├── .gitignore ├── LICENSE ├── README.md ├── _init_paths.py ├── cfgs ├── res101.yml ├── res101_ls.yml ├── res50.yml └── vgg16.yml ├── demo_global.py ├── docs └── swda.png ├── lib ├── datasets │ ├── VOCdevkit-matlab-wrapper │ │ ├── get_voc_opts.m │ │ ├── voc_eval.m │ │ └── xVOCap.m │ ├── __init__.py │ ├── cityscape.py │ ├── cityscape_car.py │ ├── clipart.py │ ├── coco.py │ ├── config_dataset.py │ ├── ds_utils.py │ ├── factory.py │ ├── foggy_cityscape.py │ ├── imdb.py │ ├── pascal_voc.py │ ├── pascal_voc_cycleclipart.py │ ├── pascal_voc_cyclewater.py │ ├── pascal_voc_water.py │ ├── sim10k.py │ ├── sim10k_cycle.py │ ├── tools │ │ ├── list_all_images.py │ │ ├── mcg_munge.py │ │ └── multilabel_list.py │ ├── voc_eval.py │ └── water.py ├── make.sh ├── model │ ├── __init__.py │ ├── faster_rcnn │ │ ├── __init__.py │ │ ├── faster_rcnn.py │ │ ├── faster_rcnn_global.py │ │ ├── faster_rcnn_global_local.py │ │ ├── faster_rcnn_local.py │ │ ├── resnet.py │ │ ├── resnet_dafrcnn.py │ │ ├── resnet_global.py │ │ ├── resnet_global_local.py │ │ ├── resnet_local.py │ │ ├── vgg16.py │ │ ├── vgg16_global.py │ │ ├── vgg16_global_local.py │ │ └── vgg16_local.py │ ├── nms │ │ ├── .gitignore │ │ ├── __init__.py │ │ ├── _ext │ │ │ ├── __init__.py │ │ │ └── nms │ │ │ │ └── __init__.py │ │ ├── build.py │ │ ├── make.sh │ │ ├── nms_cpu.py │ │ ├── nms_gpu.py │ │ ├── nms_kernel.cu │ │ ├── nms_wrapper.py │ │ └── src │ │ │ ├── nms_cuda.c │ │ │ ├── nms_cuda.h │ │ │ ├── nms_cuda_kernel.cu │ │ │ └── nms_cuda_kernel.h │ ├── roi_align │ │ ├── __init__.py │ │ ├── _ext │ │ │ ├── __init__.py │ │ │ └── roi_align │ │ │ │ └── __init__.py │ │ ├── build.py │ │ ├── functions │ │ │ ├── __init__.py │ │ │ └── roi_align.py │ │ ├── make.sh │ │ ├── modules │ │ │ ├── __init__.py │ │ │ └── roi_align.py │ │ └── src │ │ │ ├── roi_align.c │ │ │ ├── roi_align.h │ │ │ ├── roi_align_cuda.c │ │ │ ├── roi_align_cuda.h │ │ │ ├── roi_align_kernel.cu │ │ │ └── roi_align_kernel.h │ ├── roi_crop │ │ ├── __init__.py │ │ ├── _ext │ │ │ ├── __init__.py │ │ │ ├── crop_resize │ │ │ │ └── __init__.py │ │ │ └── roi_crop │ │ │ │ └── __init__.py │ │ ├── build.py │ │ ├── functions │ │ │ ├── __init__.py │ │ │ ├── crop_resize.py │ │ │ ├── gridgen.py │ │ │ └── roi_crop.py │ │ ├── make.sh │ │ ├── modules │ │ │ ├── __init__.py │ │ │ ├── gridgen.py │ │ │ └── roi_crop.py │ │ └── src │ │ │ ├── roi_crop.c │ │ │ ├── roi_crop.h │ │ │ ├── roi_crop_cuda.c │ │ │ ├── roi_crop_cuda.h │ │ │ ├── roi_crop_cuda_kernel.cu │ │ │ └── roi_crop_cuda_kernel.h │ ├── roi_pooling │ │ ├── __init__.py │ │ ├── _ext │ │ │ ├── __init__.py │ │ │ └── roi_pooling │ │ │ │ └── __init__.py │ │ ├── build.py │ │ ├── functions │ │ │ ├── __init__.py │ │ │ └── roi_pool.py │ │ ├── modules │ │ │ ├── __init__.py │ │ │ └── roi_pool.py │ │ └── src │ │ │ ├── roi_pooling.c │ │ │ ├── roi_pooling.h │ │ │ ├── roi_pooling_cuda.c │ │ │ ├── roi_pooling_cuda.h │ │ │ ├── roi_pooling_kernel.cu │ │ │ └── roi_pooling_kernel.h │ ├── rpn │ │ ├── __init__.py │ │ ├── anchor_target_layer.py │ │ ├── bbox_transform.py │ │ ├── generate_anchors.py │ │ ├── proposal_layer.py │ │ ├── proposal_target_layer_cascade.py │ │ └── rpn.py │ └── utils │ │ ├── .gitignore │ │ ├── __init__.py │ │ ├── bbox.pyx │ │ ├── blob.py │ │ ├── config.py │ │ ├── net_utils.py │ │ └── parser_func.py ├── pycocotools │ ├── UPSTREAM_REV │ ├── __init__.py │ ├── _mask.pyx │ ├── coco.py │ ├── cocoeval.py │ ├── license.txt │ ├── mask.py │ ├── maskApi.c │ └── maskApi.h ├── roi_data_layer │ ├── __init__.py │ ├── minibatch.py │ ├── roibatchLoader.py │ └── roidb.py └── setup.py ├── requirements.txt ├── test_net.py ├── test_net_global.py ├── test_net_global_local.py ├── test_net_local.py ├── test_net_so.py ├── test_scripts ├── city2foggycity_sample.sh ├── clipart_sample.sh ├── sim10k2cityscape_sample.sh └── watercolor_sample.sh ├── train_scripts ├── city2foggycity_sample.sh ├── clipart_sample.sh ├── sim10k2cityscape_sample.sh └── watercolor_sample.sh ├── trainval_net_global.py ├── trainval_net_global_local.py ├── trainval_net_local.py └── trainval_net_so.py /.gitignore: -------------------------------------------------------------------------------- 1 | data/* 2 | 3 | # READ THIS BEFORE YOU REFACTOR ME 4 | # 5 | # setup.py uses the list of patterns in this file to decide 6 | # what to delete, but it's not 100% sound. So, for example, 7 | # if you delete aten/build/ because it's redundant with build/, 8 | # aten/build/ will stop being cleaned. So be careful when 9 | # refactoring this file! 10 | 11 | ## PyTorch 12 | 13 | .mypy_cache 14 | *.pyc 15 | */*.pyc 16 | */*.so* 17 | */**/__pycache__ 18 | */**/*.dylib* 19 | */**/*.pyc 20 | */**/*.pyd 21 | */**/*.so* 22 | */**/**/*.pyc 23 | */**/**/**/*.pyc 24 | */**/**/**/**/*.pyc 25 | aten/build/ 26 | aten/src/ATen/Config.h 27 | aten/src/ATen/cuda/CUDAConfig.h 28 | build/ 29 | dist/ 30 | docs/src/**/* 31 | test/.coverage 32 | test/cpp/api/mnist 33 | test/data/gpu_tensors.pt 34 | test/data/legacy_modules.t7 35 | test/data/legacy_serialized.pt 36 | test/data/linear.pt 37 | test/htmlcov 38 | third_party/build/ 39 | tools/shared/_utils_internal.py 40 | torch.egg-info/ 41 | torch/csrc/autograd/generated/* 42 | torch/csrc/cudnn/cuDNN.cpp 43 | torch/csrc/generated 44 | torch/csrc/generic/TensorMethods.cpp 45 | torch/csrc/jit/generated/* 46 | torch/csrc/nn/THCUNN.cpp 47 | torch/csrc/nn/THCUNN.cwrap 48 | torch/csrc/nn/THNN_generic.cpp 49 | torch/csrc/nn/THNN_generic.cwrap 50 | torch/csrc/nn/THNN_generic.h 51 | torch/csrc/nn/THNN.cpp 52 | torch/csrc/nn/THNN.cwrap 53 | torch/lib/*.a* 54 | torch/lib/*.dll* 55 | torch/lib/*.dylib* 56 | torch/lib/*.h 57 | torch/lib/*.lib 58 | torch/lib/*.so* 59 | torch/lib/build 60 | torch/lib/cmake 61 | torch/lib/include 62 | torch/lib/pkgconfig 63 | torch/lib/protoc 64 | torch/lib/tmp_install 65 | torch/lib/torch_shm_manager 66 | torch/version.py 67 | 68 | # IPython notebook checkpoints 69 | .ipynb_checkpoints 70 | 71 | # Editor temporaries 72 | *.swn 73 | *.swo 74 | *.swp 75 | *.swm 76 | *~ 77 | 78 | # macOS dir files 79 | .DS_Store 80 | 81 | # Symbolic files 82 | tools/shared/cwrap_common.py 83 | 84 | # Ninja files 85 | .ninja_deps 86 | .ninja_log 87 | compile_commands.json 88 | *.egg-info/ 89 | docs/source/scripts/activation_images/ 90 | 91 | ## General 92 | 93 | # Compiled Object files 94 | *.slo 95 | *.lo 96 | *.o 97 | *.cuo 98 | *.obj 99 | 100 | # Compiled Dynamic libraries 101 | *.so 102 | *.dylib 103 | *.dll 104 | 105 | # Compiled Static libraries 106 | *.lai 107 | *.la 108 | *.a 109 | *.lib 110 | 111 | # Compiled protocol buffers 112 | *.pb.h 113 | *.pb.cc 114 | *_pb2.py 115 | 116 | # Compiled python 117 | *.pyc 118 | *.pyd 119 | 120 | # Compiled MATLAB 121 | *.mex* 122 | 123 | # IPython notebook checkpoints 124 | .ipynb_checkpoints 125 | 126 | # Editor temporaries 127 | *.swn 128 | *.swo 129 | *.swp 130 | *~ 131 | 132 | # Sublime Text settings 133 | *.sublime-workspace 134 | *.sublime-project 135 | 136 | # Eclipse Project settings 137 | *.*project 138 | .settings 139 | 140 | # QtCreator files 141 | *.user 142 | 143 | # PyCharm files 144 | .idea 145 | 146 | # Visual Studio Code files 147 | .vscode 148 | .vs 149 | 150 | # OSX dir files 151 | .DS_Store 152 | 153 | ## Caffe2 154 | 155 | # build, distribute, and bins (+ python proto bindings) 156 | build 157 | build_host_protoc 158 | build_android 159 | build_ios 160 | /build_* 161 | .build_debug/* 162 | .build_release/* 163 | distribute/* 164 | *.testbin 165 | *.bin 166 | cmake_build 167 | .cmake_build 168 | gen 169 | .setuptools-cmake-build 170 | .pytest_cache 171 | aten/build/* 172 | 173 | # Bram 174 | plsdontbreak 175 | 176 | # Generated documentation 177 | docs/_site 178 | docs/gathered 179 | _site 180 | doxygen 181 | docs/dev 182 | 183 | # LevelDB files 184 | *.sst 185 | *.ldb 186 | LOCK 187 | LOG* 188 | CURRENT 189 | MANIFEST-* 190 | 191 | # generated version file 192 | caffe2/version.py 193 | 194 | # setup.py intermediates 195 | .eggs 196 | caffe2.egg-info 197 | 198 | # Atom/Watchman required file 199 | .watchmanconfig 200 | 201 | # cython generated files 202 | lib/model/utils/bbox.c 203 | lib/pycocotools/_mask.c -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 Jianwei Yang 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # A Pytorch Implementation of [Strong-Weak Distribution Alignment for Adaptive Object Detection](https://arxiv.org/pdf/1812.04798.pdf) (CVPR 2019) 2 | 3 | 4 | 5 | ## Introduction 6 | Follow [faster-rcnn repository](https://github.com/jwyang/faster-rcnn.pytorch) 7 | to setup the environment. When installing pytorch-faster-rcnn, you may encounter some issues. 8 | Many issues have been reported there to setup the environment. We used Pytorch 0.4.0 for this project. 9 | The different version of pytorch will cause some errors, which have to be handled based on each envirionment. 10 | 11 | ### Data Preparation 12 | 13 | * **PASCAL_VOC 07+12**: Please follow the instructions in [py-faster-rcnn](https://github.com/rbgirshick/py-faster-rcnn#beyond-the-demo-installation-for-training-and-testing-models) to prepare VOC datasets. 14 | * **Clipart, WaterColor**: Dataset preparation instruction link [Cross Domain Detection ](https://github.com/naoto0804/cross-domain-detection/tree/master/datasets). Images translated by Cyclegan are available in the website. 15 | * **Sim10k**: Website [Sim10k](https://fcav.engin.umich.edu/sim-dataset/) 16 | * **Cityscape-Translated Sim10k**: TBA 17 | * **CitysScape, FoggyCityscape**: Download website [Cityscape](https://www.cityscapes-dataset.com/), see dataset preparation code in [DA-Faster RCNN](https://github.com/yuhuayc/da-faster-rcnn/tree/master/prepare_data) 18 | 19 | All codes are written to fit for the format of PASCAL_VOC. 20 | For example, the dataset [Sim10k](https://fcav.engin.umich.edu/sim-dataset/) is stored as follows. 21 | 22 | ``` 23 | $ cd Sim10k/VOC2012/ 24 | $ ls 25 | Annotations ImageSets JPEGImages 26 | $ cat ImageSets/Main/val.txt 27 | 3384827.jpg 28 | 3384828.jpg 29 | 3384829.jpg 30 | . 31 | . 32 | . 33 | ``` 34 | If you want to test the code on your own dataset, arange the dataset 35 | in the format of PASCAL, make dataset class in lib/datasets/. and add 36 | it to lib/datasets/factory.py, lib/datasets/config_dataset.py. Then, add the dataset option to lib/model/utils/parser_func.py. 37 | ### Data Path 38 | Write your dataset directories' paths in lib/datasets/config_dataset.py. 39 | 40 | ### Pretrained Model 41 | 42 | We used two models pre-trained on ImageNet in our experiments, VGG and ResNet101. You can download these two models from: 43 | 44 | * VGG16: [Dropbox](https://www.dropbox.com/s/s3brpk0bdq60nyb/vgg16_caffe.pth?dl=0), [VT Server](https://filebox.ece.vt.edu/~jw2yang/faster-rcnn/pretrained-base-models/vgg16_caffe.pth) 45 | 46 | * ResNet101: [Dropbox](https://www.dropbox.com/s/iev3tkbz5wyyuz9/resnet101_caffe.pth?dl=0), [VT Server](https://filebox.ece.vt.edu/~jw2yang/faster-rcnn/pretrained-base-models/resnet101_caffe.pth) 47 | 48 | Download them and write the path in __C.VGG_PATH and __C.RESNET_PATH at lib/model/utils/config.py. 49 | 50 | #### sample model 51 | Global-local alignment model for watercolor dataset. 52 | 53 | * ResNet101 (adapted to water color) [GoogleDrive](https://drive.google.com/file/d/1pzj2jKFwtGzwjZTeEyeDSnNlPU1MZ4t9/view?usp=sharing) 54 | 55 | ## Train 56 | * Sample training script is in a folder, train_scripts. 57 | * With only local alignment loss, 58 | ``` 59 | CUDA_VISIBLE_DEVICES=$GPU_ID python trainval_net_local.py \ 60 | --dataset source_dataset --dataset_t target_dataset --net vgg16 \ 61 | --cuda 62 | ``` 63 | Add --lc when using context-vector based regularization loss. 64 | 65 | * With only global alignment loss, 66 | ``` 67 | CUDA_VISIBLE_DEVICES=$GPU_ID python trainval_net_global.py \ 68 | --dataset source_dataset --dataset_t target_dataset --net vgg16 \ 69 | --cuda 70 | ``` 71 | Add --gc when using context-vector based regularization loss. 72 | * With global and local alignment loss, 73 | ``` 74 | CUDA_VISIBLE_DEVICES=$GPU_ID python trainval_net_global_local.py \ 75 | --dataset source_dataset --dataset_t target_dataset --net vgg16 \ 76 | --cuda 77 | ``` 78 | Add --lc and --gc when using context-vector based regularization loss. 79 | ## Test 80 | * Sample test script is in a folder, test_scripts. 81 | 82 | ``` 83 | CUDA_VISIBLE_DEVICES=$GPU_ID python test_net_global_local.py \ 84 | --dataset target_dataset --net vgg16 \ 85 | --cuda --lc --gc --load_name path_to_model 86 | ``` 87 | 88 | ## Citation 89 | Please cite the following reference if you utilize this repository for your project. 90 | 91 | ``` 92 | @article{saito2018strong, 93 | title={Strong-Weak Distribution Alignment for Adaptive Object Detection}, 94 | author={Saito, Kuniaki and Ushiku, Yoshitaka and Harada, Tatsuya and Saenko, Kate}, 95 | journal={arXiv}, 96 | year={2018} 97 | } 98 | ``` 99 | -------------------------------------------------------------------------------- /_init_paths.py: -------------------------------------------------------------------------------- 1 | import os.path as osp 2 | import sys 3 | 4 | def add_path(path): 5 | if path not in sys.path: 6 | sys.path.insert(0, path) 7 | 8 | this_dir = osp.dirname(__file__) 9 | 10 | # Add lib to PYTHONPATH 11 | lib_path = osp.join(this_dir, 'lib') 12 | add_path(lib_path) 13 | 14 | coco_path = osp.join(this_dir, 'data', 'coco', 'PythonAPI') 15 | add_path(coco_path) 16 | -------------------------------------------------------------------------------- /cfgs/res101.yml: -------------------------------------------------------------------------------- 1 | EXP_DIR: res101 2 | TRAIN: 3 | HAS_RPN: True 4 | BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True 5 | RPN_POSITIVE_OVERLAP: 0.7 6 | RPN_BATCHSIZE: 256 7 | PROPOSAL_METHOD: gt 8 | BG_THRESH_LO: 0.0 9 | DISPLAY: 20 10 | BATCH_SIZE: 128 11 | RPN_POST_NMS_TOP_N_TARGET: 128 12 | WEIGHT_DECAY: 0.0001 13 | DOUBLE_BIAS: False 14 | LEARNING_RATE: 0.001 15 | TEST: 16 | HAS_RPN: True 17 | POOLING_SIZE: 7 18 | POOLING_MODE: align 19 | CROP_RESIZE_WITH_MAX_POOL: False 20 | -------------------------------------------------------------------------------- /cfgs/res101_ls.yml: -------------------------------------------------------------------------------- 1 | EXP_DIR: res101 2 | TRAIN: 3 | HAS_RPN: True 4 | BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True 5 | RPN_POSITIVE_OVERLAP: 0.7 6 | RPN_BATCHSIZE: 256 7 | PROPOSAL_METHOD: gt 8 | BG_THRESH_LO: 0.0 9 | DISPLAY: 20 10 | BATCH_SIZE: 128 11 | WEIGHT_DECAY: 0.0001 12 | SCALES: [800] 13 | DOUBLE_BIAS: False 14 | LEARNING_RATE: 0.001 15 | TEST: 16 | HAS_RPN: True 17 | SCALES: [800] 18 | MAX_SIZE: 1200 19 | RPN_POST_NMS_TOP_N: 1000 20 | POOLING_SIZE: 7 21 | POOLING_MODE: align 22 | CROP_RESIZE_WITH_MAX_POOL: False 23 | -------------------------------------------------------------------------------- /cfgs/res50.yml: -------------------------------------------------------------------------------- 1 | EXP_DIR: res50 2 | TRAIN: 3 | HAS_RPN: True 4 | # IMS_PER_BATCH: 1 5 | BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True 6 | RPN_POSITIVE_OVERLAP: 0.7 7 | RPN_BATCHSIZE: 256 8 | PROPOSAL_METHOD: gt 9 | BG_THRESH_LO: 0.0 10 | DISPLAY: 20 11 | BATCH_SIZE: 256 12 | WEIGHT_DECAY: 0.0001 13 | DOUBLE_BIAS: False 14 | SNAPSHOT_PREFIX: res50_faster_rcnn 15 | TEST: 16 | HAS_RPN: True 17 | POOLING_MODE: crop 18 | -------------------------------------------------------------------------------- /cfgs/vgg16.yml: -------------------------------------------------------------------------------- 1 | EXP_DIR: vgg16 2 | TRAIN: 3 | HAS_RPN: True 4 | BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True 5 | RPN_POSITIVE_OVERLAP: 0.7 6 | RPN_BATCHSIZE: 256 7 | PROPOSAL_METHOD: gt 8 | BG_THRESH_LO: 0.0 9 | BATCH_SIZE: 256 10 | RPN_POST_NMS_TOP_N_TARGET: 256 11 | LEARNING_RATE: 0.001 12 | TEST: 13 | HAS_RPN: True 14 | POOLING_MODE: align 15 | CROP_RESIZE_WITH_MAX_POOL: False 16 | -------------------------------------------------------------------------------- /docs/swda.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/VisionLearningGroup/DA_Detection/730eaca8528d22ed3aa6b4dbc1965828a697cf9a/docs/swda.png -------------------------------------------------------------------------------- /lib/datasets/VOCdevkit-matlab-wrapper/get_voc_opts.m: -------------------------------------------------------------------------------- 1 | function VOCopts = get_voc_opts(path) 2 | 3 | tmp = pwd; 4 | cd(path); 5 | try 6 | addpath('VOCcode'); 7 | VOCinit; 8 | catch 9 | rmpath('VOCcode'); 10 | cd(tmp); 11 | error(sprintf('VOCcode directory not found under %s', path)); 12 | end 13 | rmpath('VOCcode'); 14 | cd(tmp); 15 | -------------------------------------------------------------------------------- /lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m: -------------------------------------------------------------------------------- 1 | function res = voc_eval(path, comp_id, test_set, output_dir) 2 | 3 | VOCopts = get_voc_opts(path); 4 | VOCopts.testset = test_set; 5 | 6 | for i = 1:length(VOCopts.classes) 7 | cls = VOCopts.classes{i}; 8 | res(i) = voc_eval_cls(cls, VOCopts, comp_id, output_dir); 9 | end 10 | 11 | fprintf('\n~~~~~~~~~~~~~~~~~~~~\n'); 12 | fprintf('Results:\n'); 13 | aps = [res(:).ap]'; 14 | fprintf('%.1f\n', aps * 100); 15 | fprintf('%.1f\n', mean(aps) * 100); 16 | fprintf('~~~~~~~~~~~~~~~~~~~~\n'); 17 | 18 | function res = voc_eval_cls(cls, VOCopts, comp_id, output_dir) 19 | 20 | test_set = VOCopts.testset; 21 | year = VOCopts.dataset(4:end); 22 | 23 | addpath(fullfile(VOCopts.datadir, 'VOCcode')); 24 | 25 | res_fn = sprintf(VOCopts.detrespath, comp_id, cls); 26 | 27 | recall = []; 28 | prec = []; 29 | ap = 0; 30 | ap_auc = 0; 31 | 32 | do_eval = (str2num(year) <= 2007) | ~strcmp(test_set, 'test'); 33 | if do_eval 34 | % Bug in VOCevaldet requires that tic has been called first 35 | tic; 36 | [recall, prec, ap] = VOCevaldet(VOCopts, comp_id, cls, true); 37 | ap_auc = xVOCap(recall, prec); 38 | 39 | % force plot limits 40 | ylim([0 1]); 41 | xlim([0 1]); 42 | 43 | print(gcf, '-djpeg', '-r0', ... 44 | [output_dir '/' cls '_pr.jpg']); 45 | end 46 | fprintf('!!! %s : %.4f %.4f\n', cls, ap, ap_auc); 47 | 48 | res.recall = recall; 49 | res.prec = prec; 50 | res.ap = ap; 51 | res.ap_auc = ap_auc; 52 | 53 | save([output_dir '/' cls '_pr.mat'], ... 54 | 'res', 'recall', 'prec', 'ap', 'ap_auc'); 55 | 56 | rmpath(fullfile(VOCopts.datadir, 'VOCcode')); 57 | -------------------------------------------------------------------------------- /lib/datasets/VOCdevkit-matlab-wrapper/xVOCap.m: -------------------------------------------------------------------------------- 1 | function ap = xVOCap(rec,prec) 2 | % From the PASCAL VOC 2011 devkit 3 | 4 | mrec=[0 ; rec ; 1]; 5 | mpre=[0 ; prec ; 0]; 6 | for i=numel(mpre)-1:-1:1 7 | mpre(i)=max(mpre(i),mpre(i+1)); 8 | end 9 | i=find(mrec(2:end)~=mrec(1:end-1))+1; 10 | ap=sum((mrec(i)-mrec(i-1)).*mpre(i)); 11 | -------------------------------------------------------------------------------- /lib/datasets/__init__.py: -------------------------------------------------------------------------------- 1 | # -------------------------------------------------------- 2 | # Fast R-CNN 3 | # Copyright (c) 2015 Microsoft 4 | # Licensed under The MIT License [see LICENSE for details] 5 | # Written by Ross Girshick 6 | # -------------------------------------------------------- 7 | -------------------------------------------------------------------------------- /lib/datasets/config_dataset.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | 5 | import os 6 | import os.path as osp 7 | import numpy as np 8 | # `pip install easydict` if you don't have it 9 | from easydict import EasyDict as edict 10 | 11 | __D = edict() 12 | # Consumers can get config by: 13 | # from fast_rcnn_config import cfg 14 | cfg_d = __D 15 | # 16 | # Training options 17 | #with regard to pascal, the directories under the path will be ./VOC2007, ./VOC2012" 18 | __D.PASCAL = "/VOCdevkit" 19 | __D.PASCALCLIP = "" 20 | __D.PASCALWATER = "/VOCdevkit" 21 | 22 | #For these datasets, the directories under the path will be Annotations ImageSets JPEGImages." 23 | __D.CLIPART = "/clipart" 24 | __D.WATER = "/watercolor" 25 | __D.SIM10K = "Sim10k/VOC2012" 26 | __D.CITYSCAPE_CAR = "/VOC2007" 27 | __D.CITYSCAPE = "VOC2007" 28 | __D.FOGGYCITY = "VOC2007" 29 | 30 | 31 | def _merge_a_into_b(a, b): 32 | """Merge config dictionary a into config dictionary b, clobbering the 33 | options in b whenever they are also specified in a. 34 | """ 35 | if type(a) is not edict: 36 | return 37 | 38 | for k, v in a.items(): 39 | # a must specify keys that are in b 40 | if k not in b: 41 | raise KeyError('{} is not a valid config key'.format(k)) 42 | 43 | # the types must match, too 44 | old_type = type(b[k]) 45 | if old_type is not type(v): 46 | if isinstance(b[k], np.ndarray): 47 | v = np.array(v, dtype=b[k].dtype) 48 | else: 49 | raise ValueError(('Type mismatch ({} vs. {}) ' 50 | 'for config key: {}').format(type(b[k]), 51 | type(v), k)) 52 | 53 | # recursively merge dicts 54 | if type(v) is edict: 55 | try: 56 | _merge_a_into_b(a[k], b[k]) 57 | except: 58 | print(('Error under config key: {}'.format(k))) 59 | raise 60 | else: 61 | b[k] = v 62 | 63 | 64 | def cfg_from_file(filename): 65 | """Load a config file and merge it into the default options.""" 66 | import yaml 67 | with open(filename, 'r') as f: 68 | yaml_cfg = edict(yaml.load(f)) 69 | 70 | _merge_a_into_b(yaml_cfg, __D) 71 | 72 | 73 | def cfg_from_list(cfg_list): 74 | """Set config keys via list (e.g., from command line).""" 75 | from ast import literal_eval 76 | assert len(cfg_list) % 2 == 0 77 | for k, v in zip(cfg_list[0::2], cfg_list[1::2]): 78 | key_list = k.split('.') 79 | d = __D 80 | for subkey in key_list[:-1]: 81 | assert subkey in d 82 | d = d[subkey] 83 | subkey = key_list[-1] 84 | assert subkey in d 85 | try: 86 | value = literal_eval(v) 87 | except: 88 | # handle the case when v is a string literal 89 | value = v 90 | assert type(value) == type(d[subkey]), \ 91 | 'type {} does not match original type {}'.format( 92 | type(value), type(d[subkey])) 93 | d[subkey] = value 94 | -------------------------------------------------------------------------------- /lib/datasets/ds_utils.py: -------------------------------------------------------------------------------- 1 | # -------------------------------------------------------- 2 | # Fast/er R-CNN 3 | # Licensed under The MIT License [see LICENSE for details] 4 | # Written by Ross Girshick 5 | # -------------------------------------------------------- 6 | from __future__ import absolute_import 7 | from __future__ import division 8 | from __future__ import print_function 9 | 10 | import numpy as np 11 | 12 | 13 | def unique_boxes(boxes, scale=1.0): 14 | """Return indices of unique boxes.""" 15 | v = np.array([1, 1e3, 1e6, 1e9]) 16 | hashes = np.round(boxes * scale).dot(v) 17 | _, index = np.unique(hashes, return_index=True) 18 | return np.sort(index) 19 | 20 | 21 | def xywh_to_xyxy(boxes): 22 | """Convert [x y w h] box format to [x1 y1 x2 y2] format.""" 23 | return np.hstack((boxes[:, 0:2], boxes[:, 0:2] + boxes[:, 2:4] - 1)) 24 | 25 | 26 | def xyxy_to_xywh(boxes): 27 | """Convert [x1 y1 x2 y2] box format to [x y w h] format.""" 28 | return np.hstack((boxes[:, 0:2], boxes[:, 2:4] - boxes[:, 0:2] + 1)) 29 | 30 | 31 | def validate_boxes(boxes, width=0, height=0): 32 | """Check that a set of boxes are valid.""" 33 | x1 = boxes[:, 0] 34 | y1 = boxes[:, 1] 35 | x2 = boxes[:, 2] 36 | y2 = boxes[:, 3] 37 | assert (x1 >= 0).all() 38 | assert (y1 >= 0).all() 39 | assert (x2 >= x1).all() 40 | assert (y2 >= y1).all() 41 | assert (x2 < width).all() 42 | assert (y2 < height).all() 43 | 44 | 45 | def filter_small_boxes(boxes, min_size): 46 | w = boxes[:, 2] - boxes[:, 0] 47 | h = boxes[:, 3] - boxes[:, 1] 48 | keep = np.where((w >= min_size) & (h > min_size))[0] 49 | return keep 50 | -------------------------------------------------------------------------------- /lib/datasets/factory.py: -------------------------------------------------------------------------------- 1 | # -------------------------------------------------------- 2 | # Fast R-CNN 3 | # Copyright (c) 2015 Microsoft 4 | # Licensed under The MIT License [see LICENSE for details] 5 | # Written by Ross Girshick 6 | # -------------------------------------------------------- 7 | 8 | """Factory method for easily getting imdbs by name.""" 9 | from __future__ import absolute_import 10 | from __future__ import division 11 | from __future__ import print_function 12 | 13 | __sets = {} 14 | from datasets.pascal_voc import pascal_voc 15 | from datasets.pascal_voc_water import pascal_voc_water 16 | from datasets.pascal_voc_cyclewater import pascal_voc_cyclewater 17 | from datasets.pascal_voc_cycleclipart import pascal_voc_cycleclipart 18 | from datasets.sim10k import sim10k 19 | from datasets.water import water 20 | from datasets.clipart import clipart 21 | from datasets.sim10k_cycle import sim10k_cycle 22 | from datasets.cityscape import cityscape 23 | from datasets.cityscape_car import cityscape_car 24 | from datasets.foggy_cityscape import foggy_cityscape 25 | 26 | import numpy as np 27 | for split in ['train', 'trainval','val','test']: 28 | name = 'cityscape_{}'.format(split) 29 | __sets[name] = (lambda split=split : cityscape(split)) 30 | for split in ['train', 'trainval','val','test']: 31 | name = 'cityscape_car_{}'.format(split) 32 | __sets[name] = (lambda split=split : cityscape_car(split)) 33 | for split in ['train', 'trainval','test']: 34 | name = 'foggy_cityscape_{}'.format(split) 35 | __sets[name] = (lambda split=split : foggy_cityscape(split)) 36 | for split in ['train','val']: 37 | name = 'sim10k_{}'.format(split) 38 | __sets[name] = (lambda split=split : sim10k(split)) 39 | for split in ['train', 'val']: 40 | name = 'sim10k_cycle_{}'.format(split) 41 | __sets[name] = (lambda split=split: sim10k_cycle(split)) 42 | for year in ['2007', '2012']: 43 | for split in ['train', 'val', 'trainval', 'test']: 44 | name = 'voc_{}_{}'.format(year, split) 45 | __sets[name] = (lambda split=split, year=year: pascal_voc(split, year)) 46 | for year in ['2007', '2012']: 47 | for split in ['train', 'val', 'trainval', 'test']: 48 | name = 'voc_water_{}_{}'.format(year, split) 49 | __sets[name] = (lambda split=split, year=year: pascal_voc_water(split, year)) 50 | for year in ['2007', '2012']: 51 | for split in ['train', 'val', 'trainval', 'test']: 52 | name = 'voc_cycleclipart_{}_{}'.format(year, split) 53 | __sets[name] = (lambda split=split, year=year: pascal_voc_cycleclipart(split, year)) 54 | for year in ['2007', '2012']: 55 | for split in ['train', 'val', 'trainval', 'test']: 56 | name = 'voc_cyclewater_{}_{}'.format(year, split) 57 | __sets[name] = (lambda split=split, year=year: pascal_voc_cyclewater(split, year)) 58 | for year in ['2007']: 59 | for split in ['trainval', 'test']: 60 | name = 'clipart_{}'.format(split) 61 | __sets[name] = (lambda split=split : clipart(split,year)) 62 | for year in ['2007']: 63 | for split in ['train', 'test']: 64 | name = 'water_{}'.format(split) 65 | __sets[name] = (lambda split=split : water(split,year)) 66 | def get_imdb(name): 67 | """Get an imdb (image database) by name.""" 68 | if name not in __sets: 69 | raise KeyError('Unknown dataset: {}'.format(name)) 70 | return __sets[name]() 71 | 72 | 73 | def list_imdbs(): 74 | """List all registered imdbs.""" 75 | return list(__sets.keys()) 76 | -------------------------------------------------------------------------------- /lib/datasets/tools/list_all_images.py: -------------------------------------------------------------------------------- 1 | import os 2 | p_path = '/scratch4/keisaito/visda/train' 3 | dir_list = os.listdir(p_path) 4 | write_name = open('/scratch4/keisaito/visda/all_images_train.txt','w') 5 | for direc in dir_list: 6 | if not '.txt' in direc: 7 | files = os.listdir(os.path.join(p_path,direc)) 8 | for file in files: 9 | class_name = direc 10 | #if class_name == 'motorcycle': 11 | # class_name = 'motorbike' 12 | #if class_name == 'plant': 13 | # class_name = 'pottedplant' 14 | file_name = os.path.join(p_path,direc,file) 15 | write_name.write('%s %s\n'%(file_name,class_name)) 16 | 17 | -------------------------------------------------------------------------------- /lib/datasets/tools/mcg_munge.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | import os 3 | import sys 4 | 5 | """Hacky tool to convert file system layout of MCG boxes downloaded from 6 | http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/mcg/ 7 | so that it's consistent with those computed by Jan Hosang (see: 8 | http://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal- 9 | computing/research/object-recognition-and-scene-understanding/how- 10 | good-are-detection-proposals-really/) 11 | 12 | NB: Boxes from the MCG website are in (y1, x1, y2, x2) order. 13 | Boxes from Hosang et al. are in (x1, y1, x2, y2) order. 14 | """ 15 | 16 | def munge(src_dir): 17 | # stored as: ./MCG-COCO-val2014-boxes/COCO_val2014_000000193401.mat 18 | # want: ./MCG/mat/COCO_val2014_0/COCO_val2014_000000141/COCO_val2014_000000141334.mat 19 | 20 | files = os.listdir(src_dir) 21 | for fn in files: 22 | base, ext = os.path.splitext(fn) 23 | # first 14 chars / first 22 chars / all chars + .mat 24 | # COCO_val2014_0/COCO_val2014_000000447/COCO_val2014_000000447991.mat 25 | first = base[:14] 26 | second = base[:22] 27 | dst_dir = os.path.join('MCG', 'mat', first, second) 28 | if not os.path.exists(dst_dir): 29 | os.makedirs(dst_dir) 30 | src = os.path.join(src_dir, fn) 31 | dst = os.path.join(dst_dir, fn) 32 | print('MV: {} -> {}'.format(src, dst)) 33 | os.rename(src, dst) 34 | 35 | if __name__ == '__main__': 36 | # src_dir should look something like: 37 | # src_dir = 'MCG-COCO-val2014-boxes' 38 | src_dir = sys.argv[1] 39 | munge(src_dir) 40 | -------------------------------------------------------------------------------- /lib/datasets/tools/multilabel_list.py: -------------------------------------------------------------------------------- 1 | import os 2 | import xml.etree.ElementTree as ET 3 | import sys 4 | argvs = sys.argv 5 | 6 | def load_image_set_index(ref): 7 | """ 8 | Load the indexes listed in this dataset's image set file. 9 | """ 10 | # Example path to image set file: 11 | # self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt 12 | image_set_file = os.path.join(ref) 13 | assert os.path.exists(image_set_file), \ 14 | 'Path does not exist: {}'.format(image_set_file) 15 | with open(image_set_file) as f: 16 | image_index = [x.strip() for x in f.readlines()] 17 | return image_index 18 | 19 | def load_pascal_annotation(ref_path, index): 20 | """ 21 | Load image and bounding boxes info from XML file in the PASCAL VOC 22 | format. 23 | """ 24 | filename = os.path.join(ref_path, 'Annotations', index + '.xml') 25 | tree = ET.parse(filename) 26 | objs = tree.findall('object') 27 | obj_list = [] 28 | for ix, obj in enumerate(objs): 29 | cls = obj.find('name').text.lower().strip() 30 | obj_list.append(cls) 31 | return list(set(obj_list)) 32 | 33 | indexes = load_image_set_index(argvs[1]) 34 | images_list = open(argvs[3],'w') 35 | for index in indexes: 36 | objs = load_pascal_annotation(argvs[2],index) 37 | write_word = os.path.join('/research/masaito/detection_dataset/VOCdevkit/VOC2007/JPEGImages', index + '.jpg' + ' ') 38 | for name in objs: 39 | write_word = write_word + name + ' ' 40 | images_list.write(write_word + '\n') 41 | 42 | -------------------------------------------------------------------------------- /lib/make.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | # CUDA_PATH=/usr/local/cuda/ 4 | 5 | export CUDA_PATH=/usr/local/cuda/ 6 | #You may also want to ad the following 7 | #export C_INCLUDE_PATH=/opt/cuda/include 8 | 9 | export CXXFLAGS="-std=c++11" 10 | export CFLAGS="-std=c99" 11 | 12 | python setup.py build_ext --inplace 13 | rm -rf build 14 | 15 | CUDA_ARCH="-gencode arch=compute_30,code=sm_30 \ 16 | -gencode arch=compute_35,code=sm_35 \ 17 | -gencode arch=compute_50,code=sm_50 \ 18 | -gencode arch=compute_52,code=sm_52 \ 19 | -gencode arch=compute_60,code=sm_60 \ 20 | -gencode arch=compute_61,code=sm_61 " 21 | 22 | # compile NMS 23 | cd model/nms/src 24 | echo "Compiling nms kernels by nvcc..." 25 | nvcc -c -o nms_cuda_kernel.cu.o nms_cuda_kernel.cu \ 26 | -D GOOGLE_CUDA=1 -x cu -Xcompiler -fPIC $CUDA_ARCH 27 | 28 | cd ../ 29 | python build.py 30 | 31 | # compile roi_pooling 32 | cd ../../ 33 | cd model/roi_pooling/src 34 | echo "Compiling roi pooling kernels by nvcc..." 35 | nvcc -c -o roi_pooling.cu.o roi_pooling_kernel.cu \ 36 | -D GOOGLE_CUDA=1 -x cu -Xcompiler -fPIC $CUDA_ARCH 37 | cd ../ 38 | python build.py 39 | 40 | # compile roi_align 41 | cd ../../ 42 | cd model/roi_align/src 43 | echo "Compiling roi align kernels by nvcc..." 44 | nvcc -c -o roi_align_kernel.cu.o roi_align_kernel.cu \ 45 | -D GOOGLE_CUDA=1 -x cu -Xcompiler -fPIC $CUDA_ARCH 46 | cd ../ 47 | python build.py 48 | 49 | # compile roi_crop 50 | cd ../../ 51 | cd model/roi_crop/src 52 | echo "Compiling roi crop kernels by nvcc..." 53 | nvcc -c -o roi_crop_cuda_kernel.cu.o roi_crop_cuda_kernel.cu \ 54 | -D GOOGLE_CUDA=1 -x cu -Xcompiler -fPIC $CUDA_ARCH 55 | cd ../ 56 | python build.py 57 | -------------------------------------------------------------------------------- /lib/model/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/VisionLearningGroup/DA_Detection/730eaca8528d22ed3aa6b4dbc1965828a697cf9a/lib/model/__init__.py -------------------------------------------------------------------------------- /lib/model/faster_rcnn/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/VisionLearningGroup/DA_Detection/730eaca8528d22ed3aa6b4dbc1965828a697cf9a/lib/model/faster_rcnn/__init__.py -------------------------------------------------------------------------------- /lib/model/faster_rcnn/faster_rcnn.py: -------------------------------------------------------------------------------- 1 | import random 2 | import torch 3 | import torch.nn as nn 4 | import torch.nn.functional as F 5 | from torch.autograd import Variable 6 | import torchvision.models as models 7 | from torch.autograd import Variable 8 | import numpy as np 9 | from model.rpn.rpn import _RPN 10 | from model.roi_pooling.modules.roi_pool import _RoIPooling 11 | from model.roi_crop.modules.roi_crop import _RoICrop 12 | from model.roi_align.modules.roi_align import RoIAlignAvg 13 | from model.rpn.proposal_target_layer_cascade import _ProposalTargetLayer 14 | import time 15 | import pdb 16 | from model.utils.net_utils import _smooth_l1_loss, _crop_pool_layer, _affine_grid_gen, _affine_theta 17 | 18 | class _fasterRCNN(nn.Module): 19 | """ faster RCNN """ 20 | def __init__(self, classes, class_agnostic): 21 | super(_fasterRCNN, self).__init__() 22 | self.classes = classes 23 | self.n_classes = len(classes) 24 | self.class_agnostic = class_agnostic 25 | # loss 26 | self.RCNN_loss_cls = 0 27 | self.RCNN_loss_bbox = 0 28 | 29 | # define rpn 30 | self.RCNN_rpn = _RPN(self.dout_base_model) 31 | self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes) 32 | self.RCNN_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0) 33 | self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0) 34 | 35 | self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE 36 | self.RCNN_roi_crop = _RoICrop() 37 | 38 | def forward(self, im_data, im_info, gt_boxes, num_boxes): 39 | batch_size = im_data.size(0) 40 | 41 | im_info = im_info.data 42 | gt_boxes = gt_boxes.data 43 | num_boxes = num_boxes.data 44 | 45 | # feed image data to base model to obtain base feature map 46 | base_feat = self.RCNN_base(im_data) 47 | 48 | # feed base feature map tp RPN to obtain rois 49 | rois, rpn_loss_cls, rpn_loss_bbox = self.RCNN_rpn(base_feat, im_info, gt_boxes, num_boxes) 50 | 51 | # if it is training phrase, then use ground trubut bboxes for refining 52 | if self.training: 53 | roi_data = self.RCNN_proposal_target(rois, gt_boxes, num_boxes) 54 | rois, rois_label, rois_target, rois_inside_ws, rois_outside_ws = roi_data 55 | 56 | rois_label = Variable(rois_label.view(-1).long()) 57 | rois_target = Variable(rois_target.view(-1, rois_target.size(2))) 58 | rois_inside_ws = Variable(rois_inside_ws.view(-1, rois_inside_ws.size(2))) 59 | rois_outside_ws = Variable(rois_outside_ws.view(-1, rois_outside_ws.size(2))) 60 | else: 61 | rois_label = None 62 | rois_target = None 63 | rois_inside_ws = None 64 | rois_outside_ws = None 65 | rpn_loss_cls = 0 66 | rpn_loss_bbox = 0 67 | 68 | rois = Variable(rois) 69 | # do roi pooling based on predicted rois 70 | 71 | if cfg.POOLING_MODE == 'crop': 72 | # pdb.set_trace() 73 | # pooled_feat_anchor = _crop_pool_layer(base_feat, rois.view(-1, 5)) 74 | grid_xy = _affine_grid_gen(rois.view(-1, 5), base_feat.size()[2:], self.grid_size) 75 | grid_yx = torch.stack([grid_xy.data[:,:,:,1], grid_xy.data[:,:,:,0]], 3).contiguous() 76 | pooled_feat = self.RCNN_roi_crop(base_feat, Variable(grid_yx).detach()) 77 | if cfg.CROP_RESIZE_WITH_MAX_POOL: 78 | pooled_feat = F.max_pool2d(pooled_feat, 2, 2) 79 | elif cfg.POOLING_MODE == 'align': 80 | pooled_feat = self.RCNN_roi_align(base_feat, rois.view(-1, 5)) 81 | elif cfg.POOLING_MODE == 'pool': 82 | pooled_feat = self.RCNN_roi_pool(base_feat, rois.view(-1,5)) 83 | 84 | # feed pooled features to top model 85 | pooled_feat = self._head_to_tail(pooled_feat) 86 | 87 | # compute bbox offset 88 | bbox_pred = self.RCNN_bbox_pred(pooled_feat) 89 | if self.training and not self.class_agnostic: 90 | # select the corresponding columns according to roi labels 91 | bbox_pred_view = bbox_pred.view(bbox_pred.size(0), int(bbox_pred.size(1) / 4), 4) 92 | bbox_pred_select = torch.gather(bbox_pred_view, 1, rois_label.view(rois_label.size(0), 1, 1).expand(rois_label.size(0), 1, 4)) 93 | bbox_pred = bbox_pred_select.squeeze(1) 94 | 95 | # compute object classification probability 96 | cls_score = self.RCNN_cls_score(pooled_feat) 97 | cls_prob = F.softmax(cls_score, 1) 98 | 99 | RCNN_loss_cls = 0 100 | RCNN_loss_bbox = 0 101 | 102 | if self.training: 103 | # classification loss 104 | RCNN_loss_cls = F.cross_entropy(cls_score, rois_label) 105 | 106 | # bounding box regression L1 loss 107 | RCNN_loss_bbox = _smooth_l1_loss(bbox_pred, rois_target, rois_inside_ws, rois_outside_ws) 108 | 109 | 110 | cls_prob = cls_prob.view(batch_size, rois.size(1), -1) 111 | bbox_pred = bbox_pred.view(batch_size, rois.size(1), -1) 112 | 113 | return rois, cls_prob, bbox_pred, rpn_loss_cls, rpn_loss_bbox, RCNN_loss_cls, RCNN_loss_bbox, rois_label 114 | 115 | def _init_weights(self): 116 | def normal_init(m, mean, stddev, truncated=False): 117 | """ 118 | weight initalizer: truncated normal and random normal. 119 | """ 120 | # x is a parameter 121 | if truncated: 122 | m.weight.data.normal_().fmod_(2).mul_(stddev).add_(mean) # not a perfect approximation 123 | else: 124 | m.weight.data.normal_(mean, stddev) 125 | m.bias.data.zero_() 126 | 127 | normal_init(self.RCNN_rpn.RPN_Conv, 0, 0.01, cfg.TRAIN.TRUNCATED) 128 | normal_init(self.RCNN_rpn.RPN_cls_score, 0, 0.01, cfg.TRAIN.TRUNCATED) 129 | normal_init(self.RCNN_rpn.RPN_bbox_pred, 0, 0.01, cfg.TRAIN.TRUNCATED) 130 | normal_init(self.RCNN_cls_score, 0, 0.01, cfg.TRAIN.TRUNCATED) 131 | normal_init(self.RCNN_bbox_pred, 0, 0.001, cfg.TRAIN.TRUNCATED) 132 | 133 | def create_architecture(self): 134 | self._init_modules() 135 | self._init_weights() 136 | -------------------------------------------------------------------------------- /lib/model/faster_rcnn/faster_rcnn_global.py: -------------------------------------------------------------------------------- 1 | import random 2 | import torch 3 | import torch.nn as nn 4 | import torch.nn.functional as F 5 | from torch.autograd import Variable 6 | import torchvision.models as models 7 | from torch.autograd import Variable 8 | import numpy as np 9 | from model.utils.config import cfg 10 | from model.rpn.rpn import _RPN 11 | from model.roi_pooling.modules.roi_pool import _RoIPooling 12 | from model.roi_crop.modules.roi_crop import _RoICrop 13 | from model.roi_align.modules.roi_align import RoIAlignAvg 14 | from model.rpn.proposal_target_layer_cascade import _ProposalTargetLayer 15 | import time 16 | import pdb 17 | from model.utils.net_utils import _smooth_l1_loss, _crop_pool_layer, _affine_grid_gen, _affine_theta,grad_reverse 18 | 19 | class _fasterRCNN(nn.Module): 20 | """ faster RCNN """ 21 | def __init__(self, classes, class_agnostic,context): 22 | super(_fasterRCNN, self).__init__() 23 | self.classes = classes 24 | self.n_classes = len(classes) 25 | self.class_agnostic = class_agnostic 26 | # loss 27 | self.RCNN_loss_cls = 0 28 | self.RCNN_loss_bbox = 0 29 | self.context = context 30 | # define rpn 31 | self.RCNN_rpn = _RPN(self.dout_base_model) 32 | self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes) 33 | self.RCNN_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0) 34 | self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0) 35 | 36 | self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE 37 | self.RCNN_roi_crop = _RoICrop() 38 | 39 | def forward(self, im_data, im_info, gt_boxes, num_boxes,target=False,eta=1.0): 40 | batch_size = im_data.size(0) 41 | 42 | im_info = im_info.data 43 | gt_boxes = gt_boxes.data 44 | num_boxes = num_boxes.data 45 | 46 | # feed image data to base model to obtain base feature map 47 | base_feat = self.RCNN_base(im_data) 48 | 49 | if self.context: 50 | domain_p,_ = self.netD(grad_reverse(base_feat,lambd=eta)) 51 | if target: 52 | domain_p, _ = self.netD(grad_reverse(base_feat, lambd=eta)) 53 | return domain_p#, diff 54 | _,feat = self.netD(base_feat.detach()) 55 | else: 56 | domain_p = self.netD(grad_reverse(base_feat,lambd=eta)) 57 | if target: 58 | return domain_p#,diff 59 | # feed base feature map tp RPN to obtain rois 60 | rois, rpn_loss_cls, rpn_loss_bbox = self.RCNN_rpn(base_feat, im_info, gt_boxes, num_boxes) 61 | 62 | # if it is training phrase, then use ground trubut bboxes for refining 63 | if self.training: 64 | roi_data = self.RCNN_proposal_target(rois, gt_boxes, num_boxes) 65 | rois, rois_label, rois_target, rois_inside_ws, rois_outside_ws = roi_data 66 | 67 | rois_label = Variable(rois_label.view(-1).long()) 68 | rois_target = Variable(rois_target.view(-1, rois_target.size(2))) 69 | rois_inside_ws = Variable(rois_inside_ws.view(-1, rois_inside_ws.size(2))) 70 | rois_outside_ws = Variable(rois_outside_ws.view(-1, rois_outside_ws.size(2))) 71 | else: 72 | rois_label = None 73 | rois_target = None 74 | rois_inside_ws = None 75 | rois_outside_ws = None 76 | rpn_loss_cls = 0 77 | rpn_loss_bbox = 0 78 | 79 | rois = Variable(rois) 80 | # do roi pooling based on predicted rois 81 | 82 | if cfg.POOLING_MODE == 'crop': 83 | # pdb.set_trace() 84 | # pooled_feat_anchor = _crop_pool_layer(base_feat, rois.view(-1, 5)) 85 | grid_xy = _affine_grid_gen(rois.view(-1, 5), base_feat.size()[2:], self.grid_size) 86 | grid_yx = torch.stack([grid_xy.data[:,:,:,1], grid_xy.data[:,:,:,0]], 3).contiguous() 87 | pooled_feat = self.RCNN_roi_crop(base_feat, Variable(grid_yx).detach()) 88 | if cfg.CROP_RESIZE_WITH_MAX_POOL: 89 | pooled_feat = F.max_pool2d(pooled_feat, 2, 2) 90 | elif cfg.POOLING_MODE == 'align': 91 | pooled_feat = self.RCNN_roi_align(base_feat, rois.view(-1, 5)) 92 | elif cfg.POOLING_MODE == 'pool': 93 | pooled_feat = self.RCNN_roi_pool(base_feat, rois.view(-1,5)) 94 | 95 | # feed pooled features to top model 96 | pooled_feat = self._head_to_tail(pooled_feat) 97 | if self.context: 98 | feat = feat.view(1, -1).repeat(pooled_feat.size(0), 1) 99 | pooled_feat = torch.cat((feat, pooled_feat), 1) 100 | # compute bbox offset 101 | bbox_pred = self.RCNN_bbox_pred(pooled_feat) 102 | if self.training and not self.class_agnostic: 103 | # select the corresponding columns according to roi labels 104 | bbox_pred_view = bbox_pred.view(bbox_pred.size(0), int(bbox_pred.size(1) / 4), 4) 105 | bbox_pred_select = torch.gather(bbox_pred_view, 1, rois_label.view(rois_label.size(0), 1, 1).expand(rois_label.size(0), 1, 4)) 106 | bbox_pred = bbox_pred_select.squeeze(1) 107 | 108 | # compute object classification probability 109 | cls_score = self.RCNN_cls_score(pooled_feat) 110 | cls_prob = F.softmax(cls_score, 1) 111 | 112 | RCNN_loss_cls = 0 113 | RCNN_loss_bbox = 0 114 | 115 | if self.training: 116 | # classification loss 117 | RCNN_loss_cls = F.cross_entropy(cls_score, rois_label) 118 | 119 | # bounding box regression L1 loss 120 | RCNN_loss_bbox = _smooth_l1_loss(bbox_pred, rois_target, rois_inside_ws, rois_outside_ws) 121 | 122 | 123 | cls_prob = cls_prob.view(batch_size, rois.size(1), -1) 124 | bbox_pred = bbox_pred.view(batch_size, rois.size(1), -1) 125 | 126 | return rois, cls_prob, bbox_pred, rpn_loss_cls, rpn_loss_bbox, RCNN_loss_cls, RCNN_loss_bbox, rois_label,domain_p#,diff 127 | 128 | def _init_weights(self): 129 | def normal_init(m, mean, stddev, truncated=False): 130 | """ 131 | weight initalizer: truncated normal and random normal. 132 | """ 133 | # x is a parameter 134 | if truncated: 135 | m.weight.data.normal_().fmod_(2).mul_(stddev).add_(mean) # not a perfect approximation 136 | else: 137 | m.weight.data.normal_(mean, stddev) 138 | m.bias.data.zero_() 139 | 140 | normal_init(self.RCNN_rpn.RPN_Conv, 0, 0.01, cfg.TRAIN.TRUNCATED) 141 | normal_init(self.RCNN_rpn.RPN_cls_score, 0, 0.01, cfg.TRAIN.TRUNCATED) 142 | normal_init(self.RCNN_rpn.RPN_bbox_pred, 0, 0.01, cfg.TRAIN.TRUNCATED) 143 | normal_init(self.RCNN_cls_score, 0, 0.01, cfg.TRAIN.TRUNCATED) 144 | normal_init(self.RCNN_bbox_pred, 0, 0.001, cfg.TRAIN.TRUNCATED) 145 | 146 | def create_architecture(self): 147 | self._init_modules() 148 | self._init_weights() 149 | -------------------------------------------------------------------------------- /lib/model/faster_rcnn/faster_rcnn_global_local.py: -------------------------------------------------------------------------------- 1 | import random 2 | import torch 3 | import torch.nn as nn 4 | import torch.nn.functional as F 5 | from torch.autograd import Variable 6 | import torchvision.models as models 7 | from torch.autograd import Variable 8 | import numpy as np 9 | from model.utils.config import cfg 10 | from model.rpn.rpn import _RPN 11 | from model.roi_pooling.modules.roi_pool import _RoIPooling 12 | from model.roi_crop.modules.roi_crop import _RoICrop 13 | from model.roi_align.modules.roi_align import RoIAlignAvg 14 | from model.rpn.proposal_target_layer_cascade import _ProposalTargetLayer 15 | import time 16 | import pdb 17 | from model.utils.net_utils import _smooth_l1_loss, _crop_pool_layer, _affine_grid_gen, _affine_theta,grad_reverse 18 | 19 | class _fasterRCNN(nn.Module): 20 | """ faster RCNN """ 21 | def __init__(self, classes, class_agnostic,lc,gc): 22 | super(_fasterRCNN, self).__init__() 23 | self.classes = classes 24 | self.n_classes = len(classes) 25 | self.class_agnostic = class_agnostic 26 | # loss 27 | self.RCNN_loss_cls = 0 28 | self.RCNN_loss_bbox = 0 29 | self.lc = lc 30 | self.gc = gc 31 | # define rpn 32 | self.RCNN_rpn = _RPN(self.dout_base_model) 33 | self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes) 34 | self.RCNN_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0) 35 | self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0) 36 | 37 | self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE 38 | self.RCNN_roi_crop = _RoICrop() 39 | 40 | def forward(self, im_data, im_info, gt_boxes, num_boxes,target=False,eta=1.0): 41 | batch_size = im_data.size(0) 42 | 43 | im_info = im_info.data 44 | gt_boxes = gt_boxes.data 45 | num_boxes = num_boxes.data 46 | 47 | # feed image data to base model to obtain base feature map 48 | base_feat1 = self.RCNN_base1(im_data) 49 | if self.lc: 50 | d_pixel, _ = self.netD_pixel(grad_reverse(base_feat1, lambd=eta)) 51 | #print(d_pixel) 52 | if not target: 53 | _, feat_pixel = self.netD_pixel(base_feat1.detach()) 54 | else: 55 | d_pixel = self.netD_pixel(grad_reverse(base_feat1, lambd=eta)) 56 | base_feat = self.RCNN_base2(base_feat1) 57 | if self.gc: 58 | domain_p, _ = self.netD(grad_reverse(base_feat, lambd=eta)) 59 | if target: 60 | return d_pixel,domain_p#, diff 61 | _,feat = self.netD(base_feat.detach()) 62 | else: 63 | domain_p = self.netD(grad_reverse(base_feat, lambd=eta)) 64 | if target: 65 | return d_pixel,domain_p#,diff 66 | # feed base feature map tp RPN to obtain rois 67 | rois, rpn_loss_cls, rpn_loss_bbox = self.RCNN_rpn(base_feat, im_info, gt_boxes, num_boxes) 68 | 69 | # if it is training phrase, then use ground trubut bboxes for refining 70 | if self.training: 71 | roi_data = self.RCNN_proposal_target(rois, gt_boxes, num_boxes) 72 | rois, rois_label, rois_target, rois_inside_ws, rois_outside_ws = roi_data 73 | 74 | rois_label = Variable(rois_label.view(-1).long()) 75 | rois_target = Variable(rois_target.view(-1, rois_target.size(2))) 76 | rois_inside_ws = Variable(rois_inside_ws.view(-1, rois_inside_ws.size(2))) 77 | rois_outside_ws = Variable(rois_outside_ws.view(-1, rois_outside_ws.size(2))) 78 | else: 79 | rois_label = None 80 | rois_target = None 81 | rois_inside_ws = None 82 | rois_outside_ws = None 83 | rpn_loss_cls = 0 84 | rpn_loss_bbox = 0 85 | 86 | rois = Variable(rois) 87 | # do roi pooling based on predicted rois 88 | 89 | if cfg.POOLING_MODE == 'crop': 90 | # pdb.set_trace() 91 | # pooled_feat_anchor = _crop_pool_layer(base_feat, rois.view(-1, 5)) 92 | grid_xy = _affine_grid_gen(rois.view(-1, 5), base_feat.size()[2:], self.grid_size) 93 | grid_yx = torch.stack([grid_xy.data[:,:,:,1], grid_xy.data[:,:,:,0]], 3).contiguous() 94 | pooled_feat = self.RCNN_roi_crop(base_feat, Variable(grid_yx).detach()) 95 | if cfg.CROP_RESIZE_WITH_MAX_POOL: 96 | pooled_feat = F.max_pool2d(pooled_feat, 2, 2) 97 | elif cfg.POOLING_MODE == 'align': 98 | pooled_feat = self.RCNN_roi_align(base_feat, rois.view(-1, 5)) 99 | elif cfg.POOLING_MODE == 'pool': 100 | pooled_feat = self.RCNN_roi_pool(base_feat, rois.view(-1,5)) 101 | 102 | # feed pooled features to top model 103 | pooled_feat = self._head_to_tail(pooled_feat) 104 | #feat_pixel = torch.zeros(feat_pixel.size()).cuda() 105 | if self.lc: 106 | feat_pixel = feat_pixel.view(1, -1).repeat(pooled_feat.size(0), 1) 107 | pooled_feat = torch.cat((feat_pixel, pooled_feat), 1) 108 | if self.gc: 109 | feat = feat.view(1, -1).repeat(pooled_feat.size(0), 1) 110 | pooled_feat = torch.cat((feat, pooled_feat), 1) 111 | # compute bbox offset 112 | 113 | # compute bbox offset 114 | bbox_pred = self.RCNN_bbox_pred(pooled_feat) 115 | if self.training and not self.class_agnostic: 116 | bbox_pred_view = bbox_pred.view(bbox_pred.size(0), int(bbox_pred.size(1) / 4), 4) 117 | bbox_pred_select = torch.gather(bbox_pred_view, 1, rois_label.view(rois_label.size(0), 1, 1).expand(rois_label.size(0), 1, 4)) 118 | bbox_pred = bbox_pred_select.squeeze(1) 119 | 120 | # compute object classification probability 121 | cls_score = self.RCNN_cls_score(pooled_feat) 122 | cls_prob = F.softmax(cls_score, 1) 123 | 124 | RCNN_loss_cls = 0 125 | RCNN_loss_bbox = 0 126 | 127 | if self.training: 128 | # classification loss 129 | RCNN_loss_cls = F.cross_entropy(cls_score, rois_label) 130 | 131 | # bounding box regression L1 loss 132 | RCNN_loss_bbox = _smooth_l1_loss(bbox_pred, rois_target, rois_inside_ws, rois_outside_ws) 133 | 134 | 135 | cls_prob = cls_prob.view(batch_size, rois.size(1), -1) 136 | bbox_pred = bbox_pred.view(batch_size, rois.size(1), -1) 137 | 138 | return rois, cls_prob, bbox_pred, rpn_loss_cls, rpn_loss_bbox, RCNN_loss_cls, RCNN_loss_bbox, rois_label,d_pixel, domain_p#,diff 139 | 140 | def _init_weights(self): 141 | def normal_init(m, mean, stddev, truncated=False): 142 | """ 143 | weight initalizer: truncated normal and random normal. 144 | """ 145 | # x is a parameter 146 | if truncated: 147 | m.weight.data.normal_().fmod_(2).mul_(stddev).add_(mean) # not a perfect approximation 148 | else: 149 | m.weight.data.normal_(mean, stddev) 150 | m.bias.data.zero_() 151 | 152 | normal_init(self.RCNN_rpn.RPN_Conv, 0, 0.01, cfg.TRAIN.TRUNCATED) 153 | normal_init(self.RCNN_rpn.RPN_cls_score, 0, 0.01, cfg.TRAIN.TRUNCATED) 154 | normal_init(self.RCNN_rpn.RPN_bbox_pred, 0, 0.01, cfg.TRAIN.TRUNCATED) 155 | normal_init(self.RCNN_cls_score, 0, 0.01, cfg.TRAIN.TRUNCATED) 156 | normal_init(self.RCNN_bbox_pred, 0, 0.001, cfg.TRAIN.TRUNCATED) 157 | 158 | def create_architecture(self): 159 | self._init_modules() 160 | self._init_weights() 161 | -------------------------------------------------------------------------------- /lib/model/faster_rcnn/faster_rcnn_local.py: -------------------------------------------------------------------------------- 1 | import random 2 | import torch 3 | import torch.nn as nn 4 | import torch.nn.functional as F 5 | from torch.autograd import Variable 6 | import torchvision.models as models 7 | from torch.autograd import Variable 8 | import numpy as np 9 | from model.utils.config import cfg 10 | from model.rpn.rpn import _RPN 11 | from model.roi_pooling.modules.roi_pool import _RoIPooling 12 | from model.roi_crop.modules.roi_crop import _RoICrop 13 | from model.roi_align.modules.roi_align import RoIAlignAvg 14 | from model.rpn.proposal_target_layer_cascade import _ProposalTargetLayer 15 | import time 16 | import pdb 17 | from model.utils.net_utils import _smooth_l1_loss, _crop_pool_layer, _affine_grid_gen, _affine_theta,grad_reverse 18 | 19 | class _fasterRCNN(nn.Module): 20 | """ faster RCNN """ 21 | def __init__(self, classes, class_agnostic,lc): 22 | super(_fasterRCNN, self).__init__() 23 | self.classes = classes 24 | self.n_classes = len(classes) 25 | self.class_agnostic = class_agnostic 26 | # loss 27 | self.RCNN_loss_cls = 0 28 | self.RCNN_loss_bbox = 0 29 | self.lc = lc 30 | # define rpn 31 | self.RCNN_rpn = _RPN(self.dout_base_model) 32 | self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes) 33 | self.RCNN_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0) 34 | self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0) 35 | 36 | self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE 37 | self.RCNN_roi_crop = _RoICrop() 38 | 39 | def forward(self, im_data, im_info, gt_boxes, num_boxes,target=False,eta=1.0): 40 | batch_size = im_data.size(0) 41 | 42 | im_info = im_info.data 43 | gt_boxes = gt_boxes.data 44 | num_boxes = num_boxes.data 45 | 46 | # feed image data to base model to obtain base feature map 47 | base_feat1 = self.RCNN_base1(im_data) 48 | 49 | if self.lc: 50 | d_pixel, _ = self.netD_pixel(grad_reverse(base_feat1, lambd=eta)) 51 | if not target: 52 | _, feat_pixel = self.netD_pixel(base_feat1.detach()) 53 | else: 54 | d_pixel = self.netD_pixel(grad_reverse(base_feat1, lambd=eta)) 55 | if target: 56 | return d_pixel 57 | base_feat = self.RCNN_base2(base_feat1) 58 | 59 | # feed base feature map tp RPN to obtain rois 60 | rois, rpn_loss_cls, rpn_loss_bbox = self.RCNN_rpn(base_feat, im_info, gt_boxes, num_boxes) 61 | 62 | # if it is training phrase, then use ground trubut bboxes for refining 63 | if self.training: 64 | roi_data = self.RCNN_proposal_target(rois, gt_boxes, num_boxes) 65 | rois, rois_label, rois_target, rois_inside_ws, rois_outside_ws = roi_data 66 | 67 | rois_label = Variable(rois_label.view(-1).long()) 68 | rois_target = Variable(rois_target.view(-1, rois_target.size(2))) 69 | rois_inside_ws = Variable(rois_inside_ws.view(-1, rois_inside_ws.size(2))) 70 | rois_outside_ws = Variable(rois_outside_ws.view(-1, rois_outside_ws.size(2))) 71 | else: 72 | rois_label = None 73 | rois_target = None 74 | rois_inside_ws = None 75 | rois_outside_ws = None 76 | rpn_loss_cls = 0 77 | rpn_loss_bbox = 0 78 | 79 | rois = Variable(rois) 80 | # do roi pooling based on predicted rois 81 | 82 | if cfg.POOLING_MODE == 'crop': 83 | # pdb.set_trace() 84 | # pooled_feat_anchor = _crop_pool_layer(base_feat, rois.view(-1, 5)) 85 | grid_xy = _affine_grid_gen(rois.view(-1, 5), base_feat.size()[2:], self.grid_size) 86 | grid_yx = torch.stack([grid_xy.data[:,:,:,1], grid_xy.data[:,:,:,0]], 3).contiguous() 87 | pooled_feat = self.RCNN_roi_crop(base_feat, Variable(grid_yx).detach()) 88 | if cfg.CROP_RESIZE_WITH_MAX_POOL: 89 | pooled_feat = F.max_pool2d(pooled_feat, 2, 2) 90 | elif cfg.POOLING_MODE == 'align': 91 | pooled_feat = self.RCNN_roi_align(base_feat, rois.view(-1, 5)) 92 | elif cfg.POOLING_MODE == 'pool': 93 | pooled_feat = self.RCNN_roi_pool(base_feat, rois.view(-1,5)) 94 | 95 | # feed pooled features to top model 96 | pooled_feat = self._head_to_tail(pooled_feat) 97 | if self.lc: 98 | feat_pixel = feat_pixel.view(1, -1).repeat(pooled_feat.size(0), 1) 99 | pooled_feat = torch.cat((feat_pixel, pooled_feat), 1) 100 | 101 | # compute bbox offset 102 | bbox_pred = self.RCNN_bbox_pred(pooled_feat) 103 | if self.training and not self.class_agnostic: 104 | # select the corresponding columns according to roi labels 105 | bbox_pred_view = bbox_pred.view(bbox_pred.size(0), int(bbox_pred.size(1) / 4), 4) 106 | bbox_pred_select = torch.gather(bbox_pred_view, 1, rois_label.view(rois_label.size(0), 1, 1).expand(rois_label.size(0), 1, 4)) 107 | bbox_pred = bbox_pred_select.squeeze(1) 108 | 109 | # compute object classification probability 110 | cls_score = self.RCNN_cls_score(pooled_feat) 111 | cls_prob = F.softmax(cls_score, 1) 112 | 113 | RCNN_loss_cls = 0 114 | RCNN_loss_bbox = 0 115 | 116 | if self.training: 117 | # classification loss 118 | RCNN_loss_cls = F.cross_entropy(cls_score, rois_label) 119 | 120 | # bounding box regression L1 loss 121 | RCNN_loss_bbox = _smooth_l1_loss(bbox_pred, rois_target, rois_inside_ws, rois_outside_ws) 122 | 123 | 124 | cls_prob = cls_prob.view(batch_size, rois.size(1), -1) 125 | bbox_pred = bbox_pred.view(batch_size, rois.size(1), -1) 126 | 127 | return rois, cls_prob, bbox_pred, rpn_loss_cls, rpn_loss_bbox, RCNN_loss_cls, RCNN_loss_bbox, rois_label,d_pixel#,diff 128 | 129 | def _init_weights(self): 130 | def normal_init(m, mean, stddev, truncated=False): 131 | """ 132 | weight initalizer: truncated normal and random normal. 133 | """ 134 | # x is a parameter 135 | if truncated: 136 | m.weight.data.normal_().fmod_(2).mul_(stddev).add_(mean) # not a perfect approximation 137 | else: 138 | m.weight.data.normal_(mean, stddev) 139 | m.bias.data.zero_() 140 | 141 | normal_init(self.RCNN_rpn.RPN_Conv, 0, 0.01, cfg.TRAIN.TRUNCATED) 142 | normal_init(self.RCNN_rpn.RPN_cls_score, 0, 0.01, cfg.TRAIN.TRUNCATED) 143 | normal_init(self.RCNN_rpn.RPN_bbox_pred, 0, 0.01, cfg.TRAIN.TRUNCATED) 144 | normal_init(self.RCNN_cls_score, 0, 0.01, cfg.TRAIN.TRUNCATED) 145 | normal_init(self.RCNN_bbox_pred, 0, 0.001, cfg.TRAIN.TRUNCATED) 146 | 147 | def create_architecture(self): 148 | self._init_modules() 149 | self._init_weights() 150 | -------------------------------------------------------------------------------- /lib/model/faster_rcnn/vgg16.py: -------------------------------------------------------------------------------- 1 | # -------------------------------------------------------- 2 | # Tensorflow Faster R-CNN 3 | # Licensed under The MIT License [see LICENSE for details] 4 | # Written by Xinlei Chen 5 | # -------------------------------------------------------- 6 | from __future__ import absolute_import 7 | from __future__ import division 8 | from __future__ import print_function 9 | 10 | import torch 11 | import torch.nn as nn 12 | import torch.nn.functional as F 13 | from torch.autograd import Variable 14 | import math 15 | import torchvision.models as models 16 | from model.utils.config import cfg 17 | 18 | from model.faster_rcnn.faster_rcnn import _fasterRCNN 19 | import pdb 20 | 21 | class vgg16(_fasterRCNN): 22 | def __init__(self, classes, pretrained=False, class_agnostic=False): 23 | self.model_path = cfg.VGG_PATH 24 | self.dout_base_model = 512 25 | self.pretrained = pretrained 26 | self.class_agnostic = class_agnostic 27 | 28 | _fasterRCNN.__init__(self, classes, class_agnostic) 29 | 30 | def _init_modules(self): 31 | vgg = models.vgg16() 32 | if self.pretrained: 33 | print("Loading pretrained weights from %s" %(self.model_path)) 34 | state_dict = torch.load(self.model_path) 35 | vgg.load_state_dict({k:v for k,v in state_dict.items() if k in vgg.state_dict()}) 36 | 37 | vgg.classifier = nn.Sequential(*list(vgg.classifier._modules.values())[:-1]) 38 | 39 | # not using the last maxpool layer 40 | self.RCNN_base = nn.Sequential(*list(vgg.features._modules.values())[:-1]) 41 | 42 | # Fix the layers before conv3: 43 | for layer in range(10): 44 | for p in self.RCNN_base[layer].parameters(): p.requires_grad = False 45 | 46 | # self.RCNN_base = _RCNN_base(vgg.features, self.classes, self.dout_base_model) 47 | 48 | self.RCNN_top = vgg.classifier 49 | 50 | # not using the last maxpool layer 51 | self.RCNN_cls_score = nn.Linear(4096, self.n_classes) 52 | 53 | if self.class_agnostic: 54 | self.RCNN_bbox_pred = nn.Linear(4096, 4) 55 | else: 56 | self.RCNN_bbox_pred = nn.Linear(4096, 4 * self.n_classes) 57 | 58 | def _head_to_tail(self, pool5): 59 | 60 | pool5_flat = pool5.view(pool5.size(0), -1) 61 | fc7 = self.RCNN_top(pool5_flat) 62 | 63 | return fc7 64 | 65 | -------------------------------------------------------------------------------- /lib/model/faster_rcnn/vgg16_global.py: -------------------------------------------------------------------------------- 1 | # -------------------------------------------------------- 2 | # Tensorflow Faster R-CNN 3 | # Licensed under The MIT License [see LICENSE for details] 4 | # Written by Xinlei Chen 5 | # -------------------------------------------------------- 6 | from __future__ import absolute_import 7 | from __future__ import division 8 | from __future__ import print_function 9 | 10 | import torch 11 | import torch.nn as nn 12 | import torch.nn.functional as F 13 | from torch.autograd import Variable 14 | import math 15 | import torchvision.models as models 16 | from model.utils.config import cfg 17 | 18 | from model.faster_rcnn.faster_rcnn_global import _fasterRCNN 19 | import pdb 20 | def conv3x3(in_planes, out_planes, stride=1): 21 | "3x3 convolution with padding" 22 | return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, 23 | padding=1, bias=False) 24 | class netD(nn.Module): 25 | def __init__(self,context=False): 26 | super(netD, self).__init__() 27 | self.conv1 = conv3x3(512, 512, stride=2) 28 | self.bn1 = nn.BatchNorm2d(512) 29 | self.conv2 = conv3x3(512, 128, stride=2) 30 | self.bn2 = nn.BatchNorm2d(128) 31 | self.conv3 = conv3x3(128, 128, stride=2) 32 | self.bn3 = nn.BatchNorm2d(128) 33 | self.fc = nn.Linear(128,2) 34 | self.context = context 35 | def forward(self, x): 36 | x = F.dropout(F.relu(self.bn1(self.conv1(x))),training=self.training) 37 | x = F.dropout(F.relu(self.bn2(self.conv2(x))),training=self.training) 38 | x = F.dropout(F.relu(self.bn3(self.conv3(x))),training=self.training) 39 | x = F.avg_pool2d(x,(x.size(2),x.size(3))) 40 | x = x.view(-1,128) 41 | if self.context: 42 | feat = x 43 | x = self.fc(x) 44 | if self.context: 45 | return x,feat#torch.cat((feat1,feat2),1)#F 46 | else: 47 | return x 48 | class netD_dc(nn.Module): 49 | def __init__(self): 50 | super(netD_dc, self).__init__() 51 | self.fc1 = nn.Linear(2048,100) 52 | self.bn1 = nn.BatchNorm1d(100) 53 | self.fc2 = nn.Linear(100,100) 54 | self.bn2 = nn.BatchNorm1d(100) 55 | self.fc3 = nn.Linear(100,2) 56 | def forward(self, x): 57 | x = F.dropout(F.relu(self.bn1(self.fc1(x))),training=self.training) 58 | x = F.dropout(F.relu(self.bn2(self.fc2(x))),training=self.training) 59 | x = self.fc3(x) 60 | return x 61 | 62 | class vgg16(_fasterRCNN): 63 | def __init__(self, classes, pretrained=False, class_agnostic=False,gc=False): 64 | self.model_path = cfg.VGG_PATH 65 | self.dout_base_model = 512 66 | self.pretrained = pretrained 67 | self.class_agnostic = class_agnostic 68 | self.gc = gc 69 | 70 | _fasterRCNN.__init__(self, classes, class_agnostic,self.gc) 71 | 72 | def _init_modules(self): 73 | vgg = models.vgg16() 74 | if self.pretrained: 75 | print("Loading pretrained weights from %s" %(self.model_path)) 76 | state_dict = torch.load(self.model_path) 77 | vgg.load_state_dict({k:v for k,v in state_dict.items() if k in vgg.state_dict()}) 78 | 79 | vgg.classifier = nn.Sequential(*list(vgg.classifier._modules.values())[:-1]) 80 | 81 | # not using the last maxpool layer 82 | self.RCNN_base = nn.Sequential(*list(vgg.features._modules.values())[:-1]) 83 | self.netD = netD(context=self.context) 84 | feat_d = 4096 85 | 86 | # Fix the layers before conv3: 87 | for layer in range(10): 88 | for p in self.RCNN_base[layer].parameters(): p.requires_grad = False 89 | 90 | # self.RCNN_base = _RCNN_base(vgg.features, self.classes, self.dout_base_model) 91 | 92 | self.RCNN_top = vgg.classifier 93 | if self.gc: 94 | feat_d += 128 95 | self.RCNN_cls_score = nn.Linear(feat_d, self.n_classes) 96 | if self.class_agnostic: 97 | self.RCNN_bbox_pred = nn.Linear(feat_d, 4) 98 | else: 99 | self.RCNN_bbox_pred = nn.Linear(feat_d, 4 * self.n_classes) 100 | 101 | 102 | def _head_to_tail(self, pool5): 103 | 104 | pool5_flat = pool5.view(pool5.size(0), -1) 105 | fc7 = self.RCNN_top(pool5_flat) 106 | 107 | return fc7 108 | 109 | -------------------------------------------------------------------------------- /lib/model/faster_rcnn/vgg16_global_local.py: -------------------------------------------------------------------------------- 1 | # -------------------------------------------------------- 2 | # Tensorflow Faster R-CNN 3 | # Licensed under The MIT License [see LICENSE for details] 4 | # Written by Xinlei Chen 5 | # -------------------------------------------------------- 6 | from __future__ import absolute_import 7 | from __future__ import division 8 | from __future__ import print_function 9 | 10 | import torch 11 | import torch.nn as nn 12 | import torch.nn.functional as F 13 | from torch.autograd import Variable 14 | import math 15 | import torchvision.models as models 16 | from model.faster_rcnn.faster_rcnn_global_local import _fasterRCNN 17 | #from model.faster_rcnn.faster_rcnn_imgandpixellevel_gradcam import _fasterRCNN 18 | from model.utils.config import cfg 19 | 20 | import pdb 21 | def conv3x3(in_planes, out_planes, stride=1): 22 | "3x3 convolution with padding" 23 | return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, 24 | padding=1, bias=False) 25 | def conv1x1(in_planes, out_planes, stride=1): 26 | "3x3 convolution with padding" 27 | return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, 28 | padding=0, bias=False) 29 | class netD_pixel(nn.Module): 30 | def __init__(self,context=False): 31 | super(netD_pixel, self).__init__() 32 | self.conv1 = conv1x1(256, 256) 33 | #self.bn1 = nn.BatchNorm2d(256) 34 | self.conv2 = conv1x1(256, 128) 35 | #self.bn2 = nn.BatchNorm2d(128) 36 | self.conv3 = conv1x1(128, 1) 37 | 38 | self.context = context 39 | def forward(self, x): 40 | x = F.relu(x) 41 | x = F.relu(self.conv1(x)) 42 | x = F.relu(self.conv2(x)) 43 | if self.context: 44 | feat = F.avg_pool2d(x, (x.size(2), x.size(3))) 45 | # feat = x 46 | x = F.sigmoid(self.conv3(x)) 47 | return x.view(-1,1), feat # torch.cat((feat1,feat2),1)#F 48 | else: 49 | x = F.sigmoid(self.conv3(x)) 50 | return x.view(-1,1)#F.sigmoid(x) 51 | 52 | 53 | class netD(nn.Module): 54 | def __init__(self,context=False): 55 | super(netD, self).__init__() 56 | self.conv1 = conv3x3(512, 512, stride=2) 57 | self.bn1 = nn.BatchNorm2d(512) 58 | self.conv2 = conv3x3(512, 128, stride=2) 59 | self.bn2 = nn.BatchNorm2d(128) 60 | self.conv3 = conv3x3(128, 128, stride=2) 61 | self.bn3 = nn.BatchNorm2d(128) 62 | self.fc = nn.Linear(128,2) 63 | self.context = context 64 | def forward(self, x): 65 | x = F.dropout(F.relu(self.bn1(self.conv1(x))),training=self.training) 66 | x = F.dropout(F.relu(self.bn2(self.conv2(x))),training=self.training) 67 | x = F.dropout(F.relu(self.bn3(self.conv3(x))),training=self.training) 68 | x = F.avg_pool2d(x,(x.size(2),x.size(3))) 69 | x = x.view(-1,128) 70 | if self.context: 71 | feat = x 72 | x = self.fc(x) 73 | if self.context: 74 | return x,feat#torch.cat((feat1,feat2),1)#F 75 | else: 76 | return x 77 | class netD_dc(nn.Module): 78 | def __init__(self): 79 | super(netD_dc, self).__init__() 80 | self.fc1 = nn.Linear(2048,100) 81 | self.bn1 = nn.BatchNorm1d(100) 82 | self.fc2 = nn.Linear(100,100) 83 | self.bn2 = nn.BatchNorm1d(100) 84 | self.fc3 = nn.Linear(100,2) 85 | def forward(self, x): 86 | x = F.dropout(F.relu(self.bn1(self.fc1(x))),training=self.training) 87 | x = F.dropout(F.relu(self.bn2(self.fc2(x))),training=self.training) 88 | x = self.fc3(x) 89 | return x 90 | 91 | class vgg16(_fasterRCNN): 92 | def __init__(self, classes, pretrained=False, class_agnostic=False,lc=False,gc=False): 93 | self.model_path = cfg.VGG_PATH 94 | self.dout_base_model = 512 95 | self.pretrained = pretrained 96 | self.class_agnostic = class_agnostic 97 | self.lc = lc 98 | self.gc = gc 99 | 100 | _fasterRCNN.__init__(self, classes, class_agnostic,self.lc,self.gc) 101 | 102 | def _init_modules(self): 103 | vgg = models.vgg16() 104 | if self.pretrained: 105 | print("Loading pretrained weights from %s" %(self.model_path)) 106 | state_dict = torch.load(self.model_path) 107 | vgg.load_state_dict({k:v for k,v in state_dict.items() if k in vgg.state_dict()}) 108 | 109 | vgg.classifier = nn.Sequential(*list(vgg.classifier._modules.values())[:-1]) 110 | 111 | # not using the last maxpool layer 112 | #print(vgg.features) 113 | self.RCNN_base1 = nn.Sequential(*list(vgg.features._modules.values())[:14]) 114 | 115 | self.RCNN_base2 = nn.Sequential(*list(vgg.features._modules.values())[14:-1]) 116 | #print(self.RCNN_base1) 117 | #print(self.RCNN_base2) 118 | self.netD = netD(context=self.gc) 119 | self.netD_pixel = netD_pixel(context=self.lc) 120 | feat_d = 4096 121 | if self.lc: 122 | feat_d += 128 123 | if self.gc: 124 | feat_d += 128 125 | # Fix the layers before conv3: 126 | for layer in range(10): 127 | for p in self.RCNN_base1[layer].parameters(): p.requires_grad = False 128 | 129 | # self.RCNN_base = _RCNN_base(vgg.features, self.classes, self.dout_base_model) 130 | 131 | self.RCNN_top = vgg.classifier 132 | 133 | self.RCNN_cls_score = nn.Linear(feat_d, self.n_classes) 134 | if self.class_agnostic: 135 | self.RCNN_bbox_pred = nn.Linear(feat_d, 4) 136 | else: 137 | self.RCNN_bbox_pred = nn.Linear(feat_d, 4 * self.n_classes) 138 | 139 | 140 | def _head_to_tail(self, pool5): 141 | 142 | pool5_flat = pool5.view(pool5.size(0), -1) 143 | fc7 = self.RCNN_top(pool5_flat) 144 | 145 | return fc7 146 | 147 | -------------------------------------------------------------------------------- /lib/model/faster_rcnn/vgg16_local.py: -------------------------------------------------------------------------------- 1 | # -------------------------------------------------------- 2 | # Tensorflow Faster R-CNN 3 | # Licensed under The MIT License [see LICENSE for details] 4 | # Written by Xinlei Chen 5 | # -------------------------------------------------------- 6 | from __future__ import absolute_import 7 | from __future__ import division 8 | from __future__ import print_function 9 | 10 | import torch 11 | import torch.nn as nn 12 | import torch.nn.functional as F 13 | from torch.autograd import Variable 14 | import math 15 | import torchvision.models as models 16 | from model.utils.config import cfg 17 | 18 | from model.faster_rcnn.faster_rcnn_local import _fasterRCNN 19 | import pdb 20 | def conv3x3(in_planes, out_planes, stride=1): 21 | "3x3 convolution with padding" 22 | return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, 23 | padding=1, bias=False) 24 | def conv1x1(in_planes, out_planes, stride=1): 25 | "3x3 convolution with padding" 26 | return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, 27 | padding=0, bias=False) 28 | class netD_pixel(nn.Module): 29 | def __init__(self,context=False): 30 | super(netD_pixel, self).__init__() 31 | self.conv1 = conv1x1(256, 256) 32 | self.conv2 = conv1x1(256, 128) 33 | self.conv3 = conv1x1(128, 1) 34 | self.context = context 35 | def forward(self, x): 36 | x = F.relu(x) 37 | x = F.relu(self.conv1(x)) 38 | x = F.relu(self.conv2(x)) 39 | if self.context: 40 | feat = F.avg_pool2d(x, (x.size(2), x.size(3))) 41 | x = self.conv3(x) 42 | return F.sigmoid(x), feat 43 | else: 44 | x = self.conv3(x) 45 | return F.sigmoid(x) 46 | 47 | 48 | class netD(nn.Module): 49 | def __init__(self,context=False): 50 | super(netD, self).__init__() 51 | self.conv1 = conv3x3(512, 512, stride=2) 52 | self.bn1 = nn.BatchNorm2d(512) 53 | self.conv2 = conv3x3(512, 128, stride=2) 54 | self.bn2 = nn.BatchNorm2d(128) 55 | self.conv3 = conv3x3(128, 128, stride=2) 56 | self.bn3 = nn.BatchNorm2d(128) 57 | self.fc = nn.Linear(128,2) 58 | self.context = context 59 | def forward(self, x): 60 | x = F.dropout(F.relu(self.bn1(self.conv1(x))),training=self.training) 61 | x = F.dropout(F.relu(self.bn2(self.conv2(x))),training=self.training) 62 | x = F.dropout(F.relu(self.bn3(self.conv3(x))),training=self.training) 63 | x = F.avg_pool2d(x,(x.size(2),x.size(3))) 64 | x = x.view(-1,128) 65 | if self.context: 66 | feat = x 67 | x = self.fc(x) 68 | if self.context: 69 | return x,feat#torch.cat((feat1,feat2),1)#F 70 | else: 71 | return x 72 | class netD_dc(nn.Module): 73 | def __init__(self): 74 | super(netD_dc, self).__init__() 75 | self.fc1 = nn.Linear(2048,100) 76 | self.bn1 = nn.BatchNorm1d(100) 77 | self.fc2 = nn.Linear(100,100) 78 | self.bn2 = nn.BatchNorm1d(100) 79 | self.fc3 = nn.Linear(100,2) 80 | def forward(self, x): 81 | x = F.dropout(F.relu(self.bn1(self.fc1(x))),training=self.training) 82 | x = F.dropout(F.relu(self.bn2(self.fc2(x))),training=self.training) 83 | x = self.fc3(x) 84 | return x 85 | 86 | class vgg16(_fasterRCNN): 87 | def __init__(self, classes, pretrained=False, class_agnostic=False,lc=False): 88 | self.model_path = cfg.VGG_PATH 89 | self.dout_base_model = 512 90 | self.pretrained = pretrained 91 | self.class_agnostic = class_agnostic 92 | self.lc = lc 93 | self.gc = gc 94 | 95 | _fasterRCNN.__init__(self, classes, class_agnostic,self.lc) 96 | 97 | def _init_modules(self): 98 | vgg = models.vgg16() 99 | if self.pretrained: 100 | print("Loading pretrained weights from %s" %(self.model_path)) 101 | state_dict = torch.load(self.model_path) 102 | vgg.load_state_dict({k:v for k,v in state_dict.items() if k in vgg.state_dict()}) 103 | 104 | vgg.classifier = nn.Sequential(*list(vgg.classifier._modules.values())[:-1]) 105 | 106 | # not using the last maxpool layer 107 | print(vgg.features) 108 | self.RCNN_base1 = nn.Sequential(*list(vgg.features._modules.values())[:14]) 109 | self.RCNN_base2 = nn.Sequential(*list(vgg.features._modules.values())[14:-1]) 110 | #print(self.RCNN_base1) 111 | #print(self.RCNN_base2) 112 | self.netD_pixel = netD_pixel(context=self.lc) 113 | feat_d = 4096 114 | if self.lc: 115 | feat_d += 128 116 | #if self.gc: 117 | # feat_d += 128 118 | # Fix the layers before conv3: 119 | for layer in range(10): 120 | for p in self.RCNN_base1[layer].parameters(): p.requires_grad = False 121 | 122 | # self.RCNN_base = _RCNN_base(vgg.features, self.classes, self.dout_base_model) 123 | 124 | self.RCNN_top = vgg.classifier 125 | 126 | self.RCNN_cls_score = nn.Linear(feat_d, self.n_classes) 127 | if self.class_agnostic: 128 | self.RCNN_bbox_pred = nn.Linear(feat_d, 4) 129 | else: 130 | self.RCNN_bbox_pred = nn.Linear(feat_d, 4 * self.n_classes) 131 | 132 | 133 | def _head_to_tail(self, pool5): 134 | 135 | pool5_flat = pool5.view(pool5.size(0), -1) 136 | fc7 = self.RCNN_top(pool5_flat) 137 | 138 | return fc7 139 | 140 | -------------------------------------------------------------------------------- /lib/model/nms/.gitignore: -------------------------------------------------------------------------------- 1 | *.c 2 | *.cpp 3 | *.so 4 | -------------------------------------------------------------------------------- /lib/model/nms/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/VisionLearningGroup/DA_Detection/730eaca8528d22ed3aa6b4dbc1965828a697cf9a/lib/model/nms/__init__.py -------------------------------------------------------------------------------- /lib/model/nms/_ext/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/VisionLearningGroup/DA_Detection/730eaca8528d22ed3aa6b4dbc1965828a697cf9a/lib/model/nms/_ext/__init__.py -------------------------------------------------------------------------------- /lib/model/nms/_ext/nms/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | from torch.utils.ffi import _wrap_function 3 | from ._nms import lib as _lib, ffi as _ffi 4 | 5 | __all__ = [] 6 | def _import_symbols(locals): 7 | for symbol in dir(_lib): 8 | fn = getattr(_lib, symbol) 9 | if callable(fn): 10 | locals[symbol] = _wrap_function(fn, _ffi) 11 | else: 12 | locals[symbol] = fn 13 | __all__.append(symbol) 14 | 15 | _import_symbols(locals()) 16 | -------------------------------------------------------------------------------- /lib/model/nms/build.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | import os 3 | import torch 4 | from torch.utils.ffi import create_extension 5 | 6 | #this_file = os.path.dirname(__file__) 7 | 8 | sources = [] 9 | headers = [] 10 | defines = [] 11 | with_cuda = False 12 | 13 | if torch.cuda.is_available(): 14 | print('Including CUDA code.') 15 | sources += ['src/nms_cuda.c'] 16 | headers += ['src/nms_cuda.h'] 17 | defines += [('WITH_CUDA', None)] 18 | with_cuda = True 19 | 20 | this_file = os.path.dirname(os.path.realpath(__file__)) 21 | print(this_file) 22 | extra_objects = ['src/nms_cuda_kernel.cu.o'] 23 | extra_objects = [os.path.join(this_file, fname) for fname in extra_objects] 24 | print(extra_objects) 25 | 26 | ffi = create_extension( 27 | '_ext.nms', 28 | headers=headers, 29 | sources=sources, 30 | define_macros=defines, 31 | relative_to=__file__, 32 | with_cuda=with_cuda, 33 | extra_objects=extra_objects 34 | ) 35 | 36 | if __name__ == '__main__': 37 | ffi.build() 38 | -------------------------------------------------------------------------------- /lib/model/nms/make.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | # CUDA_PATH=/usr/local/cuda/ 4 | 5 | cd src 6 | echo "Compiling stnm kernels by nvcc..." 7 | nvcc -c -o nms_cuda_kernel.cu.o nms_cuda_kernel.cu -x cu -Xcompiler -fPIC -arch=sm_52 8 | 9 | cd ../ 10 | python build.py 11 | -------------------------------------------------------------------------------- /lib/model/nms/nms_cpu.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import numpy as np 4 | import torch 5 | 6 | def nms_cpu(dets, thresh): 7 | dets = dets.numpy() 8 | x1 = dets[:, 0] 9 | y1 = dets[:, 1] 10 | x2 = dets[:, 2] 11 | y2 = dets[:, 3] 12 | scores = dets[:, 4] 13 | 14 | areas = (x2 - x1 + 1) * (y2 - y1 + 1) 15 | order = scores.argsort()[::-1] 16 | 17 | keep = [] 18 | while order.size > 0: 19 | i = order.item(0) 20 | keep.append(i) 21 | xx1 = np.maximum(x1[i], x1[order[1:]]) 22 | yy1 = np.maximum(y1[i], y1[order[1:]]) 23 | xx2 = np.minimum(x2[i], x2[order[1:]]) 24 | yy2 = np.minimum(y2[i], y2[order[1:]]) 25 | 26 | w = np.maximum(0.0, xx2 - xx1 + 1) 27 | h = np.maximum(0.0, yy2 - yy1 + 1) 28 | inter = w * h 29 | ovr = inter / (areas[i] + areas[order[1:]] - inter) 30 | 31 | inds = np.where(ovr <= thresh)[0] 32 | order = order[inds + 1] 33 | 34 | return torch.IntTensor(keep) 35 | -------------------------------------------------------------------------------- /lib/model/nms/nms_gpu.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | import torch 3 | import numpy as np 4 | from ._ext import nms 5 | import pdb 6 | 7 | def nms_gpu(dets, thresh): 8 | keep = dets.new(dets.size(0), 1).zero_().int() 9 | num_out = dets.new(1).zero_().int() 10 | nms.nms_cuda(keep, dets, num_out, thresh) 11 | keep = keep[:num_out[0]] 12 | return keep 13 | -------------------------------------------------------------------------------- /lib/model/nms/nms_kernel.cu: -------------------------------------------------------------------------------- 1 | // ------------------------------------------------------------------ 2 | // Faster R-CNN 3 | // Copyright (c) 2015 Microsoft 4 | // Licensed under The MIT License [see fast-rcnn/LICENSE for details] 5 | // Written by Shaoqing Ren 6 | // ------------------------------------------------------------------ 7 | 8 | #include "gpu_nms.hpp" 9 | #include 10 | #include 11 | 12 | #define CUDA_CHECK(condition) \ 13 | /* Code block avoids redefinition of cudaError_t error */ \ 14 | do { \ 15 | cudaError_t error = condition; \ 16 | if (error != cudaSuccess) { \ 17 | std::cout << cudaGetErrorString(error) << std::endl; \ 18 | } \ 19 | } while (0) 20 | 21 | #define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0)) 22 | int const threadsPerBlock = sizeof(unsigned long long) * 8; 23 | 24 | __device__ inline float devIoU(float const * const a, float const * const b) { 25 | float left = max(a[0], b[0]), right = min(a[2], b[2]); 26 | float top = max(a[1], b[1]), bottom = min(a[3], b[3]); 27 | float width = max(right - left + 1, 0.f), height = max(bottom - top + 1, 0.f); 28 | float interS = width * height; 29 | float Sa = (a[2] - a[0] + 1) * (a[3] - a[1] + 1); 30 | float Sb = (b[2] - b[0] + 1) * (b[3] - b[1] + 1); 31 | return interS / (Sa + Sb - interS); 32 | } 33 | 34 | __global__ void nms_kernel(const int n_boxes, const float nms_overlap_thresh, 35 | const float *dev_boxes, unsigned long long *dev_mask) { 36 | const int row_start = blockIdx.y; 37 | const int col_start = blockIdx.x; 38 | 39 | // if (row_start > col_start) return; 40 | 41 | const int row_size = 42 | min(n_boxes - row_start * threadsPerBlock, threadsPerBlock); 43 | const int col_size = 44 | min(n_boxes - col_start * threadsPerBlock, threadsPerBlock); 45 | 46 | __shared__ float block_boxes[threadsPerBlock * 5]; 47 | if (threadIdx.x < col_size) { 48 | block_boxes[threadIdx.x * 5 + 0] = 49 | dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 0]; 50 | block_boxes[threadIdx.x * 5 + 1] = 51 | dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 1]; 52 | block_boxes[threadIdx.x * 5 + 2] = 53 | dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 2]; 54 | block_boxes[threadIdx.x * 5 + 3] = 55 | dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 3]; 56 | block_boxes[threadIdx.x * 5 + 4] = 57 | dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 4]; 58 | } 59 | __syncthreads(); 60 | 61 | if (threadIdx.x < row_size) { 62 | const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x; 63 | const float *cur_box = dev_boxes + cur_box_idx * 5; 64 | int i = 0; 65 | unsigned long long t = 0; 66 | int start = 0; 67 | if (row_start == col_start) { 68 | start = threadIdx.x + 1; 69 | } 70 | for (i = start; i < col_size; i++) { 71 | if (devIoU(cur_box, block_boxes + i * 5) > nms_overlap_thresh) { 72 | t |= 1ULL << i; 73 | } 74 | } 75 | const int col_blocks = DIVUP(n_boxes, threadsPerBlock); 76 | dev_mask[cur_box_idx * col_blocks + col_start] = t; 77 | } 78 | } 79 | 80 | void _set_device(int device_id) { 81 | int current_device; 82 | CUDA_CHECK(cudaGetDevice(¤t_device)); 83 | if (current_device == device_id) { 84 | return; 85 | } 86 | // The call to cudaSetDevice must come before any calls to Get, which 87 | // may perform initialization using the GPU. 88 | CUDA_CHECK(cudaSetDevice(device_id)); 89 | } 90 | 91 | void _nms(int* keep_out, int* num_out, const float* boxes_host, int boxes_num, 92 | int boxes_dim, float nms_overlap_thresh, int device_id) { 93 | _set_device(device_id); 94 | 95 | float* boxes_dev = NULL; 96 | unsigned long long* mask_dev = NULL; 97 | 98 | const int col_blocks = DIVUP(boxes_num, threadsPerBlock); 99 | 100 | CUDA_CHECK(cudaMalloc(&boxes_dev, 101 | boxes_num * boxes_dim * sizeof(float))); 102 | CUDA_CHECK(cudaMemcpy(boxes_dev, 103 | boxes_host, 104 | boxes_num * boxes_dim * sizeof(float), 105 | cudaMemcpyHostToDevice)); 106 | 107 | CUDA_CHECK(cudaMalloc(&mask_dev, 108 | boxes_num * col_blocks * sizeof(unsigned long long))); 109 | 110 | dim3 blocks(DIVUP(boxes_num, threadsPerBlock), 111 | DIVUP(boxes_num, threadsPerBlock)); 112 | dim3 threads(threadsPerBlock); 113 | nms_kernel<<>>(boxes_num, 114 | nms_overlap_thresh, 115 | boxes_dev, 116 | mask_dev); 117 | 118 | std::vector mask_host(boxes_num * col_blocks); 119 | CUDA_CHECK(cudaMemcpy(&mask_host[0], 120 | mask_dev, 121 | sizeof(unsigned long long) * boxes_num * col_blocks, 122 | cudaMemcpyDeviceToHost)); 123 | 124 | std::vector remv(col_blocks); 125 | memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks); 126 | 127 | int num_to_keep = 0; 128 | for (int i = 0; i < boxes_num; i++) { 129 | int nblock = i / threadsPerBlock; 130 | int inblock = i % threadsPerBlock; 131 | 132 | if (!(remv[nblock] & (1ULL << inblock))) { 133 | keep_out[num_to_keep++] = i; 134 | unsigned long long *p = &mask_host[0] + i * col_blocks; 135 | for (int j = nblock; j < col_blocks; j++) { 136 | remv[j] |= p[j]; 137 | } 138 | } 139 | } 140 | *num_out = num_to_keep; 141 | 142 | CUDA_CHECK(cudaFree(boxes_dev)); 143 | CUDA_CHECK(cudaFree(mask_dev)); 144 | } 145 | -------------------------------------------------------------------------------- /lib/model/nms/nms_wrapper.py: -------------------------------------------------------------------------------- 1 | # -------------------------------------------------------- 2 | # Fast R-CNN 3 | # Copyright (c) 2015 Microsoft 4 | # Licensed under The MIT License [see LICENSE for details] 5 | # Written by Ross Girshick 6 | # -------------------------------------------------------- 7 | import torch 8 | from model.utils.config import cfg 9 | if torch.cuda.is_available(): 10 | from model.nms.nms_gpu import nms_gpu 11 | from model.nms.nms_cpu import nms_cpu 12 | 13 | def nms(dets, thresh, force_cpu=False): 14 | """Dispatch to either CPU or GPU NMS implementations.""" 15 | if dets.shape[0] == 0: 16 | return [] 17 | # ---numpy version--- 18 | # original: return gpu_nms(dets, thresh, device_id=cfg.GPU_ID) 19 | # ---pytorch version--- 20 | 21 | return nms_gpu(dets, thresh) if force_cpu == False else nms_cpu(dets, thresh) 22 | -------------------------------------------------------------------------------- /lib/model/nms/src/nms_cuda.c: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include "nms_cuda_kernel.h" 4 | 5 | // this symbol will be resolved automatically from PyTorch libs 6 | extern THCState *state; 7 | 8 | int nms_cuda(THCudaIntTensor *keep_out, THCudaTensor *boxes_host, 9 | THCudaIntTensor *num_out, float nms_overlap_thresh) { 10 | 11 | nms_cuda_compute(THCudaIntTensor_data(state, keep_out), 12 | THCudaIntTensor_data(state, num_out), 13 | THCudaTensor_data(state, boxes_host), 14 | THCudaTensor_size(state, boxes_host, 0), 15 | THCudaTensor_size(state, boxes_host, 1), 16 | nms_overlap_thresh); 17 | 18 | return 1; 19 | } 20 | -------------------------------------------------------------------------------- /lib/model/nms/src/nms_cuda.h: -------------------------------------------------------------------------------- 1 | // int nms_cuda(THCudaTensor *keep_out, THCudaTensor *num_out, 2 | // THCudaTensor *boxes_host, THCudaTensor *nms_overlap_thresh); 3 | 4 | int nms_cuda(THCudaIntTensor *keep_out, THCudaTensor *boxes_host, 5 | THCudaIntTensor *num_out, float nms_overlap_thresh); 6 | -------------------------------------------------------------------------------- /lib/model/nms/src/nms_cuda_kernel.cu: -------------------------------------------------------------------------------- 1 | // ------------------------------------------------------------------ 2 | // Faster R-CNN 3 | // Copyright (c) 2015 Microsoft 4 | // Licensed under The MIT License [see fast-rcnn/LICENSE for details] 5 | // Written by Shaoqing Ren 6 | // ------------------------------------------------------------------ 7 | 8 | #include 9 | #include 10 | #include 11 | #include 12 | #include "nms_cuda_kernel.h" 13 | 14 | #define CUDA_WARN(XXX) \ 15 | do { if (XXX != cudaSuccess) std::cout << "CUDA Error: " << \ 16 | cudaGetErrorString(XXX) << ", at line " << __LINE__ \ 17 | << std::endl; cudaDeviceSynchronize(); } while (0) 18 | 19 | #define CUDA_CHECK(condition) \ 20 | /* Code block avoids redefinition of cudaError_t error */ \ 21 | do { \ 22 | cudaError_t error = condition; \ 23 | if (error != cudaSuccess) { \ 24 | std::cout << cudaGetErrorString(error) << std::endl; \ 25 | } \ 26 | } while (0) 27 | 28 | #define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0)) 29 | int const threadsPerBlock = sizeof(unsigned long long) * 8; 30 | 31 | __device__ inline float devIoU(float const * const a, float const * const b) { 32 | float left = max(a[0], b[0]), right = min(a[2], b[2]); 33 | float top = max(a[1], b[1]), bottom = min(a[3], b[3]); 34 | float width = max(right - left + 1, 0.f), height = max(bottom - top + 1, 0.f); 35 | float interS = width * height; 36 | float Sa = (a[2] - a[0] + 1) * (a[3] - a[1] + 1); 37 | float Sb = (b[2] - b[0] + 1) * (b[3] - b[1] + 1); 38 | return interS / (Sa + Sb - interS); 39 | } 40 | 41 | __global__ void nms_kernel(int n_boxes, float nms_overlap_thresh, 42 | float *dev_boxes, unsigned long long *dev_mask) { 43 | const int row_start = blockIdx.y; 44 | const int col_start = blockIdx.x; 45 | 46 | // if (row_start > col_start) return; 47 | 48 | const int row_size = 49 | min(n_boxes - row_start * threadsPerBlock, threadsPerBlock); 50 | const int col_size = 51 | min(n_boxes - col_start * threadsPerBlock, threadsPerBlock); 52 | 53 | __shared__ float block_boxes[threadsPerBlock * 5]; 54 | if (threadIdx.x < col_size) { 55 | block_boxes[threadIdx.x * 5 + 0] = 56 | dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 0]; 57 | block_boxes[threadIdx.x * 5 + 1] = 58 | dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 1]; 59 | block_boxes[threadIdx.x * 5 + 2] = 60 | dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 2]; 61 | block_boxes[threadIdx.x * 5 + 3] = 62 | dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 3]; 63 | block_boxes[threadIdx.x * 5 + 4] = 64 | dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 4]; 65 | } 66 | __syncthreads(); 67 | 68 | if (threadIdx.x < row_size) { 69 | const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x; 70 | const float *cur_box = dev_boxes + cur_box_idx * 5; 71 | int i = 0; 72 | unsigned long long t = 0; 73 | int start = 0; 74 | if (row_start == col_start) { 75 | start = threadIdx.x + 1; 76 | } 77 | for (i = start; i < col_size; i++) { 78 | if (devIoU(cur_box, block_boxes + i * 5) > nms_overlap_thresh) { 79 | t |= 1ULL << i; 80 | } 81 | } 82 | const int col_blocks = DIVUP(n_boxes, threadsPerBlock); 83 | dev_mask[cur_box_idx * col_blocks + col_start] = t; 84 | } 85 | } 86 | 87 | void nms_cuda_compute(int* keep_out, int *num_out, float* boxes_host, int boxes_num, 88 | int boxes_dim, float nms_overlap_thresh) { 89 | 90 | float* boxes_dev = NULL; 91 | unsigned long long* mask_dev = NULL; 92 | 93 | const int col_blocks = DIVUP(boxes_num, threadsPerBlock); 94 | 95 | CUDA_CHECK(cudaMalloc(&boxes_dev, 96 | boxes_num * boxes_dim * sizeof(float))); 97 | CUDA_CHECK(cudaMemcpy(boxes_dev, 98 | boxes_host, 99 | boxes_num * boxes_dim * sizeof(float), 100 | cudaMemcpyHostToDevice)); 101 | 102 | CUDA_CHECK(cudaMalloc(&mask_dev, 103 | boxes_num * col_blocks * sizeof(unsigned long long))); 104 | 105 | dim3 blocks(DIVUP(boxes_num, threadsPerBlock), 106 | DIVUP(boxes_num, threadsPerBlock)); 107 | dim3 threads(threadsPerBlock); 108 | 109 | // printf("i am at line %d\n", boxes_num); 110 | // printf("i am at line %d\n", boxes_dim); 111 | 112 | nms_kernel<<>>(boxes_num, 113 | nms_overlap_thresh, 114 | boxes_dev, 115 | mask_dev); 116 | 117 | std::vector mask_host(boxes_num * col_blocks); 118 | CUDA_CHECK(cudaMemcpy(&mask_host[0], 119 | mask_dev, 120 | sizeof(unsigned long long) * boxes_num * col_blocks, 121 | cudaMemcpyDeviceToHost)); 122 | 123 | std::vector remv(col_blocks); 124 | memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks); 125 | 126 | // we need to create a memory for keep_out on cpu 127 | // otherwise, the following code cannot run 128 | 129 | int* keep_out_cpu = new int[boxes_num]; 130 | 131 | int num_to_keep = 0; 132 | for (int i = 0; i < boxes_num; i++) { 133 | int nblock = i / threadsPerBlock; 134 | int inblock = i % threadsPerBlock; 135 | 136 | if (!(remv[nblock] & (1ULL << inblock))) { 137 | // orignal: keep_out[num_to_keep++] = i; 138 | keep_out_cpu[num_to_keep++] = i; 139 | unsigned long long *p = &mask_host[0] + i * col_blocks; 140 | for (int j = nblock; j < col_blocks; j++) { 141 | remv[j] |= p[j]; 142 | } 143 | } 144 | } 145 | 146 | // copy keep_out_cpu to keep_out on gpu 147 | CUDA_WARN(cudaMemcpy(keep_out, keep_out_cpu, boxes_num * sizeof(int),cudaMemcpyHostToDevice)); 148 | 149 | // *num_out = num_to_keep; 150 | 151 | // original: *num_out = num_to_keep; 152 | // copy num_to_keep to num_out on gpu 153 | 154 | CUDA_WARN(cudaMemcpy(num_out, &num_to_keep, 1 * sizeof(int),cudaMemcpyHostToDevice)); 155 | 156 | // release cuda memory 157 | CUDA_CHECK(cudaFree(boxes_dev)); 158 | CUDA_CHECK(cudaFree(mask_dev)); 159 | // release cpu memory 160 | delete []keep_out_cpu; 161 | } 162 | -------------------------------------------------------------------------------- /lib/model/nms/src/nms_cuda_kernel.h: -------------------------------------------------------------------------------- 1 | #ifdef __cplusplus 2 | extern "C" { 3 | #endif 4 | 5 | void nms_cuda_compute(int* keep_out, int *num_out, float* boxes_host, int boxes_num, 6 | int boxes_dim, float nms_overlap_thresh); 7 | 8 | #ifdef __cplusplus 9 | } 10 | #endif 11 | -------------------------------------------------------------------------------- /lib/model/roi_align/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/VisionLearningGroup/DA_Detection/730eaca8528d22ed3aa6b4dbc1965828a697cf9a/lib/model/roi_align/__init__.py -------------------------------------------------------------------------------- /lib/model/roi_align/_ext/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/VisionLearningGroup/DA_Detection/730eaca8528d22ed3aa6b4dbc1965828a697cf9a/lib/model/roi_align/_ext/__init__.py -------------------------------------------------------------------------------- /lib/model/roi_align/_ext/roi_align/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | from torch.utils.ffi import _wrap_function 3 | from ._roi_align import lib as _lib, ffi as _ffi 4 | 5 | __all__ = [] 6 | def _import_symbols(locals): 7 | for symbol in dir(_lib): 8 | fn = getattr(_lib, symbol) 9 | if callable(fn): 10 | locals[symbol] = _wrap_function(fn, _ffi) 11 | else: 12 | locals[symbol] = fn 13 | __all__.append(symbol) 14 | 15 | _import_symbols(locals()) 16 | -------------------------------------------------------------------------------- /lib/model/roi_align/build.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | import os 3 | import torch 4 | from torch.utils.ffi import create_extension 5 | 6 | sources = ['src/roi_align.c'] 7 | headers = ['src/roi_align.h'] 8 | extra_objects = [] 9 | #sources = [] 10 | #headers = [] 11 | defines = [] 12 | with_cuda = False 13 | 14 | this_file = os.path.dirname(os.path.realpath(__file__)) 15 | print(this_file) 16 | 17 | if torch.cuda.is_available(): 18 | print('Including CUDA code.') 19 | sources += ['src/roi_align_cuda.c'] 20 | headers += ['src/roi_align_cuda.h'] 21 | defines += [('WITH_CUDA', None)] 22 | with_cuda = True 23 | 24 | extra_objects = ['src/roi_align_kernel.cu.o'] 25 | extra_objects = [os.path.join(this_file, fname) for fname in extra_objects] 26 | 27 | ffi = create_extension( 28 | '_ext.roi_align', 29 | headers=headers, 30 | sources=sources, 31 | define_macros=defines, 32 | relative_to=__file__, 33 | with_cuda=with_cuda, 34 | extra_objects=extra_objects 35 | ) 36 | 37 | if __name__ == '__main__': 38 | ffi.build() 39 | -------------------------------------------------------------------------------- /lib/model/roi_align/functions/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/VisionLearningGroup/DA_Detection/730eaca8528d22ed3aa6b4dbc1965828a697cf9a/lib/model/roi_align/functions/__init__.py -------------------------------------------------------------------------------- /lib/model/roi_align/functions/roi_align.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.autograd import Function 3 | from .._ext import roi_align 4 | 5 | 6 | # TODO use save_for_backward instead 7 | class RoIAlignFunction(Function): 8 | def __init__(self, aligned_height, aligned_width, spatial_scale): 9 | self.aligned_width = int(aligned_width) 10 | self.aligned_height = int(aligned_height) 11 | self.spatial_scale = float(spatial_scale) 12 | self.rois = None 13 | self.feature_size = None 14 | 15 | def forward(self, features, rois): 16 | self.rois = rois 17 | self.feature_size = features.size() 18 | 19 | batch_size, num_channels, data_height, data_width = features.size() 20 | num_rois = rois.size(0) 21 | 22 | output = features.new(num_rois, num_channels, self.aligned_height, self.aligned_width).zero_() 23 | if features.is_cuda: 24 | roi_align.roi_align_forward_cuda(self.aligned_height, 25 | self.aligned_width, 26 | self.spatial_scale, features, 27 | rois, output) 28 | else: 29 | roi_align.roi_align_forward(self.aligned_height, 30 | self.aligned_width, 31 | self.spatial_scale, features, 32 | rois, output) 33 | # raise NotImplementedError 34 | 35 | return output 36 | 37 | def backward(self, grad_output): 38 | assert(self.feature_size is not None and grad_output.is_cuda) 39 | 40 | batch_size, num_channels, data_height, data_width = self.feature_size 41 | 42 | grad_input = self.rois.new(batch_size, num_channels, data_height, 43 | data_width).zero_() 44 | roi_align.roi_align_backward_cuda(self.aligned_height, 45 | self.aligned_width, 46 | self.spatial_scale, grad_output, 47 | self.rois, grad_input) 48 | 49 | # print grad_input 50 | 51 | return grad_input, None 52 | -------------------------------------------------------------------------------- /lib/model/roi_align/make.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | CUDA_PATH=/usr/local/cuda/ 4 | 5 | cd src 6 | echo "Compiling my_lib kernels by nvcc..." 7 | nvcc -c -o roi_align_kernel.cu.o roi_align_kernel.cu -x cu -Xcompiler -fPIC -arch=sm_52 8 | 9 | cd ../ 10 | python build.py 11 | -------------------------------------------------------------------------------- /lib/model/roi_align/modules/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/VisionLearningGroup/DA_Detection/730eaca8528d22ed3aa6b4dbc1965828a697cf9a/lib/model/roi_align/modules/__init__.py -------------------------------------------------------------------------------- /lib/model/roi_align/modules/roi_align.py: -------------------------------------------------------------------------------- 1 | from torch.nn.modules.module import Module 2 | from torch.nn.functional import avg_pool2d, max_pool2d 3 | from ..functions.roi_align import RoIAlignFunction 4 | 5 | 6 | class RoIAlign(Module): 7 | def __init__(self, aligned_height, aligned_width, spatial_scale): 8 | super(RoIAlign, self).__init__() 9 | 10 | self.aligned_width = int(aligned_width) 11 | self.aligned_height = int(aligned_height) 12 | self.spatial_scale = float(spatial_scale) 13 | 14 | def forward(self, features, rois): 15 | return RoIAlignFunction(self.aligned_height, self.aligned_width, 16 | self.spatial_scale)(features, rois) 17 | 18 | class RoIAlignAvg(Module): 19 | def __init__(self, aligned_height, aligned_width, spatial_scale): 20 | super(RoIAlignAvg, self).__init__() 21 | 22 | self.aligned_width = int(aligned_width) 23 | self.aligned_height = int(aligned_height) 24 | self.spatial_scale = float(spatial_scale) 25 | 26 | def forward(self, features, rois): 27 | x = RoIAlignFunction(self.aligned_height+1, self.aligned_width+1, 28 | self.spatial_scale)(features, rois) 29 | return avg_pool2d(x, kernel_size=2, stride=1) 30 | 31 | class RoIAlignMax(Module): 32 | def __init__(self, aligned_height, aligned_width, spatial_scale): 33 | super(RoIAlignMax, self).__init__() 34 | 35 | self.aligned_width = int(aligned_width) 36 | self.aligned_height = int(aligned_height) 37 | self.spatial_scale = float(spatial_scale) 38 | 39 | def forward(self, features, rois): 40 | x = RoIAlignFunction(self.aligned_height+1, self.aligned_width+1, 41 | self.spatial_scale)(features, rois) 42 | return max_pool2d(x, kernel_size=2, stride=1) 43 | -------------------------------------------------------------------------------- /lib/model/roi_align/src/roi_align.c: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | 5 | 6 | void ROIAlignForwardCpu(const float* bottom_data, const float spatial_scale, const int num_rois, 7 | const int height, const int width, const int channels, 8 | const int aligned_height, const int aligned_width, const float * bottom_rois, 9 | float* top_data); 10 | 11 | void ROIAlignBackwardCpu(const float* top_diff, const float spatial_scale, const int num_rois, 12 | const int height, const int width, const int channels, 13 | const int aligned_height, const int aligned_width, const float * bottom_rois, 14 | float* top_data); 15 | 16 | int roi_align_forward(int aligned_height, int aligned_width, float spatial_scale, 17 | THFloatTensor * features, THFloatTensor * rois, THFloatTensor * output) 18 | { 19 | //Grab the input tensor 20 | float * data_flat = THFloatTensor_data(features); 21 | float * rois_flat = THFloatTensor_data(rois); 22 | 23 | float * output_flat = THFloatTensor_data(output); 24 | 25 | // Number of ROIs 26 | int num_rois = THFloatTensor_size(rois, 0); 27 | int size_rois = THFloatTensor_size(rois, 1); 28 | if (size_rois != 5) 29 | { 30 | return 0; 31 | } 32 | 33 | // data height 34 | int data_height = THFloatTensor_size(features, 2); 35 | // data width 36 | int data_width = THFloatTensor_size(features, 3); 37 | // Number of channels 38 | int num_channels = THFloatTensor_size(features, 1); 39 | 40 | // do ROIAlignForward 41 | ROIAlignForwardCpu(data_flat, spatial_scale, num_rois, data_height, data_width, num_channels, 42 | aligned_height, aligned_width, rois_flat, output_flat); 43 | 44 | return 1; 45 | } 46 | 47 | int roi_align_backward(int aligned_height, int aligned_width, float spatial_scale, 48 | THFloatTensor * top_grad, THFloatTensor * rois, THFloatTensor * bottom_grad) 49 | { 50 | //Grab the input tensor 51 | float * top_grad_flat = THFloatTensor_data(top_grad); 52 | float * rois_flat = THFloatTensor_data(rois); 53 | 54 | float * bottom_grad_flat = THFloatTensor_data(bottom_grad); 55 | 56 | // Number of ROIs 57 | int num_rois = THFloatTensor_size(rois, 0); 58 | int size_rois = THFloatTensor_size(rois, 1); 59 | if (size_rois != 5) 60 | { 61 | return 0; 62 | } 63 | 64 | // batch size 65 | // int batch_size = THFloatTensor_size(bottom_grad, 0); 66 | // data height 67 | int data_height = THFloatTensor_size(bottom_grad, 2); 68 | // data width 69 | int data_width = THFloatTensor_size(bottom_grad, 3); 70 | // Number of channels 71 | int num_channels = THFloatTensor_size(bottom_grad, 1); 72 | 73 | // do ROIAlignBackward 74 | ROIAlignBackwardCpu(top_grad_flat, spatial_scale, num_rois, data_height, 75 | data_width, num_channels, aligned_height, aligned_width, rois_flat, bottom_grad_flat); 76 | 77 | return 1; 78 | } 79 | 80 | void ROIAlignForwardCpu(const float* bottom_data, const float spatial_scale, const int num_rois, 81 | const int height, const int width, const int channels, 82 | const int aligned_height, const int aligned_width, const float * bottom_rois, 83 | float* top_data) 84 | { 85 | const int output_size = num_rois * aligned_height * aligned_width * channels; 86 | 87 | int idx = 0; 88 | for (idx = 0; idx < output_size; ++idx) 89 | { 90 | // (n, c, ph, pw) is an element in the aligned output 91 | int pw = idx % aligned_width; 92 | int ph = (idx / aligned_width) % aligned_height; 93 | int c = (idx / aligned_width / aligned_height) % channels; 94 | int n = idx / aligned_width / aligned_height / channels; 95 | 96 | float roi_batch_ind = bottom_rois[n * 5 + 0]; 97 | float roi_start_w = bottom_rois[n * 5 + 1] * spatial_scale; 98 | float roi_start_h = bottom_rois[n * 5 + 2] * spatial_scale; 99 | float roi_end_w = bottom_rois[n * 5 + 3] * spatial_scale; 100 | float roi_end_h = bottom_rois[n * 5 + 4] * spatial_scale; 101 | 102 | // Force malformed ROI to be 1x1 103 | float roi_width = fmaxf(roi_end_w - roi_start_w + 1., 0.); 104 | float roi_height = fmaxf(roi_end_h - roi_start_h + 1., 0.); 105 | float bin_size_h = roi_height / (aligned_height - 1.); 106 | float bin_size_w = roi_width / (aligned_width - 1.); 107 | 108 | float h = (float)(ph) * bin_size_h + roi_start_h; 109 | float w = (float)(pw) * bin_size_w + roi_start_w; 110 | 111 | int hstart = fminf(floor(h), height - 2); 112 | int wstart = fminf(floor(w), width - 2); 113 | 114 | int img_start = roi_batch_ind * channels * height * width; 115 | 116 | // bilinear interpolation 117 | if (h < 0 || h >= height || w < 0 || w >= width) 118 | { 119 | top_data[idx] = 0.; 120 | } 121 | else 122 | { 123 | float h_ratio = h - (float)(hstart); 124 | float w_ratio = w - (float)(wstart); 125 | int upleft = img_start + (c * height + hstart) * width + wstart; 126 | int upright = upleft + 1; 127 | int downleft = upleft + width; 128 | int downright = downleft + 1; 129 | 130 | top_data[idx] = bottom_data[upleft] * (1. - h_ratio) * (1. - w_ratio) 131 | + bottom_data[upright] * (1. - h_ratio) * w_ratio 132 | + bottom_data[downleft] * h_ratio * (1. - w_ratio) 133 | + bottom_data[downright] * h_ratio * w_ratio; 134 | } 135 | } 136 | } 137 | 138 | void ROIAlignBackwardCpu(const float* top_diff, const float spatial_scale, const int num_rois, 139 | const int height, const int width, const int channels, 140 | const int aligned_height, const int aligned_width, const float * bottom_rois, 141 | float* bottom_diff) 142 | { 143 | const int output_size = num_rois * aligned_height * aligned_width * channels; 144 | 145 | int idx = 0; 146 | for (idx = 0; idx < output_size; ++idx) 147 | { 148 | // (n, c, ph, pw) is an element in the aligned output 149 | int pw = idx % aligned_width; 150 | int ph = (idx / aligned_width) % aligned_height; 151 | int c = (idx / aligned_width / aligned_height) % channels; 152 | int n = idx / aligned_width / aligned_height / channels; 153 | 154 | float roi_batch_ind = bottom_rois[n * 5 + 0]; 155 | float roi_start_w = bottom_rois[n * 5 + 1] * spatial_scale; 156 | float roi_start_h = bottom_rois[n * 5 + 2] * spatial_scale; 157 | float roi_end_w = bottom_rois[n * 5 + 3] * spatial_scale; 158 | float roi_end_h = bottom_rois[n * 5 + 4] * spatial_scale; 159 | 160 | // Force malformed ROI to be 1x1 161 | float roi_width = fmaxf(roi_end_w - roi_start_w + 1., 0.); 162 | float roi_height = fmaxf(roi_end_h - roi_start_h + 1., 0.); 163 | float bin_size_h = roi_height / (aligned_height - 1.); 164 | float bin_size_w = roi_width / (aligned_width - 1.); 165 | 166 | float h = (float)(ph) * bin_size_h + roi_start_h; 167 | float w = (float)(pw) * bin_size_w + roi_start_w; 168 | 169 | int hstart = fminf(floor(h), height - 2); 170 | int wstart = fminf(floor(w), width - 2); 171 | 172 | int img_start = roi_batch_ind * channels * height * width; 173 | 174 | // bilinear interpolation 175 | if (h < 0 || h >= height || w < 0 || w >= width) 176 | { 177 | float h_ratio = h - (float)(hstart); 178 | float w_ratio = w - (float)(wstart); 179 | int upleft = img_start + (c * height + hstart) * width + wstart; 180 | int upright = upleft + 1; 181 | int downleft = upleft + width; 182 | int downright = downleft + 1; 183 | 184 | bottom_diff[upleft] += top_diff[idx] * (1. - h_ratio) * (1. - w_ratio); 185 | bottom_diff[upright] += top_diff[idx] * (1. - h_ratio) * w_ratio; 186 | bottom_diff[downleft] += top_diff[idx] * h_ratio * (1. - w_ratio); 187 | bottom_diff[downright] += top_diff[idx] * h_ratio * w_ratio; 188 | } 189 | } 190 | } 191 | -------------------------------------------------------------------------------- /lib/model/roi_align/src/roi_align.h: -------------------------------------------------------------------------------- 1 | int roi_align_forward(int aligned_height, int aligned_width, float spatial_scale, 2 | THFloatTensor * features, THFloatTensor * rois, THFloatTensor * output); 3 | 4 | int roi_align_backward(int aligned_height, int aligned_width, float spatial_scale, 5 | THFloatTensor * top_grad, THFloatTensor * rois, THFloatTensor * bottom_grad); 6 | -------------------------------------------------------------------------------- /lib/model/roi_align/src/roi_align_cuda.c: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include "roi_align_kernel.h" 4 | 5 | extern THCState *state; 6 | 7 | int roi_align_forward_cuda(int aligned_height, int aligned_width, float spatial_scale, 8 | THCudaTensor * features, THCudaTensor * rois, THCudaTensor * output) 9 | { 10 | // Grab the input tensor 11 | float * data_flat = THCudaTensor_data(state, features); 12 | float * rois_flat = THCudaTensor_data(state, rois); 13 | 14 | float * output_flat = THCudaTensor_data(state, output); 15 | 16 | // Number of ROIs 17 | int num_rois = THCudaTensor_size(state, rois, 0); 18 | int size_rois = THCudaTensor_size(state, rois, 1); 19 | if (size_rois != 5) 20 | { 21 | return 0; 22 | } 23 | 24 | // data height 25 | int data_height = THCudaTensor_size(state, features, 2); 26 | // data width 27 | int data_width = THCudaTensor_size(state, features, 3); 28 | // Number of channels 29 | int num_channels = THCudaTensor_size(state, features, 1); 30 | 31 | cudaStream_t stream = THCState_getCurrentStream(state); 32 | 33 | ROIAlignForwardLaucher( 34 | data_flat, spatial_scale, num_rois, data_height, 35 | data_width, num_channels, aligned_height, 36 | aligned_width, rois_flat, 37 | output_flat, stream); 38 | 39 | return 1; 40 | } 41 | 42 | int roi_align_backward_cuda(int aligned_height, int aligned_width, float spatial_scale, 43 | THCudaTensor * top_grad, THCudaTensor * rois, THCudaTensor * bottom_grad) 44 | { 45 | // Grab the input tensor 46 | float * top_grad_flat = THCudaTensor_data(state, top_grad); 47 | float * rois_flat = THCudaTensor_data(state, rois); 48 | 49 | float * bottom_grad_flat = THCudaTensor_data(state, bottom_grad); 50 | 51 | // Number of ROIs 52 | int num_rois = THCudaTensor_size(state, rois, 0); 53 | int size_rois = THCudaTensor_size(state, rois, 1); 54 | if (size_rois != 5) 55 | { 56 | return 0; 57 | } 58 | 59 | // batch size 60 | int batch_size = THCudaTensor_size(state, bottom_grad, 0); 61 | // data height 62 | int data_height = THCudaTensor_size(state, bottom_grad, 2); 63 | // data width 64 | int data_width = THCudaTensor_size(state, bottom_grad, 3); 65 | // Number of channels 66 | int num_channels = THCudaTensor_size(state, bottom_grad, 1); 67 | 68 | cudaStream_t stream = THCState_getCurrentStream(state); 69 | ROIAlignBackwardLaucher( 70 | top_grad_flat, spatial_scale, batch_size, num_rois, data_height, 71 | data_width, num_channels, aligned_height, 72 | aligned_width, rois_flat, 73 | bottom_grad_flat, stream); 74 | 75 | return 1; 76 | } 77 | -------------------------------------------------------------------------------- /lib/model/roi_align/src/roi_align_cuda.h: -------------------------------------------------------------------------------- 1 | int roi_align_forward_cuda(int aligned_height, int aligned_width, float spatial_scale, 2 | THCudaTensor * features, THCudaTensor * rois, THCudaTensor * output); 3 | 4 | int roi_align_backward_cuda(int aligned_height, int aligned_width, float spatial_scale, 5 | THCudaTensor * top_grad, THCudaTensor * rois, THCudaTensor * bottom_grad); 6 | -------------------------------------------------------------------------------- /lib/model/roi_align/src/roi_align_kernel.cu: -------------------------------------------------------------------------------- 1 | #ifdef __cplusplus 2 | extern "C" { 3 | #endif 4 | 5 | #include 6 | #include 7 | #include 8 | #include "roi_align_kernel.h" 9 | 10 | #define CUDA_1D_KERNEL_LOOP(i, n) \ 11 | for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; \ 12 | i += blockDim.x * gridDim.x) 13 | 14 | 15 | __global__ void ROIAlignForward(const int nthreads, const float* bottom_data, const float spatial_scale, const int height, const int width, 16 | const int channels, const int aligned_height, const int aligned_width, const float* bottom_rois, float* top_data) { 17 | CUDA_1D_KERNEL_LOOP(index, nthreads) { 18 | // (n, c, ph, pw) is an element in the aligned output 19 | // int n = index; 20 | // int pw = n % aligned_width; 21 | // n /= aligned_width; 22 | // int ph = n % aligned_height; 23 | // n /= aligned_height; 24 | // int c = n % channels; 25 | // n /= channels; 26 | 27 | int pw = index % aligned_width; 28 | int ph = (index / aligned_width) % aligned_height; 29 | int c = (index / aligned_width / aligned_height) % channels; 30 | int n = index / aligned_width / aligned_height / channels; 31 | 32 | // bottom_rois += n * 5; 33 | float roi_batch_ind = bottom_rois[n * 5 + 0]; 34 | float roi_start_w = bottom_rois[n * 5 + 1] * spatial_scale; 35 | float roi_start_h = bottom_rois[n * 5 + 2] * spatial_scale; 36 | float roi_end_w = bottom_rois[n * 5 + 3] * spatial_scale; 37 | float roi_end_h = bottom_rois[n * 5 + 4] * spatial_scale; 38 | 39 | // Force malformed ROIs to be 1x1 40 | float roi_width = fmaxf(roi_end_w - roi_start_w + 1., 0.); 41 | float roi_height = fmaxf(roi_end_h - roi_start_h + 1., 0.); 42 | float bin_size_h = roi_height / (aligned_height - 1.); 43 | float bin_size_w = roi_width / (aligned_width - 1.); 44 | 45 | float h = (float)(ph) * bin_size_h + roi_start_h; 46 | float w = (float)(pw) * bin_size_w + roi_start_w; 47 | 48 | int hstart = fminf(floor(h), height - 2); 49 | int wstart = fminf(floor(w), width - 2); 50 | 51 | int img_start = roi_batch_ind * channels * height * width; 52 | 53 | // bilinear interpolation 54 | if (h < 0 || h >= height || w < 0 || w >= width) { 55 | top_data[index] = 0.; 56 | } else { 57 | float h_ratio = h - (float)(hstart); 58 | float w_ratio = w - (float)(wstart); 59 | int upleft = img_start + (c * height + hstart) * width + wstart; 60 | int upright = upleft + 1; 61 | int downleft = upleft + width; 62 | int downright = downleft + 1; 63 | 64 | top_data[index] = bottom_data[upleft] * (1. - h_ratio) * (1. - w_ratio) 65 | + bottom_data[upright] * (1. - h_ratio) * w_ratio 66 | + bottom_data[downleft] * h_ratio * (1. - w_ratio) 67 | + bottom_data[downright] * h_ratio * w_ratio; 68 | } 69 | } 70 | } 71 | 72 | 73 | int ROIAlignForwardLaucher(const float* bottom_data, const float spatial_scale, const int num_rois, const int height, const int width, 74 | const int channels, const int aligned_height, const int aligned_width, const float* bottom_rois, float* top_data, cudaStream_t stream) { 75 | const int kThreadsPerBlock = 1024; 76 | const int output_size = num_rois * aligned_height * aligned_width * channels; 77 | cudaError_t err; 78 | 79 | 80 | ROIAlignForward<<<(output_size + kThreadsPerBlock - 1) / kThreadsPerBlock, kThreadsPerBlock, 0, stream>>>( 81 | output_size, bottom_data, spatial_scale, height, width, channels, 82 | aligned_height, aligned_width, bottom_rois, top_data); 83 | 84 | err = cudaGetLastError(); 85 | if(cudaSuccess != err) { 86 | fprintf( stderr, "cudaCheckError() failed : %s\n", cudaGetErrorString( err ) ); 87 | exit( -1 ); 88 | } 89 | 90 | return 1; 91 | } 92 | 93 | 94 | __global__ void ROIAlignBackward(const int nthreads, const float* top_diff, const float spatial_scale, const int height, const int width, 95 | const int channels, const int aligned_height, const int aligned_width, float* bottom_diff, const float* bottom_rois) { 96 | CUDA_1D_KERNEL_LOOP(index, nthreads) { 97 | 98 | // (n, c, ph, pw) is an element in the aligned output 99 | int pw = index % aligned_width; 100 | int ph = (index / aligned_width) % aligned_height; 101 | int c = (index / aligned_width / aligned_height) % channels; 102 | int n = index / aligned_width / aligned_height / channels; 103 | 104 | float roi_batch_ind = bottom_rois[n * 5 + 0]; 105 | float roi_start_w = bottom_rois[n * 5 + 1] * spatial_scale; 106 | float roi_start_h = bottom_rois[n * 5 + 2] * spatial_scale; 107 | float roi_end_w = bottom_rois[n * 5 + 3] * spatial_scale; 108 | float roi_end_h = bottom_rois[n * 5 + 4] * spatial_scale; 109 | /* int roi_start_w = round(bottom_rois[1] * spatial_scale); */ 110 | /* int roi_start_h = round(bottom_rois[2] * spatial_scale); */ 111 | /* int roi_end_w = round(bottom_rois[3] * spatial_scale); */ 112 | /* int roi_end_h = round(bottom_rois[4] * spatial_scale); */ 113 | 114 | // Force malformed ROIs to be 1x1 115 | float roi_width = fmaxf(roi_end_w - roi_start_w + 1., 0.); 116 | float roi_height = fmaxf(roi_end_h - roi_start_h + 1., 0.); 117 | float bin_size_h = roi_height / (aligned_height - 1.); 118 | float bin_size_w = roi_width / (aligned_width - 1.); 119 | 120 | float h = (float)(ph) * bin_size_h + roi_start_h; 121 | float w = (float)(pw) * bin_size_w + roi_start_w; 122 | 123 | int hstart = fminf(floor(h), height - 2); 124 | int wstart = fminf(floor(w), width - 2); 125 | 126 | int img_start = roi_batch_ind * channels * height * width; 127 | 128 | // bilinear interpolation 129 | if (!(h < 0 || h >= height || w < 0 || w >= width)) { 130 | float h_ratio = h - (float)(hstart); 131 | float w_ratio = w - (float)(wstart); 132 | int upleft = img_start + (c * height + hstart) * width + wstart; 133 | int upright = upleft + 1; 134 | int downleft = upleft + width; 135 | int downright = downleft + 1; 136 | 137 | atomicAdd(bottom_diff + upleft, top_diff[index] * (1. - h_ratio) * (1 - w_ratio)); 138 | atomicAdd(bottom_diff + upright, top_diff[index] * (1. - h_ratio) * w_ratio); 139 | atomicAdd(bottom_diff + downleft, top_diff[index] * h_ratio * (1 - w_ratio)); 140 | atomicAdd(bottom_diff + downright, top_diff[index] * h_ratio * w_ratio); 141 | } 142 | } 143 | } 144 | 145 | int ROIAlignBackwardLaucher(const float* top_diff, const float spatial_scale, const int batch_size, const int num_rois, const int height, const int width, 146 | const int channels, const int aligned_height, const int aligned_width, const float* bottom_rois, float* bottom_diff, cudaStream_t stream) { 147 | const int kThreadsPerBlock = 1024; 148 | const int output_size = num_rois * aligned_height * aligned_width * channels; 149 | cudaError_t err; 150 | 151 | ROIAlignBackward<<<(output_size + kThreadsPerBlock - 1) / kThreadsPerBlock, kThreadsPerBlock, 0, stream>>>( 152 | output_size, top_diff, spatial_scale, height, width, channels, 153 | aligned_height, aligned_width, bottom_diff, bottom_rois); 154 | 155 | err = cudaGetLastError(); 156 | if(cudaSuccess != err) { 157 | fprintf( stderr, "cudaCheckError() failed : %s\n", cudaGetErrorString( err ) ); 158 | exit( -1 ); 159 | } 160 | 161 | return 1; 162 | } 163 | 164 | 165 | #ifdef __cplusplus 166 | } 167 | #endif 168 | -------------------------------------------------------------------------------- /lib/model/roi_align/src/roi_align_kernel.h: -------------------------------------------------------------------------------- 1 | #ifndef _ROI_ALIGN_KERNEL 2 | #define _ROI_ALIGN_KERNEL 3 | 4 | #ifdef __cplusplus 5 | extern "C" { 6 | #endif 7 | 8 | __global__ void ROIAlignForward(const int nthreads, const float* bottom_data, 9 | const float spatial_scale, const int height, const int width, 10 | const int channels, const int aligned_height, const int aligned_width, 11 | const float* bottom_rois, float* top_data); 12 | 13 | int ROIAlignForwardLaucher( 14 | const float* bottom_data, const float spatial_scale, const int num_rois, const int height, 15 | const int width, const int channels, const int aligned_height, 16 | const int aligned_width, const float* bottom_rois, 17 | float* top_data, cudaStream_t stream); 18 | 19 | __global__ void ROIAlignBackward(const int nthreads, const float* top_diff, 20 | const float spatial_scale, const int height, const int width, 21 | const int channels, const int aligned_height, const int aligned_width, 22 | float* bottom_diff, const float* bottom_rois); 23 | 24 | int ROIAlignBackwardLaucher(const float* top_diff, const float spatial_scale, const int batch_size, const int num_rois, 25 | const int height, const int width, const int channels, const int aligned_height, 26 | const int aligned_width, const float* bottom_rois, 27 | float* bottom_diff, cudaStream_t stream); 28 | 29 | #ifdef __cplusplus 30 | } 31 | #endif 32 | 33 | #endif 34 | 35 | -------------------------------------------------------------------------------- /lib/model/roi_crop/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/VisionLearningGroup/DA_Detection/730eaca8528d22ed3aa6b4dbc1965828a697cf9a/lib/model/roi_crop/__init__.py -------------------------------------------------------------------------------- /lib/model/roi_crop/_ext/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/VisionLearningGroup/DA_Detection/730eaca8528d22ed3aa6b4dbc1965828a697cf9a/lib/model/roi_crop/_ext/__init__.py -------------------------------------------------------------------------------- /lib/model/roi_crop/_ext/crop_resize/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | from torch.utils.ffi import _wrap_function 3 | from ._crop_resize import lib as _lib, ffi as _ffi 4 | 5 | __all__ = [] 6 | def _import_symbols(locals): 7 | for symbol in dir(_lib): 8 | fn = getattr(_lib, symbol) 9 | locals[symbol] = _wrap_function(fn, _ffi) 10 | __all__.append(symbol) 11 | 12 | _import_symbols(locals()) 13 | -------------------------------------------------------------------------------- /lib/model/roi_crop/_ext/roi_crop/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | from torch.utils.ffi import _wrap_function 3 | from ._roi_crop import lib as _lib, ffi as _ffi 4 | 5 | __all__ = [] 6 | def _import_symbols(locals): 7 | for symbol in dir(_lib): 8 | fn = getattr(_lib, symbol) 9 | if callable(fn): 10 | locals[symbol] = _wrap_function(fn, _ffi) 11 | else: 12 | locals[symbol] = fn 13 | __all__.append(symbol) 14 | 15 | _import_symbols(locals()) 16 | -------------------------------------------------------------------------------- /lib/model/roi_crop/build.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | import os 3 | import torch 4 | from torch.utils.ffi import create_extension 5 | 6 | #this_file = os.path.dirname(__file__) 7 | 8 | sources = ['src/roi_crop.c'] 9 | headers = ['src/roi_crop.h'] 10 | defines = [] 11 | with_cuda = False 12 | 13 | if torch.cuda.is_available(): 14 | print('Including CUDA code.') 15 | sources += ['src/roi_crop_cuda.c'] 16 | headers += ['src/roi_crop_cuda.h'] 17 | defines += [('WITH_CUDA', None)] 18 | with_cuda = True 19 | 20 | this_file = os.path.dirname(os.path.realpath(__file__)) 21 | print(this_file) 22 | extra_objects = ['src/roi_crop_cuda_kernel.cu.o'] 23 | extra_objects = [os.path.join(this_file, fname) for fname in extra_objects] 24 | 25 | ffi = create_extension( 26 | '_ext.roi_crop', 27 | headers=headers, 28 | sources=sources, 29 | define_macros=defines, 30 | relative_to=__file__, 31 | with_cuda=with_cuda, 32 | extra_objects=extra_objects 33 | ) 34 | 35 | if __name__ == '__main__': 36 | ffi.build() 37 | -------------------------------------------------------------------------------- /lib/model/roi_crop/functions/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/VisionLearningGroup/DA_Detection/730eaca8528d22ed3aa6b4dbc1965828a697cf9a/lib/model/roi_crop/functions/__init__.py -------------------------------------------------------------------------------- /lib/model/roi_crop/functions/crop_resize.py: -------------------------------------------------------------------------------- 1 | # functions/add.py 2 | import torch 3 | from torch.autograd import Function 4 | from .._ext import roi_crop 5 | from cffi import FFI 6 | ffi = FFI() 7 | 8 | class RoICropFunction(Function): 9 | def forward(self, input1, input2): 10 | self.input1 = input1 11 | self.input2 = input2 12 | self.device_c = ffi.new("int *") 13 | output = torch.zeros(input2.size()[0], input1.size()[1], input2.size()[1], input2.size()[2]) 14 | #print('decice %d' % torch.cuda.current_device()) 15 | if input1.is_cuda: 16 | self.device = torch.cuda.current_device() 17 | else: 18 | self.device = -1 19 | self.device_c[0] = self.device 20 | if not input1.is_cuda: 21 | roi_crop.BilinearSamplerBHWD_updateOutput(input1, input2, output) 22 | else: 23 | output = output.cuda(self.device) 24 | roi_crop.BilinearSamplerBHWD_updateOutput_cuda(input1, input2, output) 25 | return output 26 | 27 | def backward(self, grad_output): 28 | grad_input1 = torch.zeros(self.input1.size()) 29 | grad_input2 = torch.zeros(self.input2.size()) 30 | #print('backward decice %d' % self.device) 31 | if not grad_output.is_cuda: 32 | roi_crop.BilinearSamplerBHWD_updateGradInput(self.input1, self.input2, grad_input1, grad_input2, grad_output) 33 | else: 34 | grad_input1 = grad_input1.cuda(self.device) 35 | grad_input2 = grad_input2.cuda(self.device) 36 | roi_crop.BilinearSamplerBHWD_updateGradInput_cuda(self.input1, self.input2, grad_input1, grad_input2, grad_output) 37 | return grad_input1, grad_input2 38 | -------------------------------------------------------------------------------- /lib/model/roi_crop/functions/gridgen.py: -------------------------------------------------------------------------------- 1 | # functions/add.py 2 | import torch 3 | from torch.autograd import Function 4 | import numpy as np 5 | 6 | 7 | class AffineGridGenFunction(Function): 8 | def __init__(self, height, width,lr=1): 9 | super(AffineGridGenFunction, self).__init__() 10 | self.lr = lr 11 | self.height, self.width = height, width 12 | self.grid = np.zeros( [self.height, self.width, 3], dtype=np.float32) 13 | self.grid[:,:,0] = np.expand_dims(np.repeat(np.expand_dims(np.arange(-1, 1, 2.0/(self.height)), 0), repeats = self.width, axis = 0).T, 0) 14 | self.grid[:,:,1] = np.expand_dims(np.repeat(np.expand_dims(np.arange(-1, 1, 2.0/(self.width)), 0), repeats = self.height, axis = 0), 0) 15 | # self.grid[:,:,0] = np.expand_dims(np.repeat(np.expand_dims(np.arange(-1, 1, 2.0/(self.height - 1)), 0), repeats = self.width, axis = 0).T, 0) 16 | # self.grid[:,:,1] = np.expand_dims(np.repeat(np.expand_dims(np.arange(-1, 1, 2.0/(self.width - 1)), 0), repeats = self.height, axis = 0), 0) 17 | self.grid[:,:,2] = np.ones([self.height, width]) 18 | self.grid = torch.from_numpy(self.grid.astype(np.float32)) 19 | #print(self.grid) 20 | 21 | def forward(self, input1): 22 | self.input1 = input1 23 | output = input1.new(torch.Size([input1.size(0)]) + self.grid.size()).zero_() 24 | self.batchgrid = input1.new(torch.Size([input1.size(0)]) + self.grid.size()).zero_() 25 | for i in range(input1.size(0)): 26 | self.batchgrid[i] = self.grid.astype(self.batchgrid[i]) 27 | 28 | # if input1.is_cuda: 29 | # self.batchgrid = self.batchgrid.cuda() 30 | # output = output.cuda() 31 | 32 | for i in range(input1.size(0)): 33 | output = torch.bmm(self.batchgrid.view(-1, self.height*self.width, 3), torch.transpose(input1, 1, 2)).view(-1, self.height, self.width, 2) 34 | 35 | return output 36 | 37 | def backward(self, grad_output): 38 | 39 | grad_input1 = self.input1.new(self.input1.size()).zero_() 40 | 41 | # if grad_output.is_cuda: 42 | # self.batchgrid = self.batchgrid.cuda() 43 | # grad_input1 = grad_input1.cuda() 44 | 45 | grad_input1 = torch.baddbmm(grad_input1, torch.transpose(grad_output.view(-1, self.height*self.width, 2), 1,2), self.batchgrid.view(-1, self.height*self.width, 3)) 46 | return grad_input1 47 | -------------------------------------------------------------------------------- /lib/model/roi_crop/functions/roi_crop.py: -------------------------------------------------------------------------------- 1 | # functions/add.py 2 | import torch 3 | from torch.autograd import Function 4 | from .._ext import roi_crop 5 | import pdb 6 | 7 | class RoICropFunction(Function): 8 | def forward(self, input1, input2): 9 | self.input1 = input1.clone() 10 | self.input2 = input2.clone() 11 | output = input2.new(input2.size()[0], input1.size()[1], input2.size()[1], input2.size()[2]).zero_() 12 | assert output.get_device() == input1.get_device(), "output and input1 must on the same device" 13 | assert output.get_device() == input2.get_device(), "output and input2 must on the same device" 14 | roi_crop.BilinearSamplerBHWD_updateOutput_cuda(input1, input2, output) 15 | return output 16 | 17 | def backward(self, grad_output): 18 | grad_input1 = self.input1.new(self.input1.size()).zero_() 19 | grad_input2 = self.input2.new(self.input2.size()).zero_() 20 | roi_crop.BilinearSamplerBHWD_updateGradInput_cuda(self.input1, self.input2, grad_input1, grad_input2, grad_output) 21 | return grad_input1, grad_input2 22 | -------------------------------------------------------------------------------- /lib/model/roi_crop/make.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | CUDA_PATH=/usr/local/cuda/ 4 | 5 | cd src 6 | echo "Compiling my_lib kernels by nvcc..." 7 | nvcc -c -o roi_crop_cuda_kernel.cu.o roi_crop_cuda_kernel.cu -x cu -Xcompiler -fPIC -arch=sm_52 8 | 9 | cd ../ 10 | python build.py 11 | -------------------------------------------------------------------------------- /lib/model/roi_crop/modules/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/VisionLearningGroup/DA_Detection/730eaca8528d22ed3aa6b4dbc1965828a697cf9a/lib/model/roi_crop/modules/__init__.py -------------------------------------------------------------------------------- /lib/model/roi_crop/modules/roi_crop.py: -------------------------------------------------------------------------------- 1 | from torch.nn.modules.module import Module 2 | from ..functions.roi_crop import RoICropFunction 3 | 4 | class _RoICrop(Module): 5 | def __init__(self, layout = 'BHWD'): 6 | super(_RoICrop, self).__init__() 7 | def forward(self, input1, input2): 8 | return RoICropFunction()(input1, input2) 9 | -------------------------------------------------------------------------------- /lib/model/roi_crop/src/roi_crop.h: -------------------------------------------------------------------------------- 1 | int BilinearSamplerBHWD_updateOutput(THFloatTensor *inputImages, THFloatTensor *grids, THFloatTensor *output); 2 | 3 | int BilinearSamplerBHWD_updateGradInput(THFloatTensor *inputImages, THFloatTensor *grids, THFloatTensor *gradInputImages, 4 | THFloatTensor *gradGrids, THFloatTensor *gradOutput); 5 | 6 | 7 | 8 | int BilinearSamplerBCHW_updateOutput(THFloatTensor *inputImages, THFloatTensor *grids, THFloatTensor *output); 9 | 10 | int BilinearSamplerBCHW_updateGradInput(THFloatTensor *inputImages, THFloatTensor *grids, THFloatTensor *gradInputImages, 11 | THFloatTensor *gradGrids, THFloatTensor *gradOutput); 12 | -------------------------------------------------------------------------------- /lib/model/roi_crop/src/roi_crop_cuda.c: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include "roi_crop_cuda_kernel.h" 5 | 6 | #define real float 7 | 8 | // this symbol will be resolved automatically from PyTorch libs 9 | extern THCState *state; 10 | 11 | // Bilinear sampling is done in BHWD (coalescing is not obvious in BDHW) 12 | // we assume BHWD format in inputImages 13 | // we assume BHW(YX) format on grids 14 | 15 | int BilinearSamplerBHWD_updateOutput_cuda(THCudaTensor *inputImages, THCudaTensor *grids, THCudaTensor *output){ 16 | // THCState *state = getCutorchState(L); 17 | // THCudaTensor *inputImages = (THCudaTensor *)luaT_checkudata(L, 2, "torch.CudaTensor"); 18 | // THCudaTensor *grids = (THCudaTensor *)luaT_checkudata(L, 3, "torch.CudaTensor"); 19 | // THCudaTensor *output = (THCudaTensor *)luaT_checkudata(L, 4, "torch.CudaTensor"); 20 | 21 | int success = 0; 22 | success = BilinearSamplerBHWD_updateOutput_cuda_kernel(THCudaTensor_size(state, output, 1), 23 | THCudaTensor_size(state, output, 3), 24 | THCudaTensor_size(state, output, 2), 25 | THCudaTensor_size(state, output, 0), 26 | THCudaTensor_size(state, inputImages, 1), 27 | THCudaTensor_size(state, inputImages, 2), 28 | THCudaTensor_size(state, inputImages, 3), 29 | THCudaTensor_size(state, inputImages, 0), 30 | THCudaTensor_data(state, inputImages), 31 | THCudaTensor_stride(state, inputImages, 0), 32 | THCudaTensor_stride(state, inputImages, 1), 33 | THCudaTensor_stride(state, inputImages, 2), 34 | THCudaTensor_stride(state, inputImages, 3), 35 | THCudaTensor_data(state, grids), 36 | THCudaTensor_stride(state, grids, 0), 37 | THCudaTensor_stride(state, grids, 3), 38 | THCudaTensor_stride(state, grids, 1), 39 | THCudaTensor_stride(state, grids, 2), 40 | THCudaTensor_data(state, output), 41 | THCudaTensor_stride(state, output, 0), 42 | THCudaTensor_stride(state, output, 1), 43 | THCudaTensor_stride(state, output, 2), 44 | THCudaTensor_stride(state, output, 3), 45 | THCState_getCurrentStream(state)); 46 | 47 | //check for errors 48 | if (!success) { 49 | THError("aborting"); 50 | } 51 | return 1; 52 | } 53 | 54 | int BilinearSamplerBHWD_updateGradInput_cuda(THCudaTensor *inputImages, THCudaTensor *grids, THCudaTensor *gradInputImages, 55 | THCudaTensor *gradGrids, THCudaTensor *gradOutput) 56 | { 57 | // THCState *state = getCutorchState(L); 58 | // THCudaTensor *inputImages = (THCudaTensor *)luaT_checkudata(L, 2, "torch.CudaTensor"); 59 | // THCudaTensor *grids = (THCudaTensor *)luaT_checkudata(L, 3, "torch.CudaTensor"); 60 | // THCudaTensor *gradInputImages = (THCudaTensor *)luaT_checkudata(L, 4, "torch.CudaTensor"); 61 | // THCudaTensor *gradGrids = (THCudaTensor *)luaT_checkudata(L, 5, "torch.CudaTensor"); 62 | // THCudaTensor *gradOutput = (THCudaTensor *)luaT_checkudata(L, 6, "torch.CudaTensor"); 63 | 64 | int success = 0; 65 | success = BilinearSamplerBHWD_updateGradInput_cuda_kernel(THCudaTensor_size(state, gradOutput, 1), 66 | THCudaTensor_size(state, gradOutput, 3), 67 | THCudaTensor_size(state, gradOutput, 2), 68 | THCudaTensor_size(state, gradOutput, 0), 69 | THCudaTensor_size(state, inputImages, 1), 70 | THCudaTensor_size(state, inputImages, 2), 71 | THCudaTensor_size(state, inputImages, 3), 72 | THCudaTensor_size(state, inputImages, 0), 73 | THCudaTensor_data(state, inputImages), 74 | THCudaTensor_stride(state, inputImages, 0), 75 | THCudaTensor_stride(state, inputImages, 1), 76 | THCudaTensor_stride(state, inputImages, 2), 77 | THCudaTensor_stride(state, inputImages, 3), 78 | THCudaTensor_data(state, grids), 79 | THCudaTensor_stride(state, grids, 0), 80 | THCudaTensor_stride(state, grids, 3), 81 | THCudaTensor_stride(state, grids, 1), 82 | THCudaTensor_stride(state, grids, 2), 83 | THCudaTensor_data(state, gradInputImages), 84 | THCudaTensor_stride(state, gradInputImages, 0), 85 | THCudaTensor_stride(state, gradInputImages, 1), 86 | THCudaTensor_stride(state, gradInputImages, 2), 87 | THCudaTensor_stride(state, gradInputImages, 3), 88 | THCudaTensor_data(state, gradGrids), 89 | THCudaTensor_stride(state, gradGrids, 0), 90 | THCudaTensor_stride(state, gradGrids, 3), 91 | THCudaTensor_stride(state, gradGrids, 1), 92 | THCudaTensor_stride(state, gradGrids, 2), 93 | THCudaTensor_data(state, gradOutput), 94 | THCudaTensor_stride(state, gradOutput, 0), 95 | THCudaTensor_stride(state, gradOutput, 1), 96 | THCudaTensor_stride(state, gradOutput, 2), 97 | THCudaTensor_stride(state, gradOutput, 3), 98 | THCState_getCurrentStream(state)); 99 | 100 | //check for errors 101 | if (!success) { 102 | THError("aborting"); 103 | } 104 | return 1; 105 | } 106 | -------------------------------------------------------------------------------- /lib/model/roi_crop/src/roi_crop_cuda.h: -------------------------------------------------------------------------------- 1 | // Bilinear sampling is done in BHWD (coalescing is not obvious in BDHW) 2 | // we assume BHWD format in inputImages 3 | // we assume BHW(YX) format on grids 4 | 5 | int BilinearSamplerBHWD_updateOutput_cuda(THCudaTensor *inputImages, THCudaTensor *grids, THCudaTensor *output); 6 | 7 | int BilinearSamplerBHWD_updateGradInput_cuda(THCudaTensor *inputImages, THCudaTensor *grids, THCudaTensor *gradInputImages, 8 | THCudaTensor *gradGrids, THCudaTensor *gradOutput); 9 | -------------------------------------------------------------------------------- /lib/model/roi_crop/src/roi_crop_cuda_kernel.h: -------------------------------------------------------------------------------- 1 | #ifdef __cplusplus 2 | extern "C" { 3 | #endif 4 | 5 | 6 | int BilinearSamplerBHWD_updateOutput_cuda_kernel(/*output->size[3]*/int oc, 7 | /*output->size[2]*/int ow, 8 | /*output->size[1]*/int oh, 9 | /*output->size[0]*/int ob, 10 | /*THCudaTensor_size(state, inputImages, 3)*/int ic, 11 | /*THCudaTensor_size(state, inputImages, 1)*/int ih, 12 | /*THCudaTensor_size(state, inputImages, 2)*/int iw, 13 | /*THCudaTensor_size(state, inputImages, 0)*/int ib, 14 | /*THCudaTensor *inputImages*/float *inputImages, int isb, int isc, int ish, int isw, 15 | /*THCudaTensor *grids*/float *grids, int gsb, int gsc, int gsh, int gsw, 16 | /*THCudaTensor *output*/float *output, int osb, int osc, int osh, int osw, 17 | /*THCState_getCurrentStream(state)*/cudaStream_t stream); 18 | 19 | int BilinearSamplerBHWD_updateGradInput_cuda_kernel(/*gradOutput->size[3]*/int goc, 20 | /*gradOutput->size[2]*/int gow, 21 | /*gradOutput->size[1]*/int goh, 22 | /*gradOutput->size[0]*/int gob, 23 | /*THCudaTensor_size(state, inputImages, 3)*/int ic, 24 | /*THCudaTensor_size(state, inputImages, 1)*/int ih, 25 | /*THCudaTensor_size(state, inputImages, 2)*/int iw, 26 | /*THCudaTensor_size(state, inputImages, 0)*/int ib, 27 | /*THCudaTensor *inputImages*/float *inputImages, int isb, int isc, int ish, int isw, 28 | /*THCudaTensor *grids*/float *grids, int gsb, int gsc, int gsh, int gsw, 29 | /*THCudaTensor *gradInputImages*/float *gradInputImages, int gisb, int gisc, int gish, int gisw, 30 | /*THCudaTensor *gradGrids*/float *gradGrids, int ggsb, int ggsc, int ggsh, int ggsw, 31 | /*THCudaTensor *gradOutput*/float *gradOutput, int gosb, int gosc, int gosh, int gosw, 32 | /*THCState_getCurrentStream(state)*/cudaStream_t stream); 33 | 34 | 35 | #ifdef __cplusplus 36 | } 37 | #endif 38 | -------------------------------------------------------------------------------- /lib/model/roi_pooling/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/VisionLearningGroup/DA_Detection/730eaca8528d22ed3aa6b4dbc1965828a697cf9a/lib/model/roi_pooling/__init__.py -------------------------------------------------------------------------------- /lib/model/roi_pooling/_ext/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/VisionLearningGroup/DA_Detection/730eaca8528d22ed3aa6b4dbc1965828a697cf9a/lib/model/roi_pooling/_ext/__init__.py -------------------------------------------------------------------------------- /lib/model/roi_pooling/_ext/roi_pooling/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | from torch.utils.ffi import _wrap_function 3 | from ._roi_pooling import lib as _lib, ffi as _ffi 4 | 5 | __all__ = [] 6 | def _import_symbols(locals): 7 | for symbol in dir(_lib): 8 | fn = getattr(_lib, symbol) 9 | if callable(fn): 10 | locals[symbol] = _wrap_function(fn, _ffi) 11 | else: 12 | locals[symbol] = fn 13 | __all__.append(symbol) 14 | 15 | _import_symbols(locals()) 16 | -------------------------------------------------------------------------------- /lib/model/roi_pooling/build.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | import os 3 | import torch 4 | from torch.utils.ffi import create_extension 5 | 6 | 7 | sources = ['src/roi_pooling.c'] 8 | headers = ['src/roi_pooling.h'] 9 | extra_objects = [] 10 | defines = [] 11 | with_cuda = False 12 | 13 | this_file = os.path.dirname(os.path.realpath(__file__)) 14 | print(this_file) 15 | 16 | if torch.cuda.is_available(): 17 | print('Including CUDA code.') 18 | sources += ['src/roi_pooling_cuda.c'] 19 | headers += ['src/roi_pooling_cuda.h'] 20 | defines += [('WITH_CUDA', None)] 21 | with_cuda = True 22 | extra_objects = ['src/roi_pooling.cu.o'] 23 | extra_objects = [os.path.join(this_file, fname) for fname in extra_objects] 24 | 25 | ffi = create_extension( 26 | '_ext.roi_pooling', 27 | headers=headers, 28 | sources=sources, 29 | define_macros=defines, 30 | relative_to=__file__, 31 | with_cuda=with_cuda, 32 | extra_objects=extra_objects 33 | ) 34 | 35 | if __name__ == '__main__': 36 | ffi.build() 37 | -------------------------------------------------------------------------------- /lib/model/roi_pooling/functions/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/VisionLearningGroup/DA_Detection/730eaca8528d22ed3aa6b4dbc1965828a697cf9a/lib/model/roi_pooling/functions/__init__.py -------------------------------------------------------------------------------- /lib/model/roi_pooling/functions/roi_pool.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.autograd import Function 3 | from .._ext import roi_pooling 4 | import pdb 5 | 6 | class RoIPoolFunction(Function): 7 | def __init__(ctx, pooled_height, pooled_width, spatial_scale): 8 | ctx.pooled_width = pooled_width 9 | ctx.pooled_height = pooled_height 10 | ctx.spatial_scale = spatial_scale 11 | ctx.feature_size = None 12 | 13 | def forward(ctx, features, rois): 14 | ctx.feature_size = features.size() 15 | batch_size, num_channels, data_height, data_width = ctx.feature_size 16 | num_rois = rois.size(0) 17 | output = features.new(num_rois, num_channels, ctx.pooled_height, ctx.pooled_width).zero_() 18 | ctx.argmax = features.new(num_rois, num_channels, ctx.pooled_height, ctx.pooled_width).zero_().int() 19 | ctx.rois = rois 20 | if not features.is_cuda: 21 | _features = features.permute(0, 2, 3, 1) 22 | roi_pooling.roi_pooling_forward(ctx.pooled_height, ctx.pooled_width, ctx.spatial_scale, 23 | _features, rois, output) 24 | else: 25 | roi_pooling.roi_pooling_forward_cuda(ctx.pooled_height, ctx.pooled_width, ctx.spatial_scale, 26 | features, rois, output, ctx.argmax) 27 | 28 | return output 29 | 30 | def backward(ctx, grad_output): 31 | assert(ctx.feature_size is not None and grad_output.is_cuda) 32 | batch_size, num_channels, data_height, data_width = ctx.feature_size 33 | grad_input = grad_output.new(batch_size, num_channels, data_height, data_width).zero_() 34 | 35 | roi_pooling.roi_pooling_backward_cuda(ctx.pooled_height, ctx.pooled_width, ctx.spatial_scale, 36 | grad_output, ctx.rois, grad_input, ctx.argmax) 37 | 38 | return grad_input, None 39 | -------------------------------------------------------------------------------- /lib/model/roi_pooling/modules/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/VisionLearningGroup/DA_Detection/730eaca8528d22ed3aa6b4dbc1965828a697cf9a/lib/model/roi_pooling/modules/__init__.py -------------------------------------------------------------------------------- /lib/model/roi_pooling/modules/roi_pool.py: -------------------------------------------------------------------------------- 1 | from torch.nn.modules.module import Module 2 | from ..functions.roi_pool import RoIPoolFunction 3 | 4 | 5 | class _RoIPooling(Module): 6 | def __init__(self, pooled_height, pooled_width, spatial_scale): 7 | super(_RoIPooling, self).__init__() 8 | 9 | self.pooled_width = int(pooled_width) 10 | self.pooled_height = int(pooled_height) 11 | self.spatial_scale = float(spatial_scale) 12 | 13 | def forward(self, features, rois): 14 | return RoIPoolFunction(self.pooled_height, self.pooled_width, self.spatial_scale)(features, rois) 15 | -------------------------------------------------------------------------------- /lib/model/roi_pooling/src/roi_pooling.c: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | 4 | int roi_pooling_forward(int pooled_height, int pooled_width, float spatial_scale, 5 | THFloatTensor * features, THFloatTensor * rois, THFloatTensor * output) 6 | { 7 | // Grab the input tensor 8 | float * data_flat = THFloatTensor_data(features); 9 | float * rois_flat = THFloatTensor_data(rois); 10 | 11 | float * output_flat = THFloatTensor_data(output); 12 | 13 | // Number of ROIs 14 | int num_rois = THFloatTensor_size(rois, 0); 15 | int size_rois = THFloatTensor_size(rois, 1); 16 | // batch size 17 | int batch_size = THFloatTensor_size(features, 0); 18 | if(batch_size != 1) 19 | { 20 | return 0; 21 | } 22 | // data height 23 | int data_height = THFloatTensor_size(features, 1); 24 | // data width 25 | int data_width = THFloatTensor_size(features, 2); 26 | // Number of channels 27 | int num_channels = THFloatTensor_size(features, 3); 28 | 29 | // Set all element of the output tensor to -inf. 30 | THFloatStorage_fill(THFloatTensor_storage(output), -1); 31 | 32 | // For each ROI R = [batch_index x1 y1 x2 y2]: max pool over R 33 | int index_roi = 0; 34 | int index_output = 0; 35 | int n; 36 | for (n = 0; n < num_rois; ++n) 37 | { 38 | int roi_batch_ind = rois_flat[index_roi + 0]; 39 | int roi_start_w = round(rois_flat[index_roi + 1] * spatial_scale); 40 | int roi_start_h = round(rois_flat[index_roi + 2] * spatial_scale); 41 | int roi_end_w = round(rois_flat[index_roi + 3] * spatial_scale); 42 | int roi_end_h = round(rois_flat[index_roi + 4] * spatial_scale); 43 | // CHECK_GE(roi_batch_ind, 0); 44 | // CHECK_LT(roi_batch_ind, batch_size); 45 | 46 | int roi_height = fmaxf(roi_end_h - roi_start_h + 1, 1); 47 | int roi_width = fmaxf(roi_end_w - roi_start_w + 1, 1); 48 | float bin_size_h = (float)(roi_height) / (float)(pooled_height); 49 | float bin_size_w = (float)(roi_width) / (float)(pooled_width); 50 | 51 | int index_data = roi_batch_ind * data_height * data_width * num_channels; 52 | const int output_area = pooled_width * pooled_height; 53 | 54 | int c, ph, pw; 55 | for (ph = 0; ph < pooled_height; ++ph) 56 | { 57 | for (pw = 0; pw < pooled_width; ++pw) 58 | { 59 | int hstart = (floor((float)(ph) * bin_size_h)); 60 | int wstart = (floor((float)(pw) * bin_size_w)); 61 | int hend = (ceil((float)(ph + 1) * bin_size_h)); 62 | int wend = (ceil((float)(pw + 1) * bin_size_w)); 63 | 64 | hstart = fminf(fmaxf(hstart + roi_start_h, 0), data_height); 65 | hend = fminf(fmaxf(hend + roi_start_h, 0), data_height); 66 | wstart = fminf(fmaxf(wstart + roi_start_w, 0), data_width); 67 | wend = fminf(fmaxf(wend + roi_start_w, 0), data_width); 68 | 69 | const int pool_index = index_output + (ph * pooled_width + pw); 70 | int is_empty = (hend <= hstart) || (wend <= wstart); 71 | if (is_empty) 72 | { 73 | for (c = 0; c < num_channels * output_area; c += output_area) 74 | { 75 | output_flat[pool_index + c] = 0; 76 | } 77 | } 78 | else 79 | { 80 | int h, w, c; 81 | for (h = hstart; h < hend; ++h) 82 | { 83 | for (w = wstart; w < wend; ++w) 84 | { 85 | for (c = 0; c < num_channels; ++c) 86 | { 87 | const int index = (h * data_width + w) * num_channels + c; 88 | if (data_flat[index_data + index] > output_flat[pool_index + c * output_area]) 89 | { 90 | output_flat[pool_index + c * output_area] = data_flat[index_data + index]; 91 | } 92 | } 93 | } 94 | } 95 | } 96 | } 97 | } 98 | 99 | // Increment ROI index 100 | index_roi += size_rois; 101 | index_output += pooled_height * pooled_width * num_channels; 102 | } 103 | return 1; 104 | } -------------------------------------------------------------------------------- /lib/model/roi_pooling/src/roi_pooling.h: -------------------------------------------------------------------------------- 1 | int roi_pooling_forward(int pooled_height, int pooled_width, float spatial_scale, 2 | THFloatTensor * features, THFloatTensor * rois, THFloatTensor * output); -------------------------------------------------------------------------------- /lib/model/roi_pooling/src/roi_pooling_cuda.c: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include "roi_pooling_kernel.h" 4 | 5 | extern THCState *state; 6 | 7 | int roi_pooling_forward_cuda(int pooled_height, int pooled_width, float spatial_scale, 8 | THCudaTensor * features, THCudaTensor * rois, THCudaTensor * output, THCudaIntTensor * argmax) 9 | { 10 | // Grab the input tensor 11 | float * data_flat = THCudaTensor_data(state, features); 12 | float * rois_flat = THCudaTensor_data(state, rois); 13 | 14 | float * output_flat = THCudaTensor_data(state, output); 15 | int * argmax_flat = THCudaIntTensor_data(state, argmax); 16 | 17 | // Number of ROIs 18 | int num_rois = THCudaTensor_size(state, rois, 0); 19 | int size_rois = THCudaTensor_size(state, rois, 1); 20 | if (size_rois != 5) 21 | { 22 | return 0; 23 | } 24 | 25 | // batch size 26 | // int batch_size = THCudaTensor_size(state, features, 0); 27 | // if (batch_size != 1) 28 | // { 29 | // return 0; 30 | // } 31 | // data height 32 | int data_height = THCudaTensor_size(state, features, 2); 33 | // data width 34 | int data_width = THCudaTensor_size(state, features, 3); 35 | // Number of channels 36 | int num_channels = THCudaTensor_size(state, features, 1); 37 | 38 | cudaStream_t stream = THCState_getCurrentStream(state); 39 | 40 | ROIPoolForwardLaucher( 41 | data_flat, spatial_scale, num_rois, data_height, 42 | data_width, num_channels, pooled_height, 43 | pooled_width, rois_flat, 44 | output_flat, argmax_flat, stream); 45 | 46 | return 1; 47 | } 48 | 49 | int roi_pooling_backward_cuda(int pooled_height, int pooled_width, float spatial_scale, 50 | THCudaTensor * top_grad, THCudaTensor * rois, THCudaTensor * bottom_grad, THCudaIntTensor * argmax) 51 | { 52 | // Grab the input tensor 53 | float * top_grad_flat = THCudaTensor_data(state, top_grad); 54 | float * rois_flat = THCudaTensor_data(state, rois); 55 | 56 | float * bottom_grad_flat = THCudaTensor_data(state, bottom_grad); 57 | int * argmax_flat = THCudaIntTensor_data(state, argmax); 58 | 59 | // Number of ROIs 60 | int num_rois = THCudaTensor_size(state, rois, 0); 61 | int size_rois = THCudaTensor_size(state, rois, 1); 62 | if (size_rois != 5) 63 | { 64 | return 0; 65 | } 66 | 67 | // batch size 68 | int batch_size = THCudaTensor_size(state, bottom_grad, 0); 69 | // if (batch_size != 1) 70 | // { 71 | // return 0; 72 | // } 73 | // data height 74 | int data_height = THCudaTensor_size(state, bottom_grad, 2); 75 | // data width 76 | int data_width = THCudaTensor_size(state, bottom_grad, 3); 77 | // Number of channels 78 | int num_channels = THCudaTensor_size(state, bottom_grad, 1); 79 | 80 | cudaStream_t stream = THCState_getCurrentStream(state); 81 | ROIPoolBackwardLaucher( 82 | top_grad_flat, spatial_scale, batch_size, num_rois, data_height, 83 | data_width, num_channels, pooled_height, 84 | pooled_width, rois_flat, 85 | bottom_grad_flat, argmax_flat, stream); 86 | 87 | return 1; 88 | } 89 | -------------------------------------------------------------------------------- /lib/model/roi_pooling/src/roi_pooling_cuda.h: -------------------------------------------------------------------------------- 1 | int roi_pooling_forward_cuda(int pooled_height, int pooled_width, float spatial_scale, 2 | THCudaTensor * features, THCudaTensor * rois, THCudaTensor * output, THCudaIntTensor * argmax); 3 | 4 | int roi_pooling_backward_cuda(int pooled_height, int pooled_width, float spatial_scale, 5 | THCudaTensor * top_grad, THCudaTensor * rois, THCudaTensor * bottom_grad, THCudaIntTensor * argmax); -------------------------------------------------------------------------------- /lib/model/roi_pooling/src/roi_pooling_kernel.h: -------------------------------------------------------------------------------- 1 | #ifndef _ROI_POOLING_KERNEL 2 | #define _ROI_POOLING_KERNEL 3 | 4 | #ifdef __cplusplus 5 | extern "C" { 6 | #endif 7 | 8 | int ROIPoolForwardLaucher( 9 | const float* bottom_data, const float spatial_scale, const int num_rois, const int height, 10 | const int width, const int channels, const int pooled_height, 11 | const int pooled_width, const float* bottom_rois, 12 | float* top_data, int* argmax_data, cudaStream_t stream); 13 | 14 | 15 | int ROIPoolBackwardLaucher(const float* top_diff, const float spatial_scale, const int batch_size, const int num_rois, 16 | const int height, const int width, const int channels, const int pooled_height, 17 | const int pooled_width, const float* bottom_rois, 18 | float* bottom_diff, const int* argmax_data, cudaStream_t stream); 19 | 20 | #ifdef __cplusplus 21 | } 22 | #endif 23 | 24 | #endif 25 | 26 | -------------------------------------------------------------------------------- /lib/model/rpn/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/VisionLearningGroup/DA_Detection/730eaca8528d22ed3aa6b4dbc1965828a697cf9a/lib/model/rpn/__init__.py -------------------------------------------------------------------------------- /lib/model/rpn/generate_anchors.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | # -------------------------------------------------------- 3 | # Faster R-CNN 4 | # Copyright (c) 2015 Microsoft 5 | # Licensed under The MIT License [see LICENSE for details] 6 | # Written by Ross Girshick and Sean Bell 7 | # -------------------------------------------------------- 8 | 9 | import numpy as np 10 | import pdb 11 | 12 | # Verify that we compute the same anchors as Shaoqing's matlab implementation: 13 | # 14 | # >> load output/rpn_cachedir/faster_rcnn_VOC2007_ZF_stage1_rpn/anchors.mat 15 | # >> anchors 16 | # 17 | # anchors = 18 | # 19 | # -83 -39 100 56 20 | # -175 -87 192 104 21 | # -359 -183 376 200 22 | # -55 -55 72 72 23 | # -119 -119 136 136 24 | # -247 -247 264 264 25 | # -35 -79 52 96 26 | # -79 -167 96 184 27 | # -167 -343 184 360 28 | 29 | #array([[ -83., -39., 100., 56.], 30 | # [-175., -87., 192., 104.], 31 | # [-359., -183., 376., 200.], 32 | # [ -55., -55., 72., 72.], 33 | # [-119., -119., 136., 136.], 34 | # [-247., -247., 264., 264.], 35 | # [ -35., -79., 52., 96.], 36 | # [ -79., -167., 96., 184.], 37 | # [-167., -343., 184., 360.]]) 38 | 39 | try: 40 | xrange # Python 2 41 | except NameError: 42 | xrange = range # Python 3 43 | 44 | 45 | def generate_anchors(base_size=16, ratios=[0.5, 1, 2], 46 | scales=2**np.arange(3, 6)): 47 | """ 48 | Generate anchor (reference) windows by enumerating aspect ratios X 49 | scales wrt a reference (0, 0, 15, 15) window. 50 | """ 51 | 52 | base_anchor = np.array([1, 1, base_size, base_size]) - 1 53 | ratio_anchors = _ratio_enum(base_anchor, ratios) 54 | anchors = np.vstack([_scale_enum(ratio_anchors[i, :], scales) 55 | for i in xrange(ratio_anchors.shape[0])]) 56 | return anchors 57 | 58 | def _whctrs(anchor): 59 | """ 60 | Return width, height, x center, and y center for an anchor (window). 61 | """ 62 | 63 | w = anchor[2] - anchor[0] + 1 64 | h = anchor[3] - anchor[1] + 1 65 | x_ctr = anchor[0] + 0.5 * (w - 1) 66 | y_ctr = anchor[1] + 0.5 * (h - 1) 67 | return w, h, x_ctr, y_ctr 68 | 69 | def _mkanchors(ws, hs, x_ctr, y_ctr): 70 | """ 71 | Given a vector of widths (ws) and heights (hs) around a center 72 | (x_ctr, y_ctr), output a set of anchors (windows). 73 | """ 74 | 75 | ws = ws[:, np.newaxis] 76 | hs = hs[:, np.newaxis] 77 | anchors = np.hstack((x_ctr - 0.5 * (ws - 1), 78 | y_ctr - 0.5 * (hs - 1), 79 | x_ctr + 0.5 * (ws - 1), 80 | y_ctr + 0.5 * (hs - 1))) 81 | return anchors 82 | 83 | def _ratio_enum(anchor, ratios): 84 | """ 85 | Enumerate a set of anchors for each aspect ratio wrt an anchor. 86 | """ 87 | 88 | w, h, x_ctr, y_ctr = _whctrs(anchor) 89 | size = w * h 90 | size_ratios = size / ratios 91 | ws = np.round(np.sqrt(size_ratios)) 92 | hs = np.round(ws * ratios) 93 | anchors = _mkanchors(ws, hs, x_ctr, y_ctr) 94 | return anchors 95 | 96 | def _scale_enum(anchor, scales): 97 | """ 98 | Enumerate a set of anchors for each scale wrt an anchor. 99 | """ 100 | 101 | w, h, x_ctr, y_ctr = _whctrs(anchor) 102 | ws = w * scales 103 | hs = h * scales 104 | anchors = _mkanchors(ws, hs, x_ctr, y_ctr) 105 | return anchors 106 | 107 | if __name__ == '__main__': 108 | import time 109 | t = time.time() 110 | a = generate_anchors() 111 | print(time.time() - t) 112 | print(a) 113 | from IPython import embed; embed() 114 | -------------------------------------------------------------------------------- /lib/model/rpn/proposal_layer.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | # -------------------------------------------------------- 3 | # Faster R-CNN 4 | # Copyright (c) 2015 Microsoft 5 | # Licensed under The MIT License [see LICENSE for details] 6 | # Written by Ross Girshick and Sean Bell 7 | # -------------------------------------------------------- 8 | # -------------------------------------------------------- 9 | # Reorganized and modified by Jianwei Yang and Jiasen Lu 10 | # -------------------------------------------------------- 11 | 12 | import torch 13 | import torch.nn as nn 14 | import numpy as np 15 | import math 16 | import yaml 17 | from model.utils.config import cfg 18 | from .generate_anchors import generate_anchors 19 | from .bbox_transform import bbox_transform_inv, clip_boxes, clip_boxes_batch 20 | from model.nms.nms_wrapper import nms 21 | 22 | import pdb 23 | 24 | DEBUG = False 25 | 26 | class _ProposalLayer(nn.Module): 27 | """ 28 | Outputs object detection proposals by applying estimated bounding-box 29 | transformations to a set of regular boxes (called "anchors"). 30 | """ 31 | 32 | def __init__(self, feat_stride, scales, ratios): 33 | super(_ProposalLayer, self).__init__() 34 | 35 | self._feat_stride = feat_stride 36 | self._anchors = torch.from_numpy(generate_anchors(scales=np.array(scales), 37 | ratios=np.array(ratios))).float() 38 | self._num_anchors = self._anchors.size(0) 39 | 40 | # rois blob: holds R regions of interest, each is a 5-tuple 41 | # (n, x1, y1, x2, y2) specifying an image batch index n and a 42 | # rectangle (x1, y1, x2, y2) 43 | # top[0].reshape(1, 5) 44 | # 45 | # # scores blob: holds scores for R regions of interest 46 | # if len(top) > 1: 47 | # top[1].reshape(1, 1, 1, 1) 48 | 49 | def forward(self, input,target=False): 50 | 51 | # Algorithm: 52 | # 53 | # for each (H, W) location i 54 | # generate A anchor boxes centered on cell i 55 | # apply predicted bbox deltas at cell i to each of the A anchors 56 | # clip predicted boxes to image 57 | # remove predicted boxes with either height or width < threshold 58 | # sort all (proposal, score) pairs by score from highest to lowest 59 | # take top pre_nms_topN proposals before NMS 60 | # apply NMS with threshold 0.7 to remaining proposals 61 | # take after_nms_topN proposals after NMS 62 | # return the top proposals (-> RoIs top, scores top) 63 | 64 | 65 | # the first set of _num_anchors channels are bg probs 66 | # the second set are the fg probs 67 | scores = input[0][:, self._num_anchors:, :, :] 68 | bbox_deltas = input[1] 69 | im_info = input[2] 70 | cfg_key = input[3] 71 | 72 | pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N 73 | post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N 74 | if target: 75 | post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N_TARGET 76 | nms_thresh = cfg[cfg_key].RPN_NMS_THRESH 77 | min_size = cfg[cfg_key].RPN_MIN_SIZE 78 | 79 | batch_size = bbox_deltas.size(0) 80 | 81 | feat_height, feat_width = scores.size(2), scores.size(3) 82 | shift_x = np.arange(0, feat_width) * self._feat_stride 83 | shift_y = np.arange(0, feat_height) * self._feat_stride 84 | shift_x, shift_y = np.meshgrid(shift_x, shift_y) 85 | shifts = torch.from_numpy(np.vstack((shift_x.ravel(), shift_y.ravel(), 86 | shift_x.ravel(), shift_y.ravel())).transpose()) 87 | shifts = shifts.contiguous().type_as(scores).float() 88 | 89 | A = self._num_anchors 90 | K = shifts.size(0) 91 | 92 | self._anchors = self._anchors.type_as(scores) 93 | # anchors = self._anchors.view(1, A, 4) + shifts.view(1, K, 4).permute(1, 0, 2).contiguous() 94 | anchors = self._anchors.view(1, A, 4) + shifts.view(K, 1, 4) 95 | anchors = anchors.view(1, K * A, 4).expand(batch_size, K * A, 4) 96 | 97 | # Transpose and reshape predicted bbox transformations to get them 98 | # into the same order as the anchors: 99 | 100 | bbox_deltas = bbox_deltas.permute(0, 2, 3, 1).contiguous() 101 | bbox_deltas = bbox_deltas.view(batch_size, -1, 4) 102 | 103 | # Same story for the scores: 104 | scores = scores.permute(0, 2, 3, 1).contiguous() 105 | scores = scores.view(batch_size, -1) 106 | 107 | # Convert anchors into proposals via bbox transformations 108 | proposals = bbox_transform_inv(anchors, bbox_deltas, batch_size) 109 | 110 | # 2. clip predicted boxes to image 111 | proposals = clip_boxes(proposals, im_info, batch_size) 112 | # proposals = clip_boxes_batch(proposals, im_info, batch_size) 113 | 114 | # assign the score to 0 if it's non keep. 115 | # keep = self._filter_boxes(proposals, min_size * im_info[:, 2]) 116 | 117 | # trim keep index to make it euqal over batch 118 | # keep_idx = torch.cat(tuple(keep_idx), 0) 119 | 120 | # scores_keep = scores.view(-1)[keep_idx].view(batch_size, trim_size) 121 | # proposals_keep = proposals.view(-1, 4)[keep_idx, :].contiguous().view(batch_size, trim_size, 4) 122 | 123 | # _, order = torch.sort(scores_keep, 1, True) 124 | 125 | scores_keep = scores 126 | proposals_keep = proposals 127 | _, order = torch.sort(scores_keep, 1, True) 128 | 129 | output = scores.new(batch_size, post_nms_topN, 5).zero_() 130 | for i in range(batch_size): 131 | # # 3. remove predicted boxes with either height or width < threshold 132 | # # (NOTE: convert min_size to input image scale stored in im_info[2]) 133 | proposals_single = proposals_keep[i] 134 | scores_single = scores_keep[i] 135 | 136 | # # 4. sort all (proposal, score) pairs by score from highest to lowest 137 | # # 5. take top pre_nms_topN (e.g. 6000) 138 | order_single = order[i] 139 | 140 | if pre_nms_topN > 0 and pre_nms_topN < scores_keep.numel(): 141 | order_single = order_single[:pre_nms_topN] 142 | 143 | proposals_single = proposals_single[order_single, :] 144 | scores_single = scores_single[order_single].view(-1,1) 145 | 146 | # 6. apply nms (e.g. threshold = 0.7) 147 | # 7. take after_nms_topN (e.g. 300) 148 | # 8. return the top proposals (-> RoIs top) 149 | 150 | keep_idx_i = nms(torch.cat((proposals_single, scores_single), 1), nms_thresh, force_cpu=not cfg.USE_GPU_NMS) 151 | keep_idx_i = keep_idx_i.long().view(-1) 152 | 153 | if post_nms_topN > 0: 154 | keep_idx_i = keep_idx_i[:post_nms_topN] 155 | proposals_single = proposals_single[keep_idx_i, :] 156 | scores_single = scores_single[keep_idx_i, :] 157 | 158 | # padding 0 at the end. 159 | num_proposal = proposals_single.size(0) 160 | output[i,:,0] = i 161 | output[i,:num_proposal,1:] = proposals_single 162 | 163 | return output 164 | 165 | def backward(self, top, propagate_down, bottom): 166 | """This layer does not propagate gradients.""" 167 | pass 168 | 169 | def reshape(self, bottom, top): 170 | """Reshaping happens during the call to forward.""" 171 | pass 172 | 173 | def _filter_boxes(self, boxes, min_size): 174 | """Remove all boxes with any side smaller than min_size.""" 175 | ws = boxes[:, :, 2] - boxes[:, :, 0] + 1 176 | hs = boxes[:, :, 3] - boxes[:, :, 1] + 1 177 | keep = ((ws >= min_size.view(-1,1).expand_as(ws)) & (hs >= min_size.view(-1,1).expand_as(hs))) 178 | return keep 179 | -------------------------------------------------------------------------------- /lib/model/rpn/rpn.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | import torch 3 | import torch.nn as nn 4 | import torch.nn.functional as F 5 | from torch.autograd import Variable 6 | 7 | from model.utils.config import cfg 8 | from .proposal_layer import _ProposalLayer 9 | from .anchor_target_layer import _AnchorTargetLayer 10 | from model.utils.net_utils import _smooth_l1_loss 11 | 12 | import numpy as np 13 | import math 14 | import pdb 15 | import time 16 | 17 | class _RPN(nn.Module): 18 | """ region proposal network """ 19 | def __init__(self, din): 20 | super(_RPN, self).__init__() 21 | 22 | self.din = din # get depth of input feature map, e.g., 512 23 | self.anchor_scales = cfg.ANCHOR_SCALES 24 | self.anchor_ratios = cfg.ANCHOR_RATIOS 25 | self.feat_stride = cfg.FEAT_STRIDE[0] 26 | 27 | # define the convrelu layers processing input feature map 28 | self.RPN_Conv = nn.Conv2d(self.din, 512, 3, 1, 1, bias=True) 29 | 30 | # define bg/fg classifcation score layer 31 | self.nc_score_out = len(self.anchor_scales) * len(self.anchor_ratios) * 2 # 2(bg/fg) * 9 (anchors) 32 | self.RPN_cls_score = nn.Conv2d(512, self.nc_score_out, 1, 1, 0) 33 | 34 | # define anchor box offset prediction layer 35 | self.nc_bbox_out = len(self.anchor_scales) * len(self.anchor_ratios) * 4 # 4(coords) * 9 (anchors) 36 | self.RPN_bbox_pred = nn.Conv2d(512, self.nc_bbox_out, 1, 1, 0) 37 | 38 | # define proposal layer 39 | self.RPN_proposal = _ProposalLayer(self.feat_stride, self.anchor_scales, self.anchor_ratios) 40 | 41 | # define anchor target layer 42 | self.RPN_anchor_target = _AnchorTargetLayer(self.feat_stride, self.anchor_scales, self.anchor_ratios) 43 | 44 | self.rpn_loss_cls = 0 45 | self.rpn_loss_box = 0 46 | 47 | @staticmethod 48 | def reshape(x, d): 49 | input_shape = x.size() 50 | x = x.view( 51 | input_shape[0], 52 | int(d), 53 | int(float(input_shape[1] * input_shape[2]) / float(d)), 54 | input_shape[3] 55 | ) 56 | return x 57 | 58 | def forward(self, base_feat, im_info, gt_boxes, num_boxes,target=False): 59 | 60 | batch_size = base_feat.size(0) 61 | 62 | # return feature map after convrelu layer 63 | rpn_conv1 = F.relu(self.RPN_Conv(base_feat), inplace=True) 64 | # get rpn classification score 65 | rpn_cls_score = self.RPN_cls_score(rpn_conv1) 66 | 67 | rpn_cls_score_reshape = self.reshape(rpn_cls_score, 2) 68 | rpn_cls_prob_reshape = F.softmax(rpn_cls_score_reshape, 1) 69 | rpn_cls_prob = self.reshape(rpn_cls_prob_reshape, self.nc_score_out) 70 | 71 | # get rpn offsets to the anchor boxes 72 | rpn_bbox_pred = self.RPN_bbox_pred(rpn_conv1) 73 | 74 | # proposal layer 75 | cfg_key = 'TRAIN' if self.training else 'TEST' 76 | 77 | rois = self.RPN_proposal((rpn_cls_prob.data, rpn_bbox_pred.data, 78 | im_info, cfg_key),target=target) 79 | 80 | self.rpn_loss_cls = 0 81 | self.rpn_loss_box = 0 82 | 83 | # generating training labels and build the rpn loss 84 | if self.training: 85 | assert gt_boxes is not None 86 | 87 | rpn_data = self.RPN_anchor_target((rpn_cls_score.data, gt_boxes, im_info, num_boxes)) 88 | 89 | # compute classification loss 90 | rpn_cls_score = rpn_cls_score_reshape.permute(0, 2, 3, 1).contiguous().view(batch_size, -1, 2) 91 | rpn_label = rpn_data[0].view(batch_size, -1) 92 | 93 | rpn_keep = Variable(rpn_label.view(-1).ne(-1).nonzero().view(-1)) 94 | rpn_cls_score = torch.index_select(rpn_cls_score.view(-1,2), 0, rpn_keep) 95 | rpn_label = torch.index_select(rpn_label.view(-1), 0, rpn_keep.data) 96 | rpn_label = Variable(rpn_label.long()) 97 | self.rpn_loss_cls = F.cross_entropy(rpn_cls_score, rpn_label) 98 | fg_cnt = torch.sum(rpn_label.data.ne(0)) 99 | 100 | rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = rpn_data[1:] 101 | 102 | # compute bbox regression loss 103 | rpn_bbox_inside_weights = Variable(rpn_bbox_inside_weights) 104 | rpn_bbox_outside_weights = Variable(rpn_bbox_outside_weights) 105 | rpn_bbox_targets = Variable(rpn_bbox_targets) 106 | 107 | self.rpn_loss_box = _smooth_l1_loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights, 108 | rpn_bbox_outside_weights, sigma=3, dim=[1,2,3]) 109 | 110 | return rois, self.rpn_loss_cls, self.rpn_loss_box 111 | -------------------------------------------------------------------------------- /lib/model/utils/.gitignore: -------------------------------------------------------------------------------- 1 | *.c 2 | *.cpp 3 | *.so 4 | -------------------------------------------------------------------------------- /lib/model/utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/VisionLearningGroup/DA_Detection/730eaca8528d22ed3aa6b4dbc1965828a697cf9a/lib/model/utils/__init__.py -------------------------------------------------------------------------------- /lib/model/utils/bbox.pyx: -------------------------------------------------------------------------------- 1 | # -------------------------------------------------------- 2 | # Fast R-CNN 3 | # Copyright (c) 2015 Microsoft 4 | # Licensed under The MIT License [see LICENSE for details] 5 | # Written by Sergey Karayev 6 | # -------------------------------------------------------- 7 | 8 | cimport cython 9 | import numpy as np 10 | cimport numpy as np 11 | 12 | DTYPE = np.float 13 | ctypedef np.float_t DTYPE_t 14 | 15 | def bbox_overlaps(np.ndarray[DTYPE_t, ndim=2] boxes, 16 | np.ndarray[DTYPE_t, ndim=2] query_boxes): 17 | return bbox_overlaps_c(boxes, query_boxes) 18 | 19 | cdef np.ndarray[DTYPE_t, ndim=2] bbox_overlaps_c( 20 | np.ndarray[DTYPE_t, ndim=2] boxes, 21 | np.ndarray[DTYPE_t, ndim=2] query_boxes): 22 | """ 23 | Parameters 24 | ---------- 25 | boxes: (N, 4) ndarray of float 26 | query_boxes: (K, 4) ndarray of float 27 | Returns 28 | ------- 29 | overlaps: (N, K) ndarray of overlap between boxes and query_boxes 30 | """ 31 | cdef unsigned int N = boxes.shape[0] 32 | cdef unsigned int K = query_boxes.shape[0] 33 | cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE) 34 | cdef DTYPE_t iw, ih, box_area 35 | cdef DTYPE_t ua 36 | cdef unsigned int k, n 37 | for k in range(K): 38 | box_area = ( 39 | (query_boxes[k, 2] - query_boxes[k, 0] + 1) * 40 | (query_boxes[k, 3] - query_boxes[k, 1] + 1) 41 | ) 42 | for n in range(N): 43 | iw = ( 44 | min(boxes[n, 2], query_boxes[k, 2]) - 45 | max(boxes[n, 0], query_boxes[k, 0]) + 1 46 | ) 47 | if iw > 0: 48 | ih = ( 49 | min(boxes[n, 3], query_boxes[k, 3]) - 50 | max(boxes[n, 1], query_boxes[k, 1]) + 1 51 | ) 52 | if ih > 0: 53 | ua = float( 54 | (boxes[n, 2] - boxes[n, 0] + 1) * 55 | (boxes[n, 3] - boxes[n, 1] + 1) + 56 | box_area - iw * ih 57 | ) 58 | overlaps[n, k] = iw * ih / ua 59 | return overlaps 60 | 61 | 62 | def bbox_intersections( 63 | np.ndarray[DTYPE_t, ndim=2] boxes, 64 | np.ndarray[DTYPE_t, ndim=2] query_boxes): 65 | return bbox_intersections_c(boxes, query_boxes) 66 | 67 | 68 | cdef np.ndarray[DTYPE_t, ndim=2] bbox_intersections_c( 69 | np.ndarray[DTYPE_t, ndim=2] boxes, 70 | np.ndarray[DTYPE_t, ndim=2] query_boxes): 71 | """ 72 | For each query box compute the intersection ratio covered by boxes 73 | ---------- 74 | Parameters 75 | ---------- 76 | boxes: (N, 4) ndarray of float 77 | query_boxes: (K, 4) ndarray of float 78 | Returns 79 | ------- 80 | overlaps: (N, K) ndarray of intersec between boxes and query_boxes 81 | """ 82 | cdef unsigned int N = boxes.shape[0] 83 | cdef unsigned int K = query_boxes.shape[0] 84 | cdef np.ndarray[DTYPE_t, ndim=2] intersec = np.zeros((N, K), dtype=DTYPE) 85 | cdef DTYPE_t iw, ih, box_area 86 | cdef DTYPE_t ua 87 | cdef unsigned int k, n 88 | for k in range(K): 89 | box_area = ( 90 | (query_boxes[k, 2] - query_boxes[k, 0] + 1) * 91 | (query_boxes[k, 3] - query_boxes[k, 1] + 1) 92 | ) 93 | for n in range(N): 94 | iw = ( 95 | min(boxes[n, 2], query_boxes[k, 2]) - 96 | max(boxes[n, 0], query_boxes[k, 0]) + 1 97 | ) 98 | if iw > 0: 99 | ih = ( 100 | min(boxes[n, 3], query_boxes[k, 3]) - 101 | max(boxes[n, 1], query_boxes[k, 1]) + 1 102 | ) 103 | if ih > 0: 104 | intersec[n, k] = iw * ih / box_area 105 | return intersec -------------------------------------------------------------------------------- /lib/model/utils/blob.py: -------------------------------------------------------------------------------- 1 | # -------------------------------------------------------- 2 | # Fast R-CNN 3 | # Copyright (c) 2015 Microsoft 4 | # Licensed under The MIT License [see LICENSE for details] 5 | # Written by Ross Girshick 6 | # -------------------------------------------------------- 7 | 8 | """Blob helper functions.""" 9 | 10 | import numpy as np 11 | # from scipy.misc import imread, imresize 12 | import cv2 13 | 14 | try: 15 | xrange # Python 2 16 | except NameError: 17 | xrange = range # Python 3 18 | 19 | 20 | def im_list_to_blob(ims): 21 | """Convert a list of images into a network input. 22 | 23 | Assumes images are already prepared (means subtracted, BGR order, ...). 24 | """ 25 | max_shape = np.array([im.shape for im in ims]).max(axis=0) 26 | num_images = len(ims) 27 | blob = np.zeros((num_images, max_shape[0], max_shape[1], 3), 28 | dtype=np.float32) 29 | for i in xrange(num_images): 30 | im = ims[i] 31 | blob[i, 0:im.shape[0], 0:im.shape[1], :] = im 32 | 33 | return blob 34 | 35 | def prep_im_for_blob(im, pixel_means, target_size, max_size): 36 | """Mean subtract and scale an image for use in a blob.""" 37 | 38 | im = im.astype(np.float32, copy=False) 39 | im -= pixel_means 40 | # im = im[:, :, ::-1] 41 | im_shape = im.shape 42 | im_size_min = np.min(im_shape[0:2]) 43 | im_size_max = np.max(im_shape[0:2]) 44 | im_scale = float(target_size) / float(im_size_min) 45 | # Prevent the biggest axis from being more than MAX_SIZE 46 | #if np.round(im_scale * im_size_max) > max_size: 47 | # im_scale = float(max_size) / float(im_size_max) 48 | # im = imresize(im, im_scale) 49 | im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, 50 | interpolation=cv2.INTER_LINEAR) 51 | 52 | return im, im_scale 53 | -------------------------------------------------------------------------------- /lib/pycocotools/UPSTREAM_REV: -------------------------------------------------------------------------------- 1 | https://github.com/pdollar/coco/commit/3ac47c77ebd5a1ed4254a98b7fbf2ef4765a3574 2 | -------------------------------------------------------------------------------- /lib/pycocotools/__init__.py: -------------------------------------------------------------------------------- 1 | __author__ = 'tylin' 2 | -------------------------------------------------------------------------------- /lib/pycocotools/license.txt: -------------------------------------------------------------------------------- 1 | Copyright (c) 2014, Piotr Dollar and Tsung-Yi Lin 2 | All rights reserved. 3 | 4 | Redistribution and use in source and binary forms, with or without 5 | modification, are permitted provided that the following conditions are met: 6 | 7 | 1. Redistributions of source code must retain the above copyright notice, this 8 | list of conditions and the following disclaimer. 9 | 2. Redistributions in binary form must reproduce the above copyright notice, 10 | this list of conditions and the following disclaimer in the documentation 11 | and/or other materials provided with the distribution. 12 | 13 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND 14 | ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED 15 | WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 16 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR 17 | ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES 18 | (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 19 | LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND 20 | ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT 21 | (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 22 | SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 23 | 24 | The views and conclusions contained in the software and documentation are those 25 | of the authors and should not be interpreted as representing official policies, 26 | either expressed or implied, of the FreeBSD Project. 27 | -------------------------------------------------------------------------------- /lib/pycocotools/mask.py: -------------------------------------------------------------------------------- 1 | __author__ = 'tsungyi' 2 | 3 | from . import _mask 4 | 5 | # Interface for manipulating masks stored in RLE format. 6 | # 7 | # RLE is a simple yet efficient format for storing binary masks. RLE 8 | # first divides a vector (or vectorized image) into a series of piecewise 9 | # constant regions and then for each piece simply stores the length of 10 | # that piece. For example, given M=[0 0 1 1 1 0 1] the RLE counts would 11 | # be [2 3 1 1], or for M=[1 1 1 1 1 1 0] the counts would be [0 6 1] 12 | # (note that the odd counts are always the numbers of zeros). Instead of 13 | # storing the counts directly, additional compression is achieved with a 14 | # variable bitrate representation based on a common scheme called LEB128. 15 | # 16 | # Compression is greatest given large piecewise constant regions. 17 | # Specifically, the size of the RLE is proportional to the number of 18 | # *boundaries* in M (or for an image the number of boundaries in the y 19 | # direction). Assuming fairly simple shapes, the RLE representation is 20 | # O(sqrt(n)) where n is number of pixels in the object. Hence space usage 21 | # is substantially lower, especially for large simple objects (large n). 22 | # 23 | # Many common operations on masks can be computed directly using the RLE 24 | # (without need for decoding). This includes computations such as area, 25 | # union, intersection, etc. All of these operations are linear in the 26 | # size of the RLE, in other words they are O(sqrt(n)) where n is the area 27 | # of the object. Computing these operations on the original mask is O(n). 28 | # Thus, using the RLE can result in substantial computational savings. 29 | # 30 | # The following API functions are defined: 31 | # encode - Encode binary masks using RLE. 32 | # decode - Decode binary masks encoded via RLE. 33 | # merge - Compute union or intersection of encoded masks. 34 | # iou - Compute intersection over union between masks. 35 | # area - Compute area of encoded masks. 36 | # toBbox - Get bounding boxes surrounding encoded masks. 37 | # frPyObjects - Convert polygon, bbox, and uncompressed RLE to encoded RLE mask. 38 | # 39 | # Usage: 40 | # Rs = encode( masks ) 41 | # masks = decode( Rs ) 42 | # R = merge( Rs, intersect=false ) 43 | # o = iou( dt, gt, iscrowd ) 44 | # a = area( Rs ) 45 | # bbs = toBbox( Rs ) 46 | # Rs = frPyObjects( [pyObjects], h, w ) 47 | # 48 | # In the API the following formats are used: 49 | # Rs - [dict] Run-length encoding of binary masks 50 | # R - dict Run-length encoding of binary mask 51 | # masks - [hxwxn] Binary mask(s) (must have type np.ndarray(dtype=uint8) in column-major order) 52 | # iscrowd - [nx1] list of np.ndarray. 1 indicates corresponding gt image has crowd region to ignore 53 | # bbs - [nx4] Bounding box(es) stored as [x y w h] 54 | # poly - Polygon stored as [[x1 y1 x2 y2...],[x1 y1 ...],...] (2D list) 55 | # dt,gt - May be either bounding boxes or encoded masks 56 | # Both poly and bbs are 0-indexed (bbox=[0 0 1 1] encloses first pixel). 57 | # 58 | # Finally, a note about the intersection over union (iou) computation. 59 | # The standard iou of a ground truth (gt) and detected (dt) object is 60 | # iou(gt,dt) = area(intersect(gt,dt)) / area(union(gt,dt)) 61 | # For "crowd" regions, we use a modified criteria. If a gt object is 62 | # marked as "iscrowd", we allow a dt to match any subregion of the gt. 63 | # Choosing gt' in the crowd gt that best matches the dt can be done using 64 | # gt'=intersect(dt,gt). Since by definition union(gt',dt)=dt, computing 65 | # iou(gt,dt,iscrowd) = iou(gt',dt) = area(intersect(gt,dt)) / area(dt) 66 | # For crowd gt regions we use this modified criteria above for the iou. 67 | # 68 | # To compile run "python setup.py build_ext --inplace" 69 | # Please do not contact us for help with compiling. 70 | # 71 | # Microsoft COCO Toolbox. version 2.0 72 | # Data, paper, and tutorials available at: http://mscoco.org/ 73 | # Code written by Piotr Dollar and Tsung-Yi Lin, 2015. 74 | # Licensed under the Simplified BSD License [see coco/license.txt] 75 | 76 | encode = _mask.encode 77 | decode = _mask.decode 78 | iou = _mask.iou 79 | merge = _mask.merge 80 | area = _mask.area 81 | toBbox = _mask.toBbox 82 | frPyObjects = _mask.frPyObjects -------------------------------------------------------------------------------- /lib/pycocotools/maskApi.c: -------------------------------------------------------------------------------- 1 | /************************************************************************** 2 | * Microsoft COCO Toolbox. version 2.0 3 | * Data, paper, and tutorials available at: http://mscoco.org/ 4 | * Code written by Piotr Dollar and Tsung-Yi Lin, 2015. 5 | * Licensed under the Simplified BSD License [see coco/license.txt] 6 | **************************************************************************/ 7 | #include "maskApi.h" 8 | #include 9 | #include 10 | 11 | uint umin( uint a, uint b ) { return (ab) ? a : b; } 13 | 14 | void rleInit( RLE *R, siz h, siz w, siz m, uint *cnts ) { 15 | R->h=h; R->w=w; R->m=m; R->cnts=(m==0)?0:malloc(sizeof(uint)*m); 16 | if(cnts) for(siz j=0; jcnts[j]=cnts[j]; 17 | } 18 | 19 | void rleFree( RLE *R ) { 20 | free(R->cnts); R->cnts=0; 21 | } 22 | 23 | void rlesInit( RLE **R, siz n ) { 24 | *R = (RLE*) malloc(sizeof(RLE)*n); 25 | for(siz i=0; i0 ) { 61 | c=umin(ca,cb); cc+=c; ct=0; 62 | ca-=c; if(!ca && a0) { 83 | crowd=iscrowd!=NULL && iscrowd[g]; 84 | if(dt[d].h!=gt[g].h || dt[d].w!=gt[g].w) { o[g*m+d]=-1; continue; } 85 | siz ka, kb, a, b; uint c, ca, cb, ct, i, u; bool va, vb; 86 | ca=dt[d].cnts[0]; ka=dt[d].m; va=vb=0; 87 | cb=gt[g].cnts[0]; kb=gt[g].m; a=b=1; i=u=0; ct=1; 88 | while( ct>0 ) { 89 | c=umin(ca,cb); if(va||vb) { u+=c; if(va&&vb) i+=c; } ct=0; 90 | ca-=c; if(!ca && ad?1:c=dy && xs>xe) || (dxye); 151 | if(flip) { t=xs; xs=xe; xe=t; t=ys; ys=ye; ye=t; } 152 | s = dx>=dy ? (double)(ye-ys)/dx : (double)(xe-xs)/dy; 153 | if(dx>=dy) for( int d=0; d<=dx; d++ ) { 154 | t=flip?dx-d:d; u[m]=t+xs; v[m]=(int)(ys+s*t+.5); m++; 155 | } else for( int d=0; d<=dy; d++ ) { 156 | t=flip?dy-d:d; v[m]=t+ys; u[m]=(int)(xs+s*t+.5); m++; 157 | } 158 | } 159 | // get points along y-boundary and downsample 160 | free(x); free(y); k=m; m=0; double xd, yd; 161 | x=malloc(sizeof(int)*k); y=malloc(sizeof(int)*k); 162 | for( j=1; jw-1 ) continue; 165 | yd=(double)(v[j]h) yd=h; yd=ceil(yd); 167 | x[m]=(int) xd; y[m]=(int) yd; m++; 168 | } 169 | // compute rle encoding given y-boundary points 170 | k=m; a=malloc(sizeof(uint)*(k+1)); 171 | for( j=0; j0) b[m++]=a[j++]; else { 177 | j++; if(jm, p=0; long x; bool more; 184 | char *s=malloc(sizeof(char)*m*6); 185 | for( i=0; icnts[i]; if(i>2) x-=(long) R->cnts[i-2]; more=1; 187 | while( more ) { 188 | char c=x & 0x1f; x >>= 5; more=(c & 0x10) ? x!=-1 : x!=0; 189 | if(more) c |= 0x20; c+=48; s[p++]=c; 190 | } 191 | } 192 | s[p]=0; return s; 193 | } 194 | 195 | void rleFrString( RLE *R, char *s, siz h, siz w ) { 196 | siz m=0, p=0, k; long x; bool more; uint *cnts; 197 | while( s[m] ) m++; cnts=malloc(sizeof(uint)*m); m=0; 198 | while( s[p] ) { 199 | x=0; k=0; more=1; 200 | while( more ) { 201 | char c=s[p]-48; x |= (c & 0x1f) << 5*k; 202 | more = c & 0x20; p++; k++; 203 | if(!more && (c & 0x10)) x |= -1 << 5*k; 204 | } 205 | if(m>2) x+=(long) cnts[m-2]; cnts[m++]=(uint) x; 206 | } 207 | rleInit(R,h,w,m,cnts); free(cnts); 208 | } 209 | -------------------------------------------------------------------------------- /lib/pycocotools/maskApi.h: -------------------------------------------------------------------------------- 1 | /************************************************************************** 2 | * Microsoft COCO Toolbox. version 2.0 3 | * Data, paper, and tutorials available at: http://mscoco.org/ 4 | * Code written by Piotr Dollar and Tsung-Yi Lin, 2015. 5 | * Licensed under the Simplified BSD License [see coco/license.txt] 6 | **************************************************************************/ 7 | #pragma once 8 | #include 9 | 10 | typedef unsigned int uint; 11 | typedef unsigned long siz; 12 | typedef unsigned char byte; 13 | typedef double* BB; 14 | typedef struct { siz h, w, m; uint *cnts; } RLE; 15 | 16 | // Initialize/destroy RLE. 17 | void rleInit( RLE *R, siz h, siz w, siz m, uint *cnts ); 18 | void rleFree( RLE *R ); 19 | 20 | // Initialize/destroy RLE array. 21 | void rlesInit( RLE **R, siz n ); 22 | void rlesFree( RLE **R, siz n ); 23 | 24 | // Encode binary masks using RLE. 25 | void rleEncode( RLE *R, const byte *mask, siz h, siz w, siz n ); 26 | 27 | // Decode binary masks encoded via RLE. 28 | void rleDecode( const RLE *R, byte *mask, siz n ); 29 | 30 | // Compute union or intersection of encoded masks. 31 | void rleMerge( const RLE *R, RLE *M, siz n, bool intersect ); 32 | 33 | // Compute area of encoded masks. 34 | void rleArea( const RLE *R, siz n, uint *a ); 35 | 36 | // Compute intersection over union between masks. 37 | void rleIou( RLE *dt, RLE *gt, siz m, siz n, byte *iscrowd, double *o ); 38 | 39 | // Compute intersection over union between bounding boxes. 40 | void bbIou( BB dt, BB gt, siz m, siz n, byte *iscrowd, double *o ); 41 | 42 | // Get bounding boxes surrounding encoded masks. 43 | void rleToBbox( const RLE *R, BB bb, siz n ); 44 | 45 | // Convert bounding boxes to encoded masks. 46 | void rleFrBbox( RLE *R, const BB bb, siz h, siz w, siz n ); 47 | 48 | // Convert polygon to encoded mask. 49 | void rleFrPoly( RLE *R, const double *xy, siz k, siz h, siz w ); 50 | 51 | // Get compressed string representation of encoded mask. 52 | char* rleToString( const RLE *R ); 53 | 54 | // Convert from compressed string representation of encoded mask. 55 | void rleFrString( RLE *R, char *s, siz h, siz w ); 56 | -------------------------------------------------------------------------------- /lib/roi_data_layer/__init__.py: -------------------------------------------------------------------------------- 1 | # -------------------------------------------------------- 2 | # Fast R-CNN 3 | # Copyright (c) 2015 Microsoft 4 | # Licensed under The MIT License [see LICENSE for details] 5 | # Written by Ross Girshick 6 | # -------------------------------------------------------- 7 | -------------------------------------------------------------------------------- /lib/roi_data_layer/minibatch.py: -------------------------------------------------------------------------------- 1 | # -------------------------------------------------------- 2 | # Fast R-CNN 3 | # Copyright (c) 2015 Microsoft 4 | # Licensed under The MIT License [see LICENSE for details] 5 | # Written by Ross Girshick and Xinlei Chen 6 | # -------------------------------------------------------- 7 | 8 | """Compute minibatch blobs for training a Fast R-CNN network.""" 9 | from __future__ import absolute_import 10 | from __future__ import division 11 | from __future__ import print_function 12 | 13 | import numpy as np 14 | import numpy.random as npr 15 | from scipy.misc import imread 16 | from model.utils.config import cfg 17 | from model.utils.blob import prep_im_for_blob, im_list_to_blob 18 | import pdb 19 | def get_minibatch(roidb, num_classes,seg_return=False): 20 | """Given a roidb, construct a minibatch sampled from it.""" 21 | num_images = len(roidb) 22 | # Sample random scales to use for each image in this batch 23 | random_scale_inds = npr.randint(0, high=len(cfg.TRAIN.SCALES), 24 | size=num_images) 25 | assert(cfg.TRAIN.BATCH_SIZE % num_images == 0), \ 26 | 'num_images ({}) must divide BATCH_SIZE ({})'. \ 27 | format(num_images, cfg.TRAIN.BATCH_SIZE) 28 | 29 | # Get the input image blob, formatted for caffe 30 | im_blob, im_scales = _get_image_blob(roidb, random_scale_inds) 31 | 32 | blobs = {'data': im_blob} 33 | 34 | assert len(im_scales) == 1, "Single batch only" 35 | assert len(roidb) == 1, "Single batch only" 36 | 37 | # gt boxes: (x1, y1, x2, y2, cls) 38 | if cfg.TRAIN.USE_ALL_GT: 39 | # Include all ground truth boxes 40 | gt_inds = np.where(roidb[0]['gt_classes'] != 0)[0] 41 | else: 42 | # For the COCO ground truth boxes, exclude the ones that are ''iscrowd'' 43 | gt_inds = np.where((roidb[0]['gt_classes'] != 0) & np.all(roidb[0]['gt_overlaps'].toarray() > -1.0, axis=1))[0] 44 | gt_boxes = np.empty((len(gt_inds), 5), dtype=np.float32) 45 | gt_boxes[:, 0:4] = roidb[0]['boxes'][gt_inds, :] * im_scales[0] 46 | gt_boxes[:, 4] = roidb[0]['gt_classes'][gt_inds] 47 | blobs['gt_boxes'] = gt_boxes 48 | blobs['im_info'] = np.array( 49 | [[im_blob.shape[1], im_blob.shape[2], im_scales[0]]], 50 | dtype=np.float32) 51 | if seg_return: 52 | blobs['seg_map'] = roidb[0]['seg_map'] 53 | blobs['img_id'] = roidb[0]['img_id'] 54 | blobs['path'] = roidb[0]['image'] 55 | 56 | return blobs 57 | 58 | def _get_image_blob(roidb, scale_inds): 59 | """Builds an input blob from the images in the roidb at the specified 60 | scales. 61 | """ 62 | num_images = len(roidb) 63 | 64 | processed_ims = [] 65 | im_scales = [] 66 | for i in range(num_images): 67 | #im = cv2.imread(roidb[i]['image']) 68 | im = imread(roidb[i]['image']) 69 | 70 | if len(im.shape) == 2: 71 | im = im[:,:,np.newaxis] 72 | im = np.concatenate((im,im,im), axis=2) 73 | # flip the channel, since the original one using cv2 74 | # rgb -> bgr 75 | im = im[:,:,::-1] 76 | 77 | if roidb[i]['flipped']: 78 | im = im[:, ::-1, :] 79 | target_size = cfg.TRAIN.SCALES[scale_inds[i]] 80 | im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size, 81 | cfg.TRAIN.MAX_SIZE) 82 | im_scales.append(im_scale) 83 | processed_ims.append(im) 84 | 85 | # Create a blob to hold the input images 86 | blob = im_list_to_blob(processed_ims) 87 | 88 | return blob, im_scales 89 | -------------------------------------------------------------------------------- /lib/roi_data_layer/roidb.py: -------------------------------------------------------------------------------- 1 | """Transform a roidb into a trainable roidb by adding a bunch of metadata.""" 2 | from __future__ import absolute_import 3 | from __future__ import division 4 | from __future__ import print_function 5 | 6 | import datasets 7 | import numpy as np 8 | from model.utils.config import cfg 9 | from datasets.factory import get_imdb 10 | import PIL 11 | import pdb 12 | 13 | def prepare_roidb(imdb): 14 | """Enrich the imdb's roidb by adding some derived quantities that 15 | are useful for training. This function precomputes the maximum 16 | overlap, taken over ground-truth boxes, between each ROI and 17 | each ground-truth box. The class with maximum overlap is also 18 | recorded. 19 | """ 20 | 21 | roidb = imdb.roidb 22 | #if not (imdb.name.startswith('coco')): 23 | sizes = [PIL.Image.open(imdb.image_path_at(i)).size 24 | for i in range(imdb.num_images)] 25 | 26 | for i in range(len(imdb.image_index)): 27 | roidb[i]['img_id'] = imdb.image_id_at(i) 28 | roidb[i]['image'] = imdb.image_path_at(i) 29 | #if not (imdb.name.startswith('coco')): 30 | roidb[i]['width'] = sizes[i][0] 31 | roidb[i]['height'] = sizes[i][1] 32 | # need gt_overlaps as a dense array for argmax 33 | gt_overlaps = roidb[i]['gt_overlaps'].toarray() 34 | # max overlap with gt over classes (columns) 35 | max_overlaps = gt_overlaps.max(axis=1) 36 | # gt class that had the max overlap 37 | max_classes = gt_overlaps.argmax(axis=1) 38 | roidb[i]['max_classes'] = max_classes 39 | roidb[i]['max_overlaps'] = max_overlaps 40 | # sanity checks 41 | # max overlap of 0 => class should be zero (background) 42 | zero_inds = np.where(max_overlaps == 0)[0] 43 | assert all(max_classes[zero_inds] == 0) 44 | # max overlap > 0 => class should not be zero (must be a fg class) 45 | nonzero_inds = np.where(max_overlaps > 0)[0] 46 | assert all(max_classes[nonzero_inds] != 0) 47 | 48 | 49 | def rank_roidb_ratio(roidb): 50 | # rank roidb based on the ratio between width and height. 51 | ratio_large = 2 # largest ratio to preserve. 52 | ratio_small = 0.5 # smallest ratio to preserve. 53 | 54 | ratio_list = [] 55 | for i in range(len(roidb)): 56 | width = roidb[i]['width'] 57 | height = roidb[i]['height'] 58 | ratio = width / float(height) 59 | 60 | if ratio > ratio_large: 61 | roidb[i]['need_crop'] = 1 62 | ratio = ratio_large 63 | elif ratio < ratio_small: 64 | roidb[i]['need_crop'] = 1 65 | ratio = ratio_small 66 | else: 67 | roidb[i]['need_crop'] = 0 68 | 69 | ratio_list.append(ratio) 70 | 71 | ratio_list = np.array(ratio_list) 72 | ratio_index = np.argsort(ratio_list) 73 | return ratio_list[ratio_index], ratio_index 74 | 75 | def filter_roidb(roidb): 76 | # filter the image without bounding box. 77 | print('before filtering, there are %d images...' % (len(roidb))) 78 | i = 0 79 | while i < len(roidb): 80 | if len(roidb[i]['boxes']) == 0: 81 | del roidb[i] 82 | i -= 1 83 | i += 1 84 | 85 | print('after filtering, there are %d images...' % (len(roidb))) 86 | return roidb 87 | 88 | def combined_roidb(imdb_names, training=True): 89 | """ 90 | Combine multiple roidbs 91 | """ 92 | 93 | def get_training_roidb(imdb): 94 | """Returns a roidb (Region of Interest database) for use in training.""" 95 | if cfg.TRAIN.USE_FLIPPED: 96 | print('Appending horizontally-flipped training examples...') 97 | imdb.append_flipped_images() 98 | print('done') 99 | 100 | print('Preparing training data...') 101 | 102 | prepare_roidb(imdb) 103 | #ratio_index = rank_roidb_ratio(imdb) 104 | print('done') 105 | 106 | return imdb.roidb 107 | 108 | def get_roidb(imdb_name): 109 | imdb = get_imdb(imdb_name) 110 | print('Loaded dataset `{:s}` for training'.format(imdb.name)) 111 | imdb.set_proposal_method(cfg.TRAIN.PROPOSAL_METHOD) 112 | print('Set proposal method: {:s}'.format(cfg.TRAIN.PROPOSAL_METHOD)) 113 | roidb = get_training_roidb(imdb) 114 | return roidb 115 | #print(imdb_names.split('+')) 116 | roidbs = [get_roidb(s) for s in imdb_names.split('+')] 117 | roidb = roidbs[0] 118 | 119 | if len(roidbs) > 1: 120 | for r in roidbs[1:]: 121 | roidb.extend(r) 122 | tmp = get_imdb(imdb_names.split('+')[1]) 123 | imdb = datasets.imdb.imdb(imdb_names, tmp.classes) 124 | else: 125 | imdb = get_imdb(imdb_names) 126 | 127 | if training: 128 | roidb = filter_roidb(roidb) 129 | 130 | ratio_list, ratio_index = rank_roidb_ratio(roidb) 131 | 132 | return imdb, roidb, ratio_list, ratio_index 133 | -------------------------------------------------------------------------------- /lib/setup.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | # -------------------------------------------------------- 3 | # Fast R-CNN 4 | # Copyright (c) 2015 Microsoft 5 | # Licensed under The MIT License [see LICENSE for details] 6 | # Written by Ross Girshick 7 | # -------------------------------------------------------- 8 | 9 | import os 10 | from os.path import join as pjoin 11 | import numpy as np 12 | from distutils.core import setup 13 | from distutils.extension import Extension 14 | from Cython.Distutils import build_ext 15 | 16 | 17 | def find_in_path(name, path): 18 | "Find a file in a search path" 19 | # adapted fom http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/ 20 | for dir in path.split(os.pathsep): 21 | binpath = pjoin(dir, name) 22 | if os.path.exists(binpath): 23 | return os.path.abspath(binpath) 24 | return None 25 | 26 | 27 | # def locate_cuda(): 28 | # """Locate the CUDA environment on the system 29 | # 30 | # Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64' 31 | # and values giving the absolute path to each directory. 32 | # 33 | # Starts by looking for the CUDAHOME env variable. If not found, everything 34 | # is based on finding 'nvcc' in the PATH. 35 | # """ 36 | # 37 | # # first check if the CUDAHOME env variable is in use 38 | # if 'CUDAHOME' in os.environ: 39 | # home = os.environ['CUDAHOME'] 40 | # nvcc = pjoin(home, 'bin', 'nvcc') 41 | # else: 42 | # # otherwise, search the PATH for NVCC 43 | # default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin') 44 | # nvcc = find_in_path('nvcc', os.environ['PATH'] + os.pathsep + default_path) 45 | # if nvcc is None: 46 | # raise EnvironmentError('The nvcc binary could not be ' 47 | # 'located in your $PATH. Either add it to your path, or set $CUDAHOME') 48 | # home = os.path.dirname(os.path.dirname(nvcc)) 49 | # 50 | # cudaconfig = {'home': home, 'nvcc': nvcc, 51 | # 'include': pjoin(home, 'include'), 52 | # 'lib64': pjoin(home, 'lib64')} 53 | # for k, v in cudaconfig.iteritems(): 54 | # if not os.path.exists(v): 55 | # raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v)) 56 | # 57 | # return cudaconfig 58 | 59 | 60 | # CUDA = locate_cuda() 61 | 62 | # Obtain the numpy include directory. This logic works across numpy versions. 63 | try: 64 | numpy_include = np.get_include() 65 | except AttributeError: 66 | numpy_include = np.get_numpy_include() 67 | 68 | 69 | def customize_compiler_for_nvcc(self): 70 | """inject deep into distutils to customize how the dispatch 71 | to gcc/nvcc works. 72 | 73 | If you subclass UnixCCompiler, it's not trivial to get your subclass 74 | injected in, and still have the right customizations (i.e. 75 | distutils.sysconfig.customize_compiler) run on it. So instead of going 76 | the OO route, I have this. Note, it's kindof like a wierd functional 77 | subclassing going on.""" 78 | 79 | # tell the compiler it can processes .cu 80 | self.src_extensions.append('.cu') 81 | 82 | # save references to the default compiler_so and _comple methods 83 | default_compiler_so = self.compiler_so 84 | super = self._compile 85 | 86 | # now redefine the _compile method. This gets executed for each 87 | # object but distutils doesn't have the ability to change compilers 88 | # based on source extension: we add it. 89 | def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts): 90 | print(extra_postargs) 91 | if os.path.splitext(src)[1] == '.cu': 92 | # use the cuda for .cu files 93 | self.set_executable('compiler_so', CUDA['nvcc']) 94 | # use only a subset of the extra_postargs, which are 1-1 translated 95 | # from the extra_compile_args in the Extension class 96 | postargs = extra_postargs['nvcc'] 97 | else: 98 | postargs = extra_postargs['gcc'] 99 | 100 | super(obj, src, ext, cc_args, postargs, pp_opts) 101 | # reset the default compiler_so, which we might have changed for cuda 102 | self.compiler_so = default_compiler_so 103 | 104 | # inject our redefined _compile method into the class 105 | self._compile = _compile 106 | 107 | 108 | # run the customize_compiler 109 | class custom_build_ext(build_ext): 110 | def build_extensions(self): 111 | customize_compiler_for_nvcc(self.compiler) 112 | build_ext.build_extensions(self) 113 | 114 | 115 | ext_modules = [ 116 | Extension( 117 | "model.utils.cython_bbox", 118 | ["model/utils/bbox.pyx"], 119 | extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, 120 | include_dirs=[numpy_include] 121 | ), 122 | Extension( 123 | 'pycocotools._mask', 124 | sources=['pycocotools/maskApi.c', 'pycocotools/_mask.pyx'], 125 | include_dirs=[numpy_include, 'pycocotools'], 126 | extra_compile_args={ 127 | 'gcc': ['-Wno-cpp', '-Wno-unused-function', '-std=c99']}, 128 | ), 129 | ] 130 | 131 | setup( 132 | name='faster_rcnn', 133 | ext_modules=ext_modules, 134 | # inject our custom trigger 135 | cmdclass={'build_ext': custom_build_ext}, 136 | ) 137 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | cython 2 | cffi 3 | opencv-python 4 | scipy 5 | msgpack 6 | easydict 7 | matplotlib 8 | pyyaml 9 | tensorboardX 10 | -------------------------------------------------------------------------------- /test_net_global_local.py: -------------------------------------------------------------------------------- 1 | # -------------------------------------------------------- 2 | # Tensorflow Faster R-CNN 3 | # Licensed under The MIT License [see LICENSE for details] 4 | # Written by Jiasen Lu, Jianwei Yang, based on code from Ross Girshick 5 | # -------------------------------------------------------- 6 | from __future__ import absolute_import 7 | from __future__ import division 8 | from __future__ import print_function 9 | 10 | import os 11 | import sys 12 | import numpy as np 13 | import pprint 14 | import time 15 | import _init_paths 16 | 17 | import torch 18 | 19 | from torch.autograd import Variable 20 | import pickle 21 | from roi_data_layer.roidb import combined_roidb 22 | from roi_data_layer.roibatchLoader import roibatchLoader 23 | from model.utils.config import cfg, cfg_from_file, cfg_from_list, get_output_dir 24 | from model.rpn.bbox_transform import clip_boxes 25 | from model.nms.nms_wrapper import nms 26 | from model.rpn.bbox_transform import bbox_transform_inv 27 | #from model.utils.net_utils import save_net, load_net, vis_detections 28 | from model.utils.parser_func import parse_args,set_dataset_args 29 | 30 | import pdb 31 | 32 | try: 33 | xrange # Python 2 34 | except NameError: 35 | xrange = range # Python 3 36 | 37 | 38 | 39 | lr = cfg.TRAIN.LEARNING_RATE 40 | momentum = cfg.TRAIN.MOMENTUM 41 | weight_decay = cfg.TRAIN.WEIGHT_DECAY 42 | 43 | if __name__ == '__main__': 44 | 45 | args = parse_args() 46 | 47 | print('Called with args:') 48 | print(args) 49 | args = set_dataset_args(args,test=True) 50 | if torch.cuda.is_available() and not args.cuda: 51 | print("WARNING: You have a CUDA device, so you should probably run with --cuda") 52 | np.random.seed(cfg.RNG_SEED) 53 | 54 | if args.cfg_file is not None: 55 | cfg_from_file(args.cfg_file) 56 | if args.set_cfgs is not None: 57 | cfg_from_list(args.set_cfgs) 58 | 59 | print('Using config:') 60 | pprint.pprint(cfg) 61 | 62 | cfg.TRAIN.USE_FLIPPED = False 63 | imdb, roidb, ratio_list, ratio_index = combined_roidb(args.imdbval_name, False) 64 | imdb.competition_mode(on=True) 65 | 66 | print('{:d} roidb entries'.format(len(roidb))) 67 | 68 | # initilize the network here. 69 | from model.faster_rcnn.vgg16_global_local import vgg16 70 | from model.faster_rcnn.resnet_global_local import resnet 71 | 72 | if args.net == 'vgg16': 73 | fasterRCNN = vgg16(imdb.classes, pretrained=True, class_agnostic=args.class_agnostic,lc=args.lc,gc=args.gc) 74 | elif args.net == 'res101': 75 | fasterRCNN = resnet(imdb.classes, 101, pretrained=True, class_agnostic=args.class_agnostic,lc=args.lc,gc=args.gc) 76 | #elif args.net == 'res50': 77 | # fasterRCNN = resnet(imdb.classes, 50, pretrained=True, class_agnostic=args.class_agnostic,context=args.context) 78 | 79 | else: 80 | print("network is not defined") 81 | pdb.set_trace() 82 | 83 | fasterRCNN.create_architecture() 84 | 85 | print("load checkpoint %s" % (args.load_name)) 86 | checkpoint = torch.load(args.load_name) 87 | fasterRCNN.load_state_dict(checkpoint['model']) 88 | if 'pooling_mode' in checkpoint.keys(): 89 | cfg.POOLING_MODE = checkpoint['pooling_mode'] 90 | 91 | 92 | print('load model successfully!') 93 | # initilize the tensor holder here. 94 | im_data = torch.FloatTensor(1) 95 | im_info = torch.FloatTensor(1) 96 | num_boxes = torch.LongTensor(1) 97 | gt_boxes = torch.FloatTensor(1) 98 | 99 | # ship to cuda 100 | if args.cuda: 101 | im_data = im_data.cuda() 102 | im_info = im_info.cuda() 103 | num_boxes = num_boxes.cuda() 104 | gt_boxes = gt_boxes.cuda() 105 | 106 | # make variable 107 | im_data = Variable(im_data) 108 | im_info = Variable(im_info) 109 | num_boxes = Variable(num_boxes) 110 | gt_boxes = Variable(gt_boxes) 111 | 112 | if args.cuda: 113 | cfg.CUDA = True 114 | 115 | if args.cuda: 116 | fasterRCNN.cuda() 117 | 118 | start = time.time() 119 | max_per_image = 100 120 | 121 | thresh = 0.0 122 | 123 | 124 | save_name = args.load_name.split('/')[-1] 125 | num_images = len(imdb.image_index) 126 | all_boxes = [[[] for _ in xrange(num_images)] 127 | for _ in xrange(imdb.num_classes)] 128 | 129 | output_dir = get_output_dir(imdb, save_name) 130 | dataset = roibatchLoader(roidb, ratio_list, ratio_index, 1, \ 131 | imdb.num_classes, training=False, normalize = False, path_return=True) 132 | dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, 133 | shuffle=False, num_workers=0, 134 | pin_memory=True) 135 | 136 | data_iter = iter(dataloader) 137 | 138 | _t = {'im_detect': time.time(), 'misc': time.time()} 139 | det_file = os.path.join(output_dir, 'detections.pkl') 140 | 141 | fasterRCNN.eval() 142 | empty_array = np.transpose(np.array([[],[],[],[],[]]), (1,0)) 143 | for i in range(num_images): 144 | 145 | data = next(data_iter) 146 | im_data.data.resize_(data[0].size()).copy_(data[0]) 147 | #print(data[0].size()) 148 | im_info.data.resize_(data[1].size()).copy_(data[1]) 149 | gt_boxes.data.resize_(data[2].size()).copy_(data[2]) 150 | num_boxes.data.resize_(data[3].size()).copy_(data[3]) 151 | 152 | det_tic = time.time() 153 | rois, cls_prob, bbox_pred, \ 154 | rpn_loss_cls, rpn_loss_box, \ 155 | RCNN_loss_cls, RCNN_loss_bbox, \ 156 | rois_label,d_pred,_ = fasterRCNN(im_data, im_info, gt_boxes, num_boxes) 157 | 158 | scores = cls_prob.data 159 | boxes = rois.data[:, :, 1:5] 160 | d_pred = d_pred.data 161 | path = data[4] 162 | 163 | if cfg.TEST.BBOX_REG: 164 | # Apply bounding-box regression deltas 165 | box_deltas = bbox_pred.data 166 | if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED: 167 | # Optionally normalize targets by a precomputed mean and stdev 168 | if args.class_agnostic: 169 | box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \ 170 | + torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda() 171 | box_deltas = box_deltas.view(1, -1, 4) 172 | else: 173 | box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \ 174 | + torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda() 175 | box_deltas = box_deltas.view(1, -1, 4 * len(imdb.classes)) 176 | 177 | pred_boxes = bbox_transform_inv(boxes, box_deltas, 1) 178 | pred_boxes = clip_boxes(pred_boxes, im_info.data, 1) 179 | else: 180 | # Simply repeat the boxes, once for each class 181 | pred_boxes = np.tile(boxes, (1, scores.shape[1])) 182 | 183 | pred_boxes /= data[1][0][2].item() 184 | 185 | scores = scores.squeeze() 186 | pred_boxes = pred_boxes.squeeze() 187 | det_toc = time.time() 188 | detect_time = det_toc - det_tic 189 | misc_tic = time.time() 190 | 191 | for j in xrange(1, imdb.num_classes): 192 | inds = torch.nonzero(scores[:,j]>thresh).view(-1) 193 | # if there is det 194 | if inds.numel() > 0: 195 | cls_scores = scores[:,j][inds] 196 | _, order = torch.sort(cls_scores, 0, True) 197 | if args.class_agnostic: 198 | cls_boxes = pred_boxes[inds, :] 199 | else: 200 | cls_boxes = pred_boxes[inds][:, j * 4:(j + 1) * 4] 201 | 202 | cls_dets = torch.cat((cls_boxes, cls_scores.unsqueeze(1)), 1) 203 | # cls_dets = torch.cat((cls_boxes, cls_scores), 1) 204 | cls_dets = cls_dets[order] 205 | keep = nms(cls_dets, cfg.TEST.NMS) 206 | cls_dets = cls_dets[keep.view(-1).long()] 207 | 208 | all_boxes[j][i] = cls_dets.cpu().numpy() 209 | else: 210 | all_boxes[j][i] = empty_array 211 | 212 | # Limit to max_per_image detections *over all classes* 213 | if max_per_image > 0: 214 | image_scores = np.hstack([all_boxes[j][i][:, -1] 215 | for j in xrange(1, imdb.num_classes)]) 216 | if len(image_scores) > max_per_image: 217 | image_thresh = np.sort(image_scores)[-max_per_image] 218 | for j in xrange(1, imdb.num_classes): 219 | keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0] 220 | all_boxes[j][i] = all_boxes[j][i][keep, :] 221 | 222 | misc_toc = time.time() 223 | nms_time = misc_toc - misc_tic 224 | 225 | sys.stdout.write('im_detect: {:d}/{:d} {:.3f}s {:.3f}s \r' \ 226 | .format(i + 1, num_images, detect_time, nms_time)) 227 | sys.stdout.flush() 228 | 229 | 230 | 231 | with open(det_file, 'wb') as f: 232 | pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL) 233 | 234 | print('Evaluating detections') 235 | imdb.evaluate_detections(all_boxes, output_dir) 236 | 237 | end = time.time() 238 | print("test time: %0.4fs" % (end - start)) 239 | -------------------------------------------------------------------------------- /test_scripts/city2foggycity_sample.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | CUDA_VISIBLE_DEVICES=$1 python test_net_global_local.py --cuda --net vgg16 --dataset foggy_cityscape --gc --lc --load_name $2 -------------------------------------------------------------------------------- /test_scripts/clipart_sample.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | CUDA_VISIBLE_DEVICES=$1 python test_net_global.py --cuda --net res101 --dataset clipart --gc --load_name $2 -------------------------------------------------------------------------------- /test_scripts/sim10k2cityscape_sample.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | CUDA_VISIBLE_DEVICES=$1 python test_net_global_local.py --cuda --net vgg16 --dataset cityscape_car --gc --lc --load_name $2 -------------------------------------------------------------------------------- /test_scripts/watercolor_sample.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | CUDA_VISIBLE_DEVICES=$1 python test_net_global_local.py --cuda --net res101 --dataset water --gc --lc --load_name $2 -------------------------------------------------------------------------------- /train_scripts/city2foggycity_sample.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | CUDA_VISIBLE_DEVICES=$1 python trainval_net_global_local.py --cuda --net vgg16 --dataset cityscape --dataset_t foggy_cityscape --gc --lc --save_dir $2 -------------------------------------------------------------------------------- /train_scripts/clipart_sample.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | CUDA_VISIBLE_DEVICES=$1 python trainval_net_global.py --cuda --net res101 --dataset pascal_voc_0712 --dataset_t clipart --gc --save_dir $2 -------------------------------------------------------------------------------- /train_scripts/sim10k2cityscape_sample.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | CUDA_VISIBLE_DEVICES=$1 python trainval_net_global_local.py --cuda --net vgg16 --dataset sim10k --dataset_t cityscape_car --gc --lc --save_dir $2 -------------------------------------------------------------------------------- /train_scripts/watercolor_sample.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | CUDA_VISIBLE_DEVICES=$1 python trainval_net_global_local.py --cuda --net res101 --dataset pascal_voc_water --dataset_t water --gc --lc --save_dir $2 --------------------------------------------------------------------------------