├── .idea ├── HCAT.iml ├── inspectionProfiles │ └── profiles_settings.xml ├── misc.xml ├── modules.xml ├── vcs.xml └── workspace.xml ├── .ipynb_checkpoints └── README-checkpoint.md ├── AGX配置环境.md ├── LICENSE ├── README.md ├── got10k_toolkit ├── LICENSE ├── MANIFEST.in ├── README.md ├── __init__.py ├── __pycache__ │ └── __init__.cpython-37.pyc ├── requirements.txt ├── resources │ ├── .ipynb_checkpoints │ │ └── sample_batch_run-checkpoint.jpg │ └── sample_batch_run.jpg ├── setup.cfg ├── setup.py ├── tests │ ├── test_datasets.py │ ├── test_experiments.py │ ├── test_trackers.py │ └── test_utils.py └── toolkit │ ├── PLOT.py │ ├── __init__.py │ ├── __pycache__ │ └── __init__.cpython-37.pyc │ ├── datasets │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── dtb70.cpython-37.pyc │ │ ├── got10k.cpython-37.pyc │ │ ├── lasot.cpython-37.pyc │ │ ├── nfs.cpython-37.pyc │ │ ├── otb.cpython-37.pyc │ │ ├── tcolor128.cpython-37.pyc │ │ ├── trackingnet.cpython-37.pyc │ │ ├── uav123.cpython-37.pyc │ │ ├── vid.cpython-37.pyc │ │ └── vot.cpython-37.pyc │ ├── dtb70.py │ ├── got10k.py │ ├── lasot.json │ ├── lasot.py │ ├── nfs.py │ ├── otb.py │ ├── tcolor128.py │ ├── trackingnet.py │ ├── uav123.json │ ├── uav123.py │ ├── vid.py │ └── vot.py │ ├── evaluate_got.py │ ├── evaluate_got_para.py │ ├── evaluate_lasot.py │ ├── evaluate_nfs.py │ ├── evaluate_otb.py │ ├── evaluate_tc128.py │ ├── evaluate_trackingnet.py │ ├── evaluate_uav123.py │ ├── experiments │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── dtb70.cpython-37.pyc │ │ ├── got10k.cpython-37.pyc │ │ ├── lasot.cpython-37.pyc │ │ ├── nfs.cpython-37.pyc │ │ ├── otb.cpython-37.pyc │ │ ├── tcolor128.cpython-37.pyc │ │ ├── trackingnet.cpython-37.pyc │ │ ├── uav123.cpython-37.pyc │ │ └── vot.cpython-37.pyc │ ├── dtb70.py │ ├── got10k.py │ ├── lasot.py │ ├── nfs.py │ ├── otb.py │ ├── tcolor128.py │ ├── trackingnet.py │ ├── uav123.py │ └── vot.py │ ├── plot_got.py │ ├── test_got.py │ ├── test_lasot.py │ ├── test_lasot_2.py │ ├── test_nfs.py │ ├── test_otb.py │ ├── test_tc128.py │ ├── test_trackingnet.py │ ├── test_trackingnet_2.py │ ├── test_uav123.py │ ├── trackers │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── identity_tracker.cpython-37.pyc │ │ └── net_wrappers.cpython-37.pyc │ ├── identity_tracker.py │ └── net_wrappers.py │ └── utils │ ├── __init__.py │ ├── __pycache__ │ ├── __init__.cpython-37.pyc │ ├── ioutils.cpython-37.pyc │ ├── metrics.cpython-37.pyc │ └── viz.cpython-37.pyc │ ├── ioutils.py │ ├── metrics.py │ └── viz.py ├── ltr ├── .ipynb_checkpoints │ ├── run_training-checkpoint.py │ └── run_training_ddp-checkpoint.py ├── README.md ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-36.pyc │ ├── __init__.cpython-37.pyc │ └── __init__.cpython-38.pyc ├── actors │ ├── .ipynb_checkpoints │ │ └── base_actor-checkpoint.py │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── __init__.cpython-38.pyc │ │ ├── base_actor.cpython-37.pyc │ │ ├── base_actor.cpython-38.pyc │ │ ├── tracking.cpython-37.pyc │ │ └── tracking.cpython-38.pyc │ ├── base_actor.py │ └── tracking.py ├── admin │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-36.pyc │ │ ├── __init__.cpython-37.pyc │ │ ├── __init__.cpython-38.pyc │ │ ├── environment.cpython-36.pyc │ │ ├── environment.cpython-37.pyc │ │ ├── environment.cpython-38.pyc │ │ ├── loading.cpython-36.pyc │ │ ├── loading.cpython-37.pyc │ │ ├── loading.cpython-38.pyc │ │ ├── local.cpython-36.pyc │ │ ├── local.cpython-37.pyc │ │ ├── local.cpython-38.pyc │ │ ├── model_constructor.cpython-36.pyc │ │ ├── model_constructor.cpython-37.pyc │ │ ├── model_constructor.cpython-38.pyc │ │ ├── multigpu.cpython-36.pyc │ │ ├── multigpu.cpython-37.pyc │ │ ├── multigpu.cpython-38.pyc │ │ ├── settings.cpython-36.pyc │ │ ├── settings.cpython-37.pyc │ │ ├── settings.cpython-38.pyc │ │ ├── stats.cpython-36.pyc │ │ ├── stats.cpython-37.pyc │ │ ├── stats.cpython-38.pyc │ │ ├── tensorboard.cpython-37.pyc │ │ └── tensorboard.cpython-38.pyc │ ├── environment.py │ ├── loading.py │ ├── local.py │ ├── model_constructor.py │ ├── multigpu.py │ ├── settings.py │ ├── stats.py │ └── tensorboard.py ├── data │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── __init__.cpython-38.pyc │ │ ├── bounding_box_utils.cpython-37.pyc │ │ ├── bounding_box_utils.cpython-38.pyc │ │ ├── image_loader.cpython-37.pyc │ │ ├── image_loader.cpython-38.pyc │ │ ├── loader.cpython-37.pyc │ │ ├── loader.cpython-38.pyc │ │ ├── processing.cpython-37.pyc │ │ ├── processing.cpython-38.pyc │ │ ├── processing_utils.cpython-37.pyc │ │ ├── processing_utils.cpython-38.pyc │ │ ├── sampler.cpython-37.pyc │ │ ├── sampler.cpython-38.pyc │ │ ├── transforms.cpython-37.pyc │ │ └── transforms.cpython-38.pyc │ ├── augmentation.py │ ├── bbox.py │ ├── bounding_box_utils.py │ ├── image_loader.py │ ├── loader.py │ ├── processing.py │ ├── processing_utils.py │ ├── sampler.py │ └── transforms.py ├── data_specs │ ├── got10k_all.txt │ ├── got10k_train_split.txt │ ├── got10k_val_split.txt │ ├── got10k_vot_exclude.txt │ ├── got10k_vot_train_split.txt │ ├── got10k_vot_val_split.txt │ ├── lasot_train_split.txt │ ├── trackingnet_classmap.txt │ ├── youtubevos_jjtrain.txt │ └── youtubevos_jjvalid.txt ├── dataset │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── __init__.cpython-38.pyc │ │ ├── base_image_dataset.cpython-37.pyc │ │ ├── base_image_dataset.cpython-38.pyc │ │ ├── base_video_dataset.cpython-37.pyc │ │ ├── base_video_dataset.cpython-38.pyc │ │ ├── coco.cpython-37.pyc │ │ ├── coco.cpython-38.pyc │ │ ├── coco_seq.cpython-37.pyc │ │ ├── coco_seq.cpython-38.pyc │ │ ├── davis.cpython-37.pyc │ │ ├── davis.cpython-38.pyc │ │ ├── ecssd.cpython-37.pyc │ │ ├── ecssd.cpython-38.pyc │ │ ├── got10k.cpython-37.pyc │ │ ├── got10k.cpython-38.pyc │ │ ├── hku_is.cpython-37.pyc │ │ ├── hku_is.cpython-38.pyc │ │ ├── imagenetvid.cpython-37.pyc │ │ ├── imagenetvid.cpython-38.pyc │ │ ├── lasot.cpython-37.pyc │ │ ├── lasot.cpython-38.pyc │ │ ├── lvis.cpython-38.pyc │ │ ├── msra10k.cpython-37.pyc │ │ ├── msra10k.cpython-38.pyc │ │ ├── sbd.cpython-37.pyc │ │ ├── sbd.cpython-38.pyc │ │ ├── synthetic_video.cpython-37.pyc │ │ ├── synthetic_video.cpython-38.pyc │ │ ├── synthetic_video_blend.cpython-37.pyc │ │ ├── synthetic_video_blend.cpython-38.pyc │ │ ├── tracking_net.cpython-37.pyc │ │ ├── tracking_net.cpython-38.pyc │ │ ├── vos_base.cpython-37.pyc │ │ ├── vos_base.cpython-38.pyc │ │ ├── youtubevos.cpython-37.pyc │ │ └── youtubevos.cpython-38.pyc │ ├── base_image_dataset.py │ ├── base_video_dataset.py │ ├── coco.py │ ├── coco_seq.py │ ├── davis.py │ ├── ecssd.py │ ├── got10k.py │ ├── hku_is.py │ ├── imagenetvid.py │ ├── lasot.py │ ├── lvis.py │ ├── msra10k.py │ ├── sbd.py │ ├── synthetic_video.py │ ├── synthetic_video_blend.py │ ├── tracking_net.py │ ├── vos_base.py │ └── youtubevos.py ├── models │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ └── __init__.cpython-38.pyc │ ├── backbone │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-37.pyc │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── base.cpython-37.pyc │ │ │ ├── base.cpython-38.pyc │ │ │ ├── convnext.cpython-37.pyc │ │ │ ├── convnext.cpython-38.pyc │ │ │ ├── lighttrack_backbone.cpython-37.pyc │ │ │ ├── lighttrack_backbone.cpython-38.pyc │ │ │ ├── resnet.cpython-37.pyc │ │ │ ├── resnet.cpython-38.pyc │ │ │ ├── resnet18_vggm.cpython-37.pyc │ │ │ ├── resnet18_vggm.cpython-38.pyc │ │ │ ├── transt_backbone.cpython-37.pyc │ │ │ └── transt_backbone.cpython-38.pyc │ │ ├── base.py │ │ ├── convnext.py │ │ ├── lighttrack │ │ │ ├── LightTrackM.pth │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-37.pyc │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── builder.cpython-37.pyc │ │ │ │ ├── builder.cpython-38.pyc │ │ │ │ ├── childnet.cpython-37.pyc │ │ │ │ ├── childnet.cpython-38.pyc │ │ │ │ ├── model.cpython-37.pyc │ │ │ │ ├── model.cpython-38.pyc │ │ │ │ ├── units.cpython-37.pyc │ │ │ │ ├── units.cpython-38.pyc │ │ │ │ ├── utils.cpython-37.pyc │ │ │ │ └── utils.cpython-38.pyc │ │ │ ├── builder.py │ │ │ ├── childnet.py │ │ │ ├── model.py │ │ │ ├── units.py │ │ │ └── utils.py │ │ ├── lighttrack_backbone.py │ │ ├── resnet.py │ │ ├── resnet18_vggm.py │ │ └── transt_backbone.py │ ├── loss │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-37.pyc │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── matcher.cpython-37.pyc │ │ │ └── matcher.cpython-38.pyc │ │ └── matcher.py │ ├── neck │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-37.pyc │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── featurefusion_network.cpython-37.pyc │ │ │ ├── featurefusion_network.cpython-38.pyc │ │ │ ├── position_encoding.cpython-37.pyc │ │ │ └── position_encoding.cpython-38.pyc │ │ ├── featurefusion_network.py │ │ └── position_encoding.py │ └── tracking │ │ ├── .ipynb_checkpoints │ │ ├── hcat-checkpoint.py │ │ └── transt-checkpoint.py │ │ ├── __init__.py │ │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── __init__.cpython-38.pyc │ │ ├── hcat.cpython-37.pyc │ │ └── transt.cpython-37.pyc │ │ ├── hcat.py │ │ └── transt.py ├── run_training.py ├── run_training_ddp.py ├── train_settings │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ └── __init__.cpython-38.pyc │ └── hcat │ │ ├── .ipynb_checkpoints │ │ ├── hcat-checkpoint.py │ │ └── hcat_ddp-checkpoint.py │ │ ├── __init__.py │ │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ └── hcat.cpython-37.pyc │ │ ├── hcat.py │ │ └── hcat_ddp.py └── trainers │ ├── __init__.py │ ├── __pycache__ │ ├── __init__.cpython-37.pyc │ ├── __init__.cpython-38.pyc │ ├── base_trainer.cpython-37.pyc │ ├── base_trainer.cpython-38.pyc │ ├── ltr_trainer.cpython-37.pyc │ └── ltr_trainer.cpython-38.pyc │ ├── base_trainer.py │ ├── hcat_trainer │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── hcat_base_trainer.cpython-37.pyc │ │ └── hcat_ltr_trainer.cpython-37.pyc │ ├── hcat_base_trainer.py │ └── hcat_ltr_trainer.py │ └── ltr_trainer.py ├── pysot_toolkit ├── .ipynb_checkpoints │ ├── bbox-checkpoint.py │ ├── eval-checkpoint.py │ ├── generate_json-checkpoint.py │ ├── pytorch2onnx-checkpoint.py │ ├── speed_cpu_stride16-checkpoint.py │ ├── speed_onnx_cpu_stride16-checkpoint.py │ ├── speed_onnx_stride16-checkpoint.py │ ├── speed_stride16-checkpoint.py │ ├── test-checkpoint.py │ └── test_onnx-checkpoint.py ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-36.pyc │ ├── __init__.cpython-37.pyc │ ├── __init__.cpython-38.pyc │ ├── bbox.cpython-36.pyc │ ├── bbox.cpython-37.pyc │ └── bbox.cpython-38.pyc ├── bbox.py ├── eval.py ├── generate_json.py ├── pytorch2onnx.py ├── speed_cpu_stride16.py ├── speed_onnx_cpu_stride16.py ├── speed_onnx_stride16.py ├── speed_stride16.py ├── test.py ├── test_onnx.py ├── toolkit │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-36.pyc │ │ ├── __init__.cpython-37.pyc │ │ └── __init__.cpython-38.pyc │ ├── datasets │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-36.pyc │ │ │ ├── __init__.cpython-37.pyc │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── dataset.cpython-36.pyc │ │ │ ├── dataset.cpython-37.pyc │ │ │ ├── dataset.cpython-38.pyc │ │ │ ├── got10k.cpython-36.pyc │ │ │ ├── got10k.cpython-37.pyc │ │ │ ├── got10k.cpython-38.pyc │ │ │ ├── lasot.cpython-36.pyc │ │ │ ├── lasot.cpython-37.pyc │ │ │ ├── lasot.cpython-38.pyc │ │ │ ├── nfs.cpython-36.pyc │ │ │ ├── nfs.cpython-37.pyc │ │ │ ├── nfs.cpython-38.pyc │ │ │ ├── otb.cpython-36.pyc │ │ │ ├── otb.cpython-37.pyc │ │ │ ├── otb.cpython-38.pyc │ │ │ ├── trackingnet.cpython-36.pyc │ │ │ ├── trackingnet.cpython-37.pyc │ │ │ ├── trackingnet.cpython-38.pyc │ │ │ ├── uav.cpython-36.pyc │ │ │ ├── uav.cpython-37.pyc │ │ │ ├── uav.cpython-38.pyc │ │ │ ├── video.cpython-36.pyc │ │ │ ├── video.cpython-37.pyc │ │ │ ├── video.cpython-38.pyc │ │ │ ├── vot.cpython-36.pyc │ │ │ ├── vot.cpython-37.pyc │ │ │ └── vot.cpython-38.pyc │ │ ├── dataset.py │ │ ├── got10k.py │ │ ├── lasot.py │ │ ├── nfs.py │ │ ├── otb.py │ │ ├── trackingnet.py │ │ ├── uav.py │ │ ├── video.py │ │ └── vot.py │ ├── evaluation │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-37.pyc │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── ar_benchmark.cpython-37.pyc │ │ │ ├── ar_benchmark.cpython-38.pyc │ │ │ ├── eao_benchmark.cpython-37.pyc │ │ │ ├── f1_benchmark.cpython-37.pyc │ │ │ └── ope_benchmark.cpython-37.pyc │ │ ├── ar_benchmark.py │ │ ├── eao_benchmark.py │ │ ├── f1_benchmark.py │ │ └── ope_benchmark.py │ ├── utils │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-37.pyc │ │ │ ├── __init__.cpython-38.pyc │ │ │ └── statistics.cpython-37.pyc │ │ ├── c_region.pxd │ │ ├── misc.py │ │ ├── region.c │ │ ├── region.cpython-37m-x86_64-linux-gnu.so │ │ ├── region.pyx │ │ ├── src │ │ │ ├── buffer.h │ │ │ ├── region.c │ │ │ └── region.h │ │ └── statistics.py │ └── visualization │ │ ├── __init__.py │ │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── draw_eao.cpython-37.pyc │ │ ├── draw_f1.cpython-37.pyc │ │ ├── draw_success_precision.cpython-37.pyc │ │ └── draw_utils.cpython-37.pyc │ │ ├── draw_eao.py │ │ ├── draw_f1.py │ │ ├── draw_success_precision.py │ │ └── draw_utils.py └── trackers │ ├── .ipynb_checkpoints │ ├── net_wrappers-checkpoint.py │ ├── tracker-checkpoint.py │ ├── tracker_onnx-checkpoint.py │ └── tracker_onnx_cpu-checkpoint.py │ ├── __init__.py │ ├── __pycache__ │ ├── __init__.cpython-36.pyc │ ├── __init__.cpython-37.pyc │ ├── __init__.cpython-38.pyc │ ├── net_wrappers.cpython-36.pyc │ ├── net_wrappers.cpython-37.pyc │ ├── net_wrappers.cpython-38.pyc │ ├── tracker.cpython-36.pyc │ ├── tracker.cpython-37.pyc │ ├── tracker.cpython-38.pyc │ ├── tracker_cpu.cpython-37.pyc │ ├── tracker_onnx.cpython-37.pyc │ └── tracker_onnx_cpu.cpython-37.pyc │ ├── net_wrappers.py │ ├── tracker.py │ ├── tracker_cpu.py │ ├── tracker_onnx.py │ └── tracker_onnx_cpu.py ├── pytracking ├── .figs │ ├── LaSOT.png │ ├── NFS.png │ ├── OTB-100.png │ ├── UAV123.png │ ├── atom_overview.png │ ├── dimp_overview.png │ └── visdom.png ├── README.md ├── VOT │ ├── __pycache__ │ │ └── vot.cpython-37.pyc │ ├── tracker_DiMP.m │ ├── trackers.ini │ └── vot.py ├── VOT2020 │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── transt_VOT2020_ap_1.cpython-37.pyc │ │ ├── transt_VOT2020_bbox.cpython-37.pyc │ │ └── transt_bbox_class.cpython-37.pyc │ ├── hcat_VOT2020.py │ ├── hcat_onnx_bbox_class.py │ ├── transt_VOT2020.py │ ├── transt_VOT2020_ap_1.py │ ├── transt_VOT2020_bbox.py │ ├── transt_bbox_class.py │ └── transt_seg_class.py ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-37.pyc │ ├── __init__.cpython-38.pyc │ ├── run_tracker.cpython-37.pyc │ ├── run_tracker.cpython-38.pyc │ ├── run_vot.cpython-37.pyc │ ├── run_webcam.cpython-37.pyc │ ├── run_webcam.cpython-38.pyc │ └── vot20_utils.cpython-37.pyc ├── analysis │ ├── __init__.py │ ├── evaluate_vos.py │ ├── extract_results.py │ ├── playback_results.py │ ├── plot_results.py │ └── vos_utils.py ├── evaluation │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── __init__.cpython-38.pyc │ │ ├── data.cpython-37.pyc │ │ ├── data.cpython-38.pyc │ │ ├── datasets.cpython-37.pyc │ │ ├── datasets.cpython-38.pyc │ │ ├── environment.cpython-37.pyc │ │ ├── environment.cpython-38.pyc │ │ ├── got10kdataset.cpython-37.pyc │ │ ├── lasotdataset.cpython-37.pyc │ │ ├── lasotdataset.cpython-38.pyc │ │ ├── local.cpython-37.pyc │ │ ├── local.cpython-38.pyc │ │ ├── multi_object_wrapper.cpython-37.pyc │ │ ├── multi_object_wrapper.cpython-38.pyc │ │ ├── running.cpython-37.pyc │ │ ├── running.cpython-38.pyc │ │ ├── tracker.cpython-37.pyc │ │ ├── tracker.cpython-38.pyc │ │ ├── vot.cpython-37.pyc │ │ ├── vot.cpython-38.pyc │ │ ├── vot2020.cpython-37.pyc │ │ ├── votdataset.cpython-37.pyc │ │ └── votdataset.cpython-38.pyc │ ├── data.py │ ├── datasets.py │ ├── environment.py │ ├── got10kdataset.py │ ├── lasotdataset.py │ ├── local.py │ ├── mobifacedataset.py │ ├── multi_object_wrapper.py │ ├── nfsdataset.py │ ├── otbdataset.py │ ├── running.py │ ├── tpldataset.py │ ├── tracker.py │ ├── trackingnetdataset.py │ ├── uavdataset.py │ ├── vot.py │ ├── vot2020.py │ └── votdataset.py ├── experiments │ ├── __init__.py │ └── myexperiments.py ├── features │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── __init__.cpython-38.pyc │ │ ├── augmentation.cpython-37.pyc │ │ ├── augmentation.cpython-38.pyc │ │ ├── net_wrappers.cpython-37.pyc │ │ ├── net_wrappers.cpython-38.pyc │ │ ├── preprocessing.cpython-37.pyc │ │ └── preprocessing.cpython-38.pyc │ ├── augmentation.py │ ├── color.py │ ├── deep.py │ ├── extractor.py │ ├── featurebase.py │ ├── net_wrappers.py │ ├── preprocessing.py │ └── util.py ├── fig │ ├── FSHCAT.png │ ├── framework.png │ ├── speed-success_lasot_wo_r50.pdf │ └── speed-success_lasot_wo_r50.png ├── libs │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── __init__.cpython-38.pyc │ │ ├── complex.cpython-37.pyc │ │ ├── complex.cpython-38.pyc │ │ ├── dcf.cpython-37.pyc │ │ ├── dcf.cpython-38.pyc │ │ ├── fourier.cpython-37.pyc │ │ ├── fourier.cpython-38.pyc │ │ ├── operation.cpython-37.pyc │ │ ├── operation.cpython-38.pyc │ │ ├── optimization.cpython-37.pyc │ │ ├── optimization.cpython-38.pyc │ │ ├── tensordict.cpython-37.pyc │ │ ├── tensordict.cpython-38.pyc │ │ ├── tensorlist.cpython-37.pyc │ │ └── tensorlist.cpython-38.pyc │ ├── complex.py │ ├── dcf.py │ ├── fourier.py │ ├── operation.py │ ├── optimization.py │ ├── tensordict.py │ └── tensorlist.py ├── notebooks │ └── analyze_results.ipynb ├── parameter │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ └── __init__.cpython-38.pyc │ └── transt │ │ ├── __init__.py │ │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── __init__.cpython-38.pyc │ │ └── transt50.cpython-37.pyc │ │ └── transt50.py ├── run_experiment.py ├── run_tracker.py ├── run_video.py ├── run_vot.py ├── run_webcam.py ├── tracker │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ └── __init__.cpython-38.pyc │ ├── base │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-37.pyc │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── basetracker.cpython-37.pyc │ │ │ └── basetracker.cpython-38.pyc │ │ └── basetracker.py │ └── transt │ │ ├── __init__.py │ │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── __init__.cpython-38.pyc │ │ ├── config.cpython-37.pyc │ │ ├── config.cpython-38.pyc │ │ └── transt.cpython-37.pyc │ │ ├── config.py │ │ └── transt.py ├── util_scripts │ ├── __init__.py │ ├── download_results.py │ ├── pack_got10k_results.py │ └── pack_trackingnet_results.py ├── utils │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── __init__.cpython-38.pyc │ │ ├── convert_vot_anno_to_rect.cpython-37.pyc │ │ ├── convert_vot_anno_to_rect.cpython-38.pyc │ │ ├── load_text.cpython-37.pyc │ │ ├── load_text.cpython-38.pyc │ │ ├── loading.cpython-37.pyc │ │ ├── loading.cpython-38.pyc │ │ ├── params.cpython-37.pyc │ │ ├── params.cpython-38.pyc │ │ ├── plotting.cpython-37.pyc │ │ ├── plotting.cpython-38.pyc │ │ ├── visdom.cpython-37.pyc │ │ └── visdom.cpython-38.pyc │ ├── convert_vot_anno_to_rect.py │ ├── load_text.py │ ├── loading.py │ ├── params.py │ ├── plotting.py │ └── visdom.py └── vot20_utils.py └── util ├── .ipynb_checkpoints ├── box_ops-checkpoint.py └── misc-checkpoint.py ├── __init__.py ├── __pycache__ ├── __init__.cpython-37.pyc ├── __init__.cpython-38.pyc ├── box_ops.cpython-37.pyc ├── box_ops.cpython-38.pyc ├── misc.cpython-37.pyc └── misc.cpython-38.pyc ├── box_ops.py └── misc.py /.idea/HCAT.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/vcs.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | -------------------------------------------------------------------------------- /AGX配置环境.md: -------------------------------------------------------------------------------- 1 | # Nvidia AGX系列搭建深度学习环境 2 | 3 | 可以参考[agx深度学习环境搭建](https://blog.csdn.net/qq_41426807/article/details/124705416) 4 | 5 | ## 一、查看自己的AGX内置环境 6 | 7 | ``` 8 | uname -a #查看系统版本 9 | jetson_release -v#查看jetson版本 10 | nvcc -V #查看cuda版本 11 | cat /usr/include/cudnn.h |grep CUDNN_MAJOR -A 2 #查看cudnn版本 12 | ``` 13 | 14 | ## 二、下载自己对应版本的miniforge(anaconda的替代品)参考[下载安装miniforge](https://blog.csdn.net/weixin_46025237/article/details/121150859?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522166749137016782428652881%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fall.%2522%257D&request_id=166749137016782428652881&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~first_rank_ecpm_v1~rank_v31_ecpm-2-121150859-null-null.142%5Ev63%5Econtrol,201%5Ev3%5Econtrol_2,213%5Ev1%5Econtrol&utm_term=nvidia%20%E5%AE%89%E8%A3%85miniforge&spm=1018.2226.3001.4187) 15 | 16 | 下载正确之后就可以创建虚拟环境 17 | 18 | ## 三、安装pytorch,torchvision 19 | 20 | 根据[pytorch for jetson](https://forums.developer.nvidia.com/t/pytorch-for-jetson/72048) 安装自己对应版本的pytorch,并按照官网提供的安装方法进行安装 21 | 22 | torchvision同样按照[pytorch for jetson](https://forums.developer.nvidia.com/t/pytorch-for-jetson/72048) 官网提供的方法进行安装，(版本对应pytorch!!!!!) 23 | 24 | # 四、验证pytorch,torchvision是否安装成功 25 | 26 | ```python 27 | import torch 28 | print(torch.__version__) 29 | print('CUDA available: ' + str(torch.cuda.is_available())) 30 | import torchvision 31 | print(torchvision.__version__) 32 | ``` 33 | 34 | 验证成功之后就可以接着安装其他所需要的包 -------------------------------------------------------------------------------- /got10k_toolkit/LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /got10k_toolkit/MANIFEST.in: -------------------------------------------------------------------------------- 1 | include got10k/datasets/uav123.json 2 | include got10k/datasets/lasot.json 3 | -------------------------------------------------------------------------------- /got10k_toolkit/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/got10k_toolkit/__init__.py -------------------------------------------------------------------------------- /got10k_toolkit/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/got10k_toolkit/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /got10k_toolkit/requirements.txt: -------------------------------------------------------------------------------- 1 | cycler==0.10.0 2 | kiwisolver==1.0.1 3 | matplotlib==3.0.2 4 | numpy==1.15.4 5 | Pillow==6.2.0 6 | pyparsing==2.3.0 7 | python-dateutil==2.7.5 8 | Shapely==1.6.4.post2 9 | six==1.11.0 10 | -------------------------------------------------------------------------------- /got10k_toolkit/resources/.ipynb_checkpoints/sample_batch_run-checkpoint.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/got10k_toolkit/resources/.ipynb_checkpoints/sample_batch_run-checkpoint.jpg -------------------------------------------------------------------------------- /got10k_toolkit/resources/sample_batch_run.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/got10k_toolkit/resources/sample_batch_run.jpg -------------------------------------------------------------------------------- /got10k_toolkit/setup.cfg: -------------------------------------------------------------------------------- 1 | [metadata] 2 | description-file = README.md 3 | -------------------------------------------------------------------------------- /got10k_toolkit/setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup, find_packages 2 | 3 | 4 | setup(name='toolkit', 5 | version='0.1.3', 6 | description='GOT-10k benchmark official API', 7 | author='Lianghua Huang', 8 | author_email='lianghua.huang.cs@gmail.com', 9 | url='https://github.com/got-10k/toolkit', 10 | license='MIT', 11 | install_requires=[ 12 | 'numpy', 'matplotlib', 'Pillow', 'Shapely', 'fire', 'wget'], 13 | packages=find_packages(), 14 | include_package_data=True, 15 | keywords=[ 16 | 'GOT-10k', 17 | 'Generic Object Tracking', 18 | 'Benchmark',]) 19 | -------------------------------------------------------------------------------- /got10k_toolkit/tests/test_trackers.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import unittest 4 | import os 5 | import random 6 | 7 | from got10k.trackers import IdentityTracker 8 | from got10k.datasets import GOT10k 9 | 10 | 11 | class TestTrackers(unittest.TestCase): 12 | 13 | def setUp(self): 14 | self.data_dir = 'data' 15 | self.tracker = IdentityTracker() 16 | 17 | def tearDown(self): 18 | pass 19 | 20 | def test_identity_tracker(self): 21 | # setup dataset 22 | root_dir = os.path.join(self.data_dir, 'GOT-10k') 23 | dataset = GOT10k(root_dir, subset='val') 24 | # run experiment 25 | img_files, anno = random.choice(dataset) 26 | boxes, times = self.tracker.track( 27 | img_files, anno[0], visualize=True) 28 | self.assertEqual(boxes.shape, anno.shape) 29 | self.assertEqual(len(times), len(anno)) 30 | 31 | 32 | if __name__ == '__main__': 33 | unittest.main() 34 | -------------------------------------------------------------------------------- /got10k_toolkit/tests/test_utils.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import unittest 4 | import numpy as np 5 | 6 | from got10k.utils.metrics import rect_iou, poly_iou 7 | 8 | 9 | class TestUtils(unittest.TestCase): 10 | 11 | def setUp(self): 12 | pass 13 | 14 | def tearDown(self): 15 | pass 16 | 17 | def test_iou(self): 18 | rects1 = np.random.rand(1000, 4) * 100 19 | rects2 = np.random.rand(1000, 4) * 100 20 | bound = (50, 100) 21 | ious1 = rect_iou(rects1, rects2, bound=bound) 22 | ious2 = poly_iou(rects1, rects2, bound=bound) 23 | self.assertTrue((ious1 - ious2).max() < 1e-14) 24 | 25 | polys1 = self._rect2corner(rects1) 26 | polys2 = self._rect2corner(rects2) 27 | ious3 = poly_iou(polys1, polys2, bound=bound) 28 | self.assertTrue((ious1 - ious3).max() < 1e-14) 29 | 30 | def _rect2corner(self, rects): 31 | x1, y1, w, h = rects.T 32 | x2, y2 = x1 + w, y1 + h 33 | corners = np.array([x1, y1, x1, y2, x2, y2, x2, y1]).T 34 | 35 | return corners 36 | 37 | 38 | if __name__ == '__main__': 39 | unittest.main() 40 | -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/PLOT.py: -------------------------------------------------------------------------------- 1 | from got10k.experiments import ExperimentUAV123 2 | 3 | report_files = ['reports/UAV123/N4-676-sub/performance.json', 'reports/UAV123/ATOM/performance.json'] 4 | tracker_names = ['N4-676-sub', 'ATOM'] 5 | # report_files = ['reports/UAV123/ATOM/performance.json'] 6 | # tracker_names = ['ATOM'] 7 | 8 | # setup experiment and plot curves 9 | experiment = ExperimentUAV123( 10 | root_dir='/home/cx/cx2/Downloads/UAV123/UAV123_fix/Dataset_UAV123/UAV123', # GOT-10k's root directory 11 | result_dir='results', # where to store tracking results 12 | report_dir='reports' # where to store evaluation reports 13 | ) 14 | experiment.plot_curves(tracker_names) -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/got10k_toolkit/toolkit/__init__.py -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/got10k_toolkit/toolkit/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/datasets/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | from .got10k import GOT10k 4 | from .otb import OTB 5 | from .vot import VOT 6 | from .dtb70 import DTB70 7 | from .tcolor128 import TColor128 8 | from .uav123 import UAV123 9 | from .nfs import NfS 10 | from .lasot import LaSOT 11 | from .trackingnet import TrackingNet 12 | from .vid import ImageNetVID 13 | 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got10k_toolkit.toolkit.trackers.net_wrappers import NetWithBackbone 4 | import os 5 | os.environ["CUDA_VISIBLE_DEVICES"]= "0,1" 6 | 7 | #Specify the path 8 | net_path = '/home/cx/TransT/models/transt.pth' 9 | dataset_root= '/home/cx/detr-tracking-v6/pytracking/toolkit/got10k/datasets' 10 | 11 | #TransT 12 | net = NetWithBackbone(net_path=net_path, use_gpu=True) 13 | tracker = IdentityTracker(name='transt', net=net, window_penalty=0.49, exemplar_size=128, instance_size=256) 14 | 15 | #Evaluation 16 | experiment = ExperimentGOT10k( 17 | root_dir=dataset_root, # GOT-10k's root directory 18 | subset='test', # 'train' | 'val' | 'test' 19 | result_dir='results', # where to store tracking results 20 | report_dir='reports' # where to store evaluation reports 21 | ) 22 | experiment.report([tracker.name]) -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/evaluate_got_para.py: -------------------------------------------------------------------------------- 1 | from got10k_toolkit.toolkit.experiments import ExperimentGOT10k 2 | from got10k_toolkit.toolkit.trackers.identity_tracker import IdentityTracker 3 | from got10k_toolkit.toolkit.trackers.net_wrappers import NetWithBackbone 4 | import os 5 | os.environ["CUDA_VISIBLE_DEVICES"]= "0,1" 6 | 7 | #Specify the path 8 | 9 | dataset_root= '/home/cx/detr-tracking-v6/pytracking/toolkit/got10k/datasets' 10 | 11 | #Evaluation 12 | experiment = ExperimentGOT10k( 13 | root_dir=dataset_root, # GOT-10k's root directory 14 | subset='test', # 'train' | 'val' | 'test' 15 | result_dir='results', # where to store tracking results 16 | report_dir='reports' # where to store evaluation reports 17 | ) 18 | experiment.report([tracker.name]) -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/evaluate_lasot.py: -------------------------------------------------------------------------------- 1 | from got10k_toolkit.toolkit.experiments import ExperimentLaSOT 2 | import os 3 | os.environ["CUDA_VISIBLE_DEVICES"]= "0,1" 4 | 5 | experiment = ExperimentLaSOT( 6 | root_dir='/home/cx/cx3/LaSOTBenchmark', # LaSOT's root directory 7 | subset='test', # 'train' | 'val' | 'test' 8 | result_dir='results', # where to store tracking results 9 | report_dir='reports' # where to store evaluation reports 10 | ) 11 | experiment.report(['transt_N4']) -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/evaluate_nfs.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | env_path = os.path.join(os.path.dirname(__file__), '../..') 4 | print(env_path) 5 | if env_path not in sys.path: 6 | sys.path.append(env_path) 7 | from got10k_toolkit.toolkit.experiments import ExperimentNfS 8 | from got10k_toolkit.toolkit.trackers.identity_tracker import IdentityTracker 9 | from got10k_toolkit.toolkit.trackers.net_wrappers import NetWithBackbone 10 | import os 11 | os.environ["CUDA_VISIBLE_DEVICES"]= "0,1" 12 | 13 | # net = NetWithBackbone(net_path='/home/cx/TransT/transt.pth', use_gpu=True) 14 | # tracker = IdentityTracker(name='transt', net=net, window_penalty=0.49, exemplar_size=128, instance_size=256) 15 | experiment = ExperimentNfS( 16 | root_dir='/home/cx/cx2/Downloads/nfs', # GOT-10k's root directory 17 | fps=30, 18 | result_dir='/home/cx/cx1/light_transt/TransT_fix_nested_v5_b/got10k_toolkit/toolkit/results', # where to store tracking results 19 | report_dir='/home/cx/cx1/light_transt/TransT_fix_nested_v5_b/got10k_toolkit/toolkit/reports' # where to store evaluation reports 20 | ) 21 | # experiment.report([tracker.name]) 22 | # experiment.report(['TransT-N4', 'TransT-N2', 'PrDiMP', 'DiMP', 'ATOM', 'ECO', 'UPDT', 'CCOT', 'MDNet']) 23 | experiment.report(['lighttranst']) -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/evaluate_otb.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | env_path = os.path.join(os.path.dirname(__file__), '../..') 4 | print(env_path) 5 | if env_path not in sys.path: 6 | sys.path.append(env_path) 7 | from got10k_toolkit.toolkit.experiments import ExperimentOTB 8 | from got10k_toolkit.toolkit.trackers.identity_tracker import IdentityTracker 9 | from got10k_toolkit.toolkit.trackers.net_wrappers import NetWithBackbone 10 | import os 11 | os.environ["CUDA_VISIBLE_DEVICES"]= "0,1" 12 | # net = NetWithBackbone(net_path='/home/cx/TransT/transt_.pth', use_gpu=True) 13 | # tracker = IdentityTracker(name='transt', net=net, window_penalty=0.49, exemplar_size=128, instance_size=256) 14 | experiment = ExperimentOTB( 15 | root_dir='/home/cx/cx2/OTB100', # GOT-10k's root directory 16 | result_dir='/home/cx/cx1/light_transt/TransT_fix_nested_v5_b/got10k_toolkit/toolkit/results', # where to store tracking results 17 | report_dir='/home/cx/cx1/light_transt/TransT_fix_nested_v5_b/got10k_toolkit/toolkit/reports' # where to store evaluation reports 18 | ) 19 | experiment.report(['lighttranst']) 20 | # experiment.report(['TransT-N4', 'TransT-N2', 'PrDiMP', 'DiMP', 'SiamRPN++', 'ATOM', 'ECO', 'UPDT', 'CCOT', 'DaSiamRPN', 'MDNet']) -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/evaluate_tc128.py: -------------------------------------------------------------------------------- 1 | from got10k_toolkit.toolkit.experiments import ExperimentTColor128 2 | from got10k_toolkit.toolkit.trackers.identity_tracker import IdentityTracker 3 | from got10k_toolkit.toolkit.trackers.net_wrappers import NetWithBackbone 4 | import os 5 | os.environ["CUDA_VISIBLE_DEVICES"]= "0,1" 6 | 7 | net = NetWithBackbone(net_path='/home/cx/TransT/transt.pth', use_gpu=True) 8 | tracker = IdentityTracker(name='transt', net=net, window_penalty=0.49, exemplar_size=128, instance_size=256) 9 | experiment = ExperimentTColor128( 10 | root_dir='/home/cx/cx2/Temple-color-128', # GOT-10k's root directory 11 | result_dir='results', # where to store tracking results 12 | report_dir='reports' # where to store evaluation reports 13 | ) 14 | experiment.report([tracker.name]) -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/evaluate_trackingnet.py: -------------------------------------------------------------------------------- 1 | from got10k_toolkit.toolkit.experiments import ExperimentTrackingNet 2 | from got10k_toolkit.toolkit.trackers.identity_tracker import IdentityTracker 3 | from got10k_toolkit.toolkit.trackers.net_wrappers import NetWithBackbone 4 | import os 5 | os.environ["CUDA_VISIBLE_DEVICES"]= "0,1" 6 | 7 | net = NetWithBackbone(net_path='/home/cx/TransT/transt.pth', use_gpu=True) 8 | tracker = IdentityTracker(name='transt', net=net, window_penalty=0.49, exemplar_size=128, instance_size=256) 9 | experiment = ExperimentTrackingNet( 10 | root_dir='/home/cx/cx2/TrackingNet', # GOT-10k's root directory 11 | subset='test', # 'train' | 'val' | 'test' 12 | result_dir='results', # where to store tracking results 13 | report_dir='reports' # where to store evaluation reports 14 | ) 15 | experiment.report([tracker.name]) -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/evaluate_uav123.py: -------------------------------------------------------------------------------- 1 | from got10k_toolkit.toolkit.experiments import ExperimentUAV123 2 | from got10k_toolkit.toolkit.trackers.identity_tracker import IdentityTracker 3 | from got10k_toolkit.toolkit.trackers.net_wrappers import NetWithBackbone 4 | import os 5 | 6 | os.environ["CUDA_VISIBLE_DEVICES"]= "0,1" 7 | 8 | # net = NetWithBackbone(net_path='/home/cx/TransT/transt.pth', use_gpu=True) 9 | # tracker = IdentityTracker(name='transt', net=net, window_penalty=0.49, exemplar_size=128, instance_size=256) 10 | experiment = ExperimentUAV123( 11 | root_dir='/home/cx/cx2/Downloads/UAV123/UAV123_fix/Dataset_UAV123/UAV123', # GOT-10k's root directory 12 | result_dir='/home/cx/cx1/light_transt/TransT_fix_nested_v5_b/got10k_toolkit/toolkit/results', # where to store tracking results 13 | report_dir='/home/cx/cx1/light_transt/TransT_fix_nested_v5_b/got10k_toolkit/toolkit/reports' # where to store evaluation reports 14 | ) 15 | # experiment.report(['TransT-N4', 'TransT-N2', 'PrDiMP', 'DiMP', 'SiamRPN++', 'ATOM', 'ECO', 'UPDT', 'CCOT', 'DaSiamRPN']) 16 | experiment.report(['lighttranst']) -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/experiments/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | from .got10k import ExperimentGOT10k 4 | from .otb import ExperimentOTB 5 | from .vot import ExperimentVOT 6 | from .dtb70 import ExperimentDTB70 7 | from .uav123 import ExperimentUAV123 8 | from .nfs import ExperimentNfS 9 | from .tcolor128 import ExperimentTColor128 10 | 11 | from .lasot import ExperimentLaSOT 12 | from .trackingnet import ExperimentTrackingNet 13 | -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/experiments/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/got10k_toolkit/toolkit/experiments/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/got10k_toolkit/toolkit/experiments/__pycache__/uav123.cpython-37.pyc -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/experiments/__pycache__/vot.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/got10k_toolkit/toolkit/experiments/__pycache__/vot.cpython-37.pyc -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/experiments/dtb70.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import os 4 | 5 | from .otb import ExperimentOTB 6 | from ..datasets import DTB70 7 | 8 | 9 | class ExperimentDTB70(ExperimentOTB): 10 | r"""Experiment pipeline and evaluation got10k_toolkit for DTB70 dataset. 11 | 12 | Args: 13 | root_dir (string): Root directory of DTB70 dataset. 14 | result_dir (string, optional): Directory for storing tracking 15 | results. Default is ``./results``. 16 | report_dir (string, optional): Directory for storing performance 17 | evaluation results. Default is ``./reports``. 18 | """ 19 | def __init__(self, root_dir, 20 | result_dir='results', report_dir='reports'): 21 | self.dataset = DTB70(root_dir) 22 | self.result_dir = os.path.join(result_dir, 'DTB70') 23 | self.report_dir = os.path.join(report_dir, 'DTB70') 24 | # as nbins_iou increases, the success score 25 | # converges to the average overlap (AO) 26 | self.nbins_iou = 21 27 | self.nbins_ce = 51 28 | -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/experiments/nfs.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import os 4 | 5 | from .otb import ExperimentOTB 6 | from ..datasets import NfS 7 | 8 | 9 | class ExperimentNfS(ExperimentOTB): 10 | r"""Experiment pipeline and evaluation got10k_toolkit for NfS dataset. 11 | 12 | Args: 13 | root_dir (string): Root directory of NfS dataset. 14 | result_dir (string, optional): Directory for storing tracking 15 | results. Default is ``./results``. 16 | report_dir (string, optional): Directory for storing performance 17 | evaluation results. Default is ``./reports``. 18 | """ 19 | def __init__(self, root_dir, fps=240, 20 | result_dir='results', report_dir='reports'): 21 | self.dataset = NfS(root_dir, fps) 22 | self.result_dir = os.path.join(result_dir, 'NfS/%d' % fps) 23 | self.report_dir = os.path.join(report_dir, 'NfS/%d' % fps) 24 | # as nbins_iou increases, the success score 25 | # converges to the average overlap (AO) 26 | self.nbins_iou = 21 27 | self.nbins_ce = 51 28 | -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/experiments/tcolor128.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import os 4 | 5 | from .otb import ExperimentOTB 6 | from ..datasets import TColor128 7 | 8 | 9 | class ExperimentTColor128(ExperimentOTB): 10 | r"""Experiment pipeline and evaluation got10k_toolkit for TColor128 dataset. 11 | 12 | Args: 13 | root_dir (string): Root directory of TColor128 dataset. 14 | result_dir (string, optional): Directory for storing tracking 15 | results. Default is ``./results``. 16 | report_dir (string, optional): Directory for storing performance 17 | evaluation results. Default is ``./reports``. 18 | """ 19 | def __init__(self, root_dir, 20 | result_dir='results', report_dir='reports'): 21 | self.dataset = TColor128(root_dir) 22 | self.result_dir = os.path.join(result_dir, 'TColor128') 23 | self.report_dir = os.path.join(report_dir, 'TColor128') 24 | # as nbins_iou increases, the success score 25 | # converges to the average overlap (AO) 26 | self.nbins_iou = 21 27 | self.nbins_ce = 51 28 | -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/experiments/uav123.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import os 4 | import numpy as np 5 | 6 | from .otb import ExperimentOTB 7 | from ..datasets import UAV123 8 | from ..utils.metrics import rect_iou, center_error 9 | 10 | 11 | class ExperimentUAV123(ExperimentOTB): 12 | r"""Experiment pipeline and evaluation got10k_toolkit for UAV123 dataset. 13 | 14 | Args: 15 | root_dir (string): Root directory of UAV123 dataset. 16 | result_dir (string, optional): Directory for storing tracking 17 | results. Default is ``./results``. 18 | report_dir (string, optional): Directory for storing performance 19 | evaluation results. Default is ``./reports``. 20 | """ 21 | def __init__(self, root_dir, version='UAV123', 22 | result_dir='results', report_dir='reports'): 23 | assert version.upper() in ['UAV123', 'UAV20L'] 24 | self.dataset = UAV123(root_dir, version) 25 | self.result_dir = os.path.join(result_dir, version.upper()) 26 | self.report_dir = os.path.join(report_dir, version.upper()) 27 | # as nbins_iou increases, the success score 28 | # converges to the average overlap (AO) 29 | self.nbins_iou = 21 30 | self.nbins_ce = 51 31 | 32 | def _calc_metrics(self, boxes, anno): 33 | valid = ~np.any(np.isnan(anno), axis=1) 34 | if len(valid) == 0: 35 | print('Warning: no valid annotations') 36 | return None, None 37 | else: 38 | ious = rect_iou(boxes[valid, :], anno[valid, :]) 39 | center_errors = center_error( 40 | boxes[valid, :], anno[valid, :]) 41 | return ious, center_errors 42 | -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/plot_got.py: -------------------------------------------------------------------------------- 1 | from got10k_toolkit.toolkit.experiments import ExperimentGOT10k 2 | 3 | report_files = ['/home/cx/transt-tracking-v6/pytracking/FusionNet_report_2020_11_09_07_09_11.json'] 4 | tracker_names = ['TRTR'] 5 | 6 | # setup experiment and plot curves 7 | experiment = ExperimentGOT10k('/home/cx/detr-tracking-v6/pytracking/toolkit/got10k/datasets', subset='test') 8 | experiment.plot_curves(report_files, tracker_names) -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/test_got.py: -------------------------------------------------------------------------------- 1 | from got10k_toolkit.toolkit.experiments import ExperimentGOT10k 2 | from got10k_toolkit.toolkit.trackers.identity_tracker import IdentityTracker 3 | from got10k_toolkit.toolkit.trackers.net_wrappers import NetWithBackbone 4 | import os 5 | os.environ["CUDA_VISIBLE_DEVICES"]= "0,1" 6 | 7 | #Specify the path 8 | net_path = '/home/cx/TransT/models/transt.pth' 9 | dataset_root= '/home/cx/detr-tracking-v6/pytracking/toolkit/got10k/datasets' 10 | 11 | #TransT 12 | net = NetWithBackbone(net_path=net_path, use_gpu=True) 13 | tracker = IdentityTracker(name='transt', net=net, window_penalty=0.49, exemplar_size=128, instance_size=256) 14 | 15 | #Evaluation 16 | experiment = ExperimentGOT10k( 17 | root_dir=dataset_root, # GOT-10k's root directory 18 | subset='test', # 'train' | 'val' | 'test' 19 | result_dir='results', # where to store tracking results 20 | report_dir='reports' # where to store evaluation reports 21 | ) 22 | experiment.run(tracker, visualize=True) -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/test_lasot.py: -------------------------------------------------------------------------------- 1 | from got10k_toolkit.toolkit.experiments import ExperimentLaSOT 2 | from got10k_toolkit.toolkit.trackers.identity_tracker import IdentityTracker 3 | from got10k_toolkit.toolkit.trackers.net_wrappers import NetWithBackbone 4 | import os 5 | os.environ["CUDA_VISIBLE_DEVICES"]= "0" 6 | 7 | net = NetWithBackbone(net_path='/home/cx/TransT/models/transt.pth', use_gpu=True) 8 | tracker = IdentityTracker(name='transt_test', net=net, window_penalty=0.49, exemplar_size=128, instance_size=256) 9 | experiment = ExperimentLaSOT( 10 | root_dir='/home/cx/cx3/LaSOTBenchmark', # LaSOT's root directory 11 | subset='test', # 'train' | 'val' | 'test' 12 | result_dir='results', # where to store tracking results 13 | report_dir='reports' # where to store evaluation reports 14 | ) 15 | experiment.run(tracker, visualize=True) -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/test_lasot_2.py: -------------------------------------------------------------------------------- 1 | from got10k_toolkit.toolkit.experiments import ExperimentLaSOT 2 | from got10k_toolkit.toolkit.trackers.identity_tracker import IdentityTracker 3 | from got10k_toolkit.toolkit.trackers.net_wrappers import NetWithBackbone 4 | import os 5 | os.environ["CUDA_VISIBLE_DEVICES"]= "1" 6 | 7 | net = NetWithBackbone(net_path='/home/cx/TransT/models/transt.pth', use_gpu=True) 8 | tracker = IdentityTracker(name='transt_66', net=net, window_penalty=0.66, exemplar_size=128, instance_size=256) 9 | experiment = ExperimentLaSOT( 10 | root_dir='/home/cx/cx3/LaSOTBenchmark', # LaSOT's root directory 11 | subset='test', # 'train' | 'val' | 'test' 12 | result_dir='results', # where to store tracking results 13 | report_dir='reports' # where to store evaluation reports 14 | ) 15 | experiment.run(tracker, visualize=False) -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/test_nfs.py: -------------------------------------------------------------------------------- 1 | from got10k_toolkit.toolkit.experiments import ExperimentNfS 2 | from got10k_toolkit.toolkit.trackers.identity_tracker import IdentityTracker 3 | from got10k_toolkit.toolkit.trackers.net_wrappers import NetWithBackbone 4 | import os 5 | os.environ["CUDA_VISIBLE_DEVICES"]= "0,1" 6 | 7 | net = NetWithBackbone(net_path='/home/cx/TransT/transt.pth', use_gpu=True) 8 | tracker = IdentityTracker(name='transt', net=net, window_penalty=0.49, exemplar_size=128, instance_size=256) 9 | experiment = ExperimentNfS( 10 | root_dir='/home/cx/cx2/Downloads/nfs-a', # Nfs's root directory 11 | fps=30, 12 | result_dir='results', # where to store tracking results 13 | report_dir='reports' # where to store evaluation reports 14 | ) 15 | experiment.run(tracker, visualize=False) -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/test_otb.py: -------------------------------------------------------------------------------- 1 | from got10k_toolkit.toolkit.experiments import ExperimentOTB 2 | from got10k_toolkit.toolkit.trackers.identity_tracker import IdentityTracker 3 | from got10k_toolkit.toolkit.trackers.net_wrappers import NetWithBackbone 4 | import os 5 | os.environ["CUDA_VISIBLE_DEVICES"]= "0,1" 6 | 7 | net = NetWithBackbone(net_path='/home/cx/TransT/models/transt_N2.pth', use_gpu=True) 8 | tracker = IdentityTracker(name='transt_N2', net=net, window_penalty=0.49, exemplar_size=128, instance_size=256) 9 | experiment = ExperimentOTB( 10 | root_dir='/home/cx/cx2/OTB100', # OTB100's root directory 11 | result_dir='results', # where to store tracking results 12 | report_dir='reports' # where to store evaluation reports 13 | ) 14 | experiment.run(tracker, visualize=False) -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/test_tc128.py: -------------------------------------------------------------------------------- 1 | from got10k_toolkit.toolkit.experiments import ExperimentTColor128 2 | from got10k_toolkit.toolkit.trackers.identity_tracker import IdentityTracker 3 | from got10k_toolkit.toolkit.trackers.net_wrappers import NetWithBackbone 4 | import os 5 | os.environ["CUDA_VISIBLE_DEVICES"]= "0,1" 6 | 7 | net = NetWithBackbone(net_path='/home/cx/TransT/transt.pth', use_gpu=True) 8 | tracker = IdentityTracker(name='transt', net=net, window_penalty=0.49, exemplar_size=128, instance_size=256) 9 | experiment = ExperimentTColor128( 10 | root_dir='/home/cx/cx2/Temple-color-128', # tc-128's root directory 11 | result_dir='results', # where to store tracking results 12 | report_dir='reports' # where to store evaluation reports 13 | ) 14 | experiment.run(tracker, visualize=False) -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/test_trackingnet.py: -------------------------------------------------------------------------------- 1 | from got10k_toolkit.toolkit.experiments import ExperimentTrackingNet 2 | from got10k_toolkit.toolkit.trackers.identity_tracker import IdentityTracker 3 | from got10k_toolkit.toolkit.trackers.net_wrappers import NetWithBackbone 4 | import os 5 | os.environ["CUDA_VISIBLE_DEVICES"]= "0" 6 | 7 | net = NetWithBackbone(net_path='/home/cx/TransT/transt.pth', use_gpu=True) 8 | tracker = IdentityTracker(name='transt_np', net=net, window_penalty=0, exemplar_size=128, instance_size=256) 9 | experiment = ExperimentTrackingNet( 10 | root_dir='/home/cx/cx2/TrackingNet', # TrackingNet's root directory 11 | subset='test', # 'train' | 'val' | 'test' 12 | result_dir='results', # where to store tracking results 13 | report_dir='reports' # where to store evaluation reports 14 | ) 15 | experiment.run(tracker, visualize=False) -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/test_trackingnet_2.py: -------------------------------------------------------------------------------- 1 | from got10k_toolkit.toolkit.experiments import ExperimentTrackingNet 2 | from got10k_toolkit.toolkit.trackers.identity_tracker import IdentityTracker 3 | from got10k_toolkit.toolkit.trackers.net_wrappers import NetWithBackbone 4 | import os 5 | os.environ["CUDA_VISIBLE_DEVICES"]= "0" 6 | 7 | net = NetWithBackbone(net_path='/home/cx/TransT/models/transt.pth', use_gpu=True) 8 | tracker = IdentityTracker(name='transt', net=net, window_penalty=0.49, exemplar_size=128, instance_size=256) 9 | experiment = ExperimentTrackingNet( 10 | root_dir='/home/cx/cx2/TrackingNet', # TrackingNet's root directory 11 | subset='test', # 'train' | 'val' | 'test' 12 | result_dir='results', # where to store tracking results 13 | report_dir='reports' # where to store evaluation reports 14 | ) 15 | experiment.run(tracker, visualize=False) -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/test_uav123.py: -------------------------------------------------------------------------------- 1 | from got10k_toolkit.toolkit.experiments import ExperimentUAV123 2 | from got10k_toolkit.toolkit.trackers.identity_tracker import IdentityTracker 3 | from got10k_toolkit.toolkit.trackers.net_wrappers import NetWithBackbone 4 | import os 5 | os.environ["CUDA_VISIBLE_DEVICES"]= "0,1" 6 | 7 | net = NetWithBackbone(net_path='/home/cx/TransT/transt.pth', use_gpu=True) 8 | tracker = IdentityTracker(name='transt', net=net, window_penalty=0.49, exemplar_size=128, instance_size=256) 9 | experiment = ExperimentUAV123( 10 | root_dir='/home/cx/cx2/Downloads/UAV123/UAV123_fix/Dataset_UAV123/UAV123', # UAV123's root directory 11 | result_dir='results', # where to store tracking results 12 | report_dir='reports' # where to store evaluation reports 13 | ) 14 | experiment.run(tracker, visualize=False) -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/trackers/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import numpy as np 4 | import time 5 | from PIL import Image 6 | import cv2 7 | 8 | from ..utils.viz import show_frame 9 | 10 | 11 | class Tracker(object): 12 | 13 | def __init__(self, name, net, is_deterministic=False): 14 | self.name = name 15 | self.is_deterministic = is_deterministic 16 | self.net = net 17 | 18 | def init(self, image, box): 19 | raise NotImplementedError() 20 | 21 | def update(self, image): 22 | raise NotImplementedError() 23 | 24 | def track(self, img_files, box, visualize=False): 25 | frame_num = len(img_files) 26 | boxes = np.zeros((frame_num, 4)) 27 | boxes[0] = box 28 | times = np.zeros(frame_num) 29 | 30 | for f, img_file in enumerate(img_files): 31 | image = Image.open(img_file) 32 | if not image.mode == 'RGB': 33 | image = image.convert('RGB') 34 | im = np.array(image) 35 | start_time = time.time() 36 | if f == 0: 37 | self.init(im, box) 38 | else: 39 | boxes[f, :] = self.update(im) 40 | times[f] = time.time() - start_time 41 | 42 | if visualize: 43 | show_frame(image, boxes[f, :]) 44 | 45 | return boxes, times 46 | 47 | 48 | from .identity_tracker import IdentityTracker 49 | -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/trackers/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/got10k_toolkit/toolkit/trackers/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/trackers/__pycache__/identity_tracker.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/got10k_toolkit/toolkit/trackers/__pycache__/identity_tracker.cpython-37.pyc -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/trackers/__pycache__/net_wrappers.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/got10k_toolkit/toolkit/trackers/__pycache__/net_wrappers.cpython-37.pyc -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/trackers/net_wrappers.py: -------------------------------------------------------------------------------- 1 | import ltr.admin.loading as ltr_loading 2 | 3 | class NetWrapper: 4 | """Used for wrapping networks in pytracking. 5 | Network modules and functions can be accessed directly as if they were members of this class.""" 6 | _rec_iter=0 7 | def __init__(self, net_path, use_gpu=True, initialize=False, **kwargs): 8 | self.net_path = net_path 9 | self.use_gpu = use_gpu 10 | self.net = None 11 | self.net_kwargs = kwargs 12 | if initialize: 13 | self.initialize() 14 | 15 | def __getattr__(self, name): 16 | if self._rec_iter > 0: 17 | self._rec_iter = 0 18 | return None 19 | self._rec_iter += 1 20 | try: 21 | ret_val = getattr(self.net, name) 22 | except Exception as e: 23 | self._rec_iter = 0 24 | raise e 25 | self._rec_iter = 0 26 | return ret_val 27 | 28 | def load_network(self): 29 | self.net, _ = ltr_loading.load_network(self.net_path, **self.net_kwargs) 30 | if self.use_gpu: 31 | self.cuda() 32 | self.eval() 33 | 34 | def initialize(self): 35 | self.load_network() 36 | 37 | 38 | class NetWithBackbone(NetWrapper): 39 | """Wraps a network with a common backbone. 40 | Assumes the network have a 'extract_backbone_features(image)' function.""" 41 | 42 | def __init__(self, net_path, use_gpu=True, initialize=False, **kwargs): 43 | super().__init__(net_path, use_gpu, initialize, **kwargs) 44 | 45 | def initialize(self): 46 | super().initialize() 47 | 48 | def template(self, z): 49 | self.net.template(z) 50 | 51 | def track(self, image): 52 | return self.net.track(image) -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/got10k_toolkit/toolkit/utils/__init__.py -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/utils/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/got10k_toolkit/toolkit/utils/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /got10k_toolkit/toolkit/utils/__pycache__/ioutils.cpython-37.pyc: 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-------------------------------------------------------------------------------- /got10k_toolkit/toolkit/utils/ioutils.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import, division 2 | 3 | import wget 4 | import os 5 | import shutil 6 | import zipfile 7 | import sys 8 | 9 | 10 | def download(url, filename): 11 | r"""Download file from the internet. 12 | 13 | Args: 14 | url (string): URL of the internet file. 15 | filename (string): Path to store the downloaded file. 16 | """ 17 | return wget.download(url, out=filename) 18 | 19 | 20 | def extract(filename, extract_dir): 21 | r"""Extract zip file. 22 | 23 | Args: 24 | filename (string): Path of the zip file. 25 | extract_dir (string): Directory to store the extracted results. 26 | """ 27 | if os.path.splitext(filename)[1] == '.zip': 28 | if not os.path.isdir(extract_dir): 29 | os.makedirs(extract_dir) 30 | with zipfile.ZipFile(filename) as z: 31 | z.extractall(extract_dir) 32 | else: 33 | raise Exception('Unsupport extension {} of the compressed file {}.'.format( 34 | os.path.splitext(filename)[1]), filename) 35 | 36 | 37 | def compress(dirname, save_file): 38 | """Compress a folder to a zip file. 39 | 40 | Arguments: 41 | dirname {string} -- Directory of all files to be compressed. 42 | save_file {string} -- Path to store the zip file. 43 | """ 44 | shutil.make_archive(save_file, 'zip', dirname) 45 | -------------------------------------------------------------------------------- /ltr/__init__.py: -------------------------------------------------------------------------------- 1 | from .admin.loading import load_network 2 | from .admin.model_constructor import model_constructor 3 | from .admin.multigpu import MultiGPU -------------------------------------------------------------------------------- /ltr/__pycache__/__init__.cpython-36.pyc: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | from pytracking import TensorDict 2 | 3 | 4 | class BaseActor: 5 | """ Base class for actor. The actor class handles the passing of the data through the network 6 | and calculation the loss""" 7 | def __init__(self, net, objective): 8 | """ 9 | args: 10 | net - The network to train 11 | objective - The loss function 12 | """ 13 | self.net = net 14 | self.objective = objective 15 | 16 | def __call__(self, data: TensorDict): 17 | """ Called in each training iteration. Should pass in input data through the network, calculate the loss, and 18 | return the training stats for the input data 19 | args: 20 | data - A TensorDict containing all the necessary data blocks. 21 | 22 | returns: 23 | loss - loss for the input data 24 | stats - a dict containing detailed losses 25 | """ 26 | raise NotImplementedError 27 | 28 | def to(self, device): 29 | """ Move the network to device 30 | args: 31 | device - device to use. 'cpu' or 'cuda' 32 | """ 33 | self.net.to(device) 34 | 35 | def train(self, mode=True): 36 | """ Set whether the network is in train mode. 37 | args: 38 | mode (True) - Bool specifying whether in training mode. 39 | """ 40 | self.net.train(mode) 41 | self.objective.train(mode) 42 | 43 | def eval(self): 44 | """ Set network to eval mode""" 45 | self.train(False) -------------------------------------------------------------------------------- /ltr/actors/__init__.py: -------------------------------------------------------------------------------- 1 | from .base_actor import BaseActor 2 | from .tracking import HCATActor -------------------------------------------------------------------------------- /ltr/actors/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/ltr/actors/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /ltr/actors/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/ltr/actors/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /ltr/actors/__pycache__/base_actor.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/ltr/actors/__pycache__/base_actor.cpython-37.pyc -------------------------------------------------------------------------------- /ltr/actors/__pycache__/base_actor.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/ltr/actors/__pycache__/base_actor.cpython-38.pyc -------------------------------------------------------------------------------- /ltr/actors/__pycache__/tracking.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/ltr/actors/__pycache__/tracking.cpython-37.pyc -------------------------------------------------------------------------------- /ltr/actors/__pycache__/tracking.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/ltr/actors/__pycache__/tracking.cpython-38.pyc -------------------------------------------------------------------------------- /ltr/actors/base_actor.py: -------------------------------------------------------------------------------- 1 | from pytracking import TensorDict 2 | 3 | 4 | class BaseActor: 5 | """ Base class for actor. The actor class handles the passing of the data through the network 6 | and calculation the loss""" 7 | def __init__(self, net, objective): 8 | """ 9 | args: 10 | net - The network to train 11 | objective - The loss function 12 | """ 13 | self.net = net 14 | self.objective = objective 15 | 16 | def __call__(self, data: TensorDict): 17 | """ Called in each training iteration. Should pass in input data through the network, calculate the loss, and 18 | return the training stats for the input data 19 | args: 20 | data - A TensorDict containing all the necessary data blocks. 21 | 22 | returns: 23 | loss - loss for the input data 24 | stats - a dict containing detailed losses 25 | """ 26 | raise NotImplementedError 27 | 28 | def to(self, device): 29 | """ Move the network to device 30 | args: 31 | device - device to use. 'cpu' or 'cuda' 32 | """ 33 | self.net.to(device) 34 | 35 | def train(self, mode=True): 36 | """ Set whether the network is in train mode. 37 | args: 38 | mode (True) - Bool specifying whether in training mode. 39 | """ 40 | self.net.train(mode) 41 | self.objective.train(mode) 42 | 43 | def eval(self): 44 | """ Set network to eval mode""" 45 | self.train(False) -------------------------------------------------------------------------------- /ltr/admin/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/ltr/admin/__init__.py -------------------------------------------------------------------------------- /ltr/admin/__pycache__/__init__.cpython-36.pyc: -------------------------------------------------------------------------------- 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self.imagenet_dir = '' 12 | self.imagenetdet_dir = '' 13 | self.ecssd_dir = '' 14 | self.hkuis_dir = '' 15 | self.msra10k_dir = '' 16 | self.davis_dir = '' 17 | self.youtubevos_dir = '' 18 | -------------------------------------------------------------------------------- /ltr/admin/multigpu.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | 3 | '''modified by chenxin for distribute training''' 4 | def is_multi_gpu(net): 5 | return isinstance(net, (MultiGPU, nn.DataParallel, nn.parallel.DistributedDataParallel)) 6 | 7 | 8 | class MultiGPU(nn.DataParallel): 9 | """Wraps a network to allow simple multi-GPU training.""" 10 | def __getattr__(self, item): 11 | try: 12 | return super().__getattr__(item) 13 | except: 14 | pass 15 | return getattr(self.module, item) -------------------------------------------------------------------------------- /ltr/admin/settings.py: -------------------------------------------------------------------------------- 1 | from ltr.admin.environment import env_settings 2 | 3 | 4 | class Settings: 5 | """ Training settings, e.g. the paths to datasets and networks.""" 6 | def __init__(self): 7 | self.set_default() 8 | 9 | def set_default(self): 10 | self.env = env_settings() 11 | self.use_gpu = True 12 | 13 | 14 | -------------------------------------------------------------------------------- /ltr/admin/stats.py: -------------------------------------------------------------------------------- 1 | 2 | 3 | class StatValue: 4 | def __init__(self): 5 | self.clear() 6 | 7 | def reset(self): 8 | self.val = 0 9 | 10 | def clear(self): 11 | self.reset() 12 | self.history = [] 13 | 14 | def update(self, val): 15 | self.val = val 16 | self.history.append(self.val) 17 | 18 | 19 | class AverageMeter(object): 20 | """Computes and stores the average and current value""" 21 | def __init__(self): 22 | self.clear() 23 | self.has_new_data = False 24 | 25 | def reset(self): 26 | self.avg = 0 27 | self.val = 0 28 | self.sum = 0 29 | self.count = 0 30 | 31 | def clear(self): 32 | self.reset() 33 | self.history = [] 34 | 35 | def update(self, val, n=1): 36 | self.val = val 37 | self.sum += val * n 38 | self.count += n 39 | self.avg = self.sum / self.count 40 | 41 | def new_epoch(self): 42 | if self.count > 0: 43 | self.history.append(self.avg) 44 | self.reset() 45 | self.has_new_data = True 46 | else: 47 | self.has_new_data = False 48 | 49 | 50 | def topk_accuracy(output, target, topk=(1,)): 51 | """Computes the precision@k for the specified values of k""" 52 | single_input = not isinstance(topk, (tuple, list)) 53 | if single_input: 54 | topk = (topk,) 55 | 56 | maxk = max(topk) 57 | batch_size = target.size(0) 58 | 59 | _, pred = output.topk(maxk, 1, True, True) 60 | pred = pred.t() 61 | correct = pred.eq(target.view(1, -1).expand_as(pred)) 62 | 63 | res = [] 64 | for k in topk: 65 | correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)[0] 66 | res.append(correct_k * 100.0 / batch_size) 67 | 68 | if single_input: 69 | return res[0] 70 | 71 | return res 72 | -------------------------------------------------------------------------------- /ltr/admin/tensorboard.py: -------------------------------------------------------------------------------- 1 | import os 2 | from collections import OrderedDict 3 | try: 4 | from torch.utils.tensorboard import SummaryWriter 5 | except: 6 | print('WARNING: You are using tensorboardX instead sis you have a too old pytorch version.') 7 | from tensorboardX import SummaryWriter 8 | 9 | 10 | class TensorboardWriter: 11 | def __init__(self, directory, loader_names): 12 | self.directory = directory 13 | self.writer = OrderedDict({name: SummaryWriter(os.path.join(self.directory, name)) for name in loader_names}) 14 | 15 | def write_info(self, module_name, script_name, description): 16 | tb_info_writer = SummaryWriter(os.path.join(self.directory, 'info')) 17 | tb_info_writer.add_text('Modulet_name', module_name) 18 | tb_info_writer.add_text('Script_name', script_name) 19 | tb_info_writer.add_text('Description', description) 20 | tb_info_writer.close() 21 | 22 | def write_epoch(self, stats: OrderedDict, epoch: int, ind=-1): 23 | for loader_name, loader_stats in stats.items(): 24 | if loader_stats is None: 25 | continue 26 | for var_name, val in loader_stats.items(): 27 | if hasattr(val, 'history') and getattr(val, 'has_new_data', True): 28 | self.writer[loader_name].add_scalar(var_name, val.history[ind], epoch) -------------------------------------------------------------------------------- /ltr/data/__init__.py: -------------------------------------------------------------------------------- 1 | from .loader import LTRLoader -------------------------------------------------------------------------------- /ltr/data/__pycache__/__init__.cpython-37.pyc: 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-------------------------------------------------------------------------------- 1 | from .lasot import Lasot 2 | from .got10k import Got10k 3 | from .tracking_net import TrackingNet 4 | from .imagenetvid import ImagenetVID 5 | from .coco import MSCOCO 6 | from .coco_seq import MSCOCOSeq 7 | from .youtubevos import YouTubeVOS 8 | from .davis import Davis 9 | # from .lvis import LVIS 10 | from .ecssd import ECSSD 11 | from .msra10k import MSRA10k 12 | from .hku_is import HKUIS 13 | from .sbd import SBD 14 | from .synthetic_video import SyntheticVideo 15 | from .synthetic_video_blend import SyntheticVideoBlend 16 | -------------------------------------------------------------------------------- /ltr/dataset/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/ltr/dataset/__pycache__/__init__.cpython-37.pyc 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Handles freezing layers etc. 7 | args: 8 | frozen_layers - Name of layers to freeze. Either list of strings, 'none' or 'all'. Default: 'none'. 9 | """ 10 | def __init__(self, frozen_layers=()): 11 | super().__init__() 12 | 13 | if isinstance(frozen_layers, str): 14 | if frozen_layers.lower() == 'none': 15 | frozen_layers = () 16 | elif frozen_layers.lower() != 'all': 17 | raise ValueError('Unknown option for frozen layers: \"{}\". Should be \"all\", \"none\" or list of layer names.'.format(frozen_layers)) 18 | 19 | self.frozen_layers = frozen_layers 20 | self._is_frozen_nograd = False 21 | 22 | 23 | def train(self, mode=True): 24 | super().train(mode) 25 | if mode == True: 26 | self._set_frozen_to_eval() 27 | if not self._is_frozen_nograd: 28 | self._set_frozen_to_nograd() 29 | self._is_frozen_nograd = True 30 | 31 | 32 | def _set_frozen_to_eval(self): 33 | if isinstance(self.frozen_layers, str) and self.frozen_layers.lower() == 'all': 34 | self.eval() 35 | else: 36 | for layer in self.frozen_layers: 37 | getattr(self, layer).eval() 38 | 39 | 40 | def _set_frozen_to_nograd(self): 41 | if isinstance(self.frozen_layers, str) and self.frozen_layers.lower() == 'all': 42 | for p in self.parameters(): 43 | p.requires_grad_(False) 44 | else: 45 | for layer in self.frozen_layers: 46 | for p in getattr(self, layer).parameters(): 47 | p.requires_grad_(False) 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3 | warnings.filterwarnings('ignore') 4 | 5 | import os 6 | import logging 7 | import torch 8 | from collections import OrderedDict 9 | from .model import _gen_childnet 10 | 11 | 12 | def build_subnet(path_backbone, ops=None): 13 | arch_list = path_backbone 14 | 15 | arch_def = [ 16 | # stage 0, 112x112 in 17 | ['ds_r1_k3_s1_e1_c16_se0.25'], 18 | # stage 1, 112x112 in 19 | ['ir_r1_k3_s2_e4_c24_se0.25', 'ir_r1_k3_s1_e4_c24_se0.25', 20 | 'ir_r1_k3_s1_e4_c24_se0.25', 'ir_r1_k3_s1_e4_c24_se0.25'], 21 | # stage 2, 56x56 in 22 | ['ir_r1_k5_s2_e4_c40_se0.25', 'ir_r1_k5_s1_e4_c40_se0.25', 23 | 'ir_r1_k5_s2_e4_c40_se0.25', 'ir_r1_k5_s2_e4_c40_se0.25'], 24 | # stage 3, 28x28 in 25 | ['ir_r1_k3_s2_e6_c80_se0.25', 'ir_r1_k3_s1_e4_c80_se0.25', 26 | 'ir_r1_k3_s1_e4_c80_se0.25', 'ir_r1_k3_s1_e4_c80_se0.25'], 27 | # stage 4, 14x14in 28 | ['ir_r1_k3_s1_e6_c96_se0.25', 'ir_r1_k3_s1_e6_c96_se0.25', 29 | 'ir_r1_k3_s1_e6_c96_se0.25', 'ir_r1_k3_s1_e6_c96_se0.25'], 30 | # stage 5, 14x14in 31 | ['ir_r1_k5_s2_e6_c192_se0.25', 'ir_r1_k5_s1_e6_c192_se0.25', 32 | 'ir_r1_k5_s2_e6_c192_se0.25', 'ir_r1_k5_s2_e6_c192_se0.25'], 33 | # stage 6, 7x7 in 34 | ['cn_r1_k1_s1_c320_se0.25'], 35 | ] 36 | 37 | model = _gen_childnet( 38 | arch_list, 39 | arch_def, 40 | num_classes=1000, 41 | drop_rate=0, 42 | drop_path_rate=0, 43 | global_pool='avg', 44 | bn_momentum=None, 45 | bn_eps=None, 46 | pool_bn=False, 47 | zero_gamma=False, 48 | ops=ops) 49 | 50 | return model 51 | 52 | 53 | -------------------------------------------------------------------------------- /ltr/models/backbone/lighttrack_backbone.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import random 3 | import os 4 | import torch 5 | import torch.nn as nn 6 | from collections import OrderedDict 7 | from ltr.models.backbone.lighttrack import build_subnet 8 | 9 | def lightrack_backbone_M(output_layers=None, pretrained=False, **kwargs): 10 | path_backbone = [[0], [4, 5], [0, 2, 5, 1], [4, 0, 4, 4], [5, 2, 1, 0], [4, 2, 5, 4], [0]] 11 | path_ops = (3, 2) 12 | model = build_subnet(path_backbone=path_backbone, ops=path_ops) 13 | checkpoint_path = os.path.join(os.path.dirname(__file__), 'lighttrack/LightTrackM.pth') 14 | if pretrained: 15 | checkpoint = torch.load(checkpoint_path, map_location='cpu') 16 | new_state_dict = OrderedDict() 17 | for k, v in checkpoint['state_dict'].items(): 18 | if k[0:8] == 'features': 19 | name = k[9:] 20 | new_state_dict[name] = v 21 | model.load_state_dict(new_state_dict, strict=True) 22 | return model 23 | 24 | 25 | 26 | -------------------------------------------------------------------------------- /ltr/models/loss/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/ltr/models/loss/__init__.py -------------------------------------------------------------------------------- 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TrackingNet[name]['img_names'] = [] 16 | # img_names = os.listdir(os.path.join(frames_root,name)) 17 | # for i in range(len(img_names)): 18 | # TrackingNet[name]['img_names'].append(name + '/' + str(i) + '.jpg') 19 | # TrackingNet[name]['gt_rect'] = [] 20 | # length = len(TrackingNet[name]['img_names']) 21 | # for i in range(length): 22 | # if i == 0: 23 | # TrackingNet[name]['gt_rect'].append(init_rect) 24 | # else: 25 | # TrackingNet[name]['gt_rect'].append([0, 0, 0, 0]) 26 | # 27 | # TrackingNet = json.dumps(TrackingNet) 28 | # with open('/home/cx/cx2/TrackingNet/TEST/TrackingNet.json', 'w') as json_file: 29 | # json_file.write(TrackingNet) 30 | 31 | 32 | with open("/home/cx/cx2/Downloads/UAV123/UAV123_fix/Dataset_UAV123/UAV123/data_seq/UAV123/UAV.json", 'r') as f: 33 | temp = json.loads(f.read()) 34 | b=1 35 | 36 | -------------------------------------------------------------------------------- /pysot_toolkit/__init__.py: 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list(map(int, init_rect)) 14 | # TrackingNet[name]['init_rect'] = init_rect 15 | # TrackingNet[name]['img_names'] = [] 16 | # img_names = os.listdir(os.path.join(frames_root,name)) 17 | # for i in range(len(img_names)): 18 | # TrackingNet[name]['img_names'].append(name + '/' + str(i) + '.jpg') 19 | # TrackingNet[name]['gt_rect'] = [] 20 | # length = len(TrackingNet[name]['img_names']) 21 | # for i in range(length): 22 | # if i == 0: 23 | # TrackingNet[name]['gt_rect'].append(init_rect) 24 | # else: 25 | # TrackingNet[name]['gt_rect'].append([0, 0, 0, 0]) 26 | # 27 | # TrackingNet = json.dumps(TrackingNet) 28 | # with open('/home/cx/cx2/TrackingNet/TEST/TrackingNet.json', 'w') as json_file: 29 | # json_file.write(TrackingNet) 30 | 31 | 32 | with open("/home/cx/cx2/Downloads/UAV123/UAV123_fix/Dataset_UAV123/UAV123/data_seq/UAV123/UAV.json", 'r') as f: 33 | temp = json.loads(f.read()) 34 | b=1 35 | 36 | -------------------------------------------------------------------------------- /pysot_toolkit/speed_onnx_cpu_stride16.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | env_path = os.path.join(os.path.dirname(__file__), '..') 4 | print(env_path) 5 | if env_path not in sys.path: 6 | sys.path.append(env_path) 7 | import torch 8 | import time 9 | import onnx 10 | import onnxruntime 11 | import numpy as np 12 | import torch.nn as nn 13 | from ltr.models.tracking.hcat import MLP 14 | from ltr.models.backbone.transt_backbone import build_backbone,Backbone 15 | from ltr.models.neck.featurefusion_network import build_featurefusion_network 16 | import ltr.admin.settings as ws_settings 17 | from ltr.models.neck.position_encoding import build_position_encoding 18 | 19 | 20 | def getdata(b=1,x=256,z=8): 21 | x = torch.randn(1,3,x,x) 22 | feature = torch.randn(1,384,z,z) 23 | # feature = torch.randn(1,1024,z,z) 24 | # feature = torch.randn(1,256,z,z) 25 | # feature = torch.randn(1,96,z,z) 26 | pos = torch.randn(1,256,z,z) 27 | return x, feature, pos 28 | 29 | def to_numpy(tensor): 30 | return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy() 31 | 32 | def inference_speed_track(): 33 | T_w = 300 # warmup 34 | T_t = 5000 # test 35 | ort_session = onnxruntime.InferenceSession("models/com_cov.onnx_test",providers=['CPUExecutionProvider']) 36 | with torch.no_grad(): 37 | x, feature, pos = getdata() 38 | ort_inputs = {'x':to_numpy(x), 39 | 'feature_template':to_numpy(feature), 40 | 'pos_template':to_numpy(pos)} 41 | for i in range(T_w): 42 | nxp = ort_session.run(None, ort_inputs) 43 | 44 | onnx_s = time.time() 45 | for i in range(T_t): 46 | nxp = ort_session.run(None, ort_inputs) 47 | onnx_e = time.time() 48 | onnxt = onnx_e - onnx_s 49 | print('The tracking process inference speed of onnx model: %.2f FPS' % (T_t / onnxt)) 50 | 51 | if __name__ == "__main__": 52 | inference_speed_track() 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| class DatasetFactory(object): 10 | @staticmethod 11 | def create_dataset(**kwargs): 12 | """ 13 | Args: 14 | name: dataset name 'OTB2015', 'LaSOT', 'UAV123', 'NFS240', 'NFS30', 15 | 'VOT2018', 'VOT2016', 'VOT2018-LT' 16 | dataset_root: dataset root 17 | load_img: wether to load image 18 | Return: 19 | dataset 20 | """ 21 | assert 'name' in kwargs, "should provide dataset name" 22 | name = kwargs['name'] 23 | if 'OTB' in name: 24 | dataset = OTBDataset(**kwargs) 25 | elif 'LaSOT' == name: 26 | dataset = LaSOTDataset(**kwargs) 27 | elif 'UAV' in name: 28 | dataset = UAVDataset(**kwargs) 29 | elif 'NFS' in name: 30 | dataset = NFSDataset(**kwargs) 31 | elif 'VOT2018' == name or 'VOT2016' == name or 'VOT2019' == name: 32 | dataset = VOTDataset(**kwargs) 33 | elif 'VOT2018-LT' == name: 34 | dataset = VOTLTDataset(**kwargs) 35 | elif 'TrackingNet' == name: 36 | dataset = TrackingNetDataset(**kwargs) 37 | elif 'GOT-10k' == name: 38 | dataset = GOT10kDataset(**kwargs) 39 | else: 40 | raise Exception("unknow dataset {}".format(kwargs['name'])) 41 | return dataset 42 | 43 | -------------------------------------------------------------------------------- /pysot_toolkit/toolkit/datasets/__pycache__/__init__.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/pysot_toolkit/toolkit/datasets/__pycache__/__init__.cpython-36.pyc -------------------------------------------------------------------------------- /pysot_toolkit/toolkit/datasets/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/pysot_toolkit/toolkit/datasets/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- 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sorted(list(self.videos.keys())) 20 | for key in keys: 21 | yield self.videos[key] 22 | 23 | def set_tracker(self, path, tracker_names): 24 | """ 25 | Args: 26 | path: path to tracker results, 27 | tracker_names: list of tracker name 28 | """ 29 | self.tracker_path = path 30 | self.tracker_names = tracker_names 31 | # for video in tqdm(self.videos.values(), 32 | # desc='loading tacker result', ncols=100): 33 | # video.load_tracker(path, tracker_names) 34 | -------------------------------------------------------------------------------- /pysot_toolkit/toolkit/evaluation/__init__.py: -------------------------------------------------------------------------------- 1 | from .ar_benchmark import AccuracyRobustnessBenchmark 2 | from .eao_benchmark import EAOBenchmark 3 | from .ope_benchmark import OPEBenchmark 4 | from .f1_benchmark import F1Benchmark 5 | -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | from . import region 2 | from .statistics import * 3 | -------------------------------------------------------------------------------- /pysot_toolkit/toolkit/utils/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/pysot_toolkit/toolkit/utils/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /pysot_toolkit/toolkit/utils/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/pysot_toolkit/toolkit/utils/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- 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struct region_polygon: 29 | int count 30 | float *x 31 | float *y 32 | 33 | ctypedef union region_container_data: 34 | region_rectangle rectangle 35 | region_polygon polygon 36 | # region_mask mask 37 | int special 38 | 39 | ctypedef struct region_container: 40 | region_type type 41 | region_container_data data 42 | 43 | # ctypedef struct region_overlap: 44 | # float overlap 45 | # float only1 46 | # float only2 47 | 48 | # region_overlap region_compute_overlap(const region_container* ra, const region_container* rb, region_bounds bounds) 49 | 50 | float compute_polygon_overlap(const region_polygon* p1, const region_polygon* p2, float *only1, float *only2, region_bounds bounds) 51 | -------------------------------------------------------------------------------- /pysot_toolkit/toolkit/utils/misc.py: -------------------------------------------------------------------------------- 1 | """ 2 | @author fangyi.zhang@vipl.ict.ac.cn 3 | """ 4 | import numpy as np 5 | 6 | def determine_thresholds(confidence, resolution=100): 7 | """choose threshold according to confidence 8 | 9 | Args: 10 | confidence: list or numpy array or numpy array 11 | reolution: number of threshold to choose 12 | 13 | Restures: 14 | threshold: numpy array 15 | """ 16 | if isinstance(confidence, list): 17 | confidence = np.array(confidence) 18 | confidence = confidence.flatten() 19 | confidence = confidence[~np.isnan(confidence)] 20 | confidence.sort() 21 | 22 | assert len(confidence) > resolution and resolution > 2 23 | 24 | thresholds = np.ones((resolution)) 25 | thresholds[0] = - np.inf 26 | thresholds[-1] = np.inf 27 | delta = np.floor(len(confidence) / (resolution - 2)) 28 | idxs = np.linspace(delta, len(confidence)-delta, resolution-2, dtype=np.int32) 29 | thresholds[1:-1] = confidence[idxs] 30 | return thresholds 31 | -------------------------------------------------------------------------------- /pysot_toolkit/toolkit/utils/region.cpython-37m-x86_64-linux-gnu.so: 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.draw_utils import COLOR, MARKER_STYLE 7 | 8 | rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']}) 9 | rc('text', usetex=True) 10 | 11 | def draw_eao(result): 12 | fig = plt.figure() 13 | ax = fig.add_subplot(111, projection='polar') 14 | angles = np.linspace(0, 2*np.pi, 8, endpoint=True) 15 | 16 | attr2value = [] 17 | for i, (tracker_name, ret) in enumerate(result.items()): 18 | value = list(ret.values()) 19 | attr2value.append(value) 20 | value.append(value[0]) 21 | attr2value = np.array(attr2value) 22 | max_value = np.max(attr2value, axis=0) 23 | min_value = np.min(attr2value, axis=0) 24 | for i, (tracker_name, ret) in enumerate(result.items()): 25 | value = list(ret.values()) 26 | value.append(value[0]) 27 | value = np.array(value) 28 | value *= (1 / max_value) 29 | plt.plot(angles, value, linestyle='-', color=COLOR[i], marker=MARKER_STYLE[i], 30 | label=tracker_name, linewidth=1.5, markersize=6) 31 | 32 | attrs = ["Overall", "Camera motion", 33 | "Illumination change","Motion Change", 34 | "Size change","Occlusion", 35 | "Unassigned"] 36 | attr_value = [] 37 | for attr, maxv, minv in zip(attrs, max_value, min_value): 38 | attr_value.append(attr + "\n({:.3f},{:.3f})".format(minv, maxv)) 39 | ax.set_thetagrids(angles[:-1] * 180/np.pi, attr_value) 40 | ax.spines['polar'].set_visible(False) 41 | ax.legend(loc='upper center', bbox_to_anchor=(0.5,-0.07), frameon=False, ncol=5) 42 | ax.grid(b=False) 43 | ax.set_ylim(0, 1.18) 44 | ax.set_yticks([]) 45 | plt.show() 46 | 47 | if __name__ == '__main__': 48 | result = pickle.load(open("../../result.pkl", 'rb')) 49 | draw_eao(result) 50 | -------------------------------------------------------------------------------- /pysot_toolkit/toolkit/visualization/draw_utils.py: -------------------------------------------------------------------------------- 1 | 2 | COLOR = ((1, 0, 0), 3 | (0, 1, 0), 4 | (1, 0, 1), 5 | (1, 1, 0), 6 | (0 , 162/255, 232/255), 7 | (0.5, 0.5, 0.5), 8 | (0, 0, 1), 9 | (0, 1, 1), 10 | (136/255, 0 , 21/255), 11 | (255/255, 127/255, 39/255), 12 | (0, 0, 0)) 13 | 14 | LINE_STYLE = ['-', '--', ':', '-', '--', ':', '-', '--', ':', '-'] 15 | 16 | MARKER_STYLE = ['o', 'v', '<', '*', 'D', 'x', '.', 'x', '<', '.'] 17 | -------------------------------------------------------------------------------- /pysot_toolkit/trackers/.ipynb_checkpoints/net_wrappers-checkpoint.py: -------------------------------------------------------------------------------- 1 | import ltr.admin.loading as ltr_loading 2 | 3 | class NetWrapper: 4 | """Used for wrapping networks in pytracking. 5 | Network modules and functions can be accessed directly as if they were members of this class.""" 6 | _rec_iter=0 7 | def __init__(self, net_path, use_gpu=True, initialize=False, **kwargs): 8 | self.net_path = net_path 9 | self.use_gpu = use_gpu 10 | self.net = None 11 | self.net_kwargs = kwargs 12 | if initialize: 13 | self.initialize() 14 | 15 | def __getattr__(self, name): 16 | if self._rec_iter > 0: 17 | self._rec_iter = 0 18 | return None 19 | self._rec_iter += 1 20 | try: 21 | ret_val = getattr(self.net, name) 22 | except Exception as e: 23 | self._rec_iter = 0 24 | raise e 25 | self._rec_iter = 0 26 | return ret_val 27 | 28 | def load_network(self): 29 | self.net, _ = ltr_loading.load_network(self.net_path, **self.net_kwargs) 30 | if self.use_gpu: 31 | self.cuda() 32 | self.eval() 33 | 34 | def initialize(self): 35 | self.load_network() 36 | 37 | 38 | class NetWithBackbone(NetWrapper): 39 | """Wraps a network with a common backbone. 40 | Assumes the network have a 'extract_backbone_features(image)' function.""" 41 | 42 | def __init__(self, net_path, use_gpu=True, initialize=False, **kwargs): 43 | super().__init__(net_path, use_gpu, initialize, **kwargs) 44 | 45 | def initialize(self): 46 | super().initialize() 47 | 48 | def template(self, z, feature_size): 49 | self.net.template(z, feature_size) 50 | 51 | def track(self, image): 52 | return self.net.track(image) 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name) 22 | except Exception as e: 23 | self._rec_iter = 0 24 | raise e 25 | self._rec_iter = 0 26 | return ret_val 27 | 28 | def load_network(self): 29 | self.net, _ = ltr_loading.load_network(self.net_path, **self.net_kwargs) 30 | if self.use_gpu: 31 | self.cuda() 32 | self.eval() 33 | 34 | def initialize(self): 35 | self.load_network() 36 | 37 | 38 | class NetWithBackbone(NetWrapper): 39 | """Wraps a network with a common backbone. 40 | Assumes the network have a 'extract_backbone_features(image)' function.""" 41 | 42 | def __init__(self, net_path, use_gpu=True, initialize=False, **kwargs): 43 | super().__init__(net_path, use_gpu, initialize, **kwargs) 44 | 45 | def initialize(self): 46 | super().initialize() 47 | 48 | def template(self, z, feature_size): 49 | self.net.template(z, feature_size) 50 | 51 | def track(self, image): 52 | return self.net.track(image) -------------------------------------------------------------------------------- /pytracking/.figs/LaSOT.png: 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installation. Check 8 | % https://trax.readthedocs.io/en/latest/tutorial_compiling.html for 9 | % compilation information 10 | trax_path = 'PATH_TO_VOT_TOOLKIT/native/trax'; 11 | 12 | tracker_name = 'dimp'; % Name of the tracker to evaluate 13 | runfile_name = 'dimp18_vot'; % Name of the parameter file to use 14 | debug = 0; 15 | 16 | %% 17 | tracker_label = [tracker_name, '_', runfile_name]; 18 | 19 | % Generate python command 20 | tracker_command = sprintf(['%s -c "import sys; sys.path.append(''%s'');', ... 21 | 'sys.path.append(''%s/support/python'');', ... 22 | 'import run_vot;', ... 23 | 'run_vot.run_vot(''%s'', ''%s'', debug=%d)"'],... 24 | python_path, pytracking_path, trax_path, ... 25 | tracker_name, runfile_name, debug); 26 | 27 | 28 | tracker_interpreter = python_path; 29 | 30 | tracker_linkpath = {[trax_path, '/build'],... 31 | [trax_path, '/build/support/client'],... 32 | [trax_path, '/build/support/opencv']}; 33 | -------------------------------------------------------------------------------- /pytracking/VOT/trackers.ini: -------------------------------------------------------------------------------- 1 | [DiMP] # 2 | label = DiMP 3 | protocol = traxpython 4 | 5 | command = import pytracking.run_vot as run_vot; run_vot.run_vot2020('dimp', 'dimp50') # Set the tracker name and the parameter name 6 | 7 | # Specify a path to trax python wrapper if it is not visible (separate by ; if using multiple paths) 8 | paths = PATH_TO_PYTRACKING 9 | 10 | # Additional environment paths 11 | #env_PATH = ;${PATH} 12 | 13 | -------------------------------------------------------------------------------- /pytracking/VOT2020/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/pytracking/VOT2020/__init__.py 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10 | 11 | exp_name='hcat' 12 | para_w=float(604/1000) 13 | penalty_k=float(298/1000) 14 | backbone_path = '' 15 | model_path = '' 16 | save_root = '/home/cx/cx1/VOT2021/vot2021_search_1/vis/' + exp_name 17 | run_vot_exp('transt', window=para_w, penalty_k=penalty_k, backbone_path=backbone_path,model_path=model_path, save_root=save_root, VIS=False) 18 | # run_vot_exp('dimp','super_dimp','ARcm_coco_seg_only_mask_384',0.65,VIS=True) 19 | 20 | -------------------------------------------------------------------------------- /pytracking/VOT2020/transt_VOT2020.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | env_path = os.path.join(os.path.dirname(__file__), '../..') 4 | if env_path not in sys.path: 5 | sys.path.append(env_path) 6 | from pytracking.VOT2020.transt_seg_class import run_vot_exp 7 | 8 | net_path = os.path.join(env_path, 'models/TransTsegm_ep0075.pth.tar') 9 | save_root = '' 10 | run_vot_exp('transt', window=0.59, penalty_k=0.265, mask_threshold=0.5, net_path=net_path, save_root=save_root, VIS=False) 11 | -------------------------------------------------------------------------------- /pytracking/VOT2020/transt_VOT2020_ap_1.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | env_path = os.path.join(os.path.dirname(__file__), '../..') 4 | if env_path not in sys.path: 5 | sys.path.append(env_path) 6 | from pytracking.VOT2020.transt_bbox_class import run_vot_exp 7 | 8 | 9 | 10 | exp_name='ligttranst_w604_e_k298_u' 11 | para_w=float(604/1000) 12 | penalty_k=float(298/1000) 13 | net_path = '/home/kb/HCAT/pysot_toolkit/models/res18_N2_q16.pth' 14 | 15 | 16 | 17 | 18 | save_root = '/home/cx/cx1/VOT2021/vot2021_search_1/vis/' + exp_name 19 | run_vot_exp('transt', window=para_w, penalty_k=penalty_k, net_path=net_path, save_root=save_root, VIS=False) 20 | # run_vot_exp('dimp','super_dimp','ARcm_coco_seg_only_mask_384',0.65,VIS=True) 21 | 22 | -------------------------------------------------------------------------------- /pytracking/VOT2020/transt_VOT2020_bbox.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | env_path = os.path.join(os.path.dirname(__file__), '../..') 4 | if env_path not in sys.path: 5 | sys.path.append(env_path) 6 | from pytracking.VOT2020.transt_bbox_class import run_vot_exp 7 | 8 | # net_path = os.path.join(env_path, 'models/TransTsegm_ep0075.pth.tar') 9 | net_path = '/home/cx/cx2/models/TransT_light/stride16_N2_sparse_q16_res18/TransT_ep0247.pth.tar' 10 | save_root = '/home/cx/cx1/light_transt/TransT_fix_nested_v5_b/vis' 11 | run_vot_exp('transt', window=0.42, penalty_k=0, net_path=net_path, save_root=save_root, VIS=False) 12 | -------------------------------------------------------------------------------- /pytracking/__init__.py: -------------------------------------------------------------------------------- 1 | from pytracking.libs import TensorList, TensorDict 2 | import pytracking.libs.complex as complex 3 | import pytracking.libs.operation as operation 4 | import pytracking.libs.fourier as fourier 5 | import pytracking.libs.dcf as dcf 6 | import pytracking.libs.optimization as optimization 7 | from pytracking.run_tracker import run_tracker 8 | from pytracking.run_webcam import run_webcam 9 | -------------------------------------------------------------------------------- /pytracking/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/pytracking/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /pytracking/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- 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settings.got_reports_path = '' 12 | settings.lasot_path = '' 13 | settings.network_path = '/home/kb/HCAT/pytracking/networks/' # Where tracking networks are stored. 14 | settings.nfs_path = '' 15 | settings.otb_path = '' 16 | settings.result_plot_path = '/home/kb/HCAT/pytracking/result_plots/' 17 | settings.results_path = '/home/kb/HCAT/pytracking/tracking_results/' # Where to store tracking results 18 | settings.segmentation_path = '/home/kb/HCAT/pytracking/segmentation_results/' 19 | settings.tn_packed_results_path = '' 20 | settings.tpl_path = '' 21 | settings.trackingnet_path = '' 22 | settings.uav_path = '' 23 | settings.vot_path = '' 24 | settings.youtubevos_dir = '' 25 | 26 | return settings 27 | 28 | -------------------------------------------------------------------------------- /pytracking/experiments/__init__.py: -------------------------------------------------------------------------------- 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extract(self, im: torch.Tensor): 14 | return im/255 - 0.5 15 | 16 | 17 | class Grayscale(FeatureBase): 18 | """Grayscale feature normalized to [-0.5, 0.5].""" 19 | def dim(self): 20 | return 1 21 | 22 | def stride(self): 23 | return self.pool_stride 24 | 25 | def extract(self, im: torch.Tensor): 26 | return torch.mean(im/255 - 0.5, 1, keepdim=True) 27 | -------------------------------------------------------------------------------- /pytracking/features/util.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from pytracking.features.featurebase import FeatureBase 3 | 4 | 5 | class Concatenate(FeatureBase): 6 | """A feature that concatenates other features. 7 | args: 8 | features: List of features to concatenate. 9 | """ 10 | def __init__(self, features, pool_stride = None, normalize_power = None, use_for_color = True, use_for_gray = True): 11 | super(Concatenate, self).__init__(pool_stride, normalize_power, use_for_color, use_for_gray) 12 | self.features = features 13 | 14 | self.input_stride = self.features[0].stride() 15 | 16 | for feat in self.features: 17 | if self.input_stride != feat.stride(): 18 | raise ValueError('Strides for the features must be the same for a bultiresolution feature.') 19 | 20 | def dim(self): 21 | return sum([f.dim() for f in self.features]) 22 | 23 | def stride(self): 24 | return self.pool_stride * self.input_stride 25 | 26 | def extract(self, im: torch.Tensor): 27 | return torch.cat([f.get_feature(im) for f in self.features], 1) -------------------------------------------------------------------------------- /pytracking/fig/FSHCAT.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/pytracking/fig/FSHCAT.png -------------------------------------------------------------------------------- /pytracking/fig/framework.png: 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Returns the input if weight=None.""" 9 | 10 | if weight is None: 11 | return input 12 | 13 | ind = None 14 | if mode is not None: 15 | if padding != 0: 16 | raise ValueError('Cannot input both padding and mode.') 17 | if mode == 'same': 18 | padding = (weight.shape[2]//2, weight.shape[3]//2) 19 | if weight.shape[2] % 2 == 0 or weight.shape[3] % 2 == 0: 20 | ind = (slice(-1) if weight.shape[2] % 2 == 0 else slice(None), 21 | slice(-1) if weight.shape[3] % 2 == 0 else slice(None)) 22 | elif mode == 'valid': 23 | padding = (0, 0) 24 | elif mode == 'full': 25 | padding = (weight.shape[2]-1, weight.shape[3]-1) 26 | else: 27 | raise ValueError('Unknown mode for padding.') 28 | 29 | out = F.conv2d(input, weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups) 30 | if ind is None: 31 | return out 32 | return out[:,:,ind[0],ind[1]] 33 | 34 | 35 | @tensor_operation 36 | def conv1x1(input: torch.Tensor, weight: torch.Tensor): 37 | """Do a convolution with a 1x1 kernel weights. Implemented with matmul, which can be faster than using conv.""" 38 | 39 | if weight is None: 40 | return input 41 | 42 | return torch.conv2d(input, weight) 43 | -------------------------------------------------------------------------------- /pytracking/libs/tensordict.py: -------------------------------------------------------------------------------- 1 | from collections import OrderedDict 2 | import torch 3 | import copy 4 | 5 | 6 | class TensorDict(OrderedDict): 7 | """Container mainly used for dicts of torch tensors. Extends OrderedDict with pytorch functionality.""" 8 | 9 | def concat(self, other): 10 | """Concatenates two dicts without copying internal data.""" 11 | return TensorDict(self, **other) 12 | 13 | def copy(self): 14 | return TensorDict(super(TensorDict, self).copy()) 15 | 16 | def __deepcopy__(self, memodict={}): 17 | return TensorDict(copy.deepcopy(list(self), memodict)) 18 | 19 | def __getattr__(self, name): 20 | if not hasattr(torch.Tensor, name): 21 | raise AttributeError('\'TensorDict\' object has not attribute \'{}\''.format(name)) 22 | 23 | def apply_attr(*args, **kwargs): 24 | return TensorDict({n: getattr(e, name)(*args, **kwargs) if hasattr(e, name) else e for n, e in self.items()}) 25 | return apply_attr 26 | 27 | def attribute(self, attr: str, *args): 28 | return TensorDict({n: getattr(e, attr, *args) for n, e in self.items()}) 29 | 30 | def apply(self, fn, *args, **kwargs): 31 | return TensorDict({n: fn(e, *args, **kwargs) for n, e in self.items()}) 32 | 33 | @staticmethod 34 | def _iterable(a): 35 | return isinstance(a, (TensorDict, list)) 36 | 37 | -------------------------------------------------------------------------------- /pytracking/parameter/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/pytracking/parameter/__init__.py -------------------------------------------------------------------------------- /pytracking/parameter/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/pytracking/parameter/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /pytracking/parameter/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- 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NetWithBackbone 3 | 4 | def parameters(): 5 | params = TrackerParams() 6 | params.debug = 0 7 | params.visualization = False 8 | params.use_gpu = True 9 | params.net = NetWithBackbone(net_path='/home/cx/TransT/models/transt.pth', 10 | use_gpu=params.use_gpu) 11 | return params 12 | -------------------------------------------------------------------------------- /pytracking/run_experiment.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import argparse 4 | import importlib 5 | 6 | env_path = os.path.join(os.path.dirname(__file__), '..') 7 | if env_path not in sys.path: 8 | sys.path.append(env_path) 9 | 10 | from pytracking.evaluation.running import run_dataset 11 | 12 | 13 | def run_experiment(experiment_module: str, experiment_name: str, debug=0, threads=0): 14 | """Run experiment. 15 | args: 16 | experiment_module: Name of experiment module in the experiments/ folder. 17 | experiment_name: Name of the experiment function. 18 | debug: Debug level. 19 | threads: Number of threads. 20 | """ 21 | expr_module = importlib.import_module('pytracking.experiments.{}'.format(experiment_module)) 22 | expr_func = getattr(expr_module, experiment_name) 23 | trackers, dataset = expr_func() 24 | print('Running: {} {}'.format(experiment_module, experiment_name)) 25 | run_dataset(dataset, trackers, debug, threads) 26 | 27 | 28 | def main(): 29 | parser = argparse.ArgumentParser(description='Run tracker.') 30 | parser.add_argument('experiment_module', type=str, help='Name of experiment module in the experiments/ folder.') 31 | parser.add_argument('experiment_name', type=str, help='Name of the experiment function.') 32 | parser.add_argument('--debug', type=int, default=0, help='Debug level.') 33 | parser.add_argument('--threads', type=int, default=0, help='Number of threads.') 34 | 35 | args = parser.parse_args() 36 | 37 | run_experiment(args.experiment_module, args.experiment_name, args.debug, args.threads) 38 | 39 | 40 | if __name__ == '__main__': 41 | main() 42 | -------------------------------------------------------------------------------- /pytracking/run_video.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import argparse 4 | 5 | env_path = os.path.join(os.path.dirname(__file__), '..') 6 | if env_path not in sys.path: 7 | sys.path.append(env_path) 8 | 9 | from pytracking.evaluation import Tracker 10 | 11 | 12 | def run_video(tracker_name, tracker_param, videofile, optional_box=None, debug=None, save_results=False): 13 | """Run the tracker on your webcam. 14 | args: 15 | tracker_name: Name of tracking method. 16 | tracker_param: Name of parameter file. 17 | debug: Debug level. 18 | """ 19 | tracker = Tracker(tracker_name, tracker_param) 20 | tracker.run_video(videofilepath=videofile, optional_box=optional_box, debug=debug, save_results=save_results) 21 | 22 | def main(): 23 | parser = argparse.ArgumentParser(description='Run the tracker on your webcam.') 24 | parser.add_argument('tracker_name', type=str, help='Name of tracking method.') 25 | parser.add_argument('tracker_param', type=str, help='Name of parameter file.') 26 | parser.add_argument('videofile', type=str, help='path to a video file.') 27 | parser.add_argument('--optional_box', type=float, default=None, nargs="+", help='optional_box with format x y w h.') 28 | parser.add_argument('--debug', type=int, default=0, help='Debug level.') 29 | parser.add_argument('--save_results', dest='save_results', action='store_true', help='Save bounding boxes') 30 | parser.set_defaults(save_results=False) 31 | 32 | args = parser.parse_args() 33 | 34 | run_video(args.tracker_name, args.tracker_param,args.videofile, args.optional_box, args.debug, args.save_results) 35 | 36 | 37 | if __name__ == '__main__': 38 | main() 39 | -------------------------------------------------------------------------------- /pytracking/run_vot.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import argparse 4 | 5 | env_path = os.path.join(os.path.dirname(__file__), '..') 6 | if env_path not in sys.path: 7 | sys.path.append(env_path) 8 | 9 | from pytracking.evaluation import Tracker 10 | 11 | 12 | def run_vot2020(tracker_name, tracker_param, run_id=None, debug=0, visdom_info=None): 13 | tracker = Tracker(tracker_name, tracker_param, run_id) 14 | tracker.run_vot2020(debug, visdom_info) 15 | 16 | 17 | def run_vot(tracker_name, tracker_param, run_id=None): 18 | tracker = Tracker(tracker_name, tracker_param, run_id) 19 | tracker.run_vot() 20 | 21 | 22 | def main(): 23 | parser = argparse.ArgumentParser(description='Run VOT.') 24 | parser.add_argument('tracker_name', type=str) 25 | parser.add_argument('tracker_param', type=str) 26 | parser.add_argument('--run_id', type=int, default=None) 27 | 28 | args = parser.parse_args() 29 | 30 | run_vot(args.tracker_name, args.tracker_param, args.run_id) 31 | 32 | 33 | if __name__ == '__main__': 34 | main() 35 | -------------------------------------------------------------------------------- /pytracking/run_webcam.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import argparse 4 | 5 | env_path = os.path.join(os.path.dirname(__file__), '..') 6 | if env_path not in sys.path: 7 | sys.path.append(env_path) 8 | 9 | from pytracking.evaluation import Tracker 10 | 11 | 12 | def run_webcam(tracker_name, tracker_param, debug=None, visdom_info=None): 13 | """Run the tracker on your webcam. 14 | args: 15 | tracker_name: Name of tracking method. 16 | tracker_param: Name of parameter file. 17 | debug: Debug level. 18 | visdom_info: Dict optionally containing 'use_visdom', 'server' and 'port' for Visdom visualization. 19 | """ 20 | visdom_info = {} if visdom_info is None else visdom_info 21 | tracker = Tracker(tracker_name, tracker_param) 22 | tracker.run_webcam(debug, visdom_info) 23 | 24 | 25 | def main(): 26 | parser = argparse.ArgumentParser(description='Run the tracker on your webcam.') 27 | parser.add_argument('tracker_name', type=str, help='Name of tracking method.') 28 | parser.add_argument('tracker_param', type=str, help='Name of parameter file.') 29 | parser.add_argument('--debug', type=int, default=0, help='Debug level.') 30 | parser.add_argument('--use_visdom', type=bool, default=True, help='Flag to enable visdom') 31 | parser.add_argument('--visdom_server', type=str, default='127.0.0.1', help='Server for visdom') 32 | parser.add_argument('--visdom_port', type=int, default=8097, help='Port for visdom') 33 | 34 | args = parser.parse_args() 35 | 36 | visdom_info = {'use_visdom': args.use_visdom, 'server': args.visdom_server, 'port': args.visdom_port} 37 | run_webcam(args.tracker_name, args.tracker_param, args.debug, visdom_info) 38 | 39 | 40 | if __name__ == '__main__': 41 | main() 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-------------------------------------------------------------------------------- /pytracking/tracker/transt/__init__.py: -------------------------------------------------------------------------------- 1 | from .transt import TransT 2 | 3 | def get_tracker_class(): 4 | return TransT -------------------------------------------------------------------------------- /pytracking/tracker/transt/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chenxin-dlut/HCAT/475cc320c000db337903f9a8425145512450e039/pytracking/tracker/transt/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /pytracking/tracker/transt/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- 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All Rights Reserved. 2 | 3 | from __future__ import absolute_import 4 | from __future__ import division 5 | from __future__ import print_function 6 | from __future__ import unicode_literals 7 | 8 | from yacs.config import CfgNode as CN 9 | 10 | __C = CN() 11 | 12 | cfg = __C 13 | 14 | # ------------------------------------------------------------------------ # 15 | # Tracker options 16 | # ------------------------------------------------------------------------ # 17 | __C.TRACK = CN() 18 | 19 | __C.TRACK.TYPE = '' 20 | 21 | # Scale penalty 22 | __C.TRACK.PENALTY_K = 0 23 | 24 | # Window influence 25 | __C.TRACK.WINDOW_INFLUENCE = 0.49 26 | 27 | # Exemplar size 28 | __C.TRACK.EXEMPLAR_SIZE = 128 29 | 30 | # Instance size 31 | __C.TRACK.INSTANCE_SIZE = 256 -------------------------------------------------------------------------------- /pytracking/util_scripts/__init__.py: -------------------------------------------------------------------------------- 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type): 5 | if len(vot_anno) == 4: 6 | return vot_anno 7 | 8 | if type == 'union': 9 | x1 = min(vot_anno[0::2]) 10 | x2 = max(vot_anno[0::2]) 11 | y1 = min(vot_anno[1::2]) 12 | y2 = max(vot_anno[1::2]) 13 | return [x1, y1, x2 - x1, y2 - y1] 14 | elif type == 'preserve_area': 15 | if len(vot_anno) != 8: 16 | raise ValueError 17 | 18 | vot_anno = np.array(vot_anno) 19 | cx = np.mean(vot_anno[0::2]) 20 | cy = np.mean(vot_anno[1::2]) 21 | 22 | x1 = min(vot_anno[0::2]) 23 | x2 = max(vot_anno[0::2]) 24 | y1 = min(vot_anno[1::2]) 25 | y2 = max(vot_anno[1::2]) 26 | 27 | A1 = np.linalg.norm(vot_anno[0:2] - vot_anno[2: 4]) * np.linalg.norm(vot_anno[2: 4] - vot_anno[4:6]) 28 | A2 = (x2 - x1) * (y2 - y1) 29 | s = np.sqrt(A1 / A2) 30 | w = s * (x2 - x1) + 1 31 | h = s * (y2 - y1) + 1 32 | 33 | x = cx - 0.5*w 34 | y = cy - 0.5*h 35 | return [x, y, w, h] 36 | else: 37 | raise ValueError 38 | -------------------------------------------------------------------------------- /pytracking/utils/load_text.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | 4 | 5 | def load_text_numpy(path, delimiter, dtype): 6 | if isinstance(delimiter, (tuple, list)): 7 | for d in delimiter: 8 | try: 9 | ground_truth_rect = np.loadtxt(path, delimiter=d, dtype=dtype) 10 | return ground_truth_rect 11 | except: 12 | pass 13 | 14 | raise Exception('Could not read file {}'.format(path)) 15 | else: 16 | ground_truth_rect = np.loadtxt(path, delimiter=delimiter, dtype=dtype) 17 | return ground_truth_rect 18 | 19 | 20 | def load_text_pandas(path, delimiter, dtype): 21 | if isinstance(delimiter, (tuple, list)): 22 | for d in delimiter: 23 | try: 24 | ground_truth_rect = pd.read_csv(path, delimiter=d, header=None, dtype=dtype, na_filter=False, 25 | low_memory=False).values 26 | return ground_truth_rect 27 | except Exception as e: 28 | pass 29 | 30 | raise Exception('Could not read file {}'.format(path)) 31 | else: 32 | ground_truth_rect = pd.read_csv(path, delimiter=delimiter, header=None, dtype=dtype, na_filter=False, 33 | low_memory=False).values 34 | return ground_truth_rect 35 | 36 | 37 | def load_text(path, delimiter=' ', dtype=np.float32, backend='numpy'): 38 | if backend == 'numpy': 39 | return load_text_numpy(path, delimiter, dtype) 40 | elif backend == 'pandas': 41 | return load_text_pandas(path, delimiter, dtype) 42 | -------------------------------------------------------------------------------- /pytracking/utils/loading.py: -------------------------------------------------------------------------------- 1 | import os 2 | import ltr.admin.loading as ltr_loading 3 | from pytracking.evaluation.environment import env_settings 4 | 5 | 6 | def load_network(net_path, **kwargs): 7 | """Load network for tracking. 8 | args: 9 | net_path - Path to network. If it is not an absolute path, it is relative to the network_path in the local.py. 10 | See ltr.admin.loading.load_network for further details. 11 | **kwargs - Additional key-word arguments that are sent to ltr.admin.loading.load_network. 12 | """ 13 | # kwargs['backbone_pretrained'] = False 14 | if os.path.isabs(net_path): 15 | path_full = net_path 16 | net, _ = ltr_loading.load_network(path_full, **kwargs) 17 | elif isinstance(env_settings().network_path, (list, tuple)): 18 | net = None 19 | for p in env_settings().network_path: 20 | path_full = os.path.join(p, net_path) 21 | try: 22 | net, _ = ltr_loading.load_network(path_full, **kwargs) 23 | break 24 | except Exception as e: 25 | # print(e) 26 | pass 27 | 28 | assert net is not None, 'Failed to load network' 29 | else: 30 | path_full = os.path.join(env_settings().network_path, net_path) 31 | net, _ = ltr_loading.load_network(path_full, **kwargs) 32 | 33 | return net 34 | -------------------------------------------------------------------------------- /pytracking/utils/params.py: -------------------------------------------------------------------------------- 1 | from pytracking import TensorList 2 | import random 3 | 4 | 5 | class TrackerParams: 6 | """Class for tracker parameters.""" 7 | def set_default_values(self, default_vals: dict): 8 | for name, val in default_vals.items(): 9 | if not hasattr(self, name): 10 | setattr(self, name, val) 11 | 12 | def get(self, name: str, *default): 13 | """Get a parameter value with the given name. If it does not exists, it return the default value given as a 14 | second argument or returns an error if no default value is given.""" 15 | if len(default) > 1: 16 | raise ValueError('Can only give one default value.') 17 | 18 | if not default: 19 | return getattr(self, name) 20 | 21 | return getattr(self, name, default[0]) 22 | 23 | def has(self, name: str): 24 | """Check if there exist a parameter with the given name.""" 25 | return hasattr(self, name) 26 | 27 | 28 | class FeatureParams: 29 | """Class for feature specific parameters""" 30 | def __init__(self, *args, **kwargs): 31 | if len(args) > 0: 32 | raise ValueError 33 | 34 | for name, val in kwargs.items(): 35 | if isinstance(val, list): 36 | setattr(self, name, TensorList(val)) 37 | else: 38 | setattr(self, name, val) 39 | 40 | 41 | def Choice(*args): 42 | """Can be used to sample random parameter values.""" 43 | return random.choice(args) 44 | -------------------------------------------------------------------------------- /util/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 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