Full Code of waterdisappear/SARATR-X for AI

gitextract_xcoybjj8/

├── LICENSE
├── README.md
├── classification/
│   ├── .gitignore
│   ├── .idea/
│   │   ├── .gitignore
│   │   ├── CoOp.iml
│   │   ├── deployment.xml
│   │   ├── inspectionProfiles/
│   │   │   ├── Project_Default.xml
│   │   │   └── profiles_settings.xml
│   │   ├── misc.xml
│   │   ├── modules.xml
│   │   └── vcs.xml
│   ├── COCOOP.md
│   ├── COOP.md
│   ├── DATASETS.md
│   ├── MIM_linear.sh
│   ├── clip/
│   │   ├── __init__.py
│   │   ├── clip.py
│   │   ├── model.py
│   │   └── simple_tokenizer.py
│   ├── configs/
│   │   ├── datasets/
│   │   │   ├── MSTAR_EOC1.yaml
│   │   │   ├── MSTAR_EOC2C.yaml
│   │   │   ├── MSTAR_EOC2V.yaml
│   │   │   ├── MSTAR_SOC.yaml
│   │   │   ├── MyDataset.yaml
│   │   │   ├── New_FUSAR.yaml
│   │   │   ├── SAR_ACD.yaml
│   │   │   ├── caltech101.yaml
│   │   │   ├── dtd.yaml
│   │   │   ├── eurosat.yaml
│   │   │   ├── fgvc_aircraft.yaml
│   │   │   ├── food101.yaml
│   │   │   ├── imagenet.yaml
│   │   │   ├── imagenet_a.yaml
│   │   │   ├── imagenet_r.yaml
│   │   │   ├── imagenet_sketch.yaml
│   │   │   ├── imagenetv2.yaml
│   │   │   ├── oxford_flowers.yaml
│   │   │   ├── oxford_pets.yaml
│   │   │   ├── stanford_cars.yaml
│   │   │   ├── sun397.yaml
│   │   │   └── ucf101.yaml
│   │   └── trainers/
│   │       ├── CNN/
│   │       │   └── vit_b16.yaml
│   │       ├── CoCoOp/
│   │       │   ├── vit_b16_c16_ep10_batch1.yaml
│   │       │   ├── vit_b16_c4_ep10_batch1.yaml
│   │       │   ├── vit_b16_c4_ep10_batch1_ctxv1.yaml
│   │       │   └── vit_b16_c8_ep10_batch1.yaml
│   │       ├── CoOp/
│   │       │   ├── rn101.yaml
│   │       │   ├── rn101_ep50.yaml
│   │       │   ├── rn50.yaml
│   │       │   ├── rn50_ctxv1.yaml
│   │       │   ├── rn50_ep100.yaml
│   │       │   ├── rn50_ep50.yaml
│   │       │   ├── rn50_ep50_ctxv1.yaml
│   │       │   ├── rn50_val.yaml
│   │       │   ├── vit_b16.yaml
│   │       │   ├── vit_b16_ctxv1.yaml
│   │       │   ├── vit_b16_ep100.yaml
│   │       │   ├── vit_b16_ep100_ctxv1.yaml
│   │       │   ├── vit_b16_ep50.yaml
│   │       │   ├── vit_b16_ep50_ctxv1.yaml
│   │       │   ├── vit_b32.yaml
│   │       │   └── vit_b32_ep50.yaml
│   │       ├── MIM_linear/
│   │       │   └── vit_b16.yaml
│   │       ├── OneHot/
│   │       │   └── vit_b16.yaml
│   │       ├── RingMo/
│   │       │   └── vit_b16.yaml
│   │       ├── clip/
│   │       │   └── vit_b16.yaml
│   │       └── clip_adapter/
│   │           ├── rn101.yaml
│   │           ├── rn101_ep50.yaml
│   │           ├── rn50.yaml
│   │           ├── rn50_ctxv1.yaml
│   │           ├── rn50_ep100.yaml
│   │           ├── rn50_ep50.yaml
│   │           ├── rn50_ep50_ctxv1.yaml
│   │           ├── rn50_val.yaml
│   │           ├── vit_b16.yaml
│   │           ├── vit_b16_ctxv1.yaml
│   │           ├── vit_b16_ep100.yaml
│   │           ├── vit_b16_ep100_ctxv1.yaml
│   │           ├── vit_b16_ep50.yaml
│   │           ├── vit_b16_ep50_ctxv1.yaml
│   │           ├── vit_b32.yaml
│   │           └── vit_b32_ep50.yaml
│   ├── datasets/
│   │   ├── MSTAR_EOC1.py
│   │   ├── MSTAR_EOC2C.py
│   │   ├── MSTAR_EOC2V.py
│   │   ├── MSTAR_SOC.py
│   │   ├── MyDataset.py
│   │   ├── New_FUSAR.py
│   │   ├── SAR_ACD.py
│   │   ├── __init__.py
│   │   ├── caltech101.py
│   │   ├── dtd.py
│   │   ├── eurosat.py
│   │   ├── fgvc_aircraft.py
│   │   ├── food101.py
│   │   ├── imagenet.py
│   │   ├── imagenet_a.py
│   │   ├── imagenet_r.py
│   │   ├── imagenet_sketch.py
│   │   ├── imagenetv2.py
│   │   ├── oxford_flowers.py
│   │   ├── oxford_pets.py
│   │   ├── stanford_cars.py
│   │   ├── sun397.py
│   │   └── ucf101.py
│   ├── demo.py
│   ├── draw_curves.py
│   ├── interpret_prompt.py
│   ├── lpclip/
│   │   ├── README.md
│   │   ├── feat_extractor.py
│   │   ├── feat_extractor.sh
│   │   ├── linear_probe.py
│   │   └── linear_probe.sh
│   ├── model/
│   │   ├── Model.py
│   │   ├── cmt.py
│   │   ├── conformer.py
│   │   ├── cotnet.py
│   │   ├── cotnet_hybrid.py
│   │   ├── features.py
│   │   ├── helpers.py
│   │   ├── hivit.py
│   │   ├── inception_transformer.py
│   │   ├── layers/
│   │   │   ├── __init__.py
│   │   │   ├── activations.py
│   │   │   ├── activations_jit.py
│   │   │   ├── activations_me.py
│   │   │   ├── adaptive_avgmax_pool.py
│   │   │   ├── aggregation_zeropad.py
│   │   │   ├── anti_aliasing.py
│   │   │   ├── blur_pool.py
│   │   │   ├── cbam.py
│   │   │   ├── classifier.py
│   │   │   ├── cond_conv2d.py
│   │   │   ├── config.py
│   │   │   ├── conv2d_same.py
│   │   │   ├── conv_bn_act.py
│   │   │   ├── create_act.py
│   │   │   ├── create_attn.py
│   │   │   ├── create_conv2d.py
│   │   │   ├── create_norm_act.py
│   │   │   ├── drop.py
│   │   │   ├── eca.py
│   │   │   ├── evo_norm.py
│   │   │   ├── helpers.py
│   │   │   ├── inplace_abn.py
│   │   │   ├── kerv2d.py
│   │   │   ├── median_pool.py
│   │   │   ├── mixed_conv2d.py
│   │   │   ├── mlp.py
│   │   │   ├── norm_act.py
│   │   │   ├── padding.py
│   │   │   ├── patch_embed.py
│   │   │   ├── pool2d_same.py
│   │   │   ├── se.py
│   │   │   ├── selective_kernel.py
│   │   │   ├── separable_conv.py
│   │   │   ├── shiftlution.py
│   │   │   ├── space_to_depth.py
│   │   │   ├── split_attn.py
│   │   │   ├── split_batchnorm.py
│   │   │   ├── std_conv.py
│   │   │   ├── tbconv.py
│   │   │   ├── test_time_pool.py
│   │   │   ├── utils.py
│   │   │   └── weight_init.py
│   │   ├── nextvit.py
│   │   ├── registry.py
│   │   ├── res2net.py
│   │   ├── resnet.py
│   │   ├── utils.py
│   │   └── vgg.py
│   ├── parse_test_res.py
│   ├── scripts/
│   │   ├── CNN/
│   │   │   ├── README.md
│   │   │   ├── eval.sh
│   │   │   ├── main.sh
│   │   │   └── zeroshot.sh
│   │   ├── MIM_finetune/
│   │   │   ├── README.md
│   │   │   ├── eval.sh
│   │   │   ├── main.sh
│   │   │   └── zeroshot.sh
│   │   └── MIM_linear/
│   │       ├── README.md
│   │       ├── eval.sh
│   │       ├── main.sh
│   │       └── zeroshot.sh
│   ├── train.py
│   └── trainers/
│       ├── MIM_linear.py
│       ├── __init__.py
│       └── imagenet_templates.py
├── detection/
│   ├── CITATION.cff
│   ├── LICENSE
│   ├── MANIFEST.in
│   ├── MMDET_README.md
│   ├── README_zh-CN.md
│   ├── 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
│   │   ├── _hivit_/
│   │   │   ├── hivit_base_OGSOD.py
│   │   │   ├── hivit_base_SARAircraft.py
│   │   │   ├── hivit_base_SARDet.py
│   │   │   └── hivit_base_SSDD.py
│   │   ├── albu_example/
│   │   │   ├── README.md
│   │   │   └── mask_rcnn_r50_fpn_albu_1x_coco.py
│   │   ├── atss/
│   │   │   ├── README.md
│   │   │   ├── atss_r101_fpn_1x_coco.py
│   │   │   ├── atss_r50_fpn_1x_coco.py
│   │   │   └── metafile.yml
│   │   ├── autoassign/
│   │   │   ├── README.md
│   │   │   ├── autoassign_r50_fpn_8x2_1x_coco.py
│   │   │   └── metafile.yml
│   │   ├── carafe/
│   │   │   ├── README.md
│   │   │   ├── faster_rcnn_r50_fpn_carafe_1x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_carafe_1x_coco.py
│   │   │   └── metafile.yml
│   │   ├── cascade_rcnn/
│   │   │   ├── README.md
│   │   │   ├── cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py
│   │   │   ├── cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco.py
│   │   │   ├── cascade_mask_rcnn_r101_fpn_1x_coco.py
│   │   │   ├── cascade_mask_rcnn_r101_fpn_20e_coco.py
│   │   │   ├── cascade_mask_rcnn_r101_fpn_mstrain_3x_coco.py
│   │   │   ├── cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py
│   │   │   ├── cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py
│   │   │   ├── cascade_mask_rcnn_r50_fpn_1x_coco.py
│   │   │   ├── cascade_mask_rcnn_r50_fpn_20e_coco.py
│   │   │   ├── cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py
│   │   │   ├── cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py
│   │   │   ├── cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py
│   │   │   ├── cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py
│   │   │   ├── cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py
│   │   │   ├── cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py
│   │   │   ├── cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py
│   │   │   ├── cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py
│   │   │   ├── cascade_rcnn_r101_caffe_fpn_1x_coco.py
│   │   │   ├── cascade_rcnn_r101_fpn_1x_coco.py
│   │   │   ├── cascade_rcnn_r101_fpn_20e_coco.py
│   │   │   ├── cascade_rcnn_r50_caffe_fpn_1x_coco.py
│   │   │   ├── cascade_rcnn_r50_fpn_1x_coco.py
│   │   │   ├── cascade_rcnn_r50_fpn_20e_coco.py
│   │   │   ├── cascade_rcnn_x101_32x4d_fpn_1x_coco.py
│   │   │   ├── cascade_rcnn_x101_32x4d_fpn_20e_coco.py
│   │   │   ├── cascade_rcnn_x101_64x4d_fpn_1x_coco.py
│   │   │   ├── cascade_rcnn_x101_64x4d_fpn_20e_coco.py
│   │   │   └── metafile.yml
│   │   ├── cascade_rpn/
│   │   │   ├── README.md
│   │   │   ├── crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py
│   │   │   ├── crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py
│   │   │   ├── crpn_r50_caffe_fpn_1x_coco.py
│   │   │   └── metafile.yml
│   │   ├── centernet/
│   │   │   ├── README.md
│   │   │   ├── centernet_resnet18_140e_coco.py
│   │   │   ├── centernet_resnet18_dcnv2_140e_coco.py
│   │   │   └── metafile.yml
│   │   ├── centripetalnet/
│   │   │   ├── README.md
│   │   │   ├── centripetalnet_hourglass104_mstest_16x6_210e_coco.py
│   │   │   └── metafile.yml
│   │   ├── cityscapes/
│   │   │   ├── README.md
│   │   │   ├── faster_rcnn_r50_fpn_1x_cityscapes.py
│   │   │   └── mask_rcnn_r50_fpn_1x_cityscapes.py
│   │   ├── common/
│   │   │   ├── lsj_100e_coco_instance.py
│   │   │   ├── mstrain-poly_3x_coco_instance.py
│   │   │   ├── mstrain_3x_coco.py
│   │   │   ├── mstrain_3x_coco_instance.py
│   │   │   ├── ssj_270k_coco_instance.py
│   │   │   └── ssj_scp_270k_coco_instance.py
│   │   ├── cornernet/
│   │   │   ├── README.md
│   │   │   ├── cornernet_hourglass104_mstest_10x5_210e_coco.py
│   │   │   ├── cornernet_hourglass104_mstest_32x3_210e_coco.py
│   │   │   ├── cornernet_hourglass104_mstest_8x6_210e_coco.py
│   │   │   └── metafile.yml
│   │   ├── dcn/
│   │   │   ├── README.md
│   │   │   ├── cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
│   │   │   ├── cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
│   │   │   ├── cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py
│   │   │   ├── cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
│   │   │   ├── cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
│   │   │   ├── faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
│   │   │   ├── faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
│   │   │   ├── faster_rcnn_r50_fpn_dpool_1x_coco.py
│   │   │   ├── faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py
│   │   │   ├── mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py
│   │   │   └── metafile.yml
│   │   ├── dcnv2/
│   │   │   ├── README.md
│   │   │   ├── faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
│   │   │   ├── faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py
│   │   │   ├── faster_rcnn_r50_fpn_mdpool_1x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
│   │   │   └── metafile.yml
│   │   ├── deepfashion/
│   │   │   ├── README.md
│   │   │   └── mask_rcnn_r50_fpn_15e_deepfashion.py
│   │   ├── deformable_detr/
│   │   │   ├── README.md
│   │   │   ├── deformable_detr_r50_16x2_50e_coco.py
│   │   │   ├── deformable_detr_refine_r50_16x2_50e_coco.py
│   │   │   ├── deformable_detr_twostage_refine_r50_16x2_50e_coco.py
│   │   │   └── metafile.yml
│   │   ├── detectors/
│   │   │   ├── README.md
│   │   │   ├── cascade_rcnn_r50_rfp_1x_coco.py
│   │   │   ├── cascade_rcnn_r50_sac_1x_coco.py
│   │   │   ├── detectors_cascade_rcnn_r50_1x_coco.py
│   │   │   ├── detectors_htc_r101_20e_coco.py
│   │   │   ├── detectors_htc_r50_1x_coco.py
│   │   │   ├── htc_r50_rfp_1x_coco.py
│   │   │   ├── htc_r50_sac_1x_coco.py
│   │   │   └── metafile.yml
│   │   ├── detr/
│   │   │   ├── README.md
│   │   │   ├── detr_r50_8x2_150e_coco.py
│   │   │   └── metafile.yml
│   │   ├── double_heads/
│   │   │   ├── README.md
│   │   │   ├── dh_faster_rcnn_r50_fpn_1x_coco.py
│   │   │   └── metafile.yml
│   │   ├── dyhead/
│   │   │   ├── README.md
│   │   │   ├── atss_r50_caffe_fpn_dyhead_1x_coco.py
│   │   │   ├── atss_r50_fpn_dyhead_1x_coco.py
│   │   │   └── metafile.yml
│   │   ├── dynamic_rcnn/
│   │   │   ├── README.md
│   │   │   ├── dynamic_rcnn_r50_fpn_1x_coco.py
│   │   │   └── metafile.yml
│   │   ├── efficientnet/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   └── retinanet_effb3_fpn_crop896_8x4_1x_coco.py
│   │   ├── empirical_attention/
│   │   │   ├── README.md
│   │   │   ├── faster_rcnn_r50_fpn_attention_0010_1x_coco.py
│   │   │   ├── faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py
│   │   │   ├── faster_rcnn_r50_fpn_attention_1111_1x_coco.py
│   │   │   ├── faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py
│   │   │   └── metafile.yml
│   │   ├── fast_rcnn/
│   │   │   ├── README.md
│   │   │   ├── fast_rcnn_r101_caffe_fpn_1x_coco.py
│   │   │   ├── fast_rcnn_r101_fpn_1x_coco.py
│   │   │   ├── fast_rcnn_r101_fpn_2x_coco.py
│   │   │   ├── fast_rcnn_r50_caffe_fpn_1x_coco.py
│   │   │   ├── fast_rcnn_r50_fpn_1x_coco.py
│   │   │   └── fast_rcnn_r50_fpn_2x_coco.py
│   │   ├── faster_rcnn/
│   │   │   ├── README.md
│   │   │   ├── faster_rcnn_r101_caffe_fpn_1x_coco.py
│   │   │   ├── faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py
│   │   │   ├── faster_rcnn_r101_fpn_1x_coco.py
│   │   │   ├── faster_rcnn_r101_fpn_2x_coco.py
│   │   │   ├── faster_rcnn_r101_fpn_mstrain_3x_coco.py
│   │   │   ├── faster_rcnn_r50_caffe_c4_1x_coco.py
│   │   │   ├── faster_rcnn_r50_caffe_c4_mstrain_1x_coco.py
│   │   │   ├── faster_rcnn_r50_caffe_dc5_1x_coco.py
│   │   │   ├── faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py
│   │   │   ├── faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py
│   │   │   ├── faster_rcnn_r50_caffe_fpn_1x_coco.py
│   │   │   ├── faster_rcnn_r50_caffe_fpn_90k_coco.py
│   │   │   ├── faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py
│   │   │   ├── faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py
│   │   │   ├── faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py
│   │   │   ├── faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py
│   │   │   ├── faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py
│   │   │   ├── faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py
│   │   │   ├── faster_rcnn_r50_fpn_1x_coco.py
│   │   │   ├── faster_rcnn_r50_fpn_2x_coco.py
│   │   │   ├── faster_rcnn_r50_fpn_bounded_iou_1x_coco.py
│   │   │   ├── faster_rcnn_r50_fpn_ciou_1x_coco.py
│   │   │   ├── faster_rcnn_r50_fpn_fp16_1x_coco.py
│   │   │   ├── faster_rcnn_r50_fpn_giou_1x_coco.py
│   │   │   ├── faster_rcnn_r50_fpn_iou_1x_coco.py
│   │   │   ├── faster_rcnn_r50_fpn_mstrain_3x_coco.py
│   │   │   ├── faster_rcnn_r50_fpn_ohem_1x_coco.py
│   │   │   ├── faster_rcnn_r50_fpn_soft_nms_1x_coco.py
│   │   │   ├── faster_rcnn_r50_fpn_tnr-pretrain_1x_coco.py
│   │   │   ├── faster_rcnn_x101_32x4d_fpn_1x_coco.py
│   │   │   ├── faster_rcnn_x101_32x4d_fpn_2x_coco.py
│   │   │   ├── faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py
│   │   │   ├── faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py
│   │   │   ├── faster_rcnn_x101_64x4d_fpn_1x_coco.py
│   │   │   ├── faster_rcnn_x101_64x4d_fpn_2x_coco.py
│   │   │   ├── faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py
│   │   │   └── metafile.yml
│   │   ├── fcos/
│   │   │   ├── README.md
│   │   │   ├── fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py
│   │   │   ├── fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py
│   │   │   ├── fcos_center_r50_caffe_fpn_gn-head_1x_coco.py
│   │   │   ├── fcos_r101_caffe_fpn_gn-head_1x_coco.py
│   │   │   ├── fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py
│   │   │   ├── fcos_r50_caffe_fpn_gn-head_1x_coco.py
│   │   │   ├── fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py
│   │   │   ├── fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py
│   │   │   ├── fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py
│   │   │   └── metafile.yml
│   │   ├── foveabox/
│   │   │   ├── README.md
│   │   │   ├── fovea_align_r101_fpn_gn-head_4x4_2x_coco.py
│   │   │   ├── fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
│   │   │   ├── fovea_align_r50_fpn_gn-head_4x4_2x_coco.py
│   │   │   ├── fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
│   │   │   ├── fovea_r101_fpn_4x4_1x_coco.py
│   │   │   ├── fovea_r101_fpn_4x4_2x_coco.py
│   │   │   ├── fovea_r50_fpn_4x4_1x_coco.py
│   │   │   ├── fovea_r50_fpn_4x4_2x_coco.py
│   │   │   └── metafile.yml
│   │   ├── fpg/
│   │   │   ├── README.md
│   │   │   ├── faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py
│   │   │   ├── faster_rcnn_r50_fpg_crop640_50e_coco.py
│   │   │   ├── faster_rcnn_r50_fpn_crop640_50e_coco.py
│   │   │   ├── mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py
│   │   │   ├── mask_rcnn_r50_fpg_crop640_50e_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_crop640_50e_coco.py
│   │   │   ├── metafile.yml
│   │   │   ├── retinanet_r50_fpg-chn128_crop640_50e_coco.py
│   │   │   └── retinanet_r50_fpg_crop640_50e_coco.py
│   │   ├── free_anchor/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   ├── retinanet_free_anchor_r101_fpn_1x_coco.py
│   │   │   ├── retinanet_free_anchor_r50_fpn_1x_coco.py
│   │   │   └── retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py
│   │   ├── fsaf/
│   │   │   ├── README.md
│   │   │   ├── fsaf_r101_fpn_1x_coco.py
│   │   │   ├── fsaf_r50_fpn_1x_coco.py
│   │   │   ├── fsaf_x101_64x4d_fpn_1x_coco.py
│   │   │   └── metafile.yml
│   │   ├── gcnet/
│   │   │   ├── README.md
│   │   │   ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py
│   │   │   ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py
│   │   │   ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py
│   │   │   ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py
│   │   │   ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
│   │   │   ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
│   │   │   ├── mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py
│   │   │   ├── mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py
│   │   │   ├── mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py
│   │   │   ├── mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
│   │   │   ├── mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
│   │   │   ├── mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py
│   │   │   ├── mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
│   │   │   ├── mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
│   │   │   └── metafile.yml
│   │   ├── gfl/
│   │   │   ├── README.md
│   │   │   ├── gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py
│   │   │   ├── gfl_r101_fpn_mstrain_2x_coco.py
│   │   │   ├── gfl_r50_fpn_1x_coco.py
│   │   │   ├── gfl_r50_fpn_mstrain_2x_coco.py
│   │   │   ├── gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py
│   │   │   ├── gfl_x101_32x4d_fpn_mstrain_2x_coco.py
│   │   │   └── metafile.yml
│   │   ├── ghm/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   ├── retinanet_ghm_r101_fpn_1x_coco.py
│   │   │   ├── retinanet_ghm_r50_fpn_1x_coco.py
│   │   │   ├── retinanet_ghm_x101_32x4d_fpn_1x_coco.py
│   │   │   └── retinanet_ghm_x101_64x4d_fpn_1x_coco.py
│   │   ├── gn/
│   │   │   ├── README.md
│   │   │   ├── mask_rcnn_r101_fpn_gn-all_2x_coco.py
│   │   │   ├── mask_rcnn_r101_fpn_gn-all_3x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_gn-all_2x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_gn-all_3x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py
│   │   │   └── metafile.yml
│   │   ├── gn+ws/
│   │   │   ├── README.md
│   │   │   ├── faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py
│   │   │   ├── faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py
│   │   │   ├── faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py
│   │   │   ├── faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py
│   │   │   ├── mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py
│   │   │   ├── mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py
│   │   │   ├── mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py
│   │   │   ├── mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py
│   │   │   ├── mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py
│   │   │   ├── mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py
│   │   │   └── metafile.yml
│   │   ├── grid_rcnn/
│   │   │   ├── README.md
│   │   │   ├── grid_rcnn_r101_fpn_gn-head_2x_coco.py
│   │   │   ├── grid_rcnn_r50_fpn_gn-head_1x_coco.py
│   │   │   ├── grid_rcnn_r50_fpn_gn-head_2x_coco.py
│   │   │   ├── grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py
│   │   │   ├── grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py
│   │   │   └── metafile.yml
│   │   ├── groie/
│   │   │   ├── README.md
│   │   │   ├── faster_rcnn_r50_fpn_groie_1x_coco.py
│   │   │   ├── grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py
│   │   │   ├── mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_groie_1x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py
│   │   │   └── metafile.yml
│   │   ├── guided_anchoring/
│   │   │   ├── README.md
│   │   │   ├── ga_fast_r50_caffe_fpn_1x_coco.py
│   │   │   ├── ga_faster_r101_caffe_fpn_1x_coco.py
│   │   │   ├── ga_faster_r50_caffe_fpn_1x_coco.py
│   │   │   ├── ga_faster_r50_fpn_1x_coco.py
│   │   │   ├── ga_faster_x101_32x4d_fpn_1x_coco.py
│   │   │   ├── ga_faster_x101_64x4d_fpn_1x_coco.py
│   │   │   ├── ga_retinanet_r101_caffe_fpn_1x_coco.py
│   │   │   ├── ga_retinanet_r101_caffe_fpn_mstrain_2x.py
│   │   │   ├── ga_retinanet_r50_caffe_fpn_1x_coco.py
│   │   │   ├── ga_retinanet_r50_fpn_1x_coco.py
│   │   │   ├── ga_retinanet_x101_32x4d_fpn_1x_coco.py
│   │   │   ├── ga_retinanet_x101_64x4d_fpn_1x_coco.py
│   │   │   ├── ga_rpn_r101_caffe_fpn_1x_coco.py
│   │   │   ├── ga_rpn_r50_caffe_fpn_1x_coco.py
│   │   │   ├── ga_rpn_r50_fpn_1x_coco.py
│   │   │   ├── ga_rpn_x101_32x4d_fpn_1x_coco.py
│   │   │   ├── ga_rpn_x101_64x4d_fpn_1x_coco.py
│   │   │   └── metafile.yml
│   │   ├── hrnet/
│   │   │   ├── README.md
│   │   │   ├── cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py
│   │   │   ├── cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py
│   │   │   ├── cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py
│   │   │   ├── cascade_rcnn_hrnetv2p_w18_20e_coco.py
│   │   │   ├── cascade_rcnn_hrnetv2p_w32_20e_coco.py
│   │   │   ├── cascade_rcnn_hrnetv2p_w40_20e_coco.py
│   │   │   ├── faster_rcnn_hrnetv2p_w18_1x_coco.py
│   │   │   ├── faster_rcnn_hrnetv2p_w18_2x_coco.py
│   │   │   ├── faster_rcnn_hrnetv2p_w32_1x_coco.py
│   │   │   ├── faster_rcnn_hrnetv2p_w32_2x_coco.py
│   │   │   ├── faster_rcnn_hrnetv2p_w40_1x_coco.py
│   │   │   ├── faster_rcnn_hrnetv2p_w40_2x_coco.py
│   │   │   ├── fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py
│   │   │   ├── fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py
│   │   │   ├── fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py
│   │   │   ├── fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py
│   │   │   ├── fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py
│   │   │   ├── fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py
│   │   │   ├── fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py
│   │   │   ├── htc_hrnetv2p_w18_20e_coco.py
│   │   │   ├── htc_hrnetv2p_w32_20e_coco.py
│   │   │   ├── htc_hrnetv2p_w40_20e_coco.py
│   │   │   ├── htc_hrnetv2p_w40_28e_coco.py
│   │   │   ├── htc_x101_64x4d_fpn_16x1_28e_coco.py
│   │   │   ├── mask_rcnn_hrnetv2p_w18_1x_coco.py
│   │   │   ├── mask_rcnn_hrnetv2p_w18_2x_coco.py
│   │   │   ├── mask_rcnn_hrnetv2p_w32_1x_coco.py
│   │   │   ├── mask_rcnn_hrnetv2p_w32_2x_coco.py
│   │   │   ├── mask_rcnn_hrnetv2p_w40_1x_coco.py
│   │   │   ├── mask_rcnn_hrnetv2p_w40_2x_coco.py
│   │   │   └── metafile.yml
│   │   ├── htc/
│   │   │   ├── README.md
│   │   │   ├── htc_r101_fpn_20e_coco.py
│   │   │   ├── htc_r50_fpn_1x_coco.py
│   │   │   ├── htc_r50_fpn_20e_coco.py
│   │   │   ├── htc_without_semantic_r50_fpn_1x_coco.py
│   │   │   ├── htc_x101_32x4d_fpn_16x1_20e_coco.py
│   │   │   ├── htc_x101_64x4d_fpn_16x1_20e_coco.py
│   │   │   ├── htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py
│   │   │   └── metafile.yml
│   │   ├── instaboost/
│   │   │   ├── README.md
│   │   │   ├── cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py
│   │   │   ├── cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py
│   │   │   ├── cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py
│   │   │   ├── mask_rcnn_r101_fpn_instaboost_4x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_instaboost_4x_coco.py
│   │   │   ├── mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py
│   │   │   └── metafile.yml
│   │   ├── lad/
│   │   │   ├── README.md
│   │   │   ├── lad_r101_paa_r50_fpn_coco_1x.py
│   │   │   ├── lad_r50_paa_r101_fpn_coco_1x.py
│   │   │   └── metafile.yml
│   │   ├── ld/
│   │   │   ├── README.md
│   │   │   ├── ld_r101_gflv1_r101dcn_fpn_coco_2x.py
│   │   │   ├── ld_r18_gflv1_r101_fpn_coco_1x.py
│   │   │   ├── ld_r34_gflv1_r101_fpn_coco_1x.py
│   │   │   ├── ld_r50_gflv1_r101_fpn_coco_1x.py
│   │   │   └── metafile.yml
│   │   ├── legacy_1.x/
│   │   │   ├── README.md
│   │   │   ├── cascade_mask_rcnn_r50_fpn_1x_coco_v1.py
│   │   │   ├── faster_rcnn_r50_fpn_1x_coco_v1.py
│   │   │   ├── mask_rcnn_r50_fpn_1x_coco_v1.py
│   │   │   ├── retinanet_r50_caffe_fpn_1x_coco_v1.py
│   │   │   ├── retinanet_r50_fpn_1x_coco_v1.py
│   │   │   └── ssd300_coco_v1.py
│   │   ├── libra_rcnn/
│   │   │   ├── README.md
│   │   │   ├── libra_fast_rcnn_r50_fpn_1x_coco.py
│   │   │   ├── libra_faster_rcnn_r101_fpn_1x_coco.py
│   │   │   ├── libra_faster_rcnn_r50_fpn_1x_coco.py
│   │   │   ├── libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py
│   │   │   ├── libra_retinanet_r50_fpn_1x_coco.py
│   │   │   └── metafile.yml
│   │   ├── lvis/
│   │   │   ├── README.md
│   │   │   ├── mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py
│   │   │   ├── mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py
│   │   │   ├── mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py
│   │   │   ├── mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py
│   │   │   ├── mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py
│   │   │   ├── mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py
│   │   │   ├── mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py
│   │   │   └── mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py
│   │   ├── mask2former/
│   │   │   ├── README.md
│   │   │   ├── mask2former_r101_lsj_8x2_50e_coco.py
│   │   │   ├── mask2former_r50_lsj_8x2_50e_coco.py
│   │   │   ├── mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco.py
│   │   │   ├── mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco.py
│   │   │   ├── mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco.py
│   │   │   ├── mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco.py
│   │   │   ├── mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco.py
│   │   │   └── metafile.yml
│   │   ├── mask_rcnn/
│   │   │   ├── README.md
│   │   │   ├── mask_rcnn_r101_caffe_fpn_1x_coco.py
│   │   │   ├── mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py
│   │   │   ├── mask_rcnn_r101_fpn_1x_coco.py
│   │   │   ├── mask_rcnn_r101_fpn_2x_coco.py
│   │   │   ├── mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py
│   │   │   ├── mask_rcnn_r50_caffe_c4_1x_coco.py
│   │   │   ├── mask_rcnn_r50_caffe_fpn_1x_coco.py
│   │   │   ├── mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py
│   │   │   ├── mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py
│   │   │   ├── mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py
│   │   │   ├── mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py
│   │   │   ├── mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py
│   │   │   ├── mask_rcnn_r50_fpn_1x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_2x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_fp16_1x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_poly_1x_coco.py
│   │   │   ├── mask_rcnn_x101_32x4d_fpn_1x_coco.py
│   │   │   ├── mask_rcnn_x101_32x4d_fpn_2x_coco.py
│   │   │   ├── mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py
│   │   │   ├── mask_rcnn_x101_32x8d_fpn_1x_coco.py
│   │   │   ├── mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py
│   │   │   ├── mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py
│   │   │   ├── mask_rcnn_x101_64x4d_fpn_1x_coco.py
│   │   │   ├── mask_rcnn_x101_64x4d_fpn_2x_coco.py
│   │   │   ├── mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py
│   │   │   └── metafile.yml
│   │   ├── maskformer/
│   │   │   ├── README.md
│   │   │   ├── maskformer_r50_mstrain_16x1_75e_coco.py
│   │   │   ├── maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco.py
│   │   │   └── metafile.yml
│   │   ├── ms_rcnn/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   ├── ms_rcnn_r101_caffe_fpn_1x_coco.py
│   │   │   ├── ms_rcnn_r101_caffe_fpn_2x_coco.py
│   │   │   ├── ms_rcnn_r50_caffe_fpn_1x_coco.py
│   │   │   ├── ms_rcnn_r50_caffe_fpn_2x_coco.py
│   │   │   ├── ms_rcnn_r50_fpn_1x_coco.py
│   │   │   ├── ms_rcnn_x101_32x4d_fpn_1x_coco.py
│   │   │   ├── ms_rcnn_x101_64x4d_fpn_1x_coco.py
│   │   │   └── ms_rcnn_x101_64x4d_fpn_2x_coco.py
│   │   ├── nas_fcos/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   ├── nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py
│   │   │   └── nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py
│   │   ├── nas_fpn/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   ├── retinanet_r50_fpn_crop640_50e_coco.py
│   │   │   └── retinanet_r50_nasfpn_crop640_50e_coco.py
│   │   ├── openimages/
│   │   │   ├── README.md
│   │   │   ├── faster_rcnn_r50_fpn_32x2_1x_openimages.py
│   │   │   ├── faster_rcnn_r50_fpn_32x2_1x_openimages_challenge.py
│   │   │   ├── faster_rcnn_r50_fpn_32x2_cas_1x_openimages.py
│   │   │   ├── faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge.py
│   │   │   ├── metafile.yml
│   │   │   ├── retinanet_r50_fpn_32x2_1x_openimages.py
│   │   │   └── ssd300_32x8_36e_openimages.py
│   │   ├── paa/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   ├── paa_r101_fpn_1x_coco.py
│   │   │   ├── paa_r101_fpn_2x_coco.py
│   │   │   ├── paa_r101_fpn_mstrain_3x_coco.py
│   │   │   ├── paa_r50_fpn_1.5x_coco.py
│   │   │   ├── paa_r50_fpn_1x_coco.py
│   │   │   ├── paa_r50_fpn_2x_coco.py
│   │   │   └── paa_r50_fpn_mstrain_3x_coco.py
│   │   ├── pafpn/
│   │   │   ├── README.md
│   │   │   ├── faster_rcnn_r50_pafpn_1x_coco.py
│   │   │   └── metafile.yml
│   │   ├── panoptic_fpn/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   ├── panoptic_fpn_r101_fpn_1x_coco.py
│   │   │   ├── panoptic_fpn_r101_fpn_mstrain_3x_coco.py
│   │   │   ├── panoptic_fpn_r50_fpn_1x_coco.py
│   │   │   └── panoptic_fpn_r50_fpn_mstrain_3x_coco.py
│   │   ├── pascal_voc/
│   │   │   ├── README.md
│   │   │   ├── faster_rcnn_r50_caffe_c4_mstrain_18k_voc0712.py
│   │   │   ├── faster_rcnn_r50_fpn_1x_voc0712.py
│   │   │   ├── faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py
│   │   │   ├── retinanet_r50_fpn_1x_voc0712.py
│   │   │   ├── ssd300_voc0712.py
│   │   │   └── ssd512_voc0712.py
│   │   ├── pisa/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   ├── pisa_faster_rcnn_r50_fpn_1x_coco.py
│   │   │   ├── pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py
│   │   │   ├── pisa_mask_rcnn_r50_fpn_1x_coco.py
│   │   │   ├── pisa_mask_rcnn_x101_32x4d_fpn_1x_coco.py
│   │   │   ├── pisa_retinanet_r50_fpn_1x_coco.py
│   │   │   ├── pisa_retinanet_x101_32x4d_fpn_1x_coco.py
│   │   │   ├── pisa_ssd300_coco.py
│   │   │   └── pisa_ssd512_coco.py
│   │   ├── point_rend/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   ├── point_rend_r50_caffe_fpn_mstrain_1x_coco.py
│   │   │   └── point_rend_r50_caffe_fpn_mstrain_3x_coco.py
│   │   ├── pvt/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   ├── retinanet_pvt-l_fpn_1x_coco.py
│   │   │   ├── retinanet_pvt-m_fpn_1x_coco.py
│   │   │   ├── retinanet_pvt-s_fpn_1x_coco.py
│   │   │   ├── retinanet_pvt-t_fpn_1x_coco.py
│   │   │   ├── retinanet_pvtv2-b0_fpn_1x_coco.py
│   │   │   ├── retinanet_pvtv2-b1_fpn_1x_coco.py
│   │   │   ├── retinanet_pvtv2-b2_fpn_1x_coco.py
│   │   │   ├── retinanet_pvtv2-b3_fpn_1x_coco.py
│   │   │   ├── retinanet_pvtv2-b4_fpn_1x_coco.py
│   │   │   └── retinanet_pvtv2-b5_fpn_1x_coco.py
│   │   ├── queryinst/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   ├── queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
│   │   │   ├── queryinst_r101_fpn_mstrain_480-800_3x_coco.py
│   │   │   ├── queryinst_r50_fpn_1x_coco.py
│   │   │   ├── queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
│   │   │   └── queryinst_r50_fpn_mstrain_480-800_3x_coco.py
│   │   ├── regnet/
│   │   │   ├── README.md
│   │   │   ├── cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py
│   │   │   ├── cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py
│   │   │   ├── cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py
│   │   │   ├── cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py
│   │   │   ├── cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py
│   │   │   ├── faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py
│   │   │   ├── faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py
│   │   │   ├── faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py
│   │   │   ├── faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py
│   │   │   ├── faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py
│   │   │   ├── faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py
│   │   │   ├── faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py
│   │   │   ├── mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco.py
│   │   │   ├── mask_rcnn_regnetx-12GF_fpn_1x_coco.py
│   │   │   ├── mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py
│   │   │   ├── mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py
│   │   │   ├── mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py
│   │   │   ├── mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py
│   │   │   ├── mask_rcnn_regnetx-4GF_fpn_1x_coco.py
│   │   │   ├── mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco.py
│   │   │   ├── mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py
│   │   │   ├── mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py
│   │   │   ├── mask_rcnn_regnetx-8GF_fpn_1x_coco.py
│   │   │   ├── metafile.yml
│   │   │   ├── retinanet_regnetx-1.6GF_fpn_1x_coco.py
│   │   │   ├── retinanet_regnetx-3.2GF_fpn_1x_coco.py
│   │   │   └── retinanet_regnetx-800MF_fpn_1x_coco.py
│   │   ├── reppoints/
│   │   │   ├── README.md
│   │   │   ├── bbox_r50_grid_center_fpn_gn-neck+head_1x_coco.py
│   │   │   ├── bbox_r50_grid_fpn_gn-neck+head_1x_coco.py
│   │   │   ├── metafile.yml
│   │   │   ├── reppoints_minmax_r50_fpn_gn-neck+head_1x_coco.py
│   │   │   ├── reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py
│   │   │   ├── reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py
│   │   │   ├── reppoints_moment_r50_fpn_1x_coco.py
│   │   │   ├── reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py
│   │   │   ├── reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py
│   │   │   ├── reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py
│   │   │   └── reppoints_partial_minmax_r50_fpn_gn-neck+head_1x_coco.py
│   │   ├── res2net/
│   │   │   ├── README.md
│   │   │   ├── cascade_mask_rcnn_r2_101_fpn_20e_coco.py
│   │   │   ├── cascade_rcnn_r2_101_fpn_20e_coco.py
│   │   │   ├── faster_rcnn_r2_101_fpn_2x_coco.py
│   │   │   ├── htc_r2_101_fpn_20e_coco.py
│   │   │   ├── mask_rcnn_r2_101_fpn_2x_coco.py
│   │   │   └── metafile.yml
│   │   ├── resnest/
│   │   │   ├── README.md
│   │   │   ├── cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py
│   │   │   ├── cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py
│   │   │   ├── cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
│   │   │   ├── cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
│   │   │   ├── faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
│   │   │   ├── faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
│   │   │   ├── mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py
│   │   │   ├── mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py
│   │   │   └── metafile.yml
│   │   ├── resnet_strikes_back/
│   │   │   ├── README.md
│   │   │   ├── cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco.py
│   │   │   ├── faster_rcnn_r50_fpn_rsb-pretrain_1x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_rsb-pretrain_1x_coco.py
│   │   │   ├── metafile.yml
│   │   │   └── retinanet_r50_fpn_rsb-pretrain_1x_coco.py
│   │   ├── retinanet/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   ├── retinanet_r101_caffe_fpn_1x_coco.py
│   │   │   ├── retinanet_r101_caffe_fpn_mstrain_3x_coco.py
│   │   │   ├── retinanet_r101_fpn_1x_coco.py
│   │   │   ├── retinanet_r101_fpn_2x_coco.py
│   │   │   ├── retinanet_r101_fpn_mstrain_640-800_3x_coco.py
│   │   │   ├── retinanet_r18_fpn_1x8_1x_coco.py
│   │   │   ├── retinanet_r18_fpn_1x_coco.py
│   │   │   ├── retinanet_r50_caffe_fpn_1x_coco.py
│   │   │   ├── retinanet_r50_caffe_fpn_mstrain_1x_coco.py
│   │   │   ├── retinanet_r50_caffe_fpn_mstrain_2x_coco.py
│   │   │   ├── retinanet_r50_caffe_fpn_mstrain_3x_coco.py
│   │   │   ├── retinanet_r50_fpn_1x_coco.py
│   │   │   ├── retinanet_r50_fpn_2x_coco.py
│   │   │   ├── retinanet_r50_fpn_90k_coco.py
│   │   │   ├── retinanet_r50_fpn_fp16_1x_coco.py
│   │   │   ├── retinanet_r50_fpn_mstrain_640-800_3x_coco.py
│   │   │   ├── retinanet_x101_32x4d_fpn_1x_coco.py
│   │   │   ├── retinanet_x101_32x4d_fpn_2x_coco.py
│   │   │   ├── retinanet_x101_64x4d_fpn_1x_coco.py
│   │   │   ├── retinanet_x101_64x4d_fpn_2x_coco.py
│   │   │   └── retinanet_x101_64x4d_fpn_mstrain_640-800_3x_coco.py
│   │   ├── rpn/
│   │   │   ├── README.md
│   │   │   ├── rpn_r101_caffe_fpn_1x_coco.py
│   │   │   ├── rpn_r101_fpn_1x_coco.py
│   │   │   ├── rpn_r101_fpn_2x_coco.py
│   │   │   ├── rpn_r50_caffe_c4_1x_coco.py
│   │   │   ├── rpn_r50_caffe_fpn_1x_coco.py
│   │   │   ├── rpn_r50_fpn_1x_coco.py
│   │   │   ├── rpn_r50_fpn_2x_coco.py
│   │   │   ├── rpn_x101_32x4d_fpn_1x_coco.py
│   │   │   ├── rpn_x101_32x4d_fpn_2x_coco.py
│   │   │   ├── rpn_x101_64x4d_fpn_1x_coco.py
│   │   │   └── rpn_x101_64x4d_fpn_2x_coco.py
│   │   ├── sabl/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   ├── sabl_cascade_rcnn_r101_fpn_1x_coco.py
│   │   │   ├── sabl_cascade_rcnn_r50_fpn_1x_coco.py
│   │   │   ├── sabl_faster_rcnn_r101_fpn_1x_coco.py
│   │   │   ├── sabl_faster_rcnn_r50_fpn_1x_coco.py
│   │   │   ├── sabl_retinanet_r101_fpn_1x_coco.py
│   │   │   ├── sabl_retinanet_r101_fpn_gn_1x_coco.py
│   │   │   ├── sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py
│   │   │   ├── sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py
│   │   │   ├── sabl_retinanet_r50_fpn_1x_coco.py
│   │   │   └── sabl_retinanet_r50_fpn_gn_1x_coco.py
│   │   ├── scnet/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   ├── scnet_r101_fpn_20e_coco.py
│   │   │   ├── scnet_r50_fpn_1x_coco.py
│   │   │   ├── scnet_r50_fpn_20e_coco.py
│   │   │   ├── scnet_x101_64x4d_fpn_20e_coco.py
│   │   │   └── scnet_x101_64x4d_fpn_8x1_20e_coco.py
│   │   ├── scratch/
│   │   │   ├── README.md
│   │   │   ├── faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py
│   │   │   └── metafile.yml
│   │   ├── seesaw_loss/
│   │   │   ├── README.md
│   │   │   ├── cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py
│   │   │   ├── cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
│   │   │   ├── cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py
│   │   │   ├── cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
│   │   │   ├── mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py
│   │   │   ├── mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
│   │   │   ├── mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py
│   │   │   ├── mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
│   │   │   ├── mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py
│   │   │   ├── mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
│   │   │   ├── mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py
│   │   │   ├── mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
│   │   │   └── metafile.yml
│   │   ├── selfsup_pretrain/
│   │   │   ├── README.md
│   │   │   ├── mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_swav-pretrain_1x_coco.py
│   │   │   └── mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco.py
│   │   ├── simple_copy_paste/
│   │   │   ├── README.md
│   │   │   ├── mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_270k_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_90k_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_270k_coco.py
│   │   │   └── mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_90k_coco.py
│   │   ├── solo/
│   │   │   ├── README.md
│   │   │   ├── decoupled_solo_light_r50_fpn_3x_coco.py
│   │   │   ├── decoupled_solo_r50_fpn_1x_coco.py
│   │   │   ├── decoupled_solo_r50_fpn_3x_coco.py
│   │   │   ├── metafile.yml
│   │   │   ├── solo_r50_fpn_1x_coco.py
│   │   │   └── solo_r50_fpn_3x_coco.py
│   │   ├── sparse_rcnn/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   ├── sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
│   │   │   ├── sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py
│   │   │   ├── sparse_rcnn_r50_fpn_1x_coco.py
│   │   │   ├── sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
│   │   │   └── sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py
│   │   ├── ssd/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   ├── ssd300_coco.py
│   │   │   ├── ssd512_coco.py
│   │   │   └── ssdlite_mobilenetv2_scratch_600e_coco.py
│   │   ├── strong_baselines/
│   │   │   ├── README.md
│   │   │   ├── mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py
│   │   │   ├── mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_fp16_coco.py
│   │   │   ├── mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_400e_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py
│   │   │   ├── mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_fp16_coco.py
│   │   │   └── mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_50e_coco.py
│   │   ├── swin/
│   │   │   ├── README.md
│   │   │   ├── mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco.py
│   │   │   ├── mask_rcnn_swin-t-p4-w7_fpn_1x_coco.py
│   │   │   ├── mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py
│   │   │   ├── mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py
│   │   │   ├── metafile.yml
│   │   │   └── retinanet_swin-t-p4-w7_fpn_1x_coco.py
│   │   ├── timm_example/
│   │   │   ├── README.md
│   │   │   ├── retinanet_timm_efficientnet_b1_fpn_1x_coco.py
│   │   │   └── retinanet_timm_tv_resnet50_fpn_1x_coco.py
│   │   ├── tood/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   ├── tood_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py
│   │   │   ├── tood_r101_fpn_mstrain_2x_coco.py
│   │   │   ├── tood_r50_fpn_1x_coco.py
│   │   │   ├── tood_r50_fpn_anchor_based_1x_coco.py
│   │   │   ├── tood_r50_fpn_mstrain_2x_coco.py
│   │   │   ├── tood_x101_64x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py
│   │   │   └── tood_x101_64x4d_fpn_mstrain_2x_coco.py
│   │   ├── tridentnet/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   ├── tridentnet_r50_caffe_1x_coco.py
│   │   │   ├── tridentnet_r50_caffe_mstrain_1x_coco.py
│   │   │   └── tridentnet_r50_caffe_mstrain_3x_coco.py
│   │   ├── vfnet/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   ├── vfnet_r101_fpn_1x_coco.py
│   │   │   ├── vfnet_r101_fpn_2x_coco.py
│   │   │   ├── vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py
│   │   │   ├── vfnet_r101_fpn_mstrain_2x_coco.py
│   │   │   ├── vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py
│   │   │   ├── vfnet_r2_101_fpn_mstrain_2x_coco.py
│   │   │   ├── vfnet_r50_fpn_1x_coco.py
│   │   │   ├── vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py
│   │   │   ├── vfnet_r50_fpn_mstrain_2x_coco.py
│   │   │   ├── vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py
│   │   │   ├── vfnet_x101_32x4d_fpn_mstrain_2x_coco.py
│   │   │   ├── vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py
│   │   │   └── vfnet_x101_64x4d_fpn_mstrain_2x_coco.py
│   │   ├── wider_face/
│   │   │   ├── README.md
│   │   │   └── ssd300_wider_face.py
│   │   ├── yolact/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   ├── yolact_r101_1x8_coco.py
│   │   │   ├── yolact_r50_1x8_coco.py
│   │   │   └── yolact_r50_8x8_coco.py
│   │   ├── yolo/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   ├── yolov3_d53_320_273e_coco.py
│   │   │   ├── yolov3_d53_fp16_mstrain-608_273e_coco.py
│   │   │   ├── yolov3_d53_mstrain-416_273e_coco.py
│   │   │   ├── yolov3_d53_mstrain-608_273e_coco.py
│   │   │   ├── yolov3_mobilenetv2_320_300e_coco.py
│   │   │   └── yolov3_mobilenetv2_mstrain-416_300e_coco.py
│   │   ├── yolof/
│   │   │   ├── README.md
│   │   │   ├── metafile.yml
│   │   │   ├── yolof_r50_c5_8x8_1x_coco.py
│   │   │   └── yolof_r50_c5_8x8_iter-1x_coco.py
│   │   └── yolox/
│   │       ├── README.md
│   │       ├── metafile.yml
│   │       ├── yolox_l_8x8_300e_coco.py
│   │       ├── yolox_m_8x8_300e_coco.py
│   │       ├── yolox_nano_8x8_300e_coco.py
│   │       ├── yolox_s_8x8_300e_coco.py
│   │       ├── yolox_tiny_8x8_300e_coco.py
│   │       └── yolox_x_8x8_300e_coco.py
│   ├── demo/
│   │   ├── MMDet_InstanceSeg_Tutorial.ipynb
│   │   ├── MMDet_Tutorial.ipynb
│   │   ├── create_result_gif.py
│   │   ├── image_demo.py
│   │   ├── inference_demo.ipynb
│   │   ├── video_demo.py
│   │   └── webcam_demo.py
│   ├── docker/
│   │   ├── Dockerfile
│   │   └── serve/
│   │       ├── Dockerfile
│   │       ├── config.properties
│   │       └── entrypoint.sh
│   ├── docs/
│   │   ├── en/
│   │   │   ├── 1_exist_data_model.md
│   │   │   ├── 2_new_data_model.md
│   │   │   ├── 3_exist_data_new_model.md
│   │   │   ├── Makefile
│   │   │   ├── _static/
│   │   │   │   └── css/
│   │   │   │       └── readthedocs.css
│   │   │   ├── 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
│   │       ├── 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
│   ├── huge_image_infer.py
│   ├── install_apex.sh
│   ├── mmcv_custom/
│   │   ├── __init__.py
│   │   ├── checkpoint.py
│   │   ├── hivit_layer_decay_optimizer_constructor.py
│   │   ├── layer_decay_optimizer_constructor.py
│   │   └── runner/
│   │       ├── __init__.py
│   │       ├── checkpoint.py
│   │       └── epoch_based_runner.py
│   ├── mmdet/
│   │   ├── .mim/
│   │   │   ├── 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
│   │   │   │   ├── _hivit_/
│   │   │   │   │   ├── fasterrcnn.py
│   │   │   │   │   ├── hivit_SAR_AIRcraft.py
│   │   │   │   │   ├── hivit_SSDD.py
│   │   │   │   │   ├── hivit_base_AIRShip.py
│   │   │   │   │   ├── hivit_base_HRSID.py
│   │   │   │   │   ├── hivit_base_MSAR.py
│   │   │   │   │   ├── hivit_base_OGSOD.py
│   │   │   │   │   ├── hivit_base_SADD.py
│   │   │   │   │   ├── hivit_base_SARAircraft.py
│   │   │   │   │   ├── hivit_base_SARDet.py
│   │   │   │   │   ├── hivit_base_SIVED.py
│   │   │   │   │   ├── hivit_base_SSDD.py
│   │   │   │   │   ├── hivit_base_Shipdataset.py
│   │   │   │   │   └── hivit_base_mask_rcnn_fpn_3x_coco.py
│   │   │   │   ├── albu_example/
│   │   │   │   │   ├── README.md
│   │   │   │   │   └── mask_rcnn_r50_fpn_albu_1x_coco.py
│   │   │   │   ├── atss/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── atss_r101_fpn_1x_coco.py
│   │   │   │   │   ├── atss_r50_fpn_1x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── autoassign/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── autoassign_r50_fpn_8x2_1x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── carafe/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── faster_rcnn_r50_fpn_carafe_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_carafe_1x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── cascade_rcnn/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_r101_fpn_1x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_r101_fpn_20e_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_r101_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_r50_fpn_1x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_r50_fpn_20e_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── cascade_rcnn_r101_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── cascade_rcnn_r101_fpn_1x_coco.py
│   │   │   │   │   ├── cascade_rcnn_r101_fpn_20e_coco.py
│   │   │   │   │   ├── cascade_rcnn_r50_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── cascade_rcnn_r50_fpn_1x_coco.py
│   │   │   │   │   ├── cascade_rcnn_r50_fpn_20e_coco.py
│   │   │   │   │   ├── cascade_rcnn_x101_32x4d_fpn_1x_coco.py
│   │   │   │   │   ├── cascade_rcnn_x101_32x4d_fpn_20e_coco.py
│   │   │   │   │   ├── cascade_rcnn_x101_64x4d_fpn_1x_coco.py
│   │   │   │   │   ├── cascade_rcnn_x101_64x4d_fpn_20e_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── cascade_rpn/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── crpn_r50_caffe_fpn_1x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── centernet/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── centernet_resnet18_140e_coco.py
│   │   │   │   │   ├── centernet_resnet18_dcnv2_140e_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── centripetalnet/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── centripetalnet_hourglass104_mstest_16x6_210e_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── cityscapes/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── faster_rcnn_r50_fpn_1x_cityscapes.py
│   │   │   │   │   └── mask_rcnn_r50_fpn_1x_cityscapes.py
│   │   │   │   ├── common/
│   │   │   │   │   ├── lsj_100e_coco_instance.py
│   │   │   │   │   ├── mstrain-poly_3x_coco_instance.py
│   │   │   │   │   ├── mstrain_3x_coco.py
│   │   │   │   │   ├── mstrain_3x_coco_instance.py
│   │   │   │   │   ├── ssj_270k_coco_instance.py
│   │   │   │   │   └── ssj_scp_270k_coco_instance.py
│   │   │   │   ├── cornernet/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── cornernet_hourglass104_mstest_10x5_210e_coco.py
│   │   │   │   │   ├── cornernet_hourglass104_mstest_32x3_210e_coco.py
│   │   │   │   │   ├── cornernet_hourglass104_mstest_8x6_210e_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── dcn/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py
│   │   │   │   │   ├── cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
│   │   │   │   │   ├── cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_dpool_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── dcnv2/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_mdpool_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── deepfashion/
│   │   │   │   │   ├── README.md
│   │   │   │   │   └── mask_rcnn_r50_fpn_15e_deepfashion.py
│   │   │   │   ├── deformable_detr/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── deformable_detr_r50_16x2_50e_coco.py
│   │   │   │   │   ├── deformable_detr_refine_r50_16x2_50e_coco.py
│   │   │   │   │   ├── deformable_detr_twostage_refine_r50_16x2_50e_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── detectors/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── cascade_rcnn_r50_rfp_1x_coco.py
│   │   │   │   │   ├── cascade_rcnn_r50_sac_1x_coco.py
│   │   │   │   │   ├── detectors_cascade_rcnn_r50_1x_coco.py
│   │   │   │   │   ├── detectors_htc_r101_20e_coco.py
│   │   │   │   │   ├── detectors_htc_r50_1x_coco.py
│   │   │   │   │   ├── htc_r50_rfp_1x_coco.py
│   │   │   │   │   ├── htc_r50_sac_1x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── detr/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── detr_r50_8x2_150e_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── double_heads/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── dh_faster_rcnn_r50_fpn_1x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── dyhead/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── atss_r50_caffe_fpn_dyhead_1x_coco.py
│   │   │   │   │   ├── atss_r50_fpn_dyhead_1x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── dynamic_rcnn/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── dynamic_rcnn_r50_fpn_1x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── efficientnet/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   └── retinanet_effb3_fpn_crop896_8x4_1x_coco.py
│   │   │   │   ├── empirical_attention/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── faster_rcnn_r50_fpn_attention_0010_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_attention_1111_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── fast_rcnn/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── fast_rcnn_r101_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── fast_rcnn_r101_fpn_1x_coco.py
│   │   │   │   │   ├── fast_rcnn_r101_fpn_2x_coco.py
│   │   │   │   │   ├── fast_rcnn_r50_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── fast_rcnn_r50_fpn_1x_coco.py
│   │   │   │   │   └── fast_rcnn_r50_fpn_2x_coco.py
│   │   │   │   ├── faster_rcnn/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── faster_rcnn_r101_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── faster_rcnn_r101_fpn_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r101_fpn_2x_coco.py
│   │   │   │   │   ├── faster_rcnn_r101_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_caffe_c4_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_caffe_c4_mstrain_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_caffe_dc5_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_caffe_fpn_90k_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py
│   │   │   │   │   ├── faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py
│   │   │   │   │   ├── faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_2x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_bounded_iou_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_ciou_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_fp16_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_giou_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_iou_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_ohem_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_soft_nms_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_tnr-pretrain_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_x101_32x4d_fpn_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_x101_32x4d_fpn_2x_coco.py
│   │   │   │   │   ├── faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── faster_rcnn_x101_64x4d_fpn_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_x101_64x4d_fpn_2x_coco.py
│   │   │   │   │   ├── faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── fcos/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py
│   │   │   │   │   ├── fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py
│   │   │   │   │   ├── fcos_center_r50_caffe_fpn_gn-head_1x_coco.py
│   │   │   │   │   ├── fcos_r101_caffe_fpn_gn-head_1x_coco.py
│   │   │   │   │   ├── fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py
│   │   │   │   │   ├── fcos_r50_caffe_fpn_gn-head_1x_coco.py
│   │   │   │   │   ├── fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py
│   │   │   │   │   ├── fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py
│   │   │   │   │   ├── fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── foveabox/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── fovea_align_r101_fpn_gn-head_4x4_2x_coco.py
│   │   │   │   │   ├── fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
│   │   │   │   │   ├── fovea_align_r50_fpn_gn-head_4x4_2x_coco.py
│   │   │   │   │   ├── fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
│   │   │   │   │   ├── fovea_r101_fpn_4x4_1x_coco.py
│   │   │   │   │   ├── fovea_r101_fpn_4x4_2x_coco.py
│   │   │   │   │   ├── fovea_r50_fpn_4x4_1x_coco.py
│   │   │   │   │   ├── fovea_r50_fpn_4x4_2x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── fpg/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_fpg_crop640_50e_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_crop640_50e_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpg_crop640_50e_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_crop640_50e_coco.py
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── retinanet_r50_fpg-chn128_crop640_50e_coco.py
│   │   │   │   │   └── retinanet_r50_fpg_crop640_50e_coco.py
│   │   │   │   ├── free_anchor/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── retinanet_free_anchor_r101_fpn_1x_coco.py
│   │   │   │   │   ├── retinanet_free_anchor_r50_fpn_1x_coco.py
│   │   │   │   │   └── retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py
│   │   │   │   ├── fsaf/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── fsaf_r101_fpn_1x_coco.py
│   │   │   │   │   ├── fsaf_r50_fpn_1x_coco.py
│   │   │   │   │   ├── fsaf_x101_64x4d_fpn_1x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── gcnet/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── gfl/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py
│   │   │   │   │   ├── gfl_r101_fpn_mstrain_2x_coco.py
│   │   │   │   │   ├── gfl_r50_fpn_1x_coco.py
│   │   │   │   │   ├── gfl_r50_fpn_mstrain_2x_coco.py
│   │   │   │   │   ├── gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py
│   │   │   │   │   ├── gfl_x101_32x4d_fpn_mstrain_2x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── ghm/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── retinanet_ghm_r101_fpn_1x_coco.py
│   │   │   │   │   ├── retinanet_ghm_r50_fpn_1x_coco.py
│   │   │   │   │   ├── retinanet_ghm_x101_32x4d_fpn_1x_coco.py
│   │   │   │   │   └── retinanet_ghm_x101_64x4d_fpn_1x_coco.py
│   │   │   │   ├── gn/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── mask_rcnn_r101_fpn_gn-all_2x_coco.py
│   │   │   │   │   ├── mask_rcnn_r101_fpn_gn-all_3x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_gn-all_2x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_gn-all_3x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── gn+ws/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py
│   │   │   │   │   ├── mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py
│   │   │   │   │   ├── mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py
│   │   │   │   │   ├── mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py
│   │   │   │   │   ├── mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py
│   │   │   │   │   ├── mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── grid_rcnn/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── grid_rcnn_r101_fpn_gn-head_2x_coco.py
│   │   │   │   │   ├── grid_rcnn_r50_fpn_gn-head_1x_coco.py
│   │   │   │   │   ├── grid_rcnn_r50_fpn_gn-head_2x_coco.py
│   │   │   │   │   ├── grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py
│   │   │   │   │   ├── grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── groie/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── faster_rcnn_r50_fpn_groie_1x_coco.py
│   │   │   │   │   ├── grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_groie_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── guided_anchoring/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── ga_fast_r50_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── ga_faster_r101_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── ga_faster_r50_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── ga_faster_r50_fpn_1x_coco.py
│   │   │   │   │   ├── ga_faster_x101_32x4d_fpn_1x_coco.py
│   │   │   │   │   ├── ga_faster_x101_64x4d_fpn_1x_coco.py
│   │   │   │   │   ├── ga_retinanet_r101_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── ga_retinanet_r101_caffe_fpn_mstrain_2x.py
│   │   │   │   │   ├── ga_retinanet_r50_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── ga_retinanet_r50_fpn_1x_coco.py
│   │   │   │   │   ├── ga_retinanet_x101_32x4d_fpn_1x_coco.py
│   │   │   │   │   ├── ga_retinanet_x101_64x4d_fpn_1x_coco.py
│   │   │   │   │   ├── ga_rpn_r101_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── ga_rpn_r50_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── ga_rpn_r50_fpn_1x_coco.py
│   │   │   │   │   ├── ga_rpn_x101_32x4d_fpn_1x_coco.py
│   │   │   │   │   ├── ga_rpn_x101_64x4d_fpn_1x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── hrnet/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py
│   │   │   │   │   ├── cascade_rcnn_hrnetv2p_w18_20e_coco.py
│   │   │   │   │   ├── cascade_rcnn_hrnetv2p_w32_20e_coco.py
│   │   │   │   │   ├── cascade_rcnn_hrnetv2p_w40_20e_coco.py
│   │   │   │   │   ├── faster_rcnn_hrnetv2p_w18_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_hrnetv2p_w18_2x_coco.py
│   │   │   │   │   ├── faster_rcnn_hrnetv2p_w32_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_hrnetv2p_w32_2x_coco.py
│   │   │   │   │   ├── faster_rcnn_hrnetv2p_w40_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_hrnetv2p_w40_2x_coco.py
│   │   │   │   │   ├── fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py
│   │   │   │   │   ├── fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py
│   │   │   │   │   ├── fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py
│   │   │   │   │   ├── fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py
│   │   │   │   │   ├── fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py
│   │   │   │   │   ├── fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py
│   │   │   │   │   ├── fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py
│   │   │   │   │   ├── htc_hrnetv2p_w18_20e_coco.py
│   │   │   │   │   ├── htc_hrnetv2p_w32_20e_coco.py
│   │   │   │   │   ├── htc_hrnetv2p_w40_20e_coco.py
│   │   │   │   │   ├── htc_hrnetv2p_w40_28e_coco.py
│   │   │   │   │   ├── htc_x101_64x4d_fpn_16x1_28e_coco.py
│   │   │   │   │   ├── mask_rcnn_hrnetv2p_w18_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_hrnetv2p_w18_2x_coco.py
│   │   │   │   │   ├── mask_rcnn_hrnetv2p_w32_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_hrnetv2p_w32_2x_coco.py
│   │   │   │   │   ├── mask_rcnn_hrnetv2p_w40_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_hrnetv2p_w40_2x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── htc/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── htc_r101_fpn_20e_coco.py
│   │   │   │   │   ├── htc_r50_fpn_1x_coco.py
│   │   │   │   │   ├── htc_r50_fpn_20e_coco.py
│   │   │   │   │   ├── htc_without_semantic_r50_fpn_1x_coco.py
│   │   │   │   │   ├── htc_x101_32x4d_fpn_16x1_20e_coco.py
│   │   │   │   │   ├── htc_x101_64x4d_fpn_16x1_20e_coco.py
│   │   │   │   │   ├── htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── instaboost/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py
│   │   │   │   │   ├── mask_rcnn_r101_fpn_instaboost_4x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_instaboost_4x_coco.py
│   │   │   │   │   ├── mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── lad/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── lad_r101_paa_r50_fpn_coco_1x.py
│   │   │   │   │   ├── lad_r50_paa_r101_fpn_coco_1x.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── ld/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── ld_r101_gflv1_r101dcn_fpn_coco_2x.py
│   │   │   │   │   ├── ld_r18_gflv1_r101_fpn_coco_1x.py
│   │   │   │   │   ├── ld_r34_gflv1_r101_fpn_coco_1x.py
│   │   │   │   │   ├── ld_r50_gflv1_r101_fpn_coco_1x.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── legacy_1.x/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── cascade_mask_rcnn_r50_fpn_1x_coco_v1.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_1x_coco_v1.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_1x_coco_v1.py
│   │   │   │   │   ├── retinanet_r50_caffe_fpn_1x_coco_v1.py
│   │   │   │   │   ├── retinanet_r50_fpn_1x_coco_v1.py
│   │   │   │   │   └── ssd300_coco_v1.py
│   │   │   │   ├── libra_rcnn/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── libra_fast_rcnn_r50_fpn_1x_coco.py
│   │   │   │   │   ├── libra_faster_rcnn_r101_fpn_1x_coco.py
│   │   │   │   │   ├── libra_faster_rcnn_r50_fpn_1x_coco.py
│   │   │   │   │   ├── libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py
│   │   │   │   │   ├── libra_retinanet_r50_fpn_1x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── lvis/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py
│   │   │   │   │   ├── mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py
│   │   │   │   │   ├── mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py
│   │   │   │   │   ├── mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py
│   │   │   │   │   ├── mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py
│   │   │   │   │   └── mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py
│   │   │   │   ├── mask2former/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── mask2former_r101_lsj_8x2_50e_coco.py
│   │   │   │   │   ├── mask2former_r50_lsj_8x2_50e_coco.py
│   │   │   │   │   ├── mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco.py
│   │   │   │   │   ├── mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco.py
│   │   │   │   │   ├── mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco.py
│   │   │   │   │   ├── mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco.py
│   │   │   │   │   ├── mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── mask_rcnn/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── mask_rcnn_r101_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py
│   │   │   │   │   ├── mask_rcnn_r101_fpn_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r101_fpn_2x_coco.py
│   │   │   │   │   ├── mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_caffe_c4_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_2x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_fp16_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_poly_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_x101_32x4d_fpn_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_x101_32x4d_fpn_2x_coco.py
│   │   │   │   │   ├── mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py
│   │   │   │   │   ├── mask_rcnn_x101_32x8d_fpn_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py
│   │   │   │   │   ├── mask_rcnn_x101_64x4d_fpn_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_x101_64x4d_fpn_2x_coco.py
│   │   │   │   │   ├── mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── maskformer/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── maskformer_r50_mstrain_16x1_75e_coco.py
│   │   │   │   │   ├── maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── ms_rcnn/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── ms_rcnn_r101_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── ms_rcnn_r101_caffe_fpn_2x_coco.py
│   │   │   │   │   ├── ms_rcnn_r50_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── ms_rcnn_r50_caffe_fpn_2x_coco.py
│   │   │   │   │   ├── ms_rcnn_r50_fpn_1x_coco.py
│   │   │   │   │   ├── ms_rcnn_x101_32x4d_fpn_1x_coco.py
│   │   │   │   │   ├── ms_rcnn_x101_64x4d_fpn_1x_coco.py
│   │   │   │   │   └── ms_rcnn_x101_64x4d_fpn_2x_coco.py
│   │   │   │   ├── nas_fcos/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py
│   │   │   │   │   └── nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py
│   │   │   │   ├── nas_fpn/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── retinanet_r50_fpn_crop640_50e_coco.py
│   │   │   │   │   └── retinanet_r50_nasfpn_crop640_50e_coco.py
│   │   │   │   ├── openimages/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── faster_rcnn_r50_fpn_32x2_1x_openimages.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_32x2_1x_openimages_challenge.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_32x2_cas_1x_openimages.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge.py
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── retinanet_r50_fpn_32x2_1x_openimages.py
│   │   │   │   │   └── ssd300_32x8_36e_openimages.py
│   │   │   │   ├── paa/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── paa_r101_fpn_1x_coco.py
│   │   │   │   │   ├── paa_r101_fpn_2x_coco.py
│   │   │   │   │   ├── paa_r101_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── paa_r50_fpn_1.5x_coco.py
│   │   │   │   │   ├── paa_r50_fpn_1x_coco.py
│   │   │   │   │   ├── paa_r50_fpn_2x_coco.py
│   │   │   │   │   └── paa_r50_fpn_mstrain_3x_coco.py
│   │   │   │   ├── pafpn/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── faster_rcnn_r50_pafpn_1x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── panoptic_fpn/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── panoptic_fpn_r101_fpn_1x_coco.py
│   │   │   │   │   ├── panoptic_fpn_r101_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── panoptic_fpn_r50_fpn_1x_coco.py
│   │   │   │   │   └── panoptic_fpn_r50_fpn_mstrain_3x_coco.py
│   │   │   │   ├── pascal_voc/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── faster_rcnn_r50_caffe_c4_mstrain_18k_voc0712.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_1x_voc0712.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py
│   │   │   │   │   ├── retinanet_r50_fpn_1x_voc0712.py
│   │   │   │   │   ├── ssd300_voc0712.py
│   │   │   │   │   └── ssd512_voc0712.py
│   │   │   │   ├── pisa/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── pisa_faster_rcnn_r50_fpn_1x_coco.py
│   │   │   │   │   ├── pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py
│   │   │   │   │   ├── pisa_mask_rcnn_r50_fpn_1x_coco.py
│   │   │   │   │   ├── pisa_mask_rcnn_x101_32x4d_fpn_1x_coco.py
│   │   │   │   │   ├── pisa_retinanet_r50_fpn_1x_coco.py
│   │   │   │   │   ├── pisa_retinanet_x101_32x4d_fpn_1x_coco.py
│   │   │   │   │   ├── pisa_ssd300_coco.py
│   │   │   │   │   └── pisa_ssd512_coco.py
│   │   │   │   ├── point_rend/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── point_rend_r50_caffe_fpn_mstrain_1x_coco.py
│   │   │   │   │   └── point_rend_r50_caffe_fpn_mstrain_3x_coco.py
│   │   │   │   ├── pvt/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── retinanet_pvt-l_fpn_1x_coco.py
│   │   │   │   │   ├── retinanet_pvt-m_fpn_1x_coco.py
│   │   │   │   │   ├── retinanet_pvt-s_fpn_1x_coco.py
│   │   │   │   │   ├── retinanet_pvt-t_fpn_1x_coco.py
│   │   │   │   │   ├── retinanet_pvtv2-b0_fpn_1x_coco.py
│   │   │   │   │   ├── retinanet_pvtv2-b1_fpn_1x_coco.py
│   │   │   │   │   ├── retinanet_pvtv2-b2_fpn_1x_coco.py
│   │   │   │   │   ├── retinanet_pvtv2-b3_fpn_1x_coco.py
│   │   │   │   │   ├── retinanet_pvtv2-b4_fpn_1x_coco.py
│   │   │   │   │   └── retinanet_pvtv2-b5_fpn_1x_coco.py
│   │   │   │   ├── queryinst/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
│   │   │   │   │   ├── queryinst_r101_fpn_mstrain_480-800_3x_coco.py
│   │   │   │   │   ├── queryinst_r50_fpn_1x_coco.py
│   │   │   │   │   ├── queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
│   │   │   │   │   └── queryinst_r50_fpn_mstrain_480-800_3x_coco.py
│   │   │   │   ├── regnet/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py
│   │   │   │   │   ├── faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco.py
│   │   │   │   │   ├── mask_rcnn_regnetx-12GF_fpn_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py
│   │   │   │   │   ├── mask_rcnn_regnetx-4GF_fpn_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco.py
│   │   │   │   │   ├── mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py
│   │   │   │   │   ├── mask_rcnn_regnetx-8GF_fpn_1x_coco.py
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── retinanet_regnetx-1.6GF_fpn_1x_coco.py
│   │   │   │   │   ├── retinanet_regnetx-3.2GF_fpn_1x_coco.py
│   │   │   │   │   └── retinanet_regnetx-800MF_fpn_1x_coco.py
│   │   │   │   ├── reppoints/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── bbox_r50_grid_center_fpn_gn-neck+head_1x_coco.py
│   │   │   │   │   ├── bbox_r50_grid_fpn_gn-neck+head_1x_coco.py
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── reppoints_minmax_r50_fpn_gn-neck+head_1x_coco.py
│   │   │   │   │   ├── reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py
│   │   │   │   │   ├── reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py
│   │   │   │   │   ├── reppoints_moment_r50_fpn_1x_coco.py
│   │   │   │   │   ├── reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py
│   │   │   │   │   ├── reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py
│   │   │   │   │   ├── reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py
│   │   │   │   │   └── reppoints_partial_minmax_r50_fpn_gn-neck+head_1x_coco.py
│   │   │   │   ├── res2net/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── cascade_mask_rcnn_r2_101_fpn_20e_coco.py
│   │   │   │   │   ├── cascade_rcnn_r2_101_fpn_20e_coco.py
│   │   │   │   │   ├── faster_rcnn_r2_101_fpn_2x_coco.py
│   │   │   │   │   ├── htc_r2_101_fpn_20e_coco.py
│   │   │   │   │   ├── mask_rcnn_r2_101_fpn_2x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── resnest/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py
│   │   │   │   │   ├── cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py
│   │   │   │   │   ├── cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
│   │   │   │   │   ├── cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── resnet_strikes_back/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco.py
│   │   │   │   │   ├── faster_rcnn_r50_fpn_rsb-pretrain_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_rsb-pretrain_1x_coco.py
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   └── retinanet_r50_fpn_rsb-pretrain_1x_coco.py
│   │   │   │   ├── retinanet/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── retinanet_r101_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── retinanet_r101_caffe_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── retinanet_r101_fpn_1x_coco.py
│   │   │   │   │   ├── retinanet_r101_fpn_2x_coco.py
│   │   │   │   │   ├── retinanet_r101_fpn_mstrain_640-800_3x_coco.py
│   │   │   │   │   ├── retinanet_r18_fpn_1x8_1x_coco.py
│   │   │   │   │   ├── retinanet_r18_fpn_1x_coco.py
│   │   │   │   │   ├── retinanet_r50_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── retinanet_r50_caffe_fpn_mstrain_1x_coco.py
│   │   │   │   │   ├── retinanet_r50_caffe_fpn_mstrain_2x_coco.py
│   │   │   │   │   ├── retinanet_r50_caffe_fpn_mstrain_3x_coco.py
│   │   │   │   │   ├── retinanet_r50_fpn_1x_coco.py
│   │   │   │   │   ├── retinanet_r50_fpn_2x_coco.py
│   │   │   │   │   ├── retinanet_r50_fpn_90k_coco.py
│   │   │   │   │   ├── retinanet_r50_fpn_fp16_1x_coco.py
│   │   │   │   │   ├── retinanet_r50_fpn_mstrain_640-800_3x_coco.py
│   │   │   │   │   ├── retinanet_x101_32x4d_fpn_1x_coco.py
│   │   │   │   │   ├── retinanet_x101_32x4d_fpn_2x_coco.py
│   │   │   │   │   ├── retinanet_x101_64x4d_fpn_1x_coco.py
│   │   │   │   │   ├── retinanet_x101_64x4d_fpn_2x_coco.py
│   │   │   │   │   └── retinanet_x101_64x4d_fpn_mstrain_640-800_3x_coco.py
│   │   │   │   ├── rpn/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── rpn_r101_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── rpn_r101_fpn_1x_coco.py
│   │   │   │   │   ├── rpn_r101_fpn_2x_coco.py
│   │   │   │   │   ├── rpn_r50_caffe_c4_1x_coco.py
│   │   │   │   │   ├── rpn_r50_caffe_fpn_1x_coco.py
│   │   │   │   │   ├── rpn_r50_fpn_1x_coco.py
│   │   │   │   │   ├── rpn_r50_fpn_2x_coco.py
│   │   │   │   │   ├── rpn_x101_32x4d_fpn_1x_coco.py
│   │   │   │   │   ├── rpn_x101_32x4d_fpn_2x_coco.py
│   │   │   │   │   ├── rpn_x101_64x4d_fpn_1x_coco.py
│   │   │   │   │   └── rpn_x101_64x4d_fpn_2x_coco.py
│   │   │   │   ├── sabl/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── sabl_cascade_rcnn_r101_fpn_1x_coco.py
│   │   │   │   │   ├── sabl_cascade_rcnn_r50_fpn_1x_coco.py
│   │   │   │   │   ├── sabl_faster_rcnn_r101_fpn_1x_coco.py
│   │   │   │   │   ├── sabl_faster_rcnn_r50_fpn_1x_coco.py
│   │   │   │   │   ├── sabl_retinanet_r101_fpn_1x_coco.py
│   │   │   │   │   ├── sabl_retinanet_r101_fpn_gn_1x_coco.py
│   │   │   │   │   ├── sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py
│   │   │   │   │   ├── sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py
│   │   │   │   │   ├── sabl_retinanet_r50_fpn_1x_coco.py
│   │   │   │   │   └── sabl_retinanet_r50_fpn_gn_1x_coco.py
│   │   │   │   ├── scnet/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── scnet_r101_fpn_20e_coco.py
│   │   │   │   │   ├── scnet_r50_fpn_1x_coco.py
│   │   │   │   │   ├── scnet_r50_fpn_20e_coco.py
│   │   │   │   │   ├── scnet_x101_64x4d_fpn_20e_coco.py
│   │   │   │   │   └── scnet_x101_64x4d_fpn_8x1_20e_coco.py
│   │   │   │   ├── scratch/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── seesaw_loss/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py
│   │   │   │   │   ├── cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
│   │   │   │   │   ├── cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py
│   │   │   │   │   ├── cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
│   │   │   │   │   ├── mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py
│   │   │   │   │   ├── mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
│   │   │   │   │   ├── mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py
│   │   │   │   │   ├── mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
│   │   │   │   │   └── metafile.yml
│   │   │   │   ├── selfsup_pretrain/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_swav-pretrain_1x_coco.py
│   │   │   │   │   └── mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco.py
│   │   │   │   ├── simple_copy_paste/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_270k_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_90k_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_270k_coco.py
│   │   │   │   │   └── mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_90k_coco.py
│   │   │   │   ├── solo/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── decoupled_solo_light_r50_fpn_3x_coco.py
│   │   │   │   │   ├── decoupled_solo_r50_fpn_1x_coco.py
│   │   │   │   │   ├── decoupled_solo_r50_fpn_3x_coco.py
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── solo_r50_fpn_1x_coco.py
│   │   │   │   │   └── solo_r50_fpn_3x_coco.py
│   │   │   │   ├── sparse_rcnn/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
│   │   │   │   │   ├── sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py
│   │   │   │   │   ├── sparse_rcnn_r50_fpn_1x_coco.py
│   │   │   │   │   ├── sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
│   │   │   │   │   └── sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py
│   │   │   │   ├── ssd/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── ssd300_coco.py
│   │   │   │   │   ├── ssd512_coco.py
│   │   │   │   │   └── ssdlite_mobilenetv2_scratch_600e_coco.py
│   │   │   │   ├── strong_baselines/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_fp16_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_400e_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py
│   │   │   │   │   ├── mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_fp16_coco.py
│   │   │   │   │   └── mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_50e_coco.py
│   │   │   │   ├── swin/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco.py
│   │   │   │   │   ├── mask_rcnn_swin-t-p4-w7_fpn_1x_coco.py
│   │   │   │   │   ├── mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py
│   │   │   │   │   ├── mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   └── retinanet_swin-t-p4-w7_fpn_1x_coco.py
│   │   │   │   ├── timm_example/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── retinanet_timm_efficientnet_b1_fpn_1x_coco.py
│   │   │   │   │   └── retinanet_timm_tv_resnet50_fpn_1x_coco.py
│   │   │   │   ├── tood/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── tood_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py
│   │   │   │   │   ├── tood_r101_fpn_mstrain_2x_coco.py
│   │   │   │   │   ├── tood_r50_fpn_1x_coco.py
│   │   │   │   │   ├── tood_r50_fpn_anchor_based_1x_coco.py
│   │   │   │   │   ├── tood_r50_fpn_mstrain_2x_coco.py
│   │   │   │   │   ├── tood_x101_64x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py
│   │   │   │   │   └── tood_x101_64x4d_fpn_mstrain_2x_coco.py
│   │   │   │   ├── tridentnet/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── tridentnet_r50_caffe_1x_coco.py
│   │   │   │   │   ├── tridentnet_r50_caffe_mstrain_1x_coco.py
│   │   │   │   │   └── tridentnet_r50_caffe_mstrain_3x_coco.py
│   │   │   │   ├── vfnet/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── vfnet_r101_fpn_1x_coco.py
│   │   │   │   │   ├── vfnet_r101_fpn_2x_coco.py
│   │   │   │   │   ├── vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py
│   │   │   │   │   ├── vfnet_r101_fpn_mstrain_2x_coco.py
│   │   │   │   │   ├── vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py
│   │   │   │   │   ├── vfnet_r2_101_fpn_mstrain_2x_coco.py
│   │   │   │   │   ├── vfnet_r50_fpn_1x_coco.py
│   │   │   │   │   ├── vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py
│   │   │   │   │   ├── vfnet_r50_fpn_mstrain_2x_coco.py
│   │   │   │   │   ├── vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py
│   │   │   │   │   ├── vfnet_x101_32x4d_fpn_mstrain_2x_coco.py
│   │   │   │   │   ├── vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py
│   │   │   │   │   └── vfnet_x101_64x4d_fpn_mstrain_2x_coco.py
│   │   │   │   ├── wider_face/
│   │   │   │   │   ├── README.md
│   │   │   │   │   └── ssd300_wider_face.py
│   │   │   │   ├── yolact/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── yolact_r101_1x8_coco.py
│   │   │   │   │   ├── yolact_r50_1x8_coco.py
│   │   │   │   │   └── yolact_r50_8x8_coco.py
│   │   │   │   ├── yolo/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── yolov3_d53_320_273e_coco.py
│   │   │   │   │   ├── yolov3_d53_fp16_mstrain-608_273e_coco.py
│   │   │   │   │   ├── yolov3_d53_mstrain-416_273e_coco.py
│   │   │   │   │   ├── yolov3_d53_mstrain-608_273e_coco.py
│   │   │   │   │   ├── yolov3_mobilenetv2_320_300e_coco.py
│   │   │   │   │   └── yolov3_mobilenetv2_mstrain-416_300e_coco.py
│   │   │   │   ├── yolof/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── metafile.yml
│   │   │   │   │   ├── yolof_r50_c5_8x8_1x_coco.py
│   │   │   │   │   └── yolof_r50_c5_8x8_iter-1x_coco.py
│   │   │   │   └── yolox/
│   │   │   │       ├── README.md
│   │   │   │       ├── metafile.yml
│   │   │   │       ├── yolox_l_8x8_300e_coco.py
│   │   │   │       ├── yolox_m_8x8_300e_coco.py
│   │   │   │       ├── yolox_nano_8x8_300e_coco.py
│   │   │   │       ├── yolox_s_8x8_300e_coco.py
│   │   │   │       ├── yolox_tiny_8x8_300e_coco.py
│   │   │   │       └── yolox_x_8x8_300e_coco.py
│   │   │   ├── demo/
│   │   │   │   ├── MMDet_InstanceSeg_Tutorial.ipynb
│   │   │   │   ├── MMDet_Tutorial.ipynb
│   │   │   │   ├── create_result_gif.py
│   │   │   │   ├── image_demo.py
│   │   │   │   ├── inference_demo.ipynb
│   │   │   │   ├── video_demo.py
│   │   │   │   └── webcam_demo.py
│   │   │   ├── model-index.yml
│   │   │   └── tools/
│   │   │       ├── .dist_train.sh.swp
│   │   │       ├── 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
│   │   │       │   └── split_coco.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
│   │   ├── __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
│   │   │   │   ├── memory_profiler_hook.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
│   │   │   │   ├── class_aware_sampler.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
│   │   │   │   ├── csp_darknet.py
│   │   │   │   ├── darknet.py
│   │   │   │   ├── detectors_resnet.py
│   │   │   │   ├── detectors_resnext.py
│   │   │   │   ├── efficientnet.py
│   │   │   │   ├── hivit.py
│   │   │   │   ├── hourglass.py
│   │   │   │   ├── hrnet.py
│   │   │   │   ├── mobilenet_v2.py
│   │   │   │   ├── pvt.py
│   │   │   │   ├── regnet.py
│   │   │   │   ├── res2net.py
│   │   │   │   ├── resnest.py
│   │   │   │   ├── resnet.py
│   │   │   │   ├── resnext.py
│   │   │   │   ├── ssd_vgg.py
│   │   │   │   ├── swin.py
│   │   │   │   ├── trident_resnet.py
│   │   │   │   └── vit.py
│   │   │   ├── builder.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
│   │   │   ├── compat_config.py
│   │   │   ├── contextmanagers.py
│   │   │   ├── logger.py
│   │   │   ├── misc.py
│   │   │   ├── optimizer.py
│   │   │   ├── profiling.py
│   │   │   ├── setup_env.py
│   │   │   ├── split_batch.py
│   │   │   ├── util_distribution.py
│   │   │   ├── util_mixins.py
│   │   │   └── util_random.py
│   │   └── version.py
│   ├── mmdet.egg-info/
│   │   ├── PKG-INFO
│   │   ├── SOURCES.txt
│   │   ├── dependency_links.txt
│   │   ├── not-zip-safe
│   │   ├── requires.txt
│   │   └── top_level.txt
│   ├── model-index.yml
│   ├── models/
│   │   ├── __init__.py
│   │   ├── models_hivit.py
│   │   └── models_vit.py
│   ├── pytest.ini
│   ├── requirements/
│   │   ├── albu.txt
│   │   ├── build.txt
│   │   ├── docs.txt
│   │   ├── mminstall.txt
│   │   ├── optional.txt
│   │   ├── readthedocs.txt
│   │   ├── runtime.txt
│   │   └── tests.txt
│   ├── requirements.txt
│   ├── setup.cfg
│   ├── setup.py
│   ├── tests/
│   │   ├── 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
│   │   │   ├── test_head.py
│   │   │   ├── test_neck.py
│   │   │   └── utils.py
│   │   ├── test_runtime/
│   │   │   ├── async_benchmark.py
│   │   │   ├── test_apis.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_compat_config.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_split_batch.py
│   │       ├── test_version.py
│   │       └── test_visualization.py
│   └── tools/
│       ├── .dist_train.sh.swp
│       ├── 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
│       │   └── split_coco.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
├── pre-training/
│   ├── engine_finetune.py
│   ├── engine_pretrain.py
│   ├── main_finetune.py
│   ├── main_pretrain.py
│   ├── models/
│   │   ├── __init__.py
│   │   ├── masked_autoencoder.py
│   │   ├── models_hivit.py
│   │   ├── models_hivit_mae.py
│   │   ├── models_vit.py
│   │   └── models_vit_mae.py
│   └── util/
│       ├── crop.py
│       ├── datasets.py
│       ├── lars.py
│       ├── lr_decay.py
│       ├── lr_sched.py
│       ├── misc.py
│       └── pos_embed.py
├── requirements_detection.txt
└── requirements_pretrain.txt

SYMBOL INDEX (4344 symbols across 607 files)

FILE: classification/clip/clip.py
  function _download (line 39) | def _download(url: str, root: str = os.path.expanduser("~/.cache/clip")):
  function _transform (line 71) | def _transform(n_px):
  function available_models (line 81) | def available_models() -> List[str]:
  function load (line 86) | def load(name: str, device: Union[str, torch.device] = "cuda" if torch.c...
  function tokenize (line 185) | def tokenize(texts: Union[str, List[str]], context_length: int = 77, tru...

FILE: classification/clip/model.py
  class Bottleneck (line 10) | class Bottleneck(nn.Module):
    method __init__ (line 13) | def __init__(self, inplanes, planes, stride=1):
    method forward (line 40) | def forward(self, x: torch.Tensor):
  class AttentionPool2d (line 56) | class AttentionPool2d(nn.Module):
    method __init__ (line 57) | def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, o...
    method forward (line 66) | def forward(self, x):
  class ModifiedResNet (line 93) | class ModifiedResNet(nn.Module):
    method __init__ (line 101) | def __init__(self, layers, output_dim, heads, input_resolution=224, wi...
    method _make_layer (line 126) | def _make_layer(self, planes, blocks, stride=1):
    method forward (line 135) | def forward(self, x):
  class LayerNorm (line 153) | class LayerNorm(nn.LayerNorm):
    method forward (line 156) | def forward(self, x: torch.Tensor):
  class QuickGELU (line 162) | class QuickGELU(nn.Module):
    method forward (line 163) | def forward(self, x: torch.Tensor):
  class ResidualAttentionBlock (line 167) | class ResidualAttentionBlock(nn.Module):
    method __init__ (line 168) | def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor ...
    method attention (line 181) | def attention(self, x: torch.Tensor):
    method forward (line 185) | def forward(self, x: torch.Tensor):
  class Transformer (line 191) | class Transformer(nn.Module):
    method __init__ (line 192) | def __init__(self, width: int, layers: int, heads: int, attn_mask: tor...
    method forward (line 198) | def forward(self, x: torch.Tensor):
  class VisionTransformer (line 202) | class VisionTransformer(nn.Module):
    method __init__ (line 203) | def __init__(self, input_resolution: int, patch_size: int, width: int,...
    method forward (line 219) | def forward(self, x: torch.Tensor):
  class CLIP (line 239) | class CLIP(nn.Module):
    method __init__ (line 240) | def __init__(self,
    method initialize_parameters (line 295) | def initialize_parameters(self):
    method build_attention_mask (line 324) | def build_attention_mask(self):
    method dtype (line 333) | def dtype(self):
    method encode_image (line 336) | def encode_image(self, image):
    method encode_text (line 339) | def encode_text(self, text):
    method forward (line 354) | def forward(self, image, text):
  function convert_weights (line 371) | def convert_weights(model: nn.Module):
  function build_model (line 395) | def build_model(state_dict: dict):

FILE: classification/clip/simple_tokenizer.py
  function default_bpe (line 11) | def default_bpe():
  function bytes_to_unicode (line 16) | def bytes_to_unicode():
  function get_pairs (line 38) | def get_pairs(word):
  function basic_clean (line 50) | def basic_clean(text):
  function whitespace_clean (line 56) | def whitespace_clean(text):
  class SimpleTokenizer (line 62) | class SimpleTokenizer(object):
    method __init__ (line 63) | def __init__(self, bpe_path: str = default_bpe()):
    method bpe (line 80) | def bpe(self, token):
    method encode (line 121) | def encode(self, text):
    method decode (line 129) | def decode(self, tokens):

FILE: classification/datasets/MSTAR_EOC1.py
  class MSTAR_EOC1 (line 12) | class MSTAR_EOC1(DatasetBase):
    method __init__ (line 16) | def __init__(self, cfg):
    method read_data (line 56) | def read_data(self, image_dir):

FILE: classification/datasets/MSTAR_EOC2C.py
  class MSTAR_EOC2C (line 12) | class MSTAR_EOC2C(DatasetBase):
    method __init__ (line 16) | def __init__(self, cfg):
    method read_data (line 56) | def read_data(self, image_dir):

FILE: classification/datasets/MSTAR_EOC2V.py
  class MSTAR_EOC2V (line 12) | class MSTAR_EOC2V(DatasetBase):
    method __init__ (line 16) | def __init__(self, cfg):
    method read_data (line 56) | def read_data(self, image_dir):

FILE: classification/datasets/MSTAR_SOC.py
  class MSTAR_SOC (line 12) | class MSTAR_SOC(DatasetBase):
    method __init__ (line 16) | def __init__(self, cfg):
    method read_data (line 56) | def read_data(self, image_dir):

FILE: classification/datasets/MyDataset.py
  class MyDataset (line 12) | class MyDataset(DatasetBase):
    method __init__ (line 16) | def __init__(self, cfg):
    method read_data (line 56) | def read_data(self, image_dir):

FILE: classification/datasets/New_FUSAR.py
  class New_FUSAR (line 12) | class New_FUSAR(DatasetBase):
    method __init__ (line 16) | def __init__(self, cfg):
    method read_data (line 56) | def read_data(self, image_dir):

FILE: classification/datasets/SAR_ACD.py
  class SAR_ACD (line 12) | class SAR_ACD(DatasetBase):
    method __init__ (line 15) | def __init__(self, cfg):
    method read_data (line 55) | def read_data(self, image_dir):

FILE: classification/datasets/caltech101.py
  class Caltech101 (line 20) | class Caltech101(DatasetBase):
    method __init__ (line 24) | def __init__(self, cfg):

FILE: classification/datasets/dtd.py
  class DescribableTextures (line 12) | class DescribableTextures(DatasetBase):
    method __init__ (line 16) | def __init__(self, cfg):
    method read_and_split_data (line 54) | def read_and_split_data(image_dir, p_trn=0.5, p_val=0.2, ignored=[], n...

FILE: classification/datasets/eurosat.py
  class EuroSAT (line 25) | class EuroSAT(DatasetBase):
    method __init__ (line 29) | def __init__(self, cfg):
    method update_classname (line 66) | def update_classname(self, dataset_old):

FILE: classification/datasets/fgvc_aircraft.py
  class FGVCAircraft (line 11) | class FGVCAircraft(DatasetBase):
    method __init__ (line 15) | def __init__(self, cfg):
    method read_data (line 56) | def read_data(self, cname2lab, split_file):

FILE: classification/datasets/food101.py
  class Food101 (line 12) | class Food101(DatasetBase):
    method __init__ (line 16) | def __init__(self, cfg):

FILE: classification/datasets/imagenet.py
  class ImageNet (line 12) | class ImageNet(DatasetBase):
    method __init__ (line 16) | def __init__(self, cfg):
    method read_classnames (line 64) | def read_classnames(text_file):
    method read_data (line 78) | def read_data(self, classnames, split_dir):

FILE: classification/datasets/imagenet_a.py
  class ImageNetA (line 12) | class ImageNetA(DatasetBase):
    method __init__ (line 20) | def __init__(self, cfg):
    method read_data (line 32) | def read_data(self, classnames):

FILE: classification/datasets/imagenet_r.py
  class ImageNetR (line 12) | class ImageNetR(DatasetBase):
    method __init__ (line 20) | def __init__(self, cfg):
    method read_data (line 32) | def read_data(self, classnames):

FILE: classification/datasets/imagenet_sketch.py
  class ImageNetSketch (line 10) | class ImageNetSketch(DatasetBase):
    method __init__ (line 18) | def __init__(self, cfg):
    method read_data (line 30) | def read_data(self, classnames):

FILE: classification/datasets/imagenetv2.py
  class ImageNetV2 (line 10) | class ImageNetV2(DatasetBase):
    method __init__ (line 18) | def __init__(self, cfg):
    method read_data (line 31) | def read_data(self, classnames):

FILE: classification/datasets/oxford_flowers.py
  class OxfordFlowers (line 14) | class OxfordFlowers(DatasetBase):
    method __init__ (line 18) | def __init__(self, cfg):
    method read_data (line 57) | def read_data(self):

FILE: classification/datasets/oxford_pets.py
  class OxfordPets (line 12) | class OxfordPets(DatasetBase):
    method __init__ (line 16) | def __init__(self, cfg):
    method read_data (line 56) | def read_data(self, split_file):
    method split_trainval (line 77) | def split_trainval(trainval, p_val=0.2):
    method save_split (line 100) | def save_split(train, val, test, filepath, path_prefix):
    method read_split (line 123) | def read_split(filepath, path_prefix):
    method subsample_classes (line 141) | def subsample_classes(*args, subsample="all"):

FILE: classification/datasets/stanford_cars.py
  class StanfordCars (line 12) | class StanfordCars(DatasetBase):
    method __init__ (line 16) | def __init__(self, cfg):
    method read_data (line 57) | def read_data(self, image_dir, anno_file, meta_file):

FILE: classification/datasets/sun397.py
  class SUN397 (line 11) | class SUN397(DatasetBase):
    method __init__ (line 15) | def __init__(self, cfg):
    method read_data (line 61) | def read_data(self, cname2lab, text_file):

FILE: classification/datasets/ucf101.py
  class UCF101 (line 12) | class UCF101(DatasetBase):
    method __init__ (line 16) | def __init__(self, cfg):
    method read_data (line 64) | def read_data(self, cname2lab, text_file):

FILE: classification/interpret_prompt.py
  function load_clip_to_cpu (line 10) | def load_clip_to_cpu(backbone_name="RN50"):

FILE: classification/lpclip/feat_extractor.py
  function print_args (line 34) | def print_args(args, cfg):
  function reset_cfg (line 48) | def reset_cfg(cfg, args):
  function extend_cfg (line 65) | def extend_cfg(cfg):
  function setup_cfg (line 85) | def setup_cfg(args):
  function main (line 105) | def main(args):

FILE: classification/lpclip/linear_probe.py
  function binary_search (line 69) | def binary_search(c_left, c_right, seed, step, test_acc_step_list):

FILE: classification/model/Model.py
  class AlexNet (line 5) | class AlexNet(nn.Module):
    method __init__ (line 6) | def __init__(self, num_classes: int = 10, dropout: float = 0.5) -> None:
    method forward (line 35) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class A_ConvNet (line 42) | class A_ConvNet(nn.Module):
    method __init__ (line 43) | def __init__(self, in_ch=1, num_classes=10):
    method forward (line 57) | def forward(self, x):
  class A_ConvNet_BN (line 73) | class A_ConvNet_BN(nn.Module):
    method __init__ (line 74) | def __init__(self, in_ch=1, num_classes=10):
    method forward (line 88) | def forward(self, x):
  class ChannelAttention (line 104) | class ChannelAttention(nn.Module):
    method __init__ (line 105) | def __init__(self, in_planes):
    method forward (line 113) | def forward(self, x):
  class SpatialAttention (line 120) | class SpatialAttention(nn.Module):
    method __init__ (line 121) | def __init__(self, kernel_size=7):
    method forward (line 130) | def forward(self, x):
  function atten (line 138) | def atten(x, con, ca ,sa):
  class AMCNN (line 145) | class AMCNN(torch.nn.Module):
    method __init__ (line 146) | def __init__(self, num_classes=3):
    method forward (line 182) | def forward(self, x):
  class BNACNN (line 199) | class BNACNN(torch.nn.Module):
    method __init__ (line 200) | def __init__(self, num_classes=3):
    method forward (line 220) | def forward(self, x):
  class MVGGNet (line 237) | class MVGGNet(torch.nn.Module):
    method __init__ (line 238) | def __init__(self, num_classes=3):
    method forward (line 250) | def forward(self, x):
  class ResNet_18 (line 257) | class ResNet_18(torch.nn.Module):
    method __init__ (line 258) | def __init__(self, num_classes=3):
    method forward (line 266) | def forward(self, x):
  class ResNet_34 (line 272) | class ResNet_34(torch.nn.Module):
    method __init__ (line 273) | def __init__(self, num_classes=3):
    method forward (line 281) | def forward(self, x):
  class ResNet_50 (line 287) | class ResNet_50(torch.nn.Module):
    method __init__ (line 288) | def __init__(self, num_classes=3):
    method forward (line 295) | def forward(self, x):
  class convnext_1 (line 301) | class convnext_1(torch.nn.Module):
    method __init__ (line 302) | def __init__(self, num_classes=3):
    method forward (line 310) | def forward(self, x):
  class efficientnet_b0 (line 316) | class efficientnet_b0(torch.nn.Module):
    method __init__ (line 317) | def __init__(self, num_classes=3):
    method forward (line 326) | def forward(self, x):
  class efficientnet_b1 (line 332) | class efficientnet_b1(torch.nn.Module):
    method __init__ (line 333) | def __init__(self, feature_extract=True, num_classes=3):
    method forward (line 342) | def forward(self, x):
  function crop (line 358) | def crop(x1, x2):
  class BN_Conv2d (line 373) | class BN_Conv2d(nn.Module):
    method __init__ (line 378) | def __init__(self, in_channels: object, out_channels: object, kernel_s...
    method forward (line 388) | def forward(self, x):
  class DoubleConv (line 393) | class DoubleConv(nn.Module):
    method __init__ (line 394) | def __init__(self, in_ch, out_ch, padding=2, bias=False):
    method forward (line 401) | def forward(self, input):
  function squash (line 405) | def squash(x, dim=-1):
  class PrimaryCaps (line 411) | class PrimaryCaps(nn.Module):
    method __init__ (line 414) | def __init__(self, num_conv_units, in_channels, out_channels, kernel_s...
    method forward (line 425) | def forward(self, x):
  class DigitCaps (line 434) | class DigitCaps(nn.Module):
    method __init__ (line 437) | def __init__(self, in_dim, in_caps, num_caps, dim_caps, num_routing):
    method forward (line 457) | def forward(self, x):
  function MLP (line 499) | def MLP(dim, projection_size, hidden_size=64):
  function SimSiamMLP (line 507) | def SimSiamMLP(dim, projection_size, hidden_size=64):
  class Unet (line 520) | class Unet(nn.Module):
    method __init__ (line 521) | def __init__(self, in_ch=1, out_ch=1, num_classes=3):
    method forward (line 552) | def forward(self, x):

FILE: classification/model/cmt.py
  function _cfg (line 23) | def _cfg(url='', **kwargs):
  class SwishImplementation (line 35) | class SwishImplementation(torch.autograd.Function):
    method forward (line 37) | def forward(ctx, i):
    method backward (line 43) | def backward(ctx, grad_output):
  class MemoryEfficientSwish (line 49) | class MemoryEfficientSwish(nn.Module):
    method forward (line 50) | def forward(self, x):
  class Mlp (line 54) | class Mlp(nn.Module):
    method __init__ (line 55) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 73) | def forward(self, x, H, W):
  class Attention (line 87) | class Attention(nn.Module):
    method __init__ (line 88) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None,
    method forward (line 111) | def forward(self, x, H, W, relative_pos):
  class Block (line 133) | class Block(nn.Module):
    method __init__ (line 134) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
    method forward (line 148) | def forward(self, x, H, W, relative_pos):
  class PatchEmbed (line 158) | class PatchEmbed(nn.Module):
    method __init__ (line 161) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
    method forward (line 177) | def forward(self, x):
  class CMT (line 189) | class CMT(nn.Module):
    method __init__ (line 190) | def __init__(self, img_size=128, in_chans=1, num_classes=10, embed_dim...
    method _init_weights (line 278) | def _init_weights(self, m):
    method update_temperature (line 294) | def update_temperature(self):
    method no_weight_decay (line 300) | def no_weight_decay(self):
    method get_classifier (line 303) | def get_classifier(self):
    method reset_classifier (line 306) | def reset_classifier(self, num_classes, global_pool=''):
    method forward_features (line 310) | def forward_features(self, x):
    method forward (line 352) | def forward(self, x):
  function resize_pos_embed (line 358) | def resize_pos_embed(posemb, posemb_new):
  function checkpoint_filter_fn (line 378) | def checkpoint_filter_fn(state_dict, model):
  function _create_cmt_model (line 396) | def _create_cmt_model(pretrained=False, distilled=False, **kwargs):
  function cmt_ti (line 421) | def cmt_ti(pretrained=False, **kwargs):
  function cmt_xs (line 430) | def cmt_xs(pretrained=False, **kwargs):
  function cmt_s (line 441) | def cmt_s(pretrained=False, **kwargs):
  function cmt_b (line 452) | def cmt_b(pretrained=False, **kwargs):

FILE: classification/model/conformer.py
  class Mlp (line 8) | class Mlp(nn.Module):
    method __init__ (line 9) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 18) | def forward(self, x):
  class Attention (line 27) | class Attention(nn.Module):
    method __init__ (line 28) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at...
    method forward (line 40) | def forward(self, x):
  class Block (line 55) | class Block(nn.Module):
    method __init__ (line 57) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
    method forward (line 69) | def forward(self, x):
  class ConvBlock (line 75) | class ConvBlock(nn.Module):
    method __init__ (line 77) | def __init__(self, inplanes, outplanes, stride=1, res_conv=False, act_...
    method zero_init_last_bn (line 104) | def zero_init_last_bn(self):
    method forward (line 107) | def forward(self, x, x_t=None, return_x_2=True):
  class FCUDown (line 143) | class FCUDown(nn.Module):
    method __init__ (line 147) | def __init__(self, inplanes, outplanes, dw_stride, act_layer=nn.GELU,
    method forward (line 158) | def forward(self, x, x_t):
  class FCUUp (line 170) | class FCUUp(nn.Module):
    method __init__ (line 174) | def __init__(self, inplanes, outplanes, up_stride, act_layer=nn.ReLU,
    method forward (line 183) | def forward(self, x, H, W):
  class Med_ConvBlock (line 192) | class Med_ConvBlock(nn.Module):
    method __init__ (line 195) | def __init__(self, inplanes, act_layer=nn.ReLU, groups=1, norm_layer=p...
    method zero_init_last_bn (line 218) | def zero_init_last_bn(self):
    method forward (line 221) | def forward(self, x):
  class ConvTransBlock (line 250) | class ConvTransBlock(nn.Module):
    method __init__ (line 255) | def __init__(self, inplanes, outplanes, res_conv, stride, dw_stride, e...
    method forward (line 287) | def forward(self, x, x_t):
  class Conformer (line 306) | class Conformer(nn.Module):
    method __init__ (line 308) | def __init__(self, patch_size=16, in_chans=3, num_classes=1000, base_c...
    method _init_weights (line 396) | def _init_weights(self, m):
    method no_weight_decay (line 414) | def no_weight_decay(self):
    method forward (line 418) | def forward(self, x):

FILE: classification/model/cotnet.py
  function _cfg (line 21) | def _cfg(url='', **kwargs):
  class CotLayer (line 36) | class CotLayer(nn.Module):
    method __init__ (line 37) | def __init__(self, dim, kernel_size):
    method forward (line 79) | def forward(self, x):
  class CoXtLayer (line 106) | class CoXtLayer(nn.Module):
    method __init__ (line 107) | def __init__(self, dim, kernel_size):
    method forward (line 150) | def forward(self, x):
  class Bottleneck (line 181) | class Bottleneck(nn.Module):
    method __init__ (line 184) | def __init__(self, inplanes, planes, stride=1, downsample=None, cardin...
    method zero_init_last_bn (line 225) | def zero_init_last_bn(self):
    method forward (line 228) | def forward(self, x):
  function _create_cotnet (line 266) | def _create_cotnet(variant, pretrained=False, **kwargs):
  function cotnet50 (line 271) | def cotnet50(pretrained=False, **kwargs):
  function cotnext50_2x48d (line 276) | def cotnext50_2x48d(pretrained=False, **kwargs):
  function cotnet101 (line 281) | def cotnet101(pretrained=False, **kwargs):
  function cotnext101_2x48d (line 286) | def cotnext101_2x48d(pretrained=False, **kwargs):

FILE: classification/model/cotnet_hybrid.py
  function _cfg (line 23) | def _cfg(url='', **kwargs):
  class CoTLayer (line 48) | class CoTLayer(nn.Module):
    method __init__ (line 49) | def __init__(self, dim, kernel_size):
    method forward (line 91) | def forward(self, x):
  class CoTBottleneck (line 118) | class CoTBottleneck(nn.Module):
    method __init__ (line 121) | def __init__(self, block_idx, inplanes, planes, stride=1, downsample=N...
    method zero_init_last_bn (line 169) | def zero_init_last_bn(self):
    method forward (line 172) | def forward(self, x):
  function get_padding (line 204) | def get_padding(kernel_size, stride, dilation=1):
  function downsample_conv (line 208) | def downsample_conv(
  function downsample_avg (line 221) | def downsample_avg(
  function drop_blocks (line 237) | def drop_blocks(drop_block_rate=0.):
  function make_blocks (line 243) | def make_blocks(
  function make_blocks_arr (line 288) | def make_blocks_arr(
  class CoTHybridNet (line 338) | class CoTHybridNet(nn.Module):
    method __init__ (line 339) | def __init__(self, block, layers, num_classes=1000, in_chans=3,
    method get_classifier (line 420) | def get_classifier(self):
    method reset_classifier (line 423) | def reset_classifier(self, num_classes, global_pool='avg'):
    method forward_features (line 427) | def forward_features(self, x):
    method forward (line 439) | def forward(self, x):
  function _create_se_cotnetd (line 448) | def _create_se_cotnetd(variant, pretrained=False, **kwargs):
  function se_cotnetd_50 (line 453) | def se_cotnetd_50(pretrained=False, **kwargs):
  function se_cotnetd_101 (line 461) | def se_cotnetd_101(pretrained=False, **kwargs):
  function se_cotnetd_152 (line 469) | def se_cotnetd_152(pretrained=False, **kwargs):
  function se_cotnetd_152_L (line 477) | def se_cotnetd_152_L(pretrained=False, **kwargs):
  function se_cotnetd_200 (line 485) | def se_cotnetd_200(pretrained=False, **kwargs):
  function se_cotnetd_270 (line 493) | def se_cotnetd_270(pretrained=False, **kwargs):

FILE: classification/model/features.py
  class FeatureInfo (line 20) | class FeatureInfo:
    method __init__ (line 22) | def __init__(self, feature_info: List[Dict], out_indices: Tuple[int]):
    method from_other (line 33) | def from_other(self, out_indices: Tuple[int]):
    method get (line 36) | def get(self, key, idx=None):
    method get_dicts (line 49) | def get_dicts(self, keys=None, idx=None):
    method channels (line 62) | def channels(self, idx=None):
    method reduction (line 67) | def reduction(self, idx=None):
    method module_name (line 72) | def module_name(self, idx=None):
    method __getitem__ (line 77) | def __getitem__(self, item):
    method __len__ (line 80) | def __len__(self):
  class FeatureHooks (line 84) | class FeatureHooks:
    method __init__ (line 92) | def __init__(self, hooks, named_modules, out_map=None, default_hook_ty...
    method _collect_output_hook (line 109) | def _collect_output_hook(self, hook_id, *args):
    method get_output (line 115) | def get_output(self, device) -> Dict[str, torch.tensor]:
  function _module_list (line 121) | def _module_list(module, flatten_sequential=False):
  function _get_feature_info (line 135) | def _get_feature_info(net, out_indices):
  function _get_return_layers (line 145) | def _get_return_layers(feature_info, out_map):
  class FeatureDictNet (line 153) | class FeatureDictNet(nn.ModuleDict):
    method __init__ (line 177) | def __init__(
    method _collect (line 200) | def _collect(self, x) -> (Dict[str, torch.Tensor]):
    method forward (line 214) | def forward(self, x) -> Dict[str, torch.Tensor]:
  class FeatureListNet (line 218) | class FeatureListNet(FeatureDictNet):
    method __init__ (line 224) | def __init__(
    method forward (line 231) | def forward(self, x) -> (List[torch.Tensor]):
  class FeatureHookNet (line 235) | class FeatureHookNet(nn.ModuleDict):
    method __init__ (line 248) | def __init__(
    method forward (line 280) | def forward(self, x):

FILE: classification/model/helpers.py
  function load_state_dict (line 23) | def load_state_dict(checkpoint_path, use_ema=False):
  function load_checkpoint (line 46) | def load_checkpoint(model, checkpoint_path, use_ema=False, strict=True):
  function resume_checkpoint (line 51) | def resume_checkpoint(model, checkpoint_path, optimizer=None, loss_scale...
  function load_pretrained (line 91) | def load_pretrained(model, cfg=None, num_classes=1000, in_chans=3, filte...
  function extract_layer (line 156) | def extract_layer(model, layer):
  function set_layer (line 174) | def set_layer(model, layer, val):
  function adapt_model_from_string (line 198) | def adapt_model_from_string(parent_module, model_string):
  function adapt_model_from_file (line 248) | def adapt_model_from_file(parent_module, model_variant):
  function default_cfg_for_features (line 253) | def default_cfg_for_features(default_cfg):
  function overlay_external_default_cfg (line 261) | def overlay_external_default_cfg(default_cfg, kwargs):
  function model_parameters (line 270) | def model_parameters(model, exclude_head=False):
  function named_apply (line 277) | def named_apply(fn: Callable, module: nn.Module, name='', depth_first=Tr...
  function adapt_input_conv (line 287) | def adapt_input_conv(in_chans, conv_weight):
  function build_model_with_cfg (line 311) | def build_model_with_cfg(

FILE: classification/model/hivit.py
  class Attention (line 9) | class Attention(nn.Module):
    method __init__ (line 10) | def __init__(self, input_size, dim, num_heads, qkv_bias=True, qk_scale...
    method forward (line 43) | def forward(self, x, rpe_index=None, mask=None):
  class BlockWithRPE (line 69) | class BlockWithRPE(nn.Module):
    method __init__ (line 70) | def __init__(self, input_size, dim, num_heads=0., mlp_ratio=4., qkv_bi...
    method forward (line 91) | def forward(self, x, rpe_index=None, mask=None):
  class PatchEmbed (line 98) | class PatchEmbed(nn.Module):
    method __init__ (line 99) | def __init__(
    method forward (line 124) | def forward(self, x):
  class PatchMerge (line 138) | class PatchMerge(nn.Module):
    method __init__ (line 139) | def __init__(self, dim, norm_layer):
    method forward (line 144) | def forward(self, x, *args, **kwargs):
  class HiViT (line 167) | class HiViT(nn.Module):
    method __init__ (line 168) | def __init__(self, img_size=224, patch_size=16, inner_patches=4, in_ch...
    method _init_weights (line 242) | def _init_weights(self, m):
    method no_weight_decay (line 252) | def no_weight_decay(self):
    method no_weight_decay_keywords (line 256) | def no_weight_decay_keywords(self):
    method forward_features (line 259) | def forward_features(self, x, ids_keep=None, mask=None):
    method forward (line 297) | def forward(self, x):
  function hivit_base (line 304) | def hivit_base(**kwargs):

FILE: classification/model/inception_transformer.py
  function _cfg (line 42) | def _cfg(url='', **kwargs):
  function _no_grad_trunc_normal_ (line 72) | def _no_grad_trunc_normal_(tensor, mean, std, a, b):
  function trunc_normal_ (line 108) | def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
  function variance_scaling_ (line 130) | def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='no...
  function lecun_normal_ (line 153) | def lecun_normal_(tensor):
  class PatchEmbed (line 156) | class PatchEmbed(nn.Module):
    method __init__ (line 159) | def __init__(self, img_size=224, kernel_size=16,  stride=16, padding=0...
    method forward (line 169) | def forward(self, x):
  class FirstPatchEmbed (line 176) | class FirstPatchEmbed(nn.Module):
    method __init__ (line 179) | def __init__(self, kernel_size=3,  stride=2, padding=1, in_chans=3, em...
    method forward (line 188) | def forward(self, x):
  class HighMixer (line 198) | class HighMixer(nn.Module):
    method __init__ (line 199) | def __init__(self, dim, kernel_size=3, stride=1, padding=1,
    method forward (line 217) | def forward(self, x):
  class LowMixer (line 233) | class LowMixer(nn.Module):
    method __init__ (line 234) | def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., poo...
    method att_fun (line 249) | def att_fun(self, q, k, v, B, N, C):
    method forward (line 257) | def forward(self, x):
  class Mixer (line 271) | class Mixer(nn.Module):
    method __init__ (line 272) | def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., pro...
    method forward (line 289) | def forward(self, x):
  class Block (line 306) | class Block(nn.Module):
    method __init__ (line 308) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=...
    method forward (line 330) | def forward(self, x):
  class InceptionTransformer (line 339) | class InceptionTransformer(nn.Module):
    method __init__ (line 340) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe...
    method init_weights (line 415) | def init_weights(self, mode=''):
    method _init_weights (line 423) | def _init_weights(self, m):
    method no_weight_decay (line 429) | def no_weight_decay(self):
    method get_classifier (line 432) | def get_classifier(self):
    method reset_classifier (line 438) | def reset_classifier(self, num_classes, global_pool=''):
    method _get_pos_embed (line 444) | def _get_pos_embed(self, pos_embed, num_patches_def, H, W):
    method forward_features (line 452) | def forward_features(self, x):
    method forward (line 480) | def forward(self, x):
  function _init_vit_weights (line 485) | def _init_vit_weights(module: nn.Module, name: str = '', head_bias: floa...
  function iformer_small (line 512) | def iformer_small(pretrained=False, **kwargs):
  function iformer_small_384 (line 536) | def iformer_small_384(pretrained=False, **kwargs):
  function iformer_base (line 560) | def iformer_base(pretrained=False, **kwargs):
  function iformer_base_384 (line 584) | def iformer_base_384(pretrained=False, **kwargs):
  function iformer_large (line 606) | def iformer_large(pretrained=False, **kwargs):
  function iformer_large_384 (line 630) | def iformer_large_384(pretrained=False, **kwargs):

FILE: classification/model/layers/activations.py
  function swish (line 14) | def swish(x, inplace: bool = False):
  class Swish (line 20) | class Swish(nn.Module):
    method __init__ (line 21) | def __init__(self, inplace: bool = False):
    method forward (line 25) | def forward(self, x):
  function mish (line 29) | def mish(x, inplace: bool = False):
  class Mish (line 36) | class Mish(nn.Module):
    method __init__ (line 39) | def __init__(self, inplace: bool = False):
    method forward (line 42) | def forward(self, x):
  function sigmoid (line 46) | def sigmoid(x, inplace: bool = False):
  class Sigmoid (line 51) | class Sigmoid(nn.Module):
    method __init__ (line 52) | def __init__(self, inplace: bool = False):
    method forward (line 56) | def forward(self, x):
  function tanh (line 60) | def tanh(x, inplace: bool = False):
  class Tanh (line 65) | class Tanh(nn.Module):
    method __init__ (line 66) | def __init__(self, inplace: bool = False):
    method forward (line 70) | def forward(self, x):
  function hard_swish (line 74) | def hard_swish(x, inplace: bool = False):
  class HardSwish (line 79) | class HardSwish(nn.Module):
    method __init__ (line 80) | def __init__(self, inplace: bool = False):
    method forward (line 84) | def forward(self, x):
  function hard_sigmoid (line 88) | def hard_sigmoid(x, inplace: bool = False):
  class HardSigmoid (line 95) | class HardSigmoid(nn.Module):
    method __init__ (line 96) | def __init__(self, inplace: bool = False):
    method forward (line 100) | def forward(self, x):
  function hard_mish (line 104) | def hard_mish(x, inplace: bool = False):
  class HardMish (line 115) | class HardMish(nn.Module):
    method __init__ (line 116) | def __init__(self, inplace: bool = False):
    method forward (line 120) | def forward(self, x):

FILE: classification/model/layers/activations_jit.py
  function swish_jit (line 19) | def swish_jit(x, inplace: bool = False):
  function mish_jit (line 26) | def mish_jit(x, _inplace: bool = False):
  class SwishJit (line 32) | class SwishJit(nn.Module):
    method __init__ (line 33) | def __init__(self, inplace: bool = False):
    method forward (line 36) | def forward(self, x):
  class MishJit (line 40) | class MishJit(nn.Module):
    method __init__ (line 41) | def __init__(self, inplace: bool = False):
    method forward (line 44) | def forward(self, x):
  function hard_sigmoid_jit (line 49) | def hard_sigmoid_jit(x, inplace: bool = False):
  class HardSigmoidJit (line 54) | class HardSigmoidJit(nn.Module):
    method __init__ (line 55) | def __init__(self, inplace: bool = False):
    method forward (line 58) | def forward(self, x):
  function hard_swish_jit (line 63) | def hard_swish_jit(x, inplace: bool = False):
  class HardSwishJit (line 68) | class HardSwishJit(nn.Module):
    method __init__ (line 69) | def __init__(self, inplace: bool = False):
    method forward (line 72) | def forward(self, x):
  function hard_mish_jit (line 77) | def hard_mish_jit(x, inplace: bool = False):
  class HardMishJit (line 85) | class HardMishJit(nn.Module):
    method __init__ (line 86) | def __init__(self, inplace: bool = False):
    method forward (line 89) | def forward(self, x):

FILE: classification/model/layers/activations_me.py
  function swish_jit_fwd (line 18) | def swish_jit_fwd(x):
  function swish_jit_bwd (line 23) | def swish_jit_bwd(x, grad_output):
  class SwishJitAutoFn (line 28) | class SwishJitAutoFn(torch.autograd.Function):
    method forward (line 35) | def forward(ctx, x):
    method backward (line 40) | def backward(ctx, grad_output):
  function swish_me (line 45) | def swish_me(x, inplace=False):
  class SwishMe (line 49) | class SwishMe(nn.Module):
    method __init__ (line 50) | def __init__(self, inplace: bool = False):
    method forward (line 53) | def forward(self, x):
  function mish_jit_fwd (line 58) | def mish_jit_fwd(x):
  function mish_jit_bwd (line 63) | def mish_jit_bwd(x, grad_output):
  class MishJitAutoFn (line 69) | class MishJitAutoFn(torch.autograd.Function):
    method forward (line 74) | def forward(ctx, x):
    method backward (line 79) | def backward(ctx, grad_output):
  function mish_me (line 84) | def mish_me(x, inplace=False):
  class MishMe (line 88) | class MishMe(nn.Module):
    method __init__ (line 89) | def __init__(self, inplace: bool = False):
    method forward (line 92) | def forward(self, x):
  function hard_sigmoid_jit_fwd (line 97) | def hard_sigmoid_jit_fwd(x, inplace: bool = False):
  function hard_sigmoid_jit_bwd (line 102) | def hard_sigmoid_jit_bwd(x, grad_output):
  class HardSigmoidJitAutoFn (line 107) | class HardSigmoidJitAutoFn(torch.autograd.Function):
    method forward (line 109) | def forward(ctx, x):
    method backward (line 114) | def backward(ctx, grad_output):
  function hard_sigmoid_me (line 119) | def hard_sigmoid_me(x, inplace: bool = False):
  class HardSigmoidMe (line 123) | class HardSigmoidMe(nn.Module):
    method __init__ (line 124) | def __init__(self, inplace: bool = False):
    method forward (line 127) | def forward(self, x):
  function hard_swish_jit_fwd (line 132) | def hard_swish_jit_fwd(x):
  function hard_swish_jit_bwd (line 137) | def hard_swish_jit_bwd(x, grad_output):
  class HardSwishJitAutoFn (line 143) | class HardSwishJitAutoFn(torch.autograd.Function):
    method forward (line 146) | def forward(ctx, x):
    method backward (line 151) | def backward(ctx, grad_output):
  function hard_swish_me (line 156) | def hard_swish_me(x, inplace=False):
  class HardSwishMe (line 160) | class HardSwishMe(nn.Module):
    method __init__ (line 161) | def __init__(self, inplace: bool = False):
    method forward (line 164) | def forward(self, x):
  function hard_mish_jit_fwd (line 169) | def hard_mish_jit_fwd(x):
  function hard_mish_jit_bwd (line 174) | def hard_mish_jit_bwd(x, grad_output):
  class HardMishJitAutoFn (line 180) | class HardMishJitAutoFn(torch.autograd.Function):
    method forward (line 186) | def forward(ctx, x):
    method backward (line 191) | def backward(ctx, grad_output):
  function hard_mish_me (line 196) | def hard_mish_me(x, inplace: bool = False):
  class HardMishMe (line 200) | class HardMishMe(nn.Module):
    method __init__ (line 201) | def __init__(self, inplace: bool = False):
    method forward (line 204) | def forward(self, x):

FILE: classification/model/layers/adaptive_avgmax_pool.py
  function adaptive_pool_feat_mult (line 17) | def adaptive_pool_feat_mult(pool_type='avg'):
  function adaptive_avgmax_pool2d (line 24) | def adaptive_avgmax_pool2d(x, output_size=1):
  function adaptive_catavgmax_pool2d (line 30) | def adaptive_catavgmax_pool2d(x, output_size=1):
  function select_adaptive_pool2d (line 36) | def select_adaptive_pool2d(x, pool_type='avg', output_size=1):
  class FastAdaptiveAvgPool2d (line 52) | class FastAdaptiveAvgPool2d(nn.Module):
    method __init__ (line 53) | def __init__(self, flatten=False):
    method forward (line 57) | def forward(self, x):
  class AdaptiveAvgMaxPool2d (line 61) | class AdaptiveAvgMaxPool2d(nn.Module):
    method __init__ (line 62) | def __init__(self, output_size=1):
    method forward (line 66) | def forward(self, x):
  class AdaptiveCatAvgMaxPool2d (line 70) | class AdaptiveCatAvgMaxPool2d(nn.Module):
    method __init__ (line 71) | def __init__(self, output_size=1):
    method forward (line 75) | def forward(self, x):
  class SelectAdaptivePool2d (line 79) | class SelectAdaptivePool2d(nn.Module):
    method __init__ (line 82) | def __init__(self, output_size=1, pool_type='fast', flatten=False):
    method is_identity (line 103) | def is_identity(self):
    method forward (line 106) | def forward(self, x):
    method feat_mult (line 112) | def feat_mult(self):
    method __repr__ (line 115) | def __repr__(self):

FILE: classification/model/layers/aggregation_zeropad.py
  function GET_BLOCKS (line 17) | def GET_BLOCKS(N):
  class AggregationZeropad (line 112) | class AggregationZeropad(Function):
    method forward (line 114) | def forward(ctx, input, weight, kernel_size, stride, padding, dilation):
    method backward (line 148) | def backward(ctx, grad_output):
  function aggregation_zeropad (line 188) | def aggregation_zeropad(input, weight, kernel_size=3, stride=1, padding=...
  class LocalConvolution (line 199) | class LocalConvolution(torch.nn.Module):
    method __init__ (line 200) | def __init__(
    method forward (line 219) | def forward(self, input: Tensor, weight: Tensor):
  function test_aggregation_zeropad (line 238) | def test_aggregation_zeropad():
  function test_aggregation_zeropad_ks1 (line 266) | def test_aggregation_zeropad_ks1():

FILE: classification/model/layers/anti_aliasing.py
  class AntiAliasDownsampleLayer (line 7) | class AntiAliasDownsampleLayer(nn.Module):
    method __init__ (line 8) | def __init__(self, channels: int = 0, filt_size: int = 3, stride: int ...
    method forward (line 17) | def forward(self, x):
  class DownsampleJIT (line 22) | class DownsampleJIT(object):
    method __init__ (line 23) | def __init__(self, channels: int = 0, filt_size: int = 3, stride: int ...
    method _create_filter (line 31) | def _create_filter(self, like: torch.Tensor):
    method __call__ (line 37) | def __call__(self, input: torch.Tensor):
  class Downsample (line 43) | class Downsample(nn.Module):
    method __init__ (line 44) | def __init__(self, channels=None, filt_size=3, stride=2):
    method forward (line 58) | def forward(self, input):

FILE: classification/model/layers/blur_pool.py
  class BlurPool2d (line 19) | class BlurPool2d(nn.Module):
    method __init__ (line 34) | def __init__(self, channels, filt_size=3, stride=2) -> None:
    method _create_filter (line 45) | def _create_filter(self, like: torch.Tensor):
    method _apply (line 49) | def _apply(self, fn):
    method forward (line 54) | def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:

FILE: classification/model/layers/cbam.py
  class ChannelAttn (line 17) | class ChannelAttn(nn.Module):
    method __init__ (line 20) | def __init__(self, channels, reduction=16, act_layer=nn.ReLU):
    method forward (line 26) | def forward(self, x):
  class LightChannelAttn (line 35) | class LightChannelAttn(ChannelAttn):
    method __init__ (line 38) | def __init__(self, channels, reduction=16):
    method forward (line 41) | def forward(self, x):
  class SpatialAttn (line 47) | class SpatialAttn(nn.Module):
    method __init__ (line 50) | def __init__(self, kernel_size=7):
    method forward (line 54) | def forward(self, x):
  class LightSpatialAttn (line 62) | class LightSpatialAttn(nn.Module):
    method __init__ (line 65) | def __init__(self, kernel_size=7):
    method forward (line 69) | def forward(self, x):
  class CbamModule (line 77) | class CbamModule(nn.Module):
    method __init__ (line 78) | def __init__(self, channels, spatial_kernel_size=7):
    method forward (line 83) | def forward(self, x):
  class LightCbamModule (line 89) | class LightCbamModule(nn.Module):
    method __init__ (line 90) | def __init__(self, channels, spatial_kernel_size=7):
    method forward (line 95) | def forward(self, x):

FILE: classification/model/layers/classifier.py
  function create_classifier (line 11) | def create_classifier(num_features, num_classes, pool_type='avg', use_co...
  class ClassifierHead (line 28) | class ClassifierHead(nn.Module):
    method __init__ (line 31) | def __init__(self, in_chs, num_classes, pool_type='avg', drop_rate=0.):
    method forward (line 36) | def forward(self, x):

FILE: classification/model/layers/cond_conv2d.py
  function get_condconv_initializer (line 21) | def get_condconv_initializer(initializer, num_experts, expert_shape):
  class CondConv2d (line 34) | class CondConv2d(nn.Module):
    method __init__ (line 43) | def __init__(self, in_channels, out_channels, kernel_size=3,
    method reset_parameters (line 73) | def reset_parameters(self):
    method forward (line 84) | def forward(self, x, routing_weights):

FILE: classification/model/layers/config.py
  function is_no_jit (line 25) | def is_no_jit():
  class set_no_jit (line 29) | class set_no_jit:
    method __init__ (line 30) | def __init__(self, mode: bool) -> None:
    method __enter__ (line 35) | def __enter__(self) -> None:
    method __exit__ (line 38) | def __exit__(self, *args: Any) -> bool:
  function is_exportable (line 44) | def is_exportable():
  class set_exportable (line 48) | class set_exportable:
    method __init__ (line 49) | def __init__(self, mode: bool) -> None:
    method __enter__ (line 54) | def __enter__(self) -> None:
    method __exit__ (line 57) | def __exit__(self, *args: Any) -> bool:
  function is_scriptable (line 63) | def is_scriptable():
  class set_scriptable (line 67) | class set_scriptable:
    method __init__ (line 68) | def __init__(self, mode: bool) -> None:
    method __enter__ (line 73) | def __enter__(self) -> None:
    method __exit__ (line 76) | def __exit__(self, *args: Any) -> bool:
  class set_layer_config (line 82) | class set_layer_config:
    method __init__ (line 86) | def __init__(
    method __enter__ (line 106) | def __enter__(self) -> None:
    method __exit__ (line 109) | def __exit__(self, *args: Any) -> bool:

FILE: classification/model/layers/conv2d_same.py
  function conv2d_same (line 13) | def conv2d_same(
  class Conv2dSame (line 20) | class Conv2dSame(nn.Conv2d):
    method __init__ (line 24) | def __init__(self, in_channels, out_channels, kernel_size, stride=1,
    method forward (line 29) | def forward(self, x):
  function create_conv2d_pad (line 33) | def create_conv2d_pad(in_chs, out_chs, kernel_size, **kwargs):

FILE: classification/model/layers/conv_bn_act.py
  class ConvBnAct (line 11) | class ConvBnAct(nn.Module):
    method __init__ (line 12) | def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,...
    method in_channels (line 28) | def in_channels(self):
    method out_channels (line 32) | def out_channels(self):
    method forward (line 35) | def forward(self, x):

FILE: classification/model/layers/create_act.py
  function get_act_fn (line 89) | def get_act_fn(name='relu'):
  function get_act_layer (line 107) | def get_act_layer(name='relu'):
  function create_act_layer (line 123) | def create_act_layer(name, inplace=False, **kwargs):

FILE: classification/model/layers/create_attn.py
  function create_attn (line 11) | def create_attn(attn_type, channels, **kwargs):

FILE: classification/model/layers/create_conv2d.py
  function create_conv2d (line 11) | def create_conv2d(in_channels, out_channels, kernel_size, **kwargs):

FILE: classification/model/layers/create_norm_act.py
  function get_norm_act_layer (line 22) | def get_norm_act_layer(layer_class):
  function create_norm_act (line 39) | def create_norm_act(layer_type, num_features, apply_act=True, jit=False,...
  function convert_norm_act_type (line 50) | def convert_norm_act_type(norm_layer, act_layer, norm_kwargs=None):

FILE: classification/model/layers/drop.py
  function drop_block_2d (line 22) | def drop_block_2d(
  function drop_block_fast_2d (line 70) | def drop_block_fast_2d(
  class DropBlock2d (line 109) | class DropBlock2d(nn.Module):
    method __init__ (line 112) | def __init__(self,
    method forward (line 129) | def forward(self, x):
  function drop_path (line 140) | def drop_path(x, drop_prob: float = 0., training: bool = False):
  class DropPath (line 160) | class DropPath(nn.Module):
    method __init__ (line 163) | def __init__(self, drop_prob=None):
    method forward (line 167) | def forward(self, x):

FILE: classification/model/layers/eca.py
  class EcaModule (line 41) | class EcaModule(nn.Module):
    method __init__ (line 52) | def __init__(self, channels=None, kernel_size=3, gamma=2, beta=1):
    method forward (line 61) | def forward(self, x):
  class CecaModule (line 68) | class CecaModule(nn.Module):
    method __init__ (line 88) | def __init__(self, channels=None, kernel_size=3, gamma=2, beta=1):
    method forward (line 101) | def forward(self, x):

FILE: classification/model/layers/evo_norm.py
  class EvoNormBatch2d (line 16) | class EvoNormBatch2d(nn.Module):
    method __init__ (line 17) | def __init__(self, num_features, apply_act=True, momentum=0.1, eps=1e-...
    method reset_parameters (line 30) | def reset_parameters(self):
    method forward (line 36) | def forward(self, x):
  class EvoNormSample2d (line 55) | class EvoNormSample2d(nn.Module):
    method __init__ (line 56) | def __init__(self, num_features, apply_act=True, groups=8, eps=1e-5, d...
    method reset_parameters (line 68) | def reset_parameters(self):
    method forward (line 74) | def forward(self, x):

FILE: classification/model/layers/helpers.py
  function _ntuple (line 10) | def _ntuple(n):

FILE: classification/model/layers/inplace_abn.py
  function inplace_abn (line 10) | def inplace_abn(x, weight, bias, running_mean, running_var,
  function inplace_abn_sync (line 15) | def inplace_abn_sync(**kwargs):
  class InplaceAbn (line 19) | class InplaceAbn(nn.Module):
    method __init__ (line 40) | def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, ...
    method reset_parameters (line 74) | def reset_parameters(self):
    method forward (line 81) | def forward(self, x):

FILE: classification/model/layers/kerv2d.py
  class Kerv2d (line 9) | class Kerv2d(nn.Conv2d):
    method __init__ (line 10) | def __init__(
    method forward (line 35) | def forward(self, input):

FILE: classification/model/layers/median_pool.py
  class MedianPool2d (line 9) | class MedianPool2d(nn.Module):
    method __init__ (line 18) | def __init__(self, kernel_size=3, stride=1, padding=0, same=False):
    method _padding (line 25) | def _padding(self, x):
    method forward (line 45) | def forward(self, x):

FILE: classification/model/layers/mixed_conv2d.py
  function _split_channels (line 14) | def _split_channels(num_chan, num_groups):
  class MixedConv2d (line 20) | class MixedConv2d(nn.ModuleDict):
    method __init__ (line 26) | def __init__(self, in_channels, out_channels, kernel_size=3,
    method forward (line 47) | def forward(self, x):

FILE: classification/model/layers/mlp.py
  class Mlp (line 8) | class Mlp(nn.Module):
    method __init__ (line 11) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 20) | def forward(self, x):
  class GluMlp (line 29) | class GluMlp(nn.Module):
    method __init__ (line 33) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method init_weights (line 43) | def init_weights(self):
    method forward (line 49) | def forward(self, x):
  class GatedMlp (line 59) | class GatedMlp(nn.Module):
    method __init__ (line 62) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 78) | def forward(self, x):
  class ConvMlp (line 88) | class ConvMlp(nn.Module):
    method __init__ (line 91) | def __init__(
    method forward (line 102) | def forward(self, x):

FILE: classification/model/layers/norm_act.py
  class BatchNormAct2d (line 10) | class BatchNormAct2d(nn.BatchNorm2d):
    method __init__ (line 17) | def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, ...
    method _forward_jit (line 29) | def _forward_jit(self, x):
    method _forward_python (line 56) | def _forward_python(self, x):
    method forward (line 59) | def forward(self, x):
  class GroupNormAct (line 70) | class GroupNormAct(nn.GroupNorm):
    method __init__ (line 72) | def __init__(self, num_groups, num_channels, eps=1e-5, affine=True,
    method forward (line 82) | def forward(self, x):

FILE: classification/model/layers/padding.py
  function get_padding (line 12) | def get_padding(kernel_size: int, stride: int = 1, dilation: int = 1, **...
  function get_same_padding (line 18) | def get_same_padding(x: int, k: int, s: int, d: int):
  function is_static_pad (line 23) | def is_static_pad(kernel_size: int, stride: int = 1, dilation: int = 1, ...
  function pad_same (line 28) | def pad_same(x, k: List[int], s: List[int], d: List[int] = (1, 1), value...
  function get_padding_value (line 36) | def get_padding_value(padding, kernel_size, **kwargs) -> Tuple[Tuple, bo...

FILE: classification/model/layers/patch_embed.py
  class PatchEmbed (line 15) | class PatchEmbed(nn.Module):
    method __init__ (line 18) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
    method forward (line 31) | def forward(self, x):

FILE: classification/model/layers/pool2d_same.py
  function avg_pool2d_same (line 14) | def avg_pool2d_same(x, kernel_size: List[int], stride: List[int], paddin...
  class AvgPool2dSame (line 21) | class AvgPool2dSame(nn.AvgPool2d):
    method __init__ (line 24) | def __init__(self, kernel_size: int, stride=None, padding=0, ceil_mode...
    method forward (line 29) | def forward(self, x):
  function max_pool2d_same (line 34) | def max_pool2d_same(
  class MaxPool2dSame (line 41) | class MaxPool2dSame(nn.MaxPool2d):
    method __init__ (line 44) | def __init__(self, kernel_size: int, stride=None, padding=0, dilation=...
    method forward (line 50) | def forward(self, x):
  function create_pool2d (line 54) | def create_pool2d(pool_type, kernel_size, stride=None, **kwargs):

FILE: classification/model/layers/se.py
  class SEModule (line 5) | class SEModule(nn.Module):
    method __init__ (line 7) | def __init__(self, channels, reduction=16, act_layer=nn.ReLU, min_chan...
    method forward (line 16) | def forward(self, x):
  class EffectiveSEModule (line 24) | class EffectiveSEModule(nn.Module):
    method __init__ (line 28) | def __init__(self, channels, gate_layer='hard_sigmoid'):
    method forward (line 33) | def forward(self, x):

FILE: classification/model/layers/selective_kernel.py
  function _kernel_valid (line 13) | def _kernel_valid(k):
  class SelectiveKernelAttn (line 20) | class SelectiveKernelAttn(nn.Module):
    method __init__ (line 21) | def __init__(self, channels, num_paths=2, attn_channels=32,
    method forward (line 35) | def forward(self, x):
  class SelectiveKernelConv (line 48) | class SelectiveKernelConv(nn.Module):
    method __init__ (line 50) | def __init__(self, in_channels, out_channels, kernel_size=None, stride...
    method forward (line 108) | def forward(self, x):

FILE: classification/model/layers/separable_conv.py
  class SeparableConvBnAct (line 14) | class SeparableConvBnAct(nn.Module):
    method __init__ (line 17) | def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,...
    method in_channels (line 34) | def in_channels(self):
    method out_channels (line 38) | def out_channels(self):
    method forward (line 41) | def forward(self, x):
  class SeparableConv2d (line 49) | class SeparableConv2d(nn.Module):
    method __init__ (line 52) | def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,...
    method in_channels (line 64) | def in_channels(self):
    method out_channels (line 68) | def out_channels(self):
    method forward (line 71) | def forward(self, x):

FILE: classification/model/layers/shiftlution.py
  class Shiftlution (line 5) | class Shiftlution(nn.Module):
    method __init__ (line 6) | def __init__(self, channels, kernel_h, kernel_w, max_height = 400, max...
    method forward (line 49) | def forward(self, x):

FILE: classification/model/layers/space_to_depth.py
  class SpaceToDepth (line 5) | class SpaceToDepth(nn.Module):
    method __init__ (line 6) | def __init__(self, block_size=4):
    method forward (line 11) | def forward(self, x):
  class SpaceToDepthJit (line 20) | class SpaceToDepthJit(object):
    method __call__ (line 21) | def __call__(self, x: torch.Tensor):
  class SpaceToDepthModule (line 30) | class SpaceToDepthModule(nn.Module):
    method __init__ (line 31) | def __init__(self, no_jit=False):
    method forward (line 38) | def forward(self, x):
  class DepthToSpace (line 42) | class DepthToSpace(nn.Module):
    method __init__ (line 44) | def __init__(self, block_size):
    method forward (line 48) | def forward(self, x):

FILE: classification/model/layers/split_attn.py
  class RadixSoftmax (line 14) | class RadixSoftmax(nn.Module):
    method __init__ (line 15) | def __init__(self, radix, cardinality):
    method forward (line 20) | def forward(self, x):
  class SplitAttnConv2d (line 31) | class SplitAttnConv2d(nn.Module):
    method __init__ (line 34) | def __init__(self, in_channels, out_channels, kernel_size, stride=1, p...
    method in_channels (line 55) | def in_channels(self):
    method out_channels (line 59) | def out_channels(self):
    method forward (line 62) | def forward(self, x):

FILE: classification/model/layers/split_batchnorm.py
  class SplitBatchNorm2d (line 18) | class SplitBatchNorm2d(torch.nn.BatchNorm2d):
    method __init__ (line 20) | def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,
    method forward (line 28) | def forward(self, input: torch.Tensor):
  function convert_splitbn_model (line 41) | def convert_splitbn_model(module, num_splits=2):

FILE: classification/model/layers/std_conv.py
  function get_weight (line 8) | def get_weight(module):
  class StdConv2d (line 14) | class StdConv2d(nn.Conv2d):
    method __init__ (line 19) | def __init__(
    method get_weight (line 29) | def get_weight(self):
    method forward (line 34) | def forward(self, x):
  class StdConv2dSame (line 39) | class StdConv2dSame(nn.Conv2d):
    method __init__ (line 44) | def __init__(
    method get_weight (line 54) | def get_weight(self):
    method forward (line 59) | def forward(self, x):
  class ScaledStdConv2d (line 66) | class ScaledStdConv2d(nn.Conv2d):
    method __init__ (line 73) | def __init__(
    method get_weight (line 86) | def get_weight(self):
    method forward (line 94) | def forward(self, x):
  class ScaledStdConv2dSame (line 98) | class ScaledStdConv2dSame(nn.Conv2d):
    method __init__ (line 105) | def __init__(
    method get_weight (line 126) | def get_weight(self):
    method forward (line 134) | def forward(self, x):

FILE: classification/model/layers/tbconv.py
  class TBConv (line 9) | class TBConv(nn.Module):
    method __init__ (line 10) | def __init__(
    method reset_parameters (line 68) | def reset_parameters(self) -> None:
    method extra_repr (line 75) | def extra_repr(self):
    method __setstate__ (line 94) | def __setstate__(self, state):
    method forward (line 99) | def forward(self, input: Tensor, weight=None) -> Tensor:

FILE: classification/model/layers/test_time_pool.py
  class TestTimePoolHead (line 16) | class TestTimePoolHead(nn.Module):
    method __init__ (line 17) | def __init__(self, base, original_pool=7):
    method forward (line 31) | def forward(self, x):
  function apply_test_time_pool (line 39) | def apply_test_time_pool(model, config):

FILE: classification/model/layers/utils.py
  function Dtype (line 8) | def Dtype(t):
  function load_kernel (line 15) | def load_kernel(kernel_name, code, **kwargs):

FILE: classification/model/layers/weight_init.py
  function _no_grad_trunc_normal_ (line 8) | def _no_grad_trunc_normal_(tensor, mean, std, a, b):
  function trunc_normal_ (line 44) | def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
  function variance_scaling_ (line 65) | def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='no...
  function lecun_normal_ (line 88) | def lecun_normal_(tensor):

FILE: classification/model/nextvit.py
  class ConvBNReLU (line 15) | class ConvBNReLU(nn.Module):
    method __init__ (line 16) | def __init__(
    method forward (line 29) | def forward(self, x):
  function _make_divisible (line 36) | def _make_divisible(v, divisor, min_value=None):
  class PatchEmbed (line 46) | class PatchEmbed(nn.Module):
    method __init__ (line 47) | def __init__(self,
    method forward (line 66) | def forward(self, x):
  class MHCA (line 70) | class MHCA(nn.Module):
    method __init__ (line 74) | def __init__(self, out_channels, head_dim):
    method forward (line 83) | def forward(self, x):
  class Mlp (line 91) | class Mlp(nn.Module):
    method __init__ (line 92) | def __init__(self, in_features, out_features=None, mlp_ratio=None, dro...
    method merge_bn (line 101) | def merge_bn(self, pre_norm):
    method forward (line 104) | def forward(self, x):
  class NCB (line 113) | class NCB(nn.Module):
    method __init__ (line 117) | def __init__(self, in_channels, out_channels, stride=1, path_dropout=0,
    method merge_bn (line 134) | def merge_bn(self):
    method forward (line 139) | def forward(self, x):
  class E_MHSA (line 150) | class E_MHSA(nn.Module):
    method __init__ (line 154) | def __init__(self, dim, out_dim=None, head_dim=32, qkv_bias=True, qk_s...
    method merge_bn (line 175) | def merge_bn(self, pre_bn):
    method forward (line 185) | def forward(self, x):
  class NTB (line 216) | class NTB(nn.Module):
    method __init__ (line 220) | def __init__(
    method merge_bn (line 249) | def merge_bn(self):
    method forward (line 255) | def forward(self, x):
  class NextViT (line 278) | class NextViT(nn.Module):
    method __init__ (line 279) | def __init__(self, stem_chs, depths, path_dropout, attn_drop=0, drop=0...
    method merge_bn (line 342) | def merge_bn(self):
    method _initialize_weights (line 348) | def _initialize_weights(self):
    method forward (line 362) | def forward(self, x):
  function nextvit_small (line 377) | def nextvit_small(pretrained=False, pretrained_cfg=None, **kwargs):
  function nextvit_base (line 383) | def nextvit_base(pretrained=False, pretrained_cfg=None, **kwargs):
  function nextvit_large (line 389) | def nextvit_large(pretrained=False, pretrained_cfg=None, **kwargs):

FILE: classification/model/registry.py
  function register_model (line 18) | def register_model(fn):
  function _natural_key (line 45) | def _natural_key(string_):
  function list_models (line 49) | def list_models(filter='', module='', pretrained=False, exclude_filters=...
  function is_model (line 80) | def is_model(model_name):
  function model_entrypoint (line 86) | def model_entrypoint(model_name):
  function list_modules (line 92) | def list_modules():
  function is_model_in_modules (line 99) | def is_model_in_modules(model_name, module_names):

FILE: classification/model/res2net.py
  class Bottle2neck (line 19) | class Bottle2neck(nn.Module):
    method __init__ (line 22) | def __init__(self, inplanes, planes, stride=1, downsample=None, baseWi...
    method forward (line 62) | def forward(self, x):
  class Res2Net (line 99) | class Res2Net(nn.Module):
    method __init__ (line 101) | def __init__(self, block, layers, baseWidth=26, scale=4, num_classes=10):
    method _make_layer (line 125) | def _make_layer(self, block, planes, blocks, stride=1):
    method forward (line 143) | def forward(self, x):
  function res2net50 (line 161) | def res2net50(pretrained=False, **kwargs):
  function res2net50_26w_4s (line 173) | def res2net50_26w_4s(pretrained=False, **kwargs):
  function res2net101_26w_4s (line 184) | def res2net101_26w_4s(pretrained=False, **kwargs):
  function res2net50_26w_6s (line 195) | def res2net50_26w_6s(pretrained=False, **kwargs):
  function res2net50_26w_8s (line 206) | def res2net50_26w_8s(pretrained=False, **kwargs):
  function res2net50_48w_2s (line 217) | def res2net50_48w_2s(pretrained=False, **kwargs):
  function res2net50_14w_8s (line 228) | def res2net50_14w_8s(pretrained=False, **kwargs):

FILE: classification/model/resnet.py
  function _cfg (line 24) | def _cfg(url='', **kwargs):
  function get_padding (line 217) | def get_padding(kernel_size, stride, dilation=1):
  class BasicBlock (line 222) | class BasicBlock(nn.Module):
    method __init__ (line 225) | def __init__(self, inplanes, planes, stride=1, downsample=None, cardin...
    method zero_init_last_bn (line 257) | def zero_init_last_bn(self):
    method forward (line 260) | def forward(self, x):
  class Bottleneck (line 290) | class Bottleneck(nn.Module):
    method __init__ (line 293) | def __init__(self, inplanes, planes, stride=1, downsample=None, cardin...
    method zero_init_last_bn (line 327) | def zero_init_last_bn(self):
    method forward (line 330) | def forward(self, x):
  function downsample_conv (line 366) | def downsample_conv(
  function downsample_avg (line 380) | def downsample_avg(
  function drop_blocks (line 397) | def drop_blocks(drop_block_rate=0.):
  function make_blocks (line 404) | def make_blocks(
  class ResNet (line 448) | class ResNet(nn.Module):
    method __init__ (line 519) | def __init__(self, block, layers, num_classes=1000, in_chans=1,
    method get_classifier (line 587) | def get_classifier(self):
    method reset_classifier (line 590) | def reset_classifier(self, num_classes, global_pool='avg'):
    method forward_features (line 594) | def forward_features(self, x):
    method forward (line 606) | def forward(self, x):
  function _create_resnet (line 615) | def _create_resnet(variant, pretrained=False, **kwargs):
  function resnet18 (line 621) | def resnet18(pretrained=False, **kwargs):
  function resnet18d (line 629) | def resnet18d(pretrained=False, **kwargs):
  function resnet34 (line 638) | def resnet34(pretrained=False, **kwargs):
  function resnet34d (line 646) | def resnet34d(pretrained=False, **kwargs):
  function resnet26 (line 655) | def resnet26(pretrained=False, **kwargs):
  function resnet26d (line 663) | def resnet26d(pretrained=False, **kwargs):
  function resnet50 (line 671) | def resnet50(pretrained=False, **kwargs):
  function resnet50d (line 679) | def resnet50d(pretrained=False, **kwargs):
  function resnet66d (line 688) | def resnet66d(pretrained=False, **kwargs):
  function resnet101 (line 696) | def resnet101(pretrained=False, **kwargs):
  function resnet101d (line 704) | def resnet101d(pretrained=False, **kwargs):
  function resnet152 (line 712) | def resnet152(pretrained=False, **kwargs):
  function resnet152d (line 720) | def resnet152d(pretrained=False, **kwargs):
  function resnet200 (line 729) | def resnet200(pretrained=False, **kwargs):
  function resnet200d (line 737) | def resnet200d(pretrained=False, **kwargs):
  function tv_resnet34 (line 746) | def tv_resnet34(pretrained=False, **kwargs):
  function tv_resnet50 (line 754) | def tv_resnet50(pretrained=False, **kwargs):
  function tv_resnet101 (line 762) | def tv_resnet101(pretrained=False, **kwargs):
  function tv_resnet152 (line 770) | def tv_resnet152(pretrained=False, **kwargs):
  function wide_resnet50_2 (line 778) | def wide_resnet50_2(pretrained=False, **kwargs):
  function wide_resnet101_2 (line 790) | def wide_resnet101_2(pretrained=False, **kwargs):
  function resnext50_32x4d (line 801) | def resnext50_32x4d(pretrained=False, **kwargs):
  function resnext26_32x4d (line 808) | def resnext26_32x4d(pretrained=False, **kwargs):
  function resnext50d_32x4d (line 815) | def resnext50d_32x4d(pretrained=False, **kwargs):
  function resnext101_32x4d (line 825) | def resnext101_32x4d(pretrained=False, **kwargs):
  function resnext101_32x8d (line 833) | def resnext101_32x8d(pretrained=False, **kwargs):
  function resnext101_64x4d (line 841) | def resnext101_64x4d(pretrained=False, **kwargs):
  function tv_resnext50_32x4d (line 849) | def tv_resnext50_32x4d(pretrained=False, **kwargs):
  function ig_resnext101_32x8d (line 857) | def ig_resnext101_32x8d(pretrained=True, **kwargs):
  function ig_resnext101_32x16d (line 868) | def ig_resnext101_32x16d(pretrained=True, **kwargs):
  function ig_resnext101_32x32d (line 879) | def ig_resnext101_32x32d(pretrained=True, **kwargs):
  function ig_resnext101_32x48d (line 890) | def ig_resnext101_32x48d(pretrained=True, **kwargs):
  function ssl_resnet18 (line 901) | def ssl_resnet18(pretrained=True, **kwargs):
  function ssl_resnet50 (line 911) | def ssl_resnet50(pretrained=True, **kwargs):
  function ssl_resnext50_32x4d (line 921) | def ssl_resnext50_32x4d(pretrained=True, **kwargs):
  function ssl_resnext101_32x4d (line 931) | def ssl_resnext101_32x4d(pretrained=True, **kwargs):
  function ssl_resnext101_32x8d (line 941) | def ssl_resnext101_32x8d(pretrained=True, **kwargs):
  function ssl_resnext101_32x16d (line 951) | def ssl_resnext101_32x16d(pretrained=True, **kwargs):
  function swsl_resnet18 (line 961) | def swsl_resnet18(pretrained=True, **kwargs):
  function swsl_resnet50 (line 972) | def swsl_resnet50(pretrained=True, **kwargs):
  function swsl_resnext50_32x4d (line 983) | def swsl_resnext50_32x4d(pretrained=True, **kwargs):
  function swsl_resnext101_32x4d (line 994) | def swsl_resnext101_32x4d(pretrained=True, **kwargs):
  function swsl_resnext101_32x8d (line 1005) | def swsl_resnext101_32x8d(pretrained=True, **kwargs):
  function swsl_resnext101_32x16d (line 1016) | def swsl_resnext101_32x16d(pretrained=True, **kwargs):
  function ecaresnet18 (line 1027) | def ecaresnet18(pretrained=False, **kwargs):
  function ecaresnet50 (line 1035) | def ecaresnet50(pretrained=False, **kwargs):
  function ecaresnet50d (line 1043) | def ecaresnet50d(pretrained=False, **kwargs):
  function ecaresnet50d_pruned (line 1053) | def ecaresnet50d_pruned(pretrained=False, **kwargs):
  function ecaresnetlight (line 1064) | def ecaresnetlight(pretrained=False, **kwargs):
  function ecaresnet101d (line 1074) | def ecaresnet101d(pretrained=False, **kwargs):
  function ecaresnet101d_pruned (line 1084) | def ecaresnet101d_pruned(pretrained=False, **kwargs):
  function ecaresnext26tn_32x4d (line 1095) | def ecaresnext26tn_32x4d(pretrained=False, **kwargs):
  function resnetblur18 (line 1108) | def resnetblur18(pretrained=False, **kwargs):
  function resnetblur50 (line 1116) | def resnetblur50(pretrained=False, **kwargs):
  function seresnet18 (line 1124) | def seresnet18(pretrained=False, **kwargs):
  function seresnet34 (line 1130) | def seresnet34(pretrained=False, **kwargs):
  function seresnet50 (line 1136) | def seresnet50(pretrained=False, **kwargs):
  function seresnet50tn (line 1142) | def seresnet50tn(pretrained=False, **kwargs):
  function seresnet101 (line 1150) | def seresnet101(pretrained=False, **kwargs):
  function seresnet152 (line 1156) | def seresnet152(pretrained=False, **kwargs):
  function seresnext26_32x4d (line 1162) | def seresnext26_32x4d(pretrained=False, **kwargs):
  function seresnext26d_32x4d (line 1170) | def seresnext26d_32x4d(pretrained=False, **kwargs):
  function seresnext26t_32x4d (line 1182) | def seresnext26t_32x4d(pretrained=False, **kwargs):
  function seresnext26tn_32x4d (line 1194) | def seresnext26tn_32x4d(pretrained=False, **kwargs):
  function seresnext50_32x4d (line 1206) | def seresnext50_32x4d(pretrained=False, **kwargs):
  function seresnext101_32x4d (line 1214) | def seresnext101_32x4d(pretrained=False, **kwargs):
  function seresnext101_32x8d (line 1222) | def seresnext101_32x8d(pretrained=False, **kwargs):
  function senet154 (line 1230) | def senet154(pretrained=False, **kwargs):

FILE: classification/model/utils.py
  class SmoothedValue (line 18) | class SmoothedValue(object):
    method __init__ (line 23) | def __init__(self, window_size=20, fmt=None):
    method update (line 31) | def update(self, value, n=1):
    method synchronize_between_processes (line 36) | def synchronize_between_processes(self):
    method median (line 50) | def median(self):
    method avg (line 55) | def avg(self):
    method global_avg (line 60) | def global_avg(self):
    method max (line 64) | def max(self):
    method value (line 68) | def value(self):
    method __str__ (line 71) | def __str__(self):
  class MetricLogger (line 80) | class MetricLogger(object):
    method __init__ (line 81) | def __init__(self, delimiter="\t"):
    method update (line 85) | def update(self, **kwargs):
    method __getattr__ (line 92) | def __getattr__(self, attr):
    method __str__ (line 100) | def __str__(self):
    method synchronize_between_processes (line 108) | def synchronize_between_processes(self):
    method add_meter (line 112) | def add_meter(self, name, meter):
    method log_every (line 115) | def log_every(self, iterable, print_freq, header=None):
  function _load_checkpoint_for_ema (line 162) | def _load_checkpoint_for_ema(model_ema, checkpoint):
  function setup_for_distributed (line 172) | def setup_for_distributed(is_master):
  function is_dist_avail_and_initialized (line 187) | def is_dist_avail_and_initialized():
  function get_world_size (line 195) | def get_world_size():
  function get_rank (line 201) | def get_rank():
  function is_main_process (line 207) | def is_main_process():
  function save_on_master (line 211) | def save_on_master(*args, **kwargs):
  function init_distributed_mode (line 216) | def init_distributed_mode(args):
  function merge_pre_bn (line 241) | def merge_pre_bn(module, pre_bn_1, pre_bn_2=None):
  function cal_flops_params_with_fvcore (line 283) | def cal_flops_params_with_fvcore(model, inputs):

FILE: classification/model/vgg.py
  class VGG (line 33) | class VGG(nn.Module):
    method __init__ (line 34) | def __init__(
    method forward (line 63) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  function make_layers (line 71) | def make_layers(cfg: List[Union[str, int]], batch_norm: bool = False) ->...
  function _vgg (line 98) | def _vgg(arch: str, cfg: str, batch_norm: bool, pretrained: bool, progre...
  function vgg11 (line 108) | def vgg11(pretrained: bool = False, progress: bool = True, **kwargs: Any...
  function vgg11_bn (line 120) | def vgg11_bn(pretrained: bool = False, progress: bool = True, **kwargs: ...
  function vgg13 (line 132) | def vgg13(pretrained: bool = False, progress: bool = True, **kwargs: Any...
  function vgg13_bn (line 144) | def vgg13_bn(pretrained: bool = False, progress: bool = True, **kwargs: ...
  function vgg16 (line 156) | def vgg16(pretrained: bool = False, progress: bool = True, **kwargs: Any...
  function vgg16_bn (line 168) | def vgg16_bn(pretrained: bool = False, progress: bool = True, **kwargs: ...
  function vgg19 (line 180) | def vgg19(pretrained: bool = False, progress: bool = True, **kwargs: Any...
  function vgg19_bn (line 192) | def vgg19_bn(pretrained: bool = False, progress: bool = True, **kwargs: ...
  class MVGGNet (line 203) | class MVGGNet(torch.nn.Module):
    method __init__ (line 204) | def __init__(self, num_classes=3):
    method forward (line 215) | def forward(self, x):

FILE: classification/parse_test_res.py
  function compute_ci95 (line 60) | def compute_ci95(res):
  function parse_function (line 63) | def parse_function(*metrics, directory="", args=None, end_signal=None):
  function main (line 125) | def main(args, end_signal):

FILE: classification/train.py
  function print_args (line 37) | def print_args(args, cfg):
  function reset_cfg (line 51) | def reset_cfg(cfg, args):
  function extend_cfg (line 83) | def extend_cfg(cfg):
  function setup_cfg (line 111) | def setup_cfg(args):
  function main (line 134) | def main(args):

FILE: classification/trainers/MIM_linear.py
  function interpolate_pos_embed (line 28) | def interpolate_pos_embed(model, checkpoint_model):
  class CustomCLIP (line 51) | class CustomCLIP(nn.Module):
    method __init__ (line 53) | def __init__(self, cfg, classnames):
    method forward (line 123) | def forward(self, image):
  class MIM_linear (line 130) | class MIM_linear(TrainerX):
    method build_model (line 133) | def build_model(self):
    method forward_backward (line 164) | def forward_backward(self, batch):
    method parse_batch_train (line 182) | def parse_batch_train(self, batch):
    method load_model (line 189) | def load_model(self, directory, epoch=None):
    method test (line 231) | def test(self, split=None):
    method after_epoch (line 261) | def after_epoch(self):

FILE: detection/demo/create_result_gif.py
  function parse_args (line 17) | def parse_args():
  function _generate_batch_data (line 32) | def _generate_batch_data(sampler, batch_size):
  function create_gif (line 43) | def create_gif(frames, gif_name, duration=2):
  function create_frame_by_matplotlib (line 58) | def create_frame_by_matplotlib(image_dir,
  function main (line 156) | def main():

FILE: detection/demo/image_demo.py
  function parse_args (line 9) | def parse_args():
  function main (line 32) | def main(args):
  function async_main (line 47) | async def async_main(args):

FILE: detection/demo/video_demo.py
  function parse_args (line 10) | def parse_args():
  function main (line 30) | def main():

FILE: detection/demo/webcam_demo.py
  function parse_args (line 10) | def parse_args():
  function main (line 24) | def main():

FILE: detection/docs/en/conf.py
  function get_version (line 29) | def get_version():
  function builder_inited_handler (line 108) | def builder_inited_handler(app):
  function setup (line 112) | def setup(app):

FILE: detection/docs/zh_cn/conf.py
  function get_version (line 29) | def get_version():
  function builder_inited_handler (line 110) | def builder_inited_handler(app):
  function setup (line 114) | def setup(app):

FILE: detection/huge_image_infer.py
  class LargeImageDetector (line 10) | class LargeImageDetector:
    method __init__ (line 11) | def __init__(self, config_file, checkpoint_file, device='cuda:0'):
    method sliding_window_inference (line 24) | def sliding_window_inference(self,
    method nms (line 89) | def nms(self, detections, iou_threshold):
    method calculate_iou (line 122) | def calculate_iou(self, box1, box2):
    method visualize_results (line 158) | def visualize_results(self, image, results, output_path=None):
  function main (line 203) | def main():

FILE: detection/mmcv_custom/checkpoint.py
  function _get_mmcv_home (line 30) | def _get_mmcv_home():
  function load_state_dict (line 41) | def load_state_dict(module, state_dict, strict=False, logger=None):
  function load_url_dist (line 109) | def load_url_dist(url, model_dir=None):
  function load_pavimodel_dist (line 123) | def load_pavimodel_dist(model_path, map_location=None):
  function load_fileclient_dist (line 151) | def load_fileclient_dist(filename, backend, map_location):
  function get_torchvision_models (line 172) | def get_torchvision_models():
  function get_external_models (line 184) | def get_external_models():
  function get_mmcls_models (line 198) | def get_mmcls_models():
  function get_deprecated_model_names (line 205) | def get_deprecated_model_names():
  function _process_mmcls_checkpoint (line 214) | def _process_mmcls_checkpoint(checkpoint):
  function _load_checkpoint (line 225) | def _load_checkpoint(filename, map_location=None):
  function load_checkpoint (line 286) | def load_checkpoint(model,
  function weights_to_cpu (line 359) | def weights_to_cpu(state_dict):
  function _save_to_state_dict (line 374) | def _save_to_state_dict(module, destination, prefix, keep_vars):
  function get_state_dict (line 394) | def get_state_dict(module, destination=None, prefix='', keep_vars=False):
  function save_checkpoint (line 438) | def save_checkpoint(model, filename, optimizer=None, meta=None):

FILE: detection/mmcv_custom/hivit_layer_decay_optimizer_constructor.py
  function get_hivit_layer_id (line 19) | def get_hivit_layer_id(name, num_layers, main_block):
  class HiViTLayerDecayOptimizerConstructor (line 38) | class HiViTLayerDecayOptimizerConstructor(DefaultOptimizerConstructor):
    method _validate_cfg (line 39) | def _validate_cfg(self):
    method add_params (line 42) | def add_params(self, params, module, **kwargs):
  class HiViT2LayerDecayOptimizerConstructor (line 114) | class HiViT2LayerDecayOptimizerConstructor(DefaultOptimizerConstructor):
    method _validate_cfg (line 115) | def _validate_cfg(self):
    method add_params (line 118) | def add_params(self, params, module, **kwargs):

FILE: detection/mmcv_custom/layer_decay_optimizer_constructor.py
  function get_num_layer_for_vit (line 17) | def get_num_layer_for_vit(var_name, num_max_layer):
  class LayerDecayOptimizerConstructor (line 30) | class LayerDecayOptimizerConstructor(DefaultOptimizerConstructor):
    method _validate_cfg (line 31) | def _validate_cfg(self):
    method add_params (line 50) | def add_params(self, params, module, prefix='', is_dcn_module=None):

FILE: detection/mmcv_custom/runner/checkpoint.py
  function save_checkpoint (line 19) | def save_checkpoint(model, filename, optimizer=None, meta=None):

FILE: detection/mmcv_custom/runner/epoch_based_runner.py
  class EpochBasedRunnerAmp (line 20) | class EpochBasedRunnerAmp(EpochBasedRunner):
    method save_checkpoint (line 26) | def save_checkpoint(self,
    method resume (line 70) | def resume(self,

FILE: detection/mmdet/.mim/demo/create_result_gif.py
  function parse_args (line 17) | def parse_args():
  function _generate_batch_data (line 32) | def _generate_batch_data(sampler, batch_size):
  function create_gif (line 43) | def create_gif(frames, gif_name, duration=2):
  function create_frame_by_matplotlib (line 58) | def create_frame_by_matplotlib(image_dir,
  function main (line 156) | def main():

FILE: detection/mmdet/.mim/demo/image_demo.py
  function parse_args (line 9) | def parse_args():
  function main (line 32) | def main(args):
  function async_main (line 47) | async def async_main(args):

FILE: detection/mmdet/.mim/demo/video_demo.py
  function parse_args (line 10) | def parse_args():
  function main (line 30) | def main():

FILE: detection/mmdet/.mim/demo/webcam_demo.py
  function parse_args (line 10) | def parse_args():
  function main (line 24) | def main():

FILE: detection/mmdet/.mim/tools/analysis_tools/analyze_logs.py
  function cal_train_time (line 11) | def cal_train_time(log_dicts, args):
  function plot_curve (line 38) | def plot_curve(log_dicts, args):
  function add_plot_parser (line 106) | def add_plot_parser(subparsers):
  function add_time_parser (line 144) | def add_time_parser(subparsers):
  function parse_args (line 160) | def parse_args():
  function load_json_logs (line 170) | def load_json_logs(json_logs):
  function main (line 193) | def main():

FILE: detection/mmdet/.mim/tools/analysis_tools/analyze_results.py
  function bbox_map_eval (line 15) | def bbox_map_eval(det_result, annotation):
  class ResultVisualizer (line 50) | class ResultVisualizer:
    method __init__ (line 60) | def __init__(self, show=False, wait_time=0, score_thr=0):
    method _save_image_gts_results (line 65) | def _save_image_gts_results(self, dataset, results, mAPs, out_dir=None):
    method evaluate_and_show (line 97) | def evaluate_and_show(self,
  function parse_args (line 145) | def parse_args():
  function main (line 184) | def main():

FILE: detection/mmdet/.mim/tools/analysis_tools/benchmark.py
  function parse_args (line 19) | def parse_args():
  function measure_inference_speed (line 59) | def measure_inference_speed(cfg, checkpoint, max_iter, log_interval,
  function repeat_measure_inference_speed (line 136) | def repeat_measure_inference_speed(cfg,
  function main (line 170) | def main():

FILE: detection/mmdet/.mim/tools/analysis_tools/coco_error_analysis.py
  function makeplot (line 13) | def makeplot(rs, ps, outDir, class_name, iou_type):
  function autolabel (line 54) | def autolabel(ax, rects):
  function makebarplot (line 73) | def makebarplot(rs, ps, outDir, class_name, iou_type):
  function get_gt_area_group_numbers (line 108) | def get_gt_area_group_numbers(cocoEval):
  function make_gt_area_group_numbers_plot (line 123) | def make_gt_area_group_numbers_plot(cocoEval, outDir, verbose=True):
  function make_gt_area_histogram_plot (line 152) | def make_gt_area_histogram_plot(cocoEval, outDir):
  function analyze_individual_category (line 174) | def analyze_individual_category(k,
  function analyze_results (line 235) | def analyze_results(res_file,
  function main (line 308) | def main():

FILE: detection/mmdet/.mim/tools/analysis_tools/confusion_matrix.py
  function parse_args (line 16) | def parse_args():
  function calculate_confusion_matrix (line 60) | def calculate_confusion_matrix(dataset,
  function analyze_per_img_dets (line 96) | def analyze_per_img_dets(confusion_matrix,
  function plot_confusion_matrix (line 146) | def plot_confusion_matrix(confusion_matrix,
  function main (line 230) | def main():

FILE: detection/mmdet/.mim/tools/analysis_tools/eval_metric.py
  function parse_args (line 11) | def parse_args():
  function main (line 48) | def main():

FILE: detection/mmdet/.mim/tools/analysis_tools/get_flops.py
  function parse_args (line 16) | def parse_args():
  function main (line 45) | def main():

FILE: detection/mmdet/.mim/tools/analysis_tools/optimize_anchors.py
  function parse_args (line 35) | def parse_args():
  class BaseAnchorOptimizer (line 66) | class BaseAnchorOptimizer:
    method __init__ (line 80) | def __init__(self,
    method get_whs_and_shapes (line 97) | def get_whs_and_shapes(self):
    method get_zero_center_bbox_tensor (line 124) | def get_zero_center_bbox_tensor(self):
    method optimize (line 137) | def optimize(self):
    method save_result (line 140) | def save_result(self, anchors, path=None):
  class YOLOKMeansAnchorOptimizer (line 151) | class YOLOKMeansAnchorOptimizer(BaseAnchorOptimizer):
    method __init__ (line 160) | def __init__(self, num_anchors, iters, **kwargs):
    method optimize (line 166) | def optimize(self):
    method kmeans_anchors (line 170) | def kmeans_anchors(self):
    method kmeans_maximization (line 206) | def kmeans_maximization(self, bboxes, assignments, centers):
    method kmeans_expectation (line 215) | def kmeans_expectation(self, bboxes, assignments, centers):
  class YOLODEAnchorOptimizer (line 223) | class YOLODEAnchorOptimizer(BaseAnchorOptimizer):
    method __init__ (line 258) | def __init__(self,
    method optimize (line 278) | def optimize(self):
    method differential_evolution (line 282) | def differential_evolution(self):
    method avg_iou_cost (line 308) | def avg_iou_cost(anchor_params, bboxes):
  function main (line 322) | def main():

FILE: detection/mmdet/.mim/tools/analysis_tools/robustness_eval.py
  function print_coco_results (line 9) | def print_coco_results(results):
  function get_coco_style_results (line 35) | def get_coco_style_results(filename,
  function get_voc_style_results (line 112) | def get_voc_style_results(filename, prints='mPC', aggregate='benchmark'):
  function get_results (line 156) | def get_results(filename,
  function get_distortions_from_file (line 184) | def get_distortions_from_file(filename):
  function get_distortions_from_results (line 191) | def get_distortions_from_results(eval_output):
  function main (line 198) | def main():

FILE: detection/mmdet/.mim/tools/analysis_tools/test_robustness.py
  function coco_eval_with_return (line 24) | def coco_eval_with_return(result_files,
  function voc_eval_with_return (line 66) | def voc_eval_with_return(result_file,
  function parse_args (line 93) | def parse_args():
  function main (line 181) | def main():

FILE: detection/mmdet/.mim/tools/dataset_converters/cityscapes.py
  function collect_files (line 12) | def collect_files(img_dir, gt_dir):
  function collect_annotations (line 29) | def collect_annotations(files, nproc=1):
  function load_img_info (line 40) | def load_img_info(files):
  function cvt_annotations (line 85) | def cvt_annotations(image_infos, out_json_name):
  function parse_args (line 114) | def parse_args():
  function main (line 127) | def main():

FILE: detection/mmdet/.mim/tools/dataset_converters/images2coco.py
  function parse_args (line 9) | def parse_args():
  function collect_image_infos (line 30) | def collect_image_infos(path, exclude_extensions=None):
  function cvt_to_coco_json (line 49) | def cvt_to_coco_json(img_infos, classes):
  function main (line 80) | def main():

FILE: detection/mmdet/.mim/tools/dataset_converters/pascal_voc.py
  function parse_xml (line 14) | def parse_xml(args):
  function cvt_annotations (line 68) | def cvt_annotations(devkit_path, years, split, out_file):
  function cvt_to_coco_json (line 96) | def cvt_to_coco_json(annotations):
  function parse_args (line 183) | def parse_args():
  function main (line 197) | def main():

FILE: detection/mmdet/.mim/tools/deployment/mmdet2torchserve.py
  function mmdet2torchserve (line 15) | def mmdet2torchserve(
  function parse_args (line 70) | def parse_args():

FILE: detection/mmdet/.mim/tools/deployment/mmdet_handler.py
  class MMdetHandler (line 12) | class MMdetHandler(BaseHandler):
    method initialize (line 15) | def initialize(self, context):
    method preprocess (line 31) | def preprocess(self, data):
    method inference (line 43) | def inference(self, data, *args, **kwargs):
    method postprocess (line 47) | def postprocess(self, data):

FILE: detection/mmdet/.mim/tools/deployment/onnx2tensorrt.py
  function get_GiB (line 19) | def get_GiB(x: int):
  function onnx2tensorrt (line 24) | def onnx2tensorrt(onnx_file,
  function parse_normalize_cfg (line 106) | def parse_normalize_cfg(test_pipeline):
  function parse_args (line 119) | def parse_args():
  function parse_shape (line 208) | def parse_shape(shape):

FILE: detection/mmdet/.mim/tools/deployment/pytorch2onnx.py
  function pytorch2onnx (line 16) | def pytorch2onnx(model,
  function parse_normalize_cfg (line 210) | def parse_normalize_cfg(test_pipeline):
  function parse_args (line 223) | def parse_args():

FILE: detection/mmdet/.mim/tools/deployment/test.py
  function parse_args (line 15) | def parse_args():
  function main (line 67) | def main():

FILE: detection/mmdet/.mim/tools/deployment/test_torchserver.py
  function parse_args (line 10) | def parse_args():
  function parse_result (line 28) | def parse_result(input, model_class):
  function main (line 42) | def main(args):

FILE: detection/mmdet/.mim/tools/misc/browse_dataset.py
  function parse_args (line 17) | def parse_args():
  function retrieve_data_cfg (line 51) | def retrieve_data_cfg(config_path, skip_type, cfg_options):
  function main (line 78) | def main():

FILE: detection/mmdet/.mim/tools/misc/download_dataset.py
  function parse_args (line 11) | def parse_args():
  function download (line 35) | def download(url, dir, unzip=True, delete=False, threads=1):
  function main (line 65) | def main():

FILE: detection/mmdet/.mim/tools/misc/gen_coco_panoptic_test_info.py
  function parse_args (line 7) | def parse_args():
  function main (line 17) | def main():

FILE: detection/mmdet/.mim/tools/misc/get_image_metas.py
  function parse_args (line 19) | def parse_args():
  function get_metas_from_csv_style_ann_file (line 36) | def get_metas_from_csv_style_ann_file(ann_file):
  function get_metas_from_txt_style_ann_file (line 52) | def get_metas_from_txt_style_ann_file(ann_file):
  function get_image_metas (line 65) | def get_image_metas(data_info, img_prefix):
  function main (line 79) | def main():

FILE: detection/mmdet/.mim/tools/misc/print_config.py
  function parse_args (line 10) | def parse_args():
  function main (line 43) | def main():

FILE: detection/mmdet/.mim/tools/misc/split_coco.py
  function parse_args (line 15) | def parse_args():
  function split_coco (line 42) | def split_coco(data_root, out_dir, percent, fold):
  function multi_wrapper (line 100) | def multi_wrapper(args):

FILE: detection/mmdet/.mim/tools/model_converters/detectron2pytorch.py
  function convert_bn (line 11) | def convert_bn(blobs, state_dict, caffe_name, torch_name, converted_names):
  function convert_conv_fc (line 24) | def convert_conv_fc(blobs, state_dict, caffe_name, torch_name,
  function convert (line 35) | def convert(src, dst, depth):
  function main (line 73) | def main():

FILE: detection/mmdet/.mim/tools/model_converters/publish_model.py
  function parse_args (line 8) | def parse_args():
  function process_checkpoint (line 17) | def process_checkpoint(in_file, out_file):
  function main (line 37) | def main():

FILE: detection/mmdet/.mim/tools/model_converters/regnet2mmdet.py
  function convert_stem (line 8) | def convert_stem(model_key, model_weight, state_dict, converted_names):
  function convert_head (line 16) | def convert_head(model_key, model_weight, state_dict, converted_names):
  function convert_reslayer (line 23) | def convert_reslayer(model_key, model_weight, state_dict, converted_names):
  function convert (line 55) | def convert(src, dst):
  function main (line 81) | def main():

FILE: detection/mmdet/.mim/tools/model_converters/selfsup2mmdet.py
  function moco_convert (line 8) | def moco_convert(src, dst):
  function main (line 28) | def main():

FILE: detection/mmdet/.mim/tools/model_converters/upgrade_model_version.py
  function is_head (line 11) | def is_head(key):
  function parse_config (line 19) | def parse_config(config_strings):
  function reorder_cls_channel (line 46) | def reorder_cls_channel(val, num_classes=81):
  function truncate_cls_channel (line 68) | def truncate_cls_channel(val, num_classes=81):
  function truncate_reg_channel (line 90) | def truncate_reg_channel(val, num_classes=81):
  function convert (line 115) | def convert(in_file, out_file, num_classes):
  function main (line 196) | def main():

FILE: detection/mmdet/.mim/tools/model_converters/upgrade_ssd_version.py
  function parse_config (line 10) | def parse_config(config_strings):
  function convert (line 22) | def convert(in_file, out_file):
  function main (line 48) | def main():

FILE: detection/mmdet/.mim/tools/test.py
  function parse_args (line 23) | def parse_args():
  function main (line 120) | def main():

FILE: detection/mmdet/.mim/tools/train.py
  function parse_args (line 24) | def parse_args():
  function main (line 107) | def main():

FILE: detection/mmdet/__init__.py
  function digit_version (line 7) | def digit_version(version_str):

FILE: detection/mmdet/apis/inference.py
  function init_detector (line 18) | def init_detector(config, checkpoint=None, device='cuda:0', cfg_options=...
  class LoadImage (line 60) | class LoadImage:
    method __call__ (line 66) | def __call__(self, results):
  function inference_detector (line 93) | def inference_detector(model, imgs):
  function async_inference_detector (line 159) | async def async_inference_detector(model, imgs):
  function show_result_pyplot (line 216) | def show_result_pyplot(model,

FILE: detection/mmdet/apis/test.py
  function single_gpu_test (line 17) | def single_gpu_test(model,
  function multi_gpu_test (line 83) | def multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False):
  function collect_results_cpu (line 138) | def collect_results_cpu(result_part, size, tmpdir=None):
  function collect_results_gpu (line 181) | def collect_results_gpu(result_part, size):

FILE: detection/mmdet/apis/train.py
  function init_random_seed (line 24) | def init_random_seed(seed=None, device='cuda'):
  function set_random_seed (line 57) | def set_random_seed(seed, deterministic=False):
  function auto_scale_lr (line 76) | def auto_scale_lr(cfg, distributed, logger):
  function train_detector (line 122) | def train_detector(model,

FILE: detection/mmdet/core/anchor/anchor_generator.py
  class AnchorGenerator (line 13) | class AnchorGenerator:
    method __init__ (line 61) | def __init__(self,
    method num_base_anchors (line 116) | def num_base_anchors(self):
    method num_base_priors (line 121) | def num_base_priors(self):
    method num_levels (line 127) | def num_levels(self):
    method gen_base_anchors (line 131) | def gen_base_anchors(self):
    method gen_single_level_base_anchors (line 151) | def gen_single_level_base_anchors(self,
    method _meshgrid (line 196) | def _meshgrid(self, x, y, row_major=True):
    method grid_priors (line 216) | def grid_priors(self, featmap_sizes, dtype=torch.float32, device='cuda'):
    method single_level_grid_priors (line 241) | def single_level_grid_priors(self,
    method sparse_priors (line 283) | def sparse_priors(self,
    method grid_anchors (line 318) | def grid_anchors(self, featmap_sizes, device='cuda'):
    method single_level_grid_anchors (line 347) | def single_level_grid_anchors(self,
    method valid_flags (line 392) | def valid_flags(self, featmap_sizes, pad_shape, device='cuda'):
    method single_level_valid_flags (line 419) | def single_level_valid_flags(self,
    method __repr__ (line 451) | def __repr__(self):
  class SSDAnchorGenerator (line 471) | class SSDAnchorGenerator(AnchorGenerator):
    method __init__ (line 492) | def __init__(self,
    method gen_base_anchors (line 571) | def gen_base_anchors(self):
    method __repr__ (line 592) | def __repr__(self):
  class LegacyAnchorGenerator (line 610) | class LegacyAnchorGenerator(AnchorGenerator):
    method gen_single_level_base_anchors (line 658) | def gen_single_level_base_anchors(self,
  class LegacySSDAnchorGenerator (line 709) | class LegacySSDAnchorGenerator(SSDAnchorGenerator, LegacyAnchorGenerator):
    method __init__ (line 716) | def __init__(self,
  class YOLOAnchorGenerator (line 734) | class YOLOAnchorGenerator(AnchorGenerator):
    method __init__ (line 744) | def __init__(self, strides, base_sizes):
    method num_levels (line 757) | def num_levels(self):
    method gen_base_anchors (line 761) | def gen_base_anchors(self):
    method gen_single_level_base_anchors (line 778) | def gen_single_level_base_anchors(self, base_sizes_per_level, center=N...
    method responsible_flags (line 806) | def responsible_flags(self, featmap_sizes, gt_bboxes, device='cuda'):
    method single_level_responsible_flags (line 831) | def single_level_responsible_flags(self,

FILE: detection/mmdet/core/anchor/builder.py
  function build_prior_generator (line 11) | def build_prior_generator(cfg, default_args=None):
  function build_anchor_generator (line 15) | def build_anchor_generator(cfg, default_args=None):

FILE: detection/mmdet/core/anchor/point_generator.py
  class PointGenerator (line 10) | class PointGenerator:
    method _meshgrid (line 12) | def _meshgrid(self, x, y, row_major=True):
    method grid_points (line 20) | def grid_points(self, featmap_size, stride=16, device='cuda'):
    method valid_flags (line 30) | def valid_flags(self, featmap_size, valid_size, device='cuda'):
  class MlvlPointGenerator (line 44) | class MlvlPointGenerator:
    method __init__ (line 55) | def __init__(self, strides, offset=0.5):
    method num_levels (line 60) | def num_levels(self):
    method num_base_priors (line 65) | def num_base_priors(self):
    method _meshgrid (line 70) | def _meshgrid(self, x, y, row_major=True):
    method grid_priors (line 80) | def grid_priors(self,
    method single_level_grid_priors (line 119) | def single_level_grid_priors(self,
    method valid_flags (line 177) | def valid_flags(self, featmap_sizes, pad_shape, device='cuda'):
    method single_level_valid_flags (line 205) | def single_level_valid_flags(self,
    method sparse_priors (line 234) | def sparse_priors(self,

FILE: detection/mmdet/core/anchor/utils.py
  function images_to_levels (line 5) | def images_to_levels(target, num_levels):
  function anchor_inside_flags (line 21) | def anchor_inside_flags(flat_anchors,
  function calc_region (line 50) | def calc_region(bbox, ratio, featmap_size=None):

FILE: detection/mmdet/core/bbox/assigners/approx_max_iou_assigner.py
  class ApproxMaxIoUAssigner (line 10) | class ApproxMaxIoUAssigner(MaxIoUAssigner):
    method __init__ (line 40) | def __init__(self,
    method assign (line 60) | def assign(self,

FILE: detection/mmdet/core/bbox/assigners/assign_result.py
  class AssignResult (line 7) | class AssignResult(util_mixins.NiceRepr):
    method __init__ (line 43) | def __init__(self, num_gts, gt_inds, max_overlaps, labels=None):
    method num_preds (line 52) | def num_preds(self):
    method set_extra_property (line 56) | def set_extra_property(self, key, value):
    method get_extra_property (line 61) | def get_extra_property(self, key):
    method info (line 66) | def info(self):
    method __nice__ (line 78) | def __nice__(self):
    method random (line 98) | def random(cls, **kwargs):
    method add_gt_ (line 192) | def add_gt_(self, gt_labels):

FILE: detection/mmdet/core/bbox/assigners/atss_assigner.py
  class ATSSAssigner (line 11) | class ATSSAssigner(BaseAssigner):
    method __init__ (line 24) | def __init__(self,
    method assign (line 34) | def assign(self,

FILE: detection/mmdet/core/bbox/assigners/base_assigner.py
  class BaseAssigner (line 5) | class BaseAssigner(metaclass=ABCMeta):
    method assign (line 9) | def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=N...

FILE: detection/mmdet/core/bbox/assigners/center_region_assigner.py
  function scale_boxes (line 10) | def scale_boxes(bboxes, scale):
  function is_located_in (line 37) | def is_located_in(points, bboxes):
  function bboxes_area (line 55) | def bboxes_area(bboxes):
  class CenterRegionAssigner (line 72) | class CenterRegionAssigner(BaseAssigner):
    method __init__ (line 94) | def __init__(self,
    method get_gt_priorities (line 108) | def get_gt_priorities(self, gt_bboxes):
    method assign (line 126) | def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=N...
    method assign_one_hot_gt_indices (line 257) | def assign_one_hot_gt_indices(self,

FILE: detection/mmdet/core/bbox/assigners/grid_assigner.py
  class GridAssigner (line 11) | class GridAssigner(BaseAssigner):
    method __init__ (line 31) | def __init__(self,
    method assign (line 43) | def assign(self, bboxes, box_responsible_flags, gt_bboxes, gt_labels=N...

FILE: detection/mmdet/core/bbox/assigners/hungarian_assigner.py
  class HungarianAssigner (line 17) | class HungarianAssigner(BaseAssigner):
    method __init__ (line 45) | def __init__(self,
    method assign (line 53) | def assign(self,

FILE: detection/mmdet/core/bbox/assigners/mask_hungarian_assigner.py
  class MaskHungarianAssigner (line 16) | class MaskHungarianAssigner(BaseAssigner):
    method __init__ (line 37) | def __init__(self,
    method assign (line 46) | def assign(self,

FILE: detection/mmdet/core/bbox/assigners/max_iou_assigner.py
  class MaxIoUAssigner (line 11) | class MaxIoUAssigner(BaseAssigner):
    method __init__ (line 46) | def __init__(self,
    method assign (line 66) | def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=N...
    method assign_wrt_overlaps (line 133) | def assign_wrt_overlaps(self, overlaps, gt_labels=None):

FILE: detection/mmdet/core/bbox/assigners/point_assigner.py
  class PointAssigner (line 10) | class PointAssigner(BaseAssigner):
    method __init__ (line 20) | def __init__(self, scale=4, pos_num=3):
    method assign (line 24) | def assign(self, points, gt_bboxes, gt_bboxes_ignore=None, gt_labels=N...

FILE: detection/mmdet/core/bbox/assigners/region_assigner.py
  function calc_region (line 10) | def calc_region(bbox, ratio, stride, featmap_size=None):
  function anchor_ctr_inside_region_flags (line 27) | def anchor_ctr_inside_region_flags(anchors, stride, region):
  class RegionAssigner (line 38) | class RegionAssigner(BaseAssigner):
    method __init__ (line 54) | def __init__(self, center_ratio=0.2, ignore_ratio=0.5):
    method assign (line 58) | def assign(self,

FILE: detection/mmdet/core/bbox/assigners/sim_ota_assigner.py
  class SimOTAAssigner (line 14) | class SimOTAAssigner(BaseAssigner):
    method __init__ (line 28) | def __init__(self,
    method assign (line 38) | def assign(self,
    method _assign (line 95) | def _assign(self,
    method get_in_gt_and_in_center_info (line 186) | def get_in_gt_and_in_center_info(self, priors, gt_bboxes):
    method dynamic_k_matching (line 230) | def dynamic_k_matching(self, cost, pairwise_ious, num_gt, valid_mask):

FILE: detection/mmdet/core/bbox/assigners/task_aligned_assigner.py
  class TaskAlignedAssigner (line 13) | class TaskAlignedAssigner(BaseAssigner):
    method __init__ (line 31) | def __init__(self, topk, iou_calculator=dict(type='BboxOverlaps2D')):
    method assign (line 36) | def assign(self,

FILE: detection/mmdet/core/bbox/assigners/uniform_assigner.py
  class UniformAssigner (line 12) | class UniformAssigner(BaseAssigner):
    method __init__ (line 25) | def __init__(self,
    method assign (line 35) | def assign(self,

FILE: detection/mmdet/core/bbox/builder.py
  function build_assigner (line 9) | def build_assigner(cfg, **default_args):
  function build_sampler (line 14) | def build_sampler(cfg, **default_args):
  function build_bbox_coder (line 19) | def build_bbox_coder(cfg, **default_args):

FILE: detection/mmdet/core/bbox/coder/base_bbox_coder.py
  class BaseBBoxCoder (line 5) | class BaseBBoxCoder(metaclass=ABCMeta):
    method __init__ (line 8) | def __init__(self, **kwargs):
    method encode (line 12) | def encode(self, bboxes, gt_bboxes):
    method decode (line 16) | def decode(self, bboxes, bboxes_pred):

FILE: detection/mmdet/core/bbox/coder/bucketing_bbox_coder.py
  class BucketingBBoxCoder (line 13) | class BucketingBBoxCoder(BaseBBoxCoder):
    method __init__ (line 35) | def __init__(self,
    method encode (line 50) | def encode(self, bboxes, gt_bboxes):
    method decode (line 72) | def decode(self, bboxes, pred_bboxes, max_shape=None):
  function generat_buckets (line 96) | def generat_buckets(proposals, num_buckets, scale_factor=1.0):
  function bbox2bucket (line 145) | def bbox2bucket(proposals,
  function bucket2bbox (line 269) | def bucket2bbox(proposals,

FILE: detection/mmdet/core/bbox/coder/delta_xywh_bbox_coder.py
  class DeltaXYWHBBoxCoder (line 13) | class DeltaXYWHBBoxCoder(BaseBBoxCoder):
    method __init__ (line 34) | def __init__(self,
    method encode (line 47) | def encode(self, bboxes, gt_bboxes):
    method decode (line 65) | def decode(self,
  function bbox2delta (line 118) | def bbox2delta(proposals, gt, means=(0., 0., 0., 0.), stds=(1., 1., 1., ...
  function delta2bbox (line 164) | def delta2bbox(rois,
  function onnx_delta2bbox (line 263) | def onnx_delta2bbox(rois,

FILE: detection/mmdet/core/bbox/coder/distance_point_bbox_coder.py
  class DistancePointBBoxCoder (line 8) | class DistancePointBBoxCoder(BaseBBoxCoder):
    method __init__ (line 19) | def __init__(self, clip_border=True):
    method encode (line 23) | def encode(self, points, gt_bboxes, max_dis=None, eps=0.1):
    method decode (line 41) | def decode(self, points, pred_bboxes, max_shape=None):

FILE: detection/mmdet/core/bbox/coder/legacy_delta_xywh_bbox_coder.py
  class LegacyDeltaXYWHBBoxCoder (line 11) | class LegacyDeltaXYWHBBoxCoder(BaseBBoxCoder):
    method __init__ (line 34) | def __init__(self,
    method encode (line 41) | def encode(self, bboxes, gt_bboxes):
    method decode (line 59) | def decode(self,
  function legacy_bbox2delta (line 85) | def legacy_bbox2delta(proposals,
  function legacy_delta2bbox (line 134) | def legacy_delta2bbox(rois,

FILE: detection/mmdet/core/bbox/coder/pseudo_bbox_coder.py
  class PseudoBBoxCoder (line 7) | class PseudoBBoxCoder(BaseBBoxCoder):
    method __init__ (line 10) | def __init__(self, **kwargs):
    method encode (line 13) | def encode(self, bboxes, gt_bboxes):
    method decode (line 17) | def decode(self, bboxes, pred_bboxes):

FILE: detection/mmdet/core/bbox/coder/tblr_bbox_coder.py
  class TBLRBBoxCoder (line 10) | class TBLRBBoxCoder(BaseBBoxCoder):
    method __init__ (line 26) | def __init__(self, normalizer=4.0, clip_border=True):
    method encode (line 31) | def encode(self, bboxes, gt_bboxes):
    method decode (line 50) | def decode(self, bboxes, pred_bboxes, max_shape=None):
  function bboxes2tblr (line 77) | def bboxes2tblr(priors, gts, normalizer=4.0, normalize_by_wh=True):
  function tblr2bboxes (line 124) | def tblr2bboxes(priors,

FILE: detection/mmdet/core/bbox/coder/yolo_bbox_coder.py
  class YOLOBBoxCoder (line 10) | class YOLOBBoxCoder(BaseBBoxCoder):
    method __init__ (line 22) | def __init__(self, eps=1e-6):
    method encode (line 27) | def encode(self, bboxes, gt_bboxes, stride):
    method decode (line 62) | def decode(self, bboxes, pred_bboxes, stride):

FILE: detection/mmdet/core/bbox/demodata.py
  function random_boxes (line 8) | def random_boxes(num=1, scale=1, rng=None):

FILE: detection/mmdet/core/bbox/iou_calculators/builder.py
  function build_iou_calculator (line 7) | def build_iou_calculator(cfg, default_args=None):

FILE: detection/mmdet/core/bbox/iou_calculators/iou2d_calculator.py
  function cast_tensor_type (line 7) | def cast_tensor_type(x, scale=1., dtype=None):
  function fp16_clamp (line 14) | def fp16_clamp(x, min=None, max=None):
  class BboxOverlaps2D (line 23) | class BboxOverlaps2D:
    method __init__ (line 26) | def __init__(self, scale=1., dtype=None):
    method __call__ (line 30) | def __call__(self, bboxes1, bboxes2, mode='iou', is_aligned=False):
    method __repr__ (line 68) | def __repr__(self):
  function bbox_overlaps (line 75) | def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False, eps=1e...

FILE: detection/mmdet/core/bbox/match_costs/builder.py
  function build_match_cost (line 7) | def build_match_cost(cfg, default_args=None):

FILE: detection/mmdet/core/bbox/match_costs/match_cost.py
  class BBoxL1Cost (line 11) | class BBoxL1Cost:
    method __init__ (line 29) | def __init__(self, weight=1., box_format='xyxy'):
    method __call__ (line 34) | def __call__(self, bbox_pred, gt_bboxes):
  class FocalLossCost (line 55) | class FocalLossCost:
    method __init__ (line 80) | def __init__(self,
    method _focal_loss_cost (line 92) | def _focal_loss_cost(self, cls_pred, gt_labels):
    method _mask_focal_loss_cost (line 111) | def _mask_focal_loss_cost(self, cls_pred, gt_labels):
    method __call__ (line 136) | def __call__(self, cls_pred, gt_labels):
  class ClassificationCost (line 153) | class ClassificationCost:
    method __init__ (line 174) | def __init__(self, weight=1.):
    method __call__ (line 177) | def __call__(self, cls_pred, gt_labels):
  class IoUCost (line 197) | class IoUCost:
    method __init__ (line 215) | def __init__(self, iou_mode='giou', weight=1.):
    method __call__ (line 219) | def __call__(self, bboxes, gt_bboxes):
  class DiceCost (line 239) | class DiceCost:
    method __init__ (line 254) | def __init__(self, weight=1., pred_act=False, eps=1e-3, naive_dice=True):
    method binary_mask_dice_loss (line 260) | def binary_mask_dice_loss(self, mask_preds, gt_masks):
    method __call__ (line 283) | def __call__(self, mask_preds, gt_masks):
  class CrossEntropyLossCost (line 299) | class CrossEntropyLossCost:
    method __init__ (line 315) | def __init__(self, weight=1., use_sigmoid=True):
    method _binary_cross_entropy (line 320) | def _binary_cross_entropy(self, cls_pred, gt_labels):
    method __call__ (line 344) | def __call__(self, cls_pred, gt_labels):

FILE: detection/mmdet/core/bbox/samplers/base_sampler.py
  class BaseSampler (line 9) | class BaseSampler(metaclass=ABCMeta):
    method __init__ (line 12) | def __init__(self,
    method _sample_pos (line 26) | def _sample_pos(self, assign_result, num_expected, **kwargs):
    method _sample_neg (line 31) | def _sample_neg(self, assign_result, num_expected, **kwargs):
    method sample (line 35) | def sample(self,

FILE: detection/mmdet/core/bbox/samplers/combined_sampler.py
  class CombinedSampler (line 7) | class CombinedSampler(BaseSampler):
    method __init__ (line 10) | def __init__(self, pos_sampler, neg_sampler, **kwargs):
    method _sample_pos (line 15) | def _sample_pos(self, **kwargs):
    method _sample_neg (line 19) | def _sample_neg(self, **kwargs):

FILE: detection/mmdet/core/bbox/samplers/instance_balanced_pos_sampler.py
  class InstanceBalancedPosSampler (line 10) | class InstanceBalancedPosSampler(RandomSampler):
    method _sample_pos (line 14) | def _sample_pos(self, assign_result, num_expected, **kwargs):

FILE: detection/mmdet/core/bbox/samplers/iou_balanced_neg_sampler.py
  class IoUBalancedNegSampler (line 10) | class IoUBalancedNegSampler(RandomSampler):
    method __init__ (line 30) | def __init__(self,
    method sample_via_interval (line 47) | def sample_via_interval(self, max_overlaps, full_set, num_expected):
    method _sample_neg (line 89) | def _sample_neg(self, assign_result, num_expected, **kwargs):

FILE: detection/mmdet/core/bbox/samplers/mask_pseudo_sampler.py
  class MaskPseudoSampler (line 13) | class MaskPseudoSampler(BaseSampler):
    method __init__ (line 16) | def __init__(self, **kwargs):
    method _sample_pos (line 19) | def _sample_pos(self, **kwargs):
    method _sample_neg (line 23) | def _sample_neg(self, **kwargs):
    method sample (line 27) | def sample(self, assign_result, masks, gt_masks, **kwargs):

FILE: detection/mmdet/core/bbox/samplers/mask_sampling_result.py
  class MaskSamplingResult (line 10) | class MaskSamplingResult(SamplingResult):
    method __init__ (line 13) | def __init__(self, pos_inds, neg_inds, masks, gt_masks, assign_result,
    method masks (line 37) | def masks(self):
    method __nice__ (line 41) | def __nice__(self):
    method info (line 50) | def info(self):

FILE: detection/mmdet/core/bbox/samplers/ohem_sampler.py
  class OHEMSampler (line 10) | class OHEMSampler(BaseSampler):
    method __init__ (line 16) | def __init__(self,
    method hard_mining (line 34) | def hard_mining(self, inds, num_expected, bboxes, labels, feats):
    method _sample_pos (line 55) | def _sample_pos(self,
    method _sample_neg (line 83) | def _sample_neg(self,

FILE: detection/mmdet/core/bbox/samplers/pseudo_sampler.py
  class PseudoSampler (line 10) | class PseudoSampler(BaseSampler):
    method __init__ (line 13) | def __init__(self, **kwargs):
    method _sample_pos (line 16) | def _sample_pos(self, **kwargs):
    method _sample_neg (line 20) | def _sample_neg(self, **kwargs):
    method sample (line 24) | def sample(self, assign_result, bboxes, gt_bboxes, *args, **kwargs):

FILE: detection/mmdet/core/bbox/samplers/random_sampler.py
  class RandomSampler (line 9) | class RandomSampler(BaseSampler):
    method __init__ (line 21) | def __init__(self,
    method random_choice (line 32) | def random_choice(self, gallery, num):
    method _sample_pos (line 64) | def _sample_pos(self, assign_result, num_expected, **kwargs):
    method _sample_neg (line 74) | def _sample_neg(self, assign_result, num_expected, **kwargs):

FILE: detection/mmdet/core/bbox/samplers/sampling_result.py
  class SamplingResult (line 7) | class SamplingResult(util_mixins.NiceRepr):
    method __init__ (line 26) | def __init__(self, pos_inds, neg_inds, bboxes, gt_bboxes, assign_result,
    method bboxes (line 53) | def bboxes(self):
    method to (line 57) | def to(self, device):
    method __nice__ (line 72) | def __nice__(self):
    method info (line 81) | def info(self):
    method random (line 94) | def random(cls, rng=None, **kwargs):

FILE: detection/mmdet/core/bbox/samplers/score_hlr_sampler.py
  class ScoreHLRSampler (line 12) | class ScoreHLRSampler(BaseSampler):
    method __init__ (line 33) | def __init__(self,
    method random_choice (line 63) | def random_choice(gallery, num):
    method _sample_pos (line 92) | def _sample_pos(self, assign_result, num_expected, **kwargs):
    method _sample_neg (line 100) | def _sample_neg(self,
    method sample (line 216) | def sample(self,

FILE: detection/mmdet/core/bbox/transforms.py
  function find_inside_bboxes (line 6) | def find_inside_bboxes(bboxes, img_h, img_w):
  function bbox_flip (line 22) | def bbox_flip(bboxes, img_shape, direction='horizontal'):
  function bbox_mapping (line 51) | def bbox_mapping(bboxes,
  function bbox_mapping_back (line 63) | def bbox_mapping_back(bboxes,
  function bbox2roi (line 75) | def bbox2roi(bbox_list):
  function roi2bbox (line 97) | def roi2bbox(rois):
  function bbox2result (line 116) | def bbox2result(bboxes, labels, num_classes):
  function distance2bbox (line 136) | def distance2bbox(points, distance, max_shape=None):
  function bbox2distance (line 189) | def bbox2distance(points, bbox, max_dis=None, eps=0.1):
  function bbox_rescale (line 213) | def bbox_rescale(bboxes, scale_factor=1.0):
  function bbox_cxcywh_to_xyxy (line 245) | def bbox_cxcywh_to_xyxy(bbox):
  function bbox_xyxy_to_cxcywh (line 259) | def bbox_xyxy_to_cxcywh(bbox):

FILE: detection/mmdet/core/data_structures/general_data.py
  class GeneralData (line 10) | class GeneralData(NiceRepr):
    method __init__ (line 87) | def __init__(self, meta_info=None, data=None):
    method set_meta_info (line 97) | def set_meta_info(self, meta_info):
    method set_data (line 129) | def set_data(self, data):
    method new (line 141) | def new(self, meta_info=None, data=None):
    method keys (line 159) | def keys(self):
    method meta_info_keys (line 166) | def meta_info_keys(self):
    method values (line 173) | def values(self):
    method meta_info_values (line 180) | def meta_info_values(self):
    method items (line 187) | def items(self):
    method meta_info_items (line 191) | def meta_info_items(self):
    method __setattr__ (line 195) | def __setattr__(self, name, val):
    method __delattr__ (line 211) | def __delattr__(self, item):
    method __getitem__ (line 228) | def __getitem__(self, name):
    method get (line 231) | def get(self, *args):
    method pop (line 235) | def pop(self, *args):
    method __contains__ (line 252) | def __contains__(self, item):
    method to (line 257) | def to(self, *args, **kwargs):
    method cpu (line 267) | def cpu(self):
    method mlu (line 277) | def mlu(self):
    method cuda (line 287) | def cuda(self):
    method detach (line 297) | def detach(self):
    method numpy (line 307) | def numpy(self):
    method __nice__ (line 316) | def __nice__(self):

FILE: detection/mmdet/core/data_structures/instance_data.py
  class InstanceData (line 10) | class InstanceData(GeneralData):
    method __setattr__ (line 66) | def __setattr__(self, name, value):
    method __getitem__ (line 90) | def __getitem__(self, item):
    method cat (line 151) | def cat(instances_list):
    method __len__ (line 183) | def __len__(self):

FILE: detection/mmdet/core/evaluation/bbox_overlaps.py
  function bbox_overlaps (line 5) | def bbox_overlaps(bboxes1,

FILE: detection/mmdet/core/evaluation/class_names.py
  function wider_face_classes (line 5) | def wider_face_classes():
  function voc_classes (line 9) | def voc_classes():
  function imagenet_det_classes (line 17) | def imagenet_det_classes():
  function imagenet_vid_classes (line 58) | def imagenet_vid_classes():
  function coco_classes (line 68) | def coco_classes():
  function cityscapes_classes (line 86) | def cityscapes_classes():
  function oid_challenge_classes (line 93) | def oid_challenge_classes():
  function oid_v6_classes (line 188) | def oid_v6_classes():
  function get_classes (line 318) | def get_classes(dataset):

FILE: detection/mmdet/core/evaluation/eval_hooks.py
  function _calc_dynamic_intervals (line 12) | def _calc_dynamic_intervals(start_interval, dynamic_interval_list):
  class EvalHook (line 24) | class EvalHook(BaseEvalHook):
    method __init__ (line 26) | def __init__(self, *args, dynamic_intervals=None, **kwargs):
    method _decide_interval (line 34) | def _decide_interval(self, runner):
    method before_train_epoch (line 41) | def before_train_epoch(self, runner):
    method before_train_iter (line 46) | def before_train_iter(self, runner):
    method _do_evaluate (line 50) | def _do_evaluate(self, runner):
  class DistEvalHook (line 68) | class DistEvalHook(BaseDistEvalHook):
    method __init__ (line 70) | def __init__(self, *args, dynamic_intervals=None, **kwargs):
    method _decide_interval (line 78) | def _decide_interval(self, runner):
    method before_train_epoch (line 85) | def before_train_epoch(self, runner):
    method before_train_iter (line 90) | def before_train_iter(self, runner):
    method _do_evaluate (line 94) | def _do_evaluate(self, runner):

FILE: detection/mmdet/core/evaluation/mean_ap.py
  function average_precision (line 13) | def average_precision(recalls, precisions, mode='area'):
  function tpfp_imagenet (line 60) | def tpfp_imagenet(det_bboxes,
  function tpfp_default (line 168) | def tpfp_default(det_bboxes,
  function tpfp_openimages (line 270) | def tpfp_openimages(det_bboxes,
  function get_cls_results (line 474) | def get_cls_results(det_results, annotations, class_id):
  function get_cls_group_ofs (line 501) | def get_cls_group_ofs(annotations, class_id):
  function eval_map (line 522) | def eval_map(det_results,
  function print_map_summary (line 688) | def print_map_summary(mean_ap,

FILE: detection/mmdet/core/evaluation/recall.py
  function _recalls (line 11) | def _recalls(all_ious, proposal_nums, thrs):
  function set_recall_param (line 44) | def set_recall_param(proposal_nums, iou_thrs):
  function eval_recalls (line 65) | def eval_recalls(gts,
  function print_recall_summary (line 117) | def print_recall_summary(recalls,
  function plot_num_recall (line 150) | def plot_num_recall(recalls, proposal_nums):
  function plot_iou_recall (line 175) | def plot_iou_recall(recalls, iou_thrs):

FILE: detection/mmdet/core/export/model_wrappers.py
  class DeployBaseDetector (line 12) | class DeployBaseDetector(BaseDetector):
    method __init__ (line 15) | def __init__(self, class_names, device_id):
    method simple_test (line 20) | def simple_test(self, img, img_metas, **kwargs):
    method aug_test (line 23) | def aug_test(self, imgs, img_metas, **kwargs):
    method extract_feat (line 26) | def extract_feat(self, imgs):
    method forward_train (line 29) | def forward_train(self, imgs, img_metas, **kwargs):
    method val_step (line 32) | def val_step(self, data, optimizer):
    method train_step (line 35) | def train_step(self, data, optimizer):
    method forward_test (line 38) | def forward_test(self, *, img, img_metas, **kwargs):
    method async_simple_test (line 41) | def async_simple_test(self, img, img_metas, **kwargs):
    method forward (line 44) | def forward(self, img, img_metas, return_loss=True, **kwargs):
  class ONNXRuntimeDetector (line 96) | class ONNXRuntimeDetector(DeployBaseDetector):
    method __init__ (line 99) | def __init__(self, onnx_file, class_names, device_id):
    method forward_test (line 130) | def forward_test(self, imgs, img_metas, **kwargs):
  class TensorRTDetector (line 152) | class TensorRTDetector(DeployBaseDetector):
    method __init__ (line 155) | def __init__(self, engine_file, class_names, device_id, output_names=N...
    method forward_test (line 177) | def forward_test(self, imgs, img_metas, **kwargs):

FILE: detection/mmdet/core/export/onnx_helper.py
  function dynamic_clip_for_onnx (line 7) | def dynamic_clip_for_onnx(x1, y1, x2, y2, max_shape):
  function get_k_for_topk (line 46) | def get_k_for_topk(k, size):
  function add_dummy_nms_for_onnx (line 82) | def add_dummy_nms_for_onnx(boxes,
  class DummyONNXNMSop (line 201) | class DummyONNXNMSop(torch.autograd.Function):
    method forward (line 208) | def forward(ctx, boxes, scores, max_output_boxes_per_class, iou_thresh...
    method symbolic (line 214) | def symbolic(g, boxes, scores, max_output_boxes_per_class, iou_threshold,

FILE: detection/mmdet/core/export/pytorch2onnx.py
  function generate_inputs_and_wrap_model (line 10) | def generate_inputs_and_wrap_model(config_path,
  function build_model_from_cfg (line 65) | def build_model_from_cfg(config_path, checkpoint_path, cfg_options=None):
  function preprocess_example_input (line 102) | def preprocess_example_input(input_config):

FILE: detection/mmdet/core/hook/checkloss_hook.py
  class CheckInvalidLossHook (line 7) | class CheckInvalidLossHook(Hook):
    method __init__ (line 18) | def __init__(self, interval=50):
    method after_train_iter (line 21) | def after_train_iter(self, runner):

FILE: detection/mmdet/core/hook/ema.py
  class BaseEMAHook (line 8) | class BaseEMAHook(Hook):
    method __init__ (line 32) | def __init__(self,
    method before_run (line 45) | def before_run(self, runner):
    method get_momentum (line 67) | def get_momentum(self, runner):
    method after_train_iter (line 71) | def after_train_iter(self, runner):
    method after_train_epoch (line 84) | def after_train_epoch(self, runner):
    method before_train_epoch (line 89) | def before_train_epoch(self, runner):
    method _swap_ema_parameters (line 94) | def _swap_ema_parameters(self):
  class ExpMomentumEMAHook (line 104) | class ExpMomentumEMAHook(BaseEMAHook):
    method __init__ (line 112) | def __init__(self, total_iter=2000, **kwargs):
  class LinearMomentumEMAHook (line 119) | class LinearMomentumEMAHook(BaseEMAHook):
    method __init__ (line 127) | def __init__(self, warm_up=100, **kwargs):

FILE: detection/mmdet/core/hook/memory_profiler_hook.py
  class MemoryProfilerHook (line 6) | class MemoryProfilerHook(Hook):
    method __init__ (line 15) | def __init__(self, interval=50):
    method after_iter (line 34) | def after_iter(self, runner):

FILE: detection/mmdet/core/hook/set_epoch_info_hook.py
  class SetEpochInfoHook (line 7) | class SetEpochInfoHook(Hook):
    method before_train_epoch (line 10) | def before_train_epoch(self, runner):

FILE: detection/mmdet/core/hook/sync_norm_hook.py
  function get_norm_states (line 11) | def get_norm_states(module):
  class SyncNormHook (line 21) | class SyncNormHook(Hook):
    method __init__ (line 30) | def __init__(self, num_last_epochs=15, interval=1):
    method before_train_epoch (line 34) | def before_train_epoch(self, runner):
    method after_train_epoch (line 40) | def after_train_epoch(self, runner):

FILE: detection/mmdet/core/hook/sync_random_size_hook.py
  class SyncRandomSizeHook (line 12) | class SyncRandomSizeHook(Hook):
    method __init__ (line 33) | def __init__(self,
    method after_train_epoch (line 52) | def after_train_epoch(self, runner):

FILE: detection/mmdet/core/hook/yolox_lrupdater_hook.py
  class YOLOXLrUpdaterHook (line 8) | class YOLOXLrUpdaterHook(CosineAnnealingLrUpdaterHook):
    method __init__ (line 23) | def __init__(self, num_last_epochs, **kwargs):
    method get_warmup_lr (line 27) | def get_warmup_lr(self, cur_iters):
    method get_lr (line 44) | def get_lr(self, runner, base_lr):

FILE: detection/mmdet/core/hook/yolox_mode_switch_hook.py
  class YOLOXModeSwitchHook (line 7) | class YOLOXModeSwitchHook(Hook):
    method __init__ (line 21) | def __init__(self,
    method before_train_epoch (line 28) | def before_train_epoch(self, runner):

FILE: detection/mmdet/core/mask/mask_target.py
  function mask_target (line 7) | def mask_target(pos_proposals_list, pos_assigned_gt_inds_list, gt_masks_...
  function mask_target_single (line 67) | def mask_target_single(pos_proposals, pos_assigned_gt_inds, gt_masks, cfg):

FILE: detection/mmdet/core/mask/structures.py
  class BaseInstanceMasks (line 12) | class BaseInstanceMasks(metaclass=ABCMeta):
    method rescale (line 16) | def rescale(self, scale, interpolation='nearest'):
    method resize (line 29) | def resize(self, out_shape, interpolation='nearest'):
    method flip (line 41) | def flip(self, flip_direction='horizontal'):
    method pad (line 52) | def pad(self, out_shape, pad_val):
    method crop (line 64) | def crop(self, bbox):
    method crop_and_resize (line 75) | def crop_and_resize(self,
    method expand (line 104) | def expand(self, expanded_h, expanded_w, top, left):
    method areas (line 109) | def areas(self):
    method to_ndarray (line 113) | def to_ndarray(self):
    method to_tensor (line 121) | def to_tensor(self, dtype, device):
    method translate (line 133) | def translate(self,
    method shear (line 153) | def shear(self,
    method rotate (line 175) | def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0):
  class BitmapMasks (line 193) | class BitmapMasks(BaseInstanceMasks):
    method __init__ (line 222) | def __init__(self, masks, height, width):
    method __getitem__ (line 239) | def __getitem__(self, index):
    method __iter__ (line 251) | def __iter__(self):
    method __repr__ (line 254) | def __repr__(self):
    method __len__ (line 261) | def __len__(self):
    method rescale (line 265) | def rescale(self, scale, interpolation='nearest'):
    method resize (line 278) | def resize(self, out_shape, interpolation='nearest'):
    method flip (line 290) | def flip(self, flip_direction='horizontal'):
    method pad (line 303) | def pad(self, out_shape, pad_val=0):
    method crop (line 314) | def crop(self, bbox):
    method crop_and_resize (line 333) | def crop_and_resize(self,
    method expand (line 369) | def expand(self, expanded_h, expanded_w, top, left):
    method translate (line 381) | def translate(self,
    method shear (line 431) | def shear(self,
    method rotate (line 466) | def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0):
    method areas (line 499) | def areas(self):
    method to_ndarray (line 503) | def to_ndarray(self):
    method to_tensor (line 507) | def to_tensor(self, dtype, device):
    method random (line 512) | def random(cls,
    method get_bboxes (line 532) | def get_bboxes(self):
  class PolygonMasks (line 548) | class PolygonMasks(BaseInstanceMasks):
    method __init__ (line 588) | def __init__(self, masks, height, width):
    method __getitem__ (line 598) | def __getitem__(self, index):
    method __iter__ (line 621) | def __iter__(self):
    method __repr__ (line 624) | def __repr__(self):
    method __len__ (line 631) | def __len__(self):
    method rescale (line 635) | def rescale(self, scale, interpolation=None):
    method resize (line 644) | def resize(self, out_shape, interpolation=None):
    method flip (line 663) | def flip(self, flip_direction='horizontal'):
    method crop (line 687) | def crop(self, bbox):
    method pad (line 716) | def pad(self, out_shape, pad_val=0):
    method expand (line 720) | def expand(self, *args, **kwargs):
    method crop_and_resize (line 724) | def crop_and_resize(self,
    method translate (line 765) | def translate(self,
    method shear (line 799) | def shear(self,
    method rotate (line 831) | def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0):
    method to_bitmap (line 860) | def to_bitmap(self):
    method areas (line 866) | def areas(self):
    method _polygon_area (line 884) | def _polygon_area(self, x, y):
    method to_ndarray (line 900) | def to_ndarray(self):
    method to_tensor (line 910) | def to_tensor(self, dtype, device):
    method random (line 920) | def random(cls,
    method get_bboxes (line 1039) | def get_bboxes(self):
  function polygon_to_bitmap (line 1058) | def polygon_to_bitmap(polygons, height, width):
  function bitmap_to_polygon (line 1075) | def bitmap_to_polygon(bitmap):

FILE: detection/mmdet/core/mask/utils.py
  function split_combined_polys (line 8) | def split_combined_polys(polys, poly_lens, polys_per_mask):
  function encode_mask_results (line 38) | def encode_mask_results(mask_results):
  function mask2bbox (line 68) | def mask2bbox(masks):

FILE: detection/mmdet/core/post_processing/bbox_nms.py
  function multiclass_nms (line 8) | def multiclass_nms(multi_bboxes,
  function fast_nms (line 98) | def fast_nms(multi_bboxes,

FILE: detection/mmdet/core/post_processing/matrix_nms.py
  function mask_matrix_nms (line 5) | def mask_matrix_nms(masks,

FILE: detection/mmdet/core/post_processing/merge_augs.py
  function merge_aug_proposals (line 13) | def merge_aug_proposals(aug_proposals, img_metas, cfg):
  function merge_aug_bboxes (line 84) | def merge_aug_bboxes(aug_bboxes, aug_scores, img_metas, rcnn_test_cfg):
  function merge_aug_scores (line 113) | def merge_aug_scores(aug_scores):
  function merge_aug_masks (line 121) | def merge_aug_masks(aug_masks, img_metas, rcnn_test_cfg, weights=None):

FILE: detection/mmdet/core/utils/dist_utils.py
  function _allreduce_coalesced (line 15) | def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1):
  function allreduce_grads (line 37) | def allreduce_grads(params, coalesce=True, bucket_size_mb=-1):
  class DistOptimizerHook (line 59) | class DistOptimizerHook(OptimizerHook):
    method __init__ (line 62) | def __init__(self, *args, **kwargs):
  function reduce_mean (line 68) | def reduce_mean(tensor):
  function obj2tensor (line 77) | def obj2tensor(pyobj, device='cuda'):
  function tensor2obj (line 83) | def tensor2obj(tensor):
  function _get_global_gloo_group (line 89) | def _get_global_gloo_group():
  function all_reduce_dict (line 98) | def all_reduce_dict(py_dict, op='sum', group=None, to_float=True):
  function sync_random_seed (line 157) | def sync_random_seed(seed=None, device='cuda'):

FILE: detection/mmdet/core/utils/misc.py
  function multi_apply (line 11) | def multi_apply(func, *args, **kwargs):
  function unmap (line 33) | def unmap(data, count, inds, fill=0):
  function mask2ndarray (line 46) | def mask2ndarray(mask):
  function flip_tensor (line 65) | def flip_tensor(src_tensor, flip_direction):
  function select_single_mlvl (line 88) | def select_single_mlvl(mlvl_tensors, batch_id, detach=True):
  function filter_scores_and_topk (line 119) | def filter_scores_and_topk(scores, score_thr, topk, results=None):
  function center_of_mass (line 168) | def center_of_mass(mask, esp=1e-6):
  function generate_coordinate (line 190) | def generate_coordinate(featmap_sizes, device='cuda'):

FILE: detection/mmdet/core/visualization/image.py
  function color_val_matplotlib (line 23) | def color_val_matplotlib(color):
  function _get_adaptive_scales (line 38) | def _get_adaptive_scales(areas, min_area=800, max_area=30000):
  function _get_bias_color (line 61) | def _get_bias_color(base, max_dist=30):
  function draw_bboxes (line 79) | def draw_bboxes(ax, bboxes, color='g', alpha=0.8, thickness=2):
  function draw_labels (line 112) | def draw_labels(ax,
  function draw_masks (line 164) | def draw_masks(ax, img, masks, color=None, with_edge=True, alpha=0.8):
  function imshow_det_bboxes (line 206) | def imshow_det_bboxes(img,
  function imshow_gt_det_bboxes (line 376) | def imshow_gt_det_bboxes(img,

FILE: detection/mmdet/core/visualization/palette.py
  function palette_val (line 6) | def palette_val(palette):
  function get_palette (line 22) | def get_palette(palette, num_classes):

FILE: detection/mmdet/datasets/api_wrappers/coco_api.py
  class COCO (line 11) | class COCO(_COCO):
    method __init__ (line 18) | def __init__(self, annotation_file=None):
    method get_ann_ids (line 27) | def get_ann_ids(self, img_ids=[], cat_ids=[], area_rng=[], iscrowd=None):
    method get_cat_ids (line 30) | def get_cat_ids(self, cat_names=[], sup_names=[], cat_ids=[]):
    method get_img_ids (line 33) | def get_img_ids(self, img_ids=[], cat_ids=[]):
    method load_anns (line 36) | def load_anns(self, ids):
    method load_cats (line 39) | def load_cats(self, ids):
    method load_imgs (line 42) | def load_imgs(self, ids):

FILE: detection/mmdet/datasets/api_wrappers/panoptic_evaluation.py
  function pq_compute_single_core (line 23) | def pq_compute_single_core(proc_id,
  function pq_compute_multi_core (line 169) | def pq_compute_multi_core(matched_annotations_list,

FILE: detection/mmdet/datasets/builder.py
  function _concat_dataset (line 32) | def _concat_dataset(cfg, default_args=None):
  function build_dataset (line 59) | def build_dataset(cfg, default_args=None):
  function build_dataloader (line 87) | def build_dataloader(dataset,
  function worker_init_fn (line 209) | def worker_init_fn(worker_id, num_workers, rank, seed):

FILE: detection/mmdet/datasets/cityscapes.py
  class CityscapesDataset (line 21) | class CityscapesDataset(CocoDataset):
    method _filter_imgs (line 29) | def _filter_imgs(self, min_size=32):
    method _parse_ann_info (line 57) | def _parse_ann_info(self, img_info, ann_info):
    method results2txt (line 111) | def results2txt(self, results, outfile_prefix):
    method format_results (line 177) | def format_results(self, results, txtfile_prefix=None):
    method evaluate (line 211) | def evaluate(self,
    method _evaluate_cityscapes (line 278) | def _evaluate_cityscapes(self, results, txtfile_prefix, logger):

FILE: detection/mmdet/datasets/coco.py
  class CocoDataset (line 23) | class CocoDataset(CustomDataset):
    method load_annotations (line 74) | def load_annotations(self, ann_file):
    method get_ann_info (line 103) | def get_ann_info(self, idx):
    method get_cat_ids (line 118) | def get_cat_ids(self, idx):
    method _filter_imgs (line 133) | def _filter_imgs(self, min_size=32):
    method _parse_ann_info (line 157) | def _parse_ann_info(self, img_info, ann_info):
    method xyxy2xywh (line 216) | def xyxy2xywh(self, bbox):
    method _proposal2json (line 236) | def _proposal2json(self, results):
    method _det2json (line 251) | def _det2json(self, results):
    method _segm2json (line 268) | def _segm2json(self, results):
    method results2json (line 306) | def results2json(self, results, outfile_prefix):
    method fast_eval_recall (line 346) | def fast_eval_recall(self, results, proposal_nums, iou_thrs, logger=No...
    method format_results (line 370) | def format_results(self, results, jsonfile_prefix=None, **kwargs):
    method evaluate_det_segm (line 398) | def evaluate_det_segm(self,
    method evaluate (line 604) | def evaluate(self,

FILE: detection/mmdet/datasets/coco_panoptic.py
  class COCOPanoptic (line 28) | class COCOPanoptic(COCO):
    method __init__ (line 37) | def __init__(self, annotation_file=None):
    method createIndex (line 46) | def createIndex(self):
    method load_anns (line 89) | def load_anns(self, ids=[]):
  class CocoPanopticDataset (line 114) | class CocoPanopticDataset(CocoDataset):
    method __init__ (line 259) | def __init__(self,
    method load_annotations (line 284) | def load_annotations(self, ann_file):
    method get_ann_info (line 306) | def get_ann_info(self, idx):
    method _parse_ann_info (line 322) | def _parse_ann_info(self, img_info, ann_info):
    method _filter_imgs (line 385) | def _filter_imgs(self, min_size=32):
    method _pan2json (line 410) | def _pan2json(self, results, outfile_prefix):
    method results2json (line 452) | def results2json(self, results, outfile_prefix):
    method evaluate_pan_json (line 503) | def evaluate_pan_json(self,
    method evaluate (line 568) | def evaluate(self,
  function parse_pq_results (line 641) | def parse_pq_results(pq_results):
  function print_panoptic_table (line 656) | def print_panoptic_table(pq_results, classwise_results=None, logger=None):

FILE: detection/mmdet/datasets/custom.py
  class CustomDataset (line 18) | class CustomDataset(Dataset):
    method __init__ (line 59) | def __init__(self,
    method __len__ (line 131) | def __len__(self):
    method load_annotations (line 135) | def load_annotations(self, ann_file):
    method load_proposals (line 139) | def load_proposals(self, proposal_file):
    method get_ann_info (line 143) | def get_ann_info(self, idx):
    method get_cat_ids (line 155) | def get_cat_ids(self, idx):
    method pre_pipeline (line 167) | def pre_pipeline(self, results):
    method _filter_imgs (line 176) | def _filter_imgs(self, min_size=32):
    method _set_group_flag (line 187) | def _set_group_flag(self):
    method _rand_another (line 199) | def _rand_another(self, idx):
    method __getitem__ (line 204) | def __getitem__(self, idx):
    method prepare_train_img (line 224) | def prepare_train_img(self, idx):
    method prepare_test_img (line 243) | def prepare_test_img(self, idx):
    method get_classes (line 262) | def get_classes(cls, classes=None):
    method get_cat2imgs (line 288) | def get_cat2imgs(self):
    method format_results (line 307) | def format_results(self, results, **kwargs):
    method evaluate (line 310) | def evaluate(self,
    method __repr__ (line 369) | def __repr__(self):

FILE: detection/mmdet/datasets/dataset_wrappers.py
  class ConcatDataset (line 17) | class ConcatDataset(_ConcatDataset):
    method __init__ (line 30) | def __init__(self, datasets, separate_eval=True):
    method get_cat_ids (line 50) | def get_cat_ids(self, idx):
    method get_ann_info (line 72) | def get_ann_info(self, idx):
    method evaluate (line 94) | def evaluate(self, results, logger=None, **kwargs):
  class RepeatDataset (line 154) | class RepeatDataset:
    method __init__ (line 167) | def __init__(self, dataset, times):
    method __getitem__ (line 177) | def __getitem__(self, idx):
    method get_cat_ids (line 180) | def get_cat_ids(self, idx):
    method get_ann_info (line 192) | def get_ann_info(self, idx):
    method __len__ (line 204) | def __len__(self):
  class ClassBalancedDataset (line 211) | class ClassBalancedDataset:
    method __init__ (line 247) | def __init__(self, dataset, oversample_thr, filter_empty_gt=True):
    method _get_repeat_factors (line 267) | def _get_repeat_factors(self, dataset, repeat_thr):
    method __getitem__ (line 316) | def __getitem__(self, idx):
    method get_ann_info (line 320) | def get_ann_info(self, idx):
    method __len__ (line 332) | def __len__(self):
  class MultiImageMixDataset (line 338) | class MultiImageMixDataset:
    method __init__ (line 362) | def __init__(self,
    method __len__ (line 398) | def __len__(self):
    method __getitem__ (line 401) | def __getitem__(self, idx):
    method update_skip_type_keys (line 446) | def update_skip_type_keys(self, skip_type_keys):

FILE: detection/mmdet/datasets/deepfashion.py
  class DeepFashionDataset (line 7) | class DeepFashionDataset(CocoDataset):

FILE: detection/mmdet/datasets/lvis.py
  class LVISV05Dataset (line 18) | class LVISV05Dataset(CocoDataset):
    method load_annotations (line 272) | def load_annotations(self, ann_file):
    method evaluate (line 311) | def evaluate(self,
  class LVISV1Dataset (line 475) | class LVISV1Dataset(LVISDataset):
    method load_annotations (line 717) | def load_annotations(self, ann_file):

FILE: detection/mmdet/datasets/openimages.py
  class OpenImagesDataset (line 21) | class OpenImagesDataset(CustomDataset):
    method __init__ (line 63) | def __init__(self,
    method get_classes_from_csv (line 127) | def get_classes_from_csv(self, label_file):
    method load_annotations (line 150) | def load_annotations(self, ann_file):
    method get_ann_info (line 220) | def get_ann_info(self, idx):
    method get_meta_from_file (line 291) | def get_meta_from_file(self, meta_file=''):
    method get_meta_from_pipeline (line 308) | def get_meta_from_pipeline(self, results):
    method get_img_shape (line 319) | def get_img_shape(self, metas):
    method prepare_test_img (line 332) | def prepare_test_img(self, idx):
    method _filter_imgs (line 344) | def _filter_imgs(self, min_size=32):
    method _set_group_flag (line 352) | def _set_group_flag(self):
    method get_relation_matrix (line 357) | def get_relation_matrix(self, hierarchy_file):
    method _convert_hierarchy_tree (line 380) | def _convert_hierarchy_tree(self,
    method add_supercategory_ann (line 426) | def add_supercategory_ann(self, annotations):
    method process_results (line 457) | def process_results(self, det_results, annotations,
    method load_image_label_from_csv (line 503) | def load_image_label_from_csv(self, image_level_ann_file):
    method get_image_level_ann (line 538) | def get_image_level_ann(self, image_level_ann_file):
    method denormalize_gt_bboxes (line 591) | def denormalize_gt_bboxes(self, annotations):
    method get_cat_ids (line 604) | def get_cat_ids(self, idx):
    method evaluate (line 615) | def evaluate(self,
  class OpenImagesChallengeDataset (line 727) | class OpenImagesChallengeDataset(OpenImagesDataset):
    method __init__ (line 730) | def __init__(self, ann_file, **kwargs):
    method get_classes_from_csv (line 735) | def get_classes_from_csv(self, label_file):
    method load_annotations (line 765) | def load_annotations(self, ann_file):
    method prepare_train_img (line 805) | def prepare_train_img(self, idx):
    method prepare_test_img (line 817) | def prepare_test_img(self, idx):
    method get_relation_matrix (line 830) | def get_relation_matrix(self, hierarchy_file):
    method get_ann_info (line 844) | def get_ann_info(self, idx):
    method load_image_label_from_csv (line 857) | def load_image_label_from_csv(self, image_level_ann_file):

FILE: detection/mmdet/datasets/pipelines/auto_augment.py
  function level_to_value (line 14) | def level_to_value(level, max_value):
  function enhance_level_to_value (line 19) | def enhance_level_to_value(level, a=1.8, b=0.1):
  function random_negative (line 24) | def random_negative(value, random_negative_prob):
  function bbox2fields (line 29) | def bbox2fields():
  class AutoAugment (line 47) | class AutoAugment:
    method __init__ (line 90) | def __init__(self, policies):
    method __call__ (line 104) | def __call__(self, results):
    method __repr__ (line 108) | def __repr__(self):
  class Shear (line 113) | class Shear:
    method __init__ (line 136) | def __init__(self,
    method _shear_img (line 180) | def _shear_img(self,
    method _shear_bboxes (line 205) | def _shear_bboxes(self, results, magnitude):
    method _shear_masks (line 235) | def _shear_masks(self,
    method _shear_seg (line 251) | def _shear_seg(self,
    method _filter_invalid (line 267) | def _filter_invalid(self, results, min_bbox_size=0):
    method __call__ (line 286) | def __call__(self, results):
    method __repr__ (line 317) | def __repr__(self):
  class Rotate (line 331) | class Rotate:
    method __init__ (line 357) | def __init__(self,
    method _rotate_img (line 408) | def _rotate_img(self, results, angle, center=None, scale=1.0):
    method _rotate_bboxes (line 427) | def _rotate_bboxes(self, results, rotate_matrix):
    method _rotate_masks (line 462) | def _rotate_masks(self,
    method _rotate_seg (line 474) | def _rotate_seg(self,
    method _filter_invalid (line 487) | def _filter_invalid(self, results, min_bbox_size=0):
    method __call__ (line 506) | def __call__(self, results):
    method __repr__ (line 532) | def __repr__(self):
  class Translate (line 546) | class Translate:
    method __init__ (line 572) | def __init__(self,
    method _translate_img (line 613) | def _translate_img(self, results, offset, direction='horizontal'):
    method _translate_bboxes (line 628) | def _translate_bboxes(self, results, offset):
    method _translate_masks (line 646) | def _translate_masks(self,
    method _translate_seg (line 657) | def _translate_seg(self,
    method _filter_invalid (line 668) | def _filter_invalid(self, results, min_size=0):
    method __call__ (line 687) | def __call__(self, results):
  class ColorTransform (line 713) | class ColorTransform:
    method __init__ (line 722) | def __init__(self, level, prob=0.5):
    method _adjust_color_img (line 733) | def _adjust_color_img(self, results, factor=1.0):
    method __call__ (line 740) | def __call__(self, results):
    method __repr__ (line 754) | def __repr__(self):
  class EqualizeTransform (line 762) | class EqualizeTransform:
    method __init__ (line 770) | def __init__(self, prob=0.5):
    method _imequalize (line 775) | def _imequalize(self, results):
    method __call__ (line 781) | def __call__(self, results):
    method __repr__ (line 795) | def __repr__(self):
  class BrightnessTransform (line 801) | class BrightnessTransform:
    method __init__ (line 810) | def __init__(self, level, prob=0.5):
    method _adjust_brightness_img (line 821) | def _adjust_brightness_img(self, results, factor=1.0):
    method __call__ (line 828) | def __call__(self, results):
    method __repr__ (line 842) | def __repr__(self):
  class ContrastTransform (line 850) | class ContrastTransform:
    method __init__ (line 859) | def __init__(self, level, prob=0.5):
    method _adjust_contrast_img (line 870) | def _adjust_contrast_img(self, results, factor=1.0):
    method __call__ (line 876) | def __call__(self, results):
    method __repr__ (line 890) | def __repr__(self):

FILE: detection/mmdet/datasets/pipelines/compose.py
  class Compose (line 10) | class Compose:
    method __init__ (line 18) | def __init__(self, transforms):
    method __call__ (line 30) | def __call__(self, data):
    method __repr__ (line 46) | def __repr__(self):

FILE: detection/mmdet/datasets/pipelines/formatting.py
  function to_tensor (line 12) | def to_tensor(data):
  class ToTensor (line 38) | class ToTensor:
    method __init__ (line 45) | def __init__(self, keys):
    method __call__ (line 48) | def __call__(self, results):
    method __repr__ (line 62) | def __repr__(self):
  class ImageToTensor (line 67) | class ImageToTensor:
    method __init__ (line 78) | def __init__(self, keys):
    method __call__ (line 81) | def __call__(self, results):
    method __repr__ (line 99) | def __repr__(self):
  class Transpose (line 104) | class Transpose:
    method __init__ (line 112) | def __init__(self, keys, order):
    method __call__ (line 116) | def __call__(self, results):
    method __repr__ (line 130) | def __repr__(self):
  class ToDataContainer (line 136) | class ToDataContainer:
    method __init__ (line 147) | def __init__(self,
    method __call__ (line 152) | def __call__(self, results):
    method __repr__ (line 170) | def __repr__(self):
  class DefaultFormatBundle (line 175) | class DefaultFormatBundle:
    method __init__ (line 200) | def __init__(self,
    method __call__ (line 206) | def __call__(self, results):
    method _add_default_meta_keys (line 248) | def _add_default_meta_keys(self, results):
    method __repr__ (line 273) | def __repr__(self):
  class Collect (line 279) | class Collect:
    method __init__ (line 318) | def __init__(self,
    method __call__ (line 326) | def __call__(self, results):
    method __repr__ (line 349) | def __repr__(self):
  class WrapFieldsToLists (line 355) | class WrapFieldsToLists:
    method __call__ (line 375) | def __call__(self, results):
    method __repr__ (line 391) | def __repr__(self):

FILE: detection/mmdet/datasets/pipelines/instaboost.py
  class InstaBoost (line 8) | class InstaBoost:
    method __init__ (line 35) | def __init__(self,
    method _load_anns (line 56) | def _load_anns(self, results):
    method _parse_anns (line 78) | def _parse_anns(self, results, anns, img):
    method __call__ (line 99) | def __call__(self, results):
    method __repr__ (line 115) | def __repr__(self):

FILE: detection/mmdet/datasets/pipelines/loading.py
  class LoadImageFromFile (line 18) | class LoadImageFromFile:
    method __init__ (line 37) | def __init__(self,
    method __call__ (line 48) | def __call__(self, results):
    method __repr__ (line 81) | def __repr__(self):
  class LoadImageFromWebcam (line 91) | class LoadImageFromWebcam(LoadImageFromFile):
    method __call__ (line 98) | def __call__(self, results):
  class LoadMultiChannelImageFromFiles (line 123) | class LoadMultiChannelImageFromFiles:
    method __init__ (line 143) | def __init__(self,
    method __call__ (line 152) | def __call__(self, results):
    method __repr__ (line 197) | def __repr__(self):
  class LoadAnnotations (line 206) | class LoadAnnotations:
    method __init__ (line 228) | def __init__(self,
    method _load_bboxes (line 245) | def _load_bboxes(self, results):
    method _load_labels (line 277) | def _load_labels(self, results):
    method _poly2mask (line 290) | def _poly2mask(self, mask_ann, img_h, img_w):
    method process_polygons (line 317) | def process_polygons(self, polygons):
    method _load_masks (line 334) | def _load_masks(self, results):
    method _load_semantic_seg (line 359) | def _load_semantic_seg(self, results):
    method __call__ (line 380) | def __call__(self, results):
    method __repr__ (line 403) | def __repr__(self):
  class LoadPanopticAnnotations (line 415) | class LoadPanopticAnnotations(LoadAnnotations):
    method __init__ (line 432) | def __init__(self,
    method _load_masks_and_semantic_segs (line 453) | def _load_masks_and_semantic_segs(self, results):
    method __call__ (line 500) | def __call__(self, results):
  class LoadProposals (line 526) | class LoadProposals:
    method __init__ (line 536) | def __init__(self, num_max_proposals=None):
    method __call__ (line 539) | def __call__(self, results):
    method __repr__ (line 565) | def __repr__(self):
  class FilterAnnotations (line 571) | class FilterAnnotations:
    method __init__ (line 581) | def __init__(self, min_gt_bbox_wh, keep_empty=True):
    method __call__ (line 586) | def __call__(self, results):
    method __repr__ (line 606) | def __repr__(self):

FILE: detection/mmdet/datasets/pipelines/test_time_aug.py
  class MultiScaleFlipAug (line 11) | class MultiScaleFlipAug:
    method __init__ (line 54) | def __init__(self,
    method __call__ (line 85) | def __call__(self, results):
    method __repr__ (line 116) | def __repr__(self):

FILE: detection/mmdet/datasets/pipelines/transforms.py
  class Resize (line 31) | class Resize:
    method __init__ (line 76) | def __init__(self,
    method random_select (line 111) | def random_select(img_scales):
    method random_sample (line 129) | def random_sample(img_scales):
    method random_sample_ratio (line 156) | def random_sample_ratio(img_scale, ratio_range):
    method _random_scale (line 182) | def _random_scale(self, results):
    method _resize_img (line 215) | def _resize_img(self, results):
    method _resize_bboxes (line 248) | def _resize_bboxes(self, results):
    method _resize_masks (line 258) | def _resize_masks(self, results):
    method _resize_seg (line 268) | def _resize_seg(self, results):
    method __call__ (line 285) | def __call__(self, results):
    method __repr__ (line 322) | def __repr__(self):
  class RandomFlip (line 333) | class RandomFlip:
    method __init__ (line 370) | def __init__(self, flip_ratio=None, direction='horizontal'):
    method bbox_flip (line 396) | def bbox_flip(self, bboxes, img_shape, direction):
    method __call__ (line 430) | def __call__(self, results):
    method __repr__ (line 485) | def __repr__(self):
  class RandomShift (line 490) | class RandomShift:
    method __init__ (line 501) | def __init__(self, shift_ratio=0.5, max_shift_px=32, filter_thr_px=1):
    method __call__ (line 513) | def __call__(self, results):
    method __repr__ (line 573) | def __repr__(self):
  class Pad (line 580) | class Pad:
    method __init__ (line 596) | def __init__(self,
    method _pad_img (line 622) | def _pad_img(self, results):
    method _pad_masks (line 640) | def _pad_masks(self, results):
    method _pad_seg (line 647) | def _pad_seg(self, results):
    method __call__ (line 655) | def __call__(self, results):
    method __repr__ (line 669) | def __repr__(self):
  class Normalize (line 679) | class Normalize:
    method __init__ (line 691) | def __init__(self, mean, std, to_rgb=True):
    method __call__ (line 696) | def __call__(self, results):
    method __repr__ (line 713) | def __repr__(self):
  class RandomCrop (line 720) | class RandomCrop:
    method __init__ (line 756) | def __init__(self,
    method _crop_data (line 787) | def _crop_data(self, results, crop_size, allow_negative_crop):
    method _get_crop_size (line 854) | def _get_crop_size(self, image_size):
    method __call__ (line 884) | def __call__(self, results):
    method __repr__ (line 900) | def __repr__(self):
  class SegRescale (line 910) | class SegRescale:
    method __init__ (line 920) | def __init__(self, scale_factor=1, backend='cv2'):
    method __call__ (line 924) | def __call__(self, results):
    method __repr__ (line 943) | def __repr__(self):
  class PhotoMetricDistortion (line 948) | class PhotoMetricDistortion:
    method __init__ (line 969) | def __init__(self,
    method __call__ (line 979) | def __call__(self, results):
    method __repr__ (line 1040) | def __repr__(self):
  class Expand (line 1052) | class Expand:
    method __init__ (line 1065) | def __init__(self,
    method __call__ (line 1081) | def __call__(self, results):
    method __repr__ (line 1135) | def __repr__(self):
  class MinIoURandomCrop (line 1144) | class MinIoURandomCrop:
    method __init__ (line 1163) | def __init__(self,
    method __call__ (line 1181) | def __call__(self, results):
    method __repr__ (line 1275) | def __repr__(self):
  class Corrupt (line 1284) | class Corrupt:
    method __init__ (line 1295) | def __init__(self, corruption, severity=1):
    method __call__ (line 1299) | def __call__(self, results):
    method __repr__ (line 1320) | def __repr__(self):
  class Albu (line 1328) | class Albu:
    method __init__ (line 1370) | def __init__(self,
    method albu_builder (line 1413) | def albu_builder(self, cfg):
    method mapper (line 1448) | def mapper(d, keymap):
    method __call__ (line 1464) | def __call__(self, results):
    method __repr__ (line 1526) | def __repr__(self):
  class RandomCenterCropPad (line 1532) | class RandomCenterCropPad:
    method __init__ (line 1620) | def __init__(self,
    method _get_border (line 1666) | def _get_border(self, border, size):
    method _filter_boxes (line 1685) | def _filter_boxes(self, patch, boxes):
    method _crop_image_and_paste (line 1701) | def _crop_image_and_paste(self, image, center, size):
    method _train_aug (line 1752) | def _train_aug(self, results):
    method _test_aug (line 1820) | def _test_aug(self, results):
    method __call__ (line 1854) | def __call__(self, results):
    method __repr__ (line 1866) | def __repr__(self):
  class CutOut (line 1881) | class CutOut:
    method __init__ (line 1904) | def __init__(self,
    method __call__ (line 1925) | def __call__(self, results):
    method __repr__ (line 1945) | def __repr__(self):
  class Mosaic (line 1955) | class Mosaic:
    method __init__ (line 2008) | def __init__(self,
    method __call__ (line 2029) | def __call__(self, results):
    method get_indexes (line 2045) | def get_indexes(self, dataset):
    method _mosaic_transform (line 2058) | def _mosaic_transform(self, results):
    method _mosaic_combine (line 2155) | def _mosaic_combine(self, loc, center_position_xy, img_shape_wh):
    method _filter_box_candidates (line 2216) | def _filter_box_candidates(self, bboxes, labels):
    method __repr__ (line 2225) | def __repr__(self):
  class MixUp (line 2236) | class MixUp:
    method __init__ (line 2292) | def __init__(self,
    method __call__ (line 2316) | def __call__(self, results):
    method get_indexes (line 2329) | def get_indexes(self, dataset):
    method _mixup_transform (line 2347) | def _mixup_transform(self, results):
    method _filter_box_candidates (line 2470) | def _filter_box_candidates(self, bbox1, bbox2):
    method __repr__ (line 2485) | def __repr__(self):
  class RandomAffine (line 2500) | class RandomAffine:
    method __init__ (line 2539) | def __init__(self,
    method __call__ (line 2566) | def __call__(self, results):
    method filter_gt_bboxes (line 2649) | def filter_gt_bboxes(self, origin_bboxes, wrapped_bboxes):
    method __repr__ (line 2664) | def __repr__(self):
    method _get_rotation_matrix (line 2679) | def _get_rotation_matrix(rotate_degrees):
    method _get_scaling_matrix (line 2688) | def _get_scaling_matrix(scale_ratio):
    method _get_share_matrix (line 2695) | def _get_share_matrix(scale_ratio):
    method _get_shear_matrix (line 2702) | def _get_shear_matrix(x_shear_degrees, y_shear_degrees):
    method _get_translation_matrix (line 2711) | def _get_translation_matrix(x, y):
  class YOLOXHSVRandomAug (line 2718) | class YOLOXHSVRandomAug:
    method __init__ (line 2729) | def __init__(self, hue_delta=5, saturation_delta=30, value_delta=30):
    method __call__ (line 2734) | def __call__(self, results):
    method __repr__ (line 2753) | def __repr__(self):
  class CopyPaste (line 2762) | class CopyPaste:
    method __init__ (line 2794) | def __init__(
    method get_indexes (line 2806) | def get_indexes(self, dataset):
    method __call__ (line 2816) | def __call__(self, results):
    method _select_object (line 2835) | def _select_object(self, results):
    method _copy_paste (line 2854) | def _copy_paste(self, dst_results, src_results):
    method get_updated_masks (line 2907) | def get_updated_masks(self, masks, composed_mask):
    method __repr__ (line 2913) | def __repr__(self):

FILE: detection/mmdet/datasets/samplers/class_aware_sampler.py
  class ClassAwareSampler (line 11) | class ClassAwareSampler(Sampler):
    method __init__ (line 40) | def __init__(self,
    method __iter__ (line 85) | def __iter__(self):
    method __len__ (line 131) | def __len__(self):
    method set_epoch (line 134) | def set_epoch(self, epoch):
  class RandomCycleIter (line 138) | class RandomCycleIter:
    method __init__ (line 156) | def __init__(self, data, generator=None):
    method __iter__ (line 163) | def __iter__(self):
    method __len__ (line 166) | def __len__(self):
    method __next__ (line 169) | def __next__(self):

FILE: detection/mmdet/datasets/samplers/distributed_sampler.py
  class DistributedSampler (line 11) | class DistributedSampler(_DistributedSampler):
    method __init__ (line 13) | def __init__(self,
    method __iter__ (line 31) | def __iter__(self):

FILE: detection/mmdet/datasets/samplers/group_sampler.py
  class GroupSampler (line 10) | class GroupSampler(Sampler):
    method __init__ (line 12) | def __init__(self, dataset, samples_per_gpu=1):
    method __iter__ (line 23) | def __iter__(self):
    method __len__ (line 47) | def __len__(self):
  class DistributedGroupSampler (line 51) | class DistributedGroupSampler(Sampler):
    method __init__ (line 72) | def __init__(self,
    method __iter__ (line 101) | def __iter__(self):
    method __len__ (line 144) | def __len__(self):
    method set_epoch (line 147) | def set_epoch(self, epoch):

FILE: detection/mmdet/datasets/samplers/infinite_sampler.py
  class InfiniteGroupBatchSampler (line 12) | class InfiniteGroupBatchSampler(Sampler):
    method __init__ (line 37) | def __init__(self,
    method _infinite_indices (line 71) | def _infinite_indices(self):
    method _indices_of_rank (line 82) | def _indices_of_rank(self):
    method __iter__ (line 87) | def __iter__(self):
    method __len__ (line 97) | def __len__(self):
    method set_epoch (line 101) | def set_epoch(self, epoch):
  class InfiniteBatchSampler (line 106) | class InfiniteBatchSampler(Sampler):
    method __init__ (line 128) | def __init__(self,
    method _infinite_indices (line 155) | def _infinite_indices(self):
    method _indices_of_rank (line 166) | def _indices_of_rank(self):
    method __iter__ (line 171) | def __iter__(self):
    method __len__ (line 180) | def __len__(self):
    method set_epoch (line 184) | def set_epoch(self, epoch):

FILE: detection/mmdet/datasets/utils.py
  function replace_ImageToTensor (line 15) | def replace_ImageToTensor(pipelines):
  function get_loading_pipeline (line 75) | def get_loading_pipeline(pipeline):
  class NumClassCheckHook (line 118) | class NumClassCheckHook(Hook):
    method _check_head (line 120) | def _check_head(self, runner):
    method before_train_epoch (line 152) | def before_train_epoch(self, runner):
    method before_val_epoch (line 160) | def before_val_epoch(self, runner):

FILE: detection/mmdet/datasets/voc.py
  class VOCDataset (line 12) | class VOCDataset(XMLDataset):
    method __init__ (line 25) | def __init__(self, **kwargs):
    method evaluate (line 34) | def evaluate(self,

FILE: detection/mmdet/datasets/wider_face.py
  class WIDERFaceDataset (line 12) | class WIDERFaceDataset(XMLDataset):
    method __init__ (line 22) | def __init__(self, **kwargs):
    method load_annotations (line 25) | def load_annotations(self, ann_file):

FILE: detection/mmdet/datasets/xml_style.py
  class XMLDataset (line 14) | class XMLDataset(CustomDataset):
    method __init__ (line 25) | def __init__(self,
    method load_annotations (line 38) | def load_annotations(self, ann_file):
    method _filter_imgs (line 69) | def _filter_imgs(self, min_size=32):
    method get_ann_info (line 90) | def get_ann_info(self, idx):
    method get_cat_ids (line 156) | def get_cat_ids(self, idx):

FILE: detection/mmdet/models/backbones/csp_darknet.py
  class Focus (line 14) | class Focus(nn.Module):
    method __init__ (line 30) | def __init__(self,
    method forward (line 49) | def forward(self, x):
  class SPPBottleneck (line 67) | class SPPBottleneck(BaseModule):
    method __init__ (line 85) | def __init__(self,
    method forward (line 116) | def forward(self, x):
  class CSPDarknet (line 124) | class CSPDarknet(BaseModule):
    method __init__ (line 177) | def __init__(self,
    method _freeze_stages (line 261) | def _freeze_stages(self):
    method train (line 269) | def train(self, mode=True):
    method forward (line 277) | def forward(self, x):

FILE: detection/mmdet/models/backbones/darknet.py
  class ResBlock (line 14) | class ResBlock(BaseModule):
    method __init__ (line 33) | def __init__(self,
    method forward (line 50) | def forward(self, x):
  class Darknet (line 60) | class Darknet(BaseModule):
    method __init__ (line 101) | def __init__(self,
    method forward (line 153) | def forward(self, x):
    method _freeze_stages (line 163) | def _freeze_stages(self):
    method train (line 171) | def train(self, mode=True):
    method make_conv_res_block (line 180) | def make_conv_res_block(in_channels,

FILE: detection/mmdet/models/backbones/detectors_resnet.py
  class Bottleneck (line 16) | class Bottleneck(_Bottleneck):
    method __init__ (line 36) | def __init__(self,
    method rfp_forward (line 73) | def rfp_forward(self, x, rfp_feat):
  class ResLayer (line 120) | class ResLayer(Sequential):
    method __init__ (line 146) | def __init__(self,
  class DetectoRS_ResNet (line 212) | class DetectoRS_ResNet(ResNet):
    method __init__ (line 234) | def __init__(self,
    method init_weights (line 296) | def init_weights(self):
    method make_res_layer (line 325) | def make_res_layer(self, **kwargs):
    method forward (line 329) | def forward(self, x):
    method rfp_forward (line 336) | def rfp_forward(self, x, rfp_feats):

FILE: detection/mmdet/models/backbones/detectors_resnext.py
  class Bottleneck (line 11) | class Bottleneck(_Bottleneck):
    method __init__ (line 14) | def __init__(self,
  class DetectoRS_ResNeXt (line 99) | class DetectoRS_ResNeXt(DetectoRS_ResNet):
    method __init__ (line 113) | def __init__(self, groups=1, base_width=4, **kwargs):
    method make_res_layer (line 118) | def make_res_layer(self, **kwargs):

FILE: detection/mmdet/models/backbones/efficientnet.py
  class EdgeResidual (line 16) | class EdgeResidual(BaseModule):
    method __init__ (line 41) | def __init__(self,
    method forward (line 91) | def forward(self, x):
  function model_scaling (line 115) | def model_scaling(layer_setting, arch_setting):
  class EfficientNet (line 159) | class EfficientNet(BaseModule):
    method __init__ (line 254) | def __init__(self,
    method make_layer (line 327) | def make_layer(self):
    method forward (line 395) | def forward(self, x):
    method _freeze_stages (line 404) | def _freeze_stages(self):
    method train (line 411) | def train(self, mode=True):

FILE: detection/mmdet/models/backbones/hivit.py
  function window_partition (line 21) | def window_partition(x, window_size):
  function window_unpartition (line 45) | def window_unpartition(windows, window_size, pad_hw, hw):
  class LayerNorm2d (line 68) | class LayerNorm2d(nn.LayerNorm):
    method forward (line 69) | def forward(self, x):
  class HiViT (line 77) | class HiViT(models.HiViT):
    method __init__ (line 78) | def __init__(self,
    method init_weights (line 150) | def init_weights(self):
    method _freeze_stages (line 166) | def _freeze_stages(self):
    method interpolate_pos_encoding (line 179) | def interpolate_pos_encoding(self, x, h, w):
    method prepare_tokens (line 200) | def prepare_tokens(self, x, mask=None):
    method forward (line 225) | def forward(self, x):

FILE: detection/mmdet/models/backbones/hourglass.py
  class HourglassModule (line 12) | class HourglassModule(BaseModule):
    method __init__ (line 30) | def __init__(self,
    method forward (line 80) | def forward(self, x):
  class HourglassNet (line 97) | class HourglassNet(BaseModule):
    method __init__ (line 131) | def __init__(self,
    method init_weights (line 195) | def init_weights(self):
    method forward (line 203) | def forward(self, x):

FILE: detection/mmdet/models/backbones/hrnet.py
  class HRModule (line 13) | class HRModule(BaseModule):
    method __init__ (line 20) | def __init__(self,
    method _check_branches (line 49) | def _check_branches(self, num_branches, num_blocks, in_channels,
    method _make_one_branch (line 66) | def _make_one_branch(self,
    method _make_branches (line 112) | def _make_branches(self, num_branches, block, num_blocks, num_channels):
    method _make_fuse_layers (line 121) | def _make_fuse_layers(self):
    method forward (line 183) | def forward(self, x):
  class HRNet (line 204) | class HRNet(BaseModule):
    method __init__ (line 281) | def __init__(self,
    method norm1 (line 403) | def norm1(self):
    method norm2 (line 408) | def norm2(self):
    method _make_transition_layer (line 412) | def _make_transition_layer(self, num_channels_pre_layer,
    method _make_layer (line 458) | def _make_layer(self, block, inplanes, planes, blocks, stride=1):
    method _make_stage (line 505) | def _make_stage(self, layer_config, in_channels, multiscale_output=True):
    method forward (line 545) | def forward(self, x):
    method train (line 581) | def train(self, mode=True):

FILE: detection/mmdet/models/backbones/mobilenet_v2.py
  class MobileNetV2 (line 14) | class MobileNetV2(BaseModule):
    method __init__ (line 46) | def __init__(self,
    method make_layer (line 138) | def make_layer(self, out_channels, num_blocks, stride, expand_ratio):
    method _freeze_stages (line 167) | def _freeze_stages(self):
    method forward (line 177) | def forward(self, x):
    method train (line 188) | def train(self, mode=True):

FILE: detection/mmdet/models/backbones/pvt.py
  class MixFFN (line 23) | class MixFFN(BaseModule):
    method __init__ (line 47) | def __init__(self,
    method forward (line 93) | def forward(self, x, hw_shape, identity=None):
  class SpatialReductionAttention (line 102) | class SpatialReductionAttention(MultiheadAttention):
    method __init__ (line 129) | def __init__(self,
    method forward (line 169) | def forward(self, x, hw_shape, identity=None):
    method legacy_forward (line 200) | def legacy_forward(self, x, hw_shape, identity=None):
  class PVTEncoderLayer (line 219) | class PVTEncoderLayer(BaseModule):
    method __init__ (line 245) | def __init__(self,
    method forward (line 284) | def forward(self, x, hw_shape):
  class AbsolutePositionEmbedding (line 291) | class AbsolutePositionEmbedding(BaseModule):
    method __init__ (line 301) | def __init__(self, pos_shape, pos_dim, drop_rate=0., init_cfg=None):
    method init_weights (line 319) | def init_weights(self):
    method resize_pos_embed (line 322) | def resize_pos_embed(self, pos_embed, input_shape, mode='bilinear'):
    method forward (line 351) | def forward(self, x, hw_shape, mode='bilinear'):
  class PyramidVisionTransformer (line 357) | class PyramidVisionTransformer(BaseModule):
    method __init__ (line 410) | def __init__(self,
    method init_weights (line 523) | def init_weights(self):
    method forward (line 563) | def forward(self, x):
  class PyramidVisionTransformerV2 (line 580) | class PyramidVisionTransformerV2(PyramidVisionTransformer):
    method __init__ (line 584) | def __init__(self, **kwargs):

FILE: detection/mmdet/models/backbones/regnet.py
  class RegNet (line 14) | class RegNet(ResNet):
    method __init__ (line 90) | def __init__(self,
    method _make_stem_layer (line 238) | def _make_stem_layer(self, in_channels, base_channels):
    method generate_regnet (line 252) | def generate_regnet(self,
    method quantize_float (line 285) | def quantize_float(number, divisor):
    method adjust_width_group (line 297) | def adjust_width_group(self, widths, bottleneck_ratio, groups):
    method get_stages_from_blocks (line 322) | def get_stages_from_blocks(self, widths):
    method forward (line 344) | def forward(self, x):

FILE: detection/mmdet/models/backbones/res2net.py
  class Bottle2neck (line 15) | class Bottle2neck(_Bottleneck):
    method __init__ (line 18) | def __init__(self,
    method forward (line 106) | def forward(self, x):
  class Res2Layer (line 163) | class Res2Layer(Sequential):
    method __init__ (line 182) | def __init__(self,
  class Res2Net (line 243) | class Res2Net(ResNet):
    method __init__ (line 303) | def __init__(self,
    method make_res_layer (line 322) | def make_res_layer(self, **kwargs):

FILE: detection/mmdet/models/backbones/resnest.py
  class RSoftmax (line 17) | class RSoftmax(nn.Module):
    method __init__ (line 25) | def __init__(self, radix, groups):
    method forward (line 30) | def forward(self, x):
  class SplitAttentionConv2d (line 41) | class SplitAttentionConv2d(BaseModule):
    method __init__ (line 64) | def __init__(self,
    method norm0 (line 116) | def norm0(self):
    method norm1 (line 121) | def norm1(self):
    method forward (line 125) | def forward(self, x):
  class Bottleneck (line 154) | class Bottleneck(_Bottleneck):
    method __init__ (line 173) | def __init__(self,
    method forward (line 234) | def forward(self, x):
  class ResNeSt (line 278) | class ResNeSt(ResNetV1d):
    method __init__ (line 299) | def __init__(self,
    method make_res_layer (line 313) | def make_res_layer(self, **kwargs):

FILE: detection/mmdet/models/backbones/resnet.py
  class BasicBlock (line 14) | class BasicBlock(BaseModule):
    method __init__ (line 17) | def __init__(self,
    method norm1 (line 58) | def norm1(self):
    method norm2 (line 63) | def norm2(self):
    method forward (line 67) | def forward(self, x):
  class Bottleneck (line 97) | class Bottleneck(BaseModule):
    method __init__ (line 100) | def __init__(self,
    method make_block_plugins (line 219) | def make_block_plugins(self, in_channels, plugins):
    method forward_plugin (line 242) | def forward_plugin(self, x, plugin_names):
    method norm1 (line 249) | def norm1(self):
    method norm2 (line 254) | def norm2(self):
    method norm3 (line 259) | def norm3(self):
    method forward (line 263) | def forward(self, x):
  class ResNet (line 306) | class ResNet(BaseModule):
    method __init__ (line 369) | def __init__(self,
    method make_stage_plugins (line 494) | def make_stage_plugins(self, plugins, stage_idx):
    method make_res_layer (line 556) | def make_res_layer(self, **kwargs):
    method norm1 (line 561) | def norm1(self):
    method _make_stem_layer (line 565) | def _make_stem_layer(self, in_channels, stem_channels):
    method _freeze_stages (line 613) | def _freeze_stages(self):
    method forward (line 631) | def forward(self, x):
    method train (line 648) | def train(self, mode=True):
  class ResNetV1d (line 661) | class ResNetV1d(ResNet):
    method __init__ (line 670) | def __init__(self, **kwargs):

FILE: detection/mmdet/models/backbones/resnext.py
  class Bottleneck (line 12) | class Bottleneck(_Bottleneck):
    method __init__ (line 15) | def __init__(self,
    method _del_block_plugins (line 98) | def _del_block_plugins(self, plugin_names):
  class ResNeXt (line 110) | class ResNeXt(ResNet):
    method __init__ (line 143) | def __init__(self, groups=1, base_width=4, **kwargs):
    method make_res_layer (line 148) | def make_res_layer(self, **kwargs):

FILE: detection/mmdet/models/backbones/ssd_vgg.py
  class SSDVGG (line 13) | class SSDVGG(VGG, BaseModule):
    method __init__ (line 50) | def __init__(self,
    method init_weights (line 105) | def init_weights(self, pretrained=None):
    method forward (line 108) | def forward(self, x):
  class L2Norm (line 122) | class L2Norm(ssd_neck.L2Norm):
    method __init__ (line 124) | def __init__(self, **kwargs):

FILE: detection/mmdet/models/backbones/swin.py
  class WindowMSA (line 22) | class WindowMSA(BaseModule):
    method __init__ (line 41) | def __init__(self,
    method init_weights (line 78) | def init_weights(self):
    method forward (line 81) | def forward(self, x, mask=None):
    method double_step_seq (line 122) | def double_step_seq(step1, len1, step2, len2):
  class ShiftWindowMSA (line 128) | class ShiftWindowMSA(BaseModule):
    method __init__ (line 151) | def __init__(self,
    method forward (line 180) | def forward(self, query, hw_shape):
    method window_reverse (line 256) | def window_reverse(self, windows, H, W):
    method window_partition (line 272) | def window_partition(self, x):
  class SwinBlock (line 288) | class SwinBlock(BaseModule):
    method __init__ (line 313) | def __init__(self,
    method forward (line 358) | def forward(self, x, hw_shape):
  class SwinBlockSequence (line 381) | class SwinBlockSequence(BaseModule):
    method __init__ (line 410) | def __init__(self,
    method forward (line 455) | def forward(self, x, hw_shape):
  class SwinTransformer (line 467) | class SwinTransformer(BaseModule):
    method __init__ (line 524) | def __init__(self,
    method train (line 643) | def train(self, mode=True):
    method _freeze_stages (line 648) | def _freeze_stages(self):
    method init_weights (line 670) | def init_weights(self):
    method forward (line 745) | def forward(self, x):

FILE: detection/mmdet/models/backbones/trident_resnet.py
  class TridentConv (line 14) | class TridentConv(BaseModule):
    method __init__ (line 33) | def __init__(self,
    method extra_repr (line 61) | def extra_repr(self):
    method forward (line 73) | def forward(self, inputs):
  class TridentBottleneck (line 93) | class TridentBottleneck(Bottleneck):
    method __init__ (line 106) | def __init__(self, trident_dilations, test_branch_idx, concat_output,
    method forward (line 128) | def forward(self, x):
  function make_trident_res_layer (line 182) | def make_trident_res_layer(block,
  class TridentResNet (line 235) | class TridentResNet(ResNet):
    method __init__ (line 256) | def __init__(self, depth, num_branch, test_branch_idx, trident_dilations,

FILE: detection/mmdet/models/backbones/vit.py
  class PatchEmbed (line 19) | class PatchEmbed(nn.Module):
    method __init__ (line 22) | def __init__(self, img_size=[224, 224], patch_size=16, in_chans=3, emb...
    method forward (line 34) | def forward(self, x, mask=None):
  class VisionTransformer (line 40) | class VisionTransformer(VisionTransformer):
    method __init__ (line 41) | def __init__(self,
    method train (line 96) | def train(self, mode=True):
    method _freeze_stages (line 101) | def _freeze_stages(self):
    method init_weights (line 122) | def init_weights(self):
    method forward_encoder (line 137) | def forward_encoder(self, x):
    method forward (line 142) | def forward(self, x):

FILE: detection/mmdet/models/builder.py
  function build_backbone (line 18) | def build_backbone(cfg):
  function build_neck (line 23) | def build_neck(cfg):
  function build_roi_extractor (line 28) | def build_roi_extractor(cfg):
  function build_shared_head (line 33) | def build_shared_head(cfg):
  function build_head (line 38) | def build_head(cfg):
  function build_loss (line 43) | def build_loss(cfg):
  function build_detector (line 48) | def build_detector(cfg, train_cfg=None, test_cfg=None):

FILE: detection/mmdet/models/dense_heads/anchor_free_head.py
  class AnchorFreeHead (line 18) | class AnchorFreeHead(BaseDenseHead, BBoxTestMixin):
    method __init__ (line 46) | def __init__(self,
    method _init_layers (line 107) | def _init_layers(self):
    method _init_cls_convs (line 113) | def _init_cls_convs(self):
    method _init_reg_convs (line 133) | def _init_reg_convs(self):
    method _init_predictor (line 153) | def _init_predictor(self):
    method _load_from_state_dict (line 159) | def _load_from_state_dict(self, state_dict, prefix, local_metadata, st...
    method forward (line 197) | def forward(self, feats):
    method forward_single (line 215) | def forward_single(self, x):
    method loss (line 240) | def loss(self,
    method get_targets (line 268) | def get_targets(self, points, gt_bboxes_list, gt_labels_list):
    method _get_points_single (line 282) | def _get_points_single(self,
    method get_points (line 310) | def get_points(self, featmap_sizes, dtype, device, flatten=False):
    method aug_test (line 334) | def aug_test(self, feats, img_metas, rescale=False):

FILE: detection/mmdet/models/dense_heads/anchor_head.py
  class AnchorHead (line 17) | class AnchorHead(BaseDenseHead, BBoxTestMixin):
    method __init__ (line 39) | def __init__(self,
    method num_anchors (line 115) | def num_anchors(self):
    method anchor_generator (line 122) | def anchor_generator(self):
    method _init_layers (line 127) | def _init_layers(self):
    method forward_single (line 135) | def forward_single(self, x):
    method forward (line 152) | def forward(self, feats):
    method get_anchors (line 171) | def get_anchors(self, featmap_sizes, img_metas, device='cuda'):
    method _get_targets_single (line 201) | def _get_targets_single(self,
    method get_targets (line 299) | def get_targets(self,
    method loss_single (line 402) | def loss_single(self, cls_score, bbox_pred, anchors, labels, label_wei...
    method loss (line 453) | def loss(self,
    method aug_test (line 522) | def aug_test(self, feats, img_metas, rescale=False):

FILE: detection/mmdet/models/dense_heads/atss_head.py
  class ATSSHead (line 14) | class ATSSHead(AnchorHead):
    method __init__ (line 24) | def __init__(self,
    method _init_layers (line 65) | def _init_layers(self):
    method forward (line 109) | def forward(self, feats):
    method forward_single (line 127) | def forward_single(self, x, scale):
    method loss_single (line 156) | def loss_single(self, anchors, cls_score, bbox_pred, centerness, labels,
    method loss (line 230) | def loss(self,
    method centerness_target (line 305) | def centerness_target(self, anchors, gts):
    method get_targets (line 322) | def get_targets(self,
    method _get_target_single (line 386) | def _get_target_single(self,
    method get_num_level_anchors_inside (line 496) | def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):

FILE: detection/mmdet/models/dense_heads/autoassign_head.py
  class CenterPrior (line 21) | class CenterPrior(nn.Module):
    method __init__ (line 37) | def __init__(self,
    method forward (line 49) | def forward(self, anchor_points_list, gt_bboxes, labels,
  class AutoAssignHead (line 128) | class AutoAssignHead(FCOSHead):
    method __init__ (line 148) | def __init__(self,
    method init_weights (line 167) | def init_weights(self):
    method forward_single (line 179) | def forward_single(self, x, scale, stride):
    method get_pos_loss_single (line 207) | def get_pos_loss_single(self, cls_score, objectness, reg_loss, gt_labels,
    method get_neg_loss_single (line 251) | def get_neg_loss_single(self, cls_score, objectness, gt_labels, ious,
    method loss (line 307) | def loss(self,
    method get_targets (line 439) | def get_targets(self, points, gt_bboxes_list):
    method _get_target_single (line 468) | def _get_target_single(self, gt_bboxes, points):
    method _get_points_single (line 506) | def _get_points_single(self,

FILE: detection/mmdet/models/dense_heads/base_dense_head.py
  class BaseDenseHead (line 12) | class BaseDenseHead(BaseModule, metaclass=ABCMeta):
    method __init__ (line 15) | def __init__(self, init_cfg=None):
    method init_weights (line 18) | def init_weights(self):
    method loss (line 27) | def loss(self, **kwargs):
    method get_bboxes (line 32) | def get_bboxes(self,
    method _get_bboxes_single (line 109) | def _get_bboxes_single(self,
    method _bbox_post_process (line 226) | def _bbox_post_process(self,
    method forward_train (line 303) | def forward_train(self,
    method simple_test (line 343) | def simple_test(self, feats, img_metas, rescale=False):
    method onnx_export (line 363) | def onnx_export(self,

FILE: detection/mmdet/models/dense_heads/base_mask_head.py
  class BaseMaskHead (line 7) | class BaseMaskHead(BaseModule, metaclass=ABCMeta):
    method __init__ (line 10) | def __init__(self, init_cfg):
    method loss (line 14) | def loss(self, **kwargs):
    method get_results (line 18) | def get_results(self, **kwargs):
    method forward_train (line 22) | def forward_train(self,
    method simple_test (line 73) | def simple_test(self,
    method onnx_export (line 114) | def onnx_export(self, img, img_metas):

FILE: detection/mmdet/models/dense_heads/cascade_rpn_head.py
  class AdaptiveConv (line 20) | class AdaptiveConv(BaseModule):
    method __init__ (line 42) | def __init__(self,
    method forward (line 79) | def forward(self, x, offset):
  class StageCascadeRPNHead (line 96) | class StageCascadeRPNHead(RPNHead):
    method __init__ (line 112) | def __init__(self,
    method _init_layers (line 153) | def _init_layers(self):
    method forward_single (line 164) | def forward_single(self, x, offset):
    method forward (line 174) | def forward(self, feats, offset_list=None):
    method _region_targets_single (line 180) | def _region_targets_single(self,
    method region_targets (line 236) | def region_targets(self,
    method get_targets (line 286) | def get_targets(self,
    method anchor_offset (line 328) | def anchor_offset(self, anchor_list, anchor_strides, featmap_sizes):
    method loss_single (line 406) | def loss_single(self, cls_score, bbox_pred, anchors, labels, label_wei...
    method loss (line 436) | def loss(self,
    method get_bboxes (line 505) | def get_bboxes(self,
    method _get_bboxes_single (line 547) | def _get_bboxes_single(self,
    method refine_bboxes (line 672) | def refine_bboxes(self, anchor_list, bbox_preds, img_metas):
  class CascadeRPNHead (line 690) | class CascadeRPNHead(BaseDenseHead):
    method __init__ (line 705) | def __init__(self, num_stages, stages, train_cfg, test_cfg, init_cfg=N...
    method loss (line 720) | def loss(self):
    method get_bboxes (line 724) | def get_bboxes(self):
    method forward_train (line 728) | def forward_train(self,
    method simple_test_rpn (line 773) | def simple_test_rpn(self, x, img_metas):
    method aug_test_rpn (line 798) | def aug_test_rpn(self, x, img_metas):

FILE: detection/mmdet/models/dense_heads/centernet_head.py
  class CenterNetHead (line 18) | class CenterNetHead(BaseDenseHead, BBoxTestMixin):
    method __init__ (line 38) | def __init__(self,
    method _build_head (line 64) | def _build_head(self, in_channel, feat_channel, out_channel):
    method init_weights (line 72) | def init_weights(self):
    method forward (line 81) | def forward(self, feats):
    method forward_single (line 98) | def forward_single(self, feat):
    method loss (line 116) | def loss(self,
    method get_targets (line 181) | def get_targets(self, gt_bboxes, gt_labels, feat_shape, img_shape):
    method get_bboxes (line 252) | def get_bboxes(self,
    method _get_bboxes_single (line 297) | def _get_bboxes_single(self,
    method decode_heatmap (line 351) | def decode_heatmap(self,
    method _bboxes_nms (line 402) | def _bboxes_nms(self, bboxes, labels, cfg):

FILE: detection/mmdet/models/dense_heads/centripetal_head.py
  class CentripetalHead (line 13) | class CentripetalHead(CornerHead):
    method __init__ (line 48) | def __init__(self,
    method _init_centripetal_layers (line 73) | def _init_centripetal_layers(self):
    method _init_layers (line 133) | def _init_layers(self):
    method init_weights (line 141) | def init_weights(self):
    method forward_single (line 157) | def forward_single(self, x, lvl_ind):
    method loss (line 208) | def loss(self,
    method loss_single (line 284) | def loss_single(self, tl_hmp, br_hmp, tl_off, br_off, tl_guiding_shift,
    method get_bboxes (line 367) | def get_bboxes(self,

FILE: detection/mmdet/models/dense_heads/corner_head.py
  class BiCornerPool (line 21) | class BiCornerPool(BaseModule):
    method __init__ (line 34) | def __init__(self,
    method forward (line 64) | def forward(self, x):
  class CornerHead (line 85) | class CornerHead(BaseDenseHead, BBoxTestMixin):
    method __init__ (line 118) | def __init__(self,
    method _make_layers (line 158) | def _make_layers(self, out_channels, in_channels=256, feat_channels=256):
    method _init_corner_kpt_layers (line 165) | def _init_corner_kpt_layers(self):
    method _init_corner_emb_layers (line 203) | def _init_corner_emb_layers(self):
    method _init_layers (line 221) | def _init_layers(self):
    method init_weights (line 230) | def init_weights(self):
    method forward (line 248) | def forward(self, feats):
    method forward_single (line 280) | def forward_single(self, x, lvl_ind, return_pool=False):
    method get_targets (line 325) | def get_targets(self,
    method loss (line 514) | def loss(self,
    method loss_single (line 576) | def loss_single(self, tl_hmp, br_hmp, tl_emb, br_emb, tl_off, br_off,
    method get_bboxes (line 655) | def get_bboxes(self,
    method _get_bboxes_single (line 704) | def _get_bboxes_single(self,
    method _bboxes_nms (line 777) | def _bboxes_nms(self, bboxes, labels, cfg):
    method decode_heatmap (line 795) | def decode_heatmap(self,
    method onnx_export (line 1031) | def onnx_export(self,

FILE: detection/mmdet/models/dense_heads/deformable_detr_head.py
  class DeformableDETRHead (line 17) | class DeformableDETRHead(DETRHead):
    method __init__ (line 36) | def __init__(self,
    method _init_layers (line 50) | def _init_layers(self):
    method init_weights (line 83) | def init_weights(self):
    method forward (line 97) | def forward(self, mlvl_feats, img_metas):
    method loss (line 184) | def loss(self,
    method get_bboxes (line 269) | def get_bboxes(self,

FILE: detection/mmdet/models/dense_heads/dense_test_mixins.py
  class BBoxTestMixin (line 14) | class BBoxTestMixin(object):
    method simple_test_bboxes (line 17) | def simple_test_bboxes(self, feats, img_metas, rescale=False):
    method aug_test_bboxes (line 41) | def aug_test_bboxes(self, feats, img_metas, rescale=False):
    method simple_test_rpn (line 116) | def simple_test_rpn(self, x, img_metas):
    method aug_test_rpn (line 133) | def aug_test_rpn(self, feats, img_metas):
    method async_simple_test_rpn (line 169) | async def async_simple_test_rpn(self, x, img_metas):
    method merge_aug_bboxes (line 179) | def merge_aug_bboxes(self, aug_bboxes, aug_scores, img_metas):

FILE: detection/mmdet/models/dense_heads/detr_head.py
  class DETRHead (line 18) | class DETRHead(AnchorFreeHead):
    method __init__ (line 52) | def __init__(self,
    method _init_layers (line 152) | def _init_layers(self):
    method init_weights (line 167) | def init_weights(self):
    method _load_from_state_dict (line 172) | def _load_from_state_dict(self, state_dict, prefix, local_metadata, st...
    method forward (line 202) | def forward(self, feats, img_metas):
    method forward_single (line 226) | def forward_single(self, x, img_metas):
    method loss (line 268) | def loss(self,
    method loss_single (line 334) | def loss_single(self,
    method get_targets (line 419) | def get_targets(self,
    method _get_target_single (line 476) | def _get_target_single(self,
    method forward_train (line 547) | def forward_train(self,
    method get_bboxes (line 583) | def get_bboxes(self,
    method _get_bboxes_single (line 628) | def _get_bboxes_single(self,
    method simple_test_bboxes (line 685) | def simple_test_bboxes(self, feats, img_metas, rescale=False):
    method forward_onnx (line 707) | def forward_onnx(self, feats, img_metas):
    method forward_single_onnx (line 735) | def forward_single_onnx(self, x, img_metas):
    method onnx_export (line 775) | def onnx_export(self, all_cls_scores_list, all_bbox_preds_list, img_me...

FILE: detection/mmdet/models/dense_heads/embedding_rpn_head.py
  class EmbeddingRPNHead (line 11) | class EmbeddingRPNHead(BaseModule):
    method __init__ (line 26) | def __init__(self,
    method _init_layers (line 38) | def _init_layers(self):
    method init_weights (line 44) | def init_weights(self):
    method _decode_init_proposals (line 54) | def _decode_init_proposals(self, imgs, img_metas):
    method forward_dummy (line 95) | def forward_dummy(self, img, img_metas):
    method forward_train (line 102) | def forward_train(self, img, img_metas):
    method simple_test_rpn (line 106) | def simple_test_rpn(self, img, img_metas):
    method simple_test (line 110) | def simple_test(self, img, img_metas):
    method aug_test_rpn (line 114) | def aug_test_rpn(self, feats, img_metas):

FILE: detection/mmdet/models/dense_heads/fcos_head.py
  class FCOSHead (line 17) | class FCOSHead(AnchorFreeHead):
    method __init__ (line 60) | def __init__(self,
    method _init_layers (line 106) | def _init_layers(self):
    method forward (line 112) | def forward(self, feats):
    method forward_single (line 133) | def forward_single(self, x, scale, stride):
    method loss (line 168) | def loss(self,
    method get_targets (line 270) | def get_targets(self, points, gt_bboxes_list, gt_labels_list):
    method _get_target_single (line 331) | def _get_target_single(self, gt_bboxes, gt_labels, points, regress_ran...
    method centerness_target (line 415) | def centerness_target(self, pos_bbox_targets):
    method _get_points_single (line 436) | def _get_points_single(self,

FILE: detection/mmdet/models/dense_heads/fovea_head.py
  class FeatureAlign (line 18) | class FeatureAlign(BaseModule):
    method __init__ (line 20) | def __init__(self,
    method forward (line 43) | def forward(self, x, shape):
  class FoveaHead (line 50) | class FoveaHead(AnchorFreeHead):
    method __init__ (line 55) | def __init__(self,
    method _init_layers (line 81) | def _init_layers(self):
    method forward_single (line 121) | def forward_single(self, x):
    method loss (line 134) | def loss(self,
    method get_targets (line 186) | def get_targets(self, gt_bbox_list, gt_label_list, featmap_sizes, poin...
    method _get_target_single (line 208) | def _get_target_single(self,
    method _get_bboxes_single (line 272) | def _get_bboxes_single(self,
    method _bbox_decode (line 358) | def _bbox_decode(self, priors, bbox_pred, base_len, max_shape):
    method _get_points_single (line 374) | def _get_points_single(self, *args, **kwargs):

FILE: detection/mmdet/models/dense_heads/free_anchor_retina_head.py
  class FreeAnchorRetinaHead (line 13) | class FreeAnchorRetinaHead(RetinaHead):
    method __init__ (line 34) | def __init__(self,
    method loss (line 54) | def loss(self,
    method positive_bag_loss (line 221) | def positive_bag_loss(self, matched_cls_prob, matched_box_prob):
    method negative_bag_loss (line 248) | def negative_bag_loss(self, cls_prob, box_prob):

FILE: detection/mmdet/models/dense_heads/fsaf_head.py
  class FSAFHead (line 15) | class FSAFHead(RetinaHead):
    method __init__ (line 43) | def __init__(self, *args, score_threshold=None, init_cfg=None, **kwargs):
    method forward_single (line 63) | def forward_single(self, x):
    method _get_targets_single (line 80) | def _get_targets_single(self,
    method loss (line 188) | def loss(self,
    method calculate_pos_recall (line 317) | def calculate_pos_recall(self, cls_scores, labels_list, pos_inds):
    method collect_loss_level_single (line 351) | def collect_loss_level_single(self, cls_loss, reg_loss, assigned_gt_inds,
    method reweight_loss_single (line 382) | def reweight_loss_single(self, cls_loss, reg_loss, assigned_gt_inds,

FILE: detection/mmdet/models/dense_heads/ga_retina_head.py
  class GARetinaHead (line 11) | class GARetinaHead(GuidedAnchorHead):
    method __init__ (line 14) | def __init__(self,
    method _init_layers (line 45) | def _init_layers(self):
    method forward_single (line 92) | def forward_single(self, x):

FILE: detection/mmdet/models/dense_heads/ga_rpn_head.py
  class GARPNHead (line 16) | class GARPNHead(GuidedAnchorHead):
    method __init__ (line 19) | def __init__(self,
    method _init_layers (line 34) | def _init_layers(self):
    method forward_single (line 40) | def forward_single(self, x):
    method loss (line 49) | def loss(self,
    method _get_bboxes_single (line 72) | def _get_bboxes_single(self,

FILE: detection/mmdet/models/dense_heads/gfl_head.py
  class Integral (line 16) | class Integral(nn.Module):
    method __init__ (line 29) | def __init__(self, reg_max=16):
    method forward (line 35) | def forward(self, x):
  class GFLHead (line 53) | class GFLHead(AnchorHead):
    method __init__ (line 88) | def __init__(self,
    method _init_layers (line 128) | def _init_layers(self):
    method forward (line 161) | def forward(self, feats):
    method forward_single (line 179) | def forward_single(self, x, scale):
    method anchor_center (line 205) | def anchor_center(self, anchors):
    method loss_single (line 218) | def loss_single(self, anchors, cls_score, bbox_pred, labels, label_wei...
    method loss (line 307) | def loss(self,
    method _get_bboxes_single (line 380) | def _get_bboxes_single(self,
    method get_targets (line 473) | def get_targets(self,
    method _get_target_single (line 537) | def _get_target_single(self,
    method get_num_level_anchors_inside (line 643) | def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):

FILE: detection/mmdet/models/dense_heads/guided_anchor_head.py
  class FeatureAdaption (line 16) | class FeatureAdaption(BaseModule):
    method __init__ (line 31) | def __init__(self,
    method forward (line 54) | def forward(self, x, shape):
  class GuidedAnchorHead (line 61) | class GuidedAnchorHead(AnchorHead):
    method __init__ (line 97) | def __init__(
    method num_anchors (line 212) | def num_anchors(self):
    method _init_layers (line 217) | def _init_layers(self):
    method forward_single (line 233) | def forward_single(self, x):
    method forward (line 246) | def forward(self, feats):
    method get_sampled_approxs (line 249) | def get_sampled_approxs(self, featmap_sizes, img_metas, device='cuda'):
    method get_anchors (line 299) | def get_anchors(self,
    method _get_guided_anchors_single (line 350) | def _get_guided_anchors_single(self,
    method ga_loc_targets (line 384) | def ga_loc_targets(self, gt_bboxes_list, featmap_sizes):
    method _ga_shape_target_single (line 486) | def _ga_shape_target_single(self,
    method ga_shape_targets (line 549) | def ga_shape_targets(self,
    method loss_shape_single (line 615) | def loss_shape_single(self, shape_pred, bbox_anchors, bbox_gts,
    method loss_loc_single (line 639) | def loss_loc_single(self, loc_pred, loc_target, loc_weight,
    method loss (line 650) | def loss(self,
    method get_bboxes (line 756) | def get_bboxes(self,
    method _get_bboxes_single (line 800) | def _get_bboxes_single(self,

FILE: detection/mmdet/models/dense_heads/lad_head.py
  class LADHead (line 11) | class LADHead(PAAHead):
    method get_label_assignment (line 16) | def get_label_assignment(self,
    method forward_train (line 121) | def forward_train(self,
    method loss (line 160) | def loss(self,

FILE: detection/mmdet/models/dense_heads/ld_head.py
  class LDHead (line 11) | class LDHead(GFLHead):
    method __init__ (line 26) | def __init__(self,
    method loss_single (line 38) | def loss_single(self, anchors, cls_score, bbox_pred, labels, label_wei...
    method forward_train (line 142) | def forward_train(self,
    method loss (line 185) | def loss(self,

FILE: detection/mmdet/models/dense_heads/mask2former_head.py
  class Mask2FormerHead (line 21) | class Mask2FormerHead(MaskFormerHead):
    method __init__ (line 58) | def __init__(self,
    method init_weights (line 137) | def init_weights(self):
    method _get_target_single (line 148) | def _get_target_single(self, cls_score, mask_pred, gt_labels, gt_masks,
    method loss_single (line 218) | def loss_single(self, cls_scores, mask_preds, gt_labels_list,
    method forward_head (line 309) | def forward_head(self, decoder_out, mask_feature, attn_mask_target_size):
    method forward (line 351) | def forward(self, feats, img_metas):

FILE: detection/mmdet/models/dense_heads/maskformer_head.py
  class MaskFormerHead (line 17) | class MaskFormerHead(AnchorFreeHead):
    method __init__ (line 53) | def __init__(self,
    method init_weights (line 127) | def init_weights(self):
    method preprocess_gt (line 137) | def preprocess_gt(self, gt_labels_list, gt_masks_list, gt_semantic_segs):
    method get_targets (line 171) | def get_targets(self, cls_scores_list, mask_preds_list, gt_labels_list,
    method _get_target_single (line 215) | def _get_target_single(self, cls_score, mask_pred, gt_labels, gt_masks,
    method loss (line 276) | def loss(self, all_cls_scores, all_mask_preds, gt_labels_list,
    method loss_single (line 320) | def loss_single(self, cls_scores, mask_preds, gt_labels_list,
    method forward (line 412) | def forward(self, feats, img_metas):
    method forward_train (line 477) | def forward_train(self,
    method simple_test (line 524) | def simple_test(self, feats, img_metas, **kwargs):

FILE: detection/mmdet/models/dense_heads/nasfcos_head.py
  class NASFCOSHead (line 12) | class NASFCOSHead(FCOSHead):
    method __init__ (line 20) | def __init__(self, *args, init_cfg=None, **kwargs):
    method _init_layers (line 39) | def _init_layers(self):

FILE: detection/mmdet/models/dense_heads/paa_head.py
  function levels_to_images (line 18) | def levels_to_images(mlvl_tensor):
  class PAAHead (line 46) | class PAAHea

[
  {
    "path": "LICENSE",
    "chars": 17682,
    "preview": "## creative commons\n\n# Attribution-NonCommercial 4.0 International\n\nCreative Commons Corporation (“Creative Commons”) is"
  },
  {
    "path": "README.md",
    "chars": 17051,
    "preview": "\n<h1 align=\"center\"> SARATR-X: Towards Building A Foundation Model for SAR Target Recognition </h1>\n\n<h5 align=\"center\">"
  },
  {
    "path": "classification/.gitignore",
    "chars": 1826,
    "preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packagi"
  },
  {
    "path": "classification/.idea/.gitignore",
    "chars": 176,
    "preview": "# Default ignored files\n/shelf/\n/workspace.xml\n# Editor-based HTTP Client requests\n/httpRequests/\n# Datasource local sto"
  },
  {
    "path": "classification/.idea/CoOp.iml",
    "chars": 327,
    "preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<module type=\"PYTHON_MODULE\" version=\"4\">\n  <component name=\"NewModuleRootManager"
  },
  {
    "path": "classification/.idea/deployment.xml",
    "chars": 410,
    "preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n  <component name=\"PublishConfigData\" remoteFilesAllowedToD"
  },
  {
    "path": "classification/.idea/inspectionProfiles/Project_Default.xml",
    "chars": 506,
    "preview": "<component name=\"InspectionProjectProfileManager\">\n  <profile version=\"1.0\">\n    <option name=\"myName\" value=\"Project De"
  },
  {
    "path": "classification/.idea/inspectionProfiles/profiles_settings.xml",
    "chars": 174,
    "preview": "<component name=\"InspectionProjectProfileManager\">\n  <settings>\n    <option name=\"USE_PROJECT_PROFILE\" value=\"false\" />\n"
  },
  {
    "path": "classification/.idea/misc.xml",
    "chars": 195,
    "preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n  <component name=\"ProjectRootManager\" version=\"2\" project-"
  },
  {
    "path": "classification/.idea/modules.xml",
    "chars": 260,
    "preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n  <component name=\"ProjectModuleManager\">\n    <modules>\n   "
  },
  {
    "path": "classification/.idea/vcs.xml",
    "chars": 180,
    "preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n  <component name=\"VcsDirectoryMappings\">\n    <mapping dire"
  },
  {
    "path": "classification/COCOOP.md",
    "chars": 3302,
    "preview": "## How to Run\n\nThe running scripts are provided in `scripts/cocoop/`, which allow you to reproduce the results on the CV"
  },
  {
    "path": "classification/COOP.md",
    "chars": 5753,
    "preview": "## How to Run\n\nWe provide the running scripts in `scripts/coop`, which allow you to reproduce the results on the IJCV'22"
  },
  {
    "path": "classification/DATASETS.md",
    "chars": 9126,
    "preview": "# How to install datasets\n\nWe suggest putting all datasets under the same folder (say `$DATA`) to ease management and fo"
  },
  {
    "path": "classification/MIM_linear.sh",
    "chars": 2401,
    "preview": "#bash scripts/MIM_linear/main.sh MSTAR_SOC vit_b16 5\n#bash scripts/MIM_linear/main.sh MSTAR_SOC vit_b16 10\n#bash scripts"
  },
  {
    "path": "classification/clip/__init__.py",
    "chars": 20,
    "preview": "from .clip import *\n"
  },
  {
    "path": "classification/clip/clip.py",
    "chars": 8260,
    "preview": "import hashlib\nimport os\nimport urllib\nimport warnings\nfrom typing import Union, List\n\nimport torch\nfrom PIL import Imag"
  },
  {
    "path": "classification/clip/model.py",
    "chars": 17242,
    "preview": "from collections import OrderedDict\nfrom typing import Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn.fun"
  },
  {
    "path": "classification/clip/simple_tokenizer.py",
    "chars": 4628,
    "preview": "import gzip\nimport html\nimport os\nfrom functools import lru_cache\n\nimport ftfy\nimport regex as re\n\n\n@lru_cache()\ndef def"
  },
  {
    "path": "classification/configs/datasets/MSTAR_EOC1.yaml",
    "chars": 30,
    "preview": "DATASET:\n  NAME: \"MSTAR_EOC1\"\n"
  },
  {
    "path": "classification/configs/datasets/MSTAR_EOC2C.yaml",
    "chars": 31,
    "preview": "DATASET:\n  NAME: \"MSTAR_EOC2C\"\n"
  },
  {
    "path": "classification/configs/datasets/MSTAR_EOC2V.yaml",
    "chars": 31,
    "preview": "DATASET:\n  NAME: \"MSTAR_EOC2V\"\n"
  },
  {
    "path": "classification/configs/datasets/MSTAR_SOC.yaml",
    "chars": 29,
    "preview": "DATASET:\n  NAME: \"MSTAR_SOC\"\n"
  },
  {
    "path": "classification/configs/datasets/MyDataset.yaml",
    "chars": 29,
    "preview": "DATASET:\n  NAME: \"MyDataset\"\n"
  },
  {
    "path": "classification/configs/datasets/New_FUSAR.yaml",
    "chars": 29,
    "preview": "DATASET:\n  NAME: \"New_FUSAR\"\n"
  },
  {
    "path": "classification/configs/datasets/SAR_ACD.yaml",
    "chars": 27,
    "preview": "DATASET:\n  NAME: \"SAR_ACD\"\n"
  },
  {
    "path": "classification/configs/datasets/caltech101.yaml",
    "chars": 30,
    "preview": "DATASET:\n  NAME: \"Caltech101\"\n"
  },
  {
    "path": "classification/configs/datasets/dtd.yaml",
    "chars": 39,
    "preview": "DATASET:\n  NAME: \"DescribableTextures\"\n"
  },
  {
    "path": "classification/configs/datasets/eurosat.yaml",
    "chars": 27,
    "preview": "DATASET:\n  NAME: \"EuroSAT\"\n"
  },
  {
    "path": "classification/configs/datasets/fgvc_aircraft.yaml",
    "chars": 32,
    "preview": "DATASET:\n  NAME: \"FGVCAircraft\"\n"
  },
  {
    "path": "classification/configs/datasets/food101.yaml",
    "chars": 27,
    "preview": "DATASET:\n  NAME: \"Food101\"\n"
  },
  {
    "path": "classification/configs/datasets/imagenet.yaml",
    "chars": 28,
    "preview": "DATASET:\n  NAME: \"ImageNet\"\n"
  },
  {
    "path": "classification/configs/datasets/imagenet_a.yaml",
    "chars": 29,
    "preview": "DATASET:\n  NAME: \"ImageNetA\"\n"
  },
  {
    "path": "classification/configs/datasets/imagenet_r.yaml",
    "chars": 29,
    "preview": "DATASET:\n  NAME: \"ImageNetR\"\n"
  },
  {
    "path": "classification/configs/datasets/imagenet_sketch.yaml",
    "chars": 34,
    "preview": "DATASET:\n  NAME: \"ImageNetSketch\"\n"
  },
  {
    "path": "classification/configs/datasets/imagenetv2.yaml",
    "chars": 30,
    "preview": "DATASET:\n  NAME: \"ImageNetV2\"\n"
  },
  {
    "path": "classification/configs/datasets/oxford_flowers.yaml",
    "chars": 32,
    "preview": "DATASET:\n  NAME: \"OxfordFlowers\""
  },
  {
    "path": "classification/configs/datasets/oxford_pets.yaml",
    "chars": 29,
    "preview": "DATASET:\n  NAME: \"OxfordPets\""
  },
  {
    "path": "classification/configs/datasets/stanford_cars.yaml",
    "chars": 32,
    "preview": "DATASET:\n  NAME: \"StanfordCars\"\n"
  },
  {
    "path": "classification/configs/datasets/sun397.yaml",
    "chars": 26,
    "preview": "DATASET:\n  NAME: \"SUN397\"\n"
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  {
    "path": "classification/configs/datasets/ucf101.yaml",
    "chars": 26,
    "preview": "DATASET:\n  NAME: \"UCF101\"\n"
  },
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    "path": "classification/configs/trainers/CNN/vit_b16.yaml",
    "chars": 1010,
    "preview": "#DATALOADER:\n#  TRAIN_X:\n#    BATCH_SIZE: 32\n#  TEST:\n#    BATCH_SIZE: 128\n#  NUM_WORKERS: 8\n#\n#INPUT:\n#  SIZE: (224, 22"
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  },
  {
    "path": "classification/configs/trainers/CoCoOp/vit_b16_c4_ep10_batch1.yaml",
    "chars": 580,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 1\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTER"
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  {
    "path": "classification/configs/trainers/CoCoOp/vit_b16_c4_ep10_batch1_ctxv1.yaml",
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    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 1\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTER"
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    "path": "classification/configs/trainers/CoCoOp/vit_b16_c8_ep10_batch1.yaml",
    "chars": 580,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 1\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTER"
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    "path": "classification/configs/trainers/CoOp/rn50_ep100.yaml",
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    "path": "classification/configs/trainers/CoOp/rn50_ep50_ctxv1.yaml",
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    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 32\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTE"
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  {
    "path": "classification/configs/trainers/CoOp/rn50_val.yaml",
    "chars": 344,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 200\n  TEST:\n    BATCH_SIZE: 200\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INT"
  },
  {
    "path": "classification/configs/trainers/CoOp/vit_b16.yaml",
    "chars": 1227,
    "preview": "#DATALOADER:\n#  TRAIN_X:\n#    BATCH_SIZE: 32\n#  TEST:\n#    BATCH_SIZE: 100\n#  NUM_WORKERS: 8\n#\n#INPUT:\n#  SIZE: (224, 22"
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    "path": "classification/configs/trainers/CoOp/vit_b16_ctxv1.yaml",
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    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 32\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTE"
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    "path": "classification/configs/trainers/CoOp/vit_b16_ep100.yaml",
    "chars": 514,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 32\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTE"
  },
  {
    "path": "classification/configs/trainers/CoOp/vit_b16_ep100_ctxv1.yaml",
    "chars": 562,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 32\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTE"
  },
  {
    "path": "classification/configs/trainers/CoOp/vit_b16_ep50.yaml",
    "chars": 513,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 32\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTE"
  },
  {
    "path": "classification/configs/trainers/CoOp/vit_b16_ep50_ctxv1.yaml",
    "chars": 561,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 32\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTE"
  },
  {
    "path": "classification/configs/trainers/CoOp/vit_b32.yaml",
    "chars": 514,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 32\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTE"
  },
  {
    "path": "classification/configs/trainers/CoOp/vit_b32_ep50.yaml",
    "chars": 513,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 32\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTE"
  },
  {
    "path": "classification/configs/trainers/MIM_linear/vit_b16.yaml",
    "chars": 1117,
    "preview": "#DATALOADER:\n#  TRAIN_X:\n#    BATCH_SIZE: 32\n#  TEST:\n#    BATCH_SIZE: 128\n#  NUM_WORKERS: 8\n#\n#INPUT:\n#  SIZE: (224, 22"
  },
  {
    "path": "classification/configs/trainers/OneHot/vit_b16.yaml",
    "chars": 1227,
    "preview": "#DATALOADER:\n#  TRAIN_X:\n#    BATCH_SIZE: 32\n#  TEST:\n#    BATCH_SIZE: 100\n#  NUM_WORKERS: 8\n#\n#INPUT:\n#  SIZE: (224, 22"
  },
  {
    "path": "classification/configs/trainers/RingMo/vit_b16.yaml",
    "chars": 1400,
    "preview": "#DATALOADER:\n#  TRAIN_X:\n#    BATCH_SIZE: 32\n#  TEST:\n#    BATCH_SIZE: 100\n#  NUM_WORKERS: 8\n#\n#INPUT:\n#  SIZE: (224, 22"
  },
  {
    "path": "classification/configs/trainers/clip/vit_b16.yaml",
    "chars": 1227,
    "preview": "#DATALOADER:\n#  TRAIN_X:\n#    BATCH_SIZE: 32\n#  TEST:\n#    BATCH_SIZE: 100\n#  NUM_WORKERS: 8\n#\n#INPUT:\n#  SIZE: (224, 22"
  },
  {
    "path": "classification/configs/trainers/clip_adapter/rn101.yaml",
    "chars": 511,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 32\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTE"
  },
  {
    "path": "classification/configs/trainers/clip_adapter/rn101_ep50.yaml",
    "chars": 510,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 32\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTE"
  },
  {
    "path": "classification/configs/trainers/clip_adapter/rn50.yaml",
    "chars": 575,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 32\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTE"
  },
  {
    "path": "classification/configs/trainers/clip_adapter/rn50_ctxv1.yaml",
    "chars": 558,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 32\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTE"
  },
  {
    "path": "classification/configs/trainers/clip_adapter/rn50_ep100.yaml",
    "chars": 511,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 32\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTE"
  },
  {
    "path": "classification/configs/trainers/clip_adapter/rn50_ep50.yaml",
    "chars": 509,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 32\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTE"
  },
  {
    "path": "classification/configs/trainers/clip_adapter/rn50_ep50_ctxv1.yaml",
    "chars": 556,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 32\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTE"
  },
  {
    "path": "classification/configs/trainers/clip_adapter/rn50_val.yaml",
    "chars": 344,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 200\n  TEST:\n    BATCH_SIZE: 200\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INT"
  },
  {
    "path": "classification/configs/trainers/clip_adapter/vit_b16.yaml",
    "chars": 1231,
    "preview": "#DATALOADER:\n#  TRAIN_X:\n#    BATCH_SIZE: 32\n#  TEST:\n#    BATCH_SIZE: 100\n#  NUM_WORKERS: 8\n#\n#INPUT:\n#  SIZE: (224, 22"
  },
  {
    "path": "classification/configs/trainers/clip_adapter/vit_b16_ctxv1.yaml",
    "chars": 562,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 32\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTE"
  },
  {
    "path": "classification/configs/trainers/clip_adapter/vit_b16_ep100.yaml",
    "chars": 514,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 32\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTE"
  },
  {
    "path": "classification/configs/trainers/clip_adapter/vit_b16_ep100_ctxv1.yaml",
    "chars": 562,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 32\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTE"
  },
  {
    "path": "classification/configs/trainers/clip_adapter/vit_b16_ep50.yaml",
    "chars": 513,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 32\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTE"
  },
  {
    "path": "classification/configs/trainers/clip_adapter/vit_b16_ep50_ctxv1.yaml",
    "chars": 561,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 32\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTE"
  },
  {
    "path": "classification/configs/trainers/clip_adapter/vit_b32.yaml",
    "chars": 514,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 32\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTE"
  },
  {
    "path": "classification/configs/trainers/clip_adapter/vit_b32_ep50.yaml",
    "chars": 513,
    "preview": "DATALOADER:\n  TRAIN_X:\n    BATCH_SIZE: 32\n  TEST:\n    BATCH_SIZE: 100\n  NUM_WORKERS: 8\n\nINPUT:\n  SIZE: (224, 224)\n  INTE"
  },
  {
    "path": "classification/datasets/MSTAR_EOC1.py",
    "chars": 3813,
    "preview": "import os\nimport pickle\nfrom scipy.io import loadmat\nimport re\nfrom dassl.data.datasets import DATASET_REGISTRY, Datum, "
  },
  {
    "path": "classification/datasets/MSTAR_EOC2C.py",
    "chars": 3712,
    "preview": "import os\nimport pickle\nfrom scipy.io import loadmat\nimport re\nfrom dassl.data.datasets import DATASET_REGISTRY, Datum, "
  },
  {
    "path": "classification/datasets/MSTAR_EOC2V.py",
    "chars": 3721,
    "preview": "import os\nimport pickle\nfrom scipy.io import loadmat\nimport re\nfrom dassl.data.datasets import DATASET_REGISTRY, Datum, "
  },
  {
    "path": "classification/datasets/MSTAR_SOC.py",
    "chars": 3833,
    "preview": "import os\nimport pickle\nfrom scipy.io import loadmat\nimport re\nfrom dassl.data.datasets import DATASET_REGISTRY, Datum, "
  },
  {
    "path": "classification/datasets/MyDataset.py",
    "chars": 4700,
    "preview": "import os\nimport pickle\nfrom scipy.io import loadmat\nimport re\nfrom dassl.data.datasets import DATASET_REGISTRY, Datum, "
  },
  {
    "path": "classification/datasets/New_FUSAR.py",
    "chars": 7099,
    "preview": "import os\nimport pickle\nfrom scipy.io import loadmat\nimport re\nfrom dassl.data.datasets import DATASET_REGISTRY, Datum, "
  },
  {
    "path": "classification/datasets/SAR_ACD.py",
    "chars": 3669,
    "preview": "import os\nimport pickle\nfrom scipy.io import loadmat\nimport re\nfrom dassl.data.datasets import DATASET_REGISTRY, Datum, "
  },
  {
    "path": "classification/datasets/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "classification/datasets/caltech101.py",
    "chars": 2438,
    "preview": "import os\nimport pickle\n\nfrom dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase\nfrom dassl.utils import mk"
  },
  {
    "path": "classification/datasets/dtd.py",
    "chars": 3939,
    "preview": "import os\nimport pickle\nimport random\n\nfrom dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase\nfrom dassl.u"
  },
  {
    "path": "classification/datasets/eurosat.py",
    "chars": 2973,
    "preview": "import os\nimport pickle\n\nfrom dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase\nfrom dassl.utils import mk"
  },
  {
    "path": "classification/datasets/fgvc_aircraft.py",
    "chars": 2878,
    "preview": "import os\nimport pickle\n\nfrom dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase\nfrom dassl.utils import mk"
  },
  {
    "path": "classification/datasets/food101.py",
    "chars": 2203,
    "preview": "import os\nimport pickle\n\nfrom dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase\nfrom dassl.utils import mk"
  },
  {
    "path": "classification/datasets/imagenet.py",
    "chars": 3723,
    "preview": "import os\nimport pickle\nfrom collections import OrderedDict\n\nfrom dassl.data.datasets import DATASET_REGISTRY, Datum, Da"
  },
  {
    "path": "classification/datasets/imagenet_a.py",
    "chars": 1439,
    "preview": "import os\n\nfrom dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase\nfrom dassl.utils import listdir_nohidden"
  },
  {
    "path": "classification/datasets/imagenet_r.py",
    "chars": 1435,
    "preview": "import os\n\nfrom dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase\nfrom dassl.utils import listdir_nohidden"
  },
  {
    "path": "classification/datasets/imagenet_sketch.py",
    "chars": 1331,
    "preview": "import os\n\nfrom dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase\nfrom dassl.utils import listdir_nohidden"
  },
  {
    "path": "classification/datasets/imagenetv2.py",
    "chars": 1416,
    "preview": "import os\n\nfrom dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase\nfrom dassl.utils import listdir_nohidden"
  },
  {
    "path": "classification/datasets/oxford_flowers.py",
    "chars": 3763,
    "preview": "import os\nimport pickle\nimport random\nfrom scipy.io import loadmat\nfrom collections import defaultdict\n\nfrom dassl.data."
  },
  {
    "path": "classification/datasets/oxford_pets.py",
    "chars": 6693,
    "preview": "import os\nimport pickle\nimport math\nimport random\nfrom collections import defaultdict\n\nfrom dassl.data.datasets import D"
  },
  {
    "path": "classification/datasets/stanford_cars.py",
    "chars": 3322,
    "preview": "import os\nimport pickle\nfrom scipy.io import loadmat\n\nfrom dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBa"
  },
  {
    "path": "classification/datasets/sun397.py",
    "chars": 3401,
    "preview": "import os\nimport pickle\n\nfrom dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase\nfrom dassl.utils import mk"
  },
  {
    "path": "classification/datasets/ucf101.py",
    "chars": 3549,
    "preview": "import os\nimport pickle\nimport re\n\nfrom dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase\nfrom dassl.utils"
  },
  {
    "path": "classification/demo.py",
    "chars": 567,
    "preview": "import subprocess\n\n# res = subprocess.call(['./scripts/coop/main.sh caltech101 rn50_ep50 end 16 1 False', '-c', 'ls -al'"
  },
  {
    "path": "classification/draw_curves.py",
    "chars": 5970,
    "preview": "import os\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nsave_dir = \"main_curves\"\nif not os.pa"
  },
  {
    "path": "classification/interpret_prompt.py",
    "chars": 1737,
    "preview": "import os\nimport sys\nimport argparse\nimport torch\n\nfrom clip.simple_tokenizer import SimpleTokenizer\nfrom clip import cl"
  },
  {
    "path": "classification/lpclip/README.md",
    "chars": 495,
    "preview": "# Linear Probe CLIP\n\nTo run linear probe baselines, make sure that your current working directory is `lpclip/`.\n\nStep 1:"
  },
  {
    "path": "classification/lpclip/feat_extractor.py",
    "chars": 6080,
    "preview": "import os, argparse\nimport numpy as np\nimport torch\nimport sys\n\nsys.path.append(os.path.abspath(\"..\"))\n\nfrom datasets.ox"
  },
  {
    "path": "classification/lpclip/feat_extractor.sh",
    "chars": 566,
    "preview": "# sh feat_extractor.sh\nDATA=/path/to/datasets\nOUTPUT='./clip_feat/'\nSEED=1\n\n# oxford_pets oxford_flowers fgvc_aircraft d"
  },
  {
    "path": "classification/lpclip/linear_probe.py",
    "chars": 5870,
    "preview": "import numpy as np\nimport os\nfrom sklearn.linear_model import LogisticRegression\nimport argparse\n\nparser = argparse.Argu"
  },
  {
    "path": "classification/lpclip/linear_probe.sh",
    "chars": 183,
    "preview": "feature_dir=clip_feat\n\nfor DATASET in OxfordPets\ndo\n    python linear_probe.py \\\n    --dataset ${DATASET} \\\n    --featur"
  },
  {
    "path": "classification/model/Model.py",
    "chars": 20261,
    "preview": "import torch\nfrom torch import nn\nfrom torchvision import models\n\nclass AlexNet(nn.Module):\n    def __init__(self, num_c"
  },
  {
    "path": "classification/model/cmt.py",
    "chars": 19423,
    "preview": "# 2022.06.28-Changed for building CMT\n#            Huawei Technologies Co., Ltd. <foss@huawei.com>\n# Author: Jianyuan Gu"
  },
  {
    "path": "classification/model/conformer.py",
    "chars": 16889,
    "preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom functools import partial\n\nfrom timm.models.layer"
  },
  {
    "path": "classification/model/cotnet.py",
    "chars": 9979,
    "preview": "import math\nimport numpy as np\nimport torch\nfrom torch import nn as nn\n\n# from config import IMAGENET_DEFAULT_MEAN, IMAG"
  },
  {
    "path": "classification/model/cotnet_hybrid.py",
    "chars": 21316,
    "preview": "import math\nimport numpy as np\nimport torch\nfrom torch import nn as nn\n\n# from config import IMAGENET_DEFAULT_MEAN, IMAG"
  },
  {
    "path": "classification/model/features.py",
    "chars": 12155,
    "preview": "\"\"\" PyTorch Feature Extraction Helpers\n\nA collection of classes, functions, modules to help extract features from models"
  },
  {
    "path": "classification/model/helpers.py",
    "chars": 14866,
    "preview": "\"\"\" Model creation / weight loading / state_dict helpers\n\nHacked together by / Copyright 2020 Ross Wightman\n\"\"\"\nimport l"
  },
  {
    "path": "classification/model/hivit.py",
    "chars": 12271,
    "preview": "import math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.checkpoint as checkpoi"
  },
  {
    "path": "classification/model/inception_transformer.py",
    "chars": 25810,
    "preview": "# Copyright 2022 Garena Online Private Limited\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you"
  },
  {
    "path": "classification/model/layers/__init__.py",
    "chars": 1866,
    "preview": "from .activations import *\nfrom .adaptive_avgmax_pool import \\\n    adaptive_avgmax_pool2d, select_adaptive_pool2d, Adapt"
  },
  {
    "path": "classification/model/layers/activations.py",
    "chars": 3306,
    "preview": "\"\"\" Activations\n\nA collection of activations fn and modules with a common interface so that they can\neasily be swapped. "
  },
  {
    "path": "classification/model/layers/activations_jit.py",
    "chars": 2529,
    "preview": "\"\"\" Activations\n\nA collection of jit-scripted activations fn and modules with a common interface so that they can\neasily"
  },
  {
    "path": "classification/model/layers/activations_me.py",
    "chars": 5339,
    "preview": "\"\"\" Activations (memory-efficient w/ custom autograd)\n\nA collection of activations fn and modules with a common interfac"
  },
  {
    "path": "classification/model/layers/adaptive_avgmax_pool.py",
    "chars": 3903,
    "preview": "\"\"\" PyTorch selectable adaptive pooling\nAdaptive pooling with the ability to select the type of pooling from:\n    * 'avg"
  },
  {
    "path": "classification/model/layers/aggregation_zeropad.py",
    "chars": 15146,
    "preview": "import torch\nfrom torch.autograd import Function\nfrom torch.nn import functional as F\nfrom torch.nn.modules.utils import"
  },
  {
    "path": "classification/model/layers/anti_aliasing.py",
    "chars": 2293,
    "preview": "import torch\nimport torch.nn.parallel\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass AntiAliasDownsampleL"
  },
  {
    "path": "classification/model/layers/blur_pool.py",
    "chars": 2180,
    "preview": "\"\"\"\nBlurPool layer inspired by\n - Kornia's Max_BlurPool2d\n - Making Convolutional Networks Shift-Invariant Again :cite:`"
  },
  {
    "path": "classification/model/layers/cbam.py",
    "chars": 3337,
    "preview": "\"\"\" CBAM (sort-of) Attention\n\nExperimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/180"
  },
  {
    "path": "classification/model/layers/classifier.py",
    "chars": 1616,
    "preview": "\"\"\" Classifier head and layer factory\n\nHacked together by / Copyright 2020 Ross Wightman\n\"\"\"\nfrom torch import nn as nn\n"
  },
  {
    "path": "classification/model/layers/cond_conv2d.py",
    "chars": 5129,
    "preview": "\"\"\" PyTorch Conditionally Parameterized Convolution (CondConv)\n\nPaper: CondConv: Conditionally Parameterized Convolution"
  },
  {
    "path": "classification/model/layers/config.py",
    "chars": 3069,
    "preview": "\"\"\" Model / Layer Config singleton state\n\"\"\"\nfrom typing import Any, Optional\n\n__all__ = [\n    'is_exportable', 'is_scri"
  },
  {
    "path": "classification/model/layers/conv2d_same.py",
    "chars": 1490,
    "preview": "\"\"\" Conv2d w/ Same Padding\n\nHacked together by / Copyright 2020 Ross Wightman\n\"\"\"\nimport torch\nimport torch.nn as nn\nimp"
  },
  {
    "path": "classification/model/layers/conv_bn_act.py",
    "chars": 1466,
    "preview": "\"\"\" Conv2d + BN + Act\n\nHacked together by / Copyright 2020 Ross Wightman\n\"\"\"\nfrom torch import nn as nn\n\nfrom .create_co"
  },
  {
    "path": "classification/model/layers/create_act.py",
    "chars": 3599,
    "preview": "\"\"\" Activation Factory\nHacked together by / Copyright 2020 Ross Wightman\n\"\"\"\nfrom .activations import *\nfrom .activation"
  },
  {
    "path": "classification/model/layers/create_attn.py",
    "chars": 1222,
    "preview": "\"\"\" Select AttentionFactory Method\n\nHacked together by / Copyright 2020 Ross Wightman\n\"\"\"\nimport torch\nfrom .se import S"
  },
  {
    "path": "classification/model/layers/create_conv2d.py",
    "chars": 1399,
    "preview": "\"\"\" Create Conv2d Factory Method\n\nHacked together by / Copyright 2020 Ross Wightman\n\"\"\"\n\nfrom .mixed_conv2d import Mixed"
  },
  {
    "path": "classification/model/layers/create_norm_act.py",
    "chars": 3327,
    "preview": "\"\"\" NormAct (Normalizaiton + Activation Layer) Factory\n\nCreate norm + act combo modules that attempt to be backwards com"
  },
  {
    "path": "classification/model/layers/drop.py",
    "chars": 6938,
    "preview": "\"\"\" DropBlock, DropPath\n\nPyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.\n\nPa"
  },
  {
    "path": "classification/model/layers/eca.py",
    "chars": 4701,
    "preview": "\"\"\"\nECA module from ECAnet\n\npaper: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks\nhttps://a"
  },
  {
    "path": "classification/model/layers/evo_norm.py",
    "chars": 3328,
    "preview": "\"\"\"EvoNormB0 (Batched) and EvoNormS0 (Sample) in PyTorch\n\nAn attempt at getting decent performing EvoNorms running in Py"
  },
  {
    "path": "classification/model/layers/helpers.py",
    "chars": 492,
    "preview": "\"\"\" Layer/Module Helpers\n\nHacked together by / Copyright 2020 Ross Wightman\n\"\"\"\nfrom itertools import repeat\n#from torch"
  },
  {
    "path": "classification/model/layers/inplace_abn.py",
    "chars": 3353,
    "preview": "import torch\nfrom torch import nn as nn\n\ntry:\n    from inplace_abn.functions import inplace_abn, inplace_abn_sync\n    ha"
  },
  {
    "path": "classification/model/layers/kerv2d.py",
    "chars": 2092,
    "preview": "import math\nimport torch\nfrom torch import Tensor\nfrom torch import nn as nn\nfrom torch.nn import functional as F\nfrom t"
  },
  {
    "path": "classification/model/layers/median_pool.py",
    "chars": 1737,
    "preview": "\"\"\" Median Pool\nHacked together by / Copyright 2020 Ross Wightman\n\"\"\"\nimport torch.nn as nn\nimport torch.nn.functional a"
  },
  {
    "path": "classification/model/layers/mixed_conv2d.py",
    "chars": 1844,
    "preview": "\"\"\" PyTorch Mixed Convolution\n\nPaper: MixConv: Mixed Depthwise Convolutional Kernels (https://arxiv.org/abs/1907.09595)\n"
  },
  {
    "path": "classification/model/layers/mlp.py",
    "chars": 3774,
    "preview": "\"\"\" MLP module w/ dropout and configurable activation layer\n\nHacked together by / Copyright 2020 Ross Wightman\n\"\"\"\nfrom "
  },
  {
    "path": "classification/model/layers/norm_act.py",
    "chars": 3444,
    "preview": "\"\"\" Normalization + Activation Layers\n\"\"\"\nimport torch\nfrom torch import nn as nn\nfrom torch.nn import functional as F\n\n"
  },
  {
    "path": "classification/model/layers/padding.py",
    "chars": 2167,
    "preview": "\"\"\" Padding Helpers\n\nHacked together by / Copyright 2020 Ross Wightman\n\"\"\"\nimport math\nfrom typing import List, Tuple\n\ni"
  },
  {
    "path": "classification/model/layers/patch_embed.py",
    "chars": 1408,
    "preview": "\"\"\" Image to Patch Embedding using Conv2d\n\nA convolution based approach to patchifying a 2D image w/ embedding projectio"
  },
  {
    "path": "classification/model/layers/pool2d_same.py",
    "chars": 2969,
    "preview": "\"\"\" AvgPool2d w/ Same Padding\n\nHacked together by / Copyright 2020 Ross Wightman\n\"\"\"\nimport torch\nimport torch.nn as nn\n"
  },
  {
    "path": "classification/model/layers/se.py",
    "chars": 1406,
    "preview": "from torch import nn as nn\nfrom .create_act import create_act_layer\n\n\nclass SEModule(nn.Module):\n\n    def __init__(self,"
  },
  {
    "path": "classification/model/layers/selective_kernel.py",
    "chars": 5282,
    "preview": "\"\"\" Selective Kernel Convolution/Attention\n\nPaper: Selective Kernel Networks (https://arxiv.org/abs/1903.06586)\n\nHacked "
  },
  {
    "path": "classification/model/layers/separable_conv.py",
    "chars": 2641,
    "preview": "\"\"\" Depthwise Separable Conv Modules\n\nBasic DWS convs. Other variations of DWS exist with batch norm or activations betw"
  },
  {
    "path": "classification/model/layers/shiftlution.py",
    "chars": 2768,
    "preview": "import numpy as np\nimport torch\nfrom torch import nn as nn\n\nclass Shiftlution(nn.Module):\n    def __init__(self, channel"
  },
  {
    "path": "classification/model/layers/space_to_depth.py",
    "chars": 1750,
    "preview": "import torch\nimport torch.nn as nn\n\n\nclass SpaceToDepth(nn.Module):\n    def __init__(self, block_size=4):\n        super("
  },
  {
    "path": "classification/model/layers/split_attn.py",
    "chars": 3013,
    "preview": "\"\"\" Split Attention Conv2d (for ResNeSt Models)\n\nPaper: `ResNeSt: Split-Attention Networks` - /https://arxiv.org/abs/200"
  },
  {
    "path": "classification/model/layers/split_batchnorm.py",
    "chars": 3441,
    "preview": "\"\"\" Split BatchNorm\n\nA PyTorch BatchNorm layer that splits input batch into N equal parts and passes each through\na sepa"
  },
  {
    "path": "classification/model/layers/std_conv.py",
    "chars": 6517,
    "preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom .padding import get_padding, get_padding_value,"
  },
  {
    "path": "classification/model/layers/tbconv.py",
    "chars": 4044,
    "preview": "import math\nimport torch\nfrom torch import Tensor\nfrom torch import nn as nn\nfrom torch.nn import functional as F\nfrom t"
  },
  {
    "path": "classification/model/layers/test_time_pool.py",
    "chars": 1851,
    "preview": "\"\"\" Test Time Pooling (Average-Max Pool)\n\nHacked together by / Copyright 2020 Ross Wightman\n\"\"\"\n\nimport logging\nfrom tor"
  },
  {
    "path": "classification/model/layers/utils.py",
    "chars": 516,
    "preview": "import torch\nfrom string import Template\nfrom collections import namedtuple\nimport cupy\n\nStream = namedtuple('Stream', ["
  },
  {
    "path": "classification/model/layers/weight_init.py",
    "chars": 3324,
    "preview": "import torch\nimport math\nimport warnings\n\nfrom torch.nn.init import _calculate_fan_in_and_fan_out\n\n\ndef _no_grad_trunc_n"
  },
  {
    "path": "classification/model/nextvit.py",
    "chars": 14860,
    "preview": "# Copyright (c) ByteDance Inc. All rights reserved.\nfrom functools import partial\n\nimport torch\nimport torch.utils.check"
  },
  {
    "path": "classification/model/registry.py",
    "chars": 3970,
    "preview": "\"\"\" Model Registry\nHacked together by / Copyright 2020 Ross Wightman\n\"\"\"\n\nimport sys\nimport re\nimport fnmatch\nfrom colle"
  },
  {
    "path": "classification/model/res2net.py",
    "chars": 8780,
    "preview": "import torch.nn as nn\nimport math\nimport torch.utils.model_zoo as model_zoo\nimport torch\nimport torch.nn.functional as F"
  },
  {
    "path": "classification/model/resnet.py",
    "chars": 53820,
    "preview": "\"\"\"PyTorch ResNet\n\nThis started as a copy of https://github.com/pytorch/vision 'resnet.py' (BSD-3-Clause) with\nadditiona"
  },
  {
    "path": "classification/model/utils.py",
    "chars": 9568,
    "preview": "# Copyright (c) 2015-present, Facebook, Inc.\n# All rights reserved.\n\"\"\"\nMisc functions, including distributed helpers.\n\n"
  },
  {
    "path": "classification/model/vgg.py",
    "chars": 9167,
    "preview": "from typing import Union, List, Dict, Any, cast\n\nimport torch\nimport torch.nn as nn\n\n\n\n__all__ = [\n    \"VGG\",\n    \"vgg11"
  },
  {
    "path": "classification/parse_test_res.py",
    "chars": 6230,
    "preview": "\"\"\"\nGoal\n---\n1. Read test results from log.txt files\n2. Compute mean and std across different folders (seeds)\n\nUsage\n---"
  },
  {
    "path": "classification/scripts/CNN/README.md",
    "chars": 72,
    "preview": "These scripts are only for reproducing the results on the IJCV'22 paper."
  },
  {
    "path": "classification/scripts/CNN/eval.sh",
    "chars": 739,
    "preview": "#!/bin/bash\n\n# custom config\nDATA=/path/to/datasets\nTRAINER=CoOp\nSHOTS=16\nNCTX=16\nCSC=False\nCTP=end\n\nDATASET=$1\nCFG=$2\n\n"
  },
  {
    "path": "classification/scripts/CNN/main.sh",
    "chars": 653,
    "preview": "#!/bin/bash\n\n# custom config\nDATA='./data/'\nTRAINER=CNN\n\nDATASET=$1\nCFG=$2  # config file\nSHOTS=$3  # number of shots (1"
  },
  {
    "path": "classification/scripts/CNN/zeroshot.sh",
    "chars": 354,
    "preview": "#!/bin/bash\n\n# custom config\nDATA=/path/to/datasets\nTRAINER=ZeroshotCLIP\nDATASET=$1\nCFG=$2  # rn50, rn101, vit_b32 or vi"
  },
  {
    "path": "classification/scripts/MIM_finetune/README.md",
    "chars": 72,
    "preview": "These scripts are only for reproducing the results on the IJCV'22 paper."
  },
  {
    "path": "classification/scripts/MIM_finetune/eval.sh",
    "chars": 739,
    "preview": "#!/bin/bash\n\n# custom config\nDATA=/path/to/datasets\nTRAINER=CoOp\nSHOTS=16\nNCTX=16\nCSC=False\nCTP=end\n\nDATASET=$1\nCFG=$2\n\n"
  },
  {
    "path": "classification/scripts/MIM_finetune/main.sh",
    "chars": 662,
    "preview": "#!/bin/bash\n\n# custom config\nDATA='./data/'\nTRAINER=MIM_finetune\n\nDATASET=$1\nCFG=$2  # config file\nSHOTS=$3  # number of"
  },
  {
    "path": "classification/scripts/MIM_finetune/zeroshot.sh",
    "chars": 354,
    "preview": "#!/bin/bash\n\n# custom config\nDATA=/path/to/datasets\nTRAINER=ZeroshotCLIP\nDATASET=$1\nCFG=$2  # rn50, rn101, vit_b32 or vi"
  },
  {
    "path": "classification/scripts/MIM_linear/README.md",
    "chars": 72,
    "preview": "These scripts are only for reproducing the results on the IJCV'22 paper."
  },
  {
    "path": "classification/scripts/MIM_linear/eval.sh",
    "chars": 739,
    "preview": "#!/bin/bash\n\n# custom config\nDATA=/path/to/datasets\nTRAINER=CoOp\nSHOTS=16\nNCTX=16\nCSC=False\nCTP=end\n\nDATASET=$1\nCFG=$2\n\n"
  },
  {
    "path": "classification/scripts/MIM_linear/main.sh",
    "chars": 660,
    "preview": "#!/bin/bash\n\n# custom config\nDATA='./data/'\nTRAINER=MIM_linear\n\nDATASET=$1\nCFG=$2  # config file\nSHOTS=$3  # number of s"
  },
  {
    "path": "classification/scripts/MIM_linear/zeroshot.sh",
    "chars": 354,
    "preview": "#!/bin/bash\n\n# custom config\nDATA=/path/to/datasets\nTRAINER=ZeroshotCLIP\nDATASET=$1\nCFG=$2  # rn50, rn101, vit_b32 or vi"
  },
  {
    "path": "classification/train.py",
    "chars": 5931,
    "preview": "import argparse\nimport torch\n\nfrom dassl.utils import setup_logger, set_random_seed, collect_env_info\nfrom dassl.config "
  },
  {
    "path": "classification/trainers/MIM_linear.py",
    "chars": 10678,
    "preview": "import os.path as osp\n\nimport torch\nimport torch.nn as nn\nfrom torch.nn import functional as F\n\nfrom dassl.engine import"
  },
  {
    "path": "classification/trainers/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "classification/trainers/imagenet_templates.py",
    "chars": 2653,
    "preview": "# source: https://github.com/openai/CLIP/blob/main/notebooks/Prompt_Engineering_for_ImageNet.ipynb\n\nIMAGENET_TEMPLATES ="
  },
  {
    "path": "detection/CITATION.cff",
    "chars": 273,
    "preview": "cff-version: 1.2.0\nmessage: \"If you use this software, please cite it as below.\"\nauthors:\n  - name: \"MMDetection Contrib"
  },
  {
    "path": "detection/LICENSE",
    "chars": 11398,
    "preview": "Copyright 2018-2023 OpenMMLab. All rights reserved.\n\n                                 Apache License\n                   "
  },
  {
    "path": "detection/MANIFEST.in",
    "chars": 205,
    "preview": "include requirements/*.txt\ninclude mmdet/VERSION\ninclude mmdet/.mim/model-index.yml\ninclude mmdet/.mim/demo/*/*\nrecursiv"
  },
  {
    "path": "detection/MMDET_README.md",
    "chars": 16065,
    "preview": "<div align=\"center\">\n  <img src=\"resources/mmdet-logo.png\" width=\"600\"/>\n  <div>&nbsp;</div>\n  <div align=\"center\">\n    "
  },
  {
    "path": "detection/README_zh-CN.md",
    "chars": 14429,
    "preview": "<div align=\"center\">\n  <img src=\"resources/mmdet-logo.png\" width=\"600\"/>\n  <div>&nbsp;</div>\n  <div align=\"center\">\n    "
  },
  {
    "path": "detection/configs/_base_/datasets/cityscapes_detection.py",
    "chars": 1937,
    "preview": "# dataset settings\ndataset_type = 'CityscapesDataset'\ndata_root = 'data/cityscapes/'\nimg_norm_cfg = dict(\n    mean=[123."
  },
  {
    "path": "detection/configs/_base_/datasets/cityscapes_instance.py",
    "chars": 1963,
    "preview": "# dataset settings\ndataset_type = 'CityscapesDataset'\ndata_root = 'data/cityscapes/'\nimg_norm_cfg = dict(\n    mean=[123."
  },
  {
    "path": "detection/configs/_base_/datasets/coco_detection.py",
    "chars": 1711,
    "preview": "# dataset settings\ndataset_type = 'CocoDataset'\ndata_root = 'data/coco/'\nimg_norm_cfg = dict(\n    mean=[123.675, 116.28,"
  },
  {
    "path": "detection/configs/_base_/datasets/coco_instance.py",
    "chars": 1752,
    "preview": "# dataset settings\ndataset_type = 'CocoDataset'\ndata_root = 'data/coco/'\nimg_norm_cfg = dict(\n    mean=[123.675, 116.28,"
  },
  {
    "path": "detection/configs/_base_/datasets/coco_instance_semantic.py",
    "chars": 1922,
    "preview": "# dataset settings\ndataset_type = 'CocoDataset'\ndata_root = 'data/coco/'\nimg_norm_cfg = dict(\n    mean=[123.675, 116.28,"
  },
  {
    "path": "detection/configs/_base_/datasets/coco_panoptic.py",
    "chars": 2079,
    "preview": "# dataset settings\ndataset_type = 'CocoPanopticDataset'\ndata_root = 'data/coco/'\nimg_norm_cfg = dict(\n    mean=[123.675,"
  },
  {
    "path": "detection/configs/_base_/datasets/deepfashion.py",
    "chars": 1888,
    "preview": "# dataset settings\ndataset_type = 'DeepFashionDataset'\ndata_root = 'data/DeepFashion/In-shop/'\nimg_norm_cfg = dict(\n    "
  }
]

// ... and 2232 more files (download for full content)