├── .gitignore ├── LICENSE ├── README.md ├── TOV_mmdetection ├── .gitignore ├── .pre-commit-config.yaml ├── .readthedocs.yml ├── LICENSE ├── MANIFEST.in ├── README.md ├── README_old.md ├── README_zh-CN_old.md ├── configs │ ├── _base_ │ │ ├── datasets │ │ │ ├── cityscapes_detection.py │ │ │ ├── cityscapes_instance.py │ │ │ ├── coco_detection.py │ │ │ ├── coco_instance.py │ │ │ ├── coco_instance_semantic.py │ │ │ ├── deepfashion.py │ │ │ ├── lvis_v0.5_instance.py │ │ │ ├── lvis_v1_instance.py │ │ │ ├── voc0712.py │ │ │ └── wider_face.py │ │ ├── default_runtime.py │ │ ├── models │ │ │ ├── cascade_mask_rcnn_r50_fpn.py │ │ │ ├── cascade_rcnn_r50_fpn.py │ │ │ ├── fast_rcnn_r50_fpn.py │ │ │ ├── faster_rcnn_r50_caffe_c4.py │ │ │ ├── faster_rcnn_r50_caffe_dc5.py │ │ │ ├── faster_rcnn_r50_fpn.py │ │ │ ├── mask_rcnn_r50_caffe_c4.py │ │ │ ├── mask_rcnn_r50_fpn.py │ │ │ ├── retinanet_r50_fpn.py │ │ │ ├── rpn_r50_caffe_c4.py │ │ │ ├── rpn_r50_fpn.py │ │ │ └── ssd300.py │ │ └── schedules │ │ │ ├── schedule_1x.py │ │ │ ├── schedule_20e.py │ │ │ └── schedule_2x.py │ ├── albu_example │ │ ├── README.md │ │ └── mask_rcnn_r50_fpn_albu_1x_coco.py │ ├── atss │ │ ├── README.md │ │ ├── atss_r101_fpn_1x_coco.py │ │ ├── atss_r50_fpn_1x_coco.py │ │ └── metafile.yml │ ├── autoassign │ │ ├── README.md │ │ ├── autoassign_r50_fpn_8x2_1x_coco.py │ │ └── metafile.yml │ ├── carafe │ │ ├── README.md │ │ ├── faster_rcnn_r50_fpn_carafe_1x_coco.py │ │ └── mask_rcnn_r50_fpn_carafe_1x_coco.py │ ├── cascade_rcnn │ │ ├── README.md │ │ ├── cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py │ │ ├── cascade_mask_rcnn_r101_fpn_1x_coco.py │ │ ├── cascade_mask_rcnn_r101_fpn_20e_coco.py │ │ ├── cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py │ │ ├── cascade_mask_rcnn_r50_fpn_1x_coco.py │ │ ├── cascade_mask_rcnn_r50_fpn_20e_coco.py │ │ ├── cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py │ │ ├── cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py │ │ ├── cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py │ │ ├── cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py │ │ ├── cascade_rcnn_r101_caffe_fpn_1x_coco.py │ │ ├── cascade_rcnn_r101_fpn_1x_coco.py │ │ ├── cascade_rcnn_r101_fpn_20e_coco.py │ │ ├── cascade_rcnn_r50_caffe_fpn_1x_coco.py │ │ ├── cascade_rcnn_r50_fpn_1x_coco.py │ │ ├── cascade_rcnn_r50_fpn_20e_coco.py │ │ ├── cascade_rcnn_x101_32x4d_fpn_1x_coco.py │ │ ├── cascade_rcnn_x101_32x4d_fpn_20e_coco.py │ │ ├── cascade_rcnn_x101_64x4d_fpn_1x_coco.py │ │ ├── cascade_rcnn_x101_64x4d_fpn_20e_coco.py │ │ └── metafile.yml │ ├── cascade_rpn │ │ ├── README.md │ │ ├── crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py │ │ ├── crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py │ │ └── crpn_r50_caffe_fpn_1x_coco.py │ ├── centernet │ │ ├── README.md │ │ ├── centernet_resnet18_140e_coco.py │ │ ├── centernet_resnet18_dcnv2_140e_coco.py │ │ └── metafile.yml │ ├── centripetalnet │ │ ├── README.md │ │ ├── centripetalnet_hourglass104_mstest_16x6_210e_coco.py │ │ └── metafile.yml │ ├── cityscapes │ │ ├── README.md │ │ ├── faster_rcnn_r50_fpn_1x_cityscapes.py │ │ └── mask_rcnn_r50_fpn_1x_cityscapes.py │ ├── cornernet │ │ ├── README.md │ │ ├── cornernet_hourglass104_mstest_10x5_210e_coco.py │ │ ├── cornernet_hourglass104_mstest_32x3_210e_coco.py │ │ ├── cornernet_hourglass104_mstest_8x6_210e_coco.py │ │ └── metafile.yml │ ├── dcn │ │ ├── README.md │ │ ├── cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py │ │ ├── cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py │ │ ├── cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py │ │ ├── cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py │ │ ├── cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py │ │ ├── faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_dpool_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_mdpool_1x_coco.py │ │ ├── faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py │ │ ├── mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py │ │ ├── mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py │ │ ├── mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py │ │ └── metafile.yml │ ├── deepfashion │ │ ├── README.md │ │ └── mask_rcnn_r50_fpn_15e_deepfashion.py │ ├── deformable_detr │ │ ├── README.md │ │ ├── deformable_detr_r50_16x2_50e_coco.py │ │ ├── deformable_detr_refine_r50_16x2_50e_coco.py │ │ ├── deformable_detr_twostage_refine_r50_16x2_50e_coco.py │ │ └── metafile.yml │ ├── detectors │ │ ├── README.md │ │ ├── cascade_rcnn_r50_rfp_1x_coco.py │ │ ├── cascade_rcnn_r50_sac_1x_coco.py │ │ ├── detectors_cascade_rcnn_r50_1x_coco.py │ │ ├── detectors_htc_r101_20e_coco.py │ │ ├── detectors_htc_r50_1x_coco.py │ │ ├── htc_r50_rfp_1x_coco.py │ │ ├── htc_r50_sac_1x_coco.py │ │ └── metafile.yml │ ├── detr │ │ ├── README.md │ │ ├── detr_r50_8x2_150e_coco.py │ │ └── metafile.yml │ ├── double_heads │ │ ├── README.md │ │ ├── dh_faster_rcnn_r50_fpn_1x_coco.py │ │ └── metafile.yml │ ├── dynamic_rcnn │ │ ├── README.md │ │ ├── dynamic_rcnn_r50_fpn_1x_coco.py │ │ └── metafile.yml │ ├── empirical_attention │ │ ├── README.md │ │ ├── faster_rcnn_r50_fpn_attention_0010_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_attention_1111_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py │ │ └── metafile.yml │ ├── fast_rcnn │ │ ├── README.md │ │ ├── fast_rcnn_r101_caffe_fpn_1x_coco.py │ │ ├── fast_rcnn_r101_fpn_1x_coco.py │ │ ├── fast_rcnn_r101_fpn_2x_coco.py │ │ ├── fast_rcnn_r50_caffe_fpn_1x_coco.py │ │ ├── fast_rcnn_r50_fpn_1x_coco.py │ │ └── fast_rcnn_r50_fpn_2x_coco.py │ ├── faster_rcnn │ │ ├── README.md │ │ ├── faster_rcnn_r101_caffe_fpn_1x_coco.py │ │ ├── faster_rcnn_r101_fpn_1x_coco.py │ │ ├── faster_rcnn_r101_fpn_2x_coco.py │ │ ├── faster_rcnn_r50_caffe_c4_1x_coco.py │ │ ├── faster_rcnn_r50_caffe_dc5_1x_coco.py │ │ ├── faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py │ │ ├── faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py │ │ ├── faster_rcnn_r50_caffe_fpn_1x_coco.py │ │ ├── faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py │ │ ├── faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py │ │ ├── faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py │ │ ├── faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py │ │ ├── faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py │ │ ├── faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py │ │ ├── faster_rcnn_r50_fpn_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_2x_coco.py │ │ ├── faster_rcnn_r50_fpn_bounded_iou_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_giou_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_iou_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_ohem_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_soft_nms_1x_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 │ │ └── metafile.yml │ ├── fcos │ │ ├── README.md │ │ ├── fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py │ │ ├── fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py │ │ ├── fcos_center_r50_caffe_fpn_gn-head_1x_coco.py │ │ ├── fcos_r101_caffe_fpn_gn-head_1x_coco.py │ │ ├── fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py │ │ ├── fcos_r50_caffe_fpn_gn-head_1x_coco.py │ │ ├── fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py │ │ ├── fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py │ │ ├── fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py │ │ └── metafile.yml │ ├── foveabox │ │ ├── README.md │ │ ├── fovea_align_r101_fpn_gn-head_4x4_2x_coco.py │ │ ├── fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py │ │ ├── fovea_align_r50_fpn_gn-head_4x4_2x_coco.py │ │ ├── fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py │ │ ├── fovea_r101_fpn_4x4_1x_coco.py │ │ ├── fovea_r101_fpn_4x4_2x_coco.py │ │ ├── fovea_r50_fpn_4x4_1x_coco.py │ │ ├── fovea_r50_fpn_4x4_2x_coco.py │ │ └── metafile.yml │ ├── fp16 │ │ ├── README.md │ │ ├── faster_rcnn_r50_fpn_fp16_1x_coco.py │ │ ├── mask_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 │ │ ├── metafile.yml │ │ └── retinanet_r50_fpn_fp16_1x_coco.py │ ├── fpg │ │ ├── README.md │ │ ├── faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py │ │ ├── faster_rcnn_r50_fpg_crop640_50e_coco.py │ │ ├── faster_rcnn_r50_fpn_crop640_50e_coco.py │ │ ├── mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py │ │ ├── mask_rcnn_r50_fpg_crop640_50e_coco.py │ │ ├── mask_rcnn_r50_fpn_crop640_50e_coco.py │ │ ├── metafile.yml │ │ ├── retinanet_r50_fpg-chn128_crop640_50e_coco.py │ │ └── retinanet_r50_fpg_crop640_50e_coco.py │ ├── free_anchor │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── retinanet_free_anchor_r101_fpn_1x_coco.py │ │ ├── retinanet_free_anchor_r50_fpn_1x_coco.py │ │ └── retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py │ ├── fsaf │ │ ├── README.md │ │ ├── fsaf_r101_fpn_1x_coco.py │ │ ├── fsaf_r50_fpn_1x_coco.py │ │ ├── fsaf_x101_64x4d_fpn_1x_coco.py │ │ └── metafile.yml │ ├── gcnet │ │ ├── README.md │ │ ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py │ │ ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py │ │ ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py │ │ ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py │ │ ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py │ │ ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py │ │ ├── mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py │ │ ├── mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py │ │ ├── mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py │ │ ├── mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py │ │ ├── mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py │ │ ├── mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py │ │ ├── mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py │ │ ├── mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py │ │ ├── mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py │ │ ├── mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py │ │ ├── mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py │ │ ├── mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py │ │ ├── mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py │ │ └── metafile.yml │ ├── gfl │ │ ├── README.md │ │ ├── gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py │ │ ├── gfl_r101_fpn_mstrain_2x_coco.py │ │ ├── gfl_r50_fpn_1x_coco.py │ │ ├── gfl_r50_fpn_mstrain_2x_coco.py │ │ ├── gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py │ │ ├── gfl_x101_32x4d_fpn_mstrain_2x_coco.py │ │ └── metafile.yml │ ├── ghm │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── retinanet_ghm_r101_fpn_1x_coco.py │ │ ├── retinanet_ghm_r50_fpn_1x_coco.py │ │ ├── retinanet_ghm_x101_32x4d_fpn_1x_coco.py │ │ └── retinanet_ghm_x101_64x4d_fpn_1x_coco.py │ ├── gn+ws │ │ ├── README.md │ │ ├── faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py │ │ ├── faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py │ │ ├── faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py │ │ ├── mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py │ │ ├── mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py │ │ ├── mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py │ │ ├── mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py │ │ ├── mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py │ │ ├── mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py │ │ ├── mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py │ │ ├── mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py │ │ └── metafile.yml │ ├── gn │ │ ├── README.md │ │ ├── mask_rcnn_r101_fpn_gn-all_2x_coco.py │ │ ├── mask_rcnn_r101_fpn_gn-all_3x_coco.py │ │ ├── mask_rcnn_r50_fpn_gn-all_2x_coco.py │ │ ├── mask_rcnn_r50_fpn_gn-all_3x_coco.py │ │ ├── mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py │ │ ├── mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py │ │ └── metafile.yml │ ├── grid_rcnn │ │ ├── README.md │ │ ├── grid_rcnn_r101_fpn_gn-head_2x_coco.py │ │ ├── grid_rcnn_r50_fpn_gn-head_1x_coco.py │ │ ├── grid_rcnn_r50_fpn_gn-head_2x_coco.py │ │ ├── grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py │ │ ├── grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py │ │ └── metafile.yml │ ├── groie │ │ ├── README.md │ │ ├── faster_rcnn_r50_fpn_groie_1x_coco.py │ │ ├── grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py │ │ ├── mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py │ │ ├── mask_rcnn_r50_fpn_groie_1x_coco.py │ │ ├── mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py │ │ └── metafile.yml │ ├── guided_anchoring │ │ ├── README.md │ │ ├── ga_fast_r50_caffe_fpn_1x_coco.py │ │ ├── ga_faster_r101_caffe_fpn_1x_coco.py │ │ ├── ga_faster_r50_caffe_fpn_1x_coco.py │ │ ├── ga_faster_r50_fpn_1x_coco.py │ │ ├── ga_faster_x101_32x4d_fpn_1x_coco.py │ │ ├── ga_faster_x101_64x4d_fpn_1x_coco.py │ │ ├── ga_retinanet_r101_caffe_fpn_1x_coco.py │ │ ├── ga_retinanet_r101_caffe_fpn_mstrain_2x.py │ │ ├── ga_retinanet_r50_caffe_fpn_1x_coco.py │ │ ├── ga_retinanet_r50_fpn_1x_coco.py │ │ ├── ga_retinanet_x101_32x4d_fpn_1x_coco.py │ │ ├── ga_retinanet_x101_64x4d_fpn_1x_coco.py │ │ ├── ga_rpn_r101_caffe_fpn_1x_coco.py │ │ ├── ga_rpn_r50_caffe_fpn_1x_coco.py │ │ ├── ga_rpn_r50_fpn_1x_coco.py │ │ ├── ga_rpn_x101_32x4d_fpn_1x_coco.py │ │ ├── ga_rpn_x101_64x4d_fpn_1x_coco.py │ │ └── metafile.yml │ ├── hrnet │ │ ├── README.md │ │ ├── cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py │ │ ├── cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py │ │ ├── cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py │ │ ├── cascade_rcnn_hrnetv2p_w18_20e_coco.py │ │ ├── cascade_rcnn_hrnetv2p_w32_20e_coco.py │ │ ├── cascade_rcnn_hrnetv2p_w40_20e_coco.py │ │ ├── faster_rcnn_hrnetv2p_w18_1x_coco.py │ │ ├── faster_rcnn_hrnetv2p_w18_2x_coco.py │ │ ├── faster_rcnn_hrnetv2p_w32_1x_coco.py │ │ ├── faster_rcnn_hrnetv2p_w32_2x_coco.py │ │ ├── faster_rcnn_hrnetv2p_w40_1x_coco.py │ │ ├── faster_rcnn_hrnetv2p_w40_2x_coco.py │ │ ├── fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py │ │ ├── fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py │ │ ├── fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py │ │ ├── fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py │ │ ├── fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py │ │ ├── fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py │ │ ├── fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py │ │ ├── htc_hrnetv2p_w18_20e_coco.py │ │ ├── htc_hrnetv2p_w32_20e_coco.py │ │ ├── htc_hrnetv2p_w40_20e_coco.py │ │ ├── htc_hrnetv2p_w40_28e_coco.py │ │ ├── htc_x101_64x4d_fpn_16x1_28e_coco.py │ │ ├── mask_rcnn_hrnetv2p_w18_1x_coco.py │ │ ├── mask_rcnn_hrnetv2p_w18_2x_coco.py │ │ ├── mask_rcnn_hrnetv2p_w32_1x_coco.py │ │ ├── mask_rcnn_hrnetv2p_w32_2x_coco.py │ │ ├── mask_rcnn_hrnetv2p_w40_1x_coco.py │ │ ├── mask_rcnn_hrnetv2p_w40_2x_coco.py │ │ └── metafile.yml │ ├── htc │ │ ├── README.md │ │ ├── htc_r101_fpn_20e_coco.py │ │ ├── htc_r50_fpn_1x_coco.py │ │ ├── htc_r50_fpn_20e_coco.py │ │ ├── htc_without_semantic_r50_fpn_1x_coco.py │ │ ├── htc_x101_32x4d_fpn_16x1_20e_coco.py │ │ ├── htc_x101_64x4d_fpn_16x1_20e_coco.py │ │ ├── htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py │ │ └── metafile.yml │ ├── instaboost │ │ ├── README.md │ │ ├── cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py │ │ ├── cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py │ │ ├── cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py │ │ ├── mask_rcnn_r101_fpn_instaboost_4x_coco.py │ │ ├── mask_rcnn_r50_fpn_instaboost_4x_coco.py │ │ ├── mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py │ │ └── metafile.yml │ ├── ld │ │ ├── README.md │ │ ├── ld_r101_gflv1_r101dcn_fpn_coco_2x.py │ │ ├── ld_r18_gflv1_r101_fpn_coco_1x.py │ │ ├── ld_r34_gflv1_r101_fpn_coco_1x.py │ │ ├── ld_r50_gflv1_r101_fpn_coco_1x.py │ │ └── metafile.yml │ ├── legacy_1.x │ │ ├── README.md │ │ ├── cascade_mask_rcnn_r50_fpn_1x_coco_v1.py │ │ ├── faster_rcnn_r50_fpn_1x_coco_v1.py │ │ ├── mask_rcnn_r50_fpn_1x_coco_v1.py │ │ ├── retinanet_r50_caffe_fpn_1x_coco_v1.py │ │ ├── retinanet_r50_fpn_1x_coco_v1.py │ │ └── ssd300_coco_v1.py │ ├── libra_rcnn │ │ ├── README.md │ │ ├── libra_fast_rcnn_r50_fpn_1x_coco.py │ │ ├── libra_faster_rcnn_r101_fpn_1x_coco.py │ │ ├── libra_faster_rcnn_r50_fpn_1x_coco.py │ │ ├── libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py │ │ ├── libra_retinanet_r50_fpn_1x_coco.py │ │ └── metafile.yml │ ├── lvis │ │ ├── README.md │ │ ├── mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py │ │ ├── mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py │ │ ├── mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py │ │ ├── mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py │ │ ├── mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py │ │ ├── mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py │ │ ├── mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py │ │ └── mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py │ ├── mask_rcnn │ │ ├── README.md │ │ ├── mask_rcnn_r101_caffe_fpn_1x_coco.py │ │ ├── mask_rcnn_r101_fpn_1x_coco.py │ │ ├── mask_rcnn_r101_fpn_2x_coco.py │ │ ├── mask_rcnn_r50_caffe_c4_1x_coco.py │ │ ├── mask_rcnn_r50_caffe_fpn_1x_coco.py │ │ ├── mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py │ │ ├── mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py │ │ ├── mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py │ │ ├── mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py │ │ ├── mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py │ │ ├── mask_rcnn_r50_fpn_1x_coco.py │ │ ├── mask_rcnn_r50_fpn_2x_coco.py │ │ ├── mask_rcnn_r50_fpn_poly_1x_coco.py │ │ ├── mask_rcnn_x101_32x4d_fpn_1x_coco.py │ │ ├── mask_rcnn_x101_32x4d_fpn_2x_coco.py │ │ ├── mask_rcnn_x101_32x8d_fpn_1x_coco.py │ │ ├── mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py │ │ ├── mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py │ │ ├── mask_rcnn_x101_64x4d_fpn_1x_coco.py │ │ ├── mask_rcnn_x101_64x4d_fpn_2x_coco.py │ │ └── metafile.yml │ ├── ms_rcnn │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── ms_rcnn_r101_caffe_fpn_1x_coco.py │ │ ├── ms_rcnn_r101_caffe_fpn_2x_coco.py │ │ ├── ms_rcnn_r50_caffe_fpn_1x_coco.py │ │ ├── ms_rcnn_r50_caffe_fpn_2x_coco.py │ │ ├── ms_rcnn_r50_fpn_1x_coco.py │ │ ├── ms_rcnn_x101_32x4d_fpn_1x_coco.py │ │ ├── ms_rcnn_x101_64x4d_fpn_1x_coco.py │ │ └── ms_rcnn_x101_64x4d_fpn_2x_coco.py │ ├── nas_fcos │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py │ │ └── nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py │ ├── nas_fpn │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── retinanet_r50_fpn_crop640_50e_coco.py │ │ └── retinanet_r50_nasfpn_crop640_50e_coco.py │ ├── paa │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── paa_r101_fpn_1x_coco.py │ │ ├── paa_r101_fpn_2x_coco.py │ │ ├── paa_r101_fpn_mstrain_3x_coco.py │ │ ├── paa_r50_fpn_1.5x_coco.py │ │ ├── paa_r50_fpn_1x_coco.py │ │ ├── paa_r50_fpn_2x_coco.py │ │ └── paa_r50_fpn_mstrain_3x_coco.py │ ├── pafpn │ │ ├── README.md │ │ ├── faster_rcnn_r50_pafpn_1x_coco.py │ │ └── metafile.yml │ ├── pascal_voc │ │ ├── README.md │ │ ├── faster_rcnn_r50_fpn_1x_voc0712.py │ │ ├── faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py │ │ ├── retinanet_r50_fpn_1x_voc0712.py │ │ ├── ssd300_voc0712.py │ │ └── ssd512_voc0712.py │ ├── pisa │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── pisa_faster_rcnn_r50_fpn_1x_coco.py │ │ ├── pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py │ │ ├── pisa_mask_rcnn_r50_fpn_1x_coco.py │ │ ├── pisa_mask_rcnn_x101_32x4d_fpn_1x_coco.py │ │ ├── pisa_retinanet_r50_fpn_1x_coco.py │ │ ├── pisa_retinanet_x101_32x4d_fpn_1x_coco.py │ │ ├── pisa_ssd300_coco.py │ │ └── pisa_ssd512_coco.py │ ├── point_rend │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── point_rend_r50_caffe_fpn_mstrain_1x_coco.py │ │ └── point_rend_r50_caffe_fpn_mstrain_3x_coco.py │ ├── regnet │ │ ├── README.md │ │ ├── faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py │ │ ├── faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py │ │ ├── faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py │ │ ├── mask_rcnn_regnetx-12GF_fpn_1x_coco.py │ │ ├── mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py │ │ ├── mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py │ │ ├── mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py │ │ ├── mask_rcnn_regnetx-4GF_fpn_1x_coco.py │ │ ├── mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py │ │ ├── mask_rcnn_regnetx-8GF_fpn_1x_coco.py │ │ ├── metafile.yml │ │ ├── retinanet_regnetx-1.6GF_fpn_1x_coco.py │ │ ├── retinanet_regnetx-3.2GF_fpn_1x_coco.py │ │ └── retinanet_regnetx-800MF_fpn_1x_coco.py │ ├── reppoints │ │ ├── README.md │ │ ├── bbox_r50_grid_center_fpn_gn-neck+head_1x_coco.py │ │ ├── bbox_r50_grid_fpn_gn-neck+head_1x_coco.py │ │ ├── metafile.yml │ │ ├── reppoints.png │ │ ├── reppoints_minmax_r50_fpn_gn-neck+head_1x_coco.py │ │ ├── reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py │ │ ├── reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py │ │ ├── reppoints_moment_r50_fpn_1x_coco.py │ │ ├── reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py │ │ ├── reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py │ │ ├── reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py │ │ └── reppoints_partial_minmax_r50_fpn_gn-neck+head_1x_coco.py │ ├── res2net │ │ ├── README.md │ │ ├── cascade_mask_rcnn_r2_101_fpn_20e_coco.py │ │ ├── cascade_rcnn_r2_101_fpn_20e_coco.py │ │ ├── faster_rcnn_r2_101_fpn_2x_coco.py │ │ ├── htc_r2_101_fpn_20e_coco.py │ │ ├── mask_rcnn_r2_101_fpn_2x_coco.py │ │ └── metafile.yml │ ├── resnest │ │ ├── README.md │ │ ├── cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py │ │ ├── cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py │ │ ├── cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py │ │ ├── cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py │ │ ├── faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py │ │ ├── faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py │ │ ├── mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py │ │ ├── mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py │ │ └── metafile.yml │ ├── retinanet │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── retinanet_r101_caffe_fpn_1x_coco.py │ │ ├── retinanet_r101_fpn_1x_coco.py │ │ ├── retinanet_r101_fpn_2x_coco.py │ │ ├── retinanet_r50_caffe_fpn_1x_coco.py │ │ ├── retinanet_r50_caffe_fpn_mstrain_1x_coco.py │ │ ├── retinanet_r50_caffe_fpn_mstrain_2x_coco.py │ │ ├── retinanet_r50_caffe_fpn_mstrain_3x_coco.py │ │ ├── retinanet_r50_fpn_1x_coco.py │ │ ├── retinanet_r50_fpn_2x_coco.py │ │ ├── retinanet_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 │ ├── rpn │ │ ├── README.md │ │ ├── rpn_r101_caffe_fpn_1x_coco.py │ │ ├── rpn_r101_fpn_1x_coco.py │ │ ├── rpn_r101_fpn_2x_coco.py │ │ ├── rpn_r50_caffe_c4_1x_coco.py │ │ ├── rpn_r50_caffe_fpn_1x_coco.py │ │ ├── rpn_r50_fpn_1x_coco.py │ │ ├── rpn_r50_fpn_2x_coco.py │ │ ├── rpn_x101_32x4d_fpn_1x_coco.py │ │ ├── rpn_x101_32x4d_fpn_2x_coco.py │ │ ├── rpn_x101_64x4d_fpn_1x_coco.py │ │ └── rpn_x101_64x4d_fpn_2x_coco.py │ ├── sabl │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── sabl_cascade_rcnn_r101_fpn_1x_coco.py │ │ ├── sabl_cascade_rcnn_r50_fpn_1x_coco.py │ │ ├── sabl_faster_rcnn_r101_fpn_1x_coco.py │ │ ├── sabl_faster_rcnn_r50_fpn_1x_coco.py │ │ ├── sabl_retinanet_r101_fpn_1x_coco.py │ │ ├── sabl_retinanet_r101_fpn_gn_1x_coco.py │ │ ├── sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py │ │ ├── sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py │ │ ├── sabl_retinanet_r50_fpn_1x_coco.py │ │ └── sabl_retinanet_r50_fpn_gn_1x_coco.py │ ├── scnet │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── scnet_r101_fpn_20e_coco.py │ │ ├── scnet_r50_fpn_1x_coco.py │ │ ├── scnet_r50_fpn_20e_coco.py │ │ ├── scnet_x101_64x4d_fpn_20e_coco.py │ │ └── scnet_x101_64x4d_fpn_8x1_20e_coco.py │ ├── scratch │ │ ├── README.md │ │ ├── faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py │ │ ├── mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py │ │ └── metafile.yml │ ├── seesaw_loss │ │ ├── README.md │ │ ├── cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py │ │ ├── cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py │ │ ├── cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py │ │ ├── cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py │ │ ├── mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py │ │ ├── mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py │ │ ├── mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py │ │ ├── mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py │ │ ├── mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py │ │ ├── mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py │ │ ├── mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py │ │ └── mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py │ ├── sparse_rcnn │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py │ │ ├── sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py │ │ ├── sparse_rcnn_r50_fpn_1x_coco.py │ │ ├── sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py │ │ └── sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py │ ├── ssd │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── ssd300_coco.py │ │ └── ssd512_coco.py │ ├── tridentnet │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── tridentnet_r50_caffe_1x_coco.py │ │ ├── tridentnet_r50_caffe_mstrain_1x_coco.py │ │ └── tridentnet_r50_caffe_mstrain_3x_coco.py │ ├── vfnet │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── vfnet_r101_fpn_1x_coco.py │ │ ├── vfnet_r101_fpn_2x_coco.py │ │ ├── vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py │ │ ├── vfnet_r101_fpn_mstrain_2x_coco.py │ │ ├── vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py │ │ ├── vfnet_r2_101_fpn_mstrain_2x_coco.py │ │ ├── vfnet_r50_fpn_1x_coco.py │ │ ├── vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py │ │ ├── vfnet_r50_fpn_mstrain_2x_coco.py │ │ ├── vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py │ │ ├── vfnet_x101_32x4d_fpn_mstrain_2x_coco.py │ │ ├── vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py │ │ └── vfnet_x101_64x4d_fpn_mstrain_2x_coco.py │ ├── wider_face │ │ ├── README.md │ │ └── ssd300_wider_face.py │ ├── yolact │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── yolact_r101_1x8_coco.py │ │ ├── yolact_r50_1x8_coco.py │ │ └── yolact_r50_8x8_coco.py │ ├── yolo │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── yolov3_d53_320_273e_coco.py │ │ ├── yolov3_d53_fp16_mstrain-608_273e_coco.py │ │ ├── yolov3_d53_mstrain-416_273e_coco.py │ │ └── yolov3_d53_mstrain-608_273e_coco.py │ └── yolof │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── yolof_r50_c5_8x8_1x_coco.py │ │ └── yolof_r50_c5_8x8_iter-1x_coco.py ├── configs2 │ ├── COCO │ │ ├── base │ │ │ └── reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py │ │ ├── coarsepointv2 │ │ │ ├── coarse_point_refine_r101_fpn_1x_coco400.py │ │ │ ├── coarse_point_refine_r50_fpn_1x_coco400.py │ │ │ └── coarse_point_refine_r50_fpn_1x_coco400_dbg.py │ │ └── p2p │ │ │ ├── p2p_r101_fpn_1x_fl_sl1_coco400_coarse.py │ │ │ ├── p2p_r50_fpn_1x_fl_sl1_coco400_coarse.py │ │ │ └── p2p_r50_fpns4_1x_fl_sl1_coco.py │ ├── DOTA │ │ ├── coarsepointv2 │ │ │ ├── cascade_coarse_point_refine_r50_fpns4_1x_DOTA_1024.py │ │ │ ├── coarse_point_refine_r50_fpns4_1x_DOTA_1024.py │ │ │ └── coarse_point_refine_r50_fpns4_1x_DOTA_1024_debug.py │ │ └── p2p │ │ │ ├── p2p_r50_fpn_1x_fl_sl1_DOTA_center.py │ │ │ └── p2p_r50_fpn_1x_fl_sl1_DOTA_coarse.py │ ├── TinyPerson │ │ ├── TinyPerson.md │ │ ├── base │ │ │ ├── Baseline_TinyPerson.sh │ │ │ ├── faster_rcnn_r50_fpn_1x_TinyPerson640.py │ │ │ ├── fcos_r50_caffe_fpn_gn-head_1x_TinyPerson640.py │ │ │ ├── fcos_standard_r50_caffe_fpns4_gn-head_1x_TinyPerson640.py │ │ │ ├── reppoints_moment_r50_fpn_1x_TinyPerson640.py │ │ │ ├── reppoints_moment_r50_fpn_gn-neck+head_1x_TinyPerson640.py │ │ │ ├── reppoints_moment_r50_fpns4_1x_TinyPerson640.py │ │ │ ├── reppoints_moment_r50_fpns4_gn-neck+head_1x_TinyPerson640.py │ │ │ ├── retinanet_r50_fpn_1x_TinyPerson640.py │ │ │ ├── retinanet_r50_fpns4_1x_TinyPerson640.py │ │ │ └── retinanet_r50_fpns4_1x_TinyPerson640_clipg.py │ │ └── scale_match │ │ │ ├── ScaleMatch.md │ │ │ ├── ScaleMatch_TinyPerson.sh │ │ │ ├── faster_rcnn_r50_fpn_1x_coco_msm_tinyperson.py │ │ │ ├── faster_rcnn_r50_fpn_1x_coco_sm_tinyperson.py │ │ │ ├── retinanet_r50_fpns4_1x_coco_msm_tinyperson.py │ │ │ └── retinanet_r50_fpns4_1x_coco_sm_tinyperson.py │ ├── TinyPersonV2 │ │ ├── TinyPersonV2.md │ │ ├── coarsepointv2 │ │ │ ├── coarse_point_refine_base_TinyPersonV2_640.py │ │ │ ├── coarse_point_refine_r50_fpns4_0.5x_TinyPersonV2_640.py │ │ │ ├── coarse_point_refine_r50_fpns4_0.5x_TinyPersonV2_640_dbg.py │ │ │ └── coarse_point_refine_r50_fpns4_1x_TinyPersonV2_640.py │ │ └── p2p │ │ │ ├── p2p_r50_fpns4_0.5x_fl_sl1_TinyPersonV2_640.py │ │ │ └── p2p_r50_fpns4_1x_fl_sl1_TinyPersonV2_640.py │ └── _base_ │ │ ├── datasets │ │ ├── TinyCOCO │ │ │ └── TinyCOCO_detection.py │ │ ├── TinyPerson │ │ │ ├── TinyPerson_detection_640x512.py │ │ │ └── TinyPerson_detection_640x640.py │ │ ├── TinyPersonV2 │ │ │ └── TinyPersonV2_detection_640x640.py │ │ └── visDrone │ │ │ ├── visDronePerson_detection.py │ │ │ ├── visDronePerson_detection_640x640.py │ │ │ └── visDronePerson_detection_640x640_s1xtest.py │ │ └── models │ │ ├── cpr │ │ └── coarse_point_refine_r50_fpns4_1x.py │ │ └── fcos │ │ └── fcos_r50_caffe_fpn_gn-head_1x.py ├── demo │ ├── MMDet_Tutorial.ipynb │ ├── create_result_gif.py │ ├── demo.jpg │ ├── demo.mp4 │ ├── image_demo.py │ ├── inference_demo.ipynb │ ├── p2p_image_demo.py │ ├── video_demo.py │ └── webcam_demo.py ├── docker │ ├── Dockerfile │ └── serve │ │ ├── Dockerfile │ │ ├── config.properties │ │ └── entrypoint.sh ├── docs │ ├── 1_exist_data_model.md │ ├── 2_new_data_model.md │ ├── 3_exist_data_new_model.md │ ├── Makefile │ ├── api.rst │ ├── changelog.md │ ├── compatibility.md │ ├── conf.py │ ├── conventions.md │ ├── cpr │ │ └── README.md │ ├── faq.md │ ├── get_started.md │ ├── index.rst │ ├── make.bat │ ├── model_zoo.md │ ├── projects.md │ ├── robustness_benchmarking.md │ ├── stat.py │ ├── tov │ │ ├── README.md │ │ ├── code_modify.md │ │ └── evaluation_of_tiny_object.md │ ├── tutorials │ │ ├── config.md │ │ ├── customize_dataset.md │ │ ├── customize_losses.md │ │ ├── customize_models.md │ │ ├── customize_runtime.md │ │ ├── data_pipeline.md │ │ ├── finetune.md │ │ ├── index.rst │ │ ├── onnx2tensorrt.md │ │ └── pytorch2onnx.md │ └── useful_tools.md ├── exp │ └── tools │ │ ├── clear_tmp_pth.py │ │ ├── clear_trash.sh │ │ ├── result2ann.py │ │ ├── speed_script.sh │ │ └── sync_log.py ├── mmdet │ ├── __init__.py │ ├── apis │ │ ├── __init__.py │ │ ├── inference.py │ │ ├── test.py │ │ └── train.py │ ├── core │ │ ├── __init__.py │ │ ├── anchor │ │ │ ├── __init__.py │ │ │ ├── anchor_generator.py │ │ │ ├── builder.py │ │ │ ├── point_generator.py │ │ │ └── utils.py │ │ ├── bbox │ │ │ ├── __init__.py │ │ │ ├── assigners │ │ │ │ ├── __init__.py │ │ │ │ ├── approx_max_iou_assigner.py │ │ │ │ ├── assign_result.py │ │ │ │ ├── atss_assigner.py │ │ │ │ ├── base_assigner.py │ │ │ │ ├── center_region_assigner.py │ │ │ │ ├── grid_assigner.py │ │ │ │ ├── hungarian_assigner.py │ │ │ │ ├── max_iou_assigner.py │ │ │ │ ├── point_assigner.py │ │ │ │ ├── region_assigner.py │ │ │ │ └── uniform_assigner.py │ │ │ ├── builder.py │ │ │ ├── coder │ │ │ │ ├── __init__.py │ │ │ │ ├── base_bbox_coder.py │ │ │ │ ├── bouding_box.py │ │ │ │ ├── bucketing_bbox_coder.py │ │ │ │ ├── delta_xywh_bbox_coder.py │ │ │ │ ├── legacy_delta_xywh_bbox_coder.py │ │ │ │ ├── pseudo_bbox_coder.py │ │ │ │ ├── tblr_bbox_coder.py │ │ │ │ └── yolo_bbox_coder.py │ │ │ ├── demodata.py │ │ │ ├── iou_calculators │ │ │ │ ├── __init__.py │ │ │ │ ├── builder.py │ │ │ │ └── iou2d_calculator.py │ │ │ ├── match_costs │ │ │ │ ├── __init__.py │ │ │ │ ├── builder.py │ │ │ │ └── match_cost.py │ │ │ ├── samplers │ │ │ │ ├── __init__.py │ │ │ │ ├── base_sampler.py │ │ │ │ ├── combined_sampler.py │ │ │ │ ├── instance_balanced_pos_sampler.py │ │ │ │ ├── iou_balanced_neg_sampler.py │ │ │ │ ├── ohem_sampler.py │ │ │ │ ├── pseudo_sampler.py │ │ │ │ ├── random_sampler.py │ │ │ │ ├── sampling_result.py │ │ │ │ └── score_hlr_sampler.py │ │ │ └── transforms.py │ │ ├── evaluation │ │ │ ├── __init__.py │ │ │ ├── bbox_overlaps.py │ │ │ ├── class_names.py │ │ │ ├── eval_hooks.py │ │ │ ├── mean_ap.py │ │ │ └── recall.py │ │ ├── export │ │ │ ├── __init__.py │ │ │ ├── model_wrappers.py │ │ │ ├── onnx_helper.py │ │ │ └── pytorch2onnx.py │ │ ├── mask │ │ │ ├── __init__.py │ │ │ ├── mask_target.py │ │ │ ├── structures.py │ │ │ └── utils.py │ │ ├── post_processing │ │ │ ├── __init__.py │ │ │ ├── bbox_nms.py │ │ │ └── merge_augs.py │ │ ├── utils │ │ │ ├── __init__.py │ │ │ ├── dist_utils.py │ │ │ └── misc.py │ │ └── visualization │ │ │ ├── __init__.py │ │ │ └── image.py │ ├── datasets │ │ ├── __init__.py │ │ ├── api_wrappers │ │ │ ├── __init__.py │ │ │ └── coco_api.py │ │ ├── builder.py │ │ ├── cityscapes.py │ │ ├── coco.py │ │ ├── cocofmt.py │ │ ├── custom.py │ │ ├── dataset_wrappers.py │ │ ├── deepfashion.py │ │ ├── lvis.py │ │ ├── pipelines │ │ │ ├── __init__.py │ │ │ ├── auto_augment.py │ │ │ ├── compose.py │ │ │ ├── formating.py │ │ │ ├── instaboost.py │ │ │ ├── loading.py │ │ │ ├── rtest_time_aug.py │ │ │ ├── scale_match.py │ │ │ ├── test_time_aug.py │ │ │ └── transforms.py │ │ ├── samplers │ │ │ ├── __init__.py │ │ │ ├── distributed_sampler.py │ │ │ └── group_sampler.py │ │ ├── utils.py │ │ ├── voc.py │ │ ├── wider_face.py │ │ └── xml_style.py │ ├── models │ │ ├── __init__.py │ │ ├── backbones │ │ │ ├── __init__.py │ │ │ ├── darknet.py │ │ │ ├── detectors_resnet.py │ │ │ ├── detectors_resnext.py │ │ │ ├── hourglass.py │ │ │ ├── hrnet.py │ │ │ ├── mobilenet_v2.py │ │ │ ├── regnet.py │ │ │ ├── res2net.py │ │ │ ├── resnest.py │ │ │ ├── resnet.py │ │ │ ├── resnext.py │ │ │ ├── ssd_vgg.py │ │ │ └── trident_resnet.py │ │ ├── builder.py │ │ ├── dense_heads │ │ │ ├── __init__.py │ │ │ ├── anchor_free_head.py │ │ │ ├── anchor_head.py │ │ │ ├── atss_head.py │ │ │ ├── autoassign_head.py │ │ │ ├── base_dense_head.py │ │ │ ├── cascade_rpn_head.py │ │ │ ├── centernet_head.py │ │ │ ├── centripetal_head.py │ │ │ ├── corner_head.py │ │ │ ├── deformable_detr_head.py │ │ │ ├── dense_test_mixins.py │ │ │ ├── detr_head.py │ │ │ ├── embedding_rpn_head.py │ │ │ ├── fcos_head.py │ │ │ ├── fovea_head.py │ │ │ ├── free_anchor_retina_head.py │ │ │ ├── fsaf_head.py │ │ │ ├── ga_retina_head.py │ │ │ ├── ga_rpn_head.py │ │ │ ├── gfl_head.py │ │ │ ├── guided_anchor_head.py │ │ │ ├── ld_head.py │ │ │ ├── nasfcos_head.py │ │ │ ├── paa_head.py │ │ │ ├── pisa_retinanet_head.py │ │ │ ├── pisa_ssd_head.py │ │ │ ├── reppoints_head.py │ │ │ ├── retina_head.py │ │ │ ├── retina_sepbn_head.py │ │ │ ├── rpn_head.py │ │ │ ├── sabl_retina_head.py │ │ │ ├── ssd_head.py │ │ │ ├── vfnet_head.py │ │ │ ├── yolact_head.py │ │ │ ├── yolo_head.py │ │ │ └── yolof_head.py │ │ ├── detectors │ │ │ ├── __init__.py │ │ │ ├── atss.py │ │ │ ├── autoassign.py │ │ │ ├── base.py │ │ │ ├── cascade_rcnn.py │ │ │ ├── centernet.py │ │ │ ├── cornernet.py │ │ │ ├── deformable_detr.py │ │ │ ├── detr.py │ │ │ ├── fast_rcnn.py │ │ │ ├── faster_rcnn.py │ │ │ ├── fcos.py │ │ │ ├── fovea.py │ │ │ ├── fsaf.py │ │ │ ├── gfl.py │ │ │ ├── grid_rcnn.py │ │ │ ├── htc.py │ │ │ ├── kd_one_stage.py │ │ │ ├── mask_rcnn.py │ │ │ ├── mask_scoring_rcnn.py │ │ │ ├── nasfcos.py │ │ │ ├── paa.py │ │ │ ├── point_rend.py │ │ │ ├── reppoints_detector.py │ │ │ ├── retinanet.py │ │ │ ├── rpn.py │ │ │ ├── scnet.py │ │ │ ├── single_stage.py │ │ │ ├── sparse_rcnn.py │ │ │ ├── trident_faster_rcnn.py │ │ │ ├── two_stage.py │ │ │ ├── vfnet.py │ │ │ ├── yolact.py │ │ │ ├── yolo.py │ │ │ └── yolof.py │ │ ├── losses │ │ │ ├── __init__.py │ │ │ ├── accuracy.py │ │ │ ├── ae_loss.py │ │ │ ├── balanced_l1_loss.py │ │ │ ├── cross_entropy_loss.py │ │ │ ├── focal_loss.py │ │ │ ├── gaussian_focal_loss.py │ │ │ ├── gfocal_loss.py │ │ │ ├── ghm_loss.py │ │ │ ├── iou_loss.py │ │ │ ├── kd_loss.py │ │ │ ├── mse_loss.py │ │ │ ├── multi_instance_learning_loss.py │ │ │ ├── pisa_loss.py │ │ │ ├── seesaw_loss.py │ │ │ ├── smooth_l1_loss.py │ │ │ ├── utils.py │ │ │ ├── varifocal_loss.py │ │ │ └── weighted_hausdorff_distance.py │ │ ├── necks │ │ │ ├── __init__.py │ │ │ ├── bfp.py │ │ │ ├── channel_mapper.py │ │ │ ├── ct_resnet_neck.py │ │ │ ├── dilated_encoder.py │ │ │ ├── fpg.py │ │ │ ├── fpn.py │ │ │ ├── fpn_carafe.py │ │ │ ├── hrfpn.py │ │ │ ├── nas_fpn.py │ │ │ ├── nasfcos_fpn.py │ │ │ ├── pafpn.py │ │ │ ├── rfp.py │ │ │ └── yolo_neck.py │ │ ├── point │ │ │ ├── __init__.py │ │ │ ├── dense_heads │ │ │ │ ├── __init__.py │ │ │ │ ├── base_locator_head.py │ │ │ │ ├── cpr_head.py │ │ │ │ └── p2p_head.py │ │ │ └── detectors │ │ │ │ ├── __init__.py │ │ │ │ └── locator.py │ │ ├── roi_heads │ │ │ ├── __init__.py │ │ │ ├── base_roi_head.py │ │ │ ├── bbox_heads │ │ │ │ ├── __init__.py │ │ │ │ ├── bbox_head.py │ │ │ │ ├── convfc_bbox_head.py │ │ │ │ ├── dii_head.py │ │ │ │ ├── double_bbox_head.py │ │ │ │ ├── sabl_head.py │ │ │ │ └── scnet_bbox_head.py │ │ │ ├── cascade_roi_head.py │ │ │ ├── double_roi_head.py │ │ │ ├── dynamic_roi_head.py │ │ │ ├── grid_roi_head.py │ │ │ ├── htc_roi_head.py │ │ │ ├── mask_heads │ │ │ │ ├── __init__.py │ │ │ │ ├── coarse_mask_head.py │ │ │ │ ├── fcn_mask_head.py │ │ │ │ ├── feature_relay_head.py │ │ │ │ ├── fused_semantic_head.py │ │ │ │ ├── global_context_head.py │ │ │ │ ├── grid_head.py │ │ │ │ ├── htc_mask_head.py │ │ │ │ ├── mask_point_head.py │ │ │ │ ├── maskiou_head.py │ │ │ │ ├── scnet_mask_head.py │ │ │ │ └── scnet_semantic_head.py │ │ │ ├── mask_scoring_roi_head.py │ │ │ ├── pisa_roi_head.py │ │ │ ├── point_rend_roi_head.py │ │ │ ├── roi_extractors │ │ │ │ ├── __init__.py │ │ │ │ ├── base_roi_extractor.py │ │ │ │ ├── generic_roi_extractor.py │ │ │ │ └── single_level_roi_extractor.py │ │ │ ├── scnet_roi_head.py │ │ │ ├── shared_heads │ │ │ │ ├── __init__.py │ │ │ │ └── res_layer.py │ │ │ ├── sparse_roi_head.py │ │ │ ├── standard_roi_head.py │ │ │ ├── test_mixins.py │ │ │ └── trident_roi_head.py │ │ └── utils │ │ │ ├── __init__.py │ │ │ ├── builder.py │ │ │ ├── gaussian_target.py │ │ │ ├── inverted_residual.py │ │ │ ├── make_divisible.py │ │ │ ├── normed_predictor.py │ │ │ ├── positional_encoding.py │ │ │ ├── res_layer.py │ │ │ ├── se_layer.py │ │ │ └── transformer.py │ ├── utils │ │ ├── __init__.py │ │ ├── collect_env.py │ │ ├── contextmanagers.py │ │ ├── logger.py │ │ ├── profiling.py │ │ ├── util_mixins.py │ │ └── util_random.py │ └── version.py ├── model_zoo.yml ├── pytest.ini ├── requirements.txt ├── requirements │ ├── build.txt │ ├── docs.txt │ ├── mminstall.txt │ ├── optional.txt │ ├── readthedocs.txt │ ├── runtime.txt │ └── tests.txt ├── resources │ ├── coco_test_12510.jpg │ ├── corruptions_sev_3.png │ ├── data_pipeline.png │ ├── loss_curve.png │ ├── mmdet-logo.png │ ├── qq_group_qrcode.jpg │ └── zhihu_qrcode.jpg ├── setup.cfg ├── setup.py ├── tests │ ├── test_data │ │ ├── test_datasets │ │ │ ├── test_coco_dataset.py │ │ │ ├── test_common.py │ │ │ ├── test_custom_dataset.py │ │ │ ├── test_dataset_wrapper.py │ │ │ └── test_xml_dataset.py │ │ ├── test_pipelines │ │ │ ├── test_formatting.py │ │ │ ├── test_loading.py │ │ │ ├── test_sampler.py │ │ │ └── test_transform │ │ │ │ ├── test_img_augment.py │ │ │ │ ├── test_models_aug_test.py │ │ │ │ ├── test_rotate.py │ │ │ │ ├── test_shear.py │ │ │ │ ├── test_transform.py │ │ │ │ └── test_translate.py │ │ └── test_utils.py │ ├── test_metrics │ │ ├── test_box_overlap.py │ │ └── test_losses.py │ ├── test_models │ │ ├── test_backbones │ │ │ ├── __init__.py │ │ │ ├── test_hourglass.py │ │ │ ├── test_mobilenet_v2.py │ │ │ ├── test_regnet.py │ │ │ ├── test_renext.py │ │ │ ├── test_res2net.py │ │ │ ├── test_resnest.py │ │ │ ├── test_resnet.py │ │ │ ├── test_trident_resnet.py │ │ │ └── utils.py │ │ ├── test_dense_heads │ │ │ ├── test_anchor_head.py │ │ │ ├── test_atss_head.py │ │ │ ├── test_autoassign_head.py │ │ │ ├── test_centernet_head.py │ │ │ ├── test_corner_head.py │ │ │ ├── test_dense_heads_attr.py │ │ │ ├── test_detr_head.py │ │ │ ├── test_fcos_head.py │ │ │ ├── test_fsaf_head.py │ │ │ ├── test_ga_anchor_head.py │ │ │ ├── test_gfl_head.py │ │ │ ├── test_ld_head.py │ │ │ ├── test_paa_head.py │ │ │ ├── test_pisa_head.py │ │ │ ├── test_sabl_retina_head.py │ │ │ ├── test_vfnet_head.py │ │ │ ├── test_yolact_head.py │ │ │ └── test_yolof_head.py │ │ ├── test_forward.py │ │ ├── test_loss.py │ │ ├── test_necks.py │ │ ├── test_roi_heads │ │ │ ├── __init__.py │ │ │ ├── test_bbox_head.py │ │ │ ├── test_mask_head.py │ │ │ ├── test_roi_extractor.py │ │ │ ├── test_sabl_bbox_head.py │ │ │ └── utils.py │ │ └── test_utils │ │ │ ├── test_inverted_residual.py │ │ │ ├── test_position_encoding.py │ │ │ ├── test_se_layer.py │ │ │ └── test_transformer.py │ ├── test_onnx │ │ ├── __init__.py │ │ ├── test_head.py │ │ ├── test_neck.py │ │ └── utils.py │ ├── test_runtime │ │ ├── async_benchmark.py │ │ ├── test_async.py │ │ ├── test_config.py │ │ ├── test_eval_hook.py │ │ └── test_fp16.py │ └── test_utils │ │ ├── test_anchor.py │ │ ├── test_assigner.py │ │ ├── test_coder.py │ │ ├── test_masks.py │ │ ├── test_misc.py │ │ ├── test_version.py │ │ └── test_visualization.py └── tools │ ├── analysis_tools │ ├── analyze_logs.py │ ├── analyze_results.py │ ├── benchmark.py │ ├── coco_error_analysis.py │ ├── eval_metric.py │ ├── get_flops.py │ ├── robustness_eval.py │ └── test_robustness.py │ ├── dataset_converters │ ├── cityscapes.py │ └── pascal_voc.py │ ├── deployment │ ├── mmdet2torchserve.py │ ├── mmdet_handler.py │ ├── onnx2tensorrt.py │ ├── pytorch2onnx.py │ └── test.py │ ├── dist_test.sh │ ├── dist_train.sh │ ├── misc │ ├── browse_dataset.py │ └── print_config.py │ ├── model_converters │ ├── detectron2pytorch.py │ ├── publish_model.py │ ├── regnet2mmdet.py │ └── upgrade_model_version.py │ ├── slurm_test.sh │ ├── slurm_train.sh │ ├── test.py │ └── train.py ├── dataset ├── LegallyItem ├── README.md └── TinyPerson_annotation.md ├── figure ├── CPR │ ├── CPR_visualize.jpg │ ├── challenge_x21.jpg │ ├── cpr_gif │ │ ├── vis_2_000000031176.gif │ │ ├── vis_2_000000031599.gif │ │ ├── vis_2_000000074711.gif │ │ ├── vis_2_000000163020.gif │ │ └── vis_2_000000279806.gif │ ├── cpr_x10_low2.jpg │ ├── framework_x20.jpg │ ├── variance_x20.jpg │ └── vis_tiny2_01.jpg ├── annotation_rule.jpg └── dataset_anno.png └── params └── Readme.md /.gitignore: -------------------------------------------------------------------------------- 1 | pretrained_model/ 2 | params/COCOPretrain 3 | dataset/cityscapes 4 | dataset/tiny_set 5 | dataset/coco 6 | tiny_benchmark/maskrcnn_benchmark.egg-info/ 7 | tiny_benchmark/outputs/ 8 | tiny_benchamrk/log/ 9 | tiny_benchamrk/log2/ 10 | tiny_benchmark/private/ 11 | tiny_benchmark/build/ 12 | tiny_benchmark/maskrcnn_benchmark/_C.*.so 13 | tiny_benchmark/configs/TinyCOCO/.ipynb_checkpoints/ 14 | */.idea 15 | .idea/* 16 | */.ipynb_checkpoints 17 | */__pycache__ 18 | *.pyc 19 | old_tmp/* 20 | tiny_benchmark/sds/outputs/ 21 | tiny_benchmark/sds/log2/ 22 | 23 | # tiny_benchmark/sds/standard_gaussian_100w_sample.npy 24 | # tiny_benchmark/tiny_benchmark/MyPackage/notebook/ 25 | # tiny_benchmark/demo/ 26 | -------------------------------------------------------------------------------- /TOV_mmdetection/.readthedocs.yml: -------------------------------------------------------------------------------- 1 | version: 2 2 | 3 | python: 4 | version: 3.7 5 | install: 6 | - requirements: requirements/docs.txt 7 | - requirements: requirements/readthedocs.txt 8 | -------------------------------------------------------------------------------- /TOV_mmdetection/MANIFEST.in: -------------------------------------------------------------------------------- 1 | include requirements/*.txt 2 | include mmdet/VERSION 3 | include mmdet/model_zoo.yml 4 | include mmdet/demo/*/* 5 | recursive-include mmdet/configs *.py *.yml 6 | recursive-include mmdet/tools *.sh *.py 7 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/_base_/datasets/lvis_v1_instance.py: -------------------------------------------------------------------------------- 1 | # dataset settings 2 | _base_ = 'coco_instance.py' 3 | dataset_type = 'LVISV1Dataset' 4 | data_root = 'data/lvis_v1/' 5 | data = dict( 6 | samples_per_gpu=2, 7 | workers_per_gpu=2, 8 | train=dict( 9 | _delete_=True, 10 | type='ClassBalancedDataset', 11 | oversample_thr=1e-3, 12 | dataset=dict( 13 | type=dataset_type, 14 | ann_file=data_root + 'annotations/lvis_v1_train.json', 15 | img_prefix=data_root)), 16 | val=dict( 17 | type=dataset_type, 18 | ann_file=data_root + 'annotations/lvis_v1_val.json', 19 | img_prefix=data_root), 20 | test=dict( 21 | type=dataset_type, 22 | ann_file=data_root + 'annotations/lvis_v1_val.json', 23 | img_prefix=data_root)) 24 | evaluation = dict(metric=['bbox', 'segm']) 25 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/_base_/default_runtime.py: -------------------------------------------------------------------------------- 1 | checkpoint_config = dict(interval=1) 2 | # yapf:disable 3 | log_config = dict( 4 | interval=50, 5 | hooks=[ 6 | dict(type='TextLoggerHook'), 7 | # dict(type='TensorboardLoggerHook') 8 | ]) 9 | # yapf:enable 10 | custom_hooks = [dict(type='NumClassCheckHook')] 11 | 12 | dist_params = dict(backend='nccl') 13 | log_level = 'INFO' 14 | load_from = None 15 | resume_from = None 16 | workflow = [('train', 1)] 17 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/atss/atss_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './atss_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict(depth=101), 5 | ) 6 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://detectron2/resnet101_caffe', 4 | backbone=dict(depth=101)) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://detectron2/resnet101_caffe', 4 | backbone=dict(depth=101)) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_rcnn_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_rcnn_r50_fpn_20e_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_rcnn_r50_fpn_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 19]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=20) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_rcnn_r50_fpn_20e_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | type='CascadeRCNN', 4 | pretrained='open-mmlab://resnext101_64x4d', 5 | backbone=dict( 6 | type='ResNeXt', 7 | depth=101, 8 | groups=64, 9 | base_width=4, 10 | num_stages=4, 11 | out_indices=(0, 1, 2, 3), 12 | frozen_stages=1, 13 | norm_cfg=dict(type='BN', requires_grad=True), 14 | style='pytorch')) 15 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_rcnn_r50_fpn_20e_coco.py' 2 | model = dict( 3 | type='CascadeRCNN', 4 | pretrained='open-mmlab://resnext101_64x4d', 5 | backbone=dict( 6 | type='ResNeXt', 7 | depth=101, 8 | groups=64, 9 | base_width=4, 10 | num_stages=4, 11 | out_indices=(0, 1, 2, 3), 12 | frozen_stages=1, 13 | norm_cfg=dict(type='BN', requires_grad=True), 14 | style='pytorch')) 15 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/centernet/centernet_resnet18_140e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './centernet_resnet18_dcnv2_140e_coco.py' 2 | 3 | model = dict(neck=dict(use_dcn=False)) 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch', 14 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 15 | stage_with_dcn=(False, True, True, True))) 16 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'deformable_detr_r50_16x2_50e_coco.py' 2 | model = dict(bbox_head=dict(with_box_refine=True)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'deformable_detr_refine_r50_16x2_50e_coco.py' 2 | model = dict(bbox_head=dict(as_two_stage=True)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/detectors/htc_r50_rfp_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 | output_img=True), 8 | neck=dict( 9 | type='RFP', 10 | rfp_steps=2, 11 | aspp_out_channels=64, 12 | aspp_dilations=(1, 3, 6, 1), 13 | rfp_backbone=dict( 14 | rfp_inplanes=256, 15 | type='DetectoRS_ResNet', 16 | depth=50, 17 | num_stages=4, 18 | out_indices=(0, 1, 2, 3), 19 | frozen_stages=1, 20 | norm_cfg=dict(type='BN', requires_grad=True), 21 | norm_eval=True, 22 | conv_cfg=dict(type='ConvAWS'), 23 | pretrained='torchvision://resnet50', 24 | style='pytorch'))) 25 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | plugins=[ 5 | dict( 6 | cfg=dict( 7 | type='GeneralizedAttention', 8 | spatial_range=-1, 9 | num_heads=8, 10 | attention_type='0010', 11 | kv_stride=2), 12 | stages=(False, False, True, True), 13 | position='after_conv2') 14 | ], 15 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 16 | stage_with_dcn=(False, True, True, True))) 17 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | plugins=[ 5 | dict( 6 | cfg=dict( 7 | type='GeneralizedAttention', 8 | spatial_range=-1, 9 | num_heads=8, 10 | attention_type='1111', 11 | kv_stride=2), 12 | stages=(False, False, True, True), 13 | position='after_conv2') 14 | ], 15 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 16 | stage_with_dcn=(False, True, True, True))) 17 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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(depth=101)) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fast_rcnn_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fast_rcnn_r50_fpn_2x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/fast_rcnn/fast_rcnn_r50_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fast_rcnn_r50_fpn_1x_coco.py' 2 | 3 | # learning policy 4 | lr_config = dict(step=[16, 22]) 5 | runner = dict(type='EpochBasedRunner', max_epochs=24) 6 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://detectron2/resnet101_caffe', 4 | backbone=dict(depth=101)) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_2x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' 2 | model = dict(roi_head=dict(bbox_head=dict(num_classes=3))) 3 | classes = ('person', 'bicycle', 'car') 4 | data = dict( 5 | train=dict(classes=classes), 6 | val=dict(classes=classes), 7 | test=dict(classes=classes)) 8 | 9 | load_from = 'http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth' # noqa 10 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' 2 | model = dict(roi_head=dict(bbox_head=dict(num_classes=1))) 3 | classes = ('person', ) 4 | data = dict( 5 | train=dict(classes=classes), 6 | val=dict(classes=classes), 7 | test=dict(classes=classes)) 8 | 9 | load_from = 'http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth' # noqa 10 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict(train_cfg=dict(rcnn=dict(sampler=dict(type='OHEMSampler')))) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' 2 | model = dict(bbox_head=dict(center_sampling=True, center_sample_radius=1.5)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://detectron/resnet101_caffe', 4 | backbone=dict(depth=101)) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='torchvision://resnet101', 4 | backbone=dict(depth=101), 5 | bbox_head=dict( 6 | with_deform=True, 7 | norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))) 8 | # learning policy 9 | lr_config = dict(step=[16, 22]) 10 | runner = dict(type='EpochBasedRunner', max_epochs=24) 11 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fovea_r50_fpn_4x4_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fovea_r50_fpn_4x4_2x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fovea_r50_fpn_4x4_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/fp16/faster_rcnn_r50_fpn_fp16_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' 2 | # fp16 settings 3 | fp16 = dict(loss_scale=512.) 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' 2 | # fp16 settings 3 | fp16 = dict(loss_scale=512.) 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' 2 | # fp16 settings 3 | fp16 = dict(loss_scale=512.) 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | style='pytorch')) 13 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/fsaf/fsaf_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fsaf_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fsaf_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../dcn/cascade_mask_rcnn_r50_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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../dcn/cascade_rcnn_r50_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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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_r50_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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './gfl_r50_fpn_mstrain_2x_coco.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict( 5 | type='ResNet', 6 | depth=101, 7 | num_stages=4, 8 | out_indices=(0, 1, 2, 3), 9 | frozen_stages=1, 10 | norm_cfg=dict(type='BN', requires_grad=True), 11 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 12 | stage_with_dcn=(False, True, True, True), 13 | norm_eval=True, 14 | style='pytorch')) 15 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './gfl_r50_fpn_mstrain_2x_coco.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict( 5 | type='ResNet', 6 | depth=101, 7 | num_stages=4, 8 | out_indices=(0, 1, 2, 3), 9 | frozen_stages=1, 10 | norm_cfg=dict(type='BN', requires_grad=True), 11 | norm_eval=True, 12 | style='pytorch')) 13 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './gfl_r50_fpn_mstrain_2x_coco.py' 2 | model = dict( 3 | type='GFL', 4 | pretrained='open-mmlab://resnext101_32x4d', 5 | backbone=dict( 6 | type='ResNeXt', 7 | depth=101, 8 | groups=32, 9 | base_width=4, 10 | num_stages=4, 11 | out_indices=(0, 1, 2, 3), 12 | frozen_stages=1, 13 | norm_cfg=dict(type='BN', requires_grad=True), 14 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 15 | stage_with_dcn=(False, False, True, True), 16 | norm_eval=True, 17 | style='pytorch')) 18 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './gfl_r50_fpn_mstrain_2x_coco.py' 2 | model = dict( 3 | type='GFL', 4 | pretrained='open-mmlab://resnext101_32x4d', 5 | backbone=dict( 6 | type='ResNeXt', 7 | depth=101, 8 | groups=32, 9 | base_width=4, 10 | num_stages=4, 11 | out_indices=(0, 1, 2, 3), 12 | frozen_stages=1, 13 | norm_cfg=dict(type='BN', requires_grad=True), 14 | norm_eval=True, 15 | style='pytorch')) 16 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_ghm_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' 2 | model = dict( 3 | bbox_head=dict( 4 | loss_cls=dict( 5 | _delete_=True, 6 | type='GHMC', 7 | bins=30, 8 | momentum=0.75, 9 | use_sigmoid=True, 10 | loss_weight=1.0), 11 | loss_bbox=dict( 12 | _delete_=True, 13 | type='GHMR', 14 | mu=0.02, 15 | bins=10, 16 | momentum=0.7, 17 | loss_weight=10.0))) 18 | optimizer_config = dict( 19 | _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) 20 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_ghm_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_ghm_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://jhu/resnet101_gn_ws', backbone=dict(depth=101)) 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' 2 | conv_cfg = dict(type='ConvWS') 3 | norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) 4 | model = dict( 5 | pretrained='open-mmlab://jhu/resnet50_gn_ws', 6 | backbone=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), 7 | neck=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), 8 | roi_head=dict( 9 | bbox_head=dict( 10 | type='Shared4Conv1FCBBoxHead', 11 | conv_out_channels=256, 12 | conv_cfg=conv_cfg, 13 | norm_cfg=norm_cfg))) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py' 2 | conv_cfg = dict(type='ConvWS') 3 | norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) 4 | model = dict( 5 | pretrained='open-mmlab://jhu/resnext101_32x4d_gn_ws', 6 | backbone=dict( 7 | type='ResNeXt', 8 | depth=101, 9 | groups=32, 10 | base_width=4, 11 | num_stages=4, 12 | out_indices=(0, 1, 2, 3), 13 | frozen_stages=1, 14 | style='pytorch', 15 | conv_cfg=conv_cfg, 16 | norm_cfg=norm_cfg)) 17 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/gn+ws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py' 2 | conv_cfg = dict(type='ConvWS') 3 | norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) 4 | model = dict( 5 | pretrained='open-mmlab://jhu/resnext50_32x4d_gn_ws', 6 | backbone=dict( 7 | type='ResNeXt', 8 | depth=50, 9 | groups=32, 10 | base_width=4, 11 | num_stages=4, 12 | out_indices=(0, 1, 2, 3), 13 | frozen_stages=1, 14 | style='pytorch', 15 | conv_cfg=conv_cfg, 16 | norm_cfg=norm_cfg)) 17 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://jhu/resnet101_gn_ws', backbone=dict(depth=101)) 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' 2 | conv_cfg = dict(type='ConvWS') 3 | norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) 4 | model = dict( 5 | pretrained='open-mmlab://jhu/resnet50_gn_ws', 6 | backbone=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), 7 | neck=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), 8 | roi_head=dict( 9 | bbox_head=dict( 10 | type='Shared4Conv1FCBBoxHead', 11 | conv_out_channels=256, 12 | conv_cfg=conv_cfg, 13 | norm_cfg=norm_cfg), 14 | mask_head=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg))) 15 | # learning policy 16 | lr_config = dict(step=[16, 22]) 17 | runner = dict(type='EpochBasedRunner', max_epochs=24) 18 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' 2 | # model settings 3 | conv_cfg = dict(type='ConvWS') 4 | norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) 5 | model = dict( 6 | pretrained='open-mmlab://jhu/resnext101_32x4d_gn_ws', 7 | backbone=dict( 8 | type='ResNeXt', 9 | depth=101, 10 | groups=32, 11 | base_width=4, 12 | num_stages=4, 13 | out_indices=(0, 1, 2, 3), 14 | frozen_stages=1, 15 | style='pytorch', 16 | conv_cfg=conv_cfg, 17 | norm_cfg=norm_cfg)) 18 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' 2 | # model settings 3 | conv_cfg = dict(type='ConvWS') 4 | norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) 5 | model = dict( 6 | pretrained='open-mmlab://jhu/resnext50_32x4d_gn_ws', 7 | backbone=dict( 8 | type='ResNeXt', 9 | depth=50, 10 | groups=32, 11 | base_width=4, 12 | num_stages=4, 13 | out_indices=(0, 1, 2, 3), 14 | frozen_stages=1, 15 | style='pytorch', 16 | conv_cfg=conv_cfg, 17 | norm_cfg=norm_cfg)) 18 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://detectron/resnet101_gn', backbone=dict(depth=101)) 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' 2 | norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) 3 | model = dict( 4 | pretrained='open-mmlab://contrib/resnet50_gn', 5 | backbone=dict(norm_cfg=norm_cfg), 6 | neck=dict(norm_cfg=norm_cfg), 7 | roi_head=dict( 8 | bbox_head=dict( 9 | type='Shared4Conv1FCBBoxHead', 10 | conv_out_channels=256, 11 | norm_cfg=norm_cfg), 12 | mask_head=dict(norm_cfg=norm_cfg))) 13 | # learning policy 14 | lr_config = dict(step=[16, 22]) 15 | runner = dict(type='EpochBasedRunner', max_epochs=24) 16 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './grid_rcnn_r50_fpn_gn-head_2x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | style='pytorch')) 13 | # optimizer 14 | optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) 15 | optimizer_config = dict(grad_clip=None) 16 | # learning policy 17 | lr_config = dict( 18 | policy='step', 19 | warmup='linear', 20 | warmup_iters=3665, 21 | warmup_ratio=1.0 / 80, 22 | step=[17, 23]) 23 | runner = dict(type='EpochBasedRunner', max_epochs=25) 24 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | style='pytorch')) 13 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://detectron2/resnet101_caffe', 4 | backbone=dict(depth=101)) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga_faster_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga_faster_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://detectron2/resnet101_caffe', 4 | backbone=dict(depth=101)) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga_retinanet_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga_retinanet_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://detectron2/resnet101_caffe', 5 | backbone=dict(depth=101)) 6 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga_rpn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga_rpn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py' 2 | # model settings 3 | model = dict( 4 | pretrained='open-mmlab://msra/hrnetv2_w18', 5 | backbone=dict( 6 | extra=dict( 7 | stage2=dict(num_channels=(18, 36)), 8 | stage3=dict(num_channels=(18, 36, 72)), 9 | stage4=dict(num_channels=(18, 36, 72, 144)))), 10 | neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) 11 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py' 2 | # model settings 3 | model = dict( 4 | pretrained='open-mmlab://msra/hrnetv2_w40', 5 | backbone=dict( 6 | type='HRNet', 7 | extra=dict( 8 | stage2=dict(num_channels=(40, 80)), 9 | stage3=dict(num_channels=(40, 80, 160)), 10 | stage4=dict(num_channels=(40, 80, 160, 320)))), 11 | neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) 12 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_rcnn_hrnetv2p_w32_20e_coco.py' 2 | # model settings 3 | model = dict( 4 | pretrained='open-mmlab://msra/hrnetv2_w18', 5 | backbone=dict( 6 | extra=dict( 7 | stage2=dict(num_channels=(18, 36)), 8 | stage3=dict(num_channels=(18, 36, 72)), 9 | stage4=dict(num_channels=(18, 36, 72, 144)))), 10 | neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) 11 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_rcnn_hrnetv2p_w32_20e_coco.py' 2 | # model settings 3 | model = dict( 4 | pretrained='open-mmlab://msra/hrnetv2_w40', 5 | backbone=dict( 6 | type='HRNet', 7 | extra=dict( 8 | stage2=dict(num_channels=(40, 80)), 9 | stage3=dict(num_channels=(40, 80, 160)), 10 | stage4=dict(num_channels=(40, 80, 160, 320)))), 11 | neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) 12 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py' 2 | # model settings 3 | model = dict( 4 | pretrained='open-mmlab://msra/hrnetv2_w18', 5 | backbone=dict( 6 | extra=dict( 7 | stage2=dict(num_channels=(18, 36)), 8 | stage3=dict(num_channels=(18, 36, 72)), 9 | stage4=dict(num_channels=(18, 36, 72, 144)))), 10 | neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) 11 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://msra/hrnetv2_w40', 4 | backbone=dict( 5 | type='HRNet', 6 | extra=dict( 7 | stage2=dict(num_channels=(40, 80)), 8 | stage3=dict(num_channels=(40, 80, 160)), 9 | stage4=dict(num_channels=(40, 80, 160, 320)))), 10 | neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) 11 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://msra/hrnetv2_w18', 4 | backbone=dict( 5 | extra=dict( 6 | stage2=dict(num_channels=(18, 36)), 7 | stage3=dict(num_channels=(18, 36, 72)), 8 | stage4=dict(num_channels=(18, 36, 72, 144)))), 9 | neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) 10 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://msra/hrnetv2_w18', 4 | backbone=dict( 5 | extra=dict( 6 | stage2=dict(num_channels=(18, 36)), 7 | stage3=dict(num_channels=(18, 36, 72)), 8 | stage4=dict(num_channels=(18, 36, 72, 144)))), 9 | neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) 10 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://msra/hrnetv2_w40', 4 | backbone=dict( 5 | type='HRNet', 6 | extra=dict( 7 | stage2=dict(num_channels=(40, 80)), 8 | stage3=dict(num_channels=(40, 80, 160)), 9 | stage4=dict(num_channels=(40, 80, 160, 320)))), 10 | neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) 11 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './htc_hrnetv2p_w32_20e_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://msra/hrnetv2_w18', 4 | backbone=dict( 5 | extra=dict( 6 | stage2=dict(num_channels=(18, 36)), 7 | stage3=dict(num_channels=(18, 36, 72)), 8 | stage4=dict(num_channels=(18, 36, 72, 144)))), 9 | neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) 10 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './htc_hrnetv2p_w32_20e_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://msra/hrnetv2_w40', 4 | backbone=dict( 5 | type='HRNet', 6 | extra=dict( 7 | stage2=dict(num_channels=(40, 80)), 8 | stage3=dict(num_channels=(40, 80, 160)), 9 | stage4=dict(num_channels=(40, 80, 160, 320)))), 10 | neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) 11 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/hrnet/htc_hrnetv2p_w40_28e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './htc_hrnetv2p_w40_20e_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[24, 27]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=28) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_hrnetv2p_w32_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://msra/hrnetv2_w18', 4 | backbone=dict( 5 | extra=dict( 6 | stage2=dict(num_channels=(18, 36)), 7 | stage3=dict(num_channels=(18, 36, 72)), 8 | stage4=dict(num_channels=(18, 36, 72, 144)))), 9 | neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) 10 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://msra/hrnetv2_w40', 4 | backbone=dict( 5 | type='HRNet', 6 | extra=dict( 7 | stage2=dict(num_channels=(40, 80)), 8 | stage3=dict(num_channels=(40, 80, 160)), 9 | stage4=dict(num_channels=(40, 80, 160, 320)))), 10 | neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) 11 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/htc/htc_r101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './htc_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | # learning policy 4 | lr_config = dict(step=[16, 19]) 5 | runner = dict(type='EpochBasedRunner', max_epochs=20) 6 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/htc/htc_r50_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './htc_r50_fpn_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 19]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=20) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './htc_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | norm_eval=True, 14 | style='pytorch')) 15 | data = dict(samples_per_gpu=1, workers_per_gpu=1) 16 | # learning policy 17 | lr_config = dict(step=[16, 19]) 18 | runner = dict(type='EpochBasedRunner', max_epochs=20) 19 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './htc_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | norm_eval=True, 14 | style='pytorch')) 15 | data = dict(samples_per_gpu=1, workers_per_gpu=1) 16 | # learning policy 17 | lr_config = dict(step=[16, 19]) 18 | runner = dict(type='EpochBasedRunner', max_epochs=20) 19 | -------------------------------------------------------------------------------- /TOV_mmdetection/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(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/instaboost/cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_instaboost_4x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_instaboost_4x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py'] 2 | model = dict( 3 | pretrained='torchvision://resnet34', 4 | backbone=dict( 5 | type='ResNet', 6 | depth=34, 7 | num_stages=4, 8 | out_indices=(0, 1, 2, 3), 9 | frozen_stages=1, 10 | norm_cfg=dict(type='BN', requires_grad=True), 11 | norm_eval=True, 12 | style='pytorch'), 13 | neck=dict( 14 | type='FPN', 15 | in_channels=[64, 128, 256, 512], 16 | out_channels=256, 17 | start_level=1, 18 | add_extra_convs='on_output', 19 | num_outs=5)) 20 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py'] 2 | model = dict( 3 | pretrained='torchvision://resnet50', 4 | backbone=dict( 5 | type='ResNet', 6 | depth=50, 7 | num_stages=4, 8 | out_indices=(0, 1, 2, 3), 9 | frozen_stages=1, 10 | norm_cfg=dict(type='BN', requires_grad=True), 11 | norm_eval=True, 12 | style='pytorch'), 13 | neck=dict( 14 | type='FPN', 15 | in_channels=[256, 512, 1024, 2048], 16 | out_channels=256, 17 | start_level=1, 18 | add_extra_convs='on_output', 19 | num_outs=5)) 20 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.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 | model = dict( 7 | bbox_head=dict( 8 | type='RetinaHead', 9 | anchor_generator=dict( 10 | type='LegacyAnchorGenerator', 11 | center_offset=0.5, 12 | octave_base_scale=4, 13 | scales_per_octave=3, 14 | ratios=[0.5, 1.0, 2.0], 15 | strides=[8, 16, 32, 64, 128]), 16 | bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'), 17 | loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0))) 18 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './libra_faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './libra_faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' 2 | # model settings 3 | model = dict( 4 | neck=[ 5 | dict( 6 | type='FPN', 7 | in_channels=[256, 512, 1024, 2048], 8 | out_channels=256, 9 | start_level=1, 10 | add_extra_convs='on_input', 11 | num_outs=5), 12 | dict( 13 | type='BFP', 14 | in_channels=256, 15 | num_levels=5, 16 | refine_level=1, 17 | refine_type='non_local') 18 | ], 19 | bbox_head=dict( 20 | loss_bbox=dict( 21 | _delete_=True, 22 | type='BalancedL1Loss', 23 | alpha=0.5, 24 | gamma=1.5, 25 | beta=0.11, 26 | loss_weight=1.0))) 27 | -------------------------------------------------------------------------------- /TOV_mmdetection/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(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/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(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/lvis/mask_rcnn_x101_32x4d_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 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/lvis/mask_rcnn_x101_32x4d_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 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/lvis/mask_rcnn_x101_64x4d_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 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/lvis/mask_rcnn_x101_64x4d_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 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://detectron2/resnet101_caffe', 4 | backbone=dict(depth=101)) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_2x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r101_fpn_2x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://detectron2/resnet101_caffe', 4 | backbone=dict(depth=101)) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | type='MaskScoringRCNN', 4 | roi_head=dict( 5 | type='MaskScoringRoIHead', 6 | mask_iou_head=dict( 7 | type='MaskIoUHead', 8 | num_convs=4, 9 | num_fcs=2, 10 | roi_feat_size=14, 11 | in_channels=256, 12 | conv_out_channels=256, 13 | fc_out_channels=1024, 14 | num_classes=80)), 15 | # model training and testing settings 16 | train_cfg=dict(rcnn=dict(mask_thr_binary=0.5))) 17 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/ms_rcnn/ms_rcnn_r50_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | type='MaskScoringRCNN', 4 | roi_head=dict( 5 | type='MaskScoringRoIHead', 6 | mask_iou_head=dict( 7 | type='MaskIoUHead', 8 | num_convs=4, 9 | num_fcs=2, 10 | roi_feat_size=14, 11 | in_channels=256, 12 | conv_out_channels=256, 13 | fc_out_channels=1024, 14 | num_classes=80)), 15 | # model training and testing settings 16 | train_cfg=dict(rcnn=dict(mask_thr_binary=0.5))) 17 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ms_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ms_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/paa/paa_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './paa_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/paa/paa_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './paa_r101_fpn_1x_coco.py' 2 | lr_config = dict(step=[16, 22]) 3 | runner = dict(type='EpochBasedRunner', max_epochs=24) 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/paa/paa_r101_fpn_mstrain_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './paa_r50_fpn_mstrain_3x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/paa/paa_r50_fpn_1.5x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './paa_r50_fpn_1x_coco.py' 2 | lr_config = dict(step=[12, 16]) 3 | runner = dict(type='EpochBasedRunner', max_epochs=18) 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/paa/paa_r50_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './paa_r50_fpn_1x_coco.py' 2 | lr_config = dict(step=[16, 22]) 3 | runner = dict(type='EpochBasedRunner', max_epochs=24) 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/paa/paa_r50_fpn_mstrain_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './paa_r50_fpn_1x_coco.py' 2 | img_norm_cfg = dict( 3 | mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) 4 | train_pipeline = [ 5 | dict(type='LoadImageFromFile'), 6 | dict(type='LoadAnnotations', with_bbox=True), 7 | dict( 8 | type='Resize', 9 | img_scale=[(1333, 640), (1333, 800)], 10 | multiscale_mode='range', 11 | keep_ratio=True), 12 | dict(type='RandomFlip', flip_ratio=0.5), 13 | dict(type='Normalize', **img_norm_cfg), 14 | dict(type='Pad', size_divisor=32), 15 | dict(type='DefaultFormatBundle'), 16 | dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), 17 | ] 18 | data = dict(train=dict(pipeline=train_pipeline)) 19 | lr_config = dict(step=[28, 34]) 20 | runner = dict(type='EpochBasedRunner', max_epochs=36) 21 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py', 3 | '../_base_/default_runtime.py' 4 | ] 5 | model = dict(roi_head=dict(bbox_head=dict(num_classes=20))) 6 | # optimizer 7 | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) 8 | optimizer_config = dict(grad_clip=None) 9 | # learning policy 10 | # actual epoch = 3 * 3 = 9 11 | lr_config = dict(policy='step', step=[3]) 12 | # runtime settings 13 | runner = dict( 14 | type='EpochBasedRunner', max_epochs=4) # actual epoch = 4 * 3 = 12 15 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/retinanet_r50_fpn.py', '../_base_/datasets/voc0712.py', 3 | '../_base_/default_runtime.py' 4 | ] 5 | model = dict(bbox_head=dict(num_classes=20)) 6 | # optimizer 7 | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) 8 | optimizer_config = dict(grad_clip=None) 9 | # learning policy 10 | # actual epoch = 3 * 3 = 9 11 | lr_config = dict(policy='step', step=[3]) 12 | # runtime settings 13 | runner = dict( 14 | type='EpochBasedRunner', max_epochs=4) # actual epoch = 4 * 3 = 12 15 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py' 2 | lr_config = dict(step=[16, 22]) 3 | runner = dict(type='EpochBasedRunner', max_epochs=24) 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://regnetx_12gf', 4 | backbone=dict( 5 | type='RegNet', 6 | arch='regnetx_12gf', 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 | neck=dict( 13 | type='FPN', 14 | in_channels=[224, 448, 896, 2240], 15 | out_channels=256, 16 | num_outs=5)) 17 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://regnetx_3.2gf', 4 | backbone=dict( 5 | dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), 6 | stage_with_dcn=(False, True, True, True))) 7 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://regnetx_4.0gf', 4 | backbone=dict( 5 | type='RegNet', 6 | arch='regnetx_4.0gf', 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 | neck=dict( 13 | type='FPN', 14 | in_channels=[80, 240, 560, 1360], 15 | out_channels=256, 16 | num_outs=5)) 17 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://regnetx_6.4gf', 4 | backbone=dict( 5 | type='RegNet', 6 | arch='regnetx_6.4gf', 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 | neck=dict( 13 | type='FPN', 14 | in_channels=[168, 392, 784, 1624], 15 | out_channels=256, 16 | num_outs=5)) 17 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://regnetx_8.0gf', 4 | backbone=dict( 5 | type='RegNet', 6 | arch='regnetx_8.0gf', 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 | neck=dict( 13 | type='FPN', 14 | in_channels=[80, 240, 720, 1920], 15 | out_channels=256, 16 | num_outs=5)) 17 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://regnetx_1.6gf', 4 | backbone=dict( 5 | type='RegNet', 6 | arch='regnetx_1.6gf', 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 | neck=dict( 13 | type='FPN', 14 | in_channels=[72, 168, 408, 912], 15 | out_channels=256, 16 | num_outs=5)) 17 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://regnetx_800mf', 4 | backbone=dict( 5 | type='RegNet', 6 | arch='regnetx_800mf', 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 | neck=dict( 13 | type='FPN', 14 | in_channels=[64, 128, 288, 672], 15 | out_channels=256, 16 | num_outs=5)) 17 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py' 2 | model = dict( 3 | bbox_head=dict(transform_method='minmax', use_grid_points=True), 4 | # training and testing settings 5 | train_cfg=dict( 6 | init=dict( 7 | assigner=dict( 8 | _delete_=True, 9 | type='MaxIoUAssigner', 10 | pos_iou_thr=0.5, 11 | neg_iou_thr=0.4, 12 | min_pos_iou=0, 13 | ignore_iof_thr=-1)))) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/reppoints/reppoints.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ucas-vg/PointTinyBenchmark/5a2393ebed992509b3f0a18b84aad4232b5ebcdb/TOV_mmdetection/configs/reppoints/reppoints.png -------------------------------------------------------------------------------- /TOV_mmdetection/configs/reppoints/reppoints_minmax_r50_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')) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='torchvision://resnet101', 4 | backbone=dict( 5 | depth=101, 6 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 7 | stage_with_dcn=(False, True, True, True))) 8 | -------------------------------------------------------------------------------- /TOV_mmdetection/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(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/reppoints/reppoints_moment_x101_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 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch', 14 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 15 | stage_with_dcn=(False, True, True, True))) 16 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/reppoints/reppoints_partial_minmax_r50_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='partial_minmax')) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://res2net101_v1d_26w_4s', 4 | backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://res2net101_v1d_26w_4s', 4 | backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://res2net101_v1d_26w_4s', 4 | backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/res2net/htc_r2_101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../htc/htc_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://res2net101_v1d_26w_4s', 4 | backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) 5 | # learning policy 6 | lr_config = dict(step=[16, 19]) 7 | runner = dict(type='EpochBasedRunner', max_epochs=20) 8 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://res2net101_v1d_26w_4s', 4 | backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://resnest101', 4 | backbone=dict(stem_channels=128, depth=101)) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://resnest101', 4 | backbone=dict(stem_channels=128, depth=101)) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://resnest101', 4 | backbone=dict(stem_channels=128, depth=101)) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | pretrained='open-mmlab://resnest101', 4 | backbone=dict(stem_channels=128, depth=101)) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://detectron2/resnet101_caffe', 4 | backbone=dict(depth=101)) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/retinanet/retinanet_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/retinanet/retinanet_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_2x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/retinanet/retinanet_r50_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_2x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_2x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://detectron2/resnet101_caffe', 4 | backbone=dict(depth=101)) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/rpn/rpn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/rpn/rpn_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_2x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/rpn/rpn_r50_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/rpn_r50_fpn.py', '../_base_/datasets/coco_detection.py', 3 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 4 | ] 5 | img_norm_cfg = dict( 6 | mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) 7 | train_pipeline = [ 8 | dict(type='LoadImageFromFile'), 9 | dict(type='LoadAnnotations', with_bbox=True, with_label=False), 10 | dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), 11 | dict(type='RandomFlip', flip_ratio=0.5), 12 | dict(type='Normalize', **img_norm_cfg), 13 | dict(type='Pad', size_divisor=32), 14 | dict(type='DefaultFormatBundle'), 15 | dict(type='Collect', keys=['img', 'gt_bboxes']), 16 | ] 17 | data = dict(train=dict(pipeline=train_pipeline)) 18 | evaluation = dict(interval=1, metric='proposal_fast') 19 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/rpn/rpn_r50_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_1x_coco.py' 2 | 3 | # learning policy 4 | lr_config = dict(step=[16, 22]) 5 | runner = dict(type='EpochBasedRunner', max_epochs=24) 6 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch')) 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/scnet/scnet_r101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './scnet_r50_fpn_20e_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/scnet/scnet_r50_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './scnet_r50_fpn_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 19]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=20) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './scnet_r50_fpn_20e_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | norm_eval=True, 14 | style='pytorch')) 15 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/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(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/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(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/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(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/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(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'tridentnet_r50_caffe_1x_coco.py' 2 | 3 | # use caffe img_norm 4 | img_norm_cfg = dict( 5 | mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) 6 | train_pipeline = [ 7 | dict(type='LoadImageFromFile'), 8 | dict(type='LoadAnnotations', with_bbox=True), 9 | dict( 10 | type='Resize', 11 | img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), 12 | (1333, 768), (1333, 800)], 13 | multiscale_mode='value', 14 | keep_ratio=True), 15 | dict(type='RandomFlip', flip_ratio=0.5), 16 | dict(type='Normalize', **img_norm_cfg), 17 | dict(type='Pad', size_divisor=32), 18 | dict(type='DefaultFormatBundle'), 19 | dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) 20 | ] 21 | 22 | data = dict(train=dict(pipeline=train_pipeline)) 23 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'tridentnet_r50_caffe_mstrain_1x_coco.py' 2 | 3 | lr_config = dict(step=[28, 34]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=36) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/vfnet/vfnet_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/vfnet/vfnet_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict( 5 | type='ResNet', 6 | depth=101, 7 | num_stages=4, 8 | out_indices=(0, 1, 2, 3), 9 | frozen_stages=1, 10 | norm_cfg=dict(type='BN', requires_grad=True), 11 | norm_eval=True, 12 | style='pytorch', 13 | dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), 14 | stage_with_dcn=(False, True, True, True))) 15 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_mstrain_2x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/vfnet/vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://res2net101_v1d_26w_4s', 4 | backbone=dict( 5 | type='Res2Net', 6 | depth=101, 7 | scales=4, 8 | base_width=26, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | norm_eval=True, 14 | style='pytorch', 15 | dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), 16 | stage_with_dcn=(False, True, True, True))) 17 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/vfnet/vfnet_r2_101_fpn_mstrain_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_mstrain_2x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://res2net101_v1d_26w_4s', 4 | backbone=dict( 5 | type='Res2Net', 6 | depth=101, 7 | scales=4, 8 | base_width=26, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | norm_eval=True, 14 | style='pytorch')) 15 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | norm_eval=True, 14 | style='pytorch', 15 | dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), 16 | stage_with_dcn=(False, True, True, True))) 17 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/vfnet/vfnet_x101_32x4d_fpn_mstrain_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_mstrain_2x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_32x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | norm_eval=True, 14 | style='pytorch')) 15 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | norm_eval=True, 14 | style='pytorch', 15 | dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), 16 | stage_with_dcn=(False, True, True, True))) 17 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/vfnet/vfnet_x101_64x4d_fpn_mstrain_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_mstrain_2x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnext101_64x4d', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | norm_eval=True, 14 | style='pytorch')) 15 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/wider_face/ssd300_wider_face.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/ssd300.py', '../_base_/datasets/wider_face.py', 3 | '../_base_/default_runtime.py' 4 | ] 5 | model = dict(bbox_head=dict(num_classes=1)) 6 | # optimizer 7 | optimizer = dict(type='SGD', lr=0.012, momentum=0.9, weight_decay=5e-4) 8 | optimizer_config = dict() 9 | # learning policy 10 | lr_config = dict( 11 | policy='step', 12 | warmup='linear', 13 | warmup_iters=1000, 14 | warmup_ratio=0.001, 15 | step=[16, 20]) 16 | # runtime settings 17 | runner = dict(type='EpochBasedRunner', max_epochs=24) 18 | log_config = dict(interval=1) 19 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/yolact/yolact_r101_1x8_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './yolact_r50_1x8_coco.py' 2 | 3 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/yolo/yolov3_d53_fp16_mstrain-608_273e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './yolov3_d53_mstrain-608_273e_coco.py' 2 | # fp16 settings 3 | fp16 = dict(loss_scale='dynamic') 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs/yolof/yolof_r50_c5_8x8_iter-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './yolof_r50_c5_8x8_1x_coco.py' 2 | 3 | # We implemented the iter-based config according to the source code. 4 | # COCO dataset has 117266 images after filtering. We use 8 gpu and 5 | # 8 batch size training, so 22500 is equivalent to 6 | # 22500/(117266/(8x8))=12.3 epoch, 15000 is equivalent to 8.2 epoch, 7 | # 20000 is equivalent to 10.9 epoch. Due to lr(0.12) is large, 8 | # the iter-based and epoch-based setting have about 0.2 difference on 9 | # the mAP evaluation value. 10 | lr_config = dict(step=[15000, 20000]) 11 | runner = dict(_delete_=True, type='IterBasedRunner', max_iters=22500) 12 | checkpoint_config = dict(interval=2500) 13 | evaluation = dict(interval=4500) 14 | log_config = dict(interval=20) 15 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs2/COCO/base/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../../../configs/reppoints/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs2/COCO/coarsepointv2/coarse_point_refine_r101_fpn_1x_coco400.py: -------------------------------------------------------------------------------- 1 | debug = False 2 | 3 | # 1.1 train_pipeline:Resize; Collect(gt_true_bboxes); 4 | # 1.2 test_pipeline: load annotation; scale_factor; 5 | # 2. data: min_gt_size, train_ann(coarse), val_ann set as train_ann; test_mode 6 | # 3. evaluation: maxDets 7 | 8 | _base_ = [ 9 | 'coarse_point_refine_r50_fpn_1x_coco400.py' 10 | ] 11 | 12 | model = dict( 13 | type='BasicLocator', 14 | pretrained='torchvision://resnet101', 15 | backbone=dict( 16 | type='ResNet', 17 | depth=101, 18 | ), 19 | ) 20 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs2/COCO/p2p/p2p_r101_fpn_1x_fl_sl1_coco400_coarse.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | 'p2p_r50_fpn_1x_fl_sl1_coco400_coarse.py' 3 | ] 4 | 5 | model = dict( 6 | pretrained='torchvision://resnet101', 7 | backbone=dict( 8 | type='ResNet', 9 | depth=101, 10 | ), 11 | ) 12 | 13 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs2/TinyPerson/base/reppoints_moment_r50_fpn_gn-neck+head_1x_TinyPerson640.py: -------------------------------------------------------------------------------- 1 | _base_ = './reppoints_moment_r50_fpn_1x_TinyPerson640.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 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs2/TinyPerson/base/reppoints_moment_r50_fpns4_1x_TinyPerson640.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | "./reppoints_moment_r50_fpn_1x_TinyPerson640.py" 3 | ] 4 | 5 | model = dict( 6 | neck=dict( 7 | type='FPN', 8 | start_level=0, # 1 9 | ), 10 | bbox_head=dict( 11 | point_strides=[4, 8, 16, 32, 64], # [8, 16, 32, 64, 128] 12 | point_base_scale=2, # 4 13 | ), 14 | # training and testing settings 15 | train_cfg=dict( 16 | init=dict( 17 | assigner=dict(type='PointAssigner', scale=2, pos_num=1), # scale=4 18 | ) 19 | ) 20 | ) 21 | 22 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs2/TinyPerson/base/reppoints_moment_r50_fpns4_gn-neck+head_1x_TinyPerson640.py: -------------------------------------------------------------------------------- 1 | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_TinyPerson640.py' 2 | 3 | model = dict( 4 | neck=dict( 5 | type='FPN', 6 | start_level=0, # 1 7 | ), 8 | bbox_head=dict( 9 | point_strides=[4, 8, 16, 32, 64], # [8, 16, 32, 64, 128] 10 | point_base_scale=2, # 4 11 | ), 12 | # training and testing settings 13 | train_cfg=dict( 14 | init=dict( 15 | assigner=dict(type='PointAssigner', scale=2, pos_num=1), # scale=4 16 | ) 17 | ) 18 | ) 19 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs2/TinyPerson/base/retinanet_r50_fpns4_1x_TinyPerson640.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | './retinanet_r50_fpn_1x_TinyPerson640.py' 3 | ] 4 | 5 | # model settings 6 | model = dict( 7 | neck=dict( 8 | start_level=0, # start_level=1, 9 | # add_extra_convs='on_input', # note 10 | num_outs=5), 11 | bbox_head=dict( 12 | type='RetinaHead', 13 | num_classes=1, # 80 14 | anchor_generator=dict( 15 | type='AnchorGenerator', 16 | octave_base_scale=2, # 4 17 | ratios=[0.5, 1.0, 2.0], 18 | strides=[4, 8, 16, 32, 64] # [8, 16, 32, 64, 128] 19 | ) 20 | ) 21 | ) 22 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs2/TinyPerson/base/retinanet_r50_fpns4_1x_TinyPerson640_clipg.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | './retinanet_r50_fpn_1x_TinyPerson640.py' 3 | ] 4 | 5 | optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) # 4 gpu 6 | optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=1, norm_type=2)) # add grad clip 7 | 8 | # model settings 9 | model = dict( 10 | neck=dict( 11 | start_level=0, # start_level=1, 12 | # add_extra_convs='on_input', # note 13 | num_outs=5), 14 | bbox_head=dict( 15 | type='RetinaHead', 16 | num_classes=1, # 80 17 | anchor_generator=dict( 18 | type='AnchorGenerator', 19 | octave_base_scale=2, # 4 20 | ratios=[0.5, 1.0, 2.0], 21 | strides=[4, 8, 16, 32, 64] # [8, 16, 32, 64, 128] 22 | ) 23 | ) 24 | ) 25 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs2/TinyPersonV2/coarsepointv2/coarse_point_refine_r50_fpns4_0.5x_TinyPersonV2_640.py: -------------------------------------------------------------------------------- 1 | _base_ = ['coarse_point_refine_r50_fpns4_1x_TinyPersonV2_640.py'] 2 | 3 | 4 | lr_config = dict( 5 | policy='step', 6 | warmup='linear', 7 | warmup_iters=500, 8 | warmup_ratio=0.001, 9 | step=[4, 5]) 10 | runner = dict(type='EpochBasedRunner', max_epochs=6) 11 | -------------------------------------------------------------------------------- /TOV_mmdetection/configs2/TinyPersonV2/p2p/p2p_r50_fpns4_0.5x_fl_sl1_TinyPersonV2_640.py: -------------------------------------------------------------------------------- 1 | norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) # add 2 | 3 | _base_ = [ 4 | 'p2p_r50_fpns4_1x_fl_sl1_TinyPersonV2_640.py', 5 | ] 6 | 7 | lr_config = dict( 8 | step=[4, 5]) 9 | runner = dict(type='EpochBasedRunner', max_epochs=6) 10 | 11 | -------------------------------------------------------------------------------- /TOV_mmdetection/demo/demo.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ucas-vg/PointTinyBenchmark/5a2393ebed992509b3f0a18b84aad4232b5ebcdb/TOV_mmdetection/demo/demo.jpg -------------------------------------------------------------------------------- /TOV_mmdetection/demo/demo.mp4: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ucas-vg/PointTinyBenchmark/5a2393ebed992509b3f0a18b84aad4232b5ebcdb/TOV_mmdetection/demo/demo.mp4 -------------------------------------------------------------------------------- /TOV_mmdetection/docker/serve/config.properties: -------------------------------------------------------------------------------- 1 | inference_address=http://0.0.0.0:8080 2 | management_address=http://0.0.0.0:8081 3 | metrics_address=http://0.0.0.0:8082 4 | model_store=/home/model-server/model-store 5 | load_models=all 6 | -------------------------------------------------------------------------------- /TOV_mmdetection/docker/serve/entrypoint.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | set -e 3 | 4 | if [[ "$1" = "serve" ]]; then 5 | shift 1 6 | torchserve --start --ts-config /home/model-server/config.properties 7 | else 8 | eval "$@" 9 | fi 10 | 11 | # prevent docker exit 12 | tail -f /dev/null 13 | -------------------------------------------------------------------------------- /TOV_mmdetection/docs/Makefile: -------------------------------------------------------------------------------- 1 | # Minimal makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line, and also 5 | # from the environment for the first two. 6 | SPHINXOPTS ?= 7 | SPHINXBUILD ?= sphinx-build 8 | SOURCEDIR = . 9 | BUILDDIR = _build 10 | 11 | # Put it first so that "make" without argument is like "make help". 12 | help: 13 | @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 14 | 15 | .PHONY: help Makefile 16 | 17 | # Catch-all target: route all unknown targets to Sphinx using the new 18 | # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). 19 | %: Makefile 20 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 21 | -------------------------------------------------------------------------------- /TOV_mmdetection/docs/tutorials/index.rst: -------------------------------------------------------------------------------- 1 | .. toctree:: 2 | :maxdepth: 2 3 | 4 | config.md 5 | customize_dataset.md 6 | data_pipeline.md 7 | customize_models.md 8 | customize_runtime.md 9 | customize_losses.md 10 | finetune.md 11 | pytorch2onnx.md 12 | onnx2tensorrt.md 13 | -------------------------------------------------------------------------------- /TOV_mmdetection/exp/tools/clear_trash.sh: -------------------------------------------------------------------------------- 1 | rm ~/.local/share/Trash/files/* -rf 2 | -------------------------------------------------------------------------------- /TOV_mmdetection/exp/tools/speed_script.sh: -------------------------------------------------------------------------------- 1 | 2 | cd /home/hui/github/TOV_mmdetection 3 | 4 | 5 | # clear invalid log_dir and copy valid log to ./log dir 6 | python exp/tools/sync_log.py 7 | 8 | # clear pth 9 | python exp/tools/clear_tmp_pth.py ../TOV_mmdetection_cache/work_dir/ 10 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/apis/__init__.py: -------------------------------------------------------------------------------- 1 | from .inference import (async_inference_detector, inference_detector, 2 | init_detector, show_result_pyplot) 3 | from .test import multi_gpu_test, single_gpu_test 4 | from .train import get_root_logger, set_random_seed, train_detector 5 | 6 | __all__ = [ 7 | 'get_root_logger', 'set_random_seed', 'train_detector', 'init_detector', 8 | 'async_inference_detector', 'inference_detector', 'show_result_pyplot', 9 | 'multi_gpu_test', 'single_gpu_test' 10 | ] 11 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/core/anchor/__init__.py: -------------------------------------------------------------------------------- 1 | from .anchor_generator import (AnchorGenerator, LegacyAnchorGenerator, 2 | YOLOAnchorGenerator) 3 | from .builder import ANCHOR_GENERATORS, build_anchor_generator 4 | from .point_generator import PointGenerator 5 | from .utils import anchor_inside_flags, calc_region, images_to_levels 6 | 7 | __all__ = [ 8 | 'AnchorGenerator', 'LegacyAnchorGenerator', 'anchor_inside_flags', 9 | 'PointGenerator', 'images_to_levels', 'calc_region', 10 | 'build_anchor_generator', 'ANCHOR_GENERATORS', 'YOLOAnchorGenerator' 11 | ] 12 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/core/anchor/builder.py: -------------------------------------------------------------------------------- 1 | from mmcv.utils import Registry, build_from_cfg 2 | 3 | ANCHOR_GENERATORS = Registry('Anchor generator') 4 | 5 | 6 | def build_anchor_generator(cfg, default_args=None): 7 | return build_from_cfg(cfg, ANCHOR_GENERATORS, default_args) 8 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/core/bbox/assigners/__init__.py: -------------------------------------------------------------------------------- 1 | from .approx_max_iou_assigner import ApproxMaxIoUAssigner 2 | from .assign_result import AssignResult 3 | from .atss_assigner import ATSSAssigner 4 | from .base_assigner import BaseAssigner 5 | from .center_region_assigner import CenterRegionAssigner 6 | from .grid_assigner import GridAssigner 7 | from .hungarian_assigner import HungarianAssigner 8 | from .max_iou_assigner import MaxIoUAssigner 9 | from .point_assigner import PointAssigner 10 | from .region_assigner import RegionAssigner 11 | from .uniform_assigner import UniformAssigner 12 | 13 | __all__ = [ 14 | 'BaseAssigner', 'MaxIoUAssigner', 'ApproxMaxIoUAssigner', 'AssignResult', 15 | 'PointAssigner', 'ATSSAssigner', 'CenterRegionAssigner', 'GridAssigner', 16 | 'HungarianAssigner', 'RegionAssigner', 'UniformAssigner' 17 | ] 18 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/core/bbox/builder.py: -------------------------------------------------------------------------------- 1 | from mmcv.utils import Registry, build_from_cfg 2 | 3 | BBOX_ASSIGNERS = Registry('bbox_assigner') 4 | BBOX_SAMPLERS = Registry('bbox_sampler') 5 | BBOX_CODERS = Registry('bbox_coder') 6 | 7 | 8 | def build_assigner(cfg, **default_args): 9 | """Builder of box assigner.""" 10 | return build_from_cfg(cfg, BBOX_ASSIGNERS, default_args) 11 | 12 | 13 | def build_sampler(cfg, **default_args): 14 | """Builder of box sampler.""" 15 | return build_from_cfg(cfg, BBOX_SAMPLERS, default_args) 16 | 17 | 18 | def build_bbox_coder(cfg, **default_args): 19 | """Builder of box coder.""" 20 | return build_from_cfg(cfg, BBOX_CODERS, default_args) 21 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/core/bbox/coder/__init__.py: -------------------------------------------------------------------------------- 1 | from .base_bbox_coder import BaseBBoxCoder 2 | from .bucketing_bbox_coder import BucketingBBoxCoder 3 | from .delta_xywh_bbox_coder import DeltaXYWHBBoxCoder 4 | from .legacy_delta_xywh_bbox_coder import LegacyDeltaXYWHBBoxCoder 5 | from .pseudo_bbox_coder import PseudoBBoxCoder 6 | from .tblr_bbox_coder import TBLRBBoxCoder 7 | from .yolo_bbox_coder import YOLOBBoxCoder 8 | 9 | __all__ = [ 10 | 'BaseBBoxCoder', 'PseudoBBoxCoder', 'DeltaXYWHBBoxCoder', 11 | 'LegacyDeltaXYWHBBoxCoder', 'TBLRBBoxCoder', 'YOLOBBoxCoder', 12 | 'BucketingBBoxCoder' 13 | ] 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/core/bbox/coder/base_bbox_coder.py: -------------------------------------------------------------------------------- 1 | from abc import ABCMeta, abstractmethod 2 | 3 | 4 | class BaseBBoxCoder(metaclass=ABCMeta): 5 | """Base bounding box coder.""" 6 | 7 | def __init__(self, **kwargs): 8 | pass 9 | 10 | @abstractmethod 11 | def encode(self, bboxes, gt_bboxes): 12 | """Encode deltas between bboxes and ground truth boxes.""" 13 | 14 | @abstractmethod 15 | def decode(self, bboxes, bboxes_pred): 16 | """Decode the predicted bboxes according to prediction and base 17 | boxes.""" 18 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/core/bbox/coder/pseudo_bbox_coder.py: -------------------------------------------------------------------------------- 1 | from ..builder import BBOX_CODERS 2 | from .base_bbox_coder import BaseBBoxCoder 3 | 4 | 5 | @BBOX_CODERS.register_module() 6 | class PseudoBBoxCoder(BaseBBoxCoder): 7 | """Pseudo bounding box coder.""" 8 | 9 | def __init__(self, **kwargs): 10 | super(BaseBBoxCoder, self).__init__(**kwargs) 11 | 12 | def encode(self, bboxes, gt_bboxes): 13 | """torch.Tensor: return the given ``bboxes``""" 14 | return gt_bboxes 15 | 16 | def decode(self, bboxes, pred_bboxes): 17 | """torch.Tensor: return the given ``pred_bboxes``""" 18 | return pred_bboxes 19 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/core/bbox/samplers/__init__.py: -------------------------------------------------------------------------------- 1 | from .base_sampler import BaseSampler 2 | from .combined_sampler import CombinedSampler 3 | from .instance_balanced_pos_sampler import InstanceBalancedPosSampler 4 | from .iou_balanced_neg_sampler import IoUBalancedNegSampler 5 | from .ohem_sampler import OHEMSampler 6 | from .pseudo_sampler import PseudoSampler 7 | from .random_sampler import RandomSampler 8 | from .sampling_result import SamplingResult 9 | from .score_hlr_sampler import ScoreHLRSampler 10 | 11 | __all__ = [ 12 | 'BaseSampler', 'PseudoSampler', 'RandomSampler', 13 | 'InstanceBalancedPosSampler', 'IoUBalancedNegSampler', 'CombinedSampler', 14 | 'OHEMSampler', 'SamplingResult', 'ScoreHLRSampler' 15 | ] 16 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/core/bbox/samplers/combined_sampler.py: -------------------------------------------------------------------------------- 1 | from ..builder import BBOX_SAMPLERS, build_sampler 2 | from .base_sampler import BaseSampler 3 | 4 | 5 | @BBOX_SAMPLERS.register_module() 6 | class CombinedSampler(BaseSampler): 7 | """A sampler that combines positive sampler and negative sampler.""" 8 | 9 | def __init__(self, pos_sampler, neg_sampler, **kwargs): 10 | super(CombinedSampler, self).__init__(**kwargs) 11 | self.pos_sampler = build_sampler(pos_sampler, **kwargs) 12 | self.neg_sampler = build_sampler(neg_sampler, **kwargs) 13 | 14 | def _sample_pos(self, **kwargs): 15 | """Sample positive samples.""" 16 | raise NotImplementedError 17 | 18 | def _sample_neg(self, **kwargs): 19 | """Sample negative samples.""" 20 | raise NotImplementedError 21 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/core/evaluation/__init__.py: -------------------------------------------------------------------------------- 1 | from .class_names import (cityscapes_classes, coco_classes, dataset_aliases, 2 | get_classes, imagenet_det_classes, 3 | imagenet_vid_classes, voc_classes) 4 | from .eval_hooks import DistEvalHook, EvalHook 5 | from .mean_ap import average_precision, eval_map, print_map_summary 6 | from .recall import (eval_recalls, plot_iou_recall, plot_num_recall, 7 | print_recall_summary) 8 | 9 | __all__ = [ 10 | 'voc_classes', 'imagenet_det_classes', 'imagenet_vid_classes', 11 | 'coco_classes', 'cityscapes_classes', 'dataset_aliases', 'get_classes', 12 | 'DistEvalHook', 'EvalHook', 'average_precision', 'eval_map', 13 | 'print_map_summary', 'eval_recalls', 'print_recall_summary', 14 | 'plot_num_recall', 'plot_iou_recall' 15 | ] 16 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/core/visualization/__init__.py: -------------------------------------------------------------------------------- 1 | from .image import (color_val_matplotlib, imshow_det_bboxes, 2 | imshow_gt_det_bboxes) 3 | 4 | __all__ = ['imshow_det_bboxes', 'imshow_gt_det_bboxes', 'color_val_matplotlib'] 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/datasets/api_wrappers/__init__.py: -------------------------------------------------------------------------------- 1 | from .coco_api import COCO, COCOeval 2 | 3 | __all__ = ['COCO', 'COCOeval'] 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/__init__.py: -------------------------------------------------------------------------------- 1 | from .backbones import * # noqa: F401,F403 2 | from .builder import (BACKBONES, DETECTORS, HEADS, LOSSES, NECKS, 3 | ROI_EXTRACTORS, SHARED_HEADS, build_backbone, 4 | build_detector, build_head, build_loss, build_neck, 5 | build_roi_extractor, build_shared_head) 6 | from .dense_heads import * # noqa: F401,F403 7 | from .detectors import * # noqa: F401,F403 8 | from .losses import * # noqa: F401,F403 9 | from .necks import * # noqa: F401,F403 10 | from .roi_heads import * # noqa: F401,F403 11 | from .point import * 12 | 13 | __all__ = [ 14 | 'BACKBONES', 'NECKS', 'ROI_EXTRACTORS', 'SHARED_HEADS', 'HEADS', 'LOSSES', 15 | 'DETECTORS', 'build_backbone', 'build_neck', 'build_roi_extractor', 16 | 'build_shared_head', 'build_head', 'build_loss', 'build_detector' 17 | ] 18 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/backbones/__init__.py: -------------------------------------------------------------------------------- 1 | from .darknet import Darknet 2 | from .detectors_resnet import DetectoRS_ResNet 3 | from .detectors_resnext import DetectoRS_ResNeXt 4 | from .hourglass import HourglassNet 5 | from .hrnet import HRNet 6 | from .mobilenet_v2 import MobileNetV2 7 | from .regnet import RegNet 8 | from .res2net import Res2Net 9 | from .resnest import ResNeSt 10 | from .resnet import ResNet, ResNetV1d 11 | from .resnext import ResNeXt 12 | from .ssd_vgg import SSDVGG 13 | from .trident_resnet import TridentResNet 14 | 15 | __all__ = [ 16 | 'RegNet', 'ResNet', 'ResNetV1d', 'ResNeXt', 'SSDVGG', 'HRNet', 17 | 'MobileNetV2', 'Res2Net', 'HourglassNet', 'DetectoRS_ResNet', 18 | 'DetectoRS_ResNeXt', 'Darknet', 'ResNeSt', 'TridentResNet' 19 | ] 20 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/detectors/atss.py: -------------------------------------------------------------------------------- 1 | from ..builder import DETECTORS 2 | from .single_stage import SingleStageDetector 3 | 4 | 5 | @DETECTORS.register_module() 6 | class ATSS(SingleStageDetector): 7 | """Implementation of `ATSS `_.""" 8 | 9 | def __init__(self, 10 | backbone, 11 | neck, 12 | bbox_head, 13 | train_cfg=None, 14 | test_cfg=None, 15 | pretrained=None, 16 | init_cfg=None): 17 | super(ATSS, self).__init__(backbone, neck, bbox_head, train_cfg, 18 | test_cfg, pretrained, init_cfg) 19 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/detectors/autoassign.py: -------------------------------------------------------------------------------- 1 | from ..builder import DETECTORS 2 | from .single_stage import SingleStageDetector 3 | 4 | 5 | @DETECTORS.register_module() 6 | class AutoAssign(SingleStageDetector): 7 | """Implementation of `AutoAssign: Differentiable Label Assignment for Dense 8 | Object Detection `_.""" 9 | 10 | def __init__(self, 11 | backbone, 12 | neck, 13 | bbox_head, 14 | train_cfg=None, 15 | test_cfg=None, 16 | pretrained=None): 17 | super(AutoAssign, self).__init__(backbone, neck, bbox_head, train_cfg, 18 | test_cfg, pretrained) 19 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/detectors/detr.py: -------------------------------------------------------------------------------- 1 | from ..builder import DETECTORS 2 | from .single_stage import SingleStageDetector 3 | 4 | 5 | @DETECTORS.register_module() 6 | class DETR(SingleStageDetector): 7 | r"""Implementation of `DETR: End-to-End Object Detection with 8 | Transformers `_""" 9 | 10 | def __init__(self, 11 | backbone, 12 | bbox_head, 13 | train_cfg=None, 14 | test_cfg=None, 15 | pretrained=None, 16 | init_cfg=None): 17 | super(DETR, self).__init__(backbone, None, bbox_head, train_cfg, 18 | test_cfg, pretrained, init_cfg) 19 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/detectors/fcos.py: -------------------------------------------------------------------------------- 1 | from ..builder import DETECTORS 2 | from .single_stage import SingleStageDetector 3 | 4 | 5 | @DETECTORS.register_module() 6 | class FCOS(SingleStageDetector): 7 | """Implementation of `FCOS `_""" 8 | 9 | def __init__(self, 10 | backbone, 11 | neck, 12 | bbox_head, 13 | train_cfg=None, 14 | test_cfg=None, 15 | pretrained=None, 16 | init_cfg=None): 17 | super(FCOS, self).__init__(backbone, neck, bbox_head, train_cfg, 18 | test_cfg, pretrained, init_cfg) 19 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/detectors/fovea.py: -------------------------------------------------------------------------------- 1 | from ..builder import DETECTORS 2 | from .single_stage import SingleStageDetector 3 | 4 | 5 | @DETECTORS.register_module() 6 | class FOVEA(SingleStageDetector): 7 | """Implementation of `FoveaBox `_""" 8 | 9 | def __init__(self, 10 | backbone, 11 | neck, 12 | bbox_head, 13 | train_cfg=None, 14 | test_cfg=None, 15 | pretrained=None, 16 | init_cfg=None): 17 | super(FOVEA, self).__init__(backbone, neck, bbox_head, train_cfg, 18 | test_cfg, pretrained, init_cfg) 19 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/detectors/fsaf.py: -------------------------------------------------------------------------------- 1 | from ..builder import DETECTORS 2 | from .single_stage import SingleStageDetector 3 | 4 | 5 | @DETECTORS.register_module() 6 | class FSAF(SingleStageDetector): 7 | """Implementation of `FSAF `_""" 8 | 9 | def __init__(self, 10 | backbone, 11 | neck, 12 | bbox_head, 13 | train_cfg=None, 14 | test_cfg=None, 15 | pretrained=None, 16 | init_cfg=None): 17 | super(FSAF, self).__init__(backbone, neck, bbox_head, train_cfg, 18 | test_cfg, pretrained, init_cfg) 19 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/detectors/gfl.py: -------------------------------------------------------------------------------- 1 | from ..builder import DETECTORS 2 | from .single_stage import SingleStageDetector 3 | 4 | 5 | @DETECTORS.register_module() 6 | class GFL(SingleStageDetector): 7 | 8 | def __init__(self, 9 | backbone, 10 | neck, 11 | bbox_head, 12 | train_cfg=None, 13 | test_cfg=None, 14 | pretrained=None, 15 | init_cfg=None): 16 | super(GFL, self).__init__(backbone, neck, bbox_head, train_cfg, 17 | test_cfg, pretrained, init_cfg) 18 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/detectors/htc.py: -------------------------------------------------------------------------------- 1 | from ..builder import DETECTORS 2 | from .cascade_rcnn import CascadeRCNN 3 | 4 | 5 | @DETECTORS.register_module() 6 | class HybridTaskCascade(CascadeRCNN): 7 | """Implementation of `HTC `_""" 8 | 9 | def __init__(self, **kwargs): 10 | super(HybridTaskCascade, self).__init__(**kwargs) 11 | 12 | @property 13 | def with_semantic(self): 14 | """bool: whether the detector has a semantic head""" 15 | return self.roi_head.with_semantic 16 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/detectors/mask_rcnn.py: -------------------------------------------------------------------------------- 1 | from ..builder import DETECTORS 2 | from .two_stage import TwoStageDetector 3 | 4 | 5 | @DETECTORS.register_module() 6 | class MaskRCNN(TwoStageDetector): 7 | """Implementation of `Mask R-CNN `_""" 8 | 9 | def __init__(self, 10 | backbone, 11 | rpn_head, 12 | roi_head, 13 | train_cfg, 14 | test_cfg, 15 | neck=None, 16 | pretrained=None, 17 | init_cfg=None): 18 | super(MaskRCNN, self).__init__( 19 | backbone=backbone, 20 | neck=neck, 21 | rpn_head=rpn_head, 22 | roi_head=roi_head, 23 | train_cfg=train_cfg, 24 | test_cfg=test_cfg, 25 | pretrained=pretrained, 26 | init_cfg=init_cfg) 27 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/detectors/nasfcos.py: -------------------------------------------------------------------------------- 1 | from ..builder import DETECTORS 2 | from .single_stage import SingleStageDetector 3 | 4 | 5 | @DETECTORS.register_module() 6 | class NASFCOS(SingleStageDetector): 7 | """NAS-FCOS: Fast Neural Architecture Search for Object Detection. 8 | 9 | https://arxiv.org/abs/1906.0442 10 | """ 11 | 12 | def __init__(self, 13 | backbone, 14 | neck, 15 | bbox_head, 16 | train_cfg=None, 17 | test_cfg=None, 18 | pretrained=None, 19 | init_cfg=None): 20 | super(NASFCOS, self).__init__(backbone, neck, bbox_head, train_cfg, 21 | test_cfg, pretrained, init_cfg) 22 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/detectors/paa.py: -------------------------------------------------------------------------------- 1 | from ..builder import DETECTORS 2 | from .single_stage import SingleStageDetector 3 | 4 | 5 | @DETECTORS.register_module() 6 | class PAA(SingleStageDetector): 7 | """Implementation of `PAA `_.""" 8 | 9 | def __init__(self, 10 | backbone, 11 | neck, 12 | bbox_head, 13 | train_cfg=None, 14 | test_cfg=None, 15 | pretrained=None, 16 | init_cfg=None): 17 | super(PAA, self).__init__(backbone, neck, bbox_head, train_cfg, 18 | test_cfg, pretrained, init_cfg) 19 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/detectors/reppoints_detector.py: -------------------------------------------------------------------------------- 1 | from ..builder import DETECTORS 2 | from .single_stage import SingleStageDetector 3 | 4 | 5 | @DETECTORS.register_module() 6 | class RepPointsDetector(SingleStageDetector): 7 | """RepPoints: Point Set Representation for Object Detection. 8 | 9 | This detector is the implementation of: 10 | - RepPoints detector (https://arxiv.org/pdf/1904.11490) 11 | """ 12 | 13 | def __init__(self, 14 | backbone, 15 | neck, 16 | bbox_head, 17 | train_cfg=None, 18 | test_cfg=None, 19 | pretrained=None, 20 | init_cfg=None): 21 | super(RepPointsDetector, 22 | self).__init__(backbone, neck, bbox_head, train_cfg, test_cfg, 23 | pretrained, init_cfg) 24 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/detectors/retinanet.py: -------------------------------------------------------------------------------- 1 | from ..builder import DETECTORS 2 | from .single_stage import SingleStageDetector 3 | 4 | 5 | @DETECTORS.register_module() 6 | class RetinaNet(SingleStageDetector): 7 | """Implementation of `RetinaNet `_""" 8 | 9 | def __init__(self, 10 | backbone, 11 | neck, 12 | bbox_head, 13 | train_cfg=None, 14 | test_cfg=None, 15 | pretrained=None, 16 | init_cfg=None): 17 | super(RetinaNet, self).__init__(backbone, neck, bbox_head, train_cfg, 18 | test_cfg, pretrained, init_cfg) 19 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/detectors/vfnet.py: -------------------------------------------------------------------------------- 1 | from ..builder import DETECTORS 2 | from .single_stage import SingleStageDetector 3 | 4 | 5 | @DETECTORS.register_module() 6 | class VFNet(SingleStageDetector): 7 | """Implementation of `VarifocalNet 8 | (VFNet).`_""" 9 | 10 | def __init__(self, 11 | backbone, 12 | neck, 13 | bbox_head, 14 | train_cfg=None, 15 | test_cfg=None, 16 | pretrained=None, 17 | init_cfg=None): 18 | super(VFNet, self).__init__(backbone, neck, bbox_head, train_cfg, 19 | test_cfg, pretrained, init_cfg) 20 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/detectors/yolo.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2019 Western Digital Corporation or its affiliates. 2 | 3 | from ..builder import DETECTORS 4 | from .single_stage import SingleStageDetector 5 | 6 | 7 | @DETECTORS.register_module() 8 | class YOLOV3(SingleStageDetector): 9 | 10 | def __init__(self, 11 | backbone, 12 | neck, 13 | bbox_head, 14 | train_cfg=None, 15 | test_cfg=None, 16 | pretrained=None, 17 | init_cfg=None): 18 | super(YOLOV3, self).__init__(backbone, neck, bbox_head, train_cfg, 19 | test_cfg, pretrained, init_cfg) 20 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/detectors/yolof.py: -------------------------------------------------------------------------------- 1 | from ..builder import DETECTORS 2 | from .single_stage import SingleStageDetector 3 | 4 | 5 | @DETECTORS.register_module() 6 | class YOLOF(SingleStageDetector): 7 | r"""Implementation of `You Only Look One-level Feature 8 | `_""" 9 | 10 | def __init__(self, 11 | backbone, 12 | neck, 13 | bbox_head, 14 | train_cfg=None, 15 | test_cfg=None, 16 | pretrained=None): 17 | super(YOLOF, self).__init__(backbone, neck, bbox_head, train_cfg, 18 | test_cfg, pretrained) 19 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/necks/__init__.py: -------------------------------------------------------------------------------- 1 | from .bfp import BFP 2 | from .channel_mapper import ChannelMapper 3 | from .ct_resnet_neck import CTResNetNeck 4 | from .dilated_encoder import DilatedEncoder 5 | from .fpg import FPG 6 | from .fpn import FPN 7 | from .fpn_carafe import FPN_CARAFE 8 | from .hrfpn import HRFPN 9 | from .nas_fpn import NASFPN 10 | from .nasfcos_fpn import NASFCOS_FPN 11 | from .pafpn import PAFPN 12 | from .rfp import RFP 13 | from .yolo_neck import YOLOV3Neck 14 | 15 | __all__ = [ 16 | 'FPN', 'BFP', 'ChannelMapper', 'HRFPN', 'NASFPN', 'FPN_CARAFE', 'PAFPN', 17 | 'NASFCOS_FPN', 'RFP', 'YOLOV3Neck', 'FPG', 'DilatedEncoder', 'CTResNetNeck' 18 | ] 19 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/point/__init__.py: -------------------------------------------------------------------------------- 1 | from .dense_heads import * 2 | from .detectors import * -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/point/dense_heads/__init__.py: -------------------------------------------------------------------------------- 1 | from .cpr_head import CPRHead 2 | from .p2p_head import P2PHead 3 | 4 | 5 | __all__ = [ 6 | 'CPRHead', 'P2PHead', 7 | ] 8 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/point/detectors/__init__.py: -------------------------------------------------------------------------------- 1 | from .locator import BasicLocator 2 | 3 | __all__ = [ 4 | 'BasicLocator' 5 | ] -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/roi_heads/bbox_heads/__init__.py: -------------------------------------------------------------------------------- 1 | from .bbox_head import BBoxHead 2 | from .convfc_bbox_head import (ConvFCBBoxHead, Shared2FCBBoxHead, 3 | Shared4Conv1FCBBoxHead) 4 | from .dii_head import DIIHead 5 | from .double_bbox_head import DoubleConvFCBBoxHead 6 | from .sabl_head import SABLHead 7 | from .scnet_bbox_head import SCNetBBoxHead 8 | 9 | __all__ = [ 10 | 'BBoxHead', 'ConvFCBBoxHead', 'Shared2FCBBoxHead', 11 | 'Shared4Conv1FCBBoxHead', 'DoubleConvFCBBoxHead', 'SABLHead', 'DIIHead', 12 | 'SCNetBBoxHead' 13 | ] 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/roi_heads/mask_heads/__init__.py: -------------------------------------------------------------------------------- 1 | from .coarse_mask_head import CoarseMaskHead 2 | from .fcn_mask_head import FCNMaskHead 3 | from .feature_relay_head import FeatureRelayHead 4 | from .fused_semantic_head import FusedSemanticHead 5 | from .global_context_head import GlobalContextHead 6 | from .grid_head import GridHead 7 | from .htc_mask_head import HTCMaskHead 8 | from .mask_point_head import MaskPointHead 9 | from .maskiou_head import MaskIoUHead 10 | from .scnet_mask_head import SCNetMaskHead 11 | from .scnet_semantic_head import SCNetSemanticHead 12 | 13 | __all__ = [ 14 | 'FCNMaskHead', 'HTCMaskHead', 'FusedSemanticHead', 'GridHead', 15 | 'MaskIoUHead', 'CoarseMaskHead', 'MaskPointHead', 'SCNetMaskHead', 16 | 'SCNetSemanticHead', 'GlobalContextHead', 'FeatureRelayHead' 17 | ] 18 | -------------------------------------------------------------------------------- /TOV_mmdetection/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 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/models/roi_heads/shared_heads/__init__.py: -------------------------------------------------------------------------------- 1 | from .res_layer import ResLayer 2 | 3 | __all__ = ['ResLayer'] 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/utils/__init__.py: -------------------------------------------------------------------------------- 1 | from .collect_env import collect_env 2 | from .logger import get_root_logger 3 | 4 | __all__ = ['get_root_logger', 'collect_env'] 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/utils/collect_env.py: -------------------------------------------------------------------------------- 1 | from mmcv.utils import collect_env as collect_base_env 2 | from mmcv.utils import get_git_hash 3 | 4 | import mmdet 5 | 6 | 7 | def collect_env(): 8 | """Collect the information of the running environments.""" 9 | env_info = collect_base_env() 10 | env_info['MMDetection'] = mmdet.__version__ + '+' + get_git_hash()[:7] 11 | return env_info 12 | 13 | 14 | if __name__ == '__main__': 15 | for name, val in collect_env().items(): 16 | print(f'{name}: {val}') 17 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/utils/logger.py: -------------------------------------------------------------------------------- 1 | import logging 2 | 3 | from mmcv.utils import get_logger 4 | 5 | 6 | def get_root_logger(log_file=None, log_level=logging.INFO): 7 | """Get root logger. 8 | 9 | Args: 10 | log_file (str, optional): File path of log. Defaults to None. 11 | log_level (int, optional): The level of logger. 12 | Defaults to logging.INFO. 13 | 14 | Returns: 15 | :obj:`logging.Logger`: The obtained logger 16 | """ 17 | logger = get_logger(name='mmdet', log_file=log_file, log_level=log_level) 18 | 19 | return logger 20 | -------------------------------------------------------------------------------- /TOV_mmdetection/mmdet/version.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Open-MMLab. All rights reserved. 2 | 3 | __version__ = '2.13.0' 4 | short_version = __version__ 5 | 6 | 7 | def parse_version_info(version_str): 8 | version_info = [] 9 | for x in version_str.split('.'): 10 | if x.isdigit(): 11 | version_info.append(int(x)) 12 | elif x.find('rc') != -1: 13 | patch_version = x.split('rc') 14 | version_info.append(int(patch_version[0])) 15 | version_info.append(f'rc{patch_version[1]}') 16 | return tuple(version_info) 17 | 18 | 19 | version_info = parse_version_info(__version__) 20 | -------------------------------------------------------------------------------- /TOV_mmdetection/pytest.ini: -------------------------------------------------------------------------------- 1 | [pytest] 2 | addopts = --xdoctest --xdoctest-style=auto 3 | norecursedirs = .git ignore build __pycache__ data docker docs .eggs 4 | 5 | filterwarnings= default 6 | ignore:.*No cfgstr given in Cacher constructor or call.*:Warning 7 | ignore:.*Define the __nice__ method for.*:Warning 8 | -------------------------------------------------------------------------------- /TOV_mmdetection/requirements.txt: -------------------------------------------------------------------------------- 1 | -r requirements/build.txt 2 | -r requirements/optional.txt 3 | -r requirements/runtime.txt 4 | -r requirements/tests.txt 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/requirements/build.txt: -------------------------------------------------------------------------------- 1 | # These must be installed before building mmdetection 2 | cython 3 | numpy 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/requirements/docs.txt: -------------------------------------------------------------------------------- 1 | recommonmark 2 | sphinx 3 | sphinx_markdown_tables 4 | sphinx_rtd_theme 5 | -------------------------------------------------------------------------------- /TOV_mmdetection/requirements/mminstall.txt: -------------------------------------------------------------------------------- 1 | mmcv-full>=1.3.3 2 | -------------------------------------------------------------------------------- /TOV_mmdetection/requirements/optional.txt: -------------------------------------------------------------------------------- 1 | albumentations>=0.3.2 2 | cityscapesscripts 3 | imagecorruptions 4 | scipy 5 | sklearn 6 | -------------------------------------------------------------------------------- /TOV_mmdetection/requirements/readthedocs.txt: -------------------------------------------------------------------------------- 1 | mmcv 2 | torch 3 | torchvision 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/requirements/runtime.txt: -------------------------------------------------------------------------------- 1 | matplotlib 2 | numpy 3 | pycocotools; platform_system == "Linux" 4 | pycocotools-windows; platform_system == "Windows" 5 | six 6 | terminaltables 7 | -------------------------------------------------------------------------------- /TOV_mmdetection/requirements/tests.txt: -------------------------------------------------------------------------------- 1 | asynctest 2 | codecov 3 | flake8 4 | interrogate 5 | isort==4.3.21 6 | # Note: used for kwarray.group_items, this may be ported to mmcv in the future. 7 | kwarray 8 | onnx==1.16.0 9 | onnxruntime==1.5.1 10 | pytest 11 | ubelt 12 | xdoctest>=0.10.0 13 | yapf 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/resources/coco_test_12510.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ucas-vg/PointTinyBenchmark/5a2393ebed992509b3f0a18b84aad4232b5ebcdb/TOV_mmdetection/resources/coco_test_12510.jpg -------------------------------------------------------------------------------- /TOV_mmdetection/resources/corruptions_sev_3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ucas-vg/PointTinyBenchmark/5a2393ebed992509b3f0a18b84aad4232b5ebcdb/TOV_mmdetection/resources/corruptions_sev_3.png -------------------------------------------------------------------------------- /TOV_mmdetection/resources/data_pipeline.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ucas-vg/PointTinyBenchmark/5a2393ebed992509b3f0a18b84aad4232b5ebcdb/TOV_mmdetection/resources/data_pipeline.png -------------------------------------------------------------------------------- /TOV_mmdetection/resources/loss_curve.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ucas-vg/PointTinyBenchmark/5a2393ebed992509b3f0a18b84aad4232b5ebcdb/TOV_mmdetection/resources/loss_curve.png -------------------------------------------------------------------------------- /TOV_mmdetection/resources/mmdet-logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ucas-vg/PointTinyBenchmark/5a2393ebed992509b3f0a18b84aad4232b5ebcdb/TOV_mmdetection/resources/mmdet-logo.png -------------------------------------------------------------------------------- /TOV_mmdetection/resources/qq_group_qrcode.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ucas-vg/PointTinyBenchmark/5a2393ebed992509b3f0a18b84aad4232b5ebcdb/TOV_mmdetection/resources/qq_group_qrcode.jpg -------------------------------------------------------------------------------- /TOV_mmdetection/resources/zhihu_qrcode.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ucas-vg/PointTinyBenchmark/5a2393ebed992509b3f0a18b84aad4232b5ebcdb/TOV_mmdetection/resources/zhihu_qrcode.jpg -------------------------------------------------------------------------------- /TOV_mmdetection/setup.cfg: -------------------------------------------------------------------------------- 1 | [isort] 2 | line_length = 79 3 | multi_line_output = 0 4 | known_standard_library = setuptools 5 | known_first_party = mmdet 6 | known_third_party = PIL,asynctest,cityscapesscripts,cv2,gather_models,matplotlib,mmcv,numpy,onnx,onnxruntime,pycocotools,pytest,seaborn,six,terminaltables,torch,ts 7 | no_lines_before = STDLIB,LOCALFOLDER 8 | default_section = THIRDPARTY 9 | 10 | [yapf] 11 | BASED_ON_STYLE = pep8 12 | BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF = true 13 | SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true 14 | -------------------------------------------------------------------------------- /TOV_mmdetection/tests/test_data/test_datasets/test_xml_dataset.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | 3 | from mmdet.datasets import DATASETS 4 | 5 | 6 | def test_xml_dataset(): 7 | dataconfig = { 8 | 'ann_file': 'data/VOCdevkit/VOC2007/ImageSets/Main/test.txt', 9 | 'img_prefix': 'data/VOCdevkit/VOC2007/', 10 | 'pipeline': [{ 11 | 'type': 'LoadImageFromFile' 12 | }] 13 | } 14 | XMLDataset = DATASETS.get('XMLDataset') 15 | 16 | class XMLDatasetSubClass(XMLDataset): 17 | CLASSES = None 18 | 19 | # get_ann_info and _filter_imgs of XMLDataset 20 | # would use self.CLASSES, we added CLASSES not NONE 21 | with pytest.raises(AssertionError): 22 | XMLDatasetSubClass(**dataconfig) 23 | -------------------------------------------------------------------------------- /TOV_mmdetection/tests/test_data/test_pipelines/test_formatting.py: -------------------------------------------------------------------------------- 1 | import os.path as osp 2 | 3 | from mmcv.utils import build_from_cfg 4 | 5 | from mmdet.datasets.builder import PIPELINES 6 | 7 | 8 | def test_default_format_bundle(): 9 | results = dict( 10 | img_prefix=osp.join(osp.dirname(__file__), '../../data'), 11 | img_info=dict(filename='color.jpg')) 12 | load = dict(type='LoadImageFromFile') 13 | load = build_from_cfg(load, PIPELINES) 14 | bundle = dict(type='DefaultFormatBundle') 15 | bundle = build_from_cfg(bundle, PIPELINES) 16 | results = load(results) 17 | assert 'pad_shape' not in results 18 | assert 'scale_factor' not in results 19 | assert 'img_norm_cfg' not in results 20 | results = bundle(results) 21 | assert 'pad_shape' in results 22 | assert 'scale_factor' in results 23 | assert 'img_norm_cfg' in results 24 | -------------------------------------------------------------------------------- /TOV_mmdetection/tests/test_models/test_backbones/__init__.py: -------------------------------------------------------------------------------- 1 | from .utils import check_norm_state, is_block, is_norm 2 | 3 | __all__ = ['is_block', 'is_norm', 'check_norm_state'] 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/tests/test_models/test_loss.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | import torch 3 | 4 | from mmdet.models.losses import (BoundedIoULoss, CIoULoss, DIoULoss, GIoULoss, 5 | IoULoss) 6 | 7 | 8 | @pytest.mark.parametrize( 9 | 'loss_class', [IoULoss, BoundedIoULoss, GIoULoss, DIoULoss, CIoULoss]) 10 | def test_iou_type_loss_zeros_weight(loss_class): 11 | pred = torch.rand((10, 4)) 12 | target = torch.rand((10, 4)) 13 | weight = torch.zeros(10) 14 | 15 | loss = loss_class()(pred, target, weight) 16 | assert loss == 0. 17 | -------------------------------------------------------------------------------- /TOV_mmdetection/tests/test_models/test_roi_heads/__init__.py: -------------------------------------------------------------------------------- 1 | from .utils import _dummy_bbox_sampling 2 | 3 | __all__ = ['_dummy_bbox_sampling'] 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/tests/test_models/test_utils/test_se_layer.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | import torch 3 | 4 | from mmdet.models.utils import SELayer 5 | 6 | 7 | def test_se_layer(): 8 | with pytest.raises(AssertionError): 9 | # act_cfg sequence length must equal to 2 10 | SELayer(channels=32, act_cfg=(dict(type='ReLU'), )) 11 | 12 | with pytest.raises(AssertionError): 13 | # act_cfg sequence must be a tuple of dict 14 | SELayer(channels=32, act_cfg=[dict(type='ReLU'), dict(type='ReLU')]) 15 | 16 | # Test SELayer forward 17 | layer = SELayer(channels=32) 18 | layer.init_weights() 19 | layer.train() 20 | 21 | x = torch.randn((1, 32, 10, 10)) 22 | x_out = layer(x) 23 | assert x_out.shape == torch.Size((1, 32, 10, 10)) 24 | -------------------------------------------------------------------------------- /TOV_mmdetection/tests/test_onnx/__init__.py: -------------------------------------------------------------------------------- 1 | from .utils import ort_validate 2 | 3 | __all__ = ['ort_validate'] 4 | -------------------------------------------------------------------------------- /TOV_mmdetection/tests/test_utils/test_version.py: -------------------------------------------------------------------------------- 1 | from mmdet import digit_version 2 | 3 | 4 | def test_version_check(): 5 | assert digit_version('1.0.5') > digit_version('1.0.5rc0') 6 | assert digit_version('1.0.5') > digit_version('1.0.4rc0') 7 | assert digit_version('1.0.5') > digit_version('1.0rc0') 8 | assert digit_version('1.0.0') > digit_version('0.6.2') 9 | assert digit_version('1.0.0') > digit_version('0.2.16') 10 | assert digit_version('1.0.5rc0') > digit_version('1.0.0rc0') 11 | assert digit_version('1.0.0rc1') > digit_version('1.0.0rc0') 12 | assert digit_version('1.0.0rc2') > digit_version('1.0.0rc0') 13 | assert digit_version('1.0.0rc2') > digit_version('1.0.0rc1') 14 | assert digit_version('1.0.1rc1') > digit_version('1.0.0rc1') 15 | assert digit_version('1.0.0') > digit_version('1.0.0rc1') 16 | -------------------------------------------------------------------------------- /TOV_mmdetection/tools/dist_test.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | CONFIG=$1 4 | CHECKPOINT=$2 5 | GPUS=$3 6 | PORT=${PORT:-29500} 7 | 8 | PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ 9 | python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \ 10 | $(dirname "$0")/test.py $CONFIG $CHECKPOINT --launcher pytorch ${@:4} 11 | -------------------------------------------------------------------------------- /TOV_mmdetection/tools/dist_train.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | CONFIG=$1 4 | GPUS=$2 5 | PORT=${PORT:-29500} 6 | 7 | PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ 8 | python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \ 9 | $(dirname "$0")/train.py $CONFIG --launcher pytorch ${@:3} 10 | -------------------------------------------------------------------------------- /TOV_mmdetection/tools/slurm_test.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -x 4 | 5 | PARTITION=$1 6 | JOB_NAME=$2 7 | CONFIG=$3 8 | CHECKPOINT=$4 9 | GPUS=${GPUS:-8} 10 | GPUS_PER_NODE=${GPUS_PER_NODE:-8} 11 | CPUS_PER_TASK=${CPUS_PER_TASK:-5} 12 | PY_ARGS=${@:5} 13 | SRUN_ARGS=${SRUN_ARGS:-""} 14 | 15 | PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ 16 | srun -p ${PARTITION} \ 17 | --job-name=${JOB_NAME} \ 18 | --gres=gpu:${GPUS_PER_NODE} \ 19 | --ntasks=${GPUS} \ 20 | --ntasks-per-node=${GPUS_PER_NODE} \ 21 | --cpus-per-task=${CPUS_PER_TASK} \ 22 | --kill-on-bad-exit=1 \ 23 | ${SRUN_ARGS} \ 24 | python -u tools/test.py ${CONFIG} ${CHECKPOINT} --launcher="slurm" ${PY_ARGS} 25 | -------------------------------------------------------------------------------- /TOV_mmdetection/tools/slurm_train.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -x 4 | 5 | PARTITION=$1 6 | JOB_NAME=$2 7 | CONFIG=$3 8 | WORK_DIR=$4 9 | GPUS=${GPUS:-8} 10 | GPUS_PER_NODE=${GPUS_PER_NODE:-8} 11 | CPUS_PER_TASK=${CPUS_PER_TASK:-5} 12 | SRUN_ARGS=${SRUN_ARGS:-""} 13 | PY_ARGS=${@:5} 14 | 15 | PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ 16 | srun -p ${PARTITION} \ 17 | --job-name=${JOB_NAME} \ 18 | --gres=gpu:${GPUS_PER_NODE} \ 19 | --ntasks=${GPUS} \ 20 | --ntasks-per-node=${GPUS_PER_NODE} \ 21 | --cpus-per-task=${CPUS_PER_TASK} \ 22 | --kill-on-bad-exit=1 \ 23 | ${SRUN_ARGS} \ 24 | python -u tools/train.py ${CONFIG} --work-dir=${WORK_DIR} --launcher="slurm" ${PY_ARGS} 25 | -------------------------------------------------------------------------------- /dataset/LegallyItem: -------------------------------------------------------------------------------- 1 | > Even most of the instances in our dataset are unidentifiable, and there can be no privacy issues. 2 | But to deal with privacy issues thoroughly, We must highlight some control terms for the released dataset is released 3 | just like other released datasets with human faces: 4 | ``` 5 | 1) Spreading image with clear person in TinyPerson V2 on the Socaial Internet is prohibited. 6 | 2) Images of identifiable people cannot be used to make demos and promotions without deidentifical. 7 | 3) The image in TinyPersonV2 can not be used for commercial/business purpose. 8 | 9 | The downloaders may be held legally liable if violating the above items. 10 | ``` -------------------------------------------------------------------------------- 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4 | [Baidu Pan](https://pan.baidu.com/s/1EDsDkeltZ0oSqKwRCrKTIA): i22c 5 | 6 | 7 | # Object Localization under Single Coarse Point Supervision (CVPR2022) 8 | 9 | [Baidu Yun passwd:y71n](https://pan.baidu.com/s/1v9HIkfBChsUJRUGhisUrwA) --------------------------------------------------------------------------------