Full Code of bagh2178/SG-Nav for AI

gitextract_t68xqa9y/

├── .gitignore
├── GLIP/
│   ├── CODE_OF_CONDUCT.md
│   ├── DATA.md
│   ├── LICENSE
│   ├── README.md
│   ├── SECURITY.md
│   ├── SUPPORT.md
│   ├── configs/
│   │   ├── flickr/
│   │   │   ├── test.yaml
│   │   │   └── val.yaml
│   │   ├── lvis/
│   │   │   ├── minival.yaml
│   │   │   └── val.yaml
│   │   ├── odinw/
│   │   │   └── Aquarium_Aquarium_Combined.v2-raw-1024.coco.yaml
│   │   └── pretrain/
│   │       ├── glip_Swin_L.yaml
│   │       └── glip_Swin_T_O365_GoldG.yaml
│   ├── maskrcnn_benchmark/
│   │   ├── __init__.py
│   │   ├── config/
│   │   │   ├── __init__.py
│   │   │   ├── defaults.py
│   │   │   └── paths_catalog.py
│   │   ├── csrc/
│   │   │   ├── ROIAlign.h
│   │   │   ├── ROIPool.h
│   │   │   ├── SigmoidFocalLoss.h
│   │   │   ├── cpu/
│   │   │   │   ├── ROIAlign_cpu.cpp
│   │   │   │   ├── nms_cpu.cpp
│   │   │   │   ├── soft_nms.cpp
│   │   │   │   └── vision.h
│   │   │   ├── cuda/
│   │   │   │   ├── ROIAlign_cuda.cu
│   │   │   │   ├── ROIPool_cuda.cu
│   │   │   │   ├── SigmoidFocalLoss_cuda.cu
│   │   │   │   ├── deform_conv_cuda.cu
│   │   │   │   ├── deform_conv_kernel_cuda.cu
│   │   │   │   ├── deform_pool_cuda.cu
│   │   │   │   ├── deform_pool_kernel_cuda.cu
│   │   │   │   ├── ml_nms.cu
│   │   │   │   ├── nms.cu
│   │   │   │   └── vision.h
│   │   │   ├── deform_conv.h
│   │   │   ├── deform_pool.h
│   │   │   ├── ml_nms.h
│   │   │   ├── nms.h
│   │   │   └── vision.cpp
│   │   ├── data/
│   │   │   ├── __init__.py
│   │   │   ├── build.py
│   │   │   ├── collate_batch.py
│   │   │   ├── datasets/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── background.py
│   │   │   │   ├── box_label_loader.py
│   │   │   │   ├── caption.py
│   │   │   │   ├── coco.py
│   │   │   │   ├── coco_dt.py
│   │   │   │   ├── concat_dataset.py
│   │   │   │   ├── custom_distributed_sampler.py
│   │   │   │   ├── duplicate_dataset.py
│   │   │   │   ├── evaluation/
│   │   │   │   │   ├── __init__.py
│   │   │   │   │   ├── box_aug.py
│   │   │   │   │   ├── coco/
│   │   │   │   │   │   ├── __init__.py
│   │   │   │   │   │   └── coco_eval.py
│   │   │   │   │   ├── flickr/
│   │   │   │   │   │   ├── __init__.py
│   │   │   │   │   │   └── flickr_eval.py
│   │   │   │   │   ├── lvis/
│   │   │   │   │   │   ├── _change_lvis_annotation.py
│   │   │   │   │   │   ├── lvis.py
│   │   │   │   │   │   └── lvis_eval.py
│   │   │   │   │   ├── od_eval.py
│   │   │   │   │   ├── od_to_grounding/
│   │   │   │   │   │   ├── __init__.py
│   │   │   │   │   │   └── od_eval.py
│   │   │   │   │   ├── vg/
│   │   │   │   │   │   ├── __init__.py
│   │   │   │   │   │   └── vg_eval.py
│   │   │   │   │   └── voc/
│   │   │   │   │       ├── __init__.py
│   │   │   │   │       └── voc_eval.py
│   │   │   │   ├── flickr.py
│   │   │   │   ├── gqa.py
│   │   │   │   ├── imagenet.py
│   │   │   │   ├── list_dataset.py
│   │   │   │   ├── lvis.py
│   │   │   │   ├── mixed.py
│   │   │   │   ├── mixup.py
│   │   │   │   ├── modulated_coco.py
│   │   │   │   ├── object365.py
│   │   │   │   ├── od_to_grounding.py
│   │   │   │   ├── phrasecut.py
│   │   │   │   ├── pseudo_data.py
│   │   │   │   ├── refexp.py
│   │   │   │   ├── tsv.py
│   │   │   │   ├── vg.py
│   │   │   │   └── voc.py
│   │   │   ├── samplers/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── distributed.py
│   │   │   │   ├── grouped_batch_sampler.py
│   │   │   │   └── iteration_based_batch_sampler.py
│   │   │   └── transforms/
│   │   │       ├── __init__.py
│   │   │       ├── build.py
│   │   │       └── transforms.py
│   │   ├── engine/
│   │   │   ├── __init__.py
│   │   │   ├── alter_trainer.py
│   │   │   ├── evolution.py
│   │   │   ├── inference.py
│   │   │   ├── predictor.py
│   │   │   ├── predictor_glip.py
│   │   │   ├── singlepath_trainer.py
│   │   │   ├── stage_trainer.py
│   │   │   └── trainer.py
│   │   ├── layers/
│   │   │   ├── __init__.py
│   │   │   ├── batch_norm.py
│   │   │   ├── deform_conv.py
│   │   │   ├── deform_pool.py
│   │   │   ├── dropblock.py
│   │   │   ├── dyhead.py
│   │   │   ├── dyrelu.py
│   │   │   ├── evonorm.py
│   │   │   ├── iou_loss.py
│   │   │   ├── misc.py
│   │   │   ├── nms.py
│   │   │   ├── roi_align.py
│   │   │   ├── roi_pool.py
│   │   │   ├── se.py
│   │   │   ├── set_loss.py
│   │   │   ├── sigmoid_focal_loss.py
│   │   │   └── smooth_l1_loss.py
│   │   ├── modeling/
│   │   │   ├── __init__.py
│   │   │   ├── backbone/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── bifpn.py
│   │   │   │   ├── blocks.py
│   │   │   │   ├── efficientdet.py
│   │   │   │   ├── efficientnet.py
│   │   │   │   ├── fbnet.py
│   │   │   │   ├── fpn.py
│   │   │   │   ├── mixer.py
│   │   │   │   ├── ops.py
│   │   │   │   ├── resnet.py
│   │   │   │   ├── swint.py
│   │   │   │   ├── swint_v2.py
│   │   │   │   ├── swint_v2_vl.py
│   │   │   │   └── swint_vl.py
│   │   │   ├── balanced_positive_negative_sampler.py
│   │   │   ├── box_coder.py
│   │   │   ├── detector/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── generalized_rcnn.py
│   │   │   │   └── generalized_vl_rcnn.py
│   │   │   ├── language_backbone/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── backbone.py
│   │   │   │   ├── bert_model.py
│   │   │   │   ├── build.py
│   │   │   │   ├── clip_model.py
│   │   │   │   ├── hfpt_tokenizer.py
│   │   │   │   ├── rnn_model.py
│   │   │   │   ├── simple_tokenizer.py
│   │   │   │   ├── test_clip_tokenizer.py
│   │   │   │   └── word_utils.py
│   │   │   ├── make_layers.py
│   │   │   ├── matcher.py
│   │   │   ├── poolers.py
│   │   │   ├── registry.py
│   │   │   ├── roi_heads/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── box_head/
│   │   │   │   │   ├── __init__.py
│   │   │   │   │   ├── box_head.py
│   │   │   │   │   ├── inference.py
│   │   │   │   │   ├── loss.py
│   │   │   │   │   ├── roi_box_feature_extractors.py
│   │   │   │   │   └── roi_box_predictors.py
│   │   │   │   ├── keypoint_head/
│   │   │   │   │   ├── inference.py
│   │   │   │   │   ├── keypoint_head.py
│   │   │   │   │   ├── loss.py
│   │   │   │   │   ├── roi_keypoint_feature_extractors.py
│   │   │   │   │   └── roi_keypoint_predictors.py
│   │   │   │   └── mask_head/
│   │   │   │       ├── __init__.py
│   │   │   │       ├── hourglass.py
│   │   │   │       ├── inference.py
│   │   │   │       ├── loss.py
│   │   │   │       ├── mask_head.py
│   │   │   │       ├── roi_mask_feature_extractors.py
│   │   │   │       └── roi_mask_predictors.py
│   │   │   ├── rpn/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── anchor_generator.py
│   │   │   │   ├── atss.py
│   │   │   │   ├── dyhead.py
│   │   │   │   ├── fcos.py
│   │   │   │   ├── inference.py
│   │   │   │   ├── loss.py
│   │   │   │   ├── modeling_bert.py
│   │   │   │   ├── retina.py
│   │   │   │   ├── rpn.py
│   │   │   │   ├── transformer.py
│   │   │   │   └── vldyhead.py
│   │   │   └── utils.py
│   │   ├── solver/
│   │   │   ├── __init__.py
│   │   │   ├── build.py
│   │   │   └── lr_scheduler.py
│   │   ├── structures/
│   │   │   ├── __init__.py
│   │   │   ├── bounding_box.py
│   │   │   ├── boxlist_ops.py
│   │   │   ├── image_list.py
│   │   │   ├── keypoint.py
│   │   │   └── segmentation_mask.py
│   │   └── utils/
│   │       ├── README.md
│   │       ├── __init__.py
│   │       ├── amp.py
│   │       ├── big_model_loading.py
│   │       ├── c2_model_loading.py
│   │       ├── checkpoint.py
│   │       ├── collect_env.py
│   │       ├── comm.py
│   │       ├── cv2_util.py
│   │       ├── dist.py
│   │       ├── ema.py
│   │       ├── env.py
│   │       ├── flops.py
│   │       ├── fuse_helper.py
│   │       ├── imports.py
│   │       ├── logger.py
│   │       ├── mdetr_dist.py
│   │       ├── metric_logger.py
│   │       ├── miscellaneous.py
│   │       ├── model_serialization.py
│   │       ├── model_zoo.py
│   │       ├── pretrain_model_loading.py
│   │       ├── registry.py
│   │       ├── shallow_contrastive_loss_helper.py
│   │       └── stats.py
│   ├── setup.py
│   └── tools/
│       ├── cityscapes/
│       │   ├── convert_cityscapes_to_coco.py
│       │   └── instances2dict_with_polygons.py
│       ├── eval_all.py
│       ├── finetune.py
│       ├── test_grounding_net.py
│       ├── test_net.py
│       └── train_net.py
├── GroundingDINO/
│   ├── LICENSE
│   ├── README.md
│   ├── demo/
│   │   ├── gradio_app.py
│   │   └── inference_on_a_image.py
│   ├── groundingdino/
│   │   ├── __init__.py
│   │   ├── config/
│   │   │   ├── GroundingDINO_SwinB.py
│   │   │   └── GroundingDINO_SwinT_OGC.py
│   │   ├── datasets/
│   │   │   ├── __init__.py
│   │   │   └── transforms.py
│   │   ├── models/
│   │   │   ├── GroundingDINO/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── backbone/
│   │   │   │   │   ├── __init__.py
│   │   │   │   │   ├── backbone.py
│   │   │   │   │   ├── position_encoding.py
│   │   │   │   │   └── swin_transformer.py
│   │   │   │   ├── bertwarper.py
│   │   │   │   ├── csrc/
│   │   │   │   │   ├── MsDeformAttn/
│   │   │   │   │   │   ├── ms_deform_attn.h
│   │   │   │   │   │   ├── ms_deform_attn_cpu.cpp
│   │   │   │   │   │   ├── ms_deform_attn_cpu.h
│   │   │   │   │   │   ├── ms_deform_attn_cuda.cu
│   │   │   │   │   │   ├── ms_deform_attn_cuda.h
│   │   │   │   │   │   └── ms_deform_im2col_cuda.cuh
│   │   │   │   │   ├── cuda_version.cu
│   │   │   │   │   └── vision.cpp
│   │   │   │   ├── fuse_modules.py
│   │   │   │   ├── groundingdino.py
│   │   │   │   ├── ms_deform_attn.py
│   │   │   │   ├── transformer.py
│   │   │   │   ├── transformer_vanilla.py
│   │   │   │   └── utils.py
│   │   │   ├── __init__.py
│   │   │   └── registry.py
│   │   ├── util/
│   │   │   ├── __init__.py
│   │   │   ├── box_ops.py
│   │   │   ├── get_tokenlizer.py
│   │   │   ├── inference.py
│   │   │   ├── logger.py
│   │   │   ├── misc.py
│   │   │   ├── slconfig.py
│   │   │   ├── slio.py
│   │   │   ├── time_counter.py
│   │   │   ├── utils.py
│   │   │   ├── visualizer.py
│   │   │   └── vl_utils.py
│   │   └── version.py
│   ├── pyproject.toml
│   ├── requirements.txt
│   └── setup.py
├── LICENSE
├── README.md
├── SG_Nav.py
├── configs/
│   ├── challenge_objectnav2021.local.rgbd.yaml
│   ├── challenge_objectnav2022.local.rgbd.yaml
│   ├── challenge_pointnav2021.local.rgbd.yaml
│   ├── challenge_pointnav2021.local.rgbd_test_scene.yaml
│   ├── ddppo_objectnav.yaml
│   ├── ddppo_pointnav.yaml
│   └── ddppo_pointnav_.yaml
├── habitat-lab/
│   ├── .circleci/
│   │   └── config.yml
│   ├── .editorconfig
│   ├── .github/
│   │   ├── ISSUE_TEMPLATE/
│   │   │   ├── bug-report.md
│   │   │   ├── feature-request.md
│   │   │   └── questions-help-support.md
│   │   └── PULL_REQUEST_TEMPLATE.md
│   ├── .gitignore
│   ├── .pre-commit-config.yaml
│   ├── CODE_OF_CONDUCT.md
│   ├── CONTRIBUTING.md
│   ├── Dockerfile
│   ├── LICENSE
│   ├── MANIFEST.in
│   ├── README.md
│   ├── configs/
│   │   ├── baselines/
│   │   │   └── ppo.yaml
│   │   ├── datasets/
│   │   │   ├── eqa/
│   │   │   │   └── mp3d.yaml
│   │   │   ├── imagenav/
│   │   │   │   ├── gibson.yaml
│   │   │   │   └── mp3d.yaml
│   │   │   ├── objectnav/
│   │   │   │   ├── hm3d.yaml
│   │   │   │   └── mp3d.yaml
│   │   │   ├── pointnav/
│   │   │   │   ├── gibson.yaml
│   │   │   │   ├── gibson_0_plus.yaml
│   │   │   │   ├── gibson_v2.yaml
│   │   │   │   ├── habitat_test.yaml
│   │   │   │   ├── hm3d.yaml
│   │   │   │   └── mp3d.yaml
│   │   │   ├── rearrangepick/
│   │   │   │   └── replica_cad.yaml
│   │   │   ├── single_episode.yaml
│   │   │   └── vln/
│   │   │       └── mp3d_r2r.yaml
│   │   ├── tasks/
│   │   │   ├── eqa_mp3d.yaml
│   │   │   ├── imagenav.yaml
│   │   │   ├── imagenav_gibson.yaml
│   │   │   ├── objectnav_hm3d.yaml
│   │   │   ├── objectnav_mp3d.yaml
│   │   │   ├── pointnav.yaml
│   │   │   ├── pointnav_gibson.yaml
│   │   │   ├── pointnav_hm3d.yaml
│   │   │   ├── pointnav_mp3d.yaml
│   │   │   ├── pointnav_rgbd.yaml
│   │   │   ├── rearrange/
│   │   │   │   ├── pick.yaml
│   │   │   │   ├── pick_spa.yaml
│   │   │   │   ├── pick_state.yaml
│   │   │   │   └── play.yaml
│   │   │   └── vln_r2r.yaml
│   │   └── test/
│   │       ├── habitat_all_sensors_test.yaml
│   │       ├── habitat_mp3d_eqa_test.yaml
│   │       ├── habitat_mp3d_object_nav_test.yaml
│   │       ├── habitat_r2r_vln_test.yaml
│   │       └── new_keys_test.yaml
│   ├── docs/
│   │   ├── .gitignore
│   │   ├── build-public.sh
│   │   ├── build.sh
│   │   ├── conf-public.py
│   │   ├── conf.py
│   │   ├── docs.rst
│   │   └── pages/
│   │       ├── habitat-lab-demo.rst
│   │       ├── habitat-sim-demo.rst
│   │       ├── index.rst
│   │       ├── quickstart.rst
│   │       └── view-transform-warp.rst
│   ├── examples/
│   │   ├── __init__.py
│   │   ├── benchmark.py
│   │   ├── example.py
│   │   ├── example_pointnav.py
│   │   ├── interactive_play.py
│   │   ├── new_actions.py
│   │   ├── register_new_sensors_and_measures.py
│   │   ├── shortest_path_follower_example.py
│   │   ├── tutorials/
│   │   │   ├── colabs/
│   │   │   │   └── Habitat_Lab.ipynb
│   │   │   └── nb_python/
│   │   │       └── Habitat_Lab.py
│   │   ├── visualization_examples.py
│   │   ├── vln_benchmark.py
│   │   └── vln_reference_path_follower_example.py
│   ├── habitat/
│   │   ├── __init__.py
│   │   ├── config/
│   │   │   ├── __init__.py
│   │   │   └── default.py
│   │   ├── core/
│   │   │   ├── __init__.py
│   │   │   ├── agent.py
│   │   │   ├── benchmark.py
│   │   │   ├── challenge.py
│   │   │   ├── dataset.py
│   │   │   ├── embodied_task.py
│   │   │   ├── env.py
│   │   │   ├── environments.py
│   │   │   ├── logging.py
│   │   │   ├── registry.py
│   │   │   ├── simulator.py
│   │   │   ├── spaces.py
│   │   │   ├── utils.py
│   │   │   └── vector_env.py
│   │   ├── datasets/
│   │   │   ├── __init__.py
│   │   │   ├── eqa/
│   │   │   │   ├── __init__.py
│   │   │   │   └── mp3d_eqa_dataset.py
│   │   │   ├── object_nav/
│   │   │   │   ├── __init__.py
│   │   │   │   └── object_nav_dataset.py
│   │   │   ├── pointnav/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── pointnav_dataset.py
│   │   │   │   └── pointnav_generator.py
│   │   │   ├── rearrange/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── configs/
│   │   │   │   │   ├── pick/
│   │   │   │   │   │   └── counter.yaml
│   │   │   │   │   └── test_config.yaml
│   │   │   │   ├── generate_episode_inits.py
│   │   │   │   ├── rearrange_dataset.py
│   │   │   │   ├── rearrange_generator.py
│   │   │   │   ├── receptacle.py
│   │   │   │   └── samplers.py
│   │   │   ├── registration.py
│   │   │   ├── utils.py
│   │   │   └── vln/
│   │   │       ├── __init__.py
│   │   │       └── r2r_vln_dataset.py
│   │   ├── py.typed
│   │   ├── sims/
│   │   │   ├── __init__.py
│   │   │   ├── habitat_simulator/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── actions.py
│   │   │   │   ├── debug_visualizer.py
│   │   │   │   ├── habitat_simulator.py
│   │   │   │   └── sim_utilities.py
│   │   │   ├── pyrobot/
│   │   │   │   ├── __init__.py
│   │   │   │   └── pyrobot.py
│   │   │   └── registration.py
│   │   ├── tasks/
│   │   │   ├── __init__.py
│   │   │   ├── eqa/
│   │   │   │   ├── __init__.py
│   │   │   │   └── eqa.py
│   │   │   ├── nav/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── nav.py
│   │   │   │   ├── object_nav_task.py
│   │   │   │   └── shortest_path_follower.py
│   │   │   ├── rearrange/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── actions.py
│   │   │   │   ├── grip_actions.py
│   │   │   │   ├── marker_info.py
│   │   │   │   ├── policy_modules.py
│   │   │   │   ├── rearrange_grasp_manager.py
│   │   │   │   ├── rearrange_sensors.py
│   │   │   │   ├── rearrange_sim.py
│   │   │   │   ├── rearrange_task.py
│   │   │   │   ├── sub_tasks/
│   │   │   │   │   ├── pick_sensors.py
│   │   │   │   │   └── pick_task.py
│   │   │   │   └── utils.py
│   │   │   ├── registration.py
│   │   │   ├── utils.py
│   │   │   └── vln/
│   │   │       ├── __init__.py
│   │   │       └── vln.py
│   │   ├── utils/
│   │   │   ├── __init__.py
│   │   │   ├── common.py
│   │   │   ├── geometry_utils.py
│   │   │   ├── pickle5_multiprocessing.py
│   │   │   ├── profiling_wrapper.py
│   │   │   ├── test_utils.py
│   │   │   └── visualizations/
│   │   │       ├── __init__.py
│   │   │       ├── fog_of_war.py
│   │   │       ├── maps.py
│   │   │       └── utils.py
│   │   └── version.py
│   ├── habitat_baselines/
│   │   ├── README.md
│   │   ├── __init__.py
│   │   ├── agents/
│   │   │   ├── __init__.py
│   │   │   ├── benchmark_gym.py
│   │   │   ├── mp_agents.py
│   │   │   ├── ppo_agents.py
│   │   │   ├── simple_agents.py
│   │   │   └── slam_agents.py
│   │   ├── common/
│   │   │   ├── __init__.py
│   │   │   ├── base_il_trainer.py
│   │   │   ├── base_trainer.py
│   │   │   ├── baseline_registry.py
│   │   │   ├── environments.py
│   │   │   ├── obs_transformers.py
│   │   │   ├── rollout_storage.py
│   │   │   ├── tensor_dict.py
│   │   │   └── tensorboard_utils.py
│   │   ├── config/
│   │   │   ├── __init__.py
│   │   │   ├── default.py
│   │   │   ├── eqa/
│   │   │   │   ├── il_eqa_cnn_pretrain.yaml
│   │   │   │   ├── il_pacman_nav.yaml
│   │   │   │   └── il_vqa.yaml
│   │   │   ├── imagenav/
│   │   │   │   ├── ddppo_imagenav_example.yaml
│   │   │   │   ├── ddppo_imagenav_gibson.yaml
│   │   │   │   └── ppo_imagenav_example.yaml
│   │   │   ├── objectnav/
│   │   │   │   ├── ddppo_objectnav.yaml
│   │   │   │   └── ddppo_objectnav_hm3d.yaml
│   │   │   ├── pointnav/
│   │   │   │   ├── ddppo_pointnav.yaml
│   │   │   │   ├── ppo_pointnav.yaml
│   │   │   │   ├── ppo_pointnav_example.yaml
│   │   │   │   └── ppo_pointnav_habitat_iccv19.yaml
│   │   │   ├── rearrange/
│   │   │   │   ├── rl_pick.yaml
│   │   │   │   ├── rl_pick_state.yaml
│   │   │   │   └── spap_pick.yaml
│   │   │   └── test/
│   │   │       ├── ddppo_imagenav_test.yaml
│   │   │       ├── ddppo_pointnav_test.yaml
│   │   │       ├── ppo_imagenav_test.yaml
│   │   │       └── ppo_pointnav_test.yaml
│   │   ├── il/
│   │   │   ├── README.md
│   │   │   ├── __init__.py
│   │   │   ├── metrics.py
│   │   │   ├── models/
│   │   │   │   ├── __init__.py
│   │   │   │   └── models.py
│   │   │   ├── requirements.txt
│   │   │   └── trainers/
│   │   │       ├── __init__.py
│   │   │       ├── eqa_cnn_pretrain_trainer.py
│   │   │       ├── pacman_trainer.py
│   │   │       └── vqa_trainer.py
│   │   ├── motion_planning/
│   │   │   ├── __init__.py
│   │   │   ├── grasp_generator.py
│   │   │   ├── motion_plan.py
│   │   │   ├── mp_sim.py
│   │   │   ├── mp_spaces.py
│   │   │   └── robot_target.py
│   │   ├── py.typed
│   │   ├── rl/
│   │   │   ├── __init__.py
│   │   │   ├── ddppo/
│   │   │   │   ├── README.md
│   │   │   │   ├── __init__.py
│   │   │   │   ├── algo/
│   │   │   │   │   ├── __init__.py
│   │   │   │   │   └── ddppo.py
│   │   │   │   ├── data_generation/
│   │   │   │   │   ├── create_gibson_large_dataset.py
│   │   │   │   │   └── gibson_dset_with_qual.json
│   │   │   │   ├── ddp_utils.py
│   │   │   │   ├── multi_node_slurm.sh
│   │   │   │   ├── policy/
│   │   │   │   │   ├── __init__.py
│   │   │   │   │   ├── resnet.py
│   │   │   │   │   ├── resnet_policy.py
│   │   │   │   │   └── running_mean_and_var.py
│   │   │   │   ├── requirements.txt
│   │   │   │   └── single_node.sh
│   │   │   ├── models/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── rnn_state_encoder.py
│   │   │   │   └── simple_cnn.py
│   │   │   ├── ppo/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── policy.py
│   │   │   │   ├── ppo.py
│   │   │   │   └── ppo_trainer.py
│   │   │   └── requirements.txt
│   │   ├── run.py
│   │   ├── slambased/
│   │   │   ├── README.md
│   │   │   ├── __init__.py
│   │   │   ├── install_deps.sh
│   │   │   ├── mappers.py
│   │   │   ├── monodepth.py
│   │   │   ├── path_planners.py
│   │   │   ├── reprojection.py
│   │   │   ├── requirements.txt
│   │   │   └── utils.py
│   │   └── utils/
│   │       ├── __init__.py
│   │       ├── common.py
│   │       ├── env_utils.py
│   │       ├── gym_adapter.py
│   │       ├── gym_definitions.py
│   │       ├── render_wrapper.py
│   │       └── visualizations/
│   │           ├── __init__.py
│   │           └── utils.py
│   ├── mypy.ini
│   ├── pyproject.toml
│   ├── requirements.txt
│   ├── scripts/
│   │   └── generate_profile_shell_scripts.py
│   ├── setup.cfg
│   ├── setup.py
│   └── test/
│       ├── test_baseline_agents.py
│       ├── test_baseline_trainers.py
│       ├── test_config.py
│       ├── test_dataset.py
│       ├── test_ddppo_reduce.py
│       ├── test_demo_notebook.py
│       ├── test_examples.py
│       ├── test_gym_wrapper.py
│       ├── test_habitat_env.py
│       ├── test_habitat_example.py
│       ├── test_habitat_sim.py
│       ├── test_habitat_task.py
│       ├── test_install.py
│       ├── test_motion_plan.py
│       ├── test_mp3d_eqa.py
│       ├── test_object_nav_task.py
│       ├── test_pointnav_dataset.py
│       ├── test_pyrobot.py
│       ├── test_r2r_vln.py
│       ├── test_rearrange_task.py
│       ├── test_rnn_state_encoder.py
│       ├── test_sensors.py
│       ├── test_spaces.py
│       ├── test_tensor_dict.py
│       └── test_visual_utils.py
├── requirements.txt
├── scenegraph.py
├── segment_anything/
│   ├── .flake8
│   ├── CODE_OF_CONDUCT.md
│   ├── CONTRIBUTING.md
│   ├── LICENSE
│   ├── README.md
│   ├── linter.sh
│   ├── notebooks/
│   │   ├── automatic_mask_generator_example.ipynb
│   │   ├── onnx_model_example.ipynb
│   │   └── predictor_example.ipynb
│   ├── scripts/
│   │   ├── amg.py
│   │   └── export_onnx_model.py
│   ├── segment_anything/
│   │   ├── __init__.py
│   │   ├── automatic_mask_generator.py
│   │   ├── build_sam.py
│   │   ├── build_sam_hq.py
│   │   ├── modeling/
│   │   │   ├── __init__.py
│   │   │   ├── common.py
│   │   │   ├── image_encoder.py
│   │   │   ├── mask_decoder.py
│   │   │   ├── mask_decoder_hq.py
│   │   │   ├── prompt_encoder.py
│   │   │   ├── sam.py
│   │   │   └── transformer.py
│   │   ├── predictor.py
│   │   └── utils/
│   │       ├── __init__.py
│   │       ├── amg.py
│   │       ├── onnx.py
│   │       └── transforms.py
│   ├── setup.cfg
│   └── setup.py
├── tools/
│   ├── agent.py
│   ├── download_mp.py
│   ├── matterport_category_mappings.tsv
│   ├── obj.npy
│   ├── replica.yaml
│   └── room.npy
└── utils/
    ├── __init__.py
    ├── image_process.py
    ├── utils_fmm/
    │   ├── __init__.py
    │   ├── control_helper.py
    │   ├── depth_utils.py
    │   ├── fmm_planner.py
    │   ├── mapping.py
    │   ├── model.py
    │   ├── pose_utils.py
    │   └── rotation_utils.py
    ├── utils_glip.py
    └── utils_scenegraph/
        ├── __init__.py
        ├── grounded_sam_demo.py
        ├── iou.py
        ├── mapping.py
        ├── slam_classes.py
        └── utils.py

SYMBOL INDEX (4564 symbols across 391 files)

FILE: GLIP/maskrcnn_benchmark/config/paths_catalog.py
  function try_to_find (line 7) | def try_to_find(file, return_dir=False, search_path=['./DATASET', './OUT...
  class DatasetCatalog (line 30) | class DatasetCatalog(object):
    method set (line 210) | def set(name, info):
    method get (line 214) | def get(name):
  class ModelCatalog (line 392) | class ModelCatalog(object):
    method get (line 416) | def get(name):
    method get_c2_imagenet_pretrained (line 424) | def get_c2_imagenet_pretrained(name):
    method get_c2_detectron_12_2017_baselines (line 432) | def get_c2_detectron_12_2017_baselines(name):

FILE: GLIP/maskrcnn_benchmark/csrc/cpu/ROIAlign_cpu.cpp
  type PreCalc (line 6) | struct PreCalc {
  function pre_calc_for_bilinear_interpolate (line 18) | void pre_calc_for_bilinear_interpolate(
  function ROIAlignForward_cpu_kernel (line 114) | void ROIAlignForward_cpu_kernel(
  function ROIAlign_forward_cpu (line 221) | at::Tensor ROIAlign_forward_cpu(const at::Tensor& input,

FILE: GLIP/maskrcnn_benchmark/csrc/cpu/nms_cpu.cpp
  function nms_cpu_kernel (line 6) | at::Tensor nms_cpu_kernel(const at::Tensor& dets,
  function nms_cpu (line 67) | at::Tensor nms_cpu(const at::Tensor& dets,

FILE: GLIP/maskrcnn_benchmark/csrc/cpu/soft_nms.cpp
  function soft_nms_cpu_kernel (line 6) | std::pair<at::Tensor, at::Tensor> soft_nms_cpu_kernel(const at::Tensor& ...
  function soft_nms_cpu (line 108) | std::pair<at::Tensor, at::Tensor> soft_nms_cpu(const at::Tensor& dets,

FILE: GLIP/maskrcnn_benchmark/csrc/deform_conv.h
  function deform_conv_forward (line 11) | int deform_conv_forward(
  function deform_conv_backward_input (line 45) | int deform_conv_backward_input(
  function deform_conv_backward_parameters (line 80) | int deform_conv_backward_parameters(
  function modulated_deform_conv_forward (line 115) | void modulated_deform_conv_forward(
  function modulated_deform_conv_backward (line 152) | void modulated_deform_conv_backward(

FILE: GLIP/maskrcnn_benchmark/csrc/deform_pool.h
  function deform_psroi_pooling_forward (line 11) | void deform_psroi_pooling_forward(
  function deform_psroi_pooling_backward (line 41) | void deform_psroi_pooling_backward(

FILE: GLIP/maskrcnn_benchmark/csrc/ml_nms.h
  function threshold (line 13) | float threshold) {

FILE: GLIP/maskrcnn_benchmark/csrc/vision.cpp
  function PYBIND11_MODULE (line 10) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {

FILE: GLIP/maskrcnn_benchmark/data/build.py
  function build_dataset (line 21) | def build_dataset(cfg, dataset_list, transforms, dataset_catalog, is_tra...
  function build_dataset_by_group (line 124) | def build_dataset_by_group(dataset_list, transforms, dataset_catalog, is...
  function make_data_sampler (line 196) | def make_data_sampler(dataset, shuffle, distributed, num_replicas=None, ...
  function _quantize (line 207) | def _quantize(x, bins):
  function _compute_aspect_ratios (line 214) | def _compute_aspect_ratios(dataset):
  function make_batch_data_sampler (line 223) | def make_batch_data_sampler(
  function make_data_loader (line 244) | def make_data_loader(cfg, is_train=True, is_distributed=False, num_repli...

FILE: GLIP/maskrcnn_benchmark/data/collate_batch.py
  class BatchCollator (line 6) | class BatchCollator(object):
    method __init__ (line 13) | def __init__(self, size_divisible=0):
    method __call__ (line 16) | def __call__(self, batch):
  class BBoxAugCollator (line 70) | class BBoxAugCollator(object):
    method __call__ (line 77) | def __call__(self, batch):

FILE: GLIP/maskrcnn_benchmark/data/datasets/background.py
  class Background (line 11) | class Background(data.Dataset):
    method __init__ (line 21) | def __init__(self, ann_file, root, remove_images_without_annotations=N...
    method __getitem__ (line 28) | def __getitem__(self, index):
    method __len__ (line 48) | def __len__(self):
    method get_img_info (line 51) | def get_img_info(self, index):

FILE: GLIP/maskrcnn_benchmark/data/datasets/box_label_loader.py
  class LabelLoader (line 12) | class LabelLoader(object):
    method __init__ (line 13) | def __init__(self, labelmap, extra_fields=(), filter_duplicate_relatio...
    method __call__ (line 24) | def __call__(self, annotations, img_size, remove_empty=False, load_fie...
    method get_box_mask (line 60) | def get_box_mask(self, rect, img_size):
    method add_masks (line 72) | def add_masks(self, annotations, img_size):
    method add_classes (line 86) | def add_classes(self, annotations):
    method add_confidences (line 93) | def add_confidences(self, annotations):
    method add_attributes (line 102) | def add_attributes(self, annotations):
    method add_features (line 110) | def add_features(self, annotations):
    method add_scores_all (line 116) | def add_scores_all(self, annotations):
    method add_boxes_all (line 122) | def add_boxes_all(self, annotations):
    method relation_loader (line 128) | def relation_loader(self, relation_annos, target):
  class BoxLabelLoader (line 154) | class BoxLabelLoader(object):
    method __init__ (line 155) | def __init__(self, labelmap, extra_fields=(), ignore_attrs=(),
    method __call__ (line 165) | def __call__(self, annotations, img_size, remove_empty=True):
    method add_classes_with_ignore (line 197) | def add_classes_with_ignore(self, annotations):
    method add_masks (line 209) | def add_masks(self, annotations, img_size):
    method get_box_mask (line 223) | def get_box_mask(self, rect, img_size):
    method add_confidences (line 235) | def add_confidences(self, annotations):
    method add_attributes (line 246) | def add_attributes(self, annotations):

FILE: GLIP/maskrcnn_benchmark/data/datasets/caption.py
  class CaptionTSV (line 14) | class CaptionTSV(TSVYamlDataset):
    method __init__ (line 15) | def __init__(self,
    method __len__ (line 66) | def __len__(self):
    method pack_caption (line 69) | def pack_caption(self, positive_caption, negative_captions, original_t...
    method __get_negative_captions__ (line 108) | def __get_negative_captions__(self, idx, negative_size=7):
    method __getitem__ (line 117) | def __getitem__(self, idx):
    method convert_anno_from_v2_to_v1 (line 252) | def convert_anno_from_v2_to_v1(self, anno):
    method get_raw_image (line 270) | def get_raw_image(self, idx):
    method get_img_id (line 274) | def get_img_id(self, idx):

FILE: GLIP/maskrcnn_benchmark/data/datasets/coco.py
  function _count_visible_keypoints (line 20) | def _count_visible_keypoints(anno):
  function _has_only_empty_bbox (line 24) | def _has_only_empty_bbox(anno):
  function has_valid_annotation (line 28) | def has_valid_annotation(anno):
  function pil_loader (line 46) | def pil_loader(path, retry=5):
  function rgb2id (line 58) | def rgb2id(color):
  class CocoDetection (line 66) | class CocoDetection(data.Dataset):
    method __init__ (line 78) | def __init__(self, root, annFile, transform=None, target_transform=None):
    method __getitem__ (line 86) | def __getitem__(self, index, return_meta=False):
    method __len__ (line 116) | def __len__(self):
    method __repr__ (line 119) | def __repr__(self):
  class COCODataset (line 130) | class COCODataset(CocoDetection):
    method __init__ (line 131) | def __init__(self, ann_file, root, remove_images_without_annotations, ...
    method categories (line 190) | def categories(self, no_background=True):
    method __getitem__ (line 198) | def __getitem__(self, idx):
    method get_img_info (line 265) | def get_img_info(self, index):

FILE: GLIP/maskrcnn_benchmark/data/datasets/coco_dt.py
  class CocoDetectionTSV (line 19) | class CocoDetectionTSV(ODTSVDataset):
    method __init__ (line 20) | def __init__(self,
    method __len__ (line 75) | def __len__(self):
    method categories (line 78) | def categories(self, no_background=True):
    method __getitem__ (line 87) | def __getitem__(self, idx):
    method get_raw_image (line 142) | def get_raw_image(self, idx):
    method get_img_id (line 146) | def get_img_id(self, idx):

FILE: GLIP/maskrcnn_benchmark/data/datasets/concat_dataset.py
  class ConcatDataset (line 7) | class ConcatDataset(_ConcatDataset):
    method get_idxs (line 13) | def get_idxs(self, idx):
    method get_img_info (line 21) | def get_img_info(self, idx):

FILE: GLIP/maskrcnn_benchmark/data/datasets/custom_distributed_sampler.py
  class DistributedSamplerChunkByNode (line 12) | class DistributedSamplerChunkByNode(torch.utils.data.Sampler):
    method __init__ (line 14) | def __init__(self,
    method __iter__ (line 98) | def __iter__(self):
    method generate_indices_within_range_with_rank (line 130) | def generate_indices_within_range_with_rank(self, seed, epoch, process...
    method __len__ (line 173) | def __len__(self) -> int:
    method set_epoch (line 176) | def set_epoch(self, epoch: int) -> None:

FILE: GLIP/maskrcnn_benchmark/data/datasets/duplicate_dataset.py
  function create_duplicate_dataset (line 11) | def create_duplicate_dataset(DatasetBaseClass):

FILE: GLIP/maskrcnn_benchmark/data/datasets/evaluation/__init__.py
  function evaluate (line 10) | def evaluate(dataset, predictions, output_folder, **kwargs):
  function evaluate_mdetr (line 39) | def evaluate_mdetr(dataset, predictions, output_folder, cfg):

FILE: GLIP/maskrcnn_benchmark/data/datasets/evaluation/box_aug.py
  function im_detect_bbox_aug (line 12) | def im_detect_bbox_aug(model, images, device, captions=None, positive_ma...
  function im_detect_bbox (line 65) | def im_detect_bbox(model, images, target_scale, target_max_size, device,
  function im_detect_bbox_hflip (line 94) | def im_detect_bbox_hflip(model, images, target_scale, target_max_size, d...
  function im_detect_bbox_scale (line 129) | def im_detect_bbox_scale(model, images, target_scale, target_max_size, d...
  function remove_boxes (line 150) | def remove_boxes(boxlist_ts, min_scale, max_scale):
  function merge_result_from_multi_scales (line 166) | def merge_result_from_multi_scales(boxlists):
  function boxlist_nms (line 209) | def boxlist_nms(boxlist, thresh, max_proposals=-1, score_field="scores",...
  function bbox_vote (line 241) | def bbox_vote(boxes, scores, vote_thresh):
  function soft_bbox_vote (line 290) | def soft_bbox_vote(boxes, scores, vote_thresh):

FILE: GLIP/maskrcnn_benchmark/data/datasets/evaluation/coco/__init__.py
  function coco_evaluation (line 4) | def coco_evaluation(

FILE: GLIP/maskrcnn_benchmark/data/datasets/evaluation/coco/coco_eval.py
  function do_coco_evaluation (line 16) | def do_coco_evaluation(
  function prepare_for_tsv_detection (line 86) | def prepare_for_tsv_detection(predictions, dataset):
  function prepare_for_coco_detection (line 129) | def prepare_for_coco_detection(predictions, dataset):
  function prepare_for_coco_segmentation (line 160) | def prepare_for_coco_segmentation(predictions, dataset):
  function prepare_for_coco_keypoint (line 214) | def prepare_for_coco_keypoint(predictions, dataset):
  function evaluate_box_proposals (line 246) | def evaluate_box_proposals(
  function evaluate_predictions_on_coco (line 375) | def evaluate_predictions_on_coco(
  function summarize_per_category (line 400) | def summarize_per_category(coco_eval, csv_output=None):
  function filter_valid_keypoints (line 459) | def filter_valid_keypoints(coco_gt, coco_dt):
  class COCOResults (line 467) | class COCOResults(object):
    method __init__ (line 484) | def __init__(self, *iou_types):
    method update (line 494) | def update(self, coco_eval):
    method __repr__ (line 507) | def __repr__(self):
  function check_expected_results (line 512) | def check_expected_results(results, expected_results, sigma_tol):

FILE: GLIP/maskrcnn_benchmark/data/datasets/evaluation/flickr/flickr_eval.py
  function get_sentence_data (line 21) | def get_sentence_data(filename) -> List[Dict[str, Any]]:
  function get_annotations (line 89) | def get_annotations(filename) -> Dict[str, Union[int, List[str], Dict[st...
  function box_area (line 152) | def box_area(boxes: np.array) -> np.array:
  function _box_inter_union (line 171) | def _box_inter_union(boxes1: np.array, boxes2: np.array) -> Tuple[np.arr...
  function box_iou (line 186) | def box_iou(boxes1: np.array, boxes2: np.array) -> np.array:
  function _merge_boxes (line 207) | def _merge_boxes(boxes: List[List[int]]) -> List[List[int]]:
  class RecallTracker (line 220) | class RecallTracker:
    method __init__ (line 223) | def __init__(self, topk: Sequence[int]):
    method add_positive (line 232) | def add_positive(self, k: int, category: str):
    method add_negative (line 239) | def add_negative(self, k: int, category: str):
    method report (line 245) | def report(self) -> Dict[int, Dict[str, float]]:
  class Flickr30kEntitiesRecallEvaluator (line 258) | class Flickr30kEntitiesRecallEvaluator:
    method __init__ (line 259) | def __init__(
    method evaluate (line 323) | def evaluate(self, predictions: List[Dict]):
  class FlickrEvaluator (line 393) | class FlickrEvaluator(object):
    method __init__ (line 394) | def __init__(
    method accumulate (line 410) | def accumulate(self):
    method update (line 413) | def update(self, predictions):
    method synchronize_between_processes (line 416) | def synchronize_between_processes(self):
    method summarize (line 420) | def summarize(self):

FILE: GLIP/maskrcnn_benchmark/data/datasets/evaluation/lvis/lvis.py
  function _isArrayLike (line 14) | def _isArrayLike(obj):
  class LVIS (line 18) | class LVIS:
    method __init__ (line 19) | def __init__(self, annotation_path=None):
    method _load_json (line 41) | def _load_json(self, path):
    method _create_index (line 45) | def _create_index(self):
    method get_ann_ids (line 70) | def get_ann_ids(self, img_ids=None, cat_ids=None, area_rng=None):
    method get_cat_ids (line 106) | def get_cat_ids(self):
    method get_img_ids (line 113) | def get_img_ids(self):
    method _load_helper (line 120) | def _load_helper(self, _dict, ids):
    method load_anns (line 128) | def load_anns(self, ids=None):
    method load_cats (line 137) | def load_cats(self, ids):
    method load_imgs (line 147) | def load_imgs(self, ids):
    method download (line 156) | def download(self, save_dir, img_ids=None):
    method ann_to_rle (line 174) | def ann_to_rle(self, ann):
    method ann_to_mask (line 197) | def ann_to_mask(self, ann):

FILE: GLIP/maskrcnn_benchmark/data/datasets/evaluation/lvis/lvis_eval.py
  function merge (line 21) | def merge(img_ids, eval_imgs):
  class Params (line 51) | class Params:
    method __init__ (line 52) | def __init__(self, iou_type):
  class LVISResults (line 78) | class LVISResults(LVIS):
    method __init__ (line 79) | def __init__(self, lvis_gt, results, max_dets=300):
    method limit_dets_per_image (line 136) | def limit_dets_per_image(self, anns, max_dets):
    method get_top_results (line 149) | def get_top_results(self, img_id, score_thrs):
  class LVISEval (line 155) | class LVISEval:
    method __init__ (line 156) | def __init__(self, lvis_gt, lvis_dt=None, iou_type="segm"):
    method _to_mask (line 195) | def _to_mask(self, anns, lvis):
    method _prepare (line 200) | def _prepare(self):
    method _prepare_freq_group (line 244) | def _prepare_freq_group(self):
    method evaluate (line 252) | def evaluate(self):
    method _get_gt_dt (line 279) | def _get_gt_dt(self, img_id, cat_id):
    method compute_iou (line 292) | def compute_iou(self, img_id, cat_id):
    method evaluate_img (line 318) | def evaluate_img(self, img_id, cat_id, area_rng):
    method accumulate (line 412) | def accumulate(self):
    method _summarize (line 525) | def _summarize(self, summary_type, iou_thr=None, area_rng="all", freq_...
    method summarize (line 550) | def summarize(self):
    method run (line 587) | def run(self):
    method print_results (line 593) | def print_results(self):
    method get_results (line 625) | def get_results(self):
  class LvisEvaluator (line 636) | class LvisEvaluator(object):
    method __init__ (line 637) | def __init__(self, lvis_gt, iou_types):
    method update (line 650) | def update(self, predictions):
    method synchronize_between_processes (line 669) | def synchronize_between_processes(self):
    method accumulate (line 674) | def accumulate(self):
    method summarize (line 678) | def summarize(self):
    method prepare (line 683) | def prepare(self, predictions, iou_type):
    method prepare_for_lvis_detection (line 693) | def prepare_for_lvis_detection(self, predictions):
    method prepare_for_lvis_segmentation (line 717) | def prepare_for_lvis_segmentation(self, predictions):
  function _merge_lists (line 752) | def _merge_lists(listA, listB, maxN, key):
  class LvisEvaluatorFixedAP (line 766) | class LvisEvaluatorFixedAP(object):
    method __init__ (line 767) | def __init__(self, gt: LVIS, topk=10000, fixed_ap=True):
    method update (line 775) | def update(self, predictions):
    method synchronize_between_processes (line 796) | def synchronize_between_processes(self):
    method prepare (line 806) | def prepare(self, predictions):
    method summarize (line 830) | def summarize(self):
    method _summarize_standard (line 839) | def _summarize_standard(self):
    method _summarize_fixed (line 845) | def _summarize_fixed(self):
  class LvisDumper (line 874) | class LvisDumper(object):
    method __init__ (line 875) | def __init__(self, topk=10000, fixed_ap=True, out_path="lvis_eval"):
    method update (line 886) | def update(self, predictions):
    method synchronize_between_processes (line 907) | def synchronize_between_processes(self):
    method prepare (line 917) | def prepare(self, predictions):
    method summarize (line 941) | def summarize(self):
    method _summarize_standard (line 950) | def _summarize_standard(self):
    method _summarize_fixed (line 958) | def _summarize_fixed(self):
  function convert_to_xywh (line 984) | def convert_to_xywh(boxes):
  function create_common_lvis_eval (line 989) | def create_common_lvis_eval(lvis_eval, img_ids, eval_imgs):
  function lvis_evaluation (line 997) | def lvis_evaluation():

FILE: GLIP/maskrcnn_benchmark/data/datasets/evaluation/od_to_grounding/__init__.py
  function od_to_grounding_evaluation (line 4) | def od_to_grounding_evaluation(

FILE: GLIP/maskrcnn_benchmark/data/datasets/evaluation/od_to_grounding/od_eval.py
  function do_od_evaluation (line 16) | def do_od_evaluation(
  function prepare_for_tsv_detection (line 86) | def prepare_for_tsv_detection(predictions, dataset):
  function prepare_for_coco_detection (line 129) | def prepare_for_coco_detection(predictions, dataset):
  function prepare_for_coco_segmentation (line 160) | def prepare_for_coco_segmentation(predictions, dataset):
  function prepare_for_coco_keypoint (line 214) | def prepare_for_coco_keypoint(predictions, dataset):
  function evaluate_box_proposals (line 246) | def evaluate_box_proposals(
  function evaluate_predictions_on_coco (line 375) | def evaluate_predictions_on_coco(
  function summarize_per_category (line 400) | def summarize_per_category(coco_eval, csv_output=None):
  function filter_valid_keypoints (line 459) | def filter_valid_keypoints(coco_gt, coco_dt):
  class COCOResults (line 467) | class COCOResults(object):
    method __init__ (line 484) | def __init__(self, *iou_types):
    method update (line 494) | def update(self, coco_eval):
    method __repr__ (line 507) | def __repr__(self):
  function check_expected_results (line 512) | def check_expected_results(results, expected_results, sigma_tol):

FILE: GLIP/maskrcnn_benchmark/data/datasets/evaluation/vg/__init__.py
  function vg_evaluation (line 6) | def vg_evaluation(dataset, predictions, output_folder, box_only, eval_at...

FILE: GLIP/maskrcnn_benchmark/data/datasets/evaluation/vg/vg_eval.py
  function evaluate_box_proposals (line 14) | def evaluate_box_proposals(
  class VGResults (line 130) | class VGResults(object):
    method __init__ (line 137) | def __init__(self, iou_type, value):
  function do_vg_evaluation (line 145) | def do_vg_evaluation(dataset, predictions, output_folder, box_only, eval...
  function eval_detection_voc (line 253) | def eval_detection_voc(pred_boxlists, gt_boxlists, classes, iou_thresh=0...
  function calc_detection_voc_prec_rec (line 326) | def calc_detection_voc_prec_rec(pred_boxlists, gt_boxlists, classindex, ...
  function voc_ap (line 450) | def voc_ap(rec, prec, use_07_metric=False):
  function calc_detection_voc_ap (line 484) | def calc_detection_voc_ap(prec, rec, use_07_metric=False):
  function evaluate_box_proposals_for_relation (line 544) | def evaluate_box_proposals_for_relation(

FILE: GLIP/maskrcnn_benchmark/data/datasets/evaluation/voc/__init__.py
  function voc_evaluation (line 6) | def voc_evaluation(dataset, predictions, output_folder, box_only, **_):

FILE: GLIP/maskrcnn_benchmark/data/datasets/evaluation/voc/voc_eval.py
  function do_voc_evaluation (line 12) | def do_voc_evaluation(dataset, predictions, output_folder, logger):
  function eval_detection_voc (line 48) | def eval_detection_voc(pred_boxlists, gt_boxlists, iou_thresh=0.5, use_0...
  function calc_detection_voc_prec_rec (line 68) | def calc_detection_voc_prec_rec(gt_boxlists, pred_boxlists, iou_thresh=0...
  function calc_detection_voc_ap (line 160) | def calc_detection_voc_ap(prec, rec, use_07_metric=False):

FILE: GLIP/maskrcnn_benchmark/data/datasets/flickr.py
  class FlickrDataset (line 7) | class FlickrDataset(ModulatedDataset):

FILE: GLIP/maskrcnn_benchmark/data/datasets/gqa.py
  class GQADataset (line 10) | class GQADataset(ModulatedDataset):
  class GQAQuestionAnswering (line 14) | class GQAQuestionAnswering(torchvision.datasets.CocoDetection):
    method __init__ (line 15) | def __init__(self, img_folder, ann_file, transforms, return_masks, ret...
    method __getitem__ (line 25) | def __getitem__(self, idx):

FILE: GLIP/maskrcnn_benchmark/data/datasets/imagenet.py
  function pil_loader (line 8) | def pil_loader(path):
  class ImageNet (line 14) | class ImageNet(data.Dataset):
    method __init__ (line 24) | def __init__(self, ann_file, root, remove_images_without_annotations=N...
    method select_class (line 42) | def select_class(self, cls):
    method __getitem__ (line 47) | def __getitem__(self, index):
    method __len__ (line 62) | def __len__(self):

FILE: GLIP/maskrcnn_benchmark/data/datasets/list_dataset.py
  class ListDataset (line 11) | class ListDataset(object):
    method __init__ (line 12) | def __init__(self, image_lists, transforms=None):
    method __getitem__ (line 16) | def __getitem__(self, item):
    method __len__ (line 28) | def __len__(self):
    method get_img_info (line 31) | def get_img_info(self, item):

FILE: GLIP/maskrcnn_benchmark/data/datasets/lvis.py
  function _isArrayLike (line 16) | def _isArrayLike(obj):
  class LVIS (line 20) | class LVIS:
    method __init__ (line 21) | def __init__(self, annotation_path=None):
    method _load_json (line 43) | def _load_json(self, path):
    method _create_index (line 47) | def _create_index(self):
    method get_ann_ids (line 72) | def get_ann_ids(self, img_ids=None, cat_ids=None, area_rng=None):
    method get_cat_ids (line 108) | def get_cat_ids(self):
    method get_img_ids (line 115) | def get_img_ids(self):
    method _load_helper (line 122) | def _load_helper(self, _dict, ids):
    method load_anns (line 130) | def load_anns(self, ids=None):
    method load_cats (line 139) | def load_cats(self, ids):
    method load_imgs (line 149) | def load_imgs(self, ids):
    method download (line 158) | def download(self, save_dir, img_ids=None):
    method ann_to_rle (line 176) | def ann_to_rle(self, ann):
    method ann_to_mask (line 199) | def ann_to_mask(self, ann):
  class LvisDetectionBase (line 211) | class LvisDetectionBase(torchvision.datasets.VisionDataset):
    method __init__ (line 212) | def __init__(self, root, annFile, transform=None, target_transform=Non...
    method __getitem__ (line 217) | def __getitem__(self, index):
    method __len__ (line 238) | def __len__(self):
  class LvisDetection (line 242) | class LvisDetection(LvisDetectionBase):
    method __init__ (line 243) | def __init__(self, img_folder, ann_file, transforms, return_masks=Fals...
    method __getitem__ (line 249) | def __getitem__(self, idx):
    method get_raw_image (line 258) | def get_raw_image(self, idx):
    method categories (line 262) | def categories(self):

FILE: GLIP/maskrcnn_benchmark/data/datasets/mixed.py
  class CustomCocoDetection (line 15) | class CustomCocoDetection(VisionDataset):
    method __init__ (line 31) | def __init__(
    method __getitem__ (line 60) | def __getitem__(self, index):
    method __len__ (line 84) | def __len__(self):
  class MixedDataset (line 88) | class MixedDataset(CustomCocoDetection):
    method __init__ (line 91) | def __init__(self,
    method __getitem__ (line 111) | def __getitem__(self, idx):
    method get_img_info (line 142) | def get_img_info(self, index):

FILE: GLIP/maskrcnn_benchmark/data/datasets/mixup.py
  class MixupDetection (line 8) | class MixupDetection(data.Dataset):
    method __init__ (line 21) | def __init__(self, dataset, mixup=None, preproc=None, *args):
    method set_mixup (line 28) | def set_mixup(self, mixup=None, *args):
    method __len__ (line 41) | def __len__(self):
    method __getitem__ (line 45) | def __getitem__(self, idx):
    method pull_item (line 86) | def pull_item(self, idx):

FILE: GLIP/maskrcnn_benchmark/data/datasets/modulated_coco.py
  class CocoGrounding (line 22) | class CocoGrounding(torchvision.datasets.CocoDetection):
    method __init__ (line 23) | def __init__(self,
    method categories (line 116) | def categories(self, no_background=True):
    method get_box_mask (line 125) | def get_box_mask(self, rect, img_size, mode="poly"):
    method __getitem__ (line 130) | def __getitem__(self, idx):
    method get_img_info (line 232) | def get_img_info(self, index):
  class ModulatedDataset (line 238) | class ModulatedDataset(torchvision.datasets.CocoDetection):
    method __init__ (line 239) | def __init__(self,
    method __getitem__ (line 273) | def __getitem__(self, idx):
    method get_img_info (line 327) | def get_img_info(self, index):
  class CocoDetection (line 333) | class CocoDetection(data.Dataset):
    method __init__ (line 345) | def __init__(self, root, annFile, transform=None, target_transform=None):
    method __getitem__ (line 353) | def __getitem__(self, index, return_meta=False):
    method __len__ (line 383) | def __len__(self):
    method __repr__ (line 386) | def __repr__(self):
  class ConvertCocoPolysToMask (line 397) | class ConvertCocoPolysToMask(object):
    method __init__ (line 398) | def __init__(self, return_masks=False, return_tokens=False, tokenizer=...
    method get_box_mask (line 404) | def get_box_mask(self, rect, img_size, mode="poly"):
    method __call__ (line 409) | def __call__(self, image, target, ignore_box_screen=False, box_format=...
  function create_greenlight_map (line 523) | def create_greenlight_map(tok_list, tokenized):
  function create_positive_map_for_od_labels (line 561) | def create_positive_map_for_od_labels(tokenized, label_to_positions):
  function convert_coco_poly_to_mask (line 598) | def convert_coco_poly_to_mask(segmentations, height, width):
  function create_positive_map (line 615) | def create_positive_map(tokenized, tokens_positive):
  function pil_loader (line 645) | def pil_loader(path, retry=5):

FILE: GLIP/maskrcnn_benchmark/data/datasets/object365.py
  class Object365DetectionTSV (line 7) | class Object365DetectionTSV(CocoDetectionTSV):

FILE: GLIP/maskrcnn_benchmark/data/datasets/od_to_grounding.py
  function clean_name (line 9) | def clean_name(name):
  function sanity_check_target_after_processing (line 16) | def sanity_check_target_after_processing(target):
  function convert_od_to_grounding_simple (line 20) | def convert_od_to_grounding_simple(
  function check_for_positive_overflow (line 104) | def check_for_positive_overflow(target, ind_to_class, tokenizer, max_seq...
  function convert_object_detection_to_grounding_optimized_for_od (line 149) | def convert_object_detection_to_grounding_optimized_for_od(
  function generate_control_options_given_probabilities (line 336) | def generate_control_options_given_probabilities(

FILE: GLIP/maskrcnn_benchmark/data/datasets/phrasecut.py
  class PhrasecutDetection (line 7) | class PhrasecutDetection(ModulatedDataset):

FILE: GLIP/maskrcnn_benchmark/data/datasets/pseudo_data.py
  class PseudoData (line 15) | class PseudoData(TSVYamlDataset):
    method __init__ (line 16) | def __init__(self,
    method __len__ (line 72) | def __len__(self):
    method check_for_overlap (line 76) | def check_for_overlap(range1, range2):
    method divert_boxes (line 81) | def divert_boxes(self, anno):
    method __getitem__ (line 107) | def __getitem__(self, idx):
    method convert_anno_from_yiling_to_ours (line 202) | def convert_anno_from_yiling_to_ours(self, anno):
    method get_raw_image (line 219) | def get_raw_image(self, idx):
    method get_img_id (line 223) | def get_img_id(self, idx):

FILE: GLIP/maskrcnn_benchmark/data/datasets/refexp.py
  class RefExpDataset (line 14) | class RefExpDataset(ModulatedDataset):
  class RefExpEvaluator (line 18) | class RefExpEvaluator(object):
    method __init__ (line 19) | def __init__(self, refexp_gt, iou_types, k=(1, 5, 10), thresh_iou=0.5):
    method accumulate (line 29) | def accumulate(self):
    method update (line 32) | def update(self, predictions):
    method synchronize_between_processes (line 35) | def synchronize_between_processes(self):
    method summarize (line 42) | def summarize(self):

FILE: GLIP/maskrcnn_benchmark/data/datasets/tsv.py
  function load_linelist_file (line 16) | def load_linelist_file(linelist_file):
  function img_from_base64 (line 25) | def img_from_base64(imagestring):
  function load_from_yaml_file (line 33) | def load_from_yaml_file(yaml_file):
  function find_file_path_in_yaml (line 38) | def find_file_path_in_yaml(fname, root):
  function create_lineidx (line 50) | def create_lineidx(filein, idxout):
  function read_to_character (line 62) | def read_to_character(fp, c):
  class TSVFile (line 75) | class TSVFile(object):
    method __init__ (line 76) | def __init__(self, tsv_file, generate_lineidx=False):
    method __del__ (line 88) | def __del__(self):
    method __str__ (line 92) | def __str__(self):
    method __repr__ (line 95) | def __repr__(self):
    method num_rows (line 98) | def num_rows(self):
    method seek (line 102) | def seek(self, idx):
    method seek_first_column (line 113) | def seek_first_column(self, idx):
    method get_key (line 120) | def get_key(self, idx):
    method __getitem__ (line 123) | def __getitem__(self, index):
    method __len__ (line 126) | def __len__(self):
    method _ensure_lineidx_loaded (line 129) | def _ensure_lineidx_loaded(self):
    method _ensure_tsv_opened (line 135) | def _ensure_tsv_opened(self):
  class CompositeTSVFile (line 146) | class CompositeTSVFile():
    method __init__ (line 147) | def __init__(self, file_list, seq_file, root='.'):
    method get_key (line 159) | def get_key(self, index):
    method num_rows (line 164) | def num_rows(self):
    method __getitem__ (line 167) | def __getitem__(self, index):
    method __len__ (line 171) | def __len__(self):
    method initialize (line 174) | def initialize(self):
  function load_list_file (line 190) | def load_list_file(fname):
  class TSVDataset (line 199) | class TSVDataset(object):
    method __init__ (line 200) | def __init__(self, img_file, label_file=None, hw_file=None,
    method __len__ (line 226) | def __len__(self):
    method __getitem__ (line 235) | def __getitem__(self, idx):
    method get_line_no (line 250) | def get_line_no(self, idx):
    method get_image (line 253) | def get_image(self, idx):
    method get_annotations (line 266) | def get_annotations(self, idx):
    method get_target_from_annotations (line 275) | def get_target_from_annotations(self, annotations, img_size, idx):
    method apply_transforms (line 280) | def apply_transforms(self, image, target=None):
    method get_img_info (line 285) | def get_img_info(self, idx):
    method get_img_key (line 309) | def get_img_key(self, idx):
  class TSVYamlDataset (line 326) | class TSVYamlDataset(TSVDataset):
    method __init__ (line 330) | def __init__(self, yaml_file, root=None, replace_clean_label=False):
  class ODTSVDataset (line 354) | class ODTSVDataset(TSVYamlDataset):
    method __init__ (line 359) | def __init__(self, yaml_file, extra_fields=(), transforms=None,
    method get_target_from_annotations (line 411) | def get_target_from_annotations(self, annotations, img_size, idx):
    method apply_transforms (line 417) | def apply_transforms(self, img, target=None):

FILE: GLIP/maskrcnn_benchmark/data/datasets/vg.py
  class VGDetectionTSV (line 14) | class VGDetectionTSV(CocoDetectionTSV):
  function sort_key_by_val (line 18) | def sort_key_by_val(dic):
  function bbox_overlaps (line 23) | def bbox_overlaps(anchors, gt_boxes):
  function _box_filter (line 59) | def _box_filter(boxes, must_overlap=False):
  class VGTSVDataset (line 78) | class VGTSVDataset(TSVYamlDataset):
    method __init__ (line 83) | def __init__(self, yaml_file, extra_fields=None, transforms=None,
    method _get_freq_prior (line 167) | def _get_freq_prior(self, must_overlap=False):
    method relation_loader (line 204) | def relation_loader(self, relation_triplets, target):
    method get_target_from_annotations (line 228) | def get_target_from_annotations(self, annotations, img_size, idx):
    method get_groundtruth (line 241) | def get_groundtruth(self, idx, call=False):
    method apply_transforms (line 255) | def apply_transforms(self, img, target=None):
    method map_class_id_to_class_name (line 260) | def map_class_id_to_class_name(self, class_id):
    method map_attribute_id_to_attribute_name (line 263) | def map_attribute_id_to_attribute_name(self, attribute_id):
    method map_relation_id_to_relation_name (line 266) | def map_relation_id_to_relation_name(self, relation_id):

FILE: GLIP/maskrcnn_benchmark/data/datasets/voc.py
  class PascalVOCDataset (line 17) | class PascalVOCDataset(torch.utils.data.Dataset):
    method __init__ (line 43) | def __init__(self, data_dir, split, use_difficult=False, transforms=No...
    method __getitem__ (line 61) | def __getitem__(self, index):
    method __len__ (line 73) | def __len__(self):
    method get_groundtruth (line 76) | def get_groundtruth(self, index):
    method _preprocess_annotation (line 87) | def _preprocess_annotation(self, target):
    method get_img_info (line 126) | def get_img_info(self, index):
    method map_class_id_to_class_name (line 133) | def map_class_id_to_class_name(self, class_id):

FILE: GLIP/maskrcnn_benchmark/data/samplers/distributed.py
  class DistributedSampler (line 12) | class DistributedSampler(Sampler):
    method __init__ (line 27) | def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True...
    method __iter__ (line 45) | def __iter__(self):
    method __len__ (line 68) | def __len__(self):
    method set_epoch (line 71) | def set_epoch(self, epoch):

FILE: GLIP/maskrcnn_benchmark/data/samplers/grouped_batch_sampler.py
  class GroupedBatchSampler (line 9) | class GroupedBatchSampler(BatchSampler):
    method __init__ (line 24) | def __init__(self, sampler, group_ids, batch_size, drop_uneven=False):
    method _prepare_batches (line 40) | def _prepare_batches(self):
    method __iter__ (line 102) | def __iter__(self):
    method __len__ (line 111) | def __len__(self):

FILE: GLIP/maskrcnn_benchmark/data/samplers/iteration_based_batch_sampler.py
  class IterationBasedBatchSampler (line 5) | class IterationBasedBatchSampler(BatchSampler):
    method __init__ (line 11) | def __init__(self, batch_sampler, num_iterations, start_iter=0):
    method __iter__ (line 16) | def __iter__(self):
    method __len__ (line 30) | def __len__(self):

FILE: GLIP/maskrcnn_benchmark/data/transforms/build.py
  function build_transforms (line 5) | def build_transforms(cfg, is_train=True):

FILE: GLIP/maskrcnn_benchmark/data/transforms/transforms.py
  function matrix_iou (line 12) | def matrix_iou(a, b, relative=False):
  class RACompose (line 29) | class RACompose(object):
    method __init__ (line 30) | def __init__(self, pre_transforms, rand_transforms, post_transforms, c...
    method __call__ (line 36) | def __call__(self, image, target):
    method __repr__ (line 47) | def __repr__(self):
  class Compose (line 64) | class Compose(object):
    method __init__ (line 65) | def __init__(self, transforms):
    method __call__ (line 68) | def __call__(self, image, target=None):
    method __repr__ (line 75) | def __repr__(self):
  class Resize (line 84) | class Resize(object):
    method __init__ (line 85) | def __init__(self, min_size, max_size, restrict=False):
    method get_size (line 93) | def get_size(self, image_size):
    method __call__ (line 117) | def __call__(self, image, target):
  class RandomHorizontalFlip (line 130) | class RandomHorizontalFlip(object):
    method __init__ (line 131) | def __init__(self, prob=0.5):
    method __call__ (line 134) | def __call__(self, image, target):
  class RandomVerticalFlip (line 145) | class RandomVerticalFlip(object):
    method __init__ (line 146) | def __init__(self, prob=0.5):
    method __call__ (line 149) | def __call__(self, image, target):
  class ToTensor (line 158) | class ToTensor(object):
    method __call__ (line 159) | def __call__(self, image, target):
  class Normalize (line 163) | class Normalize(object):
    method __init__ (line 164) | def __init__(self, mean, std, format='rgb'):
    method __call__ (line 169) | def __call__(self, image, target):
  class ColorJitter (line 178) | class ColorJitter(object):
    method __init__ (line 179) | def __init__(self,
    method __call__ (line 191) | def __call__(self, image, target):
  class RandomCrop (line 196) | class RandomCrop(object):
    method __init__ (line 197) | def __init__(self, prob=0.5, min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_c...
    method __call__ (line 203) | def __call__(self, img, target):
  class RandomAffine (line 254) | class RandomAffine(object):
    method __init__ (line 255) | def __init__(self, prob=0.5, degrees=(-10, 10), translate=(.1, .1), sc...
    method __call__ (line 264) | def __call__(self, img, targets=None):
  class RandomErasing (line 332) | class RandomErasing:
    method __init__ (line 333) | def __init__(self, prob=0.5, era_l=0.02, era_h=1/3, min_aspect=0.3,
    method _get_pixels (line 346) | def _get_pixels(self, patch_size):
    method __call__ (line 354) | def __call__(self, image, target):

FILE: GLIP/maskrcnn_benchmark/engine/alter_trainer.py
  function reduce_loss_dict (line 13) | def reduce_loss_dict(all_loss_dict):
  function do_train (line 44) | def do_train(

FILE: GLIP/maskrcnn_benchmark/engine/evolution.py
  function gather_candidates (line 25) | def gather_candidates(all_candidates):
  function gather_stats (line 31) | def gather_stats(all_candidates):
  function compute_on_dataset (line 39) | def compute_on_dataset(model, rngs, data_loader, device=cfg.MODEL.DEVICE):
  function bn_statistic (line 54) | def bn_statistic(model, rngs, data_loader, device=cfg.MODEL.DEVICE, max_...
  function inference (line 73) | def inference(
  function fitness (line 109) | def fitness(cfg, model, rngs, val_loaders):
  class EvolutionTrainer (line 129) | class EvolutionTrainer(object):
    method __init__ (line 130) | def __init__(self, cfg, model, flops_limit=None, is_distributed=True):
    method save_checkpoint (line 155) | def save_checkpoint(self):
    method load_checkpoint (line 168) | def load_checkpoint(self):
    method legal (line 179) | def legal(self, cand):
    method update_top_k (line 195) | def update_top_k(self, candidates, *, k, key, reverse=False):
    method eval_candidates (line 203) | def eval_candidates(self, train_loader, val_loader):
    method stack_random_cand (line 221) | def stack_random_cand(self, random_func, *, batchsize=10):
    method random_can (line 227) | def random_can(self, num):
    method get_mutation (line 242) | def get_mutation(self, k, mutation_num, m_prob):
    method get_crossover (line 268) | def get_crossover(self, k, crossover_num):
    method train (line 292) | def train(self, train_loader, val_loader):
    method save_candidates (line 319) | def save_candidates(self, cand, template):

FILE: GLIP/maskrcnn_benchmark/engine/inference.py
  function inference_default (line 20) | def inference_default(
  function clean_name (line 85) | def clean_name(name):
  function create_one_hot_dict (line 92) | def create_one_hot_dict(labels, no_minus_one_for_one_hot = False):
  function create_positive_dict (line 111) | def create_positive_dict(tokenized, tokens_positive, labels):
  function chunks (line 146) | def chunks(lst, n):
  function create_queries_and_maps_from_dataset (line 159) | def create_queries_and_maps_from_dataset(dataset, cfg):
  function create_queries_and_maps (line 192) | def create_queries_and_maps(labels, label_list, additional_labels = None...
  function create_positive_map_label_to_token_from_positive_map (line 262) | def create_positive_map_label_to_token_from_positive_map(positive_map, p...
  function _accumulate_predictions_from_multiple_gpus (line 270) | def _accumulate_predictions_from_multiple_gpus(predictions_per_gpu):
  function resize_box (line 291) | def resize_box(output, targets):
  function flickr_post_process (line 299) | def flickr_post_process(output, targets, positive_map_label_to_token, pl...
  function build_flickr_evaluator (line 317) | def build_flickr_evaluator(cfg):
  function build_lvis_evaluator (line 324) | def build_lvis_evaluator(ann_file, fixed_ap=True):
  function write_lvis_results (line 331) | def write_lvis_results(results, output_file_name):
  function write_flickr_results (line 345) | def write_flickr_results(results, output_file_name):
  function inference (line 360) | def inference(

FILE: GLIP/maskrcnn_benchmark/engine/predictor.py
  class COCODemo (line 19) | class COCODemo(object):
    method __init__ (line 105) | def __init__(
    method build_transform (line 139) | def build_transform(self):
    method inference (line 169) | def inference(self, image, debug=False):
    method run_on_opencv_image (line 187) | def run_on_opencv_image(self, image):
    method compute_prediction (line 212) | def compute_prediction(self, original_image):
    method select_top_predictions (line 258) | def select_top_predictions(self, predictions):
    method compute_colors_for_labels (line 297) | def compute_colors_for_labels(self, labels):
    method overlay_boxes (line 305) | def overlay_boxes(self, image, predictions):
    method overlay_scores (line 327) | def overlay_scores(self, image, predictions):
    method overlay_cboxes (line 348) | def overlay_cboxes(self, image, predictions):
    method overlay_centers (line 370) | def overlay_centers(self, image, predictions):
    method overlay_count (line 388) | def overlay_count(self, image, predictions):
    method overlay_mask (line 405) | def overlay_mask(self, image, predictions):
    method overlay_keypoints (line 431) | def overlay_keypoints(self, image, predictions):
    method create_mask_montage (line 441) | def create_mask_montage(self, image, predictions):
    method overlay_class_names (line 477) | def overlay_class_names(self, image, predictions, names=None):
  function vis_keypoints (line 505) | def vis_keypoints(img, kps, kp_thresh=0, alpha=0.7, names=None, connecti...

FILE: GLIP/maskrcnn_benchmark/engine/predictor_glip.py
  class GLIPDemo (line 29) | class GLIPDemo(object):
    method __init__ (line 30) | def __init__(self,
    method build_transform (line 61) | def build_transform(self):
    method build_tokenizer (line 91) | def build_tokenizer(self):
    method run_ner (line 106) | def run_ner(self, caption):
    method inference (line 130) | def inference(self, original_image, original_caption, thresh=None):
    method inference_batch (line 138) | def inference_batch(self, original_image_list, original_caption, thres...
    method run_on_web_image (line 148) | def run_on_web_image(self, original_image, original_caption, thresh=0.5):
    method compute_prediction (line 162) | def compute_prediction(self, original_image, original_caption):
    method compute_prediction_batch (line 208) | def compute_prediction_batch(self, original_image_list, original_capti...
    method _post_process_fixed_thresh (line 255) | def _post_process_fixed_thresh(self, predictions):
    method _post_process (line 273) | def _post_process(self, predictions, threshold=0.5):
    method compute_colors_for_labels (line 291) | def compute_colors_for_labels(self, labels):
    method overlay_boxes (line 299) | def overlay_boxes(self, image, predictions):
    method overlay_scores (line 313) | def overlay_scores(self, image, predictions):
    method overlay_entity_names (line 326) | def overlay_entity_names(self, image, predictions, names=None):
    method overlay_mask (line 352) | def overlay_mask(self, image, predictions):
    method create_mask_montage (line 373) | def create_mask_montage(self, image, predictions):
  function create_positive_map_label_to_token_from_positive_map (line 402) | def create_positive_map_label_to_token_from_positive_map(positive_map, p...
  function create_positive_map (line 409) | def create_positive_map(tokenized, tokens_positive):
  function find_noun_phrases (line 445) | def find_noun_phrases(caption: str) -> List[str]:
  function remove_punctuation (line 462) | def remove_punctuation(text: str) -> str:

FILE: GLIP/maskrcnn_benchmark/engine/singlepath_trainer.py
  function reduce_loss_dict (line 13) | def reduce_loss_dict(loss_dict):
  function do_train (line 38) | def do_train(

FILE: GLIP/maskrcnn_benchmark/engine/stage_trainer.py
  function reduce_loss_dict (line 13) | def reduce_loss_dict(all_loss_dict):
  function do_train (line 44) | def do_train(

FILE: GLIP/maskrcnn_benchmark/engine/trainer.py
  function reduce_loss_dict (line 20) | def reduce_loss_dict(loss_dict):
  function do_train (line 45) | def do_train(

FILE: GLIP/maskrcnn_benchmark/layers/batch_norm.py
  class FrozenBatchNorm2d (line 9) | class FrozenBatchNorm2d(nn.Module):
    method __init__ (line 15) | def __init__(self, n):
    method forward (line 22) | def forward(self, x):
  class AllReduce (line 30) | class AllReduce(Function):
    method forward (line 32) | def forward(ctx, input):
    method backward (line 40) | def backward(ctx, grad_output):
  class NaiveSyncBatchNorm2d (line 45) | class NaiveSyncBatchNorm2d(nn.BatchNorm2d):
    method __init__ (line 73) | def __init__(self, *args, stats_mode="", **kwargs):
    method forward (line 78) | def forward(self, input):

FILE: GLIP/maskrcnn_benchmark/layers/deform_conv.py
  class DeformConvFunction (line 12) | class DeformConvFunction(Function):
    method forward (line 15) | def forward(
    method backward (line 75) | def backward(ctx, grad_output):
    method _output_size (line 137) | def _output_size(input, weight, padding, dilation, stride):
  class ModulatedDeformConvFunction (line 152) | class ModulatedDeformConvFunction(Function):
    method forward (line 155) | def forward(
    method backward (line 209) | def backward(ctx, grad_output):
    method _infer_shape (line 251) | def _infer_shape(ctx, input, weight):
  class DeformConv (line 267) | class DeformConv(nn.Module):
    method __init__ (line 269) | def __init__(
    method reset_parameters (line 306) | def reset_parameters(self):
    method forward (line 314) | def forward(self, input, offset):
    method __repr__ (line 319) | def __repr__(self):
  class ModulatedDeformConv (line 333) | class ModulatedDeformConv(nn.Module):
    method __init__ (line 335) | def __init__(
    method reset_parameters (line 369) | def reset_parameters(self):
    method forward (line 379) | def forward(self, input, offset, mask):
    method __repr__ (line 384) | def __repr__(self):
  class ModulatedDeformConvPack (line 398) | class ModulatedDeformConvPack(ModulatedDeformConv):
    method __init__ (line 400) | def __init__(self,
    method init_offset (line 424) | def init_offset(self):
    method forward (line 429) | def forward(self, input):

FILE: GLIP/maskrcnn_benchmark/layers/deform_pool.py
  function add_conv (line 7) | def add_conv(in_ch, out_ch, ksize, stride, leaky=True):
  class upsample (line 31) | class upsample(nn.Module):
    method __init__ (line 34) | def __init__(self, size=None, scale_factor=None, mode='nearest', align...
    method forward (line 42) | def forward(self, input):
    method extra_repr (line 45) | def extra_repr(self):
  class SPPLayer (line 53) | class SPPLayer(nn.Module):
    method __init__ (line 54) | def __init__(self):
    method forward (line 57) | def forward(self, x):
  class DropBlock (line 65) | class DropBlock(nn.Module):
    method __init__ (line 66) | def __init__(self, block_size=7, keep_prob=0.9):
    method reset (line 75) | def reset(self, block_size, keep_prob):
    method calculate_gamma (line 83) | def calculate_gamma(self, x):
    method forward (line 87) | def forward(self, x):
  class resblock (line 109) | class resblock(nn.Module):
    method __init__ (line 118) | def __init__(self, ch, nblocks=1, shortcut=True):
    method forward (line 129) | def forward(self, x):
  class RFBblock (line 138) | class RFBblock(nn.Module):
    method __init__ (line 139) | def __init__(self,in_ch,residual=False):
    method forward (line 161) | def forward(self,x):
  class FeatureAdaption (line 172) | class FeatureAdaption(nn.Module):
    method __init__ (line 173) | def __init__(self, in_ch, out_ch, n_anchors, rfb=False, sep=False):
    method forward (line 187) | def forward(self, input, wh_pred):
  class ASFFmobile (line 200) | class ASFFmobile(nn.Module):
    method __init__ (line 201) | def __init__(self, level, rfb=False, vis=False):
    method forward (line 229) | def forward(self, x_level_0, x_level_1, x_level_2):
  class ASFF (line 268) | class ASFF(nn.Module):
    method __init__ (line 269) | def __init__(self, level, rfb=False, vis=False):
    method forward (line 296) | def forward(self, x_level_0, x_level_1, x_level_2):
  function make_divisible (line 333) | def make_divisible(v, divisor, min_value=None):
  class ConvBNReLU (line 353) | class ConvBNReLU(nn.Sequential):
    method __init__ (line 354) | def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, gro...
  function add_sepconv (line 362) | def add_sepconv(in_ch, out_ch, ksize, stride):
  class InvertedResidual (line 376) | class InvertedResidual(nn.Module):
    method __init__ (line 377) | def __init__(self, inp, oup, stride, expand_ratio):
    method forward (line 398) | def forward(self, x):
  class ressepblock (line 404) | class ressepblock(nn.Module):
    method __init__ (line 405) | def __init__(self, ch, out_ch, in_ch=None, shortcut=True):
    method forward (line 416) | def forward(self, x):

FILE: GLIP/maskrcnn_benchmark/layers/dropblock.py
  class DropBlock2D (line 6) | class DropBlock2D(nn.Module):
    method __init__ (line 27) | def __init__(self, drop_prob, block_size):
    method forward (line 33) | def forward(self, x):
    method _compute_block_mask (line 62) | def _compute_block_mask(self, mask):
    method _compute_gamma (line 75) | def _compute_gamma(self, x):
  class DropBlock3D (line 79) | class DropBlock3D(DropBlock2D):
    method __init__ (line 100) | def __init__(self, drop_prob, block_size):
    method forward (line 103) | def forward(self, x):
    method _compute_block_mask (line 132) | def _compute_block_mask(self, mask):
    method _compute_gamma (line 145) | def _compute_gamma(self, x):

FILE: GLIP/maskrcnn_benchmark/layers/dyhead.py
  class Conv3x3Norm (line 9) | class Conv3x3Norm(torch.nn.Module):
    method __init__ (line 10) | def __init__(self,
    method forward (line 28) | def forward(self, input, **kwargs):
  class DyConv (line 35) | class DyConv(nn.Module):
    method __init__ (line 36) | def __init__(self,
    method init_weights (line 72) | def init_weights(self):
    method forward (line 85) | def forward(self, x):
  class DyHead (line 122) | class DyHead(nn.Module):
    method __init__ (line 123) | def __init__(self, cfg, in_channels):
    method forward (line 149) | def forward(self, x):

FILE: GLIP/maskrcnn_benchmark/layers/dyrelu.py
  function _make_divisible (line 5) | def _make_divisible(v, divisor, min_value=None):
  class swish (line 15) | class swish(nn.Module):
    method forward (line 16) | def forward(self, x):
  class h_swish (line 20) | class h_swish(nn.Module):
    method __init__ (line 21) | def __init__(self, inplace=False):
    method forward (line 25) | def forward(self, x):
  class h_sigmoid (line 29) | class h_sigmoid(nn.Module):
    method __init__ (line 30) | def __init__(self, inplace=True, h_max=1):
    method forward (line 35) | def forward(self, x):
  class DYReLU (line 39) | class DYReLU(nn.Module):
    method __init__ (line 40) | def __init__(self, inp, oup, reduction=4, lambda_a=1.0, K2=True, use_b...
    method forward (line 78) | def forward(self, x):

FILE: GLIP/maskrcnn_benchmark/layers/evonorm.py
  class EvoNorm2d (line 5) | class EvoNorm2d(nn.Module):
    method __init__ (line 8) | def __init__(self, num_features, eps=1e-5, nonlinearity=True, group=32):
    method reset_parameters (line 23) | def reset_parameters(self):
    method group_std (line 29) | def group_std(self, x, groups=32):
    method forward (line 35) | def forward(self, x):

FILE: GLIP/maskrcnn_benchmark/layers/iou_loss.py
  class IOULoss (line 5) | class IOULoss(nn.Module):
    method __init__ (line 6) | def __init__(self, loss_type="iou"):
    method forward (line 10) | def forward(self, pred, target, weight=None):
  class IOUWHLoss (line 52) | class IOUWHLoss(nn.Module):  # used for anchor guiding
    method __init__ (line 53) | def __init__(self, reduction='none'):
    method forward (line 57) | def forward(self, pred, target):

FILE: GLIP/maskrcnn_benchmark/layers/misc.py
  class _NewEmptyTensorOp (line 17) | class _NewEmptyTensorOp(torch.autograd.Function):
    method forward (line 19) | def forward(ctx, x, new_shape):
    method backward (line 24) | def backward(ctx, grad):
  class Conv2d (line 29) | class Conv2d(torch.nn.Conv2d):
    method forward (line 30) | def forward(self, x):
  class ConvTranspose2d (line 45) | class ConvTranspose2d(torch.nn.ConvTranspose2d):
    method forward (line 46) | def forward(self, x):
  class BatchNorm2d (line 66) | class BatchNorm2d(torch.nn.BatchNorm2d):
    method forward (line 67) | def forward(self, x):
  function interpolate (line 75) | def interpolate(
  class Scale (line 113) | class Scale(torch.nn.Module):
    method __init__ (line 114) | def __init__(self, init_value=1.0):
    method forward (line 118) | def forward(self, input):
  class DFConv2d (line 122) | class DFConv2d(torch.nn.Module):
    method __init__ (line 124) | def __init__(
    method forward (line 181) | def forward(self, x):

FILE: GLIP/maskrcnn_benchmark/layers/roi_align.py
  class _ROIAlign (line 10) | class _ROIAlign(Function):
    method forward (line 12) | def forward(ctx, input, roi, output_size, spatial_scale, sampling_ratio):
    method backward (line 25) | def backward(ctx, grad_output):
  class ROIAlign (line 51) | class ROIAlign(nn.Module):
    method __init__ (line 52) | def __init__(self, output_size, spatial_scale, sampling_ratio):
    method forward (line 58) | def forward(self, input, rois):
    method __repr__ (line 63) | def __repr__(self):
  class ROIAlignV2 (line 71) | class ROIAlignV2(nn.Module):
    method __init__ (line 72) | def __init__(self, output_size, spatial_scale, sampling_ratio):
    method forward (line 78) | def forward(self, input, rois):
    method __repr__ (line 83) | def __repr__(self):

FILE: GLIP/maskrcnn_benchmark/layers/roi_pool.py
  class _ROIPool (line 11) | class _ROIPool(Function):
    method forward (line 13) | def forward(ctx, input, roi, output_size, spatial_scale):
    method backward (line 25) | def backward(ctx, grad_output):
  class ROIPool (line 49) | class ROIPool(nn.Module):
    method __init__ (line 50) | def __init__(self, output_size, spatial_scale):
    method forward (line 55) | def forward(self, input, rois):
    method __repr__ (line 58) | def __repr__(self):

FILE: GLIP/maskrcnn_benchmark/layers/se.py
  class SELayer (line 4) | class SELayer(nn.Module):
    method __init__ (line 5) | def __init__(self, channel, reduction=16):
    method forward (line 15) | def forward(self, x):
  class SEBlock (line 22) | class SEBlock(nn.Module):
    method __init__ (line 23) | def __init__(self, channels, reduction=16,
    method forward (line 41) | def forward(self, x):

FILE: GLIP/maskrcnn_benchmark/layers/set_loss.py
  function box_area (line 10) | def box_area(boxes):
  function box_iou (line 15) | def box_iou(boxes1, boxes2):
  function generalized_box_iou (line 31) | def generalized_box_iou(boxes1, boxes2):
  function dice_loss (line 55) | def dice_loss(inputs, targets, num_boxes):
  function sigmoid_focal_loss (line 73) | def sigmoid_focal_loss(inputs: torch.Tensor, targets: torch.Tensor, alph...
  class HungarianMatcher (line 115) | class HungarianMatcher(nn.Module):
    method __init__ (line 123) | def __init__(self, cost_class: float = 1, cost_bbox: float = 1, cost_g...
    method forward (line 145) | def forward(self, outputs, targets):
  class SetCriterion (line 217) | class SetCriterion(nn.Module):
    method __init__ (line 223) | def __init__(self, num_classes, matcher, weight_dict, eos_coef, losses,
    method loss_labels (line 248) | def loss_labels(self, outputs, targets, indices, num_boxes, log=False):
    method loss_boxes (line 283) | def loss_boxes(self, outputs, targets, indices, num_boxes):
    method _get_src_permutation_idx (line 306) | def _get_src_permutation_idx(self, indices):
    method _get_tgt_permutation_idx (line 312) | def _get_tgt_permutation_idx(self, indices):
    method get_loss (line 318) | def get_loss(self, loss, outputs, targets, indices, num_boxes, **kwargs):
    method forward (line 327) | def forward(self, outputs, targets, *argrs, **kwargs):

FILE: GLIP/maskrcnn_benchmark/layers/sigmoid_focal_loss.py
  class _SigmoidFocalLoss (line 11) | class _SigmoidFocalLoss(Function):
    method forward (line 13) | def forward(ctx, logits, targets, gamma, alpha):
    method backward (line 27) | def backward(ctx, d_loss):
  function sigmoid_focal_loss_cpu (line 42) | def sigmoid_focal_loss_cpu(logits, targets, gamma, alpha):
  class SigmoidFocalLoss (line 55) | class SigmoidFocalLoss(nn.Module):
    method __init__ (line 56) | def __init__(self, gamma, alpha):
    method forward (line 61) | def forward(self, logits, targets):
    method __repr__ (line 70) | def __repr__(self):
  function token_sigmoid_softmax_focal_loss (line 78) | def token_sigmoid_softmax_focal_loss(pred_logits, targets, alpha, gamma,...
  function token_sigmoid_binary_focal_loss_v2 (line 110) | def token_sigmoid_binary_focal_loss_v2(pred_logits, targets, alpha, gamm...
  function token_sigmoid_binary_focal_loss (line 130) | def token_sigmoid_binary_focal_loss(pred_logits, targets, alpha, gamma, ...
  class TokenSigmoidFocalLoss (line 174) | class TokenSigmoidFocalLoss(nn.Module):
    method __init__ (line 175) | def __init__(self, alpha, gamma):
    method forward (line 180) | def forward(self, logits, targets, text_masks=None, version="binary", ...
    method __repr__ (line 192) | def __repr__(self):

FILE: GLIP/maskrcnn_benchmark/layers/smooth_l1_loss.py
  function smooth_l1_loss (line 6) | def smooth_l1_loss(input, target, beta=1. / 9, size_average=True):

FILE: GLIP/maskrcnn_benchmark/modeling/backbone/__init__.py
  function build_resnet_backbone (line 23) | def build_resnet_backbone(cfg):
  function build_resnet_c5_backbone (line 31) | def build_resnet_c5_backbone(cfg):
  function build_retinanet_swint_fpn_backbone (line 38) | def build_retinanet_swint_fpn_backbone(cfg):
  function build_swint_fpn_backbone (line 84) | def build_swint_fpn_backbone(cfg):
  function build_retinanet_cvt_fpn_backbone (line 128) | def build_retinanet_cvt_fpn_backbone(cfg):
  function build_eff_fpn_p6p7_backbone (line 170) | def build_eff_fpn_p6p7_backbone(cfg):
  function build_eff_fpn_p6p7_backbone (line 199) | def build_eff_fpn_p6p7_backbone(cfg):
  function build_efficientdet_backbone (line 220) | def build_efficientdet_backbone(cfg):
  function build_backbone (line 234) | def build_backbone(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/backbone/bifpn.py
  class BiFPN (line 7) | class BiFPN(nn.Module):
    method __init__ (line 8) | def __init__(self, in_channels_list, out_channels, first_time=False, e...
    method forward (line 120) | def forward(self, inputs):
    method _forward_fast_attention (line 151) | def _forward_fast_attention(self, inputs):
    method _forward (line 225) | def _forward(self, inputs):

FILE: GLIP/maskrcnn_benchmark/modeling/backbone/blocks.py
  class stem (line 5) | class stem(nn.Module):
    method __init__ (line 8) | def __init__(self, conv, inplanes, planes, stride=1, norm_layer=nn.Bat...
    method forward (line 15) | def forward(self, x):
  class basic (line 23) | class basic(nn.Module):
    method __init__ (line 27) | def __init__(self, conv, inplanes, planes, stride=1, midplanes=None, n...
    method forward (line 43) | def forward(self, x):
  class bottleneck (line 62) | class bottleneck(nn.Module):
    method __init__ (line 66) | def __init__(self, conv, inplanes, planes, stride=1, midplanes=None, n...
    method forward (line 84) | def forward(self, x):
  class invert (line 107) | class invert(nn.Module):
    method __init__ (line 108) | def __init__(self, conv, inp, oup, stride=1, expand_ratio=1, norm_laye...
    method forward (line 141) | def forward(self, x):
  function channel_shuffle (line 154) | def channel_shuffle(x, groups):
  class shuffle (line 165) | class shuffle(nn.Module):
    method __init__ (line 169) | def __init__(self, conv, inplanes, outplanes, stride=1, midplanes=None...
    method forward (line 201) | def forward(self, x):
  class shufflex (line 212) | class shufflex(nn.Module):
    method __init__ (line 216) | def __init__(self, conv, inplanes, outplanes, stride=1, midplanes=None...
    method forward (line 258) | def forward(self, x):

FILE: GLIP/maskrcnn_benchmark/modeling/backbone/efficientdet.py
  class MaxPool2dStaticSamePadding (line 32) | class MaxPool2dStaticSamePadding(nn.Module):
    method __init__ (line 38) | def __init__(self, kernel_size, stride):
    method forward (line 59) | def forward(self, x):
  class Conv2dStaticSamePadding (line 84) | class Conv2dStaticSamePadding(nn.Module):
    method __init__ (line 90) | def __init__(self, in_channels, out_channels, kernel_size, stride=1, b...
    method forward (line 124) | def forward(self, x):
  class SeparableConvBlock (line 149) | class SeparableConvBlock(nn.Module):
    method __init__ (line 154) | def __init__(self, in_channels, out_channels=None, norm=True, activati...
    method forward (line 177) | def forward(self, x):
  class BiFPN (line 190) | class BiFPN(nn.Module):
    method __init__ (line 195) | def __init__(self, num_channels, conv_channels, first_time=False,
    method forward (line 297) | def forward(self, inputs):
    method _forward_fast_attention (line 327) | def _forward_fast_attention(self, inputs):
    method _forward (line 433) | def _forward(self, inputs):
  class Regressor (line 484) | class Regressor(nn.Module):
    method __init__ (line 489) | def __init__(self, in_channels, num_anchors, num_layers, onnx_export=F...
    method forward (line 502) | def forward(self, inputs):
  class SwishImplementation (line 519) | class SwishImplementation(torch.autograd.Function):
    method forward (line 521) | def forward(ctx, i):
    method backward (line 527) | def backward(ctx, grad_output):
  class MemoryEfficientSwish (line 532) | class MemoryEfficientSwish(nn.Module):
    method forward (line 533) | def forward(self, x):
  class Swish (line 538) | class Swish(nn.Module):
    method forward (line 539) | def forward(self, x):
  class Classifier (line 542) | class Classifier(nn.Module):
    method __init__ (line 547) | def __init__(self, in_channels, num_anchors, num_classes, num_layers,
    method forward (line 567) | def forward(self, inputs):
  class Conv2dDynamicSamePadding (line 588) | class Conv2dDynamicSamePadding(nn.Conv2d):
    method __init__ (line 591) | def __init__(self, in_channels, out_channels, kernel_size, stride=1, d...
    method forward (line 596) | def forward(self, x):
  function get_same_padding_conv2d (line 609) | def get_same_padding_conv2d(image_size=None):
  function round_filters (line 619) | def round_filters(filters, global_params):
  function round_repeats (line 633) | def round_repeats(repeats, global_params):
  function drop_connect (line 640) | def drop_connect(inputs, p, training):
  class MBConvBlock (line 651) | class MBConvBlock(nn.Module):
    method __init__ (line 663) | def __init__(self, block_args, global_params):
    method forward (line 703) | def forward(self, inputs, drop_connect_rate=None):
    method set_swish (line 740) | def set_swish(self, memory_efficient=True):
  class BlockDecoder (line 744) | class BlockDecoder(object):
    method _decode_block_string (line 748) | def _decode_block_string(block_string):
    method _encode_block_string (line 775) | def _encode_block_string(block):
    method decode (line 792) | def decode(string_list):
    method encode (line 806) | def encode(blocks_args):
  function efficientnet (line 818) | def efficientnet(width_coefficient=None, depth_coefficient=None, dropout...
  function efficientnet_params (line 847) | def efficientnet_params(model_name):
  function get_model_params (line 865) | def get_model_params(model_name, override_params):
  function load_pretrained_weights (line 902) | def load_pretrained_weights(model, model_name, load_fc=True, advprop=Fal...
  class EfficientNet (line 919) | class EfficientNet(nn.Module):
    method __init__ (line 932) | def __init__(self, blocks_args=None, global_params=None):
    method set_swish (line 982) | def set_swish(self, memory_efficient=True):
    method extract_features (line 988) | def extract_features(self, inputs):
    method forward (line 1005) | def forward(self, inputs):
    method from_name (line 1019) | def from_name(cls, model_name, override_params=None):
    method from_pretrained (line 1025) | def from_pretrained(cls, model_name, load_weights=True, advprop=True, ...
    method get_image_size (line 1036) | def get_image_size(cls, model_name):
    method _check_model_name_is_valid (line 1042) | def _check_model_name_is_valid(cls, model_name):
  class EfficientNetD (line 1048) | class EfficientNetD(nn.Module):
    method __init__ (line 1053) | def __init__(self, compound_coef, load_weights=False):
    method forward (line 1063) | def forward(self, x):
  class Anchors (line 1087) | class Anchors(nn.Module):
    method __init__ (line 1092) | def __init__(self, anchor_scale=4., pyramid_levels=None, **kwargs):
    method forward (line 1108) | def forward(self, image, dtype=torch.float32, features=None):
    method get_key (line 1190) | def get_key(self, hint, image_shape):
  class EffNetFPN (line 1193) | class EffNetFPN(nn.Module):
    method __init__ (line 1194) | def __init__(self, compound_coef=0, start_from=3):
    method forward (line 1216) | def forward(self, inputs):
  class EfficientDetBackbone (line 1229) | class EfficientDetBackbone(nn.Module):
    method __init__ (line 1256) | def __init__(self, num_classes=80, compound_coef=0, load_weights=False,
    method freeze_bn (line 1293) | def freeze_bn(self):
    method forward (line 1298) | def forward(self, inputs):
    method init_backbone (line 1310) | def init_backbone(self, path):
  function init_weights (line 1318) | def init_weights(model):
  function calc_iou (line 1328) | def calc_iou(a, b):
  class BBoxTransform (line 1344) | class BBoxTransform(nn.Module):
    method forward (line 1345) | def forward(self, anchors, regression):
  class ClipBoxes (line 1377) | class ClipBoxes(nn.Module):
    method __init__ (line 1379) | def __init__(self):
    method forward (line 1382) | def forward(self, boxes, img):
  function postprocess2 (line 1393) | def postprocess2(x, anchors, regression, classification,
  function postprocess (line 1450) | def postprocess(x, anchors, regression, classification, regressBoxes, cl...
  function display (line 1490) | def display(preds, imgs, obj_list, imshow=True, imwrite=False):
  function calculate_focal_loss2 (line 1510) | def calculate_focal_loss2(classification, target_list, alpha, gamma):
  function calculate_focal_loss (line 1515) | def calculate_focal_loss(classification, targets, alpha, gamma):
  function calculate_giou (line 1534) | def calculate_giou(pred, gt):
  class FocalLoss (line 1556) | class FocalLoss(nn.Module):
    method __init__ (line 1557) | def __init__(self, alpha=0.25, gamma=2., cls_loss_type='FL', smooth_bc...
    method forward (line 1602) | def forward(self, classifications, regressions, anchor_info, annotatio...
  class ModelWithLoss (line 1761) | class ModelWithLoss(nn.Module):
    method __init__ (line 1762) | def __init__(self, model, criterion):
    method forward (line 1767) | def forward(self, *args):
  class TorchVisionNMS (line 1776) | class TorchVisionNMS(nn.Module):
    method __init__ (line 1777) | def __init__(self, iou_threshold):
    method forward (line 1781) | def forward(self, box, prob):
  class PostProcess (line 1785) | class PostProcess(nn.Module):
    method __init__ (line 1786) | def __init__(self, iou_threshold):
    method forward (line 1790) | def forward(self, x, anchors, regression,
  class InferenceModel (line 1846) | class InferenceModel(nn.Module):
    method __init__ (line 1847) | def __init__(self, model):
    method forward (line 1859) | def forward(self, sample):

FILE: GLIP/maskrcnn_benchmark/modeling/backbone/efficientnet.py
  function round_channels (line 17) | def round_channels(channels,
  function calc_tf_padding (line 40) | def calc_tf_padding(x,
  class ConvBlock (line 71) | class ConvBlock(nn.Module):
    method __init__ (line 100) | def __init__(self,
    method forward (line 136) | def forward(self, x):
  function conv1x1_block (line 147) | def conv1x1_block(in_channels,
  function conv3x3_block (line 193) | def conv3x3_block(in_channels,
  function dwconv3x3_block (line 243) | def dwconv3x3_block(in_channels,
  function dwconv5x5_block (line 287) | def dwconv5x5_block(in_channels,
  class EffiDwsConvUnit (line 331) | class EffiDwsConvUnit(nn.Module):
    method __init__ (line 351) | def __init__(self,
    method forward (line 378) | def forward(self, x):
  class EffiInvResUnit (line 391) | class EffiInvResUnit(nn.Module):
    method __init__ (line 416) | def __init__(self,
    method forward (line 458) | def forward(self, x):
  class EffiInitBlock (line 473) | class EffiInitBlock(nn.Module):
    method __init__ (line 491) | def __init__(self,
    method forward (line 508) | def forward(self, x):
  class EfficientNet (line 515) | class EfficientNet(nn.Module):
    method __init__ (line 547) | def __init__(self,
    method _freeze_backbone (line 608) | def _freeze_backbone(self, freeze_at):
    method forward (line 616) | def forward(self, x):
  function get_efficientnet (line 625) | def get_efficientnet(cfg, version, tf_mode = True, bn_eps=1e-5, **kwargs):

FILE: GLIP/maskrcnn_benchmark/modeling/backbone/fbnet.py
  function _py2_round (line 23) | def _py2_round(x):
  function _get_divisible_by (line 27) | def _get_divisible_by(num, divisible_by, min_val):
  class Identity (line 34) | class Identity(nn.Module):
    method __init__ (line 35) | def __init__(self, C_in, C_out, stride):
    method forward (line 52) | def forward(self, x):
  class CascadeConv3x3 (line 60) | class CascadeConv3x3(nn.Sequential):
    method __init__ (line 61) | def __init__(self, C_in, C_out, stride):
    method forward (line 73) | def forward(self, x):
  class Shift (line 80) | class Shift(nn.Module):
    method __init__ (line 81) | def __init__(self, C, kernel_size, stride, padding):
    method forward (line 108) | def forward(self, x):
  class ShiftBlock5x5 (line 134) | class ShiftBlock5x5(nn.Sequential):
    method __init__ (line 135) | def __init__(self, C_in, C_out, expansion, stride):
    method forward (line 154) | def forward(self, x):
  class ChannelShuffle (line 161) | class ChannelShuffle(nn.Module):
    method __init__ (line 162) | def __init__(self, groups):
    method forward (line 166) | def forward(self, x):
  class ConvBNRelu (line 181) | class ConvBNRelu(nn.Sequential):
    method __init__ (line 182) | def __init__(
  class SEModule (line 240) | class SEModule(nn.Module):
    method __init__ (line 243) | def __init__(self, C):
    method forward (line 253) | def forward(self, x):
  class Upsample (line 257) | class Upsample(nn.Module):
    method __init__ (line 258) | def __init__(self, scale_factor, mode, align_corners=None):
    method forward (line 264) | def forward(self, x):
  function _get_upsample_op (line 271) | def _get_upsample_op(stride):
  class IRFBlock (line 288) | class IRFBlock(nn.Module):
    method __init__ (line 289) | def __init__(
    method forward (line 392) | def forward(self, x):

FILE: GLIP/maskrcnn_benchmark/modeling/backbone/fpn.py
  class FPN (line 6) | class FPN(nn.Module):
    method __init__ (line 13) | def __init__(
    method forward (line 59) | def forward(self, x):
  class LastLevelMaxPool (line 132) | class LastLevelMaxPool(nn.Module):
    method forward (line 133) | def forward(self, x):
  class LastLevelP6P7 (line 137) | class LastLevelP6P7(nn.Module):
    method __init__ (line 141) | def __init__(self, in_channels, out_channels):
    method forward (line 150) | def forward(self, c5, p5):
  class SPPLayer (line 157) | class SPPLayer(nn.Module):
    method __init__ (line 158) | def __init__(self):
    method forward (line 161) | def forward(self, x):

FILE: GLIP/maskrcnn_benchmark/modeling/backbone/mixer.py
  class MixedOperationRandom (line 4) | class MixedOperationRandom(nn.Module):
    method __init__ (line 5) | def __init__(self, search_ops):
    method forward (line 10) | def forward(self, x, x_path=None):

FILE: GLIP/maskrcnn_benchmark/modeling/backbone/ops.py
  function conv7x7 (line 7) | def conv7x7(in_planes, out_planes, stride=1, groups=1, dilation=1):
  function conv5x5 (line 13) | def conv5x5(in_planes, out_planes, stride=1, groups=1, dilation=1):
  function conv3x3 (line 19) | def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
  function conv1x1 (line 25) | def conv1x1(in_planes, out_planes, stride=1):
  function maxpool (line 30) | def maxpool(**kwargs):
  function avgpool (line 34) | def avgpool(**kwargs):
  function dropout (line 37) | def dropout(prob):

FILE: GLIP/maskrcnn_benchmark/modeling/backbone/resnet.py
  class ResNet (line 81) | class ResNet(nn.Module):
    method __init__ (line 82) | def __init__(self, cfg):
    method _freeze_backbone (line 159) | def _freeze_backbone(self, freeze_at):
    method forward (line 170) | def forward(self, x):
  class ResNetHead (line 180) | class ResNetHead(nn.Module):
    method __init__ (line 181) | def __init__(
    method forward (line 226) | def forward(self, x):
  function _make_stage (line 232) | def _make_stage(
  class Bottleneck (line 278) | class Bottleneck(nn.Module):
    method __init__ (line 279) | def __init__(
    method forward (line 382) | def forward(self, x):
  class BaseStem (line 408) | class BaseStem(nn.Module):
    method __init__ (line 409) | def __init__(self, cfg, norm_func):
    method forward (line 435) | def forward(self, x):
  class BottleneckWithFixedBatchNorm (line 451) | class BottleneckWithFixedBatchNorm(Bottleneck):
    method __init__ (line 452) | def __init__(
  class StemWithFixedBatchNorm (line 478) | class StemWithFixedBatchNorm(BaseStem):
    method __init__ (line 479) | def __init__(self, cfg):
  class BottleneckWithBatchNorm (line 485) | class BottleneckWithBatchNorm(Bottleneck):
    method __init__ (line 486) | def __init__(
  class StemWithBatchNorm (line 512) | class StemWithBatchNorm(BaseStem):
    method __init__ (line 513) | def __init__(self, cfg):
  class BottleneckWithNaiveSyncBatchNorm (line 519) | class BottleneckWithNaiveSyncBatchNorm(Bottleneck):
    method __init__ (line 520) | def __init__(
  class StemWithNaiveSyncBatchNorm (line 546) | class StemWithNaiveSyncBatchNorm(BaseStem):
    method __init__ (line 547) | def __init__(self, cfg):
  class BottleneckWithSyncBatchNorm (line 553) | class BottleneckWithSyncBatchNorm(Bottleneck):
    method __init__ (line 554) | def __init__(
  class StemWithSyncBatchNorm (line 580) | class StemWithSyncBatchNorm(BaseStem):
    method __init__ (line 581) | def __init__(self, cfg):
  class BottleneckWithGN (line 587) | class BottleneckWithGN(Bottleneck):
    method __init__ (line 588) | def __init__(
  class StemWithGN (line 614) | class StemWithGN(BaseStem):
    method __init__ (line 615) | def __init__(self, cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/backbone/swint.py
  class Mlp (line 13) | class Mlp(nn.Module):
    method __init__ (line 16) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 25) | def forward(self, x):
  function window_partition (line 34) | def window_partition(x, window_size):
  function window_reverse (line 48) | def window_reverse(windows, window_size, H, W):
  class WindowAttention (line 64) | class WindowAttention(nn.Module):
    method __init__ (line 77) | def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scal...
    method forward (line 111) | def forward(self, x, mask=None):
  class SwinTransformerBlock (line 145) | class SwinTransformerBlock(nn.Module):
    method __init__ (line 162) | def __init__(self, dim, num_heads, window_size=7, shift_size=0,
    method forward (line 186) | def forward(self, x, mask_matrix):
  class PatchMerging (line 245) | class PatchMerging(nn.Module):
    method __init__ (line 252) | def __init__(self, dim, norm_layer=nn.LayerNorm):
    method forward (line 258) | def forward(self, x, H, W):
  class BasicLayer (line 287) | class BasicLayer(nn.Module):
    method __init__ (line 305) | def __init__(self,
    method forward (line 347) | def forward(self, x, H, W):
  class PatchEmbed (line 389) | class PatchEmbed(nn.Module):
    method __init__ (line 398) | def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=...
    method forward (line 412) | def forward(self, x):
  class SwinTransformer (line 431) | class SwinTransformer(nn.Module):
    method __init__ (line 459) | def __init__(self,
    method _freeze_stages (line 556) | def _freeze_stages(self):
    method init_weights (line 573) | def init_weights(self, pretrained=None):
    method forward (line 591) | def forward(self, x):
    method train (line 617) | def train(self, mode=True):
  function build_swint_backbone (line 623) | def build_swint_backbone(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/backbone/swint_v2.py
  class Mlp (line 14) | class Mlp(nn.Module):
    method __init__ (line 17) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 26) | def forward(self, x):
  function window_partition (line 35) | def window_partition(x, window_size):
  function window_reverse (line 49) | def window_reverse(windows, window_size, H, W):
  class WindowAttention (line 65) | class WindowAttention(nn.Module):
    method __init__ (line 78) | def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scal...
    method forward (line 112) | def forward(self, x, mask=None):
  class SwinTransformerBlock (line 146) | class SwinTransformerBlock(nn.Module):
    method __init__ (line 163) | def __init__(self, dim, num_heads, window_size=7, shift_size=0,
    method forward (line 194) | def forward(self, x, mask_matrix):
  class PatchMerging (line 253) | class PatchMerging(nn.Module):
    method __init__ (line 260) | def __init__(self, dim, norm_layer=nn.LayerNorm):
    method forward (line 266) | def forward(self, x, H, W):
  class BasicLayer (line 295) | class BasicLayer(nn.Module):
    method __init__ (line 313) | def __init__(self,
    method forward (line 358) | def forward(self, x, H, W):
  class ConvEmbed (line 442) | class ConvEmbed(nn.Module):
    method __init__ (line 446) | def __init__(
    method forward (line 467) | def forward(self, x, H=None, W=None):
  class SwinTransformer (line 499) | class SwinTransformer(nn.Module):
    method __init__ (line 527) | def __init__(self,
    method _freeze_stages (line 635) | def _freeze_stages(self):
    method init_weights (line 652) | def init_weights(self, pretrained=None):
    method forward (line 670) | def forward(self, x):
    method train (line 697) | def train(self, mode=True):
  function build_swint_backbone (line 703) | def build_swint_backbone(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/backbone/swint_v2_vl.py
  class Mlp (line 15) | class Mlp(nn.Module):
    method __init__ (line 18) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 27) | def forward(self, x):
  function window_partition (line 36) | def window_partition(x, window_size):
  function window_reverse (line 50) | def window_reverse(windows, window_size, H, W):
  class WindowAttention (line 66) | class WindowAttention(nn.Module):
    method __init__ (line 79) | def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scal...
    method forward (line 125) | def forward(self, x, mask=None, x_text=None, mask_text=None):
  class SwinTransformerBlock (line 215) | class SwinTransformerBlock(nn.Module):
    method __init__ (line 232) | def __init__(self, dim, num_heads, window_size=7, shift_size=0,
    method forward (line 276) | def forward(self, x, mask_matrix, x_text, mask_text):
  class PatchMerging (line 347) | class PatchMerging(nn.Module):
    method __init__ (line 354) | def __init__(self, dim, norm_layer=nn.LayerNorm):
    method forward (line 360) | def forward(self, x, H, W):
  class BasicLayer (line 389) | class BasicLayer(nn.Module):
    method __init__ (line 407) | def __init__(self,
    method forward (line 456) | def forward(self, x, H, W, x_text=None, mask_text=None):
  class ConvEmbed (line 542) | class ConvEmbed(nn.Module):
    method __init__ (line 546) | def __init__(
    method forward (line 567) | def forward(self, x, H=None, W=None):
  class SwinTransformer (line 599) | class SwinTransformer(nn.Module):
    method __init__ (line 627) | def __init__(self,
    method _freeze_stages (line 744) | def _freeze_stages(self):
    method init_weights (line 761) | def init_weights(self, pretrained=None):
    method forward (line 779) | def forward(self, inputs):
    method train (line 822) | def train(self, mode=True):
  function build_swint_backbone (line 828) | def build_swint_backbone(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/backbone/swint_vl.py
  class Mlp (line 14) | class Mlp(nn.Module):
    method __init__ (line 17) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 26) | def forward(self, x):
  function window_partition (line 35) | def window_partition(x, window_size):
  function window_reverse (line 49) | def window_reverse(windows, window_size, H, W):
  class WindowAttention (line 65) | class WindowAttention(nn.Module):
    method __init__ (line 78) | def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scal...
    method forward (line 124) | def forward(self, x, mask=None, x_text=None, mask_text=None):
  class SwinTransformerBlock (line 214) | class SwinTransformerBlock(nn.Module):
    method __init__ (line 231) | def __init__(self, dim, num_heads, window_size=7, shift_size=0,
    method forward (line 264) | def forward(self, x, mask_matrix, x_text, mask_text):
  class PatchMerging (line 335) | class PatchMerging(nn.Module):
    method __init__ (line 342) | def __init__(self, dim, norm_layer=nn.LayerNorm):
    method forward (line 348) | def forward(self, x, H, W):
  class BasicLayer (line 377) | class BasicLayer(nn.Module):
    method __init__ (line 395) | def __init__(self,
    method forward (line 441) | def forward(self, x, H, W, x_text=None, mask_text=None):
  class PatchEmbed (line 485) | class PatchEmbed(nn.Module):
    method __init__ (line 494) | def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=...
    method forward (line 508) | def forward(self, x):
  class SwinTransformer (line 527) | class SwinTransformer(nn.Module):
    method __init__ (line 555) | def __init__(self,
    method _freeze_stages (line 661) | def _freeze_stages(self):
    method init_weights (line 678) | def init_weights(self, pretrained=None):
    method forward (line 696) | def forward(self, inputs):
    method train (line 739) | def train(self, mode=True):
  function build_swint_backbone (line 745) | def build_swint_backbone(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/balanced_positive_negative_sampler.py
  class BalancedPositiveNegativeSampler (line 5) | class BalancedPositiveNegativeSampler(object):
    method __init__ (line 10) | def __init__(self, batch_size_per_image, positive_fraction):
    method __call__ (line 19) | def __call__(self, matched_idxs):

FILE: GLIP/maskrcnn_benchmark/modeling/box_coder.py
  class BoxCoder (line 7) | class BoxCoder(object):
    method __init__ (line 13) | def __init__(self, weights, bbox_xform_clip=math.log(1000. / 16)):
    method encode (line 22) | def encode(self, reference_boxes, proposals):
    method decode (line 52) | def decode(self, rel_codes, boxes):

FILE: GLIP/maskrcnn_benchmark/modeling/detector/__init__.py
  function build_detection_model (line 9) | def build_detection_model(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py
  class GeneralizedRCNN (line 17) | class GeneralizedRCNN(nn.Module):
    method __init__ (line 27) | def __init__(self, cfg):
    method train (line 45) | def train(self, mode=True):
    method forward (line 70) | def forward(self, images, targets=None):

FILE: GLIP/maskrcnn_benchmark/modeling/detector/generalized_vl_rcnn.py
  function random_word (line 26) | def random_word(input_ids, mask_token_id, vocabs, padding_token_id, gree...
  class GeneralizedVLRCNN (line 63) | class GeneralizedVLRCNN(nn.Module):
    method __init__ (line 73) | def __init__(self, cfg):
    method train (line 129) | def train(self, mode=True):
    method forward (line 170) | def forward(self,

FILE: GLIP/maskrcnn_benchmark/modeling/language_backbone/backbone.py
  function build_bert_backbone (line 13) | def build_bert_backbone(cfg):
  function build_bert_backbone (line 20) | def build_bert_backbone(cfg):
  function build_rnn_backbone (line 27) | def build_rnn_backbone(cfg):
  function build_clip_backbone (line 34) | def build_clip_backbone(cfg):
  function build_backbone (line 40) | def build_backbone(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/language_backbone/bert_model.py
  class BertEncoder (line 10) | class BertEncoder(nn.Module):
    method __init__ (line 11) | def __init__(self, cfg):
    method forward (line 32) | def forward(self, x):

FILE: GLIP/maskrcnn_benchmark/modeling/language_backbone/build.py
  function build_tokenizer (line 4) | def build_tokenizer(tokenizer_name):

FILE: GLIP/maskrcnn_benchmark/modeling/language_backbone/clip_model.py
  class LayerNorm (line 16) | class LayerNorm(nn.Module):
    method __init__ (line 17) | def __init__(self, hidden_size, eps=1e-12):
    method forward (line 25) | def forward(self, x):
  class QuickGELU (line 34) | class QuickGELU(nn.Module):
    method forward (line 35) | def forward(self, x: torch.Tensor):
  class ResidualAttentionBlock (line 39) | class ResidualAttentionBlock(nn.Module):
    method __init__ (line 40) | def __init__(self,
    method attention (line 58) | def attention(self, x: torch.Tensor, key_padding_mask: torch.Tensor = ...
    method forward (line 63) | def forward(self, x: torch.Tensor, key_padding_mask: torch.Tensor = No...
  class CLIPTransformer (line 69) | class CLIPTransformer(nn.Module):
    method __init__ (line 70) | def __init__(self, cfg):
    method build_attention_mask (line 113) | def build_attention_mask(self):
    method _init_weights (line 121) | def _init_weights(self, m):
    method resize_pos_embed_1d (line 129) | def resize_pos_embed_1d(self, posemb, shape_new):
    method init_weights (line 140) | def init_weights(self, pretrained="", pretrained_layers=[], verbose=Fa...
    method no_weight_decay (line 165) | def no_weight_decay(self):
    method forward (line 171) | def forward(self, text):

FILE: GLIP/maskrcnn_benchmark/modeling/language_backbone/hfpt_tokenizer.py
  class HFPTTokenizer (line 7) | class HFPTTokenizer(object):
    method __init__ (line 8) | def __init__(self, pt_name=None):
    method get_eot_token (line 36) | def get_eot_token(self):
    method get_sot_token (line 39) | def get_sot_token(self):
    method get_eot_token_list (line 42) | def get_eot_token_list(self):
    method get_sot_token_list (line 45) | def get_sot_token_list(self):
    method get_tokenizer_obj (line 48) | def get_tokenizer_obj(self):
    method check_added_tokens (line 53) | def check_added_tokens(self):
    method tokenize (line 56) | def tokenize(self, texts: Union[str, List[str]], context_length: int =...
    method get_vocab_size (line 95) | def get_vocab_size(self):
    method __call__ (line 98) | def __call__(self, texts: Union[str, List[str]], context_length: int =...

FILE: GLIP/maskrcnn_benchmark/modeling/language_backbone/rnn_model.py
  class RNNEnoder (line 7) | class RNNEnoder(nn.Module):
    method __init__ (line 8) | def __init__(self, cfg):
    method forward (line 36) | def forward(self, input, mask=None):
    method encode (line 49) | def encode(self, input_labels):
    method sort_inputs (line 103) | def sort_inputs(self, input_labels):  # sort input labels by descending

FILE: GLIP/maskrcnn_benchmark/modeling/language_backbone/simple_tokenizer.py
  function default_bpe (line 14) | def default_bpe():
  function bytes_to_unicode (line 19) | def bytes_to_unicode():
  function get_pairs (line 41) | def get_pairs(word):
  function basic_clean (line 53) | def basic_clean(text):
  function whitespace_clean (line 59) | def whitespace_clean(text):
  class SimpleTokenizer (line 65) | class SimpleTokenizer(object):
    method __init__ (line 66) | def __init__(self, bpe_path: str = default_bpe()):
    method bpe (line 85) | def bpe(self, token):
    method encode (line 126) | def encode(self, text):
    method decode (line 134) | def decode(self, tokens):
    method get_vocab_size (line 139) | def get_vocab_size(self):
    method get_eot_token (line 142) | def get_eot_token(self):
    method get_sot_token (line 145) | def get_sot_token(self):
    method check_added_tokens (line 148) | def check_added_tokens(self):
    method get_tokenizer_obj (line 151) | def get_tokenizer_obj(self):
    method tokenize (line 154) | def tokenize(self, texts: Union[str, List[str]], context_length: int =...
    method __call__ (line 172) | def __call__(self, texts: Union[str, List[str]], context_length: int =...

FILE: GLIP/maskrcnn_benchmark/modeling/language_backbone/word_utils.py
  class Dictionary (line 15) | class Dictionary(object):
    method __init__ (line 16) | def __init__(self):
    method add_word (line 20) | def add_word(self, word):
    method __len__ (line 26) | def __len__(self):
    method __getitem__ (line 29) | def __getitem__(self, a):
    method __contains__ (line 39) | def __contains__(self, word):
  class Corpus (line 43) | class Corpus(object):
    method __init__ (line 44) | def __init__(self):
    method set_max_len (line 47) | def set_max_len(self, value):
    method load_file (line 50) | def load_file(self, filename):
    method add_to_corpus (line 58) | def add_to_corpus(self, line):
    method tokenize (line 67) | def tokenize(self, line, max_len=20):
    method __len__ (line 99) | def __len__(self):

FILE: GLIP/maskrcnn_benchmark/modeling/make_layers.py
  function get_group_gn (line 14) | def get_group_gn(dim, dim_per_gp, num_groups):
  function group_norm (line 31) | def group_norm(out_channels, affine=True, divisor=1):
  function make_conv3x3 (line 44) | def make_conv3x3(
  function make_fc (line 80) | def make_fc(dim_in, hidden_dim, use_gn=False):
  function conv_with_kaiming_uniform (line 95) | def conv_with_kaiming_uniform(use_gn=False, use_relu=False, use_dyrelu=F...

FILE: GLIP/maskrcnn_benchmark/modeling/matcher.py
  class Matcher (line 5) | class Matcher(object):
    method __init__ (line 23) | def __init__(self, high_threshold, low_threshold, allow_low_quality_ma...
    method __call__ (line 42) | def __call__(self, match_quality_matrix):
    method set_low_quality_matches_ (line 86) | def set_low_quality_matches_(self, matches, all_matches, match_quality...

FILE: GLIP/maskrcnn_benchmark/modeling/poolers.py
  class LevelMapper (line 11) | class LevelMapper(object):
    method __init__ (line 16) | def __init__(self, k_min, k_max, canonical_scale=224, canonical_level=...
    method __call__ (line 31) | def __call__(self, boxlists):
  class Pooler (line 45) | class Pooler(nn.Module):
    method __init__ (line 55) | def __init__(self, output_size, scales, sampling_ratio, use_v2=False):
    method convert_to_roi_format (line 82) | def convert_to_roi_format(self, boxes):
    method forward (line 95) | def forward(self, x, boxes):

FILE: GLIP/maskrcnn_benchmark/modeling/roi_heads/__init__.py
  class CombinedROIHeads (line 9) | class CombinedROIHeads(torch.nn.ModuleDict):
    method __init__ (line 15) | def __init__(self, cfg, heads):
    method forward (line 23) | def forward(self, features, proposals, targets=None, language_dict_fea...
  function build_roi_heads (line 64) | def build_roi_heads(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/roi_heads/box_head/box_head.py
  class ROIBoxHead (line 11) | class ROIBoxHead(torch.nn.Module):
    method __init__ (line 16) | def __init__(self, cfg):
    method forward (line 25) | def forward(self, features, proposals, targets=None):
  function build_roi_box_head (line 69) | def build_roi_box_head(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/roi_heads/box_head/inference.py
  class PostProcessor (line 12) | class PostProcessor(nn.Module):
    method __init__ (line 19) | def __init__(
    method forward (line 38) | def forward(self, x, boxes):
    method prepare_boxlist (line 87) | def prepare_boxlist(self, boxes, scores, image_shape, extra_field={}):
    method filter_results (line 108) | def filter_results(self, boxlist, num_classes):
  function make_roi_box_post_processor (line 164) | def make_roi_box_post_processor(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/roi_heads/box_head/loss.py
  class FastRCNNLossComputation (line 15) | class FastRCNNLossComputation(object):
    method __init__ (line 21) | def __init__(self, proposal_matcher, fg_bg_sampler, box_coder):
    method match_targets_to_proposals (line 32) | def match_targets_to_proposals(self, proposal, target):
    method prepare_targets (line 54) | def prepare_targets(self, proposals, targets):
    method subsample (line 86) | def subsample(self, proposals, targets):
    method __call__ (line 123) | def __call__(self, class_logits, box_regression):
  function make_roi_box_loss_evaluator (line 171) | def make_roi_box_loss_evaluator(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_feature_extractors.py
  class LightheadFeatureExtractor (line 15) | class LightheadFeatureExtractor(nn.Module):
    method __init__ (line 16) | def __init__(self, cfg):
    method forward (line 46) | def forward(self, x, proposals):
  class ResNet50Conv5ROIFeatureExtractor (line 66) | class ResNet50Conv5ROIFeatureExtractor(nn.Module):
    method __init__ (line 67) | def __init__(self, config):
    method forward (line 94) | def forward(self, x, proposals):
  class FPN2MLPFeatureExtractor (line 101) | class FPN2MLPFeatureExtractor(nn.Module):
    method __init__ (line 106) | def __init__(self, cfg):
    method forward (line 124) | def forward(self, x, proposals):
  class FPNXconv1fcFeatureExtractor (line 135) | class FPNXconv1fcFeatureExtractor(nn.Module):
    method __init__ (line 140) | def __init__(self, cfg):
    method forward (line 189) | def forward(self, x, proposals):
  function make_roi_box_feature_extractor (line 197) | def make_roi_box_feature_extractor(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_predictors.py
  class FastRCNNPredictor (line 5) | class FastRCNNPredictor(nn.Module):
    method __init__ (line 6) | def __init__(self, config, pretrained=None):
    method forward (line 25) | def forward(self, x):
  class FPNPredictor (line 33) | class FPNPredictor(nn.Module):
    method __init__ (line 34) | def __init__(self, cfg):
    method forward (line 47) | def forward(self, x):
  function make_roi_box_predictor (line 60) | def make_roi_box_predictor(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/inference.py
  class KeypointPostProcessor (line 10) | class KeypointPostProcessor(nn.Module):
    method __init__ (line 11) | def __init__(self, keypointer=None):
    method forward (line 15) | def forward(self, x, boxes):
  function heatmaps_to_keypoints (line 40) | def heatmaps_to_keypoints(maps, rois):
  class Keypointer (line 97) | class Keypointer(object):
    method __init__ (line 103) | def __init__(self, padding=0):
    method __call__ (line 106) | def __call__(self, masks, boxes):
  function make_roi_keypoint_post_processor (line 118) | def make_roi_keypoint_post_processor(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/keypoint_head.py
  class ROIKeypointHead (line 9) | class ROIKeypointHead(torch.nn.Module):
    method __init__ (line 10) | def __init__(self, cfg):
    method forward (line 18) | def forward(self, features, proposals, targets=None):
  function build_roi_keypoint_head (line 49) | def build_roi_keypoint_head(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/loss.py
  function project_keypoints_to_heatmap (line 17) | def project_keypoints_to_heatmap(keypoints, proposals, discretization_si...
  function cat_boxlist_with_keypoints (line 24) | def cat_boxlist_with_keypoints(boxlists):
  function _within_box (line 39) | def _within_box(points, boxes):
  class KeypointRCNNLossComputation (line 54) | class KeypointRCNNLossComputation(object):
    method __init__ (line 55) | def __init__(self, proposal_matcher, fg_bg_sampler, discretization_size):
    method match_targets_to_proposals (line 66) | def match_targets_to_proposals(self, proposal, target):
    method prepare_targets (line 79) | def prepare_targets(self, proposals, targets):
    method subsample (line 111) | def subsample(self, proposals, targets):
    method __call__ (line 145) | def __call__(self, proposals, keypoint_logits):
  function make_roi_keypoint_loss_evaluator (line 172) | def make_roi_keypoint_loss_evaluator(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/roi_keypoint_feature_extractors.py
  class KeypointRCNNFeatureExtractor (line 10) | class KeypointRCNNFeatureExtractor(nn.Module):
    method __init__ (line 11) | def __init__(self, cfg):
    method forward (line 37) | def forward(self, x, proposals):
  class KeypointRCNNFeature2XZoomExtractor (line 43) | class KeypointRCNNFeature2XZoomExtractor(nn.Module):
    method __init__ (line 44) | def __init__(self, cfg):
    method forward (line 79) | def forward(self, x, proposals):
  function make_roi_keypoint_feature_extractor (line 92) | def make_roi_keypoint_feature_extractor(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/roi_keypoint_predictors.py
  class KeypointRCNNPredictor (line 7) | class KeypointRCNNPredictor(nn.Module):
    method __init__ (line 8) | def __init__(self, cfg):
    method forward (line 26) | def forward(self, x):
  function make_roi_keypoint_predictor (line 37) | def make_roi_keypoint_predictor(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/roi_heads/mask_head/hourglass.py
  class Residual (line 6) | class Residual(nn.Module):
    method __init__ (line 7) | def __init__(self, inp_dim, out_dim, use_gn=False):
    method forward (line 22) | def forward(self, x):
  class Hourglass (line 41) | class Hourglass(nn.Module):
    method __init__ (line 42) | def __init__(self, n, f, gn=False, increase=0):
    method forward (line 58) | def forward(self, x):

FILE: GLIP/maskrcnn_benchmark/modeling/roi_heads/mask_head/inference.py
  function convert_mask_grounding_to_od_logits (line 10) | def convert_mask_grounding_to_od_logits(logits, positive_map_label_to_to...
  class MaskPostProcessor (line 20) | class MaskPostProcessor(nn.Module):
    method __init__ (line 31) | def __init__(self, masker=None, mdetr_style_aggregate_class_num=None, ...
    method forward (line 37) | def forward(self, x, boxes, positive_map_label_to_token=None):
  class MaskPostProcessorCOCOFormat (line 81) | class MaskPostProcessorCOCOFormat(MaskPostProcessor):
    method forward (line 88) | def forward(self, x, boxes, positive_map_label_to_token=None, vl_versi...
  function expand_boxes (line 108) | def expand_boxes(boxes, scale):
  function expand_masks (line 125) | def expand_masks(mask, padding):
  function paste_mask_in_image (line 135) | def paste_mask_in_image(mask, box, im_h, im_w, thresh=0.5, padding=1):
  class Masker (line 174) | class Masker(object):
    method __init__ (line 180) | def __init__(self, threshold=0.5, padding=1):
    method forward_single_image (line 184) | def forward_single_image(self, masks, boxes):
    method __call__ (line 197) | def __call__(self, masks, boxes):
  function make_roi_mask_post_processor (line 214) | def make_roi_mask_post_processor(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/roi_heads/mask_head/loss.py
  function project_masks_on_boxes (line 11) | def project_masks_on_boxes(segmentation_masks, proposals, discretization...
  class MaskRCNNLossComputation (line 47) | class MaskRCNNLossComputation(object):
    method __init__ (line 48) | def __init__(self, proposal_matcher, discretization_size, vl_version=F...
    method match_targets_to_proposals (line 58) | def match_targets_to_proposals(self, proposal, target):
    method prepare_targets (line 74) | def prepare_targets(self, proposals, targets):
    method __call__ (line 123) | def __call__(self, proposals, mask_logits, targets):
  function make_roi_mask_loss_evaluator (line 167) | def make_roi_mask_loss_evaluator(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/roi_heads/mask_head/mask_head.py
  function keep_only_positive_boxes (line 13) | def keep_only_positive_boxes(boxes):
  class ROIMaskHead (line 36) | class ROIMaskHead(torch.nn.Module):
    method __init__ (line 37) | def __init__(self, cfg):
    method forward (line 45) | def forward(self, features, proposals, targets=None,
  function build_roi_mask_head (line 87) | def build_roi_mask_head(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_feature_extractors.py
  class MaskRCNNFPNFeatureExtractor (line 13) | class MaskRCNNFPNFeatureExtractor(nn.Module):
    method __init__ (line 18) | def __init__(self, cfg):
    method forward (line 53) | def forward(self, x, proposals):
  class HourglassFPNFeatureExtractor (line 62) | class HourglassFPNFeatureExtractor(nn.Module):
    method __init__ (line 67) | def __init__(self, cfg):
    method forward (line 99) | def forward(self, x, proposals):
  function make_roi_mask_feature_extractor (line 115) | def make_roi_mask_feature_extractor(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_predictors.py
  class MaskRCNNC4Predictor (line 11) | class MaskRCNNC4Predictor(nn.Module):
    method __init__ (line 12) | def __init__(self, cfg):
    method forward (line 38) | def forward(self, x):
  class VLMaskRCNNC4Predictor (line 43) | class VLMaskRCNNC4Predictor(nn.Module):
    method __init__ (line 44) | def __init__(self, cfg):
    method forward (line 75) | def forward(self, x, language_dict_features):
  function make_roi_mask_predictor (line 109) | def make_roi_mask_predictor(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/rpn/__init__.py
  function build_rpn (line 19) | def build_rpn(cfg):

FILE: GLIP/maskrcnn_benchmark/modeling/rpn/anchor_generator.py
  class BufferList (line 12) | class BufferList(nn.Module):
    method __init__ (line 17) | def __init__(self, buffers=None):
    method extend (line 22) | def extend(self, buffers):
    method __len__ (line 28) | def __len__(self):
    method __iter__ (line 31) | def __iter__(self):
  class AnchorGenerator (line 35) | class AnchorGenerator(nn.Module):
    method __init__ (line 41) | def __init__(
    method num_anchors_per_location (line 70) | def num_anchors_per_location(self):
    method grid_anchors (line 73) | def grid_anchors(self, grid_sizes):
    method add_visibility_to (line 97) | def add_visibility_to(self, boxlist):
    method forward (line 112) | def forward(self, image_list, feature_maps):
  function make_anchor_generator (line 139) | def make_anchor_generator(config):
  function make_anchor_generator_complex (line 157) | def make_anchor_generator_complex(config):
  class CenterAnchorGenerator (line 184) | class CenterAnchorGenerator(nn.Module):
    method __init__ (line 190) | def __init__(
    method add_visibility_to (line 208) | def add_visibility_to(self, boxlist):
    method forward (line 223) | def forward(self, centers, image_sizes, feature_maps):
  function make_center_anchor_generator (line 276) | def make_center_anchor_generator(config):
  function generate_anchors (line 356) | def generate_anchors(
  function _generate_anchors (line 370) | def _generate_anchors(base_size, scales, aspect_ratios):
  function _whctrs (line 382) | def _whctrs(anchor):
  function _mkanchors (line 391) | def _mkanchors(ws, hs, x_ctr, y_ctr):
  function _ratio_enum (line 408) | def _ratio_enum(anchor, ratios):
  function _scale_enum (line 419) | def _scale_enum(anchor, scales):

FILE: GLIP/maskrcnn_benchmark/modeling/rpn/atss.py
  class BoxCoder (line 14) | class BoxCoder(object):
    method __init__ (line 16) | def __init__(self, cfg):
    method encode (line 19) | def encode(self, gt_boxes, anchors):
    method decode (line 41) | def decode(self, preds, anchors):
  class ATSSHead (line 75) | class ATSSHead(torch.nn.Module):
    method __init__ (line 76) | def __init__(self, cfg):
    method forward (line 171) | def forward(self, x):
  class ATSSModule (line 188) | class ATSSModule(torch.nn.Module):
    method __init__ (line 190) | def __init__(self, cfg):
    method forward (line 200) | def forward(self, images, features, targets=None):
    method _forward_train (line 209) | def _forward_train(self, box_cls, box_regression, centerness, targets,...
    method _forward_test (line 231) | def _forward_test(self, box_cls, box_regression, centerness, anchors):

FILE: GLIP/maskrcnn_benchmark/modeling/rpn/dyhead.py
  class h_sigmoid (line 16) | class h_sigmoid(nn.Module):
    method __init__ (line 17) | def __init__(self, inplace=True, h_max=1):
    method forward (line 22) | def forward(self, x):
  class BoxCoder (line 26) | class BoxCoder(object):
    method __init__ (line 28) | def __init__(self, cfg):
    method encode (line 31) | def encode(self, gt_boxes, anchors):
    method decode (line 52) | def decode(self, preds, anchors):
  class Conv3x3Norm (line 85) | class Conv3x3Norm(torch.nn.Module):
    method __init__ (line 86) | def __init__(self,
    method forward (line 122) | def forward(self, input, **kwargs):
  class DyConv (line 129) | class DyConv(torch.nn.Module):
    method __init__ (line 130) | def __init__(self,
    method init_weights (line 166) | def init_weights(self):
    method forward (line 179) | def forward(self, x):
  class DyHead (line 217) | class DyHead(torch.nn.Module):
    method __init__ (line 218) | def __init__(self, cfg):
    method extract_feature (line 286) | def extract_feature(self, x):
    method forward (line 293) | def forward(self, x):
  class DyHeadModule (line 327) | class DyHeadModule(torch.nn.Module):
    method __init__ (line 329) | def __init__(self, cfg):
    method forward (line 339) | def forward(self, images, features, targets=None):
    method _forward_train (line 348) | def _forward_train(self, box_cls, box_regression, centerness, targets,...
    method _forward_test (line 375) | def _forward_test(self, box_cls, box_regression, centerness, anchors):

FILE: GLIP/maskrcnn_benchmark/modeling/rpn/fcos.py
  class FCOSHead (line 14) | class FCOSHead(torch.nn.Module):
    method __init__ (line 15) | def __init__(self, cfg):
    method forward (line 100) | def forward(self, x):
  class FCOSModule (line 126) | class FCOSModule(torch.nn.Module):
    method __init__ (line 132) | def __init__(self, cfg):
    method forward (line 152) | def forward(self, images, features, targets=None):
    method _forward_train (line 180) | def _forward_train(self, locations, box_cls, box_regression, centernes...
    method _forward_test (line 199) | def _forward_test(self, locations, box_cls, box_regression, centerness...
    method compute_locations (line 208) | def compute_locations(self, features):
    method compute_locations_per_level (line 219) | def compute_locations_per_level(self, h, w, stride, device):

FILE: GLIP/maskrcnn_benchmark/modeling/rpn/inference.py
  class RPNPostProcessor (line 16) | class RPNPostProcessor(torch.nn.Module):
    method __init__ (line 22) | def __init__(
    method add_gt_proposals (line 56) | def add_gt_proposals(self, proposals, targets):
    method forward_for_single_feature_map (line 79) | def forward_for_single_feature_map(self, anchors, objectness, box_regr...
    method forward (line 133) | def forward(self, anchors, objectness, box_regression, targets=None):
    method select_over_all_levels (line 162) | def select_over_all_levels(self, boxlists):
  function make_rpn_postprocessor (line 192) | def make_rpn_postprocessor(config, rpn_box_coder, is_train):
  class RetinaPostProcessor (line 217) | class RetinaPostProcessor(torch.nn.Module):
    method __init__ (line 223) | def __init__(
    method forward_for_single_feature_map (line 255) | def forward_for_single_feature_map(self, anchors, box_cls, box_regress...
    method select_over_all_levels (line 325) | def select_over_all_levels(self, boxlists):
    method forward (line 370) | def forward(self, anchors, objectness, box_regression, targets=None):
  function make_retina_postprocessor (line 394) | def make_retina_postprocessor(config, rpn_box_coder, is_train):
  class FCOSPostProcessor (line 414) | class FCOSPostProcessor(torch.nn.Module):
    method __init__ (line 420) | def __init__(
    method forward_for_single_feature_map (line 449) | def forward_for_single_feature_map(
    method forward (line 517) | def forward(self, locations, box_cls, box_regression, centerness, imag...
    method select_over_all_levels (line 547) | def select_over_all_levels(self, boxlists):
  function make_fcos_postprocessor (line 569) | def make_fcos_postprocessor(config, is_train=False):
  class ATSSPostProcessor (line 592) | class ATSSPostProcessor(torch.nn.Module):
    method __init__ (line 593) | def __init__(
    method forward_for_single_feature_map (line 620) | def forward_for_single_feature_map(self, box_regression, centerness, a...
    method forward (line 713) | def forward(self, box_regression, centerness, anchors,
    method select_over_all_levels (line 747) | def select_over_all_levels(self, boxlists):
  function convert_grounding_to_od_logits (line 771) | def convert_grounding_to_od_logits(logits, box_cls, positive_map, score_...
  function convert_grounding_to_od_logits_v2 (line 792) | def convert_grounding_to_od_logits_v2(logits, num_class, positive_map, s...
  function make_atss_postprocessor (line 825) | def make_atss_postprocessor(config, box_coder, is_train=False):

FILE: GLIP/maskrcnn_benchmark/modeling/rpn/loss.py
  class RPNLossComputation (line 28) | class RPNLossComputation(object):
    method __init__ (line 33) | def __init__(self, proposal_matcher, fg_bg_sampler, box_coder):
    method match_targets_to_anchors (line 45) | def match_targets_to_anchors(self, anchor, target):
    method prepare_targets (line 64) | def prepare_targets(self, anchors, targets):
    method __call__ (line 95) | def __call__(self, anchors, objectness, box_regression, targets):
  class FocalLossComputation (line 156) | class FocalLossComputation(object):
    method __init__ (line 161) | def __init__(self, proposal_matcher, box_coder,
    method match_targets_to_anchors (line 180) | def match_targets_to_anchors(self, anchor, target, copied_fields=[]):
    method prepare_targets (line 194) | def prepare_targets(self, anchors, targets):
    method __call__ (line 230) | def __call__(self, anchors, box_cls, box_regression, targets):
  class FCOSLossComputation (line 270) | class FCOSLossComputation(object):
    method __init__ (line 275) | def __init__(self, cfg):
    method get_sample_region (line 291) | def get_sample_region(self, gt, strides, num_points_per, gt_xs, gt_ys,...
    method prepare_targets (line 338) | def prepare_targets(self, points, targets):
    method compute_targets_for_locations (line 384) | def compute_targets_for_locations(self, locations, targets, object_siz...
    method compute_centerness_targets (line 443) | def compute_centerness_targets(self, reg_targets):
    method __call__ (line 451) | def __call__(self, locations, box_cls, box_regression, centerness, tar...
  class ATSSLossComputation (line 518) | class ATSSLossComputation(torch.nn.Module):
    method __init__ (line 520) | def __init__(self, cfg, box_coder):
    method NllSoftMaxLoss (line 582) | def NllSoftMaxLoss(self, logits, target):
    method ContrastiveAlignLoss (line 587) | def ContrastiveAlignLoss(self, logits, positive_map):
    method GIoULoss (line 610) | def GIoULoss(self, pred, target, anchor, weight=None):
    method prepare_targets (line 653) | def prepare_targets(self, targets, anchors, tokenized=None, positive_m...
    method compute_centerness_targets (line 832) | def compute_centerness_targets(self, reg_targets, anchors):
    method __call__ (line 848) | def __call__(self, box_cls, box_regression, centerness, targets, anchors,
  function generate_anchor_labels (line 1202) | def generate_anchor_labels(matched_targets):
  function make_focal_loss_evaluator (line 1207) | def make_focal_loss_evaluator(cfg, box_coder):
  function make_rpn_loss_evaluator (line 1229) | def make_rpn_loss_evaluator(cfg, box_coder):
  function make_fcos_loss_evaluator (line 1244) | def make_fcos_loss_evaluator(cfg):
  function make_atss_loss_evaluator (line 1249) | def make_atss_loss_evaluator(cfg, box_coder):

FILE: GLIP/maskrcnn_benchmark/modeling/rpn/modeling_bert.py
  function clamp_values (line 34) | def clamp_values(vector, min_val = -50000, max_val = 50000):
  class BertSelfAttention (line 39) | class BertSelfAttention(nn.Module):
    method __init__ (line 40) | def __init__(self, config, clamp_min_for_underflow=False, clamp_max_fo...
    method transpose_for_scores (line 66) | def transpose_for_scores(self, x):
    method forward (line 71) | def forward(
  class BertSelfOutput (line 179) | class BertSelfOutput(nn.Module):
    method __init__ (line 180) | def __init__(self, config):
    method forward (line 186) | def forward(self, hidden_states, input_tensor):
  class BertAttention (line 193) | class BertAttention(nn.Module):
    method __init__ (line 194) | def __init__(self, config, clamp_min_for_underflow=False, clamp_max_fo...
    method prune_heads (line 200) | def prune_heads(self, heads):
    method forward (line 218) | def forward(
  class BertIntermediate (line 242) | class BertIntermediate(nn.Module):
    method __init__ (line 243) | def __init__(self, config):
    method forward (line 251) | def forward(self, hidden_states):
  class BertOutput (line 259) | class BertOutput(nn.Module):
    method __init__ (line 260) | def __init__(self, config):
    method forward (line 266) | def forward(self, hidden_states, input_tensor):

FILE: GLIP/maskrcnn_benchmark/modeling/rpn/retina.py
  class RetinaNetHead (line 15) | class RetinaNetHead(torch.nn.Module):
    method __init__ (line 20) | def __init__(self, cfg):
    method forward (line 84) | def forward(self, x):
  class RetinaNetModule (line 93) | class RetinaNetModule(torch.nn.Module):
    method __init__ (line 99) | def __init__(self, cfg):
    method forward (line 118) | def forward(self, images, features, targets=None):
    method _forward_train (line 141) | def _forward_train(self, anchors, box_cls, box_regression, targets):
    method _forward_test (line 152) | def _forward_test(self, anchors, box_cls, box_regression):

FILE: GLIP/maskrcnn_benchmark/modeling/rpn/rpn.py
  class mRPNHead (line 14) | class mRPNHead(nn.Module):
    method __init__ (line 19) | def __init__(self, cfg, in_channels, num_anchors):
    method forward (line 36) | def forward(self, x):
  class RPNHead (line 47) | class RPNHead(nn.Module):
    method __init__ (line 52) | def __init__(self, cfg, in_channels, num_anchors):
    method forward (line 72) | def forward(self, x):
  class RPNModule (line 82) | class RPNModule(torch.nn.Module):
    method __init__ (line 88) | def __init__(self, cfg):
    method forward (line 114) | def forward(self, images, features, targets=None):
    method _forward_train (line 137) | def _forward_train(self, anchors, objectness, rpn_box_regression, targ...
    method _forward_test (line 160) | def _forward_test(self, anchors, objectness, rpn_box_regression):

FILE: GLIP/maskrcnn_benchmark/modeling/rpn/transformer.py
  function _get_clones (line 9) | def _get_clones(module, N):
  function _get_activation_fn (line 13) | def _get_activation_fn(activation):
  class TransformerEncoderLayer (line 24) | class TransformerEncoderLayer(nn.Module):
    method __init__ (line 25) | def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
    method forward (line 42) | def forward(self, src,

FILE: GLIP/maskrcnn_benchmark/modeling/rpn/vldyhead.py
  class h_sigmoid (line 28) | class h_sigmoid(nn.Module):
    method __init__ (line 29) | def __init__(self, inplace=True, h_max=1):
    method forward (line 34) | def forward(self, x):
  class BoxCoder (line 38) | class BoxCoder(object):
    method __init__ (line 40) | def __init__(self, cfg):
    method encode (line 43) | def encode(self, gt_boxes, anchors):
    method decode (line 64) | def decode(self, preds, anchors):
  class Conv3x3Norm (line 97) | class Conv3x3Norm(torch.nn.Module):
    method __init__ (line 98) | def __init__(self,
    method forward (line 134) | def forward(self, input, **kwargs):
  class DyConv (line 141) | class DyConv(torch.nn.Module):
    method __init__ (line 142) | def __init__(self,
    method init_weights (line 178) | def init_weights(self):
    method forward (line 191) | def forward(self, inputs):
  class BertEncoderLayer (line 236) | class BertEncoderLayer(BertPreTrainedModel):
    method __init__ (line 237) | def __init__(self, config,  clamp_min_for_underflow = False, clamp_max...
    method forward (line 250) | def forward(self, inputs):
    method feed_forward_chunk (line 284) | def feed_forward_chunk(self, attention_output):
  class CLIPTransformerLayer (line 290) | class CLIPTransformerLayer(nn.Module):
    method __init__ (line 291) | def __init__(self, config):
    method _init_weights (line 310) | def _init_weights(self, m):
    method attention (line 318) | def attention(self, x: torch.Tensor, key_padding_mask: torch.Tensor = ...
    method forward (line 323) | def forward(self, inputs):
  class DummyLayer (line 342) | class DummyLayer(nn.Module):
    method __init__ (line 343) | def __init__(self):
    method forward (line 346) | def forward(self, inputs):
  class VLFuse (line 350) | class VLFuse(torch.nn.Module):
    method __init__ (line 355) | def __init__(self, cfg):
    method init_configs (line 423) | def init_configs(self, cfg):
    method forward (line 447) | def forward(self, x):
  class VLDyHead (line 560) | class VLDyHead(torch.nn.Module):
    method __init__ (line 561) | def __init__(self, cfg):
    method forward (line 731) | def forward(self, x, language_dict_features=None, embedding=None, swin...
  class VLDyHeadModule (line 862) | class VLDyHeadModule(torch.nn.Module):
    method __init__ (line 864) | def __init__(self, cfg):
    method forward (line 892) | def forward(self, images, features, targets=None,
    method _forward_train (line 950) | def _forward_train(self, box_cls, box_regression, centerness, targets,...
    method _forward_test (line 1022) | def _forward_test(self, box_regression, centerness, anchors,

FILE: GLIP/maskrcnn_benchmark/modeling/utils.py
  function cat (line 9) | def cat(tensors, dim=0):
  function permute_and_flatten (line 19) | def permute_and_flatten(layer, N, A, C, H, W):
  function concat_box_prediction_layers (line 26) | def concat_box_prediction_layers(box_regression, box_cls=None, token_log...
  function round_channels (line 75) | def round_channels(channels, divisor=8):

FILE: GLIP/maskrcnn_benchmark/solver/build.py
  function make_optimizer (line 8) | def make_optimizer(cfg, model):
  function make_lr_scheduler (line 58) | def make_lr_scheduler(cfg, optimizer):

FILE: GLIP/maskrcnn_benchmark/solver/lr_scheduler.py
  class WarmupMultiStepLR (line 11) | class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler):
    method __init__ (line 12) | def __init__(
    method get_lr (line 40) | def get_lr(self):
  class WarmupCosineAnnealingLR (line 56) | class WarmupCosineAnnealingLR(torch.optim.lr_scheduler._LRScheduler):
    method __init__ (line 57) | def __init__(
    method get_lr (line 82) | def get_lr(self):
  class WarmupReduceLROnPlateau (line 104) | class WarmupReduceLROnPlateau(torch.optim.lr_scheduler.ReduceLROnPlateau):
    method __init__ (line 105) | def __init__(
    method step (line 140) | def step(self, metrics=None):

FILE: GLIP/maskrcnn_benchmark/structures/bounding_box.py
  class BoxList (line 9) | class BoxList(object):
    method __init__ (line 19) | def __init__(self, bbox, image_size, mode="xyxy"):
    method _jit_unwrap (line 43) | def _jit_unwrap(self):
    method _jit_wrap (line 48) | def _jit_wrap(self, input_stream):
    method add_field (line 57) | def add_field(self, field, field_data):
    method get_field (line 60) | def get_field(self, field):
    method has_field (line 63) | def has_field(self, field):
    method fields (line 66) | def fields(self):
    method _copy_extra_fields (line 69) | def _copy_extra_fields(self, bbox):
    method convert (line 73) | def convert(self, mode):
    method _split_into_xyxy (line 94) | def _split_into_xyxy(self):
    method resize (line 110) | def resize(self, size, *args, **kwargs):
    method transpose (line 148) | def transpose(self, method):
    method crop (line 186) | def crop(self, box):
    method to (line 213) | def to(self, device):
    method __getitem__ (line 221) | def __getitem__(self, item):
    method __len__ (line 227) | def __len__(self):
    method clip_to_image (line 230) | def clip_to_image(self, remove_empty=True):
    method area (line 243) | def area(self):
    method copy_with_fields (line 256) | def copy_with_fields(self, fields):
    method __repr__ (line 264) | def __repr__(self):
    method concate_box_list (line 273) | def concate_box_list(list_of_boxes):
  function _onnx_clip_boxes_to_image (line 287) | def _onnx_clip_boxes_to_image(boxes, size):

FILE: GLIP/maskrcnn_benchmark/structures/boxlist_ops.py
  function boxlist_nms (line 10) | def boxlist_nms(boxlist, nms_thresh, max_proposals=-1, score_field="scor...
  function boxlist_ml_nms (line 35) | def boxlist_ml_nms(boxlist, nms_thresh, max_proposals=-1,
  function remove_small_boxes (line 78) | def remove_small_boxes(boxlist, min_size):
  function boxlist_iou (line 97) | def boxlist_iou(boxlist1, boxlist2):
  function _cat (line 136) | def _cat(tensors, dim=0):
  function cat_boxlist (line 148) | def cat_boxlist(bboxes):
  function getUnionBBox (line 177) | def getUnionBBox(aBB, bBB, margin = 10):

FILE: GLIP/maskrcnn_benchmark/structures/image_list.py
  class ImageList (line 7) | class ImageList(object):
    method __init__ (line 15) | def __init__(self, tensors, image_sizes):
    method to (line 24) | def to(self, *args, **kwargs):
  function to_image_list (line 29) | def to_image_list(tensors, size_divisible=0):

FILE: GLIP/maskrcnn_benchmark/structures/keypoint.py
  class Keypoints (line 9) | class Keypoints(object):
    method __init__ (line 10) | def __init__(self, keypoints, size, mode=None):
    method crop (line 27) | def crop(self, box):
    method resize (line 30) | def resize(self, size, *args, **kwargs):
    method transpose (line 41) | def transpose(self, method):
    method to (line 62) | def to(self, *args, **kwargs):
    method __getitem__ (line 70) | def __getitem__(self, item):
    method add_field (line 76) | def add_field(self, field, field_data):
    method get_field (line 79) | def get_field(self, field):
    method __repr__ (line 82) | def __repr__(self):
  class PersonKeypoints (line 90) | class PersonKeypoints(Keypoints):
    method __init__ (line 121) | def __init__(self, *args, **kwargs):
    method to_coco_format (line 133) | def to_coco_format(self):
    method _create_flip_indices (line 144) | def _create_flip_indices(self, names, flip_map):
    method _kp_connections (line 152) | def _kp_connections(self, keypoints):
  function keypoints_to_heat_map (line 178) | def keypoints_to_heat_map(keypoints, rois, heatmap_size):

FILE: GLIP/maskrcnn_benchmark/structures/segmentation_mask.py
  class Mask (line 11) | class Mask(object):
    method __init__ (line 18) | def __init__(self, masks, size, mode):
    method transpose (line 23) | def transpose(self, method):
    method crop (line 41) | def crop(self, box):
    method resize (line 47) | def resize(self, size, *args, **kwargs):
  class Polygons (line 51) | class Polygons(object):
    method __init__ (line 58) | def __init__(self, polygons, size, mode):
    method transpose (line 69) | def transpose(self, method):
    method crop (line 92) | def crop(self, box):
    method resize (line 108) | def resize(self, size, *args, **kwargs):
    method convert (line 125) | def convert(self, mode):
    method __repr__ (line 137) | def __repr__(self):
  class SegmentationMask (line 146) | class SegmentationMask(object):
    method __init__ (line 151) | def __init__(self, polygons, size, mode=None):
    method transpose (line 165) | def transpose(self, method):
    method crop (line 176) | def crop(self, box):
    method resize (line 183) | def resize(self, size, *args, **kwargs):
    method to (line 189) | def to(self, *args, **kwargs):
    method __getitem__ (line 192) | def __getitem__(self, item):
    method __iter__ (line 206) | def __iter__(self):
    method __repr__ (line 209) | def __repr__(self):

FILE: GLIP/maskrcnn_benchmark/utils/amp.py
  function nullcontext (line 4) | def nullcontext(enter_result=None, **kwargs):

FILE: GLIP/maskrcnn_benchmark/utils/big_model_loading.py
  function tf2th (line 8) | def tf2th(conv_weights):
  function _rename_conv_weights_for_deformable_conv_layers (line 15) | def _rename_conv_weights_for_deformable_conv_layers(state_dict, cfg):
  function load_big_format (line 47) | def load_big_format(cfg, f):

FILE: GLIP/maskrcnn_benchmark/utils/c2_model_loading.py
  function _rename_basic_resnet_weights (line 12) | def _rename_basic_resnet_weights(layer_keys):
  function _rename_fpn_weights (line 64) | def _rename_fpn_weights(layer_keys, stage_names):
  function _rename_weights_for_resnet (line 84) | def _rename_weights_for_resnet(weights, stage_names):
  function _load_c2_pickled_weights (line 135) | def _load_c2_pickled_weights(file_path):
  function _rename_conv_weights_for_deformable_conv_layers (line 148) | def _rename_conv_weights_for_deformable_conv_layers(state_dict, cfg):
  function load_resnet_c2_format (line 193) | def load_resnet_c2_format(cfg, f):
  function load_c2_format (line 206) | def load_c2_format(cfg, f):

FILE: GLIP/maskrcnn_benchmark/utils/checkpoint.py
  class Checkpointer (line 15) | class Checkpointer(object):
    method __init__ (line 16) | def __init__(
    method save (line 34) | def save(self, name, **kwargs):
    method load (line 59) | def load(self, f=None, force=False, keyword="model", skip_optimizer =F...
    method has_checkpoint (line 93) | def has_checkpoint(self):
    method get_checkpoint_file (line 97) | def get_checkpoint_file(self):
    method tag_last_checkpoint (line 109) | def tag_last_checkpoint(self, last_filename):
    method _load_file (line 114) | def _load_file(self, f):
    method _load_model (line 117) | def _load_model(self, checkpoint, keyword="model"):
  class DetectronCheckpointer (line 121) | class DetectronCheckpointer(Checkpointer):
    method __init__ (line 122) | def __init__(
    method _load_file (line 137) | def _load_file(self, f):

FILE: GLIP/maskrcnn_benchmark/utils/collect_env.py
  function get_pil_version (line 7) | def get_pil_version():
  function collect_env_info (line 11) | def collect_env_info():

FILE: GLIP/maskrcnn_benchmark/utils/comm.py
  function get_world_size (line 15) | def get_world_size():
  function get_rank (line 23) | def get_rank():
  function is_main_process (line 31) | def is_main_process():
  function synchronize (line 35) | def synchronize():
  function all_gather (line 50) | def all_gather(data):
  function reduce_dict (line 93) | def reduce_dict(input_dict, average=True):
  function broadcast_data (line 122) | def broadcast_data(data):
  function reduce_sum (line 137) | def reduce_sum(tensor):
  function shared_random_seed (line 146) | def shared_random_seed():

FILE: GLIP/maskrcnn_benchmark/utils/cv2_util.py
  function findContours (line 8) | def findContours(*args, **kwargs):

FILE: GLIP/maskrcnn_benchmark/utils/dist.py
  function _get_global_gloo_group (line 20) | def _get_global_gloo_group():
  function all_gather (line 32) | def all_gather(data):
  function reduce_dict (line 92) | def reduce_dict(input_dict, average=True):
  function setup_for_distributed (line 119) | def setup_for_distributed(is_master):
  function is_dist_avail_and_initialized (line 135) | def is_dist_avail_and_initialized():
  function get_world_size (line 147) | def get_world_size():
  function get_rank (line 157) | def get_rank():
  function get_local_rank (line 167) | def get_local_rank() -> int:
  function get_local_size (line 180) | def get_local_size() -> int:
  function is_main_process (line 193) | def is_main_process():
  function save_on_master (line 198) | def save_on_master(*args, **kwargs):
  function init_distributed_mode (line 204) | def init_distributed_mode(args):

FILE: GLIP/maskrcnn_benchmark/utils/ema.py
  class ModelEma (line 6) | class ModelEma:
    method __init__ (line 7) | def __init__(self, model, decay=0.9999, device=''):
    method load_checkpoint (line 18) | def load_checkpoint(self, checkpoint):
    method state_dict (line 33) | def state_dict(self):
    method update (line 36) | def update(self, model):

FILE: GLIP/maskrcnn_benchmark/utils/env.py
  function setup_environment (line 7) | def setup_environment():
  function setup_custom_environment (line 20) | def setup_custom_environment(custom_module_path):

FILE: GLIP/maskrcnn_benchmark/utils/flops.py
  function profile (line 15) | def profile(model, input_size, custom_ops={}, device="cpu", verbose=Fals...
  function count_conv2d (line 79) | def count_conv2d(m, x, y):
  function count_convtranspose2d (line 100) | def count_convtranspose2d(m, x, y):
  function count_bn (line 123) | def count_bn(m, x, y):
  function count_relu (line 131) | def count_relu(m, x, y):
  function count_softmax (line 138) | def count_softmax(m, x, y):
  function count_maxpool (line 148) | def count_maxpool(m, x, y):
  function count_adap_maxpool (line 155) | def count_adap_maxpool(m, x, y):
  function count_avgpool (line 163) | def count_avgpool(m, x, y):
  function count_adap_avgpool (line 172) | def count_adap_avgpool(m, x, y):
  function count_linear (line 182) | def count_linear(m, x, y):
  function count_LastLevelMaxPool (line 191) | def count_LastLevelMaxPool(m, x, y):
  function count_ROIAlign (line 197) | def count_ROIAlign(m, x, y):

FILE: GLIP/maskrcnn_benchmark/utils/fuse_helper.py
  class BertPredictionHeadTransform (line 10) | class BertPredictionHeadTransform(nn.Module):
    method __init__ (line 11) | def __init__(self, config):
    method forward (line 20) | def forward(self, hidden_states):
  class BertLMPredictionHead (line 27) | class BertLMPredictionHead(nn.Module):
    method __init__ (line 28) | def __init__(self, config):
    method forward (line 41) | def forward(self, hidden_states):
  class FeatureResizer (line 46) | class FeatureResizer(nn.Module):
    method __init__ (line 52) | def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=T...
    method forward (line 60) | def forward(self, encoder_features):
  function _make_conv (line 68) | def _make_conv(input_dim, output_dim, k, stride=1):
  function _make_mlp (line 77) | def _make_mlp(input_dim, output_dim, drop):
  function _make_coord (line 87) | def _make_coord(batch, height, width):
  function l1norm (line 106) | def l1norm(X, dim, eps=1e-8):
  function l2norm (line 114) | def l2norm(X, dim, eps=1e-8):
  function func_attention (line 122) | def func_attention(query, context, smooth=1, raw_feature_norm="softmax",...
  class BiMultiHeadAttention (line 171) | class BiMultiHeadAttention(nn.Module):
    method __init__ (line 172) | def __init__(self, v_dim, l_dim, embed_dim, num_heads, dropout=0.1, cf...
    method _shape (line 201) | def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
    method _reset_parameters (line 204) | def _reset_parameters(self):
    method forward (line 218) | def forward(self, v, l, attention_mask_l=None):
  class BiAttentionBlock (line 307) | class BiAttentionBlock(nn.Module):
    method __init__ (line 308) | def __init__(self, v_dim, l_dim, embed_dim, num_heads, hidden_dim=None...
    method forward (line 335) | def forward(self, v, l, attention_mask_l=None, dummy_tensor=None):
  class BiAttentionBlockForCheckpoint (line 344) | class BiAttentionBlockForCheckpoint(nn.Module):
    method __init__ (line 345) | def __init__(self, v_dim, l_dim, embed_dim, num_heads, hidden_dim=None...
    method forward (line 377) | def forward(self, q0, q1, q2, q3, q4, l, attention_mask_l=None, dummy_...
    method single_attention_call (line 419) | def single_attention_call(self, v, l, attention_mask_l=None, dummy_ten...
  class MultiHeadAttention (line 430) | class MultiHeadAttention(nn.Module):
    method __init__ (line 435) | def __init__(self, q_dim, k_dim, embed_dim, num_heads, dropout=0.1,
    method _shape (line 460) | def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
    method _reset_parameters (line 463) | def _reset_parameters(self):
    method forward (line 473) | def forward(self, q, k, v, attention_mask=None, return_attention=False):
  class AttentionMLP (line 543) | class AttentionMLP(nn.Module):
    method __init__ (line 544) | def __init__(self, q_dim, hidden_dim, dropout=0.1):
    method forward (line 552) | def forward(self, hidden_states):
  class AttentionT2I (line 559) | class AttentionT2I(nn.Module):
    method __init__ (line 560) | def __init__(self, q_dim, k_dim, embed_dim, num_heads, hidden_dim=None...
    method forward (line 591) | def forward(self, q0, q1, q2, q3, q4, k, v, attention_mask, dummy_arg=...

FILE: GLIP/maskrcnn_benchmark/utils/imports.py
  function import_file (line 11) | def import_file(module_name, file_path, make_importable=False):
  function import_file (line 21) | def import_file(module_name, file_path, make_importable=None):

FILE: GLIP/maskrcnn_benchmark/utils/logger.py
  function setup_logger (line 7) | def setup_logger(name, save_dir, distributed_rank):

FILE: GLIP/maskrcnn_benchmark/utils/mdetr_dist.py
  function _get_global_gloo_group (line 21) | def _get_global_gloo_group():
  function all_gather (line 32) | def all_gather(data):
  function reduce_dict (line 92) | def reduce_dict(input_dict, average=True):
  function setup_for_distributed (line 119) | def setup_for_distributed(is_master):
  function is_dist_avail_and_initialized (line 135) | def is_dist_avail_and_initialized():
  function get_world_size (line 147) | def get_world_size():
  function get_rank (line 157) | def get_rank():
  function get_local_rank (line 167) | def get_local_rank() -> int:
  function get_local_size (line 180) | def get_local_size() -> int:
  function is_main_process (line 193) | def is_main_process():
  function save_on_master (line 198) | def save_on_master(*args, **kwargs):
  function init_distributed_mode (line 204) | def init_distributed_mode(args):

FILE: GLIP/maskrcnn_benchmark/utils/metric_logger.py
  class SmoothedValue (line 11) | class SmoothedValue(object):
    method __init__ (line 16) | def __init__(self, window_size=20):
    method update (line 22) | def update(self, value):
    method median (line 31) | def median(self):
    method avg (line 36) | def avg(self):
    method global_avg (line 41) | def global_avg(self):
  class AverageMeter (line 45) | class AverageMeter(object):
    method __init__ (line 48) | def __init__(self):
    method reset (line 51) | def reset(self):
    method update (line 57) | def update(self, val, n=1):
  class MetricLogger (line 64) | class MetricLogger(object):
    method __init__ (line 65) | def __init__(self, delimiter="\t"):
    method update (line 69) | def update(self, **kwargs):
    method __getattr__ (line 76) | def __getattr__(self, attr):
    method __str__ (line 84) | def __str__(self):
  class TensorboardLogger (line 94) | class TensorboardLogger(MetricLogger):
    method __init__ (line 95) | def __init__(self,
    method _get_tensorboard_writer (line 105) | def _get_tensorboard_writer(log_dir):
    method update (line 121) | def update(self, **kwargs):

FILE: GLIP/maskrcnn_benchmark/utils/miscellaneous.py
  function mkdir (line 6) | def mkdir(path):
  function save_config (line 14) | def save_config(cfg, path):

FILE: GLIP/maskrcnn_benchmark/utils/model_serialization.py
  function resize_2d (line 9) | def resize_2d(posemb, shape_new):
  function align_and_update_state_dicts (line 20) | def align_and_update_state_dicts(model_state_dict, loaded_state_dict, re...
  function strip_prefix_if_present (line 103) | def strip_prefix_if_present(state_dict, prefix):
  function load_state_dict (line 112) | def load_state_dict(model, loaded_state_dict):
  function _group_checkpoint_keys (line 123) | def _group_checkpoint_keys(keys):
  function _group_to_str (line 143) | def _group_to_str(group):

FILE: GLIP/maskrcnn_benchmark/utils/model_zoo.py
  function cache_url (line 20) | def cache_url(url, model_dir='model', progress=True):

FILE: GLIP/maskrcnn_benchmark/utils/pretrain_model_loading.py
  function _remove_bn_statics (line 7) | def _remove_bn_statics(state_dict):
  function _rename_conv_weights_for_deformable_conv_layers (line 17) | def _rename_conv_weights_for_deformable_conv_layers(state_dict, cfg):
  function load_pretrain_format (line 44) | def load_pretrain_format(cfg, f):

FILE: GLIP/maskrcnn_benchmark/utils/registry.py
  function _register_generic (line 4) | def _register_generic(module_dict, module_name, module):
  class Registry (line 9) | class Registry(dict):
    method __init__ (line 31) | def __init__(self, *args, **kwargs):
    method register (line 34) | def register(self, module_name, module=None):

FILE: GLIP/maskrcnn_benchmark/utils/shallow_contrastive_loss_helper.py
  function normalized_positive_map (line 5) | def normalized_positive_map(positive_map):
  function pad_tensor_given_dim_length (line 13) | def pad_tensor_given_dim_length(tensor, dim, length, padding_value=0, ba...
  function pad_random_negative_tensor_given_length (line 23) | def pad_random_negative_tensor_given_length(positive_tensor, negative_pa...
  function gather_tensors (line 28) | def gather_tensors(tensor):
  function convert_to_roi_format (line 53) | def convert_to_roi_format(boxes):

FILE: GLIP/maskrcnn_benchmark/utils/stats.py
  function get_model_complexity_info (line 18) | def get_model_complexity_info(model, input_res,
  function flops_to_string (line 58) | def flops_to_string(flops, units='GMac', precision=2):
  function params_to_string (line 79) | def params_to_string(params_num, units=None, precision=2):
  function accumulate_flops (line 96) | def accumulate_flops(self):
  function print_model_with_flops (line 106) | def print_model_with_flops(model, total_flops, total_params, units='GMac',
  function get_model_parameters_number (line 150) | def get_model_parameters_number(model):
  function add_flops_counting_methods (line 155) | def add_flops_counting_methods(net_main_module):
  function compute_average_flops_cost (line 169) | def compute_average_flops_cost(self):
  function start_flops_count (line 191) | def start_flops_count(self, **kwargs):
  function stop_flops_count (line 233) | def stop_flops_count(self):
  function reset_flops_count (line 246) | def reset_flops_count(self):
  function empty_flops_counter_hook (line 259) | def empty_flops_counter_hook(module, input, output):
  function upsample_flops_counter_hook (line 263) | def upsample_flops_counter_hook(module, input, output):
  function relu_flops_counter_hook (line 272) | def relu_flops_counter_hook(module, input, output):
  function linear_flops_counter_hook (line 277) | def linear_flops_counter_hook(module, input, output):
  function pool_flops_counter_hook (line 285) | def pool_flops_counter_hook(module, input, output):
  function bn_flops_counter_hook (line 290) | def bn_flops_counter_hook(module, input, output):
  function conv_flops_counter_hook (line 299) | def conv_flops_counter_hook(conv_module, input, output):
  function batch_counter_hook (line 330) | def batch_counter_hook(module, input, output):
  function rnn_flops (line 343) | def rnn_flops(flops, rnn_module, w_ih, w_hh, input_size):
  function rnn_flops_counter_hook (line 368) | def rnn_flops_counter_hook(rnn_module, input, output):
  function rnn_cell_flops_counter_hook (line 401) | def rnn_cell_flops_counter_hook(rnn_cell_module, input, output):
  function add_batch_counter_variables_or_reset (line 418) | def add_batch_counter_variables_or_reset(module):
  function add_batch_counter_hook_function (line 423) | def add_batch_counter_hook_function(module):
  function remove_batch_counter_hook_function (line 431) | def remove_batch_counter_hook_function(module):
  function add_flops_counter_variable_or_reset (line 437) | def add_flops_counter_variable_or_reset(module):
  function is_supported_instance (line 499) | def is_supported_instance(module):
  function remove_flops_counter_hook_function (line 506) | def remove_flops_counter_hook_function(module):

FILE: GLIP/setup.py
  function get_extensions (line 17) | def get_extensions():

FILE: GLIP/tools/cityscapes/convert_cityscapes_to_coco.py
  function parse_args (line 38) | def parse_args():
  function convert_coco_stuff_mat (line 53) | def convert_coco_stuff_mat(data_dir, out_dir):
  function getLabelID (line 94) | def getLabelID(self, instID):
  function convert_cityscapes_instance_only (line 101) | def convert_cityscapes_instance_only(

FILE: GLIP/tools/cityscapes/instances2dict_with_polygons.py
  function instances2dict_with_polygons (line 19) | def instances2dict_with_polygons(imageFileList, verbose=False):
  function main (line 72) | def main(argv):

FILE: GLIP/tools/eval_all.py
  function main (line 25) | def main():

FILE: GLIP/tools/finetune.py
  function removekey (line 35) | def removekey(d, prefix):
  function train (line 47) | def train(cfg, local_rank, distributed, zero_shot, skip_optimizer_resume...
  function test (line 177) | def test(cfg, model, distributed, verbose=False):
  function tuning_highlevel_override (line 215) | def tuning_highlevel_override(cfg,):
  function report_freeze_options (line 262) | def report_freeze_options(cfg):
  function main (line 271) | def main():

FILE: GLIP/tools/test_grounding_net.py
  function init_distributed_mode (line 31) | def init_distributed_mode(args):
  function setup_for_distributed (line 58) | def setup_for_distributed(is_master):
  function main (line 73) | def main():

FILE: GLIP/tools/test_net.py
  function run_test (line 22) | def run_test(cfg, model, distributed, log_dir):
  function main (line 57) | def main():

FILE: GLIP/tools/train_net.py
  function train (line 34) | def train(cfg, local_rank, distributed, use_tensorboard=False,):
  function setup_for_distributed (line 145) | def setup_for_distributed(is_master):
  function main (line 159) | def main():

FILE: GroundingDINO/demo/gradio_app.py
  function load_model_hf (line 42) | def load_model_hf(model_config_path, repo_id, filename, device='cpu'):
  function image_transform_grounding (line 54) | def image_transform_grounding(init_image):
  function image_transform_grounding_for_vis (line 63) | def image_transform_grounding_for_vis(init_image):
  function run_grounding (line 72) | def run_grounding(input_image, grounding_caption, box_threshold, text_th...

FILE: GroundingDINO/demo/inference_on_a_image.py
  function plot_boxes_to_image (line 16) | def plot_boxes_to_image(image_pil, tgt):
  function load_image (line 57) | def load_image(image_path):
  function load_model (line 72) | def load_model(model_config_path, model_checkpoint_path, cpu_only=False):
  function get_grounding_output (line 83) | def get_grounding_output(model, image, caption, box_threshold, text_thre...

FILE: GroundingDINO/groundingdino/datasets/transforms.py
  function crop (line 17) | def crop(image, target, region):
  function hflip (line 68) | def hflip(image, target):
  function resize (line 87) | def resize(image, target, size, max_size=None):
  function pad (line 149) | def pad(image, target, padding):
  class ResizeDebug (line 162) | class ResizeDebug(object):
    method __init__ (line 163) | def __init__(self, size):
    method __call__ (line 166) | def __call__(self, img, target):
  class RandomCrop (line 170) | class RandomCrop(object):
    method __init__ (line 171) | def __init__(self, size):
    method __call__ (line 174) | def __call__(self, img, target):
  class RandomSizeCrop (line 179) | class RandomSizeCrop(object):
    method __init__ (line 180) | def __init__(self, min_size: int, max_size: int, respect_boxes: bool =...
    method __call__ (line 187) | def __call__(self, img: PIL.Image.Image, target: dict):
  class CenterCrop (line 204) | class CenterCrop(object):
    method __init__ (line 205) | def __init__(self, size):
    method __call__ (line 208) | def __call__(self, img, target):
  class RandomHorizontalFlip (line 216) | class RandomHorizontalFlip(object):
    method __init__ (line 217) | def __init__(self, p=0.5):
    method __call__ (line 220) | def __call__(self, img, target):
  class RandomResize (line 226) | class RandomResize(object):
    method __init__ (line 227) | def __init__(self, sizes, max_size=None):
    method __call__ (line 232) | def __call__(self, img, target=None):
  class RandomPad (line 237) | class RandomPad(object):
    method __init__ (line 238) | def __init__(self, max_pad):
    method __call__ (line 241) | def __call__(self, img, target):
  class RandomSelect (line 247) | class RandomSelect(object):
    method __init__ (line 253) | def __init__(self, transforms1, transforms2, p=0.5):
    method __call__ (line 258) | def __call__(self, img, target):
  class ToTensor (line 264) | class ToTensor(object):
    method __call__ (line 265) | def __call__(self, img, target):
  class RandomErasing (line 269) | class RandomErasing(object):
    method __init__ (line 270) | def __init__(self, *args, **kwargs):
    method __call__ (line 273) | def __call__(self, img, target):
  class Normalize (line 277) | class Normalize(object):
    method __init__ (line 278) | def __init__(self, mean, std):
    method __call__ (line 282) | def __call__(self, image, target=None):
  class Compose (line 296) | class Compose(object):
    method __init__ (line 297) | def __init__(self, transforms):
    method __call__ (line 300) | def __call__(self, image, target):
    method __repr__ (line 305) | def __repr__(self):

FILE: GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py
  class FrozenBatchNorm2d (line 33) | class FrozenBatchNorm2d(torch.nn.Module):
    method __init__ (line 42) | def __init__(self, n):
    method _load_from_state_dict (line 49) | def _load_from_state_dict(
    method forward (line 60) | def forward(self, x):
  class BackboneBase (line 73) | class BackboneBase(nn.Module):
    method __init__ (line 74) | def __init__(
    method forward (line 107) | def forward(self, tensor_list: NestedTensor):
  class Backbone (line 119) | class Backbone(BackboneBase):
    method __init__ (line 122) | def __init__(
  class Joiner (line 146) | class Joiner(nn.Sequential):
    method __init__ (line 147) | def __init__(self, backbone, position_embedding):
    method forward (line 150) | def forward(self, tensor_list: NestedTensor):
  function build_backbone (line 162) | def build_backbone(args):

FILE: GroundingDINO/groundingdino/models/GroundingDINO/backbone/position_encoding.py
  class PositionEmbeddingSine (line 30) | class PositionEmbeddingSine(nn.Module):
    method __init__ (line 36) | def __init__(self, num_pos_feats=64, temperature=10000, normalize=Fals...
    method forward (line 47) | def forward(self, tensor_list: NestedTensor):
  class PositionEmbeddingSineHW (line 78) | class PositionEmbeddingSineHW(nn.Module):
    method __init__ (line 84) | def __init__(
    method forward (line 98) | def forward(self, tensor_list: NestedTensor):
  class PositionEmbeddingLearned (line 134) | class PositionEmbeddingLearned(nn.Module):
    method __init__ (line 139) | def __init__(self, num_pos_feats=256):
    method reset_parameters (line 145) | def reset_parameters(self):
    method forward (line 149) | def forward(self, tensor_list: NestedTensor):
  function build_position_encoding (line 171) | def build_position_encoding(args):

FILE: GroundingDINO/groundingdino/models/GroundingDINO/backbone/swin_transformer.py
  class Mlp (line 24) | class Mlp(nn.Module):
    method __init__ (line 27) | def __init__(
    method forward (line 38) | def forward(self, x):
  function window_partition (line 47) | def window_partition(x, window_size):
  function window_reverse (line 61) | def window_reverse(windows, window_size, H, W):
  class WindowAttention (line 77) | class WindowAttention(nn.Module):
    method __init__ (line 90) | def __init__(
    method forward (line 134) | def forward(self, x, mask=None):
  class SwinTransformerBlock (line 177) | class SwinTransformerBlock(nn.Module):
    method __init__ (line 194) | def __init__(
    method forward (line 238) | def forward(self, x, mask_matrix):
  class PatchMerging (line 301) | class PatchMerging(nn.Module):
    method __init__ (line 308) | def __init__(self, dim, norm_layer=nn.LayerNorm):
    method forward (line 314) | def forward(self, x, H, W):
  class BasicLayer (line 343) | class BasicLayer(nn.Module):
    method __init__ (line 361) | def __init__(
    method forward (line 409) | def forward(self, x, H, W):
  class PatchEmbed (line 459) | class PatchEmbed(nn.Module):
    method __init__ (line 468) | def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=...
    method forward (line 482) | def forward(self, x):
  class SwinTransformer (line 501) | class SwinTransformer(nn.Module):
    method __init__ (line 530) | def __init__(
    method _freeze_stages (line 636) | def _freeze_stages(self):
    method forward_raw (line 678) | def forward_raw(self, x):
    method forward (line 712) | def forward(self, tensor_list: NestedTensor):
    method train (line 756) | def train(self, mode=True):
  function build_swin_transformer (line 762) | def build_swin_transformer(modelname, pretrain_img_size, **kw):

FILE: GroundingDINO/groundingdino/models/GroundingDINO/bertwarper.py
  class BertModelWarper (line 17) | class BertModelWarper(nn.Module):
    method __init__ (line 18) | def __init__(self, bert_model):
    method forward (line 31) | def forward(
  class TextEncoderShell (line 169) | class TextEncoderShell(nn.Module):
    method __init__ (line 170) | def __init__(self, text_encoder):
    method forward (line 175) | def forward(self, **kw):
  function generate_masks_with_special_tokens (line 180) | def generate_masks_with_special_tokens(tokenized, special_tokens_list, t...
  function generate_masks_with_special_tokens_and_transfer_map (line 224) | def generate_masks_with_special_tokens_and_transfer_map(tokenized, speci...

FILE: GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn.h
  function namespace (line 19) | namespace groundingdino {

FILE: GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.cpp
  type groundingdino (line 16) | namespace groundingdino {
    function ms_deform_attn_cpu_forward (line 18) | at::Tensor
    function ms_deform_attn_cpu_backward (line 30) | std::vector<at::Tensor>

FILE: GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h
  function namespace (line 14) | namespace groundingdino {

FILE: GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.h
  function namespace (line 14) | namespace groundingdino {

FILE: GroundingDINO/groundingdino/models/GroundingDINO/csrc/vision.cpp
  type groundingdino (line 5) | namespace groundingdino {
    function get_cuda_version (line 11) | std::string get_cuda_version() {
    function get_compiler_version (line 32) | std::string get_compiler_version() {
    function PYBIND11_MODULE (line 53) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {

FILE: GroundingDINO/groundingdino/models/GroundingDINO/fuse_modules.py
  class FeatureResizer (line 14) | class FeatureResizer(nn.Module):
    method __init__ (line 20) | def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=T...
    method forward (line 28) | def forward(self, encoder_features):
  function l1norm (line 36) | def l1norm(X, dim, eps=1e-8):
  function l2norm (line 43) | def l2norm(X, dim, eps=1e-8):
  function func_attention (line 50) | def func_attention(query, context, smooth=1, raw_feature_norm="softmax",...
  class BiMultiHeadAttention (line 99) | class BiMultiHeadAttention(nn.Module):
    method __init__ (line 100) | def __init__(self, v_dim, l_dim, embed_dim, num_heads, dropout=0.1, cf...
    method _shape (line 129) | def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
    method _reset_parameters (line 132) | def _reset_parameters(self):
    method forward (line 146) | def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):
  class BiAttentionBlock (line 252) | class BiAttentionBlock(nn.Module):
    method __init__ (line 253) | def __init__(
    method forward (line 286) | def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):

FILE: GroundingDINO/groundingdino/models/GroundingDINO/groundingdino.py
  class GroundingDINO (line 51) | class GroundingDINO(nn.Module):
    method __init__ (line 54) | def __init__(
    method _reset_parameters (line 204) | def _reset_parameters(self):
    method init_ref_points (line 210) | def init_ref_points(self, use_num_queries):
    method forward (line 213) | def forward(self, samples: NestedTensor, targets: List = None, **kw):
    method _set_aux_loss (line 353) | def _set_aux_loss(self, outputs_class, outputs_coord):
  function build_groundingdino (line 364) | def build_groundingdino(args):

FILE: GroundingDINO/groundingdino/models/GroundingDINO/ms_deform_attn.py
  function _is_power_of_2 (line 35) | def _is_power_of_2(n):
  class MultiScaleDeformableAttnFunction (line 41) | class MultiScaleDeformableAttnFunction(Function):
    method forward (line 43) | def forward(
    method backward (line 72) | def backward(ctx, grad_output):
  function multi_scale_deformable_attn_pytorch (line 93) | def multi_scale_deformable_attn_pytorch(
  class MultiScaleDeformableAttention (line 136) | class MultiScaleDeformableAttention(nn.Module):
    method __init__ (line 154) | def __init__(
    method _reset_parameters (line 194) | def _reset_parameters(self):
    method init_weights (line 197) | def init_weights(self):
    method freeze_sampling_offsets (line 222) | def freeze_sampling_offsets(self):
    method freeze_attention_weights (line 227) | def freeze_attention_weights(self):
    method forward (line 232) | def forward(
  function create_dummy_class (line 362) | def create_dummy_class(klass, dependency, message=""):
  function create_dummy_func (line 391) | def create_dummy_func(func, dependency, message=""):

FILE: GroundingDINO/groundingdino/models/GroundingDINO/transformer.py
  class Transformer (line 40) | class Transformer(nn.Module):
    method __init__ (line 41) | def __init__(
    method _reset_parameters (line 189) | def _reset_parameters(self):
    method get_valid_ratio (line 199) | def get_valid_ratio(self, mask):
    method init_ref_points (line 208) | def init_ref_points(self, use_num_queries):
    method forward (line 211) | def forward(self, srcs, masks, refpoint_embed, pos_embeds, tgt, attn_m...
  class TransformerEncoder (line 406) | class TransformerEncoder(nn.Module):
    method __init__ (line 407) | def __init__(
    method get_reference_points (line 466) | def get_reference_points(spatial_shapes, valid_ratios, device):
    method forward (line 482) | def forward(
  class TransformerDecoder (line 599) | class TransformerDecoder(nn.Module):
    method __init__ (line 600) | def __init__(
    method forward (line 634) | def forward(
  class DeformableTransformerEncoderLayer (line 739) | class DeformableTransformerEncoderLayer(nn.Module):
    method __init__ (line 740) | def __init__(
    method with_pos_embed (line 772) | def with_pos_embed(tensor, pos):
    method forward_ffn (line 775) | def forward_ffn(self, src):
    method forward (line 781) | def forward(
  class DeformableTransformerDecoderLayer (line 803) | class DeformableTransformerDecoderLayer(nn.Module):
    method __init__ (line 804) | def __init__(
    method rm_self_attn_modules (line 853) | def rm_self_attn_modules(self):
    method with_pos_embed (line 859) | def with_pos_embed(tensor, pos):
    method forward_ffn (line 862) | def forward_ffn(self, tgt):
    method forward (line 869) | def forward(
  function build_transformer (line 931) | def build_transformer(args):

FILE: GroundingDINO/groundingdino/models/GroundingDINO/transformer_vanilla.py
  class TextTransformer (line 33) | class TextTransformer(nn.Module):
    method __init__ (line 34) | def __init__(self, num_layers, d_model=256, nheads=8, dim_feedforward=...
    method forward (line 47) | def forward(self, memory_text: torch.Tensor, text_attention_mask: torc...
  class TransformerEncoderLayer (line 72) | class TransformerEncoderLayer(nn.Module):
    method __init__ (line 73) | def __init__(
    method with_pos_embed (line 98) | def with_pos_embed(self, tensor, pos: Optional[Tensor]):
    method forward (line 101) | def forward(

FILE: GroundingDINO/groundingdino/models/GroundingDINO/utils.py
  function _get_clones (line 16) | def _get_clones(module, N, layer_share=False):
  function get_sine_pos_embed (line 24) | def get_sine_pos_embed(
  function gen_encoder_output_proposals (line 56) | def gen_encoder_output_proposals(
  class RandomBoxPerturber (line 120) | class RandomBoxPerturber:
    method __init__ (line 121) | def __init__(
    method __call__ (line 128) | def __call__(self, refanchors: Tensor) -> Tensor:
  function sigmoid_focal_loss (line 139) | def sigmoid_focal_loss(
  class MLP (line 172) | class MLP(nn.Module):
    method __init__ (line 175) | def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
    method forward (line 183) | def forward(self, x):
  function _get_activation_fn (line 189) | def _get_activation_fn(activation, d_model=256, batch_dim=0):
  function gen_sineembed_for_position (line 205) | def gen_sineembed_for_position(pos_tensor):
  class ContrastiveEmbed (line 235) | class ContrastiveEmbed(nn.Module):
    method __init__ (line 236) | def __init__(self, max_text_len=256):
    method forward (line 244) | def forward(self, x, text_dict):

FILE: GroundingDINO/groundingdino/models/__init__.py
  function build_model (line 11) | def build_model(args):

FILE: GroundingDINO/groundingdino/models/registry.py
  class Registry (line 18) | class Registry(object):
    method __init__ (line 19) | def __init__(self, name):
    method __repr__ (line 23) | def __repr__(self):
    method __len__ (line 29) | def __len__(self):
    method name (line 33) | def name(self):
    method module_dict (line 37) | def module_dict(self):
    method get (line 40) | def get(self, key):
    method registe_with_name (line 43) | def registe_with_name(self, module_name=None, force=False):
    method register (line 46) | def register(self, module_build_function, module_name=None, force=False):

FILE: GroundingDINO/groundingdino/util/box_ops.py
  function box_cxcywh_to_xyxy (line 9) | def box_cxcywh_to_xyxy(x):
  function box_xyxy_to_cxcywh (line 15) | def box_xyxy_to_cxcywh(x):
  function box_iou (line 22) | def box_iou(boxes1, boxes2):
  function generalized_box_iou (line 39) | def generalized_box_iou(boxes1, boxes2):
  function box_iou_pairwise (line 66) | def box_iou_pairwise(boxes1, boxes2):
  function generalized_box_iou_pairwise (line 82) | def generalized_box_iou_pairwise(boxes1, boxes2):
  function masks_to_boxes (line 107) | def masks_to_boxes(masks):

FILE: GroundingDINO/groundingdino/util/get_tokenlizer.py
  function get_tokenlizer (line 4) | def get_tokenlizer(text_encoder_type, bert_base_uncased_path):
  function get_pretrained_language_model (line 27) | def get_pretrained_language_model(text_encoder_type, bert_base_uncased_p...
  function is_bert_model_use_local_path (line 36) | def is_bert_model_use_local_path(bert_base_uncased_path):

FILE: GroundingDINO/groundingdino/util/inference.py
  function preprocess_caption (line 22) | def preprocess_caption(caption: str) -> str:
  function load_model (line 29) | def load_model(model_config_path: str, model_checkpoint_path: str, devic...
  function load_image (line 39) | def load_image(image_path: str) -> Tuple[np.array, torch.Tensor]:
  function predict (line 53) | def predict(
  function annotate (line 88) | def annotate(image_source: np.ndarray, boxes: torch.Tensor, logits: torc...
  class Model (line 111) | class Model:
    method __init__ (line 113) | def __init__(
    method predict_with_caption (line 126) | def predict_with_caption(
    method predict_with_classes (line 167) | def predict_with_classes(
    method preprocess_image (line 213) | def preprocess_image(image_bgr: np.ndarray) -> torch.Tensor:
    method post_process_result (line 226) | def post_process_result(
    method phrases2classes (line 238) | def phrases2classes(phrases: List[str], classes: List[str]) -> np.ndar...
    method find_index (line 249) | def find_index(string, lst):

FILE: GroundingDINO/groundingdino/util/logger.py
  class _ColorfulFormatter (line 10) | class _ColorfulFormatter(logging.Formatter):
    method __init__ (line 11) | def __init__(self, *args, **kwargs):
    method formatMessage (line 18) | def formatMessage(self, record):
  function setup_logger (line 32) | def setup_logger(output=None, distributed_rank=0, *, color=True, name="i...
  function _cached_log_stream (line 92) | def _cached_log_stream(filename):

FILE: GroundingDINO/groundingdino/util/misc.py
  class SmoothedValue (line 33) | class SmoothedValue(object):
    method __init__ (line 38) | def __init__(self, window_size=20, fmt=None):
    method update (line 46) | def update(self, value, n=1):
    method synchronize_between_processes (line 51) | def synchronize_between_processes(self):
    method median (line 65) | def median(self):
    method avg (line 72) | def avg(self):
    method global_avg (line 77) | def global_avg(self):
    method max (line 85) | def max(self):
    method value (line 89) | def value(self):
    method __str__ (line 92) | def __str__(self):
  function _get_global_gloo_group (line 103) | def _get_global_gloo_group():
  function all_gather_cpu (line 115) | def all_gather_cpu(data):
  function all_gather (line 173) | def all_gather(data):
  function reduce_dict (line 220) | def reduce_dict(input_dict, average=True):
  class MetricLogger (line 247) | class MetricLogger(object):
    method __init__ (line 248) | def __init__(self, delimiter="\t"):
    method update (line 252) | def update(self, **kwargs):
    method __getattr__ (line 259) | def __getattr__(self, attr):
    method __str__ (line 266) | def __str__(self):
    method synchronize_between_processes (line 275) | def synchronize_between_processes(self):
    method add_meter (line 279) | def add_meter(self, name, meter):
    method log_every (line 282) | def log_every(self, iterable, print_freq, header=None, logger=None):
  function get_sha (line 362) | def get_sha():
  function collate_fn (line 383) | def collate_fn(batch):
  function _max_by_axis (line 390) | def _max_by_axis(the_list):
  class NestedTensor (line 399) | class NestedTensor(object):
    method __init__ (line 400) | def __init__(self, tensors, mask: Optional[Tensor]):
    method imgsize (line 416) | def imgsize(self):
    method to (line 425) | def to(self, device):
    method to_img_list_single (line 436) | def to_img_list_single(self, tensor, mask):
    method to_img_list (line 443) | def to_img_list(self):
    method device (line 460) | def device(self):
    method decompose (line 463) | def decompose(self):
    method __repr__ (line 466) | def __repr__(self):
    method shape (line 470) | def shape(self):
  function nested_tensor_from_tensor_list (line 474) | def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
  function _onnx_nested_tensor_from_tensor_list (line 502) | def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> N...
  function setup_for_distributed (line 532) | def setup_for_distributed(is_master):
  function is_dist_avail_and_initialized (line 548) | def is_dist_avail_and_initialized():
  function get_world_size (line 556) | def get_world_size():
  function get_rank (line 562) | def get_rank():
  function is_main_process (line 568) | def is_main_process():
  function save_on_master (line 572) | def save_on_master(*args, **kwargs):
  function init_distributed_mode (line 577) | def init_distributed_mode(args):
  function accuracy (line 638) | def accuracy(output, target, topk=(1,)):
  function accuracy_onehot (line 657) | def accuracy_onehot(pred, gt):
  function interpolate (line 669) | def interpolate(input, size=None, scale_factor=None, mode="nearest", ali...
  class color_sys (line 687) | class color_sys:
    method __init__ (line 688) | def __init__(self, num_colors) -> None:
    method __call__ (line 700) | def __call__(self, idx):
  function inverse_sigmoid (line 704) | def inverse_sigmoid(x, eps=1e-3):
  function clean_state_dict (line 711) | def clean_state_dict(state_dict):

FILE: GroundingDINO/groundingdino/util/slconfig.py
  function check_file_exist (line 21) | def check_file_exist(filename, msg_tmpl='file "{}" does not exist'):
  class ConfigDict (line 26) | class ConfigDict(Dict):
    method __missing__ (line 27) | def __missing__(self, name):
    method __getattr__ (line 30) | def __getattr__(self, name):
  class SLConfig (line 42) | class SLConfig(object):
    method _validate_py_syntax (line 68) | def _validate_py_syntax(filename):
    method _file2dict (line 77) | def _file2dict(filename):
    method _merge_a_into_b (line 140) | def _merge_a_into_b(a, b):
    method fromfile (line 184) | def fromfile(filename):
    method __init__ (line 188) | def __init__(self, cfg_dict=None, cfg_text=None, filename=None):
    method filename (line 209) | def filename(self):
    method text (line 213) | def text(self):
    method pretty_text (line 217) | def pretty_text(self):
    method __repr__ (line 310) | def __repr__(self):
    method __len__ (line 313) | def __len__(self):
    method __getattr__ (line 316) | def __getattr__(self, name):
    method __getitem__ (line 329) | def __getitem__(self, name):
    method __setattr__ (line 332) | def __setattr__(self, name, value):
    method __setitem__ (line 337) | def __setitem__(self, name, value):
    method __iter__ (line 342) | def __iter__(self):
    method dump (line 345) | def dump(self, file=None):
    method merge_from_dict (line 353) | def merge_from_dict(self, options):
    method __setstate__ (line 386) | def __setstate__(self, state):
    method copy (line 389) | def copy(self):
    method deepcopy (line 392) | def deepcopy(self):
  class DictAction (line 396) | class DictAction(Action):
    method _parse_int_float_bool (line 404) | def _parse_int_float_bool(val):
    method __call__ (line 419) | def __call__(self, parser, namespace, values, option_string=None):

FILE: GroundingDINO/groundingdino/util/slio.py
  class BaseFileHandler (line 23) | class BaseFileHandler(metaclass=ABCMeta):
    method load_from_fileobj (line 25) | def load_from_fileobj(self, file, **kwargs):
    method dump_to_fileobj (line 29) | def dump_to_fileobj(self, obj, file, **kwargs):
    method dump_to_str (line 33) | def dump_to_str(self, obj, **kwargs):
    method load_from_path (line 36) | def load_from_path(self, filepath, mode="r", **kwargs):
    method dump_to_path (line 40) | def dump_to_path(self, obj, filepath, mode="w", **kwargs):
  class JsonHandler (line 45) | class JsonHandler(BaseFileHandler):
    method load_from_fileobj (line 46) | def load_from_fileobj(self, file):
    method dump_to_fileobj (line 49) | def dump_to_fileobj(self, obj, file, **kwargs):
    method dump_to_str (line 52) | def dump_to_str(self, obj, **kwargs):
  class PickleHandler (line 56) | class PickleHandler(BaseFileHandler):
    method load_from_fileobj (line 57) | def load_from_fileobj(self, file, **kwargs):
    method load_from_path (line 60) | def load_from_path(self, filepath, **kwargs):
    method dump_to_str (line 63) | def dump_to_str(self, obj, **kwargs):
    method dump_to_fileobj (line 67) | def dump_to_fileobj(self, obj, file, **kwargs):
    method dump_to_path (line 71) | def dump_to_path(self, obj, filepath, **kwargs):
  class YamlHandler (line 75) | class YamlHandler(BaseFileHandler):
    method load_from_fileobj (line 76) | def load_from_fileobj(self, file, **kwargs):
    method dump_to_fileobj (line 80) | def dump_to_fileobj(self, obj, file, **kwargs):
    method dump_to_str (line 84) | def dump_to_str(self, obj, **kwargs):
  function is_str (line 102) | def is_str(x):
  function slload (line 110) | def slload(file, file_format=None, **kwargs):
  function sldump (line 143) | def sldump(obj, file=None, file_format=None, **kwargs):

FILE: GroundingDINO/groundingdino/util/time_counter.py
  class TimeCounter (line 5) | class TimeCounter:
    method __init__ (line 6) | def __init__(self) -> None:
    method clear (line 9) | def clear(self):
    method timeit (line 13) | def timeit(self, name):
  class TimeHolder (line 19) | class TimeHolder:
    method __init__ (line 20) | def __init__(self) -> None:
    method update (line 23) | def update(self, _timedict: dict):
    method final_res (line 29) | def final_res(self):
    method __str__ (line 32) | def __str__(self):
  class AverageMeter (line 36) | class AverageMeter(object):
    method __init__ (line 39) | def __init__(self, name, fmt=":f", val_only=False):
    method reset (line 45) | def reset(self):
    method update (line 51) | def update(self, val, n=1):
    method __str__ (line 57) | def __str__(self):

FILE: GroundingDINO/groundingdino/util/utils.py
  function slprint (line 15) | def slprint(x, name="x"):
  function clean_state_dict (line 29) | def clean_state_dict(state_dict):
  function renorm (line 38) | def renorm(
  class CocoClassMapper (line 66) | class CocoClassMapper:
    method __init__ (line 67) | def __init__(self) -> None:
    method origin2compact (line 153) | def origin2compact(self, idx):
    method compact2origin (line 156) | def compact2origin(self, idx):
  function to_device (line 160) | def to_device(item, device):
  function get_gaussian_mean (line 174) | def get_gaussian_mean(x, axis, other_axis, softmax=True):
  function get_expected_points_from_map (line 200) | def get_expected_points_from_map(hm, softmax=True):
  class Embedder (line 222) | class Embedder:
    method __init__ (line 223) | def __init__(self, **kwargs):
    method create_embedding_fn (line 227) | def create_embedding_fn(self):
    method embed (line 251) | def embed(self, inputs):
  function get_embedder (line 255) | def get_embedder(multires, i=0):
  class APOPMeter (line 275) | class APOPMeter:
    method __init__ (line 276) | def __init__(self) -> None:
    method update (line 282) | def update(self, pred, gt):
    method update_cm (line 293) | def update_cm(self, tp, fp, tn, fn):
  function inverse_sigmoid (line 300) | def inverse_sigmoid(x, eps=1e-5):
  function get_raw_dict (line 307) | def get_raw_dict(args):
  function stat_tensors (line 325) | def stat_tensors(tensor):
  class NiceRepr (line 340) | class NiceRepr:
    method __nice__ (line 374) | def __nice__(self):
    method __repr__ (line 384) | def __repr__(self):
    method __str__ (line 394) | def __str__(self):
  function ensure_rng (line 405) | def ensure_rng(rng=None):
  function random_boxes (line 436) | def random_boxes(num=1, scale=1, rng=None):
  class ModelEma (line 473) | class ModelEma(torch.nn.Module):
    method __init__ (line 474) | def __init__(self, model, decay=0.9997, device=None):
    method _update (line 487) | def _update(self, model, update_fn):
    method update (line 496) | def update(self, model):
    method set (line 499) | def set(self, model):
  class BestMetricSingle (line 503) | class BestMetricSingle:
    method __init__ (line 504) | def __init__(self, init_res=0.0, better="large") -> None:
    method isbetter (line 512) | def isbetter(self, new_res, old_res):
    method update (line 518) | def update(self, new_res, ep):
    method __str__ (line 525) | def __str__(self) -> str:
    method __repr__ (line 528) | def __repr__(self) -> str:
    method summary (line 531) | def summary(self) -> dict:
  class BestMetricHolder (line 538) | class BestMetricHolder:
    method __init__ (line 539) | def __init__(self, init_res=0.0, better="large", use_ema=False) -> None:
    method update (line 546) | def update(self, new_res, epoch, is_ema=False):
    method summary (line 560) | def summary(self):
    method __repr__ (line 570) | def __repr__(self) -> str:
    method __str__ (line 573) | def __str__(self) -> str:
  function targets_to (line 577) | def targets_to(targets: List[Dict[str, Any]], device):
  function get_phrases_from_posmap (line 599) | def get_phrases_from_posmap(

FILE: GroundingDINO/groundingdino/util/visualizer.py
  function renorm (line 22) | def renorm(
  class ColorMap (line 50) | class ColorMap:
    method __init__ (line 51) | def __init__(self, basergb=[255, 255, 0]):
    method __call__ (line 54) | def __call__(self, attnmap):
  function rainbow_text (line 66) | def rainbow_text(x, y, ls, lc, **kw):
  class COCOVisualizer (line 95) | class COCOVisualizer:
    method __init__ (line 96) | def __init__(self, coco=None, tokenlizer=None) -> None:
    method visualize (line 99) | def visualize(self, img, tgt, caption=None, dpi=180, savedir="vis"):
    method addtgt (line 135) | def addtgt(self, tgt):
    method showAnns (line 225) | def showAnns(self, anns, draw_bbox=False):

FILE: GroundingDINO/groundingdino/util/vl_utils.py
  function create_positive_map_from_span (line 8) | def create_positive_map_from_span(tokenized, token_span, max_text_len=256):
  function build_captions_and_token_span (line 49) | def build_captions_and_token_span(cat_list, force_lowercase):
  function build_id2posspan_and_caption (line 90) | def build_id2posspan_and_caption(category_dict: dict):

FILE: GroundingDINO/setup.py
  function write_version_file (line 44) | def write_version_file():
  function get_extensions (line 56) | def get_extensions():
  function parse_requirements (line 114) | def parse_requirements(fname="requirements.txt", with_version=True):

FILE: SG_Nav.py
  class SG_Nav_Agent (line 40) | class SG_Nav_Agent():
    method __init__ (line 41) | def __init__(self, task_config, args=None):
    method add_predicates (line 142) | def add_predicates(self, model):
    method add_rules (line 156) | def add_rules(self, model):
    method reset (line 164) | def reset(self):
    method detect_objects (line 224) | def detect_objects(self, observations):
    method act (line 356) | def act(self, observations):
    method not_use_random_goal (line 508) | def not_use_random_goal(self):
    method get_glip_real_label (line 512) | def get_glip_real_label(self, prediction):
    method fbe (line 525) | def fbe(self, traversible, start):
    method get_goal_gps (line 572) | def get_goal_gps(self, observations, angle, distance):
    method get_relative_goal_gps (line 582) | def get_relative_goal_gps(self, observations, goal_gps=None):
    method init_map (line 592) | def init_map(self):
    method update_map (line 611) | def update_map(self, observations):
    method update_free_map (line 617) | def update_free_map(self, observations):
    method update_room_map (line 624) | def update_room_map(self, observations, room_prediction_result):
    method get_traversible (line 636) | def get_traversible(self, map_pred, pose_pred):
    method _plan (line 680) | def _plan(self, traversible, goal_map, agent_pose, start, start_o, goa...
    method _get_stg (line 746) | def _get_stg(self, traversible, start, goal, goal_found):
    method set_random_goal (line 781) | def set_random_goal(self):
    method update_metrics (line 796) | def update_metrics(self, metrics):
    method visualize (line 804) | def visualize(self, traversible, observations, number_action):
    method save_video (line 840) | def save_video(self):
    method visualize_agent_and_goal (line 852) | def visualize_agent_and_goal(self, map):
  function main (line 864) | def main():

FILE: habitat-lab/examples/benchmark.py
  class ForwardOnlyAgent (line 13) | class ForwardOnlyAgent(habitat.Agent):
    method reset (line 14) | def reset(self):
    method act (line 17) | def act(self, observations):
  function main (line 22) | def main():

FILE: habitat-lab/examples/example.py
  function example (line 10) | def example():

FILE: habitat-lab/examples/example_pointnav.py
  function example (line 10) | def example():

FILE: habitat-lab/examples/interactive_play.py
  function step_env (line 66) | def step_env(env, action_name, action_args, args):
  function get_input_vel_ctlr (line 70) | def get_input_vel_ctlr(skip_pygame, arm_action, g_args, prev_obs, env):
  function get_wrapped_prop (line 209) | def get_wrapped_prop(venv, prop):
  function play_env (line 220) | def play_env(env, args, config):
  function has_pygame (line 323) | def has_pygame():

FILE: habitat-lab/examples/new_actions.py
  class NoisyStrafeActuationSpec (line 28) | class NoisyStrafeActuationSpec:
  function _strafe_impl (line 35) | def _strafe_impl(
  class NoisyStrafeLeft (line 62) | class NoisyStrafeLeft(habitat_sim.SceneNodeControl):
    method __call__ (line 63) | def __call__(
  class NoisyStrafeRight (line 78) | class NoisyStrafeRight(habitat_sim.SceneNodeControl):
    method __call__ (line 79) | def __call__(
  class NoNoiseStrafe (line 96) | class NoNoiseStrafe(HabitatSimV1ActionSpaceConfiguration):
    method get (line 97) | def get(self):
  class NoiseStrafe (line 113) | class NoiseStrafe(HabitatSimV1ActionSpaceConfiguration):
    method get (line 114) | def get(self):
  class StrafeLeft (line 130) | class StrafeLeft(SimulatorTaskAction):
    method _get_uuid (line 131) | def _get_uuid(self, *args, **kwargs) -> str:
    method step (line 134) | def step(self, *args, **kwargs):
  class StrafeRight (line 139) | class StrafeRight(SimulatorTaskAction):
    method _get_uuid (line 140) | def _get_uuid(self, *args, **kwargs) -> str:
    method step (line 143) | def step(self, *args, **kwargs):
  function main (line 147) | def main():

FILE: habitat-lab/examples/register_new_sensors_and_measures.py
  class EpisodeInfoExample (line 18) | class EpisodeInfoExample(habitat.Measure):
    method __init__ (line 19) | def __init__(self, sim, config, **kwargs: Any):
    method _get_uuid (line 26) | def _get_uuid(self, *args: Any, **kwargs: Any) -> str:
    method reset_metric (line 30) | def reset_metric(self, *args: Any, episode, **kwargs: Any):
    method update_metric (line 37) | def update_metric(self, *args: Any, episode, action, **kwargs: Any):
  class AgentPositionSensor (line 45) | class AgentPositionSensor(habitat.Sensor):
    method __init__ (line 46) | def __init__(self, sim, config, **kwargs: Any):
    method _get_uuid (line 54) | def _get_uuid(self, *args: Any, **kwargs: Any) -> str:
    method _get_sensor_type (line 58) | def _get_sensor_type(self, *args: Any, **kwargs: Any):
    method _get_observation_space (line 62) | def _get_observation_space(self, *args: Any, **kwargs: Any):
    method get_observation (line 71) | def get_observation(
  function main (line 77) | def main():

FILE: habitat-lab/examples/shortest_path_follower_example.py
  class SimpleRLEnv (line 25) | class SimpleRLEnv(habitat.RLEnv):
    method get_reward_range (line 26) | def get_reward_range(self):
    method get_reward (line 29) | def get_reward(self, observations):
    method get_done (line 32) | def get_done(self, observations):
    method get_info (line 35) | def get_info(self, observations):
  function draw_top_down_map (line 39) | def draw_top_down_map(info, output_size):
  function shortest_path_example (line 45) | def shortest_path_example():
  function main (line 85) | def main():

FILE: habitat-lab/examples/tutorials/nb_python/Habitat_Lab.py
  function display_sample (line 76) | def display_sample(
  class NewNavigationTask (line 254) | class NewNavigationTask(NavigationTask):
    method __init__ (line 255) | def __init__(self, config, sim, dataset):
    method _check_episode_is_active (line 259) | def _check_episode_is_active(self, *args, **kwargs):
  class AgentPositionSensor (line 313) | class AgentPositionSensor(habitat.Sensor):
    method __init__ (line 314) | def __init__(self, sim, config, **kwargs):
    method _get_uuid (line 319) | def _get_uuid(self, *args, **kwargs):
    method _get_sensor_type (line 323) | def _get_sensor_type(self, *args, **kwargs):
    method _get_observation_space (line 327) | def _get_observation_space(self, *args, **kwargs):
    method get_observation (line 336) | def get_observation(self, observations, *args, episode, **kwargs):
  class ForwardOnlyAgent (line 383) | class ForwardOnlyAgent(habitat.Agent):
    method __init__ (line 384) | def __init__(self, success_distance, goal_sensor_uuid):
    method reset (line 388) | def reset(self):
    method is_goal_reached (line 391) | def is_goal_reached(self, observations):
    method act (line 395) | def act(self, observations):

FILE: habitat-lab/examples/visualization_examples.py
  function example_pointnav_draw_target_birdseye_view (line 22) | def example_pointnav_draw_target_birdseye_view():
  function example_pointnav_draw_target_birdseye_view_agent_on_border (line 48) | def example_pointnav_draw_target_birdseye_view_agent_on_border():
  function example_get_topdown_map (line 82) | def example_get_topdown_map():
  function main (line 101) | def main():

FILE: habitat-lab/examples/vln_benchmark.py
  function reference_path_benchmark (line 17) | def reference_path_benchmark(config, num_episodes=None):
  function main (line 61) | def main():

FILE: habitat-lab/examples/vln_reference_path_follower_example.py
  function save_map (line 28) | def save_map(observations, info, images):
  function reference_path_example (line 38) | def reference_path_example(mode):

FILE: habitat-lab/habitat/config/default.py
  class Config (line 13) | class Config(yacs.config.CfgNode):
    method __init__ (line 14) | def __init__(self, *args, **kwargs):
  function get_config (line 446) | def get_config(

FILE: habitat-lab/habitat/core/agent.py
  class Agent (line 16) | class Agent:
    method reset (line 22) | def reset(self) -> None:
    method act (line 26) | def act(

FILE: habitat-lab/habitat/core/benchmark.py
  class Benchmark (line 29) | class Benchmark:
    method __init__ (line 32) | def __init__(
    method remote_evaluate (line 52) | def remote_evaluate(
    method local_evaluate (line 126) | def local_evaluate(
    method evaluate (line 208) | def evaluate(

FILE: habitat-lab/habitat/core/challenge.py
  class Challenge (line 13) | class Challenge(Benchmark):
    method __init__ (line 14) | def __init__(self, eval_remote=False, split_l=-1, split_r=-1):
    method submit (line 18) | def submit(self, agent):

FILE: habitat-lab/habitat/core/dataset.py
  class Episode (line 40) | class Episode:
    method _reset_shortest_path_cache_hook (line 79) | def _reset_shortest_path_cache_hook(
    method __getstate__ (line 85) | def __getstate__(self):
    method __setstate__ (line 92) | def __setstate__(self, state):
  class Dataset (line 100) | class Dataset(Generic[T]):
    method scene_from_scene_path (line 105) | def scene_from_scene_path(scene_path: str) -> str:
    method get_scenes_to_load (line 116) | def get_scenes_to_load(cls, config: Config) -> List[str]:
    method build_content_scenes_filter (line 130) | def build_content_scenes_filter(cls, config) -> Callable[[T], bool]:
    method num_episodes (line 145) | def num_episodes(self) -> int:
    method scene_ids (line 150) | def scene_ids(self) -> List[str]:
    method get_scene_episodes (line 154) | def get_scene_episodes(self, scene_id: str) -> List[T]:
    method get_episodes (line 164) | def get_episodes(self, indexes: List[int]) -> List[T]:
    method get_episode_iterator (line 172) | def get_episode_iterator(self, *args: Any, **kwargs: Any) -> Iterator[T]:
    method to_json (line 186) | def to_json(self) -> str:
    method from_json (line 201) | def from_json(
    method filter_episodes (line 215) | def filter_episodes(self, filter_fn: Callable[[T], bool]) -> "Dataset":
    method get_splits (line 230) | def get_splits(
  class EpisodeIterator (line 328) | class EpisodeIterator(Iterator[T]):
    method __init__ (line 356) | def __init__(
    method __iter__ (line 425) | def __iter__(self) -> "EpisodeIterator":
    method __next__ (line 428) | def __next__(self) -> Episode:
    method _forced_scene_switch (line 457) | def _forced_scene_switch(self) -> None:
    method _shuffle (line 472) | def _shuffle(self) -> None:
    method _group_scenes (line 486) | def _group_scenes(
    method step_taken (line 505) | def step_taken(self) -> None:
    method _randomize_value (line 509) | def _randomize_value(value: int, value_range: float) -> int:
    method _set_shuffle_intervals (line 514) | def _set_shuffle_intervals(self) -> None:
    method _forced_scene_switch_if (line 527) | def _forced_scene_switch_if(self) -> None:

FILE: habitat-lab/habitat/core/embodied_task.py
  class Action (line 21) | class Action:
    method __init__ (line 30) | def __init__(self, *args: Any, **kwargs: Any) -> None:
    method reset (line 33) | def reset(self, *args: Any, **kwargs: Any) -> None:
    method step (line 40) | def step(self, *args: Any, **kwargs: Any) -> Observations:
    method action_space (line 51) | def action_space(self) -> Space:
  class SimulatorTaskAction (line 56) | class SimulatorTaskAction(Action):
    method __init__ (line 61) | def __init__(
    method action_space (line 68) | def action_space(self):
    method reset (line 71) | def reset(self, *args: Any, **kwargs: Any) -> None:
    method step (line 74) | def step(self, *args: Any, **kwargs: Any) -> Observations:
  class Measure (line 79) | class Measure:
    method __init__ (line 98) | def __init__(self, *args: Any, **kwargs: Any) -> None:
    method _get_uuid (line 102) | def _get_uuid(self, *args: Any, **kwargs: Any) -> str:
    method reset_metric (line 105) | def reset_metric(self, *args: Any, **kwargs: Any) -> None:
    method update_metric (line 111) | def update_metric(self, *args: Any, **kwargs: Any) -> None:
    method get_metric (line 117) | def get_metric(self):
  class Metrics (line 125) | class Metrics(dict):
    method __init__ (line 128) | def __init__(self, measures: Dict[str, Measure]) -> None:
  class Measurements (line 140) | class Measurements:
    method __init__ (line 147) | def __init__(self, measures: Iterable[Measure]) -> None:
    method reset_measures (line 160) | def reset_measures(self, *args: Any, **kwargs: Any) -> None:
    method update_measures (line 164) | def update_measures(self, *args: Any, **kwargs: Any) -> None:
    method get_metrics (line 168) | def get_metrics(self) -> Metrics:
    method _get_measure_index (line 174) | def _get_measure_index(self, measure_name):
    method check_measure_dependencies (line 177) | def check_measure_dependencies(
  class EmbodiedTask (line 200) | class EmbodiedTask:
    method __init__ (line 226) | def __init__(
    method _init_entities (line 260) | def _init_entities(
    method reset (line 283) | def reset(self, episode: Episode):
    method _step_single_action (line 298) | def _step_single_action(
    method step (line 320) | def step(self, action: Dict[str, Any], episode: Episode):
    method get_action_name (line 352) | def get_action_name(self, action_index: Union[int, np.integer]):
    method action_space (line 358) | def action_space(self) -> Space:
    method overwrite_sim_config (line 366) | def overwrite_sim_config(
    method _check_episode_is_active (line 377) | def _check_episode_is_active(
    method is_episode_active (line 387) | def is_episode_active(self):
    method seed (line 390) | def seed(self, seed: int) -> None:

FILE: habitat-lab/habitat/core/env.py
  class Env (line 26) | class Env:
    method __init__ (line 57) | def __init__(
    method _setup_episode_iterator (line 129) | def _setup_episode_iterator(self):
    method current_episode (line 141) | def current_episode(self) -> Episode:
    method current_episode (line 146) | def current_episode(self, episode: Episode) -> None:
    method episode_iterator (line 154) | def episode_iterator(self) -> Iterator[Episode]:
    method episode_iterator (line 158) | def episode_iterator(self, new_iter: Iterator[Episode]) -> None:
    method episodes (line 164) | def episodes(self) -> List[Episode]:
    method episodes (line 172) | def episodes(self, episodes: List[Episode]) -> None:
    method sim (line 186) | def sim(self) -> Simulator:
    method episode_start_time (line 190) | def episode_start_time(self) -> Optional[float]:
    method episode_over (line 194) | def episode_over(self) -> bool:
    method task (line 198) | def task(self) -> EmbodiedTask:
    method _elapsed_seconds (line 202) | def _elapsed_seconds(self) -> float:
    method get_metrics (line 208) | def get_metrics(self) -> Metrics:
    method _past_limit (line 211) | def _past_limit(self) -> bool:
    method _reset_stats (line 220) | def _reset_stats(self) -> None:
    method reset (line 225) | def reset(self) -> Observations:
    method _update_step_stats (line 261) | def _update_step_stats(self) -> None:
    method step (line 272) | def step(
    method _seed_numba (line 316) | def _seed_numba(seed: int):
    method seed (line 320) | def seed(self, seed: int) -> None:
    method reconfigure (line 327) | def reconfigure(self, config: Config) -> None:
    method render (line 338) | def render(self, mode="rgb") -> np.ndarray:
    method close (line 341) | def close(self) -> None:
    method __enter__ (line 344) | def __enter__(self):
    method __exit__ (line 347) | def __exit__(self, exc_type, exc_val, exc_tb):
  class RLEnv (line 351) | class RLEnv(gym.Env):
    method __init__ (line 365) | def __init__(
    method config (line 381) | def config(self) -> Config:
    method habitat_env (line 385) | def habitat_env(self) -> Env:
    method episodes (line 389) | def episodes(self) -> List[Episode]:
    method episodes (line 393) | def episodes(self, episodes: List[Episode]) -> None:
    method current_episode (line 397) | def current_episode(self) -> Episode:
    method reset (line 401) | def reset(self) -> Observations:
    method get_reward_range (line 404) | def get_reward_range(self):
    method get_reward (line 411) | def get_reward(self, observations: Observations) -> Any:
    method get_done (line 421) | def get_done(self, observations: Observations) -> bool:
    method get_info (line 432) | def get_info(self, observations) -> Dict[Any, Any]:
    method step (line 441) | def step(self, *args, **kwargs) -> Tuple[Observations, Any, bool, dict]:
    method seed (line 454) | def seed(self, seed: Optional[int] = None) -> None:
    method render (line 457) | def render(self, mode: str = "rgb") -> np.ndarray:
    method close (line 460) | def close(self) -> None:
    method __enter__ (line 463) | def __enter__(self):
    method __exit__ (line 466) | def __exit__(self, exc_type, exc_val, exc_tb):

FILE: habitat-lab/habitat/core/environments.py
  function get_env_class (line 21) | def get_env_class(env_name: str) -> Type[habitat.RLEnv]:
  class RearrangeRLEnv (line 34) | class RearrangeRLEnv(habitat.RLEnv):
    method __init__ (line 35) | def __init__(self, config: Config, dataset: Optional[Dataset] = None):
    method reset (line 40) | def reset(self):
    method step (line 44) | def step(self, *args, **kwargs):
    method get_reward_range (line 47) | def get_reward_range(self):
    method get_reward (line 51) | def get_reward(self, observations):
    method _episode_success (line 62) | def _episode_success(self):
    method get_done (line 65) | def get_done(self, observations):
    method get_info (line 73) | def get_info(self, observations):
  class NavRLEnv (line 78) | class NavRLEnv(habitat.RLEnv):
    method __init__ (line 79) | def __init__(self, config: Config, dataset: Optional[Dataset] = None):
    method reset (line 86) | def reset(self):
    method step (line 93) | def step(self, *args, **kwargs):
    method get_reward_range (line 96) | def get_reward_range(self):
    method get_reward (line 102) | def get_reward(self, observations):
    method _episode_success (line 115) | def _episode_success(self):
    method get_done (line 118) | def get_done(self, observations):
    method get_info (line 124) | def get_info(self, observations):

FILE: habitat-lab/habitat/core/logging.py
  class HabitatLogger (line 10) | class HabitatLogger(logging.Logger):
    method __init__ (line 11) | def __init__(
    method add_filehandler (line 31) | def add_filehandler(self, log_filename):

FILE: habitat-lab/habitat/core/registry.py
  class Registry (line 43) | class Registry(metaclass=Singleton):
    method _register_impl (line 47) | def _register_impl(
    method register_task (line 72) | def register_task(cls, to_register=None, *, name: Optional[str] = None):
    method register_simulator (line 101) | def register_simulator(
    method register_sensor (line 132) | def register_sensor(cls, to_register=None, *, name: Optional[str] = No...
    method register_measure (line 144) | def register_measure(cls, to_register=None, *, name: Optional[str] = N...
    method register_task_action (line 156) | def register_task_action(
    method register_dataset (line 173) | def register_dataset(cls, to_register=None, *, name: Optional[str] = N...
    method register_action_space_configuration (line 185) | def register_action_space_configuration(
    method register_env (line 202) | def register_env(cls, to_register=None, *, name: Optional[str] = None):
    method _get_impl (line 216) | def _get_impl(cls, _type: str, name: str) -> Type:
    method get_task (line 220) | def get_task(cls, name: str) -> Type[EmbodiedTask]:
    method get_task_action (line 224) | def get_task_action(cls, name: str) -> Type[Action]:
    method get_simulator (line 228) | def get_simulator(cls, name: str) -> Type[Simulator]:
    method get_sensor (line 232) | def get_sensor(cls, name: str) -> Type[Sensor]:
    method get_measure (line 236) | def get_measure(cls, name: str) -> Type[Measure]:
    method get_dataset (line 240) | def get_dataset(cls, name: str) -> Type[Dataset]:
    method get_action_space_configuration (line 244) | def get_action_space_configuration(
    method get_env (line 250) | def get_env(cls, name: str) -> Type["RLEnv"]:

FILE: habitat-lab/habitat/core/simulator.py
  class ActionSpaceConfiguration (line 38) | class ActionSpaceConfiguration(metaclass=abc.ABCMeta):
    method get (line 42) | def get(self) -> Any:
  class SensorTypes (line 46) | class SensorTypes(Enum):
  class Sensor (line 65) | class Sensor(metaclass=abc.ABCMeta):
    method __init__ (line 83) | def __init__(self, *args: Any, **kwargs: Any) -> None:
    method _get_uuid (line 93) | def _get_uuid(self, *args: Any, **kwargs: Any) -> str:
    method _get_sensor_type (line 96) | def _get_sensor_type(self, *args: Any, **kwargs: Any) -> SensorTypes:
    method _get_observation_space (line 99) | def _get_observation_space(self, *args: Any, **kwargs: Any) -> Space:
    method get_observation (line 103) | def get_observation(self, *args: Any, **kwargs: Any) -> Any:
  class Observations (line 111) | class Observations(Dict[str, Any]):
    method __init__ (line 114) | def __init__(
  class RGBSensor (line 130) | class RGBSensor(Sensor, metaclass=abc.ABCMeta):
    method __init__ (line 131) | def __init__(self, *args: Any, **kwargs: Any) -> None:
    method _get_uuid (line 134) | def _get_uuid(self, *args: Any, **kwargs: Any) -> str:
    method _get_sensor_type (line 137) | def _get_sensor_type(self, *args: Any, **kwargs: Any) -> SensorTypes:
    method _get_observation_space (line 140) | def _get_observation_space(self, *args: Any, **kwargs: Any) -> Space:
    method get_observation (line 143) | def get_observation(self, *args: Any, **kwargs: Any) -> VisualObservat...
  class DepthSensor (line 147) | class DepthSensor(Sensor, metaclass=abc.ABCMeta):
    method __init__ (line 148) | def __init__(self, *args: Any, **kwargs: Any) -> None:
    method _get_uuid (line 151) | def _get_uuid(self, *args: Any, **kwargs: Any) -> str:
    method _get_sensor_type (line 154) | def _get_sensor_type(self, *args: Any, **kwargs: Any) -> SensorTypes:
    method _get_observation_space (line 157) | def _get_observation_space(self, *args: Any, **kwargs: Any) -> Space:
    method get_observation (line 160) | def get_observation(self, *args: Any, **kwargs: Any) -> VisualObservat...
  class SemanticSensor (line 164) | class SemanticSensor(Sensor):
    method __init__ (line 165) | def __init__(self, *args: Any, **kwargs: Any) -> None:
    method _get_uuid (line 168) | def _get_uuid(self, *args: Any, **kwargs: Any) -> str:
    metho

[
  {
    "path": ".gitignore",
    "chars": 246,
    "preview": "**/__pycache__/\nglip_large_model.pth\n/data\n/GLIP/build\n/GLIP/maskrcnn_benchmark.egg-info\n/segment_anything/segment_anyth"
  },
  {
    "path": "GLIP/CODE_OF_CONDUCT.md",
    "chars": 444,
    "preview": "# Microsoft Open Source Code of Conduct\n\nThis project has adopted the [Microsoft Open Source Code of Conduct](https://op"
  },
  {
    "path": "GLIP/DATA.md",
    "chars": 4347,
    "preview": "We provide guidance for preparing the data used by GLIP. Note that not all data are needed for a specific experiments. P"
  },
  {
    "path": "GLIP/LICENSE",
    "chars": 1141,
    "preview": "    MIT License\n\n    Copyright (c) Microsoft Corporation.\n\n    Permission is hereby granted, free of charge, to any pers"
  },
  {
    "path": "GLIP/README.md",
    "chars": 14131,
    "preview": "# GLIP: Grounded Language-Image Pre-training  \r\n\r\n<img src=\"docs/main_model.png\" width=\"800\"> \r\n\r\n## Updates\r\n04/30/2022"
  },
  {
    "path": "GLIP/SECURITY.md",
    "chars": 2780,
    "preview": "<!-- BEGIN MICROSOFT SECURITY.MD V0.0.5 BLOCK -->\n\n## Security\n\nMicrosoft takes the security of our software products an"
  },
  {
    "path": "GLIP/SUPPORT.md",
    "chars": 1315,
    "preview": "# TODO: The maintainer of this repo has not yet edited this file\r\n\r\n**REPO OWNER**: Do you want Customer Service & Suppo"
  },
  {
    "path": "GLIP/configs/flickr/test.yaml",
    "chars": 441,
    "preview": "MODEL:\n  ATSS:\n    NUM_CLASSES: 8 # Placeholder\n  FCOS:\n    NUM_CLASSES: 8 # Placeholder\n  ROI_BOX_HEAD:\n    NUM_CLASSES"
  },
  {
    "path": "GLIP/configs/flickr/val.yaml",
    "chars": 531,
    "preview": "MODEL:\n  ATSS:\n    NUM_CLASSES: 8 # Placeholder\n  FCOS:\n    NUM_CLASSES: 8 # Placeholder\n  ROI_BOX_HEAD:\n    NUM_CLASSES"
  },
  {
    "path": "GLIP/configs/lvis/minival.yaml",
    "chars": 688,
    "preview": "MODEL:\n  ATSS:\n    NUM_CLASSES: 8 # these fields are not used; just a placeholder\n  FCOS:\n    NUM_CLASSES: 8\n  ROI_BOX_H"
  },
  {
    "path": "GLIP/configs/lvis/val.yaml",
    "chars": 678,
    "preview": "MODEL:\n  ATSS:\n    NUM_CLASSES: 8 # these fields are not used; just a placeholder\n  FCOS:\n    NUM_CLASSES: 8\n  ROI_BOX_H"
  },
  {
    "path": "GLIP/configs/odinw/Aquarium_Aquarium_Combined.v2-raw-1024.coco.yaml",
    "chars": 1497,
    "preview": "DATALOADER:\r\n  ASPECT_RATIO_GROUPING: false\r\n  SIZE_DIVISIBILITY: 32\r\n\r\nDATASETS:\r\n  GENERAL_COPY: 16\r\n  CAPTION_PROMPT:"
  },
  {
    "path": "GLIP/configs/pretrain/glip_Swin_L.yaml",
    "chars": 2778,
    "preview": "MODEL:\n  META_ARCHITECTURE: \"GeneralizedVLRCNN\"\n  WEIGHT: \"swin_large_patch4_window12_384_22k.pth\"\n  RPN_ONLY: True\n  RP"
  },
  {
    "path": "GLIP/configs/pretrain/glip_Swin_T_O365_GoldG.yaml",
    "chars": 2271,
    "preview": "MODEL:\n  META_ARCHITECTURE: \"GeneralizedVLRCNN\"\n  WEIGHT: \"swin_tiny_patch4_window7_224.pth\"\n  RPN_ONLY: True\n  RPN_ARCH"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/__init__.py",
    "chars": 73,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/config/__init__.py",
    "chars": 144,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nfrom .defaults import _C as cfg\r\nfrom .paths_ca"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/config/defaults.py",
    "chars": 33374,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport os\r\n\r\nfrom yacs.config import CfgNode as"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/config/paths_catalog.py",
    "chars": 17564,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n\"\"\"Centralized catalog of paths.\"\"\"\r\n\r\nimport o"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/csrc/ROIAlign.h",
    "chars": 1704,
    "preview": "// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n#pragma once\r\n\r\n#include \"cpu/vision.h\"\r\n\r\n#if"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/csrc/ROIPool.h",
    "chars": 1682,
    "preview": "// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n#pragma once\r\n\r\n#include \"cpu/vision.h\"\r\n\r\n#if"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/csrc/SigmoidFocalLoss.h",
    "chars": 1088,
    "preview": "#pragma once\r\n\r\n#include \"cpu/vision.h\"\r\n\r\n#ifdef WITH_CUDA\r\n#include \"cuda/vision.h\"\r\n#endif\r\n\r\n// Interface for Python"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/csrc/cpu/ROIAlign_cpu.cpp",
    "chars": 8219,
    "preview": "// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n#include \"cpu/vision.h\"\r\n\r\n// implementation t"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/csrc/cpu/nms_cpu.cpp",
    "chars": 2575,
    "preview": "// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n#include \"cpu/vision.h\"\r\n\r\n\r\ntemplate <typenam"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/csrc/cpu/soft_nms.cpp",
    "chars": 4031,
    "preview": "// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n#include \"cpu/vision.h\"\r\n\r\n\r\ntemplate <typenam"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/csrc/cpu/vision.h",
    "chars": 897,
    "preview": "// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n#pragma once\r\n#include <torch/extension.h>\r\n\r\n"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/csrc/cuda/ROIAlign_cuda.cu",
    "chars": 12707,
    "preview": "// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n#include <ATen/ATen.h>\r\n#include <ATen/cuda/CU"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/csrc/cuda/ROIPool_cuda.cu",
    "chars": 8099,
    "preview": "// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n#include <ATen/ATen.h>\r\n#include <ATen/cuda/CU"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/csrc/cuda/SigmoidFocalLoss_cuda.cu",
    "chars": 5956,
    "preview": "// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n// This file is modified from  https://github."
  },
  {
    "path": "GLIP/maskrcnn_benchmark/csrc/cuda/deform_conv_cuda.cu",
    "chars": 29406,
    "preview": "// modify from\r\n// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/de"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/csrc/cuda/deform_conv_kernel_cuda.cu",
    "chars": 43374,
    "preview": "/*!\r\n ******************* BEGIN Caffe Copyright Notice and Disclaimer ****************\r\n *\r\n * COPYRIGHT\r\n *\r\n * All con"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/csrc/cuda/deform_pool_cuda.cu",
    "chars": 3791,
    "preview": "// modify from\r\n// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/mo"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/csrc/cuda/deform_pool_kernel_cuda.cu",
    "chars": 16452,
    "preview": "/*!\r\n * Copyright (c) 2017 Microsoft\r\n * Licensed under The MIT License [see LICENSE for details]\r\n * \\file deformable_p"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/csrc/cuda/ml_nms.cu",
    "chars": 5158,
    "preview": "// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n#include <ATen/ATen.h>\r\n#include <ATen/cuda/CU"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/csrc/cuda/nms.cu",
    "chars": 4991,
    "preview": "// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n#include <ATen/ATen.h>\r\n#include <ATen/cuda/CU"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/csrc/cuda/vision.h",
    "chars": 5775,
    "preview": "// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n#pragma once\r\n#include <torch/extension.h>\r\n\r\n"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/csrc/deform_conv.h",
    "chars": 4645,
    "preview": "// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n#pragma once\r\n#include \"cpu/vision.h\"\r\n\r\n#ifde"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/csrc/deform_pool.h",
    "chars": 1844,
    "preview": "// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n#pragma once\r\n#include \"cpu/vision.h\"\r\n\r\n#ifde"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/csrc/ml_nms.h",
    "chars": 791,
    "preview": "// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n#pragma once\r\n#include \"cpu/vision.h\"\r\n\r\n#ifde"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/csrc/nms.h",
    "chars": 1255,
    "preview": "// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n#pragma once\r\n#include \"cpu/vision.h\"\r\n\r\n#ifde"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/csrc/vision.cpp",
    "chars": 1667,
    "preview": "// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n#include \"nms.h\"\r\n#include \"ml_nms.h\"\r\n#includ"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/__init__.py",
    "chars": 110,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nfrom .build import make_data_loader\r\n"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/build.py",
    "chars": 23115,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport bisect\r\nimport copy\r\nimport logging\r\nimp"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/collate_batch.py",
    "chars": 3953,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\nfrom maskrcnn_benchmark.structure"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/__init__.py",
    "chars": 1010,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nfrom .coco import COCODataset\r\nfrom .voc import"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/background.py",
    "chars": 1629,
    "preview": "import os\r\nimport os.path\r\nimport json\r\nfrom PIL import Image\r\n\r\nimport torch\r\nimport torchvision\r\nimport torch.utils.da"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/box_label_loader.py",
    "chars": 11465,
    "preview": "import torch\r\nimport numpy as np\r\nimport math\r\nimport base64\r\nimport collections\r\nimport pycocotools.mask as mask_utils\r"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/caption.py",
    "chars": 13391,
    "preview": "import torch\r\nimport torch.distributed as dist\r\nimport time\r\nfrom torchvision.ops import nms\r\nimport random\r\nimport nump"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/coco.py",
    "chars": 10605,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport os\r\nimport os.path\r\nimport math\r\nfrom PI"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/coco_dt.py",
    "chars": 6284,
    "preview": "\"\"\"\r\nCOCO dataset which returns image_id for evaluation.\r\n\r\nMostly copy-paste from https://github.com/pytorch/vision/blo"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/concat_dataset.py",
    "chars": 789,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport bisect\r\n\r\nfrom torch.utils.data.dataset "
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/custom_distributed_sampler.py",
    "chars": 7961,
    "preview": "import math\r\nfrom typing import TypeVar, Optional, Iterator\r\n\r\nimport torch\r\nfrom torch.utils.data import Sampler, Datas"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/duplicate_dataset.py",
    "chars": 894,
    "preview": "import math\r\nfrom typing import TypeVar, Optional, Iterator\r\n\r\nimport torch\r\nfrom torch.utils.data import Sampler, Datas"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/evaluation/__init__.py",
    "chars": 2317,
    "preview": "from maskrcnn_benchmark.data import datasets\r\n\r\nfrom .coco import coco_evaluation\r\nfrom .voc import voc_evaluation\r\nfrom"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/evaluation/box_aug.py",
    "chars": 13633,
    "preview": "import torch\r\nimport numpy as np\r\n\r\nfrom maskrcnn_benchmark.config import cfg\r\nfrom maskrcnn_benchmark.data import trans"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/evaluation/coco/__init__.py",
    "chars": 536,
    "preview": "from .coco_eval import do_coco_evaluation\r\n\r\n\r\ndef coco_evaluation(\r\n    dataset,\r\n    predictions,\r\n    output_folder,\r"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/evaluation/coco/coco_eval.py",
    "chars": 19712,
    "preview": "import logging\r\nimport tempfile\r\nimport os\r\nimport torch\r\nimport numpy as np\r\nimport json\r\n\r\nfrom collections import Ord"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/evaluation/flickr/__init__.py",
    "chars": 42,
    "preview": "from .flickr_eval import FlickrEvaluator\r\n"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/evaluation/flickr/flickr_eval.py",
    "chars": 16992,
    "preview": "from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou\r\nfrom maskrcnn_benchmark.structures.bounding_box impor"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/evaluation/lvis/_change_lvis_annotation.py",
    "chars": 319,
    "preview": "path = \"DATASET/coco/annotations/lvis_v1_minival.json\"\r\nimport json\r\nwith open(path) as f:\r\n    all = json.load(f)\r\n\r\nfo"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/evaluation/lvis/lvis.py",
    "chars": 6832,
    "preview": "# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved\r\n# Copyrig"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/evaluation/lvis/lvis_eval.py",
    "chars": 38463,
    "preview": "# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved\r\n# Copyrig"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/evaluation/od_eval.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/evaluation/od_to_grounding/__init__.py",
    "chars": 568,
    "preview": "from .od_eval import do_od_evaluation\r\n\r\n\r\ndef od_to_grounding_evaluation(\r\n        dataset,\r\n        predictions,\r\n    "
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/evaluation/od_to_grounding/od_eval.py",
    "chars": 19712,
    "preview": "import logging\r\nimport tempfile\r\nimport os\r\nimport torch\r\nimport numpy as np\r\nimport json\r\n\r\nfrom collections import Ord"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/evaluation/vg/__init__.py",
    "chars": 514,
    "preview": "import logging\r\n\r\nfrom .vg_eval import do_vg_evaluation\r\n\r\n\r\ndef vg_evaluation(dataset, predictions, output_folder, box_"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/evaluation/vg/vg_eval.py",
    "chars": 27419,
    "preview": "# A modification version from chainercv repository.\r\n# (See https://github.com/chainer/chainercv/blob/master/chainercv/e"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/evaluation/voc/__init__.py",
    "chars": 521,
    "preview": "import logging\r\n\r\nfrom .voc_eval import do_voc_evaluation\r\n\r\n\r\ndef voc_evaluation(dataset, predictions, output_folder, b"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/evaluation/voc/voc_eval.py",
    "chars": 8369,
    "preview": "# A modification version from chainercv repository.\r\n# (See https://github.com/chainer/chainercv/blob/master/chainercv/e"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/flickr.py",
    "chars": 199,
    "preview": "import torch\r\nimport torchvision\r\nimport torch.utils.data as data\r\nfrom maskrcnn_benchmark.data.datasets.modulated_coco "
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/gqa.py",
    "chars": 3753,
    "preview": "import json\r\nfrom pathlib import Path\r\n\r\nimport torch\r\nimport torchvision\r\n\r\nfrom .modulated_coco import ConvertCocoPoly"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/imagenet.py",
    "chars": 2027,
    "preview": "import os\r\nimport os.path\r\nimport json\r\nfrom PIL import Image\r\n\r\nimport torch.utils.data as data\r\n\r\ndef pil_loader(path)"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/list_dataset.py",
    "chars": 979,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n\"\"\"\r\nSimple dataset class that wraps a list of "
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/lvis.py",
    "chars": 9136,
    "preview": "# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved\r\n# Copyrig"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/mixed.py",
    "chars": 5431,
    "preview": "import os\r\nimport os.path\r\nfrom pathlib import Path\r\nfrom typing import Any, Callable, Optional, Tuple\r\n\r\nimport torch\r\n"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/mixup.py",
    "chars": 5008,
    "preview": "\"\"\"Mixup detection dataset wrapper.\"\"\"\r\nfrom __future__ import absolute_import\r\nimport numpy as np\r\nimport torch\r\nimport"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/modulated_coco.py",
    "chars": 27167,
    "preview": "import logging\r\nimport os\r\nimport os.path\r\nimport math\r\nfrom PIL import Image, ImageDraw\r\n\r\nimport random\r\nimport numpy "
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/object365.py",
    "chars": 200,
    "preview": "import torch\r\nimport torchvision\r\nimport torch.utils.data as data\r\nfrom maskrcnn_benchmark.data.datasets.coco_dt import "
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/od_to_grounding.py",
    "chars": 14681,
    "preview": "import numpy as np\r\nimport random\r\nimport re\r\nimport torch\r\nimport pdb\r\nimport logging\r\n\r\n\r\ndef clean_name(name):\r\n    n"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/phrasecut.py",
    "chars": 196,
    "preview": "import torch\nimport torchvision\nimport torch.utils.data as data\nfrom maskrcnn_benchmark.data.datasets.modulated_coco imp"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/pseudo_data.py",
    "chars": 9133,
    "preview": "import torch\nimport torch.distributed as dist\nimport time\nfrom torchvision.ops import nms\nimport random\nimport numpy as "
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/refexp.py",
    "chars": 3358,
    "preview": "import copy\r\nfrom collections import defaultdict\r\nfrom pathlib import Path\r\n\r\nimport torch\r\nimport torch.utils.data\r\n\r\ni"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/tsv.py",
    "chars": 15152,
    "preview": "import os\r\nimport os.path as op\r\nimport json\r\n# import logging\r\nimport base64\r\nimport yaml\r\nimport errno\r\nimport io\r\nimp"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/vg.py",
    "chars": 11011,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport collections\r\nimport json\r\nimport os.path"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/datasets/voc.py",
    "chars": 4255,
    "preview": "import os\r\n\r\nimport torch\r\nimport torch.utils.data\r\nfrom PIL import Image\r\nimport sys\r\n\r\nif sys.version_info[0] == 2:\r\n "
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/samplers/__init__.py",
    "chars": 334,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nfrom .distributed import DistributedSampler\r\nfr"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/samplers/distributed.py",
    "chars": 2866,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n# Code is copy-pasted exactly as in torch.utils"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/samplers/grouped_batch_sampler.py",
    "chars": 4960,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport itertools\r\n\r\nimport torch\r\nfrom torch.ut"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/samplers/iteration_based_batch_sampler.py",
    "chars": 1195,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nfrom torch.utils.data.sampler import BatchSampl"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/transforms/__init__.py",
    "chars": 292,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nfrom .transforms import Compose\r\nfrom .transfor"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/transforms/build.py",
    "chars": 1521,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nfrom . import transforms as T\r\n\r\n\r\ndef build_tr"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/data/transforms/transforms.py",
    "chars": 14395,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport cv2\r\nimport random\r\nimport numpy as np\r\n"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/engine/__init__.py",
    "chars": 73,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/engine/alter_trainer.py",
    "chars": 4558,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport datetime\r\nimport logging\r\nimport time\r\n\r"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/engine/evolution.py",
    "chars": 13044,
    "preview": "\r\nimport time\r\nimport pickle\r\nimport logging\r\nimport os\r\nimport numpy as np\r\nimport torch\r\nimport torch.nn as nn\r\n\r\n\r\nfr"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/engine/inference.py",
    "chars": 21833,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport datetime\r\nimport logging\r\nimport time\r\ni"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/engine/predictor.py",
    "chars": 20913,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport cv2\r\nimport torch\r\nimport numpy as np\r\nf"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/engine/predictor_glip.py",
    "chars": 18786,
    "preview": "import cv2\nimport torch\nimport re\nimport numpy as np\nfrom typing import List, Union\nimport nltk\nimport inflect\nfrom tran"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/engine/singlepath_trainer.py",
    "chars": 5076,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport datetime\r\nimport logging\r\nimport time\r\ni"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/engine/stage_trainer.py",
    "chars": 7454,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport datetime\r\nimport logging\r\nimport time\r\n\r"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/engine/trainer.py",
    "chars": 16079,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport datetime\r\nimport logging\r\nimport sys\r\nim"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/layers/__init__.py",
    "chars": 1544,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\n\r\nfrom .batch_norm import FrozenB"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/layers/batch_norm.py",
    "chars": 5041,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\nfrom torch import nn\r\n\r\nimport to"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/layers/deform_conv.py",
    "chars": 14640,
    "preview": "import torch\r\nimport math\r\nfrom torch import nn\r\nfrom torch.nn import init\r\nfrom torch.nn.modules.utils import _pair\r\nfr"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/layers/deform_pool.py",
    "chars": 17502,
    "preview": "import torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\n\r\nfrom .deform_conv import DeformConv2d\r\n\r\ndef add"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/layers/dropblock.py",
    "chars": 4584,
    "preview": "import torch\r\nimport torch.nn.functional as F\r\nfrom torch import nn\r\n\r\n\r\nclass DropBlock2D(nn.Module):\r\n    r\"\"\"Randomly"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/layers/dyhead.py",
    "chars": 5205,
    "preview": "import torch\r\nimport torch.nn.functional as F\r\nfrom torch import nn\r\n\r\nfrom .deform_conv import ModulatedDeformConv\r\nfro"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/layers/dyrelu.py",
    "chars": 3956,
    "preview": "import torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\n\r\ndef _make_divisible(v, divisor, min_value=None):"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/layers/evonorm.py",
    "chars": 1344,
    "preview": "import torch\r\nimport torch.nn as nn\r\n\r\n\r\nclass EvoNorm2d(nn.Module):\r\n    __constants__ = ['num_features', 'eps', 'nonli"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/layers/iou_loss.py",
    "chars": 2931,
    "preview": "import torch\r\nfrom torch import nn\r\n\r\n\r\nclass IOULoss(nn.Module):\r\n    def __init__(self, loss_type=\"iou\"):\r\n        sup"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/layers/misc.py",
    "chars": 6871,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n\"\"\"\r\nhelper class that supports empty tensors o"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/layers/nms.py",
    "chars": 325,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nfrom maskrcnn_benchmark import _C\r\n\r\ntry:\r\n    "
  },
  {
    "path": "GLIP/maskrcnn_benchmark/layers/roi_align.py",
    "chars": 3033,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\nfrom torch import nn\r\nfrom torch."
  },
  {
    "path": "GLIP/maskrcnn_benchmark/layers/roi_pool.py",
    "chars": 1918,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\nfrom torch import nn\r\nfrom torch."
  },
  {
    "path": "GLIP/maskrcnn_benchmark/layers/se.py",
    "chars": 1821,
    "preview": "from torch import nn\r\n\r\n\r\nclass SELayer(nn.Module):\r\n    def __init__(self, channel, reduction=16):\r\n        super(SELay"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/layers/set_loss.py",
    "chars": 16913,
    "preview": "import torch\r\nimport torch.nn.functional as F\r\nimport torch.distributed as dist\r\nfrom torch import nn\r\n\r\nfrom scipy.opti"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/layers/sigmoid_focal_loss.py",
    "chars": 7532,
    "preview": "import torch\r\nfrom torch import nn\r\nimport torch.nn.functional as F\r\nfrom torch.autograd import Function\r\nfrom torch.aut"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/layers/smooth_l1_loss.py",
    "chars": 497,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\n\r\n\r\n# TODO maybe push this to nn?"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/backbone/__init__.py",
    "chars": 9052,
    "preview": "from collections import OrderedDict\r\n\r\nfrom torch import nn\r\n\r\nfrom maskrcnn_benchmark.modeling import registry\r\nfrom ma"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/backbone/bifpn.py",
    "chars": 12109,
    "preview": "import torch.nn as nn\r\nimport torch\r\n\r\nfrom maskrcnn_benchmark.layers import swish\r\n\r\n\r\nclass BiFPN(nn.Module):\r\n    def"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/backbone/blocks.py",
    "chars": 8678,
    "preview": "import torch.nn as nn\r\nfrom .ops import *\r\n\r\n\r\nclass stem(nn.Module):\r\n    num_layer = 1\r\n\r\n    def __init__(self, conv,"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/backbone/efficientdet.py",
    "chars": 80575,
    "preview": "import torch\r\nimport re\r\nimport numpy as np\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport logging\r\nimp"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/backbone/efficientnet.py",
    "chars": 23139,
    "preview": "\"\"\"\r\n    EfficientNet for ImageNet-1K, implemented in PyTorch.\r\n    Original papers:\r\n    - 'EfficientNet: Rethinking Mo"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/backbone/fbnet.py",
    "chars": 15879,
    "preview": "\"\"\"\r\nFBNet model builder\r\n\"\"\"\r\n\r\nfrom __future__ import absolute_import, division, print_function, unicode_literals\r\n\r\ni"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/backbone/fpn.py",
    "chars": 7046,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\nimport torch.nn.functional as F\r\n"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/backbone/mixer.py",
    "chars": 1040,
    "preview": "import torch\r\nfrom torch import nn\r\n\r\nclass MixedOperationRandom(nn.Module):\r\n    def __init__(self, search_ops):\r\n     "
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/backbone/ops.py",
    "chars": 2732,
    "preview": "import math\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\n\r\n\r\ndef conv7x7(in_planes, out_planes"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/backbone/resnet.py",
    "chars": 20973,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n\"\"\"\r\nVariant of the resnet module that takes cf"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/backbone/swint.py",
    "chars": 25846,
    "preview": "# --------------------------------------------------------\n# Swin Transformer\n# modified from https://github.com/SwinTra"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/backbone/swint_v2.py",
    "chars": 28474,
    "preview": "# --------------------------------------------------------\n# Swin Transformer\n# modified from https://github.com/SwinTra"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/backbone/swint_v2_vl.py",
    "chars": 35807,
    "preview": "# --------------------------------------------------------\r\n# Swin Transformer\r\n# modified from https://github.com/SwinT"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/backbone/swint_vl.py",
    "chars": 32089,
    "preview": "# --------------------------------------------------------\n# Swin Transformer\n# modified from https://github.com/SwinTra"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/balanced_positive_negative_sampler.py",
    "chars": 2784,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\n\r\n\r\nclass BalancedPositiveNegativ"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/box_coder.py",
    "chars": 3462,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport math\r\n\r\nimport torch\r\n\r\n\r\nclass BoxCoder"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/detector/__init__.py",
    "chars": 420,
    "preview": "from .generalized_rcnn import GeneralizedRCNN\r\nfrom .generalized_vl_rcnn import GeneralizedVLRCNN\r\n\r\n_DETECTION_META_ARC"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py",
    "chars": 4915,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n\"\"\"\r\nImplements the Generalized R-CNN framework"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/detector/generalized_vl_rcnn.py",
    "chars": 14299,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n\"\"\"\r\nImplements the Generalized VL R-CNN framew"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/language_backbone/__init__.py",
    "chars": 234,
    "preview": "from .backbone import build_backbone as build_language_backbone\r\nfrom .build import build_tokenizer\r\n\r\nfrom .hfpt_tokeni"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/language_backbone/backbone.py",
    "chars": 1389,
    "preview": "from collections import OrderedDict\r\nimport torch\r\nfrom torch import nn\r\n\r\nfrom maskrcnn_benchmark.modeling import regis"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/language_backbone/bert_model.py",
    "chars": 3313,
    "preview": "from copy import deepcopy\r\nimport numpy as np\r\nimport torch\r\nfrom torch import nn\r\n\r\n# from pytorch_pretrained_bert.mode"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/language_backbone/build.py",
    "chars": 567,
    "preview": "from .simple_tokenizer import SimpleTokenizer\r\n\r\n\r\ndef build_tokenizer(tokenizer_name):\r\n    tokenizer = None\r\n    if to"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/language_backbone/clip_model.py",
    "chars": 7903,
    "preview": "from collections import OrderedDict\r\nimport logging\r\nimport os\r\n\r\nimport torch\r\nfrom torch import nn\r\nimport torch.nn.fu"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/language_backbone/hfpt_tokenizer.py",
    "chars": 3286,
    "preview": "from typing import Union, List\r\n\r\nfrom transformers import AutoTokenizer\r\nimport torch\r\n\r\n\r\nclass HFPTTokenizer(object):"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/language_backbone/rnn_model.py",
    "chars": 5807,
    "preview": "from copy import deepcopy\r\nimport numpy as np\r\nimport torch\r\nfrom torch import nn\r\n\r\n\r\nclass RNNEnoder(nn.Module):\r\n    "
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/language_backbone/simple_tokenizer.py",
    "chars": 6090,
    "preview": "import gzip\r\nimport html\r\nimport os\r\nfrom functools import lru_cache\r\n\r\nimport ftfy\r\nimport regex as re\r\nfrom typing imp"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/language_backbone/test_clip_tokenizer.py",
    "chars": 308,
    "preview": "from maskrcnn_benchmark.modeling.language_backbone import build_tokenizer\r\n\r\nif __name__ == '__main__':\r\n\r\n    tokenizer"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/language_backbone/word_utils.py",
    "chars": 3279,
    "preview": "\"\"\"\r\nLanguage-related data loading helper functions and class wrappers.\r\n\"\"\"\r\n\r\nimport re\r\nimport torch\r\nimport codecs\r\n"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/make_layers.py",
    "chars": 3829,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n\"\"\"\r\nMiscellaneous utility functions\r\n\"\"\"\r\n\r\nim"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/matcher.py",
    "chars": 5438,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\n\r\n\r\nclass Matcher(object):\r\n    \""
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/poolers.py",
    "chars": 4535,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\nimport torch.nn.functional as F\r\n"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/registry.py",
    "chars": 259,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n\r\nfrom maskrcnn_benchmark.utils.registry import"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/roi_heads/__init__.py",
    "chars": 3667,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\n\r\nfrom .box_head.box_head import "
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/roi_heads/box_head/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/roi_heads/box_head/box_head.py",
    "chars": 2992,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\nfrom torch import nn\r\n\r\nfrom .roi"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/roi_heads/box_head/inference.py",
    "chars": 7586,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\nimport torch.nn.functional as F\r\n"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/roi_heads/box_head/loss.py",
    "chars": 7500,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\nfrom torch.nn import functional a"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_feature_extractors.py",
    "chars": 7493,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\nfrom torch import nn\r\nfrom torch."
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_predictors.py",
    "chars": 2128,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nfrom torch import nn\r\n\r\n\r\nclass FastRCNNPredict"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/inference.py",
    "chars": 4542,
    "preview": "import cv2\r\nimport numpy as np\r\nimport torch\r\nfrom torch import nn\r\n\r\nfrom maskrcnn_benchmark.structures.bounding_box im"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/keypoint_head.py",
    "chars": 2003,
    "preview": "import torch\r\n\r\nfrom .roi_keypoint_feature_extractors import make_roi_keypoint_feature_extractor\r\nfrom .roi_keypoint_pre"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/loss.py",
    "chars": 7284,
    "preview": "import torch\r\nfrom torch.nn import functional as F\r\n\r\nfrom maskrcnn_benchmark.modeling.matcher import Matcher\r\n\r\nfrom ma"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/roi_keypoint_feature_extractors.py",
    "chars": 3915,
    "preview": "from torch import nn\r\nfrom torch.nn import functional as F\r\n\r\nfrom maskrcnn_benchmark.modeling.poolers import Pooler\r\n\r\n"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/roi_keypoint_predictors.py",
    "chars": 1251,
    "preview": "from torch import nn\r\nfrom torch.nn import functional as F\r\n\r\nfrom maskrcnn_benchmark import layers\r\n\r\n\r\nclass KeypointR"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/roi_heads/mask_head/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/roi_heads/mask_head/hourglass.py",
    "chars": 2156,
    "preview": "from torch import nn\n\nfrom maskrcnn_benchmark.modeling.make_layers import make_conv3x3\n\n\nclass Residual(nn.Module):\n    "
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/roi_heads/mask_head/inference.py",
    "chars": 8026,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport numpy as np\r\nimport torch\r\nfrom torch im"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/roi_heads/mask_head/loss.py",
    "chars": 7470,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\nfrom torch.nn import functional a"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/roi_heads/mask_head/mask_head.py",
    "chars": 3490,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\nfrom torch import nn\r\n\r\nfrom mask"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_feature_extractors.py",
    "chars": 4143,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nfrom torch import nn\r\nfrom torch.nn import func"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_predictors.py",
    "chars": 4961,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\nfrom torch import nn\r\nfrom torch."
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/rpn/__init__.py",
    "chars": 859,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n# from .rpn import build_rpn\r\nfrom .rpn import "
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/rpn/anchor_generator.py",
    "chars": 16408,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport math\r\n\r\nimport numpy as np\r\nimport torch"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/rpn/atss.py",
    "chars": 8913,
    "preview": "import math\r\nimport torch\r\nimport torch.nn.functional as F\r\nfrom torch import nn\r\n\r\nfrom .inference import make_atss_pos"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/rpn/dyhead.py",
    "chars": 15342,
    "preview": "import math\r\nimport torch\r\nimport torch.nn.functional as F\r\nfrom torch import nn\r\n\r\nfrom .inference import make_atss_pos"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/rpn/fcos.py",
    "chars": 8718,
    "preview": "import math\r\nimport torch\r\nimport torch.nn.functional as F\r\nfrom torch import nn\r\n\r\nfrom maskrcnn_benchmark.modeling imp"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/rpn/inference.py",
    "chars": 33226,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport logging\r\n\r\nimport torch\r\n\r\nfrom maskrcnn"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/rpn/loss.py",
    "chars": 63682,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n\"\"\"\r\nThis file contains specific functions for "
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/rpn/modeling_bert.py",
    "chars": 12874,
    "preview": "# coding=utf-8\r\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\r\n# Copyright (c) 201"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/rpn/retina.py",
    "chars": 5641,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport math\r\nimport torch\r\nimport torch.nn.func"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/rpn/rpn.py",
    "chars": 6760,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\nimport torch.nn.functional as F\r\n"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/rpn/transformer.py",
    "chars": 1913,
    "preview": "import torch\r\nimport torch.nn.functional as F\r\nfrom torch import nn, Tensor\r\n\r\nimport copy\r\nfrom typing import Optional,"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/rpn/vldyhead.py",
    "chars": 47631,
    "preview": "import torch\r\nimport torch.nn.functional as F\r\nfrom torch import nn\r\nfrom collections import defaultdict\r\n\r\nfrom .infere"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/modeling/utils.py",
    "chars": 2817,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\n\"\"\"\r\nMiscellaneous utility functions\r\n\"\"\"\r\n\r\nim"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/solver/__init__.py",
    "chars": 191,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nfrom .build import make_optimizer\r\nfrom .build "
  },
  {
    "path": "GLIP/maskrcnn_benchmark/solver/build.py",
    "chars": 4573,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\nimport itertools\r\n\r\nfrom .lr_sche"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/solver/lr_scheduler.py",
    "chars": 5963,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nfrom bisect import bisect_right\r\n\r\nimport math\r"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/structures/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "GLIP/maskrcnn_benchmark/structures/bounding_box.py",
    "chars": 12331,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\n\r\n# transpose\r\nFLIP_LEFT_RIGHT = "
  },
  {
    "path": "GLIP/maskrcnn_benchmark/structures/boxlist_ops.py",
    "chars": 5764,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\n\r\nfrom .bounding_box import BoxLi"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/structures/image_list.py",
    "chars": 2488,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nfrom __future__ import division\r\n\r\nimport torch"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/structures/keypoint.py",
    "chars": 7555,
    "preview": "import torch\r\nfrom maskrcnn_benchmark.config import cfg\r\n\r\n# transpose\r\nFLIP_LEFT_RIGHT = 0\r\nFLIP_TOP_BOTTOM = 1\r\n\r\n\r\ncl"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/structures/segmentation_mask.py",
    "chars": 7179,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport torch\r\n\r\nimport pycocotools.mask as mask"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/utils/README.md",
    "chars": 180,
    "preview": "# Utility functions\r\n\r\nThis folder contain utility functions that are not used in the\r\ncore library, but are useful for "
  },
  {
    "path": "GLIP/maskrcnn_benchmark/utils/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "GLIP/maskrcnn_benchmark/utils/amp.py",
    "chars": 433,
    "preview": "from contextlib import contextmanager\r\n\r\n@contextmanager\r\ndef nullcontext(enter_result=None, **kwargs):\r\n    yield enter"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/utils/big_model_loading.py",
    "chars": 3268,
    "preview": "import numpy as np\r\nimport torch\r\nimport torch.nn as nn\r\n\r\nfrom collections import OrderedDict\r\n\r\n\r\ndef tf2th(conv_weigh"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/utils/c2_model_loading.py",
    "chars": 8603,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport logging\r\nimport pickle\r\nfrom collections"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/utils/checkpoint.py",
    "chars": 6280,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport logging\r\nimport os\r\n\r\nimport torch\r\n\r\nfr"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/utils/collect_env.py",
    "chars": 352,
    "preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\r\nimport PIL\r\n\r\nfrom torch.utils.collect_env impo"
  },
  {
    "path": "GLIP/maskrcnn_benchmark/utils/comm.py",
    "chars": 4579,
    "preview": "\"\"\"\r\nThis file contains primitives for multi-gpu communication.\r\nThis is useful when doing distributed training.\r\n\"\"\"\r\n\r"
  }
]

// ... and 418 more files (download for full content)