├── configs ├── reppoints │ ├── reppoints.png │ ├── reppoints_minmax_r50_fpn_gn-neck+head_1x_coco.py │ ├── reppoints_partial_minmax_r50_fpn_gn-neck+head_1x_coco.py │ ├── bbox_r50_grid_center_fpn_gn-neck+head_1x_coco.py │ ├── reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py │ ├── reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py │ ├── reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py │ └── reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py ├── centernet │ └── centernet_resnet18_140e_coco.py ├── fp16 │ ├── mask_rcnn_r50_fpn_fp16_1x_coco.py │ ├── retinanet_r50_fpn_fp16_1x_coco.py │ ├── faster_rcnn_r50_fpn_fp16_1x_coco.py │ ├── mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py │ └── mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py ├── faster_rcnn │ ├── faster_rcnn_r50_fpn_mstrain_3x_coco.py │ ├── faster_rcnn_r50_fpn_ohem_1x_coco.py │ ├── faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py │ ├── faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py │ ├── faster_rcnn_r50_fpn_1x_coco.py │ ├── faster_rcnn_r50_fpn_2x_coco.py │ ├── faster_rcnn_r101_fpn_1x_coco.py │ ├── faster_rcnn_r101_fpn_2x_coco.py │ ├── faster_rcnn_r50_fpn_iou_1x_coco.py │ ├── faster_rcnn_r50_fpn_giou_1x_coco.py │ ├── faster_rcnn_r101_fpn_mstrain_3x_coco.py │ ├── faster_rcnn_r50_fpn_bounded_iou_1x_coco.py │ ├── faster_rcnn_r101_caffe_fpn_1x_coco.py │ ├── faster_rcnn_r50_fpn_soft_nms_1x_coco.py │ ├── faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py │ ├── faster_rcnn_x101_32x4d_fpn_1x_coco.py │ ├── faster_rcnn_x101_32x4d_fpn_2x_coco.py │ ├── faster_rcnn_x101_64x4d_fpn_1x_coco.py │ └── faster_rcnn_x101_64x4d_fpn_2x_coco.py ├── paa │ ├── paa_r50_fpn_2x_coco.py │ ├── paa_r101_fpn_2x_coco.py │ ├── paa_r50_fpn_1.5x_coco.py │ ├── paa_r101_fpn_1x_coco.py │ └── paa_r101_fpn_mstrain_3x_coco.py ├── yolo │ └── yolov3_d53_fp16_mstrain-608_273e_coco.py ├── deformable_detr │ ├── deformable_detr_refine_r50_16x2_50e_coco.py │ └── deformable_detr_twostage_refine_r50_16x2_50e_coco.py ├── mask_rcnn │ ├── mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py │ ├── mask_rcnn_r50_fpn_1x_coco.py │ ├── mask_rcnn_r50_fpn_2x_coco.py │ ├── mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py │ ├── mask_rcnn_r50_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_r101_caffe_fpn_1x_coco.py │ ├── mask_rcnn_x101_32x4d_fpn_1x_coco.py │ ├── mask_rcnn_x101_32x4d_fpn_2x_coco.py │ ├── mask_rcnn_x101_64x4d_fpn_1x_coco.py │ └── mask_rcnn_x101_64x4d_fpn_2x_coco.py ├── fcos │ ├── fcos_center_r50_caffe_fpn_gn-head_1x_coco.py │ ├── fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py │ └── fcos_r101_caffe_fpn_gn-head_1x_coco.py ├── htc │ ├── htc_r50_fpn_20e_coco.py │ └── htc_r101_fpn_20e_coco.py ├── rpn │ ├── rpn_r50_fpn_2x_coco.py │ ├── rpn_r101_fpn_1x_coco.py │ ├── rpn_r101_fpn_2x_coco.py │ ├── rpn_r101_caffe_fpn_1x_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 ├── scnet │ ├── scnet_r50_fpn_20e_coco.py │ ├── scnet_x101_64x4d_fpn_8x1_20e_coco.py │ └── scnet_r101_fpn_20e_coco.py ├── foveabox │ ├── fovea_r50_fpn_4x4_2x_coco.py │ ├── fovea_r101_fpn_4x4_1x_coco.py │ ├── fovea_r101_fpn_4x4_2x_coco.py │ ├── fovea_align_r50_fpn_gn-head_4x4_2x_coco.py │ └── fovea_align_r101_fpn_gn-head_4x4_2x_coco.py ├── hrnet │ ├── htc_hrnetv2p_w40_28e_coco.py │ ├── faster_rcnn_hrnetv2p_w32_2x_coco.py │ ├── faster_rcnn_hrnetv2p_w40_2x_coco.py │ ├── mask_rcnn_hrnetv2p_w18_2x_coco.py │ ├── mask_rcnn_hrnetv2p_w32_2x_coco.py │ ├── mask_rcnn_hrnetv2p_w40_2x_coco.py │ ├── faster_rcnn_hrnetv2p_w18_2x_coco.py │ ├── htc_x101_64x4d_fpn_16x1_28e_coco.py │ ├── fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py │ ├── fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py │ ├── htc_hrnetv2p_w18_20e_coco.py │ ├── mask_rcnn_hrnetv2p_w18_1x_coco.py │ └── fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py ├── regnet │ ├── faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py │ └── mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py ├── retinanet │ ├── retinanet_r50_fpn_2x_coco.py │ ├── retinanet_r50_caffe_fpn_mstrain_2x_coco.py │ ├── retinanet_r50_caffe_fpn_mstrain_3x_coco.py │ ├── retinanet_r101_fpn_1x_coco.py │ ├── retinanet_r101_fpn_2x_coco.py │ ├── retinanet_r101_caffe_fpn_1x_coco.py │ ├── retinanet_r50_fpn_1x_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 ├── fast_rcnn │ ├── fast_rcnn_r50_fpn_2x_coco.py │ ├── fast_rcnn_r101_fpn_1x_coco.py │ ├── fast_rcnn_r101_fpn_2x_coco.py │ ├── fast_rcnn_r101_caffe_fpn_1x_coco.py │ └── README.md ├── tridentnet │ └── tridentnet_r50_caffe_mstrain_3x_coco.py ├── cascade_rcnn │ ├── cascade_rcnn_r50_fpn_20e_coco.py │ ├── cascade_rcnn_r50_fpn_1x_coco.py │ ├── cascade_mask_rcnn_r50_fpn_1x_coco.py │ ├── cascade_mask_rcnn_r50_fpn_20e_coco.py │ ├── cascade_rcnn_r101_fpn_1x_coco.py │ ├── cascade_rcnn_r101_fpn_20e_coco.py │ ├── cascade_mask_rcnn_r101_fpn_1x_coco.py │ ├── cascade_mask_rcnn_r101_fpn_20e_coco.py │ ├── cascade_rcnn_r101_caffe_fpn_1x_coco.py │ ├── cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py │ └── cascade_rcnn_x101_32x4d_fpn_1x_coco.py ├── ms_rcnn │ ├── ms_rcnn_r101_caffe_fpn_2x_coco.py │ ├── ms_rcnn_r50_caffe_fpn_2x_coco.py │ ├── ms_rcnn_x101_64x4d_fpn_2x_coco.py │ ├── ms_rcnn_r101_caffe_fpn_1x_coco.py │ ├── ms_rcnn_x101_32x4d_fpn_1x_coco.py │ └── ms_rcnn_x101_64x4d_fpn_1x_coco.py ├── gn │ ├── mask_rcnn_r101_fpn_gn-all_3x_coco.py │ ├── mask_rcnn_r50_fpn_gn-all_3x_coco.py │ ├── mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py │ └── mask_rcnn_r101_fpn_gn-all_2x_coco.py ├── fpg │ ├── retinanet_r50_fpg-chn128_crop640_50e_coco.py │ ├── faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py │ └── mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py ├── gn+ws │ ├── mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py │ ├── mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py │ ├── mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py │ ├── mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py │ ├── faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py │ └── mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py ├── gcnet │ ├── mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py │ ├── mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py │ ├── mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py │ ├── 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 │ ├── mask_rcnn_r50_fpn_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_r50_fpn_r16_gcb_c3-c5_1x_coco.py │ ├── mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_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_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_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 │ ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_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_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py │ └── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py ├── point_rend │ └── point_rend_r50_caffe_fpn_mstrain_3x_coco.py ├── atss │ └── atss_r101_fpn_1x_coco.py ├── fsaf │ ├── fsaf_r101_fpn_1x_coco.py │ └── fsaf_x101_64x4d_fpn_1x_coco.py ├── vfnet │ ├── vfnet_r101_fpn_1x_coco.py │ ├── vfnet_r101_fpn_mstrain_2x_coco.py │ ├── vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py │ └── vfnet_r101_fpn_2x_coco.py ├── yolact │ ├── yolact_r101_1x8_coco.py │ └── yolact_r50_8x8_coco.py ├── ghm │ ├── retinanet_ghm_r101_fpn_1x_coco.py │ ├── retinanet_ghm_x101_32x4d_fpn_1x_coco.py │ └── retinanet_ghm_x101_64x4d_fpn_1x_coco.py ├── pafpn │ └── faster_rcnn_r50_pafpn_1x_coco.py ├── dcn │ ├── mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py │ ├── faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py │ ├── mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py │ ├── mask_rcnn_r50_fpn_mdconv_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_mdconv_c3-c5_1x_coco.py │ ├── faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py │ ├── 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 │ ├── faster_rcnn_r50_fpn_dpool_1x_coco.py │ └── faster_rcnn_r50_fpn_mdpool_1x_coco.py ├── libra_rcnn │ └── libra_faster_rcnn_r101_fpn_1x_coco.py ├── grid_rcnn │ ├── grid_rcnn_r101_fpn_gn-head_2x_coco.py │ ├── grid_rcnn_r50_fpn_gn-head_1x_coco.py │ └── grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py ├── instaboost │ ├── mask_rcnn_r101_fpn_instaboost_4x_coco.py │ └── cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py ├── free_anchor │ ├── retinanet_free_anchor_r101_fpn_1x_coco.py │ └── retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py ├── lvis │ ├── mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py │ └── mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py ├── guided_anchoring │ ├── ga_faster_r101_caffe_fpn_1x_coco.py │ ├── ga_retinanet_r101_caffe_fpn_1x_coco.py │ ├── ga_rpn_r101_caffe_fpn_1x_coco.py │ ├── ga_rpn_x101_32x4d_fpn_1x_coco.py │ ├── ga_rpn_x101_64x4d_fpn_1x_coco.py │ ├── ga_faster_x101_32x4d_fpn_1x_coco.py │ └── ga_faster_x101_64x4d_fpn_1x_coco.py ├── pisa │ ├── pisa_ssd300_coco.py │ ├── pisa_ssd512_coco.py │ ├── pisa_retinanet_r50_fpn_1x_coco.py │ └── pisa_retinanet_x101_32x4d_fpn_1x_coco.py ├── seesaw_loss │ ├── mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py │ ├── mask_rcnn_r50_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_sample1e-3_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_normed_mask_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_normed_mask_mstrain_2x_lvis_v1.py ├── sparse_rcnn │ ├── sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py │ └── sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py ├── detectors │ ├── htc_r50_sac_1x_coco.py │ └── cascade_rcnn_r50_sac_1x_coco.py ├── resnest │ ├── mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py │ ├── cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py │ ├── cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py │ └── faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py ├── res2net │ ├── mask_rcnn_r2_101_fpn_2x_coco.py │ ├── faster_rcnn_r2_101_fpn_2x_coco.py │ ├── cascade_rcnn_r2_101_fpn_20e_coco.py │ ├── cascade_mask_rcnn_r2_101_fpn_20e_coco.py │ └── htc_r2_101_fpn_20e_coco.py ├── _base_ │ └── schedules │ │ ├── schedule_1x.py │ │ ├── schedule_20e.py │ │ └── schedule_2x.py ├── deepfashion │ └── mask_rcnn_r50_fpn_15e_deepfashion.py ├── gfl │ └── gfl_r101_fpn_mstrain_2x_coco.py ├── empirical_attention │ ├── faster_rcnn_r50_fpn_attention_0010_1x_coco.py │ └── faster_rcnn_r50_fpn_attention_1111_1x_coco.py └── selfsup_pretrain │ └── mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco.py └── mmdet ├── models ├── roi_heads │ ├── shared_heads │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── __init__.cpython-37.pyc │ │ │ └── res_layer.cpython-37.pyc │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── htc_roi_head.cpython-37.pyc │ │ ├── test_mixins.cpython-37.pyc │ │ ├── base_roi_head.cpython-37.pyc │ │ ├── double_roi_head.cpython-37.pyc │ │ ├── grid_roi_head.cpython-37.pyc │ │ ├── pisa_roi_head.cpython-37.pyc │ │ ├── scnet_roi_head.cpython-37.pyc │ │ ├── sparse_roi_head.cpython-37.pyc │ │ ├── cascade_roi_head.cpython-37.pyc │ │ ├── dynamic_roi_head.cpython-37.pyc │ │ ├── standard_roi_head.cpython-37.pyc │ │ ├── trident_roi_head.cpython-37.pyc │ │ ├── point_rend_roi_head.cpython-37.pyc │ │ └── mask_scoring_roi_head.cpython-37.pyc │ ├── bbox_heads │ │ └── __pycache__ │ │ │ ├── __init__.cpython-37.pyc │ │ │ ├── bbox_head.cpython-37.pyc │ │ │ ├── dii_head.cpython-37.pyc │ │ │ ├── sabl_head.cpython-37.pyc │ │ │ ├── convfc_bbox_head.cpython-37.pyc │ │ │ ├── double_bbox_head.cpython-37.pyc │ │ │ └── scnet_bbox_head.cpython-37.pyc │ ├── mask_heads │ │ └── __pycache__ │ │ │ ├── __init__.cpython-37.pyc │ │ │ ├── grid_head.cpython-37.pyc │ │ │ ├── fcn_mask_head.cpython-37.pyc │ │ │ ├── htc_mask_head.cpython-37.pyc │ │ │ ├── maskiou_head.cpython-37.pyc │ │ │ ├── coarse_mask_head.cpython-37.pyc │ │ │ ├── mask_point_head.cpython-37.pyc │ │ │ ├── scnet_mask_head.cpython-37.pyc │ │ │ ├── feature_relay_head.cpython-37.pyc │ │ │ ├── fused_semantic_head.cpython-37.pyc │ │ │ ├── global_context_head.cpython-37.pyc │ │ │ └── scnet_semantic_head.cpython-37.pyc │ └── roi_extractors │ │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── base_roi_extractor.cpython-37.pyc │ │ ├── generic_roi_extractor.cpython-37.pyc │ │ └── single_level_roi_extractor.cpython-37.pyc │ │ └── __init__.py ├── __pycache__ │ ├── builder.cpython-37.pyc │ └── __init__.cpython-37.pyc ├── necks │ └── __pycache__ │ │ ├── bfp.cpython-37.pyc │ │ ├── fpg.cpython-37.pyc │ │ ├── fpn.cpython-37.pyc │ │ ├── rfp.cpython-37.pyc │ │ ├── hrfpn.cpython-37.pyc │ │ ├── pafpn.cpython-37.pyc │ │ ├── __init__.cpython-37.pyc │ │ ├── nas_fpn.cpython-37.pyc │ │ ├── ssd_neck.cpython-37.pyc │ │ ├── yolo_neck.cpython-37.pyc │ │ ├── fpn_carafe.cpython-37.pyc │ │ ├── nasfcos_fpn.cpython-37.pyc │ │ ├── channel_mapper.cpython-37.pyc │ │ ├── ct_resnet_neck.cpython-37.pyc │ │ └── dilated_encoder.cpython-37.pyc ├── losses │ └── __pycache__ │ │ ├── utils.cpython-37.pyc │ │ ├── EDL_ReLU.cpython-37.pyc │ │ ├── __init__.cpython-37.pyc │ │ ├── accuracy.cpython-37.pyc │ │ ├── ae_loss.cpython-37.pyc │ │ ├── ghm_loss.cpython-37.pyc │ │ ├── iou_loss.cpython-37.pyc │ │ ├── kd_loss.cpython-37.pyc │ │ ├── mse_loss.cpython-37.pyc │ │ ├── focal_loss.cpython-37.pyc │ │ ├── pisa_loss.cpython-37.pyc │ │ ├── EDL_FocalLoss.cpython-37.pyc │ │ ├── MyFocalLoss.cpython-37.pyc │ │ ├── gfocal_loss.cpython-37.pyc │ │ ├── seesaw_loss.cpython-37.pyc │ │ ├── smooth_l1_loss.cpython-37.pyc │ │ ├── varifocal_loss.cpython-37.pyc │ │ ├── balanced_l1_loss.cpython-37.pyc │ │ ├── cross_entropy_loss.cpython-37.pyc │ │ ├── gaussian_focal_loss.cpython-37.pyc │ │ └── EDL_Softmax_FocalLoss.cpython-37.pyc ├── backbones │ └── __pycache__ │ │ ├── hrnet.cpython-37.pyc │ │ ├── darknet.cpython-37.pyc │ │ ├── regnet.cpython-37.pyc │ │ ├── res2net.cpython-37.pyc │ │ ├── resnest.cpython-37.pyc │ │ ├── resnet.cpython-37.pyc │ │ ├── resnext.cpython-37.pyc │ │ ├── ssd_vgg.cpython-37.pyc │ │ ├── __init__.cpython-37.pyc │ │ ├── hourglass.cpython-37.pyc │ │ ├── mobilenet_v2.cpython-37.pyc │ │ ├── trident_resnet.cpython-37.pyc │ │ ├── detectors_resnet.cpython-37.pyc │ │ └── detectors_resnext.cpython-37.pyc ├── detectors │ ├── __pycache__ │ │ ├── atss.cpython-37.pyc │ │ ├── base.cpython-37.pyc │ │ ├── detr.cpython-37.pyc │ │ ├── fcos.cpython-37.pyc │ │ ├── fovea.cpython-37.pyc │ │ ├── fsaf.cpython-37.pyc │ │ ├── gfl.cpython-37.pyc │ │ ├── htc.cpython-37.pyc │ │ ├── paa.cpython-37.pyc │ │ ├── rpn.cpython-37.pyc │ │ ├── scnet.cpython-37.pyc │ │ ├── vfnet.cpython-37.pyc │ │ ├── yolo.cpython-37.pyc │ │ ├── yolof.cpython-37.pyc │ │ ├── nasfcos.cpython-37.pyc │ │ ├── yolact.cpython-37.pyc │ │ ├── SSL_Lambda.cpython-37.pyc │ │ ├── SSL_base.cpython-37.pyc │ │ ├── __init__.cpython-37.pyc │ │ ├── autoassign.cpython-37.pyc │ │ ├── centernet.cpython-37.pyc │ │ ├── cornernet.cpython-37.pyc │ │ ├── fast_rcnn.cpython-37.pyc │ │ ├── grid_rcnn.cpython-37.pyc │ │ ├── mask_rcnn.cpython-37.pyc │ │ ├── point_rend.cpython-37.pyc │ │ ├── retinanet.cpython-37.pyc │ │ ├── two_stage.cpython-37.pyc │ │ ├── MyRetinaBase.cpython-37.pyc │ │ ├── MyRetinanet.cpython-37.pyc │ │ ├── SelfSSL_base.cpython-37.pyc │ │ ├── cascade_rcnn.cpython-37.pyc │ │ ├── faster_rcnn.cpython-37.pyc │ │ ├── kd_one_stage.cpython-37.pyc │ │ ├── single_stage.cpython-37.pyc │ │ ├── sparse_rcnn.cpython-37.pyc │ │ ├── L_single_stage.cpython-37.pyc │ │ ├── MyMCRetinanet.cpython-37.pyc │ │ ├── SSL_L_retinanet.cpython-37.pyc │ │ ├── SSL_retinanet.cpython-37.pyc │ │ ├── deformable_detr.cpython-37.pyc │ │ ├── SSL_single_stage.cpython-37.pyc │ │ ├── SelfSSL_retinanet.cpython-37.pyc │ │ ├── mask_scoring_rcnn.cpython-37.pyc │ │ ├── MyRetinaSingleStage.cpython-37.pyc │ │ ├── SSD_Enc_single_stage.cpython-37.pyc │ │ ├── SSD_L_single_stage.cpython-37.pyc │ │ ├── SSL_L_single_stage.cpython-37.pyc │ │ ├── SelfSSL_single_stage.cpython-37.pyc │ │ ├── reppoints_detector.cpython-37.pyc │ │ ├── trident_faster_rcnn.cpython-37.pyc │ │ └── MyMCRetinaSingleStage.cpython-37.pyc │ ├── deformable_detr.py │ └── scnet.py ├── utils │ └── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── builder.cpython-37.pyc │ │ ├── res_layer.cpython-37.pyc │ │ ├── se_layer.cpython-37.pyc │ │ ├── transformer.cpython-37.pyc │ │ ├── make_divisible.cpython-37.pyc │ │ ├── gaussian_target.cpython-37.pyc │ │ ├── normed_predictor.cpython-37.pyc │ │ ├── inverted_residual.cpython-37.pyc │ │ └── positional_encoding.cpython-37.pyc └── dense_heads │ └── __pycache__ │ ├── D_RW_ST.cpython-37.pyc │ ├── __init__.cpython-37.pyc │ ├── gfl_head.cpython-37.pyc │ ├── ld_head.cpython-37.pyc │ ├── paa_head.cpython-37.pyc │ ├── rpn_head.cpython-37.pyc │ ├── ssd_head.cpython-37.pyc │ ├── D_one_conv.cpython-37.pyc │ ├── D_weak_ST.cpython-37.pyc │ ├── L_A2L_cat.cpython-37.pyc │ ├── L_ssd_head.cpython-37.pyc │ ├── Lambda_Img.cpython-37.pyc │ ├── Lambda_L1.cpython-37.pyc │ ├── Lambda_L2.cpython-37.pyc │ ├── atss_head.cpython-37.pyc │ ├── detr_head.cpython-37.pyc │ ├── fcos_head.cpython-37.pyc │ ├── fovea_head.cpython-37.pyc │ ├── fsaf_head.cpython-37.pyc │ ├── ssd_L_head.cpython-37.pyc │ ├── vfnet_head.cpython-37.pyc │ ├── yolo_head.cpython-37.pyc │ ├── yolof_head.cpython-37.pyc │ ├── D_anchor_head.cpython-37.pyc │ ├── L_A2L_downcat.cpython-37.pyc │ ├── L_anchor_head.cpython-37.pyc │ ├── Lambda_L2_noL.cpython-37.pyc │ ├── Lambda_MSLE.cpython-37.pyc │ ├── MyRetinaHead.cpython-37.pyc │ ├── My_L_ssd_head.cpython-37.pyc │ ├── My_ssd_head.cpython-37.pyc │ ├── anchor_head.cpython-37.pyc │ ├── corner_head.cpython-37.pyc │ ├── ga_rpn_head.cpython-37.pyc │ ├── nasfcos_head.cpython-37.pyc │ ├── pisa_ssd_head.cpython-37.pyc │ ├── retina_head.cpython-37.pyc │ ├── yolact_head.cpython-37.pyc │ ├── D_RW_Lambda_ST.cpython-37.pyc │ ├── EDL_retina_head.cpython-37.pyc │ ├── L_A2L_paramgen.cpython-37.pyc │ ├── Lambda_L2_ReLU.cpython-37.pyc │ ├── MyMCRetinaHead.cpython-37.pyc │ ├── My_anchor_head.cpython-37.pyc │ ├── SSD_anchor_head.cpython-37.pyc │ ├── autoassign_head.cpython-37.pyc │ ├── base_dense_head.cpython-37.pyc │ ├── centernet_head.cpython-37.pyc │ ├── ga_retina_head.cpython-37.pyc │ ├── reppoints_head.cpython-37.pyc │ ├── Lambda_L2_ablation.cpython-37.pyc │ ├── SSL_EDL_RetinaSoft.cpython-37.pyc │ ├── anchor_free_head.cpython-37.pyc │ ├── cascade_rpn_head.cpython-37.pyc │ ├── centripetal_head.cpython-37.pyc │ ├── dense_test_mixins.cpython-37.pyc │ ├── embedding_rpn_head.cpython-37.pyc │ ├── guided_anchor_head.cpython-37.pyc │ ├── retina_sepbn_head.cpython-37.pyc │ ├── sabl_retina_head.cpython-37.pyc │ ├── EDL_beta_retina_head.cpython-37.pyc │ ├── Lambda_L2_lossweight.cpython-37.pyc │ ├── SelfSSL_anchor_head.cpython-37.pyc │ ├── deformable_detr_head.cpython-37.pyc │ ├── original_anchor_head.cpython-37.pyc │ ├── pisa_retinanet_head.cpython-37.pyc │ ├── EDL_retina_head_dummy.cpython-37.pyc │ ├── EDL_retina_head_softmax.cpython-37.pyc │ ├── SelfSSL_EDL_RetinaSoft.cpython-37.pyc │ ├── free_anchor_retina_head.cpython-37.pyc │ ├── D_one_conv_Unc_Baselines.cpython-37.pyc │ ├── EDL_retina_head_ReW_softmax.cpython-37.pyc │ ├── EDL_retina_head_softmax_SL.cpython-37.pyc │ ├── EDL_retina_head_SelfW_softmax.cpython-37.pyc │ └── EDL_retina_head_softmax_dummy.cpython-37.pyc ├── datasets ├── api_wrappers │ ├── __init__.py │ └── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ └── coco_api.cpython-37.pyc ├── __pycache__ │ ├── voc.cpython-37.pyc │ ├── coco.cpython-37.pyc │ ├── lvis.cpython-37.pyc │ ├── utils.cpython-37.pyc │ ├── __init__.cpython-37.pyc │ ├── builder.cpython-37.pyc │ ├── custom.cpython-37.pyc │ ├── cityscapes.cpython-37.pyc │ ├── wider_face.cpython-37.pyc │ ├── xml_style.cpython-37.pyc │ ├── deepfashion.cpython-37.pyc │ └── dataset_wrappers.cpython-37.pyc ├── pipelines │ └── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── compose.cpython-37.pyc │ │ ├── loading.cpython-37.pyc │ │ ├── formating.cpython-37.pyc │ │ ├── instaboost.cpython-37.pyc │ │ ├── transforms.cpython-37.pyc │ │ ├── auto_augment.cpython-37.pyc │ │ └── test_time_aug.cpython-37.pyc ├── samplers │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── group_sampler.cpython-37.pyc │ │ └── distributed_sampler.cpython-37.pyc │ └── __init__.py └── deepfashion.py ├── __pycache__ ├── version.cpython-37.pyc └── __init__.cpython-37.pyc ├── apis └── __pycache__ │ ├── test.cpython-37.pyc │ ├── train.cpython-37.pyc │ ├── __init__.cpython-37.pyc │ ├── inference.cpython-37.pyc │ ├── train_SSD.cpython-37.pyc │ ├── train_Lambda.cpython-37.pyc │ ├── train_SSD_L.cpython-37.pyc │ ├── CalEnsembleUnc.cpython-37.pyc │ └── CalMCDropoutUnc.cpython-37.pyc ├── utils ├── __pycache__ │ ├── logger.cpython-37.pyc │ ├── __init__.cpython-37.pyc │ ├── collect_env.cpython-37.pyc │ ├── functions.cpython-37.pyc │ ├── util_mixins.cpython-37.pyc │ ├── RetinaRunner.cpython-37.pyc │ ├── active_datasets.cpython-37.pyc │ ├── contextmanagers.cpython-37.pyc │ ├── Epoch_Based_Runner.cpython-37.pyc │ ├── Epoch_Based_Runner_SSD.cpython-37.pyc │ ├── Epoch_Based_Runner_Lambda.cpython-37.pyc │ └── Epoch_Based_Runner_SSD_L.cpython-37.pyc └── __init__.py └── core ├── __pycache__ └── __init__.cpython-37.pyc ├── anchor └── __pycache__ │ ├── utils.cpython-37.pyc │ ├── builder.cpython-37.pyc │ ├── __init__.cpython-37.pyc │ ├── point_generator.cpython-37.pyc │ └── anchor_generator.cpython-37.pyc ├── bbox ├── __pycache__ │ ├── builder.cpython-37.pyc │ ├── __init__.cpython-37.pyc │ └── transforms.cpython-37.pyc ├── coder │ └── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── base_bbox_coder.cpython-37.pyc │ │ ├── tblr_bbox_coder.cpython-37.pyc │ │ ├── yolo_bbox_coder.cpython-37.pyc │ │ ├── pseudo_bbox_coder.cpython-37.pyc │ │ ├── delta_xywh_bbox_coder.cpython-37.pyc │ │ └── legacy_delta_xywh_bbox_coder.cpython-37.pyc ├── samplers │ └── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── base_sampler.cpython-37.pyc │ │ ├── ohem_sampler.cpython-37.pyc │ │ ├── pseudo_sampler.cpython-37.pyc │ │ ├── random_sampler.cpython-37.pyc │ │ ├── sampling_result.cpython-37.pyc │ │ ├── combined_sampler.cpython-37.pyc │ │ ├── score_hlr_sampler.cpython-37.pyc │ │ ├── iou_balanced_neg_sampler.cpython-37.pyc │ │ └── instance_balanced_pos_sampler.cpython-37.pyc ├── assigners │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── assign_result.cpython-37.pyc │ │ ├── atss_assigner.cpython-37.pyc │ │ ├── base_assigner.cpython-37.pyc │ │ ├── grid_assigner.cpython-37.pyc │ │ ├── point_assigner.cpython-37.pyc │ │ ├── max_iou_assigner.cpython-37.pyc │ │ ├── region_assigner.cpython-37.pyc │ │ ├── uniform_assigner.cpython-37.pyc │ │ ├── hungarian_assigner.cpython-37.pyc │ │ ├── center_region_assigner.cpython-37.pyc │ │ └── approx_max_iou_assigner.cpython-37.pyc │ └── base_assigner.py ├── match_costs │ ├── __pycache__ │ │ ├── builder.cpython-37.pyc │ │ ├── __init__.cpython-37.pyc │ │ └── match_cost.cpython-37.pyc │ ├── __init__.py │ └── builder.py └── iou_calculators │ ├── __pycache__ │ ├── __init__.cpython-37.pyc │ ├── builder.cpython-37.pyc │ └── iou2d_calculator.cpython-37.pyc │ ├── __init__.py │ └── builder.py ├── mask ├── __pycache__ │ ├── utils.cpython-37.pyc │ ├── __init__.cpython-37.pyc │ ├── mask_target.cpython-37.pyc │ └── structures.cpython-37.pyc └── __init__.py ├── utils ├── __pycache__ │ ├── misc.cpython-37.pyc │ ├── __init__.cpython-37.pyc │ └── dist_utils.cpython-37.pyc └── __init__.py ├── evaluation └── __pycache__ │ ├── recall.cpython-37.pyc │ ├── __init__.cpython-37.pyc │ ├── mean_ap.cpython-37.pyc │ ├── class_names.cpython-37.pyc │ ├── eval_hooks.cpython-37.pyc │ └── bbox_overlaps.cpython-37.pyc ├── export ├── __pycache__ │ ├── __init__.cpython-37.pyc │ ├── onnx_helper.cpython-37.pyc │ └── pytorch2onnx.cpython-37.pyc └── __init__.py ├── visualization ├── __pycache__ │ ├── image.cpython-37.pyc │ └── __init__.cpython-37.pyc └── __init__.py ├── post_processing ├── __pycache__ │ ├── __init__.cpython-37.pyc │ ├── bbox_nms.cpython-37.pyc │ └── merge_augs.cpython-37.pyc └── __init__.py └── __init__.py /configs/reppoints/reppoints.png: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /mmdet/models/roi_heads/shared_heads/__init__.py: -------------------------------------------------------------------------------- 1 | from .res_layer import ResLayer 2 | 3 | __all__ = ['ResLayer'] 4 | -------------------------------------------------------------------------------- /mmdet/datasets/api_wrappers/__init__.py: -------------------------------------------------------------------------------- 1 | from .coco_api import COCO, COCOeval 2 | 3 | __all__ = ['COCO', 'COCOeval'] 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'./faster_rcnn_hrnetv2p_w32_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_hrnetv2p_w40_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_hrnetv2p_w32_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_hrnetv2p_w40_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ms_rcnn_r101_caffe_fpn_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ms_rcnn_x101_64x4d_fpn_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+head_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py' 2 | model = dict(bbox_head=dict(transform_method='minmax', use_grid_points=True)) 3 | -------------------------------------------------------------------------------- /mmdet/models/dense_heads/__pycache__/EDL_retina_head_ReW_softmax.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MoonLab-YH/AOD_MEH_HUA/HEAD/mmdet/models/dense_heads/__pycache__/EDL_retina_head_ReW_softmax.cpython-37.pyc -------------------------------------------------------------------------------- /mmdet/models/dense_heads/__pycache__/EDL_retina_head_softmax_SL.cpython-37.pyc: 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-------------------------------------------------------------------------------- /mmdet/models/roi_heads/mask_heads/__pycache__/global_context_head.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MoonLab-YH/AOD_MEH_HUA/HEAD/mmdet/models/roi_heads/mask_heads/__pycache__/global_context_head.cpython-37.pyc -------------------------------------------------------------------------------- /mmdet/models/roi_heads/mask_heads/__pycache__/scnet_semantic_head.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MoonLab-YH/AOD_MEH_HUA/HEAD/mmdet/models/roi_heads/mask_heads/__pycache__/scnet_semantic_head.cpython-37.pyc -------------------------------------------------------------------------------- /configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r101_fpn_gn-all_2x_coco.py' 2 | 3 | # learning policy 4 | lr_config = dict(step=[28, 34]) 5 | runner = dict(type='EpochBasedRunner', max_epochs=36) 6 | -------------------------------------------------------------------------------- /configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_gn-all_2x_coco.py' 2 | 3 | # learning policy 4 | lr_config = dict(step=[28, 34]) 5 | runner = dict(type='EpochBasedRunner', max_epochs=36) 6 | -------------------------------------------------------------------------------- /configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_hrnetv2p_w18_1x_coco.py' 2 | 3 | # learning policy 4 | lr_config = dict(step=[16, 22]) 5 | runner = dict(type='EpochBasedRunner', max_epochs=24) 6 | -------------------------------------------------------------------------------- /configs/hrnet/htc_x101_64x4d_fpn_16x1_28e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../htc/htc_x101_64x4d_fpn_16x1_20e_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[24, 27]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=28) 5 | -------------------------------------------------------------------------------- /mmdet/core/bbox/samplers/__pycache__/instance_balanced_pos_sampler.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MoonLab-YH/AOD_MEH_HUA/HEAD/mmdet/core/bbox/samplers/__pycache__/instance_balanced_pos_sampler.cpython-37.pyc -------------------------------------------------------------------------------- /mmdet/models/dense_heads/__pycache__/EDL_retina_head_SelfW_softmax.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MoonLab-YH/AOD_MEH_HUA/HEAD/mmdet/models/dense_heads/__pycache__/EDL_retina_head_SelfW_softmax.cpython-37.pyc -------------------------------------------------------------------------------- /mmdet/models/dense_heads/__pycache__/EDL_retina_head_softmax_dummy.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MoonLab-YH/AOD_MEH_HUA/HEAD/mmdet/models/dense_heads/__pycache__/EDL_retina_head_softmax_dummy.cpython-37.pyc -------------------------------------------------------------------------------- /configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py' 2 | lr_config = dict(step=[16, 22]) 3 | runner = dict(type='EpochBasedRunner', max_epochs=24) 4 | -------------------------------------------------------------------------------- /mmdet/models/roi_heads/roi_extractors/__pycache__/base_roi_extractor.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MoonLab-YH/AOD_MEH_HUA/HEAD/mmdet/models/roi_heads/roi_extractors/__pycache__/base_roi_extractor.cpython-37.pyc -------------------------------------------------------------------------------- /configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'retinanet_r50_fpg_crop640_50e_coco.py' 2 | 3 | model = dict( 4 | neck=dict(out_channels=128, inter_channels=128), 5 | bbox_head=dict(in_channels=128)) 6 | -------------------------------------------------------------------------------- /configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[20, 23]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[20, 23]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /configs/scnet/scnet_x101_64x4d_fpn_8x1_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './scnet_x101_64x4d_fpn_20e_coco.py' 2 | data = dict(samples_per_gpu=1, workers_per_gpu=1) 3 | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) 4 | -------------------------------------------------------------------------------- /mmdet/core/bbox/iou_calculators/__init__.py: -------------------------------------------------------------------------------- 1 | from .builder import build_iou_calculator 2 | from .iou2d_calculator import BboxOverlaps2D, bbox_overlaps 3 | 4 | __all__ = ['build_iou_calculator', 'BboxOverlaps2D', 'bbox_overlaps'] 5 | -------------------------------------------------------------------------------- /mmdet/models/roi_heads/roi_extractors/__pycache__/generic_roi_extractor.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MoonLab-YH/AOD_MEH_HUA/HEAD/mmdet/models/roi_heads/roi_extractors/__pycache__/generic_roi_extractor.cpython-37.pyc -------------------------------------------------------------------------------- /configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py: -------------------------------------------------------------------------------- 1 | # TODO: Remove this config after benchmarking all related configs 2 | _base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py' 3 | 4 | data = dict(samples_per_gpu=4, workers_per_gpu=4) 5 | -------------------------------------------------------------------------------- /configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) 5 | -------------------------------------------------------------------------------- /configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) 5 | -------------------------------------------------------------------------------- /configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py' 2 | 3 | # learning policy 4 | lr_config = dict(step=[28, 34]) 5 | runner = dict(type='EpochBasedRunner', max_epochs=36) 6 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_r50_caffe_fpn_mstrain_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 23]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_r50_caffe_fpn_mstrain_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[28, 34]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=36) 5 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[28, 34]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=36) 5 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 23]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[20, 23]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/mask_rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_instance.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/mask_rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_instance.py', 4 | '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './point_rend_r50_caffe_fpn_mstrain_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[28, 34]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=36) 5 | -------------------------------------------------------------------------------- /mmdet/models/roi_heads/roi_extractors/__pycache__/single_level_roi_extractor.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MoonLab-YH/AOD_MEH_HUA/HEAD/mmdet/models/roi_heads/roi_extractors/__pycache__/single_level_roi_extractor.cpython-37.pyc -------------------------------------------------------------------------------- /configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/faster_rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_detection.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/faster_rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_detection.py', 4 | '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[20, 23]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 23]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[28, 34]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=36) 5 | -------------------------------------------------------------------------------- /mmdet/datasets/samplers/__init__.py: -------------------------------------------------------------------------------- 1 | from .distributed_sampler import DistributedSampler 2 | from .group_sampler import DistributedGroupSampler, GroupSampler 3 | 4 | __all__ = ['DistributedSampler', 'DistributedGroupSampler', 'GroupSampler'] 5 | -------------------------------------------------------------------------------- /configs/atss/atss_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './atss_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/cascade_rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_detection.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /configs/fsaf/fsaf_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fsaf_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) 5 | -------------------------------------------------------------------------------- /configs/paa/paa_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './paa_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/rpn/rpn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/rpn/rpn_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/scnet/scnet_r101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './scnet_r50_fpn_20e_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/vfnet/vfnet_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/yolact/yolact_r101_1x8_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './yolact_r50_1x8_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/cascade_mask_rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_instance.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/cascade_mask_rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_instance.py', 4 | '../_base_/schedules/schedule_20e.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fast_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fast_rcnn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fovea_r50_fpn_4x4_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fovea_r50_fpn_4x4_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/paa/paa_r101_fpn_mstrain_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './paa_r50_fpn_mstrain_3x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) 5 | -------------------------------------------------------------------------------- /configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_ghm_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../common/mstrain-poly_3x_coco_instance.py', 3 | '../_base_/models/mask_rcnn_r50_fpn.py' 4 | ] 5 | 6 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 7 | -------------------------------------------------------------------------------- /configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_mstrain_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | roi_head=dict( 4 | bbox_head=dict( 5 | reg_decoded_bbox=True, 6 | loss_bbox=dict(type='IoULoss', loss_weight=10.0)))) 7 | -------------------------------------------------------------------------------- /configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' 2 | 3 | model = dict( 4 | neck=dict( 5 | type='PAFPN', 6 | in_channels=[256, 512, 1024, 2048], 7 | out_channels=256, 8 | num_outs=5)) 9 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_rcnn_r50_fpn_20e_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | roi_head=dict( 4 | bbox_head=dict( 5 | reg_decoded_bbox=True, 6 | loss_bbox=dict(type='GIoULoss', loss_weight=10.0)))) 7 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) 5 | -------------------------------------------------------------------------------- /configs/gn+ws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', checkpoint='open-mmlab://jhu/resnet101_gn_ws'))) 7 | -------------------------------------------------------------------------------- /configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', checkpoint='open-mmlab://jhu/resnet101_gn_ws'))) 7 | -------------------------------------------------------------------------------- /configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './libra_faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/rpn/rpn_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 8 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'faster_rcnn_r50_fpn_mstrain_3x_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | roi_head=dict( 4 | bbox_head=dict( 5 | reg_decoded_bbox=True, 6 | loss_bbox=dict(type='BoundedIoULoss', loss_weight=10.0)))) 7 | -------------------------------------------------------------------------------- /configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './grid_rcnn_r50_fpn_gn-head_2x_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_instaboost_4x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /mmdet/core/bbox/match_costs/__init__.py: -------------------------------------------------------------------------------- 1 | from .builder import build_match_cost 2 | from .match_cost import BBoxL1Cost, ClassificationCost, FocalLossCost, IoUCost 3 | 4 | __all__ = [ 5 | 'build_match_cost', 'ClassificationCost', 'BBoxL1Cost', 'IoUCost', 6 | 'FocalLossCost' 7 | ] 8 | -------------------------------------------------------------------------------- /configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCNv2', deform_groups=4, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_gn-all_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron/resnet101_gn'))) 8 | -------------------------------------------------------------------------------- /configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 8 | -------------------------------------------------------------------------------- /configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './reppoints_moment_r50_fpn_1x_coco.py' 2 | norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) 3 | model = dict(neck=dict(norm_cfg=norm_cfg), bbox_head=dict(norm_cfg=norm_cfg)) 4 | optimizer = dict(lr=0.01) 5 | -------------------------------------------------------------------------------- /mmdet/core/bbox/match_costs/builder.py: -------------------------------------------------------------------------------- 1 | from mmcv.utils import Registry, build_from_cfg 2 | 3 | MATCH_COST = Registry('Match Cost') 4 | 5 | 6 | def build_match_cost(cfg, default_args=None): 7 | """Builder of IoU calculator.""" 8 | return build_from_cfg(cfg, MATCH_COST, default_args) 9 | -------------------------------------------------------------------------------- /mmdet/models/detectors/deformable_detr.py: -------------------------------------------------------------------------------- 1 | from ..builder import DETECTORS 2 | from .detr import DETR 3 | 4 | 5 | @DETECTORS.register_module() 6 | class DeformableDETR(DETR): 7 | 8 | def __init__(self, *args, **kwargs): 9 | super(DETR, self).__init__(*args, **kwargs) 10 | -------------------------------------------------------------------------------- /configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron/resnet101_caffe'))) 8 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 8 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 8 | -------------------------------------------------------------------------------- /configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /mmdet/core/__init__.py: -------------------------------------------------------------------------------- 1 | from .anchor import * # noqa: F401, F403 2 | from .bbox import * # noqa: F401, F403 3 | from .evaluation import * # noqa: F401, F403 4 | from .mask import * # noqa: F401, F403 5 | from .post_processing import * # noqa: F401, F403 6 | from .utils import * # noqa: F401, F403 7 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_rcnn_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 8 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 8 | -------------------------------------------------------------------------------- /configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga_faster_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 8 | -------------------------------------------------------------------------------- /configs/instaboost/cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/pisa/pisa_ssd300_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../ssd/ssd300_coco.py' 2 | 3 | model = dict( 4 | bbox_head=dict(type='PISASSDHead'), 5 | train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2))) 6 | 7 | optimizer_config = dict( 8 | _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) 9 | -------------------------------------------------------------------------------- /configs/pisa/pisa_ssd512_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../ssd/ssd512_coco.py' 2 | 3 | model = dict( 4 | bbox_head=dict(type='PISASSDHead'), 5 | train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2))) 6 | 7 | optimizer_config = dict( 8 | _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) 9 | -------------------------------------------------------------------------------- /configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py' 2 | model = dict( 3 | roi_head=dict( 4 | mask_head=dict( 5 | predictor_cfg=dict(type='NormedConv2d', tempearture=20)))) 6 | -------------------------------------------------------------------------------- /configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /mmdet/core/visualization/__init__.py: -------------------------------------------------------------------------------- 1 | from .image import (color_val_matplotlib, imshow_det_bboxes, 2 | imshow_gt_det_bboxes, versatile_imshow_det_bboxes) 3 | 4 | __all__ = ['imshow_det_bboxes', 'imshow_gt_det_bboxes', 'color_val_matplotlib', 5 | 'versatile_imshow_det_bboxes'] 6 | -------------------------------------------------------------------------------- /mmdet/models/roi_heads/roi_extractors/__init__.py: -------------------------------------------------------------------------------- 1 | from .base_roi_extractor import BaseRoIExtractor 2 | from .generic_roi_extractor import GenericRoIExtractor 3 | from .single_level_roi_extractor import SingleRoIExtractor 4 | 5 | __all__ = ['BaseRoIExtractor', 'SingleRoIExtractor', 'GenericRoIExtractor'] 6 | -------------------------------------------------------------------------------- /configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/detectors/htc_r50_sac_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../htc/htc_r50_fpn_1x_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | type='DetectoRS_ResNet', 6 | conv_cfg=dict(type='ConvAWS'), 7 | sac=dict(type='SAC', use_deform=True), 8 | stage_with_sac=(False, True, True, True))) 9 | -------------------------------------------------------------------------------- /configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga_retinanet_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 8 | -------------------------------------------------------------------------------- /configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py' 2 | model = dict( 3 | roi_head=dict( 4 | mask_head=dict( 5 | predictor_cfg=dict(type='NormedConv2d', tempearture=20)))) 6 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 8 | -------------------------------------------------------------------------------- /mmdet/core/bbox/iou_calculators/builder.py: -------------------------------------------------------------------------------- 1 | from mmcv.utils import Registry, build_from_cfg 2 | 3 | IOU_CALCULATORS = Registry('IoU calculator') 4 | 5 | 6 | def build_iou_calculator(cfg, default_args=None): 7 | """Builder of IoU calculator.""" 8 | return build_from_cfg(cfg, IOU_CALCULATORS, default_args) 9 | -------------------------------------------------------------------------------- /configs/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | 7 | fp16 = dict(loss_scale=512.) 8 | -------------------------------------------------------------------------------- /configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga_rpn_r50_caffe_fpn_1x_coco.py' 2 | # model settings 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict( 7 | type='Pretrained', 8 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 9 | -------------------------------------------------------------------------------- /mmdet/core/utils/__init__.py: -------------------------------------------------------------------------------- 1 | from .dist_utils import DistOptimizerHook, allreduce_grads, reduce_mean 2 | from .misc import flip_tensor, mask2ndarray, multi_apply, unmap 3 | 4 | __all__ = [ 5 | 'allreduce_grads', 'DistOptimizerHook', 'reduce_mean', 'multi_apply', 6 | 'unmap', 'mask2ndarray', 'flip_tensor' 7 | ] 8 | -------------------------------------------------------------------------------- /configs/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | 7 | fp16 = dict(loss_scale=512.) 8 | -------------------------------------------------------------------------------- /configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_mstrain_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True)), 6 | bbox_head=dict(dcn_on_last_conv=True)) 7 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_r50_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/retinanet_r50_fpn.py', 3 | '../_base_/datasets/coco_detection.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | # optimizer 7 | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) 8 | -------------------------------------------------------------------------------- /configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict(plugins=[ 4 | dict( 5 | cfg=dict(type='ContextBlock', ratio=1. / 4), 6 | stages=(False, True, True, True), 7 | position='after_conv3') 8 | ])) 9 | -------------------------------------------------------------------------------- /configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' 2 | 3 | model = dict( 4 | bbox_head=dict( 5 | type='PISARetinaHead', 6 | loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)), 7 | train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2))) 8 | -------------------------------------------------------------------------------- /configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py' # noqa: E501 2 | model = dict( 3 | roi_head=dict( 4 | mask_head=dict( 5 | predictor_cfg=dict(type='NormedConv2d', tempearture=20)))) 6 | -------------------------------------------------------------------------------- /configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict(plugins=[ 4 | dict( 5 | cfg=dict(type='ContextBlock', ratio=1. / 16), 6 | stages=(False, True, True, True), 7 | position='after_conv3') 8 | ])) 9 | -------------------------------------------------------------------------------- /configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict(plugins=[ 4 | dict( 5 | cfg=dict(type='ContextBlock', ratio=1. / 4), 6 | stages=(False, True, True, True), 7 | position='after_conv3') 8 | ])) 9 | -------------------------------------------------------------------------------- /configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict(plugins=[ 4 | dict( 5 | cfg=dict(type='ContextBlock', ratio=1. / 16), 6 | stages=(False, True, True, True), 7 | position='after_conv3') 8 | ])) 9 | -------------------------------------------------------------------------------- /configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../retinanet/retinanet_x101_32x4d_fpn_1x_coco.py' 2 | 3 | model = dict( 4 | bbox_head=dict( 5 | type='PISARetinaHead', 6 | loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)), 7 | train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2))) 8 | -------------------------------------------------------------------------------- /configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py' # noqa: E501 2 | model = dict( 3 | roi_head=dict( 4 | mask_head=dict( 5 | predictor_cfg=dict(type='NormedConv2d', tempearture=20)))) 6 | -------------------------------------------------------------------------------- /configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/vfnet/vfnet_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | lr_config = dict(step=[16, 22]) 8 | runner = dict(type='EpochBasedRunner', max_epochs=24) 9 | -------------------------------------------------------------------------------- /configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | stem_channels=128, 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='open-mmlab://resnest101'))) 8 | -------------------------------------------------------------------------------- /mmdet/core/mask/__init__.py: -------------------------------------------------------------------------------- 1 | from .mask_target import mask_target 2 | from .structures import BaseInstanceMasks, BitmapMasks, PolygonMasks 3 | from .utils import encode_mask_results, split_combined_polys 4 | 5 | __all__ = [ 6 | 'split_combined_polys', 'mask_target', 'BaseInstanceMasks', 'BitmapMasks', 7 | 'PolygonMasks', 'encode_mask_results' 8 | ] 9 | -------------------------------------------------------------------------------- /mmdet/models/detectors/scnet.py: -------------------------------------------------------------------------------- 1 | from ..builder import DETECTORS 2 | from .cascade_rcnn import CascadeRCNN 3 | 4 | 5 | @DETECTORS.register_module() 6 | class SCNet(CascadeRCNN): 7 | """Implementation of `SCNet `_""" 8 | 9 | def __init__(self, **kwargs): 10 | super(SCNet, self).__init__(**kwargs) 11 | -------------------------------------------------------------------------------- /configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fast_rcnn_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://detectron2/resnet101_caffe', 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict( 7 | type='Pretrained', 8 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 9 | -------------------------------------------------------------------------------- /configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py' # noqa: E501 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | stem_channels=128, 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='open-mmlab://resnest101'))) 8 | -------------------------------------------------------------------------------- /configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | stem_channels=128, 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='open-mmlab://resnest101'))) 8 | -------------------------------------------------------------------------------- /configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | stem_channels=128, 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='open-mmlab://resnest101'))) 8 | -------------------------------------------------------------------------------- /configs/fast_rcnn/README.md: -------------------------------------------------------------------------------- 1 | # Fast R-CNN 2 | 3 | ## Introduction 4 | 5 | 6 | 7 | ```latex 8 | @inproceedings{girshick2015fast, 9 | title={Fast r-cnn}, 10 | author={Girshick, Ross}, 11 | booktitle={Proceedings of the IEEE international conference on computer vision}, 12 | year={2015} 13 | } 14 | ``` 15 | 16 | ## Results and models 17 | -------------------------------------------------------------------------------- /configs/htc/htc_r101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './htc_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | # learning policy 8 | lr_config = dict(step=[16, 19]) 9 | runner = dict(type='EpochBasedRunner', max_epochs=20) 10 | -------------------------------------------------------------------------------- /configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py' # noqa: E501 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /mmdet/core/post_processing/__init__.py: -------------------------------------------------------------------------------- 1 | from .bbox_nms import fast_nms, multiclass_nms 2 | from .merge_augs import (merge_aug_bboxes, merge_aug_masks, 3 | merge_aug_proposals, merge_aug_scores) 4 | 5 | __all__ = [ 6 | 'multiclass_nms', 'merge_aug_proposals', 'merge_aug_bboxes', 7 | 'merge_aug_scores', 'merge_aug_masks', 'fast_nms' 8 | ] 9 | -------------------------------------------------------------------------------- /configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='Res2Net', 5 | depth=101, 6 | scales=4, 7 | base_width=26, 8 | init_cfg=dict( 9 | type='Pretrained', 10 | checkpoint='open-mmlab://res2net101_v1d_26w_4s'))) 11 | -------------------------------------------------------------------------------- /configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='Res2Net', 5 | depth=101, 6 | scales=4, 7 | base_width=26, 8 | init_cfg=dict( 9 | type='Pretrained', 10 | checkpoint='open-mmlab://res2net101_v1d_26w_4s'))) 11 | -------------------------------------------------------------------------------- /configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='Res2Net', 5 | depth=101, 6 | scales=4, 7 | base_width=26, 8 | init_cfg=dict( 9 | type='Pretrained', 10 | checkpoint='open-mmlab://res2net101_v1d_26w_4s'))) 11 | -------------------------------------------------------------------------------- /mmdet/datasets/deepfashion.py: -------------------------------------------------------------------------------- 1 | from .builder import DATASETS 2 | from .coco import CocoDataset 3 | 4 | 5 | @DATASETS.register_module() 6 | class DeepFashionDataset(CocoDataset): 7 | 8 | CLASSES = ('top', 'skirt', 'leggings', 'dress', 'outer', 'pants', 'bag', 9 | 'neckwear', 'headwear', 'eyeglass', 'belt', 'footwear', 'hair', 10 | 'skin', 'face') 11 | -------------------------------------------------------------------------------- /configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True), 6 | init_cfg=dict( 7 | type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf'))) 8 | -------------------------------------------------------------------------------- /configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='Res2Net', 5 | depth=101, 6 | scales=4, 7 | base_width=26, 8 | init_cfg=dict( 9 | type='Pretrained', 10 | checkpoint='open-mmlab://res2net101_v1d_26w_4s'))) 11 | -------------------------------------------------------------------------------- /configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = ['grid_rcnn_r50_fpn_gn-head_2x_coco.py'] 2 | # learning policy 3 | lr_config = dict( 4 | policy='step', 5 | warmup='linear', 6 | warmup_iters=500, 7 | warmup_ratio=0.001, 8 | step=[8, 11]) 9 | checkpoint_config = dict(interval=1) 10 | # runtime settings 11 | runner = dict(type='EpochBasedRunner', max_epochs=12) 12 | -------------------------------------------------------------------------------- /configs/yolact/yolact_r50_8x8_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'yolact_r50_1x8_coco.py' 2 | 3 | optimizer = dict(type='SGD', lr=8e-3, momentum=0.9, weight_decay=5e-4) 4 | optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) 5 | # learning policy 6 | lr_config = dict( 7 | policy='step', 8 | warmup='linear', 9 | warmup_iters=1000, 10 | warmup_ratio=0.1, 11 | step=[20, 42, 49, 52]) 12 | -------------------------------------------------------------------------------- /configs/_base_/schedules/schedule_1x.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) 3 | optimizer_config = dict(grad_clip=None) 4 | # learning policy 5 | lr_config = dict( 6 | policy='step', 7 | warmup='linear', 8 | warmup_iters=500, 9 | warmup_ratio=0.001, 10 | step=[8, 11]) 11 | runner = dict(type='EpochBasedRunner', max_epochs=12) 12 | -------------------------------------------------------------------------------- /configs/_base_/schedules/schedule_20e.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) 3 | optimizer_config = dict(grad_clip=None) 4 | # learning policy 5 | lr_config = dict( 6 | policy='step', 7 | warmup='linear', 8 | warmup_iters=500, 9 | warmup_ratio=0.001, 10 | step=[16, 19]) 11 | runner = dict(type='EpochBasedRunner', max_epochs=20) 12 | -------------------------------------------------------------------------------- /configs/_base_/schedules/schedule_2x.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) 3 | optimizer_config = dict(grad_clip=None) 4 | # learning policy 5 | lr_config = dict( 6 | policy='step', 7 | warmup='linear', 8 | warmup_iters=500, 9 | warmup_ratio=0.001, 10 | step=[16, 22]) 11 | runner = dict(type='EpochBasedRunner', max_epochs=24) 12 | -------------------------------------------------------------------------------- /configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'faster_rcnn_r50_fpg_crop640_50e_coco.py' 2 | 3 | norm_cfg = dict(type='BN', requires_grad=True) 4 | model = dict( 5 | neck=dict(out_channels=128, inter_channels=128), 6 | rpn_head=dict(in_channels=128), 7 | roi_head=dict( 8 | bbox_roi_extractor=dict(out_channels=128), 9 | bbox_head=dict(in_channels=128))) 10 | -------------------------------------------------------------------------------- /mmdet/core/bbox/assigners/base_assigner.py: -------------------------------------------------------------------------------- 1 | from abc import ABCMeta, abstractmethod 2 | 3 | 4 | class BaseAssigner(metaclass=ABCMeta): 5 | """Base assigner that assigns boxes to ground truth boxes.""" 6 | 7 | @abstractmethod 8 | def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None): 9 | """Assign boxes to either a ground truth boxes or a negative boxes.""" 10 | -------------------------------------------------------------------------------- /configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 6 | stage_with_dcn=(False, True, True, True), 7 | init_cfg=dict(type='Pretrained', 8 | checkpoint='torchvision://resnet101'))) 9 | -------------------------------------------------------------------------------- /configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/mask_rcnn_r50_fpn.py', 3 | '../_base_/datasets/deepfashion.py', '../_base_/schedules/schedule_1x.py', 4 | '../_base_/default_runtime.py' 5 | ] 6 | model = dict( 7 | roi_head=dict( 8 | bbox_head=dict(num_classes=15), mask_head=dict(num_classes=15))) 9 | # runtime settings 10 | runner = dict(type='EpochBasedRunner', max_epochs=15) 11 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/faster_rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_detection.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | 7 | model = dict( 8 | test_cfg=dict( 9 | rcnn=dict( 10 | score_thr=0.05, 11 | nms=dict(type='soft_nms', iou_threshold=0.5), 12 | max_per_img=100))) 13 | -------------------------------------------------------------------------------- /configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'mask_rcnn_r50_fpg_crop640_50e_coco.py' 2 | 3 | model = dict( 4 | neck=dict(out_channels=128, inter_channels=128), 5 | rpn_head=dict(in_channels=128), 6 | roi_head=dict( 7 | bbox_roi_extractor=dict(out_channels=128), 8 | bbox_head=dict(in_channels=128), 9 | mask_roi_extractor=dict(out_channels=128), 10 | mask_head=dict(in_channels=128))) 11 | -------------------------------------------------------------------------------- /configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fovea_r50_fpn_4x4_1x_coco.py' 2 | model = dict( 3 | bbox_head=dict( 4 | with_deform=True, 5 | norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))) 6 | # learning policy 7 | lr_config = dict(step=[16, 22]) 8 | runner = dict(type='EpochBasedRunner', max_epochs=24) 9 | optimizer_config = dict( 10 | _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) 11 | -------------------------------------------------------------------------------- /configs/res2net/htc_r2_101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../htc/htc_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='Res2Net', 5 | depth=101, 6 | scales=4, 7 | base_width=26, 8 | init_cfg=dict( 9 | type='Pretrained', 10 | checkpoint='open-mmlab://res2net101_v1d_26w_4s'))) 11 | # learning policy 12 | lr_config = dict(step=[16, 19]) 13 | runner = dict(type='EpochBasedRunner', max_epochs=20) 14 | -------------------------------------------------------------------------------- /configs/detectors/cascade_rcnn_r50_sac_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/cascade_rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_detection.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | 7 | model = dict( 8 | backbone=dict( 9 | type='DetectoRS_ResNet', 10 | conv_cfg=dict(type='ConvAWS'), 11 | sac=dict(type='SAC', use_deform=True), 12 | stage_with_sac=(False, True, True, True))) 13 | -------------------------------------------------------------------------------- /configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), 5 | norm_eval=False, 6 | plugins=[ 7 | dict( 8 | cfg=dict(type='ContextBlock', ratio=1. / 4), 9 | stages=(False, True, True, True), 10 | position='after_conv3') 11 | ])) 12 | -------------------------------------------------------------------------------- /configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), 5 | norm_eval=False, 6 | plugins=[ 7 | dict( 8 | cfg=dict(type='ContextBlock', ratio=1. / 16), 9 | stages=(False, True, True, True), 10 | position='after_conv3') 11 | ])) 12 | -------------------------------------------------------------------------------- /configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), 5 | norm_eval=False, 6 | plugins=[ 7 | dict( 8 | cfg=dict(type='ContextBlock', ratio=1. / 4), 9 | stages=(False, True, True, True), 10 | position='after_conv3') 11 | ])) 12 | -------------------------------------------------------------------------------- /configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), 5 | norm_eval=False, 6 | plugins=[ 7 | dict( 8 | cfg=dict(type='ContextBlock', ratio=1. / 16), 9 | stages=(False, True, True, True), 10 | position='after_conv3') 11 | ])) 12 | -------------------------------------------------------------------------------- /configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | style='pytorch', 12 | init_cfg=dict( 13 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 14 | -------------------------------------------------------------------------------- /configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | style='pytorch', 12 | init_cfg=dict( 13 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 14 | -------------------------------------------------------------------------------- /configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), 5 | norm_eval=False, 6 | plugins=[ 7 | dict( 8 | cfg=dict(type='ContextBlock', ratio=1. / 16), 9 | stages=(False, True, True, True), 10 | position='after_conv3') 11 | ])) 12 | -------------------------------------------------------------------------------- /configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), 5 | norm_eval=False, 6 | plugins=[ 7 | dict( 8 | cfg=dict(type='ContextBlock', ratio=1. / 4), 9 | stages=(False, True, True, True), 10 | position='after_conv3') 11 | ])) 12 | -------------------------------------------------------------------------------- /configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | roi_head=dict( 4 | bbox_roi_extractor=dict( 5 | type='SingleRoIExtractor', 6 | roi_layer=dict( 7 | _delete_=True, 8 | type='DeformRoIPoolPack', 9 | output_size=7, 10 | output_channels=256), 11 | out_channels=256, 12 | featmap_strides=[4, 8, 16, 32]))) 13 | -------------------------------------------------------------------------------- /configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './gfl_r50_fpn_mstrain_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNet', 5 | depth=101, 6 | num_stages=4, 7 | out_indices=(0, 1, 2, 3), 8 | frozen_stages=1, 9 | norm_cfg=dict(type='BN', requires_grad=True), 10 | norm_eval=True, 11 | style='pytorch', 12 | init_cfg=dict(type='Pretrained', 13 | checkpoint='torchvision://resnet101'))) 14 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' 2 | 3 | # learning policy 4 | lr_config = dict( 5 | policy='step', 6 | warmup='linear', 7 | warmup_iters=500, 8 | warmup_ratio=0.001, 9 | step=[60000, 80000]) 10 | 11 | # Runner type 12 | runner = dict(_delete_=True, type='IterBasedRunner', max_iters=90000) 13 | 14 | checkpoint_config = dict(interval=10000) 15 | evaluation = dict(interval=10000, metric='bbox') 16 | -------------------------------------------------------------------------------- /configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | roi_head=dict( 4 | bbox_roi_extractor=dict( 5 | type='SingleRoIExtractor', 6 | roi_layer=dict( 7 | _delete_=True, 8 | type='ModulatedDeformRoIPoolPack', 9 | output_size=7, 10 | output_channels=256), 11 | out_channels=256, 12 | featmap_strides=[4, 8, 16, 32]))) 13 | -------------------------------------------------------------------------------- /configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fsaf_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), 5 | norm_eval=False, 6 | plugins=[ 7 | dict( 8 | cfg=dict(type='ContextBlock', ratio=1. / 4), 9 | stages=(False, True, True, True), 10 | position='after_conv3') 11 | ])) 12 | -------------------------------------------------------------------------------- /configs/hrnet/htc_hrnetv2p_w18_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './htc_hrnetv2p_w32_20e_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | extra=dict( 5 | stage2=dict(num_channels=(18, 36)), 6 | stage3=dict(num_channels=(18, 36, 72)), 7 | stage4=dict(num_channels=(18, 36, 72, 144))), 8 | init_cfg=dict( 9 | type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), 10 | neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) 11 | -------------------------------------------------------------------------------- /configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), 5 | norm_eval=False, 6 | plugins=[ 7 | dict( 8 | cfg=dict(type='ContextBlock', ratio=1. / 16), 9 | stages=(False, True, True, True), 10 | position='after_conv3') 11 | ])) 12 | -------------------------------------------------------------------------------- /configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fovea_r50_fpn_4x4_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101')), 7 | bbox_head=dict( 8 | with_deform=True, 9 | norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))) 10 | # learning policy 11 | lr_config = dict(step=[16, 22]) 12 | runner = dict(type='EpochBasedRunner', max_epochs=24) 13 | -------------------------------------------------------------------------------- /configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_hrnetv2p_w32_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | extra=dict( 5 | stage2=dict(num_channels=(18, 36)), 6 | stage3=dict(num_channels=(18, 36, 72)), 7 | stage4=dict(num_channels=(18, 36, 72, 144))), 8 | init_cfg=dict( 9 | type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), 10 | neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) 11 | -------------------------------------------------------------------------------- /configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ms_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ms_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict(plugins=[ 4 | dict( 5 | cfg=dict( 6 | type='GeneralizedAttention', 7 | spatial_range=-1, 8 | num_heads=8, 9 | attention_type='0010', 10 | kv_stride=2), 11 | stages=(False, False, True, True), 12 | position='after_conv2') 13 | ])) 14 | -------------------------------------------------------------------------------- /configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict(plugins=[ 4 | dict( 5 | cfg=dict( 6 | type='GeneralizedAttention', 7 | spatial_range=-1, 8 | num_heads=8, 9 | attention_type='1111', 10 | kv_stride=2), 11 | stages=(False, False, True, True), 12 | position='after_conv2') 13 | ])) 14 | -------------------------------------------------------------------------------- /configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), 5 | norm_eval=False, 6 | plugins=[ 7 | dict( 8 | cfg=dict(type='ContextBlock', ratio=1. / 4), 9 | stages=(False, True, True, True), 10 | position='after_conv3') 11 | ])) 12 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /mmdet/core/export/__init__.py: -------------------------------------------------------------------------------- 1 | from .onnx_helper import (add_dummy_nms_for_onnx, dynamic_clip_for_onnx, 2 | get_k_for_topk) 3 | from .pytorch2onnx import (build_model_from_cfg, 4 | generate_inputs_and_wrap_model, 5 | preprocess_example_input) 6 | 7 | __all__ = [ 8 | 'build_model_from_cfg', 'generate_inputs_and_wrap_model', 9 | 'preprocess_example_input', 'get_k_for_topk', 'add_dummy_nms_for_onnx', 10 | 'dynamic_clip_for_onnx' 11 | ] 12 | -------------------------------------------------------------------------------- /configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), 5 | norm_eval=False, 6 | plugins=[ 7 | dict( 8 | cfg=dict(type='ContextBlock', ratio=1. / 16), 9 | stages=(False, True, True, True), 10 | position='after_conv3') 11 | ])) 12 | -------------------------------------------------------------------------------- /configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_ghm_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_ghm_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga_rpn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga_rpn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | extra=dict( 5 | stage2=dict(num_channels=(18, 36)), 6 | stage3=dict(num_channels=(18, 36, 72)), 7 | stage4=dict(num_channels=(18, 36, 72, 144))), 8 | init_cfg=dict( 9 | type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), 10 | neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) 11 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r101_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga_faster_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga_faster_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_x101_32x4d_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_x101_32x4d_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/mask_rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_instance.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | 7 | model = dict( 8 | backbone=dict( 9 | frozen_stages=0, 10 | norm_cfg=dict(type='SyncBN', requires_grad=True), 11 | norm_eval=False, 12 | init_cfg=dict( 13 | type='Pretrained', checkpoint='./mocov2_r50_800ep_pretrain.pth'))) 14 | --------------------------------------------------------------------------------