├── EVA-01 ├── HF_models_README.md ├── README.md ├── clip │ ├── CLIP.png │ ├── README.md │ ├── __init__.py │ ├── benchmark.md │ ├── bpe_simple_vocab_16e6.txt.gz │ ├── clip.py │ ├── eva_clip.py │ ├── eva_model.py │ ├── model.py │ ├── model_configs │ │ └── EVA_CLIP_g_14.json │ ├── simple_tokenizer.py │ └── vit_model.py ├── det │ ├── .circleci │ │ └── config.yml │ ├── .clang-format │ ├── .flake8 │ ├── .github │ │ ├── CODE_OF_CONDUCT.md │ │ ├── CONTRIBUTING.md │ │ ├── Detectron2-Logo-Horz.svg │ │ ├── ISSUE_TEMPLATE.md │ │ ├── ISSUE_TEMPLATE │ │ │ ├── bugs.md │ │ │ ├── config.yml │ │ │ ├── documentation.md │ │ │ ├── feature-request.md │ │ │ └── unexpected-problems-bugs.md │ │ ├── pull_request_template.md │ │ └── workflows │ │ │ ├── check-template.yml │ │ │ ├── levenshtein.js │ │ │ ├── needs-reply.yml │ │ │ ├── remove-needs-reply.yml │ │ │ └── workflow.yml │ ├── .gitignore │ ├── README.md │ ├── configs │ │ ├── Base-RCNN-C4.yaml │ │ ├── Base-RCNN-DilatedC5.yaml │ │ ├── Base-RCNN-FPN.yaml │ │ ├── Base-RetinaNet.yaml │ │ ├── COCO-Detection │ │ │ ├── fast_rcnn_R_50_FPN_1x.yaml │ │ │ ├── faster_rcnn_R_101_C4_3x.yaml │ │ │ ├── faster_rcnn_R_101_DC5_3x.yaml │ │ │ ├── faster_rcnn_R_101_FPN_3x.yaml │ │ │ ├── faster_rcnn_R_50_C4_1x.yaml │ │ │ ├── faster_rcnn_R_50_C4_3x.yaml │ │ │ ├── faster_rcnn_R_50_DC5_1x.yaml │ │ │ ├── faster_rcnn_R_50_DC5_3x.yaml │ │ │ ├── faster_rcnn_R_50_FPN_1x.yaml │ │ │ ├── faster_rcnn_R_50_FPN_3x.yaml │ │ │ ├── faster_rcnn_X_101_32x8d_FPN_3x.yaml │ │ │ ├── fcos_R_50_FPN_1x.py │ │ │ ├── retinanet_R_101_FPN_3x.yaml │ │ │ ├── retinanet_R_50_FPN_1x.py │ │ │ ├── retinanet_R_50_FPN_1x.yaml │ │ │ ├── retinanet_R_50_FPN_3x.yaml │ │ │ ├── rpn_R_50_C4_1x.yaml │ │ │ └── rpn_R_50_FPN_1x.yaml │ │ ├── COCO-InstanceSegmentation │ │ │ ├── mask_rcnn_R_101_C4_3x.yaml │ │ │ ├── mask_rcnn_R_101_DC5_3x.yaml │ │ │ ├── mask_rcnn_R_101_FPN_3x.yaml │ │ │ ├── mask_rcnn_R_50_C4_1x.py │ │ │ ├── mask_rcnn_R_50_C4_1x.yaml │ │ │ ├── mask_rcnn_R_50_C4_3x.yaml │ │ │ ├── mask_rcnn_R_50_DC5_1x.yaml │ │ │ ├── mask_rcnn_R_50_DC5_3x.yaml │ │ │ ├── mask_rcnn_R_50_FPN_1x.py │ │ │ ├── mask_rcnn_R_50_FPN_1x.yaml │ │ │ ├── mask_rcnn_R_50_FPN_1x_giou.yaml │ │ │ ├── mask_rcnn_R_50_FPN_3x.yaml │ │ │ ├── mask_rcnn_X_101_32x8d_FPN_3x.yaml │ │ │ ├── mask_rcnn_regnetx_4gf_dds_fpn_1x.py │ │ │ └── mask_rcnn_regnety_4gf_dds_fpn_1x.py │ │ ├── COCO-Keypoints │ │ │ ├── Base-Keypoint-RCNN-FPN.yaml │ │ │ ├── keypoint_rcnn_R_101_FPN_3x.yaml │ │ │ ├── keypoint_rcnn_R_50_FPN_1x.py │ │ │ ├── keypoint_rcnn_R_50_FPN_1x.yaml │ │ │ ├── keypoint_rcnn_R_50_FPN_3x.yaml │ │ │ └── keypoint_rcnn_X_101_32x8d_FPN_3x.yaml │ │ ├── COCO-PanopticSegmentation │ │ │ ├── Base-Panoptic-FPN.yaml │ │ │ ├── panoptic_fpn_R_101_3x.yaml │ │ │ ├── panoptic_fpn_R_50_1x.py │ │ │ ├── panoptic_fpn_R_50_1x.yaml │ │ │ └── panoptic_fpn_R_50_3x.yaml │ │ ├── Cityscapes │ │ │ └── mask_rcnn_R_50_FPN.yaml │ │ ├── Detectron1-Comparisons │ │ │ ├── README.md │ │ │ ├── faster_rcnn_R_50_FPN_noaug_1x.yaml │ │ │ ├── keypoint_rcnn_R_50_FPN_1x.yaml │ │ │ └── mask_rcnn_R_50_FPN_noaug_1x.yaml │ │ ├── LVISv0.5-InstanceSegmentation │ │ │ ├── mask_rcnn_R_101_FPN_1x.yaml │ │ │ ├── mask_rcnn_R_50_FPN_1x.yaml │ │ │ └── mask_rcnn_X_101_32x8d_FPN_1x.yaml │ │ ├── LVISv1-InstanceSegmentation │ │ │ ├── mask_rcnn_R_101_FPN_1x.yaml │ │ │ ├── mask_rcnn_R_50_FPN_1x.yaml │ │ │ └── mask_rcnn_X_101_32x8d_FPN_1x.yaml │ │ ├── Misc │ │ │ ├── cascade_mask_rcnn_R_50_FPN_1x.yaml │ │ │ ├── cascade_mask_rcnn_R_50_FPN_3x.yaml │ │ │ ├── cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv.yaml │ │ │ ├── mask_rcnn_R_50_FPN_1x_cls_agnostic.yaml │ │ │ ├── mask_rcnn_R_50_FPN_1x_dconv_c3-c5.yaml │ │ │ ├── mask_rcnn_R_50_FPN_3x_dconv_c3-c5.yaml │ │ │ ├── mask_rcnn_R_50_FPN_3x_gn.yaml │ │ │ ├── mask_rcnn_R_50_FPN_3x_syncbn.yaml │ │ │ ├── mmdet_mask_rcnn_R_50_FPN_1x.py │ │ │ ├── panoptic_fpn_R_101_dconv_cascade_gn_3x.yaml │ │ │ ├── scratch_mask_rcnn_R_50_FPN_3x_gn.yaml │ │ │ ├── scratch_mask_rcnn_R_50_FPN_9x_gn.yaml │ │ │ ├── scratch_mask_rcnn_R_50_FPN_9x_syncbn.yaml │ │ │ ├── semantic_R_50_FPN_1x.yaml │ │ │ └── torchvision_imagenet_R_50.py │ │ ├── PascalVOC-Detection │ │ │ ├── faster_rcnn_R_50_C4.yaml │ │ │ └── faster_rcnn_R_50_FPN.yaml │ │ ├── common │ │ │ ├── README.md │ │ │ ├── coco_schedule.py │ │ │ ├── data │ │ │ │ ├── coco.py │ │ │ │ ├── coco_keypoint.py │ │ │ │ ├── coco_panoptic_separated.py │ │ │ │ └── constants.py │ │ │ ├── models │ │ │ │ ├── cascade_rcnn.py │ │ │ │ ├── fcos.py │ │ │ │ ├── keypoint_rcnn_fpn.py │ │ │ │ ├── mask_rcnn_c4.py │ │ │ │ ├── mask_rcnn_fpn.py │ │ │ │ ├── mask_rcnn_vitdet.py │ │ │ │ ├── panoptic_fpn.py │ │ │ │ └── retinanet.py │ │ │ ├── optim.py │ │ │ └── train.py │ │ ├── new_baselines │ │ │ ├── mask_rcnn_R_101_FPN_100ep_LSJ.py │ │ │ ├── mask_rcnn_R_101_FPN_200ep_LSJ.py │ │ │ ├── mask_rcnn_R_101_FPN_400ep_LSJ.py │ │ │ ├── mask_rcnn_R_50_FPN_100ep_LSJ.py │ │ │ ├── mask_rcnn_R_50_FPN_200ep_LSJ.py │ │ │ ├── mask_rcnn_R_50_FPN_400ep_LSJ.py │ │ │ ├── mask_rcnn_R_50_FPN_50ep_LSJ.py │ │ │ ├── mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ.py │ │ │ ├── mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ.py │ │ │ ├── mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ.py │ │ │ ├── mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ.py │ │ │ ├── mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ.py │ │ │ └── mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ.py │ │ └── quick_schedules │ │ │ ├── README.md │ │ │ ├── cascade_mask_rcnn_R_50_FPN_inference_acc_test.yaml │ │ │ ├── cascade_mask_rcnn_R_50_FPN_instant_test.yaml │ │ │ ├── fast_rcnn_R_50_FPN_inference_acc_test.yaml │ │ │ ├── fast_rcnn_R_50_FPN_instant_test.yaml │ │ │ ├── keypoint_rcnn_R_50_FPN_inference_acc_test.yaml │ │ │ ├── keypoint_rcnn_R_50_FPN_instant_test.yaml │ │ │ ├── keypoint_rcnn_R_50_FPN_normalized_training_acc_test.yaml │ │ │ ├── keypoint_rcnn_R_50_FPN_training_acc_test.yaml │ │ │ ├── mask_rcnn_R_50_C4_GCV_instant_test.yaml │ │ │ ├── mask_rcnn_R_50_C4_inference_acc_test.yaml │ │ │ ├── mask_rcnn_R_50_C4_instant_test.yaml │ │ │ ├── mask_rcnn_R_50_C4_training_acc_test.yaml │ │ │ ├── mask_rcnn_R_50_DC5_inference_acc_test.yaml │ │ │ ├── mask_rcnn_R_50_FPN_inference_acc_test.yaml │ │ │ ├── mask_rcnn_R_50_FPN_instant_test.yaml │ │ │ ├── mask_rcnn_R_50_FPN_pred_boxes_training_acc_test.yaml │ │ │ ├── mask_rcnn_R_50_FPN_training_acc_test.yaml │ │ │ ├── panoptic_fpn_R_50_inference_acc_test.yaml │ │ │ ├── panoptic_fpn_R_50_instant_test.yaml │ │ │ ├── panoptic_fpn_R_50_training_acc_test.yaml │ │ │ ├── retinanet_R_50_FPN_inference_acc_test.yaml │ │ │ ├── retinanet_R_50_FPN_instant_test.yaml │ │ │ ├── rpn_R_50_FPN_inference_acc_test.yaml │ │ │ ├── rpn_R_50_FPN_instant_test.yaml │ │ │ ├── semantic_R_50_FPN_inference_acc_test.yaml │ │ │ ├── semantic_R_50_FPN_instant_test.yaml │ │ │ └── semantic_R_50_FPN_training_acc_test.yaml │ ├── datasets │ │ ├── README.md │ │ ├── prepare_ade20k_sem_seg.py │ │ ├── prepare_cocofied_lvis.py │ │ └── prepare_panoptic_fpn.py │ ├── demo │ │ ├── README.md │ │ ├── demo.py │ │ └── predictor.py │ ├── detectron2 │ │ ├── __init__.py │ │ ├── checkpoint │ │ │ ├── __init__.py │ │ │ ├── c2_model_loading.py │ │ │ ├── catalog.py │ │ │ └── detection_checkpoint.py │ │ ├── config │ │ │ ├── __init__.py │ │ │ ├── compat.py │ │ │ ├── config.py │ │ │ ├── defaults.py │ │ │ ├── instantiate.py │ │ │ └── lazy.py │ │ ├── data │ │ │ ├── __init__.py │ │ │ ├── benchmark.py │ │ │ ├── build.py │ │ │ ├── catalog.py │ │ │ ├── common.py │ │ │ ├── dataset_mapper.py │ │ │ ├── datasets │ │ │ │ ├── README.md │ │ │ │ ├── __init__.py │ │ │ │ ├── builtin.py │ │ │ │ ├── builtin_meta.py │ │ │ │ ├── cityscapes.py │ │ │ │ ├── cityscapes_panoptic.py │ │ │ │ ├── coco.py │ │ │ │ ├── coco_panoptic.py │ │ │ │ ├── lvis.py │ │ │ │ ├── lvis_v0_5_categories.py │ │ │ │ ├── lvis_v1_categories.py │ │ │ │ ├── lvis_v1_category_image_count.py │ │ │ │ ├── pascal_voc.py │ │ │ │ └── register_coco.py │ │ │ ├── detection_utils.py │ │ │ ├── samplers │ │ │ │ ├── __init__.py │ │ │ │ ├── distributed_sampler.py │ │ │ │ └── grouped_batch_sampler.py │ │ │ └── transforms │ │ │ │ ├── __init__.py │ │ │ │ ├── augmentation.py │ │ │ │ ├── augmentation_impl.py │ │ │ │ └── transform.py │ │ ├── engine │ │ │ ├── __init__.py │ │ │ ├── defaults.py │ │ │ ├── hooks.py │ │ │ ├── launch.py │ │ │ └── train_loop.py │ │ ├── evaluation │ │ │ ├── __init__.py │ │ │ ├── cityscapes_evaluation.py │ │ │ ├── coco_evaluation.py │ │ │ ├── evaluator.py │ │ │ ├── fast_eval_api.py │ │ │ ├── lvis_evaluation.py │ │ │ ├── panoptic_evaluation.py │ │ │ ├── pascal_voc_evaluation.py │ │ │ ├── rotated_coco_evaluation.py │ │ │ ├── sem_seg_evaluation.py │ │ │ └── testing.py │ │ ├── export │ │ │ ├── README.md │ │ │ ├── __init__.py │ │ │ ├── api.py │ │ │ ├── c10.py │ │ │ ├── caffe2_export.py │ │ │ ├── caffe2_inference.py │ │ │ ├── caffe2_modeling.py │ │ │ ├── caffe2_patch.py │ │ │ ├── flatten.py │ │ │ ├── shared.py │ │ │ ├── torchscript.py │ │ │ └── torchscript_patch.py │ │ ├── layers │ │ │ ├── __init__.py │ │ │ ├── aspp.py │ │ │ ├── batch_norm.py │ │ │ ├── blocks.py │ │ │ ├── csrc │ │ │ │ ├── README.md │ │ │ │ ├── ROIAlignRotated │ │ │ │ │ ├── ROIAlignRotated.h │ │ │ │ │ ├── ROIAlignRotated_cpu.cpp │ │ │ │ │ └── ROIAlignRotated_cuda.cu │ │ │ │ ├── box_iou_rotated │ │ │ │ │ ├── box_iou_rotated.h │ │ │ │ │ ├── box_iou_rotated_cpu.cpp │ │ │ │ │ ├── box_iou_rotated_cuda.cu │ │ │ │ │ └── box_iou_rotated_utils.h │ │ │ │ ├── cocoeval │ │ │ │ │ ├── cocoeval.cpp │ │ │ │ │ └── cocoeval.h │ │ │ │ ├── cuda_version.cu │ │ │ │ ├── deformable │ │ │ │ │ ├── deform_conv.h │ │ │ │ │ ├── deform_conv_cuda.cu │ │ │ │ │ └── deform_conv_cuda_kernel.cu │ │ │ │ ├── nms_rotated │ │ │ │ │ ├── nms_rotated.h │ │ │ │ │ ├── nms_rotated_cpu.cpp │ │ │ │ │ └── nms_rotated_cuda.cu │ │ │ │ └── vision.cpp │ │ │ ├── deform_conv.py │ │ │ ├── losses.py │ │ │ ├── mask_ops.py │ │ │ ├── nms.py │ │ │ ├── roi_align.py │ │ │ ├── roi_align_rotated.py │ │ │ ├── rotated_boxes.py │ │ │ ├── shape_spec.py │ │ │ └── wrappers.py │ │ ├── model_zoo │ │ │ ├── __init__.py │ │ │ └── model_zoo.py │ │ ├── modeling │ │ │ ├── __init__.py │ │ │ ├── anchor_generator.py │ │ │ ├── backbone │ │ │ │ ├── __init__.py │ │ │ │ ├── backbone.py │ │ │ │ ├── build.py │ │ │ │ ├── fpn.py │ │ │ │ ├── mvit.py │ │ │ │ ├── regnet.py │ │ │ │ ├── resnet.py │ │ │ │ ├── swin.py │ │ │ │ ├── utils.py │ │ │ │ └── vit.py │ │ │ ├── box_regression.py │ │ │ ├── matcher.py │ │ │ ├── meta_arch │ │ │ │ ├── __init__.py │ │ │ │ ├── build.py │ │ │ │ ├── dense_detector.py │ │ │ │ ├── fcos.py │ │ │ │ ├── panoptic_fpn.py │ │ │ │ ├── rcnn.py │ │ │ │ ├── retinanet.py │ │ │ │ └── semantic_seg.py │ │ │ ├── mmdet_wrapper.py │ │ │ ├── poolers.py │ │ │ ├── postprocessing.py │ │ │ ├── proposal_generator │ │ │ │ ├── __init__.py │ │ │ │ ├── build.py │ │ │ │ ├── proposal_utils.py │ │ │ │ ├── rpn.py │ │ │ │ └── rrpn.py │ │ │ ├── roi_heads │ │ │ │ ├── __init__.py │ │ │ │ ├── box_head.py │ │ │ │ ├── cascade_rcnn.py │ │ │ │ ├── fast_rcnn.py │ │ │ │ ├── keypoint_head.py │ │ │ │ ├── mask_head.py │ │ │ │ ├── roi_heads.py │ │ │ │ └── rotated_fast_rcnn.py │ │ │ ├── sampling.py │ │ │ └── test_time_augmentation.py │ │ ├── projects │ │ │ ├── README.md │ │ │ └── __init__.py │ │ ├── solver │ │ │ ├── __init__.py │ │ │ ├── build.py │ │ │ └── lr_scheduler.py │ │ ├── structures │ │ │ ├── __init__.py │ │ │ ├── boxes.py │ │ │ ├── image_list.py │ │ │ ├── instances.py │ │ │ ├── keypoints.py │ │ │ ├── masks.py │ │ │ └── rotated_boxes.py │ │ ├── tracking │ │ │ ├── __init__.py │ │ │ ├── base_tracker.py │ │ │ ├── bbox_iou_tracker.py │ │ │ ├── hungarian_tracker.py │ │ │ ├── iou_weighted_hungarian_bbox_iou_tracker.py │ │ │ ├── utils.py │ │ │ └── vanilla_hungarian_bbox_iou_tracker.py │ │ └── utils │ │ │ ├── README.md │ │ │ ├── __init__.py │ │ │ ├── analysis.py │ │ │ ├── collect_env.py │ │ │ ├── colormap.py │ │ │ ├── comm.py │ │ │ ├── develop.py │ │ │ ├── env.py │ │ │ ├── events.py │ │ │ ├── file_io.py │ │ │ ├── logger.py │ │ │ ├── memory.py │ │ │ ├── registry.py │ │ │ ├── serialize.py │ │ │ ├── testing.py │ │ │ ├── tracing.py │ │ │ ├── video_visualizer.py │ │ │ └── visualizer.py │ ├── dev │ │ ├── README.md │ │ └── packaging │ │ │ ├── README.md │ │ │ ├── gen_install_table.py │ │ │ └── pkg_helpers.bash │ ├── docker │ │ ├── Dockerfile │ │ ├── README.md │ │ ├── deploy.Dockerfile │ │ └── docker-compose.yml │ ├── docs │ │ ├── .gitignore │ │ ├── Makefile │ │ ├── README.md │ │ ├── _static │ │ │ └── css │ │ │ │ └── custom.css │ │ ├── conf.py │ │ ├── index.rst │ │ ├── modules │ │ │ ├── checkpoint.rst │ │ │ ├── config.rst │ │ │ ├── data.rst │ │ │ ├── data_transforms.rst │ │ │ ├── engine.rst │ │ │ ├── evaluation.rst │ │ │ ├── export.rst │ │ │ ├── fvcore.rst │ │ │ ├── index.rst │ │ │ ├── layers.rst │ │ │ ├── model_zoo.rst │ │ │ ├── modeling.rst │ │ │ ├── solver.rst │ │ │ ├── structures.rst │ │ │ └── utils.rst │ │ ├── notes │ │ │ ├── benchmarks.md │ │ │ ├── changelog.md │ │ │ ├── compatibility.md │ │ │ ├── contributing.md │ │ │ └── index.rst │ │ └── tutorials │ │ │ ├── README.md │ │ │ ├── augmentation.md │ │ │ ├── builtin_datasets.md │ │ │ ├── configs.md │ │ │ ├── data_loading.md │ │ │ ├── datasets.md │ │ │ ├── deployment.md │ │ │ ├── evaluation.md │ │ │ ├── extend.md │ │ │ ├── getting_started.md │ │ │ ├── index.rst │ │ │ ├── install.md │ │ │ ├── lazyconfigs.md │ │ │ ├── models.md │ │ │ ├── training.md │ │ │ └── write-models.md │ ├── projects │ │ └── ViTDet │ │ │ └── configs │ │ │ ├── COCO │ │ │ ├── cascade_mask_rcnn_vitdet_b_100ep.py │ │ │ ├── cascade_mask_rcnn_vitdet_eva.py │ │ │ ├── cascade_mask_rcnn_vitdet_eva_1536.py │ │ │ ├── cascade_mask_rcnn_vitdet_eva_1536_lvis_annotation.py │ │ │ └── mask_rcnn_vitdet_b_100ep.py │ │ │ ├── LVIS │ │ │ ├── cascade_mask_rcnn_vitdet_eva.py │ │ │ └── cascade_mask_rcnn_vitdet_eva_1536.py │ │ │ └── common │ │ │ ├── coco_loader_lsj.py │ │ │ ├── coco_loader_lsj_1280.py │ │ │ └── coco_loader_lsj_1536.py │ ├── setup.cfg │ ├── setup.py │ ├── tests │ │ ├── README.md │ │ ├── __init__.py │ │ ├── config │ │ │ ├── dir1 │ │ │ │ ├── dir1_a.py │ │ │ │ └── dir1_b.py │ │ │ ├── root_cfg.py │ │ │ ├── test_instantiate_config.py │ │ │ ├── test_lazy_config.py │ │ │ └── test_yacs_config.py │ │ ├── data │ │ │ ├── __init__.py │ │ │ ├── test_coco.py │ │ │ ├── test_coco_evaluation.py │ │ │ ├── test_dataset.py │ │ │ ├── test_detection_utils.py │ │ │ ├── test_rotation_transform.py │ │ │ ├── test_sampler.py │ │ │ └── test_transforms.py │ │ ├── export │ │ │ └── test_c10.py │ │ ├── layers │ │ │ ├── __init__.py │ │ │ ├── test_blocks.py │ │ │ ├── test_deformable.py │ │ │ ├── test_losses.py │ │ │ ├── test_mask_ops.py │ │ │ ├── test_nms.py │ │ │ ├── test_nms_rotated.py │ │ │ ├── test_roi_align.py │ │ │ └── test_roi_align_rotated.py │ │ ├── modeling │ │ │ ├── __init__.py │ │ │ ├── test_anchor_generator.py │ │ │ ├── test_backbone.py │ │ │ ├── test_box2box_transform.py │ │ │ ├── test_fast_rcnn.py │ │ │ ├── test_matcher.py │ │ │ ├── test_mmdet.py │ │ │ ├── test_model_e2e.py │ │ │ ├── test_roi_heads.py │ │ │ ├── test_roi_pooler.py │ │ │ └── test_rpn.py │ │ ├── structures │ │ │ ├── __init__.py │ │ │ ├── test_boxes.py │ │ │ ├── test_imagelist.py │ │ │ ├── test_instances.py │ │ │ ├── test_keypoints.py │ │ │ ├── test_masks.py │ │ │ └── test_rotated_boxes.py │ │ ├── test_checkpoint.py │ │ ├── test_engine.py │ │ ├── test_events.py │ │ ├── test_export_caffe2.py │ │ ├── test_export_onnx.py │ │ ├── test_export_torchscript.py │ │ ├── test_model_analysis.py │ │ ├── test_model_zoo.py │ │ ├── test_packaging.py │ │ ├── test_registry.py │ │ ├── test_scheduler.py │ │ ├── test_solver.py │ │ ├── test_visualizer.py │ │ └── tracking │ │ │ ├── __init__.py │ │ │ ├── test_bbox_iou_tracker.py │ │ │ ├── test_hungarian_tracker.py │ │ │ ├── test_iou_weighted_hungarian_bbox_iou_tracker.py │ │ │ └── test_vanilla_hungarian_bbox_iou_tracker.py │ └── tools │ │ ├── README.md │ │ ├── __init__.py │ │ ├── analyze_model.py │ │ ├── benchmark.py │ │ ├── convert-torchvision-to-d2.py │ │ ├── deploy │ │ ├── README.md │ │ ├── export_model.py │ │ └── torchscript_mask_rcnn.cpp │ │ ├── lazyconfig_train_net.py │ │ ├── lightning_train_net.py │ │ ├── plain_train_net.py │ │ ├── train_net.py │ │ ├── visualize_data.py │ │ └── visualize_json_results.py ├── eva │ ├── .gitignore │ ├── README.md │ ├── clip │ │ ├── README.md │ │ ├── __init__.py │ │ ├── bpe_simple_vocab_16e6.txt.gz │ │ ├── clip.py │ │ ├── model.py │ │ └── simple_tokenizer.py │ ├── clip_wrapper.py │ ├── dataset_folder.py │ ├── datasets.py │ ├── engine_for_finetuning.py │ ├── engine_for_pretraining.py │ ├── imagenet_a_r_indices.py │ ├── imagenet_classes.json │ ├── imagenet_real.py │ ├── interpolate_patch_14to16.py │ ├── masking_generator.py │ ├── mixup.py │ ├── modeling_finetune.py │ ├── modeling_mae_pretrain.py │ ├── modeling_pretrain.py │ ├── objectnet_dataset.py │ ├── optim_factory.py │ ├── real.json │ ├── requirements.txt │ ├── robustness_dataset.py │ ├── run_class_finetuning.py │ ├── run_eva_pretraining.py │ ├── transforms.py │ └── utils.py ├── logs │ ├── cls │ │ ├── eva-l_ft_1k_cls_sz196_50ep_88p0.txt │ │ ├── eva-l_ft_1k_cls_sz336_50ep_88p65.txt │ │ ├── eva-l_ft_21k_to_1k_cls_sz196_20ep_88p6.txt │ │ ├── eva-l_ft_21k_to_1k_cls_sz336_20ep_89p2.txt │ │ ├── eva-l_intermed_ft_21k_90ep_sz224.txt │ │ ├── ft_1k_cls_sz336_89p6.txt │ │ ├── ft_1k_cls_sz560_89p7.txt │ │ ├── ft_eva_clip_vision_enc_1k_cls_sz224_89p1.txt │ │ ├── ft_eva_clip_vision_enc_1k_cls_sz336_89p4.txt │ │ ├── intermed_ft_22k_60ep_sz224.txt │ │ ├── linear_eva_clip_vision_enc_1k_cls_sz224_86p5.txt │ │ └── linear_eva_clip_vision_enc_1k_cls_sz336_86p5.txt │ ├── pt │ │ ├── eva-l_mim_pt_eva-clip-target_21k_150ep_log.txt │ │ └── eva_mim_pt_30m_150ep_log.txt │ ├── sem_seg │ │ ├── ft_ade20k_sem_seg_ms_62p3.txt │ │ ├── ft_ade20k_sem_seg_ms_62p3_training_log.txt │ │ ├── ft_ade20k_sem_seg_ms_relpos_layerscale_9dec_62p3_training_log.txt │ │ ├── ft_cocstuff164k_sem_seg_ss_53p4.txt │ │ ├── ft_cocstuff164k_sem_seg_ss_53p4_training_log.txt │ │ └── ft_cocstuff164k_sem_seg_ss_relpos_layerscale_9dec_53p2_training_log.txt │ └── video │ │ ├── ft_k400_log.txt │ │ ├── ft_k600_log.txt │ │ ├── ft_k700_log.txt │ │ └── ft_k722_log.txt ├── seg │ ├── README.md │ ├── configs │ │ ├── _base_ │ │ │ ├── datasets │ │ │ │ ├── ade20k.py │ │ │ │ ├── chase_db1.py │ │ │ │ ├── cityscapes.py │ │ │ │ ├── cityscapes_1024x1024.py │ │ │ │ ├── cityscapes_768x768.py │ │ │ │ ├── cityscapes_769x769.py │ │ │ │ ├── cityscapes_832x832.py │ │ │ │ ├── cityscapes_896x896.py │ │ │ │ ├── coco-stuff10k.py │ │ │ │ ├── coco-stuff164k.py │ │ │ │ ├── drive.py │ │ │ │ ├── hrf.py │ │ │ │ ├── loveda.py │ │ │ │ ├── mapillary_896x896.py │ │ │ │ ├── nyu_depth_v2.py │ │ │ │ ├── pascal_context.py │ │ │ │ ├── pascal_context_59.py │ │ │ │ ├── pascal_voc12.py │ │ │ │ ├── pascal_voc12_aug.py │ │ │ │ ├── potsdam.py │ │ │ │ └── stare.py │ │ │ ├── default_runtime.py │ │ │ ├── models │ │ │ │ ├── ann_r50-d8.py │ │ │ │ ├── apcnet_r50-d8.py │ │ │ │ ├── bisenetv1_r18-d32.py │ │ │ │ ├── bisenetv2.py │ │ │ │ ├── ccnet_r50-d8.py │ │ │ │ ├── cgnet.py │ │ │ │ ├── danet_r50-d8.py │ │ │ │ ├── deeplabv3_r50-d8.py │ │ │ │ ├── deeplabv3_unet_s5-d16.py │ │ │ │ ├── deeplabv3plus_r50-d8.py │ │ │ │ ├── dmnet_r50-d8.py │ │ │ │ ├── dnl_r50-d8.py │ │ │ │ ├── dpt_vit-b16.py │ │ │ │ ├── emanet_r50-d8.py │ │ │ │ ├── encnet_r50-d8.py │ │ │ │ ├── erfnet_fcn.py │ │ │ │ ├── fast_scnn.py │ │ │ │ ├── fastfcn_r50-d32_jpu_psp.py │ │ │ │ ├── fcn_hr18.py │ │ │ │ ├── fcn_r50-d8.py │ │ │ │ ├── fcn_unet_s5-d16.py │ │ │ │ ├── fpn_r50.py │ │ │ │ ├── gcnet_r50-d8.py │ │ │ │ ├── icnet_r50-d8.py │ │ │ │ ├── isanet_r50-d8.py │ │ │ │ ├── lraspp_m-v3-d8.py │ │ │ │ ├── mask2former_beit.py │ │ │ │ ├── mask2former_beit_cityscapes.py │ │ │ │ ├── mask2former_beit_cocostuff.py │ │ │ │ ├── mask2former_beit_pascal.py │ │ │ │ ├── mask2former_beit_potsdam.py │ │ │ │ ├── maskformer_beit.py │ │ │ │ ├── nonlocal_r50-d8.py │ │ │ │ ├── ocrnet_hr18.py │ │ │ │ ├── ocrnet_r50-d8.py │ │ │ │ ├── pointrend_r50.py │ │ │ │ ├── psanet_r50-d8.py │ │ │ │ ├── pspnet_r50-d8.py │ │ │ │ ├── pspnet_unet_s5-d16.py │ │ │ │ ├── segformer_mit-b0.py │ │ │ │ ├── setr_mla.py │ │ │ │ ├── setr_naive.py │ │ │ │ ├── setr_pup.py │ │ │ │ ├── stdc.py │ │ │ │ ├── twins_pcpvt-s_fpn.py │ │ │ │ ├── twins_pcpvt-s_upernet.py │ │ │ │ ├── upernet_beit.py │ │ │ │ ├── upernet_r50.py │ │ │ │ ├── upernet_swin.py │ │ │ │ └── upernet_vit-b16_ln_mln.py │ │ │ └── schedules │ │ │ │ ├── schedule_10k.py │ │ │ │ ├── schedule_15k.py │ │ │ │ ├── schedule_160k.py │ │ │ │ ├── schedule_20k.py │ │ │ │ ├── schedule_30k.py │ │ │ │ ├── schedule_320k.py │ │ │ │ ├── schedule_40k.py │ │ │ │ ├── schedule_60k.py │ │ │ │ └── schedule_80k.py │ │ ├── ade20k │ │ │ ├── eva_mask2former_896_20k_coco164k2ade20k_ms.py │ │ │ ├── eva_mask2former_896_20k_coco164k2ade20k_ss.py │ │ │ ├── eva_mask2former_896_40k_coco164k2ade20k_ms_relpos_layerscale_9dec.py │ │ │ └── eva_mask2former_896_40k_coco164k2ade20k_ss_relpos_layerscale_9dec.py │ │ └── coco_stuff164k │ │ │ ├── eva_mask2former_896_60k_cocostuff164k_ss.py │ │ │ └── eva_mask2former_896_80k_cocostuff164k_ss_relpos_layerscale_9dec.py │ ├── interpolate_patch_14to16.py │ ├── mmcv_custom │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── checkpoint.cpython-38.pyc │ │ │ ├── customized_text.cpython-38.pyc │ │ │ ├── layer_decay_optimizer_constructor.cpython-38.pyc │ │ │ └── my_checkpoint.cpython-38.pyc │ │ ├── checkpoint.py │ │ ├── customized_text.py │ │ ├── layer_decay_optimizer_constructor.py │ │ └── my_checkpoint.py │ ├── mmseg_custom │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ └── __init__.cpython-38.pyc │ │ ├── core │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ └── __init__.cpython-38.pyc │ │ │ ├── anchor │ │ │ │ ├── __init__.py │ │ │ │ ├── __pycache__ │ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ │ ├── builder.cpython-38.pyc │ │ │ │ │ └── point_generator.cpython-38.pyc │ │ │ │ ├── builder.py │ │ │ │ └── point_generator.py │ │ │ ├── box │ │ │ │ ├── __init__.py │ │ │ │ ├── __pycache__ │ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ │ └── builder.cpython-38.pyc │ │ │ │ ├── builder.py │ │ │ │ └── samplers │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── __pycache__ │ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ │ ├── base_sampler.cpython-38.pyc │ │ │ │ │ ├── mask_pseudo_sampler.cpython-38.pyc │ │ │ │ │ ├── mask_sampling_result.cpython-38.pyc │ │ │ │ │ └── sampling_result.cpython-38.pyc │ │ │ │ │ ├── base_sampler.py │ │ │ │ │ ├── mask_pseudo_sampler.py │ │ │ │ │ ├── mask_sampling_result.py │ │ │ │ │ └── sampling_result.py │ │ │ ├── evaluation │ │ │ │ ├── __init__.py │ │ │ │ ├── __pycache__ │ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ │ └── panoptic_utils.cpython-38.pyc │ │ │ │ └── panoptic_utils.py │ │ │ ├── mask │ │ │ │ ├── __init__.py │ │ │ │ ├── __pycache__ │ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ │ └── utils.cpython-38.pyc │ │ │ │ └── utils.py │ │ │ └── utils │ │ │ │ ├── __init__.py │ │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── dist_utils.cpython-38.pyc │ │ │ │ └── misc.cpython-38.pyc │ │ │ │ ├── dist_utils.py │ │ │ │ └── misc.py │ │ ├── datasets │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── mapillary.cpython-38.pyc │ │ │ │ └── potsdam.cpython-38.pyc │ │ │ ├── mapillary.py │ │ │ ├── pipelines │ │ │ │ ├── __init__.py │ │ │ │ ├── __pycache__ │ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ │ ├── formatting.cpython-38.pyc │ │ │ │ │ └── transform.cpython-38.pyc │ │ │ │ ├── formatting.py │ │ │ │ └── transform.py │ │ │ └── potsdam.py │ │ └── models │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-38.pyc │ │ │ └── builder.cpython-38.pyc │ │ │ ├── backbones │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── adapter_modules.cpython-38.pyc │ │ │ │ ├── beit_adapter.cpython-38.pyc │ │ │ │ ├── beit_baseline.cpython-38.pyc │ │ │ │ ├── beit_win_adapter.cpython-38.pyc │ │ │ │ ├── vit_adapter.cpython-38.pyc │ │ │ │ └── vit_baseline.cpython-38.pyc │ │ │ ├── adapter_modules.py │ │ │ ├── base │ │ │ │ ├── __pycache__ │ │ │ │ │ ├── beit.cpython-38.pyc │ │ │ │ │ ├── beit_win.cpython-38.pyc │ │ │ │ │ └── vit.cpython-38.pyc │ │ │ │ ├── beit.py │ │ │ │ ├── beit_win.py │ │ │ │ └── vit.py │ │ │ ├── beit_adapter.py │ │ │ ├── beit_baseline.py │ │ │ ├── beit_win_adapter.py │ │ │ ├── vit_adapter.py │ │ │ └── vit_baseline.py │ │ │ ├── builder.py │ │ │ ├── decode_heads │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── mask2former_head.cpython-38.pyc │ │ │ │ └── maskformer_head.cpython-38.pyc │ │ │ ├── mask2former_head.py │ │ │ └── maskformer_head.py │ │ │ ├── losses │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── cross_entropy_loss.cpython-38.pyc │ │ │ │ ├── dice_loss.cpython-38.pyc │ │ │ │ ├── focal_loss.cpython-38.pyc │ │ │ │ └── match_costs.cpython-38.pyc │ │ │ ├── cross_entropy_loss.py │ │ │ ├── dice_loss.py │ │ │ ├── focal_loss.py │ │ │ ├── match_costs.py │ │ │ └── match_loss.py │ │ │ ├── plugins │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── msdeformattn_pixel_decoder.cpython-38.pyc │ │ │ │ └── pixel_decoder.cpython-38.pyc │ │ │ ├── msdeformattn_pixel_decoder.py │ │ │ └── pixel_decoder.py │ │ │ ├── segmentors │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── encoder_decoder_mask2former.cpython-38.pyc │ │ │ │ └── encoder_decoder_mask2former_aug.cpython-38.pyc │ │ │ ├── encoder_decoder_mask2former.py │ │ │ └── encoder_decoder_mask2former_aug.py │ │ │ └── utils │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── assigner.cpython-38.pyc │ │ │ ├── point_sample.cpython-38.pyc │ │ │ ├── positional_encoding.cpython-38.pyc │ │ │ └── transformer.cpython-38.pyc │ │ │ ├── assigner.py │ │ │ ├── point_sample.py │ │ │ ├── positional_encoding.py │ │ │ └── transformer.py │ ├── ops │ │ ├── README.md │ │ ├── functions │ │ │ ├── __init__.py │ │ │ └── ms_deform_attn_func.py │ │ ├── make.sh │ │ ├── modules │ │ │ ├── __init__.py │ │ │ └── ms_deform_attn.py │ │ ├── setup.py │ │ ├── src │ │ │ ├── cpu │ │ │ │ ├── ms_deform_attn_cpu.cpp │ │ │ │ └── ms_deform_attn_cpu.h │ │ │ ├── cuda │ │ │ │ ├── ms_deform_attn_cuda.cu │ │ │ │ ├── ms_deform_attn_cuda.h │ │ │ │ └── ms_deform_im2col_cuda.cuh │ │ │ ├── ms_deform_attn.h │ │ │ └── vision.cpp │ │ └── test.py │ ├── test.py │ └── train.py └── video │ ├── .gitignore │ ├── README.md │ ├── clip │ ├── __init__.py │ ├── bpe_simple_vocab_16e6.txt.gz │ ├── clip.py │ ├── model.py │ ├── model_zoo.py │ └── simple_tokenizer.py │ ├── configs │ ├── kinetics400_ft.yaml │ ├── kinetics600_ft.yaml │ ├── kinetics700_ft.yaml │ └── kinetics722_intermediate_ft.yaml │ ├── datasets │ ├── __init__.py │ ├── blending.py │ ├── build.py │ ├── pipeline.py │ └── rand_augment.py │ ├── labels │ ├── kinetics400_labels.csv │ ├── kinetics600_labels.csv │ ├── kinetics700_labels.csv │ └── kinetics722_labels.csv │ ├── main.py │ ├── models │ ├── beit.py │ ├── clip_mae.py │ ├── gvt.py │ └── utils.py │ ├── notebooks │ └── build_file_list.ipynb │ ├── requirements.txt │ ├── scripts │ ├── download_checkpoints.sh │ ├── run_k400_ft.sh │ ├── run_k600_ft.sh │ ├── run_k700_ft.sh │ └── run_k722_ft.sh │ └── utils │ ├── __init__.py │ ├── config.py │ ├── logger.py │ ├── optimizer.py │ └── tools.py ├── EVA-02 ├── README.md ├── assets │ ├── radar.png │ └── summary_tab.png ├── asuka │ ├── .gitignore │ ├── README.md │ ├── clip_wrapper.py │ ├── dataset_folder.py │ ├── datasets.py │ ├── engine_for_finetuning.py │ ├── engine_for_pretraining.py │ ├── eva_clip │ │ ├── CLIP.png │ │ ├── README.md │ │ ├── __init__.py │ │ ├── bpe_simple_vocab_16e6.txt.gz │ │ ├── clip.py │ │ ├── eva_clip.py │ │ ├── eva_model.py │ │ ├── model.py │ │ ├── model_configs │ │ │ ├── EVA_CLIP_g_14.json │ │ │ └── EVA_CLIP_g_14_X.json │ │ ├── simple_tokenizer.py │ │ └── vit_model.py │ ├── imagenet_a_r_indices.py │ ├── imagenet_classes.json │ ├── imagenet_real.py │ ├── masking_generator.py │ ├── mixup.py │ ├── modeling_finetune.py │ ├── modeling_pretrain.py │ ├── objectnet_dataset.py │ ├── optim_factory.py │ ├── real.json │ ├── requirements.txt │ ├── robustness_dataset.py │ ├── rope.py │ ├── run_class_finetuning.py │ ├── run_eva02_pretraining.py │ ├── transforms.py │ └── utils.py ├── det │ ├── .circleci │ │ ├── config.yml │ │ └── import-tests.sh │ ├── .clang-format │ ├── .flake8 │ ├── .github │ │ ├── CODE_OF_CONDUCT.md │ │ ├── CONTRIBUTING.md │ │ ├── Detectron2-Logo-Horz.svg │ │ ├── ISSUE_TEMPLATE.md │ │ ├── ISSUE_TEMPLATE │ │ │ ├── bugs.md │ │ │ ├── config.yml │ │ │ ├── documentation.md │ │ │ ├── feature-request.md │ │ │ └── unexpected-problems-bugs.md │ │ ├── pull_request_template.md │ │ └── workflows │ │ │ ├── check-template.yml │ │ │ ├── levenshtein.js │ │ │ ├── needs-reply.yml │ │ │ ├── remove-needs-reply.yml │ │ │ └── workflow.yml │ ├── .gitignore │ ├── README.md │ ├── configs │ │ ├── Base-RCNN-C4.yaml │ │ ├── Base-RCNN-DilatedC5.yaml │ │ ├── Base-RCNN-FPN.yaml │ │ ├── Base-RetinaNet.yaml │ │ ├── COCO-Detection │ │ │ ├── fast_rcnn_R_50_FPN_1x.yaml │ │ │ ├── faster_rcnn_R_101_C4_3x.yaml │ │ │ ├── faster_rcnn_R_101_DC5_3x.yaml │ │ │ ├── faster_rcnn_R_101_FPN_3x.yaml │ │ │ ├── faster_rcnn_R_50_C4_1x.yaml │ │ │ ├── faster_rcnn_R_50_C4_3x.yaml │ │ │ ├── faster_rcnn_R_50_DC5_1x.yaml │ │ │ ├── faster_rcnn_R_50_DC5_3x.yaml │ │ │ ├── faster_rcnn_R_50_FPN_1x.yaml │ │ │ ├── faster_rcnn_R_50_FPN_3x.yaml │ │ │ ├── faster_rcnn_X_101_32x8d_FPN_3x.yaml │ │ │ ├── fcos_R_50_FPN_1x.py │ │ │ ├── retinanet_R_101_FPN_3x.yaml │ │ │ ├── retinanet_R_50_FPN_1x.py │ │ │ ├── retinanet_R_50_FPN_1x.yaml │ │ │ ├── retinanet_R_50_FPN_3x.yaml │ │ │ ├── rpn_R_50_C4_1x.yaml │ │ │ └── rpn_R_50_FPN_1x.yaml │ │ ├── COCO-InstanceSegmentation │ │ │ ├── mask_rcnn_R_101_C4_3x.yaml │ │ │ ├── mask_rcnn_R_101_DC5_3x.yaml │ │ │ ├── mask_rcnn_R_101_FPN_3x.yaml │ │ │ ├── mask_rcnn_R_50_C4_1x.py │ │ │ ├── mask_rcnn_R_50_C4_1x.yaml │ │ │ ├── mask_rcnn_R_50_C4_3x.yaml │ │ │ ├── mask_rcnn_R_50_DC5_1x.yaml │ │ │ ├── mask_rcnn_R_50_DC5_3x.yaml │ │ │ ├── mask_rcnn_R_50_FPN_1x.py │ │ │ ├── mask_rcnn_R_50_FPN_1x.yaml │ │ │ ├── mask_rcnn_R_50_FPN_1x_giou.yaml │ │ │ ├── mask_rcnn_R_50_FPN_3x.yaml │ │ │ ├── mask_rcnn_X_101_32x8d_FPN_3x.yaml │ │ │ ├── mask_rcnn_regnetx_4gf_dds_fpn_1x.py │ │ │ └── mask_rcnn_regnety_4gf_dds_fpn_1x.py │ │ ├── COCO-Keypoints │ │ │ ├── Base-Keypoint-RCNN-FPN.yaml │ │ │ ├── keypoint_rcnn_R_101_FPN_3x.yaml │ │ │ ├── keypoint_rcnn_R_50_FPN_1x.py │ │ │ ├── keypoint_rcnn_R_50_FPN_1x.yaml │ │ │ ├── keypoint_rcnn_R_50_FPN_3x.yaml │ │ │ └── keypoint_rcnn_X_101_32x8d_FPN_3x.yaml │ │ ├── COCO-PanopticSegmentation │ │ │ ├── Base-Panoptic-FPN.yaml │ │ │ ├── panoptic_fpn_R_101_3x.yaml │ │ │ ├── panoptic_fpn_R_50_1x.py │ │ │ ├── panoptic_fpn_R_50_1x.yaml │ │ │ └── panoptic_fpn_R_50_3x.yaml │ │ ├── Cityscapes │ │ │ └── mask_rcnn_R_50_FPN.yaml │ │ ├── Detectron1-Comparisons │ │ │ ├── README.md │ │ │ ├── faster_rcnn_R_50_FPN_noaug_1x.yaml │ │ │ ├── keypoint_rcnn_R_50_FPN_1x.yaml │ │ │ └── mask_rcnn_R_50_FPN_noaug_1x.yaml │ │ ├── LVISv0.5-InstanceSegmentation │ │ │ ├── mask_rcnn_R_101_FPN_1x.yaml │ │ │ ├── mask_rcnn_R_50_FPN_1x.yaml │ │ │ └── mask_rcnn_X_101_32x8d_FPN_1x.yaml │ │ ├── LVISv1-InstanceSegmentation │ │ │ ├── mask_rcnn_R_101_FPN_1x.yaml │ │ │ ├── mask_rcnn_R_50_FPN_1x.yaml │ │ │ └── mask_rcnn_X_101_32x8d_FPN_1x.yaml │ │ ├── Misc │ │ │ ├── cascade_mask_rcnn_R_50_FPN_1x.yaml │ │ │ ├── cascade_mask_rcnn_R_50_FPN_3x.yaml │ │ │ ├── cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv.yaml │ │ │ ├── mask_rcnn_R_50_FPN_1x_cls_agnostic.yaml │ │ │ ├── mask_rcnn_R_50_FPN_1x_dconv_c3-c5.yaml │ │ │ ├── mask_rcnn_R_50_FPN_3x_dconv_c3-c5.yaml │ │ │ ├── mask_rcnn_R_50_FPN_3x_gn.yaml │ │ │ ├── mask_rcnn_R_50_FPN_3x_syncbn.yaml │ │ │ ├── mmdet_mask_rcnn_R_50_FPN_1x.py │ │ │ ├── panoptic_fpn_R_101_dconv_cascade_gn_3x.yaml │ │ │ ├── scratch_mask_rcnn_R_50_FPN_3x_gn.yaml │ │ │ ├── scratch_mask_rcnn_R_50_FPN_9x_gn.yaml │ │ │ ├── scratch_mask_rcnn_R_50_FPN_9x_syncbn.yaml │ │ │ ├── semantic_R_50_FPN_1x.yaml │ │ │ └── torchvision_imagenet_R_50.py │ │ ├── PascalVOC-Detection │ │ │ ├── faster_rcnn_R_50_C4.yaml │ │ │ └── faster_rcnn_R_50_FPN.yaml │ │ ├── common │ │ │ ├── README.md │ │ │ ├── coco_schedule.py │ │ │ ├── data │ │ │ │ ├── coco.py │ │ │ │ ├── coco_keypoint.py │ │ │ │ ├── coco_panoptic_separated.py │ │ │ │ ├── constants.py │ │ │ │ ├── objects365.py │ │ │ │ └── objects365_trainval.py │ │ │ ├── models │ │ │ │ ├── cascade_rcnn.py │ │ │ │ ├── fcos.py │ │ │ │ ├── keypoint_rcnn_fpn.py │ │ │ │ ├── mask_rcnn_c4.py │ │ │ │ ├── mask_rcnn_fpn.py │ │ │ │ ├── mask_rcnn_vitdet.py │ │ │ │ ├── panoptic_fpn.py │ │ │ │ └── retinanet.py │ │ │ ├── optim.py │ │ │ └── train.py │ │ ├── new_baselines │ │ │ ├── mask_rcnn_R_101_FPN_100ep_LSJ.py │ │ │ ├── mask_rcnn_R_101_FPN_200ep_LSJ.py │ │ │ ├── mask_rcnn_R_101_FPN_400ep_LSJ.py │ │ │ ├── mask_rcnn_R_50_FPN_100ep_LSJ.py │ │ │ ├── mask_rcnn_R_50_FPN_200ep_LSJ.py │ │ │ ├── mask_rcnn_R_50_FPN_400ep_LSJ.py │ │ │ ├── mask_rcnn_R_50_FPN_50ep_LSJ.py │ │ │ ├── mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ.py │ │ │ ├── mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ.py │ │ │ ├── mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ.py │ │ │ ├── mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ.py │ │ │ ├── mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ.py │ │ │ └── mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ.py │ │ └── quick_schedules │ │ │ ├── README.md │ │ │ ├── cascade_mask_rcnn_R_50_FPN_inference_acc_test.yaml │ │ │ ├── cascade_mask_rcnn_R_50_FPN_instant_test.yaml │ │ │ ├── fast_rcnn_R_50_FPN_inference_acc_test.yaml │ │ │ ├── fast_rcnn_R_50_FPN_instant_test.yaml │ │ │ ├── keypoint_rcnn_R_50_FPN_inference_acc_test.yaml │ │ │ ├── keypoint_rcnn_R_50_FPN_instant_test.yaml │ │ │ ├── keypoint_rcnn_R_50_FPN_normalized_training_acc_test.yaml │ │ │ ├── keypoint_rcnn_R_50_FPN_training_acc_test.yaml │ │ │ ├── mask_rcnn_R_50_C4_GCV_instant_test.yaml │ │ │ ├── mask_rcnn_R_50_C4_inference_acc_test.yaml │ │ │ ├── mask_rcnn_R_50_C4_instant_test.yaml │ │ │ ├── mask_rcnn_R_50_C4_training_acc_test.yaml │ │ │ ├── mask_rcnn_R_50_DC5_inference_acc_test.yaml │ │ │ ├── mask_rcnn_R_50_FPN_inference_acc_test.yaml │ │ │ ├── mask_rcnn_R_50_FPN_instant_test.yaml │ │ │ ├── mask_rcnn_R_50_FPN_pred_boxes_training_acc_test.yaml │ │ │ ├── mask_rcnn_R_50_FPN_training_acc_test.yaml │ │ │ ├── panoptic_fpn_R_50_inference_acc_test.yaml │ │ │ ├── panoptic_fpn_R_50_instant_test.yaml │ │ │ ├── panoptic_fpn_R_50_training_acc_test.yaml │ │ │ ├── retinanet_R_50_FPN_inference_acc_test.yaml │ │ │ ├── retinanet_R_50_FPN_instant_test.yaml │ │ │ ├── rpn_R_50_FPN_inference_acc_test.yaml │ │ │ ├── rpn_R_50_FPN_instant_test.yaml │ │ │ ├── semantic_R_50_FPN_inference_acc_test.yaml │ │ │ ├── semantic_R_50_FPN_instant_test.yaml │ │ │ └── semantic_R_50_FPN_training_acc_test.yaml │ ├── datasets │ │ ├── README.md │ │ ├── prepare_ade20k_sem_seg.py │ │ ├── prepare_cocofied_lvis.py │ │ ├── prepare_for_tests.sh │ │ └── prepare_panoptic_fpn.py │ ├── demo │ │ ├── README.md │ │ ├── demo.py │ │ └── predictor.py │ ├── detectron2 │ │ ├── __init__.py │ │ ├── checkpoint │ │ │ ├── __init__.py │ │ │ ├── c2_model_loading.py │ │ │ ├── catalog.py │ │ │ └── detection_checkpoint.py │ │ ├── config │ │ │ ├── __init__.py │ │ │ ├── compat.py │ │ │ ├── config.py │ │ │ ├── defaults.py │ │ │ ├── instantiate.py │ │ │ └── lazy.py │ │ ├── data │ │ │ ├── __init__.py │ │ │ ├── benchmark.py │ │ │ ├── build.py │ │ │ ├── catalog.py │ │ │ ├── common.py │ │ │ ├── dataset_mapper.py │ │ │ ├── datasets │ │ │ │ ├── README.md │ │ │ │ ├── __init__.py │ │ │ │ ├── builtin.py │ │ │ │ ├── builtin_meta.py │ │ │ │ ├── cityscapes.py │ │ │ │ ├── cityscapes_panoptic.py │ │ │ │ ├── coco.py │ │ │ │ ├── coco_panoptic.py │ │ │ │ ├── lvis.py │ │ │ │ ├── lvis_v0_5_categories.py │ │ │ │ ├── lvis_v1_categories.py │ │ │ │ ├── lvis_v1_category_image_count.py │ │ │ │ ├── objects365.py │ │ │ │ ├── pascal_voc.py │ │ │ │ └── register_coco.py │ │ │ ├── detection_utils.py │ │ │ ├── samplers │ │ │ │ ├── __init__.py │ │ │ │ ├── distributed_sampler.py │ │ │ │ └── grouped_batch_sampler.py │ │ │ └── transforms │ │ │ │ ├── __init__.py │ │ │ │ ├── augmentation.py │ │ │ │ ├── augmentation_impl.py │ │ │ │ └── transform.py │ │ ├── engine │ │ │ ├── __init__.py │ │ │ ├── defaults.py │ │ │ ├── hooks.py │ │ │ ├── launch.py │ │ │ └── train_loop.py │ │ ├── evaluation │ │ │ ├── __init__.py │ │ │ ├── cityscapes_evaluation.py │ │ │ ├── coco_evaluation.py │ │ │ ├── evaluator.py │ │ │ ├── fast_eval_api.py │ │ │ ├── lvis_evaluation.py │ │ │ ├── panoptic_evaluation.py │ │ │ ├── pascal_voc_evaluation.py │ │ │ ├── rotated_coco_evaluation.py │ │ │ ├── sem_seg_evaluation.py │ │ │ └── testing.py │ │ ├── export │ │ │ ├── README.md │ │ │ ├── __init__.py │ │ │ ├── api.py │ │ │ ├── c10.py │ │ │ ├── caffe2_export.py │ │ │ ├── caffe2_inference.py │ │ │ ├── caffe2_modeling.py │ │ │ ├── caffe2_patch.py │ │ │ ├── flatten.py │ │ │ ├── shared.py │ │ │ ├── torchscript.py │ │ │ └── torchscript_patch.py │ │ ├── layers │ │ │ ├── __init__.py │ │ │ ├── aspp.py │ │ │ ├── batch_norm.py │ │ │ ├── blocks.py │ │ │ ├── csrc │ │ │ │ ├── README.md │ │ │ │ ├── ROIAlignRotated │ │ │ │ │ ├── ROIAlignRotated.h │ │ │ │ │ ├── ROIAlignRotated_cpu.cpp │ │ │ │ │ └── ROIAlignRotated_cuda.cu │ │ │ │ ├── box_iou_rotated │ │ │ │ │ ├── box_iou_rotated.h │ │ │ │ │ ├── box_iou_rotated_cpu.cpp │ │ │ │ │ ├── box_iou_rotated_cuda.cu │ │ │ │ │ └── box_iou_rotated_utils.h │ │ │ │ ├── cocoeval │ │ │ │ │ ├── cocoeval.cpp │ │ │ │ │ └── cocoeval.h │ │ │ │ ├── cuda_version.cu │ │ │ │ ├── deformable │ │ │ │ │ ├── deform_conv.h │ │ │ │ │ ├── deform_conv_cuda.cu │ │ │ │ │ └── deform_conv_cuda_kernel.cu │ │ │ │ ├── nms_rotated │ │ │ │ │ ├── nms_rotated.h │ │ │ │ │ ├── nms_rotated_cpu.cpp │ │ │ │ │ └── nms_rotated_cuda.cu │ │ │ │ └── vision.cpp │ │ │ ├── deform_conv.py │ │ │ ├── losses.py │ │ │ ├── mask_ops.py │ │ │ ├── nms.py │ │ │ ├── roi_align.py │ │ │ ├── roi_align_rotated.py │ │ │ ├── rotated_boxes.py │ │ │ ├── shape_spec.py │ │ │ └── wrappers.py │ │ ├── model_zoo │ │ │ ├── __init__.py │ │ │ └── model_zoo.py │ │ ├── modeling │ │ │ ├── __init__.py │ │ │ ├── anchor_generator.py │ │ │ ├── backbone │ │ │ │ ├── __init__.py │ │ │ │ ├── backbone.py │ │ │ │ ├── build.py │ │ │ │ ├── fpn.py │ │ │ │ ├── mvit.py │ │ │ │ ├── regnet.py │ │ │ │ ├── resnet.py │ │ │ │ ├── swin.py │ │ │ │ ├── utils.py │ │ │ │ └── vit.py │ │ │ ├── box_regression.py │ │ │ ├── ema.py │ │ │ ├── matcher.py │ │ │ ├── meta_arch │ │ │ │ ├── __init__.py │ │ │ │ ├── build.py │ │ │ │ ├── dense_detector.py │ │ │ │ ├── fcos.py │ │ │ │ ├── panoptic_fpn.py │ │ │ │ ├── rcnn.py │ │ │ │ ├── retinanet.py │ │ │ │ └── semantic_seg.py │ │ │ ├── mmdet_wrapper.py │ │ │ ├── poolers.py │ │ │ ├── postprocessing.py │ │ │ ├── proposal_generator │ │ │ │ ├── __init__.py │ │ │ │ ├── build.py │ │ │ │ ├── proposal_utils.py │ │ │ │ ├── rpn.py │ │ │ │ └── rrpn.py │ │ │ ├── roi_heads │ │ │ │ ├── __init__.py │ │ │ │ ├── box_head.py │ │ │ │ ├── cascade_rcnn.py │ │ │ │ ├── fast_rcnn.py │ │ │ │ ├── keypoint_head.py │ │ │ │ ├── mask_head.py │ │ │ │ ├── roi_heads.py │ │ │ │ └── rotated_fast_rcnn.py │ │ │ ├── sampling.py │ │ │ └── test_time_augmentation.py │ │ ├── projects │ │ │ ├── README.md │ │ │ └── __init__.py │ │ ├── solver │ │ │ ├── __init__.py │ │ │ ├── build.py │ │ │ └── lr_scheduler.py │ │ ├── structures │ │ │ ├── __init__.py │ │ │ ├── boxes.py │ │ │ ├── image_list.py │ │ │ ├── instances.py │ │ │ ├── keypoints.py │ │ │ ├── masks.py │ │ │ └── rotated_boxes.py │ │ ├── tracking │ │ │ ├── __init__.py │ │ │ ├── base_tracker.py │ │ │ ├── bbox_iou_tracker.py │ │ │ ├── hungarian_tracker.py │ │ │ ├── iou_weighted_hungarian_bbox_iou_tracker.py │ │ │ ├── utils.py │ │ │ └── vanilla_hungarian_bbox_iou_tracker.py │ │ └── utils │ │ │ ├── README.md │ │ │ ├── __init__.py │ │ │ ├── analysis.py │ │ │ ├── collect_env.py │ │ │ ├── colormap.py │ │ │ ├── comm.py │ │ │ ├── develop.py │ │ │ ├── env.py │ │ │ ├── events.py │ │ │ ├── file_io.py │ │ │ ├── logger.py │ │ │ ├── memory.py │ │ │ ├── registry.py │ │ │ ├── serialize.py │ │ │ ├── testing.py │ │ │ ├── tracing.py │ │ │ ├── video_visualizer.py │ │ │ └── visualizer.py │ ├── dev │ │ ├── README.md │ │ ├── linter.sh │ │ ├── packaging │ │ │ ├── README.md │ │ │ ├── build_all_wheels.sh │ │ │ ├── build_wheel.sh │ │ │ ├── gen_install_table.py │ │ │ ├── gen_wheel_index.sh │ │ │ └── pkg_helpers.bash │ │ ├── parse_results.sh │ │ ├── run_inference_tests.sh │ │ └── run_instant_tests.sh │ ├── docker │ │ ├── Dockerfile │ │ ├── README.md │ │ ├── deploy.Dockerfile │ │ └── docker-compose.yml │ ├── docs │ │ ├── .gitignore │ │ ├── Makefile │ │ ├── README.md │ │ ├── _static │ │ │ └── css │ │ │ │ └── custom.css │ │ ├── conf.py │ │ ├── index.rst │ │ ├── modules │ │ │ ├── checkpoint.rst │ │ │ ├── config.rst │ │ │ ├── data.rst │ │ │ ├── data_transforms.rst │ │ │ ├── engine.rst │ │ │ ├── evaluation.rst │ │ │ ├── export.rst │ │ │ ├── fvcore.rst │ │ │ ├── index.rst │ │ │ ├── layers.rst │ │ │ ├── model_zoo.rst │ │ │ ├── modeling.rst │ │ │ ├── solver.rst │ │ │ ├── structures.rst │ │ │ └── utils.rst │ │ ├── notes │ │ │ ├── benchmarks.md │ │ │ ├── changelog.md │ │ │ ├── compatibility.md │ │ │ ├── contributing.md │ │ │ └── index.rst │ │ ├── requirements.txt │ │ └── tutorials │ │ │ ├── README.md │ │ │ ├── augmentation.md │ │ │ ├── builtin_datasets.md │ │ │ ├── configs.md │ │ │ ├── data_loading.md │ │ │ ├── datasets.md │ │ │ ├── deployment.md │ │ │ ├── evaluation.md │ │ │ ├── extend.md │ │ │ ├── getting_started.md │ │ │ ├── index.rst │ │ │ ├── install.md │ │ │ ├── lazyconfigs.md │ │ │ ├── models.md │ │ │ ├── training.md │ │ │ └── write-models.md │ ├── projects │ │ ├── DeepLab │ │ │ ├── README.md │ │ │ ├── configs │ │ │ │ └── Cityscapes-SemanticSegmentation │ │ │ │ │ ├── Base-DeepLabV3-OS16-Semantic.yaml │ │ │ │ │ ├── deeplab_v3_R_103_os16_mg124_poly_90k_bs16.yaml │ │ │ │ │ └── deeplab_v3_plus_R_103_os16_mg124_poly_90k_bs16.yaml │ │ │ ├── deeplab │ │ │ │ ├── __init__.py │ │ │ │ ├── build_solver.py │ │ │ │ ├── config.py │ │ │ │ ├── loss.py │ │ │ │ ├── lr_scheduler.py │ │ │ │ ├── resnet.py │ │ │ │ └── semantic_seg.py │ │ │ └── train_net.py │ │ ├── DensePose │ │ │ ├── README.md │ │ │ ├── apply_net.py │ │ │ ├── configs │ │ │ │ ├── Base-DensePose-RCNN-FPN.yaml │ │ │ │ ├── HRNet │ │ │ │ │ ├── densepose_rcnn_HRFPN_HRNet_w32_s1x.yaml │ │ │ │ │ ├── densepose_rcnn_HRFPN_HRNet_w40_s1x.yaml │ │ │ │ │ └── densepose_rcnn_HRFPN_HRNet_w48_s1x.yaml │ │ │ │ ├── cse │ │ │ │ │ ├── Base-DensePose-RCNN-FPN-Human.yaml │ │ │ │ │ ├── Base-DensePose-RCNN-FPN.yaml │ │ │ │ │ ├── densepose_rcnn_R_101_FPN_DL_s1x.yaml │ │ │ │ │ ├── densepose_rcnn_R_101_FPN_DL_soft_s1x.yaml │ │ │ │ │ ├── densepose_rcnn_R_101_FPN_s1x.yaml │ │ │ │ │ ├── densepose_rcnn_R_101_FPN_soft_s1x.yaml │ │ │ │ │ ├── densepose_rcnn_R_50_FPN_DL_s1x.yaml │ │ │ │ │ ├── densepose_rcnn_R_50_FPN_DL_soft_s1x.yaml │ │ │ │ │ ├── densepose_rcnn_R_50_FPN_s1x.yaml │ │ │ │ │ ├── densepose_rcnn_R_50_FPN_soft_animals_CA_finetune_16k.yaml │ │ │ │ │ ├── densepose_rcnn_R_50_FPN_soft_animals_CA_finetune_4k.yaml │ │ │ │ │ ├── densepose_rcnn_R_50_FPN_soft_animals_I0_finetune_16k.yaml │ │ │ │ │ ├── densepose_rcnn_R_50_FPN_soft_animals_I0_finetune_i2m_16k.yaml │ │ │ │ │ ├── densepose_rcnn_R_50_FPN_soft_animals_I0_finetune_m2m_16k.yaml │ │ │ │ │ ├── densepose_rcnn_R_50_FPN_soft_animals_finetune_16k.yaml │ │ │ │ │ ├── densepose_rcnn_R_50_FPN_soft_animals_finetune_4k.yaml │ │ │ │ │ ├── densepose_rcnn_R_50_FPN_soft_animals_finetune_maskonly_24k.yaml │ │ │ │ │ ├── densepose_rcnn_R_50_FPN_soft_chimps_finetune_4k.yaml │ │ │ │ │ └── densepose_rcnn_R_50_FPN_soft_s1x.yaml │ │ │ │ ├── densepose_rcnn_R_101_FPN_DL_WC1M_s1x.yaml │ │ │ │ ├── densepose_rcnn_R_101_FPN_DL_WC1_s1x.yaml │ │ │ │ ├── densepose_rcnn_R_101_FPN_DL_WC2M_s1x.yaml │ │ │ │ ├── densepose_rcnn_R_101_FPN_DL_WC2_s1x.yaml │ │ │ │ ├── densepose_rcnn_R_101_FPN_DL_s1x.yaml │ │ │ │ ├── densepose_rcnn_R_101_FPN_WC1M_s1x.yaml │ │ │ │ ├── densepose_rcnn_R_101_FPN_WC1_s1x.yaml │ │ │ │ ├── densepose_rcnn_R_101_FPN_WC2M_s1x.yaml │ │ │ │ ├── densepose_rcnn_R_101_FPN_WC2_s1x.yaml │ │ │ │ ├── densepose_rcnn_R_101_FPN_s1x.yaml │ │ │ │ ├── densepose_rcnn_R_101_FPN_s1x_legacy.yaml │ │ │ │ ├── densepose_rcnn_R_50_FPN_DL_WC1M_s1x.yaml │ │ │ │ ├── densepose_rcnn_R_50_FPN_DL_WC1_s1x.yaml │ │ │ │ ├── densepose_rcnn_R_50_FPN_DL_WC2M_s1x.yaml │ │ │ │ ├── densepose_rcnn_R_50_FPN_DL_WC2_s1x.yaml │ │ │ │ ├── densepose_rcnn_R_50_FPN_DL_s1x.yaml │ │ │ │ ├── densepose_rcnn_R_50_FPN_WC1M_s1x.yaml │ │ │ │ ├── densepose_rcnn_R_50_FPN_WC1_s1x.yaml │ │ │ │ ├── densepose_rcnn_R_50_FPN_WC2M_s1x.yaml │ │ │ │ ├── densepose_rcnn_R_50_FPN_WC2_s1x.yaml │ │ │ │ ├── densepose_rcnn_R_50_FPN_s1x.yaml │ │ │ │ ├── densepose_rcnn_R_50_FPN_s1x_legacy.yaml │ │ │ │ ├── evolution │ │ │ │ │ ├── Base-RCNN-FPN-Atop10P_CA.yaml │ │ │ │ │ ├── densepose_R_50_FPN_DL_WC1M_3x_Atop10P_CA.yaml │ │ │ │ │ ├── densepose_R_50_FPN_DL_WC1M_3x_Atop10P_CA_B_coarsesegm.yaml │ │ │ │ │ ├── densepose_R_50_FPN_DL_WC1M_3x_Atop10P_CA_B_finesegm.yaml │ │ │ │ │ ├── densepose_R_50_FPN_DL_WC1M_3x_Atop10P_CA_B_uniform.yaml │ │ │ │ │ └── densepose_R_50_FPN_DL_WC1M_3x_Atop10P_CA_B_uv.yaml │ │ │ │ └── quick_schedules │ │ │ │ │ ├── cse │ │ │ │ │ ├── densepose_rcnn_R_50_FPN_DL_instant_test.yaml │ │ │ │ │ └── densepose_rcnn_R_50_FPN_soft_animals_finetune_instant_test.yaml │ │ │ │ │ ├── densepose_rcnn_HRFPN_HRNet_w32_instant_test.yaml │ │ │ │ │ ├── densepose_rcnn_R_50_FPN_DL_instant_test.yaml │ │ │ │ │ ├── densepose_rcnn_R_50_FPN_TTA_inference_acc_test.yaml │ │ │ │ │ ├── densepose_rcnn_R_50_FPN_WC1_instant_test.yaml │ │ │ │ │ ├── densepose_rcnn_R_50_FPN_WC2_instant_test.yaml │ │ │ │ │ ├── densepose_rcnn_R_50_FPN_inference_acc_test.yaml │ │ │ │ │ ├── densepose_rcnn_R_50_FPN_instant_test.yaml │ │ │ │ │ └── densepose_rcnn_R_50_FPN_training_acc_test.yaml │ │ │ ├── densepose │ │ │ │ ├── __init__.py │ │ │ │ ├── config.py │ │ │ │ ├── converters │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── base.py │ │ │ │ │ ├── builtin.py │ │ │ │ │ ├── chart_output_hflip.py │ │ │ │ │ ├── chart_output_to_chart_result.py │ │ │ │ │ ├── hflip.py │ │ │ │ │ ├── segm_to_mask.py │ │ │ │ │ ├── to_chart_result.py │ │ │ │ │ └── to_mask.py │ │ │ │ ├── data │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── build.py │ │ │ │ │ ├── combined_loader.py │ │ │ │ │ ├── dataset_mapper.py │ │ │ │ │ ├── image_list_dataset.py │ │ │ │ │ ├── inference_based_loader.py │ │ │ │ │ ├── meshes │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── builtin.py │ │ │ │ │ │ └── catalog.py │ │ │ │ │ ├── samplers │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── densepose_base.py │ │ │ │ │ │ ├── densepose_confidence_based.py │ │ │ │ │ │ ├── densepose_cse_base.py │ │ │ │ │ │ ├── densepose_cse_confidence_based.py │ │ │ │ │ │ ├── densepose_cse_uniform.py │ │ │ │ │ │ ├── densepose_uniform.py │ │ │ │ │ │ ├── mask_from_densepose.py │ │ │ │ │ │ └── prediction_to_gt.py │ │ │ │ │ ├── transform │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ └── image.py │ │ │ │ │ ├── utils.py │ │ │ │ │ └── video │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── frame_selector.py │ │ │ │ │ │ └── video_keyframe_dataset.py │ │ │ │ ├── engine │ │ │ │ │ ├── __init__.py │ │ │ │ │ └── trainer.py │ │ │ │ ├── evaluation │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── d2_evaluator_adapter.py │ │ │ │ │ ├── densepose_coco_evaluation.py │ │ │ │ │ ├── evaluator.py │ │ │ │ │ ├── mesh_alignment_evaluator.py │ │ │ │ │ └── tensor_storage.py │ │ │ │ ├── modeling │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── build.py │ │ │ │ │ ├── confidence.py │ │ │ │ │ ├── cse │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── embedder.py │ │ │ │ │ │ ├── utils.py │ │ │ │ │ │ ├── vertex_direct_embedder.py │ │ │ │ │ │ └── vertex_feature_embedder.py │ │ │ │ │ ├── densepose_checkpoint.py │ │ │ │ │ ├── filter.py │ │ │ │ │ ├── hrfpn.py │ │ │ │ │ ├── hrnet.py │ │ │ │ │ ├── inference.py │ │ │ │ │ ├── losses │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── chart.py │ │ │ │ │ │ ├── chart_with_confidences.py │ │ │ │ │ │ ├── cse.py │ │ │ │ │ │ ├── cycle_pix2shape.py │ │ │ │ │ │ ├── cycle_shape2shape.py │ │ │ │ │ │ ├── embed.py │ │ │ │ │ │ ├── embed_utils.py │ │ │ │ │ │ ├── mask.py │ │ │ │ │ │ ├── mask_or_segm.py │ │ │ │ │ │ ├── registry.py │ │ │ │ │ │ ├── segm.py │ │ │ │ │ │ ├── soft_embed.py │ │ │ │ │ │ └── utils.py │ │ │ │ │ ├── predictors │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── chart.py │ │ │ │ │ │ ├── chart_confidence.py │ │ │ │ │ │ ├── chart_with_confidence.py │ │ │ │ │ │ ├── cse.py │ │ │ │ │ │ ├── cse_confidence.py │ │ │ │ │ │ ├── cse_with_confidence.py │ │ │ │ │ │ └── registry.py │ │ │ │ │ ├── roi_heads │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── deeplab.py │ │ │ │ │ │ ├── registry.py │ │ │ │ │ │ ├── roi_head.py │ │ │ │ │ │ └── v1convx.py │ │ │ │ │ ├── test_time_augmentation.py │ │ │ │ │ └── utils.py │ │ │ │ ├── structures │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── chart.py │ │ │ │ │ ├── chart_confidence.py │ │ │ │ │ ├── chart_result.py │ │ │ │ │ ├── cse.py │ │ │ │ │ ├── cse_confidence.py │ │ │ │ │ ├── data_relative.py │ │ │ │ │ ├── list.py │ │ │ │ │ ├── mesh.py │ │ │ │ │ └── transform_data.py │ │ │ │ ├── utils │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── dbhelper.py │ │ │ │ │ ├── logger.py │ │ │ │ │ └── transform.py │ │ │ │ └── vis │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── base.py │ │ │ │ │ ├── bounding_box.py │ │ │ │ │ ├── densepose_data_points.py │ │ │ │ │ ├── densepose_outputs_iuv.py │ │ │ │ │ ├── densepose_outputs_vertex.py │ │ │ │ │ ├── densepose_results.py │ │ │ │ │ ├── densepose_results_textures.py │ │ │ │ │ └── extractor.py │ │ │ ├── dev │ │ │ │ ├── README.md │ │ │ │ ├── run_inference_tests.sh │ │ │ │ └── run_instant_tests.sh │ │ │ ├── doc │ │ │ │ ├── BOOTSTRAPPING_PIPELINE.md │ │ │ │ ├── DENSEPOSE_CSE.md │ │ │ │ ├── DENSEPOSE_DATASETS.md │ │ │ │ ├── DENSEPOSE_IUV.md │ │ │ │ ├── GETTING_STARTED.md │ │ │ │ ├── RELEASE_2020_04.md │ │ │ │ ├── RELEASE_2021_03.md │ │ │ │ ├── RELEASE_2021_06.md │ │ │ │ ├── TOOL_APPLY_NET.md │ │ │ │ └── TOOL_QUERY_DB.md │ │ │ ├── query_db.py │ │ │ ├── setup.py │ │ │ ├── tests │ │ │ │ ├── common.py │ │ │ │ ├── test_chart_based_annotations_accumulator.py │ │ │ │ ├── test_combine_data_loader.py │ │ │ │ ├── test_cse_annotations_accumulator.py │ │ │ │ ├── test_dataset_loaded_annotations.py │ │ │ │ ├── test_frame_selector.py │ │ │ │ ├── test_image_list_dataset.py │ │ │ │ ├── test_image_resize_transform.py │ │ │ │ ├── test_model_e2e.py │ │ │ │ ├── test_setup.py │ │ │ │ ├── test_structures.py │ │ │ │ ├── test_tensor_storage.py │ │ │ │ └── test_video_keyframe_dataset.py │ │ │ └── train_net.py │ │ ├── MViTv2 │ │ │ ├── README.md │ │ │ └── configs │ │ │ │ ├── cascade_mask_rcnn_mvitv2_b_3x.py │ │ │ │ ├── cascade_mask_rcnn_mvitv2_b_in21k_3x.py │ │ │ │ ├── cascade_mask_rcnn_mvitv2_h_in21k_lsj_3x.py │ │ │ │ ├── cascade_mask_rcnn_mvitv2_l_in21k_lsj_50ep.py │ │ │ │ ├── cascade_mask_rcnn_mvitv2_s_3x.py │ │ │ │ ├── cascade_mask_rcnn_mvitv2_t_3x.py │ │ │ │ ├── common │ │ │ │ ├── coco_loader.py │ │ │ │ └── coco_loader_lsj.py │ │ │ │ └── mask_rcnn_mvitv2_t_3x.py │ │ ├── Panoptic-DeepLab │ │ │ ├── README.md │ │ │ ├── configs │ │ │ │ ├── COCO-PanopticSegmentation │ │ │ │ │ └── panoptic_deeplab_R_52_os16_mg124_poly_200k_bs64_crop_640_640_coco_dsconv.yaml │ │ │ │ └── Cityscapes-PanopticSegmentation │ │ │ │ │ ├── Base-PanopticDeepLab-OS16.yaml │ │ │ │ │ ├── panoptic_deeplab_R_52_os16_mg124_poly_90k_bs32_crop_512_1024.yaml │ │ │ │ │ └── panoptic_deeplab_R_52_os16_mg124_poly_90k_bs32_crop_512_1024_dsconv.yaml │ │ │ ├── panoptic_deeplab │ │ │ │ ├── __init__.py │ │ │ │ ├── config.py │ │ │ │ ├── dataset_mapper.py │ │ │ │ ├── panoptic_seg.py │ │ │ │ ├── post_processing.py │ │ │ │ └── target_generator.py │ │ │ └── train_net.py │ │ ├── PointRend │ │ │ ├── README.md │ │ │ ├── configs │ │ │ │ ├── InstanceSegmentation │ │ │ │ │ ├── Base-Implicit-PointRend.yaml │ │ │ │ │ ├── Base-PointRend-RCNN-FPN.yaml │ │ │ │ │ ├── implicit_pointrend_R_50_FPN_1x_coco.yaml │ │ │ │ │ ├── implicit_pointrend_R_50_FPN_3x_coco.yaml │ │ │ │ │ ├── pointrend_rcnn_R_101_FPN_3x_coco.yaml │ │ │ │ │ ├── pointrend_rcnn_R_50_FPN_1x_cityscapes.yaml │ │ │ │ │ ├── pointrend_rcnn_R_50_FPN_1x_coco.yaml │ │ │ │ │ ├── pointrend_rcnn_R_50_FPN_3x_coco.yaml │ │ │ │ │ └── pointrend_rcnn_X_101_32x8d_FPN_3x_coco.yaml │ │ │ │ └── SemanticSegmentation │ │ │ │ │ ├── Base-PointRend-Semantic-FPN.yaml │ │ │ │ │ └── pointrend_semantic_R_101_FPN_1x_cityscapes.yaml │ │ │ ├── point_rend │ │ │ │ ├── __init__.py │ │ │ │ ├── color_augmentation.py │ │ │ │ ├── config.py │ │ │ │ ├── mask_head.py │ │ │ │ ├── point_features.py │ │ │ │ ├── point_head.py │ │ │ │ ├── roi_heads.py │ │ │ │ └── semantic_seg.py │ │ │ └── train_net.py │ │ ├── PointSup │ │ │ ├── README.md │ │ │ ├── configs │ │ │ │ ├── implicit_pointrend_R_50_FPN_3x_point_sup_point_aug_coco.yaml │ │ │ │ ├── mask_rcnn_R_50_FPN_3x_point_sup_coco.yaml │ │ │ │ └── mask_rcnn_R_50_FPN_3x_point_sup_point_aug_coco.yaml │ │ │ ├── point_sup │ │ │ │ ├── __init__.py │ │ │ │ ├── config.py │ │ │ │ ├── dataset_mapper.py │ │ │ │ ├── detection_utils.py │ │ │ │ ├── mask_head.py │ │ │ │ ├── point_utils.py │ │ │ │ └── register_point_annotations.py │ │ │ ├── tools │ │ │ │ └── prepare_coco_point_annotations_without_masks.py │ │ │ └── train_net.py │ │ ├── Rethinking-BatchNorm │ │ │ ├── README.md │ │ │ ├── configs │ │ │ │ ├── mask_rcnn_BNhead.py │ │ │ │ ├── mask_rcnn_BNhead_batch_stats.py │ │ │ │ ├── mask_rcnn_BNhead_shuffle.py │ │ │ │ ├── mask_rcnn_SyncBNhead.py │ │ │ │ ├── retinanet_SyncBNhead.py │ │ │ │ └── retinanet_SyncBNhead_SharedTraining.py │ │ │ └── retinanet-eval-domain-specific.py │ │ ├── TensorMask │ │ │ ├── README.md │ │ │ ├── configs │ │ │ │ ├── Base-TensorMask.yaml │ │ │ │ ├── tensormask_R_50_FPN_1x.yaml │ │ │ │ └── tensormask_R_50_FPN_6x.yaml │ │ │ ├── setup.py │ │ │ ├── tensormask │ │ │ │ ├── __init__.py │ │ │ │ ├── arch.py │ │ │ │ ├── config.py │ │ │ │ └── layers │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── csrc │ │ │ │ │ ├── SwapAlign2Nat │ │ │ │ │ │ ├── SwapAlign2Nat.h │ │ │ │ │ │ └── SwapAlign2Nat_cuda.cu │ │ │ │ │ └── vision.cpp │ │ │ │ │ └── swap_align2nat.py │ │ │ ├── tests │ │ │ │ ├── __init__.py │ │ │ │ └── test_swap_align2nat.py │ │ │ └── train_net.py │ │ ├── TridentNet │ │ │ ├── README.md │ │ │ ├── configs │ │ │ │ ├── Base-TridentNet-Fast-C4.yaml │ │ │ │ ├── tridentnet_fast_R_101_C4_3x.yaml │ │ │ │ ├── tridentnet_fast_R_50_C4_1x.yaml │ │ │ │ └── tridentnet_fast_R_50_C4_3x.yaml │ │ │ ├── train_net.py │ │ │ └── tridentnet │ │ │ │ ├── __init__.py │ │ │ │ ├── config.py │ │ │ │ ├── trident_backbone.py │ │ │ │ ├── trident_conv.py │ │ │ │ ├── trident_rcnn.py │ │ │ │ └── trident_rpn.py │ │ └── ViTDet │ │ │ └── configs │ │ │ ├── COCO │ │ │ ├── cascade_mask_rcnn_mvitv2_b_in21k_100ep.py │ │ │ ├── cascade_mask_rcnn_mvitv2_h_in21k_36ep.py │ │ │ ├── cascade_mask_rcnn_mvitv2_l_in21k_50ep.py │ │ │ ├── cascade_mask_rcnn_swin_b_in21k_50ep.py │ │ │ ├── cascade_mask_rcnn_swin_l_in21k_50ep.py │ │ │ ├── cascade_mask_rcnn_vitdet_1B_1536.py │ │ │ ├── cascade_mask_rcnn_vitdet_1B_75ep.py │ │ │ ├── cascade_mask_rcnn_vitdet_1B_attn.py │ │ │ ├── cascade_mask_rcnn_vitdet_1B_attn_1280.py │ │ │ ├── cascade_mask_rcnn_vitdet_1B_attn_1408.py │ │ │ ├── cascade_mask_rcnn_vitdet_1B_attn_1536.py │ │ │ ├── cascade_mask_rcnn_vitdet_1B_attn_1664.py │ │ │ ├── cascade_mask_rcnn_vitdet_1B_attn_1792.py │ │ │ ├── cascade_mask_rcnn_vitdet_1B_attn_1920.py │ │ │ ├── cascade_mask_rcnn_vitdet_1B_attn_2048.py │ │ │ ├── cascade_mask_rcnn_vitdet_1B_attn_2176.py │ │ │ ├── cascade_mask_rcnn_vitdet_1B_attn_2304.py │ │ │ ├── cascade_mask_rcnn_vitdet_1B_attn_2432.py │ │ │ ├── cascade_mask_rcnn_vitdet_1B_attn_2560.py │ │ │ ├── cascade_mask_rcnn_vitdet_b_100ep.py │ │ │ ├── cascade_mask_rcnn_vitdet_b_ours.py │ │ │ ├── cascade_mask_rcnn_vitdet_h_75ep.py │ │ │ ├── cascade_mask_rcnn_vitdet_h_75ep_1024.py │ │ │ ├── cascade_mask_rcnn_vitdet_h_75ep_1280.py │ │ │ ├── cascade_mask_rcnn_vitdet_h_75ep_1408.py │ │ │ ├── cascade_mask_rcnn_vitdet_h_75ep_1536.py │ │ │ ├── cascade_mask_rcnn_vitdet_h_75ep_1664.py │ │ │ ├── cascade_mask_rcnn_vitdet_h_75ep_1792.py │ │ │ ├── cascade_mask_rcnn_vitdet_h_75ep_1920.py │ │ │ ├── cascade_mask_rcnn_vitdet_h_75ep_2048.py │ │ │ ├── cascade_mask_rcnn_vitdet_h_75ep_2176.py │ │ │ ├── cascade_mask_rcnn_vitdet_h_75ep_2304.py │ │ │ ├── cascade_mask_rcnn_vitdet_h_75ep_2432.py │ │ │ ├── cascade_mask_rcnn_vitdet_h_75ep_2560.py │ │ │ ├── cascade_mask_rcnn_vitdet_h_75ep_conv.py │ │ │ ├── cascade_mask_rcnn_vitdet_l_100ep.py │ │ │ ├── mask_rcnn_vitdet_b_100ep.py │ │ │ ├── mask_rcnn_vitdet_h_75ep.py │ │ │ ├── mask_rcnn_vitdet_l_100ep.py │ │ │ ├── o365_cascade_mask_rcnn_vitdet_1B_attn.py │ │ │ ├── o365_cascade_mask_rcnn_vitdet_1B_attn_1024.py │ │ │ └── o365_cascade_mask_rcnn_vitdet_1B_attn_1024to1280.py │ │ │ ├── LVIS │ │ │ ├── cascade_mask_rcnn_mvitv2_b_in21k_100ep.py │ │ │ ├── cascade_mask_rcnn_mvitv2_h_in21k_50ep.py │ │ │ ├── cascade_mask_rcnn_mvitv2_l_in21k_50ep.py │ │ │ ├── cascade_mask_rcnn_swin_b_in21k_50ep.py │ │ │ ├── cascade_mask_rcnn_swin_l_in21k_50ep.py │ │ │ ├── cascade_mask_rcnn_vitdet_1B_attn.py │ │ │ ├── cascade_mask_rcnn_vitdet_1B_attn.py.bak │ │ │ ├── cascade_mask_rcnn_vitdet_1B_attn_1536.py │ │ │ ├── cascade_mask_rcnn_vitdet_b_100ep.py │ │ │ ├── cascade_mask_rcnn_vitdet_h_100ep.py │ │ │ ├── cascade_mask_rcnn_vitdet_l_100ep.py │ │ │ ├── mask_rcnn_vitdet_b_100ep.py │ │ │ ├── mask_rcnn_vitdet_h_100ep.py │ │ │ └── mask_rcnn_vitdet_l_100ep.py │ │ │ ├── Objects365 │ │ │ └── mask_rcnn_vitdet_b_100ep.py │ │ │ ├── common │ │ │ ├── coco_loader_lsj.py │ │ │ ├── coco_loader_lsj_1024.py │ │ │ ├── coco_loader_lsj_1280.py │ │ │ ├── coco_loader_lsj_1408.py │ │ │ ├── coco_loader_lsj_1536.py │ │ │ ├── coco_loader_lsj_1664.py │ │ │ ├── coco_loader_lsj_1792.py │ │ │ ├── coco_loader_lsj_1920.py │ │ │ ├── coco_loader_lsj_2048.py │ │ │ ├── coco_loader_lsj_2176.py │ │ │ ├── coco_loader_lsj_2304.py │ │ │ ├── coco_loader_lsj_2432.py │ │ │ ├── coco_loader_lsj_2560.py │ │ │ ├── objects365_loader_lsj.py │ │ │ ├── objects365_loader_lsj_1280.py │ │ │ ├── objects365_trainval_loader_lsj_1024.py │ │ │ ├── objects365_trainval_loader_lsj_1280.py │ │ │ └── objects365_trainval_loader_lsj_1536.py │ │ │ ├── eva2_mim_to_coco │ │ │ ├── cascade_mask_rcnn_vitdet_b_100ep.py │ │ │ ├── eva2_coco_cascade_mask_rcnn_vitdet_b_4attn_1024_lrd0p7.py │ │ │ ├── eva2_coco_cascade_mask_rcnn_vitdet_b_6attn_win32_1536_lrd0p7.py │ │ │ ├── eva2_coco_cascade_mask_rcnn_vitdet_l_4attn_1024_lrd0p8.py │ │ │ ├── eva2_coco_cascade_mask_rcnn_vitdet_l_8attn_win32_1536_lrd0p8.py │ │ │ └── mask_rcnn_vitdet_b_100ep.py │ │ │ ├── eva2_mim_to_lvis │ │ │ ├── cascade_mask_rcnn_vitdet_b_100ep.py │ │ │ ├── eva2_lvis_cascade_mask_rcnn_vitdet_b_4attn_1024_lrd0p7.py │ │ │ ├── eva2_lvis_cascade_mask_rcnn_vitdet_l_4attn_1024_lrd0p8.py │ │ │ ├── eva2_lvis_cascade_mask_rcnn_vitdet_l_8attn_win32_1536_lrd0p8.py │ │ │ └── mask_rcnn_vitdet_b_100ep.py │ │ │ ├── eva2_o365 │ │ │ ├── cascade_mask_rcnn_vitdet_b_100ep.py │ │ │ ├── eva2_o365_cascade_mask_rcnn_vitdet_l_8attn_1536_lrd0p8.py │ │ │ └── mask_rcnn_vitdet_b_100ep.py │ │ │ ├── eva2_o365_to_coco │ │ │ ├── cascade_mask_rcnn_vitdet_b_100ep.py │ │ │ ├── eva2_o365_to_coco_cascade_mask_rcnn_vitdet_l_8attn_1536_lrd0p8.py │ │ │ └── mask_rcnn_vitdet_b_100ep.py │ │ │ └── eva2_o365_to_lvis │ │ │ ├── cascade_mask_rcnn_vitdet_b_100ep.py │ │ │ ├── eva2_o365_to_lvis_cascade_mask_rcnn_vitdet_l_8attn_1536_lrd0p8.py │ │ │ └── mask_rcnn_vitdet_b_100ep.py │ ├── setup.cfg │ ├── setup.py │ ├── tests │ │ ├── README.md │ │ ├── __init__.py │ │ ├── config │ │ │ ├── dir1 │ │ │ │ ├── dir1_a.py │ │ │ │ └── dir1_b.py │ │ │ ├── root_cfg.py │ │ │ ├── test_instantiate_config.py │ │ │ ├── test_lazy_config.py │ │ │ └── test_yacs_config.py │ │ ├── data │ │ │ ├── __init__.py │ │ │ ├── test_coco.py │ │ │ ├── test_coco_evaluation.py │ │ │ ├── test_dataset.py │ │ │ ├── test_detection_utils.py │ │ │ ├── test_rotation_transform.py │ │ │ ├── test_sampler.py │ │ │ └── test_transforms.py │ │ ├── export │ │ │ └── test_c10.py │ │ ├── layers │ │ │ ├── __init__.py │ │ │ ├── test_blocks.py │ │ │ ├── test_deformable.py │ │ │ ├── test_losses.py │ │ │ ├── test_mask_ops.py │ │ │ ├── test_nms.py │ │ │ ├── test_nms_rotated.py │ │ │ ├── test_roi_align.py │ │ │ └── test_roi_align_rotated.py │ │ ├── modeling │ │ │ ├── __init__.py │ │ │ ├── test_anchor_generator.py │ │ │ ├── test_backbone.py │ │ │ ├── test_box2box_transform.py │ │ │ ├── test_fast_rcnn.py │ │ │ ├── test_matcher.py │ │ │ ├── test_mmdet.py │ │ │ ├── test_model_e2e.py │ │ │ ├── test_roi_heads.py │ │ │ ├── test_roi_pooler.py │ │ │ └── test_rpn.py │ │ ├── structures │ │ │ ├── __init__.py │ │ │ ├── test_boxes.py │ │ │ ├── test_imagelist.py │ │ │ ├── test_instances.py │ │ │ ├── test_keypoints.py │ │ │ ├── test_masks.py │ │ │ └── test_rotated_boxes.py │ │ ├── test_checkpoint.py │ │ ├── test_engine.py │ │ ├── test_events.py │ │ ├── test_export_caffe2.py │ │ ├── test_export_onnx.py │ │ ├── test_export_torchscript.py │ │ ├── test_model_analysis.py │ │ ├── test_model_zoo.py │ │ ├── test_packaging.py │ │ ├── test_registry.py │ │ ├── test_scheduler.py │ │ ├── test_solver.py │ │ ├── test_visualizer.py │ │ └── tracking │ │ │ ├── __init__.py │ │ │ ├── test_bbox_iou_tracker.py │ │ │ ├── test_hungarian_tracker.py │ │ │ ├── test_iou_weighted_hungarian_bbox_iou_tracker.py │ │ │ └── test_vanilla_hungarian_bbox_iou_tracker.py │ └── tools │ │ ├── README.md │ │ ├── __init__.py │ │ ├── analyze_model.py │ │ ├── benchmark.py │ │ ├── convert-torchvision-to-d2.py │ │ ├── deploy │ │ ├── CMakeLists.txt │ │ ├── README.md │ │ ├── export_model.py │ │ └── torchscript_mask_rcnn.cpp │ │ ├── lazyconfig_train_net.py │ │ ├── lightning_train_net.py │ │ ├── plain_train_net.py │ │ ├── train_net.py │ │ ├── visualize_data.py │ │ └── visualize_json_results.py └── seg │ ├── README.md │ ├── backbone │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-38.pyc │ │ ├── beit.cpython-38.pyc │ │ └── eva2.cpython-38.pyc │ └── eva2.py │ ├── configs │ ├── _base_ │ │ ├── datasets │ │ │ ├── ade20k.py │ │ │ ├── ade20k_640x640.py │ │ │ └── ade20k_ms_eval.py │ │ ├── default_runtime.py │ │ ├── models │ │ │ └── upernet_beit.py │ │ └── schedules │ │ │ ├── schedule_60k.py │ │ │ └── schedule_80k.py │ └── eva02 │ │ └── upernet │ │ ├── upernet_eva02_base_12_512_slide_60k.py │ │ ├── upernet_eva02_large_24_512_slide_80k.py │ │ ├── upernet_eva02_large_24_512_slide_ms_eval.py │ │ └── upernetpro_eva02_large_24_640_slide_80k.py │ ├── mmcv_custom │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-38.pyc │ │ ├── checkpoint.cpython-38.pyc │ │ ├── layer_decay_optimizer_constructor.cpython-38.pyc │ │ ├── resize_transform.cpython-38.pyc │ │ └── train_api.cpython-38.pyc │ ├── apex_runner │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── apex_iter_based_runner.cpython-38.pyc │ │ │ ├── checkpoint.cpython-38.pyc │ │ │ └── optimizer.cpython-38.pyc │ │ ├── apex_iter_based_runner.py │ │ ├── checkpoint.py │ │ └── optimizer.py │ ├── checkpoint.py │ ├── layer_decay_optimizer_constructor.py │ ├── resize_transform.py │ └── train_api.py │ ├── test.py │ ├── train.py │ └── work_dirs │ ├── cfg.log │ ├── upernet_eva02_base_12_512_slide_60k │ ├── 20230318_173313.log │ ├── 20230318_173313.log.json │ └── upernet_eva02_base_12_512_slide_60k.py │ ├── upernet_eva02_large_24_512_slide_80k │ └── eval_single_scale_20230318_171005.json │ ├── upernet_eva02_large_24_512_slide_ms_eval │ └── eval_single_scale_20230318_180229.json │ ├── upernet_eva2_base_12_512_slide_60k │ └── eval_single_scale_20230318_165835.json │ ├── upernet_eva2_large_24_512_slide_ms_eval │ ├── ss58p9_ms59p6_eval_single_scale_20230115_134820.json │ └── ss59p8_ms60p4_eval_single_scale_20230117_214452.json │ ├── upernet_eva2_large_24_896_slide_ms_eval │ └── ss59p2_ms59p6_eval_single_scale_20230115_113919.json │ ├── upernetpro_eva02_large_24_640_slide_80k │ └── eval_single_scale_20230318_171859.json │ └── upernetpro_eva2_large_24_640_slide_ms_eval │ ├── best_ckpt_ss60p2_ms60p5_eval_single_scale_20230118_083618.json │ ├── eval_single_scale_20230118_113958.json │ ├── eval_single_scale_20230118_133101.json │ └── eval_single_scale_20230118_134935.json ├── EVA-CLIP-18B ├── .gitignore ├── README.md ├── assets │ ├── .DS_Store │ ├── CLIP.png │ └── teaser.png ├── requirements.txt └── shinji │ ├── eva_clip │ ├── __init__.py │ ├── bpe_simple_vocab_16e6.txt.gz │ ├── constants.py │ ├── eva_vit_model.py │ ├── factory.py │ ├── hf_configs.py │ ├── hf_model.py │ ├── loss.py │ ├── model.py │ ├── model_configs │ │ ├── EVA-CLIP-18B.json │ │ ├── EVA-CLIP-8B-plus.json │ │ ├── EVA-CLIP-8B.json │ │ ├── EVA01-CLIP-B-16.json │ │ ├── EVA01-CLIP-g-14-plus.json │ │ ├── EVA01-CLIP-g-14.json │ │ ├── EVA02-CLIP-B-16.json │ │ ├── EVA02-CLIP-L-14-336.json │ │ ├── EVA02-CLIP-L-14.json │ │ ├── EVA02-CLIP-bigE-14-plus.json │ │ └── EVA02-CLIP-bigE-14.json │ ├── modified_resnet.py │ ├── openai.py │ ├── pretrained.py │ ├── rope.py │ ├── timm_model.py │ ├── tokenizer.py │ ├── transform.py │ ├── transformer.py │ └── utils.py │ └── training │ ├── __init__.py │ ├── adan.py │ ├── anyprecision_optimizer.py │ ├── data.py │ ├── deepspeed_config.json │ ├── distributed.py │ ├── fp16.py │ ├── imagenet_zeroshot_data.py │ ├── interpolate_patch14_224to336.py │ ├── interpolate_patch_14to16.py │ ├── lamb.py │ ├── lion.py │ ├── logger.py │ ├── main.py │ ├── optim.py │ ├── params.py │ ├── precision.py │ ├── scheduler.py │ ├── train.py │ ├── utils.py │ └── zero_shot.py ├── EVA-CLIP ├── .gitignore ├── README.md ├── assets │ ├── .DS_Store │ ├── CLIP.png │ └── teaser.png ├── rei │ ├── eva_clip │ │ ├── __init__.py │ │ ├── bpe_simple_vocab_16e6.txt.gz │ │ ├── constants.py │ │ ├── eva_vit_model.py │ │ ├── factory.py │ │ ├── hf_configs.py │ │ ├── hf_model.py │ │ ├── loss.py │ │ ├── model.py │ │ ├── model_configs │ │ │ ├── EVA01-CLIP-B-16.json │ │ │ ├── EVA01-CLIP-g-14-plus.json │ │ │ ├── EVA01-CLIP-g-14.json │ │ │ ├── EVA02-CLIP-B-16.json │ │ │ ├── EVA02-CLIP-L-14-336.json │ │ │ ├── EVA02-CLIP-L-14.json │ │ │ ├── EVA02-CLIP-bigE-14-plus.json │ │ │ └── EVA02-CLIP-bigE-14.json │ │ ├── modified_resnet.py │ │ ├── openai.py │ │ ├── pretrained.py │ │ ├── rope.py │ │ ├── timm_model.py │ │ ├── tokenizer.py │ │ ├── transform.py │ │ ├── transformer.py │ │ └── utils.py │ └── training │ │ ├── __init__.py │ │ ├── adan.py │ │ ├── anyprecision_optimizer.py │ │ ├── data.py │ │ ├── deepspeed_config.json │ │ ├── distributed.py │ │ ├── fp16.py │ │ ├── imagenet_zeroshot_data.py │ │ ├── interpolate_patch14_224to336.py │ │ ├── interpolate_patch_14to16.py │ │ ├── lamb.py │ │ ├── lion.py │ │ ├── logger.py │ │ ├── main.py │ │ ├── optim.py │ │ ├── params.py │ │ ├── precision.py │ │ ├── scheduler.py │ │ ├── train.py │ │ ├── utils.py │ │ └── zero_shot.py └── requirements.txt ├── LICENSE ├── README.md └── assets └── eva_pipeline.png /EVA-01/clip/CLIP.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/clip/CLIP.png -------------------------------------------------------------------------------- /EVA-01/clip/__init__.py: -------------------------------------------------------------------------------- 1 | from .clip import * 2 | from .eva_clip import * -------------------------------------------------------------------------------- /EVA-01/clip/bpe_simple_vocab_16e6.txt.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/clip/bpe_simple_vocab_16e6.txt.gz -------------------------------------------------------------------------------- /EVA-01/clip/model_configs/EVA_CLIP_g_14.json: -------------------------------------------------------------------------------- 1 | { 2 | "embed_dim": 1024, 3 | "vision_cfg": { 4 | "image_size": 224, 5 | "layers": 40, 6 | "width": 1408, 7 | "head_width": 88, 8 | "mlp_ratio": 4.3637, 9 | "patch_size": 14, 10 | "drop_path_rate": 0.4 11 | }, 12 | "text_cfg": { 13 | "context_length": 77, 14 | "vocab_size": 49408, 15 | "width": 768, 16 | "heads": 12, 17 | "layers": 12 18 | } 19 | } -------------------------------------------------------------------------------- /EVA-01/det/.flake8: -------------------------------------------------------------------------------- 1 | # This is an example .flake8 config, used when developing *Black* itself. 2 | # Keep in sync with setup.cfg which is used for source packages. 3 | 4 | [flake8] 5 | ignore = W503, E203, E221, C901, C408, E741, C407, B017, F811, C101, EXE001, EXE002 6 | max-line-length = 100 7 | max-complexity = 18 8 | select = B,C,E,F,W,T4,B9 9 | exclude = build 10 | per-file-ignores = 11 | **/__init__.py:F401,F403,E402 12 | **/configs/**.py:F401,E402 13 | configs/**.py:F401,E402 14 | **/tests/config/**.py:F401,E402 15 | tests/config/**.py:F401,E402 16 | -------------------------------------------------------------------------------- /EVA-01/det/.github/CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Code of Conduct 2 | 3 | Facebook has adopted a Code of Conduct that we expect project participants to adhere to. 4 | Please read the [full text](https://code.fb.com/codeofconduct/) 5 | so that you can understand what actions will and will not be tolerated. 6 | -------------------------------------------------------------------------------- /EVA-01/det/.github/ISSUE_TEMPLATE.md: -------------------------------------------------------------------------------- 1 | 2 | Please select an issue template from 3 | https://github.com/facebookresearch/detectron2/issues/new/choose . 4 | 5 | Otherwise your issue will be closed. 6 | -------------------------------------------------------------------------------- /EVA-01/det/.github/ISSUE_TEMPLATE/documentation.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: "\U0001F4DA Documentation Issue" 3 | about: Report a problem about existing documentation, comments, website or tutorials. 4 | labels: documentation 5 | 6 | --- 7 | 8 | ## 📚 Documentation Issue 9 | 10 | This issue category is for problems about existing documentation, not for asking how-to questions. 11 | 12 | * Provide a link to an existing documentation/comment/tutorial: 13 | 14 | * How should the above documentation/comment/tutorial improve: 15 | -------------------------------------------------------------------------------- /EVA-01/det/.github/pull_request_template.md: -------------------------------------------------------------------------------- 1 | Thanks for your contribution! 2 | 3 | If you're sending a large PR (e.g., >100 lines), 4 | please open an issue first about the feature / bug, and indicate how you want to contribute. 5 | 6 | We do not always accept features. 7 | See https://detectron2.readthedocs.io/notes/contributing.html#pull-requests about how we handle PRs. 8 | 9 | Before submitting a PR, please run `dev/linter.sh` to lint the code. 10 | 11 | -------------------------------------------------------------------------------- /EVA-01/det/configs/Base-RCNN-C4.yaml: -------------------------------------------------------------------------------- 1 | MODEL: 2 | META_ARCHITECTURE: "GeneralizedRCNN" 3 | RPN: 4 | PRE_NMS_TOPK_TEST: 6000 5 | POST_NMS_TOPK_TEST: 1000 6 | ROI_HEADS: 7 | NAME: "Res5ROIHeads" 8 | DATASETS: 9 | TRAIN: ("coco_2017_train",) 10 | TEST: ("coco_2017_val",) 11 | SOLVER: 12 | IMS_PER_BATCH: 16 13 | BASE_LR: 0.02 14 | STEPS: (60000, 80000) 15 | MAX_ITER: 90000 16 | INPUT: 17 | MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) 18 | VERSION: 2 19 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-C4.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | MASK_ON: False 5 | RESNETS: 6 | DEPTH: 101 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-Detection/faster_rcnn_R_101_DC5_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-DilatedC5.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | MASK_ON: False 5 | RESNETS: 6 | DEPTH: 101 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | MASK_ON: False 5 | RESNETS: 6 | DEPTH: 101 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-C4.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: False 5 | RESNETS: 6 | DEPTH: 50 7 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-Detection/faster_rcnn_R_50_C4_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-C4.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: False 5 | RESNETS: 6 | DEPTH: 50 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-Detection/faster_rcnn_R_50_DC5_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-DilatedC5.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: False 5 | RESNETS: 6 | DEPTH: 50 7 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-Detection/faster_rcnn_R_50_DC5_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-DilatedC5.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: False 5 | RESNETS: 6 | DEPTH: 50 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: False 5 | RESNETS: 6 | DEPTH: 50 7 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: False 5 | RESNETS: 6 | DEPTH: 50 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | MASK_ON: False 4 | WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl" 5 | PIXEL_STD: [57.375, 57.120, 58.395] 6 | RESNETS: 7 | STRIDE_IN_1X1: False # this is a C2 model 8 | NUM_GROUPS: 32 9 | WIDTH_PER_GROUP: 8 10 | DEPTH: 101 11 | SOLVER: 12 | STEPS: (210000, 250000) 13 | MAX_ITER: 270000 14 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-Detection/fcos_R_50_FPN_1x.py: -------------------------------------------------------------------------------- 1 | from ..common.optim import SGD as optimizer 2 | from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier 3 | from ..common.data.coco import dataloader 4 | from ..common.models.fcos import model 5 | from ..common.train import train 6 | 7 | dataloader.train.mapper.use_instance_mask = False 8 | optimizer.lr = 0.01 9 | 10 | model.backbone.bottom_up.freeze_at = 2 11 | train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 12 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RetinaNet.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | RESNETS: 5 | DEPTH: 101 6 | SOLVER: 7 | STEPS: (210000, 250000) 8 | MAX_ITER: 270000 9 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-Detection/retinanet_R_50_FPN_1x.py: -------------------------------------------------------------------------------- 1 | from ..common.optim import SGD as optimizer 2 | from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier 3 | from ..common.data.coco import dataloader 4 | from ..common.models.retinanet import model 5 | from ..common.train import train 6 | 7 | dataloader.train.mapper.use_instance_mask = False 8 | model.backbone.bottom_up.freeze_at = 2 9 | optimizer.lr = 0.01 10 | 11 | train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 12 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RetinaNet.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-Detection/retinanet_R_50_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RetinaNet.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | SOLVER: 7 | STEPS: (210000, 250000) 8 | MAX_ITER: 270000 9 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-Detection/rpn_R_50_C4_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-C4.yaml" 2 | MODEL: 3 | META_ARCHITECTURE: "ProposalNetwork" 4 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 5 | MASK_ON: False 6 | RESNETS: 7 | DEPTH: 50 8 | RPN: 9 | PRE_NMS_TOPK_TEST: 12000 10 | POST_NMS_TOPK_TEST: 2000 11 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-Detection/rpn_R_50_FPN_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | META_ARCHITECTURE: "ProposalNetwork" 4 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 5 | MASK_ON: False 6 | RESNETS: 7 | DEPTH: 50 8 | RPN: 9 | POST_NMS_TOPK_TEST: 2000 10 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-C4.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 101 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-DilatedC5.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 101 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 101 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.py: -------------------------------------------------------------------------------- 1 | from ..common.train import train 2 | from ..common.optim import SGD as optimizer 3 | from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier 4 | from ..common.data.coco import dataloader 5 | from ..common.models.mask_rcnn_c4 import model 6 | 7 | model.backbone.freeze_at = 2 8 | train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 9 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-C4.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-C4.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-DilatedC5.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-DilatedC5.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.py: -------------------------------------------------------------------------------- 1 | from ..common.optim import SGD as optimizer 2 | from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier 3 | from ..common.data.coco import dataloader 4 | from ..common.models.mask_rcnn_fpn import model 5 | from ..common.train import train 6 | 7 | model.backbone.bottom_up.freeze_at = 2 8 | train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 9 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x_giou.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | RPN: 8 | BBOX_REG_LOSS_TYPE: "giou" 9 | BBOX_REG_LOSS_WEIGHT: 2.0 10 | ROI_BOX_HEAD: 11 | BBOX_REG_LOSS_TYPE: "giou" 12 | BBOX_REG_LOSS_WEIGHT: 10.0 13 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | MASK_ON: True 4 | WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl" 5 | PIXEL_STD: [57.375, 57.120, 58.395] 6 | RESNETS: 7 | STRIDE_IN_1X1: False # this is a C2 model 8 | NUM_GROUPS: 32 9 | WIDTH_PER_GROUP: 8 10 | DEPTH: 101 11 | SOLVER: 12 | STEPS: (210000, 250000) 13 | MAX_ITER: 270000 14 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-Keypoint-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | RESNETS: 5 | DEPTH: 101 6 | SOLVER: 7 | STEPS: (210000, 250000) 8 | MAX_ITER: 270000 9 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.py: -------------------------------------------------------------------------------- 1 | from ..common.optim import SGD as optimizer 2 | from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier 3 | from ..common.data.coco_keypoint import dataloader 4 | from ..common.models.keypoint_rcnn_fpn import model 5 | from ..common.train import train 6 | 7 | model.backbone.bottom_up.freeze_at = 2 8 | train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 9 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-Keypoint-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-Keypoint-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | SOLVER: 7 | STEPS: (210000, 250000) 8 | MAX_ITER: 270000 9 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-Keypoint-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl" 4 | PIXEL_STD: [57.375, 57.120, 58.395] 5 | RESNETS: 6 | STRIDE_IN_1X1: False # this is a C2 model 7 | NUM_GROUPS: 32 8 | WIDTH_PER_GROUP: 8 9 | DEPTH: 101 10 | SOLVER: 11 | STEPS: (210000, 250000) 12 | MAX_ITER: 270000 13 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | META_ARCHITECTURE: "PanopticFPN" 4 | MASK_ON: True 5 | SEM_SEG_HEAD: 6 | LOSS_WEIGHT: 0.5 7 | DATASETS: 8 | TRAIN: ("coco_2017_train_panoptic_separated",) 9 | TEST: ("coco_2017_val_panoptic_separated",) 10 | DATALOADER: 11 | FILTER_EMPTY_ANNOTATIONS: False 12 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-Panoptic-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | RESNETS: 5 | DEPTH: 101 6 | SOLVER: 7 | STEPS: (210000, 250000) 8 | MAX_ITER: 270000 9 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.py: -------------------------------------------------------------------------------- 1 | from ..common.optim import SGD as optimizer 2 | from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier 3 | from ..common.data.coco_panoptic_separated import dataloader 4 | from ..common.models.panoptic_fpn import model 5 | from ..common.train import train 6 | 7 | model.backbone.bottom_up.freeze_at = 2 8 | train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 9 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-Panoptic-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | -------------------------------------------------------------------------------- /EVA-01/det/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-Panoptic-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | SOLVER: 7 | STEPS: (210000, 250000) 8 | MAX_ITER: 270000 9 | -------------------------------------------------------------------------------- /EVA-01/det/configs/Misc/cascade_mask_rcnn_R_50_FPN_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | ROI_HEADS: 8 | NAME: CascadeROIHeads 9 | ROI_BOX_HEAD: 10 | CLS_AGNOSTIC_BBOX_REG: True 11 | RPN: 12 | POST_NMS_TOPK_TRAIN: 2000 13 | -------------------------------------------------------------------------------- /EVA-01/det/configs/Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | ROI_HEADS: 8 | NAME: CascadeROIHeads 9 | ROI_BOX_HEAD: 10 | CLS_AGNOSTIC_BBOX_REG: True 11 | RPN: 12 | POST_NMS_TOPK_TRAIN: 2000 13 | SOLVER: 14 | STEPS: (210000, 250000) 15 | MAX_ITER: 270000 16 | -------------------------------------------------------------------------------- /EVA-01/det/configs/Misc/mask_rcnn_R_50_FPN_1x_cls_agnostic.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | ROI_BOX_HEAD: 8 | CLS_AGNOSTIC_BBOX_REG: True 9 | ROI_MASK_HEAD: 10 | CLS_AGNOSTIC_MASK: True 11 | -------------------------------------------------------------------------------- /EVA-01/det/configs/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | DEFORM_ON_PER_STAGE: [False, True, True, True] # on Res3,Res4,Res5 8 | DEFORM_MODULATED: False 9 | -------------------------------------------------------------------------------- /EVA-01/det/configs/Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | DEFORM_ON_PER_STAGE: [False, True, True, True] # on Res3,Res4,Res5 8 | DEFORM_MODULATED: False 9 | SOLVER: 10 | STEPS: (210000, 250000) 11 | MAX_ITER: 270000 12 | -------------------------------------------------------------------------------- /EVA-01/det/configs/Misc/mask_rcnn_R_50_FPN_3x_gn.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "catalog://ImageNetPretrained/FAIR/R-50-GN" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | NORM: "GN" 8 | STRIDE_IN_1X1: False 9 | FPN: 10 | NORM: "GN" 11 | ROI_BOX_HEAD: 12 | NAME: "FastRCNNConvFCHead" 13 | NUM_CONV: 4 14 | NUM_FC: 1 15 | NORM: "GN" 16 | ROI_MASK_HEAD: 17 | NORM: "GN" 18 | SOLVER: 19 | # 3x schedule 20 | STEPS: (210000, 250000) 21 | MAX_ITER: 270000 22 | -------------------------------------------------------------------------------- /EVA-01/det/configs/Misc/semantic_R_50_FPN_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | META_ARCHITECTURE: "SemanticSegmentor" 4 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 5 | RESNETS: 6 | DEPTH: 50 7 | DATASETS: 8 | TRAIN: ("coco_2017_train_panoptic_stuffonly",) 9 | TEST: ("coco_2017_val_panoptic_stuffonly",) 10 | INPUT: 11 | MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) 12 | -------------------------------------------------------------------------------- /EVA-01/det/configs/common/README.md: -------------------------------------------------------------------------------- 1 | This directory provides definitions for a few common models, dataloaders, scheduler, 2 | and optimizers that are often used in training. 3 | The definition of these objects are provided in the form of lazy instantiation: 4 | their arguments can be edited by users before constructing the objects. 5 | 6 | They can be imported, or loaded by `model_zoo.get_config` API in users' own configs. 7 | -------------------------------------------------------------------------------- /EVA-01/det/configs/common/data/coco_keypoint.py: -------------------------------------------------------------------------------- 1 | from detectron2.data.detection_utils import create_keypoint_hflip_indices 2 | 3 | from .coco import dataloader 4 | 5 | dataloader.train.dataset.min_keypoints = 1 6 | dataloader.train.dataset.names = "keypoints_coco_2017_train" 7 | dataloader.test.dataset.names = "keypoints_coco_2017_val" 8 | 9 | dataloader.train.mapper.update( 10 | use_instance_mask=False, 11 | use_keypoint=True, 12 | keypoint_hflip_indices=create_keypoint_hflip_indices(dataloader.train.dataset.names), 13 | ) 14 | -------------------------------------------------------------------------------- /EVA-01/det/configs/common/data/constants.py: -------------------------------------------------------------------------------- 1 | constants = dict( 2 | imagenet_rgb256_mean=[123.675, 116.28, 103.53], 3 | imagenet_rgb256_std=[58.395, 57.12, 57.375], 4 | imagenet_bgr256_mean=[103.530, 116.280, 123.675], 5 | # When using pre-trained models in Detectron1 or any MSRA models, 6 | # std has been absorbed into its conv1 weights, so the std needs to be set 1. 7 | # Otherwise, you can use [57.375, 57.120, 58.395] (ImageNet std) 8 | imagenet_bgr256_std=[1.0, 1.0, 1.0], 9 | ) 10 | -------------------------------------------------------------------------------- /EVA-01/det/configs/new_baselines/mask_rcnn_R_101_FPN_100ep_LSJ.py: -------------------------------------------------------------------------------- 1 | from .mask_rcnn_R_50_FPN_100ep_LSJ import ( 2 | dataloader, 3 | lr_multiplier, 4 | model, 5 | optimizer, 6 | train, 7 | ) 8 | 9 | model.backbone.bottom_up.stages.depth = 101 10 | -------------------------------------------------------------------------------- /EVA-01/det/configs/new_baselines/mask_rcnn_R_101_FPN_200ep_LSJ.py: -------------------------------------------------------------------------------- 1 | from .mask_rcnn_R_101_FPN_100ep_LSJ import ( 2 | dataloader, 3 | lr_multiplier, 4 | model, 5 | optimizer, 6 | train, 7 | ) 8 | 9 | train.max_iter *= 2 # 100ep -> 200ep 10 | 11 | lr_multiplier.scheduler.milestones = [ 12 | milestone * 2 for milestone in lr_multiplier.scheduler.milestones 13 | ] 14 | lr_multiplier.scheduler.num_updates = train.max_iter 15 | -------------------------------------------------------------------------------- /EVA-01/det/configs/new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ.py: -------------------------------------------------------------------------------- 1 | from .mask_rcnn_R_101_FPN_100ep_LSJ import ( 2 | dataloader, 3 | lr_multiplier, 4 | model, 5 | optimizer, 6 | train, 7 | ) 8 | 9 | train.max_iter *= 4 # 100ep -> 400ep 10 | 11 | lr_multiplier.scheduler.milestones = [ 12 | milestone * 4 for milestone in lr_multiplier.scheduler.milestones 13 | ] 14 | lr_multiplier.scheduler.num_updates = train.max_iter 15 | -------------------------------------------------------------------------------- /EVA-01/det/configs/new_baselines/mask_rcnn_R_50_FPN_200ep_LSJ.py: -------------------------------------------------------------------------------- 1 | from .mask_rcnn_R_50_FPN_100ep_LSJ import ( 2 | dataloader, 3 | lr_multiplier, 4 | model, 5 | optimizer, 6 | train, 7 | ) 8 | 9 | train.max_iter *= 2 # 100ep -> 200ep 10 | 11 | lr_multiplier.scheduler.milestones = [ 12 | milestone * 2 for milestone in lr_multiplier.scheduler.milestones 13 | ] 14 | lr_multiplier.scheduler.num_updates = train.max_iter 15 | -------------------------------------------------------------------------------- /EVA-01/det/configs/new_baselines/mask_rcnn_R_50_FPN_400ep_LSJ.py: -------------------------------------------------------------------------------- 1 | from .mask_rcnn_R_50_FPN_100ep_LSJ import ( 2 | dataloader, 3 | lr_multiplier, 4 | model, 5 | optimizer, 6 | train, 7 | ) 8 | 9 | train.max_iter *= 4 # 100ep -> 400ep 10 | 11 | lr_multiplier.scheduler.milestones = [ 12 | milestone * 4 for milestone in lr_multiplier.scheduler.milestones 13 | ] 14 | lr_multiplier.scheduler.num_updates = train.max_iter 15 | -------------------------------------------------------------------------------- /EVA-01/det/configs/new_baselines/mask_rcnn_R_50_FPN_50ep_LSJ.py: -------------------------------------------------------------------------------- 1 | from .mask_rcnn_R_50_FPN_100ep_LSJ import ( 2 | dataloader, 3 | lr_multiplier, 4 | model, 5 | optimizer, 6 | train, 7 | ) 8 | 9 | train.max_iter //= 2 # 100ep -> 50ep 10 | 11 | lr_multiplier.scheduler.milestones = [ 12 | milestone // 2 for milestone in lr_multiplier.scheduler.milestones 13 | ] 14 | lr_multiplier.scheduler.num_updates = train.max_iter 15 | -------------------------------------------------------------------------------- /EVA-01/det/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ.py: -------------------------------------------------------------------------------- 1 | from .mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ import ( 2 | dataloader, 3 | lr_multiplier, 4 | model, 5 | optimizer, 6 | train, 7 | ) 8 | 9 | train.max_iter *= 2 # 100ep -> 200ep 10 | 11 | lr_multiplier.scheduler.milestones = [ 12 | milestone * 2 for milestone in lr_multiplier.scheduler.milestones 13 | ] 14 | lr_multiplier.scheduler.num_updates = train.max_iter 15 | -------------------------------------------------------------------------------- /EVA-01/det/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ.py: -------------------------------------------------------------------------------- 1 | from .mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ import ( 2 | dataloader, 3 | lr_multiplier, 4 | model, 5 | optimizer, 6 | train, 7 | ) 8 | 9 | train.max_iter *= 4 # 100ep -> 400ep 10 | 11 | lr_multiplier.scheduler.milestones = [ 12 | milestone * 4 for milestone in lr_multiplier.scheduler.milestones 13 | ] 14 | lr_multiplier.scheduler.num_updates = train.max_iter 15 | -------------------------------------------------------------------------------- /EVA-01/det/configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ.py: -------------------------------------------------------------------------------- 1 | from .mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ import ( 2 | dataloader, 3 | lr_multiplier, 4 | model, 5 | optimizer, 6 | train, 7 | ) 8 | 9 | train.max_iter *= 2 # 100ep -> 200ep 10 | 11 | lr_multiplier.scheduler.milestones = [ 12 | milestone * 2 for milestone in lr_multiplier.scheduler.milestones 13 | ] 14 | lr_multiplier.scheduler.num_updates = train.max_iter 15 | -------------------------------------------------------------------------------- /EVA-01/det/configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ.py: -------------------------------------------------------------------------------- 1 | from .mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ import ( 2 | dataloader, 3 | lr_multiplier, 4 | model, 5 | optimizer, 6 | train, 7 | ) 8 | 9 | train.max_iter *= 4 # 100ep -> 400ep 10 | 11 | lr_multiplier.scheduler.milestones = [ 12 | milestone * 4 for milestone in lr_multiplier.scheduler.milestones 13 | ] 14 | lr_multiplier.scheduler.num_updates = train.max_iter 15 | -------------------------------------------------------------------------------- /EVA-01/det/configs/quick_schedules/cascade_mask_rcnn_R_50_FPN_inference_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://Misc/cascade_mask_rcnn_R_50_FPN_3x/144998488/model_final_480dd8.pkl" 4 | DATASETS: 5 | TEST: ("coco_2017_val_100",) 6 | TEST: 7 | EXPECTED_RESULTS: [["bbox", "AP", 50.18, 0.02], ["segm", "AP", 43.87, 0.02]] 8 | -------------------------------------------------------------------------------- /EVA-01/det/configs/quick_schedules/cascade_mask_rcnn_R_50_FPN_instant_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml" 2 | DATASETS: 3 | TRAIN: ("coco_2017_val_100",) 4 | TEST: ("coco_2017_val_100",) 5 | SOLVER: 6 | BASE_LR: 0.005 7 | STEPS: (30,) 8 | MAX_ITER: 40 9 | IMS_PER_BATCH: 4 10 | DATALOADER: 11 | NUM_WORKERS: 2 12 | -------------------------------------------------------------------------------- /EVA-01/det/configs/quick_schedules/fast_rcnn_R_50_FPN_inference_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://COCO-Detection/fast_rcnn_R_50_FPN_1x/137635226/model_final_e5f7ce.pkl" 4 | DATASETS: 5 | TEST: ("coco_2017_val_100",) 6 | TEST: 7 | EXPECTED_RESULTS: [["bbox", "AP", 45.70, 0.02]] 8 | -------------------------------------------------------------------------------- /EVA-01/det/configs/quick_schedules/keypoint_rcnn_R_50_FPN_inference_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x/137849621/model_final_a6e10b.pkl" 4 | DATASETS: 5 | TEST: ("keypoints_coco_2017_val_100",) 6 | TEST: 7 | EXPECTED_RESULTS: [["bbox", "AP", 52.47, 0.02], ["keypoints", "AP", 67.36, 0.02]] 8 | -------------------------------------------------------------------------------- /EVA-01/det/configs/quick_schedules/keypoint_rcnn_R_50_FPN_instant_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | KEYPOINT_ON: True 5 | ROI_HEADS: 6 | NUM_CLASSES: 1 7 | DATASETS: 8 | TRAIN: ("keypoints_coco_2017_val_100",) 9 | TEST: ("keypoints_coco_2017_val_100",) 10 | SOLVER: 11 | BASE_LR: 0.005 12 | STEPS: (30,) 13 | MAX_ITER: 40 14 | IMS_PER_BATCH: 4 15 | DATALOADER: 16 | NUM_WORKERS: 2 17 | -------------------------------------------------------------------------------- /EVA-01/det/configs/quick_schedules/mask_rcnn_R_50_C4_GCV_instant_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-C4.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | DATASETS: 6 | TRAIN: ("coco_2017_val_100",) 7 | TEST: ("coco_2017_val_100",) 8 | SOLVER: 9 | BASE_LR: 0.001 10 | STEPS: (30,) 11 | MAX_ITER: 40 12 | IMS_PER_BATCH: 4 13 | CLIP_GRADIENTS: 14 | ENABLED: True 15 | CLIP_TYPE: "value" 16 | CLIP_VALUE: 1.0 17 | DATALOADER: 18 | NUM_WORKERS: 2 19 | -------------------------------------------------------------------------------- /EVA-01/det/configs/quick_schedules/mask_rcnn_R_50_C4_inference_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x/137849525/model_final_4ce675.pkl" 4 | DATASETS: 5 | TEST: ("coco_2017_val_100",) 6 | TEST: 7 | EXPECTED_RESULTS: [["bbox", "AP", 47.37, 0.02], ["segm", "AP", 40.99, 0.02]] 8 | -------------------------------------------------------------------------------- /EVA-01/det/configs/quick_schedules/mask_rcnn_R_50_C4_instant_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-C4.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | DATASETS: 6 | TRAIN: ("coco_2017_val_100",) 7 | TEST: ("coco_2017_val_100",) 8 | SOLVER: 9 | BASE_LR: 0.001 10 | STEPS: (30,) 11 | MAX_ITER: 40 12 | IMS_PER_BATCH: 4 13 | DATALOADER: 14 | NUM_WORKERS: 2 15 | -------------------------------------------------------------------------------- /EVA-01/det/configs/quick_schedules/mask_rcnn_R_50_DC5_inference_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x/137849551/model_final_84107b.pkl" 4 | DATASETS: 5 | TEST: ("coco_2017_val_100",) 6 | TEST: 7 | EXPECTED_RESULTS: [["bbox", "AP", 47.44, 0.02], ["segm", "AP", 42.94, 0.02]] 8 | -------------------------------------------------------------------------------- /EVA-01/det/configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl" 4 | DATASETS: 5 | TEST: ("coco_2017_val_100",) 6 | TEST: 7 | EXPECTED_RESULTS: [["bbox", "AP", 47.34, 0.02], ["segm", "AP", 42.67, 0.02], ["bbox_TTA", "AP", 49.11, 0.02], ["segm_TTA", "AP", 45.04, 0.02]] 8 | AUG: 9 | ENABLED: True 10 | MIN_SIZES: (700, 800) # to save some time 11 | -------------------------------------------------------------------------------- /EVA-01/det/configs/quick_schedules/mask_rcnn_R_50_FPN_instant_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | DATASETS: 6 | TRAIN: ("coco_2017_val_100",) 7 | TEST: ("coco_2017_val_100",) 8 | SOLVER: 9 | BASE_LR: 0.005 10 | STEPS: (30,) 11 | MAX_ITER: 40 12 | IMS_PER_BATCH: 4 13 | DATALOADER: 14 | NUM_WORKERS: 2 15 | -------------------------------------------------------------------------------- /EVA-01/det/configs/quick_schedules/mask_rcnn_R_50_FPN_pred_boxes_training_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "./mask_rcnn_R_50_FPN_training_acc_test.yaml" 2 | MODEL: 3 | ROI_BOX_HEAD: 4 | TRAIN_ON_PRED_BOXES: True 5 | TEST: 6 | EXPECTED_RESULTS: [["bbox", "AP", 42.6, 1.0], ["segm", "AP", 35.8, 0.8]] 7 | -------------------------------------------------------------------------------- /EVA-01/det/configs/quick_schedules/panoptic_fpn_R_50_inference_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://COCO-PanopticSegmentation/panoptic_fpn_R_50_3x/139514569/model_final_c10459.pkl" 4 | DATASETS: 5 | TEST: ("coco_2017_val_100_panoptic_separated",) 6 | TEST: 7 | EXPECTED_RESULTS: [["bbox", "AP", 46.47, 0.02], ["segm", "AP", 43.39, 0.02], ["sem_seg", "mIoU", 42.55, 0.02], ["panoptic_seg", "PQ", 38.99, 0.02]] 8 | -------------------------------------------------------------------------------- /EVA-01/det/configs/quick_schedules/retinanet_R_50_FPN_inference_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../COCO-Detection/retinanet_R_50_FPN_3x.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://COCO-Detection/retinanet_R_50_FPN_3x/190397829/model_final_5bd44e.pkl" 4 | DATASETS: 5 | TEST: ("coco_2017_val_100",) 6 | TEST: 7 | EXPECTED_RESULTS: [["bbox", "AP", 44.45, 0.02]] 8 | -------------------------------------------------------------------------------- /EVA-01/det/configs/quick_schedules/retinanet_R_50_FPN_instant_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../COCO-Detection/retinanet_R_50_FPN_1x.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | DATASETS: 5 | TRAIN: ("coco_2017_val_100",) 6 | TEST: ("coco_2017_val_100",) 7 | SOLVER: 8 | BASE_LR: 0.005 9 | STEPS: (30,) 10 | MAX_ITER: 40 11 | IMS_PER_BATCH: 4 12 | DATALOADER: 13 | NUM_WORKERS: 2 14 | -------------------------------------------------------------------------------- /EVA-01/det/configs/quick_schedules/rpn_R_50_FPN_inference_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../COCO-Detection/rpn_R_50_FPN_1x.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/model_final_02ce48.pkl" 4 | DATASETS: 5 | TEST: ("coco_2017_val_100",) 6 | TEST: 7 | EXPECTED_RESULTS: [["box_proposals", "AR@1000", 58.16, 0.02]] 8 | -------------------------------------------------------------------------------- /EVA-01/det/configs/quick_schedules/rpn_R_50_FPN_instant_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../COCO-Detection/rpn_R_50_FPN_1x.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | DATASETS: 5 | TRAIN: ("coco_2017_val_100",) 6 | TEST: ("coco_2017_val_100",) 7 | SOLVER: 8 | STEPS: (30,) 9 | MAX_ITER: 40 10 | BASE_LR: 0.005 11 | IMS_PER_BATCH: 4 12 | DATALOADER: 13 | NUM_WORKERS: 2 14 | -------------------------------------------------------------------------------- /EVA-01/det/configs/quick_schedules/semantic_R_50_FPN_inference_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | META_ARCHITECTURE: "SemanticSegmentor" 4 | WEIGHTS: "detectron2://semantic_R_50_FPN_1x/111802073/model_final_c18079783c55a94968edc28b7101c5f0.pkl" 5 | RESNETS: 6 | DEPTH: 50 7 | DATASETS: 8 | TEST: ("coco_2017_val_100_panoptic_stuffonly",) 9 | TEST: 10 | EXPECTED_RESULTS: [["sem_seg", "mIoU", 39.53, 0.02], ["sem_seg", "mACC", 51.50, 0.02]] 11 | -------------------------------------------------------------------------------- /EVA-01/det/demo/README.md: -------------------------------------------------------------------------------- 1 | 2 | ## Detectron2 Demo 3 | 4 | We provide a command line tool to run a simple demo of builtin configs. 5 | The usage is explained in [GETTING_STARTED.md](../GETTING_STARTED.md). 6 | 7 | See our [blog post](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-) 8 | for a high-quality demo generated with this tool. 9 | -------------------------------------------------------------------------------- /EVA-01/det/detectron2/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | 3 | from .utils.env import setup_environment 4 | 5 | setup_environment() 6 | 7 | 8 | # This line will be programatically read/write by setup.py. 9 | # Leave them at the bottom of this file and don't touch them. 10 | __version__ = "0.6" 11 | -------------------------------------------------------------------------------- /EVA-01/det/detectron2/checkpoint/__init__.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # File: 4 | 5 | 6 | from . import catalog as _UNUSED # register the handler 7 | from .detection_checkpoint import DetectionCheckpointer 8 | from fvcore.common.checkpoint import Checkpointer, PeriodicCheckpointer 9 | 10 | __all__ = ["Checkpointer", "PeriodicCheckpointer", "DetectionCheckpointer"] 11 | -------------------------------------------------------------------------------- /EVA-01/det/detectron2/data/datasets/README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | ### Common Datasets 4 | 5 | The dataset implemented here do not need to load the data into the final format. 6 | It should provide the minimal data structure needed to use the dataset, so it can be very efficient. 7 | 8 | For example, for an image dataset, just provide the file names and labels, but don't read the images. 9 | Let the downstream decide how to read. 10 | -------------------------------------------------------------------------------- /EVA-01/det/detectron2/data/datasets/register_coco.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | from .coco import register_coco_instances # noqa 3 | from .coco_panoptic import register_coco_panoptic_separated # noqa 4 | -------------------------------------------------------------------------------- /EVA-01/det/detectron2/data/samplers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | from .distributed_sampler import ( 3 | InferenceSampler, 4 | RandomSubsetTrainingSampler, 5 | RepeatFactorTrainingSampler, 6 | TrainingSampler, 7 | ) 8 | 9 | from .grouped_batch_sampler import GroupedBatchSampler 10 | 11 | __all__ = [ 12 | "GroupedBatchSampler", 13 | "TrainingSampler", 14 | "RandomSubsetTrainingSampler", 15 | "InferenceSampler", 16 | "RepeatFactorTrainingSampler", 17 | ] 18 | -------------------------------------------------------------------------------- /EVA-01/det/detectron2/engine/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | 3 | from .launch import * 4 | from .train_loop import * 5 | 6 | __all__ = [k for k in globals().keys() if not k.startswith("_")] 7 | 8 | 9 | # prefer to let hooks and defaults live in separate namespaces (therefore not in __all__) 10 | # but still make them available here 11 | from .hooks import * 12 | from .defaults import * 13 | -------------------------------------------------------------------------------- /EVA-01/det/detectron2/layers/csrc/README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | To add a new Op: 4 | 5 | 1. Create a new directory 6 | 2. Implement new ops there 7 | 3. Delcare its Python interface in `vision.cpp`. 8 | -------------------------------------------------------------------------------- /EVA-01/det/detectron2/model_zoo/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | """ 3 | Model Zoo API for Detectron2: a collection of functions to create common model architectures 4 | listed in `MODEL_ZOO.md `_, 5 | and optionally load their pre-trained weights. 6 | """ 7 | 8 | from .model_zoo import get, get_config_file, get_checkpoint_url, get_config 9 | 10 | __all__ = ["get_checkpoint_url", "get", "get_config_file", "get_config"] 11 | -------------------------------------------------------------------------------- /EVA-01/det/detectron2/modeling/proposal_generator/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | from .build import PROPOSAL_GENERATOR_REGISTRY, build_proposal_generator 3 | from .rpn import RPN_HEAD_REGISTRY, build_rpn_head, RPN, StandardRPNHead 4 | 5 | __all__ = list(globals().keys()) 6 | -------------------------------------------------------------------------------- /EVA-01/det/detectron2/projects/README.md: -------------------------------------------------------------------------------- 1 | 2 | Projects live in the [`projects` directory](../../projects) under the root of this repository, but not here. 3 | -------------------------------------------------------------------------------- /EVA-01/det/detectron2/solver/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | from .build import build_lr_scheduler, build_optimizer, get_default_optimizer_params 3 | from .lr_scheduler import WarmupCosineLR, WarmupMultiStepLR, LRMultiplier, WarmupParamScheduler 4 | 5 | __all__ = [k for k in globals().keys() if not k.startswith("_")] 6 | -------------------------------------------------------------------------------- /EVA-01/det/detectron2/utils/README.md: -------------------------------------------------------------------------------- 1 | # Utility functions 2 | 3 | This folder contain utility functions that are not used in the 4 | core library, but are useful for building models or training 5 | code using the config system. 6 | -------------------------------------------------------------------------------- /EVA-01/det/detectron2/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | -------------------------------------------------------------------------------- /EVA-01/det/dev/README.md: -------------------------------------------------------------------------------- 1 | 2 | ## Some scripts for developers to use, include: 3 | 4 | - `linter.sh`: lint the codebase before commit. 5 | - `run_{inference,instant}_tests.sh`: run inference/training for a few iterations. 6 | Note that these tests require 2 GPUs. 7 | - `parse_results.sh`: parse results from a log file. 8 | -------------------------------------------------------------------------------- /EVA-01/det/docs/.gitignore: -------------------------------------------------------------------------------- 1 | _build 2 | -------------------------------------------------------------------------------- /EVA-01/det/docs/README.md: -------------------------------------------------------------------------------- 1 | # Read the docs: 2 | 3 | The latest documentation built from this directory is available at [detectron2.readthedocs.io](https://detectron2.readthedocs.io/). 4 | Documents in this directory are not meant to be read on github. 5 | 6 | # Build the docs: 7 | 8 | 1. Install detectron2 according to [INSTALL.md](../INSTALL.md). 9 | 2. Install additional libraries required to build docs: 10 | - docutils==0.16 11 | - Sphinx==3.2.0 12 | - recommonmark==0.6.0 13 | - sphinx_rtd_theme 14 | 15 | 3. Run `make html` from this directory. 16 | -------------------------------------------------------------------------------- /EVA-01/det/docs/index.rst: -------------------------------------------------------------------------------- 1 | .. detectron2 documentation master file, created by 2 | sphinx-quickstart on Sat Sep 21 13:46:45 2019. 3 | You can adapt this file completely to your liking, but it should at least 4 | contain the root `toctree` directive. 5 | 6 | Welcome to detectron2's documentation! 7 | ====================================== 8 | 9 | .. toctree:: 10 | :maxdepth: 2 11 | 12 | tutorials/index 13 | notes/index 14 | modules/index 15 | -------------------------------------------------------------------------------- /EVA-01/det/docs/modules/checkpoint.rst: -------------------------------------------------------------------------------- 1 | detectron2.checkpoint 2 | ============================= 3 | 4 | .. automodule:: detectron2.checkpoint 5 | :members: 6 | :undoc-members: 7 | :show-inheritance: 8 | -------------------------------------------------------------------------------- /EVA-01/det/docs/modules/config.rst: -------------------------------------------------------------------------------- 1 | detectron2.config 2 | ========================= 3 | 4 | Related tutorials: :doc:`../tutorials/configs`, :doc:`../tutorials/extend`. 5 | 6 | .. automodule:: detectron2.config 7 | :members: 8 | :undoc-members: 9 | :show-inheritance: 10 | 11 | 12 | Yaml Config References 13 | ----------------- 14 | 15 | .. literalinclude:: ../../detectron2/config/defaults.py 16 | :language: python 17 | :linenos: 18 | :lines: 7- 19 | -------------------------------------------------------------------------------- /EVA-01/det/docs/modules/data_transforms.rst: -------------------------------------------------------------------------------- 1 | detectron2.data.transforms 2 | ==================================== 3 | 4 | Related tutorial: :doc:`../tutorials/augmentation`. 5 | 6 | .. automodule:: detectron2.data.transforms 7 | :members: 8 | :undoc-members: 9 | :show-inheritance: 10 | :imported-members: 11 | -------------------------------------------------------------------------------- /EVA-01/det/docs/modules/evaluation.rst: -------------------------------------------------------------------------------- 1 | detectron2.evaluation 2 | ============================= 3 | 4 | .. automodule:: detectron2.evaluation 5 | :members: 6 | :undoc-members: 7 | :show-inheritance: 8 | -------------------------------------------------------------------------------- /EVA-01/det/docs/modules/export.rst: -------------------------------------------------------------------------------- 1 | detectron2.export 2 | ========================= 3 | 4 | Related tutorial: :doc:`../tutorials/deployment`. 5 | 6 | .. automodule:: detectron2.export 7 | :members: 8 | :undoc-members: 9 | :show-inheritance: 10 | -------------------------------------------------------------------------------- /EVA-01/det/docs/modules/index.rst: -------------------------------------------------------------------------------- 1 | API Documentation 2 | ================== 3 | 4 | .. toctree:: 5 | 6 | checkpoint 7 | config 8 | data 9 | data_transforms 10 | engine 11 | evaluation 12 | layers 13 | model_zoo 14 | modeling 15 | solver 16 | structures 17 | utils 18 | export 19 | fvcore 20 | -------------------------------------------------------------------------------- /EVA-01/det/docs/modules/layers.rst: -------------------------------------------------------------------------------- 1 | detectron2.layers 2 | ========================= 3 | 4 | .. automodule:: detectron2.layers 5 | :members: 6 | :undoc-members: 7 | :show-inheritance: 8 | -------------------------------------------------------------------------------- /EVA-01/det/docs/modules/model_zoo.rst: -------------------------------------------------------------------------------- 1 | detectron2.model_zoo 2 | ============================ 3 | 4 | .. automodule:: detectron2.model_zoo 5 | :members: 6 | :undoc-members: 7 | :show-inheritance: 8 | -------------------------------------------------------------------------------- /EVA-01/det/docs/modules/solver.rst: -------------------------------------------------------------------------------- 1 | detectron2.solver 2 | ========================= 3 | 4 | .. automodule:: detectron2.solver 5 | :members: 6 | :undoc-members: 7 | :show-inheritance: 8 | -------------------------------------------------------------------------------- /EVA-01/det/docs/modules/structures.rst: -------------------------------------------------------------------------------- 1 | detectron2.structures 2 | ============================= 3 | 4 | .. automodule:: detectron2.structures 5 | :members: 6 | :undoc-members: 7 | :show-inheritance: 8 | -------------------------------------------------------------------------------- /EVA-01/det/docs/notes/contributing.md: -------------------------------------------------------------------------------- 1 | ../../.github/CONTRIBUTING.md -------------------------------------------------------------------------------- /EVA-01/det/docs/notes/index.rst: -------------------------------------------------------------------------------- 1 | Notes 2 | ====================================== 3 | 4 | .. toctree:: 5 | :maxdepth: 2 6 | 7 | benchmarks 8 | compatibility 9 | contributing 10 | changelog 11 | -------------------------------------------------------------------------------- /EVA-01/det/docs/tutorials/README.md: -------------------------------------------------------------------------------- 1 | # Read the docs: 2 | 3 | The latest documentation built from this directory is available at [detectron2.readthedocs.io](https://detectron2.readthedocs.io/). 4 | Documents in this directory are not meant to be read on github. 5 | -------------------------------------------------------------------------------- /EVA-01/det/docs/tutorials/builtin_datasets.md: -------------------------------------------------------------------------------- 1 | ../../datasets/README.md -------------------------------------------------------------------------------- /EVA-01/det/docs/tutorials/getting_started.md: -------------------------------------------------------------------------------- 1 | ../../GETTING_STARTED.md -------------------------------------------------------------------------------- /EVA-01/det/docs/tutorials/index.rst: -------------------------------------------------------------------------------- 1 | Tutorials 2 | ====================================== 3 | 4 | .. toctree:: 5 | :maxdepth: 2 6 | 7 | install 8 | getting_started 9 | builtin_datasets 10 | extend 11 | datasets 12 | data_loading 13 | augmentation 14 | models 15 | write-models 16 | training 17 | evaluation 18 | configs 19 | lazyconfigs 20 | deployment 21 | -------------------------------------------------------------------------------- /EVA-01/det/docs/tutorials/install.md: -------------------------------------------------------------------------------- 1 | ../../INSTALL.md -------------------------------------------------------------------------------- /EVA-01/det/tests/README.md: -------------------------------------------------------------------------------- 1 | ## Unit Tests 2 | 3 | To run the unittests, do: 4 | ``` 5 | cd detectron2 6 | python -m unittest discover -v -s ./tests 7 | ``` 8 | 9 | There are also end-to-end inference & training tests, in [dev/run_*_tests.sh](../dev). 10 | -------------------------------------------------------------------------------- /EVA-01/det/tests/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | -------------------------------------------------------------------------------- /EVA-01/det/tests/config/dir1/dir1_a.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | dir1a_str = "base_a_1" 3 | dir1a_dict = {"a": 1, "b": 2} 4 | -------------------------------------------------------------------------------- /EVA-01/det/tests/config/dir1/dir1_b.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | from detectron2.config import LazyConfig 3 | 4 | # equivalent to relative import 5 | dir1a_str, dir1a_dict = LazyConfig.load_rel("dir1_a.py", ("dir1a_str", "dir1a_dict")) 6 | 7 | dir1b_str = dir1a_str + "_from_b" 8 | dir1b_dict = dir1a_dict 9 | 10 | # Every import is a reload: not modified by other config files 11 | assert dir1a_dict.a == 1 12 | -------------------------------------------------------------------------------- /EVA-01/det/tests/config/root_cfg.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | from itertools import count 3 | 4 | from detectron2.config import LazyCall as L 5 | 6 | from .dir1.dir1_a import dir1a_dict, dir1a_str 7 | 8 | dir1a_dict.a = "modified" 9 | 10 | # modification above won't affect future imports 11 | from .dir1.dir1_b import dir1b_dict, dir1b_str 12 | 13 | 14 | lazyobj = L(count)(x=dir1a_str, y=dir1b_str) 15 | -------------------------------------------------------------------------------- /EVA-01/det/tests/data/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/det/tests/data/__init__.py -------------------------------------------------------------------------------- /EVA-01/det/tests/layers/__init__.py: 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2 | 3 | Essentially a simple "copy-paste" from OpenAI CLIP [ [code](https://github.com/openai/CLIP/tree/main/clip) \| [paper](https://arxiv.org/abs/2103.00020) \| [blog](https://openai.com/blog/clip/) ]. 4 | 5 | Here, the CLIP vision feature is used as the MIM pre-training target of EVA. 6 | 7 |
-------------------------------------------------------------------------------- /EVA-01/eva/clip/__init__.py: -------------------------------------------------------------------------------- 1 | from .clip import * 2 | -------------------------------------------------------------------------------- /EVA-01/eva/clip/bpe_simple_vocab_16e6.txt.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/eva/clip/bpe_simple_vocab_16e6.txt.gz -------------------------------------------------------------------------------- /EVA-01/eva/requirements.txt: -------------------------------------------------------------------------------- 1 | timm==0.5.4 2 | Pillow 3 | blobfile 4 | mypy 5 | numpy 6 | pytest 7 | requests 8 | einops 9 | tensorboardX 10 | deepspeed==0.6.5 11 | scipy 12 | sh 13 | wandb 14 | ftfy 15 | regex -------------------------------------------------------------------------------- /EVA-01/seg/configs/_base_/datasets/pascal_voc12_aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './pascal_voc12.py' 2 | # dataset settings 3 | data = dict( 4 | train=dict( 5 | ann_dir=['SegmentationClass', 'SegmentationClassAug'], 6 | split=[ 7 | 'ImageSets/Segmentation/train.txt', 8 | 'ImageSets/Segmentation/aug.txt' 9 | ])) 10 | -------------------------------------------------------------------------------- /EVA-01/seg/configs/_base_/default_runtime.py: -------------------------------------------------------------------------------- 1 | # yapf:disable 2 | log_config = dict( 3 | interval=50, 4 | hooks=[ 5 | dict(type='TextLoggerHook', by_epoch=False), 6 | # dict(type='TensorboardLoggerHook') 7 | ]) 8 | # yapf:enable 9 | dist_params = dict(backend='nccl') 10 | log_level = 'INFO' 11 | load_from = None 12 | resume_from = None 13 | workflow = [('train', 1)] 14 | cudnn_benchmark = True 15 | -------------------------------------------------------------------------------- /EVA-01/seg/configs/_base_/schedules/schedule_10k.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) 3 | optimizer_config = dict() 4 | # learning policy 5 | lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) 6 | # runtime settings 7 | runner = dict(type='IterBasedRunner', max_iters=10000) 8 | checkpoint_config = dict(by_epoch=False, interval=2000) 9 | evaluation = dict(interval=2000, metric='mIoU', pre_eval=True) 10 | -------------------------------------------------------------------------------- /EVA-01/seg/configs/_base_/schedules/schedule_15k.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) 3 | optimizer_config = dict() 4 | # learning policy 5 | lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) 6 | # runtime settings 7 | runner = dict(type='IterBasedRunner', max_iters=15000) 8 | checkpoint_config = dict(by_epoch=False, interval=2000) 9 | evaluation = dict(interval=2000, metric='mIoU', pre_eval=True) 10 | -------------------------------------------------------------------------------- /EVA-01/seg/configs/_base_/schedules/schedule_160k.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) 3 | optimizer_config = dict() 4 | # learning policy 5 | lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) 6 | # runtime settings 7 | runner = dict(type='IterBasedRunner', max_iters=160000) 8 | checkpoint_config = dict(by_epoch=False, interval=16000) 9 | evaluation = dict(interval=16000, metric='mIoU', pre_eval=True) 10 | -------------------------------------------------------------------------------- /EVA-01/seg/configs/_base_/schedules/schedule_20k.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) 3 | optimizer_config = dict() 4 | # learning policy 5 | lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) 6 | # runtime settings 7 | runner = dict(type='IterBasedRunner', max_iters=20000) 8 | checkpoint_config = dict(by_epoch=False, interval=2000) 9 | evaluation = dict(interval=2000, metric='mIoU', pre_eval=True) 10 | -------------------------------------------------------------------------------- /EVA-01/seg/configs/_base_/schedules/schedule_30k.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) 3 | optimizer_config = dict() 4 | # learning policy 5 | lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) 6 | # runtime settings 7 | runner = dict(type='IterBasedRunner', max_iters=30000) 8 | checkpoint_config = dict(by_epoch=False, interval=2000) 9 | evaluation = dict(interval=2000, metric='mIoU', pre_eval=True) 10 | -------------------------------------------------------------------------------- /EVA-01/seg/configs/_base_/schedules/schedule_320k.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) 3 | optimizer_config = dict() 4 | # learning policy 5 | lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) 6 | # runtime settings 7 | runner = dict(type='IterBasedRunner', max_iters=320000) 8 | checkpoint_config = dict(by_epoch=False, interval=32000) 9 | evaluation = dict(interval=32000, metric='mIoU') 10 | -------------------------------------------------------------------------------- /EVA-01/seg/configs/_base_/schedules/schedule_40k.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) 3 | optimizer_config = dict() 4 | # learning policy 5 | lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) 6 | # runtime settings 7 | runner = dict(type='IterBasedRunner', max_iters=40000) 8 | checkpoint_config = dict(by_epoch=False, interval=4000) 9 | evaluation = dict(interval=4000, metric='mIoU', pre_eval=True) 10 | -------------------------------------------------------------------------------- /EVA-01/seg/configs/_base_/schedules/schedule_60k.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) 3 | optimizer_config = dict() 4 | # learning policy 5 | lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) 6 | # runtime settings 7 | runner = dict(type='IterBasedRunner', max_iters=60000) 8 | checkpoint_config = dict(by_epoch=False, interval=4000) 9 | evaluation = dict(interval=4000, metric='mIoU', pre_eval=True) 10 | -------------------------------------------------------------------------------- /EVA-01/seg/configs/_base_/schedules/schedule_80k.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) 3 | optimizer_config = dict() 4 | # learning policy 5 | lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) 6 | # runtime settings 7 | runner = dict(type='IterBasedRunner', max_iters=80000) 8 | checkpoint_config = dict(by_epoch=False, interval=8000) 9 | evaluation = dict(interval=8000, metric='mIoU', pre_eval=True) 10 | -------------------------------------------------------------------------------- /EVA-01/seg/mmcv_custom/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Shanghai AI Lab. All rights reserved. 2 | from .checkpoint import load_checkpoint 3 | from .customized_text import CustomizedTextLoggerHook 4 | from .layer_decay_optimizer_constructor import LayerDecayOptimizerConstructor 5 | from .my_checkpoint import my_load_checkpoint 6 | 7 | __all__ = [ 8 | 'LayerDecayOptimizerConstructor', 'CustomizedTextLoggerHook', 9 | 'load_checkpoint', 'my_checkpoint' 10 | ] 11 | -------------------------------------------------------------------------------- /EVA-01/seg/mmcv_custom/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmcv_custom/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmcv_custom/__pycache__/checkpoint.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmcv_custom/__pycache__/checkpoint.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmcv_custom/__pycache__/customized_text.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmcv_custom/__pycache__/customized_text.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmcv_custom/__pycache__/layer_decay_optimizer_constructor.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmcv_custom/__pycache__/layer_decay_optimizer_constructor.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmcv_custom/__pycache__/my_checkpoint.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmcv_custom/__pycache__/my_checkpoint.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/__init__.py: -------------------------------------------------------------------------------- 1 | from .core import * # noqa: F401,F403 2 | from .datasets import * # noqa: F401,F403 3 | from .models import * # noqa: F401,F403 4 | -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Shanghai AI Lab. All rights reserved. 2 | from mmseg.core.evaluation import * # noqa: F401, F403 3 | from mmseg.core.seg import * # noqa: F401, F403 4 | 5 | from .anchor import * # noqa: F401,F403 6 | from .box import * # noqa: F401,F403 7 | from .evaluation import * # noqa: F401,F403 8 | from .mask import * # noqa: F401,F403 9 | from .utils import * # noqa: F401, F403 10 | -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/core/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/anchor/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Shanghai AI Lab. All rights reserved. 2 | from .point_generator import MlvlPointGenerator # noqa: F401,F403 3 | -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/anchor/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/core/anchor/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/anchor/__pycache__/builder.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/core/anchor/__pycache__/builder.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/anchor/__pycache__/point_generator.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/core/anchor/__pycache__/point_generator.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/box/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Shanghai AI Lab. All rights reserved. 2 | from .builder import * # noqa: F401,F403 3 | from .samplers import MaskPseudoSampler # noqa: F401,F403 4 | -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/box/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/core/box/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/box/__pycache__/builder.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/core/box/__pycache__/builder.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/box/builder.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmcv.utils import Registry, build_from_cfg 3 | 4 | BBOX_SAMPLERS = Registry('bbox_sampler') 5 | BBOX_CODERS = Registry('bbox_coder') 6 | 7 | 8 | def build_sampler(cfg, **default_args): 9 | """Builder of box sampler.""" 10 | return build_from_cfg(cfg, BBOX_SAMPLERS, default_args) 11 | 12 | 13 | def build_bbox_coder(cfg, **default_args): 14 | """Builder of box coder.""" 15 | return build_from_cfg(cfg, BBOX_CODERS, default_args) 16 | -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/box/samplers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Shanghai AI Lab. All rights reserved. 2 | from .mask_pseudo_sampler import MaskPseudoSampler # noqa: F401,F403 3 | -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/box/samplers/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/core/box/samplers/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/box/samplers/__pycache__/base_sampler.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/core/box/samplers/__pycache__/base_sampler.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/box/samplers/__pycache__/mask_pseudo_sampler.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/core/box/samplers/__pycache__/mask_pseudo_sampler.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/box/samplers/__pycache__/mask_sampling_result.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/core/box/samplers/__pycache__/mask_sampling_result.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/box/samplers/__pycache__/sampling_result.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/core/box/samplers/__pycache__/sampling_result.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/evaluation/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Shanghai AI Lab. All rights reserved. 2 | from .panoptic_utils import INSTANCE_OFFSET # noqa: F401,F403 3 | -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/evaluation/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/core/evaluation/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/evaluation/__pycache__/panoptic_utils.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/core/evaluation/__pycache__/panoptic_utils.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/evaluation/panoptic_utils.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | # A custom value to distinguish instance ID and category ID; need to 3 | # be greater than the number of categories. 4 | # For a pixel in the panoptic result map: 5 | # pan_id = ins_id * INSTANCE_OFFSET + cat_id 6 | INSTANCE_OFFSET = 1000 7 | -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/mask/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Shanghai AI Lab. All rights reserved. 2 | from .utils import mask2bbox # noqa: F401,F403 3 | -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/mask/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/core/mask/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/mask/__pycache__/utils.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/core/mask/__pycache__/utils.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .dist_utils import (DistOptimizerHook, all_reduce_dict, allreduce_grads, 3 | reduce_mean) 4 | from .misc import add_prefix, multi_apply 5 | 6 | __all__ = [ 7 | 'add_prefix', 'multi_apply', 'DistOptimizerHook', 'allreduce_grads', 8 | 'all_reduce_dict', 'reduce_mean' 9 | ] 10 | -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/utils/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/core/utils/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/utils/__pycache__/dist_utils.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/core/utils/__pycache__/dist_utils.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/core/utils/__pycache__/misc.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/core/utils/__pycache__/misc.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/datasets/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .mapillary import MapillaryDataset # noqa: F401,F403 3 | from .potsdam import PotsdamDataset # noqa: F401,F403 4 | from .pipelines import * # noqa: F401,F403 5 | -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/datasets/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/datasets/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/datasets/__pycache__/mapillary.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/datasets/__pycache__/mapillary.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/datasets/__pycache__/potsdam.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/datasets/__pycache__/potsdam.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/datasets/pipelines/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .formatting import DefaultFormatBundle, ToMask 3 | from .transform import MapillaryHack, PadShortSide, SETR_Resize 4 | 5 | __all__ = [ 6 | 'DefaultFormatBundle', 'ToMask', 'SETR_Resize', 'PadShortSide', 7 | 'MapillaryHack' 8 | ] 9 | -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/datasets/pipelines/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/datasets/pipelines/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/datasets/pipelines/__pycache__/formatting.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/datasets/pipelines/__pycache__/formatting.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/datasets/pipelines/__pycache__/transform.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/datasets/pipelines/__pycache__/transform.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/__pycache__/builder.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/__pycache__/builder.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/backbones/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Shanghai AI Lab. All rights reserved. 2 | from .beit_adapter import BEiTAdapter 3 | from .beit_win_adapter import BEiTWINAdapter 4 | from .beit_baseline import BEiTBaseline 5 | from .vit_adapter import ViTAdapter 6 | from .vit_baseline import ViTBaseline 7 | 8 | __all__ = ['ViTBaseline', 'ViTAdapter', 'BEiTAdapter', 'BEiTWINAdapter', 'BEiTBaseline'] 9 | -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/backbones/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/backbones/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/backbones/__pycache__/adapter_modules.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/backbones/__pycache__/adapter_modules.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/backbones/__pycache__/beit_adapter.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/backbones/__pycache__/beit_adapter.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/backbones/__pycache__/beit_baseline.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/backbones/__pycache__/beit_baseline.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/backbones/__pycache__/beit_win_adapter.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/backbones/__pycache__/beit_win_adapter.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/backbones/__pycache__/vit_adapter.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/backbones/__pycache__/vit_adapter.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/backbones/__pycache__/vit_baseline.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/backbones/__pycache__/vit_baseline.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/backbones/base/__pycache__/beit.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/backbones/base/__pycache__/beit.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/backbones/base/__pycache__/beit_win.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/backbones/base/__pycache__/beit_win.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/backbones/base/__pycache__/vit.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/backbones/base/__pycache__/vit.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/decode_heads/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .mask2former_head import Mask2FormerHead 3 | from .maskformer_head import MaskFormerHead 4 | 5 | __all__ = [ 6 | 'MaskFormerHead', 7 | 'Mask2FormerHead', 8 | ] 9 | -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/decode_heads/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/decode_heads/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/decode_heads/__pycache__/mask2former_head.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/decode_heads/__pycache__/mask2former_head.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/decode_heads/__pycache__/maskformer_head.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/decode_heads/__pycache__/maskformer_head.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/losses/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/losses/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/losses/__pycache__/cross_entropy_loss.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/losses/__pycache__/cross_entropy_loss.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/losses/__pycache__/dice_loss.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/losses/__pycache__/dice_loss.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/losses/__pycache__/focal_loss.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/losses/__pycache__/focal_loss.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/losses/__pycache__/match_costs.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/losses/__pycache__/match_costs.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/plugins/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Shanghai AI Lab. All rights reserved. 2 | from .msdeformattn_pixel_decoder import MSDeformAttnPixelDecoder 3 | from .pixel_decoder import PixelDecoder, TransformerEncoderPixelDecoder 4 | 5 | __all__ = [ 6 | 'PixelDecoder', 'TransformerEncoderPixelDecoder', 7 | 'MSDeformAttnPixelDecoder' 8 | ] 9 | -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/plugins/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/plugins/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/plugins/__pycache__/msdeformattn_pixel_decoder.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/plugins/__pycache__/msdeformattn_pixel_decoder.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/plugins/__pycache__/pixel_decoder.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/plugins/__pycache__/pixel_decoder.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/segmentors/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .encoder_decoder_mask2former import EncoderDecoderMask2Former 3 | from .encoder_decoder_mask2former_aug import EncoderDecoderMask2FormerAug 4 | 5 | __all__ = ['EncoderDecoderMask2Former', 'EncoderDecoderMask2FormerAug'] 6 | -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/segmentors/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/seg/mmseg_custom/models/segmentors/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-01/seg/mmseg_custom/models/segmentors/__pycache__/encoder_decoder_mask2former.cpython-38.pyc: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | ``` 2 | sh make.sh 3 | ``` 4 | -------------------------------------------------------------------------------- /EVA-01/video/.gitignore: -------------------------------------------------------------------------------- 1 | **/__pycache__/** 2 | stdout* 3 | .DS_Store -------------------------------------------------------------------------------- /EVA-01/video/clip/__init__.py: -------------------------------------------------------------------------------- 1 | from .clip import * 2 | 3 | 4 | 5 | -------------------------------------------------------------------------------- /EVA-01/video/clip/bpe_simple_vocab_16e6.txt.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/video/clip/bpe_simple_vocab_16e6.txt.gz -------------------------------------------------------------------------------- /EVA-01/video/clip/model_zoo.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | 4 | def get_model_path(ckpt): 5 | if os.path.isfile(ckpt): 6 | return ckpt 7 | else: 8 | print('not found pretrained model in {}'.format(ckpt)) 9 | raise FileNotFoundError 10 | -------------------------------------------------------------------------------- /EVA-01/video/datasets/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/video/datasets/__init__.py -------------------------------------------------------------------------------- /EVA-01/video/requirements.txt: -------------------------------------------------------------------------------- 1 | pathlib 2 | mmcv-full 3 | decord 4 | ftfy 5 | einops 6 | termcolor 7 | timm 8 | regex 9 | pandas 10 | yacs 11 | tqdm 12 | wandb 13 | imgaug -------------------------------------------------------------------------------- /EVA-01/video/scripts/run_k400_ft.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | VIDEO_CONFIG=configs/kinetics400_ft.yaml 4 | OUTPUT_ROOT=/path/to/video/output/ 5 | pretrained=pretrained/eva_video_k722.pth 6 | 7 | python -m torch.distributed.launch --nproc_per_node=8 --nnodes=$nnodes \ 8 | --node_rank=$NODE_RANK --master_addr=$MASTER_ADDR --master_port=12355 \ 9 | main.py -cfg ${VIDEO_CONFIG} \ 10 | --output ${OUTPUT_ROOT} \ 11 | --accumulation-steps 4 \ 12 | --opts MODEL.PRETRAINED ${pretrained} -------------------------------------------------------------------------------- /EVA-01/video/scripts/run_k600_ft.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | VIDEO_CONFIG=configs/kinetics600_ft.yaml 4 | OUTPUT_ROOT=/path/to/video/output/ 5 | pretrained=pretrained/eva_video_k722.pth 6 | 7 | python -m torch.distributed.launch --nproc_per_node=8 --nnodes=$nnodes \ 8 | --node_rank=$NODE_RANK --master_addr=$MASTER_ADDR --master_port=12355 \ 9 | main.py -cfg ${VIDEO_CONFIG} \ 10 | --output ${OUTPUT_ROOT} \ 11 | --accumulation-steps 4 \ 12 | --opts MODEL.PRETRAINED ${pretrained} -------------------------------------------------------------------------------- /EVA-01/video/scripts/run_k700_ft.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | VIDEO_CONFIG=configs/kinetics700_ft.yaml 4 | OUTPUT_ROOT=/path/to/video/output/ 5 | pretrained=pretrained/eva_video_k722.pth 6 | 7 | python -m torch.distributed.launch --nproc_per_node=8 --nnodes=$nnodes \ 8 | --node_rank=$NODE_RANK --master_addr=$MASTER_ADDR --master_port=12355 \ 9 | main.py -cfg ${VIDEO_CONFIG} \ 10 | --output ${OUTPUT_ROOT} \ 11 | --accumulation-steps 4 \ 12 | --opts MODEL.PRETRAINED ${pretrained} -------------------------------------------------------------------------------- /EVA-01/video/scripts/run_k722_ft.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | VIDEO_CONFIG=configs/kinetics722_intermediate_ft.yaml 4 | OUTPUT_ROOT=/path/to/video/output/ 5 | pretrained=/path/to/eva_psz14.pt 6 | 7 | python -m torch.distributed.launch --nproc_per_node=8 --nnodes=$nnodes \ 8 | --node_rank=$NODE_RANK --master_addr=$MASTER_ADDR --master_port=12355 \ 9 | main.py -cfg ${VIDEO_CONFIG} \ 10 | --output ${OUTPUT_ROOT} \ 11 | --accumulation-steps 1 \ 12 | --opts MODEL.PRETRAINED ${pretrained} -------------------------------------------------------------------------------- /EVA-01/video/utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-01/video/utils/__init__.py -------------------------------------------------------------------------------- /EVA-02/assets/radar.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-02/assets/radar.png -------------------------------------------------------------------------------- /EVA-02/assets/summary_tab.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-02/assets/summary_tab.png -------------------------------------------------------------------------------- /EVA-02/asuka/.gitignore: -------------------------------------------------------------------------------- 1 | *.swp 2 | **/__pycache__/** 3 | .dumbo.json 4 | checkpoints/ 5 | .idea/* 6 | 7 | OUTPUT/* 8 | OUTPUT 9 | 10 | wandb/ 11 | 12 | # open_clip/ 13 | **/stdout/** 14 | **/stdout_pt/** 15 | **/stdout_ft/** 16 | **/stdout_test/** 17 | **/stdout_lin/** 18 | 19 | **/work_dirs/** 20 | deepspeed_config.json -------------------------------------------------------------------------------- /EVA-02/asuka/eva_clip/CLIP.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-02/asuka/eva_clip/CLIP.png -------------------------------------------------------------------------------- /EVA-02/asuka/eva_clip/__init__.py: -------------------------------------------------------------------------------- 1 | from .clip import * 2 | from .eva_clip import * 3 | from .model import * 4 | from .simple_tokenizer import * 5 | from .vit_model import * -------------------------------------------------------------------------------- /EVA-02/asuka/eva_clip/bpe_simple_vocab_16e6.txt.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-02/asuka/eva_clip/bpe_simple_vocab_16e6.txt.gz -------------------------------------------------------------------------------- /EVA-02/asuka/eva_clip/model_configs/EVA_CLIP_g_14.json: -------------------------------------------------------------------------------- 1 | { 2 | "embed_dim": 1024, 3 | "vision_cfg": { 4 | "image_size": 224, 5 | "layers": 40, 6 | "width": 1408, 7 | "head_width": 88, 8 | "mlp_ratio": 4.3637, 9 | "patch_size": 14, 10 | "drop_path_rate": 0.0 11 | }, 12 | "text_cfg": { 13 | "context_length": 77, 14 | "vocab_size": 49408, 15 | "width": 768, 16 | "heads": 12, 17 | "layers": 12 18 | } 19 | } -------------------------------------------------------------------------------- /EVA-02/asuka/requirements.txt: -------------------------------------------------------------------------------- 1 | timm==0.5.4 2 | Pillow 3 | blobfile 4 | mypy 5 | numpy 6 | pytest 7 | requests 8 | einops 9 | tensorboardX 10 | deepspeed==0.6.5 11 | scipy 12 | sh 13 | wandb 14 | ftfy 15 | regex -------------------------------------------------------------------------------- /EVA-02/det/.flake8: -------------------------------------------------------------------------------- 1 | # This is an example .flake8 config, used when developing *Black* itself. 2 | # Keep in sync with setup.cfg which is used for source packages. 3 | 4 | [flake8] 5 | ignore = W503, E203, E221, C901, C408, E741, C407, B017, F811, C101, EXE001, EXE002 6 | max-line-length = 100 7 | max-complexity = 18 8 | select = B,C,E,F,W,T4,B9 9 | exclude = build 10 | per-file-ignores = 11 | **/__init__.py:F401,F403,E402 12 | **/configs/**.py:F401,E402 13 | configs/**.py:F401,E402 14 | **/tests/config/**.py:F401,E402 15 | tests/config/**.py:F401,E402 16 | -------------------------------------------------------------------------------- /EVA-02/det/.github/CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Code of Conduct 2 | 3 | Facebook has adopted a Code of Conduct that we expect project participants to adhere to. 4 | Please read the [full text](https://code.fb.com/codeofconduct/) 5 | so that you can understand what actions will and will not be tolerated. 6 | -------------------------------------------------------------------------------- /EVA-02/det/.github/ISSUE_TEMPLATE.md: -------------------------------------------------------------------------------- 1 | 2 | Please select an issue template from 3 | https://github.com/facebookresearch/detectron2/issues/new/choose . 4 | 5 | Otherwise your issue will be closed. 6 | -------------------------------------------------------------------------------- /EVA-02/det/.github/ISSUE_TEMPLATE/documentation.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: "\U0001F4DA Documentation Issue" 3 | about: Report a problem about existing documentation, comments, website or tutorials. 4 | labels: documentation 5 | 6 | --- 7 | 8 | ## 📚 Documentation Issue 9 | 10 | This issue category is for problems about existing documentation, not for asking how-to questions. 11 | 12 | * Provide a link to an existing documentation/comment/tutorial: 13 | 14 | * How should the above documentation/comment/tutorial improve: 15 | -------------------------------------------------------------------------------- /EVA-02/det/.github/pull_request_template.md: -------------------------------------------------------------------------------- 1 | Thanks for your contribution! 2 | 3 | If you're sending a large PR (e.g., >100 lines), 4 | please open an issue first about the feature / bug, and indicate how you want to contribute. 5 | 6 | We do not always accept features. 7 | See https://detectron2.readthedocs.io/notes/contributing.html#pull-requests about how we handle PRs. 8 | 9 | Before submitting a PR, please run `dev/linter.sh` to lint the code. 10 | 11 | -------------------------------------------------------------------------------- /EVA-02/det/configs/Base-RCNN-C4.yaml: -------------------------------------------------------------------------------- 1 | MODEL: 2 | META_ARCHITECTURE: "GeneralizedRCNN" 3 | RPN: 4 | PRE_NMS_TOPK_TEST: 6000 5 | POST_NMS_TOPK_TEST: 1000 6 | ROI_HEADS: 7 | NAME: "Res5ROIHeads" 8 | DATASETS: 9 | TRAIN: ("coco_2017_train",) 10 | TEST: ("coco_2017_val",) 11 | SOLVER: 12 | IMS_PER_BATCH: 16 13 | BASE_LR: 0.02 14 | STEPS: (60000, 80000) 15 | MAX_ITER: 90000 16 | INPUT: 17 | MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) 18 | VERSION: 2 19 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-C4.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | MASK_ON: False 5 | RESNETS: 6 | DEPTH: 101 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-Detection/faster_rcnn_R_101_DC5_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-DilatedC5.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | MASK_ON: False 5 | RESNETS: 6 | DEPTH: 101 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | MASK_ON: False 5 | RESNETS: 6 | DEPTH: 101 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-C4.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: False 5 | RESNETS: 6 | DEPTH: 50 7 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-Detection/faster_rcnn_R_50_C4_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-C4.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: False 5 | RESNETS: 6 | DEPTH: 50 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-Detection/faster_rcnn_R_50_DC5_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-DilatedC5.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: False 5 | RESNETS: 6 | DEPTH: 50 7 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-Detection/faster_rcnn_R_50_DC5_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-DilatedC5.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: False 5 | RESNETS: 6 | DEPTH: 50 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: False 5 | RESNETS: 6 | DEPTH: 50 7 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: False 5 | RESNETS: 6 | DEPTH: 50 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | MASK_ON: False 4 | WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl" 5 | PIXEL_STD: [57.375, 57.120, 58.395] 6 | RESNETS: 7 | STRIDE_IN_1X1: False # this is a C2 model 8 | NUM_GROUPS: 32 9 | WIDTH_PER_GROUP: 8 10 | DEPTH: 101 11 | SOLVER: 12 | STEPS: (210000, 250000) 13 | MAX_ITER: 270000 14 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-Detection/fcos_R_50_FPN_1x.py: -------------------------------------------------------------------------------- 1 | from ..common.optim import SGD as optimizer 2 | from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier 3 | from ..common.data.coco import dataloader 4 | from ..common.models.fcos import model 5 | from ..common.train import train 6 | 7 | dataloader.train.mapper.use_instance_mask = False 8 | optimizer.lr = 0.01 9 | 10 | model.backbone.bottom_up.freeze_at = 2 11 | train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 12 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RetinaNet.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | RESNETS: 5 | DEPTH: 101 6 | SOLVER: 7 | STEPS: (210000, 250000) 8 | MAX_ITER: 270000 9 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-Detection/retinanet_R_50_FPN_1x.py: -------------------------------------------------------------------------------- 1 | from ..common.optim import SGD as optimizer 2 | from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier 3 | from ..common.data.coco import dataloader 4 | from ..common.models.retinanet import model 5 | from ..common.train import train 6 | 7 | dataloader.train.mapper.use_instance_mask = False 8 | model.backbone.bottom_up.freeze_at = 2 9 | optimizer.lr = 0.01 10 | 11 | train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 12 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RetinaNet.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-Detection/retinanet_R_50_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RetinaNet.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | SOLVER: 7 | STEPS: (210000, 250000) 8 | MAX_ITER: 270000 9 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-Detection/rpn_R_50_C4_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-C4.yaml" 2 | MODEL: 3 | META_ARCHITECTURE: "ProposalNetwork" 4 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 5 | MASK_ON: False 6 | RESNETS: 7 | DEPTH: 50 8 | RPN: 9 | PRE_NMS_TOPK_TEST: 12000 10 | POST_NMS_TOPK_TEST: 2000 11 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-Detection/rpn_R_50_FPN_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | META_ARCHITECTURE: "ProposalNetwork" 4 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 5 | MASK_ON: False 6 | RESNETS: 7 | DEPTH: 50 8 | RPN: 9 | POST_NMS_TOPK_TEST: 2000 10 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-C4.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 101 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-DilatedC5.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 101 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 101 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.py: -------------------------------------------------------------------------------- 1 | from ..common.train import train 2 | from ..common.optim import SGD as optimizer 3 | from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier 4 | from ..common.data.coco import dataloader 5 | from ..common.models.mask_rcnn_c4 import model 6 | 7 | model.backbone.freeze_at = 2 8 | train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 9 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-C4.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-C4.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-DilatedC5.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-DilatedC5.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.py: -------------------------------------------------------------------------------- 1 | from ..common.optim import SGD as optimizer 2 | from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier 3 | from ..common.data.coco import dataloader 4 | from ..common.models.mask_rcnn_fpn import model 5 | from ..common.train import train 6 | 7 | model.backbone.bottom_up.freeze_at = 2 8 | train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 9 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x_giou.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | RPN: 8 | BBOX_REG_LOSS_TYPE: "giou" 9 | BBOX_REG_LOSS_WEIGHT: 2.0 10 | ROI_BOX_HEAD: 11 | BBOX_REG_LOSS_TYPE: "giou" 12 | BBOX_REG_LOSS_WEIGHT: 10.0 13 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | MASK_ON: True 4 | WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl" 5 | PIXEL_STD: [57.375, 57.120, 58.395] 6 | RESNETS: 7 | STRIDE_IN_1X1: False # this is a C2 model 8 | NUM_GROUPS: 32 9 | WIDTH_PER_GROUP: 8 10 | DEPTH: 101 11 | SOLVER: 12 | STEPS: (210000, 250000) 13 | MAX_ITER: 270000 14 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-Keypoint-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | RESNETS: 5 | DEPTH: 101 6 | SOLVER: 7 | STEPS: (210000, 250000) 8 | MAX_ITER: 270000 9 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.py: -------------------------------------------------------------------------------- 1 | from ..common.optim import SGD as optimizer 2 | from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier 3 | from ..common.data.coco_keypoint import dataloader 4 | from ..common.models.keypoint_rcnn_fpn import model 5 | from ..common.train import train 6 | 7 | model.backbone.bottom_up.freeze_at = 2 8 | train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 9 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-Keypoint-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-Keypoint-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | SOLVER: 7 | STEPS: (210000, 250000) 8 | MAX_ITER: 270000 9 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-Keypoint-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl" 4 | PIXEL_STD: [57.375, 57.120, 58.395] 5 | RESNETS: 6 | STRIDE_IN_1X1: False # this is a C2 model 7 | NUM_GROUPS: 32 8 | WIDTH_PER_GROUP: 8 9 | DEPTH: 101 10 | SOLVER: 11 | STEPS: (210000, 250000) 12 | MAX_ITER: 270000 13 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | META_ARCHITECTURE: "PanopticFPN" 4 | MASK_ON: True 5 | SEM_SEG_HEAD: 6 | LOSS_WEIGHT: 0.5 7 | DATASETS: 8 | TRAIN: ("coco_2017_train_panoptic_separated",) 9 | TEST: ("coco_2017_val_panoptic_separated",) 10 | DATALOADER: 11 | FILTER_EMPTY_ANNOTATIONS: False 12 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-Panoptic-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | RESNETS: 5 | DEPTH: 101 6 | SOLVER: 7 | STEPS: (210000, 250000) 8 | MAX_ITER: 270000 9 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.py: -------------------------------------------------------------------------------- 1 | from ..common.optim import SGD as optimizer 2 | from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier 3 | from ..common.data.coco_panoptic_separated import dataloader 4 | from ..common.models.panoptic_fpn import model 5 | from ..common.train import train 6 | 7 | model.backbone.bottom_up.freeze_at = 2 8 | train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 9 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-Panoptic-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | -------------------------------------------------------------------------------- /EVA-02/det/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-Panoptic-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | SOLVER: 7 | STEPS: (210000, 250000) 8 | MAX_ITER: 270000 9 | -------------------------------------------------------------------------------- /EVA-02/det/configs/Misc/cascade_mask_rcnn_R_50_FPN_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | ROI_HEADS: 8 | NAME: CascadeROIHeads 9 | ROI_BOX_HEAD: 10 | CLS_AGNOSTIC_BBOX_REG: True 11 | RPN: 12 | POST_NMS_TOPK_TRAIN: 2000 13 | -------------------------------------------------------------------------------- /EVA-02/det/configs/Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | ROI_HEADS: 8 | NAME: CascadeROIHeads 9 | ROI_BOX_HEAD: 10 | CLS_AGNOSTIC_BBOX_REG: True 11 | RPN: 12 | POST_NMS_TOPK_TRAIN: 2000 13 | SOLVER: 14 | STEPS: (210000, 250000) 15 | MAX_ITER: 270000 16 | -------------------------------------------------------------------------------- /EVA-02/det/configs/Misc/mask_rcnn_R_50_FPN_1x_cls_agnostic.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | ROI_BOX_HEAD: 8 | CLS_AGNOSTIC_BBOX_REG: True 9 | ROI_MASK_HEAD: 10 | CLS_AGNOSTIC_MASK: True 11 | -------------------------------------------------------------------------------- /EVA-02/det/configs/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | DEFORM_ON_PER_STAGE: [False, True, True, True] # on Res3,Res4,Res5 8 | DEFORM_MODULATED: False 9 | -------------------------------------------------------------------------------- /EVA-02/det/configs/Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | DEFORM_ON_PER_STAGE: [False, True, True, True] # on Res3,Res4,Res5 8 | DEFORM_MODULATED: False 9 | SOLVER: 10 | STEPS: (210000, 250000) 11 | MAX_ITER: 270000 12 | -------------------------------------------------------------------------------- /EVA-02/det/configs/Misc/mask_rcnn_R_50_FPN_3x_gn.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "catalog://ImageNetPretrained/FAIR/R-50-GN" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | NORM: "GN" 8 | STRIDE_IN_1X1: False 9 | FPN: 10 | NORM: "GN" 11 | ROI_BOX_HEAD: 12 | NAME: "FastRCNNConvFCHead" 13 | NUM_CONV: 4 14 | NUM_FC: 1 15 | NORM: "GN" 16 | ROI_MASK_HEAD: 17 | NORM: "GN" 18 | SOLVER: 19 | # 3x schedule 20 | STEPS: (210000, 250000) 21 | MAX_ITER: 270000 22 | -------------------------------------------------------------------------------- /EVA-02/det/configs/Misc/semantic_R_50_FPN_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | META_ARCHITECTURE: "SemanticSegmentor" 4 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 5 | RESNETS: 6 | DEPTH: 50 7 | DATASETS: 8 | TRAIN: ("coco_2017_train_panoptic_stuffonly",) 9 | TEST: ("coco_2017_val_panoptic_stuffonly",) 10 | INPUT: 11 | MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) 12 | -------------------------------------------------------------------------------- /EVA-02/det/configs/common/README.md: -------------------------------------------------------------------------------- 1 | This directory provides definitions for a few common models, dataloaders, scheduler, 2 | and optimizers that are often used in training. 3 | The definition of these objects are provided in the form of lazy instantiation: 4 | their arguments can be edited by users before constructing the objects. 5 | 6 | They can be imported, or loaded by `model_zoo.get_config` API in users' own configs. 7 | -------------------------------------------------------------------------------- /EVA-02/det/configs/common/data/coco_keypoint.py: -------------------------------------------------------------------------------- 1 | from detectron2.data.detection_utils import create_keypoint_hflip_indices 2 | 3 | from .coco import dataloader 4 | 5 | dataloader.train.dataset.min_keypoints = 1 6 | dataloader.train.dataset.names = "keypoints_coco_2017_train" 7 | dataloader.test.dataset.names = "keypoints_coco_2017_val" 8 | 9 | dataloader.train.mapper.update( 10 | use_instance_mask=False, 11 | use_keypoint=True, 12 | keypoint_hflip_indices=create_keypoint_hflip_indices(dataloader.train.dataset.names), 13 | ) 14 | -------------------------------------------------------------------------------- /EVA-02/det/configs/common/data/constants.py: -------------------------------------------------------------------------------- 1 | constants = dict( 2 | imagenet_rgb256_mean=[123.675, 116.28, 103.53], 3 | imagenet_rgb256_std=[58.395, 57.12, 57.375], 4 | imagenet_bgr256_mean=[103.530, 116.280, 123.675], 5 | # When using pre-trained models in Detectron1 or any MSRA models, 6 | # std has been absorbed into its conv1 weights, so the std needs to be set 1. 7 | # Otherwise, you can use [57.375, 57.120, 58.395] (ImageNet std) 8 | imagenet_bgr256_std=[1.0, 1.0, 1.0], 9 | ) 10 | -------------------------------------------------------------------------------- /EVA-02/det/configs/new_baselines/mask_rcnn_R_101_FPN_100ep_LSJ.py: -------------------------------------------------------------------------------- 1 | from .mask_rcnn_R_50_FPN_100ep_LSJ import ( 2 | dataloader, 3 | lr_multiplier, 4 | model, 5 | optimizer, 6 | train, 7 | ) 8 | 9 | model.backbone.bottom_up.stages.depth = 101 10 | -------------------------------------------------------------------------------- /EVA-02/det/configs/new_baselines/mask_rcnn_R_101_FPN_200ep_LSJ.py: -------------------------------------------------------------------------------- 1 | from .mask_rcnn_R_101_FPN_100ep_LSJ import ( 2 | dataloader, 3 | lr_multiplier, 4 | model, 5 | optimizer, 6 | train, 7 | ) 8 | 9 | train.max_iter *= 2 # 100ep -> 200ep 10 | 11 | lr_multiplier.scheduler.milestones = [ 12 | milestone * 2 for milestone in lr_multiplier.scheduler.milestones 13 | ] 14 | lr_multiplier.scheduler.num_updates = train.max_iter 15 | -------------------------------------------------------------------------------- /EVA-02/det/configs/new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ.py: -------------------------------------------------------------------------------- 1 | from .mask_rcnn_R_101_FPN_100ep_LSJ import ( 2 | dataloader, 3 | lr_multiplier, 4 | model, 5 | optimizer, 6 | train, 7 | ) 8 | 9 | train.max_iter *= 4 # 100ep -> 400ep 10 | 11 | lr_multiplier.scheduler.milestones = [ 12 | milestone * 4 for milestone in lr_multiplier.scheduler.milestones 13 | ] 14 | lr_multiplier.scheduler.num_updates = train.max_iter 15 | -------------------------------------------------------------------------------- /EVA-02/det/configs/new_baselines/mask_rcnn_R_50_FPN_200ep_LSJ.py: -------------------------------------------------------------------------------- 1 | from .mask_rcnn_R_50_FPN_100ep_LSJ import ( 2 | dataloader, 3 | lr_multiplier, 4 | model, 5 | optimizer, 6 | train, 7 | ) 8 | 9 | train.max_iter *= 2 # 100ep -> 200ep 10 | 11 | lr_multiplier.scheduler.milestones = [ 12 | milestone * 2 for milestone in lr_multiplier.scheduler.milestones 13 | ] 14 | lr_multiplier.scheduler.num_updates = train.max_iter 15 | -------------------------------------------------------------------------------- /EVA-02/det/configs/new_baselines/mask_rcnn_R_50_FPN_400ep_LSJ.py: -------------------------------------------------------------------------------- 1 | from .mask_rcnn_R_50_FPN_100ep_LSJ import ( 2 | dataloader, 3 | lr_multiplier, 4 | model, 5 | optimizer, 6 | train, 7 | ) 8 | 9 | train.max_iter *= 4 # 100ep -> 400ep 10 | 11 | lr_multiplier.scheduler.milestones = [ 12 | milestone * 4 for milestone in lr_multiplier.scheduler.milestones 13 | ] 14 | lr_multiplier.scheduler.num_updates = train.max_iter 15 | -------------------------------------------------------------------------------- /EVA-02/det/configs/new_baselines/mask_rcnn_R_50_FPN_50ep_LSJ.py: -------------------------------------------------------------------------------- 1 | from .mask_rcnn_R_50_FPN_100ep_LSJ import ( 2 | dataloader, 3 | lr_multiplier, 4 | model, 5 | optimizer, 6 | train, 7 | ) 8 | 9 | train.max_iter //= 2 # 100ep -> 50ep 10 | 11 | lr_multiplier.scheduler.milestones = [ 12 | milestone // 2 for milestone in lr_multiplier.scheduler.milestones 13 | ] 14 | lr_multiplier.scheduler.num_updates = train.max_iter 15 | -------------------------------------------------------------------------------- /EVA-02/det/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ.py: -------------------------------------------------------------------------------- 1 | from .mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ import ( 2 | dataloader, 3 | lr_multiplier, 4 | model, 5 | optimizer, 6 | train, 7 | ) 8 | 9 | train.max_iter *= 2 # 100ep -> 200ep 10 | 11 | lr_multiplier.scheduler.milestones = [ 12 | milestone * 2 for milestone in lr_multiplier.scheduler.milestones 13 | ] 14 | lr_multiplier.scheduler.num_updates = train.max_iter 15 | -------------------------------------------------------------------------------- /EVA-02/det/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ.py: -------------------------------------------------------------------------------- 1 | from .mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ import ( 2 | dataloader, 3 | lr_multiplier, 4 | model, 5 | optimizer, 6 | train, 7 | ) 8 | 9 | train.max_iter *= 4 # 100ep -> 400ep 10 | 11 | lr_multiplier.scheduler.milestones = [ 12 | milestone * 4 for milestone in lr_multiplier.scheduler.milestones 13 | ] 14 | lr_multiplier.scheduler.num_updates = train.max_iter 15 | -------------------------------------------------------------------------------- /EVA-02/det/configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ.py: -------------------------------------------------------------------------------- 1 | from .mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ import ( 2 | dataloader, 3 | lr_multiplier, 4 | model, 5 | optimizer, 6 | train, 7 | ) 8 | 9 | train.max_iter *= 2 # 100ep -> 200ep 10 | 11 | lr_multiplier.scheduler.milestones = [ 12 | milestone * 2 for milestone in lr_multiplier.scheduler.milestones 13 | ] 14 | lr_multiplier.scheduler.num_updates = train.max_iter 15 | -------------------------------------------------------------------------------- /EVA-02/det/configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ.py: -------------------------------------------------------------------------------- 1 | from .mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ import ( 2 | dataloader, 3 | lr_multiplier, 4 | model, 5 | optimizer, 6 | train, 7 | ) 8 | 9 | train.max_iter *= 4 # 100ep -> 400ep 10 | 11 | lr_multiplier.scheduler.milestones = [ 12 | milestone * 4 for milestone in lr_multiplier.scheduler.milestones 13 | ] 14 | lr_multiplier.scheduler.num_updates = train.max_iter 15 | -------------------------------------------------------------------------------- /EVA-02/det/configs/quick_schedules/cascade_mask_rcnn_R_50_FPN_inference_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://Misc/cascade_mask_rcnn_R_50_FPN_3x/144998488/model_final_480dd8.pkl" 4 | DATASETS: 5 | TEST: ("coco_2017_val_100",) 6 | TEST: 7 | EXPECTED_RESULTS: [["bbox", "AP", 50.18, 0.02], ["segm", "AP", 43.87, 0.02]] 8 | -------------------------------------------------------------------------------- /EVA-02/det/configs/quick_schedules/cascade_mask_rcnn_R_50_FPN_instant_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml" 2 | DATASETS: 3 | TRAIN: ("coco_2017_val_100",) 4 | TEST: ("coco_2017_val_100",) 5 | SOLVER: 6 | BASE_LR: 0.005 7 | STEPS: (30,) 8 | MAX_ITER: 40 9 | IMS_PER_BATCH: 4 10 | DATALOADER: 11 | NUM_WORKERS: 2 12 | -------------------------------------------------------------------------------- /EVA-02/det/configs/quick_schedules/fast_rcnn_R_50_FPN_inference_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://COCO-Detection/fast_rcnn_R_50_FPN_1x/137635226/model_final_e5f7ce.pkl" 4 | DATASETS: 5 | TEST: ("coco_2017_val_100",) 6 | TEST: 7 | EXPECTED_RESULTS: [["bbox", "AP", 45.70, 0.02]] 8 | -------------------------------------------------------------------------------- /EVA-02/det/configs/quick_schedules/keypoint_rcnn_R_50_FPN_inference_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x/137849621/model_final_a6e10b.pkl" 4 | DATASETS: 5 | TEST: ("keypoints_coco_2017_val_100",) 6 | TEST: 7 | EXPECTED_RESULTS: [["bbox", "AP", 52.47, 0.02], ["keypoints", "AP", 67.36, 0.02]] 8 | -------------------------------------------------------------------------------- /EVA-02/det/configs/quick_schedules/keypoint_rcnn_R_50_FPN_instant_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | KEYPOINT_ON: True 5 | ROI_HEADS: 6 | NUM_CLASSES: 1 7 | DATASETS: 8 | TRAIN: ("keypoints_coco_2017_val_100",) 9 | TEST: ("keypoints_coco_2017_val_100",) 10 | SOLVER: 11 | BASE_LR: 0.005 12 | STEPS: (30,) 13 | MAX_ITER: 40 14 | IMS_PER_BATCH: 4 15 | DATALOADER: 16 | NUM_WORKERS: 2 17 | -------------------------------------------------------------------------------- /EVA-02/det/configs/quick_schedules/mask_rcnn_R_50_C4_GCV_instant_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-C4.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | DATASETS: 6 | TRAIN: ("coco_2017_val_100",) 7 | TEST: ("coco_2017_val_100",) 8 | SOLVER: 9 | BASE_LR: 0.001 10 | STEPS: (30,) 11 | MAX_ITER: 40 12 | IMS_PER_BATCH: 4 13 | CLIP_GRADIENTS: 14 | ENABLED: True 15 | CLIP_TYPE: "value" 16 | CLIP_VALUE: 1.0 17 | DATALOADER: 18 | NUM_WORKERS: 2 19 | -------------------------------------------------------------------------------- /EVA-02/det/configs/quick_schedules/mask_rcnn_R_50_C4_inference_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x/137849525/model_final_4ce675.pkl" 4 | DATASETS: 5 | TEST: ("coco_2017_val_100",) 6 | TEST: 7 | EXPECTED_RESULTS: [["bbox", "AP", 47.37, 0.02], ["segm", "AP", 40.99, 0.02]] 8 | -------------------------------------------------------------------------------- /EVA-02/det/configs/quick_schedules/mask_rcnn_R_50_C4_instant_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-C4.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | DATASETS: 6 | TRAIN: ("coco_2017_val_100",) 7 | TEST: ("coco_2017_val_100",) 8 | SOLVER: 9 | BASE_LR: 0.001 10 | STEPS: (30,) 11 | MAX_ITER: 40 12 | IMS_PER_BATCH: 4 13 | DATALOADER: 14 | NUM_WORKERS: 2 15 | -------------------------------------------------------------------------------- /EVA-02/det/configs/quick_schedules/mask_rcnn_R_50_DC5_inference_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x/137849551/model_final_84107b.pkl" 4 | DATASETS: 5 | TEST: ("coco_2017_val_100",) 6 | TEST: 7 | EXPECTED_RESULTS: [["bbox", "AP", 47.44, 0.02], ["segm", "AP", 42.94, 0.02]] 8 | -------------------------------------------------------------------------------- /EVA-02/det/configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl" 4 | DATASETS: 5 | TEST: ("coco_2017_val_100",) 6 | TEST: 7 | EXPECTED_RESULTS: [["bbox", "AP", 47.34, 0.02], ["segm", "AP", 42.67, 0.02], ["bbox_TTA", "AP", 49.11, 0.02], ["segm_TTA", "AP", 45.04, 0.02]] 8 | AUG: 9 | ENABLED: True 10 | MIN_SIZES: (700, 800) # to save some time 11 | -------------------------------------------------------------------------------- /EVA-02/det/configs/quick_schedules/mask_rcnn_R_50_FPN_instant_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | DATASETS: 6 | TRAIN: ("coco_2017_val_100",) 7 | TEST: ("coco_2017_val_100",) 8 | SOLVER: 9 | BASE_LR: 0.005 10 | STEPS: (30,) 11 | MAX_ITER: 40 12 | IMS_PER_BATCH: 4 13 | DATALOADER: 14 | NUM_WORKERS: 2 15 | -------------------------------------------------------------------------------- /EVA-02/det/configs/quick_schedules/mask_rcnn_R_50_FPN_pred_boxes_training_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "./mask_rcnn_R_50_FPN_training_acc_test.yaml" 2 | MODEL: 3 | ROI_BOX_HEAD: 4 | TRAIN_ON_PRED_BOXES: True 5 | TEST: 6 | EXPECTED_RESULTS: [["bbox", "AP", 42.6, 1.0], ["segm", "AP", 35.8, 0.8]] 7 | -------------------------------------------------------------------------------- /EVA-02/det/configs/quick_schedules/panoptic_fpn_R_50_inference_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://COCO-PanopticSegmentation/panoptic_fpn_R_50_3x/139514569/model_final_c10459.pkl" 4 | DATASETS: 5 | TEST: ("coco_2017_val_100_panoptic_separated",) 6 | TEST: 7 | EXPECTED_RESULTS: [["bbox", "AP", 46.47, 0.02], ["segm", "AP", 43.39, 0.02], ["sem_seg", "mIoU", 42.55, 0.02], ["panoptic_seg", "PQ", 38.99, 0.02]] 8 | -------------------------------------------------------------------------------- /EVA-02/det/configs/quick_schedules/retinanet_R_50_FPN_inference_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../COCO-Detection/retinanet_R_50_FPN_3x.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://COCO-Detection/retinanet_R_50_FPN_3x/190397829/model_final_5bd44e.pkl" 4 | DATASETS: 5 | TEST: ("coco_2017_val_100",) 6 | TEST: 7 | EXPECTED_RESULTS: [["bbox", "AP", 44.45, 0.02]] 8 | -------------------------------------------------------------------------------- /EVA-02/det/configs/quick_schedules/retinanet_R_50_FPN_instant_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../COCO-Detection/retinanet_R_50_FPN_1x.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | DATASETS: 5 | TRAIN: ("coco_2017_val_100",) 6 | TEST: ("coco_2017_val_100",) 7 | SOLVER: 8 | BASE_LR: 0.005 9 | STEPS: (30,) 10 | MAX_ITER: 40 11 | IMS_PER_BATCH: 4 12 | DATALOADER: 13 | NUM_WORKERS: 2 14 | -------------------------------------------------------------------------------- /EVA-02/det/configs/quick_schedules/rpn_R_50_FPN_inference_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../COCO-Detection/rpn_R_50_FPN_1x.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/model_final_02ce48.pkl" 4 | DATASETS: 5 | TEST: ("coco_2017_val_100",) 6 | TEST: 7 | EXPECTED_RESULTS: [["box_proposals", "AR@1000", 58.16, 0.02]] 8 | -------------------------------------------------------------------------------- /EVA-02/det/configs/quick_schedules/rpn_R_50_FPN_instant_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../COCO-Detection/rpn_R_50_FPN_1x.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | DATASETS: 5 | TRAIN: ("coco_2017_val_100",) 6 | TEST: ("coco_2017_val_100",) 7 | SOLVER: 8 | STEPS: (30,) 9 | MAX_ITER: 40 10 | BASE_LR: 0.005 11 | IMS_PER_BATCH: 4 12 | DATALOADER: 13 | NUM_WORKERS: 2 14 | -------------------------------------------------------------------------------- /EVA-02/det/configs/quick_schedules/semantic_R_50_FPN_inference_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-RCNN-FPN.yaml" 2 | MODEL: 3 | META_ARCHITECTURE: "SemanticSegmentor" 4 | WEIGHTS: "detectron2://semantic_R_50_FPN_1x/111802073/model_final_c18079783c55a94968edc28b7101c5f0.pkl" 5 | RESNETS: 6 | DEPTH: 50 7 | DATASETS: 8 | TEST: ("coco_2017_val_100_panoptic_stuffonly",) 9 | TEST: 10 | EXPECTED_RESULTS: [["sem_seg", "mIoU", 39.53, 0.02], ["sem_seg", "mACC", 51.50, 0.02]] 11 | -------------------------------------------------------------------------------- /EVA-02/det/demo/README.md: -------------------------------------------------------------------------------- 1 | 2 | ## Detectron2 Demo 3 | 4 | We provide a command line tool to run a simple demo of builtin configs. 5 | The usage is explained in [GETTING_STARTED.md](../GETTING_STARTED.md). 6 | 7 | See our [blog post](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-) 8 | for a high-quality demo generated with this tool. 9 | -------------------------------------------------------------------------------- /EVA-02/det/detectron2/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | 3 | from .utils.env import setup_environment 4 | 5 | setup_environment() 6 | 7 | 8 | # This line will be programatically read/write by setup.py. 9 | # Leave them at the bottom of this file and don't touch them. 10 | __version__ = "0.6" 11 | -------------------------------------------------------------------------------- /EVA-02/det/detectron2/checkpoint/__init__.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # File: 4 | 5 | 6 | from . import catalog as _UNUSED # register the handler 7 | from .detection_checkpoint import DetectionCheckpointer 8 | from fvcore.common.checkpoint import Checkpointer, PeriodicCheckpointer 9 | 10 | __all__ = ["Checkpointer", "PeriodicCheckpointer", "DetectionCheckpointer"] 11 | -------------------------------------------------------------------------------- /EVA-02/det/detectron2/data/datasets/README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | ### Common Datasets 4 | 5 | The dataset implemented here do not need to load the data into the final format. 6 | It should provide the minimal data structure needed to use the dataset, so it can be very efficient. 7 | 8 | For example, for an image dataset, just provide the file names and labels, but don't read the images. 9 | Let the downstream decide how to read. 10 | -------------------------------------------------------------------------------- /EVA-02/det/detectron2/data/datasets/register_coco.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | from .coco import register_coco_instances # noqa 3 | from .coco_panoptic import register_coco_panoptic_separated # noqa 4 | -------------------------------------------------------------------------------- /EVA-02/det/detectron2/data/samplers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | from .distributed_sampler import ( 3 | InferenceSampler, 4 | RandomSubsetTrainingSampler, 5 | RepeatFactorTrainingSampler, 6 | TrainingSampler, 7 | ) 8 | 9 | from .grouped_batch_sampler import GroupedBatchSampler 10 | 11 | __all__ = [ 12 | "GroupedBatchSampler", 13 | "TrainingSampler", 14 | "RandomSubsetTrainingSampler", 15 | "InferenceSampler", 16 | "RepeatFactorTrainingSampler", 17 | ] 18 | -------------------------------------------------------------------------------- /EVA-02/det/detectron2/engine/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | 3 | from .launch import * 4 | from .train_loop import * 5 | 6 | __all__ = [k for k in globals().keys() if not k.startswith("_")] 7 | 8 | 9 | # prefer to let hooks and defaults live in separate namespaces (therefore not in __all__) 10 | # but still make them available here 11 | from .hooks import * 12 | from .defaults import * 13 | -------------------------------------------------------------------------------- /EVA-02/det/detectron2/layers/csrc/README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | To add a new Op: 4 | 5 | 1. Create a new directory 6 | 2. Implement new ops there 7 | 3. Delcare its Python interface in `vision.cpp`. 8 | -------------------------------------------------------------------------------- /EVA-02/det/detectron2/model_zoo/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | """ 3 | Model Zoo API for Detectron2: a collection of functions to create common model architectures 4 | listed in `MODEL_ZOO.md `_, 5 | and optionally load their pre-trained weights. 6 | """ 7 | 8 | from .model_zoo import get, get_config_file, get_checkpoint_url, get_config 9 | 10 | __all__ = ["get_checkpoint_url", "get", "get_config_file", "get_config"] 11 | -------------------------------------------------------------------------------- /EVA-02/det/detectron2/modeling/proposal_generator/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | from .build import PROPOSAL_GENERATOR_REGISTRY, build_proposal_generator 3 | from .rpn import RPN_HEAD_REGISTRY, build_rpn_head, RPN, StandardRPNHead 4 | 5 | __all__ = list(globals().keys()) 6 | -------------------------------------------------------------------------------- /EVA-02/det/detectron2/projects/README.md: -------------------------------------------------------------------------------- 1 | 2 | Projects live in the [`projects` directory](../../projects) under the root of this repository, but not here. 3 | -------------------------------------------------------------------------------- /EVA-02/det/detectron2/solver/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | from .build import build_lr_scheduler, build_optimizer, get_default_optimizer_params 3 | from .lr_scheduler import WarmupCosineLR, WarmupMultiStepLR, LRMultiplier, WarmupParamScheduler 4 | 5 | __all__ = [k for k in globals().keys() if not k.startswith("_")] 6 | -------------------------------------------------------------------------------- /EVA-02/det/detectron2/utils/README.md: -------------------------------------------------------------------------------- 1 | # Utility functions 2 | 3 | This folder contain utility functions that are not used in the 4 | core library, but are useful for building models or training 5 | code using the config system. 6 | -------------------------------------------------------------------------------- /EVA-02/det/detectron2/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | -------------------------------------------------------------------------------- /EVA-02/det/dev/README.md: -------------------------------------------------------------------------------- 1 | 2 | ## Some scripts for developers to use, include: 3 | 4 | - `linter.sh`: lint the codebase before commit. 5 | - `run_{inference,instant}_tests.sh`: run inference/training for a few iterations. 6 | Note that these tests require 2 GPUs. 7 | - `parse_results.sh`: parse results from a log file. 8 | -------------------------------------------------------------------------------- /EVA-02/det/docs/.gitignore: -------------------------------------------------------------------------------- 1 | _build 2 | -------------------------------------------------------------------------------- /EVA-02/det/docs/README.md: -------------------------------------------------------------------------------- 1 | # Read the docs: 2 | 3 | The latest documentation built from this directory is available at [detectron2.readthedocs.io](https://detectron2.readthedocs.io/). 4 | Documents in this directory are not meant to be read on github. 5 | 6 | # Build the docs: 7 | 8 | 1. Install detectron2 according to [INSTALL.md](../INSTALL.md). 9 | 2. Install additional libraries required to build docs: 10 | - docutils==0.16 11 | - Sphinx==3.2.0 12 | - recommonmark==0.6.0 13 | - sphinx_rtd_theme 14 | 15 | 3. Run `make html` from this directory. 16 | -------------------------------------------------------------------------------- /EVA-02/det/docs/index.rst: -------------------------------------------------------------------------------- 1 | .. detectron2 documentation master file, created by 2 | sphinx-quickstart on Sat Sep 21 13:46:45 2019. 3 | You can adapt this file completely to your liking, but it should at least 4 | contain the root `toctree` directive. 5 | 6 | Welcome to detectron2's documentation! 7 | ====================================== 8 | 9 | .. toctree:: 10 | :maxdepth: 2 11 | 12 | tutorials/index 13 | notes/index 14 | modules/index 15 | -------------------------------------------------------------------------------- /EVA-02/det/docs/modules/checkpoint.rst: -------------------------------------------------------------------------------- 1 | detectron2.checkpoint 2 | ============================= 3 | 4 | .. automodule:: detectron2.checkpoint 5 | :members: 6 | :undoc-members: 7 | :show-inheritance: 8 | -------------------------------------------------------------------------------- /EVA-02/det/docs/modules/config.rst: -------------------------------------------------------------------------------- 1 | detectron2.config 2 | ========================= 3 | 4 | Related tutorials: :doc:`../tutorials/configs`, :doc:`../tutorials/extend`. 5 | 6 | .. automodule:: detectron2.config 7 | :members: 8 | :undoc-members: 9 | :show-inheritance: 10 | 11 | 12 | Yaml Config References 13 | ----------------- 14 | 15 | .. literalinclude:: ../../detectron2/config/defaults.py 16 | :language: python 17 | :linenos: 18 | :lines: 7- 19 | -------------------------------------------------------------------------------- /EVA-02/det/docs/modules/data_transforms.rst: -------------------------------------------------------------------------------- 1 | detectron2.data.transforms 2 | ==================================== 3 | 4 | Related tutorial: :doc:`../tutorials/augmentation`. 5 | 6 | .. automodule:: detectron2.data.transforms 7 | :members: 8 | :undoc-members: 9 | :show-inheritance: 10 | :imported-members: 11 | -------------------------------------------------------------------------------- /EVA-02/det/docs/modules/evaluation.rst: -------------------------------------------------------------------------------- 1 | detectron2.evaluation 2 | ============================= 3 | 4 | .. automodule:: detectron2.evaluation 5 | :members: 6 | :undoc-members: 7 | :show-inheritance: 8 | -------------------------------------------------------------------------------- /EVA-02/det/docs/modules/export.rst: -------------------------------------------------------------------------------- 1 | detectron2.export 2 | ========================= 3 | 4 | Related tutorial: :doc:`../tutorials/deployment`. 5 | 6 | .. automodule:: detectron2.export 7 | :members: 8 | :undoc-members: 9 | :show-inheritance: 10 | -------------------------------------------------------------------------------- /EVA-02/det/docs/modules/index.rst: -------------------------------------------------------------------------------- 1 | API Documentation 2 | ================== 3 | 4 | .. toctree:: 5 | 6 | checkpoint 7 | config 8 | data 9 | data_transforms 10 | engine 11 | evaluation 12 | layers 13 | model_zoo 14 | modeling 15 | solver 16 | structures 17 | utils 18 | export 19 | fvcore 20 | -------------------------------------------------------------------------------- /EVA-02/det/docs/modules/layers.rst: -------------------------------------------------------------------------------- 1 | detectron2.layers 2 | ========================= 3 | 4 | .. automodule:: detectron2.layers 5 | :members: 6 | :undoc-members: 7 | :show-inheritance: 8 | -------------------------------------------------------------------------------- /EVA-02/det/docs/modules/model_zoo.rst: -------------------------------------------------------------------------------- 1 | detectron2.model_zoo 2 | ============================ 3 | 4 | .. automodule:: detectron2.model_zoo 5 | :members: 6 | :undoc-members: 7 | :show-inheritance: 8 | -------------------------------------------------------------------------------- /EVA-02/det/docs/modules/solver.rst: -------------------------------------------------------------------------------- 1 | detectron2.solver 2 | ========================= 3 | 4 | .. automodule:: detectron2.solver 5 | :members: 6 | :undoc-members: 7 | :show-inheritance: 8 | -------------------------------------------------------------------------------- /EVA-02/det/docs/modules/structures.rst: -------------------------------------------------------------------------------- 1 | detectron2.structures 2 | ============================= 3 | 4 | .. automodule:: detectron2.structures 5 | :members: 6 | :undoc-members: 7 | :show-inheritance: 8 | -------------------------------------------------------------------------------- /EVA-02/det/docs/notes/contributing.md: -------------------------------------------------------------------------------- 1 | ../../.github/CONTRIBUTING.md -------------------------------------------------------------------------------- /EVA-02/det/docs/notes/index.rst: -------------------------------------------------------------------------------- 1 | Notes 2 | ====================================== 3 | 4 | .. toctree:: 5 | :maxdepth: 2 6 | 7 | benchmarks 8 | compatibility 9 | contributing 10 | changelog 11 | -------------------------------------------------------------------------------- /EVA-02/det/docs/tutorials/README.md: -------------------------------------------------------------------------------- 1 | # Read the docs: 2 | 3 | The latest documentation built from this directory is available at [detectron2.readthedocs.io](https://detectron2.readthedocs.io/). 4 | Documents in this directory are not meant to be read on github. 5 | -------------------------------------------------------------------------------- /EVA-02/det/docs/tutorials/builtin_datasets.md: -------------------------------------------------------------------------------- 1 | ../../datasets/README.md -------------------------------------------------------------------------------- /EVA-02/det/docs/tutorials/getting_started.md: -------------------------------------------------------------------------------- 1 | ../../GETTING_STARTED.md -------------------------------------------------------------------------------- /EVA-02/det/docs/tutorials/index.rst: -------------------------------------------------------------------------------- 1 | Tutorials 2 | ====================================== 3 | 4 | .. toctree:: 5 | :maxdepth: 2 6 | 7 | install 8 | getting_started 9 | builtin_datasets 10 | extend 11 | datasets 12 | data_loading 13 | augmentation 14 | models 15 | write-models 16 | training 17 | evaluation 18 | configs 19 | lazyconfigs 20 | deployment 21 | -------------------------------------------------------------------------------- /EVA-02/det/docs/tutorials/install.md: -------------------------------------------------------------------------------- 1 | ../../INSTALL.md -------------------------------------------------------------------------------- /EVA-02/det/projects/DeepLab/deeplab/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | from .build_solver import build_lr_scheduler 3 | from .config import add_deeplab_config 4 | from .resnet import build_resnet_deeplab_backbone 5 | from .semantic_seg import DeepLabV3Head, DeepLabV3PlusHead 6 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/HRNet/densepose_rcnn_HRFPN_HRNet_w32_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-DensePose-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "https://1drv.ms/u/s!Aus8VCZ_C_33dYBMemi9xOUFR0w" 4 | BACKBONE: 5 | NAME: "build_hrfpn_backbone" 6 | RPN: 7 | IN_FEATURES: ['p1', 'p2', 'p3', 'p4', 'p5'] 8 | ROI_HEADS: 9 | IN_FEATURES: ['p1', 'p2', 'p3', 'p4', 'p5'] 10 | SOLVER: 11 | MAX_ITER: 130000 12 | STEPS: (100000, 120000) 13 | CLIP_GRADIENTS: 14 | ENABLED: True 15 | CLIP_TYPE: "norm" 16 | BASE_LR: 0.03 17 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/cse/densepose_rcnn_R_101_FPN_DL_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN-Human.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | RESNETS: 5 | DEPTH: 101 6 | ROI_DENSEPOSE_HEAD: 7 | NAME: "DensePoseDeepLabHead" 8 | CSE: 9 | EMBED_LOSS_NAME: "EmbeddingLoss" 10 | SOLVER: 11 | MAX_ITER: 130000 12 | STEPS: (100000, 120000) 13 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/cse/densepose_rcnn_R_101_FPN_DL_soft_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN-Human.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | RESNETS: 5 | DEPTH: 101 6 | ROI_DENSEPOSE_HEAD: 7 | NAME: "DensePoseDeepLabHead" 8 | CSE: 9 | EMBED_LOSS_NAME: "SoftEmbeddingLoss" 10 | SOLVER: 11 | MAX_ITER: 130000 12 | STEPS: (100000, 120000) 13 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/cse/densepose_rcnn_R_101_FPN_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN-Human.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | RESNETS: 5 | DEPTH: 101 6 | ROI_DENSEPOSE_HEAD: 7 | NAME: "DensePoseV1ConvXHead" 8 | CSE: 9 | EMBED_LOSS_NAME: "EmbeddingLoss" 10 | SOLVER: 11 | MAX_ITER: 130000 12 | STEPS: (100000, 120000) 13 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/cse/densepose_rcnn_R_101_FPN_soft_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN-Human.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | RESNETS: 5 | DEPTH: 101 6 | ROI_DENSEPOSE_HEAD: 7 | NAME: "DensePoseV1ConvXHead" 8 | CSE: 9 | EMBED_LOSS_NAME: "SoftEmbeddingLoss" 10 | SOLVER: 11 | MAX_ITER: 130000 12 | STEPS: (100000, 120000) 13 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/cse/densepose_rcnn_R_50_FPN_DL_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN-Human.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | ROI_DENSEPOSE_HEAD: 7 | NAME: "DensePoseDeepLabHead" 8 | CSE: 9 | EMBED_LOSS_NAME: "EmbeddingLoss" 10 | SOLVER: 11 | MAX_ITER: 130000 12 | STEPS: (100000, 120000) 13 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/cse/densepose_rcnn_R_50_FPN_DL_soft_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN-Human.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | ROI_DENSEPOSE_HEAD: 7 | NAME: "DensePoseDeepLabHead" 8 | CSE: 9 | EMBED_LOSS_NAME: "SoftEmbeddingLoss" 10 | SOLVER: 11 | MAX_ITER: 130000 12 | STEPS: (100000, 120000) 13 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/cse/densepose_rcnn_R_50_FPN_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN-Human.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | ROI_DENSEPOSE_HEAD: 7 | NAME: "DensePoseV1ConvXHead" 8 | CSE: 9 | EMBED_LOSS_NAME: "EmbeddingLoss" 10 | SOLVER: 11 | MAX_ITER: 130000 12 | STEPS: (100000, 120000) 13 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/cse/densepose_rcnn_R_50_FPN_soft_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN-Human.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | ROI_DENSEPOSE_HEAD: 7 | NAME: "DensePoseV1ConvXHead" 8 | CSE: 9 | EMBED_LOSS_NAME: "SoftEmbeddingLoss" 10 | SOLVER: 11 | MAX_ITER: 130000 12 | STEPS: (100000, 120000) 13 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/densepose_rcnn_R_101_FPN_DL_WC1_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | RESNETS: 5 | DEPTH: 101 6 | ROI_DENSEPOSE_HEAD: 7 | NAME: "DensePoseDeepLabHead" 8 | UV_CONFIDENCE: 9 | ENABLED: True 10 | TYPE: "iid_iso" 11 | POINT_REGRESSION_WEIGHTS: 0.0005 12 | SOLVER: 13 | CLIP_GRADIENTS: 14 | ENABLED: True 15 | MAX_ITER: 130000 16 | STEPS: (100000, 120000) 17 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/densepose_rcnn_R_101_FPN_DL_WC2_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | RESNETS: 5 | DEPTH: 101 6 | ROI_DENSEPOSE_HEAD: 7 | NAME: "DensePoseDeepLabHead" 8 | UV_CONFIDENCE: 9 | ENABLED: True 10 | TYPE: "indep_aniso" 11 | POINT_REGRESSION_WEIGHTS: 0.0005 12 | SOLVER: 13 | CLIP_GRADIENTS: 14 | ENABLED: True 15 | MAX_ITER: 130000 16 | STEPS: (100000, 120000) 17 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/densepose_rcnn_R_101_FPN_DL_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | RESNETS: 5 | DEPTH: 101 6 | ROI_DENSEPOSE_HEAD: 7 | NAME: "DensePoseDeepLabHead" 8 | SOLVER: 9 | MAX_ITER: 130000 10 | STEPS: (100000, 120000) 11 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/densepose_rcnn_R_101_FPN_WC1M_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | RESNETS: 5 | DEPTH: 101 6 | ROI_DENSEPOSE_HEAD: 7 | UV_CONFIDENCE: 8 | ENABLED: True 9 | TYPE: "iid_iso" 10 | SEGM_CONFIDENCE: 11 | ENABLED: True 12 | POINT_REGRESSION_WEIGHTS: 0.0005 13 | SOLVER: 14 | CLIP_GRADIENTS: 15 | ENABLED: True 16 | MAX_ITER: 130000 17 | STEPS: (100000, 120000) 18 | WARMUP_FACTOR: 0.025 19 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/densepose_rcnn_R_101_FPN_WC1_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | RESNETS: 5 | DEPTH: 101 6 | ROI_DENSEPOSE_HEAD: 7 | UV_CONFIDENCE: 8 | ENABLED: True 9 | TYPE: "iid_iso" 10 | POINT_REGRESSION_WEIGHTS: 0.0005 11 | SOLVER: 12 | CLIP_GRADIENTS: 13 | ENABLED: True 14 | MAX_ITER: 130000 15 | STEPS: (100000, 120000) 16 | WARMUP_FACTOR: 0.025 17 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/densepose_rcnn_R_101_FPN_WC2M_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | RESNETS: 5 | DEPTH: 101 6 | ROI_DENSEPOSE_HEAD: 7 | UV_CONFIDENCE: 8 | ENABLED: True 9 | TYPE: "indep_aniso" 10 | SEGM_CONFIDENCE: 11 | ENABLED: True 12 | POINT_REGRESSION_WEIGHTS: 0.0005 13 | SOLVER: 14 | CLIP_GRADIENTS: 15 | ENABLED: True 16 | MAX_ITER: 130000 17 | STEPS: (100000, 120000) 18 | WARMUP_FACTOR: 0.025 19 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/densepose_rcnn_R_101_FPN_WC2_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | RESNETS: 5 | DEPTH: 101 6 | ROI_DENSEPOSE_HEAD: 7 | UV_CONFIDENCE: 8 | ENABLED: True 9 | TYPE: "indep_aniso" 10 | POINT_REGRESSION_WEIGHTS: 0.0005 11 | SOLVER: 12 | CLIP_GRADIENTS: 13 | ENABLED: True 14 | MAX_ITER: 130000 15 | STEPS: (100000, 120000) 16 | WARMUP_FACTOR: 0.025 17 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/densepose_rcnn_R_101_FPN_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | RESNETS: 5 | DEPTH: 101 6 | SOLVER: 7 | MAX_ITER: 130000 8 | STEPS: (100000, 120000) 9 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/densepose_rcnn_R_101_FPN_s1x_legacy.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | RESNETS: 5 | DEPTH: 101 6 | ROI_DENSEPOSE_HEAD: 7 | NUM_COARSE_SEGM_CHANNELS: 15 8 | POOLER_RESOLUTION: 14 9 | HEATMAP_SIZE: 56 10 | INDEX_WEIGHTS: 2.0 11 | PART_WEIGHTS: 0.3 12 | POINT_REGRESSION_WEIGHTS: 0.1 13 | DECODER_ON: False 14 | SOLVER: 15 | BASE_LR: 0.002 16 | MAX_ITER: 130000 17 | STEPS: (100000, 120000) 18 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/densepose_rcnn_R_50_FPN_DL_WC1M_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | ROI_DENSEPOSE_HEAD: 7 | NAME: "DensePoseDeepLabHead" 8 | UV_CONFIDENCE: 9 | ENABLED: True 10 | TYPE: "iid_iso" 11 | SEGM_CONFIDENCE: 12 | ENABLED: True 13 | POINT_REGRESSION_WEIGHTS: 0.0005 14 | SOLVER: 15 | CLIP_GRADIENTS: 16 | ENABLED: True 17 | MAX_ITER: 130000 18 | STEPS: (100000, 120000) 19 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/densepose_rcnn_R_50_FPN_DL_WC1_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | ROI_DENSEPOSE_HEAD: 7 | NAME: "DensePoseDeepLabHead" 8 | UV_CONFIDENCE: 9 | ENABLED: True 10 | TYPE: "iid_iso" 11 | POINT_REGRESSION_WEIGHTS: 0.0005 12 | SOLVER: 13 | CLIP_GRADIENTS: 14 | ENABLED: True 15 | MAX_ITER: 130000 16 | STEPS: (100000, 120000) 17 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/densepose_rcnn_R_50_FPN_DL_WC2_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | ROI_DENSEPOSE_HEAD: 7 | NAME: "DensePoseDeepLabHead" 8 | UV_CONFIDENCE: 9 | ENABLED: True 10 | TYPE: "indep_aniso" 11 | POINT_REGRESSION_WEIGHTS: 0.0005 12 | SOLVER: 13 | CLIP_GRADIENTS: 14 | ENABLED: True 15 | MAX_ITER: 130000 16 | STEPS: (100000, 120000) 17 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/densepose_rcnn_R_50_FPN_DL_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | ROI_DENSEPOSE_HEAD: 7 | NAME: "DensePoseDeepLabHead" 8 | SOLVER: 9 | MAX_ITER: 130000 10 | STEPS: (100000, 120000) 11 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/densepose_rcnn_R_50_FPN_WC1_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | ROI_DENSEPOSE_HEAD: 7 | UV_CONFIDENCE: 8 | ENABLED: True 9 | TYPE: "iid_iso" 10 | POINT_REGRESSION_WEIGHTS: 0.0005 11 | SOLVER: 12 | CLIP_GRADIENTS: 13 | ENABLED: True 14 | MAX_ITER: 130000 15 | STEPS: (100000, 120000) 16 | WARMUP_FACTOR: 0.025 17 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/densepose_rcnn_R_50_FPN_WC2M_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | ROI_DENSEPOSE_HEAD: 7 | UV_CONFIDENCE: 8 | ENABLED: True 9 | TYPE: "indep_aniso" 10 | SEGM_CONFIDENCE: 11 | ENABLED: True 12 | POINT_REGRESSION_WEIGHTS: 0.0005 13 | SOLVER: 14 | CLIP_GRADIENTS: 15 | ENABLED: True 16 | MAX_ITER: 130000 17 | STEPS: (100000, 120000) 18 | WARMUP_FACTOR: 0.025 19 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/densepose_rcnn_R_50_FPN_WC2_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | ROI_DENSEPOSE_HEAD: 7 | UV_CONFIDENCE: 8 | ENABLED: True 9 | TYPE: "indep_aniso" 10 | POINT_REGRESSION_WEIGHTS: 0.0005 11 | SOLVER: 12 | CLIP_GRADIENTS: 13 | ENABLED: True 14 | MAX_ITER: 130000 15 | STEPS: (100000, 120000) 16 | WARMUP_FACTOR: 0.025 17 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/densepose_rcnn_R_50_FPN_s1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | SOLVER: 7 | MAX_ITER: 130000 8 | STEPS: (100000, 120000) 9 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/densepose_rcnn_R_50_FPN_s1x_legacy.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-DensePose-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | ROI_DENSEPOSE_HEAD: 7 | NUM_COARSE_SEGM_CHANNELS: 15 8 | POOLER_RESOLUTION: 14 9 | HEATMAP_SIZE: 56 10 | INDEX_WEIGHTS: 2.0 11 | PART_WEIGHTS: 0.3 12 | POINT_REGRESSION_WEIGHTS: 0.1 13 | DECODER_ON: False 14 | SOLVER: 15 | BASE_LR: 0.002 16 | MAX_ITER: 130000 17 | STEPS: (100000, 120000) 18 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/quick_schedules/cse/densepose_rcnn_R_50_FPN_DL_instant_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../../cse/Base-DensePose-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | ROI_DENSEPOSE_HEAD: 5 | NAME: "DensePoseDeepLabHead" 6 | DATASETS: 7 | TRAIN: ("densepose_coco_2014_minival_100_cse",) 8 | TEST: ("densepose_coco_2014_minival_100_cse",) 9 | SOLVER: 10 | MAX_ITER: 40 11 | STEPS: (30,) 12 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/quick_schedules/densepose_rcnn_HRFPN_HRNet_w32_instant_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../HRNet/densepose_rcnn_HRFPN_HRNet_w32_s1x.yaml" 2 | DATASETS: 3 | TRAIN: ("densepose_coco_2014_minival_100",) 4 | TEST: ("densepose_coco_2014_minival_100",) 5 | SOLVER: 6 | MAX_ITER: 40 7 | STEPS: (30,) 8 | IMS_PER_BATCH: 2 9 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/quick_schedules/densepose_rcnn_R_50_FPN_DL_instant_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-DensePose-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | ROI_DENSEPOSE_HEAD: 5 | NAME: "DensePoseDeepLabHead" 6 | DATASETS: 7 | TRAIN: ("densepose_coco_2014_minival_100",) 8 | TEST: ("densepose_coco_2014_minival_100",) 9 | SOLVER: 10 | MAX_ITER: 40 11 | STEPS: (30,) 12 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/quick_schedules/densepose_rcnn_R_50_FPN_inference_acc_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../densepose_rcnn_R_50_FPN_s1x.yaml" 2 | MODEL: 3 | WEIGHTS: "https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x/165712039/model_final_162be9.pkl" 4 | DATASETS: 5 | TRAIN: () 6 | TEST: ("densepose_coco_2014_minival_100",) 7 | TEST: 8 | EXPECTED_RESULTS: [["bbox", "AP", 59.27, 0.025], ["densepose_gps", "AP", 60.11, 0.02], ["densepose_gpsm", "AP", 64.09, 0.02]] 9 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/configs/quick_schedules/densepose_rcnn_R_50_FPN_instant_test.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../Base-DensePose-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | DATASETS: 5 | TRAIN: ("densepose_coco_2014_minival_100",) 6 | TEST: ("densepose_coco_2014_minival_100",) 7 | SOLVER: 8 | MAX_ITER: 40 9 | STEPS: (30,) 10 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/densepose/data/meshes/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved 2 | 3 | from . import builtin 4 | 5 | __all__ = [k for k in globals().keys() if "builtin" not in k and not k.startswith("_")] 6 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/densepose/data/samplers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | 3 | from .densepose_uniform import DensePoseUniformSampler 4 | from .densepose_confidence_based import DensePoseConfidenceBasedSampler 5 | from .densepose_cse_uniform import DensePoseCSEUniformSampler 6 | from .densepose_cse_confidence_based import DensePoseCSEConfidenceBasedSampler 7 | from .mask_from_densepose import MaskFromDensePoseSampler 8 | from .prediction_to_gt import PredictionToGroundTruthSampler 9 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/densepose/data/samplers/densepose_cse_uniform.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | 3 | from .densepose_cse_base import DensePoseCSEBaseSampler 4 | from .densepose_uniform import DensePoseUniformSampler 5 | 6 | 7 | class DensePoseCSEUniformSampler(DensePoseCSEBaseSampler, DensePoseUniformSampler): 8 | """ 9 | Uniform Sampler for CSE 10 | """ 11 | 12 | pass 13 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/densepose/data/transform/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | 3 | from .image import ImageResizeTransform 4 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/densepose/data/video/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | 3 | from .frame_selector import ( 4 | FrameSelectionStrategy, 5 | RandomKFramesSelector, 6 | FirstKFramesSelector, 7 | LastKFramesSelector, 8 | FrameTsList, 9 | FrameSelector, 10 | ) 11 | 12 | from .video_keyframe_dataset import ( 13 | VideoKeyframeDataset, 14 | video_list_from_file, 15 | list_keyframes, 16 | read_keyframes, 17 | ) 18 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/densepose/engine/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | 3 | from .trainer import Trainer 4 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/densepose/evaluation/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | 3 | from .evaluator import DensePoseCOCOEvaluator 4 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/densepose/modeling/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | 3 | from .confidence import DensePoseConfidenceModelConfig, DensePoseUVConfidenceType 4 | from .filter import DensePoseDataFilter 5 | from .inference import densepose_inference 6 | from .utils import initialize_module_params 7 | from .build import ( 8 | build_densepose_data_filter, 9 | build_densepose_embedder, 10 | build_densepose_head, 11 | build_densepose_losses, 12 | build_densepose_predictor, 13 | ) 14 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/densepose/modeling/cse/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved 2 | 3 | from .vertex_direct_embedder import VertexDirectEmbedder 4 | from .vertex_feature_embedder import VertexFeatureEmbedder 5 | from .embedder import Embedder 6 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/densepose/modeling/losses/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | 3 | from .chart import DensePoseChartLoss 4 | from .chart_with_confidences import DensePoseChartWithConfidenceLoss 5 | from .cse import DensePoseCseLoss 6 | from .registry import DENSEPOSE_LOSS_REGISTRY 7 | 8 | 9 | __all__ = [ 10 | "DensePoseChartLoss", 11 | "DensePoseChartWithConfidenceLoss", 12 | "DensePoseCseLoss", 13 | "DENSEPOSE_LOSS_REGISTRY", 14 | ] 15 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/densepose/modeling/losses/registry.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | 3 | from detectron2.utils.registry import Registry 4 | 5 | DENSEPOSE_LOSS_REGISTRY = Registry("DENSEPOSE_LOSS") 6 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/densepose/modeling/predictors/registry.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | 3 | from detectron2.utils.registry import Registry 4 | 5 | DENSEPOSE_PREDICTOR_REGISTRY = Registry("DENSEPOSE_PREDICTOR") 6 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/densepose/modeling/roi_heads/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | 3 | from .v1convx import DensePoseV1ConvXHead 4 | from .deeplab import DensePoseDeepLabHead 5 | from .registry import ROI_DENSEPOSE_HEAD_REGISTRY 6 | from .roi_head import Decoder, DensePoseROIHeads 7 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/densepose/modeling/roi_heads/registry.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | 3 | from detectron2.utils.registry import Registry 4 | 5 | ROI_DENSEPOSE_HEAD_REGISTRY = Registry("ROI_DENSEPOSE_HEAD") 6 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/densepose/modeling/utils.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | 3 | from torch import nn 4 | 5 | 6 | def initialize_module_params(module: nn.Module) -> None: 7 | for name, param in module.named_parameters(): 8 | if "bias" in name: 9 | nn.init.constant_(param, 0) 10 | elif "weight" in name: 11 | nn.init.kaiming_normal_(param, mode="fan_out", nonlinearity="relu") 12 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/densepose/utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-02/det/projects/DensePose/densepose/utils/__init__.py -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/densepose/utils/logger.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | import logging 3 | 4 | 5 | def verbosity_to_level(verbosity) -> int: 6 | if verbosity is not None: 7 | if verbosity == 0: 8 | return logging.WARNING 9 | elif verbosity == 1: 10 | return logging.INFO 11 | elif verbosity >= 2: 12 | return logging.DEBUG 13 | return logging.WARNING 14 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/densepose/vis/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-02/det/projects/DensePose/densepose/vis/__init__.py -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/dev/README.md: -------------------------------------------------------------------------------- 1 | 2 | ## Some scripts for developers to use, include: 3 | 4 | - `run_instant_tests.sh`: run training for a few iterations. 5 | - `run_inference_tests.sh`: run inference on a small dataset. 6 | - `../../dev/linter.sh`: lint the codebase before commit 7 | - `../../dev/parse_results.sh`: parse results from log file. 8 | -------------------------------------------------------------------------------- /EVA-02/det/projects/DensePose/doc/RELEASE_2020_04.md: -------------------------------------------------------------------------------- 1 | # DensePose Confidence Estimation and Model Zoo Improvements 2 | 3 | * [DensePose models with confidence estimation](doc/DENSEPOSE_IUV.md#ModelZooConfidence) 4 | * [Panoptic FPN and DeepLabV3 head implementation](doc/DENSEPOSE_IUV.md#ModelZooDeepLabV3) 5 | * Test time augmentations for DensePose 6 | * New evaluation metric (GPSm) that yields more reliable scores 7 | -------------------------------------------------------------------------------- /EVA-02/det/projects/MViTv2/configs/cascade_mask_rcnn_mvitv2_b_3x.py: -------------------------------------------------------------------------------- 1 | from .cascade_mask_rcnn_mvitv2_t_3x import model, dataloader, optimizer, lr_multiplier, train 2 | 3 | 4 | model.backbone.bottom_up.depth = 24 5 | model.backbone.bottom_up.last_block_indexes = (1, 4, 20, 23) 6 | model.backbone.bottom_up.drop_path_rate = 0.4 7 | 8 | train.init_checkpoint = "detectron2://ImageNetPretrained/mvitv2/MViTv2_B_in1k.pyth" 9 | -------------------------------------------------------------------------------- /EVA-02/det/projects/MViTv2/configs/cascade_mask_rcnn_mvitv2_b_in21k_3x.py: -------------------------------------------------------------------------------- 1 | from .cascade_mask_rcnn_mvitv2_b_3x import model, dataloader, optimizer, lr_multiplier, train 2 | 3 | train.init_checkpoint = "detectron2://ImageNetPretrained/mvitv2/MViTv2_B_in21k.pyth" 4 | -------------------------------------------------------------------------------- /EVA-02/det/projects/MViTv2/configs/cascade_mask_rcnn_mvitv2_s_3x.py: -------------------------------------------------------------------------------- 1 | from .cascade_mask_rcnn_mvitv2_t_3x import model, dataloader, optimizer, lr_multiplier, train 2 | 3 | 4 | model.backbone.bottom_up.depth = 16 5 | model.backbone.bottom_up.last_block_indexes = (0, 2, 13, 15) 6 | 7 | train.init_checkpoint = "detectron2://ImageNetPretrained/mvitv2/MViTv2_S_in1k.pyth" 8 | -------------------------------------------------------------------------------- /EVA-02/det/projects/Panoptic-DeepLab/panoptic_deeplab/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | from .config import add_panoptic_deeplab_config 3 | from .dataset_mapper import PanopticDeeplabDatasetMapper 4 | from .panoptic_seg import ( 5 | PanopticDeepLab, 6 | INS_EMBED_BRANCHES_REGISTRY, 7 | build_ins_embed_branch, 8 | PanopticDeepLabSemSegHead, 9 | PanopticDeepLabInsEmbedHead, 10 | ) 11 | -------------------------------------------------------------------------------- /EVA-02/det/projects/PointRend/configs/InstanceSegmentation/implicit_pointrend_R_50_FPN_1x_coco.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-Implicit-PointRend.yaml" 2 | MODEL: 3 | WEIGHTS: detectron2://ImageNetPretrained/MSRA/R-50.pkl 4 | RESNETS: 5 | DEPTH: 50 6 | # To add COCO AP evaluation against the higher-quality LVIS annotations. 7 | # DATASETS: 8 | # TEST: ("coco_2017_val", "lvis_v0.5_val_cocofied") 9 | -------------------------------------------------------------------------------- /EVA-02/det/projects/PointRend/configs/InstanceSegmentation/implicit_pointrend_R_50_FPN_3x_coco.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-Implicit-PointRend.yaml" 2 | MODEL: 3 | WEIGHTS: detectron2://ImageNetPretrained/MSRA/R-50.pkl 4 | RESNETS: 5 | DEPTH: 50 6 | SOLVER: 7 | STEPS: (210000, 250000) 8 | MAX_ITER: 270000 9 | # To add COCO AP evaluation against the higher-quality LVIS annotations. 10 | # DATASETS: 11 | # TEST: ("coco_2017_val", "lvis_v0.5_val_cocofied") 12 | -------------------------------------------------------------------------------- /EVA-02/det/projects/PointRend/configs/InstanceSegmentation/pointrend_rcnn_R_101_FPN_3x_coco.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: Base-PointRend-RCNN-FPN.yaml 2 | MODEL: 3 | WEIGHTS: detectron2://ImageNetPretrained/MSRA/R-101.pkl 4 | MASK_ON: true 5 | RESNETS: 6 | DEPTH: 101 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | # To add COCO AP evaluation against the higher-quality LVIS annotations. 11 | # DATASETS: 12 | # TEST: ("coco_2017_val", "lvis_v0.5_val_cocofied") 13 | -------------------------------------------------------------------------------- /EVA-02/det/projects/PointRend/configs/InstanceSegmentation/pointrend_rcnn_R_50_FPN_1x_coco.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: Base-PointRend-RCNN-FPN.yaml 2 | MODEL: 3 | WEIGHTS: detectron2://ImageNetPretrained/MSRA/R-50.pkl 4 | RESNETS: 5 | DEPTH: 50 6 | # To add COCO AP evaluation against the higher-quality LVIS annotations. 7 | # DATASETS: 8 | # TEST: ("coco_2017_val", "lvis_v0.5_val_cocofied") 9 | -------------------------------------------------------------------------------- /EVA-02/det/projects/PointRend/configs/InstanceSegmentation/pointrend_rcnn_R_50_FPN_3x_coco.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: Base-PointRend-RCNN-FPN.yaml 2 | MODEL: 3 | WEIGHTS: detectron2://ImageNetPretrained/MSRA/R-50.pkl 4 | RESNETS: 5 | DEPTH: 50 6 | SOLVER: 7 | STEPS: (210000, 250000) 8 | MAX_ITER: 270000 9 | # To add COCO AP evaluation against the higher-quality LVIS annotations. 10 | # DATASETS: 11 | # TEST: ("coco_2017_val", "lvis_v0.5_val_cocofied") 12 | 13 | -------------------------------------------------------------------------------- /EVA-02/det/projects/PointRend/point_rend/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | from .config import add_pointrend_config 3 | from .mask_head import PointRendMaskHead, ImplicitPointRendMaskHead 4 | from .semantic_seg import PointRendSemSegHead 5 | from .color_augmentation import ColorAugSSDTransform 6 | 7 | from . import roi_heads as _ # only registration 8 | -------------------------------------------------------------------------------- /EVA-02/det/projects/PointSup/configs/implicit_pointrend_R_50_FPN_3x_point_sup_point_aug_coco.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../../PointRend/configs/InstanceSegmentation/implicit_pointrend_R_50_FPN_3x_coco.yaml" 2 | MODEL: 3 | ROI_MASK_HEAD: 4 | NAME: "ImplicitPointRendPointSupHead" 5 | INPUT: 6 | POINT_SUP: True 7 | SAMPLE_POINTS: 5 8 | DATASETS: 9 | TRAIN: ("coco_2017_train_points_n10_v1_without_masks",) 10 | -------------------------------------------------------------------------------- /EVA-02/det/projects/PointSup/configs/mask_rcnn_R_50_FPN_3x_point_sup_coco.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "../../../configs/Base-RCNN-FPN.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: True 5 | RESNETS: 6 | DEPTH: 50 7 | ROI_MASK_HEAD: 8 | NAME: "MaskRCNNConvUpsamplePointSupHead" 9 | INPUT: 10 | POINT_SUP: True 11 | DATASETS: 12 | TRAIN: ("coco_2017_train_points_n10_v1_without_masks",) 13 | SOLVER: 14 | STEPS: (210000, 250000) 15 | MAX_ITER: 270000 16 | -------------------------------------------------------------------------------- /EVA-02/det/projects/PointSup/configs/mask_rcnn_R_50_FPN_3x_point_sup_point_aug_coco.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "mask_rcnn_R_50_FPN_3x_point_sup_coco.yaml" 2 | INPUT: 3 | SAMPLE_POINTS: 5 4 | -------------------------------------------------------------------------------- /EVA-02/det/projects/PointSup/point_sup/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved 2 | from . import register_point_annotations 3 | from .config import add_point_sup_config 4 | from .dataset_mapper import PointSupDatasetMapper 5 | from .mask_head import MaskRCNNConvUpsamplePointSupHead 6 | from .point_utils import get_point_coords_from_point_annotation 7 | -------------------------------------------------------------------------------- /EVA-02/det/projects/PointSup/point_sup/config.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved 3 | 4 | 5 | def add_point_sup_config(cfg): 6 | """ 7 | Add config for point supervision. 8 | """ 9 | # Use point annotation 10 | cfg.INPUT.POINT_SUP = False 11 | # Sample only part of points in each iteration. 12 | # Default: 0, use all available points. 13 | cfg.INPUT.SAMPLE_POINTS = 0 14 | -------------------------------------------------------------------------------- /EVA-02/det/projects/Rethinking-BatchNorm/configs/mask_rcnn_SyncBNhead.py: -------------------------------------------------------------------------------- 1 | from .mask_rcnn_BNhead import model, dataloader, lr_multiplier, optimizer, train 2 | 3 | model.roi_heads.box_head.conv_norm = model.roi_heads.mask_head.conv_norm = "SyncBN" 4 | -------------------------------------------------------------------------------- /EVA-02/det/projects/TensorMask/configs/tensormask_R_50_FPN_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-TensorMask.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | -------------------------------------------------------------------------------- /EVA-02/det/projects/TensorMask/configs/tensormask_R_50_FPN_6x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-TensorMask.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | RESNETS: 5 | DEPTH: 50 6 | SOLVER: 7 | STEPS: (480000, 520000) 8 | MAX_ITER: 540000 9 | INPUT: 10 | MIN_SIZE_TRAIN_SAMPLING: "range" 11 | MIN_SIZE_TRAIN: (640, 800) 12 | -------------------------------------------------------------------------------- /EVA-02/det/projects/TensorMask/tensormask/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | from .config import add_tensormask_config 3 | from .arch import TensorMask 4 | -------------------------------------------------------------------------------- /EVA-02/det/projects/TensorMask/tensormask/layers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | from .swap_align2nat import SwapAlign2Nat, swap_align2nat 3 | 4 | __all__ = [k for k in globals().keys() if not k.startswith("_")] 5 | -------------------------------------------------------------------------------- /EVA-02/det/projects/TensorMask/tests/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | -------------------------------------------------------------------------------- /EVA-02/det/projects/TridentNet/configs/tridentnet_fast_R_101_C4_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-TridentNet-Fast-C4.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" 4 | MASK_ON: False 5 | RESNETS: 6 | DEPTH: 101 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-02/det/projects/TridentNet/configs/tridentnet_fast_R_50_C4_1x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-TridentNet-Fast-C4.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: False 5 | RESNETS: 6 | DEPTH: 50 7 | -------------------------------------------------------------------------------- /EVA-02/det/projects/TridentNet/configs/tridentnet_fast_R_50_C4_3x.yaml: -------------------------------------------------------------------------------- 1 | _BASE_: "Base-TridentNet-Fast-C4.yaml" 2 | MODEL: 3 | WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" 4 | MASK_ON: False 5 | RESNETS: 6 | DEPTH: 50 7 | SOLVER: 8 | STEPS: (210000, 250000) 9 | MAX_ITER: 270000 10 | -------------------------------------------------------------------------------- /EVA-02/det/projects/TridentNet/tridentnet/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | from .config import add_tridentnet_config 3 | from .trident_backbone import ( 4 | TridentBottleneckBlock, 5 | build_trident_resnet_backbone, 6 | make_trident_stage, 7 | ) 8 | from .trident_rpn import TridentRPN 9 | from .trident_rcnn import TridentRes5ROIHeads, TridentStandardROIHeads 10 | -------------------------------------------------------------------------------- /EVA-02/det/projects/ViTDet/configs/COCO/cascade_mask_rcnn_swin_l_in21k_50ep.py: -------------------------------------------------------------------------------- 1 | from .cascade_mask_rcnn_swin_b_in21k_50ep import ( 2 | dataloader, 3 | lr_multiplier, 4 | model, 5 | train, 6 | optimizer, 7 | ) 8 | 9 | model.backbone.bottom_up.depths = [2, 2, 18, 2] 10 | model.backbone.bottom_up.drop_path_rate = 0.4 11 | model.backbone.bottom_up.embed_dim = 192 12 | model.backbone.bottom_up.num_heads = [6, 12, 24, 48] 13 | 14 | 15 | train.init_checkpoint = "detectron2://ImageNetPretrained/swin/swin_large_patch4_window7_224_22k.pth" 16 | -------------------------------------------------------------------------------- /EVA-02/det/projects/ViTDet/configs/LVIS/cascade_mask_rcnn_swin_l_in21k_50ep.py: -------------------------------------------------------------------------------- 1 | from .cascade_mask_rcnn_swin_b_in21k_50ep import ( 2 | dataloader, 3 | lr_multiplier, 4 | model, 5 | train, 6 | optimizer, 7 | ) 8 | 9 | model.backbone.bottom_up.embed_dim = 192 10 | model.backbone.bottom_up.num_heads = [6, 12, 24, 48] 11 | 12 | train.init_checkpoint = "detectron2://ImageNetPretrained/swin/swin_large_patch4_window7_224_22k.pth" 13 | -------------------------------------------------------------------------------- /EVA-02/det/tests/README.md: -------------------------------------------------------------------------------- 1 | ## Unit Tests 2 | 3 | To run the unittests, do: 4 | ``` 5 | cd detectron2 6 | python -m unittest discover -v -s ./tests 7 | ``` 8 | 9 | There are also end-to-end inference & training tests, in [dev/run_*_tests.sh](../dev). 10 | -------------------------------------------------------------------------------- /EVA-02/det/tests/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | -------------------------------------------------------------------------------- /EVA-02/det/tests/config/dir1/dir1_a.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | dir1a_str = "base_a_1" 3 | dir1a_dict = {"a": 1, "b": 2} 4 | -------------------------------------------------------------------------------- /EVA-02/det/tests/config/dir1/dir1_b.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | from detectron2.config import LazyConfig 3 | 4 | # equivalent to relative import 5 | dir1a_str, dir1a_dict = LazyConfig.load_rel("dir1_a.py", ("dir1a_str", "dir1a_dict")) 6 | 7 | dir1b_str = dir1a_str + "_from_b" 8 | dir1b_dict = dir1a_dict 9 | 10 | # Every import is a reload: not modified by other config files 11 | assert dir1a_dict.a == 1 12 | -------------------------------------------------------------------------------- /EVA-02/det/tests/config/root_cfg.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | from itertools import count 3 | 4 | from detectron2.config import LazyCall as L 5 | 6 | from .dir1.dir1_a import dir1a_dict, dir1a_str 7 | 8 | dir1a_dict.a = "modified" 9 | 10 | # modification above won't affect future imports 11 | from .dir1.dir1_b import dir1b_dict, dir1b_str 12 | 13 | 14 | lazyobj = L(count)(x=dir1a_str, y=dir1b_str) 15 | -------------------------------------------------------------------------------- /EVA-02/det/tests/data/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-02/det/tests/data/__init__.py -------------------------------------------------------------------------------- /EVA-02/det/tests/layers/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-02/det/tests/layers/__init__.py -------------------------------------------------------------------------------- /EVA-02/det/tests/modeling/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-02/det/tests/modeling/__init__.py -------------------------------------------------------------------------------- /EVA-02/det/tests/structures/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-02/det/tests/structures/__init__.py -------------------------------------------------------------------------------- /EVA-02/det/tests/tracking/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-02/det/tests/tracking/__init__.py -------------------------------------------------------------------------------- /EVA-02/det/tools/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-02/det/tools/__init__.py -------------------------------------------------------------------------------- /EVA-02/seg/backbone/__init__.py: -------------------------------------------------------------------------------- 1 | from .eva2 import EVA2 2 | 3 | 4 | __all__ = ['EVA2'] 5 | -------------------------------------------------------------------------------- /EVA-02/seg/backbone/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-02/seg/backbone/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-02/seg/backbone/__pycache__/beit.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-02/seg/backbone/__pycache__/beit.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-02/seg/backbone/__pycache__/eva2.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-02/seg/backbone/__pycache__/eva2.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-02/seg/configs/_base_/default_runtime.py: -------------------------------------------------------------------------------- 1 | # yapf:disable 2 | log_config = dict( 3 | interval=50, 4 | hooks=[ 5 | dict(type='TextLoggerHook', by_epoch=False), 6 | # dict(type='TensorboardLoggerHook') 7 | ]) 8 | # yapf:enable 9 | dist_params = dict(backend='nccl') 10 | log_level = 'INFO' 11 | load_from = None 12 | resume_from = None 13 | workflow = [('train', 1)] 14 | cudnn_benchmark = True 15 | -------------------------------------------------------------------------------- /EVA-02/seg/configs/_base_/schedules/schedule_60k.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) 3 | optimizer_config = dict() 4 | # learning policy 5 | lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) 6 | # runtime settings 7 | runner = dict(type='IterBasedRunner', max_iters=60000) 8 | checkpoint_config = dict(by_epoch=False, interval=1000, max_keep_ckpts=4) 9 | evaluation = dict(interval=1000, metric='mIoU', save_best='mIoU') 10 | -------------------------------------------------------------------------------- /EVA-02/seg/configs/_base_/schedules/schedule_80k.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) 3 | optimizer_config = dict() 4 | # learning policy 5 | lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) 6 | # runtime settings 7 | runner = dict(type='IterBasedRunner', max_iters=80000) 8 | checkpoint_config = dict(by_epoch=False, interval=1000, max_keep_ckpts=10) 9 | evaluation = dict(interval=1000, metric='mIoU', save_best='mIoU') 10 | -------------------------------------------------------------------------------- /EVA-02/seg/mmcv_custom/__init__.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | from .checkpoint import load_checkpoint 4 | from .layer_decay_optimizer_constructor import LayerDecayOptimizerConstructor 5 | from .resize_transform import SETR_Resize 6 | from .apex_runner.optimizer import DistOptimizerHook 7 | from .train_api import train_segmentor 8 | 9 | __all__ = ['load_checkpoint', 'LayerDecayOptimizerConstructor', 'SETR_Resize', 'DistOptimizerHook', 'train_segmentor'] 10 | -------------------------------------------------------------------------------- /EVA-02/seg/mmcv_custom/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-02/seg/mmcv_custom/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-02/seg/mmcv_custom/__pycache__/checkpoint.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-02/seg/mmcv_custom/__pycache__/checkpoint.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-02/seg/mmcv_custom/__pycache__/layer_decay_optimizer_constructor.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-02/seg/mmcv_custom/__pycache__/layer_decay_optimizer_constructor.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-02/seg/mmcv_custom/__pycache__/resize_transform.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-02/seg/mmcv_custom/__pycache__/resize_transform.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-02/seg/mmcv_custom/__pycache__/train_api.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-02/seg/mmcv_custom/__pycache__/train_api.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-02/seg/mmcv_custom/apex_runner/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Open-MMLab. All rights reserved. 2 | from .checkpoint import save_checkpoint 3 | from .apex_iter_based_runner import IterBasedRunnerAmp 4 | 5 | 6 | __all__ = [ 7 | 'save_checkpoint', 'IterBasedRunnerAmp', 8 | ] 9 | -------------------------------------------------------------------------------- /EVA-02/seg/mmcv_custom/apex_runner/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-02/seg/mmcv_custom/apex_runner/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /EVA-02/seg/mmcv_custom/apex_runner/__pycache__/apex_iter_based_runner.cpython-38.pyc: -------------------------------------------------------------------------------- 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"drop_path_rate": 0.0 11 | }, 12 | "text_cfg": { 13 | "context_length": 77, 14 | "vocab_size": 49408, 15 | "width": 512, 16 | "heads": 8, 17 | "layers": 12 18 | } 19 | } -------------------------------------------------------------------------------- /EVA-CLIP-18B/shinji/training/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-CLIP-18B/shinji/training/__init__.py -------------------------------------------------------------------------------- /EVA-CLIP-18B/shinji/training/precision.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from contextlib import suppress 3 | 4 | 5 | def get_autocast(precision): 6 | if precision == 'amp': 7 | return torch.cuda.amp.autocast 8 | elif precision == 'amp_bfloat16' or precision == 'amp_bf16': 9 | # amp_bfloat16 is more stable than amp float16 for clip training 10 | return lambda: torch.cuda.amp.autocast(dtype=torch.bfloat16) 11 | else: 12 | return suppress 13 | -------------------------------------------------------------------------------- /EVA-CLIP/.gitignore: -------------------------------------------------------------------------------- 1 | logs/ 2 | tools/ 3 | __pycache__/ -------------------------------------------------------------------------------- /EVA-CLIP/assets/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-CLIP/assets/.DS_Store -------------------------------------------------------------------------------- /EVA-CLIP/assets/CLIP.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-CLIP/assets/CLIP.png 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-------------------------------------------------------------------------------- /EVA-CLIP/rei/eva_clip/model_configs/EVA01-CLIP-B-16.json: -------------------------------------------------------------------------------- 1 | { 2 | "embed_dim": 512, 3 | "vision_cfg": { 4 | "image_size": 224, 5 | "layers": 12, 6 | "width": 768, 7 | "patch_size": 16, 8 | "eva_model_name": "eva-clip-b-16", 9 | "ls_init_value": 0.1, 10 | "drop_path_rate": 0.0 11 | }, 12 | "text_cfg": { 13 | "context_length": 77, 14 | "vocab_size": 49408, 15 | "width": 512, 16 | "heads": 8, 17 | "layers": 12 18 | } 19 | } -------------------------------------------------------------------------------- /EVA-CLIP/rei/training/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/EVA-CLIP/rei/training/__init__.py -------------------------------------------------------------------------------- /EVA-CLIP/rei/training/precision.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from contextlib import suppress 3 | 4 | 5 | def get_autocast(precision): 6 | if precision == 'amp': 7 | return torch.cuda.amp.autocast 8 | elif precision == 'amp_bfloat16' or precision == 'amp_bf16': 9 | # amp_bfloat16 is more stable than amp float16 for clip training 10 | return lambda: torch.cuda.amp.autocast(dtype=torch.bfloat16) 11 | else: 12 | return suppress 13 | -------------------------------------------------------------------------------- /EVA-CLIP/requirements.txt: -------------------------------------------------------------------------------- 1 | torch>=1.9.0 2 | torchvision 3 | deepspeed==0.6.5 4 | xformers 5 | apex 6 | timm==0.5.4 7 | webdataset>=0.2.5 8 | tqdm 9 | pandas 10 | braceexpand 11 | huggingface_hub 12 | transformers 13 | Pillow 14 | blobfile 15 | mypy 16 | numpy 17 | pytest 18 | requests 19 | einops 20 | tensorboardX 21 | scipy 22 | sh 23 | wandb 24 | ftfy 25 | regex 26 | iopath 27 | pyyaml 28 | -------------------------------------------------------------------------------- /assets/eva_pipeline.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/baaivision/EVA/5623d08bb08a8268c7bc18e1d9b8b4bd651fe4d9/assets/eva_pipeline.png --------------------------------------------------------------------------------