An official website of the Halide project: http://halide-lang.org/.
-## Efficiency comparison
-Measured on Intel® Core™ i7-6700K CPU @ 4.00GHz x 8.
-
-Single image forward pass (in milliseconds):
-
-| Architecture | MKL backend | Halide backend | Speed Up ratio |
-|-----------------:|------------:|---------------:|---------------:|
-| AlexNet | 16.55 | 22.38 | x0.73 |
-| ResNet-50 | 63.69 | 73.91 | x0.86 |
-| SqueezeNet v1.1 | 10.11 | 8.21 | x1.23 |
-| Inception-5h | 35.38 | 37.06 | x0.95 |
-| ENet @ 3x512x256 | 82.26 | 41.21 | x1.99 |
-
-Scheduling directives might be found @ [opencv_extra/testdata/dnn](https://github.com/opencv/opencv_extra/tree/master/testdata/dnn).
+An up to date efficiency comparison: https://github.com/opencv/opencv/wiki/DNN-Efficiency
## Requirements
### LLVM compiler
## Build OpenCV with Halide backend
When you build OpenCV add the following configuration flags:
+- `ENABLE_CXX11` - enable C++11 standard
+
- `WITH_HALIDE` - enable Halide linkage
- `HALIDE_ROOT_DIR` - path to Halide build directory
ocv_add_accuracy_tests()
ocv_add_perf_tests()
+ocv_option(${the_module}_PERF_CAFFE "Add performance tests of Caffe framework" OFF)
+ocv_option(${the_module}_PERF_CLCAFFE "Add performance tests of clCaffe framework" OFF)
+if(BUILD_PERF_TESTS)
+ if (${the_module}_PERF_CAFFE)
+ find_package(Caffe QUIET)
+ if (Caffe_FOUND)
+ add_definitions(-DHAVE_CAFFE=1)
+ ocv_target_link_libraries(opencv_perf_dnn caffe)
+ endif()
+ elseif(${the_module}_PERF_CLCAFFE)
+ find_package(Caffe QUIET)
+ if (Caffe_FOUND)
+ add_definitions(-DHAVE_CLCAFFE=1)
+ ocv_target_link_libraries(opencv_perf_dnn caffe)
+ endif()
+ endif()
+endif()
+
# ----------------------------------------------------------------------------
# Torch7 importer of blobs and models, produced by Torch.nn module
# ----------------------------------------------------------------------------
* specific target. For layers that not represented in scheduling file
* or if no manual scheduling used at all, automatic scheduling will be applied.
*/
- void setHalideScheduler(const String& scheduler);
+ CV_WRAP void setHalideScheduler(const String& scheduler);
/**
* @brief Ask network to use specific computation backend where it supported.
* @param[in] backendId backend identifier.
* @see Backend
*/
- void setPreferableBackend(int backendId);
+ CV_WRAP void setPreferableBackend(int backendId);
/**
* @brief Ask network to make computations on specific target device.
* @param[in] targetId target identifier.
* @see Target
*/
- void setPreferableTarget(int targetId);
+ CV_WRAP void setPreferableTarget(int targetId);
/** @brief Sets the new value for the layer output blob
* @param name descriptor of the updating layer output blob.
--- /dev/null
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2017, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+
+// Recommends run this performance test via
+// ./bin/opencv_perf_dnn 2> /dev/null | grep "PERFSTAT" -A 3
+// because whole output includes Caffe's logs.
+//
+// Note: Be sure that interesting version of Caffe was linked.
+// Note: There is an impact on Halide performance. Comment this tests if you
+// want to run the last one.
+//
+// How to build Intel-Caffe with MKLDNN backend
+// ============================================
+// mkdir build && cd build
+// cmake -DCMAKE_BUILD_TYPE=Release \
+// -DUSE_MKLDNN_AS_DEFAULT_ENGINE=ON \
+// -DUSE_MKL2017_AS_DEFAULT_ENGINE=OFF \
+// -DCPU_ONLY=ON \
+// -DCMAKE_INSTALL_PREFIX=/usr/local .. && make -j8
+// sudo make install
+//
+// In case of problems with cublas_v2.h at include/caffe/util/device_alternate.hpp: add line
+// #define CPU_ONLY
+// before the first line
+// #ifdef CPU_ONLY // CPU-only Caffe.
+
+#if defined(HAVE_CAFFE) || defined(HAVE_CLCAFFE)
+
+#include "perf_precomp.hpp"
+#include <iostream>
+#include <caffe/caffe.hpp>
+
+namespace cvtest
+{
+
+static caffe::Net<float>* initNet(std::string proto, std::string weights)
+{
+ proto = findDataFile(proto, false);
+ weights = findDataFile(weights, false);
+
+#ifdef HAVE_CLCAFFE
+ caffe::Caffe::set_mode(caffe::Caffe::GPU);
+ caffe::Caffe::SetDevice(0);
+
+ caffe::Net<float>* net =
+ new caffe::Net<float>(proto, caffe::TEST, caffe::Caffe::GetDefaultDevice());
+#else
+ caffe::Caffe::set_mode(caffe::Caffe::CPU);
+
+ caffe::Net<float>* net = new caffe::Net<float>(proto, caffe::TEST);
+#endif
+
+ net->CopyTrainedLayersFrom(weights);
+
+ caffe::Blob<float>* input = net->input_blobs()[0];
+
+ CV_Assert(input->num() == 1);
+ CV_Assert(input->channels() == 3);
+
+ Mat inputMat(input->height(), input->width(), CV_32FC3, (char*)input->cpu_data());
+ randu(inputMat, 0.0f, 1.0f);
+
+ net->Forward();
+ return net;
+}
+
+PERF_TEST(GoogLeNet_caffe, CaffePerfTest)
+{
+ caffe::Net<float>* net = initNet("dnn/bvlc_googlenet.prototxt",
+ "dnn/bvlc_googlenet.caffemodel");
+ TEST_CYCLE() net->Forward();
+ SANITY_CHECK_NOTHING();
+}
+
+PERF_TEST(AlexNet_caffe, CaffePerfTest)
+{
+ caffe::Net<float>* net = initNet("dnn/bvlc_alexnet.prototxt",
+ "dnn/bvlc_alexnet.caffemodel");
+ TEST_CYCLE() net->Forward();
+ SANITY_CHECK_NOTHING();
+}
+
+PERF_TEST(ResNet50_caffe, CaffePerfTest)
+{
+ caffe::Net<float>* net = initNet("dnn/ResNet-50-deploy.prototxt",
+ "dnn/ResNet-50-model.caffemodel");
+ TEST_CYCLE() net->Forward();
+ SANITY_CHECK_NOTHING();
+}
+
+PERF_TEST(SqueezeNet_v1_1_caffe, CaffePerfTest)
+{
+ caffe::Net<float>* net = initNet("dnn/squeezenet_v1.1.prototxt",
+ "dnn/squeezenet_v1.1.caffemodel");
+ TEST_CYCLE() net->Forward();
+ SANITY_CHECK_NOTHING();
+}
+
+} // namespace cvtest
+
+#endif // HAVE_CAFFE
{
Net net;
loadNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
- "", 227, 227, "prob", "caffe", DNN_TARGET_CPU, &net);
+ "", 224, 224, "prob", "caffe", DNN_TARGET_CPU, &net);
TEST_CYCLE() net.forward();
SANITY_CHECK_NOTHING();
}
{
test(findDataFile("dnn/bvlc_googlenet.caffemodel", false),
findDataFile("dnn/bvlc_googlenet.prototxt", false),
- "", 227, 227, "prob", "caffe", DNN_TARGET_CPU);
+ "", 224, 224, "prob", "caffe", DNN_TARGET_CPU);
};
TEST(Reproducibility_AlexNet_Halide, Accuracy)
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html.
-//
-// Copyright (C) 2017, Intel Corporation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-
// Sample of using Halide backend in OpenCV deep learning module.
// Based on caffe_googlenet.cpp.