├── CUHK03-NP └── README.md ├── README.md ├── caffe ├── .Doxyfile ├── .github │ └── ISSUE_TEMPLATE.md ├── .travis.yml ├── CMakeLists.txt ├── CONTRIBUTING.md ├── CONTRIBUTORS.md ├── INSTALL.md ├── LICENSE ├── Makefile ├── Makefile.config.example ├── README.md ├── 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 │ │ ├── FindNCCL.cmake │ │ ├── FindNumPy.cmake │ │ ├── FindOpenBLAS.cmake │ │ ├── FindSnappy.cmake │ │ └── FindvecLib.cmake │ ├── ProtoBuf.cmake │ ├── Summary.cmake │ ├── Targets.cmake │ ├── Templates │ │ ├── CaffeConfig.cmake.in │ │ ├── CaffeConfigVersion.cmake.in │ │ └── caffe_config.h.in │ ├── Utils.cmake │ └── lint.cmake ├── docker │ ├── README.md │ ├── cpu │ │ └── Dockerfile │ └── gpu │ │ └── Dockerfile ├── docs │ ├── CMakeLists.txt │ ├── CNAME │ ├── README.md │ ├── _config.yml │ ├── _layouts │ │ └── default.html │ ├── development.md │ ├── index.md │ ├── install_apt.md │ ├── install_apt_debian.md │ ├── install_osx.md │ ├── install_yum.md │ ├── installation.md │ ├── model_zoo.md │ ├── multigpu.md │ ├── performance_hardware.md │ ├── stylesheets │ │ ├── pygment_trac.css │ │ ├── reset.css │ │ └── styles.css │ └── tutorial │ │ ├── convolution.md │ │ ├── data.md │ │ ├── forward_backward.md │ │ ├── index.md │ │ ├── interfaces.md │ │ ├── layers.md │ │ ├── layers │ │ ├── absval.md │ │ ├── accuracy.md │ │ ├── argmax.md │ │ ├── batchnorm.md │ │ ├── batchreindex.md │ │ ├── bias.md │ │ ├── bnll.md │ │ ├── concat.md │ │ ├── contrastiveloss.md │ │ ├── convolution.md │ │ ├── crop.md │ │ ├── data.md │ │ ├── deconvolution.md │ │ ├── dropout.md │ │ ├── dummydata.md │ │ ├── eltwise.md │ │ ├── elu.md │ │ ├── embed.md │ │ ├── euclideanloss.md │ │ ├── exp.md │ │ ├── filter.md │ │ ├── flatten.md │ │ ├── hdf5data.md │ │ ├── hdf5output.md │ │ ├── hingeloss.md │ │ ├── im2col.md │ │ ├── imagedata.md │ │ ├── infogainloss.md │ │ ├── innerproduct.md │ │ ├── input.md │ │ ├── log.md │ │ ├── lrn.md │ │ ├── lstm.md │ │ ├── memorydata.md │ │ ├── multinomiallogisticloss.md │ │ ├── mvn.md │ │ ├── parameter.md │ │ ├── pooling.md │ │ ├── power.md │ │ ├── prelu.md │ │ ├── python.md │ │ ├── recurrent.md │ │ ├── reduction.md │ │ ├── relu.md │ │ ├── reshape.md │ │ ├── rnn.md │ │ ├── scale.md │ │ ├── sigmoid.md │ │ ├── sigmoidcrossentropyloss.md │ │ ├── silence.md │ │ ├── slice.md │ │ ├── softmax.md │ │ ├── softmaxwithloss.md │ │ ├── split.md │ │ ├── spp.md │ │ ├── tanh.md │ │ ├── threshold.md │ │ ├── tile.md │ │ └── windowdata.md │ │ ├── loss.md │ │ ├── net_layer_blob.md │ │ └── solver.md ├── examples │ ├── 00-classification.ipynb │ ├── 01-learning-lenet.ipynb │ ├── 02-fine-tuning.ipynb │ ├── CMakeLists.txt │ ├── brewing-logreg.ipynb │ ├── cifar10 │ │ ├── cifar10_full.prototxt │ │ ├── cifar10_full_sigmoid_solver.prototxt │ │ ├── cifar10_full_sigmoid_solver_bn.prototxt │ │ ├── cifar10_full_sigmoid_train_test.prototxt │ │ ├── cifar10_full_sigmoid_train_test_bn.prototxt │ │ ├── cifar10_full_solver.prototxt │ │ ├── cifar10_full_solver_lr1.prototxt │ │ ├── cifar10_full_solver_lr2.prototxt │ │ ├── cifar10_full_train_test.prototxt │ │ ├── cifar10_quick.prototxt │ │ ├── cifar10_quick_solver.prototxt │ │ ├── cifar10_quick_solver_lr1.prototxt │ │ ├── cifar10_quick_train_test.prototxt │ │ ├── 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_train_val.prototxt │ │ └── train_val.prototxt │ ├── imagenet │ │ ├── create_imagenet.sh │ │ ├── make_imagenet_mean.sh │ │ ├── readme.md │ │ ├── resume_training.sh │ │ └── train_caffenet.sh │ ├── 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 │ │ ├── readme.md │ │ ├── train_lenet.sh │ │ ├── train_lenet_adam.sh │ │ ├── train_lenet_consolidated.sh │ │ ├── train_lenet_docker.sh │ │ ├── train_lenet_rmsprop.sh │ │ ├── train_mnist_autoencoder.sh │ │ ├── train_mnist_autoencoder_adadelta.sh │ │ ├── train_mnist_autoencoder_adagrad.sh │ │ └── train_mnist_autoencoder_nesterov.sh │ ├── net_surgery.ipynb │ ├── net_surgery │ │ ├── bvlc_caffenet_full_conv.prototxt │ │ └── conv.prototxt │ ├── pascal-multilabel-with-datalayer.ipynb │ ├── pycaffe │ │ ├── caffenet.py │ │ ├── layers │ │ │ ├── pascal_multilabel_datalayers.py │ │ │ └── pyloss.py │ │ ├── linreg.prototxt │ │ └── tools.py │ ├── siamese │ │ ├── convert_mnist_siamese_data.cpp │ │ ├── create_mnist_siamese.sh │ │ ├── mnist_siamese.ipynb │ │ ├── mnist_siamese.prototxt │ │ ├── mnist_siamese_solver.prototxt │ │ ├── mnist_siamese_train_test.prototxt │ │ ├── readme.md │ │ └── train_mnist_siamese.sh │ └── web_demo │ │ ├── app.py │ │ ├── exifutil.py │ │ ├── readme.md │ │ ├── requirements.txt │ │ └── templates │ │ └── index.html ├── include │ └── caffe │ │ ├── blob.hpp │ │ ├── caffe.hpp │ │ ├── common.hpp │ │ ├── data_transformer.hpp │ │ ├── filler.hpp │ │ ├── internal_thread.hpp │ │ ├── layer.hpp │ │ ├── layer_factory.hpp │ │ ├── layers │ │ ├── absval_layer.hpp │ │ ├── accuracy_layer.hpp │ │ ├── argmax_layer.hpp │ │ ├── base_conv_layer.hpp │ │ ├── base_data_layer.hpp │ │ ├── batch_norm_layer.hpp │ │ ├── batch_reindex_layer.hpp │ │ ├── bias_layer.hpp │ │ ├── bnll_layer.hpp │ │ ├── concat_layer.hpp │ │ ├── contrastive_loss_layer.hpp │ │ ├── conv_layer.hpp │ │ ├── crop_layer.hpp │ │ ├── cudnn_conv_layer.hpp │ │ ├── cudnn_lcn_layer.hpp │ │ ├── cudnn_lrn_layer.hpp │ │ ├── cudnn_pooling_layer.hpp │ │ ├── cudnn_relu_layer.hpp │ │ ├── cudnn_sigmoid_layer.hpp │ │ ├── cudnn_softmax_layer.hpp │ │ ├── cudnn_tanh_layer.hpp │ │ ├── data_layer.hpp │ │ ├── deconv_layer.hpp │ │ ├── dropout_layer.hpp │ │ ├── dummy_data_layer.hpp │ │ ├── eltwise_layer.hpp │ │ ├── elu_layer.hpp │ │ ├── embed_layer.hpp │ │ ├── euclidean_loss_layer.hpp │ │ ├── exp_layer.hpp │ │ ├── filter_layer.hpp │ │ ├── flatten_layer.hpp │ │ ├── hdf5_data_layer.hpp │ │ ├── hdf5_output_layer.hpp │ │ ├── hinge_loss_layer.hpp │ │ ├── im2col_layer.hpp │ │ ├── image_data_layer.hpp │ │ ├── infogain_loss_layer.hpp │ │ ├── inner_product_layer.hpp │ │ ├── input_layer.hpp │ │ ├── log_layer.hpp │ │ ├── loss_layer.hpp │ │ ├── lrn_layer.hpp │ │ ├── lstm_layer.hpp │ │ ├── memory_data_layer.hpp │ │ ├── multinomial_logistic_loss_layer.hpp │ │ ├── mvn_layer.hpp │ │ ├── neuron_layer.hpp │ │ ├── parameter_layer.hpp │ │ ├── pooling_layer.hpp │ │ ├── power_layer.hpp │ │ ├── prelu_layer.hpp │ │ ├── python_layer.hpp │ │ ├── recurrent_layer.hpp │ │ ├── reduction_layer.hpp │ │ ├── relu_layer.hpp │ │ ├── reshape_layer.hpp │ │ ├── rnn_layer.hpp │ │ ├── scale_layer.hpp │ │ ├── sigmoid_cross_entropy_loss_layer.hpp │ │ ├── sigmoid_layer.hpp │ │ ├── silence_layer.hpp │ │ ├── slice_layer.hpp │ │ ├── softmax_layer.hpp │ │ ├── softmax_loss_layer.hpp │ │ ├── split_layer.hpp │ │ ├── spp_layer.hpp │ │ ├── tanh_layer.hpp │ │ ├── threshold_layer.hpp │ │ ├── tile_layer.hpp │ │ └── window_data_layer.hpp │ │ ├── net.hpp │ │ ├── parallel.hpp │ │ ├── sgd_solvers.hpp │ │ ├── solver.hpp │ │ ├── solver_factory.hpp │ │ ├── syncedmem.hpp │ │ ├── test │ │ ├── test_caffe_main.hpp │ │ └── test_gradient_check_util.hpp │ │ └── util │ │ ├── benchmark.hpp │ │ ├── blocking_queue.hpp │ │ ├── cudnn.hpp │ │ ├── db.hpp │ │ ├── db_leveldb.hpp │ │ ├── db_lmdb.hpp │ │ ├── device_alternate.hpp │ │ ├── format.hpp │ │ ├── gpu_util.cuh │ │ ├── hdf5.hpp │ │ ├── im2col.hpp │ │ ├── insert_splits.hpp │ │ ├── io.hpp │ │ ├── math_functions.hpp │ │ ├── mkl_alternate.hpp │ │ ├── nccl.hpp │ │ ├── rng.hpp │ │ ├── signal_handler.h │ │ └── upgrade_proto.hpp ├── 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 │ │ ├── demo.m │ │ └── store2hdf5.m ├── python │ ├── CMakeLists.txt │ ├── caffe │ │ ├── __init__.py │ │ ├── _caffe.cpp │ │ ├── classifier.py │ │ ├── coord_map.py │ │ ├── detector.py │ │ ├── draw.py │ │ ├── imagenet │ │ │ └── ilsvrc_2012_mean.npy │ │ ├── io.py │ │ ├── net_spec.py │ │ ├── pycaffe.py │ │ └── test │ │ │ ├── test_coord_map.py │ │ │ ├── test_io.py │ │ │ ├── test_layer_type_list.py │ │ │ ├── test_net.py │ │ │ ├── test_net_spec.py │ │ │ ├── test_python_layer.py │ │ │ ├── test_python_layer_with_param_str.py │ │ │ └── test_solver.py │ ├── classify.py │ ├── detect.py │ ├── draw_net.py │ ├── requirements.txt │ └── train.py ├── scripts │ ├── build_docs.sh │ ├── copy_notebook.py │ ├── cpp_lint.py │ ├── deploy_docs.sh │ ├── download_model_binary.py │ ├── download_model_from_gist.sh │ ├── gather_examples.sh │ ├── split_caffe_proto.py │ ├── travis │ │ ├── build.sh │ │ ├── configure-cmake.sh │ │ ├── configure-make.sh │ │ ├── configure.sh │ │ ├── defaults.sh │ │ ├── install-deps.sh │ │ ├── install-python-deps.sh │ │ ├── setup-venv.sh │ │ └── test.sh │ └── upload_model_to_gist.sh ├── src │ ├── caffe │ │ ├── CMakeLists.txt │ │ ├── blob.cpp │ │ ├── common.cpp │ │ ├── data_transformer.cpp │ │ ├── internal_thread.cpp │ │ ├── layer.cpp │ │ ├── layer_factory.cpp │ │ ├── layers │ │ │ ├── absval_layer.cpp │ │ │ ├── absval_layer.cu │ │ │ ├── accuracy_layer.cpp │ │ │ ├── argmax_layer.cpp │ │ │ ├── base_conv_layer.cpp │ │ │ ├── base_data_layer.cpp │ │ │ ├── base_data_layer.cu │ │ │ ├── batch_norm_layer.cpp │ │ │ ├── batch_norm_layer.cu │ │ │ ├── batch_reindex_layer.cpp │ │ │ ├── batch_reindex_layer.cu │ │ │ ├── bias_layer.cpp │ │ │ ├── bias_layer.cu │ │ │ ├── bnll_layer.cpp │ │ │ ├── bnll_layer.cu │ │ │ ├── concat_layer.cpp │ │ │ ├── concat_layer.cu │ │ │ ├── contrastive_loss_layer.cpp │ │ │ ├── contrastive_loss_layer.cu │ │ │ ├── conv_layer.cpp │ │ │ ├── conv_layer.cu │ │ │ ├── crop_layer.cpp │ │ │ ├── crop_layer.cu │ │ │ ├── cudnn_conv_layer.cpp │ │ │ ├── cudnn_conv_layer.cu │ │ │ ├── cudnn_lcn_layer.cpp │ │ │ ├── cudnn_lcn_layer.cu │ │ │ ├── cudnn_lrn_layer.cpp │ │ │ ├── cudnn_lrn_layer.cu │ │ │ ├── cudnn_pooling_layer.cpp │ │ │ ├── cudnn_pooling_layer.cu │ │ │ ├── cudnn_relu_layer.cpp │ │ │ ├── cudnn_relu_layer.cu │ │ │ ├── cudnn_sigmoid_layer.cpp │ │ │ ├── cudnn_sigmoid_layer.cu │ │ │ ├── cudnn_softmax_layer.cpp │ │ │ ├── cudnn_softmax_layer.cu │ │ │ ├── cudnn_tanh_layer.cpp │ │ │ ├── cudnn_tanh_layer.cu │ │ │ ├── data_layer.cpp │ │ │ ├── deconv_layer.cpp │ │ │ ├── deconv_layer.cu │ │ │ ├── dropout_layer.cpp │ │ │ ├── dropout_layer.cu │ │ │ ├── dummy_data_layer.cpp │ │ │ ├── eltwise_layer.cpp │ │ │ ├── eltwise_layer.cu │ │ │ ├── elu_layer.cpp │ │ │ ├── elu_layer.cu │ │ │ ├── embed_layer.cpp │ │ │ ├── embed_layer.cu │ │ │ ├── euclidean_loss_layer.cpp │ │ │ ├── euclidean_loss_layer.cu │ │ │ ├── exp_layer.cpp │ │ │ ├── exp_layer.cu │ │ │ ├── filter_layer.cpp │ │ │ ├── filter_layer.cu │ │ │ ├── flatten_layer.cpp │ │ │ ├── hdf5_data_layer.cpp │ │ │ ├── hdf5_data_layer.cu │ │ │ ├── hdf5_output_layer.cpp │ │ │ ├── hdf5_output_layer.cu │ │ │ ├── hinge_loss_layer.cpp │ │ │ ├── im2col_layer.cpp │ │ │ ├── im2col_layer.cu │ │ │ ├── image_data_layer.cpp │ │ │ ├── infogain_loss_layer.cpp │ │ │ ├── inner_product_layer.cpp │ │ │ ├── inner_product_layer.cu │ │ │ ├── input_layer.cpp │ │ │ ├── log_layer.cpp │ │ │ ├── log_layer.cu │ │ │ ├── loss_layer.cpp │ │ │ ├── lrn_layer.cpp │ │ │ ├── lrn_layer.cu │ │ │ ├── lstm_layer.cpp │ │ │ ├── lstm_unit_layer.cpp │ │ │ ├── lstm_unit_layer.cu │ │ │ ├── memory_data_layer.cpp │ │ │ ├── multinomial_logistic_loss_layer.cpp │ │ │ ├── mvn_layer.cpp │ │ │ ├── mvn_layer.cu │ │ │ ├── neuron_layer.cpp │ │ │ ├── parameter_layer.cpp │ │ │ ├── pooling_layer.cpp │ │ │ ├── pooling_layer.cu │ │ │ ├── power_layer.cpp │ │ │ ├── power_layer.cu │ │ │ ├── prelu_layer.cpp │ │ │ ├── prelu_layer.cu │ │ │ ├── recurrent_layer.cpp │ │ │ ├── recurrent_layer.cu │ │ │ ├── reduction_layer.cpp │ │ │ ├── reduction_layer.cu │ │ │ ├── relu_layer.cpp │ │ │ ├── relu_layer.cu │ │ │ ├── reshape_layer.cpp │ │ │ ├── rnn_layer.cpp │ │ │ ├── scale_layer.cpp │ │ │ ├── scale_layer.cu │ │ │ ├── sigmoid_cross_entropy_loss_layer.cpp │ │ │ ├── sigmoid_cross_entropy_loss_layer.cu │ │ │ ├── sigmoid_layer.cpp │ │ │ ├── sigmoid_layer.cu │ │ │ ├── silence_layer.cpp │ │ │ ├── silence_layer.cu │ │ │ ├── slice_layer.cpp │ │ │ ├── slice_layer.cu │ │ │ ├── softmax_layer.cpp │ │ │ ├── softmax_layer.cu │ │ │ ├── softmax_loss_layer.cpp │ │ │ ├── softmax_loss_layer.cu │ │ │ ├── split_layer.cpp │ │ │ ├── split_layer.cu │ │ │ ├── spp_layer.cpp │ │ │ ├── tanh_layer.cpp │ │ │ ├── tanh_layer.cu │ │ │ ├── threshold_layer.cpp │ │ │ ├── threshold_layer.cu │ │ │ ├── tile_layer.cpp │ │ │ ├── tile_layer.cu │ │ │ └── window_data_layer.cpp │ │ ├── net.cpp │ │ ├── parallel.cpp │ │ ├── proto │ │ │ └── caffe.proto │ │ ├── solver.cpp │ │ ├── solvers │ │ │ ├── adadelta_solver.cpp │ │ │ ├── adadelta_solver.cu │ │ │ ├── adagrad_solver.cpp │ │ │ ├── adagrad_solver.cu │ │ │ ├── adam_solver.cpp │ │ │ ├── adam_solver.cu │ │ │ ├── nesterov_solver.cpp │ │ │ ├── nesterov_solver.cu │ │ │ ├── rmsprop_solver.cpp │ │ │ ├── rmsprop_solver.cu │ │ │ ├── sgd_solver.cpp │ │ │ └── sgd_solver.cu │ │ ├── syncedmem.cpp │ │ ├── test │ │ │ ├── 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_crop_layer.cpp │ │ │ ├── test_data │ │ │ │ ├── generate_sample_data.py │ │ │ │ ├── sample_data.h5 │ │ │ │ ├── sample_data_2_gzip.h5 │ │ │ │ ├── sample_data_list.txt │ │ │ │ ├── solver_data.h5 │ │ │ │ └── solver_data_list.txt │ │ │ ├── test_data_layer.cpp │ │ │ ├── test_data_transformer.cpp │ │ │ ├── test_db.cpp │ │ │ ├── test_deconvolution_layer.cpp │ │ │ ├── test_dummy_data_layer.cpp │ │ │ ├── test_eltwise_layer.cpp │ │ │ ├── test_embed_layer.cpp │ │ │ ├── test_euclidean_loss_layer.cpp │ │ │ ├── test_filler.cpp │ │ │ ├── test_filter_layer.cpp │ │ │ ├── test_flatten_layer.cpp │ │ │ ├── test_gradient_based_solver.cpp │ │ │ ├── test_hdf5_output_layer.cpp │ │ │ ├── test_hdf5data_layer.cpp │ │ │ ├── test_hinge_loss_layer.cpp │ │ │ ├── test_im2col_kernel.cu │ │ │ ├── test_im2col_layer.cpp │ │ │ ├── test_image_data_layer.cpp │ │ │ ├── test_infogain_loss_layer.cpp │ │ │ ├── test_inner_product_layer.cpp │ │ │ ├── test_internal_thread.cpp │ │ │ ├── test_io.cpp │ │ │ ├── test_layer_factory.cpp │ │ │ ├── test_lrn_layer.cpp │ │ │ ├── test_lstm_layer.cpp │ │ │ ├── test_math_functions.cpp │ │ │ ├── test_maxpool_dropout_layers.cpp │ │ │ ├── test_memory_data_layer.cpp │ │ │ ├── test_multinomial_logistic_loss_layer.cpp │ │ │ ├── test_mvn_layer.cpp │ │ │ ├── test_net.cpp │ │ │ ├── test_neuron_layer.cpp │ │ │ ├── test_platform.cpp │ │ │ ├── test_pooling_layer.cpp │ │ │ ├── test_power_layer.cpp │ │ │ ├── test_protobuf.cpp │ │ │ ├── test_random_number_generator.cpp │ │ │ ├── test_reduction_layer.cpp │ │ │ ├── test_reshape_layer.cpp │ │ │ ├── test_rnn_layer.cpp │ │ │ ├── test_scale_layer.cpp │ │ │ ├── test_sigmoid_cross_entropy_loss_layer.cpp │ │ │ ├── test_slice_layer.cpp │ │ │ ├── test_softmax_layer.cpp │ │ │ ├── test_softmax_with_loss_layer.cpp │ │ │ ├── test_solver.cpp │ │ │ ├── test_solver_factory.cpp │ │ │ ├── test_split_layer.cpp │ │ │ ├── test_spp_layer.cpp │ │ │ ├── test_stochastic_pooling.cpp │ │ │ ├── test_syncedmem.cpp │ │ │ ├── test_tanh_layer.cpp │ │ │ ├── test_threshold_layer.cpp │ │ │ ├── test_tile_layer.cpp │ │ │ ├── test_upgrade_proto.cpp │ │ │ └── test_util_blas.cpp │ │ └── util │ │ │ ├── benchmark.cpp │ │ │ ├── blocking_queue.cpp │ │ │ ├── cudnn.cpp │ │ │ ├── db.cpp │ │ │ ├── db_leveldb.cpp │ │ │ ├── db_lmdb.cpp │ │ │ ├── hdf5.cpp │ │ │ ├── im2col.cpp │ │ │ ├── im2col.cu │ │ │ ├── insert_splits.cpp │ │ │ ├── io.cpp │ │ │ ├── math_functions.cpp │ │ │ ├── math_functions.cu │ │ │ ├── signal_handler.cpp │ │ │ └── upgrade_proto.cpp │ └── gtest │ │ ├── CMakeLists.txt │ │ ├── gtest-all.cpp │ │ ├── gtest.h │ │ └── gtest_main.cc └── tools │ ├── CMakeLists.txt │ ├── caffe.cpp │ ├── compute_image_mean.cpp │ ├── convert_imageset.cpp │ ├── device_query.cpp │ ├── extra │ ├── extract_seconds.py │ ├── launch_resize_and_crop_images.sh │ ├── parse_log.py │ ├── parse_log.sh │ ├── plot_log.gnuplot.example │ ├── plot_training_log.py.example │ ├── resize_and_crop_images.py │ └── summarize.py │ ├── extract_features.cpp │ ├── finetune_net.cpp │ ├── net_speed_benchmark.cpp │ ├── test_net.cpp │ ├── train_net.cpp │ ├── upgrade_net_proto_binary.cpp │ ├── upgrade_net_proto_text.cpp │ └── upgrade_solver_proto_text.cpp ├── data └── imagenet_models │ └── README.md ├── evaluation ├── CUHK03_evaluation.m ├── CUHK03_extract_feature.m ├── KISSME │ └── toolbox │ │ ├── COPYRIGHT │ │ ├── CrossValidatePairs.m │ │ ├── KISSME.m │ │ ├── TrainValidateMarket.m │ │ ├── evalData.m │ │ ├── helper │ │ ├── PairMetricLearning.m │ │ ├── SOPD.cpp │ │ ├── SOPD.mexa64 │ │ ├── ToyCarPairsToLabels.m │ │ ├── calcMCMC.m │ │ ├── calcmAP.m │ │ ├── cdistM.m │ │ ├── col_sum.m │ │ ├── exportAndCropFigure.m │ │ ├── icg_plotroc.m │ │ ├── icg_roc.m │ │ ├── pairsToLabels.m │ │ ├── sqdist.m │ │ └── validateCovMatrix.m │ │ ├── init.m │ │ ├── install3dpartylibs.m │ │ ├── learnAlgos │ │ ├── LearnAlgo.m │ │ ├── LearnAlgoITML.m │ │ ├── LearnAlgoKISSME.m │ │ ├── LearnAlgoLDML.m │ │ ├── LearnAlgoLMNN.m │ │ ├── LearnAlgoMLEuclidean.m │ │ ├── LearnAlgoMahal.m │ │ └── LearnAlgoSVM.m │ │ └── lib │ │ ├── LMNN │ │ ├── .DS_Store │ │ ├── .svn │ │ │ ├── all-wcprops │ │ │ ├── dir-prop-base │ │ │ ├── entries │ │ │ ├── format │ │ │ ├── prop-base │ │ │ │ ├── applypca.m.svn-base │ │ │ │ ├── demo.m.svn-base │ │ │ │ ├── energyclassify.m.svn-base │ │ │ │ ├── install.m.svn-base │ │ │ │ ├── knnclassify.m.svn-base │ │ │ │ ├── lmnn.m.svn-base │ │ │ │ ├── pca.m.svn-base │ │ │ │ ├── runlmnn.m.svn-base │ │ │ │ └── setpaths.m.svn-base │ │ │ └── text-base │ │ │ │ ├── applypca.m.svn-base │ │ │ │ ├── demo.m.svn-base │ │ │ │ ├── energyclassify.m.svn-base │ │ │ │ ├── install.m.svn-base │ │ │ │ ├── knnclassify.m.svn-base │ │ │ │ ├── lmnn.m.svn-base │ │ │ │ ├── pca.m.svn-base │ │ │ │ ├── runlmnn.m.svn-base │ │ │ │ └── setpaths.m.svn-base │ │ ├── applypca.m │ │ ├── data │ │ │ ├── .svn │ │ │ │ ├── all-wcprops │ │ │ │ ├── dir-prop-base │ │ │ │ ├── entries │ │ │ │ ├── format │ │ │ │ ├── prop-base │ │ │ │ │ ├── bal2.mat.svn-base │ │ │ │ │ └── iris2.mat.svn-base │ │ │ │ └── text-base │ │ │ │ │ ├── bal2.mat.svn-base │ │ │ │ │ └── iris2.mat.svn-base │ │ │ ├── bal2.mat │ │ │ └── iris2.mat │ │ ├── demo.m │ │ ├── energyclassify.m │ │ ├── helperfunctions │ │ │ ├── .svn │ │ │ │ ├── all-wcprops │ │ │ │ ├── dir-prop-base │ │ │ │ ├── entries │ │ │ │ ├── format │ │ │ │ ├── prop-base │ │ │ │ │ ├── extractpars.m.svn-base │ │ │ │ │ ├── mat.m.svn-base │ │ │ │ │ ├── mexall.m.svn-base │ │ │ │ │ └── vec.m.svn-base │ │ │ │ └── text-base │ │ │ │ │ ├── extractpars.m.svn-base │ │ │ │ │ ├── mat.m.svn-base │ │ │ │ │ ├── mexall.m.svn-base │ │ │ │ │ └── vec.m.svn-base │ │ │ ├── distance.m │ │ │ ├── extractpars.m │ │ │ ├── mat.m │ │ │ ├── mexall.m │ │ │ ├── mexallwindows.m │ │ │ └── vec.m │ │ ├── install.m │ │ ├── installWINDOWS.m │ │ ├── knnclassify.m │ │ ├── lmnn.m │ │ ├── mexfunctions │ │ │ ├── .svn │ │ │ │ ├── all-wcprops │ │ │ │ ├── dir-prop-base │ │ │ │ ├── entries │ │ │ │ ├── format │ │ │ │ ├── prop-base │ │ │ │ │ ├── SOD.c.svn-base │ │ │ │ │ ├── SOD.m.svn-base │ │ │ │ │ ├── SODW.c.svn-base │ │ │ │ │ ├── SODW.m.svn-base │ │ │ │ │ ├── addchv.c.svn-base │ │ │ │ │ ├── addh.c.svn-base │ │ │ │ │ ├── addv.c.svn-base │ │ │ │ │ ├── cdist.c.svn-base │ │ │ │ │ ├── cdist.m.svn-base │ │ │ │ │ ├── count.c.svn-base │ │ │ │ │ ├── count.m.svn-base │ │ │ │ │ ├── distance.c.svn-base │ │ │ │ │ ├── distance.m.svn-base │ │ │ │ │ ├── findimps3Dac.c.svn-base │ │ │ │ │ ├── findimps3Dac.m.svn-base │ │ │ │ │ ├── findlessh.c.svn-base │ │ │ │ │ ├── findlessh.m.svn-base │ │ │ │ │ ├── mink.c.svn-base │ │ │ │ │ ├── mink.m.svn-base │ │ │ │ │ ├── mulh.c.svn-base │ │ │ │ │ ├── mulh.m.svn-base │ │ │ │ │ ├── mulv.m.svn-base │ │ │ │ │ ├── sd.c.svn-base │ │ │ │ │ ├── sd.m.svn-base │ │ │ │ │ ├── sumiflessh2.c.svn-base │ │ │ │ │ └── sumiflessv2.c.svn-base │ │ │ │ └── text-base │ │ │ │ │ ├── SOD.c.svn-base │ │ │ │ │ ├── SOD.m.svn-base │ │ │ │ │ ├── SODW.c.svn-base │ │ │ │ │ ├── SODW.m.svn-base │ │ │ │ │ ├── addchv.c.svn-base │ │ │ │ │ ├── addh.c.svn-base │ │ │ │ │ ├── addv.c.svn-base │ │ │ │ │ ├── cdist.c.svn-base │ │ │ │ │ ├── cdist.m.svn-base │ │ │ │ │ ├── count.c.svn-base │ │ │ │ │ ├── count.m.svn-base │ │ │ │ │ ├── distance.c.svn-base │ │ │ │ │ ├── distance.m.svn-base │ │ │ │ │ ├── findimps3Dac.c.svn-base │ │ │ │ │ ├── findimps3Dac.m.svn-base │ │ │ │ │ ├── findlessh.c.svn-base │ │ │ │ │ ├── findlessh.m.svn-base │ │ │ │ │ ├── mink.c.svn-base │ │ │ │ │ ├── mink.m.svn-base │ │ │ │ │ ├── mulh.c.svn-base │ │ │ │ │ ├── mulh.m.svn-base │ │ │ │ │ ├── mulv.m.svn-base │ │ │ │ │ ├── sd.c.svn-base │ │ │ │ │ ├── sd.m.svn-base │ │ │ │ │ ├── sumiflessh2.c.svn-base │ │ │ │ │ └── sumiflessv2.c.svn-base │ │ │ ├── SOD.c │ │ │ ├── SOD.m │ │ │ ├── SOD.mexmaci │ │ │ ├── SOD.mexmaci64 │ │ │ ├── SOD.mexw64 │ │ │ ├── SODW.c │ │ │ ├── SODW.m │ │ │ ├── SODW.mexmaci │ │ │ ├── SODW.mexmaci64 │ │ │ ├── SODW.mexw64 │ │ │ ├── addchv.c │ │ │ ├── addchv.mexmaci │ │ │ ├── addchv.mexmaci64 │ │ │ ├── addchv.mexw64 │ │ │ ├── addh.c │ │ │ ├── addh.mexmaci │ │ │ ├── addh.mexmaci64 │ │ │ ├── addh.mexw64 │ │ │ ├── addv.c │ │ │ ├── addv.mexmaci │ │ │ ├── addv.mexmaci64 │ │ │ ├── addv.mexw64 │ │ │ ├── cdist.c │ │ │ ├── cdist.m │ │ │ ├── cdist.mexmaci │ │ │ ├── cdist.mexmaci64 │ │ │ ├── cdist.mexw64 │ │ │ ├── count.c │ │ │ ├── count.m │ │ │ ├── count.mexmaci │ │ │ ├── count.mexmaci64 │ │ │ ├── count.mexw64 │ │ │ ├── findimps3Dac.c │ │ │ ├── findimps3Dac.m │ │ │ ├── findimps3Dac.mexmaci │ │ │ ├── findimps3Dac.mexmaci64 │ │ │ ├── findimps3Dac.mexw64 │ │ │ ├── findlessh.c │ │ │ ├── findlessh.m │ │ │ ├── findlessh.mexmaci │ │ │ ├── findlessh.mexmaci64 │ │ │ ├── findlessh.mexw64 │ │ │ ├── mink.c │ │ │ ├── mink.m │ │ │ ├── mink.mexmaci │ │ │ ├── mink.mexmaci64 │ │ │ ├── mink.mexw64 │ │ │ ├── mulh.c │ │ │ ├── mulh.m │ │ │ ├── mulh.mexmaci │ │ │ ├── mulh.mexmaci64 │ │ │ ├── mulh.mexw64 │ │ │ ├── mulv.m │ │ │ ├── sd.c │ │ │ ├── sd.m │ │ │ ├── sd.mexmaci │ │ │ ├── sd.mexmaci64 │ │ │ ├── sd.mexw64 │ │ │ ├── sumiflessh2.c │ │ │ ├── sumiflessh2.mexmaci │ │ │ ├── sumiflessh2.mexmaci64 │ │ │ ├── sumiflessh2.mexw64 │ │ │ ├── sumiflessv2.c │ │ │ ├── sumiflessv2.mexmaci │ │ │ ├── sumiflessv2.mexmaci64 │ │ │ └── sumiflessv2.mexw64 │ │ ├── pca.m │ │ ├── runlmnn.m │ │ └── setpaths.m │ │ ├── MildML_0.1 │ │ ├── README │ │ ├── compute_tasks.c │ │ ├── hist_count.m │ │ ├── ldml_learn.m │ │ ├── mexall.m │ │ ├── mildml_evalg_sparse.c │ │ ├── mildml_evalg_sparse.mexa64 │ │ ├── mildml_fg.m │ │ ├── mildml_learn.m │ │ ├── minimize.m │ │ └── vec.m │ │ ├── __MACOSX │ │ └── LMNN │ │ │ ├── ._.DS_Store │ │ │ ├── ._.svn │ │ │ ├── ._applypca.m │ │ │ ├── ._data │ │ │ ├── ._demo.m │ │ │ ├── ._energyclassify.m │ │ │ ├── ._install.m │ │ │ ├── ._installWINDOWS.m │ │ │ ├── ._knnclassify.m │ │ │ ├── ._lmnn.m │ │ │ ├── ._pca.m │ │ │ ├── ._runlmnn.m │ │ │ ├── ._setpaths.m │ │ │ ├── .svn │ │ │ ├── ._all-wcprops │ │ │ ├── ._dir-prop-base │ │ │ ├── ._entries │ │ │ ├── ._format │ │ │ ├── ._prop-base │ │ │ ├── ._props │ │ │ ├── ._text-base │ │ │ ├── ._tmp │ │ │ ├── prop-base │ │ │ │ ├── ._applypca.m.svn-base │ │ │ │ ├── ._demo.m.svn-base │ │ │ │ ├── ._energyclassify.m.svn-base │ │ │ │ ├── ._install.m.svn-base │ │ │ │ ├── ._knnclassify.m.svn-base │ │ │ │ ├── ._lmnn.m.svn-base │ │ │ │ ├── ._pca.m.svn-base │ │ │ │ ├── ._runlmnn.m.svn-base │ │ │ │ └── ._setpaths.m.svn-base │ │ │ ├── text-base │ │ │ │ ├── ._applypca.m.svn-base │ │ │ │ ├── ._demo.m.svn-base │ │ │ │ ├── ._energyclassify.m.svn-base │ │ │ │ ├── ._install.m.svn-base │ │ │ │ ├── ._knnclassify.m.svn-base │ │ │ │ ├── ._lmnn.m.svn-base │ │ │ │ ├── ._pca.m.svn-base │ │ │ │ ├── ._runlmnn.m.svn-base │ │ │ │ └── ._setpaths.m.svn-base │ │ │ └── tmp │ │ │ │ ├── ._prop-base │ │ │ │ ├── ._props │ │ │ │ └── ._text-base │ │ │ ├── data │ │ │ ├── ._.svn │ │ │ ├── ._bal2.mat │ │ │ ├── ._iris2.mat │ │ │ └── .svn │ │ │ │ ├── ._all-wcprops │ │ │ │ ├── ._dir-prop-base │ │ │ │ ├── ._entries │ │ │ │ ├── ._format │ │ │ │ ├── ._prop-base │ │ │ │ ├── ._props │ │ │ │ ├── ._text-base │ │ │ │ ├── ._tmp │ │ │ │ ├── prop-base │ │ │ │ ├── ._bal2.mat.svn-base │ │ │ │ └── ._iris2.mat.svn-base │ │ │ │ ├── text-base │ │ │ │ ├── ._bal2.mat.svn-base │ │ │ │ └── ._iris2.mat.svn-base │ │ │ │ └── tmp │ │ │ │ ├── ._prop-base │ │ │ │ ├── ._props │ │ │ │ └── ._text-base │ │ │ ├── helperfunctions │ │ │ ├── ._.svn │ │ │ ├── ._extractpars.m │ │ │ ├── ._mat.m │ │ │ ├── ._mexall.m │ │ │ ├── ._mexallwindows.m │ │ │ ├── ._vec.m │ │ │ └── .svn │ │ │ │ ├── ._all-wcprops │ │ │ │ ├── ._dir-prop-base │ │ │ │ ├── ._entries │ │ │ │ ├── ._format │ │ │ │ ├── ._prop-base │ │ │ │ ├── ._props │ │ │ │ ├── ._text-base │ │ │ │ ├── ._tmp │ │ │ │ ├── prop-base │ │ │ │ ├── ._extractpars.m.svn-base │ │ │ │ ├── ._mat.m.svn-base │ │ │ │ ├── ._mexall.m.svn-base │ │ │ │ └── ._vec.m.svn-base │ │ │ │ ├── text-base │ │ │ │ ├── ._extractpars.m.svn-base │ │ │ │ ├── ._mat.m.svn-base │ │ │ │ ├── ._mexall.m.svn-base │ │ │ │ └── ._vec.m.svn-base │ │ │ │ └── tmp │ │ │ │ ├── ._prop-base │ │ │ │ ├── ._props │ │ │ │ └── ._text-base │ │ │ └── mexfunctions │ │ │ ├── ._.svn │ │ │ ├── ._SOD.c │ │ │ ├── ._SOD.m │ │ │ ├── ._SODW.c │ │ │ ├── ._SODW.m │ │ │ ├── ._addchv.c │ │ │ ├── ._addh.c │ │ │ ├── ._addv.c │ │ │ ├── ._cdist.c │ │ │ ├── ._cdist.m │ │ │ ├── ._count.c │ │ │ ├── ._count.m │ │ │ ├── ._findimps3Dac.c │ │ │ ├── ._findimps3Dac.m │ │ │ ├── ._findlessh.c │ │ │ ├── ._findlessh.m │ │ │ ├── ._mink.c │ │ │ ├── ._mink.m │ │ │ ├── ._mulh.c │ │ │ ├── ._mulh.m │ │ │ ├── ._mulv.m │ │ │ ├── ._sd.c │ │ │ ├── ._sd.m │ │ │ ├── ._sumiflessh2.c │ │ │ ├── ._sumiflessv2.c │ │ │ └── .svn │ │ │ ├── ._all-wcprops │ │ │ ├── ._dir-prop-base │ │ │ ├── ._entries │ │ │ ├── ._format │ │ │ ├── ._prop-base │ │ │ ├── ._props │ │ │ ├── ._text-base │ │ │ ├── ._tmp │ │ │ ├── prop-base │ │ │ ├── ._SOD.c.svn-base │ │ │ ├── ._SOD.m.svn-base │ │ │ ├── ._SODW.c.svn-base │ │ │ ├── ._SODW.m.svn-base │ │ │ ├── ._addchv.c.svn-base │ │ │ ├── ._addh.c.svn-base │ │ │ ├── ._addv.c.svn-base │ │ │ ├── ._cdist.c.svn-base │ │ │ ├── ._cdist.m.svn-base │ │ │ ├── ._count.c.svn-base │ │ │ ├── ._count.m.svn-base │ │ │ ├── ._distance.c.svn-base │ │ │ ├── ._distance.m.svn-base │ │ │ ├── ._findimps3Dac.c.svn-base │ │ │ ├── ._findimps3Dac.m.svn-base │ │ │ ├── ._findlessh.c.svn-base │ │ │ ├── ._findlessh.m.svn-base │ │ │ ├── ._mink.c.svn-base │ │ │ ├── ._mink.m.svn-base │ │ │ ├── ._mulh.c.svn-base │ │ │ ├── ._mulh.m.svn-base │ │ │ ├── ._mulv.m.svn-base │ │ │ ├── ._sd.c.svn-base │ │ │ ├── ._sd.m.svn-base │ │ │ ├── ._sumiflessh2.c.svn-base │ │ │ └── ._sumiflessv2.c.svn-base │ │ │ ├── text-base │ │ │ ├── ._SOD.c.svn-base │ │ │ ├── ._SOD.m.svn-base │ │ │ ├── ._SODW.c.svn-base │ │ │ ├── ._SODW.m.svn-base │ │ │ ├── ._addchv.c.svn-base │ │ │ ├── ._addh.c.svn-base │ │ │ ├── ._addv.c.svn-base │ │ │ ├── ._cdist.c.svn-base │ │ │ ├── ._cdist.m.svn-base │ │ │ ├── ._count.c.svn-base │ │ │ ├── ._count.m.svn-base │ │ │ ├── ._distance.c.svn-base │ │ │ ├── ._distance.m.svn-base │ │ │ ├── ._findimps3Dac.c.svn-base │ │ │ ├── ._findimps3Dac.m.svn-base │ │ │ ├── ._findlessh.c.svn-base │ │ │ ├── ._findlessh.m.svn-base │ │ │ ├── ._mink.c.svn-base │ │ │ ├── ._mink.m.svn-base │ │ │ ├── ._mulh.c.svn-base │ │ │ ├── ._mulh.m.svn-base │ │ │ ├── ._mulv.m.svn-base │ │ │ ├── ._sd.c.svn-base │ │ │ ├── ._sd.m.svn-base │ │ │ ├── ._sumiflessh2.c.svn-base │ │ │ └── ._sumiflessv2.c.svn-base │ │ │ └── tmp │ │ │ ├── ._prop-base │ │ │ ├── ._props │ │ │ └── ._text-base │ │ └── itml │ │ ├── ComputeDistanceExtremes.m │ │ ├── CrossValidateKNN.m │ │ ├── EuclideanDistance.m │ │ ├── GetConstraints.m │ │ ├── ItmlAlg.m │ │ ├── KNN.m │ │ ├── MetricLearning.m │ │ ├── MetricLearningAutotuneKnn.m │ │ ├── README │ │ ├── SetDefaultParams.m │ │ ├── Test.m │ │ └── data │ │ ├── iris.mtx │ │ └── iris.truth ├── LOMO_XQDA │ ├── LICENSE │ ├── README.txt │ ├── bin │ │ ├── Retinex.mexa64 │ │ ├── Retinex.mexglx │ │ ├── Retinex.mexw32 │ │ └── Retinex.mexw64 │ ├── code │ │ ├── Demo_LOMO.m │ │ ├── Demo_XQDA.m │ │ ├── EvalCMC.m │ │ ├── LOMO.m │ │ ├── MahDist.m │ │ ├── SILTP.m │ │ └── XQDA.m │ ├── images │ │ ├── 000_45_a.bmp │ │ └── 000_45_b.bmp │ └── results │ │ ├── cuhk01_lomo_xqda.mat │ │ ├── cuhk03_detected_lomo_xqda.mat │ │ ├── cuhk03_labeled_lomo_xqda.mat │ │ ├── qmul_grid_lomo_xqda.mat │ │ ├── qmul_grid_lomo_xqda_camera-network.mat │ │ └── viper_lomo_xqda.mat ├── Market_1501_evaluation.m ├── Market_1501_extract_feature.m ├── data │ ├── CUHK03 │ │ ├── cuhk03_new_protocol_config_detected.mat │ │ ├── cuhk03_new_protocol_config_labeled.mat │ │ ├── gen_train_test_split.m │ │ ├── save_img.m │ │ ├── save_train_txt.m │ │ ├── train_cuhk03_detected.txt │ │ └── train_cuhk03_labeled.txt │ └── Market-1501 │ │ ├── bounding_box_test │ │ ├── bounding_box_train │ │ ├── gt_bbox │ │ ├── gt_query │ │ ├── query │ │ ├── queryCam.mat │ │ ├── queryID.mat │ │ ├── testCam.mat │ │ ├── testID.mat │ │ ├── train.txt │ │ ├── train_cam.mat │ │ ├── train_label.mat │ │ └── val.txt └── utils │ ├── applypca2.m │ ├── compute_AP.m │ ├── compute_AP_multiCam.m │ ├── compute_r1_multiCam.m │ ├── draw_confusion_matrix.m │ ├── evaluation.m │ ├── gen_train_sample_kissme.m │ ├── gen_train_sample_xqda.m │ ├── info_mars.m │ ├── prepare_img.m │ ├── process_box_feat.m │ ├── process_box_feat_prune.m │ └── re_ranking.m ├── experiments ├── CUHK03 │ ├── train_IDE_CaffeNet_detected.sh │ ├── train_IDE_CaffeNet_detected~ │ ├── train_IDE_CaffeNet_labeled.sh │ ├── train_IDE_CaffeNet_labeled~ │ ├── train_IDE_ResNet_50_detected.sh │ ├── train_IDE_ResNet_50_labeled.sh │ └── train_IDE_ResNet_50_ld.sh └── Market-1501 │ └── train_IDE_ResNet_50.sh ├── models ├── CUHK03 │ ├── CaffeNet │ │ ├── CaffeNet_detected_solver.prototxt │ │ ├── CaffeNet_detected_train_val.prototxt │ │ ├── CaffeNet_labeled_solver.prototxt │ │ ├── CaffeNet_labeled_train_val.prototxt │ │ └── CaffeNet_test.prototxt │ └── ResNet_50 │ │ ├── ResNet_50_IDE_detected_solver.prototxt │ │ ├── ResNet_50_IDE_detected_train_val.prototxt │ │ ├── ResNet_50_IDE_labeled_solver.prototxt │ │ ├── ResNet_50_IDE_labeled_train_val.prototxt │ │ └── ResNet_50_test.prototxt └── Market-1501 │ └── ResNet_50 │ ├── ResNet_50_IDE_solver.prototxt │ ├── ResNet_50_IDE_train_val.prototxt │ └── ResNet_50_test.prototxt └── python-version ├── re_ranking_feature └── re_ranking_ranklist.py /caffe/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 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/cmake/Modules/FindNCCL.cmake: -------------------------------------------------------------------------------- 1 | set(NCCL_INC_PATHS 2 | /usr/include 3 | /usr/local/include 4 | $ENV{NCCL_DIR}/include 5 | ) 6 | 7 | set(NCCL_LIB_PATHS 8 | /lib 9 | /lib64 10 | /usr/lib 11 | /usr/lib64 12 | /usr/local/lib 13 | /usr/local/lib64 14 | $ENV{NCCL_DIR}/lib 15 | ) 16 | 17 | find_path(NCCL_INCLUDE_DIR NAMES nccl.h PATHS ${NCCL_INC_PATHS}) 18 | find_library(NCCL_LIBRARIES NAMES nccl PATHS ${NCCL_LIB_PATHS}) 19 | 20 | include(FindPackageHandleStandardArgs) 21 | find_package_handle_standard_args(NCCL DEFAULT_MSG NCCL_INCLUDE_DIR NCCL_LIBRARIES) 22 | 23 | if (NCCL_FOUND) 24 | message(STATUS "Found NCCL (include: ${NCCL_INCLUDE_DIR}, library: ${NCCL_LIBRARIES})") 25 | mark_as_advanced(NCCL_INCLUDE_DIR NCCL_LIBRARIES) 26 | endif () 27 | -------------------------------------------------------------------------------- /caffe/cmake/Templates/CaffeConfigVersion.cmake.in: -------------------------------------------------------------------------------- 1 | set(PACKAGE_VERSION "@Caffe_VERSION@") 2 | 3 | # Check whether the requested PACKAGE_FIND_VERSION is compatible 4 | if("${PACKAGE_VERSION}" VERSION_LESS "${PACKAGE_FIND_VERSION}") 5 | set(PACKAGE_VERSION_COMPATIBLE FALSE) 6 | else() 7 | set(PACKAGE_VERSION_COMPATIBLE TRUE) 8 | if ("${PACKAGE_VERSION}" VERSION_EQUAL "${PACKAGE_FIND_VERSION}") 9 | set(PACKAGE_VERSION_EXACT TRUE) 10 | endif() 11 | endif() 12 | -------------------------------------------------------------------------------- /caffe/cmake/Templates/caffe_config.h.in: -------------------------------------------------------------------------------- 1 | /* Sources directory */ 2 | #define SOURCE_FOLDER "${PROJECT_SOURCE_DIR}" 3 | 4 | /* Binaries directory */ 5 | #define BINARY_FOLDER "${PROJECT_BINARY_DIR}" 6 | 7 | /* Test device */ 8 | #define CUDA_TEST_DEVICE ${CUDA_TEST_DEVICE} 9 | 10 | /* Temporary (TODO: remove) */ 11 | #if 1 12 | #define CMAKE_SOURCE_DIR SOURCE_FOLDER "/src/" 13 | #define EXAMPLES_SOURCE_DIR BINARY_FOLDER "/examples/" 14 | #define CMAKE_EXT ".gen.cmake" 15 | #else 16 | #define CMAKE_SOURCE_DIR "src/" 17 | #define EXAMPLES_SOURCE_DIR "examples/" 18 | #define CMAKE_EXT "" 19 | #endif 20 | -------------------------------------------------------------------------------- /caffe/docs/CNAME: -------------------------------------------------------------------------------- 1 | caffe.berkeleyvision.org 2 | -------------------------------------------------------------------------------- /caffe/docs/README.md: -------------------------------------------------------------------------------- 1 | # Caffe Documentation 2 | 3 | To generate the documentation, run `$CAFFE_ROOT/scripts/build_docs.sh`. 4 | 5 | To push your changes to the documentation to the gh-pages branch of your or the BVLC repo, run `$CAFFE_ROOT/scripts/deploy_docs.sh `. 6 | -------------------------------------------------------------------------------- /caffe/docs/_config.yml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - 3 | scope: 4 | path: "" # an empty string here means all files in the project 5 | values: 6 | layout: "default" 7 | 8 | -------------------------------------------------------------------------------- /caffe/docs/stylesheets/reset.css: -------------------------------------------------------------------------------- 1 | /* MeyerWeb Reset */ 2 | 3 | html, body, div, span, applet, object, iframe, 4 | h1, h2, h3, h4, h5, h6, p, blockquote, pre, 5 | a, abbr, acronym, address, big, cite, code, 6 | del, dfn, em, img, ins, kbd, q, s, samp, 7 | small, strike, strong, sub, sup, tt, var, 8 | b, u, i, center, 9 | dl, dt, dd, ol, ul, li, 10 | fieldset, form, label, legend, 11 | table, caption, tbody, tfoot, thead, tr, th, td, 12 | article, aside, canvas, details, embed, 13 | figure, figcaption, footer, header, hgroup, 14 | menu, nav, output, ruby, section, summary, 15 | time, mark, audio, video { 16 | margin: 0; 17 | padding: 0; 18 | border: 0; 19 | font: inherit; 20 | vertical-align: baseline; 21 | } 22 | -------------------------------------------------------------------------------- /caffe/docs/tutorial/convolution.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Convolution 3 | --- 4 | # Caffeinated Convolution 5 | 6 | The Caffe strategy for convolution is to reduce the problem to matrix-matrix multiplication. 7 | This linear algebra computation is highly-tuned in BLAS libraries and efficiently computed on GPU devices. 8 | 9 | For more details read Yangqing's [Convolution in Caffe: a memo](https://github.com/Yangqing/caffe/wiki/Convolution-in-Caffe:-a-memo). 10 | 11 | As it turns out, this same reduction was independently explored in the context of conv. nets by 12 | 13 | > K. Chellapilla, S. Puri, P. Simard, et al. High performance convolutional neural networks for document processing. In Tenth International Workshop on Frontiers in Handwriting Recognition, 2006. 14 | -------------------------------------------------------------------------------- /caffe/docs/tutorial/layers/absval.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Absolute Value Layer 3 | --- 4 | 5 | # Absolute Value Layer 6 | 7 | * Layer type: `AbsVal` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1AbsValLayer.html) 9 | * Header: [`./include/caffe/layers/absval_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/absval_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/absval_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/absval_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/absval_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/absval_layer.cu) 12 | 13 | * Sample 14 | 15 | layer { 16 | name: "layer" 17 | bottom: "in" 18 | top: "out" 19 | type: "AbsVal" 20 | } 21 | 22 | The `AbsVal` layer computes the output as abs(x) for each input element x. 23 | -------------------------------------------------------------------------------- /caffe/docs/tutorial/layers/argmax.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: ArgMax Layer 3 | --- 4 | 5 | # ArgMax Layer 6 | 7 | * Layer type: `ArgMax` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ArgMaxLayer.html) 9 | * Header: [`./include/caffe/layers/argmax_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/argmax_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/argmax_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/argmax_layer.cpp) 11 | 12 | ## Parameters 13 | * Parameters (`ArgMaxParameter argmax_param`) 14 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)): 15 | 16 | {% highlight Protobuf %} 17 | {% include proto/ArgMaxParameter.txt %} 18 | {% endhighlight %} 19 | -------------------------------------------------------------------------------- /caffe/docs/tutorial/layers/batchreindex.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Batch Reindex Layer 3 | --- 4 | 5 | # Batch Reindex Layer 6 | 7 | * Layer type: `BatchReindex` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1BatchReindexLayer.html) 9 | * Header: [`./include/caffe/layers/batch_reindex_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/batch_reindex_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/batch_reindex_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/batch_reindex_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/batch_reindex_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/batch_reindex_layer.cu) 12 | 13 | 14 | ## Parameters 15 | 16 | No parameters. 17 | -------------------------------------------------------------------------------- /caffe/docs/tutorial/layers/bias.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Bias Layer 3 | --- 4 | 5 | # Bias Layer 6 | 7 | * Layer type: `Bias` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1BiasLayer.html) 9 | * Header: [`./include/caffe/layers/bias_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/bias_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/bias_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/bias_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/bias_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/bias_layer.cu) 12 | 13 | ## Parameters 14 | * Parameters (`BiasParameter bias_param`) 15 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)): 16 | 17 | {% highlight Protobuf %} 18 | {% include proto/BiasParameter.txt %} 19 | {% endhighlight %} 20 | -------------------------------------------------------------------------------- /caffe/docs/tutorial/layers/crop.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Crop Layer 3 | --- 4 | 5 | # Crop Layer 6 | 7 | * Layer type: `Crop` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1CropLayer.html) 9 | * Header: [`./include/caffe/layers/crop_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/crop_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/crop_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/crop_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/crop_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/crop_layer.cu) 12 | 13 | ## Parameters 14 | 15 | * Parameters (`CropParameter crop_param`) 16 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)): 17 | 18 | {% highlight Protobuf %} 19 | {% include proto/CropParameter.txt %} 20 | {% endhighlight %} 21 | -------------------------------------------------------------------------------- /caffe/docs/tutorial/layers/dummydata.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Dummy Data Layer 3 | --- 4 | 5 | # Dummy Data Layer 6 | 7 | * Layer type: `DummyData` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1DummyDataLayer.html) 9 | * Header: [`./include/caffe/layers/dummy_data_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/dummy_data_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/dummy_data_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/dummy_data_layer.cpp) 11 | 12 | 13 | ## Parameters 14 | 15 | * Parameters (`DummyDataParameter dummy_data_param`) 16 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)): 17 | 18 | {% highlight Protobuf %} 19 | {% include proto/DummyDataParameter.txt %} 20 | {% endhighlight %} 21 | -------------------------------------------------------------------------------- /caffe/docs/tutorial/layers/embed.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Embed Layer 3 | --- 4 | 5 | # Embed Layer 6 | 7 | * Layer type: `Embed` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1EmbedLayer.html) 9 | * Header: [`./include/caffe/layers/embed_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/embed_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/embed_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/embed_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/embed_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/embed_layer.cu) 12 | 13 | ## Parameters 14 | 15 | * Parameters (`EmbedParameter embed_param`) 16 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 17 | 18 | {% highlight Protobuf %} 19 | {% include proto/EmbedParameter.txt %} 20 | {% endhighlight %} 21 | -------------------------------------------------------------------------------- /caffe/docs/tutorial/layers/filter.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Filter Layer 3 | --- 4 | 5 | # Filter Layer 6 | 7 | * Layer type: `Filter` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1FilterLayer.html) 9 | * Header: [`./include/caffe/layers/filter_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/filter_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/filter_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/filter_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/filter_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/filter_layer.cu) 12 | 13 | ## Parameters 14 | 15 | Does not take any parameters. 16 | -------------------------------------------------------------------------------- /caffe/docs/tutorial/layers/hingeloss.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Hinge Loss Layer 3 | --- 4 | 5 | # Hinge (L1, L2) Loss Layer 6 | 7 | * Layer type: `HingeLoss` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1HingeLossLayer.html) 9 | * Header: [`./include/caffe/layers/hinge_loss_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/hinge_loss_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/hinge_loss_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/hinge_loss_layer.cpp) 11 | 12 | ## Parameters 13 | 14 | * Parameters (`HingeLossParameter hinge_loss_param`) 15 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 16 | 17 | {% highlight Protobuf %} 18 | {% include proto/HingeLossParameter.txt %} 19 | {% endhighlight %} 20 | -------------------------------------------------------------------------------- /caffe/docs/tutorial/layers/im2col.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Im2col Layer 3 | --- 4 | 5 | # im2col 6 | 7 | * File type: `Im2col` 8 | * Header: [`./include/caffe/layers/im2col_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/im2col_layer.hpp) 9 | * CPU implementation: [`./src/caffe/layers/im2col_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/im2col_layer.cpp) 10 | * CUDA GPU implementation: [`./src/caffe/layers/im2col_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/im2col_layer.cu) 11 | 12 | `Im2col` is a helper for doing the image-to-column transformation that you most 13 | likely do not need to know about. This is used in Caffe's original convolution 14 | to do matrix multiplication by laying out all patches into a matrix. 15 | 16 | 17 | -------------------------------------------------------------------------------- /caffe/docs/tutorial/layers/input.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Input Layer 3 | --- 4 | 5 | # Input Layer 6 | 7 | * Layer type: `Input` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1InputLayer.html) 9 | * Header: [`./include/caffe/layers/input_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/input_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/input_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/input_layer.cpp) 11 | 12 | ## Parameters 13 | 14 | * Parameters (`InputParameter input_param`) 15 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)): 16 | 17 | {% highlight Protobuf %} 18 | {% include proto/InputParameter.txt %} 19 | {% endhighlight %} 20 | -------------------------------------------------------------------------------- /caffe/docs/tutorial/layers/log.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Log Layer 3 | --- 4 | 5 | # Log Layer 6 | 7 | * Layer type: `Log` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1LogLayer.html) 9 | * Header: [`./include/caffe/layers/log_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/log_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/log_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/log_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/log_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/log_layer.cu) 12 | 13 | ## Parameters 14 | 15 | * Parameters (`Parameter log_param`) 16 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 17 | 18 | {% highlight Protobuf %} 19 | {% include proto/LogParameter.txt %} 20 | {% endhighlight %} 21 | -------------------------------------------------------------------------------- /caffe/docs/tutorial/layers/rnn.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: RNN Layer 3 | --- 4 | 5 | # RNN Layer 6 | 7 | * Layer type: `RNN` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1RNNLayer.html) 9 | * Header: [`./include/caffe/layers/rnn_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/rnn_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/rnn_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/rnn_layer.cpp) 11 | 12 | ## Parameters 13 | 14 | * Parameters (`RecurrentParameter recurrent_param`) 15 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 16 | 17 | {% highlight Protobuf %} 18 | {% include proto/RecurrentParameter.txt %} 19 | {% endhighlight %} 20 | -------------------------------------------------------------------------------- /caffe/docs/tutorial/layers/sigmoidcrossentropyloss.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Sigmoid Cross-Entropy Loss Layer 3 | --- 4 | 5 | # Sigmoid Cross-Entropy Loss Layer 6 | 7 | * Layer type: `SigmoidCrossEntropyLoss` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SigmoidCrossEntropyLossLayer.html) 9 | * Header: [`./include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu) 12 | 13 | To-do. 14 | -------------------------------------------------------------------------------- /caffe/docs/tutorial/layers/silence.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Silence Layer 3 | --- 4 | 5 | # Silence Layer 6 | 7 | * Layer type: `Silence` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SilenceLayer.html) 9 | * Header: [`./include/caffe/layers/silence_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/silence_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/silence_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/silence_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/silence_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/silence_layer.cu) 12 | 13 | Silences a blob, so that it is not printed. 14 | 15 | ## Parameters 16 | 17 | No parameters. 18 | -------------------------------------------------------------------------------- /caffe/docs/tutorial/layers/split.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Split Layer 3 | --- 4 | 5 | # Split Layer 6 | 7 | * Layer type: `Split` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SplitLayer.html) 9 | * Header: [`./include/caffe/layers/split_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/split_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/split_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/split_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/split_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/split_layer.cu) 12 | 13 | The `Split` layer is a utility layer that splits an input blob to multiple output blobs. This is used when a blob is fed into multiple output layers. 14 | 15 | ## Parameters 16 | 17 | Does not take any parameters. 18 | -------------------------------------------------------------------------------- /caffe/docs/tutorial/layers/spp.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Spatial Pyramid Pooling Layer 3 | --- 4 | 5 | # Spatial Pyramid Pooling Layer 6 | 7 | * Layer type: `SPP` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SPPLayer.html) 9 | * Header: [`./include/caffe/layers/spp_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/spp_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/spp_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/spp_layer.cpp) 11 | 12 | 13 | ## Parameters 14 | 15 | * Parameters (`SPPParameter spp_param`) 16 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 17 | 18 | {% highlight Protobuf %} 19 | {% include proto/SPPParameter.txt %} 20 | {% endhighlight %} 21 | -------------------------------------------------------------------------------- /caffe/docs/tutorial/layers/tanh.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: TanH Layer 3 | --- 4 | 5 | # TanH Layer 6 | 7 | * Header: [`./include/caffe/layers/tanh_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/tanh_layer.hpp) 8 | * CPU implementation: [`./src/caffe/layers/tanh_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/tanh_layer.cpp) 9 | * CUDA GPU implementation: [`./src/caffe/layers/tanh_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/tanh_layer.cu) 10 | 11 | ## Parameters 12 | 13 | * Parameters (`TanHParameter tanh_param`) 14 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 15 | 16 | {% highlight Protobuf %} 17 | {% include proto/TanHParameter.txt %} 18 | {% endhighlight %} 19 | -------------------------------------------------------------------------------- /caffe/docs/tutorial/layers/threshold.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Threshold Layer 3 | --- 4 | 5 | # Threshold Layer 6 | 7 | * Header: [`./include/caffe/layers/threshold_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/threshold_layer.hpp) 8 | * CPU implementation: [`./src/caffe/layers/threshold_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/threshold_layer.cpp) 9 | * CUDA GPU implementation: [`./src/caffe/layers/threshold_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/threshold_layer.cu) 10 | 11 | ## Parameters 12 | 13 | * Parameters (`ThresholdParameter threshold_param`) 14 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 15 | 16 | {% highlight Protobuf %} 17 | {% include proto/ThresholdParameter.txt %} 18 | {% endhighlight %} 19 | -------------------------------------------------------------------------------- /caffe/docs/tutorial/layers/tile.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Tile Layer 3 | --- 4 | 5 | # Tile Layer 6 | 7 | * Layer type: `Tile` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1TileLayer.html) 9 | * Header: [`./include/caffe/layers/tile_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/tile_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/tile_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/tile_layer.cpp) 11 | * CUDA GPU implementation: [`./src/caffe/layers/tile_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/tile_layer.cu) 12 | 13 | ## Parameters 14 | 15 | * Parameters (`TileParameter tile_param`) 16 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 17 | 18 | {% highlight Protobuf %} 19 | {% include proto/TileParameter.txt %} 20 | {% endhighlight %} 21 | -------------------------------------------------------------------------------- /caffe/docs/tutorial/layers/windowdata.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: WindowData Layer 3 | --- 4 | 5 | # WindowData Layer 6 | 7 | * Layer type: `WindowData` 8 | * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1WindowDataLayer.html) 9 | * Header: [`./include/caffe/layers/window_data_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/window_data_layer.hpp) 10 | * CPU implementation: [`./src/caffe/layers/window_data_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/window_data_layer.cpp) 11 | 12 | ## Parameters 13 | 14 | * Parameters (`WindowDataParameter`) 15 | * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto): 16 | 17 | {% highlight Protobuf %} 18 | {% include proto/WindowDataParameter.txt %} 19 | {% endhighlight %} 20 | -------------------------------------------------------------------------------- /caffe/examples/cifar10/create_cifar10.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | # This script converts the cifar data into leveldb format. 3 | set -e 4 | 5 | EXAMPLE=examples/cifar10 6 | DATA=data/cifar10 7 | DBTYPE=lmdb 8 | 9 | echo "Creating $DBTYPE..." 10 | 11 | rm -rf $EXAMPLE/cifar10_train_$DBTYPE $EXAMPLE/cifar10_test_$DBTYPE 12 | 13 | ./build/examples/cifar10/convert_cifar_data.bin $DATA $EXAMPLE $DBTYPE 14 | 15 | echo "Computing image mean..." 16 | 17 | ./build/tools/compute_image_mean -backend=$DBTYPE \ 18 | $EXAMPLE/cifar10_train_$DBTYPE $EXAMPLE/mean.binaryproto 19 | 20 | echo "Done." 21 | -------------------------------------------------------------------------------- /caffe/examples/cifar10/train_full.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | set -e 3 | 4 | TOOLS=./build/tools 5 | 6 | $TOOLS/caffe train \ 7 | --solver=examples/cifar10/cifar10_full_solver.prototxt $@ 8 | 9 | # reduce learning rate by factor of 10 10 | $TOOLS/caffe train \ 11 | --solver=examples/cifar10/cifar10_full_solver_lr1.prototxt \ 12 | --snapshot=examples/cifar10/cifar10_full_iter_60000.solverstate.h5 $@ 13 | 14 | # reduce learning rate by factor of 10 15 | $TOOLS/caffe train \ 16 | --solver=examples/cifar10/cifar10_full_solver_lr2.prototxt \ 17 | --snapshot=examples/cifar10/cifar10_full_iter_65000.solverstate.h5 $@ 18 | -------------------------------------------------------------------------------- /caffe/examples/cifar10/train_full_sigmoid.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | set -e 3 | 4 | TOOLS=./build/tools 5 | 6 | $TOOLS/caffe train \ 7 | --solver=examples/cifar10/cifar10_full_sigmoid_solver.prototxt $@ 8 | 9 | -------------------------------------------------------------------------------- /caffe/examples/cifar10/train_full_sigmoid_bn.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | set -e 3 | 4 | TOOLS=./build/tools 5 | 6 | $TOOLS/caffe train \ 7 | --solver=examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt $@ 8 | 9 | -------------------------------------------------------------------------------- /caffe/examples/cifar10/train_quick.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | set -e 3 | 4 | TOOLS=./build/tools 5 | 6 | $TOOLS/caffe train \ 7 | --solver=examples/cifar10/cifar10_quick_solver.prototxt $@ 8 | 9 | # reduce learning rate by factor of 10 after 8 epochs 10 | $TOOLS/caffe train \ 11 | --solver=examples/cifar10/cifar10_quick_solver_lr1.prototxt \ 12 | --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate.h5 $@ 13 | -------------------------------------------------------------------------------- /caffe/examples/finetune_flickr_style/flickr_style.csv.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/caffe/examples/finetune_flickr_style/flickr_style.csv.gz -------------------------------------------------------------------------------- /caffe/examples/finetune_flickr_style/style_names.txt: -------------------------------------------------------------------------------- 1 | Detailed 2 | Pastel 3 | Melancholy 4 | Noir 5 | HDR 6 | Vintage 7 | Long Exposure 8 | Horror 9 | Sunny 10 | Bright 11 | Hazy 12 | Bokeh 13 | Serene 14 | Texture 15 | Ethereal 16 | Macro 17 | Depth of Field 18 | Geometric Composition 19 | Minimal 20 | Romantic 21 | -------------------------------------------------------------------------------- /caffe/examples/finetune_pascal_detection/pascal_finetune_solver.prototxt: -------------------------------------------------------------------------------- 1 | net: "examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt" 2 | test_iter: 100 3 | test_interval: 1000 4 | base_lr: 0.001 5 | lr_policy: "step" 6 | gamma: 0.1 7 | stepsize: 20000 8 | display: 20 9 | max_iter: 100000 10 | momentum: 0.9 11 | weight_decay: 0.0005 12 | snapshot: 10000 13 | snapshot_prefix: "examples/finetune_pascal_detection/pascal_det_finetune" 14 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/examples/imagenet/resume_training.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | set -e 3 | 4 | ./build/tools/caffe train \ 5 | --solver=models/bvlc_reference_caffenet/solver.prototxt \ 6 | --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate.h5 \ 7 | $@ 8 | -------------------------------------------------------------------------------- /caffe/examples/imagenet/train_caffenet.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | set -e 3 | 4 | ./build/tools/caffe train \ 5 | --solver=models/bvlc_reference_caffenet/solver.prototxt $@ 6 | -------------------------------------------------------------------------------- /caffe/examples/mnist/create_mnist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | # This script converts the mnist data into lmdb/leveldb format, 3 | # depending on the value assigned to $BACKEND. 4 | set -e 5 | 6 | EXAMPLE=examples/mnist 7 | DATA=data/mnist 8 | BUILD=build/examples/mnist 9 | 10 | BACKEND="lmdb" 11 | 12 | echo "Creating ${BACKEND}..." 13 | 14 | rm -rf $EXAMPLE/mnist_train_${BACKEND} 15 | rm -rf $EXAMPLE/mnist_test_${BACKEND} 16 | 17 | $BUILD/convert_mnist_data.bin $DATA/train-images-idx3-ubyte \ 18 | $DATA/train-labels-idx1-ubyte $EXAMPLE/mnist_train_${BACKEND} --backend=${BACKEND} 19 | $BUILD/convert_mnist_data.bin $DATA/t10k-images-idx3-ubyte \ 20 | $DATA/t10k-labels-idx1-ubyte $EXAMPLE/mnist_test_${BACKEND} --backend=${BACKEND} 21 | 22 | echo "Done." 23 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/examples/mnist/lenet_solver.prototxt: -------------------------------------------------------------------------------- 1 | # The train/test net protocol buffer definition 2 | net: "examples/mnist/lenet_train_test.prototxt" 3 | # test_iter specifies how many forward passes the test should carry out. 4 | # In the case of MNIST, we have test batch size 100 and 100 test iterations, 5 | # covering the full 10,000 testing images. 6 | test_iter: 100 7 | # Carry out testing every 500 training iterations. 8 | test_interval: 500 9 | # The base learning rate, momentum and the weight decay of the network. 10 | base_lr: 0.01 11 | momentum: 0.9 12 | weight_decay: 0.0005 13 | # The learning rate policy 14 | lr_policy: "inv" 15 | gamma: 0.0001 16 | power: 0.75 17 | # Display every 100 iterations 18 | display: 100 19 | # The maximum number of iterations 20 | max_iter: 10000 21 | # snapshot intermediate results 22 | snapshot: 5000 23 | snapshot_prefix: "examples/mnist/lenet" 24 | # solver mode: CPU or GPU 25 | solver_mode: GPU 26 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/examples/mnist/train_lenet.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | set -e 3 | 4 | ./build/tools/caffe train --solver=examples/mnist/lenet_solver.prototxt $@ 5 | -------------------------------------------------------------------------------- /caffe/examples/mnist/train_lenet_adam.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | set -e 3 | 4 | ./build/tools/caffe train --solver=examples/mnist/lenet_solver_adam.prototxt $@ 5 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/examples/net_surgery/conv.prototxt: -------------------------------------------------------------------------------- 1 | # Simple single-layer network to showcase editing model parameters. 2 | name: "convolution" 3 | layer { 4 | name: "data" 5 | type: "Input" 6 | top: "data" 7 | input_param { shape: { dim: 1 dim: 1 dim: 100 dim: 100 } } 8 | } 9 | layer { 10 | name: "conv" 11 | type: "Convolution" 12 | bottom: "data" 13 | top: "conv" 14 | convolution_param { 15 | num_output: 3 16 | kernel_size: 5 17 | stride: 1 18 | weight_filler { 19 | type: "gaussian" 20 | std: 0.01 21 | } 22 | bias_filler { 23 | type: "constant" 24 | value: 0 25 | } 26 | } 27 | } 28 | -------------------------------------------------------------------------------- /caffe/examples/siamese/create_mnist_siamese.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | # This script converts the mnist data into leveldb format. 3 | set -e 4 | 5 | EXAMPLES=./build/examples/siamese 6 | DATA=./data/mnist 7 | 8 | echo "Creating leveldb..." 9 | 10 | rm -rf ./examples/siamese/mnist_siamese_train_leveldb 11 | rm -rf ./examples/siamese/mnist_siamese_test_leveldb 12 | 13 | $EXAMPLES/convert_mnist_siamese_data.bin \ 14 | $DATA/train-images-idx3-ubyte \ 15 | $DATA/train-labels-idx1-ubyte \ 16 | ./examples/siamese/mnist_siamese_train_leveldb 17 | $EXAMPLES/convert_mnist_siamese_data.bin \ 18 | $DATA/t10k-images-idx3-ubyte \ 19 | $DATA/t10k-labels-idx1-ubyte \ 20 | ./examples/siamese/mnist_siamese_test_leveldb 21 | 22 | echo "Done." 23 | -------------------------------------------------------------------------------- /caffe/examples/siamese/train_mnist_siamese.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | set -e 3 | 4 | TOOLS=./build/tools 5 | 6 | $TOOLS/caffe train --solver=examples/siamese/mnist_siamese_solver.prototxt $@ 7 | -------------------------------------------------------------------------------- /caffe/examples/web_demo/requirements.txt: -------------------------------------------------------------------------------- 1 | werkzeug 2 | flask 3 | tornado 4 | numpy 5 | pandas 6 | pillow 7 | pyyaml 8 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/include/caffe/util/nccl.hpp: -------------------------------------------------------------------------------- 1 | #ifndef CAFFE_UTIL_NCCL_H_ 2 | #define CAFFE_UTIL_NCCL_H_ 3 | #ifdef USE_NCCL 4 | 5 | #include 6 | 7 | #include "caffe/common.hpp" 8 | 9 | #define NCCL_CHECK(condition) \ 10 | { \ 11 | ncclResult_t result = condition; \ 12 | CHECK_EQ(result, ncclSuccess) << " " \ 13 | << ncclGetErrorString(result); \ 14 | } 15 | 16 | namespace caffe { 17 | 18 | namespace nccl { 19 | 20 | template class dataType; 21 | 22 | template<> class dataType { 23 | public: 24 | static const ncclDataType_t type = ncclFloat; 25 | }; 26 | template<> class dataType { 27 | public: 28 | static const ncclDataType_t type = ncclDouble; 29 | }; 30 | 31 | } // namespace nccl 32 | 33 | } // namespace caffe 34 | 35 | #endif // end USE_NCCL 36 | 37 | #endif // CAFFE_UTIL_NCCL_H_ 38 | -------------------------------------------------------------------------------- /caffe/include/caffe/util/signal_handler.h: -------------------------------------------------------------------------------- 1 | #ifndef INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_ 2 | #define INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_ 3 | 4 | #include "caffe/proto/caffe.pb.h" 5 | #include "caffe/solver.hpp" 6 | 7 | namespace caffe { 8 | 9 | class SignalHandler { 10 | public: 11 | // Contructor. Specify what action to take when a signal is received. 12 | SignalHandler(SolverAction::Enum SIGINT_action, 13 | SolverAction::Enum SIGHUP_action); 14 | ~SignalHandler(); 15 | ActionCallback GetActionFunction(); 16 | private: 17 | SolverAction::Enum CheckForSignals() const; 18 | SolverAction::Enum SIGINT_action_; 19 | SolverAction::Enum SIGHUP_action_; 20 | }; 21 | 22 | } // namespace caffe 23 | 24 | #endif // INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_ 25 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/matlab/+caffe/get_solver.m: -------------------------------------------------------------------------------- 1 | function solver = get_solver(solver_file) 2 | % solver = get_solver(solver_file) 3 | % Construct a Solver object from solver_file 4 | 5 | CHECK(ischar(solver_file), 'solver_file must be a string'); 6 | CHECK_FILE_EXIST(solver_file); 7 | pSolver = caffe_('get_solver', solver_file); 8 | solver = caffe.Solver(pSolver); 9 | 10 | end 11 | -------------------------------------------------------------------------------- /caffe/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/caffe/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat -------------------------------------------------------------------------------- /caffe/matlab/+caffe/private/CHECK.m: -------------------------------------------------------------------------------- 1 | function CHECK(expr, error_msg) 2 | 3 | if ~expr 4 | error(error_msg); 5 | end 6 | 7 | end 8 | -------------------------------------------------------------------------------- /caffe/matlab/+caffe/private/CHECK_FILE_EXIST.m: -------------------------------------------------------------------------------- 1 | function CHECK_FILE_EXIST(filename) 2 | 3 | if exist(filename, 'file') == 0 4 | error('%s does not exist', filename); 5 | end 6 | 7 | end 8 | -------------------------------------------------------------------------------- /caffe/matlab/+caffe/reset_all.m: -------------------------------------------------------------------------------- 1 | function reset_all() 2 | % reset_all() 3 | % clear all solvers and stand-alone nets and reset Caffe to initial status 4 | 5 | caffe_('reset'); 6 | is_valid_handle('get_new_init_key'); 7 | 8 | end 9 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/matlab/+caffe/set_device.m: -------------------------------------------------------------------------------- 1 | function set_device(device_id) 2 | % set_device(device_id) 3 | % set Caffe's GPU device ID 4 | 5 | CHECK(isscalar(device_id) && device_id >= 0, ... 6 | 'device_id must be non-negative integer'); 7 | device_id = double(device_id); 8 | 9 | caffe_('set_device', device_id); 10 | 11 | end 12 | -------------------------------------------------------------------------------- /caffe/matlab/+caffe/set_mode_cpu.m: -------------------------------------------------------------------------------- 1 | function set_mode_cpu() 2 | % set_mode_cpu() 3 | % set Caffe to CPU mode 4 | 5 | caffe_('set_mode_cpu'); 6 | 7 | end 8 | -------------------------------------------------------------------------------- /caffe/matlab/+caffe/set_mode_gpu.m: -------------------------------------------------------------------------------- 1 | function set_mode_gpu() 2 | % set_mode_gpu() 3 | % set Caffe to GPU mode 4 | 5 | caffe_('set_mode_gpu'); 6 | 7 | end 8 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/python/caffe/__init__.py: -------------------------------------------------------------------------------- 1 | from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver, NCCL, Timer 2 | from ._caffe import init_log, log, set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list, set_random_seed, solver_count, set_solver_count, solver_rank, set_solver_rank, set_multiprocess, Layer, get_solver 3 | from ._caffe import __version__ 4 | from .proto.caffe_pb2 import TRAIN, TEST 5 | from .classifier import Classifier 6 | from .detector import Detector 7 | from . import io 8 | from .net_spec import layers, params, NetSpec, to_proto 9 | -------------------------------------------------------------------------------- /caffe/python/caffe/imagenet/ilsvrc_2012_mean.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/caffe/python/caffe/imagenet/ilsvrc_2012_mean.npy -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/python/requirements.txt: -------------------------------------------------------------------------------- 1 | Cython>=0.19.2 2 | numpy>=1.7.1 3 | scipy>=0.13.2 4 | scikit-image>=0.9.3 5 | matplotlib>=1.3.1 6 | ipython>=3.0.0 7 | h5py>=2.2.0 8 | leveldb>=0.191 9 | networkx>=1.8.1 10 | nose>=1.3.0 11 | pandas>=0.12.0 12 | python-dateutil>=1.4,<2 13 | protobuf>=2.5.0 14 | python-gflags>=2.0 15 | pyyaml>=3.10 16 | Pillow>=2.3.0 17 | six>=1.1.0 -------------------------------------------------------------------------------- /caffe/scripts/build_docs.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # Build documentation for display in web browser. 3 | 4 | PORT=${1:-4000} 5 | 6 | echo "usage: build_docs.sh [port]" 7 | 8 | # Find the docs dir, no matter where the script is called 9 | ROOT_DIR="$( cd "$(dirname "$0")"/.. ; pwd -P )" 10 | cd $ROOT_DIR 11 | 12 | # Gather docs. 13 | scripts/gather_examples.sh 14 | 15 | # Split caffe.proto for inclusion by layer catalogue. 16 | scripts/split_caffe_proto.py 17 | 18 | # Generate developer docs. 19 | make docs 20 | 21 | # Display docs using web server. 22 | cd docs 23 | jekyll serve -w -s . -d _site --port=$PORT 24 | -------------------------------------------------------------------------------- /caffe/scripts/download_model_from_gist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | 3 | GIST=$1 4 | DIRNAME=${2:-./models} 5 | 6 | if [ -z $GIST ]; then 7 | echo "usage: download_model_from_gist.sh " 8 | exit 9 | fi 10 | 11 | GIST_DIR=$(echo $GIST | tr '/' '-') 12 | MODEL_DIR="$DIRNAME/$GIST_DIR" 13 | 14 | if [ -d $MODEL_DIR ]; then 15 | echo "$MODEL_DIR already exists! Please make sure you're not overwriting anything important!" 16 | exit 17 | fi 18 | 19 | echo "Downloading Caffe model info to $MODEL_DIR ..." 20 | mkdir -p $MODEL_DIR 21 | wget https://gist.github.com/$GIST/download -O $MODEL_DIR/gist.zip 22 | unzip -j $MODEL_DIR/gist.zip -d $MODEL_DIR 23 | rm $MODEL_DIR/gist.zip 24 | echo "Done" 25 | -------------------------------------------------------------------------------- /caffe/scripts/travis/build.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # build the project 3 | 4 | BASEDIR=$(dirname $0) 5 | source $BASEDIR/defaults.sh 6 | 7 | if ! $WITH_CMAKE ; then 8 | make --jobs $NUM_THREADS all test pycaffe warn 9 | else 10 | cd build 11 | make --jobs $NUM_THREADS all test.testbin 12 | fi 13 | make lint 14 | -------------------------------------------------------------------------------- /caffe/scripts/travis/configure-cmake.sh: -------------------------------------------------------------------------------- 1 | # CMake configuration 2 | 3 | mkdir -p build 4 | cd build 5 | 6 | ARGS="-DCMAKE_BUILD_TYPE=Release -DBLAS=Open" 7 | 8 | if $WITH_PYTHON3 ; then 9 | ARGS="$ARGS -Dpython_version=3" 10 | fi 11 | 12 | if $WITH_IO ; then 13 | ARGS="$ARGS -DUSE_OPENCV=On -DUSE_LMDB=On -DUSE_LEVELDB=On" 14 | else 15 | ARGS="$ARGS -DUSE_OPENCV=Off -DUSE_LMDB=Off -DUSE_LEVELDB=Off" 16 | fi 17 | 18 | if $WITH_CUDA ; then 19 | # Only build SM50 20 | ARGS="$ARGS -DCPU_ONLY=Off -DCUDA_ARCH_NAME=Manual -DCUDA_ARCH_BIN=\"50\" -DCUDA_ARCH_PTX=\"\"" 21 | else 22 | ARGS="$ARGS -DCPU_ONLY=On" 23 | fi 24 | 25 | if $WITH_CUDNN ; then 26 | ARGS="$ARGS -DUSE_CUDNN=On" 27 | else 28 | ARGS="$ARGS -DUSE_CUDNN=Off" 29 | fi 30 | 31 | cmake .. $ARGS 32 | 33 | -------------------------------------------------------------------------------- /caffe/scripts/travis/configure.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # configure the project 3 | 4 | BASEDIR=$(dirname $0) 5 | source $BASEDIR/defaults.sh 6 | 7 | if ! $WITH_CMAKE ; then 8 | source $BASEDIR/configure-make.sh 9 | else 10 | source $BASEDIR/configure-cmake.sh 11 | fi 12 | -------------------------------------------------------------------------------- /caffe/scripts/travis/defaults.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # set default environment variables 3 | 4 | set -e 5 | 6 | WITH_CMAKE=${WITH_CMAKE:-false} 7 | WITH_PYTHON3=${WITH_PYTHON3:-false} 8 | WITH_IO=${WITH_IO:-true} 9 | WITH_CUDA=${WITH_CUDA:-false} 10 | WITH_CUDNN=${WITH_CUDNN:-false} 11 | -------------------------------------------------------------------------------- /caffe/scripts/travis/install-python-deps.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # install extra Python dependencies 3 | # (must come after setup-venv) 4 | 5 | BASEDIR=$(dirname $0) 6 | source $BASEDIR/defaults.sh 7 | 8 | if ! $WITH_PYTHON3 ; then 9 | # Python2 10 | : 11 | else 12 | # Python3 13 | pip install --pre protobuf==3.0.0b3 14 | fi 15 | -------------------------------------------------------------------------------- /caffe/scripts/travis/setup-venv.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # setup a Python virtualenv 3 | # (must come after install-deps) 4 | 5 | BASEDIR=$(dirname $0) 6 | source $BASEDIR/defaults.sh 7 | 8 | VENV_DIR=${1:-~/venv} 9 | 10 | # setup our own virtualenv 11 | if $WITH_PYTHON3; then 12 | PYTHON_EXE='/usr/bin/python3' 13 | else 14 | PYTHON_EXE='/usr/bin/python2' 15 | fi 16 | 17 | # use --system-site-packages so that Python will use deb packages 18 | virtualenv $VENV_DIR -p $PYTHON_EXE --system-site-packages 19 | -------------------------------------------------------------------------------- /caffe/scripts/travis/test.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # test the project 3 | 4 | BASEDIR=$(dirname $0) 5 | source $BASEDIR/defaults.sh 6 | 7 | if $WITH_CUDA ; then 8 | echo "Skipping tests for CUDA build" 9 | exit 0 10 | fi 11 | 12 | if ! $WITH_CMAKE ; then 13 | make runtest 14 | make pytest 15 | else 16 | cd build 17 | make runtest 18 | make pytest 19 | fi 20 | -------------------------------------------------------------------------------- /caffe/src/caffe/layer.cpp: -------------------------------------------------------------------------------- 1 | #include "caffe/layer.hpp" 2 | 3 | namespace caffe { 4 | 5 | INSTANTIATE_CLASS(Layer); 6 | 7 | } // namespace caffe 8 | -------------------------------------------------------------------------------- /caffe/src/caffe/layers/loss_layer.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "caffe/layers/loss_layer.hpp" 4 | 5 | namespace caffe { 6 | 7 | template 8 | void LossLayer::LayerSetUp( 9 | const vector*>& bottom, const vector*>& top) { 10 | // LossLayers have a non-zero (1) loss by default. 11 | if (this->layer_param_.loss_weight_size() == 0) { 12 | this->layer_param_.add_loss_weight(Dtype(1)); 13 | } 14 | } 15 | 16 | template 17 | void LossLayer::Reshape( 18 | const vector*>& bottom, const vector*>& top) { 19 | CHECK_EQ(bottom[0]->shape(0), bottom[1]->shape(0)) 20 | << "The data and label should have the same first dimension."; 21 | vector loss_shape(0); // Loss layers output a scalar; 0 axes. 22 | top[0]->Reshape(loss_shape); 23 | } 24 | 25 | INSTANTIATE_CLASS(LossLayer); 26 | 27 | } // namespace caffe 28 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/src/caffe/layers/parameter_layer.cpp: -------------------------------------------------------------------------------- 1 | #include "caffe/layers/parameter_layer.hpp" 2 | 3 | namespace caffe { 4 | 5 | INSTANTIATE_CLASS(ParameterLayer); 6 | REGISTER_LAYER_CLASS(Parameter); 7 | 8 | } // namespace caffe 9 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/src/caffe/test/test_data/sample_data.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/caffe/src/caffe/test/test_data/sample_data.h5 -------------------------------------------------------------------------------- /caffe/src/caffe/test/test_data/sample_data_2_gzip.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/caffe/src/caffe/test/test_data/sample_data_2_gzip.h5 -------------------------------------------------------------------------------- /caffe/src/caffe/test/test_data/sample_data_list.txt: -------------------------------------------------------------------------------- 1 | src/caffe/test/test_data/sample_data.h5 2 | src/caffe/test/test_data/sample_data_2_gzip.h5 3 | -------------------------------------------------------------------------------- /caffe/src/caffe/test/test_data/solver_data.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/caffe/src/caffe/test/test_data/solver_data.h5 -------------------------------------------------------------------------------- /caffe/src/caffe/test/test_data/solver_data_list.txt: -------------------------------------------------------------------------------- 1 | src/caffe/test/test_data/solver_data.h5 2 | -------------------------------------------------------------------------------- /caffe/src/caffe/util/cudnn.cpp: -------------------------------------------------------------------------------- 1 | #ifdef USE_CUDNN 2 | #include "caffe/util/cudnn.hpp" 3 | 4 | namespace caffe { 5 | namespace cudnn { 6 | 7 | float dataType::oneval = 1.0; 8 | float dataType::zeroval = 0.0; 9 | const void* dataType::one = 10 | static_cast(&dataType::oneval); 11 | const void* dataType::zero = 12 | static_cast(&dataType::zeroval); 13 | 14 | double dataType::oneval = 1.0; 15 | double dataType::zeroval = 0.0; 16 | const void* dataType::one = 17 | static_cast(&dataType::oneval); 18 | const void* dataType::zero = 19 | static_cast(&dataType::zeroval); 20 | 21 | } // namespace cudnn 22 | } // namespace caffe 23 | #endif 24 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /caffe/src/gtest/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | add_library(gtest STATIC EXCLUDE_FROM_ALL gtest.h gtest-all.cpp) 2 | caffe_default_properties(gtest) 3 | target_include_directories(gtest PUBLIC ${Caffe_SRC_DIR}) 4 | target_compile_definitions(gtest PUBLIC -DGTEST_USE_OWN_TR1_TUPLE) 5 | 6 | 7 | #add_library(gtest_main gtest_main.cc) 8 | #target_link_libraries(gtest_main gtest) 9 | -------------------------------------------------------------------------------- /caffe/tools/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | # Collect source files 2 | file(GLOB_RECURSE srcs ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp) 3 | 4 | # Build each source file independently 5 | foreach(source ${srcs}) 6 | get_filename_component(name ${source} NAME_WE) 7 | 8 | # caffe target already exits 9 | if(name MATCHES "caffe") 10 | set(name ${name}.bin) 11 | endif() 12 | 13 | # target 14 | add_executable(${name} ${source}) 15 | target_link_libraries(${name} ${Caffe_LINK}) 16 | caffe_default_properties(${name}) 17 | 18 | # set back RUNTIME_OUTPUT_DIRECTORY 19 | caffe_set_runtime_directory(${name} "${PROJECT_BINARY_DIR}/tools") 20 | caffe_set_solution_folder(${name} tools) 21 | 22 | # restore output name without suffix 23 | if(name MATCHES "caffe.bin") 24 | set_target_properties(${name} PROPERTIES OUTPUT_NAME caffe) 25 | endif() 26 | 27 | # Install 28 | install(TARGETS ${name} DESTINATION bin) 29 | endforeach(source) 30 | -------------------------------------------------------------------------------- /caffe/tools/device_query.cpp: -------------------------------------------------------------------------------- 1 | #include "caffe/common.hpp" 2 | 3 | int main(int argc, char** argv) { 4 | LOG(FATAL) << "Deprecated. Use caffe device_query " 5 | "[--device_id=0] instead."; 6 | return 0; 7 | } 8 | -------------------------------------------------------------------------------- /caffe/tools/finetune_net.cpp: -------------------------------------------------------------------------------- 1 | #include "caffe/caffe.hpp" 2 | 3 | int main(int argc, char** argv) { 4 | LOG(FATAL) << "Deprecated. Use caffe train --solver=... " 5 | "[--weights=...] instead."; 6 | return 0; 7 | } 8 | -------------------------------------------------------------------------------- /caffe/tools/net_speed_benchmark.cpp: -------------------------------------------------------------------------------- 1 | #include "caffe/caffe.hpp" 2 | 3 | int main(int argc, char** argv) { 4 | LOG(FATAL) << "Deprecated. Use caffe time --model=... " 5 | "[--iterations=50] [--gpu] [--device_id=0]"; 6 | return 0; 7 | } 8 | -------------------------------------------------------------------------------- /caffe/tools/test_net.cpp: -------------------------------------------------------------------------------- 1 | #include "caffe/caffe.hpp" 2 | 3 | int main(int argc, char** argv) { 4 | LOG(FATAL) << "Deprecated. Use caffe test --model=... " 5 | "--weights=... instead."; 6 | return 0; 7 | } 8 | -------------------------------------------------------------------------------- /caffe/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 | -------------------------------------------------------------------------------- /data/imagenet_models/README.md: -------------------------------------------------------------------------------- 1 | # Imagenet-models 2 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/helper/SOPD.mexa64: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/evaluation/KISSME/toolbox/helper/SOPD.mexa64 -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/helper/ToyCarPairsToLabels.m: -------------------------------------------------------------------------------- 1 | function [X,Y] = ToyCarPairsToLabels(pairs, instance_matrix) 2 | imgs1 = [pairs.img1]; 3 | imgs2 = [pairs.img2]; 4 | 5 | keymat = [[imgs1.classId] [imgs2.classId]; [imgs1.id] [imgs2.id]]; 6 | ids = unique(keymat','rows'); 7 | 8 | X = instance_matrix(:,ids(:,2)); 9 | Y = ids(:,1)'; 10 | end -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/helper/calcMCMC.m: -------------------------------------------------------------------------------- 1 | function [ result ] = calcMCMC( M, data, idxa, idxb, idxtest ) 2 | 3 | dist = sqdist(data(:,idxa(idxtest)), data(:,idxb(idxtest)),M); 4 | 5 | result = zeros(1,size(dist,2)); 6 | for pairCounter=1:size(dist,2) 7 | distPair = dist(pairCounter,:); 8 | [tmp,idx] = sort(distPair,'ascend'); 9 | result(idx==pairCounter) = result(idx==pairCounter) + 1; 10 | end 11 | 12 | tmp = 0; 13 | for counter=1:length(result) 14 | result(counter) = result(counter) + tmp; 15 | tmp = result(counter); 16 | end 17 | 18 | end 19 | 20 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/helper/calcmAP.m: -------------------------------------------------------------------------------- 1 | function [ mAP, CMC ] = calcmAP( M, data_test, data_query, testID, testCAM, queryID, queryCAM ) 2 | dist = sqdist(data_test, data_query, M); 3 | ap = zeros(size(dist, 2), 1); 4 | CMC = zeros(size(dist, 2), size(dist, 1)); 5 | for k = 1:size(dist, 2) 6 | % find groudtruth index (good and junk) 7 | good_index = intersect(find(testID == queryID(k)), find(testCAM ~= queryCAM(k)))'; 8 | junk_index1 = find(testID == -1); 9 | junk_index2 = intersect(find(testID == queryID(k)), find(testCAM == queryCAM(k))); 10 | junk_index = [junk_index1; junk_index2]'; 11 | score = dist(:, k); 12 | [~, index] = sort(score, 'ascend');% the higher, the better 13 | [ap(k), CMC(k, :)] = compute_AP(good_index, junk_index, index);% see compute_AP 14 | end 15 | %% cmc and mAP 16 | CMC = mean(CMC); 17 | mAP = mean(ap); 18 | 19 | end 20 | 21 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/helper/cdistM.m: -------------------------------------------------------------------------------- 1 | function dist=cdistM(M,x,i,j) 2 | %CDISTM Compute the quadratic distances (x(i)-x(j))^T * M * (x(i)-x(j)) 3 | %between pairs of samples 4 | % 5 | % [ Arguments ] 6 | % - x: the sample matrices 7 | % - M: the resulting metric matrix 8 | % - i,j: index of respective samples 9 | % 10 | % [ Example ] 11 | % 12 | % M = rand(10, 10); 13 | % dist = slmetric_pw(M, x, i, j); 14 | % 15 | 16 | dist = zeros(1,length(i)); 17 | for c=1:length(i) 18 | dist(c) = (x(:,i(c)) - x(:,j(c)))' ... 19 | * M * (x(:,i(c)) - x(:,j(c))); 20 | end -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/helper/col_sum.m: -------------------------------------------------------------------------------- 1 | function s = col_sum(x) 2 | % COL_SUM Sum for each column. 3 | % A more readable alternative to sum(x,1). 4 | s = sum(x,1); 5 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/helper/exportAndCropFigure.m: -------------------------------------------------------------------------------- 1 | function exportAndCropFigure(h,name,saveDir) 2 | if nargin < 3 3 | return; 4 | end 5 | [status,message,messageid] = mkdir(saveDir); 6 | [status,message,messageid] = mkdir(fullfile(saveDir,'png')); 7 | [status,message,messageid] = mkdir(fullfile(saveDir,'pdf')); 8 | [status,message,messageid] = mkdir(fullfile(saveDir,'fig')); 9 | 10 | saveas(h,fullfile(saveDir,name)); 11 | saveas(h,fullfile(saveDir,'png',[name '.png'])); 12 | saveas(h,fullfile(saveDir,'fig',[name '.fig'])); 13 | saveas(h,fullfile(saveDir,'pdf',[name '.pdf'])); 14 | 15 | %- auto crop pdf -% 16 | cmdStr = ['pdfcrop.pl' ' ' fullfile(saveDir,'pdf',name) ... 17 | ' ' fullfile(saveDir,'pdf',name)]; 18 | [status, result] = system(cmdStr); 19 | 20 | end 21 | 22 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/helper/pairsToLabels.m: -------------------------------------------------------------------------------- 1 | function [X,Y] = pairsToLabels(pairs, instance_matrix) 2 | imgs1 = [pairs.img1]; 3 | imgs2 = [pairs.img2]; 4 | 5 | keymat = [[imgs1.classId] [imgs2.classId]; [imgs1.id] [imgs2.id]]; 6 | [ids, idsndx] = unique(keymat','rows'); 7 | 8 | X = instance_matrix(:,idsndx); 9 | Y = ids(:,1)'; 10 | end -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/helper/sqdist.m: -------------------------------------------------------------------------------- 1 | function m = sqdist(p, q, A) 2 | % SQDIST Squared Euclidean or Mahalanobis distance. 3 | % SQDIST(p,q) returns m(i,j) = (p(:,i) - q(:,j))'*(p(:,i) - q(:,j)). 4 | % SQDIST(p,q,A) returns m(i,j) = (p(:,i) - q(:,j))'*A*(p(:,i) - q(:,j)). 5 | % The Lightspeed Matlab toolbox 6 | % Written by Tom Minka 7 | 8 | [d, pn] = size(p); 9 | [d, qn] = size(q); 10 | 11 | if pn == 0 || qn == 0 12 | m = zeros(pn,qn); 13 | return 14 | end 15 | 16 | if nargin == 2 17 | 18 | pmag = col_sum(p .* p); 19 | qmag = col_sum(q .* q); 20 | m = repmat(qmag, pn, 1) + repmat(pmag', 1, qn) - 2*p'*q; 21 | %m = ones(pn,1)*qmag + pmag'*ones(1,qn) - 2*p'*q; 22 | 23 | else 24 | 25 | Ap = A*p; 26 | Aq = A*q; 27 | pmag = col_sum(p .* Ap); 28 | qmag = col_sum(q .* Aq); 29 | m = repmat(qmag, pn, 1) + repmat(pmag', 1, qn) - 2*p'*Aq; 30 | 31 | end 32 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/helper/validateCovMatrix.m: -------------------------------------------------------------------------------- 1 | function [sigma] = validateCovMatrix(sig) 2 | 3 | % [sigma] = validateCovMatrix(sig) 4 | % 5 | % -- INPUT -- 6 | % sig: sample covariance matrix 7 | % 8 | % -- OUTPUT -- 9 | % sigma: positive-definite covariance matrix 10 | % 11 | 12 | EPS = 10^-6; 13 | ZERO = 10^-10; 14 | 15 | sigma = sig; 16 | [r err] = cholcov(sigma, 0); 17 | 18 | if (err ~= 0) 19 | % the covariance matrix is not positive definite! 20 | [v d] = eig(sigma); 21 | 22 | % set any of the eigenvalues that are <= 0 to some small positive value 23 | for n = 1:size(d,1) 24 | if (d(n, n) <= ZERO) 25 | d(n, n) = EPS; 26 | end 27 | end 28 | % recompose the covariance matrix, now it should be positive definite. 29 | sigma = v*d*v'; 30 | 31 | [r err] = cholcov(sigma, 0); 32 | if (err ~= 0) 33 | disp('ERROR!'); 34 | end 35 | end -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/init.m: -------------------------------------------------------------------------------- 1 | global FRAMEWORK_DIR; 2 | FRAMEWORK_DIR = fileparts(mfilename('fullpath')); 3 | LIB_DIR = fullfile(FRAMEWORK_DIR,'lib'); 4 | 5 | set(0,'defaultAxesFontName', 'Arial'); 6 | set(0,'defaultTextFontName', 'Arial'); 7 | 8 | addpath(genpath(fullfile(FRAMEWORK_DIR))); 9 | 10 | % try compile the mex file(s) if needed 11 | bool = exist('SOPD') ~= 0; 12 | if ~bool 13 | try 14 | fprintf('performing first time installation of mex files...'); 15 | wd = cd; 16 | cd(fullfile(FRAMEWORK_DIR,'helper')); 17 | mex('SOPD.cpp'); 18 | cd(wd); 19 | fprintf(' done\n'); 20 | catch ME 21 | error('failed! You might not be able to run the code.'); 22 | end 23 | end -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/learnAlgos/LearnAlgo.m: -------------------------------------------------------------------------------- 1 | classdef LearnAlgo < handle 2 | %LEARNALGO Summary of this class goes here 3 | % Detailed explanation goes here 4 | 5 | properties 6 | 7 | end 8 | 9 | methods 10 | function obj = LearnAlgo(p) 11 | 12 | end 13 | 14 | function learnPairwise(obj,X,idxa,idxb,matches) 15 | 16 | end 17 | 18 | function learn(obj,X,y) 19 | 20 | end 21 | 22 | function d = dist(obj, X, idxa,idxb) 23 | 24 | end 25 | end 26 | 27 | end 28 | 29 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/evaluation/KISSME/toolbox/lib/LMNN/.DS_Store -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/.svn/dir-prop-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | K 10 6 | svn:ignore 7 | V 3 8 | *~ 9 | 10 | END 11 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/.svn/format: -------------------------------------------------------------------------------- 1 | 8 2 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/.svn/prop-base/applypca.m.svn-base: -------------------------------------------------------------------------------- 1 | K 14 2 | svn:executable 3 | V 1 4 | * 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/.svn/prop-base/demo.m.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/.svn/prop-base/energyclassify.m.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/.svn/prop-base/install.m.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/.svn/prop-base/knnclassify.m.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/.svn/prop-base/lmnn.m.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/.svn/prop-base/pca.m.svn-base: -------------------------------------------------------------------------------- 1 | K 14 2 | svn:executable 3 | V 1 4 | * 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/.svn/prop-base/runlmnn.m.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/.svn/prop-base/setpaths.m.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/.svn/text-base/applypca.m.svn-base: -------------------------------------------------------------------------------- 1 | function [ux,u,m] = applypca(X) 2 | % [ux,u,m] = applypca(X) 3 | % 4 | % finds principal components of 5 | % 6 | % input: 7 | % x input data (each column is an input vector) 8 | % 9 | % output: 10 | % ux data projected onto pca (1st row=leading principal component) 11 | % u the orthogonal matrix with all principal components 12 | % m mean of the data 13 | % 14 | % You can recover the original data with 15 | % 16 | % xoriginal=u*ux+repmat(m,1,size(ux,2)); 17 | % 18 | % See also pca 19 | % 20 | % copyright by Kilian Q. Weinberger, 2006 21 | 22 | [u,v]=pca(X); 23 | m=mean(X,2); 24 | ux=u'*(X-repmat(m,1,size(X,2))); 25 | 26 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/.svn/text-base/demo.m.svn-base: -------------------------------------------------------------------------------- 1 | 2 | load('data/bal2.mat'); 3 | [L,Det]=lmnn(xTr,yTr,'quiet',1); 4 | enerr=energyclassify(L,xTr,yTr,xTe,yTe,3); 5 | knnerrL=knnclassify(L,xTr,yTr,xTe,yTe,3); 6 | knnerrI=knnclassify(eye(size(L)),xTr,yTr,xTe,yTe,3); 7 | 8 | clc; 9 | fprintf('Bal data set:\n'); 10 | fprintf('3-NN Euclidean training error: %2.2f\n',knnerrI(1)*100); 11 | fprintf('3-NN Euclidean testing error: %2.2f\n',knnerrI(2)*100); 12 | fprintf('3-NN Malhalanobis training error: %2.2f\n',knnerrL(1)*100); 13 | fprintf('3-NN Malhalanobis testing error: %2.2f\n',knnerrL(2)*100); 14 | fprintf('Energy classification error: %2.2f\n',enerr*100); 15 | fprintf('Training time: %2.2fs\n (As a reality check: My desktop needs 20.53s)\n\n',Det.time); 16 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/.svn/text-base/pca.m.svn-base: -------------------------------------------------------------------------------- 1 | function [evects,evals] = pca(X) 2 | % [evects,evals] = pca(X) 3 | % 4 | % finds principal components of 5 | % 6 | % input: 7 | % X dxn matrix (each column is a dx1 input vector) 8 | % 9 | % output: 10 | % evects columns are principal components (leading from left->right) 11 | % evals corresponding eigenvalues 12 | % 13 | % See also applypca 14 | % 15 | % copyright by Kilian Q. Weinberger, 2006 16 | 17 | [d,N] = size(X); 18 | 19 | mm = mean(X,2); 20 | X = X - mm*ones(1,N); % remove mean from data 21 | 22 | cc = cov(X',1); % compute covariance 23 | [cvv,cdd] = eig(cc); % compute eignvectors 24 | [zz,ii] = sort(diag(-cdd)); % sort according to eigenvalues 25 | evects = cvv(:,ii); % pick leading eigenvectors 26 | cdd = diag(cdd); % extract eigenvalues 27 | evals = cdd(ii); % sort eigenvalues 28 | 29 | 30 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/.svn/text-base/setpaths.m.svn-base: -------------------------------------------------------------------------------- 1 | fprintf('Adding directories to path.\n'); 2 | addpath(pwd); 3 | cd mexfunctions 4 | addpath(pwd); 5 | cd .. 6 | cd helperfunctions 7 | addpath(pwd); 8 | cd .. 9 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/applypca.m: -------------------------------------------------------------------------------- 1 | function [ux,u,m] = applypca(X) 2 | % [ux,u,m] = applypca(X) 3 | % 4 | % finds principal components of 5 | % 6 | % input: 7 | % x input data (each column is an input vector) 8 | % 9 | % output: 10 | % ux data projected onto pca (1st row=leading principal component) 11 | % u the orthogonal matrix with all principal components 12 | % m mean of the data 13 | % 14 | % You can recover the original data with 15 | % 16 | % xoriginal=u*ux+repmat(m,1,size(ux,2)); 17 | % 18 | % See also pca 19 | % 20 | % copyright by Kilian Q. Weinberger, 2006 21 | 22 | [u,v]=pca(X); 23 | m=mean(X,2); 24 | ux=u'*(X-repmat(m,1,size(X,2))); 25 | 26 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/data/.svn/all-wcprops: -------------------------------------------------------------------------------- 1 | K 25 2 | svn:wc:ra_dav:version-url 3 | V 56 4 | /svnroot/lksmlg/!svn/ver/111/common/algorithms/LMNN/data 5 | END 6 | bal2.mat 7 | K 25 8 | svn:wc:ra_dav:version-url 9 | V 65 10 | /svnroot/lksmlg/!svn/ver/111/common/algorithms/LMNN/data/bal2.mat 11 | END 12 | iris2.mat 13 | K 25 14 | svn:wc:ra_dav:version-url 15 | V 66 16 | /svnroot/lksmlg/!svn/ver/111/common/algorithms/LMNN/data/iris2.mat 17 | END 18 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/data/.svn/dir-prop-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | K 10 6 | svn:ignore 7 | V 3 8 | *~ 9 | 10 | END 11 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/data/.svn/entries: -------------------------------------------------------------------------------- 1 | 8 2 | 3 | dir 4 | 455 5 | https://svn.cis.upenn.edu/svnroot/lksmlg/common/algorithms/LMNN/data 6 | https://svn.cis.upenn.edu/svnroot/lksmlg 7 | 8 | 9 | 10 | 2006-10-26T19:11:12.495119Z 11 | 111 12 | lksmlg 13 | has-props 14 | 15 | svn:special svn:externals svn:needs-lock 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 38c5f050-661f-0410-ba73-9d620db92ef7 28 | 29 | bal2.mat 30 | file 31 | 32 | 33 | 34 | 35 | 2006-10-10T05:47:58.000000Z 36 | 1e8cd38f17abc1f750bb9a201eb08a5a 37 | 2006-10-26T19:11:12.495119Z 38 | 111 39 | lksmlg 40 | has-props 41 | 42 | iris2.mat 43 | file 44 | 45 | 46 | 47 | 48 | 2006-10-10T05:47:58.000000Z 49 | 7c44f7ccb3ded8c5dd6f652f1fb8edbb 50 | 2006-10-26T19:11:12.495119Z 51 | 111 52 | lksmlg 53 | has-props 54 | 55 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/data/.svn/format: -------------------------------------------------------------------------------- 1 | 8 2 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/data/.svn/prop-base/bal2.mat.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | K 13 6 | svn:mime-type 7 | V 24 8 | application/octet-stream 9 | END 10 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/data/.svn/prop-base/iris2.mat.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | K 13 6 | svn:mime-type 7 | V 24 8 | application/octet-stream 9 | END 10 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/data/.svn/text-base/bal2.mat.svn-base: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/evaluation/KISSME/toolbox/lib/LMNN/data/.svn/text-base/bal2.mat.svn-base -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/data/.svn/text-base/iris2.mat.svn-base: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/evaluation/KISSME/toolbox/lib/LMNN/data/.svn/text-base/iris2.mat.svn-base -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/data/bal2.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/evaluation/KISSME/toolbox/lib/LMNN/data/bal2.mat -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/data/iris2.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/evaluation/KISSME/toolbox/lib/LMNN/data/iris2.mat -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/demo.m: -------------------------------------------------------------------------------- 1 | 2 | load('data/bal2.mat'); 3 | [L,Det]=lmnn(xTr,yTr,'quiet',1); 4 | enerr=energyclassify(L,xTr,yTr,xTe,yTe,3); 5 | knnerrL=knnclassify(L,xTr,yTr,xTe,yTe,3); 6 | knnerrI=knnclassify(eye(size(L)),xTr,yTr,xTe,yTe,3); 7 | 8 | clc; 9 | fprintf('Bal data set:\n'); 10 | fprintf('3-NN Euclidean training error: %2.2f\n',knnerrI(1)*100); 11 | fprintf('3-NN Euclidean testing error: %2.2f\n',knnerrI(2)*100); 12 | fprintf('3-NN Malhalanobis training error: %2.2f\n',knnerrL(1)*100); 13 | fprintf('3-NN Malhalanobis testing error: %2.2f\n',knnerrL(2)*100); 14 | fprintf('Energy classification error: %2.2f\n',enerr*100); 15 | fprintf('Training time: %2.2fs\n (As a reality check: My desktop needs 20.53s)\n\n',Det.time); 16 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/helperfunctions/.svn/all-wcprops: -------------------------------------------------------------------------------- 1 | K 25 2 | svn:wc:ra_dav:version-url 3 | V 67 4 | /svnroot/lksmlg/!svn/ver/111/common/algorithms/LMNN/helperfunctions 5 | END 6 | vec.m 7 | K 25 8 | svn:wc:ra_dav:version-url 9 | V 73 10 | /svnroot/lksmlg/!svn/ver/111/common/algorithms/LMNN/helperfunctions/vec.m 11 | END 12 | extractpars.m 13 | K 25 14 | svn:wc:ra_dav:version-url 15 | V 81 16 | /svnroot/lksmlg/!svn/ver/111/common/algorithms/LMNN/helperfunctions/extractpars.m 17 | END 18 | mat.m 19 | K 25 20 | svn:wc:ra_dav:version-url 21 | V 73 22 | /svnroot/lksmlg/!svn/ver/111/common/algorithms/LMNN/helperfunctions/mat.m 23 | END 24 | mexall.m 25 | K 25 26 | svn:wc:ra_dav:version-url 27 | V 76 28 | /svnroot/lksmlg/!svn/ver/111/common/algorithms/LMNN/helperfunctions/mexall.m 29 | END 30 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/helperfunctions/.svn/dir-prop-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | K 10 6 | svn:ignore 7 | V 3 8 | *~ 9 | 10 | END 11 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/helperfunctions/.svn/format: -------------------------------------------------------------------------------- 1 | 8 2 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/helperfunctions/.svn/prop-base/extractpars.m.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/helperfunctions/.svn/prop-base/mat.m.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/helperfunctions/.svn/prop-base/mexall.m.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/helperfunctions/.svn/prop-base/vec.m.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/helperfunctions/.svn/text-base/extractpars.m.svn-base: -------------------------------------------------------------------------------- 1 | function pars=extractpars(vars,default); 2 | % function pars=extractpars(vars,default); 3 | % 4 | % 5 | 6 | if(nargin<2) 7 | default=[]; 8 | end; 9 | 10 | pars=default; 11 | if(length(vars)==1) 12 | p=vars{1}; 13 | s=fieldnames(p); 14 | for i=1:length(s) 15 | eval(['pars.' s{i} '=p.' s{i}]); 16 | end; 17 | 18 | else 19 | 20 | 21 | 22 | 23 | for i=1:2:length(vars) 24 | if(isstr(vars{i})) 25 | if(i+1>length(vars)) error(sprintf('Parameter %s has no value\n',vars{i}));else val=vars{i+1};end; 26 | if(isstr(val)) 27 | eval(['pars.' vars{i} '=''' val ''';']); 28 | else 29 | eval(['pars.' vars{i} '=' sprintf('%i',val) ';']); 30 | end; 31 | end; 32 | end; 33 | 34 | end; 35 | 36 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/helperfunctions/.svn/text-base/mat.m.svn-base: -------------------------------------------------------------------------------- 1 | function M=mat(C); 2 | 3 | r=round(sqrt(size(C,1))); 4 | M=reshape(C,r,r); -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/helperfunctions/.svn/text-base/mexall.m.svn-base: -------------------------------------------------------------------------------- 1 | % compiles all .c files in the current directory 2 | % 3 | % Kilian Q. Weinberger, 2006 4 | 5 | dd=dir('*.c'); 6 | for i=1:length(dd) 7 | fprintf('mex %s\n',dd(i).name); 8 | eval(['try;mex ' dd(i).name '; catch; disp(lasterr); end;']); 9 | end; 10 | 11 | dd=dir('*.cpp'); 12 | for i=1:length(dd) 13 | fprintf('mex %s\n',dd(i).name); 14 | eval(['try;mex ' dd(i).name '; catch; disp(lasterr); end;']); 15 | end; 16 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/helperfunctions/.svn/text-base/vec.m.svn-base: -------------------------------------------------------------------------------- 1 | function C=vec(M); 2 | 3 | C=M(:); -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/helperfunctions/extractpars.m: -------------------------------------------------------------------------------- 1 | function pars=extractpars(vars,default); 2 | % function pars=extractpars(vars,default); 3 | % 4 | % 5 | 6 | if(nargin<2) 7 | default=[]; 8 | end; 9 | 10 | pars=default; 11 | if(length(vars)==1) 12 | p=vars{1}; 13 | s=fieldnames(p); 14 | for i=1:length(s) 15 | eval(['pars.' s{i} '=p.' s{i}]); 16 | end; 17 | 18 | else 19 | 20 | 21 | 22 | 23 | for i=1:2:length(vars) 24 | if(isstr(vars{i})) 25 | if(i+1>length(vars)) error(sprintf('Parameter %s has no value\n',vars{i}));else val=vars{i+1};end; 26 | if(isstr(val)) 27 | eval(['pars.' vars{i} '=''' val ''';']); 28 | else 29 | eval(['pars.' vars{i} '=' sprintf('%i',val) ';']); 30 | end; 31 | end; 32 | end; 33 | 34 | end; 35 | 36 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/helperfunctions/mat.m: -------------------------------------------------------------------------------- 1 | function M=mat(C); 2 | 3 | r=round(sqrt(size(C,1))); 4 | M=reshape(C,r,r); -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/helperfunctions/mexall.m: -------------------------------------------------------------------------------- 1 | % compiles all .c files in the current directory 2 | % 3 | % Kilian Q. Weinberger, 2006 4 | 5 | dd=dir('*.c'); 6 | for i=1:length(dd) 7 | fprintf('mex %s\n',dd(i).name); 8 | eval(['try;mex ' dd(i).name ' -lmwblas  -lmwlapack ; catch; disp(lasterr); end;']); 9 | end; 10 | 11 | dd=dir('*.cpp'); 12 | for i=1:length(dd) 13 | fprintf('mex %s\n',dd(i).name); 14 | eval(['try;mex ' dd(i).name ' -lmwblas  -lmwlapack ; catch; disp(lasterr); end;']); 15 | end; 16 | 17 | dd=dir('*.cc'); 18 | for i=1:length(dd) 19 | fprintf('mex %s\n',dd(i).name); 20 | eval(['try;mex ' dd(i).name ' -lmwblas  -lmwlapack ; catch; disp(lasterr); end;']); 21 | end; 22 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/helperfunctions/mexallwindows.m: -------------------------------------------------------------------------------- 1 | % compiles all .c files in the current directory 2 | % 3 | % Kilian Q. Weinberger, 2006 4 | 5 | dd=dir('*.c'); 6 | for i=1:length(dd) 7 | fprintf('mex %s\n',dd(i).name); 8 | eval(['try;mex ' dd(i).name ' -lmwblas  -lmwlapack ; catch; disp(lasterr); end;']); 9 | end; 10 | 11 | dd=dir('*.cpp'); 12 | for i=1:length(dd) 13 | fprintf('mex %s\n',dd(i).name); 14 | eval(['try;mex ' dd(i).name ' -lmwblas  -lmwlapack ; catch; disp(lasterr); end;']); 15 | end; 16 | 17 | dd=dir('*.cc'); 18 | for i=1:length(dd) 19 | fprintf('mex %s\n',dd(i).name); 20 | eval(['try;mex ' dd(i).name ' -lmwblas  -lmwlapack ; catch; disp(lasterr); end;']); 21 | end; 22 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/helperfunctions/vec.m: -------------------------------------------------------------------------------- 1 | function C=vec(M); 2 | 3 | C=M(:); -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/dir-prop-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | K 10 6 | svn:ignore 7 | V 3 8 | *~ 9 | 10 | END 11 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/format: -------------------------------------------------------------------------------- 1 | 8 2 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/SOD.c.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/SOD.m.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/SODW.c.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/SODW.m.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/addchv.c.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | K 14 6 | svn:executable 7 | V 1 8 | * 9 | END 10 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/addh.c.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/addv.c.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/cdist.c.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/cdist.m.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/count.c.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/count.m.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/distance.c.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/distance.m.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/findimps3Dac.c.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/findimps3Dac.m.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/findlessh.c.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/findlessh.m.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/mink.c.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/mink.m.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/mulh.c.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/mulh.m.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/mulv.m.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/sd.c.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/sd.m.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/sumiflessh2.c.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/prop-base/sumiflessv2.c.svn-base: -------------------------------------------------------------------------------- 1 | K 11 2 | svn::ignore 3 | V 5 4 | *.mat 5 | END 6 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/text-base/SOD.m.svn-base: -------------------------------------------------------------------------------- 1 | function res=SOD(x,a,b); 2 | % function res=SOD(x,a,b); 3 | % 4 | % Computes and sums over all outer products of the columns in x. 5 | % 6 | % equivalent to: 7 | % 8 | % res=zeros(size(x,1)); 9 | % for i=1:n 10 | % res=res+x(:,a(i))*x(:,b(i))'; 11 | % end; 12 | % 13 | % 14 | % copyright 2005 by Kilian Q. Weinberger 15 | % University of Pennsylvania 16 | % kilianw@seas.upenn.edu 17 | % ******************************************** 18 | 19 | 20 | [D,N]=size(x); 21 | B=round(2500/D^2*1000000); 22 | res=zeros(D^2,1); 23 | for i=1:B:length(a) 24 | BB=min(B,length(a)-i); 25 | Xa=x(:,a(i:i+BB)); 26 | Xb=x(:,b(i:i+BB)); 27 | XaXb=Xa*Xb'; 28 | res=res+vec(Xa*Xa'+Xb*Xb'-XaXb-XaXb'); 29 | 30 | if(i>1) fprintf('.');end; 31 | end; 32 | %res=mat(res); 33 | 34 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/text-base/cdist.m.svn-base: -------------------------------------------------------------------------------- 1 | function res=cdist(x,i,j); 2 | % function res=cdist(x,i,j); 3 | % 4 | % Computes the L2-squared distances between all the columns of x 5 | % indexed by i and j and stores them in one vector. 6 | % 7 | % res=sum((x(:,i)-x(:,j)).^2); 8 | % 9 | % 10 | % copyright 2005 by Kilian Q. Weinberger 11 | % University of Pennsylvania 12 | % kilianw@seas.upenn.edu 13 | % ******************************************** 14 | 15 | B=10; 16 | res=zeros(1,length(i)); 17 | for k=1:B:length(i) 18 | ind=k:min(k+B-1,length(i)); 19 | res(ind)=sum((x(:,i(ind))-x(:,j(ind))).^2); 20 | end; 21 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/text-base/count.m.svn-base: -------------------------------------------------------------------------------- 1 | function [l,c]=count(a); 2 | % 3 | % function [l,c]=count(a); 4 | % 5 | % INPUT: 6 | % 7 | % a : sorted list of doubles 8 | % 9 | % OUTPUT: 10 | % 11 | % l : sorted list of doubles with only unique elements 12 | % c : count how many times each element appears 13 | % 14 | % EXAMPLE: 15 | % 16 | % [l,c]=count([1 1 2 2 3 3 3 4 5 5]); 17 | % 18 | % l=1 2 3 4 5 19 | % c=2 2 3 1 2 20 | % 21 | % 22 | % copyright 2005 by Kilian Q. Weinberger 23 | % University of Pennsylvania 24 | % kilianw@seas.upenn.edu 25 | % ******************************************** 26 | 27 | error('not yet implemented!\n\n\n'); 28 | [l,index]=unique(a(1,:)); 29 | l=a(:,index); 30 | c=[index(1) diff(index)]; 31 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/.svn/text-base/findlessh.m.svn-base: -------------------------------------------------------------------------------- 1 | function res=findlessh(M,thresh); 2 | 3 | % function res=findlessh(M,thresh); 4 | %/* 5 | % * ============================================================= 6 | % * findlessh.c 7 | % 8 | % * takes two input vectors M,thresh 9 | % * 10 | % * equivalent to: find(M1) fprintf('.');end; 31 | end; 32 | %res=mat(res); 33 | 34 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/SOD.mexmaci: 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Weinberger 11 | % University of Pennsylvania 12 | % kilianw@seas.upenn.edu 13 | % ******************************************** 14 | 15 | B=10; 16 | res=zeros(1,length(i)); 17 | for k=1:B:length(i) 18 | ind=k:min(k+B-1,length(i)); 19 | res(ind)=sum((x(:,i(ind))-x(:,j(ind))).^2); 20 | end; 21 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/cdist.mexmaci: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/cdist.mexmaci -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/cdist.mexmaci64: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/cdist.mexmaci64 -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/cdist.mexw64: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/cdist.mexw64 -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/count.m: -------------------------------------------------------------------------------- 1 | function [l,c]=count(a); 2 | % 3 | % function [l,c]=count(a); 4 | % 5 | % INPUT: 6 | % 7 | % a : sorted list of doubles 8 | % 9 | % OUTPUT: 10 | % 11 | % l : sorted list of doubles with only unique elements 12 | % c : count how many times each element appears 13 | % 14 | % EXAMPLE: 15 | % 16 | % [l,c]=count([1 1 2 2 3 3 3 4 5 5]); 17 | % 18 | % l=1 2 3 4 5 19 | % c=2 2 3 1 2 20 | % 21 | % 22 | % copyright 2005 by Kilian Q. Weinberger 23 | % University of Pennsylvania 24 | % kilianw@seas.upenn.edu 25 | % ******************************************** 26 | 27 | error('not yet implemented!\n\n\n'); 28 | [l,index]=unique(a(1,:)); 29 | l=a(:,index); 30 | c=[index(1) diff(index)]; 31 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/count.mexmaci: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/count.mexmaci -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/count.mexmaci64: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/count.mexmaci64 -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/count.mexw64: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/count.mexw64 -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/findimps3Dac.mexmaci: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/findimps3Dac.mexmaci -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/findimps3Dac.mexmaci64: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/findimps3Dac.mexmaci64 -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/findimps3Dac.mexw64: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/findimps3Dac.mexw64 -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/mexfunctions/findlessh.m: -------------------------------------------------------------------------------- 1 | function res=findlessh(M,thresh); 2 | 3 | % function res=findlessh(M,thresh); 4 | %/* 5 | % * ============================================================= 6 | % * findlessh.c 7 | % 8 | % * takes two input vectors M,thresh 9 | % * 10 | % * equivalent to: find(Mright) 11 | % evals corresponding eigenvalues 12 | % 13 | % See also applypca 14 | % 15 | % copyright by Kilian Q. Weinberger, 2006 16 | 17 | [d,N] = size(X); 18 | 19 | mm = mean(X,2); 20 | X = X - mm*ones(1,N); % remove mean from data 21 | 22 | cc = cov(X',1); % compute covariance 23 | [cvv,cdd] = eig(cc); % compute eignvectors 24 | [zz,ii] = sort(diag(-cdd)); % sort according to eigenvalues 25 | evects = cvv(:,ii); % pick leading eigenvectors 26 | cdd = diag(cdd); % extract eigenvalues 27 | evals = cdd(ii); % sort eigenvalues 28 | 29 | 30 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/LMNN/setpaths.m: -------------------------------------------------------------------------------- 1 | fprintf('Adding directories to path.\n'); 2 | addpath(pwd); 3 | cd mexfunctions 4 | addpath(pwd); 5 | cd .. 6 | cd helperfunctions 7 | addpath(pwd); 8 | cd .. 9 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/MildML_0.1/hist_count.m: -------------------------------------------------------------------------------- 1 | function [ Y, Z ] = hist_count( X, d, Z ) 2 | %HIST_COUNT 3 | % Counts the occurrences of values in matrix X along dimension d with 4 | % bins given by Z. 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See ItmlAlg.m for 5 | % description of the constraint matrix format 6 | 7 | m = length(y); 8 | C = zeros(num_constraints, 4); 9 | 10 | for (k=1:num_constraints), 11 | i = ceil(rand * m); 12 | j = ceil(rand * m); 13 | if (y(i) == y(j)), 14 | C(k,:) = [i j 1 l]; 15 | else 16 | C(k,:) = [i j -1 u]; 17 | end 18 | end 19 | 20 | 21 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/itml/SetDefaultParams.m: -------------------------------------------------------------------------------- 1 | function s = SetDefaultParams(s); 2 | % s = SetDefaultParams(s); 3 | % Sets default parameters 4 | % s: user-specified parameters that are used instead of defaults 5 | 6 | if (isfield(s, 'gamma') == 0), 7 | s.gamma = 1; 8 | end 9 | 10 | if (isfield(s, 'beta') == 0), 11 | s.beta = 1; 12 | end 13 | 14 | if (isfield(s, 'const_factor') == 0), 15 | s.const_factor = 40; 16 | end 17 | 18 | if (isfield(s, 'type4_rank') == 0), 19 | s.type4_rank = 5; 20 | end 21 | 22 | if (isfield(s, 'thresh') == 0), 23 | s.thresh = 10e-3; 24 | end 25 | 26 | if (isfield(s, 'k') == 0), 27 | s.k = 4; 28 | end 29 | 30 | if (isfield(s, 'max_iters') == 0), 31 | s.max_iters = 100000; 32 | end 33 | -------------------------------------------------------------------------------- /evaluation/KISSME/toolbox/lib/itml/Test.m: -------------------------------------------------------------------------------- 1 | disp('Loading iris data'); 2 | X = load('data/iris.mtx'); 3 | y = load('data/iris.truth'); 4 | 5 | disp('Running ITML'); 6 | num_folds = 2; 7 | knn_neighbor_size = 4; 8 | acc = CrossValidateKNN(y, X, @(y,X) MetricLearningAutotuneKnn(@ItmlAlg, y, X), num_folds, knn_neighbor_size); 9 | 10 | disp(sprintf('kNN cross-validated accuracy = %f', acc)); 11 | 12 | -------------------------------------------------------------------------------- /evaluation/LOMO_XQDA/LICENSE: -------------------------------------------------------------------------------- 1 | END USER LICENSE AGREEMENT 2 | 3 | THIS SOFTWARE PACKAGE IS FREE FOR NONCOMMERCIAL USE. THIS SOFTWARE PACKAGE IS PROVIDED "AS IS", WITHOUT ANY WARRANTY OF UPGRADATION OR CUSTOMIZED DEVELOPMENT. IT IS YOUR OWN RISK OF USING THIS SOFTWARE PACKAGE. THE AUTHORS ARE NOT RESPONSIBLE FOR ANY DAMAGE CAUSED BY USING THIS SOFTWARE PACKAGE. 4 | 5 | ALL RIGHTS RESERVED. 6 | COPYRIGHT (C) 2015 SHENGCAI LIAO 7 | -------------------------------------------------------------------------------- /evaluation/LOMO_XQDA/README.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/evaluation/LOMO_XQDA/README.txt -------------------------------------------------------------------------------- /evaluation/LOMO_XQDA/bin/Retinex.mexa64: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/evaluation/LOMO_XQDA/bin/Retinex.mexa64 -------------------------------------------------------------------------------- /evaluation/LOMO_XQDA/bin/Retinex.mexglx: 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length(filelist) 20 | img_name = filelist{i}; 21 | j1 = str2num(img_name(1)); 22 | j2 = str2num(img_name(3:5)); 23 | j3 = str2num(img_name(9:10)); 24 | imwrite(img_vector{j1}{j2,j3}, [path img_name]); 25 | end -------------------------------------------------------------------------------- /evaluation/data/CUHK03/save_train_txt.m: -------------------------------------------------------------------------------- 1 | clear; 2 | 3 | detected_or_labeled = 1; 4 | 5 | if detected_or_labeled == 1 6 | load('cuhk03_new_protocol_config_detected.mat'); 7 | file_path = 'cuhk03_detected/'; 8 | savepath = 'train_cuhk03_detected'; 9 | else 10 | load('cuhk03_new_protocol_config_labeled.mat'); 11 | file_path = 'cuhk03_labeled/'; 12 | savepath = 'train_cuhk03_labeled'; 13 | end 14 | 15 | fp = fopen([savepath '.txt'],'wt'); 16 | for i = 1: length(train_idx) 17 | imgpath = [file_path filelist{train_idx(i)}]; 18 | if i == 1 19 | label = 0; 20 | else 21 | if labels(train_idx(i)) ~= labels(train_idx(i-1)) 22 | label = label +1; 23 | end 24 | end 25 | fprintf(fp, '%s\n', [imgpath ' ' num2str(label)]); 26 | end 27 | fclose(fp); 28 | -------------------------------------------------------------------------------- /evaluation/data/Market-1501/bounding_box_test: -------------------------------------------------------------------------------- 1 | /home/sky/re-id/code_for_git/Market-IDE-baseline/market_evaluation/dataset/bounding_box_test -------------------------------------------------------------------------------- /evaluation/data/Market-1501/bounding_box_train: -------------------------------------------------------------------------------- 1 | /home/sky/re-id/code_for_git/Market-IDE-baseline/market_evaluation/dataset/bounding_box_train -------------------------------------------------------------------------------- /evaluation/data/Market-1501/gt_bbox: -------------------------------------------------------------------------------- 1 | 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-------------------------------------------------------------------------------- /evaluation/utils/gen_train_sample_xqda.m: -------------------------------------------------------------------------------- 1 | function [train_sample1, train_sample2, label1, label2] = gen_train_sample_xqda(label, cam, train_feat) 2 | 3 | uni_label = unique(label); 4 | ind1 = []; 5 | ind2 = []; 6 | label1 = []; 7 | label2 = []; 8 | for n = 1:length(uni_label) 9 | pos = find(label == uni_label(n)); 10 | 11 | perm = randperm(length(pos)); 12 | tmp1 = pos(perm(1:floor(length(pos)/2))); 13 | tmp2 = pos(perm(floor(length(pos)/2)+1:floor(length(pos)/2)*2)); 14 | cam1 = cam(tmp1); 15 | cam2 = cam(tmp2); 16 | pos2 = find(cam1~=cam2); 17 | tmp1 = tmp1(pos2); 18 | tmp2 = tmp2(pos2); 19 | ind1 = [ind1; tmp1]; 20 | ind2 = [ind2; tmp2]; 21 | label1 = [label1; repmat(uni_label(n), [length(tmp1), 1])]; 22 | label2 = [label2; repmat(uni_label(n), [length(tmp2), 1])]; 23 | end 24 | train_sample1 = train_feat(:, ind1)'; 25 | train_sample2 = train_feat(:, ind2)'; -------------------------------------------------------------------------------- /evaluation/utils/prepare_img.m: -------------------------------------------------------------------------------- 1 | function [ im_data ] = prepare_img( im_data, mean_data, im_size ) 2 | %PREPARE_IMG Summary of this function goes here 3 | % Detailed explanation goes here 4 | IMAGE_DIM = im_size; 5 | % Convert an image returned by Matlab's imread to im_data in caffe's data 6 | % format: W x H x C with BGR channels 7 | im_data = im_data(:, :, [3, 2, 1]); % permute channels from RGB to BGR 8 | im_data = permute(im_data, [2, 1, 3]); % flip width and height 9 | im_data = single(im_data); % convert from uint8 to single 10 | im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear'); % resize im_data 11 | im_data = im_data - mean_data; % subtract mean_data (already in W x H x C, BGR) 12 | end 13 | 14 | -------------------------------------------------------------------------------- /evaluation/utils/process_box_feat.m: -------------------------------------------------------------------------------- 1 | function video_feat = process_box_feat(box_feat, video_info) 2 | 3 | nVideo = size(video_info, 1); 4 | video_feat = zeros(size(box_feat, 1), nVideo); 5 | for n = 1:nVideo 6 | feature_set = box_feat(:, video_info(n, 1):video_info(n, 2)); 7 | video_feat(:, n) = max(feature_set, [], 2); % max pooling 8 | % video_feat(:, n) = mean(feature_set, 2); % avg pooling 9 | end 10 | 11 | %%% normalize train and test features 12 | sum_val = sqrt(sum(video_feat.^2)); 13 | for n = 1:size(video_feat, 1) 14 | video_feat(n, :) = video_feat(n, :)./sum_val; 15 | end -------------------------------------------------------------------------------- /experiments/CUHK03/train_IDE_CaffeNet_detected.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | CAFFE=caffe 4 | DATASET=CUHK03 5 | NET=CaffeNet 6 | SNAPSHOTS_DIR=output/${DATASET}_train 7 | 8 | LOG="experiments/logs/${DATASET}_re-id_${NET}.txt.`date +'%Y-%m-%d_%H-%M-%S'`" 9 | 10 | cd $(dirname ${BASH_SOURCE[0]})/../../ 11 | 12 | mkdir -p ${SNAPSHOTS_DIR} 13 | mkdir -p experiments/logs/ 14 | 15 | GLOG_logtostderr=1 ${CAFFE}/build/tools/caffe train \ 16 | -solver models/${DATASET}/${NET}/${NET}_detected_solver.prototxt \ 17 | -weights data/imagenet_models/${NET}.v2.caffemodel 2>&1 | tee ${LOG} 18 | -------------------------------------------------------------------------------- /experiments/CUHK03/train_IDE_CaffeNet_detected~: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhunzhong07/person-re-ranking/278f2c704e7f033b767f4158dff4febc03a0b78a/experiments/CUHK03/train_IDE_CaffeNet_detected~ -------------------------------------------------------------------------------- /experiments/CUHK03/train_IDE_CaffeNet_labeled.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | CAFFE=caffe 4 | DATASET=CUHK03 5 | NET=CaffeNet 6 | SNAPSHOTS_DIR=output/${DATASET}_train 7 | 8 | LOG="experiments/logs/${DATASET}_re-id_${NET}.txt.`date +'%Y-%m-%d_%H-%M-%S'`" 9 | 10 | cd $(dirname ${BASH_SOURCE[0]})/../../ 11 | 12 | mkdir -p ${SNAPSHOTS_DIR} 13 | mkdir -p experiments/logs/ 14 | 15 | GLOG_logtostderr=1 ${CAFFE}/build/tools/caffe train \ 16 | -solver models/${DATASET}/${NET}/${NET}_labeled_solver.prototxt \ 17 | -weights data/imagenet_models/${NET}.v2.caffemodel 2>&1 | tee ${LOG} 18 | -------------------------------------------------------------------------------- /experiments/CUHK03/train_IDE_CaffeNet_labeled~: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | CAFFE=caffe 4 | DATASET=CUHK03 5 | NET=CaffeNet 6 | SNAPSHOTS_DIR=output/${DATASET}_train 7 | 8 | LOG="experiments/logs/${DATASET}_re-id_${NET}.txt.`date +'%Y-%m-%d_%H-%M-%S'`" 9 | 10 | cd $(dirname ${BASH_SOURCE[0]})/../../ 11 | 12 | mkdir -p ${SNAPSHOTS_DIR} 13 | mkdir -p experiments/logs/ 14 | 15 | GLOG_logtostderr=1 ${CAFFE}/build/tools/caffe train \ 16 | -solver models/${DATASET}/${NET}/${NET}_detected_solver.prototxt \ 17 | -weights data/imagenet_models/${NET}.v2.caffemodel 2>&1 | tee ${LOG} 18 | -------------------------------------------------------------------------------- /experiments/CUHK03/train_IDE_ResNet_50_detected.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | CAFFE=caffe 4 | DATASET=CUHK03 5 | NET=ResNet_50 6 | SNAPSHOTS_DIR=output/${DATASET}_train 7 | 8 | LOG="experiments/logs/${DATASET}_re-id_${NET}.txt.`date +'%Y-%m-%d_%H-%M-%S'`" 9 | 10 | cd $(dirname ${BASH_SOURCE[0]})/../../ 11 | 12 | mkdir -p ${SNAPSHOTS_DIR} 13 | mkdir -p experiments/logs/ 14 | 15 | GLOG_logtostderr=1 ${CAFFE}/build/tools/caffe train \ 16 | -solver models/${DATASET}/${NET}/${NET}_IDE_detected_solver.prototxt \ 17 | -weights data/imagenet_models//${NET}.caffemodel 2>&1 | tee ${LOG} 18 | -------------------------------------------------------------------------------- /experiments/CUHK03/train_IDE_ResNet_50_labeled.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | CAFFE=caffe 4 | DATASET=CUHK03 5 | NET=ResNet_50 6 | SNAPSHOTS_DIR=output/${DATASET}_train 7 | 8 | LOG="experiments/logs/${DATASET}_re-id_${NET}.txt.`date +'%Y-%m-%d_%H-%M-%S'`" 9 | 10 | cd $(dirname ${BASH_SOURCE[0]})/../../ 11 | 12 | mkdir -p ${SNAPSHOTS_DIR} 13 | mkdir -p experiments/logs/ 14 | 15 | GLOG_logtostderr=1 ${CAFFE}/build/tools/caffe train \ 16 | -solver models/${DATASET}/${NET}/${NET}_IDE_labeled_solver.prototxt \ 17 | -weights data/imagenet_models//${NET}.caffemodel 2>&1 | tee ${LOG} 18 | -------------------------------------------------------------------------------- /experiments/Market-1501/train_IDE_ResNet_50.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | CAFFE=caffe 4 | DATASET=Market-1501 5 | NET=ResNet_50 6 | SNAPSHOTS_DIR=output/${DATASET}_train 7 | 8 | LOG="experiments/logs/${DATASET}_re-id_${NET}.txt.`date +'%Y-%m-%d_%H-%M-%S'`" 9 | 10 | cd $(dirname ${BASH_SOURCE[0]})/../../ 11 | 12 | mkdir -p ${SNAPSHOTS_DIR} 13 | mkdir -p experiments/logs/ 14 | 15 | GLOG_logtostderr=1 ${CAFFE}/build/tools/caffe train \ 16 | -solver models/${DATASET}/${NET}/${NET}_IDE_solver.prototxt \ 17 | -weights data/imagenet_models/${NET}.caffemodel 2>&1 | tee ${LOG} 18 | -------------------------------------------------------------------------------- /models/CUHK03/CaffeNet/CaffeNet_detected_solver.prototxt: -------------------------------------------------------------------------------- 1 | net: "models/CUHK03/CaffeNet/CaffeNet_detected_train_val.prototxt" 2 | 3 | display: 20 4 | average_loss: 20 5 | 6 | base_lr: 0.001 7 | lr_policy: "step" 8 | gamma: 0.1 9 | 10 | stepsize: 4000 11 | max_iter: 20000 12 | momentum: 0.9 13 | weight_decay: 0.0005 14 | snapshot: 0 15 | snapshot_prefix: "output/CUHK03_train/CaffeNet_IDE_detected" 16 | solver_mode: GPU 17 | -------------------------------------------------------------------------------- /models/CUHK03/CaffeNet/CaffeNet_labeled_solver.prototxt: -------------------------------------------------------------------------------- 1 | net: "models/CUHK03/CaffeNet/CaffeNet_labeled_train_val.prototxt" 2 | 3 | display: 20 4 | average_loss: 20 5 | 6 | base_lr: 0.001 7 | lr_policy: "step" 8 | gamma: 0.1 9 | 10 | stepsize: 4000 11 | max_iter: 20000 12 | momentum: 0.9 13 | weight_decay: 0.0005 14 | snapshot: 0 15 | snapshot_prefix: "output/CUHK03_train/CaffeNet_IDE_labeled" 16 | solver_mode: GPU 17 | -------------------------------------------------------------------------------- /models/CUHK03/ResNet_50/ResNet_50_IDE_detected_solver.prototxt: -------------------------------------------------------------------------------- 1 | net: "models/CUHK03/ResNet_50/ResNet_50_IDE_detected_train_val.prototxt" 2 | 3 | display: 20 4 | average_loss: 20 5 | 6 | base_lr: 0.001 7 | lr_policy: "step" 8 | gamma: 0.1 9 | stepsize: 15000 10 | max_iter: 75000 11 | 12 | momentum: 0.9 13 | weight_decay: 0.0005 14 | snapshot: 0 15 | snapshot_prefix: "output/CUHK03_train/ResNet_50_IDE_detected" 16 | solver_mode: GPU 17 | -------------------------------------------------------------------------------- /models/CUHK03/ResNet_50/ResNet_50_IDE_labeled_solver.prototxt: -------------------------------------------------------------------------------- 1 | net: "models/CUHK03/ResNet_50/ResNet_50_IDE_labeled_train_val.prototxt" 2 | 3 | display: 20 4 | average_loss: 20 5 | 6 | base_lr: 0.001 7 | lr_policy: "step" 8 | gamma: 0.1 9 | stepsize: 15000 10 | max_iter: 75000 11 | 12 | momentum: 0.9 13 | weight_decay: 0.0005 14 | snapshot: 0 15 | snapshot_prefix: "output/CUHK03_train/ResNet_50_IDE_labeled" 16 | solver_mode: GPU 17 | -------------------------------------------------------------------------------- /models/Market-1501/ResNet_50/ResNet_50_IDE_solver.prototxt: -------------------------------------------------------------------------------- 1 | net: "models/Market-1501/ResNet_50/ResNet_50_IDE_train_val.prototxt" 2 | 3 | test_iter: 10 4 | test_interval: 1000 5 | test_initialization: false 6 | display: 20 7 | average_loss: 20 8 | 9 | base_lr: 0.001 10 | lr_policy: "step" 11 | gamma: 0.1 12 | stepsize: 20000 13 | max_iter: 50000 14 | 15 | momentum: 0.9 16 | weight_decay: 0.0005 17 | snapshot: 0 18 | snapshot_prefix: "output/Market-1501_train/ResNet_50_IDE" 19 | solver_mode: GPU 20 | --------------------------------------------------------------------------------