├── .github ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md └── ISSUE_TEMPLATE │ ├── config.yml │ ├── error-report.md │ ├── feature_request.md │ └── general_questions.md ├── .gitignore ├── .isort.cfg ├── .pre-commit-config.yaml ├── .style.yapf ├── .travis.yml ├── LICENSE ├── LICENSE.mmdet ├── README.md ├── README.mmdet.md ├── conda_env.md ├── configs ├── DetectoRS │ ├── DetectoRS_mstrain_400_1200_r50_40e.py │ └── DetectoRS_mstrain_400_1200_x101_32x4d_40e.py ├── albu_example │ └── mask_rcnn_r50_fpn_1x.py ├── atss │ ├── README.md │ └── atss_r50_fpn_1x.py ├── carafe │ ├── README.md │ ├── faster_rcnn_r50_fpn_carafe_1x.py │ └── mask_rcnn_r50_fpn_carafe_1x.py ├── cascade_mask_rcnn_r101_fpn_1x.py ├── cascade_mask_rcnn_r50_caffe_c4_1x.py ├── cascade_mask_rcnn_r50_fpn_1x.py ├── cascade_mask_rcnn_x101_32x4d_fpn_1x.py ├── cascade_mask_rcnn_x101_64x4d_fpn_1x.py ├── cascade_rcnn_r101_fpn_1x.py ├── cascade_rcnn_r50_caffe_c4_1x.py ├── cascade_rcnn_r50_fpn_1x.py ├── cascade_rcnn_x101_32x4d_fpn_1x.py ├── cascade_rcnn_x101_64x4d_fpn_1x.py ├── cityscapes │ ├── README.md │ ├── faster_rcnn_r50_fpn_1x_cityscapes.py │ └── mask_rcnn_r50_fpn_1x_cityscapes.py ├── dcn │ ├── README.md │ ├── cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x.py │ ├── cascade_rcnn_dconv_c3-c5_r50_fpn_1x.py │ ├── faster_rcnn_dconv_c3-c5_r50_fpn_1x.py │ ├── faster_rcnn_dconv_c3-c5_x101_32x4d_fpn_1x.py │ ├── faster_rcnn_dpool_r50_fpn_1x.py │ ├── faster_rcnn_mdconv_c3-c5_group4_r50_fpn_1x.py │ ├── faster_rcnn_mdconv_c3-c5_r50_fpn_1x.py │ ├── faster_rcnn_mdpool_r50_fpn_1x.py │ ├── mask_rcnn_dconv_c3-c5_r50_fpn_1x.py │ └── mask_rcnn_mdconv_c3-c5_r50_fpn_1x.py ├── double_heads │ └── dh_faster_rcnn_r50_fpn_1x.py ├── empirical_attention │ ├── README.md │ ├── faster_rcnn_r50_fpn_attention_0010_1x.py │ ├── faster_rcnn_r50_fpn_attention_0010_dcn_1x.py │ ├── faster_rcnn_r50_fpn_attention_1111_1x.py │ └── faster_rcnn_r50_fpn_attention_1111_dcn_1x.py ├── fast_mask_rcnn_r101_fpn_1x.py ├── fast_mask_rcnn_r50_caffe_c4_1x.py ├── fast_mask_rcnn_r50_fpn_1x.py ├── fast_rcnn_r101_fpn_1x.py ├── fast_rcnn_r50_caffe_c4_1x.py ├── fast_rcnn_r50_fpn_1x.py ├── faster_rcnn_ohem_r50_fpn_1x.py ├── faster_rcnn_r101_fpn_1x.py ├── faster_rcnn_r50_caffe_c4_1x.py ├── faster_rcnn_r50_fpn_1x.py ├── faster_rcnn_x101_32x4d_fpn_1x.py ├── faster_rcnn_x101_64x4d_fpn_1x.py ├── fcos │ ├── README.md │ ├── fcos_center_r50_caffe_fpn_gn_1x_4gpu.py.py │ ├── fcos_mstrain_640_800_r101_caffe_fpn_gn_2x_4gpu.py │ ├── fcos_mstrain_640_800_x101_64x4d_fpn_gn_2x.py │ └── fcos_r50_caffe_fpn_gn_1x_4gpu.py ├── foveabox │ ├── README.md │ ├── fovea_align_gn_ms_r101_fpn_4gpu_2x.py │ ├── fovea_align_gn_ms_r50_fpn_4gpu_2x.py │ ├── fovea_align_gn_r101_fpn_4gpu_2x.py │ ├── fovea_align_gn_r50_fpn_4gpu_2x.py │ └── fovea_r50_fpn_4gpu_1x.py ├── fp16 │ ├── faster_rcnn_r50_fpn_fp16_1x.py │ ├── mask_rcnn_r50_fpn_fp16_1x.py │ └── retinanet_r50_fpn_fp16_1x.py ├── free_anchor │ ├── README.md │ ├── retinanet_free_anchor_r101_fpn_1x.py │ ├── retinanet_free_anchor_r50_fpn_1x.py │ └── retinanet_free_anchor_x101-32x4d_fpn_1x.py ├── gcnet │ ├── README.md │ ├── mask_rcnn_r16_gcb_c3-c5_r50_fpn_1x.py │ ├── mask_rcnn_r16_gcb_c3-c5_r50_fpn_syncbn_1x.py │ ├── mask_rcnn_r4_gcb_c3-c5_r50_fpn_1x.py │ ├── mask_rcnn_r4_gcb_c3-c5_r50_fpn_syncbn_1x.py │ └── mask_rcnn_r50_fpn_sbn_1x.py ├── ghm │ ├── README.md │ └── retinanet_ghm_r50_fpn_1x.py ├── gn+ws │ ├── README.md │ ├── faster_rcnn_r50_fpn_gn_ws_1x.py │ ├── mask_rcnn_r50_fpn_gn_ws_20_23_24e.py │ ├── mask_rcnn_r50_fpn_gn_ws_2x.py │ └── mask_rcnn_x101_32x4d_fpn_gn_ws_2x.py ├── gn │ ├── README.md │ ├── mask_rcnn_r101_fpn_gn_2x.py │ ├── mask_rcnn_r50_fpn_gn_2x.py │ └── mask_rcnn_r50_fpn_gn_contrib_2x.py ├── grid_rcnn │ ├── README.md │ ├── grid_rcnn_gn_head_r50_fpn_2x.py │ └── grid_rcnn_gn_head_x101_32x4d_fpn_2x.py ├── guided_anchoring │ ├── README.md │ ├── ga_fast_r50_caffe_fpn_1x.py │ ├── ga_faster_r50_caffe_fpn_1x.py │ ├── ga_faster_x101_32x4d_fpn_1x.py │ ├── ga_retinanet_r101_caffe_fpn_mstrain_2x.py │ ├── ga_retinanet_r50_caffe_fpn_1x.py │ ├── ga_retinanet_x101_32x4d_fpn_1x.py │ ├── ga_rpn_r101_caffe_rpn_1x.py │ ├── ga_rpn_r50_caffe_fpn_1x.py │ └── ga_rpn_x101_32x4d_fpn_1x.py ├── hrnet │ ├── README.md │ ├── cascade_mask_rcnn_hrnetv2p_w32_20e.py │ ├── cascade_rcnn_hrnetv2p_w32_20e.py │ ├── faster_rcnn_hrnetv2p_w18_1x.py │ ├── faster_rcnn_hrnetv2p_w32_1x.py │ ├── faster_rcnn_hrnetv2p_w40_1x.py │ ├── fcos_hrnetv2p_w32_gn_1x_4gpu.py │ ├── htc_hrnetv2p_w32_20e.py │ ├── mask_rcnn_hrnetv2p_w18_1x.py │ └── mask_rcnn_hrnetv2p_w32_1x.py ├── htc │ ├── README.md │ ├── htc_dconv_c3-c5_mstrain_400_1400_x101_64x4d_fpn_20e.py │ ├── htc_r101_fpn_20e.py │ ├── htc_r50_fpn_1x.py │ ├── htc_r50_fpn_20e.py │ ├── htc_without_semantic_r50_fpn_1x.py │ ├── htc_x101_32x4d_fpn_20e_16gpu.py │ └── htc_x101_64x4d_fpn_20e_16gpu.py ├── instaboost │ ├── README.md │ ├── cascade_mask_rcnn_r50_fpn_instaboost_4x.py │ ├── mask_rcnn_r50_fpn_instaboost_4x.py │ └── ssd300_coco_instaboost_4x.py ├── libra_rcnn │ ├── README.md │ ├── libra_fast_rcnn_r50_fpn_1x.py │ ├── libra_faster_rcnn_r101_fpn_1x.py │ ├── libra_faster_rcnn_r50_fpn_1x.py │ ├── libra_faster_rcnn_x101_64x4d_fpn_1x.py │ └── libra_retinanet_r50_fpn_1x.py ├── mask_rcnn_r101_fpn_1x.py ├── mask_rcnn_r50_caffe_c4_1x.py ├── mask_rcnn_r50_fpn_1x.py ├── mask_rcnn_x101_32x4d_fpn_1x.py ├── mask_rcnn_x101_64x4d_fpn_1x.py ├── ms_rcnn │ ├── README.md │ ├── ms_rcnn_r101_caffe_fpn_1x.py │ ├── ms_rcnn_r50_caffe_fpn_1x.py │ └── ms_rcnn_x101_64x4d_fpn_1x.py ├── nas_fpn │ ├── README.md │ ├── retinanet_crop640_r50_fpn_50e.py │ └── retinanet_crop640_r50_nasfpn_50e.py ├── pascal_voc │ ├── README.md │ ├── faster_rcnn_r50_fpn_1x_voc0712.py │ ├── ssd300_voc.py │ └── ssd512_voc.py ├── reppoints │ ├── README.md │ ├── bbox_r50_grid_center_fpn_1x.py │ ├── bbox_r50_grid_fpn_1x.py │ ├── reppoints.png │ ├── reppoints_minmax_r50_fpn_1x.py │ ├── reppoints_moment_r101_dcn_fpn_2x.py │ ├── reppoints_moment_r101_dcn_fpn_2x_mt.py │ ├── reppoints_moment_r101_fpn_2x.py │ ├── reppoints_moment_r101_fpn_2x_mt.py │ ├── reppoints_moment_r50_fpn_1x.py │ ├── reppoints_moment_r50_fpn_2x.py │ ├── reppoints_moment_r50_fpn_2x_mt.py │ ├── reppoints_moment_r50_no_gn_fpn_1x.py │ ├── reppoints_moment_x101_dcn_fpn_2x.py │ ├── reppoints_moment_x101_dcn_fpn_2x_mt.py │ └── reppoints_partial_minmax_r50_fpn_1x.py ├── retinanet_r101_fpn_1x.py ├── retinanet_r50_fpn_1x.py ├── retinanet_x101_32x4d_fpn_1x.py ├── retinanet_x101_64x4d_fpn_1x.py ├── rpn_r101_fpn_1x.py ├── rpn_r50_caffe_c4_1x.py ├── rpn_r50_fpn_1x.py ├── rpn_x101_32x4d_fpn_1x.py ├── rpn_x101_64x4d_fpn_1x.py ├── scratch │ ├── README.md │ ├── scratch_faster_rcnn_r50_fpn_gn_6x.py │ └── scratch_mask_rcnn_r50_fpn_gn_6x.py ├── ssd300_coco.py ├── ssd512_coco.py └── wider_face │ ├── README.md │ └── ssd300_wider_face.py ├── demo ├── coco_test_12510.jpg ├── corruptions_sev_3.png ├── data_pipeline.png ├── demo.jpg ├── inference_demo.ipynb ├── loss_curve.png └── webcam_demo.py ├── docker └── Dockerfile ├── docs ├── CHANGELOG.md ├── GETTING_STARTED.md ├── INSTALL.md ├── MODEL_ZOO.md ├── Makefile ├── ROBUSTNESS_BENCHMARKING.md ├── TECHNICAL_DETAILS.md ├── conf.py ├── index.rst ├── make.bat └── requirements.txt ├── mmdet ├── __init__.py ├── apis │ ├── __init__.py │ ├── inference.py │ ├── test.py │ └── train.py ├── core │ ├── __init__.py │ ├── anchor │ │ ├── __init__.py │ │ ├── anchor_generator.py │ │ ├── anchor_target.py │ │ ├── guided_anchor_target.py │ │ ├── point_generator.py │ │ └── point_target.py │ ├── bbox │ │ ├── __init__.py │ │ ├── assign_sampling.py │ │ ├── assigners │ │ │ ├── __init__.py │ │ │ ├── approx_max_iou_assigner.py │ │ │ ├── assign_result.py │ │ │ ├── atss_assigner.py │ │ │ ├── base_assigner.py │ │ │ ├── max_iou_assigner.py │ │ │ └── point_assigner.py │ │ ├── bbox_target.py │ │ ├── demodata.py │ │ ├── geometry.py │ │ ├── samplers │ │ │ ├── __init__.py │ │ │ ├── base_sampler.py │ │ │ ├── combined_sampler.py │ │ │ ├── instance_balanced_pos_sampler.py │ │ │ ├── iou_balanced_neg_sampler.py │ │ │ ├── ohem_sampler.py │ │ │ ├── pseudo_sampler.py │ │ │ ├── random_sampler.py │ │ │ └── sampling_result.py │ │ └── transforms.py │ ├── evaluation │ │ ├── __init__.py │ │ ├── bbox_overlaps.py │ │ ├── class_names.py │ │ ├── eval_hooks.py │ │ ├── mean_ap.py │ │ └── recall.py │ ├── fp16 │ │ ├── __init__.py │ │ ├── decorators.py │ │ ├── hooks.py │ │ └── utils.py │ ├── mask │ │ ├── __init__.py │ │ ├── mask_target.py │ │ └── utils.py │ ├── optimizer │ │ ├── __init__.py │ │ ├── builder.py │ │ ├── copy_of_sgd.py │ │ └── registry.py │ ├── post_processing │ │ ├── __init__.py │ │ ├── bbox_nms.py │ │ └── merge_augs.py │ └── utils │ │ ├── __init__.py │ │ ├── dist_utils.py │ │ └── misc.py ├── datasets │ ├── __init__.py │ ├── builder.py │ ├── cityscapes.py │ ├── coco.py │ ├── custom.py │ ├── dataset_wrappers.py │ ├── loader │ │ ├── __init__.py │ │ ├── build_loader.py │ │ └── sampler.py │ ├── pipelines │ │ ├── __init__.py │ │ ├── compose.py │ │ ├── formating.py │ │ ├── instaboost.py │ │ ├── loading.py │ │ ├── test_aug.py │ │ └── transforms.py │ ├── registry.py │ ├── voc.py │ ├── wider_face.py │ └── xml_style.py ├── models │ ├── __init__.py │ ├── anchor_heads │ │ ├── __init__.py │ │ ├── anchor_head.py │ │ ├── atss_head.py │ │ ├── fcos_head.py │ │ ├── fovea_head.py │ │ ├── free_anchor_retina_head.py │ │ ├── ga_retina_head.py │ │ ├── ga_rpn_head.py │ │ ├── guided_anchor_head.py │ │ ├── reppoints_head.py │ │ ├── retina_head.py │ │ ├── retina_sepbn_head.py │ │ ├── rpn_head.py │ │ └── ssd_head.py │ ├── backbones │ │ ├── __init__.py │ │ ├── hrnet.py │ │ ├── resnet.py │ │ ├── resnext.py │ │ └── ssd_vgg.py │ ├── bbox_heads │ │ ├── __init__.py │ │ ├── bbox_head.py │ │ ├── convfc_bbox_head.py │ │ └── double_bbox_head.py │ ├── builder.py │ ├── detectors │ │ ├── __init__.py │ │ ├── atss.py │ │ ├── base.py │ │ ├── cascade_rcnn.py │ │ ├── double_head_rcnn.py │ │ ├── fast_rcnn.py │ │ ├── faster_rcnn.py │ │ ├── fcos.py │ │ ├── fovea.py │ │ ├── grid_rcnn.py │ │ ├── htc.py │ │ ├── mask_rcnn.py │ │ ├── mask_scoring_rcnn.py │ │ ├── reppoints_detector.py │ │ ├── retinanet.py │ │ ├── rfp.py │ │ ├── rpn.py │ │ ├── single_stage.py │ │ ├── test_mixins.py │ │ └── two_stage.py │ ├── losses │ │ ├── __init__.py │ │ ├── accuracy.py │ │ ├── balanced_l1_loss.py │ │ ├── cross_entropy_loss.py │ │ ├── focal_loss.py │ │ ├── ghm_loss.py │ │ ├── iou_loss.py │ │ ├── mse_loss.py │ │ ├── smooth_l1_loss.py │ │ └── utils.py │ ├── mask_heads │ │ ├── __init__.py │ │ ├── fcn_mask_head.py │ │ ├── fused_semantic_head.py │ │ ├── grid_head.py │ │ ├── htc_mask_head.py │ │ └── maskiou_head.py │ ├── necks │ │ ├── __init__.py │ │ ├── bfp.py │ │ ├── fpn.py │ │ ├── fpn_carafe.py │ │ ├── hrfpn.py │ │ └── nas_fpn.py │ ├── registry.py │ ├── roi_extractors │ │ ├── __init__.py │ │ └── single_level.py │ ├── shared_heads │ │ ├── __init__.py │ │ └── res_layer.py │ └── utils │ │ ├── __init__.py │ │ └── weight_init.py ├── ops │ ├── __init__.py │ ├── activation.py │ ├── affine_grid │ │ ├── __init__.py │ │ ├── affine_grid.py │ │ └── src │ │ │ └── affine_grid_cuda.cpp │ ├── carafe │ │ ├── __init__.py │ │ ├── carafe.py │ │ ├── grad_check.py │ │ ├── setup.py │ │ └── src │ │ │ ├── carafe_cuda.cpp │ │ │ ├── carafe_cuda_kernel.cu │ │ │ ├── carafe_naive_cuda.cpp │ │ │ └── carafe_naive_cuda_kernel.cu │ ├── context_block.py │ ├── conv.py │ ├── conv_module.py │ ├── conv_ws.py │ ├── dcn │ │ ├── __init__.py │ │ ├── deform_conv.py │ │ ├── deform_pool.py │ │ └── src │ │ │ ├── deform_conv_cuda.cpp │ │ │ ├── deform_conv_cuda_kernel.cu │ │ │ ├── deform_pool_cuda.cpp │ │ │ └── deform_pool_cuda_kernel.cu │ ├── generalized_attention.py │ ├── grid_sampler │ │ ├── __init__.py │ │ ├── grid_sampler.py │ │ └── src │ │ │ ├── cpu │ │ │ ├── grid_sampler_cpu.cpp │ │ │ └── grid_sampler_cpu.h │ │ │ ├── cuda │ │ │ ├── grid_sampler_cuda.cu │ │ │ └── grid_sampler_cuda.cuh │ │ │ ├── cudnn │ │ │ └── grid_sampler_cudnn.cpp │ │ │ └── grid_sampler.cpp │ ├── masked_conv │ │ ├── __init__.py │ │ ├── masked_conv.py │ │ └── src │ │ │ ├── masked_conv2d_cuda.cpp │ │ │ └── masked_conv2d_kernel.cu │ ├── nms │ │ ├── __init__.py │ │ ├── nms_wrapper.py │ │ └── src │ │ │ ├── nms_cpu.cpp │ │ │ ├── nms_cuda.cpp │ │ │ └── nms_kernel.cu │ ├── non_local.py │ ├── norm.py │ ├── roi_align │ │ ├── __init__.py │ │ ├── gradcheck.py │ │ ├── roi_align.py │ │ └── src │ │ │ ├── roi_align_cuda.cpp │ │ │ ├── roi_align_kernel.cu │ │ │ └── roi_align_kernel_v2.cu │ ├── roi_pool │ │ ├── __init__.py │ │ ├── gradcheck.py │ │ ├── roi_pool.py │ │ └── src │ │ │ ├── roi_pool_cuda.cpp │ │ │ └── roi_pool_kernel.cu │ ├── saconv.py │ ├── scale.py │ ├── sigmoid_focal_loss │ │ ├── __init__.py │ │ ├── sigmoid_focal_loss.py │ │ └── src │ │ │ ├── sigmoid_focal_loss.cpp │ │ │ └── sigmoid_focal_loss_cuda.cu │ ├── upsample.py │ └── utils │ │ ├── __init__.py │ │ └── src │ │ └── compiling_info.cpp └── utils │ ├── __init__.py │ ├── collect_env.py │ ├── contextmanagers.py │ ├── flops_counter.py │ ├── logger.py │ ├── profiling.py │ ├── registry.py │ └── util_mixins.py ├── pytest.ini ├── requirements.txt ├── requirements ├── build.txt ├── optional.txt ├── runtime.txt └── tests.txt ├── setup.py ├── tests ├── async_benchmark.py ├── test_assigner.py ├── test_async.py ├── test_config.py ├── test_forward.py ├── test_heads.py ├── test_nms.py ├── test_sampler.py ├── test_soft_nms.py └── test_utils.py └── tools ├── analyze_logs.py ├── browse_dataset.py ├── coco_error_analysis.py ├── convert_datasets ├── cityscapes.py └── pascal_voc.py ├── detectron2pytorch.py ├── dist_test.sh ├── dist_train.sh ├── fuse_conv_bn.py ├── get_flops.py ├── publish_model.py ├── pytorch2onnx.py ├── robustness_eval.py ├── slurm_test.sh ├── slurm_train.sh ├── test.py ├── test_robustness.py ├── train.py └── upgrade_model_version.py /.github/CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing to mmdetection 2 | 3 | All kinds of contributions are welcome, including but not limited to the following. 4 | 5 | - Fixes (typo, bugs) 6 | - New features and components 7 | 8 | ## Workflow 9 | 10 | 1. fork and pull the latest mmdetection 11 | 2. checkout a new branch (do not use master branch for PRs) 12 | 3. commit your changes 13 | 4. create a PR 14 | 15 | Note 16 | - If you plan to add some new features that involve large changes, it is encouraged to open an issue for discussion first. 17 | - If you are the author of some papers and would like to include your method to mmdetection, 18 | please contact Kai Chen (chenkaidev[at]gmail[dot]com). We will much appreciate your contribution. 19 | 20 | ## Code style 21 | 22 | ### Python 23 | We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style. 24 | 25 | We use the following tools for linting and formatting: 26 | - [flake8](http://flake8.pycqa.org/en/latest/): linter 27 | - [yapf](https://github.com/google/yapf): formatter 28 | - [isort](https://github.com/timothycrosley/isort): sort imports 29 | 30 | Style configurations of yapf and isort can be found in [.style.yapf](../.style.yapf) and [.isort.cfg](../.isort.cfg). 31 | 32 | We use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`, 33 | fixes `end-of-files`, sorts `requirments.txt` automatically on every commit. 34 | The config for a pre-commit hook is stored in [.pre-commit-config](../.pre-commit-config.yaml). 35 | 36 | After you clone the repository, you will need to install initialize pre-commit hook. 37 | 38 | ``` 39 | pip install -U pre-commit 40 | ``` 41 | 42 | From the repository folder 43 | ``` 44 | pre-commit install 45 | ``` 46 | 47 | After this on every commit check code linters and formatter will be enforced. 48 | 49 | 50 | >Before you create a PR, make sure that your code lints and is formatted by yapf. 51 | 52 | ### C++ and CUDA 53 | We follow the [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html). 54 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/config.yml: -------------------------------------------------------------------------------- 1 | blank_issues_enabled: false 2 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/error-report.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Error report 3 | about: Create a report to help us improve 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | Thanks for your error report and we appreciate it a lot. 11 | 12 | **Checklist** 13 | 1. I have searched related issues but cannot get the expected help. 14 | 2. The bug has not been fixed in the latest version. 15 | 16 | **Describe the bug** 17 | A clear and concise description of what the bug is. 18 | 19 | **Reproduction** 20 | 1. What command or script did you run? 21 | ``` 22 | A placeholder for the command. 23 | ``` 24 | 2. Did you make any modifications on the code or config? Did you understand what you have modified? 25 | 3. What dataset did you use? 26 | 27 | **Environment** 28 | 29 | 1. Please run `python mmdet/utils/collect_env.py` to collect necessary environment infomation and paste it here. 30 | 2. You may add addition that may be helpful for locating the problem, such as 31 | - How you installed PyTorch [e.g., pip, conda, source] 32 | - Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.) 33 | 34 | **Error traceback** 35 | If applicable, paste the error trackback here. 36 | ``` 37 | A placeholder for trackback. 38 | ``` 39 | 40 | **Bug fix** 41 | If you have already identified the reason, you can provide the information here. If you are willing to create a PR to fix it, please also leave a comment here and that would be much appreciated! 42 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/feature_request.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Feature request 3 | about: Suggest an idea for this project 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Describe the feature** 11 | 12 | **Motivation** 13 | A clear and concise description of the motivation of the feature. 14 | Ex1. It is inconvenient when [....]. 15 | Ex2. There is a recent paper [....], which is very helpful for [....]. 16 | 17 | **Related resources** 18 | If there is an official code release or third-party implementations, please also provide the information here, which would be very helpful. 19 | 20 | **Additional context** 21 | Add any other context or screenshots about the feature request here. 22 | If you would like to implement the feature and create a PR, please leave a comment here and that would be much appreciated. 23 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/general_questions.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: General questions 3 | about: Ask general questions to get help 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | 106 | mmdet/version.py 107 | data 108 | .vscode 109 | .idea 110 | 111 | # custom 112 | *.pkl 113 | *.pkl.json 114 | *.log.json 115 | work_dirs/ 116 | 117 | # Pytorch 118 | *.pth 119 | -------------------------------------------------------------------------------- /.isort.cfg: -------------------------------------------------------------------------------- 1 | [isort] 2 | line_length = 79 3 | multi_line_output = 0 4 | known_standard_library = setuptools 5 | known_first_party = mmdet 6 | known_third_party = asynctest,cityscapesscripts,cv2,matplotlib,mmcv,numpy,onnx,pycocotools,robustness_eval,roi_align,roi_pool,seaborn,six,terminaltables,torch,torchvision 7 | no_lines_before = STDLIB,LOCALFOLDER 8 | default_section = THIRDPARTY 9 | -------------------------------------------------------------------------------- /.pre-commit-config.yaml: -------------------------------------------------------------------------------- 1 | repos: 2 | - repo: https://gitlab.com/pycqa/flake8.git 3 | rev: 3.7.9 4 | hooks: 5 | - id: flake8 6 | - repo: https://github.com/asottile/seed-isort-config 7 | rev: v2.1.0 8 | hooks: 9 | - id: seed-isort-config 10 | - repo: https://github.com/timothycrosley/isort 11 | rev: 4.3.21 12 | hooks: 13 | - id: isort 14 | - repo: https://github.com/pre-commit/mirrors-yapf 15 | rev: v0.29.0 16 | hooks: 17 | - id: yapf 18 | - repo: https://github.com/pre-commit/pre-commit-hooks 19 | rev: v2.5.0 20 | hooks: 21 | - id: trailing-whitespace 22 | - id: check-yaml 23 | - id: end-of-file-fixer 24 | - id: requirements-txt-fixer 25 | - id: double-quote-string-fixer 26 | - id: fix-encoding-pragma 27 | args: ["--remove"] 28 | -------------------------------------------------------------------------------- /.style.yapf: -------------------------------------------------------------------------------- 1 | [style] 2 | BASED_ON_STYLE = pep8 3 | BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF = true 4 | SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true 5 | -------------------------------------------------------------------------------- /.travis.yml: -------------------------------------------------------------------------------- 1 | dist: bionic # ubuntu 18.04 2 | language: python 3 | 4 | python: 5 | - "3.5" 6 | - "3.6" 7 | - "3.7" 8 | 9 | env: CUDA=10.1.105-1 CUDA_SHORT=10.1 UBUNTU_VERSION=ubuntu1804 FORCE_CUDA=1 10 | cache: pip 11 | 12 | # Ref to CUDA installation in Travis: https://github.com/jeremad/cuda-travis 13 | before_install: 14 | - INSTALLER=cuda-repo-${UBUNTU_VERSION}_${CUDA}_amd64.deb 15 | - wget http://developer.download.nvidia.com/compute/cuda/repos/${UBUNTU_VERSION}/x86_64/${INSTALLER} 16 | - sudo dpkg -i ${INSTALLER} 17 | - wget https://developer.download.nvidia.com/compute/cuda/repos/${UBUNTU_VERSION}/x86_64/7fa2af80.pub 18 | - sudo apt-key add 7fa2af80.pub 19 | - sudo apt update -qq 20 | - sudo apt install -y cuda-${CUDA_SHORT/./-} cuda-cufft-dev-${CUDA_SHORT/./-} 21 | - sudo apt clean 22 | - CUDA_HOME=/usr/local/cuda-${CUDA_SHORT} 23 | - LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${CUDA_HOME}/include:${LD_LIBRARY_PATH} 24 | - PATH=${CUDA_HOME}/bin:${PATH} 25 | 26 | install: 27 | - pip install Pillow==6.2.2 # remove this line when torchvision>=0.5 28 | - pip install torch==1.2 torchvision==0.4.0 # TODO: fix CI for pytorch>1.2 29 | - pip install "git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI" 30 | - pip install -r requirements.txt 31 | 32 | before_script: 33 | - flake8 . 34 | - isort -rc --check-only --diff mmdet/ tools/ tests/ 35 | - yapf -r -d --style .style.yapf mmdet/ tools/ tests/ configs/ 36 | 37 | script: 38 | - python setup.py check -m -s 39 | - python setup.py build_ext --inplace 40 | - coverage run --source mmdet -m py.test -v --xdoctest-modules tests mmdet 41 | 42 | after_success: 43 | - coverage report 44 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # DetectoRS 2 | 3 | ## News 4 | 5 | [06/30/2020] DetectoRS is now officially supported by [MMDetection](https://github.com/open-mmlab/mmdetection). 6 | A huge thanks to [@xvjiarui](https://github.com/xvjiarui), [@ZwwWayne](https://github.com/ZwwWayne) and [@hellock](https://github.com/hellock) for helping migrating the code. 7 | 8 | [06/15/2020] We have released the implementation of DetectoRS based on mmdetection-v2 in the branch **mmdetv2**, which allows more detectors to use RFP and SAC. 9 | 10 | ## Introduction 11 | 12 | This repo holds the code for [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://arxiv.org/pdf/2006.02334.pdf). 13 | The project is based on [mmdetection codebase](https://github.com/open-mmlab/mmdetection). 14 | Please refer to [mmdetection readme](README.mmdet.md) for installation and running scripts. 15 | The code is tested with PyTorch 1.4.0. 16 | It may not run with other versions. 17 | See [conda_env.md](conda_env.md) for the versions of all the packages. 18 | 19 | ## Main Results on COCO test-dev 20 | 21 | | Method | Backbone | Config | Model | Box AP | Mask AP | 22 | |-----------|:-----------------:|--------------|--------------|:------------:|:------------:| 23 | | DetectoRS | ResNet-50 | [File Link](configs/DetectoRS/DetectoRS_mstrain_400_1200_r50_40e.py) | [Download](http://cs.jhu.edu/~syqiao/DetectoRS/DetectoRS_R50-0f1c8080.pth) | 51.3 | 44.4 | 24 | | DetectoRS | ResNeXt-101-32x4d | [File Link](configs/DetectoRS/DetectoRS_mstrain_400_1200_x101_32x4d_40e.py) | [Download](https://www.cs.jhu.edu/~syqiao/DetectoRS/DetectoRS_X101-ed983634.pth) | 53.3 | 45.8 | 25 | 26 | ## Citing DetectoRS 27 | 28 | If you think DetectoRS is useful in your project, please consider citing us. 29 | 30 | ```BibTeX 31 | @article{detectors, 32 | title={DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution}, 33 | author={Qiao, Siyuan and Chen, Liang-Chieh and Yuille, Alan}, 34 | journal={arXiv preprint arXiv:2006.02334}, 35 | year={2020} 36 | } 37 | ``` 38 | -------------------------------------------------------------------------------- /configs/atss/README.md: -------------------------------------------------------------------------------- 1 | # Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection 2 | 3 | 4 | ## Introduction 5 | 6 | ``` 7 | @article{zhang2019bridging, 8 | title = {Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection}, 9 | author = {Zhang, Shifeng and Chi, Cheng and Yao, Yongqiang and Lei, Zhen and Li, Stan Z.}, 10 | journal = {arXiv preprint arXiv:1912.02424}, 11 | year = {2019} 12 | } 13 | ``` 14 | 15 | 16 | ## Results and Models 17 | 18 | | Backbone | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download | 19 | |:---------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:| 20 | | R-50 | pytorch | 1x | 3.6 | 0.357 | 12.8 | 39.2 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/atss/atss_r50_fpn_1x_20200113-a7aa251e.pth)| 21 | -------------------------------------------------------------------------------- /configs/carafe/README.md: -------------------------------------------------------------------------------- 1 | # CARAFE: Content-Aware ReAssembly of FEatures 2 | 3 | ## Introduction 4 | 5 | We provide config files to reproduce the object detection & instance segmentation results in the ICCV 2019 Oral paper for [CARAFE: Content-Aware ReAssembly of FEatures](https://arxiv.org/abs/1905.02188). 6 | 7 | ``` 8 | @inproceedings{Wang_2019_ICCV, 9 | title = {CARAFE: Content-Aware ReAssembly of FEatures}, 10 | author = {Wang, Jiaqi and Chen, Kai and Xu, Rui and Liu, Ziwei and Loy, Chen Change and Lin, Dahua}, 11 | booktitle = {The IEEE International Conference on Computer Vision (ICCV)}, 12 | month = {October}, 13 | year = {2019} 14 | } 15 | ``` 16 | 17 | ## Results and Models 18 | 19 | The results on COCO 2017 val is shown in the below table. 20 | 21 | | Method | Backbone | Style | Lr schd | Test Proposal Num| Box AP | Mask AP | Download | 22 | | :--------------------: | :-------------: | :-----: | :-----: | :--------------: | :----: | :--------: |:----------------------------------------------------------------------------------------------------: | 23 | | Faster R-CNN w/ CARAFE | R-50-FPN | pytorch | 1x | 1000 | 37.8 | - | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/carafe/faster_rcnn_r50_fpn_carafe_1x-2ca2d094.pth) | 24 | | - | - | - | - | 2000 | 37.9 | - | - | 25 | | Mask R-CNN w/ CARAFE | R-50-FPN | pytorch | 1x | 1000 | 38.6 | 35.6| [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/carafe/mask_rcnn_r50_fpn_carafe_1x-2cc4b9fe.pth) | 26 | | - | - | - | - | 2000 | 38.6 | 35.7| - | 27 | 28 | ## Implementation 29 | 30 | The CUDA implementation of CARAFE can be find at `mmdet/ops/carafe` under this repository. 31 | 32 | ## Setup CARAFE 33 | 34 | a. Use CARAFE in mmdetection. 35 | 36 | Install mmdetection following the official guide. 37 | 38 | b. Use CARAFE in your own project. 39 | 40 | Git clone mmdetection. 41 | ```shell 42 | git clone https://github.com/open-mmlab/mmdetection.git 43 | cd mmdetection 44 | ``` 45 | Setup CARAFE in our project. 46 | ```shell 47 | cp -r ./mmdet/ops/carafe $Your_Project_Path$ 48 | cd $Your_Project_Path$/carafe 49 | python setup.py develop 50 | # or "pip install -v -e ." 51 | cd .. 52 | python ./carafe/grad_check.py 53 | ``` 54 | -------------------------------------------------------------------------------- /configs/cityscapes/README.md: -------------------------------------------------------------------------------- 1 | ## Common settings 2 | 3 | - All baselines were trained using 8 GPU with a batch size of 8 (1 images per GPU) using the [linear scaling rule](https://arxiv.org/abs/1706.02677) to scale the learning rate. 4 | - All models were trained on `cityscapes_train`, and tested on `cityscapes_val`. 5 | - 1x training schedule indicates 64 epochs which corresponds to slightly less than the 24k iterations reported in the original schedule from the [Mask R-CNN paper](https://arxiv.org/abs/1703.06870) 6 | - COCO pre-trained weights are used to initialize. 7 | - A conversion [script](../../tools/convert_datasets/cityscapes.py) is provided to convert Cityscapes into COCO format. Please refer to [INSTALL.md](../../docs/INSTALL.md#prepare-datasets) for details. 8 | - `CityscapesDataset` implemented three evaluation methods. `bbox` and `segm` are standard COCO bbox/mask AP. `cityscapes` is the cityscapes dataset official evaluation, which may be slightly higher than COCO. 9 | 10 | 11 | ### Faster R-CNN 12 | 13 | | Backbone | Style | Lr schd | Scale | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download | 14 | | :-------------: | :-----: | :-----: | :---: | :------: | :-----------------: | :------------: | :----: | :------: | 15 | | R-50-FPN | pytorch | 1x | 800-1024 | 4.9 | - | - | 41.6 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes_20200227-362cfbbf.pth) | 16 | 17 | ### Mask R-CNN 18 | 19 | | Backbone | Style | Lr schd | Scale | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download | 20 | | :-------------: | :-----: | :-----: | :------: | :------: | :-----------------: | :------------: | :----: | :-----: | :------: | 21 | | R-50-FPN | pytorch | 1x | 800-1024 | 4.9 | - | - | 41.9 | 37.1 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes_20200227-afe51d5a.pth) | 22 | -------------------------------------------------------------------------------- /configs/empirical_attention/README.md: -------------------------------------------------------------------------------- 1 | # An Empirical Study of Spatial Attention Mechanisms in Deep Networks 2 | 3 | ## Introduction 4 | 5 | ``` 6 | @article{zhu2019empirical, 7 | title={An Empirical Study of Spatial Attention Mechanisms in Deep Networks}, 8 | author={Zhu, Xizhou and Cheng, Dazhi and Zhang, Zheng and Lin, Stephen and Dai, Jifeng}, 9 | journal={arXiv preprint arXiv:1904.05873}, 10 | year={2019} 11 | } 12 | ``` 13 | 14 | 15 | ## Results and Models 16 | 17 | | Backbone | Attention Component | DCN | Lr schd | box AP | Download | 18 | |:---------:|:-------------------:|:----:|:-------:|:------:|:--------:| 19 | | R-50 | 1111 | N | 1x | 38.6 | - | 20 | | R-50 | 0010 | N | 1x | 38.2 | - | 21 | | R-50 | 1111 | Y | 1x | 41.0 | - | 22 | | R-50 | 0010 | Y | 1x | 40.8 | - | 23 | -------------------------------------------------------------------------------- /configs/fcos/README.md: -------------------------------------------------------------------------------- 1 | # FCOS: Fully Convolutional One-Stage Object Detection 2 | 3 | ## Introduction 4 | 5 | ``` 6 | @article{tian2019fcos, 7 | title={FCOS: Fully Convolutional One-Stage Object Detection}, 8 | author={Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong}, 9 | journal={arXiv preprint arXiv:1904.01355}, 10 | year={2019} 11 | } 12 | ``` 13 | 14 | ## Results and Models 15 | 16 | | Backbone | Style | GN | MS train | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download | 17 | |:---------:|:-------:|:-------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:| 18 | | R-50 | caffe | N | N | 1x | 5.5 | 0.373 | 13.7 | 35.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fcos/fcos_r50_caffe_fpn_1x_4gpu_20190516-a7cac5ff.pth) | 19 | | R-50 | caffe | Y | N | 1x | 6.9 | 0.396 | 13.6 | 36.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fcos/fcos_r50_caffe_fpn_gn_1x_4gpu_20190516-9f253a93.pth) | 20 | | R-50 | caffe | Y | N | 2x | - | - | - | 36.9 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fcos/fcos_r50_caffe_fpn_gn_2x_4gpu_20190516_-93484354.pth) | 21 | | R-101 | caffe | Y | N | 1x | 10.4 | 0.558 | 11.6 | 39.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fcos/fcos_r101_caffe_fpn_gn_1x_4gpu_20190516-e4889733.pth) | 22 | | R-101 | caffe | Y | N | 2x | - | - | - | 39.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fcos/fcos_r101_caffe_fpn_gn_2x_4gpu_20190516-c03af97b.pth) | 23 | 24 | 25 | | Backbone | Style | GN | MS train | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download | 26 | |:---------:|:-------:|:-------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:| 27 | | R-50 | caffe | Y | Y | 2x | - | - | - | 38.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fcos/fcos_mstrain_640_800_r50_caffe_fpn_gn_2x_4gpu_20190516-f7329d80.pth) | 28 | | R-101 | caffe | Y | Y | 2x | - | - | - | 40.8 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fcos/fcos_mstrain_640_800_r101_caffe_fpn_gn_2x_4gpu_20190516-42e6f62d.pth) | 29 | | X-101 | caffe | Y | Y | 2x | 9.7 | 0.892 | 7.0 | 42.8 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fcos/fcos_mstrain_640_800_x101_64x4d_fpn_gn_2x_20190516-a36c0872.pth) | 30 | 31 | **Notes:** 32 | - To be consistent with the author's implementation, we use 4 GPUs with 4 images/GPU for R-50 and R-101 models, and 8 GPUs with 2 image/GPU for X-101 models. 33 | - The X-101 backbone is X-101-64x4d. 34 | -------------------------------------------------------------------------------- /configs/free_anchor/README.md: -------------------------------------------------------------------------------- 1 | # FreeAnchor: Learning to Match Anchors for Visual Object Detection 2 | 3 | ## Introduction 4 | 5 | ``` 6 | @inproceedings{zhang2019freeanchor, 7 | title = {{FreeAnchor}: Learning to Match Anchors for Visual Object Detection}, 8 | author = {Zhang, Xiaosong and Wan, Fang and Liu, Chang and Ji, Rongrong and Ye, Qixiang}, 9 | booktitle = {Neural Information Processing Systems}, 10 | year = {2019} 11 | } 12 | ``` 13 | 14 | ## Results and Models 15 | 16 | | Backbone | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download | 17 | |:---------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:| 18 | | R-50 | pytorch | 1x | 4.7 | 0.322 | 12.0 | 38.4 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/free_anchor/retinanet_free_anchor_r50_fpn_1x_20190914-84db6585.pth) | 19 | | R-101 | pytorch | 1x | 6.6 | 0.437 | 9.7 | 40.3 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/free_anchor/retinanet_free_anchor_r101_fpn_1x_20190914-c4e4db81.pth) | 20 | | X-101-32x4d | pytorch | 1x | 7.8 | 0.640 | 8.4 | 42.0 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/free_anchor/retinanet_free_anchor_x101-32x4d_fpn_1x_20190914-eb73b804.pth) | 21 | 22 | **Notes:** 23 | - We use 8 GPUs with 2 images/GPU. 24 | - For more settings and models, please refer to the [official repo](https://github.com/zhangxiaosong18/FreeAnchor). 25 | -------------------------------------------------------------------------------- /configs/ghm/README.md: -------------------------------------------------------------------------------- 1 | # Gradient Harmonized Single-stage Detector 2 | 3 | ## Introduction 4 | 5 | ``` 6 | @inproceedings{li2019gradient, 7 | title={Gradient Harmonized Single-stage Detector}, 8 | author={Li, Buyu and Liu, Yu and Wang, Xiaogang}, 9 | booktitle={AAAI Conference on Artificial Intelligence}, 10 | year={2019} 11 | } 12 | ``` 13 | 14 | ## Results and Models 15 | 16 | | Backbone | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download | 17 | | :-------------: | :-----: | :-----: | :------: | :-----------------: | :------------: | :----: | :------: | 18 | | R-50-FPN | pytorch | 1x | 3.9 | 0.500 | 9.4 | 36.9 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/ghm/retinanet_ghm_r50_fpn_1x_20190608-b9aa5862.pth) | 19 | | R-101-FPN | pytorch | 1x | 5.8 | 0.625 | 8.5 | 39.0 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/ghm/retinanet_ghm_r101_fpn_1x_20190608-b885b74a.pth) | 20 | | X-101-32x4d-FPN | pytorch | 1x | 7.0 | 0.818 | 7.6 | 40.5 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/ghm/retinanet_ghm_x101_32x4d_fpn_1x_20190608-ed295d22.pth) | 21 | | X-101-64x4d-FPN | pytorch | 1x | 9.9 | 1.191 | 6.1 | 41.6 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/ghm/retinanet_ghm_x101_64x4d_fpn_1x_20190608-7f2037ce.pth) | 22 | -------------------------------------------------------------------------------- /configs/gn/README.md: -------------------------------------------------------------------------------- 1 | # Group Normalization 2 | 3 | ## Introduction 4 | 5 | ``` 6 | @inproceedings{wu2018group, 7 | title={Group Normalization}, 8 | author={Wu, Yuxin and He, Kaiming}, 9 | booktitle={Proceedings of the European Conference on Computer Vision (ECCV)}, 10 | year={2018} 11 | } 12 | ``` 13 | 14 | ## Results and Models 15 | 16 | | Backbone | model | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download | 17 | |:-------------:|:----------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:-------:|:--------:| 18 | | R-50-FPN (d) | Mask R-CNN | 2x | 7.2 | 0.806 | 5.4 | 39.8 | 36.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/gn/mask_rcnn_r50_fpn_gn_2x_20180113-86832cf2.pth) | 19 | | R-50-FPN (d) | Mask R-CNN | 3x | 7.2 | 0.806 | 5.4 | 40.1 | 36.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/gn/mask_rcnn_r50_fpn_gn_3x_20180113-8e82f48d.pth) | 20 | | R-101-FPN (d) | Mask R-CNN | 2x | 9.9 | 0.970 | 4.8 | 41.5 | 37.0 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/gn/mask_rcnn_r101_fpn_gn_2x_20180113-9598649c.pth) | 21 | | R-101-FPN (d) | Mask R-CNN | 3x | 9.9 | 0.970 | 4.8 | 41.6 | 37.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/gn/mask_rcnn_r101_fpn_gn_3x_20180113-a14ffb96.pth) | 22 | | R-50-FPN (c) | Mask R-CNN | 2x | 7.2 | 0.806 | 5.4 | 39.7 | 35.9 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/gn/mask_rcnn_r50_fpn_gn_contrib_2x_20180113-ec93305c.pth) | 23 | | R-50-FPN (c) | Mask R-CNN | 3x | 7.2 | 0.806 | 5.4 | 40.0 | 36.2 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/gn/mask_rcnn_r50_fpn_gn_contrib_3x_20180113-9d230cab.pth) | 24 | 25 | **Notes:** 26 | - (d) means pretrained model converted from Detectron, and (c) means the contributed model pretrained by [@thangvubk](https://github.com/thangvubk). 27 | - The `3x` schedule is epoch [28, 34, 36]. 28 | - **Memory, Train/Inf time is outdated.** 29 | -------------------------------------------------------------------------------- /configs/grid_rcnn/README.md: -------------------------------------------------------------------------------- 1 | # Grid R-CNN 2 | 3 | ## Introduction 4 | 5 | ``` 6 | @inproceedings{lu2019grid, 7 | title={Grid r-cnn}, 8 | author={Lu, Xin and Li, Buyu and Yue, Yuxin and Li, Quanquan and Yan, Junjie}, 9 | booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, 10 | year={2019} 11 | } 12 | 13 | @article{lu2019grid, 14 | title={Grid R-CNN Plus: Faster and Better}, 15 | author={Lu, Xin and Li, Buyu and Yue, Yuxin and Li, Quanquan and Yan, Junjie}, 16 | journal={arXiv preprint arXiv:1906.05688}, 17 | year={2019} 18 | } 19 | ``` 20 | 21 | ## Results and Models 22 | 23 | | Backbone | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download | 24 | |:-----------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:| 25 | | R-50 | 2x | 4.8 | 1.172 | 10.9 | 40.3 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/grid_rcnn/grid_rcnn_gn_head_r50_fpn_2x_20190619-5b29cf9d.pth) | 26 | | R-101 | 2x | 6.7 | 1.214 | 10.0 | 41.7 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/grid_rcnn/grid_rcnn_gn_head_r101_fpn_2x_20190619-a4b61645.pth) | 27 | | X-101-32x4d | 2x | 8.0 | 1.335 | 8.5 | 43.0 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/grid_rcnn/grid_rcnn_gn_head_x101_32x4d_fpn_2x_20190619-0bbfd87a.pth) | 28 | | X-101-64x4d | 2x | 10.9 | 1.753 | 6.4 | 43.1 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/grid_rcnn/grid_rcnn_gn_head_x101_64x4d_fpn_2x_20190619-8f4e20bb.pth) | 29 | 30 | **Notes:** 31 | - All models are trained with 8 GPUs instead of 32 GPUs in the original paper. 32 | - The warming up lasts for 1 epoch and `2x` here indicates 25 epochs. 33 | -------------------------------------------------------------------------------- /configs/libra_rcnn/README.md: -------------------------------------------------------------------------------- 1 | # Libra R-CNN: Towards Balanced Learning for Object Detection 2 | 3 | ## Introduction 4 | 5 | We provide config files to reproduce the results in the CVPR 2019 paper [Libra R-CNN](https://arxiv.org/pdf/1904.02701.pdf). 6 | 7 | ``` 8 | @inproceedings{pang2019libra, 9 | title={Libra R-CNN: Towards Balanced Learning for Object Detection}, 10 | author={Pang, Jiangmiao and Chen, Kai and Shi, Jianping and Feng, Huajun and Ouyang, Wanli and Dahua Lin}, 11 | booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, 12 | year={2019} 13 | } 14 | ``` 15 | 16 | ## Results and models 17 | 18 | The results on COCO 2017val are shown in the below table. (results on test-dev are usually slightly higher than val) 19 | 20 | | Architecture | Backbone | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download | 21 | |:---------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:-------:|:--------:| 22 | | Faster R-CNN | R-50-FPN | pytorch | 1x | 4.2 | 0.375 | 12.0 | 38.5 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_20190610-bf0ea559.pth) | 23 | | Fast R-CNN | R-50-FPN | pytorch | 1x | 3.7 | 0.272 | 16.3 | 38.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/libra_rcnn/libra_fast_rcnn_r50_fpn_1x_20190525-a43f88b5.pth) | 24 | | Faster R-CNN | R-101-FPN | pytorch | 1x | 6.0 | 0.495 | 10.4 | 40.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_20190525-94e94051.pth) | 25 | | Faster R-CNN | X-101-64x4d-FPN | pytorch | 1x | 10.1 | 1.050 | 6.8 | 42.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_20190525-359c134a.pth) | 26 | | RetinaNet | R-50-FPN | pytorch | 1x | 3.7 | 0.328 | 11.8 | 37.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/libra_rcnn/libra_retinanet_r50_fpn_1x_20190525-ead2a6bb.pth) | 27 | -------------------------------------------------------------------------------- /configs/ms_rcnn/README.md: -------------------------------------------------------------------------------- 1 | # Mask Scoring R-CNN 2 | 3 | ## Introduction 4 | 5 | ``` 6 | @inproceedings{huang2019msrcnn, 7 | title={Mask Scoring R-CNN}, 8 | author={Zhaojin Huang and Lichao Huang and Yongchao Gong and Chang Huang and Xinggang Wang}, 9 | booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, 10 | year={2019}, 11 | } 12 | ``` 13 | 14 | ## Results and Models 15 | 16 | | Backbone | style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download | 17 | |:-------------:|:----------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:-------:|:--------:| 18 | | R-50-FPN | caffe | 1x | 4.3 | 0.537 | 10.1 | 37.4 | 35.5 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/ms-rcnn/ms_rcnn_r50_caffe_fpn_1x_20190624-619934b5.pth) | 19 | | R-50-FPN | caffe | 2x | - | - | - | 38.2 | 35.9 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ms-rcnn/ms_rcnn_r50_caffe_fpn_2x_20190525-a07be31e.pth) | 20 | | R-101-FPN | caffe | 1x | 6.2 | 0.682 | 9.1 | 39.8 | 37.2 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/ms-rcnn/ms_rcnn_r101_caffe_fpn_1x_20190624-677a5548.pth) | 21 | | R-101-FPN | caffe | 2x | - | - | - | 40.7 | 37.8 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ms-rcnn/ms_rcnn_r101_caffe_fpn_2x_20190525-4aee1528.pth) | 22 | | R-X101-32x4d | pytorch | 2x | 7.6 | 0.844 | 8.0 | 41.7 | 38.5 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/ms-rcnn/ms_rcnn_x101_32x4d_fpn_2x_20190628-ab454d07.pth) | 23 | | R-X101-64x4d | pytorch | 1x | 10.5 | 1.214 | 6.4 | 42.0 | 39.1 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/ms-rcnn/ms_rcnn_x101_64x4d_fpn_1x_20190628-dec32bda.pth) | 24 | | R-X101-64x4d | pytorch | 2x | - | - | - | 42.2 | 38.9 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ms-rcnn/ms_rcnn_x101_64x4d_fpn_2x_20190525-c044c25a.pth) | 25 | -------------------------------------------------------------------------------- /configs/nas_fpn/README.md: -------------------------------------------------------------------------------- 1 | # NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection 2 | 3 | ## Introduction 4 | 5 | ``` 6 | @inproceedings{ghiasi2019fpn, 7 | title={Nas-fpn: Learning scalable feature pyramid architecture for object detection}, 8 | author={Ghiasi, Golnaz and Lin, Tsung-Yi and Le, Quoc V}, 9 | booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, 10 | pages={7036--7045}, 11 | year={2019} 12 | } 13 | ``` 14 | 15 | ## Results and Models 16 | 17 | We benchmark the new training schedule (crop training, large batch, unfrozen BN, 50 epochs) introduced in NAS-FPN. RetinaNet is used in the paper. 18 | 19 | | Backbone | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download | 20 | |:-----------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:| 21 | | R-50-FPN | 50e | 12.8 | 0.513 | 15.3 | 37.0 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/nas_fpn/retinanet_crop640_r50_fpn_50e_190824-4d75bfa0.pth) | 22 | | R-50-NASFPN | 50e | 14.8 | 0.662 | 13.1 | 39.8 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/nas_fpn/retinanet_crop640_r50_nasfpn_50e_20191225-b82d3a86.pth) | 23 | 24 | 25 | **Note**: We find that it is unstable to train NAS-FPN and there is a small chance that results can be 3% mAP lower. 26 | -------------------------------------------------------------------------------- /configs/pascal_voc/README.md: -------------------------------------------------------------------------------- 1 | ### SSD 2 | 3 | | Backbone | Size | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download | 4 | | :------: | :---: | :---: | :-----: | :------: | :-----------------: | :------------: | :----: | :------------------------------------------------------------------------------------------------------------------------------: | 5 | | VGG16 | 300 | caffe | 240e | 2.5 | 0.159 | 35.7 / 53.6 | 77.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ssd300_voc_vgg16_caffe_240e_20190501-7160d09a.pth) | 6 | | VGG16 | 512 | caffe | 240e | 4.3 | 0.214 | 27.5 / 35.9 | 80.0 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ssd512_voc_vgg16_caffe_240e_20190501-ff194be1.pth) | 7 | -------------------------------------------------------------------------------- /configs/reppoints/reppoints.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/joe-siyuan-qiao/DetectoRS/612916ba89ad6452b07ae52d3a6ec8d34a792608/configs/reppoints/reppoints.png -------------------------------------------------------------------------------- /configs/scratch/README.md: -------------------------------------------------------------------------------- 1 | # Rethinking ImageNet Pre-training 2 | 3 | ## Introduction 4 | 5 | ``` 6 | @article{he2018rethinking, 7 | title={Rethinking imagenet pre-training}, 8 | author={He, Kaiming and Girshick, Ross and Doll{\'a}r, Piotr}, 9 | journal={arXiv preprint arXiv:1811.08883}, 10 | year={2018} 11 | } 12 | ``` 13 | 14 | ## Results and Models 15 | 16 | | Model | Backbone | Style | Lr schd | box AP | mask AP | Download | 17 | |:------------:|:---------:|:-------:|:-------:|:------:|:-------:|:--------:| 18 | | Faster R-CNN | R-50-FPN | pytorch | 6x | 40.1 | - | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/scratch/scratch_faster_rcnn_r50_fpn_gn_6x_20190515-ff554978.pth) | 19 | | Mask R-CNN | R-50-FPN | pytorch | 6x | 41.0 | 37.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/scratch/scratch_mask_rcnn_r50_fpn_gn_6x_20190515-96743f5e.pth) | 20 | 21 | Note: 22 | - The above models are trained with 16 GPUs. 23 | -------------------------------------------------------------------------------- /configs/wider_face/README.md: -------------------------------------------------------------------------------- 1 | ## WIDER Face Dataset 2 | 3 | To use the WIDER Face dataset you need to download it 4 | and extract to the `data/WIDERFace` folder. Annotation in the VOC format 5 | can be found in this [repo](https://github.com/sovrasov/wider-face-pascal-voc-annotations.git). 6 | You should move the annotation files from `WIDER_train_annotations` and `WIDER_val_annotations` folders 7 | to the `Annotation` folders inside the corresponding directories `WIDER_train` and `WIDER_val`. 8 | Also annotation lists `val.txt` and `train.txt` should be copied to `data/WIDERFace` from `WIDER_train_annotations` and `WIDER_val_annotations`. 9 | The directory should be like this: 10 | 11 | ``` 12 | mmdetection 13 | ├── mmdet 14 | ├── tools 15 | ├── configs 16 | ├── data 17 | │ ├── WIDERFace 18 | │ │ ├── WIDER_train 19 | │ | │ ├──0--Parade 20 | │ | │ ├── ... 21 | │ | │ ├── Annotations 22 | │ │ ├── WIDER_val 23 | │ | │ ├──0--Parade 24 | │ | │ ├── ... 25 | │ | │ ├── Annotations 26 | │ │ ├── val.txt 27 | │ │ ├── train.txt 28 | 29 | ``` 30 | 31 | After that you can train the SSD300 on WIDER by launching training with the `ssd300_wider_face.py` config or 32 | create your own config based on the presented one. 33 | -------------------------------------------------------------------------------- /demo/coco_test_12510.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/joe-siyuan-qiao/DetectoRS/612916ba89ad6452b07ae52d3a6ec8d34a792608/demo/coco_test_12510.jpg -------------------------------------------------------------------------------- /demo/corruptions_sev_3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/joe-siyuan-qiao/DetectoRS/612916ba89ad6452b07ae52d3a6ec8d34a792608/demo/corruptions_sev_3.png -------------------------------------------------------------------------------- /demo/data_pipeline.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/joe-siyuan-qiao/DetectoRS/612916ba89ad6452b07ae52d3a6ec8d34a792608/demo/data_pipeline.png -------------------------------------------------------------------------------- /demo/demo.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/joe-siyuan-qiao/DetectoRS/612916ba89ad6452b07ae52d3a6ec8d34a792608/demo/demo.jpg -------------------------------------------------------------------------------- /demo/loss_curve.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/joe-siyuan-qiao/DetectoRS/612916ba89ad6452b07ae52d3a6ec8d34a792608/demo/loss_curve.png -------------------------------------------------------------------------------- /demo/webcam_demo.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | import cv2 4 | import torch 5 | 6 | from mmdet.apis import inference_detector, init_detector, show_result 7 | 8 | 9 | def parse_args(): 10 | parser = argparse.ArgumentParser(description='MMDetection webcam demo') 11 | parser.add_argument('config', help='test config file path') 12 | parser.add_argument('checkpoint', help='checkpoint file') 13 | parser.add_argument('--device', type=int, default=0, help='CUDA device id') 14 | parser.add_argument( 15 | '--camera-id', type=int, default=0, help='camera device id') 16 | parser.add_argument( 17 | '--score-thr', type=float, default=0.5, help='bbox score threshold') 18 | args = parser.parse_args() 19 | return args 20 | 21 | 22 | def main(): 23 | args = parse_args() 24 | 25 | model = init_detector( 26 | args.config, args.checkpoint, device=torch.device('cuda', args.device)) 27 | 28 | camera = cv2.VideoCapture(args.camera_id) 29 | 30 | print('Press "Esc", "q" or "Q" to exit.') 31 | while True: 32 | ret_val, img = camera.read() 33 | result = inference_detector(model, img) 34 | 35 | ch = cv2.waitKey(1) 36 | if ch == 27 or ch == ord('q') or ch == ord('Q'): 37 | break 38 | 39 | show_result( 40 | img, result, model.CLASSES, score_thr=args.score_thr, wait_time=1) 41 | 42 | 43 | if __name__ == '__main__': 44 | main() 45 | -------------------------------------------------------------------------------- /docker/Dockerfile: -------------------------------------------------------------------------------- 1 | ARG PYTORCH="1.3" 2 | ARG CUDA="10.1" 3 | ARG CUDNN="7" 4 | 5 | FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel 6 | 7 | ENV TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0+PTX" 8 | ENV TORCH_NVCC_FLAGS="-Xfatbin -compress-all" 9 | ENV CMAKE_PREFIX_PATH="$(dirname $(which conda))/../" 10 | 11 | RUN apt-get update && apt-get install -y libglib2.0-0 libsm6 libxrender-dev libxext6 \ 12 | && apt-get clean \ 13 | && rm -rf /var/lib/apt/lists/* 14 | 15 | # Install mmdetection 16 | RUN conda clean --all 17 | RUN git clone https://github.com/open-mmlab/mmdetection.git /mmdetection 18 | WORKDIR /mmdetection 19 | ENV FORCE_CUDA="1" 20 | RUN pip install --no-cache-dir -e . 21 | -------------------------------------------------------------------------------- /docs/Makefile: -------------------------------------------------------------------------------- 1 | # Minimal makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line, and also 5 | # from the environment for the first two. 6 | SPHINXOPTS ?= 7 | SPHINXBUILD ?= sphinx-build 8 | SOURCEDIR = . 9 | BUILDDIR = _build 10 | 11 | # Put it first so that "make" without argument is like "make help". 12 | help: 13 | @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 14 | 15 | .PHONY: help Makefile 16 | 17 | # Catch-all target: route all unknown targets to Sphinx using the new 18 | # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). 19 | %: Makefile 20 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 21 | -------------------------------------------------------------------------------- /docs/conf.py: -------------------------------------------------------------------------------- 1 | # Configuration file for the Sphinx documentation builder. 2 | # 3 | # This file only contains a selection of the most common options. For a full 4 | # list see the documentation: 5 | # https://www.sphinx-doc.org/en/master/usage/configuration.html 6 | 7 | # -- Path setup -------------------------------------------------------------- 8 | 9 | # If extensions (or modules to document with autodoc) are in another directory, 10 | # add these directories to sys.path here. If the directory is relative to the 11 | # documentation root, use os.path.abspath to make it absolute, like shown here. 12 | # 13 | # import os 14 | # import sys 15 | # sys.path.insert(0, os.path.abspath('.')) 16 | 17 | # -- Project information ----------------------------------------------------- 18 | 19 | project = 'MMDetection' 20 | copyright = '2018-2020, OpenMMLab' 21 | author = 'OpenMMLab' 22 | 23 | # The full version, including alpha/beta/rc tags 24 | release = '1.0.0' 25 | 26 | # -- General configuration --------------------------------------------------- 27 | 28 | # Add any Sphinx extension module names here, as strings. They can be 29 | # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom 30 | # ones. 31 | extensions = [ 32 | 'sphinx.ext.autodoc', 33 | 'sphinx.ext.napoleon', 34 | 'sphinx.ext.viewcode', 35 | 'recommonmark', 36 | 'sphinx_markdown_tables', 37 | ] 38 | 39 | autodoc_mock_imports = ['torch', 'torchvision', 'mmcv'] 40 | 41 | # Add any paths that contain templates here, relative to this directory. 42 | templates_path = ['_templates'] 43 | 44 | # The suffix(es) of source filenames. 45 | # You can specify multiple suffix as a list of string: 46 | # 47 | source_suffix = { 48 | '.rst': 'restructuredtext', 49 | '.md': 'markdown', 50 | } 51 | 52 | # The master toctree document. 53 | master_doc = 'index' 54 | 55 | # List of patterns, relative to source directory, that match files and 56 | # directories to ignore when looking for source files. 57 | # This pattern also affects html_static_path and html_extra_path. 58 | exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] 59 | 60 | # -- Options for HTML output ------------------------------------------------- 61 | 62 | # The theme to use for HTML and HTML Help pages. See the documentation for 63 | # a list of builtin themes. 64 | # 65 | html_theme = 'sphinx_rtd_theme' 66 | 67 | # Add any paths that contain custom static files (such as style sheets) here, 68 | # relative to this directory. They are copied after the builtin static files, 69 | # so a file named "default.css" will overwrite the builtin "default.css". 70 | html_static_path = ['_static'] 71 | -------------------------------------------------------------------------------- /docs/index.rst: -------------------------------------------------------------------------------- 1 | Welcome to MMDetection's documentation! 2 | ======================================= 3 | 4 | .. toctree:: 5 | :maxdepth: 2 6 | 7 | INSTALL.md 8 | GETTING_STARTED.md 9 | MODEL_ZOO.md 10 | TECHNICAL_DETAILS.md 11 | CHANGELOG.md 12 | 13 | 14 | 15 | Indices and tables 16 | ================== 17 | 18 | * :ref:`genindex` 19 | * :ref:`search` 20 | -------------------------------------------------------------------------------- /docs/make.bat: -------------------------------------------------------------------------------- 1 | @ECHO OFF 2 | 3 | pushd %~dp0 4 | 5 | REM Command file for Sphinx documentation 6 | 7 | if "%SPHINXBUILD%" == "" ( 8 | set SPHINXBUILD=sphinx-build 9 | ) 10 | set SOURCEDIR=. 11 | set BUILDDIR=_build 12 | 13 | if "%1" == "" goto help 14 | 15 | %SPHINXBUILD% >NUL 2>NUL 16 | if errorlevel 9009 ( 17 | echo. 18 | echo.The 'sphinx-build' command was not found. Make sure you have Sphinx 19 | echo.installed, then set the SPHINXBUILD environment variable to point 20 | echo.to the full path of the 'sphinx-build' executable. Alternatively you 21 | echo.may add the Sphinx directory to PATH. 22 | echo. 23 | echo.If you don't have Sphinx installed, grab it from 24 | echo.http://sphinx-doc.org/ 25 | exit /b 1 26 | ) 27 | 28 | %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% 29 | goto end 30 | 31 | :help 32 | %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% 33 | 34 | :end 35 | popd 36 | -------------------------------------------------------------------------------- /docs/requirements.txt: -------------------------------------------------------------------------------- 1 | recommonmark 2 | sphinx 3 | sphinx_markdown_tables 4 | sphinx_rtd_theme 5 | -------------------------------------------------------------------------------- /mmdet/__init__.py: -------------------------------------------------------------------------------- 1 | from .version import __version__, short_version 2 | 3 | __all__ = ['__version__', 'short_version'] 4 | -------------------------------------------------------------------------------- /mmdet/apis/__init__.py: -------------------------------------------------------------------------------- 1 | from .inference import (async_inference_detector, inference_detector, 2 | init_detector, show_result, show_result_pyplot) 3 | from .test import multi_gpu_test, single_gpu_test 4 | from .train import get_root_logger, set_random_seed, train_detector 5 | 6 | __all__ = [ 7 | 'get_root_logger', 'set_random_seed', 'train_detector', 'init_detector', 8 | 'async_inference_detector', 'inference_detector', 'show_result', 9 | 'show_result_pyplot', 'multi_gpu_test', 'single_gpu_test' 10 | ] 11 | -------------------------------------------------------------------------------- /mmdet/core/__init__.py: -------------------------------------------------------------------------------- 1 | from .anchor import * # noqa: F401, F403 2 | from .bbox import * # noqa: F401, F403 3 | from .evaluation import * # noqa: F401, F403 4 | from .fp16 import * # noqa: F401, F403 5 | from .mask import * # noqa: F401, F403 6 | from .optimizer import * # noqa: F401, F403 7 | from .post_processing import * # noqa: F401, F403 8 | from .utils import * # noqa: F401, F403 9 | -------------------------------------------------------------------------------- /mmdet/core/anchor/__init__.py: -------------------------------------------------------------------------------- 1 | from .anchor_generator import AnchorGenerator 2 | from .anchor_target import (anchor_inside_flags, anchor_target, 3 | images_to_levels, unmap) 4 | from .guided_anchor_target import ga_loc_target, ga_shape_target 5 | from .point_generator import PointGenerator 6 | from .point_target import point_target 7 | 8 | __all__ = [ 9 | 'AnchorGenerator', 'anchor_target', 'anchor_inside_flags', 'ga_loc_target', 10 | 'ga_shape_target', 'PointGenerator', 'point_target', 'images_to_levels', 11 | 'unmap' 12 | ] 13 | -------------------------------------------------------------------------------- /mmdet/core/anchor/point_generator.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | class PointGenerator(object): 5 | 6 | def _meshgrid(self, x, y, row_major=True): 7 | xx = x.repeat(len(y)) 8 | yy = y.view(-1, 1).repeat(1, len(x)).view(-1) 9 | if row_major: 10 | return xx, yy 11 | else: 12 | return yy, xx 13 | 14 | def grid_points(self, featmap_size, stride=16, device='cuda'): 15 | feat_h, feat_w = featmap_size 16 | shift_x = torch.arange(0., feat_w, device=device) * stride 17 | shift_y = torch.arange(0., feat_h, device=device) * stride 18 | shift_xx, shift_yy = self._meshgrid(shift_x, shift_y) 19 | stride = shift_x.new_full((shift_xx.shape[0], ), stride) 20 | shifts = torch.stack([shift_xx, shift_yy, stride], dim=-1) 21 | all_points = shifts.to(device) 22 | return all_points 23 | 24 | def valid_flags(self, featmap_size, valid_size, device='cuda'): 25 | feat_h, feat_w = featmap_size 26 | valid_h, valid_w = valid_size 27 | assert valid_h <= feat_h and valid_w <= feat_w 28 | valid_x = torch.zeros(feat_w, dtype=torch.uint8, device=device) 29 | valid_y = torch.zeros(feat_h, dtype=torch.uint8, device=device) 30 | valid_x[:valid_w] = 1 31 | valid_y[:valid_h] = 1 32 | valid_xx, valid_yy = self._meshgrid(valid_x, valid_y) 33 | valid = valid_xx & valid_yy 34 | return valid 35 | -------------------------------------------------------------------------------- /mmdet/core/bbox/__init__.py: -------------------------------------------------------------------------------- 1 | from .assigners import AssignResult, BaseAssigner, MaxIoUAssigner 2 | from .bbox_target import bbox_target 3 | from .geometry import bbox_overlaps 4 | from .samplers import (BaseSampler, CombinedSampler, 5 | InstanceBalancedPosSampler, IoUBalancedNegSampler, 6 | PseudoSampler, RandomSampler, SamplingResult) 7 | from .transforms import (bbox2delta, bbox2result, bbox2roi, bbox_flip, 8 | bbox_mapping, bbox_mapping_back, delta2bbox, 9 | distance2bbox, roi2bbox) 10 | 11 | from .assign_sampling import ( # isort:skip, avoid recursive imports 12 | assign_and_sample, build_assigner, build_sampler) 13 | 14 | __all__ = [ 15 | 'bbox_overlaps', 'BaseAssigner', 'MaxIoUAssigner', 'AssignResult', 16 | 'BaseSampler', 'PseudoSampler', 'RandomSampler', 17 | 'InstanceBalancedPosSampler', 'IoUBalancedNegSampler', 'CombinedSampler', 18 | 'SamplingResult', 'build_assigner', 'build_sampler', 'assign_and_sample', 19 | 'bbox2delta', 'delta2bbox', 'bbox_flip', 'bbox_mapping', 20 | 'bbox_mapping_back', 'bbox2roi', 'roi2bbox', 'bbox2result', 21 | 'distance2bbox', 'bbox_target' 22 | ] 23 | -------------------------------------------------------------------------------- /mmdet/core/bbox/assign_sampling.py: -------------------------------------------------------------------------------- 1 | import mmcv 2 | 3 | from . import assigners, samplers 4 | 5 | 6 | def build_assigner(cfg, **kwargs): 7 | if isinstance(cfg, assigners.BaseAssigner): 8 | return cfg 9 | elif isinstance(cfg, dict): 10 | return mmcv.runner.obj_from_dict(cfg, assigners, default_args=kwargs) 11 | else: 12 | raise TypeError('Invalid type {} for building a sampler'.format( 13 | type(cfg))) 14 | 15 | 16 | def build_sampler(cfg, **kwargs): 17 | if isinstance(cfg, samplers.BaseSampler): 18 | return cfg 19 | elif isinstance(cfg, dict): 20 | return mmcv.runner.obj_from_dict(cfg, samplers, default_args=kwargs) 21 | else: 22 | raise TypeError('Invalid type {} for building a sampler'.format( 23 | type(cfg))) 24 | 25 | 26 | def assign_and_sample(bboxes, gt_bboxes, gt_bboxes_ignore, gt_labels, cfg): 27 | bbox_assigner = build_assigner(cfg.assigner) 28 | bbox_sampler = build_sampler(cfg.sampler) 29 | assign_result = bbox_assigner.assign(bboxes, gt_bboxes, gt_bboxes_ignore, 30 | gt_labels) 31 | sampling_result = bbox_sampler.sample(assign_result, bboxes, gt_bboxes, 32 | gt_labels) 33 | return assign_result, sampling_result 34 | -------------------------------------------------------------------------------- /mmdet/core/bbox/assigners/__init__.py: -------------------------------------------------------------------------------- 1 | from .approx_max_iou_assigner import ApproxMaxIoUAssigner 2 | from .assign_result import AssignResult 3 | from .atss_assigner import ATSSAssigner 4 | from .base_assigner import BaseAssigner 5 | from .max_iou_assigner import MaxIoUAssigner 6 | from .point_assigner import PointAssigner 7 | 8 | __all__ = [ 9 | 'BaseAssigner', 'MaxIoUAssigner', 'ApproxMaxIoUAssigner', 'AssignResult', 10 | 'PointAssigner', 'ATSSAssigner' 11 | ] 12 | -------------------------------------------------------------------------------- /mmdet/core/bbox/assigners/base_assigner.py: -------------------------------------------------------------------------------- 1 | from abc import ABCMeta, abstractmethod 2 | 3 | 4 | class BaseAssigner(metaclass=ABCMeta): 5 | 6 | @abstractmethod 7 | def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None): 8 | pass 9 | -------------------------------------------------------------------------------- /mmdet/core/bbox/bbox_target.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from ..utils import multi_apply 4 | from .transforms import bbox2delta 5 | 6 | 7 | def bbox_target(pos_bboxes_list, 8 | neg_bboxes_list, 9 | pos_gt_bboxes_list, 10 | pos_gt_labels_list, 11 | cfg, 12 | reg_classes=1, 13 | target_means=[.0, .0, .0, .0], 14 | target_stds=[1.0, 1.0, 1.0, 1.0], 15 | concat=True): 16 | labels, label_weights, bbox_targets, bbox_weights = multi_apply( 17 | bbox_target_single, 18 | pos_bboxes_list, 19 | neg_bboxes_list, 20 | pos_gt_bboxes_list, 21 | pos_gt_labels_list, 22 | cfg=cfg, 23 | reg_classes=reg_classes, 24 | target_means=target_means, 25 | target_stds=target_stds) 26 | 27 | if concat: 28 | labels = torch.cat(labels, 0) 29 | label_weights = torch.cat(label_weights, 0) 30 | bbox_targets = torch.cat(bbox_targets, 0) 31 | bbox_weights = torch.cat(bbox_weights, 0) 32 | return labels, label_weights, bbox_targets, bbox_weights 33 | 34 | 35 | def bbox_target_single(pos_bboxes, 36 | neg_bboxes, 37 | pos_gt_bboxes, 38 | pos_gt_labels, 39 | cfg, 40 | reg_classes=1, 41 | target_means=[.0, .0, .0, .0], 42 | target_stds=[1.0, 1.0, 1.0, 1.0]): 43 | num_pos = pos_bboxes.size(0) 44 | num_neg = neg_bboxes.size(0) 45 | num_samples = num_pos + num_neg 46 | labels = pos_bboxes.new_zeros(num_samples, dtype=torch.long) 47 | label_weights = pos_bboxes.new_zeros(num_samples) 48 | bbox_targets = pos_bboxes.new_zeros(num_samples, 4) 49 | bbox_weights = pos_bboxes.new_zeros(num_samples, 4) 50 | if num_pos > 0: 51 | labels[:num_pos] = pos_gt_labels 52 | pos_weight = 1.0 if cfg.pos_weight <= 0 else cfg.pos_weight 53 | label_weights[:num_pos] = pos_weight 54 | pos_bbox_targets = bbox2delta(pos_bboxes, pos_gt_bboxes, target_means, 55 | target_stds) 56 | bbox_targets[:num_pos, :] = pos_bbox_targets 57 | bbox_weights[:num_pos, :] = 1 58 | if num_neg > 0: 59 | label_weights[-num_neg:] = 1.0 60 | 61 | return labels, label_weights, bbox_targets, bbox_weights 62 | 63 | 64 | def expand_target(bbox_targets, bbox_weights, labels, num_classes): 65 | bbox_targets_expand = bbox_targets.new_zeros( 66 | (bbox_targets.size(0), 4 * num_classes)) 67 | bbox_weights_expand = bbox_weights.new_zeros( 68 | (bbox_weights.size(0), 4 * num_classes)) 69 | for i in torch.nonzero(labels > 0).squeeze(-1): 70 | start, end = labels[i] * 4, (labels[i] + 1) * 4 71 | bbox_targets_expand[i, start:end] = bbox_targets[i, :] 72 | bbox_weights_expand[i, start:end] = bbox_weights[i, :] 73 | return bbox_targets_expand, bbox_weights_expand 74 | -------------------------------------------------------------------------------- /mmdet/core/bbox/demodata.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | 4 | 5 | def ensure_rng(rng=None): 6 | """ 7 | Simple version of the ``kwarray.ensure_rng`` 8 | 9 | Args: 10 | rng (int | numpy.random.RandomState | None): 11 | if None, then defaults to the global rng. Otherwise this can be an 12 | integer or a RandomState class 13 | Returns: 14 | (numpy.random.RandomState) : rng - 15 | a numpy random number generator 16 | 17 | References: 18 | https://gitlab.kitware.com/computer-vision/kwarray/blob/master/kwarray/util_random.py#L270 19 | """ 20 | 21 | if rng is None: 22 | rng = np.random.mtrand._rand 23 | elif isinstance(rng, int): 24 | rng = np.random.RandomState(rng) 25 | else: 26 | rng = rng 27 | return rng 28 | 29 | 30 | def random_boxes(num=1, scale=1, rng=None): 31 | """ 32 | Simple version of ``kwimage.Boxes.random`` 33 | 34 | Returns: 35 | Tensor: shape (n, 4) in x1, y1, x2, y2 format. 36 | 37 | References: 38 | https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390 39 | 40 | Example: 41 | >>> num = 3 42 | >>> scale = 512 43 | >>> rng = 0 44 | >>> boxes = random_boxes(num, scale, rng) 45 | >>> print(boxes) 46 | tensor([[280.9925, 278.9802, 308.6148, 366.1769], 47 | [216.9113, 330.6978, 224.0446, 456.5878], 48 | [405.3632, 196.3221, 493.3953, 270.7942]]) 49 | """ 50 | rng = ensure_rng(rng) 51 | 52 | tlbr = rng.rand(num, 4).astype(np.float32) 53 | 54 | tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2]) 55 | tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3]) 56 | br_x = np.maximum(tlbr[:, 0], tlbr[:, 2]) 57 | br_y = np.maximum(tlbr[:, 1], tlbr[:, 3]) 58 | 59 | tlbr[:, 0] = tl_x * scale 60 | tlbr[:, 1] = tl_y * scale 61 | tlbr[:, 2] = br_x * scale 62 | tlbr[:, 3] = br_y * scale 63 | 64 | boxes = torch.from_numpy(tlbr) 65 | return boxes 66 | -------------------------------------------------------------------------------- /mmdet/core/bbox/geometry.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False): 5 | """Calculate overlap between two set of bboxes. 6 | 7 | If ``is_aligned`` is ``False``, then calculate the ious between each bbox 8 | of bboxes1 and bboxes2, otherwise the ious between each aligned pair of 9 | bboxes1 and bboxes2. 10 | 11 | Args: 12 | bboxes1 (Tensor): shape (m, 4) in format. 13 | bboxes2 (Tensor): shape (n, 4) in format. 14 | If is_aligned is ``True``, then m and n must be equal. 15 | mode (str): "iou" (intersection over union) or iof (intersection over 16 | foreground). 17 | 18 | Returns: 19 | ious(Tensor): shape (m, n) if is_aligned == False else shape (m, 1) 20 | 21 | Example: 22 | >>> bboxes1 = torch.FloatTensor([ 23 | >>> [0, 0, 10, 10], 24 | >>> [10, 10, 20, 20], 25 | >>> [32, 32, 38, 42], 26 | >>> ]) 27 | >>> bboxes2 = torch.FloatTensor([ 28 | >>> [0, 0, 10, 20], 29 | >>> [0, 10, 10, 19], 30 | >>> [10, 10, 20, 20], 31 | >>> ]) 32 | >>> bbox_overlaps(bboxes1, bboxes2) 33 | tensor([[0.5238, 0.0500, 0.0041], 34 | [0.0323, 0.0452, 1.0000], 35 | [0.0000, 0.0000, 0.0000]]) 36 | 37 | Example: 38 | >>> empty = torch.FloatTensor([]) 39 | >>> nonempty = torch.FloatTensor([ 40 | >>> [0, 0, 10, 9], 41 | >>> ]) 42 | >>> assert tuple(bbox_overlaps(empty, nonempty).shape) == (0, 1) 43 | >>> assert tuple(bbox_overlaps(nonempty, empty).shape) == (1, 0) 44 | >>> assert tuple(bbox_overlaps(empty, empty).shape) == (0, 0) 45 | """ 46 | 47 | assert mode in ['iou', 'iof'] 48 | 49 | rows = bboxes1.size(0) 50 | cols = bboxes2.size(0) 51 | if is_aligned: 52 | assert rows == cols 53 | 54 | if rows * cols == 0: 55 | return bboxes1.new(rows, 1) if is_aligned else bboxes1.new(rows, cols) 56 | 57 | if is_aligned: 58 | lt = torch.max(bboxes1[:, :2], bboxes2[:, :2]) # [rows, 2] 59 | rb = torch.min(bboxes1[:, 2:], bboxes2[:, 2:]) # [rows, 2] 60 | 61 | wh = (rb - lt + 1).clamp(min=0) # [rows, 2] 62 | overlap = wh[:, 0] * wh[:, 1] 63 | area1 = (bboxes1[:, 2] - bboxes1[:, 0] + 1) * ( 64 | bboxes1[:, 3] - bboxes1[:, 1] + 1) 65 | 66 | if mode == 'iou': 67 | area2 = (bboxes2[:, 2] - bboxes2[:, 0] + 1) * ( 68 | bboxes2[:, 3] - bboxes2[:, 1] + 1) 69 | ious = overlap / (area1 + area2 - overlap) 70 | else: 71 | ious = overlap / area1 72 | else: 73 | lt = torch.max(bboxes1[:, None, :2], bboxes2[:, :2]) # [rows, cols, 2] 74 | rb = torch.min(bboxes1[:, None, 2:], bboxes2[:, 2:]) # [rows, cols, 2] 75 | 76 | wh = (rb - lt + 1).clamp(min=0) # [rows, cols, 2] 77 | overlap = wh[:, :, 0] * wh[:, :, 1] 78 | area1 = (bboxes1[:, 2] - bboxes1[:, 0] + 1) * ( 79 | bboxes1[:, 3] - bboxes1[:, 1] + 1) 80 | 81 | if mode == 'iou': 82 | area2 = (bboxes2[:, 2] - bboxes2[:, 0] + 1) * ( 83 | bboxes2[:, 3] - bboxes2[:, 1] + 1) 84 | ious = overlap / (area1[:, None] + area2 - overlap) 85 | else: 86 | ious = overlap / (area1[:, None]) 87 | 88 | return ious 89 | -------------------------------------------------------------------------------- /mmdet/core/bbox/samplers/__init__.py: -------------------------------------------------------------------------------- 1 | from .base_sampler import BaseSampler 2 | from .combined_sampler import CombinedSampler 3 | from .instance_balanced_pos_sampler import InstanceBalancedPosSampler 4 | from .iou_balanced_neg_sampler import IoUBalancedNegSampler 5 | from .ohem_sampler import OHEMSampler 6 | from .pseudo_sampler import PseudoSampler 7 | from .random_sampler import RandomSampler 8 | from .sampling_result import SamplingResult 9 | 10 | __all__ = [ 11 | 'BaseSampler', 'PseudoSampler', 'RandomSampler', 12 | 'InstanceBalancedPosSampler', 'IoUBalancedNegSampler', 'CombinedSampler', 13 | 'OHEMSampler', 'SamplingResult' 14 | ] 15 | -------------------------------------------------------------------------------- /mmdet/core/bbox/samplers/combined_sampler.py: -------------------------------------------------------------------------------- 1 | from ..assign_sampling import build_sampler 2 | from .base_sampler import BaseSampler 3 | 4 | 5 | class CombinedSampler(BaseSampler): 6 | 7 | def __init__(self, pos_sampler, neg_sampler, **kwargs): 8 | super(CombinedSampler, self).__init__(**kwargs) 9 | self.pos_sampler = build_sampler(pos_sampler, **kwargs) 10 | self.neg_sampler = build_sampler(neg_sampler, **kwargs) 11 | 12 | def _sample_pos(self, **kwargs): 13 | raise NotImplementedError 14 | 15 | def _sample_neg(self, **kwargs): 16 | raise NotImplementedError 17 | -------------------------------------------------------------------------------- /mmdet/core/bbox/samplers/instance_balanced_pos_sampler.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | 4 | from .random_sampler import RandomSampler 5 | 6 | 7 | class InstanceBalancedPosSampler(RandomSampler): 8 | 9 | def _sample_pos(self, assign_result, num_expected, **kwargs): 10 | pos_inds = torch.nonzero(assign_result.gt_inds > 0) 11 | if pos_inds.numel() != 0: 12 | pos_inds = pos_inds.squeeze(1) 13 | if pos_inds.numel() <= num_expected: 14 | return pos_inds 15 | else: 16 | unique_gt_inds = assign_result.gt_inds[pos_inds].unique() 17 | num_gts = len(unique_gt_inds) 18 | num_per_gt = int(round(num_expected / float(num_gts)) + 1) 19 | sampled_inds = [] 20 | for i in unique_gt_inds: 21 | inds = torch.nonzero(assign_result.gt_inds == i.item()) 22 | if inds.numel() != 0: 23 | inds = inds.squeeze(1) 24 | else: 25 | continue 26 | if len(inds) > num_per_gt: 27 | inds = self.random_choice(inds, num_per_gt) 28 | sampled_inds.append(inds) 29 | sampled_inds = torch.cat(sampled_inds) 30 | if len(sampled_inds) < num_expected: 31 | num_extra = num_expected - len(sampled_inds) 32 | extra_inds = np.array( 33 | list(set(pos_inds.cpu()) - set(sampled_inds.cpu()))) 34 | if len(extra_inds) > num_extra: 35 | extra_inds = self.random_choice(extra_inds, num_extra) 36 | extra_inds = torch.from_numpy(extra_inds).to( 37 | assign_result.gt_inds.device).long() 38 | sampled_inds = torch.cat([sampled_inds, extra_inds]) 39 | elif len(sampled_inds) > num_expected: 40 | sampled_inds = self.random_choice(sampled_inds, num_expected) 41 | return sampled_inds 42 | -------------------------------------------------------------------------------- /mmdet/core/bbox/samplers/ohem_sampler.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from ..transforms import bbox2roi 4 | from .base_sampler import BaseSampler 5 | 6 | 7 | class OHEMSampler(BaseSampler): 8 | """ 9 | Online Hard Example Mining Sampler described in [1]_. 10 | 11 | References: 12 | .. [1] https://arxiv.org/pdf/1604.03540.pdf 13 | """ 14 | 15 | def __init__(self, 16 | num, 17 | pos_fraction, 18 | context, 19 | neg_pos_ub=-1, 20 | add_gt_as_proposals=True, 21 | **kwargs): 22 | super(OHEMSampler, self).__init__(num, pos_fraction, neg_pos_ub, 23 | add_gt_as_proposals) 24 | if not hasattr(context, 'num_stages'): 25 | self.bbox_roi_extractor = context.bbox_roi_extractor 26 | self.bbox_head = context.bbox_head 27 | else: 28 | self.bbox_roi_extractor = context.bbox_roi_extractor[ 29 | context.current_stage] 30 | self.bbox_head = context.bbox_head[context.current_stage] 31 | 32 | def hard_mining(self, inds, num_expected, bboxes, labels, feats): 33 | with torch.no_grad(): 34 | rois = bbox2roi([bboxes]) 35 | bbox_feats = self.bbox_roi_extractor( 36 | feats[:self.bbox_roi_extractor.num_inputs], rois) 37 | cls_score, _ = self.bbox_head(bbox_feats) 38 | loss = self.bbox_head.loss( 39 | cls_score=cls_score, 40 | bbox_pred=None, 41 | labels=labels, 42 | label_weights=cls_score.new_ones(cls_score.size(0)), 43 | bbox_targets=None, 44 | bbox_weights=None, 45 | reduction_override='none')['loss_cls'] 46 | _, topk_loss_inds = loss.topk(num_expected) 47 | return inds[topk_loss_inds] 48 | 49 | def _sample_pos(self, 50 | assign_result, 51 | num_expected, 52 | bboxes=None, 53 | feats=None, 54 | **kwargs): 55 | # Sample some hard positive samples 56 | pos_inds = torch.nonzero(assign_result.gt_inds > 0) 57 | if pos_inds.numel() != 0: 58 | pos_inds = pos_inds.squeeze(1) 59 | if pos_inds.numel() <= num_expected: 60 | return pos_inds 61 | else: 62 | return self.hard_mining(pos_inds, num_expected, bboxes[pos_inds], 63 | assign_result.labels[pos_inds], feats) 64 | 65 | def _sample_neg(self, 66 | assign_result, 67 | num_expected, 68 | bboxes=None, 69 | feats=None, 70 | **kwargs): 71 | # Sample some hard negative samples 72 | neg_inds = torch.nonzero(assign_result.gt_inds == 0) 73 | if neg_inds.numel() != 0: 74 | neg_inds = neg_inds.squeeze(1) 75 | if len(neg_inds) <= num_expected: 76 | return neg_inds 77 | else: 78 | return self.hard_mining(neg_inds, num_expected, bboxes[neg_inds], 79 | assign_result.labels[neg_inds], feats) 80 | -------------------------------------------------------------------------------- /mmdet/core/bbox/samplers/pseudo_sampler.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from .base_sampler import BaseSampler 4 | from .sampling_result import SamplingResult 5 | 6 | 7 | class PseudoSampler(BaseSampler): 8 | 9 | def __init__(self, **kwargs): 10 | pass 11 | 12 | def _sample_pos(self, **kwargs): 13 | raise NotImplementedError 14 | 15 | def _sample_neg(self, **kwargs): 16 | raise NotImplementedError 17 | 18 | def sample(self, assign_result, bboxes, gt_bboxes, **kwargs): 19 | pos_inds = torch.nonzero( 20 | assign_result.gt_inds > 0).squeeze(-1).unique() 21 | neg_inds = torch.nonzero( 22 | assign_result.gt_inds == 0).squeeze(-1).unique() 23 | gt_flags = bboxes.new_zeros(bboxes.shape[0], dtype=torch.uint8) 24 | sampling_result = SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes, 25 | assign_result, gt_flags) 26 | return sampling_result 27 | -------------------------------------------------------------------------------- /mmdet/core/bbox/samplers/random_sampler.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from .base_sampler import BaseSampler 4 | 5 | 6 | class RandomSampler(BaseSampler): 7 | 8 | def __init__(self, 9 | num, 10 | pos_fraction, 11 | neg_pos_ub=-1, 12 | add_gt_as_proposals=True, 13 | **kwargs): 14 | from mmdet.core.bbox import demodata 15 | super(RandomSampler, self).__init__(num, pos_fraction, neg_pos_ub, 16 | add_gt_as_proposals) 17 | self.rng = demodata.ensure_rng(kwargs.get('rng', None)) 18 | 19 | def random_choice(self, gallery, num): 20 | """Random select some elements from the gallery. 21 | 22 | If `gallery` is a Tensor, the returned indices will be a Tensor; 23 | If `gallery` is a ndarray or list, the returned indices will be a 24 | ndarray. 25 | 26 | Args: 27 | gallery (Tensor | ndarray | list): indices pool. 28 | num (int): expected sample num. 29 | 30 | Returns: 31 | Tensor or ndarray: sampled indices. 32 | """ 33 | assert len(gallery) >= num 34 | 35 | is_tensor = isinstance(gallery, torch.Tensor) 36 | if not is_tensor: 37 | gallery = torch.tensor( 38 | gallery, dtype=torch.long, device=torch.cuda.current_device()) 39 | perm = torch.randperm(gallery.numel(), device=gallery.device)[:num] 40 | rand_inds = gallery[perm] 41 | if not is_tensor: 42 | rand_inds = rand_inds.cpu().numpy() 43 | return rand_inds 44 | 45 | def _sample_pos(self, assign_result, num_expected, **kwargs): 46 | """Randomly sample some positive samples.""" 47 | pos_inds = torch.nonzero(assign_result.gt_inds > 0) 48 | if pos_inds.numel() != 0: 49 | pos_inds = pos_inds.squeeze(1) 50 | if pos_inds.numel() <= num_expected: 51 | return pos_inds 52 | else: 53 | return self.random_choice(pos_inds, num_expected) 54 | 55 | def _sample_neg(self, assign_result, num_expected, **kwargs): 56 | """Randomly sample some negative samples.""" 57 | neg_inds = torch.nonzero(assign_result.gt_inds == 0) 58 | if neg_inds.numel() != 0: 59 | neg_inds = neg_inds.squeeze(1) 60 | if len(neg_inds) <= num_expected: 61 | return neg_inds 62 | else: 63 | return self.random_choice(neg_inds, num_expected) 64 | -------------------------------------------------------------------------------- /mmdet/core/evaluation/__init__.py: -------------------------------------------------------------------------------- 1 | from .class_names import (cityscapes_classes, coco_classes, dataset_aliases, 2 | get_classes, imagenet_det_classes, 3 | imagenet_vid_classes, voc_classes) 4 | from .eval_hooks import DistEvalHook, EvalHook 5 | from .mean_ap import average_precision, eval_map, print_map_summary 6 | from .recall import (eval_recalls, plot_iou_recall, plot_num_recall, 7 | print_recall_summary) 8 | 9 | __all__ = [ 10 | 'voc_classes', 'imagenet_det_classes', 'imagenet_vid_classes', 11 | 'coco_classes', 'cityscapes_classes', 'dataset_aliases', 'get_classes', 12 | 'DistEvalHook', 'EvalHook', 'average_precision', 'eval_map', 13 | 'print_map_summary', 'eval_recalls', 'print_recall_summary', 14 | 'plot_num_recall', 'plot_iou_recall' 15 | ] 16 | -------------------------------------------------------------------------------- /mmdet/core/evaluation/bbox_overlaps.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | def bbox_overlaps(bboxes1, bboxes2, mode='iou'): 5 | """Calculate the ious between each bbox of bboxes1 and bboxes2. 6 | 7 | Args: 8 | bboxes1(ndarray): shape (n, 4) 9 | bboxes2(ndarray): shape (k, 4) 10 | mode(str): iou (intersection over union) or iof (intersection 11 | over foreground) 12 | 13 | Returns: 14 | ious(ndarray): shape (n, k) 15 | """ 16 | 17 | assert mode in ['iou', 'iof'] 18 | 19 | bboxes1 = bboxes1.astype(np.float32) 20 | bboxes2 = bboxes2.astype(np.float32) 21 | rows = bboxes1.shape[0] 22 | cols = bboxes2.shape[0] 23 | ious = np.zeros((rows, cols), dtype=np.float32) 24 | if rows * cols == 0: 25 | return ious 26 | exchange = False 27 | if bboxes1.shape[0] > bboxes2.shape[0]: 28 | bboxes1, bboxes2 = bboxes2, bboxes1 29 | ious = np.zeros((cols, rows), dtype=np.float32) 30 | exchange = True 31 | area1 = (bboxes1[:, 2] - bboxes1[:, 0] + 1) * ( 32 | bboxes1[:, 3] - bboxes1[:, 1] + 1) 33 | area2 = (bboxes2[:, 2] - bboxes2[:, 0] + 1) * ( 34 | bboxes2[:, 3] - bboxes2[:, 1] + 1) 35 | for i in range(bboxes1.shape[0]): 36 | x_start = np.maximum(bboxes1[i, 0], bboxes2[:, 0]) 37 | y_start = np.maximum(bboxes1[i, 1], bboxes2[:, 1]) 38 | x_end = np.minimum(bboxes1[i, 2], bboxes2[:, 2]) 39 | y_end = np.minimum(bboxes1[i, 3], bboxes2[:, 3]) 40 | overlap = np.maximum(x_end - x_start + 1, 0) * np.maximum( 41 | y_end - y_start + 1, 0) 42 | if mode == 'iou': 43 | union = area1[i] + area2 - overlap 44 | else: 45 | union = area1[i] if not exchange else area2 46 | ious[i, :] = overlap / union 47 | if exchange: 48 | ious = ious.T 49 | return ious 50 | -------------------------------------------------------------------------------- /mmdet/core/evaluation/eval_hooks.py: -------------------------------------------------------------------------------- 1 | import os.path as osp 2 | 3 | from mmcv.runner import Hook 4 | from torch.utils.data import DataLoader 5 | 6 | 7 | class EvalHook(Hook): 8 | """Evaluation hook. 9 | 10 | Attributes: 11 | dataloader (DataLoader): A PyTorch dataloader. 12 | interval (int): Evaluation interval (by epochs). Default: 1. 13 | """ 14 | 15 | def __init__(self, dataloader, interval=1, **eval_kwargs): 16 | if not isinstance(dataloader, DataLoader): 17 | raise TypeError( 18 | 'dataloader must be a pytorch DataLoader, but got {}'.format( 19 | type(dataloader))) 20 | self.dataloader = dataloader 21 | self.interval = interval 22 | self.eval_kwargs = eval_kwargs 23 | 24 | def after_train_epoch(self, runner): 25 | if not self.every_n_epochs(runner, self.interval): 26 | return 27 | from mmdet.apis import single_gpu_test 28 | results = single_gpu_test(runner.model, self.dataloader, show=False) 29 | self.evaluate(runner, results) 30 | 31 | def evaluate(self, runner, results): 32 | eval_res = self.dataloader.dataset.evaluate( 33 | results, logger=runner.logger, **self.eval_kwargs) 34 | for name, val in eval_res.items(): 35 | runner.log_buffer.output[name] = val 36 | runner.log_buffer.ready = True 37 | 38 | 39 | class DistEvalHook(EvalHook): 40 | """Distributed evaluation hook. 41 | 42 | Attributes: 43 | dataloader (DataLoader): A PyTorch dataloader. 44 | interval (int): Evaluation interval (by epochs). Default: 1. 45 | tmpdir (str | None): Temporary directory to save the results of all 46 | processes. Default: None. 47 | gpu_collect (bool): Whether to use gpu or cpu to collect results. 48 | Default: False. 49 | """ 50 | 51 | def __init__(self, 52 | dataloader, 53 | interval=1, 54 | gpu_collect=False, 55 | **eval_kwargs): 56 | if not isinstance(dataloader, DataLoader): 57 | raise TypeError( 58 | 'dataloader must be a pytorch DataLoader, but got {}'.format( 59 | type(dataloader))) 60 | self.dataloader = dataloader 61 | self.interval = interval 62 | self.gpu_collect = gpu_collect 63 | self.eval_kwargs = eval_kwargs 64 | 65 | def after_train_epoch(self, runner): 66 | if not self.every_n_epochs(runner, self.interval): 67 | return 68 | from mmdet.apis import multi_gpu_test 69 | results = multi_gpu_test( 70 | runner.model, 71 | self.dataloader, 72 | tmpdir=osp.join(runner.work_dir, '.eval_hook'), 73 | gpu_collect=self.gpu_collect) 74 | if runner.rank == 0: 75 | print('\n') 76 | self.evaluate(runner, results) 77 | -------------------------------------------------------------------------------- /mmdet/core/fp16/__init__.py: -------------------------------------------------------------------------------- 1 | from .decorators import auto_fp16, force_fp32 2 | from .hooks import Fp16OptimizerHook, wrap_fp16_model 3 | 4 | __all__ = ['auto_fp16', 'force_fp32', 'Fp16OptimizerHook', 'wrap_fp16_model'] 5 | -------------------------------------------------------------------------------- /mmdet/core/fp16/utils.py: -------------------------------------------------------------------------------- 1 | from collections import abc 2 | 3 | import numpy as np 4 | import torch 5 | 6 | 7 | def cast_tensor_type(inputs, src_type, dst_type): 8 | if isinstance(inputs, torch.Tensor): 9 | return inputs.to(dst_type) 10 | elif isinstance(inputs, str): 11 | return inputs 12 | elif isinstance(inputs, np.ndarray): 13 | return inputs 14 | elif isinstance(inputs, abc.Mapping): 15 | return type(inputs)({ 16 | k: cast_tensor_type(v, src_type, dst_type) 17 | for k, v in inputs.items() 18 | }) 19 | elif isinstance(inputs, abc.Iterable): 20 | return type(inputs)( 21 | cast_tensor_type(item, src_type, dst_type) for item in inputs) 22 | else: 23 | return inputs 24 | -------------------------------------------------------------------------------- /mmdet/core/mask/__init__.py: -------------------------------------------------------------------------------- 1 | from .mask_target import mask_target 2 | from .utils import split_combined_polys 3 | 4 | __all__ = ['split_combined_polys', 'mask_target'] 5 | -------------------------------------------------------------------------------- /mmdet/core/mask/mask_target.py: -------------------------------------------------------------------------------- 1 | import mmcv 2 | import numpy as np 3 | import torch 4 | from torch.nn.modules.utils import _pair 5 | 6 | 7 | def mask_target(pos_proposals_list, pos_assigned_gt_inds_list, gt_masks_list, 8 | cfg): 9 | cfg_list = [cfg for _ in range(len(pos_proposals_list))] 10 | mask_targets = map(mask_target_single, pos_proposals_list, 11 | pos_assigned_gt_inds_list, gt_masks_list, cfg_list) 12 | mask_targets = torch.cat(list(mask_targets)) 13 | return mask_targets 14 | 15 | 16 | def mask_target_single(pos_proposals, pos_assigned_gt_inds, gt_masks, cfg): 17 | mask_size = _pair(cfg.mask_size) 18 | num_pos = pos_proposals.size(0) 19 | mask_targets = [] 20 | if num_pos > 0: 21 | proposals_np = pos_proposals.cpu().numpy() 22 | _, maxh, maxw = gt_masks.shape 23 | proposals_np[:, [0, 2]] = np.clip(proposals_np[:, [0, 2]], 0, maxw - 1) 24 | proposals_np[:, [1, 3]] = np.clip(proposals_np[:, [1, 3]], 0, maxh - 1) 25 | pos_assigned_gt_inds = pos_assigned_gt_inds.cpu().numpy() 26 | for i in range(num_pos): 27 | gt_mask = gt_masks[pos_assigned_gt_inds[i]] 28 | bbox = proposals_np[i, :].astype(np.int32) 29 | x1, y1, x2, y2 = bbox 30 | w = np.maximum(x2 - x1 + 1, 1) 31 | h = np.maximum(y2 - y1 + 1, 1) 32 | # mask is uint8 both before and after resizing 33 | # mask_size (h, w) to (w, h) 34 | target = mmcv.imresize(gt_mask[y1:y1 + h, x1:x1 + w], 35 | mask_size[::-1]) 36 | mask_targets.append(target) 37 | mask_targets = torch.from_numpy(np.stack(mask_targets)).float().to( 38 | pos_proposals.device) 39 | else: 40 | mask_targets = pos_proposals.new_zeros((0, ) + mask_size) 41 | return mask_targets 42 | -------------------------------------------------------------------------------- /mmdet/core/mask/utils.py: -------------------------------------------------------------------------------- 1 | import mmcv 2 | 3 | 4 | def split_combined_polys(polys, poly_lens, polys_per_mask): 5 | """Split the combined 1-D polys into masks. 6 | 7 | A mask is represented as a list of polys, and a poly is represented as 8 | a 1-D array. In dataset, all masks are concatenated into a single 1-D 9 | tensor. Here we need to split the tensor into original representations. 10 | 11 | Args: 12 | polys (list): a list (length = image num) of 1-D tensors 13 | poly_lens (list): a list (length = image num) of poly length 14 | polys_per_mask (list): a list (length = image num) of poly number 15 | of each mask 16 | 17 | Returns: 18 | list: a list (length = image num) of list (length = mask num) of 19 | list (length = poly num) of numpy array 20 | """ 21 | mask_polys_list = [] 22 | for img_id in range(len(polys)): 23 | polys_single = polys[img_id] 24 | polys_lens_single = poly_lens[img_id].tolist() 25 | polys_per_mask_single = polys_per_mask[img_id].tolist() 26 | 27 | split_polys = mmcv.slice_list(polys_single, polys_lens_single) 28 | mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single) 29 | mask_polys_list.append(mask_polys) 30 | return mask_polys_list 31 | -------------------------------------------------------------------------------- /mmdet/core/optimizer/__init__.py: -------------------------------------------------------------------------------- 1 | from .builder import build_optimizer 2 | from .copy_of_sgd import CopyOfSGD 3 | from .registry import OPTIMIZERS 4 | 5 | __all__ = ['OPTIMIZERS', 'build_optimizer', 'CopyOfSGD'] 6 | -------------------------------------------------------------------------------- /mmdet/core/optimizer/copy_of_sgd.py: -------------------------------------------------------------------------------- 1 | from torch.optim import SGD 2 | 3 | from .registry import OPTIMIZERS 4 | 5 | 6 | @OPTIMIZERS.register_module 7 | class CopyOfSGD(SGD): 8 | """A clone of torch.optim.SGD. 9 | 10 | A customized optimizer could be defined like CopyOfSGD. 11 | You may derive from built-in optimizers in torch.optim, 12 | or directly implement a new optimizer. 13 | """ 14 | -------------------------------------------------------------------------------- /mmdet/core/optimizer/registry.py: -------------------------------------------------------------------------------- 1 | import inspect 2 | 3 | import torch 4 | 5 | from mmdet.utils import Registry 6 | 7 | OPTIMIZERS = Registry('optimizer') 8 | 9 | 10 | def register_torch_optimizers(): 11 | torch_optimizers = [] 12 | for module_name in dir(torch.optim): 13 | if module_name.startswith('__'): 14 | continue 15 | _optim = getattr(torch.optim, module_name) 16 | if inspect.isclass(_optim) and issubclass(_optim, 17 | torch.optim.Optimizer): 18 | OPTIMIZERS.register_module(_optim) 19 | torch_optimizers.append(module_name) 20 | return torch_optimizers 21 | 22 | 23 | TORCH_OPTIMIZERS = register_torch_optimizers() 24 | -------------------------------------------------------------------------------- /mmdet/core/post_processing/__init__.py: -------------------------------------------------------------------------------- 1 | from .bbox_nms import multiclass_nms 2 | from .merge_augs import (merge_aug_bboxes, merge_aug_masks, 3 | merge_aug_proposals, merge_aug_scores) 4 | 5 | __all__ = [ 6 | 'multiclass_nms', 'merge_aug_proposals', 'merge_aug_bboxes', 7 | 'merge_aug_scores', 'merge_aug_masks' 8 | ] 9 | -------------------------------------------------------------------------------- /mmdet/core/post_processing/bbox_nms.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from mmdet.ops.nms import nms_wrapper 4 | 5 | 6 | def multiclass_nms(multi_bboxes, 7 | multi_scores, 8 | score_thr, 9 | nms_cfg, 10 | max_num=-1, 11 | score_factors=None): 12 | """NMS for multi-class bboxes. 13 | 14 | Args: 15 | multi_bboxes (Tensor): shape (n, #class*4) or (n, 4) 16 | multi_scores (Tensor): shape (n, #class), where the 0th column 17 | contains scores of the background class, but this will be ignored. 18 | score_thr (float): bbox threshold, bboxes with scores lower than it 19 | will not be considered. 20 | nms_thr (float): NMS IoU threshold 21 | max_num (int): if there are more than max_num bboxes after NMS, 22 | only top max_num will be kept. 23 | score_factors (Tensor): The factors multiplied to scores before 24 | applying NMS 25 | 26 | Returns: 27 | tuple: (bboxes, labels), tensors of shape (k, 5) and (k, 1). Labels 28 | are 0-based. 29 | """ 30 | num_classes = multi_scores.size(1) - 1 31 | # exclude background category 32 | if multi_bboxes.shape[1] > 4: 33 | bboxes = multi_bboxes.view(multi_scores.size(0), -1, 4)[:, 1:] 34 | else: 35 | bboxes = multi_bboxes[:, None].expand(-1, num_classes, 4) 36 | scores = multi_scores[:, 1:] 37 | 38 | # filter out boxes with low scores 39 | valid_mask = scores > score_thr 40 | bboxes = bboxes[valid_mask] 41 | if score_factors is not None: 42 | scores = scores * score_factors[:, None] 43 | scores = scores[valid_mask] 44 | labels = valid_mask.nonzero()[:, 1] 45 | 46 | if bboxes.numel() == 0: 47 | bboxes = multi_bboxes.new_zeros((0, 5)) 48 | labels = multi_bboxes.new_zeros((0, ), dtype=torch.long) 49 | return bboxes, labels 50 | 51 | # Modified from https://github.com/pytorch/vision/blob 52 | # /505cd6957711af790211896d32b40291bea1bc21/torchvision/ops/boxes.py#L39. 53 | # strategy: in order to perform NMS independently per class. 54 | # we add an offset to all the boxes. The offset is dependent 55 | # only on the class idx, and is large enough so that boxes 56 | # from different classes do not overlap 57 | max_coordinate = bboxes.max() 58 | offsets = labels.to(bboxes) * (max_coordinate + 1) 59 | bboxes_for_nms = bboxes + offsets[:, None] 60 | nms_cfg_ = nms_cfg.copy() 61 | nms_type = nms_cfg_.pop('type', 'nms') 62 | nms_op = getattr(nms_wrapper, nms_type) 63 | dets, keep = nms_op( 64 | torch.cat([bboxes_for_nms, scores[:, None]], 1), **nms_cfg_) 65 | bboxes = bboxes[keep] 66 | scores = dets[:, -1] # soft_nms will modify scores 67 | labels = labels[keep] 68 | 69 | if keep.size(0) > max_num: 70 | _, inds = scores.sort(descending=True) 71 | inds = inds[:max_num] 72 | bboxes = bboxes[inds] 73 | scores = scores[inds] 74 | labels = labels[inds] 75 | 76 | return torch.cat([bboxes, scores[:, None]], 1), labels 77 | -------------------------------------------------------------------------------- /mmdet/core/utils/__init__.py: -------------------------------------------------------------------------------- 1 | from .dist_utils import DistOptimizerHook, allreduce_grads 2 | from .misc import multi_apply, tensor2imgs, unmap 3 | 4 | __all__ = [ 5 | 'allreduce_grads', 'DistOptimizerHook', 'tensor2imgs', 'unmap', 6 | 'multi_apply' 7 | ] 8 | -------------------------------------------------------------------------------- /mmdet/core/utils/dist_utils.py: -------------------------------------------------------------------------------- 1 | from collections import OrderedDict 2 | 3 | import torch.distributed as dist 4 | from mmcv.runner import OptimizerHook 5 | from torch._utils import (_flatten_dense_tensors, _take_tensors, 6 | _unflatten_dense_tensors) 7 | 8 | 9 | def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1): 10 | if bucket_size_mb > 0: 11 | bucket_size_bytes = bucket_size_mb * 1024 * 1024 12 | buckets = _take_tensors(tensors, bucket_size_bytes) 13 | else: 14 | buckets = OrderedDict() 15 | for tensor in tensors: 16 | tp = tensor.type() 17 | if tp not in buckets: 18 | buckets[tp] = [] 19 | buckets[tp].append(tensor) 20 | buckets = buckets.values() 21 | 22 | for bucket in buckets: 23 | flat_tensors = _flatten_dense_tensors(bucket) 24 | dist.all_reduce(flat_tensors) 25 | flat_tensors.div_(world_size) 26 | for tensor, synced in zip( 27 | bucket, _unflatten_dense_tensors(flat_tensors, bucket)): 28 | tensor.copy_(synced) 29 | 30 | 31 | def allreduce_grads(params, coalesce=True, bucket_size_mb=-1): 32 | grads = [ 33 | param.grad.data for param in params 34 | if param.requires_grad and param.grad is not None 35 | ] 36 | world_size = dist.get_world_size() 37 | if coalesce: 38 | _allreduce_coalesced(grads, world_size, bucket_size_mb) 39 | else: 40 | for tensor in grads: 41 | dist.all_reduce(tensor.div_(world_size)) 42 | 43 | 44 | class DistOptimizerHook(OptimizerHook): 45 | 46 | def __init__(self, grad_clip=None, coalesce=True, bucket_size_mb=-1): 47 | self.grad_clip = grad_clip 48 | self.coalesce = coalesce 49 | self.bucket_size_mb = bucket_size_mb 50 | 51 | def after_train_iter(self, runner): 52 | runner.optimizer.zero_grad() 53 | runner.outputs['loss'].backward() 54 | if self.grad_clip is not None: 55 | self.clip_grads(runner.model.parameters()) 56 | runner.optimizer.step() 57 | -------------------------------------------------------------------------------- /mmdet/core/utils/misc.py: -------------------------------------------------------------------------------- 1 | from functools import partial 2 | 3 | import mmcv 4 | import numpy as np 5 | from six.moves import map, zip 6 | 7 | 8 | def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True): 9 | num_imgs = tensor.size(0) 10 | mean = np.array(mean, dtype=np.float32) 11 | std = np.array(std, dtype=np.float32) 12 | imgs = [] 13 | for img_id in range(num_imgs): 14 | img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0) 15 | img = mmcv.imdenormalize( 16 | img, mean, std, to_bgr=to_rgb).astype(np.uint8) 17 | imgs.append(np.ascontiguousarray(img)) 18 | return imgs 19 | 20 | 21 | def multi_apply(func, *args, **kwargs): 22 | pfunc = partial(func, **kwargs) if kwargs else func 23 | map_results = map(pfunc, *args) 24 | return tuple(map(list, zip(*map_results))) 25 | 26 | 27 | def unmap(data, count, inds, fill=0): 28 | """ Unmap a subset of item (data) back to the original set of items (of 29 | size count) """ 30 | if data.dim() == 1: 31 | ret = data.new_full((count, ), fill) 32 | ret[inds] = data 33 | else: 34 | new_size = (count, ) + data.size()[1:] 35 | ret = data.new_full(new_size, fill) 36 | ret[inds, :] = data 37 | return ret 38 | -------------------------------------------------------------------------------- /mmdet/datasets/__init__.py: -------------------------------------------------------------------------------- 1 | from .builder import build_dataset 2 | from .cityscapes import CityscapesDataset 3 | from .coco import CocoDataset 4 | from .custom import CustomDataset 5 | from .dataset_wrappers import ConcatDataset, RepeatDataset 6 | from .loader import DistributedGroupSampler, GroupSampler, build_dataloader 7 | from .registry import DATASETS 8 | from .voc import VOCDataset 9 | from .wider_face import WIDERFaceDataset 10 | from .xml_style import XMLDataset 11 | 12 | __all__ = [ 13 | 'CustomDataset', 'XMLDataset', 'CocoDataset', 'VOCDataset', 14 | 'CityscapesDataset', 'GroupSampler', 'DistributedGroupSampler', 15 | 'build_dataloader', 'ConcatDataset', 'RepeatDataset', 'WIDERFaceDataset', 16 | 'DATASETS', 'build_dataset' 17 | ] 18 | -------------------------------------------------------------------------------- /mmdet/datasets/builder.py: -------------------------------------------------------------------------------- 1 | import copy 2 | 3 | from mmdet.utils import build_from_cfg 4 | from .dataset_wrappers import ConcatDataset, RepeatDataset 5 | from .registry import DATASETS 6 | 7 | 8 | def _concat_dataset(cfg, default_args=None): 9 | ann_files = cfg['ann_file'] 10 | img_prefixes = cfg.get('img_prefix', None) 11 | seg_prefixes = cfg.get('seg_prefix', None) 12 | proposal_files = cfg.get('proposal_file', None) 13 | 14 | datasets = [] 15 | num_dset = len(ann_files) 16 | for i in range(num_dset): 17 | data_cfg = copy.deepcopy(cfg) 18 | data_cfg['ann_file'] = ann_files[i] 19 | if isinstance(img_prefixes, (list, tuple)): 20 | data_cfg['img_prefix'] = img_prefixes[i] 21 | if isinstance(seg_prefixes, (list, tuple)): 22 | data_cfg['seg_prefix'] = seg_prefixes[i] 23 | if isinstance(proposal_files, (list, tuple)): 24 | data_cfg['proposal_file'] = proposal_files[i] 25 | datasets.append(build_dataset(data_cfg, default_args)) 26 | 27 | return ConcatDataset(datasets) 28 | 29 | 30 | def build_dataset(cfg, default_args=None): 31 | if isinstance(cfg, (list, tuple)): 32 | dataset = ConcatDataset([build_dataset(c, default_args) for c in cfg]) 33 | elif cfg['type'] == 'RepeatDataset': 34 | dataset = RepeatDataset( 35 | build_dataset(cfg['dataset'], default_args), cfg['times']) 36 | elif isinstance(cfg.get('ann_file'), (list, tuple)): 37 | dataset = _concat_dataset(cfg, default_args) 38 | else: 39 | dataset = build_from_cfg(cfg, DATASETS, default_args) 40 | 41 | return dataset 42 | -------------------------------------------------------------------------------- /mmdet/datasets/dataset_wrappers.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from torch.utils.data.dataset import ConcatDataset as _ConcatDataset 3 | 4 | from .registry import DATASETS 5 | 6 | 7 | @DATASETS.register_module 8 | class ConcatDataset(_ConcatDataset): 9 | """A wrapper of concatenated dataset. 10 | 11 | Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but 12 | concat the group flag for image aspect ratio. 13 | 14 | Args: 15 | datasets (list[:obj:`Dataset`]): A list of datasets. 16 | """ 17 | 18 | def __init__(self, datasets): 19 | super(ConcatDataset, self).__init__(datasets) 20 | self.CLASSES = datasets[0].CLASSES 21 | if hasattr(datasets[0], 'flag'): 22 | flags = [] 23 | for i in range(0, len(datasets)): 24 | flags.append(datasets[i].flag) 25 | self.flag = np.concatenate(flags) 26 | 27 | 28 | @DATASETS.register_module 29 | class RepeatDataset(object): 30 | """A wrapper of repeated dataset. 31 | 32 | The length of repeated dataset will be `times` larger than the original 33 | dataset. This is useful when the data loading time is long but the dataset 34 | is small. Using RepeatDataset can reduce the data loading time between 35 | epochs. 36 | 37 | Args: 38 | dataset (:obj:`Dataset`): The dataset to be repeated. 39 | times (int): Repeat times. 40 | """ 41 | 42 | def __init__(self, dataset, times): 43 | self.dataset = dataset 44 | self.times = times 45 | self.CLASSES = dataset.CLASSES 46 | if hasattr(self.dataset, 'flag'): 47 | self.flag = np.tile(self.dataset.flag, times) 48 | 49 | self._ori_len = len(self.dataset) 50 | 51 | def __getitem__(self, idx): 52 | return self.dataset[idx % self._ori_len] 53 | 54 | def __len__(self): 55 | return self.times * self._ori_len 56 | -------------------------------------------------------------------------------- /mmdet/datasets/loader/__init__.py: -------------------------------------------------------------------------------- 1 | from .build_loader import build_dataloader 2 | from .sampler import DistributedGroupSampler, GroupSampler 3 | 4 | __all__ = ['GroupSampler', 'DistributedGroupSampler', 'build_dataloader'] 5 | -------------------------------------------------------------------------------- /mmdet/datasets/loader/build_loader.py: -------------------------------------------------------------------------------- 1 | import platform 2 | import random 3 | from functools import partial 4 | 5 | import numpy as np 6 | from mmcv.parallel import collate 7 | from mmcv.runner import get_dist_info 8 | from torch.utils.data import DataLoader 9 | 10 | from .sampler import DistributedGroupSampler, DistributedSampler, GroupSampler 11 | 12 | if platform.system() != 'Windows': 13 | # https://github.com/pytorch/pytorch/issues/973 14 | import resource 15 | rlimit = resource.getrlimit(resource.RLIMIT_NOFILE) 16 | hard_limit = rlimit[1] 17 | soft_limit = min(4096, hard_limit) 18 | resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit)) 19 | 20 | 21 | def build_dataloader(dataset, 22 | imgs_per_gpu, 23 | workers_per_gpu, 24 | num_gpus=1, 25 | dist=True, 26 | shuffle=True, 27 | seed=None, 28 | **kwargs): 29 | """Build PyTorch DataLoader. 30 | 31 | In distributed training, each GPU/process has a dataloader. 32 | In non-distributed training, there is only one dataloader for all GPUs. 33 | 34 | Args: 35 | dataset (Dataset): A PyTorch dataset. 36 | imgs_per_gpu (int): Number of images on each GPU, i.e., batch size of 37 | each GPU. 38 | workers_per_gpu (int): How many subprocesses to use for data loading 39 | for each GPU. 40 | num_gpus (int): Number of GPUs. Only used in non-distributed training. 41 | dist (bool): Distributed training/test or not. Default: True. 42 | shuffle (bool): Whether to shuffle the data at every epoch. 43 | Default: True. 44 | kwargs: any keyword argument to be used to initialize DataLoader 45 | 46 | Returns: 47 | DataLoader: A PyTorch dataloader. 48 | """ 49 | rank, world_size = get_dist_info() 50 | if dist: 51 | # DistributedGroupSampler will definitely shuffle the data to satisfy 52 | # that images on each GPU are in the same group 53 | if shuffle: 54 | sampler = DistributedGroupSampler(dataset, imgs_per_gpu, 55 | world_size, rank) 56 | else: 57 | sampler = DistributedSampler( 58 | dataset, world_size, rank, shuffle=False) 59 | batch_size = imgs_per_gpu 60 | num_workers = workers_per_gpu 61 | else: 62 | sampler = GroupSampler(dataset, imgs_per_gpu) if shuffle else None 63 | batch_size = num_gpus * imgs_per_gpu 64 | num_workers = num_gpus * workers_per_gpu 65 | 66 | init_fn = partial( 67 | worker_init_fn, num_workers=num_workers, rank=rank, 68 | seed=seed) if seed is not None else None 69 | 70 | data_loader = DataLoader( 71 | dataset, 72 | batch_size=batch_size, 73 | sampler=sampler, 74 | num_workers=num_workers, 75 | collate_fn=partial(collate, samples_per_gpu=imgs_per_gpu), 76 | pin_memory=False, 77 | worker_init_fn=init_fn, 78 | **kwargs) 79 | 80 | return data_loader 81 | 82 | 83 | def worker_init_fn(worker_id, num_workers, rank, seed): 84 | # The seed of each worker equals to 85 | # num_worker * rank + worker_id + user_seed 86 | worker_seed = num_workers * rank + worker_id + seed 87 | np.random.seed(worker_seed) 88 | random.seed(worker_seed) 89 | -------------------------------------------------------------------------------- /mmdet/datasets/pipelines/__init__.py: -------------------------------------------------------------------------------- 1 | from .compose import Compose 2 | from .formating import (Collect, ImageToTensor, ToDataContainer, ToTensor, 3 | Transpose, to_tensor) 4 | from .instaboost import InstaBoost 5 | from .loading import LoadAnnotations, LoadImageFromFile, LoadProposals 6 | from .test_aug import MultiScaleFlipAug 7 | from .transforms import (Albu, Expand, MinIoURandomCrop, Normalize, Pad, 8 | PhotoMetricDistortion, RandomCrop, RandomFlip, Resize, 9 | SegRescale) 10 | 11 | __all__ = [ 12 | 'Compose', 'to_tensor', 'ToTensor', 'ImageToTensor', 'ToDataContainer', 13 | 'Transpose', 'Collect', 'LoadAnnotations', 'LoadImageFromFile', 14 | 'LoadProposals', 'MultiScaleFlipAug', 'Resize', 'RandomFlip', 'Pad', 15 | 'RandomCrop', 'Normalize', 'SegRescale', 'MinIoURandomCrop', 'Expand', 16 | 'PhotoMetricDistortion', 'Albu', 'InstaBoost' 17 | ] 18 | -------------------------------------------------------------------------------- /mmdet/datasets/pipelines/compose.py: -------------------------------------------------------------------------------- 1 | import collections 2 | 3 | from mmdet.utils import build_from_cfg 4 | from ..registry import PIPELINES 5 | 6 | 7 | @PIPELINES.register_module 8 | class Compose(object): 9 | 10 | def __init__(self, transforms): 11 | assert isinstance(transforms, collections.abc.Sequence) 12 | self.transforms = [] 13 | for transform in transforms: 14 | if isinstance(transform, dict): 15 | transform = build_from_cfg(transform, PIPELINES) 16 | self.transforms.append(transform) 17 | elif callable(transform): 18 | self.transforms.append(transform) 19 | else: 20 | raise TypeError('transform must be callable or a dict') 21 | 22 | def __call__(self, data): 23 | for t in self.transforms: 24 | data = t(data) 25 | if data is None: 26 | return None 27 | return data 28 | 29 | def __repr__(self): 30 | format_string = self.__class__.__name__ + '(' 31 | for t in self.transforms: 32 | format_string += '\n' 33 | format_string += ' {0}'.format(t) 34 | format_string += '\n)' 35 | return format_string 36 | -------------------------------------------------------------------------------- /mmdet/datasets/pipelines/test_aug.py: -------------------------------------------------------------------------------- 1 | import mmcv 2 | 3 | from ..registry import PIPELINES 4 | from .compose import Compose 5 | 6 | 7 | @PIPELINES.register_module 8 | class MultiScaleFlipAug(object): 9 | 10 | def __init__(self, transforms, img_scale, flip=False): 11 | self.transforms = Compose(transforms) 12 | self.img_scale = img_scale if isinstance(img_scale, 13 | list) else [img_scale] 14 | assert mmcv.is_list_of(self.img_scale, tuple) 15 | self.flip = flip 16 | 17 | def __call__(self, results): 18 | aug_data = [] 19 | flip_aug = [False, True] if self.flip else [False] 20 | for scale in self.img_scale: 21 | for flip in flip_aug: 22 | _results = results.copy() 23 | _results['scale'] = scale 24 | _results['flip'] = flip 25 | data = self.transforms(_results) 26 | aug_data.append(data) 27 | # list of dict to dict of list 28 | aug_data_dict = {key: [] for key in aug_data[0]} 29 | for data in aug_data: 30 | for key, val in data.items(): 31 | aug_data_dict[key].append(val) 32 | return aug_data_dict 33 | 34 | def __repr__(self): 35 | repr_str = self.__class__.__name__ 36 | repr_str += '(transforms={}, img_scale={}, flip={})'.format( 37 | self.transforms, self.img_scale, self.flip) 38 | return repr_str 39 | -------------------------------------------------------------------------------- /mmdet/datasets/registry.py: -------------------------------------------------------------------------------- 1 | from mmdet.utils import Registry 2 | 3 | DATASETS = Registry('dataset') 4 | PIPELINES = Registry('pipeline') 5 | -------------------------------------------------------------------------------- /mmdet/datasets/voc.py: -------------------------------------------------------------------------------- 1 | from mmdet.core import eval_map, eval_recalls 2 | from .registry import DATASETS 3 | from .xml_style import XMLDataset 4 | 5 | 6 | @DATASETS.register_module 7 | class VOCDataset(XMLDataset): 8 | 9 | CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 10 | 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 11 | 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 12 | 'tvmonitor') 13 | 14 | def __init__(self, **kwargs): 15 | super(VOCDataset, self).__init__(**kwargs) 16 | if 'VOC2007' in self.img_prefix: 17 | self.year = 2007 18 | elif 'VOC2012' in self.img_prefix: 19 | self.year = 2012 20 | else: 21 | raise ValueError('Cannot infer dataset year from img_prefix') 22 | 23 | def evaluate(self, 24 | results, 25 | metric='mAP', 26 | logger=None, 27 | proposal_nums=(100, 300, 1000), 28 | iou_thr=0.5, 29 | scale_ranges=None): 30 | if not isinstance(metric, str): 31 | assert len(metric) == 1 32 | metric = metric[0] 33 | allowed_metrics = ['mAP', 'recall'] 34 | if metric not in allowed_metrics: 35 | raise KeyError('metric {} is not supported'.format(metric)) 36 | annotations = [self.get_ann_info(i) for i in range(len(self))] 37 | eval_results = {} 38 | if metric == 'mAP': 39 | assert isinstance(iou_thr, float) 40 | if self.year == 2007: 41 | ds_name = 'voc07' 42 | else: 43 | ds_name = self.dataset.CLASSES 44 | mean_ap, _ = eval_map( 45 | results, 46 | annotations, 47 | scale_ranges=None, 48 | iou_thr=iou_thr, 49 | dataset=ds_name, 50 | logger=logger) 51 | eval_results['mAP'] = mean_ap 52 | elif metric == 'recall': 53 | gt_bboxes = [ann['bboxes'] for ann in annotations] 54 | if isinstance(iou_thr, float): 55 | iou_thr = [iou_thr] 56 | recalls = eval_recalls( 57 | gt_bboxes, results, proposal_nums, iou_thr, logger=logger) 58 | for i, num in enumerate(proposal_nums): 59 | for j, iou in enumerate(iou_thr): 60 | eval_results['recall@{}@{}'.format(num, iou)] = recalls[i, 61 | j] 62 | if recalls.shape[1] > 1: 63 | ar = recalls.mean(axis=1) 64 | for i, num in enumerate(proposal_nums): 65 | eval_results['AR@{}'.format(num)] = ar[i] 66 | return eval_results 67 | -------------------------------------------------------------------------------- /mmdet/datasets/wider_face.py: -------------------------------------------------------------------------------- 1 | import os.path as osp 2 | import xml.etree.ElementTree as ET 3 | 4 | import mmcv 5 | 6 | from .registry import DATASETS 7 | from .xml_style import XMLDataset 8 | 9 | 10 | @DATASETS.register_module 11 | class WIDERFaceDataset(XMLDataset): 12 | """ 13 | Reader for the WIDER Face dataset in PASCAL VOC format. 14 | Conversion scripts can be found in 15 | https://github.com/sovrasov/wider-face-pascal-voc-annotations 16 | """ 17 | CLASSES = ('face', ) 18 | 19 | def __init__(self, **kwargs): 20 | super(WIDERFaceDataset, self).__init__(**kwargs) 21 | 22 | def load_annotations(self, ann_file): 23 | img_infos = [] 24 | img_ids = mmcv.list_from_file(ann_file) 25 | for img_id in img_ids: 26 | filename = '{}.jpg'.format(img_id) 27 | xml_path = osp.join(self.img_prefix, 'Annotations', 28 | '{}.xml'.format(img_id)) 29 | tree = ET.parse(xml_path) 30 | root = tree.getroot() 31 | size = root.find('size') 32 | width = int(size.find('width').text) 33 | height = int(size.find('height').text) 34 | folder = root.find('folder').text 35 | img_infos.append( 36 | dict( 37 | id=img_id, 38 | filename=osp.join(folder, filename), 39 | width=width, 40 | height=height)) 41 | 42 | return img_infos 43 | -------------------------------------------------------------------------------- /mmdet/datasets/xml_style.py: -------------------------------------------------------------------------------- 1 | import os.path as osp 2 | import xml.etree.ElementTree as ET 3 | 4 | import mmcv 5 | import numpy as np 6 | 7 | from .custom import CustomDataset 8 | from .registry import DATASETS 9 | 10 | 11 | @DATASETS.register_module 12 | class XMLDataset(CustomDataset): 13 | 14 | def __init__(self, min_size=None, **kwargs): 15 | super(XMLDataset, self).__init__(**kwargs) 16 | self.cat2label = {cat: i + 1 for i, cat in enumerate(self.CLASSES)} 17 | self.min_size = min_size 18 | 19 | def load_annotations(self, ann_file): 20 | img_infos = [] 21 | img_ids = mmcv.list_from_file(ann_file) 22 | for img_id in img_ids: 23 | filename = 'JPEGImages/{}.jpg'.format(img_id) 24 | xml_path = osp.join(self.img_prefix, 'Annotations', 25 | '{}.xml'.format(img_id)) 26 | tree = ET.parse(xml_path) 27 | root = tree.getroot() 28 | size = root.find('size') 29 | width = int(size.find('width').text) 30 | height = int(size.find('height').text) 31 | img_infos.append( 32 | dict(id=img_id, filename=filename, width=width, height=height)) 33 | return img_infos 34 | 35 | def get_ann_info(self, idx): 36 | img_id = self.img_infos[idx]['id'] 37 | xml_path = osp.join(self.img_prefix, 'Annotations', 38 | '{}.xml'.format(img_id)) 39 | tree = ET.parse(xml_path) 40 | root = tree.getroot() 41 | bboxes = [] 42 | labels = [] 43 | bboxes_ignore = [] 44 | labels_ignore = [] 45 | for obj in root.findall('object'): 46 | name = obj.find('name').text 47 | label = self.cat2label[name] 48 | difficult = int(obj.find('difficult').text) 49 | bnd_box = obj.find('bndbox') 50 | # Coordinates may be float type 51 | bbox = [ 52 | int(float(bnd_box.find('xmin').text)), 53 | int(float(bnd_box.find('ymin').text)), 54 | int(float(bnd_box.find('xmax').text)), 55 | int(float(bnd_box.find('ymax').text)) 56 | ] 57 | ignore = False 58 | if self.min_size: 59 | assert not self.test_mode 60 | w = bbox[2] - bbox[0] 61 | h = bbox[3] - bbox[1] 62 | if w < self.min_size or h < self.min_size: 63 | ignore = True 64 | if difficult or ignore: 65 | bboxes_ignore.append(bbox) 66 | labels_ignore.append(label) 67 | else: 68 | bboxes.append(bbox) 69 | labels.append(label) 70 | if not bboxes: 71 | bboxes = np.zeros((0, 4)) 72 | labels = np.zeros((0, )) 73 | else: 74 | bboxes = np.array(bboxes, ndmin=2) - 1 75 | labels = np.array(labels) 76 | if not bboxes_ignore: 77 | bboxes_ignore = np.zeros((0, 4)) 78 | labels_ignore = np.zeros((0, )) 79 | else: 80 | bboxes_ignore = np.array(bboxes_ignore, ndmin=2) - 1 81 | labels_ignore = np.array(labels_ignore) 82 | ann = dict( 83 | bboxes=bboxes.astype(np.float32), 84 | labels=labels.astype(np.int64), 85 | bboxes_ignore=bboxes_ignore.astype(np.float32), 86 | labels_ignore=labels_ignore.astype(np.int64)) 87 | return ann 88 | -------------------------------------------------------------------------------- /mmdet/models/__init__.py: -------------------------------------------------------------------------------- 1 | from .anchor_heads import * # noqa: F401,F403 2 | from .backbones import * # noqa: F401,F403 3 | from .bbox_heads import * # noqa: F401,F403 4 | from .builder import (build_backbone, build_detector, build_head, build_loss, 5 | build_neck, build_roi_extractor, build_shared_head) 6 | from .detectors import * # noqa: F401,F403 7 | from .losses import * # noqa: F401,F403 8 | from .mask_heads import * # noqa: F401,F403 9 | from .necks import * # noqa: F401,F403 10 | from .registry import (BACKBONES, DETECTORS, HEADS, LOSSES, NECKS, 11 | ROI_EXTRACTORS, SHARED_HEADS) 12 | from .roi_extractors import * # noqa: F401,F403 13 | from .shared_heads import * # noqa: F401,F403 14 | 15 | __all__ = [ 16 | 'BACKBONES', 'NECKS', 'ROI_EXTRACTORS', 'SHARED_HEADS', 'HEADS', 'LOSSES', 17 | 'DETECTORS', 'build_backbone', 'build_neck', 'build_roi_extractor', 18 | 'build_shared_head', 'build_head', 'build_loss', 'build_detector' 19 | ] 20 | -------------------------------------------------------------------------------- /mmdet/models/anchor_heads/__init__.py: -------------------------------------------------------------------------------- 1 | from .anchor_head import AnchorHead 2 | from .atss_head import ATSSHead 3 | from .fcos_head import FCOSHead 4 | from .fovea_head import FoveaHead 5 | from .free_anchor_retina_head import FreeAnchorRetinaHead 6 | from .ga_retina_head import GARetinaHead 7 | from .ga_rpn_head import GARPNHead 8 | from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead 9 | from .reppoints_head import RepPointsHead 10 | from .retina_head import RetinaHead 11 | from .retina_sepbn_head import RetinaSepBNHead 12 | from .rpn_head import RPNHead 13 | from .ssd_head import SSDHead 14 | 15 | __all__ = [ 16 | 'AnchorHead', 'GuidedAnchorHead', 'FeatureAdaption', 'RPNHead', 17 | 'GARPNHead', 'RetinaHead', 'RetinaSepBNHead', 'GARetinaHead', 'SSDHead', 18 | 'FCOSHead', 'RepPointsHead', 'FoveaHead', 'FreeAnchorRetinaHead', 19 | 'ATSSHead' 20 | ] 21 | -------------------------------------------------------------------------------- /mmdet/models/backbones/__init__.py: -------------------------------------------------------------------------------- 1 | from .hrnet import HRNet 2 | from .resnet import ResNet, make_res_layer 3 | from .resnext import ResNeXt 4 | from .ssd_vgg import SSDVGG 5 | 6 | __all__ = ['ResNet', 'make_res_layer', 'ResNeXt', 'SSDVGG', 'HRNet'] 7 | -------------------------------------------------------------------------------- /mmdet/models/bbox_heads/__init__.py: -------------------------------------------------------------------------------- 1 | from .bbox_head import BBoxHead 2 | from .convfc_bbox_head import ConvFCBBoxHead, SharedFCBBoxHead 3 | from .double_bbox_head import DoubleConvFCBBoxHead 4 | 5 | __all__ = [ 6 | 'BBoxHead', 'ConvFCBBoxHead', 'SharedFCBBoxHead', 'DoubleConvFCBBoxHead' 7 | ] 8 | -------------------------------------------------------------------------------- /mmdet/models/builder.py: -------------------------------------------------------------------------------- 1 | from torch import nn 2 | 3 | from mmdet.utils import build_from_cfg 4 | from .registry import (BACKBONES, DETECTORS, HEADS, LOSSES, NECKS, 5 | ROI_EXTRACTORS, SHARED_HEADS) 6 | 7 | 8 | def build(cfg, registry, default_args=None): 9 | if isinstance(cfg, list): 10 | modules = [ 11 | build_from_cfg(cfg_, registry, default_args) for cfg_ in cfg 12 | ] 13 | return nn.Sequential(*modules) 14 | else: 15 | return build_from_cfg(cfg, registry, default_args) 16 | 17 | 18 | def build_backbone(cfg): 19 | return build(cfg, BACKBONES) 20 | 21 | 22 | def build_neck(cfg): 23 | return build(cfg, NECKS) 24 | 25 | 26 | def build_roi_extractor(cfg): 27 | return build(cfg, ROI_EXTRACTORS) 28 | 29 | 30 | def build_shared_head(cfg): 31 | return build(cfg, SHARED_HEADS) 32 | 33 | 34 | def build_head(cfg): 35 | return build(cfg, HEADS) 36 | 37 | 38 | def build_loss(cfg): 39 | return build(cfg, LOSSES) 40 | 41 | 42 | def build_detector(cfg, train_cfg=None, test_cfg=None): 43 | return build(cfg, DETECTORS, dict(train_cfg=train_cfg, test_cfg=test_cfg)) 44 | -------------------------------------------------------------------------------- /mmdet/models/detectors/__init__.py: -------------------------------------------------------------------------------- 1 | from .atss import ATSS 2 | from .base import BaseDetector 3 | from .cascade_rcnn import CascadeRCNN 4 | from .double_head_rcnn import DoubleHeadRCNN 5 | from .fast_rcnn import FastRCNN 6 | from .faster_rcnn import FasterRCNN 7 | from .fcos import FCOS 8 | from .fovea import FOVEA 9 | from .grid_rcnn import GridRCNN 10 | from .htc import HybridTaskCascade 11 | from .mask_rcnn import MaskRCNN 12 | from .mask_scoring_rcnn import MaskScoringRCNN 13 | from .reppoints_detector import RepPointsDetector 14 | from .retinanet import RetinaNet 15 | from .rfp import RecursiveFeaturePyramid 16 | from .rpn import RPN 17 | from .single_stage import SingleStageDetector 18 | from .two_stage import TwoStageDetector 19 | 20 | __all__ = [ 21 | 'ATSS', 'BaseDetector', 'SingleStageDetector', 'TwoStageDetector', 'RPN', 22 | 'FastRCNN', 'FasterRCNN', 'MaskRCNN', 'CascadeRCNN', 'HybridTaskCascade', 23 | 'DoubleHeadRCNN', 'RetinaNet', 'FCOS', 'GridRCNN', 'MaskScoringRCNN', 24 | 'RepPointsDetector', 'FOVEA', "RecursiveFeaturePyramid", 25 | ] 26 | -------------------------------------------------------------------------------- /mmdet/models/detectors/atss.py: -------------------------------------------------------------------------------- 1 | from ..registry import DETECTORS 2 | from .single_stage import SingleStageDetector 3 | 4 | 5 | @DETECTORS.register_module 6 | class ATSS(SingleStageDetector): 7 | 8 | def __init__(self, 9 | backbone, 10 | neck, 11 | bbox_head, 12 | train_cfg=None, 13 | test_cfg=None, 14 | pretrained=None): 15 | super(ATSS, self).__init__(backbone, neck, bbox_head, train_cfg, 16 | test_cfg, pretrained) 17 | -------------------------------------------------------------------------------- /mmdet/models/detectors/fast_rcnn.py: -------------------------------------------------------------------------------- 1 | from ..registry import DETECTORS 2 | from .two_stage import TwoStageDetector 3 | 4 | 5 | @DETECTORS.register_module 6 | class FastRCNN(TwoStageDetector): 7 | 8 | def __init__(self, 9 | backbone, 10 | bbox_roi_extractor, 11 | bbox_head, 12 | train_cfg, 13 | test_cfg, 14 | neck=None, 15 | shared_head=None, 16 | mask_roi_extractor=None, 17 | mask_head=None, 18 | pretrained=None): 19 | super(FastRCNN, self).__init__( 20 | backbone=backbone, 21 | neck=neck, 22 | shared_head=shared_head, 23 | bbox_roi_extractor=bbox_roi_extractor, 24 | bbox_head=bbox_head, 25 | train_cfg=train_cfg, 26 | test_cfg=test_cfg, 27 | mask_roi_extractor=mask_roi_extractor, 28 | mask_head=mask_head, 29 | pretrained=pretrained) 30 | 31 | def forward_test(self, imgs, img_metas, proposals, **kwargs): 32 | """ 33 | Args: 34 | imgs (List[Tensor]): the outer list indicates test-time 35 | augmentations and inner Tensor should have a shape NxCxHxW, 36 | which contains all images in the batch. 37 | img_metas (List[List[dict]]): the outer list indicates test-time 38 | augs (multiscale, flip, etc.) and the inner list indicates 39 | images in a batch. 40 | proposals (List[List[Tensor]]): the outer list indicates test-time 41 | augs (multiscale, flip, etc.) and the inner list indicates 42 | images in a batch. The Tensor should have a shape Px4, where 43 | P is the number of proposals. 44 | """ 45 | for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]: 46 | if not isinstance(var, list): 47 | raise TypeError('{} must be a list, but got {}'.format( 48 | name, type(var))) 49 | 50 | num_augs = len(imgs) 51 | if num_augs != len(img_metas): 52 | raise ValueError( 53 | 'num of augmentations ({}) != num of image meta ({})'.format( 54 | len(imgs), len(img_metas))) 55 | # TODO: remove the restriction of imgs_per_gpu == 1 when prepared 56 | imgs_per_gpu = imgs[0].size(0) 57 | assert imgs_per_gpu == 1 58 | 59 | if num_augs == 1: 60 | return self.simple_test(imgs[0], img_metas[0], proposals[0], 61 | **kwargs) 62 | else: 63 | # TODO: support test-time augmentation 64 | assert NotImplementedError 65 | -------------------------------------------------------------------------------- /mmdet/models/detectors/faster_rcnn.py: -------------------------------------------------------------------------------- 1 | from ..registry import DETECTORS 2 | from .two_stage import TwoStageDetector 3 | 4 | 5 | @DETECTORS.register_module 6 | class FasterRCNN(TwoStageDetector): 7 | 8 | def __init__(self, 9 | backbone, 10 | rpn_head, 11 | bbox_roi_extractor, 12 | bbox_head, 13 | train_cfg, 14 | test_cfg, 15 | neck=None, 16 | shared_head=None, 17 | pretrained=None): 18 | super(FasterRCNN, self).__init__( 19 | backbone=backbone, 20 | neck=neck, 21 | shared_head=shared_head, 22 | rpn_head=rpn_head, 23 | bbox_roi_extractor=bbox_roi_extractor, 24 | bbox_head=bbox_head, 25 | train_cfg=train_cfg, 26 | test_cfg=test_cfg, 27 | pretrained=pretrained) 28 | -------------------------------------------------------------------------------- /mmdet/models/detectors/fcos.py: -------------------------------------------------------------------------------- 1 | from ..registry import DETECTORS 2 | from .single_stage import SingleStageDetector 3 | 4 | 5 | @DETECTORS.register_module 6 | class FCOS(SingleStageDetector): 7 | 8 | def __init__(self, 9 | backbone, 10 | neck, 11 | bbox_head, 12 | train_cfg=None, 13 | test_cfg=None, 14 | pretrained=None): 15 | super(FCOS, self).__init__(backbone, neck, bbox_head, train_cfg, 16 | test_cfg, pretrained) 17 | -------------------------------------------------------------------------------- /mmdet/models/detectors/fovea.py: -------------------------------------------------------------------------------- 1 | from ..registry import DETECTORS 2 | from .single_stage import SingleStageDetector 3 | 4 | 5 | @DETECTORS.register_module 6 | class FOVEA(SingleStageDetector): 7 | 8 | def __init__(self, 9 | backbone, 10 | neck, 11 | bbox_head, 12 | train_cfg=None, 13 | test_cfg=None, 14 | pretrained=None): 15 | super(FOVEA, self).__init__(backbone, neck, bbox_head, train_cfg, 16 | test_cfg, pretrained) 17 | -------------------------------------------------------------------------------- /mmdet/models/detectors/mask_rcnn.py: -------------------------------------------------------------------------------- 1 | from ..registry import DETECTORS 2 | from .two_stage import TwoStageDetector 3 | 4 | 5 | @DETECTORS.register_module 6 | class MaskRCNN(TwoStageDetector): 7 | 8 | def __init__(self, 9 | backbone, 10 | rpn_head, 11 | bbox_roi_extractor, 12 | bbox_head, 13 | mask_roi_extractor, 14 | mask_head, 15 | train_cfg, 16 | test_cfg, 17 | neck=None, 18 | shared_head=None, 19 | pretrained=None): 20 | super(MaskRCNN, self).__init__( 21 | backbone=backbone, 22 | neck=neck, 23 | shared_head=shared_head, 24 | rpn_head=rpn_head, 25 | bbox_roi_extractor=bbox_roi_extractor, 26 | bbox_head=bbox_head, 27 | mask_roi_extractor=mask_roi_extractor, 28 | mask_head=mask_head, 29 | train_cfg=train_cfg, 30 | test_cfg=test_cfg, 31 | pretrained=pretrained) 32 | -------------------------------------------------------------------------------- /mmdet/models/detectors/reppoints_detector.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from mmdet.core import bbox2result, bbox_mapping_back, multiclass_nms 4 | from ..registry import DETECTORS 5 | from .single_stage import SingleStageDetector 6 | 7 | 8 | @DETECTORS.register_module 9 | class RepPointsDetector(SingleStageDetector): 10 | """RepPoints: Point Set Representation for Object Detection. 11 | 12 | This detector is the implementation of: 13 | - RepPoints detector (https://arxiv.org/pdf/1904.11490) 14 | """ 15 | 16 | def __init__(self, 17 | backbone, 18 | neck, 19 | bbox_head, 20 | train_cfg=None, 21 | test_cfg=None, 22 | pretrained=None): 23 | super(RepPointsDetector, 24 | self).__init__(backbone, neck, bbox_head, train_cfg, test_cfg, 25 | pretrained) 26 | 27 | def merge_aug_results(self, aug_bboxes, aug_scores, img_metas): 28 | """Merge augmented detection bboxes and scores. 29 | 30 | Args: 31 | aug_bboxes (list[Tensor]): shape (n, 4*#class) 32 | aug_scores (list[Tensor] or None): shape (n, #class) 33 | img_shapes (list[Tensor]): shape (3, ). 34 | 35 | Returns: 36 | tuple: (bboxes, scores) 37 | """ 38 | recovered_bboxes = [] 39 | for bboxes, img_info in zip(aug_bboxes, img_metas): 40 | img_shape = img_info[0]['img_shape'] 41 | scale_factor = img_info[0]['scale_factor'] 42 | flip = img_info[0]['flip'] 43 | bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip) 44 | recovered_bboxes.append(bboxes) 45 | bboxes = torch.cat(recovered_bboxes, dim=0) 46 | if aug_scores is None: 47 | return bboxes 48 | else: 49 | scores = torch.cat(aug_scores, dim=0) 50 | return bboxes, scores 51 | 52 | def aug_test(self, imgs, img_metas, rescale=False): 53 | # recompute feats to save memory 54 | feats = self.extract_feats(imgs) 55 | 56 | aug_bboxes = [] 57 | aug_scores = [] 58 | for x, img_meta in zip(feats, img_metas): 59 | # only one image in the batch 60 | outs = self.bbox_head(x) 61 | bbox_inputs = outs + (img_metas, self.test_cfg, False, False) 62 | det_bboxes, det_scores = self.bbox_head.get_bboxes(*bbox_inputs)[0] 63 | aug_bboxes.append(det_bboxes) 64 | aug_scores.append(det_scores) 65 | 66 | # after merging, bboxes will be rescaled to the original image size 67 | merged_bboxes, merged_scores = self.merge_aug_results( 68 | aug_bboxes, aug_scores, img_metas) 69 | det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores, 70 | self.test_cfg.score_thr, 71 | self.test_cfg.nms, 72 | self.test_cfg.max_per_img) 73 | 74 | if rescale: 75 | _det_bboxes = det_bboxes 76 | else: 77 | _det_bboxes = det_bboxes.clone() 78 | _det_bboxes[:, :4] *= img_metas[0][0]['scale_factor'] 79 | bbox_results = bbox2result(_det_bboxes, det_labels, 80 | self.bbox_head.num_classes) 81 | return bbox_results 82 | -------------------------------------------------------------------------------- /mmdet/models/detectors/retinanet.py: -------------------------------------------------------------------------------- 1 | from ..registry import DETECTORS 2 | from .single_stage import SingleStageDetector 3 | 4 | 5 | @DETECTORS.register_module 6 | class RetinaNet(SingleStageDetector): 7 | 8 | def __init__(self, 9 | backbone, 10 | neck, 11 | bbox_head, 12 | train_cfg=None, 13 | test_cfg=None, 14 | pretrained=None): 15 | super(RetinaNet, self).__init__(backbone, neck, bbox_head, train_cfg, 16 | test_cfg, pretrained) 17 | -------------------------------------------------------------------------------- /mmdet/models/detectors/single_stage.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | 3 | from mmdet.core import bbox2result 4 | from .. import builder 5 | from ..registry import DETECTORS 6 | from .base import BaseDetector 7 | 8 | 9 | @DETECTORS.register_module 10 | class SingleStageDetector(BaseDetector): 11 | """Base class for single-stage detectors. 12 | 13 | Single-stage detectors directly and densely predict bounding boxes on the 14 | output features of the backbone+neck. 15 | """ 16 | 17 | def __init__(self, 18 | backbone, 19 | neck=None, 20 | bbox_head=None, 21 | train_cfg=None, 22 | test_cfg=None, 23 | pretrained=None): 24 | super(SingleStageDetector, self).__init__() 25 | self.backbone = builder.build_backbone(backbone) 26 | if neck is not None: 27 | self.neck = builder.build_neck(neck) 28 | self.bbox_head = builder.build_head(bbox_head) 29 | self.train_cfg = train_cfg 30 | self.test_cfg = test_cfg 31 | self.init_weights(pretrained=pretrained) 32 | 33 | def init_weights(self, pretrained=None): 34 | super(SingleStageDetector, self).init_weights(pretrained) 35 | self.backbone.init_weights(pretrained=pretrained) 36 | if self.with_neck: 37 | if isinstance(self.neck, nn.Sequential): 38 | for m in self.neck: 39 | m.init_weights() 40 | else: 41 | self.neck.init_weights() 42 | self.bbox_head.init_weights() 43 | 44 | def extract_feat(self, img): 45 | """Directly extract features from the backbone+neck 46 | """ 47 | x = self.backbone(img) 48 | if self.with_neck: 49 | x = self.neck(x) 50 | return x 51 | 52 | def forward_dummy(self, img): 53 | """Used for computing network flops. 54 | 55 | See `mmdetection/tools/get_flops.py` 56 | """ 57 | x = self.extract_feat(img) 58 | outs = self.bbox_head(x) 59 | return outs 60 | 61 | def forward_train(self, 62 | img, 63 | img_metas, 64 | gt_bboxes, 65 | gt_labels, 66 | gt_bboxes_ignore=None): 67 | x = self.extract_feat(img) 68 | outs = self.bbox_head(x) 69 | loss_inputs = outs + (gt_bboxes, gt_labels, img_metas, self.train_cfg) 70 | losses = self.bbox_head.loss( 71 | *loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore) 72 | return losses 73 | 74 | def simple_test(self, img, img_metas, rescale=False): 75 | x = self.extract_feat(img) 76 | outs = self.bbox_head(x) 77 | bbox_inputs = outs + (img_metas, self.test_cfg, rescale) 78 | bbox_list = self.bbox_head.get_bboxes(*bbox_inputs) 79 | bbox_results = [ 80 | bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes) 81 | for det_bboxes, det_labels in bbox_list 82 | ] 83 | return bbox_results[0] 84 | 85 | def aug_test(self, imgs, img_metas, rescale=False): 86 | raise NotImplementedError 87 | -------------------------------------------------------------------------------- /mmdet/models/losses/__init__.py: -------------------------------------------------------------------------------- 1 | from .accuracy import Accuracy, accuracy 2 | from .balanced_l1_loss import BalancedL1Loss, balanced_l1_loss 3 | from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy, 4 | cross_entropy, mask_cross_entropy) 5 | from .focal_loss import FocalLoss, sigmoid_focal_loss 6 | from .ghm_loss import GHMC, GHMR 7 | from .iou_loss import (BoundedIoULoss, GIoULoss, IoULoss, bounded_iou_loss, 8 | iou_loss) 9 | from .mse_loss import MSELoss, mse_loss 10 | from .smooth_l1_loss import SmoothL1Loss, smooth_l1_loss 11 | from .utils import reduce_loss, weight_reduce_loss, weighted_loss 12 | 13 | __all__ = [ 14 | 'accuracy', 'Accuracy', 'cross_entropy', 'binary_cross_entropy', 15 | 'mask_cross_entropy', 'CrossEntropyLoss', 'sigmoid_focal_loss', 16 | 'FocalLoss', 'smooth_l1_loss', 'SmoothL1Loss', 'balanced_l1_loss', 17 | 'BalancedL1Loss', 'mse_loss', 'MSELoss', 'iou_loss', 'bounded_iou_loss', 18 | 'IoULoss', 'BoundedIoULoss', 'GIoULoss', 'GHMC', 'GHMR', 'reduce_loss', 19 | 'weight_reduce_loss', 'weighted_loss' 20 | ] 21 | -------------------------------------------------------------------------------- /mmdet/models/losses/accuracy.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | 3 | 4 | def accuracy(pred, target, topk=1): 5 | assert isinstance(topk, (int, tuple)) 6 | if isinstance(topk, int): 7 | topk = (topk, ) 8 | return_single = True 9 | else: 10 | return_single = False 11 | 12 | maxk = max(topk) 13 | _, pred_label = pred.topk(maxk, dim=1) 14 | pred_label = pred_label.t() 15 | correct = pred_label.eq(target.view(1, -1).expand_as(pred_label)) 16 | 17 | res = [] 18 | for k in topk: 19 | correct_k = correct[:k].view(-1).float().sum(0, keepdim=True) 20 | res.append(correct_k.mul_(100.0 / pred.size(0))) 21 | return res[0] if return_single else res 22 | 23 | 24 | class Accuracy(nn.Module): 25 | 26 | def __init__(self, topk=(1, )): 27 | super().__init__() 28 | self.topk = topk 29 | 30 | def forward(self, pred, target): 31 | return accuracy(pred, target, self.topk) 32 | -------------------------------------------------------------------------------- /mmdet/models/losses/balanced_l1_loss.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | import torch.nn as nn 4 | 5 | from ..registry import LOSSES 6 | from .utils import weighted_loss 7 | 8 | 9 | @weighted_loss 10 | def balanced_l1_loss(pred, 11 | target, 12 | beta=1.0, 13 | alpha=0.5, 14 | gamma=1.5, 15 | reduction='mean'): 16 | assert beta > 0 17 | assert pred.size() == target.size() and target.numel() > 0 18 | 19 | diff = torch.abs(pred - target) 20 | b = np.e**(gamma / alpha) - 1 21 | loss = torch.where( 22 | diff < beta, alpha / b * 23 | (b * diff + 1) * torch.log(b * diff / beta + 1) - alpha * diff, 24 | gamma * diff + gamma / b - alpha * beta) 25 | 26 | return loss 27 | 28 | 29 | @LOSSES.register_module 30 | class BalancedL1Loss(nn.Module): 31 | """Balanced L1 Loss 32 | 33 | arXiv: https://arxiv.org/pdf/1904.02701.pdf (CVPR 2019) 34 | """ 35 | 36 | def __init__(self, 37 | alpha=0.5, 38 | gamma=1.5, 39 | beta=1.0, 40 | reduction='mean', 41 | loss_weight=1.0): 42 | super(BalancedL1Loss, self).__init__() 43 | self.alpha = alpha 44 | self.gamma = gamma 45 | self.beta = beta 46 | self.reduction = reduction 47 | self.loss_weight = loss_weight 48 | 49 | def forward(self, 50 | pred, 51 | target, 52 | weight=None, 53 | avg_factor=None, 54 | reduction_override=None, 55 | **kwargs): 56 | assert reduction_override in (None, 'none', 'mean', 'sum') 57 | reduction = ( 58 | reduction_override if reduction_override else self.reduction) 59 | loss_bbox = self.loss_weight * balanced_l1_loss( 60 | pred, 61 | target, 62 | weight, 63 | alpha=self.alpha, 64 | gamma=self.gamma, 65 | beta=self.beta, 66 | reduction=reduction, 67 | avg_factor=avg_factor, 68 | **kwargs) 69 | return loss_bbox 70 | -------------------------------------------------------------------------------- /mmdet/models/losses/focal_loss.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | import torch.nn.functional as F 3 | 4 | from mmdet.ops import sigmoid_focal_loss as _sigmoid_focal_loss 5 | from ..registry import LOSSES 6 | from .utils import weight_reduce_loss 7 | 8 | 9 | # This method is only for debugging 10 | def py_sigmoid_focal_loss(pred, 11 | target, 12 | weight=None, 13 | gamma=2.0, 14 | alpha=0.25, 15 | reduction='mean', 16 | avg_factor=None): 17 | pred_sigmoid = pred.sigmoid() 18 | target = target.type_as(pred) 19 | pt = (1 - pred_sigmoid) * target + pred_sigmoid * (1 - target) 20 | focal_weight = (alpha * target + (1 - alpha) * 21 | (1 - target)) * pt.pow(gamma) 22 | loss = F.binary_cross_entropy_with_logits( 23 | pred, target, reduction='none') * focal_weight 24 | loss = weight_reduce_loss(loss, weight, reduction, avg_factor) 25 | return loss 26 | 27 | 28 | def sigmoid_focal_loss(pred, 29 | target, 30 | weight=None, 31 | gamma=2.0, 32 | alpha=0.25, 33 | reduction='mean', 34 | avg_factor=None): 35 | # Function.apply does not accept keyword arguments, so the decorator 36 | # "weighted_loss" is not applicable 37 | loss = _sigmoid_focal_loss(pred, target, gamma, alpha) 38 | # TODO: find a proper way to handle the shape of weight 39 | if weight is not None: 40 | weight = weight.view(-1, 1) 41 | loss = weight_reduce_loss(loss, weight, reduction, avg_factor) 42 | return loss 43 | 44 | 45 | @LOSSES.register_module 46 | class FocalLoss(nn.Module): 47 | 48 | def __init__(self, 49 | use_sigmoid=True, 50 | gamma=2.0, 51 | alpha=0.25, 52 | reduction='mean', 53 | loss_weight=1.0): 54 | super(FocalLoss, self).__init__() 55 | assert use_sigmoid is True, 'Only sigmoid focal loss supported now.' 56 | self.use_sigmoid = use_sigmoid 57 | self.gamma = gamma 58 | self.alpha = alpha 59 | self.reduction = reduction 60 | self.loss_weight = loss_weight 61 | 62 | def forward(self, 63 | pred, 64 | target, 65 | weight=None, 66 | avg_factor=None, 67 | reduction_override=None): 68 | assert reduction_override in (None, 'none', 'mean', 'sum') 69 | reduction = ( 70 | reduction_override if reduction_override else self.reduction) 71 | if self.use_sigmoid: 72 | loss_cls = self.loss_weight * sigmoid_focal_loss( 73 | pred, 74 | target, 75 | weight, 76 | gamma=self.gamma, 77 | alpha=self.alpha, 78 | reduction=reduction, 79 | avg_factor=avg_factor) 80 | else: 81 | raise NotImplementedError 82 | return loss_cls 83 | -------------------------------------------------------------------------------- /mmdet/models/losses/mse_loss.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | import torch.nn.functional as F 3 | 4 | from ..registry import LOSSES 5 | from .utils import weighted_loss 6 | 7 | 8 | @weighted_loss 9 | def mse_loss(pred, target): 10 | return F.mse_loss(pred, target, reduction='none') 11 | 12 | 13 | @LOSSES.register_module 14 | class MSELoss(nn.Module): 15 | 16 | def __init__(self, reduction='mean', loss_weight=1.0): 17 | super().__init__() 18 | self.reduction = reduction 19 | self.loss_weight = loss_weight 20 | 21 | def forward(self, pred, target, weight=None, avg_factor=None): 22 | loss = self.loss_weight * mse_loss( 23 | pred, 24 | target, 25 | weight, 26 | reduction=self.reduction, 27 | avg_factor=avg_factor) 28 | return loss 29 | -------------------------------------------------------------------------------- /mmdet/models/losses/smooth_l1_loss.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | 4 | from ..registry import LOSSES 5 | from .utils import weighted_loss 6 | 7 | 8 | @weighted_loss 9 | def smooth_l1_loss(pred, target, beta=1.0): 10 | assert beta > 0 11 | assert pred.size() == target.size() and target.numel() > 0 12 | diff = torch.abs(pred - target) 13 | loss = torch.where(diff < beta, 0.5 * diff * diff / beta, 14 | diff - 0.5 * beta) 15 | return loss 16 | 17 | 18 | @LOSSES.register_module 19 | class SmoothL1Loss(nn.Module): 20 | 21 | def __init__(self, beta=1.0, reduction='mean', loss_weight=1.0): 22 | super(SmoothL1Loss, self).__init__() 23 | self.beta = beta 24 | self.reduction = reduction 25 | self.loss_weight = loss_weight 26 | 27 | def forward(self, 28 | pred, 29 | target, 30 | weight=None, 31 | avg_factor=None, 32 | reduction_override=None, 33 | **kwargs): 34 | assert reduction_override in (None, 'none', 'mean', 'sum') 35 | reduction = ( 36 | reduction_override if reduction_override else self.reduction) 37 | loss_bbox = self.loss_weight * smooth_l1_loss( 38 | pred, 39 | target, 40 | weight, 41 | beta=self.beta, 42 | reduction=reduction, 43 | avg_factor=avg_factor, 44 | **kwargs) 45 | return loss_bbox 46 | -------------------------------------------------------------------------------- /mmdet/models/losses/utils.py: -------------------------------------------------------------------------------- 1 | import functools 2 | 3 | import torch.nn.functional as F 4 | 5 | 6 | def reduce_loss(loss, reduction): 7 | """Reduce loss as specified. 8 | 9 | Args: 10 | loss (Tensor): Elementwise loss tensor. 11 | reduction (str): Options are "none", "mean" and "sum". 12 | 13 | Return: 14 | Tensor: Reduced loss tensor. 15 | """ 16 | reduction_enum = F._Reduction.get_enum(reduction) 17 | # none: 0, elementwise_mean:1, sum: 2 18 | if reduction_enum == 0: 19 | return loss 20 | elif reduction_enum == 1: 21 | return loss.mean() 22 | elif reduction_enum == 2: 23 | return loss.sum() 24 | 25 | 26 | def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None): 27 | """Apply element-wise weight and reduce loss. 28 | 29 | Args: 30 | loss (Tensor): Element-wise loss. 31 | weight (Tensor): Element-wise weights. 32 | reduction (str): Same as built-in losses of PyTorch. 33 | avg_factor (float): Avarage factor when computing the mean of losses. 34 | 35 | Returns: 36 | Tensor: Processed loss values. 37 | """ 38 | # if weight is specified, apply element-wise weight 39 | if weight is not None: 40 | loss = loss * weight 41 | 42 | # if avg_factor is not specified, just reduce the loss 43 | if avg_factor is None: 44 | loss = reduce_loss(loss, reduction) 45 | else: 46 | # if reduction is mean, then average the loss by avg_factor 47 | if reduction == 'mean': 48 | loss = loss.sum() / avg_factor 49 | # if reduction is 'none', then do nothing, otherwise raise an error 50 | elif reduction != 'none': 51 | raise ValueError('avg_factor can not be used with reduction="sum"') 52 | return loss 53 | 54 | 55 | def weighted_loss(loss_func): 56 | """Create a weighted version of a given loss function. 57 | 58 | To use this decorator, the loss function must have the signature like 59 | `loss_func(pred, target, **kwargs)`. The function only needs to compute 60 | element-wise loss without any reduction. This decorator will add weight 61 | and reduction arguments to the function. The decorated function will have 62 | the signature like `loss_func(pred, target, weight=None, reduction='mean', 63 | avg_factor=None, **kwargs)`. 64 | 65 | :Example: 66 | 67 | >>> import torch 68 | >>> @weighted_loss 69 | >>> def l1_loss(pred, target): 70 | >>> return (pred - target).abs() 71 | 72 | >>> pred = torch.Tensor([0, 2, 3]) 73 | >>> target = torch.Tensor([1, 1, 1]) 74 | >>> weight = torch.Tensor([1, 0, 1]) 75 | 76 | >>> l1_loss(pred, target) 77 | tensor(1.3333) 78 | >>> l1_loss(pred, target, weight) 79 | tensor(1.) 80 | >>> l1_loss(pred, target, reduction='none') 81 | tensor([1., 1., 2.]) 82 | >>> l1_loss(pred, target, weight, avg_factor=2) 83 | tensor(1.5000) 84 | """ 85 | 86 | @functools.wraps(loss_func) 87 | def wrapper(pred, 88 | target, 89 | weight=None, 90 | reduction='mean', 91 | avg_factor=None, 92 | **kwargs): 93 | # get element-wise loss 94 | loss = loss_func(pred, target, **kwargs) 95 | loss = weight_reduce_loss(loss, weight, reduction, avg_factor) 96 | return loss 97 | 98 | return wrapper 99 | -------------------------------------------------------------------------------- /mmdet/models/mask_heads/__init__.py: -------------------------------------------------------------------------------- 1 | from .fcn_mask_head import FCNMaskHead 2 | from .fused_semantic_head import FusedSemanticHead 3 | from .grid_head import GridHead 4 | from .htc_mask_head import HTCMaskHead 5 | from .maskiou_head import MaskIoUHead 6 | 7 | __all__ = [ 8 | 'FCNMaskHead', 'HTCMaskHead', 'FusedSemanticHead', 'GridHead', 9 | 'MaskIoUHead' 10 | ] 11 | -------------------------------------------------------------------------------- /mmdet/models/mask_heads/htc_mask_head.py: -------------------------------------------------------------------------------- 1 | from mmdet.ops import ConvModule 2 | from ..registry import HEADS 3 | from .fcn_mask_head import FCNMaskHead 4 | 5 | 6 | @HEADS.register_module 7 | class HTCMaskHead(FCNMaskHead): 8 | 9 | def __init__(self, with_conv_res=True, *args, **kwargs): 10 | super(HTCMaskHead, self).__init__(*args, **kwargs) 11 | self.with_conv_res = with_conv_res 12 | if self.with_conv_res: 13 | self.conv_res = ConvModule( 14 | self.conv_out_channels, 15 | self.conv_out_channels, 16 | 1, 17 | conv_cfg=self.conv_cfg, 18 | norm_cfg=self.norm_cfg) 19 | 20 | def init_weights(self): 21 | super(HTCMaskHead, self).init_weights() 22 | if self.with_conv_res: 23 | self.conv_res.init_weights() 24 | 25 | def forward(self, x, res_feat=None, return_logits=True, return_feat=True): 26 | if res_feat is not None: 27 | assert self.with_conv_res 28 | res_feat = self.conv_res(res_feat) 29 | x = x + res_feat 30 | for conv in self.convs: 31 | x = conv(x) 32 | res_feat = x 33 | outs = [] 34 | if return_logits: 35 | x = self.upsample(x) 36 | if self.upsample_method == 'deconv': 37 | x = self.relu(x) 38 | mask_pred = self.conv_logits(x) 39 | outs.append(mask_pred) 40 | if return_feat: 41 | outs.append(res_feat) 42 | return outs if len(outs) > 1 else outs[0] 43 | -------------------------------------------------------------------------------- /mmdet/models/necks/__init__.py: -------------------------------------------------------------------------------- 1 | from .bfp import BFP 2 | from .fpn import FPN 3 | from .fpn_carafe import FPN_CARAFE 4 | from .hrfpn import HRFPN 5 | from .nas_fpn import NASFPN 6 | 7 | __all__ = ['FPN', 'BFP', 'HRFPN', 'NASFPN', 'FPN_CARAFE'] 8 | -------------------------------------------------------------------------------- /mmdet/models/registry.py: -------------------------------------------------------------------------------- 1 | from mmdet.utils import Registry 2 | 3 | BACKBONES = Registry('backbone') 4 | NECKS = Registry('neck') 5 | ROI_EXTRACTORS = Registry('roi_extractor') 6 | SHARED_HEADS = Registry('shared_head') 7 | HEADS = Registry('head') 8 | LOSSES = Registry('loss') 9 | DETECTORS = Registry('detector') 10 | -------------------------------------------------------------------------------- /mmdet/models/roi_extractors/__init__.py: -------------------------------------------------------------------------------- 1 | from .single_level import SingleRoIExtractor 2 | 3 | __all__ = ['SingleRoIExtractor'] 4 | -------------------------------------------------------------------------------- /mmdet/models/shared_heads/__init__.py: -------------------------------------------------------------------------------- 1 | from .res_layer import ResLayer 2 | 3 | __all__ = ['ResLayer'] 4 | -------------------------------------------------------------------------------- /mmdet/models/shared_heads/res_layer.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | from mmcv.cnn import constant_init, kaiming_init 3 | from mmcv.runner import load_checkpoint 4 | 5 | from mmdet.core import auto_fp16 6 | from mmdet.utils import get_root_logger 7 | from ..backbones import ResNet, make_res_layer 8 | from ..registry import SHARED_HEADS 9 | 10 | 11 | @SHARED_HEADS.register_module 12 | class ResLayer(nn.Module): 13 | 14 | def __init__(self, 15 | depth, 16 | stage=3, 17 | stride=2, 18 | dilation=1, 19 | style='pytorch', 20 | norm_cfg=dict(type='BN', requires_grad=True), 21 | norm_eval=True, 22 | with_cp=False, 23 | dcn=None): 24 | super(ResLayer, self).__init__() 25 | self.norm_eval = norm_eval 26 | self.norm_cfg = norm_cfg 27 | self.stage = stage 28 | self.fp16_enabled = False 29 | block, stage_blocks = ResNet.arch_settings[depth] 30 | stage_block = stage_blocks[stage] 31 | planes = 64 * 2**stage 32 | inplanes = 64 * 2**(stage - 1) * block.expansion 33 | 34 | res_layer = make_res_layer( 35 | block, 36 | inplanes, 37 | planes, 38 | stage_block, 39 | stride=stride, 40 | dilation=dilation, 41 | style=style, 42 | with_cp=with_cp, 43 | norm_cfg=self.norm_cfg, 44 | dcn=dcn) 45 | self.add_module('layer{}'.format(stage + 1), res_layer) 46 | 47 | def init_weights(self, pretrained=None): 48 | if isinstance(pretrained, str): 49 | logger = get_root_logger() 50 | load_checkpoint(self, pretrained, strict=False, logger=logger) 51 | elif pretrained is None: 52 | for m in self.modules(): 53 | if isinstance(m, nn.Conv2d): 54 | kaiming_init(m) 55 | elif isinstance(m, nn.BatchNorm2d): 56 | constant_init(m, 1) 57 | else: 58 | raise TypeError('pretrained must be a str or None') 59 | 60 | @auto_fp16() 61 | def forward(self, x): 62 | res_layer = getattr(self, 'layer{}'.format(self.stage + 1)) 63 | out = res_layer(x) 64 | return out 65 | 66 | def train(self, mode=True): 67 | super(ResLayer, self).train(mode) 68 | if self.norm_eval: 69 | for m in self.modules(): 70 | if isinstance(m, nn.BatchNorm2d): 71 | m.eval() 72 | -------------------------------------------------------------------------------- /mmdet/models/utils/__init__.py: -------------------------------------------------------------------------------- 1 | from .weight_init import bias_init_with_prob 2 | 3 | __all__ = ['bias_init_with_prob'] 4 | -------------------------------------------------------------------------------- /mmdet/models/utils/weight_init.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | def bias_init_with_prob(prior_prob): 5 | """ initialize conv/fc bias value according to giving probablity""" 6 | bias_init = float(-np.log((1 - prior_prob) / prior_prob)) 7 | return bias_init 8 | -------------------------------------------------------------------------------- /mmdet/ops/__init__.py: -------------------------------------------------------------------------------- 1 | from .context_block import ContextBlock 2 | from .conv import build_conv_layer 3 | from .conv_module import ConvModule 4 | from .conv_ws import ConvWS2d, conv_ws_2d 5 | from .dcn import (DeformConv, DeformConvPack, DeformRoIPooling, 6 | DeformRoIPoolingPack, ModulatedDeformConv, 7 | ModulatedDeformConvPack, ModulatedDeformRoIPoolingPack, 8 | deform_conv, deform_roi_pooling, modulated_deform_conv) 9 | from .generalized_attention import GeneralizedAttention 10 | from .masked_conv import MaskedConv2d 11 | from .nms import nms, soft_nms 12 | from .non_local import NonLocal2D 13 | from .norm import build_norm_layer 14 | from .roi_align import RoIAlign, roi_align 15 | from .roi_pool import RoIPool, roi_pool 16 | from .scale import Scale 17 | from .sigmoid_focal_loss import SigmoidFocalLoss, sigmoid_focal_loss 18 | from .upsample import build_upsample_layer 19 | from .utils import get_compiler_version, get_compiling_cuda_version 20 | 21 | __all__ = [ 22 | 'nms', 'soft_nms', 'RoIAlign', 'roi_align', 'RoIPool', 'roi_pool', 23 | 'DeformConv', 'DeformConvPack', 'DeformRoIPooling', 'DeformRoIPoolingPack', 24 | 'ModulatedDeformRoIPoolingPack', 'ModulatedDeformConv', 25 | 'ModulatedDeformConvPack', 'deform_conv', 'modulated_deform_conv', 26 | 'deform_roi_pooling', 'SigmoidFocalLoss', 'sigmoid_focal_loss', 27 | 'MaskedConv2d', 'ContextBlock', 'GeneralizedAttention', 'NonLocal2D', 28 | 'get_compiler_version', 'get_compiling_cuda_version', 'build_conv_layer', 29 | 'ConvModule', 'ConvWS2d', 'conv_ws_2d', 'build_norm_layer', 'Scale', 30 | 'build_upsample_layer' 31 | ] 32 | -------------------------------------------------------------------------------- /mmdet/ops/activation.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | 3 | activation_cfg = { 4 | # layer_abbreviation: module 5 | 'ReLU': nn.ReLU, 6 | 'LeakyReLU': nn.LeakyReLU, 7 | 'PReLU': nn.PReLU, 8 | 'RReLU': nn.RReLU, 9 | 'ReLU6': nn.ReLU6, 10 | 'SELU': nn.SELU, 11 | 'CELU': nn.CELU 12 | } 13 | 14 | 15 | def build_activation_layer(cfg): 16 | """ Build activation layer 17 | 18 | Args: 19 | cfg (dict): cfg should contain: 20 | type (str): Identify activation layer type. 21 | layer args: args needed to instantiate a activation layer. 22 | 23 | Returns: 24 | layer (nn.Module): Created activation layer 25 | """ 26 | assert isinstance(cfg, dict) and 'type' in cfg 27 | cfg_ = cfg.copy() 28 | 29 | layer_type = cfg_.pop('type') 30 | if layer_type not in activation_cfg: 31 | raise KeyError('Unrecognized activation type {}'.format(layer_type)) 32 | else: 33 | activation = activation_cfg[layer_type] 34 | if activation is None: 35 | raise NotImplementedError 36 | 37 | layer = activation(**cfg_) 38 | return layer 39 | -------------------------------------------------------------------------------- /mmdet/ops/affine_grid/__init__.py: -------------------------------------------------------------------------------- 1 | from .affine_grid import affine_grid 2 | 3 | __all__ = ['affine_grid'] 4 | -------------------------------------------------------------------------------- /mmdet/ops/affine_grid/affine_grid.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn.functional as F 3 | from torch.autograd import Function 4 | from torch.autograd.function import once_differentiable 5 | 6 | from . import affine_grid_cuda 7 | 8 | 9 | class _AffineGridGenerator(Function): 10 | 11 | @staticmethod 12 | def forward(ctx, theta, size, align_corners): 13 | 14 | ctx.save_for_backward(theta) 15 | ctx.size = size 16 | ctx.align_corners = align_corners 17 | 18 | func = affine_grid_cuda.affine_grid_generator_forward 19 | 20 | output = func(theta, size, align_corners) 21 | 22 | return output 23 | 24 | @staticmethod 25 | @once_differentiable 26 | def backward(ctx, grad_output): 27 | theta = ctx.saved_tensors 28 | size = ctx.size 29 | align_corners = ctx.align_corners 30 | 31 | func = affine_grid_cuda.affine_grid_generator_backward 32 | 33 | grad_input = func(grad_output, theta, size, align_corners) 34 | 35 | return grad_input, None, None 36 | 37 | 38 | def affine_grid(theta, size, align_corners=False): 39 | if torch.__version__ >= '1.3': 40 | return F.affine_grid(theta, size, align_corners) 41 | elif align_corners: 42 | return F.affine_grid(theta, size) 43 | else: 44 | # enforce floating point dtype on theta 45 | if not theta.is_floating_point(): 46 | raise ValueError( 47 | 'Expected theta to have floating point type, but got {}'. 48 | format(theta.dtype)) 49 | # check that shapes and sizes match 50 | if len(size) == 4: 51 | if theta.dim() != 3 or theta.size(-2) != 2 or theta.size(-1) != 3: 52 | raise ValueError( 53 | 'Expected a batch of 2D affine matrices of shape Nx2x3 ' 54 | 'for size {}. Got {}.'.format(size, theta.shape)) 55 | elif len(size) == 5: 56 | if theta.dim() != 3 or theta.size(-2) != 3 or theta.size(-1) != 4: 57 | raise ValueError( 58 | 'Expected a batch of 3D affine matrices of shape Nx3x4 ' 59 | 'for size {}. Got {}.'.format(size, theta.shape)) 60 | else: 61 | raise NotImplementedError( 62 | 'affine_grid only supports 4D and 5D sizes, ' 63 | 'for 2D and 3D affine transforms, respectively. ' 64 | 'Got size {}.'.format(size)) 65 | if min(size) <= 0: 66 | raise ValueError( 67 | 'Expected non-zero, positive output size. Got {}'.format(size)) 68 | return _AffineGridGenerator.apply(theta, size, align_corners) 69 | -------------------------------------------------------------------------------- /mmdet/ops/carafe/__init__.py: -------------------------------------------------------------------------------- 1 | from .carafe import CARAFE, CARAFENaive, CARAFEPack, carafe, carafe_naive 2 | 3 | __all__ = ['carafe', 'carafe_naive', 'CARAFE', 'CARAFENaive', 'CARAFEPack'] 4 | -------------------------------------------------------------------------------- /mmdet/ops/carafe/grad_check.py: -------------------------------------------------------------------------------- 1 | import os.path as osp 2 | import sys 3 | 4 | import mmcv 5 | import torch 6 | from torch.autograd import gradcheck 7 | 8 | sys.path.append(osp.abspath(osp.join(__file__, '../../'))) 9 | from mmdet.ops.carafe import CARAFE, CARAFENaive # noqa: E402, isort:skip 10 | from mmdet.ops.carafe import carafe, carafe_naive # noqa: E402, isort:skip 11 | 12 | feat = torch.randn(2, 64, 3, 3, requires_grad=True, device='cuda:0').double() 13 | mask = torch.randn( 14 | 2, 100, 6, 6, requires_grad=True, device='cuda:0').sigmoid().double() 15 | 16 | print('Gradcheck for carafe...') 17 | test = gradcheck(CARAFE(5, 4, 2), (feat, mask), atol=1e-4, eps=1e-4) 18 | print(test) 19 | 20 | print('Gradcheck for carafe naive...') 21 | test = gradcheck(CARAFENaive(5, 4, 2), (feat, mask), atol=1e-4, eps=1e-4) 22 | print(test) 23 | 24 | feat = torch.randn( 25 | 2, 1024, 100, 100, requires_grad=True, device='cuda:0').float() 26 | mask = torch.randn( 27 | 2, 25, 200, 200, requires_grad=True, device='cuda:0').sigmoid().float() 28 | loop_num = 500 29 | 30 | time_forward = 0 31 | time_backward = 0 32 | bar = mmcv.ProgressBar(loop_num) 33 | timer = mmcv.Timer() 34 | for i in range(loop_num): 35 | x = carafe(feat.clone(), mask.clone(), 5, 1, 2) 36 | torch.cuda.synchronize() 37 | time_forward += timer.since_last_check() 38 | x.sum().backward(retain_graph=True) 39 | torch.cuda.synchronize() 40 | time_backward += timer.since_last_check() 41 | bar.update() 42 | print('\nCARAFE time forward: {} ms/iter | time backward: {} ms/iter'.format( 43 | (time_forward + 1e-3) * 1e3 / loop_num, 44 | (time_backward + 1e-3) * 1e3 / loop_num)) 45 | 46 | time_naive_forward = 0 47 | time_naive_backward = 0 48 | bar = mmcv.ProgressBar(loop_num) 49 | timer = mmcv.Timer() 50 | for i in range(loop_num): 51 | x = carafe_naive(feat.clone(), mask.clone(), 5, 1, 2) 52 | torch.cuda.synchronize() 53 | time_naive_forward += timer.since_last_check() 54 | x.sum().backward(retain_graph=True) 55 | torch.cuda.synchronize() 56 | time_naive_backward += timer.since_last_check() 57 | bar.update() 58 | print('\nCARAFE naive time forward: {} ms/iter | time backward: {} ms/iter'. 59 | format((time_naive_forward + 1e-3) * 1e3 / loop_num, 60 | (time_naive_backward + 1e-3) * 1e3 / loop_num)) 61 | -------------------------------------------------------------------------------- /mmdet/ops/carafe/setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup 2 | 3 | from torch.utils.cpp_extension import BuildExtension, CUDAExtension 4 | 5 | NVCC_ARGS = [ 6 | '-D__CUDA_NO_HALF_OPERATORS__', 7 | '-D__CUDA_NO_HALF_CONVERSIONS__', 8 | '-D__CUDA_NO_HALF2_OPERATORS__', 9 | ] 10 | 11 | setup( 12 | name='carafe', 13 | ext_modules=[ 14 | CUDAExtension( 15 | 'carafe_cuda', 16 | ['src/carafe_cuda.cpp', 'src/carafe_cuda_kernel.cu'], 17 | extra_compile_args={ 18 | 'cxx': [], 19 | 'nvcc': NVCC_ARGS 20 | }), 21 | CUDAExtension( 22 | 'carafe_naive_cuda', 23 | ['src/carafe_naive_cuda.cpp', 'src/carafe_naive_cuda_kernel.cu'], 24 | extra_compile_args={ 25 | 'cxx': [], 26 | 'nvcc': NVCC_ARGS 27 | }) 28 | ], 29 | cmdclass={'build_ext': BuildExtension}) 30 | -------------------------------------------------------------------------------- /mmdet/ops/carafe/src/carafe_naive_cuda.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | 4 | #include 5 | #include 6 | 7 | int CARAFENAIVEForwardLaucher(const at::Tensor features, const at::Tensor masks, 8 | const int kernel_size, const int group_size, 9 | const int scale_factor, const int batch_size, 10 | const int channels, const int height, 11 | const int width, at::Tensor output); 12 | 13 | int CARAFENAIVEBackwardLaucher(const at::Tensor top_grad, 14 | const at::Tensor features, 15 | const at::Tensor masks, const int kernel_size, 16 | const int group_size, const int scale_factor, 17 | const int batch_size, const int channels, 18 | const int height, const int width, 19 | at::Tensor bottom_grad, at::Tensor mask_grad); 20 | 21 | #define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDAtensor ") 22 | #define CHECK_CONTIGUOUS(x) \ 23 | AT_CHECK(x.is_contiguous(), #x, " must be contiguous ") 24 | #define CHECK_INPUT(x) \ 25 | CHECK_CUDA(x); \ 26 | CHECK_CONTIGUOUS(x) 27 | 28 | int carafe_naive_forward_cuda(at::Tensor features, at::Tensor masks, 29 | int kernel_size, int group_size, int scale_factor, 30 | at::Tensor output) { 31 | CHECK_INPUT(features); 32 | CHECK_INPUT(masks); 33 | CHECK_INPUT(output); 34 | at::DeviceGuard guard(features.device()); 35 | 36 | int batch_size = output.size(0); 37 | int num_channels = output.size(1); 38 | int data_height = output.size(2); 39 | int data_width = output.size(3); 40 | 41 | CARAFENAIVEForwardLaucher(features, masks, kernel_size, group_size, 42 | scale_factor, batch_size, num_channels, data_height, 43 | data_width, output); 44 | 45 | return 1; 46 | } 47 | 48 | int carafe_naive_backward_cuda(at::Tensor top_grad, at::Tensor features, 49 | at::Tensor masks, int kernel_size, 50 | int group_size, int scale_factor, 51 | at::Tensor bottom_grad, at::Tensor mask_grad) { 52 | CHECK_INPUT(top_grad); 53 | CHECK_INPUT(features); 54 | CHECK_INPUT(masks); 55 | CHECK_INPUT(bottom_grad); 56 | CHECK_INPUT(mask_grad); 57 | at::DeviceGuard guard(top_grad.device()); 58 | 59 | int batch_size = top_grad.size(0); 60 | int num_channels = top_grad.size(1); 61 | int data_height = top_grad.size(2); 62 | int data_width = top_grad.size(3); 63 | 64 | CARAFENAIVEBackwardLaucher(top_grad, features, masks, kernel_size, group_size, 65 | scale_factor, batch_size, num_channels, 66 | data_height, data_width, bottom_grad, mask_grad); 67 | 68 | return 1; 69 | } 70 | 71 | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { 72 | m.def("forward", &carafe_naive_forward_cuda, "carafe_naive forward (CUDA)"); 73 | m.def("backward", &carafe_naive_backward_cuda, 74 | "carafe_naive backward (CUDA)"); 75 | } 76 | -------------------------------------------------------------------------------- /mmdet/ops/conv.py: -------------------------------------------------------------------------------- 1 | from torch import nn as nn 2 | 3 | from .conv_ws import ConvWS2d, ConvAWS2d 4 | from .dcn import DeformConvPack, ModulatedDeformConvPack 5 | from .saconv import SAConv2d 6 | 7 | conv_cfg = { 8 | 'Conv': nn.Conv2d, 9 | 'ConvWS': ConvWS2d, 10 | 'DCN': DeformConvPack, 11 | 'DCNv2': ModulatedDeformConvPack, 12 | 'ConvAWS': ConvAWS2d, 13 | 'SAC': SAConv2d, 14 | # TODO: octave conv 15 | } 16 | 17 | 18 | def build_conv_layer(cfg, *args, **kwargs): 19 | """ Build convolution layer 20 | 21 | Args: 22 | cfg (None or dict): cfg should contain: 23 | type (str): identify conv layer type. 24 | layer args: args needed to instantiate a conv layer. 25 | 26 | Returns: 27 | layer (nn.Module): created conv layer 28 | """ 29 | if cfg is None: 30 | cfg_ = dict(type='Conv') 31 | else: 32 | assert isinstance(cfg, dict) and 'type' in cfg 33 | cfg_ = cfg.copy() 34 | 35 | layer_type = cfg_.pop('type') 36 | if layer_type not in conv_cfg: 37 | raise KeyError('Unrecognized norm type {}'.format(layer_type)) 38 | else: 39 | conv_layer = conv_cfg[layer_type] 40 | 41 | layer = conv_layer(*args, **kwargs, **cfg_) 42 | 43 | return layer 44 | -------------------------------------------------------------------------------- /mmdet/ops/dcn/__init__.py: -------------------------------------------------------------------------------- 1 | from .deform_conv import (DeformConv, DeformConvPack, ModulatedDeformConv, 2 | ModulatedDeformConvPack, deform_conv, 3 | modulated_deform_conv) 4 | from .deform_pool import (DeformRoIPooling, DeformRoIPoolingPack, 5 | ModulatedDeformRoIPoolingPack, deform_roi_pooling) 6 | 7 | __all__ = [ 8 | 'DeformConv', 'DeformConvPack', 'ModulatedDeformConv', 9 | 'ModulatedDeformConvPack', 'DeformRoIPooling', 'DeformRoIPoolingPack', 10 | 'ModulatedDeformRoIPoolingPack', 'deform_conv', 'modulated_deform_conv', 11 | 'deform_roi_pooling' 12 | ] 13 | -------------------------------------------------------------------------------- /mmdet/ops/grid_sampler/__init__.py: -------------------------------------------------------------------------------- 1 | from .grid_sampler import grid_sample 2 | 3 | __all__ = ['grid_sample'] 4 | -------------------------------------------------------------------------------- /mmdet/ops/masked_conv/__init__.py: -------------------------------------------------------------------------------- 1 | from .masked_conv import MaskedConv2d, masked_conv2d 2 | 3 | __all__ = ['masked_conv2d', 'MaskedConv2d'] 4 | -------------------------------------------------------------------------------- /mmdet/ops/masked_conv/src/masked_conv2d_cuda.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include 4 | #include 5 | 6 | int MaskedIm2colForwardLaucher(const at::Tensor im, const int height, 7 | const int width, const int channels, 8 | const int kernel_h, const int kernel_w, 9 | const int pad_h, const int pad_w, 10 | const at::Tensor mask_h_idx, 11 | const at::Tensor mask_w_idx, const int mask_cnt, 12 | at::Tensor col); 13 | 14 | int MaskedCol2imForwardLaucher(const at::Tensor col, const int height, 15 | const int width, const int channels, 16 | const at::Tensor mask_h_idx, 17 | const at::Tensor mask_w_idx, const int mask_cnt, 18 | at::Tensor im); 19 | 20 | #define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDAtensor ") 21 | #define CHECK_CONTIGUOUS(x) \ 22 | AT_CHECK(x.is_contiguous(), #x, " must be contiguous ") 23 | #define CHECK_INPUT(x) \ 24 | CHECK_CUDA(x); \ 25 | CHECK_CONTIGUOUS(x) 26 | 27 | int masked_im2col_forward_cuda(const at::Tensor im, const at::Tensor mask_h_idx, 28 | const at::Tensor mask_w_idx, const int kernel_h, 29 | const int kernel_w, const int pad_h, 30 | const int pad_w, at::Tensor col) { 31 | CHECK_INPUT(im); 32 | CHECK_INPUT(mask_h_idx); 33 | CHECK_INPUT(mask_w_idx); 34 | CHECK_INPUT(col); 35 | // im: (n, ic, h, w), kernel size (kh, kw) 36 | // kernel: (oc, ic * kh * kw), col: (kh * kw * ic, ow * oh) 37 | at::DeviceGuard guard(im.device()); 38 | 39 | int channels = im.size(1); 40 | int height = im.size(2); 41 | int width = im.size(3); 42 | int mask_cnt = mask_h_idx.size(0); 43 | 44 | MaskedIm2colForwardLaucher(im, height, width, channels, kernel_h, kernel_w, 45 | pad_h, pad_w, mask_h_idx, mask_w_idx, mask_cnt, 46 | col); 47 | 48 | return 1; 49 | } 50 | 51 | int masked_col2im_forward_cuda(const at::Tensor col, 52 | const at::Tensor mask_h_idx, 53 | const at::Tensor mask_w_idx, int height, 54 | int width, int channels, at::Tensor im) { 55 | CHECK_INPUT(col); 56 | CHECK_INPUT(mask_h_idx); 57 | CHECK_INPUT(mask_w_idx); 58 | CHECK_INPUT(im); 59 | // im: (n, ic, h, w), kernel size (kh, kw) 60 | // kernel: (oc, ic * kh * kh), col: (kh * kw * ic, ow * oh) 61 | at::DeviceGuard guard(col.device()); 62 | 63 | int mask_cnt = mask_h_idx.size(0); 64 | 65 | MaskedCol2imForwardLaucher(col, height, width, channels, mask_h_idx, 66 | mask_w_idx, mask_cnt, im); 67 | 68 | return 1; 69 | } 70 | 71 | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { 72 | m.def("masked_im2col_forward", &masked_im2col_forward_cuda, 73 | "masked_im2col forward (CUDA)"); 74 | m.def("masked_col2im_forward", &masked_col2im_forward_cuda, 75 | "masked_col2im forward (CUDA)"); 76 | } 77 | -------------------------------------------------------------------------------- /mmdet/ops/nms/__init__.py: -------------------------------------------------------------------------------- 1 | from .nms_wrapper import nms, soft_nms 2 | 3 | __all__ = ['nms', 'soft_nms'] 4 | -------------------------------------------------------------------------------- /mmdet/ops/nms/src/nms_cuda.cpp: -------------------------------------------------------------------------------- 1 | // Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. 2 | #include 3 | 4 | #define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDAtensor ") 5 | 6 | at::Tensor nms_cuda(const at::Tensor boxes, float nms_overlap_thresh); 7 | 8 | at::Tensor nms(const at::Tensor& dets, const float threshold) { 9 | CHECK_CUDA(dets); 10 | if (dets.numel() == 0) 11 | return at::empty({0}, dets.options().dtype(at::kLong).device(at::kCPU)); 12 | return nms_cuda(dets, threshold); 13 | } 14 | 15 | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { 16 | m.def("nms", &nms, "non-maximum suppression"); 17 | } 18 | -------------------------------------------------------------------------------- /mmdet/ops/norm.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | 3 | norm_cfg = { 4 | # format: layer_type: (abbreviation, module) 5 | 'BN': ('bn', nn.BatchNorm2d), 6 | 'SyncBN': ('bn', nn.SyncBatchNorm), 7 | 'GN': ('gn', nn.GroupNorm), 8 | # and potentially 'SN' 9 | } 10 | 11 | 12 | def build_norm_layer(cfg, num_features, postfix=''): 13 | """ Build normalization layer 14 | 15 | Args: 16 | cfg (dict): cfg should contain: 17 | type (str): identify norm layer type. 18 | layer args: args needed to instantiate a norm layer. 19 | requires_grad (bool): [optional] whether stop gradient updates 20 | num_features (int): number of channels from input. 21 | postfix (int, str): appended into norm abbreviation to 22 | create named layer. 23 | 24 | Returns: 25 | name (str): abbreviation + postfix 26 | layer (nn.Module): created norm layer 27 | """ 28 | assert isinstance(cfg, dict) and 'type' in cfg 29 | cfg_ = cfg.copy() 30 | 31 | layer_type = cfg_.pop('type') 32 | if layer_type not in norm_cfg: 33 | raise KeyError('Unrecognized norm type {}'.format(layer_type)) 34 | else: 35 | abbr, norm_layer = norm_cfg[layer_type] 36 | if norm_layer is None: 37 | raise NotImplementedError 38 | 39 | assert isinstance(postfix, (int, str)) 40 | name = abbr + str(postfix) 41 | 42 | requires_grad = cfg_.pop('requires_grad', True) 43 | cfg_.setdefault('eps', 1e-5) 44 | if layer_type != 'GN': 45 | layer = norm_layer(num_features, **cfg_) 46 | if layer_type == 'SyncBN': 47 | layer._specify_ddp_gpu_num(1) 48 | else: 49 | assert 'num_groups' in cfg_ 50 | layer = norm_layer(num_channels=num_features, **cfg_) 51 | 52 | for param in layer.parameters(): 53 | param.requires_grad = requires_grad 54 | 55 | return name, layer 56 | -------------------------------------------------------------------------------- /mmdet/ops/roi_align/__init__.py: -------------------------------------------------------------------------------- 1 | from .roi_align import RoIAlign, roi_align 2 | 3 | __all__ = ['roi_align', 'RoIAlign'] 4 | -------------------------------------------------------------------------------- /mmdet/ops/roi_align/gradcheck.py: -------------------------------------------------------------------------------- 1 | import os.path as osp 2 | import sys 3 | 4 | import numpy as np 5 | import torch 6 | from torch.autograd import gradcheck 7 | 8 | sys.path.append(osp.abspath(osp.join(__file__, '../../'))) 9 | from roi_align import RoIAlign # noqa: E402, isort:skip 10 | 11 | feat_size = 15 12 | spatial_scale = 1.0 / 8 13 | img_size = feat_size / spatial_scale 14 | num_imgs = 2 15 | num_rois = 20 16 | 17 | batch_ind = np.random.randint(num_imgs, size=(num_rois, 1)) 18 | rois = np.random.rand(num_rois, 4) * img_size * 0.5 19 | rois[:, 2:] += img_size * 0.5 20 | rois = np.hstack((batch_ind, rois)) 21 | 22 | feat = torch.randn( 23 | num_imgs, 16, feat_size, feat_size, requires_grad=True, device='cuda:0') 24 | rois = torch.from_numpy(rois).float().cuda() 25 | inputs = (feat, rois) 26 | print('Gradcheck for roi align...') 27 | test = gradcheck(RoIAlign(3, spatial_scale), inputs, atol=1e-3, eps=1e-3) 28 | print(test) 29 | test = gradcheck(RoIAlign(3, spatial_scale, 2), inputs, atol=1e-3, eps=1e-3) 30 | print(test) 31 | -------------------------------------------------------------------------------- /mmdet/ops/roi_pool/__init__.py: -------------------------------------------------------------------------------- 1 | from .roi_pool import RoIPool, roi_pool 2 | 3 | __all__ = ['roi_pool', 'RoIPool'] 4 | -------------------------------------------------------------------------------- /mmdet/ops/roi_pool/gradcheck.py: -------------------------------------------------------------------------------- 1 | import os.path as osp 2 | import sys 3 | 4 | import torch 5 | from torch.autograd import gradcheck 6 | 7 | sys.path.append(osp.abspath(osp.join(__file__, '../../'))) 8 | from roi_pool import RoIPool # noqa: E402, isort:skip 9 | 10 | feat = torch.randn(4, 16, 15, 15, requires_grad=True).cuda() 11 | rois = torch.Tensor([[0, 0, 0, 50, 50], [0, 10, 30, 43, 55], 12 | [1, 67, 40, 110, 120]]).cuda() 13 | inputs = (feat, rois) 14 | print('Gradcheck for roi pooling...') 15 | test = gradcheck(RoIPool(4, 1.0 / 8), inputs, eps=1e-5, atol=1e-3) 16 | print(test) 17 | -------------------------------------------------------------------------------- /mmdet/ops/roi_pool/roi_pool.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | from torch.autograd import Function 4 | from torch.autograd.function import once_differentiable 5 | from torch.nn.modules.utils import _pair 6 | 7 | from . import roi_pool_cuda 8 | 9 | 10 | class RoIPoolFunction(Function): 11 | 12 | @staticmethod 13 | def forward(ctx, features, rois, out_size, spatial_scale): 14 | assert features.is_cuda 15 | out_h, out_w = _pair(out_size) 16 | assert isinstance(out_h, int) and isinstance(out_w, int) 17 | ctx.save_for_backward(rois) 18 | num_channels = features.size(1) 19 | num_rois = rois.size(0) 20 | out_size = (num_rois, num_channels, out_h, out_w) 21 | output = features.new_zeros(out_size) 22 | argmax = features.new_zeros(out_size, dtype=torch.int) 23 | roi_pool_cuda.forward(features, rois, out_h, out_w, spatial_scale, 24 | output, argmax) 25 | ctx.spatial_scale = spatial_scale 26 | ctx.feature_size = features.size() 27 | ctx.argmax = argmax 28 | 29 | return output 30 | 31 | @staticmethod 32 | @once_differentiable 33 | def backward(ctx, grad_output): 34 | assert grad_output.is_cuda 35 | spatial_scale = ctx.spatial_scale 36 | feature_size = ctx.feature_size 37 | argmax = ctx.argmax 38 | rois = ctx.saved_tensors[0] 39 | assert feature_size is not None 40 | 41 | grad_input = grad_rois = None 42 | if ctx.needs_input_grad[0]: 43 | grad_input = grad_output.new_zeros(feature_size) 44 | roi_pool_cuda.backward(grad_output.contiguous(), rois, argmax, 45 | spatial_scale, grad_input) 46 | 47 | return grad_input, grad_rois, None, None 48 | 49 | 50 | roi_pool = RoIPoolFunction.apply 51 | 52 | 53 | class RoIPool(nn.Module): 54 | 55 | def __init__(self, out_size, spatial_scale, use_torchvision=False): 56 | super(RoIPool, self).__init__() 57 | 58 | self.out_size = _pair(out_size) 59 | self.spatial_scale = float(spatial_scale) 60 | self.use_torchvision = use_torchvision 61 | 62 | def forward(self, features, rois): 63 | if self.use_torchvision: 64 | from torchvision.ops import roi_pool as tv_roi_pool 65 | return tv_roi_pool(features, rois, self.out_size, 66 | self.spatial_scale) 67 | else: 68 | return roi_pool(features, rois, self.out_size, self.spatial_scale) 69 | 70 | def __repr__(self): 71 | format_str = self.__class__.__name__ 72 | format_str += '(out_size={}, spatial_scale={}'.format( 73 | self.out_size, self.spatial_scale) 74 | format_str += ', use_torchvision={})'.format(self.use_torchvision) 75 | return format_str 76 | -------------------------------------------------------------------------------- /mmdet/ops/roi_pool/src/roi_pool_cuda.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include 4 | #include 5 | 6 | int ROIPoolForwardLaucher(const at::Tensor features, const at::Tensor rois, 7 | const float spatial_scale, const int channels, 8 | const int height, const int width, const int num_rois, 9 | const int pooled_h, const int pooled_w, 10 | at::Tensor output, at::Tensor argmax); 11 | 12 | int ROIPoolBackwardLaucher(const at::Tensor top_grad, const at::Tensor rois, 13 | const at::Tensor argmax, const float spatial_scale, 14 | const int batch_size, const int channels, 15 | const int height, const int width, 16 | const int num_rois, const int pooled_h, 17 | const int pooled_w, at::Tensor bottom_grad); 18 | 19 | #define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDAtensor ") 20 | #define CHECK_CONTIGUOUS(x) \ 21 | AT_CHECK(x.is_contiguous(), #x, " must be contiguous ") 22 | #define CHECK_INPUT(x) \ 23 | CHECK_CUDA(x); \ 24 | CHECK_CONTIGUOUS(x) 25 | 26 | int roi_pooling_forward_cuda(at::Tensor features, at::Tensor rois, 27 | int pooled_height, int pooled_width, 28 | float spatial_scale, at::Tensor output, 29 | at::Tensor argmax) { 30 | CHECK_INPUT(features); 31 | CHECK_INPUT(rois); 32 | CHECK_INPUT(output); 33 | CHECK_INPUT(argmax); 34 | at::DeviceGuard guard(features.device()); 35 | 36 | // Number of ROIs 37 | int num_rois = rois.size(0); 38 | int size_rois = rois.size(1); 39 | 40 | if (size_rois != 5) { 41 | printf("wrong roi size\n"); 42 | return 0; 43 | } 44 | 45 | int channels = features.size(1); 46 | int height = features.size(2); 47 | int width = features.size(3); 48 | 49 | ROIPoolForwardLaucher(features, rois, spatial_scale, channels, height, width, 50 | num_rois, pooled_height, pooled_width, output, argmax); 51 | 52 | return 1; 53 | } 54 | 55 | int roi_pooling_backward_cuda(at::Tensor top_grad, at::Tensor rois, 56 | at::Tensor argmax, float spatial_scale, 57 | at::Tensor bottom_grad) { 58 | CHECK_INPUT(top_grad); 59 | CHECK_INPUT(rois); 60 | CHECK_INPUT(argmax); 61 | CHECK_INPUT(bottom_grad); 62 | at::DeviceGuard guard(top_grad.device()); 63 | 64 | int pooled_height = top_grad.size(2); 65 | int pooled_width = top_grad.size(3); 66 | int num_rois = rois.size(0); 67 | int size_rois = rois.size(1); 68 | 69 | if (size_rois != 5) { 70 | printf("wrong roi size\n"); 71 | return 0; 72 | } 73 | int batch_size = bottom_grad.size(0); 74 | int channels = bottom_grad.size(1); 75 | int height = bottom_grad.size(2); 76 | int width = bottom_grad.size(3); 77 | 78 | ROIPoolBackwardLaucher(top_grad, rois, argmax, spatial_scale, batch_size, 79 | channels, height, width, num_rois, pooled_height, 80 | pooled_width, bottom_grad); 81 | 82 | return 1; 83 | } 84 | 85 | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { 86 | m.def("forward", &roi_pooling_forward_cuda, "Roi_Pooling forward (CUDA)"); 87 | m.def("backward", &roi_pooling_backward_cuda, "Roi_Pooling backward (CUDA)"); 88 | } 89 | -------------------------------------------------------------------------------- /mmdet/ops/scale.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | 4 | 5 | class Scale(nn.Module): 6 | """ 7 | A learnable scale parameter 8 | """ 9 | 10 | def __init__(self, scale=1.0): 11 | super(Scale, self).__init__() 12 | self.scale = nn.Parameter(torch.tensor(scale, dtype=torch.float)) 13 | 14 | def forward(self, x): 15 | return x * self.scale 16 | -------------------------------------------------------------------------------- /mmdet/ops/sigmoid_focal_loss/__init__.py: -------------------------------------------------------------------------------- 1 | from .sigmoid_focal_loss import SigmoidFocalLoss, sigmoid_focal_loss 2 | 3 | __all__ = ['SigmoidFocalLoss', 'sigmoid_focal_loss'] 4 | -------------------------------------------------------------------------------- /mmdet/ops/sigmoid_focal_loss/sigmoid_focal_loss.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | from torch.autograd import Function 3 | from torch.autograd.function import once_differentiable 4 | 5 | from . import sigmoid_focal_loss_cuda 6 | 7 | 8 | class SigmoidFocalLossFunction(Function): 9 | 10 | @staticmethod 11 | def forward(ctx, input, target, gamma=2.0, alpha=0.25): 12 | ctx.save_for_backward(input, target) 13 | num_classes = input.shape[1] 14 | ctx.num_classes = num_classes 15 | ctx.gamma = gamma 16 | ctx.alpha = alpha 17 | 18 | loss = sigmoid_focal_loss_cuda.forward(input, target, num_classes, 19 | gamma, alpha) 20 | return loss 21 | 22 | @staticmethod 23 | @once_differentiable 24 | def backward(ctx, d_loss): 25 | input, target = ctx.saved_tensors 26 | num_classes = ctx.num_classes 27 | gamma = ctx.gamma 28 | alpha = ctx.alpha 29 | d_loss = d_loss.contiguous() 30 | d_input = sigmoid_focal_loss_cuda.backward(input, target, d_loss, 31 | num_classes, gamma, alpha) 32 | return d_input, None, None, None, None 33 | 34 | 35 | sigmoid_focal_loss = SigmoidFocalLossFunction.apply 36 | 37 | 38 | # TODO: remove this module 39 | class SigmoidFocalLoss(nn.Module): 40 | 41 | def __init__(self, gamma, alpha): 42 | super(SigmoidFocalLoss, self).__init__() 43 | self.gamma = gamma 44 | self.alpha = alpha 45 | 46 | def forward(self, logits, targets): 47 | assert logits.is_cuda 48 | loss = sigmoid_focal_loss(logits, targets, self.gamma, self.alpha) 49 | return loss.sum() 50 | 51 | def __repr__(self): 52 | tmpstr = self.__class__.__name__ + '(gamma={}, alpha={})'.format( 53 | self.gamma, self.alpha) 54 | return tmpstr 55 | -------------------------------------------------------------------------------- /mmdet/ops/sigmoid_focal_loss/src/sigmoid_focal_loss.cpp: -------------------------------------------------------------------------------- 1 | // modify from 2 | // https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/csrc/SigmoidFocalLoss.h 3 | #include 4 | 5 | at::Tensor SigmoidFocalLoss_forward_cuda(const at::Tensor &logits, 6 | const at::Tensor &targets, 7 | const int num_classes, 8 | const float gamma, const float alpha); 9 | 10 | at::Tensor SigmoidFocalLoss_backward_cuda(const at::Tensor &logits, 11 | const at::Tensor &targets, 12 | const at::Tensor &d_losses, 13 | const int num_classes, 14 | const float gamma, const float alpha); 15 | 16 | // Interface for Python 17 | at::Tensor SigmoidFocalLoss_forward(const at::Tensor &logits, 18 | const at::Tensor &targets, 19 | const int num_classes, const float gamma, 20 | const float alpha) { 21 | if (logits.type().is_cuda()) { 22 | at::DeviceGuard guard(logits.device()); 23 | return SigmoidFocalLoss_forward_cuda(logits, targets, num_classes, gamma, 24 | alpha); 25 | } 26 | AT_ERROR("SigmoidFocalLoss is not implemented on the CPU"); 27 | } 28 | 29 | at::Tensor SigmoidFocalLoss_backward(const at::Tensor &logits, 30 | const at::Tensor &targets, 31 | const at::Tensor &d_losses, 32 | const int num_classes, const float gamma, 33 | const float alpha) { 34 | if (logits.type().is_cuda()) { 35 | at::DeviceGuard guard(logits.device()); 36 | return SigmoidFocalLoss_backward_cuda(logits, targets, d_losses, 37 | num_classes, gamma, alpha); 38 | } 39 | AT_ERROR("SigmoidFocalLoss is not implemented on the CPU"); 40 | } 41 | 42 | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { 43 | m.def("forward", &SigmoidFocalLoss_forward, 44 | "SigmoidFocalLoss forward (CUDA)"); 45 | m.def("backward", &SigmoidFocalLoss_backward, 46 | "SigmoidFocalLoss backward (CUDA)"); 47 | } 48 | -------------------------------------------------------------------------------- /mmdet/ops/upsample.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | import torch.nn.functional as F 3 | from mmcv.cnn import xavier_init 4 | 5 | from .carafe import CARAFEPack 6 | 7 | 8 | class PixelShufflePack(nn.Module): 9 | """ Pixel Shuffle upsample layer 10 | 11 | Args: 12 | in_channels (int): Number of input channels 13 | out_channels (int): Number of output channels 14 | scale_factor (int): Upsample ratio 15 | upsample_kernel (int): Kernel size of Conv layer to expand the channels 16 | 17 | Returns: 18 | upsampled feature map 19 | """ 20 | 21 | def __init__(self, in_channels, out_channels, scale_factor, 22 | upsample_kernel): 23 | super(PixelShufflePack, self).__init__() 24 | self.in_channels = in_channels 25 | self.out_channels = out_channels 26 | self.scale_factor = scale_factor 27 | self.upsample_kernel = upsample_kernel 28 | self.upsample_conv = nn.Conv2d( 29 | self.in_channels, 30 | self.out_channels * scale_factor * scale_factor, 31 | self.upsample_kernel, 32 | padding=(self.upsample_kernel - 1) // 2) 33 | self.init_weights() 34 | 35 | def init_weights(self): 36 | xavier_init(self.upsample_conv, distribution='uniform') 37 | 38 | def forward(self, x): 39 | x = self.upsample_conv(x) 40 | x = F.pixel_shuffle(x, self.scale_factor) 41 | return x 42 | 43 | 44 | upsample_cfg = { 45 | # layer_abbreviation: module 46 | 'nearest': nn.Upsample, 47 | 'bilinear': nn.Upsample, 48 | 'deconv': nn.ConvTranspose2d, 49 | 'pixel_shuffle': PixelShufflePack, 50 | 'carafe': CARAFEPack 51 | } 52 | 53 | 54 | def build_upsample_layer(cfg): 55 | """ Build upsample layer 56 | 57 | Args: 58 | cfg (dict): cfg should contain: 59 | type (str): Identify upsample layer type. 60 | upsample ratio (int): Upsample ratio 61 | layer args: args needed to instantiate a upsample layer. 62 | 63 | Returns: 64 | layer (nn.Module): Created upsample layer 65 | """ 66 | assert isinstance(cfg, dict) and 'type' in cfg 67 | cfg_ = cfg.copy() 68 | 69 | layer_type = cfg_.pop('type') 70 | if layer_type not in upsample_cfg: 71 | raise KeyError('Unrecognized upsample type {}'.format(layer_type)) 72 | else: 73 | upsample = upsample_cfg[layer_type] 74 | if upsample is None: 75 | raise NotImplementedError 76 | 77 | layer = upsample(**cfg_) 78 | return layer 79 | -------------------------------------------------------------------------------- /mmdet/ops/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # from . import compiling_info 2 | from .compiling_info import get_compiler_version, get_compiling_cuda_version 3 | 4 | # get_compiler_version = compiling_info.get_compiler_version 5 | # get_compiling_cuda_version = compiling_info.get_compiling_cuda_version 6 | 7 | __all__ = ['get_compiler_version', 'get_compiling_cuda_version'] 8 | -------------------------------------------------------------------------------- /mmdet/ops/utils/src/compiling_info.cpp: -------------------------------------------------------------------------------- 1 | // modified from 2 | // https://github.com/facebookresearch/detectron2/blob/master/detectron2/layers/csrc/vision.cpp 3 | #include 4 | #include 5 | 6 | #ifdef WITH_CUDA 7 | int get_cudart_version() { return CUDART_VERSION; } 8 | #endif 9 | 10 | std::string get_compiling_cuda_version() { 11 | #ifdef WITH_CUDA 12 | std::ostringstream oss; 13 | 14 | // copied from 15 | // https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/detail/CUDAHooks.cpp#L231 16 | auto printCudaStyleVersion = [&](int v) { 17 | oss << (v / 1000) << "." << (v / 10 % 100); 18 | if (v % 10 != 0) { 19 | oss << "." << (v % 10); 20 | } 21 | }; 22 | printCudaStyleVersion(get_cudart_version()); 23 | return oss.str(); 24 | #else 25 | return std::string("not available"); 26 | #endif 27 | } 28 | 29 | // similar to 30 | // https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Version.cpp 31 | std::string get_compiler_version() { 32 | std::ostringstream ss; 33 | #if defined(__GNUC__) 34 | #ifndef __clang__ 35 | { ss << "GCC " << __GNUC__ << "." << __GNUC_MINOR__; } 36 | #endif 37 | #endif 38 | 39 | #if defined(__clang_major__) 40 | { 41 | ss << "clang " << __clang_major__ << "." << __clang_minor__ << "." 42 | << __clang_patchlevel__; 43 | } 44 | #endif 45 | 46 | #if defined(_MSC_VER) 47 | { ss << "MSVC " << _MSC_FULL_VER; } 48 | #endif 49 | return ss.str(); 50 | } 51 | 52 | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { 53 | m.def("get_compiler_version", &get_compiler_version, "get_compiler_version"); 54 | m.def("get_compiling_cuda_version", &get_compiling_cuda_version, 55 | "get_compiling_cuda_version"); 56 | } 57 | -------------------------------------------------------------------------------- /mmdet/utils/__init__.py: -------------------------------------------------------------------------------- 1 | from .collect_env import collect_env 2 | from .flops_counter import get_model_complexity_info 3 | from .logger import get_root_logger, print_log 4 | from .registry import Registry, build_from_cfg 5 | 6 | __all__ = [ 7 | 'Registry', 'build_from_cfg', 'get_model_complexity_info', 8 | 'get_root_logger', 'print_log', 'collect_env' 9 | ] 10 | -------------------------------------------------------------------------------- /mmdet/utils/collect_env.py: -------------------------------------------------------------------------------- 1 | import os.path as osp 2 | import subprocess 3 | import sys 4 | from collections import defaultdict 5 | 6 | import cv2 7 | import mmcv 8 | import torch 9 | import torchvision 10 | 11 | import mmdet 12 | 13 | 14 | def collect_env(): 15 | env_info = {} 16 | env_info['sys.platform'] = sys.platform 17 | env_info['Python'] = sys.version.replace('\n', '') 18 | 19 | cuda_available = torch.cuda.is_available() 20 | env_info['CUDA available'] = cuda_available 21 | 22 | if cuda_available: 23 | from torch.utils.cpp_extension import CUDA_HOME 24 | env_info['CUDA_HOME'] = CUDA_HOME 25 | 26 | if CUDA_HOME is not None and osp.isdir(CUDA_HOME): 27 | try: 28 | nvcc = osp.join(CUDA_HOME, 'bin/nvcc') 29 | nvcc = subprocess.check_output( 30 | '"{}" -V | tail -n1'.format(nvcc), shell=True) 31 | nvcc = nvcc.decode('utf-8').strip() 32 | except subprocess.SubprocessError: 33 | nvcc = 'Not Available' 34 | env_info['NVCC'] = nvcc 35 | 36 | devices = defaultdict(list) 37 | for k in range(torch.cuda.device_count()): 38 | devices[torch.cuda.get_device_name(k)].append(str(k)) 39 | for name, devids in devices.items(): 40 | env_info['GPU ' + ','.join(devids)] = name 41 | 42 | gcc = subprocess.check_output('gcc --version | head -n1', shell=True) 43 | gcc = gcc.decode('utf-8').strip() 44 | env_info['GCC'] = gcc 45 | 46 | env_info['PyTorch'] = torch.__version__ 47 | env_info['PyTorch compiling details'] = torch.__config__.show() 48 | 49 | env_info['TorchVision'] = torchvision.__version__ 50 | 51 | env_info['OpenCV'] = cv2.__version__ 52 | 53 | env_info['MMCV'] = mmcv.__version__ 54 | env_info['MMDetection'] = mmdet.__version__ 55 | from mmdet.ops import get_compiler_version, get_compiling_cuda_version 56 | env_info['MMDetection Compiler'] = get_compiler_version() 57 | env_info['MMDetection CUDA Compiler'] = get_compiling_cuda_version() 58 | return env_info 59 | 60 | 61 | if __name__ == '__main__': 62 | for name, val in collect_env().items(): 63 | print('{}: {}'.format(name, val)) 64 | -------------------------------------------------------------------------------- /mmdet/utils/logger.py: -------------------------------------------------------------------------------- 1 | import logging 2 | 3 | from mmcv.runner import get_dist_info 4 | 5 | 6 | def get_root_logger(log_file=None, log_level=logging.INFO): 7 | """Get the root logger. 8 | 9 | The logger will be initialized if it has not been initialized. By default a 10 | StreamHandler will be added. If `log_file` is specified, a FileHandler will 11 | also be added. The name of the root logger is the top-level package name, 12 | e.g., "mmdet". 13 | 14 | Args: 15 | log_file (str | None): The log filename. If specified, a FileHandler 16 | will be added to the root logger. 17 | log_level (int): The root logger level. Note that only the process of 18 | rank 0 is affected, while other processes will set the level to 19 | "Error" and be silent most of the time. 20 | 21 | Returns: 22 | logging.Logger: The root logger. 23 | """ 24 | logger = logging.getLogger(__name__.split('.')[0]) # i.e., mmdet 25 | # if the logger has been initialized, just return it 26 | if logger.hasHandlers(): 27 | return logger 28 | 29 | format_str = '%(asctime)s - %(name)s - %(levelname)s - %(message)s' 30 | logging.basicConfig(format=format_str, level=log_level) 31 | rank, _ = get_dist_info() 32 | if rank != 0: 33 | logger.setLevel('ERROR') 34 | elif log_file is not None: 35 | file_handler = logging.FileHandler(log_file, 'w') 36 | file_handler.setFormatter(logging.Formatter(format_str)) 37 | file_handler.setLevel(log_level) 38 | logger.addHandler(file_handler) 39 | 40 | return logger 41 | 42 | 43 | def print_log(msg, logger=None, level=logging.INFO): 44 | """Print a log message. 45 | 46 | Args: 47 | msg (str): The message to be logged. 48 | logger (logging.Logger | str | None): The logger to be used. Some 49 | special loggers are: 50 | - "root": the root logger obtained with `get_root_logger()`. 51 | - "silent": no message will be printed. 52 | - None: The `print()` method will be used to print log messages. 53 | level (int): Logging level. Only available when `logger` is a Logger 54 | object or "root". 55 | """ 56 | if logger is None: 57 | print(msg) 58 | elif logger == 'root': 59 | _logger = get_root_logger() 60 | _logger.log(level, msg) 61 | elif isinstance(logger, logging.Logger): 62 | logger.log(level, msg) 63 | elif logger != 'silent': 64 | raise TypeError( 65 | 'logger should be either a logging.Logger object, "root", ' 66 | '"silent" or None, but got {}'.format(logger)) 67 | -------------------------------------------------------------------------------- /mmdet/utils/profiling.py: -------------------------------------------------------------------------------- 1 | import contextlib 2 | import sys 3 | import time 4 | 5 | import torch 6 | 7 | if sys.version_info >= (3, 7): 8 | 9 | @contextlib.contextmanager 10 | def profile_time(trace_name, 11 | name, 12 | enabled=True, 13 | stream=None, 14 | end_stream=None): 15 | """Print time spent by CPU and GPU. 16 | 17 | Useful as a temporary context manager to find sweet spots of 18 | code suitable for async implementation. 19 | 20 | """ 21 | if (not enabled) or not torch.cuda.is_available(): 22 | yield 23 | return 24 | stream = stream if stream else torch.cuda.current_stream() 25 | end_stream = end_stream if end_stream else stream 26 | start = torch.cuda.Event(enable_timing=True) 27 | end = torch.cuda.Event(enable_timing=True) 28 | stream.record_event(start) 29 | try: 30 | cpu_start = time.monotonic() 31 | yield 32 | finally: 33 | cpu_end = time.monotonic() 34 | end_stream.record_event(end) 35 | end.synchronize() 36 | cpu_time = (cpu_end - cpu_start) * 1000 37 | gpu_time = start.elapsed_time(end) 38 | msg = '{} {} cpu_time {:.2f} ms '.format(trace_name, name, 39 | cpu_time) 40 | msg += 'gpu_time {:.2f} ms stream {}'.format(gpu_time, stream) 41 | print(msg, end_stream) 42 | -------------------------------------------------------------------------------- /mmdet/utils/registry.py: -------------------------------------------------------------------------------- 1 | import inspect 2 | from functools import partial 3 | 4 | import mmcv 5 | 6 | 7 | class Registry(object): 8 | 9 | def __init__(self, name): 10 | self._name = name 11 | self._module_dict = dict() 12 | 13 | def __repr__(self): 14 | format_str = self.__class__.__name__ + '(name={}, items={})'.format( 15 | self._name, list(self._module_dict.keys())) 16 | return format_str 17 | 18 | @property 19 | def name(self): 20 | return self._name 21 | 22 | @property 23 | def module_dict(self): 24 | return self._module_dict 25 | 26 | def get(self, key): 27 | return self._module_dict.get(key, None) 28 | 29 | def _register_module(self, module_class, force=False): 30 | """Register a module. 31 | 32 | Args: 33 | module (:obj:`nn.Module`): Module to be registered. 34 | """ 35 | if not inspect.isclass(module_class): 36 | raise TypeError('module must be a class, but got {}'.format( 37 | type(module_class))) 38 | module_name = module_class.__name__ 39 | if not force and module_name in self._module_dict: 40 | raise KeyError('{} is already registered in {}'.format( 41 | module_name, self.name)) 42 | self._module_dict[module_name] = module_class 43 | 44 | def register_module(self, cls=None, force=False): 45 | if cls is None: 46 | return partial(self.register_module, force=force) 47 | self._register_module(cls, force=force) 48 | return cls 49 | 50 | 51 | def build_from_cfg(cfg, registry, default_args=None): 52 | """Build a module from config dict. 53 | 54 | Args: 55 | cfg (dict): Config dict. It should at least contain the key "type". 56 | registry (:obj:`Registry`): The registry to search the type from. 57 | default_args (dict, optional): Default initialization arguments. 58 | 59 | Returns: 60 | obj: The constructed object. 61 | """ 62 | assert isinstance(cfg, dict) and 'type' in cfg 63 | assert isinstance(default_args, dict) or default_args is None 64 | args = cfg.copy() 65 | obj_type = args.pop('type') 66 | if mmcv.is_str(obj_type): 67 | obj_cls = registry.get(obj_type) 68 | if obj_cls is None: 69 | raise KeyError('{} is not in the {} registry'.format( 70 | obj_type, registry.name)) 71 | elif inspect.isclass(obj_type): 72 | obj_cls = obj_type 73 | else: 74 | raise TypeError('type must be a str or valid type, but got {}'.format( 75 | type(obj_type))) 76 | if default_args is not None: 77 | for name, value in default_args.items(): 78 | args.setdefault(name, value) 79 | return obj_cls(**args) 80 | -------------------------------------------------------------------------------- /pytest.ini: -------------------------------------------------------------------------------- 1 | [pytest] 2 | addopts = --xdoctest --xdoctest-style=auto 3 | norecursedirs = .git ignore build __pycache__ data docker docs .eggs 4 | 5 | filterwarnings= default 6 | ignore:.*No cfgstr given in Cacher constructor or call.*:Warning 7 | ignore:.*Define the __nice__ method for.*:Warning 8 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | -r requirements/build.txt 2 | -r requirements/optional.txt 3 | -r requirements/runtime.txt 4 | -r requirements/tests.txt 5 | -------------------------------------------------------------------------------- /requirements/build.txt: -------------------------------------------------------------------------------- 1 | # These must be installed before building mmdetection 2 | numpy 3 | torch>=1.2,<=1.4 4 | -------------------------------------------------------------------------------- /requirements/optional.txt: -------------------------------------------------------------------------------- 1 | albumentations>=0.3.2 2 | cityscapesscripts 3 | imagecorruptions 4 | -------------------------------------------------------------------------------- /requirements/runtime.txt: -------------------------------------------------------------------------------- 1 | matplotlib 2 | mmcv>=0.3.1 3 | numpy 4 | # need older pillow until torchvision is fixed 5 | Pillow<=6.2.2 6 | six 7 | terminaltables 8 | torch>=1.2,<=1.4 9 | torchvision 10 | -------------------------------------------------------------------------------- /requirements/tests.txt: -------------------------------------------------------------------------------- 1 | asynctest 2 | codecov 3 | flake8 4 | isort 5 | # Note: used for kwarray.group_items, this may be ported to mmcv in the future. 6 | kwarray 7 | pytest 8 | pytest-cov 9 | pytest-runner 10 | ubelt 11 | xdoctest >= 0.10.0 12 | yapf 13 | -------------------------------------------------------------------------------- /tests/test_async.py: -------------------------------------------------------------------------------- 1 | """Tests for async interface.""" 2 | 3 | import asyncio 4 | import os 5 | import sys 6 | 7 | import asynctest 8 | import mmcv 9 | import torch 10 | 11 | from mmdet.apis import async_inference_detector, init_detector 12 | 13 | if sys.version_info >= (3, 7): 14 | from mmdet.utils.contextmanagers import concurrent 15 | 16 | 17 | class AsyncTestCase(asynctest.TestCase): 18 | use_default_loop = False 19 | forbid_get_event_loop = True 20 | 21 | TEST_TIMEOUT = int(os.getenv('ASYNCIO_TEST_TIMEOUT', '30')) 22 | 23 | def _run_test_method(self, method): 24 | result = method() 25 | if asyncio.iscoroutine(result): 26 | self.loop.run_until_complete( 27 | asyncio.wait_for(result, timeout=self.TEST_TIMEOUT)) 28 | 29 | 30 | class MaskRCNNDetector: 31 | 32 | def __init__(self, 33 | model_config, 34 | checkpoint=None, 35 | streamqueue_size=3, 36 | device='cuda:0'): 37 | 38 | self.streamqueue_size = streamqueue_size 39 | self.device = device 40 | # build the model and load checkpoint 41 | self.model = init_detector( 42 | model_config, checkpoint=None, device=self.device) 43 | self.streamqueue = None 44 | 45 | async def init(self): 46 | self.streamqueue = asyncio.Queue() 47 | for _ in range(self.streamqueue_size): 48 | stream = torch.cuda.Stream(device=self.device) 49 | self.streamqueue.put_nowait(stream) 50 | 51 | if sys.version_info >= (3, 7): 52 | 53 | async def apredict(self, img): 54 | if isinstance(img, str): 55 | img = mmcv.imread(img) 56 | async with concurrent(self.streamqueue): 57 | result = await async_inference_detector(self.model, img) 58 | return result 59 | 60 | 61 | class AsyncInferenceTestCase(AsyncTestCase): 62 | 63 | if sys.version_info >= (3, 7): 64 | 65 | async def test_simple_inference(self): 66 | if not torch.cuda.is_available(): 67 | import pytest 68 | 69 | pytest.skip('test requires GPU and torch+cuda') 70 | 71 | root_dir = os.path.dirname(os.path.dirname(__name__)) 72 | model_config = os.path.join(root_dir, 73 | 'configs/mask_rcnn_r50_fpn_1x.py') 74 | detector = MaskRCNNDetector(model_config) 75 | await detector.init() 76 | img_path = os.path.join(root_dir, 'demo/demo.jpg') 77 | bboxes, _ = await detector.apredict(img_path) 78 | self.assertTrue(bboxes) 79 | -------------------------------------------------------------------------------- /tests/test_nms.py: -------------------------------------------------------------------------------- 1 | """ 2 | CommandLine: 3 | pytest tests/test_nms.py 4 | """ 5 | import numpy as np 6 | import torch 7 | 8 | from mmdet.ops.nms.nms_wrapper import nms 9 | 10 | 11 | def test_nms_device_and_dtypes_cpu(): 12 | """ 13 | CommandLine: 14 | xdoctest -m tests/test_nms.py test_nms_device_and_dtypes_cpu 15 | """ 16 | iou_thr = 0.7 17 | base_dets = np.array([[49.1, 32.4, 51.0, 35.9, 0.9], 18 | [49.3, 32.9, 51.0, 35.3, 0.9], 19 | [35.3, 11.5, 39.9, 14.5, 0.4], 20 | [35.2, 11.7, 39.7, 15.7, 0.3]]) 21 | 22 | # CPU can handle float32 and float64 23 | dets = base_dets.astype(np.float32) 24 | supressed, inds = nms(dets, iou_thr) 25 | assert dets.dtype == supressed.dtype 26 | assert len(inds) == len(supressed) == 3 27 | 28 | dets = torch.FloatTensor(base_dets) 29 | surpressed, inds = nms(dets, iou_thr) 30 | assert dets.dtype == surpressed.dtype 31 | assert len(inds) == len(surpressed) == 3 32 | 33 | dets = base_dets.astype(np.float64) 34 | supressed, inds = nms(dets, iou_thr) 35 | assert dets.dtype == supressed.dtype 36 | assert len(inds) == len(supressed) == 3 37 | 38 | dets = torch.DoubleTensor(base_dets) 39 | surpressed, inds = nms(dets, iou_thr) 40 | assert dets.dtype == surpressed.dtype 41 | assert len(inds) == len(surpressed) == 3 42 | 43 | 44 | def test_nms_device_and_dtypes_gpu(): 45 | """ 46 | CommandLine: 47 | xdoctest -m tests/test_nms.py test_nms_device_and_dtypes_gpu 48 | """ 49 | if not torch.cuda.is_available(): 50 | import pytest 51 | pytest.skip('test requires GPU and torch+cuda') 52 | 53 | iou_thr = 0.7 54 | base_dets = np.array([[49.1, 32.4, 51.0, 35.9, 0.9], 55 | [49.3, 32.9, 51.0, 35.3, 0.9], 56 | [35.3, 11.5, 39.9, 14.5, 0.4], 57 | [35.2, 11.7, 39.7, 15.7, 0.3]]) 58 | 59 | for device_id in range(torch.cuda.device_count()): 60 | print('Run NMS on device_id = {!r}'.format(device_id)) 61 | # GPU can handle float32 but not float64 62 | dets = base_dets.astype(np.float32) 63 | supressed, inds = nms(dets, iou_thr, device_id) 64 | assert dets.dtype == supressed.dtype 65 | assert len(inds) == len(supressed) == 3 66 | 67 | dets = torch.FloatTensor(base_dets).to(device_id) 68 | surpressed, inds = nms(dets, iou_thr) 69 | assert dets.dtype == surpressed.dtype 70 | assert len(inds) == len(surpressed) == 3 71 | -------------------------------------------------------------------------------- /tests/test_soft_nms.py: -------------------------------------------------------------------------------- 1 | """ 2 | CommandLine: 3 | pytest tests/test_soft_nms.py 4 | """ 5 | import numpy as np 6 | import torch 7 | 8 | from mmdet.ops.nms.nms_wrapper import soft_nms 9 | 10 | 11 | def test_soft_nms_device_and_dtypes_cpu(): 12 | """ 13 | CommandLine: 14 | xdoctest -m tests/test_soft_nms.py test_soft_nms_device_and_dtypes_cpu 15 | """ 16 | iou_thr = 0.7 17 | base_dets = np.array([[49.1, 32.4, 51.0, 35.9, 0.9], 18 | [49.3, 32.9, 51.0, 35.3, 0.9], 19 | [35.3, 11.5, 39.9, 14.5, 0.4], 20 | [35.2, 11.7, 39.7, 15.7, 0.3]]) 21 | 22 | # CPU can handle float32 and float64 23 | dets = base_dets.astype(np.float32) 24 | new_dets, inds = soft_nms(dets, iou_thr) 25 | assert dets.dtype == new_dets.dtype 26 | assert len(inds) == len(new_dets) == 4 27 | 28 | dets = torch.FloatTensor(base_dets) 29 | new_dets, inds = soft_nms(dets, iou_thr) 30 | assert dets.dtype == new_dets.dtype 31 | assert len(inds) == len(new_dets) == 4 32 | 33 | dets = base_dets.astype(np.float64) 34 | new_dets, inds = soft_nms(dets, iou_thr) 35 | assert dets.dtype == new_dets.dtype 36 | assert len(inds) == len(new_dets) == 4 37 | 38 | dets = torch.DoubleTensor(base_dets) 39 | new_dets, inds = soft_nms(dets, iou_thr) 40 | assert dets.dtype == new_dets.dtype 41 | assert len(inds) == len(new_dets) == 4 42 | -------------------------------------------------------------------------------- /tests/test_utils.py: -------------------------------------------------------------------------------- 1 | import numpy.testing as npt 2 | 3 | from mmdet.utils.flops_counter import params_to_string 4 | 5 | 6 | def test_params_to_string(): 7 | npt.assert_equal(params_to_string(1e9), '1000.0 M') 8 | npt.assert_equal(params_to_string(2e5), '200.0 k') 9 | npt.assert_equal(params_to_string(3e-9), '3e-09') 10 | -------------------------------------------------------------------------------- /tools/browse_dataset.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | from pathlib import Path 4 | 5 | import mmcv 6 | from mmcv import Config 7 | 8 | from mmdet.datasets.builder import build_dataset 9 | 10 | 11 | def parse_args(): 12 | parser = argparse.ArgumentParser(description='Browse a dataset') 13 | parser.add_argument('config', help='train config file path') 14 | parser.add_argument( 15 | '--skip-type', 16 | type=str, 17 | nargs='+', 18 | default=['DefaultFormatBundle', 'Normalize', 'Collect'], 19 | help='skip some useless pipeline') 20 | parser.add_argument( 21 | '--output-dir', 22 | default=None, 23 | type=str, 24 | help='If there is no display interface, you can save it') 25 | parser.add_argument('--not-show', default=False, action='store_true') 26 | parser.add_argument( 27 | '--show-interval', 28 | type=int, 29 | default=999, 30 | help='the interval of show (ms)') 31 | args = parser.parse_args() 32 | return args 33 | 34 | 35 | def retrieve_data_cfg(config_path, skip_type): 36 | cfg = Config.fromfile(config_path) 37 | train_data_cfg = cfg.data.train 38 | train_data_cfg['pipeline'] = [ 39 | x for x in train_data_cfg.pipeline if x['type'] not in skip_type 40 | ] 41 | 42 | return cfg 43 | 44 | 45 | def main(): 46 | args = parse_args() 47 | cfg = retrieve_data_cfg(args.config, args.skip_type) 48 | 49 | dataset = build_dataset(cfg.data.train) 50 | 51 | progress_bar = mmcv.ProgressBar(len(dataset)) 52 | for item in dataset: 53 | filename = os.path.join(args.output_dir, 54 | Path(item['filename']).name 55 | ) if args.output_dir is not None else None 56 | mmcv.imshow_det_bboxes( 57 | item['img'], 58 | item['gt_bboxes'], 59 | item['gt_labels'] - 1, 60 | class_names=dataset.CLASSES, 61 | show=not args.not_show, 62 | out_file=filename, 63 | wait_time=args.show_interval) 64 | progress_bar.update() 65 | 66 | 67 | if __name__ == '__main__': 68 | main() 69 | -------------------------------------------------------------------------------- /tools/dist_test.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | PYTHON=${PYTHON:-"python"} 4 | 5 | CONFIG=$1 6 | CHECKPOINT=$2 7 | GPUS=$3 8 | PORT=${PORT:-29500} 9 | 10 | $PYTHON -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \ 11 | $(dirname "$0")/test.py $CONFIG $CHECKPOINT --launcher pytorch ${@:4} 12 | -------------------------------------------------------------------------------- /tools/dist_train.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | PYTHON=${PYTHON:-"python"} 4 | 5 | CONFIG=$1 6 | GPUS=$2 7 | PORT=${PORT:-29500} 8 | 9 | $PYTHON -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \ 10 | $(dirname "$0")/train.py $CONFIG --launcher pytorch ${@:3} 11 | -------------------------------------------------------------------------------- /tools/fuse_conv_bn.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | import torch 4 | import torch.nn as nn 5 | from mmcv.runner import save_checkpoint 6 | 7 | from mmdet.apis import init_detector 8 | 9 | 10 | def fuse_conv_bn(conv, bn): 11 | """ During inference, the functionary of batch norm layers is turned off 12 | but only the mean and var alone channels are used, which exposes the 13 | chance to fuse it with the preceding conv layers to save computations and 14 | simplify network structures. 15 | """ 16 | conv_w = conv.weight 17 | conv_b = conv.bias if conv.bias is not None else torch.zeros_like( 18 | bn.running_mean) 19 | 20 | factor = bn.weight / torch.sqrt(bn.running_var + bn.eps) 21 | conv.weight = nn.Parameter(conv_w * 22 | factor.reshape([conv.out_channels, 1, 1, 1])) 23 | conv.bias = nn.Parameter((conv_b - bn.running_mean) * factor + bn.bias) 24 | return conv 25 | 26 | 27 | def fuse_module(m): 28 | last_conv = None 29 | last_conv_name = None 30 | 31 | for name, child in m.named_children(): 32 | if isinstance(child, (nn.BatchNorm2d, nn.SyncBatchNorm)): 33 | if last_conv is None: # only fuse BN that is after Conv 34 | continue 35 | fused_conv = fuse_conv_bn(last_conv, child) 36 | m._modules[last_conv_name] = fused_conv 37 | # To reduce changes, set BN as Identity instead of deleting it. 38 | m._modules[name] = nn.Identity() 39 | last_conv = None 40 | elif isinstance(child, nn.Conv2d): 41 | last_conv = child 42 | last_conv_name = name 43 | else: 44 | fuse_module(child) 45 | return m 46 | 47 | 48 | def parse_args(): 49 | parser = argparse.ArgumentParser( 50 | description='fuse Conv and BN layers in a model') 51 | parser.add_argument('config', help='config file path') 52 | parser.add_argument('checkpoint', help='checkpoint file path') 53 | parser.add_argument('out', help='output path of the converted model') 54 | args = parser.parse_args() 55 | return args 56 | 57 | 58 | def main(): 59 | args = parse_args() 60 | # build the model from a config file and a checkpoint file 61 | model = init_detector(args.config, args.checkpoint) 62 | # fuse conv and bn layers of the model 63 | fused_model = fuse_module(model) 64 | save_checkpoint(fused_model, args.out) 65 | 66 | 67 | if __name__ == '__main__': 68 | main() 69 | -------------------------------------------------------------------------------- /tools/get_flops.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | from mmcv import Config 4 | 5 | from mmdet.models import build_detector 6 | from mmdet.utils import get_model_complexity_info 7 | 8 | 9 | def parse_args(): 10 | parser = argparse.ArgumentParser(description='Train a detector') 11 | parser.add_argument('config', help='train config file path') 12 | parser.add_argument( 13 | '--shape', 14 | type=int, 15 | nargs='+', 16 | default=[1280, 800], 17 | help='input image size') 18 | args = parser.parse_args() 19 | return args 20 | 21 | 22 | def main(): 23 | 24 | args = parse_args() 25 | 26 | if len(args.shape) == 1: 27 | input_shape = (3, args.shape[0], args.shape[0]) 28 | elif len(args.shape) == 2: 29 | input_shape = (3, ) + tuple(args.shape) 30 | else: 31 | raise ValueError('invalid input shape') 32 | 33 | cfg = Config.fromfile(args.config) 34 | model = build_detector( 35 | cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg).cuda() 36 | model.eval() 37 | 38 | if hasattr(model, 'forward_dummy'): 39 | model.forward = model.forward_dummy 40 | else: 41 | raise NotImplementedError( 42 | 'FLOPs counter is currently not currently supported with {}'. 43 | format(model.__class__.__name__)) 44 | 45 | flops, params = get_model_complexity_info(model, input_shape) 46 | split_line = '=' * 30 47 | print('{0}\nInput shape: {1}\nFlops: {2}\nParams: {3}\n{0}'.format( 48 | split_line, input_shape, flops, params)) 49 | print('!!!Please be cautious if you use the results in papers. ' 50 | 'You may need to check if all ops are supported and verify that the ' 51 | 'flops computation is correct.') 52 | 53 | 54 | if __name__ == '__main__': 55 | main() 56 | -------------------------------------------------------------------------------- /tools/publish_model.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import subprocess 3 | 4 | import torch 5 | 6 | 7 | def parse_args(): 8 | parser = argparse.ArgumentParser( 9 | description='Process a checkpoint to be published') 10 | parser.add_argument('in_file', help='input checkpoint filename') 11 | parser.add_argument('out_file', help='output checkpoint filename') 12 | args = parser.parse_args() 13 | return args 14 | 15 | 16 | def process_checkpoint(in_file, out_file): 17 | checkpoint = torch.load(in_file, map_location='cpu') 18 | # remove optimizer for smaller file size 19 | if 'optimizer' in checkpoint: 20 | del checkpoint['optimizer'] 21 | # if it is necessary to remove some sensitive data in checkpoint['meta'], 22 | # add the code here. 23 | torch.save(checkpoint, out_file) 24 | sha = subprocess.check_output(['sha256sum', out_file]).decode() 25 | final_file = out_file.rstrip('.pth') + '-{}.pth'.format(sha[:8]) 26 | subprocess.Popen(['mv', out_file, final_file]) 27 | 28 | 29 | def main(): 30 | args = parse_args() 31 | process_checkpoint(args.in_file, args.out_file) 32 | 33 | 34 | if __name__ == '__main__': 35 | main() 36 | -------------------------------------------------------------------------------- /tools/slurm_test.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -x 4 | 5 | PARTITION=$1 6 | JOB_NAME=$2 7 | CONFIG=$3 8 | CHECKPOINT=$4 9 | GPUS=${GPUS:-8} 10 | GPUS_PER_NODE=${GPUS_PER_NODE:-8} 11 | CPUS_PER_TASK=${CPUS_PER_TASK:-5} 12 | PY_ARGS=${@:5} 13 | SRUN_ARGS=${SRUN_ARGS:-""} 14 | 15 | srun -p ${PARTITION} \ 16 | --job-name=${JOB_NAME} \ 17 | --gres=gpu:${GPUS_PER_NODE} \ 18 | --ntasks=${GPUS} \ 19 | --ntasks-per-node=${GPUS_PER_NODE} \ 20 | --cpus-per-task=${CPUS_PER_TASK} \ 21 | --kill-on-bad-exit=1 \ 22 | ${SRUN_ARGS} \ 23 | python -u tools/test.py ${CONFIG} ${CHECKPOINT} --launcher="slurm" ${PY_ARGS} 24 | -------------------------------------------------------------------------------- /tools/slurm_train.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -x 4 | 5 | PARTITION=$1 6 | JOB_NAME=$2 7 | CONFIG=$3 8 | WORK_DIR=$4 9 | GPUS=${5:-8} 10 | GPUS_PER_NODE=${GPUS_PER_NODE:-8} 11 | CPUS_PER_TASK=${CPUS_PER_TASK:-5} 12 | SRUN_ARGS=${SRUN_ARGS:-""} 13 | PY_ARGS=${PY_ARGS:-"--validate"} 14 | 15 | srun -p ${PARTITION} \ 16 | --job-name=${JOB_NAME} \ 17 | --gres=gpu:${GPUS_PER_NODE} \ 18 | --ntasks=${GPUS} \ 19 | --ntasks-per-node=${GPUS_PER_NODE} \ 20 | --cpus-per-task=${CPUS_PER_TASK} \ 21 | --kill-on-bad-exit=1 \ 22 | ${SRUN_ARGS} \ 23 | python -u tools/train.py ${CONFIG} --work_dir=${WORK_DIR} --launcher="slurm" ${PY_ARGS} 24 | -------------------------------------------------------------------------------- /tools/upgrade_model_version.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import re 3 | from collections import OrderedDict 4 | 5 | import torch 6 | 7 | 8 | def convert(in_file, out_file): 9 | """Convert keys in checkpoints. 10 | 11 | There can be some breaking changes during the development of mmdetection, 12 | and this tool is used for upgrading checkpoints trained with old versions 13 | to the latest one. 14 | """ 15 | checkpoint = torch.load(in_file) 16 | in_state_dict = checkpoint.pop('state_dict') 17 | out_state_dict = OrderedDict() 18 | for key, val in in_state_dict.items(): 19 | # Use ConvModule instead of nn.Conv2d in RetinaNet 20 | # cls_convs.0.weight -> cls_convs.0.conv.weight 21 | m = re.search(r'(cls_convs|reg_convs).\d.(weight|bias)', key) 22 | if m is not None: 23 | param = m.groups()[1] 24 | new_key = key.replace(param, 'conv.{}'.format(param)) 25 | out_state_dict[new_key] = val 26 | continue 27 | 28 | out_state_dict[key] = val 29 | checkpoint['state_dict'] = out_state_dict 30 | torch.save(checkpoint, out_file) 31 | 32 | 33 | def main(): 34 | parser = argparse.ArgumentParser(description='Upgrade model version') 35 | parser.add_argument('in_file', help='input checkpoint file') 36 | parser.add_argument('out_file', help='output checkpoint file') 37 | args = parser.parse_args() 38 | convert(args.in_file, args.out_file) 39 | 40 | 41 | if __name__ == '__main__': 42 | main() 43 | --------------------------------------------------------------------------------