- res = [res sprintf(linesep)];
- else
- res = [res sprintf(['%s' linesep],input{l}(:))];
- end
- end
-end
\ No newline at end of file
+++ /dev/null
-function res=read_cell(filename,linesep,cellsep)
-if nargin < 2, linesep='\n'; end
-if nargin < 3, cellsep = '\t'; end
-if exist(filename,'file')
- fid = fopen(filename);
-else
- % Assume that filename is either a file ide or a string
- fid = filename;
-end
-
-fileLines = textscan(fid,'%s','delimiter',linesep,'BufSize',100000);
-
-fileLines = fileLines{1};
-
-if regexp(fileLines{1},cellsep,'once')
- fileLines = regexprep(fileLines,['^' cellsep '|' cellsep '$'],'');
- res = regexp(fileLines,cellsep,'split');
- res = cell2matcell(res);
-else
- res = fileLines;
-end
--- /dev/null
+function [scores, maxlabel] = classification_demo(im, use_gpu)
+% [scores, maxlabel] = classification_demo(im, use_gpu)
+%
+% Image classification demo using BVLC CaffeNet.
+%
+% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
+% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
+%
+% ****************************************************************************
+% For detailed documentation and usage on Caffe's Matlab interface, please
+% refer to Caffe Interface Tutorial at
+% http://caffe.berkeleyvision.org/tutorial/interfaces.html#matlab
+% ****************************************************************************
+%
+% input
+% im color image as uint8 HxWx3
+% use_gpu 1 to use the GPU, 0 to use the CPU
+%
+% output
+% scores 1000-dimensional ILSVRC score vector
+% maxlabel the label of the highest score
+%
+% You may need to do the following before you start matlab:
+% $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64
+% $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
+% Or the equivalent based on where things are installed on your system
+%
+% Usage:
+% im = imread('../../examples/images/cat.jpg');
+% scores = classification_demo(im, 1);
+% [score, class] = max(scores);
+% Five things to be aware of:
+% caffe uses row-major order
+% matlab uses column-major order
+% caffe uses BGR color channel order
+% matlab uses RGB color channel order
+% images need to have the data mean subtracted
+
+% Data coming in from matlab needs to be in the order
+% [width, height, channels, images]
+% where width is the fastest dimension.
+% Here is the rough matlab for putting image data into the correct
+% format in W x H x C with BGR channels:
+% % permute channels from RGB to BGR
+% im_data = im(:, :, [3, 2, 1]);
+% % flip width and height to make width the fastest dimension
+% im_data = permute(im_data, [2, 1, 3]);
+% % convert from uint8 to single
+% im_data = single(im_data);
+% % reshape to a fixed size (e.g., 227x227).
+% im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');
+% % subtract mean_data (already in W x H x C with BGR channels)
+% im_data = im_data - mean_data;
+
+% If you have multiple images, cat them with cat(4, ...)
+
+% Add caffe/matlab to you Matlab search PATH to use matcaffe
+if exist('../+caffe', 'dir')
+ addpath('..');
+else
+ error('Please run this demo from caffe/matlab/demo');
+end
+
+% Set caffe mode
+if exist('use_gpu', 'var') && use_gpu
+ caffe.set_mode_gpu();
+ gpu_id = 0; % we will use the first gpu in this demo
+ caffe.set_device(gpu_id);
+else
+ caffe.set_mode_cpu();
+end
+
+% Initialize the network using BVLC CaffeNet for image classification
+% Weights (parameter) file needs to be downloaded from Model Zoo.
+model_dir = '../../models/bvlc_reference_caffenet/';
+net_model = [model_dir 'deploy.prototxt'];
+net_weights = [model_dir 'bvlc_reference_caffenet.caffemodel'];
+phase = 'test'; % run with phase test (so that dropout isn't applied)
+if ~exist(net_weights, 'file')
+ error('Please download CaffeNet from Model Zoo before you run this demo');
+end
+
+% Initialize a network
+net = caffe.Net(net_model, net_weights, phase);
+
+if nargin < 1
+ % For demo purposes we will use the cat image
+ fprintf('using caffe/examples/images/cat.jpg as input image\n');
+ im = imread('../../examples/images/cat.jpg');
+end
+
+% prepare oversampled input
+% input_data is Height x Width x Channel x Num
+tic;
+input_data = {prepare_image(im)};
+toc;
+
+% do forward pass to get scores
+% scores are now Channels x Num, where Channels == 1000
+tic;
+% The net forward function. It takes in a cell array of N-D arrays
+% (where N == 4 here) containing data of input blob(s) and outputs a cell
+% array containing data from output blob(s)
+scores = net.forward(input_data);
+toc;
+
+scores = scores{1};
+scores = mean(scores, 2); % take average scores over 10 crops
+
+[~, maxlabel] = max(scores);
+
+% call caffe.reset_all() to reset caffe
+caffe.reset_all();
+
+% ------------------------------------------------------------------------
+function crops_data = prepare_image(im)
+% ------------------------------------------------------------------------
+% caffe/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat contains mean_data that
+% is already in W x H x C with BGR channels
+d = load('../+caffe/imagenet/ilsvrc_2012_mean.mat');
+mean_data = d.mean_data;
+IMAGE_DIM = 256;
+CROPPED_DIM = 227;
+
+% Convert an image returned by Matlab's imread to im_data in caffe's data
+% format: W x H x C with BGR channels
+im_data = im(:, :, [3, 2, 1]); % permute channels from RGB to BGR
+im_data = permute(im_data, [2, 1, 3]); % flip width and height
+im_data = single(im_data); % convert from uint8 to single
+im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear'); % resize im_data
+im_data = im_data - mean_data; % subtract mean_data (already in W x H x C, BGR)
+
+% oversample (4 corners, center, and their x-axis flips)
+crops_data = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
+indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;
+n = 1;
+for i = indices
+ for j = indices
+ crops_data(:, :, :, n) = im_data(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :);
+ crops_data(:, :, :, n+5) = crops_data(end:-1:1, :, :, n);
+ n = n + 1;
+ end
+end
+center = floor(indices(2) / 2) + 1;
+crops_data(:,:,:,5) = ...
+ im_data(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:);
+crops_data(:,:,:,10) = crops_data(end:-1:1, :, :, 5);
fprintf('HDF5 filename listed in %s \n', 'list.txt');
% NOTE: In net definition prototxt, use list.txt as input to HDF5_DATA as:
-% layers {
+% layer {
% name: "data"
-% type: HDF5_DATA
+% type: "HDF5Data"
% top: "data"
% top: "labelvec"
% hdf5_data_param {
info=h5info(filename);
prev_dat_sz=info.Datasets(1).Dataspace.Size;
prev_lab_sz=info.Datasets(2).Dataspace.Size;
- assert(prev_dat_sz(1:end-1)==dat_dims(1:end-1), 'Data dimensions must match existing dimensions in dataset');
- assert(prev_lab_sz(1:end-1)==lab_dims(1:end-1), 'Label dimensions must match existing dimensions in dataset');
+ assert(all(prev_dat_sz(1:end-1)==dat_dims(1:end-1)), 'Data dimensions must match existing dimensions in dataset');
+ assert(all(prev_lab_sz(1:end-1)==lab_dims(1:end-1)), 'Label dimensions must match existing dimensions in dataset');
startloc.dat=[ones(1,length(dat_dims)-1), prev_dat_sz(end)+1];
startloc.lab=[ones(1,length(lab_dims)-1), prev_lab_sz(end)+1];
end
name: "AlexNet"
input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 227
-input_dim: 227
+input_shape {
+ dim: 10
+ dim: 3
+ dim: 227
+ dim: 227
+}
layer {
name: "conv1"
type: "Convolution"
name: "GoogleNet"
input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 224
-input_dim: 224
+input_shape {
+ dim: 10
+ dim: 3
+ dim: 224
+ dim: 224
+}
layer {
name: "conv1/7x7_s2"
type: "Convolution"
stride: 2
weight_filler {
type: "xavier"
- std: 0.1
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.1
}
bias_filler {
type: "constant"
kernel_size: 3
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.09
}
bias_filler {
type: "constant"
kernel_size: 3
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.2
}
bias_filler {
type: "constant"
kernel_size: 5
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.1
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.09
}
bias_filler {
type: "constant"
kernel_size: 3
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.2
}
bias_filler {
type: "constant"
kernel_size: 5
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.1
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.09
}
bias_filler {
type: "constant"
kernel_size: 3
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.2
}
bias_filler {
type: "constant"
kernel_size: 5
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.1
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.08
}
bias_filler {
type: "constant"
num_output: 1024
weight_filler {
type: "xavier"
- std: 0.02
}
bias_filler {
type: "constant"
num_output: 1000
weight_filler {
type: "xavier"
- std: 0.0009765625
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.09
}
bias_filler {
type: "constant"
kernel_size: 3
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.2
}
bias_filler {
type: "constant"
kernel_size: 5
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.1
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.09
}
bias_filler {
type: "constant"
kernel_size: 3
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.2
}
bias_filler {
type: "constant"
kernel_size: 5
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.1
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.09
}
bias_filler {
type: "constant"
kernel_size: 3
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.2
}
bias_filler {
type: "constant"
kernel_size: 5
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.1
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.08
}
bias_filler {
type: "constant"
num_output: 1024
weight_filler {
type: "xavier"
- std: 0.02
}
bias_filler {
type: "constant"
num_output: 1000
weight_filler {
type: "xavier"
- std: 0.0009765625
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.09
}
bias_filler {
type: "constant"
kernel_size: 3
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.2
}
bias_filler {
type: "constant"
kernel_size: 5
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.1
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.09
}
bias_filler {
type: "constant"
kernel_size: 3
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.2
}
bias_filler {
type: "constant"
kernel_size: 5
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.1
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.09
}
bias_filler {
type: "constant"
kernel_size: 3
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.2
}
bias_filler {
type: "constant"
kernel_size: 5
weight_filler {
type: "xavier"
- std: 0.03
}
bias_filler {
type: "constant"
kernel_size: 1
weight_filler {
type: "xavier"
- std: 0.1
}
bias_filler {
type: "constant"
name: "CaffeNet"
input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 227
-input_dim: 227
+input_shape {
+ dim: 10
+ dim: 3
+ dim: 227
+ dim: 227
+}
layer {
name: "conv1"
type: "Convolution"
# mean_value: 104
# mean_value: 117
# mean_value: 123
-# mirror: true
+# mirror: false
# }
data_param {
source: "examples/imagenet/ilsvrc12_val_lmdb"
name: "R-CNN-ilsvrc13"
input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 227
-input_dim: 227
+input_shape {
+ dim: 10
+ dim: 3
+ dim: 227
+ dim: 227
+}
layer {
name: "conv1"
type: "Convolution"
name: "FlickrStyleCaffeNet"
input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 227
-input_dim: 227
+input_shape {
+ dim: 10
+ dim: 3
+ dim: 227
+ dim: 227
+}
layer {
name: "conv1"
type: "Convolution"
}
}
layer {
- name: "loss"
- type: "SoftmaxWithLoss"
- bottom: "fc8_flickr"
- bottom: "label"
-}
-layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8_flickr"
phase: TEST
}
}
+layer {
+ name: "loss"
+ type: "SoftmaxWithLoss"
+ bottom: "fc8_flickr"
+ bottom: "label"
+ top: "loss"
+}
if(NOT HAVE_PYTHON)
- message(STATUS "Python interface is disabled or not all required dependecies found. Building without it...")
+ message(STATUS "Python interface is disabled or not all required dependencies found. Building without it...")
return()
endif()
COMMAND ${CMAKE_COMMAND} -E make_directory ${PROJECT_SOURCE_DIR}/python/caffe/proto
COMMAND touch ${PROJECT_SOURCE_DIR}/python/caffe/proto/__init__.py
COMMAND cp ${proto_gen_folder}/*.py ${PROJECT_SOURCE_DIR}/python/caffe/proto/
- COMMENT "Creating symlink ${__linkname} -> ${PROJECT_BINARY_DIR}/lib/_caffe${CAffe_POSTFIX}.so")
+ COMMENT "Creating symlink ${__linkname} -> ${PROJECT_BINARY_DIR}/lib/_caffe${Caffe_POSTFIX}.so")
endif()
# ---[ Install
-from .pycaffe import Net, SGDSolver
-from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver
+from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver
+from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list
+from ._caffe import __version__
from .proto.caffe_pb2 import TRAIN, TEST
from .classifier import Classifier
from .detector import Detector
-import io
+from . import io
+from .net_spec import layers, params, NetSpec, to_proto
#include <fstream> // NOLINT
#include "caffe/caffe.hpp"
-#include "caffe/python_layer.hpp"
+#include "caffe/layers/memory_data_layer.hpp"
+#include "caffe/layers/python_layer.hpp"
+#include "caffe/sgd_solvers.hpp"
// Temporary solution for numpy < 1.7 versions: old macro, no promises.
// You're strongly advised to upgrade to >= 1.7.
Solver<Dtype>* GetSolverFromFile(const string& filename) {
SolverParameter param;
- ReadProtoFromTextFileOrDie(filename, ¶m);
- return GetSolver<Dtype>(param);
+ ReadSolverParamsFromTextFileOrDie(filename, ¶m);
+ return SolverRegistry<Dtype>::CreateSolver(param);
}
struct NdarrayConverterGenerator {
return bp::object();
}
+bp::object BlobVec_add_blob(bp::tuple args, bp::dict kwargs) {
+ if (bp::len(kwargs) > 0) {
+ throw std::runtime_error("BlobVec.add_blob takes no kwargs");
+ }
+ typedef vector<shared_ptr<Blob<Dtype> > > BlobVec;
+ BlobVec* self = bp::extract<BlobVec*>(args[0]);
+ vector<int> shape(bp::len(args) - 1);
+ for (int i = 1; i < bp::len(args); ++i) {
+ shape[i - 1] = bp::extract<int>(args[i]);
+ }
+ self->push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+ // We need to explicitly return None to use bp::raw_function.
+ return bp::object();
+}
+
BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolveOverloads, Solve, 0, 1);
BOOST_PYTHON_MODULE(_caffe) {
// below, we prepend an underscore to methods that will be replaced
// in Python
+
+ bp::scope().attr("__version__") = AS_STRING(CAFFE_VERSION);
+
// Caffe utility functions
bp::def("set_mode_cpu", &set_mode_cpu);
bp::def("set_mode_gpu", &set_mode_gpu);
bp::def("set_device", &Caffe::SetDevice);
+ bp::def("layer_type_list", &LayerRegistry<Dtype>::LayerTypeList);
+
bp::class_<Net<Dtype>, shared_ptr<Net<Dtype> >, boost::noncopyable >("Net",
bp::no_init)
.def("__init__", bp::make_constructor(&Net_Init))
.def("copy_from", static_cast<void (Net<Dtype>::*)(const string)>(
&Net<Dtype>::CopyTrainedLayersFrom))
.def("share_with", &Net<Dtype>::ShareTrainedLayersWith)
+ .add_property("_blob_loss_weights", bp::make_function(
+ &Net<Dtype>::blob_loss_weights, bp::return_internal_reference<>()))
+ .def("_bottom_ids", bp::make_function(&Net<Dtype>::bottom_ids,
+ bp::return_value_policy<bp::copy_const_reference>()))
+ .def("_top_ids", bp::make_function(&Net<Dtype>::top_ids,
+ bp::return_value_policy<bp::copy_const_reference>()))
.add_property("_blobs", bp::make_function(&Net<Dtype>::blobs,
bp::return_internal_reference<>()))
.add_property("layers", bp::make_function(&Net<Dtype>::layers,
bp::class_<Blob<Dtype>, shared_ptr<Blob<Dtype> >, boost::noncopyable>(
"Blob", bp::no_init)
+ .add_property("shape",
+ bp::make_function(
+ static_cast<const vector<int>& (Blob<Dtype>::*)() const>(
+ &Blob<Dtype>::shape),
+ bp::return_value_policy<bp::copy_const_reference>()))
.add_property("num", &Blob<Dtype>::num)
.add_property("channels", &Blob<Dtype>::channels)
.add_property("height", &Blob<Dtype>::height)
.def("solve", static_cast<void (Solver<Dtype>::*)(const char*)>(
&Solver<Dtype>::Solve), SolveOverloads())
.def("step", &Solver<Dtype>::Step)
- .def("restore", &Solver<Dtype>::Restore);
+ .def("restore", &Solver<Dtype>::Restore)
+ .def("snapshot", &Solver<Dtype>::Snapshot);
bp::class_<SGDSolver<Dtype>, bp::bases<Solver<Dtype> >,
shared_ptr<SGDSolver<Dtype> >, boost::noncopyable>(
bp::class_<AdaGradSolver<Dtype>, bp::bases<Solver<Dtype> >,
shared_ptr<AdaGradSolver<Dtype> >, boost::noncopyable>(
"AdaGradSolver", bp::init<string>());
+ bp::class_<RMSPropSolver<Dtype>, bp::bases<Solver<Dtype> >,
+ shared_ptr<RMSPropSolver<Dtype> >, boost::noncopyable>(
+ "RMSPropSolver", bp::init<string>());
+ bp::class_<AdaDeltaSolver<Dtype>, bp::bases<Solver<Dtype> >,
+ shared_ptr<AdaDeltaSolver<Dtype> >, boost::noncopyable>(
+ "AdaDeltaSolver", bp::init<string>());
+ bp::class_<AdamSolver<Dtype>, bp::bases<Solver<Dtype> >,
+ shared_ptr<AdamSolver<Dtype> >, boost::noncopyable>(
+ "AdamSolver", bp::init<string>());
bp::def("get_solver", &GetSolverFromFile,
bp::return_value_policy<bp::manage_new_object>());
// vector wrappers for all the vector types we use
bp::class_<vector<shared_ptr<Blob<Dtype> > > >("BlobVec")
- .def(bp::vector_indexing_suite<vector<shared_ptr<Blob<Dtype> > >, true>());
+ .def(bp::vector_indexing_suite<vector<shared_ptr<Blob<Dtype> > >, true>())
+ .def("add_blob", bp::raw_function(&BlobVec_add_blob));
bp::class_<vector<Blob<Dtype>*> >("RawBlobVec")
.def(bp::vector_indexing_suite<vector<Blob<Dtype>*>, true>());
bp::class_<vector<shared_ptr<Layer<Dtype> > > >("LayerVec")
.def(bp::vector_indexing_suite<vector<string> >());
bp::class_<vector<int> >("IntVec")
.def(bp::vector_indexing_suite<vector<int> >());
+ bp::class_<vector<Dtype> >("DtypeVec")
+ .def(bp::vector_indexing_suite<vector<Dtype> >());
bp::class_<vector<shared_ptr<Net<Dtype> > > >("NetVec")
.def(bp::vector_indexing_suite<vector<shared_ptr<Net<Dtype> > >, true>());
bp::class_<vector<bool> >("BoolVec")
"""
Classifier extends Net for image class prediction
by scaling, center cropping, or oversampling.
+
+ Parameters
+ ----------
+ image_dims : dimensions to scale input for cropping/sampling.
+ Default is to scale to net input size for whole-image crop.
+ mean, input_scale, raw_scale, channel_swap: params for
+ preprocessing options.
"""
def __init__(self, model_file, pretrained_file, image_dims=None,
mean=None, input_scale=None, raw_scale=None,
channel_swap=None):
- """
- Take
- image_dims: dimensions to scale input for cropping/sampling.
- Default is to scale to net input size for whole-image crop.
- mean, input_scale, raw_scale, channel_swap: params for
- preprocessing options.
- """
caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST)
# configure pre-processing
in_ = self.inputs[0]
self.transformer = caffe.io.Transformer(
{in_: self.blobs[in_].data.shape})
- self.transformer.set_transpose(in_, (2,0,1))
+ self.transformer.set_transpose(in_, (2, 0, 1))
if mean is not None:
self.transformer.set_mean(in_, mean)
if input_scale is not None:
image_dims = self.crop_dims
self.image_dims = image_dims
-
def predict(self, inputs, oversample=True):
"""
Predict classification probabilities of inputs.
- Take
- inputs: iterable of (H x W x K) input ndarrays.
- oversample: average predictions across center, corners, and mirrors
- when True (default). Center-only prediction when False.
+ Parameters
+ ----------
+ inputs : iterable of (H x W x K) input ndarrays.
+ oversample : boolean
+ average predictions across center, corners, and mirrors
+ when True (default). Center-only prediction when False.
- Give
- predictions: (N x C) ndarray of class probabilities
- for N images and C classes.
+ Returns
+ -------
+ predictions: (N x C) ndarray of class probabilities for N images and C
+ classes.
"""
# Scale to standardize input dimensions.
input_ = np.zeros((len(inputs),
- self.image_dims[0], self.image_dims[1], inputs[0].shape[2]),
- dtype=np.float32)
+ self.image_dims[0],
+ self.image_dims[1],
+ inputs[0].shape[2]),
+ dtype=np.float32)
for ix, in_ in enumerate(inputs):
input_[ix] = caffe.io.resize_image(in_, self.image_dims)
input_ = input_[:, crop[0]:crop[2], crop[1]:crop[3], :]
# Classify
- caffe_in = np.zeros(np.array(input_.shape)[[0,3,1,2]],
+ caffe_in = np.zeros(np.array(input_.shape)[[0, 3, 1, 2]],
dtype=np.float32)
for ix, in_ in enumerate(input_):
caffe_in[ix] = self.transformer.preprocess(self.inputs[0], in_)
"""
Detector extends Net for windowed detection by a list of crops or
selective search proposals.
+
+ Parameters
+ ----------
+ mean, input_scale, raw_scale, channel_swap : params for preprocessing
+ options.
+ context_pad : amount of surrounding context to take s.t. a `context_pad`
+ sized border of pixels in the network input image is context, as in
+ R-CNN feature extraction.
"""
def __init__(self, model_file, pretrained_file, mean=None,
input_scale=None, raw_scale=None, channel_swap=None,
context_pad=None):
- """
- Take
- mean, input_scale, raw_scale, channel_swap: params for
- preprocessing options.
- context_pad: amount of surrounding context to take s.t. a `context_pad`
- sized border of pixels in the network input image is context, as in
- R-CNN feature extraction.
- """
caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST)
# configure pre-processing
in_ = self.inputs[0]
self.transformer = caffe.io.Transformer(
{in_: self.blobs[in_].data.shape})
- self.transformer.set_transpose(in_, (2,0,1))
+ self.transformer.set_transpose(in_, (2, 0, 1))
if mean is not None:
self.transformer.set_mean(in_, mean)
if input_scale is not None:
self.configure_crop(context_pad)
-
def detect_windows(self, images_windows):
"""
Do windowed detection over given images and windows. Windows are
extracted then warped to the input dimensions of the net.
- Take
+ Parameters
+ ----------
images_windows: (image filename, window list) iterable.
context_crop: size of context border to crop in pixels.
- Give
+ Returns
+ -------
detections: list of {filename: image filename, window: crop coordinates,
predictions: prediction vector} dicts.
"""
for ix, window_in in enumerate(window_inputs):
caffe_in[ix] = self.transformer.preprocess(in_, window_in)
out = self.forward_all(**{in_: caffe_in})
- predictions = out[self.outputs[0]].squeeze(axis=(2,3))
+ predictions = out[self.outputs[0]].squeeze(axis=(2, 3))
# Package predictions with images and windows.
detections = []
ix += 1
return detections
-
def detect_selective_search(self, image_fnames):
"""
Do windowed detection over Selective Search proposals by extracting
the crop and warping to the input dimensions of the net.
- Take
+ Parameters
+ ----------
image_fnames: list
- Give
+ Returns
+ -------
detections: list of {filename: image filename, window: crop coordinates,
predictions: prediction vector} dicts.
"""
# Run windowed detection on the selective search list.
return self.detect_windows(zip(image_fnames, windows_list))
-
def crop(self, im, window):
"""
Crop a window from the image for detection. Include surrounding context
according to the `context_pad` configuration.
- Take
+ Parameters
+ ----------
im: H x W x K image ndarray to crop.
window: bounding box coordinates as ymin, xmin, ymax, xmax.
- Give
+ Returns
+ -------
crop: cropped window.
"""
# Crop window from the image.
return crop
-
def configure_crop(self, context_pad):
"""
Configure crop dimensions and amount of context for cropping.
If context is included, make the special input mean for context padding.
- Take
- context_pad: amount of context for cropping.
+ Parameters
+ ----------
+ context_pad : amount of context for cropping.
"""
# crop dimensions
in_ = self.inputs[0]
crop_mean = mean.copy().transpose(inv_transpose)
if channel_order is not None:
channel_order_inverse = [channel_order.index(i)
- for i in range(crop_mean.shape[2])]
- crop_mean = crop_mean[:,:, channel_order_inverse]
+ for i in range(crop_mean.shape[2])]
+ crop_mean = crop_mean[:, :, channel_order_inverse]
if raw_scale is not None:
crop_mean /= raw_scale
self.crop_mean = crop_mean
"""
Caffe network visualization: draw the NetParameter protobuffer.
-NOTE: this requires pydot>=1.0.2, which is not included in requirements.txt
-since it requires graphviz and other prerequisites outside the scope of the
-Caffe.
+
+.. note::
+
+ This requires pydot>=1.0.2, which is not included in requirements.txt since
+ it requires graphviz and other prerequisites outside the scope of the
+ Caffe.
"""
from caffe.proto import caffe_pb2
-from google.protobuf import text_format
-import pydot
+
+"""
+pydot is not supported under python 3 and pydot2 doesn't work properly.
+pydotplus works nicely (pip install pydotplus)
+"""
+try:
+ # Try to load pydotplus
+ import pydotplus as pydot
+except ImportError:
+ import pydot
# Internal layer and blob styles.
-LAYER_STYLE_DEFAULT = {'shape': 'record', 'fillcolor': '#6495ED',
- 'style': 'filled'}
-NEURON_LAYER_STYLE = {'shape': 'record', 'fillcolor': '#90EE90',
- 'style': 'filled'}
-BLOB_STYLE = {'shape': 'octagon', 'fillcolor': '#E0E0E0',
- 'style': 'filled'}
+LAYER_STYLE_DEFAULT = {'shape': 'record',
+ 'fillcolor': '#6495ED',
+ 'style': 'filled'}
+NEURON_LAYER_STYLE = {'shape': 'record',
+ 'fillcolor': '#90EE90',
+ 'style': 'filled'}
+BLOB_STYLE = {'shape': 'octagon',
+ 'fillcolor': '#E0E0E0',
+ 'style': 'filled'}
+
def get_pooling_types_dict():
"""Get dictionary mapping pooling type number to type name
"""
desc = caffe_pb2.PoolingParameter.PoolMethod.DESCRIPTOR
d = {}
- for k,v in desc.values_by_name.items():
+ for k, v in desc.values_by_name.items():
d[v.number] = k
return d
-def determine_edge_label_by_layertype(layer, layertype):
- """Define edge label based on layer type
+def get_edge_label(layer):
+ """Define edge label based on layer type.
"""
- if layertype == 'Data':
+ if layer.type == 'Data':
edge_label = 'Batch ' + str(layer.data_param.batch_size)
- elif layertype == 'Convolution':
+ elif layer.type == 'Convolution' or layer.type == 'Deconvolution':
edge_label = str(layer.convolution_param.num_output)
- elif layertype == 'InnerProduct':
+ elif layer.type == 'InnerProduct':
edge_label = str(layer.inner_product_param.num_output)
else:
edge_label = '""'
return edge_label
-def determine_node_label_by_layertype(layer, layertype, rankdir):
- """Define node label based on layer type
+def get_layer_label(layer, rankdir):
+ """Define node label based on layer type.
+
+ Parameters
+ ----------
+ layer : ?
+ rankdir : {'LR', 'TB', 'BT'}
+ Direction of graph layout.
+
+ Returns
+ -------
+ string :
+ A label for the current layer
"""
if rankdir in ('TB', 'BT'):
else:
# If graph orientation is horizontal, vertical space is free and
# horizontal space is not; separate words with newlines
- separator = '\n'
+ separator = '\\n'
- if layertype == 'Convolution':
+ if layer.type == 'Convolution' or layer.type == 'Deconvolution':
# Outer double quotes needed or else colon characters don't parse
# properly
node_label = '"%s%s(%s)%skernel size: %d%sstride: %d%spad: %d"' %\
(layer.name,
separator,
- layertype,
+ layer.type,
separator,
- layer.convolution_param.kernel_size,
+ layer.convolution_param.kernel_size[0] if len(layer.convolution_param.kernel_size._values) else 1,
separator,
- layer.convolution_param.stride,
+ layer.convolution_param.stride[0] if len(layer.convolution_param.stride._values) else 1,
separator,
- layer.convolution_param.pad)
- elif layertype == 'Pooling':
+ layer.convolution_param.pad[0] if len(layer.convolution_param.pad._values) else 0)
+ elif layer.type == 'Pooling':
pooling_types_dict = get_pooling_types_dict()
node_label = '"%s%s(%s %s)%skernel size: %d%sstride: %d%spad: %d"' %\
(layer.name,
separator,
pooling_types_dict[layer.pooling_param.pool],
- layertype,
+ layer.type,
separator,
layer.pooling_param.kernel_size,
separator,
separator,
layer.pooling_param.pad)
else:
- node_label = '"%s%s(%s)"' % (layer.name, separator, layertype)
+ node_label = '"%s%s(%s)"' % (layer.name, separator, layer.type)
return node_label
def choose_color_by_layertype(layertype):
- """Define colors for nodes based on the layer type
+ """Define colors for nodes based on the layer type.
"""
color = '#6495ED' # Default
- if layertype == 'Convolution':
+ if layertype == 'Convolution' or layertype == 'Deconvolution':
color = '#FF5050'
elif layertype == 'Pooling':
color = '#FF9900'
def get_pydot_graph(caffe_net, rankdir, label_edges=True):
- pydot_graph = pydot.Dot(caffe_net.name, graph_type='digraph', rankdir=rankdir)
- pydot_nodes = {}
- pydot_edges = []
- for layer in caffe_net.layer:
- name = layer.name
- layertype = layer.type
- node_label = determine_node_label_by_layertype(layer, layertype, rankdir)
- if (len(layer.bottom) == 1 and len(layer.top) == 1 and
- layer.bottom[0] == layer.top[0]):
- # We have an in-place neuron layer.
- pydot_nodes[name + '_' + layertype] = pydot.Node(
- node_label, **NEURON_LAYER_STYLE)
- else:
- layer_style = LAYER_STYLE_DEFAULT
- layer_style['fillcolor'] = choose_color_by_layertype(layertype)
- pydot_nodes[name + '_' + layertype] = pydot.Node(
- node_label, **layer_style)
- for bottom_blob in layer.bottom:
- pydot_nodes[bottom_blob + '_blob'] = pydot.Node(
- '%s' % (bottom_blob), **BLOB_STYLE)
- edge_label = '""'
- pydot_edges.append({'src': bottom_blob + '_blob',
- 'dst': name + '_' + layertype,
- 'label': edge_label})
- for top_blob in layer.top:
- pydot_nodes[top_blob + '_blob'] = pydot.Node(
- '%s' % (top_blob))
- if label_edges:
- edge_label = determine_edge_label_by_layertype(layer, layertype)
- else:
- edge_label = '""'
- pydot_edges.append({'src': name + '_' + layertype,
- 'dst': top_blob + '_blob',
- 'label': edge_label})
- # Now, add the nodes and edges to the graph.
- for node in pydot_nodes.values():
- pydot_graph.add_node(node)
- for edge in pydot_edges:
- pydot_graph.add_edge(
- pydot.Edge(pydot_nodes[edge['src']], pydot_nodes[edge['dst']],
- label=edge['label']))
- return pydot_graph
+ """Create a data structure which represents the `caffe_net`.
+
+ Parameters
+ ----------
+ caffe_net : object
+ rankdir : {'LR', 'TB', 'BT'}
+ Direction of graph layout.
+ label_edges : boolean, optional
+ Label the edges (default is True).
+
+ Returns
+ -------
+ pydot graph object
+ """
+ pydot_graph = pydot.Dot(caffe_net.name,
+ graph_type='digraph',
+ rankdir=rankdir)
+ pydot_nodes = {}
+ pydot_edges = []
+ for layer in caffe_net.layer:
+ node_label = get_layer_label(layer, rankdir)
+ node_name = "%s_%s" % (layer.name, layer.type)
+ if (len(layer.bottom) == 1 and len(layer.top) == 1 and
+ layer.bottom[0] == layer.top[0]):
+ # We have an in-place neuron layer.
+ pydot_nodes[node_name] = pydot.Node(node_label,
+ **NEURON_LAYER_STYLE)
+ else:
+ layer_style = LAYER_STYLE_DEFAULT
+ layer_style['fillcolor'] = choose_color_by_layertype(layer.type)
+ pydot_nodes[node_name] = pydot.Node(node_label, **layer_style)
+ for bottom_blob in layer.bottom:
+ pydot_nodes[bottom_blob + '_blob'] = pydot.Node('%s' % bottom_blob,
+ **BLOB_STYLE)
+ edge_label = '""'
+ pydot_edges.append({'src': bottom_blob + '_blob',
+ 'dst': node_name,
+ 'label': edge_label})
+ for top_blob in layer.top:
+ pydot_nodes[top_blob + '_blob'] = pydot.Node('%s' % (top_blob))
+ if label_edges:
+ edge_label = get_edge_label(layer)
+ else:
+ edge_label = '""'
+ pydot_edges.append({'src': node_name,
+ 'dst': top_blob + '_blob',
+ 'label': edge_label})
+ # Now, add the nodes and edges to the graph.
+ for node in pydot_nodes.values():
+ pydot_graph.add_node(node)
+ for edge in pydot_edges:
+ pydot_graph.add_edge(
+ pydot.Edge(pydot_nodes[edge['src']],
+ pydot_nodes[edge['dst']],
+ label=edge['label']))
+ return pydot_graph
+
def draw_net(caffe_net, rankdir, ext='png'):
- """Draws a caffe net and returns the image string encoded using the given
- extension.
+ """Draws a caffe net and returns the image string encoded using the given
+ extension.
+
+ Parameters
+ ----------
+ caffe_net : a caffe.proto.caffe_pb2.NetParameter protocol buffer.
+ ext : string, optional
+ The image extension (the default is 'png').
+
+ Returns
+ -------
+ string :
+ Postscript representation of the graph.
+ """
+ return get_pydot_graph(caffe_net, rankdir).create(format=ext)
- Input:
- caffe_net: a caffe.proto.caffe_pb2.NetParameter protocol buffer.
- ext: the image extension. Default 'png'.
- """
- return get_pydot_graph(caffe_net, rankdir).create(format=ext)
def draw_net_to_file(caffe_net, filename, rankdir='LR'):
- """Draws a caffe net, and saves it to file using the format given as the
- file extension. Use '.raw' to output raw text that you can manually feed
- to graphviz to draw graphs.
- """
- ext = filename[filename.rfind('.')+1:]
- with open(filename, 'wb') as fid:
- fid.write(draw_net(caffe_net, rankdir, ext))
+ """Draws a caffe net, and saves it to file using the format given as the
+ file extension. Use '.raw' to output raw text that you can manually feed
+ to graphviz to draw graphs.
+
+ Parameters
+ ----------
+ caffe_net : a caffe.proto.caffe_pb2.NetParameter protocol buffer.
+ filename : string
+ The path to a file where the networks visualization will be stored.
+ rankdir : {'LR', 'TB', 'BT'}
+ Direction of graph layout.
+ """
+ ext = filename[filename.rfind('.')+1:]
+ with open(filename, 'wb') as fid:
+ fid.write(draw_net(caffe_net, rankdir, ext))
from caffe.proto import caffe_pb2
except:
import sys
- if sys.version_info >= (3,0):
+ if sys.version_info >= (3, 0):
print("Failed to include caffe_pb2, things might go wrong!")
else:
raise
-## proto / datum / ndarray conversion
+## proto / datum / ndarray conversion
def blobproto_to_array(blob, return_diff=False):
- """Convert a blob proto to an array. In default, we will just return the data,
+ """
+ Convert a blob proto to an array. In default, we will just return the data,
unless return_diff is True, in which case we will return the diff.
"""
+ # Read the data into an array
if return_diff:
- return np.array(blob.diff).reshape(
- blob.num, blob.channels, blob.height, blob.width)
+ data = np.array(blob.diff)
else:
- return np.array(blob.data).reshape(
- blob.num, blob.channels, blob.height, blob.width)
+ data = np.array(blob.data)
+ # Reshape the array
+ if blob.HasField('num') or blob.HasField('channels') or blob.HasField('height') or blob.HasField('width'):
+ # Use legacy 4D shape
+ return data.reshape(blob.num, blob.channels, blob.height, blob.width)
+ else:
+ return data.reshape(blob.shape.dim)
def array_to_blobproto(arr, diff=None):
- """Converts a 4-dimensional array to blob proto. If diff is given, also
+ """Converts a N-dimensional array to blob proto. If diff is given, also
convert the diff. You need to make sure that arr and diff have the same
shape, and this function does not do sanity check.
"""
- if arr.ndim != 4:
- raise ValueError('Incorrect array shape.')
blob = caffe_pb2.BlobProto()
- blob.num, blob.channels, blob.height, blob.width = arr.shape;
+ blob.shape.dim.extend(arr.shape)
blob.data.extend(arr.astype(float).flat)
if diff is not None:
blob.diff.extend(diff.astype(float).flat)
as one can easily get it by calling datum.label.
"""
if len(datum.data):
- return np.fromstring(datum.data, dtype = np.uint8).reshape(
+ return np.fromstring(datum.data, dtype=np.uint8).reshape(
datum.channels, datum.height, datum.width)
else:
return np.array(datum.float_data).astype(float).reshape(
Note: this is mostly for illustrative purposes and it is likely better
to define your own input preprocessing routine for your needs.
- Take
- net: a Net for which the input should be prepared
+ Parameters
+ ----------
+ net : a Net for which the input should be prepared
"""
def __init__(self, inputs):
self.inputs = inputs
self.mean = {}
self.input_scale = {}
-
def __check_input(self, in_):
if in_ not in self.inputs:
raise Exception('{} is not one of the net inputs: {}'.format(
in_, self.inputs))
-
def preprocess(self, in_, data):
"""
Format input for Caffe:
- subtract mean
- scale feature
- Take
- in_: name of input blob to preprocess for
- data: (H' x W' x K) ndarray
+ Parameters
+ ----------
+ in_ : name of input blob to preprocess for
+ data : (H' x W' x K) ndarray
- Give
- caffe_in: (K x H x W) ndarray for input to a Net
+ Returns
+ -------
+ caffe_in : (K x H x W) ndarray for input to a Net
"""
self.__check_input(in_)
caffe_in = data.astype(np.float32, copy=False)
caffe_in *= input_scale
return caffe_in
-
def deprocess(self, in_, data):
"""
Invert Caffe formatting; see preprocess().
if raw_scale is not None:
decaf_in /= raw_scale
if channel_swap is not None:
- decaf_in = decaf_in[channel_swap, :, :]
+ decaf_in = decaf_in[np.argsort(channel_swap), :, :]
if transpose is not None:
- decaf_in = decaf_in.transpose([transpose[t] for t in transpose])
+ decaf_in = decaf_in.transpose(np.argsort(transpose))
return decaf_in
-
def set_transpose(self, in_, order):
"""
Set the input channel order for e.g. RGB to BGR conversion
as needed for the reference ImageNet model.
- Take
- in_: which input to assign this channel order
- order: the order to transpose the dimensions
+ Parameters
+ ----------
+ in_ : which input to assign this channel order
+ order : the order to transpose the dimensions
"""
self.__check_input(in_)
if len(order) != len(self.inputs[in_]) - 1:
'dimensions as the input.')
self.transpose[in_] = order
-
def set_channel_swap(self, in_, order):
"""
Set the input channel order for e.g. RGB to BGR conversion
as needed for the reference ImageNet model.
N.B. this assumes the channels are the first dimension AFTER transpose.
- Take
- in_: which input to assign this channel order
- order: the order to take the channels.
+ Parameters
+ ----------
+ in_ : which input to assign this channel order
+ order : the order to take the channels.
(2,1,0) maps RGB to BGR for example.
"""
self.__check_input(in_)
'dimensions as the input channels.')
self.channel_swap[in_] = order
-
def set_raw_scale(self, in_, scale):
"""
Set the scale of raw features s.t. the input blob = input * scale.
like CaffeNet and AlexNet represent images in [0, 255] so the raw_scale
of these models must be 255.
- Take
- in_: which input to assign this scale factor
- scale: scale coefficient
+ Parameters
+ ----------
+ in_ : which input to assign this scale factor
+ scale : scale coefficient
"""
self.__check_input(in_)
self.raw_scale[in_] = scale
-
def set_mean(self, in_, mean):
"""
Set the mean to subtract for centering the data.
- Take
- in_: which input to assign this mean.
- mean: mean ndarray (input dimensional or broadcastable)
+ Parameters
+ ----------
+ in_ : which input to assign this mean.
+ mean : mean ndarray (input dimensional or broadcastable)
"""
self.__check_input(in_)
ms = mean.shape
raise ValueError('Mean shape incompatible with input shape.')
self.mean[in_] = mean
-
def set_input_scale(self, in_, scale):
"""
Set the scale of preprocessed inputs s.t. the blob = blob * scale.
N.B. input_scale is done AFTER mean subtraction and other preprocessing
while raw_scale is done BEFORE.
- Take
- in_: which input to assign this scale factor
- scale: scale coefficient
+ Parameters
+ ----------
+ in_ : which input to assign this scale factor
+ scale : scale coefficient
"""
self.__check_input(in_)
self.input_scale[in_] = scale
"""
Load an image converting from grayscale or alpha as needed.
- Take
- filename: string
- color: flag for color format. True (default) loads as RGB while False
+ Parameters
+ ----------
+ filename : string
+ color : boolean
+ flag for color format. True (default) loads as RGB while False
loads as intensity (if image is already grayscale).
- Give
- image: an image with type np.float32 in range [0, 1]
+ Returns
+ -------
+ image : an image with type np.float32 in range [0, 1]
of size (H x W x 3) in RGB or
of size (H x W x 1) in grayscale.
"""
"""
Resize an image array with interpolation.
- Take
- im: (H x W x K) ndarray
- new_dims: (height, width) tuple of new dimensions.
- interp_order: interpolation order, default is linear.
+ Parameters
+ ----------
+ im : (H x W x K) ndarray
+ new_dims : (height, width) tuple of new dimensions.
+ interp_order : interpolation order, default is linear.
- Give
- im: resized ndarray with shape (new_dims[0], new_dims[1], K)
+ Returns
+ -------
+ im : resized ndarray with shape (new_dims[0], new_dims[1], K)
"""
if im.shape[-1] == 1 or im.shape[-1] == 3:
im_min, im_max = im.min(), im.max()
if im_max > im_min:
- # skimage is fast but only understands {1,3} channel images in [0, 1].
+ # skimage is fast but only understands {1,3} channel images
+ # in [0, 1].
im_std = (im - im_min) / (im_max - im_min)
resized_std = resize(im_std, new_dims, order=interp_order)
resized_im = resized_std * (im_max - im_min) + im_min
else:
# the image is a constant -- avoid divide by 0
- ret = np.empty((new_dims[0], new_dims[1], im.shape[-1]), dtype=np.float32)
+ ret = np.empty((new_dims[0], new_dims[1], im.shape[-1]),
+ dtype=np.float32)
ret.fill(im_min)
return ret
else:
# ndimage interpolates anything but more slowly.
- scale = tuple(np.array(new_dims) / np.array(im.shape[:2]))
+ scale = tuple(np.array(new_dims, dtype=float) / np.array(im.shape[:2]))
resized_im = zoom(im, scale + (1,), order=interp_order)
return resized_im.astype(np.float32)
"""
Crop images into the four corners, center, and their mirrored versions.
- Take
- image: iterable of (H x W x K) ndarrays
- crop_dims: (height, width) tuple for the crops.
+ Parameters
+ ----------
+ image : iterable of (H x W x K) ndarrays
+ crop_dims : (height, width) tuple for the crops.
- Give
- crops: (10*N x H x W x K) ndarray of crops for number of inputs N.
+ Returns
+ -------
+ crops : (10*N x H x W x K) ndarray of crops for number of inputs N.
"""
# Dimensions and center.
im_shape = np.array(images[0].shape)
# Extract crops
crops = np.empty((10 * len(images), crop_dims[0], crop_dims[1],
- im_shape[-1]), dtype=np.float32)
+ im_shape[-1]), dtype=np.float32)
ix = 0
for im in images:
for crop in crops_ix:
--- /dev/null
+"""Python net specification.
+
+This module provides a way to write nets directly in Python, using a natural,
+functional style. See examples/pycaffe/caffenet.py for an example.
+
+Currently this works as a thin wrapper around the Python protobuf interface,
+with layers and parameters automatically generated for the "layers" and
+"params" pseudo-modules, which are actually objects using __getattr__ magic
+to generate protobuf messages.
+
+Note that when using to_proto or Top.to_proto, names of intermediate blobs will
+be automatically generated. To explicitly specify blob names, use the NetSpec
+class -- assign to its attributes directly to name layers, and call
+NetSpec.to_proto to serialize all assigned layers.
+
+This interface is expected to continue to evolve as Caffe gains new capabilities
+for specifying nets. In particular, the automatically generated layer names
+are not guaranteed to be forward-compatible.
+"""
+
+from collections import OrderedDict, Counter
+
+from .proto import caffe_pb2
+from google import protobuf
+import six
+
+
+def param_name_dict():
+ """Find out the correspondence between layer names and parameter names."""
+
+ layer = caffe_pb2.LayerParameter()
+ # get all parameter names (typically underscore case) and corresponding
+ # type names (typically camel case), which contain the layer names
+ # (note that not all parameters correspond to layers, but we'll ignore that)
+ param_names = [s for s in dir(layer) if s.endswith('_param')]
+ param_type_names = [type(getattr(layer, s)).__name__ for s in param_names]
+ # strip the final '_param' or 'Parameter'
+ param_names = [s[:-len('_param')] for s in param_names]
+ param_type_names = [s[:-len('Parameter')] for s in param_type_names]
+ return dict(zip(param_type_names, param_names))
+
+
+def to_proto(*tops):
+ """Generate a NetParameter that contains all layers needed to compute
+ all arguments."""
+
+ layers = OrderedDict()
+ autonames = Counter()
+ for top in tops:
+ top.fn._to_proto(layers, {}, autonames)
+ net = caffe_pb2.NetParameter()
+ net.layer.extend(layers.values())
+ return net
+
+
+def assign_proto(proto, name, val):
+ """Assign a Python object to a protobuf message, based on the Python
+ type (in recursive fashion). Lists become repeated fields/messages, dicts
+ become messages, and other types are assigned directly. For convenience,
+ repeated fields whose values are not lists are converted to single-element
+ lists; e.g., `my_repeated_int_field=3` is converted to
+ `my_repeated_int_field=[3]`."""
+
+ is_repeated_field = hasattr(getattr(proto, name), 'extend')
+ if is_repeated_field and not isinstance(val, list):
+ val = [val]
+ if isinstance(val, list):
+ if isinstance(val[0], dict):
+ for item in val:
+ proto_item = getattr(proto, name).add()
+ for k, v in six.iteritems(item):
+ assign_proto(proto_item, k, v)
+ else:
+ getattr(proto, name).extend(val)
+ elif isinstance(val, dict):
+ for k, v in six.iteritems(val):
+ assign_proto(getattr(proto, name), k, v)
+ else:
+ setattr(proto, name, val)
+
+
+class Top(object):
+ """A Top specifies a single output blob (which could be one of several
+ produced by a layer.)"""
+
+ def __init__(self, fn, n):
+ self.fn = fn
+ self.n = n
+
+ def to_proto(self):
+ """Generate a NetParameter that contains all layers needed to compute
+ this top."""
+
+ return to_proto(self)
+
+ def _to_proto(self, layers, names, autonames):
+ return self.fn._to_proto(layers, names, autonames)
+
+
+class Function(object):
+ """A Function specifies a layer, its parameters, and its inputs (which
+ are Tops from other layers)."""
+
+ def __init__(self, type_name, inputs, params):
+ self.type_name = type_name
+ self.inputs = inputs
+ self.params = params
+ self.ntop = self.params.get('ntop', 1)
+ # use del to make sure kwargs are not double-processed as layer params
+ if 'ntop' in self.params:
+ del self.params['ntop']
+ self.in_place = self.params.get('in_place', False)
+ if 'in_place' in self.params:
+ del self.params['in_place']
+ self.tops = tuple(Top(self, n) for n in range(self.ntop))
+
+ def _get_name(self, names, autonames):
+ if self not in names and self.ntop > 0:
+ names[self] = self._get_top_name(self.tops[0], names, autonames)
+ elif self not in names:
+ autonames[self.type_name] += 1
+ names[self] = self.type_name + str(autonames[self.type_name])
+ return names[self]
+
+ def _get_top_name(self, top, names, autonames):
+ if top not in names:
+ autonames[top.fn.type_name] += 1
+ names[top] = top.fn.type_name + str(autonames[top.fn.type_name])
+ return names[top]
+
+ def _to_proto(self, layers, names, autonames):
+ if self in layers:
+ return
+ bottom_names = []
+ for inp in self.inputs:
+ inp._to_proto(layers, names, autonames)
+ bottom_names.append(layers[inp.fn].top[inp.n])
+ layer = caffe_pb2.LayerParameter()
+ layer.type = self.type_name
+ layer.bottom.extend(bottom_names)
+
+ if self.in_place:
+ layer.top.extend(layer.bottom)
+ else:
+ for top in self.tops:
+ layer.top.append(self._get_top_name(top, names, autonames))
+ layer.name = self._get_name(names, autonames)
+
+ for k, v in six.iteritems(self.params):
+ # special case to handle generic *params
+ if k.endswith('param'):
+ assign_proto(layer, k, v)
+ else:
+ try:
+ assign_proto(getattr(layer,
+ _param_names[self.type_name] + '_param'), k, v)
+ except (AttributeError, KeyError):
+ assign_proto(layer, k, v)
+
+ layers[self] = layer
+
+
+class NetSpec(object):
+ """A NetSpec contains a set of Tops (assigned directly as attributes).
+ Calling NetSpec.to_proto generates a NetParameter containing all of the
+ layers needed to produce all of the assigned Tops, using the assigned
+ names."""
+
+ def __init__(self):
+ super(NetSpec, self).__setattr__('tops', OrderedDict())
+
+ def __setattr__(self, name, value):
+ self.tops[name] = value
+
+ def __getattr__(self, name):
+ return self.tops[name]
+
+ def to_proto(self):
+ names = {v: k for k, v in six.iteritems(self.tops)}
+ autonames = Counter()
+ layers = OrderedDict()
+ for name, top in six.iteritems(self.tops):
+ top._to_proto(layers, names, autonames)
+ net = caffe_pb2.NetParameter()
+ net.layer.extend(layers.values())
+ return net
+
+
+class Layers(object):
+ """A Layers object is a pseudo-module which generates functions that specify
+ layers; e.g., Layers().Convolution(bottom, kernel_size=3) will produce a Top
+ specifying a 3x3 convolution applied to bottom."""
+
+ def __getattr__(self, name):
+ def layer_fn(*args, **kwargs):
+ fn = Function(name, args, kwargs)
+ if fn.ntop == 0:
+ return fn
+ elif fn.ntop == 1:
+ return fn.tops[0]
+ else:
+ return fn.tops
+ return layer_fn
+
+
+class Parameters(object):
+ """A Parameters object is a pseudo-module which generates constants used
+ in layer parameters; e.g., Parameters().Pooling.MAX is the value used
+ to specify max pooling."""
+
+ def __getattr__(self, name):
+ class Param:
+ def __getattr__(self, param_name):
+ return getattr(getattr(caffe_pb2, name + 'Parameter'), param_name)
+ return Param()
+
+
+_param_names = param_name_dict()
+layers = Layers()
+params = Parameters()
from collections import OrderedDict
try:
- from itertools import izip_longest
+ from itertools import izip_longest
except:
- from itertools import zip_longest as izip_longest
+ from itertools import zip_longest as izip_longest
import numpy as np
-from ._caffe import Net, SGDSolver
+from ._caffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, \
+ RMSPropSolver, AdaDeltaSolver, AdamSolver
import caffe.io
# We directly update methods from Net here (rather than using composition or
@property
+def _Net_blob_loss_weights(self):
+ """
+ An OrderedDict (bottom to top, i.e., input to output) of network
+ blob loss weights indexed by name
+ """
+ return OrderedDict(zip(self._blob_names, self._blob_loss_weights))
+
+
+@property
def _Net_params(self):
"""
An OrderedDict (bottom to top, i.e., input to output) of network
"""
Forward pass: prepare inputs and run the net forward.
- Take
- blobs: list of blobs to return in addition to output blobs.
- kwargs: Keys are input blob names and values are blob ndarrays.
- For formatting inputs for Caffe, see Net.preprocess().
- If None, input is taken from data layers.
- start: optional name of layer at which to begin the forward pass
- end: optional name of layer at which to finish the forward pass (inclusive)
-
- Give
- outs: {blob name: blob ndarray} dict.
+ Parameters
+ ----------
+ blobs : list of blobs to return in addition to output blobs.
+ kwargs : Keys are input blob names and values are blob ndarrays.
+ For formatting inputs for Caffe, see Net.preprocess().
+ If None, input is taken from data layers.
+ start : optional name of layer at which to begin the forward pass
+ end : optional name of layer at which to finish the forward pass
+ (inclusive)
+
+ Returns
+ -------
+ outs : {blob name: blob ndarray} dict.
"""
if blobs is None:
blobs = []
"""
Backward pass: prepare diffs and run the net backward.
- Take
- diffs: list of diffs to return in addition to bottom diffs.
- kwargs: Keys are output blob names and values are diff ndarrays.
+ Parameters
+ ----------
+ diffs : list of diffs to return in addition to bottom diffs.
+ kwargs : Keys are output blob names and values are diff ndarrays.
If None, top diffs are taken from forward loss.
- start: optional name of layer at which to begin the backward pass
- end: optional name of layer at which to finish the backward pass (inclusive)
+ start : optional name of layer at which to begin the backward pass
+ end : optional name of layer at which to finish the backward pass
+ (inclusive)
- Give
+ Returns
+ -------
outs: {blob name: diff ndarray} dict.
"""
if diffs is None:
# Set top diffs according to defined shapes and make arrays single and
# C-contiguous as Caffe expects.
for top, diff in kwargs.iteritems():
- if diff.ndim != 4:
- raise Exception('{} diff is not 4-d'.format(top))
if diff.shape[0] != self.blobs[top].num:
raise Exception('Diff is not batch sized')
self.blobs[top].diff[...] = diff
"""
Run net forward in batches.
- Take
- blobs: list of blobs to extract as in forward()
- kwargs: Keys are input blob names and values are blob ndarrays.
- Refer to forward().
+ Parameters
+ ----------
+ blobs : list of blobs to extract as in forward()
+ kwargs : Keys are input blob names and values are blob ndarrays.
+ Refer to forward().
- Give
- all_outs: {blob name: list of blobs} dict.
+ Returns
+ -------
+ all_outs : {blob name: list of blobs} dict.
"""
# Collect outputs from batches
all_outs = {out: [] for out in set(self.outputs + (blobs or []))}
"""
Run net forward + backward in batches.
- Take
+ Parameters
+ ----------
blobs: list of blobs to extract as in forward()
diffs: list of diffs to extract as in backward()
kwargs: Keys are input (for forward) and output (for backward) blob names
and values are ndarrays. Refer to forward() and backward().
Prefilled variants are called for lack of input or output blobs.
- Give
+ Returns
+ -------
all_blobs: {blob name: blob ndarray} dict.
all_diffs: {blob name: diff ndarray} dict.
"""
batch_blobs = self.forward(blobs=blobs, **fb)
batch_diffs = self.backward(diffs=diffs, **bb)
for out, out_blobs in batch_blobs.iteritems():
- all_outs[out].extend(out_blobs)
+ all_outs[out].extend(out_blobs.copy())
for diff, out_diffs in batch_diffs.iteritems():
- all_diffs[diff].extend(out_diffs)
+ all_diffs[diff].extend(out_diffs.copy())
# Package in ndarray.
for out, diff in zip(all_outs, all_diffs):
all_outs[out] = np.asarray(all_outs[out])
"""
Batch blob lists according to net's batch size.
- Take
+ Parameters
+ ----------
blobs: Keys blob names and values are lists of blobs (of any length).
Naturally, all the lists should have the same length.
- Give (yield)
+ Yields
+ ------
batch: {blob name: list of blobs} dict for a single batch.
"""
num = len(blobs.itervalues().next())
padding])
yield padded_batch
+
+class _Net_IdNameWrapper:
+ """
+ A simple wrapper that allows the ids propery to be accessed as a dict
+ indexed by names. Used for top and bottom names
+ """
+ def __init__(self, net, func):
+ self.net, self.func = net, func
+
+ def __getitem__(self, name):
+ # Map the layer name to id
+ ids = self.func(self.net, list(self.net._layer_names).index(name))
+ # Map the blob id to name
+ id_to_name = list(self.net.blobs)
+ return [id_to_name[i] for i in ids]
+
# Attach methods to Net.
Net.blobs = _Net_blobs
+Net.blob_loss_weights = _Net_blob_loss_weights
Net.params = _Net_params
Net.forward = _Net_forward
Net.backward = _Net_backward
Net._batch = _Net_batch
Net.inputs = _Net_inputs
Net.outputs = _Net_outputs
+Net.top_names = property(lambda n: _Net_IdNameWrapper(n, Net._top_ids))
+Net.bottom_names = property(lambda n: _Net_IdNameWrapper(n, Net._bottom_ids))
--- /dev/null
+import numpy as np
+import unittest
+
+import caffe
+
+class TestBlobProtoToArray(unittest.TestCase):
+
+ def test_old_format(self):
+ data = np.zeros((10,10))
+ blob = caffe.proto.caffe_pb2.BlobProto()
+ blob.data.extend(list(data.flatten()))
+ shape = (1,1,10,10)
+ blob.num, blob.channels, blob.height, blob.width = shape
+
+ arr = caffe.io.blobproto_to_array(blob)
+ self.assertEqual(arr.shape, shape)
+
+ def test_new_format(self):
+ data = np.zeros((10,10))
+ blob = caffe.proto.caffe_pb2.BlobProto()
+ blob.data.extend(list(data.flatten()))
+ blob.shape.dim.extend(list(data.shape))
+
+ arr = caffe.io.blobproto_to_array(blob)
+ self.assertEqual(arr.shape, data.shape)
+
+ def test_no_shape(self):
+ data = np.zeros((10,10))
+ blob = caffe.proto.caffe_pb2.BlobProto()
+ blob.data.extend(list(data.flatten()))
+
+ with self.assertRaises(ValueError):
+ caffe.io.blobproto_to_array(blob)
+
+ def test_scalar(self):
+ data = np.ones((1)) * 123
+ blob = caffe.proto.caffe_pb2.BlobProto()
+ blob.data.extend(list(data.flatten()))
+
+ arr = caffe.io.blobproto_to_array(blob)
+ self.assertEqual(arr, 123)
--- /dev/null
+import unittest
+
+import caffe
+
+class TestLayerTypeList(unittest.TestCase):
+
+ def test_standard_types(self):
+ #removing 'Data' from list
+ for type_name in ['Data', 'Convolution', 'InnerProduct']:
+ self.assertIn(type_name, caffe.layer_type_list(),
+ '%s not in layer_type_list()' % type_name)
import tempfile
import os
import numpy as np
+import six
import caffe
+
def simple_net_file(num_output):
"""Make a simple net prototxt, based on test_net.cpp, returning the name
of the (temporary) file."""
- f = tempfile.NamedTemporaryFile(delete=False)
+ f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
f.write("""name: 'testnet' force_backward: true
layer { type: 'DummyData' name: 'data' top: 'data' top: 'label'
dummy_data_param { num: 5 channels: 2 height: 3 width: 4
f.close()
return f.name
+
class TestNet(unittest.TestCase):
def setUp(self):
self.num_output = 13
def test_memory(self):
"""Check that holding onto blob data beyond the life of a Net is OK"""
- params = sum(map(list, self.net.params.itervalues()), [])
+ params = sum(map(list, six.itervalues(self.net.params)), [])
blobs = self.net.blobs.values()
del self.net
self.assertEqual(self.net.outputs, ['loss'])
def test_save_and_read(self):
- f = tempfile.NamedTemporaryFile(delete=False)
+ f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
f.close()
self.net.save(f.name)
net_file = simple_net_file(self.num_output)
--- /dev/null
+import unittest
+import tempfile
+import caffe
+from caffe import layers as L
+from caffe import params as P
+
+def lenet(batch_size):
+ n = caffe.NetSpec()
+ n.data, n.label = L.DummyData(shape=[dict(dim=[batch_size, 1, 28, 28]),
+ dict(dim=[batch_size, 1, 1, 1])],
+ transform_param=dict(scale=1./255), ntop=2)
+ n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20,
+ weight_filler=dict(type='xavier'))
+ n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)
+ n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50,
+ weight_filler=dict(type='xavier'))
+ n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)
+ n.ip1 = L.InnerProduct(n.pool2, num_output=500,
+ weight_filler=dict(type='xavier'))
+ n.relu1 = L.ReLU(n.ip1, in_place=True)
+ n.ip2 = L.InnerProduct(n.relu1, num_output=10,
+ weight_filler=dict(type='xavier'))
+ n.loss = L.SoftmaxWithLoss(n.ip2, n.label)
+ return n.to_proto()
+
+def anon_lenet(batch_size):
+ data, label = L.DummyData(shape=[dict(dim=[batch_size, 1, 28, 28]),
+ dict(dim=[batch_size, 1, 1, 1])],
+ transform_param=dict(scale=1./255), ntop=2)
+ conv1 = L.Convolution(data, kernel_size=5, num_output=20,
+ weight_filler=dict(type='xavier'))
+ pool1 = L.Pooling(conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)
+ conv2 = L.Convolution(pool1, kernel_size=5, num_output=50,
+ weight_filler=dict(type='xavier'))
+ pool2 = L.Pooling(conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)
+ ip1 = L.InnerProduct(pool2, num_output=500,
+ weight_filler=dict(type='xavier'))
+ relu1 = L.ReLU(ip1, in_place=True)
+ ip2 = L.InnerProduct(relu1, num_output=10,
+ weight_filler=dict(type='xavier'))
+ loss = L.SoftmaxWithLoss(ip2, label)
+ return loss.to_proto()
+
+def silent_net():
+ n = caffe.NetSpec()
+ n.data, n.data2 = L.DummyData(shape=dict(dim=3), ntop=2)
+ n.silence_data = L.Silence(n.data, ntop=0)
+ n.silence_data2 = L.Silence(n.data2, ntop=0)
+ return n.to_proto()
+
+class TestNetSpec(unittest.TestCase):
+ def load_net(self, net_proto):
+ f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
+ f.write(str(net_proto))
+ f.close()
+ return caffe.Net(f.name, caffe.TEST)
+
+ def test_lenet(self):
+ """Construct and build the Caffe version of LeNet."""
+
+ net_proto = lenet(50)
+ # check that relu is in-place
+ self.assertEqual(net_proto.layer[6].bottom,
+ net_proto.layer[6].top)
+ net = self.load_net(net_proto)
+ # check that all layers are present
+ self.assertEqual(len(net.layers), 9)
+
+ # now the check the version with automatically-generated layer names
+ net_proto = anon_lenet(50)
+ self.assertEqual(net_proto.layer[6].bottom,
+ net_proto.layer[6].top)
+ net = self.load_net(net_proto)
+ self.assertEqual(len(net.layers), 9)
+
+ def test_zero_tops(self):
+ """Test net construction for top-less layers."""
+
+ net_proto = silent_net()
+ net = self.load_net(net_proto)
+ self.assertEqual(len(net.forward()), 0)
import unittest
import tempfile
import os
+import six
import caffe
+
class SimpleLayer(caffe.Layer):
"""A layer that just multiplies by ten"""
def backward(self, top, propagate_down, bottom):
bottom[0].diff[...] = 10 * top[0].diff
+
+class ExceptionLayer(caffe.Layer):
+ """A layer for checking exceptions from Python"""
+
+ def setup(self, bottom, top):
+ raise RuntimeError
+
+class ParameterLayer(caffe.Layer):
+ """A layer that just multiplies by ten"""
+
+ def setup(self, bottom, top):
+ self.blobs.add_blob(1)
+ self.blobs[0].data[0] = 0
+
+ def reshape(self, bottom, top):
+ top[0].reshape(*bottom[0].data.shape)
+
+ def forward(self, bottom, top):
+ pass
+
+ def backward(self, top, propagate_down, bottom):
+ self.blobs[0].diff[0] = 1
+
def python_net_file():
- with tempfile.NamedTemporaryFile(delete=False) as f:
+ with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
f.write("""name: 'pythonnet' force_backward: true
input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
layer { type: 'Python' name: 'one' bottom: 'data' top: 'one'
python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }""")
return f.name
+
+def exception_net_file():
+ with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
+ f.write("""name: 'pythonnet' force_backward: true
+ input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
+ layer { type: 'Python' name: 'layer' bottom: 'data' top: 'top'
+ python_param { module: 'test_python_layer' layer: 'ExceptionLayer' } }
+ """)
+ return f.name
+
+
+def parameter_net_file():
+ with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
+ f.write("""name: 'pythonnet' force_backward: true
+ input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
+ layer { type: 'Python' name: 'layer' bottom: 'data' top: 'top'
+ python_param { module: 'test_python_layer' layer: 'ParameterLayer' } }
+ """)
+ return f.name
+
+
+@unittest.skipIf('Python' not in caffe.layer_type_list(),
+ 'Caffe built without Python layer support')
class TestPythonLayer(unittest.TestCase):
def setUp(self):
net_file = python_net_file()
s = 4
self.net.blobs['data'].reshape(s, s, s, s)
self.net.forward()
- for blob in self.net.blobs.itervalues():
+ for blob in six.itervalues(self.net.blobs):
for d in blob.data.shape:
self.assertEqual(s, d)
+
+ def test_exception(self):
+ net_file = exception_net_file()
+ self.assertRaises(RuntimeError, caffe.Net, net_file, caffe.TEST)
+ os.remove(net_file)
+
+ def test_parameter(self):
+ net_file = parameter_net_file()
+ net = caffe.Net(net_file, caffe.TRAIN)
+ # Test forward and backward
+ net.forward()
+ net.backward()
+ layer = net.layers[list(net._layer_names).index('layer')]
+ self.assertEqual(layer.blobs[0].data[0], 0)
+ self.assertEqual(layer.blobs[0].diff[0], 1)
+ layer.blobs[0].data[0] += layer.blobs[0].diff[0]
+ self.assertEqual(layer.blobs[0].data[0], 1)
+
+ # Test saving and loading
+ h, caffemodel_file = tempfile.mkstemp()
+ net.save(caffemodel_file)
+ layer.blobs[0].data[0] = -1
+ self.assertEqual(layer.blobs[0].data[0], -1)
+ net.copy_from(caffemodel_file)
+ self.assertEqual(layer.blobs[0].data[0], 1)
+ os.remove(caffemodel_file)
+
+ # Test weight sharing
+ net2 = caffe.Net(net_file, caffe.TRAIN)
+ net2.share_with(net)
+ layer = net.layers[list(net2._layer_names).index('layer')]
+ self.assertEqual(layer.blobs[0].data[0], 1)
+
+ os.remove(net_file)
--- /dev/null
+import unittest
+import tempfile
+import os
+import six
+
+import caffe
+
+
+class SimpleParamLayer(caffe.Layer):
+ """A layer that just multiplies by the numeric value of its param string"""
+
+ def setup(self, bottom, top):
+ try:
+ self.value = float(self.param_str)
+ except ValueError:
+ raise ValueError("Parameter string must be a legible float")
+
+ def reshape(self, bottom, top):
+ top[0].reshape(*bottom[0].data.shape)
+
+ def forward(self, bottom, top):
+ top[0].data[...] = self.value * bottom[0].data
+
+ def backward(self, top, propagate_down, bottom):
+ bottom[0].diff[...] = self.value * top[0].diff
+
+
+def python_param_net_file():
+ with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
+ f.write("""name: 'pythonnet' force_backward: true
+ input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
+ layer { type: 'Python' name: 'mul10' bottom: 'data' top: 'mul10'
+ python_param { module: 'test_python_layer_with_param_str'
+ layer: 'SimpleParamLayer' param_str: '10' } }
+ layer { type: 'Python' name: 'mul2' bottom: 'mul10' top: 'mul2'
+ python_param { module: 'test_python_layer_with_param_str'
+ layer: 'SimpleParamLayer' param_str: '2' } }""")
+ return f.name
+
+
+@unittest.skipIf('Python' not in caffe.layer_type_list(),
+ 'Caffe built without Python layer support')
+class TestLayerWithParam(unittest.TestCase):
+ def setUp(self):
+ net_file = python_param_net_file()
+ self.net = caffe.Net(net_file, caffe.TRAIN)
+ os.remove(net_file)
+
+ def test_forward(self):
+ x = 8
+ self.net.blobs['data'].data[...] = x
+ self.net.forward()
+ for y in self.net.blobs['mul2'].data.flat:
+ self.assertEqual(y, 2 * 10 * x)
+
+ def test_backward(self):
+ x = 7
+ self.net.blobs['mul2'].diff[...] = x
+ self.net.backward()
+ for y in self.net.blobs['data'].diff.flat:
+ self.assertEqual(y, 2 * 10 * x)
import tempfile
import os
import numpy as np
+import six
import caffe
from test_net import simple_net_file
+
class TestSolver(unittest.TestCase):
def setUp(self):
self.num_output = 13
net_f = simple_net_file(self.num_output)
- f = tempfile.NamedTemporaryFile(delete=False)
+ f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
f.write("""net: '""" + net_f + """'
test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9
weight_decay: 0.0005 lr_policy: 'inv' gamma: 0.0001 power: 0.75
- display: 100 max_iter: 100 snapshot_after_train: false""")
+ display: 100 max_iter: 100 snapshot_after_train: false
+ snapshot_prefix: "model" """)
f.close()
self.solver = caffe.SGDSolver(f.name)
# also make sure get_solver runs
total = 0
for net in nets:
- for ps in net.params.itervalues():
+ for ps in six.itervalues(net.params):
for p in ps:
total += p.data.sum() + p.diff.sum()
- for bl in net.blobs.itervalues():
+ for bl in six.itervalues(net.blobs):
total += bl.data.sum() + bl.diff.sum()
+
+ def test_snapshot(self):
+ self.solver.snapshot()
+ # Check that these files exist and then remove them
+ files = ['model_iter_0.caffemodel', 'model_iter_0.solverstate']
+ for fn in files:
+ assert os.path.isfile(fn)
+ os.remove(fn)
parser.add_argument(
"--model_def",
default=os.path.join(pycaffe_dir,
- "../models/bvlc_reference_caffenet/deploy.prototxt.prototxt"),
+ "../models/bvlc_reference_caffenet/deploy.prototxt"),
help="Model definition file."
)
parser.add_argument(
"""
Draw a graph of the net architecture.
"""
-import argparse
+from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
from google.protobuf import text_format
import caffe
"""Parse input arguments
"""
- parser = argparse.ArgumentParser(description='Draw a network graph')
+ parser = ArgumentParser(description=__doc__,
+ formatter_class=ArgumentDefaultsHelpFormatter)
parser.add_argument('input_net_proto_file',
help='Input network prototxt file')
help='Output image file')
parser.add_argument('--rankdir',
help=('One of TB (top-bottom, i.e., vertical), '
- 'RL (right-left, i.e., horizontal), or another'
- 'valid dot option; see'
- 'http://www.graphviz.org/doc/info/attrs.html#k:rankdir'
- '(default: LR)'),
+ 'RL (right-left, i.e., horizontal), or another '
+ 'valid dot option; see '
+ 'http://www.graphviz.org/doc/info/'
+ 'attrs.html#k:rankdir'),
default='LR')
args = parser.parse_args()
numpy>=1.7.1
scipy>=0.13.2
scikit-image>=0.9.3
-scikit-learn>=0.14.1
matplotlib>=1.3.1
-ipython>=1.1.0
+ipython>=3.0.0
h5py>=2.2.0
leveldb>=0.191
networkx>=1.8.1
protobuf>=2.5.0
python-gflags>=2.0
pyyaml>=3.10
-Pillow>=2.7.0
+Pillow>=2.3.0
+six>=1.1.0
\ No newline at end of file
if count == 0:
start_time = time.time()
return
- duration = time.time() - start_time
+ duration = (time.time() - start_time) or 0.01
progress_size = int(count * block_size)
speed = int(progress_size / (1024 * duration))
percent = int(count * block_size * 100 / total_size)
with open(readme_filename) as f:
lines = [line.strip() for line in f.readlines()]
top = lines.index('---')
- bottom = lines[top + 1:].index('---')
+ bottom = lines.index('---', top + 1)
frontmatter = yaml.load('\n'.join(lines[top + 1:bottom]))
assert all(key in frontmatter for key in required_keys)
return dirname, frontmatter
echo "Downloading Caffe model info to $MODEL_DIR ..."
mkdir -p $MODEL_DIR
-wget https://gist.github.com/$GIST/download -O $MODEL_DIR/gist.tar.gz
-tar xzf $MODEL_DIR/gist.tar.gz --directory=$MODEL_DIR --strip-components=1
-rm $MODEL_DIR/gist.tar.gz
+wget https://gist.github.com/$GIST/download -O $MODEL_DIR/gist.zip
+unzip -j $MODEL_DIR/gist.zip -d $MODEL_DIR
+rm $MODEL_DIR/gist.zip
echo "Done"
#!/bin/bash
-# Script called by Travis to do a CPU-only build of and test Caffe.
+# Script called by Travis to build and test Caffe.
+# Travis CI tests are CPU-only for lack of compatible hardware.
set -e
MAKE="make --jobs=$NUM_THREADS --keep-going"
if $WITH_CMAKE; then
mkdir build
cd build
- cmake -DBUILD_python=ON -DCMAKE_BUILD_TYPE=Release -DCPU_ONLY=ON ..
+ CPU_ONLY=" -DCPU_ONLY=ON"
+ if ! $WITH_CUDA; then
+ CPU_ONLY=" -DCPU_ONLY=OFF"
+ fi
+ PYTHON_ARGS=""
+ if [ "$PYTHON_VERSION" = "3" ]; then
+ PYTHON_ARGS="$PYTHON_ARGS -Dpython_version=3 -DBOOST_LIBRARYDIR=$CONDA_DIR/lib/"
+ fi
+ if $WITH_IO; then
+ IO_ARGS="-DUSE_OPENCV=ON -DUSE_LMDB=ON -DUSE_LEVELDB=ON"
+ else
+ IO_ARGS="-DUSE_OPENCV=OFF -DUSE_LMDB=OFF -DUSE_LEVELDB=OFF"
+ fi
+ cmake -DBUILD_python=ON -DCMAKE_BUILD_TYPE=Release $CPU_ONLY $PYTHON_ARGS -DCMAKE_INCLUDE_PATH="$CONDA_DIR/include/" -DCMAKE_LIBRARY_PATH="$CONDA_DIR/lib/" $IO_ARGS ..
$MAKE
+ $MAKE pytest
if ! $WITH_CUDA; then
$MAKE runtest
$MAKE lint
if ! $WITH_CUDA; then
export CPU_ONLY=1
fi
+ if $WITH_IO; then
+ export USE_LMDB=1
+ export USE_LEVELDB=1
+ export USE_OPENCV=1
+ fi
$MAKE all test pycaffe warn lint || true
if ! $WITH_CUDA; then
$MAKE runtest
set -e
MAKE="make --jobs=$NUM_THREADS"
-
# Install apt packages where the Ubuntu 12.04 default and ppa works for Caffe
# This ppa is for gflags and glog
apt-get -y update
apt-get install \
wget git curl \
- python-dev python-numpy \
+ python-dev python-numpy python3-dev\
libleveldb-dev libsnappy-dev libopencv-dev \
- libboost-dev libboost-system-dev libboost-python-dev libboost-thread-dev \
libprotobuf-dev protobuf-compiler \
libatlas-dev libatlas-base-dev \
libhdf5-serial-dev libgflags-dev libgoogle-glog-dev \
# if needed. By default, Aptitude in Ubuntu 12.04 installs CMake 2.8.7, but
# Caffe requires a minimum CMake version of 2.8.8.
if $WITH_CMAKE; then
- add-apt-repository -y ppa:ubuntu-sdk-team/ppa
- apt-get -y update
- apt-get -y install cmake
+ # cmake 3 will make sure that the python interpreter and libraries match
+ wget --no-check-certificate http://www.cmake.org/files/v3.2/cmake-3.2.3-Linux-x86_64.sh -O cmake3.sh
+ chmod +x cmake3.sh
+ ./cmake3.sh --prefix=/usr/ --skip-license --exclude-subdir
fi
# Install CUDA, if needed
fi
# Install LMDB
-LMDB_URL=ftp://ftp.openldap.org/pub/OpenLDAP/openldap-release/openldap-2.4.39.tgz
-LMDB_FILE=/tmp/openldap.tgz
+LMDB_URL=https://github.com/LMDB/lmdb/archive/LMDB_0.9.14.tar.gz
+LMDB_FILE=/tmp/lmdb.tar.gz
pushd .
-curl $LMDB_URL -o $LMDB_FILE
+wget $LMDB_URL -O $LMDB_FILE
tar -C /tmp -xzvf $LMDB_FILE
-cd /tmp/openldap*/libraries/liblmdb/
+cd /tmp/lmdb*/libraries/liblmdb/
$MAKE
$MAKE install
popd
# Install the Python runtime dependencies via miniconda (this is much faster
# than using pip for everything).
-wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
-chmod +x miniconda.sh
-./miniconda.sh -b
-export PATH=/home/travis/miniconda/bin:$PATH
-conda update --yes conda
-conda install --yes numpy scipy matplotlib scikit-image pip
-pip install protobuf
-rm /home/travis/miniconda/lib/libm.*
+export PATH=$CONDA_DIR/bin:$PATH
+if [ ! -d $CONDA_DIR ]; then
+ if [ "$PYTHON_VERSION" -eq "3" ]; then
+ wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
+ else
+ wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
+ fi
+ chmod +x miniconda.sh
+ ./miniconda.sh -b -p $CONDA_DIR
+
+ conda update --yes conda
+ # The version of boost we're using for Python 3 depends on 3.4 for now.
+ if [ "$PYTHON_VERSION" -eq "3" ]; then
+ conda install --yes python=3.4
+ fi
+ conda install --yes numpy scipy matplotlib scikit-image pip
+ # Let conda install boost (so that boost_python matches)
+ conda install --yes -c https://conda.binstar.org/menpo boost=1.56.0
+fi
+
+# install protobuf 3 (just use the miniconda3 directory to avoid having to setup the path again)
+if [ "$PYTHON_VERSION" -eq "3" ] && [ ! -e "$CONDA_DIR/bin/protoc" ]; then
+ pushd .
+ wget https://github.com/google/protobuf/archive/v3.0.0-alpha-3.1.tar.gz -O protobuf-3.tar.gz
+ tar -C /tmp -xzvf protobuf-3.tar.gz
+ cd /tmp/protobuf-3*/
+ ./autogen.sh
+ ./configure --prefix=$CONDA_DIR
+ $MAKE
+ $MAKE install
+ popd
+fi
+
+if [ "$PYTHON_VERSION" -eq "3" ]; then
+ pip install --pre protobuf
+else
+ pip install protobuf
+fi
echo "CUDA_ARCH := $GENCODE" >> Makefile.config
fi
+# Remove IO library settings from Makefile.config
+# to avoid conflicts with CI configuration
+sed -i -e '/USE_LMDB/d' Makefile.config
+sed -i -e '/USE_LEVELDB/d' Makefile.config
+sed -i -e '/USE_OPENCV/d' Makefile.config
+
cat << 'EOF' >> Makefile.config
-ANACONDA_HOME := $(HOME)/miniconda
+# Travis' nvcc doesn't like newer boost versions
+NVCCFLAGS := -Xcudafe --diag_suppress=cc_clobber_ignored -Xcudafe --diag_suppress=useless_using_declaration -Xcudafe --diag_suppress=set_but_not_used
+ANACONDA_HOME := $(CONDA_DIR)
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
$(ANACONDA_HOME)/include/python2.7 \
$(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include
add_library(caffe ${srcs})
target_link_libraries(caffe proto ${Caffe_LINKER_LIBS})
caffe_default_properties(caffe)
+set_target_properties(caffe PROPERTIES
+ VERSION ${CAFFE_TARGET_VERSION}
+ SOVERSION ${CAFFE_TARGET_SOVERSION}
+ )
# ---[ Tests
add_subdirectory(test)
CHECK_LE(shape.size(), kMaxBlobAxes);
count_ = 1;
shape_.resize(shape.size());
+ if (!shape_data_ || shape_data_->size() < shape.size() * sizeof(int)) {
+ shape_data_.reset(new SyncedMemory(shape.size() * sizeof(int)));
+ }
+ int* shape_data = static_cast<int*>(shape_data_->mutable_cpu_data());
for (int i = 0; i < shape.size(); ++i) {
CHECK_GE(shape[i], 0);
+ CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX";
count_ *= shape[i];
shape_[i] = shape[i];
+ shape_data[i] = shape[i];
}
if (count_ > capacity_) {
capacity_ = count_;
}
template <typename Dtype>
+const int* Blob<Dtype>::gpu_shape() const {
+ CHECK(shape_data_);
+ return (const int*)shape_data_->gpu_data();
+}
+
+template <typename Dtype>
const Dtype* Blob<Dtype>::cpu_data() const {
CHECK(data_);
return (const Dtype*)data_->cpu_data();
}
// copy data
Dtype* data_vec = mutable_cpu_data();
- for (int i = 0; i < count_; ++i) {
- data_vec[i] = proto.data(i);
+ if (proto.double_data_size() > 0) {
+ CHECK_EQ(count_, proto.double_data_size());
+ for (int i = 0; i < count_; ++i) {
+ data_vec[i] = proto.double_data(i);
+ }
+ } else {
+ CHECK_EQ(count_, proto.data_size());
+ for (int i = 0; i < count_; ++i) {
+ data_vec[i] = proto.data(i);
+ }
}
- if (proto.diff_size() > 0) {
+ if (proto.double_diff_size() > 0) {
+ CHECK_EQ(count_, proto.double_diff_size());
+ Dtype* diff_vec = mutable_cpu_diff();
+ for (int i = 0; i < count_; ++i) {
+ diff_vec[i] = proto.double_diff(i);
+ }
+ } else if (proto.diff_size() > 0) {
+ CHECK_EQ(count_, proto.diff_size());
Dtype* diff_vec = mutable_cpu_diff();
for (int i = 0; i < count_; ++i) {
diff_vec[i] = proto.diff(i);
}
}
-template <typename Dtype>
-void Blob<Dtype>::ToProto(BlobProto* proto, bool write_diff) const {
+template <>
+void Blob<double>::ToProto(BlobProto* proto, bool write_diff) const {
+ proto->clear_shape();
+ for (int i = 0; i < shape_.size(); ++i) {
+ proto->mutable_shape()->add_dim(shape_[i]);
+ }
+ proto->clear_double_data();
+ proto->clear_double_diff();
+ const double* data_vec = cpu_data();
+ for (int i = 0; i < count_; ++i) {
+ proto->add_double_data(data_vec[i]);
+ }
+ if (write_diff) {
+ const double* diff_vec = cpu_diff();
+ for (int i = 0; i < count_; ++i) {
+ proto->add_double_diff(diff_vec[i]);
+ }
+ }
+}
+
+template <>
+void Blob<float>::ToProto(BlobProto* proto, bool write_diff) const {
proto->clear_shape();
for (int i = 0; i < shape_.size(); ++i) {
proto->mutable_shape()->add_dim(shape_[i]);
}
proto->clear_data();
proto->clear_diff();
- const Dtype* data_vec = cpu_data();
+ const float* data_vec = cpu_data();
for (int i = 0; i < count_; ++i) {
proto->add_data(data_vec[i]);
}
if (write_diff) {
- const Dtype* diff_vec = cpu_diff();
+ const float* diff_vec = cpu_diff();
for (int i = 0; i < count_; ++i) {
proto->add_diff(diff_vec[i]);
}
+#include <boost/thread.hpp>
#include <glog/logging.h>
+#include <cmath>
#include <cstdio>
#include <ctime>
namespace caffe {
-shared_ptr<Caffe> Caffe::singleton_;
+// Make sure each thread can have different values.
+static boost::thread_specific_ptr<Caffe> thread_instance_;
+
+Caffe& Caffe::Get() {
+ if (!thread_instance_.get()) {
+ thread_instance_.reset(new Caffe());
+ }
+ return *(thread_instance_.get());
+}
// random seeding
int64_t cluster_seedgen(void) {
pid = getpid();
s = time(NULL);
- seed = abs(((s * 181) * ((pid - 83) * 359)) % 104729);
+ seed = std::abs(((s * 181) * ((pid - 83) * 359)) % 104729);
return seed;
}
#ifdef CPU_ONLY // CPU-only Caffe.
Caffe::Caffe()
- : random_generator_(), mode_(Caffe::CPU) { }
+ : random_generator_(), mode_(Caffe::CPU),
+ solver_count_(1), root_solver_(true) { }
Caffe::~Caffe() { }
Caffe::Caffe()
: cublas_handle_(NULL), curand_generator_(NULL), random_generator_(),
- mode_(Caffe::CPU) {
+ mode_(Caffe::CPU), solver_count_(1), root_solver_(true) {
// Try to create a cublas handler, and report an error if failed (but we will
// keep the program running as one might just want to run CPU code).
if (cublasCreate(&cublas_handle_) != CUBLAS_STATUS_SUCCESS) {
--- /dev/null
+#include <boost/thread.hpp>
+#include <map>
+#include <string>
+#include <vector>
+
+#include "caffe/common.hpp"
+#include "caffe/data_reader.hpp"
+#include "caffe/layers/data_layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+using boost::weak_ptr;
+
+map<const string, weak_ptr<DataReader::Body> > DataReader::bodies_;
+static boost::mutex bodies_mutex_;
+
+DataReader::DataReader(const LayerParameter& param)
+ : queue_pair_(new QueuePair( //
+ param.data_param().prefetch() * param.data_param().batch_size())) {
+ // Get or create a body
+ boost::mutex::scoped_lock lock(bodies_mutex_);
+ string key = source_key(param);
+ weak_ptr<Body>& weak = bodies_[key];
+ body_ = weak.lock();
+ if (!body_) {
+ body_.reset(new Body(param));
+ bodies_[key] = weak_ptr<Body>(body_);
+ }
+ body_->new_queue_pairs_.push(queue_pair_);
+}
+
+DataReader::~DataReader() {
+ string key = source_key(body_->param_);
+ body_.reset();
+ boost::mutex::scoped_lock lock(bodies_mutex_);
+ if (bodies_[key].expired()) {
+ bodies_.erase(key);
+ }
+}
+
+//
+
+DataReader::QueuePair::QueuePair(int size) {
+ // Initialize the free queue with requested number of datums
+ for (int i = 0; i < size; ++i) {
+ free_.push(new Datum());
+ }
+}
+
+DataReader::QueuePair::~QueuePair() {
+ Datum* datum;
+ while (free_.try_pop(&datum)) {
+ delete datum;
+ }
+ while (full_.try_pop(&datum)) {
+ delete datum;
+ }
+}
+
+//
+
+DataReader::Body::Body(const LayerParameter& param)
+ : param_(param),
+ new_queue_pairs_() {
+ StartInternalThread();
+}
+
+DataReader::Body::~Body() {
+ StopInternalThread();
+}
+
+void DataReader::Body::InternalThreadEntry() {
+ shared_ptr<db::DB> db(db::GetDB(param_.data_param().backend()));
+ db->Open(param_.data_param().source(), db::READ);
+ shared_ptr<db::Cursor> cursor(db->NewCursor());
+ vector<shared_ptr<QueuePair> > qps;
+ try {
+ int solver_count = param_.phase() == TRAIN ? Caffe::solver_count() : 1;
+
+ // To ensure deterministic runs, only start running once all solvers
+ // are ready. But solvers need to peek on one item during initialization,
+ // so read one item, then wait for the next solver.
+ for (int i = 0; i < solver_count; ++i) {
+ shared_ptr<QueuePair> qp(new_queue_pairs_.pop());
+ read_one(cursor.get(), qp.get());
+ qps.push_back(qp);
+ }
+ // Main loop
+ while (!must_stop()) {
+ for (int i = 0; i < solver_count; ++i) {
+ read_one(cursor.get(), qps[i].get());
+ }
+ // Check no additional readers have been created. This can happen if
+ // more than one net is trained at a time per process, whether single
+ // or multi solver. It might also happen if two data layers have same
+ // name and same source.
+ CHECK_EQ(new_queue_pairs_.size(), 0);
+ }
+ } catch (boost::thread_interrupted&) {
+ // Interrupted exception is expected on shutdown
+ }
+}
+
+void DataReader::Body::read_one(db::Cursor* cursor, QueuePair* qp) {
+ Datum* datum = qp->free_.pop();
+ // TODO deserialize in-place instead of copy?
+ datum->ParseFromString(cursor->value());
+ qp->full_.push(datum);
+
+ // go to the next iter
+ cursor->Next();
+ if (!cursor->valid()) {
+ DLOG(INFO) << "Restarting data prefetching from start.";
+ cursor->SeekToFirst();
+ }
+}
+
+} // namespace caffe
+#ifdef USE_OPENCV
#include <opencv2/core/core.hpp>
+#endif // USE_OPENCV
#include <string>
#include <vector>
CHECK_EQ(param_.mean_value_size(), 0) <<
"Cannot specify mean_file and mean_value at the same time";
const string& mean_file = param.mean_file();
- LOG(INFO) << "Loading mean file from: " << mean_file;
+ if (Caffe::root_solver()) {
+ LOG(INFO) << "Loading mean file from: " << mean_file;
+ }
BlobProto blob_proto;
ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
data_mean_.FromProto(blob_proto);
}
}
+
template<typename Dtype>
void DataTransformer<Dtype>::Transform(const Datum& datum,
Blob<Dtype>* transformed_blob) {
+ // If datum is encoded, decoded and transform the cv::image.
+ if (datum.encoded()) {
+#ifdef USE_OPENCV
+ CHECK(!(param_.force_color() && param_.force_gray()))
+ << "cannot set both force_color and force_gray";
+ cv::Mat cv_img;
+ if (param_.force_color() || param_.force_gray()) {
+ // If force_color then decode in color otherwise decode in gray.
+ cv_img = DecodeDatumToCVMat(datum, param_.force_color());
+ } else {
+ cv_img = DecodeDatumToCVMatNative(datum);
+ }
+ // Transform the cv::image into blob.
+ return Transform(cv_img, transformed_blob);
+#else
+ LOG(FATAL) << "Encoded datum requires OpenCV; compile with USE_OPENCV.";
+#endif // USE_OPENCV
+ } else {
+ if (param_.force_color() || param_.force_gray()) {
+ LOG(ERROR) << "force_color and force_gray only for encoded datum";
+ }
+ }
+
+ const int crop_size = param_.crop_size();
const int datum_channels = datum.channels();
const int datum_height = datum.height();
const int datum_width = datum.width();
+ // Check dimensions.
const int channels = transformed_blob->channels();
const int height = transformed_blob->height();
const int width = transformed_blob->width();
CHECK_LE(width, datum_width);
CHECK_GE(num, 1);
- const int crop_size = param_.crop_size();
-
if (crop_size) {
CHECK_EQ(crop_size, height);
CHECK_EQ(crop_size, width);
}
}
+#ifdef USE_OPENCV
template<typename Dtype>
void DataTransformer<Dtype>::Transform(const vector<cv::Mat> & mat_vector,
Blob<Dtype>* transformed_blob) {
template<typename Dtype>
void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
Blob<Dtype>* transformed_blob) {
+ const int crop_size = param_.crop_size();
const int img_channels = cv_img.channels();
const int img_height = cv_img.rows;
const int img_width = cv_img.cols;
+ // Check dimensions.
const int channels = transformed_blob->channels();
const int height = transformed_blob->height();
const int width = transformed_blob->width();
CHECK(cv_img.depth() == CV_8U) << "Image data type must be unsigned byte";
- const int crop_size = param_.crop_size();
const Dtype scale = param_.scale();
const bool do_mirror = param_.mirror() && Rand(2);
const bool has_mean_file = param_.has_mean_file();
}
}
}
+#endif // USE_OPENCV
template<typename Dtype>
void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
Blob<Dtype>* transformed_blob) {
+ const int crop_size = param_.crop_size();
const int input_num = input_blob->num();
const int input_channels = input_blob->channels();
const int input_height = input_blob->height();
const int input_width = input_blob->width();
+ if (transformed_blob->count() == 0) {
+ // Initialize transformed_blob with the right shape.
+ if (crop_size) {
+ transformed_blob->Reshape(input_num, input_channels,
+ crop_size, crop_size);
+ } else {
+ transformed_blob->Reshape(input_num, input_channels,
+ input_height, input_width);
+ }
+ }
+
const int num = transformed_blob->num();
const int channels = transformed_blob->channels();
const int height = transformed_blob->height();
CHECK_GE(input_height, height);
CHECK_GE(input_width, width);
- const int crop_size = param_.crop_size();
+
const Dtype scale = param_.scale();
const bool do_mirror = param_.mirror() && Rand(2);
const bool has_mean_file = param_.has_mean_file();
}
}
+template<typename Dtype>
+vector<int> DataTransformer<Dtype>::InferBlobShape(const Datum& datum) {
+ if (datum.encoded()) {
+#ifdef USE_OPENCV
+ CHECK(!(param_.force_color() && param_.force_gray()))
+ << "cannot set both force_color and force_gray";
+ cv::Mat cv_img;
+ if (param_.force_color() || param_.force_gray()) {
+ // If force_color then decode in color otherwise decode in gray.
+ cv_img = DecodeDatumToCVMat(datum, param_.force_color());
+ } else {
+ cv_img = DecodeDatumToCVMatNative(datum);
+ }
+ // InferBlobShape using the cv::image.
+ return InferBlobShape(cv_img);
+#else
+ LOG(FATAL) << "Encoded datum requires OpenCV; compile with USE_OPENCV.";
+#endif // USE_OPENCV
+ }
+ const int crop_size = param_.crop_size();
+ const int datum_channels = datum.channels();
+ const int datum_height = datum.height();
+ const int datum_width = datum.width();
+ // Check dimensions.
+ CHECK_GT(datum_channels, 0);
+ CHECK_GE(datum_height, crop_size);
+ CHECK_GE(datum_width, crop_size);
+ // Build BlobShape.
+ vector<int> shape(4);
+ shape[0] = 1;
+ shape[1] = datum_channels;
+ shape[2] = (crop_size)? crop_size: datum_height;
+ shape[3] = (crop_size)? crop_size: datum_width;
+ return shape;
+}
+
+template<typename Dtype>
+vector<int> DataTransformer<Dtype>::InferBlobShape(
+ const vector<Datum> & datum_vector) {
+ const int num = datum_vector.size();
+ CHECK_GT(num, 0) << "There is no datum to in the vector";
+ // Use first datum in the vector to InferBlobShape.
+ vector<int> shape = InferBlobShape(datum_vector[0]);
+ // Adjust num to the size of the vector.
+ shape[0] = num;
+ return shape;
+}
+
+#ifdef USE_OPENCV
+template<typename Dtype>
+vector<int> DataTransformer<Dtype>::InferBlobShape(const cv::Mat& cv_img) {
+ const int crop_size = param_.crop_size();
+ const int img_channels = cv_img.channels();
+ const int img_height = cv_img.rows;
+ const int img_width = cv_img.cols;
+ // Check dimensions.
+ CHECK_GT(img_channels, 0);
+ CHECK_GE(img_height, crop_size);
+ CHECK_GE(img_width, crop_size);
+ // Build BlobShape.
+ vector<int> shape(4);
+ shape[0] = 1;
+ shape[1] = img_channels;
+ shape[2] = (crop_size)? crop_size: img_height;
+ shape[3] = (crop_size)? crop_size: img_width;
+ return shape;
+}
+
+template<typename Dtype>
+vector<int> DataTransformer<Dtype>::InferBlobShape(
+ const vector<cv::Mat> & mat_vector) {
+ const int num = mat_vector.size();
+ CHECK_GT(num, 0) << "There is no cv_img to in the vector";
+ // Use first cv_img in the vector to InferBlobShape.
+ vector<int> shape = InferBlobShape(mat_vector[0]);
+ // Adjust num to the size of the vector.
+ shape[0] = num;
+ return shape;
+}
+#endif // USE_OPENCV
+
template <typename Dtype>
void DataTransformer<Dtype>::InitRand() {
const bool needs_rand = param_.mirror() ||
#include <boost/thread.hpp>
+#include <exception>
+
#include "caffe/internal_thread.hpp"
+#include "caffe/util/math_functions.hpp"
namespace caffe {
InternalThread::~InternalThread() {
- WaitForInternalThreadToExit();
+ StopInternalThread();
}
bool InternalThread::is_started() const {
- return thread_.get() != NULL && thread_->joinable();
+ return thread_ && thread_->joinable();
+}
+
+bool InternalThread::must_stop() {
+ return thread_ && thread_->interruption_requested();
}
+void InternalThread::StartInternalThread() {
+ CHECK(!is_started()) << "Threads should persist and not be restarted.";
+
+ int device = 0;
+#ifndef CPU_ONLY
+ CUDA_CHECK(cudaGetDevice(&device));
+#endif
+ Caffe::Brew mode = Caffe::mode();
+ int rand_seed = caffe_rng_rand();
+ int solver_count = Caffe::solver_count();
+ bool root_solver = Caffe::root_solver();
-bool InternalThread::StartInternalThread() {
- if (!WaitForInternalThreadToExit()) {
- return false;
- }
try {
- thread_.reset(
- new boost::thread(&InternalThread::InternalThreadEntry, this));
- } catch (...) {
- return false;
+ thread_.reset(new boost::thread(&InternalThread::entry, this, device, mode,
+ rand_seed, solver_count, root_solver));
+ } catch (std::exception& e) {
+ LOG(FATAL) << "Thread exception: " << e.what();
}
- return true;
}
-/** Will not return until the internal thread has exited. */
-bool InternalThread::WaitForInternalThreadToExit() {
+void InternalThread::entry(int device, Caffe::Brew mode, int rand_seed,
+ int solver_count, bool root_solver) {
+#ifndef CPU_ONLY
+ CUDA_CHECK(cudaSetDevice(device));
+#endif
+ Caffe::set_mode(mode);
+ Caffe::set_random_seed(rand_seed);
+ Caffe::set_solver_count(solver_count);
+ Caffe::set_root_solver(root_solver);
+
+ InternalThreadEntry();
+}
+
+void InternalThread::StopInternalThread() {
if (is_started()) {
+ thread_->interrupt();
try {
thread_->join();
- } catch (...) {
- return false;
+ } catch (boost::thread_interrupted&) {
+ } catch (std::exception& e) {
+ LOG(FATAL) << "Thread exception: " << e.what();
}
}
- return true;
}
} // namespace caffe
--- /dev/null
+#include <boost/thread.hpp>
+#include "caffe/layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void Layer<Dtype>::InitMutex() {
+ forward_mutex_.reset(new boost::mutex());
+}
+
+template <typename Dtype>
+void Layer<Dtype>::Lock() {
+ if (IsShared()) {
+ forward_mutex_->lock();
+ }
+}
+
+template <typename Dtype>
+void Layer<Dtype>::Unlock() {
+ if (IsShared()) {
+ forward_mutex_->unlock();
+ }
+}
+
+INSTANTIATE_CLASS(Layer);
+
+} // namespace caffe
+// Make sure we include Python.h before any system header
+// to avoid _POSIX_C_SOURCE redefinition
+#ifdef WITH_PYTHON_LAYER
+#include <boost/python.hpp>
+#endif
#include <string>
#include "caffe/layer.hpp"
#include "caffe/layer_factory.hpp"
+#include "caffe/layers/conv_layer.hpp"
+#include "caffe/layers/lrn_layer.hpp"
+#include "caffe/layers/pooling_layer.hpp"
+#include "caffe/layers/relu_layer.hpp"
+#include "caffe/layers/sigmoid_layer.hpp"
+#include "caffe/layers/softmax_layer.hpp"
+#include "caffe/layers/tanh_layer.hpp"
#include "caffe/proto/caffe.pb.h"
-#include "caffe/vision_layers.hpp"
+
+#ifdef USE_CUDNN
+#include "caffe/layers/cudnn_conv_layer.hpp"
+#include "caffe/layers/cudnn_lcn_layer.hpp"
+#include "caffe/layers/cudnn_lrn_layer.hpp"
+#include "caffe/layers/cudnn_pooling_layer.hpp"
+#include "caffe/layers/cudnn_relu_layer.hpp"
+#include "caffe/layers/cudnn_sigmoid_layer.hpp"
+#include "caffe/layers/cudnn_softmax_layer.hpp"
+#include "caffe/layers/cudnn_tanh_layer.hpp"
+#endif
#ifdef WITH_PYTHON_LAYER
-#include "caffe/python_layer.hpp"
+#include "caffe/layers/python_layer.hpp"
#endif
namespace caffe {
template <typename Dtype>
shared_ptr<Layer<Dtype> > GetConvolutionLayer(
const LayerParameter& param) {
- ConvolutionParameter_Engine engine = param.convolution_param().engine();
+ ConvolutionParameter conv_param = param.convolution_param();
+ ConvolutionParameter_Engine engine = conv_param.engine();
+#ifdef USE_CUDNN
+ bool use_dilation = false;
+ for (int i = 0; i < conv_param.dilation_size(); ++i) {
+ if (conv_param.dilation(i) > 1) {
+ use_dilation = true;
+ }
+ }
+#endif
if (engine == ConvolutionParameter_Engine_DEFAULT) {
engine = ConvolutionParameter_Engine_CAFFE;
#ifdef USE_CUDNN
- engine = ConvolutionParameter_Engine_CUDNN;
+ if (!use_dilation) {
+ engine = ConvolutionParameter_Engine_CUDNN;
+ }
#endif
}
if (engine == ConvolutionParameter_Engine_CAFFE) {
return shared_ptr<Layer<Dtype> >(new ConvolutionLayer<Dtype>(param));
#ifdef USE_CUDNN
} else if (engine == ConvolutionParameter_Engine_CUDNN) {
+ if (use_dilation) {
+ LOG(FATAL) << "CuDNN doesn't support the dilated convolution at Layer "
+ << param.name();
+ }
return shared_ptr<Layer<Dtype> >(new CuDNNConvolutionLayer<Dtype>(param));
#endif
} else {
return shared_ptr<Layer<Dtype> >(new PoolingLayer<Dtype>(param));
#ifdef USE_CUDNN
} else if (engine == PoolingParameter_Engine_CUDNN) {
- PoolingParameter p_param = param.pooling_param();
- if (p_param.pad() || p_param.pad_h() || p_param.pad_w() ||
- param.top_size() > 1) {
- LOG(INFO) << "CUDNN does not support padding or multiple tops. "
+ if (param.top_size() > 1) {
+ LOG(INFO) << "cuDNN does not support multiple tops. "
<< "Using Caffe's own pooling layer.";
return shared_ptr<Layer<Dtype> >(new PoolingLayer<Dtype>(param));
}
- return shared_ptr<Layer<Dtype> >(new CuDNNPoolingLayer<Dtype>(param));
+ // CuDNN assumes layers are not being modified in place, thus
+ // breaking our index tracking for updates in some cases in Caffe.
+ // Until there is a workaround in Caffe (index management) or
+ // cuDNN, use Caffe layer to max pooling, or don't use in place
+ // layers after max pooling layers
+ if (param.pooling_param().pool() == PoolingParameter_PoolMethod_MAX) {
+ return shared_ptr<Layer<Dtype> >(new PoolingLayer<Dtype>(param));
+ } else {
+ return shared_ptr<Layer<Dtype> >(new CuDNNPoolingLayer<Dtype>(param));
+ }
#endif
} else {
LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
REGISTER_LAYER_CREATOR(Pooling, GetPoolingLayer);
+// Get LRN layer according to engine
+template <typename Dtype>
+shared_ptr<Layer<Dtype> > GetLRNLayer(const LayerParameter& param) {
+ LRNParameter_Engine engine = param.lrn_param().engine();
+
+ if (engine == LRNParameter_Engine_DEFAULT) {
+#ifdef USE_CUDNN
+ engine = LRNParameter_Engine_CUDNN;
+#else
+ engine = LRNParameter_Engine_CAFFE;
+#endif
+ }
+
+ if (engine == LRNParameter_Engine_CAFFE) {
+ return shared_ptr<Layer<Dtype> >(new LRNLayer<Dtype>(param));
+#ifdef USE_CUDNN
+ } else if (engine == LRNParameter_Engine_CUDNN) {
+ LRNParameter lrn_param = param.lrn_param();
+
+ if (lrn_param.norm_region() ==LRNParameter_NormRegion_WITHIN_CHANNEL) {
+ return shared_ptr<Layer<Dtype> >(new CuDNNLCNLayer<Dtype>(param));
+ } else {
+ // local size is too big to be handled through cuDNN
+ if (param.lrn_param().local_size() > CUDNN_LRN_MAX_N) {
+ return shared_ptr<Layer<Dtype> >(new LRNLayer<Dtype>(param));
+ } else {
+ return shared_ptr<Layer<Dtype> >(new CuDNNLRNLayer<Dtype>(param));
+ }
+ }
+#endif
+ } else {
+ LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
+ }
+}
+
+REGISTER_LAYER_CREATOR(LRN, GetLRNLayer);
+
// Get relu layer according to engine.
template <typename Dtype>
shared_ptr<Layer<Dtype> > GetReLULayer(const LayerParameter& param) {
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/absval_layer.hpp"
#include "caffe/util/math_functions.hpp"
namespace caffe {
#include <vector>
-#include "caffe/layer.hpp"
+#include "caffe/layers/absval_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
void AbsValLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
const int count = top[0]->count();
- const Dtype* top_data = top[0]->gpu_data();
const Dtype* top_diff = top[0]->gpu_diff();
if (propagate_down[0]) {
const Dtype* bottom_data = bottom[0]->gpu_data();
-#include <algorithm>
#include <functional>
#include <utility>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/layers/accuracy_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
<< "with integer values in {0, 1, ..., C-1}.";
vector<int> top_shape(0); // Accuracy is a scalar; 0 axes.
top[0]->Reshape(top_shape);
+ if (top.size() > 1) {
+ // Per-class accuracy is a vector; 1 axes.
+ vector<int> top_shape_per_class(1);
+ top_shape_per_class[0] = bottom[0]->shape(label_axis_);
+ top[1]->Reshape(top_shape_per_class);
+ nums_buffer_.Reshape(top_shape_per_class);
+ }
}
template <typename Dtype>
const int num_labels = bottom[0]->shape(label_axis_);
vector<Dtype> maxval(top_k_+1);
vector<int> max_id(top_k_+1);
+ if (top.size() > 1) {
+ caffe_set(nums_buffer_.count(), Dtype(0), nums_buffer_.mutable_cpu_data());
+ caffe_set(top[1]->count(), Dtype(0), top[1]->mutable_cpu_data());
+ }
int count = 0;
for (int i = 0; i < outer_num_; ++i) {
for (int j = 0; j < inner_num_; ++j) {
if (has_ignore_label_ && label_value == ignore_label_) {
continue;
}
+ if (top.size() > 1) ++nums_buffer_.mutable_cpu_data()[label_value];
DCHECK_GE(label_value, 0);
DCHECK_LT(label_value, num_labels);
// Top-k accuracy
for (int k = 0; k < top_k_; k++) {
if (bottom_data_vector[k].second == label_value) {
++accuracy;
+ if (top.size() > 1) ++top[1]->mutable_cpu_data()[label_value];
break;
}
}
// LOG(INFO) << "Accuracy: " << accuracy;
top[0]->mutable_cpu_data()[0] = accuracy / count;
+ if (top.size() > 1) {
+ for (int i = 0; i < top[1]->count(); ++i) {
+ top[1]->mutable_cpu_data()[i] =
+ nums_buffer_.cpu_data()[i] == 0 ? 0
+ : top[1]->cpu_data()[i] / nums_buffer_.cpu_data()[i];
+ }
+ }
// Accuracy layer should not be used as a loss function.
}
#include <utility>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/argmax_layer.hpp"
namespace caffe {
template <typename Dtype>
void ArgMaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
- out_max_val_ = this->layer_param_.argmax_param().out_max_val();
- top_k_ = this->layer_param_.argmax_param().top_k();
- CHECK_GE(top_k_, 1) << " top k must not be less than 1.";
- CHECK_LE(top_k_, bottom[0]->count() / bottom[0]->num())
- << "top_k must be less than or equal to the number of classes.";
+ const ArgMaxParameter& argmax_param = this->layer_param_.argmax_param();
+ out_max_val_ = argmax_param.out_max_val();
+ top_k_ = argmax_param.top_k();
+ has_axis_ = argmax_param.has_axis();
+ CHECK_GE(top_k_, 1) << "top k must not be less than 1.";
+ if (has_axis_) {
+ axis_ = bottom[0]->CanonicalAxisIndex(argmax_param.axis());
+ CHECK_GE(axis_, 0) << "axis must not be less than 0.";
+ CHECK_LE(axis_, bottom[0]->num_axes()) <<
+ "axis must be less than or equal to the number of axis.";
+ CHECK_LE(top_k_, bottom[0]->shape(axis_))
+ << "top_k must be less than or equal to the dimension of the axis.";
+ } else {
+ CHECK_LE(top_k_, bottom[0]->count(1))
+ << "top_k must be less than or equal to"
+ " the dimension of the flattened bottom blob per instance.";
+ }
}
template <typename Dtype>
void ArgMaxLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
- if (out_max_val_) {
- // Produces max_ind and max_val
- top[0]->Reshape(bottom[0]->num(), 2, top_k_, 1);
+ int num_top_axes = bottom[0]->num_axes();
+ if ( num_top_axes < 3 ) num_top_axes = 3;
+ std::vector<int> shape(num_top_axes, 1);
+ if (has_axis_) {
+ // Produces max_ind or max_val per axis
+ shape = bottom[0]->shape();
+ shape[axis_] = top_k_;
} else {
- // Produces only max_ind
- top[0]->Reshape(bottom[0]->num(), 1, top_k_, 1);
+ shape[0] = bottom[0]->shape(0);
+ // Produces max_ind
+ shape[2] = top_k_;
+ if (out_max_val_) {
+ // Produces max_ind and max_val
+ shape[1] = 2;
+ }
}
+ top[0]->Reshape(shape);
}
template <typename Dtype>
const vector<Blob<Dtype>*>& top) {
const Dtype* bottom_data = bottom[0]->cpu_data();
Dtype* top_data = top[0]->mutable_cpu_data();
- int num = bottom[0]->num();
- int dim = bottom[0]->count() / bottom[0]->num();
+ int dim, axis_dist;
+ if (has_axis_) {
+ dim = bottom[0]->shape(axis_);
+ // Distance between values of axis in blob
+ axis_dist = bottom[0]->count(axis_) / dim;
+ } else {
+ dim = bottom[0]->count(1);
+ axis_dist = 1;
+ }
+ int num = bottom[0]->count() / dim;
+ std::vector<std::pair<Dtype, int> > bottom_data_vector(dim);
for (int i = 0; i < num; ++i) {
- std::vector<std::pair<Dtype, int> > bottom_data_vector;
for (int j = 0; j < dim; ++j) {
- bottom_data_vector.push_back(
- std::make_pair(bottom_data[i * dim + j], j));
+ bottom_data_vector[j] = std::make_pair(
+ bottom_data[(i / axis_dist * dim + j) * axis_dist + i % axis_dist], j);
}
std::partial_sort(
bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_,
bottom_data_vector.end(), std::greater<std::pair<Dtype, int> >());
for (int j = 0; j < top_k_; ++j) {
- top_data[top[0]->offset(i, 0, j)] = bottom_data_vector[j].second;
- }
- if (out_max_val_) {
- for (int j = 0; j < top_k_; ++j) {
- top_data[top[0]->offset(i, 1, j)] = bottom_data_vector[j].first;
+ if (out_max_val_) {
+ if (has_axis_) {
+ // Produces max_val per axis
+ top_data[(i / axis_dist * top_k_ + j) * axis_dist + i % axis_dist]
+ = bottom_data_vector[j].first;
+ } else {
+ // Produces max_ind and max_val
+ top_data[2 * i * top_k_ + j] = bottom_data_vector[j].second;
+ top_data[2 * i * top_k_ + top_k_ + j] = bottom_data_vector[j].first;
+ }
+ } else {
+ // Produces max_ind per axis
+ top_data[(i / axis_dist * top_k_ + j) * axis_dist + i % axis_dist]
+ = bottom_data_vector[j].second;
}
}
}
+#include <algorithm>
#include <vector>
#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/layers/base_conv_layer.hpp"
#include "caffe/util/im2col.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
template <typename Dtype>
void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
- CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
- << "corresponding to (num, channels, height, width)";
// Configure the kernel size, padding, stride, and inputs.
ConvolutionParameter conv_param = this->layer_param_.convolution_param();
- CHECK(!conv_param.has_kernel_size() !=
- !(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
- << "Filter size is kernel_size OR kernel_h and kernel_w; not both";
- CHECK(conv_param.has_kernel_size() ||
- (conv_param.has_kernel_h() && conv_param.has_kernel_w()))
- << "For non-square filters both kernel_h and kernel_w are required.";
- CHECK((!conv_param.has_pad() && conv_param.has_pad_h()
- && conv_param.has_pad_w())
- || (!conv_param.has_pad_h() && !conv_param.has_pad_w()))
- << "pad is pad OR pad_h and pad_w are required.";
- CHECK((!conv_param.has_stride() && conv_param.has_stride_h()
- && conv_param.has_stride_w())
- || (!conv_param.has_stride_h() && !conv_param.has_stride_w()))
- << "Stride is stride OR stride_h and stride_w are required.";
- if (conv_param.has_kernel_size()) {
- kernel_h_ = kernel_w_ = conv_param.kernel_size();
+ force_nd_im2col_ = conv_param.force_nd_im2col();
+ channel_axis_ = bottom[0]->CanonicalAxisIndex(conv_param.axis());
+ const int first_spatial_axis = channel_axis_ + 1;
+ const int num_axes = bottom[0]->num_axes();
+ num_spatial_axes_ = num_axes - first_spatial_axis;
+ CHECK_GE(num_spatial_axes_, 0);
+ vector<int> bottom_dim_blob_shape(1, num_spatial_axes_ + 1);
+ vector<int> spatial_dim_blob_shape(1, std::max(num_spatial_axes_, 1));
+ // Setup filter kernel dimensions (kernel_shape_).
+ kernel_shape_.Reshape(spatial_dim_blob_shape);
+ int* kernel_shape_data = kernel_shape_.mutable_cpu_data();
+ if (conv_param.has_kernel_h() || conv_param.has_kernel_w()) {
+ CHECK_EQ(num_spatial_axes_, 2)
+ << "kernel_h & kernel_w can only be used for 2D convolution.";
+ CHECK_EQ(0, conv_param.kernel_size_size())
+ << "Either kernel_size or kernel_h/w should be specified; not both.";
+ kernel_shape_data[0] = conv_param.kernel_h();
+ kernel_shape_data[1] = conv_param.kernel_w();
} else {
- kernel_h_ = conv_param.kernel_h();
- kernel_w_ = conv_param.kernel_w();
+ const int num_kernel_dims = conv_param.kernel_size_size();
+ CHECK(num_kernel_dims == 1 || num_kernel_dims == num_spatial_axes_)
+ << "kernel_size must be specified once, or once per spatial dimension "
+ << "(kernel_size specified " << num_kernel_dims << " times; "
+ << num_spatial_axes_ << " spatial dims).";
+ for (int i = 0; i < num_spatial_axes_; ++i) {
+ kernel_shape_data[i] =
+ conv_param.kernel_size((num_kernel_dims == 1) ? 0 : i);
+ }
}
- CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero.";
- CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero.";
- if (!conv_param.has_pad_h()) {
- pad_h_ = pad_w_ = conv_param.pad();
+ for (int i = 0; i < num_spatial_axes_; ++i) {
+ CHECK_GT(kernel_shape_data[i], 0) << "Filter dimensions must be nonzero.";
+ }
+ // Setup stride dimensions (stride_).
+ stride_.Reshape(spatial_dim_blob_shape);
+ int* stride_data = stride_.mutable_cpu_data();
+ if (conv_param.has_stride_h() || conv_param.has_stride_w()) {
+ CHECK_EQ(num_spatial_axes_, 2)
+ << "stride_h & stride_w can only be used for 2D convolution.";
+ CHECK_EQ(0, conv_param.stride_size())
+ << "Either stride or stride_h/w should be specified; not both.";
+ stride_data[0] = conv_param.stride_h();
+ stride_data[1] = conv_param.stride_w();
} else {
- pad_h_ = conv_param.pad_h();
- pad_w_ = conv_param.pad_w();
+ const int num_stride_dims = conv_param.stride_size();
+ CHECK(num_stride_dims == 0 || num_stride_dims == 1 ||
+ num_stride_dims == num_spatial_axes_)
+ << "stride must be specified once, or once per spatial dimension "
+ << "(stride specified " << num_stride_dims << " times; "
+ << num_spatial_axes_ << " spatial dims).";
+ const int kDefaultStride = 1;
+ for (int i = 0; i < num_spatial_axes_; ++i) {
+ stride_data[i] = (num_stride_dims == 0) ? kDefaultStride :
+ conv_param.stride((num_stride_dims == 1) ? 0 : i);
+ CHECK_GT(stride_data[i], 0) << "Stride dimensions must be nonzero.";
+ }
}
- if (!conv_param.has_stride_h()) {
- stride_h_ = stride_w_ = conv_param.stride();
+ // Setup pad dimensions (pad_).
+ pad_.Reshape(spatial_dim_blob_shape);
+ int* pad_data = pad_.mutable_cpu_data();
+ if (conv_param.has_pad_h() || conv_param.has_pad_w()) {
+ CHECK_EQ(num_spatial_axes_, 2)
+ << "pad_h & pad_w can only be used for 2D convolution.";
+ CHECK_EQ(0, conv_param.pad_size())
+ << "Either pad or pad_h/w should be specified; not both.";
+ pad_data[0] = conv_param.pad_h();
+ pad_data[1] = conv_param.pad_w();
} else {
- stride_h_ = conv_param.stride_h();
- stride_w_ = conv_param.stride_w();
+ const int num_pad_dims = conv_param.pad_size();
+ CHECK(num_pad_dims == 0 || num_pad_dims == 1 ||
+ num_pad_dims == num_spatial_axes_)
+ << "pad must be specified once, or once per spatial dimension "
+ << "(pad specified " << num_pad_dims << " times; "
+ << num_spatial_axes_ << " spatial dims).";
+ const int kDefaultPad = 0;
+ for (int i = 0; i < num_spatial_axes_; ++i) {
+ pad_data[i] = (num_pad_dims == 0) ? kDefaultPad :
+ conv_param.pad((num_pad_dims == 1) ? 0 : i);
+ }
+ }
+ // Setup dilation dimensions (dilation_).
+ dilation_.Reshape(spatial_dim_blob_shape);
+ int* dilation_data = dilation_.mutable_cpu_data();
+ const int num_dilation_dims = conv_param.dilation_size();
+ CHECK(num_dilation_dims == 0 || num_dilation_dims == 1 ||
+ num_dilation_dims == num_spatial_axes_)
+ << "dilation must be specified once, or once per spatial dimension "
+ << "(dilation specified " << num_dilation_dims << " times; "
+ << num_spatial_axes_ << " spatial dims).";
+ const int kDefaultDilation = 1;
+ for (int i = 0; i < num_spatial_axes_; ++i) {
+ dilation_data[i] = (num_dilation_dims == 0) ? kDefaultDilation :
+ conv_param.dilation((num_dilation_dims == 1) ? 0 : i);
}
// Special case: im2col is the identity for 1x1 convolution with stride 1
// and no padding, so flag for skipping the buffer and transformation.
- is_1x1_ = kernel_w_ == 1 && kernel_h_ == 1
- && stride_h_ == 1 && stride_w_ == 1 && pad_h_ == 0 && pad_w_ == 0;
+ is_1x1_ = true;
+ for (int i = 0; i < num_spatial_axes_; ++i) {
+ is_1x1_ &=
+ kernel_shape_data[i] == 1 && stride_data[i] == 1 && pad_data[i] == 0;
+ if (!is_1x1_) { break; }
+ }
// Configure output channels and groups.
- channels_ = bottom[0]->channels();
+ channels_ = bottom[0]->shape(channel_axis_);
num_output_ = this->layer_param_.convolution_param().num_output();
CHECK_GT(num_output_, 0);
group_ = this->layer_param_.convolution_param().group();
// Handle the parameters: weights and biases.
// - blobs_[0] holds the filter weights
// - blobs_[1] holds the biases (optional)
+ vector<int> weight_shape(2);
+ weight_shape[0] = conv_out_channels_;
+ weight_shape[1] = conv_in_channels_ / group_;
+ for (int i = 0; i < num_spatial_axes_; ++i) {
+ weight_shape.push_back(kernel_shape_data[i]);
+ }
bias_term_ = this->layer_param_.convolution_param().bias_term();
+ vector<int> bias_shape(bias_term_, num_output_);
if (this->blobs_.size() > 0) {
+ CHECK_EQ(1 + bias_term_, this->blobs_.size())
+ << "Incorrect number of weight blobs.";
+ if (weight_shape != this->blobs_[0]->shape()) {
+ Blob<Dtype> weight_shaped_blob(weight_shape);
+ LOG(FATAL) << "Incorrect weight shape: expected shape "
+ << weight_shaped_blob.shape_string() << "; instead, shape was "
+ << this->blobs_[0]->shape_string();
+ }
+ if (bias_term_ && bias_shape != this->blobs_[1]->shape()) {
+ Blob<Dtype> bias_shaped_blob(bias_shape);
+ LOG(FATAL) << "Incorrect bias shape: expected shape "
+ << bias_shaped_blob.shape_string() << "; instead, shape was "
+ << this->blobs_[1]->shape_string();
+ }
LOG(INFO) << "Skipping parameter initialization";
} else {
if (bias_term_) {
}
// Initialize and fill the weights:
// output channels x input channels per-group x kernel height x kernel width
- this->blobs_[0].reset(new Blob<Dtype>(
- conv_out_channels_, conv_in_channels_ / group_, kernel_h_, kernel_w_));
+ this->blobs_[0].reset(new Blob<Dtype>(weight_shape));
shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
this->layer_param_.convolution_param().weight_filler()));
weight_filler->Fill(this->blobs_[0].get());
// If necessary, initialize and fill the biases.
if (bias_term_) {
- vector<int> bias_shape(1, num_output_);
this->blobs_[1].reset(new Blob<Dtype>(bias_shape));
shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
this->layer_param_.convolution_param().bias_filler()));
bias_filler->Fill(this->blobs_[1].get());
}
}
+ kernel_dim_ = this->blobs_[0]->count(1);
+ weight_offset_ = conv_out_channels_ * kernel_dim_ / group_;
// Propagate gradients to the parameters (as directed by backward pass).
this->param_propagate_down_.resize(this->blobs_.size(), true);
}
template <typename Dtype>
void BaseConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
- CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
- << "corresponding to (num, channels, height, width)";
- num_ = bottom[0]->num();
- height_ = bottom[0]->height();
- width_ = bottom[0]->width();
- CHECK_EQ(bottom[0]->channels(), channels_) << "Input size incompatible with"
- " convolution kernel.";
+ const int first_spatial_axis = channel_axis_ + 1;
+ CHECK_EQ(bottom[0]->num_axes(), first_spatial_axis + num_spatial_axes_)
+ << "bottom num_axes may not change.";
+ num_ = bottom[0]->count(0, channel_axis_);
+ CHECK_EQ(bottom[0]->shape(channel_axis_), channels_)
+ << "Input size incompatible with convolution kernel.";
// TODO: generalize to handle inputs of different shapes.
for (int bottom_id = 1; bottom_id < bottom.size(); ++bottom_id) {
- CHECK_EQ(num_, bottom[bottom_id]->num()) << "Inputs must have same num.";
- CHECK_EQ(channels_, bottom[bottom_id]->channels())
- << "Inputs must have same channels.";
- CHECK_EQ(height_, bottom[bottom_id]->height())
- << "Inputs must have same height.";
- CHECK_EQ(width_, bottom[bottom_id]->width())
- << "Inputs must have same width.";
+ CHECK(bottom[0]->shape() == bottom[bottom_id]->shape())
+ << "All inputs must have the same shape.";
}
// Shape the tops.
+ bottom_shape_ = &bottom[0]->shape();
compute_output_shape();
+ vector<int> top_shape(bottom[0]->shape().begin(),
+ bottom[0]->shape().begin() + channel_axis_);
+ top_shape.push_back(num_output_);
+ for (int i = 0; i < num_spatial_axes_; ++i) {
+ top_shape.push_back(output_shape_[i]);
+ }
for (int top_id = 0; top_id < top.size(); ++top_id) {
- top[top_id]->Reshape(num_, num_output_, height_out_, width_out_);
+ top[top_id]->Reshape(top_shape);
}
if (reverse_dimensions()) {
- conv_in_height_ = height_out_;
- conv_in_width_ = width_out_;
- conv_out_spatial_dim_ = height_ * width_;
+ conv_out_spatial_dim_ = bottom[0]->count(first_spatial_axis);
} else {
- conv_in_height_ = height_;
- conv_in_width_ = width_;
- conv_out_spatial_dim_ = height_out_ * width_out_;
+ conv_out_spatial_dim_ = top[0]->count(first_spatial_axis);
}
- kernel_dim_ = conv_in_channels_ * kernel_h_ * kernel_w_;
- weight_offset_ = conv_out_channels_ * kernel_dim_ / group_ / group_;
- col_offset_ = kernel_dim_ * conv_out_spatial_dim_ / group_;
+ col_offset_ = kernel_dim_ * conv_out_spatial_dim_;
output_offset_ = conv_out_channels_ * conv_out_spatial_dim_ / group_;
+ // Setup input dimensions (conv_input_shape_).
+ vector<int> bottom_dim_blob_shape(1, num_spatial_axes_ + 1);
+ conv_input_shape_.Reshape(bottom_dim_blob_shape);
+ int* conv_input_shape_data = conv_input_shape_.mutable_cpu_data();
+ for (int i = 0; i < num_spatial_axes_ + 1; ++i) {
+ if (reverse_dimensions()) {
+ conv_input_shape_data[i] = top[0]->shape(channel_axis_ + i);
+ } else {
+ conv_input_shape_data[i] = bottom[0]->shape(channel_axis_ + i);
+ }
+ }
// The im2col result buffer will only hold one image at a time to avoid
// overly large memory usage. In the special case of 1x1 convolution
// it goes lazily unused to save memory.
- if (reverse_dimensions()) {
- col_buffer_.Reshape(1, kernel_dim_, height_, width_);
- } else {
- col_buffer_.Reshape(1, kernel_dim_, height_out_, width_out_);
+ col_buffer_shape_.clear();
+ col_buffer_shape_.push_back(kernel_dim_ * group_);
+ for (int i = 0; i < num_spatial_axes_; ++i) {
+ if (reverse_dimensions()) {
+ col_buffer_shape_.push_back(input_shape(i + 1));
+ } else {
+ col_buffer_shape_.push_back(output_shape_[i]);
+ }
}
+ col_buffer_.Reshape(col_buffer_shape_);
+ bottom_dim_ = bottom[0]->count(channel_axis_);
+ top_dim_ = top[0]->count(channel_axis_);
+ num_kernels_im2col_ = conv_in_channels_ * conv_out_spatial_dim_;
+ num_kernels_col2im_ = reverse_dimensions() ? top_dim_ : bottom_dim_;
// Set up the all ones "bias multiplier" for adding biases by BLAS
+ out_spatial_dim_ = top[0]->count(first_spatial_axis);
if (bias_term_) {
- vector<int> bias_multiplier_shape(1, height_out_ * width_out_);
+ vector<int> bias_multiplier_shape(1, out_spatial_dim_);
bias_multiplier_.Reshape(bias_multiplier_shape);
caffe_set(bias_multiplier_.count(), Dtype(1),
bias_multiplier_.mutable_cpu_data());
}
for (int g = 0; g < group_; ++g) {
caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, conv_out_channels_ /
- group_, conv_out_spatial_dim_, kernel_dim_ / group_,
+ group_, conv_out_spatial_dim_, kernel_dim_,
(Dtype)1., weights + weight_offset_ * g, col_buff + col_offset_ * g,
(Dtype)0., output + output_offset_ * g);
}
void BaseConvolutionLayer<Dtype>::forward_cpu_bias(Dtype* output,
const Dtype* bias) {
caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
- height_out_ * width_out_, 1, (Dtype)1., bias, bias_multiplier_.cpu_data(),
+ out_spatial_dim_, 1, (Dtype)1., bias, bias_multiplier_.cpu_data(),
(Dtype)1., output);
}
col_buff = input;
}
for (int g = 0; g < group_; ++g) {
- caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, kernel_dim_ / group_,
+ caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, kernel_dim_,
conv_out_spatial_dim_, conv_out_channels_ / group_,
(Dtype)1., weights + weight_offset_ * g, output + output_offset_ * g,
(Dtype)0., col_buff + col_offset_ * g);
}
for (int g = 0; g < group_; ++g) {
caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, conv_out_channels_ / group_,
- kernel_dim_ / group_, conv_out_spatial_dim_,
+ kernel_dim_, conv_out_spatial_dim_,
(Dtype)1., output + output_offset_ * g, col_buff + col_offset_ * g,
(Dtype)1., weights + weight_offset_ * g);
}
template <typename Dtype>
void BaseConvolutionLayer<Dtype>::backward_cpu_bias(Dtype* bias,
const Dtype* input) {
- caffe_cpu_gemv<Dtype>(CblasNoTrans, num_output_, height_out_ * width_out_, 1.,
+ caffe_cpu_gemv<Dtype>(CblasNoTrans, num_output_, out_spatial_dim_, 1.,
input, bias_multiplier_.cpu_data(), 1., bias);
}
}
for (int g = 0; g < group_; ++g) {
caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, conv_out_channels_ /
- group_, conv_out_spatial_dim_, kernel_dim_ / group_,
+ group_, conv_out_spatial_dim_, kernel_dim_,
(Dtype)1., weights + weight_offset_ * g, col_buff + col_offset_ * g,
(Dtype)0., output + output_offset_ * g);
}
void BaseConvolutionLayer<Dtype>::forward_gpu_bias(Dtype* output,
const Dtype* bias) {
caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
- height_out_ * width_out_, 1, (Dtype)1., bias, bias_multiplier_.gpu_data(),
+ out_spatial_dim_, 1, (Dtype)1., bias, bias_multiplier_.gpu_data(),
(Dtype)1., output);
}
col_buff = input;
}
for (int g = 0; g < group_; ++g) {
- caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, kernel_dim_ / group_,
+ caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, kernel_dim_,
conv_out_spatial_dim_, conv_out_channels_ / group_,
(Dtype)1., weights + weight_offset_ * g, output + output_offset_ * g,
(Dtype)0., col_buff + col_offset_ * g);
}
for (int g = 0; g < group_; ++g) {
caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, conv_out_channels_ / group_,
- kernel_dim_ / group_, conv_out_spatial_dim_,
+ kernel_dim_, conv_out_spatial_dim_,
(Dtype)1., output + output_offset_ * g, col_buff + col_offset_ * g,
(Dtype)1., weights + weight_offset_ * g);
}
template <typename Dtype>
void BaseConvolutionLayer<Dtype>::backward_gpu_bias(Dtype* bias,
const Dtype* input) {
- caffe_gpu_gemv<Dtype>(CblasNoTrans, num_output_, height_out_ * width_out_, 1.,
+ caffe_gpu_gemv<Dtype>(CblasNoTrans, num_output_, out_spatial_dim_, 1.,
input, bias_multiplier_.gpu_data(), 1., bias);
}
-#include <string>
+#include <boost/thread.hpp>
#include <vector>
-#include "caffe/data_layers.hpp"
-#include "caffe/net.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/blob.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/base_data_layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/blocking_queue.hpp"
namespace caffe {
} else {
output_labels_ = true;
}
- // The subclasses should setup the size of bottom and top
- DataLayerSetUp(bottom, top);
data_transformer_.reset(
new DataTransformer<Dtype>(transform_param_, this->phase_));
data_transformer_->InitRand();
+ // The subclasses should setup the size of bottom and top
+ DataLayerSetUp(bottom, top);
+}
+
+template <typename Dtype>
+BasePrefetchingDataLayer<Dtype>::BasePrefetchingDataLayer(
+ const LayerParameter& param)
+ : BaseDataLayer<Dtype>(param),
+ prefetch_free_(), prefetch_full_() {
+ for (int i = 0; i < PREFETCH_COUNT; ++i) {
+ prefetch_free_.push(&prefetch_[i]);
+ }
}
template <typename Dtype>
void BasePrefetchingDataLayer<Dtype>::LayerSetUp(
const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
BaseDataLayer<Dtype>::LayerSetUp(bottom, top);
- // Now, start the prefetch thread. Before calling prefetch, we make two
- // cpu_data calls so that the prefetch thread does not accidentally make
- // simultaneous cudaMalloc calls when the main thread is running. In some
- // GPUs this seems to cause failures if we do not so.
- this->prefetch_data_.mutable_cpu_data();
- if (this->output_labels_) {
- this->prefetch_label_.mutable_cpu_data();
+ // Before starting the prefetch thread, we make cpu_data and gpu_data
+ // calls so that the prefetch thread does not accidentally make simultaneous
+ // cudaMalloc calls when the main thread is running. In some GPUs this
+ // seems to cause failures if we do not so.
+ for (int i = 0; i < PREFETCH_COUNT; ++i) {
+ prefetch_[i].data_.mutable_cpu_data();
+ if (this->output_labels_) {
+ prefetch_[i].label_.mutable_cpu_data();
+ }
}
+#ifndef CPU_ONLY
+ if (Caffe::mode() == Caffe::GPU) {
+ for (int i = 0; i < PREFETCH_COUNT; ++i) {
+ prefetch_[i].data_.mutable_gpu_data();
+ if (this->output_labels_) {
+ prefetch_[i].label_.mutable_gpu_data();
+ }
+ }
+ }
+#endif
DLOG(INFO) << "Initializing prefetch";
- this->CreatePrefetchThread();
+ this->data_transformer_->InitRand();
+ StartInternalThread();
DLOG(INFO) << "Prefetch initialized.";
}
template <typename Dtype>
-void BasePrefetchingDataLayer<Dtype>::CreatePrefetchThread() {
- this->data_transformer_->InitRand();
- CHECK(StartInternalThread()) << "Thread execution failed";
-}
+void BasePrefetchingDataLayer<Dtype>::InternalThreadEntry() {
+#ifndef CPU_ONLY
+ cudaStream_t stream;
+ if (Caffe::mode() == Caffe::GPU) {
+ CUDA_CHECK(cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking));
+ }
+#endif
-template <typename Dtype>
-void BasePrefetchingDataLayer<Dtype>::JoinPrefetchThread() {
- CHECK(WaitForInternalThreadToExit()) << "Thread joining failed";
+ try {
+ while (!must_stop()) {
+ Batch<Dtype>* batch = prefetch_free_.pop();
+ load_batch(batch);
+#ifndef CPU_ONLY
+ if (Caffe::mode() == Caffe::GPU) {
+ batch->data_.data().get()->async_gpu_push(stream);
+ CUDA_CHECK(cudaStreamSynchronize(stream));
+ }
+#endif
+ prefetch_full_.push(batch);
+ }
+ } catch (boost::thread_interrupted&) {
+ // Interrupted exception is expected on shutdown
+ }
+#ifndef CPU_ONLY
+ if (Caffe::mode() == Caffe::GPU) {
+ CUDA_CHECK(cudaStreamDestroy(stream));
+ }
+#endif
}
template <typename Dtype>
void BasePrefetchingDataLayer<Dtype>::Forward_cpu(
const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
- // First, join the thread
- JoinPrefetchThread();
- DLOG(INFO) << "Thread joined";
+ Batch<Dtype>* batch = prefetch_full_.pop("Data layer prefetch queue empty");
// Reshape to loaded data.
- top[0]->Reshape(this->prefetch_data_.num(), this->prefetch_data_.channels(),
- this->prefetch_data_.height(), this->prefetch_data_.width());
+ top[0]->ReshapeLike(batch->data_);
// Copy the data
- caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
+ caffe_copy(batch->data_.count(), batch->data_.cpu_data(),
top[0]->mutable_cpu_data());
DLOG(INFO) << "Prefetch copied";
if (this->output_labels_) {
- caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
- top[1]->mutable_cpu_data());
+ // Reshape to loaded labels.
+ top[1]->ReshapeLike(batch->label_);
+ // Copy the labels.
+ caffe_copy(batch->label_.count(), batch->label_.cpu_data(),
+ top[1]->mutable_cpu_data());
}
- // Start a new prefetch thread
- DLOG(INFO) << "CreatePrefetchThread";
- CreatePrefetchThread();
+
+ prefetch_free_.push(batch);
}
#ifdef CPU_ONLY
#include <vector>
-#include "caffe/data_layers.hpp"
+#include "caffe/layers/base_data_layer.hpp"
namespace caffe {
template <typename Dtype>
void BasePrefetchingDataLayer<Dtype>::Forward_gpu(
const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
- // First, join the thread
- JoinPrefetchThread();
+ Batch<Dtype>* batch = prefetch_full_.pop("Data layer prefetch queue empty");
// Reshape to loaded data.
- top[0]->Reshape(this->prefetch_data_.num(), this->prefetch_data_.channels(),
- this->prefetch_data_.height(), this->prefetch_data_.width());
+ top[0]->ReshapeLike(batch->data_);
// Copy the data
- caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
+ caffe_copy(batch->data_.count(), batch->data_.gpu_data(),
top[0]->mutable_gpu_data());
if (this->output_labels_) {
- caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
+ // Reshape to loaded labels.
+ top[1]->ReshapeLike(batch->label_);
+ // Copy the labels.
+ caffe_copy(batch->label_.count(), batch->label_.gpu_data(),
top[1]->mutable_gpu_data());
}
- // Start a new prefetch thread
- CreatePrefetchThread();
+ // Ensure the copy is synchronous wrt the host, so that the next batch isn't
+ // copied in meanwhile.
+ CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault));
+ prefetch_free_.push(batch);
}
INSTANTIATE_LAYER_GPU_FORWARD(BasePrefetchingDataLayer);
--- /dev/null
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layers/batch_norm_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void BatchNormLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ BatchNormParameter param = this->layer_param_.batch_norm_param();
+ moving_average_fraction_ = param.moving_average_fraction();
+ use_global_stats_ = this->phase_ == TEST;
+ if (param.has_use_global_stats())
+ use_global_stats_ = param.use_global_stats();
+ if (bottom[0]->num_axes() == 1)
+ channels_ = 1;
+ else
+ channels_ = bottom[0]->shape(1);
+ eps_ = param.eps();
+ if (this->blobs_.size() > 0) {
+ LOG(INFO) << "Skipping parameter initialization";
+ } else {
+ this->blobs_.resize(3);
+ vector<int> sz;
+ sz.push_back(channels_);
+ this->blobs_[0].reset(new Blob<Dtype>(sz));
+ this->blobs_[1].reset(new Blob<Dtype>(sz));
+ sz[0]=1;
+ this->blobs_[2].reset(new Blob<Dtype>(sz));
+ for (int i = 0; i < 3; ++i) {
+ caffe_set(this->blobs_[i]->count(), Dtype(0),
+ this->blobs_[i]->mutable_cpu_data());
+ }
+ }
+}
+
+template <typename Dtype>
+void BatchNormLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ if (bottom[0]->num_axes() >= 1)
+ CHECK_EQ(bottom[0]->shape(1), channels_);
+ top[0]->ReshapeLike(*bottom[0]);
+
+ vector<int> sz;
+ sz.push_back(channels_);
+ mean_.Reshape(sz);
+ variance_.Reshape(sz);
+ temp_.ReshapeLike(*bottom[0]);
+ x_norm_.ReshapeLike(*bottom[0]);
+ sz[0]=bottom[0]->shape(0);
+ batch_sum_multiplier_.Reshape(sz);
+
+ int spatial_dim = bottom[0]->count()/(channels_*bottom[0]->shape(0));
+ if (spatial_sum_multiplier_.num_axes() == 0 ||
+ spatial_sum_multiplier_.shape(0) != spatial_dim) {
+ sz[0] = spatial_dim;
+ spatial_sum_multiplier_.Reshape(sz);
+ Dtype* multiplier_data = spatial_sum_multiplier_.mutable_cpu_data();
+ caffe_set(spatial_sum_multiplier_.count(), Dtype(1), multiplier_data);
+ }
+
+ int numbychans = channels_*bottom[0]->shape(0);
+ if (num_by_chans_.num_axes() == 0 ||
+ num_by_chans_.shape(0) != numbychans) {
+ sz[0] = numbychans;
+ num_by_chans_.Reshape(sz);
+ caffe_set(batch_sum_multiplier_.count(), Dtype(1),
+ batch_sum_multiplier_.mutable_cpu_data());
+ }
+}
+
+template <typename Dtype>
+void BatchNormLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ const Dtype* bottom_data = bottom[0]->cpu_data();
+ Dtype* top_data = top[0]->mutable_cpu_data();
+ int num = bottom[0]->shape(0);
+ int spatial_dim = bottom[0]->count()/(bottom[0]->shape(0)*channels_);
+
+ if (bottom[0] != top[0]) {
+ caffe_copy(bottom[0]->count(), bottom_data, top_data);
+ }
+
+ if (use_global_stats_) {
+ // use the stored mean/variance estimates.
+ const Dtype scale_factor = this->blobs_[2]->cpu_data()[0] == 0 ?
+ 0 : 1 / this->blobs_[2]->cpu_data()[0];
+ caffe_cpu_scale(variance_.count(), scale_factor,
+ this->blobs_[0]->cpu_data(), mean_.mutable_cpu_data());
+ caffe_cpu_scale(variance_.count(), scale_factor,
+ this->blobs_[1]->cpu_data(), variance_.mutable_cpu_data());
+ } else {
+ // compute mean
+ caffe_cpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim,
+ 1. / (num * spatial_dim), bottom_data,
+ spatial_sum_multiplier_.cpu_data(), 0.,
+ num_by_chans_.mutable_cpu_data());
+ caffe_cpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+ num_by_chans_.cpu_data(), batch_sum_multiplier_.cpu_data(), 0.,
+ mean_.mutable_cpu_data());
+ }
+
+ // subtract mean
+ caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+ batch_sum_multiplier_.cpu_data(), mean_.cpu_data(), 0.,
+ num_by_chans_.mutable_cpu_data());
+ caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels_ * num,
+ spatial_dim, 1, -1, num_by_chans_.cpu_data(),
+ spatial_sum_multiplier_.cpu_data(), 1., top_data);
+
+ if (!use_global_stats_) {
+ // compute variance using var(X) = E((X-EX)^2)
+ caffe_powx(top[0]->count(), top_data, Dtype(2),
+ temp_.mutable_cpu_data()); // (X-EX)^2
+ caffe_cpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim,
+ 1. / (num * spatial_dim), temp_.cpu_data(),
+ spatial_sum_multiplier_.cpu_data(), 0.,
+ num_by_chans_.mutable_cpu_data());
+ caffe_cpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+ num_by_chans_.cpu_data(), batch_sum_multiplier_.cpu_data(), 0.,
+ variance_.mutable_cpu_data()); // E((X_EX)^2)
+
+ // compute and save moving average
+ this->blobs_[2]->mutable_cpu_data()[0] *= moving_average_fraction_;
+ this->blobs_[2]->mutable_cpu_data()[0] += 1;
+ caffe_cpu_axpby(mean_.count(), Dtype(1), mean_.cpu_data(),
+ moving_average_fraction_, this->blobs_[0]->mutable_cpu_data());
+ int m = bottom[0]->count()/channels_;
+ Dtype bias_correction_factor = m > 1 ? Dtype(m)/(m-1) : 1;
+ caffe_cpu_axpby(variance_.count(), bias_correction_factor,
+ variance_.cpu_data(), moving_average_fraction_,
+ this->blobs_[1]->mutable_cpu_data());
+ }
+
+ // normalize variance
+ caffe_add_scalar(variance_.count(), eps_, variance_.mutable_cpu_data());
+ caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5),
+ variance_.mutable_cpu_data());
+
+ // replicate variance to input size
+ caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+ batch_sum_multiplier_.cpu_data(), variance_.cpu_data(), 0.,
+ num_by_chans_.mutable_cpu_data());
+ caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels_ * num,
+ spatial_dim, 1, 1., num_by_chans_.cpu_data(),
+ spatial_sum_multiplier_.cpu_data(), 0., temp_.mutable_cpu_data());
+ caffe_div(temp_.count(), top_data, temp_.cpu_data(), top_data);
+ // TODO(cdoersch): The caching is only needed because later in-place layers
+ // might clobber the data. Can we skip this if they won't?
+ caffe_copy(x_norm_.count(), top_data,
+ x_norm_.mutable_cpu_data());
+}
+
+template <typename Dtype>
+void BatchNormLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down,
+ const vector<Blob<Dtype>*>& bottom) {
+ const Dtype* top_diff;
+ if (bottom[0] != top[0]) {
+ top_diff = top[0]->cpu_diff();
+ } else {
+ caffe_copy(x_norm_.count(), top[0]->cpu_diff(), x_norm_.mutable_cpu_diff());
+ top_diff = x_norm_.cpu_diff();
+ }
+ Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+ if (use_global_stats_) {
+ caffe_div(temp_.count(), top_diff, temp_.cpu_data(), bottom_diff);
+ return;
+ }
+ const Dtype* top_data = x_norm_.cpu_data();
+ int num = bottom[0]->shape()[0];
+ int spatial_dim = bottom[0]->count()/(bottom[0]->shape(0)*channels_);
+ // if Y = (X-mean(X))/(sqrt(var(X)+eps)), then
+ //
+ // dE(Y)/dX =
+ // (dE/dY - mean(dE/dY) - mean(dE/dY \cdot Y) \cdot Y)
+ // ./ sqrt(var(X) + eps)
+ //
+ // where \cdot and ./ are hadamard product and elementwise division,
+ // respectively, dE/dY is the top diff, and mean/var/sum are all computed
+ // along all dimensions except the channels dimension. In the above
+ // equation, the operations allow for expansion (i.e. broadcast) along all
+ // dimensions except the channels dimension where required.
+
+ // sum(dE/dY \cdot Y)
+ caffe_mul(temp_.count(), top_data, top_diff, bottom_diff);
+ caffe_cpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim, 1.,
+ bottom_diff, spatial_sum_multiplier_.cpu_data(), 0.,
+ num_by_chans_.mutable_cpu_data());
+ caffe_cpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+ num_by_chans_.cpu_data(), batch_sum_multiplier_.cpu_data(), 0.,
+ mean_.mutable_cpu_data());
+
+ // reshape (broadcast) the above
+ caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+ batch_sum_multiplier_.cpu_data(), mean_.cpu_data(), 0.,
+ num_by_chans_.mutable_cpu_data());
+ caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels_ * num,
+ spatial_dim, 1, 1., num_by_chans_.cpu_data(),
+ spatial_sum_multiplier_.cpu_data(), 0., bottom_diff);
+
+ // sum(dE/dY \cdot Y) \cdot Y
+ caffe_mul(temp_.count(), top_data, bottom_diff, bottom_diff);
+
+ // sum(dE/dY)-sum(dE/dY \cdot Y) \cdot Y
+ caffe_cpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim, 1.,
+ top_diff, spatial_sum_multiplier_.cpu_data(), 0.,
+ num_by_chans_.mutable_cpu_data());
+ caffe_cpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+ num_by_chans_.cpu_data(), batch_sum_multiplier_.cpu_data(), 0.,
+ mean_.mutable_cpu_data());
+ // reshape (broadcast) the above to make
+ // sum(dE/dY)-sum(dE/dY \cdot Y) \cdot Y
+ caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+ batch_sum_multiplier_.cpu_data(), mean_.cpu_data(), 0.,
+ num_by_chans_.mutable_cpu_data());
+ caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num * channels_,
+ spatial_dim, 1, 1., num_by_chans_.cpu_data(),
+ spatial_sum_multiplier_.cpu_data(), 1., bottom_diff);
+
+ // dE/dY - mean(dE/dY)-mean(dE/dY \cdot Y) \cdot Y
+ caffe_cpu_axpby(temp_.count(), Dtype(1), top_diff,
+ Dtype(-1. / (num * spatial_dim)), bottom_diff);
+
+ // note: temp_ still contains sqrt(var(X)+eps), computed during the forward
+ // pass.
+ caffe_div(temp_.count(), bottom_diff, temp_.cpu_data(), bottom_diff);
+}
+
+
+#ifdef CPU_ONLY
+STUB_GPU(BatchNormLayer);
+#endif
+
+INSTANTIATE_CLASS(BatchNormLayer);
+REGISTER_LAYER_CLASS(BatchNorm);
+} // namespace caffe
--- /dev/null
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layers/batch_norm_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void BatchNormLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ const Dtype* bottom_data = bottom[0]->gpu_data();
+ Dtype* top_data = top[0]->mutable_gpu_data();
+ int num = bottom[0]->shape(0);
+ int spatial_dim = bottom[0]->count()/(channels_*bottom[0]->shape(0));
+
+ if (bottom[0] != top[0]) {
+ caffe_copy(bottom[0]->count(), bottom_data, top_data);
+ }
+
+
+ if (use_global_stats_) {
+ // use the stored mean/variance estimates.
+ const Dtype scale_factor = this->blobs_[2]->cpu_data()[0] == 0 ?
+ 0 : 1 / this->blobs_[2]->cpu_data()[0];
+ caffe_gpu_scale(variance_.count(), scale_factor,
+ this->blobs_[0]->gpu_data(), mean_.mutable_gpu_data());
+ caffe_gpu_scale(variance_.count(), scale_factor,
+ this->blobs_[1]->gpu_data(), variance_.mutable_gpu_data());
+ } else {
+ // compute mean
+ caffe_gpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim,
+ 1. / (num * spatial_dim), bottom_data,
+ spatial_sum_multiplier_.gpu_data(), 0.,
+ num_by_chans_.mutable_gpu_data());
+ caffe_gpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+ num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), 0.,
+ mean_.mutable_gpu_data());
+ }
+
+ // subtract mean
+ caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+ batch_sum_multiplier_.gpu_data(), mean_.gpu_data(), 0.,
+ num_by_chans_.mutable_gpu_data());
+ caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels_ * num,
+ spatial_dim, 1, -1, num_by_chans_.gpu_data(),
+ spatial_sum_multiplier_.gpu_data(), 1., top_data);
+
+ if (!use_global_stats_) {
+ // compute variance using var(X) = E((X-EX)^2)
+ caffe_gpu_powx(top[0]->count(), top_data, Dtype(2),
+ temp_.mutable_gpu_data()); // (X-EX)^2
+ caffe_gpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim,
+ 1. / (num * spatial_dim), temp_.gpu_data(),
+ spatial_sum_multiplier_.gpu_data(), 0.,
+ num_by_chans_.mutable_gpu_data());
+ caffe_gpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+ num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), 0.,
+ variance_.mutable_gpu_data()); // E((X_EX)^2)
+
+ // compute and save moving average
+ this->blobs_[2]->mutable_cpu_data()[0] *= moving_average_fraction_;
+ this->blobs_[2]->mutable_cpu_data()[0] += 1;
+ caffe_gpu_axpby(mean_.count(), Dtype(1), mean_.gpu_data(),
+ moving_average_fraction_, this->blobs_[0]->mutable_gpu_data());
+ int m = bottom[0]->count()/channels_;
+ Dtype bias_correction_factor = m > 1 ? Dtype(m)/(m-1) : 1;
+ caffe_gpu_axpby(variance_.count(), bias_correction_factor,
+ variance_.gpu_data(), moving_average_fraction_,
+ this->blobs_[1]->mutable_gpu_data());
+ }
+
+ // normalize variance
+ caffe_gpu_add_scalar(variance_.count(), eps_, variance_.mutable_gpu_data());
+ caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
+ variance_.mutable_gpu_data());
+
+ // replicate variance to input size
+ caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+ batch_sum_multiplier_.gpu_data(), variance_.gpu_data(), 0.,
+ num_by_chans_.mutable_gpu_data());
+ caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels_ * num,
+ spatial_dim, 1, 1., num_by_chans_.gpu_data(),
+ spatial_sum_multiplier_.gpu_data(), 0., temp_.mutable_gpu_data());
+ caffe_gpu_div(temp_.count(), top_data, temp_.gpu_data(), top_data);
+ // TODO(cdoersch): The caching is only needed because later in-place layers
+ // might clobber the data. Can we skip this if they won't?
+ caffe_copy(x_norm_.count(), top_data,
+ x_norm_.mutable_gpu_data());
+}
+
+template <typename Dtype>
+void BatchNormLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down,
+ const vector<Blob<Dtype>*>& bottom) {
+ const Dtype* top_diff;
+ if (bottom[0] != top[0]) {
+ top_diff = top[0]->gpu_diff();
+ } else {
+ caffe_copy(x_norm_.count(), top[0]->gpu_diff(), x_norm_.mutable_gpu_diff());
+ top_diff = x_norm_.gpu_diff();
+ }
+ Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+ if (use_global_stats_) {
+ caffe_gpu_div(temp_.count(), top_diff, temp_.gpu_data(), bottom_diff);
+ return;
+ }
+ const Dtype* top_data = x_norm_.gpu_data();
+ int num = bottom[0]->shape()[0];
+ int spatial_dim = bottom[0]->count()/(channels_*bottom[0]->shape(0));
+ // if Y = (X-mean(X))/(sqrt(var(X)+eps)), then
+ //
+ // dE(Y)/dX =
+ // (dE/dY - mean(dE/dY) - mean(dE/dY \cdot Y) \cdot Y)
+ // ./ sqrt(var(X) + eps)
+ //
+ // where \cdot and ./ are hadamard product and elementwise division,
+ // respectively, dE/dY is the top diff, and mean/var/sum are all computed
+ // along all dimensions except the channels dimension. In the above
+ // equation, the operations allow for expansion (i.e. broadcast) along all
+ // dimensions except the channels dimension where required.
+
+ // sum(dE/dY \cdot Y)
+ caffe_gpu_mul(temp_.count(), top_data, top_diff, bottom_diff);
+ caffe_gpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim, 1.,
+ bottom_diff, spatial_sum_multiplier_.gpu_data(), 0.,
+ num_by_chans_.mutable_gpu_data());
+ caffe_gpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+ num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), 0.,
+ mean_.mutable_gpu_data());
+
+ // reshape (broadcast) the above
+ caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+ batch_sum_multiplier_.gpu_data(), mean_.gpu_data(), 0.,
+ num_by_chans_.mutable_gpu_data());
+ caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels_ * num,
+ spatial_dim, 1, 1., num_by_chans_.gpu_data(),
+ spatial_sum_multiplier_.gpu_data(), 0., bottom_diff);
+
+ // sum(dE/dY \cdot Y) \cdot Y
+ caffe_gpu_mul(temp_.count(), top_data, bottom_diff, bottom_diff);
+
+ // sum(dE/dY)-sum(dE/dY \cdot Y) \cdot Y
+ caffe_gpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim, 1.,
+ top_diff, spatial_sum_multiplier_.gpu_data(), 0.,
+ num_by_chans_.mutable_gpu_data());
+ caffe_gpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+ num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), 0.,
+ mean_.mutable_gpu_data());
+ // reshape (broadcast) the above to make
+ // sum(dE/dY)-sum(dE/dY \cdot Y) \cdot Y
+ caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+ batch_sum_multiplier_.gpu_data(), mean_.gpu_data(), 0.,
+ num_by_chans_.mutable_gpu_data());
+ caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num * channels_,
+ spatial_dim, 1, 1., num_by_chans_.gpu_data(),
+ spatial_sum_multiplier_.gpu_data(), 1., bottom_diff);
+
+ // dE/dY - mean(dE/dY)-mean(dE/dY \cdot Y) \cdot Y
+ caffe_gpu_axpby(temp_.count(), Dtype(1), top_diff,
+ Dtype(-1. / (num * spatial_dim)), bottom_diff);
+
+ // note: temp_ still contains sqrt(var(X)+eps), computed during the forward
+ // pass.
+ caffe_gpu_div(temp_.count(), bottom_diff, temp_.gpu_data(), bottom_diff);
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(BatchNormLayer);
+
+
+} // namespace caffe
--- /dev/null
+#include <vector>
+
+#include "caffe/layers/batch_reindex_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template<typename Dtype>
+void BatchReindexLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ CHECK_EQ(1, bottom[1]->num_axes());
+ vector<int> newshape;
+ newshape.push_back(bottom[1]->shape(0));
+ for (int i = 1; i < bottom[0]->shape().size(); ++i) {
+ newshape.push_back(bottom[0]->shape()[i]);
+ }
+ top[0]->Reshape(newshape);
+}
+
+template<typename Dtype>
+void BatchReindexLayer<Dtype>::check_batch_reindex(int initial_num,
+ int final_num,
+ const Dtype* ridx_data) {
+ for (int i = 0; i < final_num; ++i) {
+ CHECK_GE(ridx_data[i], 0)
+ << "Index specified for reindex layer was negative.";
+ CHECK_LT(ridx_data[i], initial_num)
+ << "Index specified for reindex layer was greater than batch size.";
+ }
+}
+
+template<typename Dtype>
+void BatchReindexLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ check_batch_reindex(bottom[0]->shape(0), bottom[1]->count(),
+ bottom[1]->cpu_data());
+ if (top[0]->count() == 0) {
+ return;
+ }
+ int inner_dim = bottom[0]->count() / bottom[0]->shape(0);
+ const Dtype* in = bottom[0]->cpu_data();
+ const Dtype* permut = bottom[1]->cpu_data();
+ Dtype* out = top[0]->mutable_cpu_data();
+ for (int index = 0; index < top[0]->count(); ++index) {
+ int n = index / (inner_dim);
+ int in_n = static_cast<int>(permut[n]);
+ out[index] = in[in_n * (inner_dim) + index % (inner_dim)];
+ }
+}
+
+template<typename Dtype>
+void BatchReindexLayer<Dtype>::Backward_cpu(
+ const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
+ const vector<Blob<Dtype>*>& bottom) {
+ CHECK(!propagate_down[1]) << "Cannot backprop to index.";
+ if (!propagate_down[0]) {
+ return;
+ }
+ int inner_dim = bottom[0]->count() / bottom[0]->shape(0);
+ Dtype* bot_diff = bottom[0]->mutable_cpu_diff();
+ const Dtype* permut = bottom[1]->cpu_data();
+ const Dtype* top_diff = top[0]->cpu_diff();
+ caffe_set(bottom[0]->count(), Dtype(0), bot_diff);
+ for (int index = 0; index < top[0]->count(); ++index) {
+ int n = index / (inner_dim);
+ int in_n = static_cast<int>(permut[n]);
+ bot_diff[in_n * (inner_dim) + index % (inner_dim)] += top_diff[index];
+ }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(BatchReindexLayer);
+#endif
+
+INSTANTIATE_CLASS(BatchReindexLayer);
+REGISTER_LAYER_CLASS(BatchReindex);
+
+} // namespace caffe
--- /dev/null
+#include <algorithm>
+#include <utility>
+#include <vector>
+
+#include "caffe/layers/batch_reindex_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template<typename Dtype>
+__global__ void BRForward(const int count, const int inner_dim, const Dtype* in,
+ const Dtype* permut, Dtype* out) {
+ CUDA_KERNEL_LOOP(index, count) {
+ int n = index / (inner_dim);
+ int in_n = static_cast<int>(permut[n]);
+ out[index] = in[in_n * (inner_dim) + index % (inner_dim)];
+ }
+}
+
+template<typename Dtype>
+void BatchReindexLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ check_batch_reindex(bottom[0]->shape(0), bottom[1]->count(),
+ bottom[1]->cpu_data());
+ if (top[0]->count() == 0) {
+ return;
+ }
+ int threads = top[0]->count();
+ // NOLINT_NEXT_LINE(whitespace/operators)
+ BRForward<Dtype> <<<CAFFE_GET_BLOCKS(threads), CAFFE_CUDA_NUM_THREADS>>>(
+ top[0]->count(), bottom[0]->count() / bottom[0]->shape(0),
+ bottom[0]->gpu_data(), bottom[1]->gpu_data(), top[0]->mutable_gpu_data());
+ CUDA_POST_KERNEL_CHECK;
+}
+
+template<typename Dtype>
+__global__ void BRBackward(const int count, const int inner_dim,
+ const Dtype* in, const Dtype* top_indexes,
+ const Dtype* begins, const Dtype* counts,
+ Dtype* out) {
+ CUDA_KERNEL_LOOP(index, count) {
+ int n = index / (inner_dim);
+ out[index] = 0;
+ int lower = static_cast<int>(begins[n]);
+ int upper = lower + static_cast<int>(counts[n]);
+ for (int i = lower; i < upper; ++i) {
+ int in_n = static_cast<int>(top_indexes[i]);
+ out[index] += in[in_n * (inner_dim) + index % (inner_dim)];
+ }
+ }
+}
+
+template<typename Dtype>
+void BatchReindexLayer<Dtype>::Backward_gpu(
+ const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
+ const vector<Blob<Dtype>*>& bottom) {
+ CHECK(!propagate_down[1]) << "Cannot backprop to index.";
+ if (!propagate_down[0]) {
+ return;
+ }
+
+ vector<std::pair<int, int> > mapping;
+ const Dtype* perm = bottom[1]->cpu_data();
+ for (int i = 0; i < bottom[1]->count(); ++i) {
+ mapping.push_back(pair<int, int>(static_cast<int>(perm[i]), i));
+ }
+ std::sort(mapping.begin(), mapping.end(), pair_sort_first());
+
+ // Each element of the bottom diff is potentially the sum of many top diffs.
+ // However, we'd like each CUDA thread to handle exactly one output. Hence,
+ // we first pre-compute a list of lists of indices that need to be summed for
+ // each output. `top_indexes` holds the data of this list of lists. The
+ // k'th element of `begins` points to the location in `top_indexes` where the
+ // list for the k'th example begin, and the k'th element of `counts` is the
+ // length of that list.
+ vector<int> shape;
+ shape.push_back(bottom[1]->count());
+ Blob<Dtype> top_indexes(shape);
+ shape[0] = bottom[0]->shape(0);
+ Blob<Dtype> counts(shape);
+ Blob<Dtype> begins(shape);
+ Dtype* t_i_data = top_indexes.mutable_cpu_data();
+ Dtype* c_data = counts.mutable_cpu_data();
+ Dtype* b_data = begins.mutable_cpu_data();
+ caffe_set(begins.count(), Dtype(-1), b_data);
+ caffe_set(counts.count(), Dtype(0), c_data);
+ for (int i = 0; i < mapping.size(); ++i) {
+ t_i_data[i] = mapping[i].second;
+ if (b_data[mapping[i].first] == -1) {
+ b_data[mapping[i].first] = i;
+ }
+ c_data[mapping[i].first] += 1;
+ }
+
+ int threads = bottom[0]->count();
+ // NOLINT_NEXT_LINE(whitespace/operators)
+ BRBackward<Dtype> <<<CAFFE_GET_BLOCKS(threads), CAFFE_CUDA_NUM_THREADS>>>(
+ bottom[0]->count(), bottom[0]->count() / bottom[0]->shape(0),
+ top[0]->gpu_diff(), top_indexes.gpu_data(), begins.gpu_data(),
+ counts.gpu_data(), bottom[0]->mutable_gpu_diff());
+ CUDA_POST_KERNEL_CHECK;
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(BatchReindexLayer);
+
+} // namespace caffe
#include <algorithm>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/bnll_layer.hpp"
namespace caffe {
#include <algorithm>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/bnll_layer.hpp"
namespace caffe {
#include <vector>
-#include "caffe/layer.hpp"
+#include "caffe/layers/concat_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
}
top[0]->Reshape(top_shape);
CHECK_EQ(bottom_count_sum, top[0]->count());
+ if (bottom.size() == 1) {
+ top[0]->ShareData(*bottom[0]);
+ top[0]->ShareDiff(*bottom[0]);
+ }
}
template <typename Dtype>
void ConcatLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
+ if (bottom.size() == 1) { return; }
Dtype* top_data = top[0]->mutable_cpu_data();
int offset_concat_axis = 0;
const int top_concat_axis = top[0]->shape(concat_axis_);
template <typename Dtype>
void ConcatLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+ if (bottom.size() == 1) { return; }
const Dtype* top_diff = top[0]->cpu_diff();
int offset_concat_axis = 0;
const int top_concat_axis = top[0]->shape(concat_axis_);
for (int i = 0; i < bottom.size(); ++i) {
- if (!propagate_down[i]) { continue; }
- Dtype* bottom_diff = bottom[i]->mutable_cpu_diff();
const int bottom_concat_axis = bottom[i]->shape(concat_axis_);
- for (int n = 0; n < num_concats_; ++n) {
- caffe_copy(bottom_concat_axis * concat_input_size_, top_diff +
- (n * top_concat_axis + offset_concat_axis) * concat_input_size_,
- bottom_diff + n * bottom_concat_axis * concat_input_size_);
+ if (propagate_down[i]) {
+ Dtype* bottom_diff = bottom[i]->mutable_cpu_diff();
+ for (int n = 0; n < num_concats_; ++n) {
+ caffe_copy(bottom_concat_axis * concat_input_size_, top_diff +
+ (n * top_concat_axis + offset_concat_axis) * concat_input_size_,
+ bottom_diff + n * bottom_concat_axis * concat_input_size_);
+ }
}
offset_concat_axis += bottom_concat_axis;
}
#include <vector>
-#include "caffe/layer.hpp"
+#include "caffe/layers/concat_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
template <typename Dtype>
+__global__ void Concat(const int nthreads, const Dtype* in_data,
+ const bool forward, const int num_concats, const int concat_size,
+ const int top_concat_axis, const int bottom_concat_axis,
+ const int offset_concat_axis, Dtype* out_data) {
+ CUDA_KERNEL_LOOP(index, nthreads) {
+ const int total_concat_size = concat_size * bottom_concat_axis;
+ const int concat_num = index / total_concat_size;
+ const int concat_index = index % total_concat_size;
+ const int top_index = concat_index +
+ (concat_num * top_concat_axis + offset_concat_axis) * concat_size;
+ if (forward) {
+ out_data[top_index] = in_data[index];
+ } else {
+ out_data[index] = in_data[top_index];
+ }
+ }
+}
+
+template <typename Dtype>
void ConcatLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
+ if (bottom.size() == 1) { return; }
Dtype* top_data = top[0]->mutable_gpu_data();
int offset_concat_axis = 0;
const int top_concat_axis = top[0]->shape(concat_axis_);
+ const bool kForward = true;
for (int i = 0; i < bottom.size(); ++i) {
const Dtype* bottom_data = bottom[i]->gpu_data();
const int bottom_concat_axis = bottom[i]->shape(concat_axis_);
- for (int n = 0; n < num_concats_; ++n) {
- caffe_copy(bottom_concat_axis * concat_input_size_,
- bottom_data + n * bottom_concat_axis * concat_input_size_,
- top_data + (n * top_concat_axis + offset_concat_axis)
- * concat_input_size_);
- }
+ const int bottom_concat_size = bottom_concat_axis * concat_input_size_;
+ const int nthreads = bottom_concat_size * num_concats_;
+ Concat<Dtype> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+ nthreads, bottom_data, kForward, num_concats_, concat_input_size_,
+ top_concat_axis, bottom_concat_axis, offset_concat_axis, top_data);
offset_concat_axis += bottom_concat_axis;
}
}
template <typename Dtype>
void ConcatLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+ if (bottom.size() == 1) { return; }
const Dtype* top_diff = top[0]->gpu_diff();
int offset_concat_axis = 0;
const int top_concat_axis = top[0]->shape(concat_axis_);
+ const bool kForward = false;
for (int i = 0; i < bottom.size(); ++i) {
- if (!propagate_down[i]) { continue; }
- Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
const int bottom_concat_axis = bottom[i]->shape(concat_axis_);
- for (int n = 0; n < num_concats_; ++n) {
- caffe_copy(bottom_concat_axis * concat_input_size_, top_diff +
- (n * top_concat_axis + offset_concat_axis) * concat_input_size_,
- bottom_diff + n * bottom_concat_axis * concat_input_size_);
+ if (propagate_down[i]) {
+ Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
+ const int bottom_concat_size = bottom_concat_axis * concat_input_size_;
+ const int nthreads = bottom_concat_size * num_concats_;
+ Concat<Dtype> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+ nthreads, top_diff, kForward, num_concats_, concat_input_size_,
+ top_concat_axis, bottom_concat_axis, offset_concat_axis, bottom_diff);
}
offset_concat_axis += bottom_concat_axis;
}
#include <algorithm>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/loss_layers.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/layers/contrastive_loss_layer.hpp"
#include "caffe/util/math_functions.hpp"
namespace caffe {
diff_.mutable_cpu_data()); // a_i-b_i
const int channels = bottom[0]->channels();
Dtype margin = this->layer_param_.contrastive_loss_param().margin();
+ bool legacy_version =
+ this->layer_param_.contrastive_loss_param().legacy_version();
Dtype loss(0.0);
for (int i = 0; i < bottom[0]->num(); ++i) {
dist_sq_.mutable_cpu_data()[i] = caffe_cpu_dot(channels,
if (static_cast<int>(bottom[2]->cpu_data()[i])) { // similar pairs
loss += dist_sq_.cpu_data()[i];
} else { // dissimilar pairs
- loss += std::max(margin-dist_sq_.cpu_data()[i], Dtype(0.0));
+ if (legacy_version) {
+ loss += std::max(margin - dist_sq_.cpu_data()[i], Dtype(0.0));
+ } else {
+ Dtype dist = std::max<Dtype>(margin - sqrt(dist_sq_.cpu_data()[i]),
+ Dtype(0.0));
+ loss += dist*dist;
+ }
}
}
loss = loss / static_cast<Dtype>(bottom[0]->num()) / Dtype(2);
void ContrastiveLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
Dtype margin = this->layer_param_.contrastive_loss_param().margin();
+ bool legacy_version =
+ this->layer_param_.contrastive_loss_param().legacy_version();
for (int i = 0; i < 2; ++i) {
if (propagate_down[i]) {
const Dtype sign = (i == 0) ? 1 : -1;
Dtype(0.0),
bout + (j*channels));
} else { // dissimilar pairs
- if ((margin-dist_sq_.cpu_data()[j]) > Dtype(0.0)) {
+ Dtype mdist(0.0);
+ Dtype beta(0.0);
+ if (legacy_version) {
+ mdist = margin - dist_sq_.cpu_data()[j];
+ beta = -alpha;
+ } else {
+ Dtype dist = sqrt(dist_sq_.cpu_data()[j]);
+ mdist = margin - dist;
+ beta = -alpha * mdist / (dist + Dtype(1e-4));
+ }
+ if (mdist > Dtype(0.0)) {
caffe_cpu_axpby(
channels,
- -alpha,
+ beta,
diff_.cpu_data() + (j*channels),
Dtype(0.0),
bout + (j*channels));
#include <algorithm>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/layers/contrastive_loss_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
Dtype(0.0),
dist_sq_.mutable_gpu_data()); // \Sum (a_i-b_i)^2
Dtype margin = this->layer_param_.contrastive_loss_param().margin();
+ bool legacy_version =
+ this->layer_param_.contrastive_loss_param().legacy_version();
Dtype loss(0.0);
for (int i = 0; i < bottom[0]->num(); ++i) {
if (static_cast<int>(bottom[2]->cpu_data()[i])) { // similar pairs
loss += dist_sq_.cpu_data()[i];
} else { // dissimilar pairs
- loss += std::max(margin-dist_sq_.cpu_data()[i], Dtype(0.0));
+ if (legacy_version) {
+ loss += std::max(margin - dist_sq_.cpu_data()[i], Dtype(0.0));
+ } else {
+ Dtype dist = std::max(margin - sqrt(dist_sq_.cpu_data()[i]),
+ Dtype(0.0));
+ loss += dist*dist;
+ }
}
}
loss = loss / static_cast<Dtype>(bottom[0]->num()) / Dtype(2);
}
template <typename Dtype>
-__global__ void CLLForward(const int count, const int channels,
- const Dtype margin, const Dtype alpha,
+__global__ void CLLBackward(const int count, const int channels,
+ const Dtype margin, const bool legacy_version, const Dtype alpha,
const Dtype* y, const Dtype* diff, const Dtype* dist_sq,
Dtype *bottom_diff) {
CUDA_KERNEL_LOOP(i, count) {
if (static_cast<int>(y[n])) { // similar pairs
bottom_diff[i] = alpha * diff[i];
} else { // dissimilar pairs
- if ((margin-dist_sq[n]) > 0.0) {
- bottom_diff[i] = -alpha * diff[i];
+ Dtype mdist(0.0);
+ Dtype beta(0.0);
+ if (legacy_version) {
+ mdist = (margin - dist_sq[n]);
+ beta = -alpha;
+ } else {
+ Dtype dist = sqrt(dist_sq[n]);
+ mdist = (margin - dist);
+ beta = -alpha * mdist / (dist + Dtype(1e-4)) * diff[i];
+ }
+ if (mdist > 0.0) {
+ bottom_diff[i] = beta;
} else {
bottom_diff[i] = 0;
}
const int count = bottom[0]->count();
const int channels = bottom[0]->channels();
Dtype margin = this->layer_param_.contrastive_loss_param().margin();
+ const bool legacy_version =
+ this->layer_param_.contrastive_loss_param().legacy_version();
const Dtype sign = (i == 0) ? 1 : -1;
const Dtype alpha = sign * top[0]->cpu_diff()[0] /
static_cast<Dtype>(bottom[0]->num());
// NOLINT_NEXT_LINE(whitespace/operators)
- CLLForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
- count, channels, margin, alpha,
+ CLLBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+ count, channels, margin, legacy_version, alpha,
bottom[2]->gpu_data(), // pair similarity 0 or 1
diff_.gpu_data(), // the cached eltwise difference between a and b
dist_sq_.gpu_data(), // the cached square distance between a and b
#include <vector>
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/conv_layer.hpp"
namespace caffe {
template <typename Dtype>
void ConvolutionLayer<Dtype>::compute_output_shape() {
- this->height_out_ = (this->height_ + 2 * this->pad_h_ - this->kernel_h_)
- / this->stride_h_ + 1;
- this->width_out_ = (this->width_ + 2 * this->pad_w_ - this->kernel_w_)
- / this->stride_w_ + 1;
+ const int* kernel_shape_data = this->kernel_shape_.cpu_data();
+ const int* stride_data = this->stride_.cpu_data();
+ const int* pad_data = this->pad_.cpu_data();
+ const int* dilation_data = this->dilation_.cpu_data();
+ this->output_shape_.clear();
+ for (int i = 0; i < this->num_spatial_axes_; ++i) {
+ // i + 1 to skip channel axis
+ const int input_dim = this->input_shape(i + 1);
+ const int kernel_extent = dilation_data[i] * (kernel_shape_data[i] - 1) + 1;
+ const int output_dim = (input_dim + 2 * pad_data[i] - kernel_extent)
+ / stride_data[i] + 1;
+ this->output_shape_.push_back(output_dim);
+ }
}
template <typename Dtype>
const Dtype* bottom_data = bottom[i]->cpu_data();
Dtype* top_data = top[i]->mutable_cpu_data();
for (int n = 0; n < this->num_; ++n) {
- this->forward_cpu_gemm(bottom_data + bottom[i]->offset(n), weight,
- top_data + top[i]->offset(n));
+ this->forward_cpu_gemm(bottom_data + n * this->bottom_dim_, weight,
+ top_data + n * this->top_dim_);
if (this->bias_term_) {
const Dtype* bias = this->blobs_[1]->cpu_data();
- this->forward_cpu_bias(top_data + top[i]->offset(n), bias);
+ this->forward_cpu_bias(top_data + n * this->top_dim_, bias);
}
}
}
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
const Dtype* weight = this->blobs_[0]->cpu_data();
Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();
- if (this->param_propagate_down_[0]) {
- caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
- }
- if (this->bias_term_ && this->param_propagate_down_[1]) {
- caffe_set(this->blobs_[1]->count(), Dtype(0),
- this->blobs_[1]->mutable_cpu_diff());
- }
for (int i = 0; i < top.size(); ++i) {
const Dtype* top_diff = top[i]->cpu_diff();
const Dtype* bottom_data = bottom[i]->cpu_data();
if (this->bias_term_ && this->param_propagate_down_[1]) {
Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff();
for (int n = 0; n < this->num_; ++n) {
- this->backward_cpu_bias(bias_diff, top_diff + top[i]->offset(n));
+ this->backward_cpu_bias(bias_diff, top_diff + n * this->top_dim_);
}
}
if (this->param_propagate_down_[0] || propagate_down[i]) {
for (int n = 0; n < this->num_; ++n) {
// gradient w.r.t. weight. Note that we will accumulate diffs.
if (this->param_propagate_down_[0]) {
- this->weight_cpu_gemm(bottom_data + bottom[i]->offset(n),
- top_diff + top[i]->offset(n), weight_diff);
+ this->weight_cpu_gemm(bottom_data + n * this->bottom_dim_,
+ top_diff + n * this->top_dim_, weight_diff);
}
// gradient w.r.t. bottom data, if necessary.
if (propagate_down[i]) {
- this->backward_cpu_gemm(top_diff + top[i]->offset(n), weight,
- bottom_diff + bottom[i]->offset(n));
+ this->backward_cpu_gemm(top_diff + n * this->top_dim_, weight,
+ bottom_diff + n * this->bottom_dim_);
}
}
}
#include <vector>
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/conv_layer.hpp"
namespace caffe {
const Dtype* bottom_data = bottom[i]->gpu_data();
Dtype* top_data = top[i]->mutable_gpu_data();
for (int n = 0; n < this->num_; ++n) {
- this->forward_gpu_gemm(bottom_data + bottom[i]->offset(n), weight,
- top_data + top[i]->offset(n));
+ this->forward_gpu_gemm(bottom_data + n * this->bottom_dim_, weight,
+ top_data + n * this->top_dim_);
if (this->bias_term_) {
const Dtype* bias = this->blobs_[1]->gpu_data();
- this->forward_gpu_bias(top_data + top[i]->offset(n), bias);
+ this->forward_gpu_bias(top_data + n * this->top_dim_, bias);
}
}
}
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
const Dtype* weight = this->blobs_[0]->gpu_data();
Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();
- if (this->param_propagate_down_[0]) {
- caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
- }
- if (this->bias_term_ && this->param_propagate_down_[1]) {
- caffe_gpu_set(this->blobs_[1]->count(), Dtype(0),
- this->blobs_[1]->mutable_gpu_diff());
- }
for (int i = 0; i < top.size(); ++i) {
const Dtype* top_diff = top[i]->gpu_diff();
// Bias gradient, if necessary.
if (this->bias_term_ && this->param_propagate_down_[1]) {
Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff();
for (int n = 0; n < this->num_; ++n) {
- this->backward_gpu_bias(bias_diff, top_diff + top[i]->offset(n));
+ this->backward_gpu_bias(bias_diff, top_diff + n * this->top_dim_);
}
}
if (this->param_propagate_down_[0] || propagate_down[i]) {
for (int n = 0; n < this->num_; ++n) {
// gradient w.r.t. weight. Note that we will accumulate diffs.
if (this->param_propagate_down_[0]) {
- this->weight_gpu_gemm(bottom_data + bottom[i]->offset(n),
- top_diff + top[i]->offset(n), weight_diff);
+ this->weight_gpu_gemm(bottom_data + n * this->bottom_dim_,
+ top_diff + n * this->top_dim_, weight_diff);
}
// gradient w.r.t. bottom data, if necessary.
if (propagate_down[i]) {
- this->backward_gpu_gemm(top_diff + top[i]->offset(n), weight,
- bottom_diff + bottom[i]->offset(n));
+ this->backward_gpu_gemm(top_diff + n * this->top_dim_, weight,
+ bottom_diff + n * this->bottom_dim_);
}
}
}
#ifdef USE_CUDNN
+#include <algorithm>
#include <vector>
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_conv_layer.hpp"
namespace caffe {
stream_ = new cudaStream_t[this->group_ * CUDNN_STREAMS_PER_GROUP];
handle_ = new cudnnHandle_t[this->group_ * CUDNN_STREAMS_PER_GROUP];
+ // Initialize algorithm arrays
+ fwd_algo_ = new cudnnConvolutionFwdAlgo_t[bottom.size()];
+ bwd_filter_algo_= new cudnnConvolutionBwdFilterAlgo_t[bottom.size()];
+ bwd_data_algo_ = new cudnnConvolutionBwdDataAlgo_t[bottom.size()];
+
+ // initialize size arrays
+ workspace_fwd_sizes_ = new size_t[bottom.size()];
+ workspace_bwd_filter_sizes_ = new size_t[bottom.size()];
+ workspace_bwd_data_sizes_ = new size_t[bottom.size()];
+
+ // workspace data
+ workspaceSizeInBytes = 0;
+ workspaceData = NULL;
+ workspace = new void*[this->group_ * CUDNN_STREAMS_PER_GROUP];
+
+ for (size_t i = 0; i < bottom.size(); ++i) {
+ // initialize all to default algorithms
+ fwd_algo_[i] = (cudnnConvolutionFwdAlgo_t)0;
+ bwd_filter_algo_[i] = (cudnnConvolutionBwdFilterAlgo_t)0;
+ bwd_data_algo_[i] = (cudnnConvolutionBwdDataAlgo_t)0;
+ // default algorithms don't require workspace
+ workspace_fwd_sizes_[i] = 0;
+ workspace_bwd_data_sizes_[i] = 0;
+ workspace_bwd_filter_sizes_[i] = 0;
+ }
+
for (int g = 0; g < this->group_ * CUDNN_STREAMS_PER_GROUP; g++) {
CUDA_CHECK(cudaStreamCreate(&stream_[g]));
CUDNN_CHECK(cudnnCreate(&handle_[g]));
CUDNN_CHECK(cudnnSetStream(handle_[g], stream_[g]));
+ workspace[g] = NULL;
}
// Set the indexing parameters.
- weight_offset_ = (this->num_output_ / this->group_)
- * (this->channels_ / this->group_) * this->kernel_h_ * this->kernel_w_;
bias_offset_ = (this->num_output_ / this->group_);
// Create filter descriptor.
+ const int* kernel_shape_data = this->kernel_shape_.cpu_data();
+ const int kernel_h = kernel_shape_data[0];
+ const int kernel_w = kernel_shape_data[1];
cudnn::createFilterDesc<Dtype>(&filter_desc_,
this->num_output_ / this->group_, this->channels_ / this->group_,
- this->kernel_h_, this->kernel_w_);
+ kernel_h, kernel_w);
// Create tensor descriptor(s) for data and corresponding convolution(s).
for (int i = 0; i < bottom.size(); i++) {
- cudnnTensor4dDescriptor_t bottom_desc;
+ cudnnTensorDescriptor_t bottom_desc;
cudnn::createTensor4dDesc<Dtype>(&bottom_desc);
bottom_descs_.push_back(bottom_desc);
- cudnnTensor4dDescriptor_t top_desc;
+ cudnnTensorDescriptor_t top_desc;
cudnn::createTensor4dDesc<Dtype>(&top_desc);
top_descs_.push_back(top_desc);
cudnnConvolutionDescriptor_t conv_desc;
void CuDNNConvolutionLayer<Dtype>::Reshape(
const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
ConvolutionLayer<Dtype>::Reshape(bottom, top);
- bottom_offset_ = (this->channels_ / this->group_)
- * this->height_ * this->width_;
- top_offset_ = (this->num_output_ / this->group_)
- * this->height_out_ * this->width_out_;
+ CHECK_EQ(2, this->num_spatial_axes_)
+ << "CuDNNConvolution input must have 2 spatial axes "
+ << "(e.g., height and width). "
+ << "Use 'engine: CAFFE' for general ND convolution.";
+ bottom_offset_ = this->bottom_dim_ / this->group_;
+ top_offset_ = this->top_dim_ / this->group_;
+ const int height = bottom[0]->shape(this->channel_axis_ + 1);
+ const int width = bottom[0]->shape(this->channel_axis_ + 2);
+ const int height_out = top[0]->shape(this->channel_axis_ + 1);
+ const int width_out = top[0]->shape(this->channel_axis_ + 2);
+ const int* pad_data = this->pad_.cpu_data();
+ const int pad_h = pad_data[0];
+ const int pad_w = pad_data[1];
+ const int* stride_data = this->stride_.cpu_data();
+ const int stride_h = stride_data[0];
+ const int stride_w = stride_data[1];
+
+ // Specify workspace limit for kernels directly until we have a
+ // planning strategy and a rewrite of Caffe's GPU memory mangagement
+ size_t workspace_limit_bytes = 8*1024*1024;
for (int i = 0; i < bottom.size(); i++) {
cudnn::setTensor4dDesc<Dtype>(&bottom_descs_[i],
this->num_,
- this->channels_ / this->group_,
- this->height_, this->width_,
- this->channels_ * this->height_ * this->width_,
- this->height_ * this->width_,
- this->width_, 1);
+ this->channels_ / this->group_, height, width,
+ this->channels_ * height * width,
+ height * width, width, 1);
cudnn::setTensor4dDesc<Dtype>(&top_descs_[i],
this->num_,
- this->num_output_ / this->group_,
- this->height_out_, this->width_out_,
- this->num_output_ * this->height_out_ * this->width_out_,
- this->height_out_ * this->width_out_,
- this->width_out_, 1);
+ this->num_output_ / this->group_, height_out, width_out,
+ this->num_output_ * this->out_spatial_dim_,
+ this->out_spatial_dim_, width_out, 1);
cudnn::setConvolutionDesc<Dtype>(&conv_descs_[i], bottom_descs_[i],
- filter_desc_, this->pad_h_, this->pad_w_,
- this->stride_h_, this->stride_w_);
+ filter_desc_, pad_h, pad_w,
+ stride_h, stride_w);
+
+ // choose forward and backward algorithms + workspace(s)
+ CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm(handle_[0],
+ bottom_descs_[i],
+ filter_desc_,
+ conv_descs_[i],
+ top_descs_[i],
+ CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
+ workspace_limit_bytes,
+ &fwd_algo_[i]));
+
+ CUDNN_CHECK(cudnnGetConvolutionForwardWorkspaceSize(handle_[0],
+ bottom_descs_[i],
+ filter_desc_,
+ conv_descs_[i],
+ top_descs_[i],
+ fwd_algo_[i],
+ &(workspace_fwd_sizes_[i])));
+
+ // choose backward algorithm for filter
+ CUDNN_CHECK(cudnnGetConvolutionBackwardFilterAlgorithm(handle_[0],
+ bottom_descs_[i], top_descs_[i], conv_descs_[i], filter_desc_,
+ CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
+ workspace_limit_bytes, &bwd_filter_algo_[i]) );
+
+ // get workspace for backwards filter algorithm
+ CUDNN_CHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(handle_[0],
+ bottom_descs_[i], top_descs_[i], conv_descs_[i], filter_desc_,
+ bwd_filter_algo_[i], &workspace_bwd_filter_sizes_[i]));
+
+ // choose backward algo for data
+ CUDNN_CHECK(cudnnGetConvolutionBackwardDataAlgorithm(handle_[0],
+ filter_desc_, top_descs_[i], conv_descs_[i], bottom_descs_[i],
+ CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
+ workspace_limit_bytes, &bwd_data_algo_[i]));
+
+ // get workspace size
+ CUDNN_CHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(handle_[0],
+ filter_desc_, top_descs_[i], conv_descs_[i], bottom_descs_[i],
+ bwd_data_algo_[i], &workspace_bwd_data_sizes_[i]) );
+ }
+
+ // reduce over all workspace sizes to get a maximum to allocate / reallocate
+ size_t total_workspace_fwd = 0;
+ size_t total_workspace_bwd_data = 0;
+ size_t total_workspace_bwd_filter = 0;
+
+ for (size_t i = 0; i < bottom.size(); i++) {
+ total_workspace_fwd = std::max(total_workspace_fwd,
+ workspace_fwd_sizes_[i]);
+ total_workspace_bwd_data = std::max(total_workspace_bwd_data,
+ workspace_bwd_data_sizes_[i]);
+ total_workspace_bwd_filter = std::max(total_workspace_bwd_filter,
+ workspace_bwd_filter_sizes_[i]);
+ }
+ // get max over all operations
+ size_t max_workspace = std::max(total_workspace_fwd,
+ total_workspace_bwd_data);
+ max_workspace = std::max(max_workspace, total_workspace_bwd_filter);
+ // ensure all groups have enough workspace
+ size_t total_max_workspace = max_workspace *
+ (this->group_ * CUDNN_STREAMS_PER_GROUP);
+
+ // this is the total amount of storage needed over all groups + streams
+ if (total_max_workspace > workspaceSizeInBytes) {
+ DLOG(INFO) << "Reallocating workspace storage: " << total_max_workspace;
+ workspaceSizeInBytes = total_max_workspace;
+
+ // free the existing workspace and allocate a new (larger) one
+ cudaFree(this->workspaceData);
+
+ cudaError_t err = cudaMalloc(&(this->workspaceData), workspaceSizeInBytes);
+ if (err != cudaSuccess) {
+ // force zero memory path
+ for (int i = 0; i < bottom.size(); i++) {
+ workspace_fwd_sizes_[i] = 0;
+ workspace_bwd_filter_sizes_[i] = 0;
+ workspace_bwd_data_sizes_[i] = 0;
+ fwd_algo_[i] = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM;
+ bwd_filter_algo_[i] = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0;
+ bwd_data_algo_[i] = CUDNN_CONVOLUTION_BWD_DATA_ALGO_0;
+ }
+
+ // NULL out all workspace pointers
+ for (int g = 0; g < (this->group_ * CUDNN_STREAMS_PER_GROUP); g++) {
+ workspace[g] = NULL;
+ }
+ // NULL out underlying data
+ workspaceData = NULL;
+ workspaceSizeInBytes = 0;
+ }
+
+ // if we succeed in the allocation, set pointer aliases for workspaces
+ for (int g = 0; g < (this->group_ * CUDNN_STREAMS_PER_GROUP); g++) {
+ workspace[g] = reinterpret_cast<char *>(workspaceData) + g*max_workspace;
+ }
}
// Tensor descriptor for bias.
if (!handles_setup_) { return; }
for (int i = 0; i < bottom_descs_.size(); i++) {
- cudnnDestroyTensor4dDescriptor(bottom_descs_[i]);
- cudnnDestroyTensor4dDescriptor(top_descs_[i]);
+ cudnnDestroyTensorDescriptor(bottom_descs_[i]);
+ cudnnDestroyTensorDescriptor(top_descs_[i]);
cudnnDestroyConvolutionDescriptor(conv_descs_[i]);
}
if (this->bias_term_) {
- cudnnDestroyTensor4dDescriptor(bias_desc_);
+ cudnnDestroyTensorDescriptor(bias_desc_);
}
cudnnDestroyFilterDescriptor(filter_desc_);
cudnnDestroy(handle_[g]);
}
+ cudaFree(workspaceData);
delete [] stream_;
delete [] handle_;
+ delete [] fwd_algo_;
+ delete [] bwd_filter_algo_;
+ delete [] bwd_data_algo_;
+ delete [] workspace_fwd_sizes_;
+ delete [] workspace_bwd_data_sizes_;
+ delete [] workspace_bwd_filter_sizes_;
}
INSTANTIATE_CLASS(CuDNNConvolutionLayer);
#ifdef USE_CUDNN
#include <vector>
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_conv_layer.hpp"
namespace caffe {
template <typename Dtype>
void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+ const Dtype* weight = this->blobs_[0]->gpu_data();
for (int i = 0; i < bottom.size(); ++i) {
const Dtype* bottom_data = bottom[i]->gpu_data();
Dtype* top_data = top[i]->mutable_gpu_data();
- const Dtype* weight = this->blobs_[0]->gpu_data();
// Forward through cuDNN in parallel over groups.
for (int g = 0; g < this->group_; g++) {
// Filters.
CUDNN_CHECK(cudnnConvolutionForward(handle_[g],
- bottom_descs_[i], bottom_data + bottom_offset_ * g,
- filter_desc_, weight + weight_offset_ * g,
- conv_descs_[i],
- top_descs_[i], top_data + top_offset_ * g,
- CUDNN_RESULT_NO_ACCUMULATE));
+ cudnn::dataType<Dtype>::one,
+ bottom_descs_[i], bottom_data + bottom_offset_ * g,
+ filter_desc_, weight + this->weight_offset_ * g,
+ conv_descs_[i],
+ fwd_algo_[i], workspace[g], workspace_fwd_sizes_[i],
+ cudnn::dataType<Dtype>::zero,
+ top_descs_[i], top_data + top_offset_ * g));
// Bias.
if (this->bias_term_) {
const Dtype* bias_data = this->blobs_[1]->gpu_data();
- Dtype alpha = 1.;
- CUDNN_CHECK(cudnnAddTensor4d(handle_[g], CUDNN_ADD_SAME_C, &alpha,
- bias_desc_, bias_data + bias_offset_ * g,
- top_descs_[i], top_data + top_offset_ * g));
+#if CUDNN_VERSION_MIN(4, 0, 0)
+ CUDNN_CHECK(cudnnAddTensor(handle_[g],
+ cudnn::dataType<Dtype>::one,
+ bias_desc_, bias_data + bias_offset_ * g,
+ cudnn::dataType<Dtype>::one,
+ top_descs_[i], top_data + top_offset_ * g));
+#else
+ CUDNN_CHECK(cudnnAddTensor(handle_[g], CUDNN_ADD_SAME_C,
+ cudnn::dataType<Dtype>::one,
+ bias_desc_, bias_data + bias_offset_ * g,
+ cudnn::dataType<Dtype>::one,
+ top_descs_[i], top_data + top_offset_ * g));
+#endif
}
}
if (this->param_propagate_down_[0]) {
weight = this->blobs_[0]->gpu_data();
weight_diff = this->blobs_[0]->mutable_gpu_diff();
- caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
}
Dtype* bias_diff = NULL;
if (this->bias_term_ && this->param_propagate_down_[1]) {
bias_diff = this->blobs_[1]->mutable_gpu_diff();
- caffe_gpu_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
}
for (int i = 0; i < top.size(); ++i) {
const Dtype* top_diff = top[i]->gpu_diff();
// Gradient w.r.t. bias.
if (this->bias_term_ && this->param_propagate_down_[1]) {
CUDNN_CHECK(cudnnConvolutionBackwardBias(handle_[0*this->group_ + g],
- top_descs_[i], top_diff + top_offset_ * g,
- bias_desc_, bias_diff + bias_offset_ * g,
- CUDNN_RESULT_ACCUMULATE));
+ cudnn::dataType<Dtype>::one,
+ top_descs_[i], top_diff + top_offset_ * g,
+ cudnn::dataType<Dtype>::one,
+ bias_desc_, bias_diff + bias_offset_ * g));
}
// Gradient w.r.t. weights.
if (this->param_propagate_down_[0]) {
const Dtype* bottom_data = bottom[i]->gpu_data();
- CUDNN_CHECK(cudnnConvolutionBackwardFilter(handle_[1*this->group_ + g],
- bottom_descs_[i], bottom_data + bottom_offset_ * g,
- top_descs_[i], top_diff + top_offset_ * g,
- conv_descs_[i],
- filter_desc_, weight_diff + weight_offset_ * g,
- CUDNN_RESULT_ACCUMULATE));
+ CUDNN_CHECK(cudnnConvolutionBackwardFilter_v3(
+ handle_[1*this->group_ + g],
+ cudnn::dataType<Dtype>::one,
+ bottom_descs_[i], bottom_data + bottom_offset_ * g,
+ top_descs_[i], top_diff + top_offset_ * g,
+ conv_descs_[i],
+ bwd_filter_algo_[i], workspace[1*this->group_ + g],
+ workspace_bwd_filter_sizes_[i],
+ cudnn::dataType<Dtype>::one,
+ filter_desc_, weight_diff + this->weight_offset_ * g));
}
// Gradient w.r.t. bottom data.
weight = this->blobs_[0]->gpu_data();
}
Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
- CUDNN_CHECK(cudnnConvolutionBackwardData(handle_[2*this->group_ + g],
- filter_desc_, weight + weight_offset_ * g,
- top_descs_[i], top_diff + top_offset_ * g,
- conv_descs_[i],
- bottom_descs_[i], bottom_diff + bottom_offset_ * g,
- CUDNN_RESULT_NO_ACCUMULATE));
+ CUDNN_CHECK(cudnnConvolutionBackwardData_v3(
+ handle_[2*this->group_ + g],
+ cudnn::dataType<Dtype>::one,
+ filter_desc_, weight + this->weight_offset_ * g,
+ top_descs_[i], top_diff + top_offset_ * g,
+ conv_descs_[i],
+ bwd_data_algo_[i], workspace[2*this->group_ + g],
+ workspace_bwd_data_sizes_[i],
+ cudnn::dataType<Dtype>::zero,
+ bottom_descs_[i], bottom_diff + bottom_offset_ * g));
}
}
--- /dev/null
+#ifdef USE_CUDNN
+#include <vector>
+
+#include "caffe/layers/cudnn_lcn_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNLCNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ LRNLayer<Dtype>::LayerSetUp(bottom, top);
+
+ CUDNN_CHECK(cudnnCreate(&handle_));
+ CUDNN_CHECK(cudnnCreateLRNDescriptor(&norm_desc_));
+ cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
+ cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+
+ // create a LRN handle
+ handles_setup_ = true;
+
+ size_ = this->layer_param().lrn_param().local_size();
+ pre_pad_ = (size_ - 1) / 2;
+ alpha_ = this->layer_param().lrn_param().alpha();
+ beta_ = this->layer_param().lrn_param().beta();
+ k_ = this->layer_param().lrn_param().k();
+}
+
+template <typename Dtype>
+void CuDNNLCNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ LRNLayer<Dtype>::Reshape(bottom, top);
+ cudnn::setTensor4dDesc<Dtype>(&bottom_desc_, bottom[0]->num(),
+ this->channels_, this->height_, this->width_);
+ cudnn::setTensor4dDesc<Dtype>(&top_desc_, bottom[0]->num(),
+ this->channels_, this->height_, this->width_);
+ CUDNN_CHECK(cudnnSetLRNDescriptor(norm_desc_, size_, alpha_, beta_, k_));
+
+ // allocate / reallocate tempData buffers
+ size_t totalSizeInBytes = sizeof(Dtype)*bottom[0]->num()* \
+ this->channels_*this->height_*this->width_;
+
+ if (totalSizeInBytes > tempDataSize) {
+ tempDataSize = totalSizeInBytes;
+
+ cudaFree(tempData1);
+ cudaFree(tempData2);
+
+ // allocate new buffers
+ CUDA_CHECK(cudaMalloc(&tempData1, totalSizeInBytes));
+ CUDA_CHECK(cudaMalloc(&tempData2, totalSizeInBytes));
+ }
+}
+
+template <typename Dtype>
+CuDNNLCNLayer<Dtype>::~CuDNNLCNLayer() {
+ // Check that handles have been setup before destroying.
+ if (!handles_setup_) { return; }
+
+ cudnnDestroyTensorDescriptor(bottom_desc_);
+ cudnnDestroyTensorDescriptor(top_desc_);
+
+ // destroy LRN handle
+ cudnnDestroy(handle_);
+
+ // free temp buffers
+ cudaFree(tempData1);
+ cudaFree(tempData2);
+}
+
+INSTANTIATE_CLASS(CuDNNLCNLayer);
+
+} // namespace caffe
+#endif
--- /dev/null
+#ifdef USE_CUDNN
+#include <vector>
+
+#include "caffe/layers/cudnn_lcn_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNLCNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ const Dtype* bottom_data = bottom[0]->gpu_data();
+ Dtype* top_data = top[0]->mutable_gpu_data();
+
+ CUDNN_CHECK(cudnnDivisiveNormalizationForward(
+ handle_, norm_desc_, CUDNN_DIVNORM_PRECOMPUTED_MEANS,
+ cudnn::dataType<Dtype>::one,
+ bottom_desc_, bottom_data,
+ NULL, // srcMeansData
+ this->tempData1, this->tempData2,
+ cudnn::dataType<Dtype>::zero,
+ top_desc_, top_data) );
+}
+
+template <typename Dtype>
+void CuDNNLCNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+ const Dtype* top_diff = top[0]->gpu_diff();
+ const Dtype* top_data = top[0]->gpu_data();
+ const Dtype* bottom_data = bottom[0]->gpu_data();
+ Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+
+ CUDNN_CHECK(cudnnDivisiveNormalizationBackward(
+ handle_, norm_desc_, CUDNN_DIVNORM_PRECOMPUTED_MEANS,
+ cudnn::dataType<Dtype>::one,
+ bottom_desc_, bottom_data,
+ NULL, top_diff, // NULL - srcMeansData
+ this->tempData1, this->tempData2,
+ cudnn::dataType<Dtype>::zero,
+ bottom_desc_, bottom_diff,
+ NULL) );
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(CuDNNLCNLayer);
+
+} // namespace caffe
+#endif
--- /dev/null
+#ifdef USE_CUDNN
+#include <vector>
+
+#include "caffe/layers/cudnn_lrn_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNLRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ LRNLayer<Dtype>::LayerSetUp(bottom, top);
+
+ CUDNN_CHECK(cudnnCreate(&handle_));
+ CUDNN_CHECK(cudnnCreateLRNDescriptor(&norm_desc_));
+ cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
+ cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+
+ // create a LRN handle
+ handles_setup_ = true;
+
+ size_ = this->layer_param().lrn_param().local_size();
+ alpha_ = this->layer_param().lrn_param().alpha();
+ beta_ = this->layer_param().lrn_param().beta();
+ k_ = this->layer_param().lrn_param().k();
+}
+
+template <typename Dtype>
+void CuDNNLRNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ LRNLayer<Dtype>::Reshape(bottom, top);
+ cudnn::setTensor4dDesc<Dtype>(&bottom_desc_, bottom[0]->num(),
+ this->channels_, this->height_, this->width_);
+ cudnn::setTensor4dDesc<Dtype>(&top_desc_, bottom[0]->num(),
+ this->channels_, this->height_, this->width_);
+ CUDNN_CHECK(cudnnSetLRNDescriptor(norm_desc_, size_, alpha_, beta_, k_));
+}
+
+template <typename Dtype>
+CuDNNLRNLayer<Dtype>::~CuDNNLRNLayer() {
+ // Check that handles have been setup before destroying.
+ if (!handles_setup_) { return; }
+
+ cudnnDestroyTensorDescriptor(bottom_desc_);
+ cudnnDestroyTensorDescriptor(top_desc_);
+
+ // destroy LRN handle
+ cudnnDestroy(handle_);
+}
+
+INSTANTIATE_CLASS(CuDNNLRNLayer);
+
+} // namespace caffe
+#endif
--- /dev/null
+#ifdef USE_CUDNN
+#include <vector>
+
+#include "caffe/layers/cudnn_lrn_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNLRNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ const Dtype* bottom_data = bottom[0]->gpu_data();
+ Dtype* top_data = top[0]->mutable_gpu_data();
+
+ CUDNN_CHECK(cudnnLRNCrossChannelForward(
+ handle_, norm_desc_, CUDNN_LRN_CROSS_CHANNEL_DIM1,
+ cudnn::dataType<Dtype>::one,
+ bottom_desc_, bottom_data,
+ cudnn::dataType<Dtype>::zero,
+ top_desc_, top_data) );
+}
+
+template <typename Dtype>
+void CuDNNLRNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+ const Dtype* top_diff = top[0]->gpu_diff();
+ const Dtype* top_data = top[0]->gpu_data();
+ const Dtype* bottom_data = bottom[0]->gpu_data();
+ Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+
+ CUDNN_CHECK(cudnnLRNCrossChannelBackward(
+ handle_, norm_desc_, CUDNN_LRN_CROSS_CHANNEL_DIM1,
+ cudnn::dataType<Dtype>::one,
+ top_desc_, top_data,
+ top_desc_, top_diff,
+ bottom_desc_, bottom_data,
+ cudnn::dataType<Dtype>::zero,
+ bottom_desc_, bottom_diff) );
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(CuDNNLRNLayer);
+
+}; // namespace caffe
+
+#endif
#ifdef USE_CUDNN
#include <vector>
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_pooling_layer.hpp"
namespace caffe {
void CuDNNPoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
PoolingLayer<Dtype>::LayerSetUp(bottom, top);
- // Sanity check: CUDNN currently only supports pad == 0.
- CHECK_EQ(this->pad_h_, 0);
- CHECK_EQ(this->pad_w_, 0);
CUDNN_CHECK(cudnnCreate(&handle_));
cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
cudnn::createTensor4dDesc<Dtype>(&top_desc_);
cudnn::createPoolingDesc<Dtype>(&pooling_desc_,
this->layer_param_.pooling_param().pool(), &mode_,
- this->kernel_h_, this->kernel_w_, this->stride_h_, this->stride_w_);
+ this->kernel_h_, this->kernel_w_, this->pad_h_, this->pad_w_,
+ this->stride_h_, this->stride_w_);
handles_setup_ = true;
}
// Check that handles have been setup before destroying.
if (!handles_setup_) { return; }
- cudnnDestroyTensor4dDescriptor(bottom_desc_);
- cudnnDestroyTensor4dDescriptor(top_desc_);
+ cudnnDestroyTensorDescriptor(bottom_desc_);
+ cudnnDestroyTensorDescriptor(top_desc_);
cudnnDestroyPoolingDescriptor(pooling_desc_);
cudnnDestroy(handle_);
}
#ifdef USE_CUDNN
#include <vector>
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_pooling_layer.hpp"
namespace caffe {
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* top_data = top[0]->mutable_gpu_data();
CUDNN_CHECK(cudnnPoolingForward(handle_, pooling_desc_,
- bottom_desc_, bottom_data, top_desc_, top_data));
+ cudnn::dataType<Dtype>::one,
+ bottom_desc_, bottom_data,
+ cudnn::dataType<Dtype>::zero,
+ top_desc_, top_data));
}
template <typename Dtype>
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
CUDNN_CHECK(cudnnPoolingBackward(handle_, pooling_desc_,
- top_desc_, top_data, top_desc_, top_diff,
- bottom_desc_, bottom_data, bottom_desc_, bottom_diff));
+ cudnn::dataType<Dtype>::one,
+ top_desc_, top_data, top_desc_, top_diff,
+ bottom_desc_, bottom_data,
+ cudnn::dataType<Dtype>::zero,
+ bottom_desc_, bottom_diff));
}
INSTANTIATE_LAYER_GPU_FUNCS(CuDNNPoolingLayer);
#ifdef USE_CUDNN
-#include <algorithm>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_relu_layer.hpp"
namespace caffe {
// Check that handles have been setup before destroying.
if (!handles_setup_) { return; }
- cudnnDestroyTensor4dDescriptor(this->bottom_desc_);
- cudnnDestroyTensor4dDescriptor(this->top_desc_);
+ cudnnDestroyTensorDescriptor(this->bottom_desc_);
+ cudnnDestroyTensorDescriptor(this->top_desc_);
cudnnDestroy(this->handle_);
}
#ifdef USE_CUDNN
-#include <algorithm>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_relu_layer.hpp"
namespace caffe {
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* top_data = top[0]->mutable_gpu_data();
CUDNN_CHECK(cudnnActivationForward(this->handle_,
- CUDNN_ACTIVATION_RELU,
- this->bottom_desc_, bottom_data, this->top_desc_, top_data));
+ CUDNN_ACTIVATION_RELU,
+ cudnn::dataType<Dtype>::one,
+ this->bottom_desc_, bottom_data,
+ cudnn::dataType<Dtype>::zero,
+ this->top_desc_, top_data));
}
template <typename Dtype>
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
CUDNN_CHECK(cudnnActivationBackward(this->handle_,
- CUDNN_ACTIVATION_RELU,
- this->top_desc_, top_data, this->top_desc_, top_diff,
- this->bottom_desc_, bottom_data, this->bottom_desc_, bottom_diff));
+ CUDNN_ACTIVATION_RELU,
+ cudnn::dataType<Dtype>::one,
+ this->top_desc_, top_data, this->top_desc_, top_diff,
+ this->bottom_desc_, bottom_data,
+ cudnn::dataType<Dtype>::zero,
+ this->bottom_desc_, bottom_diff));
}
INSTANTIATE_LAYER_GPU_FUNCS(CuDNNReLULayer);
#ifdef USE_CUDNN
-#include <algorithm>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_sigmoid_layer.hpp"
namespace caffe {
// Check that handles have been setup before destroying.
if (!handles_setup_) { return; }
- cudnnDestroyTensor4dDescriptor(this->bottom_desc_);
- cudnnDestroyTensor4dDescriptor(this->top_desc_);
+ cudnnDestroyTensorDescriptor(this->bottom_desc_);
+ cudnnDestroyTensorDescriptor(this->top_desc_);
cudnnDestroy(this->handle_);
}
#ifdef USE_CUDNN
-#include <algorithm>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_sigmoid_layer.hpp"
namespace caffe {
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* top_data = top[0]->mutable_gpu_data();
CUDNN_CHECK(cudnnActivationForward(this->handle_,
- CUDNN_ACTIVATION_SIGMOID,
- this->bottom_desc_, bottom_data, this->top_desc_, top_data));
+ CUDNN_ACTIVATION_SIGMOID,
+ cudnn::dataType<Dtype>::one,
+ this->bottom_desc_, bottom_data,
+ cudnn::dataType<Dtype>::zero,
+ this->top_desc_, top_data));
}
template <typename Dtype>
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
CUDNN_CHECK(cudnnActivationBackward(this->handle_,
- CUDNN_ACTIVATION_SIGMOID,
- this->top_desc_, top_data, this->top_desc_, top_diff,
- this->bottom_desc_, bottom_data, this->bottom_desc_, bottom_diff));
+ CUDNN_ACTIVATION_SIGMOID,
+ cudnn::dataType<Dtype>::one,
+ this->top_desc_, top_data, this->top_desc_, top_diff,
+ this->bottom_desc_, bottom_data,
+ cudnn::dataType<Dtype>::zero,
+ this->bottom_desc_, bottom_diff));
}
INSTANTIATE_LAYER_GPU_FUNCS(CuDNNSigmoidLayer);
#ifdef USE_CUDNN
-#include <algorithm>
-#include <cfloat>
#include <vector>
#include "thrust/device_vector.h"
-#include "caffe/layer.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_softmax_layer.hpp"
namespace caffe {
// Check that handles have been setup before destroying.
if (!handles_setup_) { return; }
- cudnnDestroyTensor4dDescriptor(bottom_desc_);
- cudnnDestroyTensor4dDescriptor(top_desc_);
+ cudnnDestroyTensorDescriptor(bottom_desc_);
+ cudnnDestroyTensorDescriptor(top_desc_);
cudnnDestroy(handle_);
}
#ifdef USE_CUDNN
-#include <algorithm>
-#include <cfloat>
#include <vector>
#include "thrust/device_vector.h"
-#include "caffe/layer.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_softmax_layer.hpp"
namespace caffe {
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* top_data = top[0]->mutable_gpu_data();
CUDNN_CHECK(cudnnSoftmaxForward(handle_, CUDNN_SOFTMAX_ACCURATE,
- CUDNN_SOFTMAX_MODE_CHANNEL,
- bottom_desc_, bottom_data, top_desc_, top_data));
+ CUDNN_SOFTMAX_MODE_CHANNEL,
+ cudnn::dataType<Dtype>::one,
+ bottom_desc_, bottom_data,
+ cudnn::dataType<Dtype>::zero,
+ top_desc_, top_data));
}
template <typename Dtype>
const Dtype* top_diff = top[0]->gpu_diff();
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+
CUDNN_CHECK(cudnnSoftmaxBackward(handle_, CUDNN_SOFTMAX_ACCURATE,
- CUDNN_SOFTMAX_MODE_CHANNEL,
- top_desc_, top_data, top_desc_, top_diff, bottom_desc_, bottom_diff));
+ CUDNN_SOFTMAX_MODE_CHANNEL,
+ cudnn::dataType<Dtype>::one,
+ top_desc_, top_data, top_desc_, top_diff,
+ cudnn::dataType<Dtype>::zero,
+ bottom_desc_, bottom_diff));
}
}
#ifdef USE_CUDNN
-#include <algorithm>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_tanh_layer.hpp"
namespace caffe {
// Check that handles have been setup before destroying.
if (!handles_setup_) { return; }
- cudnnDestroyTensor4dDescriptor(this->bottom_desc_);
- cudnnDestroyTensor4dDescriptor(this->top_desc_);
+ cudnnDestroyTensorDescriptor(this->bottom_desc_);
+ cudnnDestroyTensorDescriptor(this->top_desc_);
cudnnDestroy(this->handle_);
}
#ifdef USE_CUDNN
-#include <algorithm>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_tanh_layer.hpp"
namespace caffe {
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* top_data = top[0]->mutable_gpu_data();
CUDNN_CHECK(cudnnActivationForward(this->handle_,
- CUDNN_ACTIVATION_TANH,
- this->bottom_desc_, bottom_data, this->top_desc_, top_data));
+ CUDNN_ACTIVATION_TANH,
+ cudnn::dataType<Dtype>::one,
+ this->bottom_desc_, bottom_data,
+ cudnn::dataType<Dtype>::zero,
+ this->top_desc_, top_data));
}
template <typename Dtype>
const Dtype* top_diff = top[0]->gpu_diff();
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+
CUDNN_CHECK(cudnnActivationBackward(this->handle_,
- CUDNN_ACTIVATION_TANH,
- this->top_desc_, top_data, this->top_desc_, top_diff,
- this->bottom_desc_, bottom_data, this->bottom_desc_, bottom_diff));
+ CUDNN_ACTIVATION_TANH,
+ cudnn::dataType<Dtype>::one,
+ this->top_desc_, top_data, this->top_desc_, top_diff,
+ this->bottom_desc_, bottom_data,
+ cudnn::dataType<Dtype>::zero,
+ this->bottom_desc_, bottom_diff));
}
INSTANTIATE_LAYER_GPU_FUNCS(CuDNNTanHLayer);
+#ifdef USE_OPENCV
#include <opencv2/core/core.hpp>
-
+#endif // USE_OPENCV
#include <stdint.h>
-#include <string>
#include <vector>
-#include "caffe/common.hpp"
-#include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/proto/caffe.pb.h"
+#include "caffe/data_transformer.hpp"
+#include "caffe/layers/data_layer.hpp"
#include "caffe/util/benchmark.hpp"
-#include "caffe/util/io.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/util/rng.hpp"
namespace caffe {
template <typename Dtype>
-DataLayer<Dtype>::~DataLayer<Dtype>() {
- this->JoinPrefetchThread();
+DataLayer<Dtype>::DataLayer(const LayerParameter& param)
+ : BasePrefetchingDataLayer<Dtype>(param),
+ reader_(param) {
+}
+
+template <typename Dtype>
+DataLayer<Dtype>::~DataLayer() {
+ this->StopInternalThread();
}
template <typename Dtype>
void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
- // Initialize DB
- db_.reset(db::GetDB(this->layer_param_.data_param().backend()));
- db_->Open(this->layer_param_.data_param().source(), db::READ);
- cursor_.reset(db_->NewCursor());
-
- // Check if we should randomly skip a few data points
- if (this->layer_param_.data_param().rand_skip()) {
- unsigned int skip = caffe_rng_rand() %
- this->layer_param_.data_param().rand_skip();
- LOG(INFO) << "Skipping first " << skip << " data points.";
- while (skip-- > 0) {
- cursor_->Next();
- }
- }
+ const int batch_size = this->layer_param_.data_param().batch_size();
// Read a data point, and use it to initialize the top blob.
- Datum datum;
- datum.ParseFromString(cursor_->value());
-
- bool force_color = this->layer_param_.data_param().force_encoded_color();
- if ((force_color && DecodeDatum(&datum, true)) ||
- DecodeDatumNative(&datum)) {
- LOG(INFO) << "Decoding Datum";
- }
- // image
- int crop_size = this->layer_param_.transform_param().crop_size();
- if (crop_size > 0) {
- top[0]->Reshape(this->layer_param_.data_param().batch_size(),
- datum.channels(), crop_size, crop_size);
- this->prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
- datum.channels(), crop_size, crop_size);
- this->transformed_data_.Reshape(1, datum.channels(), crop_size, crop_size);
- } else {
- top[0]->Reshape(
- this->layer_param_.data_param().batch_size(), datum.channels(),
- datum.height(), datum.width());
- this->prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
- datum.channels(), datum.height(), datum.width());
- this->transformed_data_.Reshape(1, datum.channels(),
- datum.height(), datum.width());
+ Datum& datum = *(reader_.full().peek());
+
+ // Use data_transformer to infer the expected blob shape from datum.
+ vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);
+ this->transformed_data_.Reshape(top_shape);
+ // Reshape top[0] and prefetch_data according to the batch_size.
+ top_shape[0] = batch_size;
+ top[0]->Reshape(top_shape);
+ for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
+ this->prefetch_[i].data_.Reshape(top_shape);
}
LOG(INFO) << "output data size: " << top[0]->num() << ","
<< top[0]->channels() << "," << top[0]->height() << ","
<< top[0]->width();
// label
if (this->output_labels_) {
- vector<int> label_shape(1, this->layer_param_.data_param().batch_size());
+ vector<int> label_shape(1, batch_size);
top[1]->Reshape(label_shape);
- this->prefetch_label_.Reshape(label_shape);
+ for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
+ this->prefetch_[i].label_.Reshape(label_shape);
+ }
}
}
-// This function is used to create a thread that prefetches the data.
-template <typename Dtype>
-void DataLayer<Dtype>::InternalThreadEntry() {
+// This function is called on prefetch thread
+template<typename Dtype>
+void DataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
CPUTimer batch_timer;
batch_timer.Start();
double read_time = 0;
double trans_time = 0;
CPUTimer timer;
- CHECK(this->prefetch_data_.count());
+ CHECK(batch->data_.count());
CHECK(this->transformed_data_.count());
- // Reshape on single input batches for inputs of varying dimension.
+ // Reshape according to the first datum of each batch
+ // on single input batches allows for inputs of varying dimension.
const int batch_size = this->layer_param_.data_param().batch_size();
- const int crop_size = this->layer_param_.transform_param().crop_size();
- bool force_color = this->layer_param_.data_param().force_encoded_color();
- if (batch_size == 1 && crop_size == 0) {
- Datum datum;
- datum.ParseFromString(cursor_->value());
- if (datum.encoded()) {
- if (force_color) {
- DecodeDatum(&datum, true);
- } else {
- DecodeDatumNative(&datum);
- }
- }
- this->prefetch_data_.Reshape(1, datum.channels(),
- datum.height(), datum.width());
- this->transformed_data_.Reshape(1, datum.channels(),
- datum.height(), datum.width());
- }
-
- Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
+ Datum& datum = *(reader_.full().peek());
+ // Use data_transformer to infer the expected blob shape from datum.
+ vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);
+ this->transformed_data_.Reshape(top_shape);
+ // Reshape batch according to the batch_size.
+ top_shape[0] = batch_size;
+ batch->data_.Reshape(top_shape);
+
+ Dtype* top_data = batch->data_.mutable_cpu_data();
Dtype* top_label = NULL; // suppress warnings about uninitialized variables
if (this->output_labels_) {
- top_label = this->prefetch_label_.mutable_cpu_data();
+ top_label = batch->label_.mutable_cpu_data();
}
for (int item_id = 0; item_id < batch_size; ++item_id) {
timer.Start();
- // get a blob
- Datum datum;
- datum.ParseFromString(cursor_->value());
-
- cv::Mat cv_img;
- if (datum.encoded()) {
- if (force_color) {
- cv_img = DecodeDatumToCVMat(datum, true);
- } else {
- cv_img = DecodeDatumToCVMatNative(datum);
- }
- if (cv_img.channels() != this->transformed_data_.channels()) {
- LOG(WARNING) << "Your dataset contains encoded images with mixed "
- << "channel sizes. Consider adding a 'force_color' flag to the "
- << "model definition, or rebuild your dataset using "
- << "convert_imageset.";
- }
- }
+ // get a datum
+ Datum& datum = *(reader_.full().pop("Waiting for data"));
read_time += timer.MicroSeconds();
timer.Start();
-
// Apply data transformations (mirror, scale, crop...)
- int offset = this->prefetch_data_.offset(item_id);
+ int offset = batch->data_.offset(item_id);
this->transformed_data_.set_cpu_data(top_data + offset);
- if (datum.encoded()) {
- this->data_transformer_->Transform(cv_img, &(this->transformed_data_));
- } else {
- this->data_transformer_->Transform(datum, &(this->transformed_data_));
- }
+ this->data_transformer_->Transform(datum, &(this->transformed_data_));
+ // Copy label.
if (this->output_labels_) {
top_label[item_id] = datum.label();
}
trans_time += timer.MicroSeconds();
- // go to the next iter
- cursor_->Next();
- if (!cursor_->valid()) {
- DLOG(INFO) << "Restarting data prefetching from start.";
- cursor_->SeekToFirst();
- }
+
+ reader_.free().push(const_cast<Datum*>(&datum));
}
+ timer.Stop();
batch_timer.Stop();
DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << " ms.";
DLOG(INFO) << " Read time: " << read_time / 1000 << " ms.";
#include <vector>
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/deconv_layer.hpp"
namespace caffe {
template <typename Dtype>
void DeconvolutionLayer<Dtype>::compute_output_shape() {
- this->height_out_ = this->stride_h_ * (this->height_ - 1) + this->kernel_h_
- - 2 * this->pad_h_;
- this->width_out_ = this->stride_w_ * (this->width_ - 1) + this->kernel_w_
- - 2 * this->pad_w_;
+ const int* kernel_shape_data = this->kernel_shape_.cpu_data();
+ const int* stride_data = this->stride_.cpu_data();
+ const int* pad_data = this->pad_.cpu_data();
+ const int* dilation_data = this->dilation_.cpu_data();
+ this->output_shape_.clear();
+ for (int i = 0; i < this->num_spatial_axes_; ++i) {
+ // i + 1 to skip channel axis
+ const int input_dim = this->input_shape(i + 1);
+ const int kernel_extent = dilation_data[i] * (kernel_shape_data[i] - 1) + 1;
+ const int output_dim = stride_data[i] * (input_dim - 1)
+ + kernel_extent - 2 * pad_data[i];
+ this->output_shape_.push_back(output_dim);
+ }
}
template <typename Dtype>
const Dtype* bottom_data = bottom[i]->cpu_data();
Dtype* top_data = top[i]->mutable_cpu_data();
for (int n = 0; n < this->num_; ++n) {
- this->backward_cpu_gemm(bottom_data + bottom[i]->offset(n), weight,
- top_data + top[i]->offset(n));
+ this->backward_cpu_gemm(bottom_data + n * this->bottom_dim_, weight,
+ top_data + n * this->top_dim_);
if (this->bias_term_) {
const Dtype* bias = this->blobs_[1]->cpu_data();
- this->forward_cpu_bias(top_data + top[i]->offset(n), bias);
+ this->forward_cpu_bias(top_data + n * this->top_dim_, bias);
}
}
}
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
const Dtype* weight = this->blobs_[0]->cpu_data();
Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();
- if (this->param_propagate_down_[0]) {
- caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
- }
- if (this->bias_term_ && this->param_propagate_down_[1]) {
- caffe_set(this->blobs_[1]->count(), Dtype(0),
- this->blobs_[1]->mutable_cpu_diff());
- }
for (int i = 0; i < top.size(); ++i) {
const Dtype* top_diff = top[i]->cpu_diff();
const Dtype* bottom_data = bottom[i]->cpu_data();
if (this->bias_term_ && this->param_propagate_down_[1]) {
Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff();
for (int n = 0; n < this->num_; ++n) {
- this->backward_cpu_bias(bias_diff, top_diff + top[i]->offset(n));
+ this->backward_cpu_bias(bias_diff, top_diff + n * this->top_dim_);
}
}
if (this->param_propagate_down_[0] || propagate_down[i]) {
for (int n = 0; n < this->num_; ++n) {
// Gradient w.r.t. weight. Note that we will accumulate diffs.
if (this->param_propagate_down_[0]) {
- this->weight_cpu_gemm(top_diff + top[i]->offset(n),
- bottom_data + bottom[i]->offset(n), weight_diff);
+ this->weight_cpu_gemm(top_diff + n * this->top_dim_,
+ bottom_data + n * this->bottom_dim_, weight_diff);
}
// Gradient w.r.t. bottom data, if necessary, reusing the column buffer
// we might have just computed above.
if (propagate_down[i]) {
- this->forward_cpu_gemm(top_diff + top[i]->offset(n), weight,
- bottom_diff + bottom[i]->offset(n),
+ this->forward_cpu_gemm(top_diff + n * this->top_dim_, weight,
+ bottom_diff + n * this->bottom_dim_,
this->param_propagate_down_[0]);
}
}
#include <vector>
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/deconv_layer.hpp"
namespace caffe {
const Dtype* bottom_data = bottom[i]->gpu_data();
Dtype* top_data = top[i]->mutable_gpu_data();
for (int n = 0; n < this->num_; ++n) {
- this->backward_gpu_gemm(bottom_data + bottom[i]->offset(n), weight,
- top_data + top[i]->offset(n));
+ this->backward_gpu_gemm(bottom_data + n * this->bottom_dim_, weight,
+ top_data + n * this->top_dim_);
if (this->bias_term_) {
const Dtype* bias = this->blobs_[1]->gpu_data();
- this->forward_gpu_bias(top_data + top[i]->offset(n), bias);
+ this->forward_gpu_bias(top_data + n * this->top_dim_, bias);
}
}
}
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
const Dtype* weight = this->blobs_[0]->gpu_data();
Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();
- if (this->param_propagate_down_[0]) {
- caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
- }
- if (this->bias_term_ && this->param_propagate_down_[1]) {
- caffe_gpu_set(this->blobs_[1]->count(), Dtype(0),
- this->blobs_[1]->mutable_gpu_diff());
- }
for (int i = 0; i < top.size(); ++i) {
const Dtype* top_diff = top[i]->gpu_diff();
const Dtype* bottom_data = bottom[i]->gpu_data();
if (this->bias_term_ && this->param_propagate_down_[1]) {
Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff();
for (int n = 0; n < this->num_; ++n) {
- this->backward_gpu_bias(bias_diff, top_diff + top[i]->offset(n));
+ this->backward_gpu_bias(bias_diff, top_diff + n * this->top_dim_);
}
}
if (this->param_propagate_down_[0] || propagate_down[i]) {
for (int n = 0; n < this->num_; ++n) {
// gradient w.r.t. weight. Note that we will accumulate diffs.
if (this->param_propagate_down_[0]) {
- this->weight_gpu_gemm(top_diff + top[i]->offset(n),
- bottom_data + bottom[i]->offset(n), weight_diff);
+ this->weight_gpu_gemm(top_diff + n * this->top_dim_,
+ bottom_data + n * this->bottom_dim_, weight_diff);
}
// gradient w.r.t. bottom data, if necessary.
if (propagate_down[i]) {
- this->forward_gpu_gemm(top_diff + top[i]->offset(n), weight,
- bottom_diff + bottom[i]->offset(n));
+ this->forward_gpu_gemm(top_diff + n * this->top_dim_, weight,
+ bottom_diff + n * this->bottom_dim_,
+ this->param_propagate_down_[0]);
}
}
}
#include <vector>
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/syncedmem.hpp"
+#include "caffe/layers/dropout_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
-#include <algorithm>
-#include <limits>
#include <vector>
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/syncedmem.hpp"
+#include "caffe/layers/dropout_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
-
template <typename Dtype>
__global__ void DropoutForward(const int n, const Dtype* in,
const unsigned int* mask, const unsigned int threshold, const float scale,
INSTANTIATE_LAYER_GPU_FUNCS(DropoutLayer);
-
} // namespace caffe
#include <vector>
#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/dummy_data_layer.hpp"
namespace caffe {
#include <cfloat>
#include <vector>
-#include "caffe/layer.hpp"
+#include "caffe/layers/eltwise_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
#include <cfloat>
#include <vector>
-#include "caffe/layer.hpp"
+#include "caffe/layers/eltwise_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
--- /dev/null
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layers/elu_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ELULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ const Dtype* bottom_data = bottom[0]->cpu_data();
+ Dtype* top_data = top[0]->mutable_cpu_data();
+ const int count = bottom[0]->count();
+ Dtype alpha = this->layer_param_.elu_param().alpha();
+ for (int i = 0; i < count; ++i) {
+ top_data[i] = std::max(bottom_data[i], Dtype(0))
+ + alpha * (exp(std::min(bottom_data[i], Dtype(0))) - Dtype(1));
+ }
+}
+
+template <typename Dtype>
+void ELULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down,
+ const vector<Blob<Dtype>*>& bottom) {
+ if (propagate_down[0]) {
+ const Dtype* bottom_data = bottom[0]->cpu_data();
+ const Dtype* top_data = top[0]->cpu_data();
+ const Dtype* top_diff = top[0]->cpu_diff();
+ Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+ const int count = bottom[0]->count();
+ Dtype alpha = this->layer_param_.elu_param().alpha();
+ for (int i = 0; i < count; ++i) {
+ bottom_diff[i] = top_diff[i] * ((bottom_data[i] > 0)
+ + (alpha + top_data[i]) * (bottom_data[i] <= 0));
+ }
+ }
+}
+
+
+#ifdef CPU_ONLY
+STUB_GPU(ELULayer);
+#endif
+
+INSTANTIATE_CLASS(ELULayer);
+REGISTER_LAYER_CLASS(ELU);
+
+} // namespace caffe
--- /dev/null
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layers/elu_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void ELUForward(const int n, const Dtype* in, Dtype* out,
+ Dtype alpha) {
+ CUDA_KERNEL_LOOP(index, n) {
+ out[index] = in[index] > 0 ? in[index] :
+ alpha * (exp(in[index]) - 1);
+ }
+}
+
+template <typename Dtype>
+void ELULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ const Dtype* bottom_data = bottom[0]->gpu_data();
+ Dtype* top_data = top[0]->mutable_gpu_data();
+ const int count = bottom[0]->count();
+ Dtype alpha = this->layer_param_.elu_param().alpha();
+ // NOLINT_NEXT_LINE(whitespace/operators)
+ ELUForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+ count, bottom_data, top_data, alpha);
+ CUDA_POST_KERNEL_CHECK;
+}
+
+template <typename Dtype>
+__global__ void ELUBackward(const int n, const Dtype* in_diff,
+ const Dtype* out_data, const Dtype* in_data,
+ Dtype* out_diff, Dtype alpha) {
+ CUDA_KERNEL_LOOP(index, n) {
+ out_diff[index] = in_data[index] > 0 ? in_diff[index] :
+ in_diff[index] * (out_data[index] + alpha);
+ }
+}
+
+template <typename Dtype>
+void ELULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down,
+ const vector<Blob<Dtype>*>& bottom) {
+ if (propagate_down[0]) {
+ const Dtype* bottom_data = bottom[0]->gpu_data();
+ const Dtype* top_diff = top[0]->gpu_diff();
+ const Dtype* top_data = top[0]->gpu_data();
+ Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+ const int count = bottom[0]->count();
+ Dtype alpha = this->layer_param_.elu_param().alpha();
+ // NOLINT_NEXT_LINE(whitespace/operators)
+ ELUBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+ count, top_diff, top_data, bottom_data, bottom_diff, alpha);
+ CUDA_POST_KERNEL_CHECK;
+ }
+}
+
+
+INSTANTIATE_LAYER_GPU_FUNCS(ELULayer);
+
+
+} // namespace caffe
--- /dev/null
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layers/embed_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void EmbedLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ N_ = this->layer_param_.embed_param().num_output();
+ CHECK_GT(N_, 0) << "EmbedLayer num_output must be positive.";
+ K_ = this->layer_param_.embed_param().input_dim();
+ CHECK_GT(K_, 0) << "EmbedLayer input_dim must be positive.";
+ bias_term_ = this->layer_param_.embed_param().bias_term();
+ // Check if we need to set up the weights
+ if (this->blobs_.size() > 0) {
+ LOG(INFO) << "Skipping parameter initialization";
+ } else {
+ if (bias_term_) {
+ this->blobs_.resize(2);
+ } else {
+ this->blobs_.resize(1);
+ }
+ // Initialize the weights --
+ // transposed from InnerProductLayer for spatial locality.
+ vector<int> weight_shape(2);
+ weight_shape[0] = K_;
+ weight_shape[1] = N_;
+ this->blobs_[0].reset(new Blob<Dtype>(weight_shape));
+ // fill the weights
+ shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
+ this->layer_param_.embed_param().weight_filler()));
+ weight_filler->Fill(this->blobs_[0].get());
+ // If necessary, initialize and fill the bias term
+ if (bias_term_) {
+ vector<int> bias_shape(1, N_);
+ this->blobs_[1].reset(new Blob<Dtype>(bias_shape));
+ shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
+ this->layer_param_.embed_param().bias_filler()));
+ bias_filler->Fill(this->blobs_[1].get());
+ }
+ } // parameter initialization
+ this->param_propagate_down_.resize(this->blobs_.size(), true);
+}
+
+template <typename Dtype>
+void EmbedLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ // Figure out the dimensions
+ M_ = bottom[0]->count();
+ vector<int> top_shape = bottom[0]->shape();
+ top_shape.push_back(N_);
+ top[0]->Reshape(top_shape);
+ // Set up the bias multiplier
+ if (bias_term_) {
+ vector<int> bias_shape(1, M_);
+ bias_multiplier_.Reshape(bias_shape);
+ caffe_set(M_, Dtype(1), bias_multiplier_.mutable_cpu_data());
+ }
+}
+
+template <typename Dtype>
+void EmbedLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ const Dtype* bottom_data = bottom[0]->cpu_data();
+ const Dtype* weight = this->blobs_[0]->cpu_data();
+ Dtype* top_data = top[0]->mutable_cpu_data();
+ int index;
+ for (int n = 0; n < M_; ++n) {
+ index = static_cast<int>(bottom_data[n]);
+ DCHECK_GE(index, 0);
+ DCHECK_LT(index, K_);
+ DCHECK_EQ(static_cast<Dtype>(index), bottom_data[n]) << "non-integer input";
+ caffe_copy(N_, weight + index * N_, top_data + n * N_);
+ }
+ if (bias_term_) {
+ const Dtype* bias = this->blobs_[1]->cpu_data();
+ caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, Dtype(1),
+ bias_multiplier_.cpu_data(), bias, Dtype(1), top_data);
+ }
+}
+
+template <typename Dtype>
+void EmbedLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+ CHECK(!propagate_down[0]) << "Can't backpropagate to EmbedLayer input.";
+ if (this->param_propagate_down_[0]) {
+ const Dtype* top_diff = top[0]->cpu_diff();
+ const Dtype* bottom_data = bottom[0]->cpu_data();
+ // Gradient with respect to weight
+ Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();
+ int index;
+ for (int n = 0; n < M_; ++n) {
+ index = static_cast<int>(bottom_data[n]);
+ DCHECK_GE(index, 0);
+ DCHECK_LT(index, K_);
+ DCHECK_EQ(static_cast<Dtype>(index), bottom_data[n])
+ << "non-integer input";
+ caffe_axpy(N_, Dtype(1), top_diff + n * N_, weight_diff + index * N_);
+ }
+ }
+ if (bias_term_ && this->param_propagate_down_[1]) {
+ const Dtype* top_diff = top[0]->cpu_diff();
+ Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff();
+ caffe_cpu_gemv<Dtype>(CblasTrans, M_, N_, Dtype(1), top_diff,
+ bias_multiplier_.cpu_data(), Dtype(1), bias_diff);
+ }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(EmbedLayer);
+#endif
+
+INSTANTIATE_CLASS(EmbedLayer);
+REGISTER_LAYER_CLASS(Embed);
+
+} // namespace caffe
--- /dev/null
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layers/embed_layer.hpp"
+#include "caffe/util/gpu_util.cuh"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void EmbedForward(const int nthreads, const Dtype* bottom_data,
+ const Dtype* weight, const int M, const int N, const int K,
+ Dtype* top_data) {
+ CUDA_KERNEL_LOOP(top_index, nthreads) {
+ const int n = top_index / N;
+ const int d = top_index % N;
+ const int index = static_cast<int>(bottom_data[n]);
+ const int weight_index = index * N + d;
+ top_data[top_index] = weight[weight_index];
+ }
+}
+
+template <typename Dtype>
+__global__ void EmbedBackward(const int nthreads, const Dtype* bottom_data,
+ const Dtype* top_diff, const int M, const int N, const int K,
+ Dtype* weight_diff);
+
+template <typename Dtype>
+__global__ void EmbedBackward(const int nthreads, const Dtype* bottom_data,
+ const Dtype* top_diff, const int M, const int N, const int K,
+ Dtype* weight_diff) {
+ CUDA_KERNEL_LOOP(top_index, nthreads) {
+ const int n = top_index / N;
+ const int d = top_index % N;
+ const int index = static_cast<int>(bottom_data[n]);
+ const int weight_index = index * N + d;
+ caffe_gpu_atomic_add(top_diff[top_index], weight_diff + weight_index);
+ }
+}
+
+template <typename Dtype>
+void EmbedLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ const Dtype* bottom_data = bottom[0]->gpu_data();
+ Dtype* top_data = top[0]->mutable_gpu_data();
+ const Dtype* weight = this->blobs_[0]->gpu_data();
+ const int count = top[0]->count();
+ EmbedForward<Dtype> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+ count, bottom_data, weight, M_, N_, K_, top_data);
+ if (bias_term_) {
+ caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, Dtype(1),
+ bias_multiplier_.gpu_data(),
+ this->blobs_[1]->gpu_data(), Dtype(1), top_data);
+ }
+}
+
+template <typename Dtype>
+void EmbedLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+ CHECK(!propagate_down[0]) << "Can't backpropagate to EmbedLayer input.";
+ if (this->param_propagate_down_[0]) {
+ const int top_count = top[0]->count();
+ const Dtype* top_diff = top[0]->gpu_diff();
+ const Dtype* bottom_data = bottom[0]->gpu_data();
+ Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();
+ EmbedBackward<Dtype> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(top_count), CAFFE_CUDA_NUM_THREADS>>>(
+ top_count, bottom_data, top_diff, M_, N_, K_, weight_diff);
+ }
+ if (bias_term_ && this->param_propagate_down_[1]) {
+ const Dtype* top_diff = top[0]->gpu_diff();
+ Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff();
+ caffe_gpu_gemv<Dtype>(CblasTrans, M_, N_, Dtype(1), top_diff,
+ bias_multiplier_.gpu_data(), Dtype(1), bias_diff);
+ }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(EmbedLayer);
+
+} // namespace caffe
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/layers/euclidean_loss_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/layers/euclidean_loss_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
-#include <algorithm>
#include <vector>
-#include "caffe/layer.hpp"
+#include "caffe/layers/exp_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
-#include <algorithm>
#include <vector>
-#include "caffe/layer.hpp"
+#include "caffe/layers/exp_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
--- /dev/null
+#include <vector>
+
+#include "caffe/layers/filter_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void FilterLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ CHECK_EQ(top.size(), bottom.size() - 1);
+ first_reshape_ = true;
+}
+
+template <typename Dtype>
+void FilterLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ // bottom[0...k-1] are the blobs to filter
+ // bottom[last] is the "selector_blob"
+ int selector_index = bottom.size() - 1;
+ for (int i = 1; i < bottom[selector_index]->num_axes(); ++i) {
+ CHECK_EQ(bottom[selector_index]->shape(i), 1)
+ << "Selector blob dimensions must be singletons (1), except the first";
+ }
+ for (int i = 0; i < bottom.size() - 1; ++i) {
+ CHECK_EQ(bottom[selector_index]->shape(0), bottom[i]->shape(0)) <<
+ "Each bottom should have the same 0th dimension as the selector blob";
+ }
+
+ const Dtype* bottom_data_selector = bottom[selector_index]->cpu_data();
+ indices_to_forward_.clear();
+
+ // look for non-zero elements in bottom[0]. Items of each bottom that
+ // have the same index as the items in bottom[0] with value == non-zero
+ // will be forwarded
+ for (int item_id = 0; item_id < bottom[selector_index]->shape(0); ++item_id) {
+ // we don't need an offset because item size == 1
+ const Dtype* tmp_data_selector = bottom_data_selector + item_id;
+ if (*tmp_data_selector) {
+ indices_to_forward_.push_back(item_id);
+ }
+ }
+ // only filtered items will be forwarded
+ int new_tops_num = indices_to_forward_.size();
+ // init
+ if (first_reshape_) {
+ new_tops_num = bottom[0]->shape(0);
+ first_reshape_ = false;
+ }
+ for (int t = 0; t < top.size(); ++t) {
+ int num_axes = bottom[t]->num_axes();
+ vector<int> shape_top(num_axes);
+ shape_top[0] = new_tops_num;
+ for (int ts = 1; ts < num_axes; ++ts)
+ shape_top[ts] = bottom[t]->shape(ts);
+ top[t]->Reshape(shape_top);
+ }
+}
+
+template <typename Dtype>
+void FilterLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ int new_tops_num = indices_to_forward_.size();
+ // forward all filtered items for all bottoms but the Selector (bottom[last])
+ for (int t = 0; t < top.size(); ++t) {
+ const Dtype* bottom_data = bottom[t]->cpu_data();
+ Dtype* top_data = top[t]->mutable_cpu_data();
+ int dim = bottom[t]->count() / bottom[t]->shape(0);
+ for (int n = 0; n < new_tops_num; ++n) {
+ int data_offset_top = n * dim;
+ int data_offset_bottom = indices_to_forward_[n] * bottom[t]->count(1);
+ caffe_copy(dim, bottom_data + data_offset_bottom,
+ top_data + data_offset_top);
+ }
+ }
+}
+
+template <typename Dtype>
+void FilterLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+ if (propagate_down[bottom.size() - 1]) {
+ LOG(FATAL) << this->type()
+ << "Layer cannot backpropagate to filter index inputs";
+ }
+ for (int i = 0; i < top.size(); i++) {
+ // bottom[last] is the selector and never needs backpropagation
+ // so we can iterate over top vector because top.size() == bottom.size() -1
+ if (propagate_down[i]) {
+ const int dim = top[i]->count() / top[i]->shape(0);
+ int next_to_backward_offset = 0;
+ int batch_offset = 0;
+ int data_offset_bottom = 0;
+ int data_offset_top = 0;
+ for (int n = 0; n < bottom[i]->shape(0); n++) {
+ data_offset_bottom = n * dim;
+ if (next_to_backward_offset >= indices_to_forward_.size()) {
+ // we already visited all items that were been forwarded, so
+ // just set to zero remaining ones
+ caffe_set(dim, Dtype(0),
+ bottom[i]->mutable_cpu_diff() + data_offset_bottom);
+ } else {
+ batch_offset = indices_to_forward_[next_to_backward_offset];
+ if (n != batch_offset) { // this data was not been forwarded
+ caffe_set(dim, Dtype(0),
+ bottom[i]->mutable_cpu_diff() + data_offset_bottom);
+ } else { // this data was been forwarded
+ data_offset_top = next_to_backward_offset * dim;
+ next_to_backward_offset++; // point to next forwarded item index
+ caffe_copy(dim, top[i]->mutable_cpu_diff() + data_offset_top,
+ bottom[i]->mutable_cpu_diff() + data_offset_bottom);
+ }
+ }
+ }
+ }
+ }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(FilterLayer);
+#endif
+
+INSTANTIATE_CLASS(FilterLayer);
+REGISTER_LAYER_CLASS(Filter);
+
+} // namespace caffe
--- /dev/null
+#include <vector>
+
+#include "caffe/layers/filter_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void FilterLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ int new_tops_num = indices_to_forward_.size();
+ // forward all filtered items for all bottoms but the Selector (bottom[last])
+ for (int t = 0; t < top.size(); ++t) {
+ const Dtype* bottom_data = bottom[t]->gpu_data();
+ Dtype* top_data = top[t]->mutable_gpu_data();
+ int dim = bottom[t]->count() / bottom[t]->shape(0);
+ for (int n = 0; n < new_tops_num; ++n) {
+ int data_offset_top = n * dim;
+ int data_offset_bottom = indices_to_forward_[n] * dim;
+ caffe_copy(dim, bottom_data + data_offset_bottom,
+ top_data + data_offset_top);
+ }
+ }
+}
+
+template <typename Dtype>
+void FilterLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+ if (propagate_down[bottom.size() - 1]) {
+ LOG(FATAL) << this->type()
+ << "Layer cannot backpropagate to filter index inputs";
+ }
+ for (int i = 0; i < top.size(); ++i) {
+ // bottom[last] is the selector and never needs backpropagation
+ // so we can iterate over top vector because top.size() == bottom.size() -1
+ if (propagate_down[i]) {
+ const int dim = top[i]->count() / top[i]->shape(0);
+ int next_to_backward_offset = 0;
+ int batch_offset = 0;
+ int data_offset_bottom = 0;
+ int data_offset_top = 0;
+ for (int n = 0; n < bottom[i]->shape(0); ++n) {
+ if (next_to_backward_offset >= indices_to_forward_.size()) {
+ // we already visited all items that were been forwarded, so
+ // just set to zero remaining ones
+ data_offset_bottom = n * dim;
+ caffe_gpu_set(dim, Dtype(0),
+ bottom[i]->mutable_gpu_diff() + data_offset_bottom);
+ } else {
+ batch_offset = indices_to_forward_[next_to_backward_offset];
+ data_offset_bottom = n * dim;
+ if (n != batch_offset) { // this data was not been forwarded
+ caffe_gpu_set(dim, Dtype(0),
+ bottom[i]->mutable_gpu_diff() + data_offset_bottom);
+ } else { // this data was been forwarded
+ data_offset_top = next_to_backward_offset * dim;
+ ++next_to_backward_offset; // point to next forwarded item index
+ caffe_copy(dim, top[i]->mutable_gpu_diff() + data_offset_top,
+ bottom[i]->mutable_gpu_diff() + data_offset_bottom);
+ }
+ }
+ }
+ }
+ }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(FilterLayer);
+
+} // namespace caffe
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/flatten_layer.hpp"
namespace caffe {
template <typename Dtype>
void FlattenLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
- vector<int> top_shape(2);
- top_shape[0] = bottom[0]->num();
- top_shape[1] = bottom[0]->count() / bottom[0]->num();
+ CHECK_NE(top[0], bottom[0]) << this->type() << " Layer does not "
+ "allow in-place computation.";
+ const int start_axis = bottom[0]->CanonicalAxisIndex(
+ this->layer_param_.flatten_param().axis());
+ const int end_axis = bottom[0]->CanonicalAxisIndex(
+ this->layer_param_.flatten_param().end_axis());
+ vector<int> top_shape;
+ for (int i = 0; i < start_axis; ++i) {
+ top_shape.push_back(bottom[0]->shape(i));
+ }
+ const int flattened_dim = bottom[0]->count(start_axis, end_axis + 1);
+ top_shape.push_back(flattened_dim);
+ for (int i = end_axis + 1; i < bottom[0]->num_axes(); ++i) {
+ top_shape.push_back(bottom[0]->shape(i));
+ }
top[0]->Reshape(top_shape);
CHECK_EQ(top[0]->count(), bottom[0]->count());
}
#include "hdf5_hl.h"
#include "stdint.h"
-#include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/layers/hdf5_data_layer.hpp"
+#include "caffe/util/hdf5.hpp"
namespace caffe {
*/
#include <stdint.h>
-#include <string>
#include <vector>
#include "hdf5.h"
#include "hdf5_hl.h"
-#include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/layers/hdf5_data_layer.hpp"
namespace caffe {
#include "hdf5.h"
#include "hdf5_hl.h"
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/hdf5_output_layer.hpp"
+#include "caffe/util/hdf5.hpp"
namespace caffe {
#include "hdf5.h"
#include "hdf5_hl.h"
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/hdf5_output_layer.hpp"
namespace caffe {
#include <algorithm>
-#include <cfloat>
-#include <cmath>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/layers/hinge_loss_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
#include <vector>
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/layers/im2col_layer.hpp"
#include "caffe/util/im2col.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
void Im2colLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
ConvolutionParameter conv_param = this->layer_param_.convolution_param();
- CHECK(!conv_param.has_kernel_size() !=
- !(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
- << "Filter size is kernel_size OR kernel_h and kernel_w; not both";
- CHECK(conv_param.has_kernel_size() ||
- (conv_param.has_kernel_h() && conv_param.has_kernel_w()))
- << "For non-square filters both kernel_h and kernel_w are required.";
- CHECK((!conv_param.has_pad() && conv_param.has_pad_h()
- && conv_param.has_pad_w())
- || (!conv_param.has_pad_h() && !conv_param.has_pad_w()))
- << "pad is pad OR pad_h and pad_w are required.";
- CHECK((!conv_param.has_stride() && conv_param.has_stride_h()
- && conv_param.has_stride_w())
- || (!conv_param.has_stride_h() && !conv_param.has_stride_w()))
- << "Stride is stride OR stride_h and stride_w are required.";
- if (conv_param.has_kernel_size()) {
- kernel_h_ = kernel_w_ = conv_param.kernel_size();
+ force_nd_im2col_ = conv_param.force_nd_im2col();
+ const int input_num_dims = bottom[0]->shape().size();
+ channel_axis_ = bottom[0]->CanonicalAxisIndex(conv_param.axis());
+ const int first_spatial_dim = channel_axis_ + 1;
+ num_spatial_axes_ = input_num_dims - first_spatial_dim;
+ CHECK_GE(num_spatial_axes_, 1);
+ vector<int> dim_blob_shape(1, num_spatial_axes_);
+ // Setup filter kernel dimensions (kernel_shape_).
+ kernel_shape_.Reshape(dim_blob_shape);
+ int* kernel_shape_data = kernel_shape_.mutable_cpu_data();
+ if (conv_param.has_kernel_h() || conv_param.has_kernel_w()) {
+ CHECK_EQ(num_spatial_axes_, 2)
+ << "kernel_h & kernel_w can only be used for 2D convolution.";
+ CHECK_EQ(0, conv_param.kernel_size_size())
+ << "Either kernel_size or kernel_h/w should be specified; not both.";
+ kernel_shape_data[0] = conv_param.kernel_h();
+ kernel_shape_data[1] = conv_param.kernel_w();
} else {
- kernel_h_ = conv_param.kernel_h();
- kernel_w_ = conv_param.kernel_w();
+ const int num_kernel_dims = conv_param.kernel_size_size();
+ CHECK(num_kernel_dims == 1 || num_kernel_dims == num_spatial_axes_)
+ << "kernel_size must be specified once, or once per spatial dimension "
+ << "(kernel_size specified " << num_kernel_dims << " times; "
+ << num_spatial_axes_ << " spatial dims);";
+ for (int i = 0; i < num_spatial_axes_; ++i) {
+ kernel_shape_data[i] =
+ conv_param.kernel_size((num_kernel_dims == 1) ? 0 : i);
+ }
}
- CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero.";
- CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero.";
- if (!conv_param.has_pad_h()) {
- pad_h_ = pad_w_ = conv_param.pad();
+ for (int i = 0; i < num_spatial_axes_; ++i) {
+ CHECK_GT(kernel_shape_data[i], 0) << "Filter dimensions must be nonzero.";
+ }
+ // Setup stride dimensions (stride_).
+ stride_.Reshape(dim_blob_shape);
+ int* stride_data = stride_.mutable_cpu_data();
+ if (conv_param.has_stride_h() || conv_param.has_stride_w()) {
+ CHECK_EQ(num_spatial_axes_, 2)
+ << "stride_h & stride_w can only be used for 2D convolution.";
+ CHECK_EQ(0, conv_param.stride_size())
+ << "Either stride or stride_h/w should be specified; not both.";
+ stride_data[0] = conv_param.stride_h();
+ stride_data[1] = conv_param.stride_w();
} else {
- pad_h_ = conv_param.pad_h();
- pad_w_ = conv_param.pad_w();
+ const int num_stride_dims = conv_param.stride_size();
+ CHECK(num_stride_dims == 0 || num_stride_dims == 1 ||
+ num_stride_dims == num_spatial_axes_)
+ << "stride must be specified once, or once per spatial dimension "
+ << "(stride specified " << num_stride_dims << " times; "
+ << num_spatial_axes_ << " spatial dims);";
+ const int kDefaultStride = 1;
+ for (int i = 0; i < num_spatial_axes_; ++i) {
+ stride_data[i] = (num_stride_dims == 0) ? kDefaultStride :
+ conv_param.stride((num_stride_dims == 1) ? 0 : i);
+ CHECK_GT(stride_data[i], 0) << "Stride dimensions must be nonzero.";
+ }
}
- if (!conv_param.has_stride_h()) {
- stride_h_ = stride_w_ = conv_param.stride();
+ // Setup pad dimensions (pad_).
+ pad_.Reshape(dim_blob_shape);
+ int* pad_data = pad_.mutable_cpu_data();
+ if (conv_param.has_pad_h() || conv_param.has_pad_w()) {
+ CHECK_EQ(num_spatial_axes_, 2)
+ << "pad_h & pad_w can only be used for 2D convolution.";
+ CHECK_EQ(0, conv_param.pad_size())
+ << "Either pad or pad_h/w should be specified; not both.";
+ pad_data[0] = conv_param.pad_h();
+ pad_data[1] = conv_param.pad_w();
} else {
- stride_h_ = conv_param.stride_h();
- stride_w_ = conv_param.stride_w();
+ const int num_pad_dims = conv_param.pad_size();
+ CHECK(num_pad_dims == 0 || num_pad_dims == 1 ||
+ num_pad_dims == num_spatial_axes_)
+ << "pad must be specified once, or once per spatial dimension "
+ << "(pad specified " << num_pad_dims << " times; "
+ << num_spatial_axes_ << " spatial dims);";
+ const int kDefaultPad = 0;
+ for (int i = 0; i < num_spatial_axes_; ++i) {
+ pad_data[i] = (num_pad_dims == 0) ? kDefaultPad :
+ conv_param.pad((num_pad_dims == 1) ? 0 : i);
+ }
+ }
+ // Setup dilation dimensions (dilation_).
+ dilation_.Reshape(dim_blob_shape);
+ int* dilation_data = dilation_.mutable_cpu_data();
+ const int num_dilation_dims = conv_param.dilation_size();
+ CHECK(num_dilation_dims == 0 || num_dilation_dims == 1 ||
+ num_dilation_dims == num_spatial_axes_)
+ << "dilation must be specified once, or once per spatial dimension "
+ << "(dilation specified " << num_dilation_dims << " times; "
+ << num_spatial_axes_ << " spatial dims).";
+ const int kDefaultDilation = 1;
+ for (int i = 0; i < num_spatial_axes_; ++i) {
+ dilation_data[i] = (num_dilation_dims == 0) ? kDefaultDilation :
+ conv_param.dilation((num_dilation_dims == 1) ? 0 : i);
}
}
template <typename Dtype>
void Im2colLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
- CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
- << "corresponding to (num, channels, height, width)";
- channels_ = bottom[0]->channels();
- height_ = bottom[0]->height();
- width_ = bottom[0]->width();
- top[0]->Reshape(
- bottom[0]->num(), channels_ * kernel_h_ * kernel_w_,
- (height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1,
- (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1);
+ vector<int> top_shape = bottom[0]->shape();
+ const int* kernel_shape_data = kernel_shape_.cpu_data();
+ const int* stride_data = stride_.cpu_data();
+ const int* pad_data = pad_.cpu_data();
+ const int* dilation_data = dilation_.cpu_data();
+ for (int i = 0; i < num_spatial_axes_; ++i) {
+ top_shape[channel_axis_] *= kernel_shape_data[i];
+ const int input_dim = bottom[0]->shape(channel_axis_ + i + 1);
+ const int kernel_extent = dilation_data[i] * (kernel_shape_data[i] - 1) + 1;
+ const int output_dim = (input_dim + 2 * pad_data[i] - kernel_extent)
+ / stride_data[i] + 1;
+ top_shape[channel_axis_ + i + 1] = output_dim;
+ }
+ top[0]->Reshape(top_shape);
+ num_ = bottom[0]->count(0, channel_axis_);
+ bottom_dim_ = bottom[0]->count(channel_axis_);
+ top_dim_ = top[0]->count(channel_axis_);
+
+ channels_ = bottom[0]->shape(channel_axis_);
}
template <typename Dtype>
const vector<Blob<Dtype>*>& top) {
const Dtype* bottom_data = bottom[0]->cpu_data();
Dtype* top_data = top[0]->mutable_cpu_data();
- for (int n = 0; n < bottom[0]->num(); ++n) {
- im2col_cpu(bottom_data + bottom[0]->offset(n), channels_, height_,
- width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
- stride_h_, stride_w_, top_data + top[0]->offset(n));
+ for (int n = 0; n < num_; ++n) {
+ DCHECK_EQ(bottom[0]->shape().size() - channel_axis_, num_spatial_axes_ + 1);
+ DCHECK_EQ(top[0]->shape().size() - channel_axis_, num_spatial_axes_ + 1);
+ DCHECK_EQ(kernel_shape_.count(), num_spatial_axes_);
+ DCHECK_EQ(pad_.count(), num_spatial_axes_);
+ DCHECK_EQ(stride_.count(), num_spatial_axes_);
+ DCHECK_EQ(dilation_.count(), num_spatial_axes_);
+ if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+ im2col_cpu(bottom_data + n * bottom_dim_, channels_,
+ bottom[0]->shape(channel_axis_ + 1),
+ bottom[0]->shape(channel_axis_ + 2),
+ kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+ pad_.cpu_data()[0], pad_.cpu_data()[1],
+ stride_.cpu_data()[0], stride_.cpu_data()[1],
+ dilation_.cpu_data()[0], dilation_.cpu_data()[1],
+ top_data + n * top_dim_);
+ } else {
+ im2col_nd_cpu(bottom_data + n * bottom_dim_, num_spatial_axes_,
+ bottom[0]->shape().data() + channel_axis_,
+ top[0]->shape().data() + channel_axis_,
+ kernel_shape_.cpu_data(), pad_.cpu_data(), stride_.cpu_data(),
+ dilation_.cpu_data(), top_data + n * top_dim_);
+ }
}
}
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
const Dtype* top_diff = top[0]->cpu_diff();
Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
- for (int n = 0; n < top[0]->num(); ++n) {
- col2im_cpu(top_diff + top[0]->offset(n), channels_, height_, width_,
- kernel_h_, kernel_w_, pad_h_, pad_w_,
- stride_h_, stride_w_, bottom_diff + bottom[0]->offset(n));
+ for (int n = 0; n < num_; ++n) {
+ if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+ col2im_cpu(top_diff + n * top_dim_, channels_,
+ bottom[0]->shape(channel_axis_ + 1),
+ bottom[0]->shape(channel_axis_ + 2),
+ kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+ pad_.cpu_data()[0], pad_.cpu_data()[1],
+ stride_.cpu_data()[0], stride_.cpu_data()[1],
+ dilation_.cpu_data()[0], dilation_.cpu_data()[1],
+ bottom_diff + n * bottom_dim_);
+ } else {
+ col2im_nd_cpu(top_diff + n * top_dim_, num_spatial_axes_,
+ bottom[0]->shape().data() + channel_axis_,
+ top[0]->shape().data() + channel_axis_,
+ kernel_shape_.cpu_data(), pad_.cpu_data(), stride_.cpu_data(),
+ dilation_.cpu_data(), bottom_diff + n * bottom_dim_);
+ }
}
}
#include <vector>
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/layers/im2col_layer.hpp"
#include "caffe/util/im2col.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
const vector<Blob<Dtype>*>& top) {
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* top_data = top[0]->mutable_gpu_data();
- for (int n = 0; n < bottom[0]->num(); ++n) {
- im2col_gpu(bottom_data + bottom[0]->offset(n), channels_, height_,
- width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
- stride_h_, stride_w_, top_data + top[0]->offset(n));
+ const int num_kernels = channels_ * top[0]->count(channel_axis_ + 1);
+ for (int n = 0; n < num_; ++n) {
+ if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+ im2col_gpu(bottom_data + n * bottom_dim_, channels_,
+ bottom[0]->shape(channel_axis_ + 1),
+ bottom[0]->shape(channel_axis_ + 2),
+ kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+ pad_.cpu_data()[0], pad_.cpu_data()[1],
+ stride_.cpu_data()[0], stride_.cpu_data()[1],
+ dilation_.cpu_data()[0], dilation_.cpu_data()[1],
+ top_data + n * top_dim_);
+ } else {
+ im2col_nd_gpu(bottom_data + n * bottom_dim_, num_spatial_axes_,
+ num_kernels, bottom[0]->gpu_shape() + channel_axis_,
+ top[0]->gpu_shape() + channel_axis_,
+ kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(),
+ dilation_.gpu_data(), top_data + n * top_dim_);
+ }
}
}
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
const Dtype* top_diff = top[0]->gpu_diff();
Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
- for (int n = 0; n < top[0]->num(); ++n) {
- col2im_gpu(top_diff + top[0]->offset(n), channels_, height_, width_,
- kernel_h_, kernel_w_, pad_h_, pad_w_,
- stride_h_, stride_w_, bottom_diff + bottom[0]->offset(n));
+ for (int n = 0; n < num_; ++n) {
+ if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+ col2im_gpu(top_diff + n * top_dim_, channels_,
+ bottom[0]->shape(channel_axis_ + 1),
+ bottom[0]->shape(channel_axis_ + 2),
+ kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+ pad_.cpu_data()[0], pad_.cpu_data()[1],
+ stride_.cpu_data()[0], stride_.cpu_data()[1],
+ dilation_.cpu_data()[0], dilation_.cpu_data()[1],
+ bottom_diff + n * bottom_dim_);
+ } else {
+ col2im_nd_gpu(top_diff + n * top_dim_, num_spatial_axes_, bottom_dim_,
+ bottom[0]->gpu_shape() + channel_axis_,
+ top[0]->gpu_shape() + channel_axis_,
+ kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(),
+ dilation_.gpu_data(), bottom_diff + n * bottom_dim_);
+ }
}
}
+#ifdef USE_OPENCV
#include <opencv2/core/core.hpp>
#include <fstream> // NOLINT(readability/streams)
#include <utility>
#include <vector>
-#include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/layers/base_data_layer.hpp"
+#include "caffe/layers/image_data_layer.hpp"
#include "caffe/util/benchmark.hpp"
#include "caffe/util/io.hpp"
#include "caffe/util/math_functions.hpp"
template <typename Dtype>
ImageDataLayer<Dtype>::~ImageDataLayer<Dtype>() {
- this->JoinPrefetchThread();
+ this->StopInternalThread();
}
template <typename Dtype>
// Read an image, and use it to initialize the top blob.
cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
new_height, new_width, is_color);
- const int channels = cv_img.channels();
- const int height = cv_img.rows;
- const int width = cv_img.cols;
- // image
- const int crop_size = this->layer_param_.transform_param().crop_size();
+ CHECK(cv_img.data) << "Could not load " << lines_[lines_id_].first;
+ // Use data_transformer to infer the expected blob shape from a cv_image.
+ vector<int> top_shape = this->data_transformer_->InferBlobShape(cv_img);
+ this->transformed_data_.Reshape(top_shape);
+ // Reshape prefetch_data and top[0] according to the batch_size.
const int batch_size = this->layer_param_.image_data_param().batch_size();
- if (crop_size > 0) {
- top[0]->Reshape(batch_size, channels, crop_size, crop_size);
- this->prefetch_data_.Reshape(batch_size, channels, crop_size, crop_size);
- this->transformed_data_.Reshape(1, channels, crop_size, crop_size);
- } else {
- top[0]->Reshape(batch_size, channels, height, width);
- this->prefetch_data_.Reshape(batch_size, channels, height, width);
- this->transformed_data_.Reshape(1, channels, height, width);
+ CHECK_GT(batch_size, 0) << "Positive batch size required";
+ top_shape[0] = batch_size;
+ for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
+ this->prefetch_[i].data_.Reshape(top_shape);
}
+ top[0]->Reshape(top_shape);
+
LOG(INFO) << "output data size: " << top[0]->num() << ","
<< top[0]->channels() << "," << top[0]->height() << ","
<< top[0]->width();
// label
vector<int> label_shape(1, batch_size);
top[1]->Reshape(label_shape);
- this->prefetch_label_.Reshape(label_shape);
+ for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
+ this->prefetch_[i].label_.Reshape(label_shape);
+ }
}
template <typename Dtype>
shuffle(lines_.begin(), lines_.end(), prefetch_rng);
}
-// This function is used to create a thread that prefetches the data.
+// This function is called on prefetch thread
template <typename Dtype>
-void ImageDataLayer<Dtype>::InternalThreadEntry() {
+void ImageDataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
CPUTimer batch_timer;
batch_timer.Start();
double read_time = 0;
double trans_time = 0;
CPUTimer timer;
- CHECK(this->prefetch_data_.count());
+ CHECK(batch->data_.count());
CHECK(this->transformed_data_.count());
ImageDataParameter image_data_param = this->layer_param_.image_data_param();
const int batch_size = image_data_param.batch_size();
const int new_height = image_data_param.new_height();
const int new_width = image_data_param.new_width();
- const int crop_size = this->layer_param_.transform_param().crop_size();
const bool is_color = image_data_param.is_color();
string root_folder = image_data_param.root_folder();
- // Reshape on single input batches for inputs of varying dimension.
- if (batch_size == 1 && crop_size == 0 && new_height == 0 && new_width == 0) {
- cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
- 0, 0, is_color);
- this->prefetch_data_.Reshape(1, cv_img.channels(),
- cv_img.rows, cv_img.cols);
- this->transformed_data_.Reshape(1, cv_img.channels(),
- cv_img.rows, cv_img.cols);
- }
-
- Dtype* prefetch_data = this->prefetch_data_.mutable_cpu_data();
- Dtype* prefetch_label = this->prefetch_label_.mutable_cpu_data();
+ // Reshape according to the first image of each batch
+ // on single input batches allows for inputs of varying dimension.
+ cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
+ new_height, new_width, is_color);
+ CHECK(cv_img.data) << "Could not load " << lines_[lines_id_].first;
+ // Use data_transformer to infer the expected blob shape from a cv_img.
+ vector<int> top_shape = this->data_transformer_->InferBlobShape(cv_img);
+ this->transformed_data_.Reshape(top_shape);
+ // Reshape batch according to the batch_size.
+ top_shape[0] = batch_size;
+ batch->data_.Reshape(top_shape);
+
+ Dtype* prefetch_data = batch->data_.mutable_cpu_data();
+ Dtype* prefetch_label = batch->label_.mutable_cpu_data();
// datum scales
const int lines_size = lines_.size();
read_time += timer.MicroSeconds();
timer.Start();
// Apply transformations (mirror, crop...) to the image
- int offset = this->prefetch_data_.offset(item_id);
+ int offset = batch->data_.offset(item_id);
this->transformed_data_.set_cpu_data(prefetch_data + offset);
this->data_transformer_->Transform(cv_img, &(this->transformed_data_));
trans_time += timer.MicroSeconds();
REGISTER_LAYER_CLASS(ImageData);
} // namespace caffe
+#endif // USE_OPENCV
#include <algorithm>
-#include <cfloat>
#include <cmath>
#include <vector>
-#include "caffe/layer.hpp"
+#include "caffe/layers/infogain_loss_layer.hpp"
#include "caffe/util/io.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
#include <vector>
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/layers/inner_product_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
const Dtype* bottom_data = bottom[0]->cpu_data();
// Gradient with respect to weight
caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
- top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_cpu_diff());
+ top_diff, bottom_data, (Dtype)1., this->blobs_[0]->mutable_cpu_diff());
}
if (bias_term_ && this->param_propagate_down_[1]) {
const Dtype* top_diff = top[0]->cpu_diff();
// Gradient with respect to bias
caffe_cpu_gemv<Dtype>(CblasTrans, M_, N_, (Dtype)1., top_diff,
- bias_multiplier_.cpu_data(), (Dtype)0.,
+ bias_multiplier_.cpu_data(), (Dtype)1.,
this->blobs_[1]->mutable_cpu_diff());
}
if (propagate_down[0]) {
#include <vector>
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/layers/inner_product_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* top_data = top[0]->mutable_gpu_data();
const Dtype* weight = this->blobs_[0]->gpu_data();
- caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, N_, K_, (Dtype)1.,
- bottom_data, weight, (Dtype)0., top_data);
- if (bias_term_) {
- caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1.,
- bias_multiplier_.gpu_data(),
- this->blobs_[1]->gpu_data(), (Dtype)1., top_data);
+ if (M_ == 1) {
+ caffe_gpu_gemv<Dtype>(CblasNoTrans, N_, K_, (Dtype)1.,
+ weight, bottom_data, (Dtype)0., top_data);
+ if (bias_term_)
+ caffe_gpu_axpy<Dtype>(N_, bias_multiplier_.cpu_data()[0],
+ this->blobs_[1]->gpu_data(), top_data);
+ } else {
+ caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, N_, K_, (Dtype)1.,
+ bottom_data, weight, (Dtype)0., top_data);
+ if (bias_term_)
+ caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1.,
+ bias_multiplier_.gpu_data(),
+ this->blobs_[1]->gpu_data(), (Dtype)1., top_data);
}
}
const Dtype* bottom_data = bottom[0]->gpu_data();
// Gradient with respect to weight
caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
- top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_gpu_diff());
+ top_diff, bottom_data, (Dtype)1., this->blobs_[0]->mutable_gpu_diff());
}
if (bias_term_ && this->param_propagate_down_[1]) {
const Dtype* top_diff = top[0]->gpu_diff();
// Gradient with respect to bias
caffe_gpu_gemv<Dtype>(CblasTrans, M_, N_, (Dtype)1., top_diff,
- bias_multiplier_.gpu_data(), (Dtype)0.,
+ bias_multiplier_.gpu_data(), (Dtype)1.,
this->blobs_[1]->mutable_gpu_diff());
}
if (propagate_down[0]) {
--- /dev/null
+#include <vector>
+
+#include "caffe/layers/log_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void LogLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ NeuronLayer<Dtype>::LayerSetUp(bottom, top);
+ const Dtype base = this->layer_param_.log_param().base();
+ if (base != Dtype(-1)) {
+ CHECK_GT(base, 0) << "base must be strictly positive.";
+ }
+ // If base == -1, interpret the base as e and set log_base = 1 exactly.
+ // Otherwise, calculate its log explicitly.
+ const Dtype log_base = (base == Dtype(-1)) ? Dtype(1) : log(base);
+ CHECK(!isnan(log_base))
+ << "NaN result: log(base) = log(" << base << ") = " << log_base;
+ CHECK(!isinf(log_base))
+ << "Inf result: log(base) = log(" << base << ") = " << log_base;
+ base_scale_ = Dtype(1) / log_base;
+ CHECK(!isnan(base_scale_))
+ << "NaN result: 1/log(base) = 1/log(" << base << ") = " << base_scale_;
+ CHECK(!isinf(base_scale_))
+ << "Inf result: 1/log(base) = 1/log(" << base << ") = " << base_scale_;
+ input_scale_ = this->layer_param_.log_param().scale();
+ input_shift_ = this->layer_param_.log_param().shift();
+ backward_num_scale_ = input_scale_ / log_base;
+}
+
+template <typename Dtype>
+void LogLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ const int count = bottom[0]->count();
+ const Dtype* bottom_data = bottom[0]->cpu_data();
+ Dtype* top_data = top[0]->mutable_cpu_data();
+ if (input_scale_ == Dtype(1) && input_shift_ == Dtype(0)) {
+ caffe_log(count, bottom_data, top_data);
+ } else {
+ caffe_copy(count, bottom_data, top_data);
+ if (input_scale_ != Dtype(1)) {
+ caffe_scal(count, input_scale_, top_data);
+ }
+ if (input_shift_ != Dtype(0)) {
+ caffe_add_scalar(count, input_shift_, top_data);
+ }
+ caffe_log(count, top_data, top_data);
+ }
+ if (base_scale_ != Dtype(1)) {
+ caffe_scal(count, base_scale_, top_data);
+ }
+}
+
+template <typename Dtype>
+void LogLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+ if (!propagate_down[0]) { return; }
+ const int count = bottom[0]->count();
+ const Dtype* bottom_data = bottom[0]->cpu_data();
+ const Dtype* top_diff = top[0]->cpu_diff();
+ Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+ caffe_copy(count, bottom_data, bottom_diff);
+ if (input_scale_ != Dtype(1)) {
+ caffe_scal(count, input_scale_, bottom_diff);
+ }
+ if (input_shift_ != Dtype(0)) {
+ caffe_add_scalar(count, input_shift_, bottom_diff);
+ }
+ caffe_powx(count, bottom_diff, Dtype(-1), bottom_diff);
+ if (backward_num_scale_ != Dtype(1)) {
+ caffe_scal(count, backward_num_scale_, bottom_diff);
+ }
+ caffe_mul(count, top_diff, bottom_diff, bottom_diff);
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(LogLayer);
+#endif
+
+INSTANTIATE_CLASS(LogLayer);
+REGISTER_LAYER_CLASS(Log);
+
+} // namespace caffe
--- /dev/null
+#include <vector>
+
+#include "caffe/layers/log_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void LogLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ const int count = bottom[0]->count();
+ const Dtype* bottom_data = bottom[0]->gpu_data();
+ Dtype* top_data = top[0]->mutable_gpu_data();
+ if (input_scale_ == Dtype(1) && input_shift_ == Dtype(0)) {
+ caffe_gpu_log(count, bottom_data, top_data);
+ } else {
+ caffe_copy(count, bottom_data, top_data);
+ if (input_scale_ != Dtype(1)) {
+ caffe_gpu_scal(count, input_scale_, top_data);
+ }
+ if (input_shift_ != Dtype(0)) {
+ caffe_gpu_add_scalar(count, input_shift_, top_data);
+ }
+ caffe_gpu_log(count, top_data, top_data);
+ }
+ if (base_scale_ != Dtype(1)) {
+ caffe_gpu_scal(count, base_scale_, top_data);
+ }
+}
+
+template <typename Dtype>
+void LogLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+ if (!propagate_down[0]) { return; }
+ const int count = bottom[0]->count();
+ const Dtype* bottom_data = bottom[0]->gpu_data();
+ const Dtype* top_diff = top[0]->gpu_diff();
+ Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+ caffe_copy(count, bottom_data, bottom_diff);
+ if (input_scale_ != Dtype(1)) {
+ caffe_gpu_scal(count, input_scale_, bottom_diff);
+ }
+ if (input_shift_ != Dtype(0)) {
+ caffe_gpu_add_scalar(count, input_shift_, bottom_diff);
+ }
+ caffe_gpu_powx(count, bottom_diff, Dtype(-1), bottom_diff);
+ if (backward_num_scale_ != Dtype(1)) {
+ caffe_gpu_scal(count, backward_num_scale_, bottom_diff);
+ }
+ caffe_gpu_mul(count, top_diff, bottom_diff, bottom_diff);
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(LogLayer);
+
+} // namespace caffe
-#include <algorithm>
-#include <cfloat>
-#include <cmath>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/loss_layer.hpp"
namespace caffe {
#include <vector>
-#include "caffe/layer.hpp"
+#include "caffe/layers/lrn_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
#endif
INSTANTIATE_CLASS(LRNLayer);
-REGISTER_LAYER_CLASS(LRN);
} // namespace caffe
#include <vector>
-#include "caffe/layer.hpp"
+#include "caffe/layers/lrn_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
template <typename Dtype>
-__global__ void LRNFillScale(const int nthreads, const Dtype* in,
+__global__ void LRNFillScale(const int nthreads, const Dtype* const in,
const int num, const int channels, const int height,
const int width, const int size, const Dtype alpha_over_size,
- const Dtype k, Dtype* scale) {
+ const Dtype k, Dtype* const scale) {
CUDA_KERNEL_LOOP(index, nthreads) {
// find out the local offset
- int w = index % width;
- int h = (index / width) % height;
- int n = index / width / height;
- int offset = (n * channels * height + h) * width + w;
- int step = height * width;
- in += offset;
- scale += offset;
+ const int w = index % width;
+ const int h = (index / width) % height;
+ const int n = index / width / height;
+ const int offset = (n * channels * height + h) * width + w;
+ const int step = height * width;
+ const Dtype* const in_off = in + offset;
+ Dtype* const scale_off = scale + offset;
int head = 0;
- int pre_pad = (size - 1) / 2;
- int post_pad = size - pre_pad - 1;
+ const int pre_pad = (size - 1) / 2;
+ const int post_pad = size - pre_pad - 1;
Dtype accum_scale = 0;
// fill the scale at [n, :, h, w]
// accumulate values
- while (head < post_pad) {
- accum_scale += in[head * step] * in[head * step];
- ++head;
- }
- // until we reach size, nothing needs to be subtracted
- while (head < size) {
- accum_scale += in[head * step] * in[head * step];
- scale[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
+ while (head < post_pad && head < channels) {
+ accum_scale += in_off[head * step] * in_off[head * step];
++head;
}
// both add and subtract
while (head < channels) {
- accum_scale += in[head * step] * in[head * step];
- accum_scale -= in[(head - size) * step] * in[(head - size) * step];
- scale[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
+ accum_scale += in_off[head * step] * in_off[head * step];
+ if (head - size >= 0) {
+ accum_scale -= in_off[(head - size) * step]
+ * in_off[(head - size) * step];
+ }
+ scale_off[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
++head;
}
// subtract only
while (head < channels + post_pad) {
- accum_scale -= in[(head - size) * step] * in[(head - size) * step];
- scale[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
+ if (head - size >= 0) {
+ accum_scale -= in_off[(head - size) * step]
+ * in_off[(head - size) * step];
+ }
+ scale_off[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
++head;
}
}
// TODO: check if it would be faster to just put it into the previous kernel.
template <typename Dtype>
-__global__ void LRNComputeOutput(const int nthreads, const Dtype* in,
- const Dtype* scale, const Dtype negative_beta, Dtype* out) {
+__global__ void LRNComputeOutput(const int nthreads, const Dtype* const in,
+ const Dtype* const scale, const Dtype negative_beta, Dtype* const out) {
CUDA_KERNEL_LOOP(index, nthreads) {
out[index] = in[index] * pow(scale[index], negative_beta);
}
}
template <typename Dtype>
-__global__ void LRNComputeDiff(const int nthreads, const Dtype* bottom_data,
- const Dtype* top_data, const Dtype* scale, const Dtype* top_diff,
+__global__ void LRNComputeDiff(const int nthreads,
+ const Dtype* const bottom_data, const Dtype* const top_data,
+ const Dtype* const scale, const Dtype* const top_diff,
const int num, const int channels, const int height,
const int width, const int size, const Dtype negative_beta,
- const Dtype cache_ratio,
- Dtype* bottom_diff) {
+ const Dtype cache_ratio, Dtype* const bottom_diff) {
CUDA_KERNEL_LOOP(index, nthreads) {
// find out the local offset
- int w = index % width;
- int h = (index / width) % height;
- int n = index / width / height;
- int offset = (n * channels * height + h) * width + w;
- int step = height * width;
- bottom_data += offset;
- top_data += offset;
- scale += offset;
- top_diff += offset;
- bottom_diff += offset;
+ const int w = index % width;
+ const int h = (index / width) % height;
+ const int n = index / width / height;
+ const int offset = (n * channels * height + h) * width + w;
+ const int step = height * width;
+ const Dtype* const bottom_off = bottom_data + offset;
+ const Dtype* const top_off = top_data + offset;
+ const Dtype* const scale_off = scale + offset;
+ const Dtype* const top_diff_off = top_diff + offset;
+ Dtype* const bottom_diff_off = bottom_diff + offset;
int head = 0;
- int pre_pad = size - (size + 1) / 2;
- int post_pad = size - pre_pad - 1;
+ const int pre_pad = size - (size + 1) / 2;
+ const int post_pad = size - pre_pad - 1;
Dtype accum_ratio = 0;
// accumulate values
- while (head < post_pad) {
- accum_ratio += top_diff[head * step] * top_data[head * step] /
- scale[head * step];
- ++head;
- }
- // until we reach size, nothing needs to be subtracted
- while (head < size) {
- accum_ratio += top_diff[head * step] * top_data[head * step] /
- scale[head * step];
- bottom_diff[(head - post_pad) * step] = top_diff[(head - post_pad) * step]
- * pow(scale[(head - post_pad) * step], negative_beta) - cache_ratio *
- bottom_data[(head - post_pad) * step] * accum_ratio;
+ while (head < post_pad && head < channels) {
+ accum_ratio += top_diff_off[head * step] * top_off[head * step] /
+ scale_off[head * step];
++head;
}
// both add and subtract
while (head < channels) {
- accum_ratio += top_diff[head * step] * top_data[head * step] /
- scale[head * step];
- accum_ratio -= top_diff[(head - size) * step] *
- top_data[(head - size) * step] / scale[(head - size) * step];
- bottom_diff[(head - post_pad) * step] = top_diff[(head - post_pad) * step]
- * pow(scale[(head - post_pad) * step], negative_beta) - cache_ratio *
- bottom_data[(head - post_pad) * step] * accum_ratio;
+ accum_ratio += top_diff_off[head * step] * top_off[head * step] /
+ scale_off[head * step];
+ if (head - size >= 0) {
+ accum_ratio -= top_diff_off[(head - size) * step] *
+ top_off[(head - size) * step] / scale_off[(head - size) * step];
+ }
+ bottom_diff_off[(head - post_pad) * step] =
+ top_diff_off[(head - post_pad) * step]
+ * pow(scale_off[(head - post_pad) * step], negative_beta)
+ - cache_ratio * bottom_off[(head - post_pad) * step] * accum_ratio;
++head;
}
// subtract only
while (head < channels + post_pad) {
- accum_ratio -= top_diff[(head - size) * step] *
- top_data[(head - size) * step] / scale[(head - size) * step];
- bottom_diff[(head - post_pad) * step] = top_diff[(head - post_pad) * step]
- * pow(scale[(head - post_pad) * step], negative_beta) - cache_ratio *
- bottom_data[(head - post_pad) * step] * accum_ratio;
+ if (head - size >= 0) {
+ accum_ratio -= top_diff_off[(head - size) * step] *
+ top_off[(head - size) * step] / scale_off[(head - size) * step];
+ }
+ bottom_diff_off[(head - post_pad) * step] =
+ top_diff_off[(head - post_pad) * step]
+ * pow(scale_off[(head - post_pad) * step], negative_beta)
+ - cache_ratio * bottom_off[(head - post_pad) * step] * accum_ratio;
++head;
}
}
+#ifdef USE_OPENCV
#include <opencv2/core/core.hpp>
+#endif // USE_OPENCV
#include <vector>
-#include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/layers/memory_data_layer.hpp"
namespace caffe {
has_new_data_ = true;
}
+#ifdef USE_OPENCV
template <typename Dtype>
void MemoryDataLayer<Dtype>::AddMatVector(const vector<cv::Mat>& mat_vector,
const vector<int>& labels) {
Reset(top_data, top_label, num);
has_new_data_ = true;
}
+#endif // USE_OPENCV
template <typename Dtype>
void MemoryDataLayer<Dtype>::Reset(Dtype* data, Dtype* labels, int n) {
#include <algorithm>
-#include <cfloat>
#include <cmath>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/layers/multinomial_logistic_loss_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
-#include <algorithm>
#include <vector>
-#include "caffe/common_layers.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/layers/mvn_layer.hpp"
#include "caffe/util/math_functions.hpp"
namespace caffe {
1, 1);
temp_.Reshape(bottom[0]->num(), bottom[0]->channels(),
bottom[0]->height(), bottom[0]->width());
- sum_multiplier_.Reshape(1, 1,
- bottom[0]->height(), bottom[0]->width());
+ if ( this->layer_param_.mvn_param().across_channels() ) {
+ sum_multiplier_.Reshape(1, bottom[0]->channels(), bottom[0]->height(),
+ bottom[0]->width());
+ } else {
+ sum_multiplier_.Reshape(1, 1, bottom[0]->height(), bottom[0]->width());
+ }
Dtype* multiplier_data = sum_multiplier_.mutable_cpu_data();
caffe_set(sum_multiplier_.count(), Dtype(1), multiplier_data);
+ eps_ = this->layer_param_.mvn_param().eps();
}
template <typename Dtype>
num = bottom[0]->num() * bottom[0]->channels();
int dim = bottom[0]->count() / num;
- Dtype eps = 1e-10;
- if (this->layer_param_.mvn_param().normalize_variance()) {
- // put the squares of bottom into temp_
- caffe_powx(bottom[0]->count(), bottom_data, Dtype(2),
- temp_.mutable_cpu_data());
+ // subtract mean
+ caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
+ sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data()); // EX
+ caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
+ mean_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
+ temp_.mutable_cpu_data());
+ caffe_add(temp_.count(), bottom_data, temp_.cpu_data(), top_data); // X-EX
- // computes variance using var(X) = E(X^2) - (EX)^2
- caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
- sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data()); // EX
+ if (this->layer_param_.mvn_param().normalize_variance()) {
+ // compute variance using var(X) = E((X-EX)^2)
+ caffe_powx(bottom[0]->count(), top_data, Dtype(2),
+ temp_.mutable_cpu_data()); // (X-EX)^2
caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.cpu_data(),
sum_multiplier_.cpu_data(), 0.,
- variance_.mutable_cpu_data()); // E(X^2)
- caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
- temp_.mutable_cpu_data()); // (EX)^2
- caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),
- variance_.mutable_cpu_data()); // variance
-
- // do mean and variance normalization
- // subtract mean
- caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
- mean_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
- temp_.mutable_cpu_data());
-
- caffe_add(temp_.count(), bottom_data, temp_.cpu_data(), top_data);
+ variance_.mutable_cpu_data()); // E((X-EX)^2)
// normalize variance
caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5),
variance_.mutable_cpu_data());
- caffe_add_scalar(variance_.count(), eps, variance_.mutable_cpu_data());
+ caffe_add_scalar(variance_.count(), eps_, variance_.mutable_cpu_data());
caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
variance_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
temp_.mutable_cpu_data());
caffe_div(temp_.count(), top_data, temp_.cpu_data(), top_data);
- } else {
- caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
- sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data()); // EX
-
- // subtract mean
- caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
- mean_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
- temp_.mutable_cpu_data());
-
- caffe_add(temp_.count(), bottom_data, temp_.cpu_data(), top_data);
}
}
num = bottom[0]->num() * bottom[0]->channels();
int dim = bottom[0]->count() / num;
- Dtype eps = 1e-10;
if (this->layer_param_.mvn_param().normalize_variance()) {
caffe_mul(temp_.count(), top_data, top_diff, bottom_diff);
// put the squares of bottom into temp_
caffe_powx(temp_.count(), bottom_data, Dtype(2),
temp_.mutable_cpu_data());
-
- // computes variance using var(X) = E(X^2) - (EX)^2
- caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
- sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data()); // EX
- caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.cpu_data(),
- sum_multiplier_.cpu_data(), 0.,
- variance_.mutable_cpu_data()); // E(X^2)
- caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
- temp_.mutable_cpu_data()); // (EX)^2
- caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),
- variance_.mutable_cpu_data()); // variance
-
- // normalize variance
- caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5),
- variance_.mutable_cpu_data());
-
- caffe_add_scalar(variance_.count(), eps, variance_.mutable_cpu_data());
-
caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
variance_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
temp_.mutable_cpu_data());
caffe_div(temp_.count(), bottom_diff, temp_.cpu_data(), bottom_diff);
} else {
- caffe_copy(temp_.count(), top_diff, bottom_diff);
+ caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, top_diff,
+ sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());
+ caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
+ mean_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
+ temp_.mutable_cpu_data());
+ caffe_add(temp_.count(), top_diff, temp_.cpu_data(), bottom_diff);
}
}
-#include <algorithm>
#include <vector>
-#include "caffe/common_layers.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/layers/mvn_layer.hpp"
#include "caffe/util/math_functions.hpp"
namespace caffe {
int dim = bottom[0]->count() / num;
- if (this->layer_param_.mvn_param().normalize_variance()) {
- // put the squares of bottom into temp_
- caffe_gpu_powx(bottom[0]->count(), bottom_data, Dtype(2),
- temp_.mutable_gpu_data());
+ // subtract mean
+ caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
+ sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data()); // EX
+ caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
+ mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
+ temp_.mutable_gpu_data());
+ caffe_gpu_add(temp_.count(), bottom_data, temp_.gpu_data(),
+ top_data); // X-EX
- // computes variance using var(X) = E(X^2) - (EX)^2
- caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
- sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data()); // EX
+ if (this->layer_param_.mvn_param().normalize_variance()) {
+ // compute variance using var(X) = E((X-EX)^2)
+ caffe_gpu_powx(bottom[0]->count(), top_data, Dtype(2),
+ temp_.mutable_gpu_data()); // (X-EX)^2
caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.gpu_data(),
sum_multiplier_.gpu_data(), 0.,
- variance_.mutable_gpu_data()); // E(X^2)
- caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
- temp_.mutable_gpu_data()); // (EX)^2
- caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
- variance_.mutable_gpu_data()); // variance
-
- Dtype eps = 1e-10;
-
- // do mean and variance normalization
- // subtract mean
- caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
- mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
- temp_.mutable_gpu_data());
-
- caffe_gpu_add(temp_.count(), bottom_data, temp_.gpu_data(), top_data);
+ variance_.mutable_gpu_data()); // E((X-EX)^2)
// normalize variance
caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
variance_.mutable_gpu_data());
- caffe_gpu_add_scalar(variance_.count(), eps, variance_.mutable_gpu_data());
+ caffe_gpu_add_scalar(variance_.count(), eps_, variance_.mutable_gpu_data());
caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
variance_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
temp_.mutable_gpu_data());
caffe_gpu_div(temp_.count(), top_data, temp_.gpu_data(), top_data);
- } else {
- caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
- sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data()); // EX
-
- // subtract mean
- caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
- mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
- temp_.mutable_gpu_data());
-
- caffe_gpu_add(temp_.count(), bottom_data, temp_.gpu_data(), top_data);
}
}
int dim = bottom[0]->count() / num;
- Dtype eps = 1e-10;
-
if (this->layer_param_.mvn_param().normalize_variance()) {
caffe_gpu_mul(temp_.count(), top_data, top_diff, bottom_diff);
caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., bottom_diff,
caffe_gpu_powx(temp_.count(), bottom_data, Dtype(2),
temp_.mutable_gpu_data());
- // computes variance using var(X) = E(X^2) - (EX)^2
- caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
- sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data()); // EX
- caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.gpu_data(),
- sum_multiplier_.gpu_data(), 0.,
- variance_.mutable_gpu_data()); // E(X^2)
- caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
- temp_.mutable_gpu_data()); // (EX)^2
- caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
- variance_.mutable_gpu_data()); // variance
-
- // normalize variance
- caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
- variance_.mutable_gpu_data());
-
- caffe_gpu_add_scalar(variance_.count(), eps, variance_.mutable_gpu_data());
-
caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
variance_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
temp_.mutable_gpu_data());
caffe_gpu_div(temp_.count(), bottom_diff, temp_.gpu_data(), bottom_diff);
} else {
- caffe_copy(temp_.count(), top_diff, bottom_diff);
+ caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, top_diff,
+ sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());
+ caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
+ mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
+ temp_.mutable_gpu_data());
+ caffe_gpu_add(temp_.count(), top_diff, temp_.gpu_data(), bottom_diff);
}
}
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/neuron_layer.hpp"
namespace caffe {
#include <cfloat>
#include <vector>
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/syncedmem.hpp"
+#include "caffe/layers/pooling_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
#include <cfloat>
#include <vector>
-#include "caffe/layer.hpp"
+#include "caffe/layers/pooling_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
template <typename Dtype>
-__global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data,
- const int num, const int channels, const int height,
- const int width, const int pooled_height, const int pooled_width,
- const int kernel_h, const int kernel_w, const int stride_h,
- const int stride_w, const int pad_h, const int pad_w, Dtype* top_data,
- int* mask, Dtype* top_mask) {
+__global__ void MaxPoolForward(const int nthreads,
+ const Dtype* const bottom_data, const int num, const int channels,
+ const int height, const int width, const int pooled_height,
+ const int pooled_width, const int kernel_h, const int kernel_w,
+ const int stride_h, const int stride_w, const int pad_h, const int pad_w,
+ Dtype* const top_data, int* mask, Dtype* top_mask) {
CUDA_KERNEL_LOOP(index, nthreads) {
- int pw = index % pooled_width;
- int ph = (index / pooled_width) % pooled_height;
- int c = (index / pooled_width / pooled_height) % channels;
- int n = index / pooled_width / pooled_height / channels;
+ const int pw = index % pooled_width;
+ const int ph = (index / pooled_width) % pooled_height;
+ const int c = (index / pooled_width / pooled_height) % channels;
+ const int n = index / pooled_width / pooled_height / channels;
int hstart = ph * stride_h - pad_h;
int wstart = pw * stride_w - pad_w;
- int hend = min(hstart + kernel_h, height);
- int wend = min(wstart + kernel_w, width);
+ const int hend = min(hstart + kernel_h, height);
+ const int wend = min(wstart + kernel_w, width);
hstart = max(hstart, 0);
wstart = max(wstart, 0);
Dtype maxval = -FLT_MAX;
int maxidx = -1;
- bottom_data += (n * channels + c) * height * width;
+ const Dtype* const bottom_slice =
+ bottom_data + (n * channels + c) * height * width;
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
- if (bottom_data[h * width + w] > maxval) {
+ if (bottom_slice[h * width + w] > maxval) {
maxidx = h * width + w;
- maxval = bottom_data[maxidx];
+ maxval = bottom_slice[maxidx];
}
}
}
}
template <typename Dtype>
-__global__ void AvePoolForward(const int nthreads, const Dtype* bottom_data,
- const int num, const int channels, const int height,
- const int width, const int pooled_height, const int pooled_width,
- const int kernel_h, const int kernel_w, const int stride_h,
- const int stride_w, const int pad_h, const int pad_w, Dtype* top_data) {
+__global__ void AvePoolForward(const int nthreads,
+ const Dtype* const bottom_data, const int num, const int channels,
+ const int height, const int width, const int pooled_height,
+ const int pooled_width, const int kernel_h, const int kernel_w,
+ const int stride_h, const int stride_w, const int pad_h, const int pad_w,
+ Dtype* const top_data) {
CUDA_KERNEL_LOOP(index, nthreads) {
- int pw = index % pooled_width;
- int ph = (index / pooled_width) % pooled_height;
- int c = (index / pooled_width / pooled_height) % channels;
- int n = index / pooled_width / pooled_height / channels;
+ const int pw = index % pooled_width;
+ const int ph = (index / pooled_width) % pooled_height;
+ const int c = (index / pooled_width / pooled_height) % channels;
+ const int n = index / pooled_width / pooled_height / channels;
int hstart = ph * stride_h - pad_h;
int wstart = pw * stride_w - pad_w;
int hend = min(hstart + kernel_h, height + pad_h);
int wend = min(wstart + kernel_w, width + pad_w);
- int pool_size = (hend - hstart) * (wend - wstart);
+ const int pool_size = (hend - hstart) * (wend - wstart);
hstart = max(hstart, 0);
wstart = max(wstart, 0);
hend = min(hend, height);
wend = min(wend, width);
Dtype aveval = 0;
- bottom_data += (n * channels + c) * height * width;
+ const Dtype* const bottom_slice =
+ bottom_data + (n * channels + c) * height * width;
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
- aveval += bottom_data[h * width + w];
+ aveval += bottom_slice[h * width + w];
}
}
top_data[index] = aveval / pool_size;
template <typename Dtype>
__global__ void StoPoolForwardTrain(const int nthreads,
- const Dtype* bottom_data,
+ const Dtype* const bottom_data,
const int num, const int channels, const int height,
const int width, const int pooled_height, const int pooled_width,
const int kernel_h, const int kernel_w, const int stride_h,
- const int stride_w, Dtype* rand_idx, Dtype* top_data) {
+ const int stride_w, Dtype* const rand_idx, Dtype* const top_data) {
CUDA_KERNEL_LOOP(index, nthreads) {
- int pw = index % pooled_width;
- int ph = (index / pooled_width) % pooled_height;
- int c = (index / pooled_width / pooled_height) % channels;
- int n = index / pooled_width / pooled_height / channels;
- int hstart = ph * stride_h;
- int hend = min(hstart + kernel_h, height);
- int wstart = pw * stride_w;
- int wend = min(wstart + kernel_w, width);
+ const int pw = index % pooled_width;
+ const int ph = (index / pooled_width) % pooled_height;
+ const int c = (index / pooled_width / pooled_height) % channels;
+ const int n = index / pooled_width / pooled_height / channels;
+ const int hstart = ph * stride_h;
+ const int hend = min(hstart + kernel_h, height);
+ const int wstart = pw * stride_w;
+ const int wend = min(wstart + kernel_w, width);
Dtype cumsum = 0.;
- bottom_data += (n * channels + c) * height * width;
+ const Dtype* const bottom_slice =
+ bottom_data + (n * channels + c) * height * width;
// First pass: get sum
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
- cumsum += bottom_data[h * width + w];
+ cumsum += bottom_slice[h * width + w];
}
}
- float thres = rand_idx[index] * cumsum;
+ const float thres = rand_idx[index] * cumsum;
// Second pass: get value, and set index.
cumsum = 0;
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
- cumsum += bottom_data[h * width + w];
+ cumsum += bottom_slice[h * width + w];
if (cumsum >= thres) {
rand_idx[index] = ((n * channels + c) * height + h) * width + w;
- top_data[index] = bottom_data[h * width + w];
+ top_data[index] = bottom_slice[h * width + w];
return;
}
}
template <typename Dtype>
__global__ void StoPoolForwardTest(const int nthreads,
- const Dtype* bottom_data,
+ const Dtype* const bottom_data,
const int num, const int channels, const int height,
const int width, const int pooled_height, const int pooled_width,
const int kernel_h, const int kernel_w, const int stride_h,
- const int stride_w, Dtype* top_data) {
+ const int stride_w, Dtype* const top_data) {
CUDA_KERNEL_LOOP(index, nthreads) {
- int pw = index % pooled_width;
- int ph = (index / pooled_width) % pooled_height;
- int c = (index / pooled_width / pooled_height) % channels;
- int n = index / pooled_width / pooled_height / channels;
- int hstart = ph * stride_h;
- int hend = min(hstart + kernel_h, height);
- int wstart = pw * stride_w;
- int wend = min(wstart + kernel_w, width);
+ const int pw = index % pooled_width;
+ const int ph = (index / pooled_width) % pooled_height;
+ const int c = (index / pooled_width / pooled_height) % channels;
+ const int n = index / pooled_width / pooled_height / channels;
+ const int hstart = ph * stride_h;
+ const int hend = min(hstart + kernel_h, height);
+ const int wstart = pw * stride_w;
+ const int wend = min(wstart + kernel_w, width);
// We set cumsum to be 0 to avoid divide-by-zero problems
Dtype cumsum = FLT_MIN;
Dtype cumvalues = 0.;
- bottom_data += (n * channels + c) * height * width;
+ const Dtype* const bottom_slice =
+ bottom_data + (n * channels + c) * height * width;
// First pass: get sum
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
- cumsum += bottom_data[h * width + w];
- cumvalues += bottom_data[h * width + w] * bottom_data[h * width + w];
+ cumsum += bottom_slice[h * width + w];
+ cumvalues += bottom_slice[h * width + w] * bottom_slice[h * width + w];
}
}
top_data[index] = cumvalues / cumsum;
template <typename Dtype>
-__global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
- const int* mask, const Dtype* top_mask, const int num, const int channels,
- const int height, const int width, const int pooled_height,
- const int pooled_width, const int kernel_h, const int kernel_w,
- const int stride_h, const int stride_w, const int pad_h, const int pad_w,
- Dtype* bottom_diff) {
+__global__ void MaxPoolBackward(const int nthreads, const Dtype* const top_diff,
+ const int* const mask, const Dtype* const top_mask, const int num,
+ const int channels, const int height, const int width,
+ const int pooled_height, const int pooled_width, const int kernel_h,
+ const int kernel_w, const int stride_h, const int stride_w, const int pad_h,
+ const int pad_w, Dtype* const bottom_diff) {
CUDA_KERNEL_LOOP(index, nthreads) {
// find out the local index
// find out the local offset
- int w = index % width;
- int h = (index / width) % height;
- int c = (index / width / height) % channels;
- int n = index / width / height / channels;
- int phstart =
- (h + pad_h < kernel_h) ? 0 : (h + pad_h - kernel_h) / stride_h + 1;
- int phend = min((h + pad_h) / stride_h + 1, pooled_height);
- int pwstart =
- (w + pad_w < kernel_w) ? 0 : (w + pad_w - kernel_w) / stride_w + 1;
- int pwend = min((w + pad_w) / stride_w + 1, pooled_width);
+ const int w = index % width;
+ const int h = (index / width) % height;
+ const int c = (index / width / height) % channels;
+ const int n = index / width / height / channels;
+ const int phstart =
+ (h + pad_h < kernel_h) ? 0 : (h + pad_h - kernel_h) / stride_h + 1;
+ const int phend = min((h + pad_h) / stride_h + 1, pooled_height);
+ const int pwstart =
+ (w + pad_w < kernel_w) ? 0 : (w + pad_w - kernel_w) / stride_w + 1;
+ const int pwend = min((w + pad_w) / stride_w + 1, pooled_width);
Dtype gradient = 0;
- int offset = (n * channels + c) * pooled_height * pooled_width;
- top_diff += offset;
+ const int offset = (n * channels + c) * pooled_height * pooled_width;
+ const Dtype* const top_diff_slice = top_diff + offset;
if (mask) {
- mask += offset;
+ const int* const mask_slice = mask + offset;
for (int ph = phstart; ph < phend; ++ph) {
for (int pw = pwstart; pw < pwend; ++pw) {
- if (mask[ph * pooled_width + pw] == h * width + w) {
- gradient += top_diff[ph * pooled_width + pw];
+ if (mask_slice[ph * pooled_width + pw] == h * width + w) {
+ gradient += top_diff_slice[ph * pooled_width + pw];
}
}
}
} else {
- top_mask += offset;
+ const Dtype* const top_mask_slice = top_mask + offset;
for (int ph = phstart; ph < phend; ++ph) {
for (int pw = pwstart; pw < pwend; ++pw) {
- if (top_mask[ph * pooled_width + pw] == h * width + w) {
- gradient += top_diff[ph * pooled_width + pw];
+ if (top_mask_slice[ph * pooled_width + pw] == h * width + w) {
+ gradient += top_diff_slice[ph * pooled_width + pw];
}
}
}
}
template <typename Dtype>
-__global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,
+__global__ void AvePoolBackward(const int nthreads, const Dtype* const top_diff,
const int num, const int channels, const int height,
const int width, const int pooled_height, const int pooled_width,
const int kernel_h, const int kernel_w, const int stride_h,
const int stride_w, const int pad_h, const int pad_w,
- Dtype* bottom_diff) {
+ Dtype* const bottom_diff) {
CUDA_KERNEL_LOOP(index, nthreads) {
// find out the local index
// find out the local offset
- int w = index % width + pad_w;
- int h = (index / width) % height + pad_h;
- int c = (index / width / height) % channels;
- int n = index / width / height / channels;
- int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
- int phend = min(h / stride_h + 1, pooled_height);
- int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
- int pwend = min(w / stride_w + 1, pooled_width);
+ const int w = index % width + pad_w;
+ const int h = (index / width) % height + pad_h;
+ const int c = (index / width / height) % channels;
+ const int n = index / width / height / channels;
+ const int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
+ const int phend = min(h / stride_h + 1, pooled_height);
+ const int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
+ const int pwend = min(w / stride_w + 1, pooled_width);
Dtype gradient = 0;
- top_diff += (n * channels + c) * pooled_height * pooled_width;
+ const Dtype* const top_diff_slice =
+ top_diff + (n * channels + c) * pooled_height * pooled_width;
for (int ph = phstart; ph < phend; ++ph) {
for (int pw = pwstart; pw < pwend; ++pw) {
// figure out the pooling size
int hend = min(hstart + kernel_h, height + pad_h);
int wend = min(wstart + kernel_w, width + pad_w);
int pool_size = (hend - hstart) * (wend - wstart);
- gradient += top_diff[ph * pooled_width + pw] / pool_size;
+ gradient += top_diff_slice[ph * pooled_width + pw] / pool_size;
}
}
bottom_diff[index] = gradient;
template <typename Dtype>
__global__ void StoPoolBackward(const int nthreads,
- const Dtype* rand_idx, const Dtype* top_diff,
+ const Dtype* const rand_idx, const Dtype* const top_diff,
const int num, const int channels, const int height,
const int width, const int pooled_height, const int pooled_width,
const int kernel_h, const int kernel_w, const int stride_h,
- const int stride_w, Dtype* bottom_diff) {
+ const int stride_w, Dtype* const bottom_diff) {
CUDA_KERNEL_LOOP(index, nthreads) {
// find out the local index
// find out the local offset
- int w = index % width;
- int h = (index / width) % height;
- int c = (index / width / height) % channels;
- int n = index / width / height / channels;
- int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
- int phend = min(h / stride_h + 1, pooled_height);
- int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
- int pwend = min(w / stride_w + 1, pooled_width);
+ const int w = index % width;
+ const int h = (index / width) % height;
+ const int c = (index / width / height) % channels;
+ const int n = index / width / height / channels;
+ const int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
+ const int phend = min(h / stride_h + 1, pooled_height);
+ const int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
+ const int pwend = min(w / stride_w + 1, pooled_width);
Dtype gradient = 0;
- rand_idx += (n * channels + c) * pooled_height * pooled_width;
- top_diff += (n * channels + c) * pooled_height * pooled_width;
+ const Dtype* const rand_idx_slice =
+ rand_idx + (n * channels + c) * pooled_height * pooled_width;
+ const Dtype* const top_diff_slice =
+ top_diff + (n * channels + c) * pooled_height * pooled_width;
for (int ph = phstart; ph < phend; ++ph) {
for (int pw = pwstart; pw < pwend; ++pw) {
- gradient += top_diff[ph * pooled_width + pw] *
- (index == static_cast<int>(rand_idx[ph * pooled_width + pw]));
+ gradient += top_diff_slice[ph * pooled_width + pw] *
+ (index == static_cast<int>(rand_idx_slice[ph * pooled_width + pw]));
}
}
bottom_diff[index] = gradient;
-#include <algorithm>
#include <vector>
-#include "caffe/layer.hpp"
+#include "caffe/layers/power_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
-#include <algorithm>
#include <vector>
-#include "caffe/layer.hpp"
+#include "caffe/layers/power_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
#include <vector>
#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+
+#include "caffe/layers/neuron_layer.hpp"
+#include "caffe/layers/prelu_layer.hpp"
namespace caffe {
// Propagate gradients to the parameters (as directed by backward pass).
this->param_propagate_down_.resize(this->blobs_.size(), true);
- multiplier_.Reshape(vector<int>(1, bottom[0]->count() / bottom[0]->num()));
+ multiplier_.Reshape(vector<int>(1, bottom[0]->count(1)));
+ backward_buff_.Reshape(vector<int>(1, bottom[0]->count(1)));
caffe_set(multiplier_.count(), Dtype(1), multiplier_.mutable_cpu_data());
}
// keep top_diff unchanged.
if (this->param_propagate_down_[0]) {
Dtype* slope_diff = this->blobs_[0]->mutable_cpu_diff();
- caffe_set(this->blobs_[0]->count(), Dtype(0), slope_diff);
for (int i = 0; i < count; ++i) {
int c = (i / dim) % channels / div_factor;
slope_diff[c] += top_diff[i] * bottom_data[i] * (bottom_data[i] <= 0);
#include <algorithm>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/neuron_layer.hpp"
+#include "caffe/layers/prelu_layer.hpp"
namespace caffe {
// CUDA kernel for element-wise parameter backward
template <typename Dtype>
-__global__ void PReLUParamBackward(const int n, const Dtype* in_diff,
+__global__ void PReLUParamBackward(const int n,
+ const int rows, const int rowPitch, const Dtype* in_diff,
const Dtype* in_data, Dtype* out_diff) {
CUDA_KERNEL_LOOP(index, n) {
out_diff[index] = in_diff[index] * in_data[index] * (in_data[index] <= 0);
+ for ( int k = 1; k < rows; k++ ) {
+ out_diff[index] += in_diff[index + k*rowPitch]
+ * in_data[index + k*rowPitch] * (in_data[index + k*rowPitch] <= 0);
+ }
}
}
bottom_data = bottom_memory_.gpu_data();
}
- // Propagte to param
+ // Propagate to param
// Since to write bottom diff will affect top diff if top and bottom blobs
// are identical (in-place computaion), we first compute param backward to
// keep top_diff unchanged.
if (this->param_propagate_down_[0]) {
Dtype* slope_diff = this->blobs_[0]->mutable_gpu_diff();
- // slope_diff is set as 0, then accumulated over batches
- caffe_gpu_set<Dtype>(this->blobs_[0]->count(), Dtype(0), slope_diff);
int cdim = channels * dim;
- Dtype dsum = 0.;
- for (int n = 0; n < bottom[0]->num(); ++n) {
- Dtype* temp_buff = multiplier_.mutable_gpu_diff();
- // compute element-wise diff
- // NOLINT_NEXT_LINE(whitespace/operators)
- PReLUParamBackward<Dtype><<<CAFFE_GET_BLOCKS(count),
- CAFFE_CUDA_NUM_THREADS>>>(
- cdim, top_diff + top[0]->offset(n),
- bottom_data + bottom[0]->offset(n), multiplier_.mutable_gpu_diff());
- CUDA_POST_KERNEL_CHECK;
- if (channel_shared_) {
- Dtype d;
- caffe_gpu_dot<Dtype>(channels * dim, multiplier_.gpu_diff(),
- multiplier_.gpu_data(), &d);
- dsum += d;
- } else {
- caffe_gpu_gemv<Dtype>(CblasNoTrans, channels, dim, 1.,
- multiplier_.gpu_diff(), multiplier_.gpu_data(), 1.,
- slope_diff);
- }
- }
+
+ // compute element-wise diff
+ // NOLINT_NEXT_LINE(whitespace/operators)
+ PReLUParamBackward<Dtype><<<CAFFE_GET_BLOCKS(cdim),
+ CAFFE_CUDA_NUM_THREADS>>>(
+ cdim, bottom[0]->num(), top[0]->offset(1), top_diff ,
+ bottom_data ,
+ backward_buff_.mutable_gpu_diff());
+ CUDA_POST_KERNEL_CHECK;
if (channel_shared_) {
- caffe_gpu_set(this->blobs_[0]->count(), Dtype(dsum), slope_diff);
+ Dtype dsum;
+ caffe_gpu_dot<Dtype>(channels * dim, backward_buff_.gpu_diff(),
+ multiplier_.gpu_data(), &dsum);
+ caffe_gpu_add_scalar(this->blobs_[0]->count(), Dtype(dsum), slope_diff);
+ } else {
+ caffe_gpu_gemv<Dtype>(CblasNoTrans, channels, dim, 1.,
+ backward_buff_.gpu_diff(), multiplier_.gpu_data(), 1.,
+ slope_diff);
}
}
// Propagate to bottom
--- /dev/null
+#include <vector>
+
+#include "caffe/layers/reduction_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ op_ = this->layer_param_.reduction_param().operation();
+}
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ axis_ = bottom[0]->CanonicalAxisIndex(
+ this->layer_param_.reduction_param().axis());
+ // In the output, we'll keep all axes up to the reduction axis, but
+ // throw away any after that.
+ // Note: currently reducing along non-tail axes is not supported; otherwise,
+ // we'd need to also copy any axes following an "end_axis".
+ vector<int> top_shape(bottom[0]->shape().begin(),
+ bottom[0]->shape().begin() + axis_);
+ top[0]->Reshape(top_shape);
+ num_ = bottom[0]->count(0, axis_);
+ dim_ = bottom[0]->count(axis_);
+ CHECK_EQ(num_, top[0]->count());
+ if (op_ == ReductionParameter_ReductionOp_SUM ||
+ op_ == ReductionParameter_ReductionOp_MEAN) {
+ vector<int> sum_mult_shape(1, dim_);
+ sum_multiplier_.Reshape(sum_mult_shape);
+ caffe_set(dim_, Dtype(1), sum_multiplier_.mutable_cpu_data());
+ }
+ coeff_ = this->layer_param().reduction_param().coeff();
+ if (op_ == ReductionParameter_ReductionOp_MEAN) {
+ coeff_ /= dim_;
+ }
+}
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::Forward_cpu(
+ const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+ const Dtype* bottom_data = bottom[0]->cpu_data();
+ const Dtype* mult_data = NULL;
+ if (sum_multiplier_.count() > 0) {
+ mult_data = sum_multiplier_.cpu_data();
+ }
+ Dtype* top_data = top[0]->mutable_cpu_data();
+ for (int i = 0; i < num_; ++i) {
+ switch (op_) {
+ case ReductionParameter_ReductionOp_SUM:
+ case ReductionParameter_ReductionOp_MEAN:
+ *top_data = caffe_cpu_dot(dim_, mult_data, bottom_data);
+ break;
+ case ReductionParameter_ReductionOp_ASUM:
+ *top_data = caffe_cpu_asum(dim_, bottom_data);
+ break;
+ case ReductionParameter_ReductionOp_SUMSQ:
+ *top_data = caffe_cpu_dot(dim_, bottom_data, bottom_data);
+ break;
+ default:
+ LOG(FATAL) << "Unknown reduction op: "
+ << ReductionParameter_ReductionOp_Name(op_);
+ }
+ bottom_data += dim_;
+ ++top_data;
+ }
+ if (coeff_ != Dtype(1)) {
+ // Reset the top_data pointer.
+ top_data = top[0]->mutable_cpu_data();
+ caffe_scal(num_, coeff_, top_data);
+ }
+}
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+ if (!propagate_down[0]) { return; }
+ // Get bottom_data, if needed.
+ const Dtype* bottom_data = NULL;
+ switch (op_) {
+ // Operations that don't need bottom_data
+ case ReductionParameter_ReductionOp_SUM:
+ case ReductionParameter_ReductionOp_MEAN:
+ break;
+ // Operations that need bottom_data
+ case ReductionParameter_ReductionOp_ASUM:
+ case ReductionParameter_ReductionOp_SUMSQ:
+ bottom_data = bottom[0]->cpu_data();
+ break;
+ default:
+ LOG(FATAL) << "Unknown reduction op: "
+ << ReductionParameter_ReductionOp_Name(op_);
+ }
+ const Dtype* top_diff = top[0]->cpu_diff();
+ Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+ for (int i = 0; i < num_; ++i) {
+ const Dtype bottom_coeff = (*top_diff) * coeff_;
+ switch (op_) {
+ case ReductionParameter_ReductionOp_SUM:
+ case ReductionParameter_ReductionOp_MEAN:
+ caffe_set(dim_, bottom_coeff, bottom_diff);
+ break;
+ case ReductionParameter_ReductionOp_ASUM:
+ caffe_cpu_sign(dim_, bottom_data, bottom_diff);
+ caffe_scal(dim_, bottom_coeff, bottom_diff);
+ break;
+ case ReductionParameter_ReductionOp_SUMSQ:
+ caffe_cpu_scale(dim_, 2 * bottom_coeff, bottom_data, bottom_diff);
+ break;
+ default:
+ LOG(FATAL) << "Unknown reduction op: "
+ << ReductionParameter_ReductionOp_Name(op_);
+ }
+ bottom_data += dim_;
+ bottom_diff += dim_;
+ ++top_diff;
+ }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(ReductionLayer);
+#endif
+
+INSTANTIATE_CLASS(ReductionLayer);
+REGISTER_LAYER_CLASS(Reduction);
+
+} // namespace caffe
--- /dev/null
+#include <vector>
+
+#include "caffe/layers/reduction_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::Forward_gpu(
+ const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+ const Dtype* bottom_data = bottom[0]->gpu_data();
+ const Dtype* mult_data = NULL;
+ if (sum_multiplier_.count() > 0) {
+ mult_data = sum_multiplier_.gpu_data();
+ }
+ Dtype* top_data = top[0]->mutable_cpu_data();
+ for (int i = 0; i < num_; ++i) {
+ switch (op_) {
+ case ReductionParameter_ReductionOp_SUM:
+ case ReductionParameter_ReductionOp_MEAN:
+ caffe_gpu_dot(dim_, mult_data, bottom_data, top_data);
+ break;
+ case ReductionParameter_ReductionOp_ASUM:
+ caffe_gpu_asum(dim_, bottom_data, top_data);
+ break;
+ case ReductionParameter_ReductionOp_SUMSQ:
+ caffe_gpu_dot(dim_, bottom_data, bottom_data, top_data);
+ break;
+ default:
+ LOG(FATAL) << "Unknown reduction op: "
+ << ReductionParameter_ReductionOp_Name(op_);
+ }
+ bottom_data += dim_;
+ ++top_data;
+ }
+ if (coeff_ != Dtype(1)) {
+ // Reset the top_data pointer.
+ top_data = top[0]->mutable_gpu_data();
+ caffe_gpu_scal(num_, coeff_, top_data);
+ }
+}
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+ if (!propagate_down[0]) { return; }
+ // Get bottom_data, if needed.
+ const Dtype* bottom_data = NULL;
+ switch (op_) {
+ // Operations that don't need bottom_data
+ case ReductionParameter_ReductionOp_SUM:
+ case ReductionParameter_ReductionOp_MEAN:
+ break;
+ // Operations that need bottom_data
+ case ReductionParameter_ReductionOp_ASUM:
+ case ReductionParameter_ReductionOp_SUMSQ:
+ bottom_data = bottom[0]->gpu_data();
+ break;
+ default:
+ LOG(FATAL) << "Unknown reduction op: "
+ << ReductionParameter_ReductionOp_Name(op_);
+ }
+ const Dtype* top_diff = top[0]->cpu_diff();
+ Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+ for (int i = 0; i < num_; ++i) {
+ const Dtype bottom_coeff = (*top_diff) * coeff_;
+ switch (op_) {
+ case ReductionParameter_ReductionOp_SUM:
+ case ReductionParameter_ReductionOp_MEAN:
+ caffe_gpu_set(dim_, bottom_coeff, bottom_diff);
+ break;
+ case ReductionParameter_ReductionOp_ASUM:
+ caffe_gpu_sign(dim_, bottom_data, bottom_diff);
+ caffe_gpu_scal(dim_, bottom_coeff, bottom_diff);
+ break;
+ case ReductionParameter_ReductionOp_SUMSQ:
+ caffe_gpu_scale(dim_, 2 * bottom_coeff, bottom_data, bottom_diff);
+ break;
+ default:
+ LOG(FATAL) << "Unknown reduction op: "
+ << ReductionParameter_ReductionOp_Name(op_);
+ }
+ bottom_data += dim_;
+ bottom_diff += dim_;
+ ++top_diff;
+ }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(ReductionLayer);
+
+} // namespace caffe
#include <algorithm>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/relu_layer.hpp"
namespace caffe {
#include <algorithm>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/relu_layer.hpp"
namespace caffe {
--- /dev/null
+#include <vector>
+
+#include "caffe/layers/reshape_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ReshapeLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ CHECK_NE(top[0], bottom[0]) << this->type() << " Layer does not "
+ "allow in-place computation.";
+ inferred_axis_ = -1;
+ copy_axes_.clear();
+ const BlobShape& top_blob_shape = this->layer_param_.reshape_param().shape();
+ const int top_num_axes = top_blob_shape.dim_size();
+ constant_count_ = 1;
+ for (int i = 0; i < top_num_axes; ++i) {
+ const int top_dim = top_blob_shape.dim(i);
+ if (top_dim == 0) {
+ copy_axes_.push_back(i);
+ } else if (top_dim == -1) {
+ CHECK_EQ(inferred_axis_, -1) << "new shape contains multiple "
+ << "-1 dims; at most a single (1) value of -1 may be specified";
+ inferred_axis_ = i;
+ } else {
+ constant_count_ *= top_dim;
+ }
+ }
+}
+
+template <typename Dtype>
+void ReshapeLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ const int input_start_axis = this->layer_param_.reshape_param().axis();
+ const int start_axis = (input_start_axis >= 0) ? input_start_axis :
+ bottom[0]->num_axes() + input_start_axis + 1;
+ CHECK_GE(start_axis, 0) << "axis " << input_start_axis << " out of range";
+ CHECK_LE(start_axis, bottom[0]->num_axes()) << "axis " << input_start_axis
+ << " out of range for " << bottom[0]->num_axes() << "-D input blob";
+ const int num_axes = this->layer_param_.reshape_param().num_axes();
+ CHECK_GE(num_axes, -1) << "num_axes must be >= 0, or -1 for all";
+ const int end_axis =
+ (num_axes == -1) ? bottom[0]->num_axes() : (start_axis + num_axes);
+ CHECK_LE(end_axis, bottom[0]->num_axes())
+ << "end_axis = axis + num_axes is out of range";
+ const int num_axes_replaced = end_axis - start_axis;
+ const int num_axes_retained = bottom[0]->num_axes() - num_axes_replaced;
+ const BlobShape& top_blob_shape = this->layer_param_.reshape_param().shape();
+ const int num_new_axes = top_blob_shape.dim_size();
+ vector<int> top_shape(num_axes_retained + num_new_axes);
+ int top_shape_index = 0;
+ for (int i = 0; i < start_axis; ++i) {
+ top_shape[top_shape_index++] = bottom[0]->shape(i);
+ }
+ for (int i = 0; i < num_new_axes; ++i) {
+ top_shape[top_shape_index++] = top_blob_shape.dim(i);
+ }
+ for (int i = end_axis; i < bottom[0]->num_axes(); ++i) {
+ top_shape[top_shape_index++] = bottom[0]->shape(i);
+ }
+ CHECK_EQ(top_shape_index, top_shape.size());
+ for (int i = 0; i < copy_axes_.size(); ++i) {
+ const int copy_axis_index = copy_axes_[i];
+ CHECK_GT(bottom[0]->num_axes(), start_axis + copy_axis_index)
+ << "new shape contains a 0, but there was no corresponding bottom axis "
+ << "to copy";
+ top_shape[start_axis + copy_axis_index] =
+ bottom[0]->shape(start_axis + copy_axis_index);
+ }
+ if (inferred_axis_ >= 0) {
+ // A -1 dim was specified; infer the correct dimension by computing the
+ // product of the other dimensions.
+ int explicit_count = constant_count_;
+ explicit_count *= bottom[0]->count(0, start_axis);
+ explicit_count *= bottom[0]->count(end_axis);
+ for (int i = 0; i < copy_axes_.size(); ++i) {
+ const int copy_axis_index = copy_axes_[i];
+ explicit_count *= top_shape[start_axis + copy_axis_index];
+ }
+ CHECK_EQ(0, bottom[0]->count() % explicit_count) << "bottom count ("
+ << bottom[0]->count() << ") must be divisible by the product of "
+ << "the specified dimensions (" << explicit_count << ")";
+ const int inferred_dim = bottom[0]->count() / explicit_count;
+ top_shape[start_axis + inferred_axis_] = inferred_dim;
+ }
+ top[0]->Reshape(top_shape);
+ CHECK_EQ(top[0]->count(), bottom[0]->count())
+ << "output count must match input count";
+ top[0]->ShareData(*bottom[0]);
+ top[0]->ShareDiff(*bottom[0]);
+}
+
+INSTANTIATE_CLASS(ReshapeLayer);
+REGISTER_LAYER_CLASS(Reshape);
+
+} // namespace caffe
-#include <algorithm>
-#include <cfloat>
#include <vector>
-#include "caffe/layer.hpp"
+#include "caffe/layers/sigmoid_cross_entropy_loss_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
}
#ifdef CPU_ONLY
-STUB_GPU(SigmoidCrossEntropyLossLayer);
+STUB_GPU_BACKWARD(SigmoidCrossEntropyLossLayer, Backward);
#endif
INSTANTIATE_CLASS(SigmoidCrossEntropyLossLayer);
-#include <algorithm>
-#include <cfloat>
#include <vector>
-#include "caffe/layer.hpp"
+#include "caffe/layers/sigmoid_cross_entropy_loss_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
template <typename Dtype>
-void SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
- const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
- // The forward pass computes the sigmoid outputs.
- sigmoid_bottom_vec_[0] = bottom[0];
- sigmoid_layer_->Forward(sigmoid_bottom_vec_, sigmoid_top_vec_);
- // Compute the loss (negative log likelihood)
- const int count = bottom[0]->count();
- const int num = bottom[0]->num();
- // Stable version of loss computation from input data
- const Dtype* input_data = bottom[0]->cpu_data();
- const Dtype* target = bottom[1]->cpu_data();
- Dtype loss = 0;
- for (int i = 0; i < count; ++i) {
- loss -= input_data[i] * (target[i] - (input_data[i] >= 0)) -
- log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0)));
- }
- top[0]->mutable_cpu_data()[0] = loss / num;
-}
-
-template <typename Dtype>
void SigmoidCrossEntropyLossLayer<Dtype>::Backward_gpu(
const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
const vector<Blob<Dtype>*>& bottom) {
}
}
-INSTANTIATE_LAYER_GPU_FUNCS(SigmoidCrossEntropyLossLayer);
+INSTANTIATE_LAYER_GPU_BACKWARD(SigmoidCrossEntropyLossLayer);
} // namespace caffe
-#include <algorithm>
#include <cmath>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/sigmoid_layer.hpp"
namespace caffe {
-#include <algorithm>
#include <cmath>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/sigmoid_layer.hpp"
namespace caffe {
#include <vector>
-#include "caffe/common_layers.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/layers/silence_layer.hpp"
#include "caffe/util/math_functions.hpp"
namespace caffe {
for (int i = 0; i < bottom.size(); ++i) {
if (propagate_down[i]) {
caffe_set(bottom[i]->count(), Dtype(0),
- bottom[i]->mutable_cpu_data());
+ bottom[i]->mutable_cpu_diff());
}
}
}
#include <vector>
-#include "caffe/common_layers.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/layers/silence_layer.hpp"
#include "caffe/util/math_functions.hpp"
namespace caffe {
for (int i = 0; i < bottom.size(); ++i) {
if (propagate_down[i]) {
caffe_gpu_set(bottom[i]->count(), Dtype(0),
- bottom[i]->mutable_gpu_data());
+ bottom[i]->mutable_gpu_diff());
}
}
}
#include <algorithm>
#include <vector>
-#include "caffe/layer.hpp"
+#include "caffe/layers/slice_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
}
}
CHECK_EQ(count, bottom[0]->count());
+ if (top.size() == 1) {
+ top[0]->ShareData(*bottom[0]);
+ top[0]->ShareDiff(*bottom[0]);
+ }
}
template <typename Dtype>
void SliceLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
+ if (top.size() == 1) { return; }
int offset_slice_axis = 0;
const Dtype* bottom_data = bottom[0]->cpu_data();
const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
template <typename Dtype>
void SliceLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
- if (!propagate_down[0]) { return; }
+ if (!propagate_down[0] || top.size() == 1) { return; }
int offset_slice_axis = 0;
Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
#include <vector>
-#include "caffe/layer.hpp"
+#include "caffe/layers/slice_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
template <typename Dtype>
+__global__ void Slice(const int nthreads, const Dtype* in_data,
+ const bool forward, const int num_slices, const int slice_size,
+ const int bottom_slice_axis, const int top_slice_axis,
+ const int offset_slice_axis, Dtype* out_data) {
+ CUDA_KERNEL_LOOP(index, nthreads) {
+ const int total_slice_size = slice_size * top_slice_axis;
+ const int slice_num = index / total_slice_size;
+ const int slice_index = index % total_slice_size;
+ const int bottom_index = slice_index +
+ (slice_num * bottom_slice_axis + offset_slice_axis) * slice_size;
+ if (forward) {
+ out_data[index] = in_data[bottom_index];
+ } else {
+ out_data[bottom_index] = in_data[index];
+ }
+ }
+}
+
+template <typename Dtype>
void SliceLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
+ if (top.size() == 1) { return; }
int offset_slice_axis = 0;
const Dtype* bottom_data = bottom[0]->gpu_data();
const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
+ const bool kForward = true;
for (int i = 0; i < top.size(); ++i) {
Dtype* top_data = top[i]->mutable_gpu_data();
const int top_slice_axis = top[i]->shape(slice_axis_);
- for (int n = 0; n < num_slices_; ++n) {
- const int top_offset = n * top_slice_axis * slice_size_;
- const int bottom_offset =
- (n * bottom_slice_axis + offset_slice_axis) * slice_size_;
- caffe_copy(top_slice_axis * slice_size_,
- bottom_data + bottom_offset, top_data + top_offset);
- }
+ const int top_slice_size = top_slice_axis * slice_size_;
+ const int nthreads = top_slice_size * num_slices_;
+ Slice<Dtype> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+ nthreads, bottom_data, kForward, num_slices_, slice_size_,
+ bottom_slice_axis, top_slice_axis, offset_slice_axis, top_data);
offset_slice_axis += top_slice_axis;
}
}
template <typename Dtype>
void SliceLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
- if (!propagate_down[0]) { return; }
+ if (!propagate_down[0] || top.size() == 1) { return; }
int offset_slice_axis = 0;
Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
+ const bool kForward = false;
for (int i = 0; i < top.size(); ++i) {
const Dtype* top_diff = top[i]->gpu_diff();
const int top_slice_axis = top[i]->shape(slice_axis_);
- for (int n = 0; n < num_slices_; ++n) {
- const int top_offset = n * top_slice_axis * slice_size_;
- const int bottom_offset =
- (n * bottom_slice_axis + offset_slice_axis) * slice_size_;
- caffe_copy(top_slice_axis * slice_size_,
- top_diff + top_offset, bottom_diff + bottom_offset);
- }
+ const int top_slice_size = top_slice_axis * slice_size_;
+ const int nthreads = top_slice_size * num_slices_;
+ Slice<Dtype> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+ nthreads, top_diff, kForward, num_slices_, slice_size_,
+ bottom_slice_axis, top_slice_axis, offset_slice_axis, bottom_diff);
offset_slice_axis += top_slice_axis;
}
}
#include <algorithm>
#include <vector>
-#include "caffe/layer.hpp"
+#include "caffe/layers/softmax_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
#include "thrust/device_vector.h"
-#include "caffe/layer.hpp"
+#include "caffe/layers/softmax_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
#include <cfloat>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/layer_factory.hpp"
+#include "caffe/layers/softmax_loss_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
if (has_ignore_label_) {
ignore_label_ = this->layer_param_.loss_param().ignore_label();
}
- normalize_ = this->layer_param_.loss_param().normalize();
+ if (!this->layer_param_.loss_param().has_normalization() &&
+ this->layer_param_.loss_param().has_normalize()) {
+ normalization_ = this->layer_param_.loss_param().normalize() ?
+ LossParameter_NormalizationMode_VALID :
+ LossParameter_NormalizationMode_BATCH_SIZE;
+ } else {
+ normalization_ = this->layer_param_.loss_param().normalization();
+ }
}
template <typename Dtype>
}
template <typename Dtype>
+Dtype SoftmaxWithLossLayer<Dtype>::get_normalizer(
+ LossParameter_NormalizationMode normalization_mode, int valid_count) {
+ Dtype normalizer;
+ switch (normalization_mode) {
+ case LossParameter_NormalizationMode_FULL:
+ normalizer = Dtype(outer_num_ * inner_num_);
+ break;
+ case LossParameter_NormalizationMode_VALID:
+ if (valid_count == -1) {
+ normalizer = Dtype(outer_num_ * inner_num_);
+ } else {
+ normalizer = Dtype(valid_count);
+ }
+ break;
+ case LossParameter_NormalizationMode_BATCH_SIZE:
+ normalizer = Dtype(outer_num_);
+ break;
+ case LossParameter_NormalizationMode_NONE:
+ normalizer = Dtype(1);
+ break;
+ default:
+ LOG(FATAL) << "Unknown normalization mode: "
+ << LossParameter_NormalizationMode_Name(normalization_mode);
+ }
+ // Some users will have no labels for some examples in order to 'turn off' a
+ // particular loss in a multi-task setup. The max prevents NaNs in that case.
+ return std::max(Dtype(1.0), normalizer);
+}
+
+template <typename Dtype>
void SoftmaxWithLossLayer<Dtype>::Forward_cpu(
const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
// The forward pass computes the softmax prob values.
++count;
}
}
- if (normalize_) {
- top[0]->mutable_cpu_data()[0] = loss / count;
- } else {
- top[0]->mutable_cpu_data()[0] = loss / outer_num_;
- }
+ top[0]->mutable_cpu_data()[0] = loss / get_normalizer(normalization_, count);
if (top.size() == 2) {
top[1]->ShareData(prob_);
}
}
}
// Scale gradient
- const Dtype loss_weight = top[0]->cpu_diff()[0];
- if (normalize_) {
- caffe_scal(prob_.count(), loss_weight / count, bottom_diff);
- } else {
- caffe_scal(prob_.count(), loss_weight / outer_num_, bottom_diff);
- }
+ Dtype loss_weight = top[0]->cpu_diff()[0] /
+ get_normalizer(normalization_, count);
+ caffe_scal(prob_.count(), loss_weight, bottom_diff);
}
}
#include <cfloat>
#include <vector>
-#include "caffe/layer.hpp"
+#include "caffe/layers/softmax_loss_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
outer_num_, dim, inner_num_, has_ignore_label_, ignore_label_, counts);
Dtype loss;
caffe_gpu_asum(nthreads, loss_data, &loss);
- if (normalize_) {
- Dtype count;
- caffe_gpu_asum(nthreads, counts, &count);
- loss /= count;
- } else {
- loss /= outer_num_;
+ Dtype valid_count = -1;
+ // Only launch another CUDA kernel if we actually need the count of valid
+ // outputs.
+ if (normalization_ == LossParameter_NormalizationMode_VALID &&
+ has_ignore_label_) {
+ caffe_gpu_asum(nthreads, counts, &valid_count);
}
- top[0]->mutable_cpu_data()[0] = loss;
+ top[0]->mutable_cpu_data()[0] = loss / get_normalizer(normalization_,
+ valid_count);
if (top.size() == 2) {
top[1]->ShareData(prob_);
}
SoftmaxLossBackwardGPU<Dtype><<<CAFFE_GET_BLOCKS(nthreads),
CAFFE_CUDA_NUM_THREADS>>>(nthreads, top_data, label, bottom_diff,
outer_num_, dim, inner_num_, has_ignore_label_, ignore_label_, counts);
- const Dtype loss_weight = top[0]->cpu_diff()[0];
- if (normalize_) {
- Dtype count;
- caffe_gpu_asum(nthreads, counts, &count);
- caffe_gpu_scal(prob_.count(), loss_weight / count, bottom_diff);
- } else {
- caffe_gpu_scal(prob_.count(), loss_weight / outer_num_, bottom_diff);
+
+ Dtype valid_count = -1;
+ // Only launch another CUDA kernel if we actually need the count of valid
+ // outputs.
+ if (normalization_ == LossParameter_NormalizationMode_VALID &&
+ has_ignore_label_) {
+ caffe_gpu_asum(nthreads, counts, &valid_count);
}
+ const Dtype loss_weight = top[0]->cpu_diff()[0] /
+ get_normalizer(normalization_, valid_count);
+ caffe_gpu_scal(prob_.count(), loss_weight , bottom_diff);
}
}
#include <vector>
-#include "caffe/layer.hpp"
+#include "caffe/layers/split_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
#include <vector>
-#include "caffe/layer.hpp"
+#include "caffe/layers/split_layer.hpp"
#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
namespace caffe {
--- /dev/null
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/layers/concat_layer.hpp"
+#include "caffe/layers/flatten_layer.hpp"
+#include "caffe/layers/pooling_layer.hpp"
+#include "caffe/layers/split_layer.hpp"
+#include "caffe/layers/spp_layer.hpp"
+
+namespace caffe {
+
+using std::min;
+using std::max;
+
+template <typename Dtype>
+LayerParameter SPPLayer<Dtype>::GetPoolingParam(const int pyramid_level,
+ const int bottom_h, const int bottom_w, const SPPParameter spp_param) {
+ LayerParameter pooling_param;
+ int num_bins = pow(2, pyramid_level);
+
+ // find padding and kernel size so that the pooling is
+ // performed across the entire image
+ int kernel_h = ceil(bottom_h / static_cast<double>(num_bins));
+ // remainder_h is the min number of pixels that need to be padded before
+ // entire image height is pooled over with the chosen kernel dimension
+ int remainder_h = kernel_h * num_bins - bottom_h;
+ // pooling layer pads (2 * pad_h) pixels on the top and bottom of the
+ // image.
+ int pad_h = (remainder_h + 1) / 2;
+
+ // similar logic for width
+ int kernel_w = ceil(bottom_w / static_cast<double>(num_bins));
+ int remainder_w = kernel_w * num_bins - bottom_w;
+ int pad_w = (remainder_w + 1) / 2;
+
+ pooling_param.mutable_pooling_param()->set_pad_h(pad_h);
+ pooling_param.mutable_pooling_param()->set_pad_w(pad_w);
+ pooling_param.mutable_pooling_param()->set_kernel_h(kernel_h);
+ pooling_param.mutable_pooling_param()->set_kernel_w(kernel_w);
+ pooling_param.mutable_pooling_param()->set_stride_h(kernel_h);
+ pooling_param.mutable_pooling_param()->set_stride_w(kernel_w);
+
+ switch (spp_param.pool()) {
+ case SPPParameter_PoolMethod_MAX:
+ pooling_param.mutable_pooling_param()->set_pool(
+ PoolingParameter_PoolMethod_MAX);
+ break;
+ case SPPParameter_PoolMethod_AVE:
+ pooling_param.mutable_pooling_param()->set_pool(
+ PoolingParameter_PoolMethod_AVE);
+ break;
+ case SPPParameter_PoolMethod_STOCHASTIC:
+ pooling_param.mutable_pooling_param()->set_pool(
+ PoolingParameter_PoolMethod_STOCHASTIC);
+ break;
+ default:
+ LOG(FATAL) << "Unknown pooling method.";
+ }
+
+ return pooling_param;
+}
+
+template <typename Dtype>
+void SPPLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ SPPParameter spp_param = this->layer_param_.spp_param();
+
+ num_ = bottom[0]->num();
+ channels_ = bottom[0]->channels();
+ bottom_h_ = bottom[0]->height();
+ bottom_w_ = bottom[0]->width();
+ reshaped_first_time_ = false;
+ CHECK_GT(bottom_h_, 0) << "Input dimensions cannot be zero.";
+ CHECK_GT(bottom_w_, 0) << "Input dimensions cannot be zero.";
+
+ pyramid_height_ = spp_param.pyramid_height();
+ split_top_vec_.clear();
+ pooling_bottom_vecs_.clear();
+ pooling_layers_.clear();
+ pooling_top_vecs_.clear();
+ pooling_outputs_.clear();
+ flatten_layers_.clear();
+ flatten_top_vecs_.clear();
+ flatten_outputs_.clear();
+ concat_bottom_vec_.clear();
+
+ if (pyramid_height_ == 1) {
+ // pooling layer setup
+ LayerParameter pooling_param = GetPoolingParam(0, bottom_h_, bottom_w_,
+ spp_param);
+ pooling_layers_.push_back(shared_ptr<PoolingLayer<Dtype> > (
+ new PoolingLayer<Dtype>(pooling_param)));
+ pooling_layers_[0]->SetUp(bottom, top);
+ return;
+ }
+ // split layer output holders setup
+ for (int i = 0; i < pyramid_height_; i++) {
+ split_top_vec_.push_back(new Blob<Dtype>());
+ }
+
+ // split layer setup
+ LayerParameter split_param;
+ split_layer_.reset(new SplitLayer<Dtype>(split_param));
+ split_layer_->SetUp(bottom, split_top_vec_);
+
+ for (int i = 0; i < pyramid_height_; i++) {
+ // pooling layer input holders setup
+ pooling_bottom_vecs_.push_back(new vector<Blob<Dtype>*>);
+ pooling_bottom_vecs_[i]->push_back(split_top_vec_[i]);
+
+ // pooling layer output holders setup
+ pooling_outputs_.push_back(new Blob<Dtype>());
+ pooling_top_vecs_.push_back(new vector<Blob<Dtype>*>);
+ pooling_top_vecs_[i]->push_back(pooling_outputs_[i]);
+
+ // pooling layer setup
+ LayerParameter pooling_param = GetPoolingParam(
+ i, bottom_h_, bottom_w_, spp_param);
+
+ pooling_layers_.push_back(shared_ptr<PoolingLayer<Dtype> > (
+ new PoolingLayer<Dtype>(pooling_param)));
+ pooling_layers_[i]->SetUp(*pooling_bottom_vecs_[i], *pooling_top_vecs_[i]);
+
+ // flatten layer output holders setup
+ flatten_outputs_.push_back(new Blob<Dtype>());
+ flatten_top_vecs_.push_back(new vector<Blob<Dtype>*>);
+ flatten_top_vecs_[i]->push_back(flatten_outputs_[i]);
+
+ // flatten layer setup
+ LayerParameter flatten_param;
+ flatten_layers_.push_back(new FlattenLayer<Dtype>(flatten_param));
+ flatten_layers_[i]->SetUp(*pooling_top_vecs_[i], *flatten_top_vecs_[i]);
+
+ // concat layer input holders setup
+ concat_bottom_vec_.push_back(flatten_outputs_[i]);
+ }
+
+ // concat layer setup
+ LayerParameter concat_param;
+ concat_layer_.reset(new ConcatLayer<Dtype>(concat_param));
+ concat_layer_->SetUp(concat_bottom_vec_, top);
+}
+
+template <typename Dtype>
+void SPPLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
+ << "corresponding to (num, channels, height, width)";
+ // Do nothing if bottom shape is unchanged since last Reshape
+ if (num_ == bottom[0]->num() && channels_ == bottom[0]->channels() &&
+ bottom_h_ == bottom[0]->height() && bottom_w_ == bottom[0]->width() &&
+ reshaped_first_time_) {
+ return;
+ }
+ num_ = bottom[0]->num();
+ channels_ = bottom[0]->channels();
+ bottom_h_ = bottom[0]->height();
+ bottom_w_ = bottom[0]->width();
+ reshaped_first_time_ = true;
+ SPPParameter spp_param = this->layer_param_.spp_param();
+ if (pyramid_height_ == 1) {
+ LayerParameter pooling_param = GetPoolingParam(0, bottom_h_, bottom_w_,
+ spp_param);
+ pooling_layers_[0].reset(new PoolingLayer<Dtype>(pooling_param));
+ pooling_layers_[0]->SetUp(bottom, top);
+ pooling_layers_[0]->Reshape(bottom, top);
+ return;
+ }
+ split_layer_->Reshape(bottom, split_top_vec_);
+ for (int i = 0; i < pyramid_height_; i++) {
+ LayerParameter pooling_param = GetPoolingParam(
+ i, bottom_h_, bottom_w_, spp_param);
+
+ pooling_layers_[i].reset(
+ new PoolingLayer<Dtype>(pooling_param));
+ pooling_layers_[i]->SetUp(
+ *pooling_bottom_vecs_[i], *pooling_top_vecs_[i]);
+ pooling_layers_[i]->Reshape(
+ *pooling_bottom_vecs_[i], *pooling_top_vecs_[i]);
+ flatten_layers_[i]->Reshape(
+ *pooling_top_vecs_[i], *flatten_top_vecs_[i]);
+ }
+ concat_layer_->Reshape(concat_bottom_vec_, top);
+}
+
+template <typename Dtype>
+void SPPLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ if (pyramid_height_ == 1) {
+ pooling_layers_[0]->Forward(bottom, top);
+ return;
+ }
+ split_layer_->Forward(bottom, split_top_vec_);
+ for (int i = 0; i < pyramid_height_; i++) {
+ pooling_layers_[i]->Forward(
+ *pooling_bottom_vecs_[i], *pooling_top_vecs_[i]);
+ flatten_layers_[i]->Forward(
+ *pooling_top_vecs_[i], *flatten_top_vecs_[i]);
+ }
+ concat_layer_->Forward(concat_bottom_vec_, top);
+}
+
+template <typename Dtype>
+void SPPLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+ if (!propagate_down[0]) {
+ return;
+ }
+ if (pyramid_height_ == 1) {
+ pooling_layers_[0]->Backward(top, propagate_down, bottom);
+ return;
+ }
+ vector<bool> concat_propagate_down(pyramid_height_, true);
+ concat_layer_->Backward(top, concat_propagate_down, concat_bottom_vec_);
+ for (int i = 0; i < pyramid_height_; i++) {
+ flatten_layers_[i]->Backward(
+ *flatten_top_vecs_[i], propagate_down, *pooling_top_vecs_[i]);
+ pooling_layers_[i]->Backward(
+ *pooling_top_vecs_[i], propagate_down, *pooling_bottom_vecs_[i]);
+ }
+ split_layer_->Backward(split_top_vec_, propagate_down, bottom);
+}
+
+INSTANTIATE_CLASS(SPPLayer);
+REGISTER_LAYER_CLASS(SPP);
+
+} // namespace caffe
// TanH neuron activation function layer.
// Adapted from ReLU layer code written by Yangqing Jia
-#include <algorithm>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/tanh_layer.hpp"
namespace caffe {
// TanH neuron activation function layer.
// Adapted from ReLU layer code written by Yangqing Jia
-#include <algorithm>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/tanh_layer.hpp"
namespace caffe {
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
-
+#include "caffe/layers/threshold_layer.hpp"
namespace caffe {
-#include <algorithm>
#include <vector>
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/threshold_layer.hpp"
namespace caffe {
--- /dev/null
+#include <vector>
+
+#include "caffe/layers/tile_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void TileLayer<Dtype>::Reshape(
+ const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+ const TileParameter& tile_param = this->layer_param_.tile_param();
+ axis_ = bottom[0]->CanonicalAxisIndex(tile_param.axis());
+ CHECK(tile_param.has_tiles()) << "Number of tiles must be specified";
+ tiles_ = tile_param.tiles();
+ CHECK_GT(tiles_, 0) << "Number of tiles must be positive.";
+ vector<int> top_shape = bottom[0]->shape();
+ top_shape[axis_] = bottom[0]->shape(axis_) * tiles_;
+ top[0]->Reshape(top_shape);
+ outer_dim_ = bottom[0]->count(0, axis_);
+ inner_dim_ = bottom[0]->count(axis_);
+}
+
+template <typename Dtype>
+void TileLayer<Dtype>::Forward_cpu(
+ const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+ const Dtype* bottom_data = bottom[0]->cpu_data();
+ Dtype* top_data = top[0]->mutable_cpu_data();
+ for (int i = 0; i < outer_dim_; ++i) {
+ for (int t = 0; t < tiles_; ++t) {
+ caffe_copy(inner_dim_, bottom_data, top_data);
+ top_data += inner_dim_;
+ }
+ bottom_data += inner_dim_;
+ }
+}
+
+template <typename Dtype>
+void TileLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+ if (!propagate_down[0]) { return; }
+ const Dtype* top_diff = top[0]->cpu_diff();
+ Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+ for (int i = 0; i < outer_dim_; ++i) {
+ caffe_copy(inner_dim_, top_diff, bottom_diff);
+ top_diff += inner_dim_;
+ for (int t = 1; t < tiles_; ++t) {
+ caffe_axpy(inner_dim_, Dtype(1), top_diff, bottom_diff);
+ top_diff += inner_dim_;
+ }
+ bottom_diff += inner_dim_;
+ }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(TileLayer);
+#endif
+
+INSTANTIATE_CLASS(TileLayer);
+REGISTER_LAYER_CLASS(Tile);
+
+} // namespace caffe
--- /dev/null
+#include <vector>
+
+#include "caffe/layers/tile_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void Tile(const int nthreads, const Dtype* bottom_data,
+ const int tile_size, const int num_tiles, const int bottom_tile_axis,
+ Dtype* top_data) {
+ CUDA_KERNEL_LOOP(index, nthreads) {
+ const int d = index % tile_size;
+ const int b = (index / tile_size / num_tiles) % bottom_tile_axis;
+ const int n = index / tile_size / num_tiles / bottom_tile_axis;
+ const int bottom_index = (n * bottom_tile_axis + b) * tile_size + d;
+ top_data[index] = bottom_data[bottom_index];
+ }
+}
+
+template <typename Dtype>
+void TileLayer<Dtype>::Forward_gpu(
+ const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+ const Dtype* bottom_data = bottom[0]->gpu_data();
+ Dtype* top_data = top[0]->mutable_gpu_data();
+ const int bottom_tile_axis = bottom[0]->shape(axis_);
+ const int nthreads = top[0]->count();
+ Tile<Dtype> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+ nthreads, bottom_data, inner_dim_, tiles_, bottom_tile_axis, top_data);
+}
+
+template <typename Dtype>
+__global__ void TileBackward(const int nthreads, const Dtype* top_diff,
+ const int tile_size, const int num_tiles, const int bottom_tile_axis,
+ Dtype* bottom_diff) {
+ CUDA_KERNEL_LOOP(index, nthreads) {
+ const int d = index % tile_size;
+ const int b = (index / tile_size) % bottom_tile_axis;
+ const int n = index / tile_size / bottom_tile_axis;
+ bottom_diff[index] = 0;
+ int top_index = (n * num_tiles * bottom_tile_axis + b) * tile_size + d;
+ for (int t = 0; t < num_tiles; ++t) {
+ bottom_diff[index] += top_diff[top_index];
+ top_index += bottom_tile_axis * tile_size;
+ }
+ }
+}
+
+template <typename Dtype>
+void TileLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+ if (!propagate_down[0]) { return; }
+ const Dtype* top_diff = top[0]->gpu_diff();
+ Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+ const int bottom_tile_axis = bottom[0]->shape(axis_);
+ const int tile_size = inner_dim_ / bottom_tile_axis;
+ const int nthreads = bottom[0]->count();
+ TileBackward<Dtype> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+ nthreads, top_diff, tile_size, tiles_, bottom_tile_axis, bottom_diff);
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(TileLayer);
+
+} // namespace caffe
+#ifdef USE_OPENCV
#include <opencv2/highgui/highgui_c.h>
#include <stdint.h>
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
-#include "caffe/common.hpp"
-#include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/layers/base_data_layer.hpp"
+#include "caffe/layers/window_data_layer.hpp"
#include "caffe/util/benchmark.hpp"
#include "caffe/util/io.hpp"
#include "caffe/util/math_functions.hpp"
template <typename Dtype>
WindowDataLayer<Dtype>::~WindowDataLayer<Dtype>() {
- this->JoinPrefetchThread();
+ this->StopInternalThread();
}
template <typename Dtype>
CHECK_GT(crop_size, 0);
const int batch_size = this->layer_param_.window_data_param().batch_size();
top[0]->Reshape(batch_size, channels, crop_size, crop_size);
- this->prefetch_data_.Reshape(batch_size, channels, crop_size, crop_size);
+ for (int i = 0; i < this->PREFETCH_COUNT; ++i)
+ this->prefetch_[i].data_.Reshape(
+ batch_size, channels, crop_size, crop_size);
LOG(INFO) << "output data size: " << top[0]->num() << ","
<< top[0]->channels() << "," << top[0]->height() << ","
// label
vector<int> label_shape(1, batch_size);
top[1]->Reshape(label_shape);
- this->prefetch_label_.Reshape(label_shape);
+ for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
+ this->prefetch_[i].label_.Reshape(label_shape);
+ }
// data mean
has_mean_file_ = this->transform_param_.has_mean_file();
return (*prefetch_rng)();
}
-// Thread fetching the data
+// This function is called on prefetch thread
template <typename Dtype>
-void WindowDataLayer<Dtype>::InternalThreadEntry() {
+void WindowDataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
// At each iteration, sample N windows where N*p are foreground (object)
// windows and N*(1-p) are background (non-object) windows
CPUTimer batch_timer;
double read_time = 0;
double trans_time = 0;
CPUTimer timer;
- Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
- Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
+ Dtype* top_data = batch->data_.mutable_cpu_data();
+ Dtype* top_label = batch->label_.mutable_cpu_data();
const Dtype scale = this->layer_param_.window_data_param().scale();
const int batch_size = this->layer_param_.window_data_param().batch_size();
const int context_pad = this->layer_param_.window_data_param().context_pad();
bool use_square = (crop_mode == "square") ? true : false;
// zero out batch
- caffe_set(this->prefetch_data_.count(), Dtype(0), top_data);
+ caffe_set(batch->data_.count(), Dtype(0), top_data);
const int num_fg = static_cast<int>(static_cast<float>(batch_size)
* fg_fraction);
REGISTER_LAYER_CLASS(WindowData);
} // namespace caffe
+#endif // USE_OPENCV
#include <utility>
#include <vector>
+#include "hdf5.h"
+
#include "caffe/common.hpp"
#include "caffe/layer.hpp"
#include "caffe/net.hpp"
+#include "caffe/parallel.hpp"
#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/hdf5.hpp"
#include "caffe/util/insert_splits.hpp"
-#include "caffe/util/io.hpp"
#include "caffe/util/math_functions.hpp"
#include "caffe/util/upgrade_proto.hpp"
namespace caffe {
template <typename Dtype>
-Net<Dtype>::Net(const NetParameter& param) {
+Net<Dtype>::Net(const NetParameter& param, const Net* root_net)
+ : root_net_(root_net) {
Init(param);
}
template <typename Dtype>
-Net<Dtype>::Net(const string& param_file, Phase phase) {
+Net<Dtype>::Net(const string& param_file, Phase phase, const Net* root_net)
+ : root_net_(root_net) {
NetParameter param;
ReadNetParamsFromTextFileOrDie(param_file, ¶m);
param.mutable_state()->set_phase(phase);
template <typename Dtype>
void Net<Dtype>::Init(const NetParameter& in_param) {
+ CHECK(Caffe::root_solver() || root_net_)
+ << "root_net_ needs to be set for all non-root solvers";
// Set phase from the state.
phase_ = in_param.state().phase();
// Filter layers based on their include/exclude rules and
// the current NetState.
NetParameter filtered_param;
FilterNet(in_param, &filtered_param);
- LOG(INFO) << "Initializing net from parameters: " << std::endl
- << filtered_param.DebugString();
+ LOG_IF(INFO, Caffe::root_solver())
+ << "Initializing net from parameters: " << std::endl
+ << filtered_param.DebugString();
// Create a copy of filtered_param with splits added where necessary.
NetParameter param;
InsertSplits(filtered_param, ¶m);
const int layer_id = -1; // inputs have fake layer ID -1
AppendTop(param, layer_id, input_id, &available_blobs, &blob_name_to_idx);
}
- DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
// For each layer, set up its input and output
bottom_vecs_.resize(param.layer_size());
top_vecs_.resize(param.layer_size());
top_id_vecs_.resize(param.layer_size());
bottom_need_backward_.resize(param.layer_size());
for (int layer_id = 0; layer_id < param.layer_size(); ++layer_id) {
+ // For non-root solvers, whether this layer is shared from root_net_.
+ bool share_from_root = !Caffe::root_solver()
+ && root_net_->layers_[layer_id]->ShareInParallel();
// Inherit phase from net if unset.
if (!param.layer(layer_id).has_phase()) {
param.mutable_layer(layer_id)->set_phase(phase_);
}
// Setup layer.
const LayerParameter& layer_param = param.layer(layer_id);
- layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));
+ if (layer_param.propagate_down_size() > 0) {
+ CHECK_EQ(layer_param.propagate_down_size(),
+ layer_param.bottom_size())
+ << "propagate_down param must be specified "
+ << "either 0 or bottom_size times ";
+ }
+ if (share_from_root) {
+ LOG(INFO) << "Sharing layer " << layer_param.name() << " from root net";
+ layers_.push_back(root_net_->layers_[layer_id]);
+ layers_[layer_id]->SetShared(true);
+ } else {
+ layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));
+ }
layer_names_.push_back(layer_param.name());
- LOG(INFO) << "Creating Layer " << layer_param.name();
+ LOG_IF(INFO, Caffe::root_solver())
+ << "Creating Layer " << layer_param.name();
bool need_backward = false;
+
// Figure out this layer's input and output
for (int bottom_id = 0; bottom_id < layer_param.bottom_size();
++bottom_id) {
}
}
// After this layer is connected, set it up.
- LOG(INFO) << "Setting up " << layer_names_[layer_id];
- layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]);
+ if (share_from_root) {
+ // Set up size of top blobs using root_net_
+ const vector<Blob<Dtype>*>& base_top = root_net_->top_vecs_[layer_id];
+ const vector<Blob<Dtype>*>& this_top = this->top_vecs_[layer_id];
+ for (int top_id = 0; top_id < base_top.size(); ++top_id) {
+ this_top[top_id]->ReshapeLike(*base_top[top_id]);
+ LOG(INFO) << "Created top blob " << top_id << " (shape: "
+ << this_top[top_id]->shape_string() << ") for shared layer "
+ << layer_param.name();
+ }
+ } else {
+ layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]);
+ }
+ LOG_IF(INFO, Caffe::root_solver())
+ << "Setting up " << layer_names_[layer_id];
for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
if (blob_loss_weights_.size() <= top_id_vecs_[layer_id][top_id]) {
blob_loss_weights_.resize(top_id_vecs_[layer_id][top_id] + 1, Dtype(0));
}
blob_loss_weights_[top_id_vecs_[layer_id][top_id]] = layer->loss(top_id);
- LOG(INFO) << "Top shape: " << top_vecs_[layer_id][top_id]->shape_string();
+ LOG_IF(INFO, Caffe::root_solver())
+ << "Top shape: " << top_vecs_[layer_id][top_id]->shape_string();
if (layer->loss(top_id)) {
- LOG(INFO) << " with loss weight " << layer->loss(top_id);
+ LOG_IF(INFO, Caffe::root_solver())
+ << " with loss weight " << layer->loss(top_id);
}
memory_used_ += top_vecs_[layer_id][top_id]->count();
}
- DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
+ LOG_IF(INFO, Caffe::root_solver())
+ << "Memory required for data: " << memory_used_ * sizeof(Dtype);
const int param_size = layer_param.param_size();
const int num_param_blobs = layers_[layer_id]->blobs().size();
CHECK_LE(param_size, num_param_blobs)
for (int param_id = 0; param_id < num_param_blobs; ++param_id) {
const ParamSpec* param_spec = (param_id < param_size) ?
&layer_param.param(param_id) : &default_param_spec;
- const bool param_need_backward = param_spec->lr_mult() > 0;
+ const bool param_need_backward = param_spec->lr_mult() != 0;
need_backward |= param_need_backward;
layers_[layer_id]->set_param_propagate_down(param_id,
param_need_backward);
// Go through the net backwards to determine which blobs contribute to the
// loss. We can skip backward computation for blobs that don't contribute
// to the loss.
+ // Also checks if all bottom blobs don't need backward computation (possible
+ // because the skip_propagate_down param) and so we can skip bacward
+ // computation for the entire layer
set<string> blobs_under_loss;
+ set<string> blobs_skip_backp;
for (int layer_id = layers_.size() - 1; layer_id >= 0; --layer_id) {
bool layer_contributes_loss = false;
+ bool layer_skip_propagate_down = true;
for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];
if (layers_[layer_id]->loss(top_id) ||
(blobs_under_loss.find(blob_name) != blobs_under_loss.end())) {
layer_contributes_loss = true;
+ }
+ if (blobs_skip_backp.find(blob_name) == blobs_skip_backp.end()) {
+ layer_skip_propagate_down = false;
+ }
+ if (layer_contributes_loss && !layer_skip_propagate_down)
break;
+ }
+ // If this layer can skip backward computation, also all his bottom blobs
+ // don't need backpropagation
+ if (layer_need_backward_[layer_id] && layer_skip_propagate_down) {
+ layer_need_backward_[layer_id] = false;
+ for (int bottom_id = 0; bottom_id < bottom_vecs_[layer_id].size();
+ ++bottom_id) {
+ bottom_need_backward_[layer_id][bottom_id] = false;
}
}
if (!layer_contributes_loss) { layer_need_backward_[layer_id] = false; }
- if (layer_need_backward_[layer_id]) {
- LOG(INFO) << layer_names_[layer_id] << " needs backward computation.";
- } else {
- LOG(INFO) << layer_names_[layer_id]
- << " does not need backward computation.";
+ if (Caffe::root_solver()) {
+ if (layer_need_backward_[layer_id]) {
+ LOG(INFO) << layer_names_[layer_id] << " needs backward computation.";
+ } else {
+ LOG(INFO) << layer_names_[layer_id]
+ << " does not need backward computation.";
+ }
}
for (int bottom_id = 0; bottom_id < bottom_vecs_[layer_id].size();
++bottom_id) {
} else {
bottom_need_backward_[layer_id][bottom_id] = false;
}
+ if (!bottom_need_backward_[layer_id][bottom_id]) {
+ const string& blob_name =
+ blob_names_[bottom_id_vecs_[layer_id][bottom_id]];
+ blobs_skip_backp.insert(blob_name);
+ }
}
}
// Handle force_backward if needed.
// In the end, all remaining blobs are considered output blobs.
for (set<string>::iterator it = available_blobs.begin();
it != available_blobs.end(); ++it) {
- LOG(INFO) << "This network produces output " << *it;
+ LOG_IF(INFO, Caffe::root_solver())
+ << "This network produces output " << *it;
net_output_blobs_.push_back(blobs_[blob_name_to_idx[*it]].get());
net_output_blob_indices_.push_back(blob_name_to_idx[*it]);
}
for (size_t layer_id = 0; layer_id < layer_names_.size(); ++layer_id) {
layer_names_index_[layer_names_[layer_id]] = layer_id;
}
- GetLearningRateAndWeightDecay();
+ ShareWeights();
debug_info_ = param.debug_info();
- LOG(INFO) << "Network initialization done.";
- LOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
+ LOG_IF(INFO, Caffe::root_solver()) << "Network initialization done.";
}
template <typename Dtype>
// Check whether the rule is broken due to phase.
if (rule.has_phase()) {
if (rule.phase() != state.phase()) {
- LOG(INFO) << "The NetState phase (" << state.phase()
- << ") differed from the phase (" << rule.phase()
- << ") specified by a rule in layer " << layer_name;
+ LOG_IF(INFO, Caffe::root_solver())
+ << "The NetState phase (" << state.phase()
+ << ") differed from the phase (" << rule.phase()
+ << ") specified by a rule in layer " << layer_name;
return false;
}
}
// Check whether the rule is broken due to min level.
if (rule.has_min_level()) {
if (state.level() < rule.min_level()) {
- LOG(INFO) << "The NetState level (" << state.level()
+ LOG_IF(INFO, Caffe::root_solver())
+ << "The NetState level (" << state.level()
<< ") is above the min_level (" << rule.min_level()
<< ") specified by a rule in layer " << layer_name;
return false;
// Check whether the rule is broken due to max level.
if (rule.has_max_level()) {
if (state.level() > rule.max_level()) {
- LOG(INFO) << "The NetState level (" << state.level()
+ LOG_IF(INFO, Caffe::root_solver())
+ << "The NetState level (" << state.level()
<< ") is above the max_level (" << rule.max_level()
<< ") specified by a rule in layer " << layer_name;
return false;
if (rule.stage(i) == state.stage(j)) { has_stage = true; }
}
if (!has_stage) {
- LOG(INFO) << "The NetState did not contain stage '" << rule.stage(i)
- << "' specified by a rule in layer " << layer_name;
+ LOG_IF(INFO, Caffe::root_solver())
+ << "The NetState did not contain stage '" << rule.stage(i)
+ << "' specified by a rule in layer " << layer_name;
return false;
}
}
if (rule.not_stage(i) == state.stage(j)) { has_stage = true; }
}
if (has_stage) {
- LOG(INFO) << "The NetState contained a not_stage '" << rule.not_stage(i)
- << "' specified by a rule in layer " << layer_name;
+ LOG_IF(INFO, Caffe::root_solver())
+ << "The NetState contained a not_stage '" << rule.not_stage(i)
+ << "' specified by a rule in layer " << layer_name;
return false;
}
}
if (blob_name_to_idx && layer_param && layer_param->bottom_size() > top_id &&
blob_name == layer_param->bottom(top_id)) {
// In-place computation
- LOG(INFO) << layer_param->name() << " -> " << blob_name << " (in-place)";
+ LOG_IF(INFO, Caffe::root_solver())
+ << layer_param->name() << " -> " << blob_name << " (in-place)";
top_vecs_[layer_id].push_back(blobs_[(*blob_name_to_idx)[blob_name]].get());
top_id_vecs_[layer_id].push_back((*blob_name_to_idx)[blob_name]);
} else if (blob_name_to_idx &&
blob_name_to_idx->find(blob_name) != blob_name_to_idx->end()) {
// If we are not doing in-place computation but have duplicated blobs,
// raise an error.
- LOG(FATAL) << "Duplicate blobs produced by multiple sources.";
+ LOG(FATAL) << "Top blob '" << blob_name
+ << "' produced by multiple sources.";
} else {
// Normal output.
- if (layer_param) {
- LOG(INFO) << layer_param->name() << " -> " << blob_name;
- } else {
- LOG(INFO) << "Input " << top_id << " -> " << blob_name;
+ if (Caffe::root_solver()) {
+ if (layer_param) {
+ LOG(INFO) << layer_param->name() << " -> " << blob_name;
+ } else {
+ LOG(INFO) << "Input " << top_id << " -> " << blob_name;
+ }
}
shared_ptr<Blob<Dtype> > blob_pointer(new Blob<Dtype>());
const int blob_id = blobs_.size();
// Helper for Net::Init: add a new bottom blob to the net.
template <typename Dtype>
-int Net<Dtype>::AppendBottom(const NetParameter& param,
- const int layer_id, const int bottom_id,
- set<string>* available_blobs, map<string, int>* blob_name_to_idx) {
+int Net<Dtype>::AppendBottom(const NetParameter& param, const int layer_id,
+ const int bottom_id, set<string>* available_blobs,
+ map<string, int>* blob_name_to_idx) {
const LayerParameter& layer_param = param.layer(layer_id);
const string& blob_name = layer_param.bottom(bottom_id);
if (available_blobs->find(blob_name) == available_blobs->end()) {
- LOG(FATAL) << "Unknown blob input " << blob_name
- << " (at index " << bottom_id << ") to layer " << layer_id;
+ LOG(FATAL) << "Unknown bottom blob '" << blob_name << "' (layer '"
+ << layer_param.name() << "', bottom index " << bottom_id << ")";
}
const int blob_id = (*blob_name_to_idx)[blob_name];
- LOG(INFO) << layer_names_[layer_id] << " <- " << blob_name;
+ LOG_IF(INFO, Caffe::root_solver())
+ << layer_names_[layer_id] << " <- " << blob_name;
bottom_vecs_[layer_id].push_back(blobs_[blob_id].get());
bottom_id_vecs_[layer_id].push_back(blob_id);
available_blobs->erase(blob_name);
- const bool need_backward = blob_need_backward_[blob_id];
+ bool propagate_down = true;
+ // Check if the backpropagation on bottom_id should be skipped
+ if (layer_param.propagate_down_size() > 0)
+ propagate_down = layer_param.propagate_down(bottom_id);
+ const bool need_backward = blob_need_backward_[blob_id] &&
+ propagate_down;
bottom_need_backward_[layer_id].push_back(need_backward);
return blob_id;
}
params_.push_back(layers_[layer_id]->blobs()[param_id]);
param_id_vecs_[layer_id].push_back(net_param_id);
param_layer_indices_.push_back(make_pair(layer_id, param_id));
+ ParamSpec default_param_spec;
+ const ParamSpec* param_spec = (layer_param.param_size() > param_id) ?
+ &layer_param.param(param_id) : &default_param_spec;
if (!param_size || !param_name.size() || (param_name.size() &&
param_names_index_.find(param_name) == param_names_index_.end())) {
// This layer "owns" this parameter blob -- it is either anonymous
// (i.e., not given a param_name) or explicitly given a name that we
// haven't already seen.
param_owners_.push_back(-1);
- if (param_size) {
+ if (param_name.size()) {
param_names_index_[param_name] = net_param_id;
}
+ const int learnable_param_id = learnable_params_.size();
+ learnable_params_.push_back(params_[net_param_id].get());
+ learnable_param_ids_.push_back(learnable_param_id);
+ has_params_lr_.push_back(param_spec->has_lr_mult());
+ has_params_decay_.push_back(param_spec->has_decay_mult());
+ params_lr_.push_back(param_spec->lr_mult());
+ params_weight_decay_.push_back(param_spec->decay_mult());
} else {
// Named param blob with name we've seen before: share params
const int owner_net_param_id = param_names_index_[param_name];
param_layer_indices_[owner_net_param_id];
const int owner_layer_id = owner_index.first;
const int owner_param_id = owner_index.second;
- LOG(INFO) << "Sharing parameters '" << param_name << "' owned by "
- << "layer '" << layer_names_[owner_layer_id] << "', param "
- << "index " << owner_param_id;
+ LOG_IF(INFO, Caffe::root_solver()) << "Sharing parameters '" << param_name
+ << "' owned by "
+ << "layer '" << layer_names_[owner_layer_id] << "', param "
+ << "index " << owner_param_id;
Blob<Dtype>* this_blob = layers_[layer_id]->blobs()[param_id].get();
Blob<Dtype>* owner_blob =
layers_[owner_layer_id]->blobs()[owner_param_id].get();
ParamSpec_DimCheckMode_PERMISSIVE)) {
// Permissive dimension checking -- only check counts are the same.
CHECK_EQ(this_blob->count(), owner_blob->count())
- << "Shared parameter blobs must have the same count.";
+ << "Cannot share param '" << param_name << "' owned by layer '"
+ << layer_names_[owner_layer_id] << "' with layer '"
+ << layer_names_[layer_id] << "'; count mismatch. Owner layer param "
+ << "shape is " << owner_blob->shape_string() << "; sharing layer "
+ << "shape is " << this_blob->shape_string();
} else {
// Strict dimension checking -- all dims must be the same.
- CHECK(this_blob->shape() == owner_blob->shape());
+ CHECK(this_blob->shape() == owner_blob->shape())
+ << "Cannot share param '" << param_name << "' owned by layer '"
+ << layer_names_[owner_layer_id] << "' with layer '"
+ << layer_names_[layer_id] << "'; shape mismatch. Owner layer param "
+ << "shape is " << owner_blob->shape_string() << "; sharing layer "
+ << "expects shape " << this_blob->shape_string();
}
- layers_[layer_id]->blobs()[param_id]->ShareData(
- *layers_[owner_layer_id]->blobs()[owner_param_id]);
- }
-}
-
-template <typename Dtype>
-void Net<Dtype>::GetLearningRateAndWeightDecay() {
- LOG(INFO) << "Collecting Learning Rate and Weight Decay.";
- ParamSpec default_param_spec;
- for (int i = 0; i < layers_.size(); ++i) {
- vector<shared_ptr<Blob<Dtype> > >& layer_blobs = layers_[i]->blobs();
- for (int j = 0; j < layer_blobs.size(); ++j) {
- const ParamSpec* param_spec =
- (layers_[i]->layer_param().param_size() > j) ?
- &layers_[i]->layer_param().param(j) : &default_param_spec;
- params_lr_.push_back(param_spec->lr_mult());
- params_weight_decay_.push_back(param_spec->decay_mult());
+ const int learnable_param_id = learnable_param_ids_[owner_net_param_id];
+ learnable_param_ids_.push_back(learnable_param_id);
+ if (param_spec->has_lr_mult()) {
+ if (has_params_lr_[learnable_param_id]) {
+ CHECK_EQ(param_spec->lr_mult(), params_lr_[learnable_param_id])
+ << "Shared param '" << param_name << "' has mismatched lr_mult.";
+ } else {
+ has_params_lr_[learnable_param_id] = true;
+ params_lr_[learnable_param_id] = param_spec->lr_mult();
+ }
+ }
+ if (param_spec->has_decay_mult()) {
+ if (has_params_decay_[learnable_param_id]) {
+ CHECK_EQ(param_spec->decay_mult(),
+ params_weight_decay_[learnable_param_id])
+ << "Shared param '" << param_name << "' has mismatched decay_mult.";
+ } else {
+ has_params_decay_[learnable_param_id] = true;
+ params_weight_decay_[learnable_param_id] = param_spec->decay_mult();
+ }
}
}
}
}
for (int i = start; i <= end; ++i) {
// LOG(ERROR) << "Forwarding " << layer_names_[i];
- layers_[i]->Reshape(bottom_vecs_[i], top_vecs_[i]);
Dtype layer_loss = layers_[i]->Forward(bottom_vecs_[i], top_vecs_[i]);
loss += layer_loss;
if (debug_info_) { ForwardDebugInfo(i); }
const Blob<Dtype>& blob = *net_input_blobs_[input_id];
const string& blob_name = blob_names_[net_input_blob_indices_[input_id]];
const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
- LOG(INFO) << " [Forward] "
- << "Input " << blob_name << " data: " << data_abs_val_mean;
+ LOG_IF(INFO, Caffe::root_solver())
+ << " [Forward] "
+ << "Input " << blob_name << " data: " << data_abs_val_mean;
}
template <typename Dtype>
const Blob<Dtype>& blob = *top_vecs_[layer_id][top_id];
const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];
const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
- LOG(INFO) << " [Forward] "
- << "Layer " << layer_names_[layer_id] << ", top blob " << blob_name
- << " data: " << data_abs_val_mean;
+ LOG_IF(INFO, Caffe::root_solver())
+ << " [Forward] "
+ << "Layer " << layer_names_[layer_id]
+ << ", top blob " << blob_name
+ << " data: " << data_abs_val_mean;
}
for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();
++param_id) {
const int net_param_id = param_id_vecs_[layer_id][param_id];
const string& blob_name = param_display_names_[net_param_id];
const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
- LOG(INFO) << " [Forward] "
- << "Layer " << layer_names_[layer_id] << ", param blob " << blob_name
- << " data: " << data_abs_val_mean;
+ LOG_IF(INFO, Caffe::root_solver())
+ << " [Forward] "
+ << "Layer " << layer_names_[layer_id]
+ << ", param blob " << blob_name
+ << " data: " << data_abs_val_mean;
}
}
const Blob<Dtype>& blob = *bottom_vec[bottom_id];
const string& blob_name = blob_names_[bottom_id_vecs_[layer_id][bottom_id]];
const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();
- LOG(INFO) << " [Backward] "
- << "Layer " << layer_names_[layer_id] << ", bottom blob " << blob_name
+ LOG_IF(INFO, Caffe::root_solver())
+ << " [Backward] "
+ << "Layer " << layer_names_[layer_id]
+ << ", bottom blob " << blob_name
<< " diff: " << diff_abs_val_mean;
}
for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();
if (!layers_[layer_id]->param_propagate_down(param_id)) { continue; }
const Blob<Dtype>& blob = *layers_[layer_id]->blobs()[param_id];
const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();
- LOG(INFO) << " [Backward] "
- << "Layer " << layer_names_[layer_id] << ", param blob " << param_id
+ LOG_IF(INFO, Caffe::root_solver())
+ << " [Backward] "
+ << "Layer " << layer_names_[layer_id]
+ << ", param blob " << param_id
<< " diff: " << diff_abs_val_mean;
}
}
const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();
if (param_owner < 0) {
const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
- LOG(INFO) << " [Update] Layer " << layer_name
+ LOG_IF(INFO, Caffe::root_solver())
+ << " [Update] Layer " << layer_name
<< ", param " << param_display_name
- << " data: " << data_abs_val_mean << "; diff: " << diff_abs_val_mean;
+ << " data: " << data_abs_val_mean
+ << "; diff: " << diff_abs_val_mean;
} else {
const string& owner_layer_name =
layer_names_[param_layer_indices_[param_owner].first];
- LOG(INFO) << " [Update] Layer " << layer_name
+ LOG_IF(INFO, Caffe::root_solver())
+ << " [Update] Layer " << layer_name
<< ", param blob " << param_display_name
- << " (owned by layer " << owner_layer_name << ", "
- << "param " << param_display_names_[param_owners_[param_id]] << ")"
+ << " (owned by layer " << owner_layer_name << ", " << "param "
+ << param_display_names_[param_owners_[param_id]] << ")"
<< " diff: " << diff_abs_val_mean;
}
}
++target_layer_id;
}
if (target_layer_id == layer_names_.size()) {
- DLOG(INFO) << "Ignoring source layer " << source_layer_name;
+ LOG(INFO) << "Ignoring source layer " << source_layer_name;
continue;
}
DLOG(INFO) << "Copying source layer " << source_layer_name;
<< "Incompatible number of blobs for layer " << source_layer_name;
for (int j = 0; j < target_blobs.size(); ++j) {
Blob<Dtype>* source_blob = source_layer->blobs()[j].get();
- CHECK(target_blobs[j]->shape() == source_blob->shape());
+ CHECK(target_blobs[j]->shape() == source_blob->shape())
+ << "Cannot share param " << j << " weights from layer '"
+ << source_layer_name << "'; shape mismatch. Source param shape is "
+ << source_blob->shape_string() << "; target param shape is "
+ << target_blobs[j]->shape_string();
target_blobs[j]->ShareData(*source_blob);
}
}
BackwardFromTo(layers_.size() - 1, 0);
if (debug_info_) {
Dtype asum_data = 0, asum_diff = 0, sumsq_data = 0, sumsq_diff = 0;
- for (int i = 0; i < params_.size(); ++i) {
- if (param_owners_[i] >= 0) { continue; }
- asum_data += params_[i]->asum_data();
- asum_diff += params_[i]->asum_diff();
- sumsq_data += params_[i]->sumsq_data();
- sumsq_diff += params_[i]->sumsq_diff();
+ for (int i = 0; i < learnable_params_.size(); ++i) {
+ asum_data += learnable_params_[i]->asum_data();
+ asum_diff += learnable_params_[i]->asum_diff();
+ sumsq_data += learnable_params_[i]->sumsq_data();
+ sumsq_diff += learnable_params_[i]->sumsq_diff();
}
const Dtype l2norm_data = std::sqrt(sumsq_data);
const Dtype l2norm_diff = std::sqrt(sumsq_diff);
LOG(ERROR) << " [Backward] All net params (data, diff): "
- << "L1 norm = (" << asum_data << ", " << asum_diff << "); "
- << "L2 norm = (" << l2norm_data << ", " << l2norm_diff << ")";
+ << "L1 norm = (" << asum_data << ", " << asum_diff << "); "
+ << "L2 norm = (" << l2norm_data << ", " << l2norm_diff << ")";
}
}
++target_layer_id;
}
if (target_layer_id == layer_names_.size()) {
- DLOG(INFO) << "Ignoring source layer " << source_layer_name;
+ LOG(INFO) << "Ignoring source layer " << source_layer_name;
continue;
}
DLOG(INFO) << "Copying source layer " << source_layer_name;
CHECK_EQ(target_blobs.size(), source_layer.blobs_size())
<< "Incompatible number of blobs for layer " << source_layer_name;
for (int j = 0; j < target_blobs.size(); ++j) {
+ if (!target_blobs[j]->ShapeEquals(source_layer.blobs(j))) {
+ Blob<Dtype> source_blob;
+ const bool kReshape = true;
+ source_blob.FromProto(source_layer.blobs(j), kReshape);
+ LOG(FATAL) << "Cannot copy param " << j << " weights from layer '"
+ << source_layer_name << "'; shape mismatch. Source param shape is "
+ << source_blob.shape_string() << "; target param shape is "
+ << target_blobs[j]->shape_string() << ". "
+ << "To learn this layer's parameters from scratch rather than "
+ << "copying from a saved net, rename the layer.";
+ }
const bool kReshape = false;
target_blobs[j]->FromProto(source_layer.blobs(j), kReshape);
}
template <typename Dtype>
void Net<Dtype>::CopyTrainedLayersFrom(const string trained_filename) {
+ if (trained_filename.size() >= 3 &&
+ trained_filename.compare(trained_filename.size() - 3, 3, ".h5") == 0) {
+ CopyTrainedLayersFromHDF5(trained_filename);
+ } else {
+ CopyTrainedLayersFromBinaryProto(trained_filename);
+ }
+}
+
+template <typename Dtype>
+void Net<Dtype>::CopyTrainedLayersFromBinaryProto(
+ const string trained_filename) {
NetParameter param;
ReadNetParamsFromBinaryFileOrDie(trained_filename, ¶m);
CopyTrainedLayersFrom(param);
}
template <typename Dtype>
+void Net<Dtype>::CopyTrainedLayersFromHDF5(const string trained_filename) {
+ hid_t file_hid = H5Fopen(trained_filename.c_str(), H5F_ACC_RDONLY,
+ H5P_DEFAULT);
+ CHECK_GE(file_hid, 0) << "Couldn't open " << trained_filename;
+ hid_t data_hid = H5Gopen2(file_hid, "data", H5P_DEFAULT);
+ CHECK_GE(data_hid, 0) << "Error reading weights from " << trained_filename;
+ int num_layers = hdf5_get_num_links(data_hid);
+ for (int i = 0; i < num_layers; ++i) {
+ string source_layer_name = hdf5_get_name_by_idx(data_hid, i);
+ if (!layer_names_index_.count(source_layer_name)) {
+ LOG(INFO) << "Ignoring source layer " << source_layer_name;
+ continue;
+ }
+ int target_layer_id = layer_names_index_[source_layer_name];
+ DLOG(INFO) << "Copying source layer " << source_layer_name;
+ vector<shared_ptr<Blob<Dtype> > >& target_blobs =
+ layers_[target_layer_id]->blobs();
+ hid_t layer_hid = H5Gopen2(data_hid, source_layer_name.c_str(),
+ H5P_DEFAULT);
+ CHECK_GE(layer_hid, 0)
+ << "Error reading weights from " << trained_filename;
+ // Check that source layer doesn't have more params than target layer
+ int num_source_params = hdf5_get_num_links(layer_hid);
+ CHECK_LE(num_source_params, target_blobs.size())
+ << "Incompatible number of blobs for layer " << source_layer_name;
+ for (int j = 0; j < target_blobs.size(); ++j) {
+ ostringstream oss;
+ oss << j;
+ string dataset_name = oss.str();
+ int target_net_param_id = param_id_vecs_[target_layer_id][j];
+ if (!H5Lexists(layer_hid, dataset_name.c_str(), H5P_DEFAULT)) {
+ // Target param doesn't exist in source weights...
+ if (param_owners_[target_net_param_id] != -1) {
+ // ...but it's weight-shared in target, so that's fine.
+ continue;
+ } else {
+ LOG(FATAL) << "Incompatible number of blobs for layer "
+ << source_layer_name;
+ }
+ }
+ hdf5_load_nd_dataset(layer_hid, dataset_name.c_str(), 0, kMaxBlobAxes,
+ target_blobs[j].get());
+ }
+ H5Gclose(layer_hid);
+ }
+ H5Gclose(data_hid);
+ H5Fclose(file_hid);
+}
+
+template <typename Dtype>
void Net<Dtype>::ToProto(NetParameter* param, bool write_diff) const {
param->Clear();
param->set_name(name_);
DLOG(INFO) << "Serializing " << layers_.size() << " layers";
for (int i = 0; i < layers_.size(); ++i) {
LayerParameter* layer_param = param->add_layer();
- for (int j = 0; j < bottom_id_vecs_[i].size(); ++j) {
- layer_param->add_bottom(blob_names_[bottom_id_vecs_[i][j]]);
+ layers_[i]->ToProto(layer_param, write_diff);
+ }
+}
+
+template <typename Dtype>
+void Net<Dtype>::ToHDF5(const string& filename, bool write_diff) const {
+ hid_t file_hid = H5Fcreate(filename.c_str(), H5F_ACC_TRUNC, H5P_DEFAULT,
+ H5P_DEFAULT);
+ CHECK_GE(file_hid, 0)
+ << "Couldn't open " << filename << " to save weights.";
+ hid_t data_hid = H5Gcreate2(file_hid, "data", H5P_DEFAULT, H5P_DEFAULT,
+ H5P_DEFAULT);
+ CHECK_GE(data_hid, 0) << "Error saving weights to " << filename << ".";
+ hid_t diff_hid = -1;
+ if (write_diff) {
+ diff_hid = H5Gcreate2(file_hid, "diff", H5P_DEFAULT, H5P_DEFAULT,
+ H5P_DEFAULT);
+ CHECK_GE(diff_hid, 0) << "Error saving weights to " << filename << ".";
+ }
+ for (int layer_id = 0; layer_id < layers_.size(); ++layer_id) {
+ const LayerParameter& layer_param = layers_[layer_id]->layer_param();
+ string layer_name = layer_param.name();
+ hid_t layer_data_hid = H5Gcreate2(data_hid, layer_name.c_str(),
+ H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT);
+ CHECK_GE(layer_data_hid, 0)
+ << "Error saving weights to " << filename << ".";
+ hid_t layer_diff_hid = -1;
+ if (write_diff) {
+ layer_diff_hid = H5Gcreate2(diff_hid, layer_name.c_str(),
+ H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT);
+ CHECK_GE(layer_diff_hid, 0)
+ << "Error saving weights to " << filename << ".";
}
- for (int j = 0; j < top_id_vecs_[i].size(); ++j) {
- layer_param->add_top(blob_names_[top_id_vecs_[i][j]]);
+ int num_params = layers_[layer_id]->blobs().size();
+ for (int param_id = 0; param_id < num_params; ++param_id) {
+ ostringstream dataset_name;
+ dataset_name << param_id;
+ const int net_param_id = param_id_vecs_[layer_id][param_id];
+ if (param_owners_[net_param_id] == -1) {
+ // Only save params that own themselves
+ hdf5_save_nd_dataset<Dtype>(layer_data_hid, dataset_name.str(),
+ *params_[net_param_id]);
+ }
+ if (write_diff) {
+ // Write diffs regardless of weight-sharing
+ hdf5_save_nd_dataset<Dtype>(layer_diff_hid, dataset_name.str(),
+ *params_[net_param_id], true);
+ }
}
- layers_[i]->ToProto(layer_param, write_diff);
+ H5Gclose(layer_data_hid);
+ if (write_diff) {
+ H5Gclose(layer_diff_hid);
+ }
+ }
+ H5Gclose(data_hid);
+ if (write_diff) {
+ H5Gclose(diff_hid);
}
+ H5Fclose(file_hid);
}
template <typename Dtype>
void Net<Dtype>::Update() {
- // First, accumulate the diffs of any shared parameters into their owner's
- // diff. (Assumes that the learning rate, weight decay, etc. have already been
- // accounted for in the current diff.)
- for (int i = 0; i < params_.size(); ++i) {
- if (param_owners_[i] < 0) { continue; }
- if (debug_info_) { UpdateDebugInfo(i); }
- const int count = params_[i]->count();
- const Dtype* this_diff;
- Dtype* owner_diff;
+ for (int i = 0; i < learnable_params_.size(); ++i) {
+ learnable_params_[i]->Update();
+ }
+}
+
+template <typename Dtype>
+void Net<Dtype>::ClearParamDiffs() {
+ for (int i = 0; i < learnable_params_.size(); ++i) {
+ Blob<Dtype>* blob = learnable_params_[i];
switch (Caffe::mode()) {
case Caffe::CPU:
- this_diff = params_[i]->cpu_diff();
- owner_diff = params_[param_owners_[i]]->mutable_cpu_diff();
- caffe_add(count, this_diff, owner_diff, owner_diff);
+ caffe_set(blob->count(), static_cast<Dtype>(0),
+ blob->mutable_cpu_diff());
break;
-#ifndef CPU_ONLY
case Caffe::GPU:
- this_diff = params_[i]->gpu_diff();
- owner_diff = params_[param_owners_[i]]->mutable_gpu_diff();
- caffe_gpu_add(count, this_diff, owner_diff, owner_diff);
- break;
+#ifndef CPU_ONLY
+ caffe_gpu_set(blob->count(), static_cast<Dtype>(0),
+ blob->mutable_gpu_diff());
#else
NO_GPU;
#endif
- default:
- LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+ break;
}
}
- // Now, update the owned parameters.
+}
+
+template <typename Dtype>
+void Net<Dtype>::ShareWeights() {
for (int i = 0; i < params_.size(); ++i) {
- if (param_owners_[i] >= 0) { continue; }
- if (debug_info_) { UpdateDebugInfo(i); }
- params_[i]->Update();
+ if (param_owners_[i] < 0) { continue; }
+ params_[i]->ShareData(*params_[param_owners_[i]]);
+ params_[i]->ShareDiff(*params_[param_owners_[i]]);
}
}
--- /dev/null
+#ifndef CPU_ONLY
+#include <cuda_runtime.h>
+#endif
+#include <glog/logging.h>
+#include <stdio.h>
+
+#include <sstream>
+#include <string>
+#include <vector>
+
+#include "boost/thread.hpp"
+#include "caffe/caffe.hpp"
+#include "caffe/parallel.hpp"
+
+namespace caffe {
+
+enum Op {
+ copy,
+ replace_cpu,
+ replace_gpu,
+ replace_cpu_diff,
+ replace_gpu_diff
+};
+
+template<typename Dtype>
+static void apply_buffers(const vector<Blob<Dtype>*>& blobs,
+ Dtype* buffer, size_t total_size, Op op) {
+ Dtype* ptr = buffer;
+ for (int i = 0; i < blobs.size(); ++i) {
+ int size = blobs[i]->count();
+ switch (op) {
+ case copy: {
+ // Init buffer to current values of blobs
+ caffe_copy(size,
+ reinterpret_cast<const Dtype*>(blobs[i]->data()->cpu_data()),
+ ptr);
+ break;
+ }
+ case replace_cpu:
+ blobs[i]->data()->set_cpu_data(ptr);
+ break;
+ case replace_gpu:
+ blobs[i]->data()->set_gpu_data(ptr);
+ break;
+ case replace_cpu_diff:
+ blobs[i]->diff()->set_cpu_data(ptr);
+ break;
+ case replace_gpu_diff:
+ blobs[i]->diff()->set_gpu_data(ptr);
+ break;
+ }
+ ptr += size;
+ }
+ // total_size is at least one byte
+ CHECK_EQ(total_size, (ptr == buffer ? 1 : ptr - buffer));
+}
+
+// Buffer size necessary to store given blobs
+template<typename Dtype>
+static size_t total_size(const vector<Blob<Dtype>*>& params) {
+ size_t size = 0;
+ for (int i = 0; i < params.size(); ++i)
+ size += params[i]->count();
+ // Size have at least one byte, otherwise cudaMalloc fails if net has no
+ // learnable parameters.
+ return (size > 0) ? size : 1;
+}
+
+template<typename Dtype>
+Params<Dtype>::Params(shared_ptr<Solver<Dtype> > root_solver)
+ : size_(total_size<Dtype>(root_solver->net()->learnable_params())),
+ data_(),
+ diff_() {
+}
+
+template<typename Dtype>
+GPUParams<Dtype>::GPUParams(shared_ptr<Solver<Dtype> > root_solver, int device)
+ : Params<Dtype>(root_solver) {
+#ifndef CPU_ONLY
+ int initial_device;
+ CUDA_CHECK(cudaGetDevice(&initial_device));
+
+ // Allocate device buffers
+ CUDA_CHECK(cudaSetDevice(device));
+ CUDA_CHECK(cudaMalloc(&data_, size_ * sizeof(Dtype)));
+
+ // Copy blob values
+ const vector<Blob<Dtype>*>& net =
+ root_solver->net()->learnable_params();
+ apply_buffers(net, data_, size_, copy);
+
+ CUDA_CHECK(cudaMalloc(&diff_, size_ * sizeof(Dtype)));
+ caffe_gpu_set(size_, Dtype(0), diff_);
+
+ CUDA_CHECK(cudaSetDevice(initial_device));
+#else
+ NO_GPU;
+#endif
+}
+
+template<typename Dtype>
+GPUParams<Dtype>::~GPUParams() {
+#ifndef CPU_ONLY
+ CUDA_CHECK(cudaFree(data_));
+ CUDA_CHECK(cudaFree(diff_));
+#endif
+}
+
+template<typename Dtype>
+void GPUParams<Dtype>::configure(Solver<Dtype>* solver) const {
+ const vector<Blob<Dtype>*>& net =
+ solver->net()->learnable_params();
+ apply_buffers(net, data_, size_, replace_gpu);
+ apply_buffers(net, diff_, size_, replace_gpu_diff);
+}
+
+void DevicePair::compute(const vector<int> devices, vector<DevicePair>* pairs) {
+#ifndef CPU_ONLY
+ vector<int> remaining(devices);
+
+ // Depth for reduction tree
+ int remaining_depth = static_cast<int>(ceil(log2(remaining.size())));
+
+ // Group GPUs by board
+ for (int d = 0; d < remaining_depth; ++d) {
+ for (int i = 0; i < remaining.size(); ++i) {
+ for (int j = i + 1; j < remaining.size(); ++j) {
+ cudaDeviceProp a, b;
+ CUDA_CHECK(cudaGetDeviceProperties(&a, remaining[i]));
+ CUDA_CHECK(cudaGetDeviceProperties(&b, remaining[j]));
+ if (a.isMultiGpuBoard && b.isMultiGpuBoard) {
+ if (a.multiGpuBoardGroupID == b.multiGpuBoardGroupID) {
+ pairs->push_back(DevicePair(remaining[i], remaining[j]));
+ DLOG(INFO) << "GPU board: " << remaining[i] << ":" << remaining[j];
+ remaining.erase(remaining.begin() + j);
+ break;
+ }
+ }
+ }
+ }
+ }
+ ostringstream s;
+ for (int i = 0; i < remaining.size(); ++i) {
+ s << (i ? ", " : "") << remaining[i];
+ }
+ DLOG(INFO) << "GPUs paired by boards, remaining: " << s.str();
+
+ // Group by P2P accessibility
+ remaining_depth = ceil(log2(remaining.size()));
+ for (int d = 0; d < remaining_depth; ++d) {
+ for (int i = 0; i < remaining.size(); ++i) {
+ for (int j = i + 1; j < remaining.size(); ++j) {
+ int access;
+ CUDA_CHECK(
+ cudaDeviceCanAccessPeer(&access, remaining[i], remaining[j]));
+ if (access) {
+ pairs->push_back(DevicePair(remaining[i], remaining[j]));
+ DLOG(INFO) << "P2P pair: " << remaining[i] << ":" << remaining[j];
+ remaining.erase(remaining.begin() + j);
+ break;
+ }
+ }
+ }
+ }
+ s.str("");
+ for (int i = 0; i < remaining.size(); ++i) {
+ s << (i ? ", " : "") << remaining[i];
+ }
+ DLOG(INFO) << "GPUs paired by P2P access, remaining: " << s.str();
+
+ // Group remaining
+ remaining_depth = ceil(log2(remaining.size()));
+ for (int d = 0; d < remaining_depth; ++d) {
+ for (int i = 0; i < remaining.size(); ++i) {
+ pairs->push_back(DevicePair(remaining[i], remaining[i + 1]));
+ DLOG(INFO) << "Remaining pair: " << remaining[i] << ":"
+ << remaining[i + 1];
+ remaining.erase(remaining.begin() + i + 1);
+ }
+ }
+
+ // Should only be the parent node remaining
+ CHECK_EQ(remaining.size(), 1);
+
+ pairs->insert(pairs->begin(), DevicePair(-1, remaining[0]));
+
+ CHECK(pairs->size() == devices.size());
+ for (int i = 0; i < pairs->size(); ++i) {
+ CHECK((*pairs)[i].parent() != (*pairs)[i].device());
+ for (int j = i + 1; j < pairs->size(); ++j) {
+ CHECK((*pairs)[i].device() != (*pairs)[j].device());
+ }
+ }
+#else
+ NO_GPU;
+#endif
+}
+
+//
+
+template<typename Dtype>
+P2PSync<Dtype>::P2PSync(shared_ptr<Solver<Dtype> > root_solver,
+ P2PSync<Dtype>* parent, const SolverParameter& param)
+ : GPUParams<Dtype>(root_solver, param.device_id()),
+ parent_(parent),
+ children_(),
+ queue_(),
+ initial_iter_(root_solver->iter()),
+ solver_() {
+#ifndef CPU_ONLY
+ int initial_device;
+ CUDA_CHECK(cudaGetDevice(&initial_device));
+ const int self = param.device_id();
+ CUDA_CHECK(cudaSetDevice(self));
+
+ if (parent == NULL) {
+ solver_ = root_solver;
+ } else {
+ Caffe::set_root_solver(false);
+ solver_.reset(new WorkerSolver<Dtype>(param, root_solver.get()));
+ Caffe::set_root_solver(true);
+ }
+ this->configure(solver_.get());
+ solver_->add_callback(this);
+
+ if (parent) {
+ // Enable p2p access between devices
+ const int peer = parent->solver_->param().device_id();
+ int access;
+ CUDA_CHECK(cudaDeviceCanAccessPeer(&access, self, peer));
+ if (access) {
+ CUDA_CHECK(cudaDeviceEnablePeerAccess(peer, 0));
+ } else {
+ LOG(INFO)<< "GPU " << self << " does not have p2p access to GPU " << peer;
+ }
+ // Allocate receiving buffer on parent
+ CUDA_CHECK(cudaSetDevice(peer));
+ CUDA_CHECK(cudaMalloc(&parent_grads_, size_ * sizeof(Dtype)));
+ CUDA_CHECK(cudaSetDevice(self));
+ }
+
+ CUDA_CHECK(cudaSetDevice(initial_device));
+#else
+ NO_GPU;
+#endif
+}
+
+template<typename Dtype>
+P2PSync<Dtype>::~P2PSync() {
+#ifndef CPU_ONLY
+ int initial_device;
+ CUDA_CHECK(cudaGetDevice(&initial_device));
+ const int self = solver_->param().device_id();
+ CUDA_CHECK(cudaSetDevice(self));
+
+ if (parent_) {
+ CUDA_CHECK(cudaFree(parent_grads_));
+ const int peer = parent_->solver_->param().device_id();
+ int access;
+ CUDA_CHECK(cudaDeviceCanAccessPeer(&access, self, peer));
+ if (access) {
+ CUDA_CHECK(cudaDeviceDisablePeerAccess(peer));
+ }
+ }
+
+ CUDA_CHECK(cudaSetDevice(initial_device));
+#endif
+}
+
+template<typename Dtype>
+void P2PSync<Dtype>::InternalThreadEntry() {
+ Caffe::SetDevice(solver_->param().device_id());
+ CHECK(Caffe::root_solver());
+ Caffe::set_root_solver(false);
+ // See if there is a defined seed and reset random state if so
+ if (solver_->param().random_seed() >= 0) {
+ // Fetch random seed and modulate by device ID to make sure
+ // everyone doesn't have the same seed. We seem to have some
+ // solver instability if we have everyone with the same seed
+ Caffe::set_random_seed(
+ solver_->param().random_seed() + solver_->param().device_id());
+ }
+ solver_->Step(solver_->param().max_iter() - initial_iter_);
+}
+
+template<typename Dtype>
+void P2PSync<Dtype>::on_start() {
+#ifndef CPU_ONLY
+#ifdef DEBUG
+ int device;
+ CUDA_CHECK(cudaGetDevice(&device));
+ CHECK(device == solver_->param().device_id());
+#else
+// CHECK(false);
+#endif
+
+ // Wait for update from parent
+ if (parent_) {
+ P2PSync<Dtype> *parent = queue_.pop();
+ CHECK(parent == parent_);
+ }
+
+ // Update children
+ for (int i = children_.size() - 1; i >= 0; i--) {
+ Dtype* src = data_;
+ Dtype* dst = children_[i]->data_;
+
+#ifdef DEBUG
+ cudaPointerAttributes attributes;
+ CUDA_CHECK(cudaPointerGetAttributes(&attributes, src));
+ CHECK(attributes.device == device);
+ CUDA_CHECK(cudaPointerGetAttributes(&attributes, dst));
+ CHECK(attributes.device == children_[i]->solver_->param().device_id());
+#endif
+
+ CUDA_CHECK(cudaMemcpyAsync(dst, src, size_ * sizeof(Dtype),
+ cudaMemcpyDeviceToDevice, cudaStreamDefault));
+ CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault));
+ children_[i]->queue_.push(this);
+ }
+#endif
+}
+
+template<typename Dtype>
+void P2PSync<Dtype>::on_gradients_ready() {
+#ifndef CPU_ONLY
+#ifdef DEBUG
+ int device;
+ CUDA_CHECK(cudaGetDevice(&device));
+ CHECK(device == solver_->param().device_id());
+#endif
+
+ // Sum children gradients as they appear in the queue
+ for (int i = 0; i < children_.size(); ++i) {
+ P2PSync<Dtype> *child = queue_.pop();
+ Dtype* src = child->parent_grads_;
+ Dtype* dst = diff_;
+
+#ifdef DEBUG
+ bool ok = false;
+ for (int j = 0; j < children_.size(); ++j) {
+ if (child == children_[j]) {
+ ok = true;
+ }
+ }
+ CHECK(ok);
+ cudaPointerAttributes attributes;
+ CUDA_CHECK(cudaPointerGetAttributes(&attributes, src));
+ CHECK(attributes.device == device);
+ CUDA_CHECK(cudaPointerGetAttributes(&attributes, dst));
+ CHECK(attributes.device == device);
+#endif
+
+ caffe_gpu_add(size_, src, dst, dst);
+ }
+
+ // Send gradients to parent
+ if (parent_) {
+ Dtype* src = diff_;
+ Dtype* dst = parent_grads_;
+
+#ifdef DEBUG
+ cudaPointerAttributes attributes;
+ CUDA_CHECK(cudaPointerGetAttributes(&attributes, src));
+ CHECK(attributes.device == device);
+ CUDA_CHECK(cudaPointerGetAttributes(&attributes, dst));
+ CHECK(attributes.device == parent_->solver_->param().device_id());
+#endif
+
+ CUDA_CHECK(cudaMemcpyAsync(dst, src, size_ * sizeof(Dtype), //
+ cudaMemcpyDeviceToDevice, cudaStreamDefault));
+ CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault));
+ parent_->queue_.push(this);
+ } else {
+ // Loss functions divide gradients by the batch size, so to compensate
+ // for split batch, the root solver divides by number of solvers.
+ caffe_gpu_scal(size_, Dtype(1.0 / Caffe::solver_count()), diff_);
+ }
+#endif
+}
+
+template<typename Dtype>
+void P2PSync<Dtype>::run(const vector<int>& gpus) {
+ // Pair devices for map-reduce synchronization
+ vector<DevicePair> pairs;
+ DevicePair::compute(gpus, &pairs);
+ ostringstream s;
+ for (int i = 1; i < pairs.size(); ++i) {
+ s << (i == 1 ? "" : ", ") << pairs[i].parent() << ":" << pairs[i].device();
+ }
+ LOG(INFO)<< "GPUs pairs " << s.str();
+
+ SolverParameter param(solver_->param());
+ vector<shared_ptr<P2PSync<Dtype> > > syncs(gpus.size());
+
+ // Build the GPU tree by finding the parent for each solver
+ for (int attempts = 0; attempts < pairs.size(); ++attempts) {
+ for (int i = 1; i < pairs.size(); ++i) {
+ if (!syncs[i].get()) {
+ P2PSync<Dtype>* parent = NULL;
+ for (int j = 0; j < syncs.size(); ++j) {
+ P2PSync<Dtype>* sync = j == 0 ? this : syncs[j].get();
+ if (sync) {
+ const SolverParameter& p = sync->solver()->param();
+ if (p.device_id() == pairs[i].parent()) {
+ parent = sync;
+ }
+ }
+ }
+ if (parent) {
+ param.set_device_id(pairs[i].device());
+ syncs[i].reset(new P2PSync<Dtype>(solver_, parent, param));
+ parent->children_.push_back((P2PSync<Dtype>*) syncs[i].get());
+ }
+ }
+ }
+ }
+
+ LOG(INFO)<< "Starting Optimization";
+
+ for (int i = 1; i < syncs.size(); ++i) {
+ syncs[i]->StartInternalThread();
+ }
+
+ // Run root solver on current thread
+ solver_->Solve();
+
+ for (int i = 1; i < syncs.size(); ++i) {
+ syncs[i]->StopInternalThread();
+ }
+}
+
+INSTANTIATE_CLASS(Params);
+INSTANTIATE_CLASS(GPUParams);
+INSTANTIATE_CLASS(P2PSync);
+
+} // namespace caffe
optional BlobShape shape = 7;
repeated float data = 5 [packed = true];
repeated float diff = 6 [packed = true];
+ repeated double double_data = 8 [packed = true];
+ repeated double double_diff = 9 [packed = true];
// 4D dimensions -- deprecated. Use "shape" instead.
optional int32 num = 1 [default = 0];
// The expected number of non-zero output weights for a given input in
// Gaussian filler -- the default -1 means don't perform sparsification.
optional int32 sparse = 7 [default = -1];
+ // Normalize the filler variance by fan_in, fan_out, or their average.
+ // Applies to 'xavier' and 'msra' fillers.
+ enum VarianceNorm {
+ FAN_IN = 0;
+ FAN_OUT = 1;
+ AVERAGE = 2;
+ }
+ optional VarianceNorm variance_norm = 8 [default = FAN_IN];
}
message NetParameter {
// NOTE
// Update the next available ID when you add a new SolverParameter field.
//
-// SolverParameter next available ID: 36 (last added: clip_gradients)
+// SolverParameter next available ID: 41 (last added: type)
message SolverParameter {
//////////////////////////////////////////////////////////////////////////////
// Specifying the train and test networks
// Display the loss averaged over the last average_loss iterations
optional int32 average_loss = 33 [default = 1];
optional int32 max_iter = 7; // the maximum number of iterations
- optional string lr_policy = 8; // The learning rate decay policy.
+ // accumulate gradients over `iter_size` x `batch_size` instances
+ optional int32 iter_size = 36 [default = 1];
+
+ // The learning rate decay policy. The currently implemented learning rate
+ // policies are as follows:
+ // - fixed: always return base_lr.
+ // - step: return base_lr * gamma ^ (floor(iter / step))
+ // - exp: return base_lr * gamma ^ iter
+ // - inv: return base_lr * (1 + gamma * iter) ^ (- power)
+ // - multistep: similar to step but it allows non uniform steps defined by
+ // stepvalue
+ // - poly: the effective learning rate follows a polynomial decay, to be
+ // zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)
+ // - sigmoid: the effective learning rate follows a sigmod decay
+ // return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))
+ //
+ // where base_lr, max_iter, gamma, step, stepvalue and power are defined
+ // in the solver parameter protocol buffer, and iter is the current iteration.
+ optional string lr_policy = 8;
optional float gamma = 9; // The parameter to compute the learning rate.
optional float power = 10; // The parameter to compute the learning rate.
optional float momentum = 11; // The momentum value.
// whether to snapshot diff in the results or not. Snapshotting diff will help
// debugging but the final protocol buffer size will be much larger.
optional bool snapshot_diff = 16 [default = false];
+ enum SnapshotFormat {
+ HDF5 = 0;
+ BINARYPROTO = 1;
+ }
+ optional SnapshotFormat snapshot_format = 37 [default = BINARYPROTO];
// the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default.
enum SolverMode {
CPU = 0;
// (and by default) initialize using a seed derived from the system clock.
optional int64 random_seed = 20 [default = -1];
- // Solver type
- enum SolverType {
- SGD = 0;
- NESTEROV = 1;
- ADAGRAD = 2;
- }
- optional SolverType solver_type = 30 [default = SGD];
- // numerical stability for AdaGrad
+ // type of the solver
+ optional string type = 40 [default = "SGD"];
+
+ // numerical stability for RMSProp, AdaGrad and AdaDelta and Adam
optional float delta = 31 [default = 1e-8];
+ // parameters for the Adam solver
+ optional float momentum2 = 39 [default = 0.999];
+
+ // RMSProp decay value
+ // MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t)
+ optional float rms_decay = 38;
// If true, print information about the state of the net that may help with
// debugging learning problems.
// If false, don't save a snapshot after training finishes.
optional bool snapshot_after_train = 28 [default = true];
+
+ // DEPRECATED: old solver enum types, use string instead
+ enum SolverType {
+ SGD = 0;
+ NESTEROV = 1;
+ ADAGRAD = 2;
+ RMSPROP = 3;
+ ADADELTA = 4;
+ ADAM = 5;
+ }
+ // DEPRECATED: use type instead of solver_type
+ optional SolverType solver_type = 30 [default = SGD];
}
// A message that stores the solver snapshots
// NOTE
// Update the next available ID when you add a new LayerParameter field.
//
-// LayerParameter next available layer-specific ID: 132 (last added: prelu_param)
+// LayerParameter next available layer-specific ID: 141 (last added: elu_param)
message LayerParameter {
optional string name = 1; // the layer name
optional string type = 2; // the layer type
// The blobs containing the numeric parameters of the layer.
repeated BlobProto blobs = 7;
+ // Specifies on which bottoms the backpropagation should be skipped.
+ // The size must be either 0 or equal to the number of bottoms.
+ repeated bool propagate_down = 11;
+
// Rules controlling whether and when a layer is included in the network,
// based on the current NetState. You may specify a non-zero number of rules
// to include OR exclude, but not both. If no include or exclude rules are
// The default for the engine is set by the ENGINE switch at compile-time.
optional AccuracyParameter accuracy_param = 102;
optional ArgMaxParameter argmax_param = 103;
+ optional BatchNormParameter batch_norm_param = 139;
optional ConcatParameter concat_param = 104;
optional ContrastiveLossParameter contrastive_loss_param = 105;
optional ConvolutionParameter convolution_param = 106;
optional DropoutParameter dropout_param = 108;
optional DummyDataParameter dummy_data_param = 109;
optional EltwiseParameter eltwise_param = 110;
+ optional ELUParameter elu_param = 140;
+ optional EmbedParameter embed_param = 137;
optional ExpParameter exp_param = 111;
+ optional FlattenParameter flatten_param = 135;
optional HDF5DataParameter hdf5_data_param = 112;
optional HDF5OutputParameter hdf5_output_param = 113;
optional HingeLossParameter hinge_loss_param = 114;
optional ImageDataParameter image_data_param = 115;
optional InfogainLossParameter infogain_loss_param = 116;
optional InnerProductParameter inner_product_param = 117;
+ optional LogParameter log_param = 134;
optional LRNParameter lrn_param = 118;
optional MemoryDataParameter memory_data_param = 119;
optional MVNParameter mvn_param = 120;
optional PowerParameter power_param = 122;
optional PReLUParameter prelu_param = 131;
optional PythonParameter python_param = 130;
+ optional ReductionParameter reduction_param = 136;
optional ReLUParameter relu_param = 123;
+ optional ReshapeParameter reshape_param = 133;
optional SigmoidParameter sigmoid_param = 124;
optional SoftmaxParameter softmax_param = 125;
+ optional SPPParameter spp_param = 132;
optional SliceParameter slice_param = 126;
optional TanHParameter tanh_param = 127;
optional ThresholdParameter threshold_param = 128;
+ optional TileParameter tile_param = 138;
optional WindowDataParameter window_data_param = 129;
}
// or can be repeated the same number of times as channels
// (would subtract them from the corresponding channel)
repeated float mean_value = 5;
+ // Force the decoded image to have 3 color channels.
+ optional bool force_color = 6 [default = false];
+ // Force the decoded image to have 1 color channels.
+ optional bool force_gray = 7 [default = false];
}
// Message that stores parameters shared by loss layers
message LossParameter {
// If specified, ignore instances with the given label.
optional int32 ignore_label = 1;
- // If true, normalize each batch across all instances (including spatial
- // dimesions, but not ignored instances); else, divide by batch size only.
- optional bool normalize = 2 [default = true];
+ // How to normalize the loss for loss layers that aggregate across batches,
+ // spatial dimensions, or other dimensions. Currently only implemented in
+ // SoftmaxWithLoss layer.
+ enum NormalizationMode {
+ // Divide by the number of examples in the batch times spatial dimensions.
+ // Outputs that receive the ignore label will NOT be ignored in computing
+ // the normalization factor.
+ FULL = 0;
+ // Divide by the total number of output locations that do not take the
+ // ignore_label. If ignore_label is not set, this behaves like FULL.
+ VALID = 1;
+ // Divide by the batch size.
+ BATCH_SIZE = 2;
+ // Do not normalize the loss.
+ NONE = 3;
+ }
+ optional NormalizationMode normalization = 3 [default = VALID];
+ // Deprecated. Ignored if normalization is specified. If normalization
+ // is not specified, then setting this to false will be equivalent to
+ // normalization = BATCH_SIZE to be consistent with previous behavior.
+ optional bool normalize = 2;
}
-// Message that stores parameters used by AccuracyLayer
+// Messages that store parameters used by individual layer types follow, in
+// alphabetical order.
+
message AccuracyParameter {
// When computing accuracy, count as correct by comparing the true label to
// the top k scoring classes. By default, only compare to the top scoring
optional int32 ignore_label = 3;
}
-// Message that stores parameters used by ArgMaxLayer
message ArgMaxParameter {
// If true produce pairs (argmax, maxval)
optional bool out_max_val = 1 [default = false];
optional uint32 top_k = 2 [default = 1];
+ // The axis along which to maximise -- may be negative to index from the
+ // end (e.g., -1 for the last axis).
+ // By default ArgMaxLayer maximizes over the flattened trailing dimensions
+ // for each index of the first / num dimension.
+ optional int32 axis = 3;
}
-// Message that stores parameters used by ConcatLayer
message ConcatParameter {
// The axis along which to concatenate -- may be negative to index from the
// end (e.g., -1 for the last axis). Other axes must have the
optional uint32 concat_dim = 1 [default = 1];
}
-// Message that stores parameters used by ContrastiveLossLayer
+message BatchNormParameter {
+ // If false, accumulate global mean/variance values via a moving average. If
+ // true, use those accumulated values instead of computing mean/variance
+ // across the batch.
+ optional bool use_global_stats = 1;
+ // How much does the moving average decay each iteration?
+ optional float moving_average_fraction = 2 [default = .999];
+ // Small value to add to the variance estimate so that we don't divide by
+ // zero.
+ optional float eps = 3 [default = 1e-5];
+}
+
message ContrastiveLossParameter {
- //margin for dissimilar pair
+ // margin for dissimilar pair
optional float margin = 1 [default = 1.0];
+ // The first implementation of this cost did not exactly match the cost of
+ // Hadsell et al 2006 -- using (margin - d^2) instead of (margin - d)^2.
+ // legacy_version = false (the default) uses (margin - d)^2 as proposed in the
+ // Hadsell paper. New models should probably use this version.
+ // legacy_version = true uses (margin - d^2). This is kept to support /
+ // reproduce existing models and results
+ optional bool legacy_version = 2 [default = false];
}
-// Message that stores parameters used by ConvolutionLayer
message ConvolutionParameter {
optional uint32 num_output = 1; // The number of outputs for the layer
optional bool bias_term = 2 [default = true]; // whether to have bias terms
+
// Pad, kernel size, and stride are all given as a single value for equal
- // dimensions in height and width or as Y, X pairs.
- optional uint32 pad = 3 [default = 0]; // The padding size (equal in Y, X)
- optional uint32 pad_h = 9 [default = 0]; // The padding height
- optional uint32 pad_w = 10 [default = 0]; // The padding width
- optional uint32 kernel_size = 4; // The kernel size (square)
- optional uint32 kernel_h = 11; // The kernel height
- optional uint32 kernel_w = 12; // The kernel width
+ // dimensions in all spatial dimensions, or once per spatial dimension.
+ repeated uint32 pad = 3; // The padding size; defaults to 0
+ repeated uint32 kernel_size = 4; // The kernel size
+ repeated uint32 stride = 6; // The stride; defaults to 1
+ // Factor used to dilate the kernel, (implicitly) zero-filling the resulting
+ // holes. (Kernel dilation is sometimes referred to by its use in the
+ // algorithme à trous from Holschneider et al. 1987.)
+ repeated uint32 dilation = 18; // The dilation; defaults to 1
+
+ // For 2D convolution only, the *_h and *_w versions may also be used to
+ // specify both spatial dimensions.
+ optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)
+ optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)
+ optional uint32 kernel_h = 11; // The kernel height (2D only)
+ optional uint32 kernel_w = 12; // The kernel width (2D only)
+ optional uint32 stride_h = 13; // The stride height (2D only)
+ optional uint32 stride_w = 14; // The stride width (2D only)
+
optional uint32 group = 5 [default = 1]; // The group size for group conv
- optional uint32 stride = 6 [default = 1]; // The stride (equal in Y, X)
- optional uint32 stride_h = 13; // The stride height
- optional uint32 stride_w = 14; // The stride width
+
optional FillerParameter weight_filler = 7; // The filler for the weight
optional FillerParameter bias_filler = 8; // The filler for the bias
enum Engine {
CUDNN = 2;
}
optional Engine engine = 15 [default = DEFAULT];
+
+ // The axis to interpret as "channels" when performing convolution.
+ // Preceding dimensions are treated as independent inputs;
+ // succeeding dimensions are treated as "spatial".
+ // With (N, C, H, W) inputs, and axis == 1 (the default), we perform
+ // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for
+ // groups g>1) filters across the spatial axes (H, W) of the input.
+ // With (N, C, D, H, W) inputs, and axis == 1, we perform
+ // N independent 3D convolutions, sliding (C/g)-channels
+ // filters across the spatial axes (D, H, W) of the input.
+ optional int32 axis = 16 [default = 1];
+
+ // Whether to force use of the general ND convolution, even if a specific
+ // implementation for blobs of the appropriate number of spatial dimensions
+ // is available. (Currently, there is only a 2D-specific convolution
+ // implementation; for input blobs with num_axes != 2, this option is
+ // ignored and the ND implementation will be used.)
+ optional bool force_nd_im2col = 17 [default = false];
}
-// Message that stores parameters used by DataLayer
message DataParameter {
enum DB {
LEVELDB = 0;
// to avoid all asynchronous sgd clients to start at the same point. The skip
// point would be set as rand_skip * rand(0,1). Note that rand_skip should not
// be larger than the number of keys in the database.
+ // DEPRECATED. Each solver accesses a different subset of the database.
optional uint32 rand_skip = 7 [default = 0];
optional DB backend = 8 [default = LEVELDB];
// DEPRECATED. See TransformationParameter. For data pre-processing, we can do
optional bool mirror = 6 [default = false];
// Force the encoded image to have 3 color channels
optional bool force_encoded_color = 9 [default = false];
+ // Prefetch queue (Number of batches to prefetch to host memory, increase if
+ // data access bandwidth varies).
+ optional uint32 prefetch = 10 [default = 4];
}
-// Message that stores parameters used by DropoutLayer
message DropoutParameter {
optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio
}
-// Message that stores parameters used by DummyDataLayer.
// DummyDataLayer fills any number of arbitrarily shaped blobs with random
// (or constant) data generated by "Fillers" (see "message FillerParameter").
message DummyDataParameter {
repeated uint32 width = 5;
}
-// Message that stores parameters used by EltwiseLayer
message EltwiseParameter {
enum EltwiseOp {
PROD = 0;
optional bool stable_prod_grad = 3 [default = true];
}
+// Message that stores parameters used by ELULayer
+message ELUParameter {
+ // Described in:
+ // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate
+ // Deep Network Learning by Exponential Linear Units (ELUs). arXiv
+ optional float alpha = 1 [default = 1];
+}
+
+// Message that stores parameters used by EmbedLayer
+message EmbedParameter {
+ optional uint32 num_output = 1; // The number of outputs for the layer
+ // The input is given as integers to be interpreted as one-hot
+ // vector indices with dimension num_input. Hence num_input should be
+ // 1 greater than the maximum possible input value.
+ optional uint32 input_dim = 2;
+
+ optional bool bias_term = 3 [default = true]; // Whether to use a bias term
+ optional FillerParameter weight_filler = 4; // The filler for the weight
+ optional FillerParameter bias_filler = 5; // The filler for the bias
+
+}
+
// Message that stores parameters used by ExpLayer
message ExpParameter {
// ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0.
optional float shift = 3 [default = 0.0];
}
+/// Message that stores parameters used by FlattenLayer
+message FlattenParameter {
+ // The first axis to flatten: all preceding axes are retained in the output.
+ // May be negative to index from the end (e.g., -1 for the last axis).
+ optional int32 axis = 1 [default = 1];
+
+ // The last axis to flatten: all following axes are retained in the output.
+ // May be negative to index from the end (e.g., the default -1 for the last
+ // axis).
+ optional int32 end_axis = 2 [default = -1];
+}
+
// Message that stores parameters used by HDF5DataLayer
message HDF5DataParameter {
// Specify the data source.
optional bool shuffle = 3 [default = false];
}
-// Message that stores parameters used by HDF5OutputLayer
message HDF5OutputParameter {
optional string file_name = 1;
}
optional Norm norm = 1 [default = L1];
}
-// Message that stores parameters used by ImageDataLayer
message ImageDataParameter {
// Specify the data source.
optional string source = 1;
// Specify the batch size.
- optional uint32 batch_size = 4;
+ optional uint32 batch_size = 4 [default = 1];
// The rand_skip variable is for the data layer to skip a few data points
// to avoid all asynchronous sgd clients to start at the same point. The skip
// point would be set as rand_skip * rand(0,1). Note that rand_skip should not
optional string root_folder = 12 [default = ""];
}
-// Message that stores parameters InfogainLossLayer
message InfogainLossParameter {
// Specify the infogain matrix source.
optional string source = 1;
}
-// Message that stores parameters used by InnerProductLayer
message InnerProductParameter {
optional uint32 num_output = 1; // The number of outputs for the layer
optional bool bias_term = 2 [default = true]; // whether to have bias terms
optional int32 axis = 5 [default = 1];
}
+// Message that stores parameters used by LogLayer
+message LogParameter {
+ // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0.
+ // Or if base is set to the default (-1), base is set to e,
+ // so y = ln(shift + scale * x) = log_e(shift + scale * x)
+ optional float base = 1 [default = -1.0];
+ optional float scale = 2 [default = 1.0];
+ optional float shift = 3 [default = 0.0];
+}
+
// Message that stores parameters used by LRNLayer
message LRNParameter {
optional uint32 local_size = 1 [default = 5];
}
optional NormRegion norm_region = 4 [default = ACROSS_CHANNELS];
optional float k = 5 [default = 1.];
+ enum Engine {
+ DEFAULT = 0;
+ CAFFE = 1;
+ CUDNN = 2;
+ }
+ optional Engine engine = 6 [default = DEFAULT];
}
-// Message that stores parameters used by MemoryDataLayer
message MemoryDataParameter {
optional uint32 batch_size = 1;
optional uint32 channels = 2;
optional uint32 width = 4;
}
-// Message that stores parameters used by MVNLayer
message MVNParameter {
// This parameter can be set to false to normalize mean only
optional bool normalize_variance = 1 [default = true];
// This parameter can be set to true to perform DNN-like MVN
optional bool across_channels = 2 [default = false];
+
+ // Epsilon for not dividing by zero while normalizing variance
+ optional float eps = 3 [default = 1e-9];
}
-// Message that stores parameters used by PoolingLayer
message PoolingParameter {
enum PoolMethod {
MAX = 0;
optional bool global_pooling = 12 [default = false];
}
-// Message that stores parameters used by PowerLayer
message PowerParameter {
// PowerLayer computes outputs y = (shift + scale * x) ^ power.
optional float power = 1 [default = 1.0];
optional float shift = 3 [default = 0.0];
}
-// Message that stores parameters used by PythonLayer
message PythonParameter {
optional string module = 1;
optional string layer = 2;
+ // This value is set to the attribute `param_str` of the `PythonLayer` object
+ // in Python before calling the `setup()` method. This could be a number,
+ // string, dictionary in Python dict format, JSON, etc. You may parse this
+ // string in `setup` method and use it in `forward` and `backward`.
+ optional string param_str = 3 [default = ''];
+ // Whether this PythonLayer is shared among worker solvers during data parallelism.
+ // If true, each worker solver sequentially run forward from this layer.
+ // This value should be set true if you are using it as a data layer.
+ optional bool share_in_parallel = 4 [default = false];
+}
+
+// Message that stores parameters used by ReductionLayer
+message ReductionParameter {
+ enum ReductionOp {
+ SUM = 1;
+ ASUM = 2;
+ SUMSQ = 3;
+ MEAN = 4;
+ }
+
+ optional ReductionOp operation = 1 [default = SUM]; // reduction operation
+
+ // The first axis to reduce to a scalar -- may be negative to index from the
+ // end (e.g., -1 for the last axis).
+ // (Currently, only reduction along ALL "tail" axes is supported; reduction
+ // of axis M through N, where N < num_axes - 1, is unsupported.)
+ // Suppose we have an n-axis bottom Blob with shape:
+ // (d0, d1, d2, ..., d(m-1), dm, d(m+1), ..., d(n-1)).
+ // If axis == m, the output Blob will have shape
+ // (d0, d1, d2, ..., d(m-1)),
+ // and the ReductionOp operation is performed (d0 * d1 * d2 * ... * d(m-1))
+ // times, each including (dm * d(m+1) * ... * d(n-1)) individual data.
+ // If axis == 0 (the default), the output Blob always has the empty shape
+ // (count 1), performing reduction across the entire input --
+ // often useful for creating new loss functions.
+ optional int32 axis = 2 [default = 0];
+
+ optional float coeff = 3 [default = 1.0]; // coefficient for output
}
// Message that stores parameters used by ReLULayer
optional Engine engine = 2 [default = DEFAULT];
}
-// Message that stores parameters used by SigmoidLayer
+message ReshapeParameter {
+ // Specify the output dimensions. If some of the dimensions are set to 0,
+ // the corresponding dimension from the bottom layer is used (unchanged).
+ // Exactly one dimension may be set to -1, in which case its value is
+ // inferred from the count of the bottom blob and the remaining dimensions.
+ // For example, suppose we want to reshape a 2D blob "input" with shape 2 x 8:
+ //
+ // layer {
+ // type: "Reshape" bottom: "input" top: "output"
+ // reshape_param { ... }
+ // }
+ //
+ // If "input" is 2D with shape 2 x 8, then the following reshape_param
+ // specifications are all equivalent, producing a 3D blob "output" with shape
+ // 2 x 2 x 4:
+ //
+ // reshape_param { shape { dim: 2 dim: 2 dim: 4 } }
+ // reshape_param { shape { dim: 0 dim: 2 dim: 4 } }
+ // reshape_param { shape { dim: 0 dim: 2 dim: -1 } }
+ // reshape_param { shape { dim: -1 dim: 0 dim: 2 } }
+ //
+ optional BlobShape shape = 1;
+
+ // axis and num_axes control the portion of the bottom blob's shape that are
+ // replaced by (included in) the reshape. By default (axis == 0 and
+ // num_axes == -1), the entire bottom blob shape is included in the reshape,
+ // and hence the shape field must specify the entire output shape.
+ //
+ // axis may be non-zero to retain some portion of the beginning of the input
+ // shape (and may be negative to index from the end; e.g., -1 to begin the
+ // reshape after the last axis, including nothing in the reshape,
+ // -2 to include only the last axis, etc.).
+ //
+ // For example, suppose "input" is a 2D blob with shape 2 x 8.
+ // Then the following ReshapeLayer specifications are all equivalent,
+ // producing a blob "output" with shape 2 x 2 x 4:
+ //
+ // reshape_param { shape { dim: 2 dim: 2 dim: 4 } }
+ // reshape_param { shape { dim: 2 dim: 4 } axis: 1 }
+ // reshape_param { shape { dim: 2 dim: 4 } axis: -3 }
+ //
+ // num_axes specifies the extent of the reshape.
+ // If num_axes >= 0 (and axis >= 0), the reshape will be performed only on
+ // input axes in the range [axis, axis+num_axes].
+ // num_axes may also be -1, the default, to include all remaining axes
+ // (starting from axis).
+ //
+ // For example, suppose "input" is a 2D blob with shape 2 x 8.
+ // Then the following ReshapeLayer specifications are equivalent,
+ // producing a blob "output" with shape 1 x 2 x 8.
+ //
+ // reshape_param { shape { dim: 1 dim: 2 dim: 8 } }
+ // reshape_param { shape { dim: 1 dim: 2 } num_axes: 1 }
+ // reshape_param { shape { dim: 1 } num_axes: 0 }
+ //
+ // On the other hand, these would produce output blob shape 2 x 1 x 8:
+ //
+ // reshape_param { shape { dim: 2 dim: 1 dim: 8 } }
+ // reshape_param { shape { dim: 1 } axis: 1 num_axes: 0 }
+ //
+ optional int32 axis = 2 [default = 0];
+ optional int32 num_axes = 3 [default = -1];
+}
+
message SigmoidParameter {
enum Engine {
DEFAULT = 0;
optional Engine engine = 1 [default = DEFAULT];
}
-// Message that stores parameters used by SliceLayer
message SliceParameter {
// The axis along which to slice -- may be negative to index from the end
// (e.g., -1 for the last axis).
optional int32 axis = 2 [default = 1];
}
-// Message that stores parameters used by TanHLayer
message TanHParameter {
enum Engine {
DEFAULT = 0;
optional Engine engine = 1 [default = DEFAULT];
}
+// Message that stores parameters used by TileLayer
+message TileParameter {
+ // The index of the axis to tile.
+ optional int32 axis = 1 [default = 1];
+
+ // The number of copies (tiles) of the blob to output.
+ optional int32 tiles = 2;
+}
+
// Message that stores parameters used by ThresholdLayer
message ThresholdParameter {
optional float threshold = 1 [default = 0]; // Strictly positive values
}
-// Message that stores parameters used by WindowDataLayer
message WindowDataParameter {
// Specify the data source.
optional string source = 1;
optional string root_folder = 13 [default = ""];
}
+message SPPParameter {
+ enum PoolMethod {
+ MAX = 0;
+ AVE = 1;
+ STOCHASTIC = 2;
+ }
+ optional uint32 pyramid_height = 1;
+ optional PoolMethod pool = 2 [default = MAX]; // The pooling method
+ enum Engine {
+ DEFAULT = 0;
+ CAFFE = 1;
+ CUDNN = 2;
+ }
+ optional Engine engine = 6 [default = DEFAULT];
+}
+
// DEPRECATED: use LayerParameter.
message V1LayerParameter {
repeated string bottom = 2;
optional HDF5OutputParameter hdf5_output_param = 1001;
}
-// Message that stores parameters used by PReLULayer
message PReLUParameter {
// Parametric ReLU described in K. He et al, Delving Deep into Rectifiers:
// Surpassing Human-Level Performance on ImageNet Classification, 2015.
#include <cstdio>
-#include <algorithm>
#include <string>
#include <vector>
-#include "caffe/net.hpp"
-#include "caffe/proto/caffe.pb.h"
#include "caffe/solver.hpp"
+#include "caffe/util/format.hpp"
+#include "caffe/util/hdf5.hpp"
#include "caffe/util/io.hpp"
-#include "caffe/util/math_functions.hpp"
#include "caffe/util/upgrade_proto.hpp"
namespace caffe {
+template<typename Dtype>
+void Solver<Dtype>::SetActionFunction(ActionCallback func) {
+ action_request_function_ = func;
+}
+
+template<typename Dtype>
+SolverAction::Enum Solver<Dtype>::GetRequestedAction() {
+ if (action_request_function_) {
+ // If the external request function has been set, call it.
+ return action_request_function_();
+ }
+ return SolverAction::NONE;
+}
+
template <typename Dtype>
-Solver<Dtype>::Solver(const SolverParameter& param)
- : net_() {
+Solver<Dtype>::Solver(const SolverParameter& param, const Solver* root_solver)
+ : net_(), callbacks_(), root_solver_(root_solver),
+ requested_early_exit_(false) {
Init(param);
}
template <typename Dtype>
-Solver<Dtype>::Solver(const string& param_file)
- : net_() {
+Solver<Dtype>::Solver(const string& param_file, const Solver* root_solver)
+ : net_(), callbacks_(), root_solver_(root_solver),
+ requested_early_exit_(false) {
SolverParameter param;
- ReadProtoFromTextFileOrDie(param_file, ¶m);
+ ReadSolverParamsFromTextFileOrDie(param_file, ¶m);
Init(param);
}
template <typename Dtype>
void Solver<Dtype>::Init(const SolverParameter& param) {
- LOG(INFO) << "Initializing solver from parameters: " << std::endl
- << param.DebugString();
+ CHECK(Caffe::root_solver() || root_solver_)
+ << "root_solver_ needs to be set for all non-root solvers";
+ LOG_IF(INFO, Caffe::root_solver()) << "Initializing solver from parameters: "
+ << std::endl << param.DebugString();
param_ = param;
CHECK_GE(param_.average_loss(), 1) << "average_loss should be non-negative.";
- if (param_.random_seed() >= 0) {
+ CheckSnapshotWritePermissions();
+ if (Caffe::root_solver() && param_.random_seed() >= 0) {
Caffe::set_random_seed(param_.random_seed());
}
// Scaffolding code
InitTrainNet();
- InitTestNets();
- LOG(INFO) << "Solver scaffolding done.";
+ if (Caffe::root_solver()) {
+ InitTestNets();
+ LOG(INFO) << "Solver scaffolding done.";
+ }
iter_ = 0;
current_step_ = 0;
}
<< "one of these fields specifying a train_net: " << field_names;
NetParameter net_param;
if (param_.has_train_net_param()) {
- LOG(INFO) << "Creating training net specified in train_net_param.";
+ LOG_IF(INFO, Caffe::root_solver())
+ << "Creating training net specified in train_net_param.";
net_param.CopyFrom(param_.train_net_param());
} else if (param_.has_train_net()) {
- LOG(INFO) << "Creating training net from train_net file: "
- << param_.train_net();
+ LOG_IF(INFO, Caffe::root_solver())
+ << "Creating training net from train_net file: " << param_.train_net();
ReadNetParamsFromTextFileOrDie(param_.train_net(), &net_param);
}
if (param_.has_net_param()) {
- LOG(INFO) << "Creating training net specified in net_param.";
+ LOG_IF(INFO, Caffe::root_solver())
+ << "Creating training net specified in net_param.";
net_param.CopyFrom(param_.net_param());
}
if (param_.has_net()) {
- LOG(INFO) << "Creating training net from net file: " << param_.net();
+ LOG_IF(INFO, Caffe::root_solver())
+ << "Creating training net from net file: " << param_.net();
ReadNetParamsFromTextFileOrDie(param_.net(), &net_param);
}
// Set the correct NetState. We start with the solver defaults (lowest
net_state.MergeFrom(net_param.state());
net_state.MergeFrom(param_.train_state());
net_param.mutable_state()->CopyFrom(net_state);
- net_.reset(new Net<Dtype>(net_param));
+ if (Caffe::root_solver()) {
+ net_.reset(new Net<Dtype>(net_param));
+ } else {
+ net_.reset(new Net<Dtype>(net_param, root_solver_->net_.get()));
+ }
}
template <typename Dtype>
void Solver<Dtype>::InitTestNets() {
+ CHECK(Caffe::root_solver());
const bool has_net_param = param_.has_net_param();
const bool has_net_file = param_.has_net();
const int num_generic_nets = has_net_param + has_net_file;
net_params[i].mutable_state()->CopyFrom(net_state);
LOG(INFO)
<< "Creating test net (#" << i << ") specified by " << sources[i];
- test_nets_[i].reset(new Net<Dtype>(net_params[i]));
+ if (Caffe::root_solver()) {
+ test_nets_[i].reset(new Net<Dtype>(net_params[i]));
+ } else {
+ test_nets_[i].reset(new Net<Dtype>(net_params[i],
+ root_solver_->test_nets_[i].get()));
+ }
test_nets_[i]->set_debug_info(param_.debug_info());
}
}
const int start_iter = iter_;
const int stop_iter = iter_ + iters;
int average_loss = this->param_.average_loss();
- vector<Dtype> losses;
- Dtype smoothed_loss = 0;
+ losses_.clear();
+ smoothed_loss_ = 0;
- for (; iter_ < stop_iter; ++iter_) {
+ while (iter_ < stop_iter) {
+ // zero-init the params
+ net_->ClearParamDiffs();
if (param_.test_interval() && iter_ % param_.test_interval() == 0
- && (iter_ > 0 || param_.test_initialization())) {
+ && (iter_ > 0 || param_.test_initialization())
+ && Caffe::root_solver()) {
TestAll();
+ if (requested_early_exit_) {
+ // Break out of the while loop because stop was requested while testing.
+ break;
+ }
}
+ for (int i = 0; i < callbacks_.size(); ++i) {
+ callbacks_[i]->on_start();
+ }
const bool display = param_.display() && iter_ % param_.display() == 0;
net_->set_debug_info(display && param_.debug_info());
- Dtype loss = net_->ForwardBackward(bottom_vec);
- if (losses.size() < average_loss) {
- losses.push_back(loss);
- int size = losses.size();
- smoothed_loss = (smoothed_loss * (size - 1) + loss) / size;
- } else {
- int idx = (iter_ - start_iter) % average_loss;
- smoothed_loss += (loss - losses[idx]) / average_loss;
- losses[idx] = loss;
+ // accumulate the loss and gradient
+ Dtype loss = 0;
+ for (int i = 0; i < param_.iter_size(); ++i) {
+ loss += net_->ForwardBackward(bottom_vec);
}
+ loss /= param_.iter_size();
+ // average the loss across iterations for smoothed reporting
+ UpdateSmoothedLoss(loss, start_iter, average_loss);
if (display) {
- LOG(INFO) << "Iteration " << iter_ << ", loss = " << smoothed_loss;
+ LOG_IF(INFO, Caffe::root_solver()) << "Iteration " << iter_
+ << ", loss = " << smoothed_loss_;
const vector<Blob<Dtype>*>& result = net_->output_blobs();
int score_index = 0;
for (int j = 0; j < result.size(); ++j) {
loss_msg_stream << " (* " << loss_weight
<< " = " << loss_weight * result_vec[k] << " loss)";
}
- LOG(INFO) << " Train net output #"
+ LOG_IF(INFO, Caffe::root_solver()) << " Train net output #"
<< score_index++ << ": " << output_name << " = "
<< result_vec[k] << loss_msg_stream.str();
}
}
}
- ComputeUpdateValue();
- net_->Update();
+ for (int i = 0; i < callbacks_.size(); ++i) {
+ callbacks_[i]->on_gradients_ready();
+ }
+ ApplyUpdate();
+
+ // Increment the internal iter_ counter -- its value should always indicate
+ // the number of times the weights have been updated.
+ ++iter_;
+
+ SolverAction::Enum request = GetRequestedAction();
// Save a snapshot if needed.
- if (param_.snapshot() && (iter_ + 1) % param_.snapshot() == 0) {
+ if ((param_.snapshot()
+ && iter_ % param_.snapshot() == 0
+ && Caffe::root_solver()) ||
+ (request == SolverAction::SNAPSHOT)) {
Snapshot();
}
+ if (SolverAction::STOP == request) {
+ requested_early_exit_ = true;
+ // Break out of training loop.
+ break;
+ }
}
}
template <typename Dtype>
void Solver<Dtype>::Solve(const char* resume_file) {
+ CHECK(Caffe::root_solver());
LOG(INFO) << "Solving " << net_->name();
LOG(INFO) << "Learning Rate Policy: " << param_.lr_policy();
+ // Initialize to false every time we start solving.
+ requested_early_exit_ = false;
+
if (resume_file) {
LOG(INFO) << "Restoring previous solver status from " << resume_file;
Restore(resume_file);
// For a network that is trained by the solver, no bottom or top vecs
// should be given, and we will just provide dummy vecs.
+ int start_iter = iter_;
Step(param_.max_iter() - iter_);
// If we haven't already, save a snapshot after optimization, unless
// overridden by setting snapshot_after_train := false
&& (!param_.snapshot() || iter_ % param_.snapshot() != 0)) {
Snapshot();
}
+ if (requested_early_exit_) {
+ LOG(INFO) << "Optimization stopped early.";
+ return;
+ }
// After the optimization is done, run an additional train and test pass to
// display the train and test loss/outputs if appropriate (based on the
// display and test_interval settings, respectively). Unlike in the rest of
// updated the parameters "max_iter" times -- this final pass is only done to
// display the loss, which is computed in the forward pass.
if (param_.display() && iter_ % param_.display() == 0) {
+ int average_loss = this->param_.average_loss();
Dtype loss;
net_->ForwardPrefilled(&loss);
- LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss;
+
+ UpdateSmoothedLoss(loss, start_iter, average_loss);
+
+ LOG(INFO) << "Iteration " << iter_ << ", loss = " << smoothed_loss_;
}
if (param_.test_interval() && iter_ % param_.test_interval() == 0) {
TestAll();
LOG(INFO) << "Optimization Done.";
}
-
template <typename Dtype>
void Solver<Dtype>::TestAll() {
- for (int test_net_id = 0; test_net_id < test_nets_.size(); ++test_net_id) {
+ for (int test_net_id = 0;
+ test_net_id < test_nets_.size() && !requested_early_exit_;
+ ++test_net_id) {
Test(test_net_id);
}
}
template <typename Dtype>
void Solver<Dtype>::Test(const int test_net_id) {
+ CHECK(Caffe::root_solver());
LOG(INFO) << "Iteration " << iter_
<< ", Testing net (#" << test_net_id << ")";
CHECK_NOTNULL(test_nets_[test_net_id].get())->
const shared_ptr<Net<Dtype> >& test_net = test_nets_[test_net_id];
Dtype loss = 0;
for (int i = 0; i < param_.test_iter(test_net_id); ++i) {
+ SolverAction::Enum request = GetRequestedAction();
+ // Check to see if stoppage of testing/training has been requested.
+ while (request != SolverAction::NONE) {
+ if (SolverAction::SNAPSHOT == request) {
+ Snapshot();
+ } else if (SolverAction::STOP == request) {
+ requested_early_exit_ = true;
+ }
+ request = GetRequestedAction();
+ }
+ if (requested_early_exit_) {
+ // break out of test loop.
+ break;
+ }
+
Dtype iter_loss;
const vector<Blob<Dtype>*>& result =
test_net->Forward(bottom_vec, &iter_loss);
}
}
}
+ if (requested_early_exit_) {
+ LOG(INFO) << "Test interrupted.";
+ return;
+ }
if (param_.test_compute_loss()) {
loss /= param_.test_iter(test_net_id);
LOG(INFO) << "Test loss: " << loss;
<< " = " << loss_weight * mean_score << " loss)";
}
LOG(INFO) << " Test net output #" << i << ": " << output_name << " = "
- << mean_score << loss_msg_stream.str();
+ << mean_score << loss_msg_stream.str();
}
}
-
template <typename Dtype>
void Solver<Dtype>::Snapshot() {
- NetParameter net_param;
- // For intermediate results, we will also dump the gradient values.
- net_->ToProto(&net_param, param_.snapshot_diff());
- string filename(param_.snapshot_prefix());
- string model_filename, snapshot_filename;
- const int kBufferSize = 20;
- char iter_str_buffer[kBufferSize];
- // Add one to iter_ to get the number of iterations that have completed.
- snprintf(iter_str_buffer, kBufferSize, "_iter_%d", iter_ + 1);
- filename += iter_str_buffer;
- model_filename = filename + ".caffemodel";
- LOG(INFO) << "Snapshotting to " << model_filename;
- WriteProtoToBinaryFile(net_param, model_filename.c_str());
- SolverState state;
- SnapshotSolverState(&state);
- state.set_iter(iter_ + 1);
- state.set_learned_net(model_filename);
- state.set_current_step(current_step_);
- snapshot_filename = filename + ".solverstate";
- LOG(INFO) << "Snapshotting solver state to " << snapshot_filename;
- WriteProtoToBinaryFile(state, snapshot_filename.c_str());
-}
-
-template <typename Dtype>
-void Solver<Dtype>::Restore(const char* state_file) {
- SolverState state;
- NetParameter net_param;
- ReadProtoFromBinaryFile(state_file, &state);
- if (state.has_learned_net()) {
- ReadNetParamsFromBinaryFileOrDie(state.learned_net().c_str(), &net_param);
- net_->CopyTrainedLayersFrom(net_param);
- }
- iter_ = state.iter();
- current_step_ = state.current_step();
- RestoreSolverState(state);
-}
-
-
-// Return the current learning rate. The currently implemented learning rate
-// policies are as follows:
-// - fixed: always return base_lr.
-// - step: return base_lr * gamma ^ (floor(iter / step))
-// - exp: return base_lr * gamma ^ iter
-// - inv: return base_lr * (1 + gamma * iter) ^ (- power)
-// - multistep: similar to step but it allows non uniform steps defined by
-// stepvalue
-// - poly: the effective learning rate follows a polynomial decay, to be
-// zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)
-// - sigmoid: the effective learning rate follows a sigmod decay
-// return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))
-//
-// where base_lr, max_iter, gamma, step, stepvalue and power are defined
-// in the solver parameter protocol buffer, and iter is the current iteration.
-template <typename Dtype>
-Dtype SGDSolver<Dtype>::GetLearningRate() {
- Dtype rate;
- const string& lr_policy = this->param_.lr_policy();
- if (lr_policy == "fixed") {
- rate = this->param_.base_lr();
- } else if (lr_policy == "step") {
- this->current_step_ = this->iter_ / this->param_.stepsize();
- rate = this->param_.base_lr() *
- pow(this->param_.gamma(), this->current_step_);
- } else if (lr_policy == "exp") {
- rate = this->param_.base_lr() * pow(this->param_.gamma(), this->iter_);
- } else if (lr_policy == "inv") {
- rate = this->param_.base_lr() *
- pow(Dtype(1) + this->param_.gamma() * this->iter_,
- - this->param_.power());
- } else if (lr_policy == "multistep") {
- if (this->current_step_ < this->param_.stepvalue_size() &&
- this->iter_ >= this->param_.stepvalue(this->current_step_)) {
- this->current_step_++;
- LOG(INFO) << "MultiStep Status: Iteration " <<
- this->iter_ << ", step = " << this->current_step_;
- }
- rate = this->param_.base_lr() *
- pow(this->param_.gamma(), this->current_step_);
- } else if (lr_policy == "poly") {
- rate = this->param_.base_lr() * pow(Dtype(1.) -
- (Dtype(this->iter_) / Dtype(this->param_.max_iter())),
- this->param_.power());
- } else if (lr_policy == "sigmoid") {
- rate = this->param_.base_lr() * (Dtype(1.) /
- (Dtype(1.) + exp(-this->param_.gamma() * (Dtype(this->iter_) -
- Dtype(this->param_.stepsize())))));
- } else {
- LOG(FATAL) << "Unknown learning rate policy: " << lr_policy;
+ CHECK(Caffe::root_solver());
+ string model_filename;
+ switch (param_.snapshot_format()) {
+ case caffe::SolverParameter_SnapshotFormat_BINARYPROTO:
+ model_filename = SnapshotToBinaryProto();
+ break;
+ case caffe::SolverParameter_SnapshotFormat_HDF5:
+ model_filename = SnapshotToHDF5();
+ break;
+ default:
+ LOG(FATAL) << "Unsupported snapshot format.";
}
- return rate;
-}
-template <typename Dtype>
-void SGDSolver<Dtype>::PreSolve() {
- // Initialize the history
- const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
- history_.clear();
- update_.clear();
- temp_.clear();
- for (int i = 0; i < net_params.size(); ++i) {
- const vector<int>& shape = net_params[i]->shape();
- history_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
- update_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
- temp_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
- }
+ SnapshotSolverState(model_filename);
}
template <typename Dtype>
-void SGDSolver<Dtype>::ClipGradients() {
- const Dtype clip_gradients = this->param_.clip_gradients();
- if (clip_gradients < 0) { return; }
- const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
- Dtype sumsq_diff = 0;
- for (int i = 0; i < net_params.size(); ++i) {
- if (this->net_->param_owners()[i] < 0) {
- sumsq_diff += net_params[i]->sumsq_diff();
- }
- }
- const Dtype l2norm_diff = std::sqrt(sumsq_diff);
- if (l2norm_diff > clip_gradients) {
- Dtype scale_factor = clip_gradients / l2norm_diff;
- LOG(INFO) << "Gradient clipping: scaling down gradients (L2 norm "
- << l2norm_diff << " > " << clip_gradients << ") "
- << "by scale factor " << scale_factor;
- for (int i = 0; i < net_params.size(); ++i) {
- if (this->net_->param_owners()[i] < 0) {
- net_params[i]->scale_diff(scale_factor);
- }
+void Solver<Dtype>::CheckSnapshotWritePermissions() {
+ if (Caffe::root_solver() && param_.snapshot()) {
+ CHECK(param_.has_snapshot_prefix())
+ << "In solver params, snapshot is specified but snapshot_prefix is not";
+ string probe_filename = SnapshotFilename(".tempfile");
+ std::ofstream probe_ofs(probe_filename.c_str());
+ if (probe_ofs.good()) {
+ probe_ofs.close();
+ std::remove(probe_filename.c_str());
+ } else {
+ LOG(FATAL) << "Cannot write to snapshot prefix '"
+ << param_.snapshot_prefix() << "'. Make sure "
+ << "that the directory exists and is writeable.";
}
}
}
template <typename Dtype>
-void SGDSolver<Dtype>::ComputeUpdateValue() {
- const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
- const vector<float>& net_params_lr = this->net_->params_lr();
- const vector<float>& net_params_weight_decay =
- this->net_->params_weight_decay();
- // get the learning rate
- Dtype rate = GetLearningRate();
- if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
- LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
- }
- ClipGradients();
- Dtype momentum = this->param_.momentum();
- Dtype weight_decay = this->param_.weight_decay();
- string regularization_type = this->param_.regularization_type();
- switch (Caffe::mode()) {
- case Caffe::CPU:
- for (int param_id = 0; param_id < net_params.size(); ++param_id) {
- // Compute the value to history, and then copy them to the blob's diff.
- Dtype local_rate = rate * net_params_lr[param_id];
- Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
- if (local_decay) {
- if (regularization_type == "L2") {
- // add weight decay
- caffe_axpy(net_params[param_id]->count(),
- local_decay,
- net_params[param_id]->cpu_data(),
- net_params[param_id]->mutable_cpu_diff());
- } else if (regularization_type == "L1") {
- caffe_cpu_sign(net_params[param_id]->count(),
- net_params[param_id]->cpu_data(),
- temp_[param_id]->mutable_cpu_data());
- caffe_axpy(net_params[param_id]->count(),
- local_decay,
- temp_[param_id]->cpu_data(),
- net_params[param_id]->mutable_cpu_diff());
- } else {
- LOG(FATAL) << "Unknown regularization type: " << regularization_type;
- }
- }
-
- caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
- net_params[param_id]->cpu_diff(), momentum,
- history_[param_id]->mutable_cpu_data());
- // copy
- caffe_copy(net_params[param_id]->count(),
- history_[param_id]->cpu_data(),
- net_params[param_id]->mutable_cpu_diff());
- }
- break;
- case Caffe::GPU:
-#ifndef CPU_ONLY
- for (int param_id = 0; param_id < net_params.size(); ++param_id) {
- // Compute the value to history, and then copy them to the blob's diff.
- Dtype local_rate = rate * net_params_lr[param_id];
- Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
- if (local_decay) {
- if (regularization_type == "L2") {
- // add weight decay
- caffe_gpu_axpy(net_params[param_id]->count(),
- local_decay,
- net_params[param_id]->gpu_data(),
- net_params[param_id]->mutable_gpu_diff());
- } else if (regularization_type == "L1") {
- caffe_gpu_sign(net_params[param_id]->count(),
- net_params[param_id]->gpu_data(),
- temp_[param_id]->mutable_gpu_data());
- caffe_gpu_axpy(net_params[param_id]->count(),
- local_decay,
- temp_[param_id]->gpu_data(),
- net_params[param_id]->mutable_gpu_diff());
- } else {
- LOG(FATAL) << "Unknown regularization type: " << regularization_type;
- }
- }
-
- caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
- net_params[param_id]->gpu_diff(), momentum,
- history_[param_id]->mutable_gpu_data());
- // copy
- caffe_copy(net_params[param_id]->count(),
- history_[param_id]->gpu_data(),
- net_params[param_id]->mutable_gpu_diff());
- }
-#else
- NO_GPU;
-#endif
- break;
- default:
- LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
- }
+string Solver<Dtype>::SnapshotFilename(const string extension) {
+ return param_.snapshot_prefix() + "_iter_" + caffe::format_int(iter_)
+ + extension;
}
template <typename Dtype>
-void SGDSolver<Dtype>::SnapshotSolverState(SolverState* state) {
- state->clear_history();
- for (int i = 0; i < history_.size(); ++i) {
- // Add history
- BlobProto* history_blob = state->add_history();
- history_[i]->ToProto(history_blob);
- }
+string Solver<Dtype>::SnapshotToBinaryProto() {
+ string model_filename = SnapshotFilename(".caffemodel");
+ LOG(INFO) << "Snapshotting to binary proto file " << model_filename;
+ NetParameter net_param;
+ net_->ToProto(&net_param, param_.snapshot_diff());
+ WriteProtoToBinaryFile(net_param, model_filename);
+ return model_filename;
}
template <typename Dtype>
-void SGDSolver<Dtype>::RestoreSolverState(const SolverState& state) {
- CHECK_EQ(state.history_size(), history_.size())
- << "Incorrect length of history blobs.";
- LOG(INFO) << "SGDSolver: restoring history";
- for (int i = 0; i < history_.size(); ++i) {
- history_[i]->FromProto(state.history(i));
- }
+string Solver<Dtype>::SnapshotToHDF5() {
+ string model_filename = SnapshotFilename(".caffemodel.h5");
+ LOG(INFO) << "Snapshotting to HDF5 file " << model_filename;
+ net_->ToHDF5(model_filename, param_.snapshot_diff());
+ return model_filename;
}
template <typename Dtype>
-void NesterovSolver<Dtype>::ComputeUpdateValue() {
- const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
- const vector<float>& net_params_lr = this->net_->params_lr();
- const vector<float>& net_params_weight_decay =
- this->net_->params_weight_decay();
- // get the learning rate
- Dtype rate = this->GetLearningRate();
- if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
- LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
- }
- SGDSolver<Dtype>::ClipGradients();
- Dtype momentum = this->param_.momentum();
- Dtype weight_decay = this->param_.weight_decay();
- string regularization_type = this->param_.regularization_type();
- switch (Caffe::mode()) {
- case Caffe::CPU:
- for (int param_id = 0; param_id < net_params.size(); ++param_id) {
- // save history momentum for stepping back
- caffe_copy(net_params[param_id]->count(),
- this->history_[param_id]->cpu_data(),
- this->update_[param_id]->mutable_cpu_data());
-
- Dtype local_rate = rate * net_params_lr[param_id];
- Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
- if (local_decay) {
- if (regularization_type == "L2") {
- // add weight decay
- caffe_axpy(net_params[param_id]->count(),
- local_decay,
- net_params[param_id]->cpu_data(),
- net_params[param_id]->mutable_cpu_diff());
- } else if (regularization_type == "L1") {
- caffe_cpu_sign(net_params[param_id]->count(),
- net_params[param_id]->cpu_data(),
- this->temp_[param_id]->mutable_cpu_data());
- caffe_axpy(net_params[param_id]->count(),
- local_decay,
- this->temp_[param_id]->cpu_data(),
- net_params[param_id]->mutable_cpu_diff());
- } else {
- LOG(FATAL) << "Unknown regularization type: " << regularization_type;
- }
- }
-
- // update history
- caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
- net_params[param_id]->cpu_diff(), momentum,
- this->history_[param_id]->mutable_cpu_data());
-
- // compute udpate: step back then over step
- caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
- this->history_[param_id]->cpu_data(), -momentum,
- this->update_[param_id]->mutable_cpu_data());
-
- // copy
- caffe_copy(net_params[param_id]->count(),
- this->update_[param_id]->cpu_data(),
- net_params[param_id]->mutable_cpu_diff());
- }
- break;
- case Caffe::GPU:
-#ifndef CPU_ONLY
- for (int param_id = 0; param_id < net_params.size(); ++param_id) {
- // save history momentum for stepping back
- caffe_copy(net_params[param_id]->count(),
- this->history_[param_id]->gpu_data(),
- this->update_[param_id]->mutable_gpu_data());
-
- Dtype local_rate = rate * net_params_lr[param_id];
- Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
- if (local_decay) {
- if (regularization_type == "L2") {
- // add weight decay
- caffe_gpu_axpy(net_params[param_id]->count(),
- local_decay,
- net_params[param_id]->gpu_data(),
- net_params[param_id]->mutable_gpu_diff());
- } else if (regularization_type == "L1") {
- caffe_gpu_sign(net_params[param_id]->count(),
- net_params[param_id]->gpu_data(),
- this->temp_[param_id]->mutable_gpu_data());
- caffe_gpu_axpy(net_params[param_id]->count(),
- local_decay,
- this->temp_[param_id]->gpu_data(),
- net_params[param_id]->mutable_gpu_diff());
- } else {
- LOG(FATAL) << "Unknown regularization type: " << regularization_type;
- }
- }
-
- // update history
- caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
- net_params[param_id]->gpu_diff(), momentum,
- this->history_[param_id]->mutable_gpu_data());
-
- // compute udpate: step back then over step
- caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
- this->history_[param_id]->gpu_data(), -momentum,
- this->update_[param_id]->mutable_gpu_data());
-
- // copy
- caffe_copy(net_params[param_id]->count(),
- this->update_[param_id]->gpu_data(),
- net_params[param_id]->mutable_gpu_diff());
- }
-#else
- NO_GPU;
-#endif
- break;
- default:
- LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+void Solver<Dtype>::Restore(const char* state_file) {
+ CHECK(Caffe::root_solver());
+ string state_filename(state_file);
+ if (state_filename.size() >= 3 &&
+ state_filename.compare(state_filename.size() - 3, 3, ".h5") == 0) {
+ RestoreSolverStateFromHDF5(state_filename);
+ } else {
+ RestoreSolverStateFromBinaryProto(state_filename);
}
}
template <typename Dtype>
-void AdaGradSolver<Dtype>::ComputeUpdateValue() {
- const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
- const vector<float>& net_params_lr = this->net_->params_lr();
- const vector<float>& net_params_weight_decay =
- this->net_->params_weight_decay();
- // get the learning rate
- Dtype rate = this->GetLearningRate();
- Dtype delta = this->param_.delta();
- if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
- LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
- }
- SGDSolver<Dtype>::ClipGradients();
- Dtype weight_decay = this->param_.weight_decay();
- string regularization_type = this->param_.regularization_type();
- switch (Caffe::mode()) {
- case Caffe::CPU:
- for (int param_id = 0; param_id < net_params.size(); ++param_id) {
- Dtype local_rate = rate * net_params_lr[param_id];
- Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
- if (local_decay) {
- if (regularization_type == "L2") {
- // add weight decay
- caffe_axpy(net_params[param_id]->count(),
- local_decay,
- net_params[param_id]->cpu_data(),
- net_params[param_id]->mutable_cpu_diff());
- } else if (regularization_type == "L1") {
- caffe_cpu_sign(net_params[param_id]->count(),
- net_params[param_id]->cpu_data(),
- this->temp_[param_id]->mutable_cpu_data());
- caffe_axpy(net_params[param_id]->count(),
- local_decay,
- this->temp_[param_id]->cpu_data(),
- net_params[param_id]->mutable_cpu_diff());
- } else {
- LOG(FATAL) << "Unknown regularization type: " << regularization_type;
- }
- }
-
- // compute square of gradient in update
- caffe_powx(net_params[param_id]->count(),
- net_params[param_id]->cpu_diff(), Dtype(2),
- this->update_[param_id]->mutable_cpu_data());
-
- // update history
- caffe_add(net_params[param_id]->count(),
- this->update_[param_id]->cpu_data(),
- this->history_[param_id]->cpu_data(),
- this->history_[param_id]->mutable_cpu_data());
-
- // prepare update
- caffe_powx(net_params[param_id]->count(),
- this->history_[param_id]->cpu_data(), Dtype(0.5),
- this->update_[param_id]->mutable_cpu_data());
-
- caffe_add_scalar(net_params[param_id]->count(),
- delta, this->update_[param_id]->mutable_cpu_data());
-
- caffe_div(net_params[param_id]->count(),
- net_params[param_id]->cpu_diff(),
- this->update_[param_id]->cpu_data(),
- this->update_[param_id]->mutable_cpu_data());
-
- // scale and copy
- caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
- this->update_[param_id]->cpu_data(), Dtype(0),
- net_params[param_id]->mutable_cpu_diff());
- }
- break;
- case Caffe::GPU:
-#ifndef CPU_ONLY
- for (int param_id = 0; param_id < net_params.size(); ++param_id) {
- Dtype local_rate = rate * net_params_lr[param_id];
- Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
- if (local_decay) {
- if (regularization_type == "L2") {
- // add weight decay
- caffe_gpu_axpy(net_params[param_id]->count(),
- local_decay,
- net_params[param_id]->gpu_data(),
- net_params[param_id]->mutable_gpu_diff());
- } else if (regularization_type == "L1") {
- caffe_gpu_sign(net_params[param_id]->count(),
- net_params[param_id]->gpu_data(),
- this->temp_[param_id]->mutable_gpu_data());
- caffe_gpu_axpy(net_params[param_id]->count(),
- local_decay,
- this->temp_[param_id]->gpu_data(),
- net_params[param_id]->mutable_gpu_diff());
- } else {
- LOG(FATAL) << "Unknown regularization type: " << regularization_type;
- }
- }
-
- // compute square of gradient in update
- caffe_gpu_powx(net_params[param_id]->count(),
- net_params[param_id]->gpu_diff(), Dtype(2),
- this->update_[param_id]->mutable_gpu_data());
-
- // update history
- caffe_gpu_add(net_params[param_id]->count(),
- this->update_[param_id]->gpu_data(),
- this->history_[param_id]->gpu_data(),
- this->history_[param_id]->mutable_gpu_data());
-
- // prepare update
- caffe_gpu_powx(net_params[param_id]->count(),
- this->history_[param_id]->gpu_data(), Dtype(0.5),
- this->update_[param_id]->mutable_gpu_data());
-
- caffe_gpu_add_scalar(net_params[param_id]->count(),
- delta, this->update_[param_id]->mutable_gpu_data());
-
- caffe_gpu_div(net_params[param_id]->count(),
- net_params[param_id]->gpu_diff(),
- this->update_[param_id]->gpu_data(),
- this->update_[param_id]->mutable_gpu_data());
-
- // scale and copy
- caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
- this->update_[param_id]->gpu_data(), Dtype(0),
- net_params[param_id]->mutable_gpu_diff());
- }
-#else
- NO_GPU;
-#endif
- break;
- default:
- LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+void Solver<Dtype>::UpdateSmoothedLoss(Dtype loss, int start_iter,
+ int average_loss) {
+ if (losses_.size() < average_loss) {
+ losses_.push_back(loss);
+ int size = losses_.size();
+ smoothed_loss_ = (smoothed_loss_ * (size - 1) + loss) / size;
+ } else {
+ int idx = (iter_ - start_iter) % average_loss;
+ smoothed_loss_ += (loss - losses_[idx]) / average_loss;
+ losses_[idx] = loss;
}
}
INSTANTIATE_CLASS(Solver);
-INSTANTIATE_CLASS(SGDSolver);
-INSTANTIATE_CLASS(NesterovSolver);
-INSTANTIATE_CLASS(AdaGradSolver);
} // namespace caffe
--- /dev/null
+#include <vector>
+
+#include "caffe/sgd_solvers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void AdaDeltaSolver<Dtype>::AdaDeltaPreSolve() {
+ // Add the extra history entries for AdaDelta after those from
+ // SGDSolver::PreSolve
+ const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+ for (int i = 0; i < net_params.size(); ++i) {
+ const vector<int>& shape = net_params[i]->shape();
+ this->history_.push_back(
+ shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+ }
+}
+
+#ifndef CPU_ONLY
+template <typename Dtype>
+void adadelta_update_gpu(int N, Dtype* g, Dtype* h, Dtype* h2, Dtype momentum,
+ Dtype delta, Dtype local_rate);
+#endif
+
+template <typename Dtype>
+void AdaDeltaSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+ const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+ const vector<float>& net_params_lr = this->net_->params_lr();
+ Dtype delta = this->param_.delta();
+ Dtype momentum = this->param_.momentum();
+ Dtype local_rate = rate * net_params_lr[param_id];
+ size_t update_history_offset = net_params.size();
+ switch (Caffe::mode()) {
+ case Caffe::CPU: {
+ // compute square of gradient in update
+ caffe_powx(net_params[param_id]->count(),
+ net_params[param_id]->cpu_diff(), Dtype(2),
+ this->update_[param_id]->mutable_cpu_data());
+
+ // update history of gradients
+ caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
+ this->update_[param_id]->cpu_data(), momentum,
+ this->history_[param_id]->mutable_cpu_data());
+
+ // add delta to history to guard against dividing by zero later
+ caffe_set(net_params[param_id]->count(), delta,
+ this->temp_[param_id]->mutable_cpu_data());
+
+ caffe_add(net_params[param_id]->count(),
+ this->temp_[param_id]->cpu_data(),
+ this->history_[update_history_offset + param_id]->cpu_data(),
+ this->update_[param_id]->mutable_cpu_data());
+
+ caffe_add(net_params[param_id]->count(),
+ this->temp_[param_id]->cpu_data(),
+ this->history_[param_id]->cpu_data(),
+ this->temp_[param_id]->mutable_cpu_data());
+
+ // divide history of updates by history of gradients
+ caffe_div(net_params[param_id]->count(),
+ this->update_[param_id]->cpu_data(),
+ this->temp_[param_id]->cpu_data(),
+ this->update_[param_id]->mutable_cpu_data());
+
+ // jointly compute the RMS of both for update and gradient history
+ caffe_powx(net_params[param_id]->count(),
+ this->update_[param_id]->cpu_data(), Dtype(0.5),
+ this->update_[param_id]->mutable_cpu_data());
+
+ // compute the update
+ caffe_mul(net_params[param_id]->count(),
+ net_params[param_id]->cpu_diff(),
+ this->update_[param_id]->cpu_data(),
+ net_params[param_id]->mutable_cpu_diff());
+
+ // compute square of update
+ caffe_powx(net_params[param_id]->count(),
+ net_params[param_id]->cpu_diff(), Dtype(2),
+ this->update_[param_id]->mutable_cpu_data());
+
+ // update history of updates
+ caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
+ this->update_[param_id]->cpu_data(), momentum,
+ this->history_[update_history_offset + param_id]->mutable_cpu_data());
+
+ // apply learning rate
+ caffe_cpu_scale(net_params[param_id]->count(), local_rate,
+ net_params[param_id]->cpu_diff(),
+ net_params[param_id]->mutable_cpu_diff());
+ break;
+ }
+ case Caffe::GPU: {
+#ifndef CPU_ONLY
+ adadelta_update_gpu(net_params[param_id]->count(),
+ net_params[param_id]->mutable_gpu_diff(),
+ this->history_[param_id]->mutable_gpu_data(),
+ this->history_[update_history_offset + param_id]->mutable_gpu_data(),
+ momentum, delta, local_rate);
+#else
+ NO_GPU;
+#endif
+ break;
+ }
+ default:
+ LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+ }
+}
+
+INSTANTIATE_CLASS(AdaDeltaSolver);
+REGISTER_SOLVER_CLASS(AdaDelta);
+
+} // namespace caffe
--- /dev/null
+#include "caffe/util/math_functions.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void AdaDeltaUpdate(int N, Dtype* g, Dtype* h, Dtype* h2,
+ Dtype momentum, Dtype delta, Dtype local_rate) {
+ CUDA_KERNEL_LOOP(i, N) {
+ float gi = g[i];
+ float hi = h[i] = momentum * h[i] + (1-momentum) * gi * gi;
+ gi = gi * sqrt((h2[i] + delta) / (hi + delta));
+ h2[i] = momentum * h2[i] + (1-momentum) * gi * gi;
+ g[i] = local_rate * gi;
+ }
+}
+template <typename Dtype>
+void adadelta_update_gpu(int N, Dtype* g, Dtype* h, Dtype* h2, Dtype momentum,
+ Dtype delta, Dtype local_rate) {
+ AdaDeltaUpdate<Dtype> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+ N, g, h, h2, momentum, delta, local_rate);
+ CUDA_POST_KERNEL_CHECK;
+}
+template void adadelta_update_gpu<float>(int , float*, float*, float*,
+ float, float, float);
+template void adadelta_update_gpu<double>(int, double*, double*, double*,
+ double, double, double);
+
+} // namespace caffe
--- /dev/null
+#include <vector>
+
+#include "caffe/sgd_solvers.hpp"
+
+namespace caffe {
+
+#ifndef CPU_ONLY
+template <typename Dtype>
+void adagrad_update_gpu(int N, Dtype* g, Dtype* h, Dtype delta,
+ Dtype local_rate);
+#endif
+
+template <typename Dtype>
+void AdaGradSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+ CHECK(Caffe::root_solver());
+ const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+ const vector<float>& net_params_lr = this->net_->params_lr();
+ Dtype delta = this->param_.delta();
+ Dtype local_rate = rate * net_params_lr[param_id];
+ switch (Caffe::mode()) {
+ case Caffe::CPU: {
+ // compute square of gradient in update
+ caffe_powx(net_params[param_id]->count(),
+ net_params[param_id]->cpu_diff(), Dtype(2),
+ this->update_[param_id]->mutable_cpu_data());
+
+ // update history
+ caffe_add(net_params[param_id]->count(),
+ this->update_[param_id]->cpu_data(),
+ this->history_[param_id]->cpu_data(),
+ this->history_[param_id]->mutable_cpu_data());
+
+ // prepare update
+ caffe_powx(net_params[param_id]->count(),
+ this->history_[param_id]->cpu_data(), Dtype(0.5),
+ this->update_[param_id]->mutable_cpu_data());
+
+ caffe_add_scalar(net_params[param_id]->count(),
+ delta, this->update_[param_id]->mutable_cpu_data());
+
+ caffe_div(net_params[param_id]->count(),
+ net_params[param_id]->cpu_diff(),
+ this->update_[param_id]->cpu_data(),
+ this->update_[param_id]->mutable_cpu_data());
+
+ // scale and copy
+ caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+ this->update_[param_id]->cpu_data(), Dtype(0),
+ net_params[param_id]->mutable_cpu_diff());
+ break;
+ }
+ case Caffe::GPU: {
+#ifndef CPU_ONLY
+ adagrad_update_gpu(net_params[param_id]->count(),
+ net_params[param_id]->mutable_gpu_diff(),
+ this->history_[param_id]->mutable_gpu_data(), delta, local_rate);
+#else
+ NO_GPU;
+#endif
+ break;
+ }
+ default:
+ LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+ }
+}
+
+INSTANTIATE_CLASS(AdaGradSolver);
+REGISTER_SOLVER_CLASS(AdaGrad);
+
+} // namespace caffe
--- /dev/null
+#include "caffe/util/math_functions.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void AdaGradUpdate(int N, Dtype* g, Dtype* h, Dtype delta,
+ Dtype local_rate) {
+ CUDA_KERNEL_LOOP(i, N) {
+ float gi = g[i];
+ float hi = h[i] = h[i] + gi*gi;
+ g[i] = local_rate * gi / (sqrt(hi) + delta);
+ }
+}
+template <typename Dtype>
+void adagrad_update_gpu(int N, Dtype* g, Dtype* h, Dtype delta,
+ Dtype local_rate) {
+ AdaGradUpdate<Dtype> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+ N, g, h, delta, local_rate);
+ CUDA_POST_KERNEL_CHECK;
+}
+template void adagrad_update_gpu<float>(int, float*, float*, float, float);
+template void adagrad_update_gpu<double>(int, double*, double*, double, double);
+
+} // namespace caffe
--- /dev/null
+#include <vector>
+
+#include "caffe/sgd_solvers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void AdamSolver<Dtype>::AdamPreSolve() {
+ // Add the extra history entries for Adam after those from
+ // SGDSolver::PreSolve
+ const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+ for (int i = 0; i < net_params.size(); ++i) {
+ const vector<int>& shape = net_params[i]->shape();
+ this->history_.push_back(
+ shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+ }
+}
+
+#ifndef CPU_ONLY
+template <typename Dtype>
+void adam_update_gpu(int N, Dtype* g, Dtype* m, Dtype* v, Dtype beta1,
+ Dtype beta2, Dtype eps_hat, Dtype corrected_local_rate);
+#endif
+
+template <typename Dtype>
+void AdamSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+ const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+ const vector<float>& net_params_lr = this->net_->params_lr();
+ Dtype local_rate = rate * net_params_lr[param_id];
+ const Dtype beta1 = this->param_.momentum();
+ const Dtype beta2 = this->param_.momentum2();
+
+ // we create aliases for convenience
+ size_t update_history_offset = net_params.size();
+ Blob<Dtype>* val_m = this->history_[param_id].get();
+ Blob<Dtype>* val_v = this->history_[param_id + update_history_offset].get();
+ Blob<Dtype>* val_t = this->temp_[param_id].get();
+
+ const int t = this->iter_ + 1;
+ const Dtype correction = std::sqrt(Dtype(1) - pow(beta2, t)) /
+ (Dtype(1.) - pow(beta1, t));
+ const int N = net_params[param_id]->count();
+ const Dtype eps_hat = this->param_.delta();
+
+ switch (Caffe::mode()) {
+ case Caffe::CPU: {
+ // update m <- \beta_1 m_{t-1} + (1-\beta_1)g_t
+ caffe_cpu_axpby(N, Dtype(1)-beta1,
+ net_params[param_id]->cpu_diff(), beta1,
+ val_m->mutable_cpu_data());
+
+ // update v <- \beta_2 m_{t-1} + (1-\beta_2)g_t^2
+ caffe_mul(N,
+ net_params[param_id]->cpu_diff(),
+ net_params[param_id]->cpu_diff(),
+ val_t->mutable_cpu_data());
+ caffe_cpu_axpby(N, Dtype(1)-beta2,
+ val_t->cpu_data(), beta2,
+ val_v->mutable_cpu_data());
+
+ // set update
+ caffe_powx(N,
+ val_v->cpu_data(), Dtype(0.5),
+ val_t->mutable_cpu_data());
+ caffe_add_scalar(N, eps_hat, val_t->mutable_cpu_data());
+ caffe_div(N,
+ val_m->cpu_data(),
+ val_t->cpu_data(),
+ val_t->mutable_cpu_data());
+
+ caffe_cpu_scale(N, local_rate*correction,
+ val_t->cpu_data(),
+ net_params[param_id]->mutable_cpu_diff());
+ break;
+ }
+ case Caffe::GPU: {
+#ifndef CPU_ONLY
+ adam_update_gpu(N, net_params[param_id]->mutable_gpu_diff(),
+ val_m->mutable_gpu_data(), val_v->mutable_gpu_data(), beta1, beta2,
+ eps_hat, local_rate*correction);
+#else
+ NO_GPU;
+#endif
+ break;
+ }
+ default:
+ LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+ }
+}
+
+INSTANTIATE_CLASS(AdamSolver);
+REGISTER_SOLVER_CLASS(Adam);
+
+} // namespace caffe
--- /dev/null
+#include "caffe/util/math_functions.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void AdamUpdate(int N, Dtype* g, Dtype* m, Dtype* v,
+ Dtype beta1, Dtype beta2, Dtype eps_hat, Dtype corrected_local_rate) {
+ CUDA_KERNEL_LOOP(i, N) {
+ float gi = g[i];
+ float mi = m[i] = m[i]*beta1 + gi*(1-beta1);
+ float vi = v[i] = v[i]*beta2 + gi*gi*(1-beta2);
+ g[i] = corrected_local_rate * mi / (sqrt(vi) + eps_hat);
+ }
+}
+template <typename Dtype>
+void adam_update_gpu(int N, Dtype* g, Dtype* m, Dtype* v, Dtype beta1,
+ Dtype beta2, Dtype eps_hat, Dtype corrected_local_rate) {
+ AdamUpdate<Dtype> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+ N, g, m, v, beta1, beta2, eps_hat, corrected_local_rate);
+ CUDA_POST_KERNEL_CHECK;
+}
+template void adam_update_gpu<float>(int, float*, float*, float*,
+ float, float, float, float);
+template void adam_update_gpu<double>(int, double*, double*, double*,
+ double, double, double, double);
+
+} // namespace caffe
--- /dev/null
+#include <vector>
+
+#include "caffe/sgd_solvers.hpp"
+
+namespace caffe {
+
+#ifndef CPU_ONLY
+template <typename Dtype>
+void nesterov_update_gpu(int N, Dtype* g, Dtype* h, Dtype momentum,
+ Dtype local_rate);
+#endif
+
+template <typename Dtype>
+void NesterovSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+ CHECK(Caffe::root_solver());
+ const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+ const vector<float>& net_params_lr = this->net_->params_lr();
+ Dtype momentum = this->param_.momentum();
+ Dtype local_rate = rate * net_params_lr[param_id];
+ switch (Caffe::mode()) {
+ case Caffe::CPU: {
+ // save history momentum for stepping back
+ caffe_copy(net_params[param_id]->count(),
+ this->history_[param_id]->cpu_data(),
+ this->update_[param_id]->mutable_cpu_data());
+
+ // update history
+ caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+ net_params[param_id]->cpu_diff(), momentum,
+ this->history_[param_id]->mutable_cpu_data());
+
+ // compute update: step back then over step
+ caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
+ this->history_[param_id]->cpu_data(), -momentum,
+ this->update_[param_id]->mutable_cpu_data());
+
+ // copy
+ caffe_copy(net_params[param_id]->count(),
+ this->update_[param_id]->cpu_data(),
+ net_params[param_id]->mutable_cpu_diff());
+ break;
+ }
+ case Caffe::GPU: {
+#ifndef CPU_ONLY
+ nesterov_update_gpu(net_params[param_id]->count(),
+ net_params[param_id]->mutable_gpu_diff(),
+ this->history_[param_id]->mutable_gpu_data(),
+ momentum, local_rate);
+#else
+ NO_GPU;
+#endif
+ break;
+ }
+ default:
+ LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+ }
+}
+
+INSTANTIATE_CLASS(NesterovSolver);
+REGISTER_SOLVER_CLASS(Nesterov);
+
+} // namespace caffe
--- /dev/null
+#include "caffe/util/math_functions.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void NesterovUpdate(int N, Dtype* g, Dtype* h,
+ Dtype momentum, Dtype local_rate) {
+ CUDA_KERNEL_LOOP(i, N) {
+ float hi = h[i];
+ float hi_new = h[i] = momentum * hi + local_rate * g[i];
+ g[i] = (1+momentum) * hi_new - momentum * hi;
+ }
+}
+template <typename Dtype>
+void nesterov_update_gpu(int N, Dtype* g, Dtype* h, Dtype momentum,
+ Dtype local_rate) {
+ NesterovUpdate<Dtype> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+ N, g, h, momentum, local_rate);
+ CUDA_POST_KERNEL_CHECK;
+}
+template void nesterov_update_gpu<float>(int, float*, float*, float, float);
+template void nesterov_update_gpu<double>(int, double*, double*, double,
+ double);
+
+} // namespace caffe
--- /dev/null
+#include <vector>
+
+#include "caffe/sgd_solvers.hpp"
+
+namespace caffe {
+
+#ifndef CPU_ONLY
+template <typename Dtype>
+void rmsprop_update_gpu(int N, Dtype* g, Dtype* h, Dtype rms_decay,
+ Dtype delta, Dtype local_rate);
+#endif
+
+template <typename Dtype>
+void RMSPropSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+ const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+ const vector<float>& net_params_lr = this->net_->params_lr();
+
+ // get the learning rate
+ Dtype delta = this->param_.delta();
+ Dtype rms_decay = this->param_.rms_decay();
+ Dtype local_rate = rate * net_params_lr[param_id];
+
+ switch (Caffe::mode()) {
+ case Caffe::CPU:
+ // compute square of gradient in update
+ caffe_powx(net_params[param_id]->count(),
+ net_params[param_id]->cpu_diff(), Dtype(2),
+ this->update_[param_id]->mutable_cpu_data());
+
+ // update history
+ caffe_cpu_axpby(net_params[param_id] -> count(),
+ Dtype(1-rms_decay), this->update_[param_id]->cpu_data(),
+ rms_decay, this->history_[param_id]-> mutable_cpu_data());
+
+ // prepare update
+ caffe_powx(net_params[param_id]->count(),
+ this->history_[param_id]->cpu_data(), Dtype(0.5),
+ this->update_[param_id]->mutable_cpu_data());
+
+ caffe_add_scalar(net_params[param_id]->count(),
+ delta, this->update_[param_id]->mutable_cpu_data());
+
+ caffe_div(net_params[param_id]->count(),
+ net_params[param_id]->cpu_diff(), this->update_[param_id]->cpu_data(),
+ this->update_[param_id]->mutable_cpu_data());
+
+ // scale and copy
+ caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+ this->update_[param_id]->cpu_data(), Dtype(0),
+ net_params[param_id]->mutable_cpu_diff());
+ break;
+ case Caffe::GPU:
+#ifndef CPU_ONLY
+ rmsprop_update_gpu(net_params[param_id]->count(),
+ net_params[param_id]->mutable_gpu_diff(),
+ this->history_[param_id]->mutable_gpu_data(),
+ rms_decay, delta, local_rate);
+#else
+ NO_GPU;
+#endif
+ break;
+ default:
+ LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+ }
+}
+
+INSTANTIATE_CLASS(RMSPropSolver);
+REGISTER_SOLVER_CLASS(RMSProp);
+
+} // namespace caffe
--- /dev/null
+#include "caffe/util/math_functions.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void RMSPropUpdate(int N, Dtype* g, Dtype* h,
+ Dtype rms_decay, Dtype delta, Dtype local_rate) {
+ CUDA_KERNEL_LOOP(i, N) {
+ float gi = g[i];
+ float hi = h[i] = rms_decay*h[i] + (1-rms_decay)*gi*gi;
+ g[i] = local_rate * g[i] / (sqrt(hi) + delta);
+ }
+}
+template <typename Dtype>
+void rmsprop_update_gpu(int N, Dtype* g, Dtype* h, Dtype rms_decay,
+ Dtype delta, Dtype local_rate) {
+ RMSPropUpdate<Dtype> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+ N, g, h, rms_decay, delta, local_rate);
+ CUDA_POST_KERNEL_CHECK;
+}
+template void rmsprop_update_gpu<float>(int, float*, float*, float, float,
+ float);
+template void rmsprop_update_gpu<double>(int, double*, double*, double, double,
+ double);
+
+} // namespace caffe
--- /dev/null
+#include <string>
+#include <vector>
+
+#include "caffe/sgd_solvers.hpp"
+#include "caffe/util/hdf5.hpp"
+#include "caffe/util/io.hpp"
+#include "caffe/util/upgrade_proto.hpp"
+
+namespace caffe {
+
+// Return the current learning rate. The currently implemented learning rate
+// policies are as follows:
+// - fixed: always return base_lr.
+// - step: return base_lr * gamma ^ (floor(iter / step))
+// - exp: return base_lr * gamma ^ iter
+// - inv: return base_lr * (1 + gamma * iter) ^ (- power)
+// - multistep: similar to step but it allows non uniform steps defined by
+// stepvalue
+// - poly: the effective learning rate follows a polynomial decay, to be
+// zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)
+// - sigmoid: the effective learning rate follows a sigmod decay
+// return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))
+//
+// where base_lr, max_iter, gamma, step, stepvalue and power are defined
+// in the solver parameter protocol buffer, and iter is the current iteration.
+template <typename Dtype>
+Dtype SGDSolver<Dtype>::GetLearningRate() {
+ Dtype rate;
+ const string& lr_policy = this->param_.lr_policy();
+ if (lr_policy == "fixed") {
+ rate = this->param_.base_lr();
+ } else if (lr_policy == "step") {
+ this->current_step_ = this->iter_ / this->param_.stepsize();
+ rate = this->param_.base_lr() *
+ pow(this->param_.gamma(), this->current_step_);
+ } else if (lr_policy == "exp") {
+ rate = this->param_.base_lr() * pow(this->param_.gamma(), this->iter_);
+ } else if (lr_policy == "inv") {
+ rate = this->param_.base_lr() *
+ pow(Dtype(1) + this->param_.gamma() * this->iter_,
+ - this->param_.power());
+ } else if (lr_policy == "multistep") {
+ if (this->current_step_ < this->param_.stepvalue_size() &&
+ this->iter_ >= this->param_.stepvalue(this->current_step_)) {
+ this->current_step_++;
+ LOG(INFO) << "MultiStep Status: Iteration " <<
+ this->iter_ << ", step = " << this->current_step_;
+ }
+ rate = this->param_.base_lr() *
+ pow(this->param_.gamma(), this->current_step_);
+ } else if (lr_policy == "poly") {
+ rate = this->param_.base_lr() * pow(Dtype(1.) -
+ (Dtype(this->iter_) / Dtype(this->param_.max_iter())),
+ this->param_.power());
+ } else if (lr_policy == "sigmoid") {
+ rate = this->param_.base_lr() * (Dtype(1.) /
+ (Dtype(1.) + exp(-this->param_.gamma() * (Dtype(this->iter_) -
+ Dtype(this->param_.stepsize())))));
+ } else {
+ LOG(FATAL) << "Unknown learning rate policy: " << lr_policy;
+ }
+ return rate;
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::PreSolve() {
+ // Initialize the history
+ const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+ history_.clear();
+ update_.clear();
+ temp_.clear();
+ for (int i = 0; i < net_params.size(); ++i) {
+ const vector<int>& shape = net_params[i]->shape();
+ history_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+ update_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+ temp_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+ }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::ClipGradients() {
+ const Dtype clip_gradients = this->param_.clip_gradients();
+ if (clip_gradients < 0) { return; }
+ const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+ Dtype sumsq_diff = 0;
+ for (int i = 0; i < net_params.size(); ++i) {
+ sumsq_diff += net_params[i]->sumsq_diff();
+ }
+ const Dtype l2norm_diff = std::sqrt(sumsq_diff);
+ if (l2norm_diff > clip_gradients) {
+ Dtype scale_factor = clip_gradients / l2norm_diff;
+ LOG(INFO) << "Gradient clipping: scaling down gradients (L2 norm "
+ << l2norm_diff << " > " << clip_gradients << ") "
+ << "by scale factor " << scale_factor;
+ for (int i = 0; i < net_params.size(); ++i) {
+ net_params[i]->scale_diff(scale_factor);
+ }
+ }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::ApplyUpdate() {
+ CHECK(Caffe::root_solver());
+ Dtype rate = GetLearningRate();
+ if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
+ LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
+ }
+ ClipGradients();
+ for (int param_id = 0; param_id < this->net_->learnable_params().size();
+ ++param_id) {
+ Normalize(param_id);
+ Regularize(param_id);
+ ComputeUpdateValue(param_id, rate);
+ }
+ this->net_->Update();
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::Normalize(int param_id) {
+ if (this->param_.iter_size() == 1) { return; }
+ // Scale gradient to counterbalance accumulation.
+ const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+ const Dtype accum_normalization = Dtype(1.) / this->param_.iter_size();
+ switch (Caffe::mode()) {
+ case Caffe::CPU: {
+ caffe_scal(net_params[param_id]->count(), accum_normalization,
+ net_params[param_id]->mutable_cpu_diff());
+ break;
+ }
+ case Caffe::GPU: {
+#ifndef CPU_ONLY
+ caffe_gpu_scal(net_params[param_id]->count(), accum_normalization,
+ net_params[param_id]->mutable_gpu_diff());
+#else
+ NO_GPU;
+#endif
+ break;
+ }
+ default:
+ LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+ }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::Regularize(int param_id) {
+ const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+ const vector<float>& net_params_weight_decay =
+ this->net_->params_weight_decay();
+ Dtype weight_decay = this->param_.weight_decay();
+ string regularization_type = this->param_.regularization_type();
+ Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
+ switch (Caffe::mode()) {
+ case Caffe::CPU: {
+ if (local_decay) {
+ if (regularization_type == "L2") {
+ // add weight decay
+ caffe_axpy(net_params[param_id]->count(),
+ local_decay,
+ net_params[param_id]->cpu_data(),
+ net_params[param_id]->mutable_cpu_diff());
+ } else if (regularization_type == "L1") {
+ caffe_cpu_sign(net_params[param_id]->count(),
+ net_params[param_id]->cpu_data(),
+ temp_[param_id]->mutable_cpu_data());
+ caffe_axpy(net_params[param_id]->count(),
+ local_decay,
+ temp_[param_id]->cpu_data(),
+ net_params[param_id]->mutable_cpu_diff());
+ } else {
+ LOG(FATAL) << "Unknown regularization type: " << regularization_type;
+ }
+ }
+ break;
+ }
+ case Caffe::GPU: {
+#ifndef CPU_ONLY
+ if (local_decay) {
+ if (regularization_type == "L2") {
+ // add weight decay
+ caffe_gpu_axpy(net_params[param_id]->count(),
+ local_decay,
+ net_params[param_id]->gpu_data(),
+ net_params[param_id]->mutable_gpu_diff());
+ } else if (regularization_type == "L1") {
+ caffe_gpu_sign(net_params[param_id]->count(),
+ net_params[param_id]->gpu_data(),
+ temp_[param_id]->mutable_gpu_data());
+ caffe_gpu_axpy(net_params[param_id]->count(),
+ local_decay,
+ temp_[param_id]->gpu_data(),
+ net_params[param_id]->mutable_gpu_diff());
+ } else {
+ LOG(FATAL) << "Unknown regularization type: " << regularization_type;
+ }
+ }
+#else
+ NO_GPU;
+#endif
+ break;
+ }
+ default:
+ LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+ }
+}
+
+#ifndef CPU_ONLY
+template <typename Dtype>
+void sgd_update_gpu(int N, Dtype* g, Dtype* h, Dtype momentum,
+ Dtype local_rate);
+#endif
+
+template <typename Dtype>
+void SGDSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+ const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+ const vector<float>& net_params_lr = this->net_->params_lr();
+ Dtype momentum = this->param_.momentum();
+ Dtype local_rate = rate * net_params_lr[param_id];
+ // Compute the update to history, then copy it to the parameter diff.
+ switch (Caffe::mode()) {
+ case Caffe::CPU: {
+ caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+ net_params[param_id]->cpu_diff(), momentum,
+ history_[param_id]->mutable_cpu_data());
+ caffe_copy(net_params[param_id]->count(),
+ history_[param_id]->cpu_data(),
+ net_params[param_id]->mutable_cpu_diff());
+ break;
+ }
+ case Caffe::GPU: {
+#ifndef CPU_ONLY
+ sgd_update_gpu(net_params[param_id]->count(),
+ net_params[param_id]->mutable_gpu_diff(),
+ history_[param_id]->mutable_gpu_data(),
+ momentum, local_rate);
+#else
+ NO_GPU;
+#endif
+ break;
+ }
+ default:
+ LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+ }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::SnapshotSolverState(const string& model_filename) {
+ switch (this->param_.snapshot_format()) {
+ case caffe::SolverParameter_SnapshotFormat_BINARYPROTO:
+ SnapshotSolverStateToBinaryProto(model_filename);
+ break;
+ case caffe::SolverParameter_SnapshotFormat_HDF5:
+ SnapshotSolverStateToHDF5(model_filename);
+ break;
+ default:
+ LOG(FATAL) << "Unsupported snapshot format.";
+ }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::SnapshotSolverStateToBinaryProto(
+ const string& model_filename) {
+ SolverState state;
+ state.set_iter(this->iter_);
+ state.set_learned_net(model_filename);
+ state.set_current_step(this->current_step_);
+ state.clear_history();
+ for (int i = 0; i < history_.size(); ++i) {
+ // Add history
+ BlobProto* history_blob = state.add_history();
+ history_[i]->ToProto(history_blob);
+ }
+ string snapshot_filename = Solver<Dtype>::SnapshotFilename(".solverstate");
+ LOG(INFO)
+ << "Snapshotting solver state to binary proto file " << snapshot_filename;
+ WriteProtoToBinaryFile(state, snapshot_filename.c_str());
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::SnapshotSolverStateToHDF5(
+ const string& model_filename) {
+ string snapshot_filename =
+ Solver<Dtype>::SnapshotFilename(".solverstate.h5");
+ LOG(INFO) << "Snapshotting solver state to HDF5 file " << snapshot_filename;
+ hid_t file_hid = H5Fcreate(snapshot_filename.c_str(), H5F_ACC_TRUNC,
+ H5P_DEFAULT, H5P_DEFAULT);
+ CHECK_GE(file_hid, 0)
+ << "Couldn't open " << snapshot_filename << " to save solver state.";
+ hdf5_save_int(file_hid, "iter", this->iter_);
+ hdf5_save_string(file_hid, "learned_net", model_filename);
+ hdf5_save_int(file_hid, "current_step", this->current_step_);
+ hid_t history_hid = H5Gcreate2(file_hid, "history", H5P_DEFAULT, H5P_DEFAULT,
+ H5P_DEFAULT);
+ CHECK_GE(history_hid, 0)
+ << "Error saving solver state to " << snapshot_filename << ".";
+ for (int i = 0; i < history_.size(); ++i) {
+ ostringstream oss;
+ oss << i;
+ hdf5_save_nd_dataset<Dtype>(history_hid, oss.str(), *history_[i]);
+ }
+ H5Gclose(history_hid);
+ H5Fclose(file_hid);
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::RestoreSolverStateFromBinaryProto(
+ const string& state_file) {
+ SolverState state;
+ ReadProtoFromBinaryFile(state_file, &state);
+ this->iter_ = state.iter();
+ if (state.has_learned_net()) {
+ NetParameter net_param;
+ ReadNetParamsFromBinaryFileOrDie(state.learned_net().c_str(), &net_param);
+ this->net_->CopyTrainedLayersFrom(net_param);
+ }
+ this->current_step_ = state.current_step();
+ CHECK_EQ(state.history_size(), history_.size())
+ << "Incorrect length of history blobs.";
+ LOG(INFO) << "SGDSolver: restoring history";
+ for (int i = 0; i < history_.size(); ++i) {
+ history_[i]->FromProto(state.history(i));
+ }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::RestoreSolverStateFromHDF5(const string& state_file) {
+ hid_t file_hid = H5Fopen(state_file.c_str(), H5F_ACC_RDONLY, H5P_DEFAULT);
+ CHECK_GE(file_hid, 0) << "Couldn't open solver state file " << state_file;
+ this->iter_ = hdf5_load_int(file_hid, "iter");
+ if (H5LTfind_dataset(file_hid, "learned_net")) {
+ string learned_net = hdf5_load_string(file_hid, "learned_net");
+ this->net_->CopyTrainedLayersFrom(learned_net);
+ }
+ this->current_step_ = hdf5_load_int(file_hid, "current_step");
+ hid_t history_hid = H5Gopen2(file_hid, "history", H5P_DEFAULT);
+ CHECK_GE(history_hid, 0) << "Error reading history from " << state_file;
+ int state_history_size = hdf5_get_num_links(history_hid);
+ CHECK_EQ(state_history_size, history_.size())
+ << "Incorrect length of history blobs.";
+ for (int i = 0; i < history_.size(); ++i) {
+ ostringstream oss;
+ oss << i;
+ hdf5_load_nd_dataset<Dtype>(history_hid, oss.str().c_str(), 0,
+ kMaxBlobAxes, history_[i].get());
+ }
+ H5Gclose(history_hid);
+ H5Fclose(file_hid);
+}
+
+INSTANTIATE_CLASS(SGDSolver);
+REGISTER_SOLVER_CLASS(SGD);
+
+} // namespace caffe
--- /dev/null
+#include "caffe/util/math_functions.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void SGDUpdate(int N, Dtype* g, Dtype* h,
+ Dtype momentum, Dtype local_rate) {
+ CUDA_KERNEL_LOOP(i, N) {
+ g[i] = h[i] = momentum*h[i] + local_rate*g[i];
+ }
+}
+template <typename Dtype>
+void sgd_update_gpu(int N, Dtype* g, Dtype* h, Dtype momentum,
+ Dtype local_rate) {
+ SGDUpdate<Dtype> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+ N, g, h, momentum, local_rate);
+ CUDA_POST_KERNEL_CHECK;
+}
+template void sgd_update_gpu<float>(int, float*, float*, float, float);
+template void sgd_update_gpu<double>(int, double*, double*, double, double);
+
+} // namespace caffe
-#include <cstring>
-
#include "caffe/common.hpp"
#include "caffe/syncedmem.hpp"
#include "caffe/util/math_functions.hpp"
SyncedMemory::~SyncedMemory() {
if (cpu_ptr_ && own_cpu_data_) {
- CaffeFreeHost(cpu_ptr_);
+ CaffeFreeHost(cpu_ptr_, cpu_malloc_use_cuda_);
}
#ifndef CPU_ONLY
- if (gpu_ptr_) {
+ if (gpu_ptr_ && own_gpu_data_) {
+ int initial_device;
+ cudaGetDevice(&initial_device);
+ if (gpu_device_ != -1) {
+ CUDA_CHECK(cudaSetDevice(gpu_device_));
+ }
CUDA_CHECK(cudaFree(gpu_ptr_));
+ cudaSetDevice(initial_device);
}
#endif // CPU_ONLY
}
inline void SyncedMemory::to_cpu() {
switch (head_) {
case UNINITIALIZED:
- CaffeMallocHost(&cpu_ptr_, size_);
+ CaffeMallocHost(&cpu_ptr_, size_, &cpu_malloc_use_cuda_);
caffe_memset(size_, 0, cpu_ptr_);
head_ = HEAD_AT_CPU;
own_cpu_data_ = true;
case HEAD_AT_GPU:
#ifndef CPU_ONLY
if (cpu_ptr_ == NULL) {
- CaffeMallocHost(&cpu_ptr_, size_);
+ CaffeMallocHost(&cpu_ptr_, size_, &cpu_malloc_use_cuda_);
own_cpu_data_ = true;
}
caffe_gpu_memcpy(size_, gpu_ptr_, cpu_ptr_);
#ifndef CPU_ONLY
switch (head_) {
case UNINITIALIZED:
+ CUDA_CHECK(cudaGetDevice(&gpu_device_));
CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
caffe_gpu_memset(size_, 0, gpu_ptr_);
head_ = HEAD_AT_GPU;
+ own_gpu_data_ = true;
break;
case HEAD_AT_CPU:
if (gpu_ptr_ == NULL) {
+ CUDA_CHECK(cudaGetDevice(&gpu_device_));
CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
+ own_gpu_data_ = true;
}
caffe_gpu_memcpy(size_, cpu_ptr_, gpu_ptr_);
head_ = SYNCED;
void SyncedMemory::set_cpu_data(void* data) {
CHECK(data);
if (own_cpu_data_) {
- CaffeFreeHost(cpu_ptr_);
+ CaffeFreeHost(cpu_ptr_, cpu_malloc_use_cuda_);
}
cpu_ptr_ = data;
head_ = HEAD_AT_CPU;
return (const void*)gpu_ptr_;
#else
NO_GPU;
+ return NULL;
+#endif
+}
+
+void SyncedMemory::set_gpu_data(void* data) {
+#ifndef CPU_ONLY
+ CHECK(data);
+ if (own_gpu_data_) {
+ int initial_device;
+ cudaGetDevice(&initial_device);
+ if (gpu_device_ != -1) {
+ CUDA_CHECK(cudaSetDevice(gpu_device_));
+ }
+ CUDA_CHECK(cudaFree(gpu_ptr_));
+ cudaSetDevice(initial_device);
+ }
+ gpu_ptr_ = data;
+ head_ = HEAD_AT_GPU;
+ own_gpu_data_ = false;
+#else
+ NO_GPU;
#endif
}
return gpu_ptr_;
#else
NO_GPU;
+ return NULL;
#endif
}
+#ifndef CPU_ONLY
+void SyncedMemory::async_gpu_push(const cudaStream_t& stream) {
+ CHECK(head_ == HEAD_AT_CPU);
+ if (gpu_ptr_ == NULL) {
+ CUDA_CHECK(cudaGetDevice(&gpu_device_));
+ CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
+ own_gpu_data_ = true;
+ }
+ const cudaMemcpyKind put = cudaMemcpyHostToDevice;
+ CUDA_CHECK(cudaMemcpyAsync(gpu_ptr_, cpu_ptr_, size_, put, stream));
+ // Assume caller will synchronize on the stream before use
+ head_ = SYNCED;
+}
+#endif
} // namespace caffe
#include <cfloat>
-#include <cmath>
-#include <cstring>
#include <vector>
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
+#include "caffe/layers/accuracy_layer.hpp"
#include "caffe/util/rng.hpp"
-#include "caffe/vision_layers.hpp"
#include "caffe/test/test_caffe_main.hpp"
namespace caffe {
template <typename Dtype>
-class AccuracyLayerTest : public ::testing::Test {
+class AccuracyLayerTest : public CPUDeviceTest<Dtype> {
protected:
AccuracyLayerTest()
: blob_bottom_data_(new Blob<Dtype>()),
blob_bottom_label_(new Blob<Dtype>()),
blob_top_(new Blob<Dtype>()),
+ blob_top_per_class_(new Blob<Dtype>()),
top_k_(3) {
vector<int> shape(2);
shape[0] = 100;
blob_bottom_vec_.push_back(blob_bottom_data_);
blob_bottom_vec_.push_back(blob_bottom_label_);
blob_top_vec_.push_back(blob_top_);
+ blob_top_per_class_vec_.push_back(blob_top_);
+ blob_top_per_class_vec_.push_back(blob_top_per_class_);
}
virtual void FillBottoms() {
delete blob_bottom_data_;
delete blob_bottom_label_;
delete blob_top_;
+ delete blob_top_per_class_;
}
Blob<Dtype>* const blob_bottom_data_;
Blob<Dtype>* const blob_bottom_label_;
Blob<Dtype>* const blob_top_;
+ Blob<Dtype>* const blob_top_per_class_;
vector<Blob<Dtype>*> blob_bottom_vec_;
vector<Blob<Dtype>*> blob_top_vec_;
+ vector<Blob<Dtype>*> blob_top_per_class_vec_;
int top_k_;
};
EXPECT_EQ(this->blob_top_->width(), 1);
}
+TYPED_TEST(AccuracyLayerTest, TestSetupOutputPerClass) {
+ LayerParameter layer_param;
+ AccuracyLayer<TypeParam> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
+ EXPECT_EQ(this->blob_top_->num(), 1);
+ EXPECT_EQ(this->blob_top_->channels(), 1);
+ EXPECT_EQ(this->blob_top_->height(), 1);
+ EXPECT_EQ(this->blob_top_->width(), 1);
+ EXPECT_EQ(this->blob_top_per_class_->num(), 10);
+ EXPECT_EQ(this->blob_top_per_class_->channels(), 1);
+ EXPECT_EQ(this->blob_top_per_class_->height(), 1);
+ EXPECT_EQ(this->blob_top_per_class_->width(), 1);
+}
+
TYPED_TEST(AccuracyLayerTest, TestForwardCPU) {
LayerParameter layer_param;
- Caffe::set_mode(Caffe::CPU);
AccuracyLayer<TypeParam> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
}
TYPED_TEST(AccuracyLayerTest, TestForwardWithSpatialAxes) {
- Caffe::set_mode(Caffe::CPU);
this->blob_bottom_data_->Reshape(2, 10, 4, 5);
vector<int> label_shape(3);
label_shape[0] = 2; label_shape[1] = 4; label_shape[2] = 5;
}
TYPED_TEST(AccuracyLayerTest, TestForwardIgnoreLabel) {
- Caffe::set_mode(Caffe::CPU);
LayerParameter layer_param;
const TypeParam kIgnoreLabelValue = -1;
layer_param.mutable_accuracy_param()->set_ignore_label(kIgnoreLabelValue);
num_correct_labels / 100.0, 1e-4);
}
+TYPED_TEST(AccuracyLayerTest, TestForwardCPUPerClass) {
+ LayerParameter layer_param;
+ AccuracyLayer<TypeParam> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
+
+ TypeParam max_value;
+ int max_id;
+ int num_correct_labels = 0;
+ const int num_class = this->blob_top_per_class_->num();
+ vector<int> correct_per_class(num_class, 0);
+ vector<int> num_per_class(num_class, 0);
+ for (int i = 0; i < 100; ++i) {
+ max_value = -FLT_MAX;
+ max_id = 0;
+ for (int j = 0; j < 10; ++j) {
+ if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
+ max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
+ max_id = j;
+ }
+ }
+ ++num_per_class[this->blob_bottom_label_->data_at(i, 0, 0, 0)];
+ if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+ ++num_correct_labels;
+ ++correct_per_class[max_id];
+ }
+ }
+ EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+ num_correct_labels / 100.0, 1e-4);
+ for (int i = 0; i < num_class; ++i) {
+ TypeParam accuracy_per_class = (num_per_class[i] > 0 ?
+ static_cast<TypeParam>(correct_per_class[i]) / num_per_class[i] : 0);
+ EXPECT_NEAR(this->blob_top_per_class_->data_at(i, 0, 0, 0),
+ accuracy_per_class, 1e-4);
+ }
+}
+
+
+TYPED_TEST(AccuracyLayerTest, TestForwardCPUPerClassWithIgnoreLabel) {
+ LayerParameter layer_param;
+ const TypeParam kIgnoreLabelValue = -1;
+ layer_param.mutable_accuracy_param()->set_ignore_label(kIgnoreLabelValue);
+ AccuracyLayer<TypeParam> layer(layer_param);
+ // Manually set some labels to the ignore label value (-1).
+ this->blob_bottom_label_->mutable_cpu_data()[2] = kIgnoreLabelValue;
+ this->blob_bottom_label_->mutable_cpu_data()[5] = kIgnoreLabelValue;
+ this->blob_bottom_label_->mutable_cpu_data()[32] = kIgnoreLabelValue;
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
+
+ TypeParam max_value;
+ int max_id;
+ int num_correct_labels = 0;
+ const int num_class = this->blob_top_per_class_->num();
+ vector<int> correct_per_class(num_class, 0);
+ vector<int> num_per_class(num_class, 0);
+ int count = 0;
+ for (int i = 0; i < 100; ++i) {
+ if (kIgnoreLabelValue == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+ continue;
+ }
+ ++count;
+ max_value = -FLT_MAX;
+ max_id = 0;
+ for (int j = 0; j < 10; ++j) {
+ if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
+ max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
+ max_id = j;
+ }
+ }
+ ++num_per_class[this->blob_bottom_label_->data_at(i, 0, 0, 0)];
+ if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+ ++num_correct_labels;
+ ++correct_per_class[max_id];
+ }
+ }
+ EXPECT_EQ(count, 97);
+ EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+ num_correct_labels / TypeParam(count), 1e-4);
+ for (int i = 0; i < 10; ++i) {
+ TypeParam accuracy_per_class = (num_per_class[i] > 0 ?
+ static_cast<TypeParam>(correct_per_class[i]) / num_per_class[i] : 0);
+ EXPECT_NEAR(this->blob_top_per_class_->data_at(i, 0, 0, 0),
+ accuracy_per_class, 1e-4);
+ }
+}
+
} // namespace caffe
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/argmax_layer.hpp"
#include "caffe/test/test_caffe_main.hpp"
namespace caffe {
template <typename Dtype>
-class ArgMaxLayerTest : public ::testing::Test {
+class ArgMaxLayerTest : public CPUDeviceTest<Dtype> {
protected:
ArgMaxLayerTest()
- : blob_bottom_(new Blob<Dtype>(10, 20, 1, 1)),
+ : blob_bottom_(new Blob<Dtype>(10, 10, 20, 20)),
blob_top_(new Blob<Dtype>()),
top_k_(5) {
- Caffe::set_mode(Caffe::CPU);
Caffe::set_random_seed(1701);
// fill the values
FillerParameter filler_param;
EXPECT_EQ(this->blob_top_->channels(), 2);
}
+TYPED_TEST(ArgMaxLayerTest, TestSetupAxis) {
+ LayerParameter layer_param;
+ ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+ argmax_param->set_axis(0);
+ ArgMaxLayer<TypeParam> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ EXPECT_EQ(this->blob_top_->shape(0), argmax_param->top_k());
+ EXPECT_EQ(this->blob_top_->shape(1), this->blob_bottom_->shape(0));
+ EXPECT_EQ(this->blob_top_->shape(2), this->blob_bottom_->shape(2));
+ EXPECT_EQ(this->blob_top_->shape(3), this->blob_bottom_->shape(3));
+}
+
+TYPED_TEST(ArgMaxLayerTest, TestSetupAxisNegativeIndexing) {
+ LayerParameter layer_param;
+ ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+ argmax_param->set_axis(-2);
+ ArgMaxLayer<TypeParam> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ EXPECT_EQ(this->blob_top_->shape(0), this->blob_bottom_->shape(0));
+ EXPECT_EQ(this->blob_top_->shape(1), this->blob_bottom_->shape(1));
+ EXPECT_EQ(this->blob_top_->shape(2), argmax_param->top_k());
+ EXPECT_EQ(this->blob_top_->shape(3), this->blob_bottom_->shape(3));
+}
+
+TYPED_TEST(ArgMaxLayerTest, TestSetupAxisMaxVal) {
+ LayerParameter layer_param;
+ ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+ argmax_param->set_axis(2);
+ argmax_param->set_out_max_val(true);
+ ArgMaxLayer<TypeParam> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ EXPECT_EQ(this->blob_top_->shape(0), this->blob_bottom_->shape(0));
+ EXPECT_EQ(this->blob_top_->shape(1), this->blob_bottom_->shape(1));
+ EXPECT_EQ(this->blob_top_->shape(2), argmax_param->top_k());
+ EXPECT_EQ(this->blob_top_->shape(3), this->blob_bottom_->shape(3));
+}
+
TYPED_TEST(ArgMaxLayerTest, TestCPU) {
LayerParameter layer_param;
ArgMaxLayer<TypeParam> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
// Now, check values
+ const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
int max_ind;
TypeParam max_val;
int num = this->blob_bottom_->num();
EXPECT_LE(this->blob_top_->data_at(i, 0, 0, 0), dim);
for (int j = 0; j < this->top_k_; ++j) {
max_ind = this->blob_top_->data_at(i, 0, j, 0);
- max_val = this->blob_bottom_->data_at(i, max_ind, 0, 0);
+ max_val = bottom_data[i * dim + max_ind];
int count = 0;
for (int k = 0; k < dim; ++k) {
- if (this->blob_bottom_->data_at(i, k, 0, 0) > max_val) {
+ if (bottom_data[i * dim + k] > max_val) {
++count;
}
}
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
// Now, check values
+ const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
int max_ind;
TypeParam max_val;
int num = this->blob_bottom_->num();
for (int j = 0; j < this->top_k_; ++j) {
max_ind = this->blob_top_->data_at(i, 0, j, 0);
max_val = this->blob_top_->data_at(i, 1, j, 0);
- EXPECT_EQ(this->blob_bottom_->data_at(i, max_ind, 0, 0), max_val);
+ EXPECT_EQ(bottom_data[i * dim + max_ind], max_val);
int count = 0;
for (int k = 0; k < dim; ++k) {
- if (this->blob_bottom_->data_at(i, k, 0, 0) > max_val) {
+ if (bottom_data[i * dim + k] > max_val) {
++count;
}
}
}
}
+TYPED_TEST(ArgMaxLayerTest, TestCPUAxis) {
+ LayerParameter layer_param;
+ ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+ argmax_param->set_axis(0);
+ ArgMaxLayer<TypeParam> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ // Now, check values
+ int max_ind;
+ TypeParam max_val;
+ std::vector<int> shape = this->blob_bottom_->shape();
+ for (int i = 0; i < shape[1]; ++i) {
+ for (int j = 0; j < shape[2]; ++j) {
+ for (int k = 0; k < shape[3]; ++k) {
+ max_ind = this->blob_top_->data_at(0, i, j, k);
+ max_val = this->blob_bottom_->data_at(max_ind, i, j, k);
+ EXPECT_GE(max_ind, 0);
+ EXPECT_LE(max_ind, shape[0]);
+ for (int l = 0; l < shape[0]; ++l) {
+ EXPECT_LE(this->blob_bottom_->data_at(l, i, j, k), max_val);
+ }
+ }
+ }
+ }
+}
+
+TYPED_TEST(ArgMaxLayerTest, TestCPUAxisTopK) {
+ LayerParameter layer_param;
+ ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+ argmax_param->set_axis(2);
+ argmax_param->set_top_k(this->top_k_);
+ ArgMaxLayer<TypeParam> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ // Now, check values
+ int max_ind;
+ TypeParam max_val;
+ std::vector<int> shape = this->blob_bottom_->shape();
+ for (int i = 0; i < shape[0]; ++i) {
+ for (int j = 0; j < shape[1]; ++j) {
+ for (int k = 0; k < shape[3]; ++k) {
+ for (int m = 0; m < this->top_k_; ++m) {
+ max_ind = this->blob_top_->data_at(i, j, m, k);
+ max_val = this->blob_bottom_->data_at(i, j, max_ind, k);
+ EXPECT_GE(max_ind, 0);
+ EXPECT_LE(max_ind, shape[2]);
+ int count = 0;
+ for (int l = 0; l < shape[2]; ++l) {
+ if (this->blob_bottom_->data_at(i, j, l, k) > max_val) {
+ ++count;
+ }
+ }
+ EXPECT_EQ(m, count);
+ }
+ }
+ }
+ }
+}
+
+TYPED_TEST(ArgMaxLayerTest, TestCPUAxisMaxValTopK) {
+ LayerParameter layer_param;
+ ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+ argmax_param->set_axis(-1);
+ argmax_param->set_top_k(this->top_k_);
+ argmax_param->set_out_max_val(true);
+ ArgMaxLayer<TypeParam> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ // Now, check values
+ TypeParam max_val;
+ std::vector<int> shape = this->blob_bottom_->shape();
+ for (int i = 0; i < shape[0]; ++i) {
+ for (int j = 0; j < shape[1]; ++j) {
+ for (int k = 0; k < shape[2]; ++k) {
+ for (int m = 0; m < this->top_k_; ++m) {
+ max_val = this->blob_top_->data_at(i, j, k, m);
+ int count = 0;
+ for (int l = 0; l < shape[3]; ++l) {
+ if (this->blob_bottom_->data_at(i, j, k, l) > max_val) {
+ ++count;
+ }
+ }
+ EXPECT_EQ(m, count);
+ }
+ }
+ }
+ }
+}
} // namespace caffe
--- /dev/null
+#include <algorithm>
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/batch_norm_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+#define BATCH_SIZE 2
+#define INPUT_DATA_SIZE 3
+
+namespace caffe {
+
+ template <typename TypeParam>
+ class BatchNormLayerTest : public MultiDeviceTest<TypeParam> {
+ typedef typename TypeParam::Dtype Dtype;
+ protected:
+ BatchNormLayerTest()
+ : blob_bottom_(new Blob<Dtype>(5, 2, 3, 4)),
+ blob_top_(new Blob<Dtype>()) {
+ // fill the values
+ FillerParameter filler_param;
+ GaussianFiller<Dtype> filler(filler_param);
+ filler.Fill(this->blob_bottom_);
+ blob_bottom_vec_.push_back(blob_bottom_);
+ blob_top_vec_.push_back(blob_top_);
+ }
+ virtual ~BatchNormLayerTest() { delete blob_bottom_; delete blob_top_; }
+ Blob<Dtype>* const blob_bottom_;
+ Blob<Dtype>* const blob_top_;
+ vector<Blob<Dtype>*> blob_bottom_vec_;
+ vector<Blob<Dtype>*> blob_top_vec_;
+ };
+
+ TYPED_TEST_CASE(BatchNormLayerTest, TestDtypesAndDevices);
+
+ TYPED_TEST(BatchNormLayerTest, TestForward) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+
+ BatchNormLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+ // Test mean
+ int num = this->blob_bottom_->num();
+ int channels = this->blob_bottom_->channels();
+ int height = this->blob_bottom_->height();
+ int width = this->blob_bottom_->width();
+
+ for (int j = 0; j < channels; ++j) {
+ Dtype sum = 0, var = 0;
+ for (int i = 0; i < num; ++i) {
+ for ( int k = 0; k < height; ++k ) {
+ for ( int l = 0; l < width; ++l ) {
+ Dtype data = this->blob_top_->data_at(i, j, k, l);
+ sum += data;
+ var += data * data;
+ }
+ }
+ }
+ sum /= height * width * num;
+ var /= height * width * num;
+
+ const Dtype kErrorBound = 0.001;
+ // expect zero mean
+ EXPECT_NEAR(0, sum, kErrorBound);
+ // expect unit variance
+ EXPECT_NEAR(1, var, kErrorBound);
+ }
+ }
+
+ TYPED_TEST(BatchNormLayerTest, TestForwardInplace) {
+ typedef typename TypeParam::Dtype Dtype;
+ Blob<Dtype> blob_inplace(5, 2, 3, 4);
+ vector<Blob<Dtype>*> blob_bottom_vec;
+ vector<Blob<Dtype>*> blob_top_vec;
+ LayerParameter layer_param;
+ FillerParameter filler_param;
+ GaussianFiller<Dtype> filler(filler_param);
+ filler.Fill(&blob_inplace);
+ blob_bottom_vec.push_back(&blob_inplace);
+ blob_top_vec.push_back(&blob_inplace);
+
+ BatchNormLayer<Dtype> layer(layer_param);
+ layer.SetUp(blob_bottom_vec, blob_top_vec);
+ layer.Forward(blob_bottom_vec, blob_top_vec);
+
+ // Test mean
+ int num = blob_inplace.num();
+ int channels = blob_inplace.channels();
+ int height = blob_inplace.height();
+ int width = blob_inplace.width();
+
+ for (int j = 0; j < channels; ++j) {
+ Dtype sum = 0, var = 0;
+ for (int i = 0; i < num; ++i) {
+ for ( int k = 0; k < height; ++k ) {
+ for ( int l = 0; l < width; ++l ) {
+ Dtype data = blob_inplace.data_at(i, j, k, l);
+ sum += data;
+ var += data * data;
+ }
+ }
+ }
+ sum /= height * width * num;
+ var /= height * width * num;
+
+ const Dtype kErrorBound = 0.001;
+ // expect zero mean
+ EXPECT_NEAR(0, sum, kErrorBound);
+ // expect unit variance
+ EXPECT_NEAR(1, var, kErrorBound);
+ }
+ }
+
+ TYPED_TEST(BatchNormLayerTest, TestGradient) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+
+ BatchNormLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-4);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+ }
+
+} // namespace caffe
--- /dev/null
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/batch_reindex_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template<typename TypeParam>
+class BatchReindexLayerTest : public MultiDeviceTest<TypeParam> {
+ typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+ BatchReindexLayerTest()
+ : blob_bottom_(new Blob<Dtype>()),
+ blob_bottom_permute_(new Blob<Dtype>()),
+ blob_top_(new Blob<Dtype>()) {
+ }
+ virtual void SetUp() {
+ Caffe::set_random_seed(1701);
+ vector<int> sz;
+ sz.push_back(5);
+ sz.push_back(4);
+ sz.push_back(3);
+ sz.push_back(2);
+ blob_bottom_->Reshape(sz);
+ vector<int> permsz;
+ permsz.push_back(6);
+ blob_bottom_permute_->Reshape(permsz);
+
+ // fill the values
+ FillerParameter filler_param;
+ GaussianFiller<Dtype> filler(filler_param);
+ filler.Fill(this->blob_bottom_);
+ int perm[] = { 4, 0, 4, 0, 1, 2 };
+ for (int i = 0; i < blob_bottom_permute_->count(); ++i) {
+ blob_bottom_permute_->mutable_cpu_data()[i] = perm[i];
+ }
+
+ blob_bottom_vec_.push_back(blob_bottom_);
+ blob_bottom_vec_.push_back(blob_bottom_permute_);
+ blob_top_vec_.push_back(blob_top_);
+ }
+ virtual ~BatchReindexLayerTest() {
+ delete blob_bottom_permute_;
+ delete blob_bottom_;
+ delete blob_top_;
+ }
+ Blob<Dtype>* const blob_bottom_;
+ Blob<Dtype>* const blob_bottom_permute_;
+ Blob<Dtype>* const blob_top_;
+ vector<Blob<Dtype>*> blob_bottom_vec_;
+ vector<Blob<Dtype>*> blob_top_vec_;
+
+ void TestForward() {
+ LayerParameter layer_param;
+
+ vector<int> sz;
+ sz.push_back(5);
+ sz.push_back(4);
+ sz.push_back(3);
+ sz.push_back(2);
+ blob_bottom_->Reshape(sz);
+ for (int i = 0; i < blob_bottom_->count(); ++i) {
+ blob_bottom_->mutable_cpu_data()[i] = i;
+ }
+
+ vector<int> permsz;
+ permsz.push_back(6);
+ blob_bottom_permute_->Reshape(permsz);
+ int perm[] = { 4, 0, 4, 0, 1, 2 };
+ for (int i = 0; i < blob_bottom_permute_->count(); ++i) {
+ blob_bottom_permute_->mutable_cpu_data()[i] = perm[i];
+ }
+ BatchReindexLayer<Dtype> layer(layer_param);
+ layer.SetUp(blob_bottom_vec_, blob_top_vec_);
+ EXPECT_EQ(blob_top_->num(), blob_bottom_permute_->num());
+ EXPECT_EQ(blob_top_->channels(), blob_bottom_->channels());
+ EXPECT_EQ(blob_top_->height(), blob_bottom_->height());
+ EXPECT_EQ(blob_top_->width(), blob_bottom_->width());
+
+ layer.Forward(blob_bottom_vec_, blob_top_vec_);
+ int channels = blob_top_->channels();
+ int height = blob_top_->height();
+ int width = blob_top_->width();
+ for (int i = 0; i < blob_top_->count(); ++i) {
+ int n = i / (channels * width * height);
+ int inner_idx = (i % (channels * width * height));
+ EXPECT_EQ(
+ blob_top_->cpu_data()[i],
+ blob_bottom_->cpu_data()[perm[n] * channels * width * height
+ + inner_idx]);
+ }
+ }
+};
+
+TYPED_TEST_CASE(BatchReindexLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(BatchReindexLayerTest, TestForward) {
+ this->TestForward();
+}
+
+TYPED_TEST(BatchReindexLayerTest, TestGradient) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ BatchReindexLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-4, 1e-2);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_, 0);
+ }
+
+} // namespace caffe
-#include <unistd.h> // for usleep
+#include <boost/thread.hpp>
#include "gtest/gtest.h"
EXPECT_FALSE(timer.running());
EXPECT_FALSE(timer.has_run_at_least_once());
timer.Start();
- usleep(300 * 1000);
+ boost::this_thread::sleep(boost::posix_time::milliseconds(300));
EXPECT_GE(timer.MilliSeconds(), 300 - kMillisecondsThreshold);
EXPECT_LE(timer.MilliSeconds(), 300 + kMillisecondsThreshold);
EXPECT_TRUE(timer.initted());
EXPECT_FALSE(timer.running());
EXPECT_FALSE(timer.has_run_at_least_once());
timer.Start();
- usleep(300 * 1000);
+ boost::this_thread::sleep(boost::posix_time::milliseconds(300));
EXPECT_GE(timer.Seconds(), 0.3 - kMillisecondsThreshold / 1000.);
EXPECT_LE(timer.Seconds(), 0.3 + kMillisecondsThreshold / 1000.);
EXPECT_TRUE(timer.initted());
-#include <cstring>
#include <vector>
#include "gtest/gtest.h"
-#include <cstring>
-
#include "gtest/gtest.h"
#include "caffe/common.hpp"
-#include <cstring>
#include <vector>
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/concat_layer.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
EXPECT_EQ(this->blob_top_->width(), this->blob_bottom_0_->width());
}
+TYPED_TEST(ConcatLayerTest, TestForwardTrivial) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ ConcatLayer<Dtype> layer(layer_param);
+ this->blob_bottom_vec_0_.resize(1);
+ layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_0_, this->blob_top_vec_);
+ for (int i = 0; i < this->blob_bottom_0_->count(); ++i) {
+ EXPECT_EQ(this->blob_bottom_0_->cpu_data()[i],
+ this->blob_top_->cpu_data()[i]);
+ }
+}
+
TYPED_TEST(ConcatLayerTest, TestForwardNum) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
}
}
+TYPED_TEST(ConcatLayerTest, TestGradientTrivial) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ ConcatLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-2);
+ this->blob_bottom_vec_0_.resize(1);
+ checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_0_,
+ this->blob_top_vec_);
+}
+
TYPED_TEST(ConcatLayerTest, TestGradientNum) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
this->blob_top_vec_);
}
+TYPED_TEST(ConcatLayerTest, TestGradientChannelsBottomOneOnly) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ ConcatLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-2);
+ checker.CheckGradient(&layer, this->blob_bottom_vec_0_,
+ this->blob_top_vec_, 1);
+}
+
} // namespace caffe
#include <algorithm>
#include <cmath>
-#include <cstdlib>
-#include <cstring>
#include <vector>
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/contrastive_loss_layer.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
protected:
ContrastiveLossLayerTest()
- : blob_bottom_data_i_(new Blob<Dtype>(128, 10, 1, 1)),
- blob_bottom_data_j_(new Blob<Dtype>(128, 10, 1, 1)),
- blob_bottom_y_(new Blob<Dtype>(128, 1, 1, 1)),
+ : blob_bottom_data_i_(new Blob<Dtype>(512, 2, 1, 1)),
+ blob_bottom_data_j_(new Blob<Dtype>(512, 2, 1, 1)),
+ blob_bottom_y_(new Blob<Dtype>(512, 1, 1, 1)),
blob_top_loss_(new Blob<Dtype>()) {
// fill the values
FillerParameter filler_param;
- filler_param.set_mean(0.0);
- filler_param.set_std(0.3); // distances~=1.0 to test both sides of margin
- GaussianFiller<Dtype> filler(filler_param);
+ filler_param.set_min(-1.0);
+ filler_param.set_max(1.0); // distances~=1.0 to test both sides of margin
+ UniformFiller<Dtype> filler(filler_param);
filler.Fill(this->blob_bottom_data_i_);
blob_bottom_vec_.push_back(blob_bottom_data_i_);
filler.Fill(this->blob_bottom_data_j_);
if (this->blob_bottom_y_->cpu_data()[i]) { // similar pairs
loss += dist_sq;
} else {
- loss += std::max(margin-dist_sq, Dtype(0));
+ Dtype dist = std::max<Dtype>(margin - sqrt(dist_sq), 0.0);
+ loss += dist*dist;
}
}
loss /= static_cast<Dtype>(num) * Dtype(2);
this->blob_top_vec_, 1);
}
+TYPED_TEST(ContrastiveLossLayerTest, TestForwardLegacy) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_contrastive_loss_param()->set_legacy_version(true);
+ ContrastiveLossLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ // manually compute to compare
+ const Dtype margin = layer_param.contrastive_loss_param().margin();
+ const int num = this->blob_bottom_data_i_->num();
+ const int channels = this->blob_bottom_data_i_->channels();
+ Dtype loss(0);
+ for (int i = 0; i < num; ++i) {
+ Dtype dist_sq(0);
+ for (int j = 0; j < channels; ++j) {
+ Dtype diff = this->blob_bottom_data_i_->cpu_data()[i*channels+j] -
+ this->blob_bottom_data_j_->cpu_data()[i*channels+j];
+ dist_sq += diff*diff;
+ }
+ if (this->blob_bottom_y_->cpu_data()[i]) { // similar pairs
+ loss += dist_sq;
+ } else {
+ loss += std::max(margin - dist_sq, Dtype(0.0));
+ }
+ }
+ loss /= static_cast<Dtype>(num) * Dtype(2);
+ EXPECT_NEAR(this->blob_top_loss_->cpu_data()[0], loss, 1e-6);
+}
+
+TYPED_TEST(ContrastiveLossLayerTest, TestGradientLegacy) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_contrastive_loss_param()->set_legacy_version(true);
+ ContrastiveLossLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
+ // check the gradient for the first two bottom layers
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_, 0);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_, 1);
+}
+
} // namespace caffe
-#include <cstring>
#include <vector>
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/conv_layer.hpp"
+
+#ifdef USE_CUDNN
+#include "caffe/layers/cudnn_conv_layer.hpp"
+#endif
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
void caffe_conv(const Blob<Dtype>* in, ConvolutionParameter* conv_param,
const vector<shared_ptr<Blob<Dtype> > >& weights,
Blob<Dtype>* out) {
+ const bool has_depth = (out->num_axes() == 5);
+ if (!has_depth) { CHECK_EQ(4, out->num_axes()); }
// Kernel size, stride, and pad
int kernel_h, kernel_w;
- if (conv_param->has_kernel_size()) {
- kernel_h = kernel_w = conv_param->kernel_size();
- } else {
+ if (conv_param->has_kernel_h() || conv_param->has_kernel_w()) {
kernel_h = conv_param->kernel_h();
kernel_w = conv_param->kernel_w();
+ } else {
+ kernel_h = kernel_w = conv_param->kernel_size(0);
}
int pad_h, pad_w;
- if (!conv_param->has_pad_h()) {
- pad_h = pad_w = conv_param->pad();
- } else {
+ if (conv_param->has_pad_h() || conv_param->has_pad_w()) {
pad_h = conv_param->pad_h();
pad_w = conv_param->pad_w();
+ } else {
+ pad_h = pad_w = conv_param->pad_size() ? conv_param->pad(0) : 0;
}
int stride_h, stride_w;
- if (!conv_param->has_stride_h()) {
- stride_h = stride_w = conv_param->stride();
- } else {
+ if (conv_param->has_stride_h() || conv_param->has_stride_w()) {
stride_h = conv_param->stride_h();
stride_w = conv_param->stride_w();
+ } else {
+ stride_h = stride_w = conv_param->stride_size() ? conv_param->stride(0) : 1;
+ }
+ int dilation_h, dilation_w;
+ dilation_h = dilation_w = conv_param->dilation_size() ?
+ conv_param->dilation(0) : 1;
+ int kernel_d, pad_d, stride_d, dilation_d;
+ if (has_depth) {
+ kernel_d = kernel_h;
+ stride_d = stride_h;
+ pad_d = pad_h;
+ dilation_d = dilation_h;
+ } else {
+ kernel_d = stride_d = dilation_d = 1;
+ pad_d = 0;
}
// Groups
int groups = conv_param->group();
- int o_g = out->channels() / groups;
- int k_g = in->channels() / groups;
+ int o_g = out->shape(1) / groups;
+ int k_g = in->shape(1) / groups;
int o_head, k_head;
// Convolution
- const Dtype* in_data = in->cpu_data();
- const Dtype* weight_data = weights[0]->cpu_data();
+ vector<int> weight_offset(4 + has_depth);
+ vector<int> in_offset(4 + has_depth);
+ vector<int> out_offset(4 + has_depth);
Dtype* out_data = out->mutable_cpu_data();
- for (int n = 0; n < out->num(); n++) {
+ for (int n = 0; n < out->shape(0); n++) {
for (int g = 0; g < groups; g++) {
o_head = o_g * g;
k_head = k_g * g;
for (int o = 0; o < o_g; o++) {
for (int k = 0; k < k_g; k++) {
- for (int y = 0; y < out->height(); y++) {
- for (int x = 0; x < out->width(); x++) {
- for (int p = 0; p < kernel_h; p++) {
- for (int q = 0; q < kernel_w; q++) {
- int in_y = y * stride_h - pad_h + p;
- int in_x = x * stride_w - pad_w + q;
- if (in_y >= 0 && in_y < in->height()
- && in_x >= 0 && in_x < in->width()) {
- out_data[out->offset(n, o + o_head, y, x)] +=
- in_data[in->offset(n, k + k_head, in_y, in_x)]
- * weight_data[weights[0]->offset(o + o_head, k, p, q)];
+ for (int z = 0; z < (has_depth ? out->shape(2) : 1); z++) {
+ for (int y = 0; y < out->shape(2 + has_depth); y++) {
+ for (int x = 0; x < out->shape(3 + has_depth); x++) {
+ for (int r = 0; r < kernel_d; r++) {
+ for (int p = 0; p < kernel_h; p++) {
+ for (int q = 0; q < kernel_w; q++) {
+ int in_z = z * stride_d - pad_d + r * dilation_d;
+ int in_y = y * stride_h - pad_h + p * dilation_h;
+ int in_x = x * stride_w - pad_w + q * dilation_w;
+ if (in_z >= 0 && in_z < (has_depth ? in->shape(2) : 1)
+ && in_y >= 0 && in_y < in->shape(2 + has_depth)
+ && in_x >= 0 && in_x < in->shape(3 + has_depth)) {
+ weight_offset[0] = o + o_head;
+ weight_offset[1] = k;
+ if (has_depth) { weight_offset[2] = r; }
+ weight_offset[2 + has_depth] = p;
+ weight_offset[3 + has_depth] = q;
+ in_offset[0] = n;
+ in_offset[1] = k + k_head;
+ if (has_depth) { in_offset[2] = in_z; }
+ in_offset[2 + has_depth] = in_y;
+ in_offset[3 + has_depth] = in_x;
+ out_offset[0] = n;
+ out_offset[1] = o + o_head;
+ if (has_depth) { out_offset[2] = z; }
+ out_offset[2 + has_depth] = y;
+ out_offset[3 + has_depth] = x;
+ out_data[out->offset(out_offset)] +=
+ in->data_at(in_offset)
+ * weights[0]->data_at(weight_offset);
+ }
+ }
}
}
}
// Bias
if (conv_param->bias_term()) {
const Dtype* bias_data = weights[1]->cpu_data();
- for (int n = 0; n < out->num(); n++) {
- for (int o = 0; o < out->channels(); o++) {
- for (int y = 0; y < out->height(); y++) {
- for (int x = 0; x < out->width(); x++) {
- out_data[out->offset(n, o, y, x)] += bias_data[o];
+ for (int n = 0; n < out->shape(0); n++) {
+ for (int o = 0; o < out->shape(1); o++) {
+ for (int z = 0; z < (has_depth ? out->shape(2) : 1); z++) {
+ for (int y = 0; y < out->shape(2 + has_depth); y++) {
+ for (int x = 0; x < out->shape(3 + has_depth); x++) {
+ out_offset[0] = n;
+ out_offset[1] = o;
+ if (has_depth) { out_offset[2] = z; }
+ out_offset[2 + has_depth] = y;
+ out_offset[3 + has_depth] = x;
+ out_data[out->offset(out_offset)] += bias_data[o];
+ }
}
}
}
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
- convolution_param->set_kernel_size(3);
- convolution_param->set_stride(2);
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_stride(2);
convolution_param->set_num_output(4);
this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
this->blob_top_vec_.push_back(this->blob_top_2_);
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
- convolution_param->set_kernel_size(3);
- convolution_param->set_stride(2);
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_stride(2);
convolution_param->set_num_output(4);
convolution_param->mutable_weight_filler()->set_type("gaussian");
convolution_param->mutable_bias_filler()->set_type("constant");
}
}
+TYPED_TEST(ConvolutionLayerTest, TestDilatedConvolution) {
+ typedef typename TypeParam::Dtype Dtype;
+ vector<int> bottom_shape;
+ bottom_shape.push_back(2);
+ bottom_shape.push_back(3);
+ bottom_shape.push_back(8);
+ bottom_shape.push_back(7);
+ this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+ this->blob_top_vec_.push_back(this->blob_top_2_);
+ for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+ this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+ }
+ LayerParameter layer_param;
+ ConvolutionParameter* convolution_param =
+ layer_param.mutable_convolution_param();
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_dilation(2);
+ convolution_param->set_num_output(4);
+ convolution_param->mutable_weight_filler()->set_type("gaussian");
+ convolution_param->mutable_bias_filler()->set_type("constant");
+ convolution_param->mutable_bias_filler()->set_value(0.1);
+ shared_ptr<Layer<Dtype> > layer(
+ new ConvolutionLayer<Dtype>(layer_param));
+ layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ // Check against reference convolution.
+ const Dtype* top_data;
+ const Dtype* ref_top_data;
+ caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
+ this->MakeReferenceTop(this->blob_top_));
+ top_data = this->blob_top_->cpu_data();
+ ref_top_data = this->ref_blob_top_->cpu_data();
+ for (int i = 0; i < this->blob_top_->count(); ++i) {
+ EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
+ }
+ caffe_conv(this->blob_bottom_2_, convolution_param, layer->blobs(),
+ this->MakeReferenceTop(this->blob_top_2_));
+ top_data = this->blob_top_2_->cpu_data();
+ ref_top_data = this->ref_blob_top_->cpu_data();
+ for (int i = 0; i < this->blob_top_->count(); ++i) {
+ EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
+ }
+}
+
+TYPED_TEST(ConvolutionLayerTest, Test0DConvolution) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ ConvolutionParameter* convolution_param =
+ layer_param.mutable_convolution_param();
+ const int kNumOutput = 3;
+ convolution_param->set_num_output(kNumOutput);
+ convolution_param->set_axis(3);
+ convolution_param->mutable_weight_filler()->set_type("gaussian");
+ convolution_param->mutable_bias_filler()->set_type("gaussian");
+ shared_ptr<Layer<Dtype> > layer(
+ new ConvolutionLayer<Dtype>(layer_param));
+ vector<int> top_shape = this->blob_bottom_->shape();
+ top_shape[3] = kNumOutput;
+ layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ EXPECT_EQ(top_shape, this->blob_top_->shape());
+ layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ // Check against reference convolution.
+ vector<int> weight_offset(2);
+ const Blob<Dtype>* weight = layer->blobs()[0].get();
+ const Blob<Dtype>* bias = layer->blobs()[1].get();
+ const int num = this->blob_top_->count(3);
+ const int dim = this->blob_top_->shape(3);
+ const int bottom_dim = this->blob_bottom_->shape(3);
+ for (int n = 0; n < num; ++n) {
+ for (int d = 0; d < dim; ++d) {
+ weight_offset[0] = d;
+ Dtype value = bias->cpu_data()[d];
+ for (int bottom_d = 0; bottom_d < bottom_dim; ++bottom_d) {
+ weight_offset[1] = bottom_d;
+ value += weight->data_at(weight_offset) *
+ this->blob_bottom_->cpu_data()[n * bottom_dim + bottom_d];
+ }
+ EXPECT_NEAR(value, this->blob_top_->cpu_data()[n * dim + d], 1e-4);
+ }
+ }
+}
+
+TYPED_TEST(ConvolutionLayerTest, TestSimple3DConvolution) {
+ typedef typename TypeParam::Dtype Dtype;
+ this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+ this->blob_top_vec_.push_back(this->blob_top_2_);
+ vector<int> bottom_shape(5);
+ bottom_shape[0] = this->blob_bottom_vec_[0]->shape(0);
+ bottom_shape[1] = this->blob_bottom_vec_[0]->shape(1);
+ bottom_shape[2] = 5;
+ bottom_shape[3] = this->blob_bottom_vec_[0]->shape(2);
+ bottom_shape[4] = this->blob_bottom_vec_[0]->shape(3);
+ FillerParameter filler_param;
+ GaussianFiller<Dtype> filler(filler_param);
+ for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+ this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+ filler.Fill(this->blob_bottom_vec_[i]);
+ }
+ LayerParameter layer_param;
+ ConvolutionParameter* convolution_param =
+ layer_param.mutable_convolution_param();
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_stride(2);
+ convolution_param->set_num_output(4);
+ convolution_param->mutable_weight_filler()->set_type("gaussian");
+ convolution_param->mutable_bias_filler()->set_type("gaussian");
+ shared_ptr<Layer<Dtype> > layer(
+ new ConvolutionLayer<Dtype>(layer_param));
+ layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ // Check against reference convolution.
+ const Dtype* top_data;
+ const Dtype* ref_top_data;
+ caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
+ this->MakeReferenceTop(this->blob_top_));
+ top_data = this->blob_top_->cpu_data();
+ ref_top_data = this->ref_blob_top_->cpu_data();
+ for (int i = 0; i < this->blob_top_->count(); ++i) {
+ EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
+ }
+ caffe_conv(this->blob_bottom_2_, convolution_param, layer->blobs(),
+ this->MakeReferenceTop(this->blob_top_2_));
+ top_data = this->blob_top_2_->cpu_data();
+ ref_top_data = this->ref_blob_top_->cpu_data();
+ for (int i = 0; i < this->blob_top_->count(); ++i) {
+ EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
+ }
+}
+
+TYPED_TEST(ConvolutionLayerTest, TestDilated3DConvolution) {
+ typedef typename TypeParam::Dtype Dtype;
+ this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+ this->blob_top_vec_.push_back(this->blob_top_2_);
+ vector<int> bottom_shape(5);
+ bottom_shape[0] = this->blob_bottom_vec_[0]->shape(0);
+ bottom_shape[1] = this->blob_bottom_vec_[0]->shape(1);
+ bottom_shape[2] = 6;
+ bottom_shape[3] = 7;
+ bottom_shape[4] = 8;
+ FillerParameter filler_param;
+ GaussianFiller<Dtype> filler(filler_param);
+ for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+ this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+ filler.Fill(this->blob_bottom_vec_[i]);
+ }
+ LayerParameter layer_param;
+ ConvolutionParameter* convolution_param =
+ layer_param.mutable_convolution_param();
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_dilation(2);
+ convolution_param->set_num_output(4);
+ convolution_param->mutable_weight_filler()->set_type("gaussian");
+ convolution_param->mutable_bias_filler()->set_type("gaussian");
+ shared_ptr<Layer<Dtype> > layer(
+ new ConvolutionLayer<Dtype>(layer_param));
+ layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ // Check against reference convolution.
+ const Dtype* top_data;
+ const Dtype* ref_top_data;
+ caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
+ this->MakeReferenceTop(this->blob_top_));
+ top_data = this->blob_top_->cpu_data();
+ ref_top_data = this->ref_blob_top_->cpu_data();
+ for (int i = 0; i < this->blob_top_->count(); ++i) {
+ EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
+ }
+ caffe_conv(this->blob_bottom_2_, convolution_param, layer->blobs(),
+ this->MakeReferenceTop(this->blob_top_2_));
+ top_data = this->blob_top_2_->cpu_data();
+ ref_top_data = this->ref_blob_top_->cpu_data();
+ for (int i = 0; i < this->blob_top_->count(); ++i) {
+ EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
+ }
+}
+
TYPED_TEST(ConvolutionLayerTest, Test1x1Convolution) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
- convolution_param->set_kernel_size(1);
- convolution_param->set_stride(1);
+ convolution_param->add_kernel_size(1);
+ convolution_param->add_stride(1);
convolution_param->set_num_output(4);
convolution_param->mutable_weight_filler()->set_type("gaussian");
convolution_param->mutable_bias_filler()->set_type("constant");
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
- convolution_param->set_kernel_size(3);
- convolution_param->set_stride(2);
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_stride(2);
convolution_param->set_num_output(3);
convolution_param->set_group(3);
convolution_param->mutable_weight_filler()->set_type("gaussian");
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
- convolution_param->set_kernel_size(3);
- convolution_param->set_stride(2);
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_stride(2);
convolution_param->set_num_output(1);
convolution_param->set_bias_term(false);
shared_ptr<Layer<Dtype> > layer(
convolution_param->set_bias_term(false);
layer.reset(new ConvolutionLayer<Dtype>(layer_param));
layer->blobs().resize(1);
- layer->blobs()[0].reset(new Blob<Dtype>(1, 3, 1, 3));
+ layer->blobs()[0].reset(new Blob<Dtype>(1, 1, 1, 3));
Dtype* weights_2 = layer->blobs()[0]->mutable_cpu_data();
- for (int c = 0; c < 3; ++c) {
- int i = c * 3; // 1 x 3 filter
- weights_2[i + 0] = -1;
- weights_2[i + 1] = 0;
- weights_2[i + 2] = 1;
- }
+ weights_2[0] = -1;
+ weights_2[1] = 0;
+ weights_2[2] = 1;
layer->SetUp(sep_blob_bottom_vec, sep_blob_top_vec);
layer->Forward(sep_blob_bottom_vec, sep_blob_top_vec);
// Test equivalence of full and separable filters.
}
}
+TYPED_TEST(ConvolutionLayerTest, TestNDAgainst2D) {
+ typedef typename TypeParam::Dtype Dtype;
+ const int kernel_h = 11;
+ const int kernel_w = 13;
+ vector<int> bottom_shape(4);
+ bottom_shape[0] = 15;
+ bottom_shape[1] = 18;
+ bottom_shape[2] = kernel_h * 2;
+ bottom_shape[3] = kernel_w * 2;
+ FillerParameter filler_param;
+ GaussianFiller<Dtype> filler(filler_param);
+ for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+ this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+ filler.Fill(this->blob_bottom_vec_[i]);
+ }
+ LayerParameter layer_param;
+ ConvolutionParameter* convolution_param =
+ layer_param.mutable_convolution_param();
+ convolution_param->set_num_output(12);
+ convolution_param->set_bias_term(false);
+ convolution_param->set_group(6);
+ convolution_param->set_kernel_h(kernel_h);
+ convolution_param->set_kernel_w(kernel_w);
+ convolution_param->mutable_weight_filler()->set_type("gaussian");
+ Blob<Dtype> weights;
+ Blob<Dtype> top_diff;
+ // Shape and fill weights and top_diff.
+ bool copy_diff;
+ bool reshape;
+ {
+ ConvolutionLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ top_diff.ReshapeLike(*this->blob_top_);
+ filler.Fill(&top_diff);
+ ASSERT_EQ(1, layer.blobs().size());
+ copy_diff = false; reshape = true;
+ weights.CopyFrom(*layer.blobs()[0], copy_diff, reshape);
+ }
+ vector<bool> propagate_down(1, true);
+ Blob<Dtype> result_2d;
+ Blob<Dtype> backward_result_2d;
+ Blob<Dtype> backward_weight_result_2d;
+ // Test with 2D im2col
+ {
+ caffe_set(this->blob_top_->count(), Dtype(0),
+ this->blob_top_->mutable_cpu_data());
+ caffe_set(this->blob_bottom_->count(), Dtype(0),
+ this->blob_bottom_->mutable_cpu_diff());
+ caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff());
+ // Do SetUp and Forward; save Forward result in result_2d.
+ convolution_param->set_force_nd_im2col(false);
+ ConvolutionLayer<Dtype> layer_2d(layer_param);
+ layer_2d.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ ASSERT_EQ(1, layer_2d.blobs().size());
+ copy_diff = false; reshape = false;
+ layer_2d.blobs()[0]->CopyFrom(weights, copy_diff, reshape);
+ layer_2d.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ copy_diff = false; reshape = true;
+ result_2d.CopyFrom(*this->blob_top_, copy_diff, reshape);
+ // Copy pre-generated top diff into actual top diff;
+ // do Backward and save result in backward_result_2d.
+ ASSERT_EQ(this->blob_top_->shape(), top_diff.shape());
+ caffe_copy(top_diff.count(), top_diff.cpu_data(),
+ this->blob_top_->mutable_cpu_diff());
+ layer_2d.Backward(this->blob_top_vec_, propagate_down,
+ this->blob_bottom_vec_);
+ copy_diff = true; reshape = true;
+ backward_result_2d.CopyFrom(*this->blob_bottom_, copy_diff, reshape);
+ backward_weight_result_2d.CopyFrom(weights, copy_diff, reshape);
+ }
+ Blob<Dtype> result_nd;
+ Blob<Dtype> backward_result_nd;
+ Blob<Dtype> backward_weight_result_nd;
+ // Test with ND im2col
+ {
+ caffe_set(this->blob_top_->count(), Dtype(0),
+ this->blob_top_->mutable_cpu_data());
+ caffe_set(this->blob_bottom_->count(), Dtype(0),
+ this->blob_bottom_->mutable_cpu_diff());
+ caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff());
+ // Do SetUp and Forward; save Forward result in result_nd.
+ convolution_param->set_force_nd_im2col(true);
+ ConvolutionLayer<Dtype> layer_nd(layer_param);
+ layer_nd.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ ASSERT_EQ(1, layer_nd.blobs().size());
+ copy_diff = false; reshape = false;
+ layer_nd.blobs()[0]->CopyFrom(weights, copy_diff, reshape);
+ layer_nd.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ copy_diff = false; reshape = true;
+ result_nd.CopyFrom(*this->blob_top_, copy_diff, reshape);
+ // Copy pre-generated top diff into actual top diff;
+ // do Backward and save result in backward_result_nd.
+ ASSERT_EQ(this->blob_top_->shape(), top_diff.shape());
+ caffe_copy(top_diff.count(), top_diff.cpu_data(),
+ this->blob_top_->mutable_cpu_diff());
+ layer_nd.Backward(this->blob_top_vec_, propagate_down,
+ this->blob_bottom_vec_);
+ copy_diff = true; reshape = true;
+ backward_result_nd.CopyFrom(*this->blob_bottom_, copy_diff, reshape);
+ backward_weight_result_nd.CopyFrom(weights, copy_diff, reshape);
+ }
+ ASSERT_EQ(result_nd.count(), result_2d.count());
+ for (int i = 0; i < result_2d.count(); ++i) {
+ EXPECT_EQ(result_2d.cpu_data()[i], result_nd.cpu_data()[i]);
+ }
+ ASSERT_EQ(backward_result_nd.count(), backward_result_2d.count());
+ for (int i = 0; i < backward_result_2d.count(); ++i) {
+ EXPECT_EQ(backward_result_2d.cpu_diff()[i],
+ backward_result_nd.cpu_diff()[i]);
+ }
+ ASSERT_EQ(backward_weight_result_nd.count(),
+ backward_weight_result_2d.count());
+ for (int i = 0; i < backward_weight_result_2d.count(); ++i) {
+ EXPECT_EQ(backward_weight_result_2d.cpu_diff()[i],
+ backward_weight_result_nd.cpu_diff()[i]);
+ }
+}
+
TYPED_TEST(ConvolutionLayerTest, TestGradient) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
layer_param.mutable_convolution_param();
this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
this->blob_top_vec_.push_back(this->blob_top_2_);
- convolution_param->set_kernel_size(3);
- convolution_param->set_stride(2);
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_stride(2);
+ convolution_param->set_num_output(2);
+ convolution_param->mutable_weight_filler()->set_type("gaussian");
+ convolution_param->mutable_bias_filler()->set_type("gaussian");
+ ConvolutionLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-3);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+}
+
+TYPED_TEST(ConvolutionLayerTest, TestDilatedGradient) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ ConvolutionParameter* convolution_param =
+ layer_param.mutable_convolution_param();
+ vector<int> bottom_shape;
+ bottom_shape.push_back(2);
+ bottom_shape.push_back(3);
+ bottom_shape.push_back(5);
+ bottom_shape.push_back(6);
+ for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+ this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+ }
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_dilation(2);
+ convolution_param->set_num_output(2);
+ convolution_param->mutable_weight_filler()->set_type("gaussian");
+ convolution_param->mutable_bias_filler()->set_type("gaussian");
+ ConvolutionLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-3);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+}
+
+TYPED_TEST(ConvolutionLayerTest, TestGradient3D) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ ConvolutionParameter* convolution_param =
+ layer_param.mutable_convolution_param();
+ vector<int> bottom_shape(5);
+ bottom_shape[0] = this->blob_bottom_vec_[0]->shape(0);
+ bottom_shape[1] = this->blob_bottom_vec_[0]->shape(1);
+ bottom_shape[2] = 5;
+ bottom_shape[3] = this->blob_bottom_vec_[0]->shape(2);
+ bottom_shape[4] = this->blob_bottom_vec_[0]->shape(3);
+ FillerParameter filler_param;
+ GaussianFiller<Dtype> filler(filler_param);
+ for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+ this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+ filler.Fill(this->blob_bottom_vec_[i]);
+ }
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_stride(2);
convolution_param->set_num_output(2);
convolution_param->mutable_weight_filler()->set_type("gaussian");
convolution_param->mutable_bias_filler()->set_type("gaussian");
layer_param.mutable_convolution_param();
this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
this->blob_top_vec_.push_back(this->blob_top_2_);
- convolution_param->set_kernel_size(1);
- convolution_param->set_stride(1);
+ convolution_param->add_kernel_size(1);
+ convolution_param->add_stride(1);
convolution_param->set_num_output(2);
convolution_param->mutable_weight_filler()->set_type("gaussian");
convolution_param->mutable_bias_filler()->set_type("gaussian");
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
- convolution_param->set_kernel_size(3);
- convolution_param->set_stride(2);
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_stride(2);
convolution_param->set_num_output(3);
convolution_param->set_group(3);
convolution_param->mutable_weight_filler()->set_type("gaussian");
#ifdef USE_CUDNN
template <typename Dtype>
-class CuDNNConvolutionLayerTest : public ::testing::Test {
+class CuDNNConvolutionLayerTest : public GPUDeviceTest<Dtype> {
protected:
CuDNNConvolutionLayerTest()
: blob_bottom_(new Blob<Dtype>(2, 3, 6, 4)),
TYPED_TEST_CASE(CuDNNConvolutionLayerTest, TestDtypes);
TYPED_TEST(CuDNNConvolutionLayerTest, TestSetupCuDNN) {
- Caffe::set_mode(Caffe::GPU);
this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
this->blob_top_vec_.push_back(this->blob_top_2_);
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
- convolution_param->set_kernel_size(3);
- convolution_param->set_stride(2);
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_stride(2);
convolution_param->set_num_output(4);
this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
this->blob_top_vec_.push_back(this->blob_top_2_);
}
TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionCuDNN) {
- Caffe::set_mode(Caffe::GPU);
this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
this->blob_top_vec_.push_back(this->blob_top_2_);
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
- convolution_param->set_kernel_size(3);
- convolution_param->set_stride(2);
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_stride(2);
convolution_param->set_num_output(4);
convolution_param->mutable_weight_filler()->set_type("gaussian");
convolution_param->mutable_bias_filler()->set_type("constant");
}
TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionGroupCuDNN) {
- Caffe::set_mode(Caffe::GPU);
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
- convolution_param->set_kernel_size(3);
- convolution_param->set_stride(2);
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_stride(2);
convolution_param->set_num_output(3);
convolution_param->set_group(3);
convolution_param->mutable_weight_filler()->set_type("gaussian");
// Test separable convolution by computing the Sobel operator
// as a single filter then comparing the result
// as the convolution of two rectangular filters.
- Caffe::set_mode(Caffe::GPU);
+
// Fill bottoms with identical Gaussian noise.
shared_ptr<GaussianFiller<TypeParam> > filler;
FillerParameter filler_param;
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
- convolution_param->set_kernel_size(3);
- convolution_param->set_stride(2);
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_stride(2);
convolution_param->set_num_output(1);
convolution_param->set_bias_term(false);
shared_ptr<Layer<TypeParam> > layer(
convolution_param->set_bias_term(false);
layer.reset(new CuDNNConvolutionLayer<TypeParam>(layer_param));
layer->blobs().resize(1);
- layer->blobs()[0].reset(new Blob<TypeParam>(1, 3, 1, 3));
+ layer->blobs()[0].reset(new Blob<TypeParam>(1, 1, 1, 3));
TypeParam* weights_2 = layer->blobs()[0]->mutable_cpu_data();
- for (int c = 0; c < 3; ++c) {
- int i = c * 3; // 1 x 3 filter
- weights_2[i + 0] = -1;
- weights_2[i + 1] = 0;
- weights_2[i + 2] = 1;
- }
+ weights_2[0] = -1;
+ weights_2[1] = 0;
+ weights_2[2] = 1;
layer->SetUp(sep_blob_bottom_vec, sep_blob_top_vec);
layer->Forward(sep_blob_bottom_vec, sep_blob_top_vec);
// Test equivalence of full and separable filters.
}
TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientCuDNN) {
- Caffe::set_mode(Caffe::GPU);
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
this->blob_top_vec_.push_back(this->blob_top_2_);
- convolution_param->set_kernel_size(3);
- convolution_param->set_stride(2);
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_stride(2);
convolution_param->set_num_output(2);
convolution_param->mutable_weight_filler()->set_type("gaussian");
convolution_param->mutable_bias_filler()->set_type("gaussian");
}
TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientGroupCuDNN) {
- Caffe::set_mode(Caffe::GPU);
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
- convolution_param->set_kernel_size(3);
- convolution_param->set_stride(2);
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_stride(2);
convolution_param->set_num_output(3);
convolution_param->set_group(3);
convolution_param->mutable_weight_filler()->set_type("gaussian");
"""
-Generate data used in the HDF5DataLayer test.
+Generate data used in the HDF5DataLayer and GradientBasedSolver tests.
"""
import os
import numpy as np
import h5py
+script_dir = os.path.dirname(os.path.abspath(__file__))
+
+# Generate HDF5DataLayer sample_data.h5
+
num_cols = 8
num_rows = 10
height = 6
print data
print label
-with h5py.File(os.path.dirname(__file__) + '/sample_data.h5', 'w') as f:
+with h5py.File(script_dir + '/sample_data.h5', 'w') as f:
f['data'] = data
f['label'] = label
f['label2'] = label2
-with h5py.File(os.path.dirname(__file__) + '/sample_data_2_gzip.h5', 'w') as f:
+with h5py.File(script_dir + '/sample_data_2_gzip.h5', 'w') as f:
f.create_dataset(
'data', data=data + total_size,
compression='gzip', compression_opts=1
)
f.create_dataset(
'label', data=label,
- compression='gzip', compression_opts=1
+ compression='gzip', compression_opts=1,
+ dtype='uint8',
)
f.create_dataset(
'label2', data=label2,
- compression='gzip', compression_opts=1
+ compression='gzip', compression_opts=1,
+ dtype='uint8',
)
-with open(os.path.dirname(__file__) + '/sample_data_list.txt', 'w') as f:
- f.write(os.path.dirname(__file__) + '/sample_data.h5\n')
- f.write(os.path.dirname(__file__) + '/sample_data_2_gzip.h5\n')
+with open(script_dir + '/sample_data_list.txt', 'w') as f:
+ f.write('src/caffe/test/test_data/sample_data.h5\n')
+ f.write('src/caffe/test/test_data/sample_data_2_gzip.h5\n')
+
+# Generate GradientBasedSolver solver_data.h5
+
+num_cols = 3
+num_rows = 8
+height = 10
+width = 10
+
+data = np.random.randn(num_rows, num_cols, height, width)
+data = data.reshape(num_rows, num_cols, height, width)
+data = data.astype('float32')
+
+targets = np.random.randn(num_rows, 1)
+targets = targets.astype('float32')
+
+print data
+print targets
+
+with h5py.File(script_dir + '/solver_data.h5', 'w') as f:
+ f['data'] = data
+ f['targets'] = targets
+
+with open(script_dir + '/solver_data_list.txt', 'w') as f:
+ f.write('src/caffe/test/test_data/solver_data.h5\n')
--- /dev/null
+src/caffe/test/test_data/solver_data.h5
+#ifdef USE_OPENCV
#include <string>
#include <vector>
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
-#include "caffe/data_layers.hpp"
#include "caffe/filler.hpp"
+#include "caffe/layers/data_layer.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/db.hpp"
#include "caffe/util/io.hpp"
TYPED_TEST_CASE(DataLayerTest, TestDtypesAndDevices);
+#ifdef USE_LEVELDB
TYPED_TEST(DataLayerTest, TestReadLevelDB) {
const bool unique_pixels = false; // all pixels the same; images different
this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
this->TestReadCrop(TEST);
}
+#endif // USE_LEVELDB
+#ifdef USE_LMDB
TYPED_TEST(DataLayerTest, TestReadLMDB) {
const bool unique_pixels = false; // all pixels the same; images different
this->Fill(unique_pixels, DataParameter_DB_LMDB);
this->TestReadCrop(TEST);
}
+#endif // USE_LMDB
} // namespace caffe
+#endif // USE_OPENCV
+#ifdef USE_OPENCV
#include <string>
#include <vector>
int NumSequenceMatches(const TransformationParameter transform_param,
const Datum& datum, Phase phase) {
// Get crop sequence with Caffe seed 1701.
- DataTransformer<Dtype>* transformer =
- new DataTransformer<Dtype>(transform_param, phase);
+ DataTransformer<Dtype> transformer(transform_param, phase);
const int crop_size = transform_param.crop_size();
Caffe::set_random_seed(seed_);
- transformer->InitRand();
- Blob<Dtype>* blob =
- new Blob<Dtype>(1, datum.channels(), datum.height(), datum.width());
+ transformer.InitRand();
+ Blob<Dtype> blob(1, datum.channels(), datum.height(), datum.width());
if (transform_param.crop_size() > 0) {
- blob->Reshape(1, datum.channels(), crop_size, crop_size);
+ blob.Reshape(1, datum.channels(), crop_size, crop_size);
}
vector<vector<Dtype> > crop_sequence;
for (int iter = 0; iter < this->num_iter_; ++iter) {
vector<Dtype> iter_crop_sequence;
- transformer->Transform(datum, blob);
- for (int j = 0; j < blob->count(); ++j) {
- iter_crop_sequence.push_back(blob->cpu_data()[j]);
+ transformer.Transform(datum, &blob);
+ for (int j = 0; j < blob.count(); ++j) {
+ iter_crop_sequence.push_back(blob.cpu_data()[j]);
}
crop_sequence.push_back(iter_crop_sequence);
}
int num_sequence_matches = 0;
for (int iter = 0; iter < this->num_iter_; ++iter) {
vector<Dtype> iter_crop_sequence = crop_sequence[iter];
- transformer->Transform(datum, blob);
- for (int j = 0; j < blob->count(); ++j) {
- num_sequence_matches +=
- (crop_sequence[iter][j] == blob->cpu_data()[j]);
+ transformer.Transform(datum, &blob);
+ for (int j = 0; j < blob.count(); ++j) {
+ num_sequence_matches += (crop_sequence[iter][j] == blob.cpu_data()[j]);
}
}
return num_sequence_matches;
}
- virtual ~DataTransformTest() { }
-
int seed_;
int num_iter_;
};
Datum datum;
FillDatum(label, channels, height, width, unique_pixels, &datum);
- Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
- DataTransformer<TypeParam>* transformer =
- new DataTransformer<TypeParam>(transform_param, TEST);
- transformer->InitRand();
- transformer->Transform(datum, blob);
- EXPECT_EQ(blob->num(), 1);
- EXPECT_EQ(blob->channels(), datum.channels());
- EXPECT_EQ(blob->height(), datum.height());
- EXPECT_EQ(blob->width(), datum.width());
- for (int j = 0; j < blob->count(); ++j) {
- EXPECT_EQ(blob->cpu_data()[j], label);
+ Blob<TypeParam> blob(1, channels, height, width);
+ DataTransformer<TypeParam> transformer(transform_param, TEST);
+ transformer.InitRand();
+ transformer.Transform(datum, &blob);
+ EXPECT_EQ(blob.num(), 1);
+ EXPECT_EQ(blob.channels(), datum.channels());
+ EXPECT_EQ(blob.height(), datum.height());
+ EXPECT_EQ(blob.width(), datum.width());
+ for (int j = 0; j < blob.count(); ++j) {
+ EXPECT_EQ(blob.cpu_data()[j], label);
}
}
Datum datum;
FillDatum(label, channels, height, width, unique_pixels, &datum);
- Blob<TypeParam>* blob = new Blob<TypeParam>(1, 3, 4, 5);
- DataTransformer<TypeParam>* transformer =
- new DataTransformer<TypeParam>(transform_param, TEST);
- transformer->InitRand();
- transformer->Transform(datum, blob);
- EXPECT_EQ(blob->num(), 1);
- EXPECT_EQ(blob->channels(), datum.channels());
- EXPECT_EQ(blob->height(), datum.height());
- EXPECT_EQ(blob->width(), datum.width());
- for (int j = 0; j < blob->count(); ++j) {
- EXPECT_EQ(blob->cpu_data()[j], j);
+ Blob<TypeParam> blob(1, 3, 4, 5);
+ DataTransformer<TypeParam> transformer(transform_param, TEST);
+ transformer.InitRand();
+ transformer.Transform(datum, &blob);
+ EXPECT_EQ(blob.num(), 1);
+ EXPECT_EQ(blob.channels(), datum.channels());
+ EXPECT_EQ(blob.height(), datum.height());
+ EXPECT_EQ(blob.width(), datum.width());
+ for (int j = 0; j < blob.count(); ++j) {
+ EXPECT_EQ(blob.cpu_data()[j], j);
}
}
transform_param.set_crop_size(crop_size);
Datum datum;
FillDatum(label, channels, height, width, unique_pixels, &datum);
- DataTransformer<TypeParam>* transformer =
- new DataTransformer<TypeParam>(transform_param, TEST);
- transformer->InitRand();
- Blob<TypeParam>* blob =
- new Blob<TypeParam>(1, channels, crop_size, crop_size);
+ DataTransformer<TypeParam> transformer(transform_param, TEST);
+ transformer.InitRand();
+ Blob<TypeParam> blob(1, channels, crop_size, crop_size);
for (int iter = 0; iter < this->num_iter_; ++iter) {
- transformer->Transform(datum, blob);
- EXPECT_EQ(blob->num(), 1);
- EXPECT_EQ(blob->channels(), datum.channels());
- EXPECT_EQ(blob->height(), crop_size);
- EXPECT_EQ(blob->width(), crop_size);
- for (int j = 0; j < blob->count(); ++j) {
- EXPECT_EQ(blob->cpu_data()[j], label);
+ transformer.Transform(datum, &blob);
+ EXPECT_EQ(blob.num(), 1);
+ EXPECT_EQ(blob.channels(), datum.channels());
+ EXPECT_EQ(blob.height(), crop_size);
+ EXPECT_EQ(blob.width(), crop_size);
+ for (int j = 0; j < blob.count(); ++j) {
+ EXPECT_EQ(blob.cpu_data()[j], label);
}
}
}
transform_param.add_mean_value(mean_value);
Datum datum;
FillDatum(label, channels, height, width, unique_pixels, &datum);
- Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
- DataTransformer<TypeParam>* transformer =
- new DataTransformer<TypeParam>(transform_param, TEST);
- transformer->InitRand();
- transformer->Transform(datum, blob);
- for (int j = 0; j < blob->count(); ++j) {
- EXPECT_EQ(blob->cpu_data()[j], label - mean_value);
+ Blob<TypeParam> blob(1, channels, height, width);
+ DataTransformer<TypeParam> transformer(transform_param, TEST);
+ transformer.InitRand();
+ transformer.Transform(datum, &blob);
+ for (int j = 0; j < blob.count(); ++j) {
+ EXPECT_EQ(blob.cpu_data()[j], label - mean_value);
}
}
transform_param.add_mean_value(2);
Datum datum;
FillDatum(label, channels, height, width, unique_pixels, &datum);
- Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
- DataTransformer<TypeParam>* transformer =
- new DataTransformer<TypeParam>(transform_param, TEST);
- transformer->InitRand();
- transformer->Transform(datum, blob);
+ Blob<TypeParam> blob(1, channels, height, width);
+ DataTransformer<TypeParam> transformer(transform_param, TEST);
+ transformer.InitRand();
+ transformer.Transform(datum, &blob);
for (int c = 0; c < channels; ++c) {
for (int j = 0; j < height * width; ++j) {
- EXPECT_EQ(blob->cpu_data()[blob->offset(0, c) + j], label - c);
+ EXPECT_EQ(blob.cpu_data()[blob.offset(0, c) + j], label - c);
}
}
}
const int size = channels * height * width;
// Create a mean file
- string* mean_file = new string();
- MakeTempFilename(mean_file);
+ string mean_file;
+ MakeTempFilename(&mean_file);
BlobProto blob_mean;
blob_mean.set_num(1);
blob_mean.set_channels(channels);
blob_mean.add_data(j);
}
- LOG(INFO) << "Using temporary mean_file " << *mean_file;
- WriteProtoToBinaryFile(blob_mean, *mean_file);
+ LOG(INFO) << "Using temporary mean_file " << mean_file;
+ WriteProtoToBinaryFile(blob_mean, mean_file);
- transform_param.set_mean_file(*mean_file);
+ transform_param.set_mean_file(mean_file);
Datum datum;
FillDatum(label, channels, height, width, unique_pixels, &datum);
- Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
- DataTransformer<TypeParam>* transformer =
- new DataTransformer<TypeParam>(transform_param, TEST);
- transformer->InitRand();
- transformer->Transform(datum, blob);
- for (int j = 0; j < blob->count(); ++j) {
- EXPECT_EQ(blob->cpu_data()[j], 0);
+ Blob<TypeParam> blob(1, channels, height, width);
+ DataTransformer<TypeParam> transformer(transform_param, TEST);
+ transformer.InitRand();
+ transformer.Transform(datum, &blob);
+ for (int j = 0; j < blob.count(); ++j) {
+ EXPECT_EQ(blob.cpu_data()[j], 0);
}
}
} // namespace caffe
+#endif // USE_OPENCV
+#if defined(USE_LEVELDB) && defined(USE_LMDB) && defined(USE_OPENCV)
#include <string>
#include "boost/scoped_ptr.hpp"
}
} // namespace caffe
+#endif // USE_LEVELDB, USE_LMDB and USE_OPENCV
-#include <cstring>
#include <vector>
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/deconv_layer.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
- convolution_param->set_kernel_size(3);
- convolution_param->set_stride(2);
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_stride(2);
convolution_param->set_num_output(4);
this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
this->blob_top_vec_.push_back(this->blob_top_2_);
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
- convolution_param->set_kernel_size(3);
- convolution_param->set_stride(2);
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_stride(2);
convolution_param->set_num_output(4);
convolution_param->mutable_weight_filler()->set_type("constant");
convolution_param->mutable_weight_filler()->set_value(1);
layer_param.mutable_convolution_param();
this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
this->blob_top_vec_.push_back(this->blob_top_2_);
- convolution_param->set_kernel_size(2);
- convolution_param->set_stride(1);
+ convolution_param->add_kernel_size(2);
+ convolution_param->add_stride(1);
convolution_param->set_num_output(1);
convolution_param->mutable_weight_filler()->set_type("gaussian");
convolution_param->mutable_bias_filler()->set_type("gaussian");
this->blob_top_vec_);
}
+TYPED_TEST(DeconvolutionLayerTest, TestNDAgainst2D) {
+ typedef typename TypeParam::Dtype Dtype;
+ const int kernel_h = 11;
+ const int kernel_w = 13;
+ vector<int> bottom_shape(4);
+ bottom_shape[0] = 15;
+ bottom_shape[1] = 12;
+ bottom_shape[2] = kernel_h * 2;
+ bottom_shape[3] = kernel_w * 2;
+ FillerParameter filler_param;
+ GaussianFiller<Dtype> filler(filler_param);
+ for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+ this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+ filler.Fill(this->blob_bottom_vec_[i]);
+ }
+ LayerParameter layer_param;
+ ConvolutionParameter* convolution_param =
+ layer_param.mutable_convolution_param();
+ convolution_param->set_num_output(18);
+ convolution_param->set_bias_term(false);
+ convolution_param->set_group(6);
+ convolution_param->set_kernel_h(kernel_h);
+ convolution_param->set_kernel_w(kernel_w);
+ convolution_param->mutable_weight_filler()->set_type("gaussian");
+ Blob<Dtype> weights;
+ Blob<Dtype> top_diff;
+ // Shape and fill weights and top_diff.
+ bool copy_diff;
+ bool reshape;
+ {
+ DeconvolutionLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ top_diff.ReshapeLike(*this->blob_top_);
+ filler.Fill(&top_diff);
+ ASSERT_EQ(1, layer.blobs().size());
+ copy_diff = false; reshape = true;
+ weights.CopyFrom(*layer.blobs()[0], copy_diff, reshape);
+ }
+ vector<bool> propagate_down(1, true);
+ Blob<Dtype> result_2d;
+ Blob<Dtype> backward_result_2d;
+ Blob<Dtype> backward_weight_result_2d;
+ // Test with 2D im2col
+ {
+ caffe_set(this->blob_top_->count(), Dtype(0),
+ this->blob_top_->mutable_cpu_data());
+ caffe_set(this->blob_bottom_->count(), Dtype(0),
+ this->blob_bottom_->mutable_cpu_diff());
+ caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff());
+ // Do SetUp and Forward; save Forward result in result_2d.
+ convolution_param->set_force_nd_im2col(false);
+ DeconvolutionLayer<Dtype> layer_2d(layer_param);
+ layer_2d.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ ASSERT_EQ(1, layer_2d.blobs().size());
+ copy_diff = false; reshape = false;
+ layer_2d.blobs()[0]->CopyFrom(weights, copy_diff, reshape);
+ layer_2d.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ copy_diff = false; reshape = true;
+ result_2d.CopyFrom(*this->blob_top_, copy_diff, reshape);
+ // Copy pre-generated top diff into actual top diff;
+ // do Backward and save result in backward_result_2d.
+ ASSERT_EQ(this->blob_top_->shape(), top_diff.shape());
+ caffe_copy(top_diff.count(), top_diff.cpu_data(),
+ this->blob_top_->mutable_cpu_diff());
+ layer_2d.Backward(this->blob_top_vec_, propagate_down,
+ this->blob_bottom_vec_);
+ copy_diff = true; reshape = true;
+ backward_result_2d.CopyFrom(*this->blob_bottom_, copy_diff, reshape);
+ backward_weight_result_2d.CopyFrom(weights, copy_diff, reshape);
+ }
+ Blob<Dtype> result_nd;
+ Blob<Dtype> backward_result_nd;
+ Blob<Dtype> backward_weight_result_nd;
+ // Test with ND im2col
+ {
+ caffe_set(this->blob_top_->count(), Dtype(0),
+ this->blob_top_->mutable_cpu_data());
+ caffe_set(this->blob_bottom_->count(), Dtype(0),
+ this->blob_bottom_->mutable_cpu_diff());
+ caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff());
+ // Do SetUp and Forward; save Forward result in result_nd.
+ convolution_param->set_force_nd_im2col(true);
+ DeconvolutionLayer<Dtype> layer_nd(layer_param);
+ layer_nd.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ ASSERT_EQ(1, layer_nd.blobs().size());
+ copy_diff = false; reshape = false;
+ layer_nd.blobs()[0]->CopyFrom(weights, copy_diff, reshape);
+ layer_nd.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ copy_diff = false; reshape = true;
+ result_nd.CopyFrom(*this->blob_top_, copy_diff, reshape);
+ // Copy pre-generated top diff into actual top diff;
+ // do Backward and save result in backward_result_nd.
+ ASSERT_EQ(this->blob_top_->shape(), top_diff.shape());
+ caffe_copy(top_diff.count(), top_diff.cpu_data(),
+ this->blob_top_->mutable_cpu_diff());
+ layer_nd.Backward(this->blob_top_vec_, propagate_down,
+ this->blob_bottom_vec_);
+ copy_diff = true; reshape = true;
+ backward_result_nd.CopyFrom(*this->blob_bottom_, copy_diff, reshape);
+ backward_weight_result_nd.CopyFrom(weights, copy_diff, reshape);
+ }
+ ASSERT_EQ(result_nd.count(), result_2d.count());
+ for (int i = 0; i < result_2d.count(); ++i) {
+ EXPECT_EQ(result_2d.cpu_data()[i], result_nd.cpu_data()[i]);
+ }
+ ASSERT_EQ(backward_result_nd.count(), backward_result_2d.count());
+ for (int i = 0; i < backward_result_2d.count(); ++i) {
+ EXPECT_EQ(backward_result_2d.cpu_diff()[i],
+ backward_result_nd.cpu_diff()[i]);
+ }
+ ASSERT_EQ(backward_weight_result_nd.count(),
+ backward_weight_result_2d.count());
+ for (int i = 0; i < backward_weight_result_2d.count(); ++i) {
+ EXPECT_EQ(backward_weight_result_2d.cpu_diff()[i],
+ backward_weight_result_nd.cpu_diff()[i]);
+ }
+}
+
+TYPED_TEST(DeconvolutionLayerTest, TestGradient3D) {
+ typedef typename TypeParam::Dtype Dtype;
+ vector<int> bottom_shape(5);
+ bottom_shape[0] = this->blob_bottom_vec_[0]->shape(0);
+ bottom_shape[1] = this->blob_bottom_vec_[0]->shape(1);
+ bottom_shape[2] = 2;
+ bottom_shape[3] = 3;
+ bottom_shape[4] = 2;
+ FillerParameter filler_param;
+ GaussianFiller<Dtype> filler(filler_param);
+ for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+ this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+ filler.Fill(this->blob_bottom_vec_[i]);
+ }
+ LayerParameter layer_param;
+ ConvolutionParameter* convolution_param =
+ layer_param.mutable_convolution_param();
+ convolution_param->add_kernel_size(2);
+ convolution_param->add_stride(2);
+ convolution_param->add_pad(1);
+ convolution_param->set_num_output(2);
+ convolution_param->mutable_weight_filler()->set_type("gaussian");
+ convolution_param->mutable_bias_filler()->set_type("gaussian");
+ DeconvolutionLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-3);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+}
+
} // namespace caffe
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
+#include "caffe/layers/dummy_data_layer.hpp"
#include "caffe/proto/caffe.pb.h"
-#include "caffe/vision_layers.hpp"
#include "caffe/test/test_caffe_main.hpp"
namespace caffe {
template <typename Dtype>
-class DummyDataLayerTest : public ::testing::Test {
+class DummyDataLayerTest : public CPUDeviceTest<Dtype> {
protected:
DummyDataLayerTest()
: blob_top_a_(new Blob<Dtype>()),
TYPED_TEST_CASE(DummyDataLayerTest, TestDtypes);
TYPED_TEST(DummyDataLayerTest, TestOneTopConstant) {
- Caffe::set_mode(Caffe::CPU);
LayerParameter param;
DummyDataParameter* dummy_data_param = param.mutable_dummy_data_param();
dummy_data_param->add_num(5);
}
TYPED_TEST(DummyDataLayerTest, TestTwoTopConstant) {
- Caffe::set_mode(Caffe::CPU);
LayerParameter param;
DummyDataParameter* dummy_data_param = param.mutable_dummy_data_param();
dummy_data_param->add_num(5);
}
TYPED_TEST(DummyDataLayerTest, TestThreeTopConstantGaussianConstant) {
- Caffe::set_mode(Caffe::CPU);
LayerParameter param;
DummyDataParameter* dummy_data_param = param.mutable_dummy_data_param();
dummy_data_param->add_num(5);
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/eltwise_layer.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
const Dtype* in_data_b = this->blob_bottom_b_->cpu_data();
const Dtype* in_data_c = this->blob_bottom_c_->cpu_data();
for (int i = 0; i < count; ++i) {
- EXPECT_EQ(data[i], in_data_a[i] * in_data_b[i] * in_data_c[i]);
+ EXPECT_NEAR(data[i], in_data_a[i] * in_data_b[i] * in_data_c[i], 1e-4);
}
}
const Dtype* in_data_b = this->blob_bottom_b_->cpu_data();
const Dtype* in_data_c = this->blob_bottom_c_->cpu_data();
for (int i = 0; i < count; ++i) {
- EXPECT_EQ(data[i], in_data_a[i] + in_data_b[i] + in_data_c[i]);
+ EXPECT_NEAR(data[i], in_data_a[i] + in_data_b[i] + in_data_c[i], 1e-4);
}
}
--- /dev/null
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/embed_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+#ifndef CPU_ONLY
+extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+#endif
+
+template <typename TypeParam>
+class EmbedLayerTest : public MultiDeviceTest<TypeParam> {
+ typedef typename TypeParam::Dtype Dtype;
+ protected:
+ EmbedLayerTest()
+ : blob_bottom_(new Blob<Dtype>(4, 1, 1, 1)),
+ blob_top_(new Blob<Dtype>()) {
+ // fill the values
+ FillerParameter filler_param;
+ UniformFiller<Dtype> filler(filler_param);
+ filler.Fill(this->blob_bottom_);
+ blob_bottom_vec_.push_back(blob_bottom_);
+ blob_top_vec_.push_back(blob_top_);
+ }
+ virtual ~EmbedLayerTest() { delete blob_bottom_; delete blob_top_; }
+ Blob<Dtype>* const blob_bottom_;
+ Blob<Dtype>* const blob_top_;
+ vector<Blob<Dtype>*> blob_bottom_vec_;
+ vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(EmbedLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(EmbedLayerTest, TestSetUp) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ EmbedParameter* embed_param = layer_param.mutable_embed_param();
+ embed_param->set_num_output(10);
+ embed_param->set_input_dim(5);
+ shared_ptr<EmbedLayer<Dtype> > layer(new EmbedLayer<Dtype>(layer_param));
+ layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ ASSERT_EQ(this->blob_top_->num_axes(), 5);
+ EXPECT_EQ(this->blob_top_->shape(0), 4);
+ EXPECT_EQ(this->blob_top_->shape(1), 1);
+ EXPECT_EQ(this->blob_top_->shape(2), 1);
+ EXPECT_EQ(this->blob_top_->shape(3), 1);
+ EXPECT_EQ(this->blob_top_->shape(4), 10);
+}
+
+TYPED_TEST(EmbedLayerTest, TestForward) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ EmbedParameter* embed_param = layer_param.mutable_embed_param();
+ const int kNumOutput = 10;
+ const int kInputDim = 5;
+ embed_param->set_num_output(kNumOutput);
+ embed_param->set_input_dim(kInputDim);
+ embed_param->mutable_weight_filler()->set_type("uniform");
+ embed_param->mutable_weight_filler()->set_min(-10);
+ embed_param->mutable_weight_filler()->set_max(10);
+ embed_param->set_bias_term(false);
+ shared_ptr<EmbedLayer<Dtype> > layer(new EmbedLayer<Dtype>(layer_param));
+ layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ ASSERT_EQ(1, layer->blobs().size());
+ vector<int> weight_shape(2);
+ weight_shape[0] = kInputDim;
+ weight_shape[1] = kNumOutput;
+ ASSERT_TRUE(weight_shape == layer->blobs()[0]->shape());
+ for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+ this->blob_bottom_->mutable_cpu_data()[i] = caffe_rng_rand() % kInputDim;
+ }
+ layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ vector<int> weight_offset(2, 0);
+ vector<int> top_offset(5, 0);
+ for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+ weight_offset[0] = static_cast<int>(this->blob_bottom_->cpu_data()[i]);
+ weight_offset[1] = 0;
+ top_offset[0] = i;
+ top_offset[4] = 0;
+ for (int j = 0; j < kNumOutput; ++j) {
+ EXPECT_EQ(layer->blobs()[0]->data_at(weight_offset),
+ this->blob_top_->data_at(top_offset));
+ ++top_offset[4];
+ ++weight_offset[1];
+ }
+ }
+}
+
+TYPED_TEST(EmbedLayerTest, TestForwardWithBias) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ EmbedParameter* embed_param = layer_param.mutable_embed_param();
+ const int kNumOutput = 10;
+ const int kInputDim = 5;
+ embed_param->set_num_output(kNumOutput);
+ embed_param->set_input_dim(kInputDim);
+ embed_param->mutable_weight_filler()->set_type("uniform");
+ embed_param->mutable_weight_filler()->set_min(-10);
+ embed_param->mutable_weight_filler()->set_max(10);
+ embed_param->mutable_bias_filler()->CopyFrom(embed_param->weight_filler());
+ embed_param->set_bias_term(true);
+ shared_ptr<EmbedLayer<Dtype> > layer(new EmbedLayer<Dtype>(layer_param));
+ layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ ASSERT_EQ(2, layer->blobs().size());
+ vector<int> weight_shape(2);
+ weight_shape[0] = kInputDim;
+ weight_shape[1] = kNumOutput;
+ ASSERT_TRUE(weight_shape == layer->blobs()[0]->shape());
+ for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+ this->blob_bottom_->mutable_cpu_data()[i] = caffe_rng_rand() % kInputDim;
+ }
+ layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ vector<int> bias_offset(1, 0);
+ vector<int> weight_offset(2, 0);
+ vector<int> top_offset(5, 0);
+ for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+ weight_offset[0] = static_cast<int>(this->blob_bottom_->cpu_data()[i]);
+ weight_offset[1] = 0;
+ top_offset[0] = i;
+ top_offset[4] = 0;
+ bias_offset[0] = 0;
+ for (int j = 0; j < kNumOutput; ++j) {
+ EXPECT_EQ(layer->blobs()[0]->data_at(weight_offset) +
+ layer->blobs()[1]->data_at(bias_offset),
+ this->blob_top_->data_at(top_offset));
+ ++top_offset[4];
+ ++weight_offset[1];
+ ++bias_offset[0];
+ }
+ }
+}
+
+TYPED_TEST(EmbedLayerTest, TestGradient) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ EmbedParameter* embed_param = layer_param.mutable_embed_param();
+ embed_param->set_num_output(10);
+ embed_param->set_input_dim(5);
+ embed_param->set_bias_term(false);
+ embed_param->mutable_weight_filler()->set_type("uniform");
+ embed_param->mutable_weight_filler()->set_min(-10);
+ embed_param->mutable_weight_filler()->set_max(10);
+ EmbedLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-3);
+ this->blob_bottom_->mutable_cpu_data()[0] = 4;
+ this->blob_bottom_->mutable_cpu_data()[1] = 2;
+ this->blob_bottom_->mutable_cpu_data()[2] = 2;
+ this->blob_bottom_->mutable_cpu_data()[3] = 3;
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_, -2);
+}
+
+TYPED_TEST(EmbedLayerTest, TestGradientWithBias) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ EmbedParameter* embed_param = layer_param.mutable_embed_param();
+ embed_param->set_num_output(10);
+ embed_param->set_input_dim(5);
+ embed_param->set_bias_term(true);
+ embed_param->mutable_weight_filler()->set_type("uniform");
+ embed_param->mutable_weight_filler()->set_min(-10);
+ embed_param->mutable_weight_filler()->set_max(10);
+ embed_param->mutable_bias_filler()->CopyFrom(embed_param->weight_filler());
+ EmbedLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-3);
+ this->blob_bottom_->mutable_cpu_data()[0] = 4;
+ this->blob_bottom_->mutable_cpu_data()[1] = 2;
+ this->blob_bottom_->mutable_cpu_data()[2] = 2;
+ this->blob_bottom_->mutable_cpu_data()[3] = 3;
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_, -2);
+}
+
+} // namespace caffe
#include <cmath>
-#include <cstdlib>
-#include <cstring>
#include <vector>
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/euclidean_loss_layer.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
-#include <cstring>
-
#include "gtest/gtest.h"
#include "caffe/filler.hpp"
EXPECT_LE(var, target_var * 5.);
}
+template <typename Dtype>
+class XavierFillerTest : public ::testing::Test {
+ protected:
+ XavierFillerTest()
+ : blob_(new Blob<Dtype>(1000, 2, 4, 5)),
+ filler_param_() {
+ }
+ virtual void test_params(FillerParameter_VarianceNorm variance_norm,
+ Dtype n) {
+ this->filler_param_.set_variance_norm(variance_norm);
+ this->filler_.reset(new XavierFiller<Dtype>(this->filler_param_));
+ this->filler_->Fill(blob_);
+ EXPECT_TRUE(this->blob_);
+ const int count = this->blob_->count();
+ const Dtype* data = this->blob_->cpu_data();
+ Dtype mean = 0.;
+ Dtype ex2 = 0.;
+ for (int i = 0; i < count; ++i) {
+ mean += data[i];
+ ex2 += data[i] * data[i];
+ }
+ mean /= count;
+ ex2 /= count;
+ Dtype std = sqrt(ex2 - mean*mean);
+ Dtype target_std = sqrt(2.0 / n);
+ EXPECT_NEAR(mean, 0.0, 0.1);
+ EXPECT_NEAR(std, target_std, 0.1);
+ }
+ virtual ~XavierFillerTest() { delete blob_; }
+ Blob<Dtype>* const blob_;
+ FillerParameter filler_param_;
+ shared_ptr<XavierFiller<Dtype> > filler_;
+};
+
+TYPED_TEST_CASE(XavierFillerTest, TestDtypes);
+
+TYPED_TEST(XavierFillerTest, TestFillFanIn) {
+ TypeParam n = 2*4*5;
+ this->test_params(FillerParameter_VarianceNorm_FAN_IN, n);
+}
+TYPED_TEST(XavierFillerTest, TestFillFanOut) {
+ TypeParam n = 1000*4*5;
+ this->test_params(FillerParameter_VarianceNorm_FAN_OUT, n);
+}
+TYPED_TEST(XavierFillerTest, TestFillAverage) {
+ TypeParam n = (2*4*5 + 1000*4*5) / 2.0;
+ this->test_params(FillerParameter_VarianceNorm_AVERAGE, n);
+}
+
+template <typename Dtype>
+class MSRAFillerTest : public ::testing::Test {
+ protected:
+ MSRAFillerTest()
+ : blob_(new Blob<Dtype>(1000, 2, 4, 5)),
+ filler_param_() {
+ }
+ virtual void test_params(FillerParameter_VarianceNorm variance_norm,
+ Dtype n) {
+ this->filler_param_.set_variance_norm(variance_norm);
+ this->filler_.reset(new MSRAFiller<Dtype>(this->filler_param_));
+ this->filler_->Fill(blob_);
+ EXPECT_TRUE(this->blob_);
+ const int count = this->blob_->count();
+ const Dtype* data = this->blob_->cpu_data();
+ Dtype mean = 0.;
+ Dtype ex2 = 0.;
+ for (int i = 0; i < count; ++i) {
+ mean += data[i];
+ ex2 += data[i] * data[i];
+ }
+ mean /= count;
+ ex2 /= count;
+ Dtype std = sqrt(ex2 - mean*mean);
+ Dtype target_std = sqrt(2.0 / n);
+ EXPECT_NEAR(mean, 0.0, 0.1);
+ EXPECT_NEAR(std, target_std, 0.1);
+ }
+ virtual ~MSRAFillerTest() { delete blob_; }
+ Blob<Dtype>* const blob_;
+ FillerParameter filler_param_;
+ shared_ptr<MSRAFiller<Dtype> > filler_;
+};
+
+TYPED_TEST_CASE(MSRAFillerTest, TestDtypes);
+
+TYPED_TEST(MSRAFillerTest, TestFillFanIn) {
+ TypeParam n = 2*4*5;
+ this->test_params(FillerParameter_VarianceNorm_FAN_IN, n);
+}
+TYPED_TEST(MSRAFillerTest, TestFillFanOut) {
+ TypeParam n = 1000*4*5;
+ this->test_params(FillerParameter_VarianceNorm_FAN_OUT, n);
+}
+TYPED_TEST(MSRAFillerTest, TestFillAverage) {
+ TypeParam n = (2*4*5 + 1000*4*5) / 2.0;
+ this->test_params(FillerParameter_VarianceNorm_AVERAGE, n);
+}
+
} // namespace caffe
--- /dev/null
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/filter_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class FilterLayerTest : public MultiDeviceTest<TypeParam> {
+ typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+ FilterLayerTest()
+ : blob_bottom_data_(new Blob<Dtype>(4, 3, 6, 4)),
+ blob_bottom_labels_(new Blob<Dtype>(4, 1, 1, 1)),
+ blob_bottom_selector_(new Blob<Dtype>(4, 1, 1, 1)),
+ blob_top_data_(new Blob<Dtype>()),
+ blob_top_labels_(new Blob<Dtype>()) {}
+ virtual void SetUp() {
+ // fill the values
+ Caffe::set_random_seed(1890);
+ FillerParameter filler_param;
+ GaussianFiller<Dtype> filler(filler_param);
+ // fill the selector blob
+ Dtype* bottom_data_selector_ = blob_bottom_selector_->mutable_cpu_data();
+ bottom_data_selector_[0] = 0;
+ bottom_data_selector_[1] = 1;
+ bottom_data_selector_[2] = 1;
+ bottom_data_selector_[3] = 0;
+ // fill the other bottom blobs
+ filler.Fill(blob_bottom_data_);
+ for (int i = 0; i < blob_bottom_labels_->count(); ++i) {
+ blob_bottom_labels_->mutable_cpu_data()[i] = caffe_rng_rand() % 5;
+ }
+ blob_bottom_vec_.push_back(blob_bottom_data_);
+ blob_bottom_vec_.push_back(blob_bottom_labels_);
+ blob_bottom_vec_.push_back(blob_bottom_selector_);
+ blob_top_vec_.push_back(blob_top_data_);
+ blob_top_vec_.push_back(blob_top_labels_);
+ }
+ virtual ~FilterLayerTest() {
+ delete blob_bottom_data_;
+ delete blob_bottom_labels_;
+ delete blob_bottom_selector_;
+ delete blob_top_data_;
+ delete blob_top_labels_;
+ }
+ Blob<Dtype>* const blob_bottom_data_;
+ Blob<Dtype>* const blob_bottom_labels_;
+ Blob<Dtype>* const blob_bottom_selector_;
+ // blobs for the top of FilterLayer
+ Blob<Dtype>* const blob_top_data_;
+ Blob<Dtype>* const blob_top_labels_;
+ vector<Blob<Dtype>*> blob_bottom_vec_;
+ vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(FilterLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(FilterLayerTest, TestReshape) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ FilterLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
+ // In the test first and last items should have been filtered
+ // so we just expect 2 remaining items
+ EXPECT_EQ(this->blob_top_data_->shape(0), 2);
+ EXPECT_EQ(this->blob_top_labels_->shape(0), 2);
+ EXPECT_GT(this->blob_bottom_data_->shape(0),
+ this->blob_top_data_->shape(0));
+ EXPECT_GT(this->blob_bottom_labels_->shape(0),
+ this->blob_top_labels_->shape(0));
+ for (int i = 1; i < this->blob_bottom_labels_->num_axes(); i++) {
+ EXPECT_EQ(this->blob_bottom_labels_->shape(i),
+ this->blob_top_labels_->shape(i));
+ }
+}
+
+TYPED_TEST(FilterLayerTest, TestForward) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ FilterLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ EXPECT_EQ(this->blob_top_labels_->data_at(0, 0, 0, 0),
+ this->blob_bottom_labels_->data_at(1, 0, 0, 0));
+ EXPECT_EQ(this->blob_top_labels_->data_at(1, 0, 0, 0),
+ this->blob_bottom_labels_->data_at(2, 0, 0, 0));
+
+ int dim = this->blob_top_data_->count() /
+ this->blob_top_data_->shape(0);
+ const Dtype* top_data = this->blob_top_data_->cpu_data();
+ const Dtype* bottom_data = this->blob_bottom_data_->cpu_data();
+ // selector is 0 1 1 0, so we need to compare bottom(1,c,h,w)
+ // with top(0,c,h,w) and bottom(2,c,h,w) with top(1,c,h,w)
+ bottom_data += dim; // bottom(1,c,h,w)
+ for (size_t n = 0; n < dim; n++)
+ EXPECT_EQ(top_data[n], bottom_data[n]);
+
+ bottom_data += dim; // bottom(2,c,h,w)
+ top_data += dim; // top(1,c,h,w)
+ for (size_t n = 0; n < dim; n++)
+ EXPECT_EQ(top_data[n], bottom_data[n]);
+}
+
+TYPED_TEST(FilterLayerTest, TestGradient) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ FilterLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-3);
+ // check only input 0 (data) because labels and selector
+ // don't need backpropagation
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_, 0);
+}
+
+} // namespace caffe
-#include <cstring>
#include <vector>
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/flatten_layer.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
LayerParameter layer_param;
FlattenLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
- EXPECT_EQ(this->blob_top_->num(), 2);
- EXPECT_EQ(this->blob_top_->channels(), 3 * 6 * 5);
- EXPECT_EQ(this->blob_top_->height(), 1);
- EXPECT_EQ(this->blob_top_->width(), 1);
+ ASSERT_EQ(this->blob_top_->num_axes(), 2);
+ EXPECT_EQ(this->blob_top_->shape(0), 2);
+ EXPECT_EQ(this->blob_top_->shape(1), 3 * 6 * 5);
}
-TYPED_TEST(FlattenLayerTest, Test) {
+TYPED_TEST(FlattenLayerTest, TestSetupWithAxis) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_flatten_param()->set_axis(2);
+ FlattenLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ ASSERT_EQ(this->blob_top_->num_axes(), 3);
+ EXPECT_EQ(this->blob_top_->shape(0), 2);
+ EXPECT_EQ(this->blob_top_->shape(1), 3);
+ EXPECT_EQ(this->blob_top_->shape(2), 6 * 5);
+}
+
+TYPED_TEST(FlattenLayerTest, TestSetupWithEndAxis) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_flatten_param()->set_end_axis(-2);
+ FlattenLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ ASSERT_EQ(this->blob_top_->num_axes(), 3);
+ EXPECT_EQ(this->blob_top_->shape(0), 2);
+ EXPECT_EQ(this->blob_top_->shape(1), 3 * 6);
+ EXPECT_EQ(this->blob_top_->shape(2), 5);
+}
+
+TYPED_TEST(FlattenLayerTest, TestSetupWithStartAndEndAxis) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_flatten_param()->set_axis(0);
+ layer_param.mutable_flatten_param()->set_end_axis(-2);
+ FlattenLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ ASSERT_EQ(this->blob_top_->num_axes(), 2);
+ EXPECT_EQ(this->blob_top_->shape(0), 2 * 3 * 6);
+ EXPECT_EQ(this->blob_top_->shape(1), 5);
+}
+
+TYPED_TEST(FlattenLayerTest, TestForward) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
FlattenLayer<Dtype> layer(layer_param);
this->blob_top_vec_);
}
-
} // namespace caffe
#include "gtest/gtest.h"
#include "caffe/common.hpp"
+#include "caffe/parallel.hpp"
#include "caffe/proto/caffe.pb.h"
-#include "caffe/solver.hpp"
+#include "caffe/sgd_solvers.hpp"
+#include "caffe/util/io.hpp"
#include "caffe/test/test_caffe_main.hpp"
protected:
GradientBasedSolverTest() :
- seed_(1701), num_(5), channels_(3), height_(10), width_(10) {}
+ seed_(1701), num_(4), channels_(3), height_(10), width_(10),
+ share_(false) {
+ input_file_ = new string(
+ CMAKE_SOURCE_DIR "caffe/test/test_data/solver_data_list.txt" CMAKE_EXT);
+ }
+ ~GradientBasedSolverTest() {
+ delete input_file_;
+ }
+ string snapshot_prefix_;
shared_ptr<SGDSolver<Dtype> > solver_;
+ shared_ptr<P2PSync<Dtype> > sync_;
int seed_;
+ // Dimensions are determined by generate_sample_data.py
+ // TODO this is brittle and the hdf5 file should be checked instead.
int num_, channels_, height_, width_;
- Dtype delta_; // Stability constant for AdaGrad.
+ bool share_;
+ Dtype delta_; // Stability constant for RMSProp, AdaGrad, AdaDelta and Adam
+
+ // Test data: check out generate_sample_data.py in the same directory.
+ string* input_file_;
- virtual SolverParameter_SolverType solver_type() = 0;
virtual void InitSolver(const SolverParameter& param) = 0;
virtual void InitSolverFromProtoString(const string& proto) {
SolverParameter param;
CHECK(google::protobuf::TextFormat::ParseFromString(proto, ¶m));
- // Disable saving a final snapshot so the tests don't pollute the user's
- // working directory with useless snapshots.
- param.set_snapshot_after_train(false);
// Set the solver_mode according to current Caffe::mode.
switch (Caffe::mode()) {
case Caffe::CPU:
LOG(FATAL) << "Unknown Caffe mode: " << Caffe::mode();
}
InitSolver(param);
- delta_ = (solver_type() == SolverParameter_SolverType_ADAGRAD) ?
- param.delta() : 0;
+ delta_ = param.delta();
}
- void RunLeastSquaresSolver(const Dtype learning_rate,
- const Dtype weight_decay, const Dtype momentum, const int num_iters) {
+ string RunLeastSquaresSolver(const Dtype learning_rate,
+ const Dtype weight_decay, const Dtype momentum, const int num_iters,
+ const int iter_size = 1, const int devices = 1,
+ const bool snapshot = false, const char* from_snapshot = NULL) {
ostringstream proto;
+ int device_id = 0;
+#ifndef CPU_ONLY
+ if (Caffe::mode() == Caffe::GPU) {
+ CUDA_CHECK(cudaGetDevice(&device_id));
+ }
+#endif
proto <<
+ "snapshot_after_train: " << snapshot << " "
"max_iter: " << num_iters << " "
"base_lr: " << learning_rate << " "
"lr_policy: 'fixed' "
+ "iter_size: " << iter_size << " "
+ "device_id: " << device_id << " "
"net_param { "
" name: 'TestNetwork' "
" layer { "
" name: 'data' "
- " type: 'DummyData' "
- " dummy_data_param { "
- " num: " << num_ << " "
- " channels: " << channels_ << " "
- " height: " << height_ << " "
- " width: " << width_ << " "
- " channels: 1 "
- " height: 1 "
- " width: 1 "
- " data_filler { "
- " type: 'gaussian' "
- " std: 1.0 "
- " } "
+ " type: 'HDF5Data' "
+ " hdf5_data_param { "
+ " source: '" << *(this->input_file_) << "' "
+ " batch_size: " << num_ / iter_size << " "
" } "
" top: 'data' "
" top: 'targets' "
- " } "
+ " } ";
+ if (share_) {
+ proto <<
+ " layer { "
+ " name: 'slice' "
+ " type: 'Slice' "
+ " bottom: 'data' "
+ " top: 'data1' "
+ " top: 'data2' "
+ " slice_param { "
+ " axis: 0 "
+ " } "
+ " } ";
+ }
+ proto <<
" layer { "
" name: 'innerprod' "
" type: 'InnerProduct' "
+ " param { name: 'weights' } "
+ " param { name: 'bias' } "
" inner_product_param { "
" num_output: 1 "
" weight_filler { "
" std: 1.0 "
" } "
" } "
- " bottom: 'data' "
- " top: 'innerprod' "
- " } "
+ " bottom: '" << string(share_ ? "data1": "data") << "' "
+ " top: '" << string(share_ ? "innerprod1": "innerprod") << "' "
+ " } ";
+ if (share_) {
+ proto <<
+ " layer { "
+ " name: 'innerprod2' "
+ " type: 'InnerProduct' "
+ " param { name: 'weights' } "
+ " param { name: 'bias' } "
+ " inner_product_param { "
+ " num_output: 1 "
+ " weight_filler { "
+ " type: 'gaussian' "
+ " std: 1.0 "
+ " } "
+ " bias_filler { "
+ " type: 'gaussian' "
+ " std: 1.0 "
+ " } "
+ " } "
+ " bottom: 'data2' "
+ " top: 'innerprod2' "
+ " } "
+ " layer { "
+ " name: 'concat' "
+ " type: 'Concat' "
+ " bottom: 'innerprod1' "
+ " bottom: 'innerprod2' "
+ " top: 'innerprod' "
+ " concat_param { "
+ " axis: 0 "
+ " } "
+ " } ";
+ }
+ proto <<
" layer { "
" name: 'loss' "
" type: 'EuclideanLoss' "
if (momentum != 0) {
proto << "momentum: " << momentum << " ";
}
+ MakeTempDir(&snapshot_prefix_);
+ proto << "snapshot_prefix: '" << snapshot_prefix_ << "/' ";
+ if (snapshot) {
+ proto << "snapshot: " << num_iters << " ";
+ }
Caffe::set_random_seed(this->seed_);
this->InitSolverFromProtoString(proto.str());
- this->solver_->Solve();
+ if (from_snapshot != NULL) {
+ this->solver_->Restore(from_snapshot);
+ vector<Blob<Dtype>*> empty_bottom_vec;
+ for (int i = 0; i < this->solver_->iter(); ++i) {
+ this->solver_->net()->Forward(empty_bottom_vec);
+ }
+ }
+ if (devices == 1) {
+ this->solver_->Solve();
+ } else {
+ LOG(INFO) << "Multi-GPU test on " << devices << " devices";
+ vector<int> gpus;
+ // put current device at the beginning
+ int device_id = solver_->param().device_id();
+ gpus.push_back(device_id);
+ for (int i = 0; gpus.size() < devices; ++i) {
+ if (i != device_id)
+ gpus.push_back(i);
+ }
+ Caffe::set_solver_count(gpus.size());
+ this->sync_.reset(new P2PSync<Dtype>(
+ this->solver_, NULL, this->solver_->param()));
+ this->sync_->run(gpus);
+ Caffe::set_solver_count(1);
+ }
+ if (snapshot) {
+ ostringstream resume_file;
+ resume_file << snapshot_prefix_ << "/_iter_" << num_iters
+ << ".solverstate";
+ string resume_filename = resume_file.str();
+ return resume_filename;
+ }
+ return string();
}
// Compute an update value given the current state of the train net,
// updated_params will store the updated weight and bias results,
// using the blobs' diffs to hold the update values themselves.
void ComputeLeastSquaresUpdate(const Dtype learning_rate,
- const Dtype weight_decay, const Dtype momentum,
+ const Dtype weight_decay, const Dtype momentum, const int num_iters,
vector<shared_ptr<Blob<Dtype> > >* updated_params) {
const int N = num_;
const int D = channels_ * height_ * width_;
((i == D) ? bias.cpu_data()[0] : weights.cpu_data()[i]);
// Finally, compute update.
const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
- ASSERT_EQ(2, history.size()); // 1 blob for weights, 1 for bias
+ if (solver_->type() != string("AdaDelta")
+ && solver_->type() != string("Adam")) {
+ ASSERT_EQ(2, history.size()); // 1 blob for weights, 1 for bias
+ } else {
+ ASSERT_EQ(4, history.size()); // additional blobs for update history
+ }
Dtype update_value = learning_rate * grad;
const Dtype history_value = (i == D) ?
history[1]->cpu_data()[0] : history[0]->cpu_data()[i];
const Dtype temp = momentum * history_value;
- switch (solver_type()) {
- case SolverParameter_SolverType_SGD:
+ if (solver_->type() == string("SGD")) {
update_value += temp;
- break;
- case SolverParameter_SolverType_NESTEROV:
+ } else if (solver_->type() == string("Nesterov")) {
update_value += temp;
// step back then over-step
update_value = (1 + momentum) * update_value - temp;
- break;
- case SolverParameter_SolverType_ADAGRAD:
+ } else if (solver_->type() == string("AdaGrad")) {
update_value /= std::sqrt(history_value + grad * grad) + delta_;
- break;
- default:
- LOG(FATAL) << "Unknown solver type: " << solver_type();
+ } else if (solver_->type() == string("RMSProp")) {
+ const Dtype rms_decay = 0.95;
+ update_value /= std::sqrt(rms_decay*history_value
+ + grad * grad * (1 - rms_decay)) + delta_;
+ } else if (solver_->type() == string("AdaDelta")) {
+ const Dtype update_history_value = (i == D) ?
+ history[1 + num_param_blobs]->cpu_data()[0] :
+ history[0 + num_param_blobs]->cpu_data()[i];
+ const Dtype weighted_gradient_average =
+ momentum * history_value + (1 - momentum) * (grad * grad);
+ update_value = grad * std::sqrt((update_history_value + delta_) /
+ (weighted_gradient_average + delta_)) * learning_rate;
+ // not actually needed, just here for illustrative purposes
+ // const Dtype weighted_update_average =
+ // momentum * update_history_value + (1 - momentum) * (update_value);
+ } else if (solver_->type() == string("Adam")) {
+ const Dtype momentum2 = 0.999;
+ const Dtype m = history_value;
+ const Dtype v = (i == D) ?
+ history[1 + num_param_blobs]->cpu_data()[0] :
+ history[0 + num_param_blobs]->cpu_data()[i];
+ const Dtype val_m = (1 - momentum) * grad + momentum * m;
+ const Dtype val_v = (1 - momentum2) * grad * grad + momentum2 * v;
+ Dtype alpha_t = learning_rate *
+ std::sqrt(Dtype(1) - pow(momentum2, num_iters)) /
+ (Dtype(1.) - pow(momentum, num_iters));
+ update_value = alpha_t * val_m / (std::sqrt(val_v) + delta_);
+ } else {
+ LOG(FATAL) << "Unknown solver type: " << solver_->type();
}
if (i == D) {
updated_bias.mutable_cpu_diff()[0] = update_value;
EXPECT_NEAR(expected_updated_bias, solver_updated_bias, error_margin);
// Check the solver's history -- should contain the previous update value.
- if (solver_type() == SolverParameter_SolverType_SGD) {
+ if (solver_->type() == string("SGD")) {
const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
ASSERT_EQ(2, history.size());
for (int i = 0; i < D; ++i) {
}
}
+ void CheckAccumulation(const Dtype kLearningRate, const Dtype kWeightDecay,
+ const Dtype kMomentum, const int kNumIters, const int kIterSize) {
+ const double kPrecision = 1e-2;
+ const double kMinPrecision = 1e-7;
+ // Solve without accumulation and save parameters.
+ this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum,
+ kNumIters);
+ // Save parameters for comparison.
+ Net<Dtype>& net = *this->solver_->net();
+ const vector<shared_ptr<Blob<Dtype> > >& param_blobs =
+ net.layer_by_name("innerprod")->blobs();
+ vector<shared_ptr<Blob<Dtype> > > noaccum_params(param_blobs.size());
+ for (int i = 0; i < param_blobs.size(); ++i) {
+ noaccum_params[i].reset(new Blob<Dtype>());
+ noaccum_params[i]->CopyFrom(*param_blobs[i], false, true);
+ }
+ // Solve by equivalent accumulation of gradients over divided batches.
+ this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum,
+ kNumIters, kIterSize);
+ Net<Dtype>& net_accum = *this->solver_->net();
+ const vector<shared_ptr<Blob<Dtype> > >& accum_params =
+ net_accum.layer_by_name("innerprod")->blobs();
+ // Compare accumulated parameters against no accumulation standard.
+ const int D = this->channels_ * this->height_ * this->width_;
+ for (int i = 0; i < D; ++i) {
+ const Dtype expected_param = noaccum_params[0]->cpu_data()[i];
+ const Dtype accum_param = accum_params[0]->cpu_data()[i];
+ const Dtype error_margin = std::max(kMinPrecision, kPrecision *
+ std::min(fabs(expected_param), fabs(accum_param)));
+ EXPECT_NEAR(expected_param, accum_param, error_margin);
+ }
+ ASSERT_EQ(1, accum_params[1]->count());
+ const Dtype expected_bias = noaccum_params[1]->cpu_data()[0];
+ const Dtype accum_bias = accum_params[1]->cpu_data()[0];
+ const Dtype error_margin = std::max(kMinPrecision, kPrecision *
+ std::min(fabs(expected_bias), fabs(accum_bias)));
+ EXPECT_NEAR(expected_bias, accum_bias, error_margin);
+ }
+
// Test that the correct update is computed for a regularized least squares
// problem:
//
void TestLeastSquaresUpdate(const Dtype learning_rate = 1.0,
const Dtype weight_decay = 0.0, const Dtype momentum = 0.0,
const int iter_to_check = 0) {
- // Initialize the solver and run K (= iter_to_check) solver iterations.
- RunLeastSquaresSolver(learning_rate, weight_decay, momentum, iter_to_check);
+ const int kNum = num_;
+ const int kIterSize = 1;
+ // Test over all numbers of devices.
+ int available_devices = 1;
+#ifndef CPU_ONLY
+ if (Caffe::mode() == Caffe::GPU) {
+ CUDA_CHECK(cudaGetDeviceCount(&available_devices));
+ }
+#endif
+ for (int devices = 1; devices <= available_devices; ++devices) {
+ // Configure batch size for single / multi device equivalence.
+ // Constant data is needed for multi device as for accumulation.
+ num_ = kNum * devices;
+
+ // Initialize the solver and run K (= iter_to_check) solver iterations
+ // (on single device).
+ RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
+ iter_to_check, kIterSize, 1);
+
+ // Compute the (K+1)th update using the analytic least squares gradient.
+ vector<shared_ptr<Blob<Dtype> > > updated_params;
+ ComputeLeastSquaresUpdate(learning_rate, weight_decay, momentum,
+ iter_to_check + 1, &updated_params);
- // Compute the (K+1)th update using the analytic least squares gradient.
- vector<shared_ptr<Blob<Dtype> > > updated_params;
- ComputeLeastSquaresUpdate(learning_rate, weight_decay, momentum,
- &updated_params);
+ // Reinitialize the solver and run K+1 solver iterations.
+ num_ = kNum;
+ RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
+ iter_to_check + 1, kIterSize, devices);
+
+ // Check that the solver's solution matches ours.
+ CheckLeastSquaresUpdate(updated_params);
+ }
+ }
- // Reinitialize the solver and run K+1 solver iterations.
+ void TestSnapshot(const Dtype learning_rate = 1.0,
+ const Dtype weight_decay = 0.0, const Dtype momentum = 0.0,
+ const int num_iters = 1) {
+ // Run the solver for num_iters * 2 iterations.
+ const int total_num_iters = num_iters * 2;
+ bool snapshot = false;
+ const int kIterSize = 1;
+ const int kDevices = 1;
+ RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
+ total_num_iters, kIterSize, kDevices, snapshot);
+
+ // Save the resulting param values.
+ vector<shared_ptr<Blob<Dtype> > > param_copies;
+ const vector<Blob<Dtype>*>& orig_params =
+ solver_->net()->learnable_params();
+ param_copies.resize(orig_params.size());
+ for (int i = 0; i < orig_params.size(); ++i) {
+ param_copies[i].reset(new Blob<Dtype>());
+ const bool kReshape = true;
+ for (int copy_diff = false; copy_diff <= true; ++copy_diff) {
+ param_copies[i]->CopyFrom(*orig_params[i], copy_diff, kReshape);
+ }
+ }
+
+ // Save the solver history
+ vector<shared_ptr<Blob<Dtype> > > history_copies;
+ const vector<shared_ptr<Blob<Dtype> > >& orig_history = solver_->history();
+ history_copies.resize(orig_history.size());
+ for (int i = 0; i < orig_history.size(); ++i) {
+ history_copies[i].reset(new Blob<Dtype>());
+ const bool kReshape = true;
+ for (int copy_diff = false; copy_diff <= true; ++copy_diff) {
+ history_copies[i]->CopyFrom(*orig_history[i], copy_diff, kReshape);
+ }
+ }
+
+ // Run the solver for num_iters iterations and snapshot.
+ snapshot = true;
+ string snapshot_name = RunLeastSquaresSolver(learning_rate, weight_decay,
+ momentum, num_iters, kIterSize, kDevices, snapshot);
+
+ // Reinitialize the solver and run for num_iters more iterations.
+ snapshot = false;
RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
- iter_to_check + 1);
+ total_num_iters, kIterSize, kDevices,
+ snapshot, snapshot_name.c_str());
- // Check that the solver's solution matches ours.
- CheckLeastSquaresUpdate(updated_params);
+ // Check that params now match.
+ const vector<Blob<Dtype>*>& params = solver_->net()->learnable_params();
+ for (int i = 0; i < params.size(); ++i) {
+ for (int j = 0; j < params[i]->count(); ++j) {
+ EXPECT_EQ(param_copies[i]->cpu_data()[j], params[i]->cpu_data()[j])
+ << "param " << i << " data differed at dim " << j;
+ EXPECT_EQ(param_copies[i]->cpu_diff()[j], params[i]->cpu_diff()[j])
+ << "param " << i << " diff differed at dim " << j;
+ }
+ }
+
+ // Check that history now matches.
+ const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
+ for (int i = 0; i < history.size(); ++i) {
+ for (int j = 0; j < history[i]->count(); ++j) {
+ EXPECT_EQ(history_copies[i]->cpu_data()[j], history[i]->cpu_data()[j])
+ << "history blob " << i << " data differed at dim " << j;
+ EXPECT_EQ(history_copies[i]->cpu_diff()[j], history[i]->cpu_diff()[j])
+ << "history blob " << i << " diff differed at dim " << j;
+ }
+ }
}
};
virtual void InitSolver(const SolverParameter& param) {
this->solver_.reset(new SGDSolver<Dtype>(param));
}
-
- virtual SolverParameter_SolverType solver_type() {
- return SolverParameter_SolverType_SGD;
- }
};
TYPED_TEST_CASE(SGDSolverTest, TestDtypesAndDevices);
this->TestLeastSquaresUpdate();
}
-TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateLROneTenth) {
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateLROneHundredth) {
typedef typename TypeParam::Dtype Dtype;
- const Dtype kLearningRate = 0.1;
+ const Dtype kLearningRate = 0.01;
this->TestLeastSquaresUpdate(kLearningRate);
}
TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithWeightDecay) {
typedef typename TypeParam::Dtype Dtype;
- const Dtype kLearningRate = 1.0;
+ const Dtype kLearningRate = 0.01;
const Dtype kWeightDecay = 0.5;
- this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay);
+ const Dtype kMomentum = 0;
+ const int kNumIters = 1;
+ for (int i = 0; i <= kNumIters; ++i) {
+ this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithWeightDecayMultiIter) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0;
+ const int kNumIters = 4;
+ for (int i = 0; i <= kNumIters; ++i) {
+ this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+ }
}
TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentum) {
typedef typename TypeParam::Dtype Dtype;
- const Dtype kLearningRate = 1.0;
- const Dtype kWeightDecay = 0.0;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0;
const Dtype kMomentum = 0.5;
const int kNumIters = 1;
for (int i = 0; i <= kNumIters; ++i) {
TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
typedef typename TypeParam::Dtype Dtype;
- const Dtype kLearningRate = 1.0;
- const Dtype kWeightDecay = 0.0;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0;
const Dtype kMomentum = 0.5;
const int kNumIters = 4;
for (int i = 0; i <= kNumIters; ++i) {
TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverything) {
typedef typename TypeParam::Dtype Dtype;
const Dtype kLearningRate = 0.01;
- const Dtype kWeightDecay = 0.1;
- const Dtype kMomentum = 0.9;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.5;
const int kNumIters = 4;
for (int i = 0; i <= kNumIters; ++i) {
this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
}
}
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingShare) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.5;
+ const int kNumIters = 4;
+ this->share_ = true;
+ for (int i = 0; i <= kNumIters; ++i) {
+ this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.9;
+ const int kNumIters = 4;
+ const int kIterSize = 2;
+ this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+ kIterSize);
+}
+
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.9;
+ const int kNumIters = 4;
+ const int kIterSize = 2;
+ this->share_ = true;
+ this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+ kIterSize);
+}
+
+TYPED_TEST(SGDSolverTest, TestSnapshot) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.9;
+ const int kNumIters = 4;
+ for (int i = 1; i <= kNumIters; ++i) {
+ this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
+TYPED_TEST(SGDSolverTest, TestSnapshotShare) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.9;
+ const int kNumIters = 4;
+ this->share_ = true;
+ for (int i = 1; i <= kNumIters; ++i) {
+ this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
template <typename TypeParam>
class AdaGradSolverTest : public GradientBasedSolverTest<TypeParam> {
virtual void InitSolver(const SolverParameter& param) {
this->solver_.reset(new AdaGradSolver<Dtype>(param));
}
- virtual SolverParameter_SolverType solver_type() {
- return SolverParameter_SolverType_ADAGRAD;
- }
};
TYPED_TEST_CASE(AdaGradSolverTest, TestDtypesAndDevices);
this->TestLeastSquaresUpdate();
}
-TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateLROneTenth) {
+TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateLROneHundredth) {
typedef typename TypeParam::Dtype Dtype;
- const Dtype kLearningRate = 0.1;
+ const Dtype kLearningRate = 0.01;
this->TestLeastSquaresUpdate(kLearningRate);
}
TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithWeightDecay) {
typedef typename TypeParam::Dtype Dtype;
- const Dtype kLearningRate = 1.0;
+ const Dtype kLearningRate = 0.01;
const Dtype kWeightDecay = 0.5;
this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay);
}
TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithEverything) {
typedef typename TypeParam::Dtype Dtype;
const Dtype kLearningRate = 0.01;
- const Dtype kWeightDecay = 0.1;
- const Dtype kMomentum = 0.0;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0;
+ const int kNumIters = 4;
+ for (int i = 0; i <= kNumIters; ++i) {
+ this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
+TYPED_TEST(AdaGradSolverTest,
+ TestAdaGradLeastSquaresUpdateWithEverythingShare) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0;
const int kNumIters = 4;
+ this->share_ = true;
for (int i = 0; i <= kNumIters; ++i) {
this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
}
}
+TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0;
+ const int kNumIters = 4;
+ const int kIterSize = 2;
+ this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+ kIterSize);
+}
+
+TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0;
+ const int kNumIters = 4;
+ const int kIterSize = 2;
+ this->share_ = true;
+ this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+ kIterSize);
+}
+
+TYPED_TEST(AdaGradSolverTest, TestSnapshot) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0;
+ const int kNumIters = 4;
+ for (int i = 1; i <= kNumIters; ++i) {
+ this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
+TYPED_TEST(AdaGradSolverTest, TestSnapshotShare) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0;
+ const int kNumIters = 4;
+ this->share_ = true;
+ for (int i = 1; i <= kNumIters; ++i) {
+ this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
template <typename TypeParam>
class NesterovSolverTest : public GradientBasedSolverTest<TypeParam> {
virtual void InitSolver(const SolverParameter& param) {
this->solver_.reset(new NesterovSolver<Dtype>(param));
}
- virtual SolverParameter_SolverType solver_type() {
- return SolverParameter_SolverType_NESTEROV;
- }
};
TYPED_TEST_CASE(NesterovSolverTest, TestDtypesAndDevices);
this->TestLeastSquaresUpdate();
}
-TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateLROneTenth) {
+TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateLROneHundredth) {
typedef typename TypeParam::Dtype Dtype;
- const Dtype kLearningRate = 0.1;
+ const Dtype kLearningRate = 0.01;
this->TestLeastSquaresUpdate(kLearningRate);
}
TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithWeightDecay) {
typedef typename TypeParam::Dtype Dtype;
- const Dtype kLearningRate = 1.0;
+ const Dtype kLearningRate = 0.01;
const Dtype kWeightDecay = 0.5;
this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay);
}
+TYPED_TEST(NesterovSolverTest,
+ TestNesterovLeastSquaresUpdateWithWeightDecayMultiIter) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0;
+ const int kNumIters = 4;
+ for (int i = 0; i <= kNumIters; ++i) {
+ this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithMomentum) {
typedef typename TypeParam::Dtype Dtype;
- const Dtype kLearningRate = 1.0;
- const Dtype kWeightDecay = 0.0;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0;
const Dtype kMomentum = 0.5;
const int kNumIters = 1;
for (int i = 0; i <= kNumIters; ++i) {
TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
typedef typename TypeParam::Dtype Dtype;
- const Dtype kLearningRate = 1.0;
- const Dtype kWeightDecay = 0.0;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0;
const Dtype kMomentum = 0.5;
const int kNumIters = 4;
for (int i = 0; i <= kNumIters; ++i) {
TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithEverything) {
typedef typename TypeParam::Dtype Dtype;
const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.9;
+ const int kNumIters = 4;
+ for (int i = 0; i <= kNumIters; ++i) {
+ this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
+TYPED_TEST(NesterovSolverTest,
+ TestNesterovLeastSquaresUpdateWithEverythingShare) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.9;
+ const int kNumIters = 4;
+ this->share_ = true;
+ for (int i = 0; i <= kNumIters; ++i) {
+ this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
+TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.9;
+ const int kNumIters = 4;
+ const int kIterSize = 2;
+ this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+ kIterSize);
+}
+
+TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.9;
+ const int kNumIters = 4;
+ const int kIterSize = 2;
+ this->share_ = true;
+ this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+ kIterSize);
+}
+
+TYPED_TEST(NesterovSolverTest, TestSnapshot) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.9;
+ const int kNumIters = 4;
+ for (int i = 1; i <= kNumIters; ++i) {
+ this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
+TYPED_TEST(NesterovSolverTest, TestSnapshotShare) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.9;
+ const int kNumIters = 4;
+ this->share_ = true;
+ for (int i = 1; i <= kNumIters; ++i) {
+ this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
+template <typename TypeParam>
+class AdaDeltaSolverTest : public GradientBasedSolverTest<TypeParam> {
+ typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+ virtual void InitSolver(const SolverParameter& param) {
+ this->solver_.reset(new AdaDeltaSolver<Dtype>(param));
+ }
+};
+
+TYPED_TEST_CASE(AdaDeltaSolverTest, TestDtypesAndDevices);
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdate) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.1;
+ this->TestLeastSquaresUpdate(kLearningRate);
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithWeightDecay) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.1;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.95;
+ this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithHalfMomentum) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.1;
+ const Dtype kWeightDecay = 0.0;
+ const Dtype kMomentum = 0.5;
+ const int kNumIters = 1;
+ for (int i = 0; i <= kNumIters; ++i) {
+ this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+ }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithMomentum) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.1;
+ const Dtype kWeightDecay = 0.0;
+ const Dtype kMomentum = 0.95;
+ const int kNumIters = 1;
+ for (int i = 0; i <= kNumIters; ++i) {
+ this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+ }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.1;
+ const Dtype kWeightDecay = 0.0;
+ const Dtype kMomentum = 0.95;
+ const int kNumIters = 4;
+ for (int i = 0; i <= kNumIters; ++i) {
+ this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithEverything) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.1;
+ const Dtype kWeightDecay = 0.1;
+ const Dtype kMomentum = 0.95;
+ const int kNumIters = 4;
+ for (int i = 0; i <= kNumIters; ++i) {
+ this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
+TYPED_TEST(AdaDeltaSolverTest,
+ TestAdaDeltaLeastSquaresUpdateWithEverythingShare) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.1;
+ const Dtype kWeightDecay = 0.1;
+ const Dtype kMomentum = 0.95;
+ const int kNumIters = 4;
+ this->share_ = true;
+ for (int i = 0; i <= kNumIters; ++i) {
+ this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.1;
+ const Dtype kWeightDecay = 0.1;
+ const Dtype kMomentum = 0.95;
+ const int kNumIters = 4;
+ const int kIterSize = 2;
+ this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+ kIterSize);
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.1;
+ const Dtype kWeightDecay = 0.1;
+ const Dtype kMomentum = 0.95;
+ const int kNumIters = 4;
+ const int kIterSize = 2;
+ this->share_ = true;
+ this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+ kIterSize);
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestSnapshot) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.1;
+ const Dtype kWeightDecay = 0.1;
+ const Dtype kMomentum = 0.95;
+ const int kNumIters = 4;
+ for (int i = 1; i <= kNumIters; ++i) {
+ this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestSnapshotShare) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.1;
const Dtype kWeightDecay = 0.1;
+ const Dtype kMomentum = 0.95;
+ const int kNumIters = 4;
+ this->share_ = true;
+ for (int i = 1; i <= kNumIters; ++i) {
+ this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
+template <typename TypeParam>
+class AdamSolverTest : public GradientBasedSolverTest<TypeParam> {
+ typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+ virtual void InitSolver(const SolverParameter& param) {
+ SolverParameter new_param = param;
+ const Dtype momentum = 0.9;
+ new_param.set_momentum(momentum);
+ const Dtype momentum2 = 0.999;
+ new_param.set_momentum2(momentum2);
+ this->solver_.reset(new AdamSolver<Dtype>(new_param));
+ }
+};
+
+TYPED_TEST_CASE(AdamSolverTest, TestDtypesAndDevices);
+
+TYPED_TEST(AdamSolverTest, TestAdamLeastSquaresUpdate) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0;
+ const Dtype kMomentum = 0.9;
+ this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+}
+
+TYPED_TEST(AdamSolverTest, TestAdamLeastSquaresUpdateWithWeightDecay) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.9;
+ this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+}
+
+TYPED_TEST(AdamSolverTest, TestAdamLeastSquaresUpdateWithEverything) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.9;
+ const int kNumIters = 4;
+ for (int i = 0; i <= kNumIters; ++i) {
+ this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
+TYPED_TEST(AdamSolverTest, TestAdamLeastSquaresUpdateWithEverythingShare) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.9;
+ const int kNumIters = 4;
+ this->share_ = true;
+ for (int i = 0; i <= kNumIters; ++i) {
+ this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
+TYPED_TEST(AdamSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.9;
+ const int kNumIters = 4;
+ const int kIterSize = 2;
+ this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+ kIterSize);
+}
+
+TYPED_TEST(AdamSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.9;
+ const int kNumIters = 4;
+ const int kIterSize = 2;
+ this->share_ = true;
+ this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+ kIterSize);
+}
+
+TYPED_TEST(AdamSolverTest, TestSnapshot) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
const Dtype kMomentum = 0.9;
const int kNumIters = 4;
+ for (int i = 1; i <= kNumIters; ++i) {
+ this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
+TYPED_TEST(AdamSolverTest, TestSnapshotShare) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.9;
+ const int kNumIters = 4;
+ this->share_ = true;
+ for (int i = 1; i <= kNumIters; ++i) {
+ this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
+template <typename TypeParam>
+class RMSPropSolverTest : public GradientBasedSolverTest<TypeParam> {
+ typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+ virtual void InitSolver(const SolverParameter& param) {
+ const Dtype rms_decay = 0.95;
+ SolverParameter new_param = param;
+ new_param.set_rms_decay(rms_decay);
+ this->solver_.reset(new RMSPropSolver<Dtype>(new_param));
+ }
+};
+
+TYPED_TEST_CASE(RMSPropSolverTest, TestDtypesAndDevices);
+
+TYPED_TEST(RMSPropSolverTest, TestRMSPropLeastSquaresUpdateWithWeightDecay) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 1.0;
+ const Dtype kWeightDecay = 0.5;
+ this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay);
+}
+
+TYPED_TEST(RMSPropSolverTest, TestRMSPropLeastSquaresUpdateWithRmsDecay) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.0;
+ const Dtype kMomentum = 0.0;
+ const int kNumIters = 4;
+ for (int i = 0; i <= kNumIters; ++i) {
+ this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
+TYPED_TEST(RMSPropSolverTest, TestRMSPropLeastSquaresUpdateWithEverything) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.0;
+ const int kNumIters = 4;
+ for (int i = 0; i <= kNumIters; ++i) {
+ this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
+TYPED_TEST(RMSPropSolverTest,
+ TestRMSPropLeastSquaresUpdateWithEverythingShare) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.0;
+ const int kNumIters = 4;
+ this->share_ = true;
for (int i = 0; i <= kNumIters; ++i) {
this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
}
}
+TYPED_TEST(RMSPropSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.0;
+ const int kNumIters = 4;
+ const int kIterSize = 2;
+ this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+ kIterSize);
+}
+
+TYPED_TEST(RMSPropSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0.0;
+ const int kNumIters = 4;
+ const int kIterSize = 2;
+ this->share_ = true;
+ this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+ kIterSize);
+}
+
+TYPED_TEST(RMSPropSolverTest, TestSnapshot) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0;
+ const int kNumIters = 4;
+ for (int i = 1; i <= kNumIters; ++i) {
+ this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
+TYPED_TEST(RMSPropSolverTest, TestSnapshotShare) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kLearningRate = 0.01;
+ const Dtype kWeightDecay = 0.5;
+ const Dtype kMomentum = 0;
+ const int kNumIters = 4;
+ this->share_ = true;
+ for (int i = 1; i <= kNumIters; ++i) {
+ this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+ }
+}
+
} // namespace caffe
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
+#include "caffe/layers/hdf5_output_layer.hpp"
#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/hdf5.hpp"
#include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include <string>
#include <vector>
+#include "hdf5.h"
+
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
-#include "caffe/filler.hpp"
+#include "caffe/layers/hdf5_data_layer.hpp"
#include "caffe/proto/caffe.pb.h"
-#include "caffe/vision_layers.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include <cmath>
-#include <cstdlib>
-#include <cstring>
#include <vector>
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/hinge_loss_layer.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
-#include <cstring>
#include <vector>
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
+#include "caffe/layers/im2col_layer.hpp"
#include "caffe/util/im2col.hpp"
-#include "caffe/vision_layers.hpp"
#include "caffe/test/test_caffe_main.hpp"
const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w,
const int stride_h, const int stride_w,
+ const int dilation_h, const int dilation_w,
const int height_col, const int width_col,
Dtype* data_col);
+template <typename Dtype, int num_axes>
+__global__ void im2col_nd_gpu_kernel(const int n, const Dtype* data_im,
+ const int* im_shape, const int* col_shape,
+ const int* kernel_shape, const int* pad, const int* stride,
+ const int* dilation, Dtype* data_col);
+
extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
template <typename Dtype>
-class Im2colKernelTest : public ::testing::Test {
+class Im2colKernelTest : public GPUDeviceTest<Dtype> {
protected:
Im2colKernelTest()
// big so launches > 1024 threads
- : blob_bottom_(new Blob<Dtype>(5, 500, 10, 10)),
+ : blob_bottom_(new Blob<Dtype>(5, 500, 15, 15)),
+ blob_kernel_shape_(new Blob<int>()),
+ blob_stride_(new Blob<int>()),
+ blob_pad_(new Blob<int>()),
+ blob_dilation_(new Blob<int>()),
blob_top_(new Blob<Dtype>()),
blob_top_cpu_(new Blob<Dtype>()) {
FillerParameter filler_param;
GaussianFiller<Dtype> filler(filler_param);
filler.Fill(this->blob_bottom_);
+ vector<int> dim_blob_shape(1, 2);
+ blob_kernel_shape_->Reshape(dim_blob_shape);
+ blob_stride_->Reshape(dim_blob_shape);
+ blob_pad_->Reshape(dim_blob_shape);
+ blob_dilation_->Reshape(dim_blob_shape);
height_ = blob_bottom_->height();
width_ = blob_bottom_->width();
channels_ = blob_bottom_->channels();
pad_ = 0;
stride_ = 2;
+ dilation_ = 3;
kernel_size_ = 3;
- height_col_ = (height_ + 2 * pad_ - kernel_size_) / stride_ + 1;
- width_col_ = (width_ + 2 * pad_ - kernel_size_) / stride_ + 1;
+ height_col_ = (height_ + 2 * pad_ -
+ (dilation_ * (kernel_size_ - 1) + 1)) / stride_ + 1;
+ width_col_ = (width_ + 2 * pad_ -
+ (dilation_ * (kernel_size_ - 1) + 1)) / stride_ + 1;
+
+ for (int i = 0; i < 2; ++i) {
+ blob_kernel_shape_->mutable_cpu_data()[i] = kernel_size_;
+ blob_stride_->mutable_cpu_data()[i] = stride_;
+ blob_pad_->mutable_cpu_data()[i] = pad_;
+ blob_dilation_->mutable_cpu_data()[i] = dilation_;
+ }
}
virtual ~Im2colKernelTest() {
- delete blob_bottom_;
- delete blob_top_;
- delete blob_top_cpu_;
+ delete blob_bottom_;
+ delete blob_top_;
+ delete blob_top_cpu_;
+ delete blob_kernel_shape_;
+ delete blob_stride_;
+ delete blob_pad_;
+ delete blob_dilation_;
}
+ Blob<int>* const blob_kernel_shape_;
+ Blob<int>* const blob_stride_;
+ Blob<int>* const blob_pad_;
+ Blob<int>* const blob_dilation_;
Blob<Dtype>* const blob_bottom_;
Blob<Dtype>* const blob_top_;
Blob<Dtype>* const blob_top_cpu_;
int channels_;
int pad_;
int stride_;
+ int dilation_;
int kernel_size_;
int height_col_;
int width_col_;
TYPED_TEST_CASE(Im2colKernelTest, TestDtypes);
-TYPED_TEST(Im2colKernelTest, TestGPU) {
- Caffe::set_mode(Caffe::GPU);
-
+TYPED_TEST(Im2colKernelTest, Test2D) {
// Reshape the blobs to correct size for im2col output
this->blob_top_->Reshape(this->blob_bottom_->num(),
this->channels_ * this->kernel_size_ * this->kernel_size_,
im2col_cpu(this->blob_bottom_->cpu_data() + this->blob_bottom_->offset(n),
this->channels_, this->height_, this->width_,
this->kernel_size_, this->kernel_size_, this->pad_, this->pad_,
- this->stride_, this->stride_,
+ this->stride_, this->stride_, this->dilation_, this->dilation_,
cpu_data + this->blob_top_cpu_->offset(n));
}
num_kernels, bottom_data + this->blob_bottom_->offset(n),
this->height_, this->width_, this->kernel_size_, this->kernel_size_,
this->pad_, this->pad_, this->stride_, this->stride_,
+ this->dilation_, this->dilation_,
this->height_col_, this->width_col_,
top_data + this->blob_top_->offset(n));
CUDA_POST_KERNEL_CHECK;
}
}
+TYPED_TEST(Im2colKernelTest, TestND) {
+ // Reshape the blobs to correct size for im2col output
+ this->blob_top_->Reshape(this->blob_bottom_->num(),
+ this->channels_ * this->kernel_size_ * this->kernel_size_,
+ this->height_col_,
+ this->width_col_);
+
+ this->blob_top_cpu_->ReshapeLike(*this->blob_top_);
+
+ const TypeParam* bottom_data_cpu = this->blob_bottom_->cpu_data();
+ TypeParam* top_data_cpu = this->blob_top_cpu_->mutable_cpu_data();
+
+ // CPU Version
+ for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+ im2col_nd_cpu(bottom_data_cpu + this->blob_bottom_->offset(n), 2,
+ this->blob_bottom_->shape().data() + 1,
+ this->blob_top_cpu_->shape().data() + 1,
+ this->blob_kernel_shape_->cpu_data(),
+ this->blob_pad_->cpu_data(), this->blob_stride_->cpu_data(),
+ this->blob_dilation_->cpu_data(),
+ top_data_cpu + this->blob_top_cpu_->offset(n));
+ }
+
+ // GPU version
+ int num_kernels = this->channels_ * this->height_col_ * this->width_col_;
+ int default_grid_dim = CAFFE_GET_BLOCKS(num_kernels);
+ const TypeParam* bottom_data_gpu = this->blob_bottom_->gpu_data();
+
+ // Launch with different grid sizes
+ for (int grid_div = 2; grid_div <= 8; grid_div++) {
+ for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+ const int grid_dim = default_grid_dim / grid_div;
+ TypeParam* top_data_gpu = this->blob_top_->mutable_gpu_data();
+ // NOLINT_NEXT_LINE(whitespace/operators)
+ im2col_nd_gpu_kernel<TypeParam, 2><<<grid_dim, CAFFE_CUDA_NUM_THREADS>>>(
+ num_kernels, bottom_data_gpu + this->blob_bottom_->offset(n),
+ this->blob_bottom_->gpu_shape() + 1, this->blob_top_->gpu_shape() + 1,
+ this->blob_kernel_shape_->gpu_data(), this->blob_pad_->gpu_data(),
+ this->blob_stride_->gpu_data(), this->blob_dilation_->gpu_data(),
+ top_data_gpu + this->blob_top_->offset(n));
+ CUDA_POST_KERNEL_CHECK;
+ }
+
+ // Compare results against CPU version
+ for (int i = 0; i < this->blob_top_->count(); ++i) {
+ TypeParam cpuval = top_data_cpu[i];
+ TypeParam gpuval = this->blob_top_->cpu_data()[i];
+ EXPECT_EQ(cpuval, gpuval);
+ if (cpuval != gpuval) {
+ break;
+ }
+ }
+ }
+}
+
} // namespace caffe
-#include <cstring>
#include <vector>
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/im2col_layer.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
: blob_bottom_(new Blob<Dtype>(2, 3, 6, 5)),
blob_top_(new Blob<Dtype>()) {
// fill the values
+ Caffe::set_random_seed(1701);
FillerParameter filler_param;
GaussianFiller<Dtype> filler(filler_param);
filler.Fill(this->blob_bottom_);
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
- convolution_param->set_kernel_size(3);
- convolution_param->set_stride(2);
+ vector<int> bottom_shape;
+ bottom_shape.push_back(2);
+ bottom_shape.push_back(3);
+ bottom_shape.push_back(10);
+ bottom_shape.push_back(11);
+ this->blob_bottom_->Reshape(bottom_shape);
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_stride(2);
+ convolution_param->add_dilation(3);
Im2colLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
EXPECT_EQ(this->blob_top_->num(), 2);
EXPECT_EQ(this->blob_top_->channels(), 27);
EXPECT_EQ(this->blob_top_->height(), 2);
- EXPECT_EQ(this->blob_top_->width(), 2);
+ EXPECT_EQ(this->blob_top_->width(), 3);
}
TYPED_TEST(Im2colLayerTest, TestForward) {
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
- convolution_param->set_kernel_size(3);
- convolution_param->set_stride(2);
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_stride(2);
Im2colLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
- convolution_param->set_kernel_size(3);
- convolution_param->set_stride(2);
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_stride(2);
Im2colLayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-2, 1e-2);
checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
+TYPED_TEST(Im2colLayerTest, TestDilatedGradient) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ ConvolutionParameter* convolution_param =
+ layer_param.mutable_convolution_param();
+ vector<int> bottom_shape;
+ bottom_shape.push_back(2);
+ bottom_shape.push_back(3);
+ bottom_shape.push_back(10);
+ bottom_shape.push_back(9);
+ this->blob_bottom_->Reshape(bottom_shape);
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_stride(2);
+ convolution_param->add_dilation(3);
+ Im2colLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-2);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+}
+
+TYPED_TEST(Im2colLayerTest, TestGradientForceND) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ ConvolutionParameter* convolution_param =
+ layer_param.mutable_convolution_param();
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_stride(2);
+ convolution_param->set_force_nd_im2col(true);
+ Im2colLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-2);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+}
+
+TYPED_TEST(Im2colLayerTest, TestDilatedGradientForceND) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ ConvolutionParameter* convolution_param =
+ layer_param.mutable_convolution_param();
+ vector<int> bottom_shape;
+ bottom_shape.push_back(2);
+ bottom_shape.push_back(3);
+ bottom_shape.push_back(10);
+ bottom_shape.push_back(9);
+ this->blob_bottom_->Reshape(bottom_shape);
+ convolution_param->add_kernel_size(3);
+ convolution_param->add_stride(2);
+ convolution_param->add_dilation(3);
+ convolution_param->set_force_nd_im2col(true);
+ Im2colLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-2);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+}
TYPED_TEST(Im2colLayerTest, TestRect) {
typedef typename TypeParam::Dtype Dtype;
layer_param.mutable_convolution_param();
convolution_param->set_kernel_h(5);
convolution_param->set_kernel_w(3);
- convolution_param->set_stride(2);
+ convolution_param->add_stride(2);
Im2colLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
}
}
-
TYPED_TEST(Im2colLayerTest, TestRectGradient) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
layer_param.mutable_convolution_param();
convolution_param->set_kernel_h(5);
convolution_param->set_kernel_w(3);
- convolution_param->set_stride(2);
+ convolution_param->add_stride(2);
Im2colLayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-2, 1e-2);
checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+#ifdef USE_OPENCV
#include <map>
#include <string>
#include <vector>
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
+#include "caffe/layers/image_data_layer.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
#include "caffe/test/test_caffe_main.hpp"
}
} // namespace caffe
+#endif // USE_OPENCV
-#include <cmath>
-#include <cstdlib>
-#include <cstring>
#include <vector>
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/infogain_loss_layer.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
-#include <cstring>
#include <vector>
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/inner_product_layer.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
protected:
InnerProductLayerTest()
: blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
+ blob_bottom_nobatch_(new Blob<Dtype>(1, 2, 3, 4)),
blob_top_(new Blob<Dtype>()) {
// fill the values
FillerParameter filler_param;
UniformFiller<Dtype> filler(filler_param);
filler.Fill(this->blob_bottom_);
- blob_bottom_vec_.push_back(blob_bottom_);
blob_top_vec_.push_back(blob_top_);
}
- virtual ~InnerProductLayerTest() { delete blob_bottom_; delete blob_top_; }
+ virtual ~InnerProductLayerTest() {
+ delete blob_bottom_;
+ delete blob_bottom_nobatch_;
+ delete blob_top_;
+ }
Blob<Dtype>* const blob_bottom_;
+ Blob<Dtype>* const blob_bottom_nobatch_;
Blob<Dtype>* const blob_top_;
vector<Blob<Dtype>*> blob_bottom_vec_;
vector<Blob<Dtype>*> blob_top_vec_;
TYPED_TEST(InnerProductLayerTest, TestSetUp) {
typedef typename TypeParam::Dtype Dtype;
+ this->blob_bottom_vec_.push_back(this->blob_bottom_);
LayerParameter layer_param;
InnerProductParameter* inner_product_param =
layer_param.mutable_inner_product_param();
TYPED_TEST(InnerProductLayerTest, TestForward) {
typedef typename TypeParam::Dtype Dtype;
+ this->blob_bottom_vec_.push_back(this->blob_bottom_);
+ bool IS_VALID_CUDA = false;
+#ifndef CPU_ONLY
+ IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
+#endif
+ if (Caffe::mode() == Caffe::CPU ||
+ sizeof(Dtype) == 4 || IS_VALID_CUDA) {
+ LayerParameter layer_param;
+ InnerProductParameter* inner_product_param =
+ layer_param.mutable_inner_product_param();
+ inner_product_param->set_num_output(10);
+ inner_product_param->mutable_weight_filler()->set_type("uniform");
+ inner_product_param->mutable_bias_filler()->set_type("uniform");
+ inner_product_param->mutable_bias_filler()->set_min(1);
+ inner_product_param->mutable_bias_filler()->set_max(2);
+ shared_ptr<InnerProductLayer<Dtype> > layer(
+ new InnerProductLayer<Dtype>(layer_param));
+ layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ const Dtype* data = this->blob_top_->cpu_data();
+ const int count = this->blob_top_->count();
+ for (int i = 0; i < count; ++i) {
+ EXPECT_GE(data[i], 1.);
+ }
+ } else {
+ LOG(ERROR) << "Skipping test due to old architecture.";
+ }
+}
+
+TYPED_TEST(InnerProductLayerTest, TestForwardNoBatch) {
+ typedef typename TypeParam::Dtype Dtype;
+ this->blob_bottom_vec_.push_back(this->blob_bottom_nobatch_);
bool IS_VALID_CUDA = false;
#ifndef CPU_ONLY
IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
TYPED_TEST(InnerProductLayerTest, TestGradient) {
typedef typename TypeParam::Dtype Dtype;
+ this->blob_bottom_vec_.push_back(this->blob_bottom_);
bool IS_VALID_CUDA = false;
#ifndef CPU_ONLY
IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
#include "gtest/gtest.h"
#include "caffe/internal_thread.hpp"
+#include "caffe/util/math_functions.hpp"
#include "caffe/test/test_caffe_main.hpp"
TEST_F(InternalThreadTest, TestStartAndExit) {
InternalThread thread;
EXPECT_FALSE(thread.is_started());
- EXPECT_TRUE(thread.StartInternalThread());
+ thread.StartInternalThread();
EXPECT_TRUE(thread.is_started());
- EXPECT_TRUE(thread.WaitForInternalThreadToExit());
+ thread.StopInternalThread();
EXPECT_FALSE(thread.is_started());
}
+class TestThreadA : public InternalThread {
+ void InternalThreadEntry() {
+ EXPECT_EQ(4244559767, caffe_rng_rand());
+ }
+};
+
+class TestThreadB : public InternalThread {
+ void InternalThreadEntry() {
+ EXPECT_EQ(1726478280, caffe_rng_rand());
+ }
+};
+
+TEST_F(InternalThreadTest, TestRandomSeed) {
+ TestThreadA t1;
+ Caffe::set_random_seed(9658361);
+ t1.StartInternalThread();
+ t1.StopInternalThread();
+
+ TestThreadA t2;
+ Caffe::set_random_seed(9658361);
+ t2.StartInternalThread();
+ t2.StopInternalThread();
+
+ TestThreadB t3;
+ Caffe::set_random_seed(3435563);
+ t3.StartInternalThread();
+ t3.StopInternalThread();
+}
+
} // namespace caffe
+#ifdef USE_OPENCV
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/highgui/highgui_c.h>
}
} // namespace caffe
+#endif // USE_OPENCV
#include <map>
#include <string>
+#include "boost/scoped_ptr.hpp"
#include "gtest/gtest.h"
#include "caffe/common.hpp"
#include "caffe/layer.hpp"
#include "caffe/layer_factory.hpp"
+#include "caffe/util/db.hpp"
+#include "caffe/util/io.hpp"
#include "caffe/test/test_caffe_main.hpp"
typename LayerRegistry<Dtype>::CreatorRegistry& registry =
LayerRegistry<Dtype>::Registry();
shared_ptr<Layer<Dtype> > layer;
- LayerParameter layer_param;
for (typename LayerRegistry<Dtype>::CreatorRegistry::iterator iter =
registry.begin(); iter != registry.end(); ++iter) {
// Special case: PythonLayer is checked by pytest
if (iter->first == "Python") { continue; }
+ LayerParameter layer_param;
+ // Data layers expect a DB
+ if (iter->first == "Data") {
+#ifdef USE_LEVELDB
+ string tmp;
+ MakeTempDir(&tmp);
+ boost::scoped_ptr<db::DB> db(db::GetDB(DataParameter_DB_LEVELDB));
+ db->Open(tmp, db::NEW);
+ db->Close();
+ layer_param.mutable_data_param()->set_source(tmp);
+#else
+ continue;
+#endif // USE_LEVELDB
+ }
layer_param.set_type(iter->first);
layer = LayerRegistry<Dtype>::CreateLayer(layer_param);
EXPECT_EQ(iter->first, layer->type());
#include <algorithm>
-#include <cstring>
#include <vector>
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/lrn_layer.hpp"
+
+#ifdef USE_CUDNN
+#include "caffe/layers/cudnn_lcn_layer.hpp"
+#include "caffe/layers/cudnn_lrn_layer.hpp"
+#endif
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
}
}
+TYPED_TEST(LRNLayerTest, TestForwardAcrossChannelsLargeRegion) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_lrn_param()->set_local_size(15);
+ LRNLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ Blob<Dtype> top_reference;
+ this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
+ &top_reference);
+ for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+ EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i],
+ this->epsilon_);
+ }
+}
+
TYPED_TEST(LRNLayerTest, TestGradientAcrossChannels) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
this->blob_top_vec_);
}
+TYPED_TEST(LRNLayerTest, TestGradientAcrossChannelsLargeRegion) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_lrn_param()->set_local_size(15);
+ LRNLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-2);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ for (int i = 0; i < this->blob_top_->count(); ++i) {
+ this->blob_top_->mutable_cpu_diff()[i] = 1.;
+ }
+ vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
+ layer.Backward(this->blob_top_vec_, propagate_down,
+ this->blob_bottom_vec_);
+ // for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+ // std::cout << "CPU diff " << this->blob_bottom_->cpu_diff()[i]
+ // << std::endl;
+ // }
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+}
+
TYPED_TEST(LRNLayerTest, TestSetupWithinChannel) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
this->blob_top_vec_);
}
+#ifdef USE_CUDNN
+template <typename Dtype>
+class CuDNNLRNLayerTest : public GPUDeviceTest<Dtype> {
+ protected:
+ CuDNNLRNLayerTest()
+ : epsilon_(Dtype(1e-5)),
+ blob_bottom_(new Blob<Dtype>()),
+ blob_top_(new Blob<Dtype>()) {}
+ virtual void SetUp() {
+ Caffe::set_random_seed(1701);
+ blob_bottom_->Reshape(2, 7, 3, 3);
+ // fill the values
+ FillerParameter filler_param;
+ GaussianFiller<Dtype> filler(filler_param);
+ filler.Fill(this->blob_bottom_);
+ blob_bottom_vec_.push_back(blob_bottom_);
+ blob_top_vec_.push_back(blob_top_);
+ }
+ virtual ~CuDNNLRNLayerTest() { delete blob_bottom_; delete blob_top_; }
+ void ReferenceLRNForward(const Blob<Dtype>& blob_bottom,
+ const LayerParameter& layer_param, Blob<Dtype>* blob_top);
+
+ Dtype epsilon_;
+ Blob<Dtype>* const blob_bottom_;
+ Blob<Dtype>* const blob_top_;
+ vector<Blob<Dtype>*> blob_bottom_vec_;
+ vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+template <typename TypeParam>
+void CuDNNLRNLayerTest<TypeParam>::ReferenceLRNForward(
+ const Blob<TypeParam>& blob_bottom, const LayerParameter& layer_param,
+ Blob<TypeParam>* blob_top) {
+ typedef TypeParam Dtype;
+ blob_top->Reshape(blob_bottom.num(), blob_bottom.channels(),
+ blob_bottom.height(), blob_bottom.width());
+ Dtype* top_data = blob_top->mutable_cpu_data();
+ LRNParameter lrn_param = layer_param.lrn_param();
+ Dtype alpha = lrn_param.alpha();
+ Dtype beta = lrn_param.beta();
+ int size = lrn_param.local_size();
+ switch (lrn_param.norm_region()) {
+ case LRNParameter_NormRegion_ACROSS_CHANNELS:
+ for (int n = 0; n < blob_bottom.num(); ++n) {
+ for (int c = 0; c < blob_bottom.channels(); ++c) {
+ for (int h = 0; h < blob_bottom.height(); ++h) {
+ for (int w = 0; w < blob_bottom.width(); ++w) {
+ int c_start = c - (size - 1) / 2;
+ int c_end = min(c_start + size, blob_bottom.channels());
+ c_start = max(c_start, 0);
+ Dtype scale = 1.;
+ for (int i = c_start; i < c_end; ++i) {
+ Dtype value = blob_bottom.data_at(n, i, h, w);
+ scale += value * value * alpha / size;
+ }
+ *(top_data + blob_top->offset(n, c, h, w)) =
+ blob_bottom.data_at(n, c, h, w) / pow(scale, beta);
+ }
+ }
+ }
+ }
+ break;
+ case LRNParameter_NormRegion_WITHIN_CHANNEL:
+ for (int n = 0; n < blob_bottom.num(); ++n) {
+ for (int c = 0; c < blob_bottom.channels(); ++c) {
+ for (int h = 0; h < blob_bottom.height(); ++h) {
+ int h_start = h - (size - 1) / 2;
+ int h_end = min(h_start + size, blob_bottom.height());
+ h_start = max(h_start, 0);
+ for (int w = 0; w < blob_bottom.width(); ++w) {
+ Dtype scale = 1.;
+ int w_start = w - (size - 1) / 2;
+ int w_end = min(w_start + size, blob_bottom.width());
+ w_start = max(w_start, 0);
+ for (int nh = h_start; nh < h_end; ++nh) {
+ for (int nw = w_start; nw < w_end; ++nw) {
+ Dtype value = blob_bottom.data_at(n, c, nh, nw);
+ scale += value * value * alpha / (size * size);
+ }
+ }
+ *(top_data + blob_top->offset(n, c, h, w)) =
+ blob_bottom.data_at(n, c, h, w) / pow(scale, beta);
+ }
+ }
+ }
+ }
+ break;
+ default:
+ LOG(FATAL) << "Unknown normalization region.";
+ }
+}
+
+TYPED_TEST_CASE(CuDNNLRNLayerTest, TestDtypes);
+
+TYPED_TEST(CuDNNLRNLayerTest, TestForwardAcrossChannelsCuDNN) {
+ // typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ CuDNNLRNLayer<TypeParam> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ Blob<TypeParam> top_reference;
+ this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
+ &top_reference);
+ for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+ EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i],
+ this->epsilon_);
+ }
+}
+
+TYPED_TEST(CuDNNLRNLayerTest, TestForwardAcrossChannelsLargeRegionCuDNN) {
+ typedef TypeParam Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_lrn_param()->set_local_size(15);
+ CuDNNLRNLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ Blob<Dtype> top_reference;
+ this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
+ &top_reference);
+ for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+ EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i],
+ this->epsilon_);
+ }
+}
+
+TYPED_TEST(CuDNNLRNLayerTest, TestGradientAcrossChannelsCuDNN) {
+ typedef TypeParam Dtype;
+ LayerParameter layer_param;
+ CuDNNLRNLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-2);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ for (int i = 0; i < this->blob_top_->count(); ++i) {
+ this->blob_top_->mutable_cpu_diff()[i] = 1.;
+ }
+ vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
+ layer.Backward(this->blob_top_vec_, propagate_down,
+ this->blob_bottom_vec_);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+}
+
+TYPED_TEST(CuDNNLRNLayerTest, TestForwardWithinChannel) {
+ typedef TypeParam Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_lrn_param()->set_norm_region(
+ LRNParameter_NormRegion_WITHIN_CHANNEL);
+ layer_param.mutable_lrn_param()->set_local_size(3);
+ CuDNNLCNLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ Blob<Dtype> top_reference;
+ this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
+ &top_reference);
+ for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+ EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i],
+ this->epsilon_);
+ }
+}
+
+TYPED_TEST(CuDNNLRNLayerTest, TestGradientWithinChannel) {
+ typedef TypeParam Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_lrn_param()->set_norm_region(
+ LRNParameter_NormRegion_WITHIN_CHANNEL);
+ layer_param.mutable_lrn_param()->set_local_size(3);
+ CuDNNLCNLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-2);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ for (int i = 0; i < this->blob_top_->count(); ++i) {
+ this->blob_top_->mutable_cpu_diff()[i] = 1.;
+ }
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+}
+
+TYPED_TEST(CuDNNLRNLayerTest, TestGradientAcrossChannelsLargeRegionCuDNN) {
+ typedef TypeParam Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_lrn_param()->set_local_size(15);
+ CuDNNLRNLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-2);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ for (int i = 0; i < this->blob_top_->count(); ++i) {
+ this->blob_top_->mutable_cpu_diff()[i] = 1.;
+ }
+ vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
+ layer.Backward(this->blob_top_vec_, propagate_down,
+ this->blob_bottom_vec_);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+}
+
+#endif
} // namespace caffe
#include <stdint.h> // for uint32_t & uint64_t
#include <time.h>
-#include <climits>
#include <cmath> // for std::fabs
-#include <cstdlib> // for rand_r
#include "gtest/gtest.h"
namespace caffe {
-template<typename Dtype>
-class MathFunctionsTest : public ::testing::Test {
+template <typename TypeParam>
+class MathFunctionsTest : public MultiDeviceTest<TypeParam> {
+ typedef typename TypeParam::Dtype Dtype;
+
protected:
MathFunctionsTest()
: blob_bottom_(new Blob<Dtype>()),
delete blob_top_;
}
- // http://en.wikipedia.org/wiki/Hamming_distance
- int ReferenceHammingDistance(const int n, const Dtype* x, const Dtype* y) {
- int dist = 0;
- uint64_t val;
- for (int i = 0; i < n; ++i) {
- if (sizeof(Dtype) == 8) {
- val = static_cast<uint64_t>(x[i]) ^ static_cast<uint64_t>(y[i]);
- } else if (sizeof(Dtype) == 4) {
- val = static_cast<uint32_t>(x[i]) ^ static_cast<uint32_t>(y[i]);
- } else {
- LOG(FATAL) << "Unrecognized Dtype size: " << sizeof(Dtype);
- }
- // Count the number of set bits
- while (val) {
- ++dist;
- val &= val - 1;
- }
- }
- return dist;
- }
-
Blob<Dtype>* const blob_bottom_;
Blob<Dtype>* const blob_top_;
};
-TYPED_TEST_CASE(MathFunctionsTest, TestDtypes);
+template <typename Dtype>
+class CPUMathFunctionsTest
+ : public MathFunctionsTest<CPUDevice<Dtype> > {
+};
-TYPED_TEST(MathFunctionsTest, TestNothing) {
+TYPED_TEST_CASE(CPUMathFunctionsTest, TestDtypes);
+
+TYPED_TEST(CPUMathFunctionsTest, TestNothing) {
// The first test case of a test suite takes the longest time
// due to the set up overhead.
}
-TYPED_TEST(MathFunctionsTest, TestHammingDistanceCPU) {
- int n = this->blob_bottom_->count();
- const TypeParam* x = this->blob_bottom_->cpu_data();
- const TypeParam* y = this->blob_top_->cpu_data();
- EXPECT_EQ(this->ReferenceHammingDistance(n, x, y),
- caffe_cpu_hamming_distance<TypeParam>(n, x, y));
-}
-
-TYPED_TEST(MathFunctionsTest, TestAsumCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestAsum) {
int n = this->blob_bottom_->count();
const TypeParam* x = this->blob_bottom_->cpu_data();
TypeParam std_asum = 0;
EXPECT_LT((cpu_asum - std_asum) / std_asum, 1e-2);
}
-TYPED_TEST(MathFunctionsTest, TestSignCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestSign) {
int n = this->blob_bottom_->count();
const TypeParam* x = this->blob_bottom_->cpu_data();
caffe_cpu_sign<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
}
}
-TYPED_TEST(MathFunctionsTest, TestSgnbitCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestSgnbit) {
int n = this->blob_bottom_->count();
const TypeParam* x = this->blob_bottom_->cpu_data();
caffe_cpu_sgnbit<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
}
}
-TYPED_TEST(MathFunctionsTest, TestFabsCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestFabs) {
int n = this->blob_bottom_->count();
const TypeParam* x = this->blob_bottom_->cpu_data();
caffe_abs<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
}
}
-TYPED_TEST(MathFunctionsTest, TestScaleCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestScale) {
int n = this->blob_bottom_->count();
TypeParam alpha = this->blob_bottom_->cpu_diff()[caffe_rng_rand() %
this->blob_bottom_->count()];
}
}
-TYPED_TEST(MathFunctionsTest, TestCopyCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestCopy) {
const int n = this->blob_bottom_->count();
const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
TypeParam* top_data = this->blob_top_->mutable_cpu_data();
- Caffe::set_mode(Caffe::CPU);
caffe_copy(n, bottom_data, top_data);
for (int i = 0; i < n; ++i) {
EXPECT_EQ(bottom_data[i], top_data[i]);
#ifndef CPU_ONLY
-// TODO: Fix caffe_gpu_hamming_distance and re-enable this test.
-TYPED_TEST(MathFunctionsTest, DISABLED_TestHammingDistanceGPU) {
- int n = this->blob_bottom_->count();
- const TypeParam* x = this->blob_bottom_->cpu_data();
- const TypeParam* y = this->blob_top_->cpu_data();
- int reference_distance = this->ReferenceHammingDistance(n, x, y);
- x = this->blob_bottom_->gpu_data();
- y = this->blob_top_->gpu_data();
- int computed_distance = caffe_gpu_hamming_distance<TypeParam>(n, x, y);
- EXPECT_EQ(reference_distance, computed_distance);
-}
+template <typename Dtype>
+class GPUMathFunctionsTest : public MathFunctionsTest<GPUDevice<Dtype> > {
+};
+
+TYPED_TEST_CASE(GPUMathFunctionsTest, TestDtypes);
-TYPED_TEST(MathFunctionsTest, TestAsumGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestAsum) {
int n = this->blob_bottom_->count();
const TypeParam* x = this->blob_bottom_->cpu_data();
TypeParam std_asum = 0;
EXPECT_LT((gpu_asum - std_asum) / std_asum, 1e-2);
}
-TYPED_TEST(MathFunctionsTest, TestSignGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestSign) {
int n = this->blob_bottom_->count();
caffe_gpu_sign<TypeParam>(n, this->blob_bottom_->gpu_data(),
this->blob_bottom_->mutable_gpu_diff());
}
}
-TYPED_TEST(MathFunctionsTest, TestSgnbitGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestSgnbit) {
int n = this->blob_bottom_->count();
caffe_gpu_sgnbit<TypeParam>(n, this->blob_bottom_->gpu_data(),
this->blob_bottom_->mutable_gpu_diff());
}
}
-TYPED_TEST(MathFunctionsTest, TestFabsGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestFabs) {
int n = this->blob_bottom_->count();
caffe_gpu_abs<TypeParam>(n, this->blob_bottom_->gpu_data(),
this->blob_bottom_->mutable_gpu_diff());
}
}
-TYPED_TEST(MathFunctionsTest, TestScaleGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestScale) {
int n = this->blob_bottom_->count();
TypeParam alpha = this->blob_bottom_->cpu_diff()[caffe_rng_rand() %
this->blob_bottom_->count()];
}
}
-TYPED_TEST(MathFunctionsTest, TestCopyGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestCopy) {
const int n = this->blob_bottom_->count();
const TypeParam* bottom_data = this->blob_bottom_->gpu_data();
TypeParam* top_data = this->blob_top_->mutable_gpu_data();
- Caffe::set_mode(Caffe::GPU);
caffe_copy(n, bottom_data, top_data);
bottom_data = this->blob_bottom_->cpu_data();
top_data = this->blob_top_->mutable_cpu_data();
-#include <cstring>
#include <vector>
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/dropout_layer.hpp"
+#include "caffe/layers/pooling_layer.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
+#ifdef USE_OPENCV
#include <opencv2/core/core.hpp>
+#endif // USE_OPENCV
#include <string>
#include <vector>
-#include "caffe/data_layers.hpp"
#include "caffe/filler.hpp"
+#include "caffe/layers/memory_data_layer.hpp"
#include "caffe/test/test_caffe_main.hpp"
}
}
+#ifdef USE_OPENCV
TYPED_TEST(MemoryDataLayerTest, AddDatumVectorDefaultTransform) {
typedef typename TypeParam::Dtype Dtype;
}
}
}
-
+#endif // USE_OPENCV
} // namespace caffe
-#include <cmath>
-#include <cstdlib>
-#include <cstring>
#include <vector>
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/multinomial_logistic_loss_layer.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
namespace caffe {
template <typename Dtype>
-class MultinomialLogisticLossLayerTest : public ::testing::Test {
+class MultinomialLogisticLossLayerTest : public CPUDeviceTest<Dtype> {
protected:
MultinomialLogisticLossLayerTest()
: blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
TYPED_TEST(MultinomialLogisticLossLayerTest, TestGradientCPU) {
LayerParameter layer_param;
- Caffe::set_mode(Caffe::CPU);
MultinomialLogisticLossLayer<TypeParam> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
GradientChecker<TypeParam> checker(1e-2, 2*1e-2, 1701, 0, 0.05);
-#include <cmath>
-#include <cstring>
#include <vector>
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
#include "caffe/filler.hpp"
+#include "caffe/layers/mvn_layer.hpp"
+#include "google/protobuf/text_format.h"
#include "gtest/gtest.h"
#include "caffe/test/test_caffe_main.hpp"
TYPED_TEST(MVNLayerTest, TestForwardMeanOnly) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
- layer_param.ParseFromString("mvn_param{normalize_variance: false}");
+ CHECK(google::protobuf::TextFormat::ParseFromString(
+ "mvn_param{normalize_variance: false}", &layer_param));
MVNLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
TYPED_TEST(MVNLayerTest, TestForwardAcrossChannels) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
- layer_param.ParseFromString("mvn_param{across_channels: true}");
+ CHECK(google::protobuf::TextFormat::ParseFromString(
+ "mvn_param{across_channels: true}", &layer_param));
MVNLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
TYPED_TEST(MVNLayerTest, TestGradientMeanOnly) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
- layer_param.ParseFromString("mvn_param{normalize_variance: false}");
+ CHECK(google::protobuf::TextFormat::ParseFromString(
+ "mvn_param{normalize_variance: false}", &layer_param));
MVNLayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-2, 1e-3);
checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
TYPED_TEST(MVNLayerTest, TestGradientAcrossChannels) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
- layer_param.ParseFromString("mvn_param{across_channels: true}");
+ CHECK(google::protobuf::TextFormat::ParseFromString(
+ "mvn_param{across_channels: true}", &layer_param));
MVNLayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-2, 1e-3);
checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
const bool force_backward = false, const bool bias_term = false,
const Dtype blobs_lr_w1 = 1, const Dtype blobs_lr_b1 = 2,
const Dtype blobs_lr_w2 = 1, const Dtype blobs_lr_b2 = 2) {
+ string bias_str = bias_term ? "true ":"false ";
ostringstream proto;
proto << "name: 'UnsharedWeightsNetwork' ";
if (force_backward) {
" type: 'InnerProduct' "
" inner_product_param { "
" num_output: 10 "
- " bias_term: " << bias_term <<
+ " bias_term: " << bias_str <<
" weight_filler { "
" type: 'gaussian' "
" std: 10 "
" type: 'InnerProduct' "
" inner_product_param { "
" num_output: 10 "
- " bias_term: " << bias_term <<
+ " bias_term: " << bias_str <<
" weight_filler { "
" type: 'gaussian' "
" std: 10 "
InitNetFromProtoString(proto);
}
+ virtual void InitSkipPropNet(bool test_skip_true) {
+ string proto =
+ "name: 'SkipPropTestNetwork' "
+ "layer { "
+ " name: 'data' "
+ " type: 'DummyData' "
+ " dummy_data_param { "
+ " shape { "
+ " dim: 5 "
+ " dim: 2 "
+ " dim: 3 "
+ " dim: 4 "
+ " } "
+ " data_filler { "
+ " type: 'gaussian' "
+ " std: 0.01 "
+ " } "
+ " shape { "
+ " dim: 5 "
+ " } "
+ " data_filler { "
+ " type: 'constant' "
+ " value: 0 "
+ " } "
+ " } "
+ " top: 'data' "
+ " top: 'label' "
+ "} "
+ "layer { "
+ " name: 'silence' "
+ " bottom: 'label' "
+ " type: 'Silence' "
+ "} "
+ "layer { "
+ " name: 'innerproduct' "
+ " type: 'InnerProduct' "
+ " inner_product_param { "
+ " num_output: 1 "
+ " weight_filler { "
+ " type: 'gaussian' "
+ " std: 0.01 "
+ " } "
+ " bias_filler { "
+ " type: 'constant' "
+ " value: 0 "
+ " } "
+ " } "
+ " param { "
+ " lr_mult: 1 "
+ " decay_mult: 1 "
+ " } "
+ " param { "
+ " lr_mult: 2 "
+ " decay_mult: 0 "
+ " } "
+ " bottom: 'data' "
+ " top: 'innerproduct' "
+ "} "
+ "layer { "
+ " name: 'ip_fake_labels' "
+ " type: 'InnerProduct' "
+ " inner_product_param { "
+ " num_output: 1 "
+ " weight_filler { "
+ " type: 'gaussian' "
+ " std: 0.01 "
+ " } "
+ " bias_filler { "
+ " type: 'constant' "
+ " value: 0 "
+ " } "
+ " } "
+ " bottom: 'data' "
+ " top: 'fake_labels' "
+ "} "
+ "layer { "
+ " name: 'argmax' "
+ " bottom: 'fake_labels' "
+ " top: 'label_argmax' "
+ " type: 'ArgMax' "
+ "} "
+ "layer { "
+ " name: 'loss' "
+ " bottom: 'innerproduct' "
+ " bottom: 'label_argmax' ";
+ if (test_skip_true)
+ proto += " propagate_down: true "
+ " propagate_down: false ";
+ else
+ proto += " propagate_down: true "
+ " propagate_down: true ";
+ proto +=
+ " top: 'cross_entropy_loss' "
+ " type: 'SigmoidCrossEntropyLoss' "
+ " loss_weight: 0.1 "
+ "} ";
+ InitNetFromProtoString(proto);
+ }
+
int seed_;
shared_ptr<Net<Dtype> > net_;
};
EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
Blob<Dtype>* ip1_weights = this->net_->layers()[1]->blobs()[0].get();
Blob<Dtype>* ip2_weights = this->net_->layers()[2]->blobs()[0].get();
- // Check that data blobs of shared weights share the same location in memory.
+ // Check that data and diff blobs of shared weights share the same memory
+ // locations.
EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
- // Check that diff blobs of shared weights are at different locations in
- // memory. (The diffs should be accumulated at update time.)
- EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
+ EXPECT_EQ(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
this->net_->Forward(bottom);
this->net_->Backward();
// Compute the expected update as the data minus the two diffs.
// Make sure the diffs are non-trivial.
for (int i = 0; i < count; ++i) {
EXPECT_NE(0, ip1_weights->cpu_diff()[i]);
- EXPECT_NE(0, ip2_weights->cpu_diff()[i]);
- EXPECT_NE(ip1_weights->cpu_diff()[i], ip2_weights->cpu_diff()[i]);
}
- caffe_axpy(count, Dtype(1), ip2_weights->cpu_diff(),
- shared_params.mutable_cpu_diff());
caffe_axpy(count, Dtype(-1), shared_params.cpu_diff(),
shared_params.mutable_cpu_data());
const Dtype* expected_updated_params = shared_params.cpu_data();
EXPECT_NE(0, ip1_weights->cpu_diff()[i]);
EXPECT_NE(0, ip2_weights->cpu_diff()[i]);
EXPECT_NE(ip1_weights->cpu_diff()[i], ip2_weights->cpu_diff()[i]);
- EXPECT_EQ(ip1_weights->cpu_diff()[i] + ip2_weights->cpu_diff()[i],
- shared_params.cpu_diff()[i]);
+ EXPECT_FLOAT_EQ(ip1_weights->cpu_diff()[i] + ip2_weights->cpu_diff()[i],
+ shared_params.cpu_diff()[i]);
}
caffe_axpy(count, Dtype(-1), ip1_weights->cpu_diff(),
unshared_params1.mutable_cpu_data());
EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
Blob<Dtype>* ip1_weights = this->net_->layers()[1]->blobs()[0].get();
Blob<Dtype>* ip2_weights = this->net_->layers()[2]->blobs()[0].get();
- // Check that data blobs of shared weights share the same location in memory.
+ // Check that data and diff blobs of shared weights share the same memory
+ // locations.
EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
- // Check that diff blobs of shared weights are at different locations in
- // memory. (The diffs should be accumulated at update time.)
- EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
+ EXPECT_EQ(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
this->net_->ForwardBackward(bottom);
this->net_->Update();
Blob<Dtype> shared_params;
ASSERT_FALSE(NULL == ip1_weights);
ASSERT_FALSE(NULL == ip2_weights);
EXPECT_NE(ip1_weights, ip2_weights);
- // Check that data blobs of shared weights share the same location in memory.
+ // Check that data and diff blobs of shared weights share the same memory
+ // locations.
EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
+ EXPECT_EQ(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
for (int i = 0; i < count; ++i) {
EXPECT_FLOAT_EQ(shared_params.cpu_data()[i], ip1_weights->cpu_data()[i]);
}
- // Check that diff blobs of shared weights are at different locations in
- // memory. (The diffs should be accumulated at update time.)
- EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
}
TYPED_TEST(NetTest, TestParamPropagateDown) {
TYPED_TEST(NetTest, TestReshape) {
typedef typename TypeParam::Dtype Dtype;
// We set up bottom blobs of two different sizes, switch between
- // them, and check that forward and backward both run and the results
- // are the same.
+ // them, check that forward and backward both run and the results
+ // are the same, and check that the output shapes change.
Caffe::set_random_seed(this->seed_);
Caffe::set_mode(Caffe::CPU);
FillerParameter filler_param;
filler_param.set_std(1);
GaussianFiller<Dtype> filler(filler_param);
- Blob<Dtype> blob1(4, 3, 9, 11);
- Blob<Dtype> blob2(2, 3, 12, 10);
+ // Check smaller shape first as larger first could hide realloc failures.
+ Blob<Dtype> blob1(2, 3, 12, 10);
+ Blob<Dtype> blob2(4, 3, 9, 11);
+ ASSERT_LT(blob1.count(), blob2.count());
filler.Fill(&blob1);
filler.Fill(&blob2);
this->net_->ForwardPrefilled();
this->net_->Backward();
for (int i = 0; i < output1.count(); ++i) {
- CHECK_EQ(*(output1.cpu_data() + i), *(output_blob->cpu_data() + i));
+ EXPECT_FLOAT_EQ(*(output1.cpu_data() + i), *(output_blob->cpu_data() + i));
}
input_blob->Reshape(blob2.num(), blob2.channels(), blob2.height(),
this->net_->ForwardPrefilled();
this->net_->Backward();
for (int i = 0; i < output2.count(); ++i) {
- CHECK_EQ(*(output2.cpu_data() + i), *(output_blob->cpu_data() + i));
+ EXPECT_FLOAT_EQ(*(output2.cpu_data() + i), *(output_blob->cpu_data() + i));
+ }
+
+ EXPECT_EQ(output1.num(), blob1.num());
+ EXPECT_EQ(output2.num(), blob2.num());
+ bool same_spatial_shape = true;
+ const int kFirstSpatialAxis = 2;
+ for (int i = kFirstSpatialAxis; i < output1.num_axes(); ++i) {
+ if (output1.shape(i) != output2.shape(i)) {
+ same_spatial_shape = false;
+ break;
+ }
+ }
+ EXPECT_FALSE(same_spatial_shape);
+}
+
+TYPED_TEST(NetTest, TestSkipPropagateDown) {
+ // check bottom_need_backward if propagate_down is true
+ this->InitSkipPropNet(false);
+ vector<bool> vec_layer_need_backward = this->net_->layer_need_backward();
+ for (int layer_id = 0; layer_id < this->net_->layers().size(); ++layer_id) {
+ string layer_name = this->net_->layer_names()[layer_id];
+ if (layer_name == "loss") {
+ // access to bottom_need_backward coresponding to label's blob
+ bool need_back = this->net_->bottom_need_backward()[layer_id][1];
+ // if propagate_down is true, the loss layer will try to
+ // backpropagate on labels
+ EXPECT_TRUE(need_back) << "bottom_need_backward should be True";
+ }
+ // layer_need_backward should be True except for data and silence layers
+ if (layer_name.find("data") != std::string::npos ||
+ layer_name == "silence") {
+ EXPECT_FALSE(vec_layer_need_backward[layer_id])
+ << "layer_need_backward for " << layer_name << " should be False";
+ } else {
+ EXPECT_TRUE(vec_layer_need_backward[layer_id])
+ << "layer_need_backward for " << layer_name << " should be True";
+ }
+ }
+ // check bottom_need_backward if propagat_down is false
+ this->InitSkipPropNet(true);
+ vec_layer_need_backward.clear();
+ vec_layer_need_backward = this->net_->layer_need_backward();
+ for (int layer_id = 0; layer_id < this->net_->layers().size(); ++layer_id) {
+ string layer_name = this->net_->layer_names()[layer_id];
+ if (layer_name == "loss") {
+ // access to bottom_need_backward coresponding to label's blob
+ bool need_back = this->net_->bottom_need_backward()[layer_id][1];
+ // if propagate_down is false, the loss layer will not try to
+ // backpropagate on labels
+ EXPECT_FALSE(need_back) << "bottom_need_backward should be False";
+ }
+ // layer_need_backward should be False except for innerproduct and
+ // loss layers
+ if (layer_name == "innerproduct" || layer_name == "loss") {
+ EXPECT_TRUE(vec_layer_need_backward[layer_id])
+ << "layer_need_backward for " << layer_name << " should be True";
+ } else {
+ EXPECT_FALSE(vec_layer_need_backward[layer_id])
+ << "layer_need_backward for " << layer_name << " should be False";
+ }
}
}
#include <algorithm>
-#include <cstring>
#include <vector>
#include "google/protobuf/text_format.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+
+#include "caffe/layers/absval_layer.hpp"
+#include "caffe/layers/bnll_layer.hpp"
+#include "caffe/layers/dropout_layer.hpp"
+#include "caffe/layers/elu_layer.hpp"
+#include "caffe/layers/exp_layer.hpp"
+#include "caffe/layers/inner_product_layer.hpp"
+#include "caffe/layers/log_layer.hpp"
+#include "caffe/layers/power_layer.hpp"
+#include "caffe/layers/prelu_layer.hpp"
+#include "caffe/layers/relu_layer.hpp"
+#include "caffe/layers/sigmoid_layer.hpp"
+#include "caffe/layers/tanh_layer.hpp"
+#include "caffe/layers/threshold_layer.hpp"
+
+#ifdef USE_CUDNN
+#include "caffe/layers/cudnn_relu_layer.hpp"
+#include "caffe/layers/cudnn_sigmoid_layer.hpp"
+#include "caffe/layers/cudnn_tanh_layer.hpp"
+#endif
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
+ slope_data[c] * std::min(bottom_data[i], (Dtype)(0)));
}
}
+
+ void LogBottomInit() {
+ FillerParameter filler_param;
+ GaussianFiller<Dtype> filler(filler_param);
+ filler.Fill(this->blob_bottom_);
+ Dtype* bottom_data = this->blob_bottom_->mutable_cpu_data();
+ caffe_exp(this->blob_bottom_->count(), bottom_data, bottom_data);
+ }
+
+ void TestLogForward(const float base, const float scale, const float shift) {
+ LogBottomInit();
+ LayerParameter layer_param;
+ layer_param.mutable_log_param()->set_base(base);
+ layer_param.mutable_log_param()->set_scale(scale);
+ layer_param.mutable_log_param()->set_shift(shift);
+ LogLayer<Dtype> layer(layer_param);
+ layer.SetUp(blob_bottom_vec_, blob_top_vec_);
+ layer.Forward(blob_bottom_vec_, blob_top_vec_);
+ const Dtype kDelta = 2e-4;
+ const Dtype* bottom_data = blob_bottom_->cpu_data();
+ const Dtype* top_data = blob_top_->cpu_data();
+ for (int i = 0; i < blob_bottom_->count(); ++i) {
+ const Dtype bottom_val = bottom_data[i];
+ const Dtype top_val = top_data[i];
+ if (base == -1) {
+ EXPECT_NEAR(top_val, log(shift + scale * bottom_val), kDelta);
+ } else {
+ EXPECT_NEAR(top_val, log(shift + scale * bottom_val) / log(base),
+ kDelta);
+ }
+ }
+ }
+
+ void TestLogGradient(const float base, const float scale, const float shift) {
+ LogBottomInit();
+ LayerParameter layer_param;
+ layer_param.mutable_log_param()->set_base(base);
+ layer_param.mutable_log_param()->set_scale(scale);
+ layer_param.mutable_log_param()->set_shift(shift);
+ LogLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-2);
+ checker.CheckGradientEltwise(&layer, blob_bottom_vec_, blob_top_vec_);
+ }
};
TYPED_TEST_CASE(NeuronLayerTest, TestDtypesAndDevices);
this->blob_top_vec_);
}
+TYPED_TEST(NeuronLayerTest, TestELU) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ CHECK(google::protobuf::TextFormat::ParseFromString(
+ "elu_param { alpha: 0.5 }", &layer_param));
+ ELULayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ const Dtype kDelta = 2e-4;
+ // Now, check values
+ const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+ const Dtype* top_data = this->blob_top_->cpu_data();
+ for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+ if (bottom_data[i] > 0) {
+ EXPECT_FLOAT_EQ(top_data[i], bottom_data[i]);
+ } else {
+ EXPECT_NEAR(top_data[i], 0.5 * (exp(bottom_data[i]) - 1), kDelta);
+ }
+ }
+}
+
+TYPED_TEST(NeuronLayerTest, TestELUasReLU) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ CHECK(google::protobuf::TextFormat::ParseFromString(
+ "elu_param { alpha: 0 }", &layer_param));
+ ELULayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ // Now, check values
+ const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+ const Dtype* top_data = this->blob_top_->cpu_data();
+ for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+ EXPECT_GE(top_data[i], 0.);
+ EXPECT_TRUE(top_data[i] == 0 || top_data[i] == bottom_data[i]);
+ }
+}
+
+TYPED_TEST(NeuronLayerTest, TestELUGradient) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ ELULayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
+ checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+}
+
+TYPED_TEST(NeuronLayerTest, TestELUasReLUGradient) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ CHECK(google::protobuf::TextFormat::ParseFromString(
+ "elu_param { alpha: 0 }", &layer_param));
+ ELULayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
+ checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+}
+
TYPED_TEST(NeuronLayerTest, TestSigmoid) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
this->TestExpGradient(kBase, kScale, kShift);
}
+TYPED_TEST(NeuronLayerTest, TestLogLayer) {
+ typedef typename TypeParam::Dtype Dtype;
+ // Test default base of "-1" -- should actually set base := e.
+ const Dtype kBase = -1;
+ const Dtype kScale = 1;
+ const Dtype kShift = 0;
+ this->TestLogForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogGradient) {
+ typedef typename TypeParam::Dtype Dtype;
+ // Test default base of "-1" -- should actually set base := e.
+ const Dtype kBase = -1;
+ const Dtype kScale = 1;
+ const Dtype kShift = 0;
+ this->TestLogGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogLayerBase2) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kBase = 2;
+ const Dtype kScale = 1;
+ const Dtype kShift = 0;
+ this->TestLogForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogGradientBase2) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kBase = 2;
+ const Dtype kScale = 1;
+ const Dtype kShift = 0;
+ this->TestLogGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogLayerBase2Shift1) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kBase = 2;
+ const Dtype kScale = 1;
+ const Dtype kShift = 1;
+ this->TestLogForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogGradientBase2Shift1) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kBase = 2;
+ const Dtype kScale = 1;
+ const Dtype kShift = 1;
+ this->TestLogGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogLayerBase2Scale3) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kBase = 2;
+ const Dtype kScale = 3;
+ const Dtype kShift = 0;
+ this->TestLogForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogGradientBase2Scale3) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kBase = 2;
+ const Dtype kScale = 3;
+ const Dtype kShift = 0;
+ this->TestLogGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogLayerBase2Shift1Scale3) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kBase = 2;
+ const Dtype kScale = 3;
+ const Dtype kShift = 1;
+ this->TestLogForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogGradientBase2Shift1Scale3) {
+ typedef typename TypeParam::Dtype Dtype;
+ const Dtype kBase = 2;
+ const Dtype kScale = 3;
+ const Dtype kShift = 1;
+ this->TestLogGradient(kBase, kScale, kShift);
+}
+
TYPED_TEST(NeuronLayerTest, TestDropoutHalf) {
const float kDropoutRatio = 0.5;
this->TestDropoutForward(kDropoutRatio);
caffe_copy(ip2.blobs()[0]->count(), ip.blobs()[0]->cpu_data(),
ip2.blobs()[0]->mutable_cpu_data());
// Forward in-place
- ip.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
ip.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
- prelu.Reshape(this->blob_top_vec_, this->blob_top_vec_);
prelu.Forward(this->blob_top_vec_, this->blob_top_vec_);
// Forward non-in-place
- ip2.Reshape(blob_bottom_vec_2, blob_middle_vec_2);
ip2.Forward(blob_bottom_vec_2, blob_middle_vec_2);
- prelu2.Reshape(blob_middle_vec_2, blob_top_vec_2);
prelu2.Forward(blob_middle_vec_2, blob_top_vec_2);
// Check numbers
for (int s = 0; s < blob_top_2->count(); ++s) {
#ifdef USE_CUDNN
template <typename Dtype>
-class CuDNNNeuronLayerTest : public ::testing::Test {
+class CuDNNNeuronLayerTest : public GPUDeviceTest<Dtype> {
protected:
CuDNNNeuronLayerTest()
: blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
TYPED_TEST_CASE(CuDNNNeuronLayerTest, TestDtypes);
TYPED_TEST(CuDNNNeuronLayerTest, TestReLUCuDNN) {
- Caffe::set_mode(Caffe::GPU);
LayerParameter layer_param;
CuDNNReLULayer<TypeParam> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
}
TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientCuDNN) {
- Caffe::set_mode(Caffe::GPU);
LayerParameter layer_param;
CuDNNReLULayer<TypeParam> layer(layer_param);
GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
}
TYPED_TEST(CuDNNNeuronLayerTest, TestReLUWithNegativeSlopeCuDNN) {
- Caffe::set_mode(Caffe::GPU);
LayerParameter layer_param;
CHECK(google::protobuf::TextFormat::ParseFromString(
"relu_param { negative_slope: 0.01 }", &layer_param));
}
TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientWithNegativeSlopeCuDNN) {
- Caffe::set_mode(Caffe::GPU);
LayerParameter layer_param;
CHECK(google::protobuf::TextFormat::ParseFromString(
"relu_param { negative_slope: 0.01 }", &layer_param));
}
TYPED_TEST(CuDNNNeuronLayerTest, TestSigmoidCuDNN) {
- Caffe::set_mode(Caffe::GPU);
LayerParameter layer_param;
CuDNNSigmoidLayer<TypeParam> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
}
TYPED_TEST(CuDNNNeuronLayerTest, TestSigmoidGradientCuDNN) {
- Caffe::set_mode(Caffe::GPU);
LayerParameter layer_param;
CuDNNSigmoidLayer<TypeParam> layer(layer_param);
GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
}
TYPED_TEST(CuDNNNeuronLayerTest, TestTanHCuDNN) {
- Caffe::set_mode(Caffe::GPU);
LayerParameter layer_param;
CuDNNTanHLayer<TypeParam> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
}
TYPED_TEST(CuDNNNeuronLayerTest, TestTanHGradientCuDNN) {
- Caffe::set_mode(Caffe::GPU);
LayerParameter layer_param;
CuDNNTanHLayer<TypeParam> layer(layer_param);
GradientChecker<TypeParam> checker(1e-2, 1e-3);
-#include <cstring>
#include <vector>
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/pooling_layer.hpp"
+
+#ifdef USE_CUDNN
+#include "caffe/layers/cudnn_pooling_layer.hpp"
+#endif
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
#ifdef USE_CUDNN
template <typename Dtype>
-class CuDNNPoolingLayerTest : public ::testing::Test {
+class CuDNNPoolingLayerTest : public GPUDeviceTest<Dtype> {
protected:
CuDNNPoolingLayerTest()
: blob_bottom_(new Blob<Dtype>()),
TYPED_TEST_CASE(CuDNNPoolingLayerTest, TestDtypes);
TYPED_TEST(CuDNNPoolingLayerTest, TestSetupCuDNN) {
- Caffe::set_mode(Caffe::GPU);
LayerParameter layer_param;
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
pooling_param->set_kernel_size(3);
EXPECT_EQ(this->blob_top_->width(), 2);
}
-// This test and all following cuDNN pooling tests with padding are commented
-// for now, since cuDNN pooling does not currently support padding.
-/*
TYPED_TEST(CuDNNPoolingLayerTest, TestSetupPaddedCuDNN) {
- Caffe::set_mode(Caffe::GPU);
LayerParameter layer_param;
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
pooling_param->set_kernel_size(3);
EXPECT_EQ(this->blob_top_->height(), 4);
EXPECT_EQ(this->blob_top_->width(), 3);
}
-*/
/*
TYPED_TEST(CuDNNPoolingLayerTest, PrintBackwardCuDNN) {
- Caffe::set_mode(Caffe::GPU);
LayerParameter layer_param;
layer_param.set_kernelsize(3);
layer_param.set_stride(2);
*/
TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxCuDNN) {
- Caffe::set_mode(Caffe::GPU);
this->TestForwardSquare();
this->TestForwardRectHigh();
this->TestForwardRectWide();
// the corresponding backward test.
/*
TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxTopMaskCuDNN) {
- Caffe::set_mode(Caffe::GPU);
this->blob_top_vec_.push_back(this->blob_top_mask_);
this->TestForwardSquare();
this->TestForwardRectHigh();
*/
TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxCuDNN) {
- Caffe::set_mode(Caffe::GPU);
for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
LayerParameter layer_param;
}
}
-/*
TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxPaddedCuDNN) {
- Caffe::set_mode(Caffe::GPU);
LayerParameter layer_param;
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
pooling_param->set_kernel_size(3);
EXPECT_NEAR(this->blob_top_->cpu_data()[7], 4, epsilon);
EXPECT_NEAR(this->blob_top_->cpu_data()[8], 1, epsilon);
}
-*/
/*
TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxTopMaskCuDNN) {
- Caffe::set_mode(Caffe::GPU);
for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
LayerParameter layer_param;
*/
TYPED_TEST(CuDNNPoolingLayerTest, TestForwardAveCuDNN) {
- Caffe::set_mode(Caffe::GPU);
LayerParameter layer_param;
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
pooling_param->set_kernel_size(3);
}
TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAveCuDNN) {
- Caffe::set_mode(Caffe::GPU);
for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
LayerParameter layer_param;
}
}
-/*
TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAvePaddedCuDNN) {
- Caffe::set_mode(Caffe::GPU);
for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
LayerParameter layer_param;
}
}
}
-*/
#endif
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/power_layer.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
#include <cmath>
-#include <cstring>
#include "gtest/gtest.h"
--- /dev/null
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/reduction_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class ReductionLayerTest : public MultiDeviceTest<TypeParam> {
+ typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+ ReductionLayerTest()
+ : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
+ blob_top_(new Blob<Dtype>()) {
+ // fill the values
+ Caffe::set_random_seed(1701);
+ FillerParameter filler_param;
+ UniformFiller<Dtype> filler(filler_param);
+ filler.Fill(this->blob_bottom_);
+ blob_bottom_vec_.push_back(blob_bottom_);
+ blob_top_vec_.push_back(blob_top_);
+ }
+ virtual ~ReductionLayerTest() {
+ delete blob_bottom_;
+ delete blob_top_;
+ }
+
+ void TestForward(ReductionParameter_ReductionOp op,
+ float coeff = 1, int axis = 0) {
+ LayerParameter layer_param;
+ ReductionParameter* reduction_param = layer_param.mutable_reduction_param();
+ reduction_param->set_operation(op);
+ if (coeff != 1.0) { reduction_param->set_coeff(coeff); }
+ if (axis != 0) { reduction_param->set_axis(axis); }
+ shared_ptr<ReductionLayer<Dtype> > layer(
+ new ReductionLayer<Dtype>(layer_param));
+ layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ const Dtype* in_data = this->blob_bottom_->cpu_data();
+ const int num = this->blob_bottom_->count(0, axis);
+ const int dim = this->blob_bottom_->count(axis);
+ for (int n = 0; n < num; ++n) {
+ Dtype expected_result = 0;
+ for (int d = 0; d < dim; ++d) {
+ switch (op) {
+ case ReductionParameter_ReductionOp_SUM:
+ expected_result += *in_data;
+ break;
+ case ReductionParameter_ReductionOp_MEAN:
+ expected_result += *in_data / dim;
+ break;
+ case ReductionParameter_ReductionOp_ASUM:
+ expected_result += fabs(*in_data);
+ break;
+ case ReductionParameter_ReductionOp_SUMSQ:
+ expected_result += (*in_data) * (*in_data);
+ break;
+ default:
+ LOG(FATAL) << "Unknown reduction op: "
+ << ReductionParameter_ReductionOp_Name(op);
+ }
+ ++in_data;
+ }
+ expected_result *= coeff;
+ const Dtype computed_result = this->blob_top_->cpu_data()[n];
+ EXPECT_FLOAT_EQ(expected_result, computed_result)
+ << "Incorrect result computed with op "
+ << ReductionParameter_ReductionOp_Name(op) << ", coeff " << coeff;
+ }
+ }
+
+ void TestGradient(ReductionParameter_ReductionOp op,
+ float coeff = 1, int axis = 0) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ ReductionParameter* reduction_param = layer_param.mutable_reduction_param();
+ reduction_param->set_operation(op);
+ reduction_param->set_coeff(coeff);
+ reduction_param->set_axis(axis);
+ ReductionLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 2e-3);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+ }
+
+ Blob<Dtype>* const blob_bottom_;
+ Blob<Dtype>* const blob_top_;
+ vector<Blob<Dtype>*> blob_bottom_vec_;
+ vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(ReductionLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(ReductionLayerTest, TestSetUp) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ shared_ptr<ReductionLayer<Dtype> > layer(
+ new ReductionLayer<Dtype>(layer_param));
+ layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ ASSERT_EQ(this->blob_top_->num_axes(), 0);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSetUpWithAxis1) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_reduction_param()->set_axis(1);
+ shared_ptr<ReductionLayer<Dtype> > layer(
+ new ReductionLayer<Dtype>(layer_param));
+ layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ ASSERT_EQ(this->blob_top_->num_axes(), 1);
+ EXPECT_EQ(this->blob_top_->shape(0), 2);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSetUpWithAxis2) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_reduction_param()->set_axis(2);
+ shared_ptr<ReductionLayer<Dtype> > layer(
+ new ReductionLayer<Dtype>(layer_param));
+ layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ ASSERT_EQ(this->blob_top_->num_axes(), 2);
+ EXPECT_EQ(this->blob_top_->shape(0), 2);
+ EXPECT_EQ(this->blob_top_->shape(1), 3);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSum) {
+ const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+ this->TestForward(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumCoeff) {
+ const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+ const float kCoeff = 2.3;
+ this->TestForward(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumCoeffAxis1) {
+ const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+ const float kCoeff = 2.3;
+ const int kAxis = 1;
+ this->TestForward(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumGradient) {
+ const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+ this->TestGradient(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumCoeffGradient) {
+ const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+ const float kCoeff = 2.3;
+ this->TestGradient(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumCoeffAxis1Gradient) {
+ const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+ const float kCoeff = 2.3;
+ const int kAxis = 1;
+ this->TestGradient(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMean) {
+ const ReductionParameter_ReductionOp kOp =
+ ReductionParameter_ReductionOp_MEAN;
+ this->TestForward(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMeanCoeff) {
+ const ReductionParameter_ReductionOp kOp =
+ ReductionParameter_ReductionOp_MEAN;
+ const float kCoeff = 2.3;
+ this->TestForward(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMeanCoeffAxis1) {
+ const ReductionParameter_ReductionOp kOp =
+ ReductionParameter_ReductionOp_MEAN;
+ const float kCoeff = 2.3;
+ const int kAxis = 1;
+ this->TestForward(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMeanGradient) {
+ const ReductionParameter_ReductionOp kOp =
+ ReductionParameter_ReductionOp_MEAN;
+ this->TestGradient(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMeanCoeffGradient) {
+ const ReductionParameter_ReductionOp kOp =
+ ReductionParameter_ReductionOp_MEAN;
+ const float kCoeff = 2.3;
+ this->TestGradient(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMeanCoeffGradientAxis1) {
+ const ReductionParameter_ReductionOp kOp =
+ ReductionParameter_ReductionOp_MEAN;
+ const float kCoeff = 2.3;
+ const int kAxis = 1;
+ this->TestGradient(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSum) {
+ const ReductionParameter_ReductionOp kOp =
+ ReductionParameter_ReductionOp_ASUM;
+ this->TestForward(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSumCoeff) {
+ const ReductionParameter_ReductionOp kOp =
+ ReductionParameter_ReductionOp_ASUM;
+ const float kCoeff = 2.3;
+ this->TestForward(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSumCoeffAxis1) {
+ const ReductionParameter_ReductionOp kOp =
+ ReductionParameter_ReductionOp_ASUM;
+ const float kCoeff = 2.3;
+ const int kAxis = 1;
+ this->TestForward(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSumGradient) {
+ const ReductionParameter_ReductionOp kOp =
+ ReductionParameter_ReductionOp_ASUM;
+ this->TestGradient(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSumCoeffGradient) {
+ const ReductionParameter_ReductionOp kOp =
+ ReductionParameter_ReductionOp_ASUM;
+ const float kCoeff = 2.3;
+ this->TestGradient(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSumCoeffAxis1Gradient) {
+ const ReductionParameter_ReductionOp kOp =
+ ReductionParameter_ReductionOp_ASUM;
+ const float kCoeff = 2.3;
+ const int kAxis = 1;
+ this->TestGradient(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquares) {
+ const ReductionParameter_ReductionOp kOp =
+ ReductionParameter_ReductionOp_SUMSQ;
+ this->TestForward(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquaresCoeff) {
+ const ReductionParameter_ReductionOp kOp =
+ ReductionParameter_ReductionOp_SUMSQ;
+ const float kCoeff = 2.3;
+ this->TestForward(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquaresCoeffAxis1) {
+ const ReductionParameter_ReductionOp kOp =
+ ReductionParameter_ReductionOp_SUMSQ;
+ const float kCoeff = 2.3;
+ const int kAxis = 1;
+ this->TestForward(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquaresGradient) {
+ const ReductionParameter_ReductionOp kOp =
+ ReductionParameter_ReductionOp_SUMSQ;
+ this->TestGradient(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquaresCoeffGradient) {
+ const ReductionParameter_ReductionOp kOp =
+ ReductionParameter_ReductionOp_SUMSQ;
+ const float kCoeff = 2.3;
+ this->TestGradient(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquaresCoeffAxis1Gradient) {
+ const ReductionParameter_ReductionOp kOp =
+ ReductionParameter_ReductionOp_SUMSQ;
+ const float kCoeff = 2.3;
+ const int kAxis = 1;
+ this->TestGradient(kOp, kCoeff, kAxis);
+}
+
+} // namespace caffe
--- /dev/null
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/reshape_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class ReshapeLayerTest : public MultiDeviceTest<TypeParam> {
+ typedef typename TypeParam::Dtype Dtype;
+ protected:
+ ReshapeLayerTest()
+ : blob_bottom_(new Blob<Dtype>(2, 3, 6, 5)),
+ blob_top_(new Blob<Dtype>()) {
+ // fill the values
+ FillerParameter filler_param;
+ GaussianFiller<Dtype> filler(filler_param);
+ filler.Fill(this->blob_bottom_);
+ blob_bottom_vec_.push_back(blob_bottom_);
+ blob_top_vec_.push_back(blob_top_);
+ }
+
+ virtual ~ReshapeLayerTest() { delete blob_bottom_; delete blob_top_; }
+
+ Blob<Dtype>* const blob_bottom_;
+ Blob<Dtype>* const blob_top_;
+ vector<Blob<Dtype>*> blob_bottom_vec_;
+ vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(ReshapeLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(ReshapeLayerTest, TestFlattenOutputSizes) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+ blob_shape->add_dim(0);
+ blob_shape->add_dim(-1);
+ blob_shape->add_dim(1);
+ blob_shape->add_dim(1);
+
+ ReshapeLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ EXPECT_EQ(this->blob_top_->num(), 2);
+ EXPECT_EQ(this->blob_top_->channels(), 3 * 6 * 5);
+ EXPECT_EQ(this->blob_top_->height(), 1);
+ EXPECT_EQ(this->blob_top_->width(), 1);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestFlattenValues) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+ blob_shape->add_dim(0);
+ blob_shape->add_dim(-1);
+ blob_shape->add_dim(1);
+ blob_shape->add_dim(1);
+ ReshapeLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ for (int c = 0; c < 3 * 6 * 5; ++c) {
+ EXPECT_EQ(this->blob_top_->data_at(0, c, 0, 0),
+ this->blob_bottom_->data_at(0, c / (6 * 5), (c / 5) % 6, c % 5));
+ EXPECT_EQ(this->blob_top_->data_at(1, c, 0, 0),
+ this->blob_bottom_->data_at(1, c / (6 * 5), (c / 5) % 6, c % 5));
+ }
+}
+
+// Test whether setting output dimensions to 0 either explicitly or implicitly
+// copies the respective dimension of the input layer.
+TYPED_TEST(ReshapeLayerTest, TestCopyDimensions) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+ blob_shape->add_dim(0);
+ blob_shape->add_dim(0);
+ blob_shape->add_dim(0);
+ blob_shape->add_dim(0);
+ ReshapeLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+ EXPECT_EQ(this->blob_top_->num(), 2);
+ EXPECT_EQ(this->blob_top_->channels(), 3);
+ EXPECT_EQ(this->blob_top_->height(), 6);
+ EXPECT_EQ(this->blob_top_->width(), 5);
+}
+
+// When a dimension is set to -1, we should infer its value from the other
+// dimensions (including those that get copied from below).
+TYPED_TEST(ReshapeLayerTest, TestInferenceOfUnspecified) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+ blob_shape->add_dim(0);
+ blob_shape->add_dim(3);
+ blob_shape->add_dim(10);
+ blob_shape->add_dim(-1);
+
+ // Count is 180, thus height should be 180 / (2*3*10) = 3.
+
+ ReshapeLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+ EXPECT_EQ(this->blob_top_->num(), 2);
+ EXPECT_EQ(this->blob_top_->channels(), 3);
+ EXPECT_EQ(this->blob_top_->height(), 10);
+ EXPECT_EQ(this->blob_top_->width(), 3);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestInferenceOfUnspecifiedWithStartAxis) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_reshape_param()->set_axis(1);
+ BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+ blob_shape->add_dim(3);
+ blob_shape->add_dim(10);
+ blob_shape->add_dim(-1);
+
+ ReshapeLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+ ASSERT_EQ(this->blob_top_->num_axes(), 4);
+ EXPECT_EQ(this->blob_top_->num(), 2);
+ EXPECT_EQ(this->blob_top_->channels(), 3);
+ EXPECT_EQ(this->blob_top_->height(), 10);
+ EXPECT_EQ(this->blob_top_->width(), 3);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestInsertSingletonAxesStart) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_reshape_param()->set_axis(0);
+ layer_param.mutable_reshape_param()->set_num_axes(0);
+ BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+ blob_shape->add_dim(1);
+ blob_shape->add_dim(1);
+ blob_shape->add_dim(1);
+
+ ReshapeLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+ ASSERT_EQ(this->blob_top_->num_axes(), 7);
+ EXPECT_EQ(this->blob_top_->shape(0), 1);
+ EXPECT_EQ(this->blob_top_->shape(1), 1);
+ EXPECT_EQ(this->blob_top_->shape(2), 1);
+ EXPECT_EQ(this->blob_top_->shape(3), 2);
+ EXPECT_EQ(this->blob_top_->shape(4), 3);
+ EXPECT_EQ(this->blob_top_->shape(5), 6);
+ EXPECT_EQ(this->blob_top_->shape(6), 5);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestInsertSingletonAxesMiddle) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_reshape_param()->set_axis(2);
+ layer_param.mutable_reshape_param()->set_num_axes(0);
+ BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+ blob_shape->add_dim(1);
+ blob_shape->add_dim(1);
+ blob_shape->add_dim(1);
+
+ ReshapeLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+ ASSERT_EQ(this->blob_top_->num_axes(), 7);
+ EXPECT_EQ(this->blob_top_->shape(0), 2);
+ EXPECT_EQ(this->blob_top_->shape(1), 3);
+ EXPECT_EQ(this->blob_top_->shape(2), 1);
+ EXPECT_EQ(this->blob_top_->shape(3), 1);
+ EXPECT_EQ(this->blob_top_->shape(4), 1);
+ EXPECT_EQ(this->blob_top_->shape(5), 6);
+ EXPECT_EQ(this->blob_top_->shape(6), 5);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestInsertSingletonAxesEnd) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_reshape_param()->set_axis(-1);
+ layer_param.mutable_reshape_param()->set_num_axes(0);
+ BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+ blob_shape->add_dim(1);
+ blob_shape->add_dim(1);
+ blob_shape->add_dim(1);
+
+ ReshapeLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+ ASSERT_EQ(this->blob_top_->num_axes(), 7);
+ EXPECT_EQ(this->blob_top_->shape(0), 2);
+ EXPECT_EQ(this->blob_top_->shape(1), 3);
+ EXPECT_EQ(this->blob_top_->shape(2), 6);
+ EXPECT_EQ(this->blob_top_->shape(3), 5);
+ EXPECT_EQ(this->blob_top_->shape(4), 1);
+ EXPECT_EQ(this->blob_top_->shape(5), 1);
+ EXPECT_EQ(this->blob_top_->shape(6), 1);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestFlattenMiddle) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_reshape_param()->set_axis(1);
+ layer_param.mutable_reshape_param()->set_num_axes(2);
+ BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+ blob_shape->add_dim(-1);
+
+ ReshapeLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+ ASSERT_EQ(this->blob_top_->num_axes(), 3);
+ EXPECT_EQ(this->blob_top_->shape(0), 2);
+ EXPECT_EQ(this->blob_top_->shape(1), 3 * 6);
+ EXPECT_EQ(this->blob_top_->shape(2), 5);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestForward) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ BlobShape* shape = layer_param.mutable_reshape_param()->mutable_shape();
+ shape->add_dim(6);
+ shape->add_dim(2);
+ shape->add_dim(3);
+ shape->add_dim(5);
+ ReshapeLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+ EXPECT_EQ(this->blob_top_->cpu_data()[i],
+ this->blob_bottom_->cpu_data()[i]);
+ }
+}
+
+TYPED_TEST(ReshapeLayerTest, TestForwardAfterReshape) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ BlobShape* shape = layer_param.mutable_reshape_param()->mutable_shape();
+ shape->add_dim(6);
+ shape->add_dim(2);
+ shape->add_dim(3);
+ shape->add_dim(5);
+ ReshapeLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ // We know the above produced the correct result from TestForward.
+ // Reshape the bottom and call layer.Reshape, then try again.
+ vector<int> new_bottom_shape(1, 2 * 3 * 6 * 5);
+ this->blob_bottom_->Reshape(new_bottom_shape);
+ layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
+ FillerParameter filler_param;
+ GaussianFiller<Dtype> filler(filler_param);
+ filler.Fill(this->blob_bottom_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+ EXPECT_EQ(this->blob_top_->cpu_data()[i],
+ this->blob_bottom_->cpu_data()[i]);
+ }
+}
+
+TYPED_TEST(ReshapeLayerTest, TestGradient) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ BlobShape* shape = layer_param.mutable_reshape_param()->mutable_shape();
+ shape->add_dim(6);
+ shape->add_dim(2);
+ shape->add_dim(3);
+ shape->add_dim(5);
+ ReshapeLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-2);
+ checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+}
+
+} // namespace caffe
#include <cmath>
-#include <cstdlib>
-#include <cstring>
#include <vector>
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/sigmoid_cross_entropy_loss_layer.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
-#include <cstring>
#include <vector>
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/slice_layer.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
EXPECT_EQ(this->blob_bottom_->width(), this->blob_top_0_->width());
}
+TYPED_TEST(SliceLayerTest, TestTrivialSlice) {
+ // Test the trivial (single output) "slice" operation --
+ // should be the identity.
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ SliceLayer<Dtype> layer(layer_param);
+ this->blob_top_vec_0_.resize(1);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_0_);
+ ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_0_->shape());
+ for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+ EXPECT_EQ(this->blob_bottom_->cpu_data()[i],
+ this->blob_top_0_->cpu_data()[i]);
+ }
+}
+
TYPED_TEST(SliceLayerTest, TestSliceAcrossNum) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
}
}
+TYPED_TEST(SliceLayerTest, TestGradientTrivial) {
+ // Test the trivial (single output) "slice" operation --
+ // should be the identity.
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ SliceLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-3);
+ this->blob_top_vec_0_.resize(1);
+ checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_0_);
+}
+
TYPED_TEST(SliceLayerTest, TestGradientAcrossNum) {
typedef typename TypeParam::Dtype Dtype;
// Gradient checks are slow; reduce blob size.
#include <cmath>
-#include <cstring>
#include <vector>
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/softmax_layer.hpp"
+
+#ifdef USE_CUDNN
+#include "caffe/layers/cudnn_softmax_layer.hpp"
+#endif
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
#ifdef USE_CUDNN
template <typename Dtype>
-class CuDNNSoftmaxLayerTest : public ::testing::Test {
+class CuDNNSoftmaxLayerTest : public GPUDeviceTest<Dtype> {
protected:
CuDNNSoftmaxLayerTest()
: blob_bottom_(new Blob<Dtype>(2, 10, 2, 3)),
TYPED_TEST_CASE(CuDNNSoftmaxLayerTest, TestDtypes);
TYPED_TEST(CuDNNSoftmaxLayerTest, TestForwardCuDNN) {
- Caffe::set_mode(Caffe::GPU);
LayerParameter layer_param;
CuDNNSoftmaxLayer<TypeParam> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
}
TYPED_TEST(CuDNNSoftmaxLayerTest, TestGradientCuDNN) {
- Caffe::set_mode(Caffe::GPU);
LayerParameter layer_param;
CuDNNSoftmaxLayer<TypeParam> layer(layer_param);
GradientChecker<TypeParam> checker(1e-2, 1e-3);
#include <cmath>
-#include <cstdlib>
-#include <cstring>
#include <vector>
#include "boost/scoped_ptr.hpp"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/softmax_loss_layer.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
#include "caffe/common.hpp"
#include "caffe/proto/caffe.pb.h"
+#include "caffe/sgd_solvers.hpp"
#include "caffe/solver.hpp"
#include "caffe/test/test_caffe_main.hpp"
--- /dev/null
+#include <map>
+#include <string>
+
+#include "boost/scoped_ptr.hpp"
+#include "google/protobuf/text_format.h"
+#include "gtest/gtest.h"
+
+#include "caffe/common.hpp"
+#include "caffe/solver.hpp"
+#include "caffe/solver_factory.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class SolverFactoryTest : public MultiDeviceTest<TypeParam> {
+ protected:
+ SolverParameter simple_solver_param() {
+ const string solver_proto =
+ "train_net_param { "
+ " layer { "
+ " name: 'data' type: 'DummyData' top: 'data' "
+ " dummy_data_param { shape { dim: 1 } } "
+ " } "
+ "} ";
+ SolverParameter solver_param;
+ CHECK(google::protobuf::TextFormat::ParseFromString(
+ solver_proto, &solver_param));
+ return solver_param;
+ }
+};
+
+TYPED_TEST_CASE(SolverFactoryTest, TestDtypesAndDevices);
+
+TYPED_TEST(SolverFactoryTest, TestCreateSolver) {
+ typedef typename TypeParam::Dtype Dtype;
+ typename SolverRegistry<Dtype>::CreatorRegistry& registry =
+ SolverRegistry<Dtype>::Registry();
+ shared_ptr<Solver<Dtype> > solver;
+ SolverParameter solver_param = this->simple_solver_param();
+ for (typename SolverRegistry<Dtype>::CreatorRegistry::iterator iter =
+ registry.begin(); iter != registry.end(); ++iter) {
+ solver_param.set_type(iter->first);
+ solver.reset(SolverRegistry<Dtype>::CreateSolver(solver_param));
+ EXPECT_EQ(iter->first, solver->type());
+ }
+}
+
+} // namespace caffe
-#include <cstring>
#include <string>
#include <vector>
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
+#include "caffe/layers/split_layer.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/insert_splits.hpp"
-#include "caffe/vision_layers.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
--- /dev/null
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/concat_layer.hpp"
+#include "caffe/layers/flatten_layer.hpp"
+#include "caffe/layers/pooling_layer.hpp"
+#include "caffe/layers/split_layer.hpp"
+#include "caffe/layers/spp_layer.hpp"
+
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class SPPLayerTest : public MultiDeviceTest<TypeParam> {
+ typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+ SPPLayerTest()
+ : blob_bottom_(new Blob<Dtype>()),
+ blob_bottom_2_(new Blob<Dtype>()),
+ blob_bottom_3_(new Blob<Dtype>()),
+ blob_top_(new Blob<Dtype>()) {}
+ virtual void SetUp() {
+ Caffe::set_random_seed(1701);
+ blob_bottom_->Reshape(2, 3, 9, 8);
+ blob_bottom_2_->Reshape(4, 3, 1024, 765);
+ blob_bottom_3_->Reshape(10, 3, 7, 7);
+ // fill the values
+ FillerParameter filler_param;
+ GaussianFiller<Dtype> filler(filler_param);
+ filler.Fill(this->blob_bottom_);
+ blob_bottom_vec_.push_back(blob_bottom_);
+ blob_bottom_vec_2_.push_back(blob_bottom_2_);
+ blob_bottom_vec_3_.push_back(blob_bottom_3_);
+ blob_top_vec_.push_back(blob_top_);
+ }
+ virtual ~SPPLayerTest() { delete blob_bottom_; delete blob_top_; }
+
+ Blob<Dtype>* const blob_bottom_;
+ Blob<Dtype>* const blob_bottom_2_;
+ Blob<Dtype>* const blob_bottom_3_;
+ Blob<Dtype>* const blob_top_;
+ vector<Blob<Dtype>*> blob_bottom_vec_;
+ vector<Blob<Dtype>*> blob_bottom_vec_2_;
+ vector<Blob<Dtype>*> blob_bottom_vec_3_;
+ vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(SPPLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(SPPLayerTest, TestSetup) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_spp_param()->set_pyramid_height(3);
+ SPPLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ // expected number of pool results is geometric sum
+ // (1 - r ** n)/(1 - r) where r = 4 and n = pyramid_height
+ // (1 - 4 ** 3)/(1 - 4) = 21
+ // multiply bottom num_channels * expected_pool_results
+ // to get expected num_channels (3 * 21 = 63)
+ EXPECT_EQ(this->blob_top_->num(), 2);
+ EXPECT_EQ(this->blob_top_->channels(), 63);
+ EXPECT_EQ(this->blob_top_->height(), 1);
+ EXPECT_EQ(this->blob_top_->width(), 1);
+}
+
+TYPED_TEST(SPPLayerTest, TestEqualOutputDims) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_spp_param()->set_pyramid_height(5);
+ SPPLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_2_, this->blob_top_vec_);
+ // expected number of pool results is geometric sum
+ // (1 - r ** n)/(1 - r) where r = 4 and n = pyramid_height
+ // (1 - 4 ** 5)/(1 - 4) = 341
+ // multiply bottom num_channels * expected_pool_results
+ // to get expected num_channels (3 * 341 = 1023)
+ EXPECT_EQ(this->blob_top_->num(), 4);
+ EXPECT_EQ(this->blob_top_->channels(), 1023);
+ EXPECT_EQ(this->blob_top_->height(), 1);
+ EXPECT_EQ(this->blob_top_->width(), 1);
+}
+
+TYPED_TEST(SPPLayerTest, TestEqualOutputDims2) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_spp_param()->set_pyramid_height(3);
+ SPPLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_3_, this->blob_top_vec_);
+ // expected number of pool results is geometric sum
+ // (1 - r ** n)/(1 - r) where r = 4 and n = pyramid_height
+ // (1 - 4 ** 3)/(1 - 4) = 21
+ // multiply bottom num_channels * expected_pool_results
+ // to get expected num_channels (3 * 21 = 63)
+ EXPECT_EQ(this->blob_top_->num(), 10);
+ EXPECT_EQ(this->blob_top_->channels(), 63);
+ EXPECT_EQ(this->blob_top_->height(), 1);
+ EXPECT_EQ(this->blob_top_->width(), 1);
+}
+
+TYPED_TEST(SPPLayerTest, TestForwardBackward) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_spp_param()->set_pyramid_height(3);
+ SPPLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
+ layer.Backward(this->blob_top_vec_, propagate_down,
+ this->blob_bottom_vec_);
+}
+
+TYPED_TEST(SPPLayerTest, TestGradient) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ SPPParameter* spp_param = layer_param.mutable_spp_param();
+ spp_param->set_pyramid_height(3);
+ SPPLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-4, 1e-2);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+}
+
+
+} // namespace caffe
#include <algorithm>
-#include <cstring>
#include <vector>
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/pooling_layer.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
namespace caffe {
-template <typename Dtype>
-class StochasticPoolingLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class StochasticPoolingLayerTest : public MultiDeviceTest<TypeParam> {
+ typedef typename TypeParam::Dtype Dtype;
+
protected:
StochasticPoolingLayerTest()
: blob_bottom_(new Blob<Dtype>()),
vector<Blob<Dtype>*> blob_top_vec_;
};
-TYPED_TEST_CASE(StochasticPoolingLayerTest, TestDtypes);
+template <typename Dtype>
+class CPUStochasticPoolingLayerTest
+ : public StochasticPoolingLayerTest<CPUDevice<Dtype> > {
+};
+
+TYPED_TEST_CASE(CPUStochasticPoolingLayerTest, TestDtypes);
-TYPED_TEST(StochasticPoolingLayerTest, TestSetup) {
+TYPED_TEST(CPUStochasticPoolingLayerTest, TestSetup) {
LayerParameter layer_param;
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
pooling_param->set_kernel_size(3);
EXPECT_EQ(this->blob_top_->width(), 2);
}
-TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPU) {
- Caffe::set_mode(Caffe::GPU);
+#ifndef CPU_ONLY
+
+template <typename Dtype>
+class GPUStochasticPoolingLayerTest
+ : public StochasticPoolingLayerTest<GPUDevice<Dtype> > {
+};
+
+TYPED_TEST_CASE(GPUStochasticPoolingLayerTest, TestDtypes);
+
+TYPED_TEST(GPUStochasticPoolingLayerTest, TestStochastic) {
LayerParameter layer_param;
layer_param.set_phase(TRAIN);
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
EXPECT_GE(total / this->blob_top_->count(), 0.55);
}
-TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPUTestPhase) {
- Caffe::set_mode(Caffe::GPU);
+TYPED_TEST(GPUStochasticPoolingLayerTest, TestStochasticTestPhase) {
LayerParameter layer_param;
layer_param.set_phase(TEST);
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
}
}
-TYPED_TEST(StochasticPoolingLayerTest, TestGradientGPU) {
- Caffe::set_mode(Caffe::GPU);
+TYPED_TEST(GPUStochasticPoolingLayerTest, TestGradient) {
LayerParameter layer_param;
layer_param.set_phase(TRAIN);
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
this->blob_top_vec_);
}
-
+#endif
} // namespace caffe
-#include <cstring>
#include <vector>
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
#include "caffe/filler.hpp"
+#include "caffe/layers/tanh_layer.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/threshold_layer.hpp"
#include "caffe/test/test_caffe_main.hpp"
--- /dev/null
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/tile_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class TileLayerTest : public MultiDeviceTest<TypeParam> {
+ typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+ TileLayerTest()
+ : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
+ blob_top_(new Blob<Dtype>()) {}
+ virtual void SetUp() {
+ blob_bottom_vec_.push_back(blob_bottom_);
+ blob_top_vec_.push_back(blob_top_);
+ FillerParameter filler_param;
+ filler_param.set_mean(0.0);
+ filler_param.set_std(1.0);
+ GaussianFiller<Dtype> filler(filler_param);
+ filler.Fill(blob_bottom_);
+ }
+
+ virtual ~TileLayerTest() {
+ delete blob_bottom_;
+ delete blob_top_;
+ }
+
+ Blob<Dtype>* const blob_bottom_;
+ Blob<Dtype>* const blob_top_;
+ vector<Blob<Dtype>*> blob_bottom_vec_;
+ vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(TileLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(TileLayerTest, TestTrivialSetup) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ const int kNumTiles = 1;
+ layer_param.mutable_tile_param()->set_tiles(kNumTiles);
+ for (int i = 0; i < this->blob_bottom_->num_axes(); ++i) {
+ layer_param.mutable_tile_param()->set_axis(i);
+ TileLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ ASSERT_EQ(this->blob_top_->num_axes(), this->blob_bottom_->num_axes());
+ for (int j = 0; j < this->blob_bottom_->num_axes(); ++j) {
+ EXPECT_EQ(this->blob_top_->shape(j), this->blob_bottom_->shape(j));
+ }
+ }
+}
+
+TYPED_TEST(TileLayerTest, TestSetup) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ const int kNumTiles = 3;
+ layer_param.mutable_tile_param()->set_tiles(kNumTiles);
+ for (int i = 0; i < this->blob_bottom_->num_axes(); ++i) {
+ layer_param.mutable_tile_param()->set_axis(i);
+ TileLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ ASSERT_EQ(this->blob_top_->num_axes(), this->blob_bottom_->num_axes());
+ for (int j = 0; j < this->blob_bottom_->num_axes(); ++j) {
+ const int top_dim =
+ ((i == j) ? kNumTiles : 1) * this->blob_bottom_->shape(j);
+ EXPECT_EQ(top_dim, this->blob_top_->shape(j));
+ }
+ }
+}
+
+TYPED_TEST(TileLayerTest, TestForwardNum) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ const int kTileAxis = 0;
+ const int kNumTiles = 3;
+ layer_param.mutable_tile_param()->set_axis(kTileAxis);
+ layer_param.mutable_tile_param()->set_tiles(kNumTiles);
+ TileLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ for (int n = 0; n < this->blob_top_->num(); ++n) {
+ for (int c = 0; c < this->blob_top_->channels(); ++c) {
+ for (int h = 0; h < this->blob_top_->height(); ++h) {
+ for (int w = 0; w < this->blob_top_->width(); ++w) {
+ const int bottom_n = n % this->blob_bottom_->num();
+ EXPECT_EQ(this->blob_bottom_->data_at(bottom_n, c, h, w),
+ this->blob_top_->data_at(n, c, h, w));
+ }
+ }
+ }
+ }
+}
+
+TYPED_TEST(TileLayerTest, TestForwardChannels) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ const int kNumTiles = 3;
+ layer_param.mutable_tile_param()->set_tiles(kNumTiles);
+ TileLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ for (int n = 0; n < this->blob_top_->num(); ++n) {
+ for (int c = 0; c < this->blob_top_->channels(); ++c) {
+ for (int h = 0; h < this->blob_top_->height(); ++h) {
+ for (int w = 0; w < this->blob_top_->width(); ++w) {
+ const int bottom_c = c % this->blob_bottom_->channels();
+ EXPECT_EQ(this->blob_bottom_->data_at(n, bottom_c, h, w),
+ this->blob_top_->data_at(n, c, h, w));
+ }
+ }
+ }
+ }
+}
+
+TYPED_TEST(TileLayerTest, TestTrivialGradient) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ const int kNumTiles = 1;
+ layer_param.mutable_tile_param()->set_tiles(kNumTiles);
+ TileLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-2);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+}
+
+TYPED_TEST(TileLayerTest, TestGradientNum) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ const int kTileAxis = 0;
+ const int kNumTiles = 3;
+ layer_param.mutable_tile_param()->set_axis(kTileAxis);
+ layer_param.mutable_tile_param()->set_tiles(kNumTiles);
+ TileLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-2);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+}
+
+TYPED_TEST(TileLayerTest, TestGradientChannels) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ const int kTileAxis = 1;
+ const int kNumTiles = 3;
+ layer_param.mutable_tile_param()->set_axis(kTileAxis);
+ layer_param.mutable_tile_param()->set_tiles(kNumTiles);
+ TileLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-2);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+}
+
+} // namespace caffe
-#include <cstring>
#include <string>
#include <vector>
+#include "boost/scoped_ptr.hpp"
#include "google/protobuf/text_format.h"
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/layer.hpp"
+#include "caffe/util/db.hpp"
+#include "caffe/util/io.hpp"
#include "caffe/util/upgrade_proto.hpp"
#include "caffe/test/test_caffe_main.hpp"
continue; // Empty string isn't actually a valid layer type.
}
layer_param.set_type(v2_layer_type);
+ // Data layers expect a DB
+ if (v2_layer_type == "Data") {
+ #ifdef USE_LEVELDB
+ string tmp;
+ MakeTempDir(&tmp);
+ boost::scoped_ptr<db::DB> db(db::GetDB(DataParameter_DB_LEVELDB));
+ db->Open(tmp, db::NEW);
+ db->Close();
+ layer_param.mutable_data_param()->set_source(tmp);
+ #else
+ continue;
+ #endif // USE_LEVELDB
+ }
+ #ifndef USE_OPENCV
+ if (v2_layer_type == "ImageData" || v2_layer_type == "WindowData") {
+ continue;
+ }
+ #endif // !USE_OPENCV
layer = LayerRegistry<float>::CreateLayer(layer_param);
EXPECT_EQ(v2_layer_type, layer->type());
}
}
+class SolverTypeUpgradeTest : public ::testing::Test {
+ protected:
+ void RunSolverTypeUpgradeTest(
+ const string& input_param_string, const string& output_param_string) {
+ // Test upgrading old solver_type field (enum) to new type field (string)
+ SolverParameter input_param;
+ CHECK(google::protobuf::TextFormat::ParseFromString(
+ input_param_string, &input_param));
+ SolverParameter expected_output_param;
+ CHECK(google::protobuf::TextFormat::ParseFromString(
+ output_param_string, &expected_output_param));
+ SolverParameter actual_output_param = input_param;
+ UpgradeSolverType(&actual_output_param);
+ EXPECT_EQ(expected_output_param.DebugString(),
+ actual_output_param.DebugString());
+ }
+};
+
+TEST_F(SolverTypeUpgradeTest, TestSimple) {
+ const char* old_type_vec[6] = { "SGD", "ADAGRAD", "NESTEROV", "RMSPROP",
+ "ADADELTA", "ADAM" };
+ const char* new_type_vec[6] = { "SGD", "AdaGrad", "Nesterov", "RMSProp",
+ "AdaDelta", "Adam" };
+ for (int i = 0; i < 6; ++i) {
+ const string& input_proto =
+ "net: 'examples/mnist/lenet_train_test.prototxt' "
+ "test_iter: 100 "
+ "test_interval: 500 "
+ "base_lr: 0.01 "
+ "momentum: 0.0 "
+ "weight_decay: 0.0005 "
+ "lr_policy: 'inv' "
+ "gamma: 0.0001 "
+ "power: 0.75 "
+ "display: 100 "
+ "max_iter: 10000 "
+ "snapshot: 5000 "
+ "snapshot_prefix: 'examples/mnist/lenet_rmsprop' "
+ "solver_mode: GPU "
+ "solver_type: " + std::string(old_type_vec[i]) + " ";
+ const string& expected_output_proto =
+ "net: 'examples/mnist/lenet_train_test.prototxt' "
+ "test_iter: 100 "
+ "test_interval: 500 "
+ "base_lr: 0.01 "
+ "momentum: 0.0 "
+ "weight_decay: 0.0005 "
+ "lr_policy: 'inv' "
+ "gamma: 0.0001 "
+ "power: 0.75 "
+ "display: 100 "
+ "max_iter: 10000 "
+ "snapshot: 5000 "
+ "snapshot_prefix: 'examples/mnist/lenet_rmsprop' "
+ "solver_mode: GPU "
+ "type: '" + std::string(new_type_vec[i]) + "' ";
+ this->RunSolverTypeUpgradeTest(input_proto, expected_output_proto);
+ }
+}
+
} // NOLINT(readability/fn_size) // namespace caffe
#ifndef CPU_ONLY // CPU-GPU test
-#include <cstring>
-
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
--- /dev/null
+#include <boost/thread.hpp>
+#include <string>
+
+#include "caffe/data_reader.hpp"
+#include "caffe/layers/base_data_layer.hpp"
+#include "caffe/parallel.hpp"
+#include "caffe/util/blocking_queue.hpp"
+
+namespace caffe {
+
+template<typename T>
+class BlockingQueue<T>::sync {
+ public:
+ mutable boost::mutex mutex_;
+ boost::condition_variable condition_;
+};
+
+template<typename T>
+BlockingQueue<T>::BlockingQueue()
+ : sync_(new sync()) {
+}
+
+template<typename T>
+void BlockingQueue<T>::push(const T& t) {
+ boost::mutex::scoped_lock lock(sync_->mutex_);
+ queue_.push(t);
+ lock.unlock();
+ sync_->condition_.notify_one();
+}
+
+template<typename T>
+bool BlockingQueue<T>::try_pop(T* t) {
+ boost::mutex::scoped_lock lock(sync_->mutex_);
+
+ if (queue_.empty()) {
+ return false;
+ }
+
+ *t = queue_.front();
+ queue_.pop();
+ return true;
+}
+
+template<typename T>
+T BlockingQueue<T>::pop(const string& log_on_wait) {
+ boost::mutex::scoped_lock lock(sync_->mutex_);
+
+ while (queue_.empty()) {
+ if (!log_on_wait.empty()) {
+ LOG_EVERY_N(INFO, 1000)<< log_on_wait;
+ }
+ sync_->condition_.wait(lock);
+ }
+
+ T t = queue_.front();
+ queue_.pop();
+ return t;
+}
+
+template<typename T>
+bool BlockingQueue<T>::try_peek(T* t) {
+ boost::mutex::scoped_lock lock(sync_->mutex_);
+
+ if (queue_.empty()) {
+ return false;
+ }
+
+ *t = queue_.front();
+ return true;
+}
+
+template<typename T>
+T BlockingQueue<T>::peek() {
+ boost::mutex::scoped_lock lock(sync_->mutex_);
+
+ while (queue_.empty()) {
+ sync_->condition_.wait(lock);
+ }
+
+ return queue_.front();
+}
+
+template<typename T>
+size_t BlockingQueue<T>::size() const {
+ boost::mutex::scoped_lock lock(sync_->mutex_);
+ return queue_.size();
+}
+
+template class BlockingQueue<Batch<float>*>;
+template class BlockingQueue<Batch<double>*>;
+template class BlockingQueue<Datum*>;
+template class BlockingQueue<shared_ptr<DataReader::QueuePair> >;
+template class BlockingQueue<P2PSync<float>*>;
+template class BlockingQueue<P2PSync<double>*>;
+
+} // namespace caffe
--- /dev/null
+#ifdef USE_CUDNN
+#include "caffe/util/cudnn.hpp"
+
+namespace caffe {
+namespace cudnn {
+
+float dataType<float>::oneval = 1.0;
+float dataType<float>::zeroval = 0.0;
+const void* dataType<float>::one =
+ static_cast<void *>(&dataType<float>::oneval);
+const void* dataType<float>::zero =
+ static_cast<void *>(&dataType<float>::zeroval);
+
+double dataType<double>::oneval = 1.0;
+double dataType<double>::zeroval = 0.0;
+const void* dataType<double>::one =
+ static_cast<void *>(&dataType<double>::oneval);
+const void* dataType<double>::zero =
+ static_cast<void *>(&dataType<double>::zeroval);
+
+} // namespace cudnn
+} // namespace caffe
+#endif
#include "caffe/util/db.hpp"
+#include "caffe/util/db_leveldb.hpp"
+#include "caffe/util/db_lmdb.hpp"
-#include <sys/stat.h>
#include <string>
namespace caffe { namespace db {
-const size_t LMDB_MAP_SIZE = 1099511627776; // 1 TB
-
-void LevelDB::Open(const string& source, Mode mode) {
- leveldb::Options options;
- options.block_size = 65536;
- options.write_buffer_size = 268435456;
- options.max_open_files = 100;
- options.error_if_exists = mode == NEW;
- options.create_if_missing = mode != READ;
- leveldb::Status status = leveldb::DB::Open(options, source, &db_);
- CHECK(status.ok()) << "Failed to open leveldb " << source
- << std::endl << status.ToString();
- LOG(INFO) << "Opened leveldb " << source;
-}
-
-void LMDB::Open(const string& source, Mode mode) {
- MDB_CHECK(mdb_env_create(&mdb_env_));
- MDB_CHECK(mdb_env_set_mapsize(mdb_env_, LMDB_MAP_SIZE));
- if (mode == NEW) {
- CHECK_EQ(mkdir(source.c_str(), 0744), 0) << "mkdir " << source << "failed";
- }
- int flags = 0;
- if (mode == READ) {
- flags = MDB_RDONLY | MDB_NOTLS;
- }
- MDB_CHECK(mdb_env_open(mdb_env_, source.c_str(), flags, 0664));
- LOG(INFO) << "Opened lmdb " << source;
-}
-
-LMDBCursor* LMDB::NewCursor() {
- MDB_txn* mdb_txn;
- MDB_cursor* mdb_cursor;
- MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, MDB_RDONLY, &mdb_txn));
- MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_));
- MDB_CHECK(mdb_cursor_open(mdb_txn, mdb_dbi_, &mdb_cursor));
- return new LMDBCursor(mdb_txn, mdb_cursor);
-}
-
-LMDBTransaction* LMDB::NewTransaction() {
- MDB_txn* mdb_txn;
- MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, 0, &mdb_txn));
- MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_));
- return new LMDBTransaction(&mdb_dbi_, mdb_txn);
-}
-
-void LMDBTransaction::Put(const string& key, const string& value) {
- MDB_val mdb_key, mdb_value;
- mdb_key.mv_data = const_cast<char*>(key.data());
- mdb_key.mv_size = key.size();
- mdb_value.mv_data = const_cast<char*>(value.data());
- mdb_value.mv_size = value.size();
- MDB_CHECK(mdb_put(mdb_txn_, *mdb_dbi_, &mdb_key, &mdb_value, 0));
-}
-
DB* GetDB(DataParameter::DB backend) {
switch (backend) {
+#ifdef USE_LEVELDB
case DataParameter_DB_LEVELDB:
return new LevelDB();
+#endif // USE_LEVELDB
+#ifdef USE_LMDB
case DataParameter_DB_LMDB:
return new LMDB();
+#endif // USE_LMDB
default:
LOG(FATAL) << "Unknown database backend";
+ return NULL;
}
}
DB* GetDB(const string& backend) {
+#ifdef USE_LEVELDB
if (backend == "leveldb") {
return new LevelDB();
- } else if (backend == "lmdb") {
+ }
+#endif // USE_LEVELDB
+#ifdef USE_LMDB
+ if (backend == "lmdb") {
return new LMDB();
- } else {
- LOG(FATAL) << "Unknown database backend";
}
+#endif // USE_LMDB
+ LOG(FATAL) << "Unknown database backend";
+ return NULL;
}
} // namespace db
--- /dev/null
+#ifdef USE_LEVELDB
+#include "caffe/util/db_leveldb.hpp"
+
+#include <string>
+
+namespace caffe { namespace db {
+
+void LevelDB::Open(const string& source, Mode mode) {
+ leveldb::Options options;
+ options.block_size = 65536;
+ options.write_buffer_size = 268435456;
+ options.max_open_files = 100;
+ options.error_if_exists = mode == NEW;
+ options.create_if_missing = mode != READ;
+ leveldb::Status status = leveldb::DB::Open(options, source, &db_);
+ CHECK(status.ok()) << "Failed to open leveldb " << source
+ << std::endl << status.ToString();
+ LOG(INFO) << "Opened leveldb " << source;
+}
+
+} // namespace db
+} // namespace caffe
+#endif // USE_LEVELDB
--- /dev/null
+#ifdef USE_LMDB
+#include "caffe/util/db_lmdb.hpp"
+
+#include <sys/stat.h>
+
+#include <string>
+
+namespace caffe { namespace db {
+
+const size_t LMDB_MAP_SIZE = 1099511627776; // 1 TB
+
+void LMDB::Open(const string& source, Mode mode) {
+ MDB_CHECK(mdb_env_create(&mdb_env_));
+ MDB_CHECK(mdb_env_set_mapsize(mdb_env_, LMDB_MAP_SIZE));
+ if (mode == NEW) {
+ CHECK_EQ(mkdir(source.c_str(), 0744), 0) << "mkdir " << source << "failed";
+ }
+ int flags = 0;
+ if (mode == READ) {
+ flags = MDB_RDONLY | MDB_NOTLS;
+ }
+ int rc = mdb_env_open(mdb_env_, source.c_str(), flags, 0664);
+#ifndef ALLOW_LMDB_NOLOCK
+ MDB_CHECK(rc);
+#else
+ if (rc == EACCES) {
+ LOG(WARNING) << "Permission denied. Trying with MDB_NOLOCK ...";
+ // Close and re-open environment handle
+ mdb_env_close(mdb_env_);
+ MDB_CHECK(mdb_env_create(&mdb_env_));
+ // Try again with MDB_NOLOCK
+ flags |= MDB_NOLOCK;
+ MDB_CHECK(mdb_env_open(mdb_env_, source.c_str(), flags, 0664));
+ } else {
+ MDB_CHECK(rc);
+ }
+#endif
+ LOG(INFO) << "Opened lmdb " << source;
+}
+
+LMDBCursor* LMDB::NewCursor() {
+ MDB_txn* mdb_txn;
+ MDB_cursor* mdb_cursor;
+ MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, MDB_RDONLY, &mdb_txn));
+ MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_));
+ MDB_CHECK(mdb_cursor_open(mdb_txn, mdb_dbi_, &mdb_cursor));
+ return new LMDBCursor(mdb_txn, mdb_cursor);
+}
+
+LMDBTransaction* LMDB::NewTransaction() {
+ MDB_txn* mdb_txn;
+ MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, 0, &mdb_txn));
+ MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_));
+ return new LMDBTransaction(&mdb_dbi_, mdb_txn);
+}
+
+void LMDBTransaction::Put(const string& key, const string& value) {
+ MDB_val mdb_key, mdb_value;
+ mdb_key.mv_data = const_cast<char*>(key.data());
+ mdb_key.mv_size = key.size();
+ mdb_value.mv_data = const_cast<char*>(value.data());
+ mdb_value.mv_size = value.size();
+ MDB_CHECK(mdb_put(mdb_txn_, *mdb_dbi_, &mdb_key, &mdb_value, 0));
+}
+
+} // namespace db
+} // namespace caffe
+#endif // USE_LMDB
--- /dev/null
+#include "caffe/util/hdf5.hpp"
+
+#include <string>
+#include <vector>
+
+namespace caffe {
+
+// Verifies format of data stored in HDF5 file and reshapes blob accordingly.
+template <typename Dtype>
+void hdf5_load_nd_dataset_helper(
+ hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
+ Blob<Dtype>* blob) {
+ // Verify that the dataset exists.
+ CHECK(H5LTfind_dataset(file_id, dataset_name_))
+ << "Failed to find HDF5 dataset " << dataset_name_;
+ // Verify that the number of dimensions is in the accepted range.
+ herr_t status;
+ int ndims;
+ status = H5LTget_dataset_ndims(file_id, dataset_name_, &ndims);
+ CHECK_GE(status, 0) << "Failed to get dataset ndims for " << dataset_name_;
+ CHECK_GE(ndims, min_dim);
+ CHECK_LE(ndims, max_dim);
+
+ // Verify that the data format is what we expect: float or double.
+ std::vector<hsize_t> dims(ndims);
+ H5T_class_t class_;
+ status = H5LTget_dataset_info(
+ file_id, dataset_name_, dims.data(), &class_, NULL);
+ CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name_;
+ switch (class_) {
+ case H5T_FLOAT:
+ LOG_FIRST_N(INFO, 1) << "Datatype class: H5T_FLOAT";
+ break;
+ case H5T_INTEGER:
+ LOG_FIRST_N(INFO, 1) << "Datatype class: H5T_INTEGER";
+ break;
+ case H5T_TIME:
+ LOG(FATAL) << "Unsupported datatype class: H5T_TIME";
+ case H5T_STRING:
+ LOG(FATAL) << "Unsupported datatype class: H5T_STRING";
+ case H5T_BITFIELD:
+ LOG(FATAL) << "Unsupported datatype class: H5T_BITFIELD";
+ case H5T_OPAQUE:
+ LOG(FATAL) << "Unsupported datatype class: H5T_OPAQUE";
+ case H5T_COMPOUND:
+ LOG(FATAL) << "Unsupported datatype class: H5T_COMPOUND";
+ case H5T_REFERENCE:
+ LOG(FATAL) << "Unsupported datatype class: H5T_REFERENCE";
+ case H5T_ENUM:
+ LOG(FATAL) << "Unsupported datatype class: H5T_ENUM";
+ case H5T_VLEN:
+ LOG(FATAL) << "Unsupported datatype class: H5T_VLEN";
+ case H5T_ARRAY:
+ LOG(FATAL) << "Unsupported datatype class: H5T_ARRAY";
+ default:
+ LOG(FATAL) << "Datatype class unknown";
+ }
+
+ vector<int> blob_dims(dims.size());
+ for (int i = 0; i < dims.size(); ++i) {
+ blob_dims[i] = dims[i];
+ }
+ blob->Reshape(blob_dims);
+}
+
+template <>
+void hdf5_load_nd_dataset<float>(hid_t file_id, const char* dataset_name_,
+ int min_dim, int max_dim, Blob<float>* blob) {
+ hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);
+ herr_t status = H5LTread_dataset_float(
+ file_id, dataset_name_, blob->mutable_cpu_data());
+ CHECK_GE(status, 0) << "Failed to read float dataset " << dataset_name_;
+}
+
+template <>
+void hdf5_load_nd_dataset<double>(hid_t file_id, const char* dataset_name_,
+ int min_dim, int max_dim, Blob<double>* blob) {
+ hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);
+ herr_t status = H5LTread_dataset_double(
+ file_id, dataset_name_, blob->mutable_cpu_data());
+ CHECK_GE(status, 0) << "Failed to read double dataset " << dataset_name_;
+}
+
+template <>
+void hdf5_save_nd_dataset<float>(
+ const hid_t file_id, const string& dataset_name, const Blob<float>& blob,
+ bool write_diff) {
+ int num_axes = blob.num_axes();
+ hsize_t *dims = new hsize_t[num_axes];
+ for (int i = 0; i < num_axes; ++i) {
+ dims[i] = blob.shape(i);
+ }
+ const float* data;
+ if (write_diff) {
+ data = blob.cpu_diff();
+ } else {
+ data = blob.cpu_data();
+ }
+ herr_t status = H5LTmake_dataset_float(
+ file_id, dataset_name.c_str(), num_axes, dims, data);
+ CHECK_GE(status, 0) << "Failed to make float dataset " << dataset_name;
+ delete[] dims;
+}
+
+template <>
+void hdf5_save_nd_dataset<double>(
+ hid_t file_id, const string& dataset_name, const Blob<double>& blob,
+ bool write_diff) {
+ int num_axes = blob.num_axes();
+ hsize_t *dims = new hsize_t[num_axes];
+ for (int i = 0; i < num_axes; ++i) {
+ dims[i] = blob.shape(i);
+ }
+ const double* data;
+ if (write_diff) {
+ data = blob.cpu_diff();
+ } else {
+ data = blob.cpu_data();
+ }
+ herr_t status = H5LTmake_dataset_double(
+ file_id, dataset_name.c_str(), num_axes, dims, data);
+ CHECK_GE(status, 0) << "Failed to make double dataset " << dataset_name;
+ delete[] dims;
+}
+
+string hdf5_load_string(hid_t loc_id, const string& dataset_name) {
+ // Get size of dataset
+ size_t size;
+ H5T_class_t class_;
+ herr_t status = \
+ H5LTget_dataset_info(loc_id, dataset_name.c_str(), NULL, &class_, &size);
+ CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name;
+ char *buf = new char[size];
+ status = H5LTread_dataset_string(loc_id, dataset_name.c_str(), buf);
+ CHECK_GE(status, 0)
+ << "Failed to load int dataset with name " << dataset_name;
+ string val(buf);
+ delete[] buf;
+ return val;
+}
+
+void hdf5_save_string(hid_t loc_id, const string& dataset_name,
+ const string& s) {
+ herr_t status = \
+ H5LTmake_dataset_string(loc_id, dataset_name.c_str(), s.c_str());
+ CHECK_GE(status, 0)
+ << "Failed to save string dataset with name " << dataset_name;
+}
+
+int hdf5_load_int(hid_t loc_id, const string& dataset_name) {
+ int val;
+ herr_t status = H5LTread_dataset_int(loc_id, dataset_name.c_str(), &val);
+ CHECK_GE(status, 0)
+ << "Failed to load int dataset with name " << dataset_name;
+ return val;
+}
+
+void hdf5_save_int(hid_t loc_id, const string& dataset_name, int i) {
+ hsize_t one = 1;
+ herr_t status = \
+ H5LTmake_dataset_int(loc_id, dataset_name.c_str(), 1, &one, &i);
+ CHECK_GE(status, 0)
+ << "Failed to save int dataset with name " << dataset_name;
+}
+
+int hdf5_get_num_links(hid_t loc_id) {
+ H5G_info_t info;
+ herr_t status = H5Gget_info(loc_id, &info);
+ CHECK_GE(status, 0) << "Error while counting HDF5 links.";
+ return info.nlinks;
+}
+
+string hdf5_get_name_by_idx(hid_t loc_id, int idx) {
+ ssize_t str_size = H5Lget_name_by_idx(
+ loc_id, ".", H5_INDEX_NAME, H5_ITER_NATIVE, idx, NULL, 0, H5P_DEFAULT);
+ CHECK_GE(str_size, 0) << "Error retrieving HDF5 dataset at index " << idx;
+ char *c_str = new char[str_size+1];
+ ssize_t status = H5Lget_name_by_idx(
+ loc_id, ".", H5_INDEX_NAME, H5_ITER_NATIVE, idx, c_str, str_size+1,
+ H5P_DEFAULT);
+ CHECK_GE(status, 0) << "Error retrieving HDF5 dataset at index " << idx;
+ string result(c_str);
+ delete[] c_str;
+ return result;
+}
+
+} // namespace caffe
-#include <cmath>
-#include <cstdlib>
-#include <cstring>
+#include <vector>
#include "caffe/util/im2col.hpp"
#include "caffe/util/math_functions.hpp"
namespace caffe {
+// Function uses casting from int to unsigned to compare if value of
+// parameter a is greater or equal to zero and lower than value of
+// parameter b. The b parameter is of type signed and is always positive,
+// therefore its value is always lower than 0x800... where casting
+// negative value of a parameter converts it to value higher than 0x800...
+// The casting allows to use one condition instead of two.
+inline bool is_a_ge_zero_and_a_lt_b(int a, int b) {
+ return static_cast<unsigned>(a) < static_cast<unsigned>(b);
+}
+
template <typename Dtype>
void im2col_cpu(const Dtype* data_im, const int channels,
const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w,
const int stride_h, const int stride_w,
+ const int dilation_h, const int dilation_w,
Dtype* data_col) {
- int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1;
- int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1;
- int channels_col = channels * kernel_h * kernel_w;
- for (int c = 0; c < channels_col; ++c) {
- int w_offset = c % kernel_w;
- int h_offset = (c / kernel_w) % kernel_h;
- int c_im = c / kernel_h / kernel_w;
- for (int h = 0; h < height_col; ++h) {
- for (int w = 0; w < width_col; ++w) {
- int h_pad = h * stride_h - pad_h + h_offset;
- int w_pad = w * stride_w - pad_w + w_offset;
- if (h_pad >= 0 && h_pad < height && w_pad >= 0 && w_pad < width)
- data_col[(c * height_col + h) * width_col + w] =
- data_im[(c_im * height + h_pad) * width + w_pad];
- else
- data_col[(c * height_col + h) * width_col + w] = 0;
+ const int output_h = (height + 2 * pad_h -
+ (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
+ const int output_w = (width + 2 * pad_w -
+ (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
+ const int channel_size = height * width;
+ for (int channel = channels; channel--; data_im += channel_size) {
+ for (int kernel_row = 0; kernel_row < kernel_h; kernel_row++) {
+ for (int kernel_col = 0; kernel_col < kernel_w; kernel_col++) {
+ int input_row = -pad_h + kernel_row * dilation_h;
+ for (int output_rows = output_h; output_rows; output_rows--) {
+ if (!is_a_ge_zero_and_a_lt_b(input_row, height)) {
+ for (int output_cols = output_w; output_cols; output_cols--) {
+ *(data_col++) = 0;
+ }
+ } else {
+ int input_col = -pad_w + kernel_col * dilation_w;
+ for (int output_col = output_w; output_col; output_col--) {
+ if (is_a_ge_zero_and_a_lt_b(input_col, width)) {
+ *(data_col++) = data_im[input_row * width + input_col];
+ } else {
+ *(data_col++) = 0;
+ }
+ input_col += stride_w;
+ }
+ }
+ input_row += stride_h;
+ }
}
}
}
template void im2col_cpu<float>(const float* data_im, const int channels,
const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w, const int stride_h,
- const int stride_w, float* data_col);
+ const int stride_w, const int dilation_h, const int dilation_w,
+ float* data_col);
template void im2col_cpu<double>(const double* data_im, const int channels,
const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w, const int stride_h,
- const int stride_w, double* data_col);
+ const int stride_w, const int dilation_h, const int dilation_w,
+ double* data_col);
+
+template <typename Dtype>
+inline void im2col_nd_core_cpu(const Dtype* data_input, const bool im2col,
+ const int num_spatial_axes, const int* im_shape, const int* col_shape,
+ const int* kernel_shape, const int* pad, const int* stride,
+ const int* dilation, Dtype* data_output) {
+ if (!im2col) {
+ int im_size = im_shape[0];
+ for (int i = 0; i < num_spatial_axes; ++i) {
+ im_size *= im_shape[1 + i];
+ }
+ caffe_set(im_size, Dtype(0), data_output);
+ }
+ int kernel_size = 1;
+ for (int i = 0; i < num_spatial_axes; ++i) {
+ kernel_size *= kernel_shape[i];
+ }
+ const int channels_col = col_shape[0];
+ vector<int> d_offset(num_spatial_axes, 0);
+ vector<int> d_iter(num_spatial_axes, 0);
+ for (int c_col = 0; c_col < channels_col; ++c_col) {
+ // Loop over spatial axes in reverse order to compute a per-axis offset.
+ int offset = c_col;
+ for (int d_i = num_spatial_axes - 1; d_i >= 0; --d_i) {
+ if (d_i < num_spatial_axes - 1) {
+ offset /= kernel_shape[d_i + 1];
+ }
+ d_offset[d_i] = offset % kernel_shape[d_i];
+ }
+ for (bool incremented = true; incremented; ) {
+ // Loop over spatial axes in forward order to compute the indices in the
+ // image and column, and whether the index lies in the padding.
+ int index_col = c_col;
+ int index_im = c_col / kernel_size;
+ bool is_padding = false;
+ for (int d_i = 0; d_i < num_spatial_axes; ++d_i) {
+ const int d = d_iter[d_i];
+ const int d_im = d * stride[d_i] - pad[d_i] +
+ d_offset[d_i] * dilation[d_i];
+ is_padding |= d_im < 0 || d_im >= im_shape[d_i + 1];
+ index_col *= col_shape[d_i + 1];
+ index_col += d;
+ index_im *= im_shape[d_i + 1];
+ index_im += d_im;
+ }
+ if (im2col) {
+ if (is_padding) {
+ data_output[index_col] = 0;
+ } else {
+ data_output[index_col] = data_input[index_im];
+ }
+ } else if (!is_padding) { // col2im
+ data_output[index_im] += data_input[index_col];
+ }
+ // Loop over spatial axes in reverse order to choose an index,
+ // like counting.
+ incremented = false;
+ for (int d_i = num_spatial_axes - 1; d_i >= 0; --d_i) {
+ const int d_max = col_shape[d_i + 1];
+ DCHECK_LT(d_iter[d_i], d_max);
+ if (d_iter[d_i] == d_max - 1) {
+ d_iter[d_i] = 0;
+ } else { // d_iter[d_i] < d_max - 1
+ ++d_iter[d_i];
+ incremented = true;
+ break;
+ }
+ }
+ } // while(incremented) {
+ } // for (int c = 0; c < channels_col; ++c) {
+}
+
+template <typename Dtype>
+void im2col_nd_cpu(const Dtype* data_im, const int num_spatial_axes,
+ const int* im_shape, const int* col_shape,
+ const int* kernel_shape, const int* pad, const int* stride,
+ const int* dilation, Dtype* data_col) {
+ const bool kIm2Col = true;
+ im2col_nd_core_cpu(data_im, kIm2Col, num_spatial_axes, im_shape, col_shape,
+ kernel_shape, pad, stride, dilation, data_col);
+}
+
+// Explicit instantiation
+template void im2col_nd_cpu<float>(const float* data_im,
+ const int num_spatial_axes,
+ const int* im_shape, const int* col_shape,
+ const int* kernel_shape, const int* pad, const int* stride,
+ const int* dilation, float* data_col);
+template void im2col_nd_cpu<double>(const double* data_im,
+ const int num_spatial_axes,
+ const int* im_shape, const int* col_shape,
+ const int* kernel_shape, const int* pad, const int* stride,
+ const int* dilation, double* data_col);
template <typename Dtype>
void col2im_cpu(const Dtype* data_col, const int channels,
- const int height, const int width, const int patch_h, const int patch_w,
+ const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w,
const int stride_h, const int stride_w,
+ const int dilation_h, const int dilation_w,
Dtype* data_im) {
caffe_set(height * width * channels, Dtype(0), data_im);
- int height_col = (height + 2 * pad_h - patch_h) / stride_h + 1;
- int width_col = (width + 2 * pad_w - patch_w) / stride_w + 1;
- int channels_col = channels * patch_h * patch_w;
- for (int c = 0; c < channels_col; ++c) {
- int w_offset = c % patch_w;
- int h_offset = (c / patch_w) % patch_h;
- int c_im = c / patch_h / patch_w;
- for (int h = 0; h < height_col; ++h) {
- for (int w = 0; w < width_col; ++w) {
- int h_pad = h * stride_h - pad_h + h_offset;
- int w_pad = w * stride_w - pad_w + w_offset;
- if (h_pad >= 0 && h_pad < height && w_pad >= 0 && w_pad < width)
- data_im[(c_im * height + h_pad) * width + w_pad] +=
- data_col[(c * height_col + h) * width_col + w];
+ const int output_h = (height + 2 * pad_h -
+ (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
+ const int output_w = (width + 2 * pad_w -
+ (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
+ const int channel_size = height * width;
+ for (int channel = channels; channel--; data_im += channel_size) {
+ for (int kernel_row = 0; kernel_row < kernel_h; kernel_row++) {
+ for (int kernel_col = 0; kernel_col < kernel_w; kernel_col++) {
+ int input_row = -pad_h + kernel_row * dilation_h;
+ for (int output_rows = output_h; output_rows; output_rows--) {
+ if (!is_a_ge_zero_and_a_lt_b(input_row, height)) {
+ data_col += output_w;
+ } else {
+ int input_col = -pad_w + kernel_col * dilation_w;
+ for (int output_col = output_w; output_col; output_col--) {
+ if (is_a_ge_zero_and_a_lt_b(input_col, width)) {
+ data_im[input_row * width + input_col] += *data_col;
+ }
+ data_col++;
+ input_col += stride_w;
+ }
+ }
+ input_row += stride_h;
+ }
}
}
}
// Explicit instantiation
template void col2im_cpu<float>(const float* data_col, const int channels,
- const int height, const int width, const int patch_h, const int patch_w,
+ const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w, const int stride_h,
- const int stride_w, float* data_im);
+ const int stride_w, const int dilation_h, const int dilation_w,
+ float* data_im);
template void col2im_cpu<double>(const double* data_col, const int channels,
- const int height, const int width, const int patch_h, const int patch_w,
+ const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w, const int stride_h,
- const int stride_w, double* data_im);
+ const int stride_w, const int dilation_h, const int dilation_w,
+ double* data_im);
+
+template <typename Dtype>
+void col2im_nd_cpu(const Dtype* data_col, const int num_spatial_axes,
+ const int* im_shape, const int* col_shape,
+ const int* kernel_shape, const int* pad, const int* stride,
+ const int* dilation, Dtype* data_im) {
+ const bool kIm2Col = false;
+ im2col_nd_core_cpu(data_col, kIm2Col, num_spatial_axes, im_shape, col_shape,
+ kernel_shape, pad, stride, dilation, data_im);
+}
+
+// Explicit instantiation
+template void col2im_nd_cpu<float>(const float* data_col,
+ const int num_spatial_axes,
+ const int* im_shape, const int* col_shape,
+ const int* kernel_shape, const int* pad, const int* stride,
+ const int* dilation, float* data_im);
+template void col2im_nd_cpu<double>(const double* data_col,
+ const int num_spatial_axes,
+ const int* im_shape, const int* col_shape,
+ const int* kernel_shape, const int* pad, const int* stride,
+ const int* dilation, double* data_im);
+
} // namespace caffe
#include <algorithm>
-#include <cmath>
-#include <cstdlib>
-#include <cstring>
#include "caffe/common.hpp"
#include "caffe/util/im2col.hpp"
const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w,
const int stride_h, const int stride_w,
+ const int dilation_h, const int dilation_w,
const int height_col, const int width_col,
Dtype* data_col) {
CUDA_KERNEL_LOOP(index, n) {
- int w_out = index % width_col;
- int h_index = index / width_col;
- int h_out = h_index % height_col;
- int channel_in = h_index / height_col;
- int channel_out = channel_in * kernel_h * kernel_w;
- int h_in = h_out * stride_h - pad_h;
- int w_in = w_out * stride_w - pad_w;
+ const int h_index = index / width_col;
+ const int h_col = h_index % height_col;
+ const int w_col = index % width_col;
+ const int c_im = h_index / height_col;
+ const int c_col = c_im * kernel_h * kernel_w;
+ const int h_offset = h_col * stride_h - pad_h;
+ const int w_offset = w_col * stride_w - pad_w;
Dtype* data_col_ptr = data_col;
- data_col_ptr += (channel_out * height_col + h_out) * width_col + w_out;
+ data_col_ptr += (c_col * height_col + h_col) * width_col + w_col;
const Dtype* data_im_ptr = data_im;
- data_im_ptr += (channel_in * height + h_in) * width + w_in;
+ data_im_ptr += (c_im * height + h_offset) * width + w_offset;
for (int i = 0; i < kernel_h; ++i) {
for (int j = 0; j < kernel_w; ++j) {
- int h = h_in + i;
- int w = w_in + j;
- *data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?
- data_im_ptr[i * width + j] : 0;
+ int h_im = h_offset + i * dilation_h;
+ int w_im = w_offset + j * dilation_w;
+ *data_col_ptr =
+ (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) ?
+ data_im_ptr[i * dilation_h * width + j * dilation_w] : 0;
data_col_ptr += height_col * width_col;
}
}
const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w,
const int stride_h, const int stride_w,
+ const int dilation_h, const int dilation_w,
Dtype* data_col) {
// We are going to launch channels * height_col * width_col kernels, each
// kernel responsible for copying a single-channel grid.
- int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1;
- int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1;
+ int height_col = (height + 2 * pad_h -
+ (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
+ int width_col = (width + 2 * pad_w -
+ (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
int num_kernels = channels * height_col * width_col;
// NOLINT_NEXT_LINE(whitespace/operators)
im2col_gpu_kernel<Dtype><<<CAFFE_GET_BLOCKS(num_kernels),
CAFFE_CUDA_NUM_THREADS>>>(
num_kernels, data_im, height, width, kernel_h, kernel_w, pad_h,
- pad_w, stride_h, stride_w, height_col,
+ pad_w, stride_h, stride_w, dilation_h, dilation_w, height_col,
width_col, data_col);
CUDA_POST_KERNEL_CHECK;
}
-
// Explicit instantiation
template void im2col_gpu<float>(const float* data_im, const int channels,
const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w, const int stride_h, const int stride_w,
- float* data_col);
+ const int dilation_h, const int dilation_w, float* data_col);
template void im2col_gpu<double>(const double* data_im, const int channels,
const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w, const int stride_h, const int stride_w,
- double* data_col);
+ const int dilation_h, const int dilation_w, double* data_col);
+
+template <typename Dtype, int num_axes>
+__global__ void im2col_nd_gpu_kernel(const int n, const Dtype* data_im,
+ const int* im_shape, const int* col_shape,
+ const int* kernel_shape, const int* pad, const int* stride,
+ const int* dilation, Dtype* data_col) {
+ int d_temp[num_axes]; // NOLINT(runtime/arrays)
+ int d_iter[num_axes]; // NOLINT(runtime/arrays)
+
+ __shared__ int shared_dilation[num_axes];
+ __shared__ int shared_kernel_shape[num_axes];
+ __shared__ int shared_pad[num_axes];
+ __shared__ int shared_stride[num_axes];
+ __shared__ int shared_col_shape[num_axes + 1];
+ __shared__ int shared_im_shape[num_axes + 1];
+
+ if (threadIdx.x < num_axes) {
+ shared_dilation[threadIdx.x] = dilation[threadIdx.x];
+ shared_kernel_shape[threadIdx.x] = kernel_shape[threadIdx.x];
+ shared_pad[threadIdx.x] = pad[threadIdx.x];
+ shared_stride[threadIdx.x] = stride[threadIdx.x];
+ }
+ if (threadIdx.x < num_axes + 1) {
+ shared_col_shape[threadIdx.x] = col_shape[threadIdx.x];
+ shared_im_shape[threadIdx.x] = im_shape[threadIdx.x];
+ }
+ __syncthreads();
+
+ int i;
+ CUDA_KERNEL_LOOP(index, n) {
+ // Initialize channel_in, computed in the loop below, with intermediate
+ // computations used to compute the spatial indices.
+ int channel_in = index;
+ int channel_out = 1;
+ for (i = num_axes - 1; i >= 0; --i) {
+ d_temp[i] = channel_in % shared_col_shape[i + 1];
+ channel_in /= shared_col_shape[i + 1];
+ channel_out *= shared_kernel_shape[i];
+ }
+ channel_out *= channel_in;
+ int data_col_inc = 1;
+ for (i = 0; i < num_axes; ++i) {
+ channel_out *= shared_col_shape[i + 1];
+ channel_out += d_temp[i];
+ d_temp[i] = d_temp[i] * shared_stride[i] - shared_pad[i];
+ channel_in *= shared_im_shape[i + 1];
+ channel_in += d_temp[i];
+ data_col_inc *= shared_col_shape[i + 1];
+ d_iter[i] = 0;
+ }
+ Dtype* data_col_ptr = data_col + channel_out;
+ const Dtype* data_im_ptr = data_im + channel_in;
+ bool incremented;
+ do {
+ bool in_range = true;
+ for (i = 0; i < num_axes; ++i) {
+ const int d_iter_im = d_iter[i] * shared_dilation[i] + d_temp[i];
+ in_range &= d_iter_im >= 0 && d_iter_im < shared_im_shape[i + 1];
+ if (!in_range) { break; }
+ }
+ if (in_range) {
+ int data_im_offset = d_iter[0] * shared_dilation[0];
+ for (i = 1; i < num_axes; ++i) {
+ data_im_offset *= shared_im_shape[i + 1];
+ data_im_offset += d_iter[i] * shared_dilation[i];
+ }
+ *data_col_ptr = data_im_ptr[data_im_offset];
+ } else {
+ *data_col_ptr = 0;
+ }
+ data_col_ptr += data_col_inc;
+ incremented = false;
+ for (i = num_axes - 1; i >= 0; --i) {
+ const int d_max = shared_kernel_shape[i];
+ if (d_iter[i] == d_max - 1) {
+ d_iter[i] = 0;
+ } else { // d_iter[i] < d_max - 1
+ ++d_iter[i];
+ incremented = true;
+ break;
+ }
+ } // for (int i = num_axes - 1; i >= 0; --i)
+ } while (incremented); // do
+ } // CUDA_KERNEL_LOOP(index, n)
+}
+
+template <typename Dtype>
+void im2col_nd_gpu(const Dtype* data_im, const int num_spatial_axes,
+ const int num_kernels, const int* im_shape, const int* col_shape,
+ const int* kernel_shape, const int* pad, const int* stride,
+ const int* dilation, Dtype* data_col) {
+ // num_axes should be smaller than block size
+ DCHECK_LT(num_spatial_axes, CAFFE_CUDA_NUM_THREADS);
+ switch (num_spatial_axes) {
+ case 1:
+ im2col_nd_gpu_kernel<Dtype, 1> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+ num_kernels, data_im, im_shape, col_shape,
+ kernel_shape, pad, stride, dilation, data_col);
+ break;
+ case 2:
+ im2col_nd_gpu_kernel<Dtype, 2> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+ num_kernels, data_im, im_shape, col_shape,
+ kernel_shape, pad, stride, dilation, data_col);
+ break;
+ case 3:
+ im2col_nd_gpu_kernel<Dtype, 3> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+ num_kernels, data_im, im_shape, col_shape,
+ kernel_shape, pad, stride, dilation, data_col);
+ break;
+ case 4:
+ im2col_nd_gpu_kernel<Dtype, 4> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+ num_kernels, data_im, im_shape, col_shape,
+ kernel_shape, pad, stride, dilation, data_col);
+ break;
+ case 5:
+ im2col_nd_gpu_kernel<Dtype, 5> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+ num_kernels, data_im, im_shape, col_shape,
+ kernel_shape, pad, stride, dilation, data_col);
+ break;
+ case 6:
+ im2col_nd_gpu_kernel<Dtype, 6> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+ num_kernels, data_im, im_shape, col_shape,
+ kernel_shape, pad, stride, dilation, data_col);
+ break;
+ case 7:
+ im2col_nd_gpu_kernel<Dtype, 7> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+ num_kernels, data_im, im_shape, col_shape,
+ kernel_shape, pad, stride, dilation, data_col);
+ break;
+ case 8:
+ im2col_nd_gpu_kernel<Dtype, 8> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+ num_kernels, data_im, im_shape, col_shape,
+ kernel_shape, pad, stride, dilation, data_col);
+ break;
+ case 9:
+ im2col_nd_gpu_kernel<Dtype, 9> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+ num_kernels, data_im, im_shape, col_shape,
+ kernel_shape, pad, stride, dilation, data_col);
+ break;
+ case 10:
+ im2col_nd_gpu_kernel<Dtype, 10> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+ num_kernels, data_im, im_shape, col_shape,
+ kernel_shape, pad, stride, dilation, data_col);
+ break;
+ default:
+ LOG(FATAL) << "im2col_nd_gpu does not support computation with "
+ << num_spatial_axes << " spatial axes";
+ }
+ CUDA_POST_KERNEL_CHECK;
+}
+
+// Explicit instantiation
+template void im2col_nd_gpu<float>(const float* data_im,
+ const int num_spatial_axes, const int col_size,
+ const int* im_shape, const int* col_shape,
+ const int* kernel_shape, const int* pad, const int* stride,
+ const int* dilation, float* data_col);
+template void im2col_nd_gpu<double>(const double* data_im,
+ const int num_spatial_axes, const int col_size,
+ const int* im_shape, const int* col_shape,
+ const int* kernel_shape, const int* pad, const int* stride,
+ const int* dilation, double* data_col);
template <typename Dtype>
__global__ void col2im_gpu_kernel(const int n, const Dtype* data_col,
const int height, const int width, const int channels,
- const int patch_h, const int patch_w,
+ const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w,
const int stride_h, const int stride_w,
+ const int dilation_h, const int dilation_w,
const int height_col, const int width_col,
Dtype* data_im) {
CUDA_KERNEL_LOOP(index, n) {
Dtype val = 0;
- int w = index % width + pad_w;
- int h = (index / width) % height + pad_h;
- int c = index / (width * height);
+ const int w_im = index % width + pad_w;
+ const int h_im = (index / width) % height + pad_h;
+ const int c_im = index / (width * height);
+ int kernel_extent_w = (kernel_w - 1) * dilation_w + 1;
+ int kernel_extent_h = (kernel_h - 1) * dilation_h + 1;
// compute the start and end of the output
- int w_col_start = (w < patch_w) ? 0 : (w - patch_w) / stride_w + 1;
- int w_col_end = min(w / stride_w + 1, width_col);
- int h_col_start = (h < patch_h) ? 0 : (h - patch_h) / stride_h + 1;
- int h_col_end = min(h / stride_h + 1, height_col);
- /*
- for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
- for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
- // the col location: [c * width * height + h_out, w_out]
- int c_col = c * patch_h * patch_w + (h - h_col * stride_h) * ksize
- + (w - w_col * stride_w);
- val += data_col[(c_col * height_col + h_col) * width_col + w_col];
- }
- }
- */
- // equivalent implementation
- int offset =
- (c * patch_h * patch_w + h * patch_w + w) * height_col * width_col;
- int coeff_h_col = (1 - stride_h * patch_w * height_col) * width_col;
- int coeff_w_col = (1 - stride_w * height_col * width_col);
- for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
- for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
- val += data_col[offset + h_col * coeff_h_col + w_col * coeff_w_col];
+ const int w_col_start =
+ (w_im < kernel_extent_w) ? 0 : (w_im - kernel_extent_w) / stride_w + 1;
+ const int w_col_end = min(w_im / stride_w + 1, width_col);
+ const int h_col_start =
+ (h_im < kernel_extent_h) ? 0 : (h_im - kernel_extent_h) / stride_h + 1;
+ const int h_col_end = min(h_im / stride_h + 1, height_col);
+ // TODO: use LCM of stride and dilation to avoid unnecessary loops
+ for (int h_col = h_col_start; h_col < h_col_end; h_col += 1) {
+ for (int w_col = w_col_start; w_col < w_col_end; w_col += 1) {
+ int h_k = (h_im - h_col * stride_h);
+ int w_k = (w_im - w_col * stride_w);
+ if (h_k % dilation_h == 0 && w_k % dilation_w == 0) {
+ h_k /= dilation_h;
+ w_k /= dilation_w;
+ int data_col_index = (((c_im * kernel_h + h_k) * kernel_w + w_k) *
+ height_col + h_col) * width_col + w_col;
+ val += data_col[data_col_index];
+ }
}
}
data_im[index] = val;
template <typename Dtype>
void col2im_gpu(const Dtype* data_col, const int channels,
- const int height, const int width, const int patch_h, const int patch_w,
+ const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w, const int stride_h,
- const int stride_w, Dtype* data_im) {
- int height_col = (height + 2 * pad_h - patch_h) / stride_h + 1;
- int width_col = (width + 2 * pad_w - patch_w) / stride_w + 1;
+ const int stride_w, const int dilation_h, const int dilation_w,
+ Dtype* data_im) {
+ int height_col = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) /
+ stride_h + 1;
+ int width_col = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) /
+ stride_w + 1;
int num_kernels = channels * height * width;
// To avoid involving atomic operations, we will launch one kernel per
// bottom dimension, and then in the kernel add up the top dimensions.
// NOLINT_NEXT_LINE(whitespace/operators)
col2im_gpu_kernel<Dtype><<<CAFFE_GET_BLOCKS(num_kernels),
CAFFE_CUDA_NUM_THREADS>>>(
- num_kernels, data_col, height, width, channels, patch_h, patch_w,
- pad_h, pad_w, stride_h, stride_w,
+ num_kernels, data_col, height, width, channels, kernel_h, kernel_w,
+ pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w,
height_col, width_col, data_im);
CUDA_POST_KERNEL_CHECK;
}
// Explicit instantiation
template void col2im_gpu<float>(const float* data_col, const int channels,
- const int height, const int width, const int patch_h, const int patch_w,
+ const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w, const int stride_h,
- const int stride_w, float* data_im);
+ const int stride_w, const int dilation_h, const int dilation_w,
+ float* data_im);
template void col2im_gpu<double>(const double* data_col, const int channels,
- const int height, const int width, const int patch_h, const int patch_w,
+ const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w, const int stride_h,
- const int stride_w, double* data_im);
+ const int stride_w, const int dilation_h, const int dilation_w,
+ double* data_im);
+
+template <typename Dtype, int num_axes>
+__global__ void col2im_nd_gpu_kernel(const int n, const Dtype* data_col,
+ const int* im_shape, const int* col_shape,
+ const int* kernel_shape, const int* pad, const int* stride,
+ const int* dilation, Dtype* data_im) {
+ int d_im[num_axes]; // NOLINT(runtime/arrays)
+ int d_col_iter[num_axes]; // NOLINT(runtime/arrays)
+ int d_col_start[num_axes]; // NOLINT(runtime/arrays)
+ int d_col_end[num_axes]; // NOLINT(runtime/arrays)
+
+ __shared__ int shared_dilation[num_axes];
+ __shared__ int shared_kernel_shape[num_axes];
+ __shared__ int shared_pad[num_axes];
+ __shared__ int shared_stride[num_axes];
+ __shared__ int shared_col_shape[num_axes + 1];
+ __shared__ int shared_im_shape[num_axes + 1];
+
+ if (threadIdx.x < num_axes) {
+ shared_dilation[threadIdx.x] = dilation[threadIdx.x];
+ shared_kernel_shape[threadIdx.x] = kernel_shape[threadIdx.x];
+ shared_pad[threadIdx.x] = pad[threadIdx.x];
+ shared_stride[threadIdx.x] = stride[threadIdx.x];
+ }
+ if (threadIdx.x < num_axes + 1) {
+ shared_col_shape[threadIdx.x] = col_shape[threadIdx.x];
+ shared_im_shape[threadIdx.x] = im_shape[threadIdx.x];
+ }
+ __syncthreads();
+
+ CUDA_KERNEL_LOOP(index, n) {
+ // Initialize channel_in, computed in the loop below, with intermediate
+ // computations used to compute the spatial indices.
+ int c_im = index;
+ // Calculate d_im (image dimensions).
+ for (int i = num_axes - 1; i >= 0; --i) {
+ d_im[i] = c_im % shared_im_shape[i + 1] + shared_pad[i];
+ c_im /= shared_im_shape[i + 1];
+ }
+ // Calculate col start/end indices.
+ bool done = false;
+ for (int i = 0; i < num_axes; ++i) {
+ const int kernel_extent =
+ shared_dilation[i] * (shared_kernel_shape[i] - 1) + 1;
+ d_col_start[i] = d_col_iter[i] =
+ (d_im[i] < kernel_extent) ? 0 :
+ (d_im[i] - kernel_extent) / shared_stride[i] + 1;
+ d_col_end[i] =
+ min(d_im[i] / shared_stride[i] + 1, shared_col_shape[i + 1]);
+ if (d_col_start[i] >= d_col_end[i]) {
+ // Skip computation if the dimension is 0 at any spatial axis --
+ // final val will be 0.
+ data_im[index] = 0;
+ done = true;
+ break; // for (int i = 0; i < num_axes; ++i)
+ }
+ }
+ if (done) {
+ continue; // CUDA_KERNEL_LOOP(index, n)
+ }
+ // Loop over the col to compute the output val.
+ Dtype val = 0;
+ bool incremented = true;
+ bool skip = false;
+ do {
+ // Compute the final offset.
+ int final_offset = 0;
+ int kernel_shape_prod = 1;
+ int kernel_index;
+ for (int i = num_axes - 1; i >= 0; --i) {
+ kernel_index = d_im[i] - d_col_iter[i] * shared_stride[i];
+ if (kernel_index % shared_dilation[i]) {
+ skip = true;
+ break;
+ } else {
+ kernel_index /= shared_dilation[i];
+ final_offset += kernel_index * kernel_shape_prod;
+ kernel_shape_prod *= shared_kernel_shape[i];
+ }
+ }
+ if (!skip) {
+ final_offset += kernel_shape_prod * c_im;
+ for (int i = 0; i < num_axes; ++i) {
+ final_offset *= shared_col_shape[i + 1];
+ final_offset += d_col_iter[i];
+ }
+ val += data_col[final_offset];
+ }
+ skip = false;
+ incremented = false;
+ for (int i = num_axes - 1; i >= 0; --i) {
+ const int d_max = d_col_end[i];
+ if (d_col_iter[i] == d_max - 1) {
+ d_col_iter[i] = d_col_start[i];
+ } else { // d_col_iter[i] < d_max - 1
+ ++d_col_iter[i];
+ incremented = true;
+ break; // for (int i = num_axes - 1; i >= 0; --i)
+ }
+ } // for (int i = num_axes - 1; i >= 0; --i)
+ } while (incremented);
+ data_im[index] = val;
+ } // CUDA_KERNEL_LOOP(index, n)
+}
+
+template <typename Dtype>
+void col2im_nd_gpu(const Dtype* data_col, const int num_spatial_axes,
+ const int im_size, const int* im_shape, const int* col_shape,
+ const int* kernel_shape, const int* pad, const int* stride,
+ const int* dilation, Dtype* data_im) {
+ // num_axes should be smaller than block size
+ DCHECK_LT(num_spatial_axes, CAFFE_CUDA_NUM_THREADS);
+ switch (num_spatial_axes) {
+ case 1:
+ col2im_nd_gpu_kernel<Dtype, 1> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+ im_size, data_col, im_shape, col_shape,
+ kernel_shape, pad, stride, dilation, data_im);
+ break;
+ case 2:
+ col2im_nd_gpu_kernel<Dtype, 2> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+ im_size, data_col, im_shape, col_shape,
+ kernel_shape, pad, stride, dilation, data_im);
+ break;
+ case 3:
+ col2im_nd_gpu_kernel<Dtype, 3> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+ im_size, data_col, im_shape, col_shape,
+ kernel_shape, pad, stride, dilation, data_im);
+ break;
+ case 4:
+ col2im_nd_gpu_kernel<Dtype, 4> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+ im_size, data_col, im_shape, col_shape,
+ kernel_shape, pad, stride, dilation, data_im);
+ break;
+ case 5:
+ col2im_nd_gpu_kernel<Dtype, 5> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+ im_size, data_col, im_shape, col_shape,
+ kernel_shape, pad, stride, dilation, data_im);
+ break;
+ case 6:
+ col2im_nd_gpu_kernel<Dtype, 6> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+ im_size, data_col, im_shape, col_shape,
+ kernel_shape, pad, stride, dilation, data_im);
+ break;
+ case 7:
+ col2im_nd_gpu_kernel<Dtype, 7> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+ im_size, data_col, im_shape, col_shape,
+ kernel_shape, pad, stride, dilation, data_im);
+ break;
+ case 8:
+ col2im_nd_gpu_kernel<Dtype, 8> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+ im_size, data_col, im_shape, col_shape,
+ kernel_shape, pad, stride, dilation, data_im);
+ break;
+ case 9:
+ col2im_nd_gpu_kernel<Dtype, 9> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+ im_size, data_col, im_shape, col_shape,
+ kernel_shape, pad, stride, dilation, data_im);
+ break;
+ case 10:
+ col2im_nd_gpu_kernel<Dtype, 10> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+ im_size, data_col, im_shape, col_shape,
+ kernel_shape, pad, stride, dilation, data_im);
+ break;
+ default:
+ LOG(FATAL) << "col2im_nd_gpu does not support computation with "
+ << num_spatial_axes << " spatial axes";
+ }
+ CUDA_POST_KERNEL_CHECK;
+}
+
+// Explicit instantiation
+template void col2im_nd_gpu<float>(const float* data_col,
+ const int num_spatial_axes, const int im_size,
+ const int* im_shape, const int* col_shape,
+ const int* kernel_shape, const int* pad, const int* stride,
+ const int* dilation, float* data_im);
+template void col2im_nd_gpu<double>(const double* data_col,
+ const int num_spatial_axes, const int im_size,
+ const int* im_shape, const int* col_shape,
+ const int* kernel_shape, const int* pad, const int* stride,
+ const int* dilation, double* data_im);
} // namespace caffe
const string& blob_name = layer_param.bottom(j);
if (blob_name_to_last_top_idx.find(blob_name) ==
blob_name_to_last_top_idx.end()) {
- LOG(FATAL) << "Unknown blob input " << blob_name << " to layer " << j;
+ LOG(FATAL) << "Unknown bottom blob '" << blob_name << "' (layer '"
+ << layer_param.name() << "', bottom index " << j << ")";
}
const pair<int, int>& bottom_idx = make_pair(i, j);
const pair<int, int>& top_idx = blob_name_to_last_top_idx[blob_name];
#include <google/protobuf/io/coded_stream.h>
#include <google/protobuf/io/zero_copy_stream_impl.h>
#include <google/protobuf/text_format.h>
+#ifdef USE_OPENCV
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/highgui/highgui_c.h>
#include <opencv2/imgproc/imgproc.hpp>
+#endif // USE_OPENCV
#include <stdint.h>
#include <algorithm>
CHECK(proto.SerializeToOstream(&output));
}
+#ifdef USE_OPENCV
cv::Mat ReadImageToCVMat(const string& filename,
const int height, const int width, const bool is_color) {
cv::Mat cv_img;
cv::Mat ReadImageToCVMat(const string& filename) {
return ReadImageToCVMat(filename, 0, 0, true);
}
+
// Do the file extension and encoding match?
static bool matchExt(const std::string & fn,
std::string en) {
return true;
return false;
}
+
bool ReadImageToDatum(const string& filename, const int label,
const int height, const int width, const bool is_color,
const std::string & encoding, Datum* datum) {
return false;
}
}
+#endif // USE_OPENCV
bool ReadFileToDatum(const string& filename, const int label,
Datum* datum) {
}
}
+#ifdef USE_OPENCV
cv::Mat DecodeDatumToCVMatNative(const Datum& datum) {
cv::Mat cv_img;
CHECK(datum.encoded()) << "Datum not encoded";
}
datum->set_data(buffer);
}
-
-// Verifies format of data stored in HDF5 file and reshapes blob accordingly.
-template <typename Dtype>
-void hdf5_load_nd_dataset_helper(
- hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
- Blob<Dtype>* blob) {
- // Verify that the dataset exists.
- CHECK(H5LTfind_dataset(file_id, dataset_name_))
- << "Failed to find HDF5 dataset " << dataset_name_;
- // Verify that the number of dimensions is in the accepted range.
- herr_t status;
- int ndims;
- status = H5LTget_dataset_ndims(file_id, dataset_name_, &ndims);
- CHECK_GE(status, 0) << "Failed to get dataset ndims for " << dataset_name_;
- CHECK_GE(ndims, min_dim);
- CHECK_LE(ndims, max_dim);
-
- // Verify that the data format is what we expect: float or double.
- std::vector<hsize_t> dims(ndims);
- H5T_class_t class_;
- status = H5LTget_dataset_info(
- file_id, dataset_name_, dims.data(), &class_, NULL);
- CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name_;
- CHECK_EQ(class_, H5T_FLOAT) << "Expected float or double data";
-
- vector<int> blob_dims(dims.size());
- for (int i = 0; i < dims.size(); ++i) {
- blob_dims[i] = dims[i];
- }
- blob->Reshape(blob_dims);
-}
-
-template <>
-void hdf5_load_nd_dataset<float>(hid_t file_id, const char* dataset_name_,
- int min_dim, int max_dim, Blob<float>* blob) {
- hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);
- herr_t status = H5LTread_dataset_float(
- file_id, dataset_name_, blob->mutable_cpu_data());
- CHECK_GE(status, 0) << "Failed to read float dataset " << dataset_name_;
-}
-
-template <>
-void hdf5_load_nd_dataset<double>(hid_t file_id, const char* dataset_name_,
- int min_dim, int max_dim, Blob<double>* blob) {
- hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);
- herr_t status = H5LTread_dataset_double(
- file_id, dataset_name_, blob->mutable_cpu_data());
- CHECK_GE(status, 0) << "Failed to read double dataset " << dataset_name_;
-}
-
-template <>
-void hdf5_save_nd_dataset<float>(
- const hid_t file_id, const string& dataset_name, const Blob<float>& blob) {
- hsize_t dims[HDF5_NUM_DIMS];
- dims[0] = blob.num();
- dims[1] = blob.channels();
- dims[2] = blob.height();
- dims[3] = blob.width();
- herr_t status = H5LTmake_dataset_float(
- file_id, dataset_name.c_str(), HDF5_NUM_DIMS, dims, blob.cpu_data());
- CHECK_GE(status, 0) << "Failed to make float dataset " << dataset_name;
-}
-
-template <>
-void hdf5_save_nd_dataset<double>(
- const hid_t file_id, const string& dataset_name, const Blob<double>& blob) {
- hsize_t dims[HDF5_NUM_DIMS];
- dims[0] = blob.num();
- dims[1] = blob.channels();
- dims[2] = blob.height();
- dims[3] = blob.width();
- herr_t status = H5LTmake_dataset_double(
- file_id, dataset_name.c_str(), HDF5_NUM_DIMS, dims, blob.cpu_data());
- CHECK_GE(status, 0) << "Failed to make double dataset " << dataset_name;
-}
-
+#endif // USE_OPENCV
} // namespace caffe
}
template <>
+void caffe_log<float>(const int n, const float* a, float* y) {
+ vsLn(n, a, y);
+}
+
+template <>
+void caffe_log<double>(const int n, const double* a, double* y) {
+ vdLn(n, a, y);
+}
+
+template <>
void caffe_abs<float>(const int n, const float* a, float* y) {
vsAbs(n, a, y);
}
double caffe_cpu_dot<double>(const int n, const double* x, const double* y);
template <>
-int caffe_cpu_hamming_distance<float>(const int n, const float* x,
- const float* y) {
- int dist = 0;
- for (int i = 0; i < n; ++i) {
- dist += __builtin_popcount(static_cast<uint32_t>(x[i]) ^
- static_cast<uint32_t>(y[i]));
- }
- return dist;
-}
-
-template <>
-int caffe_cpu_hamming_distance<double>(const int n, const double* x,
- const double* y) {
- int dist = 0;
- for (int i = 0; i < n; ++i) {
- dist += __builtin_popcountl(static_cast<uint64_t>(x[i]) ^
- static_cast<uint64_t>(y[i]));
- }
- return dist;
-}
-
-template <>
float caffe_cpu_asum<float>(const int n, const float* x) {
return cblas_sasum(n, x, 1);
}
#include <thrust/reduce.h>
#include <cmath>
-#include <cstdlib>
-#include <cstring>
#include "caffe/common.hpp"
#include "caffe/util/math_functions.hpp"
}
template <typename Dtype>
+__global__ void log_kernel(const int n, const Dtype* a, Dtype* y) {
+ CUDA_KERNEL_LOOP(index, n) {
+ y[index] = log(a[index]);
+ }
+}
+
+template <>
+void caffe_gpu_log<float>(const int N, const float* a, float* y) {
+ // NOLINT_NEXT_LINE(whitespace/operators)
+ log_kernel<float><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+ N, a, y);
+}
+
+template <>
+void caffe_gpu_log<double>(const int N, const double* a, double* y) {
+ // NOLINT_NEXT_LINE(whitespace/operators)
+ log_kernel<double><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+ N, a, y);
+}
+
+template <typename Dtype>
__global__ void powx_kernel(const int n, const Dtype* a,
const Dtype alpha, Dtype* y) {
CUDA_KERNEL_LOOP(index, n) {
- (x[index] < Dtype(0)));
DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(sgnbit, y[index] = signbit(x[index]));
-__global__ void popc_kernel(const int n, const float* a,
- const float* b, uint8_t* y) {
- CUDA_KERNEL_LOOP(index, n) {
- y[index] = __popc(static_cast<uint32_t>(a[index]) ^
- static_cast<uint32_t>(b[index]));
- }
-}
-
-__global__ void popcll_kernel(const int n, const double* a,
- const double* b, uint8_t* y) {
- CUDA_KERNEL_LOOP(index, n) {
- y[index] = __popcll(static_cast<uint64_t>(a[index]) ^
- static_cast<uint64_t>(b[index]));
- }
-}
-
-template <>
-uint32_t caffe_gpu_hamming_distance<float>(const int n, const float* x,
- const float* y) {
- // TODO: Fix caffe_gpu_hamming_distance (see failing unit test
- // TestHammingDistanceGPU in test_math_functions.cpp).
- NOT_IMPLEMENTED;
- thrust::device_vector<uint8_t> popcounts(n);
- // NOLINT_NEXT_LINE(whitespace/operators)
- popc_kernel<<<CAFFE_GET_BLOCKS(n), CAFFE_CUDA_NUM_THREADS>>>(
- n, x, y, thrust::raw_pointer_cast(popcounts.data()));
- return thrust::reduce(popcounts.begin(), popcounts.end(),
- (uint32_t) 0, thrust::plus<uint32_t>());
-}
-
-template <>
-uint32_t caffe_gpu_hamming_distance<double>(const int n, const double* x,
- const double* y) {
- // TODO: Fix caffe_gpu_hamming_distance (see failing unit test
- // TestHammingDistanceGPU in test_math_functions.cpp).
- NOT_IMPLEMENTED;
- thrust::device_vector<uint8_t> popcounts(n);
- // NOLINT_NEXT_LINE(whitespace/operators)
- popcll_kernel<<<CAFFE_GET_BLOCKS(n), CAFFE_CUDA_NUM_THREADS>>>(
- n, x, y, thrust::raw_pointer_cast(popcounts.data()));
- return thrust::reduce(popcounts.begin(), popcounts.end(),
- /* NOLINT_NEXT_LINE(build/include_what_you_use) */
- (uint32_t) 0, thrust::plus<uint32_t>());
-}
-
void caffe_gpu_rng_uniform(const int n, unsigned int* r) {
CURAND_CHECK(curandGenerate(Caffe::curand_generator(), r, n));
}
--- /dev/null
+#include <boost/bind.hpp>
+#include <glog/logging.h>
+
+#include <signal.h>
+#include <csignal>
+
+#include "caffe/util/signal_handler.h"
+
+namespace {
+ static volatile sig_atomic_t got_sigint = false;
+ static volatile sig_atomic_t got_sighup = false;
+ static bool already_hooked_up = false;
+
+ void handle_signal(int signal) {
+ switch (signal) {
+ case SIGHUP:
+ got_sighup = true;
+ break;
+ case SIGINT:
+ got_sigint = true;
+ break;
+ }
+ }
+
+ void HookupHandler() {
+ if (already_hooked_up) {
+ LOG(FATAL) << "Tried to hookup signal handlers more than once.";
+ }
+ already_hooked_up = true;
+
+ struct sigaction sa;
+ // Setup the handler
+ sa.sa_handler = &handle_signal;
+ // Restart the system call, if at all possible
+ sa.sa_flags = SA_RESTART;
+ // Block every signal during the handler
+ sigfillset(&sa.sa_mask);
+ // Intercept SIGHUP and SIGINT
+ if (sigaction(SIGHUP, &sa, NULL) == -1) {
+ LOG(FATAL) << "Cannot install SIGHUP handler.";
+ }
+ if (sigaction(SIGINT, &sa, NULL) == -1) {
+ LOG(FATAL) << "Cannot install SIGINT handler.";
+ }
+ }
+
+ // Set the signal handlers to the default.
+ void UnhookHandler() {
+ if (already_hooked_up) {
+ struct sigaction sa;
+ // Setup the sighub handler
+ sa.sa_handler = SIG_DFL;
+ // Restart the system call, if at all possible
+ sa.sa_flags = SA_RESTART;
+ // Block every signal during the handler
+ sigfillset(&sa.sa_mask);
+ // Intercept SIGHUP and SIGINT
+ if (sigaction(SIGHUP, &sa, NULL) == -1) {
+ LOG(FATAL) << "Cannot uninstall SIGHUP handler.";
+ }
+ if (sigaction(SIGINT, &sa, NULL) == -1) {
+ LOG(FATAL) << "Cannot uninstall SIGINT handler.";
+ }
+
+ already_hooked_up = false;
+ }
+ }
+
+ // Return true iff a SIGINT has been received since the last time this
+ // function was called.
+ bool GotSIGINT() {
+ bool result = got_sigint;
+ got_sigint = false;
+ return result;
+ }
+
+ // Return true iff a SIGHUP has been received since the last time this
+ // function was called.
+ bool GotSIGHUP() {
+ bool result = got_sighup;
+ got_sighup = false;
+ return result;
+ }
+} // namespace
+
+namespace caffe {
+
+SignalHandler::SignalHandler(SolverAction::Enum SIGINT_action,
+ SolverAction::Enum SIGHUP_action):
+ SIGINT_action_(SIGINT_action),
+ SIGHUP_action_(SIGHUP_action) {
+ HookupHandler();
+}
+
+SignalHandler::~SignalHandler() {
+ UnhookHandler();
+}
+
+SolverAction::Enum SignalHandler::CheckForSignals() const {
+ if (GotSIGHUP()) {
+ return SIGHUP_action_;
+ }
+ if (GotSIGINT()) {
+ return SIGINT_action_;
+ }
+ return SolverAction::NONE;
+}
+
+// Return the function that the solver can use to find out if a snapshot or
+// early exit is being requested.
+ActionCallback SignalHandler::GetActionFunction() {
+ return boost::bind(&SignalHandler::CheckForSignals, this);
+}
+
+} // namespace caffe
return NetNeedsV0ToV1Upgrade(net_param) || NetNeedsV1ToV2Upgrade(net_param);
}
+bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
+ bool success = true;
+ if (NetNeedsV0ToV1Upgrade(*param)) {
+ // NetParameter was specified using the old style (V0LayerParameter); try to
+ // upgrade it.
+ LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
+ << "V0LayerParameter: " << param_file;
+ NetParameter original_param(*param);
+ if (!UpgradeV0Net(original_param, param)) {
+ success = false;
+ LOG(ERROR) << "Warning: had one or more problems upgrading "
+ << "V0NetParameter to NetParameter (see above); continuing anyway.";
+ } else {
+ LOG(INFO) << "Successfully upgraded file specified using deprecated "
+ << "V0LayerParameter";
+ }
+ LOG(WARNING) << "Note that future Caffe releases will not support "
+ << "V0NetParameter; use ./build/tools/upgrade_net_proto_text for "
+ << "prototxt and ./build/tools/upgrade_net_proto_binary for model "
+ << "weights upgrade this and any other net protos to the new format.";
+ }
+ // NetParameter uses old style data transformation fields; try to upgrade it.
+ if (NetNeedsDataUpgrade(*param)) {
+ LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
+ << "transformation parameters: " << param_file;
+ UpgradeNetDataTransformation(param);
+ LOG(INFO) << "Successfully upgraded file specified using deprecated "
+ << "data transformation parameters.";
+ LOG(WARNING) << "Note that future Caffe releases will only support "
+ << "transform_param messages for transformation fields.";
+ }
+ if (NetNeedsV1ToV2Upgrade(*param)) {
+ LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
+ << "V1LayerParameter: " << param_file;
+ NetParameter original_param(*param);
+ if (!UpgradeV1Net(original_param, param)) {
+ success = false;
+ LOG(ERROR) << "Warning: had one or more problems upgrading "
+ << "V1LayerParameter (see above); continuing anyway.";
+ } else {
+ LOG(INFO) << "Successfully upgraded file specified using deprecated "
+ << "V1LayerParameter";
+ }
+ }
+ return success;
+}
+
+void ReadNetParamsFromTextFileOrDie(const string& param_file,
+ NetParameter* param) {
+ CHECK(ReadProtoFromTextFile(param_file, param))
+ << "Failed to parse NetParameter file: " << param_file;
+ UpgradeNetAsNeeded(param_file, param);
+}
+
+void ReadNetParamsFromBinaryFileOrDie(const string& param_file,
+ NetParameter* param) {
+ CHECK(ReadProtoFromBinaryFile(param_file, param))
+ << "Failed to parse NetParameter file: " << param_file;
+ UpgradeNetAsNeeded(param_file, param);
+}
+
bool NetNeedsV0ToV1Upgrade(const NetParameter& net_param) {
for (int i = 0; i < net_param.layers_size(); ++i) {
if (net_param.layers(i).has_layer()) {
}
if (v0_layer_param.has_pad()) {
if (type == "conv") {
- layer_param->mutable_convolution_param()->set_pad(v0_layer_param.pad());
+ layer_param->mutable_convolution_param()->add_pad(v0_layer_param.pad());
} else if (type == "pool") {
layer_param->mutable_pooling_param()->set_pad(v0_layer_param.pad());
} else {
}
if (v0_layer_param.has_kernelsize()) {
if (type == "conv") {
- layer_param->mutable_convolution_param()->set_kernel_size(
+ layer_param->mutable_convolution_param()->add_kernel_size(
v0_layer_param.kernelsize());
} else if (type == "pool") {
layer_param->mutable_pooling_param()->set_kernel_size(
}
if (v0_layer_param.has_stride()) {
if (type == "conv") {
- layer_param->mutable_convolution_param()->set_stride(
+ layer_param->mutable_convolution_param()->add_stride(
v0_layer_param.stride());
} else if (type == "pool") {
layer_param->mutable_pooling_param()->set_stride(
}
}
-bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
- bool success = true;
- if (NetNeedsV0ToV1Upgrade(*param)) {
- // NetParameter was specified using the old style (V0LayerParameter); try to
- // upgrade it.
- LOG(ERROR) << "Attempting to upgrade input file specified using deprecated "
- << "V0LayerParameter: " << param_file;
- NetParameter original_param(*param);
- if (!UpgradeV0Net(original_param, param)) {
- success = false;
- LOG(ERROR) << "Warning: had one or more problems upgrading "
- << "V0NetParameter to NetParameter (see above); continuing anyway.";
- } else {
- LOG(INFO) << "Successfully upgraded file specified using deprecated "
- << "V0LayerParameter";
- }
- LOG(ERROR) << "Note that future Caffe releases will not support "
- << "V0NetParameter; use ./build/tools/upgrade_net_proto_text for "
- << "prototxt and ./build/tools/upgrade_net_proto_binary for model "
- << "weights upgrade this and any other net protos to the new format.";
- }
- // NetParameter uses old style data transformation fields; try to upgrade it.
- if (NetNeedsDataUpgrade(*param)) {
- LOG(ERROR) << "Attempting to upgrade input file specified using deprecated "
- << "transformation parameters: " << param_file;
- UpgradeNetDataTransformation(param);
- LOG(INFO) << "Successfully upgraded file specified using deprecated "
- << "data transformation parameters.";
- LOG(ERROR) << "Note that future Caffe releases will only support "
- << "transform_param messages for transformation fields.";
- }
- if (NetNeedsV1ToV2Upgrade(*param)) {
- LOG(ERROR) << "Attempting to upgrade input file specified using deprecated "
- << "V1LayerParameter: " << param_file;
- NetParameter original_param(*param);
- if (!UpgradeV1Net(original_param, param)) {
- success = false;
- LOG(ERROR) << "Warning: had one or more problems upgrading "
- << "V1LayerParameter (see above); continuing anyway.";
- } else {
- LOG(INFO) << "Successfully upgraded file specified using deprecated "
- << "V1LayerParameter";
- }
- }
- return success;
-}
-
bool UpgradeV1Net(const NetParameter& v1_net_param, NetParameter* net_param) {
bool is_fully_compatible = true;
if (v1_net_param.layer_size() > 0) {
}
}
-void ReadNetParamsFromTextFileOrDie(const string& param_file,
- NetParameter* param) {
- CHECK(ReadProtoFromTextFile(param_file, param))
- << "Failed to parse NetParameter file: " << param_file;
- UpgradeNetAsNeeded(param_file, param);
+// Return true iff the solver contains any old solver_type specified as enums
+bool SolverNeedsTypeUpgrade(const SolverParameter& solver_param) {
+ if (solver_param.has_solver_type()) {
+ return true;
+ }
+ return false;
}
-void ReadNetParamsFromBinaryFileOrDie(const string& param_file,
- NetParameter* param) {
- CHECK(ReadProtoFromBinaryFile(param_file, param))
- << "Failed to parse NetParameter file: " << param_file;
- UpgradeNetAsNeeded(param_file, param);
+bool UpgradeSolverType(SolverParameter* solver_param) {
+ CHECK(!solver_param->has_solver_type() || !solver_param->has_type())
+ << "Failed to upgrade solver: old solver_type field (enum) and new type "
+ << "field (string) cannot be both specified in solver proto text.";
+ if (solver_param->has_solver_type()) {
+ string type;
+ switch (solver_param->solver_type()) {
+ case SolverParameter_SolverType_SGD:
+ type = "SGD";
+ break;
+ case SolverParameter_SolverType_NESTEROV:
+ type = "Nesterov";
+ break;
+ case SolverParameter_SolverType_ADAGRAD:
+ type = "AdaGrad";
+ break;
+ case SolverParameter_SolverType_RMSPROP:
+ type = "RMSProp";
+ break;
+ case SolverParameter_SolverType_ADADELTA:
+ type = "AdaDelta";
+ break;
+ case SolverParameter_SolverType_ADAM:
+ type = "Adam";
+ break;
+ default:
+ LOG(FATAL) << "Unknown SolverParameter solver_type: " << type;
+ }
+ solver_param->set_type(type);
+ solver_param->clear_solver_type();
+ } else {
+ LOG(ERROR) << "Warning: solver type already up to date. ";
+ return false;
+ }
+ return true;
+}
+
+// Check for deprecations and upgrade the SolverParameter as needed.
+bool UpgradeSolverAsNeeded(const string& param_file, SolverParameter* param) {
+ bool success = true;
+ // Try to upgrade old style solver_type enum fields into new string type
+ if (SolverNeedsTypeUpgrade(*param)) {
+ LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
+ << "'solver_type' field (enum)': " << param_file;
+ if (!UpgradeSolverType(param)) {
+ success = false;
+ LOG(ERROR) << "Warning: had one or more problems upgrading "
+ << "SolverType (see above).";
+ } else {
+ LOG(INFO) << "Successfully upgraded file specified using deprecated "
+ << "'solver_type' field (enum) to 'type' field (string).";
+ LOG(WARNING) << "Note that future Caffe releases will only support "
+ << "'type' field (string) for a solver's type.";
+ }
+ }
+ return success;
+}
+
+// Read parameters from a file into a SolverParameter proto message.
+void ReadSolverParamsFromTextFileOrDie(const string& param_file,
+ SolverParameter* param) {
+ CHECK(ReadProtoFromTextFile(param_file, param))
+ << "Failed to parse SolverParameter file: " << param_file;
+ UpgradeSolverAsNeeded(param_file, param);
}
} // namespace caffe
+#ifdef WITH_PYTHON_LAYER
+#include "boost/python.hpp"
+namespace bp = boost::python;
+#endif
+
+#include <gflags/gflags.h>
#include <glog/logging.h>
#include <cstring>
#include "boost/algorithm/string.hpp"
#include "caffe/caffe.hpp"
+#include "caffe/util/signal_handler.h"
using caffe::Blob;
using caffe::Caffe;
using caffe::Net;
using caffe::Layer;
+using caffe::Solver;
using caffe::shared_ptr;
+using caffe::string;
using caffe::Timer;
using caffe::vector;
+using std::ostringstream;
-
-DEFINE_int32(gpu, -1,
- "Run in GPU mode on given device ID.");
+DEFINE_string(gpu, "",
+ "Optional; run in GPU mode on given device IDs separated by ','."
+ "Use '-gpu all' to run on all available GPUs. The effective training "
+ "batch size is multiplied by the number of devices.");
DEFINE_string(solver, "",
"The solver definition protocol buffer text file.");
DEFINE_string(model, "",
DEFINE_string(snapshot, "",
"Optional; the snapshot solver state to resume training.");
DEFINE_string(weights, "",
- "Optional; the pretrained weights to initialize finetuning. "
- "Cannot be set simultaneously with snapshot.");
+ "Optional; the pretrained weights to initialize finetuning, "
+ "separated by ','. Cannot be set simultaneously with snapshot.");
DEFINE_int32(iterations, 50,
"The number of iterations to run.");
+DEFINE_string(sigint_effect, "stop",
+ "Optional; action to take when a SIGINT signal is received: "
+ "snapshot, stop or none.");
+DEFINE_string(sighup_effect, "snapshot",
+ "Optional; action to take when a SIGHUP signal is received: "
+ "snapshot, stop or none.");
// A simple registry for caffe commands.
typedef int (*BrewFunction)();
}
}
+// Parse GPU ids or use all available devices
+static void get_gpus(vector<int>* gpus) {
+ if (FLAGS_gpu == "all") {
+ int count = 0;
+#ifndef CPU_ONLY
+ CUDA_CHECK(cudaGetDeviceCount(&count));
+#else
+ NO_GPU;
+#endif
+ for (int i = 0; i < count; ++i) {
+ gpus->push_back(i);
+ }
+ } else if (FLAGS_gpu.size()) {
+ vector<string> strings;
+ boost::split(strings, FLAGS_gpu, boost::is_any_of(","));
+ for (int i = 0; i < strings.size(); ++i) {
+ gpus->push_back(boost::lexical_cast<int>(strings[i]));
+ }
+ } else {
+ CHECK_EQ(gpus->size(), 0);
+ }
+}
+
// caffe commands to call by
// caffe <command> <args>
//
// Device Query: show diagnostic information for a GPU device.
int device_query() {
- CHECK_GT(FLAGS_gpu, -1) << "Need a device ID to query.";
- LOG(INFO) << "Querying device ID = " << FLAGS_gpu;
- caffe::Caffe::SetDevice(FLAGS_gpu);
- caffe::Caffe::DeviceQuery();
+ LOG(INFO) << "Querying GPUs " << FLAGS_gpu;
+ vector<int> gpus;
+ get_gpus(&gpus);
+ for (int i = 0; i < gpus.size(); ++i) {
+ caffe::Caffe::SetDevice(gpus[i]);
+ caffe::Caffe::DeviceQuery();
+ }
return 0;
}
RegisterBrewFunction(device_query);
}
}
+// Translate the signal effect the user specified on the command-line to the
+// corresponding enumeration.
+caffe::SolverAction::Enum GetRequestedAction(
+ const std::string& flag_value) {
+ if (flag_value == "stop") {
+ return caffe::SolverAction::STOP;
+ }
+ if (flag_value == "snapshot") {
+ return caffe::SolverAction::SNAPSHOT;
+ }
+ if (flag_value == "none") {
+ return caffe::SolverAction::NONE;
+ }
+ LOG(FATAL) << "Invalid signal effect \""<< flag_value << "\" was specified";
+}
+
// Train / Finetune a model.
int train() {
CHECK_GT(FLAGS_solver.size(), 0) << "Need a solver definition to train.";
"but not both.";
caffe::SolverParameter solver_param;
- caffe::ReadProtoFromTextFileOrDie(FLAGS_solver, &solver_param);
+ caffe::ReadSolverParamsFromTextFileOrDie(FLAGS_solver, &solver_param);
- // If the gpu flag is not provided, allow the mode and device to be set
+ // If the gpus flag is not provided, allow the mode and device to be set
// in the solver prototxt.
- if (FLAGS_gpu < 0
+ if (FLAGS_gpu.size() == 0
&& solver_param.solver_mode() == caffe::SolverParameter_SolverMode_GPU) {
- FLAGS_gpu = solver_param.device_id();
+ if (solver_param.has_device_id()) {
+ FLAGS_gpu = "" +
+ boost::lexical_cast<string>(solver_param.device_id());
+ } else { // Set default GPU if unspecified
+ FLAGS_gpu = "" + boost::lexical_cast<string>(0);
+ }
}
- // Set device id and mode
- if (FLAGS_gpu >= 0) {
- LOG(INFO) << "Use GPU with device ID " << FLAGS_gpu;
- Caffe::SetDevice(FLAGS_gpu);
- Caffe::set_mode(Caffe::GPU);
- } else {
+ vector<int> gpus;
+ get_gpus(&gpus);
+ if (gpus.size() == 0) {
LOG(INFO) << "Use CPU.";
Caffe::set_mode(Caffe::CPU);
+ } else {
+ ostringstream s;
+ for (int i = 0; i < gpus.size(); ++i) {
+ s << (i ? ", " : "") << gpus[i];
+ }
+ LOG(INFO) << "Using GPUs " << s.str();
+
+ solver_param.set_device_id(gpus[0]);
+ Caffe::SetDevice(gpus[0]);
+ Caffe::set_mode(Caffe::GPU);
+ Caffe::set_solver_count(gpus.size());
}
- LOG(INFO) << "Starting Optimization";
+ caffe::SignalHandler signal_handler(
+ GetRequestedAction(FLAGS_sigint_effect),
+ GetRequestedAction(FLAGS_sighup_effect));
+
shared_ptr<caffe::Solver<float> >
- solver(caffe::GetSolver<float>(solver_param));
+ solver(caffe::SolverRegistry<float>::CreateSolver(solver_param));
+
+ solver->SetActionFunction(signal_handler.GetActionFunction());
if (FLAGS_snapshot.size()) {
LOG(INFO) << "Resuming from " << FLAGS_snapshot;
- solver->Solve(FLAGS_snapshot);
+ solver->Restore(FLAGS_snapshot.c_str());
} else if (FLAGS_weights.size()) {
- CopyLayers(&*solver, FLAGS_weights);
- solver->Solve();
+ CopyLayers(solver.get(), FLAGS_weights);
+ }
+
+ if (gpus.size() > 1) {
+ caffe::P2PSync<float> sync(solver, NULL, solver->param());
+ sync.run(gpus);
} else {
+ LOG(INFO) << "Starting Optimization";
solver->Solve();
}
LOG(INFO) << "Optimization Done.";
CHECK_GT(FLAGS_weights.size(), 0) << "Need model weights to score.";
// Set device id and mode
- if (FLAGS_gpu >= 0) {
- LOG(INFO) << "Use GPU with device ID " << FLAGS_gpu;
- Caffe::SetDevice(FLAGS_gpu);
+ vector<int> gpus;
+ get_gpus(&gpus);
+ if (gpus.size() != 0) {
+ LOG(INFO) << "Use GPU with device ID " << gpus[0];
+ Caffe::SetDevice(gpus[0]);
Caffe::set_mode(Caffe::GPU);
} else {
LOG(INFO) << "Use CPU.";
for (int i = 0; i < test_score.size(); ++i) {
const std::string& output_name = caffe_net.blob_names()[
caffe_net.output_blob_indices()[test_score_output_id[i]]];
- const float loss_weight =
- caffe_net.blob_loss_weights()[caffe_net.output_blob_indices()[i]];
+ const float loss_weight = caffe_net.blob_loss_weights()[
+ caffe_net.output_blob_indices()[test_score_output_id[i]]];
std::ostringstream loss_msg_stream;
const float mean_score = test_score[i] / FLAGS_iterations;
if (loss_weight) {
CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to time.";
// Set device id and mode
- if (FLAGS_gpu >= 0) {
- LOG(INFO) << "Use GPU with device ID " << FLAGS_gpu;
- Caffe::SetDevice(FLAGS_gpu);
+ vector<int> gpus;
+ get_gpus(&gpus);
+ if (gpus.size() != 0) {
+ LOG(INFO) << "Use GPU with device ID " << gpus[0];
+ Caffe::SetDevice(gpus[0]);
Caffe::set_mode(Caffe::GPU);
} else {
LOG(INFO) << "Use CPU.";
forward_timer.Start();
for (int i = 0; i < layers.size(); ++i) {
timer.Start();
- // Although Reshape should be essentially free, we include it here
- // so that we will notice Reshape performance bugs.
- layers[i]->Reshape(bottom_vecs[i], top_vecs[i]);
layers[i]->Forward(bottom_vecs[i], top_vecs[i]);
forward_time_per_layer[i] += timer.MicroSeconds();
}
int main(int argc, char** argv) {
// Print output to stderr (while still logging).
FLAGS_alsologtostderr = 1;
+ // Set version
+ gflags::SetVersionString(AS_STRING(CAFFE_VERSION));
// Usage message.
gflags::SetUsageMessage("command line brew\n"
"usage: caffe <command> <args>\n\n"
// Run tool or show usage.
caffe::GlobalInit(&argc, &argv);
if (argc == 2) {
- return GetBrewFunction(caffe::string(argv[1]))();
+#ifdef WITH_PYTHON_LAYER
+ try {
+#endif
+ return GetBrewFunction(caffe::string(argv[1]))();
+#ifdef WITH_PYTHON_LAYER
+ } catch (bp::error_already_set) {
+ PyErr_Print();
+ return 1;
+ }
+#endif
} else {
gflags::ShowUsageWithFlagsRestrict(argv[0], "tools/caffe");
}
int main(int argc, char** argv) {
::google::InitGoogleLogging(argv[0]);
+#ifdef USE_OPENCV
#ifndef GFLAGS_GFLAGS_H_
namespace gflags = google;
#endif
}
LOG(INFO) << "mean_value channel [" << c << "]:" << mean_values[c] / dim;
}
+#else
+ LOG(FATAL) << "This tool requires OpenCV; compile with USE_OPENCV.";
+#endif // USE_OPENCV
return 0;
}
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/db.hpp"
+#include "caffe/util/format.hpp"
#include "caffe/util/io.hpp"
#include "caffe/util/rng.hpp"
"Optional: What type should we encode the image as ('png','jpg',...).");
int main(int argc, char** argv) {
+#ifdef USE_OPENCV
::google::InitGoogleLogging(argv[0]);
+ // Print output to stderr (while still logging)
+ FLAGS_alsologtostderr = 1;
#ifndef GFLAGS_GFLAGS_H_
namespace gflags = google;
std::string root_folder(argv[1]);
Datum datum;
int count = 0;
- const int kMaxKeyLength = 256;
- char key_cstr[kMaxKeyLength];
int data_size = 0;
bool data_size_initialized = false;
}
}
// sequential
- int length = snprintf(key_cstr, kMaxKeyLength, "%08d_%s", line_id,
- lines[line_id].first.c_str());
+ string key_str = caffe::format_int(line_id, 8) + "_" + lines[line_id].first;
// Put in db
string out;
CHECK(datum.SerializeToString(&out));
- txn->Put(string(key_cstr, length), out);
+ txn->Put(key_str, out);
if (++count % 1000 == 0) {
// Commit db
txn->Commit();
txn.reset(db->NewTransaction());
- LOG(ERROR) << "Processed " << count << " files.";
+ LOG(INFO) << "Processed " << count << " files.";
}
}
// write the last batch
if (count % 1000 != 0) {
txn->Commit();
- LOG(ERROR) << "Processed " << count << " files.";
+ LOG(INFO) << "Processed " << count << " files.";
}
+#else
+ LOG(FATAL) << "This tool requires OpenCV; compile with USE_OPENCV.";
+#endif // USE_OPENCV
return 0;
}
"""
Parse training log
-Competitor to parse_log.sh
+Evolved from parse_log.sh
"""
import os
import extract_seconds
import argparse
import csv
-
-
-def get_line_type(line):
- """Return either 'test' or 'train' depending on line type
- """
-
- line_type = None
- if line.find('Train') != -1:
- line_type = 'train'
- elif line.find('Test') != -1:
- line_type = 'test'
- return line_type
+from collections import OrderedDict
def parse_log(path_to_log):
for the two dict_lists
"""
- re_iteration = re.compile('Iteration (\d+)')
- re_accuracy = re.compile('output #\d+: accuracy = ([\.\d]+)')
- re_train_loss = re.compile('Iteration \d+, loss = ([\.\d]+)')
- re_output_loss = re.compile('output #\d+: loss = ([\.\d]+)')
- re_lr = re.compile('lr = ([\.\d]+)')
+ regex_iteration = re.compile('Iteration (\d+)')
+ regex_train_output = re.compile('Train net output #(\d+): (\S+) = ([\.\deE+-]+)')
+ regex_test_output = re.compile('Test net output #(\d+): (\S+) = ([\.\deE+-]+)')
+ regex_learning_rate = re.compile('lr = ([-+]?[0-9]*\.?[0-9]+([eE]?[-+]?[0-9]+)?)')
# Pick out lines of interest
iteration = -1
- test_accuracy = -1
learning_rate = float('NaN')
train_dict_list = []
test_dict_list = []
- train_dict_names = ('NumIters', 'Seconds', 'TrainingLoss', 'LearningRate')
- test_dict_names = ('NumIters', 'Seconds', 'TestAccuracy', 'TestLoss')
+ train_row = None
+ test_row = None
logfile_year = extract_seconds.get_log_created_year(path_to_log)
with open(path_to_log) as f:
start_time = extract_seconds.get_start_time(f, logfile_year)
for line in f:
- iteration_match = re_iteration.search(line)
+ iteration_match = regex_iteration.search(line)
if iteration_match:
iteration = float(iteration_match.group(1))
if iteration == -1:
- # Only look for other stuff if we've found the first iteration
+ # Only start parsing for other stuff if we've found the first
+ # iteration
continue
time = extract_seconds.extract_datetime_from_line(line,
logfile_year)
seconds = (time - start_time).total_seconds()
- lr_match = re_lr.search(line)
- if lr_match:
- learning_rate = float(lr_match.group(1))
-
- accuracy_match = re_accuracy.search(line)
- if accuracy_match and get_line_type(line) == 'test':
- test_accuracy = float(accuracy_match.group(1))
-
- train_loss_match = re_train_loss.search(line)
- if train_loss_match:
- train_loss = float(train_loss_match.group(1))
- train_dict_list.append({'NumIters': iteration,
- 'Seconds': seconds,
- 'TrainingLoss': train_loss,
- 'LearningRate': learning_rate})
-
- output_loss_match = re_output_loss.search(line)
- if output_loss_match and get_line_type(line) == 'test':
- test_loss = float(output_loss_match.group(1))
- # NOTE: we assume that (1) accuracy always comes right before
- # loss for test data so the test_accuracy variable is already
- # correctly populated and (2) there's one and only one output
- # named "accuracy" for the test net
- test_dict_list.append({'NumIters': iteration,
- 'Seconds': seconds,
- 'TestAccuracy': test_accuracy,
- 'TestLoss': test_loss})
-
- return train_dict_list, train_dict_names, test_dict_list, test_dict_names
-
-
-def save_csv_files(logfile_path, output_dir, train_dict_list, train_dict_names,
- test_dict_list, test_dict_names, verbose=False):
+ learning_rate_match = regex_learning_rate.search(line)
+ if learning_rate_match:
+ learning_rate = float(learning_rate_match.group(1))
+
+ train_dict_list, train_row = parse_line_for_net_output(
+ regex_train_output, train_row, train_dict_list,
+ line, iteration, seconds, learning_rate
+ )
+ test_dict_list, test_row = parse_line_for_net_output(
+ regex_test_output, test_row, test_dict_list,
+ line, iteration, seconds, learning_rate
+ )
+
+ fix_initial_nan_learning_rate(train_dict_list)
+ fix_initial_nan_learning_rate(test_dict_list)
+
+ return train_dict_list, test_dict_list
+
+
+def parse_line_for_net_output(regex_obj, row, row_dict_list,
+ line, iteration, seconds, learning_rate):
+ """Parse a single line for training or test output
+
+ Returns a a tuple with (row_dict_list, row)
+ row: may be either a new row or an augmented version of the current row
+ row_dict_list: may be either the current row_dict_list or an augmented
+ version of the current row_dict_list
+ """
+
+ output_match = regex_obj.search(line)
+ if output_match:
+ if not row or row['NumIters'] != iteration:
+ # Push the last row and start a new one
+ if row:
+ # If we're on a new iteration, push the last row
+ # This will probably only happen for the first row; otherwise
+ # the full row checking logic below will push and clear full
+ # rows
+ row_dict_list.append(row)
+
+ row = OrderedDict([
+ ('NumIters', iteration),
+ ('Seconds', seconds),
+ ('LearningRate', learning_rate)
+ ])
+
+ # output_num is not used; may be used in the future
+ # output_num = output_match.group(1)
+ output_name = output_match.group(2)
+ output_val = output_match.group(3)
+ row[output_name] = float(output_val)
+
+ if row and len(row_dict_list) >= 1 and len(row) == len(row_dict_list[0]):
+ # The row is full, based on the fact that it has the same number of
+ # columns as the first row; append it to the list
+ row_dict_list.append(row)
+ row = None
+
+ return row_dict_list, row
+
+
+def fix_initial_nan_learning_rate(dict_list):
+ """Correct initial value of learning rate
+
+ Learning rate is normally not printed until after the initial test and
+ training step, which means the initial testing and training rows have
+ LearningRate = NaN. Fix this by copying over the LearningRate from the
+ second row, if it exists.
+ """
+
+ if len(dict_list) > 1:
+ dict_list[0]['LearningRate'] = dict_list[1]['LearningRate']
+
+
+def save_csv_files(logfile_path, output_dir, train_dict_list, test_dict_list,
+ delimiter=',', verbose=False):
"""Save CSV files to output_dir
If the input log file is, e.g., caffe.INFO, the names will be
log_basename = os.path.basename(logfile_path)
train_filename = os.path.join(output_dir, log_basename + '.train')
- write_csv(train_filename, train_dict_list, train_dict_names, verbose)
+ write_csv(train_filename, train_dict_list, delimiter, verbose)
test_filename = os.path.join(output_dir, log_basename + '.test')
- write_csv(test_filename, test_dict_list, test_dict_names, verbose)
+ write_csv(test_filename, test_dict_list, delimiter, verbose)
-def write_csv(output_filename, dict_list, header_names, verbose=False):
+def write_csv(output_filename, dict_list, delimiter, verbose=False):
"""Write a CSV file
"""
+ dialect = csv.excel
+ dialect.delimiter = delimiter
+
with open(output_filename, 'w') as f:
- dict_writer = csv.DictWriter(f, header_names)
+ dict_writer = csv.DictWriter(f, fieldnames=dict_list[0].keys(),
+ dialect=dialect)
dict_writer.writeheader()
dict_writer.writerows(dict_list)
if verbose:
action='store_true',
help='Print some extra info (e.g., output filenames)')
+ parser.add_argument('--delimiter',
+ default=',',
+ help=('Column delimiter in output files '
+ '(default: \'%(default)s\')'))
+
args = parser.parse_args()
return args
def main():
args = parse_args()
- train_dict_list, train_dict_names, test_dict_list, test_dict_names = \
- parse_log(args.logfile_path)
+ train_dict_list, test_dict_list = parse_log(args.logfile_path)
save_csv_files(args.logfile_path, args.output_dir, train_dict_list,
- train_dict_names, test_dict_list, test_dict_names)
+ test_dict_list, delimiter=args.delimiter)
if __name__ == '__main__':
exit
fi
LOG=`basename $1`
-grep -B 1 'Test ' $1 > aux.txt
+sed -n '/Iteration .* Testing net/,/Iteration *. loss/p' $1 > aux.txt
+sed -i '/Waiting for data/d' aux.txt
+sed -i '/prefetch queue empty/d' aux.txt
+sed -i '/Iteration .* loss/d' aux.txt
+sed -i '/Iteration .* lr/d' aux.txt
+sed -i '/Train net/d' aux.txt
grep 'Iteration ' aux.txt | sed 's/.*Iteration \([[:digit:]]*\).*/\1/g' > aux0.txt
grep 'Test net output #0' aux.txt | awk '{print $11}' > aux1.txt
grep 'Test net output #1' aux.txt | awk '{print $11}' > aux2.txt
# Training loss vs. training iterations
set title "Training loss vs. training iterations"
-set xlabel "Training loss"
-set ylabel "Training iterations"
+set xlabel "Training iterations"
+set ylabel "Training loss"
plot "mnist.log.train" using 1:3 title "mnist"
# Training loss vs. training time
You had better check the data files and change the mapping from field name to
field index in create_field_index before designing your own plots.
Usage:
- ./plot_log.sh chart_type[0-%s] /where/to/save.png /path/to/first.log ...
+ ./plot_training_log.py chart_type[0-%s] /where/to/save.png /path/to/first.log ...
Notes:
1. Supporting multiple logs.
2. Log file name must end with the lower-cased "%s".
--- /dev/null
+#!/usr/bin/env python
+
+"""Net summarization tool.
+
+This tool summarizes the structure of a net in a concise but comprehensive
+tabular listing, taking a prototxt file as input.
+
+Use this tool to check at a glance that the computation you've specified is the
+computation you expect.
+"""
+
+from caffe.proto import caffe_pb2
+from google import protobuf
+import re
+import argparse
+
+# ANSI codes for coloring blobs (used cyclically)
+COLORS = ['92', '93', '94', '95', '97', '96', '42', '43;30', '100',
+ '444', '103;30', '107;30']
+DISCONNECTED_COLOR = '41'
+
+def read_net(filename):
+ net = caffe_pb2.NetParameter()
+ with open(filename) as f:
+ protobuf.text_format.Parse(f.read(), net)
+ return net
+
+def format_param(param):
+ out = []
+ if len(param.name) > 0:
+ out.append(param.name)
+ if param.lr_mult != 1:
+ out.append('x{}'.format(param.lr_mult))
+ if param.decay_mult != 1:
+ out.append('Dx{}'.format(param.decay_mult))
+ return ' '.join(out)
+
+def printed_len(s):
+ return len(re.sub(r'\033\[[\d;]+m', '', s))
+
+def print_table(table, max_width):
+ """Print a simple nicely-aligned table.
+
+ table must be a list of (equal-length) lists. Columns are space-separated,
+ and as narrow as possible, but no wider than max_width. Text may overflow
+ columns; note that unlike string.format, this will not affect subsequent
+ columns, if possible."""
+
+ max_widths = [max_width] * len(table[0])
+ column_widths = [max(printed_len(row[j]) + 1 for row in table)
+ for j in range(len(table[0]))]
+ column_widths = [min(w, max_w) for w, max_w in zip(column_widths, max_widths)]
+
+ for row in table:
+ row_str = ''
+ right_col = 0
+ for cell, width in zip(row, column_widths):
+ right_col += width
+ row_str += cell + ' '
+ row_str += ' ' * max(right_col - printed_len(row_str), 0)
+ print row_str
+
+def summarize_net(net):
+ disconnected_tops = set()
+ for lr in net.layer:
+ disconnected_tops |= set(lr.top)
+ disconnected_tops -= set(lr.bottom)
+
+ table = []
+ colors = {}
+ for lr in net.layer:
+ tops = []
+ for ind, top in enumerate(lr.top):
+ color = colors.setdefault(top, COLORS[len(colors) % len(COLORS)])
+ if top in disconnected_tops:
+ top = '\033[1;4m' + top
+ if len(lr.loss_weight) > 0:
+ top = '{} * {}'.format(lr.loss_weight[ind], top)
+ tops.append('\033[{}m{}\033[0m'.format(color, top))
+ top_str = ', '.join(tops)
+
+ bottoms = []
+ for bottom in lr.bottom:
+ color = colors.get(bottom, DISCONNECTED_COLOR)
+ bottoms.append('\033[{}m{}\033[0m'.format(color, bottom))
+ bottom_str = ', '.join(bottoms)
+
+ if lr.type == 'Python':
+ type_str = lr.python_param.module + '.' + lr.python_param.layer
+ else:
+ type_str = lr.type
+
+ # Summarize conv/pool parameters.
+ # TODO support rectangular/ND parameters
+ conv_param = lr.convolution_param
+ if (lr.type in ['Convolution', 'Deconvolution']
+ and len(conv_param.kernel_size) == 1):
+ arg_str = str(conv_param.kernel_size[0])
+ if len(conv_param.stride) > 0 and conv_param.stride[0] != 1:
+ arg_str += '/' + str(conv_param.stride[0])
+ if len(conv_param.pad) > 0 and conv_param.pad[0] != 0:
+ arg_str += '+' + str(conv_param.pad[0])
+ arg_str += ' ' + str(conv_param.num_output)
+ if conv_param.group != 1:
+ arg_str += '/' + str(conv_param.group)
+ elif lr.type == 'Pooling':
+ arg_str = str(lr.pooling_param.kernel_size)
+ if lr.pooling_param.stride != 1:
+ arg_str += '/' + str(lr.pooling_param.stride)
+ if lr.pooling_param.pad != 0:
+ arg_str += '+' + str(lr.pooling_param.pad)
+ else:
+ arg_str = ''
+
+ if len(lr.param) > 0:
+ param_strs = map(format_param, lr.param)
+ if max(map(len, param_strs)) > 0:
+ param_str = '({})'.format(', '.join(param_strs))
+ else:
+ param_str = ''
+ else:
+ param_str = ''
+
+ table.append([lr.name, type_str, param_str, bottom_str, '->', top_str,
+ arg_str])
+ return table
+
+def main():
+ parser = argparse.ArgumentParser(description="Print a concise summary of net computation.")
+ parser.add_argument('filename', help='net prototxt file to summarize')
+ parser.add_argument('-w', '--max-width', help='maximum field width',
+ type=int, default=30)
+ args = parser.parse_args()
+
+ net = read_net(args.filename)
+ table = summarize_net(net)
+ print_table(table, max_width=args.max_width)
+
+if __name__ == '__main__':
+ main()
-#include <stdio.h> // for snprintf
#include <string>
#include <vector>
#include "caffe/net.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/db.hpp"
+#include "caffe/util/format.hpp"
#include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
using caffe::Blob;
using caffe::Caffe;
using caffe::Datum;
using caffe::Net;
-using boost::shared_ptr;
using std::string;
namespace db = caffe::db;
" save_feature_dataset_name1[,name2,...] num_mini_batches db_type"
" [CPU/GPU] [DEVICE_ID=0]\n"
"Note: you can extract multiple features in one pass by specifying"
- " multiple feature blob names and dataset names seperated by ','."
+ " multiple feature blob names and dataset names separated by ','."
" The names cannot contain white space characters and the number of blobs"
" and datasets must be equal.";
return 1;
arg_pos = num_required_args;
if (argc > arg_pos && strcmp(argv[arg_pos], "GPU") == 0) {
LOG(ERROR)<< "Using GPU";
- uint device_id = 0;
+ int device_id = 0;
if (argc > arg_pos + 1) {
device_id = atoi(argv[arg_pos + 1]);
CHECK_GE(device_id, 0);
}
*/
std::string feature_extraction_proto(argv[++arg_pos]);
- shared_ptr<Net<Dtype> > feature_extraction_net(
+ boost::shared_ptr<Net<Dtype> > feature_extraction_net(
new Net<Dtype>(feature_extraction_proto, caffe::TEST));
feature_extraction_net->CopyTrainedLayersFrom(pretrained_binary_proto);
int num_mini_batches = atoi(argv[++arg_pos]);
- std::vector<shared_ptr<db::DB> > feature_dbs;
- std::vector<shared_ptr<db::Transaction> > txns;
+ std::vector<boost::shared_ptr<db::DB> > feature_dbs;
+ std::vector<boost::shared_ptr<db::Transaction> > txns;
+ const char* db_type = argv[++arg_pos];
for (size_t i = 0; i < num_features; ++i) {
LOG(INFO)<< "Opening dataset " << dataset_names[i];
- shared_ptr<db::DB> db(db::GetDB(argv[++arg_pos]));
+ boost::shared_ptr<db::DB> db(db::GetDB(db_type));
db->Open(dataset_names.at(i), db::NEW);
feature_dbs.push_back(db);
- shared_ptr<db::Transaction> txn(db->NewTransaction());
+ boost::shared_ptr<db::Transaction> txn(db->NewTransaction());
txns.push_back(txn);
}
LOG(ERROR)<< "Extacting Features";
Datum datum;
- const int kMaxKeyStrLength = 100;
- char key_str[kMaxKeyStrLength];
std::vector<Blob<float>*> input_vec;
std::vector<int> image_indices(num_features, 0);
for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) {
feature_extraction_net->Forward(input_vec);
for (int i = 0; i < num_features; ++i) {
- const shared_ptr<Blob<Dtype> > feature_blob = feature_extraction_net
- ->blob_by_name(blob_names[i]);
+ const boost::shared_ptr<Blob<Dtype> > feature_blob =
+ feature_extraction_net->blob_by_name(blob_names[i]);
int batch_size = feature_blob->num();
int dim_features = feature_blob->count() / batch_size;
const Dtype* feature_blob_data;
for (int d = 0; d < dim_features; ++d) {
datum.add_float_data(feature_blob_data[d]);
}
- int length = snprintf(key_str, kMaxKeyStrLength, "%d",
- image_indices[i]);
+ string key_str = caffe::format_int(image_indices[i], 10);
+
string out;
CHECK(datum.SerializeToString(&out));
- txns.at(i)->Put(std::string(key_str, length), out);
+ txns.at(i)->Put(key_str, out);
++image_indices[i];
if (image_indices[i] % 1000 == 0) {
txns.at(i)->Commit();
LOG(ERROR)<< "Successfully extracted the features!";
return 0;
}
-
--- /dev/null
+// This is a script to upgrade old solver prototxts to the new format.
+// Usage:
+// upgrade_solver_proto_text old_solver_proto_file_in solver_proto_file_out
+
+#include <cstring>
+#include <fstream> // NOLINT(readability/streams)
+#include <iostream> // NOLINT(readability/streams)
+#include <string>
+
+#include "caffe/caffe.hpp"
+#include "caffe/util/io.hpp"
+#include "caffe/util/upgrade_proto.hpp"
+
+using std::ofstream;
+
+using namespace caffe; // NOLINT(build/namespaces)
+
+int main(int argc, char** argv) {
+ ::google::InitGoogleLogging(argv[0]);
+ if (argc != 3) {
+ LOG(ERROR) << "Usage: upgrade_solver_proto_text "
+ << "old_solver_proto_file_in solver_proto_file_out";
+ return 1;
+ }
+
+ SolverParameter solver_param;
+ string input_filename(argv[1]);
+ if (!ReadProtoFromTextFile(input_filename, &solver_param)) {
+ LOG(ERROR) << "Failed to parse input text file as SolverParameter: "
+ << input_filename;
+ return 2;
+ }
+ bool need_upgrade = SolverNeedsTypeUpgrade(solver_param);
+ bool success = true;
+ if (need_upgrade) {
+ success = UpgradeSolverAsNeeded(input_filename, &solver_param);
+ if (!success) {
+ LOG(ERROR) << "Encountered error(s) while upgrading prototxt; "
+ << "see details above.";
+ }
+ } else {
+ LOG(ERROR) << "File already in latest proto format: " << input_filename;
+ }
+
+ // Save new format prototxt.
+ WriteProtoToTextFile(solver_param, argv[2]);
+
+ LOG(ERROR) << "Wrote upgraded SolverParameter text proto to " << argv[2];
+ return !success;
+}