├── .Doxyfile ├── .github └── ISSUE_TEMPLATE.md ├── .gitignore ├── .travis.yml ├── CMakeCache.txt ├── CMakeFiles ├── 2.8.12.2 │ ├── CMakeCCompiler.cmake │ ├── CMakeCXXCompiler.cmake │ ├── CMakeSystem.cmake │ ├── CompilerIdC │ │ ├── CMakeCCompilerId.c │ │ └── a.out │ └── CompilerIdCXX │ │ ├── CMakeCXXCompilerId.cpp │ │ └── a.out ├── CMakeDirectoryInformation.cmake ├── CMakeError.log ├── CMakeOutput.log ├── CMakeRuleHashes.txt ├── Export │ └── share │ │ └── Caffe │ │ ├── CaffeTargets-release.cmake │ │ └── CaffeTargets.cmake ├── Makefile.cmake ├── Makefile2 ├── TargetDirectories.txt ├── a.out ├── cmake.check_cache ├── lint.dir │ ├── DependInfo.cmake │ ├── build.make │ ├── cmake_clean.cmake │ └── progress.make ├── progress.marks ├── pytest.dir │ ├── DependInfo.cmake │ ├── build.make │ ├── cmake_clean.cmake │ └── progress.make └── symlink_to_build.dir │ ├── DependInfo.cmake │ ├── build.make │ ├── cmake_clean.cmake │ └── progress.make ├── CMakeLists.txt ├── CONTRIBUTING.md ├── CONTRIBUTORS.md ├── CaffeConfig.cmake ├── CaffeTargets.cmake ├── INSTALL.md ├── LICENSE ├── Makefile ├── Makefile.config.example ├── README.md ├── TC_TrueBwdlog ├── cpu_13 │ ├── BC_BN_XhatVec_0_data │ ├── BC_BN_XhatVec_0_grad │ ├── BC_BN_XhatVec_1_data │ ├── BC_BN_XhatVec_1_grad │ ├── BN_XhatVec_0_data │ ├── BN_XhatVec_0_grad │ ├── BN_XhatVec_1_data │ ├── BN_XhatVec_1_grad │ ├── Mean_BC_0_data │ ├── Mean_BC_0_grad │ ├── Mean_BC_1_data │ ├── Mean_BC_1_grad │ ├── Var_BC_0_data │ ├── Var_BC_0_grad │ ├── Var_BC_1_data │ ├── Var_BC_1_grad │ ├── batch_Mean_0_data │ ├── batch_Mean_0_grad │ ├── batch_Mean_1_data │ ├── batch_Mean_1_grad │ ├── batch_Mean_BC_0_data │ ├── batch_Mean_BC_0_grad │ ├── batch_Mean_BC_1_data │ ├── batch_Mean_BC_1_grad │ ├── batch_Var_0_data │ ├── batch_Var_0_grad │ ├── batch_Var_1_data │ ├── batch_Var_1_grad │ ├── batch_Var_BC_0_data │ ├── batch_Var_BC_0_grad │ ├── batch_Var_BC_1_data │ ├── batch_Var_BC_1_grad │ ├── bias_0_data │ ├── bias_0_grad │ ├── bias_1_data │ ├── bias_1_grad │ ├── bias_BC_0_data │ ├── bias_BC_0_grad │ ├── bias_BC_1_data │ ├── bias_BC_1_grad │ ├── filter_0_data │ ├── filter_0_grad │ ├── filter_1_data │ ├── filter_1_grad │ ├── filter_BC_0_data │ ├── filter_BC_0_grad │ ├── filter_BC_1_data │ ├── filter_BC_1_grad │ ├── merged_conv_0_data │ ├── merged_conv_0_grad │ ├── merged_conv_1_data │ ├── merged_conv_1_grad │ ├── merged_conv_2_data │ ├── merged_conv_2_grad │ ├── postBN_BCVec_0_data │ ├── postBN_BCVec_0_grad │ ├── postBN_BCVec_1_data │ ├── postBN_BCVec_1_grad │ ├── postBN_blobVec_0_data │ ├── postBN_blobVec_0_grad │ ├── postBN_blobVec_1_data │ ├── postBN_blobVec_1_grad │ ├── postConv_BCVec_0_data │ ├── postConv_BCVec_0_grad │ ├── postConv_BCVec_1_data │ ├── postConv_BCVec_1_grad │ ├── postConv_blobVec_0_data │ ├── postConv_blobVec_0_grad │ ├── postConv_blobVec_1_data │ ├── postConv_blobVec_1_grad │ ├── postReLU_BCVec_0_data │ ├── postReLU_BCVec_0_grad │ ├── postReLU_BCVec_1_data │ ├── postReLU_BCVec_1_grad │ ├── postReLU_blobVec_0_data │ ├── postReLU_blobVec_0_grad │ ├── postReLU_blobVec_1_data │ ├── postReLU_blobVec_1_grad │ ├── scaler_0_data │ ├── scaler_0_grad │ ├── scaler_1_data │ ├── scaler_1_grad │ ├── scaler_BC_0_data │ ├── scaler_BC_0_grad │ ├── scaler_BC_1_data │ └── scaler_BC_1_grad └── cpu_7 │ ├── BC_BN_XhatVec_0_data │ ├── BC_BN_XhatVec_0_grad │ ├── BC_BN_XhatVec_1_data │ ├── BC_BN_XhatVec_1_grad │ ├── BN_XhatVec_0_data │ ├── BN_XhatVec_0_grad │ ├── BN_XhatVec_1_data │ ├── BN_XhatVec_1_grad │ ├── Mean_BC_0_data │ ├── Mean_BC_0_grad │ ├── Mean_BC_1_data │ ├── Mean_BC_1_grad │ ├── Var_BC_0_data │ ├── Var_BC_0_grad │ ├── Var_BC_1_data │ ├── Var_BC_1_grad │ ├── batch_Mean_0_data │ ├── batch_Mean_0_grad │ ├── batch_Mean_1_data │ ├── batch_Mean_1_grad │ ├── batch_Mean_BC_0_data │ ├── batch_Mean_BC_0_grad │ ├── batch_Mean_BC_1_data │ ├── batch_Mean_BC_1_grad │ ├── batch_Var_0_data │ ├── batch_Var_0_grad │ ├── batch_Var_1_data │ ├── batch_Var_1_grad │ ├── batch_Var_BC_0_data │ ├── batch_Var_BC_0_grad │ ├── batch_Var_BC_1_data │ ├── batch_Var_BC_1_grad │ ├── bias_0_data │ ├── bias_0_grad │ ├── bias_1_data │ ├── bias_1_grad │ ├── bias_BC_0_data │ ├── bias_BC_0_grad │ ├── bias_BC_1_data │ ├── bias_BC_1_grad │ ├── filter_0_data │ ├── filter_0_grad │ ├── filter_1_data │ ├── filter_1_grad │ ├── filter_BC_0_data │ ├── filter_BC_0_grad │ ├── filter_BC_1_data │ ├── filter_BC_1_grad │ ├── merged_conv_0_data │ ├── merged_conv_0_grad │ ├── merged_conv_1_data │ ├── merged_conv_1_grad │ ├── merged_conv_2_data │ ├── merged_conv_2_grad │ ├── postBN_BCVec_0_data │ ├── postBN_BCVec_0_grad │ ├── postBN_BCVec_1_data │ ├── postBN_BCVec_1_grad │ ├── postBN_blobVec_0_data │ ├── postBN_blobVec_0_grad │ ├── postBN_blobVec_1_data │ ├── postBN_blobVec_1_grad │ ├── postConv_BCVec_0_data │ ├── postConv_BCVec_0_grad │ ├── postConv_BCVec_1_data │ ├── postConv_BCVec_1_grad │ ├── postConv_blobVec_0_data │ ├── postConv_blobVec_0_grad │ ├── postConv_blobVec_1_data │ ├── postConv_blobVec_1_grad │ ├── postReLU_BCVec_0_data │ ├── postReLU_BCVec_0_grad │ ├── postReLU_BCVec_1_data │ ├── postReLU_BCVec_1_grad │ ├── postReLU_blobVec_0_data │ ├── postReLU_blobVec_0_grad │ ├── postReLU_blobVec_1_data │ ├── postReLU_blobVec_1_grad │ ├── scaler_0_data │ ├── scaler_0_grad │ ├── scaler_1_data │ ├── scaler_1_grad │ ├── scaler_BC_0_data │ ├── scaler_BC_0_grad │ ├── scaler_BC_1_data │ └── scaler_BC_1_grad ├── TClogBwd ├── gpu_14 │ ├── Bias_data_BC_0 │ ├── Bias_data_BC_1 │ ├── Bias_data_gpu_0 │ ├── Bias_data_gpu_1 │ ├── Bias_grad_BC_0 │ ├── Bias_grad_BC_1 │ ├── Bias_grad_gpu_0 │ ├── Bias_grad_gpu_1 │ ├── Filter_data_BC_0 │ ├── Filter_data_BC_1 │ ├── Filter_data_gpu_0 │ ├── Filter_data_gpu_1 │ ├── Filter_grad_BC_0 │ ├── Filter_grad_BC_1 │ ├── Filter_grad_gpu_0 │ ├── Filter_grad_gpu_1 │ ├── ResultSaveInvVariance_BC_transition0 │ ├── ResultSaveInvVariance_BC_transition1 │ ├── ResultSaveInvVariance_gpu_transition0 │ ├── ResultSaveInvVariance_gpu_transition1 │ ├── ResultSaveMean_BC_transition0 │ ├── ResultSaveMean_BC_transition1 │ ├── ResultSaveMean_gpu_transition0 │ ├── ResultSaveMean_gpu_transition1 │ ├── Scaler_data_BC_0 │ ├── Scaler_data_BC_1 │ ├── Scaler_data_gpu_0 │ ├── Scaler_data_gpu_1 │ ├── Scaler_grad_BC_0 │ ├── Scaler_grad_BC_1 │ ├── Scaler_grad_gpu_0 │ ├── Scaler_grad_gpu_1 │ ├── globalMean_BC_transition0 │ ├── globalMean_BC_transition1 │ ├── globalMean_gpu_transition0 │ ├── globalMean_gpu_transition1 │ ├── globalVar_BC_transition0 │ ├── globalVar_BC_transition1 │ ├── globalVariance_gpu_transition0 │ ├── globalVariance_gpu_transition1 │ ├── postBN_4G_data_transition0 │ ├── postBN_4G_data_transition1 │ ├── postBN_4G_grad_transition0 │ ├── postBN_4G_grad_transition1 │ ├── postBN_data_gpu_transition0 │ ├── postBN_data_gpu_transition1 │ ├── postBN_grad_gpu_transition0 │ ├── postBN_grad_gpu_transition1 │ ├── postConv_4G_data_transition0 │ ├── postConv_4G_data_transition1 │ ├── postConv_4G_grad_transition0 │ ├── postConv_4G_grad_transition1 │ ├── postConv_data_gpu_transition0 │ ├── postConv_data_gpu_transition1 │ ├── postConv_grad_gpu_transition0 │ ├── postConv_grad_gpu_transition1 │ ├── postReLU_4G_data_transition0 │ ├── postReLU_4G_data_transition1 │ ├── postReLU_4G_grad_transition0 │ ├── postReLU_4G_grad_transition1 │ ├── postReLU_data_gpu_transition0 │ ├── postReLU_data_gpu_transition1 │ ├── postReLU_grad_gpu_transition0 │ └── postReLU_grad_gpu_transition1 └── gpu_8 │ ├── Bias_data_BC_0 │ ├── Bias_data_BC_1 │ ├── Bias_data_gpu_0 │ ├── Bias_data_gpu_1 │ ├── Bias_grad_BC_0 │ ├── Bias_grad_BC_1 │ ├── Bias_grad_gpu_0 │ ├── Bias_grad_gpu_1 │ ├── Filter_data_BC_0 │ ├── Filter_data_BC_1 │ ├── Filter_data_gpu_0 │ ├── Filter_data_gpu_1 │ ├── Filter_grad_BC_0 │ ├── Filter_grad_BC_1 │ ├── Filter_grad_gpu_0 │ ├── Filter_grad_gpu_1 │ ├── ResultSaveInvVariance_BC_transition0 │ ├── ResultSaveInvVariance_BC_transition1 │ ├── ResultSaveInvVariance_gpu_transition0 │ ├── ResultSaveInvVariance_gpu_transition1 │ ├── ResultSaveMean_BC_transition0 │ ├── ResultSaveMean_BC_transition1 │ ├── ResultSaveMean_gpu_transition0 │ ├── ResultSaveMean_gpu_transition1 │ ├── Scaler_data_BC_0 │ ├── Scaler_data_BC_1 │ ├── Scaler_data_gpu_0 │ ├── Scaler_data_gpu_1 │ ├── Scaler_grad_BC_0 │ ├── Scaler_grad_BC_1 │ ├── Scaler_grad_gpu_0 │ ├── Scaler_grad_gpu_1 │ ├── globalMean_BC_transition0 │ ├── globalMean_BC_transition1 │ ├── globalMean_gpu_transition0 │ ├── globalMean_gpu_transition1 │ ├── globalVar_BC_transition0 │ ├── globalVar_BC_transition1 │ ├── globalVariance_gpu_transition0 │ ├── globalVariance_gpu_transition1 │ ├── postBN_4G_data_transition0 │ ├── postBN_4G_data_transition1 │ ├── postBN_4G_grad_transition0 │ ├── postBN_4G_grad_transition1 │ ├── postBN_data_gpu_transition0 │ ├── postBN_data_gpu_transition1 │ ├── postBN_grad_gpu_transition0 │ ├── postBN_grad_gpu_transition1 │ ├── postConv_4G_data_transition0 │ ├── postConv_4G_data_transition1 │ ├── postConv_4G_grad_transition0 │ ├── postConv_4G_grad_transition1 │ ├── postConv_data_gpu_transition0 │ ├── postConv_data_gpu_transition1 │ ├── postConv_grad_gpu_transition0 │ ├── postConv_grad_gpu_transition1 │ ├── postReLU_4G_data_transition0 │ ├── postReLU_4G_data_transition1 │ ├── postReLU_4G_grad_transition0 │ ├── postReLU_4G_grad_transition1 │ ├── postReLU_data_gpu_transition0 │ ├── postReLU_data_gpu_transition1 │ ├── postReLU_grad_gpu_transition0 │ └── postReLU_grad_gpu_transition1 ├── __init__.py ├── caffe.cloc ├── caffe_config.h ├── cmake ├── CaffeConfig.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 │ ├── FindNCCL.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 ├── cmake_install.cmake ├── computeDiff.py ├── data ├── cifar10 │ └── get_cifar10.sh ├── ilsvrc12 │ └── get_ilsvrc_aux.sh └── mnist │ └── get_mnist.sh ├── detect_cuda_archs.cu ├── docker ├── README.md ├── cpu │ └── Dockerfile └── gpu │ └── Dockerfile ├── docs ├── CMakeFiles │ ├── CMakeDirectoryInformation.cmake │ ├── docs.dir │ │ ├── DependInfo.cmake │ │ ├── build.make │ │ ├── cmake_clean.cmake │ │ └── progress.make │ ├── jekyll.dir │ │ ├── DependInfo.cmake │ │ ├── build.make │ │ ├── cmake_clean.cmake │ │ └── progress.make │ └── progress.marks ├── CMakeLists.txt ├── CNAME ├── Makefile ├── README.md ├── _config.yml ├── _layouts │ └── default.html ├── cmake_install.cmake ├── development.md ├── images │ ├── GitHub-Mark-64px.png │ └── caffeine-icon.png ├── index.md ├── install_apt.md ├── install_apt_debian.md ├── install_osx.md ├── install_yum.md ├── installation.md ├── model_zoo.md ├── multigpu.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 │ ├── layers │ ├── absval.md │ ├── accuracy.md │ ├── argmax.md │ ├── batchnorm.md │ ├── batchreindex.md │ ├── bias.md │ ├── bnll.md │ ├── concat.md │ ├── contrastiveloss.md │ ├── convolution.md │ ├── crop.md │ ├── data.md │ ├── deconvolution.md │ ├── dropout.md │ ├── dummydata.md │ ├── eltwise.md │ ├── elu.md │ ├── embed.md │ ├── euclideanloss.md │ ├── exp.md │ ├── filter.md │ ├── flatten.md │ ├── hdf5data.md │ ├── hdf5output.md │ ├── hingeloss.md │ ├── im2col.md │ ├── imagedata.md │ ├── infogainloss.md │ ├── innerproduct.md │ ├── input.md │ ├── log.md │ ├── lrn.md │ ├── lstm.md │ ├── memorydata.md │ ├── multinomiallogisticloss.md │ ├── mvn.md │ ├── parameter.md │ ├── pooling.md │ ├── power.md │ ├── prelu.md │ ├── python.md │ ├── recurrent.md │ ├── reduction.md │ ├── relu.md │ ├── reshape.md │ ├── rnn.md │ ├── scale.md │ ├── sigmoid.md │ ├── sigmoidcrossentropyloss.md │ ├── silence.md │ ├── slice.md │ ├── softmax.md │ ├── softmaxwithloss.md │ ├── split.md │ ├── spp.md │ ├── tanh.md │ ├── threshold.md │ ├── tile.md │ └── windowdata.md │ ├── loss.md │ ├── net_layer_blob.md │ └── solver.md ├── examples ├── 00-classification.ipynb ├── 01-learning-lenet.ipynb ├── 02-fine-tuning.ipynb ├── CMakeFiles │ ├── CMakeDirectoryInformation.cmake │ ├── classification.dir │ │ ├── CXX.includecache │ │ ├── DependInfo.cmake │ │ ├── build.make │ │ ├── cmake_clean.cmake │ │ ├── depend.internal │ │ ├── depend.make │ │ ├── flags.make │ │ ├── link.txt │ │ └── progress.make │ ├── convert_cifar_data.dir │ │ ├── CXX.includecache │ │ ├── DependInfo.cmake │ │ ├── build.make │ │ ├── cmake_clean.cmake │ │ ├── depend.internal │ │ ├── depend.make │ │ ├── flags.make │ │ ├── link.txt │ │ └── progress.make │ ├── convert_mnist_data.dir │ │ ├── CXX.includecache │ │ ├── DependInfo.cmake │ │ ├── build.make │ │ ├── cmake_clean.cmake │ │ ├── depend.internal │ │ ├── depend.make │ │ ├── flags.make │ │ ├── link.txt │ │ └── progress.make │ ├── convert_mnist_siamese_data.dir │ │ ├── CXX.includecache │ │ ├── DependInfo.cmake │ │ ├── build.make │ │ ├── cmake_clean.cmake │ │ ├── depend.internal │ │ ├── depend.make │ │ ├── flags.make │ │ ├── link.txt │ │ └── progress.make │ └── progress.marks ├── CMakeLists.txt ├── Makefile ├── brewing-logreg.ipynb ├── cifar10 │ ├── cifar10_full.prototxt │ ├── cifar10_full_sigmoid_solver.prototxt │ ├── cifar10_full_sigmoid_solver_bn.prototxt │ ├── cifar10_full_sigmoid_train_test.prototxt │ ├── cifar10_full_sigmoid_train_test_bn.prototxt │ ├── cifar10_full_solver.prototxt │ ├── cifar10_full_solver_lr1.prototxt │ ├── cifar10_full_solver_lr2.prototxt │ ├── cifar10_full_train_test.prototxt │ ├── cifar10_quick.prototxt │ ├── cifar10_quick_solver.prototxt │ ├── cifar10_quick_solver_lr1.prototxt │ ├── cifar10_quick_train_test.prototxt │ ├── cifar10_test_leveldb_256 │ │ └── 000010.log │ ├── cifar10_test_leveldb_padding │ │ └── 000014.log │ ├── cifar10_train_leveldb_256 │ │ └── 000010.log │ ├── cifar10_train_leveldb_padding │ │ └── 000014.log │ ├── convert_cifar_data │ ├── convert_cifar_data.cpp │ ├── create_cifar10.sh │ ├── preprocessing.py │ ├── readme.md │ ├── train_full.sh │ ├── train_full_sigmoid.sh │ ├── train_full_sigmoid_bn.sh │ └── train_quick.sh ├── cmake_install.cmake ├── cpp_classification │ ├── classification │ ├── classification.cpp │ └── readme.md ├── detection.ipynb ├── feature_extraction │ ├── imagenet_val.prototxt │ └── readme.md ├── finetune_flickr_style │ ├── assemble_data.py │ ├── flickr_style.csv.gz │ ├── readme.md │ └── style_names.txt ├── finetune_pascal_detection │ ├── pascal_finetune_solver.prototxt │ └── pascal_finetune_trainval_test.prototxt ├── hdf5_classification │ ├── nonlinear_auto_test.prototxt │ ├── nonlinear_auto_train.prototxt │ ├── nonlinear_train_val.prototxt │ └── train_val.prototxt ├── imagenet │ ├── create_imagenet.sh │ ├── make_imagenet_mean.sh │ ├── readme.md │ ├── resume_training.sh │ └── train_caffenet.sh ├── images │ ├── cat gray.jpg │ ├── cat.jpg │ ├── cat_gray.jpg │ └── fish-bike.jpg ├── mnist │ ├── convert_mnist_data │ ├── convert_mnist_data.cpp │ ├── create_mnist.sh │ ├── lenet.prototxt │ ├── lenet_adadelta_solver.prototxt │ ├── lenet_auto_solver.prototxt │ ├── lenet_consolidated_solver.prototxt │ ├── lenet_multistep_solver.prototxt │ ├── lenet_solver.prototxt │ ├── lenet_solver_adam.prototxt │ ├── lenet_solver_rmsprop.prototxt │ ├── lenet_train_test.prototxt │ ├── mnist_autoencoder.prototxt │ ├── mnist_autoencoder_solver.prototxt │ ├── mnist_autoencoder_solver_adadelta.prototxt │ ├── mnist_autoencoder_solver_adagrad.prototxt │ ├── mnist_autoencoder_solver_nesterov.prototxt │ ├── readme.md │ ├── train_lenet.sh │ ├── train_lenet_adam.sh │ ├── train_lenet_consolidated.sh │ ├── train_lenet_docker.sh │ ├── train_lenet_rmsprop.sh │ ├── train_mnist_autoencoder.sh │ ├── train_mnist_autoencoder_adadelta.sh │ ├── train_mnist_autoencoder_adagrad.sh │ └── train_mnist_autoencoder_nesterov.sh ├── net_surgery.ipynb ├── net_surgery │ ├── bvlc_caffenet_full_conv.prototxt │ └── conv.prototxt ├── pascal-multilabel-with-datalayer.ipynb ├── pycaffe │ ├── caffenet.py │ ├── layers │ │ ├── pascal_multilabel_datalayers.py │ │ └── pyloss.py │ ├── linreg.prototxt │ └── tools.py ├── siamese │ ├── convert_mnist_siamese_data │ ├── convert_mnist_siamese_data.cpp │ ├── create_mnist_siamese.sh │ ├── mnist_siamese.ipynb │ ├── mnist_siamese.prototxt │ ├── mnist_siamese_solver.prototxt │ ├── mnist_siamese_train_test.prototxt │ ├── readme.md │ └── train_mnist_siamese.sh └── web_demo │ ├── app.py │ ├── exifutil.py │ ├── readme.md │ ├── requirements.txt │ └── templates │ └── index.html ├── include └── caffe │ ├── blob.hpp │ ├── caffe.hpp │ ├── common.hpp │ ├── data_transformer.hpp │ ├── filler.hpp │ ├── internal_thread.hpp │ ├── layer.hpp │ ├── layer_factory.hpp │ ├── layers │ ├── DenseBlock_layer.hpp │ ├── absval_layer.hpp │ ├── accuracy_layer.hpp │ ├── argmax_layer.hpp │ ├── base_conv_layer.hpp │ ├── base_data_layer.hpp │ ├── batch_norm_layer.hpp │ ├── batch_reindex_layer.hpp │ ├── bias_layer.hpp │ ├── bnll_layer.hpp │ ├── concat_layer.hpp │ ├── contrastive_loss_layer.hpp │ ├── conv_layer.hpp │ ├── crop_layer.hpp │ ├── cudnn_batch_norm_layer.hpp │ ├── cudnn_conv_layer.hpp │ ├── cudnn_lcn_layer.hpp │ ├── cudnn_lrn_layer.hpp │ ├── cudnn_pooling_layer.hpp │ ├── cudnn_relu_layer.hpp │ ├── cudnn_sigmoid_layer.hpp │ ├── cudnn_softmax_layer.hpp │ ├── cudnn_tanh_layer.hpp │ ├── data_layer.hpp │ ├── deconv_layer.hpp │ ├── dropout_layer.hpp │ ├── dummy_data_layer.hpp │ ├── eltwise_layer.hpp │ ├── elu_layer.hpp │ ├── embed_layer.hpp │ ├── euclidean_loss_layer.hpp │ ├── exp_layer.hpp │ ├── filter_layer.hpp │ ├── flatten_layer.hpp │ ├── hdf5_data_layer.hpp │ ├── hdf5_output_layer.hpp │ ├── hinge_loss_layer.hpp │ ├── im2col_layer.hpp │ ├── image_data_layer.hpp │ ├── infogain_loss_layer.hpp │ ├── inner_product_layer.hpp │ ├── input_layer.hpp │ ├── log_layer.hpp │ ├── loss_layer.hpp │ ├── lrn_layer.hpp │ ├── lstm_layer.hpp │ ├── memory_data_layer.hpp │ ├── multinomial_logistic_loss_layer.hpp │ ├── mvn_layer.hpp │ ├── neuron_layer.hpp │ ├── parameter_layer.hpp │ ├── pooling_layer.hpp │ ├── power_layer.hpp │ ├── prelu_layer.hpp │ ├── python_layer.hpp │ ├── recurrent_layer.hpp │ ├── reduction_layer.hpp │ ├── relu_layer.hpp │ ├── reshape_layer.hpp │ ├── rnn_layer.hpp │ ├── scale_layer.hpp │ ├── sigmoid_cross_entropy_loss_layer.hpp │ ├── sigmoid_layer.hpp │ ├── silence_layer.hpp │ ├── slice_layer.hpp │ ├── softmax_layer.hpp │ ├── softmax_loss_layer.hpp │ ├── split_layer.hpp │ ├── spp_layer.hpp │ ├── tanh_layer.hpp │ ├── threshold_layer.hpp │ ├── tile_layer.hpp │ └── window_data_layer.hpp │ ├── net.hpp │ ├── parallel.hpp │ ├── sgd_solvers.hpp │ ├── solver.hpp │ ├── solver_factory.hpp │ ├── syncedmem.hpp │ ├── test │ ├── test_caffe_main.hpp │ └── test_gradient_check_util.hpp │ └── util │ ├── benchmark.hpp │ ├── blocking_queue.hpp │ ├── cudnn.hpp │ ├── db.hpp │ ├── db_leveldb.hpp │ ├── db_lmdb.hpp │ ├── device_alternate.hpp │ ├── format.hpp │ ├── gpu_util.cuh │ ├── hdf5.hpp │ ├── im2col.hpp │ ├── insert_splits.hpp │ ├── io.hpp │ ├── math_functions.hpp │ ├── mkl_alternate.hpp │ ├── nccl.hpp │ ├── rng.hpp │ ├── signal_handler.h │ └── upgrade_proto.hpp ├── lib └── libcaffe.so.1.0.0-rc4 ├── matlab ├── +caffe │ ├── +test │ │ ├── test_io.m │ │ ├── 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 │ └── version.m ├── CMakeFiles │ ├── CMakeDirectoryInformation.cmake │ └── progress.marks ├── CMakeLists.txt ├── Makefile ├── cmake_install.cmake ├── demo │ └── classification_demo.m └── hdf5creation │ ├── .gitignore │ ├── demo.m │ └── store2hdf5.m ├── models ├── bvlc_alexnet │ ├── deploy.prototxt │ ├── readme.md │ ├── solver.prototxt │ └── train_val.prototxt ├── bvlc_googlenet │ ├── deploy.prototxt │ ├── quick_solver.prototxt │ ├── readme.md │ ├── solver.prototxt │ └── train_val.prototxt ├── bvlc_reference_caffenet │ ├── deploy.prototxt │ ├── readme.md │ ├── solver.prototxt │ └── train_val.prototxt ├── bvlc_reference_rcnn_ilsvrc13 │ ├── deploy.prototxt │ └── readme.md └── finetune_flickr_style │ ├── deploy.prototxt │ ├── readme.md │ ├── solver.prototxt │ └── train_val.prototxt ├── python ├── CMakeFiles │ ├── CMakeDirectoryInformation.cmake │ └── progress.marks ├── CMakeLists.txt ├── Makefile ├── caffe │ ├── __init__.py │ ├── _caffe.cpp │ ├── classifier.py │ ├── coord_map.py │ ├── detector.py │ ├── draw.py │ ├── imagenet │ │ └── ilsvrc_2012_mean.npy │ ├── io.py │ ├── net_spec.py │ ├── pycaffe.py │ └── test │ │ ├── test_coord_map.py │ │ ├── test_io.py │ │ ├── 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 ├── cmake_install.cmake ├── detect.py ├── draw_net.py ├── requirements.txt └── train.py ├── removeCmakeDep.sh ├── run.sh ├── 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 ├── split_caffe_proto.py ├── travis │ ├── build.sh │ ├── configure-cmake.sh │ ├── configure-make.sh │ ├── configure.sh │ ├── defaults.sh │ ├── install-deps.sh │ ├── install-python-deps.sh │ ├── setup-venv.sh │ └── test.sh └── upload_model_to_gist.sh ├── src ├── caffe │ ├── CMakeFiles │ │ ├── CMakeDirectoryInformation.cmake │ │ ├── caffe.dir │ │ │ ├── CXX.includecache │ │ │ ├── DependInfo.cmake │ │ │ ├── build.make │ │ │ ├── cmake_clean.cmake │ │ │ ├── depend.internal │ │ │ ├── depend.make │ │ │ ├── flags.make │ │ │ ├── link.txt │ │ │ └── progress.make │ │ ├── cuda_compile.dir │ │ │ ├── layers │ │ │ │ ├── cuda_compile_generated_DenseBlock_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_DenseBlock_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_absval_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_absval_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_base_data_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_base_data_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_batch_norm_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_batch_norm_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_batch_reindex_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_batch_reindex_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_bias_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_bias_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_bnll_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_bnll_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_concat_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_concat_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_contrastive_loss_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_contrastive_loss_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_conv_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_conv_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_crop_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_crop_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_cudnn_batch_norm_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_cudnn_batch_norm_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_cudnn_conv_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_cudnn_conv_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_cudnn_lcn_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_cudnn_lcn_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_cudnn_lrn_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_cudnn_lrn_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_cudnn_pooling_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_cudnn_pooling_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_cudnn_relu_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_cudnn_relu_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_cudnn_sigmoid_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_cudnn_sigmoid_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_cudnn_softmax_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_cudnn_softmax_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_cudnn_tanh_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_cudnn_tanh_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_deconv_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_deconv_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_dropout_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_dropout_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_eltwise_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_eltwise_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_elu_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_elu_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_embed_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_embed_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_euclidean_loss_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_euclidean_loss_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_exp_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_exp_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_filter_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_filter_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_hdf5_data_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_hdf5_data_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_hdf5_output_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_hdf5_output_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_im2col_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_im2col_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_inner_product_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_inner_product_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_log_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_log_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_lrn_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_lrn_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_lstm_unit_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_lstm_unit_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_mvn_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_mvn_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_pooling_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_pooling_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_power_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_power_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_prelu_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_prelu_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_recurrent_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_recurrent_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_reduction_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_reduction_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_relu_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_relu_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_scale_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_scale_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_sigmoid_cross_entropy_loss_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_sigmoid_cross_entropy_loss_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_sigmoid_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_sigmoid_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_silence_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_silence_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_slice_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_slice_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_softmax_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_softmax_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_softmax_loss_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_softmax_loss_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_split_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_split_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_tanh_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_tanh_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_threshold_layer.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_threshold_layer.cu.o.depend │ │ │ │ ├── cuda_compile_generated_tile_layer.cu.o.cmake │ │ │ │ └── cuda_compile_generated_tile_layer.cu.o.depend │ │ │ ├── solvers │ │ │ │ ├── cuda_compile_generated_adadelta_solver.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_adadelta_solver.cu.o.depend │ │ │ │ ├── cuda_compile_generated_adagrad_solver.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_adagrad_solver.cu.o.depend │ │ │ │ ├── cuda_compile_generated_adam_solver.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_adam_solver.cu.o.depend │ │ │ │ ├── cuda_compile_generated_nesterov_solver.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_nesterov_solver.cu.o.depend │ │ │ │ ├── cuda_compile_generated_rmsprop_solver.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_rmsprop_solver.cu.o.depend │ │ │ │ ├── cuda_compile_generated_sgd_solver.cu.o.cmake │ │ │ │ └── cuda_compile_generated_sgd_solver.cu.o.depend │ │ │ └── util │ │ │ │ ├── cuda_compile_generated_im2col.cu.o.cmake │ │ │ │ ├── cuda_compile_generated_im2col.cu.o.depend │ │ │ │ ├── cuda_compile_generated_math_functions.cu.o.cmake │ │ │ │ └── cuda_compile_generated_math_functions.cu.o.depend │ │ ├── progress.marks │ │ └── proto.dir │ │ │ ├── CXX.includecache │ │ │ ├── DependInfo.cmake │ │ │ ├── build.make │ │ │ ├── cmake_clean.cmake │ │ │ ├── cmake_clean_target.cmake │ │ │ ├── depend.internal │ │ │ ├── depend.make │ │ │ ├── flags.make │ │ │ ├── link.txt │ │ │ └── progress.make │ ├── CMakeLists.txt │ ├── Makefile │ ├── blob.cpp │ ├── cmake_install.cmake │ ├── common.cpp │ ├── data_transformer.cpp │ ├── internal_thread.cpp │ ├── layer.cpp │ ├── layer_factory.cpp │ ├── layers │ │ ├── DenseBlock_layer.cpp │ │ ├── DenseBlock_layer.cu │ │ ├── absval_layer.cpp │ │ ├── absval_layer.cu │ │ ├── accuracy_layer.cpp │ │ ├── argmax_layer.cpp │ │ ├── base_conv_layer.cpp │ │ ├── base_data_layer.cpp │ │ ├── base_data_layer.cu │ │ ├── batch_norm_layer.cpp │ │ ├── batch_norm_layer.cu │ │ ├── batch_reindex_layer.cpp │ │ ├── batch_reindex_layer.cu │ │ ├── bias_layer.cpp │ │ ├── bias_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_batch_norm_layer.cpp │ │ ├── cudnn_batch_norm_layer.cu │ │ ├── cudnn_conv_layer.cpp │ │ ├── cudnn_conv_layer.cu │ │ ├── cudnn_lcn_layer.cpp │ │ ├── cudnn_lcn_layer.cu │ │ ├── cudnn_lrn_layer.cpp │ │ ├── cudnn_lrn_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 │ │ ├── elu_layer.cpp │ │ ├── elu_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 │ │ ├── infogain_loss_layer.cpp │ │ ├── inner_product_layer.cpp │ │ ├── inner_product_layer.cu │ │ ├── input_layer.cpp │ │ ├── log_layer.cpp │ │ ├── log_layer.cu │ │ ├── loss_layer.cpp │ │ ├── lrn_layer.cpp │ │ ├── lrn_layer.cu │ │ ├── lstm_layer.cpp │ │ ├── lstm_unit_layer.cpp │ │ ├── lstm_unit_layer.cu │ │ ├── memory_data_layer.cpp │ │ ├── multinomial_logistic_loss_layer.cpp │ │ ├── mvn_layer.cpp │ │ ├── mvn_layer.cu │ │ ├── neuron_layer.cpp │ │ ├── parameter_layer.cpp │ │ ├── pooling_layer.cpp │ │ ├── pooling_layer.cu │ │ ├── power_layer.cpp │ │ ├── power_layer.cu │ │ ├── prelu_layer.cpp │ │ ├── prelu_layer.cu │ │ ├── recurrent_layer.cpp │ │ ├── recurrent_layer.cu │ │ ├── reduction_layer.cpp │ │ ├── reduction_layer.cu │ │ ├── relu_layer.cpp │ │ ├── relu_layer.cu │ │ ├── reshape_layer.cpp │ │ ├── rnn_layer.cpp │ │ ├── scale_layer.cpp │ │ ├── scale_layer.cu │ │ ├── sigmoid_cross_entropy_loss_layer.cpp │ │ ├── sigmoid_cross_entropy_loss_layer.cu │ │ ├── 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.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 │ ├── solvers │ │ ├── adadelta_solver.cpp │ │ ├── adadelta_solver.cu │ │ ├── adagrad_solver.cpp │ │ ├── adagrad_solver.cu │ │ ├── adam_solver.cpp │ │ ├── adam_solver.cu │ │ ├── nesterov_solver.cpp │ │ ├── nesterov_solver.cu │ │ ├── rmsprop_solver.cpp │ │ ├── rmsprop_solver.cu │ │ ├── sgd_solver.cpp │ │ └── sgd_solver.cu │ ├── syncedmem.cpp │ ├── test │ │ ├── CMakeFiles │ │ │ ├── CMakeDirectoryInformation.cmake │ │ │ ├── cuda_compile.dir │ │ │ │ ├── cuda_compile_generated_test_im2col_kernel.cu.o.cmake │ │ │ │ └── cuda_compile_generated_test_im2col_kernel.cu.o.depend │ │ │ ├── progress.marks │ │ │ ├── runtest.dir │ │ │ │ ├── DependInfo.cmake │ │ │ │ ├── build.make │ │ │ │ ├── cmake_clean.cmake │ │ │ │ └── progress.make │ │ │ └── test.testbin.dir │ │ │ │ ├── DependInfo.cmake │ │ │ │ ├── build.make │ │ │ │ ├── cmake_clean.cmake │ │ │ │ ├── depend.make │ │ │ │ ├── flags.make │ │ │ │ ├── link.txt │ │ │ │ └── progress.make │ │ ├── CMakeLists.txt │ │ ├── Makefile │ │ ├── cmake_install.cmake │ │ ├── test_DenseBlock_layer.cpp │ │ ├── test_accuracy_layer.cpp │ │ ├── test_argmax_layer.cpp │ │ ├── test_batch_norm_layer.cpp │ │ ├── test_batch_reindex_layer.cpp │ │ ├── test_benchmark.cpp │ │ ├── test_bias_layer.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_crop_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_lstm_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_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_rnn_layer.cpp │ │ ├── test_scale_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_solver_factory.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 │ ├── CMakeFiles │ ├── CMakeDirectoryInformation.cmake │ ├── gtest.dir │ │ ├── DependInfo.cmake │ │ ├── build.make │ │ ├── cmake_clean.cmake │ │ ├── cmake_clean_target.cmake │ │ ├── depend.make │ │ ├── flags.make │ │ ├── link.txt │ │ └── progress.make │ └── progress.marks │ ├── CMakeLists.txt │ ├── Makefile │ ├── cmake_install.cmake │ ├── gtest-all.cpp │ ├── gtest.h │ └── gtest_main.cc └── tools ├── CMakeFiles ├── CMakeDirectoryInformation.cmake ├── caffe.bin.dir │ ├── CXX.includecache │ ├── DependInfo.cmake │ ├── build.make │ ├── cmake_clean.cmake │ ├── depend.internal │ ├── depend.make │ ├── flags.make │ ├── link.txt │ └── progress.make ├── compute_image_mean.dir │ ├── CXX.includecache │ ├── DependInfo.cmake │ ├── build.make │ ├── cmake_clean.cmake │ ├── depend.internal │ ├── depend.make │ ├── flags.make │ ├── link.txt │ └── progress.make ├── convert_imageset.dir │ ├── CXX.includecache │ ├── DependInfo.cmake │ ├── build.make │ ├── cmake_clean.cmake │ ├── depend.internal │ ├── depend.make │ ├── flags.make │ ├── link.txt │ └── progress.make ├── device_query.dir │ ├── CXX.includecache │ ├── DependInfo.cmake │ ├── build.make │ ├── cmake_clean.cmake │ ├── depend.internal │ ├── depend.make │ ├── flags.make │ ├── link.txt │ └── progress.make ├── extract_features.dir │ ├── CXX.includecache │ ├── DependInfo.cmake │ ├── build.make │ ├── cmake_clean.cmake │ ├── depend.internal │ ├── depend.make │ ├── flags.make │ ├── link.txt │ └── progress.make ├── finetune_net.dir │ ├── CXX.includecache │ ├── DependInfo.cmake │ ├── build.make │ ├── cmake_clean.cmake │ ├── depend.internal │ ├── depend.make │ ├── flags.make │ ├── link.txt │ └── progress.make ├── net_speed_benchmark.dir │ ├── CXX.includecache │ ├── DependInfo.cmake │ ├── build.make │ ├── cmake_clean.cmake │ ├── depend.internal │ ├── depend.make │ ├── flags.make │ ├── link.txt │ └── progress.make ├── progress.marks ├── test_net.dir │ ├── CXX.includecache │ ├── DependInfo.cmake │ ├── build.make │ ├── cmake_clean.cmake │ ├── depend.internal │ ├── depend.make │ ├── flags.make │ ├── link.txt │ └── progress.make ├── train_net.dir │ ├── CXX.includecache │ ├── DependInfo.cmake │ ├── build.make │ ├── cmake_clean.cmake │ ├── depend.internal │ ├── depend.make │ ├── flags.make │ ├── link.txt │ └── progress.make ├── upgrade_net_proto_binary.dir │ ├── CXX.includecache │ ├── DependInfo.cmake │ ├── build.make │ ├── cmake_clean.cmake │ ├── depend.internal │ ├── depend.make │ ├── flags.make │ ├── link.txt │ └── progress.make ├── upgrade_net_proto_text.dir │ ├── CXX.includecache │ ├── DependInfo.cmake │ ├── build.make │ ├── cmake_clean.cmake │ ├── depend.internal │ ├── depend.make │ ├── flags.make │ ├── link.txt │ └── progress.make └── upgrade_solver_proto_text.dir │ ├── CXX.includecache │ ├── DependInfo.cmake │ ├── build.make │ ├── cmake_clean.cmake │ ├── depend.internal │ ├── depend.make │ ├── flags.make │ ├── link.txt │ └── progress.make ├── CMakeLists.txt ├── Makefile ├── caffe ├── caffe.cpp ├── cmake_install.cmake ├── compute_image_mean ├── compute_image_mean.cpp ├── convert_imageset ├── convert_imageset.cpp ├── device_query ├── device_query.cpp ├── extra ├── extract_seconds.py ├── 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-------------------------------------------------------------------------------- 1 | CMAKE_PROGRESS_1 = 2 | 3 | -------------------------------------------------------------------------------- /CONTRIBUTORS.md: -------------------------------------------------------------------------------- 1 | # Contributors 2 | 3 | Caffe is developed by a core set of BVLC members and the open-source community. 4 | 5 | We thank all of our [contributors](https://github.com/BVLC/caffe/graphs/contributors)! 6 | 7 | **For the detailed history of contributions** of a given file, try 8 | 9 | git blame file 10 | 11 | to see line-by-line credits and 12 | 13 | git log --follow file 14 | 15 | to see the change log even across renames and rewrites. 16 | 17 | Please refer to the [acknowledgements](http://caffe.berkeleyvision.org/#acknowledgements) on the Caffe site for further details. 18 | 19 | **Copyright** is held by the original contributor according to the versioning history; see LICENSE. 20 | 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-------------------------------------------------------------------------------- /TC_TrueBwdlog/cpu_13/scaler_0_data: -------------------------------------------------------------------------------- 1 | 1,1,1, 2 | -------------------------------------------------------------------------------- /TC_TrueBwdlog/cpu_13/scaler_0_grad: -------------------------------------------------------------------------------- 1 | 3.72995,28.5425,-30.1781, 2 | -------------------------------------------------------------------------------- /TC_TrueBwdlog/cpu_13/scaler_1_data: -------------------------------------------------------------------------------- 1 | 1,1,1,1,1, 2 | -------------------------------------------------------------------------------- /TC_TrueBwdlog/cpu_13/scaler_1_grad: -------------------------------------------------------------------------------- 1 | 8.62892,-16.6552,18.5726,-10.1845,-0.0976993, 2 | -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /TC_TrueBwdlog/cpu_7/postConv_blobVec_0_grad: -------------------------------------------------------------------------------- 1 | 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-------------------------------------------------------------------------------- /TC_TrueBwdlog/cpu_7/postConv_blobVec_1_grad: -------------------------------------------------------------------------------- 1 | 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-------------------------------------------------------------------------------- /TC_TrueBwdlog/cpu_7/postReLU_blobVec_0_data: -------------------------------------------------------------------------------- 1 | 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-------------------------------------------------------------------------------- 1 | 1,1,1, 2 | -------------------------------------------------------------------------------- /TC_TrueBwdlog/cpu_7/scaler_0_grad: -------------------------------------------------------------------------------- 1 | 7.93454,-54.0876,46.195, 2 | -------------------------------------------------------------------------------- /TC_TrueBwdlog/cpu_7/scaler_1_data: -------------------------------------------------------------------------------- 1 | 1,1,1,1,1, 2 | -------------------------------------------------------------------------------- /TC_TrueBwdlog/cpu_7/scaler_1_grad: -------------------------------------------------------------------------------- 1 | -4.39151,-4.3476,17.9921,23.4802,-35.3548, 2 | -------------------------------------------------------------------------------- /TC_TrueBwdlog/cpu_7/scaler_BC_0_data: -------------------------------------------------------------------------------- 1 | 1,1,1,1,1,1,1,1, 2 | -------------------------------------------------------------------------------- /TC_TrueBwdlog/cpu_7/scaler_BC_0_grad: -------------------------------------------------------------------------------- 1 | 32.8855,-21.8953,23.1114,35.6868,-10.1974,-9.49985,-33.4536,-14.5227, 2 | -------------------------------------------------------------------------------- /TC_TrueBwdlog/cpu_7/scaler_BC_1_data: -------------------------------------------------------------------------------- 1 | 1,1,1,1,1,1,1,1, 2 | -------------------------------------------------------------------------------- /TC_TrueBwdlog/cpu_7/scaler_BC_1_grad: -------------------------------------------------------------------------------- 1 | -2.68337,-34.1879,-24.8947,-20.2719,-11.049,5.89607,-9.18766,-34.6654, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/Bias_data_BC_0: -------------------------------------------------------------------------------- 1 | 0,0,0,0,0,0,0,0, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/Bias_data_BC_1: -------------------------------------------------------------------------------- 1 | 0,0,0,0,0,0,0,0, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/Bias_data_gpu_0: -------------------------------------------------------------------------------- 1 | 0,0,0, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/Bias_data_gpu_1: -------------------------------------------------------------------------------- 1 | 0,0,0,0,0, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/Bias_grad_BC_0: -------------------------------------------------------------------------------- 1 | -4.2416,1.69328,-18.9348,11.1157,27.8039,22.0693,9.39346,34.3969, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/Bias_grad_BC_1: -------------------------------------------------------------------------------- 1 | -20.7163,-11.2586,22.9046,4.58007,-19.1703,-15.4093,-5.5453,2.90946, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/Bias_grad_gpu_0: -------------------------------------------------------------------------------- 1 | -10.9202,25.2884,-24.408, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/Bias_grad_gpu_1: -------------------------------------------------------------------------------- 1 | 0.746865,8.9916,25.3088,-18.7972,18.956, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/Filter_data_BC_0: -------------------------------------------------------------------------------- 1 | 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-------------------------------------------------------------------------------- 1 | -1.77958,-11.4119,-25.6107,1.99752,13.0561,9.0633,19.224,7.12805,-8.53276,10.645,3.11713,8.44066,23.2747,10.8393,1.28314,0.431737,2.37608,-2.76066,10.5887,19.9376,12.3734,0.112715,-5.58123,-43.6824, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/Filter_grad_BC_1: -------------------------------------------------------------------------------- 1 | -1.97118,0.402989,-4.15573,-10.5308,-1.44234,3.27929,0.515307,3.05476,2.39036,3.40939,-2.35117,-11.1165,8.79902,-12.0166,0.686586,6.62331,4.71827,-0.0227947,-5.95194,-1.38117,16.0285,0.0506114,-23.1493,-3.12975,-21.6367,-1.94534,8.202,17.6499,10.2419,5.03899,0.967384,-6.16171,-8.07231,-13.2473,-8.89375,-43.0301,-43.2228,-17.9206,1.98944,2.71745, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/ResultSaveInvVariance_BC_transition0: -------------------------------------------------------------------------------- 1 | 2.27789,2.57131,0.746493,0.724423,1.56661,1.49585,1.16706,1.26149, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/ResultSaveInvVariance_BC_transition1: -------------------------------------------------------------------------------- 1 | 0.627678,0.419308,0.704121,1.70522,1.74534,0.78466,1.16199,5.42743, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/ResultSaveInvVariance_gpu_transition0: -------------------------------------------------------------------------------- 1 | 1.0735,0.948114,0.964921, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/ResultSaveInvVariance_gpu_transition1: -------------------------------------------------------------------------------- 1 | 1.0735,0.948114,0.964921,0.257241,0.247552, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/ResultSaveMean_BC_transition0: -------------------------------------------------------------------------------- 1 | 0.16947,0.104042,1.49772,-0.340093,-0.453948,-0.689003,-0.220631,-0.601387, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/ResultSaveMean_BC_transition1: -------------------------------------------------------------------------------- 1 | -0.833635,-0.549101,0.392179,0.42607,-0.593064,-0.543993,-0.448456,0.0971656, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/ResultSaveMean_gpu_transition0: -------------------------------------------------------------------------------- 1 | -0.0794602,-0.0177175,0.412587, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/ResultSaveMean_gpu_transition1: -------------------------------------------------------------------------------- 1 | -0.0794602,-0.0177175,0.412587,-3.96516,0.449738, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/Scaler_data_BC_0: -------------------------------------------------------------------------------- 1 | 1,1,1,1,1,1,1,1, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/Scaler_data_BC_1: -------------------------------------------------------------------------------- 1 | 1,1,1,1,1,1,1,1, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/Scaler_data_gpu_0: -------------------------------------------------------------------------------- 1 | 1,1,1, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/Scaler_data_gpu_1: -------------------------------------------------------------------------------- 1 | 1,1,1,1,1, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/Scaler_grad_BC_0: -------------------------------------------------------------------------------- 1 | -2.31693,11.8461,-18.6937,8.01733,22.8064,20.518,38.1896,25.4993, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/Scaler_grad_BC_1: -------------------------------------------------------------------------------- 1 | -17.5175,15.142,11.7916,13.9703,-5.56434,-8.65786,-2.18669,6.26838, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/Scaler_grad_gpu_0: -------------------------------------------------------------------------------- 1 | 3.44534,27.7074,-31.1502, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/Scaler_grad_gpu_1: -------------------------------------------------------------------------------- 1 | 8.67243,-16.8338,18.6471,-10.2609,-0.22278, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/globalMean_BC_transition0: -------------------------------------------------------------------------------- 1 | 0.016947,0.0104042,0.149772,-0.0340093,-0.0453948,-0.0689003,-0.0220631,-0.0601387, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/globalMean_BC_transition1: -------------------------------------------------------------------------------- 1 | -0.0833635,-0.0549101,0.0392179,0.042607,-0.0593064,-0.0543993,-0.0448456,0.00971656, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/globalMean_gpu_transition0: -------------------------------------------------------------------------------- 1 | -0.00794602,-0.00177175,0.0412587, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/globalMean_gpu_transition1: -------------------------------------------------------------------------------- 1 | -0.00794602,-0.00177175,0.0412587,-0.396516,0.0449738, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/globalVar_BC_transition0: -------------------------------------------------------------------------------- 1 | 0.919665,0.915432,1.08311,1.09444,0.941576,0.945602,0.974917,0.964121, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/globalVar_BC_transition1: -------------------------------------------------------------------------------- 1 | 1.159,1.48037,1.10581,0.935091,0.933496,1.06573,0.975572,0.903463, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/globalVariance_gpu_transition0: -------------------------------------------------------------------------------- 1 | 0.988545,1.01351,1.00959, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_14/globalVariance_gpu_transition1: -------------------------------------------------------------------------------- 1 | 0.988545,1.01351,1.00959,2.44203,2.5651, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/Bias_data_BC_0: -------------------------------------------------------------------------------- 1 | 0,0,0,0,0,0,0,0, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/Bias_data_BC_1: -------------------------------------------------------------------------------- 1 | 0,0,0,0,0,0,0,0, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/Bias_data_gpu_0: -------------------------------------------------------------------------------- 1 | 0,0,0, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/Bias_data_gpu_1: -------------------------------------------------------------------------------- 1 | 0,0,0,0,0, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/Bias_grad_BC_0: -------------------------------------------------------------------------------- 1 | 36.3605,-31.177,5.97617,28.3214,4.17538,-12.7853,-44.1146,-18.2323, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/Bias_grad_BC_1: -------------------------------------------------------------------------------- 1 | 4.45846,-20.2596,-17.4595,-29.6092,-4.49544,-4.85002,-13.6607,-50.4519, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/Bias_grad_gpu_0: -------------------------------------------------------------------------------- 1 | 10.4104,-17.3999,-4.59422, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/Bias_grad_gpu_1: -------------------------------------------------------------------------------- 1 | 6.16954,24.5058,10.3144,20.5125,-55.4348, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/Filter_data_BC_0: -------------------------------------------------------------------------------- 1 | 0.521487,1.21022,1.07866,0.213391,0.582366,0.932893,-1.68854,1.77427,-0.564097,0.161822,-0.392131,1.29209,0.950311,2.1989,-0.126638,-0.510417,-0.606509,-0.680004,-0.662611,-0.484541,-1.33551,-1.11833,0.890412,-2.10766, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/Filter_data_BC_1: -------------------------------------------------------------------------------- 1 | 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4.87668,3.05189,-7.91994,-2.52296,1.58439,-3.54102,2.5112,-3.0559,-5.61074,0.718587,2.37359,8.06944,-6.5779,7.78955,3.58976,2.89537,-3.96765,13.8719,8.17748,-6.26936,-2.4403,2.70742,-9.24835,3.31098,-6.22199,2.91012,5.81777,12.6457,-12.599,-0.349648,-0.415705,0.233518,0.465622,5.36009,-6.66257,-3.90794,0.304154,-3.73557,-6.24446,2.7353, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/ResultSaveInvVariance_BC_transition0: -------------------------------------------------------------------------------- 1 | 1.01171,1.54592,0.602809,1.21148,0.703876,1.64933,1.08293,0.61799, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/ResultSaveInvVariance_BC_transition1: -------------------------------------------------------------------------------- 1 | 0.920305,0.759685,0.737486,0.495748,0.768431,0.815679,0.481958,0.850488, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/ResultSaveInvVariance_gpu_transition0: -------------------------------------------------------------------------------- 1 | 1.10404,1.03278,1.01386, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/ResultSaveInvVariance_gpu_transition1: -------------------------------------------------------------------------------- 1 | 1.10404,1.03278,1.01386,0.18588,0.245958, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/ResultSaveMean_BC_transition0: -------------------------------------------------------------------------------- 1 | 1.0674,0.665803,-0.235463,0.44102,1.10094,-0.686178,-0.964309,-0.955795, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/ResultSaveMean_BC_transition1: -------------------------------------------------------------------------------- 1 | -1.30243,1.16066,1.03942,2.39293,1.49375,1.13524,0.864446,-0.826611, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/ResultSaveMean_gpu_transition0: -------------------------------------------------------------------------------- 1 | -0.0993675,-0.0783306,0.209015, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/ResultSaveMean_gpu_transition1: -------------------------------------------------------------------------------- 1 | -0.0993675,-0.0783306,0.209015,0.620066,1.24894, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/Scaler_data_BC_0: -------------------------------------------------------------------------------- 1 | 1,1,1,1,1,1,1,1, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/Scaler_data_BC_1: -------------------------------------------------------------------------------- 1 | 1,1,1,1,1,1,1,1, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/Scaler_data_gpu_0: -------------------------------------------------------------------------------- 1 | 1,1,1, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/Scaler_data_gpu_1: -------------------------------------------------------------------------------- 1 | 1,1,1,1,1, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/Scaler_grad_BC_0: -------------------------------------------------------------------------------- 1 | 33.0481,-22.0603,23.3499,36.0745,-10.2088,-9.50631,-33.6935,-14.6272, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/Scaler_grad_BC_1: -------------------------------------------------------------------------------- 1 | -2.71061,-34.535,-25.1474,-20.4777,-11.1612,5.95593,-9.28094,-35.0173, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/Scaler_grad_gpu_0: -------------------------------------------------------------------------------- 1 | 8.02449,-54.8129,46.7881, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/Scaler_grad_gpu_1: -------------------------------------------------------------------------------- 1 | -3.94914,-3.58639,18.682,24.2166,-35.3638, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/globalMean_BC_transition0: -------------------------------------------------------------------------------- 1 | 0.10674,0.0665803,-0.0235463,0.044102,0.110094,-0.0686178,-0.0964309,-0.0955795, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/globalMean_BC_transition1: -------------------------------------------------------------------------------- 1 | -0.130243,0.116066,0.103942,0.239293,0.149375,0.113524,0.0864446,-0.0826611, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/globalMean_gpu_transition0: -------------------------------------------------------------------------------- 1 | -0.00993675,-0.00783306,0.0209015, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/globalMean_gpu_transition1: -------------------------------------------------------------------------------- 1 | -0.00993675,-0.00783306,0.0209015,0.0620066,0.124894, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/globalVar_BC_transition0: -------------------------------------------------------------------------------- 1 | 0.999691,0.942696,1.18081,0.969525,1.10596,0.93751,0.98701,1.16718, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/globalVar_BC_transition1: -------------------------------------------------------------------------------- 1 | 1.02048,1.07681,1.08761,1.31519,1.07281,1.05337,1.33929,1.04107, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/globalVariance_gpu_transition0: -------------------------------------------------------------------------------- 1 | 0.983714,0.995666,0.999268, 2 | -------------------------------------------------------------------------------- /TClogBwd/gpu_8/globalVariance_gpu_transition1: -------------------------------------------------------------------------------- 1 | 0.983714,0.995666,0.999268,3.85331,2.58675, 2 | -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/__init__.py -------------------------------------------------------------------------------- /cmake/Modules/FindNCCL.cmake: -------------------------------------------------------------------------------- 1 | set(NCCL_INC_PATHS 2 | /usr/include 3 | /usr/local/include 4 | $ENV{NCCL_DIR}/include 5 | ) 6 | 7 | set(NCCL_LIB_PATHS 8 | /lib 9 | /lib64 10 | /usr/lib 11 | /usr/lib64 12 | /usr/local/lib 13 | /usr/local/lib64 14 | $ENV{NCCL_DIR}/lib 15 | ) 16 | 17 | find_path(NCCL_INCLUDE_DIR NAMES nccl.h PATHS ${NCCL_INC_PATHS}) 18 | find_library(NCCL_LIBRARIES NAMES nccl PATHS ${NCCL_LIB_PATHS}) 19 | 20 | include(FindPackageHandleStandardArgs) 21 | find_package_handle_standard_args(NCCL DEFAULT_MSG NCCL_INCLUDE_DIR NCCL_LIBRARIES) 22 | 23 | if (NCCL_FOUND) 24 | message(STATUS "Found NCCL (include: ${NCCL_INCLUDE_DIR}, library: ${NCCL_LIBRARIES})") 25 | mark_as_advanced(NCCL_INCLUDE_DIR NCCL_LIBRARIES) 26 | endif () 27 | -------------------------------------------------------------------------------- /cmake/Templates/CaffeConfigVersion.cmake.in: -------------------------------------------------------------------------------- 1 | set(PACKAGE_VERSION "@Caffe_VERSION@") 2 | 3 | # Check whether the requested PACKAGE_FIND_VERSION is compatible 4 | if("${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 "${PACKAGE_FIND_VERSION}") 9 | set(PACKAGE_VERSION_EXACT TRUE) 10 | endif() 11 | endif() 12 | -------------------------------------------------------------------------------- /data/cifar10/get_cifar10.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | # This scripts downloads the CIFAR10 (binary version) data and unzips it. 3 | 4 | DIR="$( cd "$(dirname "$0")" ; pwd -P )" 5 | cd "$DIR" 6 | 7 | echo "Downloading..." 8 | 9 | wget --no-check-certificate http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz 10 | 11 | echo "Unzipping..." 12 | 13 | tar -xf cifar-10-binary.tar.gz && rm -f cifar-10-binary.tar.gz 14 | mv cifar-10-batches-bin/* . && rm -rf cifar-10-batches-bin 15 | 16 | # Creation is split out because leveldb sometimes causes segfault 17 | # and needs to be re-created. 18 | 19 | echo "Done." 20 | -------------------------------------------------------------------------------- /data/ilsvrc12/get_ilsvrc_aux.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | # 3 | # N.B. This does not download the ilsvrcC12 data set, as it is gargantuan. 4 | # This script downloads the imagenet example auxiliary files including: 5 | # - the ilsvrc12 image mean, binaryproto 6 | # - synset ids and words 7 | # - Python pickle-format data of ImageNet graph structure and relative infogain 8 | # - the training splits with labels 9 | 10 | DIR="$( cd "$(dirname "$0")" ; pwd -P )" 11 | cd "$DIR" 12 | 13 | echo "Downloading..." 14 | 15 | wget -c http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz 16 | 17 | echo "Unzipping..." 18 | 19 | tar -xf caffe_ilsvrc12.tar.gz && rm -f caffe_ilsvrc12.tar.gz 20 | 21 | echo "Done." 22 | -------------------------------------------------------------------------------- /data/mnist/get_mnist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | # This scripts downloads the mnist data and unzips it. 3 | 4 | DIR="$( cd "$(dirname "$0")" ; pwd -P )" 5 | cd "$DIR" 6 | 7 | echo "Downloading..." 8 | 9 | for fname in train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubyte 10 | do 11 | if [ ! -e $fname ]; then 12 | wget --no-check-certificate http://yann.lecun.com/exdb/mnist/${fname}.gz 13 | gunzip ${fname}.gz 14 | fi 15 | done 16 | -------------------------------------------------------------------------------- /detect_cuda_archs.cu: -------------------------------------------------------------------------------- 1 | #include 2 | int main() 3 | { 4 | int count = 0; 5 | if (cudaSuccess != cudaGetDeviceCount(&count)) return -1; 6 | if (count == 0) return -1; 7 | for (int device = 0; device < count; ++device) 8 | { 9 | cudaDeviceProp prop; 10 | if (cudaSuccess == cudaGetDeviceProperties(&prop, device)) 11 | std::printf("%d.%d ", prop.major, prop.minor); 12 | } 13 | return 0; 14 | } 15 | -------------------------------------------------------------------------------- /docs/CMakeFiles/CMakeDirectoryInformation.cmake: -------------------------------------------------------------------------------- 1 | # 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Caffe Documentation 2 | 3 | To generate the documentation, run `$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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /docs/images/GitHub-Mark-64px.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/docs/images/GitHub-Mark-64px.png -------------------------------------------------------------------------------- /docs/images/caffeine-icon.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/docs/images/caffeine-icon.png -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /docs/tutorial/fig/.gitignore: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/docs/tutorial/fig/.gitignore -------------------------------------------------------------------------------- /docs/tutorial/fig/backward.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/docs/tutorial/fig/backward.jpg -------------------------------------------------------------------------------- /docs/tutorial/fig/forward.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/docs/tutorial/fig/forward.jpg -------------------------------------------------------------------------------- /docs/tutorial/fig/forward_backward.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/docs/tutorial/fig/forward_backward.png -------------------------------------------------------------------------------- /docs/tutorial/fig/layer.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/docs/tutorial/fig/layer.jpg -------------------------------------------------------------------------------- /docs/tutorial/fig/logreg.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/docs/tutorial/fig/logreg.jpg -------------------------------------------------------------------------------- /docs/tutorial/layers/absval.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Absolute Value Layer 3 | --- 4 | 5 | # Absolute Value Layer 6 | 7 | * Layer type: `AbsVal` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1AbsValLayer.html) 9 | * Header: [`./include/caffe/layers/absval_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/absval_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/absval_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/absval_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/absval_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/absval_layer.cu) 12 | 13 | * Sample 14 | 15 | layer { 16 | name: "layer" 17 | bottom: "in" 18 | top: "out" 19 | type: "AbsVal" 20 | } 21 | 22 | The `AbsVal` layer computes the output as abs(x) for each input element x. 23 | -------------------------------------------------------------------------------- /docs/tutorial/layers/accuracy.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Accuracy and Top-k 3 | --- 4 | 5 | # Accuracy and Top-k 6 | 7 | `Accuracy` scores the output as the accuracy of output with respect to target -- it is not actually a loss and has no backward step. 8 | 9 | * Layer type: `Accuracy` 10 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1AccuracyLayer.html) 11 | * Header: [`./include/caffe/layers/accuracy_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/accuracy_layer.hpp) 12 | * CPU implementation: [`./src/caffe/layers/accuracy_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/accuracy_layer.cpp) 13 | 14 | ## Parameters 15 | * Parameters (`AccuracyParameter accuracy_param`) 16 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)): 17 | 18 | {% highlight Protobuf %} 19 | {% include proto/AccuracyParameter.txt %} 20 | {% endhighlight %} 21 | -------------------------------------------------------------------------------- /docs/tutorial/layers/argmax.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: ArgMax Layer 3 | --- 4 | 5 | # ArgMax Layer 6 | 7 | * Layer type: `ArgMax` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ArgMaxLayer.html) 9 | * Header: [`./include/caffe/layers/argmax_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/argmax_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/argmax_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/argmax_layer.cpp) 11 | 12 | ## Parameters 13 | * Parameters (`ArgMaxParameter argmax_param`) 14 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)): 15 | 16 | {% highlight Protobuf %} 17 | {% include proto/ArgMaxParameter.txt %} 18 | {% endhighlight %} 19 | -------------------------------------------------------------------------------- /docs/tutorial/layers/batchreindex.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Batch Reindex Layer 3 | --- 4 | 5 | # Batch Reindex Layer 6 | 7 | * Layer type: `BatchReindex` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1BatchReindexLayer.html) 9 | * Header: [`./include/caffe/layers/batch_reindex_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/batch_reindex_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/batch_reindex_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/batch_reindex_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/batch_reindex_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/batch_reindex_layer.cu) 12 | 13 | 14 | ## Parameters 15 | 16 | No parameters. 17 | -------------------------------------------------------------------------------- /docs/tutorial/layers/bias.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Bias Layer 3 | --- 4 | 5 | # Bias Layer 6 | 7 | * Layer type: `Bias` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1BiasLayer.html) 9 | * Header: [`./include/caffe/layers/bias_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/bias_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/bias_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/bias_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/bias_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/bias_layer.cu) 12 | 13 | ## Parameters 14 | * Parameters (`BiasParameter bias_param`) 15 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)): 16 | 17 | {% highlight Protobuf %} 18 | {% include proto/BiasParameter.txt %} 19 | {% endhighlight %} 20 | -------------------------------------------------------------------------------- /docs/tutorial/layers/bnll.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: BNLL Layer 3 | --- 4 | 5 | # BNLL Layer 6 | 7 | * Layer type: `BNLL` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1BNLLLayer.html) 9 | * Header: [`./include/caffe/layers/bnll_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/bnll_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/bnll_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/bnll_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/bnll_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/bnll_layer.cu) 12 | 13 | The `BNLL` (binomial normal log likelihood) layer computes the output as log(1 + exp(x)) for each input element x. 14 | 15 | ## Parameters 16 | No parameters. 17 | 18 | ## Sample 19 | 20 | layer { 21 | name: "layer" 22 | bottom: "in" 23 | top: "out" 24 | type: BNLL 25 | } 26 | -------------------------------------------------------------------------------- /docs/tutorial/layers/crop.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Crop Layer 3 | --- 4 | 5 | # Crop Layer 6 | 7 | * Layer type: `Crop` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1CropLayer.html) 9 | * Header: [`./include/caffe/layers/crop_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/crop_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/crop_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/crop_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/crop_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/crop_layer.cu) 12 | 13 | ## Parameters 14 | 15 | * Parameters (`CropParameter crop_param`) 16 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)): 17 | 18 | {% highlight Protobuf %} 19 | {% include proto/CropParameter.txt %} 20 | {% endhighlight %} 21 | -------------------------------------------------------------------------------- /docs/tutorial/layers/dropout.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Dropout Layer 3 | --- 4 | 5 | # Dropout Layer 6 | 7 | * Layer type: `Dropout` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1DropoutLayer.html) 9 | * Header: [`./include/caffe/layers/dropout_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/dropout_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/dropout_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/dropout_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/dropout_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/dropout_layer.cu) 12 | 13 | ## Parameters 14 | 15 | * Parameters (`DropoutParameter dropout_param`) 16 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)): 17 | 18 | {% highlight Protobuf %} 19 | {% include proto/DropoutParameter.txt %} 20 | {% endhighlight %} 21 | -------------------------------------------------------------------------------- /docs/tutorial/layers/dummydata.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Dummy Data Layer 3 | --- 4 | 5 | # Dummy Data Layer 6 | 7 | * Layer type: `DummyData` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1DummyDataLayer.html) 9 | * Header: [`./include/caffe/layers/dummy_data_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/dummy_data_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/dummy_data_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/dummy_data_layer.cpp) 11 | 12 | 13 | ## Parameters 14 | 15 | * Parameters (`DummyDataParameter dummy_data_param`) 16 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)): 17 | 18 | {% highlight Protobuf %} 19 | {% include proto/DummyDataParameter.txt %} 20 | {% endhighlight %} 21 | -------------------------------------------------------------------------------- /docs/tutorial/layers/eltwise.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Eltwise Layer 3 | --- 4 | 5 | # Eltwise Layer 6 | 7 | * Layer type: `Eltwise` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1EltwiseLayer.html) 9 | * Header: [`./include/caffe/layers/eltwise_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/eltwise_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/eltwise_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/eltwise_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/eltwise_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/eltwise_layer.cu) 12 | 13 | ## Parameters 14 | 15 | * Parameters (`EltwiseParameter eltwise_param`) 16 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)): 17 | 18 | {% highlight Protobuf %} 19 | {% include proto/EltwiseParameter.txt %} 20 | {% endhighlight %} 21 | -------------------------------------------------------------------------------- /docs/tutorial/layers/embed.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Embed Layer 3 | --- 4 | 5 | # Embed Layer 6 | 7 | * Layer type: `Embed` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1EmbedLayer.html) 9 | * Header: [`./include/caffe/layers/embed_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/embed_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/embed_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/embed_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/embed_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/embed_layer.cu) 12 | 13 | ## Parameters 14 | 15 | * Parameters (`EmbedParameter embed_param`) 16 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 17 | 18 | {% highlight Protobuf %} 19 | {% include proto/EmbedParameter.txt %} 20 | {% endhighlight %} 21 | -------------------------------------------------------------------------------- /docs/tutorial/layers/euclideanloss.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Euclidean Loss Layer 3 | --- 4 | # Sum-of-Squares / Euclidean Loss Layer 5 | 6 | * Layer type: `EuclideanLoss` 7 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1EuclideanLossLayer.html) 8 | * Header: [`./include/caffe/layers/euclidean_loss_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/euclidean_loss_layer.hpp) 9 | * CPU implementation: [`./src/caffe/layers/euclidean_loss_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/euclidean_loss_layer.cpp) 10 | * CUDA GPU implementation: [`./src/caffe/layers/euclidean_loss_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/euclidean_loss_layer.cu) 11 | 12 | The Euclidean loss layer computes the sum of squares of differences of its two inputs, $$\frac 1 {2N} \sum_{i=1}^N \| x^1_i - x^2_i \|_2^2$$. 13 | 14 | ## Parameters 15 | 16 | Does not take any parameters. 17 | -------------------------------------------------------------------------------- /docs/tutorial/layers/exp.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Exponential Layer 3 | --- 4 | 5 | # Exponential Layer 6 | 7 | * Layer type: `Exp` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ExpLayer.html) 9 | * Header: [`./include/caffe/layers/exp_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/exp_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/exp_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/exp_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/exp_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/exp_layer.cu) 12 | 13 | ## Parameters 14 | 15 | * Parameters (`Parameter exp_param`) 16 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 17 | 18 | {% highlight Protobuf %} 19 | {% include proto/ExpParameter.txt %} 20 | {% endhighlight %} 21 | 22 | ## See also 23 | 24 | * [Power layer](power.html) 25 | -------------------------------------------------------------------------------- /docs/tutorial/layers/filter.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Filter Layer 3 | --- 4 | 5 | # Filter Layer 6 | 7 | * Layer type: `Filter` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1FilterLayer.html) 9 | * Header: [`./include/caffe/layers/filter_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/filter_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/filter_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/filter_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/filter_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/filter_layer.cu) 12 | 13 | ## Parameters 14 | 15 | Does not take any parameters. 16 | -------------------------------------------------------------------------------- /docs/tutorial/layers/flatten.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Flatten Layer 3 | --- 4 | 5 | # Flatten Layer 6 | 7 | * Layer type: `Flatten` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1FlattenLayer.html) 9 | * Header: [`./include/caffe/layers/flatten_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/flatten_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/flatten_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/flatten_layer.cpp) 11 | 12 | The `Flatten` layer is a utility layer that flattens an input of shape `n * c * h * w` to a simple vector output of shape `n * (c*h*w)`. 13 | 14 | ## Parameters 15 | 16 | * Parameters (`FlattenParameter flatten_param`) 17 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 18 | 19 | {% highlight Protobuf %} 20 | {% include proto/FlattenParameter.txt %} 21 | {% endhighlight %} 22 | -------------------------------------------------------------------------------- /docs/tutorial/layers/hdf5data.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: HDF5 Data Layer 3 | --- 4 | 5 | # HDF5 Data Layer 6 | 7 | * Layer type: `HDF5Data` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1HDF5DataLayer.html) 9 | * Header: [`./include/caffe/layers/hdf5_data_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/hdf5_data_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/hdf5_data_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/hdf5_data_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/hdf5_data_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/hdf5_data_layer.cu) 12 | 13 | ## Parameters 14 | 15 | * Parameters (`HDF5DataParameter hdf5_data_param`) 16 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 17 | 18 | {% highlight Protobuf %} 19 | {% include proto/HDF5DataParameter.txt %} 20 | {% endhighlight %} 21 | -------------------------------------------------------------------------------- /docs/tutorial/layers/hingeloss.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Hinge Loss Layer 3 | --- 4 | 5 | # Hinge (L1, L2) Loss Layer 6 | 7 | * Layer type: `HingeLoss` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1HingeLossLayer.html) 9 | * Header: [`./include/caffe/layers/hinge_loss_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/hinge_loss_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/hinge_loss_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/hinge_loss_layer.cpp) 11 | 12 | ## Parameters 13 | 14 | * Parameters (`HingeLossParameter hinge_loss_param`) 15 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 16 | 17 | {% highlight Protobuf %} 18 | {% include proto/HingeLossParameter.txt %} 19 | {% endhighlight %} 20 | -------------------------------------------------------------------------------- /docs/tutorial/layers/im2col.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Im2col Layer 3 | --- 4 | 5 | # im2col 6 | 7 | * File type: `Im2col` 8 | * Header: [`./include/caffe/layers/im2col_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/im2col_layer.hpp) 9 | * CPU implementation: [`./src/caffe/layers/im2col_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/im2col_layer.cpp) 10 | * CUDA GPU implementation: [`./src/caffe/layers/im2col_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/im2col_layer.cu) 11 | 12 | `Im2col` is a helper for doing the image-to-column transformation that you most 13 | likely do not need to know about. This is used in Caffe's original convolution 14 | to do matrix multiplication by laying out all patches into a matrix. 15 | 16 | 17 | -------------------------------------------------------------------------------- /docs/tutorial/layers/input.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Input Layer 3 | --- 4 | 5 | # Input Layer 6 | 7 | * Layer type: `Input` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1InputLayer.html) 9 | * Header: [`./include/caffe/layers/input_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/input_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/input_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/input_layer.cpp) 11 | 12 | ## Parameters 13 | 14 | * Parameters (`InputParameter input_param`) 15 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)): 16 | 17 | {% highlight Protobuf %} 18 | {% include proto/InputParameter.txt %} 19 | {% endhighlight %} 20 | -------------------------------------------------------------------------------- /docs/tutorial/layers/log.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Log Layer 3 | --- 4 | 5 | # Log Layer 6 | 7 | * Layer type: `Log` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1LogLayer.html) 9 | * Header: [`./include/caffe/layers/log_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/log_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/log_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/log_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/log_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/log_layer.cu) 12 | 13 | ## Parameters 14 | 15 | * Parameters (`Parameter log_param`) 16 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 17 | 18 | {% highlight Protobuf %} 19 | {% include proto/LogParameter.txt %} 20 | {% endhighlight %} 21 | -------------------------------------------------------------------------------- /docs/tutorial/layers/multinomiallogisticloss.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Multinomial Logistic Loss Layer 3 | --- 4 | 5 | # Multinomial Logistic Loss Layer 6 | 7 | * Layer type: `MultinomialLogisticLoss` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1MultinomialLogisticLossLayer.html) 9 | * Header: [`./include/caffe/layers/multinomial_logistic_loss_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/multinomial_logistic_loss_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/multinomial_logistic_loss_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/multinomial_logistic_loss_layer.cpp) 11 | 12 | ## Parameters 13 | 14 | * Parameters (`LossParameter loss_param`) 15 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 16 | 17 | {% highlight Protobuf %} 18 | {% include proto/LossParameter.txt %} 19 | {% endhighlight %} 20 | -------------------------------------------------------------------------------- /docs/tutorial/layers/mvn.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Mean-Variance Normalization (MVN) Layer 3 | --- 4 | 5 | # Mean-Variance Normalization (MVN) Layer 6 | 7 | * Layer type: `MVN` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1MVNLayer.html) 9 | * Header: [`./include/caffe/layers/mvn_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/mvn_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/mvn_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/mvn_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/mvn_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/mvn_layer.cu) 12 | 13 | ## Parameters 14 | 15 | * Parameters (`MVNParameter mvn_param`) 16 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 17 | 18 | {% highlight Protobuf %} 19 | {% include proto/MVNParameter.txt %} 20 | {% endhighlight %} 21 | -------------------------------------------------------------------------------- /docs/tutorial/layers/parameter.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Parameter Layer 3 | --- 4 | 5 | # Parameter Layer 6 | 7 | * Layer type: `Parameter` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ParameterLayer.html) 9 | * Header: [`./include/caffe/layers/parameter_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/parameter_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/parameter_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/parameter_layer.cpp) 11 | 12 | See [https://github.com/BVLC/caffe/pull/2079](https://github.com/BVLC/caffe/pull/2079). 13 | 14 | ## Parameters 15 | 16 | * Parameters (`ParameterParameter parameter_param`) 17 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 18 | 19 | {% highlight Protobuf %} 20 | {% include proto/ParameterParameter.txt %} 21 | {% endhighlight %} 22 | -------------------------------------------------------------------------------- /docs/tutorial/layers/prelu.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: PReLU Layer 3 | --- 4 | 5 | # PReLU Layer 6 | 7 | * Layer type: `PReLU` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1PReLULayer.html) 9 | * Header: [`./include/caffe/layers/prelu_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/prelu_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/prelu_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/prelu_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/prelu_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/prelu_layer.cu) 12 | 13 | ## Parameters 14 | 15 | * Parameters (`PReLUParameter prelu_param`) 16 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 17 | 18 | {% highlight Protobuf %} 19 | {% include proto/PReLUParameter.txt %} 20 | {% endhighlight %} 21 | -------------------------------------------------------------------------------- /docs/tutorial/layers/rnn.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: RNN Layer 3 | --- 4 | 5 | # RNN Layer 6 | 7 | * Layer type: `RNN` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1RNNLayer.html) 9 | * Header: [`./include/caffe/layers/rnn_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/rnn_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/rnn_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/rnn_layer.cpp) 11 | 12 | ## Parameters 13 | 14 | * Parameters (`RecurrentParameter recurrent_param`) 15 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 16 | 17 | {% highlight Protobuf %} 18 | {% include proto/RecurrentParameter.txt %} 19 | {% endhighlight %} 20 | -------------------------------------------------------------------------------- /docs/tutorial/layers/scale.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Scale Layer 3 | --- 4 | 5 | # Scale Layer 6 | 7 | * Layer type: `Scale` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ScaleLayer.html) 9 | * Header: [`./include/caffe/layers/scale_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/scale_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/scale_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/scale_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/scale_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/scale_layer.cu) 12 | 13 | ## Parameters 14 | 15 | * Parameters (`ScaleParameter scale_param`) 16 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 17 | 18 | {% highlight Protobuf %} 19 | {% include proto/ScaleParameter.txt %} 20 | {% endhighlight %} 21 | -------------------------------------------------------------------------------- /docs/tutorial/layers/sigmoid.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Sigmoid Layer 3 | --- 4 | 5 | # Sigmoid Layer 6 | 7 | * Layer type: `Sigmoid` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SigmoidLayer.html) 9 | * Header: [`./include/caffe/layers/sigmoid_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/sigmoid_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/sigmoid_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/sigmoid_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/sigmoid_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/sigmoid_layer.cu) 12 | 13 | ## Parameters 14 | 15 | * Parameters (`SigmoidParameter sigmoid_param`) 16 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 17 | 18 | {% highlight Protobuf %} 19 | {% include proto/SigmoidParameter.txt %} 20 | {% endhighlight %} 21 | -------------------------------------------------------------------------------- /docs/tutorial/layers/sigmoidcrossentropyloss.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Sigmoid Cross-Entropy Loss Layer 3 | --- 4 | 5 | # Sigmoid Cross-Entropy Loss Layer 6 | 7 | * Layer type: `SigmoidCrossEntropyLoss` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SigmoidCrossEntropyLossLayer.html) 9 | * Header: [`./include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu) 12 | 13 | To-do. 14 | -------------------------------------------------------------------------------- /docs/tutorial/layers/silence.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Silence Layer 3 | --- 4 | 5 | # Silence Layer 6 | 7 | * Layer type: `Silence` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SilenceLayer.html) 9 | * Header: [`./include/caffe/layers/silence_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/silence_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/silence_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/silence_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/silence_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/silence_layer.cu) 12 | 13 | Silences a blob, so that it is not printed. 14 | 15 | ## Parameters 16 | 17 | No parameters. 18 | -------------------------------------------------------------------------------- /docs/tutorial/layers/split.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Split Layer 3 | --- 4 | 5 | # Split Layer 6 | 7 | * Layer type: `Split` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SplitLayer.html) 9 | * Header: [`./include/caffe/layers/split_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/split_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/split_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/split_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/split_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/split_layer.cu) 12 | 13 | The `Split` layer is a utility layer that splits an input blob to multiple output blobs. This is used when a blob is fed into multiple output layers. 14 | 15 | ## Parameters 16 | 17 | Does not take any parameters. 18 | -------------------------------------------------------------------------------- /docs/tutorial/layers/spp.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Spatial Pyramid Pooling Layer 3 | --- 4 | 5 | # Spatial Pyramid Pooling Layer 6 | 7 | * Layer type: `SPP` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SPPLayer.html) 9 | * Header: [`./include/caffe/layers/spp_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/spp_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/spp_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/spp_layer.cpp) 11 | 12 | 13 | ## Parameters 14 | 15 | * Parameters (`SPPParameter spp_param`) 16 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 17 | 18 | {% highlight Protobuf %} 19 | {% include proto/SPPParameter.txt %} 20 | {% endhighlight %} 21 | -------------------------------------------------------------------------------- /docs/tutorial/layers/tanh.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: TanH Layer 3 | --- 4 | 5 | # TanH Layer 6 | 7 | * Header: [`./include/caffe/layers/tanh_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/tanh_layer.hpp) 8 | * CPU implementation: [`./src/caffe/layers/tanh_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/tanh_layer.cpp) 9 | * CUDA GPU implementation: [`./src/caffe/layers/tanh_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/tanh_layer.cu) 10 | 11 | ## Parameters 12 | 13 | * Parameters (`TanHParameter tanh_param`) 14 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 15 | 16 | {% highlight Protobuf %} 17 | {% include proto/TanHParameter.txt %} 18 | {% endhighlight %} 19 | -------------------------------------------------------------------------------- /docs/tutorial/layers/threshold.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Threshold Layer 3 | --- 4 | 5 | # Threshold Layer 6 | 7 | * Header: [`./include/caffe/layers/threshold_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/threshold_layer.hpp) 8 | * CPU implementation: [`./src/caffe/layers/threshold_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/threshold_layer.cpp) 9 | * CUDA GPU implementation: [`./src/caffe/layers/threshold_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/threshold_layer.cu) 10 | 11 | ## Parameters 12 | 13 | * Parameters (`ThresholdParameter threshold_param`) 14 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 15 | 16 | {% highlight Protobuf %} 17 | {% include proto/ThresholdParameter.txt %} 18 | {% endhighlight %} 19 | -------------------------------------------------------------------------------- /docs/tutorial/layers/tile.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Tile Layer 3 | --- 4 | 5 | # Tile Layer 6 | 7 | * Layer type: `Tile` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1TileLayer.html) 9 | * Header: [`./include/caffe/layers/tile_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/tile_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/tile_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/tile_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/tile_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/tile_layer.cu) 12 | 13 | ## Parameters 14 | 15 | * Parameters (`TileParameter tile_param`) 16 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 17 | 18 | {% highlight Protobuf %} 19 | {% include proto/TileParameter.txt %} 20 | {% endhighlight %} 21 | -------------------------------------------------------------------------------- /docs/tutorial/layers/windowdata.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: WindowData Layer 3 | --- 4 | 5 | # WindowData Layer 6 | 7 | * Layer type: `WindowData` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1WindowDataLayer.html) 9 | * Header: [`./include/caffe/layers/window_data_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/window_data_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/window_data_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/window_data_layer.cpp) 11 | 12 | ## Parameters 13 | 14 | * Parameters (`WindowDataParameter`) 15 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 16 | 17 | {% highlight Protobuf %} 18 | {% include proto/WindowDataParameter.txt %} 19 | {% endhighlight %} 20 | -------------------------------------------------------------------------------- /examples/CMakeFiles/CMakeDirectoryInformation.cmake: -------------------------------------------------------------------------------- 1 | # CMAKE generated file: DO NOT EDIT! 2 | # 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This script converts the cifar data into leveldb format. 3 | set -e 4 | 5 | EXAMPLE=examples/cifar10 6 | DATA=data/cifar10 7 | DBTYPE=lmdb 8 | 9 | echo "Creating $DBTYPE..." 10 | 11 | rm -rf $EXAMPLE/cifar10_train_$DBTYPE $EXAMPLE/cifar10_test_$DBTYPE 12 | 13 | ./build/examples/cifar10/convert_cifar_data.bin $DATA $EXAMPLE $DBTYPE 14 | 15 | echo "Computing image mean..." 16 | 17 | ./build/tools/compute_image_mean -backend=$DBTYPE \ 18 | $EXAMPLE/cifar10_train_$DBTYPE $EXAMPLE/mean.binaryproto 19 | 20 | echo "Done." 21 | -------------------------------------------------------------------------------- /examples/cifar10/train_full.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | set -e 3 | 4 | TOOLS=./build/tools 5 | 6 | $TOOLS/caffe train \ 7 | --solver=examples/cifar10/cifar10_full_solver.prototxt $@ 8 | 9 | # reduce learning rate by factor of 10 10 | $TOOLS/caffe train \ 11 | --solver=examples/cifar10/cifar10_full_solver_lr1.prototxt \ 12 | --snapshot=examples/cifar10/cifar10_full_iter_60000.solverstate.h5 $@ 13 | 14 | # reduce learning rate by factor of 10 15 | $TOOLS/caffe train \ 16 | --solver=examples/cifar10/cifar10_full_solver_lr2.prototxt \ 17 | --snapshot=examples/cifar10/cifar10_full_iter_65000.solverstate.h5 $@ 18 | -------------------------------------------------------------------------------- /examples/cifar10/train_full_sigmoid.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | set -e 3 | 4 | TOOLS=./build/tools 5 | 6 | $TOOLS/caffe train \ 7 | --solver=examples/cifar10/cifar10_full_sigmoid_solver.prototxt $@ 8 | 9 | -------------------------------------------------------------------------------- /examples/cifar10/train_full_sigmoid_bn.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | set -e 3 | 4 | TOOLS=./build/tools 5 | 6 | $TOOLS/caffe train \ 7 | --solver=examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt $@ 8 | 9 | -------------------------------------------------------------------------------- /examples/cifar10/train_quick.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | set -e 3 | 4 | TOOLS=./build/tools 5 | 6 | $TOOLS/caffe train \ 7 | --solver=examples/cifar10/cifar10_quick_solver.prototxt $@ 8 | 9 | # reduce learning rate by factor of 10 after 8 epochs 10 | $TOOLS/caffe train \ 11 | --solver=examples/cifar10/cifar10_quick_solver_lr1.prototxt \ 12 | --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate.h5 $@ 13 | -------------------------------------------------------------------------------- /examples/cpp_classification/classification: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/examples/cpp_classification/classification -------------------------------------------------------------------------------- /examples/finetune_flickr_style/flickr_style.csv.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/examples/finetune_flickr_style/flickr_style.csv.gz -------------------------------------------------------------------------------- /examples/finetune_flickr_style/style_names.txt: -------------------------------------------------------------------------------- 1 | Detailed 2 | Pastel 3 | Melancholy 4 | Noir 5 | HDR 6 | Vintage 7 | Long Exposure 8 | Horror 9 | Sunny 10 | Bright 11 | Hazy 12 | Bokeh 13 | Serene 14 | Texture 15 | Ethereal 16 | Macro 17 | Depth of Field 18 | Geometric Composition 19 | Minimal 20 | Romantic 21 | -------------------------------------------------------------------------------- /examples/finetune_pascal_detection/pascal_finetune_solver.prototxt: -------------------------------------------------------------------------------- 1 | net: "examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt" 2 | test_iter: 100 3 | test_interval: 1000 4 | base_lr: 0.001 5 | lr_policy: "step" 6 | gamma: 0.1 7 | stepsize: 20000 8 | display: 20 9 | max_iter: 100000 10 | momentum: 0.9 11 | weight_decay: 0.0005 12 | snapshot: 10000 13 | snapshot_prefix: "examples/finetune_pascal_detection/pascal_det_finetune" 14 | -------------------------------------------------------------------------------- /examples/imagenet/make_imagenet_mean.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | # Compute the mean image from the imagenet training lmdb 3 | # N.B. this is available in data/ilsvrc12 4 | 5 | EXAMPLE=examples/imagenet 6 | DATA=data/ilsvrc12 7 | TOOLS=build/tools 8 | 9 | $TOOLS/compute_image_mean $EXAMPLE/ilsvrc12_train_lmdb \ 10 | $DATA/imagenet_mean.binaryproto 11 | 12 | echo "Done." 13 | -------------------------------------------------------------------------------- /examples/imagenet/resume_training.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | set -e 3 | 4 | ./build/tools/caffe train \ 5 | --solver=models/bvlc_reference_caffenet/solver.prototxt \ 6 | --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate.h5 \ 7 | $@ 8 | -------------------------------------------------------------------------------- /examples/imagenet/train_caffenet.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | set -e 3 | 4 | ./build/tools/caffe train \ 5 | --solver=models/bvlc_reference_caffenet/solver.prototxt $@ 6 | -------------------------------------------------------------------------------- /examples/images/cat gray.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/examples/images/cat gray.jpg -------------------------------------------------------------------------------- /examples/images/cat.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/examples/images/cat.jpg -------------------------------------------------------------------------------- /examples/images/cat_gray.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/examples/images/cat_gray.jpg -------------------------------------------------------------------------------- /examples/images/fish-bike.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/examples/images/fish-bike.jpg -------------------------------------------------------------------------------- /examples/mnist/convert_mnist_data: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/examples/mnist/convert_mnist_data -------------------------------------------------------------------------------- /examples/mnist/create_mnist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | # This script converts the mnist data into lmdb/leveldb format, 3 | # depending on the value assigned to $BACKEND. 4 | set -e 5 | 6 | EXAMPLE=examples/mnist 7 | DATA=data/mnist 8 | BUILD=build/examples/mnist 9 | 10 | BACKEND="lmdb" 11 | 12 | echo "Creating ${BACKEND}..." 13 | 14 | rm -rf $EXAMPLE/mnist_train_${BACKEND} 15 | rm -rf $EXAMPLE/mnist_test_${BACKEND} 16 | 17 | $BUILD/convert_mnist_data.bin $DATA/train-images-idx3-ubyte \ 18 | $DATA/train-labels-idx1-ubyte $EXAMPLE/mnist_train_${BACKEND} --backend=${BACKEND} 19 | $BUILD/convert_mnist_data.bin $DATA/t10k-images-idx3-ubyte \ 20 | $DATA/t10k-labels-idx1-ubyte $EXAMPLE/mnist_test_${BACKEND} --backend=${BACKEND} 21 | 22 | echo "Done." 23 | -------------------------------------------------------------------------------- /examples/mnist/lenet_adadelta_solver.prototxt: -------------------------------------------------------------------------------- 1 | # The train/test net protocol buffer definition 2 | net: "examples/mnist/lenet_train_test.prototxt" 3 | # test_iter specifies how many forward passes the test should carry out. 4 | # In the case of MNIST, we have test batch size 100 and 100 test iterations, 5 | # covering the full 10,000 testing images. 6 | test_iter: 100 7 | # Carry out testing every 500 training iterations. 8 | test_interval: 500 9 | # The base learning rate, momentum and the weight decay of the network. 10 | base_lr: 1.0 11 | lr_policy: "fixed" 12 | momentum: 0.95 13 | weight_decay: 0.0005 14 | # Display every 100 iterations 15 | display: 100 16 | # The maximum number of iterations 17 | max_iter: 10000 18 | # snapshot intermediate results 19 | snapshot: 5000 20 | snapshot_prefix: "examples/mnist/lenet_adadelta" 21 | # solver mode: CPU or GPU 22 | solver_mode: GPU 23 | type: "AdaDelta" 24 | delta: 1e-6 25 | -------------------------------------------------------------------------------- /examples/mnist/lenet_auto_solver.prototxt: -------------------------------------------------------------------------------- 1 | # The train/test net protocol buffer definition 2 | train_net: "mnist/lenet_auto_train.prototxt" 3 | test_net: "mnist/lenet_auto_test.prototxt" 4 | # test_iter specifies how many forward passes the test should carry out. 5 | # In the case of MNIST, we have test batch size 100 and 100 test iterations, 6 | # covering the full 10,000 testing images. 7 | test_iter: 100 8 | # Carry out testing every 500 training iterations. 9 | test_interval: 500 10 | # The base learning rate, momentum and the weight decay of the network. 11 | base_lr: 0.01 12 | momentum: 0.9 13 | weight_decay: 0.0005 14 | # The learning rate policy 15 | lr_policy: "inv" 16 | gamma: 0.0001 17 | power: 0.75 18 | # Display every 100 iterations 19 | display: 100 20 | # The maximum number of iterations 21 | max_iter: 10000 22 | # snapshot intermediate results 23 | snapshot: 5000 24 | snapshot_prefix: "mnist/lenet" 25 | -------------------------------------------------------------------------------- /examples/mnist/lenet_solver.prototxt: -------------------------------------------------------------------------------- 1 | # The train/test net protocol buffer definition 2 | net: "examples/mnist/lenet_train_test.prototxt" 3 | # test_iter specifies how many forward passes the test should carry out. 4 | # In the case of MNIST, we have test batch size 100 and 100 test iterations, 5 | # covering the full 10,000 testing images. 6 | test_iter: 100 7 | # Carry out testing every 500 training iterations. 8 | test_interval: 500 9 | # The base learning rate, momentum and the weight decay of the network. 10 | base_lr: 0.01 11 | momentum: 0.9 12 | weight_decay: 0.0005 13 | # The learning rate policy 14 | lr_policy: "inv" 15 | gamma: 0.0001 16 | power: 0.75 17 | # Display every 100 iterations 18 | display: 100 19 | # The maximum number of iterations 20 | max_iter: 10000 21 | # snapshot intermediate results 22 | snapshot: 5000 23 | snapshot_prefix: "examples/mnist/lenet" 24 | # solver mode: CPU or GPU 25 | solver_mode: GPU 26 | -------------------------------------------------------------------------------- /examples/mnist/mnist_autoencoder_solver.prototxt: -------------------------------------------------------------------------------- 1 | net: "examples/mnist/mnist_autoencoder.prototxt" 2 | test_state: { stage: 'test-on-train' } 3 | test_iter: 500 4 | test_state: { stage: 'test-on-test' } 5 | test_iter: 100 6 | test_interval: 500 7 | test_compute_loss: true 8 | base_lr: 0.01 9 | lr_policy: "step" 10 | gamma: 0.1 11 | stepsize: 10000 12 | display: 100 13 | max_iter: 65000 14 | weight_decay: 0.0005 15 | snapshot: 10000 16 | snapshot_prefix: "examples/mnist/mnist_autoencoder" 17 | momentum: 0.9 18 | # solver mode: CPU or GPU 19 | solver_mode: GPU 20 | -------------------------------------------------------------------------------- /examples/mnist/mnist_autoencoder_solver_adadelta.prototxt: -------------------------------------------------------------------------------- 1 | net: "examples/mnist/mnist_autoencoder.prototxt" 2 | test_state: { stage: 'test-on-train' } 3 | test_iter: 500 4 | test_state: { stage: 'test-on-test' } 5 | test_iter: 100 6 | test_interval: 500 7 | test_compute_loss: true 8 | base_lr: 1.0 9 | lr_policy: "fixed" 10 | momentum: 0.95 11 | delta: 1e-8 12 | display: 100 13 | max_iter: 65000 14 | weight_decay: 0.0005 15 | snapshot: 10000 16 | snapshot_prefix: "examples/mnist/mnist_autoencoder_adadelta_train" 17 | # solver mode: CPU or GPU 18 | solver_mode: GPU 19 | type: "AdaDelta" 20 | -------------------------------------------------------------------------------- /examples/mnist/mnist_autoencoder_solver_adagrad.prototxt: -------------------------------------------------------------------------------- 1 | net: "examples/mnist/mnist_autoencoder.prototxt" 2 | test_state: { stage: 'test-on-train' } 3 | test_iter: 500 4 | test_state: { stage: 'test-on-test' } 5 | test_iter: 100 6 | test_interval: 500 7 | test_compute_loss: true 8 | base_lr: 0.01 9 | lr_policy: "fixed" 10 | display: 100 11 | max_iter: 65000 12 | weight_decay: 0.0005 13 | snapshot: 10000 14 | snapshot_prefix: "examples/mnist/mnist_autoencoder_adagrad_train" 15 | # solver mode: CPU or GPU 16 | solver_mode: GPU 17 | type: "AdaGrad" 18 | -------------------------------------------------------------------------------- /examples/mnist/mnist_autoencoder_solver_nesterov.prototxt: -------------------------------------------------------------------------------- 1 | net: "examples/mnist/mnist_autoencoder.prototxt" 2 | test_state: { stage: 'test-on-train' } 3 | test_iter: 500 4 | test_state: { stage: 'test-on-test' } 5 | test_iter: 100 6 | test_interval: 500 7 | test_compute_loss: true 8 | base_lr: 0.01 9 | lr_policy: "step" 10 | gamma: 0.1 11 | stepsize: 10000 12 | display: 100 13 | max_iter: 65000 14 | weight_decay: 0.0005 15 | snapshot: 10000 16 | snapshot_prefix: "examples/mnist/mnist_autoencoder_nesterov_train" 17 | momentum: 0.95 18 | # solver mode: CPU or GPU 19 | solver_mode: GPU 20 | type: "Nesterov" 21 | -------------------------------------------------------------------------------- /examples/mnist/train_lenet.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | set -e 3 | 4 | ./build/tools/caffe train --solver=examples/mnist/lenet_solver.prototxt $@ 5 | -------------------------------------------------------------------------------- /examples/mnist/train_lenet_adam.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | set -e 3 | 4 | ./build/tools/caffe train --solver=examples/mnist/lenet_solver_adam.prototxt $@ 5 | -------------------------------------------------------------------------------- /examples/mnist/train_lenet_consolidated.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | set -e 3 | 4 | ./build/tools/caffe train \ 5 | --solver=examples/mnist/lenet_consolidated_solver.prototxt $@ 6 | -------------------------------------------------------------------------------- /examples/mnist/train_lenet_rmsprop.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | set -e 3 | 4 | ./build/tools/caffe train \ 5 | --solver=examples/mnist/lenet_solver_rmsprop.prototxt $@ 6 | -------------------------------------------------------------------------------- /examples/mnist/train_mnist_autoencoder.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | set -e 3 | 4 | ./build/tools/caffe train \ 5 | --solver=examples/mnist/mnist_autoencoder_solver.prototxt $@ 6 | -------------------------------------------------------------------------------- /examples/mnist/train_mnist_autoencoder_adadelta.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | set -e 3 | 4 | ./build/tools/caffe train \ 5 | --solver=examples/mnist/mnist_autoencoder_solver_adadelta.prototxt $@ 6 | -------------------------------------------------------------------------------- /examples/mnist/train_mnist_autoencoder_adagrad.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | set -e 3 | 4 | ./build/tools/caffe train \ 5 | --solver=examples/mnist/mnist_autoencoder_solver_adagrad.prototxt $@ 6 | -------------------------------------------------------------------------------- /examples/mnist/train_mnist_autoencoder_nesterov.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | set -e 3 | 4 | ./build/tools/caffe train \ 5 | --solver=examples/mnist/mnist_autoencoder_solver_nesterov.prototxt $@ 6 | -------------------------------------------------------------------------------- /examples/net_surgery/conv.prototxt: -------------------------------------------------------------------------------- 1 | # Simple single-layer network to showcase editing model parameters. 2 | name: "convolution" 3 | layer { 4 | name: "data" 5 | type: "Input" 6 | top: "data" 7 | input_param { shape: { dim: 1 dim: 1 dim: 100 dim: 100 } } 8 | } 9 | layer { 10 | name: "conv" 11 | type: "Convolution" 12 | bottom: "data" 13 | top: "conv" 14 | convolution_param { 15 | num_output: 3 16 | kernel_size: 5 17 | stride: 1 18 | weight_filler { 19 | type: "gaussian" 20 | std: 0.01 21 | } 22 | bias_filler { 23 | type: "constant" 24 | value: 0 25 | } 26 | } 27 | } 28 | -------------------------------------------------------------------------------- /examples/siamese/convert_mnist_siamese_data: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/examples/siamese/convert_mnist_siamese_data -------------------------------------------------------------------------------- /examples/siamese/create_mnist_siamese.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | # This script converts the mnist data into leveldb format. 3 | set -e 4 | 5 | EXAMPLES=./build/examples/siamese 6 | DATA=./data/mnist 7 | 8 | echo "Creating leveldb..." 9 | 10 | rm -rf ./examples/siamese/mnist_siamese_train_leveldb 11 | rm -rf ./examples/siamese/mnist_siamese_test_leveldb 12 | 13 | $EXAMPLES/convert_mnist_siamese_data.bin \ 14 | $DATA/train-images-idx3-ubyte \ 15 | $DATA/train-labels-idx1-ubyte \ 16 | ./examples/siamese/mnist_siamese_train_leveldb 17 | $EXAMPLES/convert_mnist_siamese_data.bin \ 18 | $DATA/t10k-images-idx3-ubyte \ 19 | $DATA/t10k-labels-idx1-ubyte \ 20 | ./examples/siamese/mnist_siamese_test_leveldb 21 | 22 | echo "Done." 23 | -------------------------------------------------------------------------------- /examples/siamese/mnist_siamese_solver.prototxt: -------------------------------------------------------------------------------- 1 | # The train/test net protocol buffer definition 2 | net: "examples/siamese/mnist_siamese_train_test.prototxt" 3 | # test_iter specifies how many forward passes the test should carry out. 4 | # In the case of MNIST, we have test batch size 100 and 100 test iterations, 5 | # covering the full 10,000 testing images. 6 | test_iter: 100 7 | # Carry out testing every 500 training iterations. 8 | test_interval: 500 9 | # The base learning rate, momentum and the weight decay of the network. 10 | base_lr: 0.01 11 | momentum: 0.9 12 | weight_decay: 0.0000 13 | # The learning rate policy 14 | lr_policy: "inv" 15 | gamma: 0.0001 16 | power: 0.75 17 | # Display every 100 iterations 18 | display: 100 19 | # The maximum number of iterations 20 | max_iter: 50000 21 | # snapshot intermediate results 22 | snapshot: 5000 23 | snapshot_prefix: "examples/siamese/mnist_siamese" 24 | # solver mode: CPU or GPU 25 | solver_mode: GPU 26 | -------------------------------------------------------------------------------- /examples/siamese/train_mnist_siamese.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | set -e 3 | 4 | TOOLS=./build/tools 5 | 6 | $TOOLS/caffe train --solver=examples/siamese/mnist_siamese_solver.prototxt $@ 7 | -------------------------------------------------------------------------------- /examples/web_demo/requirements.txt: -------------------------------------------------------------------------------- 1 | werkzeug 2 | flask 3 | tornado 4 | numpy 5 | pandas 6 | pillow 7 | pyyaml 8 | -------------------------------------------------------------------------------- /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/solver_factory.hpp" 17 | #include "caffe/util/benchmark.hpp" 18 | #include "caffe/util/io.hpp" 19 | #include "caffe/util/upgrade_proto.hpp" 20 | 21 | #endif // CAFFE_CAFFE_HPP_ 22 | -------------------------------------------------------------------------------- /include/caffe/util/format.hpp: -------------------------------------------------------------------------------- 1 | #ifndef CAFFE_UTIL_FORMAT_H_ 2 | #define CAFFE_UTIL_FORMAT_H_ 3 | 4 | #include // NOLINT(readability/streams) 5 | #include // NOLINT(readability/streams) 6 | #include 7 | 8 | namespace caffe { 9 | 10 | inline std::string format_int(int n, int numberOfLeadingZeros = 0 ) { 11 | std::ostringstream s; 12 | s << std::setw(numberOfLeadingZeros) << std::setfill('0') << n; 13 | return s.str(); 14 | } 15 | 16 | } 17 | 18 | #endif // CAFFE_UTIL_FORMAT_H_ 19 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /include/caffe/util/nccl.hpp: -------------------------------------------------------------------------------- 1 | #ifndef CAFFE_UTIL_NCCL_H_ 2 | #define CAFFE_UTIL_NCCL_H_ 3 | #ifdef USE_NCCL 4 | 5 | #include 6 | 7 | #include "caffe/common.hpp" 8 | 9 | #define NCCL_CHECK(condition) \ 10 | { \ 11 | ncclResult_t result = condition; \ 12 | CHECK_EQ(result, ncclSuccess) << " " \ 13 | << ncclGetErrorString(result); \ 14 | } 15 | 16 | namespace caffe { 17 | 18 | namespace nccl { 19 | 20 | template class dataType; 21 | 22 | template<> class dataType { 23 | public: 24 | static const ncclDataType_t type = ncclFloat; 25 | }; 26 | template<> class dataType { 27 | public: 28 | static const ncclDataType_t type = ncclDouble; 29 | }; 30 | 31 | } // namespace nccl 32 | 33 | } // namespace caffe 34 | 35 | #endif // end USE_NCCL 36 | 37 | #endif // CAFFE_UTIL_NCCL_H_ 38 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /lib/libcaffe.so.1.0.0-rc4: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/lib/libcaffe.so.1.0.0-rc4 -------------------------------------------------------------------------------- /matlab/+caffe/+test/test_io.m: -------------------------------------------------------------------------------- 1 | classdef test_io < matlab.unittest.TestCase 2 | methods (Test) 3 | function test_read_write_mean(self) 4 | % randomly generate mean data 5 | width = 200; 6 | height = 300; 7 | channels = 3; 8 | mean_data_write = 255 * rand(width, height, channels, 'single'); 9 | % write mean data to binary proto 10 | mean_proto_file = tempname(); 11 | caffe.io.write_mean(mean_data_write, mean_proto_file); 12 | % read mean data from saved binary proto and test whether they are equal 13 | mean_data_read = caffe.io.read_mean(mean_proto_file); 14 | self.verifyEqual(mean_data_write, mean_data_read) 15 | delete(mean_proto_file); 16 | end 17 | end 18 | end 19 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /matlab/+caffe/imagenet/ilsvrc_2012_mean.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | run(caffe.test.test_io) ]; 16 | 17 | % reset caffe after testing 18 | caffe.reset_all(); 19 | 20 | end 21 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /matlab/+caffe/version.m: -------------------------------------------------------------------------------- 1 | function version_str = version() 2 | % version() 3 | % show Caffe's version. 4 | 5 | version_str = caffe_('version'); 6 | 7 | end 8 | -------------------------------------------------------------------------------- /matlab/CMakeFiles/CMakeDirectoryInformation.cmake: -------------------------------------------------------------------------------- 1 | # CMAKE generated file: DO NOT EDIT! 2 | # Generated by "Unix Makefiles" Generator, CMake Version 2.8 3 | 4 | # Relative path conversion top directories. 5 | SET(CMAKE_RELATIVE_PATH_TOP_SOURCE "/home/tl486/git_TCcaffe/caffe_solverOriginal/caffe") 6 | SET(CMAKE_RELATIVE_PATH_TOP_BINARY "/home/tl486/git_TCcaffe/caffe_solverOriginal/caffe") 7 | 8 | # Force unix paths in dependencies. 9 | SET(CMAKE_FORCE_UNIX_PATHS 1) 10 | 11 | 12 | # The C and CXX include file regular expressions for this directory. 13 | SET(CMAKE_C_INCLUDE_REGEX_SCAN "^.*$") 14 | SET(CMAKE_C_INCLUDE_REGEX_COMPLAIN "^$") 15 | SET(CMAKE_CXX_INCLUDE_REGEX_SCAN ${CMAKE_C_INCLUDE_REGEX_SCAN}) 16 | SET(CMAKE_CXX_INCLUDE_REGEX_COMPLAIN ${CMAKE_C_INCLUDE_REGEX_COMPLAIN}) 17 | -------------------------------------------------------------------------------- /matlab/CMakeFiles/progress.marks: -------------------------------------------------------------------------------- 1 | 0 2 | -------------------------------------------------------------------------------- /matlab/hdf5creation/.gitignore: -------------------------------------------------------------------------------- 1 | *.h5 2 | list.txt 3 | -------------------------------------------------------------------------------- /models/bvlc_alexnet/solver.prototxt: -------------------------------------------------------------------------------- 1 | net: "models/bvlc_alexnet/train_val.prototxt" 2 | test_iter: 1000 3 | test_interval: 1000 4 | base_lr: 0.01 5 | lr_policy: "step" 6 | gamma: 0.1 7 | stepsize: 100000 8 | display: 20 9 | max_iter: 450000 10 | momentum: 0.9 11 | weight_decay: 0.0005 12 | snapshot: 10000 13 | snapshot_prefix: "models/bvlc_alexnet/caffe_alexnet_train" 14 | solver_mode: GPU 15 | -------------------------------------------------------------------------------- /models/bvlc_googlenet/quick_solver.prototxt: -------------------------------------------------------------------------------- 1 | net: "models/bvlc_googlenet/train_val.prototxt" 2 | test_iter: 1000 3 | test_interval: 4000 4 | test_initialization: false 5 | display: 40 6 | average_loss: 40 7 | base_lr: 0.01 8 | lr_policy: "poly" 9 | power: 0.5 10 | max_iter: 2400000 11 | momentum: 0.9 12 | weight_decay: 0.0002 13 | snapshot: 40000 14 | snapshot_prefix: "models/bvlc_googlenet/bvlc_googlenet_quick" 15 | solver_mode: GPU 16 | -------------------------------------------------------------------------------- /models/bvlc_googlenet/solver.prototxt: -------------------------------------------------------------------------------- 1 | net: "models/bvlc_googlenet/train_val.prototxt" 2 | test_iter: 1000 3 | test_interval: 4000 4 | test_initialization: false 5 | display: 40 6 | average_loss: 40 7 | base_lr: 0.01 8 | lr_policy: "step" 9 | stepsize: 320000 10 | gamma: 0.96 11 | max_iter: 10000000 12 | momentum: 0.9 13 | weight_decay: 0.0002 14 | snapshot: 40000 15 | snapshot_prefix: "models/bvlc_googlenet/bvlc_googlenet" 16 | solver_mode: GPU 17 | -------------------------------------------------------------------------------- /models/bvlc_reference_caffenet/solver.prototxt: -------------------------------------------------------------------------------- 1 | net: "models/bvlc_reference_caffenet/train_val.prototxt" 2 | test_iter: 1000 3 | test_interval: 1000 4 | base_lr: 0.01 5 | lr_policy: "step" 6 | gamma: 0.1 7 | stepsize: 100000 8 | display: 20 9 | max_iter: 450000 10 | momentum: 0.9 11 | weight_decay: 0.0005 12 | snapshot: 10000 13 | snapshot_prefix: "models/bvlc_reference_caffenet/caffenet_train" 14 | solver_mode: GPU 15 | -------------------------------------------------------------------------------- /models/finetune_flickr_style/solver.prototxt: -------------------------------------------------------------------------------- 1 | net: "models/finetune_flickr_style/train_val.prototxt" 2 | test_iter: 100 3 | test_interval: 1000 4 | # lr for fine-tuning should be lower than when starting from scratch 5 | base_lr: 0.001 6 | lr_policy: "step" 7 | gamma: 0.1 8 | # stepsize should also be lower, as we're closer to being done 9 | stepsize: 20000 10 | display: 20 11 | max_iter: 100000 12 | momentum: 0.9 13 | weight_decay: 0.0005 14 | snapshot: 10000 15 | snapshot_prefix: "models/finetune_flickr_style/finetune_flickr_style" 16 | # uncomment the following to default to CPU mode solving 17 | # solver_mode: CPU 18 | -------------------------------------------------------------------------------- /python/CMakeFiles/CMakeDirectoryInformation.cmake: -------------------------------------------------------------------------------- 1 | # CMAKE generated file: DO NOT EDIT! 2 | # Generated by "Unix Makefiles" Generator, CMake Version 2.8 3 | 4 | # Relative path conversion top directories. 5 | SET(CMAKE_RELATIVE_PATH_TOP_SOURCE "/home/tl486/git_TCcaffe/caffe_solverOriginal/caffe") 6 | SET(CMAKE_RELATIVE_PATH_TOP_BINARY "/home/tl486/git_TCcaffe/caffe_solverOriginal/caffe") 7 | 8 | # Force unix paths in dependencies. 9 | SET(CMAKE_FORCE_UNIX_PATHS 1) 10 | 11 | 12 | # The C and CXX include file regular expressions for this directory. 13 | SET(CMAKE_C_INCLUDE_REGEX_SCAN "^.*$") 14 | SET(CMAKE_C_INCLUDE_REGEX_COMPLAIN "^$") 15 | SET(CMAKE_CXX_INCLUDE_REGEX_SCAN ${CMAKE_C_INCLUDE_REGEX_SCAN}) 16 | SET(CMAKE_CXX_INCLUDE_REGEX_COMPLAIN ${CMAKE_C_INCLUDE_REGEX_COMPLAIN}) 17 | -------------------------------------------------------------------------------- /python/CMakeFiles/progress.marks: -------------------------------------------------------------------------------- 1 | 0 2 | -------------------------------------------------------------------------------- /python/caffe/__init__.py: -------------------------------------------------------------------------------- 1 | from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver, NCCL, Timer 2 | from ._caffe import init_log, log, set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list, set_random_seed, solver_count, set_solver_count, solver_rank, set_solver_rank, set_multiprocess, Layer, get_solver 3 | from ._caffe import __version__ 4 | from .proto.caffe_pb2 import TRAIN, TEST 5 | from .classifier import Classifier 6 | from .detector import Detector 7 | from . import io 8 | from .net_spec import layers, params, NetSpec, to_proto 9 | -------------------------------------------------------------------------------- /python/caffe/imagenet/ilsvrc_2012_mean.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/python/caffe/imagenet/ilsvrc_2012_mean.npy -------------------------------------------------------------------------------- /python/caffe/test/test_layer_type_list.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | 3 | import caffe 4 | 5 | class TestLayerTypeList(unittest.TestCase): 6 | 7 | def test_standard_types(self): 8 | #removing 'Data' from list 9 | for type_name in ['Data', 'Convolution', 'InnerProduct']: 10 | self.assertIn(type_name, caffe.layer_type_list(), 11 | '%s not in layer_type_list()' % type_name) 12 | -------------------------------------------------------------------------------- /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 -------------------------------------------------------------------------------- /removeCmakeDep.sh: -------------------------------------------------------------------------------- 1 | rm -r CMakeFiles 2 | rm *.cmake 3 | rm CMakeCache.txt 4 | -------------------------------------------------------------------------------- /run.sh: -------------------------------------------------------------------------------- 1 | rm -r TClogFwd 2 | rm -r TClogBwd 3 | rm -r TC_TrueFwdlog 4 | rm -r TC_TrueBwdlog 5 | make -j64 6 | make test -j64 7 | export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/OpenBLAS/lib 8 | make runtest GTEST_FILTER='DenseBlockLayerTest/*' 9 | 10 | -------------------------------------------------------------------------------- /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 $ROOT_DIR 11 | 12 | # Gather docs. 13 | scripts/gather_examples.sh 14 | 15 | # Split caffe.proto for inclusion by layer catalogue. 16 | scripts/split_caffe_proto.py 17 | 18 | # Generate developer docs. 19 | make docs 20 | 21 | # Display docs using web server. 22 | cd docs 23 | jekyll serve -w -s . -d _site --port=$PORT 24 | -------------------------------------------------------------------------------- /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 $MODEL_DIR/gist.zip 24 | echo "Done" 25 | -------------------------------------------------------------------------------- /scripts/travis/build.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # build the project 3 | 4 | BASEDIR=$(dirname $0) 5 | source $BASEDIR/defaults.sh 6 | 7 | if ! $WITH_CMAKE ; then 8 | make --jobs $NUM_THREADS all test pycaffe warn 9 | else 10 | cd build 11 | make --jobs $NUM_THREADS all test.testbin 12 | fi 13 | make lint 14 | -------------------------------------------------------------------------------- /scripts/travis/configure-cmake.sh: -------------------------------------------------------------------------------- 1 | # CMake configuration 2 | 3 | mkdir -p build 4 | cd build 5 | 6 | ARGS="-DCMAKE_BUILD_TYPE=Release -DBLAS=Open" 7 | 8 | if $WITH_PYTHON3 ; then 9 | ARGS="$ARGS -Dpython_version=3" 10 | fi 11 | 12 | if $WITH_IO ; then 13 | ARGS="$ARGS -DUSE_OPENCV=On -DUSE_LMDB=On -DUSE_LEVELDB=On" 14 | else 15 | ARGS="$ARGS -DUSE_OPENCV=Off -DUSE_LMDB=Off -DUSE_LEVELDB=Off" 16 | fi 17 | 18 | if $WITH_CUDA ; then 19 | # Only build SM50 20 | ARGS="$ARGS -DCPU_ONLY=Off -DCUDA_ARCH_NAME=Manual -DCUDA_ARCH_BIN=\"50\" -DCUDA_ARCH_PTX=\"\"" 21 | else 22 | ARGS="$ARGS -DCPU_ONLY=On" 23 | fi 24 | 25 | if $WITH_CUDNN ; then 26 | ARGS="$ARGS -DUSE_CUDNN=On" 27 | else 28 | ARGS="$ARGS -DUSE_CUDNN=Off" 29 | fi 30 | 31 | cmake .. $ARGS 32 | 33 | -------------------------------------------------------------------------------- /scripts/travis/configure.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # configure the project 3 | 4 | BASEDIR=$(dirname $0) 5 | source $BASEDIR/defaults.sh 6 | 7 | if ! $WITH_CMAKE ; then 8 | source $BASEDIR/configure-make.sh 9 | else 10 | source $BASEDIR/configure-cmake.sh 11 | fi 12 | -------------------------------------------------------------------------------- /scripts/travis/defaults.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # set default environment variables 3 | 4 | set -e 5 | 6 | WITH_CMAKE=${WITH_CMAKE:-false} 7 | WITH_PYTHON3=${WITH_PYTHON3:-false} 8 | WITH_IO=${WITH_IO:-true} 9 | WITH_CUDA=${WITH_CUDA:-false} 10 | WITH_CUDNN=${WITH_CUDNN:-false} 11 | -------------------------------------------------------------------------------- /scripts/travis/install-python-deps.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # install extra Python dependencies 3 | # (must come after setup-venv) 4 | 5 | BASEDIR=$(dirname $0) 6 | source $BASEDIR/defaults.sh 7 | 8 | if ! $WITH_PYTHON3 ; then 9 | # Python2 10 | : 11 | else 12 | # Python3 13 | pip install --pre protobuf==3.0.0b3 14 | fi 15 | -------------------------------------------------------------------------------- /scripts/travis/setup-venv.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # setup a Python virtualenv 3 | # (must come after install-deps) 4 | 5 | BASEDIR=$(dirname $0) 6 | source $BASEDIR/defaults.sh 7 | 8 | VENV_DIR=${1:-~/venv} 9 | 10 | # setup our own virtualenv 11 | if $WITH_PYTHON3; then 12 | PYTHON_EXE='/usr/bin/python3' 13 | else 14 | PYTHON_EXE='/usr/bin/python2' 15 | fi 16 | 17 | # use --system-site-packages so that Python will use deb packages 18 | virtualenv $VENV_DIR -p $PYTHON_EXE --system-site-packages 19 | -------------------------------------------------------------------------------- /scripts/travis/test.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # test the project 3 | 4 | BASEDIR=$(dirname $0) 5 | source $BASEDIR/defaults.sh 6 | 7 | if $WITH_CUDA ; then 8 | echo "Skipping tests for CUDA build" 9 | exit 0 10 | fi 11 | 12 | if ! $WITH_CMAKE ; then 13 | make runtest 14 | make pytest 15 | else 16 | cd build 17 | make runtest 18 | make pytest 19 | fi 20 | -------------------------------------------------------------------------------- /src/caffe/CMakeFiles/CMakeDirectoryInformation.cmake: -------------------------------------------------------------------------------- 1 | # CMAKE generated file: DO NOT EDIT! 2 | # Generated by "Unix Makefiles" Generator, CMake Version 2.8 3 | 4 | # Relative path conversion top directories. 5 | SET(CMAKE_RELATIVE_PATH_TOP_SOURCE "/home/tl486/git_TCcaffe/caffe_solverOriginal/caffe") 6 | 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Generator, CMake Version 2.8 3 | 4 | # compile CXX with /usr/bin/c++ 5 | CXX_FLAGS = -fPIC -Wall -Wno-sign-compare -Wno-uninitialized -O3 -DNDEBUG -I/home/tl486/git_TCcaffe/caffe_solverOriginal/caffe/src -I/home/tl486/git_TCcaffe/caffe_solverOriginal/caffe/include -isystem /usr/local/include -isystem /usr/local/cuda-8.0/include -isystem /opt/OpenBLAS/include -I/home/tl486/git_TCcaffe/caffe_solverOriginal/caffe 6 | 7 | CXX_DEFINES = -DCAFFE_VERSION=1.0.0-rc4 -DGTEST_USE_OWN_TR1_TUPLE -DUSE_CUDNN -DUSE_LEVELDB -DUSE_LMDB 8 | 9 | -------------------------------------------------------------------------------- /src/caffe/CMakeFiles/proto.dir/link.txt: -------------------------------------------------------------------------------- 1 | /usr/bin/ar cr ../../lib/libproto.a CMakeFiles/proto.dir/__/__/include/caffe/proto/caffe.pb.cc.o 2 | /usr/bin/ranlib ../../lib/libproto.a 3 | -------------------------------------------------------------------------------- /src/caffe/CMakeFiles/proto.dir/progress.make: -------------------------------------------------------------------------------- 1 | CMAKE_PROGRESS_1 = 2 | CMAKE_PROGRESS_2 = 70 3 | 4 | -------------------------------------------------------------------------------- /src/caffe/layer.cpp: -------------------------------------------------------------------------------- 1 | #include "caffe/layer.hpp" 2 | 3 | namespace caffe { 4 | 5 | INSTANTIATE_CLASS(Layer); 6 | 7 | } // namespace caffe 8 | -------------------------------------------------------------------------------- /src/caffe/layers/base_data_layer.cu: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "caffe/layers/base_data_layer.hpp" 4 | 5 | namespace caffe { 6 | 7 | template 8 | void BasePrefetchingDataLayer::Forward_gpu( 9 | const vector*>& bottom, const vector*>& top) { 10 | if (prefetch_current_) { 11 | prefetch_free_.push(prefetch_current_); 12 | } 13 | prefetch_current_ = prefetch_full_.pop("Waiting for data"); 14 | // Reshape to loaded data. 15 | top[0]->ReshapeLike(prefetch_current_->data_); 16 | top[0]->set_gpu_data(prefetch_current_->data_.mutable_gpu_data()); 17 | if (this->output_labels_) { 18 | // Reshape to loaded labels. 19 | top[1]->ReshapeLike(prefetch_current_->label_); 20 | top[1]->set_gpu_data(prefetch_current_->label_.mutable_gpu_data()); 21 | } 22 | } 23 | 24 | INSTANTIATE_LAYER_GPU_FORWARD(BasePrefetchingDataLayer); 25 | 26 | } // namespace caffe 27 | -------------------------------------------------------------------------------- /src/caffe/layers/input_layer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "caffe/layers/input_layer.hpp" 4 | 5 | namespace caffe { 6 | 7 | template 8 | void InputLayer::LayerSetUp(const vector*>& bottom, 9 | const vector*>& top) { 10 | const int num_top = top.size(); 11 | const InputParameter& param = this->layer_param_.input_param(); 12 | const int num_shape = param.shape_size(); 13 | CHECK(num_shape == 0 || num_shape == 1 || num_shape == num_top) 14 | << "Must specify 'shape' once, once per top blob, or not at all: " 15 | << num_top << " tops vs. " << num_shape << " shapes."; 16 | if (num_shape > 0) { 17 | for (int i = 0; i < num_top; ++i) { 18 | const int shape_index = (param.shape_size() == 1) ? 0 : i; 19 | top[i]->Reshape(param.shape(shape_index)); 20 | } 21 | } 22 | } 23 | 24 | INSTANTIATE_CLASS(InputLayer); 25 | REGISTER_LAYER_CLASS(Input); 26 | 27 | } // namespace caffe 28 | -------------------------------------------------------------------------------- /src/caffe/layers/loss_layer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "caffe/layers/loss_layer.hpp" 4 | 5 | namespace caffe { 6 | 7 | template 8 | void LossLayer::LayerSetUp( 9 | const vector*>& bottom, const vector*>& top) { 10 | // LossLayers have a non-zero (1) loss by default. 11 | if (this->layer_param_.loss_weight_size() == 0) { 12 | this->layer_param_.add_loss_weight(Dtype(1)); 13 | } 14 | } 15 | 16 | template 17 | void LossLayer::Reshape( 18 | const vector*>& bottom, const vector*>& top) { 19 | CHECK_EQ(bottom[0]->shape(0), bottom[1]->shape(0)) 20 | << "The data and label should have the same first dimension."; 21 | vector loss_shape(0); // Loss layers output a scalar; 0 axes. 22 | top[0]->Reshape(loss_shape); 23 | } 24 | 25 | INSTANTIATE_CLASS(LossLayer); 26 | 27 | } // namespace caffe 28 | -------------------------------------------------------------------------------- /src/caffe/layers/neuron_layer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "caffe/layers/neuron_layer.hpp" 4 | 5 | namespace caffe { 6 | 7 | template 8 | void NeuronLayer::Reshape(const vector*>& bottom, 9 | const vector*>& top) { 10 | top[0]->ReshapeLike(*bottom[0]); 11 | } 12 | 13 | INSTANTIATE_CLASS(NeuronLayer); 14 | 15 | } // namespace caffe 16 | -------------------------------------------------------------------------------- /src/caffe/layers/parameter_layer.cpp: -------------------------------------------------------------------------------- 1 | #include "caffe/layers/parameter_layer.hpp" 2 | 3 | namespace caffe { 4 | 5 | INSTANTIATE_CLASS(ParameterLayer); 6 | REGISTER_LAYER_CLASS(Parameter); 7 | 8 | } // namespace caffe 9 | -------------------------------------------------------------------------------- /src/caffe/layers/silence_layer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "caffe/layers/silence_layer.hpp" 4 | #include "caffe/util/math_functions.hpp" 5 | 6 | namespace caffe { 7 | 8 | template 9 | void SilenceLayer::Backward_cpu(const vector*>& top, 10 | const vector& propagate_down, const vector*>& bottom) { 11 | for (int i = 0; i < bottom.size(); ++i) { 12 | if (propagate_down[i]) { 13 | caffe_set(bottom[i]->count(), Dtype(0), 14 | bottom[i]->mutable_cpu_diff()); 15 | } 16 | } 17 | } 18 | 19 | #ifdef CPU_ONLY 20 | STUB_GPU(SilenceLayer); 21 | #endif 22 | 23 | INSTANTIATE_CLASS(SilenceLayer); 24 | REGISTER_LAYER_CLASS(Silence); 25 | 26 | } // namespace caffe 27 | -------------------------------------------------------------------------------- /src/caffe/layers/silence_layer.cu: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "caffe/layers/silence_layer.hpp" 4 | #include "caffe/util/math_functions.hpp" 5 | 6 | namespace caffe { 7 | 8 | template 9 | void SilenceLayer::Forward_gpu(const vector*>& bottom, 10 | const vector*>& top) { 11 | // Do nothing. 12 | } 13 | 14 | template 15 | void SilenceLayer::Backward_gpu(const vector*>& top, 16 | const vector& propagate_down, const vector*>& bottom) { 17 | for (int i = 0; i < bottom.size(); ++i) { 18 | if (propagate_down[i]) { 19 | caffe_gpu_set(bottom[i]->count(), Dtype(0), 20 | bottom[i]->mutable_gpu_diff()); 21 | } 22 | } 23 | } 24 | 25 | INSTANTIATE_LAYER_GPU_FUNCS(SilenceLayer); 26 | 27 | } // namespace caffe 28 | -------------------------------------------------------------------------------- /src/caffe/solvers/adagrad_solver.cu: -------------------------------------------------------------------------------- 1 | #include "caffe/util/math_functions.hpp" 2 | 3 | 4 | namespace caffe { 5 | 6 | template 7 | __global__ void AdaGradUpdate(int N, Dtype* g, Dtype* h, Dtype delta, 8 | Dtype local_rate) { 9 | CUDA_KERNEL_LOOP(i, N) { 10 | float gi = g[i]; 11 | float hi = h[i] = h[i] + gi*gi; 12 | g[i] = local_rate * gi / (sqrt(hi) + delta); 13 | } 14 | } 15 | template 16 | void adagrad_update_gpu(int N, Dtype* g, Dtype* h, Dtype delta, 17 | Dtype local_rate) { 18 | AdaGradUpdate // NOLINT_NEXT_LINE(whitespace/operators) 19 | <<>>( 20 | N, g, h, delta, local_rate); 21 | CUDA_POST_KERNEL_CHECK; 22 | } 23 | template void adagrad_update_gpu(int, float*, float*, float, float); 24 | template void adagrad_update_gpu(int, double*, double*, double, double); 25 | 26 | } // namespace caffe 27 | -------------------------------------------------------------------------------- /src/caffe/solvers/sgd_solver.cu: -------------------------------------------------------------------------------- 1 | #include "caffe/util/math_functions.hpp" 2 | 3 | 4 | namespace caffe { 5 | 6 | template 7 | __global__ void SGDUpdate(int N, Dtype* g, Dtype* h, 8 | Dtype momentum, Dtype local_rate) { 9 | CUDA_KERNEL_LOOP(i, N) { 10 | g[i] = h[i] = momentum*h[i] + local_rate*g[i]; 11 | } 12 | } 13 | template 14 | void sgd_update_gpu(int N, Dtype* g, Dtype* h, Dtype momentum, 15 | Dtype local_rate) { 16 | SGDUpdate // NOLINT_NEXT_LINE(whitespace/operators) 17 | <<>>( 18 | N, g, h, momentum, local_rate); 19 | CUDA_POST_KERNEL_CHECK; 20 | } 21 | template void sgd_update_gpu(int, float*, float*, float, float); 22 | template void sgd_update_gpu(int, double*, double*, double, double); 23 | 24 | } // namespace caffe 25 | -------------------------------------------------------------------------------- /src/caffe/test/CMakeFiles/CMakeDirectoryInformation.cmake: -------------------------------------------------------------------------------- 1 | # CMAKE generated file: DO NOT EDIT! 2 | # Generated by "Unix Makefiles" Generator, CMake Version 2.8 3 | 4 | # Relative path conversion top directories. 5 | SET(CMAKE_RELATIVE_PATH_TOP_SOURCE "/home/tl486/git_TCcaffe/caffe_solverOriginal/caffe") 6 | SET(CMAKE_RELATIVE_PATH_TOP_BINARY "/home/tl486/git_TCcaffe/caffe_solverOriginal/caffe") 7 | 8 | # Force unix paths in dependencies. 9 | SET(CMAKE_FORCE_UNIX_PATHS 1) 10 | 11 | 12 | # The C and CXX include file regular expressions for this directory. 13 | SET(CMAKE_C_INCLUDE_REGEX_SCAN "^.*$") 14 | SET(CMAKE_C_INCLUDE_REGEX_COMPLAIN "^$") 15 | SET(CMAKE_CXX_INCLUDE_REGEX_SCAN ${CMAKE_C_INCLUDE_REGEX_SCAN}) 16 | 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2 | # Generated by "Unix Makefiles" Generator, CMake Version 2.8 3 | 4 | # compile CXX with /usr/bin/c++ 5 | CXX_FLAGS = -fPIC -Wall -Wno-sign-compare -Wno-uninitialized -O3 -DNDEBUG -I/home/tl486/git_TCcaffe/caffe_solverOriginal/caffe/src -I/home/tl486/git_TCcaffe/caffe_solverOriginal/caffe/include -isystem /usr/local/include -isystem /usr/local/cuda-8.0/include -isystem /opt/OpenBLAS/include -I/home/tl486/git_TCcaffe/caffe_solverOriginal/caffe 6 | 7 | CXX_DEFINES = -DCAFFE_VERSION=1.0.0-rc4 -DGTEST_USE_OWN_TR1_TUPLE -DUSE_CUDNN -DUSE_LEVELDB -DUSE_LMDB 8 | 9 | -------------------------------------------------------------------------------- /src/caffe/test/test_data/sample_data.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/src/caffe/test/test_data/sample_data.h5 -------------------------------------------------------------------------------- /src/caffe/test/test_data/sample_data_2_gzip.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/src/caffe/test/test_data/sample_data_2_gzip.h5 -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /src/caffe/test/test_data/solver_data.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/src/caffe/test/test_data/solver_data.h5 -------------------------------------------------------------------------------- /src/caffe/test/test_data/solver_data_list.txt: -------------------------------------------------------------------------------- 1 | src/caffe/test/test_data/solver_data.h5 2 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /src/caffe/util/db_leveldb.cpp: -------------------------------------------------------------------------------- 1 | #ifdef USE_LEVELDB 2 | #include "caffe/util/db_leveldb.hpp" 3 | 4 | #include 5 | 6 | namespace caffe { namespace db { 7 | 8 | void LevelDB::Open(const string& source, Mode mode) { 9 | leveldb::Options options; 10 | options.block_size = 65536; 11 | options.write_buffer_size = 268435456; 12 | options.max_open_files = 100; 13 | options.error_if_exists = mode == NEW; 14 | options.create_if_missing = mode != READ; 15 | leveldb::Status status = leveldb::DB::Open(options, source, &db_); 16 | CHECK(status.ok()) << "Failed to open leveldb " << source 17 | << std::endl << status.ToString(); 18 | LOG(INFO) << "Opened leveldb " << source; 19 | } 20 | 21 | } // namespace db 22 | } // namespace caffe 23 | #endif // USE_LEVELDB 24 | -------------------------------------------------------------------------------- /src/gtest/CMakeFiles/CMakeDirectoryInformation.cmake: -------------------------------------------------------------------------------- 1 | # CMAKE generated file: DO NOT EDIT! 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Use caffe device_query " 5 | "[--device_id=0] instead."; 6 | return 0; 7 | } 8 | -------------------------------------------------------------------------------- /tools/extract_features: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/tools/extract_features -------------------------------------------------------------------------------- /tools/finetune_net: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/tools/finetune_net -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /tools/net_speed_benchmark: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/tools/net_speed_benchmark -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /tools/test_net: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/tools/test_net -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /tools/train_net: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/tools/train_net -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /tools/upgrade_net_proto_binary: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/tools/upgrade_net_proto_binary -------------------------------------------------------------------------------- /tools/upgrade_net_proto_text: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/tools/upgrade_net_proto_text -------------------------------------------------------------------------------- /tools/upgrade_solver_proto_text: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tongcheng/caffe/1625bb3bf55a766fecbb905d6fe361f3d25f78eb/tools/upgrade_solver_proto_text --------------------------------------------------------------------------------