├── README.md ├── engine_finetune.py ├── engine_pretrain.py ├── figs └── fastmim.png ├── main_finetune.py ├── main_pretrain.py ├── mim_cmt_finetune.py ├── mim_cmt_pretrain.py ├── mim_pvtv2_finetune.py ├── mim_pvtv2_pretrain.py ├── mim_swin_finetune.py ├── mim_swin_pretrain.py ├── mim_vit_pretrain.py ├── mmdet-v2.23 ├── README.md ├── README_zh-CN.md ├── build_environment.sh ├── configs │ ├── _base_ │ │ ├── datasets │ │ │ ├── cityscapes_detection.py │ │ │ ├── cityscapes_instance.py │ │ │ ├── coco_detection.py │ │ │ ├── coco_instance.py │ │ │ ├── coco_instance_semantic.py │ │ │ ├── coco_panoptic.py │ │ │ ├── deepfashion.py │ │ │ ├── lvis_v0.5_instance.py │ │ │ ├── lvis_v1_instance.py │ │ │ ├── openimages_detection.py │ │ │ ├── voc0712.py │ │ │ └── wider_face.py │ │ ├── default_runtime.py │ │ ├── models │ │ │ ├── cascade_mask_rcnn_r50_fpn.py │ │ │ ├── cascade_rcnn_r50_fpn.py │ │ │ ├── fast_rcnn_r50_fpn.py │ │ │ ├── faster_rcnn_r50_caffe_c4.py │ │ │ ├── faster_rcnn_r50_caffe_dc5.py │ │ │ ├── faster_rcnn_r50_fpn.py │ │ │ ├── mask_rcnn_r50_caffe_c4.py │ │ │ ├── mask_rcnn_r50_fpn.py │ │ │ ├── retinanet_r50_fpn.py │ │ │ ├── rpn_r50_caffe_c4.py │ │ │ ├── rpn_r50_fpn.py │ │ │ └── ssd300.py │ │ └── schedules │ │ │ ├── schedule_1x.py │ │ │ ├── schedule_20e.py │ │ │ └── schedule_2x.py │ └── swin │ │ ├── fastmim_mask_rcnn_upgraded_scp_swin_base_3x_coco.py │ │ └── simmim_mask_rcnn_upgraded_scp_swin_base_3x_coco.py ├── docs │ ├── en │ │ ├── 1_exist_data_model.md │ │ ├── 2_new_data_model.md │ │ ├── 3_exist_data_new_model.md │ │ ├── Makefile │ │ ├── _static │ │ │ ├── css │ │ │ │ └── readthedocs.css │ │ │ └── image │ │ │ │ └── mmdet-logo.png │ │ ├── api.rst │ │ ├── changelog.md │ │ ├── compatibility.md │ │ ├── conf.py │ │ ├── conventions.md │ │ ├── faq.md │ │ ├── get_started.md │ │ ├── index.rst │ │ ├── make.bat │ │ ├── model_zoo.md │ │ ├── projects.md │ │ ├── robustness_benchmarking.md │ │ ├── stat.py │ │ ├── switch_language.md │ │ ├── tutorials │ │ │ ├── config.md │ │ │ ├── customize_dataset.md │ │ │ ├── customize_losses.md │ │ │ ├── customize_models.md │ │ │ ├── customize_runtime.md │ │ │ ├── data_pipeline.md │ │ │ ├── finetune.md │ │ │ ├── how_to.md │ │ │ ├── index.rst │ │ │ ├── init_cfg.md │ │ │ ├── onnx2tensorrt.md │ │ │ ├── pytorch2onnx.md │ │ │ └── test_results_submission.md │ │ └── useful_tools.md │ └── zh_cn │ │ ├── 1_exist_data_model.md │ │ ├── 2_new_data_model.md │ │ ├── 3_exist_data_new_model.md │ │ ├── Makefile │ │ ├── _static │ │ ├── css │ │ │ └── readthedocs.css │ │ └── image │ │ │ └── mmdet-logo.png │ │ ├── api.rst │ │ ├── article.md │ │ ├── compatibility.md │ │ ├── conf.py │ │ ├── conventions.md │ │ ├── faq.md │ │ ├── get_started.md │ │ ├── index.rst │ │ ├── make.bat │ │ ├── model_zoo.md │ │ ├── projects.md │ │ ├── robustness_benchmarking.md │ │ ├── stat.py │ │ ├── switch_language.md │ │ ├── tutorials │ │ ├── config.md │ │ ├── customize_dataset.md │ │ ├── customize_losses.md │ │ ├── customize_models.md │ │ ├── customize_runtime.md │ │ ├── data_pipeline.md │ │ ├── finetune.md │ │ ├── how_to.md │ │ ├── index.rst │ │ ├── init_cfg.md │ │ ├── onnx2tensorrt.md │ │ └── pytorch2onnx.md │ │ └── useful_tools.md ├── mmdet │ ├── __init__.py │ ├── apis │ │ ├── __init__.py │ │ ├── inference.py │ │ ├── test.py │ │ └── train.py │ ├── core │ │ ├── __init__.py │ │ ├── anchor │ │ │ ├── __init__.py │ │ │ ├── anchor_generator.py │ │ │ ├── builder.py │ │ │ ├── point_generator.py │ │ │ └── utils.py │ │ ├── bbox │ │ │ ├── __init__.py │ │ │ ├── assigners │ │ │ │ ├── __init__.py │ │ │ │ ├── approx_max_iou_assigner.py │ │ │ │ ├── assign_result.py │ │ │ │ ├── atss_assigner.py │ │ │ │ ├── base_assigner.py │ │ │ │ ├── center_region_assigner.py │ │ │ │ ├── grid_assigner.py │ │ │ │ ├── hungarian_assigner.py │ │ │ │ ├── mask_hungarian_assigner.py │ │ │ │ ├── max_iou_assigner.py │ │ │ │ ├── point_assigner.py │ │ │ │ ├── region_assigner.py │ │ │ │ ├── sim_ota_assigner.py │ │ │ │ ├── task_aligned_assigner.py │ │ │ │ └── uniform_assigner.py │ │ │ ├── builder.py │ │ │ ├── coder │ │ │ │ ├── __init__.py │ │ │ │ ├── base_bbox_coder.py │ │ │ │ ├── bucketing_bbox_coder.py │ │ │ │ ├── delta_xywh_bbox_coder.py │ │ │ │ ├── distance_point_bbox_coder.py │ │ │ │ ├── legacy_delta_xywh_bbox_coder.py │ │ │ │ ├── pseudo_bbox_coder.py │ │ │ │ ├── tblr_bbox_coder.py │ │ │ │ └── yolo_bbox_coder.py │ │ │ ├── demodata.py │ │ │ ├── iou_calculators │ │ │ │ ├── __init__.py │ │ │ │ ├── builder.py │ │ │ │ └── iou2d_calculator.py │ │ │ ├── match_costs │ │ │ │ ├── __init__.py │ │ │ │ ├── builder.py │ │ │ │ └── match_cost.py │ │ │ ├── samplers │ │ │ │ ├── __init__.py │ │ │ │ ├── base_sampler.py │ │ │ │ ├── combined_sampler.py │ │ │ │ ├── instance_balanced_pos_sampler.py │ │ │ │ ├── iou_balanced_neg_sampler.py │ │ │ │ ├── mask_pseudo_sampler.py │ │ │ │ ├── mask_sampling_result.py │ │ │ │ ├── ohem_sampler.py │ │ │ │ ├── pseudo_sampler.py │ │ │ │ ├── random_sampler.py │ │ │ │ ├── sampling_result.py │ │ │ │ └── score_hlr_sampler.py │ │ │ └── transforms.py │ │ ├── data_structures │ │ │ ├── __init__.py │ │ │ ├── general_data.py │ │ │ └── instance_data.py │ │ ├── evaluation │ │ │ ├── __init__.py │ │ │ ├── bbox_overlaps.py │ │ │ ├── class_names.py │ │ │ ├── eval_hooks.py │ │ │ ├── mean_ap.py │ │ │ ├── panoptic_utils.py │ │ │ └── recall.py │ │ ├── export │ │ │ ├── __init__.py │ │ │ ├── model_wrappers.py │ │ │ ├── onnx_helper.py │ │ │ └── pytorch2onnx.py │ │ ├── hook │ │ │ ├── __init__.py │ │ │ ├── checkloss_hook.py │ │ │ ├── ema.py │ │ │ ├── set_epoch_info_hook.py │ │ │ ├── sync_norm_hook.py │ │ │ ├── sync_random_size_hook.py │ │ │ ├── yolox_lrupdater_hook.py │ │ │ └── yolox_mode_switch_hook.py │ │ ├── mask │ │ │ ├── __init__.py │ │ │ ├── mask_target.py │ │ │ ├── structures.py │ │ │ └── utils.py │ │ ├── post_processing │ │ │ ├── __init__.py │ │ │ ├── bbox_nms.py │ │ │ ├── matrix_nms.py │ │ │ └── merge_augs.py │ │ ├── utils │ │ │ ├── __init__.py │ │ │ ├── dist_utils.py │ │ │ └── misc.py │ │ └── visualization │ │ │ ├── __init__.py │ │ │ ├── image.py │ │ │ └── palette.py │ ├── datasets │ │ ├── __init__.py │ │ ├── api_wrappers │ │ │ ├── __init__.py │ │ │ ├── coco_api.py │ │ │ └── panoptic_evaluation.py │ │ ├── builder.py │ │ ├── cityscapes.py │ │ ├── coco.py │ │ ├── coco_panoptic.py │ │ ├── custom.py │ │ ├── dataset_wrappers.py │ │ ├── deepfashion.py │ │ ├── lvis.py │ │ ├── openimages.py │ │ ├── pipelines │ │ │ ├── __init__.py │ │ │ ├── auto_augment.py │ │ │ ├── compose.py │ │ │ ├── formating.py │ │ │ ├── formatting.py │ │ │ ├── instaboost.py │ │ │ ├── loading.py │ │ │ ├── test_time_aug.py │ │ │ └── transforms.py │ │ ├── samplers │ │ │ ├── __init__.py │ │ │ ├── distributed_sampler.py │ │ │ ├── group_sampler.py │ │ │ └── infinite_sampler.py │ │ ├── utils.py │ │ ├── voc.py │ │ ├── wider_face.py │ │ └── xml_style.py │ ├── models │ │ ├── __init__.py │ │ ├── backbones │ │ │ ├── __init__.py │ │ │ ├── swin.py │ │ │ └── vit.py │ │ ├── builder.py │ │ ├── datasets │ │ │ ├── __init__.py │ │ │ ├── api_wrappers │ │ │ │ ├── __init__.py │ │ │ │ ├── coco_api.py │ │ │ │ └── panoptic_evaluation.py │ │ │ ├── builder.py │ │ │ ├── cityscapes.py │ │ │ ├── coco.py │ │ │ ├── coco_panoptic.py │ │ │ ├── custom.py │ │ │ ├── dataset_wrappers.py │ │ │ ├── deepfashion.py │ │ │ ├── lvis.py │ │ │ ├── openimages.py │ │ │ ├── pipelines │ │ │ │ ├── __init__.py │ │ │ │ ├── auto_augment.py │ │ │ │ ├── compose.py │ │ │ │ ├── formating.py │ │ │ │ ├── formatting.py │ │ │ │ ├── instaboost.py │ │ │ │ ├── loading.py │ │ │ │ ├── test_time_aug.py │ │ │ │ └── transforms.py │ │ │ ├── samplers │ │ │ │ ├── __init__.py │ │ │ │ ├── distributed_sampler.py │ │ │ │ ├── group_sampler.py │ │ │ │ └── infinite_sampler.py │ │ │ ├── utils.py │ │ │ ├── voc.py │ │ │ ├── wider_face.py │ │ │ └── xml_style.py │ │ ├── dense_heads │ │ │ ├── __init__.py │ │ │ ├── anchor_free_head.py │ │ │ ├── anchor_head.py │ │ │ ├── atss_head.py │ │ │ ├── autoassign_head.py │ │ │ ├── base_dense_head.py │ │ │ ├── base_mask_head.py │ │ │ ├── cascade_rpn_head.py │ │ │ ├── centernet_head.py │ │ │ ├── centripetal_head.py │ │ │ ├── corner_head.py │ │ │ ├── deformable_detr_head.py │ │ │ ├── dense_test_mixins.py │ │ │ ├── detr_head.py │ │ │ ├── embedding_rpn_head.py │ │ │ ├── fcos_head.py │ │ │ ├── fovea_head.py │ │ │ ├── free_anchor_retina_head.py │ │ │ ├── fsaf_head.py │ │ │ ├── ga_retina_head.py │ │ │ ├── ga_rpn_head.py │ │ │ ├── gfl_head.py │ │ │ ├── guided_anchor_head.py │ │ │ ├── lad_head.py │ │ │ ├── ld_head.py │ │ │ ├── mask2former_head.py │ │ │ ├── maskformer_head.py │ │ │ ├── nasfcos_head.py │ │ │ ├── paa_head.py │ │ │ ├── pisa_retinanet_head.py │ │ │ ├── pisa_ssd_head.py │ │ │ ├── reppoints_head.py │ │ │ ├── retina_head.py │ │ │ ├── retina_sepbn_head.py │ │ │ ├── rpn_head.py │ │ │ ├── sabl_retina_head.py │ │ │ ├── solo_head.py │ │ │ ├── ssd_head.py │ │ │ ├── tood_head.py │ │ │ ├── vfnet_head.py │ │ │ ├── yolact_head.py │ │ │ ├── yolo_head.py │ │ │ ├── yolof_head.py │ │ │ └── yolox_head.py │ │ ├── detectors │ │ │ ├── __init__.py │ │ │ ├── atss.py │ │ │ ├── autoassign.py │ │ │ ├── base.py │ │ │ ├── cascade_rcnn.py │ │ │ ├── centernet.py │ │ │ ├── cornernet.py │ │ │ ├── deformable_detr.py │ │ │ ├── detr.py │ │ │ ├── fast_rcnn.py │ │ │ ├── faster_rcnn.py │ │ │ ├── fcos.py │ │ │ ├── fovea.py │ │ │ ├── fsaf.py │ │ │ ├── gfl.py │ │ │ ├── grid_rcnn.py │ │ │ ├── htc.py │ │ │ ├── kd_one_stage.py │ │ │ ├── lad.py │ │ │ ├── mask2former.py │ │ │ ├── mask_rcnn.py │ │ │ ├── mask_scoring_rcnn.py │ │ │ ├── maskformer.py │ │ │ ├── nasfcos.py │ │ │ ├── paa.py │ │ │ ├── panoptic_fpn.py │ │ │ ├── panoptic_two_stage_segmentor.py │ │ │ ├── point_rend.py │ │ │ ├── queryinst.py │ │ │ ├── reppoints_detector.py │ │ │ ├── retinanet.py │ │ │ ├── rpn.py │ │ │ ├── scnet.py │ │ │ ├── single_stage.py │ │ │ ├── single_stage_instance_seg.py │ │ │ ├── solo.py │ │ │ ├── sparse_rcnn.py │ │ │ ├── tood.py │ │ │ ├── trident_faster_rcnn.py │ │ │ ├── two_stage.py │ │ │ ├── vfnet.py │ │ │ ├── yolact.py │ │ │ ├── yolo.py │ │ │ ├── yolof.py │ │ │ └── yolox.py │ │ ├── losses │ │ │ ├── __init__.py │ │ │ ├── accuracy.py │ │ │ ├── ae_loss.py │ │ │ ├── balanced_l1_loss.py │ │ │ ├── cross_entropy_loss.py │ │ │ ├── dice_loss.py │ │ │ ├── focal_loss.py │ │ │ ├── gaussian_focal_loss.py │ │ │ ├── gfocal_loss.py │ │ │ ├── ghm_loss.py │ │ │ ├── iou_loss.py │ │ │ ├── kd_loss.py │ │ │ ├── mse_loss.py │ │ │ ├── pisa_loss.py │ │ │ ├── seesaw_loss.py │ │ │ ├── smooth_l1_loss.py │ │ │ ├── utils.py │ │ │ └── varifocal_loss.py │ │ ├── necks │ │ │ ├── __init__.py │ │ │ ├── bfp.py │ │ │ ├── channel_mapper.py │ │ │ ├── ct_resnet_neck.py │ │ │ ├── dilated_encoder.py │ │ │ ├── dyhead.py │ │ │ ├── fpg.py │ │ │ ├── fpn.py │ │ │ ├── fpn_carafe.py │ │ │ ├── hrfpn.py │ │ │ ├── nas_fpn.py │ │ │ ├── nasfcos_fpn.py │ │ │ ├── pafpn.py │ │ │ ├── rfp.py │ │ │ ├── ssd_neck.py │ │ │ ├── yolo_neck.py │ │ │ └── yolox_pafpn.py │ │ ├── plugins │ │ │ ├── __init__.py │ │ │ ├── dropblock.py │ │ │ ├── msdeformattn_pixel_decoder.py │ │ │ └── pixel_decoder.py │ │ ├── roi_heads │ │ │ ├── __init__.py │ │ │ ├── base_roi_head.py │ │ │ ├── bbox_heads │ │ │ │ ├── __init__.py │ │ │ │ ├── bbox_head.py │ │ │ │ ├── convfc_bbox_head.py │ │ │ │ ├── dii_head.py │ │ │ │ ├── double_bbox_head.py │ │ │ │ ├── sabl_head.py │ │ │ │ └── scnet_bbox_head.py │ │ │ ├── cascade_roi_head.py │ │ │ ├── double_roi_head.py │ │ │ ├── dynamic_roi_head.py │ │ │ ├── grid_roi_head.py │ │ │ ├── htc_roi_head.py │ │ │ ├── mask_heads │ │ │ │ ├── __init__.py │ │ │ │ ├── coarse_mask_head.py │ │ │ │ ├── dynamic_mask_head.py │ │ │ │ ├── fcn_mask_head.py │ │ │ │ ├── feature_relay_head.py │ │ │ │ ├── fused_semantic_head.py │ │ │ │ ├── global_context_head.py │ │ │ │ ├── grid_head.py │ │ │ │ ├── htc_mask_head.py │ │ │ │ ├── mask_point_head.py │ │ │ │ ├── maskiou_head.py │ │ │ │ ├── scnet_mask_head.py │ │ │ │ └── scnet_semantic_head.py │ │ │ ├── mask_scoring_roi_head.py │ │ │ ├── pisa_roi_head.py │ │ │ ├── point_rend_roi_head.py │ │ │ ├── roi_extractors │ │ │ │ ├── __init__.py │ │ │ │ ├── base_roi_extractor.py │ │ │ │ ├── generic_roi_extractor.py │ │ │ │ └── single_level_roi_extractor.py │ │ │ ├── scnet_roi_head.py │ │ │ ├── shared_heads │ │ │ │ ├── __init__.py │ │ │ │ └── res_layer.py │ │ │ ├── sparse_roi_head.py │ │ │ ├── standard_roi_head.py │ │ │ ├── test_mixins.py │ │ │ └── trident_roi_head.py │ │ ├── seg_heads │ │ │ ├── __init__.py │ │ │ ├── base_semantic_head.py │ │ │ ├── panoptic_fpn_head.py │ │ │ └── panoptic_fusion_heads │ │ │ │ ├── __init__.py │ │ │ │ ├── base_panoptic_fusion_head.py │ │ │ │ ├── heuristic_fusion_head.py │ │ │ │ └── maskformer_fusion_head.py │ │ └── utils │ │ │ ├── __init__.py │ │ │ ├── brick_wrappers.py │ │ │ ├── builder.py │ │ │ ├── ckpt_convert.py │ │ │ ├── conv_upsample.py │ │ │ ├── csp_layer.py │ │ │ ├── gaussian_target.py │ │ │ ├── inverted_residual.py │ │ │ ├── make_divisible.py │ │ │ ├── misc.py │ │ │ ├── normed_predictor.py │ │ │ ├── panoptic_gt_processing.py │ │ │ ├── point_sample.py │ │ │ ├── positional_encoding.py │ │ │ ├── res_layer.py │ │ │ ├── se_layer.py │ │ │ └── transformer.py │ ├── utils │ │ ├── __init__.py │ │ ├── collect_env.py │ │ ├── contextmanagers.py │ │ ├── logger.py │ │ ├── misc.py │ │ ├── profiling.py │ │ ├── setup_env.py │ │ ├── util_mixins.py │ │ └── util_random.py │ └── version.py ├── pytest.ini ├── requirements.txt ├── requirements │ ├── albu.txt │ ├── build.txt │ ├── docs.txt │ ├── mminstall.txt │ ├── optional.txt │ ├── readthedocs.txt │ ├── runtime.txt │ └── tests.txt ├── resources │ ├── coco_test_12510.jpg │ ├── corruptions_sev_3.png │ ├── data_pipeline.png │ ├── loss_curve.png │ ├── mmdet-logo.png │ ├── qq_group_qrcode.jpg │ └── zhihu_qrcode.jpg ├── setup.cfg ├── setup.py ├── tests │ ├── data │ │ ├── VOCdevkit │ │ │ ├── VOC2007 │ │ │ │ ├── Annotations │ │ │ │ │ └── 000001.xml │ │ │ │ ├── ImageSets │ │ │ │ │ └── Main │ │ │ │ │ │ ├── test.txt │ │ │ │ │ │ └── trainval.txt │ │ │ │ └── JPEGImages │ │ │ │ │ └── 000001.jpg │ │ │ └── VOC2012 │ │ │ │ ├── Annotations │ │ │ │ └── 000001.xml │ │ │ │ ├── ImageSets │ │ │ │ └── Main │ │ │ │ │ ├── test.txt │ │ │ │ │ └── trainval.txt │ │ │ │ └── JPEGImages │ │ │ │ └── 000001.jpg │ │ ├── coco_sample.json │ │ ├── color.jpg │ │ ├── configs_mmtrack │ │ │ ├── faster_rcnn_r50_dc5.py │ │ │ ├── faster_rcnn_r50_fpn.py │ │ │ ├── mot_challenge.py │ │ │ ├── selsa_faster_rcnn_r101_dc5_1x.py │ │ │ └── tracktor_faster-rcnn_r50_fpn_4e.py │ │ ├── custom_dataset │ │ │ ├── images │ │ │ │ ├── 000001.jpg │ │ │ │ └── 000001.xml │ │ │ ├── test.txt │ │ │ └── trainval.txt │ │ └── gray.jpg │ ├── test_data │ │ ├── test_datasets │ │ │ ├── test_coco_dataset.py │ │ │ ├── test_common.py │ │ │ ├── test_custom_dataset.py │ │ │ ├── test_dataset_wrapper.py │ │ │ ├── test_openimages_dataset.py │ │ │ ├── test_panoptic_dataset.py │ │ │ └── test_xml_dataset.py │ │ ├── test_pipelines │ │ │ ├── test_formatting.py │ │ │ ├── test_loading.py │ │ │ ├── test_sampler.py │ │ │ └── test_transform │ │ │ │ ├── __init__.py │ │ │ │ ├── test_img_augment.py │ │ │ │ ├── test_models_aug_test.py │ │ │ │ ├── test_rotate.py │ │ │ │ ├── test_shear.py │ │ │ │ ├── test_transform.py │ │ │ │ ├── test_translate.py │ │ │ │ └── utils.py │ │ └── test_utils.py │ ├── test_downstream │ │ └── test_mmtrack.py │ ├── test_metrics │ │ ├── test_box_overlap.py │ │ ├── test_losses.py │ │ ├── test_mean_ap.py │ │ └── test_recall.py │ ├── test_models │ │ ├── test_backbones │ │ │ ├── __init__.py │ │ │ ├── test_csp_darknet.py │ │ │ ├── test_detectors_resnet.py │ │ │ ├── test_efficientnet.py │ │ │ ├── test_hourglass.py │ │ │ ├── test_hrnet.py │ │ │ ├── test_mobilenet_v2.py │ │ │ ├── test_pvt.py │ │ │ ├── test_regnet.py │ │ │ ├── test_renext.py │ │ │ ├── test_res2net.py │ │ │ ├── test_resnest.py │ │ │ ├── test_resnet.py │ │ │ ├── test_swin.py │ │ │ ├── test_trident_resnet.py │ │ │ └── utils.py │ │ ├── test_dense_heads │ │ │ ├── test_anchor_head.py │ │ │ ├── test_atss_head.py │ │ │ ├── test_autoassign_head.py │ │ │ ├── test_centernet_head.py │ │ │ ├── test_corner_head.py │ │ │ ├── test_dense_heads_attr.py │ │ │ ├── test_detr_head.py │ │ │ ├── test_fcos_head.py │ │ │ ├── test_fsaf_head.py │ │ │ ├── test_ga_anchor_head.py │ │ │ ├── test_gfl_head.py │ │ │ ├── test_lad_head.py │ │ │ ├── test_ld_head.py │ │ │ ├── test_mask2former_head.py │ │ │ ├── test_maskformer_head.py │ │ │ ├── test_paa_head.py │ │ │ ├── test_pisa_head.py │ │ │ ├── test_sabl_retina_head.py │ │ │ ├── test_solo_head.py │ │ │ ├── test_tood_head.py │ │ │ ├── test_vfnet_head.py │ │ │ ├── test_yolact_head.py │ │ │ ├── test_yolof_head.py │ │ │ └── test_yolox_head.py │ │ ├── test_forward.py │ │ ├── test_loss.py │ │ ├── test_loss_compatibility.py │ │ ├── test_necks.py │ │ ├── test_plugins.py │ │ ├── test_roi_heads │ │ │ ├── __init__.py │ │ │ ├── test_bbox_head.py │ │ │ ├── test_mask_head.py │ │ │ ├── test_roi_extractor.py │ │ │ ├── test_sabl_bbox_head.py │ │ │ └── utils.py │ │ ├── test_seg_heads │ │ │ └── test_maskformer_fusion_head.py │ │ └── test_utils │ │ │ ├── test_brick_wrappers.py │ │ │ ├── test_conv_upsample.py │ │ │ ├── test_inverted_residual.py │ │ │ ├── test_model_misc.py │ │ │ ├── test_position_encoding.py │ │ │ ├── test_se_layer.py │ │ │ └── test_transformer.py │ ├── test_onnx │ │ ├── __init__.py │ │ ├── data │ │ │ ├── fsaf_head_get_bboxes.pkl │ │ │ ├── retina_head_get_bboxes.pkl │ │ │ ├── ssd_head_get_bboxes.pkl │ │ │ ├── yolov3_head_get_bboxes.pkl │ │ │ └── yolov3_neck.pkl │ │ ├── test_head.py │ │ ├── test_neck.py │ │ └── utils.py │ ├── test_runtime │ │ ├── async_benchmark.py │ │ ├── test_async.py │ │ ├── test_config.py │ │ ├── test_eval_hook.py │ │ └── test_fp16.py │ └── test_utils │ │ ├── test_anchor.py │ │ ├── test_assigner.py │ │ ├── test_coder.py │ │ ├── test_general_data.py │ │ ├── test_hook.py │ │ ├── test_logger.py │ │ ├── test_masks.py │ │ ├── test_misc.py │ │ ├── test_nms.py │ │ ├── test_setup_env.py │ │ ├── test_version.py │ │ └── test_visualization.py └── tools │ ├── analysis_tools │ ├── analyze_logs.py │ ├── analyze_results.py │ ├── benchmark.py │ ├── coco_error_analysis.py │ ├── confusion_matrix.py │ ├── eval_metric.py │ ├── get_flops.py │ ├── optimize_anchors.py │ ├── robustness_eval.py │ └── test_robustness.py │ ├── dataset_converters │ ├── cityscapes.py │ ├── images2coco.py │ └── pascal_voc.py │ ├── deployment │ ├── mmdet2torchserve.py │ ├── mmdet_handler.py │ ├── onnx2tensorrt.py │ ├── pytorch2onnx.py │ ├── test.py │ └── test_torchserver.py │ ├── dist_test.sh │ ├── dist_train.sh │ ├── misc │ ├── browse_dataset.py │ ├── download_dataset.py │ ├── gen_coco_panoptic_test_info.py │ ├── get_image_metas.py │ └── print_config.py │ ├── model_converters │ ├── detectron2pytorch.py │ ├── publish_model.py │ ├── regnet2mmdet.py │ ├── selfsup2mmdet.py │ ├── upgrade_model_version.py │ └── upgrade_ssd_version.py │ ├── slurm_test.sh │ ├── slurm_train.sh │ ├── test.py │ └── train.py ├── mmseg-v0.28 ├── CITATION.cff ├── LICENSE ├── LICENSES.md ├── MANIFEST.in ├── README.md ├── README_zh-CN.md ├── configs │ ├── _base_ │ │ ├── datasets │ │ │ ├── ade20k.py │ │ │ ├── ade20k_640x640.py │ │ │ ├── chase_db1.py │ │ │ ├── cityscapes.py │ │ │ ├── cityscapes_1024x1024.py │ │ │ ├── cityscapes_768x768.py │ │ │ ├── cityscapes_769x769.py │ │ │ ├── cityscapes_832x832.py │ │ │ ├── coco-stuff10k.py │ │ │ ├── coco-stuff164k.py │ │ │ ├── drive.py │ │ │ ├── hrf.py │ │ │ ├── isaid.py │ │ │ ├── loveda.py │ │ │ ├── pascal_context.py │ │ │ ├── pascal_context_59.py │ │ │ ├── pascal_voc12.py │ │ │ ├── pascal_voc12_aug.py │ │ │ ├── potsdam.py │ │ │ ├── stare.py │ │ │ └── vaihingen.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 │ │ │ ├── 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 │ │ │ ├── segmenter_vit-b16_mask.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_convnext.py │ │ │ ├── upernet_mae.py │ │ │ ├── upernet_r50.py │ │ │ ├── upernet_swin.py │ │ │ └── upernet_vit-b16_ln_mln.py │ │ └── schedules │ │ │ ├── schedule_160k.py │ │ │ ├── schedule_20k.py │ │ │ ├── schedule_320k.py │ │ │ ├── schedule_40k.py │ │ │ └── schedule_80k.py │ └── vit │ │ └── fastmim_vit_base_cfg.py ├── demo │ ├── MMSegmentation_Tutorial.ipynb │ ├── demo.png │ ├── image_demo.py │ ├── inference_demo.ipynb │ └── video_demo.py ├── docker │ ├── Dockerfile │ └── serve │ │ ├── Dockerfile │ │ ├── config.properties │ │ └── entrypoint.sh ├── docs │ ├── en │ │ ├── Makefile │ │ ├── _static │ │ │ ├── css │ │ │ │ └── readthedocs.css │ │ │ └── images │ │ │ │ └── mmsegmentation.png │ │ ├── api.rst │ │ ├── changelog.md │ │ ├── conf.py │ │ ├── dataset_prepare.md │ │ ├── faq.md │ │ ├── get_started.md │ │ ├── index.rst │ │ ├── inference.md │ │ ├── make.bat │ │ ├── model_zoo.md │ │ ├── stat.py │ │ ├── switch_language.md │ │ ├── train.md │ │ ├── tutorials │ │ │ ├── config.md │ │ │ ├── customize_datasets.md │ │ │ ├── customize_models.md │ │ │ ├── customize_runtime.md │ │ │ ├── data_pipeline.md │ │ │ ├── index.rst │ │ │ └── training_tricks.md │ │ └── useful_tools.md │ └── zh_cn │ │ ├── Makefile │ │ ├── _static │ │ ├── css │ │ │ └── readthedocs.css │ │ └── images │ │ │ └── mmsegmentation.png │ │ ├── api.rst │ │ ├── conf.py │ │ ├── dataset_prepare.md │ │ ├── faq.md │ │ ├── get_started.md │ │ ├── imgs │ │ ├── qq_group_qrcode.jpg │ │ └── zhihu_qrcode.jpg │ │ ├── index.rst │ │ ├── inference.md │ │ ├── make.bat │ │ ├── model_zoo.md │ │ ├── stat.py │ │ ├── switch_language.md │ │ ├── train.md │ │ ├── tutorials │ │ ├── config.md │ │ ├── customize_datasets.md │ │ ├── customize_models.md │ │ ├── customize_runtime.md │ │ ├── data_pipeline.md │ │ ├── index.rst │ │ └── training_tricks.md │ │ └── useful_tools.md ├── mmseg │ ├── __init__.py │ ├── apis │ │ ├── __init__.py │ │ ├── inference.py │ │ ├── test.py │ │ └── train.py │ ├── core │ │ ├── __init__.py │ │ ├── builder.py │ │ ├── evaluation │ │ │ ├── __init__.py │ │ │ ├── class_names.py │ │ │ ├── eval_hooks.py │ │ │ └── metrics.py │ │ ├── hook │ │ │ ├── __init__.py │ │ │ └── wandblogger_hook.py │ │ ├── optimizers │ │ │ ├── __init__.py │ │ │ └── layer_decay_optimizer_constructor.py │ │ ├── seg │ │ │ ├── __init__.py │ │ │ ├── builder.py │ │ │ └── sampler │ │ │ │ ├── __init__.py │ │ │ │ ├── base_pixel_sampler.py │ │ │ │ └── ohem_pixel_sampler.py │ │ └── utils │ │ │ ├── __init__.py │ │ │ ├── dist_util.py │ │ │ └── misc.py │ ├── datasets │ │ ├── __init__.py │ │ ├── ade.py │ │ ├── builder.py │ │ ├── chase_db1.py │ │ ├── cityscapes.py │ │ ├── coco_stuff.py │ │ ├── custom.py │ │ ├── dark_zurich.py │ │ ├── dataset_wrappers.py │ │ ├── drive.py │ │ ├── hrf.py │ │ ├── isaid.py │ │ ├── isprs.py │ │ ├── loveda.py │ │ ├── night_driving.py │ │ ├── pascal_context.py │ │ ├── pipelines │ │ │ ├── __init__.py │ │ │ ├── compose.py │ │ │ ├── formating.py │ │ │ ├── formatting.py │ │ │ ├── loading.py │ │ │ ├── test_time_aug.py │ │ │ └── transforms.py │ │ ├── potsdam.py │ │ ├── samplers │ │ │ ├── __init__.py │ │ │ └── distributed_sampler.py │ │ ├── stare.py │ │ └── voc.py │ ├── models │ │ ├── __init__.py │ │ ├── backbones │ │ │ ├── __init__.py │ │ │ ├── beit.py │ │ │ ├── bisenetv1.py │ │ │ ├── bisenetv2.py │ │ │ ├── cgnet.py │ │ │ ├── erfnet.py │ │ │ ├── fast_scnn.py │ │ │ ├── hrnet.py │ │ │ ├── icnet.py │ │ │ ├── mae.py │ │ │ ├── mit.py │ │ │ ├── mobilenet_v2.py │ │ │ ├── mobilenet_v3.py │ │ │ ├── resnest.py │ │ │ ├── resnet.py │ │ │ ├── resnext.py │ │ │ ├── stdc.py │ │ │ ├── swin.py │ │ │ ├── timm_backbone.py │ │ │ ├── twins.py │ │ │ ├── unet.py │ │ │ ├── vit.py │ │ │ └── vit_beit.py │ │ ├── builder.py │ │ ├── decode_heads │ │ │ ├── __init__.py │ │ │ ├── ann_head.py │ │ │ ├── apc_head.py │ │ │ ├── aspp_head.py │ │ │ ├── cascade_decode_head.py │ │ │ ├── cc_head.py │ │ │ ├── da_head.py │ │ │ ├── decode_head.py │ │ │ ├── dm_head.py │ │ │ ├── dnl_head.py │ │ │ ├── dpt_head.py │ │ │ ├── ema_head.py │ │ │ ├── enc_head.py │ │ │ ├── fcn_head.py │ │ │ ├── fpn_head.py │ │ │ ├── gc_head.py │ │ │ ├── isa_head.py │ │ │ ├── knet_head.py │ │ │ ├── lraspp_head.py │ │ │ ├── nl_head.py │ │ │ ├── ocr_head.py │ │ │ ├── point_head.py │ │ │ ├── psa_head.py │ │ │ ├── psp_head.py │ │ │ ├── segformer_head.py │ │ │ ├── segmenter_mask_head.py │ │ │ ├── sep_aspp_head.py │ │ │ ├── sep_fcn_head.py │ │ │ ├── setr_mla_head.py │ │ │ ├── setr_up_head.py │ │ │ ├── stdc_head.py │ │ │ └── uper_head.py │ │ ├── losses │ │ │ ├── __init__.py │ │ │ ├── accuracy.py │ │ │ ├── cross_entropy_loss.py │ │ │ ├── dice_loss.py │ │ │ ├── focal_loss.py │ │ │ ├── lovasz_loss.py │ │ │ ├── tversky_loss.py │ │ │ └── utils.py │ │ ├── necks │ │ │ ├── __init__.py │ │ │ ├── featurepyramid.py │ │ │ ├── fpn.py │ │ │ ├── ic_neck.py │ │ │ ├── jpu.py │ │ │ ├── mla_neck.py │ │ │ └── multilevel_neck.py │ │ ├── segmentors │ │ │ ├── __init__.py │ │ │ ├── base.py │ │ │ ├── cascade_encoder_decoder.py │ │ │ └── encoder_decoder.py │ │ └── utils │ │ │ ├── __init__.py │ │ │ ├── embed.py │ │ │ ├── inverted_residual.py │ │ │ ├── make_divisible.py │ │ │ ├── res_layer.py │ │ │ ├── se_layer.py │ │ │ ├── self_attention_block.py │ │ │ ├── shape_convert.py │ │ │ └── up_conv_block.py │ ├── ops │ │ ├── __init__.py │ │ ├── encoding.py │ │ └── wrappers.py │ ├── utils │ │ ├── __init__.py │ │ ├── collect_env.py │ │ ├── logger.py │ │ ├── misc.py │ │ ├── set_env.py │ │ └── util_distribution.py │ └── version.py ├── model-index.yml ├── pytest.ini ├── requirements.txt ├── requirements │ ├── docs.txt │ ├── mminstall.txt │ ├── optional.txt │ ├── readthedocs.txt │ ├── runtime.txt │ └── tests.txt ├── resources │ ├── 3dogs.jpg │ ├── 3dogs_mask.png │ ├── mmseg-logo.png │ └── seg_demo.gif ├── setup.cfg ├── setup.py ├── tests │ ├── __init__.py │ ├── data │ │ ├── color.jpg │ │ ├── gray.jpg │ │ ├── pseudo_cityscapes_dataset │ │ │ ├── gtFine │ │ │ │ ├── frankfurt_000000_000294_gtFine_instanceIds.png │ │ │ │ ├── frankfurt_000000_000294_gtFine_labelIds.png │ │ │ │ └── frankfurt_000000_000294_gtFine_labelTrainIds.png │ │ │ └── leftImg8bit │ │ │ │ └── frankfurt_000000_000294_leftImg8bit.png │ │ ├── pseudo_dataset │ │ │ ├── gts │ │ │ │ ├── 00000_gt.png │ │ │ │ ├── 00001_gt.png │ │ │ │ ├── 00002_gt.png │ │ │ │ ├── 00003_gt.png │ │ │ │ └── 00004_gt.png │ │ │ ├── imgs │ │ │ │ ├── 00000_img.jpg │ │ │ │ ├── 00001_img.jpg │ │ │ │ ├── 00002_img.jpg │ │ │ │ ├── 00003_img.jpg │ │ │ │ └── 00004_img.jpg │ │ │ └── splits │ │ │ │ ├── train.txt │ │ │ │ └── val.txt │ │ ├── pseudo_isaid_dataset │ │ │ ├── ann_dir │ │ │ │ ├── P0000_0_896_1024_1920_instance_color_RGB.png │ │ │ │ └── P0000_0_896_1536_2432_instance_color_RGB.png │ │ │ ├── img_dir │ │ │ │ ├── P0000_0_896_1024_1920.png │ │ │ │ └── P0000_0_896_1536_2432.png │ │ │ └── splits │ │ │ │ ├── train.txt │ │ │ │ └── val.txt │ │ ├── pseudo_loveda_dataset │ │ │ ├── ann_dir │ │ │ │ ├── 0.png │ │ │ │ ├── 1.png │ │ │ │ └── 2.png │ │ │ └── img_dir │ │ │ │ ├── 0.png │ │ │ │ ├── 1.png │ │ │ │ └── 2.png │ │ ├── pseudo_potsdam_dataset │ │ │ ├── ann_dir │ │ │ │ └── 2_10_0_0_512_512.png │ │ │ └── img_dir │ │ │ │ └── 2_10_0_0_512_512.png │ │ ├── pseudo_vaihingen_dataset │ │ │ ├── ann_dir │ │ │ │ └── area1_0_0_512_512.png │ │ │ └── img_dir │ │ │ │ └── area1_0_0_512_512.png │ │ └── seg.png │ ├── test_apis │ │ └── test_single_gpu.py │ ├── test_config.py │ ├── test_core │ │ ├── test_layer_decay_optimizer_constructor.py │ │ └── test_optimizer.py │ ├── test_data │ │ ├── test_dataset.py │ │ ├── test_dataset_builder.py │ │ ├── test_loading.py │ │ ├── test_transform.py │ │ └── test_tta.py │ ├── test_digit_version.py │ ├── test_eval_hook.py │ ├── test_inference.py │ ├── test_metrics.py │ ├── test_models │ │ ├── __init__.py │ │ ├── test_backbones │ │ │ ├── __init__.py │ │ │ ├── test_beit.py │ │ │ ├── test_bisenetv1.py │ │ │ ├── test_bisenetv2.py │ │ │ ├── test_blocks.py │ │ │ ├── test_cgnet.py │ │ │ ├── test_erfnet.py │ │ │ ├── test_fast_scnn.py │ │ │ ├── test_hrnet.py │ │ │ ├── test_icnet.py │ │ │ ├── test_mae.py │ │ │ ├── test_mit.py │ │ │ ├── test_mobilenet_v3.py │ │ │ ├── test_resnest.py │ │ │ ├── test_resnet.py │ │ │ ├── test_resnext.py │ │ │ ├── test_stdc.py │ │ │ ├── test_swin.py │ │ │ ├── test_timm_backbone.py │ │ │ ├── test_twins.py │ │ │ ├── test_unet.py │ │ │ ├── test_vit.py │ │ │ └── utils.py │ │ ├── test_forward.py │ │ ├── test_heads │ │ │ ├── __init__.py │ │ │ ├── test_ann_head.py │ │ │ ├── test_apc_head.py │ │ │ ├── test_aspp_head.py │ │ │ ├── test_cc_head.py │ │ │ ├── test_da_head.py │ │ │ ├── test_decode_head.py │ │ │ ├── test_dm_head.py │ │ │ ├── test_dnl_head.py │ │ │ ├── test_dpt_head.py │ │ │ ├── test_ema_head.py │ │ │ ├── test_enc_head.py │ │ │ ├── test_fcn_head.py │ │ │ ├── test_gc_head.py │ │ │ ├── test_isa_head.py │ │ │ ├── test_knet_head.py │ │ │ ├── test_lraspp_head.py │ │ │ ├── test_nl_head.py │ │ │ ├── test_ocr_head.py │ │ │ ├── test_point_head.py │ │ │ ├── test_psa_head.py │ │ │ ├── test_psp_head.py │ │ │ ├── test_segformer_head.py │ │ │ ├── test_segmenter_mask_head.py │ │ │ ├── test_setr_mla_head.py │ │ │ ├── test_setr_up_head.py │ │ │ ├── test_stdc_head.py │ │ │ ├── test_uper_head.py │ │ │ └── utils.py │ │ ├── test_losses │ │ │ ├── __init__.py │ │ │ ├── test_ce_loss.py │ │ │ ├── test_dice_loss.py │ │ │ ├── test_focal_loss.py │ │ │ ├── test_lovasz_loss.py │ │ │ ├── test_tversky_loss.py │ │ │ └── test_utils.py │ │ ├── test_necks │ │ │ ├── __init__.py │ │ │ ├── test_feature2pyramid.py │ │ │ ├── test_fpn.py │ │ │ ├── test_ic_neck.py │ │ │ ├── test_jpu.py │ │ │ ├── test_mla_neck.py │ │ │ └── test_multilevel_neck.py │ │ ├── test_segmentors │ │ │ ├── __init__.py │ │ │ ├── test_cascade_encoder_decoder.py │ │ │ ├── test_encoder_decoder.py │ │ │ └── utils.py │ │ └── test_utils │ │ │ ├── __init__.py │ │ │ ├── test_embed.py │ │ │ └── test_shape_convert.py │ ├── test_sampler.py │ └── test_utils │ │ ├── test_misc.py │ │ ├── test_set_env.py │ │ └── test_util_distribution.py └── tools │ ├── analyze_logs.py │ ├── benchmark.py │ ├── browse_dataset.py │ ├── confusion_matrix.py │ ├── convert_datasets │ ├── chase_db1.py │ ├── cityscapes.py │ ├── coco_stuff10k.py │ ├── coco_stuff164k.py │ ├── drive.py │ ├── hrf.py │ ├── isaid.py │ ├── loveda.py │ ├── pascal_context.py │ ├── potsdam.py │ ├── stare.py │ ├── vaihingen.py │ └── voc_aug.py │ ├── deploy_test.py │ ├── dist_test.sh │ ├── dist_train.sh │ ├── get_flops.py │ ├── model_converters │ ├── beit2mmseg.py │ ├── mit2mmseg.py │ ├── stdc2mmseg.py │ ├── swin2mmseg.py │ ├── twins2mmseg.py │ ├── vit2mmseg.py │ └── vitjax2mmseg.py │ ├── onnx2tensorrt.py │ ├── print_config.py │ ├── publish_model.py │ ├── pytorch2onnx.py │ ├── pytorch2torchscript.py │ ├── slurm_test.sh │ ├── slurm_train.sh │ ├── test.py │ ├── torchserve │ ├── mmseg2torchserve.py │ ├── mmseg_handler.py │ └── test_torchserve.py │ └── train.py ├── models_mae.py ├── models_vit.py ├── requirement_pip_install.sh └── util ├── datasets.py ├── hog_layer.py ├── lr_decay.py ├── lr_sched.py ├── misc.py └── pos_embed.py /figs/fastmim.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ggjy/FastMIM.pytorch/d4eea0dc8caf3db4be3d3ed4c7a8bc07c59b6111/figs/fastmim.png -------------------------------------------------------------------------------- /mmdet-v2.23/build_environment.sh: -------------------------------------------------------------------------------- 1 | pip install --upgrade pip; 2 | pip install torch==1.7.0 torchvision==0.8.1; 3 | pip install timm==0.3.2; 4 | pip install einops; 5 | pip install torchprofile; 6 | pip install tensorboardX; 7 | pip install cupy-cuda100; 8 | pip install addict; 9 | pip install yapf; 10 | pip install cython; 11 | pip install scikit-build; 12 | pip install opencv-python; 13 | pip install pytest-runner; 14 | pip install terminaltables==3.1.0; 15 | pip install mmpycocotools; -------------------------------------------------------------------------------- /mmdet-v2.23/configs/_base_/datasets/lvis_v0.5_instance.py: -------------------------------------------------------------------------------- 1 | # dataset settings 2 | _base_ = 'coco_instance.py' 3 | dataset_type = 'LVISV05Dataset' 4 | data_root = 'data/lvis_v0.5/' 5 | data = dict( 6 | samples_per_gpu=2, 7 | workers_per_gpu=2, 8 | train=dict( 9 | _delete_=True, 10 | type='ClassBalancedDataset', 11 | oversample_thr=1e-3, 12 | dataset=dict( 13 | type=dataset_type, 14 | ann_file=data_root + 'annotations/lvis_v0.5_train.json', 15 | img_prefix=data_root + 'train2017/')), 16 | val=dict( 17 | type=dataset_type, 18 | ann_file=data_root + 'annotations/lvis_v0.5_val.json', 19 | img_prefix=data_root + 'val2017/'), 20 | test=dict( 21 | type=dataset_type, 22 | ann_file=data_root + 'annotations/lvis_v0.5_val.json', 23 | img_prefix=data_root + 'val2017/')) 24 | evaluation = dict(metric=['bbox', 'segm']) 25 | -------------------------------------------------------------------------------- /mmdet-v2.23/configs/_base_/datasets/lvis_v1_instance.py: -------------------------------------------------------------------------------- 1 | # dataset settings 2 | _base_ = 'coco_instance.py' 3 | dataset_type = 'LVISV1Dataset' 4 | data_root = 'data/lvis_v1/' 5 | data = dict( 6 | samples_per_gpu=2, 7 | workers_per_gpu=2, 8 | train=dict( 9 | _delete_=True, 10 | type='ClassBalancedDataset', 11 | oversample_thr=1e-3, 12 | dataset=dict( 13 | type=dataset_type, 14 | ann_file=data_root + 'annotations/lvis_v1_train.json', 15 | img_prefix=data_root)), 16 | val=dict( 17 | type=dataset_type, 18 | ann_file=data_root + 'annotations/lvis_v1_val.json', 19 | img_prefix=data_root), 20 | test=dict( 21 | type=dataset_type, 22 | ann_file=data_root + 'annotations/lvis_v1_val.json', 23 | img_prefix=data_root)) 24 | evaluation = dict(metric=['bbox', 'segm']) 25 | -------------------------------------------------------------------------------- /mmdet-v2.23/configs/_base_/default_runtime.py: -------------------------------------------------------------------------------- 1 | checkpoint_config = dict(interval=1) 2 | # yapf:disable 3 | log_config = dict( 4 | interval=50, 5 | hooks=[ 6 | dict(type='TextLoggerHook'), 7 | # dict(type='TensorboardLoggerHook') 8 | ]) 9 | # yapf:enable 10 | custom_hooks = [dict(type='NumClassCheckHook')] 11 | 12 | dist_params = dict(backend='nccl') 13 | log_level = 'INFO' 14 | load_from = None 15 | resume_from = None 16 | workflow = [('train', 1)] 17 | 18 | # disable opencv multithreading to avoid system being overloaded 19 | opencv_num_threads = 0 20 | # set multi-process start method as `fork` to speed up the training 21 | mp_start_method = 'fork' 22 | -------------------------------------------------------------------------------- /mmdet-v2.23/configs/_base_/schedules/schedule_1x.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) 3 | optimizer_config = dict(grad_clip=None) 4 | # learning policy 5 | lr_config = dict( 6 | policy='step', 7 | warmup='linear', 8 | warmup_iters=500, 9 | warmup_ratio=0.001, 10 | step=[8, 11]) 11 | runner = dict(type='EpochBasedRunner', max_epochs=12) 12 | -------------------------------------------------------------------------------- /mmdet-v2.23/configs/_base_/schedules/schedule_20e.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) 3 | optimizer_config = dict(grad_clip=None) 4 | # learning policy 5 | lr_config = dict( 6 | policy='step', 7 | warmup='linear', 8 | warmup_iters=500, 9 | warmup_ratio=0.001, 10 | step=[16, 19]) 11 | runner = dict(type='EpochBasedRunner', max_epochs=20) 12 | -------------------------------------------------------------------------------- /mmdet-v2.23/configs/_base_/schedules/schedule_2x.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) 3 | optimizer_config = dict(grad_clip=None) 4 | # learning policy 5 | lr_config = dict( 6 | policy='step', 7 | warmup='linear', 8 | warmup_iters=500, 9 | warmup_ratio=0.001, 10 | step=[16, 22]) 11 | runner = dict(type='EpochBasedRunner', max_epochs=24) 12 | -------------------------------------------------------------------------------- /mmdet-v2.23/docs/en/Makefile: -------------------------------------------------------------------------------- 1 | # Minimal makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line, and also 5 | # from the environment for the first two. 6 | SPHINXOPTS ?= 7 | SPHINXBUILD ?= sphinx-build 8 | SOURCEDIR = . 9 | BUILDDIR = _build 10 | 11 | # Put it first so that "make" without argument is like "make help". 12 | help: 13 | @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 14 | 15 | .PHONY: help Makefile 16 | 17 | # Catch-all target: route all unknown targets to Sphinx using the new 18 | # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). 19 | %: Makefile 20 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 21 | -------------------------------------------------------------------------------- /mmdet-v2.23/docs/en/_static/css/readthedocs.css: -------------------------------------------------------------------------------- 1 | .header-logo { 2 | background-image: url("../image/mmdet-logo.png"); 3 | background-size: 156px 40px; 4 | height: 40px; 5 | width: 156px; 6 | } 7 | -------------------------------------------------------------------------------- /mmdet-v2.23/docs/en/_static/image/mmdet-logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ggjy/FastMIM.pytorch/d4eea0dc8caf3db4be3d3ed4c7a8bc07c59b6111/mmdet-v2.23/docs/en/_static/image/mmdet-logo.png -------------------------------------------------------------------------------- /mmdet-v2.23/docs/en/index.rst: -------------------------------------------------------------------------------- 1 | Welcome to MMDetection's documentation! 2 | ======================================= 3 | 4 | .. toctree:: 5 | :maxdepth: 2 6 | :caption: Get Started 7 | 8 | get_started.md 9 | modelzoo_statistics.md 10 | model_zoo.md 11 | 12 | .. toctree:: 13 | :maxdepth: 2 14 | :caption: Quick Run 15 | 16 | 1_exist_data_model.md 17 | 2_new_data_model.md 18 | 3_exist_data_new_model.md 19 | 20 | .. toctree:: 21 | :maxdepth: 2 22 | :caption: Tutorials 23 | 24 | tutorials/index.rst 25 | 26 | .. toctree:: 27 | :maxdepth: 2 28 | :caption: Useful Tools and Scripts 29 | 30 | useful_tools.md 31 | 32 | .. toctree:: 33 | :maxdepth: 2 34 | :caption: Notes 35 | 36 | conventions.md 37 | compatibility.md 38 | projects.md 39 | changelog.md 40 | faq.md 41 | 42 | .. toctree:: 43 | :caption: Switch Language 44 | 45 | switch_language.md 46 | 47 | .. toctree:: 48 | :maxdepth: 1 49 | :caption: API Reference 50 | 51 | api.rst 52 | 53 | Indices and tables 54 | ================== 55 | 56 | * :ref:`genindex` 57 | * :ref:`search` 58 | -------------------------------------------------------------------------------- /mmdet-v2.23/docs/en/make.bat: -------------------------------------------------------------------------------- 1 | @ECHO OFF 2 | 3 | pushd %~dp0 4 | 5 | REM Command file for Sphinx documentation 6 | 7 | if "%SPHINXBUILD%" == "" ( 8 | set SPHINXBUILD=sphinx-build 9 | ) 10 | set SOURCEDIR=. 11 | set BUILDDIR=_build 12 | 13 | if "%1" == "" goto help 14 | 15 | %SPHINXBUILD% >NUL 2>NUL 16 | if errorlevel 9009 ( 17 | echo. 18 | echo.The 'sphinx-build' command was not found. Make sure you have Sphinx 19 | echo.installed, then set the SPHINXBUILD environment variable to point 20 | echo.to the full path of the 'sphinx-build' executable. Alternatively you 21 | echo.may add the Sphinx directory to PATH. 22 | echo. 23 | echo.If you don't have Sphinx installed, grab it from 24 | echo.http://sphinx-doc.org/ 25 | exit /b 1 26 | ) 27 | 28 | %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% 29 | goto end 30 | 31 | :help 32 | %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% 33 | 34 | :end 35 | popd 36 | -------------------------------------------------------------------------------- /mmdet-v2.23/docs/en/switch_language.md: -------------------------------------------------------------------------------- 1 | ## English 2 | 3 | ## 简体中文 4 | -------------------------------------------------------------------------------- /mmdet-v2.23/docs/en/tutorials/index.rst: -------------------------------------------------------------------------------- 1 | .. toctree:: 2 | :maxdepth: 2 3 | 4 | config.md 5 | customize_dataset.md 6 | data_pipeline.md 7 | customize_models.md 8 | customize_runtime.md 9 | customize_losses.md 10 | finetune.md 11 | robustness_benchmarking.md 12 | pytorch2onnx.md 13 | onnx2tensorrt.md 14 | init_cfg.md 15 | how_to.md 16 | -------------------------------------------------------------------------------- /mmdet-v2.23/docs/zh_cn/Makefile: -------------------------------------------------------------------------------- 1 | # Minimal makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line, and also 5 | # from the environment for the first two. 6 | SPHINXOPTS ?= 7 | SPHINXBUILD ?= sphinx-build 8 | SOURCEDIR = . 9 | BUILDDIR = _build 10 | 11 | # Put it first so that "make" without argument is like "make help". 12 | help: 13 | @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 14 | 15 | .PHONY: help Makefile 16 | 17 | # Catch-all target: route all unknown targets to Sphinx using the new 18 | # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). 19 | %: Makefile 20 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 21 | -------------------------------------------------------------------------------- /mmdet-v2.23/docs/zh_cn/_static/css/readthedocs.css: -------------------------------------------------------------------------------- 1 | .header-logo { 2 | background-image: url("../image/mmdet-logo.png"); 3 | background-size: 156px 40px; 4 | height: 40px; 5 | width: 156px; 6 | } 7 | -------------------------------------------------------------------------------- /mmdet-v2.23/docs/zh_cn/_static/image/mmdet-logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ggjy/FastMIM.pytorch/d4eea0dc8caf3db4be3d3ed4c7a8bc07c59b6111/mmdet-v2.23/docs/zh_cn/_static/image/mmdet-logo.png -------------------------------------------------------------------------------- /mmdet-v2.23/docs/zh_cn/index.rst: -------------------------------------------------------------------------------- 1 | Welcome to MMDetection's documentation! 2 | ======================================= 3 | 4 | .. toctree:: 5 | :maxdepth: 2 6 | :caption: 开始你的第一步 7 | 8 | get_started.md 9 | model_zoo.md 10 | article.md 11 | 12 | .. toctree:: 13 | :maxdepth: 2 14 | :caption: 快速启动 15 | 16 | 1_exist_data_model.md 17 | 2_new_data_model.md 18 | 19 | .. toctree:: 20 | :maxdepth: 2 21 | :caption: 教程 22 | 23 | tutorials/index.rst 24 | 25 | .. toctree:: 26 | :maxdepth: 2 27 | :caption: 实用工具与脚本 28 | 29 | useful_tools.md 30 | 31 | .. toctree:: 32 | :maxdepth: 2 33 | :caption: 说明 34 | 35 | conventions.md 36 | compatibility.md 37 | faq.md 38 | 39 | .. toctree:: 40 | :caption: 语言切换 41 | 42 | switch_language.md 43 | 44 | .. toctree:: 45 | :maxdepth: 1 46 | :caption: 接口文档（英文） 47 | 48 | api.rst 49 | 50 | 51 | Indices and tables 52 | ================== 53 | 54 | * :ref:`genindex` 55 | * :ref:`search` 56 | -------------------------------------------------------------------------------- /mmdet-v2.23/docs/zh_cn/make.bat: -------------------------------------------------------------------------------- 1 | @ECHO OFF 2 | 3 | pushd %~dp0 4 | 5 | REM Command file for Sphinx documentation 6 | 7 | if "%SPHINXBUILD%" == "" ( 8 | set SPHINXBUILD=sphinx-build 9 | ) 10 | set SOURCEDIR=. 11 | set BUILDDIR=_build 12 | 13 | if "%1" == "" goto help 14 | 15 | %SPHINXBUILD% >NUL 2>NUL 16 | if errorlevel 9009 ( 17 | echo. 18 | echo.The 'sphinx-build' command was not found. Make sure you have Sphinx 19 | echo.installed, then set the SPHINXBUILD environment variable to point 20 | echo.to the full path of the 'sphinx-build' executable. Alternatively you 21 | echo.may add the Sphinx directory to PATH. 22 | echo. 23 | echo.If you don't have Sphinx installed, grab it from 24 | echo.http://sphinx-doc.org/ 25 | exit /b 1 26 | ) 27 | 28 | %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% 29 | goto end 30 | 31 | :help 32 | %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% 33 | 34 | :end 35 | popd 36 | -------------------------------------------------------------------------------- /mmdet-v2.23/docs/zh_cn/switch_language.md: -------------------------------------------------------------------------------- 1 | ## English 2 | 3 | ## 简体中文 4 | -------------------------------------------------------------------------------- /mmdet-v2.23/docs/zh_cn/tutorials/customize_runtime.md: -------------------------------------------------------------------------------- 1 | # 教程 5: 自定义训练配置 2 | -------------------------------------------------------------------------------- /mmdet-v2.23/docs/zh_cn/tutorials/index.rst: -------------------------------------------------------------------------------- 1 | .. toctree:: 2 | :maxdepth: 2 3 | 4 | config.md 5 | customize_dataset.md 6 | data_pipeline.md 7 | customize_models.md 8 | customize_runtime.md 9 | customize_losses.md 10 | finetune.md 11 | pytorch2onnx.md 12 | onnx2tensorrt.md 13 | init_cfg.md 14 | how_to.md 15 | -------------------------------------------------------------------------------- /mmdet-v2.23/docs/zh_cn/tutorials/pytorch2onnx.md: -------------------------------------------------------------------------------- 1 | # 教程 8: Pytorch 到 ONNX 的模型转换（实验性支持） 2 | 3 | 4 | > ## [尝试使用新的 MMDeploy 來部署你的模型](https://mmdeploy.readthedocs.io/) 5 | -------------------------------------------------------------------------------- /mmdet-v2.23/docs/zh_cn/useful_tools.md: -------------------------------------------------------------------------------- 1 | ## 日志分析 2 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import mmcv 3 | 4 | from .version import __version__, short_version 5 | 6 | 7 | def digit_version(version_str): 8 | digit_version = [] 9 | for x in version_str.split('.'): 10 | if x.isdigit(): 11 | digit_version.append(int(x)) 12 | elif x.find('rc') != -1: 13 | patch_version = x.split('rc') 14 | digit_version.append(int(patch_version[0]) - 1) 15 | digit_version.append(int(patch_version[1])) 16 | return digit_version 17 | 18 | 19 | mmcv_minimum_version = '1.3.17' 20 | mmcv_maximum_version = '1.5.0' 21 | mmcv_version = digit_version(mmcv.__version__) 22 | 23 | 24 | assert (mmcv_version >= digit_version(mmcv_minimum_version) 25 | and mmcv_version <= digit_version(mmcv_maximum_version)), \ 26 | f'MMCV=={mmcv.__version__} is used but incompatible. ' \ 27 | f'Please install mmcv>={mmcv_minimum_version}, <={mmcv_maximum_version}.' 28 | 29 | __all__ = ['__version__', 'short_version'] 30 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/apis/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .inference import (async_inference_detector, inference_detector, 3 | init_detector, show_result_pyplot) 4 | from .test import multi_gpu_test, single_gpu_test 5 | from .train import (get_root_logger, init_random_seed, set_random_seed, 6 | train_detector) 7 | 8 | __all__ = [ 9 | 'get_root_logger', 'set_random_seed', 'train_detector', 'init_detector', 10 | 'async_inference_detector', 'inference_detector', 'show_result_pyplot', 11 | 'multi_gpu_test', 'single_gpu_test', 'init_random_seed' 12 | ] 13 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .anchor import * # noqa: F401, F403 3 | from .bbox import * # noqa: F401, F403 4 | from .data_structures import * # noqa: F401, F403 5 | from .evaluation import * # noqa: F401, F403 6 | from .hook import * # noqa: F401, F403 7 | from .mask import * # noqa: F401, F403 8 | from .post_processing import * # noqa: F401, F403 9 | from .utils import * # noqa: F401, F403 10 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/anchor/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .anchor_generator import (AnchorGenerator, LegacyAnchorGenerator, 3 | YOLOAnchorGenerator) 4 | from .builder import (ANCHOR_GENERATORS, PRIOR_GENERATORS, 5 | build_anchor_generator, build_prior_generator) 6 | from .point_generator import MlvlPointGenerator, PointGenerator 7 | from .utils import anchor_inside_flags, calc_region, images_to_levels 8 | 9 | __all__ = [ 10 | 'AnchorGenerator', 'LegacyAnchorGenerator', 'anchor_inside_flags', 11 | 'PointGenerator', 'images_to_levels', 'calc_region', 12 | 'build_anchor_generator', 'ANCHOR_GENERATORS', 'YOLOAnchorGenerator', 13 | 'build_prior_generator', 'PRIOR_GENERATORS', 'MlvlPointGenerator' 14 | ] 15 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/anchor/builder.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import warnings 3 | 4 | from mmcv.utils import Registry, build_from_cfg 5 | 6 | PRIOR_GENERATORS = Registry('Generator for anchors and points') 7 | 8 | ANCHOR_GENERATORS = PRIOR_GENERATORS 9 | 10 | 11 | def build_prior_generator(cfg, default_args=None): 12 | return build_from_cfg(cfg, PRIOR_GENERATORS, default_args) 13 | 14 | 15 | def build_anchor_generator(cfg, default_args=None): 16 | warnings.warn( 17 | '``build_anchor_generator`` would be deprecated soon, please use ' 18 | '``build_prior_generator`` ') 19 | return build_prior_generator(cfg, default_args=default_args) 20 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/bbox/assigners/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .approx_max_iou_assigner import ApproxMaxIoUAssigner 3 | from .assign_result import AssignResult 4 | from .atss_assigner import ATSSAssigner 5 | from .base_assigner import BaseAssigner 6 | from .center_region_assigner import CenterRegionAssigner 7 | from .grid_assigner import GridAssigner 8 | from .hungarian_assigner import HungarianAssigner 9 | from .mask_hungarian_assigner import MaskHungarianAssigner 10 | from .max_iou_assigner import MaxIoUAssigner 11 | from .point_assigner import PointAssigner 12 | from .region_assigner import RegionAssigner 13 | from .sim_ota_assigner import SimOTAAssigner 14 | from .task_aligned_assigner import TaskAlignedAssigner 15 | from .uniform_assigner import UniformAssigner 16 | 17 | __all__ = [ 18 | 'BaseAssigner', 'MaxIoUAssigner', 'ApproxMaxIoUAssigner', 'AssignResult', 19 | 'PointAssigner', 'ATSSAssigner', 'CenterRegionAssigner', 'GridAssigner', 20 | 'HungarianAssigner', 'RegionAssigner', 'UniformAssigner', 'SimOTAAssigner', 21 | 'TaskAlignedAssigner', 'MaskHungarianAssigner' 22 | ] 23 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/bbox/assigners/base_assigner.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from abc import ABCMeta, abstractmethod 3 | 4 | 5 | class BaseAssigner(metaclass=ABCMeta): 6 | """Base assigner that assigns boxes to ground truth boxes.""" 7 | 8 | @abstractmethod 9 | def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None): 10 | """Assign boxes to either a ground truth boxes or a negative boxes.""" 11 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/bbox/builder.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmcv.utils import Registry, build_from_cfg 3 | 4 | BBOX_ASSIGNERS = Registry('bbox_assigner') 5 | BBOX_SAMPLERS = Registry('bbox_sampler') 6 | BBOX_CODERS = Registry('bbox_coder') 7 | 8 | 9 | def build_assigner(cfg, **default_args): 10 | """Builder of box assigner.""" 11 | return build_from_cfg(cfg, BBOX_ASSIGNERS, default_args) 12 | 13 | 14 | def build_sampler(cfg, **default_args): 15 | """Builder of box sampler.""" 16 | return build_from_cfg(cfg, BBOX_SAMPLERS, default_args) 17 | 18 | 19 | def build_bbox_coder(cfg, **default_args): 20 | """Builder of box coder.""" 21 | return build_from_cfg(cfg, BBOX_CODERS, default_args) 22 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/bbox/coder/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .base_bbox_coder import BaseBBoxCoder 3 | from .bucketing_bbox_coder import BucketingBBoxCoder 4 | from .delta_xywh_bbox_coder import DeltaXYWHBBoxCoder 5 | from .distance_point_bbox_coder import DistancePointBBoxCoder 6 | from .legacy_delta_xywh_bbox_coder import LegacyDeltaXYWHBBoxCoder 7 | from .pseudo_bbox_coder import PseudoBBoxCoder 8 | from .tblr_bbox_coder import TBLRBBoxCoder 9 | from .yolo_bbox_coder import YOLOBBoxCoder 10 | 11 | __all__ = [ 12 | 'BaseBBoxCoder', 'PseudoBBoxCoder', 'DeltaXYWHBBoxCoder', 13 | 'LegacyDeltaXYWHBBoxCoder', 'TBLRBBoxCoder', 'YOLOBBoxCoder', 14 | 'BucketingBBoxCoder', 'DistancePointBBoxCoder' 15 | ] 16 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/bbox/coder/base_bbox_coder.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from abc import ABCMeta, abstractmethod 3 | 4 | 5 | class BaseBBoxCoder(metaclass=ABCMeta): 6 | """Base bounding box coder.""" 7 | 8 | def __init__(self, **kwargs): 9 | pass 10 | 11 | @abstractmethod 12 | def encode(self, bboxes, gt_bboxes): 13 | """Encode deltas between bboxes and ground truth boxes.""" 14 | 15 | @abstractmethod 16 | def decode(self, bboxes, bboxes_pred): 17 | """Decode the predicted bboxes according to prediction and base 18 | boxes.""" 19 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/bbox/coder/pseudo_bbox_coder.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import BBOX_CODERS 3 | from .base_bbox_coder import BaseBBoxCoder 4 | 5 | 6 | @BBOX_CODERS.register_module() 7 | class PseudoBBoxCoder(BaseBBoxCoder): 8 | """Pseudo bounding box coder.""" 9 | 10 | def __init__(self, **kwargs): 11 | super(BaseBBoxCoder, self).__init__(**kwargs) 12 | 13 | def encode(self, bboxes, gt_bboxes): 14 | """torch.Tensor: return the given ``bboxes``""" 15 | return gt_bboxes 16 | 17 | def decode(self, bboxes, pred_bboxes): 18 | """torch.Tensor: return the given ``pred_bboxes``""" 19 | return pred_bboxes 20 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/bbox/demodata.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import numpy as np 3 | import torch 4 | 5 | from mmdet.utils.util_random import ensure_rng 6 | 7 | 8 | def random_boxes(num=1, scale=1, rng=None): 9 | """Simple version of ``kwimage.Boxes.random`` 10 | 11 | Returns: 12 | Tensor: shape (n, 4) in x1, y1, x2, y2 format. 13 | 14 | References: 15 | https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390 16 | 17 | Example: 18 | >>> num = 3 19 | >>> scale = 512 20 | >>> rng = 0 21 | >>> boxes = random_boxes(num, scale, rng) 22 | >>> print(boxes) 23 | tensor([[280.9925, 278.9802, 308.6148, 366.1769], 24 | [216.9113, 330.6978, 224.0446, 456.5878], 25 | [405.3632, 196.3221, 493.3953, 270.7942]]) 26 | """ 27 | rng = ensure_rng(rng) 28 | 29 | tlbr = rng.rand(num, 4).astype(np.float32) 30 | 31 | tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2]) 32 | tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3]) 33 | br_x = np.maximum(tlbr[:, 0], tlbr[:, 2]) 34 | br_y = np.maximum(tlbr[:, 1], tlbr[:, 3]) 35 | 36 | tlbr[:, 0] = tl_x * scale 37 | tlbr[:, 1] = tl_y * scale 38 | tlbr[:, 2] = br_x * scale 39 | tlbr[:, 3] = br_y * scale 40 | 41 | boxes = torch.from_numpy(tlbr) 42 | return boxes 43 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/bbox/iou_calculators/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .builder import build_iou_calculator 3 | from .iou2d_calculator import BboxOverlaps2D, bbox_overlaps 4 | 5 | __all__ = ['build_iou_calculator', 'BboxOverlaps2D', 'bbox_overlaps'] 6 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/bbox/iou_calculators/builder.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmcv.utils import Registry, build_from_cfg 3 | 4 | IOU_CALCULATORS = Registry('IoU calculator') 5 | 6 | 7 | def build_iou_calculator(cfg, default_args=None): 8 | """Builder of IoU calculator.""" 9 | return build_from_cfg(cfg, IOU_CALCULATORS, default_args) 10 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/bbox/match_costs/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .builder import build_match_cost 3 | from .match_cost import (BBoxL1Cost, ClassificationCost, CrossEntropyLossCost, 4 | DiceCost, FocalLossCost, IoUCost) 5 | 6 | __all__ = [ 7 | 'build_match_cost', 'ClassificationCost', 'BBoxL1Cost', 'IoUCost', 8 | 'FocalLossCost', 'DiceCost', 'CrossEntropyLossCost' 9 | ] 10 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/bbox/match_costs/builder.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmcv.utils import Registry, build_from_cfg 3 | 4 | MATCH_COST = Registry('Match Cost') 5 | 6 | 7 | def build_match_cost(cfg, default_args=None): 8 | """Builder of IoU calculator.""" 9 | return build_from_cfg(cfg, MATCH_COST, default_args) 10 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/bbox/samplers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .base_sampler import BaseSampler 3 | from .combined_sampler import CombinedSampler 4 | from .instance_balanced_pos_sampler import InstanceBalancedPosSampler 5 | from .iou_balanced_neg_sampler import IoUBalancedNegSampler 6 | from .mask_pseudo_sampler import MaskPseudoSampler 7 | from .mask_sampling_result import MaskSamplingResult 8 | from .ohem_sampler import OHEMSampler 9 | from .pseudo_sampler import PseudoSampler 10 | from .random_sampler import RandomSampler 11 | from .sampling_result import SamplingResult 12 | from .score_hlr_sampler import ScoreHLRSampler 13 | 14 | __all__ = [ 15 | 'BaseSampler', 'PseudoSampler', 'RandomSampler', 16 | 'InstanceBalancedPosSampler', 'IoUBalancedNegSampler', 'CombinedSampler', 17 | 'OHEMSampler', 'SamplingResult', 'ScoreHLRSampler', 'MaskPseudoSampler', 18 | 'MaskSamplingResult' 19 | ] 20 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/bbox/samplers/combined_sampler.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import BBOX_SAMPLERS, build_sampler 3 | from .base_sampler import BaseSampler 4 | 5 | 6 | @BBOX_SAMPLERS.register_module() 7 | class CombinedSampler(BaseSampler): 8 | """A sampler that combines positive sampler and negative sampler.""" 9 | 10 | def __init__(self, pos_sampler, neg_sampler, **kwargs): 11 | super(CombinedSampler, self).__init__(**kwargs) 12 | self.pos_sampler = build_sampler(pos_sampler, **kwargs) 13 | self.neg_sampler = build_sampler(neg_sampler, **kwargs) 14 | 15 | def _sample_pos(self, **kwargs): 16 | """Sample positive samples.""" 17 | raise NotImplementedError 18 | 19 | def _sample_neg(self, **kwargs): 20 | """Sample negative samples.""" 21 | raise NotImplementedError 22 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/data_structures/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .general_data import GeneralData 3 | from .instance_data import InstanceData 4 | 5 | __all__ = ['GeneralData', 'InstanceData'] 6 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/evaluation/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .class_names import (cityscapes_classes, coco_classes, dataset_aliases, 3 | get_classes, imagenet_det_classes, 4 | imagenet_vid_classes, oid_challenge_classes, 5 | oid_v6_classes, voc_classes) 6 | from .eval_hooks import DistEvalHook, EvalHook 7 | from .mean_ap import average_precision, eval_map, print_map_summary 8 | from .panoptic_utils import INSTANCE_OFFSET 9 | from .recall import (eval_recalls, plot_iou_recall, plot_num_recall, 10 | print_recall_summary) 11 | 12 | __all__ = [ 13 | 'voc_classes', 'imagenet_det_classes', 'imagenet_vid_classes', 14 | 'coco_classes', 'cityscapes_classes', 'dataset_aliases', 'get_classes', 15 | 'DistEvalHook', 'EvalHook', 'average_precision', 'eval_map', 16 | 'print_map_summary', 'eval_recalls', 'print_recall_summary', 17 | 'plot_num_recall', 'plot_iou_recall', 'oid_v6_classes', 18 | 'oid_challenge_classes', 'INSTANCE_OFFSET' 19 | ] 20 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/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 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/export/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .onnx_helper import (add_dummy_nms_for_onnx, dynamic_clip_for_onnx, 3 | get_k_for_topk) 4 | from .pytorch2onnx import (build_model_from_cfg, 5 | generate_inputs_and_wrap_model, 6 | preprocess_example_input) 7 | 8 | __all__ = [ 9 | 'build_model_from_cfg', 'generate_inputs_and_wrap_model', 10 | 'preprocess_example_input', 'get_k_for_topk', 'add_dummy_nms_for_onnx', 11 | 'dynamic_clip_for_onnx' 12 | ] 13 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/hook/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .checkloss_hook import CheckInvalidLossHook 3 | from .ema import ExpMomentumEMAHook, LinearMomentumEMAHook 4 | from .set_epoch_info_hook import SetEpochInfoHook 5 | from .sync_norm_hook import SyncNormHook 6 | from .sync_random_size_hook import SyncRandomSizeHook 7 | from .yolox_lrupdater_hook import YOLOXLrUpdaterHook 8 | from .yolox_mode_switch_hook import YOLOXModeSwitchHook 9 | 10 | __all__ = [ 11 | 'SyncRandomSizeHook', 'YOLOXModeSwitchHook', 'SyncNormHook', 12 | 'ExpMomentumEMAHook', 'LinearMomentumEMAHook', 'YOLOXLrUpdaterHook', 13 | 'CheckInvalidLossHook', 'SetEpochInfoHook' 14 | ] 15 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/hook/checkloss_hook.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import torch 3 | from mmcv.runner.hooks import HOOKS, Hook 4 | 5 | 6 | @HOOKS.register_module() 7 | class CheckInvalidLossHook(Hook): 8 | """Check invalid loss hook. 9 | 10 | This hook will regularly check whether the loss is valid 11 | during training. 12 | 13 | Args: 14 | interval (int): Checking interval (every k iterations). 15 | Default: 50. 16 | """ 17 | 18 | def __init__(self, interval=50): 19 | self.interval = interval 20 | 21 | def after_train_iter(self, runner): 22 | if self.every_n_iters(runner, self.interval): 23 | assert torch.isfinite(runner.outputs['loss']), \ 24 | runner.logger.info('loss become infinite or NaN!') 25 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/hook/set_epoch_info_hook.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmcv.parallel import is_module_wrapper 3 | from mmcv.runner import HOOKS, Hook 4 | 5 | 6 | @HOOKS.register_module() 7 | class SetEpochInfoHook(Hook): 8 | """Set runner's epoch information to the model.""" 9 | 10 | def before_train_epoch(self, runner): 11 | epoch = runner.epoch 12 | model = runner.model 13 | if is_module_wrapper(model): 14 | model = model.module 15 | model.set_epoch(epoch) 16 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/mask/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .mask_target import mask_target 3 | from .structures import BaseInstanceMasks, BitmapMasks, PolygonMasks 4 | from .utils import encode_mask_results, mask2bbox, split_combined_polys 5 | 6 | __all__ = [ 7 | 'split_combined_polys', 'mask_target', 'BaseInstanceMasks', 'BitmapMasks', 8 | 'PolygonMasks', 'encode_mask_results', 'mask2bbox' 9 | ] 10 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/post_processing/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .bbox_nms import fast_nms, multiclass_nms 3 | from .matrix_nms import mask_matrix_nms 4 | from .merge_augs import (merge_aug_bboxes, merge_aug_masks, 5 | merge_aug_proposals, merge_aug_scores) 6 | 7 | __all__ = [ 8 | 'multiclass_nms', 'merge_aug_proposals', 'merge_aug_bboxes', 9 | 'merge_aug_scores', 'merge_aug_masks', 'mask_matrix_nms', 'fast_nms' 10 | ] 11 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/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, sync_random_seed) 4 | from .misc import (center_of_mass, filter_scores_and_topk, flip_tensor, 5 | generate_coordinate, mask2ndarray, multi_apply, 6 | select_single_mlvl, unmap) 7 | 8 | __all__ = [ 9 | 'allreduce_grads', 'DistOptimizerHook', 'reduce_mean', 'multi_apply', 10 | 'unmap', 'mask2ndarray', 'flip_tensor', 'all_reduce_dict', 11 | 'center_of_mass', 'generate_coordinate', 'select_single_mlvl', 12 | 'filter_scores_and_topk', 'sync_random_seed' 13 | ] 14 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/core/visualization/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .image import (color_val_matplotlib, imshow_det_bboxes, 3 | imshow_gt_det_bboxes) 4 | from .palette import get_palette, palette_val 5 | 6 | __all__ = [ 7 | 'imshow_det_bboxes', 'imshow_gt_det_bboxes', 'color_val_matplotlib', 8 | 'palette_val', 'get_palette' 9 | ] 10 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/datasets/api_wrappers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .coco_api import COCO, COCOeval 3 | from .panoptic_evaluation import pq_compute_multi_core, pq_compute_single_core 4 | 5 | __all__ = [ 6 | 'COCO', 'COCOeval', 'pq_compute_multi_core', 'pq_compute_single_core' 7 | ] 8 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/datasets/deepfashion.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .builder import DATASETS 3 | from .coco import CocoDataset 4 | 5 | 6 | @DATASETS.register_module() 7 | class DeepFashionDataset(CocoDataset): 8 | 9 | CLASSES = ('top', 'skirt', 'leggings', 'dress', 'outer', 'pants', 'bag', 10 | 'neckwear', 'headwear', 'eyeglass', 'belt', 'footwear', 'hair', 11 | 'skin', 'face') 12 | 13 | PALETTE = [(0, 192, 64), (0, 64, 96), (128, 192, 192), (0, 64, 64), 14 | (0, 192, 224), (0, 192, 192), (128, 192, 64), (0, 192, 96), 15 | (128, 32, 192), (0, 0, 224), (0, 0, 64), (0, 160, 192), 16 | (128, 0, 96), (128, 0, 192), (0, 32, 192)] 17 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/datasets/pipelines/formating.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | # flake8: noqa 3 | import warnings 4 | 5 | from .formatting import * 6 | 7 | warnings.warn('DeprecationWarning: mmdet.datasets.pipelines.formating will be ' 8 | 'deprecated, please replace it with ' 9 | 'mmdet.datasets.pipelines.formatting.') 10 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/datasets/samplers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .distributed_sampler import DistributedSampler 3 | from .group_sampler import DistributedGroupSampler, GroupSampler 4 | from .infinite_sampler import InfiniteBatchSampler, InfiniteGroupBatchSampler 5 | 6 | __all__ = [ 7 | 'DistributedSampler', 'DistributedGroupSampler', 'GroupSampler', 8 | 'InfiniteGroupBatchSampler', 'InfiniteBatchSampler' 9 | ] 10 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .backbones import * # noqa: F401,F403 3 | from .builder import (BACKBONES, DETECTORS, HEADS, LOSSES, NECKS, 4 | ROI_EXTRACTORS, SHARED_HEADS, build_backbone, 5 | build_detector, build_head, build_loss, build_neck, 6 | build_roi_extractor, build_shared_head) 7 | from .dense_heads import * # noqa: F401,F403 8 | from .detectors import * # noqa: F401,F403 9 | from .losses import * # noqa: F401,F403 10 | from .necks import * # noqa: F401,F403 11 | from .plugins import * # noqa: F401,F403 12 | from .roi_heads import * # noqa: F401,F403 13 | from .seg_heads import * # noqa: F401,F403 14 | 15 | __all__ = [ 16 | 'BACKBONES', 'NECKS', 'ROI_EXTRACTORS', 'SHARED_HEADS', 'HEADS', 'LOSSES', 17 | 'DETECTORS', 'build_backbone', 'build_neck', 'build_roi_extractor', 18 | 'build_shared_head', 'build_head', 'build_loss', 'build_detector' 19 | ] 20 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/backbones/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .csp_darknet import CSPDarknet 3 | from .darknet import Darknet 4 | from .detectors_resnet import DetectoRS_ResNet 5 | from .detectors_resnext import DetectoRS_ResNeXt 6 | from .efficientnet import EfficientNet 7 | from .hourglass import HourglassNet 8 | from .hrnet import HRNet 9 | from .mobilenet_v2 import MobileNetV2 10 | from .pvt import PyramidVisionTransformer, PyramidVisionTransformerV2 11 | from .regnet import RegNet 12 | from .res2net import Res2Net 13 | from .resnest import ResNeSt 14 | from .resnet import ResNet, ResNetV1d 15 | from .resnext import ResNeXt 16 | from .ssd_vgg import SSDVGG 17 | from .swin import SwinTransformer 18 | from .trident_resnet import TridentResNet 19 | from .wave_dwconv import WaveDWNet 20 | from .mix_regnetg import RegNetG 21 | from .vit import VisionTransformer 22 | 23 | __all__ = [ 24 | 'RegNet', 'ResNet', 'ResNetV1d', 'ResNeXt', 'SSDVGG', 'HRNet', 25 | 'MobileNetV2', 'Res2Net', 'HourglassNet', 'DetectoRS_ResNet', 26 | 'DetectoRS_ResNeXt', 'Darknet', 'ResNeSt', 'TridentResNet', 'CSPDarknet', 27 | 'SwinTransformer', 'PyramidVisionTransformer', 28 | 'PyramidVisionTransformerV2', 'EfficientNet', 'WaveDWNet', 'RegNetG', 29 | 'VisionTransformer', 30 | ] 31 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/datasets/api_wrappers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .coco_api import COCO, COCOeval 3 | from .panoptic_evaluation import pq_compute_multi_core, pq_compute_single_core 4 | 5 | __all__ = [ 6 | 'COCO', 'COCOeval', 'pq_compute_multi_core', 'pq_compute_single_core' 7 | ] 8 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/datasets/deepfashion.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .builder import DATASETS 3 | from .coco import CocoDataset 4 | 5 | 6 | @DATASETS.register_module() 7 | class DeepFashionDataset(CocoDataset): 8 | 9 | CLASSES = ('top', 'skirt', 'leggings', 'dress', 'outer', 'pants', 'bag', 10 | 'neckwear', 'headwear', 'eyeglass', 'belt', 'footwear', 'hair', 11 | 'skin', 'face') 12 | 13 | PALETTE = [(0, 192, 64), (0, 64, 96), (128, 192, 192), (0, 64, 64), 14 | (0, 192, 224), (0, 192, 192), (128, 192, 64), (0, 192, 96), 15 | (128, 32, 192), (0, 0, 224), (0, 0, 64), (0, 160, 192), 16 | (128, 0, 96), (128, 0, 192), (0, 32, 192)] 17 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/datasets/pipelines/formating.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | # flake8: noqa 3 | import warnings 4 | 5 | from .formatting import * 6 | 7 | warnings.warn('DeprecationWarning: mmdet.datasets.pipelines.formating will be ' 8 | 'deprecated, please replace it with ' 9 | 'mmdet.datasets.pipelines.formatting.') 10 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/datasets/samplers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .distributed_sampler import DistributedSampler 3 | from .group_sampler import DistributedGroupSampler, GroupSampler 4 | from .infinite_sampler import InfiniteBatchSampler, InfiniteGroupBatchSampler 5 | 6 | __all__ = [ 7 | 'DistributedSampler', 'DistributedGroupSampler', 'GroupSampler', 8 | 'InfiniteGroupBatchSampler', 'InfiniteBatchSampler' 9 | ] 10 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/atss.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .single_stage import SingleStageDetector 4 | 5 | 6 | @DETECTORS.register_module() 7 | class ATSS(SingleStageDetector): 8 | """Implementation of `ATSS `_.""" 9 | 10 | def __init__(self, 11 | backbone, 12 | neck, 13 | bbox_head, 14 | train_cfg=None, 15 | test_cfg=None, 16 | pretrained=None, 17 | init_cfg=None): 18 | super(ATSS, self).__init__(backbone, neck, bbox_head, train_cfg, 19 | test_cfg, pretrained, init_cfg) 20 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/autoassign.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .single_stage import SingleStageDetector 4 | 5 | 6 | @DETECTORS.register_module() 7 | class AutoAssign(SingleStageDetector): 8 | """Implementation of `AutoAssign: Differentiable Label Assignment for Dense 9 | Object Detection `_.""" 10 | 11 | def __init__(self, 12 | backbone, 13 | neck, 14 | bbox_head, 15 | train_cfg=None, 16 | test_cfg=None, 17 | pretrained=None): 18 | super(AutoAssign, self).__init__(backbone, neck, bbox_head, train_cfg, 19 | test_cfg, pretrained) 20 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/deformable_detr.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .detr import DETR 4 | 5 | 6 | @DETECTORS.register_module() 7 | class DeformableDETR(DETR): 8 | 9 | def __init__(self, *args, **kwargs): 10 | super(DETR, self).__init__(*args, **kwargs) 11 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/faster_rcnn.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .two_stage import TwoStageDetector 4 | 5 | 6 | @DETECTORS.register_module() 7 | class FasterRCNN(TwoStageDetector): 8 | """Implementation of `Faster R-CNN `_""" 9 | 10 | def __init__(self, 11 | backbone, 12 | rpn_head, 13 | roi_head, 14 | train_cfg, 15 | test_cfg, 16 | neck=None, 17 | pretrained=None, 18 | init_cfg=None): 19 | super(FasterRCNN, self).__init__( 20 | backbone=backbone, 21 | neck=neck, 22 | rpn_head=rpn_head, 23 | roi_head=roi_head, 24 | train_cfg=train_cfg, 25 | test_cfg=test_cfg, 26 | pretrained=pretrained, 27 | init_cfg=init_cfg) 28 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/fcos.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .single_stage import SingleStageDetector 4 | 5 | 6 | @DETECTORS.register_module() 7 | class FCOS(SingleStageDetector): 8 | """Implementation of `FCOS `_""" 9 | 10 | def __init__(self, 11 | backbone, 12 | neck, 13 | bbox_head, 14 | train_cfg=None, 15 | test_cfg=None, 16 | pretrained=None, 17 | init_cfg=None): 18 | super(FCOS, self).__init__(backbone, neck, bbox_head, train_cfg, 19 | test_cfg, pretrained, init_cfg) 20 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/fovea.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .single_stage import SingleStageDetector 4 | 5 | 6 | @DETECTORS.register_module() 7 | class FOVEA(SingleStageDetector): 8 | """Implementation of `FoveaBox `_""" 9 | 10 | def __init__(self, 11 | backbone, 12 | neck, 13 | bbox_head, 14 | train_cfg=None, 15 | test_cfg=None, 16 | pretrained=None, 17 | init_cfg=None): 18 | super(FOVEA, self).__init__(backbone, neck, bbox_head, train_cfg, 19 | test_cfg, pretrained, init_cfg) 20 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/fsaf.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .single_stage import SingleStageDetector 4 | 5 | 6 | @DETECTORS.register_module() 7 | class FSAF(SingleStageDetector): 8 | """Implementation of `FSAF `_""" 9 | 10 | def __init__(self, 11 | backbone, 12 | neck, 13 | bbox_head, 14 | train_cfg=None, 15 | test_cfg=None, 16 | pretrained=None, 17 | init_cfg=None): 18 | super(FSAF, self).__init__(backbone, neck, bbox_head, train_cfg, 19 | test_cfg, pretrained, init_cfg) 20 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/gfl.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .single_stage import SingleStageDetector 4 | 5 | 6 | @DETECTORS.register_module() 7 | class GFL(SingleStageDetector): 8 | 9 | def __init__(self, 10 | backbone, 11 | neck, 12 | bbox_head, 13 | train_cfg=None, 14 | test_cfg=None, 15 | pretrained=None, 16 | init_cfg=None): 17 | super(GFL, self).__init__(backbone, neck, bbox_head, train_cfg, 18 | test_cfg, pretrained, init_cfg) 19 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/grid_rcnn.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .two_stage import TwoStageDetector 4 | 5 | 6 | @DETECTORS.register_module() 7 | class GridRCNN(TwoStageDetector): 8 | """Grid R-CNN. 9 | 10 | This detector is the implementation of: 11 | - Grid R-CNN (https://arxiv.org/abs/1811.12030) 12 | - Grid R-CNN Plus: Faster and Better (https://arxiv.org/abs/1906.05688) 13 | """ 14 | 15 | def __init__(self, 16 | backbone, 17 | rpn_head, 18 | roi_head, 19 | train_cfg, 20 | test_cfg, 21 | neck=None, 22 | pretrained=None, 23 | init_cfg=None): 24 | super(GridRCNN, self).__init__( 25 | backbone=backbone, 26 | neck=neck, 27 | rpn_head=rpn_head, 28 | roi_head=roi_head, 29 | train_cfg=train_cfg, 30 | test_cfg=test_cfg, 31 | pretrained=pretrained, 32 | init_cfg=init_cfg) 33 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/htc.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .cascade_rcnn import CascadeRCNN 4 | 5 | 6 | @DETECTORS.register_module() 7 | class HybridTaskCascade(CascadeRCNN): 8 | """Implementation of `HTC `_""" 9 | 10 | def __init__(self, **kwargs): 11 | super(HybridTaskCascade, self).__init__(**kwargs) 12 | 13 | @property 14 | def with_semantic(self): 15 | """bool: whether the detector has a semantic head""" 16 | return self.roi_head.with_semantic 17 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/mask2former.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .maskformer import MaskFormer 4 | 5 | 6 | @DETECTORS.register_module() 7 | class Mask2Former(MaskFormer): 8 | r"""Implementation of `Masked-attention Mask 9 | Transformer for Universal Image Segmentation 10 | `_.""" 11 | 12 | def __init__(self, 13 | backbone, 14 | neck=None, 15 | panoptic_head=None, 16 | panoptic_fusion_head=None, 17 | train_cfg=None, 18 | test_cfg=None, 19 | init_cfg=None): 20 | super().__init__( 21 | backbone, 22 | neck=neck, 23 | panoptic_head=panoptic_head, 24 | panoptic_fusion_head=panoptic_fusion_head, 25 | train_cfg=train_cfg, 26 | test_cfg=test_cfg, 27 | init_cfg=init_cfg) 28 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/mask_rcnn.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .two_stage import TwoStageDetector 4 | 5 | 6 | @DETECTORS.register_module() 7 | class MaskRCNN(TwoStageDetector): 8 | """Implementation of `Mask R-CNN `_""" 9 | 10 | def __init__(self, 11 | backbone, 12 | rpn_head, 13 | roi_head, 14 | train_cfg, 15 | test_cfg, 16 | neck=None, 17 | pretrained=None, 18 | init_cfg=None): 19 | super(MaskRCNN, self).__init__( 20 | backbone=backbone, 21 | neck=neck, 22 | rpn_head=rpn_head, 23 | roi_head=roi_head, 24 | train_cfg=train_cfg, 25 | test_cfg=test_cfg, 26 | pretrained=pretrained, 27 | init_cfg=init_cfg) 28 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/mask_scoring_rcnn.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .two_stage import TwoStageDetector 4 | 5 | 6 | @DETECTORS.register_module() 7 | class MaskScoringRCNN(TwoStageDetector): 8 | """Mask Scoring RCNN. 9 | 10 | https://arxiv.org/abs/1903.00241 11 | """ 12 | 13 | def __init__(self, 14 | backbone, 15 | rpn_head, 16 | roi_head, 17 | train_cfg, 18 | test_cfg, 19 | neck=None, 20 | pretrained=None, 21 | init_cfg=None): 22 | super(MaskScoringRCNN, self).__init__( 23 | backbone=backbone, 24 | neck=neck, 25 | rpn_head=rpn_head, 26 | roi_head=roi_head, 27 | train_cfg=train_cfg, 28 | test_cfg=test_cfg, 29 | pretrained=pretrained, 30 | init_cfg=init_cfg) 31 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/nasfcos.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .single_stage import SingleStageDetector 4 | 5 | 6 | @DETECTORS.register_module() 7 | class NASFCOS(SingleStageDetector): 8 | """NAS-FCOS: Fast Neural Architecture Search for Object Detection. 9 | 10 | https://arxiv.org/abs/1906.0442 11 | """ 12 | 13 | def __init__(self, 14 | backbone, 15 | neck, 16 | bbox_head, 17 | train_cfg=None, 18 | test_cfg=None, 19 | pretrained=None, 20 | init_cfg=None): 21 | super(NASFCOS, self).__init__(backbone, neck, bbox_head, train_cfg, 22 | test_cfg, pretrained, init_cfg) 23 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/paa.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .single_stage import SingleStageDetector 4 | 5 | 6 | @DETECTORS.register_module() 7 | class PAA(SingleStageDetector): 8 | """Implementation of `PAA `_.""" 9 | 10 | def __init__(self, 11 | backbone, 12 | neck, 13 | bbox_head, 14 | train_cfg=None, 15 | test_cfg=None, 16 | pretrained=None, 17 | init_cfg=None): 18 | super(PAA, self).__init__(backbone, neck, bbox_head, train_cfg, 19 | test_cfg, pretrained, init_cfg) 20 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/panoptic_fpn.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .panoptic_two_stage_segmentor import TwoStagePanopticSegmentor 4 | 5 | 6 | @DETECTORS.register_module() 7 | class PanopticFPN(TwoStagePanopticSegmentor): 8 | r"""Implementation of `Panoptic feature pyramid 9 | networks `_""" 10 | 11 | def __init__( 12 | self, 13 | backbone, 14 | neck=None, 15 | rpn_head=None, 16 | roi_head=None, 17 | train_cfg=None, 18 | test_cfg=None, 19 | pretrained=None, 20 | init_cfg=None, 21 | # for panoptic segmentation 22 | semantic_head=None, 23 | panoptic_fusion_head=None): 24 | super(PanopticFPN, self).__init__( 25 | backbone=backbone, 26 | neck=neck, 27 | rpn_head=rpn_head, 28 | roi_head=roi_head, 29 | train_cfg=train_cfg, 30 | test_cfg=test_cfg, 31 | pretrained=pretrained, 32 | init_cfg=init_cfg, 33 | semantic_head=semantic_head, 34 | panoptic_fusion_head=panoptic_fusion_head) 35 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/point_rend.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .two_stage import TwoStageDetector 4 | 5 | 6 | @DETECTORS.register_module() 7 | class PointRend(TwoStageDetector): 8 | """PointRend: Image Segmentation as Rendering 9 | 10 | This detector is the implementation of 11 | `PointRend `_. 12 | 13 | """ 14 | 15 | def __init__(self, 16 | backbone, 17 | rpn_head, 18 | roi_head, 19 | train_cfg, 20 | test_cfg, 21 | neck=None, 22 | pretrained=None, 23 | init_cfg=None): 24 | super(PointRend, self).__init__( 25 | backbone=backbone, 26 | neck=neck, 27 | rpn_head=rpn_head, 28 | roi_head=roi_head, 29 | train_cfg=train_cfg, 30 | test_cfg=test_cfg, 31 | pretrained=pretrained, 32 | init_cfg=init_cfg) 33 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/queryinst.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .sparse_rcnn import SparseRCNN 4 | 5 | 6 | @DETECTORS.register_module() 7 | class QueryInst(SparseRCNN): 8 | r"""Implementation of 9 | `Instances as Queries `_""" 10 | 11 | def __init__(self, 12 | backbone, 13 | rpn_head, 14 | roi_head, 15 | train_cfg, 16 | test_cfg, 17 | neck=None, 18 | pretrained=None, 19 | init_cfg=None): 20 | super(QueryInst, self).__init__( 21 | backbone=backbone, 22 | neck=neck, 23 | rpn_head=rpn_head, 24 | roi_head=roi_head, 25 | train_cfg=train_cfg, 26 | test_cfg=test_cfg, 27 | pretrained=pretrained, 28 | init_cfg=init_cfg) 29 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/reppoints_detector.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .single_stage import SingleStageDetector 4 | 5 | 6 | @DETECTORS.register_module() 7 | class RepPointsDetector(SingleStageDetector): 8 | """RepPoints: Point Set Representation for Object Detection. 9 | 10 | This detector is the implementation of: 11 | - RepPoints detector (https://arxiv.org/pdf/1904.11490) 12 | """ 13 | 14 | def __init__(self, 15 | backbone, 16 | neck, 17 | bbox_head, 18 | train_cfg=None, 19 | test_cfg=None, 20 | pretrained=None, 21 | init_cfg=None): 22 | super(RepPointsDetector, 23 | self).__init__(backbone, neck, bbox_head, train_cfg, test_cfg, 24 | pretrained, init_cfg) 25 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/retinanet.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .single_stage import SingleStageDetector 4 | 5 | 6 | @DETECTORS.register_module() 7 | class RetinaNet(SingleStageDetector): 8 | """Implementation of `RetinaNet `_""" 9 | 10 | def __init__(self, 11 | backbone, 12 | neck, 13 | bbox_head, 14 | train_cfg=None, 15 | test_cfg=None, 16 | pretrained=None, 17 | init_cfg=None): 18 | super(RetinaNet, self).__init__(backbone, neck, bbox_head, train_cfg, 19 | test_cfg, pretrained, init_cfg) 20 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/scnet.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .cascade_rcnn import CascadeRCNN 4 | 5 | 6 | @DETECTORS.register_module() 7 | class SCNet(CascadeRCNN): 8 | """Implementation of `SCNet `_""" 9 | 10 | def __init__(self, **kwargs): 11 | super(SCNet, self).__init__(**kwargs) 12 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/solo.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .single_stage_instance_seg import SingleStageInstanceSegmentor 4 | 5 | 6 | @DETECTORS.register_module() 7 | class SOLO(SingleStageInstanceSegmentor): 8 | """`SOLO: Segmenting Objects by Locations 9 | `_ 10 | 11 | """ 12 | 13 | def __init__(self, 14 | backbone, 15 | neck=None, 16 | bbox_head=None, 17 | mask_head=None, 18 | train_cfg=None, 19 | test_cfg=None, 20 | init_cfg=None, 21 | pretrained=None): 22 | super().__init__( 23 | backbone=backbone, 24 | neck=neck, 25 | bbox_head=bbox_head, 26 | mask_head=mask_head, 27 | train_cfg=train_cfg, 28 | test_cfg=test_cfg, 29 | init_cfg=init_cfg, 30 | pretrained=pretrained) 31 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/tood.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .single_stage import SingleStageDetector 4 | 5 | 6 | @DETECTORS.register_module() 7 | class TOOD(SingleStageDetector): 8 | r"""Implementation of `TOOD: Task-aligned One-stage Object Detection. 9 | `_.""" 10 | 11 | def __init__(self, 12 | backbone, 13 | neck, 14 | bbox_head, 15 | train_cfg=None, 16 | test_cfg=None, 17 | pretrained=None, 18 | init_cfg=None): 19 | super(TOOD, self).__init__(backbone, neck, bbox_head, train_cfg, 20 | test_cfg, pretrained, init_cfg) 21 | 22 | def set_epoch(self, epoch): 23 | self.bbox_head.epoch = epoch 24 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/vfnet.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .single_stage import SingleStageDetector 4 | 5 | 6 | @DETECTORS.register_module() 7 | class VFNet(SingleStageDetector): 8 | """Implementation of `VarifocalNet 9 | (VFNet).`_""" 10 | 11 | def __init__(self, 12 | backbone, 13 | neck, 14 | bbox_head, 15 | train_cfg=None, 16 | test_cfg=None, 17 | pretrained=None, 18 | init_cfg=None): 19 | super(VFNet, self).__init__(backbone, neck, bbox_head, train_cfg, 20 | test_cfg, pretrained, init_cfg) 21 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/detectors/yolof.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import DETECTORS 3 | from .single_stage import SingleStageDetector 4 | 5 | 6 | @DETECTORS.register_module() 7 | class YOLOF(SingleStageDetector): 8 | r"""Implementation of `You Only Look One-level Feature 9 | `_""" 10 | 11 | def __init__(self, 12 | backbone, 13 | neck, 14 | bbox_head, 15 | train_cfg=None, 16 | test_cfg=None, 17 | pretrained=None): 18 | super(YOLOF, self).__init__(backbone, neck, bbox_head, train_cfg, 19 | test_cfg, pretrained) 20 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/necks/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .bfp import BFP 3 | from .channel_mapper import ChannelMapper 4 | from .ct_resnet_neck import CTResNetNeck 5 | from .dilated_encoder import DilatedEncoder 6 | from .dyhead import DyHead 7 | from .fpg import FPG 8 | from .fpn import FPN 9 | from .fpn_carafe import FPN_CARAFE 10 | from .hrfpn import HRFPN 11 | from .nas_fpn import NASFPN 12 | from .nasfcos_fpn import NASFCOS_FPN 13 | from .pafpn import PAFPN 14 | from .rfp import RFP 15 | from .ssd_neck import SSDNeck 16 | from .yolo_neck import YOLOV3Neck 17 | from .yolox_pafpn import YOLOXPAFPN 18 | 19 | __all__ = [ 20 | 'FPN', 'BFP', 'ChannelMapper', 'HRFPN', 'NASFPN', 'FPN_CARAFE', 'PAFPN', 21 | 'NASFCOS_FPN', 'RFP', 'YOLOV3Neck', 'FPG', 'DilatedEncoder', 22 | 'CTResNetNeck', 'SSDNeck', 'YOLOXPAFPN', 'DyHead' 23 | ] 24 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/plugins/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .dropblock import DropBlock 3 | from .msdeformattn_pixel_decoder import MSDeformAttnPixelDecoder 4 | from .pixel_decoder import PixelDecoder, TransformerEncoderPixelDecoder 5 | 6 | __all__ = [ 7 | 'DropBlock', 'PixelDecoder', 'TransformerEncoderPixelDecoder', 8 | 'MSDeformAttnPixelDecoder' 9 | ] 10 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/roi_heads/bbox_heads/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .bbox_head import BBoxHead 3 | from .convfc_bbox_head import (ConvFCBBoxHead, Shared2FCBBoxHead, 4 | Shared4Conv1FCBBoxHead) 5 | from .dii_head import DIIHead 6 | from .double_bbox_head import DoubleConvFCBBoxHead 7 | from .sabl_head import SABLHead 8 | from .scnet_bbox_head import SCNetBBoxHead 9 | 10 | __all__ = [ 11 | 'BBoxHead', 'ConvFCBBoxHead', 'Shared2FCBBoxHead', 12 | 'Shared4Conv1FCBBoxHead', 'DoubleConvFCBBoxHead', 'SABLHead', 'DIIHead', 13 | 'SCNetBBoxHead' 14 | ] 15 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/roi_heads/double_roi_head.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ..builder import HEADS 3 | from .standard_roi_head import StandardRoIHead 4 | 5 | 6 | @HEADS.register_module() 7 | class DoubleHeadRoIHead(StandardRoIHead): 8 | """RoI head for Double Head RCNN. 9 | 10 | https://arxiv.org/abs/1904.06493 11 | """ 12 | 13 | def __init__(self, reg_roi_scale_factor, **kwargs): 14 | super(DoubleHeadRoIHead, self).__init__(**kwargs) 15 | self.reg_roi_scale_factor = reg_roi_scale_factor 16 | 17 | def _bbox_forward(self, x, rois): 18 | """Box head forward function used in both training and testing time.""" 19 | bbox_cls_feats = self.bbox_roi_extractor( 20 | x[:self.bbox_roi_extractor.num_inputs], rois) 21 | bbox_reg_feats = self.bbox_roi_extractor( 22 | x[:self.bbox_roi_extractor.num_inputs], 23 | rois, 24 | roi_scale_factor=self.reg_roi_scale_factor) 25 | if self.with_shared_head: 26 | bbox_cls_feats = self.shared_head(bbox_cls_feats) 27 | bbox_reg_feats = self.shared_head(bbox_reg_feats) 28 | cls_score, bbox_pred = self.bbox_head(bbox_cls_feats, bbox_reg_feats) 29 | 30 | bbox_results = dict( 31 | cls_score=cls_score, 32 | bbox_pred=bbox_pred, 33 | bbox_feats=bbox_cls_feats) 34 | return bbox_results 35 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/roi_heads/mask_heads/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .coarse_mask_head import CoarseMaskHead 3 | from .dynamic_mask_head import DynamicMaskHead 4 | from .fcn_mask_head import FCNMaskHead 5 | from .feature_relay_head import FeatureRelayHead 6 | from .fused_semantic_head import FusedSemanticHead 7 | from .global_context_head import GlobalContextHead 8 | from .grid_head import GridHead 9 | from .htc_mask_head import HTCMaskHead 10 | from .mask_point_head import MaskPointHead 11 | from .maskiou_head import MaskIoUHead 12 | from .scnet_mask_head import SCNetMaskHead 13 | from .scnet_semantic_head import SCNetSemanticHead 14 | 15 | __all__ = [ 16 | 'FCNMaskHead', 'HTCMaskHead', 'FusedSemanticHead', 'GridHead', 17 | 'MaskIoUHead', 'CoarseMaskHead', 'MaskPointHead', 'SCNetMaskHead', 18 | 'SCNetSemanticHead', 'GlobalContextHead', 'FeatureRelayHead', 19 | 'DynamicMaskHead' 20 | ] 21 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/roi_heads/mask_heads/scnet_mask_head.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmdet.models.builder import HEADS 3 | from mmdet.models.utils import ResLayer, SimplifiedBasicBlock 4 | from .fcn_mask_head import FCNMaskHead 5 | 6 | 7 | @HEADS.register_module() 8 | class SCNetMaskHead(FCNMaskHead): 9 | """Mask head for `SCNet `_. 10 | 11 | Args: 12 | conv_to_res (bool, optional): if True, change the conv layers to 13 | ``SimplifiedBasicBlock``. 14 | """ 15 | 16 | def __init__(self, conv_to_res=True, **kwargs): 17 | super(SCNetMaskHead, self).__init__(**kwargs) 18 | self.conv_to_res = conv_to_res 19 | if conv_to_res: 20 | assert self.conv_kernel_size == 3 21 | self.num_res_blocks = self.num_convs // 2 22 | self.convs = ResLayer( 23 | SimplifiedBasicBlock, 24 | self.in_channels, 25 | self.conv_out_channels, 26 | self.num_res_blocks, 27 | conv_cfg=self.conv_cfg, 28 | norm_cfg=self.norm_cfg) 29 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/roi_heads/mask_heads/scnet_semantic_head.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmdet.models.builder import HEADS 3 | from mmdet.models.utils import ResLayer, SimplifiedBasicBlock 4 | from .fused_semantic_head import FusedSemanticHead 5 | 6 | 7 | @HEADS.register_module() 8 | class SCNetSemanticHead(FusedSemanticHead): 9 | """Mask head for `SCNet `_. 10 | 11 | Args: 12 | conv_to_res (bool, optional): if True, change the conv layers to 13 | ``SimplifiedBasicBlock``. 14 | """ 15 | 16 | def __init__(self, conv_to_res=True, **kwargs): 17 | super(SCNetSemanticHead, self).__init__(**kwargs) 18 | self.conv_to_res = conv_to_res 19 | if self.conv_to_res: 20 | num_res_blocks = self.num_convs // 2 21 | self.convs = ResLayer( 22 | SimplifiedBasicBlock, 23 | self.in_channels, 24 | self.conv_out_channels, 25 | num_res_blocks, 26 | conv_cfg=self.conv_cfg, 27 | norm_cfg=self.norm_cfg) 28 | self.num_convs = num_res_blocks 29 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/roi_heads/roi_extractors/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .base_roi_extractor import BaseRoIExtractor 3 | from .generic_roi_extractor import GenericRoIExtractor 4 | from .single_level_roi_extractor import SingleRoIExtractor 5 | 6 | __all__ = ['BaseRoIExtractor', 'SingleRoIExtractor', 'GenericRoIExtractor'] 7 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/roi_heads/shared_heads/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .res_layer import ResLayer 3 | 4 | __all__ = ['ResLayer'] 5 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/seg_heads/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .panoptic_fpn_head import PanopticFPNHead # noqa: F401,F403 3 | from .panoptic_fusion_heads import * # noqa: F401,F403 4 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/seg_heads/panoptic_fusion_heads/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .base_panoptic_fusion_head import \ 3 | BasePanopticFusionHead # noqa: F401,F403 4 | from .heuristic_fusion_head import HeuristicFusionHead # noqa: F401,F403 5 | from .maskformer_fusion_head import MaskFormerFusionHead # noqa: F401,F403 6 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/models/utils/make_divisible.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | def make_divisible(value, divisor, min_value=None, min_ratio=0.9): 3 | """Make divisible function. 4 | 5 | This function rounds the channel number to the nearest value that can be 6 | divisible by the divisor. It is taken from the original tf repo. It ensures 7 | that all layers have a channel number that is divisible by divisor. It can 8 | be seen here: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py # noqa 9 | 10 | Args: 11 | value (int): The original channel number. 12 | divisor (int): The divisor to fully divide the channel number. 13 | min_value (int): The minimum value of the output channel. 14 | Default: None, means that the minimum value equal to the divisor. 15 | min_ratio (float): The minimum ratio of the rounded channel number to 16 | the original channel number. Default: 0.9. 17 | 18 | Returns: 19 | int: The modified output channel number. 20 | """ 21 | 22 | if min_value is None: 23 | min_value = divisor 24 | new_value = max(min_value, int(value + divisor / 2) // divisor * divisor) 25 | # Make sure that round down does not go down by more than (1-min_ratio). 26 | if new_value < min_ratio * value: 27 | new_value += divisor 28 | return new_value 29 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .collect_env import collect_env 3 | from .logger import get_caller_name, get_root_logger, log_img_scale 4 | from .misc import find_latest_checkpoint, update_data_root 5 | from .setup_env import setup_multi_processes 6 | 7 | __all__ = [ 8 | 'get_root_logger', 'collect_env', 'find_latest_checkpoint', 9 | 'update_data_root', 'setup_multi_processes', 'get_caller_name', 10 | 'log_img_scale' 11 | ] 12 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/utils/collect_env.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmcv.utils import collect_env as collect_base_env 3 | from mmcv.utils import get_git_hash 4 | 5 | import mmdet 6 | 7 | 8 | def collect_env(): 9 | """Collect the information of the running environments.""" 10 | env_info = collect_base_env() 11 | env_info['MMDetection'] = mmdet.__version__ + '+' + get_git_hash()[:7] 12 | return env_info 13 | 14 | 15 | if __name__ == '__main__': 16 | for name, val in collect_env().items(): 17 | print(f'{name}: {val}') 18 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/utils/util_random.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | """Helpers for random number generators.""" 3 | import numpy as np 4 | 5 | 6 | def ensure_rng(rng=None): 7 | """Coerces input into a random number generator. 8 | 9 | If the input is None, then a global random state is returned. 10 | 11 | If the input is a numeric value, then that is used as a seed to construct a 12 | random state. Otherwise the input is returned as-is. 13 | 14 | Adapted from [1]_. 15 | 16 | Args: 17 | rng (int | numpy.random.RandomState | None): 18 | if None, then defaults to the global rng. Otherwise this can be an 19 | integer or a RandomState class 20 | Returns: 21 | (numpy.random.RandomState) : rng - 22 | a numpy random number generator 23 | 24 | References: 25 | .. [1] https://gitlab.kitware.com/computer-vision/kwarray/blob/master/kwarray/util_random.py#L270 # noqa: E501 26 | """ 27 | 28 | if rng is None: 29 | rng = np.random.mtrand._rand 30 | elif isinstance(rng, int): 31 | rng = np.random.RandomState(rng) 32 | else: 33 | rng = rng 34 | return rng 35 | -------------------------------------------------------------------------------- /mmdet-v2.23/mmdet/version.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | 3 | __version__ = '2.23.0' 4 | short_version = __version__ 5 | 6 | 7 | def parse_version_info(version_str): 8 | version_info = [] 9 | for x in version_str.split('.'): 10 | if x.isdigit(): 11 | version_info.append(int(x)) 12 | elif x.find('rc') != -1: 13 | patch_version = x.split('rc') 14 | version_info.append(int(patch_version[0])) 15 | version_info.append(f'rc{patch_version[1]}') 16 | return tuple(version_info) 17 | 18 | 19 | version_info = parse_version_info(__version__) 20 | -------------------------------------------------------------------------------- /mmdet-v2.23/pytest.ini: -------------------------------------------------------------------------------- 1 | [pytest] 2 | addopts = --xdoctest --xdoctest-style=auto 3 | norecursedirs = .git ignore build __pycache__ data docker docs .eggs 4 | 5 | filterwarnings= default 6 | ignore:.*No cfgstr given in Cacher constructor or call.*:Warning 7 | ignore:.*Define the __nice__ method for.*:Warning 8 | -------------------------------------------------------------------------------- /mmdet-v2.23/requirements.txt: -------------------------------------------------------------------------------- 1 | -r requirements/build.txt 2 | -r requirements/optional.txt 3 | -r requirements/runtime.txt 4 | -r requirements/tests.txt 5 | -------------------------------------------------------------------------------- /mmdet-v2.23/requirements/albu.txt: -------------------------------------------------------------------------------- 1 | albumentations>=0.3.2 --no-binary qudida,albumentations 2 | -------------------------------------------------------------------------------- /mmdet-v2.23/requirements/build.txt: -------------------------------------------------------------------------------- 1 | # These must be installed before building mmdetection 2 | cython 3 | numpy 4 | -------------------------------------------------------------------------------- /mmdet-v2.23/requirements/docs.txt: -------------------------------------------------------------------------------- 1 | docutils==0.16.0 2 | -e git+https://github.com/open-mmlab/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme 3 | recommonmark 4 | sphinx==4.0.2 5 | sphinx-copybutton 6 | sphinx_markdown_tables 7 | sphinx_rtd_theme==0.5.2 8 | -------------------------------------------------------------------------------- /mmdet-v2.23/requirements/mminstall.txt: -------------------------------------------------------------------------------- 1 | mmcv-full>=1.3.17 2 | -------------------------------------------------------------------------------- /mmdet-v2.23/requirements/optional.txt: -------------------------------------------------------------------------------- 1 | cityscapesscripts 2 | imagecorruptions 3 | scipy 4 | sklearn 5 | timm 6 | -------------------------------------------------------------------------------- /mmdet-v2.23/requirements/readthedocs.txt: -------------------------------------------------------------------------------- 1 | mmcv 2 | torch 3 | torchvision 4 | -------------------------------------------------------------------------------- /mmdet-v2.23/requirements/runtime.txt: -------------------------------------------------------------------------------- 1 | matplotlib 2 | numpy 3 | pycocotools 4 | six 5 | terminaltables 6 | -------------------------------------------------------------------------------- /mmdet-v2.23/requirements/tests.txt: -------------------------------------------------------------------------------- 1 | asynctest 2 | codecov 3 | flake8 4 | interrogate 5 | isort==4.3.21 6 | # Note: used for kwarray.group_items, this may be ported to mmcv in the future. 7 | kwarray 8 | -e git+https://github.com/open-mmlab/mmtracking#egg=mmtrack 9 | onnx==1.7.0 10 | onnxruntime>=1.8.0 11 | pytest 12 | ubelt 13 | xdoctest>=0.10.0 14 | yapf 15 | 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/ggjy/FastMIM.pytorch/d4eea0dc8caf3db4be3d3ed4c7a8bc07c59b6111/mmdet-v2.23/resources/qq_group_qrcode.jpg -------------------------------------------------------------------------------- /mmdet-v2.23/resources/zhihu_qrcode.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ggjy/FastMIM.pytorch/d4eea0dc8caf3db4be3d3ed4c7a8bc07c59b6111/mmdet-v2.23/resources/zhihu_qrcode.jpg -------------------------------------------------------------------------------- /mmdet-v2.23/setup.cfg: -------------------------------------------------------------------------------- 1 | [isort] 2 | line_length = 79 3 | multi_line_output = 0 4 | extra_standard_library = setuptools 5 | known_first_party = mmdet 6 | known_third_party = PIL,asynctest,cityscapesscripts,cv2,gather_models,matplotlib,mmcv,numpy,onnx,onnxruntime,pycocotools,pytest,pytorch_sphinx_theme,requests,scipy,seaborn,six,terminaltables,torch,ts,yaml 7 | no_lines_before = STDLIB,LOCALFOLDER 8 | default_section = THIRDPARTY 9 | 10 | [yapf] 11 | BASED_ON_STYLE = pep8 12 | BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF = true 13 | SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true 14 | 15 | [codespell] 16 | skip = *.ipynb 17 | quiet-level = 3 18 | ignore-words-list = patten,nd,ty,mot,hist,formating,winn,gool,datas,wan,confids,TOOD,tood 19 | -------------------------------------------------------------------------------- /mmdet-v2.23/tests/data/VOCdevkit/VOC2007/Annotations/000001.xml: -------------------------------------------------------------------------------- 1 | 2 | VOC2007 3 | 000001.jpg 4 | 5 | The VOC2007 Database 6 | PASCAL VOC2007 7 | flickr 8 | 341012865 9 | 10 | 11 | Fried Camels 12 | Jinky the Fruit Bat 13 | 14 | 15 | 353 16 | 500 17 | 3 18 | 19 | 0 20 | 21 | dog 22 | Left 23 | 1 24 | 0 25 | 26 | 48 27 | 240 28 | 195 29 | 371 30 | 31 | 32 | 33 | person 34 | Left 35 | 1 36 | 0 37 | 38 | 8 39 | 12 40 | 352 41 | 498 42 | 43 | 44 | 45 | -------------------------------------------------------------------------------- /mmdet-v2.23/tests/data/VOCdevkit/VOC2007/ImageSets/Main/test.txt: -------------------------------------------------------------------------------- 1 | 000001 2 | -------------------------------------------------------------------------------- /mmdet-v2.23/tests/data/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt: -------------------------------------------------------------------------------- 1 | 000001 2 | -------------------------------------------------------------------------------- /mmdet-v2.23/tests/data/VOCdevkit/VOC2007/JPEGImages/000001.jpg: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /mmdet-v2.23/tests/data/VOCdevkit/VOC2012/ImageSets/Main/trainval.txt: -------------------------------------------------------------------------------- 1 | 000001 2 | -------------------------------------------------------------------------------- /mmdet-v2.23/tests/data/VOCdevkit/VOC2012/JPEGImages/000001.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ggjy/FastMIM.pytorch/d4eea0dc8caf3db4be3d3ed4c7a8bc07c59b6111/mmdet-v2.23/tests/data/VOCdevkit/VOC2012/JPEGImages/000001.jpg -------------------------------------------------------------------------------- /mmdet-v2.23/tests/data/color.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ggjy/FastMIM.pytorch/d4eea0dc8caf3db4be3d3ed4c7a8bc07c59b6111/mmdet-v2.23/tests/data/color.jpg 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-------------------------------------------------------------------------------- /mmdet-v2.23/tests/data/custom_dataset/test.txt: -------------------------------------------------------------------------------- 1 | 000001 2 | -------------------------------------------------------------------------------- /mmdet-v2.23/tests/data/custom_dataset/trainval.txt: -------------------------------------------------------------------------------- 1 | 000001 2 | -------------------------------------------------------------------------------- /mmdet-v2.23/tests/data/gray.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ggjy/FastMIM.pytorch/d4eea0dc8caf3db4be3d3ed4c7a8bc07c59b6111/mmdet-v2.23/tests/data/gray.jpg -------------------------------------------------------------------------------- /mmdet-v2.23/tests/test_data/test_datasets/test_xml_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import pytest 3 | 4 | from mmdet.datasets import DATASETS 5 | 6 | 7 | def test_xml_dataset(): 8 | dataconfig = { 9 | 'ann_file': 'data/VOCdevkit/VOC2007/ImageSets/Main/test.txt', 10 | 'img_prefix': 'data/VOCdevkit/VOC2007/', 11 | 'pipeline': [{ 12 | 'type': 'LoadImageFromFile' 13 | }] 14 | } 15 | XMLDataset = DATASETS.get('XMLDataset') 16 | 17 | class XMLDatasetSubClass(XMLDataset): 18 | CLASSES = None 19 | 20 | # get_ann_info and _filter_imgs of XMLDataset 21 | # would use self.CLASSES, we added CLASSES not NONE 22 | with pytest.raises(AssertionError): 23 | XMLDatasetSubClass(**dataconfig) 24 | -------------------------------------------------------------------------------- /mmdet-v2.23/tests/test_data/test_pipelines/test_formatting.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import os.path as osp 3 | 4 | from mmcv.utils import build_from_cfg 5 | 6 | from mmdet.datasets.builder import PIPELINES 7 | 8 | 9 | def test_default_format_bundle(): 10 | results = dict( 11 | img_prefix=osp.join(osp.dirname(__file__), '../../data'), 12 | img_info=dict(filename='color.jpg')) 13 | load = dict(type='LoadImageFromFile') 14 | load = build_from_cfg(load, PIPELINES) 15 | bundle = dict(type='DefaultFormatBundle') 16 | bundle = build_from_cfg(bundle, PIPELINES) 17 | results = load(results) 18 | assert 'pad_shape' not in results 19 | assert 'scale_factor' not in results 20 | assert 'img_norm_cfg' not in results 21 | results = bundle(results) 22 | assert 'pad_shape' in results 23 | assert 'scale_factor' in results 24 | assert 'img_norm_cfg' in results 25 | -------------------------------------------------------------------------------- /mmdet-v2.23/tests/test_data/test_pipelines/test_transform/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .utils import check_result_same, construct_toy_data, create_random_bboxes 3 | 4 | __all__ = ['create_random_bboxes', 'construct_toy_data', 'check_result_same'] 5 | -------------------------------------------------------------------------------- /mmdet-v2.23/tests/test_models/test_backbones/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .utils import check_norm_state, is_block, is_norm 3 | 4 | __all__ = ['is_block', 'is_norm', 'check_norm_state'] 5 | -------------------------------------------------------------------------------- /mmdet-v2.23/tests/test_models/test_backbones/test_efficientnet.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | import torch 3 | 4 | from mmdet.models.backbones import EfficientNet 5 | 6 | 7 | def test_efficientnet_backbone(): 8 | """Test EfficientNet backbone.""" 9 | with pytest.raises(AssertionError): 10 | # EfficientNet arch should be a key in EfficientNet.arch_settings 11 | EfficientNet(arch='c3') 12 | 13 | model = EfficientNet(arch='b0', out_indices=(0, 1, 2, 3, 4, 5, 6)) 14 | model.train() 15 | 16 | imgs = torch.randn(2, 3, 32, 32) 17 | feat = model(imgs) 18 | assert len(feat) == 7 19 | assert feat[0].shape == torch.Size([2, 32, 16, 16]) 20 | assert feat[1].shape == torch.Size([2, 16, 16, 16]) 21 | assert feat[2].shape == torch.Size([2, 24, 8, 8]) 22 | assert feat[3].shape == torch.Size([2, 40, 4, 4]) 23 | assert feat[4].shape == torch.Size([2, 112, 2, 2]) 24 | assert feat[5].shape == torch.Size([2, 320, 1, 1]) 25 | assert feat[6].shape == torch.Size([2, 1280, 1, 1]) 26 | -------------------------------------------------------------------------------- /mmdet-v2.23/tests/test_models/test_backbones/utils.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from torch.nn.modules import GroupNorm 3 | from torch.nn.modules.batchnorm import _BatchNorm 4 | 5 | from mmdet.models.backbones.res2net import Bottle2neck 6 | from mmdet.models.backbones.resnet import BasicBlock, Bottleneck 7 | from mmdet.models.backbones.resnext import Bottleneck as BottleneckX 8 | from mmdet.models.utils import SimplifiedBasicBlock 9 | 10 | 11 | def is_block(modules): 12 | """Check if is ResNet building block.""" 13 | if isinstance(modules, (BasicBlock, Bottleneck, BottleneckX, Bottle2neck, 14 | SimplifiedBasicBlock)): 15 | return True 16 | return False 17 | 18 | 19 | def is_norm(modules): 20 | """Check if is one of the norms.""" 21 | if isinstance(modules, (GroupNorm, _BatchNorm)): 22 | return True 23 | return False 24 | 25 | 26 | def check_norm_state(modules, train_state): 27 | """Check if norm layer is in correct train state.""" 28 | for mod in modules: 29 | if isinstance(mod, _BatchNorm): 30 | if mod.training != train_state: 31 | return False 32 | return True 33 | -------------------------------------------------------------------------------- /mmdet-v2.23/tests/test_models/test_roi_heads/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .utils import _dummy_bbox_sampling 3 | 4 | __all__ = ['_dummy_bbox_sampling'] 5 | -------------------------------------------------------------------------------- /mmdet-v2.23/tests/test_models/test_roi_heads/utils.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import torch 3 | 4 | from mmdet.core import build_assigner, build_sampler 5 | 6 | 7 | def _dummy_bbox_sampling(proposal_list, gt_bboxes, gt_labels): 8 | """Create sample results that can be passed to BBoxHead.get_targets.""" 9 | num_imgs = 1 10 | feat = torch.rand(1, 1, 3, 3) 11 | assign_config = dict( 12 | type='MaxIoUAssigner', 13 | pos_iou_thr=0.5, 14 | neg_iou_thr=0.5, 15 | min_pos_iou=0.5, 16 | ignore_iof_thr=-1) 17 | sampler_config = dict( 18 | type='RandomSampler', 19 | num=512, 20 | pos_fraction=0.25, 21 | neg_pos_ub=-1, 22 | add_gt_as_proposals=True) 23 | bbox_assigner = build_assigner(assign_config) 24 | bbox_sampler = build_sampler(sampler_config) 25 | gt_bboxes_ignore = [None for _ in range(num_imgs)] 26 | sampling_results = [] 27 | for i in range(num_imgs): 28 | assign_result = bbox_assigner.assign(proposal_list[i], gt_bboxes[i], 29 | gt_bboxes_ignore[i], gt_labels[i]) 30 | sampling_result = bbox_sampler.sample( 31 | assign_result, 32 | proposal_list[i], 33 | gt_bboxes[i], 34 | gt_labels[i], 35 | feats=feat) 36 | sampling_results.append(sampling_result) 37 | 38 | return sampling_results 39 | -------------------------------------------------------------------------------- /mmdet-v2.23/tests/test_models/test_utils/test_conv_upsample.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import pytest 3 | import torch 4 | 5 | from mmdet.models.utils import ConvUpsample 6 | 7 | 8 | @pytest.mark.parametrize('num_layers', [0, 1, 2]) 9 | def test_conv_upsample(num_layers): 10 | num_upsample = num_layers if num_layers > 0 else 0 11 | num_layers = num_layers if num_layers > 0 else 1 12 | layer = ConvUpsample( 13 | 10, 14 | 5, 15 | num_layers=num_layers, 16 | num_upsample=num_upsample, 17 | conv_cfg=None, 18 | norm_cfg=None) 19 | 20 | size = 5 21 | x = torch.randn((1, 10, size, size)) 22 | size = size * pow(2, num_upsample) 23 | x = layer(x) 24 | assert x.shape[-2:] == (size, size) 25 | -------------------------------------------------------------------------------- /mmdet-v2.23/tests/test_models/test_utils/test_model_misc.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import numpy as np 3 | import torch 4 | from torch.autograd import gradcheck 5 | 6 | from mmdet.models.utils import interpolate_as, sigmoid_geometric_mean 7 | 8 | 9 | def test_interpolate_as(): 10 | source = torch.rand((1, 5, 4, 4)) 11 | target = torch.rand((1, 1, 16, 16)) 12 | 13 | # Test 4D source and target 14 | result = interpolate_as(source, target) 15 | assert result.shape == torch.Size((1, 5, 16, 16)) 16 | 17 | # Test 3D target 18 | result = interpolate_as(source, target.squeeze(0)) 19 | assert result.shape == torch.Size((1, 5, 16, 16)) 20 | 21 | # Test 3D source 22 | result = interpolate_as(source.squeeze(0), target) 23 | assert result.shape == torch.Size((5, 16, 16)) 24 | 25 | # Test type(target) == np.ndarray 26 | target = np.random.rand(16, 16) 27 | result = interpolate_as(source.squeeze(0), target) 28 | assert result.shape == torch.Size((5, 16, 16)) 29 | 30 | 31 | def test_sigmoid_geometric_mean(): 32 | x = torch.randn(20, 20, dtype=torch.double, requires_grad=True) 33 | y = torch.randn(20, 20, dtype=torch.double, requires_grad=True) 34 | inputs = (x, y) 35 | test = gradcheck(sigmoid_geometric_mean, inputs, eps=1e-6, atol=1e-4) 36 | assert test 37 | -------------------------------------------------------------------------------- /mmdet-v2.23/tests/test_onnx/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .utils import ort_validate 3 | 4 | __all__ = ['ort_validate'] 5 | -------------------------------------------------------------------------------- /mmdet-v2.23/tests/test_onnx/data/fsaf_head_get_bboxes.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ggjy/FastMIM.pytorch/d4eea0dc8caf3db4be3d3ed4c7a8bc07c59b6111/mmdet-v2.23/tests/test_onnx/data/fsaf_head_get_bboxes.pkl -------------------------------------------------------------------------------- /mmdet-v2.23/tests/test_onnx/data/retina_head_get_bboxes.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ggjy/FastMIM.pytorch/d4eea0dc8caf3db4be3d3ed4c7a8bc07c59b6111/mmdet-v2.23/tests/test_onnx/data/retina_head_get_bboxes.pkl -------------------------------------------------------------------------------- /mmdet-v2.23/tests/test_onnx/data/ssd_head_get_bboxes.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ggjy/FastMIM.pytorch/d4eea0dc8caf3db4be3d3ed4c7a8bc07c59b6111/mmdet-v2.23/tests/test_onnx/data/ssd_head_get_bboxes.pkl -------------------------------------------------------------------------------- /mmdet-v2.23/tests/test_onnx/data/yolov3_head_get_bboxes.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ggjy/FastMIM.pytorch/d4eea0dc8caf3db4be3d3ed4c7a8bc07c59b6111/mmdet-v2.23/tests/test_onnx/data/yolov3_head_get_bboxes.pkl -------------------------------------------------------------------------------- /mmdet-v2.23/tests/test_onnx/data/yolov3_neck.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ggjy/FastMIM.pytorch/d4eea0dc8caf3db4be3d3ed4c7a8bc07c59b6111/mmdet-v2.23/tests/test_onnx/data/yolov3_neck.pkl -------------------------------------------------------------------------------- /mmdet-v2.23/tests/test_utils/test_logger.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import pytest 3 | 4 | from mmdet.utils import get_caller_name, log_img_scale 5 | 6 | 7 | def callee_func(): 8 | caller_name = get_caller_name() 9 | return caller_name 10 | 11 | 12 | class CallerClassForTest: 13 | 14 | def __init__(self): 15 | self.caller_name = callee_func() 16 | 17 | 18 | def test_get_caller_name(): 19 | # test the case that caller is a function 20 | caller_name = callee_func() 21 | assert caller_name == 'test_get_caller_name' 22 | 23 | # test the case that caller is a method in a class 24 | caller_class = CallerClassForTest() 25 | assert caller_class.caller_name == 'CallerClassForTest.__init__' 26 | 27 | 28 | def test_log_img_scale(): 29 | img_scale = (800, 1333) 30 | done_logging = log_img_scale(img_scale) 31 | assert done_logging 32 | 33 | img_scale = (1333, 800) 34 | done_logging = log_img_scale(img_scale, shape_order='wh') 35 | assert done_logging 36 | 37 | with pytest.raises(ValueError): 38 | img_scale = (1333, 800) 39 | done_logging = log_img_scale(img_scale, shape_order='xywh') 40 | 41 | img_scale = (640, 640) 42 | done_logging = log_img_scale(img_scale, skip_square=False) 43 | assert done_logging 44 | 45 | img_scale = (640, 640) 46 | done_logging = log_img_scale(img_scale, skip_square=True) 47 | assert not done_logging 48 | -------------------------------------------------------------------------------- /mmdet-v2.23/tests/test_utils/test_version.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmdet import digit_version 3 | 4 | 5 | def test_version_check(): 6 | assert digit_version('1.0.5') > digit_version('1.0.5rc0') 7 | assert digit_version('1.0.5') > digit_version('1.0.4rc0') 8 | assert digit_version('1.0.5') > digit_version('1.0rc0') 9 | assert digit_version('1.0.0') > digit_version('0.6.2') 10 | assert digit_version('1.0.0') > digit_version('0.2.16') 11 | assert digit_version('1.0.5rc0') > digit_version('1.0.0rc0') 12 | assert digit_version('1.0.0rc1') > digit_version('1.0.0rc0') 13 | assert digit_version('1.0.0rc2') > digit_version('1.0.0rc0') 14 | assert digit_version('1.0.0rc2') > digit_version('1.0.0rc1') 15 | assert digit_version('1.0.1rc1') > digit_version('1.0.0rc1') 16 | assert digit_version('1.0.0') > digit_version('1.0.0rc1') 17 | -------------------------------------------------------------------------------- /mmdet-v2.23/tools/dist_test.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | CONFIG=$1 4 | CHECKPOINT=$2 5 | GPUS=$3 6 | NNODES=${NNODES:-1} 7 | NODE_RANK=${NODE_RANK:-0} 8 | PORT=${PORT:-29500} 9 | MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"} 10 | 11 | PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ 12 | python -m torch.distributed.launch \ 13 | --nnodes=$NNODES \ 14 | --node_rank=$NODE_RANK \ 15 | --master_addr=$MASTER_ADDR \ 16 | --nproc_per_node=$GPUS \ 17 | --master_port=$PORT \ 18 | $(dirname "$0")/test.py \ 19 | $CONFIG \ 20 | $CHECKPOINT \ 21 | --launcher pytorch \ 22 | ${@:4} 23 | -------------------------------------------------------------------------------- /mmdet-v2.23/tools/dist_train.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | CONFIG=$1 4 | GPUS=$2 5 | NNODES=${NNODES:-1} 6 | NODE_RANK=${NODE_RANK:-0} 7 | PORT=${PORT:-29500} 8 | MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"} 9 | 10 | PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ 11 | python -m torch.distributed.launch \ 12 | --nnodes=$NNODES \ 13 | --node_rank=$NODE_RANK \ 14 | --master_addr=$MASTER_ADDR \ 15 | --nproc_per_node=$GPUS \ 16 | --master_port=$PORT \ 17 | $(dirname "$0")/train.py \ 18 | $CONFIG \ 19 | --seed 0 \ 20 | --launcher pytorch ${@:3} 21 | -------------------------------------------------------------------------------- /mmdet-v2.23/tools/misc/gen_coco_panoptic_test_info.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os.path as osp 3 | 4 | import mmcv 5 | 6 | 7 | def parse_args(): 8 | parser = argparse.ArgumentParser( 9 | description='Generate COCO test image information ' 10 | 'for COCO panoptic segmentation.') 11 | parser.add_argument('data_root', help='Path to COCO annotation directory.') 12 | args = parser.parse_args() 13 | 14 | return args 15 | 16 | 17 | def main(): 18 | args = parse_args() 19 | data_root = args.data_root 20 | val_info = mmcv.load(osp.join(data_root, 'panoptic_val2017.json')) 21 | test_old_info = mmcv.load( 22 | osp.join(data_root, 'image_info_test-dev2017.json')) 23 | 24 | # replace categories from image_info_test-dev2017.json 25 | # with categories from panoptic_val2017.json which 26 | # has attribute `isthing`. 27 | test_info = test_old_info 28 | test_info.update({'categories': val_info['categories']}) 29 | mmcv.dump(test_info, 30 | osp.join(data_root, 'panoptic_image_info_test-dev2017.json')) 31 | 32 | 33 | if __name__ == '__main__': 34 | main() 35 | -------------------------------------------------------------------------------- /mmdet-v2.23/tools/model_converters/selfsup2mmdet.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import argparse 3 | from collections import OrderedDict 4 | 5 | import torch 6 | 7 | 8 | def moco_convert(src, dst): 9 | """Convert keys in pycls pretrained moco models to mmdet style.""" 10 | # load caffe model 11 | moco_model = torch.load(src) 12 | blobs = moco_model['state_dict'] 13 | # convert to pytorch style 14 | state_dict = OrderedDict() 15 | for k, v in blobs.items(): 16 | if not k.startswith('module.encoder_q.'): 17 | continue 18 | old_k = k 19 | k = k.replace('module.encoder_q.', '') 20 | state_dict[k] = v 21 | print(old_k, '->', k) 22 | # save checkpoint 23 | checkpoint = dict() 24 | checkpoint['state_dict'] = state_dict 25 | torch.save(checkpoint, dst) 26 | 27 | 28 | def main(): 29 | parser = argparse.ArgumentParser(description='Convert model keys') 30 | parser.add_argument('src', help='src detectron model path') 31 | parser.add_argument('dst', help='save path') 32 | parser.add_argument( 33 | '--selfsup', type=str, choices=['moco', 'swav'], help='save path') 34 | args = parser.parse_args() 35 | if args.selfsup == 'moco': 36 | moco_convert(args.src, args.dst) 37 | elif args.selfsup == 'swav': 38 | print('SWAV does not need to convert the keys') 39 | 40 | 41 | if __name__ == '__main__': 42 | main() 43 | -------------------------------------------------------------------------------- /mmdet-v2.23/tools/slurm_test.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -x 4 | 5 | PARTITION=$1 6 | JOB_NAME=$2 7 | CONFIG=$3 8 | CHECKPOINT=$4 9 | GPUS=${GPUS:-8} 10 | GPUS_PER_NODE=${GPUS_PER_NODE:-8} 11 | CPUS_PER_TASK=${CPUS_PER_TASK:-5} 12 | PY_ARGS=${@:5} 13 | SRUN_ARGS=${SRUN_ARGS:-""} 14 | 15 | PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ 16 | srun -p ${PARTITION} \ 17 | --job-name=${JOB_NAME} \ 18 | --gres=gpu:${GPUS_PER_NODE} \ 19 | --ntasks=${GPUS} \ 20 | --ntasks-per-node=${GPUS_PER_NODE} \ 21 | --cpus-per-task=${CPUS_PER_TASK} \ 22 | --kill-on-bad-exit=1 \ 23 | ${SRUN_ARGS} \ 24 | python -u tools/test.py ${CONFIG} ${CHECKPOINT} --launcher="slurm" ${PY_ARGS} 25 | -------------------------------------------------------------------------------- /mmdet-v2.23/tools/slurm_train.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -x 4 | 5 | PARTITION=$1 6 | JOB_NAME=$2 7 | CONFIG=$3 8 | WORK_DIR=$4 9 | GPUS=${GPUS:-8} 10 | GPUS_PER_NODE=${GPUS_PER_NODE:-8} 11 | CPUS_PER_TASK=${CPUS_PER_TASK:-5} 12 | SRUN_ARGS=${SRUN_ARGS:-""} 13 | PY_ARGS=${@:5} 14 | 15 | PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ 16 | srun -p ${PARTITION} \ 17 | --job-name=${JOB_NAME} \ 18 | --gres=gpu:${GPUS_PER_NODE} \ 19 | --ntasks=${GPUS} \ 20 | --ntasks-per-node=${GPUS_PER_NODE} \ 21 | --cpus-per-task=${CPUS_PER_TASK} \ 22 | --kill-on-bad-exit=1 \ 23 | ${SRUN_ARGS} \ 24 | python -u tools/train.py ${CONFIG} --work-dir=${WORK_DIR} --launcher="slurm" ${PY_ARGS} 25 | -------------------------------------------------------------------------------- /mmseg-v0.28/CITATION.cff: -------------------------------------------------------------------------------- 1 | cff-version: 1.2.0 2 | message: "If you use this software, please cite it as below." 3 | authors: 4 | - name: "MMSegmentation Contributors" 5 | title: "OpenMMLab Semantic Segmentation Toolbox and Benchmark" 6 | date-released: 2020-07-10 7 | url: "https://github.com/open-mmlab/mmsegmentation" 8 | license: Apache-2.0 9 | -------------------------------------------------------------------------------- /mmseg-v0.28/LICENSES.md: -------------------------------------------------------------------------------- 1 | # Licenses for special features 2 | 3 | In this file, we list the features with other licenses instead of Apache 2.0. Users should be careful about adopting these features in any commercial matters. 4 | 5 | | Feature | Files | License | 6 | | :-------: | :-------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------: | 7 | | SegFormer | [mmseg/models/decode_heads/segformer_head.py](https://github.com/open-mmlab/mmsegmentation/blob/master/mmseg/models/decode_heads/segformer_head.py) | [NVIDIA License](https://github.com/NVlabs/SegFormer#license) | 8 | -------------------------------------------------------------------------------- /mmseg-v0.28/MANIFEST.in: -------------------------------------------------------------------------------- 1 | include requirements/*.txt 2 | include mmseg/.mim/model-index.yml 3 | recursive-include mmseg/.mim/configs *.py *.yml 4 | recursive-include mmseg/.mim/tools *.py *.sh 5 | -------------------------------------------------------------------------------- /mmseg-v0.28/configs/_base_/datasets/cityscapes_1024x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './cityscapes.py' 2 | img_norm_cfg = dict( 3 | mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) 4 | crop_size = (1024, 1024) 5 | train_pipeline = [ 6 | dict(type='LoadImageFromFile'), 7 | dict(type='LoadAnnotations'), 8 | dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)), 9 | dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), 10 | dict(type='RandomFlip', prob=0.5), 11 | dict(type='PhotoMetricDistortion'), 12 | dict(type='Normalize', **img_norm_cfg), 13 | dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), 14 | dict(type='DefaultFormatBundle'), 15 | dict(type='Collect', keys=['img', 'gt_semantic_seg']), 16 | ] 17 | test_pipeline = [ 18 | dict(type='LoadImageFromFile'), 19 | dict( 20 | type='MultiScaleFlipAug', 21 | img_scale=(2048, 1024), 22 | # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75], 23 | flip=False, 24 | transforms=[ 25 | dict(type='Resize', keep_ratio=True), 26 | dict(type='RandomFlip'), 27 | dict(type='Normalize', **img_norm_cfg), 28 | dict(type='ImageToTensor', keys=['img']), 29 | dict(type='Collect', keys=['img']), 30 | ]) 31 | ] 32 | data = dict( 33 | train=dict(pipeline=train_pipeline), 34 | val=dict(pipeline=test_pipeline), 35 | test=dict(pipeline=test_pipeline)) 36 | -------------------------------------------------------------------------------- /mmseg-v0.28/configs/_base_/datasets/cityscapes_768x768.py: -------------------------------------------------------------------------------- 1 | _base_ = './cityscapes.py' 2 | img_norm_cfg = dict( 3 | mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) 4 | crop_size = (768, 768) 5 | train_pipeline = [ 6 | dict(type='LoadImageFromFile'), 7 | dict(type='LoadAnnotations'), 8 | dict(type='Resize', img_scale=(2049, 1025), ratio_range=(0.5, 2.0)), 9 | dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), 10 | dict(type='RandomFlip', prob=0.5), 11 | dict(type='PhotoMetricDistortion'), 12 | dict(type='Normalize', **img_norm_cfg), 13 | dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), 14 | dict(type='DefaultFormatBundle'), 15 | dict(type='Collect', keys=['img', 'gt_semantic_seg']), 16 | ] 17 | test_pipeline = [ 18 | dict(type='LoadImageFromFile'), 19 | dict( 20 | type='MultiScaleFlipAug', 21 | img_scale=(2049, 1025), 22 | # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75], 23 | flip=False, 24 | transforms=[ 25 | dict(type='Resize', keep_ratio=True), 26 | dict(type='RandomFlip'), 27 | dict(type='Normalize', **img_norm_cfg), 28 | dict(type='ImageToTensor', keys=['img']), 29 | dict(type='Collect', keys=['img']), 30 | ]) 31 | ] 32 | data = dict( 33 | train=dict(pipeline=train_pipeline), 34 | val=dict(pipeline=test_pipeline), 35 | test=dict(pipeline=test_pipeline)) 36 | -------------------------------------------------------------------------------- /mmseg-v0.28/configs/_base_/datasets/cityscapes_769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './cityscapes.py' 2 | img_norm_cfg = dict( 3 | mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) 4 | crop_size = (769, 769) 5 | train_pipeline = [ 6 | dict(type='LoadImageFromFile'), 7 | dict(type='LoadAnnotations'), 8 | dict(type='Resize', img_scale=(2049, 1025), ratio_range=(0.5, 2.0)), 9 | dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), 10 | dict(type='RandomFlip', prob=0.5), 11 | dict(type='PhotoMetricDistortion'), 12 | dict(type='Normalize', **img_norm_cfg), 13 | dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), 14 | dict(type='DefaultFormatBundle'), 15 | dict(type='Collect', keys=['img', 'gt_semantic_seg']), 16 | ] 17 | test_pipeline = [ 18 | dict(type='LoadImageFromFile'), 19 | dict( 20 | type='MultiScaleFlipAug', 21 | img_scale=(2049, 1025), 22 | # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75], 23 | flip=False, 24 | transforms=[ 25 | dict(type='Resize', keep_ratio=True), 26 | dict(type='RandomFlip'), 27 | dict(type='Normalize', **img_norm_cfg), 28 | dict(type='ImageToTensor', keys=['img']), 29 | dict(type='Collect', keys=['img']), 30 | ]) 31 | ] 32 | data = dict( 33 | train=dict(pipeline=train_pipeline), 34 | val=dict(pipeline=test_pipeline), 35 | test=dict(pipeline=test_pipeline)) 36 | -------------------------------------------------------------------------------- /mmseg-v0.28/configs/_base_/datasets/cityscapes_832x832.py: -------------------------------------------------------------------------------- 1 | _base_ = './cityscapes.py' 2 | img_norm_cfg = dict( 3 | mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) 4 | crop_size = (832, 832) 5 | train_pipeline = [ 6 | dict(type='LoadImageFromFile'), 7 | dict(type='LoadAnnotations'), 8 | dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)), 9 | dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), 10 | dict(type='RandomFlip', prob=0.5), 11 | dict(type='PhotoMetricDistortion'), 12 | dict(type='Normalize', **img_norm_cfg), 13 | dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), 14 | dict(type='DefaultFormatBundle'), 15 | dict(type='Collect', keys=['img', 'gt_semantic_seg']), 16 | ] 17 | test_pipeline = [ 18 | dict(type='LoadImageFromFile'), 19 | dict( 20 | type='MultiScaleFlipAug', 21 | img_scale=(2048, 1024), 22 | # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75], 23 | flip=False, 24 | transforms=[ 25 | dict(type='Resize', keep_ratio=True), 26 | dict(type='RandomFlip'), 27 | dict(type='Normalize', **img_norm_cfg), 28 | dict(type='ImageToTensor', keys=['img']), 29 | dict(type='Collect', keys=['img']), 30 | ]) 31 | ] 32 | data = dict( 33 | train=dict(pipeline=train_pipeline), 34 | val=dict(pipeline=test_pipeline), 35 | test=dict(pipeline=test_pipeline)) 36 | -------------------------------------------------------------------------------- /mmseg-v0.28/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 | -------------------------------------------------------------------------------- /mmseg-v0.28/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 | # dict(type='PaviLoggerHook') # for internal services 8 | ]) 9 | # yapf:enable 10 | dist_params = dict(backend='nccl') 11 | log_level = 'INFO' 12 | load_from = None 13 | resume_from = None 14 | workflow = [('train', 1)] 15 | cudnn_benchmark = True 16 | -------------------------------------------------------------------------------- /mmseg-v0.28/configs/_base_/models/cgnet.py: -------------------------------------------------------------------------------- 1 | # model settings 2 | norm_cfg = dict(type='SyncBN', eps=1e-03, requires_grad=True) 3 | model = dict( 4 | type='EncoderDecoder', 5 | backbone=dict( 6 | type='CGNet', 7 | norm_cfg=norm_cfg, 8 | in_channels=3, 9 | num_channels=(32, 64, 128), 10 | num_blocks=(3, 21), 11 | dilations=(2, 4), 12 | reductions=(8, 16)), 13 | decode_head=dict( 14 | type='FCNHead', 15 | in_channels=256, 16 | in_index=2, 17 | channels=256, 18 | num_convs=0, 19 | concat_input=False, 20 | dropout_ratio=0, 21 | num_classes=19, 22 | norm_cfg=norm_cfg, 23 | loss_decode=dict( 24 | type='CrossEntropyLoss', 25 | use_sigmoid=False, 26 | loss_weight=1.0, 27 | class_weight=[ 28 | 2.5959933, 6.7415504, 3.5354059, 9.8663225, 9.690899, 9.369352, 29 | 10.289121, 9.953208, 4.3097677, 9.490387, 7.674431, 9.396905, 30 | 10.347791, 6.3927646, 10.226669, 10.241062, 10.280587, 31 | 10.396974, 10.055647 32 | ])), 33 | # model training and testing settings 34 | train_cfg=dict(sampler=None), 35 | test_cfg=dict(mode='whole')) 36 | -------------------------------------------------------------------------------- /mmseg-v0.28/configs/_base_/models/dpt_vit-b16.py: -------------------------------------------------------------------------------- 1 | norm_cfg = dict(type='SyncBN', requires_grad=True) 2 | model = dict( 3 | type='EncoderDecoder', 4 | pretrained='pretrain/vit-b16_p16_224-80ecf9dd.pth', # noqa 5 | backbone=dict( 6 | type='VisionTransformer', 7 | img_size=224, 8 | embed_dims=768, 9 | num_layers=12, 10 | num_heads=12, 11 | out_indices=(2, 5, 8, 11), 12 | final_norm=False, 13 | with_cls_token=True, 14 | output_cls_token=True), 15 | decode_head=dict( 16 | type='DPTHead', 17 | in_channels=(768, 768, 768, 768), 18 | channels=256, 19 | embed_dims=768, 20 | post_process_channels=[96, 192, 384, 768], 21 | num_classes=150, 22 | readout_type='project', 23 | input_transform='multiple_select', 24 | in_index=(0, 1, 2, 3), 25 | norm_cfg=norm_cfg, 26 | loss_decode=dict( 27 | type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), 28 | auxiliary_head=None, 29 | # model training and testing settings 30 | train_cfg=dict(), 31 | test_cfg=dict(mode='whole')) # yapf: disable 32 | -------------------------------------------------------------------------------- /mmseg-v0.28/configs/_base_/models/erfnet_fcn.py: -------------------------------------------------------------------------------- 1 | # model settings 2 | norm_cfg = dict(type='SyncBN', requires_grad=True) 3 | model = dict( 4 | type='EncoderDecoder', 5 | pretrained=None, 6 | backbone=dict( 7 | type='ERFNet', 8 | in_channels=3, 9 | enc_downsample_channels=(16, 64, 128), 10 | enc_stage_non_bottlenecks=(5, 8), 11 | enc_non_bottleneck_dilations=(2, 4, 8, 16), 12 | enc_non_bottleneck_channels=(64, 128), 13 | dec_upsample_channels=(64, 16), 14 | dec_stages_non_bottleneck=(2, 2), 15 | dec_non_bottleneck_channels=(64, 16), 16 | dropout_ratio=0.1, 17 | init_cfg=None), 18 | decode_head=dict( 19 | type='FCNHead', 20 | in_channels=16, 21 | channels=128, 22 | num_convs=1, 23 | concat_input=False, 24 | dropout_ratio=0.1, 25 | num_classes=19, 26 | norm_cfg=norm_cfg, 27 | align_corners=False, 28 | loss_decode=dict( 29 | type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), 30 | # model training and testing settings 31 | train_cfg=dict(), 32 | test_cfg=dict(mode='whole')) 33 | -------------------------------------------------------------------------------- /mmseg-v0.28/configs/_base_/models/fpn_r50.py: -------------------------------------------------------------------------------- 1 | # model settings 2 | norm_cfg = dict(type='SyncBN', requires_grad=True) 3 | model = dict( 4 | type='EncoderDecoder', 5 | pretrained='open-mmlab://resnet50_v1c', 6 | backbone=dict( 7 | type='ResNetV1c', 8 | depth=50, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | dilations=(1, 1, 1, 1), 12 | strides=(1, 2, 2, 2), 13 | norm_cfg=norm_cfg, 14 | norm_eval=False, 15 | style='pytorch', 16 | contract_dilation=True), 17 | neck=dict( 18 | type='FPN', 19 | in_channels=[256, 512, 1024, 2048], 20 | out_channels=256, 21 | num_outs=4), 22 | decode_head=dict( 23 | type='FPNHead', 24 | in_channels=[256, 256, 256, 256], 25 | in_index=[0, 1, 2, 3], 26 | feature_strides=[4, 8, 16, 32], 27 | channels=128, 28 | dropout_ratio=0.1, 29 | num_classes=19, 30 | norm_cfg=norm_cfg, 31 | align_corners=False, 32 | loss_decode=dict( 33 | type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), 34 | # model training and testing settings 35 | train_cfg=dict(), 36 | test_cfg=dict(mode='whole')) 37 | -------------------------------------------------------------------------------- /mmseg-v0.28/configs/_base_/models/lraspp_m-v3-d8.py: -------------------------------------------------------------------------------- 1 | # model settings 2 | norm_cfg = dict(type='SyncBN', eps=0.001, requires_grad=True) 3 | model = dict( 4 | type='EncoderDecoder', 5 | backbone=dict( 6 | type='MobileNetV3', 7 | arch='large', 8 | out_indices=(1, 3, 16), 9 | norm_cfg=norm_cfg), 10 | decode_head=dict( 11 | type='LRASPPHead', 12 | in_channels=(16, 24, 960), 13 | in_index=(0, 1, 2), 14 | channels=128, 15 | input_transform='multiple_select', 16 | dropout_ratio=0.1, 17 | num_classes=19, 18 | norm_cfg=norm_cfg, 19 | act_cfg=dict(type='ReLU'), 20 | align_corners=False, 21 | loss_decode=dict( 22 | type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), 23 | # model training and testing settings 24 | train_cfg=dict(), 25 | test_cfg=dict(mode='whole')) 26 | -------------------------------------------------------------------------------- /mmseg-v0.28/configs/_base_/models/segformer_mit-b0.py: -------------------------------------------------------------------------------- 1 | # model settings 2 | norm_cfg = dict(type='SyncBN', requires_grad=True) 3 | model = dict( 4 | type='EncoderDecoder', 5 | pretrained=None, 6 | backbone=dict( 7 | type='MixVisionTransformer', 8 | in_channels=3, 9 | embed_dims=32, 10 | num_stages=4, 11 | num_layers=[2, 2, 2, 2], 12 | num_heads=[1, 2, 5, 8], 13 | patch_sizes=[7, 3, 3, 3], 14 | sr_ratios=[8, 4, 2, 1], 15 | out_indices=(0, 1, 2, 3), 16 | mlp_ratio=4, 17 | qkv_bias=True, 18 | drop_rate=0.0, 19 | attn_drop_rate=0.0, 20 | drop_path_rate=0.1), 21 | decode_head=dict( 22 | type='SegformerHead', 23 | in_channels=[32, 64, 160, 256], 24 | in_index=[0, 1, 2, 3], 25 | channels=256, 26 | dropout_ratio=0.1, 27 | num_classes=19, 28 | norm_cfg=norm_cfg, 29 | align_corners=False, 30 | loss_decode=dict( 31 | type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), 32 | # model training and testing settings 33 | train_cfg=dict(), 34 | test_cfg=dict(mode='whole')) 35 | -------------------------------------------------------------------------------- /mmseg-v0.28/configs/_base_/models/segmenter_vit-b16_mask.py: -------------------------------------------------------------------------------- 1 | checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segmenter/vit_base_p16_384_20220308-96dfe169.pth' # noqa 2 | # model settings 3 | backbone_norm_cfg = dict(type='LN', eps=1e-6, requires_grad=True) 4 | model = dict( 5 | type='EncoderDecoder', 6 | pretrained=checkpoint, 7 | backbone=dict( 8 | type='VisionTransformer', 9 | img_size=(512, 512), 10 | patch_size=16, 11 | in_channels=3, 12 | embed_dims=768, 13 | num_layers=12, 14 | num_heads=12, 15 | drop_path_rate=0.1, 16 | attn_drop_rate=0.0, 17 | drop_rate=0.0, 18 | final_norm=True, 19 | norm_cfg=backbone_norm_cfg, 20 | with_cls_token=True, 21 | interpolate_mode='bicubic', 22 | ), 23 | decode_head=dict( 24 | type='SegmenterMaskTransformerHead', 25 | in_channels=768, 26 | channels=768, 27 | num_classes=150, 28 | num_layers=2, 29 | num_heads=12, 30 | embed_dims=768, 31 | dropout_ratio=0.0, 32 | loss_decode=dict( 33 | type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), 34 | ), 35 | test_cfg=dict(mode='slide', crop_size=(512, 512), stride=(480, 480)), 36 | ) 37 | -------------------------------------------------------------------------------- /mmseg-v0.28/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 | -------------------------------------------------------------------------------- /mmseg-v0.28/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 | -------------------------------------------------------------------------------- /mmseg-v0.28/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 | -------------------------------------------------------------------------------- /mmseg-v0.28/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 | -------------------------------------------------------------------------------- /mmseg-v0.28/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 | -------------------------------------------------------------------------------- /mmseg-v0.28/demo/demo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ggjy/FastMIM.pytorch/d4eea0dc8caf3db4be3d3ed4c7a8bc07c59b6111/mmseg-v0.28/demo/demo.png -------------------------------------------------------------------------------- /mmseg-v0.28/docker/Dockerfile: -------------------------------------------------------------------------------- 1 | ARG PYTORCH="1.11.0" 2 | ARG CUDA="11.3" 3 | ARG CUDNN="8" 4 | 5 | FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel 6 | 7 | ENV TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0+PTX" 8 | ENV TORCH_NVCC_FLAGS="-Xfatbin -compress-all" 9 | ENV CMAKE_PREFIX_PATH="$(dirname $(which conda))/../" 10 | 11 | # To fix GPG key error when running apt-get update 12 | RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub 13 | RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub 14 | 15 | RUN apt-get update && apt-get install -y git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libgl1-mesa-glx \ 16 | && apt-get clean \ 17 | && rm -rf /var/lib/apt/lists/* 18 | 19 | RUN conda clean --all 20 | 21 | # Install MMCV 22 | ARG PYTORCH 23 | ARG CUDA 24 | ARG MMCV 25 | RUN ["/bin/bash", "-c", "pip install --no-cache-dir mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu${CUDA//./}/torch${PYTORCH}/index.html"] 26 | 27 | # Install MMSegmentation 28 | RUN git clone https://github.com/open-mmlab/mmsegmentation.git /mmsegmentation 29 | WORKDIR /mmsegmentation 30 | ENV FORCE_CUDA="1" 31 | RUN pip install -r requirements.txt 32 | RUN pip install --no-cache-dir -e . 33 | -------------------------------------------------------------------------------- /mmseg-v0.28/docker/serve/config.properties: -------------------------------------------------------------------------------- 1 | inference_address=http://0.0.0.0:8080 2 | management_address=http://0.0.0.0:8081 3 | metrics_address=http://0.0.0.0:8082 4 | model_store=/home/model-server/model-store 5 | load_models=all 6 | -------------------------------------------------------------------------------- /mmseg-v0.28/docker/serve/entrypoint.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | set -e 3 | 4 | if [[ "$1" = "serve" ]]; then 5 | shift 1 6 | torchserve --start --ts-config /home/model-server/config.properties 7 | else 8 | eval "$@" 9 | fi 10 | 11 | # prevent docker exit 12 | tail -f /dev/null 13 | -------------------------------------------------------------------------------- /mmseg-v0.28/docs/en/Makefile: -------------------------------------------------------------------------------- 1 | # Minimal makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line, and also 5 | # from the environment for the first two. 6 | SPHINXOPTS ?= 7 | SPHINXBUILD ?= sphinx-build 8 | SOURCEDIR = . 9 | BUILDDIR = _build 10 | 11 | # Put it first so that "make" without argument is like "make help". 12 | help: 13 | @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 14 | 15 | .PHONY: help Makefile 16 | 17 | # Catch-all target: route all unknown targets to Sphinx using the new 18 | # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). 19 | %: Makefile 20 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 21 | -------------------------------------------------------------------------------- /mmseg-v0.28/docs/en/_static/css/readthedocs.css: -------------------------------------------------------------------------------- 1 | .header-logo { 2 | background-image: url("../images/mmsegmentation.png"); 3 | background-size: 201px 40px; 4 | height: 40px; 5 | width: 201px; 6 | } 7 | -------------------------------------------------------------------------------- /mmseg-v0.28/docs/en/_static/images/mmsegmentation.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ggjy/FastMIM.pytorch/d4eea0dc8caf3db4be3d3ed4c7a8bc07c59b6111/mmseg-v0.28/docs/en/_static/images/mmsegmentation.png -------------------------------------------------------------------------------- /mmseg-v0.28/docs/en/api.rst: -------------------------------------------------------------------------------- 1 | mmseg.apis 2 | -------------- 3 | .. automodule:: mmseg.apis 4 | :members: 5 | 6 | mmseg.core 7 | -------------- 8 | 9 | seg 10 | ^^^^^^^^^^ 11 | .. automodule:: mmseg.core.seg 12 | :members: 13 | 14 | evaluation 15 | ^^^^^^^^^^ 16 | .. automodule:: mmseg.core.evaluation 17 | :members: 18 | 19 | utils 20 | ^^^^^^^^^^ 21 | .. automodule:: mmseg.core.utils 22 | :members: 23 | 24 | mmseg.datasets 25 | -------------- 26 | 27 | datasets 28 | ^^^^^^^^^^ 29 | .. automodule:: mmseg.datasets 30 | :members: 31 | 32 | pipelines 33 | ^^^^^^^^^^ 34 | .. automodule:: mmseg.datasets.pipelines 35 | :members: 36 | 37 | mmseg.models 38 | -------------- 39 | 40 | segmentors 41 | ^^^^^^^^^^ 42 | .. automodule:: mmseg.models.segmentors 43 | :members: 44 | 45 | backbones 46 | ^^^^^^^^^^ 47 | .. automodule:: mmseg.models.backbones 48 | :members: 49 | 50 | decode_heads 51 | ^^^^^^^^^^^^ 52 | .. automodule:: mmseg.models.decode_heads 53 | :members: 54 | 55 | losses 56 | ^^^^^^^^^^ 57 | .. automodule:: mmseg.models.losses 58 | :members: 59 | -------------------------------------------------------------------------------- /mmseg-v0.28/docs/en/index.rst: -------------------------------------------------------------------------------- 1 | Welcome to MMSegmentation's documentation! 2 | ======================================= 3 | 4 | .. toctree:: 5 | :maxdepth: 2 6 | :caption: Get Started 7 | 8 | get_started.md 9 | 10 | .. toctree:: 11 | :maxdepth: 1 12 | :caption: Dataset Preparation 13 | 14 | dataset_prepare.md 15 | 16 | .. toctree:: 17 | :maxdepth: 1 18 | :caption: Model Zoo 19 | 20 | model_zoo.md 21 | modelzoo_statistics.md 22 | 23 | .. toctree:: 24 | :maxdepth: 2 25 | :caption: Quick Run 26 | 27 | train.md 28 | inference.md 29 | 30 | .. toctree:: 31 | :maxdepth: 2 32 | :caption: Tutorials 33 | 34 | tutorials/index.rst 35 | 36 | .. toctree:: 37 | :maxdepth: 2 38 | :caption: Useful Tools and Scripts 39 | 40 | useful_tools.md 41 | 42 | .. toctree:: 43 | :maxdepth: 2 44 | :caption: Notes 45 | 46 | changelog.md 47 | faq.md 48 | 49 | .. toctree:: 50 | :caption: Switch Language 51 | 52 | switch_language.md 53 | 54 | .. toctree:: 55 | :caption: API Reference 56 | 57 | api.rst 58 | 59 | Indices and tables 60 | ================== 61 | 62 | * :ref:`genindex` 63 | * :ref:`search` 64 | -------------------------------------------------------------------------------- /mmseg-v0.28/docs/en/make.bat: -------------------------------------------------------------------------------- 1 | @ECHO OFF 2 | 3 | pushd %~dp0 4 | 5 | REM Command file for Sphinx documentation 6 | 7 | if "%SPHINXBUILD%" == "" ( 8 | set SPHINXBUILD=sphinx-build 9 | ) 10 | set SOURCEDIR=. 11 | set BUILDDIR=_build 12 | 13 | if "%1" == "" goto help 14 | 15 | %SPHINXBUILD% >NUL 2>NUL 16 | if errorlevel 9009 ( 17 | echo. 18 | echo.The 'sphinx-build' command was not found. Make sure you have Sphinx 19 | echo.installed, then set the SPHINXBUILD environment variable to point 20 | echo.to the full path of the 'sphinx-build' executable. Alternatively you 21 | echo.may add the Sphinx directory to PATH. 22 | echo. 23 | echo.If you don't have Sphinx installed, grab it from 24 | echo.http://sphinx-doc.org/ 25 | exit /b 1 26 | ) 27 | 28 | %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% 29 | goto end 30 | 31 | :help 32 | %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% 33 | 34 | :end 35 | popd 36 | -------------------------------------------------------------------------------- /mmseg-v0.28/docs/en/switch_language.md: -------------------------------------------------------------------------------- 1 | ## English 2 | 3 | ## 简体中文 4 | -------------------------------------------------------------------------------- /mmseg-v0.28/docs/en/tutorials/index.rst: -------------------------------------------------------------------------------- 1 | .. toctree:: 2 | :maxdepth: 2 3 | 4 | config.md 5 | customize_datasets.md 6 | data_pipeline.md 7 | customize_models.md 8 | training_tricks.md 9 | customize_runtime.md 10 | -------------------------------------------------------------------------------- /mmseg-v0.28/docs/zh_cn/Makefile: -------------------------------------------------------------------------------- 1 | # Minimal makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line, and also 5 | # from the environment for the first two. 6 | SPHINXOPTS ?= 7 | SPHINXBUILD ?= sphinx-build 8 | SOURCEDIR = . 9 | BUILDDIR = _build 10 | 11 | # Put it first so that "make" without argument is like "make help". 12 | help: 13 | @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 14 | 15 | .PHONY: help Makefile 16 | 17 | # Catch-all target: route all unknown targets to Sphinx using the new 18 | # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). 19 | %: Makefile 20 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 21 | -------------------------------------------------------------------------------- /mmseg-v0.28/docs/zh_cn/_static/css/readthedocs.css: -------------------------------------------------------------------------------- 1 | .header-logo { 2 | background-image: url("../images/mmsegmentation.png"); 3 | background-size: 201px 40px; 4 | height: 40px; 5 | width: 201px; 6 | } 7 | -------------------------------------------------------------------------------- /mmseg-v0.28/docs/zh_cn/_static/images/mmsegmentation.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ggjy/FastMIM.pytorch/d4eea0dc8caf3db4be3d3ed4c7a8bc07c59b6111/mmseg-v0.28/docs/zh_cn/_static/images/mmsegmentation.png -------------------------------------------------------------------------------- /mmseg-v0.28/docs/zh_cn/api.rst: -------------------------------------------------------------------------------- 1 | mmseg.apis 2 | -------------- 3 | .. automodule:: mmseg.apis 4 | :members: 5 | 6 | mmseg.core 7 | -------------- 8 | 9 | seg 10 | ^^^^^^^^^^ 11 | .. automodule:: mmseg.core.seg 12 | :members: 13 | 14 | evaluation 15 | ^^^^^^^^^^ 16 | .. automodule:: mmseg.core.evaluation 17 | :members: 18 | 19 | utils 20 | ^^^^^^^^^^ 21 | .. automodule:: mmseg.core.utils 22 | :members: 23 | 24 | mmseg.datasets 25 | -------------- 26 | 27 | datasets 28 | ^^^^^^^^^^ 29 | .. automodule:: mmseg.datasets 30 | :members: 31 | 32 | pipelines 33 | ^^^^^^^^^^ 34 | .. automodule:: mmseg.datasets.pipelines 35 | :members: 36 | 37 | mmseg.models 38 | -------------- 39 | 40 | segmentors 41 | ^^^^^^^^^^ 42 | .. automodule:: mmseg.models.segmentors 43 | :members: 44 | 45 | backbones 46 | ^^^^^^^^^^ 47 | .. automodule:: mmseg.models.backbones 48 | :members: 49 | 50 | decode_heads 51 | ^^^^^^^^^^^^ 52 | .. automodule:: mmseg.models.decode_heads 53 | :members: 54 | 55 | losses 56 | ^^^^^^^^^^ 57 | .. automodule:: mmseg.models.losses 58 | :members: 59 | -------------------------------------------------------------------------------- /mmseg-v0.28/docs/zh_cn/imgs/qq_group_qrcode.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ggjy/FastMIM.pytorch/d4eea0dc8caf3db4be3d3ed4c7a8bc07c59b6111/mmseg-v0.28/docs/zh_cn/imgs/qq_group_qrcode.jpg -------------------------------------------------------------------------------- /mmseg-v0.28/docs/zh_cn/imgs/zhihu_qrcode.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ggjy/FastMIM.pytorch/d4eea0dc8caf3db4be3d3ed4c7a8bc07c59b6111/mmseg-v0.28/docs/zh_cn/imgs/zhihu_qrcode.jpg -------------------------------------------------------------------------------- /mmseg-v0.28/docs/zh_cn/index.rst: -------------------------------------------------------------------------------- 1 | 欢迎来到 MMSegmentation 的文档! 2 | ======================================= 3 | 4 | .. toctree:: 5 | :maxdepth: 2 6 | :caption: 开始你的第一步 7 | 8 | get_started.md 9 | 10 | .. toctree:: 11 | :maxdepth: 1 12 | :caption: 数据集准备 13 | 14 | dataset_prepare.md 15 | 16 | .. toctree:: 17 | :maxdepth: 1 18 | :caption: 模型库 19 | 20 | model_zoo.md 21 | modelzoo_statistics.md 22 | 23 | .. toctree:: 24 | :maxdepth: 2 25 | :caption: 快速启动 26 | 27 | train.md 28 | inference.md 29 | 30 | .. toctree:: 31 | :maxdepth: 2 32 | :caption: 教程 33 | 34 | tutorials/index.rst 35 | 36 | .. toctree:: 37 | :maxdepth: 2 38 | :caption: 实用工具与脚本 39 | 40 | useful_tools.md 41 | 42 | .. toctree:: 43 | :maxdepth: 2 44 | :caption: 说明 45 | 46 | changelog.md 47 | faq.md 48 | 49 | .. toctree:: 50 | :caption: 语言切换 51 | 52 | switch_language.md 53 | 54 | .. toctree:: 55 | :caption: 接口文档（英文） 56 | 57 | api.rst 58 | 59 | Indices and tables 60 | ================== 61 | 62 | * :ref:`genindex` 63 | * :ref:`search` 64 | -------------------------------------------------------------------------------- /mmseg-v0.28/docs/zh_cn/make.bat: -------------------------------------------------------------------------------- 1 | @ECHO OFF 2 | 3 | pushd %~dp0 4 | 5 | REM Command file for Sphinx documentation 6 | 7 | if "%SPHINXBUILD%" == "" ( 8 | set SPHINXBUILD=sphinx-build 9 | ) 10 | set SOURCEDIR=. 11 | set BUILDDIR=_build 12 | 13 | if "%1" == "" goto help 14 | 15 | %SPHINXBUILD% >NUL 2>NUL 16 | if errorlevel 9009 ( 17 | echo. 18 | echo.The 'sphinx-build' command was not found. Make sure you have Sphinx 19 | echo.installed, then set the SPHINXBUILD environment variable to point 20 | echo.to the full path of the 'sphinx-build' executable. Alternatively you 21 | echo.may add the Sphinx directory to PATH. 22 | echo. 23 | echo.If you don't have Sphinx installed, grab it from 24 | echo.http://sphinx-doc.org/ 25 | exit /b 1 26 | ) 27 | 28 | %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% 29 | goto end 30 | 31 | :help 32 | %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% 33 | 34 | :end 35 | popd 36 | -------------------------------------------------------------------------------- /mmseg-v0.28/docs/zh_cn/switch_language.md: -------------------------------------------------------------------------------- 1 | ## English 2 | 3 | ## 简体中文 4 | -------------------------------------------------------------------------------- /mmseg-v0.28/docs/zh_cn/tutorials/index.rst: -------------------------------------------------------------------------------- 1 | .. toctree:: 2 | :maxdepth: 2 3 | 4 | config.md 5 | customize_datasets.md 6 | data_pipeline.md 7 | customize_models.md 8 | training_tricks.md 9 | customize_runtime.md 10 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/apis/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .inference import inference_segmentor, init_segmentor, show_result_pyplot 3 | from .test import multi_gpu_test, single_gpu_test 4 | from .train import (get_root_logger, init_random_seed, set_random_seed, 5 | train_segmentor) 6 | 7 | __all__ = [ 8 | 'get_root_logger', 'set_random_seed', 'train_segmentor', 'init_segmentor', 9 | 'inference_segmentor', 'multi_gpu_test', 'single_gpu_test', 10 | 'show_result_pyplot', 'init_random_seed' 11 | ] 12 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/core/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .builder import (OPTIMIZER_BUILDERS, build_optimizer, 3 | build_optimizer_constructor) 4 | from .evaluation import * # noqa: F401, F403 5 | from .hook import * # noqa: F401, F403 6 | from .optimizers import * # noqa: F401, F403 7 | from .seg import * # noqa: F401, F403 8 | from .utils import * # noqa: F401, F403 9 | 10 | __all__ = [ 11 | 'OPTIMIZER_BUILDERS', 'build_optimizer', 'build_optimizer_constructor' 12 | ] 13 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/core/builder.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import copy 3 | 4 | from mmcv.runner.optimizer import OPTIMIZER_BUILDERS as MMCV_OPTIMIZER_BUILDERS 5 | from mmcv.utils import Registry, build_from_cfg 6 | 7 | OPTIMIZER_BUILDERS = Registry( 8 | 'optimizer builder', parent=MMCV_OPTIMIZER_BUILDERS) 9 | 10 | 11 | def build_optimizer_constructor(cfg): 12 | constructor_type = cfg.get('type') 13 | if constructor_type in OPTIMIZER_BUILDERS: 14 | return build_from_cfg(cfg, OPTIMIZER_BUILDERS) 15 | elif constructor_type in MMCV_OPTIMIZER_BUILDERS: 16 | return build_from_cfg(cfg, MMCV_OPTIMIZER_BUILDERS) 17 | else: 18 | raise KeyError(f'{constructor_type} is not registered ' 19 | 'in the optimizer builder registry.') 20 | 21 | 22 | def build_optimizer(model, cfg): 23 | optimizer_cfg = copy.deepcopy(cfg) 24 | constructor_type = optimizer_cfg.pop('constructor', 25 | 'DefaultOptimizerConstructor') 26 | paramwise_cfg = optimizer_cfg.pop('paramwise_cfg', None) 27 | optim_constructor = build_optimizer_constructor( 28 | dict( 29 | type=constructor_type, 30 | optimizer_cfg=optimizer_cfg, 31 | paramwise_cfg=paramwise_cfg)) 32 | optimizer = optim_constructor(model) 33 | return optimizer 34 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/core/evaluation/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .class_names import get_classes, get_palette 3 | from .eval_hooks import DistEvalHook, EvalHook 4 | from .metrics import (eval_metrics, intersect_and_union, mean_dice, 5 | mean_fscore, mean_iou, pre_eval_to_metrics) 6 | 7 | __all__ = [ 8 | 'EvalHook', 'DistEvalHook', 'mean_dice', 'mean_iou', 'mean_fscore', 9 | 'eval_metrics', 'get_classes', 'get_palette', 'pre_eval_to_metrics', 10 | 'intersect_and_union' 11 | ] 12 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/core/hook/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .wandblogger_hook import MMSegWandbHook 3 | 4 | __all__ = ['MMSegWandbHook'] 5 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/core/optimizers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .layer_decay_optimizer_constructor import ( 3 | LayerDecayOptimizerConstructor, LearningRateDecayOptimizerConstructor) 4 | 5 | __all__ = [ 6 | 'LearningRateDecayOptimizerConstructor', 'LayerDecayOptimizerConstructor' 7 | ] 8 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/core/seg/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .builder import build_pixel_sampler 3 | from .sampler import BasePixelSampler, OHEMPixelSampler 4 | 5 | __all__ = ['build_pixel_sampler', 'BasePixelSampler', 'OHEMPixelSampler'] 6 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/core/seg/builder.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmcv.utils import Registry, build_from_cfg 3 | 4 | PIXEL_SAMPLERS = Registry('pixel sampler') 5 | 6 | 7 | def build_pixel_sampler(cfg, **default_args): 8 | """Build pixel sampler for segmentation map.""" 9 | return build_from_cfg(cfg, PIXEL_SAMPLERS, default_args) 10 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/core/seg/sampler/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .base_pixel_sampler import BasePixelSampler 3 | from .ohem_pixel_sampler import OHEMPixelSampler 4 | 5 | __all__ = ['BasePixelSampler', 'OHEMPixelSampler'] 6 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/core/seg/sampler/base_pixel_sampler.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from abc import ABCMeta, abstractmethod 3 | 4 | 5 | class BasePixelSampler(metaclass=ABCMeta): 6 | """Base class of pixel sampler.""" 7 | 8 | def __init__(self, **kwargs): 9 | pass 10 | 11 | @abstractmethod 12 | def sample(self, seg_logit, seg_label): 13 | """Placeholder for sample function.""" 14 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/core/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .dist_util import check_dist_init, sync_random_seed 3 | from .misc import add_prefix 4 | 5 | __all__ = ['add_prefix', 'check_dist_init', 'sync_random_seed'] 6 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/core/utils/misc.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | def add_prefix(inputs, prefix): 3 | """Add prefix for dict. 4 | 5 | Args: 6 | inputs (dict): The input dict with str keys. 7 | prefix (str): The prefix to add. 8 | 9 | Returns: 10 | 11 | dict: The dict with keys updated with ``prefix``. 12 | """ 13 | 14 | outputs = dict() 15 | for name, value in inputs.items(): 16 | outputs[f'{prefix}.{name}'] = value 17 | 18 | return outputs 19 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/datasets/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .ade import ADE20KDataset 3 | from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset 4 | from .chase_db1 import ChaseDB1Dataset 5 | from .cityscapes import CityscapesDataset 6 | from .coco_stuff import COCOStuffDataset 7 | from .custom import CustomDataset 8 | from .dark_zurich import DarkZurichDataset 9 | from .dataset_wrappers import (ConcatDataset, MultiImageMixDataset, 10 | RepeatDataset) 11 | from .drive import DRIVEDataset 12 | from .hrf import HRFDataset 13 | from .isaid import iSAIDDataset 14 | from .isprs import ISPRSDataset 15 | from .loveda import LoveDADataset 16 | from .night_driving import NightDrivingDataset 17 | from .pascal_context import PascalContextDataset, PascalContextDataset59 18 | from .potsdam import PotsdamDataset 19 | from .stare import STAREDataset 20 | from .voc import PascalVOCDataset 21 | 22 | __all__ = [ 23 | 'CustomDataset', 'build_dataloader', 'ConcatDataset', 'RepeatDataset', 24 | 'DATASETS', 'build_dataset', 'PIPELINES', 'CityscapesDataset', 25 | 'PascalVOCDataset', 'ADE20KDataset', 'PascalContextDataset', 26 | 'PascalContextDataset59', 'ChaseDB1Dataset', 'DRIVEDataset', 'HRFDataset', 27 | 'STAREDataset', 'DarkZurichDataset', 'NightDrivingDataset', 28 | 'COCOStuffDataset', 'LoveDADataset', 'MultiImageMixDataset', 29 | 'iSAIDDataset', 'ISPRSDataset', 'PotsdamDataset' 30 | ] 31 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/datasets/chase_db1.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | 3 | from .builder import DATASETS 4 | from .custom import CustomDataset 5 | 6 | 7 | @DATASETS.register_module() 8 | class ChaseDB1Dataset(CustomDataset): 9 | """Chase_db1 dataset. 10 | 11 | In segmentation map annotation for Chase_db1, 0 stands for background, 12 | which is included in 2 categories. ``reduce_zero_label`` is fixed to False. 13 | The ``img_suffix`` is fixed to '.png' and ``seg_map_suffix`` is fixed to 14 | '_1stHO.png'. 15 | """ 16 | 17 | CLASSES = ('background', 'vessel') 18 | 19 | PALETTE = [[120, 120, 120], [6, 230, 230]] 20 | 21 | def __init__(self, **kwargs): 22 | super(ChaseDB1Dataset, self).__init__( 23 | img_suffix='.png', 24 | seg_map_suffix='_1stHO.png', 25 | reduce_zero_label=False, 26 | **kwargs) 27 | assert self.file_client.exists(self.img_dir) 28 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/datasets/dark_zurich.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .builder import DATASETS 3 | from .cityscapes import CityscapesDataset 4 | 5 | 6 | @DATASETS.register_module() 7 | class DarkZurichDataset(CityscapesDataset): 8 | """DarkZurichDataset dataset.""" 9 | 10 | def __init__(self, **kwargs): 11 | super().__init__( 12 | img_suffix='_rgb_anon.png', 13 | seg_map_suffix='_gt_labelTrainIds.png', 14 | **kwargs) 15 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/datasets/drive.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | 3 | from .builder import DATASETS 4 | from .custom import CustomDataset 5 | 6 | 7 | @DATASETS.register_module() 8 | class DRIVEDataset(CustomDataset): 9 | """DRIVE dataset. 10 | 11 | In segmentation map annotation for DRIVE, 0 stands for background, which is 12 | included in 2 categories. ``reduce_zero_label`` is fixed to False. The 13 | ``img_suffix`` is fixed to '.png' and ``seg_map_suffix`` is fixed to 14 | '_manual1.png'. 15 | """ 16 | 17 | CLASSES = ('background', 'vessel') 18 | 19 | PALETTE = [[120, 120, 120], [6, 230, 230]] 20 | 21 | def __init__(self, **kwargs): 22 | super(DRIVEDataset, self).__init__( 23 | img_suffix='.png', 24 | seg_map_suffix='_manual1.png', 25 | reduce_zero_label=False, 26 | **kwargs) 27 | assert self.file_client.exists(self.img_dir) 28 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/datasets/hrf.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | 3 | from .builder import DATASETS 4 | from .custom import CustomDataset 5 | 6 | 7 | @DATASETS.register_module() 8 | class HRFDataset(CustomDataset): 9 | """HRF dataset. 10 | 11 | In segmentation map annotation for HRF, 0 stands for background, which is 12 | included in 2 categories. ``reduce_zero_label`` is fixed to False. The 13 | ``img_suffix`` is fixed to '.png' and ``seg_map_suffix`` is fixed to 14 | '.png'. 15 | """ 16 | 17 | CLASSES = ('background', 'vessel') 18 | 19 | PALETTE = [[120, 120, 120], [6, 230, 230]] 20 | 21 | def __init__(self, **kwargs): 22 | super(HRFDataset, self).__init__( 23 | img_suffix='.png', 24 | seg_map_suffix='.png', 25 | reduce_zero_label=False, 26 | **kwargs) 27 | assert self.file_client.exists(self.img_dir) 28 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/datasets/isprs.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .builder import DATASETS 3 | from .custom import CustomDataset 4 | 5 | 6 | @DATASETS.register_module() 7 | class ISPRSDataset(CustomDataset): 8 | """ISPRS dataset. 9 | 10 | In segmentation map annotation for LoveDA, 0 is the ignore index. 11 | ``reduce_zero_label`` should be set to True. The ``img_suffix`` and 12 | ``seg_map_suffix`` are both fixed to '.png'. 13 | """ 14 | CLASSES = ('impervious_surface', 'building', 'low_vegetation', 'tree', 15 | 'car', 'clutter') 16 | 17 | PALETTE = [[255, 255, 255], [0, 0, 255], [0, 255, 255], [0, 255, 0], 18 | [255, 255, 0], [255, 0, 0]] 19 | 20 | def __init__(self, **kwargs): 21 | super(ISPRSDataset, self).__init__( 22 | img_suffix='.png', 23 | seg_map_suffix='.png', 24 | reduce_zero_label=True, 25 | **kwargs) 26 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/datasets/night_driving.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .builder import DATASETS 3 | from .cityscapes import CityscapesDataset 4 | 5 | 6 | @DATASETS.register_module() 7 | class NightDrivingDataset(CityscapesDataset): 8 | """NightDrivingDataset dataset.""" 9 | 10 | def __init__(self, **kwargs): 11 | super().__init__( 12 | img_suffix='_leftImg8bit.png', 13 | seg_map_suffix='_gtCoarse_labelTrainIds.png', 14 | **kwargs) 15 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/datasets/pipelines/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .compose import Compose 3 | from .formatting import (Collect, ImageToTensor, ToDataContainer, ToTensor, 4 | Transpose, to_tensor) 5 | from .loading import LoadAnnotations, LoadImageFromFile 6 | from .test_time_aug import MultiScaleFlipAug 7 | from .transforms import (CLAHE, AdjustGamma, Normalize, Pad, 8 | PhotoMetricDistortion, RandomCrop, RandomCutOut, 9 | RandomFlip, RandomMosaic, RandomRotate, Rerange, 10 | Resize, RGB2Gray, SegRescale) 11 | 12 | __all__ = [ 13 | 'Compose', 'to_tensor', 'ToTensor', 'ImageToTensor', 'ToDataContainer', 14 | 'Transpose', 'Collect', 'LoadAnnotations', 'LoadImageFromFile', 15 | 'MultiScaleFlipAug', 'Resize', 'RandomFlip', 'Pad', 'RandomCrop', 16 | 'Normalize', 'SegRescale', 'PhotoMetricDistortion', 'RandomRotate', 17 | 'AdjustGamma', 'CLAHE', 'Rerange', 'RGB2Gray', 'RandomCutOut', 18 | 'RandomMosaic' 19 | ] 20 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/datasets/pipelines/formating.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | # flake8: noqa 3 | import warnings 4 | 5 | from .formatting import * 6 | 7 | warnings.warn('DeprecationWarning: mmseg.datasets.pipelines.formating will be ' 8 | 'deprecated in 2021, please replace it with ' 9 | 'mmseg.datasets.pipelines.formatting.') 10 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/datasets/potsdam.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .builder import DATASETS 3 | from .custom import CustomDataset 4 | 5 | 6 | @DATASETS.register_module() 7 | class PotsdamDataset(CustomDataset): 8 | """ISPRS Potsdam dataset. 9 | 10 | In segmentation map annotation for Potsdam dataset, 0 is the ignore index. 11 | ``reduce_zero_label`` should be set to True. The ``img_suffix`` and 12 | ``seg_map_suffix`` are both fixed to '.png'. 13 | """ 14 | CLASSES = ('impervious_surface', 'building', 'low_vegetation', 'tree', 15 | 'car', 'clutter') 16 | 17 | PALETTE = [[255, 255, 255], [0, 0, 255], [0, 255, 255], [0, 255, 0], 18 | [255, 255, 0], [255, 0, 0]] 19 | 20 | def __init__(self, **kwargs): 21 | super(PotsdamDataset, self).__init__( 22 | img_suffix='.png', 23 | seg_map_suffix='.png', 24 | reduce_zero_label=True, 25 | **kwargs) 26 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/datasets/samplers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .distributed_sampler import DistributedSampler 3 | 4 | __all__ = ['DistributedSampler'] 5 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/datasets/stare.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import os.path as osp 3 | 4 | from .builder import DATASETS 5 | from .custom import CustomDataset 6 | 7 | 8 | @DATASETS.register_module() 9 | class STAREDataset(CustomDataset): 10 | """STARE dataset. 11 | 12 | In segmentation map annotation for STARE, 0 stands for background, which is 13 | included in 2 categories. ``reduce_zero_label`` is fixed to False. The 14 | ``img_suffix`` is fixed to '.png' and ``seg_map_suffix`` is fixed to 15 | '.ah.png'. 16 | """ 17 | 18 | CLASSES = ('background', 'vessel') 19 | 20 | PALETTE = [[120, 120, 120], [6, 230, 230]] 21 | 22 | def __init__(self, **kwargs): 23 | super(STAREDataset, self).__init__( 24 | img_suffix='.png', 25 | seg_map_suffix='.ah.png', 26 | reduce_zero_label=False, 27 | **kwargs) 28 | assert osp.exists(self.img_dir) 29 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/datasets/voc.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import os.path as osp 3 | 4 | from .builder import DATASETS 5 | from .custom import CustomDataset 6 | 7 | 8 | @DATASETS.register_module() 9 | class PascalVOCDataset(CustomDataset): 10 | """Pascal VOC dataset. 11 | 12 | Args: 13 | split (str): Split txt file for Pascal VOC. 14 | """ 15 | 16 | CLASSES = ('background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 17 | 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 18 | 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 19 | 'train', 'tvmonitor') 20 | 21 | PALETTE = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128], 22 | [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0], 23 | [192, 0, 0], [64, 128, 0], [192, 128, 0], [64, 0, 128], 24 | [192, 0, 128], [64, 128, 128], [192, 128, 128], [0, 64, 0], 25 | [128, 64, 0], [0, 192, 0], [128, 192, 0], [0, 64, 128]] 26 | 27 | def __init__(self, split, **kwargs): 28 | super(PascalVOCDataset, self).__init__( 29 | img_suffix='.jpg', seg_map_suffix='.png', split=split, **kwargs) 30 | assert osp.exists(self.img_dir) and self.split is not None 31 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/models/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .backbones import * # noqa: F401,F403 3 | from .builder import (BACKBONES, HEADS, LOSSES, SEGMENTORS, build_backbone, 4 | build_head, build_loss, build_segmentor) 5 | from .decode_heads import * # noqa: F401,F403 6 | from .losses import * # noqa: F401,F403 7 | from .necks import * # noqa: F401,F403 8 | from .segmentors import * # noqa: F401,F403 9 | 10 | __all__ = [ 11 | 'BACKBONES', 'HEADS', 'LOSSES', 'SEGMENTORS', 'build_backbone', 12 | 'build_head', 'build_loss', 'build_segmentor' 13 | ] 14 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/models/backbones/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .beit import BEiT 3 | from .bisenetv1 import BiSeNetV1 4 | from .bisenetv2 import BiSeNetV2 5 | from .cgnet import CGNet 6 | from .erfnet import ERFNet 7 | from .fast_scnn import FastSCNN 8 | from .hrnet import HRNet 9 | from .icnet import ICNet 10 | from .mae import MAE 11 | from .mit import MixVisionTransformer 12 | from .mobilenet_v2 import MobileNetV2 13 | from .mobilenet_v3 import MobileNetV3 14 | from .resnest import ResNeSt 15 | from .resnet import ResNet, ResNetV1c, ResNetV1d 16 | from .resnext import ResNeXt 17 | from .stdc import STDCContextPathNet, STDCNet 18 | from .swin import SwinTransformer 19 | from .timm_backbone import TIMMBackbone 20 | from .twins import PCPVT, SVT 21 | from .unet import UNet 22 | from .vit import VisionTransformer 23 | from .vit_beit import ViTBEiT 24 | 25 | __all__ = [ 26 | 'ResNet', 'ResNetV1c', 'ResNetV1d', 'ResNeXt', 'HRNet', 'FastSCNN', 27 | 'ResNeSt', 'MobileNetV2', 'UNet', 'CGNet', 'MobileNetV3', 28 | 'VisionTransformer', 'SwinTransformer', 'MixVisionTransformer', 29 | 'BiSeNetV1', 'BiSeNetV2', 'ICNet', 'TIMMBackbone', 'ERFNet', 'PCPVT', 30 | 'SVT', 'STDCNet', 'STDCContextPathNet', 'BEiT', 'MAE', 'ViTBEiT', 31 | ] 32 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/models/losses/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .accuracy import Accuracy, accuracy 3 | from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy, 4 | cross_entropy, mask_cross_entropy) 5 | from .dice_loss import DiceLoss 6 | from .focal_loss import FocalLoss 7 | from .lovasz_loss import LovaszLoss 8 | from .tversky_loss import TverskyLoss 9 | from .utils import reduce_loss, weight_reduce_loss, weighted_loss 10 | 11 | __all__ = [ 12 | 'accuracy', 'Accuracy', 'cross_entropy', 'binary_cross_entropy', 13 | 'mask_cross_entropy', 'CrossEntropyLoss', 'reduce_loss', 14 | 'weight_reduce_loss', 'weighted_loss', 'LovaszLoss', 'DiceLoss', 15 | 'FocalLoss', 'TverskyLoss' 16 | ] 17 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/models/necks/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .featurepyramid import Feature2Pyramid 3 | from .fpn import FPN 4 | from .ic_neck import ICNeck 5 | from .jpu import JPU 6 | from .mla_neck import MLANeck 7 | from .multilevel_neck import MultiLevelNeck 8 | 9 | __all__ = [ 10 | 'FPN', 'MultiLevelNeck', 'MLANeck', 'ICNeck', 'JPU', 'Feature2Pyramid' 11 | ] 12 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/models/segmentors/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .base import BaseSegmentor 3 | from .cascade_encoder_decoder import CascadeEncoderDecoder 4 | from .encoder_decoder import EncoderDecoder 5 | 6 | __all__ = ['BaseSegmentor', 'EncoderDecoder', 'CascadeEncoderDecoder'] 7 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/models/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .embed import PatchEmbed 3 | from .inverted_residual import InvertedResidual, InvertedResidualV3 4 | from .make_divisible import make_divisible 5 | from .res_layer import ResLayer 6 | from .se_layer import SELayer 7 | from .self_attention_block import SelfAttentionBlock 8 | from .shape_convert import (nchw2nlc2nchw, nchw_to_nlc, nlc2nchw2nlc, 9 | nlc_to_nchw) 10 | from .up_conv_block import UpConvBlock 11 | 12 | __all__ = [ 13 | 'ResLayer', 'SelfAttentionBlock', 'make_divisible', 'InvertedResidual', 14 | 'UpConvBlock', 'InvertedResidualV3', 'SELayer', 'PatchEmbed', 15 | 'nchw_to_nlc', 'nlc_to_nchw', 'nchw2nlc2nchw', 'nlc2nchw2nlc' 16 | ] 17 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/models/utils/make_divisible.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | def make_divisible(value, divisor, min_value=None, min_ratio=0.9): 3 | """Make divisible function. 4 | 5 | This function rounds the channel number to the nearest value that can be 6 | divisible by the divisor. It is taken from the original tf repo. It ensures 7 | that all layers have a channel number that is divisible by divisor. It can 8 | be seen here: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py # noqa 9 | 10 | Args: 11 | value (int): The original channel number. 12 | divisor (int): The divisor to fully divide the channel number. 13 | min_value (int): The minimum value of the output channel. 14 | Default: None, means that the minimum value equal to the divisor. 15 | min_ratio (float): The minimum ratio of the rounded channel number to 16 | the original channel number. Default: 0.9. 17 | 18 | Returns: 19 | int: The modified output channel number. 20 | """ 21 | 22 | if min_value is None: 23 | min_value = divisor 24 | new_value = max(min_value, int(value + divisor / 2) // divisor * divisor) 25 | # Make sure that round down does not go down by more than (1-min_ratio). 26 | if new_value < min_ratio * value: 27 | new_value += divisor 28 | return new_value 29 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/ops/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .encoding import Encoding 3 | from .wrappers import Upsample, resize 4 | 5 | __all__ = ['Upsample', 'resize', 'Encoding'] 6 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .collect_env import collect_env 3 | from .logger import get_root_logger 4 | from .misc import find_latest_checkpoint 5 | from .set_env import setup_multi_processes 6 | from .util_distribution import build_ddp, build_dp, get_device 7 | 8 | __all__ = [ 9 | 'get_root_logger', 'collect_env', 'find_latest_checkpoint', 10 | 'setup_multi_processes', 'build_ddp', 'build_dp', 'get_device' 11 | ] 12 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/utils/collect_env.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmcv.utils import collect_env as collect_base_env 3 | from mmcv.utils import get_git_hash 4 | 5 | import mmseg 6 | 7 | 8 | def collect_env(): 9 | """Collect the information of the running environments.""" 10 | env_info = collect_base_env() 11 | env_info['MMSegmentation'] = f'{mmseg.__version__}+{get_git_hash()[:7]}' 12 | 13 | return env_info 14 | 15 | 16 | if __name__ == '__main__': 17 | for name, val in collect_env().items(): 18 | print('{}: {}'.format(name, val)) 19 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/utils/logger.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import logging 3 | 4 | from mmcv.utils import get_logger 5 | 6 | 7 | def get_root_logger(log_file=None, log_level=logging.INFO): 8 | """Get the root logger. 9 | 10 | The logger will be initialized if it has not been initialized. By default a 11 | StreamHandler will be added. If `log_file` is specified, a FileHandler will 12 | also be added. The name of the root logger is the top-level package name, 13 | e.g., "mmseg". 14 | 15 | Args: 16 | log_file (str | None): The log filename. If specified, a FileHandler 17 | will be added to the root logger. 18 | log_level (int): The root logger level. Note that only the process of 19 | rank 0 is affected, while other processes will set the level to 20 | "Error" and be silent most of the time. 21 | 22 | Returns: 23 | logging.Logger: The root logger. 24 | """ 25 | 26 | logger = get_logger(name='mmseg', log_file=log_file, log_level=log_level) 27 | 28 | return logger 29 | -------------------------------------------------------------------------------- /mmseg-v0.28/mmseg/version.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Open-MMLab. All rights reserved. 2 | 3 | __version__ = '0.28.0' 4 | 5 | 6 | def parse_version_info(version_str): 7 | version_info = [] 8 | for x in version_str.split('.'): 9 | if x.isdigit(): 10 | version_info.append(int(x)) 11 | elif x.find('rc') != -1: 12 | patch_version = x.split('rc') 13 | version_info.append(int(patch_version[0])) 14 | version_info.append(f'rc{patch_version[1]}') 15 | return tuple(version_info) 16 | 17 | 18 | version_info = parse_version_info(__version__) 19 | -------------------------------------------------------------------------------- /mmseg-v0.28/pytest.ini: -------------------------------------------------------------------------------- 1 | [pytest] 2 | addopts = --xdoctest --xdoctest-style=auto 3 | norecursedirs = .git ignore build __pycache__ data docker docs .eggs 4 | 5 | filterwarnings= default 6 | ignore:.*No cfgstr given in Cacher constructor or call.*:Warning 7 | ignore:.*Define the __nice__ method for.*:Warning 8 | -------------------------------------------------------------------------------- /mmseg-v0.28/requirements.txt: -------------------------------------------------------------------------------- 1 | -r requirements/optional.txt 2 | -r requirements/runtime.txt 3 | -r requirements/tests.txt 4 | -------------------------------------------------------------------------------- /mmseg-v0.28/requirements/docs.txt: -------------------------------------------------------------------------------- 1 | docutils==0.16.0 2 | myst-parser 3 | -e git+https://github.com/gaotongxiao/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme 4 | sphinx==4.0.2 5 | sphinx_copybutton 6 | sphinx_markdown_tables 7 | -------------------------------------------------------------------------------- /mmseg-v0.28/requirements/mminstall.txt: -------------------------------------------------------------------------------- 1 | mmcls>=0.20.1 2 | mmcv-full>=1.4.4,<1.7.0 3 | -------------------------------------------------------------------------------- /mmseg-v0.28/requirements/optional.txt: -------------------------------------------------------------------------------- 1 | cityscapesscripts 2 | -------------------------------------------------------------------------------- /mmseg-v0.28/requirements/readthedocs.txt: -------------------------------------------------------------------------------- 1 | mmcv 2 | prettytable 3 | torch 4 | torchvision 5 | -------------------------------------------------------------------------------- /mmseg-v0.28/requirements/runtime.txt: -------------------------------------------------------------------------------- 1 | matplotlib 2 | mmcls>=0.20.1 3 | numpy 4 | packaging 5 | prettytable 6 | -------------------------------------------------------------------------------- /mmseg-v0.28/requirements/tests.txt: -------------------------------------------------------------------------------- 1 | codecov 2 | flake8 3 | interrogate 4 | pytest 5 | xdoctest>=0.10.0 6 | yapf 7 | -------------------------------------------------------------------------------- /mmseg-v0.28/resources/3dogs.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ggjy/FastMIM.pytorch/d4eea0dc8caf3db4be3d3ed4c7a8bc07c59b6111/mmseg-v0.28/resources/3dogs.jpg -------------------------------------------------------------------------------- /mmseg-v0.28/resources/3dogs_mask.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ggjy/FastMIM.pytorch/d4eea0dc8caf3db4be3d3ed4c7a8bc07c59b6111/mmseg-v0.28/resources/3dogs_mask.png -------------------------------------------------------------------------------- /mmseg-v0.28/resources/mmseg-logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ggjy/FastMIM.pytorch/d4eea0dc8caf3db4be3d3ed4c7a8bc07c59b6111/mmseg-v0.28/resources/mmseg-logo.png -------------------------------------------------------------------------------- /mmseg-v0.28/resources/seg_demo.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ggjy/FastMIM.pytorch/d4eea0dc8caf3db4be3d3ed4c7a8bc07c59b6111/mmseg-v0.28/resources/seg_demo.gif -------------------------------------------------------------------------------- /mmseg-v0.28/setup.cfg: -------------------------------------------------------------------------------- 1 | [yapf] 2 | based_on_style = pep8 3 | blank_line_before_nested_class_or_def = true 4 | split_before_expression_after_opening_paren = true 5 | 6 | [isort] 7 | line_length = 79 8 | multi_line_output = 0 9 | extra_standard_library = setuptools 10 | known_first_party = mmseg 11 | known_third_party = PIL,cityscapesscripts,cv2,detail,matplotlib,mmcv,numpy,onnxruntime,packaging,prettytable,pytest,pytorch_sphinx_theme,requests,scipy,seaborn,torch,ts 12 | no_lines_before = STDLIB,LOCALFOLDER 13 | default_section = THIRDPARTY 14 | 15 | # ignore-words-list needs to be lowercase format. For example, if we want to 16 | # ignore word "BA", then we need to append "ba" to ignore-words-list rather 17 | # than "BA" 18 | [codespell] 19 | skip = *.po,*.ts,*.ipynb 20 | count = 21 | quiet-level = 3 22 | ignore-words-list = formating,sur,hist,dota,ba 23 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. 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All rights reserved. 2 | from mmseg import digit_version 3 | 4 | 5 | def test_digit_version(): 6 | assert digit_version('0.2.16') == (0, 2, 16, 0, 0, 0) 7 | assert digit_version('1.2.3') == (1, 2, 3, 0, 0, 0) 8 | assert digit_version('1.2.3rc0') == (1, 2, 3, 0, -1, 0) 9 | assert digit_version('1.2.3rc1') == (1, 2, 3, 0, -1, 1) 10 | assert digit_version('1.0rc0') == (1, 0, 0, 0, -1, 0) 11 | assert digit_version('1.0') == digit_version('1.0.0') 12 | assert digit_version('1.5.0+cuda90_cudnn7.6.3_lms') == digit_version('1.5') 13 | assert digit_version('1.0.0dev') < digit_version('1.0.0a') 14 | assert digit_version('1.0.0a') < digit_version('1.0.0a1') 15 | assert digit_version('1.0.0a') < digit_version('1.0.0b') 16 | assert digit_version('1.0.0b') < digit_version('1.0.0rc') 17 | assert digit_version('1.0.0rc1') < digit_version('1.0.0') 18 | assert digit_version('1.0.0') < digit_version('1.0.0post') 19 | assert digit_version('1.0.0post') < digit_version('1.0.0post1') 20 | assert digit_version('v1') == (1, 0, 0, 0, 0, 0) 21 | assert digit_version('v1.1.5') == (1, 1, 5, 0, 0, 0) 22 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_inference.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import os.path as osp 3 | 4 | import mmcv 5 | 6 | from mmseg.apis import inference_segmentor, init_segmentor 7 | 8 | 9 | def test_test_time_augmentation_on_cpu(): 10 | config_file = 'configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py' 11 | config = mmcv.Config.fromfile(config_file) 12 | 13 | # Remove pretrain model download for testing 14 | config.model.pretrained = None 15 | # Replace SyncBN with BN to inference on CPU 16 | norm_cfg = dict(type='BN', requires_grad=True) 17 | config.model.backbone.norm_cfg = norm_cfg 18 | config.model.decode_head.norm_cfg = norm_cfg 19 | config.model.auxiliary_head.norm_cfg = norm_cfg 20 | 21 | # Enable test time augmentation 22 | config.data.test.pipeline[1].flip = True 23 | 24 | checkpoint_file = None 25 | model = init_segmentor(config, checkpoint_file, device='cpu') 26 | 27 | img = mmcv.imread( 28 | osp.join(osp.dirname(__file__), 'data/color.jpg'), 'color') 29 | result = inference_segmentor(model, img) 30 | assert result[0].shape == (288, 512) 31 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_backbones/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .utils import all_zeros, check_norm_state, is_block, is_norm 3 | 4 | __all__ = ['is_norm', 'is_block', 'all_zeros', 'check_norm_state'] 5 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_backbones/test_fast_scnn.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import pytest 3 | import torch 4 | 5 | from mmseg.models.backbones import FastSCNN 6 | 7 | 8 | def test_fastscnn_backbone(): 9 | with pytest.raises(AssertionError): 10 | # Fast-SCNN channel constraints. 11 | FastSCNN( 12 | 3, (32, 48), 13 | 64, (64, 96, 128), (2, 2, 1), 14 | global_out_channels=127, 15 | higher_in_channels=64, 16 | lower_in_channels=128) 17 | 18 | # Test FastSCNN Standard Forward 19 | model = FastSCNN( 20 | in_channels=3, 21 | downsample_dw_channels=(4, 6), 22 | global_in_channels=8, 23 | global_block_channels=(8, 12, 16), 24 | global_block_strides=(2, 2, 1), 25 | global_out_channels=16, 26 | higher_in_channels=8, 27 | lower_in_channels=16, 28 | fusion_out_channels=16, 29 | ) 30 | model.init_weights() 31 | model.train() 32 | batch_size = 4 33 | imgs = torch.randn(batch_size, 3, 64, 128) 34 | feat = model(imgs) 35 | 36 | assert len(feat) == 3 37 | # higher-res 38 | assert feat[0].shape == torch.Size([batch_size, 8, 8, 16]) 39 | # lower-res 40 | assert feat[1].shape == torch.Size([batch_size, 16, 2, 4]) 41 | # FFM output 42 | assert feat[2].shape == torch.Size([batch_size, 16, 8, 16]) 43 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_heads/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_heads/test_ann_head.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import torch 3 | 4 | from mmseg.models.decode_heads import ANNHead 5 | from .utils import to_cuda 6 | 7 | 8 | def test_ann_head(): 9 | 10 | inputs = [torch.randn(1, 4, 45, 45), torch.randn(1, 8, 21, 21)] 11 | head = ANNHead( 12 | in_channels=[4, 8], 13 | channels=2, 14 | num_classes=19, 15 | in_index=[-2, -1], 16 | project_channels=8) 17 | if torch.cuda.is_available(): 18 | head, inputs = to_cuda(head, inputs) 19 | outputs = head(inputs) 20 | assert outputs.shape == (1, head.num_classes, 21, 21) 21 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_heads/test_cc_head.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import pytest 3 | import torch 4 | 5 | from mmseg.models.decode_heads import CCHead 6 | from .utils import to_cuda 7 | 8 | 9 | def test_cc_head(): 10 | head = CCHead(in_channels=16, channels=8, num_classes=19) 11 | assert len(head.convs) == 2 12 | assert hasattr(head, 'cca') 13 | if not torch.cuda.is_available(): 14 | pytest.skip('CCHead requires CUDA') 15 | inputs = [torch.randn(1, 16, 23, 23)] 16 | head, inputs = to_cuda(head, inputs) 17 | outputs = head(inputs) 18 | assert outputs.shape == (1, head.num_classes, 23, 23) 19 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_heads/test_da_head.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import torch 3 | 4 | from mmseg.models.decode_heads import DAHead 5 | from .utils import to_cuda 6 | 7 | 8 | def test_da_head(): 9 | 10 | inputs = [torch.randn(1, 16, 23, 23)] 11 | head = DAHead(in_channels=16, channels=8, num_classes=19, pam_channels=8) 12 | if torch.cuda.is_available(): 13 | head, inputs = to_cuda(head, inputs) 14 | outputs = head(inputs) 15 | assert isinstance(outputs, tuple) and len(outputs) == 3 16 | for output in outputs: 17 | assert output.shape == (1, head.num_classes, 23, 23) 18 | test_output = head.forward_test(inputs, None, None) 19 | assert test_output.shape == (1, head.num_classes, 23, 23) 20 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_heads/test_ema_head.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import torch 3 | 4 | from mmseg.models.decode_heads import EMAHead 5 | from .utils import to_cuda 6 | 7 | 8 | def test_emanet_head(): 9 | head = EMAHead( 10 | in_channels=4, 11 | ema_channels=3, 12 | channels=2, 13 | num_stages=3, 14 | num_bases=2, 15 | num_classes=19) 16 | for param in head.ema_mid_conv.parameters(): 17 | assert not param.requires_grad 18 | assert hasattr(head, 'ema_module') 19 | inputs = [torch.randn(1, 4, 23, 23)] 20 | if torch.cuda.is_available(): 21 | head, inputs = to_cuda(head, inputs) 22 | outputs = head(inputs) 23 | assert outputs.shape == (1, head.num_classes, 23, 23) 24 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_heads/test_gc_head.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import torch 3 | 4 | from mmseg.models.decode_heads import GCHead 5 | from .utils import to_cuda 6 | 7 | 8 | def test_gc_head(): 9 | head = GCHead(in_channels=4, channels=4, num_classes=19) 10 | assert len(head.convs) == 2 11 | assert hasattr(head, 'gc_block') 12 | inputs = [torch.randn(1, 4, 23, 23)] 13 | if torch.cuda.is_available(): 14 | head, inputs = to_cuda(head, inputs) 15 | outputs = head(inputs) 16 | assert outputs.shape == (1, head.num_classes, 23, 23) 17 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_heads/test_isa_head.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import torch 3 | 4 | from mmseg.models.decode_heads import ISAHead 5 | from .utils import to_cuda 6 | 7 | 8 | def test_isa_head(): 9 | 10 | inputs = [torch.randn(1, 8, 23, 23)] 11 | isa_head = ISAHead( 12 | in_channels=8, 13 | channels=4, 14 | num_classes=19, 15 | isa_channels=4, 16 | down_factor=(8, 8)) 17 | if torch.cuda.is_available(): 18 | isa_head, inputs = to_cuda(isa_head, inputs) 19 | output = isa_head(inputs) 20 | assert output.shape == (1, isa_head.num_classes, 23, 23) 21 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_heads/test_nl_head.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import torch 3 | 4 | from mmseg.models.decode_heads import NLHead 5 | from .utils import to_cuda 6 | 7 | 8 | def test_nl_head(): 9 | head = NLHead(in_channels=8, channels=4, num_classes=19) 10 | assert len(head.convs) == 2 11 | assert hasattr(head, 'nl_block') 12 | inputs = [torch.randn(1, 8, 23, 23)] 13 | if torch.cuda.is_available(): 14 | head, inputs = to_cuda(head, inputs) 15 | outputs = head(inputs) 16 | assert outputs.shape == (1, head.num_classes, 23, 23) 17 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_heads/test_ocr_head.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import torch 3 | 4 | from mmseg.models.decode_heads import FCNHead, OCRHead 5 | from .utils import to_cuda 6 | 7 | 8 | def test_ocr_head(): 9 | 10 | inputs = [torch.randn(1, 8, 23, 23)] 11 | ocr_head = OCRHead( 12 | in_channels=8, channels=4, num_classes=19, ocr_channels=8) 13 | fcn_head = FCNHead(in_channels=8, channels=4, num_classes=19) 14 | if torch.cuda.is_available(): 15 | head, inputs = to_cuda(ocr_head, inputs) 16 | head, inputs = to_cuda(fcn_head, inputs) 17 | prev_output = fcn_head(inputs) 18 | output = ocr_head(inputs, prev_output) 19 | assert output.shape == (1, ocr_head.num_classes, 23, 23) 20 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_heads/test_psp_head.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import pytest 3 | import torch 4 | 5 | from mmseg.models.decode_heads import PSPHead 6 | from .utils import _conv_has_norm, to_cuda 7 | 8 | 9 | def test_psp_head(): 10 | 11 | with pytest.raises(AssertionError): 12 | # pool_scales must be list|tuple 13 | PSPHead(in_channels=4, channels=2, num_classes=19, pool_scales=1) 14 | 15 | # test no norm_cfg 16 | head = PSPHead(in_channels=4, channels=2, num_classes=19) 17 | assert not _conv_has_norm(head, sync_bn=False) 18 | 19 | # test with norm_cfg 20 | head = PSPHead( 21 | in_channels=4, 22 | channels=2, 23 | num_classes=19, 24 | norm_cfg=dict(type='SyncBN')) 25 | assert _conv_has_norm(head, sync_bn=True) 26 | 27 | inputs = [torch.randn(1, 4, 23, 23)] 28 | head = PSPHead( 29 | in_channels=4, channels=2, num_classes=19, pool_scales=(1, 2, 3)) 30 | if torch.cuda.is_available(): 31 | head, inputs = to_cuda(head, inputs) 32 | assert head.psp_modules[0][0].output_size == 1 33 | assert head.psp_modules[1][0].output_size == 2 34 | assert head.psp_modules[2][0].output_size == 3 35 | outputs = head(inputs) 36 | assert outputs.shape == (1, head.num_classes, 23, 23) 37 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_heads/test_segformer_head.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import pytest 3 | import torch 4 | 5 | from mmseg.models.decode_heads import SegformerHead 6 | 7 | 8 | def test_segformer_head(): 9 | with pytest.raises(AssertionError): 10 | # `in_channels` must have same length as `in_index` 11 | SegformerHead( 12 | in_channels=(1, 2, 3), in_index=(0, 1), channels=5, num_classes=2) 13 | 14 | H, W = (64, 64) 15 | in_channels = (32, 64, 160, 256) 16 | shapes = [(H // 2**(i + 2), W // 2**(i + 2)) 17 | for i in range(len(in_channels))] 18 | model = SegformerHead( 19 | in_channels=in_channels, 20 | in_index=[0, 1, 2, 3], 21 | channels=256, 22 | num_classes=19) 23 | 24 | with pytest.raises(IndexError): 25 | # in_index must match the input feature maps. 26 | inputs = [ 27 | torch.randn((1, in_channel, *shape)) 28 | for in_channel, shape in zip(in_channels, shapes) 29 | ][:3] 30 | temp = model(inputs) 31 | 32 | # Normal Input 33 | # ((1, 32, 16, 16), (1, 64, 8, 8), (1, 160, 4, 4), (1, 256, 2, 2) 34 | inputs = [ 35 | torch.randn((1, in_channel, *shape)) 36 | for in_channel, shape in zip(in_channels, shapes) 37 | ] 38 | temp = model(inputs) 39 | 40 | assert temp.shape == (1, 19, H // 4, W // 4) 41 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_heads/test_segmenter_mask_head.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import torch 3 | 4 | from mmseg.models.decode_heads import SegmenterMaskTransformerHead 5 | from .utils import _conv_has_norm, to_cuda 6 | 7 | 8 | def test_segmenter_mask_transformer_head(): 9 | head = SegmenterMaskTransformerHead( 10 | in_channels=2, 11 | channels=2, 12 | num_classes=150, 13 | num_layers=2, 14 | num_heads=3, 15 | embed_dims=192, 16 | dropout_ratio=0.0) 17 | assert _conv_has_norm(head, sync_bn=True) 18 | head.init_weights() 19 | 20 | inputs = [torch.randn(1, 2, 32, 32)] 21 | if torch.cuda.is_available(): 22 | head, inputs = to_cuda(head, inputs) 23 | outputs = head(inputs) 24 | assert outputs.shape == (1, head.num_classes, 32, 32) 25 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_heads/test_stdc_head.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import torch 3 | 4 | from mmseg.models.decode_heads import STDCHead 5 | from .utils import to_cuda 6 | 7 | 8 | def test_stdc_head(): 9 | inputs = [torch.randn(1, 32, 21, 21)] 10 | head = STDCHead( 11 | in_channels=32, 12 | channels=8, 13 | num_convs=1, 14 | num_classes=2, 15 | in_index=-1, 16 | loss_decode=[ 17 | dict( 18 | type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), 19 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=1.0) 20 | ]) 21 | if torch.cuda.is_available(): 22 | head, inputs = to_cuda(head, inputs) 23 | outputs = head(inputs) 24 | assert isinstance(outputs, torch.Tensor) and len(outputs) == 1 25 | assert outputs.shape == torch.Size([1, head.num_classes, 21, 21]) 26 | 27 | fake_label = torch.ones_like( 28 | outputs[:, 0:1, :, :], dtype=torch.int16).long() 29 | loss = head.losses(seg_logit=outputs, seg_label=fake_label) 30 | assert loss['loss_ce'] != torch.zeros_like(loss['loss_ce']) 31 | assert loss['loss_dice'] != torch.zeros_like(loss['loss_dice']) 32 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_heads/test_uper_head.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import pytest 3 | import torch 4 | 5 | from mmseg.models.decode_heads import UPerHead 6 | from .utils import _conv_has_norm, to_cuda 7 | 8 | 9 | def test_uper_head(): 10 | 11 | with pytest.raises(AssertionError): 12 | # fpn_in_channels must be list|tuple 13 | UPerHead(in_channels=4, channels=2, num_classes=19) 14 | 15 | # test no norm_cfg 16 | head = UPerHead( 17 | in_channels=[4, 2], channels=2, num_classes=19, in_index=[-2, -1]) 18 | assert not _conv_has_norm(head, sync_bn=False) 19 | 20 | # test with norm_cfg 21 | head = UPerHead( 22 | in_channels=[4, 2], 23 | channels=2, 24 | num_classes=19, 25 | norm_cfg=dict(type='SyncBN'), 26 | in_index=[-2, -1]) 27 | assert _conv_has_norm(head, sync_bn=True) 28 | 29 | inputs = [torch.randn(1, 4, 45, 45), torch.randn(1, 2, 21, 21)] 30 | head = UPerHead( 31 | in_channels=[4, 2], channels=2, num_classes=19, in_index=[-2, -1]) 32 | if torch.cuda.is_available(): 33 | head, inputs = to_cuda(head, inputs) 34 | outputs = head(inputs) 35 | assert outputs.shape == (1, head.num_classes, 45, 45) 36 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_heads/utils.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmcv.cnn import ConvModule 3 | from mmcv.utils.parrots_wrapper import SyncBatchNorm 4 | 5 | 6 | def _conv_has_norm(module, sync_bn): 7 | for m in module.modules(): 8 | if isinstance(m, ConvModule): 9 | if not m.with_norm: 10 | return False 11 | if sync_bn: 12 | if not isinstance(m.bn, SyncBatchNorm): 13 | return False 14 | return True 15 | 16 | 17 | def to_cuda(module, data): 18 | module = module.cuda() 19 | if isinstance(data, list): 20 | for i in range(len(data)): 21 | data[i] = data[i].cuda() 22 | return module, data 23 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_losses/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_necks/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_necks/test_fpn.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import torch 3 | 4 | from mmseg.models import FPN 5 | 6 | 7 | def test_fpn(): 8 | in_channels = [64, 128, 256, 512] 9 | inputs = [ 10 | torch.randn(1, c, 56 // 2**i, 56 // 2**i) 11 | for i, c in enumerate(in_channels) 12 | ] 13 | 14 | fpn = FPN(in_channels, 64, len(in_channels)) 15 | outputs = fpn(inputs) 16 | assert outputs[0].shape == torch.Size([1, 64, 56, 56]) 17 | assert outputs[1].shape == torch.Size([1, 64, 28, 28]) 18 | assert outputs[2].shape == torch.Size([1, 64, 14, 14]) 19 | assert outputs[3].shape == torch.Size([1, 64, 7, 7]) 20 | 21 | fpn = FPN( 22 | in_channels, 23 | 64, 24 | len(in_channels), 25 | upsample_cfg=dict(mode='nearest', scale_factor=2.0)) 26 | outputs = fpn(inputs) 27 | assert outputs[0].shape == torch.Size([1, 64, 56, 56]) 28 | assert outputs[1].shape == torch.Size([1, 64, 28, 28]) 29 | assert outputs[2].shape == torch.Size([1, 64, 14, 14]) 30 | assert outputs[3].shape == torch.Size([1, 64, 7, 7]) 31 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_necks/test_mla_neck.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import torch 3 | 4 | from mmseg.models import MLANeck 5 | 6 | 7 | def test_mla(): 8 | in_channels = [4, 4, 4, 4] 9 | mla = MLANeck(in_channels, 32) 10 | 11 | inputs = [torch.randn(1, c, 12, 12) for i, c in enumerate(in_channels)] 12 | outputs = mla(inputs) 13 | assert outputs[0].shape == torch.Size([1, 32, 12, 12]) 14 | assert outputs[1].shape == torch.Size([1, 32, 12, 12]) 15 | assert outputs[2].shape == torch.Size([1, 32, 12, 12]) 16 | assert outputs[3].shape == torch.Size([1, 32, 12, 12]) 17 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_necks/test_multilevel_neck.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import torch 3 | 4 | from mmseg.models import MultiLevelNeck 5 | 6 | 7 | def test_multilevel_neck(): 8 | 9 | # Test init_weights 10 | MultiLevelNeck([266], 32).init_weights() 11 | 12 | # Test multi feature maps 13 | in_channels = [32, 64, 128, 256] 14 | inputs = [torch.randn(1, c, 14, 14) for i, c in enumerate(in_channels)] 15 | 16 | neck = MultiLevelNeck(in_channels, 32) 17 | outputs = neck(inputs) 18 | assert outputs[0].shape == torch.Size([1, 32, 7, 7]) 19 | assert outputs[1].shape == torch.Size([1, 32, 14, 14]) 20 | assert outputs[2].shape == torch.Size([1, 32, 28, 28]) 21 | assert outputs[3].shape == torch.Size([1, 32, 56, 56]) 22 | 23 | # Test one feature map 24 | in_channels = [768] 25 | inputs = [torch.randn(1, 768, 14, 14)] 26 | 27 | neck = MultiLevelNeck(in_channels, 32) 28 | outputs = neck(inputs) 29 | assert outputs[0].shape == torch.Size([1, 32, 7, 7]) 30 | assert outputs[1].shape == torch.Size([1, 32, 14, 14]) 31 | assert outputs[2].shape == torch.Size([1, 32, 28, 28]) 32 | assert outputs[3].shape == torch.Size([1, 32, 56, 56]) 33 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_segmentors/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | -------------------------------------------------------------------------------- /mmseg-v0.28/tests/test_models/test_utils/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | -------------------------------------------------------------------------------- /mmseg-v0.28/tools/dist_test.sh: -------------------------------------------------------------------------------- 1 | CONFIG=$1 2 | CHECKPOINT=$2 3 | GPUS=$3 4 | NNODES=${NNODES:-1} 5 | NODE_RANK=${NODE_RANK:-0} 6 | PORT=${PORT:-29500} 7 | MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"} 8 | 9 | PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ 10 | python -m torch.distributed.launch \ 11 | --nnodes=$NNODES \ 12 | --node_rank=$NODE_RANK \ 13 | --master_addr=$MASTER_ADDR \ 14 | --nproc_per_node=$GPUS \ 15 | --master_port=$PORT \ 16 | $(dirname "$0")/test.py \ 17 | $CONFIG \ 18 | $CHECKPOINT \ 19 | --launcher pytorch \ 20 | ${@:4} 21 | -------------------------------------------------------------------------------- /mmseg-v0.28/tools/dist_train.sh: -------------------------------------------------------------------------------- 1 | CONFIG=$1 2 | GPUS=$2 3 | NNODES=${NNODES:-1} 4 | NODE_RANK=${NODE_RANK:-0} 5 | PORT=${PORT:-29500} 6 | MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"} 7 | 8 | PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ 9 | python -m torch.distributed.launch \ 10 | --nnodes=$NNODES \ 11 | --node_rank=$NODE_RANK \ 12 | --master_addr=$MASTER_ADDR \ 13 | --nproc_per_node=$GPUS \ 14 | --master_port=$PORT \ 15 | $(dirname "$0")/train.py \ 16 | $CONFIG \ 17 | --launcher pytorch ${@:3} 18 | -------------------------------------------------------------------------------- /mmseg-v0.28/tools/publish_model.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import argparse 3 | import subprocess 4 | 5 | import torch 6 | 7 | 8 | def parse_args(): 9 | parser = argparse.ArgumentParser( 10 | description='Process a checkpoint to be published') 11 | parser.add_argument('in_file', help='input checkpoint filename') 12 | parser.add_argument('out_file', help='output checkpoint filename') 13 | args = parser.parse_args() 14 | return args 15 | 16 | 17 | def process_checkpoint(in_file, out_file): 18 | checkpoint = torch.load(in_file, map_location='cpu') 19 | # remove optimizer for smaller file size 20 | if 'optimizer' in checkpoint: 21 | del checkpoint['optimizer'] 22 | # if it is necessary to remove some sensitive data in checkpoint['meta'], 23 | # add the code here. 24 | torch.save(checkpoint, out_file) 25 | sha = subprocess.check_output(['sha256sum', out_file]).decode() 26 | final_file = out_file.rstrip('.pth') + '-{}.pth'.format(sha[:8]) 27 | subprocess.Popen(['mv', out_file, final_file]) 28 | 29 | 30 | def main(): 31 | args = parse_args() 32 | process_checkpoint(args.in_file, args.out_file) 33 | 34 | 35 | if __name__ == '__main__': 36 | main() 37 | -------------------------------------------------------------------------------- /mmseg-v0.28/tools/slurm_test.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -x 4 | 5 | PARTITION=$1 6 | JOB_NAME=$2 7 | CONFIG=$3 8 | CHECKPOINT=$4 9 | GPUS=${GPUS:-4} 10 | GPUS_PER_NODE=${GPUS_PER_NODE:-4} 11 | CPUS_PER_TASK=${CPUS_PER_TASK:-5} 12 | PY_ARGS=${@:5} 13 | SRUN_ARGS=${SRUN_ARGS:-""} 14 | 15 | PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ 16 | srun -p ${PARTITION} \ 17 | --job-name=${JOB_NAME} \ 18 | --gres=gpu:${GPUS_PER_NODE} \ 19 | --ntasks=${GPUS} \ 20 | --ntasks-per-node=${GPUS_PER_NODE} \ 21 | --cpus-per-task=${CPUS_PER_TASK} \ 22 | --kill-on-bad-exit=1 \ 23 | ${SRUN_ARGS} \ 24 | python -u tools/test.py ${CONFIG} ${CHECKPOINT} --launcher="slurm" ${PY_ARGS} 25 | -------------------------------------------------------------------------------- /mmseg-v0.28/tools/slurm_train.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -x 4 | 5 | PARTITION=$1 6 | JOB_NAME=$2 7 | CONFIG=$3 8 | GPUS=${GPUS:-4} 9 | GPUS_PER_NODE=${GPUS_PER_NODE:-4} 10 | CPUS_PER_TASK=${CPUS_PER_TASK:-5} 11 | SRUN_ARGS=${SRUN_ARGS:-""} 12 | PY_ARGS=${@:4} 13 | 14 | PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ 15 | srun -p ${PARTITION} \ 16 | --job-name=${JOB_NAME} \ 17 | --gres=gpu:${GPUS_PER_NODE} \ 18 | --ntasks=${GPUS} \ 19 | --ntasks-per-node=${GPUS_PER_NODE} \ 20 | --cpus-per-task=${CPUS_PER_TASK} \ 21 | --kill-on-bad-exit=1 \ 22 | ${SRUN_ARGS} \ 23 | python -u tools/train.py ${CONFIG} --launcher="slurm" ${PY_ARGS} 24 | -------------------------------------------------------------------------------- /requirement_pip_install.sh: -------------------------------------------------------------------------------- 1 | pip install --upgrade pip; 2 | pip install torch==1.7.0 torchvision==0.8.1; 3 | pip install timm==0.3.2; 4 | pip install cupy-cuda101; 5 | pip install torchprofile; 6 | pip install einops; 7 | pip install tensorboardX; 8 | pip install yapf; 9 | pip install cython; 10 | pip install opencv-python; 11 | pip install pytest-runner; 12 | pip install terminaltables; 13 | pip install mmpycocotools; 14 | pip install packaging==21.0; 15 | pip install mmcls==0.21.0; 16 | pip install mmcv-full==1.4.4; -------------------------------------------------------------------------------- /util/lr_sched.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # All rights reserved. 3 | 4 | # This source code is licensed under the license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | # Modified by Jianyuan Guo (jyguo@pku.edu.cn) 7 | 8 | import math 9 | 10 | def adjust_learning_rate(optimizer, epoch, args): 11 | """Decay the learning rate with half-cycle cosine after warmup""" 12 | if epoch < args.warmup_epochs: 13 | lr = args.lr * epoch / args.warmup_epochs 14 | else: 15 | lr = args.min_lr + (args.lr - args.min_lr) * 0.5 * \ 16 | (1. + math.cos(math.pi * (epoch - args.warmup_epochs) / (args.epochs - args.warmup_epochs))) 17 | for param_group in optimizer.param_groups: 18 | if "lr_scale" in param_group: 19 | param_group["lr"] = lr * param_group["lr_scale"] 20 | else: 21 | param_group["lr"] = lr 22 | return lr 23 | 24 | def adjust_learning_rate_step(optimizer, epoch, args): 25 | if epoch < args.warmup_epochs: 26 | lr = args.lr * epoch / args.warmup_epochs 27 | else: 28 | lr = args.lr * (0.1 ** (epoch // args.decay_epoch)) 29 | for param_group in optimizer.param_groups: 30 | if "lr_scale" in param_group: 31 | param_group["lr"] = lr * param_group["lr_scale"] 32 | else: 33 | param_group["lr"] = lr 34 | return lr 35 | --------------------------------------------------------------------------------