├── README.md ├── 第一赛季 ├── README.md ├── code │ ├── ensemble.py │ ├── gen_label_csv.py │ ├── main_inception_v4.py │ ├── main_resnext_64.py │ ├── main_se_resnet50.py │ ├── main_senet154.py │ ├── model_v4.py │ ├── resnext.py │ ├── resnext_features │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-35.pyc │ │ │ ├── __init__.cpython-36.pyc │ │ │ ├── resnext101_32x4d_features.cpython-35.pyc │ │ │ ├── resnext101_32x4d_features.cpython-36.pyc │ │ │ ├── resnext101_64x4d_features.cpython-35.pyc │ │ │ └── resnext101_64x4d_features.cpython-36.pyc │ │ ├── resnext101_32x4d_features.py │ │ └── resnext101_64x4d_features.py │ ├── senet.py │ └── stacking.py └── version.txt └── 第二赛季 ├── README.md └── code ├── data-preprocess ├── DataAugment.py ├── Set_create.py ├── Txt_To_XML.py ├── json_To_txt.py └── xml_helper.py └── detect-model └── FPN+RCNN ├── .ipynb_checkpoints ├── fpn_0.1-checkpoint.json └── test-checkpoint.sh ├── README.md ├── caffe-fpn ├── .Doxyfile ├── .travis.yml ├── CMakeLists.txt ├── CONTRIBUTING.md ├── CONTRIBUTORS.md ├── INSTALL.md ├── LICENSE ├── Makefile ├── Makefile.config ├── Makefile.config.example ├── README.md ├── build │ ├── CMakeCache.txt │ ├── CMakeFiles │ │ ├── 3.5.1 │ │ │ ├── CMakeCCompiler.cmake │ │ │ ├── CMakeCXXCompiler.cmake │ │ │ ├── CMakeDetermineCompilerABI_C.bin │ │ │ ├── CMakeDetermineCompilerABI_CXX.bin │ │ │ ├── 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 │ │ ├── feature_tests.bin │ │ ├── feature_tests.c │ │ ├── feature_tests.cxx │ │ ├── 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 │ ├── CaffeConfig.cmake │ ├── CaffeTargets.cmake │ ├── Makefile │ ├── __init__.py │ ├── caffe_config.h │ ├── cmake │ │ └── CaffeConfig.cmake │ ├── cmake_install.cmake │ ├── detect_cuda_archs.cu │ ├── docs │ │ ├── CMakeFiles │ │ │ ├── CMakeDirectoryInformation.cmake │ │ │ └── progress.marks │ │ ├── Makefile │ │ └── cmake_install.cmake │ ├── examples │ │ ├── CMakeFiles │ │ │ ├── CMakeDirectoryInformation.cmake │ │ │ ├── classification.dir │ │ │ │ ├── CXX.includecache │ │ │ │ ├── DependInfo.cmake │ │ │ │ ├── build.make │ │ │ │ ├── cmake_clean.cmake │ │ │ │ ├── cpp_classification │ │ │ │ │ └── classification.cpp.o │ │ │ │ ├── depend.internal │ │ │ │ ├── depend.make │ │ │ │ ├── flags.make │ │ │ │ ├── link.txt │ │ │ │ └── progress.make │ │ │ ├── convert_cifar_data.dir │ │ │ │ ├── CXX.includecache │ │ │ │ ├── DependInfo.cmake │ │ │ │ ├── build.make │ │ │ │ ├── cifar10 │ │ │ │ │ └── convert_cifar_data.cpp.o │ │ │ │ ├── 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 │ │ │ │ ├── mnist │ │ │ │ │ └── convert_mnist_data.cpp.o │ │ │ │ └── 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 │ │ │ │ └── siamese │ │ │ │ │ └── convert_mnist_siamese_data.cpp.o │ │ │ └── progress.marks │ │ ├── Makefile │ │ ├── cifar10 │ │ │ ├── convert_cifar_data │ │ │ └── convert_cifar_data.bin │ │ ├── cmake_install.cmake │ │ ├── cpp_classification │ │ │ ├── classification │ │ │ └── classification.bin │ │ ├── mnist │ │ │ ├── convert_mnist_data │ │ │ └── convert_mnist_data.bin │ │ └── siamese │ │ │ ├── convert_mnist_siamese_data │ │ │ └── convert_mnist_siamese_data.bin │ ├── include │ │ └── caffe │ │ │ └── proto │ │ │ ├── caffe.pb.cc │ │ │ ├── caffe.pb.h │ │ │ └── caffe_pb2.py │ ├── lib │ │ ├── _caffe.so │ │ ├── libcaffe.so │ │ ├── libcaffe.so.1.0.0-rc3 │ │ └── libproto.a │ ├── matlab │ │ ├── CMakeFiles │ │ │ ├── CMakeDirectoryInformation.cmake │ │ │ └── progress.marks │ │ ├── Makefile │ │ └── cmake_install.cmake │ ├── python │ │ ├── CMakeFiles │ │ │ ├── CMakeDirectoryInformation.cmake │ │ │ ├── progress.marks │ │ │ └── pycaffe.dir │ │ │ │ ├── CXX.includecache │ │ │ │ ├── DependInfo.cmake │ │ │ │ ├── build.make │ │ │ │ ├── caffe │ │ │ │ └── _caffe.cpp.o │ │ │ │ ├── cmake_clean.cmake │ │ │ │ ├── depend.internal │ │ │ │ ├── depend.make │ │ │ │ ├── flags.make │ │ │ │ ├── link.txt │ │ │ │ └── progress.make │ │ ├── Makefile │ │ └── cmake_install.cmake │ ├── src │ │ ├── caffe │ │ │ ├── CMakeFiles │ │ │ │ ├── CMakeDirectoryInformation.cmake │ │ │ │ ├── caffe.dir │ │ │ │ │ ├── CXX.includecache │ │ │ │ │ ├── DependInfo.cmake │ │ │ │ │ ├── blob.cpp.o │ │ │ │ │ ├── build.make │ │ │ │ │ ├── cmake_clean.cmake │ │ │ │ │ ├── common.cpp.o │ │ │ │ │ ├── data_reader.cpp.o │ │ │ │ │ ├── data_transformer.cpp.o │ │ │ │ │ ├── depend.internal │ │ │ │ │ ├── depend.make │ │ │ │ │ ├── flags.make │ │ │ │ │ ├── internal_thread.cpp.o │ │ │ │ │ ├── layer.cpp.o │ │ │ │ │ ├── layer_factory.cpp.o │ │ │ │ │ ├── layers │ │ │ │ │ │ ├── absval_layer.cpp.o │ │ │ │ │ │ ├── accuracy_layer.cpp.o │ │ │ │ │ │ ├── argmax_layer.cpp.o │ │ │ │ │ │ ├── base_conv_layer.cpp.o │ │ │ │ │ │ ├── base_data_layer.cpp.o │ │ │ │ │ │ ├── batch_norm_layer.cpp.o │ │ │ │ │ │ ├── batch_reindex_layer.cpp.o │ │ │ │ │ │ ├── bias_layer.cpp.o │ │ │ │ │ │ ├── bnll_layer.cpp.o │ │ │ │ │ │ ├── conadd_layer.cpp.o │ │ │ │ │ │ ├── concat_layer.cpp.o │ │ │ │ │ │ ├── contrastive_loss_layer.cpp.o │ │ │ │ │ │ ├── conv_layer.cpp.o │ │ │ │ │ │ ├── crop_layer.cpp.o │ │ │ │ │ │ ├── cudnn_conv_layer.cpp.o │ │ │ │ │ │ ├── cudnn_lcn_layer.cpp.o │ │ │ │ │ │ ├── cudnn_lrn_layer.cpp.o │ │ │ │ │ │ ├── cudnn_pooling_layer.cpp.o │ │ │ │ │ │ ├── cudnn_relu_layer.cpp.o │ │ │ │ │ │ ├── cudnn_sigmoid_layer.cpp.o │ │ │ │ │ │ ├── cudnn_softmax_layer.cpp.o │ │ │ │ │ │ ├── cudnn_tanh_layer.cpp.o │ │ │ │ │ │ ├── data_layer.cpp.o │ │ │ │ │ │ ├── deconv_layer.cpp.o │ │ │ │ │ │ ├── deformable_conv_layer.cpp.o │ │ │ │ │ │ ├── dropout_layer.cpp.o │ │ │ │ │ │ ├── dummy_data_layer.cpp.o │ │ │ │ │ │ ├── eltwise_layer.cpp.o │ │ │ │ │ │ ├── elu_layer.cpp.o │ │ │ │ │ │ ├── embed_layer.cpp.o │ │ │ │ │ │ ├── euclidean_loss_layer.cpp.o │ │ │ │ │ │ ├── exp_layer.cpp.o │ │ │ │ │ │ ├── filter_layer.cpp.o │ │ │ │ │ │ ├── flatten_layer.cpp.o │ │ │ │ │ │ ├── hdf5_data_layer.cpp.o │ │ │ │ │ │ ├── hdf5_output_layer.cpp.o │ │ │ │ │ │ ├── hinge_loss_layer.cpp.o │ │ │ │ │ │ ├── im2col_layer.cpp.o │ │ │ │ │ │ ├── image_data_layer.cpp.o │ │ │ │ │ │ ├── infogain_loss_layer.cpp.o │ │ │ │ │ │ ├── inner_product_layer.cpp.o │ │ │ │ │ │ ├── log_layer.cpp.o │ │ │ │ │ │ ├── loss_layer.cpp.o │ │ │ │ │ │ ├── lrn_layer.cpp.o │ │ │ │ │ │ ├── memory_data_layer.cpp.o │ │ │ │ │ │ ├── multinomial_logistic_loss_layer.cpp.o │ │ │ │ │ │ ├── mvn_layer.cpp.o │ │ │ │ │ │ ├── neuron_layer.cpp.o │ │ │ │ │ │ ├── pooling_layer.cpp.o │ │ │ │ │ │ ├── power_layer.cpp.o │ │ │ │ │ │ ├── prelu_layer.cpp.o │ │ │ │ │ │ ├── reduction_layer.cpp.o │ │ │ │ │ │ ├── relu_layer.cpp.o │ │ │ │ │ │ ├── reshape_layer.cpp.o │ │ │ │ │ │ ├── roi_pooling_layer.cpp.o │ │ │ │ │ │ ├── scale_layer.cpp.o │ │ │ │ │ │ ├── sigmoid_cross_entropy_loss_layer.cpp.o │ │ │ │ │ │ ├── sigmoid_layer.cpp.o │ │ │ │ │ │ ├── silence_layer.cpp.o │ │ │ │ │ │ ├── slice_layer.cpp.o │ │ │ │ │ │ ├── smooth_L1_loss_layer.cpp.o │ │ │ │ │ │ ├── softmax_layer.cpp.o │ │ │ │ │ │ ├── softmax_loss_layer.cpp.o │ │ │ │ │ │ ├── split_layer.cpp.o │ │ │ │ │ │ ├── spp_layer.cpp.o │ │ │ │ │ │ ├── tanh_layer.cpp.o │ │ │ │ │ │ ├── threshold_layer.cpp.o │ │ │ │ │ │ ├── tile_layer.cpp.o │ │ │ │ │ │ └── window_data_layer.cpp.o │ │ │ │ │ ├── link.txt │ │ │ │ │ ├── net.cpp.o │ │ │ │ │ ├── parallel.cpp.o │ │ │ │ │ ├── progress.make │ │ │ │ │ ├── solver.cpp.o │ │ │ │ │ ├── solvers │ │ │ │ │ │ ├── adadelta_solver.cpp.o │ │ │ │ │ │ ├── adagrad_solver.cpp.o │ │ │ │ │ │ ├── adam_solver.cpp.o │ │ │ │ │ │ ├── nesterov_solver.cpp.o │ │ │ │ │ │ ├── rmsprop_solver.cpp.o │ │ │ │ │ │ └── sgd_solver.cpp.o │ │ │ │ │ ├── syncedmem.cpp.o │ │ │ │ │ └── util │ │ │ │ │ │ ├── benchmark.cpp.o │ │ │ │ │ │ ├── blocking_queue.cpp.o │ │ │ │ │ │ ├── cudnn.cpp.o │ │ │ │ │ │ ├── db.cpp.o │ │ │ │ │ │ ├── db_leveldb.cpp.o │ │ │ │ │ │ ├── db_lmdb.cpp.o │ │ │ │ │ │ ├── hdf5.cpp.o │ │ │ │ │ │ ├── im2col.cpp.o │ │ │ │ │ │ ├── insert_splits.cpp.o │ │ │ │ │ │ ├── io.cpp.o │ │ │ │ │ │ ├── math_functions.cpp.o │ │ │ │ │ │ ├── signal_handler.cpp.o │ │ │ │ │ │ └── upgrade_proto.cpp.o │ │ │ │ ├── cuda_compile.dir │ │ │ │ │ ├── layers │ │ │ │ │ │ ├── cuda_compile_generated_absval_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_absval_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_absval_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_base_data_layer.cu.o │ │ │ │ │ │ ├── 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 │ │ │ │ │ │ ├── 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 │ │ │ │ │ │ ├── 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 │ │ │ │ │ │ ├── cuda_compile_generated_bias_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_bias_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_bnll_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_bnll_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_bnll_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_conadd_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_conadd_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_conadd_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_concat_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_concat_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_concat_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_contrastive_loss_layer.cu.o │ │ │ │ │ │ ├── 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 │ │ │ │ │ │ ├── cuda_compile_generated_conv_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_conv_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_crop_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_crop_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_crop_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_cudnn_conv_layer.cu.o │ │ │ │ │ │ ├── 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 │ │ │ │ │ │ ├── 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 │ │ │ │ │ │ ├── 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 │ │ │ │ │ │ ├── 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 │ │ │ │ │ │ ├── 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 │ │ │ │ │ │ ├── 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 │ │ │ │ │ │ ├── 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 │ │ │ │ │ │ ├── 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 │ │ │ │ │ │ ├── cuda_compile_generated_deconv_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_deconv_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_deformable_conv_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_deformable_conv_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_deformable_conv_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_dropout_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_dropout_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_dropout_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_eltwise_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_eltwise_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_eltwise_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_elu_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_elu_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_elu_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_embed_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_embed_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_embed_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_euclidean_loss_layer.cu.o │ │ │ │ │ │ ├── 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 │ │ │ │ │ │ ├── cuda_compile_generated_exp_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_exp_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_filter_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_filter_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_filter_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_hdf5_data_layer.cu.o │ │ │ │ │ │ ├── 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 │ │ │ │ │ │ ├── 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 │ │ │ │ │ │ ├── cuda_compile_generated_im2col_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_im2col_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_inner_product_layer.cu.o │ │ │ │ │ │ ├── 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 │ │ │ │ │ │ ├── cuda_compile_generated_log_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_log_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_lrn_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_lrn_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_lrn_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_mvn_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_mvn_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_mvn_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_pooling_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_pooling_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_pooling_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_power_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_power_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_power_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_prelu_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_prelu_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_prelu_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_reduction_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_reduction_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_reduction_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_relu_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_relu_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_relu_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_roi_pooling_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_roi_pooling_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_roi_pooling_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_scale_layer.cu.o │ │ │ │ │ │ ├── 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 │ │ │ │ │ │ ├── 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 │ │ │ │ │ │ ├── cuda_compile_generated_sigmoid_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_sigmoid_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_silence_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_silence_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_silence_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_slice_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_slice_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_slice_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_smooth_L1_loss_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_smooth_L1_loss_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_smooth_L1_loss_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_softmax_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_softmax_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_softmax_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_softmax_loss_layer.cu.o │ │ │ │ │ │ ├── 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 │ │ │ │ │ │ ├── cuda_compile_generated_split_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_split_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_tanh_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_tanh_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_tanh_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_threshold_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_threshold_layer.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_threshold_layer.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_tile_layer.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_tile_layer.cu.o.cmake │ │ │ │ │ │ └── cuda_compile_generated_tile_layer.cu.o.depend │ │ │ │ │ ├── solvers │ │ │ │ │ │ ├── cuda_compile_generated_adadelta_solver.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_adadelta_solver.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_adadelta_solver.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_adagrad_solver.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_adagrad_solver.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_adagrad_solver.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_adam_solver.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_adam_solver.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_adam_solver.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_nesterov_solver.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_nesterov_solver.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_nesterov_solver.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_rmsprop_solver.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_rmsprop_solver.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_rmsprop_solver.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_sgd_solver.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_sgd_solver.cu.o.cmake │ │ │ │ │ │ └── cuda_compile_generated_sgd_solver.cu.o.depend │ │ │ │ │ └── util │ │ │ │ │ │ ├── cuda_compile_generated_deformable_im2col.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_deformable_im2col.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_deformable_im2col.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_im2col.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_im2col.cu.o.cmake │ │ │ │ │ │ ├── cuda_compile_generated_im2col.cu.o.depend │ │ │ │ │ │ ├── cuda_compile_generated_math_functions.cu.o │ │ │ │ │ │ ├── cuda_compile_generated_math_functions.cu.o.cmake │ │ │ │ │ │ └── cuda_compile_generated_math_functions.cu.o.depend │ │ │ │ ├── progress.marks │ │ │ │ └── proto.dir │ │ │ │ │ ├── CXX.includecache │ │ │ │ │ ├── DependInfo.cmake │ │ │ │ │ ├── __ │ │ │ │ │ └── __ │ │ │ │ │ │ └── include │ │ │ │ │ │ └── caffe │ │ │ │ │ │ └── proto │ │ │ │ │ │ └── caffe.pb.cc.o │ │ │ │ │ ├── build.make │ │ │ │ │ ├── cmake_clean.cmake │ │ │ │ │ ├── cmake_clean_target.cmake │ │ │ │ │ ├── depend.internal │ │ │ │ │ ├── depend.make │ │ │ │ │ ├── flags.make │ │ │ │ │ ├── link.txt │ │ │ │ │ └── progress.make │ │ │ ├── Makefile │ │ │ ├── cmake_install.cmake │ │ │ └── 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 │ │ │ │ ├── Makefile │ │ │ │ └── cmake_install.cmake │ │ └── 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 │ │ │ ├── Makefile │ │ │ └── cmake_install.cmake │ └── tools │ │ ├── CMakeFiles │ │ ├── CMakeDirectoryInformation.cmake │ │ ├── caffe.bin.dir │ │ │ ├── CXX.includecache │ │ │ ├── DependInfo.cmake │ │ │ ├── build.make │ │ │ ├── caffe.cpp.o │ │ │ ├── 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 │ │ │ ├── compute_image_mean.cpp.o │ │ │ ├── depend.internal │ │ │ ├── depend.make │ │ │ ├── flags.make │ │ │ ├── link.txt │ │ │ └── progress.make │ │ ├── convert_imageset.dir │ │ │ ├── CXX.includecache │ │ │ ├── DependInfo.cmake │ │ │ ├── build.make │ │ │ ├── cmake_clean.cmake │ │ │ ├── convert_imageset.cpp.o │ │ │ ├── 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 │ │ │ ├── device_query.cpp.o │ │ │ ├── flags.make │ │ │ ├── link.txt │ │ │ └── progress.make │ │ ├── extract_features.dir │ │ │ ├── CXX.includecache │ │ │ ├── DependInfo.cmake │ │ │ ├── build.make │ │ │ ├── cmake_clean.cmake │ │ │ ├── depend.internal │ │ │ ├── depend.make │ │ │ ├── extract_features.cpp.o │ │ │ ├── flags.make │ │ │ ├── link.txt │ │ │ └── progress.make │ │ ├── finetune_net.dir │ │ │ ├── CXX.includecache │ │ │ ├── DependInfo.cmake │ │ │ ├── build.make │ │ │ ├── cmake_clean.cmake │ │ │ ├── depend.internal │ │ │ ├── depend.make │ │ │ ├── finetune_net.cpp.o │ │ │ ├── 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 │ │ │ ├── net_speed_benchmark.cpp.o │ │ │ └── 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 │ │ │ └── test_net.cpp.o │ │ ├── train_net.dir │ │ │ ├── CXX.includecache │ │ │ ├── DependInfo.cmake │ │ │ ├── build.make │ │ │ ├── cmake_clean.cmake │ │ │ ├── depend.internal │ │ │ ├── depend.make │ │ │ ├── flags.make │ │ │ ├── link.txt │ │ │ ├── progress.make │ │ │ └── train_net.cpp.o │ │ ├── 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_binary.cpp.o │ │ ├── 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_net_proto_text.cpp.o │ │ └── upgrade_solver_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.cpp.o │ │ ├── Makefile │ │ ├── caffe │ │ ├── cmake_install.cmake │ │ ├── compute_image_mean │ │ ├── convert_imageset │ │ ├── device_query │ │ ├── extract_features │ │ ├── finetune_net │ │ ├── net_speed_benchmark │ │ ├── test_net │ │ ├── train_net │ │ ├── upgrade_net_proto_binary │ │ ├── upgrade_net_proto_text │ │ └── upgrade_solver_proto_text ├── caffe.cloc ├── cmake │ ├── ConfigGen.cmake │ ├── Cuda.cmake │ ├── Dependencies.cmake │ ├── External │ │ ├── gflags.cmake │ │ └── glog.cmake │ ├── Misc.cmake │ ├── Modules │ │ ├── FindAtlas.cmake │ │ ├── FindGFlags.cmake │ │ ├── FindGlog.cmake │ │ ├── FindLAPACK.cmake │ │ ├── FindLMDB.cmake │ │ ├── FindLevelDB.cmake │ │ ├── FindMKL.cmake │ │ ├── FindMatlabMex.cmake │ │ ├── FindNumPy.cmake │ │ ├── FindOpenBLAS.cmake │ │ ├── FindSnappy.cmake │ │ └── FindvecLib.cmake │ ├── ProtoBuf.cmake │ ├── Summary.cmake │ ├── Targets.cmake │ ├── Templates │ │ ├── CaffeConfig.cmake.in │ │ ├── CaffeConfigVersion.cmake.in │ │ └── caffe_config.h.in │ ├── Utils.cmake │ └── lint.cmake ├── data │ ├── cifar10 │ │ └── get_cifar10.sh │ └── ilsvrc12 │ │ └── get_ilsvrc_aux.sh ├── docs │ ├── CMakeLists.txt │ ├── CNAME │ ├── README.md │ ├── _config.yml │ ├── _layouts │ │ └── default.html │ ├── development.md │ ├── images │ │ ├── GitHub-Mark-64px.png │ │ └── caffeine-icon.png │ ├── index.md │ ├── install_apt.md │ ├── install_osx.md │ ├── install_yum.md │ ├── installation.md │ ├── model_zoo.md │ ├── 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 │ │ ├── loss.md │ │ ├── net_layer_blob.md │ │ └── solver.md ├── examples │ ├── 00-classification.ipynb │ ├── 01-learning-lenet.ipynb │ ├── 02-brewing-logreg.ipynb │ ├── 03-fine-tuning.ipynb │ ├── CMakeLists.txt │ ├── 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 │ │ ├── convert_cifar_data.cpp │ │ ├── create_cifar10.sh │ │ ├── readme.md │ │ ├── train_full.sh │ │ ├── train_full_sigmoid.sh │ │ ├── train_full_sigmoid_bn.sh │ │ └── train_quick.sh │ ├── cpp_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_solver.prototxt │ │ ├── nonlinear_train_val.prototxt │ │ ├── solver.prototxt │ │ └── train_val.prototxt │ ├── imagenet │ │ ├── create_imagenet.sh │ │ ├── make_imagenet_mean.sh │ │ ├── readme.md │ │ ├── resume_training.sh │ │ └── train_caffenet.sh │ ├── images │ │ ├── cat.jpg │ │ ├── cat_gray.jpg │ │ └── fish-bike.jpg │ ├── mnist │ │ ├── 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 │ │ ├── mnist_train_lmdb │ │ │ ├── data.mdb │ │ │ └── lock.mdb │ │ ├── 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 │ ├── pycaffe │ │ ├── caffenet.py │ │ ├── layers │ │ │ └── pyloss.py │ │ └── linreg.prototxt │ ├── siamese │ │ ├── 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_reader.hpp │ │ ├── data_transformer.hpp │ │ ├── fast_rcnn_layers.hpp │ │ ├── filler.hpp │ │ ├── internal_thread.hpp │ │ ├── layer.hpp │ │ ├── layer_factory.hpp │ │ ├── layers │ │ ├── .conadd_layer.hpp.swo │ │ ├── .conadd_layer.hpp.swp │ │ ├── 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 │ │ ├── conadd_layer.hpp │ │ ├── concat_layer.hpp │ │ ├── contrastive_loss_layer.hpp │ │ ├── conv_layer.hpp │ │ ├── crop_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 │ │ ├── deformable_conv_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 │ │ ├── log_layer.hpp │ │ ├── loss_layer.hpp │ │ ├── lrn_layer.hpp │ │ ├── memory_data_layer.hpp │ │ ├── multinomial_logistic_loss_layer.hpp │ │ ├── mvn_layer.hpp │ │ ├── neuron_layer.hpp │ │ ├── pooling_layer.hpp │ │ ├── power_layer.hpp │ │ ├── prelu_layer.hpp │ │ ├── python_layer.hpp │ │ ├── reduction_layer.hpp │ │ ├── relu_layer.hpp │ │ ├── reshape_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 │ │ ├── deformable_im2col.hpp │ │ ├── device_alternate.hpp │ │ ├── format.hpp │ │ ├── gpu_util.cuh │ │ ├── hdf5.hpp │ │ ├── im2col.hpp │ │ ├── insert_splits.hpp │ │ ├── io.hpp │ │ ├── math_functions.hpp │ │ ├── mkl_alternate.hpp │ │ ├── rng.hpp │ │ ├── signal_handler.h │ │ └── upgrade_proto.hpp ├── 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 │ ├── CMakeLists.txt │ ├── 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 │ ├── CMakeLists.txt │ ├── caffe │ │ ├── __init__.py │ │ ├── __init__.pyc │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-35.pyc │ │ │ └── pycaffe.cpython-35.pyc │ │ ├── _caffe.cpp │ │ ├── _caffe.so │ │ ├── classifier.py │ │ ├── classifier.pyc │ │ ├── detector.py │ │ ├── detector.pyc │ │ ├── draw.py │ │ ├── imagenet │ │ │ └── ilsvrc_2012_mean.npy │ │ ├── io.py │ │ ├── io.pyc │ │ ├── net_spec.py │ │ ├── net_spec.pyc │ │ ├── proto │ │ │ ├── __init__.py │ │ │ ├── __init__.pyc │ │ │ ├── caffe_pb2.py │ │ │ └── caffe_pb2.pyc │ │ ├── pycaffe.py │ │ ├── pycaffe.pyc │ │ └── test │ │ │ ├── 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 │ ├── detect.py │ ├── draw_net.py │ └── requirements.txt ├── scripts │ ├── build_docs.sh │ ├── copy_notebook.py │ ├── cpp_lint.py │ ├── deploy_docs.sh │ ├── download_model_binary.py │ ├── download_model_from_gist.sh │ ├── gather_examples.sh │ ├── travis │ │ ├── travis_build_and_test.sh │ │ ├── travis_install.sh │ │ └── travis_setup_makefile_config.sh │ └── upload_model_to_gist.sh ├── src │ ├── caffe │ │ ├── CMakeLists.txt │ │ ├── blob.cpp │ │ ├── common.cpp │ │ ├── data_reader.cpp │ │ ├── data_transformer.cpp │ │ ├── internal_thread.cpp │ │ ├── layer.cpp │ │ ├── layer_factory.cpp │ │ ├── layers │ │ │ ├── .conadd_layer.cpp.swo │ │ │ ├── .conadd_layer.cpp.swp │ │ │ ├── .concat_layer.cpp.swp │ │ │ ├── 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 │ │ │ ├── conadd_layer.cpp │ │ │ ├── conadd_layer.cu │ │ │ ├── concat_layer.cpp │ │ │ ├── concat_layer.cu │ │ │ ├── contrastive_loss_layer.cpp │ │ │ ├── contrastive_loss_layer.cu │ │ │ ├── conv_layer.cpp │ │ │ ├── conv_layer.cu │ │ │ ├── crop_layer.cpp │ │ │ ├── crop_layer.cu │ │ │ ├── cudnn_conv_layer.cpp │ │ │ ├── cudnn_conv_layer.cu │ │ │ ├── cudnn_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 │ │ │ ├── deformable_conv_layer.cpp │ │ │ ├── deformable_conv_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 │ │ │ ├── log_layer.cpp │ │ │ ├── log_layer.cu │ │ │ ├── loss_layer.cpp │ │ │ ├── lrn_layer.cpp │ │ │ ├── lrn_layer.cu │ │ │ ├── memory_data_layer.cpp │ │ │ ├── multinomial_logistic_loss_layer.cpp │ │ │ ├── mvn_layer.cpp │ │ │ ├── mvn_layer.cu │ │ │ ├── neuron_layer.cpp │ │ │ ├── pooling_layer.cpp │ │ │ ├── pooling_layer.cu │ │ │ ├── power_layer.cpp │ │ │ ├── power_layer.cu │ │ │ ├── prelu_layer.cpp │ │ │ ├── prelu_layer.cu │ │ │ ├── reduction_layer.cpp │ │ │ ├── reduction_layer.cu │ │ │ ├── relu_layer.cpp │ │ │ ├── relu_layer.cu │ │ │ ├── reshape_layer.cpp │ │ │ ├── roi_pooling_layer.cpp │ │ │ ├── roi_pooling_layer.cu │ │ │ ├── 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 │ │ │ ├── smooth_L1_loss_layer.cpp │ │ │ ├── smooth_L1_loss_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.swp │ │ │ └── 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 │ │ │ ├── CMakeLists.txt │ │ │ ├── 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_data │ │ │ │ ├── generate_sample_data.py │ │ │ │ ├── sample_data.h5 │ │ │ │ ├── sample_data_2_gzip.h5 │ │ │ │ ├── sample_data_list.txt │ │ │ │ ├── solver_data.h5 │ │ │ │ └── solver_data_list.txt │ │ │ ├── test_data_layer.cpp │ │ │ ├── test_data_transformer.cpp │ │ │ ├── test_db.cpp │ │ │ ├── test_deconvolution_layer.cpp │ │ │ ├── test_dummy_data_layer.cpp │ │ │ ├── test_eltwise_layer.cpp │ │ │ ├── test_embed_layer.cpp │ │ │ ├── test_euclidean_loss_layer.cpp │ │ │ ├── test_filler.cpp │ │ │ ├── test_filter_layer.cpp │ │ │ ├── test_flatten_layer.cpp │ │ │ ├── test_gradient_based_solver.cpp │ │ │ ├── test_hdf5_output_layer.cpp │ │ │ ├── test_hdf5data_layer.cpp │ │ │ ├── test_hinge_loss_layer.cpp │ │ │ ├── test_im2col_kernel.cu │ │ │ ├── test_im2col_layer.cpp │ │ │ ├── test_image_data_layer.cpp │ │ │ ├── test_infogain_loss_layer.cpp │ │ │ ├── test_inner_product_layer.cpp │ │ │ ├── test_internal_thread.cpp │ │ │ ├── test_io.cpp │ │ │ ├── test_layer_factory.cpp │ │ │ ├── test_lrn_layer.cpp │ │ │ ├── test_math_functions.cpp │ │ │ ├── test_maxpool_dropout_layers.cpp │ │ │ ├── test_memory_data_layer.cpp │ │ │ ├── test_multinomial_logistic_loss_layer.cpp │ │ │ ├── test_mvn_layer.cpp │ │ │ ├── test_net.cpp │ │ │ ├── test_neuron_layer.cpp │ │ │ ├── test_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_roi_pooling_layer.cpp │ │ │ ├── test_scale_layer.cpp │ │ │ ├── test_sigmoid_cross_entropy_loss_layer.cpp │ │ │ ├── test_slice_layer.cpp │ │ │ ├── test_smooth_L1_loss_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 │ │ │ ├── deformable_im2col.cu │ │ │ ├── hdf5.cpp │ │ │ ├── im2col.cpp │ │ │ ├── im2col.cu │ │ │ ├── insert_splits.cpp │ │ │ ├── io.cpp │ │ │ ├── math_functions.cpp │ │ │ ├── math_functions.cu │ │ │ ├── signal_handler.cpp │ │ │ └── upgrade_proto.cpp │ └── gtest │ │ ├── CMakeLists.txt │ │ ├── gtest-all.cpp │ │ ├── gtest.h │ │ └── gtest_main.cc └── tools │ ├── CMakeLists.txt │ ├── caffe.cpp │ ├── compute_image_mean.cpp │ ├── convert_imageset.cpp │ ├── device_query.cpp │ ├── extra │ ├── extract_seconds.py │ ├── launch_resize_and_crop_images.sh │ ├── parse_log.py │ ├── parse_log.sh │ ├── plot_log.gnuplot.example │ ├── plot_training_log.py │ ├── resize_and_crop_images.py │ ├── summarize.py │ ├── train.log │ └── train.log.train │ ├── extract_features.cpp │ ├── finetune_net.cpp │ ├── net_speed_benchmark.cpp │ ├── test_net.cpp │ ├── train_net.cpp │ ├── upgrade_net_proto_binary.cpp │ ├── upgrade_net_proto_text.cpp │ └── upgrade_solver_proto_text.cpp ├── experiments ├── README.md ├── cfgs │ ├── .ipynb_checkpoints │ │ ├── FP_Net_end2end-checkpoint.yml │ │ └── FP_Net_end2end_merge_rcnn-checkpoint.yml │ ├── FP_Net_end2end.yml │ └── FP_Net_end2end_merge_rcnn.yml └── scripts │ ├── .ipynb_checkpoints │ ├── FP_Net_end2end-checkpoint.sh │ └── FP_Net_end2end_merge_rcnn-checkpoint.sh │ ├── FP_Net_end2end.sh │ └── FP_Net_end2end_merge_rcnn.sh ├── framework.png ├── lib ├── Makefile ├── datasets │ ├── .ipynb_checkpoints │ │ ├── factory-checkpoint.py │ │ ├── imdb-checkpoint.py │ │ └── pascal_voc-checkpoint.py │ ├── VOCdevkit-matlab-wrapper │ │ ├── get_voc_opts.m │ │ ├── voc_eval.m │ │ └── xVOCap.m │ ├── __init__.py │ ├── __init__.pyc │ ├── coco.py │ ├── coco.pyc │ ├── ds_utils.py │ ├── ds_utils.pyc │ ├── factory.py │ ├── factory.pyc │ ├── imdb.py │ ├── imdb.pyc │ ├── pascal_voc.py │ ├── pascal_voc.pyc │ ├── tools │ │ └── mcg_munge.py │ ├── voc_eval.py │ └── voc_eval.pyc ├── fast_rcnn │ ├── .ipynb_checkpoints │ │ ├── config-checkpoint.py │ │ ├── lvcai_test-checkpoint.py │ │ ├── my_demo-checkpoint.py │ │ ├── nms_wrapper-checkpoint.py │ │ ├── test-checkpoint.py │ │ └── train-checkpoint.py │ ├── FP_Net_end2end.sh │ ├── __init__.py │ ├── __init__.pyc │ ├── __pycache__ │ │ ├── __init__.cpython-35.pyc │ │ ├── bbox_transform.cpython-35.pyc │ │ ├── config.cpython-35.pyc │ │ └── lvcai_test.cpython-35.pyc │ ├── bbox_transform.py │ ├── bbox_transform.pyc │ ├── config.py │ ├── config.pyc │ ├── lvcai_test.py │ ├── lvcai_test.pyc │ ├── nms_wrapper.py │ ├── nms_wrapper.pyc │ ├── test.py │ ├── test.pyc │ ├── train.py │ └── train.pyc ├── nms │ ├── .gitignore │ ├── .ipynb_checkpoints │ │ └── cpu_nms-checkpoint.pyx │ ├── __init__.py │ ├── __init__.pyc │ ├── cpu_nms.pyx │ ├── gpu_nms.hpp │ ├── gpu_nms.pyx │ ├── nms_kernel.cu │ └── py_cpu_nms.py ├── pycocotools │ ├── UPSTREAM_REV │ ├── __init__.py │ ├── __init__.pyc │ ├── _mask.c │ ├── _mask.pyx │ ├── _mask.so │ ├── coco.py │ ├── coco.pyc │ ├── cocoeval.py │ ├── cocoeval.pyc │ ├── license.txt │ ├── mask.py │ ├── mask.pyc │ ├── maskApi.c │ └── maskApi.h ├── roi_data_layer │ ├── __init__.py │ ├── __init__.pyc │ ├── layer.py │ ├── layer.pyc │ ├── minibatch.py │ ├── minibatch.pyc │ ├── roidb.py │ └── roidb.pyc ├── rpn │ ├── .ipynb_checkpoints │ │ ├── __init__-checkpoint.py │ │ ├── anchor_target_layer-checkpoint.py │ │ ├── as_rois-checkpoint.py │ │ ├── as_rois_mrcnn-checkpoint.py │ │ ├── generate_anchors-checkpoint.py │ │ ├── proposal_layer-checkpoint.py │ │ └── proposal_target_layer-checkpoint.py │ ├── __init__.py │ ├── __init__.pyc │ ├── anchor_target_layer.py │ ├── anchor_target_layer.pyc │ ├── as_rois.py │ ├── as_rois.pyc │ ├── as_rois_mrcnn.py │ ├── as_rois_mrcnn.pyc │ ├── generate.py │ ├── generate_anchors.py │ ├── generate_anchors.pyc │ ├── proposal_layer.py │ ├── proposal_layer.pyc │ ├── proposal_target_layer.py │ ├── proposal_target_layer.pyc │ ├── proposal_target_layer_mrcnn.py │ └── proposal_target_layer_mrcnn.pyc ├── setup.py ├── transform │ ├── __init__.py │ └── torch_image_transform_layer.py └── utils │ ├── .gitignore │ ├── __init__.py │ ├── __init__.pyc │ ├── __pycache__ │ ├── __init__.cpython-35.pyc │ └── timer.cpython-35.pyc │ ├── bbox.pyx │ ├── blob.py │ ├── blob.pyc │ ├── timer.py │ └── timer.pyc ├── models ├── .DS_Store ├── README.md └── pascal_voc │ ├── .DS_Store │ └── FPN │ ├── .DS_Store │ ├── FP_Net_end2end │ ├── .ipynb_checkpoints │ │ ├── solver-checkpoint.prototxt │ │ └── solver_mergercnn-checkpoint.prototxt │ ├── solver.prototxt │ ├── solver_mergercnn.prototxt │ ├── test.prototxt │ ├── test_mergercnn.prototxt │ ├── train.prototxt │ └── train_mergercnn.prototxt │ └── FP_Net_end2end_merge_rcnn │ ├── solver_mergercnn.prototxt │ ├── test_mergercnn.prototxt │ └── train_mergercnn.prototxt ├── test.sh ├── test_mergercnn.sh └── tools ├── README.md ├── __pycache__ └── _init_paths.cpython-35.pyc ├── _init_paths.py ├── _init_paths.pyc ├── compress_net.py ├── demo.py ├── eval_recall.py ├── my_demo.py ├── reval.py ├── rpn_generate.py ├── test_net.py ├── train_faster_rcnn_alt_opt.py ├── train_net.py └── train_svms.py /README.md: -------------------------------------------------------------------------------- 1 | # TianChi-Lvcai 2 | ## 2018广东工业智造大数据创新大赛——智能算法赛 3 | 4 | ## 第一赛季排名:32/2972 5 | 6 | ## 第二赛季排名:33/2972 7 | -------------------------------------------------------------------------------- /第一赛季/README.md: -------------------------------------------------------------------------------- 1 | # 2018广东工业智造大数据创新大赛——智能算法赛 2 | ## 瑕疵分类任务 3 | 4 | ## 用到的模型有inception_v4、senet154、se_resnet50、resnext\_64 -------------------------------------------------------------------------------- /第一赛季/code/resnext_features/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function, division, absolute_import 2 | from .resnext101_32x4d_features import resnext101_32x4d_features 3 | from .resnext101_64x4d_features import resnext101_64x4d_features -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | # 2018广东工业智造大数据创新大赛——智能算法赛 2 | ## 瑕疵检测任务 3 | 4 | ## base框架：FPN+RCNN 5 | ## 一些tricks 6 | 7 | ### multi-scale train 8 | ### anchor的定制化 9 | ### multi-scale test 10 | ### soft nms -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/.ipynb_checkpoints/fpn_0.1-checkpoint.json: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FakerYFX/TianChi-Lvcai/7c773bb4da8fb901265cb03344576a300423ff91/第二赛季/code/detect-model/FPN+RCNN/.ipynb_checkpoints/fpn_0.1-checkpoint.json -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/INSTALL.md: -------------------------------------------------------------------------------- 1 | # Installation 2 | 3 | See http://caffe.berkeleyvision.org/installation.html for the latest 4 | installation instructions. 5 | 6 | Check the users group in case you need help: 7 | https://groups.google.com/forum/#!forum/caffe-users 8 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/README.md: -------------------------------------------------------------------------------- 1 | deformable convolution net on caffe 2 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/build/CMakeFiles/3.5.1/CMakeDetermineCompilerABI_C.bin: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FakerYFX/TianChi-Lvcai/7c773bb4da8fb901265cb03344576a300423ff91/第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/build/CMakeFiles/3.5.1/CMakeDetermineCompilerABI_C.bin -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/build/CMakeFiles/3.5.1/CMakeDetermineCompilerABI_CXX.bin: 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"${PACKAGE_FIND_VERSION}") 9 | set(PACKAGE_VERSION_EXACT TRUE) 10 | endif() 11 | endif() 12 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/data/cifar10/get_cifar10.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | # This scripts downloads the CIFAR10 (binary version) data and unzips it. 3 | 4 | DIR="$( cd "$(dirname "$0")" ; pwd -P )" 5 | cd $DIR 6 | 7 | echo "Downloading..." 8 | 9 | wget --no-check-certificate http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz 10 | 11 | echo "Unzipping..." 12 | 13 | tar -xf cifar-10-binary.tar.gz && rm -f cifar-10-binary.tar.gz 14 | mv cifar-10-batches-bin/* . && rm -rf cifar-10-batches-bin 15 | 16 | # Creation is split out because leveldb sometimes causes segfault 17 | # and needs to be re-created. 18 | 19 | echo "Done." 20 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/data/ilsvrc12/get_ilsvrc_aux.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | # 3 | # N.B. This does not download the ilsvrcC12 data set, as it is gargantuan. 4 | # This script downloads the imagenet example auxiliary files including: 5 | # - the ilsvrc12 image mean, binaryproto 6 | # - synset ids and words 7 | # - Python pickle-format data of ImageNet graph structure and relative infogain 8 | # - the training splits with labels 9 | 10 | DIR="$( cd "$(dirname "$0")" ; pwd -P )" 11 | cd $DIR 12 | 13 | echo "Downloading..." 14 | 15 | wget -c http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz 16 | 17 | echo "Unzipping..." 18 | 19 | tar -xf caffe_ilsvrc12.tar.gz && rm -f caffe_ilsvrc12.tar.gz 20 | 21 | echo "Done." 22 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/docs/CNAME: -------------------------------------------------------------------------------- 1 | caffe.berkeleyvision.org 2 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/docs/README.md: -------------------------------------------------------------------------------- 1 | # Caffe Documentation 2 | 3 | To generate the documentation, run `$CAFFE_ROOT/scripts/build_docs.sh`. 4 | 5 | To push your changes to the documentation to the gh-pages branch of your or the BVLC repo, run `$CAFFE_ROOT/scripts/deploy_docs.sh `. 6 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/docs/images/GitHub-Mark-64px.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FakerYFX/TianChi-Lvcai/7c773bb4da8fb901265cb03344576a300423ff91/第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/docs/images/GitHub-Mark-64px.png -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/docs/images/caffeine-icon.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FakerYFX/TianChi-Lvcai/7c773bb4da8fb901265cb03344576a300423ff91/第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/docs/images/caffeine-icon.png -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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. 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$DBTYPE..." 9 | 10 | rm -rf $EXAMPLE/cifar10_train_$DBTYPE $EXAMPLE/cifar10_test_$DBTYPE 11 | 12 | ./build/examples/cifar10/convert_cifar_data.bin $DATA $EXAMPLE $DBTYPE 13 | 14 | echo "Computing image mean..." 15 | 16 | ./build/tools/compute_image_mean -backend=$DBTYPE \ 17 | $EXAMPLE/cifar10_train_$DBTYPE $EXAMPLE/mean.binaryproto 18 | 19 | echo "Done." 20 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/examples/cifar10/train_full.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | 3 | TOOLS=./build/tools 4 | 5 | $TOOLS/caffe train \ 6 | --solver=examples/cifar10/cifar10_full_solver.prototxt 7 | 8 | # reduce learning rate by factor of 10 9 | $TOOLS/caffe train \ 10 | --solver=examples/cifar10/cifar10_full_solver_lr1.prototxt \ 11 | --snapshot=examples/cifar10/cifar10_full_iter_60000.solverstate.h5 12 | 13 | # reduce learning rate by factor of 10 14 | $TOOLS/caffe train \ 15 | --solver=examples/cifar10/cifar10_full_solver_lr2.prototxt \ 16 | --snapshot=examples/cifar10/cifar10_full_iter_65000.solverstate.h5 17 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/examples/cifar10/train_full_sigmoid.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | 3 | TOOLS=./build/tools 4 | 5 | $TOOLS/caffe train \ 6 | --solver=examples/cifar10/cifar10_full_sigmoid_solver.prototxt 7 | 8 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/examples/cifar10/train_full_sigmoid_bn.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | 3 | TOOLS=./build/tools 4 | 5 | $TOOLS/caffe train \ 6 | --solver=examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt 7 | 8 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/examples/cifar10/train_quick.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | 3 | TOOLS=./build/tools 4 | 5 | $TOOLS/caffe train \ 6 | --solver=examples/cifar10/cifar10_quick_solver.prototxt 7 | 8 | # reduce learning rate by factor of 10 after 8 epochs 9 | $TOOLS/caffe train \ 10 | --solver=examples/cifar10/cifar10_quick_solver_lr1.prototxt \ 11 | --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate.h5 12 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/examples/finetune_flickr_style/flickr_style.csv.gz: -------------------------------------------------------------------------------- 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"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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/examples/hdf5_classification/nonlinear_solver.prototxt: -------------------------------------------------------------------------------- 1 | train_net: "examples/hdf5_classification/nonlinear_auto_train.prototxt" 2 | test_net: "examples/hdf5_classification/nonlinear_auto_test.prototxt" 3 | test_iter: 250 4 | test_interval: 1000 5 | base_lr: 0.01 6 | lr_policy: "step" 7 | gamma: 0.1 8 | stepsize: 5000 9 | display: 1000 10 | max_iter: 10000 11 | momentum: 0.9 12 | weight_decay: 0.0005 13 | snapshot: 10000 14 | snapshot_prefix: "examples/hdf5_classification/data/train" 15 | solver_mode: CPU 16 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/examples/hdf5_classification/solver.prototxt: -------------------------------------------------------------------------------- 1 | train_net: "examples/hdf5_classification/logreg_auto_train.prototxt" 2 | test_net: "examples/hdf5_classification/logreg_auto_test.prototxt" 3 | test_iter: 250 4 | test_interval: 1000 5 | base_lr: 0.01 6 | lr_policy: "step" 7 | gamma: 0.1 8 | stepsize: 5000 9 | display: 1000 10 | max_iter: 10000 11 | momentum: 0.9 12 | weight_decay: 0.0005 13 | snapshot: 10000 14 | snapshot_prefix: "examples/hdf5_classification/data/train" 15 | solver_mode: CPU 16 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/examples/imagenet/resume_training.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | 3 | ./build/tools/caffe train \ 4 | --solver=models/bvlc_reference_caffenet/solver.prototxt \ 5 | --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate.h5 6 | -------------------------------------------------------------------------------- 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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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/examples/mnist/mnist_train_lmdb/data.mdb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FakerYFX/TianChi-Lvcai/7c773bb4da8fb901265cb03344576a300423ff91/第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/examples/mnist/mnist_train_lmdb/data.mdb -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/examples/mnist/mnist_train_lmdb/lock.mdb: -------------------------------------------------------------------------------- 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/第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/examples/net_surgery/conv.prototxt: -------------------------------------------------------------------------------- 1 | # Simple single-layer network to showcase editing model parameters. 2 | name: "convolution" 3 | input: "data" 4 | input_shape { 5 | dim: 1 6 | dim: 1 7 | dim: 100 8 | dim: 100 9 | } 10 | layer { 11 | name: "conv" 12 | type: "Convolution" 13 | bottom: "data" 14 | top: "conv" 15 | convolution_param { 16 | num_output: 3 17 | kernel_size: 5 18 | stride: 1 19 | weight_filler { 20 | type: "gaussian" 21 | std: 0.01 22 | } 23 | bias_filler { 24 | type: "constant" 25 | value: 0 26 | } 27 | } 28 | } 29 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/examples/siamese/create_mnist_siamese.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | # This script converts the mnist data into leveldb format. 3 | 4 | EXAMPLES=./build/examples/siamese 5 | DATA=./data/mnist 6 | 7 | echo "Creating leveldb..." 8 | 9 | rm -rf ./examples/siamese/mnist_siamese_train_leveldb 10 | rm -rf ./examples/siamese/mnist_siamese_test_leveldb 11 | 12 | $EXAMPLES/convert_mnist_siamese_data.bin \ 13 | $DATA/train-images-idx3-ubyte \ 14 | $DATA/train-labels-idx1-ubyte \ 15 | ./examples/siamese/mnist_siamese_train_leveldb 16 | $EXAMPLES/convert_mnist_siamese_data.bin \ 17 | $DATA/t10k-images-idx3-ubyte \ 18 | $DATA/t10k-labels-idx1-ubyte \ 19 | ./examples/siamese/mnist_siamese_test_leveldb 20 | 21 | echo "Done." 22 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/examples/siamese/train_mnist_siamese.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | 3 | TOOLS=./build/tools 4 | 5 | $TOOLS/caffe train --solver=examples/siamese/mnist_siamese_solver.prototxt 6 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/examples/web_demo/requirements.txt: -------------------------------------------------------------------------------- 1 | werkzeug 2 | flask 3 | tornado 4 | numpy 5 | pandas 6 | pillow 7 | pyyaml 8 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/include/caffe/layers/.conadd_layer.hpp.swo: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FakerYFX/TianChi-Lvcai/7c773bb4da8fb901265cb03344576a300423ff91/第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/include/caffe/layers/.conadd_layer.hpp.swo -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/include/caffe/layers/.conadd_layer.hpp.swp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FakerYFX/TianChi-Lvcai/7c773bb4da8fb901265cb03344576a300423ff91/第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/include/caffe/layers/.conadd_layer.hpp.swp -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FakerYFX/TianChi-Lvcai/7c773bb4da8fb901265cb03344576a300423ff91/第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/matlab/hdf5creation/.gitignore: -------------------------------------------------------------------------------- 1 | *.h5 2 | list.txt 3 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/python/caffe/__init__.py: -------------------------------------------------------------------------------- 1 | from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver 2 | from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list, set_random_seed 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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/python/caffe/__init__.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FakerYFX/TianChi-Lvcai/7c773bb4da8fb901265cb03344576a300423ff91/第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/python/caffe/__init__.pyc -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/scripts/build_docs.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # Build documentation for display in web browser. 3 | 4 | PORT=${1:-4000} 5 | 6 | echo "usage: build_docs.sh [port]" 7 | 8 | # Find the docs dir, no matter where the script is called 9 | ROOT_DIR="$( cd "$(dirname "$0")"/.. ; pwd -P )" 10 | cd $ROOT_DIR 11 | 12 | # Gather docs. 13 | scripts/gather_examples.sh 14 | 15 | # Generate developer docs. 16 | make docs 17 | 18 | # Display docs using web server. 19 | cd docs 20 | jekyll serve -w -s . -d _site --port=$PORT 21 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/src/caffe/layer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include "caffe/layer.hpp" 3 | 4 | namespace caffe { 5 | 6 | template 7 | void Layer::InitMutex() { 8 | forward_mutex_.reset(new boost::mutex()); 9 | } 10 | 11 | template 12 | void Layer::Lock() { 13 | if (IsShared()) { 14 | forward_mutex_->lock(); 15 | } 16 | } 17 | 18 | template 19 | void Layer::Unlock() { 20 | if (IsShared()) { 21 | forward_mutex_->unlock(); 22 | } 23 | } 24 | 25 | INSTANTIATE_CLASS(Layer); 26 | 27 | } // namespace caffe 28 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/src/caffe/layers/.conadd_layer.cpp.swo: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FakerYFX/TianChi-Lvcai/7c773bb4da8fb901265cb03344576a300423ff91/第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/src/caffe/layers/.conadd_layer.cpp.swo -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/src/caffe/layers/.conadd_layer.cpp.swp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FakerYFX/TianChi-Lvcai/7c773bb4da8fb901265cb03344576a300423ff91/第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/src/caffe/layers/.conadd_layer.cpp.swp -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/src/caffe/layers/.concat_layer.cpp.swp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FakerYFX/TianChi-Lvcai/7c773bb4da8fb901265cb03344576a300423ff91/第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/src/caffe/layers/.concat_layer.cpp.swp -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/src/caffe/proto/.caffe.proto.swp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FakerYFX/TianChi-Lvcai/7c773bb4da8fb901265cb03344576a300423ff91/第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/src/caffe/proto/.caffe.proto.swp -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/src/caffe/test/test_data/sample_data.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FakerYFX/TianChi-Lvcai/7c773bb4da8fb901265cb03344576a300423ff91/第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/src/caffe/test/test_data/sample_data.h5 -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/src/caffe/test/test_data/sample_data_2_gzip.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FakerYFX/TianChi-Lvcai/7c773bb4da8fb901265cb03344576a300423ff91/第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/src/caffe/test/test_data/sample_data_2_gzip.h5 -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/src/caffe/test/test_data/solver_data.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FakerYFX/TianChi-Lvcai/7c773bb4da8fb901265cb03344576a300423ff91/第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/src/caffe/test/test_data/solver_data.h5 -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/src/caffe/test/test_data/solver_data_list.txt: -------------------------------------------------------------------------------- 1 | src/caffe/test/test_data/solver_data.h5 2 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/src/gtest/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | add_library(gtest STATIC EXCLUDE_FROM_ALL gtest.h gtest-all.cpp) 2 | caffe_default_properties(gtest) 3 | 4 | #add_library(gtest_main gtest_main.cc) 5 | #target_link_libraries(gtest_main gtest) 6 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/tools/device_query.cpp: -------------------------------------------------------------------------------- 1 | #include "caffe/common.hpp" 2 | 3 | int main(int argc, char** argv) { 4 | LOG(FATAL) << "Deprecated. Use caffe device_query " 5 | "[--device_id=0] instead."; 6 | return 0; 7 | } 8 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/caffe-fpn/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 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/experiments/README.md: -------------------------------------------------------------------------------- 1 | Scripts are under `experiments/scripts`. 2 | 3 | Each script saves a log file under `experiments/logs`. 4 | 5 | Configuration override files used in the experiments are stored in `experiments/cfgs`. 6 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/experiments/cfgs/.ipynb_checkpoints/FP_Net_end2end-checkpoint.yml: -------------------------------------------------------------------------------- 1 | EXP_DIR: FP_Net_end2end 2 | TRAIN: 3 | HAS_RPN: True 4 | IMS_PER_BATCH: 1 5 | BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True 6 | RPN_POSITIVE_OVERLAP: 0.7 7 | RPN_BATCHSIZE: 256 8 | PROPOSAL_METHOD: gt 9 | BG_THRESH_LO: 0.0 10 | TEST: 11 | HAS_RPN: True 12 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/experiments/cfgs/.ipynb_checkpoints/FP_Net_end2end_merge_rcnn-checkpoint.yml: -------------------------------------------------------------------------------- 1 | EXP_DIR: FP_Net_end2end_merge_rcnn 2 | TRAIN: 3 | HAS_RPN: True 4 | IMS_PER_BATCH: 1 5 | BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True 6 | RPN_POSITIVE_OVERLAP: 0.7 7 | RPN_BATCHSIZE: 256 8 | PROPOSAL_METHOD: gt 9 | BG_THRESH_LO: 0.0 10 | TEST: 11 | HAS_RPN: True 12 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/experiments/cfgs/FP_Net_end2end.yml: -------------------------------------------------------------------------------- 1 | EXP_DIR: FP_Net_end2end 2 | TRAIN: 3 | HAS_RPN: True 4 | IMS_PER_BATCH: 1 5 | BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True 6 | RPN_POSITIVE_OVERLAP: 0.7 7 | RPN_BATCHSIZE: 256 8 | PROPOSAL_METHOD: gt 9 | BG_THRESH_LO: 0.0 10 | TEST: 11 | HAS_RPN: True 12 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/experiments/cfgs/FP_Net_end2end_merge_rcnn.yml: -------------------------------------------------------------------------------- 1 | EXP_DIR: FP_Net_end2end_merge_rcnn 2 | TRAIN: 3 | HAS_RPN: True 4 | IMS_PER_BATCH: 1 5 | BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True 6 | RPN_POSITIVE_OVERLAP: 0.7 7 | RPN_BATCHSIZE: 256 8 | PROPOSAL_METHOD: gt 9 | BG_THRESH_LO: 0.0 10 | TEST: 11 | HAS_RPN: True 12 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/framework.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FakerYFX/TianChi-Lvcai/7c773bb4da8fb901265cb03344576a300423ff91/第二赛季/code/detect-model/FPN+RCNN/framework.png -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/lib/Makefile: -------------------------------------------------------------------------------- 1 | all: 2 | python setup.py build_ext --inplace 3 | rm -rf build 4 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/lib/datasets/VOCdevkit-matlab-wrapper/get_voc_opts.m: -------------------------------------------------------------------------------- 1 | function VOCopts = get_voc_opts(path) 2 | 3 | tmp = pwd; 4 | cd(path); 5 | try 6 | addpath('VOCcode'); 7 | VOCinit; 8 | catch 9 | rmpath('VOCcode'); 10 | cd(tmp); 11 | error(sprintf('VOCcode directory not found under %s', path)); 12 | end 13 | rmpath('VOCcode'); 14 | cd(tmp); 15 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/lib/datasets/VOCdevkit-matlab-wrapper/xVOCap.m: -------------------------------------------------------------------------------- 1 | function ap = xVOCap(rec,prec) 2 | % From the PASCAL VOC 2011 devkit 3 | 4 | mrec=[0 ; rec ; 1]; 5 | mpre=[0 ; prec ; 0]; 6 | for i=numel(mpre)-1:-1:1 7 | mpre(i)=max(mpre(i),mpre(i+1)); 8 | end 9 | i=find(mrec(2:end)~=mrec(1:end-1))+1; 10 | ap=sum((mrec(i)-mrec(i-1)).*mpre(i)); 11 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/lib/datasets/__init__.py: -------------------------------------------------------------------------------- 1 | # -------------------------------------------------------- 2 | # Fast R-CNN 3 | # Copyright (c) 2015 Microsoft 4 | # Licensed under The MIT License [see LICENSE for details] 5 | # Written by Ross Girshick 6 | # -------------------------------------------------------- 7 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/lib/datasets/__init__.pyc: -------------------------------------------------------------------------------- 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average_loss: 100 8 | # iter_size: 1 9 | momentum: 0.9 10 | weight_decay: 0.0001 11 | # We disable standard caffe solver snapshotting and implement our own snapshot 12 | # function 13 | snapshot: 0 14 | # We still use the snapshot prefix, though 15 | snapshot_prefix: "fpn" 16 | iter_size: 2 17 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/models/pascal_voc/FPN/FP_Net_end2end/.ipynb_checkpoints/solver_mergercnn-checkpoint.prototxt: -------------------------------------------------------------------------------- 1 | train_net: "models/pascal_voc/FPN/FP_Net_end2end/train_mergercnn.prototxt" 2 | base_lr: 0.001 3 | lr_policy: "step" 4 | gamma: 0.1 5 | stepsize: 50000 6 | display: 20 7 | average_loss: 100 8 | # iter_size: 1 9 | momentum: 0.9 10 | weight_decay: 0.0001 11 | # We disable standard caffe solver snapshotting and implement our own snapshot 12 | # function 13 | snapshot: 0 14 | # We still use the snapshot prefix, though 15 | snapshot_prefix: "fpn" 16 | iter_size: 2 17 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/models/pascal_voc/FPN/FP_Net_end2end/solver.prototxt: -------------------------------------------------------------------------------- 1 | train_net: "models/pascal_voc/FPN/FP_Net_end2end/train.prototxt" 2 | base_lr: 0.001 3 | lr_policy: "step" 4 | gamma: 0.1 5 | stepsize: 50000 6 | display: 20 7 | average_loss: 100 8 | # iter_size: 1 9 | momentum: 0.9 10 | weight_decay: 0.0001 11 | # We disable standard caffe solver snapshotting and implement our own snapshot 12 | # function 13 | snapshot: 0 14 | # We still use the snapshot prefix, though 15 | snapshot_prefix: "fpn" 16 | iter_size: 2 17 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/models/pascal_voc/FPN/FP_Net_end2end/solver_mergercnn.prototxt: -------------------------------------------------------------------------------- 1 | train_net: "models/pascal_voc/FPN/FP_Net_end2end/train_mergercnn.prototxt" 2 | base_lr: 0.001 3 | lr_policy: "step" 4 | gamma: 0.1 5 | stepsize: 50000 6 | display: 20 7 | average_loss: 100 8 | # iter_size: 1 9 | momentum: 0.9 10 | weight_decay: 0.0001 11 | # We disable standard caffe solver snapshotting and implement our own snapshot 12 | # function 13 | snapshot: 0 14 | # We still use the snapshot prefix, though 15 | snapshot_prefix: "fpn" 16 | iter_size: 2 17 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/models/pascal_voc/FPN/FP_Net_end2end_merge_rcnn/solver_mergercnn.prototxt: -------------------------------------------------------------------------------- 1 | train_net: "models/pascal_voc/FPN/FP_Net_end2end/train_mergercnn.prototxt" 2 | base_lr: 0.001 3 | lr_policy: "step" 4 | gamma: 0.1 5 | stepsize: 50000 6 | display: 20 7 | average_loss: 100 8 | # iter_size: 1 9 | momentum: 0.9 10 | weight_decay: 0.0001 11 | # We disable standard caffe solver snapshotting and implement our own snapshot 12 | # function 13 | snapshot: 0 14 | # We still use the snapshot prefix, though 15 | snapshot_prefix: "fpn" 16 | iter_size: 2 17 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/tools/README.md: -------------------------------------------------------------------------------- 1 | Tools for training, testing, and compressing Fast R-CNN networks. 2 | -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/tools/__pycache__/_init_paths.cpython-35.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FakerYFX/TianChi-Lvcai/7c773bb4da8fb901265cb03344576a300423ff91/第二赛季/code/detect-model/FPN+RCNN/tools/__pycache__/_init_paths.cpython-35.pyc -------------------------------------------------------------------------------- /第二赛季/code/detect-model/FPN+RCNN/tools/_init_paths.py: -------------------------------------------------------------------------------- 1 | # -------------------------------------------------------- 2 | # Fast R-CNN 3 | # Copyright (c) 2015 Microsoft 4 | # Licensed under The MIT License [see LICENSE for details] 5 | # Written by Ross Girshick 6 | # -------------------------------------------------------- 7 | 8 | """Set up paths for Fast R-CNN.""" 9 | 10 | import os.path as osp 11 | import sys 12 | 13 | def add_path(path): 14 | if path not in sys.path: 15 | sys.path.insert(0, path) 16 | 17 | this_dir = osp.dirname(__file__) 18 | 19 | # Add caffe to PYTHONPATH 20 | caffe_path = osp.join(this_dir, '..', 'caffe-fpn', 'python') 21 | add_path(caffe_path) 22 | 23 | # Add lib to PYTHONPATH 24 | lib_path = osp.join(this_dir, '..', 'lib') 25 | add_path(lib_path) 26 | 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