INCLUDE_DIRS := . /usr/local/include $(CUDA_INCLUDE_DIR) $(MKL_INCLUDE_DIR)
LIBRARY_DIRS := . /usr/lib /usr/local/lib $(CUDA_LIB_DIR) $(MKL_LIB_DIR)
LIBRARIES := cuda cudart cublas protobuf glog mkl_rt mkl_intel_thread curand \
- leveldb snappy opencv_core opencv_highgui pthread tcmalloc
+ leveldb snappy pthread tcmalloc
WARNINGS := -Wall
COMMON_FLAGS := $(foreach includedir,$(INCLUDE_DIRS),-I$(includedir))
$(PROGRAM_BINS): %.bin : %.o $(STATIC_NAME)
$(CXX) $< $(STATIC_NAME) -o $@ $(LDFLAGS) $(WARNINGS)
+$(OBJS): $(PROTO_GEN_CC)
+
+$(PROGRAM_OBJS): $(PROTO_GEN_CC)
+
$(CU_OBJS): %.cuo: %.cu
$(NVCC) -c $< -o $@
namespace caffe {
-// Two classes whose purpose are solely for instantiating blob template
-// functions.
-class GPUBrewer {};
-class CPUBrewer {};
// We will use the boost shared_ptr instead of the new C++11 one mainly
// because cuda does not work (at least now) well with C++11 features.
for (int c = 0; c < channels; ++c) {
for (int h = 0; h < cropsize; ++h) {
for (int w = 0; w < cropsize; ++w) {
- top_data[((itemid * channels + c) * cropsize + h) * cropsize + cropsize - 1 - w] =
- static_cast<Dtype>((uint8_t)data[
- (c * height + h + h_offset) * width + w + w_offset]
- ) * scale - subtraction;
+ top_data[((itemid * channels + c) * cropsize + h) * cropsize
+ + cropsize - 1 - w] =
+ static_cast<Dtype>(
+ (uint8_t)data[(c * height + h + h_offset) * width
+ + w + w_offset])
+ * scale - subtraction;
}
}
}
for (int c = 0; c < channels; ++c) {
for (int h = 0; h < cropsize; ++h) {
for (int w = 0; w < cropsize; ++w) {
- top_data[((itemid * channels + c) * cropsize + h) * cropsize + w] =
- static_cast<Dtype>((uint8_t)data[
- (c * height + h + h_offset) * width + w + w_offset]
- ) * scale - subtraction;
+ top_data[((itemid * channels + c) * cropsize + h) * cropsize + w]
+ = static_cast<Dtype>(
+ (uint8_t)data[(c * height + h + h_offset) * width
+ + w + w_offset])
+ * scale - subtraction;
}
}
}
CHECK_GT(datum_height_, cropsize);
CHECK_GT(datum_width_, cropsize);
// Now, start the prefetch thread.
- //LOG(INFO) << "Initializing prefetch";
- CHECK(!pthread_create(&thread_, NULL, DataLayerPrefetch<Dtype>, (void*)this))
- << "Pthread execution failed.";
- //LOG(INFO) << "Prefetch initialized.";
+ // LOG(INFO) << "Initializing prefetch";
+ CHECK(!pthread_create(&thread_, NULL, DataLayerPrefetch<Dtype>,
+ reinterpret_cast<void*>(this))) << "Pthread execution failed.";
+ // LOG(INFO) << "Prefetch initialized.";
}
template <typename Dtype>
memcpy((*top)[1]->mutable_cpu_data(), prefetch_label_->cpu_data(),
sizeof(Dtype) * prefetch_label_->count());
// Start a new prefetch thread
- CHECK(!pthread_create(&thread_, NULL, DataLayerPrefetch<Dtype>, (void*)this))
- << "Pthread execution failed.";
+ CHECK(!pthread_create(&thread_, NULL, DataLayerPrefetch<Dtype>,
+ reinterpret_cast<void*>(this))) << "Pthread execution failed.";
}
template <typename Dtype>
// First, join the thread
CHECK(!pthread_join(thread_, NULL)) << "Pthread joining failed.";
// Copy the data
- CUDA_CHECK(cudaMemcpy((*top)[0]->mutable_gpu_data(), prefetch_data_->cpu_data(),
- sizeof(Dtype) * prefetch_data_->count(), cudaMemcpyHostToDevice));
- CUDA_CHECK(cudaMemcpy((*top)[1]->mutable_gpu_data(), prefetch_label_->cpu_data(),
- sizeof(Dtype) * prefetch_label_->count(), cudaMemcpyHostToDevice));
+ CUDA_CHECK(cudaMemcpy((*top)[0]->mutable_gpu_data(),
+ prefetch_data_->cpu_data(), sizeof(Dtype) * prefetch_data_->count(),
+ cudaMemcpyHostToDevice));
+ CUDA_CHECK(cudaMemcpy((*top)[1]->mutable_gpu_data(),
+ prefetch_label_->cpu_data(), sizeof(Dtype) * prefetch_label_->count(),
+ cudaMemcpyHostToDevice));
// Start a new prefetch thread
- CHECK(!pthread_create(&thread_, NULL, DataLayerPrefetch<Dtype>, (void*)this))
- << "Pthread execution failed.";
+ CHECK(!pthread_create(&thread_, NULL, DataLayerPrefetch<Dtype>,
+ reinterpret_cast<void*>(this))) << "Pthread execution failed.";
}
// The backward operations are dummy - they do not carry any computation.
GetFiller<Dtype>(this->layer_param_.bias_filler()));
bias_filler->Fill(this->blobs_[1].get());
}
- } // parameter initialization
+ } // parameter initialization
// Setting up the bias multiplier
if (biasterm_) {
bias_multiplier_.reset(new SyncedMemory(M_ * sizeof(Dtype)));
+++ /dev/null
-// Copyright 2013 Yangqing Jia
-
-#include <gtest/gtest.h>
-
-#include <cstring>
-
-#include "caffe/common.hpp"
-#include "caffe/blob.hpp"
-#include "caffe/net.hpp"
-#include "caffe/proto/caffe.pb.h"
-#include "caffe/util/io.hpp"
-
-#include "caffe/test/test_caffe_main.hpp"
-
-namespace caffe {
-
-template <typename Dtype>
-class NetProtoTest : public ::testing::Test {};
-
-typedef ::testing::Types<float> Dtypes;
-TYPED_TEST_CASE(NetProtoTest, Dtypes);
-
-TYPED_TEST(NetProtoTest, TestLoadFromText) {
- NetParameter net_param;
- ReadProtoFromTextFile("data/simple_conv.prototxt", &net_param);
- Blob<TypeParam> lena_image;
- ReadImageToBlob<TypeParam>(string("data/lena_256.jpg"), &lena_image);
- vector<Blob<TypeParam>*> bottom_vec;
- bottom_vec.push_back(&lena_image);
-
- for (int i = 0; i < lena_image.count(); ++i) {
- EXPECT_GE(lena_image.cpu_data()[i], 0);
- EXPECT_LE(lena_image.cpu_data()[i], 1);
- }
-
- Caffe::set_mode(Caffe::CPU);
- // Initialize the network, and then does smoothing
- Net<TypeParam> caffe_net(net_param, bottom_vec);
- LOG(ERROR) << "Start Forward.";
- const vector<Blob<TypeParam>*>& output = caffe_net.Forward(bottom_vec);
- LOG(ERROR) << "Forward Done.";
- EXPECT_EQ(output[0]->num(), 1);
- EXPECT_EQ(output[0]->channels(), 1);
- EXPECT_EQ(output[0]->height(), 252);
- EXPECT_EQ(output[0]->width(), 252);
- for (int i = 0; i < output[0]->count(); ++i) {
- EXPECT_GE(output[0]->cpu_data()[i], 0);
- EXPECT_LE(output[0]->cpu_data()[i], 1);
- }
- WriteBlobToImage<TypeParam>(string("lena_smoothed.png"), *output[0]);
-}
-
-
-} // namespace caffe
+++ /dev/null
-// Copyright Yangqing Jia 2013
-//
-// This is a working version of the math functions that would hopefully replace
-// the cpu and gpu separate version, that would eventually replace the old
-// math_functions wrapper.
-
-#include "caffe/common.hpp"
-#include "caffe/syncedmem.hpp"
-
-namespace caffe {
-
-namespace blobmath {
-
-
-// Decaf gemm provides a simpler interface to the gemm functions, with the
-// limitation that the data has to be contiguous in memory.
-template <class Brewer, typename Dtype>
-void gemm(const CBLAS_TRANSPOSE TransA,
- const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
- const Dtype alpha, const Dtype* A, const Dtype* B, const Dtype beta,
- Dtype* C);
-
-
-
-
-template <typename Dtype>
-void caffe_cpu_gemv(const CBLAS_TRANSPOSE TransA, const int M, const int N,
- const Dtype alpha, const Dtype* A, const Dtype* x, const Dtype beta,
- Dtype* y);
-
-template <typename Dtype>
-void caffe_gpu_gemv(const CBLAS_TRANSPOSE TransA, const int M, const int N,
- const Dtype alpha, const Dtype* A, const Dtype* x, const Dtype beta,
- Dtype* y);
-
-template <typename Dtype>
-void caffe_axpy(const int N, const Dtype alpha, const Dtype* X,
- Dtype* Y);
-
-template <typename Dtype>
-void caffe_gpu_axpy(const int N, const Dtype alpha, const Dtype* X,
- Dtype* Y);
-
-template <typename Dtype>
-void caffe_axpby(const int N, const Dtype alpha, const Dtype* X,
- const Dtype beta, Dtype* Y);
-
-template <typename Dtype>
-void caffe_gpu_axpby(const int N, const Dtype alpha, const Dtype* X,
- const Dtype beta, Dtype* Y);
-
-template <typename Dtype>
-void caffe_copy(const int N, const Dtype *X, Dtype *Y);
-
-template <typename Dtype>
-void caffe_gpu_copy(const int N, const Dtype *X, Dtype *Y);
-
-template <typename Dtype>
-void caffe_scal(const int N, const Dtype alpha, Dtype *X);
-
-template <typename Dtype>
-void caffe_gpu_scal(const int N, const Dtype alpha, Dtype *X);
-
-template <typename Dtype>
-void caffe_sqr(const int N, const Dtype* a, Dtype* y);
-
-template <typename Dtype>
-void caffe_add(const int N, const Dtype* a, const Dtype* b, Dtype* y);
-
-template <typename Dtype>
-void caffe_sub(const int N, const Dtype* a, const Dtype* b, Dtype* y);
-
-template <typename Dtype>
-void caffe_mul(const int N, const Dtype* a, const Dtype* b, Dtype* y);
-
-template <typename Dtype>
-void caffe_gpu_mul(const int N, const Dtype* a, const Dtype* b, Dtype* y);
-
-template <typename Dtype>
-void caffe_div(const int N, const Dtype* a, const Dtype* b, Dtype* y);
-
-template <typename Dtype>
-void caffe_powx(const int n, const Dtype* a, const Dtype b, Dtype* y);
-
-template <typename Dtype>
-void caffe_vRngUniform(const int n, Dtype* r, const Dtype a, const Dtype b);
-
-template <typename Dtype>
-void caffe_vRngGaussian(const int n, Dtype* r, const Dtype a,
- const Dtype sigma);
-
-template <typename Dtype>
-void caffe_exp(const int n, const Dtype* a, Dtype* y);
-
-template <typename Dtype>
-Dtype caffe_cpu_dot(const int n, const Dtype* x, const Dtype* y);
-
-template <typename Dtype>
-void caffe_gpu_dot(const int n, const Dtype* x, const Dtype* y, Dtype* out);
-
-
-} // namespace blobmath
-
-} // namespace caffe
\ No newline at end of file
#include <google/protobuf/text_format.h>
#include <google/protobuf/io/zero_copy_stream_impl.h>
#include <google/protobuf/io/coded_stream.h>
-#include <opencv2/core/core.hpp>
-#include <opencv2/highgui/highgui.hpp>
#include <algorithm>
#include <string>
#include "caffe/util/io.hpp"
#include "caffe/proto/caffe.pb.h"
-using cv::Mat;
-using cv::Vec3b;
using std::fstream;
using std::ios;
using std::max;
namespace caffe {
-void ReadImageToProto(const string& filename, BlobProto* proto) {
- Mat cv_img;
- cv_img = cv::imread(filename, CV_LOAD_IMAGE_COLOR);
- CHECK(cv_img.data) << "Could not open or find the image.";
- DCHECK_EQ(cv_img.channels(), 3);
- proto->set_num(1);
- proto->set_channels(3);
- proto->set_height(cv_img.rows);
- proto->set_width(cv_img.cols);
- proto->clear_data();
- proto->clear_diff();
- for (int c = 0; c < 3; ++c) {
- for (int h = 0; h < cv_img.rows; ++h) {
- for (int w = 0; w < cv_img.cols; ++w) {
- proto->add_data(static_cast<float>(cv_img.at<Vec3b>(h, w)[c]) / 255.);
- }
- }
- }
-}
-
-void ReadImageToDatum(const string& filename, const int label, Datum* datum) {
- Mat cv_img;
- cv_img = cv::imread(filename, CV_LOAD_IMAGE_COLOR);
- CHECK(cv_img.data) << "Could not open or find the image.";
- DCHECK_EQ(cv_img.channels(), 3);
- datum->set_channels(3);
- datum->set_height(cv_img.rows);
- datum->set_width(cv_img.cols);
- datum->set_label(label);
- datum->clear_data();
- datum->clear_float_data();
- string* datum_string = datum->mutable_data();
- for (int c = 0; c < 3; ++c) {
- for (int h = 0; h < cv_img.rows; ++h) {
- for (int w = 0; w < cv_img.cols; ++w) {
- datum_string->push_back(static_cast<char>(cv_img.at<Vec3b>(h, w)[c]));
- }
- }
- }
-}
-
-
-void WriteProtoToImage(const string& filename, const BlobProto& proto) {
- CHECK_EQ(proto.num(), 1);
- CHECK(proto.channels() == 3 || proto.channels() == 1);
- CHECK_GT(proto.height(), 0);
- CHECK_GT(proto.width(), 0);
- Mat cv_img(proto.height(), proto.width(), CV_8UC3);
- if (proto.channels() == 1) {
- for (int c = 0; c < 3; ++c) {
- for (int h = 0; h < cv_img.rows; ++h) {
- for (int w = 0; w < cv_img.cols; ++w) {
- cv_img.at<Vec3b>(h, w)[c] =
- uint8_t(proto.data(h * cv_img.cols + w) * 255.);
- }
- }
- }
- } else {
- for (int c = 0; c < 3; ++c) {
- for (int h = 0; h < cv_img.rows; ++h) {
- for (int w = 0; w < cv_img.cols; ++w) {
- cv_img.at<Vec3b>(h, w)[c] =
- uint8_t(proto.data((c * cv_img.rows + h) * cv_img.cols + w)
- * 255.);
- }
- }
- }
- }
- CHECK(cv::imwrite(filename, cv_img));
-}
-
void ReadProtoFromTextFile(const char* filename,
::google::protobuf::Message* proto) {
int fd = open(filename, O_RDONLY);
namespace caffe {
-void ReadImageToProto(const string& filename, BlobProto* proto);
-
-template <typename Dtype>
-inline void ReadImageToBlob(const string& filename, Blob<Dtype>* blob) {
- BlobProto proto;
- ReadImageToProto(filename, &proto);
- blob->FromProto(proto);
-}
-
-void WriteProtoToImage(const string& filename, const BlobProto& proto);
-
-template <typename Dtype>
-inline void WriteBlobToImage(const string& filename, const Blob<Dtype>& blob) {
- BlobProto proto;
- blob.ToProto(&proto);
- WriteProtoToImage(filename, proto);
-}
-
-void ReadImageToDatum(const string& filename, const int label, Datum* datum);
-
void ReadProtoFromTextFile(const char* filename,
Message* proto);
inline void ReadProtoFromTextFile(const string& filename,
+++ /dev/null
-// Copyright 2013 Yangqing Jia
-// This program converts a set of images to a leveldb by storing them as Datum
-// proto buffers.
-// Usage:
-// convert_dataset ROOTFOLDER LISTFILE DB_NAME
-// where ROOTFOLDER is the root folder that holds all the images, and LISTFILE
-// should be a list of files as well as their labels, in the format as
-// subfolder1/file1.JPEG 0
-// ....
-// You are responsible for shuffling the files yourself.
-
-#include <glog/logging.h>
-#include <leveldb/db.h>
-#include <leveldb/write_batch.h>
-
-#include <string>
-#include <iostream>
-#include <fstream>
-
-#include "caffe/proto/caffe.pb.h"
-#include "caffe/util/io.hpp"
-
-using namespace caffe;
-using std::string;
-using std::stringstream;
-
-// A utility function to generate random strings
-void GenerateRandomPrefix(const int n, string* key) {
- const char* kCHARS = "abcdefghijklmnopqrstuvwxyz";
- key->clear();
- for (int i = 0; i < n; ++i) {
- key->push_back(kCHARS[rand() % 26]);
- }
- key->push_back('_');
-}
-
-int main(int argc, char** argv) {
- ::google::InitGoogleLogging(argv[0]);
- std::ifstream infile(argv[2]);
- leveldb::DB* db;
- leveldb::Options options;
- options.error_if_exists = true;
- options.create_if_missing = true;
- options.create_if_missing = true;
- options.write_buffer_size = 268435456;
- LOG(INFO) << "Opening leveldb " << argv[3];
- leveldb::Status status = leveldb::DB::Open(
- options, argv[3], &db);
- CHECK(status.ok()) << "Failed to open leveldb " << argv[3];
-
- string root_folder(argv[1]);
- string filename;
- int label;
- Datum datum;
- int count = 0;
- char key_cstr[100];
- leveldb::WriteBatch* batch = new leveldb::WriteBatch();
- while (infile >> filename >> label) {
- ReadImageToDatum(root_folder + filename, label, &datum);
- // sequential
- sprintf(key_cstr, "%08d_%s", count, filename.c_str());
- string key(key_cstr);
- // random
- // string key;
- // GenerateRandomPrefix(8, &key);
- // key += filename;
- string value;
- // get the value
- datum.SerializeToString(&value);
- batch->Put(key, value);
- if (++count % 1000 == 0) {
- db->Write(leveldb::WriteOptions(), batch);
- LOG(ERROR) << "Processed " << count << " files.";
- delete batch;
- batch = new leveldb::WriteBatch();
- }
- }
-
- delete db;
- return 0;
-}
using namespace caffe;
int main(int argc, char** argv) {
- cudaSetDevice(0);
+ cudaSetDevice(1);
Caffe::set_mode(Caffe::GPU);
Caffe::set_phase(Caffe::TRAIN);
SolverParameter solver_param;
solver_param.set_base_lr(0.01);
- solver_param.set_display(1);
+ solver_param.set_display(100);
solver_param.set_max_iter(6000);
solver_param.set_lr_policy("inv");
solver_param.set_gamma(0.0001);
projects / platform / upstream / caffeonacl.git / commitdiff