├── .gitmodules ├── README.md ├── caffe_DOC ├── CMakeLists.txt ├── LICENSE ├── Makefile ├── Makefile.config ├── Makefile.config.example ├── caffe.cloc ├── cmake │ ├── ConfigGen.cmake │ ├── Cuda.cmake │ ├── Dependencies.cmake │ ├── External │ │ ├── gflags.cmake │ │ └── glog.cmake │ ├── Misc.cmake │ ├── Modules │ │ ├── FindAtlas.cmake │ │ ├── FindGFlags.cmake │ │ ├── FindGlog.cmake │ │ ├── FindLAPACK.cmake │ │ ├── FindLMDB.cmake │ │ ├── FindLevelDB.cmake │ │ ├── FindMKL.cmake │ │ ├── FindMatlabMex.cmake │ │ ├── FindNumPy.cmake │ │ ├── FindOpenBLAS.cmake │ │ ├── FindSnappy.cmake │ │ └── FindvecLib.cmake │ ├── ProtoBuf.cmake │ ├── Summary.cmake │ ├── Targets.cmake │ ├── Templates │ │ ├── CaffeConfig.cmake.in │ │ ├── CaffeConfigVersion.cmake.in │ │ └── caffe_config.h.in │ ├── Utils.cmake │ └── lint.cmake ├── docs │ ├── CMakeLists.txt │ ├── CNAME │ ├── README.md │ ├── _config.yml │ ├── _layouts │ │ └── default.html │ ├── development.md │ ├── images │ │ ├── GitHub-Mark-64px.png │ │ └── caffeine-icon.png │ ├── index.md │ ├── install_apt.md │ ├── install_osx.md │ ├── install_yum.md │ ├── installation.md │ ├── model_zoo.md │ ├── performance_hardware.md │ ├── stylesheets │ │ ├── pygment_trac.css │ │ ├── reset.css │ │ └── styles.css │ └── tutorial │ │ ├── convolution.md │ │ ├── data.md │ │ ├── fig │ │ ├── .gitignore │ │ ├── backward.jpg │ │ ├── forward.jpg │ │ ├── forward_backward.png │ │ ├── layer.jpg │ │ └── logreg.jpg │ │ ├── forward_backward.md │ │ ├── index.md │ │ ├── interfaces.md │ │ ├── layers.md │ │ ├── loss.md │ │ ├── net_layer_blob.md │ │ └── solver.md ├── include │ └── caffe │ │ ├── blob.hpp │ │ ├── caffe.hpp │ │ ├── common.hpp │ │ ├── common_layers.hpp │ │ ├── data_layers.hpp │ │ ├── data_reader.hpp │ │ ├── data_transformer.hpp │ │ ├── filler.hpp │ │ ├── internal_thread.hpp │ │ ├── layer.hpp │ │ ├── layer_factory.hpp │ │ ├── loss_layers.hpp │ │ ├── net.hpp │ │ ├── neuron_layers.hpp │ │ ├── parallel.hpp │ │ ├── python_layer.hpp │ │ ├── solver.hpp │ │ ├── syncedmem.hpp │ │ ├── test │ │ ├── test_caffe_main.hpp │ │ └── test_gradient_check_util.hpp │ │ ├── util │ │ ├── benchmark.hpp │ │ ├── blocking_queue.hpp │ │ ├── coords.hpp │ │ ├── cudnn.hpp │ │ ├── db.hpp │ │ ├── db_leveldb.hpp │ │ ├── db_lmdb.hpp │ │ ├── device_alternate.hpp │ │ ├── gpu_util.cuh │ │ ├── hdf5.hpp │ │ ├── im2col.hpp │ │ ├── insert_splits.hpp │ │ ├── io.hpp │ │ ├── math_functions.hpp │ │ ├── mkl_alternate.hpp │ │ ├── rng.hpp │ │ ├── signal_handler.h │ │ └── upgrade_proto.hpp │ │ └── vision_layers.hpp ├── matlab │ ├── +caffe │ │ ├── +test │ │ │ ├── test_net.m │ │ │ └── test_solver.m │ │ ├── Blob.m │ │ ├── Layer.m │ │ ├── Net.m │ │ ├── Solver.m │ │ ├── get_net.m │ │ ├── get_solver.m │ │ ├── imagenet │ │ │ └── ilsvrc_2012_mean.mat │ │ ├── io.m │ │ ├── private │ │ │ ├── CHECK.m │ │ │ ├── CHECK_FILE_EXIST.m │ │ │ ├── caffe_.cpp │ │ │ └── is_valid_handle.m │ │ ├── reset_all.m │ │ ├── run_tests.m │ │ ├── set_device.m │ │ ├── set_mode_cpu.m │ │ └── set_mode_gpu.m │ ├── CMakeLists.txt │ ├── demo │ │ └── classification_demo.m │ └── hdf5creation │ │ ├── .gitignore │ │ ├── demo.m │ │ └── store2hdf5.m ├── python │ ├── CMakeLists.txt │ ├── caffe │ │ ├── __init__.py │ │ ├── __init__.pyc │ │ ├── _caffe.cpp │ │ ├── classifier.py │ │ ├── classifier.pyc │ │ ├── detector.py │ │ ├── detector.pyc │ │ ├── draw.py │ │ ├── imagenet │ │ │ └── ilsvrc_2012_mean.npy │ │ ├── io.py │ │ ├── io.pyc │ │ ├── net_spec.py │ │ ├── net_spec.pyc │ │ ├── pycaffe.py │ │ ├── pycaffe.pyc │ │ └── test │ │ │ ├── test_layer_type_list.py │ │ │ ├── test_net.py │ │ │ ├── test_net_spec.py │ │ │ ├── test_python_layer.py │ │ │ ├── test_python_layer_with_param_str.py │ │ │ └── test_solver.py │ ├── classify.py │ ├── detect.py │ ├── draw_net.py │ └── requirements.txt ├── scripts │ ├── build_docs.sh │ ├── copy_notebook.py │ ├── cpp_lint.py │ ├── deploy_docs.sh │ ├── download_model_binary.py │ ├── download_model_from_gist.sh │ ├── gather_examples.sh │ ├── travis │ │ ├── travis_build_and_test.sh │ │ ├── travis_install.sh │ │ └── travis_setup_makefile_config.sh │ └── upload_model_to_gist.sh ├── src │ ├── caffe │ │ ├── CMakeLists.txt │ │ ├── blob.cpp │ │ ├── common.cpp │ │ ├── data_reader.cpp │ │ ├── data_transformer.cpp │ │ ├── internal_thread.cpp │ │ ├── layer.cpp │ │ ├── layer_factory.cpp │ │ ├── layers │ │ │ ├── absval_layer.cpp │ │ │ ├── absval_layer.cu │ │ │ ├── accuracy_layer.cpp │ │ │ ├── argmax_layer.cpp │ │ │ ├── base_conv_layer.cpp │ │ │ ├── base_data_layer.cpp │ │ │ ├── base_data_layer.cu │ │ │ ├── bnll_layer.cpp │ │ │ ├── bnll_layer.cu │ │ │ ├── concat_layer.cpp │ │ │ ├── concat_layer.cu │ │ │ ├── contrastive_loss_layer.cpp │ │ │ ├── contrastive_loss_layer.cu │ │ │ ├── conv_layer.cpp │ │ │ ├── conv_layer.cu │ │ │ ├── crop_layer.cpp │ │ │ ├── crop_layer.cu │ │ │ ├── cudnn_conv_layer.cpp │ │ │ ├── cudnn_conv_layer.cu │ │ │ ├── cudnn_pooling_layer.cpp │ │ │ ├── cudnn_pooling_layer.cu │ │ │ ├── cudnn_relu_layer.cpp │ │ │ ├── cudnn_relu_layer.cu │ │ │ ├── cudnn_sigmoid_layer.cpp │ │ │ ├── cudnn_sigmoid_layer.cu │ │ │ ├── cudnn_softmax_layer.cpp │ │ │ ├── cudnn_softmax_layer.cu │ │ │ ├── cudnn_tanh_layer.cpp │ │ │ ├── cudnn_tanh_layer.cu │ │ │ ├── data_layer.cpp │ │ │ ├── deconv_layer.cpp │ │ │ ├── deconv_layer.cu │ │ │ ├── dropout_layer.cpp │ │ │ ├── dropout_layer.cu │ │ │ ├── dummy_data_layer.cpp │ │ │ ├── eltwise_layer.cpp │ │ │ ├── eltwise_layer.cu │ │ │ ├── embed_layer.cpp │ │ │ ├── embed_layer.cu │ │ │ ├── euclidean_loss_layer.cpp │ │ │ ├── euclidean_loss_layer.cu │ │ │ ├── exp_layer.cpp │ │ │ ├── exp_layer.cu │ │ │ ├── filter_layer.cpp │ │ │ ├── filter_layer.cu │ │ │ ├── flatten_layer.cpp │ │ │ ├── hdf5_data_layer.cpp │ │ │ ├── hdf5_data_layer.cu │ │ │ ├── hdf5_output_layer.cpp │ │ │ ├── hdf5_output_layer.cu │ │ │ ├── hinge_loss_layer.cpp │ │ │ ├── im2col_layer.cpp │ │ │ ├── im2col_layer.cu │ │ │ ├── image_data_layer.cpp │ │ │ ├── image_labelmap_data_layer.cpp │ │ │ ├── infogain_loss_layer.cpp │ │ │ ├── inner_product_layer.cpp │ │ │ ├── inner_product_layer.cu │ │ │ ├── log_layer.cpp │ │ │ ├── log_layer.cu │ │ │ ├── loss_layer.cpp │ │ │ ├── lrn_layer.cpp │ │ │ ├── lrn_layer.cu │ │ │ ├── memory_data_layer.cpp │ │ │ ├── multinomial_logistic_loss_layer.cpp │ │ │ ├── mvn_layer.cpp │ │ │ ├── mvn_layer.cu │ │ │ ├── neuron_layer.cpp │ │ │ ├── orientation_loss_layer.cpp │ │ │ ├── pooling_layer.cpp │ │ │ ├── pooling_layer.cu │ │ │ ├── power_layer.cpp │ │ │ ├── power_layer.cu │ │ │ ├── prelu_layer.cpp │ │ │ ├── prelu_layer.cu │ │ │ ├── reduction_layer.cpp │ │ │ ├── reduction_layer.cu │ │ │ ├── relu_layer.cpp │ │ │ ├── relu_layer.cu │ │ │ ├── reshape_layer.cpp │ │ │ ├── sigmoid_cross_entropy_loss_layer.cpp │ │ │ ├── sigmoid_layer.cpp │ │ │ ├── sigmoid_layer.cu │ │ │ ├── silence_layer.cpp │ │ │ ├── silence_layer.cu │ │ │ ├── slice_layer.cpp │ │ │ ├── slice_layer.cu │ │ │ ├── softmax_layer.cpp │ │ │ ├── softmax_layer.cu │ │ │ ├── softmax_loss_layer.cpp │ │ │ ├── softmax_loss_layer.cpp~ │ │ │ ├── softmax_loss_layer.cu │ │ │ ├── split_layer.cpp │ │ │ ├── split_layer.cu │ │ │ ├── spp_layer.cpp │ │ │ ├── tanh_layer.cpp │ │ │ ├── tanh_layer.cu │ │ │ ├── threshold_layer.cpp │ │ │ ├── threshold_layer.cu │ │ │ ├── tile_layer.cpp │ │ │ ├── tile_layer.cu │ │ │ └── window_data_layer.cpp │ │ ├── net.cpp │ │ ├── parallel.cpp │ │ ├── proto │ │ │ └── caffe.proto │ │ ├── solver.cpp │ │ ├── syncedmem.cpp │ │ ├── test │ │ │ ├── CMakeLists.txt │ │ │ ├── test_accuracy_layer.cpp │ │ │ ├── test_argmax_layer.cpp │ │ │ ├── test_benchmark.cpp │ │ │ ├── test_blob.cpp │ │ │ ├── test_caffe_main.cpp │ │ │ ├── test_common.cpp │ │ │ ├── test_concat_layer.cpp │ │ │ ├── test_contrastive_loss_layer.cpp │ │ │ ├── test_convolution_layer.cpp │ │ │ ├── test_data │ │ │ │ ├── generate_sample_data.py │ │ │ │ ├── sample_data.h5 │ │ │ │ ├── sample_data_2_gzip.h5 │ │ │ │ ├── sample_data_list.txt │ │ │ │ ├── solver_data.h5 │ │ │ │ └── solver_data_list.txt │ │ │ ├── test_data_layer.cpp │ │ │ ├── test_data_transformer.cpp │ │ │ ├── test_db.cpp │ │ │ ├── test_deconvolution_layer.cpp │ │ │ ├── test_dummy_data_layer.cpp │ │ │ ├── test_eltwise_layer.cpp │ │ │ ├── test_embed_layer.cpp │ │ │ ├── test_euclidean_loss_layer.cpp │ │ │ ├── test_filler.cpp │ │ │ ├── test_filter_layer.cpp │ │ │ ├── test_flatten_layer.cpp │ │ │ ├── test_gradient_based_solver.cpp │ │ │ ├── test_hdf5_output_layer.cpp │ │ │ ├── test_hdf5data_layer.cpp │ │ │ ├── test_hinge_loss_layer.cpp │ │ │ ├── test_im2col_kernel.cu │ │ │ ├── test_im2col_layer.cpp │ │ │ ├── test_image_data_layer.cpp │ │ │ ├── test_infogain_loss_layer.cpp │ │ │ ├── test_inner_product_layer.cpp │ │ │ ├── test_internal_thread.cpp │ │ │ ├── test_io.cpp │ │ │ ├── test_layer_factory.cpp │ │ │ ├── test_lrn_layer.cpp │ │ │ ├── test_math_functions.cpp │ │ │ ├── test_maxpool_dropout_layers.cpp │ │ │ ├── test_memory_data_layer.cpp │ │ │ ├── test_multinomial_logistic_loss_layer.cpp │ │ │ ├── test_mvn_layer.cpp │ │ │ ├── test_net.cpp │ │ │ ├── test_neuron_layer.cpp │ │ │ ├── test_orientation_loss_layer.cpp │ │ │ ├── test_platform.cpp │ │ │ ├── test_pooling_layer.cpp │ │ │ ├── test_power_layer.cpp │ │ │ ├── test_protobuf.cpp │ │ │ ├── test_random_number_generator.cpp │ │ │ ├── test_reduction_layer.cpp │ │ │ ├── test_reshape_layer.cpp │ │ │ ├── test_sigmoid_cross_entropy_loss_layer.cpp │ │ │ ├── test_slice_layer.cpp │ │ │ ├── test_softmax_layer.cpp │ │ │ ├── test_softmax_with_loss_layer.cpp │ │ │ ├── test_solver.cpp │ │ │ ├── test_split_layer.cpp │ │ │ ├── test_spp_layer.cpp │ │ │ ├── test_stochastic_pooling.cpp │ │ │ ├── test_syncedmem.cpp │ │ │ ├── test_tanh_layer.cpp │ │ │ ├── test_threshold_layer.cpp │ │ │ ├── test_tile_layer.cpp │ │ │ ├── test_upgrade_proto.cpp │ │ │ └── test_util_blas.cpp │ │ └── util │ │ │ ├── benchmark.cpp │ │ │ ├── blocking_queue.cpp │ │ │ ├── cudnn.cpp │ │ │ ├── db.cpp │ │ │ ├── db_leveldb.cpp │ │ │ ├── db_lmdb.cpp │ │ │ ├── hdf5.cpp │ │ │ ├── im2col.cpp │ │ │ ├── im2col.cu │ │ │ ├── insert_splits.cpp │ │ │ ├── io.cpp │ │ │ ├── math_functions.cpp │ │ │ ├── math_functions.cu │ │ │ ├── signal_handler.cpp │ │ │ └── upgrade_proto.cpp │ └── gtest │ │ ├── CMakeLists.txt │ │ ├── gtest-all.cpp │ │ ├── gtest.h │ │ └── gtest_main.cc └── tools │ ├── CMakeLists.txt │ ├── caffe.cpp │ ├── compute_image_mean.cpp │ ├── convert_imageset.cpp │ ├── device_query.cpp │ ├── extra │ ├── extract_seconds.py │ ├── launch_resize_and_crop_images.sh │ ├── parse_log.py │ ├── parse_log.sh │ ├── plot_log.gnuplot.example │ ├── plot_training_log.py.example │ └── resize_and_crop_images.py │ ├── extract_features.cpp │ ├── finetune_net.cpp │ ├── net_speed_benchmark.cpp │ ├── test_net.cpp │ ├── train_net.cpp │ ├── upgrade_net_proto_binary.cpp │ └── upgrade_net_proto_text.cpp ├── demo_occ.m ├── lib ├── GetDilateEdgeGT.m ├── GetOrderFrags.m ├── OrderByConnect.m ├── edge_nms.m ├── get_config_info.m ├── im2occedge.m ├── set_path.m └── subplot_tight.m ├── output └── PIOD │ ├── doc_edge │ └── 2008_000123.mat │ └── doc_ori │ └── 2008_000123.mat └── tools ├── CatVarargin.m ├── catstruct.m └── edge_nms.m /.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "tools/piotr_toolbox"] 2 | path = tools/piotr_toolbox 3 | url = https://github.com/pdollar/toolbox 4 | [submodule "tools/edges"] 5 | path = tools/edges 6 | url = https://github.com/pdollar/edges 7 | -------------------------------------------------------------------------------- /caffe_DOC/LICENSE: -------------------------------------------------------------------------------- 1 | COPYRIGHT 2 | 3 | All contributions by the University of California: 4 | Copyright (c) 2014, 2015, The Regents of the University of California (Regents) 5 | All rights reserved. 6 | 7 | All other contributions: 8 | Copyright (c) 2014, 2015, the respective contributors 9 | All rights reserved. 10 | 11 | Caffe uses a shared copyright model: each contributor holds copyright over 12 | their contributions to Caffe. The project versioning records all such 13 | contribution and copyright details. If a contributor wants to further mark 14 | their specific copyright on a particular contribution, they should indicate 15 | their copyright solely in the commit message of the change when it is 16 | committed. 17 | 18 | LICENSE 19 | 20 | Redistribution and use in source and binary forms, with or without 21 | modification, are permitted provided that the following conditions are met: 22 | 23 | 1. Redistributions of source code must retain the above copyright notice, this 24 | list of conditions and the following disclaimer. 25 | 2. Redistributions in binary form must reproduce the above copyright notice, 26 | this list of conditions and the following disclaimer in the documentation 27 | and/or other materials provided with the distribution. 28 | 29 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND 30 | ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED 31 | WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 32 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR 33 | ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES 34 | (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 35 | LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND 36 | ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT 37 | (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 38 | SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 39 | 40 | CONTRIBUTION AGREEMENT 41 | 42 | By contributing to the BVLC/caffe repository through pull-request, comment, 43 | or otherwise, the contributor releases their content to the 44 | license and copyright terms herein. 45 | -------------------------------------------------------------------------------- /caffe_DOC/caffe.cloc: -------------------------------------------------------------------------------- 1 | Bourne Shell 2 | filter remove_matches ^\s*# 3 | filter remove_inline #.*$ 4 | extension sh 5 | script_exe sh 6 | C 7 | filter remove_matches ^\s*// 8 | filter call_regexp_common C 9 | filter remove_inline //.*$ 10 | extension c 11 | extension ec 12 | extension pgc 13 | C++ 14 | filter remove_matches ^\s*// 15 | filter remove_inline //.*$ 16 | filter call_regexp_common C 17 | extension C 18 | extension cc 19 | extension cpp 20 | extension cxx 21 | extension pcc 22 | C/C++ Header 23 | filter remove_matches ^\s*// 24 | filter call_regexp_common C 25 | filter remove_inline //.*$ 26 | extension H 27 | extension h 28 | extension hh 29 | extension hpp 30 | CUDA 31 | filter remove_matches ^\s*// 32 | filter remove_inline //.*$ 33 | filter call_regexp_common C 34 | extension cu 35 | Python 36 | filter remove_matches ^\s*# 37 | filter docstring_to_C 38 | filter call_regexp_common C 39 | filter remove_inline #.*$ 40 | extension py 41 | make 42 | filter remove_matches ^\s*# 43 | filter remove_inline #.*$ 44 | extension Gnumakefile 45 | extension Makefile 46 | extension am 47 | extension gnumakefile 48 | extension makefile 49 | filename Gnumakefile 50 | filename Makefile 51 | filename gnumakefile 52 | filename makefile 53 | script_exe make 54 | -------------------------------------------------------------------------------- /caffe_DOC/cmake/External/gflags.cmake: -------------------------------------------------------------------------------- 1 | if (NOT __GFLAGS_INCLUDED) # guard against multiple includes 2 | set(__GFLAGS_INCLUDED TRUE) 3 | 4 | # use the system-wide gflags if present 5 | find_package(GFlags) 6 | if (GFLAGS_FOUND) 7 | set(GFLAGS_EXTERNAL FALSE) 8 | else() 9 | # gflags will use pthreads if it's available in the system, so we must link with it 10 | find_package(Threads) 11 | 12 | # build directory 13 | set(gflags_PREFIX ${CMAKE_BINARY_DIR}/external/gflags-prefix) 14 | # install directory 15 | set(gflags_INSTALL ${CMAKE_BINARY_DIR}/external/gflags-install) 16 | 17 | # we build gflags statically, but want to link it into the caffe shared library 18 | # this requires position-independent code 19 | if (UNIX) 20 | set(GFLAGS_EXTRA_COMPILER_FLAGS "-fPIC") 21 | endif() 22 | 23 | set(GFLAGS_CXX_FLAGS ${CMAKE_CXX_FLAGS} ${GFLAGS_EXTRA_COMPILER_FLAGS}) 24 | set(GFLAGS_C_FLAGS ${CMAKE_C_FLAGS} ${GFLAGS_EXTRA_COMPILER_FLAGS}) 25 | 26 | ExternalProject_Add(gflags 27 | PREFIX ${gflags_PREFIX} 28 | GIT_REPOSITORY "https://github.com/gflags/gflags.git" 29 | GIT_TAG "v2.1.2" 30 | UPDATE_COMMAND "" 31 | INSTALL_DIR ${gflags_INSTALL} 32 | CMAKE_ARGS -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE} 33 | -DCMAKE_INSTALL_PREFIX=${gflags_INSTALL} 34 | -DBUILD_SHARED_LIBS=OFF 35 | -DBUILD_STATIC_LIBS=ON 36 | -DBUILD_PACKAGING=OFF 37 | -DBUILD_TESTING=OFF 38 | -DBUILD_NC_TESTS=OFF 39 | -BUILD_CONFIG_TESTS=OFF 40 | -DINSTALL_HEADERS=ON 41 | -DCMAKE_C_FLAGS=${GFLAGS_C_FLAGS} 42 | -DCMAKE_CXX_FLAGS=${GFLAGS_CXX_FLAGS} 43 | LOG_DOWNLOAD 1 44 | LOG_INSTALL 1 45 | ) 46 | 47 | set(GFLAGS_FOUND TRUE) 48 | set(GFLAGS_INCLUDE_DIRS ${gflags_INSTALL}/include) 49 | set(GFLAGS_LIBRARIES ${gflags_INSTALL}/lib/libgflags.a ${CMAKE_THREAD_LIBS_INIT}) 50 | set(GFLAGS_LIBRARY_DIRS ${gflags_INSTALL}/lib) 51 | set(GFLAGS_EXTERNAL TRUE) 52 | 53 | list(APPEND external_project_dependencies gflags) 54 | endif() 55 | 56 | endif() 57 | -------------------------------------------------------------------------------- /caffe_DOC/cmake/External/glog.cmake: -------------------------------------------------------------------------------- 1 | # glog depends on gflags 2 | include("cmake/External/gflags.cmake") 3 | 4 | if (NOT __GLOG_INCLUDED) 5 | set(__GLOG_INCLUDED TRUE) 6 | 7 | # try the system-wide glog first 8 | find_package(Glog) 9 | if (GLOG_FOUND) 10 | set(GLOG_EXTERNAL FALSE) 11 | else() 12 | # fetch and build glog from github 13 | 14 | # build directory 15 | set(glog_PREFIX ${CMAKE_BINARY_DIR}/external/glog-prefix) 16 | # install directory 17 | set(glog_INSTALL ${CMAKE_BINARY_DIR}/external/glog-install) 18 | 19 | # we build glog statically, but want to link it into the caffe shared library 20 | # this requires position-independent code 21 | if (UNIX) 22 | set(GLOG_EXTRA_COMPILER_FLAGS "-fPIC") 23 | endif() 24 | 25 | set(GLOG_CXX_FLAGS ${CMAKE_CXX_FLAGS} ${GLOG_EXTRA_COMPILER_FLAGS}) 26 | set(GLOG_C_FLAGS ${CMAKE_C_FLAGS} ${GLOG_EXTRA_COMPILER_FLAGS}) 27 | 28 | # depend on gflags if we're also building it 29 | if (GFLAGS_EXTERNAL) 30 | set(GLOG_DEPENDS gflags) 31 | endif() 32 | 33 | ExternalProject_Add(glog 34 | DEPENDS ${GLOG_DEPENDS} 35 | PREFIX ${glog_PREFIX} 36 | GIT_REPOSITORY "https://github.com/google/glog" 37 | GIT_TAG "v0.3.4" 38 | UPDATE_COMMAND "" 39 | INSTALL_DIR ${gflags_INSTALL} 40 | CONFIGURE_COMMAND env "CFLAGS=${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}/.. 41 | LOG_DOWNLOAD 1 42 | LOG_CONFIGURE 1 43 | LOG_INSTALL 1 44 | ) 45 | 46 | set(GLOG_FOUND TRUE) 47 | set(GLOG_INCLUDE_DIRS ${glog_INSTALL}/include) 48 | set(GLOG_LIBRARIES ${GFLAGS_LIBRARIES} ${glog_INSTALL}/lib/libglog.a) 49 | set(GLOG_LIBRARY_DIRS ${glog_INSTALL}/lib) 50 | set(GLOG_EXTERNAL TRUE) 51 | 52 | list(APPEND external_project_dependencies glog) 53 | endif() 54 | 55 | endif() 56 | 57 | -------------------------------------------------------------------------------- /caffe_DOC/cmake/Misc.cmake: -------------------------------------------------------------------------------- 1 | # ---[ Configuration types 2 | set(CMAKE_CONFIGURATION_TYPES "Debug;Release" CACHE STRING "Possible configurations" FORCE) 3 | mark_as_advanced(CMAKE_CONFIGURATION_TYPES) 4 | 5 | if(DEFINED CMAKE_BUILD_TYPE) 6 | set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS ${CMAKE_CONFIGURATION_TYPES}) 7 | endif() 8 | 9 | # --[ If user doesn't specify build type then assume release 10 | if("${CMAKE_BUILD_TYPE}" STREQUAL "") 11 | set(CMAKE_BUILD_TYPE Release) 12 | endif() 13 | 14 | if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang") 15 | set(CMAKE_COMPILER_IS_CLANGXX TRUE) 16 | endif() 17 | 18 | # ---[ Solution folders 19 | caffe_option(USE_PROJECT_FOLDERS "IDE Solution folders" (MSVC_IDE OR CMAKE_GENERATOR MATCHES Xcode) ) 20 | 21 | if(USE_PROJECT_FOLDERS) 22 | set_property(GLOBAL PROPERTY USE_FOLDERS ON) 23 | set_property(GLOBAL PROPERTY PREDEFINED_TARGETS_FOLDER "CMakeTargets") 24 | endif() 25 | 26 | # ---[ Install options 27 | if(CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT) 28 | set(CMAKE_INSTALL_PREFIX "${PROJECT_BINARY_DIR}/install" CACHE PATH "Default install path" FORCE) 29 | endif() 30 | 31 | # ---[ RPATH settings 32 | set(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE CACHE BOOLEAN "Use link paths for shared library rpath") 33 | set(CMAKE_MACOSX_RPATH TRUE) 34 | 35 | list(FIND CMAKE_PLATFORM_IMPLICIT_LINK_DIRECTORIES ${CMAKE_INSTALL_PREFIX}/lib __is_systtem_dir) 36 | if(${__is_systtem_dir} STREQUAL -1) 37 | set(CMAKE_INSTALL_RPATH ${CMAKE_INSTALL_PREFIX}/lib) 38 | endif() 39 | 40 | # ---[ Funny target 41 | if(UNIX OR APPLE) 42 | add_custom_target(symlink_to_build COMMAND "ln" "-sf" "${PROJECT_BINARY_DIR}" "${PROJECT_SOURCE_DIR}/build" 43 | COMMENT "Adding symlink: /build -> ${PROJECT_BINARY_DIR}" ) 44 | endif() 45 | 46 | # ---[ Set debug postfix 47 | set(Caffe_DEBUG_POSTFIX "-d") 48 | 49 | set(CAffe_POSTFIX "") 50 | if(CMAKE_BUILD_TYPE MATCHES "Debug") 51 | set(CAffe_POSTFIX ${Caffe_DEBUG_POSTFIX}) 52 | endif() 53 | -------------------------------------------------------------------------------- /caffe_DOC/cmake/Modules/FindAtlas.cmake: -------------------------------------------------------------------------------- 1 | # Find the Atlas (and Lapack) libraries 2 | # 3 | # The following variables are optionally searched for defaults 4 | # Atlas_ROOT_DIR: Base directory where all Atlas components are found 5 | # 6 | # The following are set after configuration is done: 7 | # Atlas_FOUND 8 | # Atlas_INCLUDE_DIRS 9 | # Atlas_LIBRARIES 10 | # Atlas_LIBRARYRARY_DIRS 11 | 12 | set(Atlas_INCLUDE_SEARCH_PATHS 13 | /usr/include/atlas 14 | /usr/include/atlas-base 15 | $ENV{Atlas_ROOT_DIR} 16 | $ENV{Atlas_ROOT_DIR}/include 17 | ) 18 | 19 | set(Atlas_LIB_SEARCH_PATHS 20 | /usr/lib/atlas 21 | /usr/lib/atlas-base 22 | $ENV{Atlas_ROOT_DIR} 23 | $ENV{Atlas_ROOT_DIR}/lib 24 | ) 25 | 26 | find_path(Atlas_CBLAS_INCLUDE_DIR NAMES cblas.h PATHS ${Atlas_INCLUDE_SEARCH_PATHS}) 27 | find_path(Atlas_CLAPACK_INCLUDE_DIR NAMES clapack.h PATHS ${Atlas_INCLUDE_SEARCH_PATHS}) 28 | 29 | find_library(Atlas_CBLAS_LIBRARY NAMES ptcblas_r ptcblas cblas_r cblas PATHS ${Atlas_LIB_SEARCH_PATHS}) 30 | find_library(Atlas_BLAS_LIBRARY NAMES atlas_r atlas PATHS ${Atlas_LIB_SEARCH_PATHS}) 31 | find_library(Atlas_LAPACK_LIBRARY NAMES alapack_r alapack lapack_atlas PATHS ${Atlas_LIB_SEARCH_PATHS}) 32 | 33 | set(LOOKED_FOR 34 | Atlas_CBLAS_INCLUDE_DIR 35 | Atlas_CLAPACK_INCLUDE_DIR 36 | 37 | Atlas_CBLAS_LIBRARY 38 | Atlas_BLAS_LIBRARY 39 | Atlas_LAPACK_LIBRARY 40 | ) 41 | 42 | include(FindPackageHandleStandardArgs) 43 | find_package_handle_standard_args(Atlas DEFAULT_MSG ${LOOKED_FOR}) 44 | 45 | if(ATLAS_FOUND) 46 | set(Atlas_INCLUDE_DIR ${Atlas_CBLAS_INCLUDE_DIR} ${Atlas_CLAPACK_INCLUDE_DIR}) 47 | set(Atlas_LIBRARIES ${Atlas_LAPACK_LIBRARY} ${Atlas_CBLAS_LIBRARY} ${Atlas_BLAS_LIBRARY}) 48 | mark_as_advanced(${LOOKED_FOR}) 49 | 50 | message(STATUS "Found Atlas (include: ${Atlas_CBLAS_INCLUDE_DIR}, library: ${Atlas_BLAS_LIBRARY})") 51 | endif(ATLAS_FOUND) 52 | 53 | -------------------------------------------------------------------------------- /caffe_DOC/cmake/Modules/FindGFlags.cmake: -------------------------------------------------------------------------------- 1 | # - Try to find GFLAGS 2 | # 3 | # The following variables are optionally searched for defaults 4 | # GFLAGS_ROOT_DIR: Base directory where all GFLAGS components are found 5 | # 6 | # The following are set after configuration is done: 7 | # GFLAGS_FOUND 8 | # GFLAGS_INCLUDE_DIRS 9 | # GFLAGS_LIBRARIES 10 | # GFLAGS_LIBRARYRARY_DIRS 11 | 12 | include(FindPackageHandleStandardArgs) 13 | 14 | set(GFLAGS_ROOT_DIR "" CACHE PATH "Folder contains Gflags") 15 | 16 | # We are testing only a couple of files in the include directories 17 | if(WIN32) 18 | find_path(GFLAGS_INCLUDE_DIR gflags/gflags.h 19 | PATHS ${GFLAGS_ROOT_DIR}/src/windows) 20 | else() 21 | find_path(GFLAGS_INCLUDE_DIR gflags/gflags.h 22 | PATHS ${GFLAGS_ROOT_DIR}) 23 | endif() 24 | 25 | if(MSVC) 26 | find_library(GFLAGS_LIBRARY_RELEASE 27 | NAMES libgflags 28 | PATHS ${GFLAGS_ROOT_DIR} 29 | PATH_SUFFIXES Release) 30 | 31 | find_library(GFLAGS_LIBRARY_DEBUG 32 | NAMES libgflags-debug 33 | PATHS ${GFLAGS_ROOT_DIR} 34 | PATH_SUFFIXES Debug) 35 | 36 | set(GFLAGS_LIBRARY optimized ${GFLAGS_LIBRARY_RELEASE} debug ${GFLAGS_LIBRARY_DEBUG}) 37 | else() 38 | find_library(GFLAGS_LIBRARY gflags) 39 | endif() 40 | 41 | find_package_handle_standard_args(GFlags DEFAULT_MSG GFLAGS_INCLUDE_DIR GFLAGS_LIBRARY) 42 | 43 | 44 | if(GFLAGS_FOUND) 45 | set(GFLAGS_INCLUDE_DIRS ${GFLAGS_INCLUDE_DIR}) 46 | set(GFLAGS_LIBRARIES ${GFLAGS_LIBRARY}) 47 | message(STATUS "Found gflags (include: ${GFLAGS_INCLUDE_DIR}, library: ${GFLAGS_LIBRARY})") 48 | mark_as_advanced(GFLAGS_LIBRARY_DEBUG GFLAGS_LIBRARY_RELEASE 49 | GFLAGS_LIBRARY GFLAGS_INCLUDE_DIR GFLAGS_ROOT_DIR) 50 | endif() 51 | -------------------------------------------------------------------------------- /caffe_DOC/cmake/Modules/FindGlog.cmake: -------------------------------------------------------------------------------- 1 | # - Try to find Glog 2 | # 3 | # The following variables are optionally searched for defaults 4 | # GLOG_ROOT_DIR: Base directory where all GLOG components are found 5 | # 6 | # The following are set after configuration is done: 7 | # GLOG_FOUND 8 | # GLOG_INCLUDE_DIRS 9 | # GLOG_LIBRARIES 10 | # GLOG_LIBRARYRARY_DIRS 11 | 12 | include(FindPackageHandleStandardArgs) 13 | 14 | set(GLOG_ROOT_DIR "" CACHE PATH "Folder contains Google glog") 15 | 16 | if(WIN32) 17 | find_path(GLOG_INCLUDE_DIR glog/logging.h 18 | PATHS ${GLOG_ROOT_DIR}/src/windows) 19 | else() 20 | find_path(GLOG_INCLUDE_DIR glog/logging.h 21 | PATHS ${GLOG_ROOT_DIR}) 22 | endif() 23 | 24 | if(MSVC) 25 | find_library(GLOG_LIBRARY_RELEASE libglog_static 26 | PATHS ${GLOG_ROOT_DIR} 27 | PATH_SUFFIXES Release) 28 | 29 | find_library(GLOG_LIBRARY_DEBUG libglog_static 30 | PATHS ${GLOG_ROOT_DIR} 31 | PATH_SUFFIXES Debug) 32 | 33 | set(GLOG_LIBRARY optimized ${GLOG_LIBRARY_RELEASE} debug ${GLOG_LIBRARY_DEBUG}) 34 | else() 35 | find_library(GLOG_LIBRARY glog 36 | PATHS ${GLOG_ROOT_DIR} 37 | PATH_SUFFIXES lib lib64) 38 | endif() 39 | 40 | find_package_handle_standard_args(Glog DEFAULT_MSG GLOG_INCLUDE_DIR GLOG_LIBRARY) 41 | 42 | if(GLOG_FOUND) 43 | set(GLOG_INCLUDE_DIRS ${GLOG_INCLUDE_DIR}) 44 | set(GLOG_LIBRARIES ${GLOG_LIBRARY}) 45 | message(STATUS "Found glog (include: ${GLOG_INCLUDE_DIR}, library: ${GLOG_LIBRARY})") 46 | mark_as_advanced(GLOG_ROOT_DIR GLOG_LIBRARY_RELEASE GLOG_LIBRARY_DEBUG 47 | GLOG_LIBRARY GLOG_INCLUDE_DIR) 48 | endif() 49 | -------------------------------------------------------------------------------- /caffe_DOC/cmake/Modules/FindLMDB.cmake: -------------------------------------------------------------------------------- 1 | # Try to find the LMBD libraries and headers 2 | # LMDB_FOUND - system has LMDB lib 3 | # LMDB_INCLUDE_DIR - the LMDB include directory 4 | # LMDB_LIBRARIES - Libraries needed to use LMDB 5 | 6 | # FindCWD based on FindGMP by: 7 | # Copyright (c) 2006, Laurent Montel, 8 | # 9 | # Redistribution and use is allowed according to the terms of the BSD license. 10 | 11 | # Adapted from FindCWD by: 12 | # Copyright 2013 Conrad Steenberg 13 | # Aug 31, 2013 14 | 15 | find_path(LMDB_INCLUDE_DIR NAMES lmdb.h PATHS "$ENV{LMDB_DIR}/include") 16 | find_library(LMDB_LIBRARIES NAMES lmdb PATHS "$ENV{LMDB_DIR}/lib" ) 17 | 18 | include(FindPackageHandleStandardArgs) 19 | find_package_handle_standard_args(LMDB DEFAULT_MSG LMDB_INCLUDE_DIR LMDB_LIBRARIES) 20 | 21 | if(LMDB_FOUND) 22 | message(STATUS "Found lmdb (include: ${LMDB_INCLUDE_DIR}, library: ${LMDB_LIBRARIES})") 23 | mark_as_advanced(LMDB_INCLUDE_DIR LMDB_LIBRARIES) 24 | 25 | caffe_parse_header(${LMDB_INCLUDE_DIR}/lmdb.h 26 | LMDB_VERSION_LINES MDB_VERSION_MAJOR MDB_VERSION_MINOR MDB_VERSION_PATCH) 27 | set(LMDB_VERSION "${MDB_VERSION_MAJOR}.${MDB_VERSION_MINOR}.${MDB_VERSION_PATCH}") 28 | endif() 29 | -------------------------------------------------------------------------------- /caffe_DOC/cmake/Modules/FindLevelDB.cmake: -------------------------------------------------------------------------------- 1 | # - Find LevelDB 2 | # 3 | # LevelDB_INCLUDES - List of LevelDB includes 4 | # LevelDB_LIBRARIES - List of libraries when using LevelDB. 5 | # LevelDB_FOUND - True if LevelDB found. 6 | 7 | # Look for the header file. 8 | find_path(LevelDB_INCLUDE NAMES leveldb/db.h 9 | PATHS $ENV{LEVELDB_ROOT}/include /opt/local/include /usr/local/include /usr/include 10 | DOC "Path in which the file leveldb/db.h is located." ) 11 | 12 | # Look for the library. 13 | find_library(LevelDB_LIBRARY NAMES leveldb 14 | PATHS /usr/lib $ENV{LEVELDB_ROOT}/lib 15 | DOC "Path to leveldb library." ) 16 | 17 | include(FindPackageHandleStandardArgs) 18 | find_package_handle_standard_args(LevelDB DEFAULT_MSG LevelDB_INCLUDE LevelDB_LIBRARY) 19 | 20 | if(LEVELDB_FOUND) 21 | message(STATUS "Found LevelDB (include: ${LevelDB_INCLUDE}, library: ${LevelDB_LIBRARY})") 22 | set(LevelDB_INCLUDES ${LevelDB_INCLUDE}) 23 | set(LevelDB_LIBRARIES ${LevelDB_LIBRARY}) 24 | mark_as_advanced(LevelDB_INCLUDE LevelDB_LIBRARY) 25 | 26 | if(EXISTS "${LevelDB_INCLUDE}/leveldb/db.h") 27 | file(STRINGS "${LevelDB_INCLUDE}/leveldb/db.h" __version_lines 28 | REGEX "static const int k[^V]+Version[ \t]+=[ \t]+[0-9]+;") 29 | 30 | foreach(__line ${__version_lines}) 31 | if(__line MATCHES "[^k]+kMajorVersion[ \t]+=[ \t]+([0-9]+);") 32 | set(LEVELDB_VERSION_MAJOR ${CMAKE_MATCH_1}) 33 | elseif(__line MATCHES "[^k]+kMinorVersion[ \t]+=[ \t]+([0-9]+);") 34 | set(LEVELDB_VERSION_MINOR ${CMAKE_MATCH_1}) 35 | endif() 36 | endforeach() 37 | 38 | if(LEVELDB_VERSION_MAJOR AND LEVELDB_VERSION_MINOR) 39 | set(LEVELDB_VERSION "${LEVELDB_VERSION_MAJOR}.${LEVELDB_VERSION_MINOR}") 40 | endif() 41 | 42 | caffe_clear_vars(__line __version_lines) 43 | endif() 44 | endif() 45 | -------------------------------------------------------------------------------- /caffe_DOC/cmake/Modules/FindMatlabMex.cmake: -------------------------------------------------------------------------------- 1 | # This module looks for MatlabMex compiler 2 | # Defines variables: 3 | # Matlab_DIR - Matlab root dir 4 | # Matlab_mex - path to mex compiler 5 | # Matlab_mexext - path to mexext 6 | 7 | if(MSVC) 8 | foreach(__ver "9.30" "7.14" "7.11" "7.10" "7.9" "7.8" "7.7") 9 | get_filename_component(__matlab_root "[HKEY_LOCAL_MACHINE\\SOFTWARE\\MathWorks\\MATLAB\\${__ver};MATLABROOT]" ABSOLUTE) 10 | if(__matlab_root) 11 | break() 12 | endif() 13 | endforeach() 14 | endif() 15 | 16 | if(APPLE) 17 | foreach(__ver "R2014b" "R2014a" "R2013b" "R2013a" "R2012b" "R2012a" "R2011b" "R2011a" "R2010b" "R2010a") 18 | if(EXISTS /Applications/MATLAB_${__ver}.app) 19 | set(__matlab_root /Applications/MATLAB_${__ver}.app) 20 | break() 21 | endif() 22 | endforeach() 23 | endif() 24 | 25 | if(UNIX) 26 | execute_process(COMMAND which matlab OUTPUT_STRIP_TRAILING_WHITESPACE 27 | OUTPUT_VARIABLE __out RESULT_VARIABLE __res) 28 | 29 | if(__res MATCHES 0) # Suppress `readlink` warning if `which` returned nothing 30 | execute_process(COMMAND which matlab COMMAND xargs readlink 31 | COMMAND xargs dirname COMMAND xargs dirname COMMAND xargs echo -n 32 | OUTPUT_VARIABLE __matlab_root OUTPUT_STRIP_TRAILING_WHITESPACE) 33 | endif() 34 | endif() 35 | 36 | 37 | find_path(Matlab_DIR NAMES bin/mex bin/mexext PATHS ${__matlab_root} 38 | DOC "Matlab directory" NO_DEFAULT_PATH) 39 | 40 | find_program(Matlab_mex NAMES mex mex.bat HINTS ${Matlab_DIR} PATH_SUFFIXES bin NO_DEFAULT_PATH) 41 | find_program(Matlab_mexext NAMES mexext mexext.bat HINTS ${Matlab_DIR} PATH_SUFFIXES bin NO_DEFAULT_PATH) 42 | 43 | include(FindPackageHandleStandardArgs) 44 | find_package_handle_standard_args(MatlabMex DEFAULT_MSG Matlab_mex Matlab_mexext) 45 | 46 | if(MATLABMEX_FOUND) 47 | mark_as_advanced(Matlab_mex Matlab_mexext) 48 | endif() 49 | -------------------------------------------------------------------------------- /caffe_DOC/cmake/Modules/FindOpenBLAS.cmake: -------------------------------------------------------------------------------- 1 | 2 | 3 | SET(Open_BLAS_INCLUDE_SEARCH_PATHS 4 | /usr/include 5 | /usr/include/openblas-base 6 | /usr/local/include 7 | /usr/local/include/openblas-base 8 | /opt/OpenBLAS/include 9 | $ENV{OpenBLAS_HOME} 10 | $ENV{OpenBLAS_HOME}/include 11 | ) 12 | 13 | SET(Open_BLAS_LIB_SEARCH_PATHS 14 | /lib/ 15 | /lib/openblas-base 16 | /lib64/ 17 | /usr/lib 18 | /usr/lib/openblas-base 19 | /usr/lib64 20 | /usr/local/lib 21 | /usr/local/lib64 22 | /opt/OpenBLAS/lib 23 | $ENV{OpenBLAS}cd 24 | $ENV{OpenBLAS}/lib 25 | $ENV{OpenBLAS_HOME} 26 | $ENV{OpenBLAS_HOME}/lib 27 | ) 28 | 29 | FIND_PATH(OpenBLAS_INCLUDE_DIR NAMES cblas.h PATHS ${Open_BLAS_INCLUDE_SEARCH_PATHS}) 30 | FIND_LIBRARY(OpenBLAS_LIB NAMES openblas PATHS ${Open_BLAS_LIB_SEARCH_PATHS}) 31 | 32 | SET(OpenBLAS_FOUND ON) 33 | 34 | # Check include files 35 | IF(NOT OpenBLAS_INCLUDE_DIR) 36 | SET(OpenBLAS_FOUND OFF) 37 | MESSAGE(STATUS "Could not find OpenBLAS include. Turning OpenBLAS_FOUND off") 38 | ENDIF() 39 | 40 | # Check libraries 41 | IF(NOT OpenBLAS_LIB) 42 | SET(OpenBLAS_FOUND OFF) 43 | MESSAGE(STATUS "Could not find OpenBLAS lib. Turning OpenBLAS_FOUND off") 44 | ENDIF() 45 | 46 | IF (OpenBLAS_FOUND) 47 | IF (NOT OpenBLAS_FIND_QUIETLY) 48 | MESSAGE(STATUS "Found OpenBLAS libraries: ${OpenBLAS_LIB}") 49 | MESSAGE(STATUS "Found OpenBLAS include: ${OpenBLAS_INCLUDE_DIR}") 50 | ENDIF (NOT OpenBLAS_FIND_QUIETLY) 51 | ELSE (OpenBLAS_FOUND) 52 | IF (OpenBLAS_FIND_REQUIRED) 53 | MESSAGE(FATAL_ERROR "Could not find OpenBLAS") 54 | ENDIF (OpenBLAS_FIND_REQUIRED) 55 | ENDIF (OpenBLAS_FOUND) 56 | 57 | MARK_AS_ADVANCED( 58 | OpenBLAS_INCLUDE_DIR 59 | OpenBLAS_LIB 60 | OpenBLAS 61 | ) 62 | 63 | -------------------------------------------------------------------------------- /caffe_DOC/cmake/Modules/FindSnappy.cmake: -------------------------------------------------------------------------------- 1 | # Find the Snappy libraries 2 | # 3 | # The following variables are optionally searched for defaults 4 | # Snappy_ROOT_DIR: Base directory where all Snappy components are found 5 | # 6 | # The following are set after configuration is done: 7 | # SNAPPY_FOUND 8 | # Snappy_INCLUDE_DIR 9 | # Snappy_LIBRARIES 10 | 11 | find_path(Snappy_INCLUDE_DIR NAMES snappy.h 12 | PATHS ${SNAPPY_ROOT_DIR} ${SNAPPY_ROOT_DIR}/include) 13 | 14 | find_library(Snappy_LIBRARIES NAMES snappy 15 | PATHS ${SNAPPY_ROOT_DIR} ${SNAPPY_ROOT_DIR}/lib) 16 | 17 | include(FindPackageHandleStandardArgs) 18 | find_package_handle_standard_args(Snappy DEFAULT_MSG Snappy_INCLUDE_DIR Snappy_LIBRARIES) 19 | 20 | if(SNAPPY_FOUND) 21 | message(STATUS "Found Snappy (include: ${Snappy_INCLUDE_DIR}, library: ${Snappy_LIBRARIES})") 22 | mark_as_advanced(Snappy_INCLUDE_DIR Snappy_LIBRARIES) 23 | 24 | caffe_parse_header(${Snappy_INCLUDE_DIR}/snappy-stubs-public.h 25 | SNAPPY_VERION_LINES SNAPPY_MAJOR SNAPPY_MINOR SNAPPY_PATCHLEVEL) 26 | set(Snappy_VERSION "${SNAPPY_MAJOR}.${SNAPPY_MINOR}.${SNAPPY_PATCHLEVEL}") 27 | endif() 28 | 29 | -------------------------------------------------------------------------------- /caffe_DOC/cmake/Modules/FindvecLib.cmake: -------------------------------------------------------------------------------- 1 | # Find the vecLib libraries as part of Accelerate.framework or as standalon framework 2 | # 3 | # The following are set after configuration is done: 4 | # VECLIB_FOUND 5 | # vecLib_INCLUDE_DIR 6 | # vecLib_LINKER_LIBS 7 | 8 | 9 | if(NOT APPLE) 10 | return() 11 | endif() 12 | 13 | set(__veclib_include_suffix "Frameworks/vecLib.framework/Versions/Current/Headers") 14 | 15 | find_path(vecLib_INCLUDE_DIR vecLib.h 16 | DOC "vecLib include directory" 17 | PATHS /System/Library/${__veclib_include_suffix} 18 | /System/Library/Frameworks/Accelerate.framework/Versions/Current/${__veclib_include_suffix} 19 | /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.9.sdk/System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/) 20 | 21 | include(FindPackageHandleStandardArgs) 22 | find_package_handle_standard_args(vecLib DEFAULT_MSG vecLib_INCLUDE_DIR) 23 | 24 | if(VECLIB_FOUND) 25 | if(vecLib_INCLUDE_DIR MATCHES "^/System/Library/Frameworks/vecLib.framework.*") 26 | set(vecLib_LINKER_LIBS -lcblas "-framework vecLib") 27 | message(STATUS "Found standalone vecLib.framework") 28 | else() 29 | set(vecLib_LINKER_LIBS -lcblas "-framework Accelerate") 30 | message(STATUS "Found vecLib as part of Accelerate.framework") 31 | endif() 32 | 33 | mark_as_advanced(vecLib_INCLUDE_DIR) 34 | endif() 35 | -------------------------------------------------------------------------------- /caffe_DOC/cmake/Templates/CaffeConfig.cmake.in: -------------------------------------------------------------------------------- 1 | # Config file for the Caffe package. 2 | # 3 | # Note: 4 | # Caffe and this config file depends on opencv, 5 | # so put `find_package(OpenCV)` before searching Caffe 6 | # via `find_package(Caffe)`. All other lib/includes 7 | # dependencies are hard coded in the file 8 | # 9 | # After successful configuration the following variables 10 | # will be defined: 11 | # 12 | # Caffe_INCLUDE_DIRS - Caffe include directories 13 | # Caffe_LIBRARIES - libraries to link against 14 | # Caffe_DEFINITIONS - a list of definitions to pass to compiler 15 | # 16 | # Caffe_HAVE_CUDA - signals about CUDA support 17 | # Caffe_HAVE_CUDNN - signals about cuDNN support 18 | 19 | 20 | # OpenCV dependency 21 | 22 | if(NOT OpenCV_FOUND) 23 | set(Caffe_OpenCV_CONFIG_PATH "@OpenCV_CONFIG_PATH@") 24 | if(Caffe_OpenCV_CONFIG_PATH) 25 | get_filename_component(Caffe_OpenCV_CONFIG_PATH ${Caffe_OpenCV_CONFIG_PATH} ABSOLUTE) 26 | 27 | if(EXISTS ${Caffe_OpenCV_CONFIG_PATH} AND NOT TARGET opencv_core) 28 | message(STATUS "Caffe: using OpenCV config from ${Caffe_OpenCV_CONFIG_PATH}") 29 | include(${Caffe_OpenCV_CONFIG_PATH}/OpenCVModules.cmake) 30 | endif() 31 | 32 | else() 33 | find_package(OpenCV REQUIRED) 34 | endif() 35 | unset(Caffe_OpenCV_CONFIG_PATH) 36 | endif() 37 | 38 | # Compute paths 39 | get_filename_component(Caffe_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH) 40 | set(Caffe_INCLUDE_DIRS "@Caffe_INCLUDE_DIRS@") 41 | 42 | @Caffe_INSTALL_INCLUDE_DIR_APPEND_COMMAND@ 43 | 44 | # Our library dependencies 45 | if(NOT TARGET caffe AND NOT caffe_BINARY_DIR) 46 | include("${Caffe_CMAKE_DIR}/CaffeTargets.cmake") 47 | endif() 48 | 49 | # List of IMPORTED libs created by CaffeTargets.cmake 50 | set(Caffe_LIBRARIES caffe) 51 | 52 | # Definitions 53 | set(Caffe_DEFINITIONS "@Caffe_DEFINITIONS@") 54 | 55 | # Cuda support variables 56 | set(Caffe_CPU_ONLY @CPU_ONLY@) 57 | set(Caffe_HAVE_CUDA @HAVE_CUDA@) 58 | set(Caffe_HAVE_CUDNN @HAVE_CUDNN@) 59 | -------------------------------------------------------------------------------- /caffe_DOC/cmake/Templates/CaffeConfigVersion.cmake.in: -------------------------------------------------------------------------------- 1 | set(PACKAGE_VERSION "@Caffe_VERSION@") 2 | 3 | # Check whether the requested PACKAGE_FIND_VERSION is compatible 4 | if("${PACKAGE_VERSION}" VERSION_LESS "${PACKAGE_FIND_VERSION}") 5 | set(PACKAGE_VERSION_COMPATIBLE FALSE) 6 | else() 7 | set(PACKAGE_VERSION_COMPATIBLE TRUE) 8 | if ("${PACKAGE_VERSION}" VERSION_EQUAL "${PACKAGE_FIND_VERSION}") 9 | set(PACKAGE_VERSION_EXACT TRUE) 10 | endif() 11 | endif() 12 | -------------------------------------------------------------------------------- /caffe_DOC/cmake/Templates/caffe_config.h.in: -------------------------------------------------------------------------------- 1 | /* Sources directory */ 2 | #define SOURCE_FOLDER "${PROJECT_SOURCE_DIR}" 3 | 4 | /* Binaries directory */ 5 | #define BINARY_FOLDER "${PROJECT_BINARY_DIR}" 6 | 7 | /* NVIDA Cuda */ 8 | #cmakedefine HAVE_CUDA 9 | 10 | /* NVIDA cuDNN */ 11 | #cmakedefine HAVE_CUDNN 12 | #cmakedefine USE_CUDNN 13 | 14 | /* NVIDA cuDNN */ 15 | #cmakedefine CPU_ONLY 16 | 17 | /* Test device */ 18 | #define CUDA_TEST_DEVICE ${CUDA_TEST_DEVICE} 19 | 20 | /* Temporary (TODO: remove) */ 21 | #if 1 22 | #define CMAKE_SOURCE_DIR SOURCE_FOLDER "/src/" 23 | #define EXAMPLES_SOURCE_DIR BINARY_FOLDER "/examples/" 24 | #define CMAKE_EXT ".gen.cmake" 25 | #else 26 | #define CMAKE_SOURCE_DIR "src/" 27 | #define EXAMPLES_SOURCE_DIR "examples/" 28 | #define CMAKE_EXT "" 29 | #endif 30 | 31 | /* Matlab */ 32 | #cmakedefine HAVE_MATLAB 33 | -------------------------------------------------------------------------------- /caffe_DOC/cmake/lint.cmake: -------------------------------------------------------------------------------- 1 | 2 | set(CMAKE_SOURCE_DIR ..) 3 | set(LINT_COMMAND ${CMAKE_SOURCE_DIR}/scripts/cpp_lint.py) 4 | set(SRC_FILE_EXTENSIONS h hpp hu c cpp cu cc) 5 | set(EXCLUDE_FILE_EXTENSTIONS pb.h pb.cc) 6 | set(LINT_DIRS include src/caffe examples tools python matlab) 7 | 8 | cmake_policy(SET CMP0009 NEW) # suppress cmake warning 9 | 10 | # find all files of interest 11 | foreach(ext ${SRC_FILE_EXTENSIONS}) 12 | foreach(dir ${LINT_DIRS}) 13 | file(GLOB_RECURSE FOUND_FILES ${CMAKE_SOURCE_DIR}/${dir}/*.${ext}) 14 | set(LINT_SOURCES ${LINT_SOURCES} ${FOUND_FILES}) 15 | endforeach() 16 | endforeach() 17 | 18 | # find all files that should be excluded 19 | foreach(ext ${EXCLUDE_FILE_EXTENSTIONS}) 20 | file(GLOB_RECURSE FOUND_FILES ${CMAKE_SOURCE_DIR}/*.${ext}) 21 | set(EXCLUDED_FILES ${EXCLUDED_FILES} ${FOUND_FILES}) 22 | endforeach() 23 | 24 | # exclude generated pb files 25 | list(REMOVE_ITEM LINT_SOURCES ${EXCLUDED_FILES}) 26 | 27 | execute_process( 28 | COMMAND ${LINT_COMMAND} ${LINT_SOURCES} 29 | ERROR_VARIABLE LINT_OUTPUT 30 | ERROR_STRIP_TRAILING_WHITESPACE 31 | ) 32 | 33 | string(REPLACE "\n" ";" LINT_OUTPUT ${LINT_OUTPUT}) 34 | 35 | list(GET LINT_OUTPUT -1 LINT_RESULT) 36 | list(REMOVE_AT LINT_OUTPUT -1) 37 | string(REPLACE " " ";" LINT_RESULT ${LINT_RESULT}) 38 | list(GET LINT_RESULT -1 NUM_ERRORS) 39 | if(NUM_ERRORS GREATER 0) 40 | foreach(msg ${LINT_OUTPUT}) 41 | string(FIND ${msg} "Done" result) 42 | if(result LESS 0) 43 | message(STATUS ${msg}) 44 | endif() 45 | endforeach() 46 | message(FATAL_ERROR "Lint found ${NUM_ERRORS} errors!") 47 | else() 48 | message(STATUS "Lint did not find any errors!") 49 | endif() 50 | 51 | -------------------------------------------------------------------------------- /caffe_DOC/docs/CNAME: -------------------------------------------------------------------------------- 1 | caffe.berkeleyvision.org 2 | -------------------------------------------------------------------------------- /caffe_DOC/docs/README.md: -------------------------------------------------------------------------------- 1 | # Caffe Documentation 2 | 3 | To generate the documentation, run `$CAFFE_ROOT/scripts/build_docs.sh`. 4 | 5 | To push your changes to the documentation to the gh-pages branch of your or the BVLC repo, run `$CAFFE_ROOT/scripts/deploy_docs.sh `. 6 | -------------------------------------------------------------------------------- /caffe_DOC/docs/_config.yml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - 3 | scope: 4 | path: "" # an empty string here means all files in the project 5 | values: 6 | layout: "default" 7 | 8 | -------------------------------------------------------------------------------- /caffe_DOC/docs/_layouts/default.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 8 | 9 | 10 | 11 | Caffe {% if page contains 'title' %}| {{ page.title }}{% endif %} 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 24 | 25 | 26 | 35 |
36 |
37 |

Caffe

38 |

39 | Deep learning framework by the BVLC 40 |

41 |

42 | Created by 43 |
44 | Yangqing Jia 45 |
46 | Lead Developer 47 |
48 | Evan Shelhamer 49 |

54 |
55 |
56 | 57 | {{ content }} 58 | 59 |
60 |
61 | 62 | 63 | -------------------------------------------------------------------------------- /caffe_DOC/docs/images/GitHub-Mark-64px.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pengwangucla/DOC/9bf812a6d30c2a2c46afb453713150f1c4d05869/caffe_DOC/docs/images/GitHub-Mark-64px.png -------------------------------------------------------------------------------- /caffe_DOC/docs/images/caffeine-icon.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pengwangucla/DOC/9bf812a6d30c2a2c46afb453713150f1c4d05869/caffe_DOC/docs/images/caffeine-icon.png -------------------------------------------------------------------------------- /caffe_DOC/docs/install_apt.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Installation: Ubuntu 3 | --- 4 | 5 | # Ubuntu Installation 6 | 7 | **General dependencies** 8 | 9 | sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler 10 | sudo apt-get install --no-install-recommends libboost-all-dev 11 | 12 | **CUDA**: Install via the NVIDIA package instead of `apt-get` to be certain of the library and driver versions. 13 | Install the library and latest driver separately; the driver bundled with the library is usually out-of-date. 14 | This can be skipped for CPU-only installation. 15 | 16 | **BLAS**: install ATLAS by `sudo apt-get install libatlas-base-dev` or install OpenBLAS or MKL for better CPU performance. 17 | 18 | **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. 19 | 20 | **Remaining dependencies, 14.04** 21 | 22 | Everything is packaged in 14.04. 23 | 24 | sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev 25 | 26 | **Remaining dependencies, 12.04** 27 | 28 | These dependencies need manual installation in 12.04. 29 | 30 | # glog 31 | wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz 32 | tar zxvf glog-0.3.3.tar.gz 33 | cd glog-0.3.3 34 | ./configure 35 | make && make install 36 | # gflags 37 | wget https://github.com/schuhschuh/gflags/archive/master.zip 38 | unzip master.zip 39 | cd gflags-master 40 | mkdir build && cd build 41 | export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1 42 | make && make install 43 | # lmdb 44 | git clone https://github.com/LMDB/lmdb 45 | cd lmdb/libraries/liblmdb 46 | make && make install 47 | 48 | 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. 49 | 50 | Continue with [compilation](installation.html#compilation). 51 | -------------------------------------------------------------------------------- /caffe_DOC/docs/install_yum.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Installation: RHEL / Fedora / CentOS 3 | --- 4 | 5 | # RHEL / Fedora / CentOS Installation 6 | 7 | **General dependencies** 8 | 9 | sudo yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel 10 | 11 | **Remaining dependencies, recent OS** 12 | 13 | sudo yum install gflags-devel glog-devel lmdb-devel 14 | 15 | **Remaining dependencies, if not found** 16 | 17 | # glog 18 | wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz 19 | tar zxvf glog-0.3.3.tar.gz 20 | cd glog-0.3.3 21 | ./configure 22 | make && make install 23 | # gflags 24 | wget https://github.com/schuhschuh/gflags/archive/master.zip 25 | unzip master.zip 26 | cd gflags-master 27 | mkdir build && cd build 28 | export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1 29 | make && make install 30 | # lmdb 31 | git clone https://github.com/LMDB/lmdb 32 | cd lmdb/libraries/liblmdb 33 | make && make install 34 | 35 | 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. 36 | 37 | **CUDA**: Install via the NVIDIA package instead of `yum` to be certain of the library and driver versions. 38 | Install the library and latest driver separately; the driver bundled with the library is usually out-of-date. 39 | + CentOS/RHEL/Fedora: 40 | 41 | **BLAS**: install ATLAS by `sudo yum install atlas-devel` or install OpenBLAS or MKL for better CPU performance. For the Makefile build, uncomment and set `BLAS_LIB` accordingly as ATLAS is usually installed under `/usr/lib[64]/atlas`). 42 | 43 | **Python** (optional): if you use the default Python you will need to `sudo yum install` the `python-devel` package to have the Python headers for building the pycaffe wrapper. 44 | 45 | Continue with [compilation](installation.html#compilation). 46 | -------------------------------------------------------------------------------- /caffe_DOC/docs/stylesheets/reset.css: -------------------------------------------------------------------------------- 1 | /* MeyerWeb Reset */ 2 | 3 | html, body, div, span, applet, object, iframe, 4 | h1, h2, h3, h4, h5, h6, p, blockquote, pre, 5 | a, abbr, acronym, address, big, cite, code, 6 | del, dfn, em, img, ins, kbd, q, s, samp, 7 | small, strike, strong, sub, sup, tt, var, 8 | b, u, i, center, 9 | dl, dt, dd, ol, ul, li, 10 | fieldset, form, label, legend, 11 | table, caption, tbody, tfoot, thead, tr, th, td, 12 | article, aside, canvas, details, embed, 13 | figure, figcaption, footer, header, hgroup, 14 | menu, nav, output, ruby, section, summary, 15 | time, mark, audio, video { 16 | margin: 0; 17 | padding: 0; 18 | border: 0; 19 | font: inherit; 20 | vertical-align: baseline; 21 | } 22 | -------------------------------------------------------------------------------- /caffe_DOC/docs/tutorial/convolution.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Convolution 3 | --- 4 | # Caffeinated Convolution 5 | 6 | The Caffe strategy for convolution is to reduce the problem to matrix-matrix multiplication. 7 | This linear algebra computation is highly-tuned in BLAS libraries and efficiently computed on GPU devices. 8 | 9 | For more details read Yangqing's [Convolution in Caffe: a memo](https://github.com/Yangqing/caffe/wiki/Convolution-in-Caffe:-a-memo). 10 | 11 | As it turns out, this same reduction was independently explored in the context of conv. nets by 12 | 13 | > K. Chellapilla, S. Puri, P. Simard, et al. High performance convolutional neural networks for document processing. In Tenth International Workshop on Frontiers in Handwriting Recognition, 2006. 14 | -------------------------------------------------------------------------------- /caffe_DOC/docs/tutorial/fig/.gitignore: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pengwangucla/DOC/9bf812a6d30c2a2c46afb453713150f1c4d05869/caffe_DOC/docs/tutorial/fig/.gitignore -------------------------------------------------------------------------------- /caffe_DOC/docs/tutorial/fig/backward.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pengwangucla/DOC/9bf812a6d30c2a2c46afb453713150f1c4d05869/caffe_DOC/docs/tutorial/fig/backward.jpg -------------------------------------------------------------------------------- /caffe_DOC/docs/tutorial/fig/forward.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pengwangucla/DOC/9bf812a6d30c2a2c46afb453713150f1c4d05869/caffe_DOC/docs/tutorial/fig/forward.jpg -------------------------------------------------------------------------------- /caffe_DOC/docs/tutorial/fig/forward_backward.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pengwangucla/DOC/9bf812a6d30c2a2c46afb453713150f1c4d05869/caffe_DOC/docs/tutorial/fig/forward_backward.png -------------------------------------------------------------------------------- /caffe_DOC/docs/tutorial/fig/layer.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pengwangucla/DOC/9bf812a6d30c2a2c46afb453713150f1c4d05869/caffe_DOC/docs/tutorial/fig/layer.jpg -------------------------------------------------------------------------------- /caffe_DOC/docs/tutorial/fig/logreg.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pengwangucla/DOC/9bf812a6d30c2a2c46afb453713150f1c4d05869/caffe_DOC/docs/tutorial/fig/logreg.jpg -------------------------------------------------------------------------------- /caffe_DOC/include/caffe/caffe.hpp: -------------------------------------------------------------------------------- 1 | // caffe.hpp is the header file that you need to include in your code. It wraps 2 | // all the internal caffe header files into one for simpler inclusion. 3 | 4 | #ifndef CAFFE_CAFFE_HPP_ 5 | #define CAFFE_CAFFE_HPP_ 6 | 7 | #include "caffe/blob.hpp" 8 | #include "caffe/common.hpp" 9 | #include "caffe/filler.hpp" 10 | #include "caffe/layer.hpp" 11 | #include "caffe/layer_factory.hpp" 12 | #include "caffe/net.hpp" 13 | #include "caffe/parallel.hpp" 14 | #include "caffe/proto/caffe.pb.h" 15 | #include "caffe/solver.hpp" 16 | #include "caffe/util/benchmark.hpp" 17 | #include "caffe/util/io.hpp" 18 | #include "caffe/vision_layers.hpp" 19 | 20 | #endif // CAFFE_CAFFE_HPP_ 21 | -------------------------------------------------------------------------------- /caffe_DOC/include/caffe/data_reader.hpp: -------------------------------------------------------------------------------- 1 | #ifndef CAFFE_DATA_READER_HPP_ 2 | #define CAFFE_DATA_READER_HPP_ 3 | 4 | #include 5 | #include 6 | #include 7 | 8 | #include "caffe/common.hpp" 9 | #include "caffe/internal_thread.hpp" 10 | #include "caffe/util/blocking_queue.hpp" 11 | #include "caffe/util/db.hpp" 12 | 13 | namespace caffe { 14 | 15 | /** 16 | * @brief Reads data from a source to queues available to data layers. 17 | * A single reading thread is created per source, even if multiple solvers 18 | * are running in parallel, e.g. for multi-GPU training. This makes sure 19 | * databases are read sequentially, and that each solver accesses a different 20 | * subset of the database. Data is distributed to solvers in a round-robin 21 | * way to keep parallel training deterministic. 22 | */ 23 | class DataReader { 24 | public: 25 | explicit DataReader(const LayerParameter& param); 26 | ~DataReader(); 27 | 28 | inline BlockingQueue& free() const { 29 | return queue_pair_->free_; 30 | } 31 | inline BlockingQueue& full() const { 32 | return queue_pair_->full_; 33 | } 34 | 35 | protected: 36 | // Queue pairs are shared between a body and its readers 37 | class QueuePair { 38 | public: 39 | explicit QueuePair(int size); 40 | ~QueuePair(); 41 | 42 | BlockingQueue free_; 43 | BlockingQueue full_; 44 | 45 | DISABLE_COPY_AND_ASSIGN(QueuePair); 46 | }; 47 | 48 | // A single body is created per source 49 | class Body : public InternalThread { 50 | public: 51 | explicit Body(const LayerParameter& param); 52 | virtual ~Body(); 53 | 54 | protected: 55 | void InternalThreadEntry(); 56 | void read_one(db::Cursor* cursor, QueuePair* qp); 57 | 58 | const LayerParameter param_; 59 | BlockingQueue > new_queue_pairs_; 60 | 61 | friend class DataReader; 62 | 63 | DISABLE_COPY_AND_ASSIGN(Body); 64 | }; 65 | 66 | // A source is uniquely identified by its layer name + path, in case 67 | // the same database is read from two different locations in the net. 68 | static inline string source_key(const LayerParameter& param) { 69 | return param.name() + ":" + param.data_param().source(); 70 | } 71 | 72 | const shared_ptr queue_pair_; 73 | shared_ptr body_; 74 | 75 | static map > bodies_; 76 | 77 | DISABLE_COPY_AND_ASSIGN(DataReader); 78 | }; 79 | 80 | } // namespace caffe 81 | 82 | #endif // CAFFE_DATA_READER_HPP_ 83 | -------------------------------------------------------------------------------- /caffe_DOC/include/caffe/internal_thread.hpp: -------------------------------------------------------------------------------- 1 | #ifndef CAFFE_INTERNAL_THREAD_HPP_ 2 | #define CAFFE_INTERNAL_THREAD_HPP_ 3 | 4 | #include "caffe/common.hpp" 5 | 6 | /** 7 | Forward declare boost::thread instead of including boost/thread.hpp 8 | to avoid a boost/NVCC issues (#1009, #1010) on OSX. 9 | */ 10 | namespace boost { class thread; } 11 | 12 | namespace caffe { 13 | 14 | /** 15 | * Virtual class encapsulate boost::thread for use in base class 16 | * The child class will acquire the ability to run a single thread, 17 | * by reimplementing the virtual function InternalThreadEntry. 18 | */ 19 | class InternalThread { 20 | public: 21 | InternalThread() : thread_() {} 22 | virtual ~InternalThread(); 23 | 24 | /** 25 | * Caffe's thread local state will be initialized using the current 26 | * thread values, e.g. device id, solver index etc. The random seed 27 | * is initialized using caffe_rng_rand. 28 | */ 29 | void StartInternalThread(); 30 | 31 | /** Will not return until the internal thread has exited. */ 32 | void StopInternalThread(); 33 | 34 | bool is_started() const; 35 | 36 | protected: 37 | /* Implement this method in your subclass 38 | with the code you want your thread to run. */ 39 | virtual void InternalThreadEntry() {} 40 | 41 | /* Should be tested when running loops to exit when requested. */ 42 | bool must_stop(); 43 | 44 | private: 45 | void entry(int device, Caffe::Brew mode, int rand_seed, int solver_count, 46 | bool root_solver); 47 | 48 | shared_ptr thread_; 49 | }; 50 | 51 | } // namespace caffe 52 | 53 | #endif // CAFFE_INTERNAL_THREAD_HPP_ 54 | -------------------------------------------------------------------------------- /caffe_DOC/include/caffe/python_layer.hpp: -------------------------------------------------------------------------------- 1 | #ifndef CAFFE_PYTHON_LAYER_HPP_ 2 | #define CAFFE_PYTHON_LAYER_HPP_ 3 | 4 | #include 5 | #include 6 | 7 | #include "caffe/layer.hpp" 8 | 9 | namespace bp = boost::python; 10 | 11 | namespace caffe { 12 | 13 | template 14 | class PythonLayer : public Layer { 15 | public: 16 | PythonLayer(PyObject* self, const LayerParameter& param) 17 | : Layer(param), self_(bp::handle<>(bp::borrowed(self))) { } 18 | 19 | virtual void LayerSetUp(const vector*>& bottom, 20 | const vector*>& top) { 21 | self_.attr("param_str") = bp::str( 22 | this->layer_param_.python_param().param_str()); 23 | self_.attr("setup")(bottom, top); 24 | } 25 | virtual void Reshape(const vector*>& bottom, 26 | const vector*>& top) { 27 | self_.attr("reshape")(bottom, top); 28 | } 29 | 30 | virtual inline bool ShareInParallel() const { 31 | return this->layer_param_.python_param().share_in_parallel(); 32 | } 33 | 34 | virtual inline const char* type() const { return "Python"; } 35 | 36 | protected: 37 | virtual void Forward_cpu(const vector*>& bottom, 38 | const vector*>& top) { 39 | self_.attr("forward")(bottom, top); 40 | } 41 | virtual void Backward_cpu(const vector*>& top, 42 | const vector& propagate_down, const vector*>& bottom) { 43 | self_.attr("backward")(top, propagate_down, bottom); 44 | } 45 | 46 | private: 47 | bp::object self_; 48 | }; 49 | 50 | } // namespace caffe 51 | 52 | #endif 53 | -------------------------------------------------------------------------------- /caffe_DOC/include/caffe/test/test_caffe_main.hpp: -------------------------------------------------------------------------------- 1 | // The main caffe test code. Your test cpp code should include this hpp 2 | // to allow a main function to be compiled into the binary. 3 | #ifndef CAFFE_TEST_TEST_CAFFE_MAIN_HPP_ 4 | #define CAFFE_TEST_TEST_CAFFE_MAIN_HPP_ 5 | 6 | #include 7 | #include 8 | 9 | #include 10 | #include 11 | 12 | #include "caffe/common.hpp" 13 | 14 | using std::cout; 15 | using std::endl; 16 | 17 | #ifdef CMAKE_BUILD 18 | #include "caffe_config.h" 19 | #else 20 | #define CUDA_TEST_DEVICE -1 21 | #define CMAKE_SOURCE_DIR "src/" 22 | #define EXAMPLES_SOURCE_DIR "examples/" 23 | #define CMAKE_EXT "" 24 | #endif 25 | 26 | int main(int argc, char** argv); 27 | 28 | namespace caffe { 29 | 30 | template 31 | class MultiDeviceTest : public ::testing::Test { 32 | public: 33 | typedef typename TypeParam::Dtype Dtype; 34 | protected: 35 | MultiDeviceTest() { 36 | Caffe::set_mode(TypeParam::device); 37 | } 38 | virtual ~MultiDeviceTest() {} 39 | }; 40 | 41 | typedef ::testing::Types TestDtypes; 42 | 43 | template 44 | struct CPUDevice { 45 | typedef TypeParam Dtype; 46 | static const Caffe::Brew device = Caffe::CPU; 47 | }; 48 | 49 | template 50 | class CPUDeviceTest : public MultiDeviceTest > { 51 | }; 52 | 53 | #ifdef CPU_ONLY 54 | 55 | typedef ::testing::Types, 56 | CPUDevice > TestDtypesAndDevices; 57 | 58 | #else 59 | 60 | template 61 | struct GPUDevice { 62 | typedef TypeParam Dtype; 63 | static const Caffe::Brew device = Caffe::GPU; 64 | }; 65 | 66 | template 67 | class GPUDeviceTest : public MultiDeviceTest > { 68 | }; 69 | 70 | typedef ::testing::Types, CPUDevice, 71 | GPUDevice, GPUDevice > 72 | TestDtypesAndDevices; 73 | 74 | #endif 75 | 76 | } // namespace caffe 77 | 78 | #endif // CAFFE_TEST_TEST_CAFFE_MAIN_HPP_ 79 | -------------------------------------------------------------------------------- /caffe_DOC/include/caffe/util/benchmark.hpp: -------------------------------------------------------------------------------- 1 | #ifndef CAFFE_UTIL_BENCHMARK_H_ 2 | #define CAFFE_UTIL_BENCHMARK_H_ 3 | 4 | #include 5 | 6 | #include "caffe/util/device_alternate.hpp" 7 | 8 | namespace caffe { 9 | 10 | class Timer { 11 | public: 12 | Timer(); 13 | virtual ~Timer(); 14 | virtual void Start(); 15 | virtual void Stop(); 16 | virtual float MilliSeconds(); 17 | virtual float MicroSeconds(); 18 | virtual float Seconds(); 19 | 20 | inline bool initted() { return initted_; } 21 | inline bool running() { return running_; } 22 | inline bool has_run_at_least_once() { return has_run_at_least_once_; } 23 | 24 | protected: 25 | void Init(); 26 | 27 | bool initted_; 28 | bool running_; 29 | bool has_run_at_least_once_; 30 | #ifndef CPU_ONLY 31 | cudaEvent_t start_gpu_; 32 | cudaEvent_t stop_gpu_; 33 | #endif 34 | boost::posix_time::ptime start_cpu_; 35 | boost::posix_time::ptime stop_cpu_; 36 | float elapsed_milliseconds_; 37 | float elapsed_microseconds_; 38 | }; 39 | 40 | class CPUTimer : public Timer { 41 | public: 42 | explicit CPUTimer(); 43 | virtual ~CPUTimer() {} 44 | virtual void Start(); 45 | virtual void Stop(); 46 | virtual float MilliSeconds(); 47 | virtual float MicroSeconds(); 48 | }; 49 | 50 | } // namespace caffe 51 | 52 | #endif // CAFFE_UTIL_BENCHMARK_H_ 53 | -------------------------------------------------------------------------------- /caffe_DOC/include/caffe/util/blocking_queue.hpp: -------------------------------------------------------------------------------- 1 | #ifndef CAFFE_UTIL_BLOCKING_QUEUE_HPP_ 2 | #define CAFFE_UTIL_BLOCKING_QUEUE_HPP_ 3 | 4 | #include 5 | #include 6 | 7 | #include "caffe/common.hpp" 8 | 9 | namespace caffe { 10 | 11 | template 12 | class BlockingQueue { 13 | public: 14 | explicit BlockingQueue(); 15 | 16 | void push(const T& t); 17 | 18 | bool try_pop(T* t); 19 | 20 | // This logs a message if the threads needs to be blocked 21 | // useful for detecting e.g. when data feeding is too slow 22 | T pop(const string& log_on_wait = ""); 23 | 24 | bool try_peek(T* t); 25 | 26 | // Return element without removing it 27 | T peek(); 28 | 29 | size_t size() const; 30 | 31 | protected: 32 | /** 33 | Move synchronization fields out instead of including boost/thread.hpp 34 | to avoid a boost/NVCC issues (#1009, #1010) on OSX. Also fails on 35 | Linux CUDA 7.0.18. 36 | */ 37 | class sync; 38 | 39 | std::queue queue_; 40 | shared_ptr sync_; 41 | 42 | DISABLE_COPY_AND_ASSIGN(BlockingQueue); 43 | }; 44 | 45 | } // namespace caffe 46 | 47 | #endif 48 | -------------------------------------------------------------------------------- /caffe_DOC/include/caffe/util/coords.hpp: -------------------------------------------------------------------------------- 1 | #ifndef CAFFE_UTIL_COORDS_H_ 2 | #define CAFFE_UTIL_COORDS_H_ 3 | 4 | #include 5 | #include 6 | #include 7 | 8 | namespace caffe { 9 | 10 | template 11 | class DiagonalAffineMap { 12 | public: 13 | explicit DiagonalAffineMap(const vector > coefs) 14 | : coefs_(coefs) { } 15 | static DiagonalAffineMap identity(const int nd) { 16 | return DiagonalAffineMap(vector >(nd, make_pair(1, 0))); 17 | } 18 | 19 | inline DiagonalAffineMap compose(const DiagonalAffineMap& other) const { 20 | CHECK_EQ(coefs_.size(), other.coefs_.size()) 21 | << "Attempt to compose DiagonalAffineMaps of different dimensions"; 22 | DiagonalAffineMap out; 23 | transform(coefs_.begin(), coefs_.end(), other.coefs_.begin(), 24 | std::back_inserter(out.coefs_), &compose_coefs); 25 | return out; 26 | } 27 | inline DiagonalAffineMap inv() const { 28 | DiagonalAffineMap out; 29 | transform(coefs_.begin(), coefs_.end(), std::back_inserter(out.coefs_), 30 | &inv_coefs); 31 | return out; 32 | } 33 | inline vector > coefs() { return coefs_; } 34 | 35 | private: 36 | DiagonalAffineMap() { } 37 | static inline pair compose_coefs(pair left, 38 | pair right) { 39 | return make_pair(left.first * right.first, 40 | left.first * right.second + left.second); 41 | } 42 | static inline pair inv_coefs(pair coefs) { 43 | return make_pair(1 / coefs.first, - coefs.second / coefs.first); 44 | } 45 | vector > coefs_; 46 | }; 47 | 48 | template 49 | DiagonalAffineMap FilterMap(const int kernel_h, const int kernel_w, 50 | const int stride_h, const int stride_w, const int pad_h, const int pad_w) { 51 | vector > coefs; 52 | coefs.push_back(make_pair(stride_h, 53 | static_cast(kernel_h - 1) / 2 - pad_h)); 54 | coefs.push_back(make_pair(stride_w, 55 | static_cast(kernel_w - 1) / 2 - pad_w)); 56 | return DiagonalAffineMap(coefs); 57 | } 58 | 59 | } // namespace caffe 60 | 61 | #endif // CAFFE_UTIL_COORDS_H_ 62 | -------------------------------------------------------------------------------- /caffe_DOC/include/caffe/util/db.hpp: -------------------------------------------------------------------------------- 1 | #ifndef CAFFE_UTIL_DB_HPP 2 | #define CAFFE_UTIL_DB_HPP 3 | 4 | #include 5 | 6 | #include "caffe/common.hpp" 7 | #include "caffe/proto/caffe.pb.h" 8 | 9 | namespace caffe { namespace db { 10 | 11 | enum Mode { READ, WRITE, NEW }; 12 | 13 | class Cursor { 14 | public: 15 | Cursor() { } 16 | virtual ~Cursor() { } 17 | virtual void SeekToFirst() = 0; 18 | virtual void Next() = 0; 19 | virtual string key() = 0; 20 | virtual string value() = 0; 21 | virtual bool valid() = 0; 22 | 23 | DISABLE_COPY_AND_ASSIGN(Cursor); 24 | }; 25 | 26 | class Transaction { 27 | public: 28 | Transaction() { } 29 | virtual ~Transaction() { } 30 | virtual void Put(const string& key, const string& value) = 0; 31 | virtual void Commit() = 0; 32 | 33 | DISABLE_COPY_AND_ASSIGN(Transaction); 34 | }; 35 | 36 | class DB { 37 | public: 38 | DB() { } 39 | virtual ~DB() { } 40 | virtual void Open(const string& source, Mode mode) = 0; 41 | virtual void Close() = 0; 42 | virtual Cursor* NewCursor() = 0; 43 | virtual Transaction* NewTransaction() = 0; 44 | 45 | DISABLE_COPY_AND_ASSIGN(DB); 46 | }; 47 | 48 | DB* GetDB(DataParameter::DB backend); 49 | DB* GetDB(const string& backend); 50 | 51 | } // namespace db 52 | } // namespace caffe 53 | 54 | #endif // CAFFE_UTIL_DB_HPP 55 | -------------------------------------------------------------------------------- /caffe_DOC/include/caffe/util/db_leveldb.hpp: -------------------------------------------------------------------------------- 1 | #ifndef CAFFE_UTIL_DB_LEVELDB_HPP 2 | #define CAFFE_UTIL_DB_LEVELDB_HPP 3 | 4 | #include 5 | 6 | #include "leveldb/db.h" 7 | #include "leveldb/write_batch.h" 8 | 9 | #include "caffe/util/db.hpp" 10 | 11 | namespace caffe { namespace db { 12 | 13 | class LevelDBCursor : public Cursor { 14 | public: 15 | explicit LevelDBCursor(leveldb::Iterator* iter) 16 | : iter_(iter) { SeekToFirst(); } 17 | ~LevelDBCursor() { delete iter_; } 18 | virtual void SeekToFirst() { iter_->SeekToFirst(); } 19 | virtual void Next() { iter_->Next(); } 20 | virtual string key() { return iter_->key().ToString(); } 21 | virtual string value() { return iter_->value().ToString(); } 22 | virtual bool valid() { return iter_->Valid(); } 23 | 24 | private: 25 | leveldb::Iterator* iter_; 26 | }; 27 | 28 | class LevelDBTransaction : public Transaction { 29 | public: 30 | explicit LevelDBTransaction(leveldb::DB* db) : db_(db) { CHECK_NOTNULL(db_); } 31 | virtual void Put(const string& key, const string& value) { 32 | batch_.Put(key, value); 33 | } 34 | virtual void Commit() { 35 | leveldb::Status status = db_->Write(leveldb::WriteOptions(), &batch_); 36 | CHECK(status.ok()) << "Failed to write batch to leveldb " 37 | << std::endl << status.ToString(); 38 | } 39 | 40 | private: 41 | leveldb::DB* db_; 42 | leveldb::WriteBatch batch_; 43 | 44 | DISABLE_COPY_AND_ASSIGN(LevelDBTransaction); 45 | }; 46 | 47 | class LevelDB : public DB { 48 | public: 49 | LevelDB() : db_(NULL) { } 50 | virtual ~LevelDB() { Close(); } 51 | virtual void Open(const string& source, Mode mode); 52 | virtual void Close() { 53 | if (db_ != NULL) { 54 | delete db_; 55 | db_ = NULL; 56 | } 57 | } 58 | virtual LevelDBCursor* NewCursor() { 59 | return new LevelDBCursor(db_->NewIterator(leveldb::ReadOptions())); 60 | } 61 | virtual LevelDBTransaction* NewTransaction() { 62 | return new LevelDBTransaction(db_); 63 | } 64 | 65 | private: 66 | leveldb::DB* db_; 67 | }; 68 | 69 | 70 | } // namespace db 71 | } // namespace caffe 72 | 73 | #endif // CAFFE_UTIL_DB_LEVELDB_HPP 74 | -------------------------------------------------------------------------------- /caffe_DOC/include/caffe/util/gpu_util.cuh: -------------------------------------------------------------------------------- 1 | #ifndef CAFFE_UTIL_GPU_UTIL_H_ 2 | #define CAFFE_UTIL_GPU_UTIL_H_ 3 | 4 | namespace caffe { 5 | 6 | template 7 | inline __device__ Dtype caffe_gpu_atomic_add(const Dtype val, Dtype* address); 8 | 9 | template <> 10 | inline __device__ 11 | float caffe_gpu_atomic_add(const float val, float* address) { 12 | return atomicAdd(address, val); 13 | } 14 | 15 | // double atomicAdd implementation taken from: 16 | // http://docs.nvidia.com/cuda/cuda-c-programming-guide/#axzz3PVCpVsEG 17 | template <> 18 | inline __device__ 19 | double caffe_gpu_atomic_add(const double val, double* address) { 20 | unsigned long long int* address_as_ull = // NOLINT(runtime/int) 21 | // NOLINT_NEXT_LINE(runtime/int) 22 | reinterpret_cast(address); 23 | unsigned long long int old = *address_as_ull; // NOLINT(runtime/int) 24 | unsigned long long int assumed; // NOLINT(runtime/int) 25 | do { 26 | assumed = old; 27 | old = atomicCAS(address_as_ull, assumed, 28 | __double_as_longlong(val + __longlong_as_double(assumed))); 29 | } while (assumed != old); 30 | return __longlong_as_double(old); 31 | } 32 | 33 | } // namespace caffe 34 | 35 | #endif // CAFFE_UTIL_GPU_UTIL_H_ 36 | -------------------------------------------------------------------------------- /caffe_DOC/include/caffe/util/hdf5.hpp: -------------------------------------------------------------------------------- 1 | #ifndef CAFFE_UTIL_HDF5_H_ 2 | #define CAFFE_UTIL_HDF5_H_ 3 | 4 | #include 5 | 6 | #include "hdf5.h" 7 | #include "hdf5_hl.h" 8 | 9 | #include "caffe/blob.hpp" 10 | 11 | namespace caffe { 12 | 13 | template 14 | void hdf5_load_nd_dataset_helper( 15 | hid_t file_id, const char* dataset_name_, int min_dim, int max_dim, 16 | Blob* blob); 17 | 18 | template 19 | void hdf5_load_nd_dataset( 20 | hid_t file_id, const char* dataset_name_, int min_dim, int max_dim, 21 | Blob* blob); 22 | 23 | template 24 | void hdf5_save_nd_dataset( 25 | const hid_t file_id, const string& dataset_name, const Blob& blob, 26 | bool write_diff = false); 27 | 28 | int hdf5_load_int(hid_t loc_id, const string& dataset_name); 29 | void hdf5_save_int(hid_t loc_id, const string& dataset_name, int i); 30 | string hdf5_load_string(hid_t loc_id, const string& dataset_name); 31 | void hdf5_save_string(hid_t loc_id, const string& dataset_name, 32 | const string& s); 33 | 34 | int hdf5_get_num_links(hid_t loc_id); 35 | string hdf5_get_name_by_idx(hid_t loc_id, int idx); 36 | 37 | } // namespace caffe 38 | 39 | #endif // CAFFE_UTIL_HDF5_H_ 40 | -------------------------------------------------------------------------------- /caffe_DOC/include/caffe/util/im2col.hpp: -------------------------------------------------------------------------------- 1 | #ifndef _CAFFE_UTIL_IM2COL_HPP_ 2 | #define _CAFFE_UTIL_IM2COL_HPP_ 3 | 4 | namespace caffe { 5 | 6 | template 7 | void im2col_cpu(const Dtype* data_im, const int channels, 8 | const int height, const int width, const int kernel_h, const int kernel_w, 9 | const int pad_h, const int pad_w, const int stride_h, 10 | const int stride_w, Dtype* data_col); 11 | 12 | template 13 | void col2im_cpu(const Dtype* data_col, const int channels, 14 | const int height, const int width, const int patch_h, const int patch_w, 15 | const int pad_h, const int pad_w, const int stride_h, 16 | const int stride_w, Dtype* data_im); 17 | 18 | template 19 | void im2col_gpu(const Dtype* data_im, const int channels, 20 | const int height, const int width, const int kernel_h, const int kernel_w, 21 | const int pad_h, const int pad_w, const int stride_h, 22 | const int stride_w, Dtype* data_col); 23 | 24 | template 25 | void col2im_gpu(const Dtype* data_col, const int channels, 26 | const int height, const int width, const int patch_h, const int patch_w, 27 | const int pad_h, const int pad_w, const int stride_h, 28 | const int stride_w, Dtype* data_im); 29 | 30 | } // namespace caffe 31 | 32 | #endif // CAFFE_UTIL_IM2COL_HPP_ 33 | -------------------------------------------------------------------------------- /caffe_DOC/include/caffe/util/insert_splits.hpp: -------------------------------------------------------------------------------- 1 | #ifndef _CAFFE_UTIL_INSERT_SPLITS_HPP_ 2 | #define _CAFFE_UTIL_INSERT_SPLITS_HPP_ 3 | 4 | #include 5 | 6 | #include "caffe/proto/caffe.pb.h" 7 | 8 | namespace caffe { 9 | 10 | // Copy NetParameters with SplitLayers added to replace any shared bottom 11 | // blobs with unique bottom blobs provided by the SplitLayer. 12 | void InsertSplits(const NetParameter& param, NetParameter* param_split); 13 | 14 | void ConfigureSplitLayer(const string& layer_name, const string& blob_name, 15 | const int blob_idx, const int split_count, const float loss_weight, 16 | LayerParameter* split_layer_param); 17 | 18 | string SplitLayerName(const string& layer_name, const string& blob_name, 19 | const int blob_idx); 20 | 21 | string SplitBlobName(const string& layer_name, const string& blob_name, 22 | const int blob_idx, const int split_idx); 23 | 24 | } // namespace caffe 25 | 26 | #endif // CAFFE_UTIL_INSERT_SPLITS_HPP_ 27 | -------------------------------------------------------------------------------- /caffe_DOC/include/caffe/util/rng.hpp: -------------------------------------------------------------------------------- 1 | #ifndef CAFFE_RNG_CPP_HPP_ 2 | #define CAFFE_RNG_CPP_HPP_ 3 | 4 | #include 5 | #include 6 | 7 | #include "boost/random/mersenne_twister.hpp" 8 | #include "boost/random/uniform_int.hpp" 9 | 10 | #include "caffe/common.hpp" 11 | 12 | namespace caffe { 13 | 14 | typedef boost::mt19937 rng_t; 15 | 16 | inline rng_t* caffe_rng() { 17 | return static_cast(Caffe::rng_stream().generator()); 18 | } 19 | 20 | // Fisher–Yates algorithm 21 | template 22 | inline void shuffle(RandomAccessIterator begin, RandomAccessIterator end, 23 | RandomGenerator* gen) { 24 | typedef typename std::iterator_traits::difference_type 25 | difference_type; 26 | typedef typename boost::uniform_int dist_type; 27 | 28 | difference_type length = std::distance(begin, end); 29 | if (length <= 0) return; 30 | 31 | for (difference_type i = length - 1; i > 0; --i) { 32 | dist_type dist(0, i); 33 | std::iter_swap(begin + i, begin + dist(*gen)); 34 | } 35 | } 36 | 37 | template 38 | inline void shuffle(RandomAccessIterator begin, RandomAccessIterator end) { 39 | shuffle(begin, end, caffe_rng()); 40 | } 41 | } // namespace caffe 42 | 43 | #endif // CAFFE_RNG_HPP_ 44 | -------------------------------------------------------------------------------- /caffe_DOC/include/caffe/util/signal_handler.h: -------------------------------------------------------------------------------- 1 | #ifndef INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_ 2 | #define INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_ 3 | 4 | #include "caffe/proto/caffe.pb.h" 5 | #include "caffe/solver.hpp" 6 | 7 | namespace caffe { 8 | 9 | class SignalHandler { 10 | public: 11 | // Contructor. Specify what action to take when a signal is received. 12 | SignalHandler(SolverAction::Enum SIGINT_action, 13 | SolverAction::Enum SIGHUP_action); 14 | ~SignalHandler(); 15 | ActionCallback GetActionFunction(); 16 | private: 17 | SolverAction::Enum CheckForSignals() const; 18 | SolverAction::Enum SIGINT_action_; 19 | SolverAction::Enum SIGHUP_action_; 20 | }; 21 | 22 | } // namespace caffe 23 | 24 | #endif // INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_ 25 | -------------------------------------------------------------------------------- /caffe_DOC/matlab/+caffe/+test/test_solver.m: -------------------------------------------------------------------------------- 1 | classdef test_solver < matlab.unittest.TestCase 2 | 3 | properties 4 | num_output 5 | solver 6 | end 7 | 8 | methods 9 | function self = test_solver() 10 | self.num_output = 13; 11 | model_file = caffe.test.test_net.simple_net_file(self.num_output); 12 | solver_file = tempname(); 13 | 14 | fid = fopen(solver_file, 'w'); 15 | fprintf(fid, [ ... 16 | 'net: "' model_file '"\n' ... 17 | 'test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9\n' ... 18 | 'weight_decay: 0.0005 lr_policy: "inv" gamma: 0.0001 power: 0.75\n' ... 19 | 'display: 100 max_iter: 100 snapshot_after_train: false\n' ]); 20 | fclose(fid); 21 | 22 | self.solver = caffe.Solver(solver_file); 23 | % also make sure get_solver runs 24 | caffe.get_solver(solver_file); 25 | caffe.set_mode_cpu(); 26 | % fill in valid labels 27 | self.solver.net.blobs('label').set_data(randi( ... 28 | self.num_output - 1, self.solver.net.blobs('label').shape)); 29 | self.solver.test_nets(1).blobs('label').set_data(randi( ... 30 | self.num_output - 1, self.solver.test_nets(1).blobs('label').shape)); 31 | 32 | delete(solver_file); 33 | delete(model_file); 34 | end 35 | end 36 | methods (Test) 37 | function test_solve(self) 38 | self.verifyEqual(self.solver.iter(), 0) 39 | self.solver.step(30); 40 | self.verifyEqual(self.solver.iter(), 30) 41 | self.solver.solve() 42 | self.verifyEqual(self.solver.iter(), 100) 43 | end 44 | end 45 | end 46 | -------------------------------------------------------------------------------- /caffe_DOC/matlab/+caffe/Layer.m: -------------------------------------------------------------------------------- 1 | classdef Layer < handle 2 | % Wrapper class of caffe::Layer in matlab 3 | 4 | properties (Access = private) 5 | hLayer_self 6 | attributes 7 | % attributes fields: 8 | % hBlob_blobs 9 | end 10 | properties (SetAccess = private) 11 | params 12 | end 13 | 14 | methods 15 | function self = Layer(hLayer_layer) 16 | CHECK(is_valid_handle(hLayer_layer), 'invalid Layer handle'); 17 | 18 | % setup self handle and attributes 19 | self.hLayer_self = hLayer_layer; 20 | self.attributes = caffe_('layer_get_attr', self.hLayer_self); 21 | 22 | % setup weights 23 | self.params = caffe.Blob.empty(); 24 | for n = 1:length(self.attributes.hBlob_blobs) 25 | self.params(n) = caffe.Blob(self.attributes.hBlob_blobs(n)); 26 | end 27 | end 28 | function layer_type = type(self) 29 | layer_type = caffe_('layer_get_type', self.hLayer_self); 30 | end 31 | end 32 | end 33 | -------------------------------------------------------------------------------- /caffe_DOC/matlab/+caffe/Solver.m: -------------------------------------------------------------------------------- 1 | classdef Solver < handle 2 | % Wrapper class of caffe::SGDSolver in matlab 3 | 4 | properties (Access = private) 5 | hSolver_self 6 | attributes 7 | % attribute fields 8 | % hNet_net 9 | % hNet_test_nets 10 | end 11 | properties (SetAccess = private) 12 | net 13 | test_nets 14 | end 15 | 16 | methods 17 | function self = Solver(varargin) 18 | % decide whether to construct a solver from solver_file or handle 19 | if ~(nargin == 1 && isstruct(varargin{1})) 20 | % construct a solver from solver_file 21 | self = caffe.get_solver(varargin{:}); 22 | return 23 | end 24 | % construct a solver from handle 25 | hSolver_solver = varargin{1}; 26 | CHECK(is_valid_handle(hSolver_solver), 'invalid Solver handle'); 27 | 28 | % setup self handle and attributes 29 | self.hSolver_self = hSolver_solver; 30 | self.attributes = caffe_('solver_get_attr', self.hSolver_self); 31 | 32 | % setup net and test_nets 33 | self.net = caffe.Net(self.attributes.hNet_net); 34 | self.test_nets = caffe.Net.empty(); 35 | for n = 1:length(self.attributes.hNet_test_nets) 36 | self.test_nets(n) = caffe.Net(self.attributes.hNet_test_nets(n)); 37 | end 38 | end 39 | function iter = iter(self) 40 | iter = caffe_('solver_get_iter', self.hSolver_self); 41 | end 42 | function restore(self, snapshot_filename) 43 | CHECK(ischar(snapshot_filename), 'snapshot_filename must be a string'); 44 | CHECK_FILE_EXIST(snapshot_filename); 45 | caffe_('solver_restore', self.hSolver_self, snapshot_filename); 46 | end 47 | function solve(self) 48 | caffe_('solver_solve', self.hSolver_self); 49 | end 50 | function step(self, iters) 51 | CHECK(isscalar(iters) && iters > 0, 'iters must be positive integer'); 52 | iters = double(iters); 53 | caffe_('solver_step', self.hSolver_self, iters); 54 | end 55 | end 56 | end 57 | -------------------------------------------------------------------------------- /caffe_DOC/matlab/+caffe/get_net.m: -------------------------------------------------------------------------------- 1 | function net = get_net(varargin) 2 | % net = get_net(model_file, phase_name) or 3 | % net = get_net(model_file, weights_file, phase_name) 4 | % Construct a net from model_file, and load weights from weights_file 5 | % phase_name can only be 'train' or 'test' 6 | 7 | CHECK(nargin == 2 || nargin == 3, ['usage: ' ... 8 | 'net = get_net(model_file, phase_name) or ' ... 9 | 'net = get_net(model_file, weights_file, phase_name)']); 10 | if nargin == 3 11 | model_file = varargin{1}; 12 | weights_file = varargin{2}; 13 | phase_name = varargin{3}; 14 | elseif nargin == 2 15 | model_file = varargin{1}; 16 | phase_name = varargin{2}; 17 | end 18 | 19 | CHECK(ischar(model_file), 'model_file must be a string'); 20 | CHECK(ischar(phase_name), 'phase_name must be a string'); 21 | CHECK_FILE_EXIST(model_file); 22 | CHECK(strcmp(phase_name, 'train') || strcmp(phase_name, 'test'), ... 23 | sprintf('phase_name can only be %strain%s or %stest%s', ... 24 | char(39), char(39), char(39), char(39))); 25 | 26 | % construct caffe net from model_file 27 | hNet = caffe_('get_net', model_file, phase_name); 28 | net = caffe.Net(hNet); 29 | 30 | % load weights from weights_file 31 | if nargin == 3 32 | CHECK(ischar(weights_file), 'weights_file must be a string'); 33 | CHECK_FILE_EXIST(weights_file); 34 | net.copy_from(weights_file); 35 | end 36 | 37 | end 38 | -------------------------------------------------------------------------------- /caffe_DOC/matlab/+caffe/get_solver.m: -------------------------------------------------------------------------------- 1 | function solver = get_solver(solver_file) 2 | % solver = get_solver(solver_file) 3 | % Construct a Solver object from solver_file 4 | 5 | CHECK(ischar(solver_file), 'solver_file must be a string'); 6 | CHECK_FILE_EXIST(solver_file); 7 | pSolver = caffe_('get_solver', solver_file); 8 | solver = caffe.Solver(pSolver); 9 | 10 | end 11 | -------------------------------------------------------------------------------- /caffe_DOC/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pengwangucla/DOC/9bf812a6d30c2a2c46afb453713150f1c4d05869/caffe_DOC/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat -------------------------------------------------------------------------------- /caffe_DOC/matlab/+caffe/io.m: -------------------------------------------------------------------------------- 1 | classdef io 2 | % a class for input and output functions 3 | 4 | methods (Static) 5 | function im_data = load_image(im_file) 6 | % im_data = load_image(im_file) 7 | % load an image from disk into Caffe-supported data format 8 | % switch channels from RGB to BGR, make width the fastest dimension 9 | % and convert to single 10 | % returns im_data in W x H x C. For colored images, C = 3 in BGR 11 | % channels, and for grayscale images, C = 1 12 | CHECK(ischar(im_file), 'im_file must be a string'); 13 | CHECK_FILE_EXIST(im_file); 14 | im_data = imread(im_file); 15 | % permute channels from RGB to BGR for colored images 16 | if size(im_data, 3) == 3 17 | im_data = im_data(:, :, [3, 2, 1]); 18 | end 19 | % flip width and height to make width the fastest dimension 20 | im_data = permute(im_data, [2, 1, 3]); 21 | % convert from uint8 to single 22 | im_data = single(im_data); 23 | end 24 | function mean_data = read_mean(mean_proto_file) 25 | % mean_data = read_mean(mean_proto_file) 26 | % read image mean data from binaryproto file 27 | % returns mean_data in W x H x C with BGR channels 28 | CHECK(ischar(mean_proto_file), 'mean_proto_file must be a string'); 29 | CHECK_FILE_EXIST(mean_proto_file); 30 | mean_data = caffe_('read_mean', mean_proto_file); 31 | end 32 | end 33 | end 34 | -------------------------------------------------------------------------------- /caffe_DOC/matlab/+caffe/private/CHECK.m: -------------------------------------------------------------------------------- 1 | function CHECK(expr, error_msg) 2 | 3 | if ~expr 4 | error(error_msg); 5 | end 6 | 7 | end 8 | -------------------------------------------------------------------------------- /caffe_DOC/matlab/+caffe/private/CHECK_FILE_EXIST.m: -------------------------------------------------------------------------------- 1 | function CHECK_FILE_EXIST(filename) 2 | 3 | if exist(filename, 'file') == 0 4 | error('%s does not exist', filename); 5 | end 6 | 7 | end 8 | -------------------------------------------------------------------------------- /caffe_DOC/matlab/+caffe/private/is_valid_handle.m: -------------------------------------------------------------------------------- 1 | function valid = is_valid_handle(hObj) 2 | % valid = is_valid_handle(hObj) or is_valid_handle('get_new_init_key') 3 | % Check if a handle is valid (has the right data type and init_key matches) 4 | % Use is_valid_handle('get_new_init_key') to get new init_key from C++; 5 | 6 | % a handle is a struct array with the following fields 7 | % (uint64) ptr : the pointer to the C++ object 8 | % (double) init_key : caffe initialization key 9 | 10 | persistent init_key; 11 | if isempty(init_key) 12 | init_key = caffe_('get_init_key'); 13 | end 14 | 15 | % is_valid_handle('get_new_init_key') to get new init_key from C++; 16 | if ischar(hObj) && strcmp(hObj, 'get_new_init_key') 17 | init_key = caffe_('get_init_key'); 18 | return 19 | else 20 | % check whether data types are correct and init_key matches 21 | valid = isstruct(hObj) ... 22 | && isscalar(hObj.ptr) && isa(hObj.ptr, 'uint64') ... 23 | && isscalar(hObj.init_key) && isa(hObj.init_key, 'double') ... 24 | && hObj.init_key == init_key; 25 | end 26 | 27 | end 28 | -------------------------------------------------------------------------------- /caffe_DOC/matlab/+caffe/reset_all.m: -------------------------------------------------------------------------------- 1 | function reset_all() 2 | % reset_all() 3 | % clear all solvers and stand-alone nets and reset Caffe to initial status 4 | 5 | caffe_('reset'); 6 | is_valid_handle('get_new_init_key'); 7 | 8 | end 9 | -------------------------------------------------------------------------------- /caffe_DOC/matlab/+caffe/run_tests.m: -------------------------------------------------------------------------------- 1 | function results = run_tests() 2 | % results = run_tests() 3 | % run all tests in this caffe matlab wrapper package 4 | 5 | % use CPU for testing 6 | caffe.set_mode_cpu(); 7 | 8 | % reset caffe before testing 9 | caffe.reset_all(); 10 | 11 | % put all test cases here 12 | results = [... 13 | run(caffe.test.test_net) ... 14 | run(caffe.test.test_solver) ]; 15 | 16 | % reset caffe after testing 17 | caffe.reset_all(); 18 | 19 | end 20 | -------------------------------------------------------------------------------- /caffe_DOC/matlab/+caffe/set_device.m: -------------------------------------------------------------------------------- 1 | function set_device(device_id) 2 | % set_device(device_id) 3 | % set Caffe's GPU device ID 4 | 5 | CHECK(isscalar(device_id) && device_id >= 0, ... 6 | 'device_id must be non-negative integer'); 7 | device_id = double(device_id); 8 | 9 | caffe_('set_device', device_id); 10 | 11 | end 12 | -------------------------------------------------------------------------------- /caffe_DOC/matlab/+caffe/set_mode_cpu.m: -------------------------------------------------------------------------------- 1 | function set_mode_cpu() 2 | % set_mode_cpu() 3 | % set Caffe to CPU mode 4 | 5 | caffe_('set_mode_cpu'); 6 | 7 | end 8 | -------------------------------------------------------------------------------- /caffe_DOC/matlab/+caffe/set_mode_gpu.m: -------------------------------------------------------------------------------- 1 | function set_mode_gpu() 2 | % set_mode_gpu() 3 | % set Caffe to GPU mode 4 | 5 | caffe_('set_mode_gpu'); 6 | 7 | end 8 | -------------------------------------------------------------------------------- /caffe_DOC/matlab/hdf5creation/.gitignore: -------------------------------------------------------------------------------- 1 | *.h5 2 | list.txt 3 | -------------------------------------------------------------------------------- /caffe_DOC/matlab/hdf5creation/demo.m: -------------------------------------------------------------------------------- 1 | %% WRITING TO HDF5 2 | filename='trial.h5'; 3 | 4 | num_total_samples=10000; 5 | % to simulate data being read from disk / generated etc. 6 | data_disk=rand(5,5,1,num_total_samples); 7 | label_disk=rand(10,num_total_samples); 8 | 9 | chunksz=100; 10 | created_flag=false; 11 | totalct=0; 12 | for batchno=1:num_total_samples/chunksz 13 | fprintf('batch no. %d\n', batchno); 14 | last_read=(batchno-1)*chunksz; 15 | 16 | % to simulate maximum data to be held in memory before dumping to hdf5 file 17 | batchdata=data_disk(:,:,1,last_read+1:last_read+chunksz); 18 | batchlabs=label_disk(:,last_read+1:last_read+chunksz); 19 | 20 | % store to hdf5 21 | startloc=struct('dat',[1,1,1,totalct+1], 'lab', [1,totalct+1]); 22 | curr_dat_sz=store2hdf5(filename, batchdata, batchlabs, ~created_flag, startloc, chunksz); 23 | created_flag=true;% flag set so that file is created only once 24 | totalct=curr_dat_sz(end);% updated dataset size (#samples) 25 | end 26 | 27 | % display structure of the stored HDF5 file 28 | h5disp(filename); 29 | 30 | %% READING FROM HDF5 31 | 32 | % Read data and labels for samples #1000 to 1999 33 | data_rd=h5read(filename, '/data', [1 1 1 1000], [5, 5, 1, 1000]); 34 | label_rd=h5read(filename, '/label', [1 1000], [10, 1000]); 35 | fprintf('Testing ...\n'); 36 | try 37 | assert(isequal(data_rd, single(data_disk(:,:,:,1000:1999))), 'Data do not match'); 38 | assert(isequal(label_rd, single(label_disk(:,1000:1999))), 'Labels do not match'); 39 | 40 | fprintf('Success!\n'); 41 | catch err 42 | fprintf('Test failed ...\n'); 43 | getReport(err) 44 | end 45 | 46 | %delete(filename); 47 | 48 | % CREATE list.txt containing filename, to be used as source for HDF5_DATA_LAYER 49 | FILE=fopen('list.txt', 'w'); 50 | fprintf(FILE, '%s', filename); 51 | fclose(FILE); 52 | fprintf('HDF5 filename listed in %s \n', 'list.txt'); 53 | 54 | % NOTE: In net definition prototxt, use list.txt as input to HDF5_DATA as: 55 | % layer { 56 | % name: "data" 57 | % type: "HDF5Data" 58 | % top: "data" 59 | % top: "labelvec" 60 | % hdf5_data_param { 61 | % source: "/path/to/list.txt" 62 | % batch_size: 64 63 | % } 64 | % } 65 | -------------------------------------------------------------------------------- /caffe_DOC/python/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | if(NOT HAVE_PYTHON) 2 | message(STATUS "Python interface is disabled or not all required dependecies found. Building without it...") 3 | return() 4 | endif() 5 | 6 | include_directories(${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDE_DIR} ${Boost_INCLUDE_DIRS}) 7 | file(GLOB_RECURSE python_srcs ${PROJECT_SOURCE_DIR}/python/*.cpp) 8 | 9 | add_library(pycaffe SHARED ${python_srcs}) 10 | target_link_libraries(pycaffe ${Caffe_LINK} ${PYTHON_LIBRARIES} ${Boost_LIBRARIES}) 11 | set_target_properties(pycaffe PROPERTIES PREFIX "" OUTPUT_NAME "_caffe") 12 | caffe_default_properties(pycaffe) 13 | 14 | if(UNIX OR APPLE) 15 | set(__linkname "${PROJECT_SOURCE_DIR}/python/caffe/_caffe.so") 16 | add_custom_command(TARGET pycaffe POST_BUILD 17 | COMMAND ln -sf $ "${__linkname}" 18 | COMMAND ${CMAKE_COMMAND} -E make_directory ${PROJECT_SOURCE_DIR}/python/caffe/proto 19 | COMMAND touch ${PROJECT_SOURCE_DIR}/python/caffe/proto/__init__.py 20 | COMMAND cp ${proto_gen_folder}/*.py ${PROJECT_SOURCE_DIR}/python/caffe/proto/ 21 | COMMENT "Creating symlink ${__linkname} -> ${PROJECT_BINARY_DIR}/lib/_caffe${CAffe_POSTFIX}.so") 22 | endif() 23 | 24 | # ---[ Install 25 | file(GLOB files1 *.py requirements.txt) 26 | install(FILES ${files1} DESTINATION python) 27 | 28 | file(GLOB files2 caffe/*.py) 29 | install(FILES ${files2} DESTINATION python/caffe) 30 | install(TARGETS pycaffe DESTINATION python/caffe) 31 | install(DIRECTORY caffe/imagenet caffe/proto caffe/test DESTINATION python/caffe) 32 | 33 | 34 | 35 | -------------------------------------------------------------------------------- /caffe_DOC/python/caffe/__init__.py: -------------------------------------------------------------------------------- 1 | from .pycaffe import Net, SGDSolver 2 | from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list 3 | from .proto.caffe_pb2 import TRAIN, TEST 4 | from .classifier import Classifier 5 | from .detector import Detector 6 | from . import io 7 | from .net_spec import layers, params, NetSpec, to_proto 8 | 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-------------------------------------------------------------------------------- /caffe_DOC/python/caffe/test/test_python_layer_with_param_str.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | import tempfile 3 | import os 4 | import six 5 | 6 | import caffe 7 | 8 | 9 | class SimpleParamLayer(caffe.Layer): 10 | """A layer that just multiplies by the numeric value of its param string""" 11 | 12 | def setup(self, bottom, top): 13 | try: 14 | self.value = float(self.param_str) 15 | except ValueError: 16 | raise ValueError("Parameter string must be a legible float") 17 | 18 | def reshape(self, bottom, top): 19 | top[0].reshape(*bottom[0].data.shape) 20 | 21 | def forward(self, bottom, top): 22 | top[0].data[...] = self.value * bottom[0].data 23 | 24 | def backward(self, top, propagate_down, bottom): 25 | bottom[0].diff[...] = self.value * top[0].diff 26 | 27 | 28 | def python_param_net_file(): 29 | with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f: 30 | f.write("""name: 'pythonnet' force_backward: true 31 | input: 'data' input_shape { dim: 10 dim: 9 dim: 8 } 32 | layer { type: 'Python' name: 'mul10' bottom: 'data' top: 'mul10' 33 | python_param { module: 'test_python_layer_with_param_str' 34 | layer: 'SimpleParamLayer' param_str: '10' } } 35 | layer { type: 'Python' name: 'mul2' bottom: 'mul10' top: 'mul2' 36 | python_param { module: 'test_python_layer_with_param_str' 37 | layer: 'SimpleParamLayer' param_str: '2' } }""") 38 | return f.name 39 | 40 | 41 | class TestLayerWithParam(unittest.TestCase): 42 | def setUp(self): 43 | net_file = python_param_net_file() 44 | self.net = caffe.Net(net_file, caffe.TRAIN) 45 | os.remove(net_file) 46 | 47 | def test_forward(self): 48 | x = 8 49 | self.net.blobs['data'].data[...] = x 50 | self.net.forward() 51 | for y in self.net.blobs['mul2'].data.flat: 52 | self.assertEqual(y, 2 * 10 * x) 53 | 54 | def test_backward(self): 55 | x = 7 56 | self.net.blobs['mul2'].diff[...] = x 57 | self.net.backward() 58 | for y in self.net.blobs['data'].diff.flat: 59 | self.assertEqual(y, 2 * 10 * x) 60 | -------------------------------------------------------------------------------- /caffe_DOC/python/caffe/test/test_solver.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | import tempfile 3 | import os 4 | import numpy as np 5 | import six 6 | 7 | import caffe 8 | from test_net import simple_net_file 9 | 10 | 11 | class TestSolver(unittest.TestCase): 12 | def setUp(self): 13 | self.num_output = 13 14 | net_f = simple_net_file(self.num_output) 15 | f = tempfile.NamedTemporaryFile(mode='w+', delete=False) 16 | f.write("""net: '""" + net_f + """' 17 | test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9 18 | weight_decay: 0.0005 lr_policy: 'inv' gamma: 0.0001 power: 0.75 19 | display: 100 max_iter: 100 snapshot_after_train: false""") 20 | f.close() 21 | self.solver = caffe.SGDSolver(f.name) 22 | # also make sure get_solver runs 23 | caffe.get_solver(f.name) 24 | caffe.set_mode_cpu() 25 | # fill in valid labels 26 | self.solver.net.blobs['label'].data[...] = \ 27 | np.random.randint(self.num_output, 28 | size=self.solver.net.blobs['label'].data.shape) 29 | self.solver.test_nets[0].blobs['label'].data[...] = \ 30 | np.random.randint(self.num_output, 31 | size=self.solver.test_nets[0].blobs['label'].data.shape) 32 | os.remove(f.name) 33 | os.remove(net_f) 34 | 35 | def test_solve(self): 36 | self.assertEqual(self.solver.iter, 0) 37 | self.solver.solve() 38 | self.assertEqual(self.solver.iter, 100) 39 | 40 | def test_net_memory(self): 41 | """Check that nets survive after the solver is destroyed.""" 42 | 43 | nets = [self.solver.net] + list(self.solver.test_nets) 44 | self.assertEqual(len(nets), 2) 45 | del self.solver 46 | 47 | total = 0 48 | for net in nets: 49 | for ps in six.itervalues(net.params): 50 | for p in ps: 51 | total += p.data.sum() + p.diff.sum() 52 | for bl in six.itervalues(net.blobs): 53 | total += bl.data.sum() + bl.diff.sum() 54 | -------------------------------------------------------------------------------- /caffe_DOC/python/draw_net.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | """ 3 | Draw a graph of the net architecture. 4 | """ 5 | from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter 6 | from google.protobuf import text_format 7 | 8 | import caffe 9 | import caffe.draw 10 | from caffe.proto import caffe_pb2 11 | 12 | 13 | def parse_args(): 14 | """Parse input arguments 15 | """ 16 | 17 | parser = ArgumentParser(description=__doc__, 18 | formatter_class=ArgumentDefaultsHelpFormatter) 19 | 20 | parser.add_argument('input_net_proto_file', 21 | help='Input network prototxt file') 22 | parser.add_argument('output_image_file', 23 | help='Output image file') 24 | parser.add_argument('--rankdir', 25 | help=('One of TB (top-bottom, i.e., vertical), ' 26 | 'RL (right-left, i.e., horizontal), or another ' 27 | 'valid dot option; see ' 28 | 'http://www.graphviz.org/doc/info/' 29 | 'attrs.html#k:rankdir'), 30 | default='LR') 31 | 32 | args = parser.parse_args() 33 | return args 34 | 35 | 36 | def main(): 37 | args = parse_args() 38 | net = caffe_pb2.NetParameter() 39 | text_format.Merge(open(args.input_net_proto_file).read(), net) 40 | print('Drawing net to %s' % args.output_image_file) 41 | caffe.draw.draw_net_to_file(net, args.output_image_file, args.rankdir) 42 | 43 | 44 | if __name__ == '__main__': 45 | main() 46 | -------------------------------------------------------------------------------- /caffe_DOC/python/requirements.txt: -------------------------------------------------------------------------------- 1 | Cython>=0.19.2 2 | numpy>=1.7.1 3 | scipy>=0.13.2 4 | scikit-image>=0.9.3 5 | matplotlib>=1.3.1 6 | ipython>=3.0.0 7 | h5py>=2.2.0 8 | leveldb>=0.191 9 | networkx>=1.8.1 10 | nose>=1.3.0 11 | pandas>=0.12.0 12 | python-dateutil>=1.4,<2 13 | protobuf>=2.5.0 14 | python-gflags>=2.0 15 | pyyaml>=3.10 16 | Pillow>=2.3.0 17 | six>=1.1.0 -------------------------------------------------------------------------------- /caffe_DOC/scripts/build_docs.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # Build documentation for display in web browser. 3 | 4 | PORT=${1:-4000} 5 | 6 | echo "usage: build_docs.sh [port]" 7 | 8 | # Find the docs dir, no matter where the script is called 9 | ROOT_DIR="$( cd "$(dirname "$0")"/.. ; pwd -P )" 10 | cd $ROOT_DIR 11 | 12 | # Gather docs. 13 | scripts/gather_examples.sh 14 | 15 | # Generate developer docs. 16 | make docs 17 | 18 | # Display docs using web server. 19 | cd docs 20 | jekyll serve -w -s . -d _site --port=$PORT 21 | -------------------------------------------------------------------------------- /caffe_DOC/scripts/copy_notebook.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | """ 3 | Takes as arguments: 4 | 1. the path to a JSON file (such as an IPython notebook). 5 | 2. the path to output file 6 | 7 | If 'metadata' dict in the JSON file contains 'include_in_docs': true, 8 | then copies the file to output file, appending the 'metadata' property 9 | as YAML front-matter, adding the field 'category' with value 'notebook'. 10 | """ 11 | import os 12 | import sys 13 | import json 14 | 15 | filename = sys.argv[1] 16 | output_filename = sys.argv[2] 17 | content = json.load(open(filename)) 18 | 19 | if 'include_in_docs' in content['metadata'] and content['metadata']['include_in_docs']: 20 | yaml_frontmatter = ['---'] 21 | for key, val in content['metadata'].iteritems(): 22 | if key == 'example_name': 23 | key = 'title' 24 | if val == '': 25 | val = os.path.basename(filename) 26 | yaml_frontmatter.append('{}: {}'.format(key, val)) 27 | yaml_frontmatter += ['category: notebook'] 28 | yaml_frontmatter += ['original_path: ' + filename] 29 | 30 | with open(output_filename, 'w') as fo: 31 | fo.write('\n'.join(yaml_frontmatter + ['---']) + '\n') 32 | fo.write(open(filename).read()) 33 | -------------------------------------------------------------------------------- /caffe_DOC/scripts/deploy_docs.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # Publish documentation to the gh-pages site. 3 | 4 | # The remote for pushing the docs (defaults to origin). 5 | # This is where you will submit the PR to BVLC:gh-pages from. 6 | REMOTE=${1:-origin} 7 | 8 | echo "Generating docs and pushing to $REMOTE:gh-pages..." 9 | echo "To build and view docs when not on master, simply do 'jekyll serve -s docs'." 10 | echo 11 | 12 | REMOTE_URL=`git config --get remote.${REMOTE}.url` 13 | BRANCH=`git rev-parse --abbrev-ref HEAD` 14 | MSG=`git log --oneline -1` 15 | 16 | if [[ $BRANCH = 'master' ]]; then 17 | # Find the docs dir, no matter where the script is called 18 | DIR="$( cd "$(dirname "$0")" ; pwd -P )" 19 | DOCS_SITE_DIR=$DIR/../docs/_site 20 | 21 | # Make sure that docs/_site tracks remote:gh-pages. 22 | # If not, then we make a new repo and check out just that branch. 23 | mkdir -p $DOCS_SITE_DIR 24 | cd $DOCS_SITE_DIR 25 | SITE_REMOTE_URL=`git config --get remote.${REMOTE}.url` 26 | SITE_BRANCH=`git rev-parse --abbrev-ref HEAD` 27 | 28 | echo $SITE_REMOTE_URL 29 | echo $SITE_BRANCH 30 | echo `pwd` 31 | 32 | if [[ ( $SITE_REMOTE_URL = $REMOTE_URL ) && ( $SITE_BRANCH = 'gh-pages' ) ]]; then 33 | echo "Confirmed that docs/_site has same remote as main repo, and is on gh-pages." 34 | else 35 | echo "Checking out $REMOTE:gh-pages into docs/_site (will take a little time)." 36 | git init . 37 | git remote add -t gh-pages -f $REMOTE $REMOTE_URL 38 | git checkout gh-pages 39 | fi 40 | 41 | echo "Building the site into docs/_site, and committing the changes." 42 | jekyll build -s .. -d . 43 | git add --all . 44 | git commit -m "$MSG" 45 | git push $REMOTE gh-pages 46 | 47 | echo "All done!" 48 | cd ../.. 49 | else echo "You must run this deployment script from the 'master' branch." 50 | fi 51 | -------------------------------------------------------------------------------- /caffe_DOC/scripts/download_model_from_gist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | 3 | GIST=$1 4 | DIRNAME=${2:-./models} 5 | 6 | if [ -z $GIST ]; then 7 | echo "usage: download_model_from_gist.sh " 8 | exit 9 | fi 10 | 11 | GIST_DIR=$(echo $GIST | tr '/' '-') 12 | MODEL_DIR="$DIRNAME/$GIST_DIR" 13 | 14 | if [ -d $MODEL_DIR ]; then 15 | echo "$MODEL_DIR already exists! Please make sure you're not overwriting anything important!" 16 | exit 17 | fi 18 | 19 | echo "Downloading Caffe model info to $MODEL_DIR ..." 20 | mkdir -p $MODEL_DIR 21 | wget https://gist.github.com/$GIST/download -O $MODEL_DIR/gist.zip 22 | unzip -j $MODEL_DIR/gist.zip -d $MODEL_DIR 23 | rm $MODEL_DIR/gist.zip 24 | echo "Done" 25 | -------------------------------------------------------------------------------- /caffe_DOC/scripts/gather_examples.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # Assemble documentation for the project into one directory via symbolic links. 3 | 4 | # Find the docs dir, no matter where the script is called 5 | ROOT_DIR="$( cd "$(dirname "$0")"/.. ; pwd -P )" 6 | cd $ROOT_DIR 7 | 8 | # Gather docs from examples/**/readme.md 9 | GATHERED_DIR=docs/gathered 10 | rm -r $GATHERED_DIR 11 | mkdir $GATHERED_DIR 12 | for README_FILENAME in $(find examples -iname "readme.md"); do 13 | # Only use file if it is to be included in docs. 14 | if grep -Fxq "include_in_docs: true" $README_FILENAME; then 15 | # Make link to readme.md in docs/gathered/. 16 | # Since everything is called readme.md, rename it by its dirname. 17 | README_DIRNAME=`dirname $README_FILENAME` 18 | DOCS_FILENAME=$GATHERED_DIR/$README_DIRNAME.md 19 | mkdir -p `dirname $DOCS_FILENAME` 20 | ln -s $ROOT_DIR/$README_FILENAME $DOCS_FILENAME 21 | fi 22 | done 23 | 24 | # Gather docs from examples/*.ipynb and add YAML front-matter. 25 | for NOTEBOOK_FILENAME in $(find examples -depth -iname "*.ipynb"); do 26 | DOCS_FILENAME=$GATHERED_DIR/$NOTEBOOK_FILENAME 27 | mkdir -p `dirname $DOCS_FILENAME` 28 | python scripts/copy_notebook.py $NOTEBOOK_FILENAME $DOCS_FILENAME 29 | done 30 | -------------------------------------------------------------------------------- /caffe_DOC/scripts/travis/travis_build_and_test.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # Script called by Travis to do a CPU-only build of and test Caffe. 3 | 4 | set -e 5 | MAKE="make --jobs=$NUM_THREADS --keep-going" 6 | 7 | if $WITH_CMAKE; then 8 | mkdir build 9 | cd build 10 | CPU_ONLY=" -DCPU_ONLY=ON" 11 | if ! $WITH_CUDA; then 12 | CPU_ONLY=" -DCPU_ONLY=OFF" 13 | fi 14 | PYTHON_ARGS="" 15 | if [ "$PYTHON_VERSION" = "3" ]; then 16 | PYTHON_ARGS="$PYTHON_ARGS -Dpython_version=3 -DBOOST_LIBRARYDIR=$CONDA_DIR/lib/" 17 | fi 18 | cmake -DBUILD_python=ON -DCMAKE_BUILD_TYPE=Release $CPU_ONLY $PYTHON_ARGS -DCMAKE_INCLUDE_PATH="$CONDA_DIR/include/" -DCMAKE_LIBRARY_PATH="$CONDA_DIR/lib/" .. 19 | $MAKE 20 | $MAKE pytest 21 | if ! $WITH_CUDA; then 22 | $MAKE runtest 23 | $MAKE lint 24 | fi 25 | $MAKE clean 26 | cd - 27 | else 28 | if ! $WITH_CUDA; then 29 | export CPU_ONLY=1 30 | fi 31 | $MAKE all test pycaffe warn lint || true 32 | if ! $WITH_CUDA; then 33 | $MAKE runtest 34 | fi 35 | $MAKE all 36 | $MAKE test 37 | $MAKE pycaffe 38 | $MAKE pytest 39 | $MAKE warn 40 | if ! $WITH_CUDA; then 41 | $MAKE lint 42 | fi 43 | fi 44 | -------------------------------------------------------------------------------- /caffe_DOC/scripts/travis/travis_setup_makefile_config.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | set -e 4 | 5 | mv Makefile.config.example Makefile.config 6 | 7 | if $WITH_CUDA; then 8 | # Only generate compute_50. 9 | GENCODE="-gencode arch=compute_50,code=sm_50" 10 | GENCODE="$GENCODE -gencode arch=compute_50,code=compute_50" 11 | echo "CUDA_ARCH := $GENCODE" >> Makefile.config 12 | fi 13 | 14 | cat << 'EOF' >> Makefile.config 15 | # Travis' nvcc doesn't like newer boost versions 16 | NVCCFLAGS := -Xcudafe --diag_suppress=cc_clobber_ignored -Xcudafe --diag_suppress=useless_using_declaration -Xcudafe --diag_suppress=set_but_not_used 17 | ANACONDA_HOME := $(CONDA_DIR) 18 | PYTHON_INCLUDE := $(ANACONDA_HOME)/include \ 19 | $(ANACONDA_HOME)/include/python2.7 \ 20 | $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include 21 | PYTHON_LIB := $(ANACONDA_HOME)/lib 22 | INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include 23 | LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib 24 | WITH_PYTHON_LAYER := 1 25 | EOF 26 | -------------------------------------------------------------------------------- /caffe_DOC/scripts/upload_model_to_gist.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # Check for valid directory 4 | DIRNAME=$1 5 | if [ ! -f $DIRNAME/readme.md ]; then 6 | echo "usage: upload_model_to_gist.sh " 7 | echo " /readme.md must exist" 8 | fi 9 | cd $DIRNAME 10 | FILES=`find . -maxdepth 1 -type f ! -name "*.caffemodel*" | xargs echo` 11 | 12 | # Check for gist tool. 13 | gist -v >/dev/null 2>&1 || { echo >&2 "I require 'gist' but it's not installed. Do 'gem install gist'."; exit 1; } 14 | 15 | NAME=`sed -n 's/^name:[[:space:]]*//p' readme.md` 16 | if [ -z "$NAME" ]; then 17 | echo " /readme.md must contain name field in the front-matter." 18 | fi 19 | 20 | GIST=`sed -n 's/^gist_id:[[:space:]]*//p' readme.md` 21 | if [ -z "$GIST" ]; then 22 | echo "Uploading new Gist" 23 | gist -p -d "$NAME" $FILES 24 | else 25 | echo "Updating existing Gist, id $GIST" 26 | gist -u $GIST -d "$NAME" $FILES 27 | fi 28 | 29 | RESULT=$? 30 | if [ $RESULT -eq 0 ]; then 31 | echo "You've uploaded your model!" 32 | echo "Don't forget to add the gist_id field to your /readme.md now!" 33 | echo "Run the command again after you do that, to make sure the Gist id propagates." 34 | echo "" 35 | echo "And do share your model over at https://github.com/BVLC/caffe/wiki/Model-Zoo" 36 | else 37 | echo "Something went wrong!" 38 | fi 39 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | # generate protobuf sources 2 | file(GLOB proto_files proto/*.proto) 3 | caffe_protobuf_generate_cpp_py(${proto_gen_folder} proto_srcs proto_hdrs proto_python ${proto_files}) 4 | 5 | # include python files either to force generation 6 | add_library(proto STATIC ${proto_hdrs} ${proto_srcs} ${proto_python}) 7 | set(Caffe_LINKER_LIBS proto ${Caffe_LINKER_LIBS}) # note, crucial to prepend! 8 | caffe_default_properties(proto) 9 | 10 | # --[ Caffe library 11 | 12 | # creates 'test_srcs', 'srcs', 'test_cuda', 'cuda' lists 13 | caffe_pickup_caffe_sources(${PROJECT_SOURCE_DIR}) 14 | 15 | if(HAVE_CUDA) 16 | caffe_cuda_compile(cuda_objs ${cuda}) 17 | list(APPEND srcs ${cuda_objs} ${cuda}) 18 | endif() 19 | 20 | add_library(caffe ${srcs}) 21 | target_link_libraries(caffe proto ${Caffe_LINKER_LIBS}) 22 | caffe_default_properties(caffe) 23 | 24 | # ---[ Tests 25 | add_subdirectory(test) 26 | 27 | # ---[ Install 28 | install(DIRECTORY ${Caffe_INCLUDE_DIR}/caffe DESTINATION include) 29 | install(FILES ${proto_hdrs} DESTINATION include/caffe/proto) 30 | install(TARGETS caffe proto EXPORT CaffeTargets DESTINATION lib) 31 | 32 | file(WRITE ${PROJECT_BINARY_DIR}/__init__.py) 33 | list(APPEND proto_python ${PROJECT_BINARY_DIR}/__init__.py) 34 | install(PROGRAMS ${proto_python} DESTINATION python/caffe/proto) 35 | 36 | 37 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/internal_thread.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | 4 | #include "caffe/internal_thread.hpp" 5 | #include "caffe/util/math_functions.hpp" 6 | 7 | namespace caffe { 8 | 9 | InternalThread::~InternalThread() { 10 | StopInternalThread(); 11 | } 12 | 13 | bool InternalThread::is_started() const { 14 | return thread_ && thread_->joinable(); 15 | } 16 | 17 | bool InternalThread::must_stop() { 18 | return thread_ && thread_->interruption_requested(); 19 | } 20 | 21 | void InternalThread::StartInternalThread() { 22 | CHECK(!is_started()) << "Threads should persist and not be restarted."; 23 | 24 | int device = 0; 25 | #ifndef CPU_ONLY 26 | CUDA_CHECK(cudaGetDevice(&device)); 27 | #endif 28 | Caffe::Brew mode = Caffe::mode(); 29 | int rand_seed = caffe_rng_rand(); 30 | int solver_count = Caffe::solver_count(); 31 | bool root_solver = Caffe::root_solver(); 32 | 33 | try { 34 | thread_.reset(new boost::thread(&InternalThread::entry, this, device, mode, 35 | rand_seed, solver_count, root_solver)); 36 | } catch (std::exception& e) { 37 | LOG(FATAL) << "Thread exception: " << e.what(); 38 | } 39 | } 40 | 41 | void InternalThread::entry(int device, Caffe::Brew mode, int rand_seed, 42 | int solver_count, bool root_solver) { 43 | #ifndef CPU_ONLY 44 | CUDA_CHECK(cudaSetDevice(device)); 45 | #endif 46 | Caffe::set_mode(mode); 47 | Caffe::set_random_seed(rand_seed); 48 | Caffe::set_solver_count(solver_count); 49 | Caffe::set_root_solver(root_solver); 50 | 51 | InternalThreadEntry(); 52 | } 53 | 54 | void InternalThread::StopInternalThread() { 55 | if (is_started()) { 56 | thread_->interrupt(); 57 | try { 58 | thread_->join(); 59 | } catch (boost::thread_interrupted&) { 60 | } catch (std::exception& e) { 61 | LOG(FATAL) << "Thread exception: " << e.what(); 62 | } 63 | } 64 | } 65 | 66 | } // namespace caffe 67 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include "caffe/layer.hpp" 3 | 4 | namespace caffe { 5 | 6 | template 7 | void Layer::InitMutex() { 8 | forward_mutex_.reset(new boost::mutex()); 9 | } 10 | 11 | template 12 | void Layer::Lock() { 13 | if (IsShared()) { 14 | forward_mutex_->lock(); 15 | } 16 | } 17 | 18 | template 19 | void Layer::Unlock() { 20 | if (IsShared()) { 21 | forward_mutex_->unlock(); 22 | } 23 | } 24 | 25 | INSTANTIATE_CLASS(Layer); 26 | 27 | } // namespace caffe 28 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/absval_layer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "caffe/layer.hpp" 4 | #include "caffe/neuron_layers.hpp" 5 | #include "caffe/util/math_functions.hpp" 6 | 7 | namespace caffe { 8 | 9 | template 10 | void AbsValLayer::LayerSetUp(const vector*>& bottom, 11 | const vector*>& top) { 12 | NeuronLayer::LayerSetUp(bottom, top); 13 | CHECK_NE(top[0], bottom[0]) << this->type() << " Layer does not " 14 | "allow in-place computation."; 15 | } 16 | 17 | template 18 | void AbsValLayer::Forward_cpu( 19 | const vector*>& bottom, const vector*>& top) { 20 | const int count = top[0]->count(); 21 | Dtype* top_data = top[0]->mutable_cpu_data(); 22 | caffe_abs(count, bottom[0]->cpu_data(), top_data); 23 | } 24 | 25 | template 26 | void AbsValLayer::Backward_cpu(const vector*>& top, 27 | const vector& propagate_down, const vector*>& bottom) { 28 | const int count = top[0]->count(); 29 | const Dtype* top_diff = top[0]->cpu_diff(); 30 | if (propagate_down[0]) { 31 | const Dtype* bottom_data = bottom[0]->cpu_data(); 32 | Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); 33 | caffe_cpu_sign(count, bottom_data, bottom_diff); 34 | caffe_mul(count, bottom_diff, top_diff, bottom_diff); 35 | } 36 | } 37 | 38 | #ifdef CPU_ONLY 39 | STUB_GPU(AbsValLayer); 40 | #endif 41 | 42 | INSTANTIATE_CLASS(AbsValLayer); 43 | REGISTER_LAYER_CLASS(AbsVal); 44 | 45 | } // namespace caffe 46 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/absval_layer.cu: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "caffe/layer.hpp" 4 | #include "caffe/util/math_functions.hpp" 5 | #include "caffe/vision_layers.hpp" 6 | 7 | namespace caffe { 8 | 9 | template 10 | void AbsValLayer::Forward_gpu( 11 | const vector*>& bottom, const vector*>& top) { 12 | const int count = top[0]->count(); 13 | Dtype* top_data = top[0]->mutable_gpu_data(); 14 | caffe_gpu_abs(count, bottom[0]->gpu_data(), top_data); 15 | } 16 | 17 | template 18 | void AbsValLayer::Backward_gpu(const vector*>& top, 19 | const vector& propagate_down, const vector*>& bottom) { 20 | const int count = top[0]->count(); 21 | const Dtype* top_diff = top[0]->gpu_diff(); 22 | if (propagate_down[0]) { 23 | const Dtype* bottom_data = bottom[0]->gpu_data(); 24 | Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); 25 | caffe_gpu_sign(count, bottom_data, bottom_diff); 26 | caffe_gpu_mul(count, bottom_diff, top_diff, bottom_diff); 27 | } 28 | } 29 | 30 | INSTANTIATE_LAYER_GPU_FUNCS(AbsValLayer); 31 | 32 | 33 | } // namespace caffe 34 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/argmax_layer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | 6 | #include "caffe/layer.hpp" 7 | #include "caffe/vision_layers.hpp" 8 | 9 | namespace caffe { 10 | 11 | template 12 | void ArgMaxLayer::LayerSetUp(const vector*>& bottom, 13 | const vector*>& top) { 14 | out_max_val_ = this->layer_param_.argmax_param().out_max_val(); 15 | top_k_ = this->layer_param_.argmax_param().top_k(); 16 | CHECK_GE(top_k_, 1) << " top k must not be less than 1."; 17 | CHECK_LE(top_k_, bottom[0]->count() / bottom[0]->num()) 18 | << "top_k must be less than or equal to the number of classes."; 19 | } 20 | 21 | template 22 | void ArgMaxLayer::Reshape(const vector*>& bottom, 23 | const vector*>& top) { 24 | if (out_max_val_) { 25 | // Produces max_ind and max_val 26 | top[0]->Reshape(bottom[0]->num(), 2, top_k_, 1); 27 | } else { 28 | // Produces only max_ind 29 | top[0]->Reshape(bottom[0]->num(), 1, top_k_, 1); 30 | } 31 | } 32 | 33 | template 34 | void ArgMaxLayer::Forward_cpu(const vector*>& bottom, 35 | const vector*>& top) { 36 | const Dtype* bottom_data = bottom[0]->cpu_data(); 37 | Dtype* top_data = top[0]->mutable_cpu_data(); 38 | int num = bottom[0]->num(); 39 | int dim = bottom[0]->count() / bottom[0]->num(); 40 | for (int i = 0; i < num; ++i) { 41 | std::vector > bottom_data_vector; 42 | for (int j = 0; j < dim; ++j) { 43 | bottom_data_vector.push_back( 44 | std::make_pair(bottom_data[i * dim + j], j)); 45 | } 46 | std::partial_sort( 47 | bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_, 48 | bottom_data_vector.end(), std::greater >()); 49 | for (int j = 0; j < top_k_; ++j) { 50 | top_data[top[0]->offset(i, 0, j)] = bottom_data_vector[j].second; 51 | } 52 | if (out_max_val_) { 53 | for (int j = 0; j < top_k_; ++j) { 54 | top_data[top[0]->offset(i, 1, j)] = bottom_data_vector[j].first; 55 | } 56 | } 57 | } 58 | } 59 | 60 | INSTANTIATE_CLASS(ArgMaxLayer); 61 | REGISTER_LAYER_CLASS(ArgMax); 62 | 63 | } // namespace caffe 64 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/base_data_layer.cu: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "caffe/data_layers.hpp" 4 | 5 | namespace caffe { 6 | 7 | template 8 | void BasePrefetchingDataLayer::Forward_gpu( 9 | const vector*>& bottom, const vector*>& top) { 10 | Batch* batch = prefetch_full_.pop("Data layer prefetch queue empty"); 11 | // Reshape to loaded data. 12 | top[0]->ReshapeLike(batch->data_); 13 | // Copy the data 14 | caffe_copy(batch->data_.count(), batch->data_.gpu_data(), 15 | top[0]->mutable_gpu_data()); 16 | if (this->output_labels_) { 17 | // Reshape to loaded labels. 18 | top[1]->ReshapeLike(batch->label_); 19 | // Copy the labels. 20 | caffe_copy(batch->label_.count(), batch->label_.gpu_data(), 21 | top[1]->mutable_gpu_data()); 22 | } 23 | // Ensure the copy is synchronous wrt the host, so that the next batch isn't 24 | // copied in meanwhile. 25 | CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault)); 26 | prefetch_free_.push(batch); 27 | } 28 | 29 | template 30 | void BasePrefetchingLabelmapDataLayer::Forward_gpu( 31 | const vector*>& bottom, const vector*>& top) { 32 | LabelmapBatch* batch = prefetch_full_.pop("Data layer prefetch queue empty"); 33 | // Reshape to loaded data. 34 | top[0]->ReshapeLike(batch->data_); 35 | // Copy the data 36 | caffe_copy(batch->data_.count(), batch->data_.gpu_data(), 37 | top[0]->mutable_gpu_data()); 38 | top[1]->ReshapeLike(batch->labelmap_); 39 | // Copy the labels. 40 | caffe_copy(batch->labelmap_.count(), batch->labelmap_.gpu_data(), 41 | top[1]->mutable_gpu_data()); 42 | // Ensure the copy is synchronous wrt the host, so that the next batch isn't 43 | // copied in meanwhile. 44 | CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault)); 45 | prefetch_free_.push(batch); 46 | } 47 | 48 | INSTANTIATE_LAYER_GPU_FORWARD(BasePrefetchingDataLayer); 49 | INSTANTIATE_LAYER_GPU_FORWARD(BasePrefetchingLabelmapDataLayer); 50 | 51 | } // namespace caffe 52 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/bnll_layer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | 4 | #include "caffe/layer.hpp" 5 | #include "caffe/vision_layers.hpp" 6 | 7 | namespace caffe { 8 | 9 | const float kBNLL_THRESHOLD = 50.; 10 | 11 | template 12 | void BNLLLayer::Forward_cpu(const vector*>& bottom, 13 | const vector*>& top) { 14 | const Dtype* bottom_data = bottom[0]->cpu_data(); 15 | Dtype* top_data = top[0]->mutable_cpu_data(); 16 | const int count = bottom[0]->count(); 17 | for (int i = 0; i < count; ++i) { 18 | top_data[i] = bottom_data[i] > 0 ? 19 | bottom_data[i] + log(1. + exp(-bottom_data[i])) : 20 | log(1. + exp(bottom_data[i])); 21 | } 22 | } 23 | 24 | template 25 | void BNLLLayer::Backward_cpu(const vector*>& top, 26 | const vector& propagate_down, 27 | const vector*>& bottom) { 28 | if (propagate_down[0]) { 29 | const Dtype* bottom_data = bottom[0]->cpu_data(); 30 | const Dtype* top_diff = top[0]->cpu_diff(); 31 | Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); 32 | const int count = bottom[0]->count(); 33 | Dtype expval; 34 | for (int i = 0; i < count; ++i) { 35 | expval = exp(std::min(bottom_data[i], Dtype(kBNLL_THRESHOLD))); 36 | bottom_diff[i] = top_diff[i] * expval / (expval + 1.); 37 | } 38 | } 39 | } 40 | 41 | #ifdef CPU_ONLY 42 | STUB_GPU(BNLLLayer); 43 | #endif 44 | 45 | INSTANTIATE_CLASS(BNLLLayer); 46 | REGISTER_LAYER_CLASS(BNLL); 47 | 48 | } // namespace caffe 49 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/bnll_layer.cu: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | 4 | #include "caffe/layer.hpp" 5 | #include "caffe/vision_layers.hpp" 6 | 7 | namespace caffe { 8 | 9 | const float kBNLL_THRESHOLD = 50.; 10 | 11 | template 12 | __global__ void BNLLForward(const int n, const Dtype* in, Dtype* out) { 13 | CUDA_KERNEL_LOOP(index, n) { 14 | out[index] = in[index] > 0 ? 15 | in[index] + log(1. + exp(-in[index])) : 16 | log(1. + exp(in[index])); 17 | } 18 | } 19 | 20 | template 21 | void BNLLLayer::Forward_gpu(const vector*>& bottom, 22 | const vector*>& top) { 23 | const Dtype* bottom_data = bottom[0]->gpu_data(); 24 | Dtype* top_data = top[0]->mutable_gpu_data(); 25 | const int count = bottom[0]->count(); 26 | // NOLINT_NEXT_LINE(whitespace/operators) 27 | BNLLForward<<>>( 28 | count, bottom_data, top_data); 29 | CUDA_POST_KERNEL_CHECK; 30 | } 31 | 32 | template 33 | __global__ void BNLLBackward(const int n, const Dtype* in_diff, 34 | const Dtype* in_data, Dtype* out_diff) { 35 | CUDA_KERNEL_LOOP(index, n) { 36 | Dtype expval = exp(min(in_data[index], Dtype(kBNLL_THRESHOLD))); 37 | out_diff[index] = in_diff[index] * expval / (expval + 1.); 38 | } 39 | } 40 | 41 | template 42 | void BNLLLayer::Backward_gpu(const vector*>& top, 43 | const vector& propagate_down, 44 | const vector*>& bottom) { 45 | if (propagate_down[0]) { 46 | const Dtype* bottom_data = bottom[0]->gpu_data(); 47 | const Dtype* top_diff = top[0]->gpu_diff(); 48 | Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); 49 | const int count = bottom[0]->count(); 50 | // NOLINT_NEXT_LINE(whitespace/operators) 51 | BNLLBackward<<>>( 52 | count, top_diff, bottom_data, bottom_diff); 53 | CUDA_POST_KERNEL_CHECK; 54 | } 55 | } 56 | 57 | INSTANTIATE_LAYER_GPU_FUNCS(BNLLLayer); 58 | 59 | 60 | } // namespace caffe 61 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/cudnn_pooling_layer.cpp: -------------------------------------------------------------------------------- 1 | #ifdef USE_CUDNN 2 | #include 3 | 4 | #include "caffe/filler.hpp" 5 | #include "caffe/layer.hpp" 6 | #include "caffe/util/im2col.hpp" 7 | #include "caffe/util/math_functions.hpp" 8 | #include "caffe/vision_layers.hpp" 9 | 10 | namespace caffe { 11 | 12 | template 13 | void CuDNNPoolingLayer::LayerSetUp(const vector*>& bottom, 14 | const vector*>& top) { 15 | PoolingLayer::LayerSetUp(bottom, top); 16 | CUDNN_CHECK(cudnnCreate(&handle_)); 17 | cudnn::createTensor4dDesc(&bottom_desc_); 18 | cudnn::createTensor4dDesc(&top_desc_); 19 | cudnn::createPoolingDesc(&pooling_desc_, 20 | this->layer_param_.pooling_param().pool(), &mode_, 21 | this->kernel_h_, this->kernel_w_, this->pad_h_, this->pad_w_, 22 | this->stride_h_, this->stride_w_); 23 | handles_setup_ = true; 24 | } 25 | 26 | template 27 | void CuDNNPoolingLayer::Reshape(const vector*>& bottom, 28 | const vector*>& top) { 29 | PoolingLayer::Reshape(bottom, top); 30 | cudnn::setTensor4dDesc(&bottom_desc_, bottom[0]->num(), 31 | this->channels_, this->height_, this->width_); 32 | cudnn::setTensor4dDesc(&top_desc_, bottom[0]->num(), 33 | this->channels_, this->pooled_height_, this->pooled_width_); 34 | } 35 | 36 | template 37 | CuDNNPoolingLayer::~CuDNNPoolingLayer() { 38 | // Check that handles have been setup before destroying. 39 | if (!handles_setup_) { return; } 40 | 41 | cudnnDestroyTensorDescriptor(bottom_desc_); 42 | cudnnDestroyTensorDescriptor(top_desc_); 43 | cudnnDestroyPoolingDescriptor(pooling_desc_); 44 | cudnnDestroy(handle_); 45 | } 46 | 47 | INSTANTIATE_CLASS(CuDNNPoolingLayer); 48 | 49 | } // namespace caffe 50 | #endif 51 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/cudnn_pooling_layer.cu: -------------------------------------------------------------------------------- 1 | #ifdef USE_CUDNN 2 | #include 3 | 4 | #include "caffe/filler.hpp" 5 | #include "caffe/layer.hpp" 6 | #include "caffe/util/im2col.hpp" 7 | #include "caffe/util/math_functions.hpp" 8 | #include "caffe/vision_layers.hpp" 9 | 10 | namespace caffe { 11 | 12 | template 13 | void CuDNNPoolingLayer::Forward_gpu(const vector*>& bottom, 14 | const vector*>& top) { 15 | const Dtype* bottom_data = bottom[0]->gpu_data(); 16 | Dtype* top_data = top[0]->mutable_gpu_data(); 17 | CUDNN_CHECK(cudnnPoolingForward(handle_, pooling_desc_, 18 | cudnn::dataType::one, 19 | bottom_desc_, bottom_data, 20 | cudnn::dataType::zero, 21 | top_desc_, top_data)); 22 | } 23 | 24 | template 25 | void CuDNNPoolingLayer::Backward_gpu(const vector*>& top, 26 | const vector& propagate_down, const vector*>& bottom) { 27 | if (!propagate_down[0]) { 28 | return; 29 | } 30 | const Dtype* top_diff = top[0]->gpu_diff(); 31 | const Dtype* top_data = top[0]->gpu_data(); 32 | const Dtype* bottom_data = bottom[0]->gpu_data(); 33 | Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); 34 | CUDNN_CHECK(cudnnPoolingBackward(handle_, pooling_desc_, 35 | cudnn::dataType::one, 36 | top_desc_, top_data, top_desc_, top_diff, 37 | bottom_desc_, bottom_data, 38 | cudnn::dataType::zero, 39 | bottom_desc_, bottom_diff)); 40 | } 41 | 42 | INSTANTIATE_LAYER_GPU_FUNCS(CuDNNPoolingLayer); 43 | 44 | } // namespace caffe 45 | #endif 46 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/cudnn_relu_layer.cpp: -------------------------------------------------------------------------------- 1 | #ifdef USE_CUDNN 2 | #include 3 | #include 4 | 5 | #include "caffe/layer.hpp" 6 | #include "caffe/vision_layers.hpp" 7 | 8 | namespace caffe { 9 | 10 | template 11 | void CuDNNReLULayer::LayerSetUp(const vector*>& bottom, 12 | const vector*>& top) { 13 | ReLULayer::LayerSetUp(bottom, top); 14 | // initialize cuDNN 15 | CUDNN_CHECK(cudnnCreate(&handle_)); 16 | cudnn::createTensor4dDesc(&bottom_desc_); 17 | cudnn::createTensor4dDesc(&top_desc_); 18 | handles_setup_ = true; 19 | } 20 | 21 | template 22 | void CuDNNReLULayer::Reshape(const vector*>& bottom, 23 | const vector*>& top) { 24 | ReLULayer::Reshape(bottom, top); 25 | const int N = bottom[0]->num(); 26 | const int K = bottom[0]->channels(); 27 | const int H = bottom[0]->height(); 28 | const int W = bottom[0]->width(); 29 | cudnn::setTensor4dDesc(&bottom_desc_, N, K, H, W); 30 | cudnn::setTensor4dDesc(&top_desc_, N, K, H, W); 31 | } 32 | 33 | template 34 | CuDNNReLULayer::~CuDNNReLULayer() { 35 | // Check that handles have been setup before destroying. 36 | if (!handles_setup_) { return; } 37 | 38 | cudnnDestroyTensorDescriptor(this->bottom_desc_); 39 | cudnnDestroyTensorDescriptor(this->top_desc_); 40 | cudnnDestroy(this->handle_); 41 | } 42 | 43 | INSTANTIATE_CLASS(CuDNNReLULayer); 44 | 45 | } // namespace caffe 46 | #endif 47 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/cudnn_relu_layer.cu: -------------------------------------------------------------------------------- 1 | #ifdef USE_CUDNN 2 | #include 3 | #include 4 | 5 | #include "caffe/layer.hpp" 6 | #include "caffe/vision_layers.hpp" 7 | 8 | namespace caffe { 9 | 10 | template 11 | void CuDNNReLULayer::Forward_gpu(const vector*>& bottom, 12 | const vector*>& top) { 13 | // Fallback to standard Caffe for leaky ReLU. 14 | if (ReLULayer::layer_param_.relu_param().negative_slope() != 0) { 15 | return ReLULayer::Forward_gpu(bottom, top); 16 | } 17 | 18 | const Dtype* bottom_data = bottom[0]->gpu_data(); 19 | Dtype* top_data = top[0]->mutable_gpu_data(); 20 | CUDNN_CHECK(cudnnActivationForward(this->handle_, 21 | CUDNN_ACTIVATION_RELU, 22 | cudnn::dataType::one, 23 | this->bottom_desc_, bottom_data, 24 | cudnn::dataType::zero, 25 | this->top_desc_, top_data)); 26 | } 27 | 28 | template 29 | void CuDNNReLULayer::Backward_gpu(const vector*>& top, 30 | const vector& propagate_down, 31 | const vector*>& bottom) { 32 | if (!propagate_down[0]) { 33 | return; 34 | } 35 | 36 | // Fallback to standard Caffe for leaky ReLU. 37 | if (ReLULayer::layer_param_.relu_param().negative_slope() != 0) { 38 | return ReLULayer::Backward_gpu(top, propagate_down, bottom); 39 | } 40 | 41 | const Dtype* top_data = top[0]->gpu_data(); 42 | const Dtype* top_diff = top[0]->gpu_diff(); 43 | const Dtype* bottom_data = bottom[0]->gpu_data(); 44 | Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); 45 | CUDNN_CHECK(cudnnActivationBackward(this->handle_, 46 | CUDNN_ACTIVATION_RELU, 47 | cudnn::dataType::one, 48 | this->top_desc_, top_data, this->top_desc_, top_diff, 49 | this->bottom_desc_, bottom_data, 50 | cudnn::dataType::zero, 51 | this->bottom_desc_, bottom_diff)); 52 | } 53 | 54 | INSTANTIATE_LAYER_GPU_FUNCS(CuDNNReLULayer); 55 | 56 | } // namespace caffe 57 | #endif 58 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/cudnn_sigmoid_layer.cpp: -------------------------------------------------------------------------------- 1 | #ifdef USE_CUDNN 2 | #include 3 | #include 4 | 5 | #include "caffe/layer.hpp" 6 | #include "caffe/vision_layers.hpp" 7 | 8 | namespace caffe { 9 | 10 | template 11 | void CuDNNSigmoidLayer::LayerSetUp(const vector*>& bottom, 12 | const vector*>& top) { 13 | SigmoidLayer::LayerSetUp(bottom, top); 14 | // initialize cuDNN 15 | CUDNN_CHECK(cudnnCreate(&handle_)); 16 | cudnn::createTensor4dDesc(&bottom_desc_); 17 | cudnn::createTensor4dDesc(&top_desc_); 18 | handles_setup_ = true; 19 | } 20 | 21 | template 22 | void CuDNNSigmoidLayer::Reshape(const vector*>& bottom, 23 | const vector*>& top) { 24 | SigmoidLayer::Reshape(bottom, top); 25 | const int N = bottom[0]->num(); 26 | const int K = bottom[0]->channels(); 27 | const int H = bottom[0]->height(); 28 | const int W = bottom[0]->width(); 29 | cudnn::setTensor4dDesc(&bottom_desc_, N, K, H, W); 30 | cudnn::setTensor4dDesc(&top_desc_, N, K, H, W); 31 | } 32 | 33 | template 34 | CuDNNSigmoidLayer::~CuDNNSigmoidLayer() { 35 | // Check that handles have been setup before destroying. 36 | if (!handles_setup_) { return; } 37 | 38 | cudnnDestroyTensorDescriptor(this->bottom_desc_); 39 | cudnnDestroyTensorDescriptor(this->top_desc_); 40 | cudnnDestroy(this->handle_); 41 | } 42 | 43 | INSTANTIATE_CLASS(CuDNNSigmoidLayer); 44 | 45 | } // namespace caffe 46 | #endif 47 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/cudnn_sigmoid_layer.cu: -------------------------------------------------------------------------------- 1 | #ifdef USE_CUDNN 2 | #include 3 | #include 4 | 5 | #include "caffe/layer.hpp" 6 | #include "caffe/vision_layers.hpp" 7 | 8 | namespace caffe { 9 | 10 | template 11 | void CuDNNSigmoidLayer::Forward_gpu(const vector*>& bottom, 12 | const vector*>& top) { 13 | const Dtype* bottom_data = bottom[0]->gpu_data(); 14 | Dtype* top_data = top[0]->mutable_gpu_data(); 15 | CUDNN_CHECK(cudnnActivationForward(this->handle_, 16 | CUDNN_ACTIVATION_SIGMOID, 17 | cudnn::dataType::one, 18 | this->bottom_desc_, bottom_data, 19 | cudnn::dataType::zero, 20 | this->top_desc_, top_data)); 21 | } 22 | 23 | template 24 | void CuDNNSigmoidLayer::Backward_gpu(const vector*>& top, 25 | const vector& propagate_down, 26 | const vector*>& bottom) { 27 | if (!propagate_down[0]) { 28 | return; 29 | } 30 | 31 | const Dtype* top_data = top[0]->gpu_data(); 32 | const Dtype* top_diff = top[0]->gpu_diff(); 33 | const Dtype* bottom_data = bottom[0]->gpu_data(); 34 | Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); 35 | CUDNN_CHECK(cudnnActivationBackward(this->handle_, 36 | CUDNN_ACTIVATION_SIGMOID, 37 | cudnn::dataType::one, 38 | this->top_desc_, top_data, this->top_desc_, top_diff, 39 | this->bottom_desc_, bottom_data, 40 | cudnn::dataType::zero, 41 | this->bottom_desc_, bottom_diff)); 42 | } 43 | 44 | INSTANTIATE_LAYER_GPU_FUNCS(CuDNNSigmoidLayer); 45 | 46 | } // namespace caffe 47 | #endif 48 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/cudnn_softmax_layer.cpp: -------------------------------------------------------------------------------- 1 | #ifdef USE_CUDNN 2 | #include 3 | #include 4 | #include 5 | 6 | #include "thrust/device_vector.h" 7 | 8 | #include "caffe/layer.hpp" 9 | #include "caffe/util/math_functions.hpp" 10 | #include "caffe/vision_layers.hpp" 11 | 12 | namespace caffe { 13 | 14 | template 15 | void CuDNNSoftmaxLayer::LayerSetUp(const vector*>& bottom, 16 | const vector*>& top) { 17 | SoftmaxLayer::LayerSetUp(bottom, top); 18 | // Initialize CUDNN. 19 | CUDNN_CHECK(cudnnCreate(&handle_)); 20 | cudnn::createTensor4dDesc(&bottom_desc_); 21 | cudnn::createTensor4dDesc(&top_desc_); 22 | handles_setup_ = true; 23 | } 24 | 25 | template 26 | void CuDNNSoftmaxLayer::Reshape(const vector*>& bottom, 27 | const vector*>& top) { 28 | SoftmaxLayer::Reshape(bottom, top); 29 | int N = this->outer_num_; 30 | int K = bottom[0]->shape(this->softmax_axis_); 31 | int H = this->inner_num_; 32 | int W = 1; 33 | cudnn::setTensor4dDesc(&bottom_desc_, N, K, H, W); 34 | cudnn::setTensor4dDesc(&top_desc_, N, K, H, W); 35 | } 36 | 37 | template 38 | CuDNNSoftmaxLayer::~CuDNNSoftmaxLayer() { 39 | // Check that handles have been setup before destroying. 40 | if (!handles_setup_) { return; } 41 | 42 | cudnnDestroyTensorDescriptor(bottom_desc_); 43 | cudnnDestroyTensorDescriptor(top_desc_); 44 | cudnnDestroy(handle_); 45 | } 46 | 47 | INSTANTIATE_CLASS(CuDNNSoftmaxLayer); 48 | 49 | } // namespace caffe 50 | #endif 51 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/cudnn_softmax_layer.cu: -------------------------------------------------------------------------------- 1 | #ifdef USE_CUDNN 2 | #include 3 | #include 4 | #include 5 | 6 | #include "thrust/device_vector.h" 7 | 8 | #include "caffe/layer.hpp" 9 | #include "caffe/util/math_functions.hpp" 10 | #include "caffe/vision_layers.hpp" 11 | 12 | namespace caffe { 13 | 14 | template 15 | void CuDNNSoftmaxLayer::Forward_gpu(const vector*>& bottom, 16 | const vector*>& top) { 17 | const Dtype* bottom_data = bottom[0]->gpu_data(); 18 | Dtype* top_data = top[0]->mutable_gpu_data(); 19 | CUDNN_CHECK(cudnnSoftmaxForward(handle_, CUDNN_SOFTMAX_ACCURATE, 20 | CUDNN_SOFTMAX_MODE_CHANNEL, 21 | cudnn::dataType::one, 22 | bottom_desc_, bottom_data, 23 | cudnn::dataType::zero, 24 | top_desc_, top_data)); 25 | } 26 | 27 | template 28 | void CuDNNSoftmaxLayer::Backward_gpu(const vector*>& top, 29 | const vector& propagate_down, const vector*>& bottom) { 30 | if (propagate_down[0]) { 31 | const Dtype* top_data = top[0]->gpu_data(); 32 | const Dtype* top_diff = top[0]->gpu_diff(); 33 | const Dtype* bottom_data = bottom[0]->gpu_data(); 34 | Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); 35 | 36 | CUDNN_CHECK(cudnnSoftmaxBackward(handle_, CUDNN_SOFTMAX_ACCURATE, 37 | CUDNN_SOFTMAX_MODE_CHANNEL, 38 | cudnn::dataType::one, 39 | top_desc_, top_data, top_desc_, top_diff, 40 | cudnn::dataType::zero, 41 | bottom_desc_, bottom_diff)); 42 | } 43 | } 44 | 45 | INSTANTIATE_LAYER_GPU_FUNCS(CuDNNSoftmaxLayer); 46 | 47 | } // namespace caffe 48 | #endif 49 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/cudnn_tanh_layer.cpp: -------------------------------------------------------------------------------- 1 | #ifdef USE_CUDNN 2 | #include 3 | #include 4 | 5 | #include "caffe/layer.hpp" 6 | #include "caffe/vision_layers.hpp" 7 | 8 | namespace caffe { 9 | 10 | template 11 | void CuDNNTanHLayer::LayerSetUp(const vector*>& bottom, 12 | const vector*>& top) { 13 | TanHLayer::LayerSetUp(bottom, top); 14 | // initialize cuDNN 15 | CUDNN_CHECK(cudnnCreate(&handle_)); 16 | cudnn::createTensor4dDesc(&bottom_desc_); 17 | cudnn::createTensor4dDesc(&top_desc_); 18 | handles_setup_ = true; 19 | } 20 | 21 | template 22 | void CuDNNTanHLayer::Reshape(const vector*>& bottom, 23 | const vector*>& top) { 24 | TanHLayer::Reshape(bottom, top); 25 | const int N = bottom[0]->num(); 26 | const int K = bottom[0]->channels(); 27 | const int H = bottom[0]->height(); 28 | const int W = bottom[0]->width(); 29 | cudnn::setTensor4dDesc(&bottom_desc_, N, K, H, W); 30 | cudnn::setTensor4dDesc(&top_desc_, N, K, H, W); 31 | } 32 | 33 | template 34 | CuDNNTanHLayer::~CuDNNTanHLayer() { 35 | // Check that handles have been setup before destroying. 36 | if (!handles_setup_) { return; } 37 | 38 | cudnnDestroyTensorDescriptor(this->bottom_desc_); 39 | cudnnDestroyTensorDescriptor(this->top_desc_); 40 | cudnnDestroy(this->handle_); 41 | } 42 | 43 | INSTANTIATE_CLASS(CuDNNTanHLayer); 44 | 45 | } // namespace caffe 46 | #endif 47 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/cudnn_tanh_layer.cu: -------------------------------------------------------------------------------- 1 | #ifdef USE_CUDNN 2 | #include 3 | #include 4 | 5 | #include "caffe/layer.hpp" 6 | #include "caffe/vision_layers.hpp" 7 | 8 | namespace caffe { 9 | 10 | template 11 | void CuDNNTanHLayer::Forward_gpu(const vector*>& bottom, 12 | const vector*>& top) { 13 | const Dtype* bottom_data = bottom[0]->gpu_data(); 14 | Dtype* top_data = top[0]->mutable_gpu_data(); 15 | CUDNN_CHECK(cudnnActivationForward(this->handle_, 16 | CUDNN_ACTIVATION_TANH, 17 | cudnn::dataType::one, 18 | this->bottom_desc_, bottom_data, 19 | cudnn::dataType::zero, 20 | this->top_desc_, top_data)); 21 | } 22 | 23 | template 24 | void CuDNNTanHLayer::Backward_gpu(const vector*>& top, 25 | const vector& propagate_down, 26 | const vector*>& bottom) { 27 | if (!propagate_down[0]) { 28 | return; 29 | } 30 | 31 | const Dtype* top_data = top[0]->gpu_data(); 32 | const Dtype* top_diff = top[0]->gpu_diff(); 33 | const Dtype* bottom_data = bottom[0]->gpu_data(); 34 | Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); 35 | 36 | CUDNN_CHECK(cudnnActivationBackward(this->handle_, 37 | CUDNN_ACTIVATION_TANH, 38 | cudnn::dataType::one, 39 | this->top_desc_, top_data, this->top_desc_, top_diff, 40 | this->bottom_desc_, bottom_data, 41 | cudnn::dataType::zero, 42 | this->bottom_desc_, bottom_diff)); 43 | } 44 | 45 | INSTANTIATE_LAYER_GPU_FUNCS(CuDNNTanHLayer); 46 | 47 | } // namespace caffe 48 | #endif 49 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/euclidean_loss_layer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "caffe/layer.hpp" 4 | #include "caffe/util/io.hpp" 5 | #include "caffe/util/math_functions.hpp" 6 | #include "caffe/vision_layers.hpp" 7 | 8 | namespace caffe { 9 | 10 | template 11 | void EuclideanLossLayer::Reshape( 12 | const vector*>& bottom, const vector*>& top) { 13 | LossLayer::Reshape(bottom, top); 14 | CHECK_EQ(bottom[0]->count(1), bottom[1]->count(1)) 15 | << "Inputs must have the same dimension."; 16 | diff_.ReshapeLike(*bottom[0]); 17 | } 18 | 19 | template 20 | void EuclideanLossLayer::Forward_cpu(const vector*>& bottom, 21 | const vector*>& top) { 22 | int count = bottom[0]->count(); 23 | caffe_sub( 24 | count, 25 | bottom[0]->cpu_data(), 26 | bottom[1]->cpu_data(), 27 | diff_.mutable_cpu_data()); 28 | Dtype dot = caffe_cpu_dot(count, diff_.cpu_data(), diff_.cpu_data()); 29 | Dtype loss = dot / bottom[0]->num() / Dtype(2); 30 | top[0]->mutable_cpu_data()[0] = loss; 31 | } 32 | 33 | template 34 | void EuclideanLossLayer::Backward_cpu(const vector*>& top, 35 | const vector& propagate_down, const vector*>& bottom) { 36 | for (int i = 0; i < 2; ++i) { 37 | if (propagate_down[i]) { 38 | const Dtype sign = (i == 0) ? 1 : -1; 39 | const Dtype alpha = sign * top[0]->cpu_diff()[0] / bottom[i]->num(); 40 | caffe_cpu_axpby( 41 | bottom[i]->count(), // count 42 | alpha, // alpha 43 | diff_.cpu_data(), // a 44 | Dtype(0), // beta 45 | bottom[i]->mutable_cpu_diff()); // b 46 | } 47 | } 48 | } 49 | 50 | #ifdef CPU_ONLY 51 | STUB_GPU(EuclideanLossLayer); 52 | #endif 53 | 54 | INSTANTIATE_CLASS(EuclideanLossLayer); 55 | REGISTER_LAYER_CLASS(EuclideanLoss); 56 | 57 | } // namespace caffe 58 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/euclidean_loss_layer.cu: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "caffe/layer.hpp" 4 | #include "caffe/util/io.hpp" 5 | #include "caffe/util/math_functions.hpp" 6 | #include "caffe/vision_layers.hpp" 7 | 8 | namespace caffe { 9 | 10 | template 11 | void EuclideanLossLayer::Forward_gpu(const vector*>& bottom, 12 | const vector*>& top) { 13 | int count = bottom[0]->count(); 14 | caffe_gpu_sub( 15 | count, 16 | bottom[0]->gpu_data(), 17 | bottom[1]->gpu_data(), 18 | diff_.mutable_gpu_data()); 19 | Dtype dot; 20 | caffe_gpu_dot(count, diff_.gpu_data(), diff_.gpu_data(), &dot); 21 | Dtype loss = dot / bottom[0]->num() / Dtype(2); 22 | top[0]->mutable_cpu_data()[0] = loss; 23 | } 24 | 25 | template 26 | void EuclideanLossLayer::Backward_gpu(const vector*>& top, 27 | const vector& propagate_down, const vector*>& bottom) { 28 | for (int i = 0; i < 2; ++i) { 29 | if (propagate_down[i]) { 30 | const Dtype sign = (i == 0) ? 1 : -1; 31 | const Dtype alpha = sign * top[0]->cpu_diff()[0] / bottom[i]->num(); 32 | caffe_gpu_axpby( 33 | bottom[i]->count(), // count 34 | alpha, // alpha 35 | diff_.gpu_data(), // a 36 | Dtype(0), // beta 37 | bottom[i]->mutable_gpu_diff()); // b 38 | } 39 | } 40 | } 41 | 42 | INSTANTIATE_LAYER_GPU_FUNCS(EuclideanLossLayer); 43 | 44 | } // namespace caffe 45 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/exp_layer.cu: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | 4 | #include "caffe/layer.hpp" 5 | #include "caffe/util/math_functions.hpp" 6 | #include "caffe/vision_layers.hpp" 7 | 8 | namespace caffe { 9 | 10 | template 11 | void ExpLayer::Forward_gpu(const vector*>& bottom, 12 | const vector*>& top) { 13 | const int count = bottom[0]->count(); 14 | const Dtype* bottom_data = bottom[0]->gpu_data(); 15 | Dtype* top_data = top[0]->mutable_gpu_data(); 16 | if (inner_scale_ == Dtype(1)) { 17 | caffe_gpu_exp(count, bottom_data, top_data); 18 | } else { 19 | caffe_gpu_scale(count, inner_scale_, bottom_data, top_data); 20 | caffe_gpu_exp(count, top_data, top_data); 21 | } 22 | if (outer_scale_ != Dtype(1)) { 23 | caffe_gpu_scal(count, outer_scale_, top_data); 24 | } 25 | } 26 | 27 | template 28 | void ExpLayer::Backward_gpu(const vector*>& top, 29 | const vector& propagate_down, const vector*>& bottom) { 30 | if (!propagate_down[0]) { return; } 31 | const int count = bottom[0]->count(); 32 | const Dtype* top_data = top[0]->gpu_data(); 33 | const Dtype* top_diff = top[0]->gpu_diff(); 34 | Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); 35 | caffe_gpu_mul(count, top_data, top_diff, bottom_diff); 36 | if (inner_scale_ != Dtype(1)) { 37 | caffe_gpu_scal(count, inner_scale_, bottom_diff); 38 | } 39 | } 40 | 41 | INSTANTIATE_LAYER_GPU_FUNCS(ExpLayer); 42 | 43 | 44 | } // namespace caffe 45 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/flatten_layer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "caffe/layer.hpp" 4 | #include "caffe/util/math_functions.hpp" 5 | #include "caffe/vision_layers.hpp" 6 | 7 | namespace caffe { 8 | 9 | template 10 | void FlattenLayer::Reshape(const vector*>& bottom, 11 | const vector*>& top) { 12 | const int start_axis = bottom[0]->CanonicalAxisIndex( 13 | this->layer_param_.flatten_param().axis()); 14 | const int end_axis = bottom[0]->CanonicalAxisIndex( 15 | this->layer_param_.flatten_param().end_axis()); 16 | vector top_shape; 17 | for (int i = 0; i < start_axis; ++i) { 18 | top_shape.push_back(bottom[0]->shape(i)); 19 | } 20 | const int flattened_dim = bottom[0]->count(start_axis, end_axis + 1); 21 | top_shape.push_back(flattened_dim); 22 | for (int i = end_axis + 1; i < bottom[0]->num_axes(); ++i) { 23 | top_shape.push_back(bottom[0]->shape(i)); 24 | } 25 | top[0]->Reshape(top_shape); 26 | CHECK_EQ(top[0]->count(), bottom[0]->count()); 27 | } 28 | 29 | template 30 | void FlattenLayer::Forward_cpu(const vector*>& bottom, 31 | const vector*>& top) { 32 | top[0]->ShareData(*bottom[0]); 33 | } 34 | 35 | template 36 | void FlattenLayer::Backward_cpu(const vector*>& top, 37 | const vector& propagate_down, const vector*>& bottom) { 38 | bottom[0]->ShareDiff(*top[0]); 39 | } 40 | 41 | INSTANTIATE_CLASS(FlattenLayer); 42 | REGISTER_LAYER_CLASS(Flatten); 43 | 44 | } // namespace caffe 45 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/hdf5_data_layer.cu: -------------------------------------------------------------------------------- 1 | /* 2 | TODO: 3 | - only load parts of the file, in accordance with a prototxt param "max_mem" 4 | */ 5 | 6 | #include 7 | #include 8 | #include 9 | 10 | #include "hdf5.h" 11 | #include "hdf5_hl.h" 12 | 13 | #include "caffe/data_layers.hpp" 14 | #include "caffe/layer.hpp" 15 | #include "caffe/util/io.hpp" 16 | 17 | namespace caffe { 18 | 19 | template 20 | void HDF5DataLayer::Forward_gpu(const vector*>& bottom, 21 | const vector*>& top) { 22 | const int batch_size = this->layer_param_.hdf5_data_param().batch_size(); 23 | for (int i = 0; i < batch_size; ++i, ++current_row_) { 24 | if (current_row_ == hdf_blobs_[0]->shape(0)) { 25 | if (num_files_ > 1) { 26 | current_file_ += 1; 27 | if (current_file_ == num_files_) { 28 | current_file_ = 0; 29 | if (this->layer_param_.hdf5_data_param().shuffle()) { 30 | std::random_shuffle(file_permutation_.begin(), 31 | file_permutation_.end()); 32 | } 33 | DLOG(INFO) << "Looping around to first file."; 34 | } 35 | LoadHDF5FileData( 36 | hdf_filenames_[file_permutation_[current_file_]].c_str()); 37 | } 38 | current_row_ = 0; 39 | if (this->layer_param_.hdf5_data_param().shuffle()) 40 | std::random_shuffle(data_permutation_.begin(), data_permutation_.end()); 41 | } 42 | for (int j = 0; j < this->layer_param_.top_size(); ++j) { 43 | int data_dim = top[j]->count() / top[j]->shape(0); 44 | caffe_copy(data_dim, 45 | &hdf_blobs_[j]->cpu_data()[data_permutation_[current_row_] 46 | * data_dim], &top[j]->mutable_gpu_data()[i * data_dim]); 47 | } 48 | } 49 | } 50 | 51 | INSTANTIATE_LAYER_GPU_FUNCS(HDF5DataLayer); 52 | 53 | } // namespace caffe 54 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/hdf5_output_layer.cu: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "hdf5.h" 4 | #include "hdf5_hl.h" 5 | 6 | #include "caffe/blob.hpp" 7 | #include "caffe/common.hpp" 8 | #include "caffe/layer.hpp" 9 | #include "caffe/vision_layers.hpp" 10 | 11 | namespace caffe { 12 | 13 | template 14 | void HDF5OutputLayer::Forward_gpu(const vector*>& bottom, 15 | const vector*>& top) { 16 | CHECK_GE(bottom.size(), 2); 17 | CHECK_EQ(bottom[0]->num(), bottom[1]->num()); 18 | data_blob_.Reshape(bottom[0]->num(), bottom[0]->channels(), 19 | bottom[0]->height(), bottom[0]->width()); 20 | label_blob_.Reshape(bottom[1]->num(), bottom[1]->channels(), 21 | bottom[1]->height(), bottom[1]->width()); 22 | const int data_datum_dim = bottom[0]->count() / bottom[0]->num(); 23 | const int label_datum_dim = bottom[1]->count() / bottom[1]->num(); 24 | 25 | for (int i = 0; i < bottom[0]->num(); ++i) { 26 | caffe_copy(data_datum_dim, &bottom[0]->gpu_data()[i * data_datum_dim], 27 | &data_blob_.mutable_cpu_data()[i * data_datum_dim]); 28 | caffe_copy(label_datum_dim, &bottom[1]->gpu_data()[i * label_datum_dim], 29 | &label_blob_.mutable_cpu_data()[i * label_datum_dim]); 30 | } 31 | SaveBlobs(); 32 | } 33 | 34 | template 35 | void HDF5OutputLayer::Backward_gpu(const vector*>& top, 36 | const vector& propagate_down, const vector*>& bottom) { 37 | return; 38 | } 39 | 40 | INSTANTIATE_LAYER_GPU_FUNCS(HDF5OutputLayer); 41 | 42 | } // namespace caffe 43 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/im2col_layer.cu: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "caffe/common.hpp" 4 | #include "caffe/layer.hpp" 5 | #include "caffe/util/im2col.hpp" 6 | #include "caffe/vision_layers.hpp" 7 | 8 | namespace caffe { 9 | 10 | template 11 | void Im2colLayer::Forward_gpu(const vector*>& bottom, 12 | const vector*>& top) { 13 | const Dtype* bottom_data = bottom[0]->gpu_data(); 14 | Dtype* top_data = top[0]->mutable_gpu_data(); 15 | for (int n = 0; n < bottom[0]->num(); ++n) { 16 | im2col_gpu(bottom_data + bottom[0]->offset(n), channels_, height_, 17 | width_, kernel_h_, kernel_w_, pad_h_, pad_w_, 18 | stride_h_, stride_w_, top_data + top[0]->offset(n)); 19 | } 20 | } 21 | 22 | template 23 | void Im2colLayer::Backward_gpu(const vector*>& top, 24 | const vector& propagate_down, const vector*>& bottom) { 25 | const Dtype* top_diff = top[0]->gpu_diff(); 26 | Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); 27 | for (int n = 0; n < top[0]->num(); ++n) { 28 | col2im_gpu(top_diff + top[0]->offset(n), channels_, height_, width_, 29 | kernel_h_, kernel_w_, pad_h_, pad_w_, 30 | stride_h_, stride_w_, bottom_diff + bottom[0]->offset(n)); 31 | } 32 | } 33 | 34 | 35 | INSTANTIATE_LAYER_GPU_FUNCS(Im2colLayer); 36 | 37 | } // namespace caffe 38 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/log_layer.cu: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | 4 | #include "caffe/layer.hpp" 5 | #include "caffe/neuron_layers.hpp" 6 | #include "caffe/util/math_functions.hpp" 7 | 8 | namespace caffe { 9 | 10 | template 11 | void LogLayer::Forward_gpu(const vector*>& bottom, 12 | const vector*>& top) { 13 | const int count = bottom[0]->count(); 14 | const Dtype* bottom_data = bottom[0]->gpu_data(); 15 | Dtype* top_data = top[0]->mutable_gpu_data(); 16 | if (input_scale_ == Dtype(1) && input_shift_ == Dtype(0)) { 17 | caffe_gpu_log(count, bottom_data, top_data); 18 | } else { 19 | caffe_copy(count, bottom_data, top_data); 20 | if (input_scale_ != Dtype(1)) { 21 | caffe_gpu_scal(count, input_scale_, top_data); 22 | } 23 | if (input_shift_ != Dtype(0)) { 24 | caffe_gpu_add_scalar(count, input_shift_, top_data); 25 | } 26 | caffe_gpu_log(count, top_data, top_data); 27 | } 28 | if (base_scale_ != Dtype(1)) { 29 | caffe_gpu_scal(count, base_scale_, top_data); 30 | } 31 | } 32 | 33 | template 34 | void LogLayer::Backward_gpu(const vector*>& top, 35 | const vector& propagate_down, const vector*>& bottom) { 36 | if (!propagate_down[0]) { return; } 37 | const int count = bottom[0]->count(); 38 | const Dtype* bottom_data = bottom[0]->gpu_data(); 39 | const Dtype* top_diff = top[0]->gpu_diff(); 40 | Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); 41 | caffe_copy(count, bottom_data, bottom_diff); 42 | if (input_scale_ != Dtype(1)) { 43 | caffe_gpu_scal(count, input_scale_, bottom_diff); 44 | } 45 | if (input_shift_ != Dtype(0)) { 46 | caffe_gpu_add_scalar(count, input_shift_, bottom_diff); 47 | } 48 | caffe_gpu_powx(count, bottom_diff, Dtype(-1), bottom_diff); 49 | if (backward_num_scale_ != Dtype(1)) { 50 | caffe_gpu_scal(count, backward_num_scale_, bottom_diff); 51 | } 52 | caffe_gpu_mul(count, top_diff, bottom_diff, bottom_diff); 53 | } 54 | 55 | INSTANTIATE_LAYER_GPU_FUNCS(LogLayer); 56 | 57 | } // namespace caffe 58 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/loss_layer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | 6 | #include "caffe/layer.hpp" 7 | #include "caffe/util/io.hpp" 8 | #include "caffe/util/math_functions.hpp" 9 | #include "caffe/vision_layers.hpp" 10 | 11 | namespace caffe { 12 | 13 | template 14 | void LossLayer::LayerSetUp( 15 | const vector*>& bottom, const vector*>& top) { 16 | // LossLayers have a non-zero (1) loss by default. 17 | if (this->layer_param_.loss_weight_size() == 0) { 18 | this->layer_param_.add_loss_weight(Dtype(1)); 19 | } 20 | } 21 | 22 | template 23 | void LossLayer::Reshape( 24 | const vector*>& bottom, const vector*>& top) { 25 | CHECK_EQ(bottom[0]->num(), bottom[1]->num()) 26 | << "The data and label should have the same number."; 27 | vector loss_shape(0); // Loss layers output a scalar; 0 axes. 28 | top[0]->Reshape(loss_shape); 29 | } 30 | 31 | INSTANTIATE_CLASS(LossLayer); 32 | 33 | } // namespace caffe 34 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/multinomial_logistic_loss_layer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | 6 | #include "caffe/layer.hpp" 7 | #include "caffe/util/io.hpp" 8 | #include "caffe/util/math_functions.hpp" 9 | #include "caffe/vision_layers.hpp" 10 | 11 | namespace caffe { 12 | 13 | template 14 | void MultinomialLogisticLossLayer::Reshape( 15 | const vector*>& bottom, const vector*>& top) { 16 | LossLayer::Reshape(bottom, top); 17 | CHECK_EQ(bottom[1]->channels(), 1); 18 | CHECK_EQ(bottom[1]->height(), 1); 19 | CHECK_EQ(bottom[1]->width(), 1); 20 | } 21 | 22 | template 23 | void MultinomialLogisticLossLayer::Forward_cpu( 24 | const vector*>& bottom, const vector*>& top) { 25 | const Dtype* bottom_data = bottom[0]->cpu_data(); 26 | const Dtype* bottom_label = bottom[1]->cpu_data(); 27 | int num = bottom[0]->num(); 28 | int dim = bottom[0]->count() / bottom[0]->num(); 29 | Dtype loss = 0; 30 | for (int i = 0; i < num; ++i) { 31 | int label = static_cast(bottom_label[i]); 32 | Dtype prob = std::max( 33 | bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD)); 34 | loss -= log(prob); 35 | } 36 | top[0]->mutable_cpu_data()[0] = loss / num; 37 | } 38 | 39 | template 40 | void MultinomialLogisticLossLayer::Backward_cpu( 41 | const vector*>& top, const vector& propagate_down, 42 | const vector*>& bottom) { 43 | if (propagate_down[1]) { 44 | LOG(FATAL) << this->type() 45 | << " Layer cannot backpropagate to label inputs."; 46 | } 47 | if (propagate_down[0]) { 48 | const Dtype* bottom_data = bottom[0]->cpu_data(); 49 | const Dtype* bottom_label = bottom[1]->cpu_data(); 50 | Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); 51 | int num = bottom[0]->num(); 52 | int dim = bottom[0]->count() / bottom[0]->num(); 53 | caffe_set(bottom[0]->count(), Dtype(0), bottom_diff); 54 | const Dtype scale = - top[0]->cpu_diff()[0] / num; 55 | for (int i = 0; i < num; ++i) { 56 | int label = static_cast(bottom_label[i]); 57 | Dtype prob = std::max( 58 | bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD)); 59 | bottom_diff[i * dim + label] = scale / prob; 60 | } 61 | } 62 | } 63 | 64 | INSTANTIATE_CLASS(MultinomialLogisticLossLayer); 65 | REGISTER_LAYER_CLASS(MultinomialLogisticLoss); 66 | 67 | } // namespace caffe 68 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/neuron_layer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "caffe/layer.hpp" 4 | #include "caffe/vision_layers.hpp" 5 | 6 | namespace caffe { 7 | 8 | template 9 | void NeuronLayer::Reshape(const vector*>& bottom, 10 | const vector*>& top) { 11 | top[0]->ReshapeLike(*bottom[0]); 12 | } 13 | 14 | INSTANTIATE_CLASS(NeuronLayer); 15 | 16 | } // namespace caffe 17 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/relu_layer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | 4 | #include "caffe/layer.hpp" 5 | #include "caffe/vision_layers.hpp" 6 | 7 | namespace caffe { 8 | 9 | template 10 | void ReLULayer::Forward_cpu(const vector*>& bottom, 11 | const vector*>& top) { 12 | const Dtype* bottom_data = bottom[0]->cpu_data(); 13 | Dtype* top_data = top[0]->mutable_cpu_data(); 14 | const int count = bottom[0]->count(); 15 | Dtype negative_slope = this->layer_param_.relu_param().negative_slope(); 16 | for (int i = 0; i < count; ++i) { 17 | top_data[i] = std::max(bottom_data[i], Dtype(0)) 18 | + negative_slope * std::min(bottom_data[i], Dtype(0)); 19 | } 20 | } 21 | 22 | template 23 | void ReLULayer::Backward_cpu(const vector*>& top, 24 | const vector& propagate_down, 25 | const vector*>& bottom) { 26 | if (propagate_down[0]) { 27 | const Dtype* bottom_data = bottom[0]->cpu_data(); 28 | const Dtype* top_diff = top[0]->cpu_diff(); 29 | Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); 30 | const int count = bottom[0]->count(); 31 | Dtype negative_slope = this->layer_param_.relu_param().negative_slope(); 32 | for (int i = 0; i < count; ++i) { 33 | bottom_diff[i] = top_diff[i] * ((bottom_data[i] > 0) 34 | + negative_slope * (bottom_data[i] <= 0)); 35 | } 36 | } 37 | } 38 | 39 | 40 | #ifdef CPU_ONLY 41 | STUB_GPU(ReLULayer); 42 | #endif 43 | 44 | INSTANTIATE_CLASS(ReLULayer); 45 | 46 | } // namespace caffe 47 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/relu_layer.cu: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | 4 | #include "caffe/layer.hpp" 5 | #include "caffe/vision_layers.hpp" 6 | 7 | namespace caffe { 8 | 9 | template 10 | __global__ void ReLUForward(const int n, const Dtype* in, Dtype* out, 11 | Dtype negative_slope) { 12 | CUDA_KERNEL_LOOP(index, n) { 13 | out[index] = in[index] > 0 ? in[index] : in[index] * negative_slope; 14 | } 15 | } 16 | 17 | template 18 | void ReLULayer::Forward_gpu(const vector*>& bottom, 19 | const vector*>& top) { 20 | const Dtype* bottom_data = bottom[0]->gpu_data(); 21 | Dtype* top_data = top[0]->mutable_gpu_data(); 22 | const int count = bottom[0]->count(); 23 | Dtype negative_slope = this->layer_param_.relu_param().negative_slope(); 24 | // NOLINT_NEXT_LINE(whitespace/operators) 25 | ReLUForward<<>>( 26 | count, bottom_data, top_data, negative_slope); 27 | CUDA_POST_KERNEL_CHECK; 28 | // << " count: " << count << " bottom_data: " 29 | // << (unsigned long)bottom_data 30 | // << " top_data: " << (unsigned long)top_data 31 | // << " blocks: " << CAFFE_GET_BLOCKS(count) 32 | // << " threads: " << CAFFE_CUDA_NUM_THREADS; 33 | } 34 | 35 | template 36 | __global__ void ReLUBackward(const int n, const Dtype* in_diff, 37 | const Dtype* in_data, Dtype* out_diff, Dtype negative_slope) { 38 | CUDA_KERNEL_LOOP(index, n) { 39 | out_diff[index] = in_diff[index] * ((in_data[index] > 0) 40 | + (in_data[index] <= 0) * negative_slope); 41 | } 42 | } 43 | 44 | template 45 | void ReLULayer::Backward_gpu(const vector*>& top, 46 | const vector& propagate_down, 47 | const vector*>& bottom) { 48 | if (propagate_down[0]) { 49 | const Dtype* bottom_data = bottom[0]->gpu_data(); 50 | const Dtype* top_diff = top[0]->gpu_diff(); 51 | Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); 52 | const int count = bottom[0]->count(); 53 | Dtype negative_slope = this->layer_param_.relu_param().negative_slope(); 54 | // NOLINT_NEXT_LINE(whitespace/operators) 55 | ReLUBackward<<>>( 56 | count, top_diff, bottom_data, bottom_diff, negative_slope); 57 | CUDA_POST_KERNEL_CHECK; 58 | } 59 | } 60 | 61 | 62 | INSTANTIATE_LAYER_GPU_FUNCS(ReLULayer); 63 | 64 | 65 | } // namespace caffe 66 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/sigmoid_layer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | 5 | #include "caffe/layer.hpp" 6 | #include "caffe/vision_layers.hpp" 7 | 8 | namespace caffe { 9 | 10 | template 11 | inline Dtype sigmoid(Dtype x) { 12 | return 1. / (1. + exp(-x)); 13 | } 14 | 15 | template 16 | void SigmoidLayer::Forward_cpu(const vector*>& bottom, 17 | const vector*>& top) { 18 | const Dtype* bottom_data = bottom[0]->cpu_data(); 19 | Dtype* top_data = top[0]->mutable_cpu_data(); 20 | const int count = bottom[0]->count(); 21 | for (int i = 0; i < count; ++i) { 22 | top_data[i] = sigmoid(bottom_data[i]); 23 | } 24 | } 25 | 26 | template 27 | void SigmoidLayer::Backward_cpu(const vector*>& top, 28 | const vector& propagate_down, 29 | const vector*>& bottom) { 30 | if (propagate_down[0]) { 31 | const Dtype* top_data = top[0]->cpu_data(); 32 | const Dtype* top_diff = top[0]->cpu_diff(); 33 | Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); 34 | const int count = bottom[0]->count(); 35 | for (int i = 0; i < count; ++i) { 36 | const Dtype sigmoid_x = top_data[i]; 37 | bottom_diff[i] = top_diff[i] * sigmoid_x * (1. - sigmoid_x); 38 | } 39 | } 40 | } 41 | 42 | #ifdef CPU_ONLY 43 | STUB_GPU(SigmoidLayer); 44 | #endif 45 | 46 | INSTANTIATE_CLASS(SigmoidLayer); 47 | 48 | 49 | } // namespace caffe 50 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/sigmoid_layer.cu: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | 5 | #include "caffe/layer.hpp" 6 | #include "caffe/vision_layers.hpp" 7 | 8 | namespace caffe { 9 | 10 | template 11 | __global__ void SigmoidForward(const int n, const Dtype* in, Dtype* out) { 12 | CUDA_KERNEL_LOOP(index, n) { 13 | out[index] = 1. / (1. + exp(-in[index])); 14 | } 15 | } 16 | 17 | template 18 | void SigmoidLayer::Forward_gpu(const vector*>& bottom, 19 | const vector*>& top) { 20 | const Dtype* bottom_data = bottom[0]->gpu_data(); 21 | Dtype* top_data = top[0]->mutable_gpu_data(); 22 | const int count = bottom[0]->count(); 23 | // NOLINT_NEXT_LINE(whitespace/operators) 24 | SigmoidForward<<>>( 25 | count, bottom_data, top_data); 26 | CUDA_POST_KERNEL_CHECK; 27 | // << " count: " << count << " bottom_data: " 28 | // << (unsigned long)bottom_data 29 | // << " top_data: " << (unsigned long)top_data 30 | // << " blocks: " << CAFFE_GET_BLOCKS(count) 31 | // << " threads: " << CAFFE_CUDA_NUM_THREADS; 32 | } 33 | 34 | template 35 | __global__ void SigmoidBackward(const int n, const Dtype* in_diff, 36 | const Dtype* out_data, Dtype* out_diff) { 37 | CUDA_KERNEL_LOOP(index, n) { 38 | const Dtype sigmoid_x = out_data[index]; 39 | out_diff[index] = in_diff[index] * sigmoid_x * (1 - sigmoid_x); 40 | } 41 | } 42 | 43 | template 44 | void SigmoidLayer::Backward_gpu(const vector*>& top, 45 | const vector& propagate_down, 46 | const vector*>& bottom) { 47 | if (propagate_down[0]) { 48 | const Dtype* top_data = top[0]->gpu_data(); 49 | const Dtype* top_diff = top[0]->gpu_diff(); 50 | Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); 51 | const int count = bottom[0]->count(); 52 | // NOLINT_NEXT_LINE(whitespace/operators) 53 | SigmoidBackward<<>>( 54 | count, top_diff, top_data, bottom_diff); 55 | CUDA_POST_KERNEL_CHECK; 56 | } 57 | } 58 | 59 | INSTANTIATE_LAYER_GPU_FUNCS(SigmoidLayer); 60 | 61 | 62 | } // namespace caffe 63 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/silence_layer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "caffe/common_layers.hpp" 4 | #include "caffe/layer.hpp" 5 | #include "caffe/util/math_functions.hpp" 6 | 7 | namespace caffe { 8 | 9 | template 10 | void SilenceLayer::Backward_cpu(const vector*>& top, 11 | const vector& propagate_down, const vector*>& bottom) { 12 | for (int i = 0; i < bottom.size(); ++i) { 13 | if (propagate_down[i]) { 14 | caffe_set(bottom[i]->count(), Dtype(0), 15 | bottom[i]->mutable_cpu_data()); 16 | } 17 | } 18 | } 19 | 20 | #ifdef CPU_ONLY 21 | STUB_GPU(SilenceLayer); 22 | #endif 23 | 24 | INSTANTIATE_CLASS(SilenceLayer); 25 | REGISTER_LAYER_CLASS(Silence); 26 | 27 | } // namespace caffe 28 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/silence_layer.cu: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "caffe/common_layers.hpp" 4 | #include "caffe/layer.hpp" 5 | #include "caffe/util/math_functions.hpp" 6 | 7 | namespace caffe { 8 | 9 | template 10 | void SilenceLayer::Forward_gpu(const vector*>& bottom, 11 | const vector*>& top) { 12 | // Do nothing. 13 | } 14 | 15 | template 16 | void SilenceLayer::Backward_gpu(const vector*>& top, 17 | const vector& propagate_down, const vector*>& bottom) { 18 | for (int i = 0; i < bottom.size(); ++i) { 19 | if (propagate_down[i]) { 20 | caffe_gpu_set(bottom[i]->count(), Dtype(0), 21 | bottom[i]->mutable_gpu_data()); 22 | } 23 | } 24 | } 25 | 26 | INSTANTIATE_LAYER_GPU_FUNCS(SilenceLayer); 27 | 28 | } // namespace caffe 29 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/split_layer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "caffe/layer.hpp" 4 | #include "caffe/util/math_functions.hpp" 5 | #include "caffe/vision_layers.hpp" 6 | 7 | namespace caffe { 8 | 9 | template 10 | void SplitLayer::Reshape(const vector*>& bottom, 11 | const vector*>& top) { 12 | count_ = bottom[0]->count(); 13 | for (int i = 0; i < top.size(); ++i) { 14 | // Do not allow in-place computation in the SplitLayer. Instead, share data 15 | // by reference in the forward pass, and keep separate diff allocations in 16 | // the backward pass. (Technically, it should be possible to share the diff 17 | // blob of the first split output with the input, but this seems to cause 18 | // some strange effects in practice...) 19 | CHECK_NE(top[i], bottom[0]) << this->type() << " Layer does not " 20 | "allow in-place computation."; 21 | top[i]->ReshapeLike(*bottom[0]); 22 | CHECK_EQ(count_, top[i]->count()); 23 | } 24 | } 25 | 26 | template 27 | void SplitLayer::Forward_cpu(const vector*>& bottom, 28 | const vector*>& top) { 29 | for (int i = 0; i < top.size(); ++i) { 30 | top[i]->ShareData(*bottom[0]); 31 | } 32 | } 33 | 34 | template 35 | void SplitLayer::Backward_cpu(const vector*>& top, 36 | const vector& propagate_down, const vector*>& bottom) { 37 | if (!propagate_down[0]) { return; } 38 | if (top.size() == 1) { 39 | caffe_copy(count_, top[0]->cpu_diff(), bottom[0]->mutable_cpu_diff()); 40 | return; 41 | } 42 | caffe_add(count_, top[0]->cpu_diff(), top[1]->cpu_diff(), 43 | bottom[0]->mutable_cpu_diff()); 44 | // Add remaining top blob diffs. 45 | for (int i = 2; i < top.size(); ++i) { 46 | const Dtype* top_diff = top[i]->cpu_diff(); 47 | Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); 48 | caffe_axpy(count_, Dtype(1.), top_diff, bottom_diff); 49 | } 50 | } 51 | 52 | 53 | #ifdef CPU_ONLY 54 | STUB_GPU(SplitLayer); 55 | #endif 56 | 57 | INSTANTIATE_CLASS(SplitLayer); 58 | REGISTER_LAYER_CLASS(Split); 59 | 60 | } // namespace caffe 61 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/split_layer.cu: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "caffe/layer.hpp" 4 | #include "caffe/util/math_functions.hpp" 5 | #include "caffe/vision_layers.hpp" 6 | 7 | namespace caffe { 8 | 9 | template 10 | void SplitLayer::Forward_gpu(const vector*>& bottom, 11 | const vector*>& top) { 12 | for (int i = 0; i < top.size(); ++i) { 13 | top[i]->ShareData(*bottom[0]); 14 | } 15 | } 16 | 17 | template 18 | void SplitLayer::Backward_gpu(const vector*>& top, 19 | const vector& propagate_down, const vector*>& bottom) { 20 | if (!propagate_down[0]) { return; } 21 | if (top.size() == 1) { 22 | caffe_copy(count_, top[0]->gpu_diff(), bottom[0]->mutable_gpu_diff()); 23 | return; 24 | } 25 | caffe_gpu_add(count_, top[0]->gpu_diff(), top[1]->gpu_diff(), 26 | bottom[0]->mutable_gpu_diff()); 27 | // Add remaining top blob diffs. 28 | for (int i = 2; i < top.size(); ++i) { 29 | const Dtype* top_diff = top[i]->gpu_diff(); 30 | Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); 31 | caffe_gpu_axpy(count_, Dtype(1.), top_diff, bottom_diff); 32 | } 33 | } 34 | 35 | 36 | INSTANTIATE_LAYER_GPU_FUNCS(SplitLayer); 37 | 38 | } // namespace caffe 39 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/tanh_layer.cpp: -------------------------------------------------------------------------------- 1 | // TanH neuron activation function layer. 2 | // Adapted from ReLU layer code written by Yangqing Jia 3 | 4 | #include 5 | #include 6 | 7 | #include "caffe/layer.hpp" 8 | #include "caffe/vision_layers.hpp" 9 | 10 | namespace caffe { 11 | 12 | template 13 | void TanHLayer::Forward_cpu(const vector*>& bottom, 14 | const vector*>& top) { 15 | const Dtype* bottom_data = bottom[0]->cpu_data(); 16 | Dtype* top_data = top[0]->mutable_cpu_data(); 17 | const int count = bottom[0]->count(); 18 | for (int i = 0; i < count; ++i) { 19 | top_data[i] = tanh(bottom_data[i]); 20 | } 21 | } 22 | 23 | template 24 | void TanHLayer::Backward_cpu(const vector*>& top, 25 | const vector& propagate_down, 26 | const vector*>& bottom) { 27 | if (propagate_down[0]) { 28 | const Dtype* top_data = top[0]->cpu_data(); 29 | const Dtype* top_diff = top[0]->cpu_diff(); 30 | Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); 31 | const int count = bottom[0]->count(); 32 | Dtype tanhx; 33 | for (int i = 0; i < count; ++i) { 34 | tanhx = top_data[i]; 35 | bottom_diff[i] = top_diff[i] * (1 - tanhx * tanhx); 36 | } 37 | } 38 | } 39 | 40 | #ifdef CPU_ONLY 41 | STUB_GPU(TanHLayer); 42 | #endif 43 | 44 | INSTANTIATE_CLASS(TanHLayer); 45 | 46 | } // namespace caffe 47 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/tanh_layer.cu: -------------------------------------------------------------------------------- 1 | // TanH neuron activation function layer. 2 | // Adapted from ReLU layer code written by Yangqing Jia 3 | 4 | #include 5 | #include 6 | 7 | #include "caffe/layer.hpp" 8 | #include "caffe/vision_layers.hpp" 9 | 10 | namespace caffe { 11 | 12 | template 13 | __global__ void TanHForward(const int n, const Dtype* in, Dtype* out) { 14 | CUDA_KERNEL_LOOP(index, n) { 15 | out[index] = tanh(in[index]); 16 | } 17 | } 18 | 19 | template 20 | void TanHLayer::Forward_gpu(const vector*>& bottom, 21 | const vector*>& top) { 22 | const Dtype* bottom_data = bottom[0]->gpu_data(); 23 | Dtype* top_data = top[0]->mutable_gpu_data(); 24 | const int count = bottom[0]->count(); 25 | // NOLINT_NEXT_LINE(whitespace/operators) 26 | TanHForward<<>>( 27 | count, bottom_data, top_data); 28 | CUDA_POST_KERNEL_CHECK; 29 | } 30 | 31 | template 32 | __global__ void TanHBackward(const int n, const Dtype* in_diff, 33 | const Dtype* out_data, Dtype* out_diff) { 34 | CUDA_KERNEL_LOOP(index, n) { 35 | Dtype tanhx = out_data[index]; 36 | out_diff[index] = in_diff[index] * (1 - tanhx * tanhx); 37 | } 38 | } 39 | 40 | template 41 | void TanHLayer::Backward_gpu(const vector*>& top, 42 | const vector& propagate_down, 43 | const vector*>& bottom) { 44 | if (propagate_down[0]) { 45 | const Dtype* top_data = top[0]->gpu_data(); 46 | const Dtype* top_diff = top[0]->gpu_diff(); 47 | Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); 48 | const int count = bottom[0]->count(); 49 | // NOLINT_NEXT_LINE(whitespace/operators) 50 | TanHBackward<<>>( 51 | count, top_diff, top_data, bottom_diff); 52 | CUDA_POST_KERNEL_CHECK; 53 | } 54 | } 55 | 56 | INSTANTIATE_LAYER_GPU_FUNCS(TanHLayer); 57 | 58 | 59 | } // namespace caffe 60 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/threshold_layer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "caffe/layer.hpp" 4 | #include "caffe/vision_layers.hpp" 5 | 6 | 7 | namespace caffe { 8 | 9 | template 10 | void ThresholdLayer::LayerSetUp(const vector*>& bottom, 11 | const vector*>& top) { 12 | NeuronLayer::LayerSetUp(bottom, top); 13 | threshold_ = this->layer_param_.threshold_param().threshold(); 14 | } 15 | 16 | template 17 | void ThresholdLayer::Forward_cpu(const vector*>& bottom, 18 | const vector*>& top) { 19 | const Dtype* bottom_data = bottom[0]->cpu_data(); 20 | Dtype* top_data = top[0]->mutable_cpu_data(); 21 | const int count = bottom[0]->count(); 22 | for (int i = 0; i < count; ++i) { 23 | top_data[i] = (bottom_data[i] > threshold_) ? Dtype(1) : Dtype(0); 24 | } 25 | } 26 | 27 | #ifdef CPU_ONLY 28 | STUB_GPU_FORWARD(ThresholdLayer, Forward); 29 | #endif 30 | 31 | INSTANTIATE_CLASS(ThresholdLayer); 32 | REGISTER_LAYER_CLASS(Threshold); 33 | 34 | } // namespace caffe 35 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/threshold_layer.cu: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | 4 | #include "caffe/layer.hpp" 5 | #include "caffe/vision_layers.hpp" 6 | 7 | namespace caffe { 8 | 9 | template 10 | __global__ void ThresholdForward(const int n, const Dtype threshold, 11 | const Dtype* in, Dtype* out) { 12 | CUDA_KERNEL_LOOP(index, n) { 13 | out[index] = in[index] > threshold ? 1 : 0; 14 | } 15 | } 16 | 17 | template 18 | void ThresholdLayer::Forward_gpu(const vector*>& bottom, 19 | const vector*>& top) { 20 | const Dtype* bottom_data = bottom[0]->gpu_data(); 21 | Dtype* top_data = top[0]->mutable_gpu_data(); 22 | const int count = bottom[0]->count(); 23 | // NOLINT_NEXT_LINE(whitespace/operators) 24 | ThresholdForward<<>>( 25 | count, threshold_, bottom_data, top_data); 26 | CUDA_POST_KERNEL_CHECK; 27 | } 28 | 29 | 30 | INSTANTIATE_LAYER_GPU_FORWARD(ThresholdLayer); 31 | 32 | 33 | } // namespace caffe 34 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/layers/tile_layer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "caffe/common_layers.hpp" 4 | #include "caffe/layer.hpp" 5 | #include "caffe/util/math_functions.hpp" 6 | 7 | namespace caffe { 8 | 9 | template 10 | void TileLayer::Reshape( 11 | const vector*>& bottom, const vector*>& top) { 12 | const TileParameter& tile_param = this->layer_param_.tile_param(); 13 | axis_ = bottom[0]->CanonicalAxisIndex(tile_param.axis()); 14 | CHECK(tile_param.has_tiles()) << "Number of tiles must be specified"; 15 | tiles_ = tile_param.tiles(); 16 | CHECK_GT(tiles_, 0) << "Number of tiles must be positive."; 17 | vector top_shape = bottom[0]->shape(); 18 | top_shape[axis_] = bottom[0]->shape(axis_) * tiles_; 19 | top[0]->Reshape(top_shape); 20 | outer_dim_ = bottom[0]->count(0, axis_); 21 | inner_dim_ = bottom[0]->count(axis_); 22 | } 23 | 24 | template 25 | void TileLayer::Forward_cpu( 26 | const vector*>& bottom, const vector*>& top) { 27 | const Dtype* bottom_data = bottom[0]->cpu_data(); 28 | Dtype* top_data = top[0]->mutable_cpu_data(); 29 | for (int i = 0; i < outer_dim_; ++i) { 30 | for (int t = 0; t < tiles_; ++t) { 31 | caffe_copy(inner_dim_, bottom_data, top_data); 32 | top_data += inner_dim_; 33 | } 34 | bottom_data += inner_dim_; 35 | } 36 | } 37 | 38 | template 39 | void TileLayer::Backward_cpu(const vector*>& top, 40 | const vector& propagate_down, const vector*>& bottom) { 41 | if (!propagate_down[0]) { return; } 42 | const Dtype* top_diff = top[0]->cpu_diff(); 43 | Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); 44 | for (int i = 0; i < outer_dim_; ++i) { 45 | caffe_copy(inner_dim_, top_diff, bottom_diff); 46 | top_diff += inner_dim_; 47 | for (int t = 1; t < tiles_; ++t) { 48 | caffe_axpy(inner_dim_, Dtype(1), top_diff, bottom_diff); 49 | top_diff += inner_dim_; 50 | } 51 | bottom_diff += inner_dim_; 52 | } 53 | } 54 | 55 | #ifdef CPU_ONLY 56 | STUB_GPU(TileLayer); 57 | #endif 58 | 59 | INSTANTIATE_CLASS(TileLayer); 60 | REGISTER_LAYER_CLASS(Tile); 61 | 62 | } // namespace caffe 63 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/test/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | # The option allows to include in build only selected test files and exclude all others 2 | # Usage example: 3 | # cmake -DBUILD_only_tests="common,net,blob,im2col_kernel" 4 | set(BUILD_only_tests "" CACHE STRING "Blank or comma-separated list of test files to build without 'test_' prefix and extention") 5 | caffe_leave_only_selected_tests(test_srcs ${BUILD_only_tests}) 6 | caffe_leave_only_selected_tests(test_cuda ${BUILD_only_tests}) 7 | 8 | # For 'make runtest' target we don't need to embed test data paths to 9 | # source files, because test target is executed in source directory 10 | # That's why the lines below are commented. TODO: remove them 11 | 12 | # definition needed to include CMake generated files 13 | #add_definitions(-DCMAKE_BUILD) 14 | 15 | # generates test_data/sample_data_list.txt.gen.cmake 16 | #caffe_configure_testdatafile(test_data/sample_data_list.txt) 17 | 18 | set(the_target test.testbin) 19 | set(test_args --gtest_shuffle) 20 | 21 | if(HAVE_CUDA) 22 | caffe_cuda_compile(test_cuda_objs ${test_cuda}) 23 | list(APPEND test_srcs ${test_cuda_objs} ${test_cuda}) 24 | else() 25 | list(APPEND test_args --gtest_filter="-*GPU*") 26 | endif() 27 | 28 | # ---[ Adding test target 29 | add_executable(${the_target} EXCLUDE_FROM_ALL ${test_srcs}) 30 | target_link_libraries(${the_target} gtest ${Caffe_LINK}) 31 | caffe_default_properties(${the_target}) 32 | caffe_set_runtime_directory(${the_target} "${PROJECT_BINARY_DIR}/test") 33 | 34 | # ---[ Adding runtest 35 | add_custom_target(runtest COMMAND ${the_target} ${test_args} 36 | WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}) 37 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/test/test_caffe_main.cpp: -------------------------------------------------------------------------------- 1 | // The main caffe test code. Your test cpp code should include this hpp 2 | // to allow a main function to be compiled into the binary. 3 | 4 | #include "caffe/caffe.hpp" 5 | #include "caffe/test/test_caffe_main.hpp" 6 | 7 | namespace caffe { 8 | #ifndef CPU_ONLY 9 | cudaDeviceProp CAFFE_TEST_CUDA_PROP; 10 | #endif 11 | } 12 | 13 | #ifndef CPU_ONLY 14 | using caffe::CAFFE_TEST_CUDA_PROP; 15 | #endif 16 | 17 | int main(int argc, char** argv) { 18 | ::testing::InitGoogleTest(&argc, argv); 19 | caffe::GlobalInit(&argc, &argv); 20 | #ifndef CPU_ONLY 21 | // Before starting testing, let's first print out a few cuda defice info. 22 | int device; 23 | cudaGetDeviceCount(&device); 24 | cout << "Cuda number of devices: " << device << endl; 25 | if (argc > 1) { 26 | // Use the given device 27 | device = atoi(argv[1]); 28 | cudaSetDevice(device); 29 | cout << "Setting to use device " << device << endl; 30 | } else if (CUDA_TEST_DEVICE >= 0) { 31 | // Use the device assigned in build configuration; but with a lower priority 32 | device = CUDA_TEST_DEVICE; 33 | } 34 | cudaGetDevice(&device); 35 | cout << "Current device id: " << device << endl; 36 | cudaGetDeviceProperties(&CAFFE_TEST_CUDA_PROP, device); 37 | #endif 38 | // invoke the test. 39 | return RUN_ALL_TESTS(); 40 | } 41 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/test/test_common.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "gtest/gtest.h" 4 | 5 | #include "caffe/common.hpp" 6 | #include "caffe/syncedmem.hpp" 7 | #include "caffe/util/math_functions.hpp" 8 | 9 | #include "caffe/test/test_caffe_main.hpp" 10 | 11 | namespace caffe { 12 | 13 | class CommonTest : public ::testing::Test {}; 14 | 15 | #ifndef CPU_ONLY // GPU Caffe singleton test. 16 | 17 | TEST_F(CommonTest, TestCublasHandlerGPU) { 18 | int cuda_device_id; 19 | CUDA_CHECK(cudaGetDevice(&cuda_device_id)); 20 | EXPECT_TRUE(Caffe::cublas_handle()); 21 | } 22 | 23 | #endif 24 | 25 | TEST_F(CommonTest, TestBrewMode) { 26 | Caffe::set_mode(Caffe::CPU); 27 | EXPECT_EQ(Caffe::mode(), Caffe::CPU); 28 | Caffe::set_mode(Caffe::GPU); 29 | EXPECT_EQ(Caffe::mode(), Caffe::GPU); 30 | } 31 | 32 | TEST_F(CommonTest, TestRandSeedCPU) { 33 | SyncedMemory data_a(10 * sizeof(int)); 34 | SyncedMemory data_b(10 * sizeof(int)); 35 | Caffe::set_random_seed(1701); 36 | caffe_rng_bernoulli(10, 0.5, static_cast(data_a.mutable_cpu_data())); 37 | 38 | Caffe::set_random_seed(1701); 39 | caffe_rng_bernoulli(10, 0.5, static_cast(data_b.mutable_cpu_data())); 40 | 41 | for (int i = 0; i < 10; ++i) { 42 | EXPECT_EQ(static_cast(data_a.cpu_data())[i], 43 | static_cast(data_b.cpu_data())[i]); 44 | } 45 | } 46 | 47 | #ifndef CPU_ONLY // GPU Caffe singleton test. 48 | 49 | TEST_F(CommonTest, TestRandSeedGPU) { 50 | SyncedMemory data_a(10 * sizeof(unsigned int)); 51 | SyncedMemory data_b(10 * sizeof(unsigned int)); 52 | Caffe::set_random_seed(1701); 53 | CURAND_CHECK(curandGenerate(Caffe::curand_generator(), 54 | static_cast(data_a.mutable_gpu_data()), 10)); 55 | Caffe::set_random_seed(1701); 56 | CURAND_CHECK(curandGenerate(Caffe::curand_generator(), 57 | static_cast(data_b.mutable_gpu_data()), 10)); 58 | for (int i = 0; i < 10; ++i) { 59 | EXPECT_EQ(((const unsigned int*)(data_a.cpu_data()))[i], 60 | ((const unsigned int*)(data_b.cpu_data()))[i]); 61 | } 62 | } 63 | 64 | #endif 65 | 66 | } // namespace caffe 67 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/test/test_data/generate_sample_data.py: -------------------------------------------------------------------------------- 1 | """ 2 | Generate data used in the HDF5DataLayer and GradientBasedSolver tests. 3 | """ 4 | import os 5 | import numpy as np 6 | import h5py 7 | 8 | script_dir = os.path.dirname(os.path.abspath(__file__)) 9 | 10 | # Generate HDF5DataLayer sample_data.h5 11 | 12 | num_cols = 8 13 | num_rows = 10 14 | height = 6 15 | width = 5 16 | total_size = num_cols * num_rows * height * width 17 | 18 | data = np.arange(total_size) 19 | data = data.reshape(num_rows, num_cols, height, width) 20 | data = data.astype('float32') 21 | 22 | # We had a bug where data was copied into label, but the tests weren't 23 | # catching it, so let's make label 1-indexed. 24 | label = 1 + np.arange(num_rows)[:, np.newaxis] 25 | label = label.astype('float32') 26 | 27 | # We add an extra label2 dataset to test HDF5 layer's ability 28 | # to handle arbitrary number of output ("top") Blobs. 29 | label2 = label + 1 30 | 31 | print data 32 | print label 33 | 34 | with h5py.File(script_dir + '/sample_data.h5', 'w') as f: 35 | f['data'] = data 36 | f['label'] = label 37 | f['label2'] = label2 38 | 39 | with h5py.File(script_dir + '/sample_data_2_gzip.h5', 'w') as f: 40 | f.create_dataset( 41 | 'data', data=data + total_size, 42 | compression='gzip', compression_opts=1 43 | ) 44 | f.create_dataset( 45 | 'label', data=label, 46 | compression='gzip', compression_opts=1 47 | ) 48 | f.create_dataset( 49 | 'label2', data=label2, 50 | compression='gzip', compression_opts=1 51 | ) 52 | 53 | with open(script_dir + '/sample_data_list.txt', 'w') as f: 54 | f.write(script_dir + '/sample_data.h5\n') 55 | f.write(script_dir + '/sample_data_2_gzip.h5\n') 56 | 57 | # Generate GradientBasedSolver solver_data.h5 58 | 59 | num_cols = 3 60 | num_rows = 8 61 | height = 10 62 | width = 10 63 | 64 | data = np.random.randn(num_rows, num_cols, height, width) 65 | data = data.reshape(num_rows, num_cols, height, width) 66 | data = data.astype('float32') 67 | 68 | targets = np.random.randn(num_rows, 1) 69 | targets = targets.astype('float32') 70 | 71 | print data 72 | print targets 73 | 74 | with h5py.File(script_dir + '/solver_data.h5', 'w') as f: 75 | f['data'] = data 76 | f['targets'] = targets 77 | 78 | with open(script_dir + '/solver_data_list.txt', 'w') as f: 79 | f.write(script_dir + '/solver_data.h5\n') 80 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/test/test_data/sample_data.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pengwangucla/DOC/9bf812a6d30c2a2c46afb453713150f1c4d05869/caffe_DOC/src/caffe/test/test_data/sample_data.h5 -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/test/test_data/sample_data_2_gzip.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pengwangucla/DOC/9bf812a6d30c2a2c46afb453713150f1c4d05869/caffe_DOC/src/caffe/test/test_data/sample_data_2_gzip.h5 -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/test/test_data/sample_data_list.txt: -------------------------------------------------------------------------------- 1 | src/caffe/test/test_data/sample_data.h5 2 | src/caffe/test/test_data/sample_data_2_gzip.h5 3 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/test/test_data/solver_data.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pengwangucla/DOC/9bf812a6d30c2a2c46afb453713150f1c4d05869/caffe_DOC/src/caffe/test/test_data/solver_data.h5 -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/test/test_data/solver_data_list.txt: -------------------------------------------------------------------------------- 1 | src/caffe/test/test_data/solver_data.h5 2 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/test/test_internal_thread.cpp: -------------------------------------------------------------------------------- 1 | #include "glog/logging.h" 2 | #include "gtest/gtest.h" 3 | 4 | #include "caffe/internal_thread.hpp" 5 | #include "caffe/util/math_functions.hpp" 6 | 7 | #include "caffe/test/test_caffe_main.hpp" 8 | 9 | namespace caffe { 10 | 11 | 12 | class InternalThreadTest : public ::testing::Test {}; 13 | 14 | TEST_F(InternalThreadTest, TestStartAndExit) { 15 | InternalThread thread; 16 | EXPECT_FALSE(thread.is_started()); 17 | thread.StartInternalThread(); 18 | EXPECT_TRUE(thread.is_started()); 19 | thread.StopInternalThread(); 20 | EXPECT_FALSE(thread.is_started()); 21 | } 22 | 23 | class TestThreadA : public InternalThread { 24 | void InternalThreadEntry() { 25 | EXPECT_EQ(4244559767, caffe_rng_rand()); 26 | } 27 | }; 28 | 29 | class TestThreadB : public InternalThread { 30 | void InternalThreadEntry() { 31 | EXPECT_EQ(1726478280, caffe_rng_rand()); 32 | } 33 | }; 34 | 35 | TEST_F(InternalThreadTest, TestRandomSeed) { 36 | TestThreadA t1; 37 | Caffe::set_random_seed(9658361); 38 | t1.StartInternalThread(); 39 | t1.StopInternalThread(); 40 | 41 | TestThreadA t2; 42 | Caffe::set_random_seed(9658361); 43 | t2.StartInternalThread(); 44 | t2.StopInternalThread(); 45 | 46 | TestThreadB t3; 47 | Caffe::set_random_seed(3435563); 48 | t3.StartInternalThread(); 49 | t3.StopInternalThread(); 50 | } 51 | 52 | } // namespace caffe 53 | 54 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/test/test_layer_factory.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | 4 | #include "boost/scoped_ptr.hpp" 5 | #include "gtest/gtest.h" 6 | 7 | #include "caffe/common.hpp" 8 | #include "caffe/layer.hpp" 9 | #include "caffe/layer_factory.hpp" 10 | #include "caffe/util/db.hpp" 11 | #include "caffe/util/io.hpp" 12 | 13 | #include "caffe/test/test_caffe_main.hpp" 14 | 15 | namespace caffe { 16 | 17 | template 18 | class LayerFactoryTest : public MultiDeviceTest {}; 19 | 20 | TYPED_TEST_CASE(LayerFactoryTest, TestDtypesAndDevices); 21 | 22 | TYPED_TEST(LayerFactoryTest, TestCreateLayer) { 23 | typedef typename TypeParam::Dtype Dtype; 24 | typename LayerRegistry::CreatorRegistry& registry = 25 | LayerRegistry::Registry(); 26 | shared_ptr > layer; 27 | for (typename LayerRegistry::CreatorRegistry::iterator iter = 28 | registry.begin(); iter != registry.end(); ++iter) { 29 | // Special case: PythonLayer is checked by pytest 30 | if (iter->first == "Python") { continue; } 31 | LayerParameter layer_param; 32 | // Data layers expect a DB 33 | if (iter->first == "Data") { 34 | string tmp; 35 | MakeTempDir(&tmp); 36 | boost::scoped_ptr db(db::GetDB(DataParameter_DB_LEVELDB)); 37 | db->Open(tmp, db::NEW); 38 | db->Close(); 39 | layer_param.mutable_data_param()->set_source(tmp); 40 | } 41 | layer_param.set_type(iter->first); 42 | layer = LayerRegistry::CreateLayer(layer_param); 43 | EXPECT_EQ(iter->first, layer->type()); 44 | } 45 | } 46 | 47 | } // namespace caffe 48 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/test/test_multinomial_logistic_loss_layer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | 6 | #include "gtest/gtest.h" 7 | 8 | #include "caffe/blob.hpp" 9 | #include "caffe/common.hpp" 10 | #include "caffe/filler.hpp" 11 | #include "caffe/vision_layers.hpp" 12 | 13 | #include "caffe/test/test_caffe_main.hpp" 14 | #include "caffe/test/test_gradient_check_util.hpp" 15 | 16 | namespace caffe { 17 | 18 | template 19 | class MultinomialLogisticLossLayerTest : public CPUDeviceTest { 20 | protected: 21 | MultinomialLogisticLossLayerTest() 22 | : blob_bottom_data_(new Blob(10, 5, 1, 1)), 23 | blob_bottom_label_(new Blob(10, 1, 1, 1)), 24 | blob_top_loss_(new Blob()) { 25 | Caffe::set_random_seed(1701); 26 | // fill the values 27 | FillerParameter filler_param; 28 | PositiveUnitballFiller filler(filler_param); 29 | filler.Fill(this->blob_bottom_data_); 30 | blob_bottom_vec_.push_back(blob_bottom_data_); 31 | for (int i = 0; i < blob_bottom_label_->count(); ++i) { 32 | blob_bottom_label_->mutable_cpu_data()[i] = caffe_rng_rand() % 5; 33 | } 34 | blob_bottom_vec_.push_back(blob_bottom_label_); 35 | blob_top_vec_.push_back(blob_top_loss_); 36 | } 37 | virtual ~MultinomialLogisticLossLayerTest() { 38 | delete blob_bottom_data_; 39 | delete blob_bottom_label_; 40 | delete blob_top_loss_; 41 | } 42 | Blob* const blob_bottom_data_; 43 | Blob* const blob_bottom_label_; 44 | Blob* const blob_top_loss_; 45 | vector*> blob_bottom_vec_; 46 | vector*> blob_top_vec_; 47 | }; 48 | 49 | TYPED_TEST_CASE(MultinomialLogisticLossLayerTest, TestDtypes); 50 | 51 | 52 | TYPED_TEST(MultinomialLogisticLossLayerTest, TestGradientCPU) { 53 | LayerParameter layer_param; 54 | MultinomialLogisticLossLayer layer(layer_param); 55 | layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); 56 | GradientChecker checker(1e-2, 2*1e-2, 1701, 0, 0.05); 57 | checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, 58 | this->blob_top_vec_, 0); 59 | } 60 | 61 | } // namespace caffe 62 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/test/test_platform.cpp: -------------------------------------------------------------------------------- 1 | #ifndef CPU_ONLY 2 | 3 | #include 4 | #include 5 | 6 | #include "glog/logging.h" 7 | #include "gtest/gtest.h" 8 | 9 | #include "caffe/test/test_caffe_main.hpp" 10 | 11 | namespace caffe { 12 | 13 | extern cudaDeviceProp CAFFE_TEST_CUDA_PROP; 14 | 15 | class PlatformTest : public ::testing::Test {}; 16 | 17 | TEST_F(PlatformTest, TestInitialization) { 18 | printf("Major revision number: %d\n", CAFFE_TEST_CUDA_PROP.major); 19 | printf("Minor revision number: %d\n", CAFFE_TEST_CUDA_PROP.minor); 20 | printf("Name: %s\n", CAFFE_TEST_CUDA_PROP.name); 21 | printf("Total global memory: %lu\n", 22 | CAFFE_TEST_CUDA_PROP.totalGlobalMem); 23 | printf("Total shared memory per block: %lu\n", 24 | CAFFE_TEST_CUDA_PROP.sharedMemPerBlock); 25 | printf("Total registers per block: %d\n", 26 | CAFFE_TEST_CUDA_PROP.regsPerBlock); 27 | printf("Warp size: %d\n", 28 | CAFFE_TEST_CUDA_PROP.warpSize); 29 | printf("Maximum memory pitch: %lu\n", 30 | CAFFE_TEST_CUDA_PROP.memPitch); 31 | printf("Maximum threads per block: %d\n", 32 | CAFFE_TEST_CUDA_PROP.maxThreadsPerBlock); 33 | for (int i = 0; i < 3; ++i) 34 | printf("Maximum dimension %d of block: %d\n", i, 35 | CAFFE_TEST_CUDA_PROP.maxThreadsDim[i]); 36 | for (int i = 0; i < 3; ++i) 37 | printf("Maximum dimension %d of grid: %d\n", i, 38 | CAFFE_TEST_CUDA_PROP.maxGridSize[i]); 39 | printf("Clock rate: %d\n", CAFFE_TEST_CUDA_PROP.clockRate); 40 | printf("Total constant memory: %lu\n", 41 | CAFFE_TEST_CUDA_PROP.totalConstMem); 42 | printf("Texture alignment: %lu\n", 43 | CAFFE_TEST_CUDA_PROP.textureAlignment); 44 | printf("Concurrent copy and execution: %s\n", 45 | (CAFFE_TEST_CUDA_PROP.deviceOverlap ? "Yes" : "No")); 46 | printf("Number of multiprocessors: %d\n", 47 | CAFFE_TEST_CUDA_PROP.multiProcessorCount); 48 | printf("Kernel execution timeout: %s\n", 49 | (CAFFE_TEST_CUDA_PROP.kernelExecTimeoutEnabled ? "Yes" : "No")); 50 | printf("Unified virtual addressing: %s\n", 51 | (CAFFE_TEST_CUDA_PROP.unifiedAddressing ? "Yes" : "No")); 52 | EXPECT_TRUE(true); 53 | } 54 | 55 | } // namespace caffe 56 | 57 | #endif // CPU_ONLY 58 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/test/test_protobuf.cpp: -------------------------------------------------------------------------------- 1 | // This is simply a script that tries serializing protocol buffer in text 2 | // format. Nothing special here and no actual code is being tested. 3 | #include 4 | 5 | #include "google/protobuf/text_format.h" 6 | #include "gtest/gtest.h" 7 | 8 | #include "caffe/proto/caffe.pb.h" 9 | 10 | #include "caffe/test/test_caffe_main.hpp" 11 | 12 | namespace caffe { 13 | 14 | class ProtoTest : public ::testing::Test {}; 15 | 16 | TEST_F(ProtoTest, TestSerialization) { 17 | LayerParameter param; 18 | param.set_name("test"); 19 | param.set_type("Test"); 20 | std::cout << "Printing in binary format." << std::endl; 21 | std::cout << param.SerializeAsString() << std::endl; 22 | std::cout << "Printing in text format." << std::endl; 23 | std::string str; 24 | google::protobuf::TextFormat::PrintToString(param, &str); 25 | std::cout << str << std::endl; 26 | EXPECT_TRUE(true); 27 | } 28 | 29 | } // namespace caffe 30 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/util/blocking_queue.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | 4 | #include "caffe/data_layers.hpp" 5 | #include "caffe/data_reader.hpp" 6 | #include "caffe/parallel.hpp" 7 | #include "caffe/util/blocking_queue.hpp" 8 | 9 | namespace caffe { 10 | 11 | template 12 | class BlockingQueue::sync { 13 | public: 14 | mutable boost::mutex mutex_; 15 | boost::condition_variable condition_; 16 | }; 17 | 18 | template 19 | BlockingQueue::BlockingQueue() 20 | : sync_(new sync()) { 21 | } 22 | 23 | template 24 | void BlockingQueue::push(const T& t) { 25 | boost::mutex::scoped_lock lock(sync_->mutex_); 26 | queue_.push(t); 27 | lock.unlock(); 28 | sync_->condition_.notify_one(); 29 | } 30 | 31 | template 32 | bool BlockingQueue::try_pop(T* t) { 33 | boost::mutex::scoped_lock lock(sync_->mutex_); 34 | 35 | if (queue_.empty()) { 36 | return false; 37 | } 38 | 39 | *t = queue_.front(); 40 | queue_.pop(); 41 | return true; 42 | } 43 | 44 | template 45 | T BlockingQueue::pop(const string& log_on_wait) { 46 | boost::mutex::scoped_lock lock(sync_->mutex_); 47 | 48 | while (queue_.empty()) { 49 | if (!log_on_wait.empty()) { 50 | LOG_EVERY_N(INFO, 1000)<< log_on_wait; 51 | } 52 | sync_->condition_.wait(lock); 53 | } 54 | 55 | T t = queue_.front(); 56 | queue_.pop(); 57 | return t; 58 | } 59 | 60 | template 61 | bool BlockingQueue::try_peek(T* t) { 62 | boost::mutex::scoped_lock lock(sync_->mutex_); 63 | 64 | if (queue_.empty()) { 65 | return false; 66 | } 67 | 68 | *t = queue_.front(); 69 | return true; 70 | } 71 | 72 | template 73 | T BlockingQueue::peek() { 74 | boost::mutex::scoped_lock lock(sync_->mutex_); 75 | 76 | while (queue_.empty()) { 77 | sync_->condition_.wait(lock); 78 | } 79 | 80 | return queue_.front(); 81 | } 82 | 83 | template 84 | size_t BlockingQueue::size() const { 85 | boost::mutex::scoped_lock lock(sync_->mutex_); 86 | return queue_.size(); 87 | } 88 | 89 | template class BlockingQueue*>; 90 | template class BlockingQueue*>; 91 | template class BlockingQueue*>; 92 | template class BlockingQueue*>; 93 | template class BlockingQueue; 94 | template class BlockingQueue >; 95 | template class BlockingQueue*>; 96 | template class BlockingQueue*>; 97 | 98 | } // namespace caffe 99 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/util/cudnn.cpp: -------------------------------------------------------------------------------- 1 | #ifdef USE_CUDNN 2 | #include "caffe/util/cudnn.hpp" 3 | 4 | namespace caffe { 5 | namespace cudnn { 6 | 7 | float dataType::oneval = 1.0; 8 | float dataType::zeroval = 0.0; 9 | const void* dataType::one = 10 | static_cast(&dataType::oneval); 11 | const void* dataType::zero = 12 | static_cast(&dataType::zeroval); 13 | 14 | double dataType::oneval = 1.0; 15 | double dataType::zeroval = 0.0; 16 | const void* dataType::one = 17 | static_cast(&dataType::oneval); 18 | const void* dataType::zero = 19 | static_cast(&dataType::zeroval); 20 | 21 | } // namespace cudnn 22 | } // namespace caffe 23 | #endif 24 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/util/db.cpp: -------------------------------------------------------------------------------- 1 | #include "caffe/util/db.hpp" 2 | #include "caffe/util/db_leveldb.hpp" 3 | #include "caffe/util/db_lmdb.hpp" 4 | 5 | #include 6 | 7 | namespace caffe { namespace db { 8 | 9 | DB* GetDB(DataParameter::DB backend) { 10 | switch (backend) { 11 | case DataParameter_DB_LEVELDB: 12 | return new LevelDB(); 13 | case DataParameter_DB_LMDB: 14 | return new LMDB(); 15 | default: 16 | LOG(FATAL) << "Unknown database backend"; 17 | } 18 | } 19 | 20 | DB* GetDB(const string& backend) { 21 | if (backend == "leveldb") { 22 | return new LevelDB(); 23 | } else if (backend == "lmdb") { 24 | return new LMDB(); 25 | } else { 26 | LOG(FATAL) << "Unknown database backend"; 27 | } 28 | } 29 | 30 | } // namespace db 31 | } // namespace caffe 32 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/util/db_leveldb.cpp: -------------------------------------------------------------------------------- 1 | #include "caffe/util/db_leveldb.hpp" 2 | 3 | #include 4 | 5 | namespace caffe { namespace db { 6 | 7 | void LevelDB::Open(const string& source, Mode mode) { 8 | leveldb::Options options; 9 | options.block_size = 65536; 10 | options.write_buffer_size = 268435456; 11 | options.max_open_files = 100; 12 | options.error_if_exists = mode == NEW; 13 | options.create_if_missing = mode != READ; 14 | leveldb::Status status = leveldb::DB::Open(options, source, &db_); 15 | CHECK(status.ok()) << "Failed to open leveldb " << source 16 | << std::endl << status.ToString(); 17 | LOG(INFO) << "Opened leveldb " << source; 18 | } 19 | 20 | } // namespace db 21 | } // namespace caffe 22 | -------------------------------------------------------------------------------- /caffe_DOC/src/caffe/util/db_lmdb.cpp: -------------------------------------------------------------------------------- 1 | #include "caffe/util/db_lmdb.hpp" 2 | 3 | #include 4 | 5 | #include 6 | 7 | namespace caffe { namespace db { 8 | 9 | const size_t LMDB_MAP_SIZE = 1099511627776; // 1 TB 10 | 11 | void LMDB::Open(const string& source, Mode mode) { 12 | MDB_CHECK(mdb_env_create(&mdb_env_)); 13 | MDB_CHECK(mdb_env_set_mapsize(mdb_env_, LMDB_MAP_SIZE)); 14 | if (mode == NEW) { 15 | CHECK_EQ(mkdir(source.c_str(), 0744), 0) << "mkdir " << source << "failed"; 16 | } 17 | int flags = 0; 18 | if (mode == READ) { 19 | flags = MDB_RDONLY | MDB_NOTLS; 20 | } 21 | MDB_CHECK(mdb_env_open(mdb_env_, source.c_str(), flags, 0664)); 22 | LOG(INFO) << "Opened lmdb " << source; 23 | } 24 | 25 | LMDBCursor* LMDB::NewCursor() { 26 | MDB_txn* mdb_txn; 27 | MDB_cursor* mdb_cursor; 28 | MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, MDB_RDONLY, &mdb_txn)); 29 | MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_)); 30 | MDB_CHECK(mdb_cursor_open(mdb_txn, mdb_dbi_, &mdb_cursor)); 31 | return new LMDBCursor(mdb_txn, mdb_cursor); 32 | } 33 | 34 | LMDBTransaction* LMDB::NewTransaction() { 35 | MDB_txn* mdb_txn; 36 | MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, 0, &mdb_txn)); 37 | MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_)); 38 | return new LMDBTransaction(&mdb_dbi_, mdb_txn); 39 | } 40 | 41 | void LMDBTransaction::Put(const string& key, const string& value) { 42 | MDB_val mdb_key, mdb_value; 43 | mdb_key.mv_data = const_cast(key.data()); 44 | mdb_key.mv_size = key.size(); 45 | mdb_value.mv_data = const_cast(value.data()); 46 | mdb_value.mv_size = value.size(); 47 | MDB_CHECK(mdb_put(mdb_txn_, *mdb_dbi_, &mdb_key, &mdb_value, 0)); 48 | } 49 | 50 | } // namespace db 51 | } // namespace caffe 52 | -------------------------------------------------------------------------------- /caffe_DOC/src/gtest/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | add_library(gtest STATIC EXCLUDE_FROM_ALL gtest.h gtest-all.cpp) 2 | caffe_default_properties(gtest) 3 | 4 | #add_library(gtest_main gtest_main.cc) 5 | #target_link_libraries(gtest_main gtest) 6 | -------------------------------------------------------------------------------- /caffe_DOC/src/gtest/gtest_main.cc: -------------------------------------------------------------------------------- 1 | // Copyright 2006, Google Inc. 2 | // All rights reserved. 3 | // 4 | // Redistribution and use in source and binary forms, with or without 5 | // modification, are permitted provided that the following conditions are 6 | // met: 7 | // 8 | // * Redistributions of source code must retain the above copyright 9 | // notice, this list of conditions and the following disclaimer. 10 | // * Redistributions in binary form must reproduce the above 11 | // copyright notice, this list of conditions and the following disclaimer 12 | // in the documentation and/or other materials provided with the 13 | // distribution. 14 | // * Neither the name of Google Inc. nor the names of its 15 | // contributors may be used to endorse or promote products derived from 16 | // this software without specific prior written permission. 17 | // 18 | // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 19 | // "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 20 | // LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 21 | // A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT 22 | // OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 23 | // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT 24 | // LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, 25 | // DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY 26 | // THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT 27 | // (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 28 | // OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 29 | 30 | #include 31 | 32 | #include "gtest/gtest.h" 33 | 34 | GTEST_API_ int main(int argc, char **argv) { 35 | std::cout << "Running main() from gtest_main.cc\n"; 36 | 37 | testing::InitGoogleTest(&argc, argv); 38 | return RUN_ALL_TESTS(); 39 | } 40 | -------------------------------------------------------------------------------- /caffe_DOC/tools/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | # Collect source files 2 | file(GLOB_RECURSE srcs ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp) 3 | 4 | # Build each source file independently 5 | foreach(source ${srcs}) 6 | get_filename_component(name ${source} NAME_WE) 7 | 8 | # caffe target already exits 9 | if(name MATCHES "caffe") 10 | set(name ${name}.bin) 11 | endif() 12 | 13 | # target 14 | add_executable(${name} ${source}) 15 | target_link_libraries(${name} ${Caffe_LINK}) 16 | caffe_default_properties(${name}) 17 | 18 | # set back RUNTIME_OUTPUT_DIRECTORY 19 | caffe_set_runtime_directory(${name} "${PROJECT_BINARY_DIR}/tools") 20 | caffe_set_solution_folder(${name} tools) 21 | 22 | # restore output name without suffix 23 | if(name MATCHES "caffe.bin") 24 | set_target_properties(${name} PROPERTIES OUTPUT_NAME caffe) 25 | endif() 26 | 27 | # Install 28 | install(TARGETS ${name} DESTINATION bin) 29 | endforeach(source) 30 | -------------------------------------------------------------------------------- /caffe_DOC/tools/device_query.cpp: -------------------------------------------------------------------------------- 1 | #include "caffe/common.hpp" 2 | 3 | int main(int argc, char** argv) { 4 | LOG(FATAL) << "Deprecated. Use caffe device_query " 5 | "[--device_id=0] instead."; 6 | return 0; 7 | } 8 | -------------------------------------------------------------------------------- /caffe_DOC/tools/extra/extract_seconds.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | import datetime 3 | import os 4 | import sys 5 | 6 | def extract_datetime_from_line(line, year): 7 | # Expected format: I0210 13:39:22.381027 25210 solver.cpp:204] Iteration 100, lr = 0.00992565 8 | line = line.strip().split() 9 | month = int(line[0][1:3]) 10 | day = int(line[0][3:]) 11 | timestamp = line[1] 12 | pos = timestamp.rfind('.') 13 | ts = [int(x) for x in timestamp[:pos].split(':')] 14 | hour = ts[0] 15 | minute = ts[1] 16 | second = ts[2] 17 | microsecond = int(timestamp[pos + 1:]) 18 | dt = datetime.datetime(year, month, day, hour, minute, second, microsecond) 19 | return dt 20 | 21 | 22 | def get_log_created_year(input_file): 23 | """Get year from log file system timestamp 24 | """ 25 | 26 | log_created_time = os.path.getctime(input_file) 27 | log_created_year = datetime.datetime.fromtimestamp(log_created_time).year 28 | return log_created_year 29 | 30 | 31 | def get_start_time(line_iterable, year): 32 | """Find start time from group of lines 33 | """ 34 | 35 | start_datetime = None 36 | for line in line_iterable: 37 | line = line.strip() 38 | if line.find('Solving') != -1: 39 | start_datetime = extract_datetime_from_line(line, year) 40 | break 41 | return start_datetime 42 | 43 | 44 | def extract_seconds(input_file, output_file): 45 | with open(input_file, 'r') as f: 46 | lines = f.readlines() 47 | log_created_year = get_log_created_year(input_file) 48 | start_datetime = get_start_time(lines, log_created_year) 49 | assert start_datetime, 'Start time not found' 50 | 51 | out = open(output_file, 'w') 52 | for line in lines: 53 | line = line.strip() 54 | if line.find('Iteration') != -1: 55 | dt = extract_datetime_from_line(line, log_created_year) 56 | elapsed_seconds = (dt - start_datetime).total_seconds() 57 | out.write('%f\n' % elapsed_seconds) 58 | out.close() 59 | 60 | if __name__ == '__main__': 61 | if len(sys.argv) < 3: 62 | print('Usage: ./extract_seconds input_file output_file') 63 | exit(1) 64 | extract_seconds(sys.argv[1], sys.argv[2]) 65 | -------------------------------------------------------------------------------- /caffe_DOC/tools/extra/launch_resize_and_crop_images.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #### https://github.com/Yangqing/mincepie/wiki/Launch-Your-Mapreducer 3 | 4 | # If you encounter error that the address already in use, kill the process. 5 | # 11235 is the port of server process 6 | # https://github.com/Yangqing/mincepie/blob/master/mincepie/mince.py 7 | # sudo netstat -ap | grep 11235 8 | # The last column of the output is PID/Program name 9 | # kill -9 PID 10 | # Second solution: 11 | # nmap localhost 12 | # fuser -k 11235/tcp 13 | # Or just wait a few seconds. 14 | 15 | ## Launch your Mapreduce locally 16 | # num_clients: number of processes 17 | # image_lib: OpenCV or PIL, case insensitive. The default value is the faster OpenCV. 18 | # input: the file containing one image path relative to input_folder each line 19 | # input_folder: where are the original images 20 | # output_folder: where to save the resized and cropped images 21 | ./resize_and_crop_images.py --num_clients=8 --image_lib=opencv --input=/home/user/Datasets/ImageNet/ILSVRC2010/ILSVRC2010_images.txt --input_folder=/home/user/Datasets/ImageNet/ILSVRC2010/ILSVRC2010_images_train/ --output_folder=/home/user/Datasets/ImageNet/ILSVRC2010/ILSVRC2010_images_train_resized/ 22 | 23 | ## Launch your Mapreduce with MPI 24 | # mpirun -n 8 --launch=mpi resize_and_crop_images.py --image_lib=opencv --input=/home/user/Datasets/ImageNet/ILSVRC2010/ILSVRC2010_images.txt --input_folder=/home/user/Datasets/ImageNet/ILSVRC2010/ILSVRC2010_images_train/ --output_folder=/home/user/Datasets/ImageNet/ILSVRC2010/ILSVRC2010_images_train_resized/ 25 | -------------------------------------------------------------------------------- /caffe_DOC/tools/extra/parse_log.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # Usage parse_log.sh caffe.log 3 | # It creates the following two text files, each containing a table: 4 | # caffe.log.test (columns: '#Iters Seconds TestAccuracy TestLoss') 5 | # caffe.log.train (columns: '#Iters Seconds TrainingLoss LearningRate') 6 | 7 | 8 | # get the dirname of the script 9 | DIR="$( cd "$(dirname "$0")" ; pwd -P )" 10 | 11 | if [ "$#" -lt 1 ] 12 | then 13 | echo "Usage parse_log.sh /path/to/your.log" 14 | exit 15 | fi 16 | LOG=`basename $1` 17 | grep -B 1 'Test ' $1 > aux.txt 18 | grep 'Iteration ' aux.txt | sed 's/.*Iteration \([[:digit:]]*\).*/\1/g' > aux0.txt 19 | grep 'Test net output #0' aux.txt | awk '{print $11}' > aux1.txt 20 | grep 'Test net output #1' aux.txt | awk '{print $11}' > aux2.txt 21 | 22 | # Extracting elapsed seconds 23 | # For extraction of time since this line contains the start time 24 | grep '] Solving ' $1 > aux3.txt 25 | grep 'Testing net' $1 >> aux3.txt 26 | $DIR/extract_seconds.py aux3.txt aux4.txt 27 | 28 | # Generating 29 | echo '#Iters Seconds TestAccuracy TestLoss'> $LOG.test 30 | paste aux0.txt aux4.txt aux1.txt aux2.txt | column -t >> $LOG.test 31 | rm aux.txt aux0.txt aux1.txt aux2.txt aux3.txt aux4.txt 32 | 33 | # For extraction of time since this line contains the start time 34 | grep '] Solving ' $1 > aux.txt 35 | grep ', loss = ' $1 >> aux.txt 36 | grep 'Iteration ' aux.txt | sed 's/.*Iteration \([[:digit:]]*\).*/\1/g' > aux0.txt 37 | grep ', loss = ' $1 | awk '{print $9}' > aux1.txt 38 | grep ', lr = ' $1 | awk '{print $9}' > aux2.txt 39 | 40 | # Extracting elapsed seconds 41 | $DIR/extract_seconds.py aux.txt aux3.txt 42 | 43 | # Generating 44 | echo '#Iters Seconds TrainingLoss LearningRate'> $LOG.train 45 | paste aux0.txt aux3.txt aux1.txt aux2.txt | column -t >> $LOG.train 46 | rm aux.txt aux0.txt aux1.txt aux2.txt aux3.txt 47 | -------------------------------------------------------------------------------- /caffe_DOC/tools/finetune_net.cpp: -------------------------------------------------------------------------------- 1 | #include "caffe/caffe.hpp" 2 | 3 | int main(int argc, char** argv) { 4 | LOG(FATAL) << "Deprecated. Use caffe train --solver=... " 5 | "[--weights=...] instead."; 6 | return 0; 7 | } 8 | -------------------------------------------------------------------------------- /caffe_DOC/tools/net_speed_benchmark.cpp: -------------------------------------------------------------------------------- 1 | #include "caffe/caffe.hpp" 2 | 3 | int main(int argc, char** argv) { 4 | LOG(FATAL) << "Deprecated. Use caffe time --model=... " 5 | "[--iterations=50] [--gpu] [--device_id=0]"; 6 | return 0; 7 | } 8 | -------------------------------------------------------------------------------- /caffe_DOC/tools/test_net.cpp: -------------------------------------------------------------------------------- 1 | #include "caffe/caffe.hpp" 2 | 3 | int main(int argc, char** argv) { 4 | LOG(FATAL) << "Deprecated. Use caffe test --model=... " 5 | "--weights=... instead."; 6 | return 0; 7 | } 8 | -------------------------------------------------------------------------------- /caffe_DOC/tools/train_net.cpp: -------------------------------------------------------------------------------- 1 | #include "caffe/caffe.hpp" 2 | 3 | int main(int argc, char** argv) { 4 | LOG(FATAL) << "Deprecated. Use caffe train --solver=... " 5 | "[--snapshot=...] instead."; 6 | return 0; 7 | } 8 | -------------------------------------------------------------------------------- /caffe_DOC/tools/upgrade_net_proto_binary.cpp: -------------------------------------------------------------------------------- 1 | // This is a script to upgrade "V0" network prototxts to the new format. 2 | // Usage: 3 | // upgrade_net_proto_binary v0_net_proto_file_in net_proto_file_out 4 | 5 | #include 6 | #include // NOLINT(readability/streams) 7 | #include // NOLINT(readability/streams) 8 | #include 9 | 10 | #include "caffe/caffe.hpp" 11 | #include "caffe/util/io.hpp" 12 | #include "caffe/util/upgrade_proto.hpp" 13 | 14 | using std::ofstream; 15 | 16 | using namespace caffe; // NOLINT(build/namespaces) 17 | 18 | int main(int argc, char** argv) { 19 | ::google::InitGoogleLogging(argv[0]); 20 | if (argc != 3) { 21 | LOG(ERROR) << "Usage: " 22 | << "upgrade_net_proto_binary v0_net_proto_file_in net_proto_file_out"; 23 | return 1; 24 | } 25 | 26 | NetParameter net_param; 27 | string input_filename(argv[1]); 28 | if (!ReadProtoFromBinaryFile(input_filename, &net_param)) { 29 | LOG(ERROR) << "Failed to parse input binary file as NetParameter: " 30 | << input_filename; 31 | return 2; 32 | } 33 | bool need_upgrade = NetNeedsUpgrade(net_param); 34 | bool success = true; 35 | if (need_upgrade) { 36 | success = UpgradeNetAsNeeded(input_filename, &net_param); 37 | if (!success) { 38 | LOG(ERROR) << "Encountered error(s) while upgrading prototxt; " 39 | << "see details above."; 40 | } 41 | } else { 42 | LOG(ERROR) << "File already in V1 proto format: " << argv[1]; 43 | } 44 | 45 | WriteProtoToBinaryFile(net_param, argv[2]); 46 | 47 | LOG(ERROR) << "Wrote upgraded NetParameter binary proto to " << argv[2]; 48 | return !success; 49 | } 50 | -------------------------------------------------------------------------------- /caffe_DOC/tools/upgrade_net_proto_text.cpp: -------------------------------------------------------------------------------- 1 | // This is a script to upgrade "V0" network prototxts to the new format. 2 | // Usage: 3 | // upgrade_net_proto_text v0_net_proto_file_in net_proto_file_out 4 | 5 | #include 6 | #include // NOLINT(readability/streams) 7 | #include // NOLINT(readability/streams) 8 | #include 9 | 10 | #include "caffe/caffe.hpp" 11 | #include "caffe/util/io.hpp" 12 | #include "caffe/util/upgrade_proto.hpp" 13 | 14 | using std::ofstream; 15 | 16 | using namespace caffe; // NOLINT(build/namespaces) 17 | 18 | int main(int argc, char** argv) { 19 | ::google::InitGoogleLogging(argv[0]); 20 | if (argc != 3) { 21 | LOG(ERROR) << "Usage: " 22 | << "upgrade_net_proto_text v0_net_proto_file_in net_proto_file_out"; 23 | return 1; 24 | } 25 | 26 | NetParameter net_param; 27 | string input_filename(argv[1]); 28 | if (!ReadProtoFromTextFile(input_filename, &net_param)) { 29 | LOG(ERROR) << "Failed to parse input text file as NetParameter: " 30 | << input_filename; 31 | return 2; 32 | } 33 | bool need_upgrade = NetNeedsUpgrade(net_param); 34 | bool need_data_upgrade = NetNeedsDataUpgrade(net_param); 35 | bool success = true; 36 | if (need_upgrade) { 37 | success = UpgradeNetAsNeeded(input_filename, &net_param); 38 | if (!success) { 39 | LOG(ERROR) << "Encountered error(s) while upgrading prototxt; " 40 | << "see details above."; 41 | } 42 | } else { 43 | LOG(ERROR) << "File already in latest proto format: " << input_filename; 44 | } 45 | 46 | if (need_data_upgrade) { 47 | UpgradeNetDataTransformation(&net_param); 48 | } 49 | 50 | // Save new format prototxt. 51 | WriteProtoToTextFile(net_param, argv[2]); 52 | 53 | LOG(ERROR) << "Wrote upgraded NetParameter text proto to " << argv[2]; 54 | return !success; 55 | } 56 | -------------------------------------------------------------------------------- /demo_occ.m: -------------------------------------------------------------------------------- 1 | addpath('./lib') 2 | addpath(genpath('./tools')) 3 | 4 | dataset='PIOD'; 5 | set_path 6 | %% set up 7 | [occ_model, edge_model, opt] = get_config_info(); 8 | 9 | %% 10 | edge_path = [ResPath, edge_model, '/']; 11 | occ_path = [ResPath, occ_model, '/']; 12 | 13 | for iimg = 1:length(image_list) 14 | fprintf([image_list{iimg}, '\n']); 15 | im = imread([ImgPath, image_list{iimg}, '.jpg']); 16 | occ_file = [occ_path, image_list{iimg}, '.mat']; 17 | edge_file = [edge_path, image_list{iimg}, '.mat']; 18 | doc_res = im2occedge(occ_file, edge_file, opt); 19 | 20 | % load ground truth 21 | 22 | load([GTPath, image_list{iimg}, '.mat'], 'bndinfo_pascal'); 23 | gt_img = GetDilateEdgeGT(bndinfo_pascal); 24 | 25 | s_num = 1; 26 | r= 3; 27 | c = 2; 28 | subplot_tight(r, c, 1); imshow(im); 29 | subplot_tight(r,c,3); imshow(doc_res{1,1},[]); 30 | subplot_tight(r,c,4); imagesc(doc_res{1,2}); 31 | colormap(parula(100)); axis('image'); 32 | colorbar('FontSize',12); 33 | 34 | subplot_tight(r,c,5); imshow(gt_img(:,:,1)); 35 | subplot_tight(r,c,6); imagesc(gt_img(:,:,2)); axis('image'); 36 | colormap(parula(100)); 37 | colorbar('FontSize',12); 38 | 39 | pause; 40 | close all; 41 | 42 | end 43 | -------------------------------------------------------------------------------- /lib/GetDilateEdgeGT.m: -------------------------------------------------------------------------------- 1 | function sem_map = GetDilateEdgeGT(bndinfo, varargin) 2 | opt = struct('dilate', 1); 3 | opt = CatVarargin(opt, varargin); 4 | 5 | se = strel('disk', opt.dilate); 6 | 7 | if bndinfo.ne == 0 8 | sem_map = zeros([bndinfo.imsize, 2], 'single'); 9 | 10 | else 11 | % generate prediction ground truth map 12 | sem_map = zeros(bndinfo.imsize, 'single'); 13 | edgemap = zeros(bndinfo.imsize, 'single'); 14 | for iedge = 1:length(bndinfo.edges.indices); 15 | edgemap(bndinfo.edges.indices{iedge}) = 1; 16 | end 17 | 18 | if opt.dilate % whether dilate the ground truth 19 | sem_map(:,:,1) = imdilate(edgemap, se); 20 | 21 | % for orientation map 22 | temp = bndinfo.OrientMap; 23 | temp(temp ~= 0) = temp( temp ~=0) + 2*pi; 24 | temp = imdilate(temp, se); 25 | temp(temp ~= 0) = temp( temp ~= 0) - 2*pi; 26 | sem_map(:,:,2) = temp; 27 | else 28 | sem_map(:,:,1) = edgemap; 29 | sem_map(:,:,2) = bndinfo.OrientMap; 30 | end 31 | %... can add other ground truth here 32 | end 33 | 34 | end -------------------------------------------------------------------------------- /lib/GetOrderFrags.m: -------------------------------------------------------------------------------- 1 | function [frags, new_edge] = GetOrderFrags(thin_edge, len_thresh) 2 | if ~exist('len_thresh','var'); len_thresh = 5; end 3 | new_edge = zeros(size(thin_edge)); 4 | max_range = 8; 5 | imsize = size(thin_edge); 6 | CC = bwconncomp(thin_edge,max_range); 7 | frags = []; 8 | for icc = 1:length(CC.PixelIdxList) 9 | if length(CC.PixelIdxList{icc}) < len_thresh; continue; end 10 | frags = [frags, OrderByConnect(CC.PixelIdxList{icc}, imsize )]; 11 | end 12 | 13 | for ifrag = 1:length(frags) 14 | new_edge(frags{ifrag}) = 1; 15 | end 16 | 17 | end -------------------------------------------------------------------------------- /lib/OrderByConnect.m: -------------------------------------------------------------------------------- 1 | function frags = OrderByConnect(ind, imsize); 2 | 3 | [y, x] = ind2sub(imsize, ind); 4 | x = single(x); 5 | y = single(y); 6 | x_dis = single(abs(bsxfun(@minus, x(:), x(:)'))); 7 | y_dis = single(abs(bsxfun(@minus, y(:), y(:)'))); 8 | x_dis = setdiag(x_dis,inf); 9 | y_dis = setdiag(y_dis,inf); 10 | 11 | dis_mat = single(cat(3, x_dis, y_dis)); 12 | adjmat = max(dis_mat, [], 3) <= 1; 13 | dis_mat = sum(dis_mat, 3); 14 | frags = PropAdj(adjmat, dis_mat); 15 | 16 | for ifrag = 1:length(frags) 17 | frags{ifrag} = ind(frags{ifrag}); 18 | end 19 | end 20 | 21 | function groups = PropAdj(adj, dismat) 22 | 23 | np = size(adj,1); 24 | 25 | set_flag = false(np, 1); 26 | groups = cell(1); 27 | len = ones(1); 28 | igrp = 1; 29 | for ipx = 1:np 30 | if set_flag(ipx) 31 | continue; 32 | end 33 | cur_id = ipx; 34 | ind = [ipx]; 35 | set_flag(ipx) = 1; 36 | len(igrp) =1 ; 37 | while 1 38 | id_next = find(adj(cur_id, :)); 39 | if numel(id_next) == 0 40 | break; 41 | end 42 | if numel(id_next) > 1; 43 | [~, idx] = min(dismat(cur_id, id_next)); id_next = id_next(idx); 44 | end 45 | % can not back to self 46 | adj(cur_id,:) = 0; adj(:, cur_id) = 0; 47 | ind = [ind, id_next]; 48 | set_flag(id_next) = 1; 49 | len(igrp) = len(igrp) + 1; 50 | cur_id = id_next; 51 | end 52 | groups{igrp} = ind ; 53 | igrp = igrp + 1; 54 | end 55 | 56 | % rm the isolated one 57 | groups( len <= 2) = []; 58 | end 59 | -------------------------------------------------------------------------------- /lib/edge_nms.m: -------------------------------------------------------------------------------- 1 | function thin_edge = edge_nms(edge_pr, thresh) 2 | edge_pr = single(edge_pr); 3 | bw_edge = edge_pr <= thresh; 4 | edge_pr(bw_edge) = 0; 5 | [Ox,Oy]=gradient2(convTri(edge_pr,4)); 6 | [Oxx,~]=gradient2(Ox); [Oxy,Oyy]=gradient2(Oy); 7 | O=mod(atan(Oyy.*sign(-Oxy)./(Oxx+1e-5)),pi); 8 | thin_edge=edgesNmsMex(edge_pr,O,1,5,1.01,4); 9 | -------------------------------------------------------------------------------- /lib/get_config_info.m: -------------------------------------------------------------------------------- 1 | function [occ_model, edge_model, config] = get_config_info() 2 | 3 | occ_model = 'doc_ori'; 4 | edge_model = 'doc_edge'; 5 | config.thresh = 0.3; 6 | % one may extend to multi scale for occlusion inference which we 7 | % do not include in this version. 8 | config.occ_scale = 1; 9 | 10 | end -------------------------------------------------------------------------------- /lib/im2occedge.m: -------------------------------------------------------------------------------- 1 | function doc_res = im2occedge(occ_file, edge_file, varargin) 2 | 3 | model = struct('scales', 1, 'thresh', 0.7, 'occ_normalized',..., 4 | 0, 'imsize', []); 5 | model = CatVarargin(model, varargin); 6 | 7 | docmaps = cell(length(model.scales),2); 8 | load(edge_file, 'edgemap'); 9 | docmaps{1,1} = edgemap; % the first 10 | 11 | load(occ_file, 'occmap'); 12 | if ~all(size(occmap) - size(docmaps{1,1}) == 0); 13 | occmap = imresize(occmap, size(docmaps{1,1})); 14 | end 15 | docmaps{1,2} = occmap; 16 | docmaps{1,2} = max(min(docmaps{1,2}, 3*pi/2), -3*pi/2); 17 | 18 | doc_res = cell(size(docmaps,1), 4); 19 | 20 | [doc_res{1,4},doc_res{1, 2}, doc_res{1, 1}, doc_res{1,3}] = ..., 21 | post_process(docmaps{1, 1}, docmaps{1, 2}, ..., 22 | model.thresh); 23 | 24 | end 25 | 26 | 27 | function [new_edge, new_occ, varargout] = post_process(edge_pred, ..., 28 | edge_occ, thresh) 29 | 30 | assert(all(size(edge_pred(:,:,1)) - size(edge_occ(:,:,1)) == 0)); 31 | thin_edge = edge_nms(edge_pred, thresh); 32 | 33 | new_occ = zeros(size(thin_edge), 'single'); 34 | sim_score = zeros(size(thin_edge), 'single'); 35 | 36 | [frags, new_edge] = GetOrderFrags(thin_edge, 5); 37 | new_edge = single(new_edge); 38 | 39 | for ifrag = 1:length(frags) 40 | [theta, s_score] = getTheta(frags{ifrag}, edge_occ); 41 | sim_score(frags{ifrag}) = s_score; 42 | new_occ(frags{ifrag}) = theta; 43 | end 44 | 45 | if nargout > 2 46 | varargout{1} = thin_edge; 47 | varargout{2} = sim_score; % whether the edge prediction and orientation prediction is similar 48 | end 49 | end 50 | 51 | function [theta, varargout] = getTheta(pixel_id, edge_occ) 52 | 53 | imsize = size(edge_occ); 54 | neighbor = 5; 55 | 56 | npix = length(pixel_id); 57 | idx = 1:npix; 58 | [y1, x1] = ind2sub(imsize, pixel_id(min(idx+neighbor, npix))); 59 | [y2, x2] = ind2sub(imsize(1:2), pixel_id(max(idx-neighbor,1))); 60 | 61 | theta_1 = atan2(y2-y1, x2-x1); 62 | theta_2 = atan2(y1-y2, x1-x2); 63 | 64 | theta_pred = edge_occ(pixel_id); 65 | abs_diff = abs(theta_pred-theta_1); abs_diff = mod(abs_diff, 2*pi); 66 | ind_same = abs_diff <= pi/2 | abs_diff > 3*pi/2; 67 | theta = ind_same .* theta_1 + ~ind_same .* theta_2 ; 68 | 69 | if nargout > 1 70 | varargout{1} = abs(cos(abs_diff)); 71 | end 72 | 73 | end 74 | -------------------------------------------------------------------------------- /lib/set_path.m: -------------------------------------------------------------------------------- 1 | 2 | switch dataset 3 | case 'PIOD' 4 | image_list = {'2008_000123'}; 5 | ImgPath = './data/PIOD/Images/'; % test set 6 | ResPath = './output/PIOD/'; 7 | GTPath = './data/PIOD/Data/'; 8 | 9 | end -------------------------------------------------------------------------------- /output/PIOD/doc_edge/2008_000123.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pengwangucla/DOC/9bf812a6d30c2a2c46afb453713150f1c4d05869/output/PIOD/doc_edge/2008_000123.mat -------------------------------------------------------------------------------- /output/PIOD/doc_ori/2008_000123.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pengwangucla/DOC/9bf812a6d30c2a2c46afb453713150f1c4d05869/output/PIOD/doc_ori/2008_000123.mat -------------------------------------------------------------------------------- /tools/CatVarargin.m: -------------------------------------------------------------------------------- 1 | function opt = CatVarargin(opt, varargin) 2 | % use to cat the field generated by varargin 3 | warning off 4 | if ~isempty(varargin{1}) 5 | opt = catstruct(varargin{1}{1}, opt); 6 | end 7 | 8 | end -------------------------------------------------------------------------------- /tools/edge_nms.m: -------------------------------------------------------------------------------- 1 | function thin_edge = edge_nms(edge_pr, thresh) 2 | edge_pr = single(edge_pr); 3 | bw_edge = edge_pr <= thresh; 4 | edge_pr(bw_edge) = 0; 5 | [Ox,Oy]=gradient2(convTri(edge_pr,4)); 6 | [Oxx,~]=gradient2(Ox); [Oxy,Oyy]=gradient2(Oy); 7 | O=mod(atan(Oyy.*sign(-Oxy)./(Oxx+1e-5)),pi); 8 | thin_edge=edgesNmsMex(edge_pr,O,1,5,1.01,4); 9 | --------------------------------------------------------------------------------