Repository: MIV-XJTU/ARTrack Branch: main Commit: 5931f4af8dd3 Files: 756 Total size: 5.3 MB Directory structure: gitextract_o26t4wgy/ ├── ARTrack_env_cuda113.yaml ├── LICENSE ├── README.md ├── artrackv2_mindspore/ │ ├── .gitignore │ ├── README.md │ ├── experiments/ │ │ └── ostrack/ │ │ ├── 2stage_256_got.yaml │ │ ├── best_384.yaml │ │ ├── finetune.yaml │ │ ├── finetune_384.yaml │ │ ├── finetune_384_got.yaml │ │ ├── vitb_256_mae_32x4_ep300.yaml │ │ ├── vitb_256_mae_ce_32x4_ep300.yaml │ │ ├── vitb_256_mae_ce_32x4_got10k_ep100.yaml │ │ ├── vitb_384_mae_32x4_ep300.yaml │ │ ├── vitb_384_mae_ce_32x4_ep300.yaml │ │ └── vitb_384_mae_ce_32x4_got10k_ep100.yaml │ ├── external/ │ │ ├── AR/ │ │ │ ├── README.md │ │ │ ├── __init__.py │ │ │ ├── ltr/ │ │ │ │ ├── README.md │ │ │ │ ├── __init__.py │ │ │ │ ├── actors/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── base_actor.py │ │ │ │ │ ├── bbreg.py │ │ │ │ │ └── tracking.py │ │ │ │ ├── admin/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── environment.py │ │ │ │ │ ├── loading.py │ │ │ │ │ ├── model_constructor.py │ │ │ │ │ ├── multigpu.py │ │ │ │ │ ├── settings.py │ │ │ │ │ ├── stats.py │ │ │ │ │ └── tensorboard.py │ │ │ │ ├── data/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── bounding_box_utils.py │ │ │ │ │ ├── image_loader.py │ │ │ │ │ ├── loader.py │ │ │ │ │ ├── processing.py │ │ │ │ │ ├── processing_utils.py │ │ │ │ │ ├── processing_utils_SE.py │ │ │ │ │ ├── sampler.py │ │ │ │ │ └── transforms.py │ │ │ │ ├── dataset/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── 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 │ │ │ │ ├── external/ │ │ │ │ │ └── PreciseRoIPooling/ │ │ │ │ │ ├── .gitignore │ │ │ │ │ ├── LICENSE │ │ │ │ │ ├── README.md │ │ │ │ │ ├── pytorch/ │ │ │ │ │ │ ├── prroi_pool/ │ │ │ │ │ │ │ ├── .gitignore │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ ├── functional.py │ │ │ │ │ │ │ ├── prroi_pool.py │ │ │ │ │ │ │ └── src/ │ │ │ │ │ │ │ ├── prroi_pooling_gpu.c │ │ │ │ │ │ │ ├── prroi_pooling_gpu.h │ │ │ │ │ │ │ ├── prroi_pooling_gpu_impl.cu │ │ │ │ │ │ │ └── prroi_pooling_gpu_impl.cuh │ │ │ │ │ │ └── tests/ │ │ │ │ │ │ └── test_prroi_pooling2d.py │ │ │ │ │ └── src/ │ │ │ │ │ ├── prroi_pooling_gpu_impl.cu │ │ │ │ │ └── prroi_pooling_gpu_impl.cuh │ │ │ │ ├── models/ │ │ │ │ │ ├── AR_seg_mask/ │ │ │ │ │ │ ├── AR_seg_mask.py │ │ │ │ │ │ └── __init__.py │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── backbone/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── base.py │ │ │ │ │ │ ├── resnet.py │ │ │ │ │ │ ├── resnet18_vggm.py │ │ │ │ │ │ └── resnet_seg.py │ │ │ │ │ ├── bbreg/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── atom.py │ │ │ │ │ │ └── atom_iou_net.py │ │ │ │ │ ├── head/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── seg_network.py │ │ │ │ │ │ └── utils.py │ │ │ │ │ ├── layers/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── activation.py │ │ │ │ │ │ ├── blocks.py │ │ │ │ │ │ ├── distance.py │ │ │ │ │ │ ├── filter.py │ │ │ │ │ │ ├── normalization.py │ │ │ │ │ │ └── transform.py │ │ │ │ │ ├── loss/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── kl_regression.py │ │ │ │ │ │ └── target_classification.py │ │ │ │ │ ├── meta/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ └── steepestdescent.py │ │ │ │ │ ├── neck/ │ │ │ │ │ │ ├── CorrNL.py │ │ │ │ │ │ └── neck_utils.py │ │ │ │ │ ├── target_classifier/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── features.py │ │ │ │ │ │ ├── initializer.py │ │ │ │ │ │ ├── linear_filter.py │ │ │ │ │ │ ├── optimizer.py │ │ │ │ │ │ └── residual_modules.py │ │ │ │ │ └── tracking/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ └── dimpnet.py │ │ │ │ ├── run_training.py │ │ │ │ ├── train_settings/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── bbreg/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── atom.py │ │ │ │ │ │ ├── atom_gmm_sampl.py │ │ │ │ │ │ ├── atom_paper.py │ │ │ │ │ │ └── atom_prob_ml.py │ │ │ │ │ └── dimp/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── dimp18.py │ │ │ │ │ ├── dimp50.py │ │ │ │ │ ├── prdimp18.py │ │ │ │ │ ├── prdimp50.py │ │ │ │ │ └── super_dimp.py │ │ │ │ └── trainers/ │ │ │ │ ├── __init__.py │ │ │ │ ├── base_trainer.py │ │ │ │ └── ltr_trainer.py │ │ │ └── pytracking/ │ │ │ ├── ARcm_seg.py │ │ │ ├── VOT/ │ │ │ │ ├── tracker_DiMP.m │ │ │ │ ├── trackers.ini │ │ │ │ └── vot.py │ │ │ ├── VOT2020_super_only_mask_384_HP/ │ │ │ │ ├── dimp_alpha_065.py │ │ │ │ ├── dimp_alpha_seg_class.py │ │ │ │ ├── mixformer_alpha_seg_class.py │ │ │ │ ├── mixformer_large_alpha_seg_class.py │ │ │ │ └── vot.py │ │ │ ├── __init__.py │ │ │ ├── analysis/ │ │ │ │ ├── __init__.py │ │ │ │ ├── evaluate_vos.py │ │ │ │ ├── extract_results.py │ │ │ │ ├── playback_results.py │ │ │ │ ├── plot_results.py │ │ │ │ └── vos_utils.py │ │ │ ├── evaluation/ │ │ │ │ ├── __init__.py │ │ │ │ ├── data.py │ │ │ │ ├── datasets.py │ │ │ │ ├── environment.py │ │ │ │ ├── got10kdataset.py │ │ │ │ ├── lasotdataset.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 │ │ │ │ ├── augmentation.py │ │ │ │ ├── color.py │ │ │ │ ├── deep.py │ │ │ │ ├── extractor.py │ │ │ │ ├── featurebase.py │ │ │ │ ├── net_wrappers.py │ │ │ │ ├── preprocessing.py │ │ │ │ └── util.py │ │ │ ├── libs/ │ │ │ │ ├── __init__.py │ │ │ │ ├── complex.py │ │ │ │ ├── dcf.py │ │ │ │ ├── fourier.py │ │ │ │ ├── operation.py │ │ │ │ ├── optimization.py │ │ │ │ ├── tensordict.py │ │ │ │ └── tensorlist.py │ │ │ ├── parameter/ │ │ │ │ ├── __init__.py │ │ │ │ ├── atom/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── atom_gmm_sampl.py │ │ │ │ │ ├── atom_prob_ml.py │ │ │ │ │ ├── default.py │ │ │ │ │ ├── default_vot.py │ │ │ │ │ └── multiscale_no_iounet.py │ │ │ │ ├── dimp/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── dimp18.py │ │ │ │ │ ├── dimp18_vot.py │ │ │ │ │ ├── dimp50.py │ │ │ │ │ ├── dimp50_vot.py │ │ │ │ │ ├── dimp50_vot19.py │ │ │ │ │ ├── prdimp18.py │ │ │ │ │ ├── prdimp50.py │ │ │ │ │ └── super_dimp.py │ │ │ │ └── eco/ │ │ │ │ ├── __init__.py │ │ │ │ └── default.py │ │ │ ├── tracker/ │ │ │ │ ├── __init__.py │ │ │ │ ├── atom/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── atom.py │ │ │ │ │ └── optim.py │ │ │ │ ├── base/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ └── basetracker.py │ │ │ │ ├── dimp/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ └── dimp.py │ │ │ │ └── eco/ │ │ │ │ ├── __init__.py │ │ │ │ ├── eco.py │ │ │ │ └── optim.py │ │ │ ├── util_scripts/ │ │ │ │ ├── __init__.py │ │ │ │ ├── download_results.py │ │ │ │ ├── pack_got10k_results.py │ │ │ │ └── pack_trackingnet_results.py │ │ │ ├── utils/ │ │ │ │ ├── __init__.py │ │ │ │ ├── convert_vot_anno_to_rect.py │ │ │ │ ├── load_text.py │ │ │ │ ├── loading.py │ │ │ │ ├── params.py │ │ │ │ ├── plotting.py │ │ │ │ └── visdom.py │ │ │ └── vot20_utils.py │ │ ├── PreciseRoIPooling/ │ │ │ ├── .gitignore │ │ │ ├── LICENSE │ │ │ ├── README.md │ │ │ ├── pytorch/ │ │ │ │ ├── prroi_pool/ │ │ │ │ │ ├── .gitignore │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── functional.py │ │ │ │ │ ├── prroi_pool.py │ │ │ │ │ └── src/ │ │ │ │ │ ├── prroi_pooling_gpu.c │ │ │ │ │ ├── prroi_pooling_gpu.h │ │ │ │ │ ├── prroi_pooling_gpu_impl.cu │ │ │ │ │ └── prroi_pooling_gpu_impl.cuh │ │ │ │ └── tests/ │ │ │ │ └── test_prroi_pooling2d.py │ │ │ └── src/ │ │ │ ├── prroi_pooling_gpu_impl.cu │ │ │ └── prroi_pooling_gpu_impl.cuh │ │ └── vot20/ │ │ └── cttrack/ │ │ ├── config.yaml │ │ └── trackers.ini │ ├── lib/ │ │ ├── __init__.py │ │ ├── config/ │ │ │ ├── __init__.py │ │ │ └── ostrack/ │ │ │ └── config.py │ │ ├── models/ │ │ │ ├── __init__.py │ │ │ ├── component/ │ │ │ │ ├── __init__.py │ │ │ │ ├── attention.py │ │ │ │ ├── block.py │ │ │ │ ├── drop.py │ │ │ │ ├── mlp.py │ │ │ │ ├── norm.py │ │ │ │ ├── patch_embed.py │ │ │ │ ├── pos_embed.py │ │ │ │ └── weight_init.py │ │ │ ├── layers/ │ │ │ │ ├── __init__.py │ │ │ │ ├── attn.py │ │ │ │ ├── attn_blocks.py │ │ │ │ ├── head.py │ │ │ │ ├── mask_decoder.py │ │ │ │ ├── patch_embed.py │ │ │ │ ├── rpe.py │ │ │ │ └── self_practice.py │ │ │ ├── ostrack/ │ │ │ │ ├── Vit_model_test.py │ │ │ │ ├── __init__.py │ │ │ │ ├── base_backbone.py │ │ │ │ ├── load_parameter_test.py │ │ │ │ ├── ostrack.py │ │ │ │ ├── ostrack_test.py │ │ │ │ ├── utils.py │ │ │ │ ├── vit.py │ │ │ │ └── vit_ce.py │ │ │ └── timm.py │ │ ├── train/ │ │ │ ├── __init__.py │ │ │ ├── _init_paths.py │ │ │ ├── actors/ │ │ │ │ ├── __init__.py │ │ │ │ ├── base_actor.py │ │ │ │ └── ostrack.py │ │ │ ├── admin/ │ │ │ │ ├── __init__.py │ │ │ │ ├── environment.py │ │ │ │ ├── local.py │ │ │ │ ├── settings.py │ │ │ │ ├── stats.py │ │ │ │ └── tensorboard.py │ │ │ ├── base_functions.py │ │ │ ├── data/ │ │ │ │ ├── __init__.py │ │ │ │ ├── bounding_box_utils.py │ │ │ │ ├── image_loader.py │ │ │ │ ├── loader.py │ │ │ │ ├── processing.py │ │ │ │ ├── processing_utils.py │ │ │ │ ├── sampler.py │ │ │ │ ├── sequence_sampler.py │ │ │ │ ├── transforms.py │ │ │ │ └── wandb_logger.py │ │ │ ├── data_specs/ │ │ │ │ ├── README.md │ │ │ │ ├── got10k_train_full_split.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 │ │ │ ├── dataset/ │ │ │ │ ├── COCO_tool.py │ │ │ │ ├── __init__.py │ │ │ │ ├── base_image_dataset.py │ │ │ │ ├── base_video_dataset.py │ │ │ │ ├── coco.py │ │ │ │ ├── coco_seq.py │ │ │ │ ├── coco_seq_lmdb.py │ │ │ │ ├── got10k.py │ │ │ │ ├── got10k_lmdb.py │ │ │ │ ├── imagenetvid.py │ │ │ │ ├── imagenetvid_lmdb.py │ │ │ │ ├── lasot.py │ │ │ │ ├── lasot_lmdb.py │ │ │ │ ├── tracking_net.py │ │ │ │ └── tracking_net_lmdb.py │ │ │ ├── run_training.py │ │ │ ├── train_script.py │ │ │ ├── train_script_distill.py │ │ │ └── trainers/ │ │ │ ├── __init__.py │ │ │ ├── base_trainer.py │ │ │ └── ltr_trainer.py │ │ ├── utils/ │ │ │ ├── __init__.py │ │ │ ├── box_ops.py │ │ │ ├── ce_utils.py │ │ │ ├── focal_loss.py │ │ │ ├── heapmap_utils.py │ │ │ ├── image.py │ │ │ ├── lmdb_utils.py │ │ │ ├── merge.py │ │ │ ├── misc.py │ │ │ ├── tensor.py │ │ │ └── variable_hook.py │ │ └── vis/ │ │ ├── __init__.py │ │ ├── plotting.py │ │ └── utils.py │ └── tracking/ │ ├── _init_paths.py │ ├── analysis_results.ipynb │ ├── analysis_results.py │ ├── analysis_results_ITP.py │ ├── convert_transt.py │ ├── create_default_local_file.py │ ├── download_pytracking_results.py │ ├── pre_read_datasets.py │ ├── profile_model.py │ ├── test.py │ ├── test_exp.py │ ├── train.py │ ├── video_demo.py │ └── vis_results.py ├── experiments/ │ ├── artrack/ │ │ ├── artrack_256_full.yaml │ │ ├── artrack_256_got.yaml │ │ ├── artrack_384_full.yaml │ │ └── artrack_large_384_full.yaml │ ├── artrack_seq/ │ │ ├── artrack_seq_256_full.yaml │ │ ├── artrack_seq_256_got.yaml │ │ ├── artrack_seq_384_full.yaml │ │ └── artrack_seq_large_384_full.yaml │ ├── artrackv2/ │ │ ├── artrackv2_256_full.yaml │ │ ├── artrackv2_256_got.yaml │ │ └── artrackv2_large_384_got.yaml │ └── artrackv2_seq/ │ ├── artrackv2_seq_256_full.yaml │ ├── artrackv2_seq_256_got.yaml │ └── artrackv2_seq_large_384_got.yaml ├── external/ │ ├── AR/ │ │ ├── README.md │ │ ├── __init__.py │ │ ├── ltr/ │ │ │ ├── README.md │ │ │ ├── __init__.py │ │ │ ├── actors/ │ │ │ │ ├── __init__.py │ │ │ │ ├── base_actor.py │ │ │ │ ├── bbreg.py │ │ │ │ └── tracking.py │ │ │ ├── admin/ │ │ │ │ ├── __init__.py │ │ │ │ ├── environment.py │ │ │ │ ├── loading.py │ │ │ │ ├── model_constructor.py │ │ │ │ ├── multigpu.py │ │ │ │ ├── settings.py │ │ │ │ ├── stats.py │ │ │ │ └── tensorboard.py │ │ │ ├── data/ │ │ │ │ ├── __init__.py │ │ │ │ ├── bounding_box_utils.py │ │ │ │ ├── image_loader.py │ │ │ │ ├── loader.py │ │ │ │ ├── processing.py │ │ │ │ ├── processing_utils.py │ │ │ │ ├── processing_utils_SE.py │ │ │ │ ├── sampler.py │ │ │ │ └── transforms.py │ │ │ ├── dataset/ │ │ │ │ ├── __init__.py │ │ │ │ ├── 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 │ │ │ ├── external/ │ │ │ │ └── PreciseRoIPooling/ │ │ │ │ ├── .gitignore │ │ │ │ ├── LICENSE │ │ │ │ ├── README.md │ │ │ │ ├── pytorch/ │ │ │ │ │ ├── prroi_pool/ │ │ │ │ │ │ ├── .gitignore │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── functional.py │ │ │ │ │ │ ├── prroi_pool.py │ │ │ │ │ │ └── src/ │ │ │ │ │ │ ├── prroi_pooling_gpu.c │ │ │ │ │ │ ├── prroi_pooling_gpu.h │ │ │ │ │ │ ├── prroi_pooling_gpu_impl.cu │ │ │ │ │ │ └── prroi_pooling_gpu_impl.cuh │ │ │ │ │ └── tests/ │ │ │ │ │ └── test_prroi_pooling2d.py │ │ │ │ └── src/ │ │ │ │ ├── prroi_pooling_gpu_impl.cu │ │ │ │ └── prroi_pooling_gpu_impl.cuh │ │ │ ├── models/ │ │ │ │ ├── AR_seg_mask/ │ │ │ │ │ ├── AR_seg_mask.py │ │ │ │ │ └── __init__.py │ │ │ │ ├── __init__.py │ │ │ │ ├── backbone/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── base.py │ │ │ │ │ ├── resnet.py │ │ │ │ │ ├── resnet18_vggm.py │ │ │ │ │ └── resnet_seg.py │ │ │ │ ├── bbreg/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── atom.py │ │ │ │ │ └── atom_iou_net.py │ │ │ │ ├── head/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── seg_network.py │ │ │ │ │ └── utils.py │ │ │ │ ├── layers/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── activation.py │ │ │ │ │ ├── blocks.py │ │ │ │ │ ├── distance.py │ │ │ │ │ ├── filter.py │ │ │ │ │ ├── normalization.py │ │ │ │ │ └── transform.py │ │ │ │ ├── loss/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── kl_regression.py │ │ │ │ │ └── target_classification.py │ │ │ │ ├── meta/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ └── steepestdescent.py │ │ │ │ ├── neck/ │ │ │ │ │ ├── CorrNL.py │ │ │ │ │ └── neck_utils.py │ │ │ │ ├── target_classifier/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── features.py │ │ │ │ │ ├── initializer.py │ │ │ │ │ ├── linear_filter.py │ │ │ │ │ ├── optimizer.py │ │ │ │ │ └── residual_modules.py │ │ │ │ └── tracking/ │ │ │ │ ├── __init__.py │ │ │ │ └── dimpnet.py │ │ │ ├── run_training.py │ │ │ ├── train_settings/ │ │ │ │ ├── __init__.py │ │ │ │ ├── bbreg/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── atom.py │ │ │ │ │ ├── atom_gmm_sampl.py │ │ │ │ │ ├── atom_paper.py │ │ │ │ │ └── atom_prob_ml.py │ │ │ │ └── dimp/ │ │ │ │ ├── __init__.py │ │ │ │ ├── dimp18.py │ │ │ │ ├── dimp50.py │ │ │ │ ├── prdimp18.py │ │ │ │ ├── prdimp50.py │ │ │ │ └── super_dimp.py │ │ │ └── trainers/ │ │ │ ├── __init__.py │ │ │ ├── base_trainer.py │ │ │ └── ltr_trainer.py │ │ └── pytracking/ │ │ ├── ARcm_seg.py │ │ ├── VOT/ │ │ │ ├── tracker_DiMP.m │ │ │ ├── trackers.ini │ │ │ └── vot.py │ │ ├── VOT2020_super_only_mask_384_HP/ │ │ │ ├── dimp_alpha_065.py │ │ │ ├── dimp_alpha_seg_class.py │ │ │ ├── mixformer_alpha_seg_class.py │ │ │ ├── mixformer_large_alpha_seg_class.py │ │ │ └── vot.py │ │ ├── __init__.py │ │ ├── analysis/ │ │ │ ├── __init__.py │ │ │ ├── evaluate_vos.py │ │ │ ├── extract_results.py │ │ │ ├── playback_results.py │ │ │ ├── plot_results.py │ │ │ └── vos_utils.py │ │ ├── evaluation/ │ │ │ ├── __init__.py │ │ │ ├── data.py │ │ │ ├── datasets.py │ │ │ ├── environment.py │ │ │ ├── got10kdataset.py │ │ │ ├── lasotdataset.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 │ │ │ ├── augmentation.py │ │ │ ├── color.py │ │ │ ├── deep.py │ │ │ ├── extractor.py │ │ │ ├── featurebase.py │ │ │ ├── net_wrappers.py │ │ │ ├── preprocessing.py │ │ │ └── util.py │ │ ├── libs/ │ │ │ ├── __init__.py │ │ │ ├── complex.py │ │ │ ├── dcf.py │ │ │ ├── fourier.py │ │ │ ├── operation.py │ │ │ ├── optimization.py │ │ │ ├── tensordict.py │ │ │ └── tensorlist.py │ │ ├── parameter/ │ │ │ ├── __init__.py │ │ │ ├── atom/ │ │ │ │ ├── __init__.py │ │ │ │ ├── atom_gmm_sampl.py │ │ │ │ ├── atom_prob_ml.py │ │ │ │ ├── default.py │ │ │ │ ├── default_vot.py │ │ │ │ └── multiscale_no_iounet.py │ │ │ ├── dimp/ │ │ │ │ ├── __init__.py │ │ │ │ ├── dimp18.py │ │ │ │ ├── dimp18_vot.py │ │ │ │ ├── dimp50.py │ │ │ │ ├── dimp50_vot.py │ │ │ │ ├── dimp50_vot19.py │ │ │ │ ├── prdimp18.py │ │ │ │ ├── prdimp50.py │ │ │ │ └── super_dimp.py │ │ │ └── eco/ │ │ │ ├── __init__.py │ │ │ └── default.py │ │ ├── tracker/ │ │ │ ├── __init__.py │ │ │ ├── atom/ │ │ │ │ ├── __init__.py │ │ │ │ ├── atom.py │ │ │ │ └── optim.py │ │ │ ├── base/ │ │ │ │ ├── __init__.py │ │ │ │ └── basetracker.py │ │ │ ├── dimp/ │ │ │ │ ├── __init__.py │ │ │ │ └── dimp.py │ │ │ └── eco/ │ │ │ ├── __init__.py │ │ │ ├── eco.py │ │ │ └── optim.py │ │ ├── util_scripts/ │ │ │ ├── __init__.py │ │ │ ├── download_results.py │ │ │ ├── pack_got10k_results.py │ │ │ └── pack_trackingnet_results.py │ │ ├── utils/ │ │ │ ├── __init__.py │ │ │ ├── convert_vot_anno_to_rect.py │ │ │ ├── load_text.py │ │ │ ├── loading.py │ │ │ ├── params.py │ │ │ ├── plotting.py │ │ │ └── visdom.py │ │ └── vot20_utils.py │ ├── PreciseRoIPooling/ │ │ ├── .gitignore │ │ ├── LICENSE │ │ ├── README.md │ │ ├── pytorch/ │ │ │ ├── prroi_pool/ │ │ │ │ ├── .gitignore │ │ │ │ ├── __init__.py │ │ │ │ ├── functional.py │ │ │ │ ├── prroi_pool.py │ │ │ │ └── src/ │ │ │ │ ├── prroi_pooling_gpu.c │ │ │ │ ├── prroi_pooling_gpu.h │ │ │ │ ├── prroi_pooling_gpu_impl.cu │ │ │ │ └── prroi_pooling_gpu_impl.cuh │ │ │ └── tests/ │ │ │ └── test_prroi_pooling2d.py │ │ └── src/ │ │ ├── prroi_pooling_gpu_impl.cu │ │ └── prroi_pooling_gpu_impl.cuh │ └── vot20/ │ └── cttrack/ │ ├── config.yaml │ └── trackers.ini ├── lib/ │ ├── __init__.py │ ├── config/ │ │ ├── __init__.py │ │ ├── artrack/ │ │ │ └── config.py │ │ ├── artrack_seq/ │ │ │ └── config.py │ │ ├── artrackv2/ │ │ │ └── config.py │ │ └── artrackv2_seq/ │ │ └── config.py │ ├── models/ │ │ ├── __init__.py │ │ ├── artrack/ │ │ │ ├── __init__.py │ │ │ ├── artrack.py │ │ │ ├── base_backbone.py │ │ │ ├── utils.py │ │ │ ├── vit.py │ │ │ └── vit_ce.py │ │ ├── artrack_seq/ │ │ │ ├── __init__.py │ │ │ ├── artrack_seq.py │ │ │ ├── base_backbone.py │ │ │ ├── utils.py │ │ │ ├── vit.py │ │ │ └── vit_ce.py │ │ ├── artrackv2/ │ │ │ ├── __init__.py │ │ │ ├── artrackv2.py │ │ │ ├── base_backbone.py │ │ │ ├── utils.py │ │ │ └── vit.py │ │ ├── artrackv2_seq/ │ │ │ ├── __init__.py │ │ │ ├── artrackv2_seq.py │ │ │ ├── base_backbone.py │ │ │ ├── utils.py │ │ │ └── vit.py │ │ ├── layers/ │ │ │ ├── __init__.py │ │ │ ├── attn.py │ │ │ ├── attn_blocks.py │ │ │ ├── frozen_bn.py │ │ │ ├── head.py │ │ │ ├── head_seq.py │ │ │ ├── mask_decoder.py │ │ │ ├── patch_embed.py │ │ │ └── rpe.py │ │ └── mask_decoder/ │ │ ├── __init__.py │ │ ├── attention.py │ │ ├── block.py │ │ ├── drop.py │ │ ├── mlp.py │ │ ├── norm.py │ │ ├── patch_embed.py │ │ ├── pos_embed.py │ │ └── weight_init.py │ ├── test/ │ │ ├── __init__.py │ │ ├── analysis/ │ │ │ ├── __init__.py │ │ │ ├── extract_results.py │ │ │ └── plot_results.py │ │ ├── evaluation/ │ │ │ ├── __init__.py │ │ │ ├── data.py │ │ │ ├── datasets.py │ │ │ ├── environment.py │ │ │ ├── got10kdataset.py │ │ │ ├── itbdataset.py │ │ │ ├── lasot_lmdbdataset.py │ │ │ ├── lasotdataset.py │ │ │ ├── lasotextensionsubsetdataset.py │ │ │ ├── local.py │ │ │ ├── nfsdataset.py │ │ │ ├── otbdataset.py │ │ │ ├── running.py │ │ │ ├── tc128cedataset.py │ │ │ ├── tc128dataset.py │ │ │ ├── tnl2kdataset.py │ │ │ ├── tracker.py │ │ │ ├── trackingnetdataset.py │ │ │ ├── uavdataset.py │ │ │ └── votdataset.py │ │ ├── parameter/ │ │ │ ├── __init__.py │ │ │ ├── artrack.py │ │ │ ├── artrack_seq.py │ │ │ ├── artrackv2.py │ │ │ └── artrackv2_seq.py │ │ ├── tracker/ │ │ │ ├── __init__.py │ │ │ ├── artrack.py │ │ │ ├── artrack_seq.py │ │ │ ├── artrackv2.py │ │ │ ├── artrackv2_seq.py │ │ │ ├── basetracker.py │ │ │ ├── data_utils.py │ │ │ └── vis_utils.py │ │ └── utils/ │ │ ├── __init__.py │ │ ├── _init_paths.py │ │ ├── hann.py │ │ ├── load_text.py │ │ ├── params.py │ │ ├── transform_got10k.py │ │ └── transform_trackingnet.py │ ├── train/ │ │ ├── __init__.py │ │ ├── _init_paths.py │ │ ├── actors/ │ │ │ ├── __init__.py │ │ │ ├── artrack.py │ │ │ ├── artrack_seq.py │ │ │ ├── artrackv2.py │ │ │ ├── artrackv2_seq.py │ │ │ └── base_actor.py │ │ ├── admin/ │ │ │ ├── __init__.py │ │ │ ├── environment.py │ │ │ ├── local.py │ │ │ ├── multigpu.py │ │ │ ├── settings.py │ │ │ ├── stats.py │ │ │ └── tensorboard.py │ │ ├── base_functions.py │ │ ├── data/ │ │ │ ├── __init__.py │ │ │ ├── bounding_box_utils.py │ │ │ ├── image_loader.py │ │ │ ├── loader.py │ │ │ ├── processing.py │ │ │ ├── processing_utils.py │ │ │ ├── sampler.py │ │ │ ├── sequence_sampler.py │ │ │ ├── sequence_sampler_v2.py │ │ │ ├── transforms.py │ │ │ └── wandb_logger.py │ │ ├── data_specs/ │ │ │ ├── README.md │ │ │ ├── got10k_train_full_split.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 │ │ ├── dataset/ │ │ │ ├── COCO_tool.py │ │ │ ├── __init__.py │ │ │ ├── base_image_dataset.py │ │ │ ├── base_video_dataset.py │ │ │ ├── coco.py │ │ │ ├── coco_seq.py │ │ │ ├── coco_seq_lmdb.py │ │ │ ├── got10k.py │ │ │ ├── got10k_lmdb.py │ │ │ ├── imagenetvid.py │ │ │ ├── imagenetvid_lmdb.py │ │ │ ├── lasot.py │ │ │ ├── lasot_lmdb.py │ │ │ ├── tracking_net.py │ │ │ └── tracking_net_lmdb.py │ │ ├── run_training.py │ │ ├── train_script.py │ │ ├── train_script_distill.py │ │ └── trainers/ │ │ ├── __init__.py │ │ ├── base_trainer.py │ │ ├── ltr_seq_trainer.py │ │ ├── ltr_seq_trainer_v2.py │ │ └── ltr_trainer.py │ ├── utils/ │ │ ├── __init__.py │ │ ├── box_ops.py │ │ ├── ce_utils.py │ │ ├── focal_loss.py │ │ ├── heapmap_utils.py │ │ ├── image.py │ │ ├── lmdb_utils.py │ │ ├── merge.py │ │ ├── misc.py │ │ ├── tensor.py │ │ └── variable_hook.py │ └── vis/ │ ├── __init__.py │ ├── plotting.py │ ├── utils.py │ └── visdom_cus.py └── tracking/ ├── _init_paths.py ├── analysis_results.py ├── analysis_results_ITP.py ├── convert_transt.py ├── create_default_local_file.py ├── download_pytracking_results.py ├── pre_read_datasets.py ├── test.py ├── test_exp.py └── train.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: ARTrack_env_cuda113.yaml ================================================ name: artrack channels: - http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch - http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - defaults dependencies: - ca-certificates=2021.10.8=ha878542_0 - certifi=2021.10.8=py39hf3d152e_2 - fvcore=0.1.5.post20220305=pyhd8ed1ab_0 - portalocker=2.4.0=py39hf3d152e_0 - python_abi=3.9=2_cp39 - tabulate=0.8.9=pyhd8ed1ab_0 - termcolor=1.1.0=py_2 - yacs=0.1.8=pyhd8ed1ab_0 - ffmpeg=4.3=hf484d3e_0 - pytorch=1.11.0=py3.9_cuda11.3_cudnn8.2.0_0 - pytorch-mutex=1.0=cuda - torchvision=0.12.0=py39_cu113 - _anaconda_depends=2021.11=py39_0 - _libgcc_mutex=0.1=main - _openmp_mutex=4.5=1_gnu - alabaster=0.7.12=pyhd3eb1b0_0 - 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zope=1.0=py39h06a4308_1 - zope.event=4.5.0=py39h06a4308_0 - zope.interface=5.4.0=py39h7f8727e_0 - zstd=1.4.9=haebb681_0 - pip: - astor==0.8.1 - configparser==5.2.0 - data==0.4 - docker-pycreds==0.4.0 - easydict==1.9 - einops==0.4.1 - formulaic==0.5.2 - funcsigs==1.0.2 - future==0.18.2 - gitdb==4.0.9 - gitpython==3.1.27 - interface-meta==1.3.0 - iopath==0.1.9 - jpeg4py==0.1.4 - jsonpatch==1.32 - jsonpointer==2.3 - latex==0.7.0 - libarchive-c==2.9 - linearmodels==4.29 - lmdb==1.3.0 - loguru==0.6.0 - mat73==0.59 - memory-profiler==0.60.0 - msgpack==1.0.2 - ninja==1.11.1 - opencv-python==4.5.5.64 - pathtools==0.1.2 - promise==2.3 - property-cached==1.6.4 - protobuf==3.20.0 - pycocotools==2.0.4 - pyhdfe==0.1.2 - ruamel-yaml-conda==0.15.100 - sentry-sdk==1.5.8 - setproctitle==1.2.2 - setuptools-scm==7.1.0 - shapely==1.8.1.post1 - shortuuid==1.0.8 - shutilwhich==1.1.0 - smmap==5.0.0 - tables==3.6.1 - tempdir==0.7.1 - tensorboardx==2.5.1 - thop==0.1.0.post2207010342 - tikzplotlib==0.10.1 - timm==0.5.4 - tomli==2.0.1 - torch==1.11.0 - torchfile==0.1.0 - visdom==0.1.8.9 - wandb==0.12.11 - webcolors==1.12 - yaspin==2.1.0 prefix: /public/baiyifan/conda_envs/artrack ================================================ FILE: LICENSE ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: README.md ================================================ # ARTrack [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/artrackv2-prompting-autoregressive-tracker/visual-object-tracking-on-got-10k)](https://paperswithcode.com/sota/visual-object-tracking-on-got-10k?p=artrackv2-prompting-autoregressive-tracker) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/artrackv2-prompting-autoregressive-tracker/visual-object-tracking-on-lasot)](https://paperswithcode.com/sota/visual-object-tracking-on-lasot?p=artrackv2-prompting-autoregressive-tracker) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/artrackv2-prompting-autoregressive-tracker/visual-object-tracking-on-trackingnet)](https://paperswithcode.com/sota/visual-object-tracking-on-trackingnet?p=artrackv2-prompting-autoregressive-tracker) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/artrackv2-prompting-autoregressive-tracker/visual-object-tracking-on-needforspeed)](https://paperswithcode.com/sota/visual-object-tracking-on-needforspeed?p=artrackv2-prompting-autoregressive-tracker) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/artrackv2-prompting-autoregressive-tracker/visual-object-tracking-on-tnl2k)](https://paperswithcode.com/sota/visual-object-tracking-on-tnl2k?p=artrackv2-prompting-autoregressive-tracker) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/artrackv2-prompting-autoregressive-tracker/visual-object-tracking-on-lasot-ext)](https://paperswithcode.com/sota/visual-object-tracking-on-lasot-ext?p=artrackv2-prompting-autoregressive-tracker) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/artrackv2-prompting-autoregressive-tracker/visual-object-tracking-on-uav123)](https://paperswithcode.com/sota/visual-object-tracking-on-uav123?p=artrackv2-prompting-autoregressive-tracker) The official PyTorch implementation of our **CVPR 2023 Highlight** and **CVPR 2024 Poster** paper: **Autoregressive Visual Tracking** **ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe** GitHub maintainer: [Yifan Bai](https://github.com/AlexDotHam) [[CVPR2023](https://openaccess.thecvf.com/content/CVPR2023/papers/Wei_Autoregressive_Visual_Tracking_CVPR_2023_paper.pdf)] [[CVPR2024](https://artrackv2.github.io/)] ### :bookmark: Update List: - [ ] Fastitnp - [ ] VastTrack and More Datasets ### :bookmark: Checkpoints in Baidu Pan [[Baidu Pan](https://pan.baidu.com/s/1bpdLdwO39kHD5iH06aGIzg?pwd=g06j)] passwd: g06j Google Drive: [ARTrackV2-B-256](https://drive.google.com/file/d/1tGaY5jQxZOTzJDWXgOgoHtBwc5l4NLQ2/view?usp=drive_link) | [ARTrackV2-B-256-GOT](https://drive.google.com/file/d/1RqsjHqTBsIN5ivD-C5tXDyhZlGZjJN88/view?usp=drive_link) | [ARTrackV2-L-384-GOT](https://drive.google.com/file/d/1KJ-TXFSn6K_OhchoRE29ePZbSm9sBHVS/view?usp=drive_link) | ### :bookmark:Our ARTrackV2 is accepted by CVPR2024!!! [[Deprecated Code](https://drive.google.com/file/d/15PHRN9utPfm1L4djr7U6MLHWIkx90EVD/view?usp=sharing)] [[Raw Result](https://drive.google.com/drive/folders/1Kd6IA60OQStfYCgsi42I20VRDTB7VcWl?usp=sharing)] We have released the training code for ARTrackV2 and merged it into this repository. You can adjust the config to use the corresponding ARTrackV2-marked modules. The training process remains the same as V1. | Variant | [ARTrackV2-B-256](https://drive.google.com/file/d/1tGaY5jQxZOTzJDWXgOgoHtBwc5l4NLQ2/view?usp=drive_link) | [ARTrackV2-B-256-GOT](https://drive.google.com/file/d/1RqsjHqTBsIN5ivD-C5tXDyhZlGZjJN88/view?usp=drive_link) | [ARTrackV2-L-384-GOT](https://drive.google.com/file/d/1KJ-TXFSn6K_OhchoRE29ePZbSm9sBHVS/view?usp=drive_link) | |:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:| | Model Config | ViT-B, 256^2 resolution | ViT-B, 256^2 resolution | ViT-L, 384^2 resolution | | GOT-10k (AO / SR 0.5 / SR 0.75) | - / - / - | 76.1 / 85.5 / 72.9 | 79.6 / 88.0 / 78.7 | | LaSOT (AUC / Norm P / P) | 71.5 / 80.3 / 77.5 | - / - / - | - / - / - | | TrackingNet (AUC / Norm P / P) | 84.3 / 89.1 / 83.6 | - / - / - | - / - / - | | LaSOT_ext (AUC / Norm P / P) | 51.1 / 58.4 / 61.5 | - / - / - | - / - / - | ## Highlight ![](figure/overview.jpg) ### :bookmark:Brief Introduction We present **ARTrack**, an autoregressive framework for visual object tracking. ARTrack tackles tracking as a coordinate sequence interpretation task that estimates object trajectories progressively, where the current estimate is induced by previous states and in turn affects subsequences. This time-autoregressive approach models the sequential evolution of trajectories to keep tracing the object **across frames**, making it superior to existing template matching based trackers that only consider the **per-frame** localization accuracy. ARTrack is simple and direct, eliminating customized localization heads and post-processings. Despite its simplicity, ARTrack achieves state-of-the-art performance on prevailing benchmark datasets. ### :bookmark:Strong Performance | Variant | ARTrack-256 | ARTrack-384 | ARTrack-L-384 | |:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:| | Model Config | ViT-B, 256^2 resolution | ViT-B, 384^2 resolution | ViT-L, 384^2 resolution | | GOT-10k (AO / SR 0.5 / SR 0.75) | 73.5 / 82.2 / 70.9 | 75.5 / 84.3 / 74.3 | 78.5 / 87.4 / 77.8 | | LaSOT (AUC / Norm P / P) | 70.4 / 79.5 / 76.6 | 72.6 / 81.7 / 79.1 | 73.1 / 82.2 / 80.3 | | TrackingNet (AUC / Norm P / P) | 84.2 / 88.7 / 83.5 | 85.1 / 89.1 / 84.8 | 85.6 / 89.6 / 84.8 | | LaSOT_ext (AUC / Norm P / P) | 46.4 / 56.5 / 52.3 | 51.9 / 62.0 / 58.5 | 52.8 / 62.9 / 59.7 | | TNL-2K (AUC) | 57.5 | 59.8 | 60.3 | | NfS30 (AUC) | 64.3 | 66.8 | 67.9 | | UAV123 (AUC) | 67.7 | 70.5 | 71.2 | ### :bookmark:Inference Speed Our baseline model (backbone: ViT-B, resolution: 256x256) can run at **26 fps** (frames per second) on a single NVIDIA GeForce RTX 3090, our alter decoder version can run at **45 fps** on a single NVIDIA GeForce RTX 3090. ## Bug of array of inhomogeneous shape Thanks to [MrtXue](https://github.com/MrtXue), if you meet the "ValueError: setting an array element with a sequence." when you train in the second stage, you can try to reduce your numpy version to 1.23. ## Update for checkpoint(ARTrack_large_384_full): You can download the model weights from [Google Drive](https://drive.google.com/drive/folders/1KsH_MIZIdgjZpUZBmR4P88yeYDqM8yNW?usp=sharing) | Variant | ARTrack-L-384 | |:-------------------------------:|:-----------------------:| | Model Config | ViT-L, 384^2 resolution | | GOT-10k (AO / SR 0.5 / SR 0.75) | 80.0 / 88.5 / 80.0 | | LaSOT (AUC / Norm P / P) | 73.5 / 82.4 / 80.6 | | TrackingNet (AUC / Norm P / P) | 85.5 / 90.1 / 85.9 | | LaSOT_ext (AUC / Norm P / P) | 51.8 / 62.3 / 58.8 | ## Update for checkpoint and raw_result(ARTrack_base_256_full): You can download the model weights and raw_result from [Google Drive](https://drive.google.com/drive/folders/1KsH_MIZIdgjZpUZBmR4P88yeYDqM8yNW?usp=sharing) | Variant | ARTrack-256 | ARTrack-256-got | |:-------------------------------:|:-----------------------:|:-----------------------:| | Model Config | ViT-B, 256^2 resolution | ViT-B, 256^2 resolution | | GOT-10k (AO / SR 0.5 / SR 0.75) | 76.7 / 85.7 / 74.8 | 74.1 / 83.1 / 70.0 | | LaSOT (AUC / Norm P / P) | 70.8 / 79.6 / 76.3 | - / - / - | | TrackingNet (AUC / Norm P / P) | 84.3 / 88.7 / 83.4 | - / - / - | | LaSOT_ext (AUC / Norm P / P) | 48.4 / 57.7 / 53.7 | - / - / - | ## Install the environment Use the Anaconda (CUDA 11.3) ``` conda env create -f ARTrack_env_cuda113.yaml ``` ## Set project paths Run the following command to set paths for this project ``` python tracking/create_default_local_file.py --workspace_dir . --data_dir ./data --save_dir ./output ``` After running this command, you can also modify paths by editing these two files ``` lib/train/admin/local.py # paths about training lib/test/evaluation/local.py # paths about testing ``` ## Data Preparation Put the tracking datasets in ./data. It should look like this: ``` ${PROJECT_ROOT} -- data -- lasot |-- airplane |-- basketball |-- bear ... -- got10k |-- test |-- train |-- val -- coco |-- annotations |-- images -- trackingnet |-- TRAIN_0 |-- TRAIN_1 ... |-- TRAIN_11 |-- TEST ``` ## Training Download pre-trained [MAE ViT-Base weights](https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_base.pth) and put it under `$PROJECT_ROOT$/pretrained_models` (different pretrained models can also be used, see [MAE](https://github.com/facebookresearch/mae) for more details). ### One-stage pair-level training Since sequence-level training requires video input, and the COCO dataset contains only images, traditional training methods were first used to train the model so that it could be fairly compared to other trackers. ``` python tracking/train.py --script artrack --config artrack_256_full --save_dir ./output --mode multiple --nproc_per_node 4 --use_wandb 0 ``` Replace `--config` with the desired model config under `experiments/artrack`. We use [wandb](https://github.com/wandb/client) to record detailed training logs, in case you don't want to use wandb, set `--use_wandb 0`. ### Two-stage sequence-level training To enable sequence-level training, replace 'experience/artrack_seq/*.yaml' PRETRAIN_PTH in the yaml configuration file with the path to your pretrained checkpoint, such as './output/artrack_256_full/checkpoints/train/artrack/artrack_256_full/ARTrack_ep0240.pth.tar'. ``` python tracking/train.py --script artrack_seq --config artrack_seq_256_full --save_dir ./output --mode multiple --nproc_per_node 4 --use_wandb 0 ``` ## Evaluation Change the corresponding values of `lib/test/evaluation/local.py` to the actual benchmark saving paths Some testing examples: - LaSOT or other off-line evaluated benchmarks (modify `--dataset` correspondingly) ``` python tracking/test.py artrack_seq artrack_seq_256_full --dataset lasot --threads 16 --num_gpus 4 python tracking/analysis_results.py # need to modify tracker configs and names ``` - GOT10K-test ``` python tracking/test.py artrack_seq artrack_seq_256_full --dataset got10k_test --threads 16 --num_gpus 4 python lib/test/utils/transform_got10k.py --tracker_name ostrack --cfg_name vitb_384_mae_ce_32x4_got10k_ep100 ``` - TrackingNet ``` python tracking/test.py artrack_seq artrack_seq_256_full --dataset trackingnet --threads 16 --num_gpus 4 python lib/test/utils/transform_trackingnet.py --tracker_name ostrack --cfg_name vitb_384_mae_ce_32x4_ep300 ``` ## Acknowledgement :heart::heart::heart:Our idea is implemented base on the following projects. We really appreciate their excellent open-source works! - [SIoU](https://github.com/AlexDotHam/SIoU-loss) [[related paper](https://arxiv.org/abs/2205.12740)] - [OSTrack](https://github.com/botaoye/OSTrack) [[related paper](https://arxiv.org/abs/2203.11991)] - [PyTracking](https://github.com/visionml/pytracking) [[related paper](https://arxiv.org/abs/2208.06888)] :heart::heart::heart:This project is not for commercial use. For commercial use, please contact the author. :heart::heart::heart:This project is not for commercial use. For commercial use, please contact the author. :heart::heart::heart:This project is not for commercial use. For commercial use, please contact the author. ## Citation If any parts of our paper and code help your research, please consider citing us and giving a star to our repository. ``` @InProceedings{Wei_2023_CVPR, author = {Wei, Xing and Bai, Yifan and Zheng, Yongchao and Shi, Dahu and Gong, Yihong}, title = {Autoregressive Visual Tracking}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2023}, pages = {9697-9706} } @InProceedings{Bai_2024_CVPR, author = {Bai, Yifan and Zhao, Zeyang and Gong, Yihong and Wei, Xing}, title = {ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024} } ``` ## Contact If you have any questions or concerns, feel free to open issues or directly contact me through the ways on my GitHub homepage **provide below paper's title**. ================================================ FILE: artrackv2_mindspore/.gitignore ================================================ test/ lib/models/__pycache__/ lib/config/__pycache__/ lib/test/tracker/__pycache__/ lib/models/__pycache__/ lib/config/ostrack/__pycache__/ lib/config/ostrack/__pycache__/ ================================================ FILE: artrackv2_mindspore/README.md ================================================ # ARTrackV2 ## Evaluation Make sure you have installed the GPU version of MindSpore according to [link](https://www.mindspore.cn/install/en). Change the corresponding values of `lib/test/evaluation/local.py` to the actual benchmark saving paths Some testing examples: - GOT10K-test ```python cd tracking python test.py ostrack 2stage_256_got --dataset got10k_test --thread 0 --num_gpus 1 ``` ================================================ FILE: artrackv2_mindspore/experiments/ostrack/2stage_256_got.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 3 FACTOR: 4.0 SCALE_JITTER: 0.25 SIZE: 256 NUMBER: 14 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 128 NUMBER: 2 TRAIN: DATASETS_NAME: #- LASOT - GOT10K_train_full #- TRACKINGNET DATASETS_RATIO: #- 1 - 1 #- 1 SAMPLE_PER_EPOCH: 1000 VAL: DATASETS_NAME: - GOT10K_official_val DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: PRETRAIN_FILE: "../checkpoint1.ckpt" EXTRA_MERGER: False RETURN_INTER: False DECODER: TYPE: "mask" MASK_RATIO: 0.75 EMBEDDIM: 512 DEPTH: 8 NUMHEADS: 16 MLPRATIO: 4 BACKBONE: TYPE: vit_base_patch16_224 STRIDE: 16 HEAD: TYPE: PIX NUM_CHANNELS: 768 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 8 EPOCH: 120 GIOU_WEIGHT: 2.0 L1_WEIGHT: 0.0 GRAD_CLIP_NORM: 0.1 LR: 0.00008 LR_DROP_EPOCH: 90 NUM_WORKER: 6 OPTIMIZER: ADAMW PRINT_INTERVAL: 1 SCHEDULER: TYPE: step DECAY_RATE: 0.05 VAL_EPOCH_INTERVAL: 10 WEIGHT_DECAY: 0.05 AMP: False TEST: EPOCH: 300 SEARCH_FACTOR: 3.95 SEARCH_SIZE: 256 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 128 ================================================ FILE: artrackv2_mindspore/experiments/ostrack/best_384.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 4.5 FACTOR: 5.0 SCALE_JITTER: 0.5 SIZE: 384 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 192 TRAIN: DATASETS_NAME: - LASOT - GOT10K_vottrain - COCO17 - TRACKINGNET DATASETS_RATIO: - 1 - 1 - 1 - 1 SAMPLE_PER_EPOCH: 60000 VAL: DATASETS_NAME: - GOT10K_votval DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10240 MODEL: PRETRAIN_FILE: "mae_pretrain_vit_base.pth" EXTRA_MERGER: False RETURN_INTER: False BACKBONE: TYPE: vit_base_patch16_224 STRIDE: 16 HEAD: TYPE: PIX NUM_CHANNELS: 768 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 16 EPOCH: 500 GIOU_WEIGHT: 2.0 L1_WEIGHT: 0.0 GRAD_CLIP_NORM: 0.1 LR: 0.00008 LR_DROP_EPOCH: 400 NUM_WORKER: 16 OPTIMIZER: ADAMW PRINT_INTERVAL: 10 SCHEDULER: TYPE: step DECAY_RATE: 0.1 VAL_EPOCH_INTERVAL: 10 WEIGHT_DECAY: 0.0001 AMP: False TEST: EPOCH: 500 SEARCH_FACTOR: 5.0 SEARCH_SIZE: 384 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 192 ================================================ FILE: artrackv2_mindspore/experiments/ostrack/finetune.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 3 FACTOR: 4.0 SCALE_JITTER: 0.25 SIZE: 256 NUMBER: 8 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 128 TRAIN: DATASETS_NAME: - LASOT - GOT10K_vottrain - TRACKINGNET DATASETS_RATIO: - 1 - 1 - 1 SAMPLE_PER_EPOCH: 960 VAL: DATASETS_NAME: - GOT10K_official_val DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: PRETRAIN_FILE: "mae_pretrain_vit_base.pth" EXTRA_MERGER: False RETURN_INTER: False BACKBONE: TYPE: vit_base_patch16_224 STRIDE: 16 HEAD: TYPE: PIX NUM_CHANNELS: 768 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 8 EPOCH: 120 GIOU_WEIGHT: 2.0 L1_WEIGHT: 0.0 GRAD_CLIP_NORM: 0.1 LR: 0.000004 LR_DROP_EPOCH: 400 NUM_WORKER: 8 OPTIMIZER: ADAMW PRINT_INTERVAL: 1 SCHEDULER: TYPE: step DECAY_RATE: 0.1 VAL_EPOCH_INTERVAL: 10 WEIGHT_DECAY: 0.05 AMP: False TEST: EPOCH: 30 SEARCH_FACTOR: 4.0 SEARCH_SIZE: 256 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 128 ================================================ FILE: artrackv2_mindspore/experiments/ostrack/finetune_384.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 4.5 FACTOR: 5.0 SCALE_JITTER: 0.5 SIZE: 384 NUMBER: 12 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 192 TRAIN: DATASETS_NAME: - LASOT - GOT10K_vottrain - TRACKINGNET DATASETS_RATIO: - 1 - 1 - 1 SAMPLE_PER_EPOCH: 960 VAL: DATASETS_NAME: - GOT10K_official_val DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: PRETRAIN_FILE: "mae_pretrain_vit_large.pth" EXTRA_MERGER: False RETURN_INTER: False DECODER: TYPE: "mask" MASK_RATIO: 0.75 EMBEDDIM: 512 DEPTH: 8 NUMHEADS: 16 MLPRATIO: 4 BACKBONE: TYPE: vit_large_patch16_224 STRIDE: 16 EMBEDDIM: 1024 HEAD: TYPE: PIX NUM_CHANNELS: 1024 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 4 EPOCH: 120 GIOU_WEIGHT: 2.0 L1_WEIGHT: 0.0 GRAD_CLIP_NORM: 0.1 LR: 0.00008 LR_DROP_EPOCH: 400 NUM_WORKER: 8 OPTIMIZER: ADAMW PRINT_INTERVAL: 1 SCHEDULER: TYPE: step DECAY_RATE: 0.1 VAL_EPOCH_INTERVAL: 10 WEIGHT_DECAY: 0.05 AMP: False TEST: EPOCH: 500 SEARCH_FACTOR: 5.0 SEARCH_SIZE: 384 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 192 ================================================ FILE: artrackv2_mindspore/experiments/ostrack/finetune_384_got.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 4.5 FACTOR: 5.0 SCALE_JITTER: 0.5 SIZE: 384 NUMBER: 17 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 192 TRAIN: DATASETS_NAME: - GOT10K_train_full DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 960 VAL: DATASETS_NAME: - GOT10K_official_val DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: PRETRAIN_FILE: "mae_pretrain_vit_base.pth" EXTRA_MERGER: False RETURN_INTER: False BACKBONE: TYPE: vit_base_patch16_224 STRIDE: 16 HEAD: TYPE: PIX NUM_CHANNELS: 768 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 4 EPOCH: 120 GIOU_WEIGHT: 2.0 L1_WEIGHT: 0.0 GRAD_CLIP_NORM: 0.1 LR: 0.000004 LR_DROP_EPOCH: 400 NUM_WORKER: 4 OPTIMIZER: ADAMW PRINT_INTERVAL: 1 SCHEDULER: TYPE: step DECAY_RATE: 0.1 VAL_EPOCH_INTERVAL: 10 WEIGHT_DECAY: 0.05 AMP: False TEST: EPOCH: 500 SEARCH_FACTOR: 5.0 SEARCH_SIZE: 384 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 192 ================================================ FILE: artrackv2_mindspore/experiments/ostrack/vitb_256_mae_32x4_ep300.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 3 FACTOR: 4.0 SCALE_JITTER: 0.25 SIZE: 256 NUMBER: 1 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 128 TRAIN: DATASETS_NAME: - GOT10K_train_full DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 60000 # TRAIN: # DATASETS_NAME: # - LASOT # - GOT10K_vottrain # - COCO17 # - TRACKINGNET # DATASETS_RATIO: # - 1 # - 1 # - 1 # - 1 # SAMPLE_PER_EPOCH: 60000 VAL: DATASETS_NAME: - GOT10K_votval DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: PRETRAIN_FILE: "mae_pretrain_vit_base.pth" EXTRA_MERGER: False RETURN_INTER: False BACKBONE: TYPE: vit_base_patch16_224 STRIDE: 16 HEAD: TYPE: CENTER NUM_CHANNELS: 256 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 32 EPOCH: 300 GIOU_WEIGHT: 2.0 L1_WEIGHT: 5.0 GRAD_CLIP_NORM: 0.1 LR: 0.0004 LR_DROP_EPOCH: 240 NUM_WORKER: 10 OPTIMIZER: ADAMW PRINT_INTERVAL: 50 SCHEDULER: TYPE: step DECAY_RATE: 0.1 VAL_EPOCH_INTERVAL: 20 WEIGHT_DECAY: 0.0001 AMP: False TEST: EPOCH: 300 SEARCH_FACTOR: 4.0 SEARCH_SIZE: 256 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 128 ================================================ FILE: artrackv2_mindspore/experiments/ostrack/vitb_256_mae_ce_32x4_ep300.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 3 FACTOR: 4.0 SCALE_JITTER: 0.25 SIZE: 256 NUMBER: 1 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 128 # TRAIN: # DATASETS_NAME: # - GOT10K_train_full # DATASETS_RATIO: # - 1 # SAMPLE_PER_EPOCH: 60000 TRAIN: DATASETS_NAME: - LASOT - GOT10K_vottrain - COCO17 - TRACKINGNET DATASETS_RATIO: - 1 - 1 - 1 - 1 SAMPLE_PER_EPOCH: 60000 VAL: DATASETS_NAME: - GOT10K_votval DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: PRETRAIN_FILE: "mae_pretrain_vit_base.pth" EXTRA_MERGER: False RETURN_INTER: False BACKBONE: TYPE: vit_base_patch16_224 STRIDE: 16 HEAD: TYPE: PIX NUM_CHANNELS: 768 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 CE_START_EPOCH: 600 # candidate elimination start epoch CE_WARM_EPOCH: 560 # candidate elimination warm up epoch BATCH_SIZE: 48 EPOCH: 500 GIOU_WEIGHT: 2.0 L1_WEIGHT: 0.0 GRAD_CLIP_NORM: 0.1 LR: 0.00008 LR_DROP_EPOCH: 400 NUM_WORKER: 8 OPTIMIZER: ADAMW PRINT_INTERVAL: 10 SCHEDULER: TYPE: step DECAY_RATE: 0.1 VAL_EPOCH_INTERVAL: 20 WEIGHT_DECAY: 0.0001 AMP: False TEST: EPOCH: 500 SEARCH_FACTOR: 4.0 SEARCH_SIZE: 256 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 128 ================================================ FILE: artrackv2_mindspore/experiments/ostrack/vitb_256_mae_ce_32x4_got10k_ep100.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 3 FACTOR: 4.0 SCALE_JITTER: 0.25 SIZE: 256 NUMBER: 24 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 128 TRAIN: DATASETS_NAME: - LASOT - GOT10K_vottrain - TRACKINGNET DATASETS_RATIO: - 1 - 1 - 1 SAMPLE_PER_EPOCH: 1000 VAL: DATASETS_NAME: - GOT10K_official_val DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: PRETRAIN_FILE: "mae_pretrain_vit_base.pth" EXTRA_MERGER: False RETURN_INTER: False BACKBONE: TYPE: vit_base_patch16_224 STRIDE: 16 HEAD: TYPE: PIX NUM_CHANNELS: 768 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 8 EPOCH: 120 GIOU_WEIGHT: 2.0 L1_WEIGHT: 0.0 GRAD_CLIP_NORM: 0.1 LR: 0.000001 LR_DROP_EPOCH: 400 NUM_WORKER: 4 OPTIMIZER: ADAMW PRINT_INTERVAL: 1 SCHEDULER: TYPE: step DECAY_RATE: 0.1 VAL_EPOCH_INTERVAL: 10 WEIGHT_DECAY: 0.0001 AMP: False TEST: EPOCH: 30 SEARCH_FACTOR: 4.2 SEARCH_SIZE: 256 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 128 ================================================ FILE: artrackv2_mindspore/experiments/ostrack/vitb_384_mae_32x4_ep300.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 4.5 FACTOR: 5.0 SCALE_JITTER: 0.5 SIZE: 384 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 192 # TRAIN: # DATASETS_NAME: # - GOT10K_train_full # DATASETS_RATIO: # - 1 # SAMPLE_PER_EPOCH: 60000 TRAIN: DATASETS_NAME: - LASOT - GOT10K_vottrain - COCO17 - TRACKINGNET DATASETS_RATIO: - 1 - 1 - 1 - 1 SAMPLE_PER_EPOCH: 60000 VAL: DATASETS_NAME: - GOT10K_votval DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: PRETRAIN_FILE: "mae_pretrain_vit_base.pth" EXTRA_MERGER: False RETURN_INTER: False BACKBONE: TYPE: vit_base_patch16_224 STRIDE: 16 HEAD: TYPE: CENTER NUM_CHANNELS: 256 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 32 EPOCH: 300 GIOU_WEIGHT: 2.0 L1_WEIGHT: 5.0 GRAD_CLIP_NORM: 0.1 LR: 0.0004 LR_DROP_EPOCH: 240 NUM_WORKER: 10 OPTIMIZER: ADAMW PRINT_INTERVAL: 50 SCHEDULER: TYPE: step DECAY_RATE: 0.1 VAL_EPOCH_INTERVAL: 20 WEIGHT_DECAY: 0.0001 AMP: False TEST: EPOCH: 300 SEARCH_FACTOR: 5.0 SEARCH_SIZE: 384 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 192 ================================================ FILE: artrackv2_mindspore/experiments/ostrack/vitb_384_mae_ce_32x4_ep300.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 4.5 FACTOR: 5.0 SCALE_JITTER: 0.5 SIZE: 384 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 192 # TRAIN: # DATASETS_NAME: # - GOT10K_train_full # DATASETS_RATIO: # - 1 # SAMPLE_PER_EPOCH: 60000 TRAIN: DATASETS_NAME: - LASOT - GOT10K_vottrain - COCO17 - TRACKINGNET DATASETS_RATIO: - 1 - 1 - 1 - 1 SAMPLE_PER_EPOCH: 60000 VAL: DATASETS_NAME: - GOT10K_votval DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: PRETRAIN_FILE: "mae_pretrain_vit_base.pth" EXTRA_MERGER: False RETURN_INTER: False BACKBONE: TYPE: vit_base_patch16_224_ce STRIDE: 16 CE_LOC: [3, 6, 9] CE_KEEP_RATIO: [0.7, 0.7, 0.7] CE_TEMPLATE_RANGE: 'CTR_POINT' # choose between ALL, CTR_POINT, CTR_REC, GT_BOX HEAD: TYPE: CENTER NUM_CHANNELS: 256 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 32 EPOCH: 300 GIOU_WEIGHT: 2.0 L1_WEIGHT: 5.0 GRAD_CLIP_NORM: 0.1 LR: 0.0004 LR_DROP_EPOCH: 240 NUM_WORKER: 10 OPTIMIZER: ADAMW PRINT_INTERVAL: 50 SCHEDULER: TYPE: step DECAY_RATE: 0.1 VAL_EPOCH_INTERVAL: 20 WEIGHT_DECAY: 0.0001 AMP: False TEST: EPOCH: 300 SEARCH_FACTOR: 5.0 SEARCH_SIZE: 384 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 192 ================================================ FILE: artrackv2_mindspore/experiments/ostrack/vitb_384_mae_ce_32x4_got10k_ep100.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 4.5 FACTOR: 5.0 SCALE_JITTER: 0.5 SIZE: 384 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 192 TRAIN: DATASETS_NAME: - GOT10K_train_full DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 60000 VAL: DATASETS_NAME: - GOT10K_official_val DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: PRETRAIN_FILE: "mae_pretrain_vit_base.pth" EXTRA_MERGER: False RETURN_INTER: False BACKBONE: TYPE: vit_base_patch16_224_ce STRIDE: 16 CE_LOC: [3, 6, 9] CE_KEEP_RATIO: [0.7, 0.7, 0.7] CE_TEMPLATE_RANGE: 'CTR_POINT' # choose between ALL, CTR_POINT, CTR_REC, GT_BOX HEAD: TYPE: CENTER NUM_CHANNELS: 256 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 CE_START_EPOCH: 20 # candidate elimination start epoch CE_WARM_EPOCH: 50 # candidate elimination warm up epoch BATCH_SIZE: 32 EPOCH: 100 GIOU_WEIGHT: 2.0 L1_WEIGHT: 5.0 GRAD_CLIP_NORM: 0.1 LR: 0.0004 LR_DROP_EPOCH: 80 NUM_WORKER: 10 OPTIMIZER: ADAMW PRINT_INTERVAL: 50 SCHEDULER: TYPE: step DECAY_RATE: 0.1 VAL_EPOCH_INTERVAL: 20 WEIGHT_DECAY: 0.0001 AMP: False TEST: EPOCH: 100 SEARCH_FACTOR: 5.0 SEARCH_SIZE: 384 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 192 ================================================ FILE: artrackv2_mindspore/external/AR/README.md ================================================ # Alpha-Refine ## Introduction Alpha-Refine is the winner of the VOT Real-Time Challenge 2020, which has great ability to predict high-quality masks. In this work, we combine the STARK tracker with Alpha-Refine to test on the VOT2020 benchamark. ## Installation After the environment has been installed according to the README.md of STARK, you only need to install a few more packages as shown below. * Install ninja-build for Precise ROI pooling ```bash sudo apt-get install ninja-build ``` In case of issues, we refer to https://github.com/vacancy/PreciseRoIPooling. * Install the Precise ROI pooling ``` cd ltr/external git clone https://github.com/vacancy/PreciseRoIPooling.git cd ../.. ``` * Add the project path to environment variables ``` export PYTHONPATH=:$PYTHONPATH ``` * Setup the environment Create the default environment setting files. ```bash # Environment settings for pytracking. Saved at pytracking/evaluation/local.py python -c "from pytracking.evaluation.environment import create_default_local_file; create_default_local_file()" # Environment settings for ltr. Saved at ltr/admin/local.py python -c "from ltr.admin.environment import create_default_local_file; create_default_local_file()" ``` You can modify these files to set the paths to datasets, results paths etc. * Download the pre-trained Alpha-Refine network Download the network for [Alpha-Refine](https://drive.google.com/open?id=1qOQRfaRMbQ2nmgX1NFjoQHfXOAn609QM) and put it under the ltr/checkpoints/ltr/ARcm_seg/ARcm_coco_seg_only_mask_384 dir. ================================================ FILE: artrackv2_mindspore/external/AR/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/external/AR/ltr/README.md ================================================ # LTR A general PyTorch based framework for learning tracking representations. ## Table of Contents * [Quick Start](#quick-start) * [Overview](#overview) * [Trackers](#trackers) * [PrDiMP](#PrDiMP) * [DiMP](#DiMP) * [ATOM](#ATOM) * [Training your own networks](#training-your-own-networks) ## Quick Start The installation script will automatically generate a local configuration file "admin/local.py". In case the file was not generated, run ```admin.environment.create_default_local_file()``` to generate it. Next, set the paths to the training workspace, i.e. the directory where the checkpoints will be saved. Also set the paths to the datasets you want to use. If all the dependencies have been correctly installed, you can train a network using the run_training.py script in the correct conda environment. ```bash conda activate pytracking python run_training.py train_module train_name ``` Here, ```train_module``` is the sub-module inside ```train_settings``` and ```train_name``` is the name of the train setting file to be used. For example, you can train using the included default ATOM settings by running: ```bash python run_training bbreg atom_default ``` ## Overview The framework consists of the following sub-modules. - [actors](actors): Contains the actor classes for different trainings. The actor class is responsible for passing the input data through the network can calculating losses. - [admin](admin): Includes functions for loading networks, tensorboard etc. and also contains environment settings. - [dataset](dataset): Contains integration of a number of training datasets, namely [TrackingNet](https://tracking-net.org/), [GOT-10k](http://got-10k.aitestunion.com/), [LaSOT](https://cis.temple.edu/lasot/), [ImageNet-VID](http://image-net.org/), [DAVIS](https://davischallenge.org), [YouTube-VOS](https://youtube-vos.org), [MS-COCO](http://cocodataset.org/#home), [SBD](http://home.bharathh.info/pubs/codes/SBD), [LVIS](https://www.lvisdataset.org), [ECSSD](http://www.cse.cuhk.edu.hk/leojia/projects/hsaliency/dataset.html), [MSRA10k](https://mmcheng.net/msra10k), and [HKU-IS](https://sites.google.com/site/ligb86/hkuis). Additionally, it includes modules to generate synthetic videos from image datasets. - [data_specs](data_specs): Information about train/val splits of different datasets. - [data](data): Contains functions for processing data, e.g. loading images, data augmentations, sampling frames from videos. - [external](external): External libraries needed for training. Added as submodules. - [models](models): Contains different layers and network definitions. - [trainers](trainers): The main class which runs the training. - [train_settings](train_settings): Contains settings files, specifying the training of a network. ## Trackers The framework currently contains the training code for the following trackers. ### PrDiMP The following setting files can be used train the DiMP networks, or to know the exact training details. - [dimp.prdimp18](train_settings/dimp/prdimp18.py): The default settings used for training the PrDiMP model with ResNet-18 backbone. - [dimp.prdimp50](train_settings/dimp/prdimp50.py): The default settings used for training the PrDiMP model with ResNet-50 backbone. - [dimp.super_dimp](train_settings/dimp/super_dimp.py): Combines the bounding-box regressor of PrDiMP with the standard DiMP classifier and better training and inference settings. ### DiMP The following setting files can be used train the DiMP networks, or to know the exact training details. - [dimp.dimp18](train_settings/dimp/dimp18.py): The default settings used for training the DiMP model with ResNet-18 backbone. - [dimp.dimp50](train_settings/dimp/dimp50.py): The default settings used for training the DiMP model with ResNet-50 backbone. ### ATOM The following setting file can be used train the ATOM network, or to know the exact training details. - [bbreg.atom](train_settings/bbreg/atom_paper.py): The settings used in the paper for training the network in ATOM. - [bbreg.atom](train_settings/bbreg/atom.py): Newer settings used for training the network in ATOM, also utilizing the GOT10k dataset. - [bbreg.atom](train_settings/bbreg/atom_prob_ml.py): Settings for ATOM with the probabilistic bounding box regression proposed in [this paper](https://arxiv.org/abs/1909.12297). - [bbreg.atom](train_settings/bbreg/atom_paper.py): The baseline ATOM* setting evaluated in [this paper](https://arxiv.org/abs/1909.12297). ## Training your own networks To train a custom network using the toolkit, the following components need to be specified in the train settings. For reference, see [atom.py](train_settings/bbreg/atom.py). - Datasets: The datasets to be used for training. A number of standard tracking datasets are already available in ```dataset``` module. - Processing: This function should perform the necessary post-processing of the data, e.g. cropping of target region, data augmentations etc. - Sampler: Determines how the frames are sampled from a video sequence to form the batches. - Network: The network module to be trained. - Objective: The training objective. - Actor: The trainer passes the training batch to the actor who is responsible for passing the data through the network correctly, and calculating the training loss. - Optimizer: Optimizer to be used, e.g. Adam. - Trainer: The main class which runs the epochs and saves checkpoints. ================================================ FILE: artrackv2_mindspore/external/AR/ltr/__init__.py ================================================ from .admin.loading import load_network from .admin.model_constructor import model_constructor from .admin.multigpu import MultiGPU ================================================ FILE: artrackv2_mindspore/external/AR/ltr/actors/__init__.py ================================================ from .base_actor import BaseActor from .bbreg import AtomActor from .tracking import DiMPActor ================================================ FILE: artrackv2_mindspore/external/AR/ltr/actors/base_actor.py ================================================ from pytracking import TensorDict class BaseActor: """ Base class for actor. The actor class handles the passing of the data through the network and calculation the loss""" def __init__(self, net, objective): """ args: net - The network to train objective - The loss function """ self.net = net self.objective = objective def __call__(self, data: TensorDict): """ Called in each training iteration. Should pass in input data through the network, calculate the loss, and return the training stats for the input data args: data - A TensorDict containing all the necessary data blocks. returns: loss - loss for the input data stats - a dict containing detailed losses """ raise NotImplementedError def to(self, device): """ Move the network to device args: device - device to use. 'cpu' or 'cuda' """ self.net.to(device) def train(self, mode=True): """ Set whether the network is in train mode. args: mode (True) - Bool specifying whether in training mode. """ self.net.train(mode) def eval(self): """ Set network to eval mode""" self.train(False) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/actors/bbreg.py ================================================ from . import BaseActor class AtomActor(BaseActor): """ Actor for training the IoU-Net in ATOM""" def __call__(self, data): """ args: data - The input data, should contain the fields 'train_images', 'test_images', 'train_anno', 'test_proposals' and 'proposal_iou'. returns: loss - the training loss states - dict containing detailed losses """ # Run network to obtain IoU prediction for each proposal in 'test_proposals' iou_pred = self.net(data['train_images'], data['test_images'], data['train_anno'], data['test_proposals']) iou_pred = iou_pred.view(-1, iou_pred.shape[2]) iou_gt = data['proposal_iou'].view(-1, data['proposal_iou'].shape[2]) # Compute loss loss = self.objective(iou_pred, iou_gt) # Return training stats stats = {'Loss/total': loss.item(), 'Loss/iou': loss.item()} return loss, stats class AtomBBKLActor(BaseActor): """ Actor for training the IoU-Net in ATOM with BBKL""" def __call__(self, data): """ args: data - The input data, should contain the fields 'train_images', 'test_images', 'train_anno', 'test_proposals', 'proposal_density', and 'gt_density'. returns: loss - the training loss states - dict containing detailed losses """ # Run network to obtain IoU prediction for each proposal in 'test_proposals' bb_scores = self.net(data['train_images'], data['test_images'], data['train_anno'], data['test_proposals']) bb_scores = bb_scores.view(-1, bb_scores.shape[2]) proposal_density = data['proposal_density'].view(-1, data['proposal_density'].shape[2]) gt_density = data['gt_density'].view(-1, data['gt_density'].shape[2]) # Compute loss loss = self.objective(bb_scores, sample_density=proposal_density, gt_density=gt_density, mc_dim=1) # Return training stats stats = {'Loss/total': loss.item(), 'Loss/bb_ce': loss.item()} return loss, stats ================================================ FILE: artrackv2_mindspore/external/AR/ltr/actors/tracking.py ================================================ from . import BaseActor import torch class DiMPActor(BaseActor): """Actor for training the DiMP network.""" def __init__(self, net, objective, loss_weight=None): super().__init__(net, objective) if loss_weight is None: loss_weight = {'iou': 1.0, 'test_clf': 1.0} self.loss_weight = loss_weight def __call__(self, data): """ args: data - The input data, should contain the fields 'train_images', 'test_images', 'train_anno', 'test_proposals', 'proposal_iou' and 'test_label'. returns: loss - the training loss stats - dict containing detailed losses """ # Run network target_scores, iou_pred = self.net(train_imgs=data['train_images'], test_imgs=data['test_images'], train_bb=data['train_anno'], test_proposals=data['test_proposals']) # Classification losses for the different optimization iterations clf_losses_test = [self.objective['test_clf'](s, data['test_label'], data['test_anno']) for s in target_scores] # Loss of the final filter clf_loss_test = clf_losses_test[-1] loss_target_classifier = self.loss_weight['test_clf'] * clf_loss_test # Compute loss for ATOM IoUNet loss_iou = self.loss_weight['iou'] * self.objective['iou'](iou_pred, data['proposal_iou']) # Loss for the initial filter iteration loss_test_init_clf = 0 if 'test_init_clf' in self.loss_weight.keys(): loss_test_init_clf = self.loss_weight['test_init_clf'] * clf_losses_test[0] # Loss for the intermediate filter iterations loss_test_iter_clf = 0 if 'test_iter_clf' in self.loss_weight.keys(): test_iter_weights = self.loss_weight['test_iter_clf'] if isinstance(test_iter_weights, list): loss_test_iter_clf = sum([a*b for a, b in zip(test_iter_weights, clf_losses_test[1:-1])]) else: loss_test_iter_clf = (test_iter_weights / (len(clf_losses_test) - 2)) * sum(clf_losses_test[1:-1]) # Total loss loss = loss_iou + loss_target_classifier + loss_test_init_clf + loss_test_iter_clf # Log stats stats = {'Loss/total': loss.item(), 'Loss/iou': loss_iou.item(), 'Loss/target_clf': loss_target_classifier.item()} if 'test_init_clf' in self.loss_weight.keys(): stats['Loss/test_init_clf'] = loss_test_init_clf.item() if 'test_iter_clf' in self.loss_weight.keys(): stats['Loss/test_iter_clf'] = loss_test_iter_clf.item() stats['ClfTrain/test_loss'] = clf_loss_test.item() if len(clf_losses_test) > 0: stats['ClfTrain/test_init_loss'] = clf_losses_test[0].item() if len(clf_losses_test) > 2: stats['ClfTrain/test_iter_loss'] = sum(clf_losses_test[1:-1]).item() / (len(clf_losses_test) - 2) return loss, stats class KLDiMPActor(BaseActor): """Actor for training the DiMP network.""" def __init__(self, net, objective, loss_weight=None): super().__init__(net, objective) if loss_weight is None: loss_weight = {'bb_ce': 1.0} self.loss_weight = loss_weight def __call__(self, data): """ args: data - The input data, should contain the fields 'train_images', 'test_images', 'train_anno', 'test_proposals', 'proposal_iou' and 'test_label'. returns: loss - the training loss stats - dict containing detailed losses """ # Run network target_scores, bb_scores = self.net(train_imgs=data['train_images'], test_imgs=data['test_images'], train_bb=data['train_anno'], test_proposals=data['test_proposals']) # Reshape bb reg variables is_valid = data['test_anno'][:, :, 0] < 99999.0 bb_scores = bb_scores[is_valid, :] proposal_density = data['proposal_density'][is_valid, :] gt_density = data['gt_density'][is_valid, :] # Compute loss bb_ce = self.objective['bb_ce'](bb_scores, sample_density=proposal_density, gt_density=gt_density, mc_dim=1) loss_bb_ce = self.loss_weight['bb_ce'] * bb_ce # If standard DiMP classifier is used loss_target_classifier = 0 loss_test_init_clf = 0 loss_test_iter_clf = 0 if 'test_clf' in self.loss_weight.keys(): # Classification losses for the different optimization iterations clf_losses_test = [self.objective['test_clf'](s, data['test_label'], data['test_anno']) for s in target_scores] # Loss of the final filter clf_loss_test = clf_losses_test[-1] loss_target_classifier = self.loss_weight['test_clf'] * clf_loss_test # Loss for the initial filter iteration if 'test_init_clf' in self.loss_weight.keys(): loss_test_init_clf = self.loss_weight['test_init_clf'] * clf_losses_test[0] # Loss for the intermediate filter iterations if 'test_iter_clf' in self.loss_weight.keys(): test_iter_weights = self.loss_weight['test_iter_clf'] if isinstance(test_iter_weights, list): loss_test_iter_clf = sum([a * b for a, b in zip(test_iter_weights, clf_losses_test[1:-1])]) else: loss_test_iter_clf = (test_iter_weights / (len(clf_losses_test) - 2)) * sum(clf_losses_test[1:-1]) # If PrDiMP classifier is used loss_clf_ce = 0 loss_clf_ce_init = 0 loss_clf_ce_iter = 0 if 'clf_ce' in self.loss_weight.keys(): # Classification losses for the different optimization iterations clf_ce_losses = [self.objective['clf_ce'](s, data['test_label_density'], grid_dim=(-2,-1)) for s in target_scores] # Loss of the final filter clf_ce = clf_ce_losses[-1] loss_clf_ce = self.loss_weight['clf_ce'] * clf_ce # Loss for the initial filter iteration if 'clf_ce_init' in self.loss_weight.keys(): loss_clf_ce_init = self.loss_weight['clf_ce_init'] * clf_ce_losses[0] # Loss for the intermediate filter iterations if 'clf_ce_iter' in self.loss_weight.keys() and len(clf_ce_losses) > 2: test_iter_weights = self.loss_weight['clf_ce_iter'] if isinstance(test_iter_weights, list): loss_clf_ce_iter = sum([a * b for a, b in zip(test_iter_weights, clf_ce_losses[1:-1])]) else: loss_clf_ce_iter = (test_iter_weights / (len(clf_ce_losses) - 2)) * sum(clf_ce_losses[1:-1]) # Total loss loss = loss_bb_ce + loss_clf_ce + loss_clf_ce_init + loss_clf_ce_iter + \ loss_target_classifier + loss_test_init_clf + loss_test_iter_clf if torch.isinf(loss) or torch.isnan(loss): raise Exception('ERROR: Loss was nan or inf!!!') # Log stats stats = {'Loss/total': loss.item(), 'Loss/bb_ce': bb_ce.item(), 'Loss/loss_bb_ce': loss_bb_ce.item()} if 'test_clf' in self.loss_weight.keys(): stats['Loss/target_clf'] = loss_target_classifier.item() if 'test_init_clf' in self.loss_weight.keys(): stats['Loss/test_init_clf'] = loss_test_init_clf.item() if 'test_iter_clf' in self.loss_weight.keys(): stats['Loss/test_iter_clf'] = loss_test_iter_clf.item() if 'clf_ce' in self.loss_weight.keys(): stats['Loss/clf_ce'] = loss_clf_ce.item() if 'clf_ce_init' in self.loss_weight.keys(): stats['Loss/clf_ce_init'] = loss_clf_ce_init.item() if 'clf_ce_iter' in self.loss_weight.keys() and len(clf_ce_losses) > 2: stats['Loss/clf_ce_iter'] = loss_clf_ce_iter.item() if 'test_clf' in self.loss_weight.keys(): stats['ClfTrain/test_loss'] = clf_loss_test.item() if len(clf_losses_test) > 0: stats['ClfTrain/test_init_loss'] = clf_losses_test[0].item() if len(clf_losses_test) > 2: stats['ClfTrain/test_iter_loss'] = sum(clf_losses_test[1:-1]).item() / (len(clf_losses_test) - 2) if 'clf_ce' in self.loss_weight.keys(): stats['ClfTrain/clf_ce'] = clf_ce.item() if len(clf_ce_losses) > 0: stats['ClfTrain/clf_ce_init'] = clf_ce_losses[0].item() if len(clf_ce_losses) > 2: stats['ClfTrain/clf_ce_iter'] = sum(clf_ce_losses[1:-1]).item() / (len(clf_ce_losses) - 2) return loss, stats ================================================ FILE: artrackv2_mindspore/external/AR/ltr/admin/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/external/AR/ltr/admin/environment.py ================================================ import importlib import os from collections import OrderedDict def create_default_local_file(): path = os.path.join(os.path.dirname(__file__), 'local.py') empty_str = '\'\'' default_settings = OrderedDict({ 'workspace_dir': empty_str, 'tensorboard_dir': 'self.workspace_dir + \'/tensorboard/\'', 'lasot_dir': empty_str, 'got10k_dir': empty_str, 'trackingnet_dir': empty_str, 'coco_dir': empty_str, 'lvis_dir': empty_str, 'sbd_dir': empty_str, 'imagenet_dir': empty_str, 'imagenetdet_dir': empty_str, 'ecssd_dir': empty_str, 'hkuis_dir': empty_str, 'msra10k_dir': empty_str, 'davis_dir': empty_str, 'youtubevos_dir': empty_str}) comment = {'workspace_dir': 'Base directory for saving network checkpoints.', 'tensorboard_dir': 'Directory for tensorboard files.'} with open(path, 'w') as f: f.write('class EnvironmentSettings:\n') f.write(' def __init__(self):\n') for attr, attr_val in default_settings.items(): comment_str = None if attr in comment: comment_str = comment[attr] if comment_str is None: f.write(' self.{} = {}\n'.format(attr, attr_val)) else: f.write(' self.{} = {} # {}\n'.format(attr, attr_val, comment_str)) def env_settings(): env_module_name = 'ltr.admin.local' try: env_module = importlib.import_module(env_module_name) return env_module.EnvironmentSettings() except: env_file = os.path.join(os.path.dirname(__file__), 'local.py') create_default_local_file() raise RuntimeError('YOU HAVE NOT SETUP YOUR local.py!!!\n Go to "{}" and set all the paths you need. Then try to run again.'.format(env_file)) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/admin/loading.py ================================================ import torch import os import sys from pathlib import Path import importlib import inspect from ltr.admin import settings as ws_settings def load_trained_network(workspace_dir, network_path, checkpoint=None): """OUTDATED. Use load_pretrained instead!""" checkpoint_dir = os.path.join(workspace_dir, 'checkpoints') directory = '{}/{}'.format(checkpoint_dir, network_path) net, _ = load_network(directory, checkpoint) return net def load_pretrained(module, name, checkpoint=None, **kwargs): """Load a network trained using the LTR framework. This is useful when you want to initialize your new network with a previously trained model. args: module - Name of the train script module. I.e. the name of the folder in ltr/train_scripts. name - The name of the train_script. checkpoint - You can supply the checkpoint number or the full path to the checkpoint file (see load_network). **kwargs - These are passed to load_network (see that function). """ settings = ws_settings.Settings() network_dir = os.path.join(settings.env.workspace_dir, 'checkpoints', 'ltr', module, name) return load_network(network_dir=network_dir, checkpoint=checkpoint, **kwargs) def load_network(network_dir=None, checkpoint=None, constructor_fun_name=None, constructor_module=None, **kwargs): """Loads a network checkpoint file. Can be called in two different ways: load_checkpoint(network_dir): Loads the checkpoint file given by the path. If checkpoint_dir is a directory, it tries to find the latest checkpoint in that directory. load_checkpoint(network_dir, checkpoint=epoch_num): Loads the network at the given epoch number (int). The extra keyword arguments are supplied to the network constructor to replace saved ones. """ if network_dir is not None: net_path = Path(network_dir) else: net_path = None if net_path.is_file(): checkpoint = str(net_path) if checkpoint is None: # Load most recent checkpoint checkpoint_list = sorted(net_path.glob('*.pth.tar')) if checkpoint_list: checkpoint_path = checkpoint_list[-1] else: raise Exception('No matching checkpoint file found') elif isinstance(checkpoint, int): # Checkpoint is the epoch number checkpoint_list = sorted(net_path.glob('*_ep{:04d}.pth.tar'.format(checkpoint))) if not checkpoint_list or len(checkpoint_list) == 0: raise Exception('No matching checkpoint file found') if len(checkpoint_list) > 1: raise Exception('Multiple matching checkpoint files found') else: checkpoint_path = checkpoint_list[0] elif isinstance(checkpoint, str): # Checkpoint is the path checkpoint_path = os.path.expanduser(checkpoint) else: raise TypeError # Load network checkpoint_dict = torch_load_legacy(checkpoint_path) # Construct network model if 'constructor' in checkpoint_dict and checkpoint_dict['constructor'] is not None: net_constr = checkpoint_dict['constructor'] if constructor_fun_name is not None: net_constr.fun_name = constructor_fun_name if constructor_module is not None: net_constr.fun_module = constructor_module # Legacy networks before refactoring if net_constr.fun_module.startswith('dlframework.'): net_constr.fun_module = net_constr.fun_module[len('dlframework.'):] net_fun = getattr(importlib.import_module(net_constr.fun_module), net_constr.fun_name) net_fun_args = list(inspect.signature(net_fun).parameters.keys()) for arg, val in kwargs.items(): if arg in net_fun_args: net_constr.kwds[arg] = val else: print('WARNING: Keyword argument "{}" not found when loading network. It was ignored.'.format(arg)) net = net_constr.get() else: raise RuntimeError('No constructor for the given network.') net.load_state_dict(checkpoint_dict['net']) net.constructor = checkpoint_dict['constructor'] if 'net_info' in checkpoint_dict and checkpoint_dict['net_info'] is not None: net.info = checkpoint_dict['net_info'] return net, checkpoint_dict def load_weights(net, path, strict=True): checkpoint_dict = torch.load(path) weight_dict = checkpoint_dict['net'] net.load_state_dict(weight_dict, strict=strict) return net def torch_load_legacy(path): """Load network with legacy environment.""" # Setup legacy env (for older networks) _setup_legacy_env() # Load network checkpoint_dict = torch.load(path, map_location='cpu') # Cleanup legacy _cleanup_legacy_env() return checkpoint_dict def _setup_legacy_env(): importlib.import_module('ltr') sys.modules['dlframework'] = sys.modules['ltr'] sys.modules['dlframework.common'] = sys.modules['ltr'] importlib.import_module('ltr.admin') sys.modules['dlframework.common.utils'] = sys.modules['ltr.admin'] for m in ('model_constructor', 'stats', 'settings', 'local'): importlib.import_module('ltr.admin.' + m) sys.modules['dlframework.common.utils.' + m] = sys.modules['ltr.admin.' + m] def _cleanup_legacy_env(): del_modules = [] for m in sys.modules.keys(): if m.startswith('dlframework'): del_modules.append(m) for m in del_modules: del sys.modules[m] ================================================ FILE: artrackv2_mindspore/external/AR/ltr/admin/model_constructor.py ================================================ from functools import wraps import importlib def model_constructor(f): """ Wraps the function 'f' which returns the network. An extra field 'constructor' is added to the network returned by 'f'. This field contains an instance of the 'NetConstructor' class, which contains the information needed to re-construct the network, such as the name of the function 'f', the function arguments etc. Thus, the network can be easily constructed from a saved checkpoint by calling NetConstructor.get() function. """ @wraps(f) def f_wrapper(*args, **kwds): net_constr = NetConstructor(f.__name__, f.__module__, args, kwds) output = f(*args, **kwds) if isinstance(output, (tuple, list)): # Assume first argument is the network output[0].constructor = net_constr else: output.constructor = net_constr return output return f_wrapper class NetConstructor: """ Class to construct networks. Takes as input the function name (e.g. atom_resnet18), the name of the module which contains the network function (e.g. ltr.models.bbreg.atom) and the arguments for the network function. The class object can then be stored along with the network weights to re-construct the network.""" def __init__(self, fun_name, fun_module, args, kwds): """ args: fun_name - The function which returns the network fun_module - the module which contains the network function args - arguments which are passed to the network function kwds - arguments which are passed to the network function """ self.fun_name = fun_name self.fun_module = fun_module self.args = args self.kwds = kwds def get(self): """ Rebuild the network by calling the network function with the correct arguments. """ net_module = importlib.import_module(self.fun_module) net_fun = getattr(net_module, self.fun_name) return net_fun(*self.args, **self.kwds) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/admin/multigpu.py ================================================ import torch.nn as nn def is_multi_gpu(net): return isinstance(net, (MultiGPU, nn.DataParallel)) class MultiGPU(nn.DataParallel): """Wraps a network to allow simple multi-GPU training.""" def __getattr__(self, item): try: return super().__getattr__(item) except: pass return getattr(self.module, item) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/admin/settings.py ================================================ from ltr.admin.environment import env_settings class Settings: """ Training settings, e.g. the paths to datasets and networks.""" def __init__(self): self.set_default() def set_default(self): self.env = env_settings() self.use_gpu = True ================================================ FILE: artrackv2_mindspore/external/AR/ltr/admin/stats.py ================================================ class StatValue: def __init__(self): self.clear() def reset(self): self.val = 0 def clear(self): self.reset() self.history = [] def update(self, val): self.val = val self.history.append(self.val) class AverageMeter(object): """Computes and stores the average and current value""" def __init__(self): self.clear() self.has_new_data = False def reset(self): self.avg = 0 self.val = 0 self.sum = 0 self.count = 0 def clear(self): self.reset() self.history = [] def update(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count def new_epoch(self): if self.count > 0: self.history.append(self.avg) self.reset() self.has_new_data = True else: self.has_new_data = False def topk_accuracy(output, target, topk=(1,)): """Computes the precision@k for the specified values of k""" single_input = not isinstance(topk, (tuple, list)) if single_input: topk = (topk,) maxk = max(topk) batch_size = target.size(0) _, pred = output.topk(maxk, 1, True, True) pred = pred.t() correct = pred.eq(target.view(1, -1).expand_as(pred)) res = [] for k in topk: correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)[0] res.append(correct_k * 100.0 / batch_size) if single_input: return res[0] return res ================================================ FILE: artrackv2_mindspore/external/AR/ltr/admin/tensorboard.py ================================================ import os from collections import OrderedDict try: from torch.utils.tensorboard import SummaryWriter except: print('WARNING: You are using tensorboardX instead sis you have a too old pytorch version.') from tensorboardX import SummaryWriter class TensorboardWriter: def __init__(self, directory, loader_names): self.directory = directory self.writer = OrderedDict({name: SummaryWriter(os.path.join(self.directory, name)) for name in loader_names}) def write_info(self, module_name, script_name, description): tb_info_writer = SummaryWriter(os.path.join(self.directory, 'info')) tb_info_writer.add_text('Modulet_name', module_name) tb_info_writer.add_text('Script_name', script_name) tb_info_writer.add_text('Description', description) tb_info_writer.close() def write_epoch(self, stats: OrderedDict, epoch: int, ind=-1): for loader_name, loader_stats in stats.items(): if loader_stats is None: continue for var_name, val in loader_stats.items(): if hasattr(val, 'history') and getattr(val, 'has_new_data', True): self.writer[loader_name].add_scalar(var_name, val.history[ind], epoch) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/data/__init__.py ================================================ from .loader import LTRLoader ================================================ FILE: artrackv2_mindspore/external/AR/ltr/data/bounding_box_utils.py ================================================ import torch def rect_to_rel(bb, sz_norm=None): """Convert standard rectangular parametrization of the bounding box [x, y, w, h] to relative parametrization [cx/sw, cy/sh, log(w), log(h)], where [cx, cy] is the center coordinate. args: bb - N x 4 tensor of boxes. sz_norm - [N] x 2 tensor of value of [sw, sh] (optional). sw=w and sh=h if not given. """ c = bb[...,:2] + 0.5 * bb[...,2:] if sz_norm is None: c_rel = c / bb[...,2:] else: c_rel = c / sz_norm sz_rel = torch.log(bb[...,2:]) return torch.cat((c_rel, sz_rel), dim=-1) def rel_to_rect(bb, sz_norm=None): """Inverts the effect of rect_to_rel. See above.""" sz = torch.exp(bb[...,2:]) if sz_norm is None: c = bb[...,:2] * sz else: c = bb[...,:2] * sz_norm tl = c - 0.5 * sz return torch.cat((tl, sz), dim=-1) def masks_to_bboxes(mask, fmt='c'): """ Convert a mask tensor to one or more bounding boxes. Note: This function is a bit new, make sure it does what it says. /Andreas :param mask: Tensor of masks, shape = (..., H, W) :param fmt: bbox layout. 'c' => "center + size" or (x_center, y_center, width, height) 't' => "top left + size" or (x_left, y_top, width, height) 'v' => "vertices" or (x_left, y_top, x_right, y_bottom) :return: tensor containing a batch of bounding boxes, shape = (..., 4) """ batch_shape = mask.shape[:-2] mask = mask.reshape((-1, *mask.shape[-2:])) bboxes = [] for m in mask: mx = m.sum(dim=-2).nonzero() my = m.sum(dim=-1).nonzero() bb = [mx.min(), my.min(), mx.max(), my.max()] if (len(mx) > 0 and len(my) > 0) else [0, 0, 0, 0] bboxes.append(bb) bboxes = torch.tensor(bboxes, dtype=torch.float32, device=mask.device) bboxes = bboxes.reshape(batch_shape + (4,)) if fmt == 'v': return bboxes x1 = bboxes[..., :2] s = bboxes[..., 2:] - x1 + 1 if fmt == 'c': return torch.cat((x1 + 0.5 * s, s), dim=-1) elif fmt == 't': return torch.cat((x1, s), dim=-1) raise ValueError("Undefined bounding box layout '%s'" % fmt) def masks_to_bboxes_multi(mask, ids, fmt='c'): assert mask.dim() == 2 bboxes = [] for id in ids: mx = (mask == id).sum(dim=-2).nonzero() my = (mask == id).float().sum(dim=-1).nonzero() bb = [mx.min(), my.min(), mx.max(), my.max()] if (len(mx) > 0 and len(my) > 0) else [0, 0, 0, 0] bb = torch.tensor(bb, dtype=torch.float32, device=mask.device) x1 = bb[:2] s = bb[2:] - x1 + 1 if fmt == 'v': pass elif fmt == 'c': bb = torch.cat((x1 + 0.5 * s, s), dim=-1) elif fmt == 't': bb = torch.cat((x1, s), dim=-1) else: raise ValueError("Undefined bounding box layout '%s'" % fmt) bboxes.append(bb) return bboxes ================================================ FILE: artrackv2_mindspore/external/AR/ltr/data/image_loader.py ================================================ import jpeg4py import cv2 as cv from PIL import Image import numpy as np davis_palette = np.repeat(np.expand_dims(np.arange(0,256), 1), 3, 1).astype(np.uint8) davis_palette[:22, :] = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0], [191, 0, 0], [64, 128, 0], [191, 128, 0], [64, 0, 128], [191, 0, 128], [64, 128, 128], [191, 128, 128], [0, 64, 0], [128, 64, 0], [0, 191, 0], [128, 191, 0], [0, 64, 128], [128, 64, 128]] def default_image_loader(path): """The default image loader, reads the image from the given path. It first tries to use the jpeg4py_loader, but reverts to the opencv_loader if the former is not available.""" if default_image_loader.use_jpeg4py is None: # Try using jpeg4py im = jpeg4py_loader(path) if im is None: default_image_loader.use_jpeg4py = False print('Using opencv_loader instead.') else: default_image_loader.use_jpeg4py = True return im if default_image_loader.use_jpeg4py: return jpeg4py_loader(path) return opencv_loader(path) default_image_loader.use_jpeg4py = None def jpeg4py_loader(path): """ Image reading using jpeg4py https://github.com/ajkxyz/jpeg4py""" try: return jpeg4py.JPEG(path).decode() except Exception as e: print('ERROR: Could not read image "{}"'.format(path)) print(e) return None def opencv_loader(path): """ Read image using opencv's imread function and returns it in rgb format""" try: im = cv.imread(path, cv.IMREAD_COLOR) # convert to rgb and return return cv.cvtColor(im, cv.COLOR_BGR2RGB) except Exception as e: print('ERROR: Could not read image "{}"'.format(path)) print(e) return None def jpeg4py_loader_w_failsafe(path): """ Image reading using jpeg4py https://github.com/ajkxyz/jpeg4py""" try: return jpeg4py.JPEG(path).decode() except: try: im = cv.imread(path, cv.IMREAD_COLOR) # convert to rgb and return return cv.cvtColor(im, cv.COLOR_BGR2RGB) except Exception as e: print('ERROR: Could not read image "{}"'.format(path)) print(e) return None def opencv_seg_loader(path): """ Read segmentation annotation using opencv's imread function""" try: return cv.imread(path) except Exception as e: print('ERROR: Could not read image "{}"'.format(path)) print(e) return None def imread_indexed(filename): """ Load indexed image with given filename. Used to read segmentation annotations.""" im = Image.open(filename) annotation = np.atleast_3d(im)[...,0] return annotation def imwrite_indexed(filename, array, color_palette=None): """ Save indexed image as png. Used to save segmentation annotation.""" if color_palette is None: color_palette = davis_palette if np.atleast_3d(array).shape[2] != 1: raise Exception("Saving indexed PNGs requires 2D array.") im = Image.fromarray(array) im.putpalette(color_palette.ravel()) im.save(filename, format='PNG') ================================================ FILE: artrackv2_mindspore/external/AR/ltr/data/loader.py ================================================ import torch import torch.utils.data.dataloader import importlib import collections from torch._six import string_classes, int_classes from pytracking import TensorDict, TensorList def _check_use_shared_memory(): if hasattr(torch.utils.data.dataloader, '_use_shared_memory'): return getattr(torch.utils.data.dataloader, '_use_shared_memory') collate_lib = importlib.import_module('torch.utils.data._utils.collate') if hasattr(collate_lib, '_use_shared_memory'): return getattr(collate_lib, '_use_shared_memory') return torch.utils.data.get_worker_info() is not None def ltr_collate(batch): """Puts each data field into a tensor with outer dimension batch size""" error_msg = "batch must contain tensors, numbers, dicts or lists; found {}" elem_type = type(batch[0]) if isinstance(batch[0], torch.Tensor): out = None if _check_use_shared_memory(): # If we're in a background process, concatenate directly into a # shared memory tensor to avoid an extra copy numel = sum([x.numel() for x in batch]) storage = batch[0].storage()._new_shared(numel) out = batch[0].new(storage) return torch.stack(batch, 0, out=out) # if batch[0].dim() < 4: # return torch.stack(batch, 0, out=out) # return torch.cat(batch, 0, out=out) elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ and elem_type.__name__ != 'string_': elem = batch[0] if elem_type.__name__ == 'ndarray': # array of string classes and object if torch.utils.data.dataloader.re.search('[SaUO]', elem.dtype.str) is not None: raise TypeError(error_msg.format(elem.dtype)) return torch.stack([torch.from_numpy(b) for b in batch], 0) if elem.shape == (): # scalars py_type = float if elem.dtype.name.startswith('float') else int return torch.utils.data.dataloader.numpy_type_map[elem.dtype.name](list(map(py_type, batch))) elif isinstance(batch[0], int_classes): return torch.LongTensor(batch) elif isinstance(batch[0], float): return torch.DoubleTensor(batch) elif isinstance(batch[0], string_classes): return batch elif isinstance(batch[0], TensorDict): return TensorDict({key: ltr_collate([d[key] for d in batch]) for key in batch[0]}) elif isinstance(batch[0], collections.Mapping): return {key: ltr_collate([d[key] for d in batch]) for key in batch[0]} elif isinstance(batch[0], TensorList): transposed = zip(*batch) return TensorList([ltr_collate(samples) for samples in transposed]) elif isinstance(batch[0], collections.Sequence): transposed = zip(*batch) return [ltr_collate(samples) for samples in transposed] elif batch[0] is None: return batch raise TypeError((error_msg.format(type(batch[0])))) def ltr_collate_stack1(batch): """Puts each data field into a tensor. The tensors are stacked at dim=1 to form the batch""" error_msg = "batch must contain tensors, numbers, dicts or lists; found {}" elem_type = type(batch[0]) if isinstance(batch[0], torch.Tensor): out = None if _check_use_shared_memory(): # If we're in a background process, concatenate directly into a # shared memory tensor to avoid an extra copy numel = sum([x.numel() for x in batch]) storage = batch[0].storage()._new_shared(numel) out = batch[0].new(storage) return torch.stack(batch, 1, out=out) # if batch[0].dim() < 4: # return torch.stack(batch, 0, out=out) # return torch.cat(batch, 0, out=out) elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ and elem_type.__name__ != 'string_': elem = batch[0] if elem_type.__name__ == 'ndarray': # array of string classes and object if torch.utils.data.dataloader.re.search('[SaUO]', elem.dtype.str) is not None: raise TypeError(error_msg.format(elem.dtype)) return torch.stack([torch.from_numpy(b) for b in batch], 1) if elem.shape == (): # scalars py_type = float if elem.dtype.name.startswith('float') else int return torch.utils.data.dataloader.numpy_type_map[elem.dtype.name](list(map(py_type, batch))) elif isinstance(batch[0], int_classes): return torch.LongTensor(batch) elif isinstance(batch[0], float): return torch.DoubleTensor(batch) elif isinstance(batch[0], string_classes): return batch elif isinstance(batch[0], TensorDict): return TensorDict({key: ltr_collate_stack1([d[key] for d in batch]) for key in batch[0]}) elif isinstance(batch[0], collections.Mapping): return {key: ltr_collate_stack1([d[key] for d in batch]) for key in batch[0]} elif isinstance(batch[0], TensorList): transposed = zip(*batch) return TensorList([ltr_collate_stack1(samples) for samples in transposed]) elif isinstance(batch[0], collections.Sequence): transposed = zip(*batch) return [ltr_collate_stack1(samples) for samples in transposed] elif batch[0] is None: return batch raise TypeError((error_msg.format(type(batch[0])))) class LTRLoader(torch.utils.data.dataloader.DataLoader): """ Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset. Note: The only difference with default pytorch DataLoader is that an additional option stack_dim is available to select along which dimension the data should be stacked to form a batch. Arguments: dataset (Dataset): dataset from which to load the data. batch_size (int, optional): how many samples per batch to load (default: 1). shuffle (bool, optional): set to ``True`` to have the data reshuffled at every epoch (default: False). sampler (Sampler, optional): defines the strategy to draw samples from the dataset. If specified, ``shuffle`` must be False. batch_sampler (Sampler, optional): like sampler, but returns a batch of indices at a time. Mutually exclusive with batch_size, shuffle, sampler, and drop_last. num_workers (int, optional): how many subprocesses to use for data loading. 0 means that the data will be loaded in the main process. (default: 0) collate_fn (callable, optional): merges a list of samples to form a mini-batch. stack_dim (int): Dimension along which to stack to form the batch. (default: 0) pin_memory (bool, optional): If ``True``, the data loader will copy tensors into CUDA pinned memory before returning them. drop_last (bool, optional): set to ``True`` to drop the last incomplete batch, if the dataset size is not divisible by the batch size. If ``False`` and the size of dataset is not divisible by the batch size, then the last batch will be smaller. (default: False) timeout (numeric, optional): if positive, the timeout value for collecting a batch from workers. Should always be non-negative. (default: 0) worker_init_fn (callable, optional): If not None, this will be called on each worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as input, after seeding and before data loading. (default: None) .. note:: By default, each worker will have its PyTorch seed set to ``base_seed + worker_id``, where ``base_seed`` is a long generated by main process using its RNG. However, seeds for other libraies may be duplicated upon initializing workers (w.g., NumPy), causing each worker to return identical random numbers. (See :ref:`dataloader-workers-random-seed` section in FAQ.) You may use ``torch.initial_seed()`` to access the PyTorch seed for each worker in :attr:`worker_init_fn`, and use it to set other seeds before data loading. .. warning:: If ``spawn`` start method is used, :attr:`worker_init_fn` cannot be an unpicklable object, e.g., a lambda function. """ __initialized = False def __init__(self, name, dataset, training=True, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, epoch_interval=1, collate_fn=None, stack_dim=0, pin_memory=False, drop_last=False, timeout=0, worker_init_fn=None): if collate_fn is None: if stack_dim == 0: collate_fn = ltr_collate elif stack_dim == 1: collate_fn = ltr_collate_stack1 else: raise ValueError('Stack dim no supported. Must be 0 or 1.') super(LTRLoader, self).__init__(dataset, batch_size, shuffle, sampler, batch_sampler, num_workers, collate_fn, pin_memory, drop_last, timeout, worker_init_fn) self.name = name self.training = training self.epoch_interval = epoch_interval self.stack_dim = stack_dim ================================================ FILE: artrackv2_mindspore/external/AR/ltr/data/processing.py ================================================ import torch import torchvision.transforms as transforms from pytracking import TensorDict import ltr.data.processing_utils as prutils def stack_tensors(x): if isinstance(x, (list, tuple)) and isinstance(x[0], torch.Tensor): return torch.stack(x) return x class BaseProcessing: """ Base class for Processing. Processing class is used to process the data returned by a dataset, before passing it through the network. For example, it can be used to crop a search region around the object, apply various data augmentations, etc.""" def __init__(self, transform=transforms.ToTensor(), train_transform=None, test_transform=None, joint_transform=None): """ args: transform - The set of transformations to be applied on the images. Used only if train_transform or test_transform is None. train_transform - The set of transformations to be applied on the train images. If None, the 'transform' argument is used instead. test_transform - The set of transformations to be applied on the test images. If None, the 'transform' argument is used instead. joint_transform - The set of transformations to be applied 'jointly' on the train and test images. For example, it can be used to convert both test and train images to grayscale. """ self.transform = {'train': transform if train_transform is None else train_transform, 'test': transform if test_transform is None else test_transform, 'joint': joint_transform} def __call__(self, data: TensorDict): raise NotImplementedError class ATOMProcessing(BaseProcessing): """ The processing class used for training ATOM. The images are processed in the following way. First, the target bounding box is jittered by adding some noise. Next, a square region (called search region ) centered at the jittered target center, and of area search_area_factor^2 times the area of the jittered box is cropped from the image. The reason for jittering the target box is to avoid learning the bias that the target is always at the center of the search region. The search region is then resized to a fixed size given by the argument output_sz. A set of proposals are then generated for the test images by jittering the ground truth box. """ def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor, proposal_params, mode='pair', *args, **kwargs): """ args: search_area_factor - The size of the search region relative to the target size. output_sz - An integer, denoting the size to which the search region is resized. The search region is always square. center_jitter_factor - A dict containing the amount of jittering to be applied to the target center before extracting the search region. See _get_jittered_box for how the jittering is done. scale_jitter_factor - A dict containing the amount of jittering to be applied to the target size before extracting the search region. See _get_jittered_box for how the jittering is done. proposal_params - Arguments for the proposal generation process. See _generate_proposals for details. mode - Either 'pair' or 'sequence'. If mode='sequence', then output has an extra dimension for frames """ super().__init__(*args, **kwargs) self.search_area_factor = search_area_factor self.output_sz = output_sz self.center_jitter_factor = center_jitter_factor self.scale_jitter_factor = scale_jitter_factor self.proposal_params = proposal_params self.mode = mode def _get_jittered_box(self, box, mode): """ Jitter the input box args: box - input bounding box mode - string 'train' or 'test' indicating train or test data returns: torch.Tensor - jittered box """ jittered_size = box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_factor[mode]) max_offset = (jittered_size.prod().sqrt() * torch.tensor(self.center_jitter_factor[mode]).float()) jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (torch.rand(2) - 0.5) return torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0) def _generate_proposals(self, box): """ Generates proposals by adding noise to the input box args: box - input box returns: torch.Tensor - Array of shape (num_proposals, 4) containing proposals torch.Tensor - Array of shape (num_proposals,) containing IoU overlap of each proposal with the input box. The IoU is mapped to [-1, 1] """ # Generate proposals num_proposals = self.proposal_params['boxes_per_frame'] proposal_method = self.proposal_params.get('proposal_method', 'default') if proposal_method == 'default': proposals = torch.zeros((num_proposals, 4)) gt_iou = torch.zeros(num_proposals) for i in range(num_proposals): proposals[i, :], gt_iou[i] = prutils.perturb_box(box, min_iou=self.proposal_params['min_iou'], sigma_factor=self.proposal_params['sigma_factor']) elif proposal_method == 'gmm': proposals, _, _ = prutils.sample_box_gmm(box, self.proposal_params['proposal_sigma'], num_samples=num_proposals) gt_iou = prutils.iou(box.view(1,4), proposals.view(-1,4)) # Map to [-1, 1] gt_iou = gt_iou * 2 - 1 return proposals, gt_iou def __call__(self, data: TensorDict): """ args: data - The input data, should contain the following fields: 'train_images', test_images', 'train_anno', 'test_anno' returns: TensorDict - output data block with following fields: 'train_images', 'test_images', 'train_anno', 'test_anno', 'test_proposals', 'proposal_iou' """ # Apply joint transforms if self.transform['joint'] is not None: data['train_images'], data['train_anno'] = self.transform['joint'](image=data['train_images'], bbox=data['train_anno']) data['test_images'], data['test_anno'] = self.transform['joint'](image=data['test_images'], bbox=data['test_anno'], new_roll=False) for s in ['train', 'test']: assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \ "In pair mode, num train/test frames must be 1" # Add a uniform noise to the center pos jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']] # Crop image region centered at jittered_anno box crops, boxes = prutils.jittered_center_crop(data[s + '_images'], jittered_anno, data[s + '_anno'], self.search_area_factor, self.output_sz) # Apply transforms data[s + '_images'], data[s + '_anno'] = self.transform[s](image=crops, bbox=boxes, joint=False) # Generate proposals frame2_proposals, gt_iou = zip(*[self._generate_proposals(a) for a in data['test_anno']]) data['test_proposals'] = list(frame2_proposals) data['proposal_iou'] = list(gt_iou) # Prepare output if self.mode == 'sequence': data = data.apply(stack_tensors) else: data = data.apply(lambda x: x[0] if isinstance(x, list) else x) return data class KLBBregProcessing(BaseProcessing): """ Based on ATOMProcessing. It supports training ATOM using the Maximum Likelihood or KL-divergence based learning introduced in [https://arxiv.org/abs/1909.12297] and in PrDiMP [https://arxiv.org/abs/2003.12565]. """ def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor, proposal_params, mode='pair', *args, **kwargs): """ args: search_area_factor - The size of the search region relative to the target size. output_sz - An integer, denoting the size to which the search region is resized. The search region is always square. center_jitter_factor - A dict containing the amount of jittering to be applied to the target center before extracting the search region. See _get_jittered_box for how the jittering is done. scale_jitter_factor - A dict containing the amount of jittering to be applied to the target size before extracting the search region. See _get_jittered_box for how the jittering is done. proposal_params - Arguments for the proposal generation process. See _generate_proposals for details. mode - Either 'pair' or 'sequence'. If mode='sequence', then output has an extra dimension for frames """ super().__init__(*args, **kwargs) self.search_area_factor = search_area_factor self.output_sz = output_sz self.center_jitter_factor = center_jitter_factor self.scale_jitter_factor = scale_jitter_factor self.proposal_params = proposal_params self.mode = mode def _get_jittered_box(self, box, mode): """ Jitter the input box args: box - input bounding box mode - string 'train' or 'test' indicating train or test data returns: torch.Tensor - jittered box """ jittered_size = box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_factor[mode]) max_offset = (jittered_size.prod().sqrt() * torch.tensor(self.center_jitter_factor[mode]).float()) jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (torch.rand(2) - 0.5) return torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0) def _generate_proposals(self, box): """ """ # Generate proposals proposals, proposal_density, gt_density = prutils.sample_box_gmm(box, self.proposal_params['proposal_sigma'], gt_sigma=self.proposal_params['gt_sigma'], num_samples=self.proposal_params[ 'boxes_per_frame'], add_mean_box=self.proposal_params.get( 'add_mean_box', False)) return proposals, proposal_density, gt_density def __call__(self, data: TensorDict): """ args: data - The input data, should contain the following fields: 'train_images', test_images', 'train_anno', 'test_anno' returns: TensorDict - output data block with following fields: 'train_images', 'test_images', 'train_anno', 'test_anno', 'test_proposals', 'proposal_density', 'gt_density' """ # Apply joint transforms if self.transform['joint'] is not None: data['train_images'], data['train_anno'] = self.transform['joint'](image=data['train_images'], bbox=data['train_anno']) data['test_images'], data['test_anno'] = self.transform['joint'](image=data['test_images'], bbox=data['test_anno'], new_roll=False) for s in ['train', 'test']: assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \ "In pair mode, num train/test frames must be 1" # Add a uniform noise to the center pos jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']] # Crop image region centered at jittered_anno box crops, boxes, _ = prutils.jittered_center_crop(data[s + '_images'], jittered_anno, data[s + '_anno'], self.search_area_factor, self.output_sz) # Apply transforms data[s + '_images'], data[s + '_anno'] = self.transform[s](image=crops, bbox=boxes, joint=False) # Generate proposals proposals, proposal_density, gt_density = zip(*[self._generate_proposals(a) for a in data['test_anno']]) data['test_proposals'] = proposals data['proposal_density'] = proposal_density data['gt_density'] = gt_density # Prepare output if self.mode == 'sequence': data = data.apply(stack_tensors) else: data = data.apply(lambda x: x[0] if isinstance(x, list) else x) return data class ATOMwKLProcessing(BaseProcessing): """Same as ATOMProcessing but using the GMM-based sampling of proposal boxes used in KLBBregProcessing.""" def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor, proposal_params, mode='pair', *args, **kwargs): super().__init__(*args, **kwargs) self.search_area_factor = search_area_factor self.output_sz = output_sz self.center_jitter_factor = center_jitter_factor self.scale_jitter_factor = scale_jitter_factor self.proposal_params = proposal_params self.mode = mode def _get_jittered_box(self, box, mode): """ Jitter the input box args: box - input bounding box mode - string 'train' or 'test' indicating train or test data returns: torch.Tensor - jittered box """ jittered_size = box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_factor[mode]) max_offset = (jittered_size.prod().sqrt() * torch.tensor(self.center_jitter_factor[mode]).float()) jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (torch.rand(2) - 0.5) return torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0) def _generate_proposals(self, box): """ """ # Generate proposals proposals, proposal_density, gt_density = prutils.sample_box_gmm(box, self.proposal_params['proposal_sigma'], self.proposal_params['gt_sigma'], self.proposal_params['boxes_per_frame']) iou = prutils.iou_gen(proposals, box.view(1, 4)) return proposals, proposal_density, gt_density, iou def __call__(self, data: TensorDict): # Apply joint transforms if self.transform['joint'] is not None: data['train_images'], data['train_anno'] = self.transform['joint'](image=data['train_images'], bbox=data['train_anno']) data['test_images'], data['test_anno'] = self.transform['joint'](image=data['test_images'], bbox=data['test_anno'], new_roll=False) for s in ['train', 'test']: assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \ "In pair mode, num train/test frames must be 1" # Add a uniform noise to the center pos jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']] # Crop image region centered at jittered_anno box crops, boxes = prutils.jittered_center_crop(data[s + '_images'], jittered_anno, data[s + '_anno'], self.search_area_factor, self.output_sz) # Apply transforms data[s + '_images'], data[s + '_anno'] = self.transform[s](image=crops, bbox=boxes, joint=False) # Generate proposals proposals, proposal_density, gt_density, proposal_iou = zip( *[self._generate_proposals(a) for a in data['test_anno']]) data['test_proposals'] = proposals data['proposal_density'] = proposal_density data['gt_density'] = gt_density data['proposal_iou'] = proposal_iou # Prepare output if self.mode == 'sequence': data = data.apply(stack_tensors) else: data = data.apply(lambda x: x[0] if isinstance(x, list) else x) return data class DiMPProcessing(BaseProcessing): """ The processing class used for training DiMP. The images are processed in the following way. First, the target bounding box is jittered by adding some noise. Next, a square region (called search region ) centered at the jittered target center, and of area search_area_factor^2 times the area of the jittered box is cropped from the image. The reason for jittering the target box is to avoid learning the bias that the target is always at the center of the search region. The search region is then resized to a fixed size given by the argument output_sz. A Gaussian label centered at the target is generated for each image. These label functions are used for computing the loss of the predicted classification model on the test images. A set of proposals are also generated for the test images by jittering the ground truth box. These proposals are used to train the bounding box estimating branch. """ def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor, crop_type='replicate', max_scale_change=None, mode='pair', proposal_params=None, label_function_params=None, *args, **kwargs): """ args: search_area_factor - The size of the search region relative to the target size. output_sz - An integer, denoting the size to which the search region is resized. The search region is always square. center_jitter_factor - A dict containing the amount of jittering to be applied to the target center before extracting the search region. See _get_jittered_box for how the jittering is done. scale_jitter_factor - A dict containing the amount of jittering to be applied to the target size before extracting the search region. See _get_jittered_box for how the jittering is done. crop_type - If 'replicate', the boundary pixels are replicated in case the search region crop goes out of image. If 'inside', the search region crop is shifted/shrunk to fit completely inside the image. If 'inside_major', the search region crop is shifted/shrunk to fit completely inside one axis of the image. max_scale_change - Maximum allowed scale change when performing the crop (only applicable for 'inside' and 'inside_major') mode - Either 'pair' or 'sequence'. If mode='sequence', then output has an extra dimension for frames proposal_params - Arguments for the proposal generation process. See _generate_proposals for details. label_function_params - Arguments for the label generation process. See _generate_label_function for details. """ super().__init__(*args, **kwargs) self.search_area_factor = search_area_factor self.output_sz = output_sz self.center_jitter_factor = center_jitter_factor self.scale_jitter_factor = scale_jitter_factor self.crop_type = crop_type self.mode = mode self.max_scale_change = max_scale_change self.proposal_params = proposal_params self.label_function_params = label_function_params def _get_jittered_box(self, box, mode): """ Jitter the input box args: box - input bounding box mode - string 'train' or 'test' indicating train or test data returns: torch.Tensor - jittered box """ jittered_size = box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_factor[mode]) max_offset = (jittered_size.prod().sqrt() * torch.tensor(self.center_jitter_factor[mode]).float()) jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (torch.rand(2) - 0.5) return torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0) def _generate_proposals(self, box): """ Generates proposals by adding noise to the input box args: box - input box returns: torch.Tensor - Array of shape (num_proposals, 4) containing proposals torch.Tensor - Array of shape (num_proposals,) containing IoU overlap of each proposal with the input box. The IoU is mapped to [-1, 1] """ # Generate proposals num_proposals = self.proposal_params['boxes_per_frame'] proposal_method = self.proposal_params.get('proposal_method', 'default') if proposal_method == 'default': proposals = torch.zeros((num_proposals, 4)) gt_iou = torch.zeros(num_proposals) for i in range(num_proposals): proposals[i, :], gt_iou[i] = prutils.perturb_box(box, min_iou=self.proposal_params['min_iou'], sigma_factor=self.proposal_params['sigma_factor']) elif proposal_method == 'gmm': proposals, _, _ = prutils.sample_box_gmm(box, self.proposal_params['proposal_sigma'], num_samples=num_proposals) gt_iou = prutils.iou(box.view(1, 4), proposals.view(-1, 4)) else: raise ValueError('Unknown proposal method.') # Map to [-1, 1] gt_iou = gt_iou * 2 - 1 return proposals, gt_iou def _generate_label_function(self, target_bb): """ Generates the gaussian label function centered at target_bb args: target_bb - target bounding box (num_images, 4) returns: torch.Tensor - Tensor of shape (num_images, label_sz, label_sz) containing the label for each sample """ gauss_label = prutils.gaussian_label_function(target_bb.view(-1, 4), self.label_function_params['sigma_factor'], self.label_function_params['kernel_sz'], self.label_function_params['feature_sz'], self.output_sz, end_pad_if_even=self.label_function_params.get('end_pad_if_even', True)) return gauss_label def __call__(self, data: TensorDict): """ args: data - The input data, should contain the following fields: 'train_images', test_images', 'train_anno', 'test_anno' returns: TensorDict - output data block with following fields: 'train_images', 'test_images', 'train_anno', 'test_anno', 'test_proposals', 'proposal_iou', 'test_label' (optional), 'train_label' (optional), 'test_label_density' (optional), 'train_label_density' (optional) """ if self.transform['joint'] is not None: data['train_images'], data['train_anno'] = self.transform['joint'](image=data['train_images'], bbox=data['train_anno']) data['test_images'], data['test_anno'] = self.transform['joint'](image=data['test_images'], bbox=data['test_anno'], new_roll=False) for s in ['train', 'test']: assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \ "In pair mode, num train/test frames must be 1" # Add a uniform noise to the center pos jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']] crops, boxes = prutils.target_image_crop(data[s + '_images'], jittered_anno, data[s + '_anno'], self.search_area_factor, self.output_sz, mode=self.crop_type, max_scale_change=self.max_scale_change) data[s + '_images'], data[s + '_anno'] = self.transform[s](image=crops, bbox=boxes, joint=False) # Generate proposals if self.proposal_params: frame2_proposals, gt_iou = zip(*[self._generate_proposals(a) for a in data['test_anno']]) data['test_proposals'] = list(frame2_proposals) data['proposal_iou'] = list(gt_iou) # Prepare output if self.mode == 'sequence': data = data.apply(stack_tensors) else: data = data.apply(lambda x: x[0] if isinstance(x, list) else x) # Generate label functions if self.label_function_params is not None: data['train_label'] = self._generate_label_function(data['train_anno']) data['test_label'] = self._generate_label_function(data['test_anno']) return data class KLDiMPProcessing(BaseProcessing): """ The processing class used for training PrDiMP that additionally supports the probabilistic classifier and bounding box regressor. See DiMPProcessing for details. """ def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor, crop_type='replicate', max_scale_change=None, mode='pair', proposal_params=None, label_function_params=None, label_density_params=None, *args, **kwargs): """ args: search_area_factor - The size of the search region relative to the target size. output_sz - An integer, denoting the size to which the search region is resized. The search region is always square. center_jitter_factor - A dict containing the amount of jittering to be applied to the target center before extracting the search region. See _get_jittered_box for how the jittering is done. scale_jitter_factor - A dict containing the amount of jittering to be applied to the target size before extracting the search region. See _get_jittered_box for how the jittering is done. crop_type - If 'replicate', the boundary pixels are replicated in case the search region crop goes out of image. If 'inside', the search region crop is shifted/shrunk to fit completely inside the image. If 'inside_major', the search region crop is shifted/shrunk to fit completely inside one axis of the image. max_scale_change - Maximum allowed scale change when performing the crop (only applicable for 'inside' and 'inside_major') mode - Either 'pair' or 'sequence'. If mode='sequence', then output has an extra dimension for frames proposal_params - Arguments for the proposal generation process. See _generate_proposals for details. label_function_params - Arguments for the label generation process. See _generate_label_function for details. label_density_params - Arguments for the label density generation process. See _generate_label_function for details. """ super().__init__(*args, **kwargs) self.search_area_factor = search_area_factor self.output_sz = output_sz self.center_jitter_factor = center_jitter_factor self.scale_jitter_factor = scale_jitter_factor self.crop_type = crop_type self.mode = mode self.max_scale_change = max_scale_change self.proposal_params = proposal_params self.label_function_params = label_function_params self.label_density_params = label_density_params def _get_jittered_box(self, box, mode): """ Jitter the input box args: box - input bounding box mode - string 'train' or 'test' indicating train or test data returns: torch.Tensor - jittered box """ jittered_size = box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_factor[mode]) max_offset = (jittered_size.prod().sqrt() * torch.tensor(self.center_jitter_factor[mode]).float()) jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (torch.rand(2) - 0.5) return torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0) def _generate_proposals(self, box): """ Generate proposal sample boxes from a GMM proposal distribution and compute their ground-truth density. This is used for ML and KL based regression learning of the bounding box regressor. args: box - input bounding box """ # Generate proposals proposals, proposal_density, gt_density = prutils.sample_box_gmm(box, self.proposal_params['proposal_sigma'], gt_sigma=self.proposal_params['gt_sigma'], num_samples=self.proposal_params['boxes_per_frame'], add_mean_box=self.proposal_params.get('add_mean_box', False)) return proposals, proposal_density, gt_density def _generate_label_function(self, target_bb): """ Generates the gaussian label function centered at target_bb args: target_bb - target bounding box (num_images, 4) returns: torch.Tensor - Tensor of shape (num_images, label_sz, label_sz) containing the label for each sample """ gauss_label = prutils.gaussian_label_function(target_bb.view(-1, 4), self.label_function_params['sigma_factor'], self.label_function_params['kernel_sz'], self.label_function_params['feature_sz'], self.output_sz, end_pad_if_even=self.label_function_params.get('end_pad_if_even', True)) return gauss_label def _generate_label_density(self, target_bb): """ Generates the gaussian label density centered at target_bb args: target_bb - target bounding box (num_images, 4) returns: torch.Tensor - Tensor of shape (num_images, label_sz, label_sz) containing the label for each sample """ feat_sz = self.label_density_params['feature_sz'] * self.label_density_params.get('interp_factor', 1) gauss_label = prutils.gaussian_label_function(target_bb.view(-1, 4), self.label_density_params['sigma_factor'], self.label_density_params['kernel_sz'], feat_sz, self.output_sz, end_pad_if_even=self.label_density_params.get('end_pad_if_even', True), density=True, uni_bias=self.label_density_params.get('uni_weight', 0.0)) gauss_label *= (gauss_label > self.label_density_params.get('threshold', 0.0)).float() if self.label_density_params.get('normalize', False): g_sum = gauss_label.sum(dim=(-2,-1)) valid = g_sum>0.01 gauss_label[valid, :, :] /= g_sum[valid].view(-1, 1, 1) gauss_label[~valid, :, :] = 1.0 / (gauss_label.shape[-2] * gauss_label.shape[-1]) gauss_label *= 1.0 - self.label_density_params.get('shrink', 0.0) return gauss_label def __call__(self, data: TensorDict): """ args: data - The input data, should contain the following fields: 'train_images', test_images', 'train_anno', 'test_anno' returns: TensorDict - output data block with following fields: 'train_images', 'test_images', 'train_anno', 'test_anno', 'test_proposals', 'proposal_density', 'gt_density', 'test_label' (optional), 'train_label' (optional), 'test_label_density' (optional), 'train_label_density' (optional) """ if self.transform['joint'] is not None: data['train_images'], data['train_anno'] = self.transform['joint'](image=data['train_images'], bbox=data['train_anno']) data['test_images'], data['test_anno'] = self.transform['joint'](image=data['test_images'], bbox=data['test_anno'], new_roll=False) for s in ['train', 'test']: assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \ "In pair mode, num train/test frames must be 1" # Add a uniform noise to the center pos jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']] crops, boxes = prutils.target_image_crop(data[s + '_images'], jittered_anno, data[s + '_anno'], self.search_area_factor, self.output_sz, mode=self.crop_type, max_scale_change=self.max_scale_change) data[s + '_images'], data[s + '_anno'] = self.transform[s](image=crops, bbox=boxes, joint=False) # Generate proposals proposals, proposal_density, gt_density = zip(*[self._generate_proposals(a) for a in data['test_anno']]) data['test_proposals'] = proposals data['proposal_density'] = proposal_density data['gt_density'] = gt_density for s in ['train', 'test']: is_distractor = data.get('is_distractor_{}_frame'.format(s), None) if is_distractor is not None: for is_dist, box in zip(is_distractor, data[s+'_anno']): if is_dist: box[0] = 99999999.9 box[1] = 99999999.9 # Prepare output if self.mode == 'sequence': data = data.apply(stack_tensors) else: data = data.apply(lambda x: x[0] if isinstance(x, list) else x) # Generate label functions if self.label_function_params is not None: data['train_label'] = self._generate_label_function(data['train_anno']) data['test_label'] = self._generate_label_function(data['test_anno']) if self.label_density_params is not None: data['train_label_density'] = self._generate_label_density(data['train_anno']) data['test_label_density'] = self._generate_label_density(data['test_anno']) return data ================================================ FILE: artrackv2_mindspore/external/AR/ltr/data/processing_utils.py ================================================ import torch import math import cv2 as cv import random import torch.nn.functional as F from .bounding_box_utils import rect_to_rel, rel_to_rect def sample_target(im, target_bb, search_area_factor, output_sz=None, mask=None): """ Extracts a square crop centered at target_bb box, of area search_area_factor^2 times target_bb area args: im - cv image target_bb - target box [x, y, w, h] search_area_factor - Ratio of crop size to target size output_sz - (float) Size to which the extracted crop is resized (always square). If None, no resizing is done. returns: cv image - extracted crop float - the factor by which the crop has been resized to make the crop size equal output_size """ x, y, w, h = target_bb.tolist() # Crop image crop_sz = math.ceil(math.sqrt(w * h) * search_area_factor) if crop_sz < 1: raise Exception('Too small bounding box.') x1 = round(x + 0.5 * w - crop_sz * 0.5) x2 = x1 + crop_sz y1 = round(y + 0.5 * h - crop_sz * 0.5) y2 = y1 + crop_sz x1_pad = max(0, -x1) x2_pad = max(x2 - im.shape[1] + 1, 0) y1_pad = max(0, -y1) y2_pad = max(y2 - im.shape[0] + 1, 0) # Crop target im_crop = im[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad, :] if mask is not None: mask_crop = mask[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad] # Pad im_crop_padded = cv.copyMakeBorder(im_crop, y1_pad, y2_pad, x1_pad, x2_pad, cv.BORDER_REPLICATE) if mask is not None: mask_crop_padded = F.pad(mask_crop, pad=(x1_pad, x2_pad, y1_pad, y2_pad), mode='constant', value=0) if output_sz is not None: resize_factor = output_sz / crop_sz im_crop_padded = cv.resize(im_crop_padded, (output_sz, output_sz)) if mask is None: return im_crop_padded, resize_factor mask_crop_padded = \ F.interpolate(mask_crop_padded[None, None], (output_sz, output_sz), mode='bilinear', align_corners=False)[0, 0] return im_crop_padded, resize_factor, mask_crop_padded else: if mask is None: return im_crop_padded, 1.0 return im_crop_padded, 1.0, mask_crop_padded def transform_image_to_crop(box_in: torch.Tensor, box_extract: torch.Tensor, resize_factor: float, crop_sz: torch.Tensor) -> torch.Tensor: """ Transform the box co-ordinates from the original image co-ordinates to the co-ordinates of the cropped image args: box_in - the box for which the co-ordinates are to be transformed box_extract - the box about which the image crop has been extracted. resize_factor - the ratio between the original image scale and the scale of the image crop crop_sz - size of the cropped image returns: torch.Tensor - transformed co-ordinates of box_in """ box_extract_center = box_extract[0:2] + 0.5 * box_extract[2:4] box_in_center = box_in[0:2] + 0.5 * box_in[2:4] box_out_center = (crop_sz - 1) / 2 + (box_in_center - box_extract_center) * resize_factor box_out_wh = box_in[2:4] * resize_factor box_out = torch.cat((box_out_center - 0.5 * box_out_wh, box_out_wh)) return box_out def jittered_center_crop(frames, box_extract, box_gt, search_area_factor, output_sz, masks=None): """ For each frame in frames, extracts a square crop centered at box_extract, of area search_area_factor^2 times box_extract area. The extracted crops are then resized to output_sz. Further, the co-ordinates of the box box_gt are transformed to the image crop co-ordinates args: frames - list of frames box_extract - list of boxes of same length as frames. The crops are extracted using anno_extract box_gt - list of boxes of same length as frames. The co-ordinates of these boxes are transformed from image co-ordinates to the crop co-ordinates search_area_factor - The area of the extracted crop is search_area_factor^2 times box_extract area output_sz - The size to which the extracted crops are resized returns: list - list of image crops list - box_gt location in the crop co-ordinates """ if masks is None: crops_resize_factors = [sample_target(f, a, search_area_factor, output_sz) for f, a in zip(frames, box_extract)] frames_crop, resize_factors = zip(*crops_resize_factors) masks_crop = None else: crops_resize_factors = [sample_target(f, a, search_area_factor, output_sz, m) for f, a, m in zip(frames, box_extract, masks)] frames_crop, resize_factors, masks_crop = zip(*crops_resize_factors) crop_sz = torch.Tensor([output_sz, output_sz]) # find the bb location in the crop box_crop = [transform_image_to_crop(a_gt, a_ex, rf, crop_sz) for a_gt, a_ex, rf in zip(box_gt, box_extract, resize_factors)] return frames_crop, box_crop, masks_crop def sample_target_adaptive(im, target_bb, search_area_factor, output_sz, mode: str = 'replicate', max_scale_change=None, mask=None): """ Extracts a crop centered at target_bb box, of area search_area_factor^2. If the crop area contains regions outside the image, it is shifted so that the it is inside the image. Further, if the crop area exceeds the image size, a smaller crop which fits the image is returned instead. args: im - Input numpy image to crop. target_bb - target box [x, y, w, h] search_area_factor - Ratio of crop size to target size output_sz - (float) Size to which the extracted crop is resized (always square). If None, no resizing is done. mode - If 'replicate', the boundary pixels are replicated in case the search region crop goes out of image. If 'inside', the search region crop is shifted/shrunk to fit completely inside the image. If 'inside_major', the search region crop is shifted/shrunk to fit completely inside one axis of the image. max_scale_change - Maximum allowed scale change when performing the crop (only applicable for 'inside' and 'inside_major') mask - Optional mask to apply the same crop. returns: numpy image - Extracted crop. torch.Tensor - A bounding box denoting the cropped region in the image. numpy mask - Cropped mask returned only if mask is not None. """ if max_scale_change is None: max_scale_change = float('inf') if isinstance(output_sz, (float, int)): output_sz = (output_sz, output_sz) output_sz = torch.Tensor(output_sz) im_h = im.shape[0] im_w = im.shape[1] bbx, bby, bbw, bbh = target_bb.tolist() # Crop image crop_sz_x, crop_sz_y = (output_sz * ( target_bb[2:].prod() / output_sz.prod()).sqrt() * search_area_factor).ceil().long().tolist() # Get new sample size if forced inside the image if mode == 'inside' or mode == 'inside_major': # Calculate rescaling factor if outside the image rescale_factor = [crop_sz_x / im_w, crop_sz_y / im_h] if mode == 'inside': rescale_factor = max(rescale_factor) elif mode == 'inside_major': rescale_factor = min(rescale_factor) rescale_factor = min(max(1, rescale_factor), max_scale_change) crop_sz_x = math.floor(crop_sz_x / rescale_factor) crop_sz_y = math.floor(crop_sz_y / rescale_factor) if crop_sz_x < 1 or crop_sz_y < 1: raise Exception('Too small bounding box.') x1 = round(bbx + 0.5 * bbw - crop_sz_x * 0.5) x2 = x1 + crop_sz_x y1 = round(bby + 0.5 * bbh - crop_sz_y * 0.5) y2 = y1 + crop_sz_y # Move box inside image shift_x = max(0, -x1) + min(0, im_w - x2) x1 += shift_x x2 += shift_x shift_y = max(0, -y1) + min(0, im_h - y2) y1 += shift_y y2 += shift_y out_x = (max(0, -x1) + max(0, x2 - im_w)) // 2 out_y = (max(0, -y1) + max(0, y2 - im_h)) // 2 shift_x = (-x1 - out_x) * (out_x > 0) shift_y = (-y1 - out_y) * (out_y > 0) x1 += shift_x x2 += shift_x y1 += shift_y y2 += shift_y x1_pad = max(0, -x1) x2_pad = max(x2 - im.shape[1] + 1, 0) y1_pad = max(0, -y1) y2_pad = max(y2 - im.shape[0] + 1, 0) # Crop target im_crop = im[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad, :] if mask is not None: mask_crop = mask[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad] # Pad im_crop_padded = cv.copyMakeBorder(im_crop, y1_pad, y2_pad, x1_pad, x2_pad, cv.BORDER_REPLICATE) if mask is not None: mask_crop_padded = F.pad(mask_crop, pad=(x1_pad, x2_pad, y1_pad, y2_pad), mode='constant', value=0) # Resize image im_out = cv.resize(im_crop_padded, tuple(output_sz.long().tolist())) if mask is not None: mask_out = \ F.interpolate(mask_crop_padded[None, None], tuple(output_sz.flip(0).long().tolist()), mode='nearest')[0, 0] crop_box = torch.Tensor([x1, y1, x2 - x1, y2 - y1]) if mask is None: return im_out, crop_box else: return im_out, crop_box, mask_out def crop_and_resize(im, box, crop_bb, output_sz, mask=None): if isinstance(output_sz, (float, int)): output_sz = (output_sz, output_sz) im_h = im.shape[0] im_w = im.shape[1] if crop_bb[2] < 1 or crop_bb[3] < 1: raise Exception('Too small bounding box.') x1 = crop_bb[0] x2 = crop_bb[0] + crop_bb[2] y1 = crop_bb[1] y2 = crop_bb[1] + crop_bb[3] x1_pad = max(0, -x1) x2_pad = max(x2 - im.shape[1] + 1, 0) y1_pad = max(0, -y1) y2_pad = max(y2 - im.shape[0] + 1, 0) # Crop target im_crop = im[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad, :] if mask is not None: mask_crop = mask[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad] # Pad im_crop_padded = cv.copyMakeBorder(im_crop, y1_pad, y2_pad, x1_pad, x2_pad, cv.BORDER_REPLICATE) if mask is not None: mask_crop_padded = F.pad(mask_crop, pad=(x1_pad, x2_pad, y1_pad, y2_pad), mode='constant', value=0) # Resize image im_out = cv.resize(im_crop_padded, output_sz) if mask is not None: mask_out = F.interpolate(mask_crop_padded[None, None], (output_sz[1], output_sz[0]), mode='nearest')[0, 0] rescale_factor = output_sz[0] / crop_bb[2] # Hack if box is not None: box_crop = box.clone() box_crop[0] -= crop_bb[0] box_crop[1] -= crop_bb[1] box_crop *= rescale_factor else: box_crop = None if mask is None: return im_out, box_crop else: return im_out, box_crop, mask_out def transform_box_to_crop(box: torch.Tensor, crop_box: torch.Tensor, crop_sz: torch.Tensor) -> torch.Tensor: """ Transform the box co-ordinates from the original image co-ordinates to the co-ordinates of the cropped image args: box - the box for which the co-ordinates are to be transformed crop_box - bounding box defining the crop in the original image crop_sz - size of the cropped image returns: torch.Tensor - transformed co-ordinates of box_in """ box_out = box.clone() box_out[:2] -= crop_box[:2] scale_factor = crop_sz / crop_box[2:] box_out[:2] *= scale_factor box_out[2:] *= scale_factor return box_out def target_image_crop(frames, box_extract, box_gt, search_area_factor, output_sz, mode: str = 'replicate', max_scale_change=None, masks=None): """ For each frame in frames, extracts a square crop centered at box_extract, of area search_area_factor^2 times box_extract area. If the crop area contains regions outside the image, it is shifted / shrunk so that it completely fits inside the image. The extracted crops are then resized to output_sz. Further, the co-ordinates of the box box_gt are transformed to the image crop co-ordinates args: frames - list of frames box_extract - list of boxes of same length as frames. The crops are extracted using anno_extract box_gt - list of boxes of same length as frames. The co-ordinates of these boxes are transformed from image co-ordinates to the crop co-ordinates search_area_factor - The area of the extracted crop is search_area_factor^2 times box_extract area output_sz - The size to which the extracted crops are resized mode - If 'replicate', the boundary pixels are replicated in case the search region crop goes out of image. If 'inside', the search region crop is shifted/shrunk to fit completely inside the image. If 'inside_major', the search region crop is shifted/shrunk to fit completely inside one axis of the image. max_scale_change - Maximum allowed scale change when performing the crop (only applicable for 'inside' and 'inside_major') masks - Optional masks to apply the same crop. returns: list - list of image crops list - box_gt location in the crop co-ordinates """ if isinstance(output_sz, (float, int)): output_sz = (output_sz, output_sz) if masks is None: frame_crops_boxes = [sample_target_adaptive(f, a, search_area_factor, output_sz, mode, max_scale_change) for f, a in zip(frames, box_extract)] frames_crop, crop_boxes = zip(*frame_crops_boxes) else: frame_crops_boxes_masks = [ sample_target_adaptive(f, a, search_area_factor, output_sz, mode, max_scale_change, mask=m) for f, a, m in zip(frames, box_extract, masks)] frames_crop, crop_boxes, masks_crop = zip(*frame_crops_boxes_masks) crop_sz = torch.Tensor(output_sz) # find the bb location in the crop box_crop = [transform_box_to_crop(bb_gt, crop_bb, crop_sz) for bb_gt, crop_bb in zip(box_gt, crop_boxes)] if masks is None: return frames_crop, box_crop else: return frames_crop, box_crop, masks_crop def iou(reference, proposals): """Compute the IoU between a reference box with multiple proposal boxes. args: reference - Tensor of shape (1, 4). proposals - Tensor of shape (num_proposals, 4) returns: torch.Tensor - Tensor of shape (num_proposals,) containing IoU of reference box with each proposal box. """ # Intersection box tl = torch.max(reference[:, :2], proposals[:, :2]) br = torch.min(reference[:, :2] + reference[:, 2:], proposals[:, :2] + proposals[:, 2:]) sz = (br - tl).clamp(0) # Area intersection = sz.prod(dim=1) union = reference[:, 2:].prod(dim=1) + proposals[:, 2:].prod(dim=1) - intersection return intersection / union def rand_uniform(a, b, shape=1): """ sample numbers uniformly between a and b. args: a - lower bound b - upper bound shape - shape of the output tensor returns: torch.Tensor - tensor of shape=shape """ return (b - a) * torch.rand(shape) + a def perturb_box(box, min_iou=0.5, sigma_factor=0.1): """ Perturb the input box by adding gaussian noise to the co-ordinates args: box - input box min_iou - minimum IoU overlap between input box and the perturbed box sigma_factor - amount of perturbation, relative to the box size. Can be either a single element, or a list of sigma_factors, in which case one of them will be uniformly sampled. Further, each of the sigma_factor element can be either a float, or a tensor of shape (4,) specifying the sigma_factor per co-ordinate returns: torch.Tensor - the perturbed box """ if isinstance(sigma_factor, list): # If list, sample one sigma_factor as current sigma factor c_sigma_factor = random.choice(sigma_factor) else: c_sigma_factor = sigma_factor if not isinstance(c_sigma_factor, torch.Tensor): c_sigma_factor = c_sigma_factor * torch.ones(4) perturb_factor = torch.sqrt(box[2] * box[3]) * c_sigma_factor # multiple tries to ensure that the perturbed box has iou > min_iou with the input box for i_ in range(100): c_x = box[0] + 0.5 * box[2] c_y = box[1] + 0.5 * box[3] c_x_per = random.gauss(c_x, perturb_factor[0]) c_y_per = random.gauss(c_y, perturb_factor[1]) w_per = random.gauss(box[2], perturb_factor[2]) h_per = random.gauss(box[3], perturb_factor[3]) if w_per <= 1: w_per = box[2] * rand_uniform(0.15, 0.5) if h_per <= 1: h_per = box[3] * rand_uniform(0.15, 0.5) box_per = torch.Tensor([c_x_per - 0.5 * w_per, c_y_per - 0.5 * h_per, w_per, h_per]).round() if box_per[2] <= 1: box_per[2] = box[2] * rand_uniform(0.15, 0.5) if box_per[3] <= 1: box_per[3] = box[3] * rand_uniform(0.15, 0.5) box_iou = iou(box.view(1, 4), box_per.view(1, 4)) # if there is sufficient overlap, return if box_iou > min_iou: return box_per, box_iou # else reduce the perturb factor perturb_factor *= 0.9 return box_per, box_iou def gauss_1d(sz, sigma, center, end_pad=0, density=False): k = torch.arange(-(sz - 1) / 2, (sz + 1) / 2 + end_pad).reshape(1, -1) gauss = torch.exp(-1.0 / (2 * sigma ** 2) * (k - center.reshape(-1, 1)) ** 2) if density: gauss /= math.sqrt(2 * math.pi) * sigma return gauss def gauss_2d(sz, sigma, center, end_pad=(0, 0), density=False): if isinstance(sigma, (float, int)): sigma = (sigma, sigma) return gauss_1d(sz[0].item(), sigma[0], center[:, 0], end_pad[0], density).reshape(center.shape[0], 1, -1) * \ gauss_1d(sz[1].item(), sigma[1], center[:, 1], end_pad[1], density).reshape(center.shape[0], -1, 1) def gaussian_label_function(target_bb, sigma_factor, kernel_sz, feat_sz, image_sz, end_pad_if_even=True, density=False, uni_bias=0): """Construct Gaussian label function.""" if isinstance(kernel_sz, (float, int)): kernel_sz = (kernel_sz, kernel_sz) if isinstance(feat_sz, (float, int)): feat_sz = (feat_sz, feat_sz) if isinstance(image_sz, (float, int)): image_sz = (image_sz, image_sz) image_sz = torch.Tensor(image_sz) feat_sz = torch.Tensor(feat_sz) target_center = target_bb[:, 0:2] + 0.5 * target_bb[:, 2:4] target_center_norm = (target_center - image_sz / 2) / image_sz center = feat_sz * target_center_norm + 0.5 * \ torch.Tensor([(kernel_sz[0] + 1) % 2, (kernel_sz[1] + 1) % 2]) sigma = sigma_factor * feat_sz.prod().sqrt().item() if end_pad_if_even: end_pad = (int(kernel_sz[0] % 2 == 0), int(kernel_sz[1] % 2 == 0)) else: end_pad = (0, 0) gauss_label = gauss_2d(feat_sz, sigma, center, end_pad, density=density) if density: sz = (feat_sz + torch.Tensor(end_pad)).prod() label = (1.0 - uni_bias) * gauss_label + uni_bias / sz else: label = gauss_label + uni_bias return label def gauss_density_centered(x, std): """Evaluate the probability density of a Gaussian centered at zero. args: x - Samples. std - List of standard deviations """ return torch.exp(-0.5 * (x / std) ** 2) / (math.sqrt(2 * math.pi) * std) def gmm_density_centered(x, std): """Evaluate the probability density of a GMM centered at zero. args: x - Samples. Assumes dim=-1 is the component dimension and dim=-2 is feature dimension. Rest are sample dimension. std - Tensor of standard deviations """ if x.dim() == std.dim() - 1: x = x.unsqueeze(-1) elif not (x.dim() == std.dim() and x.shape[-1] == 1): raise ValueError('Last dimension must be the gmm stds.') return gauss_density_centered(x, std).prod(-2).mean(-1) def sample_gmm_centered(std, num_samples=1): """Sample from a GMM distribution centered at zero: args: std - Tensor of standard deviations num_samples - number of samples """ num_components = std.shape[-1] num_dims = std.numel() // num_components std = std.view(1, num_dims, num_components) # Sample component ids k = torch.randint(num_components, (num_samples,), dtype=torch.int64) std_samp = std[0, :, k].t() # Sample x_centered = std_samp * torch.randn(num_samples, num_dims) prob_dens = gmm_density_centered(x_centered, std) return x_centered, prob_dens def sample_gmm(mean, std, num_samples=1): """Sample from a GMM distribution: args: mean - a single mean vector std - Tensor of standard deviations num_samples - number of samples """ num_dims = mean.numel() num_components = std.shape[-1] mean = mean.view(1, num_dims) std = std.view(1, -1, num_components) # Sample component ids k = torch.randint(num_components, (num_samples,), dtype=torch.int64) std_samp = std[0, :, k].t() # Sample x_centered = std_samp * torch.randn(num_samples, num_dims) x = x_centered + mean prob_dens = gmm_density_centered(x_centered, std) return x, prob_dens def sample_box_gmm(mean_box, proposal_sigma, gt_sigma=None, num_samples=1, add_mean_box=False): """Sample boxes from a Gaussian mixture model. args: mean_box - Center (or mean) bounding box proposal_sigma - List of standard deviations for each Gaussian gt_sigma - Standard deviation of the ground truth distribution num_samples - Number of sampled boxes add_mean_box - Also add mean box as first element returns: proposals, proposal density and ground truth density for all samples """ center_std = torch.Tensor([s[0] for s in proposal_sigma]) sz_std = torch.Tensor([s[1] for s in proposal_sigma]) std = torch.stack([center_std, center_std, sz_std, sz_std]) mean_box = mean_box.view(1, 4) sz_norm = mean_box[:, 2:].clone() # Sample boxes proposals_rel_centered, proposal_density = sample_gmm_centered(std, num_samples) # Add mean and map back mean_box_rel = rect_to_rel(mean_box, sz_norm) proposals_rel = proposals_rel_centered + mean_box_rel proposals = rel_to_rect(proposals_rel, sz_norm) if gt_sigma is None or gt_sigma[0] == 0 and gt_sigma[1] == 0: gt_density = torch.zeros_like(proposal_density) else: std_gt = torch.Tensor([gt_sigma[0], gt_sigma[0], gt_sigma[1], gt_sigma[1]]).view(1, 4) gt_density = gauss_density_centered(proposals_rel_centered, std_gt).prod(-1) if add_mean_box: proposals = torch.cat((mean_box, proposals)) proposal_density = torch.cat((torch.Tensor([-1]), proposal_density)) gt_density = torch.cat((torch.Tensor([1]), gt_density)) return proposals, proposal_density, gt_density ================================================ FILE: artrackv2_mindspore/external/AR/ltr/data/processing_utils_SE.py ================================================ import torch import math import cv2 as cv import random import numpy as np def stack_tensors(x): if isinstance(x, list) and isinstance(x[0], torch.Tensor): return torch.stack(x) return x '''Added on 2019.12.23''' def sample_target_SE(im, target_bb, search_area_factor, output_sz=None, mode=cv.BORDER_REPLICATE): """ Extracts a crop centered at target_bb box, of size search_area_factor times target_bb(Both height and width) args: im - cv image target_bb - target box [x, y, w, h] search_area_factor - Ratio of crop size to target size output_sz - (float) Size to which the extracted crop is resized (always square). If None, no resizing is done. returns: cv image - extracted crop float - the factor by which the crop has been resized to make the crop size equal output_size """ x, y, w, h = target_bb.tolist() # Crop image ws = math.ceil(search_area_factor * w) hs = math.ceil(search_area_factor * h) if ws < 1 or hs < 1: raise Exception('Too small bounding box.') x1 = round(x + 0.5*w - ws*0.5) x2 = x1 + ws y1 = round(y + 0.5 * h - hs * 0.5) y2 = y1 + hs x1_pad = max(0, -x1) x2_pad = max(x2-im.shape[1]+1, 0) y1_pad = max(0, -y1) y2_pad = max(y2-im.shape[0]+1, 0) # Crop target im_crop = im[y1+y1_pad:y2-y2_pad, x1+x1_pad:x2-x2_pad, :] # Pad im_crop_padded = cv.copyMakeBorder(im_crop, y1_pad, y2_pad, x1_pad, x2_pad, mode) if output_sz is not None: w_rsz_f = output_sz / ws h_rsz_f = output_sz / hs im_crop_padded_rsz = cv.resize(im_crop_padded, (output_sz, output_sz)) if len(im_crop_padded_rsz.shape)==2: im_crop_padded_rsz = im_crop_padded_rsz[...,np.newaxis] return im_crop_padded_rsz, h_rsz_f, w_rsz_f else: return im_crop_padded, 1.0, 1.0 '''把mask映射到原图上''' def map_mask_back(im, target_bb, search_area_factor, mask, mode=cv.BORDER_REPLICATE): """ Extracts a crop centered at target_bb box, of size search_area_factor times target_bb(Both height and width) args: im - cv image target_bb - target box [x, y, w, h] search_area_factor - Ratio of crop size to target size output_sz - (float) Size to which the extracted crop is resized (always square). If None, no resizing is done. returns: cv image - extracted crop float - the factor by which the crop has been resized to make the crop size equal output_size """ H,W = (im.shape[0],im.shape[1]) base = np.zeros((H,W)) x, y, w, h = target_bb.tolist() # Crop image ws = math.ceil(search_area_factor * w) hs = math.ceil(search_area_factor * h) if ws < 1 or hs < 1: raise Exception('Too small bounding box.') x1 = round(x + 0.5*w - ws*0.5) x2 = x1 + ws y1 = round(y + 0.5 * h - hs * 0.5) y2 = y1 + hs x1_pad = max(0, -x1) x2_pad = max(x2-im.shape[1]+1, 0) y1_pad = max(0, -y1) y2_pad = max(y2-im.shape[0]+1, 0) '''pad base''' base_padded = cv.copyMakeBorder(base, y1_pad, y2_pad, x1_pad, x2_pad, mode) '''Resize mask''' mask_rsz = cv.resize(mask,(ws,hs)) '''fill region with mask''' base_padded[y1+y1_pad:y2+y1_pad, x1+x1_pad:x2+x1_pad] = mask_rsz.copy() '''crop base_padded to get final mask''' final_mask = base_padded[y1_pad:y1_pad+H,x1_pad:x1_pad+W] assert (final_mask.shape == (H,W)) return final_mask '''Added on 2019.12.23''' def transform_image_to_crop_SE(box_in: torch.Tensor, box_extract: torch.Tensor, resize_factor_h: float, resize_factor_w: float, crop_sz: torch.Tensor) -> torch.Tensor: """ Transform the box co-ordinates from the original image co-ordinates to the co-ordinates of the cropped image args: box_in - the box for which the co-ordinates are to be transformed box_extract - the box about which the image crop has been extracted. resize_factor - the ratio between the original image scale and the scale of the image crop crop_sz - size of the cropped image returns: torch.Tensor - transformed co-ordinates of box_in """ box_extract_center = box_extract[0:2] + 0.5*box_extract[2:4] box_in_center = box_in[0:2] + 0.5*box_in[2:4] box_out_xc = (crop_sz[0] -1)/2 + (box_in_center[0] - box_extract_center[0])*resize_factor_w box_out_yc = (crop_sz[0] -1)/2 + (box_in_center[1] - box_extract_center[1])*resize_factor_h box_out_w = box_in[2] * resize_factor_w box_out_h = box_in[3] * resize_factor_h '''2019.12.28 为了避免出现(x1,y1)小于0,或者(x2,y2)大于256的情况,这里我对它们加上了一些限制''' max_sz = crop_sz[0].item() box_out_x1 = torch.clamp(box_out_xc - 0.5 * box_out_w,0,max_sz) box_out_y1 = torch.clamp(box_out_yc - 0.5 * box_out_h,0,max_sz) box_out_x2 = torch.clamp(box_out_xc + 0.5 * box_out_w,0,max_sz) box_out_y2 = torch.clamp(box_out_yc + 0.5 * box_out_h,0,max_sz) box_out_w_new = box_out_x2 - box_out_x1 box_out_h_new = box_out_y2 - box_out_y1 box_out = torch.stack((box_out_x1, box_out_y1, box_out_w_new, box_out_h_new)) return box_out def centered_crop(frames, anno, area_factor, output_sz): crops_resize_factors = [sample_target(f, a, area_factor, output_sz) for f, a in zip(frames, anno)] frames_crop, resize_factors = zip(*crops_resize_factors) crop_sz = torch.Tensor([output_sz, output_sz]) # find the bb location in the crop anno_crop = [transform_image_to_crop(a, a, rf, crop_sz) for a, rf in zip(anno, resize_factors)] return frames_crop, anno_crop '''Added by Bin Yan 2019.12.23, changed on 2020.1.4(add a new args: "get_bbox_coord")''' def jittered_center_crop_SE(frames, box_extract, box_gt, search_area_factor, output_sz, get_bbox_coord=True, mode=cv.BORDER_REPLICATE): """ Crop a patch centered at box_extract. The height and width of cropped region is search_area_factor times that of box_extract. The extracted crops are then resized to output_sz. Further, the co-ordinates of the box box_gt are transformed to the image crop co-ordinates args: frames - list of frames box_extract - list of boxes of same length as frames. The crops are extracted using anno_extract box_gt - list of boxes of same length as frames. The co-ordinates of these boxes are transformed from image co-ordinates to the crop co-ordinates search_area_factor - The area of the extracted crop is search_area_factor^2 times box_extract area output_sz - The size to which the extracted crops are resized returns: list - list of image crops list - box_gt location in the crop co-ordinates """ '''call function "sample_target_SE" and function "transform_image_to_crop_SE"''' '''get cropped patch(fixed size)''' crops_resize_factors = [sample_target_SE(f, a, search_area_factor, output_sz, mode=mode) for f, a in zip(frames, box_extract)] frames_crop, resize_factors_h, resize_factors_w = zip(*crops_resize_factors) if get_bbox_coord: crop_sz = torch.Tensor([output_sz, output_sz]) # find the bb location in the crop '''get GT's cooridinate on the cropped patch''' box_crop = [transform_image_to_crop_SE(a_gt, a_ex, h_rsf, w_rsf, crop_sz) for a_gt, a_ex, h_rsf, w_rsf in zip(box_gt, box_extract, resize_factors_h, resize_factors_w)] return frames_crop, box_crop else: return frames_crop def sample_target_nopad(im, target_bb, search_area_factor, output_sz): """ Extracts a crop centered at target_bb box, of area search_area_factor^2. If the crop area contains regions outside the image, it is shifted so that the it is inside the image. Further, if the crop area exceeds the image size, a smaller crop which fits the image is returned instead. args: im - cv image target_bb - target box [x, y, w, h] search_area_factor - Ratio of crop size to target size output_sz - (float) Size to which the extracted crop is resized (always square). If None, no resizing is done. returns: cv image - extracted crop torch.Tensor - a bounding box denoting the cropped region in the image. """ if isinstance(output_sz, (float, int)): output_sz = (output_sz, output_sz) output_sz = torch.Tensor(output_sz) im_h = im.shape[0] im_w = im.shape[1] bbx, bby, bbw, bbh = target_bb.tolist() # Crop image crop_sz_x, crop_sz_y = (output_sz * (target_bb[2:].prod()/output_sz.prod()).sqrt() * search_area_factor).ceil() # Calculate rescaling factor if outside the image rescale_factor = max(1, crop_sz_x/im_w, crop_sz_y/im_h) crop_sz_x = math.floor(crop_sz_x / rescale_factor) crop_sz_y = math.floor(crop_sz_y / rescale_factor) if crop_sz_x < 1 or crop_sz_y < 1: raise Exception('Too small bounding box.') x1 = round(bbx + 0.5*bbw - crop_sz_x*0.5) x2 = x1 + crop_sz_x y1 = round(bby + 0.5*bbh - crop_sz_y*0.5) y2 = y1 + crop_sz_y # Move box inside image shift_x = max(0, -x1) + min(0, im_w - x2) x1 += shift_x x2 += shift_x shift_y = max(0, -y1) + min(0, im_h - y2) y1 += shift_y y2 += shift_y # Crop and resize image im_crop = im[y1:y2, x1:x2, :] im_out = cv.resize(im_crop, tuple(output_sz.long().tolist())) crop_box = torch.Tensor([x1, y1, x2-x1, y2-y1]) return im_out, crop_box def transform_box_to_crop(box: torch.Tensor, crop_box: torch.Tensor, crop_sz: torch.Tensor) -> torch.Tensor: """ Transform the box co-ordinates from the original image co-ordinates to the co-ordinates of the cropped image args: box - the box for which the co-ordinates are to be transformed crop_box - bounding box defining the crop in the original image crop_sz - size of the cropped image returns: torch.Tensor - transformed co-ordinates of box_in """ box_out = box.clone() box_out[:2] -= crop_box[:2] scale_factor = crop_sz / crop_box[2:] box_out[:2] *= scale_factor box_out[2:] *= scale_factor return box_out def jittered_center_crop_nopad(frames, box_extract, box_gt, search_area_factor, output_sz): """ For each frame in frames, extracts a square crop centered at box_extract, of area search_area_factor^2 times box_extract area. If the crop area contains regions outside the image, it is shifted / shrunk so that it completely fits inside the image. The extracted crops are then resized to output_sz. Further, the co-ordinates of the box box_gt are transformed to the image crop co-ordinates args: frames - list of frames box_extract - list of boxes of same length as frames. The crops are extracted using anno_extract box_gt - list of boxes of same length as frames. The co-ordinates of these boxes are transformed from image co-ordinates to the crop co-ordinates search_area_factor - The area of the extracted crop is search_area_factor^2 times box_extract area output_sz - The size to which the extracted crops are resized returns: list - list of image crops list - box_gt location in the crop co-ordinates """ if isinstance(output_sz, (float, int)): output_sz = (output_sz, output_sz) frame_crops_boxes = [sample_target_nopad(f, a, search_area_factor, output_sz) for f, a in zip(frames, box_extract)] frames_crop, crop_boxes = zip(*frame_crops_boxes) crop_sz = torch.Tensor(output_sz) # find the bb location in the crop box_crop = [transform_box_to_crop(bb_gt, crop_bb, crop_sz) for bb_gt, crop_bb in zip(box_gt, crop_boxes)] return frames_crop, box_crop def iou(reference, proposals): """Compute the IoU between a reference box with multiple proposal boxes. args: reference - Tensor of shape (1, 4). proposals - Tensor of shape (num_proposals, 4) returns: torch.Tensor - Tensor of shape (num_proposals,) containing IoU of reference box with each proposal box. """ # Intersection box tl = torch.max(reference[:,:2], proposals[:,:2]) br = torch.min(reference[:,:2] + reference[:,2:], proposals[:,:2] + proposals[:,2:]) sz = (br - tl).clamp(0) # Area intersection = sz.prod(dim=1) union = reference[:,2:].prod(dim=1) + proposals[:,2:].prod(dim=1) - intersection return intersection / union def rand_uniform(a, b, shape=1): """ sample numbers uniformly between a and b. args: a - lower bound b - upper bound shape - shape of the output tensor returns: torch.Tensor - tensor of shape=shape """ return (b - a) * torch.rand(shape) + a def perturb_box(box, min_iou=0.5, sigma_factor=0.1): """ Perturb the input box by adding gaussian noise to the co-ordinates args: box - input box min_iou - minimum IoU overlap between input box and the perturbed box sigma_factor - amount of perturbation, relative to the box size. Can be either a single element, or a list of sigma_factors, in which case one of them will be uniformly sampled. Further, each of the sigma_factor element can be either a float, or a tensor of shape (4,) specifying the sigma_factor per co-ordinate returns: torch.Tensor - the perturbed box """ if isinstance(sigma_factor, list): # If list, sample one sigma_factor as current sigma factor c_sigma_factor = random.choice(sigma_factor) else: c_sigma_factor = sigma_factor if not isinstance(c_sigma_factor, torch.Tensor): c_sigma_factor = c_sigma_factor * torch.ones(4) perturb_factor = torch.sqrt(box[2]*box[3])*c_sigma_factor # multiple tries to ensure that the perturbed box has iou > min_iou with the input box for i_ in range(100): c_x = box[0] + 0.5*box[2] c_y = box[1] + 0.5 * box[3] c_x_per = random.gauss(c_x, perturb_factor[0]) c_y_per = random.gauss(c_y, perturb_factor[1]) w_per = random.gauss(box[2], perturb_factor[2]) h_per = random.gauss(box[3], perturb_factor[3]) if w_per <= 1: w_per = box[2]*rand_uniform(0.15, 0.5) if h_per <= 1: h_per = box[3]*rand_uniform(0.15, 0.5) box_per = torch.Tensor([c_x_per - 0.5*w_per, c_y_per - 0.5*h_per, w_per, h_per]).round() if box_per[2] <= 1: box_per[2] = box[2]*rand_uniform(0.15, 0.5) if box_per[3] <= 1: box_per[3] = box[3]*rand_uniform(0.15, 0.5) box_iou = iou(box.view(1, 4), box_per.view(1, 4)) # if there is sufficient overlap, return if box_iou > min_iou: return box_per, box_iou # else reduce the perturb factor perturb_factor *= 0.9 return box_per, box_iou def gauss_1d(sz, sigma, center, end_pad=0): k = torch.arange(-(sz-1)/2, (sz+1)/2 + end_pad).reshape(1, -1) return torch.exp(-1.0/(2*sigma**2) * (k - center.reshape(-1, 1))**2) def gauss_2d(sz, sigma, center, end_pad=(0, 0)): if isinstance(sigma, (float, int)): sigma = (sigma, sigma) return gauss_1d(sz[0].item(), sigma[0], center[:, 0], end_pad[0]).reshape(center.shape[0], 1, -1) * \ gauss_1d(sz[1].item(), sigma[1], center[:, 1], end_pad[1]).reshape(center.shape[0], -1, 1) def gaussian_label_function(target_bb, sigma_factor, kernel_sz, feat_sz, image_sz, end_pad_if_even=True): """Construct Gaussian label function.""" if isinstance(kernel_sz, (float, int)): kernel_sz = (kernel_sz, kernel_sz) if isinstance(feat_sz, (float, int)): feat_sz = (feat_sz, feat_sz) if isinstance(image_sz, (float, int)): image_sz = (image_sz, image_sz) image_sz = torch.Tensor(image_sz) feat_sz = torch.Tensor(feat_sz) target_center = target_bb[:, 0:2] + 0.5 * target_bb[:, 2:4] target_center_norm = (target_center - image_sz / 2) / image_sz center = feat_sz * target_center_norm + 0.5 * \ torch.Tensor([(kernel_sz[0] + 1) % 2, (kernel_sz[1] + 1) % 2]) sigma = sigma_factor * feat_sz.prod().sqrt().item() if end_pad_if_even: end_pad = (int(kernel_sz[0]%2 == 0), int(kernel_sz[1]%2 == 0)) else: end_pad = (0, 0) gauss_label = gauss_2d(feat_sz, sigma, center, end_pad) return gauss_label ================================================ FILE: artrackv2_mindspore/external/AR/ltr/data/sampler.py ================================================ import random import torch.utils.data from pytracking import TensorDict def no_processing(data): return data class TrackingSampler(torch.utils.data.Dataset): """ Class responsible for sampling frames from training sequences to form batches. Each training sample is a tuple consisting of i) a set of train frames, used to learn the DiMP classification model and obtain the modulation vector for IoU-Net, and ii) a set of test frames on which target classification loss for the predicted DiMP model, and the IoU prediction loss for the IoU-Net is calculated. The sampling is done in the following ways. First a dataset is selected at random. Next, a sequence is selected from that dataset. A base frame is then sampled randomly from the sequence. Next, a set of 'train frames' and 'test frames' are sampled from the sequence from the range [base_frame_id - max_gap, base_frame_id] and (base_frame_id, base_frame_id + max_gap] respectively. Only the frames in which the target is visible are sampled. If enough visible frames are not found, the 'max_gap' is increased gradually till enough frames are found. The sampled frames are then passed through the input 'processing' function for the necessary processing- """ def __init__(self, datasets, p_datasets, samples_per_epoch, max_gap, num_test_frames, num_train_frames=1, processing=no_processing, frame_sample_mode='causal'): """ args: datasets - List of datasets to be used for training p_datasets - List containing the probabilities by which each dataset will be sampled samples_per_epoch - Number of training samples per epoch max_gap - Maximum gap, in frame numbers, between the train frames and the test frames. num_test_frames - Number of test frames to sample. num_train_frames - Number of train frames to sample. processing - An instance of Processing class which performs the necessary processing of the data. frame_sample_mode - Either 'causal' or 'interval'. If 'causal', then the test frames are sampled in a causally, otherwise randomly within the interval. """ self.datasets = datasets # If p not provided, sample uniformly from all videos if p_datasets is None: p_datasets = [len(d) for d in self.datasets] # Normalize p_total = sum(p_datasets) self.p_datasets = [x / p_total for x in p_datasets] self.samples_per_epoch = samples_per_epoch self.max_gap = max_gap self.num_test_frames = num_test_frames self.num_train_frames = num_train_frames self.processing = processing self.frame_sample_mode = frame_sample_mode def __len__(self): return self.samples_per_epoch def _sample_visible_ids(self, visible, num_ids=1, min_id=None, max_id=None): """ Samples num_ids frames between min_id and max_id for which target is visible args: visible - 1d Tensor indicating whether target is visible for each frame num_ids - number of frames to be samples min_id - Minimum allowed frame number max_id - Maximum allowed frame number returns: list - List of sampled frame numbers. None if not sufficient visible frames could be found. """ if num_ids == 0: return [] if min_id is None or min_id < 0: min_id = 0 if max_id is None or max_id > len(visible): max_id = len(visible) valid_ids = [i for i in range(min_id, max_id) if visible[i]] # No visible ids if len(valid_ids) == 0: return None return random.choices(valid_ids, k=num_ids) def __getitem__(self, index): """ args: index (int): Index (Ignored since we sample randomly) returns: TensorDict - dict containing all the data blocks """ # Select a dataset dataset = random.choices(self.datasets, self.p_datasets)[0] is_video_dataset = dataset.is_video_sequence() # Sample a sequence with enough visible frames enough_visible_frames = False while not enough_visible_frames: # Sample a sequence seq_id = random.randint(0, dataset.get_num_sequences() - 1) # Sample frames seq_info_dict = dataset.get_sequence_info(seq_id) visible = seq_info_dict['visible'] enough_visible_frames = visible.type(torch.int64).sum().item() > 2 * ( self.num_test_frames + self.num_train_frames) and len(visible) >= 20 enough_visible_frames = enough_visible_frames or not is_video_dataset if is_video_dataset: train_frame_ids = None test_frame_ids = None gap_increase = 0 if self.frame_sample_mode == 'interval': # Sample frame numbers within interval defined by the first frame while test_frame_ids is None: base_frame_id = self._sample_visible_ids(visible, num_ids=1) extra_train_frame_ids = self._sample_visible_ids(visible, num_ids=self.num_train_frames - 1, min_id=base_frame_id[ 0] - self.max_gap - gap_increase, max_id=base_frame_id[ 0] + self.max_gap + gap_increase) if extra_train_frame_ids is None: gap_increase += 5 continue train_frame_ids = base_frame_id + extra_train_frame_ids test_frame_ids = self._sample_visible_ids(visible, num_ids=self.num_test_frames, min_id=train_frame_ids[0] - self.max_gap - gap_increase, max_id=train_frame_ids[0] + self.max_gap + gap_increase) gap_increase += 5 # Increase gap until a frame is found elif self.frame_sample_mode == 'causal': # Sample test and train frames in a causal manner, i.e. test_frame_ids > train_frame_ids while test_frame_ids is None: base_frame_id = self._sample_visible_ids(visible, num_ids=1, min_id=self.num_train_frames - 1, max_id=len(visible) - self.num_test_frames) prev_frame_ids = self._sample_visible_ids(visible, num_ids=self.num_train_frames - 1, min_id=base_frame_id[0] - self.max_gap - gap_increase, max_id=base_frame_id[0]) if prev_frame_ids is None: gap_increase += 5 continue train_frame_ids = base_frame_id + prev_frame_ids test_frame_ids = self._sample_visible_ids(visible, min_id=train_frame_ids[0] + 1, max_id=train_frame_ids[0] + self.max_gap + gap_increase, num_ids=self.num_test_frames) # Increase gap until a frame is found gap_increase += 5 else: # In case of image dataset, just repeat the image to generate synthetic video train_frame_ids = [1] * self.num_train_frames test_frame_ids = [1] * self.num_test_frames train_frames, train_anno, meta_obj_train = dataset.get_frames(seq_id, train_frame_ids, seq_info_dict) test_frames, test_anno, meta_obj_test = dataset.get_frames(seq_id, test_frame_ids, seq_info_dict) data = TensorDict({'train_images': train_frames, 'train_anno': train_anno['bbox'], 'test_images': test_frames, 'test_anno': test_anno['bbox'], 'dataset': dataset.get_name(), 'test_class': meta_obj_test.get('object_class_name')}) return self.processing(data) class DiMPSampler(TrackingSampler): """ See TrackingSampler.""" def __init__(self, datasets, p_datasets, samples_per_epoch, max_gap, num_test_frames, num_train_frames=1, processing=no_processing, frame_sample_mode='causal'): super().__init__(datasets=datasets, p_datasets=p_datasets, samples_per_epoch=samples_per_epoch, max_gap=max_gap, num_test_frames=num_test_frames, num_train_frames=num_train_frames, processing=processing, frame_sample_mode=frame_sample_mode) class ATOMSampler(TrackingSampler): """ See TrackingSampler.""" def __init__(self, datasets, p_datasets, samples_per_epoch, max_gap, num_test_frames=1, num_train_frames=1, processing=no_processing, frame_sample_mode='interval'): super().__init__(datasets=datasets, p_datasets=p_datasets, samples_per_epoch=samples_per_epoch, max_gap=max_gap, num_test_frames=num_test_frames, num_train_frames=num_train_frames, processing=processing, frame_sample_mode=frame_sample_mode) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/data/transforms.py ================================================ import random import numpy as np import math import cv2 as cv import torch import torch.nn.functional as F import torchvision.transforms.functional as tvisf class Transform: """A set of transformations, used for e.g. data augmentation. Args of constructor: transforms: An arbitrary number of transformations, derived from the TransformBase class. They are applied in the order they are given. The Transform object can jointly transform images, bounding boxes and segmentation masks. This is done by calling the object with the following key-word arguments (all are optional). The following arguments are inputs to be transformed. They are either supplied as a single instance, or a list of instances. image - Image coords - 2xN dimensional Tensor of 2D image coordinates [y, x] bbox - Bounding box on the form [x, y, w, h] mask - Segmentation mask with discrete classes The following parameters can be supplied with calling the transform object: joint [Bool] - If True then transform all images/coords/bbox/mask in the list jointly using the same transformation. Otherwise each tuple (images, coords, bbox, mask) will be transformed independently using different random rolls. Default: True. new_roll [Bool] - If False, then no new random roll is performed, and the saved result from the previous roll is used instead. Default: True. Check the DiMPProcessing class for examples. """ def __init__(self, *transforms): if len(transforms) == 1 and isinstance(transforms[0], (list, tuple)): transforms = transforms[0] self.transforms = transforms self._valid_inputs = ['image', 'coords', 'bbox', 'mask'] self._valid_args = ['joint', 'new_roll'] self._valid_all = self._valid_inputs + self._valid_args def __call__(self, **inputs): var_names = [k for k in inputs.keys() if k in self._valid_inputs] for v in inputs.keys(): if v not in self._valid_all: raise ValueError('Incorrect input \"{}\" to transform. Only supports inputs {} and arguments {}.'.format(v, self._valid_inputs, self._valid_args)) joint_mode = inputs.get('joint', True) new_roll = inputs.get('new_roll', True) if not joint_mode: out = zip(*[self(**inp) for inp in self._split_inputs(inputs)]) return tuple(list(o) for o in out) out = {k: v for k, v in inputs.items() if k in self._valid_inputs} for t in self.transforms: out = t(**out, joint=joint_mode, new_roll=new_roll) if len(var_names) == 1: return out[var_names[0]] # Make sure order is correct return tuple(out[v] for v in var_names) def _split_inputs(self, inputs): var_names = [k for k in inputs.keys() if k in self._valid_inputs] split_inputs = [{k: v for k, v in zip(var_names, vals)} for vals in zip(*[inputs[vn] for vn in var_names])] for arg_name, arg_val in filter(lambda it: it[0]!='joint' and it[0] in self._valid_args, inputs.items()): if isinstance(arg_val, list): for inp, av in zip(split_inputs, arg_val): inp[arg_name] = av else: for inp in split_inputs: inp[arg_name] = arg_val return split_inputs def __repr__(self): format_string = self.__class__.__name__ + '(' for t in self.transforms: format_string += '\n' format_string += ' {0}'.format(t) format_string += '\n)' return format_string class TransformBase: """Base class for transformation objects. See the Transform class for details.""" def __init__(self): self._valid_inputs = ['image', 'coords', 'bbox', 'mask'] self._valid_args = ['new_roll'] self._valid_all = self._valid_inputs + self._valid_args self._rand_params = None def __call__(self, **inputs): # Split input input_vars = {k: v for k, v in inputs.items() if k in self._valid_inputs} input_args = {k: v for k, v in inputs.items() if k in self._valid_args} # Roll random parameters for the transform if input_args.get('new_roll', True): rand_params = self.roll() if rand_params is None: rand_params = () elif not isinstance(rand_params, tuple): rand_params = (rand_params,) self._rand_params = rand_params outputs = dict() for var_name, var in input_vars.items(): if var is not None: transform_func = getattr(self, 'transform_' + var_name) if var_name in ['coords', 'bbox']: params = (self._get_image_size(input_vars),) + self._rand_params else: params = self._rand_params if isinstance(var, (list, tuple)): outputs[var_name] = [transform_func(x, *params) for x in var] else: outputs[var_name] = transform_func(var, *params) return outputs def _get_image_size(self, inputs): im = None for var_name in ['image', 'mask']: if inputs.get(var_name) is not None: im = inputs[var_name] break if im is None: return None if isinstance(im, (list, tuple)): im = im[0] if isinstance(im, np.ndarray): return im.shape[:2] if torch.is_tensor(im): return (im.shape[-2], im.shape[-1]) raise Exception('Unknown image type') def roll(self): return None def transform_image(self, image, *rand_params): """Must be deterministic""" return image def transform_coords(self, coords, image_shape, *rand_params): """Must be deterministic""" return coords def transform_bbox(self, bbox, image_shape, *rand_params): """Assumes [x, y, w, h]""" # Check if not overloaded if self.transform_coords.__code__ == TransformBase.transform_coords.__code__: return bbox coord = bbox.clone().view(-1,2).t().flip(0) x1 = coord[1, 0] x2 = coord[1, 0] + coord[1, 1] y1 = coord[0, 0] y2 = coord[0, 0] + coord[0, 1] coord_all = torch.tensor([[y1, y1, y2, y2], [x1, x2, x2, x1]]) coord_transf = self.transform_coords(coord_all, image_shape, *rand_params).flip(0) tl = torch.min(coord_transf, dim=1)[0] sz = torch.max(coord_transf, dim=1)[0] - tl bbox_out = torch.cat((tl, sz), dim=-1).reshape(bbox.shape) return bbox_out def transform_mask(self, mask, *rand_params): """Must be deterministic""" return mask class ToTensor(TransformBase): """Convert to a Tensor""" def transform_image(self, image): # handle numpy array if image.ndim == 2: image = image[:, :, None] image = torch.from_numpy(image.transpose((2, 0, 1))) # backward compatibility if isinstance(image, torch.ByteTensor): return image.float().div(255) else: return image def transfrom_mask(self, mask): if isinstance(mask, np.ndarray): return torch.from_numpy(mask) class ToTensorAndJitter(TransformBase): """Convert to a Tensor and jitter brightness""" def __init__(self, brightness_jitter=0.0, normalize=True): super().__init__() self.brightness_jitter = brightness_jitter self.normalize = normalize def roll(self): return np.random.uniform(max(0, 1 - self.brightness_jitter), 1 + self.brightness_jitter) def transform_image(self, image, brightness_factor): # handle numpy array image = torch.from_numpy(image.transpose((2, 0, 1))) # backward compatibility if self.normalize: return image.float().mul(brightness_factor/255.0).clamp(0.0, 1.0) else: return image.float().mul(brightness_factor).clamp(0.0, 255.0) def transform_mask(self, mask, brightness_factor): if isinstance(mask, np.ndarray): return torch.from_numpy(mask) else: return mask class Normalize(TransformBase): """Normalize image""" def __init__(self, mean, std, inplace=False): super().__init__() self.mean = mean self.std = std self.inplace = inplace def transform_image(self, image): return tvisf.normalize(image, self.mean, self.std, self.inplace) class ToGrayscale(TransformBase): """Converts image to grayscale with probability""" def __init__(self, probability = 0.5): super().__init__() self.probability = probability self.color_weights = np.array([0.2989, 0.5870, 0.1140], dtype=np.float32) def roll(self): return random.random() < self.probability def transform_image(self, image, do_grayscale): if do_grayscale: if torch.is_tensor(image): raise NotImplementedError('Implement torch variant.') img_gray = cv.cvtColor(image, cv.COLOR_RGB2GRAY) return np.stack([img_gray, img_gray, img_gray], axis=2) # return np.repeat(np.sum(img * self.color_weights, axis=2, keepdims=True).astype(np.uint8), 3, axis=2) return image class ToBGR(TransformBase): """Converts image to BGR""" def transform_image(self, image): if torch.is_tensor(image): raise NotImplementedError('Implement torch variant.') img_bgr = cv.cvtColor(image, cv.COLOR_RGB2BGR) return img_bgr class RandomHorizontalFlip(TransformBase): """Horizontally flip image randomly with a probability p.""" def __init__(self, probability = 0.5): super().__init__() self.probability = probability def roll(self): return random.random() < self.probability def transform_image(self, image, do_flip): if do_flip: if torch.is_tensor(image): return image.flip((2,)) return np.fliplr(image).copy() return image def transform_coords(self, coords, image_shape, do_flip): if do_flip: coords = coords.clone() coords[1,:] = (image_shape[1] - 1) - coords[1,:] return coords def transform_mask(self, mask, do_flip): if do_flip: if torch.is_tensor(mask): return mask.flip((-1,)) return np.fliplr(mask).copy() return mask class Blur(TransformBase): """ Blur the image by applying a gaussian kernel with given sigma""" def __init__(self, sigma): super().__init__() if isinstance(sigma, (float, int)): sigma = (sigma, sigma) self.sigma = sigma self.filter_size = [math.ceil(2*s) for s in self.sigma] x_coord = [torch.arange(-sz, sz+1, dtype=torch.float32) for sz in self.filter_size] self.filter = [torch.exp(-(x**2)/(2*s**2)) for x, s in zip(x_coord, self.sigma)] self.filter[0] = self.filter[0].view(1,1,-1,1) / self.filter[0].sum() self.filter[1] = self.filter[1].view(1,1,1,-1) / self.filter[1].sum() def transform_image(self, image): if torch.is_tensor(image): sz = image.shape[2:] im1 = F.conv2d(image.view(-1, 1, sz[0], sz[1]), self.filter[0], padding=(self.filter_size[0], 0)) return F.conv2d(im1, self.filter[1], padding=(0,self.filter_size[1])).view(-1,sz[0],sz[1]) else: raise NotImplementedError class RandomBlur(TransformBase): """ Blur the image, with a given probability, by applying a gaussian kernel with given sigma""" def __init__(self, sigma, probability=0.1): super().__init__() self.probability = probability if isinstance(sigma, (float, int)): sigma = (sigma, sigma) self.sigma = sigma self.filter_size = [math.ceil(2*s) for s in self.sigma] x_coord = [torch.arange(-sz, sz+1, dtype=torch.float32) for sz in self.filter_size] self.filter = [torch.exp(-(x**2)/(2*s**2)) for x, s in zip(x_coord, self.sigma)] self.filter[0] = self.filter[0].view(1,1,-1,1) / self.filter[0].sum() self.filter[1] = self.filter[1].view(1,1,1,-1) / self.filter[1].sum() def roll(self): return random.random() < self.probability def transform(self, image, do_blur=None): if do_blur is None: do_blur = False if do_blur: if torch.is_tensor(image): sz = image.shape[1:] im1 = F.conv2d(image.view(-1, 1, sz[0], sz[1]), self.filter[0], padding=(self.filter_size[0], 0)) return F.conv2d(im1, self.filter[1], padding=(0,self.filter_size[1])).view(-1,sz[0],sz[1]) else: raise NotImplementedError else: return image class RandomAffine(TransformBase): """Apply random affine transformation.""" def __init__(self, p_flip=0.0, max_rotation=0.0, max_shear=0.0, max_scale=0.0, max_ar_factor=0.0, border_mode='constant', pad_amount=0): super().__init__() self.p_flip = p_flip self.max_rotation = max_rotation self.max_shear = max_shear self.max_scale = max_scale self.max_ar_factor = max_ar_factor if border_mode == 'constant': self.border_flag = cv.BORDER_CONSTANT elif border_mode == 'replicate': self.border_flag == cv.BORDER_REPLICATE else: raise Exception self.pad_amount = pad_amount def roll(self): do_flip = random.random() < self.p_flip theta = random.uniform(-self.max_rotation, self.max_rotation) shear_x = random.uniform(-self.max_shear, self.max_shear) shear_y = random.uniform(-self.max_shear, self.max_shear) ar_factor = np.exp(random.uniform(-self.max_ar_factor, self.max_ar_factor)) scale_factor = np.exp(random.uniform(-self.max_scale, self.max_scale)) return do_flip, theta, (shear_x, shear_y), (scale_factor, scale_factor * ar_factor) def _construct_t_mat(self, image_shape, do_flip, theta, shear_values, scale_factors): im_h, im_w = image_shape t_mat = np.identity(3) if do_flip: if do_flip: t_mat[0, 0] = -1.0 t_mat[0, 2] = im_w t_rot = cv.getRotationMatrix2D((im_w * 0.5, im_h * 0.5), theta, 1.0) t_rot = np.concatenate((t_rot, np.array([0.0, 0.0, 1.0]).reshape(1, 3))) t_shear = np.array([[1.0, shear_values[0], -shear_values[0] * 0.5 * im_w], [shear_values[1], 1.0, -shear_values[1] * 0.5 * im_h], [0.0, 0.0, 1.0]]) t_scale = np.array([[scale_factors[0], 0.0, (1.0 - scale_factors[0]) * 0.5 * im_w], [0.0, scale_factors[1], (1.0 - scale_factors[1]) * 0.5 * im_h], [0.0, 0.0, 1.0]]) t_mat = t_scale @ t_rot @ t_shear @ t_mat t_mat[0, 2] += self.pad_amount t_mat[1, 2] += self.pad_amount t_mat = t_mat[:2, :] return t_mat def transform_image(self, image, do_flip, theta, shear_values, scale_factors): if torch.is_tensor(image): raise Exception('Only supported for numpy input') t_mat = self._construct_t_mat(image.shape[:2], do_flip, theta, shear_values, scale_factors) output_sz = (image.shape[1] + 2*self.pad_amount, image.shape[0] + 2*self.pad_amount) image_t = cv.warpAffine(image, t_mat, output_sz, flags=cv.INTER_LINEAR, borderMode=self.border_flag) return image_t def transform_coords(self, coords, image_shape, do_flip, theta, shear_values, scale_factors): t_mat = self._construct_t_mat(image_shape, do_flip, theta, shear_values, scale_factors) t_mat_tensor = torch.from_numpy(t_mat).float() coords_xy1 = torch.stack((coords[1, :], coords[0, :], torch.ones_like(coords[1, :]))) coords_xy_t = torch.mm(t_mat_tensor, coords_xy1) return coords_xy_t[[1, 0], :] def transform_mask(self, mask, do_flip, theta, shear_values, scale_factors): t_mat = self._construct_t_mat(mask.shape[:2], do_flip, theta, shear_values, scale_factors) output_sz = (mask.shape[1] + 2*self.pad_amount, mask.shape[0] + 2*self.pad_amount) mask_t = cv.warpAffine(mask.numpy(), t_mat, output_sz, flags=cv.INTER_NEAREST, borderMode=self.border_flag) return torch.from_numpy(mask_t) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/dataset/__init__.py ================================================ from .lasot import Lasot from .got10k import Got10k from .tracking_net import TrackingNet from .imagenetvid import ImagenetVID from .coco import MSCOCO from .coco_seq import MSCOCOSeq from .youtubevos import YouTubeVOS from .davis import Davis from .lvis import LVIS from .ecssd import ECSSD from .msra10k import MSRA10k from .hku_is import HKUIS from .sbd import SBD from .synthetic_video import SyntheticVideo from .synthetic_video_blend import SyntheticVideoBlend ================================================ FILE: artrackv2_mindspore/external/AR/ltr/dataset/base_image_dataset.py ================================================ import torch.utils.data from ltr.data.image_loader import jpeg4py_loader class BaseImageDataset(torch.utils.data.Dataset): """ Base class for image datasets """ def __init__(self, name, root, image_loader=jpeg4py_loader): """ args: root - The root path to the dataset image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. """ self.name = name self.root = root self.image_loader = image_loader self.image_list = [] # Contains the list of sequences. self.class_list = [] def __len__(self): """ Returns size of the dataset returns: int - number of samples in the dataset """ return self.get_num_images() def __getitem__(self, index): """ Not to be used! Check get_frames() instead. """ return None def get_name(self): """ Name of the dataset returns: string - Name of the dataset """ raise NotImplementedError def get_num_images(self): """ Number of sequences in a dataset returns: int - number of sequences in the dataset.""" return len(self.image_list) def has_class_info(self): return False def get_class_name(self, image_id): return None def get_num_classes(self): return len(self.class_list) def get_class_list(self): return self.class_list def get_images_in_class(self, class_name): raise NotImplementedError def has_segmentation_info(self): return False def get_image_info(self, seq_id): """ Returns information about a particular image, args: seq_id - index of the image returns: Dict """ raise NotImplementedError def get_image(self, image_id, anno=None): """ Get a image args: image_id - index of image anno(None) - The annotation for the sequence (see get_sequence_info). If None, they will be loaded. returns: image - anno - dict - A dict containing meta information about the sequence, e.g. class of the target object. """ raise NotImplementedError ================================================ FILE: artrackv2_mindspore/external/AR/ltr/dataset/base_video_dataset.py ================================================ import torch.utils.data from ltr.data.image_loader import jpeg4py_loader class BaseVideoDataset(torch.utils.data.Dataset): """ Base class for video datasets """ def __init__(self, name, root, image_loader=jpeg4py_loader): """ args: root - The root path to the dataset image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. """ self.name = name self.root = root self.image_loader = image_loader self.sequence_list = [] # Contains the list of sequences. self.class_list = [] def __len__(self): """ Returns size of the dataset returns: int - number of samples in the dataset """ return self.get_num_sequences() def __getitem__(self, index): """ Not to be used! Check get_frames() instead. """ return None def is_video_sequence(self): """ Returns whether the dataset is a video dataset or an image dataset returns: bool - True if a video dataset """ return True def is_synthetic_video_dataset(self): """ Returns whether the dataset contains real videos or synthetic returns: bool - True if a video dataset """ return False def get_name(self): """ Name of the dataset returns: string - Name of the dataset """ raise NotImplementedError def get_num_sequences(self): """ Number of sequences in a dataset returns: int - number of sequences in the dataset.""" return len(self.sequence_list) def has_class_info(self): return False def has_occlusion_info(self): return False def get_num_classes(self): return len(self.class_list) def get_class_list(self): return self.class_list def get_sequences_in_class(self, class_name): raise NotImplementedError def has_segmentation_info(self): return False def get_sequence_info(self, seq_id): """ Returns information about a particular sequences, args: seq_id - index of the sequence returns: Dict """ raise NotImplementedError def get_frames(self, seq_id, frame_ids, anno=None): """ Get a set of frames from a particular sequence args: seq_id - index of sequence frame_ids - a list of frame numbers anno(None) - The annotation for the sequence (see get_sequence_info). If None, they will be loaded. returns: list - List of frames corresponding to frame_ids list - List of dicts for each frame dict - A dict containing meta information about the sequence, e.g. class of the target object. """ raise NotImplementedError ================================================ FILE: artrackv2_mindspore/external/AR/ltr/dataset/coco.py ================================================ import os from .base_image_dataset import BaseImageDataset from ltr.data.image_loader import jpeg4py_loader import torch from pycocotools.coco import COCO import random from collections import OrderedDict from ltr.admin.environment import env_settings class MSCOCO(BaseImageDataset): """ The COCO object detection dataset. Publication: Microsoft COCO: Common Objects in Context. Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar and C. Lawrence Zitnick ECCV, 2014 https://arxiv.org/pdf/1405.0312.pdf Download the images along with annotations from http://cocodataset.org/#download. The root folder should be organized as follows. - coco_root - annotations - instances_train2014.json - instances_train2017.json - images - train2014 - train2017 Note: You also have to install the coco pythonAPI from https://github.com/cocodataset/cocoapi. """ def __init__(self, root=None, image_loader=jpeg4py_loader, data_fraction=None, min_area=None, split="train", version="2014"): """ args: root - path to coco root folder image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. data_fraction - Fraction of dataset to be used. The complete dataset is used by default min_area - Objects with area less than min_area are filtered out. Default is 0.0 split - 'train' or 'val'. version - version of coco dataset (2014 or 2017) """ root = env_settings().coco_dir if root is None else root super().__init__('COCO', root, image_loader) self.img_pth = os.path.join(root, 'images/{}{}/'.format(split, version)) self.anno_path = os.path.join(root, 'annotations/instances_{}{}.json'.format(split, version)) self.coco_set = COCO(self.anno_path) self.cats = self.coco_set.cats self.class_list = self.get_class_list() # the parent class thing would happen in the sampler self.image_list = self._get_image_list(min_area=min_area) if data_fraction is not None: self.image_list = random.sample(self.image_list, int(len(self.image_list) * data_fraction)) self.im_per_class = self._build_im_per_class() def _get_image_list(self, min_area=None): ann_list = list(self.coco_set.anns.keys()) image_list = [a for a in ann_list if self.coco_set.anns[a]['iscrowd'] == 0] if min_area is not None: image_list = [a for a in image_list if self.coco_set.anns[a]['area'] > min_area] return image_list def get_num_classes(self): return len(self.class_list) def get_name(self): return 'coco' def has_class_info(self): return True def has_segmentation_info(self): return True def get_class_list(self): class_list = [] for cat_id in self.cats.keys(): class_list.append(self.cats[cat_id]['name']) return class_list def _build_im_per_class(self): im_per_class = {} for i, im in enumerate(self.image_list): class_name = self.cats[self.coco_set.anns[im]['category_id']]['name'] if class_name not in im_per_class: im_per_class[class_name] = [i] else: im_per_class[class_name].append(i) return im_per_class def get_images_in_class(self, class_name): return self.im_per_class[class_name] def get_image_info(self, im_id): anno = self._get_anno(im_id) bbox = torch.Tensor(anno['bbox']).view(4,) mask = torch.Tensor(self.coco_set.annToMask(anno)) valid = (bbox[2] > 0) & (bbox[3] > 0) visible = valid.clone().byte() return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible} def _get_anno(self, im_id): anno = self.coco_set.anns[self.image_list[im_id]] return anno def _get_image(self, im_id): path = self.coco_set.loadImgs([self.coco_set.anns[self.image_list[im_id]]['image_id']])[0]['file_name'] img = self.image_loader(os.path.join(self.img_pth, path)) return img def get_meta_info(self, im_id): try: cat_dict_current = self.cats[self.coco_set.anns[self.image_list[im_id]]['category_id']] object_meta = OrderedDict({'object_class_name': cat_dict_current['name'], 'motion_class': None, 'major_class': cat_dict_current['supercategory'], 'root_class': None, 'motion_adverb': None}) except: object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def get_class_name(self, im_id): cat_dict_current = self.cats[self.coco_set.anns[self.image_list[im_id]]['category_id']] return cat_dict_current['name'] def get_image(self, image_id, anno=None): frame = self._get_image(image_id) if anno is None: anno = self.get_image_info(image_id) object_meta = self.get_meta_info(image_id) return frame, anno, object_meta ================================================ FILE: artrackv2_mindspore/external/AR/ltr/dataset/coco_seq.py ================================================ import os from .base_video_dataset import BaseVideoDataset from ltr.data.image_loader import jpeg4py_loader import torch import random from pycocotools.coco import COCO from collections import OrderedDict from ltr.admin.environment import env_settings class MSCOCOSeq(BaseVideoDataset): """ The COCO dataset. COCO is an image dataset. Thus, we treat each image as a sequence of length 1. Publication: Microsoft COCO: Common Objects in Context. Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar and C. Lawrence Zitnick ECCV, 2014 https://arxiv.org/pdf/1405.0312.pdf Download the images along with annotations from http://cocodataset.org/#download. The root folder should be organized as follows. - coco_root - annotations - instances_train2014.json - instances_train2017.json - images - train2014 - train2017 Note: You also have to install the coco pythonAPI from https://github.com/cocodataset/cocoapi. """ def __init__(self, root=None, image_loader=jpeg4py_loader, data_fraction=None, split="train", version="2014"): """ args: root - path to the coco dataset. image_loader (default_image_loader) - The function to read the images. If installed, jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else, opencv's imread is used. data_fraction (None) - Fraction of images to be used. The images are selected randomly. If None, all the images will be used split - 'train' or 'val'. version - version of coco dataset (2014 or 2017) """ root = env_settings().coco_dir if root is None else root super().__init__('COCO', root, image_loader) self.img_pth = os.path.join(root, 'images/{}{}/'.format(split, version)) self.anno_path = os.path.join(root, 'annotations/instances_{}{}.json'.format(split, version)) # Load the COCO set. self.coco_set = COCO(self.anno_path) self.cats = self.coco_set.cats self.class_list = self.get_class_list() self.sequence_list = self._get_sequence_list() if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction)) self.seq_per_class = self._build_seq_per_class() def _get_sequence_list(self): ann_list = list(self.coco_set.anns.keys()) seq_list = [a for a in ann_list if self.coco_set.anns[a]['iscrowd'] == 0] return seq_list def is_video_sequence(self): return False def get_num_classes(self): return len(self.class_list) def get_name(self): return 'coco' def has_class_info(self): return True def get_class_list(self): class_list = [] for cat_id in self.cats.keys(): class_list.append(self.cats[cat_id]['name']) return class_list def has_segmentation_info(self): return True def get_num_sequences(self): return len(self.sequence_list) def _build_seq_per_class(self): seq_per_class = {} for i, seq in enumerate(self.sequence_list): class_name = self.cats[self.coco_set.anns[seq]['category_id']]['name'] if class_name not in seq_per_class: seq_per_class[class_name] = [i] else: seq_per_class[class_name].append(i) return seq_per_class def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def get_sequence_info(self, seq_id): anno = self._get_anno(seq_id) bbox = torch.Tensor(anno['bbox']).view(1, 4) mask = torch.Tensor(self.coco_set.annToMask(anno)).unsqueeze(dim=0) valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0) visible = valid.clone().byte() return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible} def _get_anno(self, seq_id): anno = self.coco_set.anns[self.sequence_list[seq_id]] return anno def _get_frames(self, seq_id): path = self.coco_set.loadImgs([self.coco_set.anns[self.sequence_list[seq_id]]['image_id']])[0]['file_name'] img = self.image_loader(os.path.join(self.img_pth, path)) return img def get_meta_info(self, seq_id): try: cat_dict_current = self.cats[self.coco_set.anns[self.sequence_list[seq_id]]['category_id']] object_meta = OrderedDict({'object_class_name': cat_dict_current['name'], 'motion_class': None, 'major_class': cat_dict_current['supercategory'], 'root_class': None, 'motion_adverb': None}) except: object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def get_class_name(self, seq_id): cat_dict_current = self.cats[self.coco_set.anns[self.sequence_list[seq_id]]['category_id']] return cat_dict_current['name'] def get_frames(self, seq_id=None, frame_ids=None, anno=None): # COCO is an image dataset. Thus we replicate the image denoted by seq_id len(frame_ids) times, and return a # list containing these replicated images. frame = self._get_frames(seq_id) frame_list = [frame.copy() for _ in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[0, ...] for _ in frame_ids] object_meta = self.get_meta_info(seq_id) return frame_list, anno_frames, object_meta ================================================ FILE: artrackv2_mindspore/external/AR/ltr/dataset/davis.py ================================================ from pathlib import Path from ltr.dataset.vos_base import VOSDatasetBase, VOSMeta from pytracking.evaluation import Sequence from ltr.admin.environment import env_settings from ltr.data.image_loader import jpeg4py_loader class Davis(VOSDatasetBase): """ The Davis VOS dataset Publication: A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung CVPR, 2016 http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Perazzi_A_Benchmark_Dataset_CVPR_2016_paper.pdf Download the dataset from https://davischallenge.org/davis2017/code.html """ def __init__(self, root=None, sequences=None, version='2017', split='train', multiobj=True, vis_threshold=10, image_loader=jpeg4py_loader): """ args: root - Dataset root path. If unset, it uses the path in your local.py config. sequences - List of sequence names. Limit to a subset of sequences if not None. version - '2016' or '2017 split - Any name in DAVIS/ImageSets/ multiobj - Whether the dataset will return all objects in a sequence or multiple sequences with one object in each. vis_threshold - Minimum number of pixels required to consider a target object "visible". image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. """ if version == '2017': if split in ['train', 'val']: root = env_settings().davis_dir if root is None else root elif split in ['test-dev']: root = env_settings().davis_testdev_dir if root is None else root else: raise Exception('Unknown split {}'.format(split)) else: root = env_settings().davis16_dir if root is None else root super().__init__(name='DAVIS', root=Path(root), version=version, split=split, multiobj=multiobj, vis_threshold=vis_threshold, image_loader=image_loader) dset_path = self.root self._jpeg_path = dset_path / 'JPEGImages' / '480p' self._anno_path = dset_path / 'Annotations' / '480p' meta_path = dset_path / "generated_meta.json" if meta_path.exists(): self.gmeta = VOSMeta(filename=meta_path) else: self.gmeta = VOSMeta.generate('DAVIS', self._jpeg_path, self._anno_path) self.gmeta.save(meta_path) if sequences is None: if self.split != 'all': fname = dset_path / 'ImageSets' / self.version / (self.split + '.txt') sequences = open(fname).read().splitlines() else: sequences = [p for p in sorted(self._jpeg_path.glob("*")) if p.is_dir()] self.sequence_names = sequences self._samples = [] for seq in sequences: obj_ids = self.gmeta.get_obj_ids(seq) if self.multiobj: # Multiple objects per sample self._samples.append((seq, obj_ids)) else: # One object per sample self._samples.extend([(seq, [obj_id]) for obj_id in obj_ids]) print("%s loaded." % self.get_name()) def _construct_sequence(self, sequence_info): seq_name = sequence_info['sequence'] images, gt_labels, gt_bboxes = self.get_paths_and_bboxes(sequence_info) return Sequence(name=seq_name, frames=images, dataset='DAVIS', ground_truth_rect=gt_bboxes, ground_truth_seg=gt_labels, object_ids=sequence_info['object_ids'], multiobj_mode=self.multiobj) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/dataset/ecssd.py ================================================ import os from .base_image_dataset import BaseImageDataset from ltr.data.image_loader import jpeg4py_loader, opencv_loader, imread_indexed import torch from collections import OrderedDict from ltr.admin.environment import env_settings from ltr.data.bounding_box_utils import masks_to_bboxes class ECSSD(BaseImageDataset): """ Extended Complex Scene Saliency Dataset (ECSSD) Publication: Hierarchical Image Saliency Detection on Extended CSSD Jianping Shi, Qiong Yan, Li Xu, Jiaya Jia TPAMI, 2016 https://arxiv.org/pdf/1408.5418.pdf Download the dataset from http://www.cse.cuhk.edu.hk/leojia/projects/hsaliency/dataset.html """ def __init__(self, root=None, image_loader=jpeg4py_loader, data_fraction=None, min_area=None): """ args: root - path to ECSSD root folder image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. data_fraction - Fraction of dataset to be used. The complete dataset is used by default min_area - Objects with area less than min_area are filtered out. Default is 0.0 """ root = env_settings().ecssd_dir if root is None else root super().__init__('ECSSD', root, image_loader) self.image_list = self._load_dataset(min_area=min_area) if data_fraction is not None: raise NotImplementedError def _load_dataset(self, min_area=None): images = [] for i in range(1, 1001): a = imread_indexed(os.path.join(self.root, 'ground_truth_mask', '{:04d}.png'.format(i))) if min_area is None or (a > 0).sum() > min_area: images.append(i) return images def get_name(self): return 'ecssd' def has_segmentation_info(self): return True def get_image_info(self, im_id): mask = imread_indexed(os.path.join(self.root, 'ground_truth_mask', '{:04d}.png'.format(self.image_list[im_id]))) mask = torch.Tensor(mask == 255) bbox = masks_to_bboxes(mask, fmt='t').view(4,) valid = (bbox[2] > 0) & (bbox[3] > 0) visible = valid.clone().byte() return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible} def get_meta_info(self, im_id): object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def get_image(self, image_id, anno=None): frame = self.image_loader(os.path.join(self.root, 'images', '{:04d}.jpg'.format(self.image_list[image_id]))) if anno is None: anno = self.get_image_info(image_id) object_meta = self.get_meta_info(image_id) return frame, anno, object_meta ================================================ FILE: artrackv2_mindspore/external/AR/ltr/dataset/got10k.py ================================================ import os import os.path import numpy as np import torch import csv import pandas import random from collections import OrderedDict from .base_video_dataset import BaseVideoDataset from ltr.data.image_loader import jpeg4py_loader from ltr.admin.environment import env_settings class Got10k(BaseVideoDataset): """ GOT-10k dataset. Publication: GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild Lianghua Huang, Xin Zhao, and Kaiqi Huang arXiv:1810.11981, 2018 https://arxiv.org/pdf/1810.11981.pdf Download dataset from http://got-10k.aitestunion.com/downloads """ def __init__(self, root=None, image_loader=jpeg4py_loader, split=None, seq_ids=None, data_fraction=None): """ args: root - path to the got-10k training data. Note: This should point to the 'train' folder inside GOT-10k image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. split - 'train' or 'val'. Note: The validation split here is a subset of the official got-10k train split, not NOT the official got-10k validation split. To use the official validation split, provide that as the root folder instead. seq_ids - List containing the ids of the videos to be used for training. Note: Only one of 'split' or 'seq_ids' options can be used at the same time. data_fraction - Fraction of dataset to be used. The complete dataset is used by default """ root = env_settings().got10k_dir if root is None else root super().__init__('GOT10k', root, image_loader) # all folders inside the root self.sequence_list = self._get_sequence_list() # seq_id is the index of the folder inside the got10k root path if split is not None: if seq_ids is not None: raise ValueError('Cannot set both split_name and seq_ids.') ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') if split == 'train': file_path = os.path.join(ltr_path, 'data_specs', 'got10k_train_split.txt') elif split == 'val': file_path = os.path.join(ltr_path, 'data_specs', 'got10k_val_split.txt') elif split == 'vottrain': file_path = os.path.join(ltr_path, 'data_specs', 'got10k_vot_train_split.txt') elif split == 'votval': file_path = os.path.join(ltr_path, 'data_specs', 'got10k_vot_val_split.txt') else: raise ValueError('Unknown split name.') seq_ids = pandas.read_csv(file_path, header=None, squeeze=True, dtype=np.int64).values.tolist() elif seq_ids is None: seq_ids = list(range(0, len(self.sequence_list))) self.sequence_list = [self.sequence_list[i] for i in seq_ids] if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction)) self.sequence_meta_info = self._load_meta_info() self.seq_per_class = self._build_seq_per_class() self.class_list = list(self.seq_per_class.keys()) self.class_list.sort() def get_name(self): return 'got10k' def has_class_info(self): return True def has_occlusion_info(self): return True def _load_meta_info(self): sequence_meta_info = {s: self._read_meta(os.path.join(self.root, s)) for s in self.sequence_list} return sequence_meta_info def _read_meta(self, seq_path): try: with open(os.path.join(seq_path, 'meta_info.ini')) as f: meta_info = f.readlines() object_meta = OrderedDict({'object_class_name': meta_info[5].split(': ')[-1][:-1], 'motion_class': meta_info[6].split(': ')[-1][:-1], 'major_class': meta_info[7].split(': ')[-1][:-1], 'root_class': meta_info[8].split(': ')[-1][:-1], 'motion_adverb': meta_info[9].split(': ')[-1][:-1]}) except: object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def _build_seq_per_class(self): seq_per_class = {} for i, s in enumerate(self.sequence_list): object_class = self.sequence_meta_info[s]['object_class_name'] if object_class in seq_per_class: seq_per_class[object_class].append(i) else: seq_per_class[object_class] = [i] return seq_per_class def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def _get_sequence_list(self): with open(os.path.join(self.root, 'list.txt')) as f: dir_list = list(csv.reader(f)) dir_list = [dir_name[0] for dir_name in dir_list] return dir_list def _read_bb_anno(self, seq_path): bb_anno_file = os.path.join(seq_path, "groundtruth.txt") gt = pandas.read_csv(bb_anno_file, delimiter=',', header=None, dtype=np.float32, na_filter=False, low_memory=False).values return torch.tensor(gt) def _read_target_visible(self, seq_path): # Read full occlusion and out_of_view occlusion_file = os.path.join(seq_path, "absence.label") cover_file = os.path.join(seq_path, "cover.label") with open(occlusion_file, 'r', newline='') as f: occlusion = torch.ByteTensor([int(v[0]) for v in csv.reader(f)]) with open(cover_file, 'r', newline='') as f: cover = torch.ByteTensor([int(v[0]) for v in csv.reader(f)]) target_visible = ~occlusion & (cover>0).byte() visible_ratio = cover.float() / 8 return target_visible, visible_ratio def _get_sequence_path(self, seq_id): return os.path.join(self.root, self.sequence_list[seq_id]) def get_sequence_info(self, seq_id): seq_path = self._get_sequence_path(seq_id) bbox = self._read_bb_anno(seq_path) valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0) visible, visible_ratio = self._read_target_visible(seq_path) visible = visible & valid.byte() return {'bbox': bbox, 'valid': valid, 'visible': visible, 'visible_ratio': visible_ratio} def _get_frame_path(self, seq_path, frame_id): return os.path.join(seq_path, '{:08}.jpg'.format(frame_id+1)) # frames start from 1 def _get_frame(self, seq_path, frame_id): return self.image_loader(self._get_frame_path(seq_path, frame_id)) def get_class_name(self, seq_id): obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]] return obj_meta['object_class_name'] def get_frames(self, seq_id, frame_ids, anno=None): seq_path = self._get_sequence_path(seq_id) obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]] frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] return frame_list, anno_frames, obj_meta ================================================ FILE: artrackv2_mindspore/external/AR/ltr/dataset/hku_is.py ================================================ import os from .base_image_dataset import BaseImageDataset from ltr.data.image_loader import jpeg4py_loader, opencv_loader, imread_indexed import torch from collections import OrderedDict from ltr.admin.environment import env_settings from ltr.data.bounding_box_utils import masks_to_bboxes class HKUIS(BaseImageDataset): """ HKU-IS salient object detection dataset Publication: Visual saliency based on multiscale deep features Guanbin Li and Yizhou Yu CVPR, 2015 https://arxiv.org/pdf/1503.08663.pdf Dowload dataset from https://sites.google.com/site/ligb86/hkuis """ def __init__(self, root=None, image_loader=jpeg4py_loader, data_fraction=None, min_area=None): """ args: root - path to HKU-IS root folder image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. data_fraction - Fraction of dataset to be used. The complete dataset is used by default min_area - Objects with area less than min_area are filtered out. Default is 0.0 """ root = env_settings().hkuis_dir if root is None else root super().__init__('HKUIS', root, image_loader) self.image_list, self.anno_list = self._load_dataset(min_area=min_area) if data_fraction is not None: raise NotImplementedError def _load_dataset(self, min_area=None): files_list = os.listdir(os.path.join(self.root, 'imgs')) image_list = [f[:-4] for f in files_list] images = [] annos = [] for f in image_list: a = imread_indexed(os.path.join(self.root, 'gt', '{}.png'.format(f))) if min_area is None or (a > 0).sum() > min_area: im = opencv_loader(os.path.join(self.root, 'imgs', '{}.png'.format(f))) images.append(im) annos.append(a) return images, annos def get_name(self): return 'hku-is' def has_segmentation_info(self): return True def get_image_info(self, im_id): mask = self.anno_list[im_id] mask = torch.Tensor(mask == 255) bbox = masks_to_bboxes(mask, fmt='t').view(4,) valid = (bbox[2] > 0) & (bbox[3] > 0) visible = valid.clone().byte() return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible} def get_meta_info(self, im_id): object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def get_image(self, image_id, anno=None): frame = self.image_list[image_id] if anno is None: anno = self.get_image_info(image_id) object_meta = self.get_meta_info(image_id) return frame, anno, object_meta ================================================ FILE: artrackv2_mindspore/external/AR/ltr/dataset/imagenetvid.py ================================================ import os from .base_video_dataset import BaseVideoDataset from ltr.data.image_loader import default_image_loader import xml.etree.ElementTree as ET import json import torch import random from collections import OrderedDict from ltr.admin.environment import env_settings def get_target_to_image_ratio(seq): anno = torch.Tensor(seq['anno']) img_sz = torch.Tensor(seq['image_size']) return (anno[0, 2:4].prod() / (img_sz.prod())).sqrt() class ImagenetVID(BaseVideoDataset): """ Imagenet VID dataset. Publication: ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei IJCV, 2015 https://arxiv.org/pdf/1409.0575.pdf Download the dataset from http://image-net.org/ """ def __init__(self, root=None, image_loader=default_image_loader, min_length=0, max_target_area=1): """ args: root - path to the imagenet vid dataset. image_loader (default_image_loader) - The function to read the images. If installed, jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else, opencv's imread is used. min_length - Minimum allowed sequence length. max_target_area - max allowed ratio between target area and image area. Can be used to filter out targets which cover complete image. """ root = env_settings().imagenet_dir if root is None else root super().__init__(root, image_loader) cache_file = os.path.join(root, 'cache.json') if os.path.isfile(cache_file): # If available, load the pre-processed cache file containing meta-info for each sequence with open(cache_file, 'r') as f: sequence_list_dict = json.load(f) self.sequence_list = sequence_list_dict else: # Else process the imagenet annotations and generate the cache file self.sequence_list = self._process_anno(root) with open(cache_file, 'w') as f: json.dump(self.sequence_list, f) # Filter the sequences based on min_length and max_target_area in the first frame self.sequence_list = [x for x in self.sequence_list if len(x['anno']) >= min_length and get_target_to_image_ratio(x) < max_target_area] def get_name(self): return 'imagenetvid' def get_num_sequences(self): return len(self.sequence_list) def get_sequence_info(self, seq_id): bb_anno = torch.Tensor(self.sequence_list[seq_id]['anno']) valid = (bb_anno[:, 2] > 0) & (bb_anno[:, 3] > 0) visible = torch.ByteTensor(self.sequence_list[seq_id]['target_visible']) & valid.byte() return {'bbox': bb_anno, 'valid': valid, 'visible': visible} def _get_frame(self, sequence, frame_id): set_name = 'ILSVRC2015_VID_train_{:04d}'.format(sequence['set_id']) vid_name = 'ILSVRC2015_train_{:08d}'.format(sequence['vid_id']) frame_number = frame_id + sequence['start_frame'] frame_path = os.path.join(self.root, 'Data', 'VID', 'train', set_name, vid_name, '{:06d}.JPEG'.format(frame_number)) return self.image_loader(frame_path) def get_frames(self, seq_id, frame_ids, anno=None): sequence = self.sequence_list[seq_id] frame_list = [self._get_frame(sequence, f) for f in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) # Create anno dict anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] # added the class info to the meta info object_meta = OrderedDict({'object_class': sequence['class_name'], 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return frame_list, anno_frames, object_meta def _process_anno(self, root): # Builds individual tracklets base_vid_anno_path = os.path.join(root, 'Annotations', 'VID', 'train') all_sequences = [] for set in sorted(os.listdir(base_vid_anno_path)): set_id = int(set.split('_')[-1]) for vid in sorted(os.listdir(os.path.join(base_vid_anno_path, set))): vid_id = int(vid.split('_')[-1]) anno_files = sorted(os.listdir(os.path.join(base_vid_anno_path, set, vid))) frame1_anno = ET.parse(os.path.join(base_vid_anno_path, set, vid, anno_files[0])) image_size = [int(frame1_anno.find('size/width').text), int(frame1_anno.find('size/height').text)] objects = [ET.ElementTree(file=os.path.join(base_vid_anno_path, set, vid, f)).findall('object') for f in anno_files] tracklets = {} # Find all tracklets along with start frame for f_id, all_targets in enumerate(objects): for target in all_targets: tracklet_id = target.find('trackid').text if tracklet_id not in tracklets: tracklets[tracklet_id] = f_id for tracklet_id, tracklet_start in tracklets.items(): tracklet_anno = [] target_visible = [] class_name_id = None for f_id in range(tracklet_start, len(objects)): found = False for target in objects[f_id]: if target.find('trackid').text == tracklet_id: if not class_name_id: class_name_id = target.find('name').text x1 = int(target.find('bndbox/xmin').text) y1 = int(target.find('bndbox/ymin').text) x2 = int(target.find('bndbox/xmax').text) y2 = int(target.find('bndbox/ymax').text) tracklet_anno.append([x1, y1, x2 - x1, y2 - y1]) target_visible.append(target.find('occluded').text == '0') found = True break if not found: break new_sequence = {'set_id': set_id, 'vid_id': vid_id, 'class_name': class_name_id, 'start_frame': tracklet_start, 'anno': tracklet_anno, 'target_visible': target_visible, 'image_size': image_size} all_sequences.append(new_sequence) return all_sequences ================================================ FILE: artrackv2_mindspore/external/AR/ltr/dataset/lasot.py ================================================ import os import os.path import torch import numpy as np import pandas import csv import random from collections import OrderedDict from .base_video_dataset import BaseVideoDataset from ltr.data.image_loader import jpeg4py_loader from ltr.admin.environment import env_settings class Lasot(BaseVideoDataset): """ LaSOT dataset. Publication: LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Hexin Bai, Yong Xu, Chunyuan Liao and Haibin Ling CVPR, 2019 https://arxiv.org/pdf/1809.07845.pdf Download the dataset from https://cis.temple.edu/lasot/download.html """ def __init__(self, root=None, image_loader=jpeg4py_loader, vid_ids=None, split=None, data_fraction=None): """ args: root - path to the lasot dataset. image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. vid_ids - List containing the ids of the videos (1 - 20) used for training. If vid_ids = [1, 3, 5], then the videos with subscripts -1, -3, and -5 from each class will be used for training. split - If split='train', the official train split (protocol-II) is used for training. Note: Only one of vid_ids or split option can be used at a time. data_fraction - Fraction of dataset to be used. The complete dataset is used by default """ root = env_settings().lasot_dir if root is None else root super().__init__('LaSOT', root, image_loader) # Keep a list of all classes self.class_list = [f for f in os.listdir(self.root)] self.class_to_id = {cls_name: cls_id for cls_id, cls_name in enumerate(self.class_list)} self.sequence_list = self._build_sequence_list(vid_ids, split) if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction)) self.seq_per_class = self._build_class_list() def _build_sequence_list(self, vid_ids=None, split=None): if split is not None: if vid_ids is not None: raise ValueError('Cannot set both split_name and vid_ids.') ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') if split == 'train': file_path = os.path.join(ltr_path, 'data_specs', 'lasot_train_split.txt') else: raise ValueError('Unknown split name.') sequence_list = pandas.read_csv(file_path, header=None, squeeze=True).values.tolist() elif vid_ids is not None: sequence_list = [c+'-'+str(v) for c in self.class_list for v in vid_ids] else: raise ValueError('Set either split_name or vid_ids.') return sequence_list def _build_class_list(self): seq_per_class = {} for seq_id, seq_name in enumerate(self.sequence_list): class_name = seq_name.split('-')[0] if class_name in seq_per_class: seq_per_class[class_name].append(seq_id) else: seq_per_class[class_name] = [seq_id] return seq_per_class def get_name(self): return 'lasot' def has_class_info(self): return True def has_occlusion_info(self): return True def get_num_sequences(self): return len(self.sequence_list) def get_num_classes(self): return len(self.class_list) def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def _read_bb_anno(self, seq_path): bb_anno_file = os.path.join(seq_path, "groundtruth.txt") gt = pandas.read_csv(bb_anno_file, delimiter=',', header=None, dtype=np.float32, na_filter=False, low_memory=False).values return torch.tensor(gt) def _read_target_visible(self, seq_path): # Read full occlusion and out_of_view occlusion_file = os.path.join(seq_path, "full_occlusion.txt") out_of_view_file = os.path.join(seq_path, "out_of_view.txt") with open(occlusion_file, 'r', newline='') as f: occlusion = torch.ByteTensor([int(v) for v in list(csv.reader(f))[0]]) with open(out_of_view_file, 'r') as f: out_of_view = torch.ByteTensor([int(v) for v in list(csv.reader(f))[0]]) target_visible = ~occlusion & ~out_of_view return target_visible def _get_sequence_path(self, seq_id): seq_name = self.sequence_list[seq_id] class_name = seq_name.split('-')[0] vid_id = seq_name.split('-')[1] return os.path.join(self.root, class_name, class_name + '-' + vid_id) def get_sequence_info(self, seq_id): seq_path = self._get_sequence_path(seq_id) bbox = self._read_bb_anno(seq_path) valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0) visible = self._read_target_visible(seq_path) & valid.byte() return {'bbox': bbox, 'valid': valid, 'visible': visible} def _get_frame_path(self, seq_path, frame_id): return os.path.join(seq_path, 'img', '{:08}.jpg'.format(frame_id+1)) # frames start from 1 def _get_frame(self, seq_path, frame_id): return self.image_loader(self._get_frame_path(seq_path, frame_id)) def _get_class(self, seq_path): raw_class = seq_path.split('/')[-2] return raw_class def get_class_name(self, seq_id): seq_path = self._get_sequence_path(seq_id) obj_class = self._get_class(seq_path) return obj_class def get_frames(self, seq_id, frame_ids, anno=None): seq_path = self._get_sequence_path(seq_id) obj_class = self._get_class(seq_path) frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] object_meta = OrderedDict({'object_class_name': obj_class, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return frame_list, anno_frames, object_meta ================================================ FILE: artrackv2_mindspore/external/AR/ltr/dataset/lvis.py ================================================ import os from .base_image_dataset import BaseImageDataset from ltr.data.image_loader import jpeg4py_loader_w_failsafe import torch import random import lvis.lvis as lvis_pk from collections import OrderedDict from ltr.admin.environment import env_settings class LVIS(BaseImageDataset): """ The LVIS object detection dataset Publication: LVIS: A Dataset for Large Vocabulary Instance Segmentation Agrim Gupta, Piotr Dollár, and Ross Girshick CVPR, 2019 https://arxiv.org/pdf/1908.03195.pdf Download the images along with annotations from https://www.lvisdataset.org/dataset. The root folder should be organized as follows. - lvis_root - annotations - lvis_v0.5_train.json - lvis_v0.5_val.json - images - val2017 - train2017 Note: You also have to install the lvis Python API from https://github.com/lvis-dataset/lvis-api """ def __init__(self, root=None, image_loader=jpeg4py_loader_w_failsafe, data_fraction=None, min_area=None, split="train"): """ args: root - path to lvis root folder image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. data_fraction - Fraction of dataset to be used. The complete dataset is used by default min_area - Objects with area less than min_area are filtered out. Default is 0.0 split - 'train' or 'val'. """ root = env_settings().lvis_dir if root is None else root super().__init__('LVIS', root, image_loader) self.img_pth = os.path.join(root, 'images', f'{split}2017/') self.anno_path = os.path.join(root, 'annotations', f'lvis_v0.5_{split}.json') # Load the LVIS set. self.lvis_set = lvis_pk.LVIS(self.anno_path) self.cats = self.lvis_set.cats self.class_list = self.get_class_list() # the parent class thing would happen in the sampler self.image_list = self._get_image_list(min_area=min_area) if data_fraction is not None: self.image_list = random.sample(self.image_list, int(len(self.image_list) * data_fraction)) self.im_per_class = self._build_im_per_class() def _get_image_list(self, min_area=None): im_list = list(self.lvis_set.anns.keys()) # No 'iscrowd' information in LVIS if min_area is not None: im_list = [s for s in im_list if self.lvis_set.anns[s]['area'] > min_area] return im_list def get_num_classes(self): return len(self.class_list) def get_name(self): return 'lvis' def has_class_info(self): return True def get_class_list(self): class_list = [] for cat_id in self.cats.keys(): class_list.append(self.cats[cat_id]['name']) return class_list def has_segmentation_info(self): return True def _build_im_per_class(self): im_per_class = {} for i, im in enumerate(self.image_list): class_name = self.cats[self.lvis_set.anns[im]['category_id']]['name'] if class_name not in im_per_class: im_per_class[class_name] = [i] else: im_per_class[class_name].append(i) return im_per_class def get_images_in_class(self, class_name): return self.im_per_class[class_name] def get_image_info(self, im_id): anno = self._get_anno(im_id) bbox = torch.Tensor(anno['bbox']).view(4,) mask = torch.Tensor(self.lvis_set.ann_to_mask(anno)) valid = (bbox[2] > 0) & (bbox[3] > 0) visible = valid.clone().byte() return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible} def _get_anno(self, im_id): anno = self.lvis_set.anns[self.image_list[im_id]] return anno def _get_image(self, im_id): path = self.lvis_set.load_imgs([self.lvis_set.anns[self.image_list[im_id]]['image_id']])[0]['file_name'] img = self.image_loader(os.path.join(self.img_pth, path)) return img def get_meta_info(self, im_id): try: cat_dict_current = self.cats[self.lvis_set.anns[self.image_list[im_id]]['category_id']] object_meta = OrderedDict({'object_class_name': cat_dict_current['name'], 'motion_class': None, 'major_class': None, # No 'supercategory' information available in LVIS 'root_class': None, 'motion_adverb': None}) except: object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def get_class_name(self, im_id): cat_dict_current = self.cats[self.lvis_set.anns[self.image_list[im_id]]['category_id']] return cat_dict_current['name'] def get_image(self, image_id, anno=None): frame = self._get_image(image_id) if anno is None: anno = self.get_image_info(image_id) object_meta = self.get_meta_info(image_id) return frame, anno, object_meta ================================================ FILE: artrackv2_mindspore/external/AR/ltr/dataset/msra10k.py ================================================ import os from .base_image_dataset import BaseImageDataset from ltr.data.image_loader import jpeg4py_loader, imread_indexed import torch from collections import OrderedDict from ltr.admin.environment import env_settings from ltr.data.bounding_box_utils import masks_to_bboxes class MSRA10k(BaseImageDataset): """ MSRA10k salient object detection dataset Publication: Global contrast based salient region detection Ming-Ming Cheng, Niloy J. Mitra, Xiaolei Huang, Philip H. S. Torr, and Shi-Min Hu TPAMI, 2015 https://mmcheng.net/mftp/Papers/SaliencyTPAMI.pdf Download dataset from https://mmcheng.net/msra10k/ """ def __init__(self, root=None, image_loader=jpeg4py_loader, data_fraction=None, min_area=None): """ args: root - path to MSRA10k root folder image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. data_fraction - Fraction of dataset to be used. The complete dataset is used by default min_area - Objects with area less than min_area are filtered out. Default is 0.0 """ root = env_settings().msra10k_dir if root is None else root super().__init__('MSRA10k', root, image_loader) self.image_list = self._load_dataset(min_area=min_area) if data_fraction is not None: raise NotImplementedError def _load_dataset(self, min_area=None): files_list = os.listdir(os.path.join(self.root, 'Imgs')) image_list = [f[:-4] for f in files_list if f[-3:] == 'jpg'] images = [] for f in image_list: a = imread_indexed(os.path.join(self.root, 'Imgs', '{}.png'.format(f))) if min_area is None or (a > 0).sum() > min_area: images.append(f) return images def get_name(self): return 'msra10k' def has_segmentation_info(self): return True def get_image_info(self, im_id): mask = imread_indexed(os.path.join(self.root, 'Imgs', '{}.png'.format(self.image_list[im_id]))) mask = torch.Tensor(mask == 255) bbox = masks_to_bboxes(mask, fmt='t').view(4,) valid = (bbox[2] > 0) & (bbox[3] > 0) visible = valid.clone().byte() return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible} def get_meta_info(self, im_id): object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def get_image(self, image_id, anno=None): frame = self.image_loader(os.path.join(self.root, 'Imgs', '{}.jpg'.format(self.image_list[image_id]))) if anno is None: anno = self.get_image_info(image_id) object_meta = self.get_meta_info(image_id) return frame, anno, object_meta ================================================ FILE: artrackv2_mindspore/external/AR/ltr/dataset/sbd.py ================================================ from .base_image_dataset import BaseImageDataset from ltr.data.image_loader import jpeg4py_loader_w_failsafe import torch from collections import OrderedDict import os from scipy.io import loadmat from ltr.data.bounding_box_utils import masks_to_bboxes from ltr.admin.environment import env_settings class SBD(BaseImageDataset): """ Semantic Boundaries Dataset and Benchmark (SBD) Publication: Semantic contours from inverse detectors Bharath Hariharan, Pablo Arbelaez, Lubomir Bourdev, Subhransu Maji and Jitendra Malik ICCV, 2011 http://home.bharathh.info/pubs/pdfs/BharathICCV2011.pdf Download dataset from: http://home.bharathh.info/pubs/codes/SBD/download.html """ def __init__(self, root=None, image_loader=jpeg4py_loader_w_failsafe, data_fraction=None, split="train"): """ args: root - path to SBD root folder image_loader - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. data_fraction - Fraction of dataset to be used. The complete dataset is used by default split - dataset split ("train", "train_noval", "val") """ root = env_settings().sbd_dir if root is None else root super().__init__('SBD', root, image_loader) assert split in ["train", "train_noval", "val"] self.root = root self.image_path_list, self.anno_file_list = self._load_dataset(split) # Load mat fine anno_list = [loadmat(a) for a in self.anno_file_list] self.image_list = self._construct_image_list(anno_list) if data_fraction is not None: raise NotImplementedError def _load_dataset(self, split): split_f = os.path.join(self.root, split.rstrip('\n') + '.txt') with open(os.path.join(split_f), "r") as f: file_names = [x.strip() for x in f.readlines()] image_list = [os.path.join(self.root, 'img', x + ".jpg") for x in file_names] anno_list = [os.path.join(self.root, 'inst', x + ".mat") for x in file_names] assert (len(image_list) == len(anno_list)) return image_list, anno_list def _get_mask_from_mat(self, mat): return torch.tensor(mat['GTinst'][0]['Segmentation'][0]) def _construct_image_list(self, anno_list): image_list = [] for im_id, a in enumerate(anno_list): mask = self._get_mask_from_mat(a) for instance_id in range(1, mask.max().item() + 1): image_list.append((im_id, instance_id)) return image_list def get_name(self): return 'sbd' def has_segmentation_info(self): return True def get_image_info(self, im_id): image_id, instance_id = self.image_list[im_id] anno_mat = loadmat(self.anno_file_list[image_id]) mask = self._get_mask_from_mat(anno_mat) mask = (mask == instance_id).float() bbox = masks_to_bboxes(mask, fmt='t') valid = (bbox[2] > 0) & (bbox[3] > 0) visible = valid.clone().byte() return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible} def _get_image(self, im_id): image_id, _ = self.image_list[im_id] img = self.image_loader(self.image_path_list[image_id]) return img def get_meta_info(self, im_id): object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def get_image(self, image_id, anno=None): image = self._get_image(image_id) if anno is None: anno = self.get_image_info(image_id) object_meta = self.get_meta_info(image_id) return image, anno, object_meta ================================================ FILE: artrackv2_mindspore/external/AR/ltr/dataset/synthetic_video.py ================================================ from collections import OrderedDict from .base_video_dataset import BaseVideoDataset from ltr.data.bounding_box_utils import masks_to_bboxes class SyntheticVideo(BaseVideoDataset): """ Create a synthetic video dataset from an image dataset by applying a random transformation to images. """ def __init__(self, base_image_dataset, transform=None): """ args: base_image_dataset - Image dataset used for generating synthetic videos transform - Set of transforms to be applied to the images to generate synthetic video. """ super().__init__(base_image_dataset.get_name() + '_syn_vid', base_image_dataset.root, base_image_dataset.image_loader) self.base_image_dataset = base_image_dataset self.transform = transform def get_name(self): return self.name def is_video_sequence(self): return False def has_class_info(self): return self.base_image_dataset.has_class_info() def has_occlusion_info(self): return True def get_num_sequences(self): return self.base_image_dataset.get_num_images() def get_num_classes(self): return len(self.class_list) def get_sequences_in_class(self, class_name): return self.get_images_in_class[class_name] def get_sequence_info(self, seq_id): image_info = self.base_image_dataset.get_image_info(seq_id) image_info = {k: v.unsqueeze(0) for k, v in image_info.items()} return image_info def get_class_name(self, seq_id): return self.base_image_dataset.get_class_name(seq_id) def get_frames(self, seq_id, frame_ids, anno=None): frame, anno, object_meta = self.base_image_dataset.get_image(seq_id, anno=anno) frame_list = [frame.copy() for _ in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[0].clone() for f_id in frame_ids] if self.transform is not None: if 'mask' in anno_frames.keys(): frame_list, anno_frames['bbox'], anno_frames['mask'] = self.transform(image=frame_list, bbox=anno_frames['bbox'], mask=anno_frames['mask'], joint=False) anno_frames['bbox'] = [masks_to_bboxes(m, fmt='t') for m in anno_frames['mask']] else: frame_list, anno_frames['bbox'] = self.transform(image=frame_list, bbox=anno_frames['bbox'], joint=False) object_meta = OrderedDict({'object_class_name': self.get_class_name(seq_id), 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return frame_list, anno_frames, object_meta ================================================ FILE: artrackv2_mindspore/external/AR/ltr/dataset/synthetic_video_blend.py ================================================ from collections import OrderedDict from .base_video_dataset import BaseVideoDataset from ltr.data.bounding_box_utils import masks_to_bboxes import random import torch class SyntheticVideoBlend(BaseVideoDataset): """ Create a synthetic video by applying random transformations to an object (foreground) and pasting it in a background image. Currently, the foreground object is pasted at random locations in different frames. """ def __init__(self, foreground_image_dataset, background_image_dataset, foreground_transform=None, background_transform=None): """ args: foreground_image_dataset - A segmentation dataset from which foreground objects are cropped using the segmentation mask background_image_dataset - Dataset used to sample background image for the synthetic video foreground_transform - Random transformations to be applied to the foreground object in every frame background_transform - Random transformations to be applied to the background image in every frame """ assert foreground_image_dataset.has_segmentation_info() super().__init__(foreground_image_dataset.get_name() + '_syn_vid_blend', foreground_image_dataset.root, foreground_image_dataset.image_loader) self.foreground_image_dataset = foreground_image_dataset self.background_image_dataset = background_image_dataset self.foreground_transform = foreground_transform self.background_transform = background_transform def get_name(self): return self.name def is_video_sequence(self): return False def has_class_info(self): return self.foreground_image_dataset.has_class_info() def has_occlusion_info(self): return True def get_num_sequences(self): return self.foreground_image_dataset.get_num_images() def get_num_classes(self): return len(self.class_list) def get_sequences_in_class(self, class_name): return self.get_images_in_class[class_name] def get_sequence_info(self, seq_id): image_info = self.foreground_image_dataset.get_image_info(seq_id) image_info = {k: v.unsqueeze(0) for k, v in image_info.items()} return image_info def get_class_name(self, seq_id): return self.foreground_image_dataset.get_class_name(seq_id) def _paste_target(self, fg_image, fg_box, fg_mask, bg_image, paste_loc): fg_mask = fg_mask.view(fg_mask.shape[0], fg_mask.shape[1], 1) fg_box = fg_box.long().tolist() x1 = int(paste_loc[0] - 0.5 * fg_box[2]) x2 = x1 + fg_box[2] y1 = int(paste_loc[1] - 0.5 * fg_box[3]) y2 = y1 + fg_box[3] x1_pad = max(-x1, 0) y1_pad = max(-y1, 0) x2_pad = max(x2 - bg_image.shape[1], 0) y2_pad = max(y2 - bg_image.shape[0], 0) bg_mask = torch.zeros((bg_image.shape[0], bg_image.shape[1], 1), dtype=fg_mask.dtype, device=fg_mask.device) if x1_pad >= fg_mask.shape[1] or x2_pad >= fg_mask.shape[1] or y1_pad >= fg_mask.shape[0] or y2_pad >= \ fg_mask.shape[0]: return bg_image, bg_mask.squeeze(-1) fg_mask_patch = fg_mask[fg_box[1] + y1_pad:fg_box[1] + fg_box[3] - y2_pad, fg_box[0] + x1_pad:fg_box[0] + fg_box[2] - x2_pad, :] fg_image_patch = fg_image[fg_box[1] + y1_pad:fg_box[1] + fg_box[3] - y2_pad, fg_box[0] + x1_pad:fg_box[0] + fg_box[2] - x2_pad, :] bg_image[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad, :] = \ bg_image[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad, :] * (1 - fg_mask_patch.numpy()) \ + fg_mask_patch.numpy() * fg_image_patch bg_mask[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad, :] = fg_mask_patch return bg_image, bg_mask.squeeze(-1) def get_frames(self, seq_id, frame_ids, anno=None): # Handle foreground fg_frame, fg_anno, fg_object_meta = self.foreground_image_dataset.get_image(seq_id, anno=anno) fg_frame_list = [fg_frame.copy() for _ in frame_ids] fg_anno_frames = {} for key, value in fg_anno.items(): fg_anno_frames[key] = [value[0].clone() for f_id in frame_ids] if self.foreground_transform is not None: fg_frame_list, fg_anno_frames['bbox'], fg_anno_frames['mask'] = self.foreground_transform( image=fg_frame_list, bbox=fg_anno_frames['bbox'], mask=fg_anno_frames['mask'], joint=False) # Sample a random background bg_seq_id = random.randint(0, self.background_image_dataset.get_num_images() - 1) bg_frame, bg_anno, _ = self.background_image_dataset.get_image(bg_seq_id) bg_frame_list = [bg_frame.copy() for _ in frame_ids] bg_anno_frames = {} for key, value in bg_anno.items(): # Note: Since we get bg anno from image dataset, it does not has frame dimension bg_anno_frames[key] = [value.clone() for f_id in frame_ids] if self.background_transform is not None: if 'mask' in bg_anno_frames.keys(): bg_frame_list, bg_anno_frames['bbox'], bg_anno_frames['mask'] = self.background_transform( image=bg_frame_list, bbox=bg_anno_frames['bbox'], mask=bg_anno_frames['mask'], joint=False) else: bg_frame_list, bg_anno_frames['bbox'] = self.background_transform( image=bg_frame_list, bbox=bg_anno_frames['bbox'], joint=False) for i in range(len(frame_ids)): # To be safe, get target bb for the mask bbox = masks_to_bboxes(fg_anno_frames['mask'][i], fmt='t') loc_y = random.randint(0, bg_frame_list[i].shape[0] - 1) loc_x = random.randint(0, bg_frame_list[i].shape[1] - 1) paste_loc = (loc_x, loc_y) fg_frame_list[i], fg_anno_frames['mask'][i] = self._paste_target(fg_frame_list[i], bbox, fg_anno_frames['mask'][i], bg_frame_list[i], paste_loc) fg_anno_frames['bbox'][i] = masks_to_bboxes(fg_anno_frames['mask'][i], fmt='t') object_meta = OrderedDict({'object_class_name': self.get_class_name(seq_id), 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return fg_frame_list, fg_anno_frames, object_meta ================================================ FILE: artrackv2_mindspore/external/AR/ltr/dataset/tracking_net.py ================================================ import torch import os import os.path import numpy as np import pandas import random from collections import OrderedDict from ltr.data.image_loader import jpeg4py_loader from .base_video_dataset import BaseVideoDataset from ltr.admin.environment import env_settings def list_sequences(root, set_ids): """ Lists all the videos in the input set_ids. Returns a list of tuples (set_id, video_name) args: root: Root directory to TrackingNet set_ids: Sets (0-11) which are to be used returns: list - list of tuples (set_id, video_name) containing the set_id and video_name for each sequence """ sequence_list = [] for s in set_ids: anno_dir = os.path.join(root, "TRAIN_" + str(s), "anno") sequences_cur_set = [(s, os.path.splitext(f)[0]) for f in os.listdir(anno_dir) if f.endswith('.txt')] sequence_list += sequences_cur_set return sequence_list class TrackingNet(BaseVideoDataset): """ TrackingNet dataset. Publication: TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild. Matthias Mueller,Adel Bibi, Silvio Giancola, Salman Al-Subaihi and Bernard Ghanem ECCV, 2018 https://ivul.kaust.edu.sa/Documents/Publications/2018/TrackingNet%20A%20Large%20Scale%20Dataset%20and%20Benchmark%20for%20Object%20Tracking%20in%20the%20Wild.pdf Download the dataset using the toolkit https://github.com/SilvioGiancola/TrackingNet-devkit. """ def __init__(self, root=None, image_loader=jpeg4py_loader, set_ids=None, data_fraction=None): """ args: root - The path to the TrackingNet folder, containing the training sets. image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. set_ids (None) - List containing the ids of the TrackingNet sets to be used for training. If None, all the sets (0 - 11) will be used. data_fraction - Fraction of dataset to be used. The complete dataset is used by default """ root = env_settings().trackingnet_dir if root is None else root super().__init__('TrackingNet', root, image_loader) if set_ids is None: set_ids = [i for i in range(12)] self.set_ids = set_ids # Keep a list of all videos. Sequence list is a list of tuples (set_id, video_name) containing the set_id and # video_name for each sequence self.sequence_list = list_sequences(self.root, self.set_ids) if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list) * data_fraction)) self.seq_to_class_map, self.seq_per_class = self._load_class_info() # we do not have the class_lists for the tracking net self.class_list = list(self.seq_per_class.keys()) self.class_list.sort() def _load_class_info(self): ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') class_map_path = os.path.join(ltr_path, 'data_specs', 'trackingnet_classmap.txt') with open(class_map_path, 'r') as f: seq_to_class_map = {seq_class.split('\t')[0]: seq_class.rstrip().split('\t')[1] for seq_class in f} seq_per_class = {} for i, seq in enumerate(self.sequence_list): class_name = seq_to_class_map[seq[1]] if class_name not in seq_per_class: seq_per_class[class_name] = [i] else: seq_per_class[class_name].append(i) return seq_to_class_map, seq_per_class def get_name(self): return 'trackingnet' def has_class_info(self): return True def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def _read_bb_anno(self, seq_id): set_id = self.sequence_list[seq_id][0] vid_name = self.sequence_list[seq_id][1] bb_anno_file = os.path.join(self.root, "TRAIN_" + str(set_id), "anno", vid_name + ".txt") gt = pandas.read_csv(bb_anno_file, delimiter=',', header=None, dtype=np.float32, na_filter=False, low_memory=False).values return torch.tensor(gt) def get_sequence_info(self, seq_id): bbox = self._read_bb_anno(seq_id) valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0) visible = valid.clone().byte() return {'bbox': bbox, 'valid': valid, 'visible': visible} def _get_frame(self, seq_id, frame_id): set_id = self.sequence_list[seq_id][0] vid_name = self.sequence_list[seq_id][1] frame_path = os.path.join(self.root, "TRAIN_" + str(set_id), "frames", vid_name, str(frame_id) + ".jpg") return self.image_loader(frame_path) def _get_class(self, seq_id): seq_name = self.sequence_list[seq_id][1] return self.seq_to_class_map[seq_name] def get_class_name(self, seq_id): obj_class = self._get_class(seq_id) return obj_class def get_frames(self, seq_id, frame_ids, anno=None): frame_list = [self._get_frame(seq_id, f) for f in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] obj_class = self._get_class(seq_id) object_meta = OrderedDict({'object_class_name': obj_class, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return frame_list, anno_frames, object_meta ================================================ FILE: artrackv2_mindspore/external/AR/ltr/dataset/vos_base.py ================================================ import torch from pathlib import Path from collections import OrderedDict, defaultdict import json import numpy as np import os from .base_video_dataset import BaseVideoDataset from ltr.data.image_loader import jpeg4py_loader, imread_indexed from ltr.data.bounding_box_utils import masks_to_bboxes class VOSMeta: def __init__(self, data=None, filename=None): if filename is not None: self.load(filename) elif data is not None: self._data = data else: raise ValueError("Must set either data or filename parameter") def save(self, gen_meta: Path): gen_meta.parent.mkdir(exist_ok=True, parents=True) json.dump(self._data, open(gen_meta, "w")) def load(self, gen_meta: Path): if not gen_meta.exists(): print("Generated metadata file %s is not found." % gen_meta) print("Find and run VOSMeta.generate() to create it.") raise FileNotFoundError(gen_meta) self._data = json.load(open(gen_meta), object_pairs_hook=OrderedDict) @classmethod def generate(cls, dset_name: str, dset_images_path: Path, dset_annos_path: Path): """ Count the annotation mask pixels per object, per frame, in all sequences in a dataset :param dset_name: Dataset name, for printing the progress bar. :param dset_annos_path: Path to annotations directory, containing sequence directories, with annotation frames in them. :return: Dataset meta dict: {'sequence0': { 'shape': (height, width) 'obj_sizes': # Object pixels per frame {'frame0': {'object0': px_count, 'object1': px_count, ...}, 'frame1': {'object0': px_count, 'object1': px_count, ...}, ... }, 'bboxes': # Bounding boxes per frame {'frame0': {'object0': bbox, 'object1': bbox, ...}, 'frame1': {'object0': bbox, 'object1': bbox, ...}, ... }, ... } """ assert(dset_annos_path.exists()) dset_meta = OrderedDict() sequences = [p.stem for p in sorted(dset_annos_path.glob("*")) if p.is_dir()] try: from tqdm import tqdm except: def tqdm(x, *args, **kwargs): return x for seq in tqdm(sequences, desc=dset_name, unit="seq"): obj_sizes2 = defaultdict(OrderedDict) bboxes = defaultdict(OrderedDict) shape = None frame_names = [file.stem for file in sorted((dset_images_path / seq).glob("*.jpg"))] anno_paths = list(sorted((dset_annos_path / seq).glob("*.png"))) # Extract information from the given label frames for path in anno_paths: f_id = path.stem # Count label-pixels per frame labels = imread_indexed(path) # labels = np.array(Image.open(path)) obj_ids, obj_sizes = np.unique(labels, return_counts=True) obj_ids = [str(oid) for oid in obj_ids] obj_sizes = obj_sizes.tolist() if '0' in obj_ids: # Remove background id obj_ids = obj_ids[1:] obj_sizes = obj_sizes[1:] obj_sizes2[f_id] = OrderedDict(zip(obj_ids, obj_sizes)) # Generate per-label bounding boxes for obj_id in obj_ids: bboxes[f_id][obj_id] = cls._mask_to_bbox(labels == int(obj_id)) if shape is None: shape = labels.shape[:2] # Format result dset_meta[seq] = dict(shape=shape, obj_sizes=obj_sizes2, bboxes=bboxes, frame_names=frame_names) return VOSMeta(dset_meta) @staticmethod def _mask_to_bbox(mask: np.ndarray): mask = mask.astype(int) xs = mask.sum(axis=-2).nonzero()[0].tolist() ys = mask.sum(axis=-1).nonzero()[0].tolist() if len(ys) > 0 and len(xs) > 0: x, y, w, h = xs[0], ys[0], xs[-1] - xs[0], ys[-1] - ys[0] else: x, y, w, h = 0, 0, 0, 0 return [x, y, w, h] @staticmethod def _transpose_nested_dict(d): """ Permute a 2-level nested dict such that the inner and outer keys swap places. """ d2 = defaultdict(OrderedDict) for key1, inner in d.items(): for key2, value in inner.items(): d2[key2][key1] = value return d2 def select_split(self, dataset_name, split): ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') sequences = set([s.strip() for s in open(os.path.join(ltr_path, 'data_specs', dataset_name + '_' + split + '.txt')).readlines()]) all_sequences = set(self._data.keys()) to_remove = all_sequences.difference(sequences) for seq_name in to_remove: self._data.pop(seq_name) def get_sequence_names(self): return list(self._data.keys()) def get_shape(self, seq_name): """ Sequence image shape (h,w) """ h, w = self._data[seq_name]['shape'] return h, w def get_obj_ids(self, seq_name): """ All objects in the sequence """ return list(self.get_obj_sizes_per_object(seq_name).keys()) def get_frame_names(self, seq_name): """ All filename stems of the frames in the sequence """ return self._data[seq_name]['frame_names'] def enable_all_frames(self, dset_images_path): """ For YouTubeVOS: Update the frame names with (jpeg) files from the _all_frames set :param dset_images_path: /path/to/train_all_frames/JPEGImages (or valid or test) :param seq: Sequence name :return: """ # Try load the cached index idx_file = dset_images_path.parent / "frame_names.json" if idx_file.exists(): print('Loading cached frame names from %s' % idx_file) all_frame_names = json.load(open(idx_file)) else: # Cache the data to the user's home directory (guaranteed to be writable) all_frame_names = dict() user_idx_file = Path.home() / (dset_images_path.parent.stem + "_frame_names.json") print('Indexing YouTubeVOS "all_frames" frame names to %s' % user_idx_file) for seq in self._data: all_frame_names[seq] = [file.stem for file in sorted((dset_images_path / seq).glob("*.jpg"))] json.dump(all_frame_names, open(user_idx_file, "w")) print('Done. Move %s to %s to load faster next time.' % (user_idx_file, idx_file)) for seq, frame_names in all_frame_names.items(): self._data[seq]['frame_names'] = frame_names def get_aspect_ratio(self, seq_name): """ Sequence aspect ratio """ h, w = self._data[seq_name]['shape'] return w / h def get_obj_sizes_per_frame(self, seq_name): """ Get object pixel counts, grouped by frame names """ return self._data[seq_name]['obj_sizes'] def get_bboxes_per_frame(self, seq_name): """ Object bounding boxes, grouped by frame names """ return self._data[seq_name]['bboxes'] def get_obj_sizes_per_object(self, seq_name): """ Object pixel counts, grouped by object """ return self._transpose_nested_dict(self.get_obj_sizes_per_frame(seq_name)) def get_bboxes_per_object(self, seq_name): """ Object bounding boxes, grouped by object """ return self._transpose_nested_dict(self.get_bboxes_per_frame(seq_name)) @staticmethod def generate_datasets_meta(src, dst=Path("~/vosdataset_meta").expanduser()): VOSMeta.generate("SyntheticCoco", src / "JPEGImages", src / "Annotations").save(src / "generated_meta.json") class VOSDatasetBase(BaseVideoDataset): """ Generic VOS dataset reader base class, for both DAVIS and YouTubeVOS """ def __init__(self, name: str, root: Path, version=None, split='train', multiobj=True, vis_threshold=10, image_loader=jpeg4py_loader): """ :param root: Dataset root path, eg /path/to/DAVIS or /path/to/YouTubeVOS/ Note: YouTubeVOS 2018 and 2019 are expected to be in /path/to/YouTubeVOS/2018 and /path/to/YouTubeVOS/2019, respectively :param name: 'DAVIS' or 'YouTubeVOS' (case sensitive) :param version: DAVIS: '2016', '2017, YouTubeVOS: '2018' or '2019' :param split: DAVIS: Any name in DAVIS/ImageSets/, YouTubeVOS: 'test', 'train', 'valid' or 'jjtrain', 'jjvalid' :param multiobj: Whether the dataset will return all objects in a sequence or multiple sequences with one object in each. :param vis_threshold: Minimum number of pixels required to consider a target object "visible". :param image_loader: Image loader. """ assert root.exists() and root.is_dir() super().__init__(name, root, image_loader) self.version = version self.split = split self.vis_threshold = vis_threshold self.multiobj = multiobj def _load_image(self, path): im = self.image_loader(str(path)) assert im is not None im = np.atleast_3d(im) return im @staticmethod def _load_anno(path): if not path.exists(): return None # im = np.atleast_3d(np.array(Image.open(path))) im = imread_indexed(path) return im def get_num_sequences(self): return len(self._samples) def get_sequence_info(self, sample_id): """ Get sample meta data. :param sample_id: Sample to query. :return: dict of metadata: sequence: Sequence name frame_shape: (height, width) of the images frame_names: List of frame filename stems in the sequence object_ids: Id numbers of all objects occurring in the sequence obj_sizes: Matrix shape=(frames, object) of the number of pixels for each object in each frame Coordinates in this matrix relate to the frame_names and object_ids visible: Boolean matrix of the same shape as obj_sizes. Entries with more pixels than self.visible_threshold are True. """ m = self.gmeta seq_name, obj_ids = self._samples[sample_id] f_names = m.get_frame_names(seq_name) # All frames f2i = {f: i for i, f in enumerate(f_names)} # Frame name to matrix index o2i = {o: i for i, o in enumerate(obj_ids)} # Object id to matrix index # Get a matrix of object sizes: shape=(frames, objects) obj_sizes = torch.zeros((len(f_names), len(obj_ids)), dtype=torch.int) sizes_per_object = m.get_obj_sizes_per_object(seq_name) for obj_id in obj_ids: frames = sizes_per_object[obj_id] oid = o2i[obj_id] for f, sz in frames.items(): obj_sizes[f2i[f], oid] = sz visible = (obj_sizes > self.vis_threshold).byte() return dict(sequence=seq_name, frame_shape=m.get_shape(seq_name), frame_names=f_names, object_ids=obj_ids, object_sizes=obj_sizes, visible=visible, valid=visible) def get_paths_and_bboxes(self, sequence_info): seq_name = sequence_info['sequence'] annos_root = self._anno_path / seq_name images_root = self._jpeg_path / seq_name frame_names = sequence_info['frame_names'] f2i = {f: i for i, f in enumerate(frame_names)} images = [str(images_root / (f + ".jpg")) for f in frame_names] # Find the frames where ground truth is available and # get the bounding boxes and segmentation labels of those frames all_bboxes = self.gmeta.get_bboxes_per_frame(seq_name) gt_labels = [str(annos_root / (f + ".png")) if f in all_bboxes.keys() else None for f in frame_names] gt_bboxes = OrderedDict() for obj_id in sequence_info['object_ids']: gt_bboxes[obj_id] = np.array([all_bboxes.get(frame, {}).get(obj_id, [-1, -1, -1, -1]) for frame in frame_names]) return images, gt_labels, gt_bboxes def _construct_sequence(self, sequence_info): raise NotImplementedError def get_sequence_list(self): if len(self.sequence_list) > 0: return self.sequence_list self.sequence_list = [self._construct_sequence(self.get_sequence_info(i)) for i in range(len(self._samples))] return self.sequence_list def __len__(self): return len(self._samples) def _get_image_path(self, meta, frame_id): return self._jpeg_path / meta['sequence'] / (meta['frame_names'][frame_id] + ".jpg") def _get_anno_path(self, meta, frame_id): return self._anno_path / meta['sequence'] / (meta['frame_names'][frame_id] + ".png") def get_frames(self, sample_id, frame_ids, anno=None): """ Fetch frames with the given ids. :param sample_id: Sample to get. :param frame_ids: List of frame indices in the sequence belonging to the sample_id :return: dict of metadata and data: sequence: Sequence name images: List of images. No entries may be None labels: List of label/mask images. Entries may be None if the data is missing bboxes: List of bounding boxes. Entries may be None if the data is missing """ seq_name, obj_ids = self._samples[sample_id] meta = self.get_sequence_info(sample_id) if anno is None else anno frame_names = meta['frame_names'] images = [self._load_image(self._jpeg_path / seq_name / (frame_names[f] + ".jpg")) for f in frame_ids] labels = [self._load_anno(self._anno_path / seq_name / (frame_names[f] + ".png")) for f in frame_ids] # Generate bounding boxes for the requested objects bboxes = [] for lb in labels: lb = torch.from_numpy(lb.squeeze()) frame_bbs = {} for obj_id in obj_ids: bbox = masks_to_bboxes(lb == int(obj_id), fmt='t') if bbox[3] == 0 or bbox[2] == 0: print("!") frame_bbs[obj_id] = bbox bboxes.append(frame_bbs) # Insert empty bboxes for missing object ids for bbox in bboxes: for obj_id in obj_ids: if obj_id not in bbox: bbox[obj_id] = torch.zeros(4, dtype=torch.float32) # Remap to object id 1, if requested - for training if not self.multiobj: assert len(obj_ids) == 1 obj_id = obj_ids[0] labels = [torch.Tensor(lb == int(obj_id)) for lb in labels] bboxes = [bbox[obj_id] for bbox in bboxes] else: labels = [torch.Tensor(lb) for lb in labels] object_meta = {key: meta[key] for key in ['sequence', 'frame_shape', 'frame_names', 'object_ids']} anno_frames = dict(bbox=bboxes, mask=labels) for key in ['object_sizes', 'visible', 'valid']: value = meta[key] anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] return images, anno_frames, object_meta def get_name(self): return "%s/%s/%s" % (self.name, self.version, self.split) def has_class_info(self): return False def has_occlusion_info(self): return True def get_num_classes(self): return 0 def get_class_list(self): return [] def get_sequences_in_class(self, class_name): raise [] def has_segmentation_info(self): return True ================================================ FILE: artrackv2_mindspore/external/AR/ltr/dataset/youtubevos.py ================================================ from pathlib import Path import os from ltr.dataset.vos_base import VOSDatasetBase, VOSMeta from pytracking.evaluation import Sequence import json from ltr.admin.environment import env_settings from ltr.data.image_loader import jpeg4py_loader class YouTubeVOSMeta: """ Thin wrapper for YouTubeVOS meta data meta.json { "videos": { "": { "objects": { "": { "category": "", "frames": [ "", "", ] } } } } } # is the same as the pixel values of object in annotated segmentation PNG files. # is the 5-digit index of frame in video, and not necessary to start from 0. """ def __init__(self, dset_split_path): self._data = json.load(open(dset_split_path / 'meta.json'))['videos'] def sequences(self): return list(self._data.keys()) def seq_frames(self, seq_name): """ All filename stems of the frames in the sequence """ frames = set() for obj_id in self.object_ids(seq_name): for f in self.object_frames(seq_name, obj_id): frames.add(f) return list(sorted(frames)) def object_ids(self, seq_name): """ All objects in the sequence """ return list(self._data[seq_name]['objects'].keys()) def object_category(self, seq_name, obj_id): return self._data[seq_name]['objects'][str(obj_id)]['category'] def object_frames(self, seq_name, obj_id): return self._data[seq_name]['objects'][str(obj_id)]['frames'] def object_first_frame(self, seq_name, obj_id): return self.object_frames(seq_name, obj_id)[0] class YouTubeVOS(VOSDatasetBase): """ YoutubeVOS video object segmentation dataset. Publication: YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark Ning Xu, Linjie Yang, Yuchen Fan, Dingcheng Yue, Yuchen Liang, Jianchao Yang, and Thomas Huang ECCV, 2018 https://arxiv.org/pdf/1809.03327.pdf Download dataset from: https://youtube-vos.org/dataset/ """ def __init__(self, root=None, version='2019', split='train', cleanup=None, all_frames=False, sequences=None, multiobj=True, vis_threshold=10, image_loader=jpeg4py_loader): """ args: root - Dataset root path. If unset, it uses the path in your local.py config. version - '2018' or '2019' split - 'test', 'train', 'valid', or 'jjtrain', 'jjvalid'. 'jjvalid' corresponds to a custom validation dataset consisting of 300 videos randomly sampled from the train set. 'jjtrain' contains the remaining videos used for training. cleanup - List of actions to take to to clean up known problems in the dataset. 'aspects': remove frames with weird aspect ratios, 'starts': fix up start frames from original meta data all_frames - Whether to use an "all_frames" split. sequences - List of sequence names. Limit to a subset of sequences if not None. multiobj - Whether the dataset will return all objects in a sequence or multiple sequences with one object in each. vis_threshold - Minimum number of pixels required to consider a target object "visible". image_loader - Image loader. """ root = env_settings().youtubevos_dir if root is None else root super().__init__(name="YouTubeVOS", root=Path(root), version=version, split=split, multiobj=multiobj, vis_threshold=vis_threshold, image_loader=image_loader) split_folder = self.split if self.split.startswith("jj"): split_folder = "train" dset_path = self.root / self.version / split_folder self._anno_path = dset_path / 'Annotations' if all_frames: self._jpeg_path = self.root / self.version / (split_folder + "_all_frames") / 'JPEGImages' else: self._jpeg_path = dset_path / 'JPEGImages' self.meta = YouTubeVOSMeta(dset_path) meta_path = dset_path / "generated_meta.json" if meta_path.exists(): self.gmeta = VOSMeta(filename=meta_path) else: self.gmeta = VOSMeta.generate('YouTubeVOS', self._jpeg_path, self._anno_path) self.gmeta.save(meta_path) if all_frames: self.gmeta.enable_all_frames(self._jpeg_path) if self.split not in ['train', 'valid', 'test']: self.gmeta.select_split('youtubevos', self.split) if sequences is None: sequences = self.gmeta.get_sequence_names() to_remove = set() cleanup = {} if cleanup is None else set(cleanup) if 'aspect' in cleanup: # Remove sequences with unusual aspect ratios for seq_name in sequences: a = self.gmeta.get_aspect_ratio(seq_name) if a < 1.45 or a > 1.9: to_remove.add(seq_name) if 'starts' in cleanup: # Fix incorrect start frames for some objects found with ytvos_start_frames_test() bad_start_frames = [("0e27472bea", '2', ['00055', '00060'], '00065'), ("5937b08d69", '4', ['00000'], '00005'), ("5e1ce354fd", '5', ['00010', '00015'], '00020'), ("7053e4f41e", '2', ['00000', '00005', '00010', '00015'], '00020'), ("720e3fa04c", '2', ['00050'], '00055'), ("c73c8e747f", '2', ['00035'], '00040')] for seq_name, obj_id, bad_frames, good_frame in bad_start_frames: # bad_frames is from meta.json included with the dataset # good_frame is from the generated meta - and the first actual frame where the object was seen. if seq_name in self.meta._data: frames = self.meta.object_frames(seq_name, obj_id) for f in bad_frames: frames.remove(f) assert frames[0] == good_frame sequences = [seq for seq in sequences if seq not in to_remove] self.sequence_names = sequences self._samples = [] for seq in sequences: obj_ids = self.meta.object_ids(seq) if self.multiobj: # Multiple objects per sample self._samples.append((seq, obj_ids)) else: # One object per sample self._samples.extend([(seq, [obj_id]) for obj_id in obj_ids]) print("%s loaded." % self.get_name()) if len(to_remove) > 0: print(" %d sequences were removed, (%d remaining)." % (len(to_remove), len(sequences))) def _construct_sequence(self, sequence_info): seq_name = sequence_info['sequence'] frame_names = sequence_info['frame_names'] fname_to_fid = {f: i for i, f in enumerate(frame_names)} images, gt_segs, gt_bboxes = self.get_paths_and_bboxes(sequence_info) init_data = dict() for obj_id in sequence_info['object_ids']: if obj_id == '0': print("!") f_name = self.meta.object_first_frame(seq_name, obj_id) f_id = fname_to_fid[f_name] if f_id not in init_data: init_data[f_id] = {'object_ids': [obj_id], 'bbox': {obj_id: gt_bboxes[obj_id][f_id,:]}, 'mask': os.path.join(os.path.dirname(gt_segs[f_id]), (f_name + ".png"))} assert init_data[f_id]['mask'] in gt_segs # If this fails, some file is missing else: init_data[f_id]['object_ids'].append(obj_id) init_data[f_id]['bbox'][obj_id] = gt_bboxes[obj_id][f_id,:] return Sequence(name=seq_name, frames=images, dataset='YouTubeVOS', ground_truth_rect=gt_bboxes, init_data=init_data, ground_truth_seg=gt_segs, object_ids=sequence_info['object_ids'], multiobj_mode=self.multiobj) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/external/PreciseRoIPooling/.gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class .vim-template* # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ .pytest_cache/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ ================================================ FILE: artrackv2_mindspore/external/AR/ltr/external/PreciseRoIPooling/LICENSE ================================================ MIT License Copyright (c) 2018 Jiayuan Mao Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: artrackv2_mindspore/external/AR/ltr/external/PreciseRoIPooling/README.md ================================================ # PreciseRoIPooling This repo implements the **Precise RoI Pooling** (PrRoI Pooling), proposed in the paper **Acquisition of Localization Confidence for Accurate Object Detection** published at ECCV 2018 (Oral Presentation). **Acquisition of Localization Confidence for Accurate Object Detection** _Borui Jiang*, Ruixuan Luo*, Jiayuan Mao*, Tete Xiao, Yuning Jiang_ (* indicates equal contribution.) https://arxiv.org/abs/1807.11590 ## Brief In short, Precise RoI Pooling is an integration-based (bilinear interpolation) average pooling method for RoI Pooling. It avoids any quantization and has a continuous gradient on bounding box coordinates. It is: - different from the original RoI Pooling proposed in [Fast R-CNN](https://arxiv.org/abs/1504.08083). PrRoI Pooling uses average pooling instead of max pooling for each bin and has a continuous gradient on bounding box coordinates. That is, one can take the derivatives of some loss function w.r.t the coordinates of each RoI and optimize the RoI coordinates. - different from the RoI Align proposed in [Mask R-CNN](https://arxiv.org/abs/1703.06870). PrRoI Pooling uses a full integration-based average pooling instead of sampling a constant number of points. This makes the gradient w.r.t. the coordinates continuous. For a better illustration, we illustrate RoI Pooling, RoI Align and PrRoI Pooing in the following figure. More details including the gradient computation can be found in our paper.
## Implementation PrRoI Pooling was originally implemented by [Tete Xiao](http://tetexiao.com/) based on MegBrain, an (internal) deep learning framework built by Megvii Inc. It was later adapted into open-source deep learning frameworks. Currently, we only support PyTorch. Unfortunately, we don't have any specific plan for the adaptation into other frameworks such as TensorFlow, but any contributions (pull requests) will be more than welcome. ## Usage (PyTorch 1.0) In the directory `pytorch/`, we provide a PyTorch-based implementation of PrRoI Pooling. It requires PyTorch 1.0+ and only supports CUDA (CPU mode is not implemented). Since we use PyTorch JIT for cxx/cuda code compilation, to use the module in your code, simply do: ``` from prroi_pool import PrRoIPool2D avg_pool = PrRoIPool2D(window_height, window_width, spatial_scale) roi_features = avg_pool(features, rois) # for those who want to use the "functional" from prroi_pool.functional import prroi_pool2d roi_features = prroi_pool2d(features, rois, window_height, window_width, spatial_scale) ``` ## Usage (PyTorch 0.4) **!!! Please first checkout to the branch pytorch0.4.** In the directory `pytorch/`, we provide a PyTorch-based implementation of PrRoI Pooling. It requires PyTorch 0.4 and only supports CUDA (CPU mode is not implemented). To use the PrRoI Pooling module, first goto `pytorch/prroi_pool` and execute `./travis.sh` to compile the essential components (you may need `nvcc` for this step). To use the module in your code, simply do: ``` from prroi_pool import PrRoIPool2D avg_pool = PrRoIPool2D(window_height, window_width, spatial_scale) roi_features = avg_pool(features, rois) # for those who want to use the "functional" from prroi_pool.functional import prroi_pool2d roi_features = prroi_pool2d(features, rois, window_height, window_width, spatial_scale) ``` Here, - RoI is an `m * 5` float tensor of format `(batch_index, x0, y0, x1, y1)`, following the convention in the original Caffe implementation of RoI Pooling, although in some frameworks the batch indices are provided by an integer tensor. - `spatial_scale` is multiplied to the RoIs. For example, if your feature maps are down-sampled by a factor of 16 (w.r.t. the input image), you should use a spatial scale of `1/16`. - The coordinates for RoI follows the [L, R) convension. That is, `(0, 0, 4, 4)` denotes a box of size `4x4`. ================================================ FILE: artrackv2_mindspore/external/AR/ltr/external/PreciseRoIPooling/pytorch/prroi_pool/.gitignore ================================================ *.o /_prroi_pooling ================================================ FILE: artrackv2_mindspore/external/AR/ltr/external/PreciseRoIPooling/pytorch/prroi_pool/__init__.py ================================================ #! /usr/bin/env python3 # -*- coding: utf-8 -*- # File : __init__.py # Author : Jiayuan Mao, Tete Xiao # Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com # Date : 07/13/2018 # # This file is part of PreciseRoIPooling. # Distributed under terms of the MIT license. # Copyright (c) 2017 Megvii Technology Limited. from .prroi_pool import * ================================================ FILE: artrackv2_mindspore/external/AR/ltr/external/PreciseRoIPooling/pytorch/prroi_pool/functional.py ================================================ #! /usr/bin/env python3 # -*- coding: utf-8 -*- # File : functional.py # Author : Jiayuan Mao, Tete Xiao # Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com # Date : 07/13/2018 # # This file is part of PreciseRoIPooling. # Distributed under terms of the MIT license. # Copyright (c) 2017 Megvii Technology Limited. import torch import torch.autograd as ag __all__ = ['prroi_pool2d'] _prroi_pooling = None def _import_prroi_pooling(): global _prroi_pooling if _prroi_pooling is None: try: from os.path import join as pjoin, dirname from torch.utils.cpp_extension import load as load_extension root_dir = pjoin(dirname(__file__), 'src') _prroi_pooling = load_extension( '_prroi_pooling', [pjoin(root_dir, 'prroi_pooling_gpu.c'), pjoin(root_dir, 'prroi_pooling_gpu_impl.cu')], verbose=True ) except ImportError: raise ImportError('Can not compile Precise RoI Pooling library.') return _prroi_pooling class PrRoIPool2DFunction(ag.Function): @staticmethod def forward(ctx, features, rois, pooled_height, pooled_width, spatial_scale): _prroi_pooling = _import_prroi_pooling() assert 'FloatTensor' in features.type() and 'FloatTensor' in rois.type(), \ 'Precise RoI Pooling only takes float input, got {} for features and {} for rois.'.format(features.type(), rois.type()) pooled_height = int(pooled_height) pooled_width = int(pooled_width) spatial_scale = float(spatial_scale) features = features.contiguous() rois = rois.contiguous() params = (pooled_height, pooled_width, spatial_scale) if features.is_cuda: output = _prroi_pooling.prroi_pooling_forward_cuda(features, rois, *params) ctx.params = params # everything here is contiguous. ctx.save_for_backward(features, rois, output) else: raise NotImplementedError('Precise RoI Pooling only supports GPU (cuda) implememtations.') return output @staticmethod def backward(ctx, grad_output): _prroi_pooling = _import_prroi_pooling() features, rois, output = ctx.saved_tensors grad_input = grad_coor = None if features.requires_grad: grad_output = grad_output.contiguous() grad_input = _prroi_pooling.prroi_pooling_backward_cuda(features, rois, output, grad_output, *ctx.params) if rois.requires_grad: grad_output = grad_output.contiguous() grad_coor = _prroi_pooling.prroi_pooling_coor_backward_cuda(features, rois, output, grad_output, *ctx.params) return grad_input, grad_coor, None, None, None prroi_pool2d = PrRoIPool2DFunction.apply ================================================ FILE: artrackv2_mindspore/external/AR/ltr/external/PreciseRoIPooling/pytorch/prroi_pool/prroi_pool.py ================================================ #! /usr/bin/env python3 # -*- coding: utf-8 -*- # File : prroi_pool.py # Author : Jiayuan Mao, Tete Xiao # Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com # Date : 07/13/2018 # # This file is part of PreciseRoIPooling. # Distributed under terms of the MIT license. # Copyright (c) 2017 Megvii Technology Limited. import torch.nn as nn from .functional import prroi_pool2d __all__ = ['PrRoIPool2D'] class PrRoIPool2D(nn.Module): def __init__(self, pooled_height, pooled_width, spatial_scale): super().__init__() self.pooled_height = int(pooled_height) self.pooled_width = int(pooled_width) self.spatial_scale = float(spatial_scale) def forward(self, features, rois): return prroi_pool2d(features, rois, self.pooled_height, self.pooled_width, self.spatial_scale) def extra_repr(self): return 'kernel_size=({pooled_height}, {pooled_width}), spatial_scale={spatial_scale}'.format(**self.__dict__) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/external/PreciseRoIPooling/pytorch/prroi_pool/src/prroi_pooling_gpu.c ================================================ /* * File : prroi_pooling_gpu.c * Author : Jiayuan Mao, Tete Xiao * Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com * Date : 07/13/2018 * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ #include #include #include #include #include #include "prroi_pooling_gpu_impl.cuh" at::Tensor prroi_pooling_forward_cuda(const at::Tensor &features, const at::Tensor &rois, int pooled_height, int pooled_width, float spatial_scale) { int nr_rois = rois.size(0); int nr_channels = features.size(1); int height = features.size(2); int width = features.size(3); int top_count = nr_rois * nr_channels * pooled_height * pooled_width; auto output = at::zeros({nr_rois, nr_channels, pooled_height, pooled_width}, features.options()); if (output.numel() == 0) { THCudaCheck(cudaGetLastError()); return output; } cudaStream_t stream = at::cuda::getCurrentCUDAStream(); PrRoIPoolingForwardGpu( stream, features.data(), rois.data(), output.data(), nr_channels, height, width, pooled_height, pooled_width, spatial_scale, top_count ); THCudaCheck(cudaGetLastError()); return output; } at::Tensor prroi_pooling_backward_cuda( const at::Tensor &features, const at::Tensor &rois, const at::Tensor &output, const at::Tensor &output_diff, int pooled_height, int pooled_width, float spatial_scale) { auto features_diff = at::zeros_like(features); int nr_rois = rois.size(0); int batch_size = features.size(0); int nr_channels = features.size(1); int height = features.size(2); int width = features.size(3); int top_count = nr_rois * nr_channels * pooled_height * pooled_width; int bottom_count = batch_size * nr_channels * height * width; if (output.numel() == 0) { THCudaCheck(cudaGetLastError()); return features_diff; } cudaStream_t stream = at::cuda::getCurrentCUDAStream(); PrRoIPoolingBackwardGpu( stream, features.data(), rois.data(), output.data(), output_diff.data(), features_diff.data(), nr_channels, height, width, pooled_height, pooled_width, spatial_scale, top_count, bottom_count ); THCudaCheck(cudaGetLastError()); return features_diff; } at::Tensor prroi_pooling_coor_backward_cuda( const at::Tensor &features, const at::Tensor &rois, const at::Tensor &output, const at::Tensor &output_diff, int pooled_height, int pooled_width, float spatial_scale) { auto coor_diff = at::zeros_like(rois); int nr_rois = rois.size(0); int nr_channels = features.size(1); int height = features.size(2); int width = features.size(3); int top_count = nr_rois * nr_channels * pooled_height * pooled_width; int bottom_count = nr_rois * 5; if (output.numel() == 0) { THCudaCheck(cudaGetLastError()); return coor_diff; } cudaStream_t stream = at::cuda::getCurrentCUDAStream(); PrRoIPoolingCoorBackwardGpu( stream, features.data(), rois.data(), output.data(), output_diff.data(), coor_diff.data(), nr_channels, height, width, pooled_height, pooled_width, spatial_scale, top_count, bottom_count ); THCudaCheck(cudaGetLastError()); return coor_diff; } PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("prroi_pooling_forward_cuda", &prroi_pooling_forward_cuda, "PRRoIPooling_forward"); m.def("prroi_pooling_backward_cuda", &prroi_pooling_backward_cuda, "PRRoIPooling_backward"); m.def("prroi_pooling_coor_backward_cuda", &prroi_pooling_coor_backward_cuda, "PRRoIPooling_backward_coor"); } ================================================ FILE: artrackv2_mindspore/external/AR/ltr/external/PreciseRoIPooling/pytorch/prroi_pool/src/prroi_pooling_gpu.h ================================================ /* * File : prroi_pooling_gpu.h * Author : Jiayuan Mao, Tete Xiao * Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com * Date : 07/13/2018 * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ int prroi_pooling_forward_cuda(THCudaTensor *features, THCudaTensor *rois, THCudaTensor *output, int pooled_height, int pooled_width, float spatial_scale); int prroi_pooling_backward_cuda( THCudaTensor *features, THCudaTensor *rois, THCudaTensor *output, THCudaTensor *output_diff, THCudaTensor *features_diff, int pooled_height, int pooled_width, float spatial_scale ); int prroi_pooling_coor_backward_cuda( THCudaTensor *features, THCudaTensor *rois, THCudaTensor *output, THCudaTensor *output_diff, THCudaTensor *features_diff, int pooled_height, int pooled_width, float spatial_scal ); ================================================ FILE: artrackv2_mindspore/external/AR/ltr/external/PreciseRoIPooling/pytorch/prroi_pool/src/prroi_pooling_gpu_impl.cu ================================================ /* * File : prroi_pooling_gpu_impl.cu * Author : Tete Xiao, Jiayuan Mao * Email : jasonhsiao97@gmail.com * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ #include "prroi_pooling_gpu_impl.cuh" #include #include #define CUDA_KERNEL_LOOP(i, n) \ for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ i < (n); \ i += blockDim.x * gridDim.x) #define CUDA_POST_KERNEL_CHECK \ do { \ cudaError_t err = cudaGetLastError(); \ if (cudaSuccess != err) { \ fprintf(stderr, "cudaCheckError() failed : %s\n", cudaGetErrorString(err)); \ exit(-1); \ } \ } while(0) #define CUDA_NUM_THREADS 512 namespace { static int CUDA_NUM_BLOCKS(const int N) { return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; } __device__ static float PrRoIPoolingGetData(F_DEVPTR_IN data, const int h, const int w, const int height, const int width) { bool overflow = (h < 0) || (w < 0) || (h >= height) || (w >= width); float retVal = overflow ? 0.0f : data[h * width + w]; return retVal; } __device__ static float PrRoIPoolingGetCoeff(float dh, float dw){ dw = dw > 0 ? dw : -dw; dh = dh > 0 ? dh : -dh; return (1.0f - dh) * (1.0f - dw); } __device__ static float PrRoIPoolingSingleCoorIntegral(float s, float t, float c1, float c2) { return 0.5 * (t * t - s * s) * c2 + (t - 0.5 * t * t - s + 0.5 * s * s) * c1; } __device__ static float PrRoIPoolingInterpolation(F_DEVPTR_IN data, const float h, const float w, const int height, const int width){ float retVal = 0.0f; int h1 = floorf(h); int w1 = floorf(w); retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h)+1; w1 = floorf(w); retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h); w1 = floorf(w)+1; retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h)+1; w1 = floorf(w)+1; retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); return retVal; } __device__ static float PrRoIPoolingMatCalculation(F_DEVPTR_IN this_data, const int s_h, const int s_w, const int e_h, const int e_w, const float y0, const float x0, const float y1, const float x1, const int h0, const int w0) { float alpha, beta, lim_alpha, lim_beta, tmp; float sum_out = 0; alpha = x0 - float(s_w); beta = y0 - float(s_h); lim_alpha = x1 - float(s_w); lim_beta = y1 - float(s_h); tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, s_h, s_w, h0, w0) * tmp; alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, s_h, e_w, h0, w0) * tmp; alpha = x0 - float(s_w); beta = float(e_h) - y1; lim_alpha = x1 - float(s_w); lim_beta = float(e_h) - y0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, e_h, s_w, h0, w0) * tmp; alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, e_h, e_w, h0, w0) * tmp; return sum_out; } __device__ static void PrRoIPoolingDistributeDiff(F_DEVPTR_OUT diff, const float top_diff, const int h, const int w, const int height, const int width, const float coeff) { bool overflow = (h < 0) || (w < 0) || (h >= height) || (w >= width); if (!overflow) atomicAdd(diff + h * width + w, top_diff * coeff); } __device__ static void PrRoIPoolingMatDistributeDiff(F_DEVPTR_OUT diff, const float top_diff, const int s_h, const int s_w, const int e_h, const int e_w, const float y0, const float x0, const float y1, const float x1, const int h0, const int w0) { float alpha, beta, lim_alpha, lim_beta, tmp; alpha = x0 - float(s_w); beta = y0 - float(s_h); lim_alpha = x1 - float(s_w); lim_beta = y1 - float(s_h); tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, s_h, s_w, h0, w0, tmp); alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, s_h, e_w, h0, w0, tmp); alpha = x0 - float(s_w); beta = float(e_h) - y1; lim_alpha = x1 - float(s_w); lim_beta = float(e_h) - y0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, e_h, s_w, h0, w0, tmp); alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, e_h, e_w, h0, w0, tmp); } __global__ void PrRoIPoolingForward( const int nthreads, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, ((float)0.0)); float roi_height = max(roi_end_h - roi_start_h, ((float)0.0)); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_data = bottom_data + (roi_batch_ind * channels + c) * height * width; float *this_out = top_data + index; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); if (win_size == 0) { *this_out = 0; return; } float sum_out = 0; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); for (int w_iter = s_w; w_iter < e_w; ++w_iter) for (int h_iter = s_h; h_iter < e_h; ++h_iter) sum_out += PrRoIPoolingMatCalculation(this_data, h_iter, w_iter, h_iter + 1, w_iter + 1, max(win_start_h, float(h_iter)), max(win_start_w, float(w_iter)), min(win_end_h, float(h_iter) + 1.0), min(win_end_w, float(w_iter + 1.0)), height, width); *this_out = sum_out / win_size; } } __global__ void PrRoIPoolingBackward( const int nthreads, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, (float)0); float roi_height = max(roi_end_h - roi_start_h, (float)0); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_out_grad = top_diff + index; float *this_data_grad = bottom_diff + (roi_batch_ind * channels + c) * height * width; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); float sum_out = win_size == float(0) ? float(0) : *this_out_grad / win_size; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); for (int w_iter = s_w; w_iter < e_w; ++w_iter) for (int h_iter = s_h; h_iter < e_h; ++h_iter) PrRoIPoolingMatDistributeDiff(this_data_grad, sum_out, h_iter, w_iter, h_iter + 1, w_iter + 1, max(win_start_h, float(h_iter)), max(win_start_w, float(w_iter)), min(win_end_h, float(h_iter) + 1.0), min(win_end_w, float(w_iter + 1.0)), height, width); } } __global__ void PrRoIPoolingCoorBackward( const int nthreads, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, (float)0); float roi_height = max(roi_end_h - roi_start_h, (float)0); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_out_grad = top_diff + index; const float *this_bottom_data = bottom_data + (roi_batch_ind * channels + c) * height * width; const float *this_top_data = top_data + index; float *this_data_grad = bottom_diff + n * 5; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); float sum_out = win_size == float(0) ? float(0) : *this_out_grad / win_size; // WARNING: to be discussed if (sum_out == 0) return; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); float g_x1_y = 0, g_x2_y = 0, g_x_y1 = 0, g_x_y2 = 0; for (int h_iter = s_h; h_iter < e_h; ++h_iter) { g_x1_y += PrRoIPoolingSingleCoorIntegral(max(win_start_h, float(h_iter)) - h_iter, min(win_end_h, float(h_iter + 1)) - h_iter, PrRoIPoolingInterpolation(this_bottom_data, h_iter, win_start_w, height, width), PrRoIPoolingInterpolation(this_bottom_data, h_iter + 1, win_start_w, height, width)); g_x2_y += PrRoIPoolingSingleCoorIntegral(max(win_start_h, float(h_iter)) - h_iter, min(win_end_h, float(h_iter + 1)) - h_iter, PrRoIPoolingInterpolation(this_bottom_data, h_iter, win_end_w, height, width), PrRoIPoolingInterpolation(this_bottom_data, h_iter + 1, win_end_w, height, width)); } for (int w_iter = s_w; w_iter < e_w; ++w_iter) { g_x_y1 += PrRoIPoolingSingleCoorIntegral(max(win_start_w, float(w_iter)) - w_iter, min(win_end_w, float(w_iter + 1)) - w_iter, PrRoIPoolingInterpolation(this_bottom_data, win_start_h, w_iter, height, width), PrRoIPoolingInterpolation(this_bottom_data, win_start_h, w_iter + 1, height, width)); g_x_y2 += PrRoIPoolingSingleCoorIntegral(max(win_start_w, float(w_iter)) - w_iter, min(win_end_w, float(w_iter + 1)) - w_iter, PrRoIPoolingInterpolation(this_bottom_data, win_end_h, w_iter, height, width), PrRoIPoolingInterpolation(this_bottom_data, win_end_h, w_iter + 1, height, width)); } float partial_x1 = -g_x1_y + (win_end_h - win_start_h) * (*this_top_data); float partial_y1 = -g_x_y1 + (win_end_w - win_start_w) * (*this_top_data); float partial_x2 = g_x2_y - (win_end_h - win_start_h) * (*this_top_data); float partial_y2 = g_x_y2 - (win_end_w - win_start_w) * (*this_top_data); partial_x1 = partial_x1 / win_size * spatial_scale; partial_x2 = partial_x2 / win_size * spatial_scale; partial_y1 = partial_y1 / win_size * spatial_scale; partial_y2 = partial_y2 / win_size * spatial_scale; // (b, x1, y1, x2, y2) this_data_grad[0] = 0; atomicAdd(this_data_grad + 1, (partial_x1 * (1.0 - float(pw) / pooled_width) + partial_x2 * (1.0 - float(pw + 1) / pooled_width)) * (*this_out_grad)); atomicAdd(this_data_grad + 2, (partial_y1 * (1.0 - float(ph) / pooled_height) + partial_y2 * (1.0 - float(ph + 1) / pooled_height)) * (*this_out_grad)); atomicAdd(this_data_grad + 3, (partial_x2 * float(pw + 1) / pooled_width + partial_x1 * float(pw) / pooled_width) * (*this_out_grad)); atomicAdd(this_data_grad + 4, (partial_y2 * float(ph + 1) / pooled_height + partial_y1 * float(ph) / pooled_height) * (*this_out_grad)); } } } /* !anonymous namespace */ #ifdef __cplusplus extern "C" { #endif void PrRoIPoolingForwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count) { PrRoIPoolingForward<<>>( top_count, bottom_data, bottom_rois, top_data, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } void PrRoIPoolingBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count) { cudaMemsetAsync(bottom_diff, 0, sizeof(float) * bottom_count, stream); PrRoIPoolingBackward<<>>( top_count, bottom_rois, top_diff, bottom_diff, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } void PrRoIPoolingCoorBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count) { cudaMemsetAsync(bottom_diff, 0, sizeof(float) * bottom_count, stream); PrRoIPoolingCoorBackward<<>>( top_count, bottom_data, bottom_rois, top_data, top_diff, bottom_diff, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } } /* !extern "C" */ ================================================ FILE: artrackv2_mindspore/external/AR/ltr/external/PreciseRoIPooling/pytorch/prroi_pool/src/prroi_pooling_gpu_impl.cuh ================================================ /* * File : prroi_pooling_gpu_impl.cuh * Author : Tete Xiao, Jiayuan Mao * Email : jasonhsiao97@gmail.com * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ #ifndef PRROI_POOLING_GPU_IMPL_CUH #define PRROI_POOLING_GPU_IMPL_CUH #ifdef __cplusplus extern "C" { #endif #define F_DEVPTR_IN const float * #define F_DEVPTR_OUT float * void PrRoIPoolingForwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count); void PrRoIPoolingBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count); void PrRoIPoolingCoorBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count); #ifdef __cplusplus } /* !extern "C" */ #endif #endif /* !PRROI_POOLING_GPU_IMPL_CUH */ ================================================ FILE: artrackv2_mindspore/external/AR/ltr/external/PreciseRoIPooling/pytorch/tests/test_prroi_pooling2d.py ================================================ # -*- coding: utf-8 -*- # File : test_prroi_pooling2d.py # Author : Jiayuan Mao # Email : maojiayuan@gmail.com # Date : 18/02/2018 # # This file is part of Jacinle. import unittest import torch import torch.nn as nn import torch.nn.functional as F from jactorch.utils.unittest import TorchTestCase from prroi_pool import PrRoIPool2D class TestPrRoIPool2D(TorchTestCase): def test_forward(self): pool = PrRoIPool2D(7, 7, spatial_scale=0.5) features = torch.rand((4, 16, 24, 32)).cuda() rois = torch.tensor([ [0, 0, 0, 14, 14], [1, 14, 14, 28, 28], ]).float().cuda() out = pool(features, rois) out_gold = F.avg_pool2d(features, kernel_size=2, stride=1) self.assertTensorClose(out, torch.stack(( out_gold[0, :, :7, :7], out_gold[1, :, 7:14, 7:14], ), dim=0)) def test_backward_shapeonly(self): pool = PrRoIPool2D(2, 2, spatial_scale=0.5) features = torch.rand((4, 2, 24, 32)).cuda() rois = torch.tensor([ [0, 0, 0, 4, 4], [1, 14, 14, 18, 18], ]).float().cuda() features.requires_grad = rois.requires_grad = True out = pool(features, rois) loss = out.sum() loss.backward() self.assertTupleEqual(features.size(), features.grad.size()) self.assertTupleEqual(rois.size(), rois.grad.size()) if __name__ == '__main__': unittest.main() ================================================ FILE: artrackv2_mindspore/external/AR/ltr/external/PreciseRoIPooling/src/prroi_pooling_gpu_impl.cu ================================================ /* * File : prroi_pooling_gpu_impl.cu * Author : Tete Xiao, Jiayuan Mao * Email : jasonhsiao97@gmail.com * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ #include "prroi_pooling_gpu_impl.cuh" #include #include #define CUDA_KERNEL_LOOP(i, n) \ for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ i < (n); \ i += blockDim.x * gridDim.x) #define CUDA_POST_KERNEL_CHECK \ do { \ cudaError_t err = cudaGetLastError(); \ if (cudaSuccess != err) { \ fprintf(stderr, "cudaCheckError() failed : %s\n", cudaGetErrorString(err)); \ exit(-1); \ } \ } while(0) #define CUDA_NUM_THREADS 512 namespace { static int CUDA_NUM_BLOCKS(const int N) { return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; } __device__ static float PrRoIPoolingGetData(F_DEVPTR_IN data, const int h, const int w, const int height, const int width) { bool overflow = (h < 0) || (w < 0) || (h >= height) || (w >= width); float retVal = overflow ? 0.0f : data[h * width + w]; return retVal; } __device__ static float PrRoIPoolingGetCoeff(float dh, float dw){ dw = dw > 0 ? dw : -dw; dh = dh > 0 ? dh : -dh; return (1.0f - dh) * (1.0f - dw); } __device__ static float PrRoIPoolingSingleCoorIntegral(float s, float t, float c1, float c2) { return 0.5 * (t * t - s * s) * c2 + (t - 0.5 * t * t - s + 0.5 * s * s) * c1; } __device__ static float PrRoIPoolingInterpolation(F_DEVPTR_IN data, const float h, const float w, const int height, const int width){ float retVal = 0.0f; int h1 = floorf(h); int w1 = floorf(w); retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h)+1; w1 = floorf(w); retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h); w1 = floorf(w)+1; retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h)+1; w1 = floorf(w)+1; retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); return retVal; } __device__ static float PrRoIPoolingMatCalculation(F_DEVPTR_IN this_data, const int s_h, const int s_w, const int e_h, const int e_w, const float y0, const float x0, const float y1, const float x1, const int h0, const int w0) { float alpha, beta, lim_alpha, lim_beta, tmp; float sum_out = 0; alpha = x0 - float(s_w); beta = y0 - float(s_h); lim_alpha = x1 - float(s_w); lim_beta = y1 - float(s_h); tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, s_h, s_w, h0, w0) * tmp; alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, s_h, e_w, h0, w0) * tmp; alpha = x0 - float(s_w); beta = float(e_h) - y1; lim_alpha = x1 - float(s_w); lim_beta = float(e_h) - y0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, e_h, s_w, h0, w0) * tmp; alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, e_h, e_w, h0, w0) * tmp; return sum_out; } __device__ static void PrRoIPoolingDistributeDiff(F_DEVPTR_OUT diff, const float top_diff, const int h, const int w, const int height, const int width, const float coeff) { bool overflow = (h < 0) || (w < 0) || (h >= height) || (w >= width); if (!overflow) atomicAdd(diff + h * width + w, top_diff * coeff); } __device__ static void PrRoIPoolingMatDistributeDiff(F_DEVPTR_OUT diff, const float top_diff, const int s_h, const int s_w, const int e_h, const int e_w, const float y0, const float x0, const float y1, const float x1, const int h0, const int w0) { float alpha, beta, lim_alpha, lim_beta, tmp; alpha = x0 - float(s_w); beta = y0 - float(s_h); lim_alpha = x1 - float(s_w); lim_beta = y1 - float(s_h); tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, s_h, s_w, h0, w0, tmp); alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, s_h, e_w, h0, w0, tmp); alpha = x0 - float(s_w); beta = float(e_h) - y1; lim_alpha = x1 - float(s_w); lim_beta = float(e_h) - y0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, e_h, s_w, h0, w0, tmp); alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, e_h, e_w, h0, w0, tmp); } __global__ void PrRoIPoolingForward( const int nthreads, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, ((float)0.0)); float roi_height = max(roi_end_h - roi_start_h, ((float)0.0)); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_data = bottom_data + (roi_batch_ind * channels + c) * height * width; float *this_out = top_data + index; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); if (win_size == 0) { *this_out = 0; return; } float sum_out = 0; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); for (int w_iter = s_w; w_iter < e_w; ++w_iter) for (int h_iter = s_h; h_iter < e_h; ++h_iter) sum_out += PrRoIPoolingMatCalculation(this_data, h_iter, w_iter, h_iter + 1, w_iter + 1, max(win_start_h, float(h_iter)), max(win_start_w, float(w_iter)), min(win_end_h, float(h_iter) + 1.0), min(win_end_w, float(w_iter + 1.0)), height, width); *this_out = sum_out / win_size; } } __global__ void PrRoIPoolingBackward( const int nthreads, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, (float)0); float roi_height = max(roi_end_h - roi_start_h, (float)0); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_out_grad = top_diff + index; float *this_data_grad = bottom_diff + (roi_batch_ind * channels + c) * height * width; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); float sum_out = win_size == float(0) ? float(0) : *this_out_grad / win_size; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); for (int w_iter = s_w; w_iter < e_w; ++w_iter) for (int h_iter = s_h; h_iter < e_h; ++h_iter) PrRoIPoolingMatDistributeDiff(this_data_grad, sum_out, h_iter, w_iter, h_iter + 1, w_iter + 1, max(win_start_h, float(h_iter)), max(win_start_w, float(w_iter)), min(win_end_h, float(h_iter) + 1.0), min(win_end_w, float(w_iter + 1.0)), height, width); } } __global__ void PrRoIPoolingCoorBackward( const int nthreads, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, (float)0); float roi_height = max(roi_end_h - roi_start_h, (float)0); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_out_grad = top_diff + index; const float *this_bottom_data = bottom_data + (roi_batch_ind * channels + c) * height * width; const float *this_top_data = top_data + index; float *this_data_grad = bottom_diff + n * 5; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); float sum_out = win_size == float(0) ? float(0) : *this_out_grad / win_size; // WARNING: to be discussed if (sum_out == 0) return; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); float g_x1_y = 0, g_x2_y = 0, g_x_y1 = 0, g_x_y2 = 0; for (int h_iter = s_h; h_iter < e_h; ++h_iter) { g_x1_y += PrRoIPoolingSingleCoorIntegral(max(win_start_h, float(h_iter)) - h_iter, min(win_end_h, float(h_iter + 1)) - h_iter, PrRoIPoolingInterpolation(this_bottom_data, h_iter, win_start_w, height, width), PrRoIPoolingInterpolation(this_bottom_data, h_iter + 1, win_start_w, height, width)); g_x2_y += PrRoIPoolingSingleCoorIntegral(max(win_start_h, float(h_iter)) - h_iter, min(win_end_h, float(h_iter + 1)) - h_iter, PrRoIPoolingInterpolation(this_bottom_data, h_iter, win_end_w, height, width), PrRoIPoolingInterpolation(this_bottom_data, h_iter + 1, win_end_w, height, width)); } for (int w_iter = s_w; w_iter < e_w; ++w_iter) { g_x_y1 += PrRoIPoolingSingleCoorIntegral(max(win_start_w, float(w_iter)) - w_iter, min(win_end_w, float(w_iter + 1)) - w_iter, PrRoIPoolingInterpolation(this_bottom_data, win_start_h, w_iter, height, width), PrRoIPoolingInterpolation(this_bottom_data, win_start_h, w_iter + 1, height, width)); g_x_y2 += PrRoIPoolingSingleCoorIntegral(max(win_start_w, float(w_iter)) - w_iter, min(win_end_w, float(w_iter + 1)) - w_iter, PrRoIPoolingInterpolation(this_bottom_data, win_end_h, w_iter, height, width), PrRoIPoolingInterpolation(this_bottom_data, win_end_h, w_iter + 1, height, width)); } float partial_x1 = -g_x1_y + (win_end_h - win_start_h) * (*this_top_data); float partial_y1 = -g_x_y1 + (win_end_w - win_start_w) * (*this_top_data); float partial_x2 = g_x2_y - (win_end_h - win_start_h) * (*this_top_data); float partial_y2 = g_x_y2 - (win_end_w - win_start_w) * (*this_top_data); partial_x1 = partial_x1 / win_size * spatial_scale; partial_x2 = partial_x2 / win_size * spatial_scale; partial_y1 = partial_y1 / win_size * spatial_scale; partial_y2 = partial_y2 / win_size * spatial_scale; // (b, x1, y1, x2, y2) this_data_grad[0] = 0; atomicAdd(this_data_grad + 1, (partial_x1 * (1.0 - float(pw) / pooled_width) + partial_x2 * (1.0 - float(pw + 1) / pooled_width)) * (*this_out_grad)); atomicAdd(this_data_grad + 2, (partial_y1 * (1.0 - float(ph) / pooled_height) + partial_y2 * (1.0 - float(ph + 1) / pooled_height)) * (*this_out_grad)); atomicAdd(this_data_grad + 3, (partial_x2 * float(pw + 1) / pooled_width + partial_x1 * float(pw) / pooled_width) * (*this_out_grad)); atomicAdd(this_data_grad + 4, (partial_y2 * float(ph + 1) / pooled_height + partial_y1 * float(ph) / pooled_height) * (*this_out_grad)); } } } /* !anonymous namespace */ #ifdef __cplusplus extern "C" { #endif void PrRoIPoolingForwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count) { PrRoIPoolingForward<<>>( top_count, bottom_data, bottom_rois, top_data, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } void PrRoIPoolingBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count) { cudaMemsetAsync(bottom_diff, 0, sizeof(float) * bottom_count, stream); PrRoIPoolingBackward<<>>( top_count, bottom_rois, top_diff, bottom_diff, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } void PrRoIPoolingCoorBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count) { cudaMemsetAsync(bottom_diff, 0, sizeof(float) * bottom_count, stream); PrRoIPoolingCoorBackward<<>>( top_count, bottom_data, bottom_rois, top_data, top_diff, bottom_diff, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } } /* !extern "C" */ ================================================ FILE: artrackv2_mindspore/external/AR/ltr/external/PreciseRoIPooling/src/prroi_pooling_gpu_impl.cuh ================================================ /* * File : prroi_pooling_gpu_impl.cuh * Author : Tete Xiao, Jiayuan Mao * Email : jasonhsiao97@gmail.com * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ #ifndef PRROI_POOLING_GPU_IMPL_CUH #define PRROI_POOLING_GPU_IMPL_CUH #ifdef __cplusplus extern "C" { #endif #define F_DEVPTR_IN const float * #define F_DEVPTR_OUT float * void PrRoIPoolingForwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count); void PrRoIPoolingBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count); void PrRoIPoolingCoorBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count); #ifdef __cplusplus } /* !extern "C" */ #endif #endif /* !PRROI_POOLING_GPU_IMPL_CUH */ ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/AR_seg_mask/AR_seg_mask.py ================================================ import torch.nn as nn from ltr.models.neck import CorrNL from ltr import model_constructor import torch import ltr.models.backbone.resnet_seg as resnet_seg from ltr.models.head import seg_network from easydict import EasyDict as edict '''2020.4.14 replace mask head with frtm for higher-quality mask''' '''2020.4.22 Only use the mask branch''' class ARnet_seg_mask(nn.Module): """ Scale Estimation network module with three branches: bbox, coner and mask. """ def __init__(self, feature_extractor, neck_module, head_module, used_layers, extractor_grad=True,output_size=(256,256)): """ args: feature_extractor - backbone feature extractor bb_regressor - IoU prediction module bb_regressor_layer - List containing the name of the layers from feature_extractor, which are input to bb_regressor extractor_grad - Bool indicating whether backbone feature extractor requires gradients """ super(ARnet_seg_mask, self).__init__() self.feature_extractor = feature_extractor self.neck = neck_module self.refiner = head_module self.used_layers = used_layers self.output_size = output_size if not extractor_grad: for p in self.feature_extractor.parameters(): p.requires_grad_(False) def forward(self, train_imgs, test_imgs, train_bb, mode='train'): """ Forward pass Note: If the training is done in sequence mode, that is, test_imgs.dim() == 5, then the batch dimension corresponds to the first dimensions. test_imgs is thus of the form [sequence, batch, feature, row, col] """ self.forward_ref(train_imgs, train_bb) pred_dict = self.forward_test(test_imgs, mode) return pred_dict def forward_ref(self, train_imgs, train_bb): """ Forward pass of reference branch. size of train_imgs is (1,batch,3,H,W), train_bb is (1,batch,4)""" num_sequences = train_imgs.shape[-4] # batch num_train_images = train_imgs.shape[0] if train_imgs.dim() == 5 else 1 # 1 # Extract backbone features '''train_feat OrderedDict, key:'layer4' ''' train_feat_dict = self.extract_backbone_features(train_imgs.view(-1, *train_imgs.shape[-3:])) # 输入size是(batch,3,256,256) train_feat_list = [feat for feat in train_feat_dict.values()] #list,其中每个元素对应一层输出的特征(tensor) # get reference feature self.neck.get_ref_kernel(train_feat_list, train_bb.view(num_train_images, num_sequences, 4)) def forward_test(self, test_imgs, mode='train'): """ Forward pass of test branch. size of test_imgs is (1,batch,3,256,256)""" output = {} # Extract backbone features test_feat_dict = self.extract_backbone_features(test_imgs.view(-1, *test_imgs.shape[-3:]), layers=['layer1','layer2','layer3','layer4','layer5'])# 输入size是(batch,3,256,256) '''list,tensor''' # Save low-level feature list # Lfeat_list = [feat for name, feat in test_feat_dict.items() if name != 'layer3'] # fuse feature from two branches fusion_feat = self.neck.fuse_feat([test_feat_dict['layer4']]) # Obtain bbox prediction if mode=='train': output['mask'] = torch.sigmoid(self.refiner(fusion_feat, test_feat_dict, self.output_size)) elif mode == 'mask': output = torch.sigmoid(self.refiner(fusion_feat, test_feat_dict, self.output_size)) else: raise ValueError("mode should be train or test") return output def extract_backbone_features(self, im, layers=None): if layers is None: layers = self.used_layers return self.feature_extractor(im, layers) def extract_features(self, im, layers): return self.feature_extractor(im, layers) @model_constructor def ARnet_seg_mask_resnet50(backbone_pretrained=True,used_layers=('layer4',),pool_size=None): # backbone backbone_net = resnet_seg.resnet50(pretrained=backbone_pretrained) # neck neck_net = CorrNL.CorrNL(pool_size=pool_size) # multiple heads '''create segnet''' in_channels = 1024 # disc_params = edict(layer="layer4", in_channels=in_channels, c_channels=96, out_channels=64) # non-local feat (64 channels rather than 1) '''2020.4.22 change "out_channels" to pool_size * pool_size''' disc_params = edict(layer="layer4", in_channels=in_channels, c_channels=96, out_channels=pool_size*pool_size) # non-local feat (64 channels rather than 1) refnet_params = edict( layers=("layer5", "layer4", "layer3", "layer2"), nchannels=64, use_batch_norm=True) disc_params.in_channels = backbone_net.get_out_channels()[disc_params.layer] p = refnet_params refinement_layers_channels = {L: nch for L, nch in backbone_net.get_out_channels().items() if L in p.layers} refiner = seg_network.SegNetwork(disc_params.out_channels, p.nchannels, refinement_layers_channels, p.use_batch_norm) '''create Alpha-Refine''' net = ARnet_seg_mask(feature_extractor=backbone_net, neck_module=neck_net, head_module=refiner, used_layers=used_layers, extractor_grad=True, output_size=(int(pool_size*2*16),int(pool_size*2*16))) return net ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/AR_seg_mask/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/backbone/__init__.py ================================================ from .resnet import resnet18, resnet50, resnet_baby from .resnet18_vggm import resnet18_vggmconv1 ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/backbone/base.py ================================================ import torch import torch.nn as nn class Backbone(nn.Module): """Base class for backbone networks. Handles freezing layers etc. args: frozen_layers - Name of layers to freeze. Either list of strings, 'none' or 'all'. Default: 'none'. """ def __init__(self, frozen_layers=()): super().__init__() if isinstance(frozen_layers, str): if frozen_layers.lower() == 'none': frozen_layers = () elif frozen_layers.lower() != 'all': raise ValueError('Unknown option for frozen layers: \"{}\". Should be \"all\", \"none\" or list of layer names.'.format(frozen_layers)) self.frozen_layers = frozen_layers self._is_frozen_nograd = False def train(self, mode=True): super().train(mode) if mode == True: self._set_frozen_to_eval() if not self._is_frozen_nograd: self._set_frozen_to_nograd() self._is_frozen_nograd = True def _set_frozen_to_eval(self): if isinstance(self.frozen_layers, str) and self.frozen_layers.lower() == 'all': self.eval() else: for layer in self.frozen_layers: getattr(self, layer).eval() def _set_frozen_to_nograd(self): if isinstance(self.frozen_layers, str) and self.frozen_layers.lower() == 'all': for p in self.parameters(): p.requires_grad_(False) else: for layer in self.frozen_layers: for p in getattr(self, layer).parameters(): p.requires_grad_(False) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/backbone/resnet.py ================================================ import math import torch.nn as nn from collections import OrderedDict import torch.utils.model_zoo as model_zoo from torchvision.models.resnet import model_urls from .base import Backbone def conv3x3(in_planes, out_planes, stride=1, dilation=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, bias=False, dilation=dilation) class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1, use_bn=True): super(BasicBlock, self).__init__() self.use_bn = use_bn self.conv1 = conv3x3(inplanes, planes, stride, dilation=dilation) if use_bn: self.bn1 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes, dilation=dilation) if use_bn: self.bn2 = nn.BatchNorm2d(planes) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) if self.use_bn: out = self.bn1(out) out = self.relu(out) out = self.conv2(out) if self.use_bn: out = self.bn2(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out class Bottleneck(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=dilation, bias=False, dilation=dilation) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes * 4) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out class ResNet(Backbone): """ ResNet network module. Allows extracting specific feature blocks.""" def __init__(self, block, layers, output_layers, num_classes=1000, inplanes=64, dilation_factor=1, frozen_layers=()): self.inplanes = inplanes super(ResNet, self).__init__(frozen_layers=frozen_layers) self.output_layers = output_layers self.conv1 = nn.Conv2d(3, inplanes, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(inplanes) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) stride = [1 + (dilation_factor < l) for l in (8, 4, 2)] self.layer1 = self._make_layer(block, inplanes, layers[0], dilation=max(dilation_factor//8, 1)) self.layer2 = self._make_layer(block, inplanes*2, layers[1], stride=stride[0], dilation=max(dilation_factor//4, 1)) self.layer3 = self._make_layer(block, inplanes*4, layers[2], stride=stride[1], dilation=max(dilation_factor//2, 1)) self.layer4 = self._make_layer(block, inplanes*8, layers[3], stride=stride[2], dilation=dilation_factor) out_feature_strides = {'conv1': 4, 'layer1': 4, 'layer2': 4*stride[0], 'layer3': 4*stride[0]*stride[1], 'layer4': 4*stride[0]*stride[1]*stride[2]} # TODO better way? if isinstance(self.layer1[0], BasicBlock): out_feature_channels = {'conv1': inplanes, 'layer1': inplanes, 'layer2': inplanes*2, 'layer3': inplanes*4, 'layer4': inplanes*8} elif isinstance(self.layer1[0], Bottleneck): base_num_channels = 4 * inplanes out_feature_channels = {'conv1': inplanes, 'layer1': base_num_channels, 'layer2': base_num_channels * 2, 'layer3': base_num_channels * 4, 'layer4': base_num_channels * 8} else: raise Exception('block not supported') self._out_feature_strides = out_feature_strides self._out_feature_channels = out_feature_channels # self.avgpool = nn.AvgPool2d(7, stride=1) self.avgpool = nn.AdaptiveAvgPool2d((1,1)) self.fc = nn.Linear(inplanes*8 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def out_feature_strides(self, layer=None): if layer is None: return self._out_feature_strides else: return self._out_feature_strides[layer] def out_feature_channels(self, layer=None): if layer is None: return self._out_feature_channels else: return self._out_feature_channels[layer] def _make_layer(self, block, planes, blocks, stride=1, dilation=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample, dilation=dilation)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def _add_output_and_check(self, name, x, outputs, output_layers): if name in output_layers: outputs[name] = x return len(output_layers) == len(outputs) def forward(self, x, output_layers=None): """ Forward pass with input x. The output_layers specify the feature blocks which must be returned """ outputs = OrderedDict() if output_layers is None: output_layers = self.output_layers x = self.conv1(x) x = self.bn1(x) x = self.relu(x) if self._add_output_and_check('conv1', x, outputs, output_layers): return outputs x = self.maxpool(x) x = self.layer1(x) if self._add_output_and_check('layer1', x, outputs, output_layers): return outputs x = self.layer2(x) if self._add_output_and_check('layer2', x, outputs, output_layers): return outputs x = self.layer3(x) if self._add_output_and_check('layer3', x, outputs, output_layers): return outputs x = self.layer4(x) if self._add_output_and_check('layer4', x, outputs, output_layers): return outputs x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc(x) if self._add_output_and_check('fc', x, outputs, output_layers): return outputs if len(output_layers) == 1 and output_layers[0] == 'default': return x raise ValueError('output_layer is wrong.') def resnet_baby(output_layers=None, pretrained=False, inplanes=16, **kwargs): """Constructs a ResNet-18 model. """ if output_layers is None: output_layers = ['default'] else: for l in output_layers: if l not in ['conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']: raise ValueError('Unknown layer: {}'.format(l)) model = ResNet(BasicBlock, [2, 2, 2, 2], output_layers, inplanes=inplanes, **kwargs) if pretrained: raise NotImplementedError return model def resnet18(output_layers=None, pretrained=False, **kwargs): """Constructs a ResNet-18 model. """ if output_layers is None: output_layers = ['default'] else: for l in output_layers: if l not in ['conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']: raise ValueError('Unknown layer: {}'.format(l)) model = ResNet(BasicBlock, [2, 2, 2, 2], output_layers, **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet18'])) return model def resnet50(output_layers=None, pretrained=False, **kwargs): """Constructs a ResNet-50 model. """ if output_layers is None: output_layers = ['default'] else: for l in output_layers: if l not in ['conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']: raise ValueError('Unknown layer: {}'.format(l)) model = ResNet(Bottleneck, [3, 4, 6, 3], output_layers, **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet50'])) return model ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/backbone/resnet18_vggm.py ================================================ import math import torch import torch.nn as nn from collections import OrderedDict from torchvision.models.resnet import BasicBlock from .base import Backbone class SpatialCrossMapLRN(nn.Module): def __init__(self, local_size=1, alpha=1.0, beta=0.75, k=1, ACROSS_CHANNELS=True): super(SpatialCrossMapLRN, self).__init__() self.ACROSS_CHANNELS = ACROSS_CHANNELS if ACROSS_CHANNELS: self.average=nn.AvgPool3d(kernel_size=(local_size, 1, 1), stride=1, padding=(int((local_size-1.0)/2), 0, 0)) else: self.average=nn.AvgPool2d(kernel_size=local_size, stride=1, padding=int((local_size-1.0)/2)) self.alpha = alpha self.beta = beta self.k = k def forward(self, x): if self.ACROSS_CHANNELS: div = x.pow(2).unsqueeze(1) div = self.average(div).squeeze(1) div = div.mul(self.alpha).add(self.k).pow(self.beta) else: div = x.pow(2) div = self.average(div) div = div.mul(self.alpha).add(self.k).pow(self.beta) x = x.div(div) return x class ResNetVGGm1(Backbone): def __init__(self, block, layers, output_layers, num_classes=1000, frozen_layers=()): self.inplanes = 64 super(ResNetVGGm1, self).__init__(frozen_layers=frozen_layers) self.output_layers = output_layers self.vggmconv1 = nn.Conv2d(3,96,(7, 7),(2, 2), padding=3) self.vgglrn = SpatialCrossMapLRN(5, 0.0005, 0.75, 2) self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer(block, 128, layers[1], stride=2) self.layer3 = self._make_layer(block, 256, layers[2], stride=2) self.layer4 = self._make_layer(block, 512, layers[3], stride=2) # self.avgpool = nn.AvgPool2d(7, stride=1) self.avgpool = nn.AdaptiveAvgPool2d((1,1)) self.fc = nn.Linear(512 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def _add_output_and_check(self, name, x, outputs, output_layers): if name in output_layers: outputs[name] = x return len(output_layers) == len(outputs) def forward(self, x, output_layers=None): outputs = OrderedDict() if output_layers is None: output_layers = self.output_layers if 'vggconv1' in output_layers: c1 = self.vgglrn(self.relu(self.vggmconv1(x))) if self._add_output_and_check('vggconv1', c1, outputs, output_layers): return outputs x = self.conv1(x) x = self.bn1(x) x = self.relu(x) if self._add_output_and_check('conv1', x, outputs, output_layers): return outputs x = self.maxpool(x) x = self.layer1(x) if self._add_output_and_check('layer1', x, outputs, output_layers): return outputs x = self.layer2(x) if self._add_output_and_check('layer2', x, outputs, output_layers): return outputs x = self.layer3(x) if self._add_output_and_check('layer3', x, outputs, output_layers): return outputs x = self.layer4(x) if self._add_output_and_check('layer4', x, outputs, output_layers): return outputs x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc(x) if self._add_output_and_check('fc', x, outputs, output_layers): return outputs if len(output_layers) == 1 and output_layers[0] == 'default': return x raise ValueError('output_layer is wrong.') def resnet18_vggmconv1(output_layers=None, path=None, **kwargs): """Constructs a ResNet-18 model with first-layer VGGm features. """ if output_layers is None: output_layers = ['default'] else: for l in output_layers: if l not in ['vggconv1', 'conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']: raise ValueError('Unknown layer: {}'.format(l)) model = ResNetVGGm1(BasicBlock, [2, 2, 2, 2], output_layers, **kwargs) if path is not None: model.load_state_dict(torch.load(path), strict=False) return model ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/backbone/resnet_seg.py ================================================ import math import torch.nn as nn from collections import OrderedDict import torch.utils.model_zoo as model_zoo from torchvision.models.resnet import model_urls '''2020.4.14 newly added''' from collections import OrderedDict as odict from ltr.models.head.utils import get_out_channels def conv3x3(in_planes, out_planes, stride=1, dilation=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, bias=False, dilation=dilation) class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1): super(BasicBlock, self).__init__() self.conv1 = conv3x3(inplanes, planes, stride, dilation=dilation) self.bn1 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes, dilation=dilation) self.bn2 = nn.BatchNorm2d(planes) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out class Bottleneck(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=dilation, bias=False, dilation=dilation) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes * 4) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out class ResNet(nn.Module): """ ResNet network module. Allows extracting specific feature blocks.""" def __init__(self, block, layers, output_layers, num_classes=1000, inplanes=64, dilation_factor=1): self.inplanes = inplanes super(ResNet, self).__init__() self.output_layers = output_layers self.conv1 = nn.Conv2d(3, inplanes, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(inplanes) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) stride = [1 + (dilation_factor < l) for l in (8, 4, 2)] self.layer1 = self._make_layer(block, inplanes, layers[0], dilation=max(dilation_factor//8, 1)) self.layer2 = self._make_layer(block, inplanes*2, layers[1], stride=stride[0], dilation=max(dilation_factor//4, 1)) self.layer3 = self._make_layer(block, inplanes*4, layers[2], stride=stride[1], dilation=max(dilation_factor//2, 1)) self.layer4 = self._make_layer(block, inplanes*8, layers[3], stride=stride[2], dilation=dilation_factor) # self.avgpool = nn.AvgPool2d(7, stride=1) self.avgpool = nn.AdaptiveAvgPool2d((1,1)) self.fc = nn.Linear(inplanes*8 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() '''2020.4.14 newly added''' self._out_channels = odict( # Deep-to-shallow order is required by SegNetwork layer5=get_out_channels(self.layer4), layer4=get_out_channels(self.layer3), layer3=get_out_channels(self.layer2), layer2=get_out_channels(self.layer1), layer1=get_out_channels(self.conv1)) def _make_layer(self, block, planes, blocks, stride=1, dilation=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample, dilation=dilation)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def _add_output_and_check(self, name, x, outputs, output_layers): if name in output_layers: outputs[name] = x return len(output_layers) == len(outputs) def forward(self, x, output_layers=None): """ Forward pass with input x. The output_layers specify the feature blocks which must be returned """ outputs = OrderedDict() if output_layers is None: output_layers = self.output_layers x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x)# conv1: (batch,64,128,128) '''2020.4.14 change names for every layers''' if self._add_output_and_check('layer1', x, outputs, output_layers): return outputs x = self.layer1(x) # (batch,256,64,64) if self._add_output_and_check('layer2', x, outputs, output_layers): return outputs x = self.layer2(x) # (batch,512,32,32) if self._add_output_and_check('layer3', x, outputs, output_layers): return outputs x = self.layer3(x) # (batch,1024,16,16) if self._add_output_and_check('layer4', x, outputs, output_layers): return outputs x = self.layer4(x) if self._add_output_and_check('layer5', x, outputs, output_layers): return outputs x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc(x) if self._add_output_and_check('fc', x, outputs, output_layers): return outputs if len(output_layers) == 1 and output_layers[0] == 'default': return x raise ValueError('output_layer is wrong.') '''2020.4.14 newly added''' def get_out_channels(self): return self._out_channels def resnet18(output_layers=None, pretrained=False, dilation_factor=1): """Constructs a ResNet-18 model. """ if output_layers is None: output_layers = ['default'] else: for l in output_layers: if l not in ['conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']: raise ValueError('Unknown layer: {}'.format(l)) model = ResNet(BasicBlock, [2, 2, 2, 2], output_layers, dilation_factor=dilation_factor) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet18'])) return model def resnet50(output_layers=None, pretrained=False, dilation_factor=1): """Constructs a ResNet-50 model. """ if output_layers is None: output_layers = ['default'] else: for l in output_layers: if l not in ['conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']: raise ValueError('Unknown layer: {}'.format(l)) model = ResNet(Bottleneck, [3, 4, 6, 3], output_layers, dilation_factor=dilation_factor) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet50'])) return model '''newly added''' def resnet101(output_layers=None, pretrained=False, dilation_factor=1): """Constructs a ResNet-101 model. """ if output_layers is None: output_layers = ['default'] else: for l in output_layers: if l not in ['conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']: raise ValueError('Unknown layer: {}'.format(l)) model = ResNet(Bottleneck, [3, 4, 23, 3], output_layers, dilation_factor=dilation_factor) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet101'])) return model ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/bbreg/__init__.py ================================================ from .atom_iou_net import AtomIoUNet ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/bbreg/atom.py ================================================ import torch.nn as nn import ltr.models.backbone as backbones import ltr.models.bbreg as bbmodels from ltr import model_constructor class ATOMnet(nn.Module): """ ATOM network module""" def __init__(self, feature_extractor, bb_regressor, bb_regressor_layer, extractor_grad=True): """ args: feature_extractor - backbone feature extractor bb_regressor - IoU prediction module bb_regressor_layer - List containing the name of the layers from feature_extractor, which are input to bb_regressor extractor_grad - Bool indicating whether backbone feature extractor requires gradients """ super(ATOMnet, self).__init__() self.feature_extractor = feature_extractor self.bb_regressor = bb_regressor self.bb_regressor_layer = bb_regressor_layer if not extractor_grad: for p in self.feature_extractor.parameters(): p.requires_grad_(False) def forward(self, train_imgs, test_imgs, train_bb, test_proposals): """ Forward pass Note: If the training is done in sequence mode, that is, test_imgs.dim() == 5, then the batch dimension corresponds to the first dimensions. test_imgs is thus of the form [sequence, batch, feature, row, col] """ num_sequences = train_imgs.shape[-4] num_train_images = train_imgs.shape[0] if train_imgs.dim() == 5 else 1 num_test_images = test_imgs.shape[0] if test_imgs.dim() == 5 else 1 # Extract backbone features train_feat = self.extract_backbone_features(train_imgs.reshape(-1, *train_imgs.shape[-3:])) test_feat = self.extract_backbone_features(test_imgs.reshape(-1, *test_imgs.shape[-3:])) train_feat_iou = [feat for feat in train_feat.values()] test_feat_iou = [feat for feat in test_feat.values()] # Obtain iou prediction iou_pred = self.bb_regressor(train_feat_iou, test_feat_iou, train_bb.reshape(num_train_images, num_sequences, 4), test_proposals.reshape(num_train_images, num_sequences, -1, 4)) return iou_pred def extract_backbone_features(self, im, layers=None): if layers is None: layers = self.bb_regressor_layer return self.feature_extractor(im, layers) def extract_features(self, im, layers): return self.feature_extractor(im, layers) @model_constructor def atom_resnet18(iou_input_dim=(256,256), iou_inter_dim=(256,256), backbone_pretrained=True): # backbone backbone_net = backbones.resnet18(pretrained=backbone_pretrained) # Bounding box regressor iou_predictor = bbmodels.AtomIoUNet(pred_input_dim=iou_input_dim, pred_inter_dim=iou_inter_dim) net = ATOMnet(feature_extractor=backbone_net, bb_regressor=iou_predictor, bb_regressor_layer=['layer2', 'layer3'], extractor_grad=False) return net @model_constructor def atom_resnet50(iou_input_dim=(256,256), iou_inter_dim=(256,256), backbone_pretrained=True): # backbone backbone_net = backbones.resnet50(pretrained=backbone_pretrained) # Bounding box regressor iou_predictor = bbmodels.AtomIoUNet(input_dim=(4*128,4*256), pred_input_dim=iou_input_dim, pred_inter_dim=iou_inter_dim) net = ATOMnet(feature_extractor=backbone_net, bb_regressor=iou_predictor, bb_regressor_layer=['layer2', 'layer3'], extractor_grad=False) return net ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/bbreg/atom_iou_net.py ================================================ import torch.nn as nn import torch from ltr.models.layers.blocks import LinearBlock from ltr.external.PreciseRoIPooling.pytorch.prroi_pool import PrRoIPool2D def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1): return nn.Sequential( nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=True), nn.BatchNorm2d(out_planes), nn.ReLU(inplace=True)) class AtomIoUNet(nn.Module): """Network module for IoU prediction. Refer to the ATOM paper for an illustration of the architecture. It uses two backbone feature layers as input. args: input_dim: Feature dimensionality of the two input backbone layers. pred_input_dim: Dimensionality input the the prediction network. pred_inter_dim: Intermediate dimensionality in the prediction network.""" def __init__(self, input_dim=(128,256), pred_input_dim=(256,256), pred_inter_dim=(256,256)): super().__init__() # _r for reference, _t for test self.conv3_1r = conv(input_dim[0], 128, kernel_size=3, stride=1) self.conv3_1t = conv(input_dim[0], 256, kernel_size=3, stride=1) self.conv3_2t = conv(256, pred_input_dim[0], kernel_size=3, stride=1) self.prroi_pool3r = PrRoIPool2D(3, 3, 1/8) self.prroi_pool3t = PrRoIPool2D(5, 5, 1/8) self.fc3_1r = conv(128, 256, kernel_size=3, stride=1, padding=0) self.conv4_1r = conv(input_dim[1], 256, kernel_size=3, stride=1) self.conv4_1t = conv(input_dim[1], 256, kernel_size=3, stride=1) self.conv4_2t = conv(256, pred_input_dim[1], kernel_size=3, stride=1) self.prroi_pool4r = PrRoIPool2D(1, 1, 1/16) self.prroi_pool4t = PrRoIPool2D(3, 3, 1 / 16) self.fc34_3r = conv(256 + 256, pred_input_dim[0], kernel_size=1, stride=1, padding=0) self.fc34_4r = conv(256 + 256, pred_input_dim[1], kernel_size=1, stride=1, padding=0) self.fc3_rt = LinearBlock(pred_input_dim[0], pred_inter_dim[0], 5) self.fc4_rt = LinearBlock(pred_input_dim[1], pred_inter_dim[1], 3) self.iou_predictor = nn.Linear(pred_inter_dim[0]+pred_inter_dim[1], 1, bias=True) # Init weights for m in self.modules(): if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d) or isinstance(m, nn.Linear): nn.init.kaiming_normal_(m.weight.data, mode='fan_in') if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): # In earlier versions batch norm parameters was initialized with default initialization, # which changed in pytorch 1.2. In 1.1 and earlier the weight was set to U(0,1). # So we use the same initialization here. # m.weight.data.fill_(1) m.weight.data.uniform_() m.bias.data.zero_() def forward(self, feat1, feat2, bb1, proposals2): """Runs the ATOM IoUNet during training operation. This forward pass is mainly used for training. Call the individual functions during tracking instead. args: feat1: Features from the reference frames (4 or 5 dims). feat2: Features from the test frames (4 or 5 dims). bb1: Target boxes (x,y,w,h) in image coords in the reference samples. Dims (images, sequences, 4). proposals2: Proposal boxes for which the IoU will be predicted (images, sequences, num_proposals, 4).""" assert bb1.dim() == 3 assert proposals2.dim() == 4 num_images = proposals2.shape[0] num_sequences = proposals2.shape[1] # Extract first train sample feat1 = [f[0,...] if f.dim()==5 else f.reshape(-1, num_sequences, *f.shape[-3:])[0,...] for f in feat1] bb1 = bb1[0,...] # Get modulation vector modulation = self.get_modulation(feat1, bb1) iou_feat = self.get_iou_feat(feat2) modulation = [f.reshape(1, num_sequences, -1).repeat(num_images, 1, 1).reshape(num_sequences*num_images, -1) for f in modulation] proposals2 = proposals2.reshape(num_sequences*num_images, -1, 4) pred_iou = self.predict_iou(modulation, iou_feat, proposals2) return pred_iou.reshape(num_images, num_sequences, -1) def predict_iou(self, modulation, feat, proposals): """Predicts IoU for the give proposals. args: modulation: Modulation vectors for the targets. Dims (batch, feature_dim). feat: IoU features (from get_iou_feat) for test images. Dims (batch, feature_dim, H, W). proposals: Proposal boxes for which the IoU will be predicted (batch, num_proposals, 4).""" fc34_3_r, fc34_4_r = modulation c3_t, c4_t = feat batch_size = c3_t.size()[0] # Modulation c3_t_att = c3_t * fc34_3_r.reshape(batch_size, -1, 1, 1) c4_t_att = c4_t * fc34_4_r.reshape(batch_size, -1, 1, 1) # Add batch_index to rois batch_index = torch.arange(batch_size, dtype=torch.float32).reshape(-1, 1).to(c3_t.device) # Push the different rois for the same image along the batch dimension num_proposals_per_batch = proposals.shape[1] # input proposals2 is in format xywh, convert it to x0y0x1y1 format proposals_xyxy = torch.cat((proposals[:, :, 0:2], proposals[:, :, 0:2] + proposals[:, :, 2:4]), dim=2) # Add batch index roi2 = torch.cat((batch_index.reshape(batch_size, -1, 1).expand(-1, num_proposals_per_batch, -1), proposals_xyxy), dim=2) roi2 = roi2.reshape(-1, 5).to(proposals_xyxy.device) roi3t = self.prroi_pool3t(c3_t_att, roi2) roi4t = self.prroi_pool4t(c4_t_att, roi2) fc3_rt = self.fc3_rt(roi3t) fc4_rt = self.fc4_rt(roi4t) fc34_rt_cat = torch.cat((fc3_rt, fc4_rt), dim=1) iou_pred = self.iou_predictor(fc34_rt_cat).reshape(batch_size, num_proposals_per_batch) return iou_pred def get_modulation(self, feat, bb): """Get modulation vectors for the targets. args: feat: Backbone features from reference images. Dims (batch, feature_dim, H, W). bb: Target boxes (x,y,w,h) in image coords in the reference samples. Dims (batch, 4).""" feat3_r, feat4_r = feat c3_r = self.conv3_1r(feat3_r) # Add batch_index to rois batch_size = bb.shape[0] batch_index = torch.arange(batch_size, dtype=torch.float32).reshape(-1, 1).to(bb.device) # input bb is in format xywh, convert it to x0y0x1y1 format bb = bb.clone() bb[:, 2:4] = bb[:, 0:2] + bb[:, 2:4] roi1 = torch.cat((batch_index, bb), dim=1) roi3r = self.prroi_pool3r(c3_r, roi1) c4_r = self.conv4_1r(feat4_r) roi4r = self.prroi_pool4r(c4_r, roi1) fc3_r = self.fc3_1r(roi3r) # Concatenate from block 3 and 4 fc34_r = torch.cat((fc3_r, roi4r), dim=1) fc34_3_r = self.fc34_3r(fc34_r) fc34_4_r = self.fc34_4r(fc34_r) return fc34_3_r, fc34_4_r def get_iou_feat(self, feat2): """Get IoU prediction features from a 4 or 5 dimensional backbone input.""" feat2 = [f.reshape(-1, *f.shape[-3:]) if f.dim()==5 else f for f in feat2] feat3_t, feat4_t = feat2 c3_t = self.conv3_2t(self.conv3_1t(feat3_t)) c4_t = self.conv4_2t(self.conv4_1t(feat4_t)) return c3_t, c4_t ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/head/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/head/seg_network.py ================================================ import torch from torch import nn as nn from torch.nn import functional as F from ltr.models.head.utils import conv, relu, interpolate, adaptive_cat class TSE(nn.Module): def __init__(self, fc, ic, oc): super().__init__() nc = ic + oc self.reduce = nn.Sequential(conv(fc, oc, 1), relu(), conv(oc, oc, 1)) self.transform = nn.Sequential(conv(nc, nc, 3), relu(), conv(nc, nc, 3), relu(), conv(nc, oc, 3), relu()) def forward(self, ft, score, x=None): h = self.reduce(ft) hpool = F.adaptive_avg_pool2d(h, (1, 1)) if x is None else x h = adaptive_cat((h, score), dim=1, ref_tensor=0) h = self.transform(h) return h, hpool class CAB(nn.Module): def __init__(self, oc, deepest): super().__init__() self.convreluconv = nn.Sequential(conv(2 * oc, oc, 1), relu(), conv(oc, oc, 1)) self.deepest = deepest def forward(self, deeper, shallower, att_vec=None): shallow_pool = F.adaptive_avg_pool2d(shallower, (1, 1)) deeper_pool = deeper if self.deepest else F.adaptive_avg_pool2d(deeper, (1, 1)) if att_vec is not None: global_pool = torch.cat([shallow_pool, deeper_pool, att_vec], dim=1) else: global_pool = torch.cat((shallow_pool, deeper_pool), dim=1) conv_1x1 = self.convreluconv(global_pool) inputs = shallower * torch.sigmoid(conv_1x1) out = inputs + interpolate(deeper, inputs.shape[-2:]) return out class RRB(nn.Module): def __init__(self, oc, use_bn=False): super().__init__() self.conv1x1 = conv(oc, oc, 1) if use_bn: self.bblock = nn.Sequential(conv(oc, oc, 3), nn.BatchNorm2d(oc), relu(), conv(oc, oc, 3, bias=False)) else: self.bblock = nn.Sequential(conv(oc, oc, 3), relu(), conv(oc, oc, 3, bias=False)) # Basic block def forward(self, x): h = self.conv1x1(x) return F.relu(h + self.bblock(h)) class Upsampler(nn.Module): def __init__(self, in_channels=64): super().__init__() self.conv1 = conv(in_channels, in_channels // 2, 3) self.conv2 = conv(in_channels // 2, 1, 3) def forward(self, x, image_size): print(x.shape) x = F.interpolate(x, (2 * x.shape[-2], 2 * x.shape[-1]), mode='bicubic', align_corners=False) x = F.relu(self.conv1(x)) x = F.interpolate(x, image_size[-2:], mode='bicubic', align_corners=False) x = self.conv2(x) return x class PyrUpBicubic2d(nn.Module): def __init__(self, channels): super().__init__() self.channels = channels def kernel(d): x = d + torch.arange(-1, 3, dtype=torch.float32) x = torch.abs(x) a = -0.75 f = (x < 1).float() * ((a + 2) * x * x * x - (a + 3) * x * x + 1) + \ ((x >= 1) * (x < 2)).float() * (a * x * x * x - 5 * a * x * x + 8 * a * x - 4 * a) W = f.reshape(1, 1, 1, len(x)).float() Wt = W.permute(0, 1, 3, 2) return W, Wt We, We_t = kernel(-0.25) Wo, Wo_t = kernel(-0.25 - 0.5) # Building non-separable filters for now. It would make sense to # have separable filters if it proves to be faster. # .contiguous() is needed until a bug is fixed in nn.Conv2d. self.W00 = (We_t @ We).expand(channels, 1, 4, 4).contiguous() self.W01 = (We_t @ Wo).expand(channels, 1, 4, 4).contiguous() self.W10 = (Wo_t @ We).expand(channels, 1, 4, 4).contiguous() self.W11 = (Wo_t @ Wo).expand(channels, 1, 4, 4).contiguous() def forward(self, input): if input.device != self.W00.device: self.W00 = self.W00.to(input.device) self.W01 = self.W01.to(input.device) self.W10 = self.W10.to(input.device) self.W11 = self.W11.to(input.device) a = F.pad(input, (2, 2, 2, 2), 'replicate') I00 = F.conv2d(a, self.W00, groups=self.channels) I01 = F.conv2d(a, self.W01, groups=self.channels) I10 = F.conv2d(a, self.W10, groups=self.channels) I11 = F.conv2d(a, self.W11, groups=self.channels) n, c, h, w = I11.shape J0 = torch.stack((I00, I01), dim=-1).view(n, c, h, 2 * w) J1 = torch.stack((I10, I11), dim=-1).view(n, c, h, 2 * w) out = torch.stack((J0, J1), dim=-2).view(n, c, 2 * h, 2 * w) out = F.pad(out, (-1, -1, -1, -1)) return out class BackwardCompatibleUpsampler(nn.Module): """ Upsampler with bicubic interpolation that works with Pytorch 1.0.1 """ def __init__(self, in_channels=64): super().__init__() self.conv1 = conv(in_channels, in_channels // 2, 3) self.up1 = PyrUpBicubic2d(in_channels) self.conv2 = conv(in_channels // 2, 1, 3) self.up2 = PyrUpBicubic2d(in_channels // 2) def forward(self, x, image_size): x = self.up1(x) x = F.relu(self.conv1(x)) x = self.up2(x) x = F.interpolate(x, image_size[-2:], mode='bilinear', align_corners=False) x = self.conv2(x) return x class SegNetwork(nn.Module): def __init__(self, in_channels=1, out_channels=32, ft_channels=None, use_bn=False): super().__init__() assert ft_channels is not None self.ft_channels = ft_channels self.TSE = nn.ModuleDict() self.RRB1 = nn.ModuleDict() self.CAB = nn.ModuleDict() self.RRB2 = nn.ModuleDict() ic = in_channels oc = out_channels for L, fc in self.ft_channels.items(): self.TSE[L] = TSE(fc, ic, oc) self.RRB1[L] = RRB(oc, use_bn=use_bn) self.CAB[L] = CAB(oc, L == 'layer5') self.RRB2[L] = RRB(oc, use_bn=use_bn) #if torch.__version__ == '1.0.1' self.project = BackwardCompatibleUpsampler(out_channels) #self.project = Upsampler(out_channels) def forward(self, scores, features, image_size): num_targets = scores.shape[0] num_fmaps = features[next(iter(self.ft_channels))].shape[0] if num_targets > num_fmaps: multi_targets = True else: multi_targets = False x = None for i, L in enumerate(self.ft_channels): ft = features[L] s = interpolate(scores, ft.shape[-2:]) # Resample scores to match features size if multi_targets: h, hpool = self.TSE[L](ft.repeat(num_targets, 1, 1, 1), s, x) else: h, hpool = self.TSE[L](ft, s, x) h = self.RRB1[L](h) h = self.CAB[L](hpool, h) x = self.RRB2[L](h) x = self.project(x, image_size) return x ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/head/utils.py ================================================ from collections import OrderedDict as odict import numpy as np import torch from torch import nn as nn from torch.nn import functional as F def text_bargraph(values): blocks = np.array(('u', ' ', '▁', '▂', '▃', '▄', '▅', '▆', '▇', '█', 'o')) nsteps = len(blocks) - 2 - 1 hstep = 1 / (2 * nsteps) values = np.array(values) nans = np.isnan(values) values[nans] = 0 # '░' indices = ((values + hstep) * nsteps + 1).astype(np.int) indices[values < 0] = 0 indices[values > 1] = len(blocks) - 1 graph = blocks[indices] graph[nans] = '░' graph = str.join('', graph) return graph class ModuleWrapper: """ A wrapper for hiding modules from PyTorch, so that the same module can be used in multiple places. and yet saved only once in a checkpoint, or not at all. """ # https://stackoverflow.com/questions/1466676/create-a-wrapper-class-to-call-a-pre-and-post-function-around-existing-functions def __init__(self, wrapped_module): self.__wrapped_module__ = wrapped_module def __getattr__(self, attr): orig_attr = self.__wrapped_module__.__getattribute__(attr) if callable(orig_attr): def hooked(*args, **kwargs): result = orig_attr(*args, **kwargs) # prevent wrapped_class from becoming unwrapped if result == self.__wrapped_module__: return self return result return hooked else: return orig_attr def __call__(self, *args, **kwargs): return self.__wrapped_module__(*args, **kwargs) def conv(ic, oc, ksize, bias=True, dilation=1, stride=1): return nn.Conv2d(ic, oc, ksize, padding=ksize // 2, bias=bias, dilation=dilation, stride=stride) def relu(negative_slope=0.0, inplace=False): return nn.LeakyReLU(negative_slope, inplace=inplace) def interpolate(t, sz): sz = sz.tolist() if torch.is_tensor(sz) else sz return F.interpolate(t, sz, mode='bilinear', align_corners=False) if t.shape[-2:] != sz else t def adaptive_cat(seq, dim=0, ref_tensor=0): sz = seq[ref_tensor].shape[-2:] t = torch.cat([interpolate(t, sz) for t in seq], dim=dim) return t def get_out_channels(layer): if hasattr(layer, 'out_channels'): oc = layer.out_channels elif hasattr(layer, '_modules'): oc = get_out_channels(layer._modules) else: ocs = [] for key in reversed(layer): ocs.append(get_out_channels(layer[key])) oc = 0 for elem in ocs: if elem: return elem return oc def is_finite(t): return (torch.isnan(t) + torch.isinf(t)) == 0 class AverageMeter: """Computes and stores the average and current value""" def __init__(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 self.seq_avg = [] def reset(self): self.__init__() def update(self, val, n=1): if not np.isnan(val): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count def update_multi(self, val): val = np.array(val) v = val[~np.isnan(val)] n = len(v) self.val = val self.sum += np.nansum(v) self.count += n self.avg = self.sum / self.count ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/layers/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/layers/activation.py ================================================ import math import torch import torch.nn as nn import torch.nn.functional as F def softmax_reg(x: torch.Tensor, dim, reg=None): """Softmax with optinal denominator regularization.""" if reg is None: return torch.softmax(x, dim=dim) dim %= x.dim() if isinstance(reg, (float, int)): reg = x.new_tensor([reg]) reg = reg.expand([1 if d==dim else x.shape[d] for d in range(x.dim())]) x = torch.cat((x, reg), dim=dim) return torch.softmax(x, dim=dim)[[slice(-1) if d==dim else slice(None) for d in range(x.dim())]] class MLU(nn.Module): r"""MLU activation """ def __init__(self, min_val, inplace=False): super().__init__() self.min_val = min_val self.inplace = inplace def forward(self, input): return F.elu(F.leaky_relu(input, 1/self.min_val, inplace=self.inplace), self.min_val, inplace=self.inplace) class LeakyReluPar(nn.Module): r"""LeakyRelu parametric activation """ def forward(self, x, a): return (1.0 - a)/2.0 * torch.abs(x) + (1.0 + a)/2.0 * x class LeakyReluParDeriv(nn.Module): r"""Derivative of the LeakyRelu parametric activation, wrt x. """ def forward(self, x, a): return (1.0 - a)/2.0 * torch.sign(x.detach()) + (1.0 + a)/2.0 class BentIdentPar(nn.Module): r"""BentIdent parametric activation """ def __init__(self, b=1.0): super().__init__() self.b = b def forward(self, x, a): return (1.0 - a)/2.0 * (torch.sqrt(x*x + 4.0*self.b*self.b) - 2.0*self.b) + (1.0 + a)/2.0 * x class BentIdentParDeriv(nn.Module): r"""BentIdent parametric activation deriv """ def __init__(self, b=1.0): super().__init__() self.b = b def forward(self, x, a): return (1.0 - a)/2.0 * (x / torch.sqrt(x*x + 4.0*self.b*self.b)) + (1.0 + a)/2.0 ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/layers/blocks.py ================================================ from torch import nn def conv_block(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1, bias=True, batch_norm=True, relu=True, padding_mode='zeros'): layers = [] assert padding_mode == 'zeros' or padding_mode == 'replicate' if padding_mode == 'replicate' and padding > 0: assert isinstance(padding, int) layers.append(nn.ReflectionPad2d(padding)) padding = 0 layers.append(nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias)) if batch_norm: layers.append(nn.BatchNorm2d(out_planes)) if relu: layers.append(nn.ReLU(inplace=True)) return nn.Sequential(*layers) class LinearBlock(nn.Module): def __init__(self, in_planes, out_planes, input_sz, bias=True, batch_norm=True, relu=True): super().__init__() self.linear = nn.Linear(in_planes*input_sz*input_sz, out_planes, bias=bias) self.bn = nn.BatchNorm2d(out_planes) if batch_norm else None self.relu = nn.ReLU(inplace=True) if relu else None def forward(self, x): x = self.linear(x.reshape(x.shape[0], -1)) if self.bn is not None: x = self.bn(x.reshape(x.shape[0], x.shape[1], 1, 1)) if self.relu is not None: x = self.relu(x) return x.reshape(x.shape[0], -1) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/layers/distance.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F class DistanceMap(nn.Module): """Generate a distance map from a origin center location. args: num_bins: Number of bins in the map. bin_displacement: Displacement of the bins. """ def __init__(self, num_bins, bin_displacement=1.0): super().__init__() self.num_bins = num_bins self.bin_displacement = bin_displacement def forward(self, center, output_sz): """Create the distance map. args: center: Torch tensor with (y,x) center position. Dims (batch, 2) output_sz: Size of output distance map. 2-dimensional tuple.""" center = center.view(-1,2) bin_centers = torch.arange(self.num_bins, dtype=torch.float32, device=center.device).view(1, -1, 1, 1) k0 = torch.arange(output_sz[0], dtype=torch.float32, device=center.device).view(1,1,-1,1) k1 = torch.arange(output_sz[1], dtype=torch.float32, device=center.device).view(1,1,1,-1) d0 = k0 - center[:,0].view(-1,1,1,1) d1 = k1 - center[:,1].view(-1,1,1,1) dist = torch.sqrt(d0*d0 + d1*d1) bin_diff = dist / self.bin_displacement - bin_centers bin_val = torch.cat((F.relu(1.0 - torch.abs(bin_diff[:,:-1,:,:]), inplace=True), (1.0 + bin_diff[:,-1:,:,:]).clamp(0, 1)), dim=1) return bin_val ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/layers/filter.py ================================================ import torch import torch.nn.functional as F def apply_filter(feat, filter, dilation_factors=None): """Applies the filter on the input features (feat). The number of groups is automatically calculated. args: feat: These are the input features. Must have dimensions (images_in_sequence, sequences, feat_dim, H, W) filter: The filter to apply. Must have dimensions (sequences, feat_dim, fH, fW) or (sequences, filters, feat_dim/groups, fH, fW) output: scores: Output of filtering. Dimensions (images_in_sequence, sequences, yH, yW) or (images_in_sequence, sequences, filters, yH, yW) """ multiple_filters = (filter.dim() == 5) padding = (filter.shape[-2] // 2, filter.shape[-1] // 2) num_images = feat.shape[0] num_sequences = feat.shape[1] if feat.dim() == 5 else 1 num_filters = filter.shape[1] if multiple_filters else 1 num_channels = feat.shape[-3] groups = num_channels // filter.shape[-3] assert num_filters % groups == 0 and num_channels % groups == 0 if multiple_filters: if dilation_factors is None: scores = F.conv2d(feat.reshape(num_images, -1, feat.shape[-2], feat.shape[-1]), filter.view(-1, *filter.shape[-3:]), padding=padding, groups=num_sequences*groups) return scores.view(num_images, num_sequences, -1, scores.shape[-2], scores.shape[-1]) else: scores_all = [] start_id = 0 for d_factor, num_filters_with_d in dilation_factors.items(): f_d = filter[:, start_id:start_id+num_filters_with_d, ...].contiguous() padding_d = [p+d_factor-1 for p in padding] scores_d = F.conv2d(feat.reshape(num_images, -1, feat.shape[-2], feat.shape[-1]), f_d.view(-1, *f_d.shape[-3:]), padding=padding_d, groups=num_sequences * groups, dilation=d_factor) scores_d = scores_d.view(num_images, num_sequences, -1, scores_d.shape[-2], scores_d.shape[-1]) scores_all.append(scores_d) start_id += num_filters_with_d scores = torch.cat(scores_all, dim=2) return scores scores = F.conv2d(feat.reshape(num_images, -1, feat.shape[-2], feat.shape[-1]), filter, padding=padding, groups=num_sequences) return scores.view(num_images, num_sequences, scores.shape[-2], scores.shape[-1]) def apply_feat_transpose(feat, input, filter_ksz, training=True, groups=1): """Applies the transposed operation off apply_filter w.r.t. filter itself. Can be used to compute the filter gradient. args: feat: These are the input features. Must have dimensions (images_in_sequence, sequences, feat_dim, H, W) input: Input activation (e.g. residuals). Must have dimensions (images_in_sequence, sequences, yH, yW) or (images_in_sequence, sequences, filters, yH, yW) training: Choose the faster implementation whether training or not. output: Output of transposed operation. Dimensions (sequences, feat_dim, fH, fW) """ if groups != 1: raise NotImplementedError('Not implemented other values of group.') if training or input.dim() == 5: return _apply_feat_transpose_v3(feat, input, filter_ksz) return _apply_feat_transpose_v2(feat, input, filter_ksz) def _apply_feat_transpose_v1(feat, input, filter_ksz): """This one is slow as hell!!!!""" num_images = feat.shape[0] num_sequences = feat.shape[1] if feat.dim() == 5 else 1 feat_sz = (feat.shape[-2], feat.shape[-1]) if isinstance(filter_ksz, int): filter_ksz = (filter_ksz, filter_ksz) # trans_pad = sz + padding - filter_ksz trans_pad = [sz + ksz//2 - ksz for sz, ksz in zip(feat_sz, filter_ksz)] filter_grad = F.conv_transpose2d(input.flip((2, 3)).view(1, -1, input.shape[-2], input.shape[-1]), feat.reshape(-1, feat.shape[-3], feat.shape[-2], feat.shape[-1]), padding=trans_pad, groups=num_images * num_sequences) return filter_grad.view(num_images, num_sequences, -1, filter_grad.shape[-2], filter_grad.shape[-1]).sum(dim=0) def _apply_feat_transpose_v2(feat, input, filter_ksz): """Fast forward and slow backward""" multiple_filters = (input.dim() == 5) num_images = feat.shape[0] num_sequences = feat.shape[1] if feat.dim() == 5 else 1 num_filters = input.shape[2] if multiple_filters else 1 if isinstance(filter_ksz, int): filter_ksz = (filter_ksz, filter_ksz) trans_pad = [(ksz-1)//2 for ksz in filter_ksz] if multiple_filters: filter_grad = F.conv2d(input.reshape(-1, num_filters, input.shape[-2], input.shape[-1]).permute(1,0,2,3), feat.reshape(-1, 1, feat.shape[-2], feat.shape[-1]), padding=trans_pad, groups=num_images * num_sequences) if num_images == 1: return filter_grad.view(num_filters, num_sequences, -1, filter_grad.shape[-2], filter_grad.shape[-1]).flip((3,4)).permute(1,0,2,3,4) return filter_grad.view(num_filters, num_images, num_sequences, -1, filter_grad.shape[-2], filter_grad.shape[-1]).sum(dim=1).flip((3,4)).permute(1,0,2,3,4) filter_grad = F.conv2d(input.reshape(1, -1, input.shape[-2], input.shape[-1]), feat.reshape(-1, 1, feat.shape[-2], feat.shape[-1]), padding=trans_pad, groups=num_images * num_sequences) return filter_grad.view(num_images, num_sequences, -1, filter_grad.shape[-2], filter_grad.shape[-1]).sum(dim=0).flip((2,3)) def _apply_feat_transpose_v3(feat, input, filter_ksz): """Slow forward fast backward""" multiple_filters = (input.dim() == 5) num_images = feat.shape[0] num_sequences = feat.shape[1] if feat.dim() == 5 else 1 num_filters = input.shape[2] if multiple_filters else 1 if isinstance(filter_ksz, int): filter_ksz = (filter_ksz, filter_ksz) trans_pad = [ksz//2 for ksz in filter_ksz] filter_grad = F.conv2d(feat.reshape(-1, feat.shape[-3], feat.shape[-2], feat.shape[-1]).permute(1,0,2,3), input.reshape(-1, 1, input.shape[-2], input.shape[-1]), padding=trans_pad, groups=num_images * num_sequences) if multiple_filters: if num_images == 1: return filter_grad.view(-1, num_sequences, num_filters, filter_grad.shape[-2], filter_grad.shape[-1]).permute(1,2,0,3,4) return filter_grad.view(-1, num_images, num_sequences, num_filters, filter_grad.shape[-2], filter_grad.shape[-1]).sum(dim=1).permute(1,2,0,3,4) if num_images == 1: return filter_grad.permute(1,0,2,3) return filter_grad.view(-1, num_images, num_sequences, filter_grad.shape[-2], filter_grad.shape[-1]).sum(dim=1).permute(1,0,2,3) def _apply_feat_transpose_v4(feat, input, filter_ksz): """Slow forward fast backward""" num_images = feat.shape[0] num_sequences = feat.shape[1] if feat.dim() == 5 else 1 if isinstance(filter_ksz, int): filter_ksz = (filter_ksz, filter_ksz) trans_pad = [ksz//2 for ksz in filter_ksz] filter_grad = F.conv2d(feat.permute(2,1,0,3,4).reshape(feat.shape[-3], -1, feat.shape[-2], feat.shape[-1]), input.permute(1,0,2,3), padding=trans_pad, groups=num_sequences) return filter_grad.permute(1,0,2,3) def filter_gradient(feat, filter, label=None, training=True): """Computes gradient of the filter when applied on the input features and ground truth label. args: feat: These are the input features. Must have dimensions (images_in_sequence, sequences, feat_dim, H, W) filter: The filter to apply. Must have dimensions (sequences, feat_dim, fH, fW) label: Ground truth label in the L2 loss. Dimensions (images_in_sequence, sequences, yH, yW) output: filter_gradient: Dimensions same as input filter (sequences, feat_dim, fH, fW) """ residuals = apply_filter(feat, filter) if label is not None: residuals = residuals - label filter_ksz = (filter.shape[-2], filter.shape[-1]) return apply_feat_transpose(feat, residuals, filter_ksz, training=training) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/layers/normalization.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F class InstanceL2Norm(nn.Module): """Instance L2 normalization. """ def __init__(self, size_average=True, eps=1e-5, scale=1.0): super().__init__() self.size_average = size_average self.eps = eps self.scale = scale def forward(self, input): if self.size_average: return input * (self.scale * ((input.shape[1] * input.shape[2] * input.shape[3]) / ( torch.sum((input * input).view(input.shape[0], 1, 1, -1), dim=3, keepdim=True) + self.eps)).sqrt()) else: return input * (self.scale / (torch.sum((input * input).view(input.shape[0], 1, 1, -1), dim=3, keepdim=True) + self.eps).sqrt()) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/layers/transform.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from collections import OrderedDict def interpolate(x, sz): """Interpolate 4D tensor x to size sz.""" sz = sz.tolist() if torch.is_tensor(sz) else sz return F.interpolate(x, sz, mode='bilinear', align_corners=False) if x.shape[-2:] != sz else x class InterpCat(nn.Module): """Interpolate and concatenate features of different resolutions.""" def forward(self, input): if isinstance(input, (dict, OrderedDict)): input = list(input.values()) output_shape = None for x in input: if output_shape is None or output_shape[0] > x.shape[-2]: output_shape = x.shape[-2:] return torch.cat([interpolate(x, output_shape) for x in input], dim=-3) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/loss/__init__.py ================================================ from .target_classification import LBHinge ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/loss/kl_regression.py ================================================ import math import torch import torch.nn as nn from torch.nn import functional as F class KLRegression(nn.Module): """KL-divergence loss for probabilistic regression. It is computed using Monte Carlo (MC) samples from an arbitrary distribution.""" def __init__(self, eps=0.0): super().__init__() self.eps = eps def forward(self, scores, sample_density, gt_density, mc_dim=-1): """Args: scores: predicted score values sample_density: probability density of the sample distribution gt_density: probability density of the ground truth distribution mc_dim: dimension of the MC samples""" exp_val = scores - torch.log(sample_density + self.eps) L = torch.logsumexp(exp_val, dim=mc_dim) - math.log(scores.shape[mc_dim]) - \ torch.mean(scores * (gt_density / (sample_density + self.eps)), dim=mc_dim) return L.mean() class MLRegression(nn.Module): """Maximum likelihood loss for probabilistic regression. It is computed using Monte Carlo (MC) samples from an arbitrary distribution.""" def __init__(self, eps=0.0): super().__init__() self.eps = eps def forward(self, scores, sample_density, gt_density=None, mc_dim=-1): """Args: scores: predicted score values. First sample must be ground-truth sample_density: probability density of the sample distribution gt_density: not used mc_dim: dimension of the MC samples. Only mc_dim=1 supported""" assert mc_dim == 1 assert (sample_density[:,0,...] == -1).all() exp_val = scores[:, 1:, ...] - torch.log(sample_density[:, 1:, ...] + self.eps) L = torch.logsumexp(exp_val, dim=mc_dim) - math.log(scores.shape[mc_dim] - 1) - scores[:, 0, ...] loss = L.mean() return loss class KLRegressionGrid(nn.Module): """KL-divergence loss for probabilistic regression. It is computed using the grid integration strategy.""" def forward(self, scores, gt_density, grid_dim=-1, grid_scale=1.0): """Args: scores: predicted score values gt_density: probability density of the ground truth distribution grid_dim: dimension(s) of the grid grid_scale: area of one grid cell""" score_corr = grid_scale * torch.sum(scores * gt_density, dim=grid_dim) L = torch.logsumexp(scores, dim=grid_dim) + math.log(grid_scale) - score_corr return L.mean() ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/loss/target_classification.py ================================================ import torch.nn as nn import torch from torch.nn import functional as F class LBHinge(nn.Module): """Loss that uses a 'hinge' on the lower bound. This means that for samples with a label value smaller than the threshold, the loss is zero if the prediction is also smaller than that threshold. args: error_matric: What base loss to use (MSE by default). threshold: Threshold to use for the hinge. clip: Clip the loss if it is above this value. """ def __init__(self, error_metric=nn.MSELoss(), threshold=None, clip=None): super().__init__() self.error_metric = error_metric self.threshold = threshold if threshold is not None else -100 self.clip = clip def forward(self, prediction, label, target_bb=None): negative_mask = (label < self.threshold).float() positive_mask = (1.0 - negative_mask) prediction = negative_mask * F.relu(prediction) + positive_mask * prediction loss = self.error_metric(prediction, positive_mask * label) if self.clip is not None: loss = torch.min(loss, torch.tensor([self.clip], device=loss.device)) return loss ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/meta/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/meta/steepestdescent.py ================================================ import math import torch import torch.nn as nn from pytracking import TensorList from ltr.models.layers import activation class GNSteepestDescent(nn.Module): """General module for steepest descent based meta learning.""" def __init__(self, residual_module, num_iter=1, compute_losses=False, detach_length=float('Inf'), parameter_batch_dim=0, residual_batch_dim=0, steplength_reg=0.0, filter_dilation_factors=None): super().__init__() self.residual_module = residual_module self.num_iter = num_iter self.compute_losses = compute_losses self.detach_length = detach_length self.steplength_reg = steplength_reg self._parameter_batch_dim = parameter_batch_dim self._residual_batch_dim = residual_batch_dim self.filter_dilation_factors = filter_dilation_factors def _sqr_norm(self, x: TensorList, batch_dim=0): sum_keep_batch_dim = lambda e: e.sum(dim=[d for d in range(e.dim()) if d != batch_dim]) return sum((x * x).apply(sum_keep_batch_dim)) def _compute_loss(self, res): return sum((res * res).sum()) / sum(res.numel()) def forward(self, meta_parameter: TensorList, num_iter=None, *args, **kwargs): # Make sure grad is enabled torch_grad_enabled = torch.is_grad_enabled() torch.set_grad_enabled(True) num_iter = self.num_iter if num_iter is None else num_iter meta_parameter_iterates = [meta_parameter] losses = [] for i in range(num_iter): if i > 0 and i % self.detach_length == 0: meta_parameter = meta_parameter.detach() meta_parameter.requires_grad_(True) # Compute residual vector r = self.residual_module(meta_parameter, filter_dilation_factors=self.filter_dilation_factors, **kwargs) if self.compute_losses: losses.append(self._compute_loss(r)) # Compute gradient of loss u = r.clone() g = TensorList(torch.autograd.grad(r, meta_parameter, u, create_graph=True)) # Multiply gradient with Jacobian h = TensorList(torch.autograd.grad(g, u, g, create_graph=True)) # Compute squared norms ip_gg = self._sqr_norm(g, batch_dim=self._parameter_batch_dim) ip_hh = self._sqr_norm(h, batch_dim=self._residual_batch_dim) # Compute step length alpha = ip_gg / (ip_hh + self.steplength_reg * ip_gg).clamp(1e-8) # Compute optimization step step = g.apply(lambda e: alpha.reshape([-1 if d==self._parameter_batch_dim else 1 for d in range(e.dim())]) * e) # Add step to parameter meta_parameter = meta_parameter - step meta_parameter_iterates.append(meta_parameter) if self.compute_losses: losses.append(self._compute_loss(self.residual_module(meta_parameter, filter_dilation_factors=self.filter_dilation_factors, **kwargs))) # Reset the grad enabled flag torch.set_grad_enabled(torch_grad_enabled) if not torch_grad_enabled: meta_parameter.detach_() for w in meta_parameter_iterates: w.detach_() for l in losses: l.detach_() return meta_parameter, meta_parameter_iterates, losses ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/neck/CorrNL.py ================================================ import torch.nn as nn import torch from ltr.external.PreciseRoIPooling.pytorch.prroi_pool import PrRoIPool2D from torch.nn import functional as F from ltr.models.neck.neck_utils import * class CorrNL(nn.Module): """Network module for IoU prediction. Refer to the ATOM paper for an illustration of the architecture. It uses two backbone feature layers as input. args: input_dim: Feature dimensionality of the two input backbone layers. pred_input_dim: Dimensionality input the the prediction network. pred_inter_dim: Intermediate dimensionality in the prediction network.""" def __init__(self, pool_size=8, use_NL=True): super().__init__() self.prroi_pool = PrRoIPool2D(pool_size, pool_size, 1/16) num_corr_channel = pool_size*pool_size self.channel_attention = SEModule(num_corr_channel,reduction=4) self.spatial_attention = NONLocalBlock2D(in_channels=num_corr_channel) self.use_NL = use_NL def forward(self, feat1, feat2, bb1): """Runs the ATOM IoUNet during training operation. This forward pass is mainly used for training. Call the individual functions during tracking instead. args: feat1: Features from the reference frames (4 or 5 dims). feat2: Features from the test frames (4 or 5 dims). bb1: Target boxes (x,y,w,h) in image coords in the reference samples. Dims (images, sequences, 4). proposals2: Proposal boxes for which the IoU will be predicted (images, sequences, num_proposals, 4).""" assert bb1.dim() == 3 # num_images, num_sequences = bb1.size()[:2] # 1, 64 # Extract first train sample if len(feat1)==1: feat1 = feat1[0] # size为(64,C,H,W) feat2 = feat2[0] # size为(64,C,H,W) bb1 = bb1[0,...] # (64,4) else: raise ValueError("Only support single-layer feature map") '''get PrRoIPool feature ''' # Add batch_index to rois batch_size = bb1.shape[0] batch_index = torch.arange(batch_size, dtype=torch.float32).view(-1, 1).to(bb1.device) # (64,1) # input bb is in format xywh, convert it to x0y0x1y1 format bb1 = bb1.clone() bb1[:, 2:4] = bb1[:, 0:2] + bb1[:, 2:4] roi1 = torch.cat((batch_index, bb1), dim=1) #(64,1),(64,4) ---> (64,5) feat_roi1 = self.prroi_pool(feat1, roi1) # (64,C,H,W) feat_corr,_ = self.corr_fun(feat_roi1, feat2) # print('相关后的特征维度是:',feat_corr.size())#(batch,StxSt,Sr,Sr) '''channel attention: Squeeze and Excitation''' feat_ca = self.channel_attention(feat_corr) # 计算通道注意力特征 '''spatial attention: Non-local 2D''' feat_sa = self.spatial_attention(feat_ca) return feat_sa def get_ref_kernel(self, feat1, bb1): assert bb1.dim() == 3 # num_images, num_sequences = bb1.size()[:2] # 1, 64 # Extract first train sample if len(feat1) == 1: feat1 = feat1[0] # size为(64,C,H,W) bb1 = bb1[0, ...] # (64,4) else: raise ValueError("Only support single-layer feature map") '''get PrRoIPool feature ''' # Add batch_index to rois batch_size = bb1.shape[0] batch_index = torch.arange(batch_size, dtype=torch.float32).view(-1, 1).to(bb1.device) # (64,1) # input bb is in format xywh, convert it to x0y0x1y1 format bb1 = bb1.clone() bb1[:, 2:4] = bb1[:, 0:2] + bb1[:, 2:4] roi1 = torch.cat((batch_index, bb1), dim=1) # (64,1),(64,4) ---> (64,5) '''注意: feat1 and roi1 must be cuda tensor''' self.ref_kernel = self.prroi_pool(feat1.float(), roi1) # (64,C,H,W) # self.ref_kernel.half() def fuse_feat(self, feat2): '''fuse features from reference and test branch''' if len(feat2) == 1: feat2 = feat2[0] '''Step1: pixel-wise correlation''' feat_corr,_ = self.corr_fun(self.ref_kernel, feat2) # print('相关后的特征维度是:',feat_corr.size())#(batch,StxSt,Sr,Sr) (batch,64,16,16) '''Step2: channel attention: Squeeze and Excitation''' feat_ca = self.channel_attention(feat_corr) # 计算通道注意力特征 if not self.use_NL: # print('not use non-local') return feat_ca else: '''Step3: spatial attention: Non-local 2D''' feat_sa = self.spatial_attention(feat_ca) return feat_sa def corr_fun(self, Kernel_tmp, Feature, KERs=None): size = Kernel_tmp.size() CORR = [] Kernel = [] for i in range(len(Feature)): ker = Kernel_tmp[i:i + 1] fea = Feature[i:i + 1] ker = ker.view(size[1], size[2] * size[3]).transpose(0, 1) ker = ker.unsqueeze(2).unsqueeze(3) if not (type(KERs) == type(None)): ker = torch.cat([ker, KERs[i]], 0) co = F.conv2d(fea, ker.contiguous()) CORR.append(co) ker = ker.unsqueeze(0) Kernel.append(ker) corr = torch.cat(CORR, 0) Kernel = torch.cat(Kernel, 0) return corr, Kernel ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/neck/neck_utils.py ================================================ import torch.nn as nn import torch from torch.nn import functional as F def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1): return nn.Sequential( nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=True), nn.BatchNorm2d(out_planes), nn.ReLU(inplace=True)) '''Channel attention module''' class SEModule(nn.Module): def __init__(self, channels, reduction=4): super(SEModule, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1, padding=0) self.relu = nn.ReLU(inplace=True) self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1, padding=0) self.sigmoid = nn.Sigmoid() def forward(self, x): module_input = x x = self.avg_pool(x) x = self.fc1(x) x = self.relu(x) x = self.fc2(x) x = self.sigmoid(x) return module_input * x '''Non-local module''' class _NonLocalBlockND(nn.Module): def __init__(self, in_channels, inter_channels=None, dimension=3, sub_sample=True, bn_layer=True): """ :param in_channels: :param inter_channels: :param dimension: :param sub_sample: :param bn_layer: """ super(_NonLocalBlockND, self).__init__() assert dimension in [1, 2, 3] self.dimension = dimension self.sub_sample = sub_sample self.in_channels = in_channels self.inter_channels = inter_channels if self.inter_channels is None: self.inter_channels = in_channels // 2 if self.inter_channels == 0: self.inter_channels = 1 if dimension == 3: conv_nd = nn.Conv3d max_pool_layer = nn.MaxPool3d(kernel_size=(1, 2, 2)) bn = nn.BatchNorm3d elif dimension == 2: conv_nd = nn.Conv2d max_pool_layer = nn.MaxPool2d(kernel_size=(2, 2)) bn = nn.BatchNorm2d else: conv_nd = nn.Conv1d max_pool_layer = nn.MaxPool1d(kernel_size=(2)) bn = nn.BatchNorm1d self.g = conv_nd(in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1, padding=0) if bn_layer: self.W = nn.Sequential( conv_nd(in_channels=self.inter_channels, out_channels=self.in_channels, kernel_size=1, stride=1, padding=0), bn(self.in_channels) ) nn.init.constant_(self.W[1].weight, 0) nn.init.constant_(self.W[1].bias, 0) else: self.W = conv_nd(in_channels=self.inter_channels, out_channels=self.in_channels, kernel_size=1, stride=1, padding=0) nn.init.constant_(self.W.weight, 0) nn.init.constant_(self.W.bias, 0) self.theta = conv_nd(in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1, padding=0) self.phi = conv_nd(in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1, padding=0) if sub_sample: self.g = nn.Sequential(self.g, max_pool_layer) self.phi = nn.Sequential(self.phi, max_pool_layer) def forward(self, x, return_nl_map=False): """ :param x: (b, c, t, h, w) :param return_nl_map: if True return z, nl_map, else only return z. :return: """ batch_size = x.size(0) g_x = self.g(x).view(batch_size, self.inter_channels, -1) g_x = g_x.permute(0, 2, 1) theta_x = self.theta(x).view(batch_size, self.inter_channels, -1) theta_x = theta_x.permute(0, 2, 1) phi_x = self.phi(x).view(batch_size, self.inter_channels, -1) f = torch.matmul(theta_x, phi_x) f_div_C = F.softmax(f, -1) y = torch.matmul(f_div_C, g_x) y = y.permute(0, 2, 1).contiguous() y = y.view(batch_size, self.inter_channels, *x.size()[2:]) W_y = self.W(y) z = W_y + x if return_nl_map: return z, f_div_C return z class NONLocalBlock2D(_NonLocalBlockND): def __init__(self, in_channels, inter_channels=None, sub_sample=True, bn_layer=True): super(NONLocalBlock2D, self).__init__(in_channels, inter_channels=inter_channels, dimension=2, sub_sample=sub_sample, bn_layer=bn_layer,) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/target_classifier/__init__.py ================================================ from .linear_filter import LinearFilter ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/target_classifier/features.py ================================================ import torch from torch import nn import torch.nn.functional as F from torchvision.models.resnet import BasicBlock, Bottleneck from ltr.models.layers.normalization import InstanceL2Norm from ltr.models.layers.transform import InterpCat def residual_basic_block(feature_dim=256, num_blocks=1, l2norm=True, final_conv=False, norm_scale=1.0, out_dim=None, interp_cat=False, final_relu=False, init_pool=False): """Construct a network block based on the BasicBlock used in ResNet 18 and 34.""" if out_dim is None: out_dim = feature_dim feat_layers = [] if interp_cat: feat_layers.append(InterpCat()) if init_pool: feat_layers.append(nn.MaxPool2d(kernel_size=3, stride=2, padding=1)) for i in range(num_blocks): odim = feature_dim if i < num_blocks - 1 + int(final_conv) else out_dim feat_layers.append(BasicBlock(feature_dim, odim)) if final_conv: feat_layers.append(nn.Conv2d(feature_dim, out_dim, kernel_size=3, padding=1, bias=False)) if final_relu: feat_layers.append(nn.ReLU(inplace=True)) if l2norm: feat_layers.append(InstanceL2Norm(scale=norm_scale)) return nn.Sequential(*feat_layers) def residual_basic_block_pool(feature_dim=256, num_blocks=1, l2norm=True, final_conv=False, norm_scale=1.0, out_dim=None, pool=True): """Construct a network block based on the BasicBlock used in ResNet.""" if out_dim is None: out_dim = feature_dim feat_layers = [] for i in range(num_blocks): odim = feature_dim if i < num_blocks - 1 + int(final_conv) else out_dim feat_layers.append(BasicBlock(feature_dim, odim)) if final_conv: feat_layers.append(nn.Conv2d(feature_dim, out_dim, kernel_size=3, padding=1, bias=False)) if pool: feat_layers.append(nn.MaxPool2d(kernel_size=3, stride=2, padding=1)) if l2norm: feat_layers.append(InstanceL2Norm(scale=norm_scale)) return nn.Sequential(*feat_layers) def residual_bottleneck(feature_dim=256, num_blocks=1, l2norm=True, final_conv=False, norm_scale=1.0, out_dim=None, interp_cat=False, final_relu=False, final_pool=False): """Construct a network block based on the Bottleneck block used in ResNet.""" if out_dim is None: out_dim = feature_dim feat_layers = [] if interp_cat: feat_layers.append(InterpCat()) for i in range(num_blocks): planes = feature_dim if i < num_blocks - 1 + int(final_conv) else out_dim // 4 feat_layers.append(Bottleneck(4*feature_dim, planes)) if final_conv: feat_layers.append(nn.Conv2d(4*feature_dim, out_dim, kernel_size=3, padding=1, bias=False)) if final_relu: feat_layers.append(nn.ReLU(inplace=True)) if final_pool: feat_layers.append(nn.MaxPool2d(kernel_size=3, stride=2, padding=1)) if l2norm: feat_layers.append(InstanceL2Norm(scale=norm_scale)) return nn.Sequential(*feat_layers) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/target_classifier/initializer.py ================================================ import torch.nn as nn import torch import torch.nn.functional as F from ltr.external.PreciseRoIPooling.pytorch.prroi_pool import PrRoIPool2D from ltr.models.layers.blocks import conv_block import math class FilterPool(nn.Module): """Pool the target region in a feature map. args: filter_size: Size of the filter. feature_stride: Input feature stride. pool_square: Do a square pooling instead of pooling the exact target region.""" def __init__(self, filter_size=1, feature_stride=16, pool_square=False): super().__init__() self.prroi_pool = PrRoIPool2D(filter_size, filter_size, 1/feature_stride) self.pool_square = pool_square def forward(self, feat, bb): """Pool the regions in bb. args: feat: Input feature maps. Dims (num_samples, feat_dim, H, W). bb: Target bounding boxes (x, y, w, h) in the image coords. Dims (num_samples, 4). returns: pooled_feat: Pooled features. Dims (num_samples, feat_dim, wH, wW).""" # Add batch_index to rois bb = bb.reshape(-1,4) num_images_total = bb.shape[0] batch_index = torch.arange(num_images_total, dtype=torch.float32).reshape(-1, 1).to(bb.device) # input bb is in format xywh, convert it to x0y0x1y1 format pool_bb = bb.clone() if self.pool_square: bb_sz = pool_bb[:, 2:4].prod(dim=1, keepdim=True).sqrt() pool_bb[:, :2] += pool_bb[:, 2:]/2 - bb_sz/2 pool_bb[:, 2:] = bb_sz pool_bb[:, 2:4] = pool_bb[:, 0:2] + pool_bb[:, 2:4] roi1 = torch.cat((batch_index, pool_bb), dim=1) return self.prroi_pool(feat, roi1) class FilterInitializer(nn.Module): """Initializes a target classification filter by applying a number of conv layers before and after pooling the target region. args: filter_size: Size of the filter. feature_dim: Input feature dimentionality. feature_stride: Input feature stride. pool_square: Do a square pooling instead of pooling the exact target region. filter_norm: Normalize the output filter with its size in the end. num_filter_pre_convs: Conv layers before pooling. num_filter_post_convs: Conv layers after pooling.""" def __init__(self, filter_size=1, feature_dim=256, feature_stride=16, pool_square=False, filter_norm=True, num_filter_pre_convs=1, num_filter_post_convs=0): super().__init__() self.filter_pool = FilterPool(filter_size=filter_size, feature_stride=feature_stride, pool_square=pool_square) self.filter_norm = filter_norm # Make pre conv pre_conv_layers = [] for i in range(num_filter_pre_convs): pre_conv_layers.append(conv_block(feature_dim, feature_dim, kernel_size=3, padding=1)) self.filter_pre_layers = nn.Sequential(*pre_conv_layers) if pre_conv_layers else None # Make post conv post_conv_layers = [] for i in range(num_filter_post_convs): post_conv_layers.append(conv_block(feature_dim, feature_dim, kernel_size=1, padding=0)) post_conv_layers.append(nn.Conv2d(feature_dim, feature_dim, kernel_size=1, padding=0)) self.filter_post_layers = nn.Sequential(*post_conv_layers) # Init weights for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def forward(self, feat, bb): """Runs the initializer module. Note that [] denotes an optional dimension. args: feat: Input feature maps. Dims (images_in_sequence, [sequences], feat_dim, H, W). bb: Target bounding boxes (x, y, w, h) in the image coords. Dims (images_in_sequence, [sequences], 4). returns: weights: The output weights. Dims (sequences, feat_dim, wH, wW).""" num_images = bb.shape[0] if bb.dim() == 3 else 1 if self.filter_pre_layers is not None: feat = self.filter_pre_layers(feat.reshape(-1, feat.shape[-3], feat.shape[-2], feat.shape[-1])) feat_post = self.filter_pool(feat, bb) weights = self.filter_post_layers(feat_post) if num_images > 1: weights = torch.mean(weights.reshape(num_images, -1, weights.shape[-3], weights.shape[-2], weights.shape[-1]), dim=0) if self.filter_norm: weights = weights / (weights.shape[1] * weights.shape[2] * weights.shape[3]) return weights class FilterInitializerLinear(nn.Module): """Initializes a target classification filter by applying a linear conv layer and then pooling the target region. args: filter_size: Size of the filter. feature_dim: Input feature dimentionality. feature_stride: Input feature stride. pool_square: Do a square pooling instead of pooling the exact target region. filter_norm: Normalize the output filter with its size in the end. conv_ksz: Kernel size of the conv layer before pooling.""" def __init__(self, filter_size=1, feature_dim=256, feature_stride=16, pool_square=False, filter_norm=True, conv_ksz=3, init_weights='default'): super().__init__() self.filter_conv = nn.Conv2d(feature_dim, feature_dim, kernel_size=conv_ksz, padding=conv_ksz // 2) self.filter_pool = FilterPool(filter_size=filter_size, feature_stride=feature_stride, pool_square=pool_square) self.filter_norm = filter_norm # Init weights for m in self.modules(): if isinstance(m, nn.Conv2d): if init_weights == 'default': n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif init_weights == 'zero': m.weight.data.zero_() if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def forward(self, feat, bb): """Runs the initializer module. Note that [] denotes an optional dimension. args: feat: Input feature maps. Dims (images_in_sequence, [sequences], feat_dim, H, W). bb: Target bounding boxes (x, y, w, h) in the image coords. Dims (images_in_sequence, [sequences], 4). returns: weights: The output weights. Dims (sequences, feat_dim, wH, wW).""" num_images = feat.shape[0] feat = self.filter_conv(feat.reshape(-1, feat.shape[-3], feat.shape[-2], feat.shape[-1])) weights = self.filter_pool(feat, bb) # If multiple input images, compute the initial filter as the average filter. if num_images > 1: weights = torch.mean(weights.reshape(num_images, -1, weights.shape[-3], weights.shape[-2], weights.shape[-1]), dim=0) if self.filter_norm: weights = weights / (weights.shape[1] * weights.shape[2] * weights.shape[3]) return weights class FilterInitializerZero(nn.Module): """Initializes a target classification filter with zeros. args: filter_size: Size of the filter. feature_dim: Input feature dimentionality.""" def __init__(self, filter_size=1, feature_dim=256): super().__init__() self.filter_size = (feature_dim, filter_size, filter_size) def forward(self, feat, bb): """Runs the initializer module. Note that [] denotes an optional dimension. args: feat: Input feature maps. Dims (images_in_sequence, [sequences], feat_dim, H, W). bb: Target bounding boxes (x, y, w, h) in the image coords. Dims (images_in_sequence, [sequences], 4). returns: weights: The output weights. Dims (sequences, feat_dim, wH, wW).""" num_sequences = feat.shape[1] if feat.dim() == 5 else 1 return feat.new_zeros(num_sequences, self.filter_size[0], self.filter_size[1], self.filter_size[2]) class FilterInitializerSiamese(nn.Module): """Initializes a target classification filter by only pooling the target region (similar to Siamese trackers). args: filter_size: Size of the filter. feature_stride: Input feature stride. pool_square: Do a square pooling instead of pooling the exact target region. filter_norm: Normalize the output filter with its size in the end.""" def __init__(self, filter_size=1, feature_stride=16, pool_square=False, filter_norm=True): super().__init__() self.filter_pool = FilterPool(filter_size=filter_size, feature_stride=feature_stride, pool_square=pool_square) self.filter_norm = filter_norm # Init weights for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def forward(self, feat, bb): """Runs the initializer module. Note that [] denotes an optional dimension. args: feat: Input feature maps. Dims (images_in_sequence, [sequences], feat_dim, H, W). bb: Target bounding boxes (x, y, w, h) in the image coords. Dims (images_in_sequence, [sequences], 4). returns: weights: The output weights. Dims (sequences, feat_dim, wH, wW).""" num_images = feat.shape[0] feat = feat.reshape(-1, feat.shape[-3], feat.shape[-2], feat.shape[-1]) weights = self.filter_pool(feat, bb) if num_images > 1: weights = torch.mean(weights.reshape(num_images, -1, weights.shape[-3], weights.shape[-2], weights.shape[-1]), dim=0) if self.filter_norm: weights = weights / (weights.shape[1] * weights.shape[2] * weights.shape[3]) return weights ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/target_classifier/linear_filter.py ================================================ import torch.nn as nn import ltr.models.layers.filter as filter_layer import math class LinearFilter(nn.Module): """Target classification filter module. args: filter_size: Size of filter (int). filter_initialize: Filter initializer module. filter_optimizer: Filter optimizer module. feature_extractor: Feature extractor module applied to the input backbone features.""" def __init__(self, filter_size, filter_initializer, filter_optimizer=None, feature_extractor=None): super().__init__() self.filter_size = filter_size # Modules self.filter_initializer = filter_initializer self.filter_optimizer = filter_optimizer self.feature_extractor = feature_extractor # Init weights for m in self.feature_extractor.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def forward(self, train_feat, test_feat, train_bb, *args, **kwargs): """Learns a target classification filter based on the train samples and return the resulting classification scores on the test samples. The forward function is ONLY used for training. Call the individual functions during tracking. args: train_feat: Backbone features for the train samples (4 or 5 dims). test_feat: Backbone features for the test samples (4 or 5 dims). trian_bb: Target boxes (x,y,w,h) for the train samples in image coordinates. Dims (images, sequences, 4). *args, **kwargs: These are passed to the optimizer module. returns: test_scores: Classification scores on the test samples.""" assert train_bb.dim() == 3 num_sequences = train_bb.shape[1] if train_feat.dim() == 5: train_feat = train_feat.reshape(-1, *train_feat.shape[-3:]) if test_feat.dim() == 5: test_feat = test_feat.reshape(-1, *test_feat.shape[-3:]) # Extract features train_feat = self.extract_classification_feat(train_feat, num_sequences) test_feat = self.extract_classification_feat(test_feat, num_sequences) # Train filter filter, filter_iter, losses = self.get_filter(train_feat, train_bb, *args, **kwargs) # Classify samples using all return filters test_scores = [self.classify(f, test_feat) for f in filter_iter] return test_scores def extract_classification_feat(self, feat, num_sequences=None): """Extract classification features based on the input backbone features.""" if self.feature_extractor is None: return feat if num_sequences is None: return self.feature_extractor(feat) output = self.feature_extractor(feat) return output.reshape(-1, num_sequences, *output.shape[-3:]) def classify(self, weights, feat): """Run classifier (filter) on the features (feat).""" scores = filter_layer.apply_filter(feat, weights) return scores def get_filter(self, feat, bb, *args, **kwargs): """Outputs the learned filter based on the input features (feat) and target boxes (bb) by running the filter initializer and optimizer. Note that [] denotes an optional dimension. args: feat: Input feature maps. Dims (images_in_sequence, [sequences], feat_dim, H, W). bb: Target bounding boxes (x, y, w, h) in the image coords. Dims (images_in_sequence, [sequences], 4). *args, **kwargs: These are passed to the optimizer module. returns: weights: The final oprimized weights. Dims (sequences, feat_dim, wH, wW). weight_iterates: The weights computed in each iteration (including initial input and final output). losses: Train losses.""" weights = self.filter_initializer(feat, bb) if self.filter_optimizer is not None: weights, weights_iter, losses = self.filter_optimizer(weights, feat=feat, bb=bb, *args, **kwargs) else: weights_iter = [weights] losses = None return weights, weights_iter, losses def train_classifier(self, backbone_feat, bb): num_sequences = bb.shape[1] if backbone_feat.dim() == 5: backbone_feat = backbone_feat.reshape(-1, *backbone_feat.shape[-3:]) # Extract features train_feat = self.extract_classification_feat(backbone_feat, num_sequences) # Get filters from each iteration final_filter, _, train_losses = self.get_filter(train_feat, bb) return final_filter, train_losses def track_frame(self, filter_weights, backbone_feat): if backbone_feat.dim() == 5: num_sequences = backbone_feat.shape[1] backbone_feat = backbone_feat.reshape(-1, *backbone_feat.shape[-3:]) else: num_sequences = None test_feat = self.extract_classification_feat(backbone_feat, num_sequences) scores = filter_layer.apply_filter(test_feat, filter_weights) return scores ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/target_classifier/optimizer.py ================================================ import torch.nn as nn import torch import torch.nn.functional as F import ltr.models.layers.filter as filter_layer import ltr.models.layers.activation as activation from ltr.models.layers.distance import DistanceMap import math class DiMPSteepestDescentGN(nn.Module): """Optimizer module for DiMP. It unrolls the steepest descent with Gauss-Newton iterations to optimize the target filter. Moreover it learns parameters in the loss itself, as described in the DiMP paper. args: num_iter: Number of default optimization iterations. feat_stride: The stride of the input feature. init_step_length: Initial scaling of the step length (which is then learned). init_filter_reg: Initial filter regularization weight (which is then learned). init_gauss_sigma: The standard deviation to use for the initialization of the label function. num_dist_bins: Number of distance bins used for learning the loss label, mask and weight. bin_displacement: The displacement of the bins (level of discritization). mask_init_factor: Parameter controlling the initialization of the target mask. score_act: Type of score activation (target mask computation) to use. The default 'relu' is what is described in the paper. act_param: Parameter for the score_act. min_filter_reg: Enforce a minimum value on the regularization (helps stability sometimes). mask_act: What activation to do on the output of the mask computation ('sigmoid' or 'linear'). detach_length: Detach the filter every n-th iteration. Default is to never detech, i.e. 'Inf'. alpha_eps: Term in the denominator of the steepest descent that stabalizes learning. """ def __init__(self, num_iter=1, feat_stride=16, init_step_length=1.0, init_filter_reg=1e-2, init_gauss_sigma=1.0, num_dist_bins=5, bin_displacement=1.0, mask_init_factor=4.0, score_act='relu', act_param=None, min_filter_reg=1e-3, mask_act='sigmoid', detach_length=float('Inf'), alpha_eps=0): super().__init__() self.num_iter = num_iter self.feat_stride = feat_stride self.log_step_length = nn.Parameter(math.log(init_step_length) * torch.ones(1)) self.filter_reg = nn.Parameter(init_filter_reg * torch.ones(1)) self.distance_map = DistanceMap(num_dist_bins, bin_displacement) self.min_filter_reg = min_filter_reg self.detach_length = detach_length self.alpha_eps = alpha_eps # Distance coordinates d = torch.arange(num_dist_bins, dtype=torch.float32).reshape(1,-1,1,1) * bin_displacement if init_gauss_sigma == 0: init_gauss = torch.zeros_like(d) init_gauss[0,0,0,0] = 1 else: init_gauss = torch.exp(-1/2 * (d / init_gauss_sigma)**2) # Module that predicts the target label function (y in the paper) self.label_map_predictor = nn.Conv2d(num_dist_bins, 1, kernel_size=1, bias=False) self.label_map_predictor.weight.data = init_gauss - init_gauss.min() # Module that predicts the target mask (m in the paper) mask_layers = [nn.Conv2d(num_dist_bins, 1, kernel_size=1, bias=False)] if mask_act == 'sigmoid': mask_layers.append(nn.Sigmoid()) init_bias = 0.0 elif mask_act == 'linear': init_bias = 0.5 else: raise ValueError('Unknown activation') self.target_mask_predictor = nn.Sequential(*mask_layers) self.target_mask_predictor[0].weight.data = mask_init_factor * torch.tanh(2.0 - d) + init_bias # Module that predicts the residual weights (v in the paper) self.spatial_weight_predictor = nn.Conv2d(num_dist_bins, 1, kernel_size=1, bias=False) self.spatial_weight_predictor.weight.data.fill_(1.0) # The score actvation and its derivative if score_act == 'bentpar': self.score_activation = activation.BentIdentPar(act_param) self.score_activation_deriv = activation.BentIdentParDeriv(act_param) elif score_act == 'relu': self.score_activation = activation.LeakyReluPar() self.score_activation_deriv = activation.LeakyReluParDeriv() else: raise ValueError('Unknown score activation') def forward(self, weights, feat, bb, sample_weight=None, num_iter=None, compute_losses=True): """Runs the optimizer module. Note that [] denotes an optional dimension. args: weights: Initial weights. Dims (sequences, feat_dim, wH, wW). feat: Input feature maps. Dims (images_in_sequence, [sequences], feat_dim, H, W). bb: Target bounding boxes (x, y, w, h) in the image coords. Dims (images_in_sequence, [sequences], 4). sample_weight: Optional weight for each sample. Dims: (images_in_sequence, [sequences]). num_iter: Number of iterations to run. compute_losses: Whether to compute the (train) loss in each iteration. returns: weights: The final oprimized weights. weight_iterates: The weights computed in each iteration (including initial input and final output). losses: Train losses.""" # Sizes num_iter = self.num_iter if num_iter is None else num_iter num_images = feat.shape[0] num_sequences = feat.shape[1] if feat.dim() == 5 else 1 filter_sz = (weights.shape[-2], weights.shape[-1]) output_sz = (feat.shape[-2] + (weights.shape[-2] + 1) % 2, feat.shape[-1] + (weights.shape[-1] + 1) % 2) # Get learnable scalars step_length_factor = torch.exp(self.log_step_length) reg_weight = (self.filter_reg*self.filter_reg).clamp(min=self.min_filter_reg**2) # Compute distance map dmap_offset = (torch.Tensor(filter_sz).to(bb.device) % 2) / 2.0 center = ((bb[..., :2] + bb[..., 2:] / 2) / self.feat_stride).reshape(-1, 2).flip((1,)) - dmap_offset dist_map = self.distance_map(center, output_sz) # Compute label map masks and weight label_map = self.label_map_predictor(dist_map).reshape(num_images, num_sequences, *dist_map.shape[-2:]) target_mask = self.target_mask_predictor(dist_map).reshape(num_images, num_sequences, *dist_map.shape[-2:]) spatial_weight = self.spatial_weight_predictor(dist_map).reshape(num_images, num_sequences, *dist_map.shape[-2:]) # Get total sample weights if sample_weight is None: sample_weight = math.sqrt(1.0 / num_images) * spatial_weight elif isinstance(sample_weight, torch.Tensor): sample_weight = sample_weight.sqrt().reshape(num_images, num_sequences, 1, 1) * spatial_weight backprop_through_learning = (self.detach_length > 0) weight_iterates = [weights] losses = [] for i in range(num_iter): if not backprop_through_learning or (i > 0 and i % self.detach_length == 0): weights = weights.detach() # Compute residuals scores = filter_layer.apply_filter(feat, weights) scores_act = self.score_activation(scores, target_mask) score_mask = self.score_activation_deriv(scores, target_mask) residuals = sample_weight * (scores_act - label_map) if compute_losses: losses.append(((residuals**2).sum() + reg_weight * (weights**2).sum())/num_sequences) # Compute gradient residuals_mapped = score_mask * (sample_weight * residuals) weights_grad = filter_layer.apply_feat_transpose(feat, residuals_mapped, filter_sz, training=self.training) + \ reg_weight * weights # Map the gradient with the Jacobian scores_grad = filter_layer.apply_filter(feat, weights_grad) scores_grad = sample_weight * (score_mask * scores_grad) # Compute optimal step length alpha_num = (weights_grad * weights_grad).sum(dim=(1,2,3)) alpha_den = ((scores_grad * scores_grad).reshape(num_images, num_sequences, -1).sum(dim=(0,2)) + (reg_weight + self.alpha_eps) * alpha_num).clamp(1e-8) alpha = alpha_num / alpha_den # Update filter weights = weights - (step_length_factor * alpha.reshape(-1, 1, 1, 1)) * weights_grad # Add the weight iterate weight_iterates.append(weights) if compute_losses: scores = filter_layer.apply_filter(feat, weights) scores = self.score_activation(scores, target_mask) losses.append((((sample_weight * (scores - label_map))**2).sum() + reg_weight * (weights**2).sum())/num_sequences) return weights, weight_iterates, losses class DiMPL2SteepestDescentGN(nn.Module): """A simpler optimizer module that uses L2 loss. args: num_iter: Number of default optimization iterations. feat_stride: The stride of the input feature. init_step_length: Initial scaling of the step length (which is then learned). gauss_sigma: The standard deviation of the label function. hinge_threshold: Threshold for the hinge-based loss (see DiMP paper). init_filter_reg: Initial filter regularization weight (which is then learned). min_filter_reg: Enforce a minimum value on the regularization (helps stability sometimes). detach_length: Detach the filter every n-th iteration. Default is to never detech, i.e. 'Inf'. alpha_eps: Term in the denominator of the steepest descent that stabalizes learning. """ def __init__(self, num_iter=1, feat_stride=16, init_step_length=1.0, gauss_sigma=1.0, hinge_threshold=-999, init_filter_reg=1e-2, min_filter_reg=1e-3, detach_length=float('Inf'), alpha_eps=0.0): super().__init__() self.num_iter = num_iter self.feat_stride = feat_stride self.log_step_length = nn.Parameter(math.log(init_step_length) * torch.ones(1)) self.filter_reg = nn.Parameter(init_filter_reg * torch.ones(1)) self.min_filter_reg = min_filter_reg self.detach_length = detach_length self.hinge_threshold = hinge_threshold self.gauss_sigma = gauss_sigma self.alpha_eps = alpha_eps def get_label(self, center, output_sz): center = center.reshape(center.shape[0], -1, center.shape[-1]) k0 = torch.arange(output_sz[0], dtype=torch.float32).reshape(1, 1, -1, 1).to(center.device) k1 = torch.arange(output_sz[1], dtype=torch.float32).reshape(1, 1, 1, -1).to(center.device) g0 = torch.exp(-1.0 / (2 * self.gauss_sigma ** 2) * (k0 - center[:,:,0].reshape(*center.shape[:2], 1, 1)) ** 2) g1 = torch.exp(-1.0 / (2 * self.gauss_sigma ** 2) * (k1 - center[:,:,1].reshape(*center.shape[:2], 1, 1)) ** 2) gauss = g0 * g1 return gauss def forward(self, weights, feat, bb, sample_weight=None, num_iter=None, compute_losses=True): """Runs the optimizer module. Note that [] denotes an optional dimension. args: weights: Initial weights. Dims (sequences, feat_dim, wH, wW). feat: Input feature maps. Dims (images_in_sequence, [sequences], feat_dim, H, W). bb: Target bounding boxes (x, y, w, h) in the image coords. Dims (images_in_sequence, [sequences], 4). sample_weight: Optional weight for each sample. Dims: (images_in_sequence, [sequences]). num_iter: Number of iterations to run. compute_losses: Whether to compute the (train) loss in each iteration. returns: weights: The final oprimized weights. weight_iterates: The weights computed in each iteration (including initial input and final output). losses: Train losses.""" # Sizes num_iter = self.num_iter if num_iter is None else num_iter num_images = feat.shape[0] num_sequences = feat.shape[1] if feat.dim() == 5 else 1 filter_sz = (weights.shape[-2], weights.shape[-1]) output_sz = (feat.shape[-2] + (weights.shape[-2] + 1) % 2, feat.shape[-1] + (weights.shape[-1] + 1) % 2) # Get learnable scalars step_length_factor = torch.exp(self.log_step_length) reg_weight = (self.filter_reg*self.filter_reg).clamp(min=self.min_filter_reg**2) # Compute distance map dmap_offset = (torch.Tensor(filter_sz).to(bb.device) % 2) / 2.0 center = ((bb[..., :2] + bb[..., 2:] / 2) / self.feat_stride).flip((-1,)) - dmap_offset label_map = self.get_label(center, output_sz) target_mask = (label_map > self.hinge_threshold).float() label_map *= target_mask # Get total sample weights if sample_weight is None: sample_weight = math.sqrt(1.0 / num_images) elif isinstance(sample_weight, torch.Tensor): sample_weight = sample_weight.sqrt().reshape(num_images, num_sequences, 1, 1) weight_iterates = [weights] losses = [] for i in range(num_iter): if i > 0 and i % self.detach_length == 0: weights = weights.detach() # Compute residuals scores = filter_layer.apply_filter(feat, weights) scores_act = target_mask * scores + (1.0 - target_mask) * F.relu(scores) score_mask = target_mask + (1.0 - target_mask) * (scores.detach() > 0).float() residuals = sample_weight * (scores_act - label_map) if compute_losses: losses.append(((residuals**2).sum() + reg_weight * (weights**2).sum())/num_sequences) # Compute gradient residuals_mapped = score_mask * (sample_weight * residuals) weights_grad = filter_layer.apply_feat_transpose(feat, residuals_mapped, filter_sz, training=self.training) + \ reg_weight * weights # Map the gradient with the Jacobian scores_grad = filter_layer.apply_filter(feat, weights_grad) scores_grad = sample_weight * (score_mask * scores_grad) # Compute optimal step length alpha_num = (weights_grad * weights_grad).sum(dim=(1,2,3)) alpha_den = ((scores_grad * scores_grad).reshape(num_images, num_sequences, -1).sum(dim=(0,2)) + (reg_weight + self.alpha_eps) * alpha_num).clamp(1e-8) alpha = alpha_num / alpha_den # Update filter weights = weights - (step_length_factor * alpha.reshape(-1, 1, 1, 1)) * weights_grad # Add the weight iterate weight_iterates.append(weights) if compute_losses: scores = filter_layer.apply_filter(feat, weights) scores = target_mask * scores + (1.0 - target_mask) * F.relu(scores) losses.append((((sample_weight * (scores - label_map))**2).sum() + reg_weight * (weights**2).sum())/num_sequences) return weights, weight_iterates, losses class PrDiMPSteepestDescentNewton(nn.Module): """Optimizer module for PrDiMP. It unrolls the steepest descent with Newton iterations to optimize the target filter. See the PrDiMP paper. args: num_iter: Number of default optimization iterations. feat_stride: The stride of the input feature. init_step_length: Initial scaling of the step length (which is then learned). init_filter_reg: Initial filter regularization weight (which is then learned). gauss_sigma: The standard deviation to use for the label density function. min_filter_reg: Enforce a minimum value on the regularization (helps stability sometimes). detach_length: Detach the filter every n-th iteration. Default is to never detech, i.e. 'Inf'. alpha_eps: Term in the denominator of the steepest descent that stabalizes learning. init_uni_weight: Weight of uniform label distribution. normalize_label: Wheter to normalize the label distribution. label_shrink: How much to shrink to label distribution. softmax_reg: Regularization in the denominator of the SoftMax. label_threshold: Threshold probabilities smaller than this. """ def __init__(self, num_iter=1, feat_stride=16, init_step_length=1.0, init_filter_reg=1e-2, gauss_sigma=1.0, min_filter_reg=1e-3, detach_length=float('Inf'), alpha_eps=0.0, init_uni_weight=None, normalize_label=False, label_shrink=0, softmax_reg=None, label_threshold=0.0): super().__init__() self.num_iter = num_iter self.feat_stride = feat_stride self.log_step_length = nn.Parameter(math.log(init_step_length) * torch.ones(1)) self.filter_reg = nn.Parameter(init_filter_reg * torch.ones(1)) self.gauss_sigma = gauss_sigma self.min_filter_reg = min_filter_reg self.detach_length = detach_length self.alpha_eps = alpha_eps self.uni_weight = 0 if init_uni_weight is None else init_uni_weight self.normalize_label = normalize_label self.label_shrink = label_shrink self.softmax_reg = softmax_reg self.label_threshold = label_threshold def get_label_density(self, center, output_sz): center = center.reshape(center.shape[0], -1, center.shape[-1]) k0 = torch.arange(output_sz[0], dtype=torch.float32).reshape(1, 1, -1, 1).to(center.device) k1 = torch.arange(output_sz[1], dtype=torch.float32).reshape(1, 1, 1, -1).to(center.device) dist0 = (k0 - center[:,:,0].reshape(*center.shape[:2], 1, 1)) ** 2 dist1 = (k1 - center[:,:,1].reshape(*center.shape[:2], 1, 1)) ** 2 if self.gauss_sigma == 0: dist0_view = dist0.reshape(-1, dist0.shape[-2]) dist1_view = dist1.reshape(-1, dist1.shape[-1]) one_hot0 = torch.zeros_like(dist0_view) one_hot1 = torch.zeros_like(dist1_view) one_hot0[torch.arange(one_hot0.shape[0]), dist0_view.argmin(dim=-1)] = 1.0 one_hot1[torch.arange(one_hot1.shape[0]), dist1_view.argmin(dim=-1)] = 1.0 gauss = one_hot0.reshape(dist0.shape) * one_hot1.reshape(dist1.shape) else: g0 = torch.exp(-1.0 / (2 * self.gauss_sigma ** 2) * dist0) g1 = torch.exp(-1.0 / (2 * self.gauss_sigma ** 2) * dist1) gauss = (g0 / (2*math.pi*self.gauss_sigma**2)) * g1 gauss = gauss * (gauss > self.label_threshold).float() if self.normalize_label: gauss /= (gauss.sum(dim=(-2,-1), keepdim=True) + 1e-8) label_dens = (1.0 - self.label_shrink)*((1.0 - self.uni_weight) * gauss + self.uni_weight / (output_sz[0]*output_sz[1])) return label_dens def forward(self, weights, feat, bb, sample_weight=None, num_iter=None, compute_losses=True): """Runs the optimizer module. Note that [] denotes an optional dimension. args: weights: Initial weights. Dims (sequences, feat_dim, wH, wW). feat: Input feature maps. Dims (images_in_sequence, [sequences], feat_dim, H, W). bb: Target bounding boxes (x, y, w, h) in the image coords. Dims (images_in_sequence, [sequences], 4). sample_weight: Optional weight for each sample. Dims: (images_in_sequence, [sequences]). num_iter: Number of iterations to run. compute_losses: Whether to compute the (train) loss in each iteration. returns: weights: The final oprimized weights. weight_iterates: The weights computed in each iteration (including initial input and final output). losses: Train losses.""" # Sizes num_iter = self.num_iter if num_iter is None else num_iter num_images = feat.shape[0] num_sequences = feat.shape[1] if feat.dim() == 5 else 1 filter_sz = (weights.shape[-2], weights.shape[-1]) output_sz = (feat.shape[-2] + (weights.shape[-2] + 1) % 2, feat.shape[-1] + (weights.shape[-1] + 1) % 2) # Get learnable scalars step_length_factor = torch.exp(self.log_step_length) reg_weight = (self.filter_reg*self.filter_reg).clamp(min=self.min_filter_reg**2) # Compute label density offset = (torch.Tensor(filter_sz).to(bb.device) % 2) / 2.0 center = ((bb[..., :2] + bb[..., 2:] / 2) / self.feat_stride).flip((-1,)) - offset label_density = self.get_label_density(center, output_sz) # Get total sample weights if sample_weight is None: sample_weight = torch.Tensor([1.0 / num_images]).to(feat.device) elif isinstance(sample_weight, torch.Tensor): sample_weight = sample_weight.reshape(num_images, num_sequences, 1, 1) exp_reg = 0 if self.softmax_reg is None else math.exp(self.softmax_reg) def _compute_loss(scores, weights): return torch.sum(sample_weight.reshape(sample_weight.shape[0], -1) * (torch.log(scores.exp().sum(dim=(-2, -1)) + exp_reg) - (label_density * scores).sum(dim=(-2, -1)))) / num_sequences +\ reg_weight * (weights ** 2).sum() / num_sequences weight_iterates = [weights] losses = [] for i in range(num_iter): if i > 0 and i % self.detach_length == 0: weights = weights.detach() # Compute "residuals" scores = filter_layer.apply_filter(feat, weights) scores_softmax = activation.softmax_reg(scores.reshape(num_images, num_sequences, -1), dim=2, reg=self.softmax_reg).reshape(scores.shape) res = sample_weight*(scores_softmax - label_density) if compute_losses: losses.append(_compute_loss(scores, weights)) # Compute gradient weights_grad = filter_layer.apply_feat_transpose(feat, res, filter_sz, training=self.training) + \ reg_weight * weights # Map the gradient with the Hessian scores_grad = filter_layer.apply_filter(feat, weights_grad) sm_scores_grad = scores_softmax * scores_grad hes_scores_grad = sm_scores_grad - scores_softmax * torch.sum(sm_scores_grad, dim=(-2,-1), keepdim=True) grad_hes_grad = (scores_grad * hes_scores_grad).reshape(num_images, num_sequences, -1).sum(dim=2).clamp(min=0) grad_hes_grad = (sample_weight.reshape(sample_weight.shape[0], -1) * grad_hes_grad).sum(dim=0) # Compute optimal step length alpha_num = (weights_grad * weights_grad).sum(dim=(1,2,3)) alpha_den = (grad_hes_grad + (reg_weight + self.alpha_eps) * alpha_num).clamp(1e-8) alpha = alpha_num / alpha_den # Update filter weights = weights - (step_length_factor * alpha.reshape(-1, 1, 1, 1)) * weights_grad # Add the weight iterate weight_iterates.append(weights) if compute_losses: scores = filter_layer.apply_filter(feat, weights) losses.append(_compute_loss(scores, weights)) return weights, weight_iterates, losses ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/target_classifier/residual_modules.py ================================================ import torch import torch.nn as nn import math import ltr.models.layers.filter as filter_layer import ltr.models.layers.activation as activation from ltr.models.layers.distance import DistanceMap from pytracking import TensorList class LinearFilterLearnGen(nn.Module): def __init__(self, feat_stride=16, init_filter_reg=1e-2, init_gauss_sigma=1.0, num_dist_bins=5, bin_displacement=1.0, mask_init_factor=4.0, score_act='bentpar', act_param=None, mask_act='sigmoid'): super().__init__() self.filter_reg = nn.Parameter(init_filter_reg * torch.ones(1)) self.feat_stride = feat_stride self.distance_map = DistanceMap(num_dist_bins, bin_displacement) # Distance coordinates d = torch.arange(num_dist_bins, dtype=torch.float32).reshape(1,-1,1,1) * bin_displacement if init_gauss_sigma == 0: init_gauss = torch.zeros_like(d) init_gauss[0,0,0,0] = 1 else: init_gauss = torch.exp(-1/2 * (d / init_gauss_sigma)**2) self.label_map_predictor = nn.Conv2d(num_dist_bins, 1, kernel_size=1, bias=False) self.label_map_predictor.weight.data = init_gauss - init_gauss.min() mask_layers = [nn.Conv2d(num_dist_bins, 1, kernel_size=1, bias=False)] if mask_act == 'sigmoid': mask_layers.append(nn.Sigmoid()) init_bias = 0.0 elif mask_act == 'linear': init_bias = 0.5 else: raise ValueError('Unknown activation') self.target_mask_predictor = nn.Sequential(*mask_layers) self.target_mask_predictor[0].weight.data = mask_init_factor * torch.tanh(2.0 - d) + init_bias self.spatial_weight_predictor = nn.Conv2d(num_dist_bins, 1, kernel_size=1, bias=False) self.spatial_weight_predictor.weight.data.fill_(1.0) if score_act == 'bentpar': self.score_activation = activation.BentIdentPar(act_param) elif score_act == 'relu': self.score_activation = activation.LeakyReluPar() else: raise ValueError('Unknown activation') def forward(self, meta_parameter: TensorList, feat, bb, sample_weight=None, is_distractor=None): filter = meta_parameter[0] num_images = feat.shape[0] num_sequences = feat.shape[1] if feat.dim() == 5 else 1 filter_sz = (filter.shape[-2], filter.shape[-1]) # Compute scores scores = filter_layer.apply_filter(feat, filter) # Compute distance map center = ((bb[..., :2] + bb[..., 2:] / 2) / self.feat_stride).reshape(-1, 2).flip((1,)) if is_distractor is not None: center[is_distractor.reshape(-1), :] = 99999 dist_map = self.distance_map(center, scores.shape[-2:]) # Compute label map masks and weight label_map = self.label_map_predictor(dist_map).reshape(num_images, num_sequences, dist_map.shape[-2], dist_map.shape[-1]) target_mask = self.target_mask_predictor(dist_map).reshape(num_images, num_sequences, dist_map.shape[-2], dist_map.shape[-1]) spatial_weight = self.spatial_weight_predictor(dist_map).reshape(num_images, num_sequences, dist_map.shape[-2], dist_map.shape[-1]) if sample_weight is None: sample_weight = math.sqrt(1.0 / num_images) * spatial_weight elif isinstance(sample_weight, torch.Tensor): sample_weight = sample_weight.sqrt().reshape(-1, 1, 1, 1) * spatial_weight # Compute data residual scores_act = self.score_activation(scores, target_mask) data_residual = sample_weight * (scores_act - label_map) # Compute regularization residual. Put batch in second dimension reg_residual = self.filter_reg*filter.reshape(1, num_sequences, -1) return TensorList([data_residual, reg_residual]) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/tracking/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/external/AR/ltr/models/tracking/dimpnet.py ================================================ import math import torch import torch.nn as nn from collections import OrderedDict from ltr.models.meta import steepestdescent import ltr.models.target_classifier.linear_filter as target_clf import ltr.models.target_classifier.features as clf_features import ltr.models.target_classifier.initializer as clf_initializer import ltr.models.target_classifier.optimizer as clf_optimizer import ltr.models.bbreg as bbmodels import ltr.models.backbone as backbones from ltr import model_constructor class DiMPnet(nn.Module): """The DiMP network. args: feature_extractor: Backbone feature extractor network. Must return a dict of feature maps classifier: Target classification module. bb_regressor: Bounding box regression module. classification_layer: Name of the backbone feature layer to use for classification. bb_regressor_layer: Names of the backbone layers to use for bounding box regression.""" def __init__(self, feature_extractor, classifier, bb_regressor, classification_layer, bb_regressor_layer): super().__init__() self.feature_extractor = feature_extractor self.classifier = classifier self.bb_regressor = bb_regressor self.classification_layer = [classification_layer] if isinstance(classification_layer, str) else classification_layer self.bb_regressor_layer = bb_regressor_layer self.output_layers = sorted(list(set(self.classification_layer + self.bb_regressor_layer))) def forward(self, train_imgs, test_imgs, train_bb, test_proposals, *args, **kwargs): """Runs the DiMP network the way it is applied during training. The forward function is ONLY used for training. Call the individual functions during tracking. args: train_imgs: Train image samples (images, sequences, 3, H, W). test_imgs: Test image samples (images, sequences, 3, H, W). trian_bb: Target boxes (x,y,w,h) for the train images. Dims (images, sequences, 4). test_proposals: Proposal boxes to use for the IoUNet (bb_regressor) module. *args, **kwargs: These are passed to the classifier module. returns: test_scores: Classification scores on the test samples. iou_pred: Predicted IoU scores for the test_proposals.""" assert train_imgs.dim() == 5 and test_imgs.dim() == 5, 'Expect 5 dimensional inputs' # Extract backbone features train_feat = self.extract_backbone_features(train_imgs.reshape(-1, *train_imgs.shape[-3:])) test_feat = self.extract_backbone_features(test_imgs.reshape(-1, *test_imgs.shape[-3:])) # Classification features train_feat_clf = self.get_backbone_clf_feat(train_feat) test_feat_clf = self.get_backbone_clf_feat(test_feat) # Run classifier module target_scores = self.classifier(train_feat_clf, test_feat_clf, train_bb, *args, **kwargs) # Get bb_regressor features train_feat_iou = self.get_backbone_bbreg_feat(train_feat) test_feat_iou = self.get_backbone_bbreg_feat(test_feat) # Run the IoUNet module iou_pred = self.bb_regressor(train_feat_iou, test_feat_iou, train_bb, test_proposals) return target_scores, iou_pred def get_backbone_clf_feat(self, backbone_feat): feat = OrderedDict({l: backbone_feat[l] for l in self.classification_layer}) if len(self.classification_layer) == 1: return feat[self.classification_layer[0]] return feat def get_backbone_bbreg_feat(self, backbone_feat): return [backbone_feat[l] for l in self.bb_regressor_layer] def extract_classification_feat(self, backbone_feat): return self.classifier.extract_classification_feat(self.get_backbone_clf_feat(backbone_feat)) def extract_backbone_features(self, im, layers=None): if layers is None: layers = self.output_layers return self.feature_extractor(im, layers) def extract_features(self, im, layers=None): if layers is None: layers = self.bb_regressor_layer + ['classification'] if 'classification' not in layers: return self.feature_extractor(im, layers) backbone_layers = sorted(list(set([l for l in layers + self.classification_layer if l != 'classification']))) all_feat = self.feature_extractor(im, backbone_layers) all_feat['classification'] = self.extract_classification_feat(all_feat) return OrderedDict({l: all_feat[l] for l in layers}) @model_constructor def dimpnet18(filter_size=1, optim_iter=5, optim_init_step=1.0, optim_init_reg=0.01, classification_layer='layer3', feat_stride=16, backbone_pretrained=True, clf_feat_blocks=1, clf_feat_norm=True, init_filter_norm=False, final_conv=True, out_feature_dim=256, init_gauss_sigma=1.0, num_dist_bins=5, bin_displacement=1.0, mask_init_factor=4.0, iou_input_dim=(256, 256), iou_inter_dim=(256, 256), score_act='relu', act_param=None, target_mask_act='sigmoid', detach_length=float('Inf'), frozen_backbone_layers=()): # Backbone backbone_net = backbones.resnet18(pretrained=backbone_pretrained, frozen_layers=frozen_backbone_layers) # Feature normalization norm_scale = math.sqrt(1.0 / (out_feature_dim * filter_size * filter_size)) # Classifier features clf_feature_extractor = clf_features.residual_basic_block(num_blocks=clf_feat_blocks, l2norm=clf_feat_norm, final_conv=final_conv, norm_scale=norm_scale, out_dim=out_feature_dim) # Initializer for the DiMP classifier initializer = clf_initializer.FilterInitializerLinear(filter_size=filter_size, filter_norm=init_filter_norm, feature_dim=out_feature_dim) # Optimizer for the DiMP classifier optimizer = clf_optimizer.DiMPSteepestDescentGN(num_iter=optim_iter, feat_stride=feat_stride, init_step_length=optim_init_step, init_filter_reg=optim_init_reg, init_gauss_sigma=init_gauss_sigma, num_dist_bins=num_dist_bins, bin_displacement=bin_displacement, mask_init_factor=mask_init_factor, score_act=score_act, act_param=act_param, mask_act=target_mask_act, detach_length=detach_length) # The classifier module classifier = target_clf.LinearFilter(filter_size=filter_size, filter_initializer=initializer, filter_optimizer=optimizer, feature_extractor=clf_feature_extractor) # Bounding box regressor bb_regressor = bbmodels.AtomIoUNet(pred_input_dim=iou_input_dim, pred_inter_dim=iou_inter_dim) # DiMP network net = DiMPnet(feature_extractor=backbone_net, classifier=classifier, bb_regressor=bb_regressor, classification_layer=classification_layer, bb_regressor_layer=['layer2', 'layer3']) return net @model_constructor def dimpnet50(filter_size=1, optim_iter=5, optim_init_step=1.0, optim_init_reg=0.01, classification_layer='layer3', feat_stride=16, backbone_pretrained=True, clf_feat_blocks=0, clf_feat_norm=True, init_filter_norm=False, final_conv=True, out_feature_dim=512, init_gauss_sigma=1.0, num_dist_bins=5, bin_displacement=1.0, mask_init_factor=4.0, iou_input_dim=(256, 256), iou_inter_dim=(256, 256), score_act='relu', act_param=None, target_mask_act='sigmoid', detach_length=float('Inf'), frozen_backbone_layers=()): # Backbone backbone_net = backbones.resnet50(pretrained=backbone_pretrained, frozen_layers=frozen_backbone_layers) # Feature normalization norm_scale = math.sqrt(1.0 / (out_feature_dim * filter_size * filter_size)) # Classifier features if classification_layer == 'layer3': feature_dim = 256 elif classification_layer == 'layer4': feature_dim = 512 else: raise Exception clf_feature_extractor = clf_features.residual_bottleneck(feature_dim=feature_dim, num_blocks=clf_feat_blocks, l2norm=clf_feat_norm, final_conv=final_conv, norm_scale=norm_scale, out_dim=out_feature_dim) # Initializer for the DiMP classifier initializer = clf_initializer.FilterInitializerLinear(filter_size=filter_size, filter_norm=init_filter_norm, feature_dim=out_feature_dim) # Optimizer for the DiMP classifier optimizer = clf_optimizer.DiMPSteepestDescentGN(num_iter=optim_iter, feat_stride=feat_stride, init_step_length=optim_init_step, init_filter_reg=optim_init_reg, init_gauss_sigma=init_gauss_sigma, num_dist_bins=num_dist_bins, bin_displacement=bin_displacement, mask_init_factor=mask_init_factor, score_act=score_act, act_param=act_param, mask_act=target_mask_act, detach_length=detach_length) # The classifier module classifier = target_clf.LinearFilter(filter_size=filter_size, filter_initializer=initializer, filter_optimizer=optimizer, feature_extractor=clf_feature_extractor) # Bounding box regressor bb_regressor = bbmodels.AtomIoUNet(input_dim=(4*128,4*256), pred_input_dim=iou_input_dim, pred_inter_dim=iou_inter_dim) # DiMP network net = DiMPnet(feature_extractor=backbone_net, classifier=classifier, bb_regressor=bb_regressor, classification_layer=classification_layer, bb_regressor_layer=['layer2', 'layer3']) return net @model_constructor def L2dimpnet18(filter_size=1, optim_iter=5, optim_init_step=1.0, optim_init_reg=0.01, classification_layer='layer3', feat_stride=16, backbone_pretrained=True, clf_feat_blocks=1, clf_feat_norm=True, init_filter_norm=False, final_conv=True, out_feature_dim=256, iou_input_dim=(256, 256), iou_inter_dim=(256, 256), detach_length=float('Inf'), hinge_threshold=-999, gauss_sigma=1.0, alpha_eps=0): # Backbone backbone_net = backbones.resnet18(pretrained=backbone_pretrained) # Feature normalization norm_scale = math.sqrt(1.0 / (out_feature_dim * filter_size * filter_size)) # Classifier features clf_feature_extractor = clf_features.residual_basic_block(num_blocks=clf_feat_blocks, l2norm=clf_feat_norm, final_conv=final_conv, norm_scale=norm_scale, out_dim=out_feature_dim) # Initializer for the DiMP classifier initializer = clf_initializer.FilterInitializerLinear(filter_size=filter_size, filter_norm=init_filter_norm, feature_dim=out_feature_dim) # Optimizer for the DiMP classifier optimizer = clf_optimizer.DiMPL2SteepestDescentGN(num_iter=optim_iter, feat_stride=feat_stride, init_step_length=optim_init_step, hinge_threshold=hinge_threshold, init_filter_reg=optim_init_reg, gauss_sigma=gauss_sigma, detach_length=detach_length, alpha_eps=alpha_eps) # The classifier module classifier = target_clf.LinearFilter(filter_size=filter_size, filter_initializer=initializer, filter_optimizer=optimizer, feature_extractor=clf_feature_extractor) # Bounding box regressor bb_regressor = bbmodels.AtomIoUNet(pred_input_dim=iou_input_dim, pred_inter_dim=iou_inter_dim) # DiMP network net = DiMPnet(feature_extractor=backbone_net, classifier=classifier, bb_regressor=bb_regressor, classification_layer=classification_layer, bb_regressor_layer=['layer2', 'layer3']) return net @model_constructor def klcedimpnet18(filter_size=1, optim_iter=5, optim_init_step=1.0, optim_init_reg=0.01, classification_layer='layer3', feat_stride=16, backbone_pretrained=True, clf_feat_blocks=1, clf_feat_norm=True, init_filter_norm=False, final_conv=True, out_feature_dim=256, gauss_sigma=1.0, iou_input_dim=(256, 256), iou_inter_dim=(256, 256), detach_length=float('Inf'), alpha_eps=0.0, train_feature_extractor=True, init_uni_weight=None, optim_min_reg=1e-3, init_initializer='default', normalize_label=False, label_shrink=0, softmax_reg=None, label_threshold=0, final_relu=False, init_pool_square=False, frozen_backbone_layers=()): if not train_feature_extractor: frozen_backbone_layers = 'all' # Backbone backbone_net = backbones.resnet18(pretrained=backbone_pretrained, frozen_layers=frozen_backbone_layers) # Feature normalization norm_scale = math.sqrt(1.0 / (out_feature_dim * filter_size * filter_size)) # Classifier features clf_feature_extractor = clf_features.residual_basic_block(num_blocks=clf_feat_blocks, l2norm=clf_feat_norm, final_conv=final_conv, norm_scale=norm_scale, out_dim=out_feature_dim, final_relu=final_relu) # Initializer for the DiMP classifier initializer = clf_initializer.FilterInitializerLinear(filter_size=filter_size, filter_norm=init_filter_norm, feature_dim=out_feature_dim, init_weights=init_initializer, pool_square=init_pool_square) # Optimizer for the DiMP classifier optimizer = clf_optimizer.PrDiMPSteepestDescentNewton(num_iter=optim_iter, feat_stride=feat_stride, init_step_length=optim_init_step, init_filter_reg=optim_init_reg, gauss_sigma=gauss_sigma, detach_length=detach_length, alpha_eps=alpha_eps, init_uni_weight=init_uni_weight, min_filter_reg=optim_min_reg, normalize_label=normalize_label, label_shrink=label_shrink, softmax_reg=softmax_reg, label_threshold=label_threshold) # The classifier module classifier = target_clf.LinearFilter(filter_size=filter_size, filter_initializer=initializer, filter_optimizer=optimizer, feature_extractor=clf_feature_extractor) # Bounding box regressor bb_regressor = bbmodels.AtomIoUNet(pred_input_dim=iou_input_dim, pred_inter_dim=iou_inter_dim) # DiMP network net = DiMPnet(feature_extractor=backbone_net, classifier=classifier, bb_regressor=bb_regressor, classification_layer=classification_layer, bb_regressor_layer=['layer2', 'layer3']) return net @model_constructor def klcedimpnet50(filter_size=1, optim_iter=5, optim_init_step=1.0, optim_init_reg=0.01, classification_layer='layer3', feat_stride=16, backbone_pretrained=True, clf_feat_blocks=0, clf_feat_norm=True, init_filter_norm=False, final_conv=True, out_feature_dim=512, gauss_sigma=1.0, iou_input_dim=(256, 256), iou_inter_dim=(256, 256), detach_length=float('Inf'), alpha_eps=0.0, train_feature_extractor=True, init_uni_weight=None, optim_min_reg=1e-3, init_initializer='default', normalize_label=False, label_shrink=0, softmax_reg=None, label_threshold=0, final_relu=False, frozen_backbone_layers=()): if not train_feature_extractor: frozen_backbone_layers = 'all' # Backbone backbone_net = backbones.resnet50(pretrained=backbone_pretrained, frozen_layers=frozen_backbone_layers) # Feature normalization norm_scale = math.sqrt(1.0 / (out_feature_dim * filter_size * filter_size)) # Classifier features clf_feature_extractor = clf_features.residual_bottleneck(num_blocks=clf_feat_blocks, l2norm=clf_feat_norm, final_conv=final_conv, norm_scale=norm_scale, out_dim=out_feature_dim, final_relu=final_relu) # Initializer for the DiMP classifier initializer = clf_initializer.FilterInitializerLinear(filter_size=filter_size, filter_norm=init_filter_norm, feature_dim=out_feature_dim, init_weights=init_initializer) # Optimizer for the DiMP classifier optimizer = clf_optimizer.PrDiMPSteepestDescentNewton(num_iter=optim_iter, feat_stride=feat_stride, init_step_length=optim_init_step, init_filter_reg=optim_init_reg, gauss_sigma=gauss_sigma, detach_length=detach_length, alpha_eps=alpha_eps, init_uni_weight=init_uni_weight, min_filter_reg=optim_min_reg, normalize_label=normalize_label, label_shrink=label_shrink, softmax_reg=softmax_reg, label_threshold=label_threshold) # The classifier module classifier = target_clf.LinearFilter(filter_size=filter_size, filter_initializer=initializer, filter_optimizer=optimizer, feature_extractor=clf_feature_extractor) # Bounding box regressor bb_regressor = bbmodels.AtomIoUNet(input_dim=(4*128,4*256), pred_input_dim=iou_input_dim, pred_inter_dim=iou_inter_dim) # DiMP network net = DiMPnet(feature_extractor=backbone_net, classifier=classifier, bb_regressor=bb_regressor, classification_layer=classification_layer, bb_regressor_layer=['layer2', 'layer3']) return net ================================================ FILE: artrackv2_mindspore/external/AR/ltr/run_training.py ================================================ import os import sys import argparse import importlib import multiprocessing import cv2 as cv import torch.backends.cudnn env_path = os.path.join(os.path.dirname(__file__), '..') if env_path not in sys.path: sys.path.append(env_path) import ltr.admin.settings as ws_settings def run_training(train_module, train_name, cudnn_benchmark=True): """Run a train scripts in train_settings. args: train_module: Name of module in the "train_settings/" folder. train_name: Name of the train settings file. cudnn_benchmark: Use cudnn benchmark or not (default is True). """ # This is needed to avoid strange crashes related to opencv cv.setNumThreads(0) torch.backends.cudnn.benchmark = cudnn_benchmark print('Training: {} {}'.format(train_module, train_name)) settings = ws_settings.Settings() settings.module_name = train_module settings.script_name = train_name settings.project_path = 'ltr/{}/{}'.format(train_module, train_name) expr_module = importlib.import_module('ltr.train_settings.{}.{}'.format(train_module, train_name)) expr_func = getattr(expr_module, 'run') expr_func(settings) def main(): parser = argparse.ArgumentParser(description='Run a train scripts in train_settings.') parser.add_argument('train_module', type=str, help='Name of module in the "train_settings/" folder.') parser.add_argument('train_name', type=str, help='Name of the train settings file.') parser.add_argument('--cudnn_benchmark', type=bool, default=True, help='Set cudnn benchmark on (1) or off (0) (default is on).') args = parser.parse_args() run_training(args.train_module, args.train_name, args.cudnn_benchmark) if __name__ == '__main__': multiprocessing.set_start_method('spawn', force=True) main() ================================================ FILE: artrackv2_mindspore/external/AR/ltr/train_settings/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/external/AR/ltr/train_settings/bbreg/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/external/AR/ltr/train_settings/bbreg/atom.py ================================================ import torch.nn as nn import torch.optim as optim from ltr.dataset import Lasot, TrackingNet, MSCOCOSeq, Got10k from ltr.data import processing, sampler, LTRLoader import ltr.models.bbreg.atom as atom_models from ltr import actors from ltr.trainers import LTRTrainer import ltr.data.transforms as tfm def run(settings): # Most common settings are assigned in the settings struct settings.description = 'ATOM IoUNet with default settings, but additionally using GOT10k for training.' settings.batch_size = 64 settings.num_workers = 8 settings.print_interval = 1 settings.normalize_mean = [0.485, 0.456, 0.406] settings.normalize_std = [0.229, 0.224, 0.225] settings.search_area_factor = 5.0 settings.feature_sz = 18 settings.output_sz = settings.feature_sz * 16 settings.center_jitter_factor = {'train': 0, 'test': 4.5} settings.scale_jitter_factor = {'train': 0, 'test': 0.5} # Train datasets lasot_train = Lasot(settings.env.lasot_dir, split='train') got10k_train = Got10k(settings.env.got10k_dir, split='vottrain') trackingnet_train = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(4))) coco_train = MSCOCOSeq(settings.env.coco_dir) # Validation datasets got10k_val = Got10k(settings.env.got10k_dir, split='votval') # The joint augmentation transform, that is applied to the pairs jointly transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05)) # The augmentation transform applied to the training set (individually to each image in the pair) transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # The augmentation transform applied to the validation set (individually to each image in the pair) transform_val = tfm.Transform(tfm.ToTensor(), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # Data processing to do on the training pairs proposal_params = {'min_iou': 0.1, 'boxes_per_frame': 16, 'sigma_factor': [0.01, 0.05, 0.1, 0.2, 0.3]} data_processing_train = processing.ATOMProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, transform=transform_train, joint_transform=transform_joint) # Data processing to do on the validation pairs data_processing_val = processing.ATOMProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, transform=transform_val, joint_transform=transform_joint) # The sampler for training dataset_train = sampler.ATOMSampler([lasot_train, got10k_train, trackingnet_train, coco_train], [1,1,1,1], samples_per_epoch=1000*settings.batch_size, max_gap=50, processing=data_processing_train) # The loader for training loader_train = LTRLoader('train', dataset_train, training=True, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=True, drop_last=True, stack_dim=1) # The sampler for validation dataset_val = sampler.ATOMSampler([got10k_val], [1], samples_per_epoch=500*settings.batch_size, max_gap=50, processing=data_processing_val) # The loader for validation loader_val = LTRLoader('val', dataset_val, training=False, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=False, drop_last=True, epoch_interval=5, stack_dim=1) # Create network and actor net = atom_models.atom_resnet18(backbone_pretrained=True) objective = nn.MSELoss() actor = actors.AtomActor(net=net, objective=objective) # Optimizer optimizer = optim.Adam(actor.net.bb_regressor.parameters(), lr=1e-3) lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.2) # Create trainer trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler) # Run training (set fail_safe=False if you are debugging) trainer.train(50, load_latest=True, fail_safe=True) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/train_settings/bbreg/atom_gmm_sampl.py ================================================ import torch.nn as nn import torch.optim as optim from ltr.dataset import Lasot, TrackingNet, MSCOCOSeq, Got10k from ltr.data import processing, sampler, LTRLoader import ltr.models.bbreg.atom as atom_models from ltr import actors from ltr.trainers import LTRTrainer import ltr.data.transforms as tfm def run(settings): # Most common settings are assigned in the settings struct settings.description = 'ATOM IoUNet using the baseline ATOM* settings in [https://arxiv.org/abs/1909.12297].' \ 'Unlike standard ATOM, it employs the GMM-based proposal sampling and minor parameter changes.' settings.batch_size = 64 settings.num_workers = 8 settings.print_interval = 1 settings.normalize_mean = [0.485, 0.456, 0.406] settings.normalize_std = [0.229, 0.224, 0.225] settings.search_area_factor = 5.0 settings.feature_sz = 18 settings.output_sz = settings.feature_sz * 16 settings.center_jitter_factor = {'train': 0, 'test': 4.5} settings.scale_jitter_factor = {'train': 0, 'test': 0.5} # Train datasets lasot_train = Lasot(settings.env.lasot_dir, split='train') got10k_train = Got10k(settings.env.got10k_dir, split='vottrain') trackingnet_train = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(4))) coco_train = MSCOCOSeq(settings.env.coco_dir) # Validation datasets got10k_val = Got10k(settings.env.got10k_dir, split='votval') # The joint augmentation transform, that is applied to the pairs jointly transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05)) # The augmentation transform applied to the training set (individually to each image in the pair) transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # The augmentation transform applied to the validation set (individually to each image in the pair) transform_val = tfm.Transform(tfm.ToTensor(), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # Data processing to do on the training pairs proposal_params = {'proposal_method': 'gmm', 'boxes_per_frame': 128, 'proposal_sigma': [(0.05, 0.05), (0.5, 0.5)]} data_processing_train = processing.ATOMProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, transform=transform_train, joint_transform=transform_joint) # Data processing to do on the validation pairs data_processing_val = processing.ATOMProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, transform=transform_val, joint_transform=transform_joint) # The sampler for training dataset_train = sampler.ATOMSampler([lasot_train, got10k_train, trackingnet_train, coco_train], [1,1,1,1], samples_per_epoch=1000*settings.batch_size, max_gap=200, processing=data_processing_train) # The loader for training loader_train = LTRLoader('train', dataset_train, training=True, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=True, drop_last=True, stack_dim=1) # The sampler for validation dataset_val = sampler.ATOMSampler([got10k_val], [1], samples_per_epoch=500*settings.batch_size, max_gap=200, processing=data_processing_val) # The loader for validation loader_val = LTRLoader('val', dataset_val, training=False, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=False, drop_last=True, epoch_interval=5, stack_dim=1) # Create network and actor net = atom_models.atom_resnet18(backbone_pretrained=True) objective = nn.MSELoss() actor = actors.AtomActor(net=net, objective=objective) # Optimizer optimizer = optim.Adam(actor.net.bb_regressor.parameters(), lr=1e-3) lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.2) # Create trainer trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler) # Run training (set fail_safe=False if you are debugging) trainer.train(50, load_latest=True, fail_safe=True) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/train_settings/bbreg/atom_paper.py ================================================ import torch.nn as nn import torch.optim as optim from ltr.dataset import Lasot, TrackingNet, MSCOCOSeq from ltr.data import processing, sampler, LTRLoader import ltr.models.bbreg.atom as atom_models from ltr import actors from ltr.trainers import LTRTrainer import ltr.data.transforms as tfm def run(settings): # Most common settings are assigned in the settings struct settings.description = 'ATOM IoUNet with default settings according to the paper.' settings.batch_size = 64 settings.num_workers = 8 settings.print_interval = 1 settings.normalize_mean = [0.485, 0.456, 0.406] settings.normalize_std = [0.229, 0.224, 0.225] settings.search_area_factor = 5.0 settings.feature_sz = 18 settings.output_sz = settings.feature_sz * 16 settings.center_jitter_factor = {'train': 0, 'test': 4.5} settings.scale_jitter_factor = {'train': 0, 'test': 0.5} # Train datasets lasot_train = Lasot(settings.env.lasot_dir, split='train') trackingnet_train = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(11))) coco_train = MSCOCOSeq(settings.env.coco_dir) # Validation datasets trackingnet_val = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(11,12))) # The joint augmentation transform, that is applied to the pairs jointly transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05)) # The augmentation transform applied to the training set (individually to each image in the pair) transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # The augmentation transform applied to the validation set (individually to each image in the pair) transform_val = tfm.Transform(tfm.ToTensor(), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # Data processing to do on the training pairs proposal_params = {'min_iou': 0.1, 'boxes_per_frame': 16, 'sigma_factor': [0.01, 0.05, 0.1, 0.2, 0.3]} data_processing_train = processing.ATOMProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, transform=transform_train, joint_transform=transform_joint) # Data processing to do on the validation pairs data_processing_val = processing.ATOMProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, transform=transform_val, joint_transform=transform_joint) # The sampler for training dataset_train = sampler.ATOMSampler([lasot_train, trackingnet_train, coco_train], [1,1,1], samples_per_epoch=1000*settings.batch_size, max_gap=50, processing=data_processing_train) # The loader for training loader_train = LTRLoader('train', dataset_train, training=True, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=True, drop_last=True, stack_dim=1) # The sampler for validation dataset_val = sampler.ATOMSampler([trackingnet_val], [1], samples_per_epoch=500*settings.batch_size, max_gap=50, processing=data_processing_val) # The loader for validation loader_val = LTRLoader('val', dataset_val, training=False, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=False, drop_last=True, epoch_interval=5, stack_dim=1) # Create network and actor net = atom_models.atom_resnet18(backbone_pretrained=True) objective = nn.MSELoss() actor = actors.AtomActor(net=net, objective=objective) # Optimizer optimizer = optim.Adam(actor.net.bb_regressor.parameters(), lr=1e-3) lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.2) # Create trainer trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler) # Run training (set fail_safe=False if you are debugging) trainer.train(50, load_latest=True, fail_safe=True) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/train_settings/bbreg/atom_prob_ml.py ================================================ import torch.optim as optim from ltr.dataset import Lasot, TrackingNet, MSCOCOSeq, Got10k from ltr.data import processing, sampler, LTRLoader import ltr.models.bbreg.atom as atom_models import ltr.models.loss.kl_regression as klreg_losses import ltr.actors.bbreg as bbreg_actors from ltr.trainers import LTRTrainer import ltr.data.transforms as tfm def run(settings): # Most common settings are assigned in the settings struct settings.description = 'ATOM using the probabilistic maximum likelihood trained regression model for bounding-box' \ 'regression presented in [https://arxiv.org/abs/1909.12297].' settings.batch_size = 64 settings.num_workers = 8 settings.print_interval = 1 settings.normalize_mean = [0.485, 0.456, 0.406] settings.normalize_std = [0.229, 0.224, 0.225] settings.search_area_factor = 5.0 settings.feature_sz = 18 settings.output_sz = settings.feature_sz * 16 settings.center_jitter_factor = {'train': 0, 'test': 4.5} settings.scale_jitter_factor = {'train': 0, 'test': 0.5} # Train datasets lasot_train = Lasot(settings.env.lasot_dir, split='train') got10k_train = Got10k(settings.env.got10k_dir, split='vottrain') trackingnet_train = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(4))) coco_train = MSCOCOSeq(settings.env.coco_dir) # Validation datasets got10k_val = Got10k(settings.env.got10k_dir, split='votval') # The joint augmentation transform, that is applied to the pairs jointly transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05)) # The augmentation transform applied to the training set (individually to each image in the pair) transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # The augmentation transform applied to the validation set (individually to each image in the pair) transform_val = tfm.Transform(tfm.ToTensor(), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # Data processing to do on the training pairs proposal_params = {'boxes_per_frame': 128, 'gt_sigma': (0, 0), 'proposal_sigma': [(0.05, 0.05), (0.5, 0.5)], 'add_mean_box': True} data_processing_train = processing.KLBBregProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, transform=transform_train, joint_transform=transform_joint) # Data processing to do on the validation pairs data_processing_val = processing.KLBBregProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, transform=transform_val, joint_transform=transform_joint) # The sampler for training dataset_train = sampler.ATOMSampler([lasot_train, got10k_train, trackingnet_train, coco_train], [1,1,1,1], samples_per_epoch=1000*settings.batch_size, max_gap=200, processing=data_processing_train) # The loader for training loader_train = LTRLoader('train', dataset_train, training=True, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=True, drop_last=True, stack_dim=1) # The sampler for validation dataset_val = sampler.ATOMSampler([got10k_val], [1], samples_per_epoch=500*settings.batch_size, max_gap=200, processing=data_processing_val) # The loader for validation loader_val = LTRLoader('val', dataset_val, training=False, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=False, drop_last=True, epoch_interval=5, stack_dim=1) # Create network and actor net = atom_models.atom_resnet18(backbone_pretrained=True) objective = klreg_losses.MLRegression() actor = bbreg_actors.AtomBBKLActor(net=net, objective=objective) # Optimizer optimizer = optim.Adam(actor.net.bb_regressor.parameters(), lr=1e-3) lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.2) # Create trainer trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler) # Run training (set fail_safe=False if you are debugging) trainer.train(50, load_latest=True, fail_safe=True) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/train_settings/dimp/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/external/AR/ltr/train_settings/dimp/dimp18.py ================================================ import torch.nn as nn import torch.optim as optim from ltr.dataset import Lasot, Got10k, TrackingNet, MSCOCOSeq from ltr.data import processing, sampler, LTRLoader from ltr.models.tracking import dimpnet import ltr.models.loss as ltr_losses from ltr import actors from ltr.trainers import LTRTrainer import ltr.data.transforms as tfm from ltr import MultiGPU def run(settings): settings.description = 'Default train settings for DiMP with ResNet18 as backbone.' settings.batch_size = 26 settings.num_workers = 8 settings.multi_gpu = False settings.print_interval = 1 settings.normalize_mean = [0.485, 0.456, 0.406] settings.normalize_std = [0.229, 0.224, 0.225] settings.search_area_factor = 5.0 settings.output_sigma_factor = 1/4 settings.target_filter_sz = 4 settings.feature_sz = 18 settings.output_sz = settings.feature_sz * 16 settings.center_jitter_factor = {'train': 3, 'test': 4.5} settings.scale_jitter_factor = {'train': 0.25, 'test': 0.5} settings.hinge_threshold = 0.05 # settings.print_stats = ['Loss/total', 'Loss/iou', 'ClfTrain/init_loss', 'ClfTrain/test_loss'] # Train datasets lasot_train = Lasot(settings.env.lasot_dir, split='train') got10k_train = Got10k(settings.env.got10k_dir, split='vottrain') trackingnet_train = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(4))) coco_train = MSCOCOSeq(settings.env.coco_dir) # Validation datasets got10k_val = Got10k(settings.env.got10k_dir, split='votval') # Data transform transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05)) transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) transform_val = tfm.Transform(tfm.ToTensor(), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # The tracking pairs processing module output_sigma = settings.output_sigma_factor / settings.search_area_factor proposal_params = {'min_iou': 0.1, 'boxes_per_frame': 8, 'sigma_factor': [0.01, 0.05, 0.1, 0.2, 0.3]} label_params = {'feature_sz': settings.feature_sz, 'sigma_factor': output_sigma, 'kernel_sz': settings.target_filter_sz} data_processing_train = processing.DiMPProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, label_function_params=label_params, transform=transform_train, joint_transform=transform_joint) data_processing_val = processing.DiMPProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, label_function_params=label_params, transform=transform_val, joint_transform=transform_joint) # Train sampler and loader dataset_train = sampler.DiMPSampler([lasot_train, got10k_train, trackingnet_train, coco_train], [0.25,1,1,1], samples_per_epoch=26000, max_gap=30, num_test_frames=3, num_train_frames=3, processing=data_processing_train) loader_train = LTRLoader('train', dataset_train, training=True, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=True, drop_last=True, stack_dim=1) # Validation samplers and loaders dataset_val = sampler.DiMPSampler([got10k_val], [1], samples_per_epoch=5000, max_gap=30, num_test_frames=3, num_train_frames=3, processing=data_processing_val) loader_val = LTRLoader('val', dataset_val, training=False, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=False, drop_last=True, epoch_interval=5, stack_dim=1) # Create network and actor net = dimpnet.dimpnet18(filter_size=settings.target_filter_sz, backbone_pretrained=True, optim_iter=5, clf_feat_norm=True, final_conv=True, optim_init_step=0.9, optim_init_reg=0.1, init_gauss_sigma=output_sigma * settings.feature_sz, num_dist_bins=100, bin_displacement=0.1, mask_init_factor=3.0, target_mask_act='sigmoid', score_act='relu') # Wrap the network for multi GPU training if settings.multi_gpu: net = MultiGPU(net, dim=1) objective = {'iou': nn.MSELoss(), 'test_clf': ltr_losses.LBHinge(threshold=settings.hinge_threshold)} loss_weight = {'iou': 1, 'test_clf': 100, 'test_init_clf': 100, 'test_iter_clf': 400} actor = actors.DiMPActor(net=net, objective=objective, loss_weight=loss_weight) # Optimizer optimizer = optim.Adam([{'params': actor.net.classifier.filter_initializer.parameters(), 'lr': 5e-5}, {'params': actor.net.classifier.filter_optimizer.parameters(), 'lr': 5e-4}, {'params': actor.net.classifier.feature_extractor.parameters(), 'lr': 5e-5}, {'params': actor.net.bb_regressor.parameters(), 'lr': 1e-3}, {'params': actor.net.feature_extractor.parameters()}], lr=2e-4) lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.2) trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler) trainer.train(50, load_latest=True, fail_safe=True) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/train_settings/dimp/dimp50.py ================================================ import torch.nn as nn import torch.optim as optim from ltr.dataset import Lasot, Got10k, TrackingNet, MSCOCOSeq from ltr.data import processing, sampler, LTRLoader from ltr.models.tracking import dimpnet import ltr.models.loss as ltr_losses from ltr import actors from ltr.trainers import LTRTrainer import ltr.data.transforms as tfm from ltr import MultiGPU def run(settings): settings.description = 'Default train settings for DiMP with ResNet50 as backbone.' settings.batch_size = 10 settings.num_workers = 8 settings.multi_gpu = False settings.print_interval = 1 settings.normalize_mean = [0.485, 0.456, 0.406] settings.normalize_std = [0.229, 0.224, 0.225] settings.search_area_factor = 5.0 settings.output_sigma_factor = 1/4 settings.target_filter_sz = 4 settings.feature_sz = 18 settings.output_sz = settings.feature_sz * 16 settings.center_jitter_factor = {'train': 3, 'test': 4.5} settings.scale_jitter_factor = {'train': 0.25, 'test': 0.5} settings.hinge_threshold = 0.05 # settings.print_stats = ['Loss/total', 'Loss/iou', 'ClfTrain/clf_ce', 'ClfTrain/test_loss'] # Train datasets lasot_train = Lasot(settings.env.lasot_dir, split='train') got10k_train = Got10k(settings.env.got10k_dir, split='vottrain') trackingnet_train = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(4))) coco_train = MSCOCOSeq(settings.env.coco_dir) # Validation datasets got10k_val = Got10k(settings.env.got10k_dir, split='votval') # Data transform transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05)) transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) transform_val = tfm.Transform(tfm.ToTensor(), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # The tracking pairs processing module output_sigma = settings.output_sigma_factor / settings.search_area_factor proposal_params = {'min_iou': 0.1, 'boxes_per_frame': 8, 'sigma_factor': [0.01, 0.05, 0.1, 0.2, 0.3]} label_params = {'feature_sz': settings.feature_sz, 'sigma_factor': output_sigma, 'kernel_sz': settings.target_filter_sz} data_processing_train = processing.DiMPProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, label_function_params=label_params, transform=transform_train, joint_transform=transform_joint) data_processing_val = processing.DiMPProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, label_function_params=label_params, transform=transform_val, joint_transform=transform_joint) # Train sampler and loader dataset_train = sampler.DiMPSampler([lasot_train, got10k_train, trackingnet_train, coco_train], [0.25,1,1,1], samples_per_epoch=26000, max_gap=30, num_test_frames=3, num_train_frames=3, processing=data_processing_train) loader_train = LTRLoader('train', dataset_train, training=True, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=True, drop_last=True, stack_dim=1) # Validation samplers and loaders dataset_val = sampler.DiMPSampler([got10k_val], [1], samples_per_epoch=5000, max_gap=30, num_test_frames=3, num_train_frames=3, processing=data_processing_val) loader_val = LTRLoader('val', dataset_val, training=False, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=False, drop_last=True, epoch_interval=5, stack_dim=1) # Create network and actor net = dimpnet.dimpnet50(filter_size=settings.target_filter_sz, backbone_pretrained=True, optim_iter=5, clf_feat_norm=True, clf_feat_blocks=0, final_conv=True, out_feature_dim=512, optim_init_step=0.9, optim_init_reg=0.1, init_gauss_sigma=output_sigma * settings.feature_sz, num_dist_bins=100, bin_displacement=0.1, mask_init_factor=3.0, target_mask_act='sigmoid', score_act='relu') # Wrap the network for multi GPU training if settings.multi_gpu: net = MultiGPU(net, dim=1) objective = {'iou': nn.MSELoss(), 'test_clf': ltr_losses.LBHinge(threshold=settings.hinge_threshold)} loss_weight = {'iou': 1, 'test_clf': 100, 'test_init_clf': 100, 'test_iter_clf': 400} actor = actors.DiMPActor(net=net, objective=objective, loss_weight=loss_weight) # Optimizer optimizer = optim.Adam([{'params': actor.net.classifier.filter_initializer.parameters(), 'lr': 5e-5}, {'params': actor.net.classifier.filter_optimizer.parameters(), 'lr': 5e-4}, {'params': actor.net.classifier.feature_extractor.parameters(), 'lr': 5e-5}, {'params': actor.net.bb_regressor.parameters()}, {'params': actor.net.feature_extractor.parameters(), 'lr': 2e-5}], lr=2e-4) lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.2) trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler) trainer.train(50, load_latest=True, fail_safe=True) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/train_settings/dimp/prdimp18.py ================================================ import torch.optim as optim from ltr.dataset import Lasot, Got10k, TrackingNet, MSCOCOSeq from ltr.data import processing, sampler, LTRLoader from ltr.models.tracking import dimpnet import ltr.models.loss as ltr_losses import ltr.models.loss.kl_regression as klreg_losses import ltr.actors.tracking as tracking_actors from ltr.trainers import LTRTrainer import ltr.data.transforms as tfm from ltr import MultiGPU def run(settings): settings.description = 'Default train settings for PrDiMP with ResNet18 as backbone.' settings.batch_size = 26 settings.num_workers = 8 settings.multi_gpu = False settings.print_interval = 1 settings.normalize_mean = [0.485, 0.456, 0.406] settings.normalize_std = [0.229, 0.224, 0.225] settings.search_area_factor = 5.0 settings.output_sigma_factor = 1/4 settings.target_filter_sz = 4 settings.feature_sz = 18 settings.output_sz = settings.feature_sz * 16 settings.center_jitter_factor = {'train': 3, 'test': 4.5} settings.scale_jitter_factor = {'train': 0.25, 'test': 0.5} settings.hinge_threshold = 0.05 settings.print_stats = ['Loss/total', 'Loss/bb_ce', 'ClfTrain/clf_ce'] # Train datasets lasot_train = Lasot(settings.env.lasot_dir, split='train') got10k_train = Got10k(settings.env.got10k_dir, split='vottrain') trackingnet_train = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(4))) coco_train = MSCOCOSeq(settings.env.coco_dir) # Validation datasets got10k_val = Got10k(settings.env.got10k_dir, split='votval') # Data transform transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05)) transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) transform_val = tfm.Transform(tfm.ToTensor(), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # The tracking pairs processing module output_sigma = settings.output_sigma_factor / settings.search_area_factor proposal_params = {'boxes_per_frame': 128, 'gt_sigma': (0.05, 0.05), 'proposal_sigma': [(0.05, 0.05), (0.5, 0.5)]} label_params = {'feature_sz': settings.feature_sz, 'sigma_factor': output_sigma, 'kernel_sz': settings.target_filter_sz} label_density_params = {'feature_sz': settings.feature_sz, 'sigma_factor': output_sigma, 'kernel_sz': settings.target_filter_sz, 'normalize': True} data_processing_train = processing.KLDiMPProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, label_function_params=label_params, label_density_params=label_density_params, transform=transform_train, joint_transform=transform_joint) data_processing_val = processing.KLDiMPProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, label_function_params=label_params, label_density_params=label_density_params, transform=transform_val, joint_transform=transform_joint) # Train sampler and loader dataset_train = sampler.DiMPSampler([lasot_train, got10k_train, trackingnet_train, coco_train], [0.25,1,1,1], samples_per_epoch=26000, max_gap=200, num_test_frames=3, num_train_frames=3, processing=data_processing_train) loader_train = LTRLoader('train', dataset_train, training=True, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=True, drop_last=True, stack_dim=1) # Validation samplers and loaders dataset_val = sampler.DiMPSampler([got10k_val], [1], samples_per_epoch=5000, max_gap=200, num_test_frames=3, num_train_frames=3, processing=data_processing_val) loader_val = LTRLoader('val', dataset_val, training=False, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=False, drop_last=True, epoch_interval=5, stack_dim=1) # Create network and actor net = dimpnet.klcedimpnet18(filter_size=settings.target_filter_sz, backbone_pretrained=True, optim_iter=5, clf_feat_norm=True, final_conv=True, optim_init_step=1.0, optim_init_reg=0.05, optim_min_reg=0.05, gauss_sigma=output_sigma * settings.feature_sz, alpha_eps=0.05, normalize_label=True, init_initializer='zero') # Wrap the network for multi GPU training if settings.multi_gpu: net = MultiGPU(net, dim=1) objective = {'bb_ce': klreg_losses.KLRegression(), 'clf_ce': klreg_losses.KLRegressionGrid()} loss_weight = {'bb_ce': 0.0025, 'clf_ce': 0.25, 'clf_ce_init': 0.25, 'clf_ce_iter': 1.0} actor = tracking_actors.KLDiMPActor(net=net, objective=objective, loss_weight=loss_weight) # Optimizer optimizer = optim.Adam([{'params': actor.net.classifier.parameters(), 'lr': 1e-3}, {'params': actor.net.bb_regressor.parameters(), 'lr': 1e-3}, {'params': actor.net.feature_extractor.parameters()}], lr=2e-4) lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.2) trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler) trainer.train(50, load_latest=True, fail_safe=True) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/train_settings/dimp/prdimp50.py ================================================ import torch.optim as optim from ltr.dataset import Lasot, Got10k, TrackingNet, MSCOCOSeq from ltr.data import processing, sampler, LTRLoader from ltr.models.tracking import dimpnet import ltr.models.loss as ltr_losses import ltr.models.loss.kl_regression as klreg_losses import ltr.actors.tracking as tracking_actors from ltr.trainers import LTRTrainer import ltr.data.transforms as tfm from ltr import MultiGPU def run(settings): settings.description = 'Default train settings for PrDiMP with ResNet50 as backbone.' settings.batch_size = 10 settings.num_workers = 8 settings.multi_gpu = False settings.print_interval = 1 settings.normalize_mean = [0.485, 0.456, 0.406] settings.normalize_std = [0.229, 0.224, 0.225] settings.search_area_factor = 5.0 settings.output_sigma_factor = 1/4 settings.target_filter_sz = 4 settings.feature_sz = 18 settings.output_sz = settings.feature_sz * 16 settings.center_jitter_factor = {'train': 3, 'test': 4.5} settings.scale_jitter_factor = {'train': 0.25, 'test': 0.5} settings.hinge_threshold = 0.05 settings.print_stats = ['Loss/total', 'Loss/bb_ce', 'ClfTrain/clf_ce'] # Train datasets lasot_train = Lasot(settings.env.lasot_dir, split='train') got10k_train = Got10k(settings.env.got10k_dir, split='vottrain') trackingnet_train = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(4))) coco_train = MSCOCOSeq(settings.env.coco_dir) # Validation datasets got10k_val = Got10k(settings.env.got10k_dir, split='votval') # Data transform transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05)) transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) transform_val = tfm.Transform(tfm.ToTensor(), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # The tracking pairs processing module output_sigma = settings.output_sigma_factor / settings.search_area_factor proposal_params = {'boxes_per_frame': 128, 'gt_sigma': (0.05, 0.05), 'proposal_sigma': [(0.05, 0.05), (0.5, 0.5)]} label_params = {'feature_sz': settings.feature_sz, 'sigma_factor': output_sigma, 'kernel_sz': settings.target_filter_sz} label_density_params = {'feature_sz': settings.feature_sz, 'sigma_factor': output_sigma, 'kernel_sz': settings.target_filter_sz, 'normalize': True} data_processing_train = processing.KLDiMPProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, label_function_params=label_params, label_density_params=label_density_params, transform=transform_train, joint_transform=transform_joint) data_processing_val = processing.KLDiMPProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, label_function_params=label_params, label_density_params=label_density_params, transform=transform_val, joint_transform=transform_joint) # Train sampler and loader dataset_train = sampler.DiMPSampler([lasot_train, got10k_train, trackingnet_train, coco_train], [0.25,1,1,1], samples_per_epoch=26000, max_gap=200, num_test_frames=3, num_train_frames=3, processing=data_processing_train) loader_train = LTRLoader('train', dataset_train, training=True, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=True, drop_last=True, stack_dim=1) # Validation samplers and loaders dataset_val = sampler.DiMPSampler([got10k_val], [1], samples_per_epoch=5000, max_gap=200, num_test_frames=3, num_train_frames=3, processing=data_processing_val) loader_val = LTRLoader('val', dataset_val, training=False, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=False, drop_last=True, epoch_interval=5, stack_dim=1) # Create network and actor net = dimpnet.klcedimpnet50(filter_size=settings.target_filter_sz, backbone_pretrained=True, optim_iter=5, clf_feat_norm=True, clf_feat_blocks=0, final_conv=True, out_feature_dim=512, optim_init_step=1.0, optim_init_reg=0.05, optim_min_reg=0.05, gauss_sigma=output_sigma * settings.feature_sz, alpha_eps=0.05, normalize_label=True, init_initializer='zero') # Wrap the network for multi GPU training if settings.multi_gpu: net = MultiGPU(net, dim=1) objective = {'bb_ce': klreg_losses.KLRegression(), 'clf_ce': klreg_losses.KLRegressionGrid()} loss_weight = {'bb_ce': 0.0025, 'clf_ce': 0.25, 'clf_ce_init': 0.25, 'clf_ce_iter': 1.0} actor = tracking_actors.KLDiMPActor(net=net, objective=objective, loss_weight=loss_weight) # Optimizer optimizer = optim.Adam([{'params': actor.net.classifier.parameters(), 'lr': 1e-3}, {'params': actor.net.bb_regressor.parameters(), 'lr': 1e-3}, {'params': actor.net.feature_extractor.parameters(), 'lr': 2e-5}], lr=2e-4) lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.2) trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler) trainer.train(50, load_latest=True, fail_safe=True) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/train_settings/dimp/super_dimp.py ================================================ import torch.optim as optim from ltr.dataset import Lasot, Got10k, TrackingNet, MSCOCOSeq from ltr.data import processing, sampler, LTRLoader from ltr.models.tracking import dimpnet import ltr.models.loss as ltr_losses import ltr.models.loss.kl_regression as klreg_losses import ltr.actors.tracking as tracking_actors from ltr.trainers import LTRTrainer import ltr.data.transforms as tfm from ltr import MultiGPU def run(settings): settings.description = 'SuperDiMP: Combines the DiMP classifier with the PrDiMP bounding box regressor and better' \ 'training settings (larger batch size, inside_major cropping, and flipping augmentation.' \ 'Gives results significantly better than both DiMP-50 and PrDiMP-50.' settings.batch_size = 20 settings.num_workers = 8 settings.multi_gpu = False settings.print_interval = 1 settings.normalize_mean = [0.485, 0.456, 0.406] settings.normalize_std = [0.229, 0.224, 0.225] settings.search_area_factor = 6.0 settings.output_sigma_factor = 1/4 settings.target_filter_sz = 4 settings.feature_sz = 22 settings.output_sz = settings.feature_sz * 16 settings.center_jitter_factor = {'train': 3, 'test': 5.5} settings.scale_jitter_factor = {'train': 0.25, 'test': 0.5} settings.hinge_threshold = 0.05 # settings.print_stats = ['Loss/total', 'Loss/iou', 'ClfTrain/init_loss', 'ClfTrain/test_loss'] # Train datasets lasot_train = Lasot(settings.env.lasot_dir, split='train') got10k_train = Got10k(settings.env.got10k_dir, split='vottrain') trackingnet_train = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(4))) coco_train = MSCOCOSeq(settings.env.coco_dir) # Validation datasets got10k_val = Got10k(settings.env.got10k_dir, split='votval') # Data transform transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05), tfm.RandomHorizontalFlip(probability=0.5)) transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2), tfm.RandomHorizontalFlip(probability=0.5), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) transform_val = tfm.Transform(tfm.ToTensor(), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # The tracking pairs processing module output_sigma = settings.output_sigma_factor / settings.search_area_factor proposal_params = {'boxes_per_frame': 128, 'gt_sigma': (0.05, 0.05), 'proposal_sigma': [(0.05, 0.05), (0.5, 0.5)]} label_params = {'feature_sz': settings.feature_sz, 'sigma_factor': output_sigma, 'kernel_sz': settings.target_filter_sz} label_density_params = {'feature_sz': settings.feature_sz, 'sigma_factor': output_sigma, 'kernel_sz': settings.target_filter_sz} data_processing_train = processing.KLDiMPProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, crop_type='inside_major', max_scale_change=1.5, mode='sequence', proposal_params=proposal_params, label_function_params=label_params, label_density_params=label_density_params, transform=transform_train, joint_transform=transform_joint) data_processing_val = processing.KLDiMPProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, crop_type='inside_major', max_scale_change=1.5, mode='sequence', proposal_params=proposal_params, label_function_params=label_params, label_density_params=label_density_params, transform=transform_val, joint_transform=transform_joint) # Train sampler and loader dataset_train = sampler.DiMPSampler([lasot_train, got10k_train, trackingnet_train, coco_train], [1,1,1,1], samples_per_epoch=40000, max_gap=200, num_test_frames=3, num_train_frames=3, processing=data_processing_train) loader_train = LTRLoader('train', dataset_train, training=True, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=True, drop_last=True, stack_dim=1) # Validation samplers and loaders dataset_val = sampler.DiMPSampler([got10k_val], [1], samples_per_epoch=10000, max_gap=200, num_test_frames=3, num_train_frames=3, processing=data_processing_val) loader_val = LTRLoader('val', dataset_val, training=False, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=False, drop_last=True, epoch_interval=5, stack_dim=1) # Create network and actor net = dimpnet.dimpnet50(filter_size=settings.target_filter_sz, backbone_pretrained=True, optim_iter=5, clf_feat_norm=True, clf_feat_blocks=0, final_conv=True, out_feature_dim=512, optim_init_step=0.9, optim_init_reg=0.1, init_gauss_sigma=output_sigma * settings.feature_sz, num_dist_bins=100, bin_displacement=0.1, mask_init_factor=3.0, target_mask_act='sigmoid', score_act='relu', frozen_backbone_layers=['conv1', 'bn1', 'layer1', 'layer2']) # Wrap the network for multi GPU training if settings.multi_gpu: net = MultiGPU(net, dim=1) objective = {'bb_ce': klreg_losses.KLRegression(), 'test_clf': ltr_losses.LBHinge(threshold=settings.hinge_threshold)} loss_weight = {'bb_ce': 0.01, 'test_clf': 100, 'test_init_clf': 100, 'test_iter_clf': 400} actor = tracking_actors.KLDiMPActor(net=net, objective=objective, loss_weight=loss_weight) # Optimizer optimizer = optim.Adam([{'params': actor.net.classifier.filter_initializer.parameters(), 'lr': 5e-5}, {'params': actor.net.classifier.filter_optimizer.parameters(), 'lr': 5e-4}, {'params': actor.net.classifier.feature_extractor.parameters(), 'lr': 5e-5}, {'params': actor.net.bb_regressor.parameters(), 'lr': 1e-3}, {'params': actor.net.feature_extractor.layer3.parameters(), 'lr': 2e-5}], lr=2e-4) lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.2) trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler) trainer.train(50, load_latest=True, fail_safe=True) ================================================ FILE: artrackv2_mindspore/external/AR/ltr/trainers/__init__.py ================================================ from .base_trainer import BaseTrainer from .ltr_trainer import LTRTrainer ================================================ FILE: artrackv2_mindspore/external/AR/ltr/trainers/base_trainer.py ================================================ import os import glob import torch import traceback from ltr.admin import loading, multigpu class BaseTrainer: """Base trainer class. Contains functions for training and saving/loading chackpoints. Trainer classes should inherit from this one and overload the train_epoch function.""" def __init__(self, actor, loaders, optimizer, settings, lr_scheduler=None): """ args: actor - The actor for training the network loaders - list of dataset loaders, e.g. [train_loader, val_loader]. In each epoch, the trainer runs one epoch for each loader. optimizer - The optimizer used for training, e.g. Adam settings - Training settings lr_scheduler - Learning rate scheduler """ self.actor = actor self.optimizer = optimizer self.lr_scheduler = lr_scheduler self.loaders = loaders self.update_settings(settings) self.epoch = 0 self.stats = {} self.device = getattr(settings, 'device', None) if self.device is None: self.device = torch.device("cuda:0" if torch.cuda.is_available() and settings.use_gpu else "cpu") self.actor.to(self.device) def update_settings(self, settings=None): """Updates the trainer settings. Must be called to update internal settings.""" if settings is not None: self.settings = settings if self.settings.env.workspace_dir is not None: self.settings.env.workspace_dir = os.path.expanduser(self.settings.env.workspace_dir) self._checkpoint_dir = os.path.join(self.settings.env.workspace_dir, 'checkpoints') if not os.path.exists(self._checkpoint_dir): os.makedirs(self._checkpoint_dir) else: self._checkpoint_dir = None def train(self, max_epochs, load_latest=False, fail_safe=True): """Do training for the given number of epochs. args: max_epochs - Max number of training epochs, load_latest - Bool indicating whether to resume from latest epoch. fail_safe - Bool indicating whether the training to automatically restart in case of any crashes. """ epoch = -1 num_tries = 10 for i in range(num_tries): try: if load_latest: self.load_checkpoint() for epoch in range(self.epoch+1, max_epochs+1): self.epoch = epoch self.train_epoch() if self.lr_scheduler is not None: self.lr_scheduler.step() if self._checkpoint_dir: self.save_checkpoint() except: print('Training crashed at epoch {}'.format(epoch)) if fail_safe: self.epoch -= 1 load_latest = True print('Traceback for the error!') print(traceback.format_exc()) print('Restarting training from last epoch ...') else: raise print('Finished training!') def train_epoch(self): raise NotImplementedError def save_checkpoint(self): """Saves a checkpoint of the network and other variables.""" net = self.actor.net.module if multigpu.is_multi_gpu(self.actor.net) else self.actor.net actor_type = type(self.actor).__name__ net_type = type(net).__name__ state = { 'epoch': self.epoch, 'actor_type': actor_type, 'net_type': net_type, 'net': net.state_dict(), 'net_info': getattr(net, 'info', None), 'constructor': getattr(net, 'constructor', None), 'optimizer': self.optimizer.state_dict(), 'stats': self.stats, 'settings': self.settings } directory = '{}/{}'.format(self._checkpoint_dir, self.settings.project_path) if not os.path.exists(directory): os.makedirs(directory) # First save as a tmp file tmp_file_path = '{}/{}_ep{:04d}.tmp'.format(directory, net_type, self.epoch) torch.save(state, tmp_file_path) file_path = '{}/{}_ep{:04d}.pth.tar'.format(directory, net_type, self.epoch) # Now rename to actual checkpoint. os.rename seems to be atomic if files are on same filesystem. Not 100% sure os.rename(tmp_file_path, file_path) def load_checkpoint(self, checkpoint = None, fields = None, ignore_fields = None, load_constructor = False): """Loads a network checkpoint file. Can be called in three different ways: load_checkpoint(): Loads the latest epoch from the workspace. Use this to continue training. load_checkpoint(epoch_num): Loads the network at the given epoch number (int). load_checkpoint(path_to_checkpoint): Loads the file from the given absolute path (str). """ net = self.actor.net.module if multigpu.is_multi_gpu(self.actor.net) else self.actor.net actor_type = type(self.actor).__name__ net_type = type(net).__name__ if checkpoint is None: # Load most recent checkpoint checkpoint_list = sorted(glob.glob('{}/{}/{}_ep*.pth.tar'.format(self._checkpoint_dir, self.settings.project_path, net_type))) if checkpoint_list: checkpoint_path = checkpoint_list[-1] else: print('No matching checkpoint file found') return elif isinstance(checkpoint, int): # Checkpoint is the epoch number checkpoint_path = '{}/{}/{}_ep{:04d}.pth.tar'.format(self._checkpoint_dir, self.settings.project_path, net_type, checkpoint) elif isinstance(checkpoint, str): # checkpoint is the path if os.path.isdir(checkpoint): checkpoint_list = sorted(glob.glob('{}/*_ep*.pth.tar'.format(checkpoint))) if checkpoint_list: checkpoint_path = checkpoint_list[-1] else: raise Exception('No checkpoint found') else: checkpoint_path = os.path.expanduser(checkpoint) else: raise TypeError # Load network checkpoint_dict = loading.torch_load_legacy(checkpoint_path) assert net_type == checkpoint_dict['net_type'], 'Network is not of correct type.' if fields is None: fields = checkpoint_dict.keys() if ignore_fields is None: ignore_fields = ['settings'] # Never load the scheduler. It exists in older checkpoints. ignore_fields.extend(['lr_scheduler', 'constructor', 'net_type', 'actor_type', 'net_info']) # Load all fields for key in fields: if key in ignore_fields: continue if key == 'net': net.load_state_dict(checkpoint_dict[key]) elif key == 'optimizer': self.optimizer.load_state_dict(checkpoint_dict[key]) else: setattr(self, key, checkpoint_dict[key]) # Set the net info if load_constructor and 'constructor' in checkpoint_dict and checkpoint_dict['constructor'] is not None: net.constructor = checkpoint_dict['constructor'] if 'net_info' in checkpoint_dict and checkpoint_dict['net_info'] is not None: net.info = checkpoint_dict['net_info'] # Update the epoch in lr scheduler if 'epoch' in fields: self.lr_scheduler.last_epoch = self.epoch return True ================================================ FILE: artrackv2_mindspore/external/AR/ltr/trainers/ltr_trainer.py ================================================ import os from collections import OrderedDict from ltr.trainers import BaseTrainer from ltr.admin.stats import AverageMeter, StatValue from ltr.admin.tensorboard import TensorboardWriter import torch import time class LTRTrainer(BaseTrainer): def __init__(self, actor, loaders, optimizer, settings, lr_scheduler=None): """ args: actor - The actor for training the network loaders - list of dataset loaders, e.g. [train_loader, val_loader]. In each epoch, the trainer runs one epoch for each loader. optimizer - The optimizer used for training, e.g. Adam settings - Training settings lr_scheduler - Learning rate scheduler """ super().__init__(actor, loaders, optimizer, settings, lr_scheduler) self._set_default_settings() # Initialize statistics variables self.stats = OrderedDict({loader.name: None for loader in self.loaders}) # Initialize tensorboard tensorboard_writer_dir = os.path.join(self.settings.env.tensorboard_dir, self.settings.project_path) self.tensorboard_writer = TensorboardWriter(tensorboard_writer_dir, [l.name for l in loaders]) self.move_data_to_gpu = getattr(settings, 'move_data_to_gpu', True) def _set_default_settings(self): # Dict of all default values default = {'print_interval': 10, 'print_stats': None, 'description': ''} for param, default_value in default.items(): if getattr(self.settings, param, None) is None: setattr(self.settings, param, default_value) def cycle_dataset(self, loader): """Do a cycle of training or validation.""" self.actor.train(loader.training) torch.set_grad_enabled(loader.training) self._init_timing() for i, data in enumerate(loader, 1): # get inputs if self.move_data_to_gpu: data = data.to(self.device) data['epoch'] = self.epoch data['settings'] = self.settings # forward pass loss, stats = self.actor(data) # backward pass and update weights if loader.training: self.optimizer.zero_grad() loss.backward() self.optimizer.step() # update statistics batch_size = data['train_images'].shape[loader.stack_dim] self._update_stats(stats, batch_size, loader) # print statistics self._print_stats(i, loader, batch_size) def train_epoch(self): """Do one epoch for each loader.""" for loader in self.loaders: if self.epoch % loader.epoch_interval == 0: self.cycle_dataset(loader) self._stats_new_epoch() self._write_tensorboard() def _init_timing(self): self.num_frames = 0 self.start_time = time.time() self.prev_time = self.start_time def _update_stats(self, new_stats: OrderedDict, batch_size, loader): # Initialize stats if not initialized yet if loader.name not in self.stats.keys() or self.stats[loader.name] is None: self.stats[loader.name] = OrderedDict({name: AverageMeter() for name in new_stats.keys()}) for name, val in new_stats.items(): if name not in self.stats[loader.name].keys(): self.stats[loader.name][name] = AverageMeter() self.stats[loader.name][name].update(val, batch_size) def _print_stats(self, i, loader, batch_size): self.num_frames += batch_size current_time = time.time() batch_fps = batch_size / (current_time - self.prev_time) average_fps = self.num_frames / (current_time - self.start_time) self.prev_time = current_time if i % self.settings.print_interval == 0 or i == loader.__len__(): print_str = '[%s: %d, %d / %d] ' % (loader.name, self.epoch, i, loader.__len__()) print_str += 'FPS: %.1f (%.1f) , ' % (average_fps, batch_fps) for name, val in self.stats[loader.name].items(): if (self.settings.print_stats is None or name in self.settings.print_stats) and hasattr(val, 'avg'): print_str += '%s: %.5f , ' % (name, val.avg) print(print_str[:-5]) def _stats_new_epoch(self): # Record learning rate for loader in self.loaders: if loader.training: lr_list = self.lr_scheduler.get_lr() for i, lr in enumerate(lr_list): var_name = 'LearningRate/group{}'.format(i) if var_name not in self.stats[loader.name].keys(): self.stats[loader.name][var_name] = StatValue() self.stats[loader.name][var_name].update(lr) for loader_stats in self.stats.values(): if loader_stats is None: continue for stat_value in loader_stats.values(): if hasattr(stat_value, 'new_epoch'): stat_value.new_epoch() def _write_tensorboard(self): if self.epoch == 1: self.tensorboard_writer.write_info(self.settings.module_name, self.settings.script_name, self.settings.description) self.tensorboard_writer.write_epoch(self.stats, self.epoch) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/ARcm_seg.py ================================================ import os import sys import torch import numpy as np import cv2 import torch.nn as nn from external.AR.pytracking.utils.loading import load_network from external.AR.ltr.data.processing_utils_SE import sample_target_SE, transform_image_to_crop_SE, map_mask_back env_path = os.path.join(os.path.dirname(__file__), '..') if env_path not in sys.path: sys.path.append(env_path) def mask_torch2numpy(Pmask): Pmask_arr = np.array(Pmask.squeeze().cpu()) # (H,W) (0,1) return Pmask_arr class ARcm_seg(object): def __init__(self, refine_net_dir, search_factor=2.0, input_sz=256): self.refine_network = self.get_network(refine_net_dir) self.search_factor = search_factor self.input_sz = input_sz self.mean = np.array([0.485, 0.456, 0.406]).reshape((1,1,3)) self.std = np.array([0.229, 0.224, 0.225]).reshape((1,1,3)) def initialize(self, frame1, bbox1): ''' :param frame1: cv array (H,W,3) :param bbox1: ndarray (4,) :return: ''' '''Step1: get cropped patch(tensor)''' patch1, h_f, w_f = sample_target_SE(frame1, bbox1, self.search_factor, self.input_sz, mode=cv2.BORDER_CONSTANT) patch1_tensor = self.img_preprocess(patch1) '''Step2: get GT's cooridinate on the cropped patch(tensor)''' crop_sz = torch.Tensor((self.input_sz, self.input_sz)) bbox1_tensor = self.gt_preprocess(bbox1) # (4,) bbox1_crop_tensor = transform_image_to_crop_SE(bbox1_tensor, bbox1_tensor, h_f, w_f, crop_sz).cuda() '''Step3: forward prop (reference branch)''' with torch.no_grad(): self.refine_network.forward_ref(patch1_tensor, bbox1_crop_tensor) '''refine''' def get_mask(self, Cframe, Cbbox, dtm=None, vis=False): ''' :param Cframe: Current frame(cv2 array) :param Cbbox: Current bbox (ndarray) (x1,y1,w,h) :return: mask ''' '''Step1: get cropped patch(tensor)''' Cpatch, h_f, w_f = sample_target_SE(Cframe, Cbbox, self.search_factor, self.input_sz, mode=cv2.BORDER_CONSTANT) Cpatch_tensor = self.img_preprocess(Cpatch) '''Step2: forward prop (test branch)''' with torch.no_grad(): if dtm is not None: '''2020.4.26 support input dtm''' pred = self.refine_network.forward_test(Cpatch_tensor, dtm, mode='mask') else: pred = self.refine_network.forward_test(Cpatch_tensor,mode='mask') Pmask_arr = mask_torch2numpy(pred) mask_arr = map_mask_back(Cframe, Cbbox, self.search_factor, Pmask_arr, mode=cv2.BORDER_CONSTANT) if vis: return mask_arr, Cpatch, Pmask_arr else: return mask_arr def get_network(self,checkpoint_dir): network = load_network(checkpoint_dir) network.cuda() network.eval() return network def img_preprocess(self,img_arr): '''---> Pytorch tensor(RGB),Normal(-1 to 1,subtract mean, divide std) input img_arr (H,W,3) output (1,1,3,H,W) ''' norm_img = ((img_arr/255.0) - self.mean)/(self.std) img_f32 = norm_img.astype(np.float32) img_tensor = torch.from_numpy(img_f32).cuda() img_tensor = img_tensor.permute((2,0,1)) return img_tensor.unsqueeze(dim=0).unsqueeze(dim=0) def gt_preprocess(self,gt_arr): ''' :param gt: ndarray (4,) :return: torch tensor (4,) ''' return torch.from_numpy(gt_arr.astype(np.float32)) def add_frame_mask(frame, mask, threshold=0.5): mask_new = (mask>threshold)*255 #(H,W) frame_new = frame.copy().astype(np.float) frame_new[...,1] += 0.3*mask_new frame_new = frame_new.clip(0,255).astype(np.uint8) return frame_new def add_frame_bbox(frame, refined_box, color): x1, y1, w, h = refined_box.tolist() cv2.rectangle(frame, (int(x1), int(y1)), (int(x1 + w), int(y1 + h)), color, 2) return frame ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/VOT/tracker_DiMP.m ================================================ % Set path to the python in the pytracking conda environment python_path = 'PATH_TO_CONDA_INSTALLATION/envs/pytracking/bin/python'; % Set path to pytracking pytracking_path = 'PATH_TO_VISIONML/pytracking'; % Set path to trax installation. Check % https://trax.readthedocs.io/en/latest/tutorial_compiling.html for % compilation information trax_path = 'PATH_TO_VOT_TOOLKIT/native/trax'; tracker_name = 'dimp'; % Name of the tracker to evaluate runfile_name = 'dimp18_vot'; % Name of the parameter file to use debug = 0; %% tracker_label = [tracker_name, '_', runfile_name]; % Generate python command tracker_command = sprintf(['%s -c "import sys; sys.path.append(''%s'');', ... 'sys.path.append(''%s/support/python'');', ... 'import run_vot;', ... 'run_vot.run_vot(''%s'', ''%s'', debug=%d)"'],... python_path, pytracking_path, trax_path, ... tracker_name, runfile_name, debug); tracker_interpreter = python_path; tracker_linkpath = {[trax_path, '/build'],... [trax_path, '/build/support/client'],... [trax_path, '/build/support/opencv']}; ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/VOT/trackers.ini ================================================ [DiMP] # label = DiMP protocol = traxpython command = run_vot; run_vot.run_vot2020('dimp', 'dimp50') # Set the tracker name and the parameter name # Specify a path to trax python wrapper if it is not visible (separate by ; if using multiple paths) paths = PATH_TO_PYTRACKING # Additional environment paths #env_PATH = ;${PATH} ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/VOT/vot.py ================================================ """ \file vot.py @brief Python utility functions for VOT integration @author Luka Cehovin, Alessio Dore @date 2016, 2019 """ import sys import copy import collections try: import trax except ImportError: raise Exception('TraX support not found. Please add trax module to Python path.') Rectangle = collections.namedtuple('Rectangle', ['x', 'y', 'width', 'height']) Point = collections.namedtuple('Point', ['x', 'y']) Polygon = collections.namedtuple('Polygon', ['points']) class VOT(object): """ Base class for Python VOT integration """ def __init__(self, region_format, channels=None): """ Constructor Args: region_format: Region format options """ assert(region_format in [trax.Region.RECTANGLE, trax.Region.POLYGON]) if channels is None: channels = ['color'] elif channels == 'rgbd': channels = ['color', 'depth'] elif channels == 'rgbt': channels = ['color', 'ir'] elif channels == 'ir': channels = ['ir'] else: raise Exception('Illegal configuration {}.'.format(channels)) self._trax = trax.Server([region_format], [trax.Image.PATH], channels) request = self._trax.wait() assert(request.type == 'initialize') if isinstance(request.region, trax.Polygon): self._region = Polygon([Point(x[0], x[1]) for x in request.region]) else: self._region = Rectangle(*request.region.bounds()) self._image = [str(x) for k, x in request.image.items()] if len(self._image) == 1: self._image = self._image[0] self._trax.status(request.region) def region(self): """ Send configuration message to the client and receive the initialization region and the path of the first image Returns: initialization region """ return self._region def report(self, region, confidence = None): """ Report the tracking results to the client Arguments: region: region for the frame """ assert(isinstance(region, Rectangle) or isinstance(region, Polygon)) if isinstance(region, Polygon): tregion = trax.Polygon.create([(x.x, x.y) for x in region.points]) else: tregion = trax.Rectangle.create(region.x, region.y, region.width, region.height) properties = {} if not confidence is None: properties['confidence'] = confidence self._trax.status(tregion, properties) def frame(self): """ Get a frame (image path) from client Returns: absolute path of the image """ if hasattr(self, "_image"): image = self._image del self._image return tuple(image) request = self._trax.wait() if request.type == 'frame': image = [str(x) for k, x in request.image.items()] if len(image) == 1: image = image[0] return tuple(image) else: return None def quit(self): if hasattr(self, '_trax'): self._trax.quit() def __del__(self): self.quit() ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/VOT2020_super_only_mask_384_HP/dimp_alpha_065.py ================================================ from pytracking.VOT2020_super_only_mask_384_HP.dimp_alpha_seg_class import run_vot_exp import os os.environ['CUDA_VISIBLE_DEVICES'] = '1' # run_vot_exp('dimp','dimp50_vot19','SEbcm',0.60,VIS=False) run_vot_exp('dimp','super_dimp','ARcm_coco_seg_only_mask_384',0.65,VIS=False) # run_vot_exp('dimp','super_dimp','ARcm_coco_seg_only_mask_384',0.65,VIS=True) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/VOT2020_super_only_mask_384_HP/dimp_alpha_seg_class.py ================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import cv2 import torch import vot import sys import time '''Refine module & Pytracking base trackers''' import os from pytracking.evaluation import Tracker from pytracking.ARcm_seg import ARcm_seg from pytracking.vot20_utils import * '''''' '''DiMP-alpha class''' class DIMP_ALPHA(object): def __init__(self, tracker_name='dimp', para_name='dimp50_vot19', refine_model_name='ARcm_coco_seg', threshold=0.15): self.THRES = threshold '''create tracker''' '''DIMP''' tracker_info = Tracker(tracker_name, para_name, None) params = tracker_info.get_parameters() params.visualization = False params.debug = False params.visdom_info = {'use_visdom': False, 'server': '127.0.0.1', 'port': 8097} self.dimp = tracker_info.tracker_class(params) '''Alpha-Refine''' project_path = os.path.join(os.path.dirname(__file__), '..', '..') refine_root = os.path.join(project_path, 'ltr/checkpoints/ltr/ARcm_seg/') refine_path = os.path.join(refine_root, refine_model_name) '''2020.4.25 input size: 384x384''' self.alpha = ARcm_seg(refine_path, input_sz=384) def initialize(self, img_RGB, mask): region = rect_from_mask(mask) self.H, self.W, _ = img_RGB.shape gt_bbox_np = np.array(region).astype(np.float32) '''Initialize dimp for specific video''' gt_bbox_torch = torch.from_numpy(gt_bbox_np) init_info = {} init_info['init_bbox'] = gt_bbox_torch _ = self.dimp.initialize(img_RGB, init_info) '''initilize refinement module for specific video''' self.alpha.initialize(img_RGB, np.array(gt_bbox_np)) def track(self, img_RGB): '''TRACK''' '''base tracker''' outputs = self.dimp.track(img_RGB) pred_bbox = outputs['target_bbox'] '''Step1: Post-Process''' x1, y1, w, h = pred_bbox # add boundary and min size limit x1, y1, x2, y2 = bbox_clip(x1, y1, x1 + w, y1 + h, (self.H, self.W)) w = x2 - x1 h = y2 - y1 new_pos = torch.from_numpy(np.array([y1 + h / 2, x1 + w / 2]).astype(np.float32)) new_target_sz = torch.from_numpy(np.array([h, w]).astype(np.float32)) new_scale = torch.sqrt(new_target_sz.prod() / self.dimp.base_target_sz.prod()) ##### update self.dimp.pos = new_pos.clone() self.dimp.target_sz = new_target_sz self.dimp.target_scale = new_scale bbox_new = [x1, y1, w, h] '''Step2: Mask report''' pred_mask, search, search_mask = self.alpha.get_mask(img_RGB, np.array(bbox_new), vis=True) final_mask = (pred_mask > self.THRES).astype(np.uint8) search_region = search.astype(np.uint8) search_mask = (search_mask > self.THRES).astype(np.uint8) return bbox_new, final_mask, search_region, search_mask def run_vot_exp(tracker_name, para_name, refine_model_name, threshold, VIS=False): torch.set_num_threads(1) # torch.cuda.set_device(CUDA_ID) # set GPU id save_root = os.path.join('', para_name) if VIS and (not os.path.exists(save_root)): os.mkdir(save_root) tracker = DIMP_ALPHA(tracker_name=tracker_name, para_name=para_name, refine_model_name=refine_model_name, threshold=threshold) handle = vot.VOT("mask") selection = handle.region() imagefile = handle.frame() if not imagefile: sys.exit(0) if VIS: '''for vis''' seq_name = imagefile.split('/')[-3] save_v_dir = os.path.join(save_root, seq_name) if not os.path.exists(save_v_dir): os.mkdir(save_v_dir) cur_time = int(time.time() % 10000) save_dir = os.path.join(save_v_dir, str(cur_time)) if not os.path.exists(save_dir): os.makedirs(save_dir) image = cv2.cvtColor(cv2.imread(imagefile), cv2.COLOR_BGR2RGB) # Right # mask given by the toolkit ends with the target (zero-padding to the right and down is needed) mask = make_full_size(selection, (image.shape[1], image.shape[0])) tracker.initialize(image, mask) while True: imagefile = handle.frame() if not imagefile: break image = cv2.cvtColor(cv2.imread(imagefile), cv2.COLOR_BGR2RGB) # Right b1, m, search, search_m = tracker.track(image) handle.report(m) if VIS: '''Visualization''' # original image image_ori = image[:, :, ::-1].copy() # RGB --> BGR image_name = imagefile.split('/')[-1] save_path = os.path.join(save_dir, image_name) cv2.imwrite(save_path, image_ori) # dimp box image_b = image_ori.copy() cv2.rectangle(image_b, (int(b1[0]), int(b1[1])), (int(b1[0] + b1[2]), int(b1[1] + b1[3])), (0, 0, 255), 2) image_b_name = image_name.replace('.jpg', '_bbox.jpg') save_path = os.path.join(save_dir, image_b_name) cv2.imwrite(save_path, image_b) # search region search_bgr = search[:, :, ::-1].copy() search_name = image_name.replace('.jpg', '_search.jpg') save_path = os.path.join(save_dir, search_name) cv2.imwrite(save_path, search_bgr) # search region mask search_bgr_m = search_bgr.astype(np.float32) search_bgr_m[:, :, 1] += 127.0 * search_m search_bgr_m[:, :, 2] += 127.0 * search_m contours, _ = cv2.findContours(search_m, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) search_bgr_m = cv2.drawContours(search_bgr_m, contours, -1, (0, 255, 255), 4) search_bgr_m = search_bgr_m.clip(0, 255).astype(np.uint8) search_name_m = image_name.replace('.jpg', '_search_mask.jpg') save_path = os.path.join(save_dir, search_name_m) cv2.imwrite(save_path, search_bgr_m) # original image + mask image_m = image_ori.copy().astype(np.float32) image_m[:, :, 1] += 127.0 * m image_m[:, :, 2] += 127.0 * m contours, _ = cv2.findContours(m, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) image_m = cv2.drawContours(image_m, contours, -1, (0, 255, 255), 2) image_m = image_m.clip(0, 255).astype(np.uint8) image_mask_name_m = image_name.replace('.jpg', '_mask.jpg') save_path = os.path.join(save_dir, image_mask_name_m) cv2.imwrite(save_path, image_m) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/VOT2020_super_only_mask_384_HP/mixformer_alpha_seg_class.py ================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import cv2 import torch import vot import sys import time import os import numpy as np from lib.test.tracker.mixformer_online import MixFormerOnline from pytracking.ARcm_seg import ARcm_seg from pytracking.vot20_utils import * import lib.test.parameter.mixformer_online as vot_params class MIXFORMER_ALPHA_SEG(object): def __init__(self, tracker, refine_model_name='ARcm_coco_seg', threshold=0.6): self.THRES = threshold self.tracker = tracker '''create tracker''' '''Alpha-Refine''' project_path = os.path.join(os.path.dirname(__file__), '..', '..') refine_root = os.path.join(project_path, 'ltr/checkpoints/ltr/ARcm_seg/') refine_path = os.path.join(refine_root, refine_model_name) '''2020.4.25 input size: 384x384''' self.alpha = ARcm_seg(refine_path, input_sz=384) def initialize(self, image, mask): region = rect_from_mask(mask) # init_info = {'init_bbox': region} # self.tracker.initialize(image, init_info) self.H, self.W, _ = image.shape gt_bbox_np = np.array(region).astype(np.float32) '''Initialize STARK for specific video''' init_info = {'init_bbox': list(gt_bbox_np)} self.tracker.initialize(image, init_info) '''initilize refinement module for specific video''' self.alpha.initialize(image, np.array(gt_bbox_np)) def track(self, img_RGB): '''TRACK''' '''base tracker''' outputs = self.tracker.track(img_RGB) pred_bbox = outputs['target_bbox'] '''Step2: Mask report''' pred_mask, search, search_mask = self.alpha.get_mask(img_RGB, np.array(pred_bbox), vis=True) final_mask = (pred_mask > self.THRES).astype(np.uint8) return final_mask, 1 def make_full_size(x, output_sz): ''' zero-pad input x (right and down) to match output_sz x: numpy array e.g., binary mask output_sz: size of the output [width, height] ''' if x.shape[0] == output_sz[1] and x.shape[1] == output_sz[0]: return x pad_x = output_sz[0] - x.shape[1] if pad_x < 0: x = x[:, :x.shape[1] + pad_x] # padding has to be set to zero, otherwise pad function fails pad_x = 0 pad_y = output_sz[1] - x.shape[0] if pad_y < 0: x = x[:x.shape[0] + pad_y, :] # padding has to be set to zero, otherwise pad function fails pad_y = 0 return np.pad(x, ((0, pad_y), (0, pad_x)), 'constant', constant_values=0) refine_model_name = 'ARcm_coco_seg_only_mask_384' params = vot_params.parameters("baseline", model="mixformer_online_22k.pth.tar") # params = vot_params.parameters("baseline") mixformer = MixFormerOnline(params, "VOT20") tracker = MIXFORMER_ALPHA_SEG(tracker=mixformer, refine_model_name=refine_model_name) handle = vot.VOT("mask") selection = handle.region() imagefile = handle.frame() if not imagefile: sys.exit(0) image = cv2.cvtColor(cv2.imread(imagefile), cv2.COLOR_BGR2RGB) # Right # mask given by the toolkit ends with the target (zero-padding to the right and down is needed) mask = make_full_size(selection, (image.shape[1], image.shape[0])) tracker.H = image.shape[0] tracker.W = image.shape[1] tracker.initialize(image, mask) while True: imagefile = handle.frame() if not imagefile: break image = cv2.cvtColor(cv2.imread(imagefile), cv2.COLOR_BGR2RGB) # Right region, confidence = tracker.track(image) handle.report(region, confidence) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/VOT2020_super_only_mask_384_HP/mixformer_large_alpha_seg_class.py ================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import cv2 import torch import vot import sys import time import os import numpy as np from lib.test.tracker.mixformer_online import MixFormerOnline from pytracking.ARcm_seg import ARcm_seg from pytracking.vot20_utils import * import lib.test.parameter.mixformer_online as vot_params class MIXFORMER_ALPHA_SEG(object): def __init__(self, tracker, refine_model_name='ARcm_coco_seg', threshold=0.6): self.THRES = threshold self.tracker = tracker '''create tracker''' '''Alpha-Refine''' project_path = os.path.join(os.path.dirname(__file__), '..', '..') refine_root = os.path.join(project_path, 'ltr/checkpoints/ltr/ARcm_seg/') refine_path = os.path.join(refine_root, refine_model_name) '''2020.4.25 input size: 384x384''' self.alpha = ARcm_seg(refine_path, input_sz=384) def initialize(self, image, mask): region = rect_from_mask(mask) # init_info = {'init_bbox': region} # self.tracker.initialize(image, init_info) self.H, self.W, _ = image.shape gt_bbox_np = np.array(region).astype(np.float32) '''Initialize STARK for specific video''' init_info = {'init_bbox': list(gt_bbox_np)} self.tracker.initialize(image, init_info) '''initilize refinement module for specific video''' self.alpha.initialize(image, np.array(gt_bbox_np)) def track(self, img_RGB): '''TRACK''' '''base tracker''' outputs = self.tracker.track(img_RGB) pred_bbox = outputs['target_bbox'] '''Step2: Mask report''' pred_mask, search, search_mask = self.alpha.get_mask(img_RGB, np.array(pred_bbox), vis=True) final_mask = (pred_mask > self.THRES).astype(np.uint8) return final_mask, 1 def make_full_size(x, output_sz): ''' zero-pad input x (right and down) to match output_sz x: numpy array e.g., binary mask output_sz: size of the output [width, height] ''' if x.shape[0] == output_sz[1] and x.shape[1] == output_sz[0]: return x pad_x = output_sz[0] - x.shape[1] if pad_x < 0: x = x[:, :x.shape[1] + pad_x] # padding has to be set to zero, otherwise pad function fails pad_x = 0 pad_y = output_sz[1] - x.shape[0] if pad_y < 0: x = x[:x.shape[0] + pad_y, :] # padding has to be set to zero, otherwise pad function fails pad_y = 0 return np.pad(x, ((0, pad_y), (0, pad_x)), 'constant', constant_values=0) refine_model_name = 'ARcm_coco_seg_only_mask_384' # params = vot_params.parameters("baseline_large") params = vot_params.parameters("baseline_large", model="mixformerL_online_22k.pth.tar") mixformer = MixFormerOnline(params, "VOT20") tracker = MIXFORMER_ALPHA_SEG(tracker=mixformer, refine_model_name=refine_model_name) handle = vot.VOT("mask") selection = handle.region() imagefile = handle.frame() if not imagefile: sys.exit(0) image = cv2.cvtColor(cv2.imread(imagefile), cv2.COLOR_BGR2RGB) # Right # mask given by the toolkit ends with the target (zero-padding to the right and down is needed) mask = make_full_size(selection, (image.shape[1], image.shape[0])) tracker.H = image.shape[0] tracker.W = image.shape[1] tracker.initialize(image, mask) while True: imagefile = handle.frame() if not imagefile: break image = cv2.cvtColor(cv2.imread(imagefile), cv2.COLOR_BGR2RGB) # Right region, confidence = tracker.track(image) handle.report(region, confidence) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/VOT2020_super_only_mask_384_HP/vot.py ================================================ """ \file vot.py @brief Python utility functions for VOT integration @author Luka Cehovin, Alessio Dore @date 2016 """ import sys import copy import collections import numpy as np try: import trax except ImportError: raise Exception('TraX support not found. Please add trax module to Python path.') Rectangle = collections.namedtuple('Rectangle', ['x', 'y', 'width', 'height']) Point = collections.namedtuple('Point', ['x', 'y']) Polygon = collections.namedtuple('Polygon', ['points']) class VOT(object): """ Base class for Python VOT integration """ def __init__(self, region_format, channels=None): """ Constructor Args: region_format: Region format options """ assert(region_format in [trax.Region.RECTANGLE, trax.Region.POLYGON, trax.Region.MASK]) if channels is None: channels = ['color'] elif channels == 'rgbd': channels = ['color', 'depth'] elif channels == 'rgbt': channels = ['color', 'ir'] elif channels == 'ir': channels = ['ir'] else: raise Exception('Illegal configuration {}.'.format(channels)) self._trax = trax.Server([region_format], [trax.Image.PATH], channels, customMetadata=dict(vot="python")) request = self._trax.wait() assert(request.type == 'initialize') if isinstance(request.region, trax.Polygon): self._region = Polygon([Point(x[0], x[1]) for x in request.region]) elif isinstance(request.region, trax.Mask): self._region = request.region.array(True) else: self._region = Rectangle(*request.region.bounds()) self._image = [x.path() for k, x in request.image.items()] if len(self._image) == 1: self._image = self._image[0] self._trax.status(request.region) def region(self): """ Send configuration message to the client and receive the initialization region and the path of the first image Returns: initialization region """ return self._region def report(self, region, confidence = None): """ Report the tracking results to the client Arguments: region: region for the frame """ assert(isinstance(region, (Rectangle, Polygon, np.ndarray))) if isinstance(region, Polygon): tregion = trax.Polygon.create([(x.x, x.y) for x in region.points]) elif isinstance(region, np.ndarray): tregion = trax.Mask.create(region) else: tregion = trax.Rectangle.create(region.x, region.y, region.width, region.height) properties = {} if not confidence is None: properties['confidence'] = confidence self._trax.status(tregion, properties) def frame(self): """ Get a frame (image path) from client Returns: absolute path of the image """ if hasattr(self, "_image"): image = self._image del self._image return image request = self._trax.wait() if request.type == 'frame': image = [x.path() for k, x in request.image.items()] if len(image) == 1: return image[0] return image else: return None def quit(self): if hasattr(self, '_trax'): self._trax.quit() def __del__(self): self.quit() ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/__init__.py ================================================ from pytracking.libs import TensorList, TensorDict ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/analysis/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/analysis/evaluate_vos.py ================================================ import os import numpy as np import torch import pandas as pd from collections import OrderedDict from ltr.data.image_loader import imread_indexed from pytracking.evaluation import get_dataset from pathlib import Path from pytracking.analysis.plot_results import generate_formatted_report import pytracking.analysis.vos_utils as utils # Originally db_eval_sequence() in the davis challenge toolkit: def evaluate_sequence(seq_name, segmentations, annotations, object_info, measure='J'): """ Evaluate video sequence results. Arguments: segmentations (dict of ndarray): segmentation labels. annotations (dict of ndarray): ground-truth labels. object_info dict: {object_id: first_frame_index} measure evaluation metric (J,F) """ results = dict(raw=OrderedDict()) _measures = {'J': utils.davis_jaccard_measure, 'F': utils.davis_f_measure} _statistics = {'decay': utils.decay, 'mean': utils.mean, 'recall': utils.recall, 'std': utils.std} for obj_id, first_frame in object_info.items(): r = np.ones((len(annotations))) * np.nan for i, (an, sg) in enumerate(zip(annotations, segmentations)): if list(annotations.keys()).index(first_frame) < i < len(annotations) - 1: r[i] = _measures[measure](annotations[an] == obj_id, segmentations[sg] == obj_id) results['raw'][obj_id] = r for stat, stat_fn in _statistics.items(): results[stat] = [float(stat_fn(r)) for r in results['raw'].values()] return results def evaluate_dataset(results_path, dset_name, measure='J', to_file=True, scores=False, sequences=None, quiet=False): dset = get_dataset(dset_name) results = OrderedDict() dset_scores = [] dset_decay = [] dset_recall = [] if to_file: f = open(results_path / ("evaluation-%s.txt" % measure), "w") def _print(msg): if not quiet: print(msg) if to_file: print(msg, file=f) if sequences is not None: sequences = [sequences] if not isinstance(sequences, (list, tuple)) else sequences target_names = [] for j, sequence in enumerate(dset): if (sequences is not None) and (sequence.name not in sequences): continue # Load all frames frames = sequence.ground_truth_seg annotations = OrderedDict() segmentations = OrderedDict() for f in frames: if f is None: continue file = Path(f) annotations[file.name] = imread_indexed(file) if not scores: segmentations[file.name] = imread_indexed(os.path.join(results_path, sequence.name, file.name)) else: raise NotImplementedError # Find object ids and starting frames object_info = dict() for f_id, d in sequence.init_data.items(): for obj_id in d['object_ids']: object_info[int(obj_id)] = Path(d['mask']).name if 0 in object_info: # Remove background object_info.pop(0) # Evaluate n_seqs = len(dset) n_objs = len(object_info) seq_name = sequence.name _print("%d/%d: %s: %d object%s" % (j + 1, n_seqs, seq_name, n_objs, "s" if n_objs > 1 else "")) r = evaluate_sequence(seq_name, segmentations, annotations, object_info, measure=measure) results[seq_name] = r # Print scores, per frame and object, ignoring NaNs per_obj_score = [] # Per-object accuracies, averaged over the sequence per_frame_score = [] # Per-frame accuracies, averaged over the objects for obj_id, score in r['raw'].items(): target_names.append('{}_{}'.format(seq_name, obj_id)) per_frame_score.append(score) s = utils.mean(score) # Sequence average for one object per_obj_score.append(s) if n_objs > 1: _print("joint {obj}: acc {score:.3f} ┊{apf}┊".format(obj=obj_id, score=s, apf=utils.text_bargraph(score))) # Print mean object score per frame and final score dset_decay.extend(r['decay']) dset_recall.extend(r['recall']) dset_scores.extend(per_obj_score) seq_score = utils.mean(per_obj_score) # Final score seq_mean_score = utils.nanmean(np.array(per_frame_score), axis=0) # Mean object score per frame # Print sequence results _print("final : acc {seq:.3f} ({dset:.3f}) ┊{apf}┊".format( seq=seq_score, dset=np.mean(dset_scores), apf=utils.text_bargraph(seq_mean_score))) _print("%s: %.3f, recall: %.3f, decay: %.3f" % (measure, utils.mean(dset_scores), utils.mean(dset_recall), utils.mean(dset_decay))) if to_file: f.close() return target_names, dset_scores, dset_recall, dset_decay def evaluate_vos(trackers, dataset='yt2019_jjval', force=False): """ evaluate a list of trackers on a vos dataset. args: trackers - list of trackers to evaluate dataset - name of the dataset force - Force re-evaluation. If False, the pre-computed results are loaded if available """ csv_name_global = f'{dataset}_global_results.csv' csv_name_per_sequence = f'{dataset}_per-sequence_results.csv' table_g_all = [] table_seq_all = [] scores = {'J-Mean': [], 'J-Recall': [], 'J-Decay': []} display_names = [] for t in trackers: if t.display_name is not None: disp_name = t.display_name elif t.run_id is not None: disp_name = '{} {}_{:03d}'.format(t.name, t.parameter_name, t.run_id) else: disp_name = '{} {}'.format(t.name, t.parameter_name) display_names.append(disp_name) results_path = t.segmentation_dir csv_name_global_path = os.path.join(results_path, csv_name_global) csv_name_per_sequence_path = os.path.join(results_path, csv_name_per_sequence) if os.path.exists(csv_name_global_path) and os.path.exists(csv_name_per_sequence_path) and not force: table_g = pd.read_csv(csv_name_global_path) table_seq = pd.read_csv(csv_name_per_sequence_path) else: seq_names, dset_scores, dset_recall, dset_decay = evaluate_dataset(results_path, dataset, measure='J', to_file=False, scores=False, sequences=None) g_measures = ['J-Mean', 'J-Recall', 'J-Decay'] g_res = np.array([utils.mean(dset_scores), utils.mean(dset_recall), utils.mean(dset_decay)]) g_res = np.reshape(g_res, [1, len(g_res)]) table_g = pd.DataFrame(data=g_res, columns=g_measures) with open(csv_name_global_path, 'w') as f: table_g.to_csv(f, index=False, float_format="%.3f") seq_measures = ['Sequence', 'J-Mean', 'J-Recall', 'J-Decay'] table_seq = pd.DataFrame(data=list(zip(seq_names, dset_scores, dset_recall, dset_decay)), columns=seq_measures) with open(csv_name_per_sequence_path, 'w') as f: table_seq.to_csv(f, index=False, float_format="%.3f") scores['J-Mean'].append(table_g['J-Mean'].values[0]*100) scores['J-Recall'].append(table_g['J-Recall'].values[0]*100) scores['J-Decay'].append(table_g['J-Decay'].values[0]*100) table_g_all.append(table_g) table_seq_all.append(table_seq) report = generate_formatted_report(display_names, scores) print(report) return table_g_all, table_seq_all ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/analysis/extract_results.py ================================================ import os import sys import importlib import numpy as np from pytracking.utils.load_text import load_text import torch import pickle from tqdm import tqdm env_path = os.path.join(os.path.dirname(__file__), '../..') if env_path not in sys.path: sys.path.append(env_path) from pytracking.evaluation.environment import env_settings def calc_err_center(pred_bb, anno_bb, normalized=False): pred_center = pred_bb[:, :2] + 0.5 * (pred_bb[:, 2:] - 1.0) anno_center = anno_bb[:, :2] + 0.5 * (anno_bb[:, 2:] - 1.0) if normalized: pred_center = pred_center / anno_bb[:, 2:] anno_center = anno_center / anno_bb[:, 2:] err_center = ((pred_center - anno_center)**2).sum(1).sqrt() return err_center def calc_iou_overlap(pred_bb, anno_bb): tl = torch.max(pred_bb[:, :2], anno_bb[:, :2]) br = torch.min(pred_bb[:, :2] + pred_bb[:, 2:] - 1.0, anno_bb[:, :2] + anno_bb[:, 2:] - 1.0) sz = (br - tl + 1.0).clamp(0) # Area intersection = sz.prod(dim=1) union = pred_bb[:, 2:].prod(dim=1) + anno_bb[:, 2:].prod(dim=1) - intersection return intersection / union def calc_seq_err_robust(pred_bb, anno_bb, dataset, target_visible=None): pred_bb = pred_bb.clone() # Check if invalid values are present if torch.isnan(pred_bb).any() or (pred_bb[:, 2:] < 0.0).any(): raise Exception('Error: Invalid results') if torch.isnan(anno_bb).any(): if dataset == 'uav': pass else: raise Exception('Warning: NaNs in annotation') if (pred_bb[:, 2:] == 0.0).any(): for i in range(1, pred_bb.shape[0]): if (pred_bb[i, 2:] == 0.0).any() and not torch.isnan(anno_bb[i, :]).any(): pred_bb[i, :] = pred_bb[i-1, :] if pred_bb.shape[0] != anno_bb.shape[0]: if dataset == 'lasot': if pred_bb.shape[0] > anno_bb.shape[0]: # For monkey-17, there is a mismatch for some trackers. pred_bb = pred_bb[:anno_bb.shape[0], :] else: raise Exception('Mis-match in tracker prediction and GT lengths') else: # print('Warning: Mis-match in tracker prediction and GT lengths') if pred_bb.shape[0] > anno_bb.shape[0]: pred_bb = pred_bb[:anno_bb.shape[0], :] else: pad = torch.zeros((anno_bb.shape[0] - pred_bb.shape[0], 4)).type_as(pred_bb) pred_bb = torch.cat((pred_bb, pad), dim=0) pred_bb[0, :] = anno_bb[0, :] if target_visible is not None: target_visible = target_visible.bool() valid = ((anno_bb[:, 2:] > 0.0).sum(1) == 2) & target_visible else: valid = ((anno_bb[:, 2:] > 0.0).sum(1) == 2) err_center = calc_err_center(pred_bb, anno_bb) err_center_normalized = calc_err_center(pred_bb, anno_bb, normalized=True) err_overlap = calc_iou_overlap(pred_bb, anno_bb) # handle invalid anno cases if dataset in ['uav']: err_center[~valid] = -1.0 else: err_center[~valid] = float("Inf") err_center_normalized[~valid] = -1.0 err_overlap[~valid] = -1.0 if dataset == 'lasot': err_center_normalized[~target_visible] = float("Inf") err_center[~target_visible] = float("Inf") if torch.isnan(err_overlap).any(): raise Exception('Nans in calculated overlap') return err_overlap, err_center, err_center_normalized, valid def extract_results(trackers, dataset, report_name, skip_missing_seq=False, plot_bin_gap=0.05, exclude_invalid_frames=False): settings = env_settings() eps = 1e-16 result_plot_path = os.path.join(settings.result_plot_path, report_name) if not os.path.exists(result_plot_path): os.makedirs(result_plot_path) threshold_set_overlap = torch.arange(0.0, 1.0 + plot_bin_gap, plot_bin_gap, dtype=torch.float64) threshold_set_center = torch.arange(0, 51, dtype=torch.float64) threshold_set_center_norm = torch.arange(0, 51, dtype=torch.float64) / 100.0 avg_overlap_all = torch.zeros((len(dataset), len(trackers)), dtype=torch.float64) ave_success_rate_plot_overlap = torch.zeros((len(dataset), len(trackers), threshold_set_overlap.numel()), dtype=torch.float32) ave_success_rate_plot_center = torch.zeros((len(dataset), len(trackers), threshold_set_center.numel()), dtype=torch.float32) ave_success_rate_plot_center_norm = torch.zeros((len(dataset), len(trackers), threshold_set_center.numel()), dtype=torch.float32) valid_sequence = torch.ones(len(dataset), dtype=torch.uint8) for seq_id, seq in enumerate(tqdm(dataset)): # Load anno anno_bb = torch.tensor(seq.ground_truth_rect) target_visible = torch.tensor(seq.target_visible, dtype=torch.uint8) if seq.target_visible is not None else None for trk_id, trk in enumerate(trackers): # Load results base_results_path = '{}/{}'.format(trk.results_dir, seq.name) results_path = '{}.txt'.format(base_results_path) if os.path.isfile(results_path): pred_bb = torch.tensor(load_text(str(results_path), delimiter=('\t', ','), dtype=np.float64)) else: if skip_missing_seq: valid_sequence[seq_id] = 0 break else: raise Exception('Result not found. {}'.format(results_path)) # Calculate measures err_overlap, err_center, err_center_normalized, valid_frame = calc_seq_err_robust( pred_bb, anno_bb, seq.dataset, target_visible) avg_overlap_all[seq_id, trk_id] = err_overlap[valid_frame].mean() if exclude_invalid_frames: seq_length = valid_frame.long().sum() else: seq_length = anno_bb.shape[0] if seq_length <= 0: raise Exception('Seq length zero') ave_success_rate_plot_overlap[seq_id, trk_id, :] = (err_overlap.view(-1, 1) > threshold_set_overlap.view(1, -1)).sum(0).float() / seq_length ave_success_rate_plot_center[seq_id, trk_id, :] = (err_center.view(-1, 1) <= threshold_set_center.view(1, -1)).sum(0).float() / seq_length ave_success_rate_plot_center_norm[seq_id, trk_id, :] = (err_center_normalized.view(-1, 1) <= threshold_set_center_norm.view(1, -1)).sum(0).float() / seq_length print('\n\nComputed results over {} / {} sequences'.format(valid_sequence.long().sum().item(), valid_sequence.shape[0])) # Prepare dictionary for saving data seq_names = [s.name for s in dataset] tracker_names = [{'name': t.name, 'param': t.parameter_name, 'run_id': t.run_id, 'disp_name': t.display_name} for t in trackers] eval_data = {'sequences': seq_names, 'trackers': tracker_names, 'valid_sequence': valid_sequence.tolist(), 'ave_success_rate_plot_overlap': ave_success_rate_plot_overlap.tolist(), 'ave_success_rate_plot_center': ave_success_rate_plot_center.tolist(), 'ave_success_rate_plot_center_norm': ave_success_rate_plot_center_norm.tolist(), 'avg_overlap_all': avg_overlap_all.tolist(), 'threshold_set_overlap': threshold_set_overlap.tolist(), 'threshold_set_center': threshold_set_center.tolist(), 'threshold_set_center_norm': threshold_set_center_norm.tolist()} with open(result_plot_path + '/eval_data.pkl', 'wb') as fh: pickle.dump(eval_data, fh) return eval_data ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/analysis/playback_results.py ================================================ import os import sys import importlib import numpy as np import torch import time import matplotlib.patches as patches import cv2 as cv import matplotlib.pyplot as plt from pytracking.analysis.plot_results import get_plot_draw_styles from pytracking.utils.plotting import draw_figure from pytracking.evaluation import get_dataset, trackerlist env_path = os.path.join(os.path.dirname(__file__), '../..') if env_path not in sys.path: sys.path.append(env_path) class Display: def __init__(self, sequence_length, plot_draw_styles, sequence_name): self.active = True self.frame_number = 0 self.pause_mode = True self.step_size = 0 self.step_direction = 'forward' self.fig, self.ax = plt.subplots(1) self.fig.canvas.mpl_connect('key_press_event', self.key_callback_fn) plt.tight_layout() self.sequence_length = sequence_length self.sequence_name = sequence_name self.plot_draw_styles = plot_draw_styles def key_callback_fn(self, event): if event.key == ' ': self.pause_mode = not self.pause_mode self.step_size = 0 self.step_direction = 'forward' elif event.key == 'right': if self.pause_mode: self.frame_number += 1 if self.frame_number >= self.sequence_length: self.frame_number = self.sequence_length - 1 elif self.step_direction == 'stop': self.step_direction = 'forward' self.step_size = 0 elif self.step_direction == 'backward' and self.step_size == 0: self.step_direction = 'stop' else: self.step_size += 1 elif event.key == 'left': if self.pause_mode: self.frame_number -= 1 if self.frame_number < 0: self.frame_number = 0 elif self.step_direction == 'stop': self.step_direction = 'backward' self.step_size = 0 elif self.step_direction == 'forward' and self.step_size == 0: self.step_direction = 'stop' else: self.step_size -= 1 elif event.key == 'escape' or event.key == 'q': self.active = False def _get_speed(self): delta = 0 if self.step_direction == 'forward': delta = 2 ** abs(self.step_size) elif self.step_direction == 'backward': delta = -1 * 2 ** abs(self.step_size) return delta def step(self): delta = self._get_speed() self.frame_number += delta if self.frame_number < 0: self.frame_number = 0 elif self.frame_number >= self.sequence_length: self.frame_number = self.sequence_length - 1 def show(self, image, bb_list, trackers, gt=None): self.ax.cla() self.ax.imshow(image) # Draw rects rect_handles = [] for i, bb in enumerate(bb_list): rect = patches.Rectangle((bb[0], bb[1]), bb[2], bb[3], linewidth=1, edgecolor=self.plot_draw_styles[i]['color'], facecolor='none') self.ax.add_patch(rect) rect_handles.append(patches.Rectangle((bb[0], bb[1]), bb[2], bb[3], linewidth=1, edgecolor=self.plot_draw_styles[i]['color'], facecolor=self.plot_draw_styles[i]['color'], label=trackers[i])) if gt is not None: rect = patches.Rectangle((gt[0], gt[1]), gt[2], gt[3], linewidth=2, edgecolor='g', facecolor='none') self.ax.add_patch(rect) rect_handles.append(rect) self.ax.set_axis_off() self.ax.axis('equal') plt.legend(handles=rect_handles, loc=4, borderaxespad=0.) mode = 'manual' if self.pause_mode else 'auto ' speed = self._get_speed() self.fig.suptitle('Sequence: {} Mode: {} Speed: {:d}x'.format(self.sequence_name, mode, speed), fontsize=14) draw_figure(self.fig) def read_image(image_file: str): im = cv.imread(image_file) return cv.cvtColor(im, cv.COLOR_BGR2RGB) def _get_display_name(tracker): if tracker.display_name is None: if tracker.run_id is not None: return '{}_{}_{:03d}'.format(tracker.name, tracker.parameter_name, tracker.run_id) else: return '{}_{}'.format(tracker.name, tracker.parameter_name) else: return tracker.display_name def playback_results(trackers, sequence): """ Playback saved results of input trackers for a particular sequence. You can navigate the sequence using left/right arrow keys. You can also change to 'auto' mode by pressing space bar, in which case the sequence will be replayed at a particular speed. The speed for playback in 'auto' mode can be controlled using the left/right arrow keys. You can exit the application using escape or q keys. """ plot_draw_styles = get_plot_draw_styles() tracker_results = [] # Load results for trk_id, trk in enumerate(trackers): # Load results base_results_path = '{}/{}'.format(trk.results_dir, sequence.name) results_path = '{}.txt'.format(base_results_path) if os.path.isfile(results_path): try: pred_bb = torch.tensor(np.loadtxt(str(results_path), dtype=np.float64)) except: pred_bb = torch.tensor(np.loadtxt(str(results_path), delimiter=',', dtype=np.float64)) else: raise Exception('Result not found. {}'.format(results_path)) tracker_results.append(pred_bb) # Convert to list of shape seq_length * num_trackers * 4 tracker_results = torch.stack(tracker_results, dim=1).tolist() tracker_names = [_get_display_name(t) for t in trackers] display = Display(len(tracker_results), plot_draw_styles, sequence.name) while display.active: frame_number = display.frame_number image = read_image(sequence.frames[frame_number]) display.show(image, tracker_results[frame_number], tracker_names) time.sleep(0.01) if display.pause_mode and display.frame_number == frame_number: time.sleep(0.1) elif not display.pause_mode: display.step() ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/analysis/plot_results.py ================================================ import tikzplotlib import matplotlib import matplotlib.pyplot as plt import os import torch import pickle import json from pytracking.evaluation.environment import env_settings from pytracking.analysis.extract_results import extract_results def get_plot_draw_styles(): plot_draw_style = [{'color': (1.0, 0.0, 0.0), 'line_style': '-'}, {'color': (0.0, 1.0, 0.0), 'line_style': '-'}, {'color': (0.0, 0.0, 1.0), 'line_style': '-'}, {'color': (0.0, 0.0, 0.0), 'line_style': '-'}, {'color': (1.0, 0.0, 1.0), 'line_style': '-'}, {'color': (0.0, 1.0, 1.0), 'line_style': '-'}, {'color': (0.5, 0.5, 0.5), 'line_style': '-'}, {'color': (136.0 / 255.0, 0.0, 21.0 / 255.0), 'line_style': '-'}, {'color': (1.0, 127.0 / 255.0, 39.0 / 255.0), 'line_style': '-'}, {'color': (0.0, 162.0 / 255.0, 232.0 / 255.0), 'line_style': '-'}, {'color': (0.0, 0.5, 0.0), 'line_style': '-'}, {'color': (1.0, 0.5, 0.2), 'line_style': '-'}, {'color': (0.1, 0.4, 0.0), 'line_style': '-'}, {'color': (0.6, 0.3, 0.9), 'line_style': '-'}, {'color': (0.4, 0.7, 0.1), 'line_style': '-'}, {'color': (0.2, 0.1, 0.7), 'line_style': '-'}, {'color': (0.7, 0.6, 0.2), 'line_style': '-'}] return plot_draw_style def check_eval_data_is_valid(eval_data, trackers, dataset): """ Checks if the pre-computed results are valid""" seq_names = [s.name for s in dataset] seq_names_saved = eval_data['sequences'] tracker_names_f = [(t.name, t.parameter_name, t.run_id) for t in trackers] tracker_names_f_saved = [(t['name'], t['param'], t['run_id']) for t in eval_data['trackers']] return seq_names == seq_names_saved and tracker_names_f == tracker_names_f_saved def merge_multiple_runs(eval_data): new_tracker_names = [] ave_success_rate_plot_overlap_merged = [] ave_success_rate_plot_center_merged = [] ave_success_rate_plot_center_norm_merged = [] avg_overlap_all_merged = [] ave_success_rate_plot_overlap = torch.tensor(eval_data['ave_success_rate_plot_overlap']) ave_success_rate_plot_center = torch.tensor(eval_data['ave_success_rate_plot_center']) ave_success_rate_plot_center_norm = torch.tensor(eval_data['ave_success_rate_plot_center_norm']) avg_overlap_all = torch.tensor(eval_data['avg_overlap_all']) trackers = eval_data['trackers'] merged = torch.zeros(len(trackers), dtype=torch.uint8) for i in range(len(trackers)): if merged[i]: continue base_tracker = trackers[i] new_tracker_names.append(base_tracker) match = [t['name'] == base_tracker['name'] and t['param'] == base_tracker['param'] for t in trackers] match = torch.tensor(match) ave_success_rate_plot_overlap_merged.append(ave_success_rate_plot_overlap[:, match, :].mean(1)) ave_success_rate_plot_center_merged.append(ave_success_rate_plot_center[:, match, :].mean(1)) ave_success_rate_plot_center_norm_merged.append(ave_success_rate_plot_center_norm[:, match, :].mean(1)) avg_overlap_all_merged.append(avg_overlap_all[:, match].mean(1)) merged[match] = 1 ave_success_rate_plot_overlap_merged = torch.stack(ave_success_rate_plot_overlap_merged, dim=1) ave_success_rate_plot_center_merged = torch.stack(ave_success_rate_plot_center_merged, dim=1) ave_success_rate_plot_center_norm_merged = torch.stack(ave_success_rate_plot_center_norm_merged, dim=1) avg_overlap_all_merged = torch.stack(avg_overlap_all_merged, dim=1) eval_data['trackers'] = new_tracker_names eval_data['ave_success_rate_plot_overlap'] = ave_success_rate_plot_overlap_merged.tolist() eval_data['ave_success_rate_plot_center'] = ave_success_rate_plot_center_merged.tolist() eval_data['ave_success_rate_plot_center_norm'] = ave_success_rate_plot_center_norm_merged.tolist() eval_data['avg_overlap_all'] = avg_overlap_all_merged.tolist() return eval_data def get_tracker_display_name(tracker): if tracker['disp_name'] is None: if tracker['run_id'] is None: disp_name = '{}_{}'.format(tracker['name'], tracker['param']) else: disp_name = '{}_{}_{:03d}'.format(tracker['name'], tracker['param'], tracker['run_id']) else: disp_name = tracker['disp_name'] return disp_name def plot_draw_save(y, x, scores, trackers, plot_draw_styles, result_plot_path, plot_opts): # Plot settings font_size = plot_opts.get('font_size', 12) font_size_axis = plot_opts.get('font_size_axis', 13) line_width = plot_opts.get('line_width', 2) font_size_legend = plot_opts.get('font_size_legend', 13) plot_type = plot_opts['plot_type'] legend_loc = plot_opts['legend_loc'] xlabel = plot_opts['xlabel'] ylabel = plot_opts['ylabel'] xlim = plot_opts['xlim'] ylim = plot_opts['ylim'] title = plot_opts['title'] matplotlib.rcParams.update({'font.size': font_size}) matplotlib.rcParams.update({'axes.titlesize': font_size_axis}) matplotlib.rcParams.update({'axes.titleweight': 'black'}) matplotlib.rcParams.update({'axes.labelsize': font_size_axis}) fig, ax = plt.subplots() index_sort = scores.argsort(descending=False) plotted_lines = [] legend_text = [] for id, id_sort in enumerate(index_sort): line = ax.plot(x.tolist(), y[id_sort, :].tolist(), linewidth=line_width, color=plot_draw_styles[index_sort.numel() - id - 1]['color'], linestyle=plot_draw_styles[index_sort.numel() - id - 1]['line_style']) plotted_lines.append(line[0]) tracker = trackers[id_sort] disp_name = get_tracker_display_name(tracker) legend_text.append('{} [{:.1f}]'.format(disp_name, scores[id_sort])) ax.legend(plotted_lines[::-1], legend_text[::-1], loc=legend_loc, fancybox=False, edgecolor='black', fontsize=font_size_legend, framealpha=1.0) ax.set(xlabel=xlabel, ylabel=ylabel, xlim=xlim, ylim=ylim, title=title) ax.grid(True, linestyle='-.') fig.tight_layout() tikzplotlib.save('{}/{}_plot.tex'.format(result_plot_path, plot_type)) fig.savefig('{}/{}_plot.pdf'.format(result_plot_path, plot_type), dpi=300, format='pdf', transparent=True) plt.draw() def check_and_load_precomputed_results(trackers, dataset, report_name, force_evaluation=False, **kwargs): # Load data settings = env_settings() # Load pre-computed results result_plot_path = os.path.join(settings.result_plot_path, report_name) eval_data_path = os.path.join(result_plot_path, 'eval_data.pkl') if os.path.isfile(eval_data_path) and not force_evaluation: with open(eval_data_path, 'rb') as fh: eval_data = pickle.load(fh) else: # print('Pre-computed evaluation data not found. Computing results!') eval_data = extract_results(trackers, dataset, report_name, **kwargs) if not check_eval_data_is_valid(eval_data, trackers, dataset): # print('Pre-computed evaluation data invalid. Re-computing results!') eval_data = extract_results(trackers, dataset, report_name, **kwargs) else: # Update display names tracker_names = [{'name': t.name, 'param': t.parameter_name, 'run_id': t.run_id, 'disp_name': t.display_name} for t in trackers] eval_data['trackers'] = tracker_names return eval_data def get_auc_curve(ave_success_rate_plot_overlap, valid_sequence): ave_success_rate_plot_overlap = ave_success_rate_plot_overlap[valid_sequence, :, :] auc_curve = ave_success_rate_plot_overlap.mean(0) * 100.0 auc = auc_curve.mean(-1) return auc_curve, auc def get_prec_curve(ave_success_rate_plot_center, valid_sequence): ave_success_rate_plot_center = ave_success_rate_plot_center[valid_sequence, :, :] prec_curve = ave_success_rate_plot_center.mean(0) * 100.0 prec_score = prec_curve[:, 20] return prec_curve, prec_score def plot_results(trackers, dataset, report_name, merge_results=False, plot_types=('success'), force_evaluation=False, **kwargs): """ Plot results for the given trackers args: trackers - List of trackers to evaluate dataset - List of sequences to evaluate report_name - Name of the folder in env_settings.perm_mat_path where the computed results and plots are saved merge_results - If True, multiple random runs for a non-deterministic trackers are averaged plot_types - List of scores to display. Can contain 'success', 'prec' (precision), and 'norm_prec' (normalized precision) """ # Load data settings = env_settings() plot_draw_styles = get_plot_draw_styles() # Load pre-computed results result_plot_path = os.path.join(settings.result_plot_path, report_name) eval_data = check_and_load_precomputed_results(trackers, dataset, report_name, force_evaluation, **kwargs) # Merge results from multiple runs if merge_results: eval_data = merge_multiple_runs(eval_data) tracker_names = eval_data['trackers'] valid_sequence = torch.tensor(eval_data['valid_sequence'], dtype=torch.bool) print('\nPlotting results over {} / {} sequences'.format(valid_sequence.long().sum().item(), valid_sequence.shape[0])) print('\nGenerating plots for: {}'.format(report_name)) # ******************************** Success Plot ************************************** if 'success' in plot_types: ave_success_rate_plot_overlap = torch.tensor(eval_data['ave_success_rate_plot_overlap']) # Index out valid sequences auc_curve, auc = get_auc_curve(ave_success_rate_plot_overlap, valid_sequence) threshold_set_overlap = torch.tensor(eval_data['threshold_set_overlap']) success_plot_opts = {'plot_type': 'success', 'legend_loc': 'lower left', 'xlabel': 'Overlap threshold', 'ylabel': 'Overlap Precision [%]', 'xlim': (0, 1.0), 'ylim': (0, 100), 'title': 'Success plot'} plot_draw_save(auc_curve, threshold_set_overlap, auc, tracker_names, plot_draw_styles, result_plot_path, success_plot_opts) # ******************************** Precision Plot ************************************** if 'prec' in plot_types: ave_success_rate_plot_center = torch.tensor(eval_data['ave_success_rate_plot_center']) # Index out valid sequences prec_curve, prec_score = get_prec_curve(ave_success_rate_plot_center, valid_sequence) threshold_set_center = torch.tensor(eval_data['threshold_set_center']) precision_plot_opts = {'plot_type': 'precision', 'legend_loc': 'lower right', 'xlabel': 'Location error threshold [pixels]', 'ylabel': 'Distance Precision [%]', 'xlim': (0, 50), 'ylim': (0, 100), 'title': 'Precision plot'} plot_draw_save(prec_curve, threshold_set_center, prec_score, tracker_names, plot_draw_styles, result_plot_path, precision_plot_opts) # ******************************** Norm Precision Plot ************************************** if 'norm_prec' in plot_types: ave_success_rate_plot_center_norm = torch.tensor(eval_data['ave_success_rate_plot_center_norm']) # Index out valid sequences prec_curve, prec_score = get_prec_curve(ave_success_rate_plot_center_norm, valid_sequence) threshold_set_center_norm = torch.tensor(eval_data['threshold_set_center_norm']) norm_precision_plot_opts = {'plot_type': 'norm_precision', 'legend_loc': 'lower right', 'xlabel': 'Location error threshold', 'ylabel': 'Distance Precision [%]', 'xlim': (0, 0.5), 'ylim': (0, 100), 'title': 'Normalized Precision plot'} plot_draw_save(prec_curve, threshold_set_center_norm, prec_score, tracker_names, plot_draw_styles, result_plot_path, norm_precision_plot_opts) plt.show() def generate_formatted_report(row_labels, scores, table_name=''): name_width = max([len(d) for d in row_labels] + [len(table_name)]) + 5 min_score_width = 10 report_text = '\n{label: <{width}} |'.format(label=table_name, width=name_width) score_widths = [max(min_score_width, len(k) + 3) for k in scores.keys()] for s, s_w in zip(scores.keys(), score_widths): report_text = '{prev} {s: <{width}} |'.format(prev=report_text, s=s, width=s_w) report_text = '{prev}\n'.format(prev=report_text) for trk_id, d_name in enumerate(row_labels): # display name report_text = '{prev}{tracker: <{width}} |'.format(prev=report_text, tracker=d_name, width=name_width) for (score_type, score_value), s_w in zip(scores.items(), score_widths): report_text = '{prev} {score: <{width}} |'.format(prev=report_text, score='{:0.2f}'.format(score_value[trk_id].item()), width=s_w) report_text = '{prev}\n'.format(prev=report_text) return report_text def print_results(trackers, dataset, report_name, merge_results=False, plot_types=('success'), **kwargs): """ Print the results for the given trackers in a formatted table args: trackers - List of trackers to evaluate dataset - List of sequences to evaluate report_name - Name of the folder in env_settings.perm_mat_path where the computed results and plots are saved merge_results - If True, multiple random runs for a non-deterministic trackers are averaged plot_types - List of scores to display. Can contain 'success' (prints AUC, OP50, and OP75 scores), 'prec' (prints precision score), and 'norm_prec' (prints normalized precision score) """ # Load pre-computed results eval_data = check_and_load_precomputed_results(trackers, dataset, report_name, **kwargs) # Merge results from multiple runs if merge_results: eval_data = merge_multiple_runs(eval_data) tracker_names = eval_data['trackers'] valid_sequence = torch.tensor(eval_data['valid_sequence'], dtype=torch.bool) print('\nReporting results over {} / {} sequences'.format(valid_sequence.long().sum().item(), valid_sequence.shape[0])) scores = {} # ******************************** Success Plot ************************************** if 'success' in plot_types: threshold_set_overlap = torch.tensor(eval_data['threshold_set_overlap']) ave_success_rate_plot_overlap = torch.tensor(eval_data['ave_success_rate_plot_overlap']) # Index out valid sequences auc_curve, auc = get_auc_curve(ave_success_rate_plot_overlap, valid_sequence) scores['AUC'] = auc scores['OP50'] = auc_curve[:, threshold_set_overlap == 0.50] scores['OP75'] = auc_curve[:, threshold_set_overlap == 0.75] # ******************************** Precision Plot ************************************** if 'prec' in plot_types: ave_success_rate_plot_center = torch.tensor(eval_data['ave_success_rate_plot_center']) # Index out valid sequences prec_curve, prec_score = get_prec_curve(ave_success_rate_plot_center, valid_sequence) scores['Precision'] = prec_score # ******************************** Norm Precision Plot ********************************* if 'norm_prec' in plot_types: ave_success_rate_plot_center_norm = torch.tensor(eval_data['ave_success_rate_plot_center_norm']) # Index out valid sequences norm_prec_curve, norm_prec_score = get_prec_curve(ave_success_rate_plot_center_norm, valid_sequence) scores['Norm Precision'] = norm_prec_score # Print tracker_disp_names = [get_tracker_display_name(trk) for trk in tracker_names] report_text = generate_formatted_report(tracker_disp_names, scores, table_name=report_name) print(report_text) def plot_got_success(trackers, report_name): """ Plot success plot for GOT-10k dataset using the json reports. Save the json reports from http://got-10k.aitestunion.com/leaderboard in the directory set to env_settings.got_reports_path The tracker name in the experiment file should be set to the name of the report file for that tracker, e.g. DiMP50_report_2019_09_02_15_44_25 if the report is name DiMP50_report_2019_09_02_15_44_25.json args: trackers - List of trackers to evaluate report_name - Name of the folder in env_settings.perm_mat_path where the computed results and plots are saved """ # Load data settings = env_settings() plot_draw_styles = get_plot_draw_styles() result_plot_path = os.path.join(settings.result_plot_path, report_name) auc_curve = torch.zeros((len(trackers), 101)) scores = torch.zeros(len(trackers)) # Load results tracker_names = [] for trk_id, trk in enumerate(trackers): json_path = '{}/{}.json'.format(settings.got_reports_path, trk.name) if os.path.isfile(json_path): with open(json_path, 'r') as f: eval_data = json.load(f) else: raise Exception('Report not found {}'.format(json_path)) if len(eval_data.keys()) > 1: raise Exception # First field is the tracker name. Index it out eval_data = eval_data[list(eval_data.keys())[0]] if 'succ_curve' in eval_data.keys(): curve = eval_data['succ_curve'] ao = eval_data['ao'] elif 'overall' in eval_data.keys() and 'succ_curve' in eval_data['overall'].keys(): curve = eval_data['overall']['succ_curve'] ao = eval_data['overall']['ao'] else: raise Exception('Invalid JSON file {}'.format(json_path)) auc_curve[trk_id, :] = torch.tensor(curve) * 100.0 scores[trk_id] = ao * 100.0 tracker_names.append({'name': trk.name, 'param': trk.parameter_name, 'run_id': trk.run_id, 'disp_name': trk.display_name}) threshold_set_overlap = torch.arange(0.0, 1.01, 0.01, dtype=torch.float64) success_plot_opts = {'plot_type': 'success', 'legend_loc': 'lower left', 'xlabel': 'Overlap threshold', 'ylabel': 'Overlap Precision [%]', 'xlim': (0, 1.0), 'ylim': (0, 100), 'title': 'Success plot'} plot_draw_save(auc_curve, threshold_set_overlap, scores, tracker_names, plot_draw_styles, result_plot_path, success_plot_opts) plt.show() def print_per_sequence_results(trackers, dataset, report_name, merge_results=False, filter_criteria=None, **kwargs): """ Print per-sequence results for the given trackers. Additionally, the sequences to list can be filtered using the filter criteria. args: trackers - List of trackers to evaluate dataset - List of sequences to evaluate report_name - Name of the folder in env_settings.perm_mat_path where the computed results and plots are saved merge_results - If True, multiple random runs for a non-deterministic trackers are averaged filter_criteria - Filter sequence results which are reported. Following modes are supported None: No filtering. Display results for all sequences in dataset 'ao_min': Only display sequences for which the minimum average overlap (AO) score over the trackers is less than a threshold filter_criteria['threshold']. This mode can be used to select sequences where at least one tracker performs poorly. 'ao_max': Only display sequences for which the maximum average overlap (AO) score over the trackers is less than a threshold filter_criteria['threshold']. This mode can be used to select sequences all tracker performs poorly. 'delta_ao': Only display sequences for which the performance of different trackers vary by at least filter_criteria['threshold'] in average overlap (AO) score. This mode can be used to select sequences where the behaviour of the trackers greatly differ between each other. """ # Load pre-computed results eval_data = check_and_load_precomputed_results(trackers, dataset, report_name, **kwargs) # Merge results from multiple runs if merge_results: eval_data = merge_multiple_runs(eval_data) tracker_names = eval_data['trackers'] valid_sequence = torch.tensor(eval_data['valid_sequence'], dtype=torch.bool) sequence_names = eval_data['sequences'] avg_overlap_all = torch.tensor(eval_data['avg_overlap_all']) * 100.0 # Filter sequences if filter_criteria is not None: if filter_criteria['mode'] == 'ao_min': min_ao = avg_overlap_all.min(dim=1)[0] valid_sequence = valid_sequence & (min_ao < filter_criteria['threshold']) elif filter_criteria['mode'] == 'ao_max': max_ao = avg_overlap_all.max(dim=1)[0] valid_sequence = valid_sequence & (max_ao < filter_criteria['threshold']) elif filter_criteria['mode'] == 'delta_ao': min_ao = avg_overlap_all.min(dim=1)[0] max_ao = avg_overlap_all.max(dim=1)[0] valid_sequence = valid_sequence & ((max_ao - min_ao) > filter_criteria['threshold']) else: raise Exception avg_overlap_all = avg_overlap_all[valid_sequence, :] sequence_names = [s + ' (ID={})'.format(i) for i, (s, v) in enumerate(zip(sequence_names, valid_sequence.tolist())) if v] tracker_disp_names = [get_tracker_display_name(trk) for trk in tracker_names] scores_per_tracker = {k: avg_overlap_all[:, i] for i, k in enumerate(tracker_disp_names)} report_text = generate_formatted_report(sequence_names, scores_per_tracker) print(report_text) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/analysis/vos_utils.py ================================================ import warnings import numpy as np from skimage.morphology import binary_dilation, disk from math import floor def text_bargraph(values): blocks = np.array(('u', ' ', '▁', '▂', '▃', '▄', '▅', '▆', '▇', '█', 'o')) nsteps = len(blocks)-2-1 hstep = 1 / (2*nsteps) values = np.array(values) nans = np.isnan(values) values[nans] = 0 # '░' indices = ((values + hstep) * nsteps + 1).astype(np.int) indices[values < 0] = 0 indices[values > 1] = len(blocks)-1 graph = blocks[indices] graph[nans] = '░' graph = str.join('', graph) return graph # ---------------------------------------------------------------------------- # The 2017 DAVIS Challenge on Video Object Segmentation # ----------------------------------------------------------------------------- # Copyright (c) 2017 Federico Perazzi # Licensed under the BSD License [see LICENSE for details] # Written by Federico Perazzi (federico@disneyresearch.com) # Adapted from DAVIS 2016 (Federico Perazzi) # ---------------------------------------------------------------------------- # Originally db_eval_iou() in the davis challenge toolkit: def davis_jaccard_measure(fg_mask, gt_mask): """ Compute region similarity as the Jaccard Index. :param fg_mask: (ndarray): binary segmentation map. :param gt_mask: (ndarray): binary annotation map. :return: jaccard (float): region similarity """ gt_mask = gt_mask.astype(np.bool) fg_mask = fg_mask.astype(np.bool) if np.isclose(np.sum(gt_mask), 0) and np.isclose(np.sum(fg_mask), 0): return 1 else: return np.sum((gt_mask & fg_mask)) / \ np.sum((gt_mask | fg_mask), dtype=np.float32) def davis_jaccard_measure_torch(fg_mask, gt_mask): """ Compute region similarity as the Jaccard Index. :param fg_mask: (ndarray): binary segmentation map. :param gt_mask: (ndarray): binary annotation map. :return: jaccard (float): region similarity """ #gt_mask = gt_mask.astype(np.bool) #fg_mask = fg_mask.astype(np.bool) if gt_mask.sum() == 0 and fg_mask.sum() == 0: return 1 else: return (gt_mask & fg_mask).sum() / \ (gt_mask | fg_mask).sum().float() # Originally db_eval_boundary() in the davis challenge toolkit: def davis_f_measure(foreground_mask, gt_mask, bound_th=0.008): """ Compute mean,recall and decay from per-frame evaluation. Calculates precision/recall for boundaries between foreground_mask and gt_mask using morphological operators to speed it up. Arguments: foreground_mask (ndarray): binary segmentation image. gt_mask (ndarray): binary annotated image. Returns: F (float): boundaries F-measure P (float): boundaries precision R (float): boundaries recall """ assert np.atleast_3d(foreground_mask).shape[2] == 1 bound_pix = bound_th if bound_th >= 1 else \ np.ceil(bound_th * np.linalg.norm(foreground_mask.shape)) # Get the pixel boundaries of both masks fg_boundary = seg2bmap(foreground_mask) gt_boundary = seg2bmap(gt_mask) fg_dil = binary_dilation(fg_boundary, disk(bound_pix)) gt_dil = binary_dilation(gt_boundary, disk(bound_pix)) # Get the intersection gt_match = gt_boundary * fg_dil fg_match = fg_boundary * gt_dil # Area of the intersection n_fg = np.sum(fg_boundary) n_gt = np.sum(gt_boundary) # % Compute precision and recall if n_fg == 0 and n_gt > 0: precision = 1 recall = 0 elif n_fg > 0 and n_gt == 0: precision = 0 recall = 1 elif n_fg == 0 and n_gt == 0: precision = 1 recall = 1 else: precision = np.sum(fg_match) / float(n_fg) recall = np.sum(gt_match) / float(n_gt) # Compute F measure if precision + recall == 0: F = 0 else: F = 2 * precision * recall / (precision + recall) return F def seg2bmap(seg, width=None, height=None): """ From a segmentation, compute a binary boundary map with 1 pixel wide boundaries. The boundary pixels are offset by 1/2 pixel towards the origin from the actual segment boundary. Arguments: seg : Segments labeled from 1..k. width : Width of desired bmap <= seg.shape[1] height : Height of desired bmap <= seg.shape[0] Returns: bmap (ndarray): Binary boundary map. David Martin January 2003 """ seg = seg.astype(np.bool) seg[seg > 0] = 1 assert np.atleast_3d(seg).shape[2] == 1 width = seg.shape[1] if width is None else width height = seg.shape[0] if height is None else height h, w = seg.shape[:2] ar1 = float(width) / float(height) ar2 = float(w) / float(h) assert not (width > w | height > h | abs(ar1 - ar2) > 0.01), \ 'Can''t convert %dx%d seg to %dx%d bmap.' % (w, h, width, height) e = np.zeros_like(seg) s = np.zeros_like(seg) se = np.zeros_like(seg) e[:, :-1] = seg[:, 1:] s[:-1, :] = seg[1:, :] se[:-1, :-1] = seg[1:, 1:] b = seg ^ e | seg ^ s | seg ^ se b[-1, :] = seg[-1, :] ^ e[-1, :] b[:, -1] = seg[:, -1] ^ s[:, -1] b[-1, -1] = 0 if w == width and h == height: bmap = b else: bmap = np.zeros((height, width)) for x in range(w): for y in range(h): if b[y, x]: j = 1 + floor((y - 1) + height / h) i = 1 + floor((x - 1) + width / h) bmap[j, i] = 1 return bmap def nanmean(*args, **kwargs): with warnings.catch_warnings(): warnings.simplefilter("ignore", category=RuntimeWarning) return np.nanmean(*args, **kwargs) def mean(X): """ Compute average ignoring NaN values. """ return np.nanmean(X) def recall(X, threshold=0.5): """ Fraction of values of X scoring higher than 'threshold' """ with warnings.catch_warnings(): warnings.simplefilter("ignore", category=RuntimeWarning) x = X[~np.isnan(X)] x = mean(x > threshold) return x def decay(X, n_bins=4): """ Performance loss over time. """ X = X[~np.isnan(X)] ids = np.round(np.linspace(1, len(X), n_bins + 1) + 1e-10) - 1 ids = ids.astype(np.uint8) D_bins = [X[ids[i]:ids[i + 1] + 1] for i in range(0, 4)] with warnings.catch_warnings(): warnings.simplefilter("ignore", category=RuntimeWarning) D = np.nanmean(D_bins[0]) - np.nanmean(D_bins[3]) return D def std(X): """ Compute standard deviation. """ return np.nanstd(X) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/evaluation/__init__.py ================================================ from .data import Sequence from .tracker import Tracker, trackerlist from .datasets import get_dataset ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/evaluation/data.py ================================================ import numpy as np from pytracking.evaluation.environment import env_settings from ltr.data.image_loader import imread_indexed from collections import OrderedDict class BaseDataset: """Base class for all datasets.""" def __init__(self): self.env_settings = env_settings() def __len__(self): """Overload this function in your dataset. This should return number of sequences in the dataset.""" raise NotImplementedError def get_sequence_list(self): """Overload this in your dataset. Should return the list of sequences in the dataset.""" raise NotImplementedError class Sequence: """Class for the sequence in an evaluation.""" def __init__(self, name, frames, dataset, ground_truth_rect, ground_truth_seg=None, init_data=None, object_class=None, target_visible=None, object_ids=None, multiobj_mode=False): self.name = name self.frames = frames self.dataset = dataset self.ground_truth_rect = ground_truth_rect self.ground_truth_seg = ground_truth_seg self.object_class = object_class self.target_visible = target_visible self.object_ids = object_ids self.multiobj_mode = multiobj_mode self.init_data = self._construct_init_data(init_data) self._ensure_start_frame() def _ensure_start_frame(self): # Ensure start frame is 0 start_frame = min(list(self.init_data.keys())) if start_frame > 0: self.frames = self.frames[start_frame:] if self.ground_truth_rect is not None: if isinstance(self.ground_truth_rect, (dict, OrderedDict)): for obj_id, gt in self.ground_truth_rect.items(): self.ground_truth_rect[obj_id] = gt[start_frame:,:] else: self.ground_truth_rect = self.ground_truth_rect[start_frame:,:] if self.ground_truth_seg is not None: self.ground_truth_seg = self.ground_truth_seg[start_frame:] assert len(self.frames) == len(self.ground_truth_seg) if self.target_visible is not None: self.target_visible = self.target_visible[start_frame:] self.init_data = {frame-start_frame: val for frame, val in self.init_data.items()} def _construct_init_data(self, init_data): if init_data is not None: if not self.multiobj_mode: assert self.object_ids is None or len(self.object_ids) == 1 for frame, init_val in init_data.items(): if 'bbox' in init_val and isinstance(init_val['bbox'], (dict, OrderedDict)): init_val['bbox'] = init_val['bbox'][self.object_ids[0]] # convert to list for frame, init_val in init_data.items(): if 'bbox' in init_val: if isinstance(init_val['bbox'], (dict, OrderedDict)): init_val['bbox'] = OrderedDict({obj_id: list(init) for obj_id, init in init_val['bbox'].items()}) else: init_val['bbox'] = list(init_val['bbox']) else: init_data = {0: dict()} # Assume start from frame 0 if self.object_ids is not None: init_data[0]['object_ids'] = self.object_ids if self.ground_truth_rect is not None: if self.multiobj_mode: assert isinstance(self.ground_truth_rect, (dict, OrderedDict)) init_data[0]['bbox'] = OrderedDict({obj_id: list(gt[0,:]) for obj_id, gt in self.ground_truth_rect.items()}) else: assert self.object_ids is None or len(self.object_ids) == 1 if isinstance(self.ground_truth_rect, (dict, OrderedDict)): init_data[0]['bbox'] = list(self.ground_truth_rect[self.object_ids[0]][0, :]) else: init_data[0]['bbox'] = list(self.ground_truth_rect[0,:]) if self.ground_truth_seg is not None: init_data[0]['mask'] = self.ground_truth_seg[0] return init_data def init_info(self): info = self.frame_info(frame_num=0) return info def frame_info(self, frame_num): info = self.object_init_data(frame_num=frame_num) return info def init_bbox(self, frame_num=0): return self.object_init_data(frame_num=frame_num).get('init_bbox') def init_mask(self, frame_num=0): return self.object_init_data(frame_num=frame_num).get('init_mask') def get_info(self, keys, frame_num=None): info = dict() for k in keys: val = self.get(k, frame_num=frame_num) if val is not None: info[k] = val return info def object_init_data(self, frame_num=None) -> dict: if frame_num is None: frame_num = 0 if frame_num not in self.init_data: return dict() init_data = dict() for key, val in self.init_data[frame_num].items(): if val is None: continue init_data['init_'+key] = val if 'init_mask' in init_data and init_data['init_mask'] is not None: anno = imread_indexed(init_data['init_mask']) if not self.multiobj_mode and self.object_ids is not None: assert len(self.object_ids) == 1 anno = (anno == int(self.object_ids[0])).astype(np.uint8) init_data['init_mask'] = anno if self.object_ids is not None: init_data['object_ids'] = self.object_ids init_data['sequence_object_ids'] = self.object_ids return init_data def target_class(self, frame_num=None): return self.object_class def get(self, name, frame_num=None): return getattr(self, name)(frame_num) def __repr__(self): return "{self.__class__.__name__} {self.name}, length={len} frames".format(self=self, len=len(self.frames)) class SequenceList(list): """List of sequences. Supports the addition operator to concatenate sequence lists.""" def __getitem__(self, item): if isinstance(item, str): for seq in self: if seq.name == item: return seq raise IndexError('Sequence name not in the dataset.') elif isinstance(item, int): return super(SequenceList, self).__getitem__(item) elif isinstance(item, (tuple, list)): return SequenceList([super(SequenceList, self).__getitem__(i) for i in item]) else: return SequenceList(super(SequenceList, self).__getitem__(item)) def __add__(self, other): return SequenceList(super(SequenceList, self).__add__(other)) def copy(self): return SequenceList(super(SequenceList, self).copy()) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/evaluation/datasets.py ================================================ from collections import namedtuple import importlib from pytracking.evaluation.data import SequenceList DatasetInfo = namedtuple('DatasetInfo', ['module', 'class_name', 'kwargs']) pt = "pytracking.evaluation.%sdataset" # Useful abbreviations to reduce the clutter dataset_dict = dict( otb=DatasetInfo(module=pt % "otb", class_name="OTBDataset", kwargs=dict()), nfs=DatasetInfo(module=pt % "nfs", class_name="NFSDataset", kwargs=dict()), uav=DatasetInfo(module=pt % "uav", class_name="UAVDataset", kwargs=dict()), tpl=DatasetInfo(module=pt % "tpl", class_name="TPLDataset", kwargs=dict()), tpl_nootb=DatasetInfo(module=pt % "tpl", class_name="TPLDataset", kwargs=dict(exclude_otb=True)), vot=DatasetInfo(module=pt % "vot", class_name="VOTDataset", kwargs=dict()), trackingnet=DatasetInfo(module=pt % "trackingnet", class_name="TrackingNetDataset", kwargs=dict()), got10k_test=DatasetInfo(module=pt % "got10k", class_name="GOT10KDataset", kwargs=dict(split='test')), got10k_val=DatasetInfo(module=pt % "got10k", class_name="GOT10KDataset", kwargs=dict(split='val')), got10k_ltrval=DatasetInfo(module=pt % "got10k", class_name="GOT10KDataset", kwargs=dict(split='ltrval')), lasot=DatasetInfo(module=pt % "lasot", class_name="LaSOTDataset", kwargs=dict()), dv2017_val=DatasetInfo(module="ltr.dataset.davis", class_name="Davis", kwargs=dict(version='2017', split='val')), dv2016_val=DatasetInfo(module="ltr.dataset.davis", class_name="Davis", kwargs=dict(version='2016', split='val')), dv2017_test_dev=DatasetInfo(module="ltr.dataset.davis", class_name="Davis", kwargs=dict(version='2017', split='test-dev')), dv2017_test_chal=DatasetInfo(module="ltr.dataset.davis", class_name="Davis", kwargs=dict(version='2017', split='test-challenge')), yt2019_test=DatasetInfo(module="ltr.dataset.youtubevos", class_name="YouTubeVOS", kwargs=dict(version='2019', split='test')), yt2019_valid=DatasetInfo(module="ltr.dataset.youtubevos", class_name="YouTubeVOS", kwargs=dict(version='2019', split='valid')), yt2019_valid_all=DatasetInfo(module="ltr.dataset.youtubevos", class_name="YouTubeVOS", kwargs=dict(version='2019', split='valid', all_frames=True)), yt2018_valid_all=DatasetInfo(module="ltr.dataset.youtubevos", class_name="YouTubeVOS", kwargs=dict(version='2018', split='valid', all_frames=True)), yt2018_jjval=DatasetInfo(module="ltr.dataset.youtubevos", class_name="YouTubeVOS", kwargs=dict(version='2018', split='jjvalid')), yt2019_jjval=DatasetInfo(module="ltr.dataset.youtubevos", class_name="YouTubeVOS", kwargs=dict(version='2019', split='jjvalid', cleanup=['starts'])), yt2019_jjval_all=DatasetInfo(module="ltr.dataset.youtubevos", class_name="YouTubeVOS", kwargs=dict(version='2019', split='jjvalid', all_frames=True, cleanup=['starts'])), ) def load_dataset(name: str): """ Import and load a single dataset.""" name = name.lower() dset_info = dataset_dict.get(name) if dset_info is None: raise ValueError('Unknown dataset \'%s\'' % name) m = importlib.import_module(dset_info.module) dataset = getattr(m, dset_info.class_name)(**dset_info.kwargs) # Call the constructor return dataset.get_sequence_list() def get_dataset(*args): """ Get a single or set of datasets.""" dset = SequenceList() for name in args: dset.extend(load_dataset(name)) return dset ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/evaluation/environment.py ================================================ import importlib import os class EnvSettings: def __init__(self): pytracking_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..')) self.results_path = '{}/tracking_results/'.format(pytracking_path) self.segmentation_path = '{}/segmentation_results/'.format(pytracking_path) self.network_path = '{}/networks/'.format(pytracking_path) self.result_plot_path = '{}/result_plots/'.format(pytracking_path) self.otb_path = '' self.nfs_path = '' self.uav_path = '' self.tpl_path = '' self.vot_path = '' self.got10k_path = '' self.lasot_path = '' self.trackingnet_path = '' self.davis_dir = '' self.youtubevos_dir = '' self.got_packed_results_path = '' self.got_reports_path = '' self.tn_packed_results_path = '' def create_default_local_file(): comment = {'results_path': 'Where to store tracking results', 'network_path': 'Where tracking networks are stored.'} path = os.path.join(os.path.dirname(__file__), 'local.py') with open(path, 'w') as f: settings = EnvSettings() f.write('from pytracking.evaluation.environment import EnvSettings\n\n') f.write('def local_env_settings():\n') f.write(' settings = EnvSettings()\n\n') f.write(' # Set your local paths here.\n\n') for attr in dir(settings): comment_str = None if attr in comment: comment_str = comment[attr] attr_val = getattr(settings, attr) if not attr.startswith('__') and not callable(attr_val): if comment_str is None: f.write(' settings.{} = \'{}\'\n'.format(attr, attr_val)) else: f.write(' settings.{} = \'{}\' # {}\n'.format(attr, attr_val, comment_str)) f.write('\n return settings\n\n') def env_settings(): env_module_name = 'pytracking.evaluation.local' try: env_module = importlib.import_module(env_module_name) return env_module.local_env_settings() except: env_file = os.path.join(os.path.dirname(__file__), 'local.py') # Create a default file create_default_local_file() raise RuntimeError('YOU HAVE NOT SETUP YOUR local.py!!!\n Go to "{}" and set all the paths you need. ' 'Then try to run again.'.format(env_file)) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/evaluation/got10kdataset.py ================================================ import numpy as np from pytracking.evaluation.data import Sequence, BaseDataset, SequenceList from pytracking.utils.load_text import load_text import os class GOT10KDataset(BaseDataset): """ GOT-10k dataset. Publication: GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild Lianghua Huang, Xin Zhao, and Kaiqi Huang arXiv:1810.11981, 2018 https://arxiv.org/pdf/1810.11981.pdf Download dataset from http://got-10k.aitestunion.com/downloads """ def __init__(self, split): super().__init__() # Split can be test, val, or ltrval (a validation split consisting of videos from the official train set) if split == 'test' or split == 'val': self.base_path = os.path.join(self.env_settings.got10k_path, split) else: self.base_path = os.path.join(self.env_settings.got10k_path, 'train') self.sequence_list = self._get_sequence_list(split) self.split = split def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_list]) def _construct_sequence(self, sequence_name): anno_path = '{}/{}/groundtruth.txt'.format(self.base_path, sequence_name) ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64) frames_path = '{}/{}'.format(self.base_path, sequence_name) frame_list = [frame for frame in os.listdir(frames_path) if frame.endswith(".jpg")] frame_list.sort(key=lambda f: int(f[:-4])) frames_list = [os.path.join(frames_path, frame) for frame in frame_list] return Sequence(sequence_name, frames_list, 'got10k', ground_truth_rect.reshape(-1, 4)) def __len__(self): return len(self.sequence_list) def _get_sequence_list(self, split): with open('{}/list.txt'.format(self.base_path)) as f: sequence_list = f.read().splitlines() if split == 'ltrval': with open('{}/got10k_val_split.txt'.format(self.env_settings.dataspec_path)) as f: seq_ids = f.read().splitlines() sequence_list = [sequence_list[int(x)] for x in seq_ids] return sequence_list ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/evaluation/lasotdataset.py ================================================ import numpy as np from pytracking.evaluation.data import Sequence, BaseDataset, SequenceList from pytracking.utils.load_text import load_text class LaSOTDataset(BaseDataset): """ LaSOT test set consisting of 280 videos (see Protocol-II in the LaSOT paper) Publication: LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Hexin Bai, Yong Xu, Chunyuan Liao and Haibin Ling CVPR, 2019 https://arxiv.org/pdf/1809.07845.pdf Download the dataset from https://cis.temple.edu/lasot/download.html """ def __init__(self): super().__init__() self.base_path = self.env_settings.lasot_path self.sequence_list = self._get_sequence_list() self.clean_list = self.clean_seq_list() def clean_seq_list(self): clean_lst = [] for i in range(len(self.sequence_list)): cls, _ = self.sequence_list[i].split('-') clean_lst.append(cls) return clean_lst def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_list]) def _construct_sequence(self, sequence_name): class_name = sequence_name.split('-')[0] anno_path = '{}/{}/{}/groundtruth.txt'.format(self.base_path, class_name, sequence_name) ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64) occlusion_label_path = '{}/{}/{}/full_occlusion.txt'.format(self.base_path, class_name, sequence_name) # NOTE: pandas backed seems super super slow for loading occlusion/oov masks full_occlusion = load_text(str(occlusion_label_path), delimiter=',', dtype=np.float64, backend='numpy') out_of_view_label_path = '{}/{}/{}/out_of_view.txt'.format(self.base_path, class_name, sequence_name) out_of_view = load_text(str(out_of_view_label_path), delimiter=',', dtype=np.float64, backend='numpy') target_visible = np.logical_and(full_occlusion == 0, out_of_view == 0) frames_path = '{}/{}/{}/img'.format(self.base_path, class_name, sequence_name) frames_list = ['{}/{:08d}.jpg'.format(frames_path, frame_number) for frame_number in range(1, ground_truth_rect.shape[0] + 1)] target_class = class_name return Sequence(sequence_name, frames_list, 'lasot', ground_truth_rect.reshape(-1, 4), object_class=target_class, target_visible=target_visible) def __len__(self): return len(self.sequence_list) def _get_sequence_list(self): sequence_list = ['airplane-1', 'airplane-9', 'airplane-13', 'airplane-15', 'basketball-1', 'basketball-6', 'basketball-7', 'basketball-11', 'bear-2', 'bear-4', 'bear-6', 'bear-17', 'bicycle-2', 'bicycle-7', 'bicycle-9', 'bicycle-18', 'bird-2', 'bird-3', 'bird-15', 'bird-17', 'boat-3', 'boat-4', 'boat-12', 'boat-17', 'book-3', 'book-10', 'book-11', 'book-19', 'bottle-1', 'bottle-12', 'bottle-14', 'bottle-18', 'bus-2', 'bus-5', 'bus-17', 'bus-19', 'car-2', 'car-6', 'car-9', 'car-17', 'cat-1', 'cat-3', 'cat-18', 'cat-20', 'cattle-2', 'cattle-7', 'cattle-12', 'cattle-13', 'spider-14', 'spider-16', 'spider-18', 'spider-20', 'coin-3', 'coin-6', 'coin-7', 'coin-18', 'crab-3', 'crab-6', 'crab-12', 'crab-18', 'surfboard-12', 'surfboard-4', 'surfboard-5', 'surfboard-8', 'cup-1', 'cup-4', 'cup-7', 'cup-17', 'deer-4', 'deer-8', 'deer-10', 'deer-14', 'dog-1', 'dog-7', 'dog-15', 'dog-19', 'guitar-3', 'guitar-8', 'guitar-10', 'guitar-16', 'person-1', 'person-5', 'person-10', 'person-12', 'pig-2', 'pig-10', 'pig-13', 'pig-18', 'rubicCube-1', 'rubicCube-6', 'rubicCube-14', 'rubicCube-19', 'swing-10', 'swing-14', 'swing-17', 'swing-20', 'drone-13', 'drone-15', 'drone-2', 'drone-7', 'pool-12', 'pool-15', 'pool-3', 'pool-7', 'rabbit-10', 'rabbit-13', 'rabbit-17', 'rabbit-19', 'racing-10', 'racing-15', 'racing-16', 'racing-20', 'robot-1', 'robot-19', 'robot-5', 'robot-8', 'sepia-13', 'sepia-16', 'sepia-6', 'sepia-8', 'sheep-3', 'sheep-5', 'sheep-7', 'sheep-9', 'skateboard-16', 'skateboard-19', 'skateboard-3', 'skateboard-8', 'tank-14', 'tank-16', 'tank-6', 'tank-9', 'tiger-12', 'tiger-18', 'tiger-4', 'tiger-6', 'train-1', 'train-11', 'train-20', 'train-7', 'truck-16', 'truck-3', 'truck-6', 'truck-7', 'turtle-16', 'turtle-5', 'turtle-8', 'turtle-9', 'umbrella-17', 'umbrella-19', 'umbrella-2', 'umbrella-9', 'yoyo-15', 'yoyo-17', 'yoyo-19', 'yoyo-7', 'zebra-10', 'zebra-14', 'zebra-16', 'zebra-17', 'elephant-1', 'elephant-12', 'elephant-16', 'elephant-18', 'goldfish-3', 'goldfish-7', 'goldfish-8', 'goldfish-10', 'hat-1', 'hat-2', 'hat-5', 'hat-18', 'kite-4', 'kite-6', 'kite-10', 'kite-15', 'motorcycle-1', 'motorcycle-3', 'motorcycle-9', 'motorcycle-18', 'mouse-1', 'mouse-8', 'mouse-9', 'mouse-17', 'flag-3', 'flag-9', 'flag-5', 'flag-2', 'frog-3', 'frog-4', 'frog-20', 'frog-9', 'gametarget-1', 'gametarget-2', 'gametarget-7', 'gametarget-13', 'hand-2', 'hand-3', 'hand-9', 'hand-16', 'helmet-5', 'helmet-11', 'helmet-19', 'helmet-13', 'licenseplate-6', 'licenseplate-12', 'licenseplate-13', 'licenseplate-15', 'electricfan-1', 'electricfan-10', 'electricfan-18', 'electricfan-20', 'chameleon-3', 'chameleon-6', 'chameleon-11', 'chameleon-20', 'crocodile-3', 'crocodile-4', 'crocodile-10', 'crocodile-14', 'gecko-1', 'gecko-5', 'gecko-16', 'gecko-19', 'fox-2', 'fox-3', 'fox-5', 'fox-20', 'giraffe-2', 'giraffe-10', 'giraffe-13', 'giraffe-15', 'gorilla-4', 'gorilla-6', 'gorilla-9', 'gorilla-13', 'hippo-1', 'hippo-7', 'hippo-9', 'hippo-20', 'horse-1', 'horse-4', 'horse-12', 'horse-15', 'kangaroo-2', 'kangaroo-5', 'kangaroo-11', 'kangaroo-14', 'leopard-1', 'leopard-7', 'leopard-16', 'leopard-20', 'lion-1', 'lion-5', 'lion-12', 'lion-20', 'lizard-1', 'lizard-3', 'lizard-6', 'lizard-13', 'microphone-2', 'microphone-6', 'microphone-14', 'microphone-16', 'monkey-3', 'monkey-4', 'monkey-9', 'monkey-17', 'shark-2', 'shark-3', 'shark-5', 'shark-6', 'squirrel-8', 'squirrel-11', 'squirrel-13', 'squirrel-19', 'volleyball-1', 'volleyball-13', 'volleyball-18', 'volleyball-19'] return sequence_list ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/evaluation/mobifacedataset.py ================================================ from pytracking.evaluation.data import Sequence, BaseDataset, SequenceList import glob import numpy as np import os.path as osp from collections import OrderedDict import pandas as pd class MobifaceDataset(BaseDataset): """ Mobiface dataset. Publication: MobiFace: A Novel Dataset for Mobile Face Tracking in the Wild Yiming Lin, Shiyang Cheng, Jie Shen, Maja Pantic arXiv:1805.09749, 2018 https://arxiv.org/pdf/1805.09749v2 Download dataset from https://mobiface.github.io/ """ def __init__(self, split): """ args: split - Split to use. Can be i) 'train': official training set, ii) 'test': official test set, iii) 'all': whole dataset. """ super().__init__() self.base_path = self.env_settings.mobiface_path self.sequence_list = self._get_sequence_list(split) self.split = split def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_list]) def _get_sequence_list(self, split): self.train_meta_fn = osp.join(self.base_path, 'train.meta.csv') self.test_meta_fn = osp.join(self.base_path, 'test.meta.csv') self.train_meta = pd.read_csv(self.train_meta_fn,index_col=0).transpose().to_dict() self.test_meta = pd.read_csv(self.test_meta_fn,index_col=0).transpose().to_dict() if split == 'train': self.meta = self.train_meta elif split == 'test': self.meta = self.test_meta else: self.meta = {**self.train_meta, **self.test_meta} # In Python 3.5 or greater self.meta = OrderedDict(sorted(self.meta.items(), key=lambda t: t[0])) self.anno_files = [] for k,v in self.meta.items(): if k in self.train_meta.keys(): self.anno_files.append(osp.abspath(osp.join(self.base_path,'train', k+'.annot.csv'))) else: self.anno_files.append(osp.abspath(osp.join(self.base_path,'test', k+'.annot.csv'))) self.seq_names = sorted(list(self.meta.keys())) self.seq_dirs = [fn[:-len('.annot.csv')] for fn in self.anno_files] return self.seq_names def _construct_sequence(self, sequence_name): index = self.seq_names.index(sequence_name) img_files = sorted(glob.glob(self.seq_dirs[index]+'/*.jpg')) if len(img_files) == 0: img_files = sorted(glob.glob(self.seq_dirs[index]+'.png')) with open(self.anno_files[index], 'r') as f: anno = np.loadtxt(f, delimiter=',', skiprows=1, dtype=int) anno = anno[:,1:] assert anno.shape[1] == 4 return Sequence(sequence_name, img_files, anno.reshape(-1, 4)) def __len__(self): return len(self.sequence_list) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/evaluation/multi_object_wrapper.py ================================================ import numpy as np from collections import OrderedDict import time import copy class MultiObjectWrapper: def __init__(self, base_tracker_class, params, visdom=None, fast_load=False): self.base_tracker_class = base_tracker_class self.params = params self.visdom = visdom self.initialized_ids = [] self.trackers = OrderedDict() self.fast_load = fast_load if self.fast_load: self.tracker_copy = self.base_tracker_class(self.params) if hasattr(self.tracker_copy, 'initialize_features'): self.tracker_copy.initialize_features() def create_tracker(self): tracker = None if self.fast_load: try: tracker = copy.deepcopy(self.tracker_copy) except: pass if tracker is None: tracker = self.base_tracker_class(self.params) tracker.visdom = self.visdom return tracker def _split_info(self, info): info_split = OrderedDict() init_other = OrderedDict() # Init other contains init info for all other objects for obj_id in info['init_object_ids']: info_split[obj_id] = dict() init_other[obj_id] = dict() info_split[obj_id]['object_ids'] = [obj_id] info_split[obj_id]['sequence_object_ids'] = info['sequence_object_ids'] if 'init_bbox' in info: info_split[obj_id]['init_bbox'] = info['init_bbox'][obj_id] init_other[obj_id]['init_bbox'] = info['init_bbox'][obj_id] if 'init_mask' in info: info_split[obj_id]['init_mask'] = (info['init_mask'] == int(obj_id)).astype(np.uint8) init_other[obj_id]['init_mask'] = info_split[obj_id]['init_mask'] for obj_info in info_split.values(): obj_info['init_other'] = init_other return info_split def _set_defaults(self, tracker_out: dict, defaults=None): defaults = {} if defaults is None else defaults for key, val in defaults.items(): if tracker_out.get(key) is None: tracker_out[key] = val return tracker_out def default_merge(self, out_all): out_merged = OrderedDict() out_first = list(out_all.values())[0] out_types = out_first.keys() # Merge segmentation mask if 'segmentation' in out_types and out_first['segmentation'] is not None: # Stack all masks # If a tracker outputs soft segmentation mask, use that. Else use the binary segmentation segmentation_maps = [out.get('segmentation_soft', out['segmentation']) for out in out_all.values()] segmentation_maps = np.stack(segmentation_maps) obj_ids = np.array([0, *map(int, out_all.keys())], dtype=np.uint8) segm_threshold = getattr(self.params, 'segmentation_threshold', 0.5) merged_segmentation = obj_ids[np.where(segmentation_maps.max(axis=0) > segm_threshold, segmentation_maps.argmax(axis=0) + 1, 0)] out_merged['segmentation'] = merged_segmentation # Merge other fields for key in out_types: if key == 'segmentation': pass else: out_merged[key] = {obj_id: out[key] for obj_id, out in out_all.items()} return out_merged def merge_outputs(self, out_all): if hasattr(self.base_tracker_class, 'merge_results'): out_merged = self.base_tracker_class.merge_results(out_all) else: out_merged = self.default_merge(out_all) return out_merged def initialize(self, image, info: dict) -> dict: self.initialized_ids = [] self.trackers = OrderedDict() if len(info['init_object_ids']) == 0: return None object_ids = info['object_ids'] init_info_split = self._split_info(info) self.trackers = OrderedDict({obj_id: self.create_tracker() for obj_id in object_ids}) out_all = OrderedDict() # Run individual trackers for each object for obj_id in info['init_object_ids']: start_time = time.time() out = self.trackers[obj_id].initialize(image, init_info_split[obj_id]) if out is None: out = {} init_default = {'target_bbox': init_info_split[obj_id].get('init_bbox'), 'time': time.time() - start_time, 'segmentation': init_info_split[obj_id].get('init_mask')} out = self._set_defaults(out, init_default) out_all[obj_id] = out # Merge results out_merged = self.merge_outputs(out_all) self.initialized_ids = info['init_object_ids'].copy() return out_merged def track(self, image, info: dict = None) -> dict: if info is None: info = {} prev_output = info.get('previous_output', OrderedDict()) if info.get('init_object_ids', False): init_info_split = self._split_info(info) for obj_init_info in init_info_split.values(): obj_init_info['previous_output'] = prev_output info['init_other'] = list(init_info_split.values())[0]['init_other'] out_all = OrderedDict() for obj_id in self.initialized_ids: start_time = time.time() out = self.trackers[obj_id].track(image, info) default = {'time': time.time() - start_time} out = self._set_defaults(out, default) out_all[obj_id] = out # Initialize new if info.get('init_object_ids', False): for obj_id in info['init_object_ids']: if not obj_id in self.trackers: self.trackers[obj_id] = self.create_tracker() start_time = time.time() out = self.trackers[obj_id].initialize(image, init_info_split[obj_id]) if out is None: out = {} init_default = {'target_bbox': init_info_split[obj_id].get('init_bbox'), 'time': time.time() - start_time, 'segmentation': init_info_split[obj_id].get('init_mask')} out = self._set_defaults(out, init_default) out_all[obj_id] = out self.initialized_ids.extend(info['init_object_ids']) # Merge results out_merged = self.merge_outputs(out_all) return out_merged def visdom_draw_tracking(self, image, box, segmentation): if isinstance(box, (OrderedDict, dict)): box = [v for k, v in box.items()] else: box = (box,) if segmentation is None: self.visdom.register((image, *box), 'Tracking', 1, 'Tracking') else: self.visdom.register((image, *box, segmentation), 'Tracking', 1, 'Tracking') ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/evaluation/nfsdataset.py ================================================ import numpy as np from pytracking.evaluation.data import Sequence, BaseDataset, SequenceList from pytracking.utils.load_text import load_text class NFSDataset(BaseDataset): """ NFS dataset. Publication: Need for Speed: A Benchmark for Higher Frame Rate Object Tracking H. Kiani Galoogahi, A. Fagg, C. Huang, D. Ramanan, and S.Lucey ICCV, 2017 http://openaccess.thecvf.com/content_ICCV_2017/papers/Galoogahi_Need_for_Speed_ICCV_2017_paper.pdf Download the dataset from http://ci2cv.net/nfs/index.html """ def __init__(self): super().__init__() self.base_path = self.env_settings.nfs_path self.sequence_info_list = self._get_sequence_info_list() def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_info_list]) def _construct_sequence(self, sequence_info): sequence_path = sequence_info['path'] nz = sequence_info['nz'] ext = sequence_info['ext'] start_frame = sequence_info['startFrame'] end_frame = sequence_info['endFrame'] init_omit = 0 if 'initOmit' in sequence_info: init_omit = sequence_info['initOmit'] frames = ['{base_path}/{sequence_path}/{frame:0{nz}}.{ext}'.format(base_path=self.base_path, sequence_path=sequence_path, frame=frame_num, nz=nz, ext=ext) for frame_num in range(start_frame+init_omit, end_frame+1)] anno_path = '{}/{}'.format(self.base_path, sequence_info['anno_path']) ground_truth_rect = load_text(str(anno_path), delimiter='\t', dtype=np.float64) return Sequence(sequence_info['name'], frames, 'nfs', ground_truth_rect[init_omit:,:], object_class=sequence_info['object_class']) def __len__(self): return len(self.sequence_info_list) def _get_sequence_info_list(self): sequence_info_list = [ {"name": "nfs_Gymnastics", "path": "sequences/Gymnastics", "startFrame": 1, "endFrame": 368, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_Gymnastics.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_MachLoop_jet", "path": "sequences/MachLoop_jet", "startFrame": 1, "endFrame": 99, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_MachLoop_jet.txt", "object_class": "aircraft", 'occlusion': False}, {"name": "nfs_Skiing_red", "path": "sequences/Skiing_red", "startFrame": 1, "endFrame": 69, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_Skiing_red.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_Skydiving", "path": "sequences/Skydiving", "startFrame": 1, "endFrame": 196, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_Skydiving.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_airboard_1", "path": "sequences/airboard_1", "startFrame": 1, "endFrame": 425, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_airboard_1.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_airplane_landing", "path": "sequences/airplane_landing", "startFrame": 1, "endFrame": 81, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_airplane_landing.txt", "object_class": "aircraft", 'occlusion': False}, {"name": "nfs_airtable_3", "path": "sequences/airtable_3", "startFrame": 1, "endFrame": 482, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_airtable_3.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_basketball_1", "path": "sequences/basketball_1", "startFrame": 1, "endFrame": 282, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_1.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_basketball_2", "path": "sequences/basketball_2", "startFrame": 1, "endFrame": 102, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_2.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_basketball_3", "path": "sequences/basketball_3", "startFrame": 1, "endFrame": 421, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_3.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_basketball_6", "path": "sequences/basketball_6", "startFrame": 1, "endFrame": 224, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_6.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_basketball_7", "path": "sequences/basketball_7", "startFrame": 1, "endFrame": 240, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_7.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_basketball_player", "path": "sequences/basketball_player", "startFrame": 1, "endFrame": 369, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_player.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_basketball_player_2", "path": "sequences/basketball_player_2", "startFrame": 1, "endFrame": 437, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_player_2.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_beach_flipback_person", "path": "sequences/beach_flipback_person", "startFrame": 1, "endFrame": 61, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_beach_flipback_person.txt", "object_class": "person head", 'occlusion': False}, {"name": "nfs_bee", "path": "sequences/bee", "startFrame": 1, "endFrame": 45, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bee.txt", "object_class": "insect", 'occlusion': False}, {"name": "nfs_biker_acrobat", "path": "sequences/biker_acrobat", "startFrame": 1, "endFrame": 128, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_acrobat.txt", "object_class": "bicycle", 'occlusion': False}, {"name": "nfs_biker_all_1", "path": "sequences/biker_all_1", "startFrame": 1, "endFrame": 113, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_all_1.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_biker_head_2", "path": "sequences/biker_head_2", "startFrame": 1, "endFrame": 132, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_head_2.txt", "object_class": "person head", 'occlusion': False}, {"name": "nfs_biker_head_3", "path": "sequences/biker_head_3", "startFrame": 1, "endFrame": 254, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_head_3.txt", "object_class": "person head", 'occlusion': False}, {"name": "nfs_biker_upper_body", "path": "sequences/biker_upper_body", "startFrame": 1, "endFrame": 194, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_upper_body.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_biker_whole_body", "path": "sequences/biker_whole_body", "startFrame": 1, "endFrame": 572, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_whole_body.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_billiard_2", "path": "sequences/billiard_2", "startFrame": 1, "endFrame": 604, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_billiard_2.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_billiard_3", "path": "sequences/billiard_3", "startFrame": 1, "endFrame": 698, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_billiard_3.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_billiard_6", "path": "sequences/billiard_6", "startFrame": 1, "endFrame": 771, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_billiard_6.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_billiard_7", "path": "sequences/billiard_7", "startFrame": 1, "endFrame": 724, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_billiard_7.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_billiard_8", "path": "sequences/billiard_8", "startFrame": 1, "endFrame": 778, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_billiard_8.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_bird_2", "path": "sequences/bird_2", "startFrame": 1, "endFrame": 476, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bird_2.txt", "object_class": "bird", 'occlusion': False}, {"name": "nfs_book", "path": "sequences/book", "startFrame": 1, "endFrame": 288, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_book.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_bottle", "path": "sequences/bottle", "startFrame": 1, "endFrame": 2103, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bottle.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_bowling_1", "path": "sequences/bowling_1", "startFrame": 1, "endFrame": 303, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bowling_1.txt", "object_class": "ball", 'occlusion': True}, {"name": "nfs_bowling_2", "path": "sequences/bowling_2", "startFrame": 1, "endFrame": 710, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bowling_2.txt", "object_class": "ball", 'occlusion': True}, {"name": "nfs_bowling_3", "path": "sequences/bowling_3", "startFrame": 1, "endFrame": 271, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bowling_3.txt", "object_class": "ball", 'occlusion': True}, {"name": "nfs_bowling_6", "path": "sequences/bowling_6", "startFrame": 1, "endFrame": 260, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bowling_6.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_bowling_ball", "path": "sequences/bowling_ball", "startFrame": 1, "endFrame": 275, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bowling_ball.txt", "object_class": "ball", 'occlusion': True}, {"name": "nfs_bunny", "path": "sequences/bunny", "startFrame": 1, "endFrame": 705, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bunny.txt", "object_class": "mammal", 'occlusion': False}, {"name": "nfs_car", "path": "sequences/car", "startFrame": 1, "endFrame": 2020, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car.txt", "object_class": "car", 'occlusion': True}, {"name": "nfs_car_camaro", "path": "sequences/car_camaro", "startFrame": 1, "endFrame": 36, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_camaro.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_car_drifting", "path": "sequences/car_drifting", "startFrame": 1, "endFrame": 173, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_drifting.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_car_jumping", "path": "sequences/car_jumping", "startFrame": 1, "endFrame": 22, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_jumping.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_car_rc_rolling", "path": "sequences/car_rc_rolling", "startFrame": 1, "endFrame": 62, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_rc_rolling.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_car_rc_rotating", "path": "sequences/car_rc_rotating", "startFrame": 1, "endFrame": 80, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_rc_rotating.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_car_side", "path": "sequences/car_side", "startFrame": 1, "endFrame": 108, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_side.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_car_white", "path": "sequences/car_white", "startFrame": 1, "endFrame": 2063, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_white.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_cheetah", "path": "sequences/cheetah", "startFrame": 1, "endFrame": 167, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_cheetah.txt", "object_class": "mammal", 'occlusion': True}, {"name": "nfs_cup", "path": "sequences/cup", "startFrame": 1, "endFrame": 1281, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_cup.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_cup_2", "path": "sequences/cup_2", "startFrame": 1, "endFrame": 182, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_cup_2.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_dog", "path": "sequences/dog", "startFrame": 1, "endFrame": 1030, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dog.txt", "object_class": "dog", 'occlusion': True}, {"name": "nfs_dog_1", "path": "sequences/dog_1", "startFrame": 1, "endFrame": 168, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dog_1.txt", "object_class": "dog", 'occlusion': False}, {"name": "nfs_dog_2", "path": "sequences/dog_2", "startFrame": 1, "endFrame": 594, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dog_2.txt", "object_class": "dog", 'occlusion': True}, {"name": "nfs_dog_3", "path": "sequences/dog_3", "startFrame": 1, "endFrame": 200, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dog_3.txt", "object_class": "dog", 'occlusion': False}, {"name": "nfs_dogs", "path": "sequences/dogs", "startFrame": 1, "endFrame": 198, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dogs.txt", "object_class": "dog", 'occlusion': True}, {"name": "nfs_dollar", "path": "sequences/dollar", "startFrame": 1, "endFrame": 1426, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dollar.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_drone", "path": "sequences/drone", "startFrame": 1, "endFrame": 70, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_drone.txt", "object_class": "aircraft", 'occlusion': False}, {"name": "nfs_ducks_lake", "path": "sequences/ducks_lake", "startFrame": 1, "endFrame": 107, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_ducks_lake.txt", "object_class": "bird", 'occlusion': False}, {"name": "nfs_exit", "path": "sequences/exit", "startFrame": 1, "endFrame": 359, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_exit.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_first", "path": "sequences/first", "startFrame": 1, "endFrame": 435, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_first.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_flower", "path": "sequences/flower", "startFrame": 1, "endFrame": 448, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_flower.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_footbal_skill", "path": "sequences/footbal_skill", "startFrame": 1, "endFrame": 131, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_footbal_skill.txt", "object_class": "ball", 'occlusion': True}, {"name": "nfs_helicopter", "path": "sequences/helicopter", "startFrame": 1, "endFrame": 310, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_helicopter.txt", "object_class": "aircraft", 'occlusion': False}, {"name": "nfs_horse_jumping", "path": "sequences/horse_jumping", "startFrame": 1, "endFrame": 117, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_horse_jumping.txt", "object_class": "horse", 'occlusion': True}, {"name": "nfs_horse_running", "path": "sequences/horse_running", "startFrame": 1, "endFrame": 139, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_horse_running.txt", "object_class": "horse", 'occlusion': False}, {"name": "nfs_iceskating_6", "path": "sequences/iceskating_6", "startFrame": 1, "endFrame": 603, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_iceskating_6.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_jellyfish_5", "path": "sequences/jellyfish_5", "startFrame": 1, "endFrame": 746, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_jellyfish_5.txt", "object_class": "invertebrate", 'occlusion': False}, {"name": "nfs_kid_swing", "path": "sequences/kid_swing", "startFrame": 1, "endFrame": 169, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_kid_swing.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_motorcross", "path": "sequences/motorcross", "startFrame": 1, "endFrame": 39, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_motorcross.txt", "object_class": "vehicle", 'occlusion': True}, {"name": "nfs_motorcross_kawasaki", "path": "sequences/motorcross_kawasaki", "startFrame": 1, "endFrame": 65, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_motorcross_kawasaki.txt", "object_class": "vehicle", 'occlusion': False}, {"name": "nfs_parkour", "path": "sequences/parkour", "startFrame": 1, "endFrame": 58, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_parkour.txt", "object_class": "person head", 'occlusion': False}, {"name": "nfs_person_scooter", "path": "sequences/person_scooter", "startFrame": 1, "endFrame": 413, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_person_scooter.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_pingpong_2", "path": "sequences/pingpong_2", "startFrame": 1, "endFrame": 1277, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_pingpong_2.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_pingpong_7", "path": "sequences/pingpong_7", "startFrame": 1, "endFrame": 1290, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_pingpong_7.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_pingpong_8", "path": "sequences/pingpong_8", "startFrame": 1, "endFrame": 296, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_pingpong_8.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_purse", "path": "sequences/purse", "startFrame": 1, "endFrame": 968, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_purse.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_rubber", "path": "sequences/rubber", "startFrame": 1, "endFrame": 1328, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_rubber.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_running", "path": "sequences/running", "startFrame": 1, "endFrame": 677, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_running.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_running_100_m", "path": "sequences/running_100_m", "startFrame": 1, "endFrame": 313, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_running_100_m.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_running_100_m_2", "path": "sequences/running_100_m_2", "startFrame": 1, "endFrame": 337, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_running_100_m_2.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_running_2", "path": "sequences/running_2", "startFrame": 1, "endFrame": 363, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_running_2.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_shuffleboard_1", "path": "sequences/shuffleboard_1", "startFrame": 1, "endFrame": 42, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffleboard_1.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_shuffleboard_2", "path": "sequences/shuffleboard_2", "startFrame": 1, "endFrame": 41, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffleboard_2.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_shuffleboard_4", "path": "sequences/shuffleboard_4", "startFrame": 1, "endFrame": 62, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffleboard_4.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_shuffleboard_5", "path": "sequences/shuffleboard_5", "startFrame": 1, "endFrame": 32, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffleboard_5.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_shuffleboard_6", "path": "sequences/shuffleboard_6", "startFrame": 1, "endFrame": 52, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffleboard_6.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_shuffletable_2", "path": "sequences/shuffletable_2", "startFrame": 1, "endFrame": 372, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffletable_2.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_shuffletable_3", "path": "sequences/shuffletable_3", "startFrame": 1, "endFrame": 368, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffletable_3.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_shuffletable_4", "path": "sequences/shuffletable_4", "startFrame": 1, "endFrame": 101, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffletable_4.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_ski_long", "path": "sequences/ski_long", "startFrame": 1, "endFrame": 274, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_ski_long.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_soccer_ball", "path": "sequences/soccer_ball", "startFrame": 1, "endFrame": 163, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_soccer_ball.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_soccer_ball_2", "path": "sequences/soccer_ball_2", "startFrame": 1, "endFrame": 1934, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_soccer_ball_2.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_soccer_ball_3", "path": "sequences/soccer_ball_3", "startFrame": 1, "endFrame": 1381, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_soccer_ball_3.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_soccer_player_2", "path": "sequences/soccer_player_2", "startFrame": 1, "endFrame": 475, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_soccer_player_2.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_soccer_player_3", "path": "sequences/soccer_player_3", "startFrame": 1, "endFrame": 319, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_soccer_player_3.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_stop_sign", "path": "sequences/stop_sign", "startFrame": 1, "endFrame": 302, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_stop_sign.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_suv", "path": "sequences/suv", "startFrame": 1, "endFrame": 2584, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_suv.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_tiger", "path": "sequences/tiger", "startFrame": 1, "endFrame": 1556, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_tiger.txt", "object_class": "mammal", 'occlusion': False}, {"name": "nfs_walking", "path": "sequences/walking", "startFrame": 1, "endFrame": 555, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_walking.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_walking_3", "path": "sequences/walking_3", "startFrame": 1, "endFrame": 1427, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_walking_3.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_water_ski_2", "path": "sequences/water_ski_2", "startFrame": 1, "endFrame": 47, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_water_ski_2.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_yoyo", "path": "sequences/yoyo", "startFrame": 1, "endFrame": 67, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_yoyo.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_zebra_fish", "path": "sequences/zebra_fish", "startFrame": 1, "endFrame": 671, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_zebra_fish.txt", "object_class": "fish", 'occlusion': False}, ] return sequence_info_list ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/evaluation/otbdataset.py ================================================ import numpy as np from pytracking.evaluation.data import Sequence, BaseDataset, SequenceList from pytracking.utils.load_text import load_text class OTBDataset(BaseDataset): """ OTB-2015 dataset Publication: Object Tracking Benchmark Wu, Yi, Jongwoo Lim, and Ming-hsuan Yan TPAMI, 2015 http://faculty.ucmerced.edu/mhyang/papers/pami15_tracking_benchmark.pdf Download the dataset from http://cvlab.hanyang.ac.kr/tracker_benchmark/index.html """ def __init__(self): super().__init__() self.base_path = self.env_settings.otb_path self.sequence_info_list = self._get_sequence_info_list() def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_info_list]) def _construct_sequence(self, sequence_info): sequence_path = sequence_info['path'] nz = sequence_info['nz'] ext = sequence_info['ext'] start_frame = sequence_info['startFrame'] end_frame = sequence_info['endFrame'] init_omit = 0 if 'initOmit' in sequence_info: init_omit = sequence_info['initOmit'] frames = ['{base_path}/{sequence_path}/{frame:0{nz}}.{ext}'.format(base_path=self.base_path, sequence_path=sequence_path, frame=frame_num, nz=nz, ext=ext) for frame_num in range(start_frame+init_omit, end_frame+1)] anno_path = '{}/{}'.format(self.base_path, sequence_info['anno_path']) # NOTE: OTB has some weird annos which panda cannot handle ground_truth_rect = load_text(str(anno_path), delimiter=(',', None), dtype=np.float64, backend='numpy') return Sequence(sequence_info['name'], frames, 'otb', ground_truth_rect[init_omit:,:], object_class=sequence_info['object_class']) def __len__(self): return len(self.sequence_info_list) def _get_sequence_info_list(self): sequence_info_list = [ {"name": "Basketball", "path": "Basketball/img", "startFrame": 1, "endFrame": 725, "nz": 4, "ext": "jpg", "anno_path": "Basketball/groundtruth_rect.txt", "object_class": "person"}, {"name": "Biker", "path": "Biker/img", "startFrame": 1, "endFrame": 142, "nz": 4, "ext": "jpg", "anno_path": "Biker/groundtruth_rect.txt", "object_class": "person head"}, {"name": "Bird1", "path": "Bird1/img", "startFrame": 1, "endFrame": 408, "nz": 4, "ext": "jpg", "anno_path": "Bird1/groundtruth_rect.txt", "object_class": "bird"}, {"name": "Bird2", "path": "Bird2/img", "startFrame": 1, "endFrame": 99, "nz": 4, "ext": "jpg", "anno_path": "Bird2/groundtruth_rect.txt", "object_class": "bird"}, {"name": "BlurBody", "path": "BlurBody/img", "startFrame": 1, "endFrame": 334, "nz": 4, "ext": "jpg", "anno_path": "BlurBody/groundtruth_rect.txt", "object_class": "person"}, {"name": "BlurCar1", "path": "BlurCar1/img", "startFrame": 247, "endFrame": 988, "nz": 4, "ext": "jpg", "anno_path": "BlurCar1/groundtruth_rect.txt", "object_class": "car"}, {"name": "BlurCar2", "path": "BlurCar2/img", "startFrame": 1, "endFrame": 585, "nz": 4, "ext": "jpg", "anno_path": "BlurCar2/groundtruth_rect.txt", "object_class": "car"}, {"name": "BlurCar3", "path": "BlurCar3/img", "startFrame": 3, "endFrame": 359, "nz": 4, "ext": "jpg", "anno_path": "BlurCar3/groundtruth_rect.txt", "object_class": "car"}, {"name": "BlurCar4", "path": "BlurCar4/img", "startFrame": 18, "endFrame": 397, "nz": 4, "ext": "jpg", "anno_path": "BlurCar4/groundtruth_rect.txt", "object_class": "car"}, {"name": "BlurFace", "path": "BlurFace/img", "startFrame": 1, "endFrame": 493, "nz": 4, "ext": "jpg", "anno_path": "BlurFace/groundtruth_rect.txt", "object_class": "face"}, {"name": "BlurOwl", "path": "BlurOwl/img", "startFrame": 1, "endFrame": 631, "nz": 4, "ext": "jpg", "anno_path": "BlurOwl/groundtruth_rect.txt", "object_class": "other"}, {"name": "Board", "path": "Board/img", "startFrame": 1, "endFrame": 698, "nz": 5, "ext": "jpg", "anno_path": "Board/groundtruth_rect.txt", "object_class": "other"}, {"name": "Bolt", "path": "Bolt/img", "startFrame": 1, "endFrame": 350, "nz": 4, "ext": "jpg", "anno_path": "Bolt/groundtruth_rect.txt", "object_class": "person"}, {"name": "Bolt2", "path": "Bolt2/img", "startFrame": 1, "endFrame": 293, "nz": 4, "ext": "jpg", "anno_path": "Bolt2/groundtruth_rect.txt", "object_class": "person"}, {"name": "Box", "path": "Box/img", "startFrame": 1, "endFrame": 1161, "nz": 4, "ext": "jpg", "anno_path": "Box/groundtruth_rect.txt", "object_class": "other"}, {"name": "Boy", "path": "Boy/img", "startFrame": 1, "endFrame": 602, "nz": 4, "ext": "jpg", "anno_path": "Boy/groundtruth_rect.txt", "object_class": "face"}, {"name": "Car1", "path": "Car1/img", "startFrame": 1, "endFrame": 1020, "nz": 4, "ext": "jpg", "anno_path": "Car1/groundtruth_rect.txt", "object_class": "car"}, {"name": "Car2", "path": "Car2/img", "startFrame": 1, "endFrame": 913, "nz": 4, "ext": "jpg", "anno_path": "Car2/groundtruth_rect.txt", "object_class": "car"}, {"name": "Car24", "path": "Car24/img", "startFrame": 1, "endFrame": 3059, "nz": 4, "ext": "jpg", "anno_path": "Car24/groundtruth_rect.txt", "object_class": "car"}, {"name": "Car4", "path": "Car4/img", "startFrame": 1, "endFrame": 659, "nz": 4, "ext": "jpg", "anno_path": "Car4/groundtruth_rect.txt", "object_class": "car"}, {"name": "CarDark", "path": "CarDark/img", "startFrame": 1, "endFrame": 393, "nz": 4, "ext": "jpg", "anno_path": "CarDark/groundtruth_rect.txt", "object_class": "car"}, {"name": "CarScale", "path": "CarScale/img", "startFrame": 1, "endFrame": 252, "nz": 4, "ext": "jpg", "anno_path": "CarScale/groundtruth_rect.txt", "object_class": "car"}, {"name": "ClifBar", "path": "ClifBar/img", "startFrame": 1, "endFrame": 472, "nz": 4, "ext": "jpg", "anno_path": "ClifBar/groundtruth_rect.txt", "object_class": "other"}, {"name": "Coke", "path": "Coke/img", "startFrame": 1, "endFrame": 291, "nz": 4, "ext": "jpg", "anno_path": "Coke/groundtruth_rect.txt", "object_class": "other"}, {"name": "Couple", "path": "Couple/img", "startFrame": 1, "endFrame": 140, "nz": 4, "ext": "jpg", "anno_path": "Couple/groundtruth_rect.txt", "object_class": "person"}, {"name": "Coupon", "path": "Coupon/img", "startFrame": 1, "endFrame": 327, "nz": 4, "ext": "jpg", "anno_path": "Coupon/groundtruth_rect.txt", "object_class": "other"}, {"name": "Crossing", "path": "Crossing/img", "startFrame": 1, "endFrame": 120, "nz": 4, "ext": "jpg", "anno_path": "Crossing/groundtruth_rect.txt", "object_class": "person"}, {"name": "Crowds", "path": "Crowds/img", "startFrame": 1, "endFrame": 347, "nz": 4, "ext": "jpg", "anno_path": "Crowds/groundtruth_rect.txt", "object_class": "person"}, {"name": "Dancer", "path": "Dancer/img", "startFrame": 1, "endFrame": 225, "nz": 4, "ext": "jpg", "anno_path": "Dancer/groundtruth_rect.txt", "object_class": "person"}, {"name": "Dancer2", "path": "Dancer2/img", "startFrame": 1, "endFrame": 150, "nz": 4, "ext": "jpg", "anno_path": "Dancer2/groundtruth_rect.txt", "object_class": "person"}, {"name": "David", "path": "David/img", "startFrame": 300, "endFrame": 770, "nz": 4, "ext": "jpg", "anno_path": "David/groundtruth_rect.txt", "object_class": "face"}, {"name": "David2", "path": "David2/img", "startFrame": 1, "endFrame": 537, "nz": 4, "ext": "jpg", "anno_path": "David2/groundtruth_rect.txt", "object_class": "face"}, {"name": "David3", "path": "David3/img", "startFrame": 1, "endFrame": 252, "nz": 4, "ext": "jpg", "anno_path": "David3/groundtruth_rect.txt", "object_class": "person"}, {"name": "Deer", "path": "Deer/img", "startFrame": 1, "endFrame": 71, "nz": 4, "ext": "jpg", "anno_path": "Deer/groundtruth_rect.txt", "object_class": "mammal"}, {"name": "Diving", "path": "Diving/img", "startFrame": 1, "endFrame": 215, "nz": 4, "ext": "jpg", "anno_path": "Diving/groundtruth_rect.txt", "object_class": "person"}, {"name": "Dog", "path": "Dog/img", "startFrame": 1, "endFrame": 127, "nz": 4, "ext": "jpg", "anno_path": "Dog/groundtruth_rect.txt", "object_class": "dog"}, {"name": "Dog1", "path": "Dog1/img", "startFrame": 1, "endFrame": 1350, "nz": 4, "ext": "jpg", "anno_path": "Dog1/groundtruth_rect.txt", "object_class": "dog"}, {"name": "Doll", "path": "Doll/img", "startFrame": 1, "endFrame": 3872, "nz": 4, "ext": "jpg", "anno_path": "Doll/groundtruth_rect.txt", "object_class": "other"}, {"name": "DragonBaby", "path": "DragonBaby/img", "startFrame": 1, "endFrame": 113, "nz": 4, "ext": "jpg", "anno_path": "DragonBaby/groundtruth_rect.txt", "object_class": "face"}, {"name": "Dudek", "path": "Dudek/img", "startFrame": 1, "endFrame": 1145, "nz": 4, "ext": "jpg", "anno_path": "Dudek/groundtruth_rect.txt", "object_class": "face"}, {"name": "FaceOcc1", "path": "FaceOcc1/img", "startFrame": 1, "endFrame": 892, "nz": 4, "ext": "jpg", "anno_path": "FaceOcc1/groundtruth_rect.txt", "object_class": "face"}, {"name": "FaceOcc2", "path": "FaceOcc2/img", "startFrame": 1, "endFrame": 812, "nz": 4, "ext": "jpg", "anno_path": "FaceOcc2/groundtruth_rect.txt", "object_class": "face"}, {"name": "Fish", "path": "Fish/img", "startFrame": 1, "endFrame": 476, "nz": 4, "ext": "jpg", "anno_path": "Fish/groundtruth_rect.txt", "object_class": "other"}, {"name": "FleetFace", "path": "FleetFace/img", "startFrame": 1, "endFrame": 707, "nz": 4, "ext": "jpg", "anno_path": "FleetFace/groundtruth_rect.txt", "object_class": "face"}, {"name": "Football", "path": "Football/img", "startFrame": 1, "endFrame": 362, "nz": 4, "ext": "jpg", "anno_path": "Football/groundtruth_rect.txt", "object_class": "person head"}, {"name": "Football1", "path": "Football1/img", "startFrame": 1, "endFrame": 74, "nz": 4, "ext": "jpg", "anno_path": "Football1/groundtruth_rect.txt", "object_class": "face"}, {"name": "Freeman1", "path": "Freeman1/img", "startFrame": 1, "endFrame": 326, "nz": 4, "ext": "jpg", "anno_path": "Freeman1/groundtruth_rect.txt", "object_class": "face"}, {"name": "Freeman3", "path": "Freeman3/img", "startFrame": 1, "endFrame": 460, "nz": 4, "ext": "jpg", "anno_path": "Freeman3/groundtruth_rect.txt", "object_class": "face"}, {"name": "Freeman4", "path": "Freeman4/img", "startFrame": 1, "endFrame": 283, "nz": 4, "ext": "jpg", "anno_path": "Freeman4/groundtruth_rect.txt", "object_class": "face"}, {"name": "Girl", "path": "Girl/img", "startFrame": 1, "endFrame": 500, "nz": 4, "ext": "jpg", "anno_path": "Girl/groundtruth_rect.txt", "object_class": "face"}, {"name": "Girl2", "path": "Girl2/img", "startFrame": 1, "endFrame": 1500, "nz": 4, "ext": "jpg", "anno_path": "Girl2/groundtruth_rect.txt", "object_class": "person"}, {"name": "Gym", "path": "Gym/img", "startFrame": 1, "endFrame": 767, "nz": 4, "ext": "jpg", "anno_path": "Gym/groundtruth_rect.txt", "object_class": "person"}, {"name": "Human2", "path": "Human2/img", "startFrame": 1, "endFrame": 1128, "nz": 4, "ext": "jpg", "anno_path": "Human2/groundtruth_rect.txt", "object_class": "person"}, {"name": "Human3", "path": "Human3/img", "startFrame": 1, "endFrame": 1698, "nz": 4, "ext": "jpg", "anno_path": "Human3/groundtruth_rect.txt", "object_class": "person"}, {"name": "Human4_2", "path": "Human4/img", "startFrame": 1, "endFrame": 667, "nz": 4, "ext": "jpg", "anno_path": "Human4/groundtruth_rect.2.txt", "object_class": "person"}, {"name": "Human5", "path": "Human5/img", "startFrame": 1, "endFrame": 713, "nz": 4, "ext": "jpg", "anno_path": "Human5/groundtruth_rect.txt", "object_class": "person"}, {"name": "Human6", "path": "Human6/img", "startFrame": 1, "endFrame": 792, "nz": 4, "ext": "jpg", "anno_path": "Human6/groundtruth_rect.txt", "object_class": "person"}, {"name": "Human7", "path": "Human7/img", "startFrame": 1, "endFrame": 250, "nz": 4, "ext": "jpg", "anno_path": "Human7/groundtruth_rect.txt", "object_class": "person"}, {"name": "Human8", "path": "Human8/img", "startFrame": 1, "endFrame": 128, "nz": 4, "ext": "jpg", "anno_path": "Human8/groundtruth_rect.txt", "object_class": "person"}, {"name": "Human9", "path": "Human9/img", "startFrame": 1, "endFrame": 305, "nz": 4, "ext": "jpg", "anno_path": "Human9/groundtruth_rect.txt", "object_class": "person"}, {"name": "Ironman", "path": "Ironman/img", "startFrame": 1, "endFrame": 166, "nz": 4, "ext": "jpg", "anno_path": "Ironman/groundtruth_rect.txt", "object_class": "person head"}, {"name": "Jogging_1", "path": "Jogging/img", "startFrame": 1, "endFrame": 307, "nz": 4, "ext": "jpg", "anno_path": "Jogging/groundtruth_rect.1.txt", "object_class": "person"}, {"name": "Jogging_2", "path": "Jogging/img", "startFrame": 1, "endFrame": 307, "nz": 4, "ext": "jpg", "anno_path": "Jogging/groundtruth_rect.2.txt", "object_class": "person"}, {"name": "Jump", "path": "Jump/img", "startFrame": 1, "endFrame": 122, "nz": 4, "ext": "jpg", "anno_path": "Jump/groundtruth_rect.txt", "object_class": "person"}, {"name": "Jumping", "path": "Jumping/img", "startFrame": 1, "endFrame": 313, "nz": 4, "ext": "jpg", "anno_path": "Jumping/groundtruth_rect.txt", "object_class": "face"}, {"name": "KiteSurf", "path": "KiteSurf/img", "startFrame": 1, "endFrame": 84, "nz": 4, "ext": "png", "anno_path": "KiteSurf/groundtruth_rect.txt", "object_class": "face"}, {"name": "Lemming", "path": "Lemming/img", "startFrame": 1, "endFrame": 1336, "nz": 4, "ext": "jpg", "anno_path": "Lemming/groundtruth_rect.txt", "object_class": "other"}, {"name": "Liquor", "path": "Liquor/img", "startFrame": 1, "endFrame": 1741, "nz": 4, "ext": "jpg", "anno_path": "Liquor/groundtruth_rect.txt", "object_class": "other"}, {"name": "Man", "path": "Man/img", "startFrame": 1, "endFrame": 134, "nz": 4, "ext": "jpg", "anno_path": "Man/groundtruth_rect.txt", "object_class": "face"}, {"name": "Matrix", "path": "Matrix/img", "startFrame": 1, "endFrame": 100, "nz": 4, "ext": "jpg", "anno_path": "Matrix/groundtruth_rect.txt", "object_class": "person head"}, {"name": "Mhyang", "path": "Mhyang/img", "startFrame": 1, "endFrame": 1490, "nz": 4, "ext": "jpg", "anno_path": "Mhyang/groundtruth_rect.txt", "object_class": "face"}, {"name": "MotorRolling", "path": "MotorRolling/img", "startFrame": 1, "endFrame": 164, "nz": 4, "ext": "jpg", "anno_path": "MotorRolling/groundtruth_rect.txt", "object_class": "vehicle"}, {"name": "MountainBike", "path": "MountainBike/img", "startFrame": 1, "endFrame": 228, "nz": 4, "ext": "jpg", "anno_path": "MountainBike/groundtruth_rect.txt", "object_class": "bicycle"}, {"name": "Panda", "path": "Panda/img", "startFrame": 1, "endFrame": 1000, "nz": 4, "ext": "jpg", "anno_path": "Panda/groundtruth_rect.txt", "object_class": "mammal"}, {"name": "RedTeam", "path": "RedTeam/img", "startFrame": 1, "endFrame": 1918, "nz": 4, "ext": "jpg", "anno_path": "RedTeam/groundtruth_rect.txt", "object_class": "vehicle"}, {"name": "Rubik", "path": "Rubik/img", "startFrame": 1, "endFrame": 1997, "nz": 4, "ext": "jpg", "anno_path": "Rubik/groundtruth_rect.txt", "object_class": "other"}, {"name": "Shaking", "path": "Shaking/img", "startFrame": 1, "endFrame": 365, "nz": 4, "ext": "jpg", "anno_path": "Shaking/groundtruth_rect.txt", "object_class": "face"}, {"name": "Singer1", "path": "Singer1/img", "startFrame": 1, "endFrame": 351, "nz": 4, "ext": "jpg", "anno_path": "Singer1/groundtruth_rect.txt", "object_class": "person"}, {"name": "Singer2", "path": "Singer2/img", "startFrame": 1, "endFrame": 366, "nz": 4, "ext": "jpg", "anno_path": "Singer2/groundtruth_rect.txt", "object_class": "person"}, {"name": "Skater", "path": "Skater/img", "startFrame": 1, "endFrame": 160, "nz": 4, "ext": "jpg", "anno_path": "Skater/groundtruth_rect.txt", "object_class": "person"}, {"name": "Skater2", "path": "Skater2/img", "startFrame": 1, "endFrame": 435, "nz": 4, "ext": "jpg", "anno_path": "Skater2/groundtruth_rect.txt", "object_class": "person"}, {"name": "Skating1", "path": "Skating1/img", "startFrame": 1, "endFrame": 400, "nz": 4, "ext": "jpg", "anno_path": "Skating1/groundtruth_rect.txt", "object_class": "person"}, {"name": "Skating2_1", "path": "Skating2/img", "startFrame": 1, "endFrame": 473, "nz": 4, "ext": "jpg", "anno_path": "Skating2/groundtruth_rect.1.txt", "object_class": "person"}, {"name": "Skating2_2", "path": "Skating2/img", "startFrame": 1, "endFrame": 473, "nz": 4, "ext": "jpg", "anno_path": "Skating2/groundtruth_rect.2.txt", "object_class": "person"}, {"name": "Skiing", "path": "Skiing/img", "startFrame": 1, "endFrame": 81, "nz": 4, "ext": "jpg", "anno_path": "Skiing/groundtruth_rect.txt", "object_class": "person"}, {"name": "Soccer", "path": "Soccer/img", "startFrame": 1, "endFrame": 392, "nz": 4, "ext": "jpg", "anno_path": "Soccer/groundtruth_rect.txt", "object_class": "face"}, {"name": "Subway", "path": "Subway/img", "startFrame": 1, "endFrame": 175, "nz": 4, "ext": "jpg", "anno_path": "Subway/groundtruth_rect.txt", "object_class": "person"}, {"name": "Surfer", "path": "Surfer/img", "startFrame": 1, "endFrame": 376, "nz": 4, "ext": "jpg", "anno_path": "Surfer/groundtruth_rect.txt", "object_class": "person head"}, {"name": "Suv", "path": "Suv/img", "startFrame": 1, "endFrame": 945, "nz": 4, "ext": "jpg", "anno_path": "Suv/groundtruth_rect.txt", "object_class": "car"}, {"name": "Sylvester", "path": "Sylvester/img", "startFrame": 1, "endFrame": 1345, "nz": 4, "ext": "jpg", "anno_path": "Sylvester/groundtruth_rect.txt", "object_class": "other"}, {"name": "Tiger1", "path": "Tiger1/img", "startFrame": 1, "endFrame": 354, "nz": 4, "ext": "jpg", "anno_path": "Tiger1/groundtruth_rect.txt", "initOmit": 5, "object_class": "other"}, {"name": "Tiger2", "path": "Tiger2/img", "startFrame": 1, "endFrame": 365, "nz": 4, "ext": "jpg", "anno_path": "Tiger2/groundtruth_rect.txt", "object_class": "other"}, {"name": "Toy", "path": "Toy/img", "startFrame": 1, "endFrame": 271, "nz": 4, "ext": "jpg", "anno_path": "Toy/groundtruth_rect.txt", "object_class": "other"}, {"name": "Trans", "path": "Trans/img", "startFrame": 1, "endFrame": 124, "nz": 4, "ext": "jpg", "anno_path": "Trans/groundtruth_rect.txt", "object_class": "other"}, {"name": "Trellis", "path": "Trellis/img", "startFrame": 1, "endFrame": 569, "nz": 4, "ext": "jpg", "anno_path": "Trellis/groundtruth_rect.txt", "object_class": "face"}, {"name": "Twinnings", "path": "Twinnings/img", "startFrame": 1, "endFrame": 472, "nz": 4, "ext": "jpg", "anno_path": "Twinnings/groundtruth_rect.txt", "object_class": "other"}, {"name": "Vase", "path": "Vase/img", "startFrame": 1, "endFrame": 271, "nz": 4, "ext": "jpg", "anno_path": "Vase/groundtruth_rect.txt", "object_class": "other"}, {"name": "Walking", "path": "Walking/img", "startFrame": 1, "endFrame": 412, "nz": 4, "ext": "jpg", "anno_path": "Walking/groundtruth_rect.txt", "object_class": "person"}, {"name": "Walking2", "path": "Walking2/img", "startFrame": 1, "endFrame": 500, "nz": 4, "ext": "jpg", "anno_path": "Walking2/groundtruth_rect.txt", "object_class": "person"}, {"name": "Woman", "path": "Woman/img", "startFrame": 1, "endFrame": 597, "nz": 4, "ext": "jpg", "anno_path": "Woman/groundtruth_rect.txt", "object_class": "person"} ] return sequence_info_list ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/evaluation/running.py ================================================ import numpy as np import multiprocessing import os import sys from itertools import product from collections import OrderedDict from pytracking.evaluation import Sequence, Tracker from ltr.data.image_loader import imwrite_indexed def _save_tracker_output(seq: Sequence, tracker: Tracker, output: dict): """Saves the output of the tracker.""" if not os.path.exists(tracker.results_dir): os.makedirs(tracker.results_dir) base_results_path = os.path.join(tracker.results_dir, seq.name) segmentation_path = os.path.join(tracker.segmentation_dir, seq.name) frame_names = [os.path.splitext(os.path.basename(f))[0] for f in seq.frames] def save_bb(file, data): tracked_bb = np.array(data).astype(int) np.savetxt(file, tracked_bb, delimiter='\t', fmt='%d') def save_time(file, data): exec_times = np.array(data).astype(float) np.savetxt(file, exec_times, delimiter='\t', fmt='%f') def _convert_dict(input_dict): data_dict = {} for elem in input_dict: for k, v in elem.items(): if k in data_dict.keys(): data_dict[k].append(v) else: data_dict[k] = [v, ] return data_dict for key, data in output.items(): # If data is empty if not data: continue if key == 'target_bbox': if isinstance(data[0], (dict, OrderedDict)): data_dict = _convert_dict(data) for obj_id, d in data_dict.items(): bbox_file = '{}_{}.txt'.format(base_results_path, obj_id) save_bb(bbox_file, d) else: # Single-object mode bbox_file = '{}.txt'.format(base_results_path) save_bb(bbox_file, data) elif key == 'time': if isinstance(data[0], dict): data_dict = _convert_dict(data) for obj_id, d in data_dict.items(): timings_file = '{}_{}_time.txt'.format(base_results_path, obj_id) save_time(timings_file, d) else: timings_file = '{}_time.txt'.format(base_results_path) save_time(timings_file, data) elif key == 'segmentation': assert len(frame_names) == len(data) if not os.path.exists(segmentation_path): os.makedirs(segmentation_path) for frame_name, frame_seg in zip(frame_names, data): imwrite_indexed(os.path.join(segmentation_path, '{}.png'.format(frame_name)), frame_seg) def run_sequence(seq: Sequence, tracker: Tracker, debug=False, visdom_info=None): """Runs a tracker on a sequence.""" def _results_exist(): if seq.object_ids is None: bbox_file = '{}/{}.txt'.format(tracker.results_dir, seq.name) return os.path.isfile(bbox_file) else: bbox_files = ['{}/{}_{}.txt'.format(tracker.results_dir, seq.name, obj_id) for obj_id in seq.object_ids] missing = [not os.path.isfile(f) for f in bbox_files] return sum(missing) == 0 visdom_info = {} if visdom_info is None else visdom_info if _results_exist() and not debug: return print('Tracker: {} {} {} , Sequence: {}'.format(tracker.name, tracker.parameter_name, tracker.run_id, seq.name)) if debug: output = tracker.run_sequence(seq, debug=debug, visdom_info=visdom_info) else: try: output = tracker.run_sequence(seq, debug=debug, visdom_info=visdom_info) except Exception as e: print(e) return sys.stdout.flush() if isinstance(output['time'][0], (dict, OrderedDict)): exec_time = sum([sum(times.values()) for times in output['time']]) num_frames = len(output['time']) else: exec_time = sum(output['time']) num_frames = len(output['time']) print('FPS: {}'.format(num_frames / exec_time)) if not debug: _save_tracker_output(seq, tracker, output) def run_dataset(dataset, trackers, debug=False, threads=0, visdom_info=None): """Runs a list of trackers on a dataset. args: dataset: List of Sequence instances, forming a dataset. trackers: List of Tracker instances. debug: Debug level. threads: Number of threads to use (default 0). visdom_info: Dict containing information about the server for visdom """ multiprocessing.set_start_method('spawn', force=True) print('Evaluating {:4d} trackers on {:5d} sequences'.format(len(trackers), len(dataset))) multiprocessing.set_start_method('spawn', force=True) visdom_info = {} if visdom_info is None else visdom_info if threads == 0: mode = 'sequential' else: mode = 'parallel' if mode == 'sequential': for seq in dataset: for tracker_info in trackers: run_sequence(seq, tracker_info, debug=debug, visdom_info=visdom_info) elif mode == 'parallel': param_list = [(seq, tracker_info, debug, visdom_info) for seq, tracker_info in product(dataset, trackers)] with multiprocessing.Pool(processes=threads) as pool: pool.starmap(run_sequence, param_list) print('Done') ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/evaluation/tpldataset.py ================================================ import numpy as np from pytracking.evaluation.data import Sequence, BaseDataset, SequenceList from pytracking.utils.load_text import load_text class TPLDataset(BaseDataset): """ Temple Color 128 dataset Publication: Encoding Color Information for Visual Tracking: Algorithms and Benchmark P. Liang, E. Blasch, and H. Ling TIP, 2015 http://www.dabi.temple.edu/~hbling/publication/TColor-128.pdf Download the dataset from http://www.dabi.temple.edu/~hbling/data/TColor-128/TColor-128.html """ def __init__(self, exclude_otb=False): """ args: exclude_otb (bool) - If True, sequences overlapping with the OTB dataset are excluded """ super().__init__() self.base_path = self.env_settings.tpl_path self.sequence_info_list = self._get_sequence_info_list(exclude_otb) def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_info_list]) def _construct_sequence(self, sequence_info): sequence_path = sequence_info['path'] nz = sequence_info['nz'] ext = sequence_info['ext'] start_frame = sequence_info['startFrame'] end_frame = sequence_info['endFrame'] init_omit = 0 if 'initOmit' in sequence_info: init_omit = sequence_info['initOmit'] frames = ['{base_path}/{sequence_path}/{frame:0{nz}}.{ext}'.format(base_path=self.base_path, sequence_path=sequence_path, frame=frame_num, nz=nz, ext=ext) for frame_num in range(start_frame+init_omit, end_frame+1)] anno_path = '{}/{}'.format(self.base_path, sequence_info['anno_path']) ground_truth_rect = load_text(str(anno_path), delimiter=(',', None), dtype=np.float64, backend='numpy') return Sequence(sequence_info['name'], frames, 'tpl', ground_truth_rect[init_omit:,:]) def __len__(self): return len(self.sequence_info_list) def _get_sequence_info_list(self, exclude_otb=False): sequence_info_list = [ {"name": "tpl_Skating2", "path": "tpl_Skating2/img", "startFrame": 1, "endFrame": 707, "nz": 4, "ext": "jpg", "anno_path": "tpl_Skating2/Skating2_gt.txt"}, {"name": "tpl_Pool_ce3", "path": "tpl_Pool_ce3/img", "startFrame": 1, "endFrame": 124, "nz": 4, "ext": "jpg", "anno_path": "tpl_Pool_ce3/Pool_ce3_gt.txt"}, {"name": "tpl_Microphone_ce1", "path": "tpl_Microphone_ce1/img", "startFrame": 1, "endFrame": 204, "nz": 4, "ext": "jpg", "anno_path": "tpl_Microphone_ce1/Microphone_ce1_gt.txt"}, {"name": "tpl_Torus", "path": "tpl_Torus/img", "startFrame": 1, "endFrame": 264, "nz": 4, "ext": "jpg", "anno_path": "tpl_Torus/Torus_gt.txt"}, {"name": "tpl_Lemming", "path": "tpl_Lemming/img", "startFrame": 1, "endFrame": 1336, "nz": 4, "ext": "jpg", "anno_path": "tpl_Lemming/Lemming_gt.txt"}, {"name": "tpl_Eagle_ce", "path": "tpl_Eagle_ce/img", "startFrame": 1, "endFrame": 112, "nz": 4, "ext": "jpg", "anno_path": "tpl_Eagle_ce/Eagle_ce_gt.txt"}, {"name": "tpl_Skating_ce2", "path": "tpl_Skating_ce2/img", "startFrame": 1, "endFrame": 497, "nz": 4, "ext": "jpg", "anno_path": "tpl_Skating_ce2/Skating_ce2_gt.txt"}, {"name": "tpl_Yo_yos_ce3", "path": "tpl_Yo_yos_ce3/img", "startFrame": 1, "endFrame": 201, "nz": 4, "ext": "jpg", "anno_path": "tpl_Yo_yos_ce3/Yo-yos_ce3_gt.txt"}, {"name": "tpl_Board", "path": "tpl_Board/img", "startFrame": 1, "endFrame": 598, "nz": 4, "ext": "jpg", "anno_path": "tpl_Board/Board_gt.txt"}, {"name": "tpl_Tennis_ce3", "path": "tpl_Tennis_ce3/img", "startFrame": 1, "endFrame": 204, "nz": 4, "ext": "jpg", "anno_path": "tpl_Tennis_ce3/Tennis_ce3_gt.txt"}, {"name": "tpl_SuperMario_ce", "path": "tpl_SuperMario_ce/img", "startFrame": 1, "endFrame": 146, "nz": 4, "ext": "jpg", "anno_path": "tpl_SuperMario_ce/SuperMario_ce_gt.txt"}, {"name": "tpl_Yo_yos_ce1", "path": "tpl_Yo_yos_ce1/img", "startFrame": 1, "endFrame": 235, "nz": 4, "ext": "jpg", "anno_path": "tpl_Yo_yos_ce1/Yo-yos_ce1_gt.txt"}, {"name": "tpl_Soccer", "path": "tpl_Soccer/img", "startFrame": 1, "endFrame": 392, "nz": 4, "ext": "jpg", "anno_path": "tpl_Soccer/Soccer_gt.txt"}, {"name": "tpl_Fish_ce2", "path": "tpl_Fish_ce2/img", "startFrame": 1, "endFrame": 573, "nz": 4, "ext": "jpg", "anno_path": "tpl_Fish_ce2/Fish_ce2_gt.txt"}, {"name": "tpl_Liquor", "path": "tpl_Liquor/img", "startFrame": 1, "endFrame": 1741, "nz": 4, "ext": "jpg", "anno_path": "tpl_Liquor/Liquor_gt.txt"}, {"name": "tpl_Plane_ce2", "path": "tpl_Plane_ce2/img", "startFrame": 1, "endFrame": 653, "nz": 4, "ext": "jpg", "anno_path": "tpl_Plane_ce2/Plane_ce2_gt.txt"}, {"name": "tpl_Couple", "path": "tpl_Couple/img", "startFrame": 1, "endFrame": 140, "nz": 4, "ext": "jpg", "anno_path": "tpl_Couple/Couple_gt.txt"}, {"name": "tpl_Logo_ce", "path": "tpl_Logo_ce/img", "startFrame": 1, "endFrame": 610, "nz": 4, "ext": "jpg", "anno_path": "tpl_Logo_ce/Logo_ce_gt.txt"}, {"name": "tpl_Hand_ce2", "path": "tpl_Hand_ce2/img", "startFrame": 1, "endFrame": 251, "nz": 4, "ext": "jpg", "anno_path": "tpl_Hand_ce2/Hand_ce2_gt.txt"}, {"name": "tpl_Kite_ce2", "path": "tpl_Kite_ce2/img", "startFrame": 1, "endFrame": 658, "nz": 4, "ext": "jpg", "anno_path": "tpl_Kite_ce2/Kite_ce2_gt.txt"}, {"name": "tpl_Walking", "path": "tpl_Walking/img", "startFrame": 1, "endFrame": 412, "nz": 4, "ext": "jpg", "anno_path": "tpl_Walking/Walking_gt.txt"}, {"name": "tpl_David", "path": "tpl_David/img", "startFrame": 300, "endFrame": 770, "nz": 4, "ext": "jpg", "anno_path": "tpl_David/David_gt.txt"}, {"name": "tpl_Boat_ce1", "path": "tpl_Boat_ce1/img", "startFrame": 1, "endFrame": 377, "nz": 4, "ext": "jpg", "anno_path": "tpl_Boat_ce1/Boat_ce1_gt.txt"}, {"name": "tpl_Airport_ce", "path": "tpl_Airport_ce/img", "startFrame": 1, "endFrame": 148, "nz": 4, "ext": "jpg", "anno_path": "tpl_Airport_ce/Airport_ce_gt.txt"}, {"name": "tpl_Tiger2", "path": "tpl_Tiger2/img", "startFrame": 1, "endFrame": 365, "nz": 4, "ext": "jpg", "anno_path": "tpl_Tiger2/Tiger2_gt.txt"}, {"name": "tpl_Suitcase_ce", "path": "tpl_Suitcase_ce/img", "startFrame": 1, "endFrame": 184, "nz": 4, "ext": "jpg", "anno_path": "tpl_Suitcase_ce/Suitcase_ce_gt.txt"}, {"name": "tpl_TennisBall_ce", "path": "tpl_TennisBall_ce/img", "startFrame": 1, "endFrame": 288, "nz": 4, "ext": "jpg", "anno_path": "tpl_TennisBall_ce/TennisBall_ce_gt.txt"}, {"name": "tpl_Singer_ce1", "path": "tpl_Singer_ce1/img", "startFrame": 1, "endFrame": 214, "nz": 4, "ext": "jpg", "anno_path": "tpl_Singer_ce1/Singer_ce1_gt.txt"}, {"name": "tpl_Pool_ce2", "path": "tpl_Pool_ce2/img", "startFrame": 1, "endFrame": 133, "nz": 4, "ext": "jpg", "anno_path": "tpl_Pool_ce2/Pool_ce2_gt.txt"}, {"name": "tpl_Surf_ce3", "path": "tpl_Surf_ce3/img", "startFrame": 1, "endFrame": 279, "nz": 4, "ext": "jpg", "anno_path": "tpl_Surf_ce3/Surf_ce3_gt.txt"}, {"name": "tpl_Bird", "path": "tpl_Bird/img", "startFrame": 1, "endFrame": 99, "nz": 4, "ext": "jpg", "anno_path": "tpl_Bird/Bird_gt.txt"}, {"name": "tpl_Crossing", "path": "tpl_Crossing/img", "startFrame": 1, "endFrame": 120, "nz": 4, "ext": "jpg", "anno_path": "tpl_Crossing/Crossing_gt.txt"}, {"name": "tpl_Plate_ce1", "path": "tpl_Plate_ce1/img", "startFrame": 1, "endFrame": 142, "nz": 4, "ext": "jpg", "anno_path": "tpl_Plate_ce1/Plate_ce1_gt.txt"}, {"name": "tpl_Cup", "path": "tpl_Cup/img", "startFrame": 1, "endFrame": 303, "nz": 4, "ext": "jpg", "anno_path": "tpl_Cup/Cup_gt.txt"}, {"name": "tpl_Surf_ce2", "path": "tpl_Surf_ce2/img", "startFrame": 1, "endFrame": 391, "nz": 4, "ext": "jpg", "anno_path": "tpl_Surf_ce2/Surf_ce2_gt.txt"}, {"name": "tpl_Busstation_ce2", "path": "tpl_Busstation_ce2/img", "startFrame": 6, "endFrame": 400, "nz": 4, "ext": "jpg", "anno_path": "tpl_Busstation_ce2/Busstation_ce2_gt.txt"}, {"name": "tpl_Charger_ce", "path": "tpl_Charger_ce/img", "startFrame": 1, "endFrame": 298, "nz": 4, "ext": "jpg", "anno_path": "tpl_Charger_ce/Charger_ce_gt.txt"}, {"name": "tpl_Pool_ce1", "path": "tpl_Pool_ce1/img", "startFrame": 1, "endFrame": 166, "nz": 4, "ext": "jpg", "anno_path": "tpl_Pool_ce1/Pool_ce1_gt.txt"}, {"name": "tpl_MountainBike", "path": "tpl_MountainBike/img", "startFrame": 1, "endFrame": 228, "nz": 4, "ext": "jpg", "anno_path": "tpl_MountainBike/MountainBike_gt.txt"}, {"name": "tpl_Guitar_ce1", "path": "tpl_Guitar_ce1/img", "startFrame": 1, "endFrame": 268, "nz": 4, "ext": "jpg", "anno_path": "tpl_Guitar_ce1/Guitar_ce1_gt.txt"}, {"name": "tpl_Busstation_ce1", "path": "tpl_Busstation_ce1/img", "startFrame": 1, "endFrame": 363, "nz": 4, "ext": "jpg", "anno_path": "tpl_Busstation_ce1/Busstation_ce1_gt.txt"}, {"name": "tpl_Diving", "path": "tpl_Diving/img", "startFrame": 1, "endFrame": 231, "nz": 4, "ext": "jpg", "anno_path": "tpl_Diving/Diving_gt.txt"}, {"name": "tpl_Skating_ce1", "path": "tpl_Skating_ce1/img", "startFrame": 1, "endFrame": 409, "nz": 4, "ext": "jpg", "anno_path": "tpl_Skating_ce1/Skating_ce1_gt.txt"}, {"name": "tpl_Hurdle_ce2", "path": "tpl_Hurdle_ce2/img", "startFrame": 27, "endFrame": 330, "nz": 4, "ext": "jpg", "anno_path": "tpl_Hurdle_ce2/Hurdle_ce2_gt.txt"}, {"name": "tpl_Plate_ce2", "path": "tpl_Plate_ce2/img", "startFrame": 1, "endFrame": 181, "nz": 4, "ext": "jpg", "anno_path": "tpl_Plate_ce2/Plate_ce2_gt.txt"}, {"name": "tpl_CarDark", "path": "tpl_CarDark/img", "startFrame": 1, "endFrame": 393, "nz": 4, "ext": "jpg", "anno_path": "tpl_CarDark/CarDark_gt.txt"}, {"name": "tpl_Singer_ce2", "path": "tpl_Singer_ce2/img", "startFrame": 1, "endFrame": 999, "nz": 4, "ext": "jpg", "anno_path": "tpl_Singer_ce2/Singer_ce2_gt.txt"}, {"name": "tpl_Shaking", "path": "tpl_Shaking/img", "startFrame": 1, "endFrame": 365, "nz": 4, "ext": "jpg", "anno_path": "tpl_Shaking/Shaking_gt.txt"}, {"name": "tpl_Iceskater", "path": "tpl_Iceskater/img", "startFrame": 1, "endFrame": 500, "nz": 4, "ext": "jpg", "anno_path": "tpl_Iceskater/Iceskater_gt.txt"}, {"name": "tpl_Badminton_ce2", "path": "tpl_Badminton_ce2/img", "startFrame": 1, "endFrame": 705, "nz": 4, "ext": "jpg", "anno_path": "tpl_Badminton_ce2/Badminton_ce2_gt.txt"}, {"name": "tpl_Spiderman_ce", "path": "tpl_Spiderman_ce/img", "startFrame": 1, "endFrame": 351, "nz": 4, "ext": "jpg", "anno_path": "tpl_Spiderman_ce/Spiderman_ce_gt.txt"}, {"name": "tpl_Kite_ce1", "path": "tpl_Kite_ce1/img", "startFrame": 1, "endFrame": 484, "nz": 4, "ext": "jpg", "anno_path": "tpl_Kite_ce1/Kite_ce1_gt.txt"}, {"name": "tpl_Skyjumping_ce", "path": "tpl_Skyjumping_ce/img", "startFrame": 1, "endFrame": 938, "nz": 4, "ext": "jpg", "anno_path": "tpl_Skyjumping_ce/Skyjumping_ce_gt.txt"}, {"name": "tpl_Ball_ce1", "path": "tpl_Ball_ce1/img", "startFrame": 1, "endFrame": 391, "nz": 4, "ext": "jpg", "anno_path": "tpl_Ball_ce1/Ball_ce1_gt.txt"}, {"name": "tpl_Yo_yos_ce2", "path": "tpl_Yo_yos_ce2/img", "startFrame": 1, "endFrame": 454, "nz": 4, "ext": "jpg", "anno_path": "tpl_Yo_yos_ce2/Yo-yos_ce2_gt.txt"}, {"name": "tpl_Ironman", "path": "tpl_Ironman/img", "startFrame": 1, "endFrame": 166, "nz": 4, "ext": "jpg", "anno_path": "tpl_Ironman/Ironman_gt.txt"}, {"name": "tpl_FaceOcc1", "path": "tpl_FaceOcc1/img", "startFrame": 1, "endFrame": 892, "nz": 4, "ext": "jpg", "anno_path": "tpl_FaceOcc1/FaceOcc1_gt.txt"}, {"name": "tpl_Surf_ce1", "path": "tpl_Surf_ce1/img", "startFrame": 1, "endFrame": 404, "nz": 4, "ext": "jpg", "anno_path": "tpl_Surf_ce1/Surf_ce1_gt.txt"}, {"name": "tpl_Ring_ce", "path": "tpl_Ring_ce/img", "startFrame": 1, "endFrame": 201, "nz": 4, "ext": "jpg", "anno_path": "tpl_Ring_ce/Ring_ce_gt.txt"}, {"name": "tpl_Surf_ce4", "path": "tpl_Surf_ce4/img", "startFrame": 1, "endFrame": 135, "nz": 4, "ext": "jpg", "anno_path": "tpl_Surf_ce4/Surf_ce4_gt.txt"}, {"name": "tpl_Ball_ce4", "path": "tpl_Ball_ce4/img", "startFrame": 1, "endFrame": 538, "nz": 4, "ext": "jpg", "anno_path": "tpl_Ball_ce4/Ball_ce4_gt.txt"}, {"name": "tpl_Bikeshow_ce", "path": "tpl_Bikeshow_ce/img", "startFrame": 1, "endFrame": 361, "nz": 4, "ext": "jpg", "anno_path": "tpl_Bikeshow_ce/Bikeshow_ce_gt.txt"}, {"name": "tpl_Kobe_ce", "path": "tpl_Kobe_ce/img", "startFrame": 1, "endFrame": 582, "nz": 4, "ext": "jpg", "anno_path": "tpl_Kobe_ce/Kobe_ce_gt.txt"}, {"name": "tpl_Tiger1", "path": "tpl_Tiger1/img", "startFrame": 1, "endFrame": 354, "nz": 4, "ext": "jpg", "anno_path": "tpl_Tiger1/Tiger1_gt.txt"}, {"name": "tpl_Skiing", "path": "tpl_Skiing/img", "startFrame": 1, "endFrame": 81, "nz": 4, "ext": "jpg", "anno_path": "tpl_Skiing/Skiing_gt.txt"}, {"name": "tpl_Tennis_ce1", "path": "tpl_Tennis_ce1/img", "startFrame": 1, "endFrame": 454, "nz": 4, "ext": "jpg", "anno_path": "tpl_Tennis_ce1/Tennis_ce1_gt.txt"}, {"name": "tpl_Carchasing_ce4", "path": "tpl_Carchasing_ce4/img", "startFrame": 1, "endFrame": 442, "nz": 4, "ext": "jpg", "anno_path": "tpl_Carchasing_ce4/Carchasing_ce4_gt.txt"}, {"name": "tpl_Walking2", "path": "tpl_Walking2/img", "startFrame": 1, "endFrame": 500, "nz": 4, "ext": "jpg", "anno_path": "tpl_Walking2/Walking2_gt.txt"}, {"name": "tpl_Sailor_ce", "path": "tpl_Sailor_ce/img", "startFrame": 1, "endFrame": 402, "nz": 4, "ext": "jpg", "anno_path": "tpl_Sailor_ce/Sailor_ce_gt.txt"}, {"name": "tpl_Railwaystation_ce", "path": "tpl_Railwaystation_ce/img", "startFrame": 1, "endFrame": 413, "nz": 4, "ext": "jpg", "anno_path": "tpl_Railwaystation_ce/Railwaystation_ce_gt.txt"}, {"name": "tpl_Bee_ce", "path": "tpl_Bee_ce/img", "startFrame": 1, "endFrame": 90, "nz": 4, "ext": "jpg", "anno_path": "tpl_Bee_ce/Bee_ce_gt.txt"}, {"name": "tpl_Girl", "path": "tpl_Girl/img", "startFrame": 1, "endFrame": 500, "nz": 4, "ext": "jpg", "anno_path": "tpl_Girl/Girl_gt.txt"}, {"name": "tpl_Subway", "path": "tpl_Subway/img", "startFrame": 1, "endFrame": 175, "nz": 4, "ext": "jpg", "anno_path": "tpl_Subway/Subway_gt.txt"}, {"name": "tpl_David3", "path": "tpl_David3/img", "startFrame": 1, "endFrame": 252, "nz": 4, "ext": "jpg", "anno_path": "tpl_David3/David3_gt.txt"}, {"name": "tpl_Electricalbike_ce", "path": "tpl_Electricalbike_ce/img", "startFrame": 1, "endFrame": 818, "nz": 4, "ext": "jpg", "anno_path": "tpl_Electricalbike_ce/Electricalbike_ce_gt.txt"}, {"name": "tpl_Michaeljackson_ce", "path": "tpl_Michaeljackson_ce/img", "startFrame": 1, "endFrame": 393, "nz": 4, "ext": "jpg", "anno_path": "tpl_Michaeljackson_ce/Michaeljackson_ce_gt.txt"}, {"name": "tpl_Woman", "path": "tpl_Woman/img", "startFrame": 1, "endFrame": 597, "nz": 4, "ext": "jpg", "anno_path": "tpl_Woman/Woman_gt.txt"}, {"name": "tpl_TableTennis_ce", "path": "tpl_TableTennis_ce/img", "startFrame": 1, "endFrame": 198, "nz": 4, "ext": "jpg", "anno_path": "tpl_TableTennis_ce/TableTennis_ce_gt.txt"}, {"name": "tpl_Motorbike_ce", "path": "tpl_Motorbike_ce/img", "startFrame": 1, "endFrame": 563, "nz": 4, "ext": "jpg", "anno_path": "tpl_Motorbike_ce/Motorbike_ce_gt.txt"}, {"name": "tpl_Baby_ce", "path": "tpl_Baby_ce/img", "startFrame": 1, "endFrame": 296, "nz": 4, "ext": "jpg", "anno_path": "tpl_Baby_ce/Baby_ce_gt.txt"}, {"name": "tpl_Gym", "path": "tpl_Gym/img", "startFrame": 1, "endFrame": 766, "nz": 4, "ext": "jpg", "anno_path": "tpl_Gym/Gym_gt.txt"}, {"name": "tpl_Matrix", "path": "tpl_Matrix/img", "startFrame": 1, "endFrame": 100, "nz": 4, "ext": "jpg", "anno_path": "tpl_Matrix/Matrix_gt.txt"}, {"name": "tpl_Kite_ce3", "path": "tpl_Kite_ce3/img", "startFrame": 1, "endFrame": 528, "nz": 4, "ext": "jpg", "anno_path": "tpl_Kite_ce3/Kite_ce3_gt.txt"}, {"name": "tpl_Fish_ce1", "path": "tpl_Fish_ce1/img", "startFrame": 1, "endFrame": 401, "nz": 4, "ext": "jpg", "anno_path": "tpl_Fish_ce1/Fish_ce1_gt.txt"}, {"name": "tpl_Hand_ce1", "path": "tpl_Hand_ce1/img", "startFrame": 1, "endFrame": 401, "nz": 4, "ext": "jpg", "anno_path": "tpl_Hand_ce1/Hand_ce1_gt.txt"}, {"name": "tpl_Doll", "path": "tpl_Doll/img", "startFrame": 1, "endFrame": 3872, "nz": 4, "ext": "jpg", "anno_path": "tpl_Doll/Doll_gt.txt"}, {"name": "tpl_Carchasing_ce3", "path": "tpl_Carchasing_ce3/img", "startFrame": 1, "endFrame": 572, "nz": 4, "ext": "jpg", "anno_path": "tpl_Carchasing_ce3/Carchasing_ce3_gt.txt"}, {"name": "tpl_Thunder_ce", "path": "tpl_Thunder_ce/img", "startFrame": 1, "endFrame": 375, "nz": 4, "ext": "jpg", "anno_path": "tpl_Thunder_ce/Thunder_ce_gt.txt"}, {"name": "tpl_Singer2", "path": "tpl_Singer2/img", "startFrame": 1, "endFrame": 366, "nz": 4, "ext": "jpg", "anno_path": "tpl_Singer2/Singer2_gt.txt"}, {"name": "tpl_Basketball", "path": "tpl_Basketball/img", "startFrame": 1, "endFrame": 725, "nz": 4, "ext": "jpg", "anno_path": "tpl_Basketball/Basketball_gt.txt"}, {"name": "tpl_Hand", "path": "tpl_Hand/img", "startFrame": 1, "endFrame": 244, "nz": 4, "ext": "jpg", "anno_path": "tpl_Hand/Hand_gt.txt"}, {"name": "tpl_Cup_ce", "path": "tpl_Cup_ce/img", "startFrame": 1, "endFrame": 338, "nz": 4, "ext": "jpg", "anno_path": "tpl_Cup_ce/Cup_ce_gt.txt"}, {"name": "tpl_MotorRolling", "path": "tpl_MotorRolling/img", "startFrame": 1, "endFrame": 164, "nz": 4, "ext": "jpg", "anno_path": "tpl_MotorRolling/MotorRolling_gt.txt"}, {"name": "tpl_Boat_ce2", "path": "tpl_Boat_ce2/img", "startFrame": 1, "endFrame": 412, "nz": 4, "ext": "jpg", "anno_path": "tpl_Boat_ce2/Boat_ce2_gt.txt"}, {"name": "tpl_CarScale", "path": "tpl_CarScale/img", "startFrame": 1, "endFrame": 252, "nz": 4, "ext": "jpg", "anno_path": "tpl_CarScale/CarScale_gt.txt"}, {"name": "tpl_Sunshade", "path": "tpl_Sunshade/img", "startFrame": 1, "endFrame": 172, "nz": 4, "ext": "jpg", "anno_path": "tpl_Sunshade/Sunshade_gt.txt"}, {"name": "tpl_Football1", "path": "tpl_Football1/img", "startFrame": 1, "endFrame": 74, "nz": 4, "ext": "jpg", "anno_path": "tpl_Football1/Football1_gt.txt"}, {"name": "tpl_Singer1", "path": "tpl_Singer1/img", "startFrame": 1, "endFrame": 351, "nz": 4, "ext": "jpg", "anno_path": "tpl_Singer1/Singer1_gt.txt"}, {"name": "tpl_Hurdle_ce1", "path": "tpl_Hurdle_ce1/img", "startFrame": 1, "endFrame": 300, "nz": 4, "ext": "jpg", "anno_path": "tpl_Hurdle_ce1/Hurdle_ce1_gt.txt"}, {"name": "tpl_Basketball_ce3", "path": "tpl_Basketball_ce3/img", "startFrame": 1, "endFrame": 441, "nz": 4, "ext": "jpg", "anno_path": "tpl_Basketball_ce3/Basketball_ce3_gt.txt"}, {"name": "tpl_Toyplane_ce", "path": "tpl_Toyplane_ce/img", "startFrame": 1, "endFrame": 405, "nz": 4, "ext": "jpg", "anno_path": "tpl_Toyplane_ce/Toyplane_ce_gt.txt"}, {"name": "tpl_Skating1", "path": "tpl_Skating1/img", "startFrame": 1, "endFrame": 400, "nz": 4, "ext": "jpg", "anno_path": "tpl_Skating1/Skating1_gt.txt"}, {"name": "tpl_Juice", "path": "tpl_Juice/img", "startFrame": 1, "endFrame": 404, "nz": 4, "ext": "jpg", "anno_path": "tpl_Juice/Juice_gt.txt"}, {"name": "tpl_Biker", "path": "tpl_Biker/img", "startFrame": 1, "endFrame": 180, "nz": 4, "ext": "jpg", "anno_path": "tpl_Biker/Biker_gt.txt"}, {"name": "tpl_Boy", "path": "tpl_Boy/img", "startFrame": 1, "endFrame": 602, "nz": 4, "ext": "jpg", "anno_path": "tpl_Boy/Boy_gt.txt"}, {"name": "tpl_Jogging1", "path": "tpl_Jogging1/img", "startFrame": 1, "endFrame": 307, "nz": 4, "ext": "jpg", "anno_path": "tpl_Jogging1/Jogging1_gt.txt"}, {"name": "tpl_Deer", "path": "tpl_Deer/img", "startFrame": 1, "endFrame": 71, "nz": 4, "ext": "jpg", "anno_path": "tpl_Deer/Deer_gt.txt"}, {"name": "tpl_Panda", "path": "tpl_Panda/img", "startFrame": 1, "endFrame": 241, "nz": 4, "ext": "jpg", "anno_path": "tpl_Panda/Panda_gt.txt"}, {"name": "tpl_Coke", "path": "tpl_Coke/img", "startFrame": 1, "endFrame": 291, "nz": 4, "ext": "jpg", "anno_path": "tpl_Coke/Coke_gt.txt"}, {"name": "tpl_Carchasing_ce1", "path": "tpl_Carchasing_ce1/img", "startFrame": 1, "endFrame": 501, "nz": 4, "ext": "jpg", "anno_path": "tpl_Carchasing_ce1/Carchasing_ce1_gt.txt"}, {"name": "tpl_Badminton_ce1", "path": "tpl_Badminton_ce1/img", "startFrame": 1, "endFrame": 579, "nz": 4, "ext": "jpg", "anno_path": "tpl_Badminton_ce1/Badminton_ce1_gt.txt"}, {"name": "tpl_Trellis", "path": "tpl_Trellis/img", "startFrame": 1, "endFrame": 569, "nz": 4, "ext": "jpg", "anno_path": "tpl_Trellis/Trellis_gt.txt"}, {"name": "tpl_Face_ce2", "path": "tpl_Face_ce2/img", "startFrame": 1, "endFrame": 148, "nz": 4, "ext": "jpg", "anno_path": "tpl_Face_ce2/Face_ce2_gt.txt"}, {"name": "tpl_Ball_ce2", "path": "tpl_Ball_ce2/img", "startFrame": 1, "endFrame": 603, "nz": 4, "ext": "jpg", "anno_path": "tpl_Ball_ce2/Ball_ce2_gt.txt"}, {"name": "tpl_Skiing_ce", "path": "tpl_Skiing_ce/img", "startFrame": 1, "endFrame": 511, "nz": 4, "ext": "jpg", "anno_path": "tpl_Skiing_ce/Skiing_ce_gt.txt"}, {"name": "tpl_Jogging2", "path": "tpl_Jogging2/img", "startFrame": 1, "endFrame": 307, "nz": 4, "ext": "jpg", "anno_path": "tpl_Jogging2/Jogging2_gt.txt"}, {"name": "tpl_Bike_ce1", "path": "tpl_Bike_ce1/img", "startFrame": 1, "endFrame": 801, "nz": 4, "ext": "jpg", "anno_path": "tpl_Bike_ce1/Bike_ce1_gt.txt"}, {"name": "tpl_Bike_ce2", "path": "tpl_Bike_ce2/img", "startFrame": 1, "endFrame": 812, "nz": 4, "ext": "jpg", "anno_path": "tpl_Bike_ce2/Bike_ce2_gt.txt"}, {"name": "tpl_Ball_ce3", "path": "tpl_Ball_ce3/img", "startFrame": 1, "endFrame": 273, "nz": 4, "ext": "jpg", "anno_path": "tpl_Ball_ce3/Ball_ce3_gt.txt"}, {"name": "tpl_Girlmov", "path": "tpl_Girlmov/img", "startFrame": 1, "endFrame": 1500, "nz": 4, "ext": "jpg", "anno_path": "tpl_Girlmov/Girlmov_gt.txt"}, {"name": "tpl_Bolt", "path": "tpl_Bolt/img", "startFrame": 1, "endFrame": 350, "nz": 4, "ext": "jpg", "anno_path": "tpl_Bolt/Bolt_gt.txt"}, {"name": "tpl_Basketball_ce2", "path": "tpl_Basketball_ce2/img", "startFrame": 1, "endFrame": 455, "nz": 4, "ext": "jpg", "anno_path": "tpl_Basketball_ce2/Basketball_ce2_gt.txt"}, {"name": "tpl_Bicycle", "path": "tpl_Bicycle/img", "startFrame": 1, "endFrame": 271, "nz": 4, "ext": "jpg", "anno_path": "tpl_Bicycle/Bicycle_gt.txt"}, {"name": "tpl_Face_ce", "path": "tpl_Face_ce/img", "startFrame": 1, "endFrame": 620, "nz": 4, "ext": "jpg", "anno_path": "tpl_Face_ce/Face_ce_gt.txt"}, {"name": "tpl_Basketball_ce1", "path": "tpl_Basketball_ce1/img", "startFrame": 1, "endFrame": 496, "nz": 4, "ext": "jpg", "anno_path": "tpl_Basketball_ce1/Basketball_ce1_gt.txt"}, {"name": "tpl_Messi_ce", "path": "tpl_Messi_ce/img", "startFrame": 1, "endFrame": 272, "nz": 4, "ext": "jpg", "anno_path": "tpl_Messi_ce/Messi_ce_gt.txt"}, {"name": "tpl_Tennis_ce2", "path": "tpl_Tennis_ce2/img", "startFrame": 1, "endFrame": 305, "nz": 4, "ext": "jpg", "anno_path": "tpl_Tennis_ce2/Tennis_ce2_gt.txt"}, {"name": "tpl_Microphone_ce2", "path": "tpl_Microphone_ce2/img", "startFrame": 1, "endFrame": 103, "nz": 4, "ext": "jpg", "anno_path": "tpl_Microphone_ce2/Microphone_ce2_gt.txt"}, {"name": "tpl_Guitar_ce2", "path": "tpl_Guitar_ce2/img", "startFrame": 1, "endFrame": 313, "nz": 4, "ext": "jpg", "anno_path": "tpl_Guitar_ce2/Guitar_ce2_gt.txt"} ] otb_sequences = ['tpl_Skating2', 'tpl_Lemming', 'tpl_Board', 'tpl_Soccer', 'tpl_Liquor', 'tpl_Couple', 'tpl_Walking', 'tpl_David', 'tpl_Tiger2', 'tpl_Bird', 'tpl_Crossing', 'tpl_MountainBike', 'tpl_Diving', 'tpl_CarDark', 'tpl_Shaking', 'tpl_Ironman', 'tpl_FaceOcc1', 'tpl_Tiger1', 'tpl_Skiing', 'tpl_Walking2', 'tpl_Girl', 'tpl_Girlmov', 'tpl_Subway', 'tpl_David3', 'tpl_Woman', 'tpl_Gym', 'tpl_Matrix', 'tpl_Doll', 'tpl_Singer2', 'tpl_Basketball', 'tpl_MotorRolling', 'tpl_CarScale', 'tpl_Football1', 'tpl_Singer1', 'tpl_Skating1', 'tpl_Biker', 'tpl_Boy', 'tpl_Jogging1', 'tpl_Deer', 'tpl_Panda', 'tpl_Coke', 'tpl_Trellis', 'tpl_Jogging2', 'tpl_Bolt', ] if exclude_otb: sequence_info_list_nootb = [] for seq in sequence_info_list: if seq['name'] not in otb_sequences: sequence_info_list_nootb.append(seq) sequence_info_list = sequence_info_list_nootb return sequence_info_list ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/evaluation/tracker.py ================================================ import importlib import os import numpy as np from collections import OrderedDict from pytracking.evaluation.environment import env_settings import time import cv2 as cv from pytracking.utils.visdom import Visdom import matplotlib.pyplot as plt import matplotlib.patches as patches from pytracking.utils.plotting import draw_figure, overlay_mask from pytracking.utils.convert_vot_anno_to_rect import convert_vot_anno_to_rect from ltr.data.bounding_box_utils import masks_to_bboxes from pytracking.evaluation.multi_object_wrapper import MultiObjectWrapper import torch _tracker_disp_colors = {1: (0, 255, 0), 2: (0, 0, 255), 3: (255, 0, 0), 4: (255, 255, 255), 5: (0, 0, 0), 6: (0, 255, 128), 7: (123, 123, 123), 8: (255, 128, 0), 9: (128, 0, 255)} def trackerlist(name: str, parameter_name: str, run_ids = None, display_name: str = None): """Generate list of trackers. args: name: Name of tracking method. parameter_name: Name of parameter file. run_ids: A single or list of run_ids. display_name: Name to be displayed in the result plots. """ if run_ids is None or isinstance(run_ids, int): run_ids = [run_ids] return [Tracker(name, parameter_name, run_id, display_name) for run_id in run_ids] class Tracker: """Wraps the tracker for evaluation and running purposes. args: name: Name of tracking method. parameter_name: Name of parameter file. run_id: The run id. display_name: Name to be displayed in the result plots. """ def __init__(self, name: str, parameter_name: str, run_id: int = None, display_name: str = None): assert run_id is None or isinstance(run_id, int) self.name = name self.parameter_name = parameter_name self.run_id = run_id self.display_name = display_name env = env_settings() if self.run_id is None: self.results_dir = '{}/{}/{}'.format(env.results_path, self.name, self.parameter_name) self.segmentation_dir = '{}/{}/{}'.format(env.segmentation_path, self.name, self.parameter_name) else: self.results_dir = '{}/{}/{}_{:03d}'.format(env.results_path, self.name, self.parameter_name, self.run_id) self.segmentation_dir = '{}/{}/{}_{:03d}'.format(env.segmentation_path, self.name, self.parameter_name, self.run_id) tracker_module_abspath = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'tracker', self.name)) if os.path.isdir(tracker_module_abspath): tracker_module = importlib.import_module('pytracking.tracker.{}'.format(self.name)) self.tracker_class = tracker_module.get_tracker_class() else: self.tracker_class = None self.visdom = None def _init_visdom(self, visdom_info, debug): visdom_info = {} if visdom_info is None else visdom_info self.pause_mode = False self.step = False if debug > 0 and visdom_info.get('use_visdom', True): try: self.visdom = Visdom(debug, {'handler': self._visdom_ui_handler, 'win_id': 'Tracking'}, visdom_info=visdom_info) # Show help help_text = 'You can pause/unpause the tracker by pressing ''space'' with the ''Tracking'' window ' \ 'selected. During paused mode, you can track for one frame by pressing the right arrow key.' \ 'To enable/disable plotting of a data block, tick/untick the corresponding entry in ' \ 'block list.' self.visdom.register(help_text, 'text', 1, 'Help') except: time.sleep(0.5) print('!!! WARNING: Visdom could not start, so using matplotlib visualization instead !!!\n' '!!! Start Visdom in a separate terminal window by typing \'visdom\' !!!') def _visdom_ui_handler(self, data): if data['event_type'] == 'KeyPress': if data['key'] == ' ': self.pause_mode = not self.pause_mode elif data['key'] == 'ArrowRight' and self.pause_mode: self.step = True def create_tracker(self, params): tracker = self.tracker_class(params) tracker.visdom = self.visdom return tracker def run_sequence(self, seq, visualization=None, debug=None, visdom_info=None, multiobj_mode=None): """Run tracker on sequence. args: seq: Sequence to run the tracker on. visualization: Set visualization flag (None means default value specified in the parameters). debug: Set debug level (None means default value specified in the parameters). visdom_info: Visdom info. multiobj_mode: Which mode to use for multiple objects. """ params = self.get_parameters() visualization_ = visualization debug_ = debug if debug is None: debug_ = getattr(params, 'debug', 0) if visualization is None: if debug is None: visualization_ = getattr(params, 'visualization', False) else: visualization_ = True if debug else False params.visualization = visualization_ params.debug = debug_ self._init_visdom(visdom_info, debug_) if visualization_ and self.visdom is None: self.init_visualization() # Get init information init_info = seq.init_info() is_single_object = not seq.multiobj_mode if multiobj_mode is None: multiobj_mode = getattr(params, 'multiobj_mode', getattr(self.tracker_class, 'multiobj_mode', 'default')) if multiobj_mode == 'default' or is_single_object: tracker = self.create_tracker(params) elif multiobj_mode == 'parallel': tracker = MultiObjectWrapper(self.tracker_class, params, self.visdom) else: raise ValueError('Unknown multi object mode {}'.format(multiobj_mode)) output = self._track_sequence(tracker, seq, init_info) return output def _track_sequence(self, tracker, seq, init_info): # Define outputs # Each field in output is a list containing tracker prediction for each frame. # In case of single object tracking mode: # target_bbox[i] is the predicted bounding box for frame i # time[i] is the processing time for frame i # segmentation[i] is the segmentation mask for frame i (numpy array) # In case of multi object tracking mode: # target_bbox[i] is an OrderedDict, where target_bbox[i][obj_id] is the predicted box for target obj_id in # frame i # time[i] is either the processing time for frame i, or an OrderedDict containing processing times for each # object in frame i # segmentation[i] is the multi-label segmentation mask for frame i (numpy array) output = {'target_bbox': [], 'time': [], 'segmentation': []} def _store_outputs(tracker_out: dict, defaults=None): defaults = {} if defaults is None else defaults for key in output.keys(): val = tracker_out.get(key, defaults.get(key, None)) if key in tracker_out or val is not None: output[key].append(val) # Initialize image = self._read_image(seq.frames[0]) if tracker.params.visualization and self.visdom is None: self.visualize(image, init_info.get('init_bbox')) start_time = time.time() out = tracker.initialize(image, init_info) if out is None: out = {} prev_output = OrderedDict(out) init_default = {'target_bbox': init_info.get('init_bbox'), 'time': time.time() - start_time, 'segmentation': init_info.get('init_mask')} _store_outputs(out, init_default) for frame_num, frame_path in enumerate(seq.frames[1:], start=1): while True: if not self.pause_mode: break elif self.step: self.step = False break else: time.sleep(0.1) image = self._read_image(frame_path) start_time = time.time() info = seq.frame_info(frame_num) info['previous_output'] = prev_output out = tracker.track(image, info) prev_output = OrderedDict(out) _store_outputs(out, {'time': time.time() - start_time}) segmentation = out['segmentation'] if 'segmentation' in out else None if self.visdom is not None: tracker.visdom_draw_tracking(image, out['target_bbox'], segmentation) elif tracker.params.visualization: self.visualize(image, out['target_bbox'], segmentation) for key in ['target_bbox', 'segmentation']: if key in output and len(output[key]) <= 1: output.pop(key) return output def run_video(self, videofilepath, optional_box=None, debug=None, visdom_info=None): """Run the tracker with the vieofile. args: debug: Debug level. """ params = self.get_parameters() debug_ = debug if debug is None: debug_ = getattr(params, 'debug', 0) params.debug = debug_ params.tracker_name = self.name params.param_name = self.parameter_name self._init_visdom(visdom_info, debug_) multiobj_mode = getattr(params, 'multiobj_mode', getattr(self.tracker_class, 'multiobj_mode', 'default')) if multiobj_mode == 'default': tracker = self.create_tracker(params) if hasattr(tracker, 'initialize_features'): tracker.initialize_features() elif multiobj_mode == 'parallel': tracker = MultiObjectWrapper(self.tracker_class, params, self.visdom, fast_load=True) else: raise ValueError('Unknown multi object mode {}'.format(multiobj_mode)) assert os.path.isfile(videofilepath), "Invalid param {}".format(videofilepath) ", videofilepath must be a valid videofile" cap = cv.VideoCapture(videofilepath) display_name = 'Display: ' + tracker.params.tracker_name cv.namedWindow(display_name, cv.WINDOW_NORMAL | cv.WINDOW_KEEPRATIO) cv.resizeWindow(display_name, 960, 720) success, frame = cap.read() cv.imshow(display_name, frame) def _build_init_info(box): return {'init_bbox': OrderedDict({1: box}), 'init_object_ids': [1, ], 'object_ids': [1, ], 'sequence_object_ids': [1, ]} if success is not True: print("Read frame from {} failed.".format(videofilepath)) exit(-1) if optional_box is not None: assert isinstance(optional_box, list, tuple) assert len(optional_box) == 4, "valid box's foramt is [x,y,w,h]" tracker.initialize(frame, _build_init_info(optional_box)) else: while True: # cv.waitKey() frame_disp = frame.copy() cv.putText(frame_disp, 'Select target ROI and press ENTER', (20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1.5, (0, 0, 0), 1) x, y, w, h = cv.selectROI(display_name, frame_disp, fromCenter=False) init_state = [x, y, w, h] tracker.initialize(frame, _build_init_info(init_state)) break while True: ret, frame = cap.read() if frame is None: return frame_disp = frame.copy() # Draw box out = tracker.track(frame) state = [int(s) for s in out['target_bbox'][1]] cv.rectangle(frame_disp, (state[0], state[1]), (state[2] + state[0], state[3] + state[1]), (0, 255, 0), 5) font_color = (0, 0, 0) cv.putText(frame_disp, 'Tracking!', (20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1) cv.putText(frame_disp, 'Press r to reset', (20, 55), cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1) cv.putText(frame_disp, 'Press q to quit', (20, 80), cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1) # Display the resulting frame cv.imshow(display_name, frame_disp) key = cv.waitKey(1) if key == ord('q'): break elif key == ord('r'): ret, frame = cap.read() frame_disp = frame.copy() cv.putText(frame_disp, 'Select target ROI and press ENTER', (20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1.5, (0, 0, 0), 1) cv.imshow(display_name, frame_disp) x, y, w, h = cv.selectROI(display_name, frame_disp, fromCenter=False) init_state = [x, y, w, h] tracker.initialize(frame, _build_init_info(init_state)) # When everything done, release the capture cap.release() cv.destroyAllWindows() def run_webcam(self, debug=None, visdom_info=None): """Run the tracker with the webcam. args: debug: Debug level. """ params = self.get_parameters() debug_ = debug if debug is None: debug_ = getattr(params, 'debug', 0) params.debug = debug_ params.tracker_name = self.name params.param_name = self.parameter_name self._init_visdom(visdom_info, debug_) multiobj_mode = getattr(params, 'multiobj_mode', getattr(self.tracker_class, 'multiobj_mode', 'default')) if multiobj_mode == 'default': tracker = self.create_tracker(params) elif multiobj_mode == 'parallel': tracker = MultiObjectWrapper(self.tracker_class, params, self.visdom, fast_load=True) else: raise ValueError('Unknown multi object mode {}'.format(multiobj_mode)) class UIControl: def __init__(self): self.mode = 'init' # init, select, track self.target_tl = (-1, -1) self.target_br = (-1, -1) self.new_init = False def mouse_callback(self, event, x, y, flags, param): if event == cv.EVENT_LBUTTONDOWN and self.mode == 'init': self.target_tl = (x, y) self.target_br = (x, y) self.mode = 'select' elif event == cv.EVENT_MOUSEMOVE and self.mode == 'select': self.target_br = (x, y) elif event == cv.EVENT_LBUTTONDOWN and self.mode == 'select': self.target_br = (x, y) self.mode = 'init' self.new_init = True def get_tl(self): return self.target_tl if self.target_tl[0] < self.target_br[0] else self.target_br def get_br(self): return self.target_br if self.target_tl[0] < self.target_br[0] else self.target_tl def get_bb(self): tl = self.get_tl() br = self.get_br() bb = [min(tl[0], br[0]), min(tl[1], br[1]), abs(br[0] - tl[0]), abs(br[1] - tl[1])] return bb ui_control = UIControl() cap = cv.VideoCapture(0) display_name = 'Display: ' + self.name cv.namedWindow(display_name, cv.WINDOW_NORMAL | cv.WINDOW_KEEPRATIO) cv.resizeWindow(display_name, 960, 720) cv.setMouseCallback(display_name, ui_control.mouse_callback) next_object_id = 1 sequence_object_ids = [] prev_output = OrderedDict() while True: # Capture frame-by-frame ret, frame = cap.read() frame_disp = frame.copy() info = OrderedDict() info['previous_output'] = prev_output if ui_control.new_init: ui_control.new_init = False init_state = ui_control.get_bb() info['init_object_ids'] = [next_object_id, ] info['init_bbox'] = OrderedDict({next_object_id: init_state}) sequence_object_ids.append(next_object_id) next_object_id += 1 # Draw box if ui_control.mode == 'select': cv.rectangle(frame_disp, ui_control.get_tl(), ui_control.get_br(), (255, 0, 0), 2) if len(sequence_object_ids) > 0: info['sequence_object_ids'] = sequence_object_ids out = tracker.track(frame, info) prev_output = OrderedDict(out) if 'segmentation' in out: frame_disp = overlay_mask(frame_disp, out['segmentation']) if 'target_bbox' in out: for obj_id, state in out['target_bbox'].items(): state = [int(s) for s in state] cv.rectangle(frame_disp, (state[0], state[1]), (state[2] + state[0], state[3] + state[1]), _tracker_disp_colors[obj_id], 5) # Put text font_color = (0, 0, 0) cv.putText(frame_disp, 'Select target', (20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1) cv.putText(frame_disp, 'Press r to reset', (20, 55), cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1) cv.putText(frame_disp, 'Press q to quit', (20, 85), cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1) # Display the resulting frame cv.imshow(display_name, frame_disp) key = cv.waitKey(1) if key == ord('q'): break elif key == ord('r'): next_object_id = 1 sequence_object_ids = [] prev_output = OrderedDict() info = OrderedDict() info['object_ids'] = [] info['init_object_ids'] = [] info['init_bbox'] = OrderedDict() tracker.initialize(frame, info) ui_control.mode = 'init' # When everything done, release the capture cap.release() cv.destroyAllWindows() def run_vot2020(self, debug=None, visdom_info=None): params = self.get_parameters() params.tracker_name = self.name params.param_name = self.parameter_name params.run_id = self.run_id debug_ = debug if debug is None: debug_ = getattr(params, 'debug', 0) if debug is None: visualization_ = getattr(params, 'visualization', False) else: visualization_ = True if debug else False params.visualization = visualization_ params.debug = debug_ self._init_visdom(visdom_info, debug_) tracker = self.create_tracker(params) tracker.initialize_features() output_segmentation = tracker.predicts_segmentation_mask() import pytracking.evaluation.vot2020 as vot def _convert_anno_to_list(vot_anno): vot_anno = [vot_anno[0], vot_anno[1], vot_anno[2], vot_anno[3]] return vot_anno def _convert_image_path(image_path): return image_path """Run tracker on VOT.""" if output_segmentation: handle = vot.VOT("mask") else: handle = vot.VOT("rectangle") vot_anno = handle.region() image_path = handle.frame() if not image_path: return image_path = _convert_image_path(image_path) image = self._read_image(image_path) if output_segmentation: vot_anno_mask = vot.make_full_size(vot_anno, (image.shape[1], image.shape[0])) bbox = masks_to_bboxes(torch.from_numpy(vot_anno_mask), fmt='t').squeeze().tolist() else: bbox = _convert_anno_to_list(vot_anno) vot_anno_mask = None out = tracker.initialize(image, {'init_mask': vot_anno_mask, 'init_bbox': bbox}) if out is None: out = {} prev_output = OrderedDict(out) # Track while True: image_path = handle.frame() if not image_path: break image_path = _convert_image_path(image_path) image = self._read_image(image_path) info = OrderedDict() info['previous_output'] = prev_output out = tracker.track(image, info) prev_output = OrderedDict(out) if output_segmentation: pred = out['segmentation'].astype(np.uint8) else: state = out['target_bbox'] pred = vot.Rectangle(*state) handle.report(pred, 1.0) segmentation = out['segmentation'] if 'segmentation' in out else None if self.visdom is not None: tracker.visdom_draw_tracking(image, out['target_bbox'], segmentation) elif tracker.params.visualization: self.visualize(image, out['target_bbox'], segmentation) def run_vot(self, debug=None, visdom_info=None): params = self.get_parameters() params.tracker_name = self.name params.param_name = self.parameter_name params.run_id = self.run_id debug_ = debug if debug is None: debug_ = getattr(params, 'debug', 0) if debug is None: visualization_ = getattr(params, 'visualization', False) else: visualization_ = True if debug else False params.visualization = visualization_ params.debug = debug_ self._init_visdom(visdom_info, debug_) tracker = self.create_tracker(params) tracker.initialize_features() import pytracking.evaluation.vot as vot def _convert_anno_to_list(vot_anno): vot_anno = [vot_anno[0][0][0], vot_anno[0][0][1], vot_anno[0][1][0], vot_anno[0][1][1], vot_anno[0][2][0], vot_anno[0][2][1], vot_anno[0][3][0], vot_anno[0][3][1]] return vot_anno def _convert_image_path(image_path): image_path_new = image_path[20:- 2] return "".join(image_path_new) """Run tracker on VOT.""" handle = vot.VOT("polygon") vot_anno_polygon = handle.region() vot_anno_polygon = _convert_anno_to_list(vot_anno_polygon) init_state = convert_vot_anno_to_rect(vot_anno_polygon, tracker.params.vot_anno_conversion_type) image_path = handle.frame() if not image_path: return image_path = _convert_image_path(image_path) image = self._read_image(image_path) tracker.initialize(image, {'init_bbox': init_state}) # Track while True: image_path = handle.frame() if not image_path: break image_path = _convert_image_path(image_path) image = self._read_image(image_path) out = tracker.track(image) state = out['target_bbox'] handle.report(vot.Rectangle(state[0], state[1], state[2], state[3])) segmentation = out['segmentation'] if 'segmentation' in out else None if self.visdom is not None: tracker.visdom_draw_tracking(image, out['target_bbox'], segmentation) elif tracker.params.visualization: self.visualize(image, out['target_bbox'], segmentation) def get_parameters(self): """Get parameters.""" param_module = importlib.import_module('pytracking.parameter.{}.{}'.format(self.name, self.parameter_name)) params = param_module.parameters() return params def init_visualization(self): self.pause_mode = False self.fig, self.ax = plt.subplots(1) self.fig.canvas.mpl_connect('key_press_event', self.press) plt.tight_layout() def visualize(self, image, state, segmentation=None): self.ax.cla() self.ax.imshow(image) if segmentation is not None: self.ax.imshow(segmentation, alpha=0.5) if isinstance(state, (OrderedDict, dict)): boxes = [v for k, v in state.items()] else: boxes = (state,) for i, box in enumerate(boxes, start=1): col = _tracker_disp_colors[i] col = [float(c) / 255.0 for c in col] rect = patches.Rectangle((box[0], box[1]), box[2], box[3], linewidth=1, edgecolor=col, facecolor='none') self.ax.add_patch(rect) if getattr(self, 'gt_state', None) is not None: gt_state = self.gt_state rect = patches.Rectangle((gt_state[0], gt_state[1]), gt_state[2], gt_state[3], linewidth=1, edgecolor='g', facecolor='none') self.ax.add_patch(rect) self.ax.set_axis_off() self.ax.axis('equal') draw_figure(self.fig) if self.pause_mode: keypress = False while not keypress: keypress = plt.waitforbuttonpress() def reset_tracker(self): pass def press(self, event): if event.key == 'p': self.pause_mode = not self.pause_mode print("Switching pause mode!") elif event.key == 'r': self.reset_tracker() print("Resetting target pos to gt!") def _read_image(self, image_file: str): im = cv.imread(image_file) return cv.cvtColor(im, cv.COLOR_BGR2RGB) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/evaluation/trackingnetdataset.py ================================================ import numpy as np from pytracking.evaluation.data import Sequence, BaseDataset, SequenceList import os from pytracking.utils.load_text import load_text class TrackingNetDataset(BaseDataset): """ TrackingNet test set. Publication: TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild. Matthias Mueller,Adel Bibi, Silvio Giancola, Salman Al-Subaihi and Bernard Ghanem ECCV, 2018 https://ivul.kaust.edu.sa/Documents/Publications/2018/TrackingNet%20A%20Large%20Scale%20Dataset%20and%20Benchmark%20for%20Object%20Tracking%20in%20the%20Wild.pdf Download the dataset using the toolkit https://github.com/SilvioGiancola/TrackingNet-devkit. """ def __init__(self): super().__init__() self.base_path = self.env_settings.trackingnet_path sets = 'TEST' if not isinstance(sets, (list, tuple)): if sets == 'TEST': sets = ['TEST'] elif sets == 'TRAIN': sets = ['TRAIN_{}'.format(i) for i in range(5)] self.sequence_list = self._list_sequences(self.base_path, sets) def get_sequence_list(self): return SequenceList([self._construct_sequence(set, seq_name) for set, seq_name in self.sequence_list]) def _construct_sequence(self, set, sequence_name): anno_path = '{}/{}/anno/{}.txt'.format(self.base_path, set, sequence_name) ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64, backend='numpy') frames_path = '{}/{}/frames/{}'.format(self.base_path, set, sequence_name) frame_list = [frame for frame in os.listdir(frames_path) if frame.endswith(".jpg")] frame_list.sort(key=lambda f: int(f[:-4])) frames_list = [os.path.join(frames_path, frame) for frame in frame_list] return Sequence(sequence_name, frames_list, 'trackingnet', ground_truth_rect.reshape(-1, 4)) def __len__(self): return len(self.sequence_list) def _list_sequences(self, root, set_ids): sequence_list = [] for s in set_ids: anno_dir = os.path.join(root, s, "anno") sequences_cur_set = [(s, os.path.splitext(f)[0]) for f in os.listdir(anno_dir) if f.endswith('.txt')] sequence_list += sequences_cur_set return sequence_list ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/evaluation/uavdataset.py ================================================ import numpy as np from pytracking.evaluation.data import Sequence, BaseDataset, SequenceList from pytracking.utils.load_text import load_text class UAVDataset(BaseDataset): """ UAV123 dataset. Publication: A Benchmark and Simulator for UAV Tracking. Matthias Mueller, Neil Smith and Bernard Ghanem ECCV, 2016 https://ivul.kaust.edu.sa/Documents/Publications/2016/A%20Benchmark%20and%20Simulator%20for%20UAV%20Tracking.pdf Download the dataset from https://ivul.kaust.edu.sa/Pages/pub-benchmark-simulator-uav.aspx """ def __init__(self): super().__init__() self.base_path = self.env_settings.uav_path self.sequence_info_list = self._get_sequence_info_list() def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_info_list]) def _construct_sequence(self, sequence_info): sequence_path = sequence_info['path'] nz = sequence_info['nz'] ext = sequence_info['ext'] start_frame = sequence_info['startFrame'] end_frame = sequence_info['endFrame'] init_omit = 0 if 'initOmit' in sequence_info: init_omit = sequence_info['initOmit'] frames = ['{base_path}/{sequence_path}/{frame:0{nz}}.{ext}'.format(base_path=self.base_path, sequence_path=sequence_path, frame=frame_num, nz=nz, ext=ext) for frame_num in range(start_frame+init_omit, end_frame+1)] anno_path = '{}/{}'.format(self.base_path, sequence_info['anno_path']) ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64, backend='numpy') return Sequence(sequence_info['name'], frames, 'uav', ground_truth_rect[init_omit:,:], object_class=sequence_info['object_class']) def __len__(self): return len(self.sequence_info_list) def _get_sequence_info_list(self): sequence_info_list = [ {"name": "uav_bike1", "path": "data_seq/UAV123/bike1", "startFrame": 1, "endFrame": 3085, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/bike1.txt", "object_class": "vehicle"}, {"name": "uav_bike2", "path": "data_seq/UAV123/bike2", "startFrame": 1, "endFrame": 553, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/bike2.txt", "object_class": "vehicle"}, {"name": "uav_bike3", "path": "data_seq/UAV123/bike3", "startFrame": 1, "endFrame": 433, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/bike3.txt", "object_class": "vehicle"}, {"name": "uav_bird1_1", "path": "data_seq/UAV123/bird1", "startFrame": 1, "endFrame": 253, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/bird1_1.txt", "object_class": "bird"}, {"name": "uav_bird1_2", "path": "data_seq/UAV123/bird1", "startFrame": 775, "endFrame": 1477, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/bird1_2.txt", "object_class": "bird"}, {"name": "uav_bird1_3", "path": "data_seq/UAV123/bird1", "startFrame": 1573, "endFrame": 2437, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/bird1_3.txt", "object_class": "bird"}, {"name": "uav_boat1", "path": "data_seq/UAV123/boat1", "startFrame": 1, "endFrame": 901, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat1.txt", "object_class": "vessel"}, {"name": "uav_boat2", "path": "data_seq/UAV123/boat2", "startFrame": 1, "endFrame": 799, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat2.txt", "object_class": "vessel"}, {"name": "uav_boat3", "path": "data_seq/UAV123/boat3", "startFrame": 1, "endFrame": 901, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat3.txt", "object_class": "vessel"}, {"name": "uav_boat4", "path": "data_seq/UAV123/boat4", "startFrame": 1, "endFrame": 553, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat4.txt", "object_class": "vessel"}, {"name": "uav_boat5", "path": "data_seq/UAV123/boat5", "startFrame": 1, "endFrame": 505, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat5.txt", "object_class": "vessel"}, {"name": "uav_boat6", "path": "data_seq/UAV123/boat6", "startFrame": 1, "endFrame": 805, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat6.txt", "object_class": "vessel"}, {"name": "uav_boat7", "path": "data_seq/UAV123/boat7", "startFrame": 1, "endFrame": 535, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat7.txt", "object_class": "vessel"}, {"name": "uav_boat8", "path": "data_seq/UAV123/boat8", "startFrame": 1, "endFrame": 685, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat8.txt", "object_class": "vessel"}, {"name": "uav_boat9", "path": "data_seq/UAV123/boat9", "startFrame": 1, "endFrame": 1399, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat9.txt", "object_class": "vessel"}, {"name": "uav_building1", "path": "data_seq/UAV123/building1", "startFrame": 1, "endFrame": 469, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/building1.txt", "object_class": "other"}, {"name": "uav_building2", "path": "data_seq/UAV123/building2", "startFrame": 1, "endFrame": 577, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/building2.txt", "object_class": "other"}, {"name": "uav_building3", "path": "data_seq/UAV123/building3", "startFrame": 1, "endFrame": 829, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/building3.txt", "object_class": "other"}, {"name": "uav_building4", "path": "data_seq/UAV123/building4", "startFrame": 1, "endFrame": 787, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/building4.txt", "object_class": "other"}, {"name": "uav_building5", "path": "data_seq/UAV123/building5", "startFrame": 1, "endFrame": 481, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/building5.txt", "object_class": "other"}, {"name": "uav_car1_1", "path": "data_seq/UAV123/car1", "startFrame": 1, "endFrame": 751, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car1_1.txt", "object_class": "car"}, {"name": "uav_car1_2", "path": "data_seq/UAV123/car1", "startFrame": 751, "endFrame": 1627, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car1_2.txt", "object_class": "car"}, {"name": "uav_car1_3", "path": "data_seq/UAV123/car1", "startFrame": 1627, "endFrame": 2629, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car1_3.txt", "object_class": "car"}, {"name": "uav_car10", "path": "data_seq/UAV123/car10", "startFrame": 1, "endFrame": 1405, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car10.txt", "object_class": "car"}, {"name": "uav_car11", "path": "data_seq/UAV123/car11", "startFrame": 1, "endFrame": 337, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car11.txt", "object_class": "car"}, {"name": "uav_car12", "path": "data_seq/UAV123/car12", "startFrame": 1, "endFrame": 499, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car12.txt", "object_class": "car"}, {"name": "uav_car13", "path": "data_seq/UAV123/car13", "startFrame": 1, "endFrame": 415, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car13.txt", "object_class": "car"}, {"name": "uav_car14", "path": "data_seq/UAV123/car14", "startFrame": 1, "endFrame": 1327, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car14.txt", "object_class": "car"}, {"name": "uav_car15", "path": "data_seq/UAV123/car15", "startFrame": 1, "endFrame": 469, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car15.txt", "object_class": "car"}, {"name": "uav_car16_1", "path": "data_seq/UAV123/car16", "startFrame": 1, "endFrame": 415, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car16_1.txt", "object_class": "car"}, {"name": "uav_car16_2", "path": "data_seq/UAV123/car16", "startFrame": 415, "endFrame": 1993, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car16_2.txt", "object_class": "car"}, {"name": "uav_car17", "path": "data_seq/UAV123/car17", "startFrame": 1, "endFrame": 1057, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car17.txt", "object_class": "car"}, {"name": "uav_car18", "path": "data_seq/UAV123/car18", "startFrame": 1, "endFrame": 1207, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car18.txt", "object_class": "car"}, {"name": "uav_car1_s", "path": "data_seq/UAV123/car1_s", "startFrame": 1, "endFrame": 1475, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car1_s.txt", "object_class": "car"}, {"name": "uav_car2", "path": "data_seq/UAV123/car2", "startFrame": 1, "endFrame": 1321, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car2.txt", "object_class": "car"}, {"name": "uav_car2_s", "path": "data_seq/UAV123/car2_s", "startFrame": 1, "endFrame": 320, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car2_s.txt", "object_class": "car"}, {"name": "uav_car3", "path": "data_seq/UAV123/car3", "startFrame": 1, "endFrame": 1717, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car3.txt", "object_class": "car"}, {"name": "uav_car3_s", "path": "data_seq/UAV123/car3_s", "startFrame": 1, "endFrame": 1300, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car3_s.txt", "object_class": "car"}, {"name": "uav_car4", "path": "data_seq/UAV123/car4", "startFrame": 1, "endFrame": 1345, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car4.txt", "object_class": "car"}, {"name": "uav_car4_s", "path": "data_seq/UAV123/car4_s", "startFrame": 1, "endFrame": 830, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car4_s.txt", "object_class": "car"}, {"name": "uav_car5", "path": "data_seq/UAV123/car5", "startFrame": 1, "endFrame": 745, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car5.txt", "object_class": "car"}, {"name": "uav_car6_1", "path": "data_seq/UAV123/car6", "startFrame": 1, "endFrame": 487, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car6_1.txt", "object_class": "car"}, {"name": "uav_car6_2", "path": "data_seq/UAV123/car6", "startFrame": 487, "endFrame": 1807, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car6_2.txt", "object_class": "car"}, {"name": "uav_car6_3", "path": "data_seq/UAV123/car6", "startFrame": 1807, "endFrame": 2953, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car6_3.txt", "object_class": "car"}, {"name": "uav_car6_4", "path": "data_seq/UAV123/car6", "startFrame": 2953, "endFrame": 3925, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car6_4.txt", "object_class": "car"}, {"name": "uav_car6_5", "path": "data_seq/UAV123/car6", "startFrame": 3925, "endFrame": 4861, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car6_5.txt", "object_class": "car"}, {"name": "uav_car7", "path": "data_seq/UAV123/car7", "startFrame": 1, "endFrame": 1033, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car7.txt", "object_class": "car"}, {"name": "uav_car8_1", "path": "data_seq/UAV123/car8", "startFrame": 1, "endFrame": 1357, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car8_1.txt", "object_class": "car"}, {"name": "uav_car8_2", "path": "data_seq/UAV123/car8", "startFrame": 1357, "endFrame": 2575, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car8_2.txt", "object_class": "car"}, {"name": "uav_car9", "path": "data_seq/UAV123/car9", "startFrame": 1, "endFrame": 1879, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car9.txt", "object_class": "car"}, {"name": "uav_group1_1", "path": "data_seq/UAV123/group1", "startFrame": 1, "endFrame": 1333, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group1_1.txt", "object_class": "person"}, {"name": "uav_group1_2", "path": "data_seq/UAV123/group1", "startFrame": 1333, "endFrame": 2515, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group1_2.txt", "object_class": "person"}, {"name": "uav_group1_3", "path": "data_seq/UAV123/group1", "startFrame": 2515, "endFrame": 3925, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group1_3.txt", "object_class": "person"}, {"name": "uav_group1_4", "path": "data_seq/UAV123/group1", "startFrame": 3925, "endFrame": 4873, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group1_4.txt", "object_class": "person"}, {"name": "uav_group2_1", "path": "data_seq/UAV123/group2", "startFrame": 1, "endFrame": 907, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group2_1.txt", "object_class": "person"}, {"name": "uav_group2_2", "path": "data_seq/UAV123/group2", "startFrame": 907, "endFrame": 1771, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group2_2.txt", "object_class": "person"}, {"name": "uav_group2_3", "path": "data_seq/UAV123/group2", "startFrame": 1771, "endFrame": 2683, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group2_3.txt", "object_class": "person"}, {"name": "uav_group3_1", "path": "data_seq/UAV123/group3", "startFrame": 1, "endFrame": 1567, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group3_1.txt", "object_class": "person"}, {"name": "uav_group3_2", "path": "data_seq/UAV123/group3", "startFrame": 1567, "endFrame": 2827, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group3_2.txt", "object_class": "person"}, {"name": "uav_group3_3", "path": "data_seq/UAV123/group3", "startFrame": 2827, "endFrame": 4369, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group3_3.txt", "object_class": "person"}, {"name": "uav_group3_4", "path": "data_seq/UAV123/group3", "startFrame": 4369, "endFrame": 5527, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group3_4.txt", "object_class": "person"}, {"name": "uav_person1", "path": "data_seq/UAV123/person1", "startFrame": 1, "endFrame": 799, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person1.txt", "object_class": "person"}, {"name": "uav_person10", "path": "data_seq/UAV123/person10", "startFrame": 1, "endFrame": 1021, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person10.txt", "object_class": "person"}, {"name": "uav_person11", "path": "data_seq/UAV123/person11", "startFrame": 1, "endFrame": 721, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person11.txt", "object_class": "person"}, {"name": "uav_person12_1", "path": "data_seq/UAV123/person12", "startFrame": 1, "endFrame": 601, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person12_1.txt", "object_class": "person"}, {"name": "uav_person12_2", "path": "data_seq/UAV123/person12", "startFrame": 601, "endFrame": 1621, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person12_2.txt", "object_class": "person"}, {"name": "uav_person13", "path": "data_seq/UAV123/person13", "startFrame": 1, "endFrame": 883, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person13.txt", "object_class": "person"}, {"name": "uav_person14_1", "path": "data_seq/UAV123/person14", "startFrame": 1, "endFrame": 847, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person14_1.txt", "object_class": "person"}, {"name": "uav_person14_2", "path": "data_seq/UAV123/person14", "startFrame": 847, "endFrame": 1813, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person14_2.txt", "object_class": "person"}, {"name": "uav_person14_3", "path": "data_seq/UAV123/person14", "startFrame": 1813, "endFrame": 2923, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person14_3.txt", "object_class": "person"}, {"name": "uav_person15", "path": "data_seq/UAV123/person15", "startFrame": 1, "endFrame": 1339, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person15.txt", "object_class": "person"}, {"name": "uav_person16", "path": "data_seq/UAV123/person16", "startFrame": 1, "endFrame": 1147, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person16.txt", "object_class": "person"}, {"name": "uav_person17_1", "path": "data_seq/UAV123/person17", "startFrame": 1, "endFrame": 1501, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person17_1.txt", "object_class": "person"}, {"name": "uav_person17_2", "path": "data_seq/UAV123/person17", "startFrame": 1501, "endFrame": 2347, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person17_2.txt", "object_class": "person"}, {"name": "uav_person18", "path": "data_seq/UAV123/person18", "startFrame": 1, "endFrame": 1393, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person18.txt", "object_class": "person"}, {"name": "uav_person19_1", "path": "data_seq/UAV123/person19", "startFrame": 1, "endFrame": 1243, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person19_1.txt", "object_class": "person"}, {"name": "uav_person19_2", "path": "data_seq/UAV123/person19", "startFrame": 1243, "endFrame": 2791, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person19_2.txt", "object_class": "person"}, {"name": "uav_person19_3", "path": "data_seq/UAV123/person19", "startFrame": 2791, "endFrame": 4357, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person19_3.txt", "object_class": "person"}, {"name": "uav_person1_s", "path": "data_seq/UAV123/person1_s", "startFrame": 1, "endFrame": 1600, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person1_s.txt", "object_class": "person"}, {"name": "uav_person2_1", "path": "data_seq/UAV123/person2", "startFrame": 1, "endFrame": 1189, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person2_1.txt", "object_class": "person"}, {"name": "uav_person2_2", "path": "data_seq/UAV123/person2", "startFrame": 1189, "endFrame": 2623, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person2_2.txt", "object_class": "person"}, {"name": "uav_person20", "path": "data_seq/UAV123/person20", "startFrame": 1, "endFrame": 1783, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person20.txt", "object_class": "person"}, {"name": "uav_person21", "path": "data_seq/UAV123/person21", "startFrame": 1, "endFrame": 487, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person21.txt", "object_class": "person"}, {"name": "uav_person22", "path": "data_seq/UAV123/person22", "startFrame": 1, "endFrame": 199, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person22.txt", "object_class": "person"}, {"name": "uav_person23", "path": "data_seq/UAV123/person23", "startFrame": 1, "endFrame": 397, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person23.txt", "object_class": "person"}, {"name": "uav_person2_s", "path": "data_seq/UAV123/person2_s", "startFrame": 1, "endFrame": 250, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person2_s.txt", "object_class": "person"}, {"name": "uav_person3", "path": "data_seq/UAV123/person3", "startFrame": 1, "endFrame": 643, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person3.txt", "object_class": "person"}, {"name": "uav_person3_s", "path": "data_seq/UAV123/person3_s", "startFrame": 1, "endFrame": 505, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person3_s.txt", "object_class": "person"}, {"name": "uav_person4_1", "path": "data_seq/UAV123/person4", "startFrame": 1, "endFrame": 1501, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person4_1.txt", "object_class": "person"}, {"name": "uav_person4_2", "path": "data_seq/UAV123/person4", "startFrame": 1501, "endFrame": 2743, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person4_2.txt", "object_class": "person"}, {"name": "uav_person5_1", "path": "data_seq/UAV123/person5", "startFrame": 1, "endFrame": 877, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person5_1.txt", "object_class": "person"}, {"name": "uav_person5_2", "path": "data_seq/UAV123/person5", "startFrame": 877, "endFrame": 2101, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person5_2.txt", "object_class": "person"}, {"name": "uav_person6", "path": "data_seq/UAV123/person6", "startFrame": 1, "endFrame": 901, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person6.txt", "object_class": "person"}, {"name": "uav_person7_1", "path": "data_seq/UAV123/person7", "startFrame": 1, "endFrame": 1249, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person7_1.txt", "object_class": "person"}, {"name": "uav_person7_2", "path": "data_seq/UAV123/person7", "startFrame": 1249, "endFrame": 2065, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person7_2.txt", "object_class": "person"}, {"name": "uav_person8_1", "path": "data_seq/UAV123/person8", "startFrame": 1, "endFrame": 1075, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person8_1.txt", "object_class": "person"}, {"name": "uav_person8_2", "path": "data_seq/UAV123/person8", "startFrame": 1075, "endFrame": 1525, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person8_2.txt", "object_class": "person"}, {"name": "uav_person9", "path": "data_seq/UAV123/person9", "startFrame": 1, "endFrame": 661, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person9.txt", "object_class": "person"}, {"name": "uav_truck1", "path": "data_seq/UAV123/truck1", "startFrame": 1, "endFrame": 463, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/truck1.txt", "object_class": "truck"}, {"name": "uav_truck2", "path": "data_seq/UAV123/truck2", "startFrame": 1, "endFrame": 385, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/truck2.txt", "object_class": "truck"}, {"name": "uav_truck3", "path": "data_seq/UAV123/truck3", "startFrame": 1, "endFrame": 535, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/truck3.txt", "object_class": "truck"}, {"name": "uav_truck4_1", "path": "data_seq/UAV123/truck4", "startFrame": 1, "endFrame": 577, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/truck4_1.txt", "object_class": "truck"}, {"name": "uav_truck4_2", "path": "data_seq/UAV123/truck4", "startFrame": 577, "endFrame": 1261, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/truck4_2.txt", "object_class": "truck"}, {"name": "uav_uav1_1", "path": "data_seq/UAV123/uav1", "startFrame": 1, "endFrame": 1555, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav1_1.txt", "object_class": "aircraft"}, {"name": "uav_uav1_2", "path": "data_seq/UAV123/uav1", "startFrame": 1555, "endFrame": 2377, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav1_2.txt", "object_class": "aircraft"}, {"name": "uav_uav1_3", "path": "data_seq/UAV123/uav1", "startFrame": 2473, "endFrame": 3469, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav1_3.txt", "object_class": "aircraft"}, {"name": "uav_uav2", "path": "data_seq/UAV123/uav2", "startFrame": 1, "endFrame": 133, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav2.txt", "object_class": "aircraft"}, {"name": "uav_uav3", "path": "data_seq/UAV123/uav3", "startFrame": 1, "endFrame": 265, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav3.txt", "object_class": "aircraft"}, {"name": "uav_uav4", "path": "data_seq/UAV123/uav4", "startFrame": 1, "endFrame": 157, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav4.txt", "object_class": "aircraft"}, {"name": "uav_uav5", "path": "data_seq/UAV123/uav5", "startFrame": 1, "endFrame": 139, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav5.txt", "object_class": "aircraft"}, {"name": "uav_uav6", "path": "data_seq/UAV123/uav6", "startFrame": 1, "endFrame": 109, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav6.txt", "object_class": "aircraft"}, {"name": "uav_uav7", "path": "data_seq/UAV123/uav7", "startFrame": 1, "endFrame": 373, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav7.txt", "object_class": "aircraft"}, {"name": "uav_uav8", "path": "data_seq/UAV123/uav8", "startFrame": 1, "endFrame": 301, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav8.txt", "object_class": "aircraft"}, {"name": "uav_wakeboard1", "path": "data_seq/UAV123/wakeboard1", "startFrame": 1, "endFrame": 421, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard1.txt", "object_class": "person"}, {"name": "uav_wakeboard10", "path": "data_seq/UAV123/wakeboard10", "startFrame": 1, "endFrame": 469, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard10.txt", "object_class": "person"}, {"name": "uav_wakeboard2", "path": "data_seq/UAV123/wakeboard2", "startFrame": 1, "endFrame": 733, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard2.txt", "object_class": "person"}, {"name": "uav_wakeboard3", "path": "data_seq/UAV123/wakeboard3", "startFrame": 1, "endFrame": 823, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard3.txt", "object_class": "person"}, {"name": "uav_wakeboard4", "path": "data_seq/UAV123/wakeboard4", "startFrame": 1, "endFrame": 697, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard4.txt", "object_class": "person"}, {"name": "uav_wakeboard5", "path": "data_seq/UAV123/wakeboard5", "startFrame": 1, "endFrame": 1675, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard5.txt", "object_class": "person"}, {"name": "uav_wakeboard6", "path": "data_seq/UAV123/wakeboard6", "startFrame": 1, "endFrame": 1165, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard6.txt", "object_class": "person"}, {"name": "uav_wakeboard7", "path": "data_seq/UAV123/wakeboard7", "startFrame": 1, "endFrame": 199, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard7.txt", "object_class": "person"}, {"name": "uav_wakeboard8", "path": "data_seq/UAV123/wakeboard8", "startFrame": 1, "endFrame": 1543, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard8.txt", "object_class": "person"}, {"name": "uav_wakeboard9", "path": "data_seq/UAV123/wakeboard9", "startFrame": 1, "endFrame": 355, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard9.txt", "object_class": "person"} ] return sequence_info_list ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/evaluation/vot.py ================================================ """ \file vot.py @brief Python utility functions for VOT integration @author Luka Cehovin, Alessio Dore @date 2016 """ import sys import copy import collections try: import trax import trax.server TRAX = True except ImportError: TRAX = False Rectangle = collections.namedtuple('Rectangle', ['x', 'y', 'width', 'height']) Point = collections.namedtuple('Point', ['x', 'y']) Polygon = collections.namedtuple('Polygon', ['points']) def parse_region(string): tokens = map(float, string.split(',')) if len(tokens) == 4: return Rectangle(tokens[0], tokens[1], tokens[2], tokens[3]) elif len(tokens) % 2 == 0 and len(tokens) > 4: return Polygon([Point(tokens[i],tokens[i+1]) for i in xrange(0,len(tokens),2)]) return None def encode_region(region): if isinstance(region, Polygon): return ','.join(['{},{}'.format(p.x,p.y) for p in region.points]) elif isinstance(region, Rectangle): return '{},{},{},{}'.format(region.x, region.y, region.width, region.height) else: return "" def convert_region(region, to): if to == 'rectangle': if isinstance(region, Rectangle): return copy.copy(region) elif isinstance(region, Polygon): top = sys.float_info.max bottom = sys.float_info.min left = sys.float_info.max right = sys.float_info.min for point in region.points: top = min(top, point.y) bottom = max(bottom, point.y) left = min(left, point.x) right = max(right, point.x) return Rectangle(left, top, right - left, bottom - top) else: return None if to == 'polygon': if isinstance(region, Rectangle): points = [] points.append((region.x, region.y)) points.append((region.x + region.width, region.y)) points.append((region.x + region.width, region.y + region.height)) points.append((region.x, region.y + region.height)) return Polygon(points) elif isinstance(region, Polygon): return copy.copy(region) else: return None return None class VOT(object): """ Base class for Python VOT integration """ def __init__(self, region_format): """ Constructor Args: region_format: Region format options """ assert(region_format in ['rectangle', 'polygon']) if TRAX: options = trax.server.ServerOptions(region_format, trax.image.PATH) self._trax = trax.server.Server(options) request = self._trax.wait() assert(request.type == 'initialize') if request.region.type == 'polygon': self._region = Polygon([Point(x[0], x[1]) for x in request.region.points]) else: self._region = Rectangle(request.region.x, request.region.y, request.region.width, request.region.height) self._image = str(request.image) self._trax.status(request.region) else: self._files = [x.strip('\n') for x in open('images.txt', 'r').readlines()] self._frame = 0 self._region = convert_region(parse_region(open('region.txt', 'r').readline()), region_format) self._result = [] def region(self): """ Send configuration message to the client and receive the initialization region and the path of the first image Returns: initialization region """ return self._region def report(self, region, confidence = 0): """ Report the tracking results to the client Arguments: region: region for the frame """ assert(isinstance(region, Rectangle) or isinstance(region, Polygon)) if TRAX: if isinstance(region, Polygon): tregion = trax.region.Polygon([(x.x, x.y) for x in region.points]) else: tregion = trax.region.Rectangle(region.x, region.y, region.width, region.height) self._trax.status(tregion, {"confidence" : confidence}) else: self._result.append(region) self._frame += 1 def frame(self): """ Get a frame (image path) from client Returns: absolute path of the image """ if TRAX: if hasattr(self, "_image"): image = str(self._image) del self._image return image request = self._trax.wait() if request.type == 'frame': return str(request.image) else: return None else: if self._frame >= len(self._files): return None return self._files[self._frame] def quit(self): if TRAX: self._trax.quit() elif hasattr(self, '_result'): with open('output.txt', 'w') as f: for r in self._result: f.write(encode_region(r)) f.write('\n') def __del__(self): self.quit() ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/evaluation/vot2020.py ================================================ """ \file vot.py @brief Python utility functions for VOT integration @author Luka Cehovin, Alessio Dore @date 2016 """ import sys import copy import collections import numpy as np try: import trax except ImportError: raise Exception('TraX support not found. Please add trax module to Python path.') def make_full_size(x, output_sz): ''' zero-pad input x (right and down) to match output_sz x: numpy array e.g., binary mask output_sz: size of the output [width, height] ''' if x.shape[0] == output_sz[1] and x.shape[1] == output_sz[0]: return x pad_x = output_sz[0] - x.shape[1] if pad_x < 0: x = x[:, :x.shape[1] + pad_x] # padding has to be set to zero, otherwise pad function fails pad_x = 0 pad_y = output_sz[1] - x.shape[0] if pad_y < 0: x = x[:x.shape[0] + pad_y, :] # padding has to be set to zero, otherwise pad function fails pad_y = 0 return np.pad(x, ((0, pad_y), (0, pad_x)), 'constant', constant_values=0) Rectangle = collections.namedtuple('Rectangle', ['x', 'y', 'width', 'height']) Point = collections.namedtuple('Point', ['x', 'y']) Polygon = collections.namedtuple('Polygon', ['points']) class VOT(object): """ Base class for Python VOT integration """ def __init__(self, region_format, channels=None): """ Constructor Args: region_format: Region format options """ assert(region_format in [trax.Region.RECTANGLE, trax.Region.POLYGON, trax.Region.MASK]) if channels is None: channels = ['color'] elif channels == 'rgbd': channels = ['color', 'depth'] elif channels == 'rgbt': channels = ['color', 'ir'] elif channels == 'ir': channels = ['ir'] else: raise Exception('Illegal configuration {}.'.format(channels)) self._trax = trax.Server([region_format], [trax.Image.PATH], channels, customMetadata=dict(vot="python")) request = self._trax.wait() assert(request.type == 'initialize') if isinstance(request.region, trax.Polygon): self._region = Polygon([Point(x[0], x[1]) for x in request.region]) if isinstance(request.region, trax.Mask): self._region = request.region.array(True) else: self._region = Rectangle(*request.region.bounds()) self._image = [x.path() for k, x in request.image.items()] if len(self._image) == 1: self._image = self._image[0] self._trax.status(request.region) def region(self): """ Send configuration message to the client and receive the initialization region and the path of the first image Returns: initialization region """ return self._region def report(self, region, confidence = None): """ Report the tracking results to the client Arguments: region: region for the frame """ assert(isinstance(region, (Rectangle, Polygon, np.ndarray))) if isinstance(region, Polygon): tregion = trax.Polygon.create([(x.x, x.y) for x in region.points]) if isinstance(region, np.ndarray): tregion = trax.Mask.create(region) else: tregion = trax.Rectangle.create(region.x, region.y, region.width, region.height) properties = {} if not confidence is None: properties['confidence'] = confidence self._trax.status(tregion, properties) def frame(self): """ Get a frame (image path) from client Returns: absolute path of the image """ if hasattr(self, "_image"): image = self._image del self._image return image request = self._trax.wait() if request.type == 'frame': image = [x.path() for k, x in request.image.items()] if len(image) == 1: return image[0] return image else: return None def quit(self): if hasattr(self, '_trax'): self._trax.quit() def __del__(self): self.quit() ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/evaluation/votdataset.py ================================================ import numpy as np from pytracking.evaluation.data import Sequence, BaseDataset, SequenceList class VOTDataset(BaseDataset): """ VOT2018 dataset Publication: The sixth Visual Object Tracking VOT2018 challenge results. Matej Kristan, Ales Leonardis, Jiri Matas, Michael Felsberg, Roman Pfugfelder, Luka Cehovin Zajc, Tomas Vojir, Goutam Bhat, Alan Lukezic et al. ECCV, 2018 https://prints.vicos.si/publications/365 Download the dataset from http://www.votchallenge.net/vot2018/dataset.html """ def __init__(self): super().__init__() self.base_path = self.env_settings.vot_path self.sequence_list = self._get_sequence_list() def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_list]) def _construct_sequence(self, sequence_name): sequence_path = sequence_name nz = 8 ext = 'jpg' start_frame = 1 anno_path = '{}/{}/groundtruth.txt'.format(self.base_path, sequence_name) try: ground_truth_rect = np.loadtxt(str(anno_path), dtype=np.float64) except: ground_truth_rect = np.loadtxt(str(anno_path), delimiter=',', dtype=np.float64) end_frame = ground_truth_rect.shape[0] frames = ['{base_path}/{sequence_path}/color/{frame:0{nz}}.{ext}'.format(base_path=self.base_path, sequence_path=sequence_path, frame=frame_num, nz=nz, ext=ext) for frame_num in range(start_frame, end_frame+1)] # Convert gt if ground_truth_rect.shape[1] > 4: gt_x_all = ground_truth_rect[:, [0, 2, 4, 6]] gt_y_all = ground_truth_rect[:, [1, 3, 5, 7]] x1 = np.amin(gt_x_all, 1).reshape(-1,1) y1 = np.amin(gt_y_all, 1).reshape(-1,1) x2 = np.amax(gt_x_all, 1).reshape(-1,1) y2 = np.amax(gt_y_all, 1).reshape(-1,1) ground_truth_rect = np.concatenate((x1, y1, x2-x1, y2-y1), 1) return Sequence(sequence_name, frames, 'vot', ground_truth_rect) def __len__(self): return len(self.sequence_list) def _get_sequence_list(self): sequence_list= ['ants1', 'ants3', 'bag', 'ball1', 'ball2', 'basketball', 'birds1', 'blanket', 'bmx', 'bolt1', 'bolt2', 'book', 'butterfly', 'car1', 'conduction1', 'crabs1', 'crossing', 'dinosaur', 'drone_across', 'drone_flip', 'drone1', 'fernando', 'fish1', 'fish2', 'fish3', 'flamingo1', 'frisbee', 'girl', 'glove', 'godfather', 'graduate', 'gymnastics1', 'gymnastics2', 'gymnastics3', 'hand', 'handball1', 'handball2', 'helicopter', 'iceskater1', 'iceskater2', 'leaves', 'matrix', 'motocross1', 'motocross2', 'nature', 'pedestrian1', 'rabbit', 'racing', 'road', 'shaking', 'sheep', 'singer2', 'singer3', 'soccer1', 'soccer2', 'soldier', 'tiger', 'traffic', 'wiper', 'zebrafish1'] return sequence_list ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/experiments/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/experiments/myexperiments.py ================================================ from pytracking.evaluation import Tracker, get_dataset, trackerlist def atom_nfs_uav(): # Run three runs of ATOM on NFS and UAV datasets trackers = trackerlist('atom', 'default', range(3)) dataset = get_dataset('nfs', 'uav') return trackers, dataset def uav_test(): # Run DiMP18, ATOM and ECO on the UAV dataset trackers = trackerlist('dimp', 'dimp18', range(1)) + \ trackerlist('atom', 'default', range(1)) + \ trackerlist('eco', 'default', range(1)) dataset = get_dataset('uav') return trackers, dataset ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/features/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/features/augmentation.py ================================================ import numpy as np import math import torch import torch.nn.functional as F import cv2 as cv import random from pytracking.features.preprocessing import numpy_to_torch, torch_to_numpy class Transform: """Base data augmentation transform class.""" def __init__(self, output_sz = None, shift = None): self.output_sz = output_sz self.shift = (0,0) if shift is None else shift def __call__(self, image, is_mask=False): raise NotImplementedError def crop_to_output(self, image): if isinstance(image, torch.Tensor): imsz = image.shape[2:] if self.output_sz is None: pad_h = 0 pad_w = 0 else: pad_h = (self.output_sz[0] - imsz[0]) / 2 pad_w = (self.output_sz[1] - imsz[1]) / 2 pad_left = math.floor(pad_w) + self.shift[1] pad_right = math.ceil(pad_w) - self.shift[1] pad_top = math.floor(pad_h) + self.shift[0] pad_bottom = math.ceil(pad_h) - self.shift[0] return F.pad(image, (pad_left, pad_right, pad_top, pad_bottom), 'replicate') else: raise NotImplementedError class Identity(Transform): """Identity transformation.""" def __call__(self, image, is_mask=False): return self.crop_to_output(image) class FlipHorizontal(Transform): """Flip along horizontal axis.""" def __call__(self, image, is_mask=False): if isinstance(image, torch.Tensor): return self.crop_to_output(image.flip((3,))) else: return np.fliplr(image) class FlipVertical(Transform): """Flip along vertical axis.""" def __call__(self, image: torch.Tensor, is_mask=False): if isinstance(image, torch.Tensor): return self.crop_to_output(image.flip((2,))) else: return np.flipud(image) class Translation(Transform): """Translate.""" def __init__(self, translation, output_sz = None, shift = None): super().__init__(output_sz, shift) self.shift = (self.shift[0] + translation[0], self.shift[1] + translation[1]) def __call__(self, image, is_mask=False): if isinstance(image, torch.Tensor): return self.crop_to_output(image) else: raise NotImplementedError class Scale(Transform): """Scale.""" def __init__(self, scale_factor, output_sz = None, shift = None): super().__init__(output_sz, shift) self.scale_factor = scale_factor def __call__(self, image, is_mask=False): if isinstance(image, torch.Tensor): # Calculate new size. Ensure that it is even so that crop/pad becomes easier h_orig, w_orig = image.shape[2:] if h_orig != w_orig: raise NotImplementedError h_new = round(h_orig /self.scale_factor) h_new += (h_new - h_orig) % 2 w_new = round(w_orig /self.scale_factor) w_new += (w_new - w_orig) % 2 image_resized = F.interpolate(image, [h_new, w_new], mode='bilinear') return self.crop_to_output(image_resized) else: raise NotImplementedError class Affine(Transform): """Affine transformation.""" def __init__(self, transform_matrix, output_sz = None, shift = None): super().__init__(output_sz, shift) self.transform_matrix = transform_matrix def __call__(self, image, is_mask=False): if isinstance(image, torch.Tensor): return self.crop_to_output(numpy_to_torch(self(torch_to_numpy(image)))) else: return cv.warpAffine(image, self.transform_matrix, image.shape[1::-1], borderMode=cv.BORDER_REPLICATE) class Rotate(Transform): """Rotate with given angle.""" def __init__(self, angle, output_sz = None, shift = None): super().__init__(output_sz, shift) self.angle = math.pi * angle/180 def __call__(self, image, is_mask=False): if isinstance(image, torch.Tensor): return self.crop_to_output(numpy_to_torch(self(torch_to_numpy(image)))) else: c = (np.expand_dims(np.array(image.shape[:2]),1)-1)/2 R = np.array([[math.cos(self.angle), math.sin(self.angle)], [-math.sin(self.angle), math.cos(self.angle)]]) H =np.concatenate([R, c - R @ c], 1) return cv.warpAffine(image, H, image.shape[1::-1], borderMode=cv.BORDER_REPLICATE) class Blur(Transform): """Blur with given sigma (can be axis dependent).""" def __init__(self, sigma, output_sz = None, shift = None): super().__init__(output_sz, shift) if isinstance(sigma, (float, int)): sigma = (sigma, sigma) self.sigma = sigma self.filter_size = [math.ceil(2*s) for s in self.sigma] x_coord = [torch.arange(-sz, sz+1, dtype=torch.float32) for sz in self.filter_size] self.filter = [torch.exp(-(x**2)/(2*s**2)) for x, s in zip(x_coord, self.sigma)] self.filter[0] = self.filter[0].view(1,1,-1,1) / self.filter[0].sum() self.filter[1] = self.filter[1].view(1,1,1,-1) / self.filter[1].sum() def __call__(self, image, is_mask=False): if isinstance(image, torch.Tensor): sz = image.shape[2:] im1 = F.conv2d(image.view(-1,1,sz[0],sz[1]), self.filter[0], padding=(self.filter_size[0],0)) return self.crop_to_output(F.conv2d(im1, self.filter[1], padding=(0,self.filter_size[1])).view(1,-1,sz[0],sz[1])) else: raise NotImplementedError class RandomAffine(Transform): """Affine transformation.""" def __init__(self, p_flip=0.0, max_rotation=0.0, max_shear=0.0, max_scale=0.0, max_ar_factor=0.0, border_mode='constant', output_sz = None, shift = None): super().__init__(output_sz, shift) self.p_flip = p_flip self.max_rotation = max_rotation self.max_shear = max_shear self.max_scale = max_scale self.max_ar_factor = max_ar_factor self.pad_amount = 0 if border_mode == 'constant': self.border_flag = cv.BORDER_CONSTANT elif border_mode == 'replicate': self.border_flag == cv.BORDER_REPLICATE else: raise Exception self.roll_values = self.roll() def roll(self): do_flip = random.random() < self.p_flip theta = random.uniform(-self.max_rotation, self.max_rotation) shear_x = random.uniform(-self.max_shear, self.max_shear) shear_y = random.uniform(-self.max_shear, self.max_shear) ar_factor = np.exp(random.uniform(-self.max_ar_factor, self.max_ar_factor)) scale_factor = np.exp(random.uniform(-self.max_scale, self.max_scale)) return do_flip, theta, (shear_x, shear_y), (scale_factor, scale_factor * ar_factor) def _construct_t_mat(self, image_shape, do_flip, theta, shear_values, scale_factors): im_h, im_w = image_shape t_mat = np.identity(3) if do_flip: if do_flip: t_mat[0, 0] = -1.0 t_mat[0, 2] = im_w t_rot = cv.getRotationMatrix2D((im_w * 0.5, im_h * 0.5), theta, 1.0) t_rot = np.concatenate((t_rot, np.array([0.0, 0.0, 1.0]).reshape(1, 3))) t_shear = np.array([[1.0, shear_values[0], -shear_values[0] * 0.5 * im_w], [shear_values[1], 1.0, -shear_values[1] * 0.5 * im_h], [0.0, 0.0, 1.0]]) t_scale = np.array([[scale_factors[0], 0.0, (1.0 - scale_factors[0]) * 0.5 * im_w], [0.0, scale_factors[1], (1.0 - scale_factors[1]) * 0.5 * im_h], [0.0, 0.0, 1.0]]) t_mat = t_scale @ t_rot @ t_shear @ t_mat t_mat[0, 2] += self.pad_amount t_mat[1, 2] += self.pad_amount t_mat = t_mat[:2, :] return t_mat def __call__(self, image, is_mask=False): input_tensor = torch.is_tensor(image) if input_tensor: image = torch_to_numpy(image) do_flip, theta, shear_values, scale_factors = self.roll_values t_mat = self._construct_t_mat(image.shape[:2], do_flip, theta, shear_values, scale_factors) output_sz = (image.shape[1] + 2*self.pad_amount, image.shape[0] + 2*self.pad_amount) if not is_mask: image_t = cv.warpAffine(image, t_mat, output_sz, flags=cv.INTER_LINEAR, borderMode=self.border_flag) else: image_t = cv.warpAffine(image, t_mat, output_sz, flags=cv.INTER_NEAREST, borderMode=self.border_flag) image_t = image_t.reshape(image.shape) if input_tensor: image_t = numpy_to_torch(image_t) return self.crop_to_output(image_t) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/features/color.py ================================================ import torch from pytracking.features.featurebase import FeatureBase class RGB(FeatureBase): """RGB feature normalized to [-0.5, 0.5].""" def dim(self): return 3 def stride(self): return self.pool_stride def extract(self, im: torch.Tensor): return im/255 - 0.5 class Grayscale(FeatureBase): """Grayscale feature normalized to [-0.5, 0.5].""" def dim(self): return 1 def stride(self): return self.pool_stride def extract(self, im: torch.Tensor): return torch.mean(im/255 - 0.5, 1, keepdim=True) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/features/deep.py ================================================ from pytracking.features.featurebase import FeatureBase, MultiFeatureBase import torch import torchvision from pytracking import TensorList from pytracking.evaluation.environment import env_settings import os from pytracking.utils.loading import load_network from ltr.models.backbone.resnet18_vggm import resnet18_vggmconv1 normalize = torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) class ResNet18m1(MultiFeatureBase): """ResNet18 feature together with the VGG-m conv1 layer. args: output_layers: List of layers to output. net_path: Relative or absolute net path (default should be fine). use_gpu: Use GPU or CPU. """ def __init__(self, output_layers, net_path=None, use_gpu=True, *args, **kwargs): super(ResNet18m1, self).__init__(*args, **kwargs) for l in output_layers: if l not in ['vggconv1', 'conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']: raise ValueError('Unknown layer') self.output_layers = list(output_layers) self.use_gpu = use_gpu self.net_path = 'resnet18_vggmconv1/resnet18_vggmconv1.pth' if net_path is None else net_path def initialize(self): if isinstance(self.pool_stride, int) and self.pool_stride == 1: self.pool_stride = [1] * len(self.output_layers) self.layer_stride = {'vggconv1': 2, 'conv1': 2, 'layer1': 4, 'layer2': 8, 'layer3': 16, 'layer4': 32, 'fc': None} self.layer_dim = {'vggconv1': 96, 'conv1': 64, 'layer1': 64, 'layer2': 128, 'layer3': 256, 'layer4': 512, 'fc': None} self.mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, -1, 1, 1) self.std = torch.Tensor([0.229, 0.224, 0.225]).view(1, -1, 1, 1) if os.path.isabs(self.net_path): net_path_full = [self.net_path] else: root_paths = env_settings().network_path if isinstance(root_paths, str): root_paths = [root_paths] net_path_full = [os.path.join(root, self.net_path) for root in root_paths] self.net = None for net_path in net_path_full: try: self.net = resnet18_vggmconv1(self.output_layers, path=net_path) break except: pass if self.net is None: raise Exception('Did not find network file {}'.format(self.net_path)) if self.use_gpu: self.net.cuda() self.net.eval() def dim(self): return TensorList([self.layer_dim[l] for l in self.output_layers]) def stride(self): return TensorList([s * self.layer_stride[l] for l, s in zip(self.output_layers, self.pool_stride)]) def extract(self, im: torch.Tensor): im = im / 255 im -= self.mean im /= self.std if self.use_gpu: im = im.cuda() with torch.no_grad(): return TensorList(self.net(im).values()) class ATOMResNet18(MultiFeatureBase): """ResNet18 feature with the ATOM IoUNet. args: output_layers: List of layers to output. net_path: Relative or absolute net path (default should be fine). use_gpu: Use GPU or CPU. """ def __init__(self, output_layers=('layer3',), net_path='atom_iou', use_gpu=True, *args, **kwargs): super().__init__(*args, **kwargs) self.output_layers = list(output_layers) self.use_gpu = use_gpu self.net_path = net_path def initialize(self): self.net = load_network(self.net_path) if self.use_gpu: self.net.cuda() self.net.eval() self.iou_predictor = self.net.bb_regressor self.layer_stride = {'conv1': 2, 'layer1': 4, 'layer2': 8, 'layer3': 16, 'layer4': 32, 'classification': 16, 'fc': None} self.layer_dim = {'conv1': 64, 'layer1': 64, 'layer2': 128, 'layer3': 256, 'layer4': 512, 'classification': 256, 'fc': None} self.iounet_feature_layers = self.net.bb_regressor_layer if isinstance(self.pool_stride, int) and self.pool_stride == 1: self.pool_stride = [1] * len(self.output_layers) self.feature_layers = sorted(list(set(self.output_layers + self.iounet_feature_layers))) self.mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, -1, 1, 1) self.std = torch.Tensor([0.229, 0.224, 0.225]).view(1, -1, 1, 1) def dim(self): return TensorList([self.layer_dim[l] for l in self.output_layers]) def stride(self): return TensorList([s * self.layer_stride[l] for l, s in zip(self.output_layers, self.pool_stride)]) def extract(self, im: torch.Tensor): im = im / 255 im -= self.mean im /= self.std if self.use_gpu: im = im.cuda() with torch.no_grad(): output_features = self.net.extract_features(im, self.feature_layers) # Store the raw resnet features which are input to iounet self.iounet_backbone_features = TensorList( [output_features[layer].clone() for layer in self.iounet_feature_layers]) # Store the processed features from iounet, just before pooling with torch.no_grad(): self.iounet_features = TensorList(self.iou_predictor.get_iou_feat(self.iounet_backbone_features)) return TensorList([output_features[layer] for layer in self.output_layers]) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/features/extractor.py ================================================ import torch from pytracking.features.preprocessing import sample_patch from pytracking import TensorList class ExtractorBase: """Base feature extractor class. args: features: List of features. """ def __init__(self, features): self.features = features def initialize(self): for f in self.features: f.initialize() class SingleResolutionExtractor(ExtractorBase): """Single resolution feature extractor. args: features: List of features. """ def __init__(self, features): super().__init__(features) self.feature_stride = self.features[0].stride() if isinstance(self.feature_stride, (list, TensorList)): self.feature_stride = self.feature_stride[0] def stride(self): return self.feature_stride def size(self, input_sz): return input_sz // self.stride() def extract(self, im, pos, scales, image_sz): if isinstance(scales, (int, float)): scales = [scales] # Get image patches im_patches = torch.cat([sample_patch(im, pos, s*image_sz, image_sz) for s in scales]) # Compute features feature_map = torch.cat(TensorList([f.get_feature(im_patches) for f in self.features]).unroll(), dim=1) return feature_map class MultiResolutionExtractor(ExtractorBase): """Multi-resolution feature extractor. args: features: List of features. """ def __init__(self, features, patch_mode='replicate', max_scale_change=None): super().__init__(features) self.patch_mode = patch_mode self.max_scale_change = max_scale_change self.is_color = None def stride(self): return torch.Tensor(TensorList([f.stride() for f in self.features if self._return_feature(f)]).unroll().list()) def size(self, input_sz): return TensorList([f.size(input_sz) for f in self.features if self._return_feature(f)]).unroll() def dim(self): return TensorList([f.dim() for f in self.features if self._return_feature(f)]).unroll() def get_fparams(self, name: str = None): if name is None: return [f.fparams for f in self.features if self._return_feature(f)] return TensorList([getattr(f.fparams, name) for f in self.features if self._return_feature(f)]).unroll() def get_attribute(self, name: str, ignore_missing: bool = False): if ignore_missing: return TensorList([getattr(f, name) for f in self.features if self._return_feature(f) and hasattr(f, name)]) else: return TensorList([getattr(f, name, None) for f in self.features if self._return_feature(f)]) def get_unique_attribute(self, name: str): feat = None for f in self.features: if self._return_feature(f) and hasattr(f, name): if feat is not None: raise RuntimeError('The attribute was not unique.') feat = f if feat is None: raise RuntimeError('The attribute did not exist') return getattr(feat, name) def _return_feature(self, f): return self.is_color is None or self.is_color and f.use_for_color or not self.is_color and f.use_for_gray def set_is_color(self, is_color: bool): self.is_color = is_color def extract(self, im, pos, scales, image_sz, return_patches=False): """Extract features. args: im: Image. pos: Center position for extraction. scales: Image scales to extract features from. image_sz: Size to resize the image samples to before extraction. """ if isinstance(scales, (int, float)): scales = [scales] # Get image patches patch_iter, coord_iter = zip(*(sample_patch(im, pos, s*image_sz, image_sz, mode=self.patch_mode, max_scale_change=self.max_scale_change) for s in scales)) im_patches = torch.cat(list(patch_iter)) patch_coords = torch.cat(list(coord_iter)) # im_patches = torch.cat([sample_patch(im, pos, s*image_sz, image_sz) for s in scales]) # Compute features feature_map = TensorList([f.get_feature(im_patches) for f in self.features]).unroll() if return_patches: return feature_map, patch_coords, im_patches else: return feature_map, patch_coords def extract_transformed(self, im, pos, scale, image_sz, transforms): """Extract features from a set of transformed image samples. args: im: Image. pos: Center position for extraction. scale: Image scale to extract features from. image_sz: Size to resize the image samples to before extraction. transforms: A set of image transforms to apply. """ # Get image patche im_patch, _ = sample_patch(im, pos, scale*image_sz, image_sz) # Apply transforms im_patches = torch.cat([T(im_patch) for T in transforms]) # Compute features feature_map = TensorList([f.get_feature(im_patches) for f in self.features]).unroll() return feature_map ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/features/featurebase.py ================================================ import torch import torch.nn.functional as F from pytracking import TensorList class FeatureBase: """Base feature class. args: fparams: Feature specific parameters. pool_stride: Amount of average pooling to apply do downsample the feature map. output_size: Alternatively, specify the output size of the feature map. Adaptive average pooling will be applied. normalize_power: The power exponent for the normalization. None means no normalization (default). use_for_color: Use this feature for color images. use_for_gray: Use this feature for grayscale images. """ def __init__(self, fparams = None, pool_stride = None, output_size = None, normalize_power = None, use_for_color = True, use_for_gray = True): self.fparams = fparams self.pool_stride = 1 if pool_stride is None else pool_stride self.output_size = output_size self.normalize_power = normalize_power self.use_for_color = use_for_color self.use_for_gray = use_for_gray def initialize(self): pass def dim(self): raise NotImplementedError def stride(self): raise NotImplementedError def size(self, im_sz): if self.output_size is None: return im_sz // self.stride() if isinstance(im_sz, torch.Tensor): return torch.Tensor([self.output_size[0], self.output_size[1]]) return self.output_size def extract(self, im): """Performs feature extraction.""" raise NotImplementedError def get_feature(self, im: torch.Tensor): """Get the feature. Generally, call this function. args: im: image patch as a torch.Tensor. """ # Return empty tensor if it should not be used is_color = im.shape[1] == 3 if is_color and not self.use_for_color or not is_color and not self.use_for_gray: return torch.Tensor([]) # Extract feature feat = self.extract(im) # Pool/downsample if self.output_size is not None: feat = F.adaptive_avg_pool2d(feat, self.output_size) elif self.pool_stride != 1: feat = F.avg_pool2d(feat, self.pool_stride, self.pool_stride) # Normalize if self.normalize_power is not None: feat /= (torch.sum(feat.abs().view(feat.shape[0],1,1,-1)**self.normalize_power, dim=3, keepdim=True) / (feat.shape[1]*feat.shape[2]*feat.shape[3]) + 1e-10)**(1/self.normalize_power) return feat class MultiFeatureBase(FeatureBase): """Base class for features potentially having multiple feature blocks as output (like CNNs). See FeatureBase for more info. """ def size(self, im_sz): if self.output_size is None: return TensorList([im_sz // s for s in self.stride()]) if isinstance(im_sz, torch.Tensor): return TensorList([im_sz // s if sz is None else torch.Tensor([sz[0], sz[1]]) for sz, s in zip(self.output_size, self.stride())]) def get_feature(self, im: torch.Tensor): """Get the feature. Generally, call this function. args: im: image patch as a torch.Tensor. """ # Return empty tensor if it should not be used is_color = im.shape[1] == 3 if is_color and not self.use_for_color or not is_color and not self.use_for_gray: return torch.Tensor([]) feat_list = self.extract(im) output_sz = [None]*len(feat_list) if self.output_size is None else self.output_size # Pool/downsample for i, (sz, s) in enumerate(zip(output_sz, self.pool_stride)): if sz is not None: feat_list[i] = F.adaptive_avg_pool2d(feat_list[i], sz) elif s != 1: feat_list[i] = F.avg_pool2d(feat_list[i], s, s) # Normalize if self.normalize_power is not None: for feat in feat_list: feat /= (torch.sum(feat.abs().view(feat.shape[0],1,1,-1)**self.normalize_power, dim=3, keepdim=True) / (feat.shape[1]*feat.shape[2]*feat.shape[3]) + 1e-10)**(1/self.normalize_power) return feat_list ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/features/net_wrappers.py ================================================ import torch from pytracking.utils.loading import load_network class NetWrapper: """Used for wrapping networks in pytracking. Network modules and functions can be accessed directly as if they were members of this class.""" _rec_iter=0 def __init__(self, net_path, use_gpu=True, initialize=False, **kwargs): self.net_path = net_path self.use_gpu = use_gpu self.net = None self.net_kwargs = kwargs if initialize: self.initialize() def __getattr__(self, name): if self._rec_iter > 0: self._rec_iter = 0 return None self._rec_iter += 1 try: ret_val = getattr(self.net, name) except Exception as e: self._rec_iter = 0 raise e self._rec_iter = 0 return ret_val def load_network(self): self.net = load_network(self.net_path, **self.net_kwargs) if self.use_gpu: self.cuda() self.eval() def initialize(self): self.load_network() class NetWithBackbone(NetWrapper): """Wraps a network with a common backbone. Assumes the network have a 'extract_backbone_features(image)' function.""" def __init__(self, net_path, use_gpu=True, initialize=False, image_format='rgb', mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), **kwargs): super().__init__(net_path, use_gpu, initialize, **kwargs) self.image_format = image_format self._mean = torch.Tensor(mean).view(1, -1, 1, 1) self._std = torch.Tensor(std).view(1, -1, 1, 1) def initialize(self, image_format='rgb', mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)): super().initialize() def preprocess_image(self, im: torch.Tensor): """Normalize the image with the mean and standard deviation used by the network.""" if self.image_format in ['rgb', 'bgr']: im = im/255 if self.image_format in ['bgr', 'bgr255']: im = im[:, [2, 1, 0], :, :] im -= self._mean im /= self._std if self.use_gpu: im = im.cuda() return im def extract_backbone(self, im: torch.Tensor): """Extract backbone features from the network. Expects a float tensor image with pixel range [0, 255].""" im = self.preprocess_image(im) return self.net.extract_backbone_features(im) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/features/preprocessing.py ================================================ import torch import torch.nn.functional as F import numpy as np def numpy_to_torch(a: np.ndarray): return torch.from_numpy(a).float().permute(2, 0, 1).unsqueeze(0) def torch_to_numpy(a: torch.Tensor): return a.squeeze(0).permute(1,2,0).numpy() def sample_patch_transformed(im, pos, scale, image_sz, transforms, is_mask=False): """Extract transformed image samples. args: im: Image. pos: Center position for extraction. scale: Image scale to extract features from. image_sz: Size to resize the image samples to before extraction. transforms: A set of image transforms to apply. """ # Get image patche im_patch, _ = sample_patch(im, pos, scale*image_sz, image_sz, is_mask=is_mask) # Apply transforms im_patches = torch.cat([T(im_patch, is_mask=is_mask) for T in transforms]) return im_patches def sample_patch_multiscale(im, pos, scales, image_sz, mode: str='replicate', max_scale_change=None): """Extract image patches at multiple scales. args: im: Image. pos: Center position for extraction. scales: Image scales to extract image patches from. image_sz: Size to resize the image samples to mode: how to treat image borders: 'replicate' (default), 'inside' or 'inside_major' max_scale_change: maximum allowed scale change when using 'inside' and 'inside_major' mode """ if isinstance(scales, (int, float)): scales = [scales] # Get image patches patch_iter, coord_iter = zip(*(sample_patch(im, pos, s*image_sz, image_sz, mode=mode, max_scale_change=max_scale_change) for s in scales)) im_patches = torch.cat(list(patch_iter)) patch_coords = torch.cat(list(coord_iter)) return im_patches, patch_coords def sample_patch(im: torch.Tensor, pos: torch.Tensor, sample_sz: torch.Tensor, output_sz: torch.Tensor = None, mode: str = 'replicate', max_scale_change=None, is_mask=False): """Sample an image patch. args: im: Image pos: center position of crop sample_sz: size to crop output_sz: size to resize to mode: how to treat image borders: 'replicate' (default), 'inside' or 'inside_major' max_scale_change: maximum allowed scale change when using 'inside' and 'inside_major' mode """ # if mode not in ['replicate', 'inside']: # raise ValueError('Unknown border mode \'{}\'.'.format(mode)) # copy and convert posl = pos.long().clone() pad_mode = mode # Get new sample size if forced inside the image if mode == 'inside' or mode == 'inside_major': pad_mode = 'replicate' im_sz = torch.Tensor([im.shape[2], im.shape[3]]) shrink_factor = (sample_sz.float() / im_sz) if mode == 'inside': shrink_factor = shrink_factor.max() elif mode == 'inside_major': shrink_factor = shrink_factor.min() shrink_factor.clamp_(min=1, max=max_scale_change) sample_sz = (sample_sz.float() / shrink_factor).long() # Compute pre-downsampling factor if output_sz is not None: resize_factor = torch.min(sample_sz.float() / output_sz.float()).item() df = int(max(int(resize_factor - 0.1), 1)) else: df = int(1) sz = sample_sz.float() / df # new size # Do downsampling if df > 1: os = posl % df # offset posl = (posl - os) / df # new position im2 = im[..., os[0].item()::df, os[1].item()::df] # downsample else: im2 = im # compute size to crop szl = torch.max(sz.round(), torch.Tensor([2])).long() # Extract top and bottom coordinates tl = posl - (szl - 1)/2 br = posl + szl/2 + 1 # Shift the crop to inside if mode == 'inside' or mode == 'inside_major': im2_sz = torch.LongTensor([im2.shape[2], im2.shape[3]]) shift = (-tl).clamp(0) - (br - im2_sz).clamp(0) tl += shift br += shift outside = ((-tl).clamp(0) + (br - im2_sz).clamp(0)) // 2 shift = (-tl - outside) * (outside > 0).long() tl += shift br += shift # Get image patch # im_patch = im2[...,tl[0].item():br[0].item(),tl[1].item():br[1].item()] # Get image patch if not is_mask: im_patch = F.pad(im2, (-tl[1].item(), br[1].item() - im2.shape[3], -tl[0].item(), br[0].item() - im2.shape[2]), pad_mode) else: im_patch = F.pad(im2, (-tl[1].item(), br[1].item() - im2.shape[3], -tl[0].item(), br[0].item() - im2.shape[2])) # Get image coordinates patch_coord = df * torch.cat((tl, br)).view(1,4) if output_sz is None or (im_patch.shape[-2] == output_sz[0] and im_patch.shape[-1] == output_sz[1]): return im_patch.clone(), patch_coord # Resample if not is_mask: im_patch = F.interpolate(im_patch, output_sz.long().tolist(), mode='bilinear') else: im_patch = F.interpolate(im_patch, output_sz.long().tolist(), mode='nearest') return im_patch, patch_coord ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/features/util.py ================================================ import torch from pytracking.features.featurebase import FeatureBase class Concatenate(FeatureBase): """A feature that concatenates other features. args: features: List of features to concatenate. """ def __init__(self, features, pool_stride = None, normalize_power = None, use_for_color = True, use_for_gray = True): super(Concatenate, self).__init__(pool_stride, normalize_power, use_for_color, use_for_gray) self.features = features self.input_stride = self.features[0].stride() for feat in self.features: if self.input_stride != feat.stride(): raise ValueError('Strides for the features must be the same for a bultiresolution feature.') def dim(self): return sum([f.dim() for f in self.features]) def stride(self): return self.pool_stride * self.input_stride def extract(self, im: torch.Tensor): return torch.cat([f.get_feature(im) for f in self.features], 1) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/libs/__init__.py ================================================ from .tensorlist import TensorList from .tensordict import TensorDict ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/libs/complex.py ================================================ import torch from pytracking.libs.tensorlist import tensor_operation def is_complex(a: torch.Tensor) -> bool: return a.dim() >= 4 and a.shape[-1] == 2 def is_real(a: torch.Tensor) -> bool: return not is_complex(a) @tensor_operation def mult(a: torch.Tensor, b: torch.Tensor): """Pointwise complex multiplication of complex tensors.""" if is_real(a): if a.dim() >= b.dim(): raise ValueError('Incorrect dimensions.') # a is real return mult_real_cplx(a, b) if is_real(b): if b.dim() >= a.dim(): raise ValueError('Incorrect dimensions.') # b is real return mult_real_cplx(b, a) # Both complex c = mult_real_cplx(a[..., 0], b) c[..., 0] -= a[..., 1] * b[..., 1] c[..., 1] += a[..., 1] * b[..., 0] return c @tensor_operation def mult_conj(a: torch.Tensor, b: torch.Tensor): """Pointwise complex multiplication of complex tensors, with conjugate on b: a*conj(b).""" if is_real(a): if a.dim() >= b.dim(): raise ValueError('Incorrect dimensions.') # a is real return mult_real_cplx(a, conj(b)) if is_real(b): if b.dim() >= a.dim(): raise ValueError('Incorrect dimensions.') # b is real return mult_real_cplx(b, a) # Both complex c = mult_real_cplx(b[...,0], a) c[..., 0] += a[..., 1] * b[..., 1] c[..., 1] -= a[..., 0] * b[..., 1] return c @tensor_operation def mult_real_cplx(a: torch.Tensor, b: torch.Tensor): """Pointwise complex multiplication of real tensor a with complex tensor b.""" if is_real(b): raise ValueError('Last dimension must have length 2.') return a.unsqueeze(-1) * b @tensor_operation def div(a: torch.Tensor, b: torch.Tensor): """Pointwise complex division of complex tensors.""" if is_real(b): if b.dim() >= a.dim(): raise ValueError('Incorrect dimensions.') # b is real return div_cplx_real(a, b) return div_cplx_real(mult_conj(a, b), abs_sqr(b)) @tensor_operation def div_cplx_real(a: torch.Tensor, b: torch.Tensor): """Pointwise complex division of complex tensor a with real tensor b.""" if is_real(a): raise ValueError('Last dimension must have length 2.') return a / b.unsqueeze(-1) @tensor_operation def abs_sqr(a: torch.Tensor): """Squared absolute value.""" if is_real(a): raise ValueError('Last dimension must have length 2.') return torch.sum(a*a, -1) @tensor_operation def abs(a: torch.Tensor): """Absolute value.""" if is_real(a): raise ValueError('Last dimension must have length 2.') return torch.sqrt(abs_sqr(a)) @tensor_operation def conj(a: torch.Tensor): """Complex conjugate.""" if is_real(a): raise ValueError('Last dimension must have length 2.') # return a * torch.Tensor([1, -1], device=a.device) return complex(a[...,0], -a[...,1]) @tensor_operation def real(a: torch.Tensor): """Real part.""" if is_real(a): raise ValueError('Last dimension must have length 2.') return a[..., 0] @tensor_operation def imag(a: torch.Tensor): """Imaginary part.""" if is_real(a): raise ValueError('Last dimension must have length 2.') return a[..., 1] @tensor_operation def complex(a: torch.Tensor, b: torch.Tensor = None): """Create complex tensor from real and imaginary part.""" if b is None: b = a.new_zeros(a.shape) elif a is None: a = b.new_zeros(b.shape) return torch.cat((a.unsqueeze(-1), b.unsqueeze(-1)), -1) @tensor_operation def mtimes(a: torch.Tensor, b: torch.Tensor, conj_a=False, conj_b=False): """Complex matrix multiplication of complex tensors. The dimensions (-3, -2) are matrix multiplied. -1 is the complex dimension.""" if is_real(a): if a.dim() >= b.dim(): raise ValueError('Incorrect dimensions.') return mtimes_real_complex(a, b, conj_b=conj_b) if is_real(b): if b.dim() >= a.dim(): raise ValueError('Incorrect dimensions.') return mtimes_complex_real(a, b, conj_a=conj_a) if not conj_a and not conj_b: return complex(torch.matmul(a[..., 0], b[..., 0]) - torch.matmul(a[..., 1], b[..., 1]), torch.matmul(a[..., 0], b[..., 1]) + torch.matmul(a[..., 1], b[..., 0])) if conj_a and not conj_b: return complex(torch.matmul(a[..., 0], b[..., 0]) + torch.matmul(a[..., 1], b[..., 1]), torch.matmul(a[..., 0], b[..., 1]) - torch.matmul(a[..., 1], b[..., 0])) if not conj_a and conj_b: return complex(torch.matmul(a[..., 0], b[..., 0]) + torch.matmul(a[..., 1], b[..., 1]), torch.matmul(a[..., 1], b[..., 0]) - torch.matmul(a[..., 0], b[..., 1])) if conj_a and conj_b: return complex(torch.matmul(a[..., 0], b[..., 0]) - torch.matmul(a[..., 1], b[..., 1]), -torch.matmul(a[..., 0], b[..., 1]) - torch.matmul(a[..., 1], b[..., 0])) @tensor_operation def mtimes_real_complex(a: torch.Tensor, b: torch.Tensor, conj_b=False): if is_real(b): raise ValueError('Incorrect dimensions.') if not conj_b: return complex(torch.matmul(a, b[..., 0]), torch.matmul(a, b[..., 1])) if conj_b: return complex(torch.matmul(a, b[..., 0]), -torch.matmul(a, b[..., 1])) @tensor_operation def mtimes_complex_real(a: torch.Tensor, b: torch.Tensor, conj_a=False): if is_real(a): raise ValueError('Incorrect dimensions.') if not conj_a: return complex(torch.matmul(a[..., 0], b), torch.matmul(a[..., 1], b)) if conj_a: return complex(torch.matmul(a[..., 0], b), -torch.matmul(a[..., 1], b)) @tensor_operation def exp_imag(a: torch.Tensor): """Complex exponential with imaginary input: e^(i*a)""" a = a.unsqueeze(-1) return torch.cat((torch.cos(a), torch.sin(a)), -1) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/libs/dcf.py ================================================ import torch import math from pytracking import fourier from pytracking import complex import torch.nn.functional as F def hann1d(sz: int, centered = True) -> torch.Tensor: """1D cosine window.""" if centered: return 0.5 * (1 - torch.cos((2 * math.pi / (sz + 1)) * torch.arange(1, sz + 1).float())) w = 0.5 * (1 + torch.cos((2 * math.pi / (sz + 2)) * torch.arange(0, sz//2 + 1).float())) return torch.cat([w, w[1:sz-sz//2].flip((0,))]) def hann2d(sz: torch.Tensor, centered = True) -> torch.Tensor: """2D cosine window.""" return hann1d(sz[0].item(), centered).reshape(1, 1, -1, 1) * hann1d(sz[1].item(), centered).reshape(1, 1, 1, -1) def hann2d_clipped(sz: torch.Tensor, effective_sz: torch.Tensor, centered = True) -> torch.Tensor: """1D clipped cosine window.""" # Ensure that the difference is even effective_sz += (effective_sz - sz) % 2 effective_window = hann1d(effective_sz[0].item(), True).reshape(1, 1, -1, 1) * hann1d(effective_sz[1].item(), True).reshape(1, 1, 1, -1) pad = (sz - effective_sz) / 2 window = F.pad(effective_window, (pad[1].item(), pad[1].item(), pad[0].item(), pad[0].item()), 'replicate') if centered: return window else: mid = (sz / 2).int() window_shift_lr = torch.cat((window[:, :, :, mid[1]:], window[:, :, :, :mid[1]]), 3) return torch.cat((window_shift_lr[:, :, mid[0]:, :], window_shift_lr[:, :, :mid[0], :]), 2) def gauss_fourier(sz: int, sigma: float, half: bool = False) -> torch.Tensor: if half: k = torch.arange(0, int(sz/2+1)) else: k = torch.arange(-int((sz-1)/2), int(sz/2+1)) return (math.sqrt(2*math.pi) * sigma / sz) * torch.exp(-2 * (math.pi * sigma * k.float() / sz)**2) def gauss_spatial(sz, sigma, center=0, end_pad=0): k = torch.arange(-(sz-1)/2, (sz+1)/2+end_pad) return torch.exp(-1.0/(2*sigma**2) * (k - center)**2) def label_function(sz: torch.Tensor, sigma: torch.Tensor): return gauss_fourier(sz[0].item(), sigma[0].item()).reshape(1, 1, -1, 1) * gauss_fourier(sz[1].item(), sigma[1].item(), True).reshape(1, 1, 1, -1) def label_function_spatial(sz: torch.Tensor, sigma: torch.Tensor, center: torch.Tensor = torch.zeros(2), end_pad: torch.Tensor = torch.zeros(2)): """The origin is in the middle of the image.""" return gauss_spatial(sz[0].item(), sigma[0].item(), center[0], end_pad[0].item()).reshape(1, 1, -1, 1) * \ gauss_spatial(sz[1].item(), sigma[1].item(), center[1], end_pad[1].item()).reshape(1, 1, 1, -1) def cubic_spline_fourier(f, a): """The continuous Fourier transform of a cubic spline kernel.""" bf = (6*(1 - torch.cos(2 * math.pi * f)) + 3*a*(1 - torch.cos(4 * math.pi * f)) - (6 + 8*a)*math.pi*f*torch.sin(2 * math.pi * f) - 2*a*math.pi*f*torch.sin(4 * math.pi * f)) \ / (4 * math.pi**4 * f**4) bf[f == 0] = 1 return bf def get_interp_fourier(sz: torch.Tensor, method='ideal', bicubic_param=0.5, centering=True, windowing=False, device='cpu'): ky, kx = fourier.get_frequency_coord(sz) if method=='ideal': interp_y = torch.ones(ky.shape) / sz[0] interp_x = torch.ones(kx.shape) / sz[1] elif method=='bicubic': interp_y = cubic_spline_fourier(ky / sz[0], bicubic_param) / sz[0] interp_x = cubic_spline_fourier(kx / sz[1], bicubic_param) / sz[1] else: raise ValueError('Unknown method.') if centering: interp_y = complex.mult(interp_y, complex.exp_imag((-math.pi/sz[0]) * ky)) interp_x = complex.mult(interp_x, complex.exp_imag((-math.pi/sz[1]) * kx)) if windowing: raise NotImplementedError return interp_y.to(device), interp_x.to(device) def interpolate_dft(a: torch.Tensor, interp_fs) -> torch.Tensor: if isinstance(interp_fs, torch.Tensor): return complex.mult(a, interp_fs) if isinstance(interp_fs, (tuple, list)): return complex.mult(complex.mult(a, interp_fs[0]), interp_fs[1]) raise ValueError('"interp_fs" must be tensor or tuple of tensors.') def get_reg_filter(sz: torch.Tensor, target_sz: torch.Tensor, params): """Computes regularization filter in CCOT and ECO.""" if not params.use_reg_window: return params.reg_window_min * torch.ones(1,1,1,1) if getattr(params, 'reg_window_square', False): target_sz = target_sz.prod().sqrt() * torch.ones(2) # Normalization factor reg_scale = 0.5 * target_sz # Construct grid if getattr(params, 'reg_window_centered', True): wrg = torch.arange(-int((sz[0]-1)/2), int(sz[0]/2+1), dtype=torch.float32).view(1,1,-1,1) wcg = torch.arange(-int((sz[1]-1)/2), int(sz[1]/2+1), dtype=torch.float32).view(1,1,1,-1) else: wrg = torch.cat([torch.arange(0, int(sz[0]/2+1), dtype=torch.float32), torch.arange(-int((sz[0] - 1) / 2), 0, dtype=torch.float32)]).view(1,1,-1,1) wcg = torch.cat([torch.arange(0, int(sz[1]/2+1), dtype=torch.float32), torch.arange(-int((sz[1] - 1) / 2), 0, dtype=torch.float32)]).view(1,1,1,-1) # Construct regularization window reg_window = (params.reg_window_edge - params.reg_window_min) * \ (torch.abs(wrg/reg_scale[0])**params.reg_window_power + torch.abs(wcg/reg_scale[1])**params.reg_window_power) + params.reg_window_min # Compute DFT and enforce sparsity reg_window_dft = torch.rfft(reg_window, 2) / sz.prod() reg_window_dft_abs = complex.abs(reg_window_dft) reg_window_dft[reg_window_dft_abs < params.reg_sparsity_threshold * reg_window_dft_abs.max(), :] = 0 # Do the inverse transform to correct for the window minimum reg_window_sparse = torch.irfft(reg_window_dft, 2, signal_sizes=sz.long().tolist()) reg_window_dft[0,0,0,0,0] += params.reg_window_min - sz.prod() * reg_window_sparse.min() reg_window_dft = complex.real(fourier.rfftshift2(reg_window_dft)) # Remove zeros max_inds,_ = reg_window_dft.nonzero().max(dim=0) mid_ind = int((reg_window_dft.shape[2]-1)/2) top = max_inds[-2].item() + 1 bottom = 2*mid_ind - max_inds[-2].item() right = max_inds[-1].item() + 1 reg_window_dft = reg_window_dft[..., bottom:top, :right] if reg_window_dft.shape[-1] > 1: reg_window_dft = torch.cat([reg_window_dft[..., 1:].flip((2, 3)), reg_window_dft], -1) return reg_window_dft def max2d(a: torch.Tensor) -> (torch.Tensor, torch.Tensor): """Computes maximum and argmax in the last two dimensions.""" max_val_row, argmax_row = torch.max(a, dim=-2) max_val, argmax_col = torch.max(max_val_row, dim=-1) argmax_row = argmax_row.view(argmax_col.numel(),-1)[torch.arange(argmax_col.numel()), argmax_col.view(-1)] argmax_row = argmax_row.reshape(argmax_col.shape) argmax = torch.cat((argmax_row.unsqueeze(-1), argmax_col.unsqueeze(-1)), -1) return max_val, argmax ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/libs/fourier.py ================================================ import torch import torch.nn.functional as F from pytracking import complex, TensorList from pytracking.libs.tensorlist import tensor_operation @tensor_operation def rfftshift2(a: torch.Tensor): h = a.shape[2] + 2 return torch.cat((a[:,:,(h-1)//2:,...], a[:,:,:h//2,...]), 2) @tensor_operation def irfftshift2(a: torch.Tensor): mid = int((a.shape[2]-1)/2) return torch.cat((a[:,:,mid:,...], a[:,:,:mid,...]), 2) @tensor_operation def cfft2(a): """Do FFT and center the low frequency component. Always produces odd (full) output sizes.""" return rfftshift2(torch.rfft(a, 2)) @tensor_operation def cifft2(a, signal_sizes=None): """Do inverse FFT corresponding to cfft2.""" return torch.irfft(irfftshift2(a), 2, signal_sizes=signal_sizes) @tensor_operation def sample_fs(a: torch.Tensor, grid_sz: torch.Tensor = None, rescale = True): """Samples the Fourier series.""" # Size of the fourier series sz = torch.Tensor([a.shape[2], 2*a.shape[3]-1]).float() # Default grid if grid_sz is None or sz[0] == grid_sz[0] and sz[1] == grid_sz[1]: if rescale: return sz.prod().item() * cifft2(a) return cifft2(a) if sz[0] > grid_sz[0] or sz[1] > grid_sz[1]: raise ValueError("Only grid sizes that are smaller than the Fourier series size are supported.") tot_pad = (grid_sz - sz).tolist() is_even = [s.item() % 2 == 0 for s in sz] # Compute paddings pad_top = int((tot_pad[0]+1)/2) if is_even[0] else int(tot_pad[0]/2) pad_bottom = int(tot_pad[0] - pad_top) pad_right = int((tot_pad[1]+1)/2) if rescale: return grid_sz.prod().item() * cifft2(F.pad(a, (0, 0, 0, pad_right, pad_top, pad_bottom)), signal_sizes=grid_sz.long().tolist()) else: return cifft2(F.pad(a, (0, 0, 0, pad_right, pad_top, pad_bottom)), signal_sizes=grid_sz.long().tolist()) def get_frequency_coord(sz, add_complex_dim = False, device='cpu'): """Frequency coordinates.""" ky = torch.arange(-int((sz[0]-1)/2), int(sz[0]/2+1), dtype=torch.float32, device=device).view(1,1,-1,1) kx = torch.arange(0, int(sz[1]/2+1), dtype=torch.float32, device=device).view(1,1,1,-1) if add_complex_dim: ky = ky.unsqueeze(-1) kx = kx.unsqueeze(-1) return ky, kx @tensor_operation def shift_fs(a: torch.Tensor, shift: torch.Tensor): """Shift a sample a in the Fourier domain. Params: a : The fourier coefficiens of the sample. shift : The shift to be performed normalized to the range [-pi, pi].""" if a.dim() != 5: raise ValueError('a must be the Fourier coefficients, a 5-dimensional tensor.') if shift[0] == 0 and shift[1] == 0: return a ky, kx = get_frequency_coord((a.shape[2], 2*a.shape[3]-1), device=a.device) return complex.mult(complex.mult(a, complex.exp_imag(shift[0].item()*ky)), complex.exp_imag(shift[1].item()*kx)) def sum_fs(a: TensorList) -> torch.Tensor: """Sum a list of Fourier series expansions.""" s = None mid = None for e in sorted(a, key=lambda elem: elem.shape[-3], reverse=True): if s is None: s = e.clone() mid = int((s.shape[-3] - 1) / 2) else: # Compute coordinates top = mid - int((e.shape[-3] - 1) / 2) bottom = mid + int(e.shape[-3] / 2) + 1 right = e.shape[-2] # Add the data s[..., top:bottom, :right, :] += e return s def sum_fs12(a: TensorList) -> torch.Tensor: """Sum a list of Fourier series expansions.""" s = None mid = None for e in sorted(a, key=lambda elem: elem.shape[0], reverse=True): if s is None: s = e.clone() mid = int((s.shape[0] - 1) / 2) else: # Compute coordinates top = mid - int((e.shape[0] - 1) / 2) bottom = mid + int(e.shape[0] / 2) + 1 right = e.shape[1] # Add the data s[top:bottom, :right, ...] += e return s @tensor_operation def inner_prod_fs(a: torch.Tensor, b: torch.Tensor): if complex.is_complex(a) and complex.is_complex(b): return 2 * (a.reshape(-1) @ b.reshape(-1)) - a[:, :, :, 0, :].reshape(-1) @ b[:, :, :, 0, :].reshape(-1) elif complex.is_real(a) and complex.is_real(b): return 2 * (a.reshape(-1) @ b.reshape(-1)) - a[:, :, :, 0].reshape(-1) @ b[:, :, :, 0].reshape(-1) else: raise NotImplementedError('Not implemented for mixed real and complex.') ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/libs/operation.py ================================================ import torch import torch.nn.functional as F from pytracking.libs.tensorlist import tensor_operation, TensorList @tensor_operation def conv2d(input: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor = None, stride=1, padding=0, dilation=1, groups=1, mode=None): """Standard conv2d. Returns the input if weight=None.""" if weight is None: return input ind = None if mode is not None: if padding != 0: raise ValueError('Cannot input both padding and mode.') if mode == 'same': padding = (weight.shape[2]//2, weight.shape[3]//2) if weight.shape[2] % 2 == 0 or weight.shape[3] % 2 == 0: ind = (slice(-1) if weight.shape[2] % 2 == 0 else slice(None), slice(-1) if weight.shape[3] % 2 == 0 else slice(None)) elif mode == 'valid': padding = (0, 0) elif mode == 'full': padding = (weight.shape[2]-1, weight.shape[3]-1) else: raise ValueError('Unknown mode for padding.') out = F.conv2d(input, weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups) if ind is None: return out return out[:,:,ind[0],ind[1]] @tensor_operation def conv1x1(input: torch.Tensor, weight: torch.Tensor): """Do a convolution with a 1x1 kernel weights. Implemented with matmul, which can be faster than using conv.""" if weight is None: return input return torch.conv2d(input, weight) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/libs/optimization.py ================================================ import torch import torch.autograd import math from pytracking.libs import TensorList from pytracking.utils.plotting import plot_graph from ltr.models.layers.activation import softmax_reg class L2Problem: """Base class for representing an L2 optimization problem.""" def __call__(self, x: TensorList) -> TensorList: """Shall compute the residuals of the problem.""" raise NotImplementedError def ip_input(self, a, b): """Inner product of the input space.""" return sum(a.view(-1) @ b.view(-1)) def ip_output(self, a, b): """Inner product of the output space.""" return sum(a.view(-1) @ b.view(-1)) def M1(self, x): """M1 preconditioner.""" return x def M2(self, x): """M2 preconditioner.""" return x class MinimizationProblem: """General minimization problem.""" def __call__(self, x: TensorList) -> TensorList: """Shall compute the loss.""" raise NotImplementedError def ip_input(self, a, b): """Inner product of the input space.""" return sum(a.view(-1) @ b.view(-1)) def M1(self, x): return x def M2(self, x): return x class ConjugateGradientBase: """Conjugate Gradient optimizer base class. Implements the CG loop.""" def __init__(self, fletcher_reeves = True, standard_alpha = True, direction_forget_factor = 0, debug = False): self.fletcher_reeves = fletcher_reeves self.standard_alpha = standard_alpha self.direction_forget_factor = direction_forget_factor self.debug = debug # State self.p = None self.rho = torch.ones(1) self.r_prev = None # Right hand side self.b = None def reset_state(self): self.p = None self.rho = torch.ones(1) self.r_prev = None def run_CG(self, num_iter, x=None, eps=0.0): """Main conjugate gradient method. args: num_iter: Number of iterations. x: Initial guess. Assumed zero if None. eps: Stop if the residual norm gets smaller than this. """ # Apply forgetting factor if self.direction_forget_factor == 0: self.reset_state() elif self.p is not None: self.rho /= self.direction_forget_factor if x is None: r = self.b.clone() else: r = self.b - self.A(x) # Norms of residuals etc for debugging resvec = None if self.debug: normr = self.residual_norm(r) resvec = torch.zeros(num_iter+1) resvec[0] = normr # Loop over iterations for ii in range(num_iter): # Preconditioners y = self.M1(r) z = self.M2(y) rho1 = self.rho self.rho = self.ip(r, z) if self.check_zero(self.rho): if self.debug: print('Stopped CG since rho = 0') if resvec is not None: resvec = resvec[:ii+1] return x, resvec if self.p is None: self.p = z.clone() else: if self.fletcher_reeves: beta = self.rho / rho1 else: rho2 = self.ip(self.r_prev, z) beta = (self.rho - rho2) / rho1 beta = beta.clamp(0) self.p = z + self.p * beta q = self.A(self.p) pq = self.ip(self.p, q) if self.standard_alpha: alpha = self.rho / pq else: alpha = self.ip(self.p, r) / pq # Save old r for PR formula if not self.fletcher_reeves: self.r_prev = r.clone() # Form new iterate if x is None: x = self.p * alpha else: x += self.p * alpha if ii < num_iter - 1 or self.debug: r -= q * alpha if eps > 0.0 or self.debug: normr = self.residual_norm(r) if self.debug: self.evaluate_CG_iteration(x) resvec[ii+1] = normr if eps > 0 and normr <= eps: if self.debug: print('Stopped CG since norm smaller than eps') break if resvec is not None: resvec = resvec[:ii+2] return x, resvec def A(self, x): # Implements the left hand operation raise NotImplementedError def ip(self, a, b): # Implements the inner product return a.view(-1) @ b.view(-1) def residual_norm(self, r): res = self.ip(r, r).sum() if isinstance(res, (TensorList, list, tuple)): res = sum(res) return res.sqrt() def check_zero(self, s, eps = 0.0): ss = s.abs() <= eps if isinstance(ss, (TensorList, list, tuple)): ss = sum(ss) return ss.item() > 0 def M1(self, x): # M1 preconditioner return x def M2(self, x): # M2 preconditioner return x def evaluate_CG_iteration(self, x): pass class ConjugateGradient(ConjugateGradientBase): """Conjugate Gradient optimizer, performing single linearization of the residuals in the start.""" def __init__(self, problem: L2Problem, variable: TensorList, cg_eps = 0.0, fletcher_reeves = True, standard_alpha = True, direction_forget_factor = 0, debug = False, plotting = False, visdom=None): super().__init__(fletcher_reeves, standard_alpha, direction_forget_factor, debug or plotting) self.problem = problem self.x = variable self.plotting = plotting self.fig_num = (10,11) self.visdom = visdom self.cg_eps = cg_eps self.f0 = None self.g = None self.dfdxt_g = None self.residuals = torch.zeros(0) self.losses = torch.zeros(0) def clear_temp(self): self.f0 = None self.g = None self.dfdxt_g = None def run(self, num_cg_iter): """Run the oprimizer with the provided number of iterations.""" if num_cg_iter == 0: return lossvec = None if self.debug: lossvec = torch.zeros(2) self.x.requires_grad_(True) # Evaluate function at current estimate self.f0 = self.problem(self.x) # Create copy with graph detached self.g = self.f0.detach() if self.debug: lossvec[0] = self.problem.ip_output(self.g, self.g) self.g.requires_grad_(True) # Get df/dx^t @ f0 self.dfdxt_g = TensorList(torch.autograd.grad(self.f0, self.x, self.g, create_graph=True)) # Get the right hand side self.b = - self.dfdxt_g.detach() # Run CG delta_x, res = self.run_CG(num_cg_iter, eps=self.cg_eps) self.x.detach_() self.x += delta_x if self.debug: self.f0 = self.problem(self.x) lossvec[-1] = self.problem.ip_output(self.f0, self.f0) self.residuals = torch.cat((self.residuals, res)) self.losses = torch.cat((self.losses, lossvec)) if self.visdom is not None: self.visdom.register(self.losses, 'lineplot', 3, 'Loss') self.visdom.register(self.residuals, 'lineplot', 3, 'CG residuals') elif self.plotting: plot_graph(self.losses, self.fig_num[0], title='Loss') plot_graph(self.residuals, self.fig_num[1], title='CG residuals') self.x.detach_() self.clear_temp() def A(self, x): dfdx_x = torch.autograd.grad(self.dfdxt_g, self.g, x, retain_graph=True) return TensorList(torch.autograd.grad(self.f0, self.x, dfdx_x, retain_graph=True)) def ip(self, a, b): return self.problem.ip_input(a, b) def M1(self, x): return self.problem.M1(x) def M2(self, x): return self.problem.M2(x) class GaussNewtonCG(ConjugateGradientBase): """Gauss-Newton with Conjugate Gradient optimizer.""" def __init__(self, problem: L2Problem, variable: TensorList, cg_eps = 0.0, fletcher_reeves = True, standard_alpha = True, direction_forget_factor = 0, debug = False, analyze = False, plotting = False, visdom=None): super().__init__(fletcher_reeves, standard_alpha, direction_forget_factor, debug or analyze or plotting) self.problem = problem self.x = variable self.analyze_convergence = analyze self.plotting = plotting self.fig_num = (10,11,12) self.visdom = visdom self.cg_eps = cg_eps self.f0 = None self.g = None self.dfdxt_g = None self.residuals = torch.zeros(0) self.losses = torch.zeros(0) self.gradient_mags = torch.zeros(0) def clear_temp(self): self.f0 = None self.g = None self.dfdxt_g = None def run_GN(self, *args, **kwargs): return self.run(*args, **kwargs) def run(self, num_cg_iter, num_gn_iter=None): """Run the optimizer. args: num_cg_iter: Number of CG iterations per GN iter. If list, then each entry specifies number of CG iterations and number of GN iterations is given by the length of the list. num_gn_iter: Number of GN iterations. Shall only be given if num_cg_iter is an integer. """ if isinstance(num_cg_iter, int): if num_gn_iter is None: raise ValueError('Must specify number of GN iter if CG iter is constant') num_cg_iter = [num_cg_iter]*num_gn_iter num_gn_iter = len(num_cg_iter) if num_gn_iter == 0: return if self.analyze_convergence: self.evaluate_CG_iteration(0) # Outer loop for running the GN iterations. for cg_iter in num_cg_iter: self.run_GN_iter(cg_iter) if self.debug: if not self.analyze_convergence: self.f0 = self.problem(self.x) loss = self.problem.ip_output(self.f0, self.f0) self.losses = torch.cat((self.losses, loss.detach().cpu().view(-1))) if self.visdom is not None: self.visdom.register(self.losses, 'lineplot', 3, 'Loss') self.visdom.register(self.residuals, 'lineplot', 3, 'CG residuals') if self.analyze_convergence: self.visdom.register(self.gradient_mags, 'lineplot', 4, 'Gradient magnitude') elif self.plotting: plot_graph(self.losses, self.fig_num[0], title='Loss') plot_graph(self.residuals, self.fig_num[1], title='CG residuals') if self.analyze_convergence: plot_graph(self.gradient_mags, self.fig_num[2], 'Gradient magnitude') self.x.detach_() self.clear_temp() return self.losses, self.residuals def run_GN_iter(self, num_cg_iter): """Runs a single GN iteration.""" self.x.requires_grad_(True) # Evaluate function at current estimate self.f0 = self.problem(self.x) # Create copy with graph detached self.g = self.f0.detach() if self.debug and not self.analyze_convergence: loss = self.problem.ip_output(self.g, self.g) self.losses = torch.cat((self.losses, loss.detach().cpu().view(-1))) self.g.requires_grad_(True) # Get df/dx^t @ f0 self.dfdxt_g = TensorList(torch.autograd.grad(self.f0, self.x, self.g, create_graph=True)) # Get the right hand side self.b = - self.dfdxt_g.detach() # Run CG delta_x, res = self.run_CG(num_cg_iter, eps=self.cg_eps) self.x.detach_() self.x += delta_x if self.debug: self.residuals = torch.cat((self.residuals, res)) def A(self, x): dfdx_x = torch.autograd.grad(self.dfdxt_g, self.g, x, retain_graph=True) return TensorList(torch.autograd.grad(self.f0, self.x, dfdx_x, retain_graph=True)) def ip(self, a, b): return self.problem.ip_input(a, b) def M1(self, x): return self.problem.M1(x) def M2(self, x): return self.problem.M2(x) def evaluate_CG_iteration(self, delta_x): if self.analyze_convergence: x = (self.x + delta_x).detach() x.requires_grad_(True) # compute loss and gradient f = self.problem(x) loss = self.problem.ip_output(f, f) grad = TensorList(torch.autograd.grad(loss, x)) # store in the vectors self.losses = torch.cat((self.losses, loss.detach().cpu().view(-1))) self.gradient_mags = torch.cat((self.gradient_mags, sum(grad.view(-1) @ grad.view(-1)).cpu().sqrt().detach().view(-1))) class GradientDescentL2: """Gradient descent with momentum for L2 problems.""" def __init__(self, problem: L2Problem, variable: TensorList, step_length: float, momentum: float = 0.0, debug = False, plotting = False, visdom=None): self.problem = problem self.x = variable self.step_legnth = step_length self.momentum = momentum self.debug = debug or plotting self.plotting = plotting self.fig_num = (10,11) self.visdom = visdom self.losses = torch.zeros(0) self.gradient_mags = torch.zeros(0) self.residuals = None self.clear_temp() def clear_temp(self): self.f0 = None self.dir = None def run(self, num_iter, dummy = None): if num_iter == 0: return lossvec = None if self.debug: lossvec = torch.zeros(num_iter+1) grad_mags = torch.zeros(num_iter+1) for i in range(num_iter): self.x.requires_grad_(True) # Evaluate function at current estimate self.f0 = self.problem(self.x) # Compute loss loss = self.problem.ip_output(self.f0, self.f0) # Compute grad grad = TensorList(torch.autograd.grad(loss, self.x)) # Update direction if self.dir is None: self.dir = grad else: self.dir = grad + self.momentum * self.dir self.x.detach_() self.x -= self.step_legnth * self.dir if self.debug: lossvec[i] = loss.item() grad_mags[i] = sum(grad.view(-1) @ grad.view(-1)).sqrt().item() if self.debug: self.x.requires_grad_(True) self.f0 = self.problem(self.x) loss = self.problem.ip_output(self.f0, self.f0) grad = TensorList(torch.autograd.grad(loss, self.x)) lossvec[-1] = self.problem.ip_output(self.f0, self.f0).item() grad_mags[-1] = sum(grad.view(-1) @ grad.view(-1)).cpu().sqrt().item() self.losses = torch.cat((self.losses, lossvec)) self.gradient_mags = torch.cat((self.gradient_mags, grad_mags)) if self.visdom is not None: self.visdom.register(self.losses, 'lineplot', 3, 'Loss') self.visdom.register(self.gradient_mags, 'lineplot', 4, 'Gradient magnitude') elif self.plotting: plot_graph(self.losses, self.fig_num[0], title='Loss') plot_graph(self.gradient_mags, self.fig_num[1], title='Gradient magnitude') self.x.detach_() self.clear_temp() class NewtonCG(ConjugateGradientBase): """Newton with Conjugate Gradient. Handels general minimization problems.""" def __init__(self, problem: MinimizationProblem, variable: TensorList, init_hessian_reg = 0.0, hessian_reg_factor = 1.0, cg_eps = 0.0, fletcher_reeves = True, standard_alpha = True, direction_forget_factor = 0, debug = False, analyze = False, plotting = False, fig_num=(10, 11, 12)): super().__init__(fletcher_reeves, standard_alpha, direction_forget_factor, debug or analyze or plotting) self.problem = problem self.x = variable self.analyze_convergence = analyze self.plotting = plotting self.fig_num = fig_num self.hessian_reg = init_hessian_reg self.hessian_reg_factor = hessian_reg_factor self.cg_eps = cg_eps self.f0 = None self.g = None self.residuals = torch.zeros(0) self.losses = torch.zeros(0) self.gradient_mags = torch.zeros(0) def clear_temp(self): self.f0 = None self.g = None def run(self, num_cg_iter, num_newton_iter=None): if isinstance(num_cg_iter, int): if num_cg_iter == 0: return if num_newton_iter is None: num_newton_iter = 1 num_cg_iter = [num_cg_iter] * num_newton_iter num_newton_iter = len(num_cg_iter) if num_newton_iter == 0: return if self.analyze_convergence: self.evaluate_CG_iteration(0) for cg_iter in num_cg_iter: self.run_newton_iter(cg_iter) self.hessian_reg *= self.hessian_reg_factor if self.debug: if not self.analyze_convergence: loss = self.problem(self.x) self.losses = torch.cat((self.losses, loss.detach().cpu().view(-1))) if self.plotting: plot_graph(self.losses, self.fig_num[0], title='Loss') plot_graph(self.residuals, self.fig_num[1], title='CG residuals') if self.analyze_convergence: plot_graph(self.gradient_mags, self.fig_num[2], 'Gradient magnitude') self.x.detach_() self.clear_temp() return self.losses, self.residuals def run_newton_iter(self, num_cg_iter): self.x.requires_grad_(True) # Evaluate function at current estimate self.f0 = self.problem(self.x) if self.debug and not self.analyze_convergence: self.losses = torch.cat((self.losses, self.f0.detach().cpu().view(-1))) # Gradient of loss self.g = TensorList(torch.autograd.grad(self.f0, self.x, create_graph=True)) # Get the right hand side self.b = - self.g.detach() # Run CG delta_x, res = self.run_CG(num_cg_iter, eps=self.cg_eps) self.x.detach_() self.x += delta_x if self.debug: self.residuals = torch.cat((self.residuals, res)) def A(self, x): return TensorList(torch.autograd.grad(self.g, self.x, x, retain_graph=True)) + self.hessian_reg * x def ip(self, a, b): # Implements the inner product return self.problem.ip_input(a, b) def M1(self, x): return self.problem.M1(x) def M2(self, x): return self.problem.M2(x) def evaluate_CG_iteration(self, delta_x): if self.analyze_convergence: x = (self.x + delta_x).detach() x.requires_grad_(True) # compute loss and gradient loss = self.problem(x) grad = TensorList(torch.autograd.grad(loss, x)) # store in the vectors self.losses = torch.cat((self.losses, loss.detach().cpu().view(-1))) self.gradient_mags = torch.cat((self.gradient_mags, sum(grad.view(-1) @ grad.view(-1)).cpu().sqrt().detach().view(-1))) class GradientDescent: """Gradient descent for general minimization problems.""" def __init__(self, problem: MinimizationProblem, variable: TensorList, step_length: float, momentum: float = 0.0, debug = False, plotting = False, fig_num=(10,11)): self.problem = problem self.x = variable self.step_legnth = step_length self.momentum = momentum self.debug = debug or plotting self.plotting = plotting self.fig_num = fig_num self.losses = torch.zeros(0) self.gradient_mags = torch.zeros(0) self.residuals = None self.clear_temp() def clear_temp(self): self.dir = None def run(self, num_iter, dummy = None): if num_iter == 0: return lossvec = None if self.debug: lossvec = torch.zeros(num_iter+1) grad_mags = torch.zeros(num_iter+1) for i in range(num_iter): self.x.requires_grad_(True) # Evaluate function at current estimate loss = self.problem(self.x) # Compute grad grad = TensorList(torch.autograd.grad(loss, self.x)) # Update direction if self.dir is None: self.dir = grad else: self.dir = grad + self.momentum * self.dir self.x.detach_() self.x -= self.step_legnth * self.dir if self.debug: lossvec[i] = loss.item() grad_mags[i] = sum(grad.view(-1) @ grad.view(-1)).sqrt().item() if self.debug: self.x.requires_grad_(True) loss = self.problem(self.x) grad = TensorList(torch.autograd.grad(loss, self.x)) lossvec[-1] = loss.item() grad_mags[-1] = sum(grad.view(-1) @ grad.view(-1)).cpu().sqrt().item() self.losses = torch.cat((self.losses, lossvec)) self.gradient_mags = torch.cat((self.gradient_mags, grad_mags)) if self.plotting: plot_graph(self.losses, self.fig_num[0], title='Loss') plot_graph(self.gradient_mags, self.fig_num[1], title='Gradient magnitude') self.x.detach_() self.clear_temp() ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/libs/tensordict.py ================================================ from collections import OrderedDict import torch import copy class TensorDict(OrderedDict): """Container mainly used for dicts of torch tensors. Extends OrderedDict with pytorch functionality.""" def concat(self, other): """Concatenates two dicts without copying internal data.""" return TensorDict(self, **other) def copy(self): return TensorDict(super(TensorDict, self).copy()) def __deepcopy__(self, memodict={}): return TensorDict(copy.deepcopy(list(self), memodict)) def __getattr__(self, name): if not hasattr(torch.Tensor, name): raise AttributeError('\'TensorDict\' object has not attribute \'{}\''.format(name)) def apply_attr(*args, **kwargs): return TensorDict({n: getattr(e, name)(*args, **kwargs) if hasattr(e, name) else e for n, e in self.items()}) return apply_attr def attribute(self, attr: str, *args): return TensorDict({n: getattr(e, attr, *args) for n, e in self.items()}) def apply(self, fn, *args, **kwargs): return TensorDict({n: fn(e, *args, **kwargs) for n, e in self.items()}) @staticmethod def _iterable(a): return isinstance(a, (TensorDict, list)) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/libs/tensorlist.py ================================================ import functools import torch import copy class TensorList(list): """Container mainly used for lists of torch tensors. Extends lists with pytorch functionality.""" def __init__(self, list_of_tensors = None): if list_of_tensors is None: list_of_tensors = list() super(TensorList, self).__init__(list_of_tensors) def __deepcopy__(self, memodict={}): return TensorList(copy.deepcopy(list(self), memodict)) def __getitem__(self, item): if isinstance(item, int): return super(TensorList, self).__getitem__(item) elif isinstance(item, (tuple, list)): return TensorList([super(TensorList, self).__getitem__(i) for i in item]) else: return TensorList(super(TensorList, self).__getitem__(item)) def __add__(self, other): if TensorList._iterable(other): return TensorList([e1 + e2 for e1, e2 in zip(self, other)]) return TensorList([e + other for e in self]) def __radd__(self, other): if TensorList._iterable(other): return TensorList([e2 + e1 for e1, e2 in zip(self, other)]) return TensorList([other + e for e in self]) def __iadd__(self, other): if TensorList._iterable(other): for i, e2 in enumerate(other): self[i] += e2 else: for i in range(len(self)): self[i] += other return self def __sub__(self, other): if TensorList._iterable(other): return TensorList([e1 - e2 for e1, e2 in zip(self, other)]) return TensorList([e - other for e in self]) def __rsub__(self, other): if TensorList._iterable(other): return TensorList([e2 - e1 for e1, e2 in zip(self, other)]) return TensorList([other - e for e in self]) def __isub__(self, other): if TensorList._iterable(other): for i, e2 in enumerate(other): self[i] -= e2 else: for i in range(len(self)): self[i] -= other return self def __mul__(self, other): if TensorList._iterable(other): return TensorList([e1 * e2 for e1, e2 in zip(self, other)]) return TensorList([e * other for e in self]) def __rmul__(self, other): if TensorList._iterable(other): return TensorList([e2 * e1 for e1, e2 in zip(self, other)]) return TensorList([other * e for e in self]) def __imul__(self, other): if TensorList._iterable(other): for i, e2 in enumerate(other): self[i] *= e2 else: for i in range(len(self)): self[i] *= other return self def __truediv__(self, other): if TensorList._iterable(other): return TensorList([e1 / e2 for e1, e2 in zip(self, other)]) return TensorList([e / other for e in self]) def __rtruediv__(self, other): if TensorList._iterable(other): return TensorList([e2 / e1 for e1, e2 in zip(self, other)]) return TensorList([other / e for e in self]) def __itruediv__(self, other): if TensorList._iterable(other): for i, e2 in enumerate(other): self[i] /= e2 else: for i in range(len(self)): self[i] /= other return self def __matmul__(self, other): if TensorList._iterable(other): return TensorList([e1 @ e2 for e1, e2 in zip(self, other)]) return TensorList([e @ other for e in self]) def __rmatmul__(self, other): if TensorList._iterable(other): return TensorList([e2 @ e1 for e1, e2 in zip(self, other)]) return TensorList([other @ e for e in self]) def __imatmul__(self, other): if TensorList._iterable(other): for i, e2 in enumerate(other): self[i] @= e2 else: for i in range(len(self)): self[i] @= other return self def __mod__(self, other): if TensorList._iterable(other): return TensorList([e1 % e2 for e1, e2 in zip(self, other)]) return TensorList([e % other for e in self]) def __rmod__(self, other): if TensorList._iterable(other): return TensorList([e2 % e1 for e1, e2 in zip(self, other)]) return TensorList([other % e for e in self]) def __pos__(self): return TensorList([+e for e in self]) def __neg__(self): return TensorList([-e for e in self]) def __le__(self, other): if TensorList._iterable(other): return TensorList([e1 <= e2 for e1, e2 in zip(self, other)]) return TensorList([e <= other for e in self]) def __ge__(self, other): if TensorList._iterable(other): return TensorList([e1 >= e2 for e1, e2 in zip(self, other)]) return TensorList([e >= other for e in self]) def concat(self, other): return TensorList(super(TensorList, self).__add__(other)) def copy(self): return TensorList(super(TensorList, self).copy()) def unroll(self): if not any(isinstance(t, TensorList) for t in self): return self new_list = TensorList() for t in self: if isinstance(t, TensorList): new_list.extend(t.unroll()) else: new_list.append(t) return new_list def list(self): return list(self) def attribute(self, attr: str, *args): return TensorList([getattr(e, attr, *args) for e in self]) def apply(self, fn): return TensorList([fn(e) for e in self]) def __getattr__(self, name): if not hasattr(torch.Tensor, name): raise AttributeError('\'TensorList\' object has not attribute \'{}\''.format(name)) def apply_attr(*args, **kwargs): return TensorList([getattr(e, name)(*args, **kwargs) for e in self]) return apply_attr @staticmethod def _iterable(a): return isinstance(a, (TensorList, list)) def tensor_operation(op): def islist(a): return isinstance(a, TensorList) @functools.wraps(op) def oplist(*args, **kwargs): if len(args) == 0: raise ValueError('Must be at least one argument without keyword (i.e. operand).') if len(args) == 1: if islist(args[0]): return TensorList([op(a, **kwargs) for a in args[0]]) else: # Multiple operands, assume max two if islist(args[0]) and islist(args[1]): return TensorList([op(a, b, *args[2:], **kwargs) for a, b in zip(*args[:2])]) if islist(args[0]): return TensorList([op(a, *args[1:], **kwargs) for a in args[0]]) if islist(args[1]): return TensorList([op(args[0], b, *args[2:], **kwargs) for b in args[1]]) # None of the operands are lists return op(*args, **kwargs) return oplist ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/parameter/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/parameter/atom/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/parameter/atom/atom_gmm_sampl.py ================================================ from pytracking.utils import TrackerParams, FeatureParams, Choice from pytracking.features.extractor import MultiResolutionExtractor from pytracking.features import deep import torch def parameters(): params = TrackerParams() # These are usually set from outside params.debug = 0 # Debug level params.visualization = False # Do visualization # Use GPU or not (IoUNet requires this to be True) params.use_gpu = True # Feature specific parameters deep_params = TrackerParams() # Patch sampling parameters params.max_image_sample_size = (18*16)**2 # Maximum image sample size params.min_image_sample_size = (18*16)**2 # Minimum image sample size params.search_area_scale = 5 # Scale relative to target size params.feature_size_odd = False # Good to use False for even-sized kernels and vice versa # Optimization parameters params.CG_iter = 5 # The number of Conjugate Gradient iterations in each update after the first frame params.init_CG_iter = 60 # The total number of Conjugate Gradient iterations used in the first frame params.init_GN_iter = 6 # The number of Gauss-Newton iterations used in the first frame (only if the projection matrix is updated) params.post_init_CG_iter = 0 # CG iterations to run after GN params.fletcher_reeves = False # Use the Fletcher-Reeves (true) or Polak-Ribiere (false) formula in the Conjugate Gradient params.standard_alpha = True # Use the standard formula for computing the step length in Conjugate Gradient params.CG_forgetting_rate = None # Forgetting rate of the last conjugate direction # Learning parameters for each feature type deep_params.learning_rate = 0.01 # Learning rate deep_params.init_samples_minimum_weight = 0.25 # Minimum weight of initial samples in memory deep_params.output_sigma_factor = 1/4 # Standard deviation of Gaussian label relative to target size # Training parameters params.sample_memory_size = 250 # Memory size params.train_skipping = 10 # How often to run training (every n-th frame) # Online model parameters deep_params.kernel_size = (4,4) # Kernel size of filter deep_params.compressed_dim = 64 # Dimension output of projection matrix deep_params.filter_reg = 1e-1 # Filter regularization factor deep_params.projection_reg = 1e-4 # Projection regularization factor # Windowing params.feature_window = False # Perform windowing of features params.window_output = False # Perform windowing of output scores # Detection parameters params.scale_factors = torch.ones(1) # What scales to use for localization (only one scale if IoUNet is used) params.score_upsample_factor = 1 # How much Fourier upsampling to use # Init data augmentation parameters params.augmentation = {'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45,-45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3,1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6,-0.6)], 'dropout': (7, 0.2)} params.augmentation_expansion_factor = 2 # How much to expand sample when doing augmentation params.random_shift_factor = 1/3 # How much random shift to do on each augmented sample deep_params.use_augmentation = True # Whether to use augmentation for this feature # Factorized convolution parameters # params.use_projection_matrix = True # Use projection matrix, i.e. use the factorized convolution formulation params.update_projection_matrix = True # Whether the projection matrix should be optimized or not params.proj_init_method = 'randn' # Method for initializing the projection matrix params.filter_init_method = 'randn' # Method for initializing the spatial filter params.projection_activation = 'none' # Activation function after projection ('none', 'relu', 'elu' or 'mlu') params.response_activation = ('mlu', 0.05) # Activation function on the output scores ('none', 'relu', 'elu' or 'mlu') # Advanced localization parameters params.advanced_localization = True # Use this or not params.target_not_found_threshold = 0.25 # Absolute score threshold to detect target missing params.distractor_threshold = 0.8 # Relative threshold to find distractors params.hard_negative_threshold = 0.5 # Relative threshold to find hard negative samples params.target_neighborhood_scale = 2.2 # Target neighborhood to remove params.dispalcement_scale = 0.8 # Dispacement to consider for distractors params.hard_negative_learning_rate = 0.02 # Learning rate if hard negative detected params.hard_negative_CG_iter = 5 # Number of optimization iterations to use if hard negative detected params.update_scale_when_uncertain = True # Update scale or not if distractor is close # IoUNet parameters params.use_iou_net = True # Use IoU net or not params.box_refinement_space = 'relative' params.iounet_augmentation = False # Use the augmented samples to compute the modulation vector params.iounet_k = 3 # Top-k average to estimate final box params.num_init_random_boxes = 9 # Num extra random boxes in addition to the classifier prediction params.box_jitter_pos = 0.1 # How much to jitter the translation for random boxes params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes params.maximal_aspect_ratio = 6 # Limit on the aspect ratio params.box_refinement_iter = 10 # Number of iterations for refining the boxes params.box_refinement_step_length = (1e-2, 5e-2) # 1 # Gradient step length in the bounding box refinement 5e-3 2e-2 params.box_refinement_step_decay = 1 # Multiplicative step length decay (1 means no decay) # Setup the feature extractor (which includes the IoUNet) deep_fparams = FeatureParams(feature_params=[deep_params]) deep_feat = deep.ATOMResNet18(net_path='atom_gmm_sampl', output_layers=['layer3'], fparams=deep_fparams, normalize_power=2) params.features = MultiResolutionExtractor([deep_feat]) return params ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/parameter/atom/atom_prob_ml.py ================================================ from pytracking.utils import TrackerParams, FeatureParams, Choice from pytracking.features.extractor import MultiResolutionExtractor from pytracking.features import deep import torch def parameters(): params = TrackerParams() # These are usually set from outside params.debug = 0 # Debug level params.visualization = False # Do visualization # Use GPU or not (IoUNet requires this to be True) params.use_gpu = True # Feature specific parameters deep_params = TrackerParams() # Patch sampling parameters params.max_image_sample_size = (18*16)**2 # Maximum image sample size params.min_image_sample_size = (18*16)**2 # Minimum image sample size params.search_area_scale = 5 # Scale relative to target size params.feature_size_odd = False # Good to use False for even-sized kernels and vice versa # Optimization parameters params.CG_iter = 5 # The number of Conjugate Gradient iterations in each update after the first frame params.init_CG_iter = 60 # The total number of Conjugate Gradient iterations used in the first frame params.init_GN_iter = 6 # The number of Gauss-Newton iterations used in the first frame (only if the projection matrix is updated) params.post_init_CG_iter = 0 # CG iterations to run after GN params.fletcher_reeves = False # Use the Fletcher-Reeves (true) or Polak-Ribiere (false) formula in the Conjugate Gradient params.standard_alpha = True # Use the standard formula for computing the step length in Conjugate Gradient params.CG_forgetting_rate = None # Forgetting rate of the last conjugate direction # Learning parameters for each feature type deep_params.learning_rate = 0.01 # Learning rate deep_params.init_samples_minimum_weight = 0.25 # Minimum weight of initial samples in memory deep_params.output_sigma_factor = 1/4 # Standard deviation of Gaussian label relative to target size # Training parameters params.sample_memory_size = 250 # Memory size params.train_skipping = 10 # How often to run training (every n-th frame) # Online model parameters deep_params.kernel_size = (4,4) # Kernel size of filter deep_params.compressed_dim = 64 # Dimension output of projection matrix deep_params.filter_reg = 1e-1 # Filter regularization factor deep_params.projection_reg = 1e-4 # Projection regularization factor # Windowing params.feature_window = False # Perform windowing of features params.window_output = False # Perform windowing of output scores # Detection parameters params.scale_factors = torch.ones(1) # What scales to use for localization (only one scale if IoUNet is used) params.score_upsample_factor = 1 # How much Fourier upsampling to use # Init data augmentation parameters params.augmentation = {'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45,-45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3,1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6,-0.6)], 'dropout': (7, 0.2)} params.augmentation_expansion_factor = 2 # How much to expand sample when doing augmentation params.random_shift_factor = 1/3 # How much random shift to do on each augmented sample deep_params.use_augmentation = True # Whether to use augmentation for this feature # Factorized convolution parameters # params.use_projection_matrix = True # Use projection matrix, i.e. use the factorized convolution formulation params.update_projection_matrix = True # Whether the projection matrix should be optimized or not params.proj_init_method = 'randn' # Method for initializing the projection matrix params.filter_init_method = 'randn' # Method for initializing the spatial filter params.projection_activation = 'none' # Activation function after projection ('none', 'relu', 'elu' or 'mlu') params.response_activation = ('mlu', 0.05) # Activation function on the output scores ('none', 'relu', 'elu' or 'mlu') # Advanced localization parameters params.advanced_localization = True # Use this or not params.target_not_found_threshold = 0.25 # Absolute score threshold to detect target missing params.distractor_threshold = 0.8 # Relative threshold to find distractors params.hard_negative_threshold = 0.5 # Relative threshold to find hard negative samples params.target_neighborhood_scale = 2.2 # Target neighborhood to remove params.dispalcement_scale = 0.8 # Dispacement to consider for distractors params.hard_negative_learning_rate = 0.02 # Learning rate if hard negative detected params.hard_negative_CG_iter = 5 # Number of optimization iterations to use if hard negative detected params.update_scale_when_uncertain = True # Update scale or not if distractor is close # IoUNet parameters params.use_iou_net = True # Use IoU net or not params.box_refinement_space = 'relative' params.iounet_augmentation = False # Use the augmented samples to compute the modulation vector params.iounet_k = 3 # Top-k average to estimate final box params.num_init_random_boxes = 9 # Num extra random boxes in addition to the classifier prediction params.box_jitter_pos = 0.1 # How much to jitter the translation for random boxes params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes params.maximal_aspect_ratio = 6 # Limit on the aspect ratio params.box_refinement_iter = 10 # Number of iterations for refining the boxes params.box_refinement_step_length = (2e-4, 10e-4) # 1 # Gradient step length in the bounding box refinement params.box_refinement_step_decay = 1 # Multiplicative step length decay (1 means no decay) # Setup the feature extractor (which includes the IoUNet) deep_fparams = FeatureParams(feature_params=[deep_params]) deep_feat = deep.ATOMResNet18(net_path='atom_prob_ml', output_layers=['layer3'], fparams=deep_fparams, normalize_power=2) params.features = MultiResolutionExtractor([deep_feat]) return params ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/parameter/atom/default.py ================================================ from pytracking.utils import TrackerParams, FeatureParams, Choice from pytracking.features.extractor import MultiResolutionExtractor from pytracking.features import deep import torch def parameters(): params = TrackerParams() # These are usually set from outside params.debug = 0 # Debug level params.visualization = False # Do visualization # Use GPU or not (IoUNet requires this to be True) params.use_gpu = True # Feature specific parameters deep_params = TrackerParams() # Patch sampling parameters params.max_image_sample_size = (18*16)**2 # Maximum image sample size params.min_image_sample_size = (18*16)**2 # Minimum image sample size params.search_area_scale = 5 # Scale relative to target size params.feature_size_odd = False # Good to use False for even-sized kernels and vice versa # Optimization parameters params.CG_iter = 5 # The number of Conjugate Gradient iterations in each update after the first frame params.init_CG_iter = 60 # The total number of Conjugate Gradient iterations used in the first frame params.init_GN_iter = 6 # The number of Gauss-Newton iterations used in the first frame (only if the projection matrix is updated) params.post_init_CG_iter = 0 # CG iterations to run after GN params.fletcher_reeves = False # Use the Fletcher-Reeves (true) or Polak-Ribiere (false) formula in the Conjugate Gradient params.standard_alpha = True # Use the standard formula for computing the step length in Conjugate Gradient params.CG_forgetting_rate = None # Forgetting rate of the last conjugate direction # Learning parameters for each feature type deep_params.learning_rate = 0.01 # Learning rate deep_params.init_samples_minimum_weight = 0.25 # Minimum weight of initial samples in memory deep_params.output_sigma_factor = 1/4 # Standard deviation of Gaussian label relative to target size # Training parameters params.sample_memory_size = 250 # Memory size params.train_skipping = 10 # How often to run training (every n-th frame) # Online model parameters deep_params.kernel_size = (4,4) # Kernel size of filter deep_params.compressed_dim = 64 # Dimension output of projection matrix deep_params.filter_reg = 1e-1 # Filter regularization factor deep_params.projection_reg = 1e-4 # Projection regularization factor # Windowing params.feature_window = False # Perform windowing of features params.window_output = False # Perform windowing of output scores # Detection parameters params.scale_factors = torch.ones(1) # What scales to use for localization (only one scale if IoUNet is used) params.score_upsample_factor = 1 # How much Fourier upsampling to use # Init data augmentation parameters params.augmentation = {'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45,-45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3,1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6,-0.6)], 'dropout': (7, 0.2)} params.augmentation_expansion_factor = 2 # How much to expand sample when doing augmentation params.random_shift_factor = 1/3 # How much random shift to do on each augmented sample deep_params.use_augmentation = True # Whether to use augmentation for this feature # Factorized convolution parameters # params.use_projection_matrix = True # Use projection matrix, i.e. use the factorized convolution formulation params.update_projection_matrix = True # Whether the projection matrix should be optimized or not params.proj_init_method = 'randn' # Method for initializing the projection matrix params.filter_init_method = 'randn' # Method for initializing the spatial filter params.projection_activation = 'none' # Activation function after projection ('none', 'relu', 'elu' or 'mlu') params.response_activation = ('mlu', 0.05) # Activation function on the output scores ('none', 'relu', 'elu' or 'mlu') # Advanced localization parameters params.advanced_localization = True # Use this or not params.target_not_found_threshold = 0.25 # Absolute score threshold to detect target missing params.distractor_threshold = 0.8 # Relative threshold to find distractors params.hard_negative_threshold = 0.5 # Relative threshold to find hard negative samples params.target_neighborhood_scale = 2.2 # Target neighborhood to remove params.dispalcement_scale = 0.8 # Dispacement to consider for distractors params.hard_negative_learning_rate = 0.02 # Learning rate if hard negative detected params.hard_negative_CG_iter = 5 # Number of optimization iterations to use if hard negative detected params.update_scale_when_uncertain = True # Update scale or not if distractor is close # IoUNet parameters params.use_iou_net = True # Use IoU net or not params.iounet_augmentation = False # Use the augmented samples to compute the modulation vector params.iounet_k = 3 # Top-k average to estimate final box params.num_init_random_boxes = 9 # Num extra random boxes in addition to the classifier prediction params.box_jitter_pos = 0.1 # How much to jitter the translation for random boxes params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes params.maximal_aspect_ratio = 6 # Limit on the aspect ratio params.box_refinement_iter = 5 # Number of iterations for refining the boxes params.box_refinement_step_length = 1 # Gradient step length in the bounding box refinement params.box_refinement_step_decay = 1 # Multiplicative step length decay (1 means no decay) # Setup the feature extractor (which includes the IoUNet) deep_fparams = FeatureParams(feature_params=[deep_params]) deep_feat = deep.ATOMResNet18(net_path='atom_default.pth', output_layers=['layer3'], fparams=deep_fparams, normalize_power=2) params.features = MultiResolutionExtractor([deep_feat]) return params ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/parameter/atom/default_vot.py ================================================ from pytracking.utils import TrackerParams, FeatureParams, Choice from pytracking.features.extractor import MultiResolutionExtractor from pytracking.features import deep import torch def parameters(): params = TrackerParams() # These are usually set from outside params.debug = 0 # Debug level params.visualization = False # Do visualization # Use GPU or not (IoUNet requires this to be True) params.use_gpu = True # Feature specific parameters deep_params = TrackerParams() # Patch sampling parameters params.max_image_sample_size = (14 * 16) ** 2 # Maximum image sample size params.min_image_sample_size = (14 * 16) ** 2 # Minimum image sample size params.search_area_scale = 4 # Scale relative to target size params.feature_size_odd = False # Good to use False for even-sized kernels and vice versa # Optimization parameters params.CG_iter = 5 # The number of Conjugate Gradient iterations in each update after the first frame params.init_CG_iter = 60 # The total number of Conjugate Gradient iterations used in the first frame params.init_GN_iter = 6 # The number of Gauss-Newton iterations used in the first frame (only if the projection matrix is updated) params.post_init_CG_iter = 0 # CG iterations to run after GN params.fletcher_reeves = False # Use the Fletcher-Reeves (true) or Polak-Ribiere (false) formula in the Conjugate Gradient params.standard_alpha = True # Use the standard formula for computing the step length in Conjugate Gradient params.CG_forgetting_rate = None # Forgetting rate of the last conjugate direction # Learning parameters for each feature type deep_params.learning_rate = 0.0075 # Learning rate deep_params.output_sigma_factor = 1/4 # Standard deviation of Gaussian label relative to target size # Training parameters params.sample_memory_size = 250 # Memory size params.train_skipping = 10 # How often to run training (every n-th frame) # Online model parameters deep_params.kernel_size = (4, 4) # Kernel size of filter deep_params.compressed_dim = 64 # Dimension output of projection matrix deep_params.filter_reg = 1e-1 # Filter regularization factor deep_params.projection_reg = 1e-4 # Projection regularization factor # Windowing params.feature_window = False # Perform windowing of features params.window_output = True # Perform windowing of output scores # Detection parameters params.scale_factors = torch.ones(1) # What scales to use for localization (only one scale if IoUNet is used) params.score_upsample_factor = 1 # How much Fourier upsampling to use # Init data augmentation parameters params.augmentation = {'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45,-45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3,1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6,-0.6)], 'dropout': (7, 0.2)} params.augmentation_expansion_factor = 2 # How much to expand sample when doing augmentation params.random_shift_factor = 1 / 3 # How much random shift to do on each augmented sample deep_params.use_augmentation = True # Whether to use augmentation for this feature # Factorized convolution parameters # params.use_projection_matrix = True # Use projection matrix, i.e. use the factorized convolution formulation params.update_projection_matrix = True # Whether the projection matrix should be optimized or not params.proj_init_method = 'randn' # Method for initializing the projection matrix params.filter_init_method = 'randn' # Method for initializing the spatial filter params.projection_activation = 'none' # Activation function after projection ('none', 'relu', 'elu' or 'mlu') params.response_activation = ('mlu', 0.05) # Activation function on the output scores ('none', 'relu', 'elu' or 'mlu') # Advanced localization parameters params.advanced_localization = True # Use this or not params.target_not_found_threshold = -1 # Absolute score threshold to detect target missing params.distractor_threshold = 100 # Relative threshold to find distractors params.hard_negative_threshold = 0.3 # Relative threshold to find hard negative samples params.target_neighborhood_scale = 2.2 # Target neighborhood to remove params.dispalcement_scale = 0.7 # Dispacement to consider for distractors params.hard_negative_learning_rate = 0.02 # Learning rate if hard negative detected params.hard_negative_CG_iter = 5 # Number of optimization iterations to use if hard negative detected params.update_scale_when_uncertain = True # Update scale or not if distractor is close # IoUNet parameters params.iounet_augmentation = False # Use the augmented samples to compute the modulation vector params.iounet_k = 3 # Top-k average to estimate final box params.num_init_random_boxes = 9 # Num extra random boxes in addition to the classifier prediction params.box_jitter_pos = 0.1 # How much to jitter the translation for random boxes params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes params.maximal_aspect_ratio = 6 # Limit on the aspect ratio params.box_refinement_iter = 5 # Number of iterations for refining the boxes params.box_refinement_step_length = 1 # Gradient step length in the bounding box refinement params.box_refinement_step_decay = 1 # Multiplicative step length decay (1 means no decay) # Setup the feature extractor (which includes the IoUNet) deep_fparams = FeatureParams(feature_params=[deep_params]) deep_feat = deep.ATOMResNet18(net_path='atom_default.pth', output_layers=['layer3'], fparams=deep_fparams, normalize_power=2) params.features = MultiResolutionExtractor([deep_feat]) params.vot_anno_conversion_type = 'preserve_area' return params ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/parameter/atom/multiscale_no_iounet.py ================================================ from pytracking.utils import TrackerParams, FeatureParams, Choice from pytracking.features.extractor import MultiResolutionExtractor from pytracking.features import deep import torch def parameters(): params = TrackerParams() # These are usually set from outside params.debug = 0 # Debug level params.visualization = False # Do visualization # Use GPU or not (IoUNet requires this to be True) params.use_gpu = True # Feature specific parameters deep_params = TrackerParams() # Patch sampling parameters params.max_image_sample_size = (18*16)**2 # Maximum image sample size params.min_image_sample_size = (18*16)**2 # Minimum image sample size params.search_area_scale = 5 # Scale relative to target size params.feature_size_odd = False # Good to use False for even-sized kernels and vice versa # Optimization parameters params.CG_iter = 5 # The number of Conjugate Gradient iterations in each update after the first frame params.init_CG_iter = 60 # The total number of Conjugate Gradient iterations used in the first frame params.init_GN_iter = 6 # The number of Gauss-Newton iterations used in the first frame (only if the projection matrix is updated) params.post_init_CG_iter = 0 # CG iterations to run after GN params.fletcher_reeves = False # Use the Fletcher-Reeves (true) or Polak-Ribiere (false) formula in the Conjugate Gradient params.standard_alpha = True # Use the standard formula for computing the step length in Conjugate Gradient params.CG_forgetting_rate = None # Forgetting rate of the last conjugate direction # Learning parameters for each feature type deep_params.learning_rate = 0.01 # Learning rate deep_params.init_samples_minimum_weight = 0.25 # Minimum weight of initial samples in memory deep_params.output_sigma_factor = 1/4 # Standard deviation of Gaussian label relative to target size # Training parameters params.sample_memory_size = 250 # Memory size params.train_skipping = 10 # How often to run training (every n-th frame) # Online model parameters deep_params.kernel_size = (4,4) # Kernel size of filter deep_params.compressed_dim = 64 # Dimension output of projection matrix deep_params.filter_reg = 1e-1 # Filter regularization factor deep_params.projection_reg = 1e-4 # Projection regularization factor # Windowing params.feature_window = False # Perform windowing of features params.window_output = False # Perform windowing of output scores # Detection parameters params.scale_factors = 1.02**torch.arange(-2, 3).float() # What scales to use for localization (only one scale if IoUNet is used) params.score_upsample_factor = 1 # How much Fourier upsampling to use # Init data augmentation parameters params.augmentation = {'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45,-45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3,1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6,-0.6)], 'dropout': (7, 0.2)} params.augmentation_expansion_factor = 2 # How much to expand sample when doing augmentation params.random_shift_factor = 1/3 # How much random shift to do on each augmented sample deep_params.use_augmentation = True # Whether to use augmentation for this feature # Factorized convolution parameters # params.use_projection_matrix = True # Use projection matrix, i.e. use the factorized convolution formulation params.update_projection_matrix = True # Whether the projection matrix should be optimized or not params.proj_init_method = 'randn' # Method for initializing the projection matrix params.filter_init_method = 'randn' # Method for initializing the spatial filter params.projection_activation = 'none' # Activation function after projection ('none', 'relu', 'elu' or 'mlu') params.response_activation = ('mlu', 0.05) # Activation function on the output scores ('none', 'relu', 'elu' or 'mlu') # Advanced localization parameters params.advanced_localization = True # Use this or not params.target_not_found_threshold = 0.25 # Absolute score threshold to detect target missing params.distractor_threshold = 0.8 # Relative threshold to find distractors params.hard_negative_threshold = 0.5 # Relative threshold to find hard negative samples params.target_neighborhood_scale = 2.2 # Target neighborhood to remove params.dispalcement_scale = 0.8 # Dispacement to consider for distractors params.hard_negative_learning_rate = 0.02 # Learning rate if hard negative detected params.hard_negative_CG_iter = 5 # Number of optimization iterations to use if hard negative detected params.update_scale_when_uncertain = True # Update scale or not if distractor is close # IoUNet parameters params.use_iou_net = False # Use IoU net or not params.iounet_augmentation = False # Use the augmented samples to compute the modulation vector params.iounet_k = 3 # Top-k average to estimate final box params.num_init_random_boxes = 9 # Num extra random boxes in addition to the classifier prediction params.box_jitter_pos = 0.1 # How much to jitter the translation for random boxes params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes params.maximal_aspect_ratio = 6 # Limit on the aspect ratio params.box_refinement_iter = 5 # Number of iterations for refining the boxes params.box_refinement_step_length = 1 # Gradient step length in the bounding box refinement params.box_refinement_step_decay = 1 # Multiplicative step length decay (1 means no decay) # Setup the feature extractor (which includes the IoUNet) deep_fparams = FeatureParams(feature_params=[deep_params]) deep_feat = deep.ATOMResNet18(net_path='atom_default.pth', output_layers=['layer3'], fparams=deep_fparams, normalize_power=2) params.features = MultiResolutionExtractor([deep_feat]) return params ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/parameter/dimp/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/parameter/dimp/dimp18.py ================================================ from pytracking.utils import TrackerParams from pytracking.features.net_wrappers import NetWithBackbone def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.image_sample_size = 18*16 params.search_area_scale = 5 # Learning parameters params.sample_memory_size = 50 params.learning_rate = 0.01 params.init_samples_minimum_weight = 0.25 params.train_skipping = 20 # Net optimization params params.update_classifier = True params.net_opt_iter = 10 params.net_opt_update_iter = 2 params.net_opt_hn_iter = 1 # Detection parameters params.window_output = False # Init augmentation parameters params.use_augmentation = True params.augmentation = {'fliplr': True, 'rotate': [10, -10, 45, -45], 'blur': [(3,1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6,-0.6)], 'dropout': (2, 0.2)} params.augmentation_expansion_factor = 2 params.random_shift_factor = 1/3 # Advanced localization parameters params.advanced_localization = True params.target_not_found_threshold = 0.25 params.distractor_threshold = 0.8 params.hard_negative_threshold = 0.5 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.8 params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True # IoUnet parameters params.iounet_augmentation = False params.iounet_use_log_scale = True params.iounet_k = 3 params.num_init_random_boxes = 9 params.box_jitter_pos = 0.1 params.box_jitter_sz = 0.5 params.maximal_aspect_ratio = 6 params.box_refinement_iter = 5 params.box_refinement_step_length = 1 params.box_refinement_step_decay = 1 params.net = NetWithBackbone(net_path='dimp18.pth', use_gpu=params.use_gpu) params.vot_anno_conversion_type = 'preserve_area' return params ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/parameter/dimp/dimp18_vot.py ================================================ from pytracking.utils import TrackerParams from pytracking.features.net_wrappers import NetWithBackbone def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.image_sample_size = 14 * 16 params.search_area_scale = 4 params.feature_size_odd = False # Learning parameters params.sample_memory_size = 250 params.learning_rate = 0.0075 params.init_samples_minimum_weight = 0.0 params.train_skipping = 10 # Net optimization params params.update_classifier = True params.net_opt_iter = 25 params.net_opt_update_iter = 3 params.net_opt_hn_iter = 3 # Detection parameters params.window_output = True # Init augmentation parameters params.use_augmentation = True params.augmentation = {'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45, -45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3, 1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6, -0.6)], 'dropout': (7, 0.2)} params.augmentation_expansion_factor = 2 params.random_shift_factor = 1/3 # Advanced localization parameters params.advanced_localization = True params.target_not_found_threshold = 0.0 params.distractor_threshold = 100 params.hard_negative_threshold = 0.45 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.7 params.perform_hn_without_windowing = True params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True # IoUnet parameters params.iounet_augmentation = False params.iounet_use_log_scale = True params.iounet_k = 3 params.num_init_random_boxes = 9 params.box_jitter_pos = 0.1 params.box_jitter_sz = 0.5 params.maximal_aspect_ratio = 6 params.box_refinement_iter = 5 params.box_refinement_step_length = 1 params.box_refinement_step_decay = 1 params.net = NetWithBackbone(net_path='dimp18.pth', use_gpu=params.use_gpu) params.vot_anno_conversion_type = 'preserve_area' return params ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/parameter/dimp/dimp50.py ================================================ from pytracking.utils import TrackerParams from pytracking.features.net_wrappers import NetWithBackbone def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.image_sample_size = 18*16 params.search_area_scale = 5 # Learning parameters params.sample_memory_size = 50 params.learning_rate = 0.01 params.init_samples_minimum_weight = 0.25 params.train_skipping = 20 # Net optimization params params.update_classifier = True params.net_opt_iter = 10 params.net_opt_update_iter = 2 params.net_opt_hn_iter = 1 # Detection parameters params.window_output = False # Init augmentation parameters params.use_augmentation = True params.augmentation = {'fliplr': True, 'rotate': [10, -10, 45, -45], 'blur': [(3,1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6,-0.6)], 'dropout': (2, 0.2)} params.augmentation_expansion_factor = 2 params.random_shift_factor = 1/3 # Advanced localization parameters params.advanced_localization = True params.target_not_found_threshold = 0.25 params.distractor_threshold = 0.8 params.hard_negative_threshold = 0.5 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.8 params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True # IoUnet parameters params.iounet_augmentation = False params.iounet_use_log_scale = True params.iounet_k = 3 params.num_init_random_boxes = 9 params.box_jitter_pos = 0.1 params.box_jitter_sz = 0.5 params.maximal_aspect_ratio = 6 params.box_refinement_iter = 5 params.box_refinement_step_length = 1 params.box_refinement_step_decay = 1 params.net = NetWithBackbone(net_path='dimp50.pth', use_gpu=params.use_gpu) params.vot_anno_conversion_type = 'preserve_area' return params ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/parameter/dimp/dimp50_vot.py ================================================ from pytracking.utils import TrackerParams from pytracking.features.net_wrappers import NetWithBackbone def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.image_sample_size = 14 * 16 params.search_area_scale = 4 # Learning parameters params.sample_memory_size = 250 params.learning_rate = 0.0075 params.init_samples_minimum_weight = 0.0 params.train_skipping = 10 # Net optimization params params.update_classifier = True params.net_opt_iter = 25 params.net_opt_update_iter = 3 params.net_opt_hn_iter = 3 # Detection parameters params.window_output = True # Init augmentation parameters params.use_augmentation = True params.augmentation = {'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45, -45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3, 1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6, -0.6)], 'dropout': (7, 0.2)} params.augmentation_expansion_factor = 2 params.random_shift_factor = 1/3 # Advanced localization parameters params.advanced_localization = True params.target_not_found_threshold = 0.0 params.distractor_threshold = 100 params.hard_negative_threshold = 0.45 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.7 params.perform_hn_without_windowing = True params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True # IoUnet parameters params.iounet_augmentation = False params.iounet_use_log_scale = True params.iounet_k = 3 params.num_init_random_boxes = 9 params.box_jitter_pos = 0.1 params.box_jitter_sz = 0.5 params.maximal_aspect_ratio = 6 params.box_refinement_iter = 5 params.box_refinement_step_length = 1 params.box_refinement_step_decay = 1 params.net = NetWithBackbone(net_path='dimp50.pth', use_gpu=params.use_gpu) params.vot_anno_conversion_type = 'preserve_area' return params ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/parameter/dimp/dimp50_vot19.py ================================================ from pytracking.utils import TrackerParams from pytracking.features.net_wrappers import NetWithBackbone def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.image_sample_size = 16 * 16 params.search_area_scale = 4.5 # Learning parameters params.sample_memory_size = 100 params.learning_rate = 0.0075 params.init_samples_minimum_weight = 0.0 params.train_skipping = 10 # Net optimization params params.update_classifier = True params.net_opt_iter = 15 params.net_opt_update_iter = 2 params.net_opt_hn_iter = 2 # Detection parameters params.window_output = True # Init augmentation parameters params.use_augmentation = True params.augmentation = {'fliplr': True, 'rotate': [-5, 10, -30, 60], 'blur': [(2, 0.2), (1, 3)], 'relativeshift': [(0.6, 0.6), (-0.6, -0.6)], 'dropout': (3, 0.2)} params.augmentation_expansion_factor = 1.4 params.random_shift_factor = 1/3 # Advanced localization parameters params.advanced_localization = True params.target_not_found_threshold = 0.0 params.distractor_threshold = 100 params.hard_negative_threshold = 0.45 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.7 params.perform_hn_without_windowing = True params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True # IoUnet parameters params.iounet_augmentation = False params.iounet_use_log_scale = True params.iounet_k = 3 params.num_init_random_boxes = 9 params.box_jitter_pos = 0.1 params.box_jitter_sz = 0.5 params.maximal_aspect_ratio = 6 params.box_refinement_iter = 3 params.box_refinement_step_length = 1 params.box_refinement_step_decay = 1 params.net = NetWithBackbone(net_path='dimp50.pth', use_gpu=params.use_gpu) params.vot_anno_conversion_type = 'preserve_area' return params ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/parameter/dimp/prdimp18.py ================================================ from pytracking.utils import TrackerParams from pytracking.features.net_wrappers import NetWithBackbone def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.image_sample_size = 18*16 params.search_area_scale = 5 # Learning parameters params.sample_memory_size = 50 params.learning_rate = 0.01 params.init_samples_minimum_weight = 0.25 params.train_skipping = 20 # Net optimization params params.update_classifier = True params.net_opt_iter = 10 params.net_opt_update_iter = 2 params.net_opt_hn_iter = 1 # Detection parameters params.window_output = False # Init augmentation parameters params.use_augmentation = True params.augmentation = {'fliplr': True, 'rotate': [10, -10, 45, -45], 'blur': [(3,1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6,-0.6)], 'dropout': (2, 0.2)} params.augmentation_expansion_factor = 2 params.random_shift_factor = 1/3 # Advanced localization parameters params.advanced_localization = True params.score_preprocess = 'softmax' params.target_not_found_threshold = 0.04 params.distractor_threshold = 0.8 params.hard_negative_threshold = 0.5 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.8 params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True # IoUnet parameters params.box_refinement_space = 'relative' params.iounet_augmentation = False # Use the augmented samples to compute the modulation vector params.iounet_k = 3 # Top-k average to estimate final box params.num_init_random_boxes = 9 # Num extra random boxes in addition to the classifier prediction params.box_jitter_pos = 0.1 # How much to jitter the translation for random boxes params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes params.maximal_aspect_ratio = 6 # Limit on the aspect ratio params.box_refinement_iter = 10 # Number of iterations for refining the boxes params.box_refinement_step_length = 2.5e-3 # 1 # Gradient step length in the bounding box refinement params.box_refinement_step_decay = 1 # Multiplicative step length decay (1 means no decay) params.net = NetWithBackbone(net_path='prdimp18.pth.tar', use_gpu=params.use_gpu) params.vot_anno_conversion_type = 'preserve_area' return params ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/parameter/dimp/prdimp50.py ================================================ from pytracking.utils import TrackerParams from pytracking.features.net_wrappers import NetWithBackbone def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.image_sample_size = 22*16 params.search_area_scale = 6 params.border_mode = 'inside_major' params.patch_max_scale_change = 1.5 # Learning parameters params.sample_memory_size = 50 params.learning_rate = 0.01 params.init_samples_minimum_weight = 0.25 params.train_skipping = 20 # Net optimization params params.update_classifier = True params.net_opt_iter = 10 params.net_opt_update_iter = 2 params.net_opt_hn_iter = 1 # Detection parameters params.window_output = False # Init augmentation parameters params.use_augmentation = True params.augmentation = {'fliplr': True, 'rotate': [10, -10, 45, -45], 'blur': [(3,1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6,-0.6)], 'dropout': (2, 0.2)} params.augmentation_expansion_factor = 2 params.random_shift_factor = 1/3 # Advanced localization parameters params.advanced_localization = True params.score_preprocess = 'softmax' params.target_not_found_threshold = 0.04 params.distractor_threshold = 0.8 params.hard_negative_threshold = 0.5 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.8 params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True # IoUnet parameters params.box_refinement_space = 'relative' params.iounet_augmentation = False # Use the augmented samples to compute the modulation vector params.iounet_k = 3 # Top-k average to estimate final box params.num_init_random_boxes = 9 # Num extra random boxes in addition to the classifier prediction params.box_jitter_pos = 0.1 # How much to jitter the translation for random boxes params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes params.maximal_aspect_ratio = 6 # Limit on the aspect ratio params.box_refinement_iter = 10 # Number of iterations for refining the boxes params.box_refinement_step_length = 2.5e-3 # 1 # Gradient step length in the bounding box refinement params.box_refinement_step_decay = 1 # Multiplicative step length decay (1 means no decay) params.net = NetWithBackbone(net_path='prdimp50.pth.tar', use_gpu=params.use_gpu) params.vot_anno_conversion_type = 'preserve_area' return params ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/parameter/dimp/super_dimp.py ================================================ from pytracking.utils import TrackerParams from pytracking.features.net_wrappers import NetWithBackbone def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.image_sample_size = 22*16 params.search_area_scale = 6 params.border_mode = 'inside_major' params.patch_max_scale_change = 1.5 # Learning parameters params.sample_memory_size = 50 params.learning_rate = 0.01 params.init_samples_minimum_weight = 0.25 params.train_skipping = 20 # Net optimization params params.update_classifier = True params.net_opt_iter = 10 params.net_opt_update_iter = 2 params.net_opt_hn_iter = 1 # Detection parameters params.window_output = False # Init augmentation parameters params.use_augmentation = True params.augmentation = {'fliplr': True, 'rotate': [10, -10, 45, -45], 'blur': [(3,1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6,-0.6)], 'dropout': (2, 0.2)} params.augmentation_expansion_factor = 2 params.random_shift_factor = 1/3 # Advanced localization parameters params.advanced_localization = True params.target_not_found_threshold = 0.25 params.distractor_threshold = 0.8 params.hard_negative_threshold = 0.5 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.8 params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True # IoUnet parameters params.box_refinement_space = 'relative' params.iounet_augmentation = False # Use the augmented samples to compute the modulation vector params.iounet_k = 3 # Top-k average to estimate final box params.num_init_random_boxes = 9 # Num extra random boxes in addition to the classifier prediction params.box_jitter_pos = 0.1 # How much to jitter the translation for random boxes params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes params.maximal_aspect_ratio = 6 # Limit on the aspect ratio params.box_refinement_iter = 10 # Number of iterations for refining the boxes params.box_refinement_step_length = 2.5e-3 # 1 # Gradient step length in the bounding box refinement params.box_refinement_step_decay = 1 # Multiplicative step length decay (1 means no decay) params.net = NetWithBackbone(net_path='super_dimp.pth.tar', use_gpu=params.use_gpu) params.vot_anno_conversion_type = 'preserve_area' return params ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/parameter/eco/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/parameter/eco/default.py ================================================ from pytracking.utils import TrackerParams, FeatureParams from pytracking.features.extractor import MultiResolutionExtractor from pytracking.features import deep import torch def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True # Feature specific parameters shallow_params = TrackerParams() deep_params = TrackerParams() # Patch sampling parameters params.max_image_sample_size = 250**2 # Maximum image sample size params.min_image_sample_size = 200**2 # Minimum image sample size params.search_area_scale = 4.5 # Scale relative to target size # Conjugate Gradient parameters params.CG_iter = 5 # The number of Conjugate Gradient iterations in each update after the first frame params.init_CG_iter = 100 # The total number of Conjugate Gradient iterations used in the first frame params.init_GN_iter = 10 # The number of Gauss-Newton iterations used in the first frame (only if the projection matrix is updated) params.post_init_CG_iter = 0 # CG iterations to run after GN params.fletcher_reeves = False # Use the Fletcher-Reeves (true) or Polak-Ribiere (false) formula in the Conjugate Gradient params.standard_alpha = True # Use the standard formula for computing the step length in Conjugate Gradient params.CG_forgetting_rate = 75 # Forgetting rate of the last conjugate direction params.precond_data_param = 0.3 # Weight of the data term in the preconditioner params.precond_reg_param = 0.15 # Weight of the regularization term in the preconditioner params.precond_proj_param = 35 # Weight of the projection matrix part in the preconditioner # Learning parameters shallow_params.learning_rate = 0.025 deep_params.learning_rate = 0.0075 shallow_params.output_sigma_factor = 1/16 deep_params.output_sigma_factor = 1/4 # Training parameters params.sample_memory_size = 200 # Memory size params.train_skipping = 10 # How often to run training (every n-th frame) # Detection parameters params.scale_factors = 1.02**torch.arange(-2, 3).float() # What scales to use for localization params.score_upsample_factor = 1 # How much Fourier upsampling to use params.score_fusion_strategy = 'weightedsum' # Fusion strategy shallow_params.translation_weight = 0.4 # Weight of this feature deep_params.translation_weight = 1 - shallow_params.translation_weight # Init augmentation parameters params.augmentation = {'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45,-45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3,1), (1, 3), (2, 2)], 'shift': [(6, 6), (-6, 6), (6, -6), (-6,-6)], 'dropout': (7, 0.2)} # Whether to use augmentation for this feature deep_params.use_augmentation = True shallow_params.use_augmentation = True # Factorized convolution parameters # params.use_projection_matrix = True # Use projection matrix, i.e. use the factorized convolution formulation params.update_projection_matrix = True # Whether the projection matrix should be optimized or not # params.proj_init_method = 'pca' # Method for initializing the projection matrix params.projection_reg = 5e-8 # Regularization paremeter of the projection matrix shallow_params.compressed_dim = 16 # Dimension output of projection matrix for shallow features deep_params.compressed_dim = 64 # Dimension output of projection matrix for deep features # Interpolation parameters params.interpolation_method = 'bicubic' # The kind of interpolation kernel params.interpolation_bicubic_a = -0.75 # The parameter for the bicubic interpolation kernel params.interpolation_centering = True # Center the kernel at the feature sample params.interpolation_windowing = False # Do additional windowing on the Fourier coefficients of the kernel # Regularization parameters shallow_params.use_reg_window = True # Use spatial regularization or not shallow_params.reg_window_min = 1e-4 # The minimum value of the regularization window shallow_params.reg_window_edge = 10e-3 # The impact of the spatial regularization shallow_params.reg_window_power = 2 # The degree of the polynomial to use (e.g. 2 is a quadratic window) shallow_params.reg_sparsity_threshold = 0.05 # A relative threshold of which DFT coefficients that should be set to zero deep_params.use_reg_window = True # Use spatial regularization or not deep_params.reg_window_min = 10e-4 # The minimum value of the regularization window deep_params.reg_window_edge = 50e-3 # The impact of the spatial regularization deep_params.reg_window_power = 2 # The degree of the polynomial to use (e.g. 2 is a quadratic window) deep_params.reg_sparsity_threshold = 0.1 # A relative threshold of which DFT coefficients that should be set to zero fparams = FeatureParams(feature_params=[shallow_params, deep_params]) features = deep.ResNet18m1(output_layers=['vggconv1', 'layer3'], use_gpu=params.use_gpu, fparams=fparams, pool_stride=[2, 1], normalize_power=2) params.features = MultiResolutionExtractor([features]) return params ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/tracker/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/tracker/atom/__init__.py ================================================ from .atom import ATOM def get_tracker_class(): return ATOM ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/tracker/atom/atom.py ================================================ from pytracking.tracker.base import BaseTracker import torch import torch.nn.functional as F import torch.nn import math import time from pytracking import dcf, fourier, TensorList, operation from pytracking.features.preprocessing import numpy_to_torch from pytracking.utils.plotting import show_tensor from pytracking.libs.optimization import GaussNewtonCG, ConjugateGradient, GradientDescentL2 from .optim import ConvProblem, FactorizedConvProblem from pytracking.features import augmentation import ltr.data.bounding_box_utils as bbutils class ATOM(BaseTracker): multiobj_mode = 'parallel' def initialize_features(self): if not getattr(self, 'features_initialized', False): self.params.features.initialize() self.features_initialized = True def initialize(self, image, info: dict) -> dict: state = info['init_bbox'] # Initialize some stuff self.frame_num = 1 if not self.params.has('device'): self.params.device = 'cuda' if self.params.use_gpu else 'cpu' # Initialize features self.initialize_features() # Check if image is color self.params.features.set_is_color(image.shape[2] == 3) # Get feature specific params self.fparams = self.params.features.get_fparams('feature_params') tic = time.time() # Get position and size self.pos = torch.Tensor([state[1] + (state[3] - 1)/2, state[0] + (state[2] - 1)/2]) self.target_sz = torch.Tensor([state[3], state[2]]) # Set search area self.target_scale = 1.0 search_area = torch.prod(self.target_sz * self.params.search_area_scale).item() if search_area > self.params.max_image_sample_size: self.target_scale = math.sqrt(search_area / self.params.max_image_sample_size) elif search_area < self.params.min_image_sample_size: self.target_scale = math.sqrt(search_area / self.params.min_image_sample_size) # Check if IoUNet is used self.use_iou_net = self.params.get('use_iou_net', True) # Target size in base scale self.base_target_sz = self.target_sz / self.target_scale # Use odd square search area and set sizes feat_max_stride = max(self.params.features.stride()) if self.params.get('search_area_shape', 'square') == 'square': self.img_sample_sz = torch.round(torch.sqrt(torch.prod(self.base_target_sz * self.params.search_area_scale))) * torch.ones(2) elif self.params.search_area_shape == 'initrect': self.img_sample_sz = torch.round(self.base_target_sz * self.params.search_area_scale) else: raise ValueError('Unknown search area shape') if self.params.feature_size_odd: self.img_sample_sz += feat_max_stride - self.img_sample_sz % (2 * feat_max_stride) else: self.img_sample_sz += feat_max_stride - (self.img_sample_sz + feat_max_stride) % (2 * feat_max_stride) # Set sizes self.img_support_sz = self.img_sample_sz self.feature_sz = self.params.features.size(self.img_sample_sz) self.output_sz = self.params.score_upsample_factor * self.img_support_sz # Interpolated size of the output self.kernel_size = self.fparams.attribute('kernel_size') self.iou_img_sample_sz = self.img_sample_sz # Optimization options self.params.precond_learning_rate = self.fparams.attribute('learning_rate') if self.params.CG_forgetting_rate is None or max(self.params.precond_learning_rate) >= 1: self.params.direction_forget_factor = 0 else: self.params.direction_forget_factor = (1 - max(self.params.precond_learning_rate))**self.params.CG_forgetting_rate self.output_window = None if self.params.get('window_output', False): if self.params.get('use_clipped_window', False): self.output_window = dcf.hann2d_clipped(self.output_sz.long(), self.output_sz.long()*self.params.effective_search_area / self.params.search_area_scale, centered=False).to(self.params.device) else: self.output_window = dcf.hann2d(self.output_sz.long(), centered=False).to(self.params.device) # Initialize some learning things self.init_learning() # Convert image im = numpy_to_torch(image) self.im = im # For debugging only # Setup scale bounds self.image_sz = torch.Tensor([im.shape[2], im.shape[3]]) self.min_scale_factor = torch.max(10 / self.base_target_sz) self.max_scale_factor = torch.min(self.image_sz / self.base_target_sz) # Extract and transform sample x = self.generate_init_samples(im) # Initialize iounet if self.use_iou_net: self.init_iou_net() # Initialize projection matrix self.init_projection_matrix(x) # Transform to get the training sample train_x = self.preprocess_sample(x) # Generate label function init_y = self.init_label_function(train_x) # Init memory self.init_memory(train_x) # Init optimizer and do initial optimization self.init_optimization(train_x, init_y) self.pos_iounet = self.pos.clone() out = {'time': time.time() - tic} return out def init_optimization(self, train_x, init_y): # Initialize filter filter_init_method = self.params.get('filter_init_method', 'zeros') self.filter = TensorList( [x.new_zeros(1, cdim, sz[0], sz[1]) for x, cdim, sz in zip(train_x, self.compressed_dim, self.kernel_size)]) if filter_init_method == 'zeros': pass elif filter_init_method == 'randn': for f in self.filter: f.normal_(0, 1/f.numel()) else: raise ValueError('Unknown "filter_init_method"') # Get parameters self.params.update_projection_matrix = self.params.get('update_projection_matrix', True) and self.params.use_projection_matrix optimizer = self.params.get('optimizer', 'GaussNewtonCG') # Setup factorized joint optimization if self.params.update_projection_matrix: self.joint_problem = FactorizedConvProblem(self.init_training_samples, init_y, self.filter_reg, self.fparams.attribute('projection_reg'), self.params, self.init_sample_weights, self.projection_activation, self.response_activation) # Variable containing both filter and projection matrix joint_var = self.filter.concat(self.projection_matrix) # Initialize optimizer analyze_convergence = self.params.get('analyze_convergence', False) if optimizer == 'GaussNewtonCG': self.joint_optimizer = GaussNewtonCG(self.joint_problem, joint_var, debug=(self.params.debug >= 1), plotting=(self.params.debug >= 3), analyze=analyze_convergence, visdom=self.visdom) elif optimizer == 'GradientDescentL2': self.joint_optimizer = GradientDescentL2(self.joint_problem, joint_var, self.params.optimizer_step_length, self.params.optimizer_momentum, plotting=(self.params.debug >= 3), debug=(self.params.debug >= 1), visdom=self.visdom) # Do joint optimization if isinstance(self.params.init_CG_iter, (list, tuple)): self.joint_optimizer.run(self.params.init_CG_iter) else: self.joint_optimizer.run(self.params.init_CG_iter // self.params.init_GN_iter, self.params.init_GN_iter) if analyze_convergence: opt_name = 'CG' if self.params.get('CG_optimizer', True) else 'GD' for val_name, values in zip(['loss', 'gradient'], [self.joint_optimizer.losses, self.joint_optimizer.gradient_mags]): val_str = ' '.join(['{:.8e}'.format(v.item()) for v in values]) file_name = '{}_{}.txt'.format(opt_name, val_name) with open(file_name, 'a') as f: f.write(val_str + '\n') raise RuntimeError('Exiting') # Re-project samples with the new projection matrix compressed_samples = self.project_sample(self.init_training_samples, self.projection_matrix) for train_samp, init_samp in zip(self.training_samples, compressed_samples): train_samp[:init_samp.shape[0],...] = init_samp self.hinge_mask = None # Initialize optimizer self.conv_problem = ConvProblem(self.training_samples, self.y, self.filter_reg, self.sample_weights, self.response_activation) if optimizer == 'GaussNewtonCG': self.filter_optimizer = ConjugateGradient(self.conv_problem, self.filter, fletcher_reeves=self.params.fletcher_reeves, direction_forget_factor=self.params.direction_forget_factor, debug=(self.params.debug>=1), plotting=(self.params.debug>=3), visdom=self.visdom) elif optimizer == 'GradientDescentL2': self.filter_optimizer = GradientDescentL2(self.conv_problem, self.filter, self.params.optimizer_step_length, self.params.optimizer_momentum, debug=(self.params.debug >= 1), plotting=(self.params.debug>=3), visdom=self.visdom) # Transfer losses from previous optimization if self.params.update_projection_matrix: self.filter_optimizer.residuals = self.joint_optimizer.residuals self.filter_optimizer.losses = self.joint_optimizer.losses if not self.params.update_projection_matrix: self.filter_optimizer.run(self.params.init_CG_iter) # Post optimization self.filter_optimizer.run(self.params.post_init_CG_iter) # Free memory del self.init_training_samples if self.params.use_projection_matrix: del self.joint_problem, self.joint_optimizer def track(self, image, info: dict = None) -> dict: self.debug_info = {} self.frame_num += 1 self.debug_info['frame_num'] = self.frame_num # Convert image im = numpy_to_torch(image) self.im = im # For debugging only # ------- LOCALIZATION ------- # # Get sample sample_pos = self.pos.round() sample_scales = self.target_scale * self.params.scale_factors test_x = self.extract_processed_sample(im, self.pos, sample_scales, self.img_sample_sz) # Compute scores scores_raw = self.apply_filter(test_x) translation_vec, scale_ind, s, flag = self.localize_target(scores_raw) # Update position and scale if flag != 'not_found': if self.use_iou_net: update_scale_flag = self.params.get('update_scale_when_uncertain', True) or flag != 'uncertain' if self.params.get('use_classifier', True): self.update_state(sample_pos + translation_vec) self.refine_target_box(sample_pos, sample_scales[scale_ind], scale_ind, update_scale_flag) elif self.params.get('use_classifier', True): self.update_state(sample_pos + translation_vec, sample_scales[scale_ind]) score_map = s[scale_ind, ...] max_score = torch.max(score_map).item() self.debug_info['max_score'] = max_score self.debug_info['flag'] = flag if self.visdom is not None: self.visdom.register(score_map, 'heatmap', 2, 'Score Map') self.visdom.register(self.debug_info, 'info_dict', 1, 'Status') elif self.params.debug >= 2: show_tensor(score_map, 5, title='Max score = {:.2f}'.format(max_score)) # ------- UPDATE ------- # # Check flags and set learning rate if hard negative update_flag = flag not in ['not_found', 'uncertain'] hard_negative = (flag == 'hard_negative') learning_rate = self.params.hard_negative_learning_rate if hard_negative else None if update_flag: # Get train sample train_x = TensorList([x[scale_ind:scale_ind+1, ...] for x in test_x]) # Create label for sample train_y = self.get_label_function(sample_pos, sample_scales[scale_ind]) # Update memory self.update_memory(train_x, train_y, learning_rate) # Train filter if hard_negative: self.filter_optimizer.run(self.params.hard_negative_CG_iter) elif (self.frame_num-1) % self.params.train_skipping == 0: self.filter_optimizer.run(self.params.CG_iter) # Set the pos of the tracker to iounet pos if self.use_iou_net and flag != 'not_found': self.pos = self.pos_iounet.clone() # Return new state new_state = torch.cat((self.pos[[1,0]] - (self.target_sz[[1,0]]-1)/2, self.target_sz[[1,0]])) out = {'target_bbox': new_state.tolist()} return out def apply_filter(self, sample_x: TensorList): return operation.conv2d(sample_x, self.filter, mode='same') def localize_target(self, scores_raw): # Weighted sum (if multiple features) with interpolation in fourier domain weight = self.fparams.attribute('translation_weight', 1.0) scores_raw = weight * scores_raw sf_weighted = fourier.cfft2(scores_raw) / (scores_raw.size(2) * scores_raw.size(3)) for i, (sz, ksz) in enumerate(zip(self.feature_sz, self.kernel_size)): sf_weighted[i] = fourier.shift_fs(sf_weighted[i], math.pi * (1 - torch.Tensor([ksz[0]%2, ksz[1]%2]) / sz)) scores_fs = fourier.sum_fs(sf_weighted) scores = fourier.sample_fs(scores_fs, self.output_sz) if self.output_window is not None and not self.params.get('perform_hn_without_windowing', False): scores *= self.output_window if self.params.get('advanced_localization', False): return self.localize_advanced(scores) # Get maximum max_score, max_disp = dcf.max2d(scores) _, scale_ind = torch.max(max_score, dim=0) max_disp = max_disp.float().cpu() # Convert to displacements in the base scale disp = (max_disp + self.output_sz / 2) % self.output_sz - self.output_sz / 2 # Compute translation vector and scale change factor translation_vec = disp[scale_ind, ...].view(-1) * (self.img_support_sz / self.output_sz) * self.target_scale translation_vec *= self.params.scale_factors[scale_ind] # Shift the score output for visualization purposes if self.params.debug >= 2: sz = scores.shape[-2:] scores = torch.cat([scores[...,sz[0]//2:,:], scores[...,:sz[0]//2,:]], -2) scores = torch.cat([scores[...,:,sz[1]//2:], scores[...,:,:sz[1]//2]], -1) return translation_vec, scale_ind, scores, None def localize_advanced(self, scores): """Dows the advanced localization with hard negative detection and target not found.""" sz = scores.shape[-2:] if self.output_window is not None and self.params.get('perform_hn_without_windowing', False): scores_orig = scores.clone() scores_orig = torch.cat([scores_orig[..., (sz[0] + 1) // 2:, :], scores_orig[..., :(sz[0] + 1) // 2, :]], -2) scores_orig = torch.cat([scores_orig[..., :, (sz[1] + 1) // 2:], scores_orig[..., :, :(sz[1] + 1) // 2]], -1) scores *= self.output_window # Shift scores back scores = torch.cat([scores[...,(sz[0]+1)//2:,:], scores[...,:(sz[0]+1)//2,:]], -2) scores = torch.cat([scores[...,:,(sz[1]+1)//2:], scores[...,:,:(sz[1]+1)//2]], -1) # Find maximum max_score1, max_disp1 = dcf.max2d(scores) _, scale_ind = torch.max(max_score1, dim=0) max_score1 = max_score1[scale_ind] max_disp1 = max_disp1[scale_ind,...].float().cpu().view(-1) target_disp1 = max_disp1 - self.output_sz // 2 translation_vec1 = target_disp1 * (self.img_support_sz / self.output_sz) * self.target_scale if max_score1.item() < self.params.target_not_found_threshold: return translation_vec1, scale_ind, scores, 'not_found' if self.output_window is not None and self.params.get('perform_hn_without_windowing', False): scores = scores_orig # Mask out target neighborhood target_neigh_sz = self.params.target_neighborhood_scale * self.target_sz / self.target_scale tneigh_top = max(round(max_disp1[0].item() - target_neigh_sz[0].item() / 2), 0) tneigh_bottom = min(round(max_disp1[0].item() + target_neigh_sz[0].item() / 2 + 1), sz[0]) tneigh_left = max(round(max_disp1[1].item() - target_neigh_sz[1].item() / 2), 0) tneigh_right = min(round(max_disp1[1].item() + target_neigh_sz[1].item() / 2 + 1), sz[1]) scores_masked = scores[scale_ind:scale_ind+1,...].clone() scores_masked[...,tneigh_top:tneigh_bottom,tneigh_left:tneigh_right] = 0 # Find new maximum max_score2, max_disp2 = dcf.max2d(scores_masked) max_disp2 = max_disp2.float().cpu().view(-1) target_disp2 = max_disp2 - self.output_sz // 2 translation_vec2 = target_disp2 * (self.img_support_sz / self.output_sz) * self.target_scale # Handle the different cases if max_score2 > self.params.distractor_threshold * max_score1: disp_norm1 = torch.sqrt(torch.sum(target_disp1**2)) disp_norm2 = torch.sqrt(torch.sum(target_disp2**2)) disp_threshold = self.params.dispalcement_scale * math.sqrt(sz[0] * sz[1]) / 2 if disp_norm2 > disp_threshold and disp_norm1 < disp_threshold: return translation_vec1, scale_ind, scores, 'hard_negative' if disp_norm2 < disp_threshold and disp_norm1 > disp_threshold: return translation_vec2, scale_ind, scores, 'hard_negative' if disp_norm2 > disp_threshold and disp_norm1 > disp_threshold: return translation_vec1, scale_ind, scores, 'uncertain' # If also the distractor is close, return with highest score return translation_vec1, scale_ind, scores, 'uncertain' if max_score2 > self.params.hard_negative_threshold * max_score1 and max_score2 > self.params.target_not_found_threshold: return translation_vec1, scale_ind, scores, 'hard_negative' return translation_vec1, scale_ind, scores, None def extract_sample(self, im: torch.Tensor, pos: torch.Tensor, scales, sz: torch.Tensor): return self.params.features.extract(im, pos, scales, sz)[0] def get_iou_features(self): return self.params.features.get_unique_attribute('iounet_features') def get_iou_backbone_features(self): return self.params.features.get_unique_attribute('iounet_backbone_features') def extract_processed_sample(self, im: torch.Tensor, pos: torch.Tensor, scales, sz: torch.Tensor) -> (TensorList, TensorList): x = self.extract_sample(im, pos, scales, sz) return self.preprocess_sample(self.project_sample(x)) def preprocess_sample(self, x: TensorList) -> (TensorList, TensorList): if self.params.get('_feature_window', False): x = x * self.feature_window return x def project_sample(self, x: TensorList, proj_matrix = None): # Apply projection matrix if proj_matrix is None: proj_matrix = self.projection_matrix return operation.conv2d(x, proj_matrix).apply(self.projection_activation) def init_learning(self): # Get window function self.feature_window = TensorList([dcf.hann2d(sz).to(self.params.device) for sz in self.feature_sz]) # Filter regularization self.filter_reg = self.fparams.attribute('filter_reg') # Activation function after the projection matrix (phi_1 in the paper) projection_activation = self.params.get('projection_activation', 'none') if isinstance(projection_activation, tuple): projection_activation, act_param = projection_activation if projection_activation == 'none': self.projection_activation = lambda x: x elif projection_activation == 'relu': self.projection_activation = torch.nn.ReLU(inplace=True) elif projection_activation == 'elu': self.projection_activation = torch.nn.ELU(inplace=True) elif projection_activation == 'mlu': self.projection_activation = lambda x: F.elu(F.leaky_relu(x, 1 / act_param), act_param) else: raise ValueError('Unknown activation') # Activation function after the output scores (phi_2 in the paper) response_activation = self.params.get('response_activation', 'none') if isinstance(response_activation, tuple): response_activation, act_param = response_activation if response_activation == 'none': self.response_activation = lambda x: x elif response_activation == 'relu': self.response_activation = torch.nn.ReLU(inplace=True) elif response_activation == 'elu': self.response_activation = torch.nn.ELU(inplace=True) elif response_activation == 'mlu': self.response_activation = lambda x: F.elu(F.leaky_relu(x, 1 / act_param), act_param) else: raise ValueError('Unknown activation') def generate_init_samples(self, im: torch.Tensor) -> TensorList: """Generate augmented initial samples.""" # Compute augmentation size aug_expansion_factor = self.params.get('augmentation_expansion_factor', None) aug_expansion_sz = self.img_sample_sz.clone() aug_output_sz = None if aug_expansion_factor is not None and aug_expansion_factor != 1: aug_expansion_sz = (self.img_sample_sz * aug_expansion_factor).long() aug_expansion_sz += (aug_expansion_sz - self.img_sample_sz.long()) % 2 aug_expansion_sz = aug_expansion_sz.float() aug_output_sz = self.img_sample_sz.long().tolist() # Random shift operator get_rand_shift = lambda: None random_shift_factor = self.params.get('random_shift_factor', 0) if random_shift_factor > 0: get_rand_shift = lambda: ((torch.rand(2) - 0.5) * self.img_sample_sz * random_shift_factor).long().tolist() # Create transofmations self.transforms = [augmentation.Identity(aug_output_sz)] if 'shift' in self.params.augmentation: self.transforms.extend([augmentation.Translation(shift, aug_output_sz) for shift in self.params.augmentation['shift']]) if 'relativeshift' in self.params.augmentation: get_absolute = lambda shift: (torch.Tensor(shift) * self.img_sample_sz/2).long().tolist() self.transforms.extend([augmentation.Translation(get_absolute(shift), aug_output_sz) for shift in self.params.augmentation['relativeshift']]) if 'fliplr' in self.params.augmentation and self.params.augmentation['fliplr']: self.transforms.append(augmentation.FlipHorizontal(aug_output_sz, get_rand_shift())) if 'blur' in self.params.augmentation: self.transforms.extend([augmentation.Blur(sigma, aug_output_sz, get_rand_shift()) for sigma in self.params.augmentation['blur']]) if 'scale' in self.params.augmentation: self.transforms.extend([augmentation.Scale(scale_factor, aug_output_sz, get_rand_shift()) for scale_factor in self.params.augmentation['scale']]) if 'rotate' in self.params.augmentation: self.transforms.extend([augmentation.Rotate(angle, aug_output_sz, get_rand_shift()) for angle in self.params.augmentation['rotate']]) # Generate initial samples init_samples = self.params.features.extract_transformed(im, self.pos, self.target_scale, aug_expansion_sz, self.transforms) # Remove augmented samples for those that shall not have for i, use_aug in enumerate(self.fparams.attribute('use_augmentation')): if not use_aug: init_samples[i] = init_samples[i][0:1, ...] # Add dropout samples if 'dropout' in self.params.augmentation: num, prob = self.params.augmentation['dropout'] self.transforms.extend(self.transforms[:1]*num) for i, use_aug in enumerate(self.fparams.attribute('use_augmentation')): if use_aug: init_samples[i] = torch.cat([init_samples[i], F.dropout2d(init_samples[i][0:1,...].expand(num,-1,-1,-1), p=prob, training=True)]) return init_samples def init_projection_matrix(self, x): # Set if using projection matrix self.params.use_projection_matrix = self.params.get('use_projection_matrix', True) if self.params.use_projection_matrix: self.compressed_dim = self.fparams.attribute('compressed_dim', None) proj_init_method = self.params.get('proj_init_method', 'pca') if proj_init_method == 'pca': x_mat = TensorList([e.permute(1, 0, 2, 3).reshape(e.shape[1], -1).clone() for e in x]) x_mat -= x_mat.mean(dim=1, keepdim=True) cov_x = x_mat @ x_mat.t() self.projection_matrix = TensorList( [None if cdim is None else torch.svd(C)[0][:, :cdim].t().unsqueeze(-1).unsqueeze(-1).clone() for C, cdim in zip(cov_x, self.compressed_dim)]) elif proj_init_method == 'randn': self.projection_matrix = TensorList( [None if cdim is None else ex.new_zeros(cdim,ex.shape[1],1,1).normal_(0,1/math.sqrt(ex.shape[1])) for ex, cdim in zip(x, self.compressed_dim)]) else: self.compressed_dim = x.size(1) self.projection_matrix = TensorList([None]*len(x)) def init_label_function(self, train_x): # Allocate label function self.y = TensorList([x.new_zeros(self.params.sample_memory_size, 1, x.shape[2], x.shape[3]) for x in train_x]) # Output sigma factor output_sigma_factor = self.fparams.attribute('output_sigma_factor') self.sigma = (self.feature_sz / self.img_support_sz * self.base_target_sz).prod().sqrt() * output_sigma_factor * torch.ones(2) # Center pos in normalized coords target_center_norm = (self.pos - self.pos.round()) / (self.target_scale * self.img_support_sz) # Generate label functions for y, sig, sz, ksz, x in zip(self.y, self.sigma, self.feature_sz, self.kernel_size, train_x): center_pos = sz * target_center_norm + 0.5 * torch.Tensor([(ksz[0] + 1) % 2, (ksz[1] + 1) % 2]) for i, T in enumerate(self.transforms[:x.shape[0]]): sample_center = center_pos + torch.Tensor(T.shift) / self.img_support_sz * sz y[i, 0, ...] = dcf.label_function_spatial(sz, sig, sample_center) # Return only the ones to use for initial training return TensorList([y[:x.shape[0], ...] for y, x in zip(self.y, train_x)]) def init_memory(self, train_x): # Initialize first-frame training samples self.num_init_samples = train_x.size(0) self.init_sample_weights = TensorList([x.new_ones(1) / x.shape[0] for x in train_x]) self.init_training_samples = train_x # Sample counters and weights self.num_stored_samples = self.num_init_samples.copy() self.previous_replace_ind = [None] * len(self.num_stored_samples) self.sample_weights = TensorList([x.new_zeros(self.params.sample_memory_size) for x in train_x]) for sw, init_sw, num in zip(self.sample_weights, self.init_sample_weights, self.num_init_samples): sw[:num] = init_sw # Initialize memory self.training_samples = TensorList( [x.new_zeros(self.params.sample_memory_size, cdim, x.shape[2], x.shape[3]) for x, cdim in zip(train_x, self.compressed_dim)]) def update_memory(self, sample_x: TensorList, sample_y: TensorList, learning_rate = None): replace_ind = self.update_sample_weights(self.sample_weights, self.previous_replace_ind, self.num_stored_samples, self.num_init_samples, self.fparams, learning_rate) self.previous_replace_ind = replace_ind for train_samp, x, ind in zip(self.training_samples, sample_x, replace_ind): train_samp[ind:ind+1,...] = x for y_memory, y, ind in zip(self.y, sample_y, replace_ind): y_memory[ind:ind+1,...] = y if self.hinge_mask is not None: for m, y, ind in zip(self.hinge_mask, sample_y, replace_ind): m[ind:ind+1,...] = (y >= self.params.hinge_threshold).float() self.num_stored_samples += 1 def update_sample_weights(self, sample_weights, previous_replace_ind, num_stored_samples, num_init_samples, fparams, learning_rate = None): # Update weights and get index to replace in memory replace_ind = [] for sw, prev_ind, num_samp, num_init, fpar in zip(sample_weights, previous_replace_ind, num_stored_samples, num_init_samples, fparams): lr = learning_rate if lr is None: lr = fpar.learning_rate init_samp_weight = getattr(fpar, 'init_samples_minimum_weight', None) if init_samp_weight == 0: init_samp_weight = None s_ind = 0 if init_samp_weight is None else num_init if num_samp == 0 or lr == 1: sw[:] = 0 sw[0] = 1 r_ind = 0 else: # Get index to replace _, r_ind = torch.min(sw[s_ind:], 0) r_ind = r_ind.item() + s_ind # Update weights if prev_ind is None: sw /= 1 - lr sw[r_ind] = lr else: sw[r_ind] = sw[prev_ind] / (1 - lr) sw /= sw.sum() if init_samp_weight is not None and sw[:num_init].sum() < init_samp_weight: sw /= init_samp_weight + sw[num_init:].sum() sw[:num_init] = init_samp_weight / num_init replace_ind.append(r_ind) return replace_ind def get_label_function(self, sample_pos, sample_scale): # Generate label function train_y = TensorList() target_center_norm = (self.pos - sample_pos) / (sample_scale * self.img_support_sz) for sig, sz, ksz in zip(self.sigma, self.feature_sz, self.kernel_size): center = sz * target_center_norm + 0.5 * torch.Tensor([(ksz[0] + 1) % 2, (ksz[1] + 1) % 2]) train_y.append(dcf.label_function_spatial(sz, sig, center)) return train_y def update_state(self, new_pos, new_scale = None): # Update scale if new_scale is not None: self.target_scale = new_scale.clamp(self.min_scale_factor, self.max_scale_factor) self.target_sz = self.base_target_sz * self.target_scale # Update pos inside_ratio = 0.2 inside_offset = (inside_ratio - 0.5) * self.target_sz self.pos = torch.max(torch.min(new_pos, self.image_sz - inside_offset), inside_offset) def get_iounet_box(self, pos, sz, sample_pos, sample_scale): """All inputs in original image coordinates""" box_center = (pos - sample_pos) / sample_scale + (self.iou_img_sample_sz - 1) / 2 box_sz = sz / sample_scale target_ul = box_center - (box_sz - 1) / 2 return torch.cat([target_ul.flip((0,)), box_sz.flip((0,))]) def init_iou_net(self): # Setup IoU net self.iou_predictor = self.params.features.get_unique_attribute('iou_predictor') for p in self.iou_predictor.parameters(): p.requires_grad = False # Get target boxes for the different augmentations self.iou_target_box = self.get_iounet_box(self.pos, self.target_sz, self.pos.round(), self.target_scale) target_boxes = TensorList() if self.params.iounet_augmentation: for T in self.transforms: if not isinstance(T, (augmentation.Identity, augmentation.Translation, augmentation.FlipHorizontal, augmentation.FlipVertical, augmentation.Blur)): break target_boxes.append(self.iou_target_box + torch.Tensor([T.shift[1], T.shift[0], 0, 0])) else: target_boxes.append(self.iou_target_box.clone()) target_boxes = torch.cat(target_boxes.view(1,4), 0).to(self.params.device) # Get iou features iou_backbone_features = self.get_iou_backbone_features() # Remove other augmentations such as rotation iou_backbone_features = TensorList([x[:target_boxes.shape[0],...] for x in iou_backbone_features]) # Extract target feat with torch.no_grad(): target_feat = self.iou_predictor.get_modulation(iou_backbone_features, target_boxes) self.target_feat = TensorList([x.detach().mean(0) for x in target_feat]) if self.params.get('iounet_not_use_reference', False): self.target_feat = TensorList([torch.full_like(tf, tf.norm() / tf.numel()) for tf in self.target_feat]) def refine_target_box(self, sample_pos, sample_scale, scale_ind, update_scale = True): # Initial box for refinement init_box = self.get_iounet_box(self.pos, self.target_sz, sample_pos, sample_scale) # Extract features from the relevant scale iou_features = self.get_iou_features() iou_features = TensorList([x[scale_ind:scale_ind+1,...] for x in iou_features]) init_boxes = init_box.view(1,4).clone() if self.params.num_init_random_boxes > 0: # Get random initial boxes square_box_sz = init_box[2:].prod().sqrt() rand_factor = square_box_sz * torch.cat([self.params.box_jitter_pos * torch.ones(2), self.params.box_jitter_sz * torch.ones(2)]) minimal_edge_size = init_box[2:].min()/3 rand_bb = (torch.rand(self.params.num_init_random_boxes, 4) - 0.5) * rand_factor new_sz = (init_box[2:] + rand_bb[:,2:]).clamp(minimal_edge_size) new_center = (init_box[:2] + init_box[2:]/2) + rand_bb[:,:2] init_boxes = torch.cat([new_center - new_sz/2, new_sz], 1) init_boxes = torch.cat([init_box.view(1,4), init_boxes]) # Refine boxes by maximizing iou output_boxes, output_iou = self.optimize_boxes(iou_features, init_boxes) # Remove weird boxes with extreme aspect ratios output_boxes[:, 2:].clamp_(1) aspect_ratio = output_boxes[:,2] / output_boxes[:,3] keep_ind = (aspect_ratio < self.params.maximal_aspect_ratio) * (aspect_ratio > 1/self.params.maximal_aspect_ratio) output_boxes = output_boxes[keep_ind,:] output_iou = output_iou[keep_ind] # If no box found if output_boxes.shape[0] == 0: return # Take average of top k boxes k = self.params.get('iounet_k', 5) topk = min(k, output_boxes.shape[0]) _, inds = torch.topk(output_iou, topk) predicted_box = output_boxes[inds, :].mean(0) predicted_iou = output_iou.view(-1, 1)[inds, :].mean(0) # Update position new_pos = predicted_box[:2] + predicted_box[2:]/2 - (self.iou_img_sample_sz - 1) / 2 new_pos = new_pos.flip((0,)) * sample_scale + sample_pos new_target_sz = predicted_box[2:].flip((0,)) * sample_scale new_scale = torch.sqrt(new_target_sz.prod() / self.base_target_sz.prod()) self.pos_iounet = new_pos.clone() if self.params.get('use_iounet_pos_for_learning', True): self.pos = new_pos.clone() self.target_sz = new_target_sz if update_scale: self.target_scale = new_scale def optimize_boxes(self, iou_features, init_boxes): # Optimize iounet boxes output_boxes = init_boxes.view(1, -1, 4).to(self.params.device) step_length = self.params.box_refinement_step_length init_step_length = self.params.box_refinement_step_length if isinstance(step_length, (tuple, list)): init_step_length = torch.Tensor([step_length[0], step_length[0], step_length[1], step_length[1]]).to( self.params.device).view(1, 1, 4) box_refinement_space = self.params.get('box_refinement_space', 'default') step_length = init_step_length * output_boxes.new_ones(1, output_boxes.shape[1], 1) outputs_prev = -99999999 * output_boxes.new_ones(1, output_boxes.shape[1]) step = torch.zeros_like(output_boxes) if box_refinement_space == 'default': # Optimization using bounding box space used in original IoUNet for i_ in range(self.params.box_refinement_iter): # forward pass bb_init = output_boxes.clone().detach() bb_init.requires_grad = True outputs = self.iou_predictor.predict_iou(self.target_feat, iou_features, bb_init) if isinstance(outputs, (list, tuple)): outputs = outputs[0] outputs.backward(gradient=torch.ones_like(outputs)) # Update mask and step length update_mask = (outputs.detach() > outputs_prev) | (self.params.box_refinement_step_decay >= 1) update_mask_float = update_mask.view(1, -1, 1).float() step_length[~update_mask, :] *= self.params.box_refinement_step_decay outputs_prev = outputs.detach().clone() # Update proposal step = update_mask_float * step_length * bb_init.grad * bb_init[:, :, 2:].repeat(1, 1, 2) - ( 1.0 - update_mask_float) * step output_boxes = bb_init + step output_boxes.detach_() elif box_refinement_space == 'relative': # Optimization using relative bounding box space sz_norm = output_boxes[:, :1, 2:].clone() output_boxes_rel = bbutils.rect_to_rel(output_boxes, sz_norm) for i_ in range(self.params.box_refinement_iter): # forward pass bb_init_rel = output_boxes_rel.clone().detach() bb_init_rel.requires_grad = True bb_init = bbutils.rel_to_rect(bb_init_rel, sz_norm) outputs = self.iou_predictor.predict_iou(self.target_feat, iou_features, bb_init) if isinstance(outputs, (list, tuple)): outputs = outputs[0] outputs.backward(gradient=torch.ones_like(outputs)) # Update mask and step length update_mask = (outputs.detach() > outputs_prev) | (self.params.box_refinement_step_decay >= 1) update_mask_float = update_mask.view(1, -1, 1).float() step_length[~update_mask, :] *= self.params.box_refinement_step_decay outputs_prev = outputs.detach().clone() # Update proposal step = update_mask_float * step_length * bb_init_rel.grad - (1.0 - update_mask_float) * step output_boxes_rel = bb_init_rel + step output_boxes_rel.detach_() # for s in outputs.view(-1): # print('{:.2f} '.format(s.item()), end='') # print('') # print('') output_boxes = bbutils.rel_to_rect(output_boxes_rel, sz_norm) else: raise ValueError('Unknown box_refinement_space {}'.format(box_refinement_space)) return output_boxes.view(-1, 4).cpu(), outputs.detach().view(-1).cpu() ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/tracker/atom/optim.py ================================================ import torch from pytracking import optimization, TensorList, operation import math class FactorizedConvProblem(optimization.L2Problem): def __init__(self, training_samples: TensorList, y:TensorList, filter_reg: torch.Tensor, projection_reg, params, sample_weights: TensorList, projection_activation, response_activation): self.training_samples = training_samples self.y = y self.filter_reg = filter_reg self.sample_weights = sample_weights self.params = params self.projection_reg = projection_reg self.projection_activation = projection_activation self.response_activation = response_activation self.diag_M = self.filter_reg.concat(projection_reg) def __call__(self, x: TensorList): """ Compute residuals :param x: [filters, projection_matrices] :return: [data_terms, filter_regularizations, proj_mat_regularizations] """ filter = x[:len(x)//2] # w2 in paper P = x[len(x)//2:] # w1 in paper # Do first convolution compressed_samples = operation.conv1x1(self.training_samples, P).apply(self.projection_activation) # Do second convolution residuals = operation.conv2d(compressed_samples, filter, mode='same').apply(self.response_activation) # Compute data residuals residuals = residuals - self.y residuals = self.sample_weights.sqrt().view(-1, 1, 1, 1) * residuals # Add regularization for projection matrix residuals.extend(self.filter_reg.apply(math.sqrt) * filter) # Add regularization for projection matrix residuals.extend(self.projection_reg.apply(math.sqrt) * P) return residuals def ip_input(self, a: TensorList, b: TensorList): num = len(a) // 2 # Number of filters a_filter = a[:num] b_filter = b[:num] a_P = a[num:] b_P = b[num:] # Filter inner product # ip_out = a_filter.reshape(-1) @ b_filter.reshape(-1) ip_out = operation.conv2d(a_filter, b_filter).view(-1) # Add projection matrix part # ip_out += a_P.reshape(-1) @ b_P.reshape(-1) ip_out += operation.conv2d(a_P.view(1,-1,1,1), b_P.view(1,-1,1,1)).view(-1) # Have independent inner products for each filter return ip_out.concat(ip_out.clone()) def M1(self, x: TensorList): return x / self.diag_M class ConvProblem(optimization.L2Problem): def __init__(self, training_samples: TensorList, y:TensorList, filter_reg: torch.Tensor, sample_weights: TensorList, response_activation): self.training_samples = training_samples self.y = y self.filter_reg = filter_reg self.sample_weights = sample_weights self.response_activation = response_activation def __call__(self, x: TensorList): """ Compute residuals :param x: [filters] :return: [data_terms, filter_regularizations] """ # Do convolution and compute residuals residuals = operation.conv2d(self.training_samples, x, mode='same').apply(self.response_activation) residuals = residuals - self.y residuals = self.sample_weights.sqrt().view(-1, 1, 1, 1) * residuals # Add regularization for projection matrix residuals.extend(self.filter_reg.apply(math.sqrt) * x) return residuals def ip_input(self, a: TensorList, b: TensorList): # return a.reshape(-1) @ b.reshape(-1) # return (a * b).sum() return operation.conv2d(a, b).view(-1) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/tracker/base/__init__.py ================================================ from .basetracker import BaseTracker ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/tracker/base/basetracker.py ================================================ from _collections import OrderedDict class BaseTracker: """Base class for all trackers.""" def __init__(self, params): self.params = params self.visdom = None def initialize(self, image, info: dict) -> dict: """Overload this function in your tracker. This should initialize the model.""" raise NotImplementedError def track(self, image, info: dict = None) -> dict: """Overload this function in your tracker. This should track in the frame and update the model.""" raise NotImplementedError def visdom_draw_tracking(self, image, box, segmentation=None): if isinstance(box, OrderedDict): box = [v for k, v in box.items()] else: box = (box,) if segmentation is None: self.visdom.register((image, *box), 'Tracking', 1, 'Tracking') else: self.visdom.register((image, *box, segmentation), 'Tracking', 1, 'Tracking') ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/tracker/dimp/__init__.py ================================================ from .dimp import DiMP def get_tracker_class(): return DiMP ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/tracker/dimp/dimp.py ================================================ from pytracking.tracker.base import BaseTracker import torch import torch.nn.functional as F import math import time from pytracking import dcf, TensorList from pytracking.features.preprocessing import numpy_to_torch from pytracking.utils.plotting import show_tensor, plot_graph from pytracking.features.preprocessing import sample_patch_multiscale, sample_patch_transformed from pytracking.features import augmentation import ltr.data.bounding_box_utils as bbutils from ltr.models.target_classifier.initializer import FilterInitializerZero from ltr.models.layers import activation class DiMP(BaseTracker): multiobj_mode = 'parallel' def initialize_features(self): if not getattr(self, 'features_initialized', False): self.params.net.initialize() self.features_initialized = True def initialize(self, image, info: dict) -> dict: # Initialize some stuff self.frame_num = 1 if not self.params.has('device'): self.params.device = 'cuda' if self.params.use_gpu else 'cpu' # Initialize network self.initialize_features() # The DiMP network self.net = self.params.net # Time initialization tic = time.time() # Convert image im = numpy_to_torch(image) # Get target position and size state = info['init_bbox'] self.pos = torch.Tensor([state[1] + (state[3] - 1)/2, state[0] + (state[2] - 1)/2]) self.target_sz = torch.Tensor([state[3], state[2]]) # Get object id self.object_id = info.get('object_ids', [None])[0] self.id_str = '' if self.object_id is None else ' {}'.format(self.object_id) # Set sizes self.image_sz = torch.Tensor([im.shape[2], im.shape[3]]) sz = self.params.image_sample_size sz = torch.Tensor([sz, sz] if isinstance(sz, int) else sz) if self.params.get('use_image_aspect_ratio', False): sz = self.image_sz * sz.prod().sqrt() / self.image_sz.prod().sqrt() stride = self.params.get('feature_stride', 32) sz = torch.round(sz / stride) * stride self.img_sample_sz = sz self.img_support_sz = self.img_sample_sz # Set search area search_area = torch.prod(self.target_sz * self.params.search_area_scale).item() self.target_scale = math.sqrt(search_area) / self.img_sample_sz.prod().sqrt() # Target size in base scale self.base_target_sz = self.target_sz / self.target_scale # Setup scale factors if not self.params.has('scale_factors'): self.params.scale_factors = torch.ones(1) elif isinstance(self.params.scale_factors, (list, tuple)): self.params.scale_factors = torch.Tensor(self.params.scale_factors) # Setup scale bounds self.min_scale_factor = torch.max(10 / self.base_target_sz) self.max_scale_factor = torch.min(self.image_sz / self.base_target_sz) # Extract and transform sample init_backbone_feat = self.generate_init_samples(im) # Initialize classifier self.init_classifier(init_backbone_feat) # Initialize IoUNet if self.params.get('use_iou_net', True): self.init_iou_net(init_backbone_feat) out = {'time': time.time() - tic} return out def track(self, image, info: dict = None) -> dict: self.debug_info = {} self.frame_num += 1 self.debug_info['frame_num'] = self.frame_num # Convert image im = numpy_to_torch(image) # ------- LOCALIZATION ------- # # Extract backbone features backbone_feat, sample_coords, im_patches = self.extract_backbone_features(im, self.get_centered_sample_pos(), self.target_scale * self.params.scale_factors, self.img_sample_sz) # Extract classification features test_x = self.get_classification_features(backbone_feat) # Location of sample sample_pos, sample_scales = self.get_sample_location(sample_coords) # Compute classification scores scores_raw = self.classify_target(test_x) # Localize the target translation_vec, scale_ind, s, flag = self.localize_target(scores_raw, sample_pos, sample_scales) new_pos = sample_pos[scale_ind,:] + translation_vec # Update position and scale if flag != 'not_found': if self.params.get('use_iou_net', True): update_scale_flag = self.params.get('update_scale_when_uncertain', True) or flag != 'uncertain' if self.params.get('use_classifier', True): self.update_state(new_pos) self.refine_target_box(backbone_feat, sample_pos[scale_ind,:], sample_scales[scale_ind], scale_ind, update_scale_flag) elif self.params.get('use_classifier', True): self.update_state(new_pos, sample_scales[scale_ind]) # ------- UPDATE ------- # update_flag = flag not in ['not_found', 'uncertain'] hard_negative = (flag == 'hard_negative') learning_rate = self.params.get('hard_negative_learning_rate', None) if hard_negative else None if update_flag and self.params.get('update_classifier', False): # Get train sample train_x = test_x[scale_ind:scale_ind+1, ...] # Create target_box and label for spatial sample target_box = self.get_iounet_box(self.pos, self.target_sz, sample_pos[scale_ind,:], sample_scales[scale_ind]) # Update the classifier model self.update_classifier(train_x, target_box, learning_rate, s[scale_ind,...]) # Set the pos of the tracker to iounet pos if self.params.get('use_iou_net', True) and flag != 'not_found' and hasattr(self, 'pos_iounet'): self.pos = self.pos_iounet.clone() score_map = s[scale_ind, ...] max_score = torch.max(score_map).item() # Visualize and set debug info self.search_area_box = torch.cat((sample_coords[scale_ind,[1,0]], sample_coords[scale_ind,[3,2]] - sample_coords[scale_ind,[1,0]] - 1)) self.debug_info['flag' + self.id_str] = flag self.debug_info['max_score' + self.id_str] = max_score if self.visdom is not None: self.visdom.register(score_map, 'heatmap', 2, 'Score Map' + self.id_str) self.visdom.register(self.debug_info, 'info_dict', 1, 'Status') elif self.params.debug >= 2: show_tensor(score_map, 5, title='Max score = {:.2f}'.format(max_score)) # Compute output bounding box new_state = torch.cat((self.pos[[1,0]] - (self.target_sz[[1,0]]-1)/2, self.target_sz[[1,0]])) if self.params.get('output_not_found_box', False) and flag == 'not_found': output_state = [-1, -1, -1, -1] else: output_state = new_state.tolist() '''2020.4.26 ''' out = {'target_bbox': output_state, 'dcf_center':new_pos[[1,0]]} return out def get_sample_location(self, sample_coord): """Get the location of the extracted sample.""" sample_coord = sample_coord.float() sample_pos = 0.5*(sample_coord[:,:2] + sample_coord[:,2:] - 1) sample_scales = ((sample_coord[:,2:] - sample_coord[:,:2]) / self.img_sample_sz).prod(dim=1).sqrt() return sample_pos, sample_scales def get_centered_sample_pos(self): """Get the center position for the new sample. Make sure the target is correctly centered.""" return self.pos + ((self.feature_sz + self.kernel_size) % 2) * self.target_scale * \ self.img_support_sz / (2*self.feature_sz) def classify_target(self, sample_x: TensorList): """Classify target by applying the DiMP filter.""" with torch.no_grad(): scores = self.net.classifier.classify(self.target_filter, sample_x) return scores def localize_target(self, scores, sample_pos, sample_scales): """Run the target localization.""" scores = scores.squeeze(1) preprocess_method = self.params.get('score_preprocess', 'none') if preprocess_method == 'none': pass elif preprocess_method == 'exp': scores = scores.exp() elif preprocess_method == 'softmax': reg_val = getattr(self.net.classifier.filter_optimizer, 'softmax_reg', None) scores_view = scores.view(scores.shape[0], -1) scores_softmax = activation.softmax_reg(scores_view, dim=-1, reg=reg_val) scores = scores_softmax.view(scores.shape) else: raise Exception('Unknown score_preprocess in params.') score_filter_ksz = self.params.get('score_filter_ksz', 1) if score_filter_ksz > 1: assert score_filter_ksz % 2 == 1 kernel = scores.new_ones(1,1,score_filter_ksz,score_filter_ksz) scores = F.conv2d(scores.view(-1,1,*scores.shape[-2:]), kernel, padding=score_filter_ksz//2).view(scores.shape) if self.params.get('advanced_localization', False): return self.localize_advanced(scores, sample_pos, sample_scales) # Get maximum score_sz = torch.Tensor(list(scores.shape[-2:])) score_center = (score_sz - 1)/2 max_score, max_disp = dcf.max2d(scores) _, scale_ind = torch.max(max_score, dim=0) max_disp = max_disp[scale_ind,...].float().cpu().view(-1) target_disp = max_disp - score_center # Compute translation vector and scale change factor output_sz = score_sz - (self.kernel_size + 1) % 2 translation_vec = target_disp * (self.img_support_sz / output_sz) * sample_scales[scale_ind] return translation_vec, scale_ind, scores, None def localize_advanced(self, scores, sample_pos, sample_scales): """Run the target advanced localization (as in ATOM).""" sz = scores.shape[-2:] score_sz = torch.Tensor(list(sz)) output_sz = score_sz - (self.kernel_size + 1) % 2 score_center = (score_sz - 1)/2 scores_hn = scores if self.output_window is not None and self.params.get('perform_hn_without_windowing', False): scores_hn = scores.clone() scores *= self.output_window max_score1, max_disp1 = dcf.max2d(scores) _, scale_ind = torch.max(max_score1, dim=0) sample_scale = sample_scales[scale_ind] max_score1 = max_score1[scale_ind] max_disp1 = max_disp1[scale_ind,...].float().cpu().view(-1) target_disp1 = max_disp1 - score_center translation_vec1 = target_disp1 * (self.img_support_sz / output_sz) * sample_scale if max_score1.item() < self.params.target_not_found_threshold: return translation_vec1, scale_ind, scores_hn, 'not_found' if max_score1.item() < self.params.get('uncertain_threshold', -float('inf')): return translation_vec1, scale_ind, scores_hn, 'uncertain' if max_score1.item() < self.params.get('hard_sample_threshold', -float('inf')): return translation_vec1, scale_ind, scores_hn, 'hard_negative' # Mask out target neighborhood target_neigh_sz = self.params.target_neighborhood_scale * (self.target_sz / sample_scale) * (output_sz / self.img_support_sz) tneigh_top = max(round(max_disp1[0].item() - target_neigh_sz[0].item() / 2), 0) tneigh_bottom = min(round(max_disp1[0].item() + target_neigh_sz[0].item() / 2 + 1), sz[0]) tneigh_left = max(round(max_disp1[1].item() - target_neigh_sz[1].item() / 2), 0) tneigh_right = min(round(max_disp1[1].item() + target_neigh_sz[1].item() / 2 + 1), sz[1]) scores_masked = scores_hn[scale_ind:scale_ind + 1, ...].clone() scores_masked[...,tneigh_top:tneigh_bottom,tneigh_left:tneigh_right] = 0 # Find new maximum max_score2, max_disp2 = dcf.max2d(scores_masked) max_disp2 = max_disp2.float().cpu().view(-1) target_disp2 = max_disp2 - score_center translation_vec2 = target_disp2 * (self.img_support_sz / output_sz) * sample_scale prev_target_vec = (self.pos - sample_pos[scale_ind,:]) / ((self.img_support_sz / output_sz) * sample_scale) # Handle the different cases if max_score2 > self.params.distractor_threshold * max_score1: disp_norm1 = torch.sqrt(torch.sum((target_disp1-prev_target_vec)**2)) disp_norm2 = torch.sqrt(torch.sum((target_disp2-prev_target_vec)**2)) disp_threshold = self.params.dispalcement_scale * math.sqrt(sz[0] * sz[1]) / 2 if disp_norm2 > disp_threshold and disp_norm1 < disp_threshold: return translation_vec1, scale_ind, scores_hn, 'hard_negative' if disp_norm2 < disp_threshold and disp_norm1 > disp_threshold: return translation_vec2, scale_ind, scores_hn, 'hard_negative' if disp_norm2 > disp_threshold and disp_norm1 > disp_threshold: return translation_vec1, scale_ind, scores_hn, 'uncertain' # If also the distractor is close, return with highest score return translation_vec1, scale_ind, scores_hn, 'uncertain' if max_score2 > self.params.hard_negative_threshold * max_score1 and max_score2 > self.params.target_not_found_threshold: return translation_vec1, scale_ind, scores_hn, 'hard_negative' return translation_vec1, scale_ind, scores_hn, 'normal' def extract_backbone_features(self, im: torch.Tensor, pos: torch.Tensor, scales, sz: torch.Tensor): im_patches, patch_coords = sample_patch_multiscale(im, pos, scales, sz, mode=self.params.get('border_mode', 'replicate'), max_scale_change=self.params.get('patch_max_scale_change', None)) with torch.no_grad(): backbone_feat = self.net.extract_backbone(im_patches) return backbone_feat, patch_coords, im_patches def get_classification_features(self, backbone_feat): with torch.no_grad(): return self.net.extract_classification_feat(backbone_feat) def get_iou_backbone_features(self, backbone_feat): return self.net.get_backbone_bbreg_feat(backbone_feat) def get_iou_features(self, backbone_feat): with torch.no_grad(): return self.net.bb_regressor.get_iou_feat(self.get_iou_backbone_features(backbone_feat)) def get_iou_modulation(self, iou_backbone_feat, target_boxes): with torch.no_grad(): return self.net.bb_regressor.get_modulation(iou_backbone_feat, target_boxes) def generate_init_samples(self, im: torch.Tensor) -> TensorList: """Perform data augmentation to generate initial training samples.""" mode = self.params.get('border_mode', 'replicate') if mode == 'inside': # Get new sample size if forced inside the image im_sz = torch.Tensor([im.shape[2], im.shape[3]]) sample_sz = self.target_scale * self.img_sample_sz shrink_factor = (sample_sz.float() / im_sz) if mode == 'inside': shrink_factor = shrink_factor.max() elif mode == 'inside_major': shrink_factor = shrink_factor.min() shrink_factor.clamp_(min=1, max=self.params.get('patch_max_scale_change', None)) sample_sz = (sample_sz.float() / shrink_factor) self.init_sample_scale = (sample_sz / self.img_sample_sz).prod().sqrt() tl = self.pos - (sample_sz - 1) / 2 br = self.pos + sample_sz / 2 + 1 global_shift = - ((-tl).clamp(0) - (br - im_sz).clamp(0)) / self.init_sample_scale else: self.init_sample_scale = self.target_scale global_shift = torch.zeros(2) self.init_sample_pos = self.pos.round() # Compute augmentation size aug_expansion_factor = self.params.get('augmentation_expansion_factor', None) aug_expansion_sz = self.img_sample_sz.clone() aug_output_sz = None if aug_expansion_factor is not None and aug_expansion_factor != 1: aug_expansion_sz = (self.img_sample_sz * aug_expansion_factor).long() aug_expansion_sz += (aug_expansion_sz - self.img_sample_sz.long()) % 2 aug_expansion_sz = aug_expansion_sz.float() aug_output_sz = self.img_sample_sz.long().tolist() # Random shift for each sample get_rand_shift = lambda: None random_shift_factor = self.params.get('random_shift_factor', 0) if random_shift_factor > 0: get_rand_shift = lambda: ((torch.rand(2) - 0.5) * self.img_sample_sz * random_shift_factor + global_shift).long().tolist() # Always put identity transformation first, since it is the unaugmented sample that is always used self.transforms = [augmentation.Identity(aug_output_sz, global_shift.long().tolist())] augs = self.params.augmentation if self.params.get('use_augmentation', True) else {} # Add all augmentations if 'shift' in augs: self.transforms.extend([augmentation.Translation(shift, aug_output_sz, global_shift.long().tolist()) for shift in augs['shift']]) if 'relativeshift' in augs: get_absolute = lambda shift: (torch.Tensor(shift) * self.img_sample_sz/2).long().tolist() self.transforms.extend([augmentation.Translation(get_absolute(shift), aug_output_sz, global_shift.long().tolist()) for shift in augs['relativeshift']]) if 'fliplr' in augs and augs['fliplr']: self.transforms.append(augmentation.FlipHorizontal(aug_output_sz, get_rand_shift())) if 'blur' in augs: self.transforms.extend([augmentation.Blur(sigma, aug_output_sz, get_rand_shift()) for sigma in augs['blur']]) if 'scale' in augs: self.transforms.extend([augmentation.Scale(scale_factor, aug_output_sz, get_rand_shift()) for scale_factor in augs['scale']]) if 'rotate' in augs: self.transforms.extend([augmentation.Rotate(angle, aug_output_sz, get_rand_shift()) for angle in augs['rotate']]) # Extract augmented image patches im_patches = sample_patch_transformed(im, self.init_sample_pos, self.init_sample_scale, aug_expansion_sz, self.transforms) # Extract initial backbone features with torch.no_grad(): init_backbone_feat = self.net.extract_backbone(im_patches) return init_backbone_feat def init_target_boxes(self): """Get the target bounding boxes for the initial augmented samples.""" self.classifier_target_box = self.get_iounet_box(self.pos, self.target_sz, self.init_sample_pos, self.init_sample_scale) init_target_boxes = TensorList() for T in self.transforms: init_target_boxes.append(self.classifier_target_box + torch.Tensor([T.shift[1], T.shift[0], 0, 0])) init_target_boxes = torch.cat(init_target_boxes.view(1, 4), 0).to(self.params.device) self.target_boxes = init_target_boxes.new_zeros(self.params.sample_memory_size, 4) self.target_boxes[:init_target_boxes.shape[0],:] = init_target_boxes return init_target_boxes def init_memory(self, train_x: TensorList): # Initialize first-frame spatial training samples self.num_init_samples = train_x.size(0) init_sample_weights = TensorList([x.new_ones(1) / x.shape[0] for x in train_x]) # Sample counters and weights for spatial self.num_stored_samples = self.num_init_samples.copy() self.previous_replace_ind = [None] * len(self.num_stored_samples) self.sample_weights = TensorList([x.new_zeros(self.params.sample_memory_size) for x in train_x]) for sw, init_sw, num in zip(self.sample_weights, init_sample_weights, self.num_init_samples): sw[:num] = init_sw # Initialize memory self.training_samples = TensorList( [x.new_zeros(self.params.sample_memory_size, x.shape[1], x.shape[2], x.shape[3]) for x in train_x]) for ts, x in zip(self.training_samples, train_x): ts[:x.shape[0],...] = x def update_memory(self, sample_x: TensorList, target_box, learning_rate = None): # Update weights and get replace ind replace_ind = self.update_sample_weights(self.sample_weights, self.previous_replace_ind, self.num_stored_samples, self.num_init_samples, learning_rate) self.previous_replace_ind = replace_ind # Update sample and label memory for train_samp, x, ind in zip(self.training_samples, sample_x, replace_ind): train_samp[ind:ind+1,...] = x # Update bb memory self.target_boxes[replace_ind[0],:] = target_box self.num_stored_samples += 1 def update_sample_weights(self, sample_weights, previous_replace_ind, num_stored_samples, num_init_samples, learning_rate = None): # Update weights and get index to replace replace_ind = [] for sw, prev_ind, num_samp, num_init in zip(sample_weights, previous_replace_ind, num_stored_samples, num_init_samples): lr = learning_rate if lr is None: lr = self.params.learning_rate init_samp_weight = self.params.get('init_samples_minimum_weight', None) if init_samp_weight == 0: init_samp_weight = None s_ind = 0 if init_samp_weight is None else num_init if num_samp == 0 or lr == 1: sw[:] = 0 sw[0] = 1 r_ind = 0 else: # Get index to replace if num_samp < sw.shape[0]: r_ind = num_samp else: _, r_ind = torch.min(sw[s_ind:], 0) r_ind = r_ind.item() + s_ind # Update weights if prev_ind is None: sw /= 1 - lr sw[r_ind] = lr else: sw[r_ind] = sw[prev_ind] / (1 - lr) sw /= sw.sum() if init_samp_weight is not None and sw[:num_init].sum() < init_samp_weight: sw /= init_samp_weight + sw[num_init:].sum() sw[:num_init] = init_samp_weight / num_init replace_ind.append(r_ind) return replace_ind def update_state(self, new_pos, new_scale = None): # Update scale if new_scale is not None: self.target_scale = new_scale.clamp(self.min_scale_factor, self.max_scale_factor) self.target_sz = self.base_target_sz * self.target_scale # Update pos inside_ratio = self.params.get('target_inside_ratio', 0.2) inside_offset = (inside_ratio - 0.5) * self.target_sz self.pos = torch.max(torch.min(new_pos, self.image_sz - inside_offset), inside_offset) def get_iounet_box(self, pos, sz, sample_pos, sample_scale): """All inputs in original image coordinates. Generates a box in the cropped image sample reference frame, in the format used by the IoUNet.""" box_center = (pos - sample_pos) / sample_scale + (self.img_sample_sz - 1) / 2 box_sz = sz / sample_scale target_ul = box_center - (box_sz - 1) / 2 return torch.cat([target_ul.flip((0,)), box_sz.flip((0,))]) def init_iou_net(self, backbone_feat): # Setup IoU net and objective for p in self.net.bb_regressor.parameters(): p.requires_grad = False # Get target boxes for the different augmentations self.classifier_target_box = self.get_iounet_box(self.pos, self.target_sz, self.init_sample_pos, self.init_sample_scale) target_boxes = TensorList() if self.params.iounet_augmentation: for T in self.transforms: if not isinstance(T, (augmentation.Identity, augmentation.Translation, augmentation.FlipHorizontal, augmentation.FlipVertical, augmentation.Blur)): break target_boxes.append(self.classifier_target_box + torch.Tensor([T.shift[1], T.shift[0], 0, 0])) else: target_boxes.append(self.classifier_target_box + torch.Tensor([self.transforms[0].shift[1], self.transforms[0].shift[0], 0, 0])) target_boxes = torch.cat(target_boxes.view(1,4), 0).to(self.params.device) # Get iou features iou_backbone_feat = self.get_iou_backbone_features(backbone_feat) # Remove other augmentations such as rotation iou_backbone_feat = TensorList([x[:target_boxes.shape[0],...] for x in iou_backbone_feat]) # Get modulation vector self.iou_modulation = self.get_iou_modulation(iou_backbone_feat, target_boxes) if torch.is_tensor(self.iou_modulation[0]): self.iou_modulation = TensorList([x.detach().mean(0) for x in self.iou_modulation]) def init_classifier(self, init_backbone_feat): # Get classification features x = self.get_classification_features(init_backbone_feat) # Overwrite some parameters in the classifier. (These are not generally changed) self._overwrite_classifier_params(feature_dim=x.shape[-3]) # Add the dropout augmentation here, since it requires extraction of the classification features if 'dropout' in self.params.augmentation and self.params.get('use_augmentation', True): num, prob = self.params.augmentation['dropout'] self.transforms.extend(self.transforms[:1]*num) x = torch.cat([x, F.dropout2d(x[0:1,...].expand(num,-1,-1,-1), p=prob, training=True)]) # Set feature size and other related sizes self.feature_sz = torch.Tensor(list(x.shape[-2:])) ksz = self.net.classifier.filter_size self.kernel_size = torch.Tensor([ksz, ksz] if isinstance(ksz, (int, float)) else ksz) self.output_sz = self.feature_sz + (self.kernel_size + 1)%2 # Construct output window self.output_window = None if self.params.get('window_output', False): if self.params.get('use_clipped_window', False): self.output_window = dcf.hann2d_clipped(self.output_sz.long(), (self.output_sz*self.params.effective_search_area / self.params.search_area_scale).long(), centered=True).to(self.params.device) else: self.output_window = dcf.hann2d(self.output_sz.long(), centered=True).to(self.params.device) self.output_window = self.output_window.squeeze(0) # Get target boxes for the different augmentations target_boxes = self.init_target_boxes() # Set number of iterations plot_loss = self.params.debug > 0 num_iter = self.params.get('net_opt_iter', None) # Get target filter by running the discriminative model prediction module with torch.no_grad(): self.target_filter, _, losses = self.net.classifier.get_filter(x, target_boxes, num_iter=num_iter, compute_losses=plot_loss) # Init memory if self.params.get('update_classifier', True): self.init_memory(TensorList([x])) if plot_loss: if isinstance(losses, dict): losses = losses['train'] self.losses = torch.cat(losses) if self.visdom is not None: self.visdom.register((self.losses, torch.arange(self.losses.numel())), 'lineplot', 3, 'Training Loss' + self.id_str) elif self.params.debug >= 3: plot_graph(self.losses, 10, title='Training Loss' + self.id_str) def _overwrite_classifier_params(self, feature_dim): # Overwrite some parameters in the classifier. (These are not generally changed) pred_module = getattr(self.net.classifier.filter_optimizer, 'score_predictor', self.net.classifier.filter_optimizer) if self.params.get('label_threshold', None) is not None: self.net.classifier.filter_optimizer.label_threshold = self.params.label_threshold if self.params.get('label_shrink', None) is not None: self.net.classifier.filter_optimizer.label_shrink = self.params.label_shrink if self.params.get('softmax_reg', None) is not None: self.net.classifier.filter_optimizer.softmax_reg = self.params.softmax_reg if self.params.get('filter_reg', None) is not None: pred_module.filter_reg[0] = self.params.filter_reg pred_module.min_filter_reg = self.params.filter_reg if self.params.get('filter_init_zero', False): self.net.classifier.filter_initializer = FilterInitializerZero(self.net.classifier.filter_size, feature_dim) def update_classifier(self, train_x, target_box, learning_rate=None, scores=None): # Set flags and learning rate hard_negative_flag = learning_rate is not None if learning_rate is None: learning_rate = self.params.learning_rate # Update the tracker memory if hard_negative_flag or self.frame_num % self.params.get('train_sample_interval', 1) == 0: self.update_memory(TensorList([train_x]), target_box, learning_rate) # Decide the number of iterations to run num_iter = 0 low_score_th = self.params.get('low_score_opt_threshold', None) if hard_negative_flag: num_iter = self.params.get('net_opt_hn_iter', None) elif low_score_th is not None and low_score_th > scores.max().item(): num_iter = self.params.get('net_opt_low_iter', None) elif (self.frame_num - 1) % self.params.train_skipping == 0: num_iter = self.params.get('net_opt_update_iter', None) plot_loss = self.params.debug > 0 if num_iter > 0: # Get inputs for the DiMP filter optimizer module samples = self.training_samples[0][:self.num_stored_samples[0],...] target_boxes = self.target_boxes[:self.num_stored_samples[0],:].clone() sample_weights = self.sample_weights[0][:self.num_stored_samples[0]] # Run the filter optimizer module with torch.no_grad(): self.target_filter, _, losses = self.net.classifier.filter_optimizer(self.target_filter, num_iter=num_iter, feat=samples, bb=target_boxes, sample_weight=sample_weights, compute_losses=plot_loss) if plot_loss: if isinstance(losses, dict): losses = losses['train'] self.losses = torch.cat((self.losses, torch.cat(losses))) if self.visdom is not None: self.visdom.register((self.losses, torch.arange(self.losses.numel())), 'lineplot', 3, 'Training Loss' + self.id_str) elif self.params.debug >= 3: plot_graph(self.losses, 10, title='Training Loss' + self.id_str) def refine_target_box(self, backbone_feat, sample_pos, sample_scale, scale_ind, update_scale = True): """Run the ATOM IoUNet to refine the target bounding box.""" if hasattr(self.net.bb_regressor, 'predict_bb'): return self.direct_box_regression(backbone_feat, sample_pos, sample_scale, scale_ind, update_scale) # Initial box for refinement init_box = self.get_iounet_box(self.pos, self.target_sz, sample_pos, sample_scale) # Extract features from the relevant scale iou_features = self.get_iou_features(backbone_feat) iou_features = TensorList([x[scale_ind:scale_ind+1,...] for x in iou_features]) # Generate random initial boxes init_boxes = init_box.view(1,4).clone() if self.params.num_init_random_boxes > 0: square_box_sz = init_box[2:].prod().sqrt() rand_factor = square_box_sz * torch.cat([self.params.box_jitter_pos * torch.ones(2), self.params.box_jitter_sz * torch.ones(2)]) minimal_edge_size = init_box[2:].min()/3 rand_bb = (torch.rand(self.params.num_init_random_boxes, 4) - 0.5) * rand_factor new_sz = (init_box[2:] + rand_bb[:,2:]).clamp(minimal_edge_size) new_center = (init_box[:2] + init_box[2:]/2) + rand_bb[:,:2] init_boxes = torch.cat([new_center - new_sz/2, new_sz], 1) init_boxes = torch.cat([init_box.view(1,4), init_boxes]) # Optimize the boxes output_boxes, output_iou = self.optimize_boxes(iou_features, init_boxes) # Remove weird boxes output_boxes[:, 2:].clamp_(1) aspect_ratio = output_boxes[:,2] / output_boxes[:,3] keep_ind = (aspect_ratio < self.params.maximal_aspect_ratio) * (aspect_ratio > 1/self.params.maximal_aspect_ratio) output_boxes = output_boxes[keep_ind,:] output_iou = output_iou[keep_ind] # If no box found if output_boxes.shape[0] == 0: return # Predict box k = self.params.get('iounet_k', 5) topk = min(k, output_boxes.shape[0]) _, inds = torch.topk(output_iou, topk) predicted_box = output_boxes[inds, :].mean(0) predicted_iou = output_iou.view(-1, 1)[inds, :].mean(0) # Get new position and size new_pos = predicted_box[:2] + predicted_box[2:] / 2 new_pos = (new_pos.flip((0,)) - (self.img_sample_sz - 1) / 2) * sample_scale + sample_pos new_target_sz = predicted_box[2:].flip((0,)) * sample_scale new_scale = torch.sqrt(new_target_sz.prod() / self.base_target_sz.prod()) self.pos_iounet = new_pos.clone() if self.params.get('use_iounet_pos_for_learning', True): self.pos = new_pos.clone() self.target_sz = new_target_sz if update_scale: self.target_scale = new_scale # self.visualize_iou_pred(iou_features, predicted_box) def optimize_boxes(self, iou_features, init_boxes): box_refinement_space = self.params.get('box_refinement_space', 'default') if box_refinement_space == 'default': return self.optimize_boxes_default(iou_features, init_boxes) if box_refinement_space == 'relative': return self.optimize_boxes_relative(iou_features, init_boxes) raise ValueError('Unknown box_refinement_space {}'.format(box_refinement_space)) def optimize_boxes_default(self, iou_features, init_boxes): """Optimize iounet boxes with the default parametrization""" output_boxes = init_boxes.view(1, -1, 4).to(self.params.device) step_length = self.params.box_refinement_step_length if isinstance(step_length, (tuple, list)): step_length = torch.Tensor([step_length[0], step_length[0], step_length[1], step_length[1]], device=self.params.device).view(1,1,4) for i_ in range(self.params.box_refinement_iter): # forward pass bb_init = output_boxes.clone().detach() bb_init.requires_grad = True outputs = self.net.bb_regressor.predict_iou(self.iou_modulation, iou_features, bb_init) if isinstance(outputs, (list, tuple)): outputs = outputs[0] outputs.backward(gradient = torch.ones_like(outputs)) # Update proposal output_boxes = bb_init + step_length * bb_init.grad * bb_init[:, :, 2:].repeat(1, 1, 2) output_boxes.detach_() step_length *= self.params.box_refinement_step_decay return output_boxes.view(-1,4).cpu(), outputs.detach().view(-1).cpu() def optimize_boxes_relative(self, iou_features, init_boxes): """Optimize iounet boxes with the relative parametrization ised in PrDiMP""" output_boxes = init_boxes.view(1, -1, 4).to(self.params.device) step_length = self.params.box_refinement_step_length if isinstance(step_length, (tuple, list)): step_length = torch.Tensor([step_length[0], step_length[0], step_length[1], step_length[1]]).to(self.params.device).view(1,1,4) sz_norm = output_boxes[:,:1,2:].clone() output_boxes_rel = bbutils.rect_to_rel(output_boxes, sz_norm) for i_ in range(self.params.box_refinement_iter): # forward pass bb_init_rel = output_boxes_rel.clone().detach() bb_init_rel.requires_grad = True bb_init = bbutils.rel_to_rect(bb_init_rel, sz_norm) outputs = self.net.bb_regressor.predict_iou(self.iou_modulation, iou_features, bb_init) if isinstance(outputs, (list, tuple)): outputs = outputs[0] outputs.backward(gradient = torch.ones_like(outputs)) # Update proposal output_boxes_rel = bb_init_rel + step_length * bb_init_rel.grad output_boxes_rel.detach_() step_length *= self.params.box_refinement_step_decay # for s in outputs.view(-1): # print('{:.2f} '.format(s.item()), end='') # print('') # print('') output_boxes = bbutils.rel_to_rect(output_boxes_rel, sz_norm) return output_boxes.view(-1,4).cpu(), outputs.detach().view(-1).cpu() def direct_box_regression(self, backbone_feat, sample_pos, sample_scale, scale_ind, update_scale = True): """Implementation of direct bounding box regression.""" # Initial box for refinement init_box = self.get_iounet_box(self.pos, self.target_sz, sample_pos, sample_scale) # Extract features from the relevant scale iou_features = self.get_iou_features(backbone_feat) iou_features = TensorList([x[scale_ind:scale_ind+1,...] for x in iou_features]) # Generate random initial boxes init_boxes = init_box.view(1, 1, 4).clone().to(self.params.device) # Optimize the boxes output_boxes = self.net.bb_regressor.predict_bb(self.iou_modulation, iou_features, init_boxes).view(-1,4).cpu() # Remove weird boxes output_boxes[:, 2:].clamp_(1) predicted_box = output_boxes[0, :] # Get new position and size new_pos = predicted_box[:2] + predicted_box[2:] / 2 new_pos = (new_pos.flip((0,)) - (self.img_sample_sz - 1) / 2) * sample_scale + sample_pos new_target_sz = predicted_box[2:].flip((0,)) * sample_scale new_scale_bbr = torch.sqrt(new_target_sz.prod() / self.base_target_sz.prod()) new_scale = new_scale_bbr self.pos_iounet = new_pos.clone() if self.params.get('use_iounet_pos_for_learning', True): self.pos = new_pos.clone() self.target_sz = new_target_sz if update_scale: self.target_scale = new_scale def visualize_iou_pred(self, iou_features, center_box): center_box = center_box.view(1,1,4) sz_norm = center_box[...,2:].clone() center_box_rel = bbutils.rect_to_rel(center_box, sz_norm) pos_dist = 1.0 sz_dist = math.log(3.0) pos_step = 0.01 sz_step = 0.01 pos_scale = torch.arange(-pos_dist, pos_dist+pos_step, step=pos_step) sz_scale = torch.arange(-sz_dist, sz_dist+sz_step, step=sz_step) bbx = torch.zeros(1, pos_scale.numel(), 4) bbx[0,:,0] = pos_scale.clone() bby = torch.zeros(pos_scale.numel(), 1, 4) bby[:,0,1] = pos_scale.clone() bbw = torch.zeros(1, sz_scale.numel(), 4) bbw[0,:,2] = sz_scale.clone() bbh = torch.zeros(sz_scale.numel(), 1, 4) bbh[:,0,3] = sz_scale.clone() pos_boxes = bbutils.rel_to_rect((center_box_rel + bbx) + bby, sz_norm).view(1,-1,4).to(self.params.device) sz_boxes = bbutils.rel_to_rect((center_box_rel + bbw) + bbh, sz_norm).view(1,-1,4).to(self.params.device) pos_scores = self.net.bb_regressor.predict_iou(self.iou_modulation, iou_features, pos_boxes).exp() sz_scores = self.net.bb_regressor.predict_iou(self.iou_modulation, iou_features, sz_boxes).exp() show_tensor(pos_scores.view(pos_scale.numel(),-1), title='Position scores', fig_num=21) show_tensor(sz_scores.view(sz_scale.numel(),-1), title='Size scores', fig_num=22) def visdom_draw_tracking(self, image, box, segmentation=None): if hasattr(self, 'search_area_box'): self.visdom.register((image, box, self.search_area_box), 'Tracking', 1, 'Tracking') else: self.visdom.register((image, box), 'Tracking', 1, 'Tracking') ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/tracker/eco/__init__.py ================================================ from .eco import ECO def get_tracker_class(): return ECO ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/tracker/eco/eco.py ================================================ from pytracking.tracker.base import BaseTracker import torch import torch.nn.functional as F import math from pytracking import complex, dcf, fourier, TensorList from pytracking.libs.tensorlist import tensor_operation from pytracking.features.preprocessing import numpy_to_torch from pytracking.utils.plotting import show_tensor from pytracking.libs.optimization import GaussNewtonCG from .optim import FilterOptim, FactorizedConvProblem from pytracking.features import augmentation class ECO(BaseTracker): multiobj_mode = 'parallel' def initialize_features(self): if not getattr(self, 'features_initialized', False): self.params.features.initialize() self.features_initialized = True def initialize(self, image, info: dict) -> dict: state = info['init_bbox'] # Initialize some stuff self.frame_num = 1 if not self.params.has('device'): self.params.device = 'cuda' if self.params.use_gpu else 'cpu' # Initialize features self.initialize_features() # Chack if image is color self.params.features.set_is_color(image.shape[2] == 3) # Get feature specific params self.fparams = self.params.features.get_fparams('feature_params') # Get position and size self.pos = torch.Tensor([state[1] + (state[3] - 1)/2, state[0] + (state[2] - 1)/2]) self.target_sz = torch.Tensor([state[3], state[2]]) # Set search area self.target_scale = 1.0 search_area = torch.prod(self.target_sz * self.params.search_area_scale).item() if search_area > self.params.max_image_sample_size: self.target_scale = math.sqrt(search_area / self.params.max_image_sample_size) elif search_area < self.params.min_image_sample_size: self.target_scale = math.sqrt(search_area / self.params.min_image_sample_size) # Target size in base scale self.base_target_sz = self.target_sz / self.target_scale # Use odd square search area and set sizes feat_max_stride = max(self.params.features.stride()) self.img_sample_sz = torch.round(torch.sqrt(torch.prod(self.base_target_sz * self.params.search_area_scale))) * torch.ones(2) self.img_sample_sz += feat_max_stride - self.img_sample_sz % (2 * feat_max_stride) # Set other sizes (corresponds to ECO code) self.img_support_sz = self.img_sample_sz self.feature_sz = self.params.features.size(self.img_sample_sz) self.filter_sz = self.feature_sz + (self.feature_sz + 1) % 2 self.output_sz = self.params.score_upsample_factor * self.img_support_sz # Interpolated size of the output self.compressed_dim = self.fparams.attribute('compressed_dim') # Number of filters self.num_filters = len(self.filter_sz) # Get window function self.window = TensorList([dcf.hann2d(sz).to(self.params.device) for sz in self.feature_sz]) # Get interpolation function self.interp_fs = TensorList([dcf.get_interp_fourier(sz, self.params.interpolation_method, self.params.interpolation_bicubic_a, self.params.interpolation_centering, self.params.interpolation_windowing, self.params.device) for sz in self.filter_sz]) # Get regularization filter self.reg_filter = TensorList([dcf.get_reg_filter(self.img_support_sz, self.base_target_sz, fparams).to(self.params.device) for fparams in self.fparams]) self.reg_energy = self.reg_filter.view(-1) @ self.reg_filter.view(-1) # Get label function output_sigma_factor = self.fparams.attribute('output_sigma_factor') sigma = (self.filter_sz / self.img_support_sz) * torch.sqrt(self.base_target_sz.prod()) * output_sigma_factor self.yf = TensorList([dcf.label_function(sz, sig).to(self.params.device) for sz, sig in zip(self.filter_sz, sigma)]) # Optimization options self.params.precond_learning_rate = self.fparams.attribute('learning_rate') if self.params.CG_forgetting_rate is None or max(self.params.precond_learning_rate) >= 1: self.params.direction_forget_factor = 0 else: self.params.direction_forget_factor = (1 - max(self.params.precond_learning_rate))**self.params.CG_forgetting_rate # Convert image im = numpy_to_torch(image) # Setup bounds self.image_sz = torch.Tensor([im.shape[2], im.shape[3]]) self.min_scale_factor = torch.max(10 / self.base_target_sz) self.max_scale_factor = torch.min(self.image_sz / self.base_target_sz) # Extract and transform sample x = self.generate_init_samples(im) # Initialize projection matrix x_mat = TensorList([e.permute(1,0,2,3).reshape(e.shape[1], -1).clone() for e in x]) x_mat -= x_mat.mean(dim=1, keepdim=True) cov_x = x_mat @ x_mat.t() self.projection_matrix = TensorList([torch.svd(C)[0][:,:cdim].clone() for C, cdim in zip(cov_x, self.compressed_dim)]) # Transform to get the training sample train_xf = self.preprocess_sample(x) # Shift the samples back if 'shift' in self.params.augmentation: for xf in train_xf: if xf.shape[0] == 1: continue for i, shift in enumerate(self.params.augmentation['shift']): shift_samp = 2 * math.pi * torch.Tensor(shift) / self.img_support_sz xf[1+i:2+i,...] = fourier.shift_fs(xf[1+i:2+i,...], shift=shift_samp) # Shift sample shift_samp = 2*math.pi * (self.pos - self.pos.round()) / (self.target_scale * self.img_support_sz) train_xf = fourier.shift_fs(train_xf, shift=shift_samp) # Initialize first-frame training samples num_init_samples = train_xf.size(0) self.init_sample_weights = TensorList([xf.new_ones(1) / xf.shape[0] for xf in train_xf]) self.init_training_samples = train_xf.permute(2, 3, 0, 1, 4) # Sample counters and weights self.num_stored_samples = num_init_samples self.previous_replace_ind = [None]*len(self.num_stored_samples) self.sample_weights = TensorList([xf.new_zeros(self.params.sample_memory_size) for xf in train_xf]) for sw, init_sw, num in zip(self.sample_weights, self.init_sample_weights, num_init_samples): sw[:num] = init_sw # Initialize memory self.training_samples = TensorList( [xf.new_zeros(xf.shape[2], xf.shape[3], self.params.sample_memory_size, cdim, 2) for xf, cdim in zip(train_xf, self.compressed_dim)]) # Initialize filter self.filter = TensorList( [xf.new_zeros(1, cdim, xf.shape[2], xf.shape[3], 2) for xf, cdim in zip(train_xf, self.compressed_dim)]) # Do joint optimization self.joint_problem = FactorizedConvProblem(self.init_training_samples, self.yf, self.reg_filter, self.projection_matrix, self.params, self.init_sample_weights) joint_var = self.filter.concat(self.projection_matrix) self.joint_optimizer = GaussNewtonCG(self.joint_problem, joint_var, debug=(self.params.debug>=1), visdom=self.visdom) if self.params.update_projection_matrix: self.joint_optimizer.run(self.params.init_CG_iter // self.params.init_GN_iter, self.params.init_GN_iter) # Re-project samples with the new projection matrix compressed_samples = complex.mtimes(self.init_training_samples, self.projection_matrix) for train_samp, init_samp in zip(self.training_samples, compressed_samples): train_samp[:,:,:init_samp.shape[2],:,:] = init_samp # Initialize optimizer self.filter_optimizer = FilterOptim(self.params, self.reg_energy) self.filter_optimizer.register(self.filter, self.training_samples, self.yf, self.sample_weights, self.reg_filter) self.filter_optimizer.sample_energy = self.joint_problem.sample_energy self.filter_optimizer.residuals = self.joint_optimizer.residuals.clone() if not self.params.update_projection_matrix: self.filter_optimizer.run(self.params.init_CG_iter) # Post optimization self.filter_optimizer.run(self.params.post_init_CG_iter) self.symmetrize_filter() def track(self, image, info: dict = None) -> dict: self.debug_info = {} self.frame_num += 1 self.debug_info['frame_num'] = self.frame_num # Convert image im = numpy_to_torch(image) # ------- LOCALIZATION ------- # # Get sample sample_pos = self.pos.round() sample_scales = self.target_scale * self.params.scale_factors test_xf = self.extract_fourier_sample(im, self.pos, sample_scales, self.img_sample_sz) # Compute scores sf = self.apply_filter(test_xf) translation_vec, scale_ind, s = self.localize_target(sf) scale_change_factor = self.params.scale_factors[scale_ind] # Update position and scale self.update_state(sample_pos + translation_vec, self.target_scale * scale_change_factor) score_map = s[scale_ind, ...] max_score = torch.max(score_map).item() self.debug_info['max_score'] = max_score if self.visdom is not None: self.visdom.register(score_map, 'heatmap', 2, 'Score Map') self.visdom.register(self.debug_info, 'info_dict', 1, 'Status') elif self.params.debug >= 2: show_tensor(score_map, 5, title='Max score = {:.2f}'.format(max_score)) # if self.params.debug >= 3: # for i, hf in enumerate(self.filter): # show_tensor(fourier.sample_fs(hf).abs().mean(1), 6+i) # ------- UPDATE ------- # # Get train sample train_xf = TensorList([xf[scale_ind:scale_ind+1, ...] for xf in test_xf]) # Shift the sample shift_samp = 2*math.pi * (self.pos - sample_pos) / (sample_scales[scale_ind] * self.img_support_sz) train_xf = fourier.shift_fs(train_xf, shift=shift_samp) # Update memory self.update_memory(train_xf) # Train filter if self.frame_num % self.params.train_skipping == 1: self.filter_optimizer.run(self.params.CG_iter, train_xf) self.symmetrize_filter() # Return new state new_state = torch.cat((self.pos[[1,0]] - (self.target_sz[[1,0]]-1)/2, self.target_sz[[1,0]])) out = {'target_bbox': new_state.tolist()} return out def apply_filter(self, sample_xf: TensorList) -> torch.Tensor: return complex.mult(self.filter, sample_xf).sum(1, keepdim=True) def localize_target(self, sf: TensorList): if self.params.score_fusion_strategy == 'sum': scores = fourier.sample_fs(fourier.sum_fs(sf), self.output_sz) elif self.params.score_fusion_strategy == 'weightedsum': weight = self.fparams.attribute('translation_weight') scores = fourier.sample_fs(fourier.sum_fs(weight * sf), self.output_sz) elif self.params.score_fusion_strategy == 'transcale': alpha = self.fparams.attribute('scale_weight') beta = self.fparams.attribute('translation_weight') sample_sz = torch.round(self.output_sz.view(1,-1) * self.params.scale_factors.view(-1,1)) scores = 0 for sfe, a, b in zip(sf, alpha, beta): sfe = fourier.shift_fs(sfe, math.pi*torch.ones(2)) scores_scales = [] for sind, sz in enumerate(sample_sz): pd = (self.output_sz-sz)/2 scores_scales.append(F.pad(fourier.sample_fs(sfe[sind:sind+1,...], sz), (math.floor(pd[1].item()), math.ceil(pd[1].item()), math.floor(pd[0].item()), math.ceil(pd[0].item())))) scores_cat = torch.cat(scores_scales) scores = scores + (b - a) * scores_cat.mean(dim=0, keepdim=True) + a * scores_cat else: raise ValueError('Unknown score fusion strategy.') # Get maximum max_score, max_disp = dcf.max2d(scores) _, scale_ind = torch.max(max_score, dim=0) max_disp = max_disp.float().cpu() # Convert to displacements in the base scale if self.params.score_fusion_strategy in ['sum', 'weightedsum']: disp = (max_disp + self.output_sz / 2) % self.output_sz - self.output_sz / 2 elif self.params.score_fusion_strategy == 'transcale': disp = max_disp - self.output_sz / 2 # Compute translation vector and scale change factor translation_vec = disp[scale_ind, ...].view(-1) * (self.img_support_sz / self.output_sz) * self.target_scale if self.params.score_fusion_strategy in ['sum', 'weightedsum']: translation_vec *= self.params.scale_factors[scale_ind] return translation_vec, scale_ind, scores def extract_sample(self, im: torch.Tensor, pos: torch.Tensor, scales, sz: torch.Tensor): return self.params.features.extract(im, pos, scales, sz)[0] def extract_fourier_sample(self, im: torch.Tensor, pos: torch.Tensor, scales, sz: torch.Tensor) -> TensorList: x = self.extract_sample(im, pos, scales, sz) return self.preprocess_sample(self.project_sample(x)) def preprocess_sample(self, x: TensorList) -> TensorList: x *= self.window sample_xf = fourier.cfft2(x) return TensorList([dcf.interpolate_dft(xf, bf) for xf, bf in zip(sample_xf, self.interp_fs)]) def project_sample(self, x: TensorList): @tensor_operation def _project_sample(x: torch.Tensor, P: torch.Tensor): if P is None: return x return torch.matmul(x.permute(2, 3, 0, 1), P).permute(2, 3, 0, 1) return _project_sample(x, self.projection_matrix) def generate_init_samples(self, im: torch.Tensor) -> TensorList: # Do data augmentation transforms = [augmentation.Identity()] if 'shift' in self.params.augmentation: transforms.extend([augmentation.Translation(shift) for shift in self.params.augmentation['shift']]) if 'fliplr' in self.params.augmentation and self.params.augmentation['fliplr']: transforms.append(augmentation.FlipHorizontal()) if 'rotate' in self.params.augmentation: transforms.extend([augmentation.Rotate(angle) for angle in self.params.augmentation['rotate']]) if 'blur' in self.params.augmentation: transforms.extend([augmentation.Blur(sigma) for sigma in self.params.augmentation['blur']]) init_samples = self.params.features.extract_transformed(im, self.pos, self.target_scale, self.img_sample_sz, transforms) # Remove augmented samples for those that shall not have for i, use_aug in enumerate(self.fparams.attribute('use_augmentation')): if not use_aug: init_samples[i] = init_samples[i][0:1, ...] if 'dropout' in self.params.augmentation: num, prob = self.params.augmentation['dropout'] for i, use_aug in enumerate(self.fparams.attribute('use_augmentation')): if use_aug: init_samples[i] = torch.cat([init_samples[i], F.dropout2d(init_samples[i][0:1,...].expand(num,-1,-1,-1), p=prob, training=True)]) return init_samples def update_memory(self, sample_xf: TensorList): # Update weights and get index to replace replace_ind = self.update_sample_weights() for train_samp, xf, ind in zip(self.training_samples, sample_xf, replace_ind): train_samp[:,:,ind:ind+1,:,:] = xf.permute(2, 3, 0, 1, 4) def update_sample_weights(self): replace_ind = [] for sw, prev_ind, num_samp, fparams in zip(self.sample_weights, self.previous_replace_ind, self.num_stored_samples, self.fparams): if num_samp == 0 or fparams.learning_rate == 1: sw[:] = 0 sw[0] = 1 r_ind = 0 else: # Get index to replace _, r_ind = torch.min(sw, 0) r_ind = r_ind.item() # Update weights if prev_ind is None: sw /= 1 - fparams.learning_rate sw[r_ind] = fparams.learning_rate else: sw[r_ind] = sw[prev_ind] / (1 - fparams.learning_rate) sw /= sw.sum() replace_ind.append(r_ind) self.previous_replace_ind = replace_ind.copy() self.num_stored_samples += 1 return replace_ind def update_state(self, new_pos, new_scale): # Update scale self.target_scale = new_scale.clamp(self.min_scale_factor, self.max_scale_factor) self.target_sz = self.base_target_sz * self.target_scale # Update pos inside_ratio = 0.2 inside_offset = (inside_ratio - 0.5) * self.target_sz self.pos = torch.max(torch.min(new_pos, self.image_sz - inside_offset), inside_offset) def symmetrize_filter(self): for hf in self.filter: hf[:,:,:,0,:] /= 2 hf[:,:,:,0,:] += complex.conj(hf[:,:,:,0,:].flip((2,))) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/tracker/eco/optim.py ================================================ import torch import torch.nn.functional as F from pytracking import complex, optimization, fourier, TensorList from pytracking.utils.plotting import plot_graph import math class FactorizedConvProblem(optimization.L2Problem): def __init__(self, training_samples: TensorList, yf:TensorList, reg_filter: torch.Tensor, init_proj_mat: TensorList, params, sample_weights: torch.Tensor = None): self.training_samples = training_samples self.yf = complex.complex(yf).permute(2, 3, 0, 1, 4) self.reg_filter = reg_filter self.sample_weights_sqrt = None if sample_weights is None else sample_weights.sqrt() self.params = params # Sample energy for preconditioner compressed_samples = complex.mtimes(self.training_samples, init_proj_mat) self.sample_energy = complex.abs_sqr(compressed_samples).mean(dim=2, keepdim=True).permute(2, 3, 0, 1) self.reg_energy = self.reg_filter.view(-1) @ self.reg_filter.view(-1) # Projection energy for preconditioner self.proj_energy = 2 * fourier.inner_prod_fs(yf, yf) / self.training_samples.size(3) # Filter part of preconditioner self.diag_M = (1 - self.params.precond_reg_param) * (self.params.precond_data_param * self.sample_energy + (1 - self.params.precond_data_param) * self.sample_energy.mean(1, keepdim=True)) + \ self.params.precond_reg_param * self.reg_energy self.diag_M.unsqueeze_(-1) # Projection matrix part of preconditioner self.diag_M.extend(self.params.precond_proj_param * (self.proj_energy + self.params.projection_reg)) def __call__(self, x: TensorList): """ Compute residuals :param x: [filters, projection_matrices] :return: [data_terms, filter_regularizations, proj_mat_regularizations] """ hf = x[:len(x)//2] P = x[len(x)//2:] compressed_samples = complex.mtimes(self.training_samples, P) residuals = complex.mtimes(compressed_samples, hf.permute(2, 3, 1, 0, 4)) # (h, w, num_samp, num_filt, 2) residuals = residuals - self.yf if self.sample_weights_sqrt is not None: residuals = complex.mult(self.sample_weights_sqrt.view(1, 1, -1, 1), residuals) # Add spatial regularization for hfe, reg_filter in zip(hf, self.reg_filter): reg_pad1 = min(reg_filter.shape[-2] - 1, hfe.shape[-3] - 1) reg_pad2 = min(reg_filter.shape[-1] - 1, hfe.shape[-2] - 1) # Add part needed for convolution if reg_pad2 > 0: hfe_left_padd = complex.conj(hfe[...,1:reg_pad2+1,:].clone().detach().flip((2,3))) hfe_conv = torch.cat([hfe_left_padd, hfe], -2) else: hfe_conv = hfe.clone() # Shift data to batch dimension hfe_conv = hfe_conv.permute(0,1,4,2,3).reshape(-1, 1, hfe_conv.shape[-3], hfe_conv.shape[-2]) # Do first convolution hfe_conv = F.conv2d(hfe_conv, reg_filter, padding=(reg_pad1, reg_pad2)) residuals.append(hfe_conv) # Add regularization for projection matrix residuals.extend(math.sqrt(self.params.projection_reg) * P) return residuals def ip_input(self, a: TensorList, b: TensorList): num = len(a) // 2 # Number of filters a_filter = a[:num] b_filter = b[:num] a_P = a[num:] b_P = b[num:] # Filter inner product ip_out = fourier.inner_prod_fs(a_filter, b_filter) # Add projection matrix part ip_out += a_P.reshape(-1) @ b_P.reshape(-1) # Have independent inner products for each filter return ip_out.concat(ip_out.clone()) def ip_output(self, a: TensorList, b: TensorList): num = len(a) // 3 # Number of filters a_data = a[:num].permute(2,3,0,1,4) b_data = b[:num].permute(2,3,0,1,4) a_filt_reg = a[num:2*num] b_filt_reg = b[num:2*num] a_P_reg = a[2*num:] b_P_reg = b[2*num:] ip_data = sum(fourier.inner_prod_fs(a_data, b_data)) ip_filt_reg = ip_data.new_zeros(1) for ar, br, res_data, reg_filter in zip(a_filt_reg, b_filt_reg, a_data, self.reg_filter): reg_pad2 = min(reg_filter.shape[-1] - 1, res_data.shape[-2] - 1) arp = ar.reshape(1, -1, 2, ar.shape[2], ar.shape[3]).permute(0, 1, 3, 4, 2) brp = br.reshape(1, -1, 2, br.shape[2], br.shape[3]).permute(0, 1, 3, 4, 2) ip_filt_reg += fourier.inner_prod_fs(arp[:,:,:,2*reg_pad2:,:], brp[:,:,:,2*reg_pad2:,:]) ip_P_reg = sum(a_P_reg.view(-1) @ b_P_reg.view(-1)) return ip_data + ip_filt_reg + ip_P_reg def M1(self, x: TensorList): return x / self.diag_M class FilterOptim(optimization.ConjugateGradientBase): def __init__(self, params, reg_energy): super(FilterOptim, self).__init__(params.fletcher_reeves, params.standard_alpha, params.direction_forget_factor, (params.debug >= 3)) # Parameters self.params = params self.reg_energy = reg_energy self.sample_energy = None self.residuals = torch.zeros(0) def register(self, filter, training_samples, yf, sample_weights, reg_filter): self.filter = filter self.training_samples = training_samples # (h, w, num_samples, num_channels, 2) self.yf = yf self.sample_weights = sample_weights self.reg_filter = reg_filter def run(self, num_iter, new_xf: TensorList = None): if num_iter == 0: return if new_xf is not None: new_sample_energy = complex.abs_sqr(new_xf) if self.sample_energy is None: self.sample_energy = new_sample_energy else: self.sample_energy = (1 - self.params.precond_learning_rate) * self.sample_energy + self.params.precond_learning_rate * new_sample_energy # Compute right hand side self.b = complex.mtimes(self.sample_weights.view(1,1,1,-1), self.training_samples).permute(2,3,0,1,4) self.b = complex.mult_conj(self.yf, self.b) self.diag_M = (1 - self.params.precond_reg_param) * (self.params.precond_data_param * self.sample_energy + (1 - self.params.precond_data_param) * self.sample_energy.mean(1, keepdim=True)) + self.params.precond_reg_param * self.reg_energy _, res = self.run_CG(num_iter, self.filter) if self.debug: self.residuals = torch.cat((self.residuals, res)) plot_graph(self.residuals, 9) def A(self, hf: TensorList): # Classify sh = complex.mtimes(self.training_samples, hf.permute(2,3,1,0,4)) # (h, w, num_samp, num_filt, 2) sh = complex.mult(self.sample_weights.view(1,1,-1,1), sh) # Multiply with transpose hf_out = complex.mtimes(sh.permute(0,1,3,2,4), self.training_samples, conj_b=True).permute(2,3,0,1,4) # Add regularization for hfe, hfe_out, reg_filter in zip(hf, hf_out, self.reg_filter): reg_pad1 = min(reg_filter.shape[-2] - 1, hfe.shape[-3] - 1) reg_pad2 = min(reg_filter.shape[-1] - 1, 2*hfe.shape[-2]- 2) # Add part needed for convolution if reg_pad2 > 0: hfe_conv = torch.cat([complex.conj(hfe[...,1:reg_pad2+1,:].flip((2,3))), hfe], -2) else: hfe_conv = hfe.clone() # Shift data to batch dimension hfe_conv = hfe_conv.permute(0,1,4,2,3).reshape(-1, 1, hfe_conv.shape[-3], hfe_conv.shape[-2]) # Do first convolution hfe_conv = F.conv2d(hfe_conv, reg_filter, padding=(reg_pad1, reg_pad2)) # Do second convolution remove_size = min(reg_pad2, hfe.shape[-2]-1) hfe_conv = F.conv2d(hfe_conv[...,remove_size:], reg_filter) # Reshape back and add hfe_out += hfe_conv.reshape(hfe.shape[0], hfe.shape[1], 2, hfe.shape[2], hfe.shape[3]).permute(0,1,3,4,2) return hf_out def ip(self, a: torch.Tensor, b: torch.Tensor): return fourier.inner_prod_fs(a, b) def M1(self, hf): return complex.div(hf, self.diag_M) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/util_scripts/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/util_scripts/download_results.py ================================================ import os import sys import gdown import re import shutil import argparse import tempfile env_path = os.path.join(os.path.dirname(__file__), '../..') if env_path not in sys.path: sys.path.append(env_path) from pytracking.evaluation.environment import env_settings results_link_dict = { "dimp": { "prdimp50_003.zip": "1p13j3iwcOCubBi3ms0hLwqnP6-x0J8Mc", "prdimp50_002.zip": "1PPKgrAepbuyM2kjfzYAozQKTL6AjcQOz", "prdimp50_001.zip": "17NFBObEDeK6mW4Mk2vN5Ekk1SGbFvxRS", "prdimp50_000.zip": "1r3Efq7AumML2yGQ_KV4zmf4ATKVE1bo6", "prdimp18_004.zip": "1DF4ZJQAa4CwvN_OiT4te33AV0kpsO7JM", "prdimp18_003.zip": "1RgwJAN4TxnzgVgsfvrHIg1OUXD1EBZkO", "prdimp18_002.zip": "17lMllYhygCqgE81DoHX4BZar3xc3auzM", "prdimp18_001.zip": "1Yg7DmGYOnn2k0MYtSjjKlGyzO1Uimj4G", "prdimp18_000.zip": "1DuZJSBJ-23WJBQTOWSAaoPYSbGAJJN2Z", "prdimp50_004.zip": "1f9bx9-dtx3B5_IvIJhjjJyp-cnXciqLO", "dimp50_004.zip": "1Lj3p8mYCoIqxzdQXZkWFTw-MA8c6eeLa", "dimp50_000.zip": "1LCgf5sg453Z4bY37A_W5mbXeG68U1fET", "dimp18_000.zip": "17M7dJZ1oKrIY4-O5lL_mlQPEubUn034g", "dimp18_001.zip": "1AsiliVgISyDTouYOQYVOXA0srj3YskhJ", "dimp50_got_001.zip": "1EE5FcPXqMBkv_0ghfzytCMmbKxWxy04p", "dimp18_002.zip": "1I0GrBaPnySOyPWSvItHhXH8182tFCi_Y", "dimp50_got_002.zip": "1ALXzVkn58GZ1E0I22vrbXkEXwy5u0xOc", "dimp18_got_000.zip": "1BxowlgGEonnuaVXwiDwiYr7VV7BRWLvr", "dimp50_001.zip": "1XfPvwAcymW88J1rq7RlhyKmqsawJDK-K", "dimp18_got_002.zip": "1awqXQnFRr5NwjLfI-Ngtt3zT7XmQIwzs", "dimp18_got_001.zip": "1rr2J6NuuYJ5E4wDUw-PrxaNKjIsfgAyk", "dimp50_got_000.zip": "1ruP8XJOu0woq-bvKdHJ9_Y9RceHDrDjm", "dimp18_004.zip": "1EztF6bpROFwZ1PSJWgMB7bQ4G_Z08YIg", "dimp18_003.zip": "1iuiFLv04WE7GfBjm8UkZXFq4gheG2Ru8", "dimp50_003.zip": "1rLsgeQXyKpD6ryl9BjlIVdO3vd27ekwy", "dimp50_002.zip": "1wj2jUwlpHgsP1hAcuxXAVriUPuEspsu4", }, "atom": { "default_004.zip": "1BapnQh_8iRM44DXj862eOZV4q8zQLdmT", "default_003.zip": "1YpfOBLBEUQQiX0fWMPA5pnW3dm0NG3E5", "default_got_000.zip": "1uJnC0PPQhavwRbAL7VQ2Zow8YdLVzeCb", "default_got_001.zip": "1YzJm0H31veDW-lMxwy8MYNpMULgsYHKf", "default_000.zip": "1x6fKGZk3V839mX99Gl_pw7JUaiMaTxc5", "default_002.zip": "1QIlQFv3p6MBTwsYdIMYmzUDBDQGxGsUC", "default_001.zip": "1-K2--GNCURDKEgUuiEF18K4DcCLvDEVt", "default_got_002.zip": "1qGtArxdAy0uWSd-HqFT5zmXpR6TCm4Vc", }, } def _download_file(file_id, path): link = 'https://drive.google.com/uc?id=' + file_id gdown.download(link, path, quiet=True) def download_results(download_path, trackers='all'): """ Script to automatically download tracker results for PyTracking. args: download_path - Directory where the zipped results are downloaded trackers - Tracker results which are to be downloaded. If set to 'all', all available results are downloaded. If set to a name of a tracker (e.g. atom), all results for that tracker are downloaded. Otherwise, it can be set to a dict, where the keys are the names of the trackers for which results are downloaded. The value can be set to either 'all', in which case all available results for the tracker are downloaded. Else the value should be a list of parameter file names. """ print('Using download path ''{}'''.format(download_path)) os.makedirs(download_path, exist_ok=True) if isinstance(trackers, str): if trackers == 'all': trackers = {k: 'all' for k in results_link_dict.keys()} elif trackers in results_link_dict: trackers = {trackers: 'all'} else: raise Exception('tracker_list must be set to ''all'', a tracker name, or be a dict') elif isinstance(trackers, dict): pass else: raise Exception('tracker_list must be set to ''all'', or be a dict') for trk, runfiles in trackers.items(): trk_path = os.path.join(download_path, trk) if not os.path.exists(trk_path): os.makedirs(trk_path) if runfiles == 'all': for params, fileid in results_link_dict[trk].items(): print('Downloading: {}/{}'.format(trk, params)) _download_file(fileid, os.path.join(trk_path, params)) elif isinstance(runfiles, (list, tuple)): for p in runfiles: for params, fileid in results_link_dict[trk].items(): if re.match(r'{}(|_(\d\d\d)).zip'.format(p), params) is not None: print('Downloading: {}/{}'.format(trk, params)) _download_file(fileid, os.path.join(trk_path, params)) else: raise Exception('tracker_list values must either be set to ''all'', or be a list of param names') def unpack_tracking_results(download_path, output_path=None): """ Unpacks zipped benchmark results. The directory 'download_path' should have the following structure - root - tracker1 - param1.zip - param2.zip . . - tracker2 - param1.zip - param2.zip . . args: download_path - Path to the directory where the zipped results are stored output_path - Path to the directory where the results will be unpacked. Set to env_settings().results_path by default """ if output_path is None: output_path = env_settings().results_path if not os.path.exists(output_path): os.makedirs(output_path) trackers = os.listdir(download_path) for t in trackers: runfiles = os.listdir(os.path.join(download_path, t)) for r in runfiles: save_path = os.path.join(output_path, t) if not os.path.exists(save_path): os.makedirs(save_path) shutil.unpack_archive(os.path.join(download_path, t, r), os.path.join(save_path, r[:-4]), 'zip') def main(): parser = argparse.ArgumentParser(description='Download and unpack zipped results') parser.add_argument('--tracker', type=str, default='all', help='Name of tracker results to download, or ''all''.') parser.add_argument('--output_path', type=str, default=None, help='Path to the directory where the results will be unpacked.') parser.add_argument('--temp_download_path', type=str, default=None, help='Temporary path used for downloading the Zip files.') parser.add_argument('--download', type=bool, default=True, help='Whether to download results or unpack existing downloaded files.') args = parser.parse_args() download_path = args.temp_download_path if download_path is None: download_path = '{}/pytracking_results/'.format(tempfile.gettempdir()) if args.download: download_results(download_path, args.tracker) unpack_tracking_results(download_path, args.output_path) if __name__ == '__main__': main() ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/util_scripts/pack_got10k_results.py ================================================ import numpy as np import os import shutil from pytracking.evaluation.environment import env_settings def pack_got10k_results(tracker_name, param_name, output_name): """ Packs got10k results into a zip folder which can be directly uploaded to the evaluation server. The packed file is saved in the folder env_settings().got_packed_results_path args: tracker_name - name of the tracker param_name - name of the parameter file output_name - name of the packed zip file """ output_path = os.path.join(env_settings().got_packed_results_path, output_name) if not os.path.exists(output_path): os.makedirs(output_path) results_path = env_settings().results_path for i in range(1,181): seq_name = 'GOT-10k_Test_{:06d}'.format(i) seq_output_path = '{}/{}'.format(output_path, seq_name) if not os.path.exists(seq_output_path): os.makedirs(seq_output_path) for run_id in range(3): res = np.loadtxt('{}/{}/{}_{:03d}/{}.txt'.format(results_path, tracker_name, param_name, run_id, seq_name), dtype=np.float64) times = np.loadtxt( '{}/{}/{}_{:03d}/{}_time.txt'.format(results_path, tracker_name, param_name, run_id, seq_name), dtype=np.float64) np.savetxt('{}/{}_{:03d}.txt'.format(seq_output_path, seq_name, run_id+1), res, delimiter=',', fmt='%f') np.savetxt('{}/{}_time.txt'.format(seq_output_path, seq_name), times, fmt='%f') # Generate ZIP file shutil.make_archive(output_path, 'zip', output_path) # Remove raw text files shutil.rmtree(output_path) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/util_scripts/pack_trackingnet_results.py ================================================ import numpy as np import os import shutil from pytracking.evaluation.environment import env_settings from pytracking.evaluation.datasets import get_dataset def pack_trackingnet_results(tracker_name, param_name, run_id=None, output_name=None): """ Packs trackingnet results into a zip folder which can be directly uploaded to the evaluation server. The packed file is saved in the folder env_settings().tn_packed_results_path args: tracker_name - name of the tracker param_name - name of the parameter file run_id - run id for the tracker output_name - name of the packed zip file """ if output_name is None: if run_id is None: output_name = '{}_{}'.format(tracker_name, param_name) else: output_name = '{}_{}_{:03d}'.format(tracker_name, param_name, run_id) output_path = os.path.join(env_settings().tn_packed_results_path, output_name) if not os.path.exists(output_path): os.makedirs(output_path) results_path = env_settings().results_path tn_dataset = get_dataset('trackingnet') for seq in tn_dataset: seq_name = seq.name if run_id is None: seq_results_path = '{}/{}/{}/{}.txt'.format(results_path, tracker_name, param_name, seq_name) else: seq_results_path = '{}/{}/{}_{:03d}/{}.txt'.format(results_path, tracker_name, param_name, run_id, seq_name) results = np.loadtxt(seq_results_path, dtype=np.float64) np.savetxt('{}/{}.txt'.format(output_path, seq_name), results, delimiter=',', fmt='%.2f') # Generate ZIP file shutil.make_archive(output_path, 'zip', output_path) # Remove raw text files shutil.rmtree(output_path) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/utils/__init__.py ================================================ from .params import TrackerParams, FeatureParams, Choice ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/utils/convert_vot_anno_to_rect.py ================================================ import numpy as np def convert_vot_anno_to_rect(vot_anno, type): if len(vot_anno) == 4: return vot_anno if type == 'union': x1 = min(vot_anno[0::2]) x2 = max(vot_anno[0::2]) y1 = min(vot_anno[1::2]) y2 = max(vot_anno[1::2]) return [x1, y1, x2 - x1, y2 - y1] elif type == 'preserve_area': if len(vot_anno) != 8: raise ValueError vot_anno = np.array(vot_anno) cx = np.mean(vot_anno[0::2]) cy = np.mean(vot_anno[1::2]) x1 = min(vot_anno[0::2]) x2 = max(vot_anno[0::2]) y1 = min(vot_anno[1::2]) y2 = max(vot_anno[1::2]) A1 = np.linalg.norm(vot_anno[0:2] - vot_anno[2: 4]) * np.linalg.norm(vot_anno[2: 4] - vot_anno[4:6]) A2 = (x2 - x1) * (y2 - y1) s = np.sqrt(A1 / A2) w = s * (x2 - x1) + 1 h = s * (y2 - y1) + 1 x = cx - 0.5*w y = cy - 0.5*h return [x, y, w, h] else: raise ValueError ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/utils/load_text.py ================================================ import numpy as np import pandas as pd def load_text_numpy(path, delimiter, dtype): if isinstance(delimiter, (tuple, list)): for d in delimiter: try: ground_truth_rect = np.loadtxt(path, delimiter=d, dtype=dtype) return ground_truth_rect except: pass raise Exception('Could not read file {}'.format(path)) else: ground_truth_rect = np.loadtxt(path, delimiter=delimiter, dtype=dtype) return ground_truth_rect def load_text_pandas(path, delimiter, dtype): if isinstance(delimiter, (tuple, list)): for d in delimiter: try: ground_truth_rect = pd.read_csv(path, delimiter=d, header=None, dtype=dtype, na_filter=False, low_memory=False).values return ground_truth_rect except Exception as e: pass raise Exception('Could not read file {}'.format(path)) else: ground_truth_rect = pd.read_csv(path, delimiter=delimiter, header=None, dtype=dtype, na_filter=False, low_memory=False).values return ground_truth_rect def load_text(path, delimiter=' ', dtype=np.float32, backend='numpy'): if backend == 'numpy': return load_text_numpy(path, delimiter, dtype) elif backend == 'pandas': return load_text_pandas(path, delimiter, dtype) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/utils/loading.py ================================================ import os import ltr.admin.loading as ltr_loading from pytracking.evaluation.environment import env_settings def load_network(net_path, **kwargs): """Load network for tracking. args: net_path - Path to network. If it is not an absolute path, it is relative to the network_path in the local.py. See ltr.admin.loading.load_network for further details. **kwargs - Additional key-word arguments that are sent to ltr.admin.loading.load_network. """ kwargs['backbone_pretrained'] = False if os.path.isabs(net_path): path_full = net_path net, _ = ltr_loading.load_network(path_full, **kwargs) elif isinstance(env_settings().network_path, (list, tuple)): net = None for p in env_settings().network_path: path_full = os.path.join(p, net_path) try: net, _ = ltr_loading.load_network(path_full, **kwargs) break except Exception as e: # print(e) pass assert net is not None, 'Failed to load network' else: path_full = os.path.join(env_settings().network_path, net_path) net, _ = ltr_loading.load_network(path_full, **kwargs) return net ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/utils/params.py ================================================ from pytracking import TensorList import random class TrackerParams: """Class for tracker parameters.""" def set_default_values(self, default_vals: dict): for name, val in default_vals.items(): if not hasattr(self, name): setattr(self, name, val) def get(self, name: str, *default): """Get a parameter value with the given name. If it does not exists, it return the default value given as a second argument or returns an error if no default value is given.""" if len(default) > 1: raise ValueError('Can only give one default value.') if not default: return getattr(self, name) return getattr(self, name, default[0]) def has(self, name: str): """Check if there exist a parameter with the given name.""" return hasattr(self, name) class FeatureParams: """Class for feature specific parameters""" def __init__(self, *args, **kwargs): if len(args) > 0: raise ValueError for name, val in kwargs.items(): if isinstance(val, list): setattr(self, name, TensorList(val)) else: setattr(self, name, val) def Choice(*args): """Can be used to sample random parameter values.""" return random.choice(args) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/utils/plotting.py ================================================ import matplotlib.pyplot as plt import numpy as np import torch import cv2 def draw_figure(fig): fig.canvas.draw() fig.canvas.flush_events() plt.pause(0.001) def show_tensor(a: torch.Tensor, fig_num = None, title = None, range=(None, None), ax=None): """Display a 2D tensor. args: fig_num: Figure number. title: Title of figure. """ a_np = a.squeeze().cpu().clone().detach().numpy() if a_np.ndim == 3: a_np = np.transpose(a_np, (1, 2, 0)) if ax is None: fig = plt.figure(fig_num) plt.tight_layout() plt.cla() plt.imshow(a_np, vmin=range[0], vmax=range[1]) plt.axis('off') plt.axis('equal') if title is not None: plt.title(title) draw_figure(fig) else: ax.cla() ax.imshow(a_np, vmin=range[0], vmax=range[1]) ax.set_axis_off() ax.axis('equal') if title is not None: ax.set_title(title) draw_figure(plt.gcf()) def plot_graph(a: torch.Tensor, fig_num = None, title = None): """Plot graph. Data is a 1D tensor. args: fig_num: Figure number. title: Title of figure. """ a_np = a.squeeze().cpu().clone().detach().numpy() if a_np.ndim > 1: raise ValueError fig = plt.figure(fig_num) # plt.tight_layout() plt.cla() plt.plot(a_np) if title is not None: plt.title(title) draw_figure(fig) def show_image_with_boxes(im, boxes, iou_pred=None, disp_ids=None): im_np = im.clone().cpu().squeeze().numpy() im_np = np.ascontiguousarray(im_np.transpose(1, 2, 0).astype(np.uint8)) boxes = boxes.view(-1, 4).cpu().numpy().round().astype(int) # Draw proposals for i_ in range(boxes.shape[0]): if disp_ids is None or disp_ids[i_]: bb = boxes[i_, :] disp_color = (i_*38 % 256, (255 - i_*97) % 256, (123 + i_*66) % 256) cv2.rectangle(im_np, (bb[0], bb[1]), (bb[0] + bb[2], bb[1] + bb[3]), disp_color, 1) if iou_pred is not None: text_pos = (bb[0], bb[1] - 5) cv2.putText(im_np, 'ID={} IOU = {:3.2f}'.format(i_, iou_pred[i_]), text_pos, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1, bottomLeftOrigin=False) im_tensor = torch.from_numpy(im_np.transpose(2, 0, 1)).float() return im_tensor def _pascal_color_map(N=256, normalized=False): """ Python implementation of the color map function for the PASCAL VOC data set. Official Matlab version can be found in the PASCAL VOC devkit http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html#devkit """ def bitget(byteval, idx): return (byteval & (1 << idx)) != 0 dtype = 'float32' if normalized else 'uint8' cmap = np.zeros((N, 3), dtype=dtype) for i in range(N): r = g = b = 0 c = i for j in range(8): r = r | (bitget(c, 0) << 7 - j) g = g | (bitget(c, 1) << 7 - j) b = b | (bitget(c, 2) << 7 - j) c = c >> 3 cmap[i] = np.array([r, g, b]) cmap = cmap / 255 if normalized else cmap return cmap def overlay_mask(im, ann, alpha=0.5, colors=None, contour_thickness=None): """ Overlay mask over image. Source: https://github.com/albertomontesg/davis-interactive/blob/master/davisinteractive/utils/visualization.py This function allows you to overlay a mask over an image with some transparency. # Arguments im: Numpy Array. Array with the image. The shape must be (H, W, 3) and the pixels must be represented as `np.uint8` data type. ann: Numpy Array. Array with the mask. The shape must be (H, W) and the values must be intergers alpha: Float. Proportion of alpha to apply at the overlaid mask. colors: Numpy Array. Optional custom colormap. It must have shape (N, 3) being N the maximum number of colors to represent. contour_thickness: Integer. Thickness of each object index contour draw over the overlay. This function requires to have installed the package `opencv-python`. # Returns Numpy Array: Image of the overlay with shape (H, W, 3) and data type `np.uint8`. """ im, ann = np.asarray(im, dtype=np.uint8), np.asarray(ann, dtype=np.int) if im.shape[:-1] != ann.shape: raise ValueError('First two dimensions of `im` and `ann` must match') if im.shape[-1] != 3: raise ValueError('im must have three channels at the 3 dimension') colors = colors or _pascal_color_map() colors = np.asarray(colors, dtype=np.uint8) mask = colors[ann] fg = im * alpha + (1 - alpha) * mask img = im.copy() img[ann > 0] = fg[ann > 0] if contour_thickness: # pragma: no cover import cv2 for obj_id in np.unique(ann[ann > 0]): contours = cv2.findContours((ann == obj_id).astype( np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:] cv2.drawContours(img, contours[0], -1, colors[obj_id].tolist(), contour_thickness) return img ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/utils/visdom.py ================================================ import visdom import visdom.server from pytracking.features.preprocessing import numpy_to_torch from pytracking.utils.plotting import show_image_with_boxes, overlay_mask import cv2 import torch import copy import numpy as np from collections import OrderedDict class VisBase: def __init__(self, visdom, show_data, title): self.visdom = visdom self.show_data = show_data self.title = title self.raw_data = None def update(self, data, **kwargs): self.save_data(data, **kwargs) if self.show_data: self.draw_data() def save_data(self, data, **kwargs): raise NotImplementedError def draw_data(self): raise NotImplementedError def toggle_display(self, new_mode=None): if new_mode is not None: self.show_data = new_mode else: self.show_data = not self.show_data if self.show_data: self.draw_data() else: self.visdom.close(self.title) class VisImage(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) def save_data(self, data): data = data.float() self.raw_data = data def draw_data(self): self.visdom.image(self.raw_data.clone(), opts={'title': self.title}, win=self.title) class VisHeatmap(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) def save_data(self, data): data = data.squeeze().flip(0) self.raw_data = data def draw_data(self): self.visdom.heatmap(self.raw_data.clone(), opts={'title': self.title}, win=self.title) class VisFeaturemap(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) self.block_list = None def block_list_callback_handler(self, data): self.block_list[data['propertyId']]['value'] = data['value'] self.visdom.properties(self.block_list, opts={'title': 'Featuremap UI'}, win='featuremap_ui') self.draw_data() def save_data(self, data): data = data.view(-1, *data.shape[-2:]) data = data.flip(1) if self.block_list is None: self.block_list = [] self.draw_feat = [] for i in range(data.shape[0]): self.block_list.append({'type': 'checkbox', 'name': 'Channel {:04d}'.format(i), 'value': False}) self.visdom.properties(self.block_list, opts={'title': 'Featuremap UI'}, win='featuremap_ui') self.visdom.register_event_handler(self.block_list_callback_handler, 'featuremap_ui') self.raw_data = data def draw_data(self): if self.block_list is not None and self.show_data: for i, d in enumerate(self.block_list): if d['value']: fig_title = '{} ch: {:04d}'.format(self.title, i) self.visdom.heatmap(self.raw_data[i, :, :].clone(), opts={'title': fig_title}, win=fig_title) class VisCostVolume(VisBase): def __init__(self, visdom, show_data, title, flip=False): super().__init__(visdom, show_data, title) self.show_slice = False self.slice_pos = None self.flip = flip def show_cost_volume(self): data = self.raw_data.clone() # data_perm = data.permute(2, 0, 3, 1).contiguous() data_perm = data.permute(0, 2, 1, 3).contiguous() if self.flip: data_perm = data_perm.permute(2, 3, 0, 1).contiguous() data_perm = data_perm.view(data_perm.shape[0] * data_perm.shape[1], -1) self.visdom.heatmap(data_perm.flip(0), opts={'title': self.title}, win=self.title) def set_zoom_pos(self, slice_pos): self.slice_pos = slice_pos def toggle_show_slice(self, new_mode=None): if new_mode is not None: self.show_slice = new_mode else: self.show_slice = not self.show_slice def show_cost_volume_slice(self): slice_pos = self.slice_pos # slice_pos: [row, col] cost_volume_data = self.raw_data.clone() if self.flip: cost_volume_slice = cost_volume_data[:, :, slice_pos[0], slice_pos[1]] else: cost_volume_slice = cost_volume_data[slice_pos[0], slice_pos[1], :, :] self.visdom.heatmap(cost_volume_slice.flip(0), opts={'title': self.title}, win=self.title) def save_data(self, data): data = data.view(data.shape[-2], data.shape[-1], data.shape[-2], data.shape[-1]) self.raw_data = data def draw_data(self): if self.show_slice: self.show_cost_volume_slice() else: self.show_cost_volume() class VisCostVolumeUI(VisBase): def cv_ui_handler(self, data): zoom_toggled = False if data['event_type'] == 'KeyPress': if data['key'] == 'ArrowRight': self.zoom_pos[1] = min(self.zoom_pos[1] + 1, self.feat_shape[1]-1) elif data['key'] == 'ArrowLeft': self.zoom_pos[1] = max(self.zoom_pos[1] - 1, 0) elif data['key'] == 'ArrowUp': self.zoom_pos[0] = max(self.zoom_pos[0] - 1, 0) elif data['key'] == 'ArrowDown': self.zoom_pos[0] = min(self.zoom_pos[0] + 1, self.feat_shape[0]-1) elif data['key'] == 'Enter': self.zoom_mode = not self.zoom_mode zoom_toggled = True # Update image self.show_image() # Update cost volumes for block_title, block in self.registered_blocks.items(): if isinstance(block, VisCostVolume): block.set_zoom_pos(self.zoom_pos) block.toggle_show_slice(self.zoom_mode) if (self.zoom_mode or zoom_toggled) and block.show_data: block.draw_data() def __init__(self, visdom, show_data, title, feat_shape, registered_blocks): super().__init__(visdom, show_data, title) self.feat_shape = feat_shape self.zoom_mode = False self.zoom_pos = [int((feat_shape[0] - 1) / 2), int((feat_shape[1] - 1) / 2)] self.registered_blocks = registered_blocks self.visdom.register_event_handler(self.cv_ui_handler, title) def draw_grid(self, data): stride_r = int(data.shape[1] / self.feat_shape[0]) stride_c = int(data.shape[2] / self.feat_shape[1]) # Draw grid data[:, list(range(0, data.shape[1], stride_r)), :] = 0 data[:, :, list(range(0, data.shape[2], stride_c))] = 0 data[0, list(range(0, data.shape[1], stride_r)), :] = 255 data[0, :, list(range(0, data.shape[2], stride_c))] = 255 return data def shade_cell(self, data): stride_r = int(data.shape[1] / self.feat_shape[0]) stride_c = int(data.shape[2] / self.feat_shape[1]) r1 = self.zoom_pos[0]*stride_r r2 = min((self.zoom_pos[0] + 1)*stride_r, data.shape[1]) c1 = self.zoom_pos[1] * stride_c c2 = min((self.zoom_pos[1] + 1) * stride_c, data.shape[2]) factor = 0.8 if self.zoom_mode else 0.5 data[:, r1:r2, c1:c2] = data[:, r1:r2, c1:c2] * (1 - factor) + torch.tensor([255.0, 0.0, 0.0]).view(3, 1, 1).to(data.device) * factor return data def show_image(self, data=None): if data is None: data = self.raw_data.clone() data = self.draw_grid(data) data = self.shade_cell(data) self.visdom.image(data, opts={'title': self.title}, win=self.title) def save_data(self, data): # Ignore feat shape data = data[0] data = data.float() self.raw_data = data def draw_data(self): self.show_image(self.raw_data.clone()) class VisInfoDict(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) self.raw_data = OrderedDict() def generate_display_text(self, data): display_text = '' for key, value in data.items(): key = key.replace('_', ' ') if value is None: display_text += '{}: {}
'.format(key, 'None') elif isinstance(value, (str, int)): display_text += '{}: {}
'.format(key, value) else: display_text += '{}: {:.2f}
'.format(key, value) return display_text def save_data(self, data): for key, val in data.items(): self.raw_data[key] = val def draw_data(self): data = copy.deepcopy(self.raw_data) display_text = self.generate_display_text(data) self.visdom.text(display_text, opts={'title': self.title}, win=self.title) class VisText(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) def save_data(self, data): self.raw_data = data def draw_data(self): data = copy.deepcopy(self.raw_data) self.visdom.text(data, opts={'title': self.title}, win=self.title) class VisLinePlot(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) def save_data(self, data): self.raw_data = data def draw_data(self): if isinstance(self.raw_data, (list, tuple)): data_y = self.raw_data[0].clone() data_x = self.raw_data[1].clone() else: data_y = self.raw_data.clone() data_x = torch.arange(data_y.shape[0]) self.visdom.line(data_y, data_x, opts={'title': self.title}, win=self.title) class VisTracking(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) def save_data(self, data): image = data[0] boxes_masks = data[1:] boxes, masks = [], [] for bm in boxes_masks: if bm is None: continue if isinstance(bm, list): boxes.append(torch.Tensor(bm)); continue if len(bm.shape) > 1: # Binarize segmentation if a float tensor is provided if bm.dtype != np.uint8: bm = (bm > 0.5).astype(np.uint8) masks.append(bm); continue boxes.append(bm.float()) self.raw_data = [image, boxes, masks] def draw_data(self): disp_image = self.raw_data[0].copy() resize_factor = 1 if max(disp_image.shape) > 480: resize_factor = 480.0 / float(max(disp_image.shape)) disp_image = cv2.resize(disp_image, None, fx=resize_factor, fy=resize_factor) for i, mask in enumerate(self.raw_data[2]): self.raw_data[2][i] = cv2.resize(mask, None, fx=resize_factor, fy=resize_factor) boxes = [resize_factor * b.clone() for b in self.raw_data[1]] for i, disp_rect in enumerate(boxes): color = ((255*((i%3)>0)), 255*((i+1)%2), (255*(i%5))//4) cv2.rectangle(disp_image, (int(disp_rect[0]), int(disp_rect[1])), (int(disp_rect[0] + disp_rect[2]), int(disp_rect[1] + disp_rect[3])), color, 2) for i, mask in enumerate(self.raw_data[2], 1): disp_image = overlay_mask(disp_image, mask * i) disp_image = numpy_to_torch(disp_image).squeeze(0) disp_image = disp_image.float() self.visdom.image(disp_image, opts={'title': self.title}, win=self.title) class VisBBReg(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) self.block_list = [] def block_list_callback_handler(self, data): self.block_list[data['propertyId']]['value'] = data['value'] self.visdom.properties(self.block_list, opts={'title': 'BBReg Vis'}, win='bbreg_vis') self.draw_data() def save_data(self, data): self.image = data[0].float() self.init_boxes = data[1] self.final_boxes = data[2] self.final_ious = data[3] def draw_data(self): if len(self.block_list) == 0: self.block_list.append({'type': 'checkbox', 'name': 'ID 0', 'value': True}) self.block_list.append({'type': 'checkbox', 'name': 'ID 1', 'value': True}) self.visdom.properties(self.block_list, opts={'title': 'BBReg Vis'}, win='bbreg_vis') self.visdom.register_event_handler(self.block_list_callback_handler, 'bbreg_vis') disp_image = self.image ids = [x['value'] for x in self.block_list] init_box_image = show_image_with_boxes(disp_image.clone(), self.init_boxes.clone(), disp_ids=ids) final_box_image = show_image_with_boxes(disp_image.clone(), self.final_boxes.clone(), self.final_ious.clone(), disp_ids=ids) self.visdom.image(init_box_image, opts={'title': 'Init Boxes'}, win='Init Boxes') self.visdom.image(final_box_image, opts={'title': 'Final Boxes'}, win='Final Boxes') class Visdom: def __init__(self, debug=0, ui_info=None, visdom_info=None): self.debug = debug self.visdom = visdom.Visdom(server=visdom_info.get('server', '127.0.0.1'), port=visdom_info.get('port', 8097)) self.registered_blocks = {} self.blocks_list = [] self.visdom.properties(self.blocks_list, opts={'title': 'Block List'}, win='block_list') self.visdom.register_event_handler(self.block_list_callback_handler, 'block_list') if ui_info is not None: self.visdom.register_event_handler(ui_info['handler'], ui_info['win_id']) def block_list_callback_handler(self, data): field_name = self.blocks_list[data['propertyId']]['name'] self.registered_blocks[field_name].toggle_display(data['value']) self.blocks_list[data['propertyId']]['value'] = data['value'] self.visdom.properties(self.blocks_list, opts={'title': 'Block List'}, win='block_list') def register(self, data, mode, debug_level=0, title='Data', **kwargs): if title not in self.registered_blocks.keys(): show_data = self.debug >= debug_level if title != 'Tracking': self.blocks_list.append({'type': 'checkbox', 'name': title, 'value': show_data}) self.visdom.properties(self.blocks_list, opts={'title': 'Block List'}, win='block_list') if mode == 'image': self.registered_blocks[title] = VisImage(self.visdom, show_data, title) elif mode == 'heatmap': self.registered_blocks[title] = VisHeatmap(self.visdom, show_data, title) elif mode == 'cost_volume': self.registered_blocks[title] = VisCostVolume(self.visdom, show_data, title) elif mode == 'cost_volume_flip': self.registered_blocks[title] = VisCostVolume(self.visdom, show_data, title, flip=True) elif mode == 'cost_volume_ui': self.registered_blocks[title] = VisCostVolumeUI(self.visdom, show_data, title, data[1], self.registered_blocks) elif mode == 'info_dict': self.registered_blocks[title] = VisInfoDict(self.visdom, show_data, title) elif mode == 'text': self.registered_blocks[title] = VisText(self.visdom, show_data, title) elif mode == 'lineplot': self.registered_blocks[title] = VisLinePlot(self.visdom, show_data, title) elif mode == 'Tracking': self.registered_blocks[title] = VisTracking(self.visdom, show_data, title) elif mode == 'bbreg': self.registered_blocks[title] = VisBBReg(self.visdom, show_data, title) elif mode == 'featmap': self.registered_blocks[title] = VisFeaturemap(self.visdom, show_data, title) else: raise ValueError('Visdom Error: Unknown data mode {}'.format(mode)) # Update self.registered_blocks[title].update(data, **kwargs) ================================================ FILE: artrackv2_mindspore/external/AR/pytracking/vot20_utils.py ================================================ import numpy as np def make_full_size(x, output_sz): ''' zero-pad input x (right and down) to match output_sz x: numpy array e.g., binary mask output_sz: size of the output [width, height] ''' if x.shape[0] == output_sz[1] and x.shape[1] == output_sz[0]: return x pad_x = output_sz[0] - x.shape[1] if pad_x < 0: x = x[:, :x.shape[1] + pad_x] # padding has to be set to zero, otherwise pad function fails pad_x = 0 pad_y = output_sz[1] - x.shape[0] if pad_y < 0: x = x[:x.shape[0] + pad_y, :] # padding has to be set to zero, otherwise pad function fails pad_y = 0 return np.pad(x, ((0, pad_y), (0, pad_x)), 'constant', constant_values=0) def rect_from_mask(mask): ''' create an axis-aligned rectangle from a given binary mask mask in created as a minimal rectangle containing all non-zero pixels ''' x_ = np.sum(mask, axis=0) y_ = np.sum(mask, axis=1) x0 = np.min(np.nonzero(x_)) x1 = np.max(np.nonzero(x_)) y0 = np.min(np.nonzero(y_)) y1 = np.max(np.nonzero(y_)) return [x0, y0, x1 - x0 + 1, y1 - y0 + 1] def mask_from_rect(rect, output_sz): ''' create a binary mask from a given rectangle rect: axis-aligned rectangle [x0, y0, width, height] output_sz: size of the output [width, height] ''' mask = np.zeros((output_sz[1], output_sz[0]), dtype=np.uint8) x0 = max(int(round(rect[0])), 0) y0 = max(int(round(rect[1])), 0) x1 = min(int(round(rect[0] + rect[2])), output_sz[0]) y1 = min(int(round(rect[1] + rect[3])), output_sz[1]) mask[y0:y1, x0:x1] = 1 return mask def bbox_clip(x1, y1, x2, y2, boundary, min_sz=10): '''boundary (H,W)''' x1_new = max(0, min(x1, boundary[1] - min_sz)) y1_new = max(0, min(y1, boundary[0] - min_sz)) x2_new = max(min_sz, min(x2, boundary[1])) y2_new = max(min_sz, min(y2, boundary[0])) return x1_new, y1_new, x2_new, y2_new ================================================ FILE: artrackv2_mindspore/external/PreciseRoIPooling/.gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class .vim-template* # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ .pytest_cache/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ ================================================ FILE: artrackv2_mindspore/external/PreciseRoIPooling/LICENSE ================================================ MIT License Copyright (c) 2018 Jiayuan Mao Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: artrackv2_mindspore/external/PreciseRoIPooling/README.md ================================================ # PreciseRoIPooling This repo implements the **Precise RoI Pooling** (PrRoI Pooling), proposed in the paper **Acquisition of Localization Confidence for Accurate Object Detection** published at ECCV 2018 (Oral Presentation). **Acquisition of Localization Confidence for Accurate Object Detection** _Borui Jiang*, Ruixuan Luo*, Jiayuan Mao*, Tete Xiao, Yuning Jiang_ (* indicates equal contribution.) https://arxiv.org/abs/1807.11590 ## Brief In short, Precise RoI Pooling is an integration-based (bilinear interpolation) average pooling method for RoI Pooling. It avoids any quantization and has a continuous gradient on bounding box coordinates. It is: - different from the original RoI Pooling proposed in [Fast R-CNN](https://arxiv.org/abs/1504.08083). PrRoI Pooling uses average pooling instead of max pooling for each bin and has a continuous gradient on bounding box coordinates. That is, one can take the derivatives of some loss function w.r.t the coordinates of each RoI and optimize the RoI coordinates. - different from the RoI Align proposed in [Mask R-CNN](https://arxiv.org/abs/1703.06870). PrRoI Pooling uses a full integration-based average pooling instead of sampling a constant number of points. This makes the gradient w.r.t. the coordinates continuous. For a better illustration, we illustrate RoI Pooling, RoI Align and PrRoI Pooing in the following figure. More details including the gradient computation can be found in our paper.
## Implementation PrRoI Pooling was originally implemented by [Tete Xiao](http://tetexiao.com/) based on MegBrain, an (internal) deep learning framework built by Megvii Inc. It was later adapted into open-source deep learning frameworks. Currently, we only support PyTorch. Unfortunately, we don't have any specific plan for the adaptation into other frameworks such as TensorFlow, but any contributions (pull requests) will be more than welcome. ## Usage (PyTorch 1.0) In the directory `pytorch/`, we provide a PyTorch-based implementation of PrRoI Pooling. It requires PyTorch 1.0+ and only supports CUDA (CPU mode is not implemented). Since we use PyTorch JIT for cxx/cuda code compilation, to use the module in your code, simply do: ``` from prroi_pool import PrRoIPool2D avg_pool = PrRoIPool2D(window_height, window_width, spatial_scale) roi_features = avg_pool(features, rois) # for those who want to use the "functional" from prroi_pool.functional import prroi_pool2d roi_features = prroi_pool2d(features, rois, window_height, window_width, spatial_scale) ``` ## Usage (PyTorch 0.4) **!!! Please first checkout to the branch pytorch0.4.** In the directory `pytorch/`, we provide a PyTorch-based implementation of PrRoI Pooling. It requires PyTorch 0.4 and only supports CUDA (CPU mode is not implemented). To use the PrRoI Pooling module, first goto `pytorch/prroi_pool` and execute `./travis.sh` to compile the essential components (you may need `nvcc` for this step). To use the module in your code, simply do: ``` from prroi_pool import PrRoIPool2D avg_pool = PrRoIPool2D(window_height, window_width, spatial_scale) roi_features = avg_pool(features, rois) # for those who want to use the "functional" from prroi_pool.functional import prroi_pool2d roi_features = prroi_pool2d(features, rois, window_height, window_width, spatial_scale) ``` Here, - RoI is an `m * 5` float tensor of format `(batch_index, x0, y0, x1, y1)`, following the convention in the original Caffe implementation of RoI Pooling, although in some frameworks the batch indices are provided by an integer tensor. - `spatial_scale` is multiplied to the RoIs. For example, if your feature maps are down-sampled by a factor of 16 (w.r.t. the input image), you should use a spatial scale of `1/16`. - The coordinates for RoI follows the [L, R) convension. That is, `(0, 0, 4, 4)` denotes a box of size `4x4`. ================================================ FILE: artrackv2_mindspore/external/PreciseRoIPooling/pytorch/prroi_pool/.gitignore ================================================ *.o /_prroi_pooling ================================================ FILE: artrackv2_mindspore/external/PreciseRoIPooling/pytorch/prroi_pool/__init__.py ================================================ #! /usr/bin/env python3 # -*- coding: utf-8 -*- # File : __init__.py # Author : Jiayuan Mao, Tete Xiao # Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com # Date : 07/13/2018 # # This file is part of PreciseRoIPooling. # Distributed under terms of the MIT license. # Copyright (c) 2017 Megvii Technology Limited. from .prroi_pool import * ================================================ FILE: artrackv2_mindspore/external/PreciseRoIPooling/pytorch/prroi_pool/functional.py ================================================ #! /usr/bin/env python3 # -*- coding: utf-8 -*- # File : functional.py # Author : Jiayuan Mao, Tete Xiao # Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com # Date : 07/13/2018 # # This file is part of PreciseRoIPooling. # Distributed under terms of the MIT license. # Copyright (c) 2017 Megvii Technology Limited. import torch import torch.autograd as ag __all__ = ['prroi_pool2d'] _prroi_pooling = None def _import_prroi_pooling(): global _prroi_pooling if _prroi_pooling is None: try: from os.path import join as pjoin, dirname from torch.utils.cpp_extension import load as load_extension root_dir = pjoin(dirname(__file__), 'src') _prroi_pooling = load_extension( '_prroi_pooling', [pjoin(root_dir, 'prroi_pooling_gpu.c'), pjoin(root_dir, 'prroi_pooling_gpu_impl.cu')], verbose=True ) except ImportError: raise ImportError('Can not compile Precise RoI Pooling library.') return _prroi_pooling class PrRoIPool2DFunction(ag.Function): @staticmethod def forward(ctx, features, rois, pooled_height, pooled_width, spatial_scale): _prroi_pooling = _import_prroi_pooling() assert 'FloatTensor' in features.type() and 'FloatTensor' in rois.type(), \ 'Precise RoI Pooling only takes float input, got {} for features and {} for rois.'.format(features.type(), rois.type()) pooled_height = int(pooled_height) pooled_width = int(pooled_width) spatial_scale = float(spatial_scale) features = features.contiguous() rois = rois.contiguous() params = (pooled_height, pooled_width, spatial_scale) if features.is_cuda: output = _prroi_pooling.prroi_pooling_forward_cuda(features, rois, *params) ctx.params = params # everything here is contiguous. ctx.save_for_backward(features, rois, output) else: raise NotImplementedError('Precise RoI Pooling only supports GPU (cuda) implememtations.') return output @staticmethod def backward(ctx, grad_output): _prroi_pooling = _import_prroi_pooling() features, rois, output = ctx.saved_tensors grad_input = grad_coor = None if features.requires_grad: grad_output = grad_output.contiguous() grad_input = _prroi_pooling.prroi_pooling_backward_cuda(features, rois, output, grad_output, *ctx.params) if rois.requires_grad: grad_output = grad_output.contiguous() grad_coor = _prroi_pooling.prroi_pooling_coor_backward_cuda(features, rois, output, grad_output, *ctx.params) return grad_input, grad_coor, None, None, None prroi_pool2d = PrRoIPool2DFunction.apply ================================================ FILE: artrackv2_mindspore/external/PreciseRoIPooling/pytorch/prroi_pool/prroi_pool.py ================================================ #! /usr/bin/env python3 # -*- coding: utf-8 -*- # File : prroi_pool.py # Author : Jiayuan Mao, Tete Xiao # Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com # Date : 07/13/2018 # # This file is part of PreciseRoIPooling. # Distributed under terms of the MIT license. # Copyright (c) 2017 Megvii Technology Limited. import torch.nn as nn from .functional import prroi_pool2d __all__ = ['PrRoIPool2D'] class PrRoIPool2D(nn.Module): def __init__(self, pooled_height, pooled_width, spatial_scale): super().__init__() self.pooled_height = int(pooled_height) self.pooled_width = int(pooled_width) self.spatial_scale = float(spatial_scale) def forward(self, features, rois): return prroi_pool2d(features, rois, self.pooled_height, self.pooled_width, self.spatial_scale) def extra_repr(self): return 'kernel_size=({pooled_height}, {pooled_width}), spatial_scale={spatial_scale}'.format(**self.__dict__) ================================================ FILE: artrackv2_mindspore/external/PreciseRoIPooling/pytorch/prroi_pool/src/prroi_pooling_gpu.c ================================================ /* * File : prroi_pooling_gpu.c * Author : Jiayuan Mao, Tete Xiao * Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com * Date : 07/13/2018 * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ #include #include #include #include #include "prroi_pooling_gpu_impl.cuh" at::Tensor prroi_pooling_forward_cuda(const at::Tensor &features, const at::Tensor &rois, int pooled_height, int pooled_width, float spatial_scale) { int nr_rois = rois.size(0); int nr_channels = features.size(1); int height = features.size(2); int width = features.size(3); int top_count = nr_rois * nr_channels * pooled_height * pooled_width; auto output = at::zeros({nr_rois, nr_channels, pooled_height, pooled_width}, features.options()); if (output.numel() == 0) { AT_CUDA_CHECK(cudaGetLastError()); return output; } cudaStream_t stream = at::cuda::getCurrentCUDAStream(); PrRoIPoolingForwardGpu( stream, features.data(), rois.data(), output.data(), nr_channels, height, width, pooled_height, pooled_width, spatial_scale, top_count ); AT_CUDA_CHECK(cudaGetLastError()); return output; } at::Tensor prroi_pooling_backward_cuda( const at::Tensor &features, const at::Tensor &rois, const at::Tensor &output, const at::Tensor &output_diff, int pooled_height, int pooled_width, float spatial_scale) { auto features_diff = at::zeros_like(features); int nr_rois = rois.size(0); int batch_size = features.size(0); int nr_channels = features.size(1); int height = features.size(2); int width = features.size(3); int top_count = nr_rois * nr_channels * pooled_height * pooled_width; int bottom_count = batch_size * nr_channels * height * width; if (output.numel() == 0) { AT_CUDA_CHECK(cudaGetLastError()); return features_diff; } cudaStream_t stream = at::cuda::getCurrentCUDAStream(); PrRoIPoolingBackwardGpu( stream, features.data(), rois.data(), output.data(), output_diff.data(), features_diff.data(), nr_channels, height, width, pooled_height, pooled_width, spatial_scale, top_count, bottom_count ); AT_CUDA_CHECK(cudaGetLastError()); return features_diff; } at::Tensor prroi_pooling_coor_backward_cuda( const at::Tensor &features, const at::Tensor &rois, const at::Tensor &output, const at::Tensor &output_diff, int pooled_height, int pooled_width, float spatial_scale) { auto coor_diff = at::zeros_like(rois); int nr_rois = rois.size(0); int nr_channels = features.size(1); int height = features.size(2); int width = features.size(3); int top_count = nr_rois * nr_channels * pooled_height * pooled_width; int bottom_count = nr_rois * 5; if (output.numel() == 0) { AT_CUDA_CHECK(cudaGetLastError()); return coor_diff; } cudaStream_t stream = at::cuda::getCurrentCUDAStream(); PrRoIPoolingCoorBackwardGpu( stream, features.data(), rois.data(), output.data(), output_diff.data(), coor_diff.data(), nr_channels, height, width, pooled_height, pooled_width, spatial_scale, top_count, bottom_count ); AT_CUDA_CHECK(cudaGetLastError()); return coor_diff; } PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("prroi_pooling_forward_cuda", &prroi_pooling_forward_cuda, "PRRoIPooling_forward"); m.def("prroi_pooling_backward_cuda", &prroi_pooling_backward_cuda, "PRRoIPooling_backward"); m.def("prroi_pooling_coor_backward_cuda", &prroi_pooling_coor_backward_cuda, "PRRoIPooling_backward_coor"); } ================================================ FILE: artrackv2_mindspore/external/PreciseRoIPooling/pytorch/prroi_pool/src/prroi_pooling_gpu.h ================================================ /* * File : prroi_pooling_gpu.h * Author : Jiayuan Mao, Tete Xiao * Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com * Date : 07/13/2018 * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ int prroi_pooling_forward_cuda(THCudaTensor *features, THCudaTensor *rois, THCudaTensor *output, int pooled_height, int pooled_width, float spatial_scale); int prroi_pooling_backward_cuda( THCudaTensor *features, THCudaTensor *rois, THCudaTensor *output, THCudaTensor *output_diff, THCudaTensor *features_diff, int pooled_height, int pooled_width, float spatial_scale ); int prroi_pooling_coor_backward_cuda( THCudaTensor *features, THCudaTensor *rois, THCudaTensor *output, THCudaTensor *output_diff, THCudaTensor *features_diff, int pooled_height, int pooled_width, float spatial_scal ); ================================================ FILE: artrackv2_mindspore/external/PreciseRoIPooling/pytorch/prroi_pool/src/prroi_pooling_gpu_impl.cu ================================================ /* * File : prroi_pooling_gpu_impl.cu * Author : Tete Xiao, Jiayuan Mao * Email : jasonhsiao97@gmail.com * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ #include "prroi_pooling_gpu_impl.cuh" #include #include #define CUDA_KERNEL_LOOP(i, n) \ for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ i < (n); \ i += blockDim.x * gridDim.x) #define CUDA_POST_KERNEL_CHECK \ do { \ cudaError_t err = cudaGetLastError(); \ if (cudaSuccess != err) { \ fprintf(stderr, "cudaCheckError() failed : %s\n", cudaGetErrorString(err)); \ exit(-1); \ } \ } while(0) #define CUDA_NUM_THREADS 512 namespace { static int CUDA_NUM_BLOCKS(const int N) { return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; } __device__ static float PrRoIPoolingGetData(F_DEVPTR_IN data, const int h, const int w, const int height, const int width) { bool overflow = (h < 0) || (w < 0) || (h >= height) || (w >= width); float retVal = overflow ? 0.0f : data[h * width + w]; return retVal; } __device__ static float PrRoIPoolingGetCoeff(float dh, float dw){ dw = dw > 0 ? dw : -dw; dh = dh > 0 ? dh : -dh; return (1.0f - dh) * (1.0f - dw); } __device__ static float PrRoIPoolingSingleCoorIntegral(float s, float t, float c1, float c2) { return 0.5 * (t * t - s * s) * c2 + (t - 0.5 * t * t - s + 0.5 * s * s) * c1; } __device__ static float PrRoIPoolingInterpolation(F_DEVPTR_IN data, const float h, const float w, const int height, const int width){ float retVal = 0.0f; int h1 = floorf(h); int w1 = floorf(w); retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h)+1; w1 = floorf(w); retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h); w1 = floorf(w)+1; retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h)+1; w1 = floorf(w)+1; retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); return retVal; } __device__ static float PrRoIPoolingMatCalculation(F_DEVPTR_IN this_data, const int s_h, const int s_w, const int e_h, const int e_w, const float y0, const float x0, const float y1, const float x1, const int h0, const int w0) { float alpha, beta, lim_alpha, lim_beta, tmp; float sum_out = 0; alpha = x0 - float(s_w); beta = y0 - float(s_h); lim_alpha = x1 - float(s_w); lim_beta = y1 - float(s_h); tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, s_h, s_w, h0, w0) * tmp; alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, s_h, e_w, h0, w0) * tmp; alpha = x0 - float(s_w); beta = float(e_h) - y1; lim_alpha = x1 - float(s_w); lim_beta = float(e_h) - y0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, e_h, s_w, h0, w0) * tmp; alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, e_h, e_w, h0, w0) * tmp; return sum_out; } __device__ static void PrRoIPoolingDistributeDiff(F_DEVPTR_OUT diff, const float top_diff, const int h, const int w, const int height, const int width, const float coeff) { bool overflow = (h < 0) || (w < 0) || (h >= height) || (w >= width); if (!overflow) atomicAdd(diff + h * width + w, top_diff * coeff); } __device__ static void PrRoIPoolingMatDistributeDiff(F_DEVPTR_OUT diff, const float top_diff, const int s_h, const int s_w, const int e_h, const int e_w, const float y0, const float x0, const float y1, const float x1, const int h0, const int w0) { float alpha, beta, lim_alpha, lim_beta, tmp; alpha = x0 - float(s_w); beta = y0 - float(s_h); lim_alpha = x1 - float(s_w); lim_beta = y1 - float(s_h); tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, s_h, s_w, h0, w0, tmp); alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, s_h, e_w, h0, w0, tmp); alpha = x0 - float(s_w); beta = float(e_h) - y1; lim_alpha = x1 - float(s_w); lim_beta = float(e_h) - y0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, e_h, s_w, h0, w0, tmp); alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, e_h, e_w, h0, w0, tmp); } __global__ void PrRoIPoolingForward( const int nthreads, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, ((float)0.0)); float roi_height = max(roi_end_h - roi_start_h, ((float)0.0)); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_data = bottom_data + (roi_batch_ind * channels + c) * height * width; float *this_out = top_data + index; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); if (win_size == 0) { *this_out = 0; return; } float sum_out = 0; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); for (int w_iter = s_w; w_iter < e_w; ++w_iter) for (int h_iter = s_h; h_iter < e_h; ++h_iter) sum_out += PrRoIPoolingMatCalculation(this_data, h_iter, w_iter, h_iter + 1, w_iter + 1, max(win_start_h, float(h_iter)), max(win_start_w, float(w_iter)), min(win_end_h, float(h_iter) + 1.0), min(win_end_w, float(w_iter + 1.0)), height, width); *this_out = sum_out / win_size; } } __global__ void PrRoIPoolingBackward( const int nthreads, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, (float)0); float roi_height = max(roi_end_h - roi_start_h, (float)0); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_out_grad = top_diff + index; float *this_data_grad = bottom_diff + (roi_batch_ind * channels + c) * height * width; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); float sum_out = win_size == float(0) ? float(0) : *this_out_grad / win_size; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); for (int w_iter = s_w; w_iter < e_w; ++w_iter) for (int h_iter = s_h; h_iter < e_h; ++h_iter) PrRoIPoolingMatDistributeDiff(this_data_grad, sum_out, h_iter, w_iter, h_iter + 1, w_iter + 1, max(win_start_h, float(h_iter)), max(win_start_w, float(w_iter)), min(win_end_h, float(h_iter) + 1.0), min(win_end_w, float(w_iter + 1.0)), height, width); } } __global__ void PrRoIPoolingCoorBackward( const int nthreads, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, (float)0); float roi_height = max(roi_end_h - roi_start_h, (float)0); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_out_grad = top_diff + index; const float *this_bottom_data = bottom_data + (roi_batch_ind * channels + c) * height * width; const float *this_top_data = top_data + index; float *this_data_grad = bottom_diff + n * 5; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); float sum_out = win_size == float(0) ? float(0) : *this_out_grad / win_size; // WARNING: to be discussed if (sum_out == 0) return; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); float g_x1_y = 0, g_x2_y = 0, g_x_y1 = 0, g_x_y2 = 0; for (int h_iter = s_h; h_iter < e_h; ++h_iter) { g_x1_y += PrRoIPoolingSingleCoorIntegral(max(win_start_h, float(h_iter)) - h_iter, min(win_end_h, float(h_iter + 1)) - h_iter, PrRoIPoolingInterpolation(this_bottom_data, h_iter, win_start_w, height, width), PrRoIPoolingInterpolation(this_bottom_data, h_iter + 1, win_start_w, height, width)); g_x2_y += PrRoIPoolingSingleCoorIntegral(max(win_start_h, float(h_iter)) - h_iter, min(win_end_h, float(h_iter + 1)) - h_iter, PrRoIPoolingInterpolation(this_bottom_data, h_iter, win_end_w, height, width), PrRoIPoolingInterpolation(this_bottom_data, h_iter + 1, win_end_w, height, width)); } for (int w_iter = s_w; w_iter < e_w; ++w_iter) { g_x_y1 += PrRoIPoolingSingleCoorIntegral(max(win_start_w, float(w_iter)) - w_iter, min(win_end_w, float(w_iter + 1)) - w_iter, PrRoIPoolingInterpolation(this_bottom_data, win_start_h, w_iter, height, width), PrRoIPoolingInterpolation(this_bottom_data, win_start_h, w_iter + 1, height, width)); g_x_y2 += PrRoIPoolingSingleCoorIntegral(max(win_start_w, float(w_iter)) - w_iter, min(win_end_w, float(w_iter + 1)) - w_iter, PrRoIPoolingInterpolation(this_bottom_data, win_end_h, w_iter, height, width), PrRoIPoolingInterpolation(this_bottom_data, win_end_h, w_iter + 1, height, width)); } float partial_x1 = -g_x1_y + (win_end_h - win_start_h) * (*this_top_data); float partial_y1 = -g_x_y1 + (win_end_w - win_start_w) * (*this_top_data); float partial_x2 = g_x2_y - (win_end_h - win_start_h) * (*this_top_data); float partial_y2 = g_x_y2 - (win_end_w - win_start_w) * (*this_top_data); partial_x1 = partial_x1 / win_size * spatial_scale; partial_x2 = partial_x2 / win_size * spatial_scale; partial_y1 = partial_y1 / win_size * spatial_scale; partial_y2 = partial_y2 / win_size * spatial_scale; // (b, x1, y1, x2, y2) this_data_grad[0] = 0; atomicAdd(this_data_grad + 1, (partial_x1 * (1.0 - float(pw) / pooled_width) + partial_x2 * (1.0 - float(pw + 1) / pooled_width)) * (*this_out_grad)); atomicAdd(this_data_grad + 2, (partial_y1 * (1.0 - float(ph) / pooled_height) + partial_y2 * (1.0 - float(ph + 1) / pooled_height)) * (*this_out_grad)); atomicAdd(this_data_grad + 3, (partial_x2 * float(pw + 1) / pooled_width + partial_x1 * float(pw) / pooled_width) * (*this_out_grad)); atomicAdd(this_data_grad + 4, (partial_y2 * float(ph + 1) / pooled_height + partial_y1 * float(ph) / pooled_height) * (*this_out_grad)); } } } /* !anonymous namespace */ #ifdef __cplusplus extern "C" { #endif void PrRoIPoolingForwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count) { PrRoIPoolingForward<<>>( top_count, bottom_data, bottom_rois, top_data, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } void PrRoIPoolingBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count) { cudaMemsetAsync(bottom_diff, 0, sizeof(float) * bottom_count, stream); PrRoIPoolingBackward<<>>( top_count, bottom_rois, top_diff, bottom_diff, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } void PrRoIPoolingCoorBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count) { cudaMemsetAsync(bottom_diff, 0, sizeof(float) * bottom_count, stream); PrRoIPoolingCoorBackward<<>>( top_count, bottom_data, bottom_rois, top_data, top_diff, bottom_diff, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } } /* !extern "C" */ ================================================ FILE: artrackv2_mindspore/external/PreciseRoIPooling/pytorch/prroi_pool/src/prroi_pooling_gpu_impl.cuh ================================================ /* * File : prroi_pooling_gpu_impl.cuh * Author : Tete Xiao, Jiayuan Mao * Email : jasonhsiao97@gmail.com * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ #ifndef PRROI_POOLING_GPU_IMPL_CUH #define PRROI_POOLING_GPU_IMPL_CUH #ifdef __cplusplus extern "C" { #endif #define F_DEVPTR_IN const float * #define F_DEVPTR_OUT float * void PrRoIPoolingForwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count); void PrRoIPoolingBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count); void PrRoIPoolingCoorBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count); #ifdef __cplusplus } /* !extern "C" */ #endif #endif /* !PRROI_POOLING_GPU_IMPL_CUH */ ================================================ FILE: artrackv2_mindspore/external/PreciseRoIPooling/pytorch/tests/test_prroi_pooling2d.py ================================================ # -*- coding: utf-8 -*- # File : test_prroi_pooling2d.py # Author : Jiayuan Mao # Email : maojiayuan@gmail.com # Date : 18/02/2018 # # This file is part of Jacinle. import unittest import torch import torch.nn as nn import torch.nn.functional as F from jactorch.utils.unittest import TorchTestCase from prroi_pool import PrRoIPool2D class TestPrRoIPool2D(TorchTestCase): def test_forward(self): pool = PrRoIPool2D(7, 7, spatial_scale=0.5) features = torch.rand((4, 16, 24, 32)).cuda() rois = torch.tensor([ [0, 0, 0, 14, 14], [1, 14, 14, 28, 28], ]).float().cuda() out = pool(features, rois) out_gold = F.avg_pool2d(features, kernel_size=2, stride=1) self.assertTensorClose(out, torch.stack(( out_gold[0, :, :7, :7], out_gold[1, :, 7:14, 7:14], ), dim=0)) def test_backward_shapeonly(self): pool = PrRoIPool2D(2, 2, spatial_scale=0.5) features = torch.rand((4, 2, 24, 32)).cuda() rois = torch.tensor([ [0, 0, 0, 4, 4], [1, 14, 14, 18, 18], ]).float().cuda() features.requires_grad = rois.requires_grad = True out = pool(features, rois) loss = out.sum() loss.backward() self.assertTupleEqual(features.size(), features.grad.size()) self.assertTupleEqual(rois.size(), rois.grad.size()) if __name__ == '__main__': unittest.main() ================================================ FILE: artrackv2_mindspore/external/PreciseRoIPooling/src/prroi_pooling_gpu_impl.cu ================================================ /* * File : prroi_pooling_gpu_impl.cu * Author : Tete Xiao, Jiayuan Mao * Email : jasonhsiao97@gmail.com * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ #include "prroi_pooling_gpu_impl.cuh" #include #include #define CUDA_KERNEL_LOOP(i, n) \ for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ i < (n); \ i += blockDim.x * gridDim.x) #define CUDA_POST_KERNEL_CHECK \ do { \ cudaError_t err = cudaGetLastError(); \ if (cudaSuccess != err) { \ fprintf(stderr, "cudaCheckError() failed : %s\n", cudaGetErrorString(err)); \ exit(-1); \ } \ } while(0) #define CUDA_NUM_THREADS 512 namespace { static int CUDA_NUM_BLOCKS(const int N) { return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; } __device__ static float PrRoIPoolingGetData(F_DEVPTR_IN data, const int h, const int w, const int height, const int width) { bool overflow = (h < 0) || (w < 0) || (h >= height) || (w >= width); float retVal = overflow ? 0.0f : data[h * width + w]; return retVal; } __device__ static float PrRoIPoolingGetCoeff(float dh, float dw){ dw = dw > 0 ? dw : -dw; dh = dh > 0 ? dh : -dh; return (1.0f - dh) * (1.0f - dw); } __device__ static float PrRoIPoolingSingleCoorIntegral(float s, float t, float c1, float c2) { return 0.5 * (t * t - s * s) * c2 + (t - 0.5 * t * t - s + 0.5 * s * s) * c1; } __device__ static float PrRoIPoolingInterpolation(F_DEVPTR_IN data, const float h, const float w, const int height, const int width){ float retVal = 0.0f; int h1 = floorf(h); int w1 = floorf(w); retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h)+1; w1 = floorf(w); retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h); w1 = floorf(w)+1; retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h)+1; w1 = floorf(w)+1; retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); return retVal; } __device__ static float PrRoIPoolingMatCalculation(F_DEVPTR_IN this_data, const int s_h, const int s_w, const int e_h, const int e_w, const float y0, const float x0, const float y1, const float x1, const int h0, const int w0) { float alpha, beta, lim_alpha, lim_beta, tmp; float sum_out = 0; alpha = x0 - float(s_w); beta = y0 - float(s_h); lim_alpha = x1 - float(s_w); lim_beta = y1 - float(s_h); tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, s_h, s_w, h0, w0) * tmp; alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, s_h, e_w, h0, w0) * tmp; alpha = x0 - float(s_w); beta = float(e_h) - y1; lim_alpha = x1 - float(s_w); lim_beta = float(e_h) - y0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, e_h, s_w, h0, w0) * tmp; alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, e_h, e_w, h0, w0) * tmp; return sum_out; } __device__ static void PrRoIPoolingDistributeDiff(F_DEVPTR_OUT diff, const float top_diff, const int h, const int w, const int height, const int width, const float coeff) { bool overflow = (h < 0) || (w < 0) || (h >= height) || (w >= width); if (!overflow) atomicAdd(diff + h * width + w, top_diff * coeff); } __device__ static void PrRoIPoolingMatDistributeDiff(F_DEVPTR_OUT diff, const float top_diff, const int s_h, const int s_w, const int e_h, const int e_w, const float y0, const float x0, const float y1, const float x1, const int h0, const int w0) { float alpha, beta, lim_alpha, lim_beta, tmp; alpha = x0 - float(s_w); beta = y0 - float(s_h); lim_alpha = x1 - float(s_w); lim_beta = y1 - float(s_h); tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, s_h, s_w, h0, w0, tmp); alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, s_h, e_w, h0, w0, tmp); alpha = x0 - float(s_w); beta = float(e_h) - y1; lim_alpha = x1 - float(s_w); lim_beta = float(e_h) - y0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, e_h, s_w, h0, w0, tmp); alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, e_h, e_w, h0, w0, tmp); } __global__ void PrRoIPoolingForward( const int nthreads, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, ((float)0.0)); float roi_height = max(roi_end_h - roi_start_h, ((float)0.0)); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_data = bottom_data + (roi_batch_ind * channels + c) * height * width; float *this_out = top_data + index; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); if (win_size == 0) { *this_out = 0; return; } float sum_out = 0; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); for (int w_iter = s_w; w_iter < e_w; ++w_iter) for (int h_iter = s_h; h_iter < e_h; ++h_iter) sum_out += PrRoIPoolingMatCalculation(this_data, h_iter, w_iter, h_iter + 1, w_iter + 1, max(win_start_h, float(h_iter)), max(win_start_w, float(w_iter)), min(win_end_h, float(h_iter) + 1.0), min(win_end_w, float(w_iter + 1.0)), height, width); *this_out = sum_out / win_size; } } __global__ void PrRoIPoolingBackward( const int nthreads, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, (float)0); float roi_height = max(roi_end_h - roi_start_h, (float)0); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_out_grad = top_diff + index; float *this_data_grad = bottom_diff + (roi_batch_ind * channels + c) * height * width; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); float sum_out = win_size == float(0) ? float(0) : *this_out_grad / win_size; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); for (int w_iter = s_w; w_iter < e_w; ++w_iter) for (int h_iter = s_h; h_iter < e_h; ++h_iter) PrRoIPoolingMatDistributeDiff(this_data_grad, sum_out, h_iter, w_iter, h_iter + 1, w_iter + 1, max(win_start_h, float(h_iter)), max(win_start_w, float(w_iter)), min(win_end_h, float(h_iter) + 1.0), min(win_end_w, float(w_iter + 1.0)), height, width); } } __global__ void PrRoIPoolingCoorBackward( const int nthreads, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, (float)0); float roi_height = max(roi_end_h - roi_start_h, (float)0); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_out_grad = top_diff + index; const float *this_bottom_data = bottom_data + (roi_batch_ind * channels + c) * height * width; const float *this_top_data = top_data + index; float *this_data_grad = bottom_diff + n * 5; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); float sum_out = win_size == float(0) ? float(0) : *this_out_grad / win_size; // WARNING: to be discussed if (sum_out == 0) return; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); float g_x1_y = 0, g_x2_y = 0, g_x_y1 = 0, g_x_y2 = 0; for (int h_iter = s_h; h_iter < e_h; ++h_iter) { g_x1_y += PrRoIPoolingSingleCoorIntegral(max(win_start_h, float(h_iter)) - h_iter, min(win_end_h, float(h_iter + 1)) - h_iter, PrRoIPoolingInterpolation(this_bottom_data, h_iter, win_start_w, height, width), PrRoIPoolingInterpolation(this_bottom_data, h_iter + 1, win_start_w, height, width)); g_x2_y += PrRoIPoolingSingleCoorIntegral(max(win_start_h, float(h_iter)) - h_iter, min(win_end_h, float(h_iter + 1)) - h_iter, PrRoIPoolingInterpolation(this_bottom_data, h_iter, win_end_w, height, width), PrRoIPoolingInterpolation(this_bottom_data, h_iter + 1, win_end_w, height, width)); } for (int w_iter = s_w; w_iter < e_w; ++w_iter) { g_x_y1 += PrRoIPoolingSingleCoorIntegral(max(win_start_w, float(w_iter)) - w_iter, min(win_end_w, float(w_iter + 1)) - w_iter, PrRoIPoolingInterpolation(this_bottom_data, win_start_h, w_iter, height, width), PrRoIPoolingInterpolation(this_bottom_data, win_start_h, w_iter + 1, height, width)); g_x_y2 += PrRoIPoolingSingleCoorIntegral(max(win_start_w, float(w_iter)) - w_iter, min(win_end_w, float(w_iter + 1)) - w_iter, PrRoIPoolingInterpolation(this_bottom_data, win_end_h, w_iter, height, width), PrRoIPoolingInterpolation(this_bottom_data, win_end_h, w_iter + 1, height, width)); } float partial_x1 = -g_x1_y + (win_end_h - win_start_h) * (*this_top_data); float partial_y1 = -g_x_y1 + (win_end_w - win_start_w) * (*this_top_data); float partial_x2 = g_x2_y - (win_end_h - win_start_h) * (*this_top_data); float partial_y2 = g_x_y2 - (win_end_w - win_start_w) * (*this_top_data); partial_x1 = partial_x1 / win_size * spatial_scale; partial_x2 = partial_x2 / win_size * spatial_scale; partial_y1 = partial_y1 / win_size * spatial_scale; partial_y2 = partial_y2 / win_size * spatial_scale; // (b, x1, y1, x2, y2) this_data_grad[0] = 0; atomicAdd(this_data_grad + 1, (partial_x1 * (1.0 - float(pw) / pooled_width) + partial_x2 * (1.0 - float(pw + 1) / pooled_width)) * (*this_out_grad)); atomicAdd(this_data_grad + 2, (partial_y1 * (1.0 - float(ph) / pooled_height) + partial_y2 * (1.0 - float(ph + 1) / pooled_height)) * (*this_out_grad)); atomicAdd(this_data_grad + 3, (partial_x2 * float(pw + 1) / pooled_width + partial_x1 * float(pw) / pooled_width) * (*this_out_grad)); atomicAdd(this_data_grad + 4, (partial_y2 * float(ph + 1) / pooled_height + partial_y1 * float(ph) / pooled_height) * (*this_out_grad)); } } } /* !anonymous namespace */ #ifdef __cplusplus extern "C" { #endif void PrRoIPoolingForwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count) { PrRoIPoolingForward<<>>( top_count, bottom_data, bottom_rois, top_data, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } void PrRoIPoolingBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count) { cudaMemsetAsync(bottom_diff, 0, sizeof(float) * bottom_count, stream); PrRoIPoolingBackward<<>>( top_count, bottom_rois, top_diff, bottom_diff, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } void PrRoIPoolingCoorBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count) { cudaMemsetAsync(bottom_diff, 0, sizeof(float) * bottom_count, stream); PrRoIPoolingCoorBackward<<>>( top_count, bottom_data, bottom_rois, top_data, top_diff, bottom_diff, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } } /* !extern "C" */ ================================================ FILE: artrackv2_mindspore/external/PreciseRoIPooling/src/prroi_pooling_gpu_impl.cuh ================================================ /* * File : prroi_pooling_gpu_impl.cuh * Author : Tete Xiao, Jiayuan Mao * Email : jasonhsiao97@gmail.com * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ #ifndef PRROI_POOLING_GPU_IMPL_CUH #define PRROI_POOLING_GPU_IMPL_CUH #ifdef __cplusplus extern "C" { #endif #define F_DEVPTR_IN const float * #define F_DEVPTR_OUT float * void PrRoIPoolingForwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count); void PrRoIPoolingBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count); void PrRoIPoolingCoorBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count); #ifdef __cplusplus } /* !extern "C" */ #endif #endif /* !PRROI_POOLING_GPU_IMPL_CUH */ ================================================ FILE: artrackv2_mindspore/external/vot20/cttrack/config.yaml ================================================ registry: - ./trackers.ini stack: vot2020 ================================================ FILE: artrackv2_mindspore/external/vot20/cttrack/trackers.ini ================================================ [cttrack_large] # label = cttrack_large protocol = traxpython command = from cttrack_start import main;main() # Specify a path to trax python wrapper if it is not visible (separate by ; if using multiple paths) # paths = /home/lr/workspace/CTTrack: paths = # Additional environment paths env_PATH = : ================================================ FILE: artrackv2_mindspore/lib/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/lib/config/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/lib/config/ostrack/config.py ================================================ from easydict import EasyDict as edict import yaml """ Add default config for OSTrack. """ cfg = edict() # MODEL cfg.MODEL = edict() cfg.MODEL.PRETRAIN_FILE = "mae_pretrain_vit_base.pth" cfg.MODEL.EXTRA_MERGER = False cfg.MODEL.RETURN_INTER = False cfg.MODEL.RETURN_STAGES = [2, 5, 8, 11] # MODEL.BACKBONE cfg.MODEL.BACKBONE = edict() cfg.MODEL.BACKBONE.TYPE = "vit_base_patch16_224" cfg.MODEL.BACKBONE.PATCHSIZE = 16 cfg.MODEL.BACKBONE.EMBEDDIM = 768 cfg.MODEL.BACKBONE.STRIDE = 16 cfg.MODEL.BACKBONE.MID_PE = False cfg.MODEL.BACKBONE.SEP_SEG = False cfg.MODEL.BACKBONE.CAT_MODE = 'direct' cfg.MODEL.BACKBONE.MERGE_LAYER = 0 cfg.MODEL.BACKBONE.ADD_CLS_TOKEN = False cfg.MODEL.BACKBONE.CLS_TOKEN_USE_MODE = 'ignore' cfg.MODEL.BACKBONE.CE_LOC = [] cfg.MODEL.BACKBONE.CE_KEEP_RATIO = [] cfg.MODEL.BACKBONE.CE_TEMPLATE_RANGE = 'ALL' # choose between ALL, CTR_POINT, CTR_REC, GT_BOX # MODEL.DECODER cfg.MODEL.DECODER = edict() cfg.MODEL.DECODER.TYPE = "mask" cfg.MODEL.DECODER.MASK_RATIO = 0.75 cfg.MODEL.DECODER.EMBEDDIM = 512 cfg.MODEL.DECODER.DEPTH = 8 cfg.MODEL.DECODER.NUMHEADS = 16 cfg.MODEL.DECODER.MLPRATIO = 4 # MODEL.HEAD cfg.MODEL.BINS = 400 cfg.MODEL.ENCODER_LAYER = 3 cfg.MODEL.NUM_HEADS = 16 cfg.MODEL.MLP_RATIO = 4 cfg.MODEL.QKV_BIAS = True cfg.MODEL.DROP_RATE = 0.1 cfg.MODEL.ATTN_DROP = 0.0 cfg.MODEL.DROP_PATH = 0.0 cfg.MODEL.DECODER_LAYER = 6 cfg.MODEL.HEAD = edict() cfg.MODEL.HEAD.TYPE = "PIX" cfg.MODEL.HEAD.NUM_CHANNELS = 1024 # TRAIN cfg.TRAIN = edict() cfg.TRAIN.LR = 0.0001 cfg.TRAIN.WEIGHT_DECAY = 0.0001 cfg.TRAIN.EPOCH = 500 cfg.TRAIN.LR_DROP_EPOCH = 400 cfg.TRAIN.BATCH_SIZE = 16 cfg.TRAIN.NUM_WORKER = 10 cfg.TRAIN.OPTIMIZER = "ADAMW" cfg.TRAIN.BACKBONE_MULTIPLIER = 0.1 cfg.TRAIN.GIOU_WEIGHT = 2.0 cfg.TRAIN.L1_WEIGHT = 5.0 cfg.TRAIN.FREEZE_LAYERS = [0, ] cfg.TRAIN.PRINT_INTERVAL = 50 cfg.TRAIN.VAL_EPOCH_INTERVAL = 20 cfg.TRAIN.GRAD_CLIP_NORM = 0.1 cfg.TRAIN.AMP = False cfg.TRAIN.CE_START_EPOCH = 20 # candidate elimination start epoch cfg.TRAIN.CE_WARM_EPOCH = 80 # candidate elimination warm up epoch cfg.TRAIN.DROP_PATH_RATE = 0.1 # drop path rate for ViT backbone # TRAIN.SCHEDULER cfg.TRAIN.SCHEDULER = edict() cfg.TRAIN.SCHEDULER.TYPE = "step" cfg.TRAIN.SCHEDULER.DECAY_RATE = 0.1 # DATA cfg.DATA = edict() cfg.DATA.SAMPLER_MODE = "causal" # sampling methods cfg.DATA.MEAN = [0.485, 0.456, 0.406] cfg.DATA.STD = [0.229, 0.224, 0.225] cfg.DATA.MAX_SAMPLE_INTERVAL = 200 cfg.DATA.MAX_GAP = 300 cfg.DATA.MAX_INTERVAL = 5 cfg.DATA.INTERVAL_PROB = 0.0 cfg.DATA.TEMP = 2 # DATA.TRAIN cfg.DATA.TRAIN = edict() cfg.DATA.TRAIN.DATASETS_NAME = ["LASOT", "GOT10K_vottrain"] cfg.DATA.TRAIN.DATASETS_RATIO = [1, 1] cfg.DATA.TRAIN.SAMPLE_PER_EPOCH = 60000 # DATA.VAL cfg.DATA.VAL = edict() cfg.DATA.VAL.DATASETS_NAME = ["GOT10K_votval"] cfg.DATA.VAL.DATASETS_RATIO = [1] cfg.DATA.VAL.SAMPLE_PER_EPOCH = 10000 # DATA.SEARCH cfg.DATA.SEARCH = edict() cfg.DATA.SEARCH.SIZE = 256 cfg.DATA.SEARCH.FACTOR = 5.0 cfg.DATA.SEARCH.CENTER_JITTER = 4.5 cfg.DATA.SEARCH.SCALE_JITTER = 0.5 cfg.DATA.SEARCH.NUMBER = 1 # DATA.TEMPLATE cfg.DATA.TEMPLATE = edict() cfg.DATA.TEMPLATE.NUMBER = 1 cfg.DATA.TEMPLATE.SIZE = 128 cfg.DATA.TEMPLATE.FACTOR = 2.0 cfg.DATA.TEMPLATE.CENTER_JITTER = 0 cfg.DATA.TEMPLATE.SCALE_JITTER = 0 # TEST cfg.TEST = edict() cfg.TEST.TEMPLATE_FACTOR = 2.0 cfg.TEST.TEMPLATE_SIZE = 128 cfg.TEST.SEARCH_FACTOR = 5.0 cfg.TEST.SEARCH_SIZE = 256 cfg.TEST.EPOCH = 500 def _edict2dict(dest_dict, src_edict): if isinstance(dest_dict, dict) and isinstance(src_edict, dict): for k, v in src_edict.items(): if not isinstance(v, edict): dest_dict[k] = v else: dest_dict[k] = {} _edict2dict(dest_dict[k], v) else: return def gen_config(config_file): cfg_dict = {} _edict2dict(cfg_dict, cfg) with open(config_file, 'w') as f: yaml.dump(cfg_dict, f, default_flow_style=False) def _update_config(base_cfg, exp_cfg): if isinstance(base_cfg, dict) and isinstance(exp_cfg, edict): for k, v in exp_cfg.items(): if k in base_cfg: if not isinstance(v, dict): base_cfg[k] = v else: _update_config(base_cfg[k], v) else: raise ValueError("{} not exist in config.py".format(k)) else: return def update_config_from_file(filename, base_cfg=None): exp_config = None with open(filename) as f: exp_config = edict(yaml.safe_load(f)) if base_cfg is not None: _update_config(base_cfg, exp_config) else: _update_config(cfg, exp_config) ================================================ FILE: artrackv2_mindspore/lib/models/__init__.py ================================================ from .ostrack.ostrack import build_ostrack ================================================ FILE: artrackv2_mindspore/lib/models/component/__init__.py ================================================ # -*- coding:utf-8 -*- # author : Skye Song # file : __init__.py.py # Copyright (c) Skye-Song. All Rights Reserved ================================================ FILE: artrackv2_mindspore/lib/models/component/attention.py ================================================ # -*- coding:utf-8 -*- # author : Skye Song # file : attention.py # Copyright (c) Skye-Song. All Rights Reserved import sys sys.path.append("/home/baiyifan/code/AR2_mindspore_cp/2stage") import mindspore as ms import mindspore.nn as nn import mindspore.ops as ops from einops import rearrange from lib.utils.image import * class Attention(nn.Cell): def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim ** -0.5 self.qkv = nn.Dense(dim, dim * 3, has_bias=qkv_bias) self.attn_drop = nn.Dropout(p=attn_drop) self.proj = nn.Dense(dim, dim) self.proj_drop = nn.Dropout(p=proj_drop) def construct(self, x, padding_mask=None, **kwargs): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # (B, head, N, C//head) attn = (q @ k.swapaxes(-2, -1)) * self.scale # (B, head, N, N) if padding_mask is not None: assert padding_mask.size()[0] == B assert padding_mask.size()[1] == N attn = attn.masked_fill(padding_mask.unsqueeze(1).unsqueeze(2), float("-inf")) attn = ops.softmax(attn,axis=-1) attn = self.attn_drop(attn) x = (attn @ v).swapaxes(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) return x class ClsMixAttention(nn.Cell): def __init__(self, dim, num_heads, qkv_bias=False, attn_drop=0., proj_drop=0., ): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim ** -0.5 self.qkv = nn.Dense(dim, dim * 3, has_bias=qkv_bias) self.attn_drop = nn.Dropout(p=attn_drop) self.proj = nn.Dense(dim, dim) self.proj_drop = nn.Dropout(p=proj_drop) def construct(self, x, t_h, t_w, s_h, s_w, online_size=1, padding_mask=None): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # (B, head, N, C) q_cls, q_t, q_s = ops.split(q, [1, t_h * t_w * (1 + online_size), s_h * s_w], axis=2) k_cls, k_t, k_s = ops.split(k, [1, t_h * t_w * (1 + online_size), s_h * s_w], axis=2) v_cls, v_t, v_s = ops.split(v, [1, t_h * t_w * (1 + online_size), s_h * s_w], axis=2) # cls token attention attn = (q_cls @ k.swapaxes(-2, -1)) * self.scale # (B, head, N_q, N) if padding_mask is not None: assert padding_mask.size()[0] == B assert padding_mask.size()[1] == N attn = attn.masked_fill(padding_mask.unsqueeze(1).unsqueeze(2), float("-inf")) attn = ops.softmax(attn,axis=-1) attn = self.attn_drop(attn) x_cls = rearrange(attn @ v, 'b h t d -> b t (h d)') # template attention attn = (q_t @ k_t.swapaxes(-2, -1)) * self.scale # (B, head, N_q, N) if padding_mask is not None: assert padding_mask.size()[0] == B assert padding_mask.size()[1] == N attn = attn.masked_fill(padding_mask.unsqueeze(1).unsqueeze(2), float("-inf")) attn = ops.softmax(attn,axis=-1) attn = self.attn_drop(attn) x_t = rearrange(attn @ v_t, 'b h t d -> b t (h d)') # search region attention attn = (q_s @ k.swapaxes(-2, -1)) * self.scale # (B, head, N_s, N) if padding_mask is not None: assert padding_mask.size()[0] == B assert padding_mask.size()[1] == N attn = attn.masked_fill(padding_mask.unsqueeze(1).unsqueeze(2), float("-inf")) attn = ops.softmax(attn,axis=-1) attn = self.attn_drop(attn) x_s = rearrange(attn @ v, 'b h t d -> b t (h d)') x = ops.cat([x_cls, x_t, x_s], axis=1) x = self.proj(x) x = self.proj_drop(x) return x class MixAttention(nn.Cell): def __init__(self, dim, num_heads, qkv_bias=False, attn_drop=0., proj_drop=0., ): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim ** -0.5 self.qkv = nn.Dense(dim, dim * 3, has_bias=qkv_bias) self.attn_drop = nn.Dropout(p=attn_drop) self.proj = nn.Dense(dim, dim) self.proj_drop = nn.Dropout(p=proj_drop) def construct(self, x, t_h, t_w, s_h, s_w, padding_mask=None): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # (B, head, N, C) q_t, q_s = ops.split(q, [t_h * t_w * 2, s_h * s_w], axis=2) k_t, k_s = ops.split(k, [t_h * t_w * 2, s_h * s_w], axis=2) v_t, v_s = ops.split(v, [t_h * t_w * 2, s_h * s_w], axis=2) # template attention attn = (q_t @ k_t.swapaxes(-2, -1)) * self.scale # (B, head, N_q, N) if padding_mask is not None: assert padding_mask.size()[0] == B assert padding_mask.size()[1] == N attn = attn.masked_fill(padding_mask.unsqueeze(1).unsqueeze(2), float("-inf")) attn = ops.softmax(attn,axis=-1) attn = self.attn_drop(attn) x_t = rearrange(attn @ v_t, 'b h t d -> b t (h d)') # search region attention attn = (q_s @ k.swapaxes(-2, -1)) * self.scale # (B, head, N_s, N) if padding_mask is not None: assert padding_mask.size()[0] == B assert padding_mask.size()[1] == N attn = attn.masked_fill(padding_mask.unsqueeze(1).unsqueeze(2), float("-inf")) attn = ops.softmax(attn,axis=-1) attn = self.attn_drop(attn) x_s = rearrange(attn @ v, 'b h t d -> b t (h d)') x = ops.cat([x_t, x_s], axis=1) x = self.proj(x) x = self.proj_drop(x) return x class NottAttention(nn.Cell): def __init__(self, dim, num_heads, qkv_bias=False, attn_drop=0., proj_drop=0., ): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim ** -0.5 self.qkv = nn.Dense(dim, dim * 3, has_bias=qkv_bias) self.attn_drop = nn.Dropout(p=attn_drop) self.proj = nn.Dense(dim, dim) self.proj_drop = nn.Dropout(p=proj_drop) def construct(self, x, t_h, t_w, s_h, s_w, padding_mask=None): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # (B, head, N, C) q_t, q_s = ops.split(q, [t_h * t_w * 2, s_h * s_w], axis=2) k_t, k_s = ops.split(k, [t_h * t_w * 2, s_h * s_w], axis=2) v_t, v_s = ops.split(v, [t_h * t_w * 2, s_h * s_w], axis=2) # template attention attn = (q_t @ k_s.swapaxes(-2, -1)) * self.scale # (B, head, N_q, N) if padding_mask is not None: assert padding_mask.size()[0] == B assert padding_mask.size()[1] == N attn = attn.masked_fill(padding_mask.unsqueeze(1).unsqueeze(2), float("-inf")) attn = ops.softmax(attn,axis=-1) attn = self.attn_drop(attn) x_t = rearrange(attn @ v_s, 'b h t d -> b t (h d)') # search region attention attn = (q_s @ k.swapaxes(-2, -1)) * self.scale # (B, head, N_s, N) if padding_mask is not None: assert padding_mask.size()[0] == B assert padding_mask.size()[1] == N attn = attn.masked_fill(padding_mask.unsqueeze(1).unsqueeze(2), float("-inf")) attn = ops.softmax(attn,axis=-1) attn = self.attn_drop(attn) x_s = rearrange(attn @ v, 'b h t d -> b t (h d)') x = ops.cat([x_t, x_s], axis=1) x = self.proj(x) x = self.proj_drop(x) return x class NossAttention(nn.Cell): def __init__(self, dim, num_heads, qkv_bias=False, attn_drop=0., proj_drop=0., ): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim ** -0.5 self.qkv = nn.Dense(dim, dim * 3, has_bias=qkv_bias) self.attn_drop = nn.Dropout(p=attn_drop) self.proj = nn.Dense(dim, dim) self.proj_drop = nn.Dropout(p=proj_drop) def construct(self, x, t_h, t_w, s_h, s_w, padding_mask=None): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # (B, head, N, C) q_t, q_s = ops.split(q, [t_h * t_w * 2, s_h * s_w], axis=2) k_t, k_s = ops.split(k, [t_h * t_w * 2, s_h * s_w], axis=2) v_t, v_s = ops.split(v, [t_h * t_w * 2, s_h * s_w], axis=2) # template attention attn = (q_t @ k.swapaxes(-2, -1)) * self.scale # (B, head, N_q, N) if padding_mask is not None: assert padding_mask.size()[0] == B assert padding_mask.size()[1] == N attn = attn.masked_fill(padding_mask.unsqueeze(1).unsqueeze(2), float("-inf")) attn = ops.softmax(attn,axis=-1) attn = self.attn_drop(attn) x_t = rearrange(attn @ v, 'b h t d -> b t (h d)') # search region attention attn = (q_s @ k_t.swapaxes(-2, -1)) * self.scale # (B, head, N_s, N) if padding_mask is not None: assert padding_mask.size()[0] == B assert padding_mask.size()[1] == N attn = attn.masked_fill(padding_mask.unsqueeze(1).unsqueeze(2), float("-inf")) attn = ops.softmax(attn,axis=-1) attn = self.attn_drop(attn) x_s = rearrange(attn @ v_t, 'b h t d -> b t (h d)') x = ops.cat([x_t, x_s], axis=1) x = self.proj(x) x = self.proj_drop(x) return x class CrossAttention(nn.Cell): def __init__(self, dim, num_heads, qkv_bias=False, attn_drop=0., proj_drop=0., ): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim ** -0.5 self.qkv = nn.Dense(dim, dim * 3, has_bias=qkv_bias) self.attn_drop = nn.Dropout(p=attn_drop) self.proj = nn.Dense(dim, dim) self.proj_drop = nn.Dropout(p=proj_drop) def construct(self, x, t_h, t_w, s_h, s_w): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # (B, head, N, C) q_t, q_s = ops.split(q, [t_h * t_w * 2, s_h * s_w], axis=2) k_t, k_s = ops.split(k, [((t_h + 1) // 2) ** 2 * 2, s_h * s_w // 4], axis=4) v_t, v_s = ops.split(v, [((t_h + 1) // 2) ** 2 * 2, s_h * s_w // 4], axis=4) # template attention attn = (q_t @ k_s.swapaxes(-2, -1)) * self.scale attn = ops.softmax(attn,axis=-1) attn = self.attn_drop(attn) x_t = rearrange(attn @ v_s, 'b h t d -> b t (h d)') # search region attention attn = (q_s @ k_t.swapaxes(-2, -1)) * self.scale attn = ops.softmax(attn,axis=-1) attn = self.attn_drop(attn) x_s = rearrange(attn @ v_t, 'b h t d -> b t (h d)') x = ops.cat([x_t, x_s], axis=1) x = self.proj(x) x = self.proj_drop(x) return x ================================================ FILE: artrackv2_mindspore/lib/models/component/block.py ================================================ # -*- coding:utf-8 -*- # author : Skye Song # file : block.py # Copyright (c) Skye-Song. All Rights Reserved import sys sys.path.append("/home/baiyifan/weizhenhuan/2stage/lib/models/component") from attention import * from lib.models.timm import * class Block(nn.Cell): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0., attention = "Attention", act_layer=nn.GELU, norm_layer=nn.LayerNorm): super().__init__() if norm_layer is None: norm_layer = nn.LayerNorm dim_tuple=dim if isinstance(dim,int): dim_tuple=tuple([dim]) self.norm1 = norm_layer(dim_tuple) self.attn = globals()[attention](dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim_tuple) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) def construct(self, x, **kwargs): x = x + self.drop_path(self.attn(self.norm1(x), **kwargs)) x = x + self.drop_path(self.mlp(self.norm2(x))) return x ================================================ FILE: artrackv2_mindspore/lib/models/component/drop.py ================================================ """ DropBlock, DropPath PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers. Papers: DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890) Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382) Code: DropBlock impl inspired by two Tensorflow impl that I liked: - https://github.com/tensorflow/tpu/blob/master/models/official/resnet/resnet_model.py#L74 - https://github.com/clovaai/assembled-cnn/blob/master/nets/blocks.py Hacked together by / Copyright 2020 Ross Wightman """ import mindspore as ms from mindspore import nn from mindspore import ops def drop_block_2d( x, drop_prob: float = 0.1, block_size: int = 7, gamma_scale: float = 1.0, with_noise: bool = False, inplace: bool = False, batchwise: bool = False): """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf DropBlock with an experimental gaussian noise option. This layer has been tested on a few training runs with success, but needs further validation and possibly optimization for lower runtime impact. """ B, C, H, W = x.shape total_size = W * H clipped_block_size = min(block_size, min(W, H)) # seed_drop_rate, the gamma parameter gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / ( (W - block_size + 1) * (H - block_size + 1)) # Forces the block to be inside the feature map. w_i, h_i = ops.meshgrid(ops.arange(W).to(x.device), ops.arange(H).to(x.device)) valid_block = ((w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2)) & \ ((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2)) valid_block = ops.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype) if batchwise: # one mask for whole batch, quite a bit faster uniform_noise = ops.rand((1, C, H, W), dtype=x.dtype, device=x.device) else: uniform_noise = ops.rand_like(x) block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype) block_mask = -ops.max_pool2d( -block_mask, kernel_size=clipped_block_size, # block_size, stride=1, padding=clipped_block_size // 2) if with_noise: normal_noise = ops.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else ops.randn_like(x) if inplace: x.mul_(block_mask).add_(normal_noise * (1 - block_mask)) else: x = x * block_mask + normal_noise * (1 - block_mask) else: normalize_scale = (block_mask.numel() / block_mask.to(dtype=ms.float32).sum().add(1e-7)).to(x.dtype) if inplace: x.mul_(block_mask * normalize_scale) else: x = x * block_mask * normalize_scale return x def drop_block_fast_2d( x: ms.Tensor, drop_prob: float = 0.1, block_size: int = 7, gamma_scale: float = 1.0, with_noise: bool = False, inplace: bool = False, batchwise: bool = False): """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid block mask at edges. """ B, C, H, W = x.shape total_size = W * H clipped_block_size = min(block_size, min(W, H)) gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / ( (W - block_size + 1) * (H - block_size + 1)) if batchwise: # one mask for whole batch, quite a bit faster block_mask = ops.rand((1, C, H, W), dtype=x.dtype, device=x.device) < gamma else: # mask per batch element block_mask = ops.rand_like(x) < gamma block_mask = ops.max_pool2d( block_mask.to(x.dtype), kernel_size=clipped_block_size, stride=1, padding=clipped_block_size // 2) if with_noise: normal_noise = ops.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else ops.randn_like(x) if inplace: x.mul_(1. - block_mask).add_(normal_noise * block_mask) else: x = x * (1. - block_mask) + normal_noise * block_mask else: block_mask = 1 - block_mask normalize_scale = (block_mask.numel() / block_mask.to(dtype=ms.float32).sum().add(1e-7)).to(dtype=x.dtype) if inplace: x.mul_(block_mask * normalize_scale) else: x = x * block_mask * normalize_scale return x class DropBlock2d(nn.Cell): """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf """ def __init__(self, drop_prob=0.1, block_size=7, gamma_scale=1.0, with_noise=False, inplace=False, batchwise=False, fast=True): super(DropBlock2d, self).__init__() self.drop_prob = drop_prob self.gamma_scale = gamma_scale self.block_size = block_size self.with_noise = with_noise self.inplace = inplace self.batchwise = batchwise self.fast = fast # FIXME finish comparisons of fast vs not def construct(self, x): if not self.training or not self.drop_prob: return x if self.fast: return drop_block_fast_2d( x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise) else: return drop_block_2d( x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise) def drop_path(x, drop_prob: float = 0., training: bool = False): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the argument. """ if drop_prob == 0. or not training: return x keep_prob = 1 - drop_prob shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets random_tensor = keep_prob + ops.rand(shape, dtype=x.dtype, device=x.device) random_tensor.floor_() # binarize output = x.div(keep_prob) * random_tensor return output class DropPath(nn.Cell): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). """ def __init__(self, drop_prob=None): super(DropPath, self).__init__() self.drop_prob = drop_prob def construct(self, x): return drop_path(x, self.drop_prob, self.training) ================================================ FILE: artrackv2_mindspore/lib/models/component/mlp.py ================================================ """ MLP module w/ dropout and configurable activation layer Hacked together by / Copyright 2020 Ross Wightman """ import mindspore as ms from mindspore import nn from mindspore import ops class Mlp(nn.Cell): """ MLP as used in Vision Transformer, MLP-Mixer and related networks """ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Dense(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Dense(hidden_features, out_features) self.drop = nn.Dropout(p=drop) def construct(self, x): x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x class MultiLayerMlp(nn.Cell): """ Very simple multi-layer perceptron (also called FFN)""" def __init__(self, input_dim, hidden_dim, output_dim, num_layers, BN=False): super().__init__() self.num_layers = num_layers h = [hidden_dim] * (num_layers - 1) if BN: self.layers = nn.CellList(nn.SequentialCell(nn.Dense(n, k), nn.BatchNorm1d(k)) for n, k in zip([input_dim] + h, h + [output_dim])) else: self.layers = nn.CellList(nn.Dense(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) def construct(self, x): for i, layer in enumerate(self.layers): x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) return x class GluMlp(nn.Cell): """ MLP w/ GLU style gating See: https://arxiv.org/abs/1612.08083, https://arxiv.org/abs/2002.05202 """ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.Sigmoid, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features assert hidden_features % 2 == 0 self.fc1 = nn.Dense(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Dense(hidden_features // 2, out_features) self.drop = nn.Dropout(p=drop) def init_weights(self): # override init of fc1 w/ gate portion set to weight near zero, bias=1 fc1_mid = self.fc1.bias.shape[0] // 2 nn.init.ones_(self.fc1.bias[fc1_mid:]) nn.init.normal_(self.fc1.weight[fc1_mid:], std=1e-6) def construct(self, x): x = self.fc1(x) x, gates = x.chunk(2, dim=-1) x = x * self.act(gates) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x class GatedMlp(nn.Cell): """ MLP as used in gMLP """ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, gate_layer=None, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Dense(in_features, hidden_features) self.act = act_layer() if gate_layer is not None: assert hidden_features % 2 == 0 self.gate = gate_layer(hidden_features) hidden_features = hidden_features // 2 # FIXME base reduction on gate property? else: self.gate = nn.Identity() self.fc2 = nn.Dense(hidden_features, out_features) self.drop = nn.Dropout(p=drop) def construct(self, x): x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.gate(x) x = self.fc2(x) x = self.drop(x) return x class ConvMlp(nn.Cell): """ MLP using 1x1 convs that keeps spatial dims """ def __init__( self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU, norm_layer=None, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Conv2d(in_features, hidden_features, kernel_size=1, bias=True) self.norm = norm_layer(hidden_features) if norm_layer else nn.Identity() self.act = act_layer() self.fc2 = nn.Conv2d(hidden_features, out_features, kernel_size=1, bias=True) self.drop = nn.Dropout(p=drop) def construct(self, x): x = self.fc1(x) x = self.norm(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) return x ================================================ FILE: artrackv2_mindspore/lib/models/component/norm.py ================================================ # -*- coding:utf-8 -*- # author : Skye Song # file : norm.py # Copyright (c) Skye-Song. All Rights Reserved import torch class FrozenBatchNorm2d(torch.nn.Module): """ BatchNorm2d where the batch statistics and the affine parameters are fixed. Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than torchvision.models.resnet[18,34,50,101] produce nans. """ def __init__(self, n): super(FrozenBatchNorm2d, self).__init__() self.register_buffer("weight", torch.ones(n)) self.register_buffer("bias", torch.zeros(n)) self.register_buffer("running_mean", torch.zeros(n)) self.register_buffer("running_var", torch.ones(n)) def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): num_batches_tracked_key = prefix + 'num_batches_tracked' if num_batches_tracked_key in state_dict: del state_dict[num_batches_tracked_key] super(FrozenBatchNorm2d, self)._load_from_state_dict( state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) def forward(self, x): # move reshapes to the beginning # to make it fuser-friendly w = self.weight.reshape(1, -1, 1, 1) b = self.bias.reshape(1, -1, 1, 1) rv = self.running_var.reshape(1, -1, 1, 1) rm = self.running_mean.reshape(1, -1, 1, 1) eps = 1e-5 scale = w * (rv + eps).rsqrt() # rsqrt(x): 1/sqrt(x), r: reciprocal bias = b - rm * scale return x * scale + bias ================================================ FILE: artrackv2_mindspore/lib/models/component/patch_embed.py ================================================ """ Image to Patch Embedding using Conv2d A convolution based approach to patchifying a 2D image w/ embedding projection. Based on the impl in https://github.com/google-research/vision_transformer Hacked together by / Copyright 2020 Ross Wightman """ from torch import nn as nn from itertools import repeat import collections.abc # From PyTorch internals def _ntuple(n): def parse(x): if isinstance(x, collections.abc.Iterable): return x return tuple(repeat(x, n)) return parse to_1tuple = _ntuple(1) to_2tuple = _ntuple(2) to_3tuple = _ntuple(3) to_4tuple = _ntuple(4) to_ntuple = _ntuple class PatchEmbed(nn.Module): """ 2D Image to Patch Embedding """ def __init__(self, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True): super().__init__() self.flatten = flatten self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() def forward(self, x): x = self.proj(x) if self.flatten: x = x.flatten(2).transpose(1, 2) # BCHW -> BNC x = self.norm(x) return x ================================================ FILE: artrackv2_mindspore/lib/models/component/pos_embed.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # -------------------------------------------------------- # Position embedding utils # -------------------------------------------------------- import numpy as np import torch # -------------------------------------------------------- # 2D sine-cosine position embedding # References: # Transformer: https://github.com/tensorflow/models/blob/master/official/nlp/transformer/model_utils.py # MoCo v3: https://github.com/facebookresearch/moco-v3 # -------------------------------------------------------- def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False): """ grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) """ grid_h = np.arange(grid_size, dtype=np.float32) grid_w = np.arange(grid_size, dtype=np.float32) grid = np.meshgrid(grid_w, grid_h) # here w goes first grid = np.stack(grid, axis=0) grid = grid.reshape([2, 1, grid_size, grid_size]) pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) if cls_token: pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0) return pos_embed def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): assert embed_dim % 2 == 0 # use half of dimensions to encode grid_h emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) return emb def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): """ embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D) """ assert embed_dim % 2 == 0 omega = np.arange(embed_dim // 2, dtype=float) omega /= embed_dim / 2. omega = 1. / 10000**omega # (D/2,) pos = pos.reshape(-1) # (M,) out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product emb_sin = np.sin(out) # (M, D/2) emb_cos = np.cos(out) # (M, D/2) emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) return emb # -------------------------------------------------------- # Interpolate position embeddings for high-resolution # References: # DeiT: https://github.com/facebookresearch/deit # -------------------------------------------------------- def interpolate_pos_embed(model, checkpoint_model): if 'pos_embed' in checkpoint_model: pos_embed_checkpoint = checkpoint_model['pos_embed'] embedding_size = pos_embed_checkpoint.shape[-1] num_patches = model.patch_embed.num_patches num_extra_tokens = model.pos_embed.shape[-2] - num_patches # height (== width) for the checkpoint position embedding orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) # height (== width) for the new position embedding new_size = int(num_patches ** 0.5) # class_token and dist_token are kept unchanged if orig_size != new_size: print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] # only the position tokens are interpolated pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) pos_tokens = torch.nn.functional.interpolate( pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) checkpoint_model['pos_embed'] = new_pos_embed ================================================ FILE: artrackv2_mindspore/lib/models/component/weight_init.py ================================================ import torch import math import warnings from torch.nn.init import _calculate_fan_in_and_fan_out def _no_grad_trunc_normal_(tensor, mean, std, a, b): # Cut & paste from PyTorch official master until it's in a few official releases - RW # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf def norm_cdf(x): # Computes standard normal cumulative distribution function return (1. + math.erf(x / math.sqrt(2.))) / 2. if (mean < a - 2 * std) or (mean > b + 2 * std): warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " "The distribution of values may be incorrect.", stacklevel=2) with torch.no_grad(): # Values are generated by using a truncated uniform distribution and # then using the inverse CDF for the normal distribution. # Get upper and lower cdf values l = norm_cdf((a - mean) / std) u = norm_cdf((b - mean) / std) # Uniformly fill tensor with values from [l, u], then translate to # [2l-1, 2u-1]. tensor.uniform_(2 * l - 1, 2 * u - 1) # Use inverse cdf transform for normal distribution to get truncated # standard normal tensor.erfinv_() # Transform to proper mean, std tensor.mul_(std * math.sqrt(2.)) tensor.add_(mean) # Clamp to ensure it's in the proper range tensor.clamp_(min=a, max=b) return tensor def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): # type: (Tensor, float, float, float, float) -> Tensor r"""Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn from the normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` with values outside :math:`[a, b]` redrawn until they are within the bounds. The method used for generating the random values works best when :math:`a \leq \text{mean} \leq b`. Args: tensor: an n-dimensional `torch.Tensor` mean: the mean of the normal distribution std: the standard deviation of the normal distribution a: the minimum cutoff value b: the maximum cutoff value Examples: >>> w = torch.empty(3, 5) >>> nn.init.trunc_normal_(w) """ return _no_grad_trunc_normal_(tensor, mean, std, a, b) def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'): fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) if mode == 'fan_in': denom = fan_in elif mode == 'fan_out': denom = fan_out elif mode == 'fan_avg': denom = (fan_in + fan_out) / 2 variance = scale / denom if distribution == "truncated_normal": # constant is stddev of standard normal truncated to (-2, 2) trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978) elif distribution == "normal": tensor.normal_(std=math.sqrt(variance)) elif distribution == "uniform": bound = math.sqrt(3 * variance) tensor.uniform_(-bound, bound) else: raise ValueError(f"invalid distribution {distribution}") def lecun_normal_(tensor): variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal') ================================================ FILE: artrackv2_mindspore/lib/models/layers/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/lib/models/layers/attn.py ================================================ import sys sys.path.append("/home/baiyifan/code/AR2_mindspore_cp/2stage") import mindspore as ms from mindspore import ops from mindspore import nn from mindspore.common.initializer import initializer,TruncatedNormal from lib.models.layers.rpe import generate_2d_concatenated_self_attention_relative_positional_encoding_index class Attention(nn.Cell): def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0., rpe=False, z_size=7, x_size=14): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim ** -0.5 self.qkv = nn.Dense(dim, dim * 3, has_bias=qkv_bias) self.attn_drop = nn.Dropout(p=attn_drop) self.proj = nn.Dense(dim, dim) self.proj_drop = nn.Dropout(p=proj_drop) self.rpe =rpe if self.rpe: relative_position_index = \ generate_2d_concatenated_self_attention_relative_positional_encoding_index([z_size, z_size], [x_size, x_size]) self.register_buffer("relative_position_index", relative_position_index) # define a parameter table of relative position bias self.relative_position_bias_table = ops.empty((num_heads,relative_position_index.max() + 1)) relative_position_bias_table_shape = self.relative_position_bias_table.shape self.relative_position_bias_table = initializer(TruncatedNormal(sigma=0.02),relative_position_bias_table_shape) self.relative_position_bias_table = ms.Parameter(self.relative_position_bias_table) def construct(self, x, mask=None, return_attention=False): # x: B, N, C # mask: [B, N, ] torch.bool B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple) attn = (q @ k.swapaxes(-2, -1)) * self.scale if self.rpe: relative_position_bias = self.relative_position_bias_table[:, self.relative_position_index].unsqueeze(0) attn += relative_position_bias if mask is not None: attn = attn.masked_fill(mask.unsqueeze(1).unsqueeze(2), float('-inf'),) split_attn = False len_t = 49 if split_attn: attn_t = ops.softmax(attn[..., :len_t],axis=-1) attn_s = ops.softmax(attn[..., len_t:],axis=-1) attn = ops.cat([attn_t, attn_s], dim=-1) else: attn = ops.softmax(attn,axis=-1) attn = self.attn_drop(attn) x = (attn @ v).swapaxes(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) if return_attention: return x, attn else: return x class Attention_talking_head(nn.Cell): # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py # with slight modifications to add Talking Heads Attention (https://arxiv.org/pdf/2003.02436v1.pdf) def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., rpe=True, z_size=7, x_size=14): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = qk_scale or head_dim ** -0.5 self.qkv = nn.Dense(dim, dim * 3, has_bias=qkv_bias) self.attn_drop = nn.Dropout(p=attn_drop) self.proj = nn.Dense(dim, dim) self.proj_l = nn.Dense(num_heads, num_heads) self.proj_w = nn.Dense(num_heads, num_heads) self.proj_drop = nn.Dropout(p=proj_drop) self.rpe = rpe if self.rpe: relative_position_index = \ generate_2d_concatenated_self_attention_relative_positional_encoding_index([z_size, z_size], [x_size, x_size]) self.register_buffer("relative_position_index", relative_position_index) # define a parameter table of relative position bias self.relative_position_bias_table = ops.empty((num_heads,relative_position_index.max() + 1)) relative_position_bias_table_shape = self.relative_position_bias_table.shape self.relative_position_bias_table=initializer(TruncatedNormal(sigma=0.02),relative_position_bias_table_shape) self.relative_position_bias_table=ms.Parameter(self.relative_position_bias_table) def construct(self, x, mask=None): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0] * self.scale, qkv[1], qkv[2] attn = (q @ k.swapaxes(-2, -1)) if self.rpe: relative_position_bias = self.relative_position_bias_table[:, self.relative_position_index].unsqueeze(0) attn += relative_position_bias if mask is not None: attn = attn.masked_fill(mask.unsqueeze(1).unsqueeze(2), float('-inf'),) attn = self.proj_l(attn.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) attn = ops.softmax(attn,axis=-1) attn = self.proj_w(attn.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) attn = self.attn_drop(attn) x = (attn @ v).swapaxes(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) return x ================================================ FILE: artrackv2_mindspore/lib/models/layers/attn_blocks.py ================================================ import math import torch import torch.nn as nn from timm.models.layers import Mlp, DropPath, trunc_normal_, lecun_normal_ from lib.models.layers.attn import Attention def candidate_elimination(attn: torch.Tensor, tokens: torch.Tensor, lens_t: int, keep_ratio: float, global_index: torch.Tensor, box_mask_z: torch.Tensor): """ Eliminate potential background candidates for computation reduction and noise cancellation. Args: attn (torch.Tensor): [B, num_heads, L_t + L_s, L_t + L_s], attention weights tokens (torch.Tensor): [B, L_t + L_s, C], template and search region tokens lens_t (int): length of template keep_ratio (float): keep ratio of search region tokens (candidates) global_index (torch.Tensor): global index of search region tokens box_mask_z (torch.Tensor): template mask used to accumulate attention weights Returns: tokens_new (torch.Tensor): tokens after candidate elimination keep_index (torch.Tensor): indices of kept search region tokens removed_index (torch.Tensor): indices of removed search region tokens """ lens_s = attn.shape[-1] - lens_t bs, hn, _, _ = attn.shape lens_keep = math.ceil(keep_ratio * lens_s) if lens_keep == lens_s: return tokens, global_index, None attn_t = attn[:, :, :lens_t, lens_t:] if box_mask_z is not None: box_mask_z = box_mask_z.unsqueeze(1).unsqueeze(-1).expand(-1, attn_t.shape[1], -1, attn_t.shape[-1]) # attn_t = attn_t[:, :, box_mask_z, :] attn_t = attn_t[box_mask_z] attn_t = attn_t.view(bs, hn, -1, lens_s) attn_t = attn_t.mean(dim=2).mean(dim=1) # B, H, L-T, L_s --> B, L_s # attn_t = [attn_t[i, :, box_mask_z[i, :], :] for i in range(attn_t.size(0))] # attn_t = [attn_t[i].mean(dim=1).mean(dim=0) for i in range(len(attn_t))] # attn_t = torch.stack(attn_t, dim=0) else: attn_t = attn_t.mean(dim=2).mean(dim=1) # B, H, L-T, L_s --> B, L_s # use sort instead of topk, due to the speed issue # https://github.com/pytorch/pytorch/issues/22812 sorted_attn, indices = torch.sort(attn_t, dim=1, descending=True) topk_attn, topk_idx = sorted_attn[:, :lens_keep], indices[:, :lens_keep] non_topk_attn, non_topk_idx = sorted_attn[:, lens_keep:], indices[:, lens_keep:] keep_index = global_index.gather(dim=1, index=topk_idx) removed_index = global_index.gather(dim=1, index=non_topk_idx) # separate template and search tokens tokens_t = tokens[:, :lens_t] tokens_s = tokens[:, lens_t:] # obtain the attentive and inattentive tokens B, L, C = tokens_s.shape # topk_idx_ = topk_idx.unsqueeze(-1).expand(B, lens_keep, C) attentive_tokens = tokens_s.gather(dim=1, index=topk_idx.unsqueeze(-1).expand(B, -1, C)) # inattentive_tokens = tokens_s.gather(dim=1, index=non_topk_idx.unsqueeze(-1).expand(B, -1, C)) # compute the weighted combination of inattentive tokens # fused_token = non_topk_attn @ inattentive_tokens # concatenate these tokens # tokens_new = torch.cat([tokens_t, attentive_tokens, fused_token], dim=0) tokens_new = torch.cat([tokens_t, attentive_tokens], dim=1) return tokens_new, keep_index, removed_index class CEBlock(nn.Module): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, keep_ratio_search=1.0,): super().__init__() self.norm1 = norm_layer(dim) self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) self.keep_ratio_search = keep_ratio_search def forward(self, x, global_index_template, global_index_search, mask=None, ce_template_mask=None, keep_ratio_search=None): x_attn, attn = self.attn(self.norm1(x), mask, True) x = x + self.drop_path(x_attn) lens_t = global_index_template.shape[1] removed_index_search = None if self.keep_ratio_search < 1 and (keep_ratio_search is None or keep_ratio_search < 1): keep_ratio_search = self.keep_ratio_search if keep_ratio_search is None else keep_ratio_search x, global_index_search, removed_index_search = candidate_elimination(attn, x, lens_t, keep_ratio_search, global_index_search, ce_template_mask) x = x + self.drop_path(self.mlp(self.norm2(x))) return x, global_index_template, global_index_search, removed_index_search, attn class Block(nn.Module): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): super().__init__() self.norm1 = norm_layer(dim) self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) def forward(self, x, mask=None): x = x + self.drop_path(self.attn(self.norm1(x), mask)) x = x + self.drop_path(self.mlp(self.norm2(x))) return x ================================================ FILE: artrackv2_mindspore/lib/models/layers/head.py ================================================ import sys import mindspore as ms import mindspore.nn as nn from mindspore import ops from mindspore import Tensor from mindspore.nn import Identity from mindspore.nn.probability.distribution import Categorical from lib.models.timm import * import copy from typing import Optional def top_k_top_p_filtering_batch(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')): """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering Args: logits: logits distribution shape (vocabulary size) top_k > 0: keep only top k tokens with highest probability (top-k filtering). top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751) From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317 """ top_k = min(top_k, logits.size(-1)) # Safety check if top_k > 0: # Remove all tokens with a probability less than the last token of the top-k # ops.topk()返回最后一维最大的top_k个元素，返回值为二维(values,indices) # ...表示其他维度由计算机自行推断 for i in range(logits.shape[0]): indices_to_remove = logits[i] < ops.topk(logits[i], top_k)[0][..., -1, None] logits[i][indices_to_remove] = filter_value # 对于topk之外的其他元素的logits值设为负无穷 if top_p > 0.0: for i in range(logits.shape[0]): sorted_logits, sorted_indices = ops.sort(logits[i], descending=True) # 对logits进行递减排序 cumulative_probs = ops.cumsum(ops.softmax(sorted_logits, axis=-1), axis=-1) # Remove tokens with cumulative probability above the threshold sorted_indices_to_remove = cumulative_probs > top_p # Shift the indices to the right to keep also the first token above the threshold sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() sorted_indices_to_remove[..., 0] = 0 indices_to_remove = sorted_indices[sorted_indices_to_remove] logits[i][indices_to_remove] = filter_value return logits def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1, freeze_bn=False): return nn.Sequential( nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=True), nn.BatchNorm2d(out_planes), nn.ReLU(inplace=True)) class Corner_Predictor(nn.Cell): """ Corner Predictor module""" def __init__(self, inplanes=64, channel=256, feat_sz=20, stride=16, freeze_bn=False): super(Corner_Predictor, self).__init__() self.feat_sz = feat_sz self.stride = stride self.img_sz = self.feat_sz * self.stride '''top-left corner''' self.conv1_tl = conv(inplanes, channel, freeze_bn=freeze_bn) self.conv2_tl = conv(channel, channel // 2, freeze_bn=freeze_bn) self.conv3_tl = conv(channel // 2, channel // 4, freeze_bn=freeze_bn) self.conv4_tl = conv(channel // 4, channel // 8, freeze_bn=freeze_bn) self.conv5_tl = nn.Conv2d(channel // 8, 1, kernel_size=1) '''bottom-right corner''' self.conv1_br = conv(inplanes, channel, freeze_bn=freeze_bn) self.conv2_br = conv(channel, channel // 2, freeze_bn=freeze_bn) self.conv3_br = conv(channel // 2, channel // 4, freeze_bn=freeze_bn) self.conv4_br = conv(channel // 4, channel // 8, freeze_bn=freeze_bn) self.conv5_br = nn.Conv2d(channel // 8, 1, kernel_size=1) '''about coordinates and indexs''' self.indice = ops.arange(0, self.feat_sz).view(-1, 1) * self.stride # generate mesh-grid self.coord_x = self.indice.repeat((self.feat_sz, 1)).view((self.feat_sz * self.feat_sz,)).float() self.coord_y = self.indice.repeat((1, self.feat_sz)).view((self.feat_sz * self.feat_sz,)).float() def construct(self, x, return_dist=False, softmax=True): """ Forward pass with input x. """ score_map_tl, score_map_br = self.get_score_map(x) if return_dist: coorx_tl, coory_tl, prob_vec_tl = self.soft_argmax(score_map_tl, return_dist=True, softmax=softmax) coorx_br, coory_br, prob_vec_br = self.soft_argmax(score_map_br, return_dist=True, softmax=softmax) return ops.stack((coorx_tl, coory_tl, coorx_br, coory_br), axis=1) / self.img_sz, prob_vec_tl, prob_vec_br else: coorx_tl, coory_tl = self.soft_argmax(score_map_tl) coorx_br, coory_br = self.soft_argmax(score_map_br) return ops.stack((coorx_tl, coory_tl, coorx_br, coory_br), axis=1) / self.img_sz def get_score_map(self, x): # top-left branch x_tl1 = self.conv1_tl(x) x_tl2 = self.conv2_tl(x_tl1) x_tl3 = self.conv3_tl(x_tl2) x_tl4 = self.conv4_tl(x_tl3) score_map_tl = self.conv5_tl(x_tl4) # bottom-right branch x_br1 = self.conv1_br(x) x_br2 = self.conv2_br(x_br1) x_br3 = self.conv3_br(x_br2) x_br4 = self.conv4_br(x_br3) score_map_br = self.conv5_br(x_br4) return score_map_tl, score_map_br def soft_argmax(self, score_map, return_dist=False, softmax=True): """ get soft-argmax coordinate for a given heatmap """ score_vec = score_map.view((-1, self.feat_sz * self.feat_sz)) # (batch, feat_sz * feat_sz) prob_vec = ops.softmax(score_vec, axis=1) exp_x = ops.sum((self.coord_x * prob_vec), dim=1) exp_y = ops.sum((self.coord_y * prob_vec), dim=1) if return_dist: if softmax: return exp_x, exp_y, prob_vec else: return exp_x, exp_y, score_vec else: return exp_x, exp_y class CenterPredictor(nn.Cell, ): def __init__(self, inplanes=64, channel=256, feat_sz=20, stride=16, freeze_bn=False): super(CenterPredictor, self).__init__() self.feat_sz = feat_sz self.stride = stride self.img_sz = self.feat_sz * self.stride # corner predict self.conv1_ctr = conv(inplanes, channel, freeze_bn=freeze_bn) self.conv2_ctr = conv(channel, channel // 2, freeze_bn=freeze_bn) self.conv3_ctr = conv(channel // 2, channel // 4, freeze_bn=freeze_bn) self.conv4_ctr = conv(channel // 4, channel // 8, freeze_bn=freeze_bn) self.conv5_ctr = nn.Conv2d(channel // 8, 1, kernel_size=1) # size regress self.conv1_offset = conv(inplanes, channel, freeze_bn=freeze_bn) self.conv2_offset = conv(channel, channel // 2, freeze_bn=freeze_bn) self.conv3_offset = conv(channel // 2, channel // 4, freeze_bn=freeze_bn) self.conv4_offset = conv(channel // 4, channel // 8, freeze_bn=freeze_bn) self.conv5_offset = nn.Conv2d(channel // 8, 2, kernel_size=1) # size regress self.conv1_size = conv(inplanes, channel, freeze_bn=freeze_bn) self.conv2_size = conv(channel, channel // 2, freeze_bn=freeze_bn) self.conv3_size = conv(channel // 2, channel // 4, freeze_bn=freeze_bn) self.conv4_size = conv(channel // 4, channel // 8, freeze_bn=freeze_bn) self.conv5_size = nn.Conv2d(channel // 8, 2, kernel_size=1) for p in self.parameters(): if p.dim() > 1: nn.init.xavier_uniform_(p) def construct(self, x, gt_score_map=None): """ Forward pass with input x. """ score_map_ctr, size_map, offset_map = self.get_score_map(x) # assert gt_score_map is None if gt_score_map is None: bbox = self.cal_bbox(score_map_ctr, size_map, offset_map) else: bbox = self.cal_bbox(gt_score_map.unsqueeze(1), size_map, offset_map) return score_map_ctr, bbox, size_map, offset_map def cal_bbox(self, score_map_ctr, size_map, offset_map, return_score=False): max_score, idx = ops.max(score_map_ctr.flatten(1), axis=1, keepdim=True) idx_y = idx // self.feat_sz idx_x = idx % self.feat_sz idx = idx.unsqueeze(1).expand(idx.shape[0], 2, 1) size = size_map.flatten(2).gather(dim=2, index=idx) offset = offset_map.flatten(2).gather(dim=2, index=idx).squeeze(-1) # bbox = ops.cat([idx_x - size[:, 0] / 2, idx_y - size[:, 1] / 2, # idx_x + size[:, 0] / 2, idx_y + size[:, 1] / 2], axis=1) / self.feat_sz # cx, cy, w, h bbox = ops.cat([(idx_x.to(ms.float) + offset[:, :1]) / self.feat_sz, (idx_y.to(ms.float) + offset[:, 1:]) / self.feat_sz, size.squeeze(-1)], axis=1) if return_score: return bbox, max_score return bbox def get_pred(self, score_map_ctr, size_map, offset_map): max_score, idx = ops.max(score_map_ctr.flatten(1), axis=1, keepdim=True) idx_y = idx // self.feat_sz idx_x = idx % self.feat_sz idx = idx.unsqueeze(1).expand(idx.shape[0], 2, 1) size = size_map.flatten(2).gather(dim=2, index=idx) offset = offset_map.flatten(2).gather(dim=2, index=idx).squeeze(-1) # bbox = ops.cat([idx_x - size[:, 0] / 2, idx_y - size[:, 1] / 2, # idx_x + size[:, 0] / 2, idx_y + size[:, 1] / 2], dim=1) / self.feat_sz return size * self.feat_sz, offset def get_score_map(self, x): def _sigmoid(x): y = ops.clamp(x.sigmoid_(), min=1e-4, max=1 - 1e-4) return y # ctr branch x_ctr1 = self.conv1_ctr(x) x_ctr2 = self.conv2_ctr(x_ctr1) x_ctr3 = self.conv3_ctr(x_ctr2) x_ctr4 = self.conv4_ctr(x_ctr3) score_map_ctr = self.conv5_ctr(x_ctr4) # offset branch x_offset1 = self.conv1_offset(x) x_offset2 = self.conv2_offset(x_offset1) x_offset3 = self.conv3_offset(x_offset2) x_offset4 = self.conv4_offset(x_offset3) score_map_offset = self.conv5_offset(x_offset4) # size branch x_size1 = self.conv1_size(x) x_size2 = self.conv2_size(x_size1) x_size3 = self.conv3_size(x_size2) x_size4 = self.conv4_size(x_size3) score_map_size = self.conv5_size(x_size4) return _sigmoid(score_map_ctr), _sigmoid(score_map_size), score_map_offset class MLP(nn.Cell): """ Very simple multi-layer perceptron (also called FFN)""" def __init__(self, input_dim, hidden_dim, output_dim, num_layers, BN=False): super().__init__() self.num_layers = num_layers h = [hidden_dim] * (num_layers - 1) if BN: self.layers = nn.CellList(nn.Sequential(nn.Linear(n, k), nn.BatchNorm1d(k)) for n, k in zip([input_dim] + h, h + [output_dim])) else: self.layers = nn.CellList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) def construct(self, x): for i, layer in enumerate(self.layers): x = ops.relu(layer(x)) if i < self.num_layers - 1 else layer(x) return x class SelfAttention(nn.Cell): def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., attn_pos_encoding_only=False): super(SelfAttention, self).__init__() assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}." self.dim = dim self.num_heads = num_heads head_dim = dim // num_heads self.scale = qk_scale or head_dim ** -0.5 if attn_pos_encoding_only: self.qkv = nn.Linear(dim, 3 * dim, bias=qkv_bias) else: self.q = nn.Linear(dim, dim, bias=qkv_bias) self.k = nn.Linear(dim, dim, bias=qkv_bias) self.v = nn.Linear(dim, dim, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) self.attn_pos_encoding_only = attn_pos_encoding_only def construct(self, x, q_ape, k_ape, attn_pos): ''' Args: x (ms.Tensor): (B, L, C) q_ape (ms.Tensor | None): (1 or B, L, C), absolute positional encoding for q k_ape (ms.Tensor | None): (1 or B, L, C), absolute positional encoding for k attn_pos (ms.Tensor | None): (1 or B, num_heads, L, L), untied positional encoding Returns: ms.Tensor: (B, L, C) ''' B, N, C = x.shape if self.attn_pos_encoding_only: assert q_ape is None and k_ape is None qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] else: q = x + q_ape if q_ape is not None else x q = self.q(q).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) k = x + k_ape if k_ape is not None else x k = self.k(k).reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) v = self.v(x).reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) attn = q @ k.swapaxes(-2, -1) attn = attn * self.scale if attn_pos is not None: attn = attn + attn_pos attn = ops.softmax(attn,axis=-1) attn = self.attn_drop(attn) x = attn @ v x = x.swapaxes(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) return x class CrossAttention(nn.Cell): def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., attn_pos_encoding_only=False): super(CrossAttention, self).__init__() assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}." self.dim = dim self.num_heads = num_heads head_dim = dim // num_heads self.scale = qk_scale or head_dim ** -0.5 if attn_pos_encoding_only: self.q = nn.Linear(dim, dim, bias=qkv_bias) self.kv = nn.Linear(dim, 2 * dim, bias=qkv_bias) else: self.q = nn.Linear(dim, dim, bias=qkv_bias) self.k = nn.Linear(dim, dim, bias=qkv_bias) self.v = nn.Linear(dim, dim, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) self.attn_pos_encoding_only = attn_pos_encoding_only def construct(self, q, kv, q_ape, k_ape, attn_pos): ''' Args: q (ms.Tensor): (B, L_q, C) kv (ms.Tensor): (B, L_kv, C) q_ape (ms.Tensor | None): (1 or B, L_q, C), absolute positional encoding for q k_ape (ms.Tensor | None): (1 or B, L_kv, C), absolute positional encoding for k attn_pos (ms.Tensor | None): (1 or B, num_heads, L_q, L_kv), untied positional encoding Returns: ms.Tensor: (B, L_q, C) ''' B, q_N, C = q.shape kv_N = kv.shape[1] if self.attn_pos_encoding_only: assert q_ape is None and k_ape is None q = self.q(q).reshape(B, q_N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) kv = self.kv(kv).reshape(B, kv_N, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) k, v = kv[0], kv[1] else: q = q + q_ape if q_ape is not None else q q = self.q(q).reshape(B, q_N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) k = kv + k_ape if k_ape is not None else kv k = self.k(k).reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) v = self.v(kv).reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) attn = q @ k.swapaxes(-2, -1) attn = attn * self.scale if attn_pos is not None: attn = attn + attn_pos attn = ops.softmax(attn,axis=-1) attn = self.attn_drop(attn) x = attn @ v x = x.swapaxes(1, 2).reshape(B, q_N, C) x = self.proj(x) x = self.proj_drop(x) return x class Mlp(nn.Cell): """ Multilayer perceptron.""" def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def construct(self, x): ''' Args: x (ms.Tensor): (B, L, C), input tensor Returns: ms.Tensor: (B, L, C), output tensor ''' x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x class FeatureFusion(nn.Cell): def __init__(self, dim, num_heads, mlp_ratio=2., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., drop_path=nn.Identity(), act_layer=nn.GELU, norm_layer=nn.LayerNorm, attn_pos_encoding_only=False): super(FeatureFusion, self).__init__() self.z_norm1 = norm_layer(dim) self.x_norm1 = norm_layer(dim) self.z_self_attn = SelfAttention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop, attn_pos_encoding_only) self.x_self_attn = SelfAttention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop, attn_pos_encoding_only) self.z_norm2_1 = norm_layer(dim) self.z_norm2_2 = norm_layer(dim) self.x_norm2_1 = norm_layer(dim) self.x_norm2_2 = norm_layer(dim) self.z_x_cross_attention = CrossAttention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop, attn_pos_encoding_only) self.x_z_cross_attention = CrossAttention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop, attn_pos_encoding_only) mlp_hidden_dim = int(dim * mlp_ratio) self.z_norm3 = norm_layer(dim) self.x_norm3 = norm_layer(dim) print(mlp_ratio) self.z_mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) self.x_mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) self.drop_path = drop_path def construct(self, z, x, z_self_attn_pos, x_self_attn_pos, z_x_cross_attn_pos, x_z_cross_attn_pos): z = z + self.drop_path(self.z_self_attn(self.z_norm1(z), None, None, z_self_attn_pos)) x = x + self.drop_path(self.x_self_attn(self.x_norm1(x), None, None, x_self_attn_pos)) z = z + self.drop_path(self.z_x_cross_attention(self.z_norm2_1(z), self.x_norm2_1(x), None, None, z_x_cross_attn_pos)) x = x + self.drop_path(self.x_z_cross_attention(self.x_norm2_2(x), self.z_norm2_2(z), None, None, x_z_cross_attn_pos)) z = z + self.drop_path(self.z_mlp(self.z_norm3(z))) x = x + self.drop_path(self.x_mlp(self.x_norm3(x))) return z, x class FeatureFusionEncoder(nn.Cell): def __init__(self, feature_fusion_layers, z_pos_enc, x_pos_enc, z_rel_pos_index, x_rel_pos_index, z_x_rel_pos_index, x_z_rel_pos_index, z_rel_pos_bias_table, x_rel_pos_bias_table, z_x_rel_pos_bias_table, x_z_rel_pos_bias_table): super(FeatureFusionEncoder, self).__init__() self.layers = nn.CellList(feature_fusion_layers) self.z_pos_enc = z_pos_enc self.x_pos_enc = x_pos_enc self.register_buffer('z_rel_pos_index', z_rel_pos_index, False) self.register_buffer('x_rel_pos_index', x_rel_pos_index, False) self.register_buffer('z_x_rel_pos_index', z_x_rel_pos_index, False) self.register_buffer('x_z_rel_pos_index', x_z_rel_pos_index, False) self.z_rel_pos_bias_table = z_rel_pos_bias_table self.x_rel_pos_bias_table = x_rel_pos_bias_table self.z_x_rel_pos_bias_table = z_x_rel_pos_bias_table self.x_z_rel_pos_bias_table = x_z_rel_pos_bias_table #self.conv1 = ms.nn.Conv2d(384,768,1,1,0) #self.conv2 = ms.nn.Conv2d(768,768,2,1,1) #self.conv3 = ms.nn.Conv2d(768,384,1,1,0) #self.norm1 = ms.nn.LayerNorm(384) #self.norm2 = ms.nn.LayerNorm(768) #self.norm3 = ms.nn.LayerNorm(384) def construct(self, z, x, z_pos, x_pos): ''' Args: z (ms.Tensor): (B, L_z, C), template image feature tokens x (ms.Tensor): (B, L_x, C), search image feature tokens z_pos (ms.Tensor | None): (1 or B, L_z, C), optional positional encoding for z x_pos (ms.Tensor | None): (1 or B, L_x, C), optional positional encoding for x Returns: Tuple[ms.Tensor, ms.Tensor]: (B, L_z, C): template image feature tokens (B, L_x, C): search image feature tokens ''' # Support untied positional encoding only for simplicity assert z_pos is None and x_pos is None # untied positional encoding z_q_pos, z_k_pos = self.z_pos_enc() x_q_pos, x_k_pos = self.x_pos_enc() z_self_attn_pos = (z_q_pos @ z_k_pos.swapaxes(-2, -1)).unsqueeze(0) x_self_attn_pos = (x_q_pos @ x_k_pos.swapaxes(-2, -1)).unsqueeze(0) z_x_cross_attn_pos = (z_q_pos @ x_k_pos.swapaxes(-2, -1)).unsqueeze(0) x_z_cross_attn_pos = (x_q_pos @ z_k_pos.swapaxes(-2, -1)).unsqueeze(0) # relative positional encoding z_self_attn_pos = z_self_attn_pos + self.z_rel_pos_bias_table(self.z_rel_pos_index) x_self_attn_pos = x_self_attn_pos + self.x_rel_pos_bias_table(self.x_rel_pos_index) z_x_cross_attn_pos = z_x_cross_attn_pos + self.z_x_rel_pos_bias_table(self.z_x_rel_pos_index) x_z_cross_attn_pos = x_z_cross_attn_pos + self.x_z_rel_pos_bias_table(self.x_z_rel_pos_index) # x = self.norm1(x) # B,L,C = x.shape # x = x.permute(0,2,1).reshape(B,C,14,14) # x_temp = x # x = self.conv3(self.conv2((self.conv1(x)))) # x = x[:,:,1:,1:] # x = x+x_temp # x = x.reshape(B,C,L).permute(0,2,1) # x = self.norm3(x) for layer in self.layers: z, x = layer(z, x, z_self_attn_pos, x_self_attn_pos, z_x_cross_attn_pos, x_z_cross_attn_pos) return z, x class Learned2DPositionalEncoder(nn.Cell): def __init__(self, dim, w, h): super(Learned2DPositionalEncoder, self).__init__() self.w_pos = nn.Parameter(ops.empty(w, dim)) self.h_pos = nn.Parameter(ops.empty(h, dim)) trunc_normal_(self.w_pos, std=0.02) trunc_normal_(self.h_pos, std=0.02) def construct(self): w = self.w_pos.shape[0] h = self.h_pos.shape[0] return (self.w_pos[None, :, :] + self.h_pos[:, None, :]).view(h * w, -1) class Untied2DPositionalEncoder(nn.Cell): def __init__(self, dim, num_heads, w, h, scale=None, with_q=True, with_k=True): super(Untied2DPositionalEncoder, self).__init__() assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}." self.pos = Learned2DPositionalEncoder(dim, w, h) self.norm = nn.LayerNorm(dim) self.pos_q_linear = None self.pos_k_linear = None if with_q: self.pos_q_linear = nn.Linear(dim, dim) if with_k: self.pos_k_linear = nn.Linear(dim, dim) self.num_heads = num_heads head_dim = dim // num_heads self.scale = scale or head_dim ** -0.5 def construct(self): pos = self.norm(self.pos()) seq_len = pos.shape[0] if self.pos_q_linear is not None and self.pos_k_linear is not None: pos_q = self.pos_q_linear(pos).view(seq_len, self.num_heads, -1).swapaxes(0, 1) * self.scale pos_k = self.pos_k_linear(pos).view(seq_len, self.num_heads, -1).swapaxes(0, 1) return pos_q, pos_k elif self.pos_q_linear is not None: pos_q = self.pos_q_linear(pos).view(seq_len, self.num_heads, -1).swapaxes(0, 1) * self.scale return pos_q elif self.pos_k_linear is not None: pos_k = self.pos_k_linear(pos).view(seq_len, self.num_heads, -1).swapaxes(0, 1) return pos_k else: raise RuntimeError def generate_2d_relative_positional_encoding_index(z_shape, x_shape): ''' z_shape: (z_h, z_w) x_shape: (x_h, x_w) ''' z_2d_index_h, z_2d_index_w = ops.meshgrid(ops.arange(z_shape[0]), ops.arange(z_shape[1])) x_2d_index_h, x_2d_index_w = ops.meshgrid(ops.arange(x_shape[0]), ops.arange(x_shape[1])) z_2d_index_h = z_2d_index_h.flatten(0) z_2d_index_w = z_2d_index_w.flatten(0) x_2d_index_h = x_2d_index_h.flatten(0) x_2d_index_w = x_2d_index_w.flatten(0) diff_h = z_2d_index_h[:, None] - x_2d_index_h[None, :] diff_w = z_2d_index_w[:, None] - x_2d_index_w[None, :] diff = ops.stack((diff_h, diff_w), axis=-1) _, indices = ops.unique(diff.view(-1, 2), return_inverse=True, dim=0) return indices.view(z_shape[0] * z_shape[1], x_shape[0] * x_shape[1]) class RelativePosition2DEncoder(nn.Cell): def __init__(self, num_heads, embed_size): super(RelativePosition2DEncoder, self).__init__() self.relative_position_bias_table = nn.Parameter(ops.empty((num_heads, embed_size))) trunc_normal_(self.relative_position_bias_table, std=0.02) def construct(self, attn_rpe_index): ''' Args: attn_rpe_index (ms.Tensor): (*), any shape containing indices, max(attn_rpe_index) < embed_size Returns: ms.Tensor: (1, num_heads, *) ''' return self.relative_position_bias_table[:, attn_rpe_index].unsqueeze(0) class DropPathAllocator: def __init__(self, max_drop_path_rate, stochastic_depth_decay = True): self.max_drop_path_rate = max_drop_path_rate self.stochastic_depth_decay = stochastic_depth_decay self.allocated = [] self.allocating = [] def __enter__(self): self.allocating = [] def __exit__(self, exc_type, exc_val, exc_tb): if len(self.allocating) != 0: self.allocated.append(self.allocating) self.allocating = None if not self.stochastic_depth_decay: for depth_module in self.allocated: for module in depth_module: if isinstance(module, DropPath): module.drop_prob = self.max_drop_path_rate else: depth = self.get_depth() dpr = [x.item() for x in ops.linspace(0, self.max_drop_path_rate, depth)] assert len(dpr) == len(self.allocated) for drop_path_rate, depth_modules in zip(dpr, self.allocated): for module in depth_modules: if isinstance(module, DropPath): module.drop_prob = drop_path_rate def __len__(self): length = 0 for depth_modules in self.allocated: length += len(depth_modules) return length def increase_depth(self): self.allocated.append(self.allocating) self.allocating = [] def get_depth(self): return len(self.allocated) def allocate(self): if self.max_drop_path_rate == 0 or (self.stochastic_depth_decay and self.get_depth() == 0): drop_path_module = Identity() else: drop_path_module = DropPath() self.allocating.append(drop_path_module) return drop_path_module def get_all_allocated(self): allocated = [] for depth_module in self.allocated: for module in depth_module: allocated.append(module) return allocated def build_encoder(encoder_layer, num_heads, mlp_ratio, qkv_bias, drop_rate, attn_drop, dim, z_size, x_size, drop_path): z_shape = [z_size, z_size] x_shape = [x_size, x_size] encoder_layers = [] for i in range(encoder_layer): encoder_layers.append( FeatureFusion(dim, num_heads, mlp_ratio, qkv_bias, drop=drop_rate, attn_drop=attn_drop, drop_path=drop_path.allocate(), attn_pos_encoding_only=True) ) z_abs_encoder = Untied2DPositionalEncoder(dim, num_heads, z_shape[0], z_shape[1]) x_abs_encoder = Untied2DPositionalEncoder(dim, num_heads, x_shape[0], x_shape[1]) z_self_attn_rel_pos_index = generate_2d_relative_positional_encoding_index(z_shape, z_shape) x_self_attn_rel_pos_index = generate_2d_relative_positional_encoding_index(x_shape, x_shape) z_x_cross_attn_rel_pos_index = generate_2d_relative_positional_encoding_index(z_shape, x_shape) x_z_cross_attn_rel_pos_index = generate_2d_relative_positional_encoding_index(x_shape, z_shape) z_self_attn_rel_pos_bias_table = RelativePosition2DEncoder(num_heads, z_self_attn_rel_pos_index.max() + 1) x_self_attn_rel_pos_bias_table = RelativePosition2DEncoder(num_heads, x_self_attn_rel_pos_index.max() + 1) z_x_cross_attn_rel_pos_bias_table = RelativePosition2DEncoder(num_heads, z_x_cross_attn_rel_pos_index.max() + 1) x_z_cross_attn_rel_pos_bias_table = RelativePosition2DEncoder(num_heads, x_z_cross_attn_rel_pos_index.max() + 1) return FeatureFusionEncoder(encoder_layers, z_abs_encoder, x_abs_encoder, z_self_attn_rel_pos_index, x_self_attn_rel_pos_index, z_x_cross_attn_rel_pos_index, x_z_cross_attn_rel_pos_index, z_self_attn_rel_pos_bias_table, x_self_attn_rel_pos_bias_table, z_x_cross_attn_rel_pos_bias_table, x_z_cross_attn_rel_pos_bias_table) class TargetQueryDecoderLayer(nn.Cell): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., drop_path=nn.Identity(), act_layer=nn.GELU, norm_layer=nn.LayerNorm): super(TargetQueryDecoderLayer, self).__init__() self.norm_1 = norm_layer(dim) #self.self_attn1 = SelfAttention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop) self.self_attn1 = nn.MultiheadAttention(dim, num_heads, dropout=drop) self.norm_2_query = norm_layer(dim) self.norm_2_memory = norm_layer(dim) # self.cross_attn = CrossAttention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop) self.multihead_attn = nn.MultiheadAttention(dim, num_heads, dropout=drop) self.norm_3 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlpz = Mlp(dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) #self.norm_4 = norm_layer(dim) #self.self_attn2 = nn.MultiheadAttention(dim, num_heads, dropout=drop) #self.norm_5_query = norm_layer(dim) #self.norm_5_memory = norm_layer(dim) # self.cross_attn = CrossAttention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop) #self.multihead_attn2 = nn.MultiheadAttention(dim, num_heads, dropout=drop) #self.norm_6 = norm_layer(dim) #mlp_hidden_dim = int(dim * mlp_ratio) #self.mlpx = Mlp(dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) self.drop_path = drop_path def construct(self, query, memoryz, query_pos, tgt_mask: Optional[Tensor] = None, memory_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] = None, memory_key_padding_mask: Optional[Tensor] = None, ): ''' Args: query (ms.Tensor): (B, num_queries, C) memory (ms.Tensor): (B, L, C) query_pos (ms.Tensor): (1 or B, num_queries, C) memory_pos (ms.Tensor): (1 or B, L, C) Returns: ms.Tensor: (B, num_queries, C) ''' #memory = ops.cat((memoryx,memoryz),dim=1) tgt = query q = k = self.norm_1(query) + query_pos query = query + self.drop_path(self.self_attn1(q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask)[0]) q2 = self.norm_2_query(query) + query_pos memory = memoryz k2 = self.norm_2_memory(memory).permute(1, 0 ,2) memory_in = memory.permute(1, 0 ,2) query = query + self.drop_path( self.multihead_attn(query=q2, key=k2, value=memory_in, attn_mask=memory_mask, key_padding_mask=memory_key_padding_mask)[0]) query = query + self.drop_path(self.mlpz(self.norm_3(query))) return query def _get_clones(module, N): return nn.CellList([copy.deepcopy(module) for i in range(N)]) class TargetQueryDecoderBlock(nn.Cell): def __init__(self, dim, decoder_layers, num_layer): super(TargetQueryDecoderBlock, self).__init__() self.layers = nn.CellList(decoder_layers) self.num_layers = num_layer self.norm = nn.LayerNorm(dim) def construct(self, tgt, z, query_pos: Optional[Tensor] = None, tgt_mask: Optional[Tensor] = None, memory_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] = None, memory_key_padding_mask: Optional[Tensor] = None): ''' Args: z (ops.Tensor): (B, L_z, C) x (ms.Tensor): (B, L_x, C) Returns: ms.Tensor: (B, num_queries, C) ''' output = tgt for layer in self.layers: output = layer(output, z, query_pos, tgt_mask=tgt_mask, memory_mask=memory_mask, tgt_key_padding_mask=tgt_key_padding_mask, memory_key_padding_mask=memory_key_padding_mask) output = self.norm(output) return output def build_decoder(decoder_layer, drop_path, dim, num_heads, mlp_ratio, qkv_bias, drop_rate, attn_drop_rate): num_layers = decoder_layer decoder_layers = [] for _ in range(num_layers): decoder_layers.append( TargetQueryDecoderLayer(dim, num_heads, mlp_ratio, qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=drop_path.allocate())) drop_path.increase_depth() decoder = TargetQueryDecoderBlock(dim, decoder_layers, num_layers) return decoder def generate_square_subsequent_mask(sz): r"""Generate a square mask for the sequence. The masked positions are filled with float('-inf'). Unmasked positions are filled with float(0.0). """ mask = (ops.triu(ops.ones(sz, sz)) == 1).swapaxes(0, 1) #for i in range(int(sz/4 - 1)): # j = i+1 # for k in range(4): # mask[j*4+k, 0:j*4] = 0 mask = mask.float().masked_fill(mask == 0, float( '-inf')).masked_fill(mask == 1, float(0.0)) return mask class Pix2Track(nn.Cell): def __init__(self, in_channel=64, feat_sz=20, feat_tz=10, stride=16, encoder_layer=3, decoder_layer=3, bins=400,num_heads=12, mlp_ratio=2, qkv_bias=True, drop_rate=0.0,attn_drop=0.0, drop_path=nn.Identity): super(Pix2Track, self).__init__() self.bins = bins self.word_embeddings = nn.Embedding(self.bins * 3 + 2, in_channel, padding_idx=self.bins * 3, max_norm=1, norm_type=2.0) print(self.bins) self.position_embeddings = nn.Embedding( 5, in_channel) self.prev_position_embeddings = nn.Embedding(5, in_channel) self.output_bias = ms.Parameter(ops.zeros(self.bins * 3 + 2)) #self.out_norm_cls = nn.LayerNorm(in_channel) self.identity_search = ms.Parameter(ops.zeros(1, 1, 768)) self.identity_search = trunc_normal_(self.identity_search, std=.02) self.encoder_layer = encoder_layer self.drop_path = drop_path self.tz = feat_tz * feat_tz self.sz = feat_sz * feat_sz trunc_normal_(self.word_embeddings.weight, std=.02) if self.encoder_layer > 0 : self.encoder = build_encoder(encoder_layer, num_heads, mlp_ratio, qkv_bias, drop_rate, attn_drop, in_channel, feat_tz, feat_sz, self.drop_path) else: self.encoder = None self.decoder = build_decoder(decoder_layer, self.drop_path, in_channel, num_heads, mlp_ratio, qkv_bias, drop_rate, attn_drop, feat_tz, feat_sz) def construct(self, zx_feat, pos_z, pos_x, identity, seqs_input=None, head_type=None, stage=None, search_feature=None): emb_weight = self.word_embeddings.weight.clone() share_weight = emb_weight.T z_feat = zx_feat[:, :self.tz] x_feat = zx_feat[:, self.tz:] z_pos = None x_pos = None out_list = [] bs = zx_feat.shape[0] if self.encoder != None: z_feat, x_feat = self.encoder(z_feat, x_feat, None, None) output_x_feat = x_feat.clone() #print("this is original x_feat") #print(x_feat) #if search_feature == None: #print("I input none") # x_feat = ops.cat((x_feat, x_feat), dim=1) # else: #print("i input something") # x_feat = ops.cat((x_feat, search_feature), axis=1) #print("this is train_x_feat") #print(x_feat) #print(x_feat.shape) #print(x_feat) #print(x_feat.shape) #print(stage) if stage == None: seqs_input = seqs_input.to(ms.int64).to(zx_feat.device) tgt = self.word_embeddings(seqs_input).permute(1, 0, 2) query_embed_ = self.position_embeddings.weight.unsqueeze(1) prev_embed = self.prev_position_embeddings.weight.unsqueeze(1) query_embed = ops.cat([prev_embed, query_embed_], axis=0) query_embed = query_embed.repeat(1, bs, 1) #print(tgt.shape) decoder_feat_cls = self.decoder(tgt, z_feat, x_feat, pos_z, pos_x, identity, self.identity_search, query_embed[:len(tgt)], tgt_mask=generate_square_subsequent_mask(len(tgt)).to(tgt.device)) #decoder_feat = self.out_norm_cls(decoder_feat) at = ops.matmul(decoder_feat_cls, share_weight) at = at + self.output_bias output = {'feat': at, "state": "train"} #print("dododo!") else: b = seqs_input #b = seqs_input.unsqueeze(0) #print(b) a = ops.ones(bs, 1) * self.bins * 3 #print(a) a = a.to(b) #print(a.shape) #print(b.shape) c = ops.cat([b, a], axis=1) #c = a #print(c) #print(c) bs_lst = bs / 2 seqs_input = c.to(zx_feat.device).to(ms.int32) #print(seqs_input) #print("may i do this?") for i in range(5): tgt = self.word_embeddings(seqs_input).permute(1, 0, 2) #print("may i do do do!") query_embed_ = self.position_embeddings.weight.unsqueeze(1) prev_embed = self.prev_position_embeddings.weight.unsqueeze(0).repeat(4,1,1).permute(1,0,2).reshape(4*5, -1).unsqueeze(1) query_embed = ops.cat([prev_embed, query_embed_], axis=0) #query_embed = query_embed_.repeat(1, bs, 1) query_embed = query_embed.repeat(1, bs, 1) #print(tgt.shape) #print(query_embed.shape) #print(len(tgt)) #print(z_feat.shape) #print(x_feat.shape) decoder_feat_cls = self.decoder(tgt, z_feat, x_feat, pos_z, pos_x, identity, self.identity_search, query_embed[:len(tgt)], tgt_mask=generate_square_subsequent_mask(len(tgt)).to(tgt.device)) # print(decoder_feat_cls) #decoder_feat_cls = self.out_norm_cls(decoder_feat_cls) out = ops.matmul(decoder_feat_cls.swapaxes(0, 1)[:, -1, :], share_weight) + self.output_bias if i == 4: temp = ops.matmul(decoder_feat_cls, share_weight) + self.output_bias # temp = temp.softmax(-1) #out_logits = top_k_top_p_filtering_batch(out, 0, 0.4) #next_token = seqs_input[:, -1:].clone() #for j in range(next_token.shape[0]): # next_token[j] = ops.multinomial(ops.softmax(out_logits[j].squeeze(0), axis=-1), num_samples=1) #out = out.softmax(-1) #value, extra_seq = out.topk(axis=-1, k=1)[0], out.topk(axis=-1, k=1)[1] #seqs_input = ops.cat([seqs_input, next_token], axis=-1) #if i == 0: # seqs_output = next_token # values = value #else: # seqs_output = ops.cat([seqs_output, next_token], axis=-1) # values = ops.cat([values, value], axis=-1) out_list.append(out.unsqueeze(0)) out_val = ops.softmax(out[:, :self.bins*3],axis=-1) out = ops.softmax(out,axis=-1) if head_type == "half": #print("can i do that?") if i <= 3: prob_out = out_val else: prob_out = out prob = Categorical(prob_out) max_indicies = ops.argmax(prob_out, -1) samplex_indices = prob.sample() #temp_bs = len(max_indicies) // 2 #assert len(max_indicies) % 2 == 0 selected_indices = ops.cat([max_indicies], axis=0) for j in range(bs): if j == 0 : value = prob_out[j, max_indicies[j]].unsqueeze(0) else: value = ops.cat([value, prob_out[j, max_indicies[j]].unsqueeze(0)], axis=0) # else: # value = ops.cat([value, prob_out[j, samplex_indices[j]].unsqueeze(0)], axis=0) selected_indices = selected_indices.unsqueeze(1) value = value.unsqueeze(1) seqs_input = ops.cat([seqs_input, selected_indices], axis=-1) if i == 0: seqs_output = selected_indices values = value else: seqs_output = ops.cat([seqs_output, selected_indices], axis=-1) values = ops.cat([values, value], axis=-1) continue value, extra_seq = out.topk(dim=-1, k=1)[0], out.topk(dim=-1, k=1)[1] seqs_input = ops.cat([seqs_input, extra_seq], axis=-1) if i == 0: seqs_output = extra_seq values = value else: seqs_output = ops.cat([seqs_output, extra_seq], axis=-1) values = ops.cat([values, value], axis=-1) #print(seqs_input) #print(seqs_input) #print(x_feat.shape) #print(z_feat.shape) #print(seqs_input) if not(not out_list): feat = ops.cat(out_list) #print(seqs_input) output = {'seqs': seqs_output, 'class': values, 'feat': feat, "state": "val/test", "x_feat": output_x_feat.detach()} return output def build_box_head(cfg, hidden_dim): stride = cfg.MODEL.BACKBONE.STRIDE if cfg.MODEL.HEAD.TYPE == "MLP": mlp_head = MLP(hidden_dim, hidden_dim, 4, 3) # dim_in, dim_hidden, dim_out, 3 layers return mlp_head elif "CORNER" in cfg.MODEL.HEAD.TYPE: feat_sz = int(cfg.DATA.SEARCH.SIZE / stride) channel = getattr(cfg.MODEL, "NUM_CHANNELS", 256) print("head channel: %d" % channel) if cfg.MODEL.HEAD.TYPE == "CORNER": corner_head = Corner_Predictor(inplanes=cfg.MODEL.HIDDEN_DIM, channel=channel, feat_sz=feat_sz, stride=stride) else: raise ValueError() return corner_head elif cfg.MODEL.HEAD.TYPE == "CENTER": in_channel = hidden_dim out_channel = cfg.MODEL.HEAD.NUM_CHANNELS feat_sz = int(cfg.DATA.SEARCH.SIZE / stride) center_head = CenterPredictor(inplanes=in_channel, channel=out_channel, feat_sz=feat_sz, stride=stride) return center_head elif cfg.MODEL.HEAD.TYPE == "PIX": in_channel = hidden_dim feat_sz = int(cfg.DATA.SEARCH.SIZE / stride) feat_tz = int(cfg.DATA.TEMPLATE.SIZE / stride) decoder_layer = cfg.MODEL.DECODER_LAYER encoder_layer = cfg.MODEL.ENCODER_LAYER bins = cfg.MODEL.BINS num_heads = cfg.MODEL.NUM_HEADS mlp_ratio = cfg.MODEL.MLP_RATIO qkv_bias = cfg.MODEL.QKV_BIAS drop_rate = cfg.MODEL.DROP_RATE attn_drop = cfg.MODEL.ATTN_DROP drop_path = cfg.MODEL.DROP_PATH drop_path_allocator = DropPathAllocator(drop_path) pix_head = Pix2Track(in_channel=in_channel, feat_sz=feat_sz, feat_tz=feat_tz, stride=stride, encoder_layer=encoder_layer, decoder_layer=decoder_layer, bins=bins, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop_rate=drop_rate, attn_drop=attn_drop, drop_path=drop_path_allocator) return pix_head else: raise ValueError("HEAD TYPE %s is not supported." % cfg.MODEL.HEAD_TYPE) ================================================ FILE: artrackv2_mindspore/lib/models/layers/mask_decoder.py ================================================ # -*- coding:utf-8 -*- # author : Skye Song # file : vit_decoder.py # Copyright (c) Skye-Song. All Rights Reserved import mindspore as ms import mindspore.nn as nn from mindspore import ops from mindspore import Tensor import sys from lib.utils.box_ops import box_xywh_to_cxywh, box_cxcywh_to_xyxy from lib.models.component.block import Block from einops import rearrange from lib.utils.image import * from mindspore.common.initializer import initializer,Normal,XavierUniform,Constant class MaskDecoder(nn.Cell): def __init__(self, mask_ratio=0.75, patch_size=16, num_patches=8 ** 2, embed_dim=1024, decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16, pool_size=8, mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False): super().__init__() self.mask_ratio = mask_ratio self.num_patches = num_patches self.patch_size = patch_size self.decoder_embed = nn.Dense(embed_dim, decoder_embed_dim, has_bias=True) self.mask_token = ms.Parameter(ops.zeros((1, 1, decoder_embed_dim))) self.decoder_pos_embed = ms.Parameter(ops.zeros((1, num_patches, decoder_embed_dim)), requires_grad=False) # fixed sin-cos embedding self.decoder_blocks = nn.CellList([ Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer) for i in range(decoder_depth)]) decoder_embed_dim_tuple=decoder_embed_dim if isinstance(decoder_embed_dim,int): decoder_embed_dim_tuple=tuple([decoder_embed_dim]) self.decoder_norm = norm_layer(decoder_embed_dim_tuple) self.decoder_pred = nn.Dense(decoder_embed_dim, patch_size ** 2 * 3, has_bias=True) # decoder to patch self.norm_pix_loss = norm_pix_loss def random_masking(self, x): """ Perform per-sample random masking by per-sample shuffling. Per-sample shuffling is done by argsort random noise. x: [N, L, D], sequence """ N, L, D = x.shape # batch, length, dim len_keep = int(L * (1 - self.mask_ratio)) noise = ops.rand(N, L, device=x.device) # noise in [0, 1] # sort noise for each sample ids_shuffle = ops.argsort(noise, dim=1) # ascend: small is keep, large is remove ids_restore = ops.argsort(ids_shuffle, dim=1) # keep the first subset ids_keep = ids_shuffle[:, :len_keep] x_keep = ops.gather_elements(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D)) # generate the binary mask: 0 is keep, 1 is remove mask = ops.ones([N, L], device=x.device) mask[:, :len_keep] = 0 # unshuffle to get the binary mask mask = ops.gather_elements(mask, dim=1, index=ids_restore) # get the masked x mask_tokens = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] - x_keep.shape[1], 1) x_ = ops.cat([x_keep, mask_tokens], axis=1) # no cls token x_masked = ops.gather_elements(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2])) # unshuffle return x_masked, mask def forward_decoder(self, x, eval=False): # embed tokens x = self.decoder_embed(x) mask = None # append mask tokens to sequence if not eval: x, mask = self.random_masking(x) # add pos embed x = x + self.decoder_pos_embed # apply Transformer blocks for blk in self.decoder_blocks: x = blk(x) x = self.decoder_norm(x) # predictor projection x = self.decoder_pred(x) return x, mask def unpatchify(self, x): """ x: (N, L, patch_size**2 *3) imgs: (N, 3, H, W) """ p = self.patch_size h = w = int(x.shape[1] ** .5) assert h * w == x.shape[1] x = x.reshape((x.shape[0], h, w, p, p, 3)) x = ops.permute(x, (0,5,1,3,2,4)) imgs = x.reshape((x.shape[0], 3, h * p, h * p)) return imgs def patchify(self, imgs): """ imgs: (N, 3, H, W) x: (N, L, patch_size**2 *3) """ p = self.patch_size assert imgs.shape[2] == imgs.shape[3] and imgs.shape[2] % p == 0 h = w = imgs.shape[2] // p x = imgs.reshape((imgs.shape[0], 3, h, p, w, p)) x = ops.permute(x, (0,2,4,3,5,1)) x = x.reshape((imgs.shape[0], h * w, p ** 2 * 3)) return x def forward_loss(self, imgs, pred, mask=None): """ imgs: [N, 3, H, W] pred: [N, L, p*p*3] mask: [N, L], 0 is keep, 1 is remove, """ target = self.patchify(imgs) if self.norm_pix_loss: mean = target.mean(dim=-1, keepdims=True) var = target.var(dim=-1, keepdims=True) target = (target - mean) / (var + 1.e-6) ** .5 loss = (pred - target) ** 2 loss = loss.mean(dim=-1) # [N, L], mean loss per patc if mask == None: loss = loss.sum() / pred.shape[1] / pred.shape[0] # mean loss on removed patches else: loss = loss.sum() / pred.shape[1] / pred.shape[0] return loss def construct(self, x, images=None, gt_bboxes=None, eval=False,): x_numpy = x.asnumpy() x_numpy = rearrange(x_numpy, 'b c h w -> b (h w) c') x = Tensor(x_numpy) pred, mask = self.forward_decoder(x, eval) # [N, L, p*p*3] if eval: return self.unpatchify(pred) if mask != None: loss = self.forward_loss(imgs=images, pred=pred, mask=mask) else: loss = self.forward_loss(imgs=images, pred=pred) pred = self.unpatchify(pred) return pred, loss def mask_decoder(): model = MaskDecoder( mask_ratio=0.75, patch_size=16, num_patches=8 ** 2, embed_dim=1024, decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16, mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False) return model def build_maskdecoder(cfg): pool_size = int(cfg.DATA.TEMPLATE.SIZE / cfg.MODEL.BACKBONE.PATCHSIZE) num_patches = (cfg.DATA.TEMPLATE.SIZE // cfg.MODEL.BACKBONE.PATCHSIZE) ** 2 model = MaskDecoder( mask_ratio=cfg.MODEL.DECODER.MASK_RATIO, patch_size=cfg.MODEL.BACKBONE.PATCHSIZE, num_patches=num_patches, embed_dim=cfg.MODEL.BACKBONE.EMBEDDIM, decoder_embed_dim=cfg.MODEL.DECODER.EMBEDDIM, decoder_depth=cfg.MODEL.DECODER.DEPTH, decoder_num_heads=cfg.MODEL.DECODER.NUMHEADS, pool_size=pool_size, mlp_ratio=cfg.MODEL.DECODER.MLPRATIO, norm_layer=nn.LayerNorm, norm_pix_loss=False) return model ================================================ FILE: artrackv2_mindspore/lib/models/layers/patch_embed.py ================================================ import sys sys.path.append("/home/baiyifan/code/AR2_mindspore_cp/2stage") import mindspore.nn as nn from mindspore import ops from lib.models.timm import to_2tuple class PatchEmbed(nn.Cell): """ 2D Image to Patch Embedding """ def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True): super().__init__() img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) self.img_size = img_size self.patch_size = patch_size self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) self.num_patches = self.grid_size[0] * self.grid_size[1] self.flatten = flatten self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size,pad_mode='valid',has_bias=True) self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() def construct(self, x): # allow different input size # B, C, H, W = x.shape # _assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).") # _assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).") x = self.proj(x) if self.flatten: x = ops.flatten(x,start_dim=2) x = x.swapaxes(1, 2) # BCHW -> BNC x = self.norm(x) return x ================================================ FILE: artrackv2_mindspore/lib/models/layers/rpe.py ================================================ import torch import torch.nn as nn from timm.models.layers import trunc_normal_ def generate_2d_relative_positional_encoding_index(z_shape, x_shape): ''' z_shape: (z_h, z_w) x_shape: (x_h, x_w) ''' z_2d_index_h, z_2d_index_w = torch.meshgrid(torch.arange(z_shape[0]), torch.arange(z_shape[1])) x_2d_index_h, x_2d_index_w = torch.meshgrid(torch.arange(x_shape[0]), torch.arange(x_shape[1])) z_2d_index_h = z_2d_index_h.flatten(0) z_2d_index_w = z_2d_index_w.flatten(0) x_2d_index_h = x_2d_index_h.flatten(0) x_2d_index_w = x_2d_index_w.flatten(0) diff_h = z_2d_index_h[:, None] - x_2d_index_h[None, :] diff_w = z_2d_index_w[:, None] - x_2d_index_w[None, :] diff = torch.stack((diff_h, diff_w), dim=-1) _, indices = torch.unique(diff.view(-1, 2), return_inverse=True, dim=0) return indices.view(z_shape[0] * z_shape[1], x_shape[0] * x_shape[1]) def generate_2d_concatenated_self_attention_relative_positional_encoding_index(z_shape, x_shape): ''' z_shape: (z_h, z_w) x_shape: (x_h, x_w) ''' z_2d_index_h, z_2d_index_w = torch.meshgrid(torch.arange(z_shape[0]), torch.arange(z_shape[1])) x_2d_index_h, x_2d_index_w = torch.meshgrid(torch.arange(x_shape[0]), torch.arange(x_shape[1])) z_2d_index_h = z_2d_index_h.flatten(0) z_2d_index_w = z_2d_index_w.flatten(0) x_2d_index_h = x_2d_index_h.flatten(0) x_2d_index_w = x_2d_index_w.flatten(0) concatenated_2d_index_h = torch.cat((z_2d_index_h, x_2d_index_h)) concatenated_2d_index_w = torch.cat((z_2d_index_w, x_2d_index_w)) diff_h = concatenated_2d_index_h[:, None] - concatenated_2d_index_h[None, :] diff_w = concatenated_2d_index_w[:, None] - concatenated_2d_index_w[None, :] z_len = z_shape[0] * z_shape[1] x_len = x_shape[0] * x_shape[1] a = torch.empty((z_len + x_len), dtype=torch.int64) a[:z_len] = 0 a[z_len:] = 1 b=a[:, None].repeat(1, z_len + x_len) c=a[None, :].repeat(z_len + x_len, 1) diff = torch.stack((diff_h, diff_w, b, c), dim=-1) _, indices = torch.unique(diff.view((z_len + x_len) * (z_len + x_len), 4), return_inverse=True, dim=0) return indices.view((z_len + x_len), (z_len + x_len)) def generate_2d_concatenated_cross_attention_relative_positional_encoding_index(z_shape, x_shape): ''' z_shape: (z_h, z_w) x_shape: (x_h, x_w) ''' z_2d_index_h, z_2d_index_w = torch.meshgrid(torch.arange(z_shape[0]), torch.arange(z_shape[1])) x_2d_index_h, x_2d_index_w = torch.meshgrid(torch.arange(x_shape[0]), torch.arange(x_shape[1])) z_2d_index_h = z_2d_index_h.flatten(0) z_2d_index_w = z_2d_index_w.flatten(0) x_2d_index_h = x_2d_index_h.flatten(0) x_2d_index_w = x_2d_index_w.flatten(0) concatenated_2d_index_h = torch.cat((z_2d_index_h, x_2d_index_h)) concatenated_2d_index_w = torch.cat((z_2d_index_w, x_2d_index_w)) diff_h = x_2d_index_h[:, None] - concatenated_2d_index_h[None, :] diff_w = x_2d_index_w[:, None] - concatenated_2d_index_w[None, :] z_len = z_shape[0] * z_shape[1] x_len = x_shape[0] * x_shape[1] a = torch.empty(z_len + x_len, dtype=torch.int64) a[: z_len] = 0 a[z_len:] = 1 c = a[None, :].repeat(x_len, 1) diff = torch.stack((diff_h, diff_w, c), dim=-1) _, indices = torch.unique(diff.view(x_len * (z_len + x_len), 3), return_inverse=True, dim=0) return indices.view(x_len, (z_len + x_len)) class RelativePosition2DEncoder(nn.Module): def __init__(self, num_heads, embed_size): super(RelativePosition2DEncoder, self).__init__() self.relative_position_bias_table = nn.Parameter(torch.empty((num_heads, embed_size))) trunc_normal_(self.relative_position_bias_table, std=0.02) def forward(self, attn_rpe_index): ''' Args: attn_rpe_index (torch.Tensor): (*), any shape containing indices, max(attn_rpe_index) < embed_size Returns: torch.Tensor: (1, num_heads, *) ''' return self.relative_position_bias_table[:, attn_rpe_index].unsqueeze(0) ================================================ FILE: artrackv2_mindspore/lib/models/layers/self_practice.py ================================================ import torch import mindspore as ms import torch.nn as nn import mindspore.nn as msnn from mindspore import ops import numpy as np x = np.random.randn(3,5) x1 = torch.tensor(x) x2 = ms.tensor(x) length1 = torch.tensor([[2,4,1],[3,1,0],[4,2,2]]) length2 = ms.tensor([[2,4,1],[3,1,0],[4,2,2]]) y1 = torch.gather(x1,dim=1,index = length1) y2 = ops.gather_elements(x2,dim=1,index= length2) print(y1) print(y2) ================================================ FILE: artrackv2_mindspore/lib/models/ostrack/Vit_model_test.py ================================================ import sys sys.path.append("/home/djh/python-code/Artrackv2/2stage") from lib.models.ostrack.vit import * from lib.test.evaluation.tracker import Tracker from lib.models.layers.mask_decoder import build_maskdecoder from lib.models.layers.head import DropPathAllocator tracker = Tracker('ostrack', '2stage_256_got', 'got10k_test', None) param = tracker.get_parameters() cfg = param.cfg patch_start_index = 1 kwargs = {'patch_size': 16, 'embed_dim': 768, 'depth': 12, 'num_heads': 12, 'drop_path_rate': 0.1} model = VisionTransformer(**kwargs) model.finetune_track(cfg=cfg, patch_start_index=patch_start_index) cross_2_decoder = build_maskdecoder(cfg) drop_path = cfg.MODEL.DROP_PATH drop_path_allocator = DropPathAllocator(drop_path) num_heads = cfg.MODEL.NUM_HEADS mlp_ratio = cfg.MODEL.MLP_RATIO qkv_bias = cfg.MODEL.QKV_BIAS drop_rate = cfg.MODEL.DROP_RATE attn_drop = cfg.MODEL.ATTN_DROP #def __init__(self, input_dim, hidden_dim, output_dim, num_layers, BN=False): score_mlp = build_score_decoder(cfg) cover_mlp = build_score_decoder(cfg) model = OSTrack( backbone, #decoder, cross_2_decoder, score_mlp, #cover_mlp, ) for name,param in model.parameters_and_names(): print (param.name) ================================================ FILE: artrackv2_mindspore/lib/models/ostrack/__init__.py ================================================ from .ostrack import build_ostrack ================================================ FILE: artrackv2_mindspore/lib/models/ostrack/base_backbone.py ================================================ from functools import partial import mindspore as ms import mindspore.nn as nn import mindspore.ops as ops import sys sys.path.append("/home/baiyifan/code/AR2_mindspore_cp/2stage") from lib.models.timm import * from lib.models.layers.patch_embed import PatchEmbed from lib.models.ostrack.utils import combine_tokens, recover_tokens import time def generate_square_subsequent_mask(sz, sx, ss): r"""Generate a square mask for the sequence. The masked positions are filled with float('-inf'). Unmasked positions are filled with float(0.0). """ # 0 means mask, 1 means visible sum = sz + sx + ss mask = (ops.triu(ops.ones((sum, sum))) == 1).swapaxes(0, 1) mask[:, :] = 0 mask[:int(sz/2), :int(sz/2)] = 1 #template self mask[int(sz/2):sz, int(sz/2):sz] = 1 # dt self mask[int(sz/2):sz, sz:sz+sx] = 1 # dt search mask[int(sz / 2):sz, -1] = 1 # dt search mask[sz:sz+sx, :sz+sx] = 1 # sr dt-t-sr mask[sz+sx:, :] = 1 # co dt-t-sr-co # mask[sz+sx:, :sz] = 0 return ~mask class BaseBackbone(nn.Cell): def __init__(self): super().__init__() # for original ViT self.pos_embed = None self.img_size = [224, 224] self.patch_size = 16 self.embed_dim = 384 self.cat_mode = 'direct' self.pos_embed_z = None self.pos_embed_x = None self.bins = 400 in_channel = 768 self.range = 2 self.word_embeddings = nn.Embedding(self.bins * self.range + 6, in_channel, padding_idx=self.bins * self.range+4) # mindspore的nn.Embedding中没有max_norm,norm_type，所以只能删去参数max_norm=1, norm_type=2.0 print(self.bins) self.position_embeddings = nn.Embedding( 5, in_channel) self.output_bias = ms.Parameter(ops.zeros(self.bins * self.range + 6)) self.prev_position_embeddings = nn.Embedding(7 * 4, in_channel) self.template_segment_pos_embed = None self.search_segment_pos_embed = None self.return_inter = False self.return_stage = [2, 5, 8, 11] self.add_cls_token = False self.add_sep_seg = False def finetune_track(self, cfg, patch_start_index=1): search_size = to_2tuple(cfg.DATA.SEARCH.SIZE) template_size = to_2tuple(cfg.DATA.TEMPLATE.SIZE) new_patch_size = cfg.MODEL.BACKBONE.STRIDE self.cat_mode = cfg.MODEL.BACKBONE.CAT_MODE self.return_inter = cfg.MODEL.RETURN_INTER self.add_sep_seg = cfg.MODEL.BACKBONE.SEP_SEG # resize patch embedding if new_patch_size != self.patch_size: print('Inconsistent Patch Size With The Pretrained Weights, Interpolate The Weight!') old_patch_embed = {} for name, param in self.patch_embed.named_parameters(): if 'weight' in name: param = ops.interpolate(param, size=(new_patch_size, new_patch_size), mode='bicubic', align_corners=False) param = ms.Parameter(param) old_patch_embed[name] = param print("Attention:old_patch_embed:",old_patch_embed) self.patch_embed = PatchEmbed(img_size=self.img_size, patch_size=new_patch_size, in_chans=3, embed_dim=self.embed_dim) self.patch_embed.proj.bias = old_patch_embed['proj.bias'] self.patch_embed.proj.weight = old_patch_embed['proj.weight'] # for patch embedding patch_pos_embed = self.pos_embed[:, patch_start_index:, :] patch_pos_embed = patch_pos_embed.swapaxes(1, 2) B, E, Q = patch_pos_embed.shape P_H, P_W = self.img_size[0] // self.patch_size, self.img_size[1] // self.patch_size patch_pos_embed = patch_pos_embed.view(B, E, P_H, P_W) # for search region H, W = search_size new_P_H, new_P_W = H // new_patch_size, W // new_patch_size search_patch_pos_embed = ops.interpolate(patch_pos_embed, size=(new_P_H, new_P_W), mode='bicubic', align_corners=False) search_patch_pos_embed = ops.flatten(search_patch_pos_embed,start_dim=2) search_patch_pos_embed = search_patch_pos_embed.swapaxes(1, 2) # for template region H, W = template_size new_P_H, new_P_W = H // new_patch_size, W // new_patch_size template_patch_pos_embed = ops.interpolate(patch_pos_embed, size=(new_P_H, new_P_W), mode='bicubic', align_corners=False) template_patch_pos_embed = ops.flatten(template_patch_pos_embed,start_dim=2).swapaxes(1, 2) self.pos_embed_z = ms.Parameter(template_patch_pos_embed) self.pos_embed_z0 = ms.Parameter(template_patch_pos_embed) self.pos_embed_z1 = ms.Parameter(template_patch_pos_embed) self.pos_embed_x = ms.Parameter(search_patch_pos_embed) # for cls token (keep it but not used) if self.add_cls_token and patch_start_index > 0: cls_pos_embed = self.pos_embed[:, 0:1, :] self.cls_pos_embed = ms.Parameter(cls_pos_embed) # separate token and segment token if self.add_sep_seg: self.template_segment_pos_embed = ms.Parameter(ops.zeros((1, 1, self.embed_dim))) self.template_segment_pos_embed = trunc_normal_(self.template_segment_pos_embed, std=.02) self.search_segment_pos_embed = ms.Parameter(ops.zeros((1, 1, self.embed_dim))) self.search_segment_pos_embed = trunc_normal_(self.search_segment_pos_embed, std=.02) # self.cls_token = None # self.pos_embed = None if self.return_inter: for i_layer in self.fpn_stage: if i_layer != 11: norm_layer = partial(nn.LayerNorm, eps=1e-6) layer = norm_layer(self.embed_dim) layer_name = f'norm{i_layer}' self.add_module(layer_name, layer) def forward_features(self, z_0, z_1_feat, x, identity, seqs_input): share_weight = self.word_embeddings.embedding_table.T out_list = [] begin = self.bins * self.range begin_2 = self.bins * self.range + 1 begin_3 = self.bins * self.range + 2 begin_4 = self.bins * self.range + 3 score = self.bins * self.range + 5 B, H, W = x.shape[0], x.shape[2], x.shape[3] a = ops.cat((ops.ones((B, 1),dtype=x.dtype) * begin, ops.ones((B, 1),dtype=x.dtype) * begin_2, ops.ones((B, 1),dtype=x.dtype) * begin_3, ops.ones((B, 1),dtype=x.dtype) * begin_4, ops.ones((B, 1),dtype=x.dtype) * score), axis=1) b = seqs_input # c = ops.cat([a], axis=1) c = ops.cat([b, a], axis=1) seqs_input_ = c.to(ms.int64) output_x_feat = x.copy() # original:output_x_feat = x.clone() tgt = self.word_embeddings(seqs_input_).permute(1, 0, 2) x = self.patch_embed(x) z_0 = self.patch_embed(z_0) z_1 = z_1_feat s_x = x.shape[1] s_z = z_0.shape[1] + z_1.shape[1] s_s = seqs_input.shape[1] z_0 += identity[:, 0, :].tile((B, self.pos_embed_z.shape[1], 1)) z_1 += identity[:, 1, :].tile((B, self.pos_embed_z.shape[1], 1)) x += identity[:, 2, :].tile((B, self.pos_embed_x.shape[1], 1)) query_embed_ = self.position_embeddings.embedding_table.unsqueeze(1) prev_embed_ = self.prev_position_embeddings.embedding_table.unsqueeze(1) query_embed = ops.cat([prev_embed_, query_embed_], axis=0) #query_embed = ops.cat([query_embed_], axis=0) query_embed = query_embed.tile((1, B, 1)) tgt = tgt.swapaxes(0, 1) query_embed = query_embed.swapaxes(0, 1) # print(self.pos_embed_z0.value()) # print(self.pos_embed_z1.value()) # print(self.pos_embed_x.value()) z_0 += self.pos_embed_z0 z_1 += self.pos_embed_z1 x += self.pos_embed_x s_s = seqs_input_.shape[1] mask = generate_square_subsequent_mask(s_z, s_x, s_s) tgt += query_embed[:, :tgt.shape[1]] z = ops.cat((z_0, z_1), axis=1) zx = combine_tokens(z, x, mode=self.cat_mode) zxs = ops.cat((zx, tgt), axis=1) zxs = self.pos_drop(zxs) m1 = zxs[:, -5:-1] for j, blk in enumerate(self.blocks): zxs = blk(zxs, padding_mask=mask) zxs_numpy = zxs.numpy() m3 = zxs[:, -5:-1] for j, blk in enumerate(self.extension): zxs = blk(zxs, padding_mask=mask) lens_z = self.pos_embed_z.shape[1] lens_x = self.pos_embed_x.shape[1] z_0_feat = zxs[:, :lens_z] z_1_feat = zxs[:, lens_z:lens_z*2] x_feat = zxs[:, lens_z*2:lens_z*2+lens_x] m2 = zxs[:, -5:-1] x_out = self.norm(zxs[:, -5:-1]) score_feat = zxs[:, -1] seq_feat = x_out at = ops.matmul(x_out, share_weight) out = at + self.output_bias temp = out.swapaxes(0, 1) out_list.append(out.unsqueeze(0)) out = ops.softmax(out,-1) value, extra_seq = out.topk(dim=-1, k=1)[0], out.topk(dim=-1, k=1)[1] for i in range(4): value, extra_seq = out[:, i, :].topk(dim=-1, k=1)[0], out[:, i, :].topk(dim=-1, k=1)[1] if i == 0: seqs_output = extra_seq values = value else: seqs_output = ops.cat([seqs_output, extra_seq], axis=-1) values = ops.cat([values, value], axis=-1) output = {'seqs': seqs_output, 'class': values, 'feat': temp, "state": "val/test", "x_feat": ops.stop_gradient(output_x_feat), "seq_feat": seq_feat} return output, z_0_feat, z_1_feat, x_feat, score_feat def construct(self, z_0, z_1_feat, x, identity, seqs_input, **kwargs): """ Joint feature extraction and relation modeling for the basic ViT backbone. Args: z (ops.Tensor): template feature, [B, C, H_z, W_z] x (ops.Tensor): search region feature, [B, C, H_x, W_x] Returns: x (ops.Tensor): merged template and search region feature, [B, L_z+L_x, C] attn : None """ output = self.forward_features(z_0, z_1_feat, x, identity, seqs_input) return output ================================================ FILE: artrackv2_mindspore/lib/models/ostrack/load_parameter_test.py ================================================ import torch import sys sys.path.append("/home/djh/python-code/Artrackv2/2stage") from mindspore import Tensor from mindspore import save_checkpoint def get_keymap_txt(pth_file): # 如果是tar压缩文件。需要执行下面这段代码 checkpoint = torch.load(pth_file,map_location="cpu") state_dict = checkpoint['net'] # end # # 否则就是执行下面这段代码 # map_path = pth_file.split('.')[0] + '_key_map.txt' # map_file = open(map_path, 'w') # state_dict = torch.load(pth_file, map_location=torch.device('cpu')) # if 'model_state' in state_dict: # state_dict = state_dict['model_state'] # elif 'module' in state_dict: # state_dict = state_dict['module'] # elif 'model' in state_dict: # state_dict = state_dict['model'] # # end list = [] dict = {} for name,value in state_dict.items(): print(name) if name == "cross_2_decoder.decoder_blocks.0.norm1.weight": print(state_dict[name]) new_name = name.replace("norm1.weight","norm1.gamma") new_name = new_name.replace("norm1.bias","norm1.beta") new_name = new_name.replace("norm2.weight","norm2.gamma") new_name = new_name.replace("norm2.bias","norm2.beta") new_name = new_name.replace("decoder_norm.weight","decoder_norm.gamma") new_name = new_name.replace("decoder_norm.bias","decoder_norm.beta") new_name = new_name.replace("word_embeddings.weight", "word_embeddings.embedding_table") new_name = new_name.replace("position_embeddings.weight", "position_embeddings.embedding_table") new_name = new_name.replace("prev_position_embeddings.weight", "prev_position_embeddings.embedding_table") new_name = new_name.replace("norm.weight","norm.gamma") new_name = new_name.replace("norm.bias","norm.beta") list.append(new_name) ms_params_list=[] for name,value in dict.items(): param_dict={} param_dict['name'] = name param_dict['data'] = Tensor(value.numpy()) if name=="backbone.pos_embed_z0": print(param_dict['data']) ms_params_list.append(param_dict) # 要生成转换文件的话，就执行下面这句注释代码 # save_checkpoint(ms_params_list, "/home/djh/python-code/Artrackv2/checkpoint1.ckpt") pth_file = "/mnt/d/Download/OSTrack_ep0030.pth.tar" get_keymap_txt(pth_file) ================================================ FILE: artrackv2_mindspore/lib/models/ostrack/ostrack.py ================================================ """ Basic OSTrack model. """ import sys from copy import deepcopy import math import os from typing import List import mindspore as ms from mindspore import nn from lib.models.timm import * from lib.models.ostrack.vit import vit_base_patch16_224, vit_large_patch16_224 from lib.models.ostrack.vit_ce import vit_large_patch16_224_ce, vit_base_patch16_224_ce from lib.models.layers.mask_decoder import build_maskdecoder from lib.models.layers.head import build_decoder, MLP, DropPathAllocator class OSTrack(nn.Cell): """ This is the base class for OSTrack """ def __init__(self, transformer, #decoder, cross_2_decoder, score_mlp, #cover_mlp, ): """ Initializes the model. Parameters: transformer: torch module of the transformer architecture. aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used. """ super().__init__() self.backbone = transformer self.score_mlp = score_mlp self.identity = ms.Parameter(ops.zeros((1, 3, 768))) self.identity = trunc_normal_(self.identity, std=.02) self.cross_2_decoder = cross_2_decoder def construct(self, template: ms.Tensor, dz_feat: ms.Tensor, search: ms.Tensor, ce_template_mask=None, ce_keep_rate=None, return_last_attn=False, seq_input=None, head_type=None, stage=None, search_feature=None, target_in_search_img=None, gt_bboxes=None, ): template_0 = template[:, 0] out, z_0_feat, z_1_feat, x_feat, score_feat = self.backbone(z_0=template_0, z_1_feat=dz_feat, x=search, identity=self.identity, seqs_input=seq_input, ce_template_mask=ce_template_mask, ce_keep_rate=ce_keep_rate, return_last_attn=return_last_attn,) seq_feat = out['seq_feat'].permute(1, 0 ,2) share_weight = self.backbone.word_embeddings.embedding_table[:800, :].unsqueeze(0).tile((seq_feat.shape[1], 1, 1)) pos = self.backbone.position_embeddings.embedding_table.unsqueeze(0).tile((seq_feat.shape[1], 1, 1)).permute(1, 0 ,2) score = self.score_mlp(score_feat) ops.clamp(score, min=0.0, max=1.0) out['score'] = score loss = ms.tensor(0.0, dtype=ms.float32) if target_in_search_img != None: target_in_search_gt = self.backbone.patch_embed(target_in_search_img) z_1_feat = z_1_feat.reshape(z_1_feat.shape[0], int(z_1_feat.shape[1] ** 0.5), int(z_1_feat.shape[1] ** 0.5), z_1_feat.shape[2]).permute(0, 3, 1, 2) target_in_search_gt = self.cross_2_decoder.unpatchify(target_in_search_gt) update_img, loss_temp = self.cross_2_decoder(z_1_feat, target_in_search_gt) update_feat = self.cross_2_decoder.patchify(update_img) out['dz_feat'] = update_feat loss += loss_temp out['renew_loss'] = loss else: z_1_feat = z_1_feat.reshape(z_1_feat.shape[0], int(z_1_feat.shape[1] ** 0.5), int(z_1_feat.shape[1] ** 0.5), z_1_feat.shape[2]).permute(0, 3, 1, 2) update_feat = self.cross_2_decoder(z_1_feat, eval=True) update_feat = self.cross_2_decoder.patchify(update_feat) out['dz_feat'] = update_feat return out def forward_head(self, cat_feature, pos_z, pos_x, identity, seq_input=None, gt_score_map=None, head_type=None, stage=None, search_feature=None): """ cat_feature: output embeddings of the backbone, it can be (HW1+HW2, B, C) or (HW2, B, C) """ if self.head_type == "CORNER": # run the corner head pred_box, score_map = self.box_head(opt_feat, True) outputs_coord = box_xyxy_to_cxcywh(pred_box) outputs_coord_new = outputs_coord.view(bs, Nq, 4) out = {'pred_boxes': outputs_coord_new, 'score_map': score_map, } return out elif self.head_type == "CENTER": # run the center head score_map_ctr, bbox, size_map, offset_map = self.box_head(opt_feat, gt_score_map) # outputs_coord = box_xyxy_to_cxcywh(bbox) outputs_coord = bbox outputs_coord_new = outputs_coord.view(bs, Nq, 4) out = {'pred_boxes': outputs_coord_new, 'score_map': score_map_ctr, 'size_map': size_map, 'offset_map': offset_map} return out elif self.head_type == "PIX": output_dict = self.box_head(cat_feature, pos_z, pos_x, identity, seq_input, head_type, stage, search_feature) return output_dict else: raise NotImplementedError class MlpScoreDecoder(nn.Cell): def __init__(self, in_dim, hidden_dim, num_layers, bn=False): super().__init__() self.num_layers = num_layers h = [hidden_dim] * (num_layers - 1) out_dim = 1 # score if bn: self.layers = nn.SequentialCell(*[nn.SequentialCell(nn.Dense(n, k), nn.BatchNorm1d(k), nn.ReLU()) if i < num_layers - 1 else nn.SequentialCell(nn.Dense(n, k), nn.BatchNorm1d(k)) for i, (n, k) in enumerate(zip([in_dim] + h, h + [out_dim]))]) else: self.layers = nn.SequentialCell(*[nn.SequentialCell(nn.Dense(n, k), nn.ReLU()) if i < num_layers - 1 else nn.Dense(n, k) for i, (n, k) in enumerate(zip([in_dim] + h, h + [out_dim]))]) def construct(self, reg_tokens): """ reg tokens shape: (b, 4, embed_dim) """ x = self.layers(reg_tokens) # (b, 4, 1) x = ops.mean(x,axis=1) # (b, 1) return x def build_score_decoder(cfg): return MlpScoreDecoder( in_dim=cfg.MODEL.BACKBONE.EMBEDDIM, hidden_dim=cfg.MODEL.BACKBONE.EMBEDDIM, num_layers=2, bn=False ) def build_ostrack(cfg, training=True): current_dir = os.path.dirname(os.path.abspath(__file__)) # This is your Project Root pretrained_path = "/home/baiyifan/code/vitrack/" if cfg.MODEL.PRETRAIN_FILE and ('OSTrack' not in cfg.MODEL.PRETRAIN_FILE) and training: pretrained = os.path.join(pretrained_path, cfg.MODEL.PRETRAIN_FILE) else: pretrained = '' if cfg.MODEL.BACKBONE.TYPE == 'vit_base_patch16_224': backbone = vit_base_patch16_224(pretrained, drop_path_rate=cfg.TRAIN.DROP_PATH_RATE) hidden_dim = backbone.embed_dim patch_start_index = 1 elif cfg.MODEL.BACKBONE.TYPE == 'vit_large_patch16_224': print("i use vit_large") backbone = vit_large_patch16_224(pretrained, drop_path_rate=cfg.TRAIN.DROP_PATH_RATE) hidden_dim = backbone.embed_dim patch_start_index = 1 elif cfg.MODEL.BACKBONE.TYPE == 'vit_base_patch16_224_ce': backbone = vit_base_patch16_224_ce(pretrained, drop_path_rate=cfg.TRAIN.DROP_PATH_RATE, ce_loc=cfg.MODEL.BACKBONE.CE_LOC, ce_keep_ratio=cfg.MODEL.BACKBONE.CE_KEEP_RATIO, ) hidden_dim = backbone.embed_dim patch_start_index = 1 elif cfg.MODEL.BACKBONE.TYPE == 'vit_large_patch16_224_ce': backbone = vit_large_patch16_224_ce(pretrained, drop_path_rate=cfg.TRAIN.DROP_PATH_RATE, ce_loc=cfg.MODEL.BACKBONE.CE_LOC, ce_keep_ratio=cfg.MODEL.BACKBONE.CE_KEEP_RATIO, ) hidden_dim = backbone.embed_dim patch_start_index = 1 else: raise NotImplementedError backbone.finetune_track(cfg=cfg, patch_start_index=patch_start_index) #decoder = build_maskdecoder(cfg) cross_2_decoder = build_maskdecoder(cfg) drop_path = cfg.MODEL.DROP_PATH drop_path_allocator = DropPathAllocator(drop_path) num_heads = cfg.MODEL.NUM_HEADS mlp_ratio = cfg.MODEL.MLP_RATIO qkv_bias = cfg.MODEL.QKV_BIAS drop_rate = cfg.MODEL.DROP_RATE attn_drop = cfg.MODEL.ATTN_DROP score_mlp = build_score_decoder(cfg) cover_mlp = build_score_decoder(cfg) model = OSTrack( backbone, #decoder, cross_2_decoder, score_mlp, #cover_mlp, ) from mindspore.amp import auto_mixed_precision model = auto_mixed_precision(model, 'O0') load_from = cfg.MODEL.PRETRAIN_FILE param_dict = ms.load_checkpoint(load_from) param_not_load, _ = ms.load_param_into_net(model, param_dict) print("未加载权重：",param_not_load) print('Load pretrained model from: ' + load_from) model.backbone.pos_embed_z0 = model.backbone.pos_embed_z1 return model ================================================ FILE: artrackv2_mindspore/lib/models/ostrack/ostrack_test.py ================================================ import sys sys.path.append("/home/djh/python-code/Artrackv2/2stage") from lib.models.ostrack.vit import * from lib.test.evaluation.tracker import Tracker from lib.models.ostrack import * tracker = Tracker('ostrack', '2stage_256_got', 'got10k_test', None) param = tracker.get_parameters() cfg = param.cfg model = build_ostrack(cfg,training=False) ================================================ FILE: artrackv2_mindspore/lib/models/ostrack/utils.py ================================================ import math import mindspore as ms from mindspore import ops def combine_tokens(template_tokens, search_tokens, mode='direct', return_res=False): # [B, HW, C] len_t = template_tokens.shape[1] len_s = search_tokens.shape[1] if mode == 'direct': merged_feature = ops.cat((template_tokens, search_tokens), axis=1) elif mode == 'template_central': central_pivot = len_s // 2 first_half = search_tokens[:, :central_pivot, :] second_half = search_tokens[:, central_pivot:, :] merged_feature = ops.cat((first_half, template_tokens, second_half), axis=1) elif mode == 'partition': feat_size_s = int(math.sqrt(len_s)) feat_size_t = int(math.sqrt(len_t)) window_size = math.ceil(feat_size_t / 2.) # pad feature maps to multiples of window size B, _, C = template_tokens.shape H = W = feat_size_t template_tokens = template_tokens.view(B, H, W, C) pad_l = pad_b = pad_r = 0 # pad_r = (window_size - W % window_size) % window_size pad_t = (window_size - H % window_size) % window_size template_tokens = ops.pad(template_tokens, (0, 0, pad_l, pad_r, pad_t, pad_b)) _, Hp, Wp, _ = template_tokens.shape template_tokens = template_tokens.view(B, Hp // window_size, window_size, W, C) template_tokens = ops.cat([template_tokens[:, 0, ...], template_tokens[:, 1, ...]], axis=2) _, Hc, Wc, _ = template_tokens.shape template_tokens = template_tokens.view(B, -1, C) merged_feature = ops.cat([template_tokens, search_tokens], axis=1) # calculate new h and w, which may be useful for SwinT or others merged_h, merged_w = feat_size_s + Hc, feat_size_s if return_res: return merged_feature, merged_h, merged_w else: raise NotImplementedError return merged_feature def recover_tokens(merged_tokens, len_template_token, len_search_token, mode='direct'): if mode == 'direct': recovered_tokens = merged_tokens elif mode == 'template_central': central_pivot = len_search_token // 2 len_remain = len_search_token - central_pivot len_half_and_t = central_pivot + len_template_token first_half = merged_tokens[:, :central_pivot, :] second_half = merged_tokens[:, -len_remain:, :] template_tokens = merged_tokens[:, central_pivot:len_half_and_t, :] recovered_tokens = ops.cat((template_tokens, first_half, second_half), axis=1) elif mode == 'partition': recovered_tokens = merged_tokens else: raise NotImplementedError return recovered_tokens def window_partition(x, window_size: int): """ Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C) """ B, H, W, C = x.shape x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) return windows def window_reverse(windows, window_size: int, H: int, W: int): """ Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image W (int): Width of image Returns: x: (B, H, W, C) """ B = int(windows.shape[0] / (H * W / window_size / window_size)) x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) return x ================================================ FILE: artrackv2_mindspore/lib/models/ostrack/vit.py ================================================ """ Vision Transformer (ViT) in PyTorch A PyTorch implement of Vision Transformers as described in: 'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929 `How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers` - https://arxiv.org/abs/2106.10270 The official jax code is released and available at https://github.com/google-research/vision_transformer DeiT model defs and weights from https://github.com/facebookresearch/deit, paper `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877 Acknowledgments: * The paper authors for releasing code and weights, thanks! * I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out for some einops/einsum fun * Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT * Bert reference code checks against Huggingface Transformers and Tensorflow Bert Hacked together by / Copyright 2021 Ross Wightman Modified by Botao Ye """ import sys sys.path.append("/home/baiyifan/code/AR2_mindspore_cp/2stage") import math import logging from functools import partial from collections import OrderedDict from copy import deepcopy from lib.models.timm import * from lib.models.layers.patch_embed import PatchEmbed from lib.models.ostrack.base_backbone import BaseBackbone import mindspore as ms from mindspore import nn as msnn import mindspore.ops as ops import mindspore.numpy as np from mindspore.common.initializer import initializer import numpy class Attention(msnn.Cell): def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim ** -0.5 self.qkv = msnn.Dense(dim, dim * 3, has_bias=qkv_bias) self.attn_drop = msnn.Dropout(p=attn_drop) self.proj = msnn.Dense(dim, dim) self.proj_drop = msnn.Dropout(p=proj_drop) def construct(self, x, return_attention=False,padding_mask=None): B, N, C = x.shape # print("x:",x) # weight = ms.tensor(self.qkv.weight.value(),dtype=ms.float32).numpy() # bias = ms.tensor(self.qkv.bias.value(),dtype=ms.float32).numpy() # m = ops.ones_like(x,dtype=ms.float32) # test = self.qkv(m).numpy() # with numpy.printoptions (precision=12): # print("weight:",weight) # print("bias:",bias) # print("test:",test) # numpy.savez("/home/baiyifan/code/Artrackv2_mindspore/2stage/outputv1.npz",weight,bias,test) qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (camsnnot use tensor as tuple) attn = (q @ k.swapaxes(-2, -1)) * self.scale if padding_mask != None: attn = attn.masked_fill(padding_mask, float("-inf")) attn = ops.softmax(attn,axis=-1) attn = self.attn_drop(attn) x = (attn @ v).swapaxes(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) if return_attention: return x, attn return x class Block(msnn.Cell): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0., act_layer=msnn.GELU, norm_layer=msnn.LayerNorm): super().__init__() norm_layer_dim=[] norm_layer_dim.append(dim) norm_layer_dim = tuple(norm_layer_dim) self.norm1 = norm_layer(norm_layer_dim) self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here self.drop_path = DropPath(drop_path) if drop_path > 0. else msnn.Identity() self.norm2 = norm_layer(norm_layer_dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) def construct(self, x, return_attention=False,padding_mask=None): if return_attention: feat, attn = self.attn(self.norm1(x), True,padding_mask) x = x + self.drop_path(feat) x = x + self.drop_path(self.mlp(self.norm2(x))) return x, attn else: x = x + self.drop_path(self.attn(self.norm1(x),padding_mask=padding_mask)) x = x + self.drop_path(self.mlp(self.norm2(x))) return x class VisionTransformer(BaseBackbone): """ Vision Transformer A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - https://arxiv.org/abs/2010.11929 Includes distillation token & head support for `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877 """ def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None, distilled=False, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None, act_layer=None, weight_init=''): """ Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number of input chamsnnels num_classes (int): number of classes for classification head embed_dim (int): embedding dimension depth (int): depth of transformer num_heads (int): number of attention heads mlp_ratio (int): ratio of mlp hidden dim to embedding dim qkv_bias (bool): enable bias for qkv if True representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set distilled (bool): model includes a distillation token and head as in DeiT models drop_rate (float): dropout rate attn_drop_rate (float): attention dropout rate drop_path_rate (float): stochastic depth rate embed_layer (msnn.Cell): patch embedding layer norm_layer: (msnn.Cell): normalization layer weight_init: (str): weight init scheme """ super().__init__() self.num_classes = num_classes self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models self.num_tokens = 2 if distilled else 1 norm_layer = norm_layer or partial(msnn.LayerNorm, epsilon=1e-6) act_layer = act_layer or msnn.GELU self.patch_embed = embed_layer( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) num_patches = self.patch_embed.num_patches self.cls_token = ms.Parameter(ops.zeros((1, 1, embed_dim))) self.dist_token = ms.Parameter(ops.zeros((1, 1, embed_dim))) if distilled else None self.pos_embed = ms.Parameter(ops.zeros((1, num_patches + self.num_tokens, embed_dim))) self.pos_drop = msnn.Dropout(p=drop_rate) dpr = [x.item() for x in ops.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule self.blocks = msnn.SequentialCell(*[ Block( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer) for i in range(depth)]) self.extension = msnn.SequentialCell(*[ Block( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer) for i in range(3)]) norm_layer_dim=[] norm_layer_dim.append(embed_dim) self.norm = norm_layer(norm_layer_dim) # # Representation layer # if representation_size and not distilled: # self.num_features = representation_size # self.pre_logits = msnn.SequentialCell(OrderedDict([ # ('fc', msnn.Linear(embed_dim, representation_size)), # ('act', msnn.Tanh()) # ])) # else: # self.pre_logits = msnn.Identity() # # # Classifier head(s) # self.head = msnn.Linear(self.num_features, num_classes) if num_classes > 0 else msnn.Identity() # self.head_dist = None # if distilled: # self.head_dist = msnn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else msnn.Identity() self.init_weights(weight_init) def init_weights(self, mode=''): assert mode in ('jax', 'jax_nlhb', 'nlhb', '') head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0. trunc_normal_(self.pos_embed, std=.02) if self.dist_token is not None: trunc_normal_(self.dist_token, std=.02) if mode.startswith('jax'): # leave cls token as zeros to match jax impl named_apply(partial(_init_vit_weights, head_bias=head_bias, jax_impl=True), self) else: trunc_normal_(self.cls_token, std=.02) self.apply(_init_vit_weights) def _init_weights(self, m): # this fn left here for compat with downstream users _init_vit_weights(m) # @torch.jit.ignore() def load_pretrained(self, checkpoint_path, prefix=''): _load_weights(self, checkpoint_path, prefix) # @torch.jit.ignore def no_weight_decay(self): return {'pos_embed', 'cls_token', 'dist_token'} def get_classifier(self): if self.dist_token is None: return self.head else: return self.head, self.head_dist def reset_classifier(self, num_classes, global_pool=''): self.num_classes = num_classes self.head = msnn.Dense(self.embed_dim, num_classes) if num_classes > 0 else msnn.Identity() if self.num_tokens == 2: self.head_dist = msnn.Dense(self.embed_dim, self.num_classes) if num_classes > 0 else msnn.Identity() def _init_vit_weights(Cell: msnn.Cell, name: str = '', head_bias: float = 0., jax_impl: bool = False): """ ViT weight initialization * When called without n, head_bias, jax_impl args it will behave exactly the same as my original init for compatibility with prev hparam / downstream use cases (ie DeiT). * When called w/ valid n (Cell name) and jax_impl=True, will (hopefully) match JAX impl """ if isinstance(Cell, msnn.Dense): if name.startswith('head'): bias_shape = Cell.bias.shape weight_shape = Cell.weight.shape Cell.weight=initializer('zeros',weight_shape) Cell.bias=initializer(Constant(head_bias),bias_shape) elif name.startswith('pre_logits'): bias_shape = Cell.bias.shape weight_shape = Cell.weight.shape lecun_normal_(Cell.weight) Cell.bias=initializer('zeros',bias_shape) else: weight_shape = Cell.weight.shape if jax_impl: msnn.init.xavier_uniform_(Cell.weight) if Cell.bias is not None: bias_shape = Cell.bias.shape if 'mlp' in name: Cell.bias=initializer('normal',bias_shape,sigma=1e-6) else: Cell.bias=initializer('zeros',bias_shape) else: trunc_normal_(Cell.weight, std=.02) if Cell.bias is not None: bias_shape = Cell.bias.shape Cell.bias=initializer('zeros',bias_shape) elif jax_impl and isinstance(Cell, msnn.Conv2d): # NOTE conv was left to pytorch default in my original init weight_shape = Cell.weight.shape lecun_normal_(Cell.weight) if Cell.bias is not None: bias_shape = Cell.bias.shape Cell.bias=initializer('zeros',bias_shape) def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''): """ Load weights from .npz checkpoints for official Google Brain Flax implementation """ import numpy as np def _n2p(w, t=True): if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1: w = w.flatten() if t: if w.ndim == 4: w = w.swapaxes([3, 2, 0, 1]) elif w.ndim == 3: w = w.swapaxes([2, 0, 1]) elif w.ndim == 2: w = w.swapaxes([1, 0]) return ops.from_numpy(w) w = np.load(checkpoint_path) if not prefix and 'opt/target/embedding/kernel' in w: prefix = 'opt/target/' if hasattr(model.patch_embed, 'backbone'): # hybrid backbone = model.patch_embed.backbone stem_only = not hasattr(backbone, 'stem') stem = backbone if stem_only else backbone.stem stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel']))) stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale'])) stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias'])) if not stem_only: for i, stage in enumerate(backbone.stages): for j, block in enumerate(stage.blocks): bp = f'{prefix}block{i + 1}/unit{j + 1}/' for r in range(3): getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel'])) getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale'])) getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias'])) if block.downsample is not None: block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel'])) block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale'])) block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias'])) embed_conv_w = _n2p(w[f'{prefix}embedding/kernel']) else: embed_conv_w = adapt_input_conv( model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel'])) model.patch_embed.proj.weight.copy_(embed_conv_w) model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias'])) model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False)) pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False) if pos_embed_w.shape != model.pos_embed.shape: pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size) model.pos_embed.copy_(pos_embed_w) model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale'])) model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias'])) if isinstance(model.head, msnn.Dense) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]: model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel'])) model.head.bias.copy_(_n2p(w[f'{prefix}head/bias'])) if isinstance(getattr(model.pre_logits, 'fc', None), msnn.Dense) and f'{prefix}pre_logits/bias' in w: model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel'])) model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias'])) for i, block in enumerate(model.blocks.children()): block_prefix = f'{prefix}Transformer/encoderblock_{i}/' mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/' block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale'])) block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias'])) block.attn.qkv.weight.copy_(ops.cat([ _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')])) block.attn.qkv.bias.copy_(ops.cat([ _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')])) block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1)) block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias'])) for r in range(2): getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel'])) getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias'])) block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale'])) block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias'])) def resize_pos_embed(posemb, posemb_new, num_tokens=1, gs_new=()): # Rescale the grid of position embeddings when loading from state_dict. Adapted from # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224 print('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape) ntok_new = posemb_new.shape[1] if num_tokens: posemb_tok, posemb_grid = posemb[:, :num_tokens], posemb[0, num_tokens:] ntok_new -= num_tokens else: posemb_tok, posemb_grid = posemb[:, :0], posemb[0] gs_old = int(math.sqrt(len(posemb_grid))) if not len(gs_new): # backwards compatibility gs_new = [int(math.sqrt(ntok_new))] * 2 assert len(gs_new) >= 2 print('Position embedding grid-size from %s to %s', [gs_old, gs_old], gs_new) posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2) posemb_grid = ops.interpolate(posemb_grid, size=gs_new, mode='bilinear') posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1) posemb = ops.cat([posemb_tok, posemb_grid], axis=1) return posemb def checkpoint_filter_fn(state_dict, model): """ convert patch embedding weight from manual patchify + linear proj to conv""" out_dict = {} if 'model' in state_dict: # For deit models state_dict = state_dict['model'] for k, v in state_dict.items(): if 'patch_embed.proj.weight' in k and len(v.shape) < 4: # For old models that I trained prior to conv based patchification O, I, H, W = model.patch_embed.proj.weight.shape v = v.reshape(O, -1, H, W) elif k == 'pos_embed' and v.shape != model.pos_embed.shape: # To resize pos embedding when using model at different size from pretrained weights v = resize_pos_embed( v, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size) out_dict[k] = v return out_dict def _create_vision_transformer(variant, pretrained=False, default_cfg=None, **kwargs): if kwargs.get('features_only', None): raise RuntimeError('features_only not implemented for Vision Transformer models.') model = VisionTransformer(**kwargs) if pretrained: if 'npz' in pretrained: model.load_pretrained(pretrained, prefix='') else: checkpoint = ms.load_checkpoint(pretrained, map_location="cpu") missing_keys, unexpected_keys = model.load_state_dict(checkpoint["model"], strict=False) print('Load pretrained model from: ' + pretrained) return model def vit_base_patch16_224(pretrained=False, **kwargs): """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929). """ model_kwargs = dict( patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs) model = _create_vision_transformer('vit_base_patch16_224_in21k', pretrained=pretrained, **model_kwargs) return model def vit_large_patch16_224(pretrained=False, **kwargs): model_kwargs = dict( patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs) model = _create_vision_transformer('vit_large_patch16_224_in21k', pretrained=pretrained, **model_kwargs) return model ================================================ FILE: artrackv2_mindspore/lib/models/ostrack/vit_ce.py ================================================ import math import logging from functools import partial from collections import OrderedDict from copy import deepcopy import torch import torch.nn as nn import torch.nn.functional as F from timm.models.layers import to_2tuple from lib.models.layers.patch_embed import PatchEmbed from .utils import combine_tokens, recover_tokens from .vit import VisionTransformer from ..layers.attn_blocks import CEBlock _logger = logging.getLogger(__name__) class VisionTransformerCE(VisionTransformer): """ Vision Transformer with candidate elimination (CE) module A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - https://arxiv.org/abs/2010.11929 Includes distillation token & head support for `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877 """ def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None, distilled=False, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None, act_layer=None, weight_init='', ce_loc=None, ce_keep_ratio=None): """ Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number of input channels num_classes (int): number of classes for classification head embed_dim (int): embedding dimension depth (int): depth of transformer num_heads (int): number of attention heads mlp_ratio (int): ratio of mlp hidden dim to embedding dim qkv_bias (bool): enable bias for qkv if True representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set distilled (bool): model includes a distillation token and head as in DeiT models drop_rate (float): dropout rate attn_drop_rate (float): attention dropout rate drop_path_rate (float): stochastic depth rate embed_layer (nn.Module): patch embedding layer norm_layer: (nn.Module): normalization layer weight_init: (str): weight init scheme """ # super().__init__() super().__init__() if isinstance(img_size, tuple): self.img_size = img_size else: self.img_size = to_2tuple(img_size) self.patch_size = patch_size self.in_chans = in_chans self.num_classes = num_classes self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models self.num_tokens = 2 if distilled else 1 norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) act_layer = act_layer or nn.GELU self.patch_embed = embed_layer( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) num_patches = self.patch_embed.num_patches self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim)) self.pos_drop = nn.Dropout(p=drop_rate) dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule blocks = [] ce_index = 0 self.ce_loc = ce_loc for i in range(depth): ce_keep_ratio_i = 1.0 if ce_loc is not None and i in ce_loc: ce_keep_ratio_i = ce_keep_ratio[ce_index] ce_index += 1 blocks.append( CEBlock( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer, keep_ratio_search=ce_keep_ratio_i) ) self.blocks = nn.Sequential(*blocks) self.norm = norm_layer(embed_dim) self.init_weights(weight_init) def forward_features(self, z, x, mask_z=None, mask_x=None, ce_template_mask=None, ce_keep_rate=None, return_last_attn=False ): B, H, W = x.shape[0], x.shape[2], x.shape[3] x = self.patch_embed(x) z = self.patch_embed(z) # attention mask handling # B, H, W if mask_z is not None and mask_x is not None: mask_z = F.interpolate(mask_z[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0] mask_z = mask_z.flatten(1).unsqueeze(-1) mask_x = F.interpolate(mask_x[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0] mask_x = mask_x.flatten(1).unsqueeze(-1) mask_x = combine_tokens(mask_z, mask_x, mode=self.cat_mode) mask_x = mask_x.squeeze(-1) if self.add_cls_token: cls_tokens = self.cls_token.expand(B, -1, -1) cls_tokens = cls_tokens + self.cls_pos_embed z += self.pos_embed_z x += self.pos_embed_x if self.add_sep_seg: x += self.search_segment_pos_embed z += self.template_segment_pos_embed x = combine_tokens(z, x, mode=self.cat_mode) if self.add_cls_token: x = torch.cat([cls_tokens, x], dim=1) x = self.pos_drop(x) lens_z = self.pos_embed_z.shape[1] lens_x = self.pos_embed_x.shape[1] global_index_t = torch.linspace(0, lens_z - 1, lens_z).to(x.device) global_index_t = global_index_t.repeat(B, 1) global_index_s = torch.linspace(0, lens_x - 1, lens_x).to(x.device) global_index_s = global_index_s.repeat(B, 1) removed_indexes_s = [] for i, blk in enumerate(self.blocks): x, global_index_t, global_index_s, removed_index_s, attn = \ blk(x, global_index_t, global_index_s, mask_x, ce_template_mask, ce_keep_rate) if self.ce_loc is not None and i in self.ce_loc: removed_indexes_s.append(removed_index_s) x = self.norm(x) lens_x_new = global_index_s.shape[1] lens_z_new = global_index_t.shape[1] z = x[:, :lens_z_new] x = x[:, lens_z_new:] if removed_indexes_s and removed_indexes_s[0] is not None: removed_indexes_cat = torch.cat(removed_indexes_s, dim=1) pruned_lens_x = lens_x - lens_x_new pad_x = torch.zeros([B, pruned_lens_x, x.shape[2]], device=x.device) x = torch.cat([x, pad_x], dim=1) index_all = torch.cat([global_index_s, removed_indexes_cat], dim=1) # recover original token order C = x.shape[-1] # x = x.gather(1, index_all.unsqueeze(-1).expand(B, -1, C).argsort(1)) x = torch.zeros_like(x).scatter_(dim=1, index=index_all.unsqueeze(-1).expand(B, -1, C).to(torch.int64), src=x) x = recover_tokens(x, lens_z_new, lens_x, mode=self.cat_mode) # re-concatenate with the template, which may be further used by other modules x = torch.cat([z, x], dim=1) aux_dict = { "attn": attn, "removed_indexes_s": removed_indexes_s, # used for visualization } return x, aux_dict def forward(self, z, x, ce_template_mask=None, ce_keep_rate=None, tnc_keep_rate=None, return_last_attn=False): x, aux_dict = self.forward_features(z, x, ce_template_mask=ce_template_mask, ce_keep_rate=ce_keep_rate,) return x, aux_dict def _create_vision_transformer(pretrained=False, **kwargs): model = VisionTransformerCE(**kwargs) if pretrained: if 'npz' in pretrained: model.load_pretrained(pretrained, prefix='') else: checkpoint = torch.load(pretrained, map_location="cpu") missing_keys, unexpected_keys = model.load_state_dict(checkpoint["model"], strict=False) print('Load pretrained model from: ' + pretrained) return model def vit_base_patch16_224_ce(pretrained=False, **kwargs): """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929). """ model_kwargs = dict( patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs) model = _create_vision_transformer(pretrained=pretrained, **model_kwargs) return model def vit_large_patch16_224_ce(pretrained=False, **kwargs): """ ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929). """ model_kwargs = dict( patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs) model = _create_vision_transformer(pretrained=pretrained, **model_kwargs) return model ================================================ FILE: artrackv2_mindspore/lib/models/timm.py ================================================ import mindspore as ms import mindspore.nn as nn import mindspore.ops as ops import math import numpy as np def to_2tuple(input): if isinstance(input,tuple): if len(input)==2: return input else: raise ValueError("The tuple's length is not 2!") elif isinstance(input,list): if len(input)==2: return tuple(x for x in input) else: raise ValueError("The List's length is not 2!") else: return (input,input) class Mlp(nn.Cell): """ MLP as used in Vision Transformer, MLP-Mixer and related networks """ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features drop_probs = to_2tuple(drop) self.fc1 = nn.Dense(in_features, hidden_features,has_bias=True) if act_layer==nn.GELU: self.act = act_layer(approximate=False) self.drop1 = nn.Dropout(p=drop_probs[0]) self.fc2 = nn.Dense(hidden_features, out_features,has_bias=True) self.drop2 = nn.Dropout(p=drop_probs[1]) def construct(self, x): x = self.fc1(x) x = self.act(x) x = self.drop1(x) x = self.fc2(x) x = self.drop2(x) return x def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the argument. """ if drop_prob == 0. or not training: return x keep_prob = 1 - drop_prob shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets random_tensor = x.new_empty(shape).bernoulli_(keep_prob) if keep_prob > 0.0 and scale_by_keep: random_tensor.div_(keep_prob) return x * random_tensor class DropPath(nn.Cell): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). """ def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True): super(DropPath, self).__init__() self.drop_prob = drop_prob self.scale_by_keep = scale_by_keep def construct(self, x): return drop_path(x, self.drop_prob, self.training, self.scale_by_keep) def extra_repr(self): return f'drop_prob={round(self.drop_prob,3):0.3f}' def _trunc_normal_(tensor, mean, std, a, b): # Cut & paste from PyTorch official master until it's in a few official releases - RW # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf def norm_cdf(x): # Computes standard normal cumulative distribution function return (1. + math.erf(x / math.sqrt(2.))) / 2. if (mean < a - 2 * std) or (mean > b + 2 * std): warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " "The distribution of values may be incorrect.", stacklevel=2) # Values are generated by using a truncated uniform distribution and # then using the inverse CDF for the normal distribution. # Get upper and lower cdf values l = norm_cdf((a - mean) / std) u = norm_cdf((b - mean) / std) # Uniformly fill tensor with values from [l, u], then translate to # [2l-1, 2u-1]. shape = tensor.shape minval = ms.Tensor(2*l-1) maxval = ms.Tensor(2*u-1) x = ops.uniform(shape,minval, maxval) tensor = x # Use inverse cdf transform for normal distribution to get truncated # standard normal tensor = tensor.erfinv() # Transform to proper mean, std tenspr = tensor.mul(std * math.sqrt(2.)) tensor = tensor.add(mean) # Clamp to ensure it's in the proper range tensor = tensor.clamp(min=a, max=b) return tensor def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): # type: (Tensor, float, float, float, float) -> Tensor r"""Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn from the normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` with values outside :math:`[a, b]` redrawn until they are within the bounds. The method used for generating the random values works best when :math:`a \leq \text{mean} \leq b`. NOTE: this impl is similar to the PyTorch trunc_normal_, the bounds [a, b] are applied while sampling the normal with mean/std applied, therefore a, b args should be adjusted to match the range of mean, std args. Args: tensor: an n-dimensional `torch.Tensor` mean: the mean of the normal distribution std: the standard deviation of the normal distribution a: the minimum cutoff value b: the maximum cutoff value Examples: >>> w = torch.empty(3, 5) >>> nn.init.trunc_normal_(w) """ return _trunc_normal_(tensor, mean, std, a, b) def trunc_normal_tf_(tensor, mean=0., std=1., a=-2., b=2.): # type: (Tensor, float, float, float, float) -> Tensor r"""Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn from the normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` with values outside :math:`[a, b]` redrawn until they are within the bounds. The method used for generating the random values works best when :math:`a \leq \text{mean} \leq b`. NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0 and the result is subsquently scaled and shifted by the mean and std args. Args: tensor: an n-dimensional `torch.Tensor` mean: the mean of the normal distribution std: the standard deviation of the normal distribution a: the minimum cutoff value b: the maximum cutoff value Examples: >>> w = torch.empty(3, 5) >>> nn.init.trunc_normal_(w) """ _trunc_normal_(tensor, 0, 1.0, a, b) tensor.mul_(std).add_(mean) return tensor def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'): fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) if mode == 'fan_in': denom = fan_in elif mode == 'fan_out': denom = fan_out elif mode == 'fan_avg': denom = (fan_in + fan_out) / 2 variance = scale / denom if distribution == "truncated_normal": # constant is stddev of standard normal truncated to (-2, 2) trunc_normal_tf_(tensor, std=math.sqrt(variance) / .87962566103423978) elif distribution == "normal": tensor.normal_(std=math.sqrt(variance)) elif distribution == "uniform": bound = math.sqrt(3 * variance) tensor.uniform_(-bound, bound) else: raise ValueError(f"invalid distribution {distribution}") def lecun_normal_(tensor): variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal') def adapt_input_conv(in_chans, conv_weight): conv_type = conv_weight.dtype conv_weight = conv_weight.float() # Some weights are in torch.half, ensure it's float for sum on CPU O, I, J, K = conv_weight.shape if in_chans == 1: if I > 3: assert conv_weight.shape[1] % 3 == 0 # For models with space2depth stems conv_weight = conv_weight.reshape(O, I // 3, 3, J, K) conv_weight = conv_weight.sum(dim=2, keepdim=False) else: conv_weight = conv_weight.sum(dim=1, keepdim=True) elif in_chans != 3: if I != 3: raise NotImplementedError('Weight format not supported by conversion.') else: # NOTE this strategy should be better than random init, but there could be other combinations of # the original RGB input layer weights that'd work better for specific cases. repeat = int(math.ceil(in_chans / 3)) conv_weight = conv_weight.repeat(1, repeat, 1, 1)[:, :in_chans, :, :] conv_weight *= (3 / float(in_chans)) conv_weight = conv_weight.to(conv_type) return conv_weight ================================================ FILE: artrackv2_mindspore/lib/train/__init__.py ================================================ # from .admin.multigpu import MultiGPU ================================================ FILE: artrackv2_mindspore/lib/train/_init_paths.py ================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os.path as osp import sys def add_path(path): if path not in sys.path: sys.path.insert(0, path) this_dir = osp.dirname(__file__) prj_path = osp.join(this_dir, '../..') add_path(prj_path) ================================================ FILE: artrackv2_mindspore/lib/train/actors/__init__.py ================================================ from .base_actor import BaseActor from .ostrack import OSTrackActor ================================================ FILE: artrackv2_mindspore/lib/train/actors/base_actor.py ================================================ from lib.utils import TensorDict class BaseActor: """ Base class for actor. The actor class handles the passing of the data through the network and calculation the loss""" def __init__(self, net, objective): """ args: net - The network to train objective - The loss function """ self.net = net self.objective = objective def __call__(self, data: TensorDict): """ Called in each training iteration. Should pass in input data through the network, calculate the loss, and return the training stats for the input data args: data - A TensorDict containing all the necessary data blocks. returns: loss - loss for the input data stats - a dict containing detailed losses """ raise NotImplementedError def to(self, device): """ Move the network to device args: device - device to use. 'cpu' or 'cuda' """ self.net.to(device) def train(self, mode=True): """ Set whether the network is in train mode. args: mode (True) - Bool specifying whether in training mode. """ self.net.train(mode) def eval(self): """ Set network to eval mode""" self.train(False) ================================================ FILE: artrackv2_mindspore/lib/train/actors/ostrack.py ================================================ from . import BaseActor from lib.utils.misc import NestedTensor from lib.utils.box_ops import box_cxcywh_to_xyxy, box_xywh_to_xyxy import torch import math import numpy as np import numpy import cv2 import torch.nn.functional as F import torchvision.transforms.functional as tvisf import lib.train.data.bounding_box_utils as bbutils from lib.utils.merge import merge_template_search from torch.distributions.categorical import Categorical from ...utils.heapmap_utils import generate_heatmap from ...utils.ce_utils import generate_mask_cond, adjust_keep_rate def IoU(rect1, rect2): """ caculate interection over union Args: rect1: (x1, y1, x2, y2) rect2: (x1, y1, x2, y2) Returns: iou """ # overlap x1, y1, x2, y2 = rect1[0], rect1[1], rect1[2], rect1[3] tx1, ty1, tx2, ty2 = rect2[0], rect2[1], rect2[2], rect2[3] xx1 = np.maximum(tx1, x1) yy1 = np.maximum(ty1, y1) xx2 = np.minimum(tx2, x2) yy2 = np.minimum(ty2, y2) ww = np.maximum(0, xx2 - xx1) hh = np.maximum(0, yy2 - yy1) area = (x2-x1) * (y2-y1) target_a = (tx2-tx1) * (ty2 - ty1) inter = ww * hh iou = inter / (area + target_a - inter) return iou def fp16_clamp(x, min=None, max=None): if not x.is_cuda and x.dtype == torch.float16: # clamp for cpu float16, tensor fp16 has no clamp implementation return x.float().clamp(min, max).half() return x.clamp(min, max) def generate_sa_simdr(joints): ''' :param joints: [num_joints, 3] :param joints_vis: [num_joints, 3] :return: target, target_weight(1: visible, 0: invisible) ''' num_joints = 48 image_size = [256, 256] simdr_split_ratio = 1.5625 sigma = 6 target_x1 = np.zeros((num_joints, int(image_size[0] * simdr_split_ratio)), dtype=np.float32) target_y1 = np.zeros((num_joints, int(image_size[1] * simdr_split_ratio)), dtype=np.float32) target_x2 = np.zeros((num_joints, int(image_size[0] * simdr_split_ratio)), dtype=np.float32) target_y2 = np.zeros((num_joints, int(image_size[1] * simdr_split_ratio)), dtype=np.float32) zero_4_begin = np.zeros((num_joints, 1), dtype=np.float32) tmp_size = sigma * 3 for joint_id in range(num_joints): mu_x1 = joints[joint_id][0] mu_y1 = joints[joint_id][1] mu_x2 = joints[joint_id][2] mu_y2 = joints[joint_id][3] x1 = np.arange(0, int(image_size[0] * simdr_split_ratio), 1, np.float32) y1 = np.arange(0, int(image_size[1] * simdr_split_ratio), 1, np.float32) x2 = np.arange(0, int(image_size[0] * simdr_split_ratio), 1, np.float32) y2 = np.arange(0, int(image_size[1] * simdr_split_ratio), 1, np.float32) target_x1[joint_id] = (np.exp(- ((x1 - mu_x1) ** 2) / (2 * sigma ** 2))) / ( sigma * np.sqrt(np.pi * 2)) target_y1[joint_id] = (np.exp(- ((y1 - mu_y1) ** 2) / (2 * sigma ** 2))) / ( sigma * np.sqrt(np.pi * 2)) target_x2[joint_id] = (np.exp(- ((x2 - mu_x2) ** 2) / (2 * sigma ** 2))) / ( sigma * np.sqrt(np.pi * 2)) target_y2[joint_id] = (np.exp(- ((y2 - mu_y2) ** 2) / (2 * sigma ** 2))) / ( sigma * np.sqrt(np.pi * 2)) return target_x1, target_y1, target_x2, target_y2 # angle cost def SIoU_loss(test1, test2, theta=4): eps = 1e-7 cx_pred = (test1[:, 0] + test1[:, 2]) / 2 cy_pred = (test1[:, 1] + test1[:, 3]) / 2 cx_gt = (test2[:, 0] + test2[:, 2]) / 2 cy_gt = (test2[:, 1] + test2[:, 3]) / 2 dist = ((cx_pred - cx_gt)**2 + (cy_pred - cy_gt)**2) ** 0.5 ch = torch.max(cy_gt, cy_pred) - torch.min(cy_gt, cy_pred) x = ch / (dist + eps) angle = 1 - 2*torch.sin(torch.arcsin(x)-torch.pi/4)**2 # distance cost xmin = torch.min(test1[:, 0], test2[:, 0]) xmax = torch.max(test1[:, 2], test2[:, 2]) ymin = torch.min(test1[:, 1], test2[:, 1]) ymax = torch.max(test1[:, 3], test2[:, 3]) cw = xmax - xmin ch = ymax - ymin px = ((cx_gt - cx_pred) / (cw+eps))**2 py = ((cy_gt - cy_pred) / (ch+eps))**2 gama = 2 - angle dis = (1 - torch.exp(-1 * gama * px)) + (1 - torch.exp(-1 * gama * py)) #shape cost w_pred = test1[:, 2] - test1[:, 0] h_pred = test1[:, 3] - test1[:, 1] w_gt = test2[:, 2] - test2[:, 0] h_gt = test2[:, 3] - test2[:, 1] ww = torch.abs(w_pred - w_gt) / (torch.max(w_pred, w_gt) + eps) wh = torch.abs(h_gt - h_pred) / (torch.max(h_gt, h_pred) + eps) omega = (1 - torch.exp(-1 * wh)) ** theta + (1 - torch.exp(-1 * ww)) ** theta #IoU loss lt = torch.max(test1[..., :2], test2[..., :2]) # [B, rows, 2] rb = torch.min(test1[..., 2:], test2[..., 2:]) # [B, rows, 2] wh = fp16_clamp(rb - lt, min=0) overlap = wh[..., 0] * wh[..., 1] area1 = (test1[..., 2] - test1[..., 0]) * ( test1[..., 3] - test1[..., 1]) area2 = (test2[..., 2] - test2[..., 0]) * ( test2[..., 3] - test2[..., 1]) iou = overlap / (area1 + area2 - overlap) SIoU = 1 - iou + (omega + dis) / 2 return SIoU, iou def ciou(pred, target, eps=1e-7): # overlap lt = torch.max(pred[:, :2], target[:, :2]) rb = torch.min(pred[:, 2:], target[:, 2:]) wh = (rb - lt).clamp(min=0) overlap = wh[:, 0] * wh[:, 1] # union ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1]) ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1]) union = ap + ag - overlap + eps # IoU ious = overlap / union # enclose area enclose_x1y1 = torch.min(pred[:, :2], target[:, :2]) enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:]) enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0) cw = enclose_wh[:, 0] ch = enclose_wh[:, 1] c2 = cw**2 + ch**2 + eps b1_x1, b1_y1 = pred[:, 0], pred[:, 1] b1_x2, b1_y2 = pred[:, 2], pred[:, 3] b2_x1, b2_y1 = target[:, 0], target[:, 1] b2_x2, b2_y2 = target[:, 2], target[:, 3] w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2))**2 / 4 right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2))**2 / 4 rho2 = left + right factor = 4 / math.pi**2 v = factor * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2) # CIoU cious = ious - (rho2 / c2 + v**2 / (1 - ious + v)) return cious, ious class OSTrackActor(BaseActor): """ Actor for training OSTrack models """ def __init__(self, net, objective, loss_weight, settings, bins, search_size, cfg=None): super().__init__(net, objective) self.loss_weight = loss_weight self.settings = settings self.bs = self.settings.batchsize # batch size self.cfg = cfg self.bins = bins self.search_size = search_size self.logsoftmax = torch.nn.LogSoftmax(dim=1) self.focal = None self.range = 2 self.loss_weight['KL'] = 0 self.loss_weight['focal'] = 0 self.pre_num = 7 self.pre_bbox = None self.x_feat_rem = None def __call__(self, data): """ args: data - The input data, should contain the fields 'template', 'search', 'gt_bbox'. template_images: (N_t, batch, 3, H, W) search_images: (N_s, batch, 3, H, W) returns: loss - the training loss status - dict containing detailed losses """ # forward pass out_dict = self.forward_pass(data) # compute losses loss, status = self.compute_losses(out_dict, data) return loss, status def _bbox_clip(self, cx, cy, width, height, boundary): cx = max(0, min(cx, boundary[1])) cy = max(0, min(cy, boundary[0])) width = max(10, min(width, boundary[1])) height = max(10, min(height, boundary[0])) return cx, cy, width, height def get_subwindow(self, im, pos, model_sz, original_sz, avg_chans): """ args: im: bgr based image pos: center position model_sz: exemplar size s_z: original size avg_chans: channel average """ if isinstance(pos, float): pos = [pos, pos] sz = original_sz im_sz = im.shape c = (original_sz + 1) / 2 # context_xmin = round(pos[0] - c) # py2 and py3 round context_xmin = np.floor(pos[0] - c + 0.5) context_xmax = context_xmin + sz - 1 # context_ymin = round(pos[1] - c) context_ymin = np.floor(pos[1] - c + 0.5) context_ymax = context_ymin + sz - 1 left_pad = int(max(0., -context_xmin)) top_pad = int(max(0., -context_ymin)) right_pad = int(max(0., context_xmax - im_sz[1] + 1)) bottom_pad = int(max(0., context_ymax - im_sz[0] + 1)) context_xmin = context_xmin + left_pad context_xmax = context_xmax + left_pad context_ymin = context_ymin + top_pad context_ymax = context_ymax + top_pad r, c, k = im.shape if any([top_pad, bottom_pad, left_pad, right_pad]): size = (r + top_pad + bottom_pad, c + left_pad + right_pad, k) te_im = np.zeros(size, np.uint8) te_im[top_pad:top_pad + r, left_pad:left_pad + c, :] = im if top_pad: te_im[0:top_pad, left_pad:left_pad + c, :] = avg_chans if bottom_pad: te_im[r + top_pad:, left_pad:left_pad + c, :] = avg_chans if left_pad: te_im[:, 0:left_pad, :] = avg_chans if right_pad: te_im[:, c + left_pad:, :] = avg_chans im_patch = te_im[int(context_ymin):int(context_ymax + 1), int(context_xmin):int(context_xmax + 1), :] else: im_patch = im[int(context_ymin):int(context_ymax + 1), int(context_xmin):int(context_xmax + 1), :] if not np.array_equal(model_sz, original_sz): try: im_patch = cv2.resize(im_patch, (model_sz, model_sz)) except: return None im_patch = im_patch.transpose(2, 0, 1) im_patch = im_patch[np.newaxis, :, :, :] im_patch = im_patch.astype(np.float32) im_patch = torch.from_numpy(im_patch) im_patch = im_patch.cuda() return im_patch def batch_init(self, images, template_bbox, initial_bbox) -> dict: self.frame_num = 1 self.device = 'cuda' # Convert bbox (x1, y1, w, h) -> (cx, cy, w, h) template_bbox_1 = template_bbox[:, 0] template_bbox_2 = template_bbox[:, 1] template_bbox_1 = bbutils.batch_xywh2center2(template_bbox_1) # ndarray:(2*num_seq,4) template_bbox_2 = bbutils.batch_xywh2center2(template_bbox_2) # ndarray:(2*num_seq,4) initial_bbox = bbutils.batch_xywh2center2(initial_bbox) # ndarray:(2*num_seq,4) self.center_pos = initial_bbox[:, :2] # ndarray:(2*num_seq,2) self.size = initial_bbox[:, 2:] # ndarray:(2*num_seq,2) self.pre_bbox = initial_bbox for i in range(self.pre_num - 1): self.pre_bbox = numpy.concatenate((self.pre_bbox, initial_bbox), axis=1) #print(self.pre_bbox.shape) template_factor = self.cfg.DATA.TEMPLATE.FACTOR w_z_1 = template_bbox_1[:, 2] * template_factor # ndarray:(2*num_seq) h_z_1 = template_bbox_1[:, 3] * template_factor # ndarray:(2*num_seq) s_z_1 = np.ceil(np.sqrt(w_z_1 * h_z_1)) # ndarray:(2*num_seq) w_z_2 = template_bbox_2[:, 2] * template_factor # ndarray:(2*num_seq) h_z_2 = template_bbox_2[:, 3] * template_factor # ndarray:(2*num_seq) s_z_2 = np.ceil(np.sqrt(w_z_2 * h_z_2)) # ndarray:(2*num_seq) self.channel_average = [] for img in images: self.channel_average.append(np.mean(img[0], axis=(0, 1))) self.channel_average.append(np.mean(img[1], axis=(0, 1))) self.channel_average = np.array(self.channel_average) # ndarray:(2*num_seq,3) #get crop z_crop_list = [] z_1_list = [] z_2_list = [] for i in range(len(images)): here_crop_1 = self.get_subwindow(images[i][0], template_bbox_1[i, :2], self.cfg.DATA.TEMPLATE.SIZE, s_z_1[i], self.channel_average[2*i]) here_crop_2 = self.get_subwindow(images[i][1], template_bbox_2[i, :2], self.cfg.DATA.TEMPLATE.SIZE, s_z_2[i], self.channel_average[2*i+1]) z_crop_1 = here_crop_1.float().mul(1.0 / 255.0).clamp(0.0, 1.0) z_crop_2 = here_crop_2.float().mul(1.0 / 255.0).clamp(0.0, 1.0) self.mean = [0.485, 0.456, 0.406] self.std = [0.229, 0.224, 0.225] self.inplace = False z_crop_1[0] = tvisf.normalize(z_crop_1[0], self.mean, self.std, self.inplace) z_crop_2[0] = tvisf.normalize(z_crop_2[0], self.mean, self.std, self.inplace) z_1_list.append(z_crop_1.unsqueeze(1).clone()) z_2_list.append(z_crop_2.unsqueeze(1).clone()) z_crop = torch.concat([z_crop_1.unsqueeze(1), z_crop_2.unsqueeze(1)], dim=1) z_crop_list.append(z_crop.clone()) z_crop = torch.cat(z_crop_list, dim=0) # Tensor(2*num_seq,3,128,128) z_1_crop = torch.cat(z_1_list, dim=0) z_2_crop = torch.cat(z_2_list, dim=0) z_2_crop = z_2_crop.squeeze(1).to(self.net.module.backbone.word_embeddings.weight) z_2_feat = self.net.module.backbone.patch_embed(z_2_crop) out = {'template_images': z_crop, "z_1":z_1_crop, "z_2":z_2_crop, "z_2_feat":z_2_feat} return out def batch_track(self, img, gt_boxes, template, dz_feat, action_mode='max') -> dict: search_factor = self.cfg.DATA.SEARCH.FACTOR w_x = self.size[:, 0] * search_factor h_x = self.size[:, 1] * search_factor s_x = np.ceil(np.sqrt(w_x * h_x)) gt_boxes_corner = bbutils.batch_xywh2corner(gt_boxes) # ndarray:(2*num_seq,4) initial_bbox = bbutils.batch_xywh2center2(gt_boxes) x_crop_list = [] gt_in_crop_list = [] pre_seq_list = [] pre_seq_in_list = [] x_feat_list = [] target_in_search_list = [] update_feat_list = [] for i in range(len(img)): template_factor = self.cfg.DATA.TEMPLATE.FACTOR w_z_1 = initial_bbox[:, 2] * template_factor # ndarray:(2*num_seq) h_z_1 = initial_bbox[:, 3] * template_factor # ndarray:(2*num_seq) s_z_1 = np.ceil(np.sqrt(w_z_1 * h_z_1)) # ndarray:(2*num_seq) channel_avg = np.mean(img[i], axis=(0, 1)) target_in_search = self.get_subwindow(img[i], initial_bbox[i, :2], self.cfg.DATA.TEMPLATE.SIZE, round(s_z_1[i]), channel_avg) x_crop = self.get_subwindow(img[i], self.center_pos[i], self.cfg.DATA.SEARCH.SIZE, round(s_x[i]), channel_avg) if x_crop == None: return None if target_in_search == None: return None for q in range(self.pre_num): pre_seq_temp = bbutils.batch_center2corner(self.pre_bbox[:, 0+4*q:4+4*q]) if q == 0: pre_seq = pre_seq_temp else: pre_seq = numpy.concatenate((pre_seq, pre_seq_temp), axis=1) #pre_seq = bbutils.batch_center2corner(self.pre_bbox) #ndarray:(x1 y1 x2 y2) if gt_boxes_corner is not None and np.sum(np.abs(gt_boxes_corner[i] - np.zeros(4))) > 10: pre_in = np.zeros(4 * self.pre_num) for w in range(self.pre_num): #print(pre_seq[i, 0+w*4:2+w*4].shape) #print(self.center_pos[i].shape) #print(pre_in[0+w*4:2+w*4].shape) pre_in[0+w*4:2+w*4] = pre_seq[i, 0+w*4:2+w*4] - self.center_pos[i] pre_in[2+w*4:4+w*4] = pre_seq[i, 2+w*4:4+w*4] - self.center_pos[i] pre_in[0+w*4:4+w*4] = pre_in[0+w*4:4+w*4] * (self.cfg.DATA.SEARCH.SIZE / s_x[i]) + self.cfg.DATA.SEARCH.SIZE / 2 #print(pre_in) pre_in[0+w*4:4+w*4] = pre_in[0+w*4:4+w*4] / self.cfg.DATA.SEARCH.SIZE pre_seq_list.append(pre_in) gt_in_crop = np.zeros(4) gt_in_crop[:2] = gt_boxes_corner[i, :2] - self.center_pos[i] gt_in_crop[2:] = gt_boxes_corner[i, 2:] - self.center_pos[i] gt_in_crop = gt_in_crop * (self.cfg.DATA.SEARCH.SIZE / s_x[i]) + self.cfg.DATA.SEARCH.SIZE / 2 gt_in_crop[2:] = gt_in_crop[2:] - gt_in_crop[:2] # (x1,y1,x2,y2) to (x1,y1,w,h) gt_in_crop_list.append(gt_in_crop) else: pre_in = np.zeros(4 * self.pre_num) #pre_in[:2] = pre_seq[i, :2] - self.center_pos[i] #pre_in[2:] = pre_seq[i, 2:] - self.center_pos[i] #pre_in = pre_in * (self.cfg.DATA.SEARCH.SIZE / s_x[i]) + self.cfg.DATA.SEARCH.SIZE / 2 pre_seq_list.append(pre_in) gt_in_crop_list.append(np.zeros(4)) #print(gt_in_crop) pre_seq_input = torch.from_numpy(pre_in).clamp(-0.5 * self.range + 0.5, 0.5 + self.range * 0.5) pre_seq_input = (pre_seq_input + (0.5 * self.range - 0.5)) * (self.bins - 1) pre_seq_in_list.append(pre_seq_input.clone()) x_crop = x_crop.float().mul(1.0 / 255.0).clamp(0.0, 1.0) target_in_search = target_in_search.float().mul(1.0 / 255.0).clamp(0.0, 1.0) rem_x = x_crop x_crop[0] = tvisf.normalize(x_crop[0], self.mean, self.std, self.inplace) target_in_search[0] = tvisf.normalize(target_in_search[0], self.mean, self.std, self.inplace) x_crop_list.append(x_crop.clone()) target_in_search_list.append(target_in_search.clone()) x_crop = torch.cat(x_crop_list, dim=0) target_in_search = torch.cat(target_in_search_list, dim=0) pre_seq_output = torch.cat(pre_seq_in_list, dim=0).reshape(-1, 4*self.pre_num) pre = torch.zeros_like(pre_seq_output) #print("this is x_feat_rem") #print(self.x_feat_rem) #print("i do this") outputs = self.net(template, dz_feat.cuda(), x_crop, seq_input=pre_seq_output, head_type=None, stage="batch_track", search_feature=self.x_feat_rem, target_in_search_img=target_in_search, gt_bboxes=gt_boxes[-1]) selected_indices = outputs['seqs'].detach() x_feat = outputs['x_feat'].detach().cpu() self.x_feat_rem = x_feat.clone() x_feat_list.append(x_feat.clone()) update_feat = outputs['dz_feat'].detach().cpu() update_feat_list.append(update_feat.clone()) pred_bbox = selected_indices[:, 0:4].data.cpu().numpy() bbox = (pred_bbox / (self.bins-1) - (self.range * 0.5 - 0.5)) * s_x.reshape(-1, 1) cx = bbox[:, 0] + self.center_pos[:, 0] - s_x/2 cy = bbox[:, 1] + self.center_pos[:, 1] - s_x/2 width = bbox[:, 2] - bbox[:, 0] height = bbox[:, 3] - bbox[:, 1] cx = cx + width/2 cy = cy + height/2 for i in range(len(img)): cx[i], cy[i], width[i], height[i] = self._bbox_clip(cx[i], cy[i], width[i], height[i], img[i].shape[:2]) self.center_pos = np.stack([cx, cy], 1) self.size = np.stack([width, height], 1) #self.pre_bbox = np.stack([cx, cy, width, height], 1) for e in range(self.pre_num): if e != self.pre_num-1 : self.pre_bbox[:, 0+e*4:4+e*4] = self.pre_bbox[:, 4+e*4:8+e*4] else: self.pre_bbox[:, 0+e*4:4+e*4] = numpy.stack([cx, cy, width, height], 1) #print(self.pre_bbox) #print(self.pre_bbox) #print(gt_boxes) #print(gt_in_crop) bbox = np.stack([cx - width / 2, cy - height / 2, width, height], 1) #print(pre_seq_output) #print(bbox) #print(gt_boxes) out = { 'dz_feat': update_feat, 'search_images': x_crop, 'target_in_search': target_in_search, 'pred_bboxes': bbox, 'selected_indices': selected_indices.cpu(), 'gt_in_crop': torch.tensor(np.stack(gt_in_crop_list, axis=0), dtype=torch.float), 'pre_seq': torch.tensor(np.stack(pre_seq_list, axis=0), dtype=torch.float), 'x_feat': torch.tensor([item.cpu().detach().numpy() for item in x_feat_list], dtype=torch.float), } #print("i want to see this") #print(out['x_feat'].shape) #print(out['pre_seq'].shape) # import matplotlib.pyplot as plt # print(gt_in_crop) # rem_x_plt = x_crop[0].permute(1, 2, 0).cpu() # plt.imshow(rem_x_plt) # ax = plt.gca() # ax.add_patch(plt.Rectangle(gt_in_crop[0:2], gt_in_crop[2], gt_in_crop[3], color="blue", fill=False, linewidth=1)) # plt.show() # input() return out def explore(self, data): results = {} search_images_list = [] search_anno_list = [] action_tensor_list = [] iou_list = [] # cover_list = [] pre_seq_list = [] x_feat_list = [] target_in_search_list = [] template_all_list = [] dz_feat_udpate_list = [] num_frames = data['num_frames'] images = data['search_images'] gt_bbox = data['search_annos'] template = data['template_images'] template_bbox = data['template_annos'] visible_ratio = data['visible_ratio'] template = template template_bbox = template_bbox template_bbox = np.array(template_bbox) num_seq = len(num_frames) for idx in range(np.max(num_frames)): here_images = [img[idx] for img in images] #S, N here_gt_bbox = np.array([gt[idx] for gt in gt_bbox]) here_images = here_images here_gt_bbox = np.concatenate([here_gt_bbox], 0) if idx == 0: outputs_template = self.batch_init(template, template_bbox, here_gt_bbox) results['template_images'] = outputs_template['z_1'] self.template_temp = outputs_template['z_1'].clone() z_all = [outputs_template['z_1'], outputs_template['z_2']] results['z_all'] = z_all self.dz_feat_update = outputs_template['z_2_feat'] else: outputs = self.batch_track(here_images, here_gt_bbox, self.template_temp, self.dz_feat_update, action_mode='half') if outputs == None: return None template_all_list.append(self.template_temp.clone()) dz_feat_udpate_list.append(self.dz_feat_update.clone().to(outputs['dz_feat'])) x_feat = outputs['x_feat'] self.dz_feat_update = outputs['dz_feat'] #print(x_feat.shape) pred_bbox = outputs['pred_bboxes'] search_images_list.append(outputs['search_images']) target_in_search_list.append(outputs['target_in_search']) search_anno_list.append(outputs['gt_in_crop']) #action_tensor_list.append(outputs['selected_indices']) if len(outputs['pre_seq']) != 8: print(outputs['pre_seq']) print(len(outputs['pre_seq'])) print(idx) print(data['num_frames']) print(data['search_annos']) return None pre_seq_list.append(outputs['pre_seq']) pred_bbox_corner = bbutils.batch_xywh2corner(pred_bbox) gt_bbox_corner = bbutils.batch_xywh2corner(here_gt_bbox) here_iou = [] for i in range(num_seq): bbox_iou = IoU(pred_bbox_corner[i], gt_bbox_corner[i]) here_iou.append(bbox_iou) iou_list.append(here_iou) x_feat_list.append(x_feat.clone()) search_images_reverse_list = [] search_anno_reverse_list = [] action_tensor_reverse_list = [] iou_reverse_list = [] pre_seq_reverse_list = [] x_feat_reverse_list = [] target_in_search_reverse_list = [] dz_feat_update_reverse_list = [] template_all_reverse_list = [] for idx in range(np.max(num_frames)): real_idx = np.max(num_frames) - 1 - idx here_images = [img[real_idx] for img in images] # S, N here_gt_bbox = np.array([gt[real_idx] for gt in gt_bbox]) here_images = here_images here_gt_bbox = np.concatenate([here_gt_bbox], 0) if idx == 0: outputs_template = self.batch_init(template, template_bbox, here_gt_bbox) results['template_images'] = outputs_template['z_1'] self.template_temp = outputs_template['z_1'].clone() z_all = [outputs_template['z_1'], outputs_template['z_2']] results['z_all'] = z_all self.dz_feat_update = outputs_template['z_2_feat'].clone() else: outputs = self.batch_track(here_images, here_gt_bbox, self.template_temp, self.dz_feat_update, action_mode='half') if outputs == None: return None template_all_reverse_list.append(self.template_temp.clone()) dz_feat_update_reverse_list.append(self.dz_feat_update.clone().to(outputs['dz_feat'])) x_feat = outputs['x_feat'] self.dz_feat_update = outputs['dz_feat'] # print(x_feat.shape) pred_bbox = outputs['pred_bboxes'] search_images_reverse_list.append(outputs['search_images']) target_in_search_reverse_list.append(outputs['target_in_search']) search_anno_reverse_list.append(outputs['gt_in_crop']) # action_tensor_list.append(outputs['selected_indices']) if len(outputs['pre_seq']) != 8: print(outputs['pre_seq']) print(len(outputs['pre_seq'])) print(idx) print(data['num_frames']) print(data['search_annos']) return None pre_seq_reverse_list.append(outputs['pre_seq']) pred_bbox_corner = bbutils.batch_xywh2corner(pred_bbox) gt_bbox_corner = bbutils.batch_xywh2corner(here_gt_bbox) here_iou = [] for i in range(num_seq): bbox_iou = IoU(pred_bbox_corner[i], gt_bbox_corner[i]) here_iou.append(bbox_iou) iou_reverse_list.append(here_iou) x_feat_reverse_list.append(x_feat.clone()) results['x_feat'] = torch.cat([torch.stack(x_feat_list), torch.stack(x_feat_reverse_list)], dim=2) results['search_images'] = torch.cat([torch.stack(search_images_list), torch.stack(search_images_reverse_list)], dim=1) results['template_images_z0'] = torch.cat([torch.stack(template_all_list), torch.stack(template_all_reverse_list)], dim=1) results['dz_feat_update'] = torch.cat([torch.stack(dz_feat_udpate_list), torch.stack(dz_feat_update_reverse_list)], dim=1) results['search_anno'] = torch.cat([torch.stack(search_anno_list), torch.stack(search_anno_reverse_list)], dim=1) results['pre_seq'] = torch.cat([torch.stack(pre_seq_list), torch.stack(pre_seq_reverse_list)], dim=1) results['target_in_search'] = torch.cat([torch.stack(target_in_search_list), torch.stack(target_in_search_reverse_list)], dim=1) iou_tensor = torch.tensor(iou_list, dtype=torch.float) iou_tensor_reverse = torch.tensor(iou_reverse_list, dtype=torch.float) results['baseline_iou'] = torch.cat([iou_tensor[:, :num_seq], iou_tensor_reverse[:, :num_seq]], dim=1) #results['explore_iou'] = iou_tensor[:, num_seq:] #results['action_tensor'] = torch.stack(action_tensor_list) return results def forward_pass(self, data): # currently only support 1 template and 1 search region assert len(data['template_images']) == 1 assert len(data['search_images']) == 1 #print(data['dataset']) template_list = [] for i in range(self.settings.num_template): template_img_i = data['template_images'][i].view(-1, *data['template_images'].shape[2:]) # (batch, 3, 128, 128) # template_att_i = data['template_att'][i].view(-1, *data['template_att'].shape[2:]) # (batch, 128, 128) template_list.append(template_img_i) search_img = data['search_images'][0].view(-1, *data['search_images'].shape[2:]) # (batch, 3, 320, 320) # search_att = data['search_att'][0].view(-1, *data['search_att'].shape[2:]) # (batch, 320, 320) box_mask_z = None ce_keep_rate = None if self.cfg.MODEL.BACKBONE.CE_LOC: box_mask_z = generate_mask_cond(self.cfg, template_list[0].shape[0], template_list[0].device, data['template_anno'][0]) ce_start_epoch = self.cfg.TRAIN.CE_START_EPOCH ce_warm_epoch = self.cfg.TRAIN.CE_WARM_EPOCH ce_keep_rate = adjust_keep_rate(data['epoch'], warmup_epochs=ce_start_epoch, total_epochs=ce_start_epoch + ce_warm_epoch, ITERS_PER_EPOCH=1, base_keep_rate=self.cfg.MODEL.BACKBONE.CE_KEEP_RATIO[0]) if len(template_list) == 1: template_list = template_list[0] gt_bbox = data['search_anno'][-1] begin = self.bins end = self.bins + 1 gt_bbox[:, 2] = gt_bbox[:, 0] + gt_bbox[:, 2] gt_bbox[:, 3] = gt_bbox[:, 1] + gt_bbox[:, 3] gt_bbox = gt_bbox.clamp(min=0.0, max=1.0) data['real_bbox'] = gt_bbox seq_ori = gt_bbox * (self.bins - 1) seq_ori = seq_ori.int().to(search_img) B = seq_ori.shape[0] seq_ori_4_4 = seq_ori[:, 0:3] #seq_input = torch.cat([torch.ones((B, 1)).to(search_img) * begin, seq_ori_4_4], dim=1) # seq_input = torch.cat([torch.ones(1).to(target_bounding_box_label_matrix) * begin, seq_shuffle]) #seq_output = torch.cat([seq_ori], dim=1) seq_input = torch.cat([torch.ones((B, 1)).to(search_img) * begin, seq_ori], dim=1) # seq_input = torch.cat([torch.ones(1).to(target_bounding_box_label_matrix) * begin, seq_shuffle]) seq_output = torch.cat([seq_ori, torch.ones((B, 1)).to(search_img) * end], dim=1) data['seq_input'] = seq_input data['seq_output'] = seq_output out_dict = self.net(template=template_list, search=search_img, ce_template_mask=box_mask_z, ce_keep_rate=ce_keep_rate, return_last_attn=False, seq_input=seq_input) return out_dict def compute_sequence_losses(self, data): num_frames = data['search_images'].shape[0] #template_images = data['template_images'].repeat(num_frames,1,1,1,1) template_images_for = data['template_images_z0'].reshape(-1, *data['template_images_z0'].size()[2:]) dz_feat = data['dz_feat_update'].reshape(-1, *data['dz_feat_update'].size()[2:]) target_in_search = data['target_in_search'].reshape(-1, *data['target_in_search'].size()[2:]) #template_images = template_images.view(-1, *template_images.size()[2:]) search_images = data['search_images'].reshape(-1, *data['search_images'].size()[2:]) search_anno = data['search_anno'].reshape(-1, *data['search_anno'].size()[2:]) # cover_truth = data['cover'].reshape(-1, *data['cover'].size()[2:]) #print(data['pre_seq'].shape) #print(data['x_feat'].shape) pre_seq = data['pre_seq'].reshape(-1, 4*self.pre_num) x_feat = data['x_feat'].reshape(-1, *data['x_feat'].size()[2:]) # print("begin") # print(template_images.shape) # print(template_images_for.shape) # print(target_in_search.shape) # print("end") epoch = data['epoch'] if epoch < 11: self.loss_weight['focal'] = 2 plus = 1 rem_p = 1 else: self.loss_weight['focal'] = 0 plus = 1 rem_p = 0 #print("this is looking for") #print(x_feat.shape) #print(pre_seq.shape) pre_seq = pre_seq.clamp(-0.5 * self.range + 0.5, 0.5 + self.range * 0.5) pre_seq = (pre_seq+(self.range * 0.5 - 0.5)) * (self.bins - 1) #print(pre_seq) outputs = self.net(template_images_for, dz_feat, search_images, seq_input=pre_seq, stage="forward_pass", search_feature=x_feat, target_in_search_img=target_in_search) score = outputs['score'] # cover = outputs['cover'] renew_loss = outputs['renew_loss'] pred_feat = outputs["feat"] # generate labels if self.focal == None: weight = torch.ones(self.bins*self.range + 6) * 1 weight[self.bins * self.range + 4] = 0.1 weight[self.bins * self.range + 3] = 0.1 weight[self.bins * self.range + 2] = 0.1 weight[self.bins*self.range+1] = 0.1 weight[self.bins*self.range] = 0.1 weight.to(pred_feat) self.focal = torch.nn.CrossEntropyLoss(weight=weight, size_average=True).to(pred_feat) # target[:, 2] = target[:, 2] + target[:, 0] # target[:, 3] = target[:, 3] + target[:, 1] # real_target = torch.zeros_like(target) # real_target[: ,0] = target[: ,0] # real_target[:, 1] = target[:, 1] # real_target[:, 2] = target[:, 2] + target[:, 0] # real_target[:, 3] = target[:, 3] + target[:, 1] # target = target.reshape(-1).to(torch.int64) # real_target = real_target.reshape(-1).to(torch.int64) # pred = pred_feat.permute(1, 0, 2)[:, 0:4, :].reshape(-1, self.bins + 2) #print(search_anno) search_anno[:, 2] = search_anno[:, 2] + search_anno[: ,0] search_anno[:, 3] = search_anno[:, 3] + search_anno[: ,1] target = (search_anno / self.cfg.DATA.SEARCH.SIZE + (self.range * 0.5 - 0.5)) * (self.bins - 1) #target[:, 2] = target[:, 2] + target[:, 0] #target[:, 3] = target[:, 3] + target[:, 1] target = target.clamp(min=0.0, max=(self.bins * self.range-0.0001)) #print(target) target_iou = target end_flag = torch.ones((target.shape[0], 1)) * (self.bins * self.range + 1) end_flag = end_flag.to(target) target = torch.cat([target], dim=1) target = target.reshape(-1).to(torch.int64) pred = pred_feat.permute(1, 0, 2).reshape(-1, self.bins*self.range+6) #print(target) varifocal_loss = self.focal(pred, target) pred = pred_feat[0:4, :, 0:self.bins*self.range] target = target_iou[:, 0:4].to(pred_feat) / (self.bins - 1) - (self.range * 0.5 - 0.5) #print(target) #print(target) out = pred.softmax(-1).to(pred) mul = torch.range((-1*self.range * 0.5 + 0.5)+1/800, (self.range * 0.5 + 0.5)-1/800, 2/800).to(pred) #print(mul) ans = out * mul ans = ans.sum(dim=-1) ans = ans.permute(1, 0).to(pred) extra_seq = ans #print(extra_seq) extra_seq = extra_seq.to(pred) # print(extra_seq) cious, iou = SIoU_loss(extra_seq, target, 4) cious = cious.mean() score_real = score score_loss = self.objective['l1'](score_real, iou) #cover_loss = self.objective['l1'](cover, cover_truth) giou_loss = cious l1_loss = self.objective['l1'](extra_seq, target) #self.loss_weight['giou'] = 0 loss_bb = (self.loss_weight['giou'] * giou_loss + self.loss_weight['l1'] * l1_loss + self.loss_weight[ 'focal'] * varifocal_loss) * plus #pred = pred_feat[0:4, :, :].permute(1, 0, 2) #log_softmax_score = F.log_softmax(pred * self.cfg.DATA.TEMP, dim=-1) #log_softmax_score = log_softmax_score.reshape(-1, self.bins+2) #log = torch.argmax(log_softmax_score, dim=-1) #action_tensor = data['action_tensor'][:, :, 0:4].reshape(-1) #bs = log_softmax_score.shape[0] #selected_logprobs = log_softmax_score[range(bs), action_tensor.view(-1)] #print("this is true!") #print(selected_logprobs) #selected_logprobs = selected_logprobs.reshape(-1, 4) #print(selected_logprobs) #selected_logprobs = torch.mean(selected_logprobs, dim=1) #print(selected_logprobs) #selected_logprobs = selected_logprobs.reshape(-1) #cls_loss = - selected_logprobs * data['reward_tensor'].view(-1) #loss_sl = cls_loss.mean() #print(self.net.box_head.encoder.layers[0].z_self_attn.qkv.weight) #total_losses = loss_bb #total_losses = loss_bb + renew_loss * 0.3 + score_loss * 1 total_losses = loss_bb + renew_loss * 0.3 * rem_p + score_loss * 1 * rem_p# + cover_loss * 1 * rem_p #total_losses = loss_bb * 1 + renew_loss * 0.3 + score_loss * 0 mean_iou = iou.detach().mean() status = {"Loss/total": total_losses.item()/2, # "Loss/cover": cover_loss.item()/2, #"Loss/sl": loss_sl.item(), "Loss/score": score_loss.item()/2, "Loss/giou": giou_loss.item()/2, "Loss/l1": l1_loss.item()/2, "Loss/location": varifocal_loss.item()/2, "Loss/renew": renew_loss.item()/2, "IoU": mean_iou.item()/2} return total_losses, status def compute_losses(self, pred_dict, gt_dict, return_status=True): # gt gaussian map bins = self.bins gt_bbox = gt_dict['search_anno'][-1] # (Ns, batch, 4) (x1,y1,w,h) -> (batch, 4) real_bbox = gt_dict['real_bbox'] seq_output = gt_dict['seq_output'] pred_feat = pred_dict["feat"] if self.focal == None: weight = torch.ones(bins + 1) * 1 weight[bins+1] = 0.1 weight[bins] = 0.1 weight.to(pred_feat) self.klloss = torch.nn.KLDivLoss(reduction='none').to(pred_feat) self.focal = torch.nn.CrossEntropyLoss(weight=weight, size_average=True).to(pred_feat) # compute varfifocal loss pred = pred_feat.permute(1, 0, 2).reshape(-1, bins+1) #print(pred) target = seq_output.reshape(-1).to(torch.int64) #print(target) varifocal_loss = self.focal(pred, target) # compute giou and L1 loss beta = 1 pred = pred_feat[0:4, :, 0:bins] * beta target = seq_output[:, 0:4].to(pred_feat) target_box = seq_output[:, 0:4].cpu().numpy() x1,y1,x2,y2 = generate_sa_simdr(target_box) # x1_ = torch.Tensor(x1).to(pred).unsqueeze(1) # y1_ = torch.Tensor(y1).to(pred).unsqueeze(1) # x2_ = torch.Tensor(x2).to(pred).unsqueeze(1) # y2_ = torch.Tensor(y2).to(pred).unsqueeze(1) # KL_target = torch.concat((x1_, y1_, x2_, y2_), dim=1) out = pred.softmax(-1).to(pred) mul = torch.arange(0, bins, 1).to(pred) ans = out * mul ans = ans.sum(dim=-1) ans = ans.permute(1, 0).to(pred) target = target / (bins - 1) extra_seq = ans / (bins - 1) extra_seq = extra_seq.to(pred) #cious, iou = ciou(extra_seq, target) cious, iou = SIoU_loss(extra_seq, target, 4) cious = cious.mean() #cious, iou = self.objective['giou'](extra_seq, target) giou_loss = cious #giou_loss = 1 - cious #giou_loss, iou = self.objective['giou'](extra_seq, target) l1_loss = self.objective['l1'](extra_seq, target) #print(giou_loss) #print(l1_loss) #print(varifocal_loss) # gt_gaussian_maps = generate_heatmap(gt_dict['search_anno'], self.cfg.DATA.SEARCH.SIZE, self.cfg.MODEL.BACKBONE.STRIDE) # gt_gaussian_maps = gt_gaussian_maps[-1].unsqueeze(1) # # # # Get boxes # pred_boxes = pred_dict['pred_boxes'] # if torch.isnan(pred_boxes).any(): # raise ValueError("Network outputs is NAN! Stop Training") # num_queries = pred_boxes.size(1) # pred_boxes_vec = box_cxcywh_to_xyxy(pred_boxes).view(-1, 4) # (B,N,4) --> (BN,4) (x1,y1,x2,y2) # gt_boxes_vec = box_xywh_to_xyxy(gt_bbox)[:, None, :].repeat((1, num_queries, 1)).view(-1, 4).clamp(min=0.0, # max=1.0) # (B,4) --> (B,1,4) --> (B,N,4) # compute giou and iou # try: # giou_loss, iou = self.objective['giou'](pred_boxes_vec, gt_boxes_vec) # (BN,4) (BN,4) # except: # giou_loss, iou = torch.tensor(0.0).cuda(), torch.tensor(0.0).cuda() # # compute l1 loss # l1_loss = self.objective['l1'](pred_boxes_vec, gt_boxes_vec) # (BN,4) (BN,4) # # compute location loss # if 'score_map' in pred_dict: # location_loss = self.objective['focal'](pred_dict['score_map'], gt_gaussian_maps) # else: # location_loss = torch.tensor(0.0, device=l1_loss.device) # weighted sum loss = self.loss_weight['giou'] * giou_loss + self.loss_weight['l1'] * l1_loss + self.loss_weight['focal'] * varifocal_loss if return_status: # status for log mean_iou = iou.detach().mean() status = {"Loss/total": loss.item(), "Loss/giou": giou_loss.item(), "Loss/l1": l1_loss.item(), "Loss/location": varifocal_loss.item(), "IoU": mean_iou.item()} return loss, status else: return loss ================================================ FILE: artrackv2_mindspore/lib/train/admin/__init__.py ================================================ from .environment import env_settings, create_default_local_file_ITP_train from .stats import AverageMeter, StatValue #from .tensorboard import TensorboardWriter ================================================ FILE: artrackv2_mindspore/lib/train/admin/environment.py ================================================ import importlib import os from collections import OrderedDict def create_default_local_file(): path = os.path.join(os.path.dirname(__file__), 'local.py') empty_str = '\'\'' default_settings = OrderedDict({ 'workspace_dir': empty_str, 'tensorboard_dir': 'self.workspace_dir + \'/tensorboard/\'', 'pretrained_networks': 'self.workspace_dir + \'/pretrained_networks/\'', 'lasot_dir': empty_str, 'got10k_dir': empty_str, 'trackingnet_dir': empty_str, 'coco_dir': empty_str, 'lvis_dir': empty_str, 'sbd_dir': empty_str, 'imagenet_dir': empty_str, 'imagenetdet_dir': empty_str, 'ecssd_dir': empty_str, 'hkuis_dir': empty_str, 'msra10k_dir': empty_str, 'davis_dir': empty_str, 'youtubevos_dir': empty_str}) comment = {'workspace_dir': 'Base directory for saving network checkpoints.', 'tensorboard_dir': 'Directory for tensorboard files.'} with open(path, 'w') as f: f.write('class EnvironmentSettings:\n') f.write(' def __init__(self):\n') for attr, attr_val in default_settings.items(): comment_str = None if attr in comment: comment_str = comment[attr] if comment_str is None: f.write(' self.{} = {}\n'.format(attr, attr_val)) else: f.write(' self.{} = {} # {}\n'.format(attr, attr_val, comment_str)) def create_default_local_file_ITP_train(workspace_dir, data_dir): path = os.path.join(os.path.dirname(__file__), 'local.py') empty_str = '\'\'' default_settings = OrderedDict({ 'workspace_dir': workspace_dir, 'tensorboard_dir': os.path.join(workspace_dir, 'tensorboard'), # Directory for tensorboard files. 'pretrained_networks': os.path.join(workspace_dir, 'pretrained_networks'), 'lasot_dir': os.path.join(data_dir, 'lasot'), 'got10k_dir': os.path.join(data_dir, 'got10k/train'), 'got10k_val_dir': os.path.join(data_dir, 'got10k/val'), 'lasot_lmdb_dir': os.path.join(data_dir, 'lasot_lmdb'), 'got10k_lmdb_dir': os.path.join(data_dir, 'got10k_lmdb'), 'trackingnet_dir': os.path.join(data_dir, 'trackingnet'), 'trackingnet_lmdb_dir': os.path.join(data_dir, 'trackingnet_lmdb'), 'coco_dir': os.path.join(data_dir, 'coco'), 'coco_lmdb_dir': os.path.join(data_dir, 'coco_lmdb'), 'lvis_dir': empty_str, 'sbd_dir': empty_str, 'imagenet_dir': os.path.join(data_dir, 'vid'), 'imagenet_lmdb_dir': os.path.join(data_dir, 'vid_lmdb'), 'imagenetdet_dir': empty_str, 'ecssd_dir': empty_str, 'hkuis_dir': empty_str, 'msra10k_dir': empty_str, 'davis_dir': empty_str, 'youtubevos_dir': empty_str}) comment = {'workspace_dir': 'Base directory for saving network checkpoints.', 'tensorboard_dir': 'Directory for tensorboard files.'} with open(path, 'w') as f: f.write('class EnvironmentSettings:\n') f.write(' def __init__(self):\n') for attr, attr_val in default_settings.items(): comment_str = None if attr in comment: comment_str = comment[attr] if comment_str is None: if attr_val == empty_str: f.write(' self.{} = {}\n'.format(attr, attr_val)) else: f.write(' self.{} = \'{}\'\n'.format(attr, attr_val)) else: f.write(' self.{} = \'{}\' # {}\n'.format(attr, attr_val, comment_str)) def env_settings(): env_module_name = 'lib.train.admin.local' try: env_module = importlib.import_module(env_module_name) return env_module.EnvironmentSettings() except: env_file = os.path.join(os.path.dirname(__file__), 'local.py') create_default_local_file() raise RuntimeError('YOU HAVE NOT SETUP YOUR local.py!!!\n Go to "{}" and set all the paths you need. Then try to run again.'.format(env_file)) ================================================ FILE: artrackv2_mindspore/lib/train/admin/local.py ================================================ class EnvironmentSettings: def __init__(self): self.workspace_dir = '/home/baiyifan/code/prev_for_2stage' # Base directory for saving network checkpoints. self.tensorboard_dir = '/home/baiyifan/code/detrack/tensorboard' # Directory for tensorboard files. self.pretrained_networks = '/home/baiyifan/code/OSTrack/pretrained_networks' self.lasot_dir = '/home/baiyifan/LaSOT/LaSOTBenchmark' self.got10k_dir = '/home/baiyifan/GOT-10k/train' self.got10k_val_dir = '/home/baiyifan/GOT-10k/val' self.lasot_lmdb_dir = '/home/baiyifan/LaSOT/LaSOTBenchmark' self.got10k_lmdb_dir = '' self.trackingnet_dir = '/ssddata/TrackingNet/all_zip' self.trackingnet_lmdb_dir = '/ssddata/TrackingNet/all_zip' self.coco_dir = '/home/baiyifan/coco' self.coco_lmdb_dir = '' self.lvis_dir = '' self.sbd_dir = '' self.imagenet_dir = '/home/baiyifan/code/OSTrack/data/vid' self.imagenet_lmdb_dir = '/home/baiyifan/code/OSTrack/data/vid_lmdb' self.imagenetdet_dir = '' self.ecssd_dir = '' self.hkuis_dir = '' self.msra10k_dir = '' self.davis_dir = '' self.youtubevos_dir = '' ================================================ FILE: artrackv2_mindspore/lib/train/admin/settings.py ================================================ from lib.train.admin.environment import env_settings class Settings: """ Training settings, e.g. the paths to datasets and networks.""" def __init__(self): self.set_default() def set_default(self): self.env = env_settings() self.use_gpu = True ================================================ FILE: artrackv2_mindspore/lib/train/admin/stats.py ================================================ class StatValue: def __init__(self): self.clear() def reset(self): self.val = 0 def clear(self): self.reset() self.history = [] def update(self, val): self.val = val self.history.append(self.val) class AverageMeter(object): """Computes and stores the average and current value""" def __init__(self): self.clear() self.has_new_data = False def reset(self): self.avg = 0 self.val = 0 self.sum = 0 self.count = 0 def clear(self): self.reset() self.history = [] def update(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count def new_epoch(self): if self.count > 0: self.history.append(self.avg) self.reset() self.has_new_data = True else: self.has_new_data = False def topk_accuracy(output, target, topk=(1,)): """Computes the precision@k for the specified values of k""" single_input = not isinstance(topk, (tuple, list)) if single_input: topk = (topk,) maxk = max(topk) batch_size = target.size(0) _, pred = output.topk(maxk, 1, True, True) pred = pred.t() correct = pred.eq(target.view(1, -1).expand_as(pred)) res = [] for k in topk: correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)[0] res.append(correct_k * 100.0 / batch_size) if single_input: return res[0] return res ================================================ FILE: artrackv2_mindspore/lib/train/admin/tensorboard.py ================================================ #import os #from collections import OrderedDict #try: # from torch.utils.tensorboard import SummaryWriter #except: # print('WARNING: You are using tensorboardX instead sis you have a too old pytorch version.') # from tensorboardX import SummaryWriter #class TensorboardWriter: # def __init__(self, directory, loader_names): # self.directory = directory # self.writer = OrderedDict({name: SummaryWriter(os.path.join(self.directory, name)) for name in loader_names}) # def write_info(self, script_name, description): # tb_info_writer = SummaryWriter(os.path.join(self.directory, 'info')) # tb_info_writer.add_text('Script_name', script_name) # tb_info_writer.add_text('Description', description) # tb_info_writer.close() # def write_epoch(self, stats: OrderedDict, epoch: int, ind=-1): # for loader_name, loader_stats in stats.items(): # if loader_stats is None: # continue # for var_name, val in loader_stats.items(): # if hasattr(val, 'history') and getattr(val, 'has_new_data', True): # self.writer[loader_name].add_scalar(var_name, val.history[ind], epoch) ================================================ FILE: artrackv2_mindspore/lib/train/base_functions.py ================================================ import torch from torch.utils.data.distributed import DistributedSampler # datasets related from lib.train.dataset import Lasot, Got10k, MSCOCOSeq, ImagenetVID, TrackingNet from lib.train.dataset import Lasot_lmdb, Got10k_lmdb, MSCOCOSeq_lmdb, ImagenetVID_lmdb, TrackingNet_lmdb from lib.train.data import sampler, opencv_loader, processing, LTRLoader import lib.train.data.transforms as tfm from lib.utils.misc import is_main_process def update_settings(settings, cfg): settings.print_interval = cfg.TRAIN.PRINT_INTERVAL settings.search_area_factor = {'template': cfg.DATA.TEMPLATE.FACTOR, 'search': cfg.DATA.SEARCH.FACTOR} settings.output_sz = {'template': cfg.DATA.TEMPLATE.SIZE, 'search': cfg.DATA.SEARCH.SIZE} settings.center_jitter_factor = {'template': cfg.DATA.TEMPLATE.CENTER_JITTER, 'search': cfg.DATA.SEARCH.CENTER_JITTER} settings.scale_jitter_factor = {'template': cfg.DATA.TEMPLATE.SCALE_JITTER, 'search': cfg.DATA.SEARCH.SCALE_JITTER} settings.grad_clip_norm = cfg.TRAIN.GRAD_CLIP_NORM settings.print_stats = None settings.batchsize = cfg.TRAIN.BATCH_SIZE settings.scheduler_type = cfg.TRAIN.SCHEDULER.TYPE def names2datasets(name_list: list, settings, image_loader): assert isinstance(name_list, list) datasets = [] #settings.use_lmdb = True for name in name_list: assert name in ["LASOT", "GOT10K_vottrain", "GOT10K_votval", "GOT10K_train_full", "GOT10K_official_val", "COCO17", "VID", "TRACKINGNET"] if name == "LASOT": if settings.use_lmdb: print("Building lasot dataset from lmdb") datasets.append(Lasot_lmdb(settings.env.lasot_lmdb_dir, split='train', image_loader=image_loader)) else: datasets.append(Lasot(settings.env.lasot_dir, split='train', image_loader=image_loader)) if name == "GOT10K_vottrain": if settings.use_lmdb: print("Building got10k from lmdb") datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='vottrain', image_loader=image_loader)) else: datasets.append(Got10k(settings.env.got10k_dir, split='vottrain', image_loader=image_loader)) if name == "GOT10K_train_full": if settings.use_lmdb: print("Building got10k_train_full from lmdb") datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='train_full', image_loader=image_loader)) else: datasets.append(Got10k(settings.env.got10k_dir, split='train_full', image_loader=image_loader)) if name == "GOT10K_votval": if settings.use_lmdb: print("Building got10k from lmdb") datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='votval', image_loader=image_loader)) else: datasets.append(Got10k(settings.env.got10k_dir, split='votval', image_loader=image_loader)) if name == "GOT10K_official_val": if settings.use_lmdb: raise ValueError("Not implement") else: datasets.append(Got10k(settings.env.got10k_val_dir, split=None, image_loader=image_loader)) if name == "COCO17": if settings.use_lmdb: print("Building COCO2017 from lmdb") datasets.append(MSCOCOSeq_lmdb(settings.env.coco_lmdb_dir, version="2017", image_loader=image_loader)) else: datasets.append(MSCOCOSeq(settings.env.coco_dir, version="2017", image_loader=image_loader)) if name == "VID": if settings.use_lmdb: print("Building VID from lmdb") datasets.append(ImagenetVID_lmdb(settings.env.imagenet_lmdb_dir, image_loader=image_loader)) else: datasets.append(ImagenetVID(settings.env.imagenet_dir, image_loader=image_loader)) if name == "TRACKINGNET": if settings.use_lmdb: print("Building TrackingNet from lmdb") datasets.append(TrackingNet_lmdb(settings.env.trackingnet_lmdb_dir, image_loader=image_loader)) else: # raise ValueError("NOW WE CAN ONLY USE TRACKINGNET FROM LMDB") datasets.append(TrackingNet(settings.env.trackingnet_dir, image_loader=image_loader)) return datasets def build_dataloaders(cfg, settings): # Data transform transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05), tfm.RandomHorizontalFlip(probability=0.5)) transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2), tfm.RandomHorizontalFlip_Norm(probability=0.5), tfm.Normalize(mean=cfg.DATA.MEAN, std=cfg.DATA.STD)) transform_val = tfm.Transform(tfm.ToTensor(), tfm.Normalize(mean=cfg.DATA.MEAN, std=cfg.DATA.STD)) # The tracking pairs processing module output_sz = settings.output_sz search_area_factor = settings.search_area_factor data_processing_train = processing.STARKProcessing(search_area_factor=search_area_factor, output_sz=output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', transform=transform_train, joint_transform=transform_joint, settings=settings) data_processing_val = processing.STARKProcessing(search_area_factor=search_area_factor, output_sz=output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', transform=transform_val, joint_transform=transform_joint, settings=settings) # Train sampler and loader settings.num_template = getattr(cfg.DATA.TEMPLATE, "NUMBER", 1) settings.num_search = getattr(cfg.DATA.SEARCH, "NUMBER", 1) sampler_mode = getattr(cfg.DATA, "SAMPLER_MODE", "causal") train_cls = getattr(cfg.TRAIN, "TRAIN_CLS", False) print("sampler_mode", sampler_mode) dataset_train = sampler.TrackingSampler(datasets=names2datasets(cfg.DATA.TRAIN.DATASETS_NAME, settings, opencv_loader), p_datasets=cfg.DATA.TRAIN.DATASETS_RATIO, samples_per_epoch=cfg.DATA.TRAIN.SAMPLE_PER_EPOCH, max_gap=cfg.DATA.MAX_SAMPLE_INTERVAL, num_search_frames=settings.num_search, num_template_frames=settings.num_template, processing=data_processing_train, frame_sample_mode=sampler_mode, train_cls=train_cls) train_sampler = DistributedSampler(dataset_train) if settings.local_rank != -1 else None shuffle = False if settings.local_rank != -1 else True loader_train = LTRLoader('train', dataset_train, training=True, batch_size=cfg.TRAIN.BATCH_SIZE, shuffle=shuffle, num_workers=cfg.TRAIN.NUM_WORKER, drop_last=True, stack_dim=1, sampler=train_sampler) # Validation samplers and loaders dataset_val = sampler.TrackingSampler(datasets=names2datasets(cfg.DATA.VAL.DATASETS_NAME, settings, opencv_loader), p_datasets=cfg.DATA.VAL.DATASETS_RATIO, samples_per_epoch=cfg.DATA.VAL.SAMPLE_PER_EPOCH, max_gap=cfg.DATA.MAX_SAMPLE_INTERVAL, num_search_frames=settings.num_search, num_template_frames=settings.num_template, processing=data_processing_val, frame_sample_mode=sampler_mode, train_cls=train_cls) val_sampler = DistributedSampler(dataset_val) if settings.local_rank != -1 else None loader_val = LTRLoader('val', dataset_val, training=False, batch_size=cfg.TRAIN.BATCH_SIZE, num_workers=cfg.TRAIN.NUM_WORKER, drop_last=True, stack_dim=1, sampler=val_sampler, epoch_interval=cfg.TRAIN.VAL_EPOCH_INTERVAL) return loader_train, loader_val def get_optimizer_scheduler(net, cfg): train_cls = getattr(cfg.TRAIN, "TRAIN_CLS", False) if train_cls: print("Only training classification head. Learnable parameters are shown below.") param_dicts = [ {"params": [p for n, p in net.named_parameters() if "cls" in n and p.requires_grad]} ] for n, p in net.named_parameters(): if "cls" not in n: p.requires_grad = False else: print(n) else: param_dicts = [ {"params": [p for n, p in net.named_parameters() if "backbone" not in n and p.requires_grad]}, { "params": [p for n, p in net.named_parameters() if "backbone" in n and p.requires_grad], "lr": cfg.TRAIN.LR * cfg.TRAIN.BACKBONE_MULTIPLIER, }, ] if is_main_process(): print("Learnable parameters are shown below.") for n, p in net.named_parameters(): if p.requires_grad: print(n) if cfg.TRAIN.OPTIMIZER == "ADAMW": optimizer = torch.optim.AdamW(param_dicts, lr=cfg.TRAIN.LR, weight_decay=cfg.TRAIN.WEIGHT_DECAY) else: raise ValueError("Unsupported Optimizer") if cfg.TRAIN.SCHEDULER.TYPE == 'step': lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, cfg.TRAIN.LR_DROP_EPOCH) elif cfg.TRAIN.SCHEDULER.TYPE == "Mstep": lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=cfg.TRAIN.SCHEDULER.MILESTONES, gamma=cfg.TRAIN.SCHEDULER.GAMMA) else: raise ValueError("Unsupported scheduler") return optimizer, lr_scheduler ================================================ FILE: artrackv2_mindspore/lib/train/data/__init__.py ================================================ # from .loader import LTRLoader from .image_loader import jpeg4py_loader, opencv_loader, jpeg4py_loader_w_failsafe, default_image_loader ================================================ FILE: artrackv2_mindspore/lib/train/data/bounding_box_utils.py ================================================ import torch import numpy as np def batch_center2corner(boxes): xmin = boxes[:, 0] - boxes[:, 2] * 0.5 ymin = boxes[:, 1] - boxes[:, 3] * 0.5 xmax = boxes[:, 0] + boxes[:, 2] * 0.5 ymax = boxes[:, 1] + boxes[:, 3] * 0.5 if isinstance(boxes, np.ndarray): return np.stack([xmin, ymin, xmax, ymax], 1) else: return torch.stack([xmin, ymin, xmax, ymax], 1) def batch_corner2center(boxes): cx = (boxes[:, 0] + boxes[:, 2]) * 0.5 cy = (boxes[:, 1] + boxes[:, 3]) * 0.5 w = (boxes[:, 2] - boxes[:, 0]) h = (boxes[:, 3] - boxes[:, 1]) if isinstance(boxes, np.ndarray): return np.stack([cx, cy, w, h], 1) else: return torch.stack([cx, cy, w, h], 1) def batch_xywh2center(boxes): cx = boxes[:, 0] + (boxes[:, 2] - 1) / 2 cy = boxes[:, 1] + (boxes[:, 3] - 1) / 2 w = boxes[:, 2] h = boxes[:, 3] if isinstance(boxes, np.ndarray): return np.stack([cx, cy, w, h], 1) else: return torch.stack([cx, cy, w, h], 1) def batch_xywh2center2(boxes): cx = boxes[:, 0] + boxes[:, 2] / 2 cy = boxes[:, 1] + boxes[:, 3] / 2 w = boxes[:, 2] h = boxes[:, 3] if isinstance(boxes, np.ndarray): return np.stack([cx, cy, w, h], 1) else: return torch.stack([cx, cy, w, h], 1) def batch_xywh2corner(boxes): xmin = boxes[:, 0] ymin = boxes[:, 1] xmax = boxes[:, 0] + boxes[:, 2] ymax = boxes[:, 1] + boxes[:, 3] if isinstance(boxes, np.ndarray): return np.stack([xmin, ymin, xmax, ymax], 1) else: return torch.stack([xmin, ymin, xmax, ymax], 1) def rect_to_rel(bb, sz_norm=None): """Convert standard rectangular parametrization of the bounding box [x, y, w, h] to relative parametrization [cx/sw, cy/sh, log(w), log(h)], where [cx, cy] is the center coordinate. args: bb - N x 4 tensor of boxes. sz_norm - [N] x 2 tensor of value of [sw, sh] (optional). sw=w and sh=h if not given. """ c = bb[...,:2] + 0.5 * bb[...,2:] if sz_norm is None: c_rel = c / bb[...,2:] else: c_rel = c / sz_norm sz_rel = torch.log(bb[...,2:]) return torch.cat((c_rel, sz_rel), dim=-1) def rel_to_rect(bb, sz_norm=None): """Inverts the effect of rect_to_rel. See above.""" sz = torch.exp(bb[...,2:]) if sz_norm is None: c = bb[...,:2] * sz else: c = bb[...,:2] * sz_norm tl = c - 0.5 * sz return torch.cat((tl, sz), dim=-1) def masks_to_bboxes(mask, fmt='c'): """ Convert a mask tensor to one or more bounding boxes. Note: This function is a bit new, make sure it does what it says. /Andreas :param mask: Tensor of masks, shape = (..., H, W) :param fmt: bbox layout. 'c' => "center + size" or (x_center, y_center, width, height) 't' => "top left + size" or (x_left, y_top, width, height) 'v' => "vertices" or (x_left, y_top, x_right, y_bottom) :return: tensor containing a batch of bounding boxes, shape = (..., 4) """ batch_shape = mask.shape[:-2] mask = mask.reshape((-1, *mask.shape[-2:])) bboxes = [] for m in mask: mx = m.sum(dim=-2).nonzero() my = m.sum(dim=-1).nonzero() bb = [mx.min(), my.min(), mx.max(), my.max()] if (len(mx) > 0 and len(my) > 0) else [0, 0, 0, 0] bboxes.append(bb) bboxes = torch.tensor(bboxes, dtype=torch.float32, device=mask.device) bboxes = bboxes.reshape(batch_shape + (4,)) if fmt == 'v': return bboxes x1 = bboxes[..., :2] s = bboxes[..., 2:] - x1 + 1 if fmt == 'c': return torch.cat((x1 + 0.5 * s, s), dim=-1) elif fmt == 't': return torch.cat((x1, s), dim=-1) raise ValueError("Undefined bounding box layout '%s'" % fmt) def masks_to_bboxes_multi(mask, ids, fmt='c'): assert mask.dim() == 2 bboxes = [] for id in ids: mx = (mask == id).sum(dim=-2).nonzero() my = (mask == id).float().sum(dim=-1).nonzero() bb = [mx.min(), my.min(), mx.max(), my.max()] if (len(mx) > 0 and len(my) > 0) else [0, 0, 0, 0] bb = torch.tensor(bb, dtype=torch.float32, device=mask.device) x1 = bb[:2] s = bb[2:] - x1 + 1 if fmt == 'v': pass elif fmt == 'c': bb = torch.cat((x1 + 0.5 * s, s), dim=-1) elif fmt == 't': bb = torch.cat((x1, s), dim=-1) else: raise ValueError("Undefined bounding box layout '%s'" % fmt) bboxes.append(bb) return bboxes ================================================ FILE: artrackv2_mindspore/lib/train/data/image_loader.py ================================================ import jpeg4py import cv2 as cv from PIL import Image import numpy as np davis_palette = np.repeat(np.expand_dims(np.arange(0,256), 1), 3, 1).astype(np.uint8) davis_palette[:22, :] = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0], [191, 0, 0], [64, 128, 0], [191, 128, 0], [64, 0, 128], [191, 0, 128], [64, 128, 128], [191, 128, 128], [0, 64, 0], [128, 64, 0], [0, 191, 0], [128, 191, 0], [0, 64, 128], [128, 64, 128]] def default_image_loader(path): """The default image loader, reads the image from the given path. It first tries to use the jpeg4py_loader, but reverts to the opencv_loader if the former is not available.""" if default_image_loader.use_jpeg4py is None: # Try using jpeg4py im = jpeg4py_loader(path) if im is None: default_image_loader.use_jpeg4py = False print('Using opencv_loader instead.') else: default_image_loader.use_jpeg4py = True return im if default_image_loader.use_jpeg4py: return jpeg4py_loader(path) return opencv_loader(path) default_image_loader.use_jpeg4py = None def jpeg4py_loader(path): """ Image reading using jpeg4py https://github.com/ajkxyz/jpeg4py""" try: return jpeg4py.JPEG(path).decode() except Exception as e: print('ERROR: Could not read image "{}"'.format(path)) print(e) return None def opencv_loader(path): """ Read image using opencv's imread function and returns it in rgb format""" try: im = cv.imread(path, cv.IMREAD_COLOR) # convert to rgb and return return cv.cvtColor(im, cv.COLOR_BGR2RGB) except Exception as e: print('ERROR: Could not read image "{}"'.format(path)) print(e) return None def jpeg4py_loader_w_failsafe(path): """ Image reading using jpeg4py https://github.com/ajkxyz/jpeg4py""" try: return jpeg4py.JPEG(path).decode() except: try: im = cv.imread(path, cv.IMREAD_COLOR) # convert to rgb and return return cv.cvtColor(im, cv.COLOR_BGR2RGB) except Exception as e: print('ERROR: Could not read image "{}"'.format(path)) print(e) return None def opencv_seg_loader(path): """ Read segmentation annotation using opencv's imread function""" try: return cv.imread(path) except Exception as e: print('ERROR: Could not read image "{}"'.format(path)) print(e) return None def imread_indexed(filename): """ Load indexed image with given filename. Used to read segmentation annotations.""" im = Image.open(filename) annotation = np.atleast_3d(im)[...,0] return annotation def imwrite_indexed(filename, array, color_palette=None): """ Save indexed image as png. Used to save segmentation annotation.""" if color_palette is None: color_palette = davis_palette if np.atleast_3d(array).shape[2] != 1: raise Exception("Saving indexed PNGs requires 2D array.") im = Image.fromarray(array) im.putpalette(color_palette.ravel()) im.save(filename, format='PNG') ================================================ FILE: artrackv2_mindspore/lib/train/data/loader.py ================================================ import torch import torch.utils.data.dataloader import importlib import collections # from torch import string_classes from lib.utils import TensorDict, TensorList string_classes = str if float(torch.__version__[:3]) >= 1.9 or len('.'.join((torch.__version__).split('.')[0:2])) > 3: int_classes = int else: # from torch._six import int_classes int_classes = int # 原代码是没有int_classes = int，为 from torch._six import int_classes import warnings warnings.filterwarnings("ignore") def _check_use_shared_memory(): if hasattr(torch.utils.data.dataloader, '_use_shared_memory'): return getattr(torch.utils.data.dataloader, '_use_shared_memory') collate_lib = importlib.import_module('torch.utils.data._utils.collate') if hasattr(collate_lib, '_use_shared_memory'): return getattr(collate_lib, '_use_shared_memory') return torch.utils.data.get_worker_info() is not None def ltr_collate(batch): """Puts each data field into a tensor with outer dimension batch size""" error_msg = "batch must contain tensors, numbers, dicts or lists; found {}" elem_type = type(batch[0]) if isinstance(batch[0], torch.Tensor): out = None if _check_use_shared_memory(): # If we're in a background process, concatenate directly into a # shared memory tensor to avoid an extra copy numel = sum([x.numel() for x in batch]) storage = batch[0].storage()._new_shared(numel) out = batch[0].new(storage) return torch.stack(batch, 0, out=out) # if batch[0].dim() < 4: # return torch.stack(batch, 0, out=out) # return torch.cat(batch, 0, out=out) elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ and elem_type.__name__ != 'string_': elem = batch[0] if elem_type.__name__ == 'ndarray': # array of string classes and object if torch.utils.data.dataloader.re.search('[SaUO]', elem.dtype.str) is not None: raise TypeError(error_msg.format(elem.dtype)) return torch.stack([torch.from_numpy(b) for b in batch], 0) if elem.shape == (): # scalars py_type = float if elem.dtype.name.startswith('float') else int return torch.utils.data.dataloader.numpy_type_map[elem.dtype.name](list(map(py_type, batch))) elif isinstance(batch[0], int_classes): return torch.LongTensor(batch) elif isinstance(batch[0], float): return torch.DoubleTensor(batch) elif isinstance(batch[0], string_classes): return batch elif isinstance(batch[0], TensorDict): return TensorDict({key: ltr_collate([d[key] for d in batch]) for key in batch[0]}) elif isinstance(batch[0], collections.Mapping): return {key: ltr_collate([d[key] for d in batch]) for key in batch[0]} elif isinstance(batch[0], TensorList): transposed = zip(*batch) return TensorList([ltr_collate(samples) for samples in transposed]) elif isinstance(batch[0], collections.Sequence): transposed = zip(*batch) return [ltr_collate(samples) for samples in transposed] elif batch[0] is None: return batch raise TypeError((error_msg.format(type(batch[0])))) def ltr_collate_stack1(batch): """Puts each data field into a tensor. The tensors are stacked at dim=1 to form the batch""" error_msg = "batch must contain tensors, numbers, dicts or lists; found {}" elem_type = type(batch[0]) if isinstance(batch[0], torch.Tensor): out = None if _check_use_shared_memory(): # If we're in a background process, concatenate directly into a # shared memory tensor to avoid an extra copy numel = sum([x.numel() for x in batch]) storage = batch[0].storage()._new_shared(numel) out = batch[0].new(storage) return torch.stack(batch, 1, out=out) # if batch[0].dim() < 4: # return torch.stack(batch, 0, out=out) # return torch.cat(batch, 0, out=out) elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ and elem_type.__name__ != 'string_': elem = batch[0] if elem_type.__name__ == 'ndarray': # array of string classes and object if torch.utils.data.dataloader.re.search('[SaUO]', elem.dtype.str) is not None: raise TypeError(error_msg.format(elem.dtype)) return torch.stack([torch.from_numpy(b) for b in batch], 1) if elem.shape == (): # scalars py_type = float if elem.dtype.name.startswith('float') else int return torch.utils.data.dataloader.numpy_type_map[elem.dtype.name](list(map(py_type, batch))) elif isinstance(batch[0], int_classes): return torch.LongTensor(batch) elif isinstance(batch[0], float): return torch.DoubleTensor(batch) elif isinstance(batch[0], string_classes): return batch elif isinstance(batch[0], TensorDict): return TensorDict({key: ltr_collate_stack1([d[key] for d in batch]) for key in batch[0]}) elif isinstance(batch[0], collections.Mapping): return {key: ltr_collate_stack1([d[key] for d in batch]) for key in batch[0]} elif isinstance(batch[0], TensorList): transposed = zip(*batch) return TensorList([ltr_collate_stack1(samples) for samples in transposed]) elif isinstance(batch[0], collections.Sequence): transposed = zip(*batch) return [ltr_collate_stack1(samples) for samples in transposed] elif batch[0] is None: return batch raise TypeError((error_msg.format(type(batch[0])))) class LTRLoader(torch.utils.data.dataloader.DataLoader): """ Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset. Note: The only difference with default pytorch DataLoader is that an additional option stack_dim is available to select along which dimension the data should be stacked to form a batch. Arguments: dataset (Dataset): dataset from which to load the data. batch_size (int, optional): how many samples per batch to load (default: 1). shuffle (bool, optional): set to ``True`` to have the data reshuffled at every epoch (default: False). sampler (Sampler, optional): defines the strategy to draw samples from the dataset. If specified, ``shuffle`` must be False. batch_sampler (Sampler, optional): like sampler, but returns a batch of indices at a time. Mutually exclusive with batch_size, shuffle, sampler, and drop_last. num_workers (int, optional): how many subprocesses to use for data loading. 0 means that the data will be loaded in the main process. (default: 0) collate_fn (callable, optional): merges a list of samples to form a mini-batch. stack_dim (int): Dimension along which to stack to form the batch. (default: 0) pin_memory (bool, optional): If ``True``, the data loader will copy tensors into CUDA pinned memory before returning them. drop_last (bool, optional): set to ``True`` to drop the last incomplete batch, if the dataset size is not divisible by the batch size. If ``False`` and the size of dataset is not divisible by the batch size, then the last batch will be smaller. (default: False) timeout (numeric, optional): if positive, the timeout value for collecting a batch from workers. Should always be non-negative. (default: 0) worker_init_fn (callable, optional): If not None, this will be called on each worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as input, after seeding and before data loading. (default: None) .. note:: By default, each worker will have its PyTorch seed set to ``base_seed + worker_id``, where ``base_seed`` is a long generated by main process using its RNG. However, seeds for other libraries may be duplicated upon initializing workers (w.g., NumPy), causing each worker to return identical random numbers. (See :ref:`dataloader-workers-random-seed` section in FAQ.) You may use ``torch.initial_seed()`` to access the PyTorch seed for each worker in :attr:`worker_init_fn`, and use it to set other seeds before data loading. .. warning:: If ``spawn`` start method is used, :attr:`worker_init_fn` cannot be an unpicklable object, e.g., a lambda function. """ __initialized = False def __init__(self, name, dataset, training=True, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, epoch_interval=1, collate_fn=None, stack_dim=0, pin_memory=False, drop_last=True, timeout=0, worker_init_fn=None): print("pin_memory is", pin_memory) if collate_fn is None: if stack_dim == 0: collate_fn = ltr_collate elif stack_dim == 1: collate_fn = ltr_collate_stack1 else: raise ValueError('Stack dim no supported. Must be 0 or 1.') super(LTRLoader, self).__init__(dataset, batch_size, shuffle, sampler, batch_sampler, num_workers, collate_fn, pin_memory, drop_last, timeout, worker_init_fn) self.name = name self.training = training self.epoch_interval = epoch_interval self.stack_dim = stack_dim ================================================ FILE: artrackv2_mindspore/lib/train/data/processing.py ================================================ import torch import torchvision.transforms as transforms from lib.utils import TensorDict import lib.train.data.processing_utils as prutils import torch.nn.functional as F def stack_tensors(x): if isinstance(x, (list, tuple)) and isinstance(x[0], torch.Tensor): return torch.stack(x) return x class BaseProcessing: """ Base class for Processing. Processing class is used to process the data returned by a dataset, before passing it through the network. For example, it can be used to crop a search region around the object, apply various data augmentations, etc.""" def __init__(self, transform=transforms.ToTensor(), template_transform=None, search_transform=None, joint_transform=None): """ args: transform - The set of transformations to be applied on the images. Used only if template_transform or search_transform is None. template_transform - The set of transformations to be applied on the template images. If None, the 'transform' argument is used instead. search_transform - The set of transformations to be applied on the search images. If None, the 'transform' argument is used instead. joint_transform - The set of transformations to be applied 'jointly' on the template and search images. For example, it can be used to convert both template and search images to grayscale. """ self.transform = {'template': transform if template_transform is None else template_transform, 'search': transform if search_transform is None else search_transform, 'joint': joint_transform} def __call__(self, data: TensorDict): raise NotImplementedError class STARKProcessing(BaseProcessing): """ The processing class used for training LittleBoy. The images are processed in the following way. First, the target bounding box is jittered by adding some noise. Next, a square region (called search region ) centered at the jittered target center, and of area search_area_factor^2 times the area of the jittered box is cropped from the image. The reason for jittering the target box is to avoid learning the bias that the target is always at the center of the search region. The search region is then resized to a fixed size given by the argument output_sz. """ def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor, mode='pair', settings=None, *args, **kwargs): """ args: search_area_factor - The size of the search region relative to the target size. output_sz - An integer, denoting the size to which the search region is resized. The search region is always square. center_jitter_factor - A dict containing the amount of jittering to be applied to the target center before extracting the search region. See _get_jittered_box for how the jittering is done. scale_jitter_factor - A dict containing the amount of jittering to be applied to the target size before extracting the search region. See _get_jittered_box for how the jittering is done. mode - Either 'pair' or 'sequence'. If mode='sequence', then output has an extra dimension for frames """ super().__init__(*args, **kwargs) self.search_area_factor = search_area_factor self.output_sz = output_sz self.center_jitter_factor = center_jitter_factor self.scale_jitter_factor = scale_jitter_factor self.mode = mode self.settings = settings def _get_jittered_box(self, box, mode): """ Jitter the input box args: box - input bounding box mode - string 'template' or 'search' indicating template or search data returns: torch.Tensor - jittered box """ jittered_size = box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_factor[mode]) max_offset = (jittered_size.prod().sqrt() * torch.tensor(self.center_jitter_factor[mode]).float()) jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (torch.rand(2) - 0.5) return torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0) def __call__(self, data: TensorDict): """ args: data - The input data, should contain the following fields: 'template_images', search_images', 'template_anno', 'search_anno' returns: TensorDict - output data block with following fields: 'template_images', 'search_images', 'template_anno', 'search_anno', 'test_proposals', 'proposal_iou' """ # Apply joint transforms if self.transform['joint'] is not None: data['template_images'], data['template_anno'], data['template_masks'] = self.transform['joint']( image=data['template_images'], bbox=data['template_anno'], mask=data['template_masks']) data['search_images'], data['search_anno'], data['search_masks'] = self.transform['joint']( image=data['search_images'], bbox=data['search_anno'], mask=data['search_masks'], new_roll=False) data["target_in_search_images"] = data["search_images"] data["target_in_search_anno"] = data["search_anno"] data["target_in_search_masks"] = data["search_masks"] self.scale_jitter_factor["target_in_search"] = self.scale_jitter_factor["template"] self.center_jitter_factor["target_in_search"] = self.center_jitter_factor["template"] self.search_area_factor["target_in_search"] = self.search_area_factor["template"] self.output_sz["target_in_search"] = self.output_sz["template"] self.transform["target_in_search"] = self.transform["search"] for s in ['template', 'search', 'target_in_search']: assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \ "In pair mode, num train/test frames must be 1" # Add a uniform noise to the center pos jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']] # 2021.1.9 Check whether data is valid. Avoid too small bounding boxes w, h = torch.stack(jittered_anno, dim=0)[:, 2], torch.stack(jittered_anno, dim=0)[:, 3] crop_sz = torch.ceil(torch.sqrt(w * h) * self.search_area_factor[s]) if (crop_sz < 1).any(): data['valid'] = False # print("Too small box is found. Replace it with new data.") return data # Crop image region centered at jittered_anno box and get the attention mask crops, boxes, att_mask, mask_crops = prutils.jittered_center_crop(data[s + '_images'], jittered_anno, data[s + '_anno'], self.search_area_factor[s], self.output_sz[s], masks=data[s + '_masks']) # Apply transforms data[s + '_images'], data[s + '_anno'], data[s + '_att'], data[s + '_masks'] = self.transform[s]( image=crops, bbox=boxes, att=att_mask, mask=mask_crops, joint=False) # 2021.1.9 Check whether elements in data[s + '_att'] is all 1 # Note that type of data[s + '_att'] is tuple, type of ele is torch.tensor for ele in data[s + '_att']: if (ele == 1).all(): data['valid'] = False # print("Values of original attention mask are all one. Replace it with new data.") return data # 2021.1.10 more strict conditions: require the donwsampled masks not to be all 1 for ele in data[s + '_att']: feat_size = self.output_sz[s] // 16 # 16 is the backbone stride # (1,1,128,128) (1,1,256,256) --> (1,1,8,8) (1,1,16,16) mask_down = F.interpolate(ele[None, None].float(), size=feat_size).to(torch.bool)[0] if (mask_down == 1).all(): data['valid'] = False # print("Values of down-sampled attention mask are all one. " # "Replace it with new data.") return data data['valid'] = True # if we use copy-and-paste augmentation if data["template_masks"] is None or data["search_masks"] is None: data["template_masks"] = torch.zeros((1, self.output_sz["template"], self.output_sz["template"])) data["search_masks"] = torch.zeros((1, self.output_sz["search"], self.output_sz["search"])) # Prepare output if self.mode == 'sequence': data = data.apply(stack_tensors) else: data = data.apply(lambda x: x[0] if isinstance(x, list) else x) return data ================================================ FILE: artrackv2_mindspore/lib/train/data/processing_utils.py ================================================ import mindspore as ms from mindspore import ops import math import cv2 as cv import numpy as np '''modified from the original test implementation Replace cv.BORDER_REPLICATE with cv.BORDER_CONSTANT Add a variable called att_mask for computing attention and positional encoding later''' def sample_target(im, target_bb, search_area_factor, output_sz=None, mask=None): """ Extracts a square crop centered at target_bb box, of area search_area_factor^2 times target_bb area args: im - cv image target_bb - target box [x, y, w, h] search_area_factor - Ratio of crop size to target size output_sz - (float) Size to which the extracted crop is resized (always square). If None, no resizing is done. returns: cv image - extracted crop float - the factor by which the crop has been resized to make the crop size equal output_size """ if not isinstance(target_bb, list): x, y, w, h = target_bb.tolist() else: x, y, w, h = target_bb # Crop image crop_sz = math.ceil(math.sqrt(w * h) * search_area_factor) if crop_sz < 1: raise Exception('Too small bounding box.') x1 = round(x + 0.5 * w - crop_sz * 0.5) x2 = x1 + crop_sz y1 = round(y + 0.5 * h - crop_sz * 0.5) y2 = y1 + crop_sz x1_pad = max(0, -x1) x2_pad = max(x2 - im.shape[1] + 1, 0) y1_pad = max(0, -y1) y2_pad = max(y2 - im.shape[0] + 1, 0) # Crop target im_crop = im[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad, :] if mask is not None: mask_crop = mask[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad] # Pad im_crop_padded = cv.copyMakeBorder(im_crop, y1_pad, y2_pad, x1_pad, x2_pad, cv.BORDER_CONSTANT) # deal with attention mask H, W, _ = im_crop_padded.shape att_mask = np.ones((H,W)) end_x, end_y = -x2_pad, -y2_pad if y2_pad == 0: end_y = None if x2_pad == 0: end_x = None att_mask[y1_pad:end_y, x1_pad:end_x] = 0 if mask is not None: mask_crop_padded = ops.pad(mask_crop, pad=(x1_pad, x2_pad, y1_pad, y2_pad), mode='constant', value=0) if output_sz is not None: resize_factor = output_sz / crop_sz im_crop_padded = cv.resize(im_crop_padded, (output_sz, output_sz)) att_mask = cv.resize(att_mask, (output_sz, output_sz)).astype(np.bool_) if mask is None: return im_crop_padded, resize_factor, att_mask mask_crop_padded = \ ops.interpolate(mask_crop_padded[None, None], (output_sz, output_sz), mode='bilinear', align_corners=False)[0, 0] return im_crop_padded, resize_factor, att_mask, mask_crop_padded else: if mask is None: return im_crop_padded, att_mask.astype(np.bool_), 1.0 return im_crop_padded, 1.0, att_mask.astype(np.bool_), mask_crop_padded def transform_image_to_crop(box_in: ms.Tensor, box_extract: ms.Tensor, resize_factor: float, crop_sz: ms.Tensor, normalize=False) -> ms.Tensor: """ Transform the box co-ordinates from the original image co-ordinates to the co-ordinates of the cropped image args: box_in - the box for which the co-ordinates are to be transformed box_extract - the box about which the image crop has been extracted. resize_factor - the ratio between the original image scale and the scale of the image crop crop_sz - size of the cropped image returns: torch.Tensor - transformed co-ordinates of box_in """ box_extract_center = box_extract[0:2] + 0.5 * box_extract[2:4] box_in_center = box_in[0:2] + 0.5 * box_in[2:4] box_out_center = (crop_sz - 1) / 2 + (box_in_center - box_extract_center) * resize_factor box_out_wh = box_in[2:4] * resize_factor box_out = ops.cat((box_out_center - 0.5 * box_out_wh, box_out_wh)) if normalize: return box_out / crop_sz[0] else: return box_out def jittered_center_crop(frames, box_extract, box_gt, search_area_factor, output_sz, masks=None): """ For each frame in frames, extracts a square crop centered at box_extract, of area search_area_factor^2 times box_extract area. The extracted crops are then resized to output_sz. Further, the co-ordinates of the box box_gt are transformed to the image crop co-ordinates args: frames - list of frames box_extract - list of boxes of same length as frames. The crops are extracted using anno_extract box_gt - list of boxes of same length as frames. The co-ordinates of these boxes are transformed from image co-ordinates to the crop co-ordinates search_area_factor - The area of the extracted crop is search_area_factor^2 times box_extract area output_sz - The size to which the extracted crops are resized returns: list - list of image crops list - box_gt location in the crop co-ordinates """ if masks is None: crops_resize_factors = [sample_target(f, a, search_area_factor, output_sz) for f, a in zip(frames, box_extract)] frames_crop, resize_factors, att_mask = zip(*crops_resize_factors) masks_crop = None else: crops_resize_factors = [sample_target(f, a, search_area_factor, output_sz, m) for f, a, m in zip(frames, box_extract, masks)] frames_crop, resize_factors, att_mask, masks_crop = zip(*crops_resize_factors) # frames_crop: tuple of ndarray (128,128,3), att_mask: tuple of ndarray (128,128) crop_sz = ms.Tensor([output_sz, output_sz]) # find the bb location in the crop '''Note that here we use normalized coord''' box_crop = [transform_image_to_crop(a_gt, a_ex, rf, crop_sz, normalize=True) for a_gt, a_ex, rf in zip(box_gt, box_extract, resize_factors)] # (x1,y1,w,h) list of tensors return frames_crop, box_crop, att_mask, masks_crop def transform_box_to_crop(box: ms.Tensor, crop_box: ms.Tensor, crop_sz: ms.Tensor, normalize=False) -> ms.Tensor: """ Transform the box co-ordinates from the original image co-ordinates to the co-ordinates of the cropped image args: box - the box for which the co-ordinates are to be transformed crop_box - bounding box defining the crop in the original image crop_sz - size of the cropped image returns: torch.Tensor - transformed co-ordinates of box_in """ box_out = box.clone() box_out[:2] -= crop_box[:2] scale_factor = crop_sz / crop_box[2:] box_out[:2] *= scale_factor box_out[2:] *= scale_factor if normalize: return box_out / crop_sz[0] else: return box_out ================================================ FILE: artrackv2_mindspore/lib/train/data/sampler.py ================================================ import random import torch.utils.data from lib.utils import TensorDict import numpy as np def no_processing(data): return data class TrackingSampler(torch.utils.data.Dataset): """ Class responsible for sampling frames from training sequences to form batches. The sampling is done in the following ways. First a dataset is selected at random. Next, a sequence is selected from that dataset. A base frame is then sampled randomly from the sequence. Next, a set of 'train frames' and 'test frames' are sampled from the sequence from the range [base_frame_id - max_gap, base_frame_id] and (base_frame_id, base_frame_id + max_gap] respectively. Only the frames in which the target is visible are sampled. If enough visible frames are not found, the 'max_gap' is increased gradually till enough frames are found. The sampled frames are then passed through the input 'processing' function for the necessary processing- """ def __init__(self, datasets, p_datasets, samples_per_epoch, max_gap, num_search_frames, num_template_frames=1, processing=no_processing, frame_sample_mode='causal', train_cls=False, pos_prob=0.5): """ args: datasets - List of datasets to be used for training p_datasets - List containing the probabilities by which each dataset will be sampled samples_per_epoch - Number of training samples per epoch max_gap - Maximum gap, in frame numbers, between the train frames and the test frames. num_search_frames - Number of search frames to sample. num_template_frames - Number of template frames to sample. processing - An instance of Processing class which performs the necessary processing of the data. frame_sample_mode - Either 'causal' or 'interval'. If 'causal', then the test frames are sampled in a causally, otherwise randomly within the interval. """ self.datasets = datasets self.train_cls = train_cls # whether we are training classification self.pos_prob = pos_prob # probability of sampling positive class when making classification # If p not provided, sample uniformly from all videos if p_datasets is None: p_datasets = [len(d) for d in self.datasets] # Normalize p_total = sum(p_datasets) self.p_datasets = [x / p_total for x in p_datasets] self.samples_per_epoch = samples_per_epoch self.max_gap = max_gap self.num_search_frames = num_search_frames self.num_template_frames = num_template_frames self.processing = processing self.frame_sample_mode = frame_sample_mode def __len__(self): return self.samples_per_epoch def _sample_visible_ids(self, visible, num_ids=1, min_id=None, max_id=None, allow_invisible=False, force_invisible=False): """ Samples num_ids frames between min_id and max_id for which target is visible args: visible - 1d Tensor indicating whether target is visible for each frame num_ids - number of frames to be samples min_id - Minimum allowed frame number max_id - Maximum allowed frame number returns: list - List of sampled frame numbers. None if not sufficient visible frames could be found. """ if num_ids == 0: return [] if min_id is None or min_id < 0: min_id = 0 if max_id is None or max_id > len(visible): max_id = len(visible) # get valid ids if force_invisible: valid_ids = [i for i in range(min_id, max_id) if not visible[i]] else: if allow_invisible: valid_ids = [i for i in range(min_id, max_id)] else: valid_ids = [i for i in range(min_id, max_id) if visible[i]] # No visible ids if len(valid_ids) == 0: return None return random.choices(valid_ids, k=num_ids) def __getitem__(self, index): if self.train_cls: return self.getitem_cls() else: return self.getitem() def getitem(self): """ returns: TensorDict - dict containing all the data blocks """ valid = False while not valid: # Select a dataset dataset = random.choices(self.datasets, self.p_datasets)[0] is_video_dataset = dataset.is_video_sequence() # sample a sequence from the given dataset seq_id, visible, seq_info_dict = self.sample_seq_from_dataset(dataset, is_video_dataset) if is_video_dataset: template_frame_ids = None search_frame_ids = None gap_increase = 0 if self.frame_sample_mode == 'causal': # Sample test and train frames in a causal manner, i.e. search_frame_ids > template_frame_ids while search_frame_ids is None: base_frame_id = self._sample_visible_ids(visible, num_ids=1, min_id=self.num_template_frames - 1, max_id=len(visible) - self.num_search_frames) prev_frame_ids = self._sample_visible_ids(visible, num_ids=self.num_template_frames - 1, min_id=base_frame_id[0] - self.max_gap - gap_increase, max_id=base_frame_id[0]) if prev_frame_ids is None: gap_increase += 5 continue template_frame_ids = base_frame_id + prev_frame_ids search_frame_ids = self._sample_visible_ids(visible, min_id=template_frame_ids[0] + 1, max_id=template_frame_ids[0] + self.max_gap + gap_increase, num_ids=self.num_search_frames) # Increase gap until a frame is found gap_increase += 5 elif self.frame_sample_mode == "trident" or self.frame_sample_mode == "trident_pro": template_frame_ids, search_frame_ids = self.get_frame_ids_trident(visible) elif self.frame_sample_mode == "stark": template_frame_ids, search_frame_ids = self.get_frame_ids_stark(visible, seq_info_dict["valid"]) else: raise ValueError("Illegal frame sample mode") else: # In case of image dataset, just repeat the image to generate synthetic video template_frame_ids = [1] * self.num_template_frames search_frame_ids = [1] * self.num_search_frames try: template_frames, template_anno, meta_obj_train = dataset.get_frames(seq_id, template_frame_ids, seq_info_dict) search_frames, search_anno, meta_obj_test = dataset.get_frames(seq_id, search_frame_ids, seq_info_dict) H, W, _ = template_frames[0].shape template_masks = template_anno['mask'] if 'mask' in template_anno else [torch.zeros((H, W))] * self.num_template_frames search_masks = search_anno['mask'] if 'mask' in search_anno else [torch.zeros((H, W))] * self.num_search_frames data = TensorDict({'template_images': template_frames, 'template_anno': template_anno['bbox'], 'template_masks': template_masks, 'search_images': search_frames, 'search_anno': search_anno['bbox'], 'search_masks': search_masks, 'dataset': dataset.get_name(), 'test_class': meta_obj_test.get('object_class_name')}) # make data augmentation data = self.processing(data) # check whether data is valid valid = data['valid'] except: valid = False return data def getitem_cls(self): # get data for classification """ args: index (int): Index (Ignored since we sample randomly) aux (bool): whether the current data is for auxiliary use (e.g. copy-and-paste) returns: TensorDict - dict containing all the data blocks """ valid = False label = None while not valid: # Select a dataset dataset = random.choices(self.datasets, self.p_datasets)[0] is_video_dataset = dataset.is_video_sequence() # sample a sequence from the given dataset seq_id, visible, seq_info_dict = self.sample_seq_from_dataset(dataset, is_video_dataset) # sample template and search frame ids if is_video_dataset: if self.frame_sample_mode in ["trident", "trident_pro"]: template_frame_ids, search_frame_ids = self.get_frame_ids_trident(visible) elif self.frame_sample_mode == "stark": template_frame_ids, search_frame_ids = self.get_frame_ids_stark(visible, seq_info_dict["valid"]) else: raise ValueError("illegal frame sample mode") else: # In case of image dataset, just repeat the image to generate synthetic video template_frame_ids = [1] * self.num_template_frames search_frame_ids = [1] * self.num_search_frames try: # "try" is used to handle trackingnet data failure # get images and bounding boxes (for templates) template_frames, template_anno, meta_obj_train = dataset.get_frames(seq_id, template_frame_ids, seq_info_dict) H, W, _ = template_frames[0].shape template_masks = template_anno['mask'] if 'mask' in template_anno else [torch.zeros( (H, W))] * self.num_template_frames # get images and bounding boxes (for searches) # positive samples if random.random() < self.pos_prob: label = torch.ones(1,) search_frames, search_anno, meta_obj_test = dataset.get_frames(seq_id, search_frame_ids, seq_info_dict) search_masks = search_anno['mask'] if 'mask' in search_anno else [torch.zeros( (H, W))] * self.num_search_frames # negative samples else: label = torch.zeros(1,) if is_video_dataset: search_frame_ids = self._sample_visible_ids(visible, num_ids=1, force_invisible=True) if search_frame_ids is None: search_frames, search_anno, meta_obj_test = self.get_one_search() else: search_frames, search_anno, meta_obj_test = dataset.get_frames(seq_id, search_frame_ids, seq_info_dict) search_anno["bbox"] = [self.get_center_box(H, W)] else: search_frames, search_anno, meta_obj_test = self.get_one_search() H, W, _ = search_frames[0].shape search_masks = search_anno['mask'] if 'mask' in search_anno else [torch.zeros( (H, W))] * self.num_search_frames data = TensorDict({'template_images': template_frames, 'template_anno': template_anno['bbox'], 'template_masks': template_masks, 'search_images': search_frames, 'search_anno': search_anno['bbox'], 'search_masks': search_masks, 'dataset': dataset.get_name(), 'test_class': meta_obj_test.get('object_class_name')}) # make data augmentation data = self.processing(data) # add classification label data["label"] = label # check whether data is valid valid = data['valid'] except: valid = False return data def get_center_box(self, H, W, ratio=1/8): cx, cy, w, h = W/2, H/2, W * ratio, H * ratio return torch.tensor([int(cx-w/2), int(cy-h/2), int(w), int(h)]) def sample_seq_from_dataset(self, dataset, is_video_dataset): # Sample a sequence with enough visible frames enough_visible_frames = False while not enough_visible_frames: # Sample a sequence seq_id = random.randint(0, dataset.get_num_sequences() - 1) # Sample frames seq_info_dict = dataset.get_sequence_info(seq_id) visible = seq_info_dict['visible'] enough_visible_frames = visible.type(torch.int64).sum().item() > 2 * ( self.num_search_frames + self.num_template_frames) and len(visible) >= 20 enough_visible_frames = enough_visible_frames or not is_video_dataset return seq_id, visible, seq_info_dict def get_one_search(self): # Select a dataset dataset = random.choices(self.datasets, self.p_datasets)[0] is_video_dataset = dataset.is_video_sequence() # sample a sequence seq_id, visible, seq_info_dict = self.sample_seq_from_dataset(dataset, is_video_dataset) # sample a frame if is_video_dataset: if self.frame_sample_mode == "stark": search_frame_ids = self._sample_visible_ids(seq_info_dict["valid"], num_ids=1) else: search_frame_ids = self._sample_visible_ids(visible, num_ids=1, allow_invisible=True) else: search_frame_ids = [1] # get the image, bounding box and other info search_frames, search_anno, meta_obj_test = dataset.get_frames(seq_id, search_frame_ids, seq_info_dict) return search_frames, search_anno, meta_obj_test def get_frame_ids_trident(self, visible): # get template and search ids in a 'trident' manner template_frame_ids_extra = [] while None in template_frame_ids_extra or len(template_frame_ids_extra) == 0: template_frame_ids_extra = [] # first randomly sample two frames from a video template_frame_id1 = self._sample_visible_ids(visible, num_ids=1) # the initial template id search_frame_ids = self._sample_visible_ids(visible, num_ids=1) # the search region id # get the dynamic template id for max_gap in self.max_gap: if template_frame_id1[0] >= search_frame_ids[0]: min_id, max_id = search_frame_ids[0], search_frame_ids[0] + max_gap else: min_id, max_id = search_frame_ids[0] - max_gap, search_frame_ids[0] if self.frame_sample_mode == "trident_pro": f_id = self._sample_visible_ids(visible, num_ids=1, min_id=min_id, max_id=max_id, allow_invisible=True) else: f_id = self._sample_visible_ids(visible, num_ids=1, min_id=min_id, max_id=max_id) if f_id is None: template_frame_ids_extra += [None] else: template_frame_ids_extra += f_id template_frame_ids = template_frame_id1 + template_frame_ids_extra return template_frame_ids, search_frame_ids def get_frame_ids_stark(self, visible, valid): # get template and search ids in a 'stark' manner template_frame_ids_extra = [] while None in template_frame_ids_extra or len(template_frame_ids_extra) == 0: template_frame_ids_extra = [] # first randomly sample two frames from a video template_frame_id1 = self._sample_visible_ids(visible, num_ids=1) # the initial template id search_frame_ids = self._sample_visible_ids(visible, num_ids=1) # the search region id # get the dynamic template id for max_gap in self.max_gap: if template_frame_id1[0] >= search_frame_ids[0]: min_id, max_id = search_frame_ids[0], search_frame_ids[0] + max_gap else: min_id, max_id = search_frame_ids[0] - max_gap, search_frame_ids[0] """we require the frame to be valid but not necessary visible""" f_id = self._sample_visible_ids(valid, num_ids=1, min_id=min_id, max_id=max_id) if f_id is None: template_frame_ids_extra += [None] else: template_frame_ids_extra += f_id template_frame_ids = template_frame_id1 + template_frame_ids_extra return template_frame_ids, search_frame_ids ================================================ FILE: artrackv2_mindspore/lib/train/data/sequence_sampler.py ================================================ import random import torch.utils.data import numpy as np from lib.utils import TensorDict class SequenceSampler(torch.utils.data.Dataset): """ Sample sequence for sequence-level training """ def __init__(self, datasets, p_datasets, samples_per_epoch, max_gap, num_search_frames, num_template_frames=1, frame_sample_mode='sequential', max_interval=10, prob=0.7): """ args: datasets - List of datasets to be used for training p_datasets - List containing the probabilities by which each dataset will be sampled samples_per_epoch - Number of training samples per epoch max_gap - Maximum gap, in frame numbers, between the train frames and the search frames.\ max_interval - Maximum interval between sampled frames num_search_frames - Number of search frames to sample. num_template_frames - Number of template frames to sample. processing - An instance of Processing class which performs the necessary processing of the data. frame_sample_mode - Either 'causal' or 'interval'. If 'causal', then the search frames are sampled in a causally, otherwise randomly within the interval. prob - sequential sampling by prob / interval sampling by 1-prob """ self.datasets = datasets # If p not provided, sample uniformly from all videos if p_datasets is None: p_datasets = [len(d) for d in self.datasets] # Normalize p_total = sum(p_datasets) self.p_datasets = [x / p_total for x in p_datasets] self.samples_per_epoch = samples_per_epoch self.max_gap = max_gap self.max_interval = max_interval self.num_search_frames = num_search_frames self.num_template_frames = num_template_frames self.frame_sample_mode = frame_sample_mode self.prob=prob self.extra=1 def __len__(self): return self.samples_per_epoch def _sample_visible_ids(self, visible, num_ids=1, min_id=None, max_id=None): """ Samples num_ids frames between min_id and max_id for which target is visible args: visible - 1d Tensor indicating whether target is visible for each frame num_ids - number of frames to be samples min_id - Minimum allowed frame number max_id - Maximum allowed frame number returns: list - List of sampled frame numbers. None if not sufficient visible frames could be found. """ if num_ids == 0: return [] if min_id is None or min_id < 0: min_id = 0 if max_id is None or max_id > len(visible): max_id = len(visible) valid_ids = [i for i in range(min_id, max_id) if visible[i]] # No visible ids if len(valid_ids) == 0: return None return random.choices(valid_ids, k=num_ids) def _sequential_sample(self, visible): # Sample frames in sequential manner template_frame_ids = self._sample_visible_ids(visible, num_ids=1, min_id=0, max_id=len(visible) - self.num_search_frames) template_another = self._sample_visible_ids(visible, num_ids=1, min_id=template_frame_ids[0], max_id=min(len(visible) - self.num_search_frames, template_frame_ids[0] + self.max_gap)) template_frame_ids.append(template_another[0]) template_frame_ids.sort() if self.max_gap == -1: left = template_frame_ids[1] else: # template frame (1) ->(max_gap) -> search frame (num_search_frames) left_max = min(len(visible) - self.num_search_frames, template_frame_ids[1] + self.max_gap) left = self._sample_visible_ids(visible, num_ids=1, min_id=template_frame_ids[1], max_id=left_max)[0] valid_ids = [i for i in range(left, len(visible)) if visible[i]] search_frame_ids = valid_ids[:self.num_search_frames] # if length is not enough last = search_frame_ids[-1] while len(search_frame_ids) < self.num_search_frames: if last >= len(visible) - 1: search_frame_ids.append(last) else: last += 1 if visible[last]: search_frame_ids.append(last) return template_frame_ids, search_frame_ids def _random_interval_sample(self, visible): # Get valid ids valid_ids = [i for i in range(len(visible)) if visible[i]] # Sample template frame avg_interval = self.max_interval while avg_interval * (self.num_search_frames - 1) > len(visible): avg_interval = max(avg_interval - 1, 1) while True: template_frame_ids = self._sample_visible_ids(visible, num_ids=1, min_id=0, max_id=len(visible) - avg_interval * (self.num_search_frames - 1)) if template_frame_ids == None: avg_interval = avg_interval - 1 else: break if avg_interval == 0: template_frame_ids = [valid_ids[0]] break # Sample first search frame if self.max_gap == -1: search_frame_ids = template_frame_ids else: avg_interval = self.max_interval while avg_interval * (self.num_search_frames - 1) > len(visible): avg_interval = max(avg_interval - 1, 1) while True: left_max = min(max(len(visible) - avg_interval * (self.num_search_frames - 1), template_frame_ids[0] + 1), template_frame_ids[0] + self.max_gap) search_frame_ids = self._sample_visible_ids(visible, num_ids=1, min_id=template_frame_ids[0], max_id=left_max) if search_frame_ids == None: avg_interval = avg_interval - 1 else: break if avg_interval == -1: search_frame_ids = template_frame_ids break # Sample rest of the search frames with random interval last = search_frame_ids[0] while last <= len(visible) - 1 and len(search_frame_ids) < self.num_search_frames: # sample id with interval max_id = min(last + self.max_interval + 1, len(visible)) id = self._sample_visible_ids(visible, num_ids=1, min_id=last, max_id=max_id) if id is None: # If not found in current range, find from previous range last = last + self.max_interval else: search_frame_ids.append(id[0]) last = search_frame_ids[-1] # if length is not enough, randomly sample new ids if len(search_frame_ids) < self.num_search_frames: valid_ids = [x for x in valid_ids if x > search_frame_ids[0] and x not in search_frame_ids] if len(valid_ids) > 0: new_ids = random.choices(valid_ids, k=min(len(valid_ids), self.num_search_frames - len(search_frame_ids))) search_frame_ids = search_frame_ids + new_ids search_frame_ids = sorted(search_frame_ids, key=int) # if length is still not enough, duplicate last frame while len(search_frame_ids) < self.num_search_frames: search_frame_ids.append(search_frame_ids[-1]) for i in range(1, self.num_search_frames): if search_frame_ids[i] - search_frame_ids[i - 1] > self.max_interval: print(search_frame_ids[i] - search_frame_ids[i - 1]) return template_frame_ids, search_frame_ids def __getitem__(self, index): """ args: index (int): Index (Ignored since we sample randomly) returns: TensorDict - dict containing all the data blocks """ # Select a dataset dataset = random.choices(self.datasets, self.p_datasets)[0] if dataset.get_name() == 'got10k' : max_gap = self.max_gap max_interval = self.max_interval else: max_gap = self.max_gap max_interval = self.max_interval self.max_gap = max_gap * self.extra self.max_interval = max_interval * self.extra is_video_dataset = dataset.is_video_sequence() # Sample a sequence with enough visible frames enough_visible_frames = False while not enough_visible_frames: # Sample a sequence seq_id = random.randint(0, dataset.get_num_sequences() - 1) # Sample frames seq_info_dict = dataset.get_sequence_info(seq_id) visible = seq_info_dict['visible'] enough_visible_frames = visible.type(torch.int64).sum().item() > 2 * ( self.num_search_frames + self.num_template_frames) and len(visible) >= (self.num_search_frames + self.num_template_frames) enough_visible_frames = enough_visible_frames or not is_video_dataset if is_video_dataset: if self.frame_sample_mode == 'sequential': template_frame_ids, search_frame_ids = self._sequential_sample(visible) elif self.frame_sample_mode == 'random_interval': if random.random() < self.prob: template_frame_ids, search_frame_ids = self._random_interval_sample(visible) else: template_frame_ids, search_frame_ids = self._sequential_sample(visible) else: self.max_gap = max_gap self.max_interval = max_interval raise NotImplementedError else: # In case of image dataset, just repeat the image to generate synthetic video template_frame_ids = [1] * self.num_template_frames search_frame_ids = [1] * self.num_search_frames self.max_gap = max_gap self.max_interval = max_interval # print("this is template_frame_ids", template_frame_ids) # print("this is search_frame_ids", search_frame_ids) template_frames, template_anno, meta_obj_template = dataset.get_frames(seq_id, template_frame_ids, seq_info_dict) search_frames, search_anno, meta_obj_search = dataset.get_frames(seq_id, search_frame_ids, seq_info_dict) visible_ratio = search_anno['visible_ratio'] template_bbox = [bbox.numpy() for bbox in template_anno['bbox']] # tensor -> numpy array search_bbox = [bbox.numpy() for bbox in search_anno['bbox']] # tensor -> numpy array return TensorDict({'template_images': np.array(template_frames).squeeze(), # 1 template images 'template_annos': np.array(template_bbox).squeeze(), 'search_images': np.array(search_frames), # (num_frames) search images 'search_annos': np.array(search_bbox), 'seq_id': seq_id, 'dataset': dataset.get_name(), 'search_class': meta_obj_search.get('object_class_name'), 'num_frames': len(search_frames), 'visible_ratio': visible_ratio }) ================================================ FILE: artrackv2_mindspore/lib/train/data/transforms.py ================================================ import random import numpy as np import math import cv2 as cv import torch import torch.nn.functional as F import torchvision.transforms.functional as tvisf class Transform: """A set of transformations, used for e.g. data augmentation. Args of constructor: transforms: An arbitrary number of transformations, derived from the TransformBase class. They are applied in the order they are given. The Transform object can jointly transform images, bounding boxes and segmentation masks. This is done by calling the object with the following key-word arguments (all are optional). The following arguments are inputs to be transformed. They are either supplied as a single instance, or a list of instances. image - Image coords - 2xN dimensional Tensor of 2D image coordinates [y, x] bbox - Bounding box on the form [x, y, w, h] mask - Segmentation mask with discrete classes The following parameters can be supplied with calling the transform object: joint [Bool] - If True then transform all images/coords/bbox/mask in the list jointly using the same transformation. Otherwise each tuple (images, coords, bbox, mask) will be transformed independently using different random rolls. Default: True. new_roll [Bool] - If False, then no new random roll is performed, and the saved result from the previous roll is used instead. Default: True. Check the DiMPProcessing class for examples. """ def __init__(self, *transforms): if len(transforms) == 1 and isinstance(transforms[0], (list, tuple)): transforms = transforms[0] self.transforms = transforms self._valid_inputs = ['image', 'coords', 'bbox', 'mask', 'att'] self._valid_args = ['joint', 'new_roll'] self._valid_all = self._valid_inputs + self._valid_args def __call__(self, **inputs): var_names = [k for k in inputs.keys() if k in self._valid_inputs] for v in inputs.keys(): if v not in self._valid_all: raise ValueError('Incorrect input \"{}\" to transform. Only supports inputs {} and arguments {}.'.format(v, self._valid_inputs, self._valid_args)) joint_mode = inputs.get('joint', True) new_roll = inputs.get('new_roll', True) if not joint_mode: out = zip(*[self(**inp) for inp in self._split_inputs(inputs)]) return tuple(list(o) for o in out) out = {k: v for k, v in inputs.items() if k in self._valid_inputs} for t in self.transforms: out = t(**out, joint=joint_mode, new_roll=new_roll) if len(var_names) == 1: return out[var_names[0]] # Make sure order is correct return tuple(out[v] for v in var_names) def _split_inputs(self, inputs): var_names = [k for k in inputs.keys() if k in self._valid_inputs] split_inputs = [{k: v for k, v in zip(var_names, vals)} for vals in zip(*[inputs[vn] for vn in var_names])] for arg_name, arg_val in filter(lambda it: it[0]!='joint' and it[0] in self._valid_args, inputs.items()): if isinstance(arg_val, list): for inp, av in zip(split_inputs, arg_val): inp[arg_name] = av else: for inp in split_inputs: inp[arg_name] = arg_val return split_inputs def __repr__(self): format_string = self.__class__.__name__ + '(' for t in self.transforms: format_string += '\n' format_string += ' {0}'.format(t) format_string += '\n)' return format_string class TransformBase: """Base class for transformation objects. See the Transform class for details.""" def __init__(self): """2020.12.24 Add 'att' to valid inputs""" self._valid_inputs = ['image', 'coords', 'bbox', 'mask', 'att'] self._valid_args = ['new_roll'] self._valid_all = self._valid_inputs + self._valid_args self._rand_params = None def __call__(self, **inputs): # Split input input_vars = {k: v for k, v in inputs.items() if k in self._valid_inputs} input_args = {k: v for k, v in inputs.items() if k in self._valid_args} # Roll random parameters for the transform if input_args.get('new_roll', True): rand_params = self.roll() if rand_params is None: rand_params = () elif not isinstance(rand_params, tuple): rand_params = (rand_params,) self._rand_params = rand_params outputs = dict() for var_name, var in input_vars.items(): if var is not None: transform_func = getattr(self, 'transform_' + var_name) if var_name in ['coords', 'bbox']: params = (self._get_image_size(input_vars),) + self._rand_params else: params = self._rand_params if isinstance(var, (list, tuple)): outputs[var_name] = [transform_func(x, *params) for x in var] else: outputs[var_name] = transform_func(var, *params) return outputs def _get_image_size(self, inputs): im = None for var_name in ['image', 'mask']: if inputs.get(var_name) is not None: im = inputs[var_name] break if im is None: return None if isinstance(im, (list, tuple)): im = im[0] if isinstance(im, np.ndarray): return im.shape[:2] if torch.is_tensor(im): return (im.shape[-2], im.shape[-1]) raise Exception('Unknown image type') def roll(self): return None def transform_image(self, image, *rand_params): """Must be deterministic""" return image def transform_coords(self, coords, image_shape, *rand_params): """Must be deterministic""" return coords def transform_bbox(self, bbox, image_shape, *rand_params): """Assumes [x, y, w, h]""" # Check if not overloaded if self.transform_coords.__code__ == TransformBase.transform_coords.__code__: return bbox coord = bbox.clone().view(-1,2).t().flip(0) x1 = coord[1, 0] x2 = coord[1, 0] + coord[1, 1] y1 = coord[0, 0] y2 = coord[0, 0] + coord[0, 1] coord_all = torch.tensor([[y1, y1, y2, y2], [x1, x2, x2, x1]]) coord_transf = self.transform_coords(coord_all, image_shape, *rand_params).flip(0) tl = torch.min(coord_transf, dim=1)[0] sz = torch.max(coord_transf, dim=1)[0] - tl bbox_out = torch.cat((tl, sz), dim=-1).reshape(bbox.shape) return bbox_out def transform_mask(self, mask, *rand_params): """Must be deterministic""" return mask def transform_att(self, att, *rand_params): """2020.12.24 Added to deal with attention masks""" return att class ToTensor(TransformBase): """Convert to a Tensor""" def transform_image(self, image): # handle numpy array if image.ndim == 2: image = image[:, :, None] image = torch.from_numpy(image.transpose((2, 0, 1))) # backward compatibility if isinstance(image, torch.ByteTensor): return image.float().div(255) else: return image def transfrom_mask(self, mask): if isinstance(mask, np.ndarray): return torch.from_numpy(mask) def transform_att(self, att): if isinstance(att, np.ndarray): return torch.from_numpy(att).to(torch.bool) elif isinstance(att, torch.Tensor): return att.to(torch.bool) else: raise ValueError ("dtype must be np.ndarray or torch.Tensor") class ToTensorAndJitter(TransformBase): """Convert to a Tensor and jitter brightness""" def __init__(self, brightness_jitter=0.0, normalize=True): super().__init__() self.brightness_jitter = brightness_jitter self.normalize = normalize def roll(self): return np.random.uniform(max(0, 1 - self.brightness_jitter), 1 + self.brightness_jitter) def transform_image(self, image, brightness_factor): # handle numpy array image = torch.from_numpy(image.transpose((2, 0, 1))) # backward compatibility if self.normalize: return image.float().mul(brightness_factor/255.0).clamp(0.0, 1.0) else: return image.float().mul(brightness_factor).clamp(0.0, 255.0) def transform_mask(self, mask, brightness_factor): if isinstance(mask, np.ndarray): return torch.from_numpy(mask) else: return mask def transform_att(self, att, brightness_factor): if isinstance(att, np.ndarray): return torch.from_numpy(att).to(torch.bool) elif isinstance(att, torch.Tensor): return att.to(torch.bool) else: raise ValueError ("dtype must be np.ndarray or torch.Tensor") class Normalize(TransformBase): """Normalize image""" def __init__(self, mean, std, inplace=False): super().__init__() self.mean = mean self.std = std self.inplace = inplace def transform_image(self, image): return tvisf.normalize(image, self.mean, self.std, self.inplace) class ToGrayscale(TransformBase): """Converts image to grayscale with probability""" def __init__(self, probability = 0.5): super().__init__() self.probability = probability self.color_weights = np.array([0.2989, 0.5870, 0.1140], dtype=np.float32) def roll(self): return random.random() < self.probability def transform_image(self, image, do_grayscale): if do_grayscale: if torch.is_tensor(image): raise NotImplementedError('Implement torch variant.') img_gray = cv.cvtColor(image, cv.COLOR_RGB2GRAY) return np.stack([img_gray, img_gray, img_gray], axis=2) # return np.repeat(np.sum(img * self.color_weights, axis=2, keepdims=True).astype(np.uint8), 3, axis=2) return image class ToBGR(TransformBase): """Converts image to BGR""" def transform_image(self, image): if torch.is_tensor(image): raise NotImplementedError('Implement torch variant.') img_bgr = cv.cvtColor(image, cv.COLOR_RGB2BGR) return img_bgr class RandomHorizontalFlip(TransformBase): """Horizontally flip image randomly with a probability p.""" def __init__(self, probability = 0.5): super().__init__() self.probability = probability def roll(self): return random.random() < self.probability def transform_image(self, image, do_flip): if do_flip: if torch.is_tensor(image): return image.flip((2,)) return np.fliplr(image).copy() return image def transform_coords(self, coords, image_shape, do_flip): if do_flip: coords_flip = coords.clone() coords_flip[1,:] = (image_shape[1] - 1) - coords[1,:] return coords_flip return coords def transform_mask(self, mask, do_flip): if do_flip: if torch.is_tensor(mask): return mask.flip((-1,)) return np.fliplr(mask).copy() return mask def transform_att(self, att, do_flip): if do_flip: if torch.is_tensor(att): return att.flip((-1,)) return np.fliplr(att).copy() return att class RandomHorizontalFlip_Norm(RandomHorizontalFlip): """Horizontally flip image randomly with a probability p. The difference is that the coord is normalized to [0,1]""" def __init__(self, probability = 0.5): super().__init__() self.probability = probability def transform_coords(self, coords, image_shape, do_flip): """we should use 1 rather than image_shape""" if do_flip: coords_flip = coords.clone() coords_flip[1,:] = 1 - coords[1,:] return coords_flip return coords ================================================ FILE: artrackv2_mindspore/lib/train/data/wandb_logger.py ================================================ from collections import OrderedDict try: import wandb except ImportError: raise ImportError( 'Please run "pip install wandb" to install wandb') class WandbWriter: def __init__(self, exp_name, cfg, output_dir, cur_step=0, step_interval=0): self.wandb = wandb self.step = cur_step self.interval = step_interval wandb.init(project="tracking", name=exp_name, config=cfg, dir=output_dir) def write_log(self, stats: OrderedDict, epoch=-1): self.step += 1 for loader_name, loader_stats in stats.items(): if loader_stats is None: continue log_dict = {} for var_name, val in loader_stats.items(): if hasattr(val, 'avg'): log_dict.update({loader_name + '/' + var_name: val.avg}) else: log_dict.update({loader_name + '/' + var_name: val.val}) if epoch >= 0: log_dict.update({loader_name + '/epoch': epoch}) self.wandb.log(log_dict, step=self.step*self.interval) ================================================ FILE: artrackv2_mindspore/lib/train/data_specs/README.md ================================================ # README ## Description for different text files GOT10K - got10k_train_full_split.txt: the complete GOT-10K training set. (9335 videos) - got10k_train_split.txt: part of videos from the GOT-10K training set - got10k_val_split.txt: another part of videos from the GOT-10K training set - got10k_vot_exclude.txt: 1k videos that are forbidden from "using to train models then testing on VOT" (as required by [VOT Challenge](https://www.votchallenge.net/vot2020/participation.html)) - got10k_vot_train_split.txt: part of videos from the "VOT-permitted" GOT-10K training set - got10k_vot_val_split.txt: another part of videos from the "VOT-permitted" GOT-10K training set LaSOT - lasot_train_split.txt: the complete LaSOT training set TrackingNnet - trackingnet_classmap.txt: The map from the sequence name to the target class for the TrackingNet ================================================ FILE: artrackv2_mindspore/lib/train/data_specs/got10k_train_full_split.txt ================================================ 3784 8998 3906 1631 8277 8358 2338 7938 2988 8302 2662 2663 2825 7447 4781 2218 6348 5860 4517 2819 8075 5391 116 3606 7976 7941 1024 4519 1970 557 8579 6908 993 7204 1991 3674 8781 6840 5 3225 3763 8688 6778 5777 4794 2744 8126 3864 1733 2923 6829 701 683 2081 1831 2404 1459 2741 5972 3618 7462 2654 103 2174 6224 2989 2506 2766 5912 2699 3295 3986 609 4895 6673 801 1098 1602 2490 3129 8476 3186 7355 4784 4270 1812 4226 2267 8873 6544 6112 2381 4752 753 3776 6511 6016 731 2559 7369 5866 563 7731 1105 5603 50 4238 2208 8725 4994 4719 1444 8807 7298 8139 8760 8173 2332 4131 5207 1065 8562 3992 4024 2188 9095 6765 1707 6105 6922 5362 1486 7898 4135 6574 1551 998 6565 8127 8927 2544 4365 510 768 3535 3875 6808 2931 487 1088 4451 368 2470 8111 3493 7338 8281 6390 1271 4373 3667 3494 3757 2966 3756 7840 6315 7827 3300 6261 4163 2217 6549 94 7236 9136 1857 6691 3470 6271 807 516 9311 6098 3144 8420 5425 5694 2643 6696 6072 7285 3781 903 8522 6092 5979 2622 2529 855 3420 3261 8953 7866 2492 3157 359 1520 2642 7452 759 36 8931 1744 4350 1089 9199 4295 1889 1908 4868 4498 1968 9103 3273 8723 7413 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4606 2769 6970 1545 8850 4971 5489 2008 4564 8682 7784 5768 9252 901 438 3577 2765 5904 664 3348 6298 3602 2502 8617 7684 4293 5166 5805 4126 2451 6906 7234 9243 3778 2940 1087 9053 5026 2504 5283 2820 4242 797 3925 1383 8750 7861 1403 6973 7617 968 3065 5395 4347 8144 2688 6527 8597 8673 7327 6331 1422 7115 244 7013 2092 54 7970 5742 3464 4823 8588 2938 3060 6406 4149 2375 6616 8803 1555 4369 1380 3011 6144 3367 4990 7370 7131 1995 2602 985 8785 8480 9125 1927 3269 3771 1032 7378 6900 5726 2731 2020 4503 3313 6727 8793 2304 523 6036 58 7993 5512 5049 2721 8482 673 7937 1168 4472 8247 7287 9017 6421 9190 3584 1819 1792 2810 6033 638 6749 7677 981 7160 4726 1886 7845 7911 6975 568 7422 4613 4501 2569 4263 3206 4133 2420 3706 8894 2263 5774 4925 9180 8888 2945 2091 1873 6303 729 6728 2156 3267 1860 6597 1374 4930 5253 938 580 5825 4839 166 8198 6892 8701 74 7094 7284 8954 3156 6140 4279 5594 2229 7535 5466 8413 7105 8192 2632 7638 9308 8530 832 4643 2201 3268 4322 6510 2967 262 403 7973 1258 8828 4036 5838 9263 8529 2788 4202 237 3838 1291 2305 4056 5628 7281 1430 6476 7935 2850 6041 2013 4016 4576 5312 6827 6321 8669 8439 830 1942 1519 2750 6106 6993 6235 5899 7313 5331 4371 7086 4399 8600 2660 5409 3465 5499 6231 5745 1801 5337 4468 1451 4192 1275 8230 2302 1114 4960 8860 3900 6468 5058 1505 8868 5588 3858 1947 2565 1472 8499 243 8442 6583 7085 5374 2250 4291 4426 492 2311 8305 3662 5338 8780 7488 3890 5005 2442 4680 7358 9116 4397 5999 587 7902 83 3566 2134 8942 4767 6601 2456 1745 5736 5254 8017 4015 7690 3798 8947 1067 2116 7945 590 2547 2535 64 2053 5359 2493 6669 4351 6412 7473 6147 7175 6983 5196 745 2657 3497 697 3161 7528 2239 5991 3201 7681 2440 5189 2959 2044 8917 2046 6313 6333 5318 2763 4301 2555 2213 2933 4121 1340 3903 4392 7889 5323 1055 707 3857 518 6078 5134 6645 9138 1592 680 4446 7943 3461 3887 5601 2321 6621 558 4914 913 5637 6453 8511 4531 1218 5508 2603 6802 8426 8297 2947 5971 6552 5262 5935 782 7435 8357 6139 1136 1473 5008 3585 3627 2914 5356 2997 2347 881 5652 4849 8808 8351 4017 2010 6836 7616 4391 3630 3712 6099 2969 5238 4333 2301 4406 1236 1050 1864 1104 8408 8251 8795 5879 3365 7481 8206 2452 1767 8859 124 3948 4444 8962 4438 5003 1740 8428 3105 5117 1095 1480 8755 7881 3097 4877 155 1917 2455 6042 337 6724 6045 8483 7135 2242 4566 1679 834 1746 795 3548 2314 2036 4046 9129 6979 7084 5091 2413 8170 5775 1817 529 7220 813 2916 5130 8972 126 1243 2370 4831 9122 3010 5104 2613 6761 7482 909 2146 4595 5340 3512 6283 2346 653 6121 2615 7421 1869 1002 8834 2991 8992 632 1093 4543 645 2352 4115 373 1483 6966 8598 3896 3434 5987 8318 1815 1223 1548 6885 5073 6330 2573 1369 4095 1431 2185 5766 1301 7258 8048 7598 2847 1996 2378 8561 743 6381 271 1956 7439 7596 7134 6636 5804 1858 6214 4730 8536 1203 3118 9202 1875 5885 8975 168 5898 4014 4186 3346 3041 5558 9296 8157 4339 3234 1738 2604 6803 5387 5590 125 2173 8012 8005 4858 3069 651 372 378 8366 6299 1449 7793 8541 3235 8043 3086 3983 6949 4690 2176 6494 7637 8406 3856 7408 350 7021 8224 7044 7662 6697 7679 169 528 7029 2790 7138 7432 7602 8333 1582 1378 519 482 9279 8015 6592 4514 3542 2612 628 5053 6699 6227 2094 1621 847 3598 2728 8490 7276 6620 8345 9216 4278 4059 9058 5063 5816 4173 8134 1997 3182 3224 8129 5109 4494 189 7640 8243 180 2963 1123 5593 3263 4185 7140 8990 6320 9275 4601 4854 5907 1135 8083 5964 7788 1992 8069 9174 6160 35 8572 2865 46 3952 6418 2510 5783 20 3816 2715 3930 2548 5204 4122 4103 708 7756 3825 777 3550 8502 3929 5440 6751 7764 4070 7331 3743 9131 9206 3828 23 41 4197 234 5723 7622 8832 4626 2169 5599 2976 5266 1967 1150 5334 90 822 2538 3169 6771 7442 498 4967 5580 7581 7680 4728 1115 4040 1064 3106 6266 4415 9294 5597 7059 197 7218 6948 5690 4234 1653 4485 4019 3370 919 1330 6085 2078 3768 5427 4545 2435 8862 3633 8145 5221 1388 5913 8140 7471 7156 6989 1190 6832 2830 4387 3454 7469 2910 4526 5187 2410 9223 6247 6912 4681 1300 7407 8612 6523 3616 6894 7253 4515 5874 5448 7137 7957 1130 3092 7054 3516 5797 1000 2727 4336 9090 6403 7255 8919 6522 6760 8898 4803 1938 374 8686 9150 3985 7045 3475 6065 7991 1409 7851 6671 6090 5826 7857 1155 8964 1117 7072 6064 2497 4899 2397 3189 2369 15 5027 5754 8950 5617 8391 914 6264 279 6174 5184 3733 7392 5278 2924 567 7994 352 8084 2148 2723 3359 70 1870 7708 220 3994 9013 3191 9220 4155 5717 1110 2198 9179 785 5325 4770 4250 52 4634 5072 9037 601 8036 7996 2483 7232 8675 8836 1279 5346 7676 6104 1515 4603 5607 7894 5144 2628 68 440 3586 3083 4830 4378 7762 1134 4542 7850 6296 2866 4011 8751 4776 7954 7102 5697 2032 5729 5017 6962 2051 1092 764 9019 2759 8581 1484 8618 912 2382 4892 8447 8176 5491 5695 5504 1060 7064 709 578 4320 2379 7649 8416 1613 5344 7512 7865 3037 6689 6557 1569 5955 3707 9168 8566 1775 5950 6943 7804 434 6179 9300 1142 7947 6456 6291 5789 6538 9134 3049 5075 5399 5161 1623 948 6302 6063 7516 117 506 3302 7146 355 3854 1081 2827 1496 2574 6167 3183 4287 5482 1722 7319 7277 3860 3443 3298 8364 3826 7254 2360 5093 7039 6325 4230 2567 6241 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404 2691 1015 7092 7562 8624 2291 4193 5934 5503 2326 4408 2960 842 1963 3354 5568 9050 3806 439 9154 6055 6451 2190 7633 688 4354 8890 2813 2872 8102 8317 6609 1497 8389 6449 1682 3594 5103 5812 863 268 3054 8079 2260 2027 3091 7687 6703 3557 2019 8427 2799 8182 6641 3168 2284 1934 4865 1077 6507 1658 3811 1774 7897 2238 2943 191 3869 3246 4057 3188 414 8072 7838 1382 4962 6010 5363 4042 1983 4077 7429 1833 3583 4044 1109 1295 386 5481 3927 311 ================================================ FILE: artrackv2_mindspore/lib/train/data_specs/got10k_val_split.txt ================================================ 1349 5651 5878 562 2202 8904 765 1501 8654 2975 2689 3680 5180 1900 7707 4723 8912 4029 3579 869 2888 8657 6599 741 4288 2244 7357 5704 8791 208 8587 7969 4805 8526 4887 8871 7468 3343 886 7794 5764 2646 6454 6101 7885 7744 1297 4119 4856 122 2286 2925 5131 3570 5843 3027 5320 5626 540 1862 5401 7335 699 7760 9198 3259 7345 8698 1280 6479 3100 3988 1322 5737 1268 3257 6791 3326 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4900 4090 7842 ================================================ FILE: artrackv2_mindspore/lib/train/data_specs/lasot_train_split.txt ================================================ airplane-10 airplane-11 airplane-12 airplane-14 airplane-16 airplane-17 airplane-18 airplane-19 airplane-2 airplane-20 airplane-3 airplane-4 airplane-5 airplane-6 airplane-7 airplane-8 basketball-10 basketball-12 basketball-13 basketball-14 basketball-15 basketball-16 basketball-17 basketball-18 basketball-19 basketball-2 basketball-20 basketball-3 basketball-4 basketball-5 basketball-8 basketball-9 bear-1 bear-10 bear-11 bear-12 bear-13 bear-14 bear-15 bear-16 bear-18 bear-19 bear-20 bear-3 bear-5 bear-7 bear-8 bear-9 bicycle-1 bicycle-10 bicycle-11 bicycle-12 bicycle-13 bicycle-14 bicycle-15 bicycle-16 bicycle-17 bicycle-19 bicycle-20 bicycle-3 bicycle-4 bicycle-5 bicycle-6 bicycle-8 bird-1 bird-10 bird-11 bird-12 bird-13 bird-14 bird-16 bird-18 bird-19 bird-20 bird-4 bird-5 bird-6 bird-7 bird-8 bird-9 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rubicCube-10 rubicCube-11 rubicCube-12 rubicCube-13 rubicCube-15 rubicCube-16 rubicCube-17 rubicCube-18 rubicCube-2 rubicCube-20 rubicCube-3 rubicCube-4 rubicCube-5 rubicCube-7 rubicCube-8 rubicCube-9 sepia-1 sepia-10 sepia-11 sepia-12 sepia-14 sepia-15 sepia-17 sepia-18 sepia-19 sepia-2 sepia-20 sepia-3 sepia-4 sepia-5 sepia-7 sepia-9 shark-1 shark-10 shark-11 shark-12 shark-13 shark-14 shark-15 shark-16 shark-17 shark-18 shark-19 shark-20 shark-4 shark-7 shark-8 shark-9 sheep-1 sheep-10 sheep-11 sheep-12 sheep-13 sheep-14 sheep-15 sheep-16 sheep-17 sheep-18 sheep-19 sheep-2 sheep-20 sheep-4 sheep-6 sheep-8 skateboard-1 skateboard-10 skateboard-11 skateboard-12 skateboard-13 skateboard-14 skateboard-15 skateboard-17 skateboard-18 skateboard-2 skateboard-20 skateboard-4 skateboard-5 skateboard-6 skateboard-7 skateboard-9 spider-1 spider-10 spider-11 spider-12 spider-13 spider-15 spider-17 spider-19 spider-2 spider-3 spider-4 spider-5 spider-6 spider-7 spider-8 spider-9 squirrel-1 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_sV1Jd1uiYg_0 person _tZU1XTOML4_0 boat _usyDpllGBo_0 horse _vBAv8cBoqE_0 skateboard _vV0wdWq0cU_0 person _xMVx44FbT4_0 horse _xQn3TupjYs_0 cat _xy58m6yCko_0 motorcycle _yQQjARqD1s_0 boat _yfoe4GCA0Q_4 airplane _yfoe4GCA0Q_2 airplane _yv5Cwbm9EA_0 person _zIDofZkgS4_1 truck _zQt1CSSKyA_1 bicycle _0eR2vQAEqE_0 elephant _0eR2vQAEqE_1 elephant _17u-cPTYt0_0 car _17u-cPTYt0_1 car _2mIWIhbDPY_0 bus _37U5Elgnck_0 person _5fE6dP48FM_0 cow _5sIT4l5izM_0 knife _6qUuUUYvUQ_0 person _7zbbqEa3nw_1 train _7zbbqEa3nw_4 train _8VTthFkvS0_0 bird _8iyumFI4sQ_1 elephant _8iyumFI4sQ_2 elephant _8iyumFI4sQ_3 elephant _81FImml2gk_0 dog _9bypka_Q4c_0 bus _-CvwC7H730_0 person _-XcxnQLKPM_0 dog __Q5A7gExpI_0 person ================================================ FILE: artrackv2_mindspore/lib/train/dataset/COCO_tool.py ================================================ __author__ = 'tylin' __version__ = '2.0' # Interface for accessing the Microsoft COCO dataset. # Microsoft COCO is a large image dataset designed for object detection, # segmentation, and caption generation. pycocotools is a Python API that # assists in loading, parsing and visualizing the annotations in COCO. # Please visit http://mscoco.org/ for more information on COCO, including # for the data, paper, and tutorials. The exact format of the annotations # is also described on the COCO website. For example usage of the pycocotools # please see pycocotools_demo.ipynb. In addition to this API, please download both # the COCO images and annotations in order to run the demo. # An alternative to using the API is to load the annotations directly # into Python dictionary # Using the API provides additional utility functions. Note that this API # supports both *instance* and *caption* annotations. In the case of # captions not all functions are defined (e.g. categories are undefined). # The following API functions are defined: # COCO - COCO api class that loads COCO annotation file and prepare data structures. # decodeMask - Decode binary mask M encoded via run-length encoding. # encodeMask - Encode binary mask M using run-length encoding. # getAnnIds - Get ann ids that satisfy given filter conditions. # getCatIds - Get cat ids that satisfy given filter conditions. # getImgIds - Get img ids that satisfy given filter conditions. # loadAnns - Load anns with the specified ids. # loadCats - Load cats with the specified ids. # loadImgs - Load imgs with the specified ids. # annToMask - Convert segmentation in an annotation to binary mask. # showAnns - Display the specified annotations. # loadRes - Load algorithm results and create API for accessing them. # download - Download COCO images from mscoco.org server. # Throughout the API "ann"=annotation, "cat"=category, and "img"=image. # Help on each functions can be accessed by: "help COCO>function". # See also COCO>decodeMask, # COCO>encodeMask, COCO>getAnnIds, COCO>getCatIds, # COCO>getImgIds, COCO>loadAnns, COCO>loadCats, # COCO>loadImgs, COCO>annToMask, COCO>showAnns # Microsoft COCO Toolbox. version 2.0 # Data, paper, and tutorials available at: http://mscoco.org/ # Code written by Piotr Dollar and Tsung-Yi Lin, 2014. # Licensed under the Simplified BSD License [see bsd.txt] import json import time import matplotlib.pyplot as plt from matplotlib.collections import PatchCollection from matplotlib.patches import Polygon import numpy as np import copy import itertools from pycocotools import mask as maskUtils import os from collections import defaultdict import sys PYTHON_VERSION = sys.version_info[0] if PYTHON_VERSION == 2: from urllib import urlretrieve elif PYTHON_VERSION == 3: from urllib.request import urlretrieve def _isArrayLike(obj): return hasattr(obj, '__iter__') and hasattr(obj, '__len__') class COCO: def __init__(self, dataset): """ Constructor of Microsoft COCO helper class for reading and visualizing annotations. :param annotation_file (str): location of annotation file :param image_folder (str): location to the folder that hosts images. :return: """ # load dataset self.dataset,self.anns,self.cats,self.imgs = dict(),dict(),dict(),dict() self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list) assert type(dataset)==dict, 'annotation file format {} not supported'.format(type(dataset)) self.dataset = dataset self.createIndex() def createIndex(self): # create index print('creating index...') anns, cats, imgs = {}, {}, {} imgToAnns,catToImgs = defaultdict(list),defaultdict(list) if 'annotations' in self.dataset: for ann in self.dataset['annotations']: imgToAnns[ann['image_id']].append(ann) anns[ann['id']] = ann if 'images' in self.dataset: for img in self.dataset['images']: imgs[img['id']] = img if 'categories' in self.dataset: for cat in self.dataset['categories']: cats[cat['id']] = cat if 'annotations' in self.dataset and 'categories' in self.dataset: for ann in self.dataset['annotations']: catToImgs[ann['category_id']].append(ann['image_id']) print('index created!') # create class members self.anns = anns self.imgToAnns = imgToAnns self.catToImgs = catToImgs self.imgs = imgs self.cats = cats def info(self): """ Print information about the annotation file. :return: """ for key, value in self.dataset['info'].items(): print('{}: {}'.format(key, value)) def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None): """ Get ann ids that satisfy given filter conditions. default skips that filter :param imgIds (int array) : get anns for given imgs catIds (int array) : get anns for given cats areaRng (float array) : get anns for given area range (e.g. [0 inf]) iscrowd (boolean) : get anns for given crowd label (False or True) :return: ids (int array) : integer array of ann ids """ imgIds = imgIds if _isArrayLike(imgIds) else [imgIds] catIds = catIds if _isArrayLike(catIds) else [catIds] if len(imgIds) == len(catIds) == len(areaRng) == 0: anns = self.dataset['annotations'] else: if not len(imgIds) == 0: lists = [self.imgToAnns[imgId] for imgId in imgIds if imgId in self.imgToAnns] anns = list(itertools.chain.from_iterable(lists)) else: anns = self.dataset['annotations'] anns = anns if len(catIds) == 0 else [ann for ann in anns if ann['category_id'] in catIds] anns = anns if len(areaRng) == 0 else [ann for ann in anns if ann['area'] > areaRng[0] and ann['area'] < areaRng[1]] if not iscrowd == None: ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd] else: ids = [ann['id'] for ann in anns] return ids def getCatIds(self, catNms=[], supNms=[], catIds=[]): """ filtering parameters. default skips that filter. :param catNms (str array) : get cats for given cat names :param supNms (str array) : get cats for given supercategory names :param catIds (int array) : get cats for given cat ids :return: ids (int array) : integer array of cat ids """ catNms = catNms if _isArrayLike(catNms) else [catNms] supNms = supNms if _isArrayLike(supNms) else [supNms] catIds = catIds if _isArrayLike(catIds) else [catIds] if len(catNms) == len(supNms) == len(catIds) == 0: cats = self.dataset['categories'] else: cats = self.dataset['categories'] cats = cats if len(catNms) == 0 else [cat for cat in cats if cat['name'] in catNms] cats = cats if len(supNms) == 0 else [cat for cat in cats if cat['supercategory'] in supNms] cats = cats if len(catIds) == 0 else [cat for cat in cats if cat['id'] in catIds] ids = [cat['id'] for cat in cats] return ids def getImgIds(self, imgIds=[], catIds=[]): ''' Get img ids that satisfy given filter conditions. :param imgIds (int array) : get imgs for given ids :param catIds (int array) : get imgs with all given cats :return: ids (int array) : integer array of img ids ''' imgIds = imgIds if _isArrayLike(imgIds) else [imgIds] catIds = catIds if _isArrayLike(catIds) else [catIds] if len(imgIds) == len(catIds) == 0: ids = self.imgs.keys() else: ids = set(imgIds) for i, catId in enumerate(catIds): if i == 0 and len(ids) == 0: ids = set(self.catToImgs[catId]) else: ids &= set(self.catToImgs[catId]) return list(ids) def loadAnns(self, ids=[]): """ Load anns with the specified ids. :param ids (int array) : integer ids specifying anns :return: anns (object array) : loaded ann objects """ if _isArrayLike(ids): return [self.anns[id] for id in ids] elif type(ids) == int: return [self.anns[ids]] def loadCats(self, ids=[]): """ Load cats with the specified ids. :param ids (int array) : integer ids specifying cats :return: cats (object array) : loaded cat objects """ if _isArrayLike(ids): return [self.cats[id] for id in ids] elif type(ids) == int: return [self.cats[ids]] def loadImgs(self, ids=[]): """ Load anns with the specified ids. :param ids (int array) : integer ids specifying img :return: imgs (object array) : loaded img objects """ if _isArrayLike(ids): return [self.imgs[id] for id in ids] elif type(ids) == int: return [self.imgs[ids]] def showAnns(self, anns, draw_bbox=False): """ Display the specified annotations. :param anns (array of object): annotations to display :return: None """ if len(anns) == 0: return 0 if 'segmentation' in anns[0] or 'keypoints' in anns[0]: datasetType = 'instances' elif 'caption' in anns[0]: datasetType = 'captions' else: raise Exception('datasetType not supported') if datasetType == 'instances': ax = plt.gca() ax.set_autoscale_on(False) polygons = [] color = [] for ann in anns: c = (np.random.random((1, 3))*0.6+0.4).tolist()[0] if 'segmentation' in ann: if type(ann['segmentation']) == list: # polygon for seg in ann['segmentation']: poly = np.array(seg).reshape((int(len(seg)/2), 2)) polygons.append(Polygon(poly)) color.append(c) else: # mask t = self.imgs[ann['image_id']] if type(ann['segmentation']['counts']) == list: rle = maskUtils.frPyObjects([ann['segmentation']], t['height'], t['width']) else: rle = [ann['segmentation']] m = maskUtils.decode(rle) img = np.ones( (m.shape[0], m.shape[1], 3) ) if ann['iscrowd'] == 1: color_mask = np.array([2.0,166.0,101.0])/255 if ann['iscrowd'] == 0: color_mask = np.random.random((1, 3)).tolist()[0] for i in range(3): img[:,:,i] = color_mask[i] ax.imshow(np.dstack( (img, m*0.5) )) if 'keypoints' in ann and type(ann['keypoints']) == list: # turn skeleton into zero-based index sks = np.array(self.loadCats(ann['category_id'])[0]['skeleton'])-1 kp = np.array(ann['keypoints']) x = kp[0::3] y = kp[1::3] v = kp[2::3] for sk in sks: if np.all(v[sk]>0): plt.plot(x[sk],y[sk], linewidth=3, color=c) plt.plot(x[v>0], y[v>0],'o',markersize=8, markerfacecolor=c, markeredgecolor='k',markeredgewidth=2) plt.plot(x[v>1], y[v>1],'o',markersize=8, markerfacecolor=c, markeredgecolor=c, markeredgewidth=2) if draw_bbox: [bbox_x, bbox_y, bbox_w, bbox_h] = ann['bbox'] poly = [[bbox_x, bbox_y], [bbox_x, bbox_y+bbox_h], [bbox_x+bbox_w, bbox_y+bbox_h], [bbox_x+bbox_w, bbox_y]] np_poly = np.array(poly).reshape((4,2)) polygons.append(Polygon(np_poly)) color.append(c) p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4) ax.add_collection(p) p = PatchCollection(polygons, facecolor='none', edgecolors=color, linewidths=2) ax.add_collection(p) elif datasetType == 'captions': for ann in anns: print(ann['caption']) def loadRes(self, resFile): """ Load result file and return a result api object. :param resFile (str) : file name of result file :return: res (obj) : result api object """ res = COCO() res.dataset['images'] = [img for img in self.dataset['images']] print('Loading and preparing results...') tic = time.time() if type(resFile) == str or (PYTHON_VERSION == 2 and type(resFile) == unicode): with open(resFile) as f: anns = json.load(f) elif type(resFile) == np.ndarray: anns = self.loadNumpyAnnotations(resFile) else: anns = resFile assert type(anns) == list, 'results in not an array of objects' annsImgIds = [ann['image_id'] for ann in anns] assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), \ 'Results do not correspond to current coco set' if 'caption' in anns[0]: imgIds = set([img['id'] for img in res.dataset['images']]) & set([ann['image_id'] for ann in anns]) res.dataset['images'] = [img for img in res.dataset['images'] if img['id'] in imgIds] for id, ann in enumerate(anns): ann['id'] = id+1 elif 'bbox' in anns[0] and not anns[0]['bbox'] == []: res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) for id, ann in enumerate(anns): bb = ann['bbox'] x1, x2, y1, y2 = [bb[0], bb[0]+bb[2], bb[1], bb[1]+bb[3]] if not 'segmentation' in ann: ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]] ann['area'] = bb[2]*bb[3] ann['id'] = id+1 ann['iscrowd'] = 0 elif 'segmentation' in anns[0]: res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) for id, ann in enumerate(anns): # now only support compressed RLE format as segmentation results ann['area'] = maskUtils.area(ann['segmentation']) if not 'bbox' in ann: ann['bbox'] = maskUtils.toBbox(ann['segmentation']) ann['id'] = id+1 ann['iscrowd'] = 0 elif 'keypoints' in anns[0]: res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) for id, ann in enumerate(anns): s = ann['keypoints'] x = s[0::3] y = s[1::3] x0,x1,y0,y1 = np.min(x), np.max(x), np.min(y), np.max(y) ann['area'] = (x1-x0)*(y1-y0) ann['id'] = id + 1 ann['bbox'] = [x0,y0,x1-x0,y1-y0] print('DONE (t={:0.2f}s)'.format(time.time()- tic)) res.dataset['annotations'] = anns res.createIndex() return res def download(self, tarDir = None, imgIds = [] ): ''' Download COCO images from mscoco.org server. :param tarDir (str): COCO results directory name imgIds (list): images to be downloaded :return: ''' if tarDir is None: print('Please specify target directory') return -1 if len(imgIds) == 0: imgs = self.imgs.values() else: imgs = self.loadImgs(imgIds) N = len(imgs) if not os.path.exists(tarDir): os.makedirs(tarDir) for i, img in enumerate(imgs): tic = time.time() fname = os.path.join(tarDir, img['file_name']) if not os.path.exists(fname): urlretrieve(img['coco_url'], fname) print('downloaded {}/{} images (t={:0.1f}s)'.format(i, N, time.time()- tic)) def loadNumpyAnnotations(self, data): """ Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class} :param data (numpy.ndarray) :return: annotations (python nested list) """ print('Converting ndarray to lists...') assert(type(data) == np.ndarray) print(data.shape) assert(data.shape[1] == 7) N = data.shape[0] ann = [] for i in range(N): if i % 1000000 == 0: print('{}/{}'.format(i,N)) ann += [{ 'image_id' : int(data[i, 0]), 'bbox' : [ data[i, 1], data[i, 2], data[i, 3], data[i, 4] ], 'score' : data[i, 5], 'category_id': int(data[i, 6]), }] return ann def annToRLE(self, ann): """ Convert annotation which can be polygons, uncompressed RLE to RLE. :return: binary mask (numpy 2D array) """ t = self.imgs[ann['image_id']] h, w = t['height'], t['width'] segm = ann['segmentation'] if type(segm) == list: # polygon -- a single object might consist of multiple parts # we merge all parts into one mask rle code rles = maskUtils.frPyObjects(segm, h, w) rle = maskUtils.merge(rles) elif type(segm['counts']) == list: # uncompressed RLE rle = maskUtils.frPyObjects(segm, h, w) else: # rle rle = ann['segmentation'] return rle def annToMask(self, ann): """ Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask. :return: binary mask (numpy 2D array) """ rle = self.annToRLE(ann) m = maskUtils.decode(rle) return m ================================================ FILE: artrackv2_mindspore/lib/train/dataset/__init__.py ================================================ from .lasot import Lasot from .got10k import Got10k from .tracking_net import TrackingNet from .imagenetvid import ImagenetVID from .coco import MSCOCO from .coco_seq import MSCOCOSeq from .got10k_lmdb import Got10k_lmdb from .lasot_lmdb import Lasot_lmdb from .imagenetvid_lmdb import ImagenetVID_lmdb from .coco_seq_lmdb import MSCOCOSeq_lmdb from .tracking_net_lmdb import TrackingNet_lmdb ================================================ FILE: artrackv2_mindspore/lib/train/dataset/base_image_dataset.py ================================================ import torch.utils.data from lib.train.data.image_loader import jpeg4py_loader class BaseImageDataset(torch.utils.data.Dataset): """ Base class for image datasets """ def __init__(self, name, root, image_loader=jpeg4py_loader): """ args: root - The root path to the dataset image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. """ self.name = name self.root = root self.image_loader = image_loader self.image_list = [] # Contains the list of sequences. self.class_list = [] def __len__(self): """ Returns size of the dataset returns: int - number of samples in the dataset """ return self.get_num_images() def __getitem__(self, index): """ Not to be used! Check get_frames() instead. """ return None def get_name(self): """ Name of the dataset returns: string - Name of the dataset """ raise NotImplementedError def get_num_images(self): """ Number of sequences in a dataset returns: int - number of sequences in the dataset.""" return len(self.image_list) def has_class_info(self): return False def get_class_name(self, image_id): return None def get_num_classes(self): return len(self.class_list) def get_class_list(self): return self.class_list def get_images_in_class(self, class_name): raise NotImplementedError def has_segmentation_info(self): return False def get_image_info(self, seq_id): """ Returns information about a particular image, args: seq_id - index of the image returns: Dict """ raise NotImplementedError def get_image(self, image_id, anno=None): """ Get a image args: image_id - index of image anno(None) - The annotation for the sequence (see get_sequence_info). If None, they will be loaded. returns: image - anno - dict - A dict containing meta information about the sequence, e.g. class of the target object. """ raise NotImplementedError ================================================ FILE: artrackv2_mindspore/lib/train/dataset/base_video_dataset.py ================================================ import torch.utils.data # 2021.1.5 use jpeg4py_loader_w_failsafe as default from lib.train.data.image_loader import jpeg4py_loader_w_failsafe class BaseVideoDataset(torch.utils.data.Dataset): """ Base class for video datasets """ def __init__(self, name, root, image_loader=jpeg4py_loader_w_failsafe): """ args: root - The root path to the dataset image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. """ self.name = name self.root = root self.image_loader = image_loader self.sequence_list = [] # Contains the list of sequences. self.class_list = [] def __len__(self): """ Returns size of the dataset returns: int - number of samples in the dataset """ return self.get_num_sequences() def __getitem__(self, index): """ Not to be used! Check get_frames() instead. """ return None def is_video_sequence(self): """ Returns whether the dataset is a video dataset or an image dataset returns: bool - True if a video dataset """ return True def is_synthetic_video_dataset(self): """ Returns whether the dataset contains real videos or synthetic returns: bool - True if a video dataset """ return False def get_name(self): """ Name of the dataset returns: string - Name of the dataset """ raise NotImplementedError def get_num_sequences(self): """ Number of sequences in a dataset returns: int - number of sequences in the dataset.""" return len(self.sequence_list) def has_class_info(self): return False def has_occlusion_info(self): return False def get_num_classes(self): return len(self.class_list) def get_class_list(self): return self.class_list def get_sequences_in_class(self, class_name): raise NotImplementedError def has_segmentation_info(self): return False def get_sequence_info(self, seq_id): """ Returns information about a particular sequences, args: seq_id - index of the sequence returns: Dict """ raise NotImplementedError def get_frames(self, seq_id, frame_ids, anno=None): """ Get a set of frames from a particular sequence args: seq_id - index of sequence frame_ids - a list of frame numbers anno(None) - The annotation for the sequence (see get_sequence_info). If None, they will be loaded. returns: list - List of frames corresponding to frame_ids list - List of dicts for each frame dict - A dict containing meta information about the sequence, e.g. class of the target object. """ raise NotImplementedError ================================================ FILE: artrackv2_mindspore/lib/train/dataset/coco.py ================================================ import os from .base_image_dataset import BaseImageDataset import torch import random from collections import OrderedDict from lib.train.data import jpeg4py_loader from lib.train.admin import env_settings from pycocotools.coco import COCO class MSCOCO(BaseImageDataset): """ The COCO object detection dataset. Publication: Microsoft COCO: Common Objects in Context. Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar and C. Lawrence Zitnick ECCV, 2014 https://arxiv.org/pdf/1405.0312.pdf Download the images along with annotations from http://cocodataset.org/#download. The root folder should be organized as follows. - coco_root - annotations - instances_train2014.json - instances_train2017.json - images - train2014 - train2017 Note: You also have to install the coco pythonAPI from https://github.com/cocodataset/cocoapi. """ def __init__(self, root=None, image_loader=jpeg4py_loader, data_fraction=None, min_area=None, split="train", version="2014"): """ args: root - path to coco root folder image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. data_fraction - Fraction of dataset to be used. The complete dataset is used by default min_area - Objects with area less than min_area are filtered out. Default is 0.0 split - 'train' or 'val'. version - version of coco dataset (2014 or 2017) """ root = env_settings().coco_dir if root is None else root super().__init__('COCO', root, image_loader) self.img_pth = os.path.join(root, 'images/{}{}/'.format(split, version)) self.anno_path = os.path.join(root, 'annotations/instances_{}{}.json'.format(split, version)) self.coco_set = COCO(self.anno_path) self.cats = self.coco_set.cats self.class_list = self.get_class_list() # the parent class thing would happen in the sampler self.image_list = self._get_image_list(min_area=min_area) if data_fraction is not None: self.image_list = random.sample(self.image_list, int(len(self.image_list) * data_fraction)) self.im_per_class = self._build_im_per_class() def _get_image_list(self, min_area=None): ann_list = list(self.coco_set.anns.keys()) image_list = [a for a in ann_list if self.coco_set.anns[a]['iscrowd'] == 0] if min_area is not None: image_list = [a for a in image_list if self.coco_set.anns[a]['area'] > min_area] return image_list def get_num_classes(self): return len(self.class_list) def get_name(self): return 'coco' def has_class_info(self): return True def has_segmentation_info(self): return True def get_class_list(self): class_list = [] for cat_id in self.cats.keys(): class_list.append(self.cats[cat_id]['name']) return class_list def _build_im_per_class(self): im_per_class = {} for i, im in enumerate(self.image_list): class_name = self.cats[self.coco_set.anns[im]['category_id']]['name'] if class_name not in im_per_class: im_per_class[class_name] = [i] else: im_per_class[class_name].append(i) return im_per_class def get_images_in_class(self, class_name): return self.im_per_class[class_name] def get_image_info(self, im_id): anno = self._get_anno(im_id) bbox = torch.Tensor(anno['bbox']).view(4,) mask = torch.Tensor(self.coco_set.annToMask(anno)) valid = (bbox[2] > 0) & (bbox[3] > 0) visible = valid.clone().byte() return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible} def _get_anno(self, im_id): anno = self.coco_set.anns[self.image_list[im_id]] return anno def _get_image(self, im_id): path = self.coco_set.loadImgs([self.coco_set.anns[self.image_list[im_id]]['image_id']])[0]['file_name'] img = self.image_loader(os.path.join(self.img_pth, path)) return img def get_meta_info(self, im_id): try: cat_dict_current = self.cats[self.coco_set.anns[self.image_list[im_id]]['category_id']] object_meta = OrderedDict({'object_class_name': cat_dict_current['name'], 'motion_class': None, 'major_class': cat_dict_current['supercategory'], 'root_class': None, 'motion_adverb': None}) except: object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def get_class_name(self, im_id): cat_dict_current = self.cats[self.coco_set.anns[self.image_list[im_id]]['category_id']] return cat_dict_current['name'] def get_image(self, image_id, anno=None): frame = self._get_image(image_id) if anno is None: anno = self.get_image_info(image_id) object_meta = self.get_meta_info(image_id) return frame, anno, object_meta ================================================ FILE: artrackv2_mindspore/lib/train/dataset/coco_seq.py ================================================ import os from .base_video_dataset import BaseVideoDataset from lib.train.data import jpeg4py_loader import torch import random from pycocotools.coco import COCO from collections import OrderedDict from lib.train.admin import env_settings class MSCOCOSeq(BaseVideoDataset): """ The COCO dataset. COCO is an image dataset. Thus, we treat each image as a sequence of length 1. Publication: Microsoft COCO: Common Objects in Context. Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar and C. Lawrence Zitnick ECCV, 2014 https://arxiv.org/pdf/1405.0312.pdf Download the images along with annotations from http://cocodataset.org/#download. The root folder should be organized as follows. - coco_root - annotations - instances_train2014.json - instances_train2017.json - images - train2014 - train2017 Note: You also have to install the coco pythonAPI from https://github.com/cocodataset/cocoapi. """ def __init__(self, root=None, image_loader=jpeg4py_loader, data_fraction=None, split="train", version="2014"): """ args: root - path to the coco dataset. image_loader (default_image_loader) - The function to read the images. If installed, jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else, opencv's imread is used. data_fraction (None) - Fraction of images to be used. The images are selected randomly. If None, all the images will be used split - 'train' or 'val'. version - version of coco dataset (2014 or 2017) """ root = env_settings().coco_dir if root is None else root super().__init__('COCO', root, image_loader) self.img_pth = os.path.join(root, 'images/{}{}/'.format(split, version)) self.anno_path = os.path.join(root, 'annotations/instances_{}{}.json'.format(split, version)) # Load the COCO set. self.coco_set = COCO(self.anno_path) self.cats = self.coco_set.cats self.class_list = self.get_class_list() self.sequence_list = self._get_sequence_list() if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction)) self.seq_per_class = self._build_seq_per_class() def _get_sequence_list(self): ann_list = list(self.coco_set.anns.keys()) seq_list = [a for a in ann_list if self.coco_set.anns[a]['iscrowd'] == 0] return seq_list def is_video_sequence(self): return False def get_num_classes(self): return len(self.class_list) def get_name(self): return 'coco' def has_class_info(self): return True def get_class_list(self): class_list = [] for cat_id in self.cats.keys(): class_list.append(self.cats[cat_id]['name']) return class_list def has_segmentation_info(self): return True def get_num_sequences(self): return len(self.sequence_list) def _build_seq_per_class(self): seq_per_class = {} for i, seq in enumerate(self.sequence_list): class_name = self.cats[self.coco_set.anns[seq]['category_id']]['name'] if class_name not in seq_per_class: seq_per_class[class_name] = [i] else: seq_per_class[class_name].append(i) return seq_per_class def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def get_sequence_info(self, seq_id): anno = self._get_anno(seq_id) bbox = torch.Tensor(anno['bbox']).view(1, 4) mask = torch.Tensor(self.coco_set.annToMask(anno)).unsqueeze(dim=0) '''2021.1.3 To avoid too small bounding boxes. Here we change the threshold to 50 pixels''' valid = (bbox[:, 2] > 50) & (bbox[:, 3] > 50) visible = valid.clone().byte() return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible} def _get_anno(self, seq_id): anno = self.coco_set.anns[self.sequence_list[seq_id]] return anno def _get_frames(self, seq_id): path = self.coco_set.loadImgs([self.coco_set.anns[self.sequence_list[seq_id]]['image_id']])[0]['file_name'] img = self.image_loader(os.path.join(self.img_pth, path)) return img def get_meta_info(self, seq_id): try: cat_dict_current = self.cats[self.coco_set.anns[self.sequence_list[seq_id]]['category_id']] object_meta = OrderedDict({'object_class_name': cat_dict_current['name'], 'motion_class': None, 'major_class': cat_dict_current['supercategory'], 'root_class': None, 'motion_adverb': None}) except: object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def get_class_name(self, seq_id): cat_dict_current = self.cats[self.coco_set.anns[self.sequence_list[seq_id]]['category_id']] return cat_dict_current['name'] def get_frames(self, seq_id=None, frame_ids=None, anno=None): # COCO is an image dataset. Thus we replicate the image denoted by seq_id len(frame_ids) times, and return a # list containing these replicated images. frame = self._get_frames(seq_id) frame_list = [frame.copy() for _ in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[0, ...] for _ in frame_ids] object_meta = self.get_meta_info(seq_id) return frame_list, anno_frames, object_meta ================================================ FILE: artrackv2_mindspore/lib/train/dataset/coco_seq_lmdb.py ================================================ import os from .base_video_dataset import BaseVideoDataset from lib.train.data import jpeg4py_loader import torch import random from collections import OrderedDict from lib.train.admin import env_settings from lib.train.dataset.COCO_tool import COCO from lib.utils.lmdb_utils import decode_img, decode_json import time class MSCOCOSeq_lmdb(BaseVideoDataset): """ The COCO dataset. COCO is an image dataset. Thus, we treat each image as a sequence of length 1. Publication: Microsoft COCO: Common Objects in Context. Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar and C. Lawrence Zitnick ECCV, 2014 https://arxiv.org/pdf/1405.0312.pdf Download the images along with annotations from http://cocodataset.org/#download. The root folder should be organized as follows. - coco_root - annotations - instances_train2014.json - instances_train2017.json - images - train2014 - train2017 Note: You also have to install the coco pythonAPI from https://github.com/cocodataset/cocoapi. """ def __init__(self, root=None, image_loader=jpeg4py_loader, data_fraction=None, split="train", version="2014"): """ args: root - path to the coco dataset. image_loader (default_image_loader) - The function to read the images. If installed, jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else, opencv's imread is used. data_fraction (None) - Fraction of images to be used. The images are selected randomly. If None, all the images will be used split - 'train' or 'val'. version - version of coco dataset (2014 or 2017) """ root = env_settings().coco_dir if root is None else root super().__init__('COCO_lmdb', root, image_loader) self.root = root self.img_pth = 'images/{}{}/'.format(split, version) self.anno_path = 'annotations/instances_{}{}.json'.format(split, version) # Load the COCO set. print('loading annotations into memory...') tic = time.time() coco_json = decode_json(root, self.anno_path) print('Done (t={:0.2f}s)'.format(time.time() - tic)) self.coco_set = COCO(coco_json) self.cats = self.coco_set.cats self.class_list = self.get_class_list() self.sequence_list = self._get_sequence_list() if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction)) self.seq_per_class = self._build_seq_per_class() def _get_sequence_list(self): ann_list = list(self.coco_set.anns.keys()) seq_list = [a for a in ann_list if self.coco_set.anns[a]['iscrowd'] == 0] return seq_list def is_video_sequence(self): return False def get_num_classes(self): return len(self.class_list) def get_name(self): return 'coco_lmdb' def has_class_info(self): return True def get_class_list(self): class_list = [] for cat_id in self.cats.keys(): class_list.append(self.cats[cat_id]['name']) return class_list def has_segmentation_info(self): return True def get_num_sequences(self): return len(self.sequence_list) def _build_seq_per_class(self): seq_per_class = {} for i, seq in enumerate(self.sequence_list): class_name = self.cats[self.coco_set.anns[seq]['category_id']]['name'] if class_name not in seq_per_class: seq_per_class[class_name] = [i] else: seq_per_class[class_name].append(i) return seq_per_class def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def get_sequence_info(self, seq_id): anno = self._get_anno(seq_id) bbox = torch.Tensor(anno['bbox']).view(1, 4) mask = torch.Tensor(self.coco_set.annToMask(anno)).unsqueeze(dim=0) '''2021.1.3 To avoid too small bounding boxes. Here we change the threshold to 50 pixels''' valid = (bbox[:, 2] > 50) & (bbox[:, 3] > 50) visible = valid.clone().byte() return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible} def _get_anno(self, seq_id): anno = self.coco_set.anns[self.sequence_list[seq_id]] return anno def _get_frames(self, seq_id): path = self.coco_set.loadImgs([self.coco_set.anns[self.sequence_list[seq_id]]['image_id']])[0]['file_name'] # img = self.image_loader(os.path.join(self.img_pth, path)) img = decode_img(self.root, os.path.join(self.img_pth, path)) return img def get_meta_info(self, seq_id): try: cat_dict_current = self.cats[self.coco_set.anns[self.sequence_list[seq_id]]['category_id']] object_meta = OrderedDict({'object_class_name': cat_dict_current['name'], 'motion_class': None, 'major_class': cat_dict_current['supercategory'], 'root_class': None, 'motion_adverb': None}) except: object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def get_class_name(self, seq_id): cat_dict_current = self.cats[self.coco_set.anns[self.sequence_list[seq_id]]['category_id']] return cat_dict_current['name'] def get_frames(self, seq_id=None, frame_ids=None, anno=None): # COCO is an image dataset. Thus we replicate the image denoted by seq_id len(frame_ids) times, and return a # list containing these replicated images. frame = self._get_frames(seq_id) frame_list = [frame.copy() for _ in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[0, ...] for _ in frame_ids] object_meta = self.get_meta_info(seq_id) return frame_list, anno_frames, object_meta ================================================ FILE: artrackv2_mindspore/lib/train/dataset/got10k.py ================================================ import os import os.path import numpy as np import torch import csv import pandas import random from collections import OrderedDict from .base_video_dataset import BaseVideoDataset from lib.train.data import jpeg4py_loader from lib.train.admin import env_settings class Got10k(BaseVideoDataset): """ GOT-10k dataset. Publication: GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild Lianghua Huang, Xin Zhao, and Kaiqi Huang arXiv:1810.11981, 2018 https://arxiv.org/pdf/1810.11981.pdf Download dataset from http://got-10k.aitestunion.com/downloads """ def __init__(self, root=None, image_loader=jpeg4py_loader, split=None, seq_ids=None, data_fraction=None): """ args: root - path to the got-10k training data. Note: This should point to the 'train' folder inside GOT-10k image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. split - 'train' or 'val'. Note: The validation split here is a subset of the official got-10k train split, not NOT the official got-10k validation split. To use the official validation split, provide that as the root folder instead. seq_ids - List containing the ids of the videos to be used for training. Note: Only one of 'split' or 'seq_ids' options can be used at the same time. data_fraction - Fraction of dataset to be used. The complete dataset is used by default """ root = env_settings().got10k_dir if root is None else root super().__init__('GOT10k', root, image_loader) # all folders inside the root self.sequence_list = self._get_sequence_list() # seq_id is the index of the folder inside the got10k root path if split is not None: if seq_ids is not None: raise ValueError('Cannot set both split_name and seq_ids.') ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') if split == 'train': file_path = os.path.join(ltr_path, 'data_specs', 'got10k_train_split.txt') elif split == 'val': file_path = os.path.join(ltr_path, 'data_specs', 'got10k_val_split.txt') elif split == 'train_full': file_path = os.path.join(ltr_path, 'data_specs', 'got10k_train_full_split.txt') elif split == 'vottrain': file_path = os.path.join(ltr_path, 'data_specs', 'got10k_vot_train_split.txt') elif split == 'votval': file_path = os.path.join(ltr_path, 'data_specs', 'got10k_vot_val_split.txt') else: raise ValueError('Unknown split name.') # seq_ids = pandas.read_csv(file_path, header=None, squeeze=True, dtype=np.int64).values.tolist() seq_ids = pandas.read_csv(file_path, header=None, dtype=np.int64).squeeze("columns").values.tolist() elif seq_ids is None: seq_ids = list(range(0, len(self.sequence_list))) self.sequence_list = [self.sequence_list[i] for i in seq_ids] if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction)) self.sequence_meta_info = self._load_meta_info() self.seq_per_class = self._build_seq_per_class() self.class_list = list(self.seq_per_class.keys()) self.class_list.sort() def get_name(self): return 'got10k' def has_class_info(self): return True def has_occlusion_info(self): return True def _load_meta_info(self): sequence_meta_info = {s: self._read_meta(os.path.join(self.root, s)) for s in self.sequence_list} return sequence_meta_info def _read_meta(self, seq_path): try: with open(os.path.join(seq_path, 'meta_info.ini')) as f: meta_info = f.readlines() object_meta = OrderedDict({'object_class_name': meta_info[5].split(': ')[-1][:-1], 'motion_class': meta_info[6].split(': ')[-1][:-1], 'major_class': meta_info[7].split(': ')[-1][:-1], 'root_class': meta_info[8].split(': ')[-1][:-1], 'motion_adverb': meta_info[9].split(': ')[-1][:-1]}) except: object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def _build_seq_per_class(self): seq_per_class = {} for i, s in enumerate(self.sequence_list): object_class = self.sequence_meta_info[s]['object_class_name'] if object_class in seq_per_class: seq_per_class[object_class].append(i) else: seq_per_class[object_class] = [i] return seq_per_class def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def _get_sequence_list(self): with open(os.path.join(self.root, 'list.txt')) as f: dir_list = list(csv.reader(f)) dir_list = [dir_name[0] for dir_name in dir_list] return dir_list def _read_bb_anno(self, seq_path): bb_anno_file = os.path.join(seq_path, "groundtruth.txt") gt = pandas.read_csv(bb_anno_file, delimiter=',', header=None, dtype=np.float32, na_filter=False, low_memory=False).values return torch.tensor(gt) def _read_target_visible(self, seq_path): # Read full occlusion and out_of_view occlusion_file = os.path.join(seq_path, "absence.label") cover_file = os.path.join(seq_path, "cover.label") with open(occlusion_file, 'r', newline='') as f: occlusion = torch.ByteTensor([int(v[0]) for v in csv.reader(f)]) with open(cover_file, 'r', newline='') as f: cover = torch.ByteTensor([int(v[0]) for v in csv.reader(f)]) target_visible = ~occlusion & (cover>0).byte() visible_ratio = cover.float() / 8 return target_visible, visible_ratio def _get_sequence_path(self, seq_id): return os.path.join(self.root, self.sequence_list[seq_id]) def get_sequence_info(self, seq_id): seq_path = self._get_sequence_path(seq_id) bbox = self._read_bb_anno(seq_path) valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0) visible, visible_ratio = self._read_target_visible(seq_path) visible = visible & valid.byte() return {'bbox': bbox, 'valid': valid, 'visible': visible, 'visible_ratio': visible_ratio} def _get_frame_path(self, seq_path, frame_id): return os.path.join(seq_path, '{:08}.jpg'.format(frame_id+1)) # frames start from 1 def _get_frame(self, seq_path, frame_id): return self.image_loader(self._get_frame_path(seq_path, frame_id)) def get_class_name(self, seq_id): obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]] return obj_meta['object_class_name'] def get_frames(self, seq_id, frame_ids, anno=None): seq_path = self._get_sequence_path(seq_id) obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]] frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] return frame_list, anno_frames, obj_meta ================================================ FILE: artrackv2_mindspore/lib/train/dataset/got10k_lmdb.py ================================================ import os import os.path import numpy as np import torch import csv import pandas import random from collections import OrderedDict from .base_video_dataset import BaseVideoDataset from lib.train.data import jpeg4py_loader from lib.train.admin import env_settings '''2021.1.16 Gok10k for loading lmdb dataset''' from lib.utils.lmdb_utils import * class Got10k_lmdb(BaseVideoDataset): def __init__(self, root=None, image_loader=jpeg4py_loader, split=None, seq_ids=None, data_fraction=None): """ args: root - path to the got-10k training data. Note: This should point to the 'train' folder inside GOT-10k image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. split - 'train' or 'val'. Note: The validation split here is a subset of the official got-10k train split, not NOT the official got-10k validation split. To use the official validation split, provide that as the root folder instead. seq_ids - List containing the ids of the videos to be used for training. Note: Only one of 'split' or 'seq_ids' options can be used at the same time. data_fraction - Fraction of dataset to be used. The complete dataset is used by default use_lmdb - whether the dataset is stored in lmdb format """ root = env_settings().got10k_lmdb_dir if root is None else root super().__init__('GOT10k_lmdb', root, image_loader) # all folders inside the root self.sequence_list = self._get_sequence_list() # seq_id is the index of the folder inside the got10k root path if split is not None: if seq_ids is not None: raise ValueError('Cannot set both split_name and seq_ids.') train_lib_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') if split == 'train': file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_train_split.txt') elif split == 'val': file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_val_split.txt') elif split == 'train_full': file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_train_full_split.txt') elif split == 'vottrain': file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_vot_train_split.txt') elif split == 'votval': file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_vot_val_split.txt') else: raise ValueError('Unknown split name.') seq_ids = pandas.read_csv(file_path, header=None, squeeze=True, dtype=np.int64).values.tolist() elif seq_ids is None: seq_ids = list(range(0, len(self.sequence_list))) self.sequence_list = [self.sequence_list[i] for i in seq_ids] if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction)) self.sequence_meta_info = self._load_meta_info() self.seq_per_class = self._build_seq_per_class() self.class_list = list(self.seq_per_class.keys()) self.class_list.sort() def get_name(self): return 'got10k_lmdb' def has_class_info(self): return True def has_occlusion_info(self): return True def _load_meta_info(self): def _read_meta(meta_info): object_meta = OrderedDict({'object_class_name': meta_info[5].split(': ')[-1], 'motion_class': meta_info[6].split(': ')[-1], 'major_class': meta_info[7].split(': ')[-1], 'root_class': meta_info[8].split(': ')[-1], 'motion_adverb': meta_info[9].split(': ')[-1]}) return object_meta sequence_meta_info = {} for s in self.sequence_list: try: meta_str = decode_str(self.root, "train/%s/meta_info.ini" %s) sequence_meta_info[s] = _read_meta(meta_str.split('\n')) except: sequence_meta_info[s] = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return sequence_meta_info def _build_seq_per_class(self): seq_per_class = {} for i, s in enumerate(self.sequence_list): object_class = self.sequence_meta_info[s]['object_class_name'] if object_class in seq_per_class: seq_per_class[object_class].append(i) else: seq_per_class[object_class] = [i] return seq_per_class def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def _get_sequence_list(self): dir_str = decode_str(self.root, 'train/list.txt') dir_list = dir_str.split('\n') return dir_list def _read_bb_anno(self, seq_path): bb_anno_file = os.path.join(seq_path, "groundtruth.txt") gt_str_list = decode_str(self.root, bb_anno_file).split('\n')[:-1] # the last line in got10k is empty gt_list = [list(map(float, line.split(','))) for line in gt_str_list] gt_arr = np.array(gt_list).astype(np.float32) return torch.tensor(gt_arr) def _read_target_visible(self, seq_path): # full occlusion and out_of_view files occlusion_file = os.path.join(seq_path, "absence.label") cover_file = os.path.join(seq_path, "cover.label") # Read these files occ_list = list(map(int, decode_str(self.root, occlusion_file).split('\n')[:-1])) # the last line in got10k is empty occlusion = torch.ByteTensor(occ_list) cover_list = list(map(int, decode_str(self.root, cover_file).split('\n')[:-1])) # the last line in got10k is empty cover = torch.ByteTensor(cover_list) target_visible = ~occlusion & (cover>0).byte() visible_ratio = cover.float() / 8 return target_visible, visible_ratio def _get_sequence_path(self, seq_id): return os.path.join("train", self.sequence_list[seq_id]) def get_sequence_info(self, seq_id): seq_path = self._get_sequence_path(seq_id) bbox = self._read_bb_anno(seq_path) valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0) visible, visible_ratio = self._read_target_visible(seq_path) visible = visible & valid.byte() return {'bbox': bbox, 'valid': valid, 'visible': visible, 'visible_ratio': visible_ratio} def _get_frame_path(self, seq_path, frame_id): return os.path.join(seq_path, '{:08}.jpg'.format(frame_id+1)) # frames start from 1 def _get_frame(self, seq_path, frame_id): return decode_img(self.root, self._get_frame_path(seq_path, frame_id)) def get_class_name(self, seq_id): obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]] return obj_meta['object_class_name'] def get_frames(self, seq_id, frame_ids, anno=None): seq_path = self._get_sequence_path(seq_id) obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]] frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] return frame_list, anno_frames, obj_meta ================================================ FILE: artrackv2_mindspore/lib/train/dataset/imagenetvid.py ================================================ import os from .base_video_dataset import BaseVideoDataset from lib.train.data import jpeg4py_loader import xml.etree.ElementTree as ET import json import torch from collections import OrderedDict from lib.train.admin import env_settings def get_target_to_image_ratio(seq): anno = torch.Tensor(seq['anno']) img_sz = torch.Tensor(seq['image_size']) return (anno[0, 2:4].prod() / (img_sz.prod())).sqrt() class ImagenetVID(BaseVideoDataset): """ Imagenet VID dataset. Publication: ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei IJCV, 2015 https://arxiv.org/pdf/1409.0575.pdf Download the dataset from http://image-net.org/ """ def __init__(self, root=None, image_loader=jpeg4py_loader, min_length=0, max_target_area=1): """ args: root - path to the imagenet vid dataset. image_loader (default_image_loader) - The function to read the images. If installed, jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else, opencv's imread is used. min_length - Minimum allowed sequence length. max_target_area - max allowed ratio between target area and image area. Can be used to filter out targets which cover complete image. """ root = env_settings().imagenet_dir if root is None else root super().__init__("imagenetvid", root, image_loader) cache_file = os.path.join(root, 'cache.json') if os.path.isfile(cache_file): # If available, load the pre-processed cache file containing meta-info for each sequence with open(cache_file, 'r') as f: sequence_list_dict = json.load(f) self.sequence_list = sequence_list_dict else: # Else process the imagenet annotations and generate the cache file self.sequence_list = self._process_anno(root) with open(cache_file, 'w') as f: json.dump(self.sequence_list, f) # Filter the sequences based on min_length and max_target_area in the first frame self.sequence_list = [x for x in self.sequence_list if len(x['anno']) >= min_length and get_target_to_image_ratio(x) < max_target_area] def get_name(self): return 'imagenetvid' def get_num_sequences(self): return len(self.sequence_list) def get_sequence_info(self, seq_id): bb_anno = torch.Tensor(self.sequence_list[seq_id]['anno']) valid = (bb_anno[:, 2] > 0) & (bb_anno[:, 3] > 0) visible = torch.ByteTensor(self.sequence_list[seq_id]['target_visible']) & valid.byte() return {'bbox': bb_anno, 'valid': valid, 'visible': visible} def _get_frame(self, sequence, frame_id): set_name = 'ILSVRC2015_VID_train_{:04d}'.format(sequence['set_id']) vid_name = 'ILSVRC2015_train_{:08d}'.format(sequence['vid_id']) frame_number = frame_id + sequence['start_frame'] frame_path = os.path.join(self.root, 'Data', 'VID', 'train', set_name, vid_name, '{:06d}.JPEG'.format(frame_number)) return self.image_loader(frame_path) def get_frames(self, seq_id, frame_ids, anno=None): sequence = self.sequence_list[seq_id] frame_list = [self._get_frame(sequence, f) for f in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) # Create anno dict anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] # added the class info to the meta info object_meta = OrderedDict({'object_class': sequence['class_name'], 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return frame_list, anno_frames, object_meta def _process_anno(self, root): # Builds individual tracklets base_vid_anno_path = os.path.join(root, 'Annotations', 'VID', 'train') all_sequences = [] for set in sorted(os.listdir(base_vid_anno_path)): set_id = int(set.split('_')[-1]) for vid in sorted(os.listdir(os.path.join(base_vid_anno_path, set))): vid_id = int(vid.split('_')[-1]) anno_files = sorted(os.listdir(os.path.join(base_vid_anno_path, set, vid))) frame1_anno = ET.parse(os.path.join(base_vid_anno_path, set, vid, anno_files[0])) image_size = [int(frame1_anno.find('size/width').text), int(frame1_anno.find('size/height').text)] objects = [ET.ElementTree(file=os.path.join(base_vid_anno_path, set, vid, f)).findall('object') for f in anno_files] tracklets = {} # Find all tracklets along with start frame for f_id, all_targets in enumerate(objects): for target in all_targets: tracklet_id = target.find('trackid').text if tracklet_id not in tracklets: tracklets[tracklet_id] = f_id for tracklet_id, tracklet_start in tracklets.items(): tracklet_anno = [] target_visible = [] class_name_id = None for f_id in range(tracklet_start, len(objects)): found = False for target in objects[f_id]: if target.find('trackid').text == tracklet_id: if not class_name_id: class_name_id = target.find('name').text x1 = int(target.find('bndbox/xmin').text) y1 = int(target.find('bndbox/ymin').text) x2 = int(target.find('bndbox/xmax').text) y2 = int(target.find('bndbox/ymax').text) tracklet_anno.append([x1, y1, x2 - x1, y2 - y1]) target_visible.append(target.find('occluded').text == '0') found = True break if not found: break new_sequence = {'set_id': set_id, 'vid_id': vid_id, 'class_name': class_name_id, 'start_frame': tracklet_start, 'anno': tracklet_anno, 'target_visible': target_visible, 'image_size': image_size} all_sequences.append(new_sequence) return all_sequences ================================================ FILE: artrackv2_mindspore/lib/train/dataset/imagenetvid_lmdb.py ================================================ import os from .base_video_dataset import BaseVideoDataset from lib.train.data import jpeg4py_loader import torch from collections import OrderedDict from lib.train.admin import env_settings from lib.utils.lmdb_utils import decode_img, decode_json def get_target_to_image_ratio(seq): anno = torch.Tensor(seq['anno']) img_sz = torch.Tensor(seq['image_size']) return (anno[0, 2:4].prod() / (img_sz.prod())).sqrt() class ImagenetVID_lmdb(BaseVideoDataset): """ Imagenet VID dataset. Publication: ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei IJCV, 2015 https://arxiv.org/pdf/1409.0575.pdf Download the dataset from http://image-net.org/ """ def __init__(self, root=None, image_loader=jpeg4py_loader, min_length=0, max_target_area=1): """ args: root - path to the imagenet vid dataset. image_loader (default_image_loader) - The function to read the images. If installed, jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else, opencv's imread is used. min_length - Minimum allowed sequence length. max_target_area - max allowed ratio between target area and image area. Can be used to filter out targets which cover complete image. """ root = env_settings().imagenet_dir if root is None else root super().__init__("imagenetvid_lmdb", root, image_loader) sequence_list_dict = decode_json(root, "cache.json") self.sequence_list = sequence_list_dict # Filter the sequences based on min_length and max_target_area in the first frame self.sequence_list = [x for x in self.sequence_list if len(x['anno']) >= min_length and get_target_to_image_ratio(x) < max_target_area] def get_name(self): return 'imagenetvid_lmdb' def get_num_sequences(self): return len(self.sequence_list) def get_sequence_info(self, seq_id): bb_anno = torch.Tensor(self.sequence_list[seq_id]['anno']) valid = (bb_anno[:, 2] > 0) & (bb_anno[:, 3] > 0) visible = torch.ByteTensor(self.sequence_list[seq_id]['target_visible']) & valid.byte() return {'bbox': bb_anno, 'valid': valid, 'visible': visible} def _get_frame(self, sequence, frame_id): set_name = 'ILSVRC2015_VID_train_{:04d}'.format(sequence['set_id']) vid_name = 'ILSVRC2015_train_{:08d}'.format(sequence['vid_id']) frame_number = frame_id + sequence['start_frame'] frame_path = os.path.join('Data', 'VID', 'train', set_name, vid_name, '{:06d}.JPEG'.format(frame_number)) return decode_img(self.root, frame_path) def get_frames(self, seq_id, frame_ids, anno=None): sequence = self.sequence_list[seq_id] frame_list = [self._get_frame(sequence, f) for f in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) # Create anno dict anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] # added the class info to the meta info object_meta = OrderedDict({'object_class': sequence['class_name'], 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return frame_list, anno_frames, object_meta ================================================ FILE: artrackv2_mindspore/lib/train/dataset/lasot.py ================================================ import os import os.path import torch import numpy as np import pandas import csv import random from collections import OrderedDict from .base_video_dataset import BaseVideoDataset from lib.train.data import jpeg4py_loader from lib.train.admin import env_settings class Lasot(BaseVideoDataset): """ LaSOT dataset. Publication: LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Hexin Bai, Yong Xu, Chunyuan Liao and Haibin Ling CVPR, 2019 https://arxiv.org/pdf/1809.07845.pdf Download the dataset from https://cis.temple.edu/lasot/download.html """ def __init__(self, root=None, image_loader=jpeg4py_loader, vid_ids=None, split=None, data_fraction=None): """ args: root - path to the lasot dataset. image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. vid_ids - List containing the ids of the videos (1 - 20) used for training. If vid_ids = [1, 3, 5], then the videos with subscripts -1, -3, and -5 from each class will be used for training. split - If split='train', the official train split (protocol-II) is used for training. Note: Only one of vid_ids or split option can be used at a time. data_fraction - Fraction of dataset to be used. The complete dataset is used by default """ root = env_settings().lasot_dir if root is None else root super().__init__('LaSOT', root, image_loader) # Keep a list of all classes self.class_list = [f for f in os.listdir(self.root)] self.class_to_id = {cls_name: cls_id for cls_id, cls_name in enumerate(self.class_list)} self.sequence_list = self._build_sequence_list(vid_ids, split) if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction)) self.seq_per_class = self._build_class_list() def _build_sequence_list(self, vid_ids=None, split=None): if split is not None: if vid_ids is not None: raise ValueError('Cannot set both split_name and vid_ids.') ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') if split == 'train': file_path = os.path.join(ltr_path, 'data_specs', 'lasot_train_split.txt') else: raise ValueError('Unknown split name.') # sequence_list = pandas.read_csv(file_path, header=None, squeeze=True).values.tolist() sequence_list = pandas.read_csv(file_path, header=None).squeeze("columns").values.tolist() elif vid_ids is not None: sequence_list = [c+'-'+str(v) for c in self.class_list for v in vid_ids] else: raise ValueError('Set either split_name or vid_ids.') return sequence_list def _build_class_list(self): seq_per_class = {} for seq_id, seq_name in enumerate(self.sequence_list): class_name = seq_name.split('-')[0] if class_name in seq_per_class: seq_per_class[class_name].append(seq_id) else: seq_per_class[class_name] = [seq_id] return seq_per_class def get_name(self): return 'lasot' def has_class_info(self): return True def has_occlusion_info(self): return True def get_num_sequences(self): return len(self.sequence_list) def get_num_classes(self): return len(self.class_list) def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def _read_bb_anno(self, seq_path): bb_anno_file = os.path.join(seq_path, "groundtruth.txt") gt = pandas.read_csv(bb_anno_file, delimiter=',', header=None, dtype=np.float32, na_filter=False, low_memory=False).values return torch.tensor(gt) def _read_target_visible(self, seq_path): # Read full occlusion and out_of_view occlusion_file = os.path.join(seq_path, "full_occlusion.txt") out_of_view_file = os.path.join(seq_path, "out_of_view.txt") with open(occlusion_file, 'r', newline='') as f: occlusion = torch.ByteTensor([int(v) for v in list(csv.reader(f))[0]]) with open(out_of_view_file, 'r') as f: out_of_view = torch.ByteTensor([int(v) for v in list(csv.reader(f))[0]]) target_visible = ~occlusion & ~out_of_view return target_visible def _get_sequence_path(self, seq_id): seq_name = self.sequence_list[seq_id] class_name = seq_name.split('-')[0] vid_id = seq_name.split('-')[1] return os.path.join(self.root, class_name, class_name + '-' + vid_id) def get_sequence_info(self, seq_id): seq_path = self._get_sequence_path(seq_id) bbox = self._read_bb_anno(seq_path) valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0) visible = self._read_target_visible(seq_path) & valid.byte() return {'bbox': bbox, 'valid': valid, 'visible': visible} def _get_frame_path(self, seq_path, frame_id): return os.path.join(seq_path, 'img', '{:08}.jpg'.format(frame_id+1)) # frames start from 1 def _get_frame(self, seq_path, frame_id): return self.image_loader(self._get_frame_path(seq_path, frame_id)) def _get_class(self, seq_path): raw_class = seq_path.split('/')[-2] return raw_class def get_class_name(self, seq_id): seq_path = self._get_sequence_path(seq_id) obj_class = self._get_class(seq_path) return obj_class def get_frames(self, seq_id, frame_ids, anno=None): seq_path = self._get_sequence_path(seq_id) obj_class = self._get_class(seq_path) frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] object_meta = OrderedDict({'object_class_name': obj_class, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return frame_list, anno_frames, object_meta ================================================ FILE: artrackv2_mindspore/lib/train/dataset/lasot_lmdb.py ================================================ import os import os.path import torch import numpy as np import pandas import csv import random from collections import OrderedDict from .base_video_dataset import BaseVideoDataset from lib.train.data import jpeg4py_loader from lib.train.admin import env_settings '''2021.1.16 Lasot for loading lmdb dataset''' from lib.utils.lmdb_utils import * class Lasot_lmdb(BaseVideoDataset): def __init__(self, root=None, image_loader=jpeg4py_loader, vid_ids=None, split=None, data_fraction=None): """ args: root - path to the lasot dataset. image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. vid_ids - List containing the ids of the videos (1 - 20) used for training. If vid_ids = [1, 3, 5], then the videos with subscripts -1, -3, and -5 from each class will be used for training. split - If split='train', the official train split (protocol-II) is used for training. Note: Only one of vid_ids or split option can be used at a time. data_fraction - Fraction of dataset to be used. The complete dataset is used by default """ root = env_settings().lasot_lmdb_dir if root is None else root super().__init__('LaSOT_lmdb', root, image_loader) self.sequence_list = self._build_sequence_list(vid_ids, split) class_list = [seq_name.split('-')[0] for seq_name in self.sequence_list] self.class_list = [] for ele in class_list: if ele not in self.class_list: self.class_list.append(ele) # Keep a list of all classes self.class_to_id = {cls_name: cls_id for cls_id, cls_name in enumerate(self.class_list)} if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction)) self.seq_per_class = self._build_class_list() def _build_sequence_list(self, vid_ids=None, split=None): if split is not None: if vid_ids is not None: raise ValueError('Cannot set both split_name and vid_ids.') ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') if split == 'train': file_path = os.path.join(ltr_path, 'data_specs', 'lasot_train_split.txt') else: raise ValueError('Unknown split name.') sequence_list = pandas.read_csv(file_path, header=None, squeeze=True).values.tolist() elif vid_ids is not None: sequence_list = [c+'-'+str(v) for c in self.class_list for v in vid_ids] else: raise ValueError('Set either split_name or vid_ids.') return sequence_list def _build_class_list(self): seq_per_class = {} for seq_id, seq_name in enumerate(self.sequence_list): class_name = seq_name.split('-')[0] if class_name in seq_per_class: seq_per_class[class_name].append(seq_id) else: seq_per_class[class_name] = [seq_id] return seq_per_class def get_name(self): return 'lasot_lmdb' def has_class_info(self): return True def has_occlusion_info(self): return True def get_num_sequences(self): return len(self.sequence_list) def get_num_classes(self): return len(self.class_list) def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def _read_bb_anno(self, seq_path): bb_anno_file = os.path.join(seq_path, "groundtruth.txt") gt_str_list = decode_str(self.root, bb_anno_file).split('\n')[:-1] # the last line is empty gt_list = [list(map(float, line.split(','))) for line in gt_str_list] gt_arr = np.array(gt_list).astype(np.float32) return torch.tensor(gt_arr) def _read_target_visible(self, seq_path): # Read full occlusion and out_of_view occlusion_file = os.path.join(seq_path, "full_occlusion.txt") out_of_view_file = os.path.join(seq_path, "out_of_view.txt") occ_list = list(map(int, decode_str(self.root, occlusion_file).split(','))) occlusion = torch.ByteTensor(occ_list) out_view_list = list(map(int, decode_str(self.root, out_of_view_file).split(','))) out_of_view = torch.ByteTensor(out_view_list) target_visible = ~occlusion & ~out_of_view return target_visible def _get_sequence_path(self, seq_id): seq_name = self.sequence_list[seq_id] class_name = seq_name.split('-')[0] vid_id = seq_name.split('-')[1] return os.path.join(class_name, class_name + '-' + vid_id) def get_sequence_info(self, seq_id): seq_path = self._get_sequence_path(seq_id) bbox = self._read_bb_anno(seq_path) valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0) visible = self._read_target_visible(seq_path) & valid.byte() return {'bbox': bbox, 'valid': valid, 'visible': visible} def _get_frame_path(self, seq_path, frame_id): return os.path.join(seq_path, 'img', '{:08}.jpg'.format(frame_id+1)) # frames start from 1 def _get_frame(self, seq_path, frame_id): return decode_img(self.root, self._get_frame_path(seq_path, frame_id)) def _get_class(self, seq_path): raw_class = seq_path.split('/')[-2] return raw_class def get_class_name(self, seq_id): seq_path = self._get_sequence_path(seq_id) obj_class = self._get_class(seq_path) return obj_class def get_frames(self, seq_id, frame_ids, anno=None): seq_path = self._get_sequence_path(seq_id) obj_class = self._get_class(seq_path) frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] object_meta = OrderedDict({'object_class_name': obj_class, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return frame_list, anno_frames, object_meta ================================================ FILE: artrackv2_mindspore/lib/train/dataset/tracking_net.py ================================================ import torch import os import os.path import numpy as np import pandas import random from collections import OrderedDict from lib.train.data import jpeg4py_loader from .base_video_dataset import BaseVideoDataset from lib.train.admin import env_settings def list_sequences(root, set_ids): """ Lists all the videos in the input set_ids. Returns a list of tuples (set_id, video_name) args: root: Root directory to TrackingNet set_ids: Sets (0-11) which are to be used returns: list - list of tuples (set_id, video_name) containing the set_id and video_name for each sequence """ sequence_list = [] for s in set_ids: anno_dir = os.path.join(root, "TRAIN_" + str(s), "anno") sequences_cur_set = [(s, os.path.splitext(f)[0]) for f in os.listdir(anno_dir) if f.endswith('.txt')] sequence_list += sequences_cur_set return sequence_list class TrackingNet(BaseVideoDataset): """ TrackingNet dataset. Publication: TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild. Matthias Mueller,Adel Bibi, Silvio Giancola, Salman Al-Subaihi and Bernard Ghanem ECCV, 2018 https://ivul.kaust.edu.sa/Documents/Publications/2018/TrackingNet%20A%20Large%20Scale%20Dataset%20and%20Benchmark%20for%20Object%20Tracking%20in%20the%20Wild.pdf Download the dataset using the toolkit https://github.com/SilvioGiancola/TrackingNet-devkit. """ def __init__(self, root=None, image_loader=jpeg4py_loader, set_ids=None, data_fraction=None): """ args: root - The path to the TrackingNet folder, containing the training sets. image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. set_ids (None) - List containing the ids of the TrackingNet sets to be used for training. If None, all the sets (0 - 11) will be used. data_fraction - Fraction of dataset to be used. The complete dataset is used by default """ root = env_settings().trackingnet_dir if root is None else root super().__init__('TrackingNet', root, image_loader) if set_ids is None: set_ids = [i for i in range(12)] self.set_ids = set_ids # Keep a list of all videos. Sequence list is a list of tuples (set_id, video_name) containing the set_id and # video_name for each sequence self.sequence_list = list_sequences(self.root, self.set_ids) if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list) * data_fraction)) self.seq_to_class_map, self.seq_per_class = self._load_class_info() # we do not have the class_lists for the tracking net self.class_list = list(self.seq_per_class.keys()) self.class_list.sort() def _load_class_info(self): ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') class_map_path = os.path.join(ltr_path, 'data_specs', 'trackingnet_classmap.txt') with open(class_map_path, 'r') as f: seq_to_class_map = {seq_class.split('\t')[0]: seq_class.rstrip().split('\t')[1] for seq_class in f} seq_per_class = {} for i, seq in enumerate(self.sequence_list): class_name = seq_to_class_map.get(seq[1], 'Unknown') if class_name not in seq_per_class: seq_per_class[class_name] = [i] else: seq_per_class[class_name].append(i) return seq_to_class_map, seq_per_class def get_name(self): return 'trackingnet' def has_class_info(self): return True def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def _read_bb_anno(self, seq_id): set_id = self.sequence_list[seq_id][0] vid_name = self.sequence_list[seq_id][1] bb_anno_file = os.path.join(self.root, "TRAIN_" + str(set_id), "anno", vid_name + ".txt") gt = pandas.read_csv(bb_anno_file, delimiter=',', header=None, dtype=np.float32, na_filter=False, low_memory=False).values return torch.tensor(gt) def get_sequence_info(self, seq_id): bbox = self._read_bb_anno(seq_id) valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0) visible = valid.clone().byte() return {'bbox': bbox, 'valid': valid, 'visible': visible} def _get_frame(self, seq_id, frame_id): set_id = self.sequence_list[seq_id][0] vid_name = self.sequence_list[seq_id][1] frame_path = os.path.join(self.root, "TRAIN_" + str(set_id), "frames", vid_name, str(frame_id) + ".jpg") return self.image_loader(frame_path) def _get_class(self, seq_id): seq_name = self.sequence_list[seq_id][1] return self.seq_to_class_map[seq_name] def get_class_name(self, seq_id): obj_class = self._get_class(seq_id) return obj_class def get_frames(self, seq_id, frame_ids, anno=None): frame_list = [self._get_frame(seq_id, f) for f in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] obj_class = self._get_class(seq_id) object_meta = OrderedDict({'object_class_name': obj_class, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return frame_list, anno_frames, object_meta ================================================ FILE: artrackv2_mindspore/lib/train/dataset/tracking_net_lmdb.py ================================================ import torch import os import os.path import numpy as np import random from collections import OrderedDict from lib.train.data import jpeg4py_loader from .base_video_dataset import BaseVideoDataset from lib.train.admin import env_settings import json from lib.utils.lmdb_utils import decode_img, decode_str def list_sequences(root): """ Lists all the videos in the input set_ids. Returns a list of tuples (set_id, video_name) args: root: Root directory to TrackingNet returns: list - list of tuples (set_id, video_name) containing the set_id and video_name for each sequence """ fname = os.path.join(root, "seq_list.json") with open(fname, "r") as f: sequence_list = json.loads(f.read()) return sequence_list class TrackingNet_lmdb(BaseVideoDataset): """ TrackingNet dataset. Publication: TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild. Matthias Mueller,Adel Bibi, Silvio Giancola, Salman Al-Subaihi and Bernard Ghanem ECCV, 2018 https://ivul.kaust.edu.sa/Documents/Publications/2018/TrackingNet%20A%20Large%20Scale%20Dataset%20and%20Benchmark%20for%20Object%20Tracking%20in%20the%20Wild.pdf Download the dataset using the toolkit https://github.com/SilvioGiancola/TrackingNet-devkit. """ def __init__(self, root=None, image_loader=jpeg4py_loader, set_ids=None, data_fraction=None): """ args: root - The path to the TrackingNet folder, containing the training sets. image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. set_ids (None) - List containing the ids of the TrackingNet sets to be used for training. If None, all the sets (0 - 11) will be used. data_fraction - Fraction of dataset to be used. The complete dataset is used by default """ root = env_settings().trackingnet_lmdb_dir if root is None else root super().__init__('TrackingNet_lmdb', root, image_loader) if set_ids is None: set_ids = [i for i in range(12)] self.set_ids = set_ids # Keep a list of all videos. Sequence list is a list of tuples (set_id, video_name) containing the set_id and # video_name for each sequence self.sequence_list = list_sequences(self.root) if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list) * data_fraction)) self.seq_to_class_map, self.seq_per_class = self._load_class_info() # we do not have the class_lists for the tracking net self.class_list = list(self.seq_per_class.keys()) self.class_list.sort() def _load_class_info(self): ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') class_map_path = os.path.join(ltr_path, 'data_specs', 'trackingnet_classmap.txt') with open(class_map_path, 'r') as f: seq_to_class_map = {seq_class.split('\t')[0]: seq_class.rstrip().split('\t')[1] for seq_class in f} seq_per_class = {} for i, seq in enumerate(self.sequence_list): class_name = seq_to_class_map.get(seq[1], 'Unknown') if class_name not in seq_per_class: seq_per_class[class_name] = [i] else: seq_per_class[class_name].append(i) return seq_to_class_map, seq_per_class def get_name(self): return 'trackingnet_lmdb' def has_class_info(self): return True def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def _read_bb_anno(self, seq_id): set_id = self.sequence_list[seq_id][0] vid_name = self.sequence_list[seq_id][1] gt_str_list = decode_str(os.path.join(self.root, "TRAIN_%d_lmdb" % set_id), os.path.join("anno", vid_name + ".txt")).split('\n')[:-1] gt_list = [list(map(float, line.split(','))) for line in gt_str_list] gt_arr = np.array(gt_list).astype(np.float32) return torch.tensor(gt_arr) def get_sequence_info(self, seq_id): bbox = self._read_bb_anno(seq_id) valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0) visible = valid.clone().byte() return {'bbox': bbox, 'valid': valid, 'visible': visible} def _get_frame(self, seq_id, frame_id): set_id = self.sequence_list[seq_id][0] vid_name = self.sequence_list[seq_id][1] return decode_img(os.path.join(self.root, "TRAIN_%d_lmdb" % set_id), os.path.join("frames", vid_name, str(frame_id) + ".jpg")) def _get_class(self, seq_id): seq_name = self.sequence_list[seq_id][1] return self.seq_to_class_map[seq_name] def get_class_name(self, seq_id): obj_class = self._get_class(seq_id) return obj_class def get_frames(self, seq_id, frame_ids, anno=None): frame_list = [self._get_frame(seq_id, f) for f in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] obj_class = self._get_class(seq_id) object_meta = OrderedDict({'object_class_name': obj_class, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return frame_list, anno_frames, object_meta ================================================ FILE: artrackv2_mindspore/lib/train/run_training.py ================================================ import os import sys import argparse import importlib import cv2 as cv import torch.backends.cudnn import torch.distributed as dist import torch import random import numpy as np torch.backends.cudnn.benchmark = False import _init_paths import lib.train.admin.settings as ws_settings def init_seeds(seed): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False torch.set_num_threads(4) cv.setNumThreads(1) cv.ocl.setUseOpenCL(False) def run_training(script_name, config_name, cudnn_benchmark=True, local_rank=-1, save_dir=None, base_seed=None, use_lmdb=False, script_name_prv=None, config_name_prv=None, use_wandb=False, distill=None, script_teacher=None, config_teacher=None): """Run the train script. args: script_name: Name of emperiment in the "experiments/" folder. config_name: Name of the yaml file in the "experiments/". cudnn_benchmark: Use cudnn benchmark or not (default is True). """ if save_dir is None: print("save_dir dir is not given. Use the default dir instead.") # This is needed to avoid strange crashes related to opencv torch.set_num_threads(4) cv.setNumThreads(4) torch.backends.cudnn.benchmark = cudnn_benchmark print('script_name: {}.py config_name: {}.yaml'.format(script_name, config_name)) '''2021.1.5 set seed for different process''' if base_seed is not None: if local_rank != -1: init_seeds(base_seed + local_rank) else: init_seeds(base_seed) settings = ws_settings.Settings() settings.script_name = script_name settings.config_name = config_name settings.project_path = 'train/{}/{}'.format(script_name, config_name) if script_name_prv is not None and config_name_prv is not None: settings.project_path_prv = 'train/{}/{}'.format(script_name_prv, config_name_prv) settings.local_rank = local_rank settings.save_dir = os.path.abspath(save_dir) settings.use_lmdb = use_lmdb prj_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")) settings.cfg_file = os.path.join(prj_dir, 'experiments/%s/%s.yaml' % (script_name, config_name)) settings.use_wandb = use_wandb if distill: settings.distill = distill settings.script_teacher = script_teacher settings.config_teacher = config_teacher if script_teacher is not None and config_teacher is not None: settings.project_path_teacher = 'train/{}/{}'.format(script_teacher, config_teacher) settings.cfg_file_teacher = os.path.join(prj_dir, 'experiments/%s/%s.yaml' % (script_teacher, config_teacher)) expr_module = importlib.import_module('lib.train.train_script_distill') else: expr_module = importlib.import_module('lib.train.train_script') expr_func = getattr(expr_module, 'run') expr_func(settings) def main(): parser = argparse.ArgumentParser(description='Run a train scripts in train_settings.') parser.add_argument('--script', type=str, required=True, help='Name of the train script.') parser.add_argument('--config', type=str, required=True, help="Name of the config file.") parser.add_argument('--cudnn_benchmark', type=bool, default=False, help='Set cudnn benchmark on (1) or off (0) (default is on).') parser.add_argument('--local_rank', default=-1, type=int, help='node rank for distributed training') parser.add_argument('--save_dir', type=str, help='the directory to save checkpoints and logs') parser.add_argument('--seed', type=int, default=42, help='seed for random numbers') parser.add_argument('--use_lmdb', type=int, choices=[0, 1], default=0) # whether datasets are in lmdb format parser.add_argument('--script_prv', type=str, default=None, help='Name of the train script of previous model.') parser.add_argument('--config_prv', type=str, default=None, help="Name of the config file of previous model.") parser.add_argument('--use_wandb', type=int, choices=[0, 1], default=0) # whether to use wandb # for knowledge distillation parser.add_argument('--distill', type=int, choices=[0, 1], default=0) # whether to use knowledge distillation parser.add_argument('--script_teacher', type=str, help='teacher script name') parser.add_argument('--config_teacher', type=str, help='teacher yaml configure file name') args = parser.parse_args() if args.local_rank != -1: dist.init_process_group(backend='nccl') torch.cuda.set_device(args.local_rank) else: torch.cuda.set_device(0) run_training(args.script, args.config, cudnn_benchmark=args.cudnn_benchmark, local_rank=args.local_rank, save_dir=args.save_dir, base_seed=args.seed, use_lmdb=args.use_lmdb, script_name_prv=args.script_prv, config_name_prv=args.config_prv, use_wandb=args.use_wandb, distill=args.distill, script_teacher=args.script_teacher, config_teacher=args.config_teacher) if __name__ == '__main__': main() ================================================ FILE: artrackv2_mindspore/lib/train/train_script.py ================================================ import os # loss function related from lib.utils.box_ops import giou_loss from torch.nn.functional import l1_loss from torch.nn import BCEWithLogitsLoss # train pipeline related from lib.train.trainers import LTRTrainer from lib.train.dataset import Lasot, Got10k, MSCOCOSeq, ImagenetVID, TrackingNet from lib.train.dataset import Lasot_lmdb, Got10k_lmdb, MSCOCOSeq_lmdb, ImagenetVID_lmdb, TrackingNet_lmdb from lib.train.data import sampler, opencv_loader, processing, LTRLoader, sequence_sampler # distributed training related from torch.nn.parallel import DistributedDataParallel as DDP from torch.nn.parallel import DataParallel as DP # some more advanced functions from .base_functions import * # network related from lib.models.ostrack import build_ostrack # forward propagation related from lib.train.actors import OSTrackActor # for import modules import importlib from ..utils.focal_loss import FocalLoss def names2datasets(name_list: list, settings, image_loader): assert isinstance(name_list, list) datasets = [] #settings.use_lmdb = True for name in name_list: assert name in ["LASOT", "GOT10K_vottrain", "GOT10K_votval", "GOT10K_train_full", "GOT10K_official_val", "COCO17", "VID", "TRACKINGNET"] if name == "LASOT": if settings.use_lmdb: print("Building lasot dataset from lmdb") datasets.append(Lasot_lmdb(settings.env.lasot_lmdb_dir, split='train', image_loader=image_loader)) else: datasets.append(Lasot(settings.env.lasot_dir, split='train', image_loader=image_loader)) if name == "GOT10K_vottrain": if settings.use_lmdb: print("Building got10k from lmdb") datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='vottrain', image_loader=image_loader)) else: datasets.append(Got10k(settings.env.got10k_dir, split='vottrain', image_loader=image_loader)) if name == "GOT10K_train_full": if settings.use_lmdb: print("Building got10k_train_full from lmdb") datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='train_full', image_loader=image_loader)) else: datasets.append(Got10k(settings.env.got10k_dir, split='train_full', image_loader=image_loader)) if name == "GOT10K_votval": if settings.use_lmdb: print("Building got10k from lmdb") datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='votval', image_loader=image_loader)) else: datasets.append(Got10k(settings.env.got10k_dir, split='votval', image_loader=image_loader)) if name == "GOT10K_official_val": if settings.use_lmdb: raise ValueError("Not implement") else: datasets.append(Got10k(settings.env.got10k_val_dir, split=None, image_loader=image_loader)) if name == "COCO17": if settings.use_lmdb: print("Building COCO2017 from lmdb") datasets.append(MSCOCOSeq_lmdb(settings.env.coco_lmdb_dir, version="2017", image_loader=image_loader)) else: datasets.append(MSCOCOSeq(settings.env.coco_dir, version="2017", image_loader=image_loader)) if name == "VID": if settings.use_lmdb: print("Building VID from lmdb") datasets.append(ImagenetVID_lmdb(settings.env.imagenet_lmdb_dir, image_loader=image_loader)) else: datasets.append(ImagenetVID(settings.env.imagenet_dir, image_loader=image_loader)) if name == "TRACKINGNET": if settings.use_lmdb: print("Building TrackingNet from lmdb") datasets.append(TrackingNet_lmdb(settings.env.trackingnet_lmdb_dir, image_loader=image_loader)) else: # raise ValueError("NOW WE CAN ONLY USE TRACKINGNET FROM LMDB") datasets.append(TrackingNet(settings.env.trackingnet_dir, image_loader=image_loader)) return datasets def slt_collate(batch): ret = {} for k in batch[0].keys(): here_list = [] for ex in batch: here_list.append(ex[k]) ret[k] = here_list return ret class SLTLoader(torch.utils.data.dataloader.DataLoader): """ Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset. """ __initialized = False def __init__(self, name, dataset, training=True, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, epoch_interval=1, collate_fn=None, stack_dim=0, pin_memory=False, drop_last=False, timeout=0, worker_init_fn=None): if collate_fn is None: collate_fn = slt_collate super(SLTLoader, self).__init__(dataset, batch_size, shuffle, sampler, batch_sampler, num_workers, collate_fn, pin_memory, drop_last, timeout, worker_init_fn) self.name = name self.training = training self.epoch_interval = epoch_interval self.stack_dim = stack_dim def run(settings): settings.description = 'Training script for STARK-S, STARK-ST stage1, and STARK-ST stage2' # update the default configs with config file if not os.path.exists(settings.cfg_file): raise ValueError("%s doesn't exist." % settings.cfg_file) config_module = importlib.import_module("lib.config.%s.config" % settings.script_name) cfg = config_module.cfg config_module.update_config_from_file(settings.cfg_file) if settings.local_rank in [-1, 0]: print("New configuration is shown below.") for key in cfg.keys(): print("%s configuration:" % key, cfg[key]) print('\n') # update settings based on cfg update_settings(settings, cfg) # Record the training log log_dir = os.path.join(settings.save_dir, 'logs') if settings.local_rank in [-1, 0]: if not os.path.exists(log_dir): os.makedirs(log_dir) settings.log_file = os.path.join(log_dir, "%s-%s.log" % (settings.script_name, settings.config_name)) # Build dataloaders dataset_train = sequence_sampler.SequenceSampler(datasets=names2datasets(cfg.DATA.TRAIN.DATASETS_NAME, settings, opencv_loader), p_datasets=cfg.DATA.TRAIN.DATASETS_RATIO, samples_per_epoch=cfg.DATA.TRAIN.SAMPLE_PER_EPOCH, max_gap=cfg.DATA.MAX_GAP, max_interval=cfg.DATA.MAX_INTERVAL, num_search_frames=cfg.DATA.SEARCH.NUMBER, num_template_frames=cfg.DATA.TEMPLATE.NUMBER, frame_sample_mode='random_interval', prob=cfg.DATA.INTERVAL_PROB) loader_train = SLTLoader('train', dataset_train, training=True, batch_size=cfg.TRAIN.BATCH_SIZE, num_workers=cfg.TRAIN.NUM_WORKER, shuffle=False, drop_last=True, pin_memory=True) if "RepVGG" in cfg.MODEL.BACKBONE.TYPE or "swin" in cfg.MODEL.BACKBONE.TYPE or "LightTrack" in cfg.MODEL.BACKBONE.TYPE: cfg.ckpt_dir = settings.save_dir bins = cfg.MODEL.BINS search_size = cfg.DATA.SEARCH.SIZE # Create network if settings.script_name == "ostrack": net = build_ostrack(cfg) else: raise ValueError("illegal script name") # wrap networks to distributed one net.cuda() import torch.distributed as dist rank = dist.get_rank() device_id = rank % torch.cuda.device_count() net = net.to(device_id) if settings.local_rank != -1: print(f"Start running basic DDP example on rank {rank}.") # net = torch.nn.SyncBatchNorm.convert_sync_batchnorm(net) # add syncBN converter net = DDP(net, find_unused_parameters=True, broadcast_buffers=False) print(f"Finish running basic DDP example on rank {rank}.") # net = DDP(net, device_ids=[settings.local_rank], find_unused_parameters=True, broadcast_buffers=False) #net = DP(net) settings.device = torch.device("cuda:%d" % settings.local_rank) else: settings.device = torch.device("cuda:0") settings.deep_sup = getattr(cfg.TRAIN, "DEEP_SUPERVISION", False) settings.distill = getattr(cfg.TRAIN, "DISTILL", False) settings.distill_loss_type = getattr(cfg.TRAIN, "DISTILL_LOSS_TYPE", "KL") # Loss functions and Actors if settings.script_name == "ostrack": focal_loss = FocalLoss() objective = {'giou': giou_loss, 'l1': l1_loss, 'focal': focal_loss, 'cls': BCEWithLogitsLoss()} loss_weight = {'giou': cfg.TRAIN.GIOU_WEIGHT, 'l1': cfg.TRAIN.L1_WEIGHT, 'focal': 2., 'cls': 2.0} actor = OSTrackActor(net=net, objective=objective, loss_weight=loss_weight, settings=settings, cfg=cfg, bins=bins, search_size=search_size) else: raise ValueError("illegal script name") # if cfg.TRAIN.DEEP_SUPERVISION: # raise ValueError("Deep supervision is not supported now.") # Optimizer, parameters, and learning rates optimizer, lr_scheduler = get_optimizer_scheduler(net, cfg) use_amp = getattr(cfg.TRAIN, "AMP", False) trainer = LTRTrainer(actor, [loader_train], optimizer, settings, lr_scheduler, use_amp=use_amp) # train process trainer.train(cfg.TRAIN.EPOCH, load_latest=True, fail_safe=True) ================================================ FILE: artrackv2_mindspore/lib/train/train_script_distill.py ================================================ import os # loss function related from lib.utils.box_ops import giou_loss from torch.nn.functional import l1_loss from torch.nn import BCEWithLogitsLoss # train pipeline related from lib.train.trainers import LTRTrainer # distributed training related from torch.nn.parallel import DistributedDataParallel as DDP # some more advanced functions from .base_functions import * # network related from lib.models.stark import build_starks, build_starkst from lib.models.stark import build_stark_lightning_x_trt # forward propagation related from lib.train.actors import STARKLightningXtrtdistillActor # for import modules import importlib def build_network(script_name, cfg): # Create network if script_name == "stark_s": net = build_starks(cfg) elif script_name == "stark_st1" or script_name == "stark_st2": net = build_starkst(cfg) elif script_name == "stark_lightning_X_trt": net = build_stark_lightning_x_trt(cfg, phase="train") else: raise ValueError("illegal script name") return net def run(settings): settings.description = 'Training script for STARK-S, STARK-ST stage1, and STARK-ST stage2' # update the default configs with config file if not os.path.exists(settings.cfg_file): raise ValueError("%s doesn't exist." % settings.cfg_file) config_module = importlib.import_module("lib.config.%s.config" % settings.script_name) cfg = config_module.cfg config_module.update_config_from_file(settings.cfg_file) if settings.local_rank in [-1, 0]: print("New configuration is shown below.") for key in cfg.keys(): print("%s configuration:" % key, cfg[key]) print('\n') # update the default teacher configs with teacher config file if not os.path.exists(settings.cfg_file_teacher): raise ValueError("%s doesn't exist." % settings.cfg_file_teacher) config_module_teacher = importlib.import_module("lib.config.%s.config" % settings.script_teacher) cfg_teacher = config_module_teacher.cfg config_module_teacher.update_config_from_file(settings.cfg_file_teacher) if settings.local_rank in [-1, 0]: print("New teacher configuration is shown below.") for key in cfg_teacher.keys(): print("%s configuration:" % key, cfg_teacher[key]) print('\n') # update settings based on cfg update_settings(settings, cfg) # Record the training log log_dir = os.path.join(settings.save_dir, 'logs') if settings.local_rank in [-1, 0]: if not os.path.exists(log_dir): os.makedirs(log_dir) settings.log_file = os.path.join(log_dir, "%s-%s.log" % (settings.script_name, settings.config_name)) # Build dataloaders loader_train, loader_val = build_dataloaders(cfg, settings) if "RepVGG" in cfg.MODEL.BACKBONE.TYPE or "swin" in cfg.MODEL.BACKBONE.TYPE: cfg.ckpt_dir = settings.save_dir """turn on the distillation mode""" cfg.TRAIN.DISTILL = True cfg_teacher.TRAIN.DISTILL = True net = build_network(settings.script_name, cfg) net_teacher = build_network(settings.script_teacher, cfg_teacher) # wrap networks to distributed one net.cuda() net_teacher.cuda() net_teacher.eval() if settings.local_rank != -1: net = DDP(net, device_ids=[settings.local_rank], find_unused_parameters=True) net_teacher = DDP(net_teacher, device_ids=[settings.local_rank], find_unused_parameters=True) settings.device = torch.device("cuda:%d" % settings.local_rank) else: settings.device = torch.device("cuda:0") # settings.deep_sup = getattr(cfg.TRAIN, "DEEP_SUPERVISION", False) # settings.distill = getattr(cfg.TRAIN, "DISTILL", False) settings.distill_loss_type = getattr(cfg.TRAIN, "DISTILL_LOSS_TYPE", "L1") # Loss functions and Actors if settings.script_name == "stark_lightning_X_trt": objective = {'giou': giou_loss, 'l1': l1_loss} loss_weight = {'giou': cfg.TRAIN.GIOU_WEIGHT, 'l1': cfg.TRAIN.L1_WEIGHT} actor = STARKLightningXtrtdistillActor(net=net, objective=objective, loss_weight=loss_weight, settings=settings, net_teacher=net_teacher) else: raise ValueError("illegal script name") # Optimizer, parameters, and learning rates optimizer, lr_scheduler = get_optimizer_scheduler(net, cfg) use_amp = getattr(cfg.TRAIN, "AMP", False) trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler, use_amp=use_amp) # train process trainer.train(cfg.TRAIN.EPOCH, load_latest=True, fail_safe=True, distill=True) ================================================ FILE: artrackv2_mindspore/lib/train/trainers/__init__.py ================================================ from .base_trainer import BaseTrainer from .ltr_trainer import LTRTrainer ================================================ FILE: artrackv2_mindspore/lib/train/trainers/base_trainer.py ================================================ import os import glob import torch import traceback from lib.train.admin import multigpu from torch.utils.data.distributed import DistributedSampler class BaseTrainer: """Base trainer class. Contains functions for training and saving/loading checkpoints. Trainer classes should inherit from this one and overload the train_epoch function.""" def __init__(self, actor, loaders, optimizer, settings, lr_scheduler=None): """ args: actor - The actor for training the network loaders - list of dataset loaders, e.g. [train_loader, val_loader]. In each epoch, the trainer runs one epoch for each loader. optimizer - The optimizer used for training, e.g. Adam settings - Training settings lr_scheduler - Learning rate scheduler """ self.actor = actor self.optimizer = optimizer self.lr_scheduler = lr_scheduler self.loaders = loaders self.update_settings(settings) self.epoch = 0 self.stats = {} self.device = getattr(settings, 'device', None) if self.device is None: self.device = torch.device("cuda:0" if torch.cuda.is_available() and settings.use_gpu else "cpu") self.actor.to(self.device) self.settings = settings def update_settings(self, settings=None): """Updates the trainer settings. Must be called to update internal settings.""" if settings is not None: self.settings = settings if self.settings.env.workspace_dir is not None: self.settings.env.workspace_dir = os.path.expanduser(self.settings.env.workspace_dir) '''2021.1.4 New function: specify checkpoint dir''' if self.settings.save_dir is None: self._checkpoint_dir = os.path.join(self.settings.env.workspace_dir, 'checkpoints') else: self._checkpoint_dir = os.path.join(self.settings.save_dir, 'checkpoints') print("checkpoints will be saved to %s" % self._checkpoint_dir) if self.settings.local_rank in [-1, 0]: if not os.path.exists(self._checkpoint_dir): print("Training with multiple GPUs. checkpoints directory doesn't exist. " "Create checkpoints directory") os.makedirs(self._checkpoint_dir) else: self._checkpoint_dir = None def train(self, max_epochs, load_latest=False, fail_safe=True, load_previous_ckpt=False, distill=False): """Do training for the given number of epochs. args: max_epochs - Max number of training epochs, load_latest - Bool indicating whether to resume from latest epoch. fail_safe - Bool indicating whether the training to automatically restart in case of any crashes. """ epoch = -1 num_tries = 1 for i in range(num_tries): try: if load_latest: self.load_checkpoint() if load_previous_ckpt: directory = '{}/{}'.format(self._checkpoint_dir, self.settings.project_path_prv) self.load_state_dict(directory) if distill: directory_teacher = '{}/{}'.format(self._checkpoint_dir, self.settings.project_path_teacher) self.load_state_dict(directory_teacher, distill=True) for epoch in range(self.epoch+1, max_epochs+1): self.epoch = epoch self.train_epoch() if self.lr_scheduler is not None: if self.settings.scheduler_type != 'cosine': self.lr_scheduler.step() else: self.lr_scheduler.step(epoch - 1) # only save the last 10 checkpoints save_every_epoch = getattr(self.settings, "save_every_epoch", False) save_epochs = [79, 159, 239] if epoch > (max_epochs - 1) or save_every_epoch or epoch % 1 == 0 or epoch in save_epochs or epoch > (max_epochs - 5): # if epoch > (max_epochs - 10) or save_every_epoch or epoch % 100 == 0: if self._checkpoint_dir: if self.settings.local_rank in [-1, 0]: self.save_checkpoint() except: print('Training crashed at epoch {}'.format(epoch)) if fail_safe: self.epoch -= 1 load_latest = True print('Traceback for the error!') print(traceback.format_exc()) print('Restarting training from last epoch ...') else: raise print('Finished training!') def train_epoch(self): raise NotImplementedError def save_checkpoint(self): """Saves a checkpoint of the network and other variables.""" net = self.actor.net.module if multigpu.is_multi_gpu(self.actor.net) else self.actor.net actor_type = type(self.actor).__name__ net_type = type(net).__name__ state = { 'epoch': self.epoch, 'actor_type': actor_type, 'net_type': net_type, 'net': net.state_dict(), 'net_info': getattr(net, 'info', None), 'constructor': getattr(net, 'constructor', None), 'optimizer': self.optimizer.state_dict(), 'stats': self.stats, 'settings': self.settings } directory = '{}/{}'.format(self._checkpoint_dir, self.settings.project_path) print(directory) if not os.path.exists(directory): print("directory doesn't exist. creating...") os.makedirs(directory) # First save as a tmp file tmp_file_path = '{}/{}_ep{:04d}.tmp'.format(directory, net_type, self.epoch) torch.save(state, tmp_file_path) file_path = '{}/{}_ep{:04d}.pth.tar'.format(directory, net_type, self.epoch) # Now rename to actual checkpoint. os.rename seems to be atomic if files are on same filesystem. Not 100% sure os.rename(tmp_file_path, file_path) def load_checkpoint(self, checkpoint = None, fields = None, ignore_fields = None, load_constructor = False): """Loads a network checkpoint file. Can be called in three different ways: load_checkpoint(): Loads the latest epoch from the workspace. Use this to continue training. load_checkpoint(epoch_num): Loads the network at the given epoch number (int). load_checkpoint(path_to_checkpoint): Loads the file from the given absolute path (str). """ net = self.actor.net.module if multigpu.is_multi_gpu(self.actor.net) else self.actor.net actor_type = type(self.actor).__name__ net_type = type(net).__name__ if checkpoint is None: # Load most recent checkpoint checkpoint_list = sorted(glob.glob('{}/{}/{}_ep*.pth.tar'.format(self._checkpoint_dir, self.settings.project_path, net_type))) if checkpoint_list: checkpoint_path = checkpoint_list[-1] else: print('No matching checkpoint file found') return elif isinstance(checkpoint, int): # Checkpoint is the epoch number checkpoint_path = '{}/{}/{}_ep{:04d}.pth.tar'.format(self._checkpoint_dir, self.settings.project_path, net_type, checkpoint) elif isinstance(checkpoint, str): # checkpoint is the path if os.path.isdir(checkpoint): checkpoint_list = sorted(glob.glob('{}/*_ep*.pth.tar'.format(checkpoint))) if checkpoint_list: checkpoint_path = checkpoint_list[-1] else: raise Exception('No checkpoint found') else: checkpoint_path = os.path.expanduser(checkpoint) else: raise TypeError # Load network checkpoint_dict = torch.load(checkpoint_path, map_location='cpu') assert net_type == checkpoint_dict['net_type'], 'Network is not of correct type.' if fields is None: fields = checkpoint_dict.keys() if ignore_fields is None: ignore_fields = ['settings'] # Never load the scheduler. It exists in older checkpoints. ignore_fields.extend(['lr_scheduler', 'constructor', 'net_type', 'actor_type', 'net_info']) # Load all fields for key in fields: if key in ignore_fields: continue if key == 'net': net.load_state_dict(checkpoint_dict[key]) elif key == 'optimizer': self.optimizer.load_state_dict(checkpoint_dict[key]) else: setattr(self, key, checkpoint_dict[key]) # Set the net info if load_constructor and 'constructor' in checkpoint_dict and checkpoint_dict['constructor'] is not None: net.constructor = checkpoint_dict['constructor'] if 'net_info' in checkpoint_dict and checkpoint_dict['net_info'] is not None: net.info = checkpoint_dict['net_info'] # Update the epoch in lr scheduler if 'epoch' in fields: self.lr_scheduler.last_epoch = self.epoch # 2021.1.10 Update the epoch in data_samplers for loader in self.loaders: if isinstance(loader.sampler, DistributedSampler): loader.sampler.set_epoch(self.epoch) return True def load_state_dict(self, checkpoint=None, distill=False): """Loads a network checkpoint file. Can be called in three different ways: load_checkpoint(): Loads the latest epoch from the workspace. Use this to continue training. load_checkpoint(epoch_num): Loads the network at the given epoch number (int). load_checkpoint(path_to_checkpoint): Loads the file from the given absolute path (str). """ if distill: net = self.actor.net_teacher.module if multigpu.is_multi_gpu(self.actor.net_teacher) \ else self.actor.net_teacher else: net = self.actor.net.module if multigpu.is_multi_gpu(self.actor.net) else self.actor.net net_type = type(net).__name__ if isinstance(checkpoint, str): # checkpoint is the path if os.path.isdir(checkpoint): checkpoint_list = sorted(glob.glob('{}/*_ep*.pth.tar'.format(checkpoint))) if checkpoint_list: checkpoint_path = checkpoint_list[-1] else: raise Exception('No checkpoint found') else: checkpoint_path = os.path.expanduser(checkpoint) else: raise TypeError # Load network print("Loading pretrained model from ", checkpoint_path) checkpoint_dict = torch.load(checkpoint_path, map_location='cpu') assert net_type == checkpoint_dict['net_type'], 'Network is not of correct type.' missing_k, unexpected_k = net.load_state_dict(checkpoint_dict["net"], strict=False) print("previous checkpoint is loaded.") print("missing keys: ", missing_k) print("unexpected keys:", unexpected_k) return True ================================================ FILE: artrackv2_mindspore/lib/train/trainers/ltr_trainer.py ================================================ import os import datetime from collections import OrderedDict from torch.nn.utils import clip_grad_norm_ #from lib.train.data.wandb_logger import WandbWriter from lib.train.trainers import BaseTrainer from lib.train.admin import AverageMeter, StatValue from memory_profiler import profile #from lib.train.admin import TensorboardWriter import torch import time import numpy as np from torch.utils.data.distributed import DistributedSampler from torch.cuda.amp import autocast from torch.cuda.amp import GradScaler from lib.utils.misc import get_world_size class LTRTrainer(BaseTrainer): def __init__(self, actor, loaders, optimizer, settings, lr_scheduler=None, use_amp=False): """ args: actor - The actor for training the network loaders - list of dataset loaders, e.g. [train_loader, val_loader]. In each epoch, the trainer runs one epoch for each loader. optimizer - The optimizer used for training, e.g. Adam settings - Training settings lr_scheduler - Learning rate scheduler """ super().__init__(actor, loaders, optimizer, settings, lr_scheduler) self._set_default_settings() # Initialize statistics variables self.stats = OrderedDict({loader.name: None for loader in self.loaders}) # Initialize tensorboard and wandb #self.wandb_writer = None #if settings.local_rank in [-1, 0]: # tensorboard_writer_dir = os.path.join(self.settings.env.tensorboard_dir, self.settings.project_path) # if not os.path.exists(tensorboard_writer_dir): # os.makedirs(tensorboard_writer_dir) # self.tensorboard_writer = TensorboardWriter(tensorboard_writer_dir, [l.name for l in loaders]) # if settings.use_wandb: # world_size = get_world_size() # cur_train_samples = self.loaders[0].dataset.samples_per_epoch * max(0, self.epoch - 1) # interval = (world_size * settings.batchsize) # * interval # self.wandb_writer = WandbWriter(settings.project_path[6:], {}, tensorboard_writer_dir, cur_train_samples, interval) self.move_data_to_gpu = getattr(settings, 'move_data_to_gpu', True) print("move_data", self.move_data_to_gpu) self.settings = settings self.use_amp = use_amp if use_amp: self.scaler = GradScaler() def _set_default_settings(self): # Dict of all default values default = {'print_interval': 10, 'print_stats': None, 'description': ''} for param, default_value in default.items(): if getattr(self.settings, param, None) is None: setattr(self.settings, param, default_value) self.miou_list = [] def cycle_dataset(self, loader): """Do a cycle of training or validation.""" torch.autograd.set_detect_anomaly(True) self.actor.train(loader.training) torch.set_grad_enabled(loader.training) self._init_timing() for i, data in enumerate(loader, 1): self.actor.eval() self.data_read_done_time = time.time() with torch.no_grad(): explore_result = self.actor.explore(data) if explore_result == None: print("this time i skip") continue # get inputs #print(data) self.data_to_gpu_time = time.time() data['epoch'] = self.epoch data['settings'] = self.settings stats = {} reward_record = [] miou_record = [] e_miou_record = [] num_seq = len(data['num_frames']) # Calculate reward tensor #reward_tensor = torch.zeros(explore_result['baseline_iou'].size()) baseline_iou = explore_result['baseline_iou'] #explore_iou = explore_result['explore_iou'] for seq_idx in range(num_seq): num_frames = data['num_frames'][seq_idx] - 1 b_miou = torch.mean(baseline_iou[:num_frames, seq_idx]) # e_miou = torch.mean(explore_iou[:num_frames, seq_idx]) miou_record.append(b_miou.item()) # e_miou_record.append(e_miou.item()) b_reward = b_miou.item() # e_reward = e_miou.item() # iou_gap = e_reward - b_reward # reward_record.append(iou_gap) # reward_tensor[:num_frames, seq_idx] = iou_gap # Training mode cursor = 0 bs_backward = 1 #print(self.actor.net.module.box_head.decoder.layers[2].mlpx.fc1.weight) self.optimizer.zero_grad() self.actor.train() cover = torch.Tensor(data['visible_ratio']).permute(1, 0) reverse_cover = torch.flip(cover, dims=[0]) cover = cover[1:, :] reverse_cover = reverse_cover[1:, :] cover_real = torch.cat((cover, reverse_cover), dim=1) while cursor < num_seq*2: #print("now is ", cursor , "and all is ", num_seq) model_inputs = {} model_inputs['slt_loss_weight'] = 15 #if cursor < num_seq: # model_inputs['template_images'] = explore_result['template_images'][cursor:cursor + bs_backward].cuda() #else: # model_inputs['template_images'] = explore_result['template_images_reverse'][cursor - num_seq:cursor - num_seq + bs_backward].cuda() model_inputs['search_images'] = explore_result['search_images'][:, cursor:cursor + bs_backward].cuda() model_inputs['search_anno'] = explore_result['search_anno'][:, cursor:cursor + bs_backward].cuda() model_inputs['pre_seq'] = explore_result['pre_seq'][:, cursor:cursor + bs_backward].cuda() model_inputs['x_feat'] = explore_result['x_feat'].squeeze(1)[:, cursor:cursor + bs_backward].cuda() model_inputs['template_images_z0'] = explore_result['template_images_z0'][:, cursor:cursor + bs_backward].cuda() model_inputs['dz_feat_update'] = explore_result['dz_feat_update'][:, cursor:cursor + bs_backward].cuda() model_inputs['target_in_search'] = explore_result['target_in_search'][:, cursor:cursor + bs_backward].cuda() model_inputs['cover'] = cover_real[:, cursor:cursor + bs_backward].cuda() model_inputs['epoch'] = self.epoch #print("this is cursor") #print(explore_result['pre_seq'].shape) #print(explore_result['x_feat'].squeeze(1).shape) #model_inputs['action_tensor'] = explore_result['action_tensor'][:, cursor:cursor + bs_backward].cuda() #model_inputs['reward_tensor'] = reward_tensor[:, cursor:cursor + bs_backward].cuda() loss, stats_cur = self.actor.compute_sequence_losses(model_inputs) #for name, param in self.actor.net.named_parameters(): # shape, c = (param.grad.shape, param.grad.sum()) if param.grad is not None else (None, None) # print(f'{name}: {param.shape} \n\t grad: {shape} \n\t {c}') #print("i make this!") loss.backward() #print("i made that?") for key, val in stats_cur.items(): if key in stats: stats[key] += val*(bs_backward / num_seq) else: stats[key] = val*(bs_backward / num_seq) cursor += bs_backward grad_norm = clip_grad_norm_(self.actor.net.parameters(), 100) stats['grad_norm'] = grad_norm #print(self.actor.net.module.backbone.blocks[8].mlp.fc1.weight) self.optimizer.step() miou = np.mean(miou_record) self.miou_list.append(miou) #stats['reward'] = np.mean(reward_record) #stats['e_mIoU'] = np.mean(e_miou_record) stats['mIoU'] = miou stats['mIoU10'] = np.mean(self.miou_list[-10:]) stats['mIoU100'] = np.mean(self.miou_list[-100:]) batch_size = num_seq * np.max(data['num_frames']) self._update_stats(stats, batch_size, loader) self._print_stats(i, loader, batch_size) torch.cuda.empty_cache() # # forward pass # if not self.use_amp: # loss, stats = self.actor(data) # else: # with autocast(): # loss, stats = self.actor(data) # # # backward pass and update weights # if loader.training: # self.optimizer.zero_grad() # if not self.use_amp: # loss.backward() # if self.settings.grad_clip_norm > 0: # torch.nn.utils.clip_grad_norm_(self.actor.net.parameters(), self.settings.grad_clip_norm) # self.optimizer.step() # else: # self.scaler.scale(loss).backward() # self.scaler.step(self.optimizer) # self.scaler.update() # update statistics # batch_size = data['template_images'].shape[loader.stack_dim] # self._update_stats(stats, batch_size, loader) # print statistics # self._print_stats(i, loader, batch_size) # update wandb status #if self.wandb_writer is not None and i % self.settings.print_interval == 0: # if self.settings.local_rank in [-1, 0]: # self.wandb_writer.write_log(self.stats, self.epoch) # calculate ETA after every epoch # epoch_time = self.prev_time - self.start_time # print("Epoch Time: " + str(datetime.timedelta(seconds=epoch_time))) # print("Avg Data Time: %.5f" % (self.avg_date_time / self.num_frames * batch_size)) # print("Avg GPU Trans Time: %.5f" % (self.avg_gpu_trans_time / self.num_frames * batch_size)) # print("Avg Forward Time: %.5f" % (self.avg_forward_time / self.num_frames * batch_size)) def train_epoch(self): """Do one epoch for each loader.""" for loader in self.loaders: if self.epoch % loader.epoch_interval == 0: # 2021.1.10 Set epoch if isinstance(loader.sampler, DistributedSampler): loader.sampler.set_epoch(self.epoch) self.cycle_dataset(loader) self._stats_new_epoch() #if self.settings.local_rank in [-1, 0]: # self._write_tensorboard() def _init_timing(self): self.num_frames = 0 self.start_time = time.time() self.prev_time = self.start_time self.avg_date_time = 0 self.avg_gpu_trans_time = 0 self.avg_forward_time = 0 def _update_stats(self, new_stats: OrderedDict, batch_size, loader): # Initialize stats if not initialized yet if loader.name not in self.stats.keys() or self.stats[loader.name] is None: self.stats[loader.name] = OrderedDict({name: AverageMeter() for name in new_stats.keys()}) # add lr state if loader.training: lr_list = self.lr_scheduler.get_last_lr() for i, lr in enumerate(lr_list): var_name = 'LearningRate/group{}'.format(i) if var_name not in self.stats[loader.name].keys(): self.stats[loader.name][var_name] = StatValue() self.stats[loader.name][var_name].update(lr) for name, val in new_stats.items(): if name not in self.stats[loader.name].keys(): self.stats[loader.name][name] = AverageMeter() self.stats[loader.name][name].update(val, batch_size) def _print_stats(self, i, loader, batch_size): self.num_frames += batch_size current_time = time.time() batch_fps = batch_size / (current_time - self.prev_time) average_fps = self.num_frames / (current_time - self.start_time) prev_frame_time_backup = self.prev_time self.prev_time = current_time self.avg_date_time += (self.data_read_done_time - prev_frame_time_backup) self.avg_gpu_trans_time += (self.data_to_gpu_time - self.data_read_done_time) self.avg_forward_time += current_time - self.data_to_gpu_time if i % self.settings.print_interval == 0 or i == loader.__len__(): print_str = '[%s: %d, %d / %d] ' % (loader.name, self.epoch, i, loader.__len__()) print_str += 'FPS: %.1f (%.1f) , ' % (average_fps, batch_fps) # 2021.12.14 add data time print print_str += 'DataTime: %.3f (%.3f) , ' % (self.avg_date_time / self.num_frames * batch_size, self.avg_gpu_trans_time / self.num_frames * batch_size) print_str += 'ForwardTime: %.3f , ' % (self.avg_forward_time / self.num_frames * batch_size) print_str += 'TotalTime: %.3f , ' % ((current_time - self.start_time) / self.num_frames * batch_size) # print_str += 'DataTime: %.3f (%.3f) , ' % (self.data_read_done_time - prev_frame_time_backup, self.data_to_gpu_time - self.data_read_done_time) # print_str += 'ForwardTime: %.3f , ' % (current_time - self.data_to_gpu_time) # print_str += 'TotalTime: %.3f , ' % (current_time - prev_frame_time_backup) for name, val in self.stats[loader.name].items(): if (self.settings.print_stats is None or name in self.settings.print_stats): if hasattr(val, 'avg'): print_str += '%s: %.5f , ' % (name, val.avg) # else: # print_str += '%s: %r , ' % (name, val) print(print_str[:-5]) log_str = print_str[:-5] + '\n' with open(self.settings.log_file, 'a') as f: f.write(log_str) def _stats_new_epoch(self): # Record learning rate for loader in self.loaders: if loader.training: try: lr_list = self.lr_scheduler.get_last_lr() except: lr_list = self.lr_scheduler._get_lr(self.epoch) for i, lr in enumerate(lr_list): var_name = 'LearningRate/group{}'.format(i) if var_name not in self.stats[loader.name].keys(): self.stats[loader.name][var_name] = StatValue() self.stats[loader.name][var_name].update(lr) for loader_stats in self.stats.values(): if loader_stats is None: continue for stat_value in loader_stats.values(): if hasattr(stat_value, 'new_epoch'): stat_value.new_epoch() #def _write_tensorboard(self): # if self.epoch == 1: # self.tensorboard_writer.write_info(self.settings.script_name, self.settings.description) # self.tensorboard_writer.write_epoch(self.stats, self.epoch) ================================================ FILE: artrackv2_mindspore/lib/utils/__init__.py ================================================ # from .tensor import TensorDict, TensorList ================================================ FILE: artrackv2_mindspore/lib/utils/box_ops.py ================================================ import numpy as np def box_xywh_to_cxywh(x): x1, y1, w, h = x.unbind(-1) b = [x1+0.5*w, y1+0.5*h, w, h] return torch.stack(b, dim=-1) def box_cxcywh_to_xyxy(x): x_c, y_c, w, h = x.unbind(-1) b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)] return torch.stack(b, dim=-1) def box_xywh_to_xyxy(x): x1, y1, w, h = x.unbind(-1) b = [x1, y1, x1 + w, y1 + h] return torch.stack(b, dim=-1) def box_xyxy_to_xywh(x): x1, y1, x2, y2 = x.unbind(-1) b = [x1, y1, x2 - x1, y2 - y1] return torch.stack(b, dim=-1) def box_xyxy_to_cxcywh(x): x0, y0, x1, y1 = x.unbind(-1) b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)] return torch.stack(b, dim=-1) # modified from torchvision to also return the union '''Note that this function only supports shape (N,4)''' def box_iou(boxes1, boxes2): """ :param boxes1: (N, 4) (x1,y1,x2,y2) :param boxes2: (N, 4) (x1,y1,x2,y2) :return: """ area1 = box_area(boxes1) # (N,) area2 = box_area(boxes2) # (N,) lt = torch.max(boxes1[:, :2], boxes2[:, :2]) # (N,2) rb = torch.min(boxes1[:, 2:], boxes2[:, 2:]) # (N,2) wh = (rb - lt).clamp(min=0) # (N,2) inter = wh[:, 0] * wh[:, 1] # (N,) union = area1 + area2 - inter iou = inter / union return iou, union '''Note that this implementation is different from DETR's''' def generalized_box_iou(boxes1, boxes2): """ Generalized IoU from https://giou.stanford.edu/ The boxes should be in [x0, y0, x1, y1] format boxes1: (N, 4) boxes2: (N, 4) """ # degenerate boxes gives inf / nan results # so do an early check # try: #assert (boxes1[:, 2:] >= boxes1[:, :2]).all() # assert (boxes2[:, 2:] >= boxes2[:, :2]).all() iou, union = box_iou(boxes1, boxes2) # (N,) lt = torch.min(boxes1[:, :2], boxes2[:, :2]) rb = torch.max(boxes1[:, 2:], boxes2[:, 2:]) wh = (rb - lt).clamp(min=0) # (N,2) area = wh[:, 0] * wh[:, 1] # (N,) return iou - (area - union) / area, iou def giou_loss(boxes1, boxes2): """ :param boxes1: (N, 4) (x1,y1,x2,y2) :param boxes2: (N, 4) (x1,y1,x2,y2) :return: """ giou, iou = generalized_box_iou(boxes1, boxes2) return (1 - giou).mean(), iou def clip_box(box: list, H, W, margin=0): x1, y1, w, h = box x2, y2 = x1 + w, y1 + h x1 = min(max(0, x1), W-margin) x2 = min(max(margin, x2), W) y1 = min(max(0, y1), H-margin) y2 = min(max(margin, y2), H) w = max(margin, x2-x1) h = max(margin, y2-y1) return [x1, y1, w, h] ================================================ FILE: artrackv2_mindspore/lib/utils/ce_utils.py ================================================ def generate_bbox_mask(bbox_mask, bbox): b, h, w = bbox_mask.shape for i in range(b): bbox_i = bbox[i].cpu().tolist() bbox_mask[i, int(bbox_i[1]):int(bbox_i[1] + bbox_i[3] - 1), int(bbox_i[0]):int(bbox_i[0] + bbox_i[2] - 1)] = 1 return bbox_mask def generate_mask_cond(cfg, bs, device, gt_bbox): template_size = cfg.DATA.TEMPLATE.SIZE stride = cfg.MODEL.BACKBONE.STRIDE template_feat_size = template_size // stride if cfg.MODEL.BACKBONE.CE_TEMPLATE_RANGE == 'ALL': box_mask_z = None elif cfg.MODEL.BACKBONE.CE_TEMPLATE_RANGE == 'CTR_POINT': if template_feat_size == 8: index = slice(3, 4) elif template_feat_size == 12: index = slice(5, 6) elif template_feat_size == 7: index = slice(3, 4) elif template_feat_size == 14: index = slice(6, 7) else: raise NotImplementedError box_mask_z = torch.zeros([bs, template_feat_size, template_feat_size], device=device) box_mask_z[:, index, index] = 1 box_mask_z = box_mask_z.flatten(1).to(torch.bool) elif cfg.MODEL.BACKBONE.CE_TEMPLATE_RANGE == 'CTR_REC': # use fixed 4x4 region, 3:5 for 8x8 # use fixed 4x4 region 5:6 for 12x12 if template_feat_size == 8: index = slice(3, 5) elif template_feat_size == 12: index = slice(5, 7) elif template_feat_size == 7: index = slice(3, 4) else: raise NotImplementedError box_mask_z = torch.zeros([bs, template_feat_size, template_feat_size], device=device) box_mask_z[:, index, index] = 1 box_mask_z = box_mask_z.flatten(1).to(torch.bool) elif cfg.MODEL.BACKBONE.CE_TEMPLATE_RANGE == 'GT_BOX': box_mask_z = torch.zeros([bs, template_size, template_size], device=device) # box_mask_z_ori = data['template_seg'][0].view(-1, 1, *data['template_seg'].shape[2:]) # (batch, 1, 128, 128) box_mask_z = generate_bbox_mask(box_mask_z, gt_bbox * template_size).unsqueeze(1).to( torch.float) # (batch, 1, 128, 128) # box_mask_z_vis = box_mask_z.cpu().numpy() box_mask_z = F.interpolate(box_mask_z, scale_factor=1. / cfg.MODEL.BACKBONE.STRIDE, mode='bilinear', align_corners=False) box_mask_z = box_mask_z.flatten(1).to(torch.bool) # box_mask_z_vis = box_mask_z[:, 0, ...].cpu().numpy() # gaussian_maps_vis = generate_heatmap(data['template_anno'], self.cfg.DATA.TEMPLATE.SIZE, self.cfg.MODEL.STRIDE)[0].cpu().numpy() else: raise NotImplementedError return box_mask_z def adjust_keep_rate(epoch, warmup_epochs, total_epochs, ITERS_PER_EPOCH, base_keep_rate=0.5, max_keep_rate=1, iters=-1): if epoch < warmup_epochs: return 1 if epoch >= total_epochs: return base_keep_rate if iters == -1: iters = epoch * ITERS_PER_EPOCH total_iters = ITERS_PER_EPOCH * (total_epochs - warmup_epochs) iters = iters - ITERS_PER_EPOCH * warmup_epochs keep_rate = base_keep_rate + (max_keep_rate - base_keep_rate) \ * (math.cos(iters / total_iters * math.pi) + 1) * 0.5 return keep_rate ================================================ FILE: artrackv2_mindspore/lib/utils/focal_loss.py ================================================ from abc import ABC import torch import torch.nn as nn import torch.nn.functional as F class FocalLoss(nn.Module, ABC): def __init__(self, alpha=2, beta=4): super(FocalLoss, self).__init__() self.alpha = alpha self.beta = beta def forward(self, prediction, target): positive_index = target.eq(1).float() negative_index = target.lt(1).float() negative_weights = torch.pow(1 - target, self.beta) # clamp min value is set to 1e-12 to maintain the numerical stability prediction = torch.clamp(prediction, 1e-12) positive_loss = torch.log(prediction) * torch.pow(1 - prediction, self.alpha) * positive_index negative_loss = torch.log(1 - prediction) * torch.pow(prediction, self.alpha) * negative_weights * negative_index num_positive = positive_index.float().sum() positive_loss = positive_loss.sum() negative_loss = negative_loss.sum() if num_positive == 0: loss = -negative_loss else: loss = -(positive_loss + negative_loss) / num_positive return loss class LBHinge(nn.Module): """Loss that uses a 'hinge' on the lower bound. This means that for samples with a label value smaller than the threshold, the loss is zero if the prediction is also smaller than that threshold. args: error_matric: What base loss to use (MSE by default). threshold: Threshold to use for the hinge. clip: Clip the loss if it is above this value. """ def __init__(self, error_metric=nn.MSELoss(), threshold=None, clip=None): super().__init__() self.error_metric = error_metric self.threshold = threshold if threshold is not None else -100 self.clip = clip def forward(self, prediction, label, target_bb=None): negative_mask = (label < self.threshold).float() positive_mask = (1.0 - negative_mask) prediction = negative_mask * F.relu(prediction) + positive_mask * prediction loss = self.error_metric(prediction, positive_mask * label) if self.clip is not None: loss = torch.min(loss, torch.tensor([self.clip], device=loss.device)) return loss ================================================ FILE: artrackv2_mindspore/lib/utils/heapmap_utils.py ================================================ import numpy as np import torch def generate_heatmap(bboxes, patch_size=320, stride=16): """ Generate ground truth heatmap same as CenterNet Args: bboxes (torch.Tensor): shape of [num_search, bs, 4] Returns: gaussian_maps: list of generated heatmap """ gaussian_maps = [] heatmap_size = patch_size // stride for single_patch_bboxes in bboxes: bs = single_patch_bboxes.shape[0] gt_scoremap = torch.zeros(bs, heatmap_size, heatmap_size) classes = torch.arange(bs).to(torch.long) bbox = single_patch_bboxes * heatmap_size wh = bbox[:, 2:] centers_int = (bbox[:, :2] + wh / 2).round() CenterNetHeatMap.generate_score_map(gt_scoremap, classes, wh, centers_int, 0.7) gaussian_maps.append(gt_scoremap.to(bbox.device)) return gaussian_maps class CenterNetHeatMap(object): @staticmethod def generate_score_map(fmap, gt_class, gt_wh, centers_int, min_overlap): radius = CenterNetHeatMap.get_gaussian_radius(gt_wh, min_overlap) radius = torch.clamp_min(radius, 0) radius = radius.type(torch.int).cpu().numpy() for i in range(gt_class.shape[0]): channel_index = gt_class[i] CenterNetHeatMap.draw_gaussian(fmap[channel_index], centers_int[i], radius[i]) @staticmethod def get_gaussian_radius(box_size, min_overlap): """ copyed from CornerNet box_size (w, h), it could be a torch.Tensor, numpy.ndarray, list or tuple notice: we are using a bug-version, please refer to fix bug version in CornerNet """ # box_tensor = torch.Tensor(box_size) box_tensor = box_size width, height = box_tensor[..., 0], box_tensor[..., 1] a1 = 1 b1 = height + width c1 = width * height * (1 - min_overlap) / (1 + min_overlap) sq1 = torch.sqrt(b1 ** 2 - 4 * a1 * c1) r1 = (b1 + sq1) / 2 a2 = 4 b2 = 2 * (height + width) c2 = (1 - min_overlap) * width * height sq2 = torch.sqrt(b2 ** 2 - 4 * a2 * c2) r2 = (b2 + sq2) / 2 a3 = 4 * min_overlap b3 = -2 * min_overlap * (height + width) c3 = (min_overlap - 1) * width * height sq3 = torch.sqrt(b3 ** 2 - 4 * a3 * c3) r3 = (b3 + sq3) / 2 return torch.min(r1, torch.min(r2, r3)) @staticmethod def gaussian2D(radius, sigma=1): # m, n = [(s - 1.) / 2. for s in shape] m, n = radius y, x = np.ogrid[-m: m + 1, -n: n + 1] gauss = np.exp(-(x * x + y * y) / (2 * sigma * sigma)) gauss[gauss < np.finfo(gauss.dtype).eps * gauss.max()] = 0 return gauss @staticmethod def draw_gaussian(fmap, center, radius, k=1): diameter = 2 * radius + 1 gaussian = CenterNetHeatMap.gaussian2D((radius, radius), sigma=diameter / 6) gaussian = torch.Tensor(gaussian) x, y = int(center[0]), int(center[1]) height, width = fmap.shape[:2] left, right = min(x, radius), min(width - x, radius + 1) top, bottom = min(y, radius), min(height - y, radius + 1) masked_fmap = fmap[y - top: y + bottom, x - left: x + right] masked_gaussian = gaussian[radius - top: radius + bottom, radius - left: radius + right] if min(masked_gaussian.shape) > 0 and min(masked_fmap.shape) > 0: masked_fmap = torch.max(masked_fmap, masked_gaussian * k) fmap[y - top: y + bottom, x - left: x + right] = masked_fmap # return fmap def compute_grids(features, strides): """ grids regret to the input image size """ grids = [] for level, feature in enumerate(features): h, w = feature.size()[-2:] shifts_x = torch.arange( 0, w * strides[level], step=strides[level], dtype=torch.float32, device=feature.device) shifts_y = torch.arange( 0, h * strides[level], step=strides[level], dtype=torch.float32, device=feature.device) shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) shift_x = shift_x.reshape(-1) shift_y = shift_y.reshape(-1) grids_per_level = torch.stack((shift_x, shift_y), dim=1) + \ strides[level] // 2 grids.append(grids_per_level) return grids def get_center3x3(locations, centers, strides, range=3): ''' Inputs: locations: M x 2 centers: N x 2 strides: M ''' range = (range - 1) / 2 M, N = locations.shape[0], centers.shape[0] locations_expanded = locations.view(M, 1, 2).expand(M, N, 2) # M x N x 2 centers_expanded = centers.view(1, N, 2).expand(M, N, 2) # M x N x 2 strides_expanded = strides.view(M, 1, 1).expand(M, N, 2) # M x N centers_discret = ((centers_expanded / strides_expanded).int() * strides_expanded).float() + \ strides_expanded / 2 # M x N x 2 dist_x = (locations_expanded[:, :, 0] - centers_discret[:, :, 0]).abs() dist_y = (locations_expanded[:, :, 1] - centers_discret[:, :, 1]).abs() return (dist_x <= strides_expanded[:, :, 0] * range) & \ (dist_y <= strides_expanded[:, :, 0] * range) def get_pred(score_map_ctr, size_map, offset_map, feat_size): max_score, idx = torch.max(score_map_ctr.flatten(1), dim=1, keepdim=True) idx = idx.unsqueeze(1).expand(idx.shape[0], 2, 1) size = size_map.flatten(2).gather(dim=2, index=idx).squeeze(-1) offset = offset_map.flatten(2).gather(dim=2, index=idx).squeeze(-1) return size * feat_size, offset ================================================ FILE: artrackv2_mindspore/lib/utils/image.py ================================================ ================================================ FILE: artrackv2_mindspore/lib/utils/lmdb_utils.py ================================================ import lmdb import numpy as np import cv2 import json LMDB_ENVS = dict() LMDB_HANDLES = dict() LMDB_FILELISTS = dict() def get_lmdb_handle(name): global LMDB_HANDLES, LMDB_FILELISTS item = LMDB_HANDLES.get(name, None) if item is None: env = lmdb.open(name, readonly=True, lock=False, readahead=False, meminit=False) LMDB_ENVS[name] = env item = env.begin(write=False) LMDB_HANDLES[name] = item return item def decode_img(lmdb_fname, key_name): handle = get_lmdb_handle(lmdb_fname) binfile = handle.get(key_name.encode()) if binfile is None: print("Illegal data detected. %s %s" % (lmdb_fname, key_name)) s = np.frombuffer(binfile, np.uint8) x = cv2.cvtColor(cv2.imdecode(s, cv2.IMREAD_COLOR), cv2.COLOR_BGR2RGB) return x def decode_str(lmdb_fname, key_name): handle = get_lmdb_handle(lmdb_fname) binfile = handle.get(key_name.encode()) string = binfile.decode() return string def decode_json(lmdb_fname, key_name): return json.loads(decode_str(lmdb_fname, key_name)) if __name__ == "__main__": lmdb_fname = "/data/sda/v-yanbi/iccv21/LittleBoy_clean/data/got10k_lmdb" '''Decode image''' # key_name = "test/GOT-10k_Test_000001/00000001.jpg" # img = decode_img(lmdb_fname, key_name) # cv2.imwrite("001.jpg", img) '''Decode str''' # key_name = "test/list.txt" # key_name = "train/GOT-10k_Train_000001/groundtruth.txt" key_name = "train/GOT-10k_Train_000001/absence.label" str_ = decode_str(lmdb_fname, key_name) print(str_) ================================================ FILE: artrackv2_mindspore/lib/utils/merge.py ================================================ import torch def merge_template_search(inp_list, return_search=False, return_template=False): """NOTICE: search region related features must be in the last place""" seq_dict = {"feat": torch.cat([x["feat"] for x in inp_list], dim=0), "mask": torch.cat([x["mask"] for x in inp_list], dim=1), "pos": torch.cat([x["pos"] for x in inp_list], dim=0)} if return_search: x = inp_list[-1] seq_dict.update({"feat_x": x["feat"], "mask_x": x["mask"], "pos_x": x["pos"]}) if return_template: z = inp_list[0] seq_dict.update({"feat_z": z["feat"], "mask_z": z["mask"], "pos_z": z["pos"]}) return seq_dict def get_qkv(inp_list): """The 1st element of the inp_list is about the template, the 2nd (the last) element is about the search region""" dict_x = inp_list[-1] dict_c = {"feat": torch.cat([x["feat"] for x in inp_list], dim=0), "mask": torch.cat([x["mask"] for x in inp_list], dim=1), "pos": torch.cat([x["pos"] for x in inp_list], dim=0)} # concatenated dict q = dict_x["feat"] + dict_x["pos"] k = dict_c["feat"] + dict_c["pos"] v = dict_c["feat"] key_padding_mask = dict_c["mask"] return q, k, v, key_padding_mask ================================================ FILE: artrackv2_mindspore/lib/utils/misc.py ================================================ # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved """ Misc functions, including distributed helpers. Mostly copy-paste from torchvision references. """ import os import subprocess import time from collections import defaultdict, deque import datetime import pickle from typing import Optional, List import torch import torch.distributed as dist from mindspore import Tensor # needed due to empty tensor bug in pytorch and torchvision 0.5 import torchvision vers = torchvision.__version__.split('.') if int(vers[0]) <= 0 and int(vers[1]) < 7: from torchvision.ops import _new_empty_tensor from torchvision.ops.misc import _output_size class SmoothedValue(object): """Track a series of values and provide access to smoothed values over a window or the global series average. """ def __init__(self, window_size=20, fmt=None): if fmt is None: fmt = "{median:.4f} ({global_avg:.4f})" self.deque = deque(maxlen=window_size) self.total = 0.0 self.count = 0 self.fmt = fmt def update(self, value, n=1): self.deque.append(value) self.count += n self.total += value * n def synchronize_between_processes(self): """ Warning: does not synchronize the deque! """ if not is_dist_avail_and_initialized(): return t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda') dist.barrier() dist.all_reduce(t) t = t.tolist() self.count = int(t[0]) self.total = t[1] @property def median(self): d = torch.tensor(list(self.deque)) return d.median().item() @property def avg(self): d = torch.tensor(list(self.deque), dtype=torch.float32) return d.mean().item() @property def global_avg(self): return self.total / self.count @property def max(self): return max(self.deque) @property def value(self): return self.deque[-1] def __str__(self): return self.fmt.format( median=self.median, avg=self.avg, global_avg=self.global_avg, max=self.max, value=self.value) def all_gather(data): """ Run all_gather on arbitrary picklable data (not necessarily tensors) Args: data: any picklable object Returns: list[data]: list of data gathered from each rank """ world_size = get_world_size() if world_size == 1: return [data] # serialized to a Tensor buffer = pickle.dumps(data) storage = torch.ByteStorage.from_buffer(buffer) tensor = torch.ByteTensor(storage).to("cuda") # obtain Tensor size of each rank local_size = torch.tensor([tensor.numel()], device="cuda") size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)] dist.all_gather(size_list, local_size) size_list = [int(size.item()) for size in size_list] max_size = max(size_list) # receiving Tensor from all ranks # we pad the tensor because torch all_gather does not support # gathering tensors of different shapes tensor_list = [] for _ in size_list: tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda")) if local_size != max_size: padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda") tensor = torch.cat((tensor, padding), dim=0) dist.all_gather(tensor_list, tensor) data_list = [] for size, tensor in zip(size_list, tensor_list): buffer = tensor.cpu().numpy().tobytes()[:size] data_list.append(pickle.loads(buffer)) return data_list def reduce_dict(input_dict, average=True): """ Args: input_dict (dict): all the values will be reduced average (bool): whether to do average or sum Reduce the values in the dictionary from all processes so that all processes have the averaged results. Returns a dict with the same fields as input_dict, after reduction. """ world_size = get_world_size() if world_size < 2: return input_dict with torch.no_grad(): names = [] values = [] # sort the keys so that they are consistent across processes for k in sorted(input_dict.keys()): names.append(k) values.append(input_dict[k]) values = torch.stack(values, dim=0) dist.all_reduce(values) if average: values /= world_size reduced_dict = {k: v for k, v in zip(names, values)} return reduced_dict class MetricLogger(object): def __init__(self, delimiter="\t"): self.meters = defaultdict(SmoothedValue) self.delimiter = delimiter def update(self, **kwargs): for k, v in kwargs.items(): if isinstance(v, torch.Tensor): v = v.item() assert isinstance(v, (float, int)) self.meters[k].update(v) def __getattr__(self, attr): if attr in self.meters: return self.meters[attr] if attr in self.__dict__: return self.__dict__[attr] raise AttributeError("'{}' object has no attribute '{}'".format( type(self).__name__, attr)) def __str__(self): loss_str = [] for name, meter in self.meters.items(): loss_str.append( "{}: {}".format(name, str(meter)) ) return self.delimiter.join(loss_str) def synchronize_between_processes(self): for meter in self.meters.values(): meter.synchronize_between_processes() def add_meter(self, name, meter): self.meters[name] = meter def log_every(self, iterable, print_freq, header=None): i = 0 if not header: header = '' start_time = time.time() end = time.time() iter_time = SmoothedValue(fmt='{avg:.4f}') data_time = SmoothedValue(fmt='{avg:.4f}') space_fmt = ':' + str(len(str(len(iterable)))) + 'd' if torch.cuda.is_available(): log_msg = self.delimiter.join([ header, '[{0' + space_fmt + '}/{1}]', 'eta: {eta}', '{meters}', 'time: {time}', 'data: {data}', 'max mem: {memory:.0f}' ]) else: log_msg = self.delimiter.join([ header, '[{0' + space_fmt + '}/{1}]', 'eta: {eta}', '{meters}', 'time: {time}', 'data: {data}' ]) MB = 1024.0 * 1024.0 for obj in iterable: data_time.update(time.time() - end) yield obj iter_time.update(time.time() - end) if i % print_freq == 0 or i == len(iterable) - 1: eta_seconds = iter_time.global_avg * (len(iterable) - i) eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) if torch.cuda.is_available(): print(log_msg.format( i, len(iterable), eta=eta_string, meters=str(self), time=str(iter_time), data=str(data_time), memory=torch.cuda.max_memory_allocated() / MB)) else: print(log_msg.format( i, len(iterable), eta=eta_string, meters=str(self), time=str(iter_time), data=str(data_time))) i += 1 end = time.time() total_time = time.time() - start_time total_time_str = str(datetime.timedelta(seconds=int(total_time))) print('{} Total time: {} ({:.4f} s / it)'.format( header, total_time_str, total_time / len(iterable))) def get_sha(): cwd = os.path.dirname(os.path.abspath(__file__)) def _run(command): return subprocess.check_output(command, cwd=cwd).decode('ascii').strip() sha = 'N/A' diff = "clean" branch = 'N/A' try: sha = _run(['git', 'rev-parse', 'HEAD']) subprocess.check_output(['git', 'diff'], cwd=cwd) diff = _run(['git', 'diff-index', 'HEAD']) diff = "has uncommited changes" if diff else "clean" branch = _run(['git', 'rev-parse', '--abbrev-ref', 'HEAD']) except Exception: pass message = f"sha: {sha}, status: {diff}, branch: {branch}" return message def collate_fn(batch): batch = list(zip(*batch)) batch[0] = nested_tensor_from_tensor_list(batch[0]) return tuple(batch) def _max_by_axis(the_list): # type: (List[List[int]]) -> List[int] maxes = the_list[0] # get the first one for sublist in the_list[1:]: # [h,w,3] for index, item in enumerate(sublist): # index: 0,1,2 maxes[index] = max(maxes[index], item) # compare current max with the other elements in the whole return maxes class NestedTensor(object): def __init__(self, tensors, mask: Optional[Tensor]): self.tensors = tensors self.mask = mask def to(self, device): # type: (Device) -> NestedTensor # noqa cast_tensor = self.tensors.to(device) mask = self.mask if mask is not None: assert mask is not None cast_mask = mask.to(device) else: cast_mask = None return NestedTensor(cast_tensor, cast_mask) def decompose(self): return self.tensors, self.mask def __repr__(self): return str(self.tensors) def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): # TODO make this more general if tensor_list[0].ndim == 3: if torchvision._is_tracing(): # nested_tensor_from_tensor_list() does not export well to ONNX # call _onnx_nested_tensor_from_tensor_list() instead return _onnx_nested_tensor_from_tensor_list(tensor_list) # TODO make it support different-sized images max_size = _max_by_axis([list(img.shape) for img in tensor_list]) # [[3,h1,w1], [3,h2,w2], [3,h3,w3], ...] # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list])) batch_shape = [len(tensor_list)] + max_size # () b, c, h, w = batch_shape dtype = tensor_list[0].dtype device = tensor_list[0].device tensor = torch.zeros(batch_shape, dtype=dtype, device=device) mask = torch.ones((b, h, w), dtype=torch.bool, device=device) for img, pad_img, m in zip(tensor_list, tensor, mask): pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) # copy valid regions of the images to the largest padded base. m[: img.shape[1], :img.shape[2]] = False else: raise ValueError('not supported') return NestedTensor(tensor, mask) # _onnx_nested_tensor_from_tensor_list() is an implementation of # nested_tensor_from_tensor_list() that is supported by ONNX tracing. @torch.jit.unused def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor: max_size = [] for i in range(tensor_list[0].dim()): max_size_i = torch.max(torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)).to(torch.int64) max_size.append(max_size_i) max_size = tuple(max_size) # work around for # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) # m[: img.shape[1], :img.shape[2]] = False # which is not yet supported in onnx padded_imgs = [] padded_masks = [] for img in tensor_list: padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))] padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0])) padded_imgs.append(padded_img) m = torch.zeros_like(img[0], dtype=torch.int, device=img.device) padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1) padded_masks.append(padded_mask.to(torch.bool)) tensor = torch.stack(padded_imgs) mask = torch.stack(padded_masks) return NestedTensor(tensor, mask=mask) def setup_for_distributed(is_master): """ This function disables printing when not in master process """ import builtins as __builtin__ builtin_print = __builtin__.print def print(*args, **kwargs): force = kwargs.pop('force', False) if is_master or force: builtin_print(*args, **kwargs) __builtin__.print = print def is_dist_avail_and_initialized(): if not dist.is_available(): return False if not dist.is_initialized(): return False return True def get_world_size(): if not is_dist_avail_and_initialized(): return 1 return dist.get_world_size() def get_rank(): if not is_dist_avail_and_initialized(): return 0 return dist.get_rank() def is_main_process(): return get_rank() == 0 def save_on_master(*args, **kwargs): if is_main_process(): torch.save(*args, **kwargs) def init_distributed_mode(args): if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ: args.rank = int(os.environ["RANK"]) args.world_size = int(os.environ['WORLD_SIZE']) args.gpu = int(os.environ['LOCAL_RANK']) elif 'SLURM_PROCID' in os.environ: args.rank = int(os.environ['SLURM_PROCID']) args.gpu = args.rank % torch.cuda.device_count() else: print('Not using distributed mode') args.distributed = False return args.distributed = True torch.cuda.set_device(args.gpu) args.dist_backend = 'nccl' print('| distributed init (rank {}): {}'.format( args.rank, args.dist_url), flush=True) torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank) torch.distributed.barrier() setup_for_distributed(args.rank == 0) @torch.no_grad() def accuracy(output, target, topk=(1,)): """Computes the precision@k for the specified values of k""" if target.numel() == 0: return [torch.zeros([], device=output.device)] maxk = max(topk) batch_size = target.size(0) _, pred = output.topk(maxk, 1, True, True) pred = pred.t() correct = pred.eq(target.view(1, -1).expand_as(pred)) res = [] for k in topk: correct_k = correct[:k].view(-1).float().sum(0) res.append(correct_k.mul_(100.0 / batch_size)) return res def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None): # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor """ Equivalent to nn.functional.interpolate, but with support for empty batch sizes. This will eventually be supported natively by PyTorch, and this class can go away. """ if float(torchvision.__version__[:3]) < 0.7: if input.numel() > 0: return torch.nn.functional.interpolate( input, size, scale_factor, mode, align_corners ) output_shape = _output_size(2, input, size, scale_factor) output_shape = list(input.shape[:-2]) + list(output_shape) return _new_empty_tensor(input, output_shape) else: return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners) ================================================ FILE: artrackv2_mindspore/lib/utils/tensor.py ================================================ import functools import torch import copy from collections import OrderedDict class TensorDict(OrderedDict): """Container mainly used for dicts of torch tensors. Extends OrderedDict with pytorch functionality.""" def concat(self, other): """Concatenates two dicts without copying internal data.""" return TensorDict(self, **other) def copy(self): return TensorDict(super(TensorDict, self).copy()) def __deepcopy__(self, memodict={}): return TensorDict(copy.deepcopy(list(self), memodict)) def __getattr__(self, name): if not hasattr(torch.Tensor, name): raise AttributeError('\'TensorDict\' object has not attribute \'{}\''.format(name)) def apply_attr(*args, **kwargs): return TensorDict({n: getattr(e, name)(*args, **kwargs) if hasattr(e, name) else e for n, e in self.items()}) return apply_attr def attribute(self, attr: str, *args): return TensorDict({n: getattr(e, attr, *args) for n, e in self.items()}) def apply(self, fn, *args, **kwargs): return TensorDict({n: fn(e, *args, **kwargs) for n, e in self.items()}) @staticmethod def _iterable(a): return isinstance(a, (TensorDict, list)) class TensorList(list): """Container mainly used for lists of torch tensors. Extends lists with pytorch functionality.""" def __init__(self, list_of_tensors = None): if list_of_tensors is None: list_of_tensors = list() super(TensorList, self).__init__(list_of_tensors) def __deepcopy__(self, memodict={}): return TensorList(copy.deepcopy(list(self), memodict)) def __getitem__(self, item): if isinstance(item, int): return super(TensorList, self).__getitem__(item) elif isinstance(item, (tuple, list)): return TensorList([super(TensorList, self).__getitem__(i) for i in item]) else: return TensorList(super(TensorList, self).__getitem__(item)) def __add__(self, other): if TensorList._iterable(other): return TensorList([e1 + e2 for e1, e2 in zip(self, other)]) return TensorList([e + other for e in self]) def __radd__(self, other): if TensorList._iterable(other): return TensorList([e2 + e1 for e1, e2 in zip(self, other)]) return TensorList([other + e for e in self]) def __iadd__(self, other): if TensorList._iterable(other): for i, e2 in enumerate(other): self[i] += e2 else: for i in range(len(self)): self[i] += other return self def __sub__(self, other): if TensorList._iterable(other): return TensorList([e1 - e2 for e1, e2 in zip(self, other)]) return TensorList([e - other for e in self]) def __rsub__(self, other): if TensorList._iterable(other): return TensorList([e2 - e1 for e1, e2 in zip(self, other)]) return TensorList([other - e for e in self]) def __isub__(self, other): if TensorList._iterable(other): for i, e2 in enumerate(other): self[i] -= e2 else: for i in range(len(self)): self[i] -= other return self def __mul__(self, other): if TensorList._iterable(other): return TensorList([e1 * e2 for e1, e2 in zip(self, other)]) return TensorList([e * other for e in self]) def __rmul__(self, other): if TensorList._iterable(other): return TensorList([e2 * e1 for e1, e2 in zip(self, other)]) return TensorList([other * e for e in self]) def __imul__(self, other): if TensorList._iterable(other): for i, e2 in enumerate(other): self[i] *= e2 else: for i in range(len(self)): self[i] *= other return self def __truediv__(self, other): if TensorList._iterable(other): return TensorList([e1 / e2 for e1, e2 in zip(self, other)]) return TensorList([e / other for e in self]) def __rtruediv__(self, other): if TensorList._iterable(other): return TensorList([e2 / e1 for e1, e2 in zip(self, other)]) return TensorList([other / e for e in self]) def __itruediv__(self, other): if TensorList._iterable(other): for i, e2 in enumerate(other): self[i] /= e2 else: for i in range(len(self)): self[i] /= other return self def __matmul__(self, other): if TensorList._iterable(other): return TensorList([e1 @ e2 for e1, e2 in zip(self, other)]) return TensorList([e @ other for e in self]) def __rmatmul__(self, other): if TensorList._iterable(other): return TensorList([e2 @ e1 for e1, e2 in zip(self, other)]) return TensorList([other @ e for e in self]) def __imatmul__(self, other): if TensorList._iterable(other): for i, e2 in enumerate(other): self[i] @= e2 else: for i in range(len(self)): self[i] @= other return self def __mod__(self, other): if TensorList._iterable(other): return TensorList([e1 % e2 for e1, e2 in zip(self, other)]) return TensorList([e % other for e in self]) def __rmod__(self, other): if TensorList._iterable(other): return TensorList([e2 % e1 for e1, e2 in zip(self, other)]) return TensorList([other % e for e in self]) def __pos__(self): return TensorList([+e for e in self]) def __neg__(self): return TensorList([-e for e in self]) def __le__(self, other): if TensorList._iterable(other): return TensorList([e1 <= e2 for e1, e2 in zip(self, other)]) return TensorList([e <= other for e in self]) def __ge__(self, other): if TensorList._iterable(other): return TensorList([e1 >= e2 for e1, e2 in zip(self, other)]) return TensorList([e >= other for e in self]) def concat(self, other): return TensorList(super(TensorList, self).__add__(other)) def copy(self): return TensorList(super(TensorList, self).copy()) def unroll(self): if not any(isinstance(t, TensorList) for t in self): return self new_list = TensorList() for t in self: if isinstance(t, TensorList): new_list.extend(t.unroll()) else: new_list.append(t) return new_list def list(self): return list(self) def attribute(self, attr: str, *args): return TensorList([getattr(e, attr, *args) for e in self]) def apply(self, fn): return TensorList([fn(e) for e in self]) def __getattr__(self, name): if not hasattr(torch.Tensor, name): raise AttributeError('\'TensorList\' object has not attribute \'{}\''.format(name)) def apply_attr(*args, **kwargs): return TensorList([getattr(e, name)(*args, **kwargs) for e in self]) return apply_attr @staticmethod def _iterable(a): return isinstance(a, (TensorList, list)) def tensor_operation(op): def islist(a): return isinstance(a, TensorList) @functools.wraps(op) def oplist(*args, **kwargs): if len(args) == 0: raise ValueError('Must be at least one argument without keyword (i.e. operand).') if len(args) == 1: if islist(args[0]): return TensorList([op(a, **kwargs) for a in args[0]]) else: # Multiple operands, assume max two if islist(args[0]) and islist(args[1]): return TensorList([op(a, b, *args[2:], **kwargs) for a, b in zip(*args[:2])]) if islist(args[0]): return TensorList([op(a, *args[1:], **kwargs) for a in args[0]]) if islist(args[1]): return TensorList([op(args[0], b, *args[2:], **kwargs) for b in args[1]]) # None of the operands are lists return op(*args, **kwargs) return oplist ================================================ FILE: artrackv2_mindspore/lib/utils/variable_hook.py ================================================ import torch from bytecode import Bytecode, Instr class get_local(object): cache = {} is_activate = False def __init__(self, varname): self.varname = varname def __call__(self, func): if not type(self).is_activate: return func type(self).cache[func.__qualname__] = [] c = Bytecode.from_code(func.__code__) extra_code = [ Instr('STORE_FAST', '_res'), Instr('LOAD_FAST', self.varname), Instr('STORE_FAST', '_value'), Instr('LOAD_FAST', '_res'), Instr('LOAD_FAST', '_value'), Instr('BUILD_TUPLE', 2), Instr('STORE_FAST', '_result_tuple'), Instr('LOAD_FAST', '_result_tuple'), ] c[-1:-1] = extra_code func.__code__ = c.to_code() def wrapper(*args, **kwargs): res, values = func(*args, **kwargs) if isinstance(values, torch.Tensor): type(self).cache[func.__qualname__].append(values.detach().cpu().numpy()) elif isinstance(values, list): # list of Tensor type(self).cache[func.__qualname__].append([value.detach().cpu().numpy() for value in values]) else: raise NotImplementedError return res return wrapper @classmethod def clear(cls): for key in cls.cache.keys(): cls.cache[key] = [] @classmethod def activate(cls): cls.is_activate = True ================================================ FILE: artrackv2_mindspore/lib/vis/__init__.py ================================================ ================================================ FILE: artrackv2_mindspore/lib/vis/plotting.py ================================================ import matplotlib.pyplot as plt import numpy as np import torch import cv2 def draw_figure(fig): fig.canvas.draw() fig.canvas.flush_events() plt.pause(0.001) def show_tensor(a: torch.Tensor, fig_num = None, title = None, range=(None, None), ax=None): """Display a 2D tensor. args: fig_num: Figure number. title: Title of figure. """ a_np = a.squeeze().cpu().clone().detach().numpy() if a_np.ndim == 3: a_np = np.transpose(a_np, (1, 2, 0)) if ax is None: fig = plt.figure(fig_num) plt.tight_layout() plt.cla() plt.imshow(a_np, vmin=range[0], vmax=range[1]) plt.axis('off') plt.axis('equal') if title is not None: plt.title(title) draw_figure(fig) else: ax.cla() ax.imshow(a_np, vmin=range[0], vmax=range[1]) ax.set_axis_off() ax.axis('equal') if title is not None: ax.set_title(title) draw_figure(plt.gcf()) def plot_graph(a: torch.Tensor, fig_num = None, title = None): """Plot graph. Data is a 1D tensor. args: fig_num: Figure number. title: Title of figure. """ a_np = a.squeeze().cpu().clone().detach().numpy() if a_np.ndim > 1: raise ValueError fig = plt.figure(fig_num) # plt.tight_layout() plt.cla() plt.plot(a_np) if title is not None: plt.title(title) draw_figure(fig) def show_image_with_boxes(im, boxes, iou_pred=None, disp_ids=None): im_np = im.clone().cpu().squeeze().numpy() im_np = np.ascontiguousarray(im_np.transpose(1, 2, 0).astype(np.uint8)) boxes = boxes.view(-1, 4).cpu().numpy().round().astype(int) # Draw proposals for i_ in range(boxes.shape[0]): if disp_ids is None or disp_ids[i_]: bb = boxes[i_, :] disp_color = (i_*38 % 256, (255 - i_*97) % 256, (123 + i_*66) % 256) cv2.rectangle(im_np, (bb[0], bb[1]), (bb[0] + bb[2], bb[1] + bb[3]), disp_color, 1) if iou_pred is not None: text_pos = (bb[0], bb[1] - 5) cv2.putText(im_np, 'ID={} IOU = {:3.2f}'.format(i_, iou_pred[i_]), text_pos, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1, bottomLeftOrigin=False) im_tensor = torch.from_numpy(im_np.transpose(2, 0, 1)).float() return im_tensor def _pascal_color_map(N=256, normalized=False): """ Python implementation of the color map function for the PASCAL VOC data set. Official Matlab version can be found in the PASCAL VOC devkit http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html#devkit """ def bitget(byteval, idx): return (byteval & (1 << idx)) != 0 dtype = 'float32' if normalized else 'uint8' cmap = np.zeros((N, 3), dtype=dtype) for i in range(N): r = g = b = 0 c = i for j in range(8): r = r | (bitget(c, 0) << 7 - j) g = g | (bitget(c, 1) << 7 - j) b = b | (bitget(c, 2) << 7 - j) c = c >> 3 cmap[i] = np.array([r, g, b]) cmap = cmap / 255 if normalized else cmap return cmap def overlay_mask(im, ann, alpha=0.5, colors=None, contour_thickness=None): """ Overlay mask over image. Source: https://github.com/albertomontesg/davis-interactive/blob/master/davisinteractive/utils/visualization.py This function allows you to overlay a mask over an image with some transparency. # Arguments im: Numpy Array. Array with the image. The shape must be (H, W, 3) and the pixels must be represented as `np.uint8` data type. ann: Numpy Array. Array with the mask. The shape must be (H, W) and the values must be intergers alpha: Float. Proportion of alpha to apply at the overlaid mask. colors: Numpy Array. Optional custom colormap. It must have shape (N, 3) being N the maximum number of colors to represent. contour_thickness: Integer. Thickness of each object index contour draw over the overlay. This function requires to have installed the package `opencv-python`. # Returns Numpy Array: Image of the overlay with shape (H, W, 3) and data type `np.uint8`. """ im, ann = np.asarray(im, dtype=np.uint8), np.asarray(ann, dtype=np.int) if im.shape[:-1] != ann.shape: raise ValueError('First two dimensions of `im` and `ann` must match') if im.shape[-1] != 3: raise ValueError('im must have three channels at the 3 dimension') colors = colors or _pascal_color_map() colors = np.asarray(colors, dtype=np.uint8) mask = colors[ann] fg = im * alpha + (1 - alpha) * mask img = im.copy() img[ann > 0] = fg[ann > 0] if contour_thickness: # pragma: no cover import cv2 for obj_id in np.unique(ann[ann > 0]): contours = cv2.findContours((ann == obj_id).astype( np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:] cv2.drawContours(img, contours[0], -1, colors[obj_id].tolist(), contour_thickness) return img ================================================ FILE: artrackv2_mindspore/lib/vis/utils.py ================================================ import torch import numpy as np def numpy_to_torch(a: np.ndarray): return torch.from_numpy(a).float().permute(2, 0, 1).unsqueeze(0) ================================================ FILE: artrackv2_mindspore/tracking/_init_paths.py ================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os.path as osp import sys def add_path(path): if path not in sys.path: sys.path.insert(0, path) this_dir = osp.dirname(__file__) prj_path = osp.join(this_dir, '..') add_path(prj_path) ================================================ FILE: artrackv2_mindspore/tracking/analysis_results.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "%matplotlib inline\n", "import os\n", "import sys\n", "import matplotlib.pyplot as plt\n", "plt.rcParams['figure.figsize'] = [14, 8]\n", "\n", "sys.path.append('/home/yebotao/OSTrack')\n", "from lib.test.analysis.plot_results import plot_results, print_results, print_per_sequence_results, print_results_per_video\n", "from lib.test.evaluation import get_dataset, trackerlist" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "dataset_name = 'lasot'\n", "\n", "trackers = []\n", "trackers.extend(trackerlist(name='ostrack', parameter_name='vitb_256_mae_ce_32x4_ep300', dataset_name=dataset_name,\n", " run_ids=None, display_name='OSTrack256'))\n", "trackers.extend(trackerlist(name='ostrack', parameter_name='vitb_384_mae_ce_32x4_ep300', dataset_name=dataset_name,\n", " run_ids=None, display_name='OSTrack384'))\n", "\n", "dataset = get_dataset(dataset_name)\n", "# plot_results(trackers, dataset, dataset_name, merge_results=True, plot_types=('success', 'prec'),\n", "# skip_missing_seq=False, force_evaluation=True, plot_bin_gap=0.05, exclude_invalid_frames=False)\n", "print_results(trackers, dataset, dataset_name, merge_results=True, plot_types=('success', 'prec', 'norm_prec'))\n", "# print_results_per_video(trackers, dataset, dataset_name, merge_results=True, plot_types=('success', 'prec', 'norm_prec'),\n", "# per_video=True, force_evaluation=True)\n", "# print_per_sequence_results(trackers, dataset, dataset_name, merge_results=True, plot_types=('success', 'prec', 'norm_prec'))" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: artrackv2_mindspore/tracking/analysis_results.py ================================================ import _init_paths import matplotlib.pyplot as plt plt.rcParams['figure.figsize'] = [8, 8] from lib.test.analysis.plot_results import plot_results, print_results, print_per_sequence_results from lib.test.evaluation import get_dataset, trackerlist trackers = [] #dataset_name = 'lasot_extension_subset' dataset_name = 'lasot' """stark""" # trackers.extend(trackerlist(name='stark_s', parameter_name='baseline', dataset_name=dataset_name, # run_ids=None, display_name='STARK-S50')) # trackers.extend(trackerlist(name='stark_st', parameter_name='baseline', dataset_name=dataset_name, # run_ids=None, display_name='STARK-ST50')) # trackers.extend(trackerlist(name='stark_st', parameter_name='baseline_R101', dataset_name=dataset_name, # run_ids=None, display_name='STARK-ST101')) """TransT""" # trackers.extend(trackerlist(name='TransT_N2', parameter_name=None, dataset_name=None, # run_ids=None, display_name='TransT_N2', result_only=True)) # trackers.extend(trackerlist(name='TransT_N4', parameter_name=None, dataset_name=None, # run_ids=None, display_name='TransT_N4', result_only=True)) """pytracking""" # trackers.extend(trackerlist('atom', 'default', None, range(0,5), 'ATOM')) # trackers.extend(trackerlist('dimp', 'dimp18', None, range(0,5), 'DiMP18')) # trackers.extend(trackerlist('dimp', 'dimp50', None, range(0,5), 'DiMP50')) # trackers.extend(trackerlist('dimp', 'prdimp18', None, range(0,5), 'PrDiMP18')) # trackers.extend(trackerlist('dimp', 'prdimp50', None, range(0,5), 'PrDiMP50')) """ostrack""" trackers.extend(trackerlist(name='ostrack', parameter_name='finetune_384', dataset_name=dataset_name, run_ids=None, display_name='OSTrack256')) #trackers.extend(trackerlist(name='ostrack', parameter_name='vitb_384_mae_ce_32x4_ep300', dataset_name=dataset_name, #. run_ids=None, display_name='OSTrack384')) dataset = get_dataset(dataset_name) # dataset = get_dataset('otb', 'nfs', 'uav', 'tc128ce') # plot_results(trackers, dataset, 'OTB2015', merge_results=True, plot_types=('success', 'norm_prec'), # skip_missing_seq=False, force_evaluation=True, plot_bin_gap=0.05) print_results(trackers, dataset, dataset_name, merge_results=True, plot_types=('success', 'norm_prec', 'prec')) # print_results(trackers, dataset, 'UNO', merge_results=True, plot_types=('success', 'prec')) ================================================ FILE: artrackv2_mindspore/tracking/analysis_results_ITP.py ================================================ import _init_paths import argparse from lib.test.analysis.plot_results import print_results from lib.test.evaluation import get_dataset, trackerlist def parse_args(): """ args for evaluation. """ parser = argparse.ArgumentParser(description='Parse args for training') # for train parser.add_argument('--script', type=str, help='training script name') parser.add_argument('--config', type=str, default='baseline', help='yaml configure file name') args = parser.parse_args() return args if __name__ == "__main__": args = parse_args() trackers = [] trackers.extend(trackerlist(args.script, args.config, "None", None, args.config)) dataset = get_dataset('lasot') print_results(trackers, dataset, 'LaSOT', merge_results=True, plot_types=('success', 'prec', 'norm_prec')) ================================================ FILE: artrackv2_mindspore/tracking/convert_transt.py ================================================ import _init_paths import os from lib.test.evaluation import get_dataset import shutil trackers = [] # dataset_name = 'uav' dataset_name = 'nfs' root_dir = "/data/sda/v-yanbi/iccv21/STARK_Latest/Stark" base_dir = os.path.join(root_dir, "test/tracking_results/TransT_N2") dataset = get_dataset(dataset_name) for x in dataset: seq_name = x.name file_name = "%s.txt" % (seq_name.replace("nfs_", "")) file_path = os.path.join(base_dir, file_name) file_path_new = os.path.join(base_dir, "%s.txt" % seq_name) if os.path.exists(file_path): shutil.move(file_path, file_path_new) ================================================ FILE: artrackv2_mindspore/tracking/create_default_local_file.py ================================================ import argparse import os import _init_paths from lib.train.admin import create_default_local_file_ITP_train from lib.test.evaluation import create_default_local_file_ITP_test def parse_args(): parser = argparse.ArgumentParser(description='Create default local file on ITP or PAI') parser.add_argument("--workspace_dir", type=str, required=True) # workspace dir parser.add_argument("--data_dir", type=str, required=True) parser.add_argument("--save_dir", type=str, required=True) args = parser.parse_args() return args if __name__ == "__main__": args = parse_args() workspace_dir = os.path.realpath(args.workspace_dir) data_dir = os.path.realpath(args.data_dir) save_dir = os.path.realpath(args.save_dir) create_default_local_file_ITP_train(workspace_dir, data_dir) create_default_local_file_ITP_test(workspace_dir, data_dir, save_dir) ================================================ FILE: artrackv2_mindspore/tracking/download_pytracking_results.py ================================================ import os import sys import gdown import re import shutil import argparse import tempfile import _init_paths from lib.test.evaluation.environment import env_settings pytracking_results_link_dict = { "dimp": { "prdimp50_003.zip": "1p13j3iwcOCubBi3ms0hLwqnP6-x0J8Mc", "prdimp50_002.zip": "1PPKgrAepbuyM2kjfzYAozQKTL6AjcQOz", "prdimp50_001.zip": "17NFBObEDeK6mW4Mk2vN5Ekk1SGbFvxRS", "prdimp50_000.zip": "1r3Efq7AumML2yGQ_KV4zmf4ATKVE1bo6", "prdimp18_004.zip": "1DF4ZJQAa4CwvN_OiT4te33AV0kpsO7JM", "prdimp18_003.zip": "1RgwJAN4TxnzgVgsfvrHIg1OUXD1EBZkO", "prdimp18_002.zip": "17lMllYhygCqgE81DoHX4BZar3xc3auzM", "prdimp18_001.zip": "1Yg7DmGYOnn2k0MYtSjjKlGyzO1Uimj4G", "prdimp18_000.zip": "1DuZJSBJ-23WJBQTOWSAaoPYSbGAJJN2Z", "prdimp50_004.zip": "1f9bx9-dtx3B5_IvIJhjjJyp-cnXciqLO", "dimp50_004.zip": "1Lj3p8mYCoIqxzdQXZkWFTw-MA8c6eeLa", "dimp50_000.zip": "1LCgf5sg453Z4bY37A_W5mbXeG68U1fET", "dimp18_000.zip": "17M7dJZ1oKrIY4-O5lL_mlQPEubUn034g", "dimp18_001.zip": "1AsiliVgISyDTouYOQYVOXA0srj3YskhJ", "dimp18_002.zip": "1I0GrBaPnySOyPWSvItHhXH8182tFCi_Y", "dimp50_001.zip": "1XfPvwAcymW88J1rq7RlhyKmqsawJDK-K", "dimp18_004.zip": "1EztF6bpROFwZ1PSJWgMB7bQ4G_Z08YIg", "dimp18_003.zip": "1iuiFLv04WE7GfBjm8UkZXFq4gheG2Ru8", "dimp50_003.zip": "1rLsgeQXyKpD6ryl9BjlIVdO3vd27ekwy", "dimp50_002.zip": "1wj2jUwlpHgsP1hAcuxXAVriUPuEspsu4", }, "atom": { "default_004.zip": "1BapnQh_8iRM44DXj862eOZV4q8zQLdmT", "default_003.zip": "1YpfOBLBEUQQiX0fWMPA5pnW3dm0NG3E5", "default_000.zip": "1x6fKGZk3V839mX99Gl_pw7JUaiMaTxc5", "default_002.zip": "1QIlQFv3p6MBTwsYdIMYmzUDBDQGxGsUC", "default_001.zip": "1-K2--GNCURDKEgUuiEF18K4DcCLvDEVt", }, "kys": { "default_004.zip": "1QdfkA3d4MzKwdDiBOM1ZhDJWk9NmALxD", "default_000.zip": "1SCs79_ePTc8zxPDzRAgAmbbRlnmE89SN", "default_003.zip": "1TCzq38QW4YiMrgU5VR6NAEefJ85gwzfT", "default_002.zip": "1_9u1ybCFxHu0yJmW5ZzDR4-isJMEUsDf", "default_001.zip": "1utJhdosNj6vlI75dfzUxGM3Vy8OjWslT", }, } def _download_file(file_id, path): link = 'https://drive.google.com/uc?id=' + file_id gdown.download(link, path, quiet=True) def download_results(download_path, trackers='pytracking'): """ Script to automatically download tracker results for PyTracking. args: download_path - Directory where the zipped results are downloaded trackers - Tracker results which are to be downloaded. If set to 'pytracking', results for all pytracking based trackers will be downloaded. If set to 'external', results for available external trackers will be downloaded. If set to 'all', all available results are downloaded. If set to a name of a tracker (e.g. atom), all results for that tracker are downloaded. Otherwise, it can be set to a dict, where the keys are the names of the trackers for which results are downloaded. The value can be set to either 'all', in which case all available results for the tracker are downloaded. Else the value should be a list of parameter file names. """ print('Using download path ''{}'''.format(download_path)) os.makedirs(download_path, exist_ok=True) if isinstance(trackers, str): if trackers == 'all': all_trackers = list(pytracking_results_link_dict.keys()) + list(external_results_link_dict.keys()) trackers = {k: 'all' for k in all_trackers} elif trackers == 'pytracking': trackers = {k: 'all' for k in pytracking_results_link_dict.keys()} elif trackers == 'external': trackers = {k: 'all' for k in external_results_link_dict.keys()} elif trackers in pytracking_results_link_dict or trackers in external_results_link_dict: trackers = {trackers: 'all'} else: raise Exception('tracker_list must be set to ''all'', a tracker name, or be a dict') elif isinstance(trackers, dict): pass else: raise Exception('tracker_list must be set to ''all'', or be a dict') common_link_dict = pytracking_results_link_dict # for k, v in external_results_link_dict.items(): # common_link_dict[k] = v for trk, runfiles in trackers.items(): trk_path = os.path.join(download_path, trk) if not os.path.exists(trk_path): os.makedirs(trk_path) if runfiles == 'all': for params, fileid in common_link_dict[trk].items(): print('Downloading: {}/{}'.format(trk, params)) _download_file(fileid, os.path.join(trk_path, params)) elif isinstance(runfiles, (list, tuple)): for p in runfiles: for params, fileid in common_link_dict[trk].items(): if re.match(r'{}(|_(\d\d\d)).zip'.format(p), params) is not None: print('Downloading: {}/{}'.format(trk, params)) _download_file(fileid, os.path.join(trk_path, params)) else: raise Exception('tracker_list values must either be set to ''all'', or be a list of param names') def unpack_tracking_results(download_path, output_path=None): """ Unpacks zipped benchmark results. The directory 'download_path' should have the following structure - root - tracker1 - param1.zip - param2.zip . . - tracker2 - param1.zip - param2.zip . . args: download_path - Path to the directory where the zipped results are stored output_path - Path to the directory where the results will be unpacked. Set to env_settings().results_path by default """ if output_path is None: output_path = env_settings().results_path if not os.path.exists(output_path): os.makedirs(output_path) trackers = os.listdir(download_path) for t in trackers: runfiles = os.listdir(os.path.join(download_path, t)) for r in runfiles: save_path = os.path.join(output_path, t) if not os.path.exists(save_path): os.makedirs(save_path) shutil.unpack_archive(os.path.join(download_path, t, r), os.path.join(save_path, r[:-4]), 'zip') def main(): parser = argparse.ArgumentParser(description='Download and unpack zipped results') parser.add_argument('--tracker', type=str, default='pytracking', help='Name of tracker results to download, or "pytracking" (downloads results for PyTracking' ' based trackers, or "external" (downloads results for external trackers) or "all"') parser.add_argument('--output_path', type=str, default=None, help='Path to the directory where the results will be unpacked.') parser.add_argument('--temp_download_path', type=str, default=None, help='Temporary path used for downloading the Zip files.') parser.add_argument('--download', type=bool, default=True, help='Whether to download results or unpack existing downloaded files.') args = parser.parse_args() download_path = args.temp_download_path if download_path is None: download_path = '{}/pytracking_results/'.format(tempfile.gettempdir()) if args.download: download_results(download_path, args.tracker) unpack_tracking_results(download_path, args.output_path) if __name__ == '__main__': main() ================================================ FILE: artrackv2_mindspore/tracking/pre_read_datasets.py ================================================ import _init_paths import multiprocessing as mp import argparse import os from lib.utils.lmdb_utils import decode_str import time import json def parse_args(): """ args for training. """ parser = argparse.ArgumentParser(description='Parse args for training') parser.add_argument('--data_dir', type=str, help='directory where lmdb data is located') parser.add_argument('--dataset_str', type=str, help="which datasets to use") args = parser.parse_args() return args def get_trknet_dict(trknet_dir): with open(os.path.join(trknet_dir, "seq_list.json"), "r") as f: seq_list = json.loads(f.read()) res_dict = {} set_idx_pre = -1 for set_idx, seq_name in seq_list: if set_idx != set_idx_pre: res_dict[set_idx] = "anno/%s.txt" % seq_name set_idx_pre = set_idx return res_dict def target(lmdb_dir, key_name): _ = decode_str(lmdb_dir, key_name) if __name__ == "__main__": args = parse_args() data_dir = args.data_dir dataset_str = args.dataset_str key_dict = {"got10k_lmdb": "train/list.txt", "lasot_lmdb": "LaSOTBenchmark.json", "coco_lmdb": "annotations/instances_train2017.json", "vid_lmdb": "cache.json"} print("Ready to pre load datasets") start = time.time() ps = [] datasets = [] if 'g' in dataset_str: datasets.append("got10k_lmdb") if 'l' in dataset_str: datasets.append("lasot_lmdb") if 'c' in dataset_str: datasets.append("coco_lmdb") if 'v' in dataset_str: datasets.append("vid_lmdb") for dataset in datasets: lmdb_dir = os.path.join(data_dir, dataset) p = mp.Process(target=target, args=(lmdb_dir, key_dict[dataset])) print("add %s %s to job queue" % (lmdb_dir, key_dict[dataset])) ps.append(p) # deal with trackingnet if 't' in dataset_str: trknet_dict = get_trknet_dict(os.path.join(data_dir, "trackingnet_lmdb")) for set_idx, seq_path in trknet_dict.items(): lmdb_dir = os.path.join(data_dir, "trackingnet_lmdb", "TRAIN_%d_lmdb" % set_idx) p = mp.Process(target=target, args=(lmdb_dir, seq_path)) print("add %s %s to job queue" % (lmdb_dir, seq_path)) ps.append(p) for p in ps: p.start() for p in ps: p.join() print("Pre read over") end = time.time() hour = (end - start) / 3600 print("it takes %.2f hours to pre-read data" % hour) ================================================ FILE: artrackv2_mindspore/tracking/profile_model.py ================================================ import os import sys prj_path = os.path.join(os.path.dirname(__file__), '..') if prj_path not in sys.path: sys.path.append(prj_path) import argparse import torch from lib.utils.misc import NestedTensor from thop import profile from thop.utils import clever_format import time import importlib def parse_args(): """ args for training. """ parser = argparse.ArgumentParser(description='Parse args for training') # for train parser.add_argument('--script', type=str, default='ostrack', choices=['ostrack'], help='training script name') parser.add_argument('--config', type=str, default='2stage_256_got', help='yaml configure file name') args = parser.parse_args() return args def evaluate_vit(model, template, search, seq_input, stage): '''Speed Test''' #macs1, params1 = profile(model, inputs=(template, search), # custom_ops=None, verbose=False) #macs, params = clever_format([macs1, params1], "%.3f") #print('overall macs is ', macs) #print('overall params is ', params) T_w = 500 T_t = 1000 print("testing speed ...") torch.cuda.synchronize() with torch.no_grad(): # overall for i in range(T_w): _ = model(template, search, seq_input=seq_input, stage=stage) start = time.time() for i in range(T_t): _ = model(template, search, seq_input=seq_input, stage=stage) torch.cuda.synchronize() end = time.time() avg_lat = (end - start) / T_t print("The average overall latency is %.2f ms" % (avg_lat * 1000)) print("FPS is %.2f fps" % (1. / avg_lat)) # for i in range(T_w): # _ = model(template, search) # start = time.time() # for i in range(T_t): # _ = model(template, search) # end = time.time() # avg_lat = (end - start) / T_t # print("The average backbone latency is %.2f ms" % (avg_lat * 1000)) def evaluate_vit_separate(model, template, search): '''Speed Test''' T_w = 50 T_t = 1000 print("testing speed ...") z = model.forward_backbone(template, image_type='template') x = model.forward_backbone(search, image_type='search') with torch.no_grad(): # overall for i in range(T_w): _ = model.forward_backbone(search, image_type='search') _ = model.forward_cat(z, x) start = time.time() for i in range(T_t): _ = model.forward_backbone(search, image_type='search') _ = model.forward_cat(z, x) end = time.time() avg_lat = (end - start) / T_t print("The average overall latency is %.2f ms" % (avg_lat * 1000)) def get_data(bs, sz): img_patch = torch.randn(bs, 3, sz, sz) att_mask = torch.rand(bs, sz, sz) > 0.5 return NestedTensor(img_patch, att_mask) if __name__ == "__main__": device = "cuda:1" torch.cuda.set_device(device) # Compute the Flops and Params of our STARK-S model args = parse_args() '''update cfg''' yaml_fname = 'experiments/%s/%s.yaml' % (args.script, args.config) config_module = importlib.import_module('lib.config.%s.config' % args.script) cfg = config_module.cfg config_module.update_config_from_file(yaml_fname) print(cfg) '''set some values''' bs = 1 z_sz = cfg.TEST.TEMPLATE_SIZE x_sz = cfg.TEST.SEARCH_SIZE print(x_sz) print(z_sz) if args.script == "ostrack": model_module = importlib.import_module('lib.models') model_constructor = model_module.build_ostrack model = model_constructor(cfg, training=False) # get the template and search template = torch.randn(bs, 2, 3, z_sz, z_sz) search = torch.randn(bs, 3, x_sz, x_sz) # transfer to device model = model.to(device) model = model.eval() template = template.to(device) search = search.to(device) merge_layer = cfg.MODEL.BACKBONE.MERGE_LAYER #seq_input = torch.Tensor([[1]]) #seq_input = torch.Tensor([[1,2,3,4,5]]) #seq_input = torch.Tensor([[5,6,7,8,9,10,11,12,13,14,15,16,17,1,2,3]]) seq_input = torch.Tensor([[5,6,7,8,9,10,11,12,13,14,15,16,1,2,3,4,17,18,19,20]]).to(device).repeat(bs, 1) stage = "doit" #stage = None if merge_layer <= 0: evaluate_vit(model, template, search, seq_input, stage) else: evaluate_vit_separate(model, template, search) else: raise NotImplementedError ================================================ FILE: artrackv2_mindspore/tracking/test.py ================================================ import os import sys import argparse import mindspore as ms from mindspore import context import mindspore prj_path = os.path.join(os.path.dirname(__file__), '..') if prj_path not in sys.path: sys.path.append(prj_path) from lib.test.evaluation import get_dataset from lib.test.evaluation.running import run_dataset from lib.test.evaluation.tracker import Tracker def run_tracker(tracker_name, tracker_param, run_id=None, dataset_name='otb', sequence=None, debug=0, threads=0, num_gpus=8): """Run tracker on sequence or dataset. args: tracker_name: Name of tracking method. tracker_param: Name of parameter file. run_id: The run id. dataset_name: Name of dataset (otb, nfs, uav, tpl, vot, tn, gott, gotv, lasot). sequence: Sequence number or name. debug: Debug level. threads: Number of threads. """ dataset = get_dataset(dataset_name) if sequence is not None: dataset = [dataset[sequence]] trackers = [Tracker(tracker_name, tracker_param, dataset_name, run_id)] run_dataset(dataset, trackers, debug, threads, num_gpus=num_gpus) def main(): context.set_context(device_target="GPU") mindspore.run_check() parser = argparse.ArgumentParser(description='Run tracker on sequence or dataset.') parser.add_argument('tracker_name', type=str, help='Name of tracking method.') parser.add_argument('tracker_param', type=str, help='Name of config file.') parser.add_argument('--runid', type=int, default=None, help='The run id.') parser.add_argument('--dataset_name', type=str, default='otb', help='Name of dataset (otb, nfs, uav, tpl, vot, tn, gott, gotv, lasot).') parser.add_argument('--sequence', type=str, default=None, help='Sequence number or name.') parser.add_argument('--debug', type=int, default=0, help='Debug level.') parser.add_argument('--threads', type=int, default=0, help='Number of threads.') parser.add_argument('--num_gpus', type=int, default=8) args = parser.parse_args() try: seq_name = int(args.sequence) except: seq_name = args.sequence run_tracker(args.tracker_name, args.tracker_param, args.runid, args.dataset_name, seq_name, args.debug, args.threads, num_gpus=args.num_gpus) if __name__ == '__main__': main() ================================================ FILE: artrackv2_mindspore/tracking/test_exp.py ================================================ import os import sys import argparse prj_path = os.path.join(os.path.dirname(__file__), '..') if prj_path not in sys.path: sys.path.append(prj_path) from lib.test.evaluation import get_dataset from lib.test.evaluation.running import run_dataset from lib.test.evaluation.tracker import Tracker def run_tracker(tracker_name, tracker_param, run_id=None, dataset_name='otb', sequence=None, debug=0, threads=0, num_gpus=8): """Run tracker on sequence or dataset. args: tracker_name: Name of tracking method. tracker_param: Name of parameter file. run_id: The run id. dataset_name: Name of dataset (otb, nfs, uav, tpl, vot, tn, gott, gotv, lasot). sequence: Sequence number or name. debug: Debug level. threads: Number of threads. """ dataset = get_dataset(*dataset_name) if sequence is not None: dataset = [dataset[sequence]] trackers = [Tracker(tracker_name, tracker_param, dataset_name, run_id)] run_dataset(dataset, trackers, debug, threads, num_gpus=num_gpus) def main(): parser = argparse.ArgumentParser(description='Run tracker on sequence or dataset.') parser.add_argument('tracker_name', type=str, help='Name of tracking method.') parser.add_argument('tracker_param', type=str, help='Name of config file.') parser.add_argument('--runid', type=int, default=None, help='The run id.') parser.add_argument('--dataset_name', type=str, default='otb', help='Name of dataset (otb, nfs, uav, tpl, vot, tn, gott, gotv, lasot).') parser.add_argument('--sequence', type=str, default=None, help='Sequence number or name.') parser.add_argument('--debug', type=int, default=0, help='Debug level.') parser.add_argument('--threads', type=int, default=0, help='Number of threads.') parser.add_argument('--num_gpus', type=int, default=8) args = parser.parse_args() try: seq_name = int(args.sequence) except: seq_name = args.sequence args.dataset_name = ['trackingnet', 'got10k_test', 'lasot'] run_tracker(args.tracker_name, args.tracker_param, args.runid, args.dataset_name, seq_name, args.debug, args.threads, num_gpus=args.num_gpus) if __name__ == '__main__': main() ================================================ FILE: artrackv2_mindspore/tracking/train.py ================================================ import os import argparse import random import torch def parse_args(): """ args for training. """ parser = argparse.ArgumentParser(description='Parse args for training') # for train parser.add_argument('--script', type=str, help='training script name') parser.add_argument('--config', type=str, default='baseline', help='yaml configure file name') parser.add_argument('--save_dir', type=str, help='root directory to save checkpoints, logs, and tensorboard') parser.add_argument('--mode', type=str, choices=["single", "multiple", "multi_node"], default="multiple", help="train on single gpu or multiple gpus") parser.add_argument('--nproc_per_node', type=int, help="number of GPUs per node") # specify when mode is multiple parser.add_argument('--use_lmdb', type=int, choices=[0, 1], default=0) # whether datasets are in lmdb format parser.add_argument('--script_prv', type=str, help='training script name') parser.add_argument('--config_prv', type=str, default='baseline', help='yaml configure file name') parser.add_argument('--use_wandb', type=int, choices=[0, 1], default=0) # whether to use wandb # for knowledge distillation parser.add_argument('--distill', type=int, choices=[0, 1], default=0) # whether to use knowledge distillation parser.add_argument('--script_teacher', type=str, help='teacher script name') parser.add_argument('--config_teacher', type=str, help='teacher yaml configure file name') # for multiple machines parser.add_argument('--rank', type=int, help='Rank of the current process.') parser.add_argument('--world-size', type=int, help='Number of processes participating in the job.') parser.add_argument('--ip', type=str, default='127.0.0.1', help='IP of the current rank 0.') parser.add_argument('--port', type=int, default='20000', help='Port of the current rank 0.') args = parser.parse_args() return args def main(): torch.set_num_threads(8) args = parse_args() if args.mode == "single": train_cmd = "python lib/train/run_training.py --script %s --config %s --save_dir %s --use_lmdb %d " \ "--script_prv %s --config_prv %s --distill %d --script_teacher %s --config_teacher %s --use_wandb %d"\ % (args.script, args.config, args.save_dir, args.use_lmdb, args.script_prv, args.config_prv, args.distill, args.script_teacher, args.config_teacher, args.use_wandb) elif args.mode == "multiple": train_cmd = "python -m torch.distributed.launch --nproc_per_node %d --master_port %d lib/train/run_training.py " \ "--script %s --config %s --save_dir %s --use_lmdb %d --script_prv %s --config_prv %s --use_wandb %d " \ "--distill %d --script_teacher %s --config_teacher %s" \ % (args.nproc_per_node, random.randint(10000, 50000), args.script, args.config, args.save_dir, args.use_lmdb, args.script_prv, args.config_prv, args.use_wandb, args.distill, args.script_teacher, args.config_teacher) elif args.mode == "multi_node": train_cmd = "python -m torch.distributed.launch --nproc_per_node %d --master_addr %s --master_port %d --nnodes %d --node_rank %d lib/train/run_training.py " \ "--script %s --config %s --save_dir %s --use_lmdb %d --script_prv %s --config_prv %s --use_wandb %d " \ "--distill %d --script_teacher %s --config_teacher %s" \ % (args.nproc_per_node, args.ip, args.port, args.world_size, args.rank, args.script, args.config, args.save_dir, args.use_lmdb, args.script_prv, args.config_prv, args.use_wandb, args.distill, args.script_teacher, args.config_teacher) else: raise ValueError("mode should be 'single' or 'multiple'.") os.system(train_cmd) if __name__ == "__main__": main() ================================================ FILE: artrackv2_mindspore/tracking/video_demo.py ================================================ import os import sys import argparse prj_path = os.path.join(os.path.dirname(__file__), '..') if prj_path not in sys.path: sys.path.append(prj_path) from lib.test.evaluation import Tracker def run_video(tracker_name, tracker_param, videofile, optional_box=None, debug=None, save_results=False): """Run the tracker on your webcam. args: tracker_name: Name of tracking method. tracker_param: Name of parameter file. debug: Debug level. """ tracker = Tracker(tracker_name, tracker_param, "video") tracker.run_video(videofilepath=videofile, optional_box=optional_box, debug=debug, save_results=save_results) def main(): parser = argparse.ArgumentParser(description='Run the tracker on your webcam.') parser.add_argument('tracker_name', type=str, help='Name of tracking method.') parser.add_argument('tracker_param', type=str, help='Name of parameter file.') parser.add_argument('videofile', type=str, help='path to a video file.') parser.add_argument('--optional_box', type=float, default=None, nargs="+", help='optional_box with format x y w h.') parser.add_argument('--debug', type=int, default=0, help='Debug level.') parser.add_argument('--save_results', dest='save_results', action='store_true', help='Save bounding boxes') parser.set_defaults(save_results=False) args = parser.parse_args() run_video(args.tracker_name, args.tracker_param, args.videofile, args.optional_box, args.debug, args.save_results) if __name__ == '__main__': main() ================================================ FILE: artrackv2_mindspore/tracking/vis_results.py ================================================ import os import sys import time import torch import numpy as np import cv2 as cv from tqdm import tqdm from lib.vis.visdom_cus import Visdom env_path = os.path.join(os.path.dirname(__file__), '../lib') if env_path not in sys.path: sys.path.append(env_path) from lib.test.evaluation import trackerlist, get_dataset from lib.test.utils.load_text import load_text class VisResults(object): def __init__(self): self._init_visdom() def vis_dataset(self, dataset, trackers, skip_missing_seq=False, seq_list=[]): for seq_id, seq in enumerate(tqdm(dataset)): # Load anno seq_name = seq.name if seq_list: if seq_name not in seq_list: continue anno_bb = torch.tensor(seq.ground_truth_rect) target_visible = torch.tensor(seq.target_visible, dtype=torch.uint8) if seq.target_visible is not None else None all_pred_boxes = [] for trk_id, trk in enumerate(trackers): # Load results base_results_path = '{}/{}'.format(trk.results_dir, seq.name) results_path = '{}.txt'.format(base_results_path) if os.path.isfile(results_path): pred_bb = torch.tensor(load_text(str(results_path), delimiter=('\t', ','), dtype=np.float64)) all_pred_boxes.append(pred_bb) else: if skip_missing_seq: break else: raise Exception('Result not found. {}'.format(results_path)) frame_list = seq.frames for i in range(len(anno_bb)): data = [] frame = frame_list[i] im = cv.imread(frame) im = cv.cvtColor(im, cv.COLOR_BGR2RGB) # im = torch.from_numpy(im).float().permute(2, 0, 1) # im = im.numpy() data.append(im) gt_box = anno_bb[i] data.append(gt_box) for tracker_result in all_pred_boxes: data.append(tracker_result[i]) while self.pause_mode: if self.step: self.step = False break if self.next_seq: self.next_seq = False break self.update_boxes(data, seq_name + '-' + str(i).zfill(3)) # self.update_seg_result(im, frame) def update_boxes(self, data, caption): caption = 'Green: GT, Red: stark_s, Yellow: stark_motion _' + caption self.visdom.register(data, 'Tracking', 1, 'Tracking', caption=caption) def update_seg_result(self, frame_img, frame_path): seg_mask_path = os.path.join(os.path.dirname(frame_path), 'seg_mask', os.path.basename(frame_path).replace('jpg', 'png')) seg_mask = cv.imread(seg_mask_path) alpha = 0.5 out_img = (alpha * frame_img) + ((1 - alpha) * seg_mask) if max(out_img.shape) > 480: resize_factor = 480.0 / float(max(out_img.shape)) out_img = cv.resize(out_img, None, fx=resize_factor, fy=resize_factor) out_img = torch.from_numpy(out_img).float().permute(2, 0, 1) self.visdom.register(out_img, 'image', 1, 'Segmentation Result') def _init_visdom(self, visdom_info=None): visdom_info = {} if visdom_info is None else visdom_info self.pause_mode = False self.step = False self.next_seq = False try: self.visdom = Visdom(1, {'handler': self._visdom_ui_handler, 'win_id': 'Tracking'}, visdom_info=visdom_info, env='vis_results') # Show help help_text = 'You can pause/unpause the tracker by pressing ''space'' with the ''Tracking'' window ' \ 'selected. During paused mode, you can track for one frame by pressing the right arrow key.' \ 'To enable/disable plotting of a data block, tick/untick the corresponding entry in ' \ 'block list.' self.visdom.register(help_text, 'text', 1, 'Help') except: time.sleep(0.5) print('!!! WARNING: Visdom could not start, so using matplotlib visualization instead !!!\n' '!!! Start Visdom in a separate terminal window by typing \'visdom\' !!!') def _visdom_ui_handler(self, data): if data['event_type'] == 'KeyPress': if data['key'] == ' ': self.pause_mode = not self.pause_mode elif data['key'] == 'n': self.next_seq = True elif data['key'] == 'ArrowRight' and self.pause_mode: self.step = True if __name__ == '__main__': viser = VisResults() dataset_name = 'lasot' trackers = [] # trackers.extend(trackerlist('defor_stark_s', 'baseline_got10k_only', None, 'defor_stark')) # trackers.extend(trackerlist('stark_s', 'baseline_got10k_only', None, 'stark')) # trackers.extend(trackerlist('dimp', 'dimp50', dataset_name, None, 'DiMP50')) # trackers.extend(trackerlist('sa', 'attn_direct', None, 'SA')) # ori # trackers.extend(trackerlist('sa', 'attn1', None, 'SA')) # extrat conv to conver backbone feature from 1024 to 32 # trackers.extend(trackerlist('sa', 'attn_segaddlabel', None, 'SA')) # add label to seg mask # trackers.extend(trackerlist('stark_s', 'baseline_got10k_only_encoder_only_ep500', None, 'STARK-S-Encoder-EP500')) # trackers.extend(trackerlist('stark_motion', 'baseline_got10k_only_ep100_002', None, 'STARK-motion-EP100')) # trackers.extend( # trackerlist('stark_motion', 'baseline_got10k_only_offset_ep100_001', None, 'STARK-motion-offset-EP100')) # trackers.extend(trackerlist('stark_s', 'baseline', dataset_name, None, 'stark_s')) # trackers.extend(trackerlist('stark_st', 'baseline', dataset_name, None, 'stark_st')) # trackers.extend(trackerlist('stark_mem', 'baseline_roi', dataset_name, None, 'baseline')) # trackers.extend(trackerlist('stark_mem', 'baseline_roi', dataset_name, 17, 'update_template')) # trackers.extend(trackerlist('stark_mem', 'baseline_roi', dataset_name, 32, 'update_template')) # trackers.extend(trackerlist('stark_mem', 'baseline_roi', dataset_name, 36, 'update_template')) # trackers.extend(trackerlist('stark_mem', 'baseline_roi_ep300', dataset_name, None, 'update_template')) # trackers.extend(trackerlist('stark_mem', 'baseline_roi_ep300', dataset_name, 6, 'update_template')) # trackers.extend(trackerlist('stark_mem', 'baseline_roi_ep300', dataset_name, 55, 'update_template')) # trackers.extend(trackerlist('stark_mem', 'baseline_roi_ep300', dataset_name, 57, 'update_template')) # trackers.extend(trackerlist('stark_mem', 'baseline_roi_upsample_syncbn', dataset_name, None, 'baseline_roi_upsample_syncbn')) # trackers.extend(trackerlist('stark_mem', 'baseline_roi', dataset, 9, 'update_template1')) # trackers.extend(trackerlist('stark_mem', 'baseline_roi_ciou', dataset_name, None, 'update_template')) # trackers.extend(trackerlist('stark_mem', 'baseline_roi_ciou', dataset_name, 1, 'update_template')) trackers.extend(trackerlist('stark_mem', 'baseline_roi_ep500', dataset_name, None, 'baseline_roi_ep500')) trackers.extend(trackerlist('vit_tracker', 'cait_small_224_ep300', dataset_name, None, 'cait_small_224_ep300')) trackers.extend(trackerlist('vit_tracker', 'cait_small_224_fcos_new_32x4_ep300', dataset_name, None, 'cait_small_224_fcos_new_32x4_ep300')) trackers.extend(trackerlist('vit_tracker', 'cait_small_224_fcos_cn_32x4_ep300_test_cn', dataset_name, None, 'cait_small_224_fcos_cn_32x4_ep300_test_cn')) # trackers.extend(trackerlist('vit_tracker', 'cait_small_224_vfloss_64x2_ep100', dataset_name, None, 'cait_small_224_vfloss_64x2_ep100')) # trackers.extend(trackerlist('vit_tracker', 'cait_small_224_fcos_cn_32x4_ep300_retest', dataset_name, None, 'retest')) dataset = get_dataset(dataset_name) # trackers.extend(trackerlist('dimp', 'dimp50', None, 'DiMP50')) # trackers.extend(trackerlist('sa', 'attn_direct', None, 'SA')) # dataset = get_dataset('vot') # 'GOT-10k_Train_001350' viser.vis_dataset(dataset, trackers, seq_list=[]) # viser.vis_dataset(dataset, trackers, seq_list=['GOT-10k_Train_007446']) ================================================ FILE: experiments/artrack/artrack_256_full.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 3 FACTOR: 4.0 SCALE_JITTER: 0.25 SIZE: 256 NUMBER: 1 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 128 TRAIN: DATASETS_NAME: - LASOT - GOT10K_vottrain - COCO17 - TRACKINGNET DATASETS_RATIO: - 1 - 1 - 1 - 1 SAMPLE_PER_EPOCH: 60000 VAL: DATASETS_NAME: - GOT10K_official_val DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: BINS: 400 RANGE: 2 PRETRAIN_FILE: "mae_pretrain_vit_base.pth" BACKBONE: TYPE: vit_base_patch16_224 STRIDE: 16 HEAD: TYPE: PIX NUM_CHANNELS: 768 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 50 EPOCH: 240 GIOU_WEIGHT: 2.0 L1_WEIGHT: 0.0 GRAD_CLIP_NORM: 0.1 LR: 0.00008 LR_DROP_EPOCH: 192 NUM_WORKER: 4 OPTIMIZER: ADAMW PRINT_INTERVAL: 10 SCHEDULER: TYPE: step DECAY_RATE: 0.1 VAL_EPOCH_INTERVAL: 20 WEIGHT_DECAY: 0.0001 AMP: False TEST: EPOCH: 240 SEARCH_FACTOR: 4.0 SEARCH_SIZE: 256 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 128 ================================================ FILE: experiments/artrack/artrack_256_got.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 3.0 FACTOR: 4.0 SCALE_JITTER: 0.25 SIZE: 256 NUMBER: 1 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 128 TRAIN: DATASETS_NAME: - GOT10K_train_full DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 60000 VAL: DATASETS_NAME: - GOT10K_official_val DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: BINS: 400 RANGE: 2 PRETRAIN_FILE: "mae_pretrain_vit_base.pth" BACKBONE: TYPE: vit_base_patch16_224 STRIDE: 16 HEAD: TYPE: PIX NUM_CHANNELS: 768 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 50 EPOCH: 120 GIOU_WEIGHT: 2.0 L1_WEIGHT: 0.0 GRAD_CLIP_NORM: 0.1 LR: 0.00008 LR_DROP_EPOCH: 96 NUM_WORKER: 4 OPTIMIZER: ADAMW PRINT_INTERVAL: 10 SCHEDULER: TYPE: step DECAY_RATE: 0.1 VAL_EPOCH_INTERVAL: 20 WEIGHT_DECAY: 0.0001 AMP: False TEST: EPOCH: 120 SEARCH_FACTOR: 4.0 SEARCH_SIZE: 256 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 128 ================================================ FILE: experiments/artrack/artrack_384_full.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 4.5 FACTOR: 5.0 SCALE_JITTER: 0.5 SIZE: 384 NUMBER: 1 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 192 TRAIN: DATASETS_NAME: - LASOT - GOT10K_vottrain - COCO17 - TRACKINGNET DATASETS_RATIO: - 1 - 1 - 1 - 1 SAMPLE_PER_EPOCH: 60000 VAL: DATASETS_NAME: - GOT10K_official_val DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: BINS: 600 RANGE: 2 PRETRAIN_FILE: "mae_pretrain_vit_base.pth" BACKBONE: TYPE: vit_base_patch16_224 STRIDE: 16 HEAD: TYPE: PIX NUM_CHANNELS: 768 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 48 EPOCH: 240 GIOU_WEIGHT: 2.0 L1_WEIGHT: 0.0 GRAD_CLIP_NORM: 0.1 LR: 0.00008 LR_DROP_EPOCH: 192 NUM_WORKER: 4 OPTIMIZER: ADAMW PRINT_INTERVAL: 10 SCHEDULER: TYPE: step DECAY_RATE: 0.1 VAL_EPOCH_INTERVAL: 20 WEIGHT_DECAY: 0.0001 AMP: False TEST: EPOCH: 240 SEARCH_FACTOR: 5.0 SEARCH_SIZE: 384 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 192 ================================================ FILE: experiments/artrack/artrack_large_384_full.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 4.5 FACTOR: 5.0 SCALE_JITTER: 0.5 SIZE: 384 NUMBER: 1 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 192 TRAIN: DATASETS_NAME: - LASOT - GOT10K_vottrain - COCO17 - TRACKINGNET DATASETS_RATIO: - 1 - 1 - 1 - 1 SAMPLE_PER_EPOCH: 60000 VAL: DATASETS_NAME: - GOT10K_official_val DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: BINS: 600 RANGE: 2 PRETRAIN_FILE: "mae_pretrain_vit_large.pth" BACKBONE: TYPE: vit_large_patch16_224 STRIDE: 16 HEAD: TYPE: PIX NUM_CHANNELS: 1024 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 15 EPOCH: 120 GIOU_WEIGHT: 2.0 L1_WEIGHT: 0.0 GRAD_CLIP_NORM: 0.1 LR: 0.00008 LR_DROP_EPOCH: 96 NUM_WORKER: 4 OPTIMIZER: ADAMW PRINT_INTERVAL: 10 SCHEDULER: TYPE: step DECAY_RATE: 0.1 VAL_EPOCH_INTERVAL: 20 WEIGHT_DECAY: 0.0001 AMP: False TEST: EPOCH: 120 SEARCH_FACTOR: 5.0 SEARCH_SIZE: 384 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 192 ================================================ FILE: experiments/artrack_seq/artrack_seq_256_full.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 3 FACTOR: 4.0 SCALE_JITTER: 0.25 SIZE: 256 NUMBER: 36 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 128 TRAIN: DATASETS_NAME: - LASOT - GOT10K_vottrain - TRACKINGNET DATASETS_RATIO: - 1 - 1 - 1 SAMPLE_PER_EPOCH: 1000 VAL: DATASETS_NAME: - GOT10K_official_val DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: BINS: 400 RANGE: 2 PRENUM: 7 PRETRAIN_FILE: "mae_pretrain_vit_base.pth" PRETRAIN_PTH: "" EXTRA_MERGER: False RETURN_INTER: False BACKBONE: TYPE: vit_base_patch16_224 STRIDE: 16 HEAD: TYPE: PIX NUM_CHANNELS: 768 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 8 EPOCH: 60 GIOU_WEIGHT: 2.0 L1_WEIGHT: 0.0 GRAD_CLIP_NORM: 0.1 LR: 0.000004 LR_DROP_EPOCH: 999 NUM_WORKER: 4 OPTIMIZER: ADAMW PRINT_INTERVAL: 1 SCHEDULER: TYPE: step DECAY_RATE: 0.1 VAL_EPOCH_INTERVAL: 10 WEIGHT_DECAY: 0.05 AMP: False TEST: EPOCH: 60 SEARCH_FACTOR: 4.0 SEARCH_SIZE: 256 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 128 ================================================ FILE: experiments/artrack_seq/artrack_seq_256_got.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 3 FACTOR: 4.0 SCALE_JITTER: 0.25 SIZE: 256 NUMBER: 36 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 128 TRAIN: DATASETS_NAME: - GOT10K_train_full DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 1000 VAL: DATASETS_NAME: - GOT10K_official_val DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: BINS: 400 RANGE: 2 PRENUM: 7 PRETRAIN_FILE: "mae_pretrain_vit_base.pth" PRETRAIN_PTH: "" EXTRA_MERGER: False RETURN_INTER: False BACKBONE: TYPE: vit_base_patch16_224 STRIDE: 16 HEAD: TYPE: PIX NUM_CHANNELS: 768 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 8 EPOCH: 30 GIOU_WEIGHT: 2.0 L1_WEIGHT: 0.0 GRAD_CLIP_NORM: 0.1 LR: 0.000004 LR_DROP_EPOCH: 999 NUM_WORKER: 4 OPTIMIZER: ADAMW PRINT_INTERVAL: 1 SCHEDULER: TYPE: step DECAY_RATE: 0.1 VAL_EPOCH_INTERVAL: 10 WEIGHT_DECAY: 0.05 AMP: False TEST: EPOCH: 30 SEARCH_FACTOR: 4.0 SEARCH_SIZE: 256 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 128 ================================================ FILE: experiments/artrack_seq/artrack_seq_384_full.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 4.5 FACTOR: 5.0 SCALE_JITTER: 0.5 SIZE: 384 NUMBER: 32 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 192 TRAIN: DATASETS_NAME: - LASOT - GOT10K_vottrain - TRACKINGNET DATASETS_RATIO: - 1 - 1 - 1 SAMPLE_PER_EPOCH: 1000 VAL: DATASETS_NAME: - GOT10K_official_val DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: BINS: 600 RANGE: 2 PRENUM: 7 PRETRAIN_FILE: "mae_pretrain_vit_base.pth" PRETRAIN_PTH: "" EXTRA_MERGER: False RETURN_INTER: False BACKBONE: TYPE: vit_base_patch16_224 STRIDE: 16 HEAD: TYPE: PIX NUM_CHANNELS: 768 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 8 EPOCH: 60 GIOU_WEIGHT: 2.0 L1_WEIGHT: 0.0 GRAD_CLIP_NORM: 0.1 LR: 0.000004 LR_DROP_EPOCH: 999 NUM_WORKER: 4 OPTIMIZER: ADAMW PRINT_INTERVAL: 1 SCHEDULER: TYPE: step DECAY_RATE: 0.1 VAL_EPOCH_INTERVAL: 10 WEIGHT_DECAY: 0.05 AMP: False TEST: EPOCH: 60 SEARCH_FACTOR: 5.0 SEARCH_SIZE: 384 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 192 ================================================ FILE: experiments/artrack_seq/artrack_seq_large_384_full.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 4.5 FACTOR: 5.0 SCALE_JITTER: 0.5 SIZE: 384 NUMBER: 15 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 192 TRAIN: DATASETS_NAME: - LASOT - GOT10K_vottrain - TRACKINGNET DATASETS_RATIO: - 1 - 1 - 1 SAMPLE_PER_EPOCH: 1000 VAL: DATASETS_NAME: - GOT10K_official_val DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: BINS: 600 RANGE: 2 PRENUM: 7 PRETRAIN_FILE: "mae_pretrain_vit_large.pth" PRETRAIN_PTH: "" EXTRA_MERGER: False RETURN_INTER: False BACKBONE: TYPE: vit_large_patch16_224 STRIDE: 16 HEAD: TYPE: PIX NUM_CHANNELS: 1024 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 8 EPOCH: 60 GIOU_WEIGHT: 2.0 L1_WEIGHT: 0.0 GRAD_CLIP_NORM: 0.1 LR: 0.000004 LR_DROP_EPOCH: 999 NUM_WORKER: 4 OPTIMIZER: ADAMW PRINT_INTERVAL: 1 SCHEDULER: TYPE: step DECAY_RATE: 0.1 VAL_EPOCH_INTERVAL: 10 WEIGHT_DECAY: 0.05 AMP: False TEST: EPOCH: 60 SEARCH_FACTOR: 5.0 SEARCH_SIZE: 384 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 192 ================================================ FILE: experiments/artrackv2/artrackv2_256_full.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 3 FACTOR: 4.0 SCALE_JITTER: 0.25 SIZE: 256 NUMBER: 1 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 128 NUMBER: 2 TRAIN: DATASETS_NAME: - LASOT - GOT10K_vottrain - COCO17 - TRACKINGNET DATASETS_RATIO: - 1 - 1 - 1 - 1 SAMPLE_PER_EPOCH: 76800 VAL: DATASETS_NAME: - GOT10K_votval DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: BINS: 400 RANGE: 2 EXTENSION: 3 PRETRAIN_FILE: "mae_pretrain_vit_base.pth" EXTRA_MERGER: False RETURN_INTER: False BACKBONE: TYPE: vit_base_patch16_224 STRIDE: 16 HEAD: TYPE: PIX NUM_CHANNELS: 768 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 32 EPOCH: 300 GIOU_WEIGHT: 2.0 L1_WEIGHT: 0.0 GRAD_CLIP_NORM: 0.1 LR: 0.00008 LR_DROP_EPOCH: 240 NUM_WORKER: 6 OPTIMIZER: ADAMW PRINT_INTERVAL: 10 SCHEDULER: TYPE: step DECAY_RATE: 0.1 VAL_EPOCH_INTERVAL: 20 WEIGHT_DECAY: 0.0001 AMP: False TEST: EPOCH: 118 SEARCH_FACTOR: 4.0 SEARCH_SIZE: 256 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 128 ================================================ FILE: experiments/artrackv2/artrackv2_256_got.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 3 FACTOR: 4.0 SCALE_JITTER: 0.25 SIZE: 256 NUMBER: 1 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 128 NUMBER: 2 TRAIN: DATASETS_NAME: - GOT10K_train_full DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 76800 VAL: DATASETS_NAME: - GOT10K_votval DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: BINS: 400 RANGE: 2 EXTENSION: 3 PRETRAIN_FILE: "mae_pretrain_vit_base.pth" EXTRA_MERGER: False RETURN_INTER: False BACKBONE: TYPE: vit_base_patch16_224 STRIDE: 16 HEAD: TYPE: PIX NUM_CHANNELS: 768 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 32 EPOCH: 120 GIOU_WEIGHT: 2.0 L1_WEIGHT: 0.0 GRAD_CLIP_NORM: 0.1 LR: 0.00008 LR_DROP_EPOCH: 96 NUM_WORKER: 6 OPTIMIZER: ADAMW PRINT_INTERVAL: 10 SCHEDULER: TYPE: step DECAY_RATE: 0.1 VAL_EPOCH_INTERVAL: 20 WEIGHT_DECAY: 0.0001 AMP: False TEST: EPOCH: 118 SEARCH_FACTOR: 4.0 SEARCH_SIZE: 256 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 128 ================================================ FILE: experiments/artrackv2/artrackv2_large_384_got.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 4.5 FACTOR: 5.0 SCALE_JITTER: 0.5 SIZE: 384 NUMBER: 1 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 192 NUMBER: 2 TRAIN: DATASETS_NAME: - GOT10K_train_full DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 57600 VAL: DATASETS_NAME: - GOT10K_votval DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: BINS: 600 RANGE: 2 EXTENSION: 6 PRETRAIN_FILE: "mae_pretrain_vit_large.pth" EXTRA_MERGER: False RETURN_INTER: False BACKBONE: TYPE: vit_large_patch16_224 STRIDE: 16 HEAD: TYPE: PIX NUM_CHANNELS: 1024 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 12 EPOCH: 100 GIOU_WEIGHT: 2.0 L1_WEIGHT: 0.0 GRAD_CLIP_NORM: 0.1 LR: 0.000008 LR_DROP_EPOCH: 80 NUM_WORKER: 8 OPTIMIZER: ADAMW PRINT_INTERVAL: 10 SCHEDULER: TYPE: step DECAY_RATE: 0.1 VAL_EPOCH_INTERVAL: 20 WEIGHT_DECAY: 0.0001 AMP: False TEST: EPOCH: 100 SEARCH_FACTOR: 5.0 SEARCH_SIZE: 384 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 192 ================================================ FILE: experiments/artrackv2_seq/artrackv2_seq_256_full.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 3 FACTOR: 4.0 SCALE_JITTER: 0.25 SIZE: 256 NUMBER: 24 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 128 NUMBER: 2 TRAIN: DATASETS_NAME: - LASOT - GOT10K_vottrain - TRACKINGNET DATASETS_RATIO: - 1 - 1 - 1 SAMPLE_PER_EPOCH: 1000 VAL: DATASETS_NAME: - GOT10K_official_val DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: BINS: 400 RANGE: 2 EXTENSION: 3 PRENUM: 7 PRETRAIN_FILE: "mae_pretrain_vit_base.pth" PRETRAIN_PTH: "" EXTRA_MERGER: False RETURN_INTER: False DECODER: TYPE: "mask" MASK_RATIO: 0.75 EMBEDDIM: 512 DEPTH: 8 NUMHEADS: 16 MLPRATIO: 4 BACKBONE: TYPE: vit_base_patch16_224 STRIDE: 16 HEAD: TYPE: PIX NUM_CHANNELS: 768 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 8 EPOCH: 40 GIOU_WEIGHT: 2.0 L1_WEIGHT: 0.0 GRAD_CLIP_NORM: 0.1 LR: 0.00008 LR_DROP_EPOCH: 999 NUM_WORKER: 6 OPTIMIZER: ADAMW PRINT_INTERVAL: 1 SCHEDULER: TYPE: step DECAY_RATE: 0.05 VAL_EPOCH_INTERVAL: 10 WEIGHT_DECAY: 0.05 AMP: False TEST: EPOCH: 40 SEARCH_FACTOR: 4.0 SEARCH_SIZE: 256 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 128 ================================================ FILE: experiments/artrackv2_seq/artrackv2_seq_256_got.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 3 FACTOR: 4.0 SCALE_JITTER: 0.25 SIZE: 256 NUMBER: 24 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 128 NUMBER: 2 TRAIN: DATASETS_NAME: - GOT10K_train_full DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 1000 VAL: DATASETS_NAME: - GOT10K_official_val DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: BINS: 400 RANGE: 2 EXTENSION: 3 PRENUM: 7 PRETRAIN_FILE: "mae_pretrain_vit_base.pth" PRETRAIN_PTH: "" EXTRA_MERGER: False RETURN_INTER: False DECODER: TYPE: "mask" MASK_RATIO: 0.75 EMBEDDIM: 512 DEPTH: 8 NUMHEADS: 16 MLPRATIO: 4 BACKBONE: TYPE: vit_base_patch16_224 STRIDE: 16 HEAD: TYPE: PIX NUM_CHANNELS: 768 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 8 EPOCH: 30 GIOU_WEIGHT: 2.0 L1_WEIGHT: 0.0 GRAD_CLIP_NORM: 0.1 LR: 0.00008 LR_DROP_EPOCH: 999 NUM_WORKER: 6 OPTIMIZER: ADAMW PRINT_INTERVAL: 1 SCHEDULER: TYPE: step DECAY_RATE: 0.05 VAL_EPOCH_INTERVAL: 10 WEIGHT_DECAY: 0.05 AMP: False TEST: EPOCH: 30 SEARCH_FACTOR: 3.95 SEARCH_SIZE: 256 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 128 ================================================ FILE: experiments/artrackv2_seq/artrackv2_seq_large_384_got.yaml ================================================ DATA: MAX_SAMPLE_INTERVAL: 200 MEAN: - 0.485 - 0.456 - 0.406 SEARCH: CENTER_JITTER: 4.5 FACTOR: 5.0 SCALE_JITTER: 0.5 SIZE: 384 NUMBER: 12 STD: - 0.229 - 0.224 - 0.225 TEMPLATE: CENTER_JITTER: 0 FACTOR: 2.0 SCALE_JITTER: 0 SIZE: 192 NUMBER: 2 TRAIN: - LASOT - GOT10K_vottrain - TRACKINGNET - SAV DATASETS_RATIO: - 1 - 1 - 1 - 4 SAMPLE_PER_EPOCH: 1000 VAL: DATASETS_NAME: - GOT10K_official_val DATASETS_RATIO: - 1 SAMPLE_PER_EPOCH: 10000 MODEL: BINS: 600 RANGE: 2 EXTENSION: 6 PRENUM: 7 PRETRAIN_FILE: "mae_pretrain_vit_large.pth" PRETRAIN_PTH: "" EXTRA_MERGER: False RETURN_INTER: False DECODER: TYPE: "mask" MASK_RATIO: 0.75 EMBEDDIM: 512 DEPTH: 8 NUMHEADS: 16 MLPRATIO: 4 BACKBONE: TYPE: vit_base_patch16_224 STRIDE: 16 HEAD: TYPE: PIX NUM_CHANNELS: 1024 TRAIN: BACKBONE_MULTIPLIER: 0.1 DROP_PATH_RATE: 0.1 BATCH_SIZE: 8 EPOCH: 120 GIOU_WEIGHT: 2.0 L1_WEIGHT: 0.0 GRAD_CLIP_NORM: 0.1 LR: 0.00008 LR_DROP_EPOCH: 60 NUM_WORKER: 6 OPTIMIZER: ADAMW PRINT_INTERVAL: 1 SCHEDULER: TYPE: step DECAY_RATE: 0.05 VAL_EPOCH_INTERVAL: 10 WEIGHT_DECAY: 0.05 AMP: False TEST: EPOCH: 30 SEARCH_FACTOR: 4.55 SEARCH_SIZE: 384 TEMPLATE_FACTOR: 2.0 TEMPLATE_SIZE: 192 ================================================ FILE: external/AR/README.md ================================================ # Alpha-Refine ## Introduction Alpha-Refine is the winner of the VOT Real-Time Challenge 2020, which has great ability to predict high-quality masks. In this work, we combine the STARK tracker with Alpha-Refine to test on the VOT2020 benchamark. ## Installation After the environment has been installed according to the README.md of STARK, you only need to install a few more packages as shown below. * Install ninja-build for Precise ROI pooling ```bash sudo apt-get install ninja-build ``` In case of issues, we refer to https://github.com/vacancy/PreciseRoIPooling. * Install the Precise ROI pooling ``` cd ltr/external git clone https://github.com/vacancy/PreciseRoIPooling.git cd ../.. ``` * Add the project path to environment variables ``` export PYTHONPATH=:$PYTHONPATH ``` * Setup the environment Create the default environment setting files. ```bash # Environment settings for pytracking. Saved at pytracking/evaluation/local.py python -c "from pytracking.evaluation.environment import create_default_local_file; create_default_local_file()" # Environment settings for ltr. Saved at ltr/admin/local.py python -c "from ltr.admin.environment import create_default_local_file; create_default_local_file()" ``` You can modify these files to set the paths to datasets, results paths etc. * Download the pre-trained Alpha-Refine network Download the network for [Alpha-Refine](https://drive.google.com/open?id=1qOQRfaRMbQ2nmgX1NFjoQHfXOAn609QM) and put it under the ltr/checkpoints/ltr/ARcm_seg/ARcm_coco_seg_only_mask_384 dir. ================================================ FILE: external/AR/__init__.py ================================================ ================================================ FILE: external/AR/ltr/README.md ================================================ # LTR A general PyTorch based framework for learning tracking representations. ## Table of Contents * [Quick Start](#quick-start) * [Overview](#overview) * [Trackers](#trackers) * [PrDiMP](#PrDiMP) * [DiMP](#DiMP) * [ATOM](#ATOM) * [Training your own networks](#training-your-own-networks) ## Quick Start The installation script will automatically generate a local configuration file "admin/local.py". In case the file was not generated, run ```admin.environment.create_default_local_file()``` to generate it. Next, set the paths to the training workspace, i.e. the directory where the checkpoints will be saved. Also set the paths to the datasets you want to use. If all the dependencies have been correctly installed, you can train a network using the run_training.py script in the correct conda environment. ```bash conda activate pytracking python run_training.py train_module train_name ``` Here, ```train_module``` is the sub-module inside ```train_settings``` and ```train_name``` is the name of the train setting file to be used. For example, you can train using the included default ATOM settings by running: ```bash python run_training bbreg atom_default ``` ## Overview The framework consists of the following sub-modules. - [actors](actors): Contains the actor classes for different trainings. The actor class is responsible for passing the input data through the network can calculating losses. - [admin](admin): Includes functions for loading networks, tensorboard etc. and also contains environment settings. - [dataset](dataset): Contains integration of a number of training datasets, namely [TrackingNet](https://tracking-net.org/), [GOT-10k](http://got-10k.aitestunion.com/), [LaSOT](https://cis.temple.edu/lasot/), [ImageNet-VID](http://image-net.org/), [DAVIS](https://davischallenge.org), [YouTube-VOS](https://youtube-vos.org), [MS-COCO](http://cocodataset.org/#home), [SBD](http://home.bharathh.info/pubs/codes/SBD), [LVIS](https://www.lvisdataset.org), [ECSSD](http://www.cse.cuhk.edu.hk/leojia/projects/hsaliency/dataset.html), [MSRA10k](https://mmcheng.net/msra10k), and [HKU-IS](https://sites.google.com/site/ligb86/hkuis). Additionally, it includes modules to generate synthetic videos from image datasets. - [data_specs](data_specs): Information about train/val splits of different datasets. - [data](data): Contains functions for processing data, e.g. loading images, data augmentations, sampling frames from videos. - [external](external): External libraries needed for training. Added as submodules. - [models](models): Contains different layers and network definitions. - [trainers](trainers): The main class which runs the training. - [train_settings](train_settings): Contains settings files, specifying the training of a network. ## Trackers The framework currently contains the training code for the following trackers. ### PrDiMP The following setting files can be used train the DiMP networks, or to know the exact training details. - [dimp.prdimp18](train_settings/dimp/prdimp18.py): The default settings used for training the PrDiMP model with ResNet-18 backbone. - [dimp.prdimp50](train_settings/dimp/prdimp50.py): The default settings used for training the PrDiMP model with ResNet-50 backbone. - [dimp.super_dimp](train_settings/dimp/super_dimp.py): Combines the bounding-box regressor of PrDiMP with the standard DiMP classifier and better training and inference settings. ### DiMP The following setting files can be used train the DiMP networks, or to know the exact training details. - [dimp.dimp18](train_settings/dimp/dimp18.py): The default settings used for training the DiMP model with ResNet-18 backbone. - [dimp.dimp50](train_settings/dimp/dimp50.py): The default settings used for training the DiMP model with ResNet-50 backbone. ### ATOM The following setting file can be used train the ATOM network, or to know the exact training details. - [bbreg.atom](train_settings/bbreg/atom_paper.py): The settings used in the paper for training the network in ATOM. - [bbreg.atom](train_settings/bbreg/atom.py): Newer settings used for training the network in ATOM, also utilizing the GOT10k dataset. - [bbreg.atom](train_settings/bbreg/atom_prob_ml.py): Settings for ATOM with the probabilistic bounding box regression proposed in [this paper](https://arxiv.org/abs/1909.12297). - [bbreg.atom](train_settings/bbreg/atom_paper.py): The baseline ATOM* setting evaluated in [this paper](https://arxiv.org/abs/1909.12297). ## Training your own networks To train a custom network using the toolkit, the following components need to be specified in the train settings. For reference, see [atom.py](train_settings/bbreg/atom.py). - Datasets: The datasets to be used for training. A number of standard tracking datasets are already available in ```dataset``` module. - Processing: This function should perform the necessary post-processing of the data, e.g. cropping of target region, data augmentations etc. - Sampler: Determines how the frames are sampled from a video sequence to form the batches. - Network: The network module to be trained. - Objective: The training objective. - Actor: The trainer passes the training batch to the actor who is responsible for passing the data through the network correctly, and calculating the training loss. - Optimizer: Optimizer to be used, e.g. Adam. - Trainer: The main class which runs the epochs and saves checkpoints. ================================================ FILE: external/AR/ltr/__init__.py ================================================ from .admin.loading import load_network from .admin.model_constructor import model_constructor from .admin.multigpu import MultiGPU ================================================ FILE: external/AR/ltr/actors/__init__.py ================================================ from .base_actor import BaseActor from .bbreg import AtomActor from .tracking import DiMPActor ================================================ FILE: external/AR/ltr/actors/base_actor.py ================================================ from pytracking import TensorDict class BaseActor: """ Base class for actor. The actor class handles the passing of the data through the network and calculation the loss""" def __init__(self, net, objective): """ args: net - The network to train objective - The loss function """ self.net = net self.objective = objective def __call__(self, data: TensorDict): """ Called in each training iteration. Should pass in input data through the network, calculate the loss, and return the training stats for the input data args: data - A TensorDict containing all the necessary data blocks. returns: loss - loss for the input data stats - a dict containing detailed losses """ raise NotImplementedError def to(self, device): """ Move the network to device args: device - device to use. 'cpu' or 'cuda' """ self.net.to(device) def train(self, mode=True): """ Set whether the network is in train mode. args: mode (True) - Bool specifying whether in training mode. """ self.net.train(mode) def eval(self): """ Set network to eval mode""" self.train(False) ================================================ FILE: external/AR/ltr/actors/bbreg.py ================================================ from . import BaseActor class AtomActor(BaseActor): """ Actor for training the IoU-Net in ATOM""" def __call__(self, data): """ args: data - The input data, should contain the fields 'train_images', 'test_images', 'train_anno', 'test_proposals' and 'proposal_iou'. returns: loss - the training loss states - dict containing detailed losses """ # Run network to obtain IoU prediction for each proposal in 'test_proposals' iou_pred = self.net(data['train_images'], data['test_images'], data['train_anno'], data['test_proposals']) iou_pred = iou_pred.view(-1, iou_pred.shape[2]) iou_gt = data['proposal_iou'].view(-1, data['proposal_iou'].shape[2]) # Compute loss loss = self.objective(iou_pred, iou_gt) # Return training stats stats = {'Loss/total': loss.item(), 'Loss/iou': loss.item()} return loss, stats class AtomBBKLActor(BaseActor): """ Actor for training the IoU-Net in ATOM with BBKL""" def __call__(self, data): """ args: data - The input data, should contain the fields 'train_images', 'test_images', 'train_anno', 'test_proposals', 'proposal_density', and 'gt_density'. returns: loss - the training loss states - dict containing detailed losses """ # Run network to obtain IoU prediction for each proposal in 'test_proposals' bb_scores = self.net(data['train_images'], data['test_images'], data['train_anno'], data['test_proposals']) bb_scores = bb_scores.view(-1, bb_scores.shape[2]) proposal_density = data['proposal_density'].view(-1, data['proposal_density'].shape[2]) gt_density = data['gt_density'].view(-1, data['gt_density'].shape[2]) # Compute loss loss = self.objective(bb_scores, sample_density=proposal_density, gt_density=gt_density, mc_dim=1) # Return training stats stats = {'Loss/total': loss.item(), 'Loss/bb_ce': loss.item()} return loss, stats ================================================ FILE: external/AR/ltr/actors/tracking.py ================================================ from . import BaseActor import torch class DiMPActor(BaseActor): """Actor for training the DiMP network.""" def __init__(self, net, objective, loss_weight=None): super().__init__(net, objective) if loss_weight is None: loss_weight = {'iou': 1.0, 'test_clf': 1.0} self.loss_weight = loss_weight def __call__(self, data): """ args: data - The input data, should contain the fields 'train_images', 'test_images', 'train_anno', 'test_proposals', 'proposal_iou' and 'test_label'. returns: loss - the training loss stats - dict containing detailed losses """ # Run network target_scores, iou_pred = self.net(train_imgs=data['train_images'], test_imgs=data['test_images'], train_bb=data['train_anno'], test_proposals=data['test_proposals']) # Classification losses for the different optimization iterations clf_losses_test = [self.objective['test_clf'](s, data['test_label'], data['test_anno']) for s in target_scores] # Loss of the final filter clf_loss_test = clf_losses_test[-1] loss_target_classifier = self.loss_weight['test_clf'] * clf_loss_test # Compute loss for ATOM IoUNet loss_iou = self.loss_weight['iou'] * self.objective['iou'](iou_pred, data['proposal_iou']) # Loss for the initial filter iteration loss_test_init_clf = 0 if 'test_init_clf' in self.loss_weight.keys(): loss_test_init_clf = self.loss_weight['test_init_clf'] * clf_losses_test[0] # Loss for the intermediate filter iterations loss_test_iter_clf = 0 if 'test_iter_clf' in self.loss_weight.keys(): test_iter_weights = self.loss_weight['test_iter_clf'] if isinstance(test_iter_weights, list): loss_test_iter_clf = sum([a*b for a, b in zip(test_iter_weights, clf_losses_test[1:-1])]) else: loss_test_iter_clf = (test_iter_weights / (len(clf_losses_test) - 2)) * sum(clf_losses_test[1:-1]) # Total loss loss = loss_iou + loss_target_classifier + loss_test_init_clf + loss_test_iter_clf # Log stats stats = {'Loss/total': loss.item(), 'Loss/iou': loss_iou.item(), 'Loss/target_clf': loss_target_classifier.item()} if 'test_init_clf' in self.loss_weight.keys(): stats['Loss/test_init_clf'] = loss_test_init_clf.item() if 'test_iter_clf' in self.loss_weight.keys(): stats['Loss/test_iter_clf'] = loss_test_iter_clf.item() stats['ClfTrain/test_loss'] = clf_loss_test.item() if len(clf_losses_test) > 0: stats['ClfTrain/test_init_loss'] = clf_losses_test[0].item() if len(clf_losses_test) > 2: stats['ClfTrain/test_iter_loss'] = sum(clf_losses_test[1:-1]).item() / (len(clf_losses_test) - 2) return loss, stats class KLDiMPActor(BaseActor): """Actor for training the DiMP network.""" def __init__(self, net, objective, loss_weight=None): super().__init__(net, objective) if loss_weight is None: loss_weight = {'bb_ce': 1.0} self.loss_weight = loss_weight def __call__(self, data): """ args: data - The input data, should contain the fields 'train_images', 'test_images', 'train_anno', 'test_proposals', 'proposal_iou' and 'test_label'. returns: loss - the training loss stats - dict containing detailed losses """ # Run network target_scores, bb_scores = self.net(train_imgs=data['train_images'], test_imgs=data['test_images'], train_bb=data['train_anno'], test_proposals=data['test_proposals']) # Reshape bb reg variables is_valid = data['test_anno'][:, :, 0] < 99999.0 bb_scores = bb_scores[is_valid, :] proposal_density = data['proposal_density'][is_valid, :] gt_density = data['gt_density'][is_valid, :] # Compute loss bb_ce = self.objective['bb_ce'](bb_scores, sample_density=proposal_density, gt_density=gt_density, mc_dim=1) loss_bb_ce = self.loss_weight['bb_ce'] * bb_ce # If standard DiMP classifier is used loss_target_classifier = 0 loss_test_init_clf = 0 loss_test_iter_clf = 0 if 'test_clf' in self.loss_weight.keys(): # Classification losses for the different optimization iterations clf_losses_test = [self.objective['test_clf'](s, data['test_label'], data['test_anno']) for s in target_scores] # Loss of the final filter clf_loss_test = clf_losses_test[-1] loss_target_classifier = self.loss_weight['test_clf'] * clf_loss_test # Loss for the initial filter iteration if 'test_init_clf' in self.loss_weight.keys(): loss_test_init_clf = self.loss_weight['test_init_clf'] * clf_losses_test[0] # Loss for the intermediate filter iterations if 'test_iter_clf' in self.loss_weight.keys(): test_iter_weights = self.loss_weight['test_iter_clf'] if isinstance(test_iter_weights, list): loss_test_iter_clf = sum([a * b for a, b in zip(test_iter_weights, clf_losses_test[1:-1])]) else: loss_test_iter_clf = (test_iter_weights / (len(clf_losses_test) - 2)) * sum(clf_losses_test[1:-1]) # If PrDiMP classifier is used loss_clf_ce = 0 loss_clf_ce_init = 0 loss_clf_ce_iter = 0 if 'clf_ce' in self.loss_weight.keys(): # Classification losses for the different optimization iterations clf_ce_losses = [self.objective['clf_ce'](s, data['test_label_density'], grid_dim=(-2,-1)) for s in target_scores] # Loss of the final filter clf_ce = clf_ce_losses[-1] loss_clf_ce = self.loss_weight['clf_ce'] * clf_ce # Loss for the initial filter iteration if 'clf_ce_init' in self.loss_weight.keys(): loss_clf_ce_init = self.loss_weight['clf_ce_init'] * clf_ce_losses[0] # Loss for the intermediate filter iterations if 'clf_ce_iter' in self.loss_weight.keys() and len(clf_ce_losses) > 2: test_iter_weights = self.loss_weight['clf_ce_iter'] if isinstance(test_iter_weights, list): loss_clf_ce_iter = sum([a * b for a, b in zip(test_iter_weights, clf_ce_losses[1:-1])]) else: loss_clf_ce_iter = (test_iter_weights / (len(clf_ce_losses) - 2)) * sum(clf_ce_losses[1:-1]) # Total loss loss = loss_bb_ce + loss_clf_ce + loss_clf_ce_init + loss_clf_ce_iter + \ loss_target_classifier + loss_test_init_clf + loss_test_iter_clf if torch.isinf(loss) or torch.isnan(loss): raise Exception('ERROR: Loss was nan or inf!!!') # Log stats stats = {'Loss/total': loss.item(), 'Loss/bb_ce': bb_ce.item(), 'Loss/loss_bb_ce': loss_bb_ce.item()} if 'test_clf' in self.loss_weight.keys(): stats['Loss/target_clf'] = loss_target_classifier.item() if 'test_init_clf' in self.loss_weight.keys(): stats['Loss/test_init_clf'] = loss_test_init_clf.item() if 'test_iter_clf' in self.loss_weight.keys(): stats['Loss/test_iter_clf'] = loss_test_iter_clf.item() if 'clf_ce' in self.loss_weight.keys(): stats['Loss/clf_ce'] = loss_clf_ce.item() if 'clf_ce_init' in self.loss_weight.keys(): stats['Loss/clf_ce_init'] = loss_clf_ce_init.item() if 'clf_ce_iter' in self.loss_weight.keys() and len(clf_ce_losses) > 2: stats['Loss/clf_ce_iter'] = loss_clf_ce_iter.item() if 'test_clf' in self.loss_weight.keys(): stats['ClfTrain/test_loss'] = clf_loss_test.item() if len(clf_losses_test) > 0: stats['ClfTrain/test_init_loss'] = clf_losses_test[0].item() if len(clf_losses_test) > 2: stats['ClfTrain/test_iter_loss'] = sum(clf_losses_test[1:-1]).item() / (len(clf_losses_test) - 2) if 'clf_ce' in self.loss_weight.keys(): stats['ClfTrain/clf_ce'] = clf_ce.item() if len(clf_ce_losses) > 0: stats['ClfTrain/clf_ce_init'] = clf_ce_losses[0].item() if len(clf_ce_losses) > 2: stats['ClfTrain/clf_ce_iter'] = sum(clf_ce_losses[1:-1]).item() / (len(clf_ce_losses) - 2) return loss, stats ================================================ FILE: external/AR/ltr/admin/__init__.py ================================================ ================================================ FILE: external/AR/ltr/admin/environment.py ================================================ import importlib import os from collections import OrderedDict def create_default_local_file(): path = os.path.join(os.path.dirname(__file__), 'local.py') empty_str = '\'\'' default_settings = OrderedDict({ 'workspace_dir': empty_str, 'tensorboard_dir': 'self.workspace_dir + \'/tensorboard/\'', 'lasot_dir': empty_str, 'got10k_dir': empty_str, 'trackingnet_dir': empty_str, 'coco_dir': empty_str, 'lvis_dir': empty_str, 'sbd_dir': empty_str, 'imagenet_dir': empty_str, 'imagenetdet_dir': empty_str, 'ecssd_dir': empty_str, 'hkuis_dir': empty_str, 'msra10k_dir': empty_str, 'davis_dir': empty_str, 'youtubevos_dir': empty_str}) comment = {'workspace_dir': 'Base directory for saving network checkpoints.', 'tensorboard_dir': 'Directory for tensorboard files.'} with open(path, 'w') as f: f.write('class EnvironmentSettings:\n') f.write(' def __init__(self):\n') for attr, attr_val in default_settings.items(): comment_str = None if attr in comment: comment_str = comment[attr] if comment_str is None: f.write(' self.{} = {}\n'.format(attr, attr_val)) else: f.write(' self.{} = {} # {}\n'.format(attr, attr_val, comment_str)) def env_settings(): env_module_name = 'ltr.admin.local' try: env_module = importlib.import_module(env_module_name) return env_module.EnvironmentSettings() except: env_file = os.path.join(os.path.dirname(__file__), 'local.py') create_default_local_file() raise RuntimeError('YOU HAVE NOT SETUP YOUR local.py!!!\n Go to "{}" and set all the paths you need. Then try to run again.'.format(env_file)) ================================================ FILE: external/AR/ltr/admin/loading.py ================================================ import torch import os import sys from pathlib import Path import importlib import inspect from ltr.admin import settings as ws_settings def load_trained_network(workspace_dir, network_path, checkpoint=None): """OUTDATED. Use load_pretrained instead!""" checkpoint_dir = os.path.join(workspace_dir, 'checkpoints') directory = '{}/{}'.format(checkpoint_dir, network_path) net, _ = load_network(directory, checkpoint) return net def load_pretrained(module, name, checkpoint=None, **kwargs): """Load a network trained using the LTR framework. This is useful when you want to initialize your new network with a previously trained model. args: module - Name of the train script module. I.e. the name of the folder in ltr/train_scripts. name - The name of the train_script. checkpoint - You can supply the checkpoint number or the full path to the checkpoint file (see load_network). **kwargs - These are passed to load_network (see that function). """ settings = ws_settings.Settings() network_dir = os.path.join(settings.env.workspace_dir, 'checkpoints', 'ltr', module, name) return load_network(network_dir=network_dir, checkpoint=checkpoint, **kwargs) def load_network(network_dir=None, checkpoint=None, constructor_fun_name=None, constructor_module=None, **kwargs): """Loads a network checkpoint file. Can be called in two different ways: load_checkpoint(network_dir): Loads the checkpoint file given by the path. If checkpoint_dir is a directory, it tries to find the latest checkpoint in that directory. load_checkpoint(network_dir, checkpoint=epoch_num): Loads the network at the given epoch number (int). The extra keyword arguments are supplied to the network constructor to replace saved ones. """ if network_dir is not None: net_path = Path(network_dir) else: net_path = None if net_path.is_file(): checkpoint = str(net_path) if checkpoint is None: # Load most recent checkpoint checkpoint_list = sorted(net_path.glob('*.pth.tar')) if checkpoint_list: checkpoint_path = checkpoint_list[-1] else: raise Exception('No matching checkpoint file found') elif isinstance(checkpoint, int): # Checkpoint is the epoch number checkpoint_list = sorted(net_path.glob('*_ep{:04d}.pth.tar'.format(checkpoint))) if not checkpoint_list or len(checkpoint_list) == 0: raise Exception('No matching checkpoint file found') if len(checkpoint_list) > 1: raise Exception('Multiple matching checkpoint files found') else: checkpoint_path = checkpoint_list[0] elif isinstance(checkpoint, str): # Checkpoint is the path checkpoint_path = os.path.expanduser(checkpoint) else: raise TypeError # Load network checkpoint_dict = torch_load_legacy(checkpoint_path) # Construct network model if 'constructor' in checkpoint_dict and checkpoint_dict['constructor'] is not None: net_constr = checkpoint_dict['constructor'] if constructor_fun_name is not None: net_constr.fun_name = constructor_fun_name if constructor_module is not None: net_constr.fun_module = constructor_module # Legacy networks before refactoring if net_constr.fun_module.startswith('dlframework.'): net_constr.fun_module = net_constr.fun_module[len('dlframework.'):] net_fun = getattr(importlib.import_module(net_constr.fun_module), net_constr.fun_name) net_fun_args = list(inspect.signature(net_fun).parameters.keys()) for arg, val in kwargs.items(): if arg in net_fun_args: net_constr.kwds[arg] = val else: print('WARNING: Keyword argument "{}" not found when loading network. It was ignored.'.format(arg)) net = net_constr.get() else: raise RuntimeError('No constructor for the given network.') net.load_state_dict(checkpoint_dict['net']) net.constructor = checkpoint_dict['constructor'] if 'net_info' in checkpoint_dict and checkpoint_dict['net_info'] is not None: net.info = checkpoint_dict['net_info'] return net, checkpoint_dict def load_weights(net, path, strict=True): checkpoint_dict = torch.load(path) weight_dict = checkpoint_dict['net'] net.load_state_dict(weight_dict, strict=strict) return net def torch_load_legacy(path): """Load network with legacy environment.""" # Setup legacy env (for older networks) _setup_legacy_env() # Load network checkpoint_dict = torch.load(path, map_location='cpu') # Cleanup legacy _cleanup_legacy_env() return checkpoint_dict def _setup_legacy_env(): importlib.import_module('ltr') sys.modules['dlframework'] = sys.modules['ltr'] sys.modules['dlframework.common'] = sys.modules['ltr'] importlib.import_module('ltr.admin') sys.modules['dlframework.common.utils'] = sys.modules['ltr.admin'] for m in ('model_constructor', 'stats', 'settings', 'local'): importlib.import_module('ltr.admin.' + m) sys.modules['dlframework.common.utils.' + m] = sys.modules['ltr.admin.' + m] def _cleanup_legacy_env(): del_modules = [] for m in sys.modules.keys(): if m.startswith('dlframework'): del_modules.append(m) for m in del_modules: del sys.modules[m] ================================================ FILE: external/AR/ltr/admin/model_constructor.py ================================================ from functools import wraps import importlib def model_constructor(f): """ Wraps the function 'f' which returns the network. An extra field 'constructor' is added to the network returned by 'f'. This field contains an instance of the 'NetConstructor' class, which contains the information needed to re-construct the network, such as the name of the function 'f', the function arguments etc. Thus, the network can be easily constructed from a saved checkpoint by calling NetConstructor.get() function. """ @wraps(f) def f_wrapper(*args, **kwds): net_constr = NetConstructor(f.__name__, f.__module__, args, kwds) output = f(*args, **kwds) if isinstance(output, (tuple, list)): # Assume first argument is the network output[0].constructor = net_constr else: output.constructor = net_constr return output return f_wrapper class NetConstructor: """ Class to construct networks. Takes as input the function name (e.g. atom_resnet18), the name of the module which contains the network function (e.g. ltr.models.bbreg.atom) and the arguments for the network function. The class object can then be stored along with the network weights to re-construct the network.""" def __init__(self, fun_name, fun_module, args, kwds): """ args: fun_name - The function which returns the network fun_module - the module which contains the network function args - arguments which are passed to the network function kwds - arguments which are passed to the network function """ self.fun_name = fun_name self.fun_module = fun_module self.args = args self.kwds = kwds def get(self): """ Rebuild the network by calling the network function with the correct arguments. """ net_module = importlib.import_module(self.fun_module) net_fun = getattr(net_module, self.fun_name) return net_fun(*self.args, **self.kwds) ================================================ FILE: external/AR/ltr/admin/multigpu.py ================================================ import torch.nn as nn def is_multi_gpu(net): return isinstance(net, (MultiGPU, nn.DataParallel)) class MultiGPU(nn.DataParallel): """Wraps a network to allow simple multi-GPU training.""" def __getattr__(self, item): try: return super().__getattr__(item) except: pass return getattr(self.module, item) ================================================ FILE: external/AR/ltr/admin/settings.py ================================================ from ltr.admin.environment import env_settings class Settings: """ Training settings, e.g. the paths to datasets and networks.""" def __init__(self): self.set_default() def set_default(self): self.env = env_settings() self.use_gpu = True ================================================ FILE: external/AR/ltr/admin/stats.py ================================================ class StatValue: def __init__(self): self.clear() def reset(self): self.val = 0 def clear(self): self.reset() self.history = [] def update(self, val): self.val = val self.history.append(self.val) class AverageMeter(object): """Computes and stores the average and current value""" def __init__(self): self.clear() self.has_new_data = False def reset(self): self.avg = 0 self.val = 0 self.sum = 0 self.count = 0 def clear(self): self.reset() self.history = [] def update(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count def new_epoch(self): if self.count > 0: self.history.append(self.avg) self.reset() self.has_new_data = True else: self.has_new_data = False def topk_accuracy(output, target, topk=(1,)): """Computes the precision@k for the specified values of k""" single_input = not isinstance(topk, (tuple, list)) if single_input: topk = (topk,) maxk = max(topk) batch_size = target.size(0) _, pred = output.topk(maxk, 1, True, True) pred = pred.t() correct = pred.eq(target.view(1, -1).expand_as(pred)) res = [] for k in topk: correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)[0] res.append(correct_k * 100.0 / batch_size) if single_input: return res[0] return res ================================================ FILE: external/AR/ltr/admin/tensorboard.py ================================================ import os from collections import OrderedDict try: from torch.utils.tensorboard import SummaryWriter except: print('WARNING: You are using tensorboardX instead sis you have a too old pytorch version.') from tensorboardX import SummaryWriter class TensorboardWriter: def __init__(self, directory, loader_names): self.directory = directory self.writer = OrderedDict({name: SummaryWriter(os.path.join(self.directory, name)) for name in loader_names}) def write_info(self, module_name, script_name, description): tb_info_writer = SummaryWriter(os.path.join(self.directory, 'info')) tb_info_writer.add_text('Modulet_name', module_name) tb_info_writer.add_text('Script_name', script_name) tb_info_writer.add_text('Description', description) tb_info_writer.close() def write_epoch(self, stats: OrderedDict, epoch: int, ind=-1): for loader_name, loader_stats in stats.items(): if loader_stats is None: continue for var_name, val in loader_stats.items(): if hasattr(val, 'history') and getattr(val, 'has_new_data', True): self.writer[loader_name].add_scalar(var_name, val.history[ind], epoch) ================================================ FILE: external/AR/ltr/data/__init__.py ================================================ from .loader import LTRLoader ================================================ FILE: external/AR/ltr/data/bounding_box_utils.py ================================================ import torch def rect_to_rel(bb, sz_norm=None): """Convert standard rectangular parametrization of the bounding box [x, y, w, h] to relative parametrization [cx/sw, cy/sh, log(w), log(h)], where [cx, cy] is the center coordinate. args: bb - N x 4 tensor of boxes. sz_norm - [N] x 2 tensor of value of [sw, sh] (optional). sw=w and sh=h if not given. """ c = bb[...,:2] + 0.5 * bb[...,2:] if sz_norm is None: c_rel = c / bb[...,2:] else: c_rel = c / sz_norm sz_rel = torch.log(bb[...,2:]) return torch.cat((c_rel, sz_rel), dim=-1) def rel_to_rect(bb, sz_norm=None): """Inverts the effect of rect_to_rel. See above.""" sz = torch.exp(bb[...,2:]) if sz_norm is None: c = bb[...,:2] * sz else: c = bb[...,:2] * sz_norm tl = c - 0.5 * sz return torch.cat((tl, sz), dim=-1) def masks_to_bboxes(mask, fmt='c'): """ Convert a mask tensor to one or more bounding boxes. Note: This function is a bit new, make sure it does what it says. /Andreas :param mask: Tensor of masks, shape = (..., H, W) :param fmt: bbox layout. 'c' => "center + size" or (x_center, y_center, width, height) 't' => "top left + size" or (x_left, y_top, width, height) 'v' => "vertices" or (x_left, y_top, x_right, y_bottom) :return: tensor containing a batch of bounding boxes, shape = (..., 4) """ batch_shape = mask.shape[:-2] mask = mask.reshape((-1, *mask.shape[-2:])) bboxes = [] for m in mask: mx = m.sum(dim=-2).nonzero() my = m.sum(dim=-1).nonzero() bb = [mx.min(), my.min(), mx.max(), my.max()] if (len(mx) > 0 and len(my) > 0) else [0, 0, 0, 0] bboxes.append(bb) bboxes = torch.tensor(bboxes, dtype=torch.float32, device=mask.device) bboxes = bboxes.reshape(batch_shape + (4,)) if fmt == 'v': return bboxes x1 = bboxes[..., :2] s = bboxes[..., 2:] - x1 + 1 if fmt == 'c': return torch.cat((x1 + 0.5 * s, s), dim=-1) elif fmt == 't': return torch.cat((x1, s), dim=-1) raise ValueError("Undefined bounding box layout '%s'" % fmt) def masks_to_bboxes_multi(mask, ids, fmt='c'): assert mask.dim() == 2 bboxes = [] for id in ids: mx = (mask == id).sum(dim=-2).nonzero() my = (mask == id).float().sum(dim=-1).nonzero() bb = [mx.min(), my.min(), mx.max(), my.max()] if (len(mx) > 0 and len(my) > 0) else [0, 0, 0, 0] bb = torch.tensor(bb, dtype=torch.float32, device=mask.device) x1 = bb[:2] s = bb[2:] - x1 + 1 if fmt == 'v': pass elif fmt == 'c': bb = torch.cat((x1 + 0.5 * s, s), dim=-1) elif fmt == 't': bb = torch.cat((x1, s), dim=-1) else: raise ValueError("Undefined bounding box layout '%s'" % fmt) bboxes.append(bb) return bboxes ================================================ FILE: external/AR/ltr/data/image_loader.py ================================================ import jpeg4py import cv2 as cv from PIL import Image import numpy as np davis_palette = np.repeat(np.expand_dims(np.arange(0,256), 1), 3, 1).astype(np.uint8) davis_palette[:22, :] = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0], [191, 0, 0], [64, 128, 0], [191, 128, 0], [64, 0, 128], [191, 0, 128], [64, 128, 128], [191, 128, 128], [0, 64, 0], [128, 64, 0], [0, 191, 0], [128, 191, 0], [0, 64, 128], [128, 64, 128]] def default_image_loader(path): """The default image loader, reads the image from the given path. It first tries to use the jpeg4py_loader, but reverts to the opencv_loader if the former is not available.""" if default_image_loader.use_jpeg4py is None: # Try using jpeg4py im = jpeg4py_loader(path) if im is None: default_image_loader.use_jpeg4py = False print('Using opencv_loader instead.') else: default_image_loader.use_jpeg4py = True return im if default_image_loader.use_jpeg4py: return jpeg4py_loader(path) return opencv_loader(path) default_image_loader.use_jpeg4py = None def jpeg4py_loader(path): """ Image reading using jpeg4py https://github.com/ajkxyz/jpeg4py""" try: return jpeg4py.JPEG(path).decode() except Exception as e: print('ERROR: Could not read image "{}"'.format(path)) print(e) return None def opencv_loader(path): """ Read image using opencv's imread function and returns it in rgb format""" try: im = cv.imread(path, cv.IMREAD_COLOR) # convert to rgb and return return cv.cvtColor(im, cv.COLOR_BGR2RGB) except Exception as e: print('ERROR: Could not read image "{}"'.format(path)) print(e) return None def jpeg4py_loader_w_failsafe(path): """ Image reading using jpeg4py https://github.com/ajkxyz/jpeg4py""" try: return jpeg4py.JPEG(path).decode() except: try: im = cv.imread(path, cv.IMREAD_COLOR) # convert to rgb and return return cv.cvtColor(im, cv.COLOR_BGR2RGB) except Exception as e: print('ERROR: Could not read image "{}"'.format(path)) print(e) return None def opencv_seg_loader(path): """ Read segmentation annotation using opencv's imread function""" try: return cv.imread(path) except Exception as e: print('ERROR: Could not read image "{}"'.format(path)) print(e) return None def imread_indexed(filename): """ Load indexed image with given filename. Used to read segmentation annotations.""" im = Image.open(filename) annotation = np.atleast_3d(im)[...,0] return annotation def imwrite_indexed(filename, array, color_palette=None): """ Save indexed image as png. Used to save segmentation annotation.""" if color_palette is None: color_palette = davis_palette if np.atleast_3d(array).shape[2] != 1: raise Exception("Saving indexed PNGs requires 2D array.") im = Image.fromarray(array) im.putpalette(color_palette.ravel()) im.save(filename, format='PNG') ================================================ FILE: external/AR/ltr/data/loader.py ================================================ import torch import torch.utils.data.dataloader import importlib import collections from torch._six import string_classes, int_classes from pytracking import TensorDict, TensorList def _check_use_shared_memory(): if hasattr(torch.utils.data.dataloader, '_use_shared_memory'): return getattr(torch.utils.data.dataloader, '_use_shared_memory') collate_lib = importlib.import_module('torch.utils.data._utils.collate') if hasattr(collate_lib, '_use_shared_memory'): return getattr(collate_lib, '_use_shared_memory') return torch.utils.data.get_worker_info() is not None def ltr_collate(batch): """Puts each data field into a tensor with outer dimension batch size""" error_msg = "batch must contain tensors, numbers, dicts or lists; found {}" elem_type = type(batch[0]) if isinstance(batch[0], torch.Tensor): out = None if _check_use_shared_memory(): # If we're in a background process, concatenate directly into a # shared memory tensor to avoid an extra copy numel = sum([x.numel() for x in batch]) storage = batch[0].storage()._new_shared(numel) out = batch[0].new(storage) return torch.stack(batch, 0, out=out) # if batch[0].dim() < 4: # return torch.stack(batch, 0, out=out) # return torch.cat(batch, 0, out=out) elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ and elem_type.__name__ != 'string_': elem = batch[0] if elem_type.__name__ == 'ndarray': # array of string classes and object if torch.utils.data.dataloader.re.search('[SaUO]', elem.dtype.str) is not None: raise TypeError(error_msg.format(elem.dtype)) return torch.stack([torch.from_numpy(b) for b in batch], 0) if elem.shape == (): # scalars py_type = float if elem.dtype.name.startswith('float') else int return torch.utils.data.dataloader.numpy_type_map[elem.dtype.name](list(map(py_type, batch))) elif isinstance(batch[0], int_classes): return torch.LongTensor(batch) elif isinstance(batch[0], float): return torch.DoubleTensor(batch) elif isinstance(batch[0], string_classes): return batch elif isinstance(batch[0], TensorDict): return TensorDict({key: ltr_collate([d[key] for d in batch]) for key in batch[0]}) elif isinstance(batch[0], collections.Mapping): return {key: ltr_collate([d[key] for d in batch]) for key in batch[0]} elif isinstance(batch[0], TensorList): transposed = zip(*batch) return TensorList([ltr_collate(samples) for samples in transposed]) elif isinstance(batch[0], collections.Sequence): transposed = zip(*batch) return [ltr_collate(samples) for samples in transposed] elif batch[0] is None: return batch raise TypeError((error_msg.format(type(batch[0])))) def ltr_collate_stack1(batch): """Puts each data field into a tensor. The tensors are stacked at dim=1 to form the batch""" error_msg = "batch must contain tensors, numbers, dicts or lists; found {}" elem_type = type(batch[0]) if isinstance(batch[0], torch.Tensor): out = None if _check_use_shared_memory(): # If we're in a background process, concatenate directly into a # shared memory tensor to avoid an extra copy numel = sum([x.numel() for x in batch]) storage = batch[0].storage()._new_shared(numel) out = batch[0].new(storage) return torch.stack(batch, 1, out=out) # if batch[0].dim() < 4: # return torch.stack(batch, 0, out=out) # return torch.cat(batch, 0, out=out) elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ and elem_type.__name__ != 'string_': elem = batch[0] if elem_type.__name__ == 'ndarray': # array of string classes and object if torch.utils.data.dataloader.re.search('[SaUO]', elem.dtype.str) is not None: raise TypeError(error_msg.format(elem.dtype)) return torch.stack([torch.from_numpy(b) for b in batch], 1) if elem.shape == (): # scalars py_type = float if elem.dtype.name.startswith('float') else int return torch.utils.data.dataloader.numpy_type_map[elem.dtype.name](list(map(py_type, batch))) elif isinstance(batch[0], int_classes): return torch.LongTensor(batch) elif isinstance(batch[0], float): return torch.DoubleTensor(batch) elif isinstance(batch[0], string_classes): return batch elif isinstance(batch[0], TensorDict): return TensorDict({key: ltr_collate_stack1([d[key] for d in batch]) for key in batch[0]}) elif isinstance(batch[0], collections.Mapping): return {key: ltr_collate_stack1([d[key] for d in batch]) for key in batch[0]} elif isinstance(batch[0], TensorList): transposed = zip(*batch) return TensorList([ltr_collate_stack1(samples) for samples in transposed]) elif isinstance(batch[0], collections.Sequence): transposed = zip(*batch) return [ltr_collate_stack1(samples) for samples in transposed] elif batch[0] is None: return batch raise TypeError((error_msg.format(type(batch[0])))) class LTRLoader(torch.utils.data.dataloader.DataLoader): """ Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset. Note: The only difference with default pytorch DataLoader is that an additional option stack_dim is available to select along which dimension the data should be stacked to form a batch. Arguments: dataset (Dataset): dataset from which to load the data. batch_size (int, optional): how many samples per batch to load (default: 1). shuffle (bool, optional): set to ``True`` to have the data reshuffled at every epoch (default: False). sampler (Sampler, optional): defines the strategy to draw samples from the dataset. If specified, ``shuffle`` must be False. batch_sampler (Sampler, optional): like sampler, but returns a batch of indices at a time. Mutually exclusive with batch_size, shuffle, sampler, and drop_last. num_workers (int, optional): how many subprocesses to use for data loading. 0 means that the data will be loaded in the main process. (default: 0) collate_fn (callable, optional): merges a list of samples to form a mini-batch. stack_dim (int): Dimension along which to stack to form the batch. (default: 0) pin_memory (bool, optional): If ``True``, the data loader will copy tensors into CUDA pinned memory before returning them. drop_last (bool, optional): set to ``True`` to drop the last incomplete batch, if the dataset size is not divisible by the batch size. If ``False`` and the size of dataset is not divisible by the batch size, then the last batch will be smaller. (default: False) timeout (numeric, optional): if positive, the timeout value for collecting a batch from workers. Should always be non-negative. (default: 0) worker_init_fn (callable, optional): If not None, this will be called on each worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as input, after seeding and before data loading. (default: None) .. note:: By default, each worker will have its PyTorch seed set to ``base_seed + worker_id``, where ``base_seed`` is a long generated by main process using its RNG. However, seeds for other libraies may be duplicated upon initializing workers (w.g., NumPy), causing each worker to return identical random numbers. (See :ref:`dataloader-workers-random-seed` section in FAQ.) You may use ``torch.initial_seed()`` to access the PyTorch seed for each worker in :attr:`worker_init_fn`, and use it to set other seeds before data loading. .. warning:: If ``spawn`` start method is used, :attr:`worker_init_fn` cannot be an unpicklable object, e.g., a lambda function. """ __initialized = False def __init__(self, name, dataset, training=True, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, epoch_interval=1, collate_fn=None, stack_dim=0, pin_memory=False, drop_last=False, timeout=0, worker_init_fn=None): if collate_fn is None: if stack_dim == 0: collate_fn = ltr_collate elif stack_dim == 1: collate_fn = ltr_collate_stack1 else: raise ValueError('Stack dim no supported. Must be 0 or 1.') super(LTRLoader, self).__init__(dataset, batch_size, shuffle, sampler, batch_sampler, num_workers, collate_fn, pin_memory, drop_last, timeout, worker_init_fn) self.name = name self.training = training self.epoch_interval = epoch_interval self.stack_dim = stack_dim ================================================ FILE: external/AR/ltr/data/processing.py ================================================ import torch import torchvision.transforms as transforms from pytracking import TensorDict import ltr.data.processing_utils as prutils def stack_tensors(x): if isinstance(x, (list, tuple)) and isinstance(x[0], torch.Tensor): return torch.stack(x) return x class BaseProcessing: """ Base class for Processing. Processing class is used to process the data returned by a dataset, before passing it through the network. For example, it can be used to crop a search region around the object, apply various data augmentations, etc.""" def __init__(self, transform=transforms.ToTensor(), train_transform=None, test_transform=None, joint_transform=None): """ args: transform - The set of transformations to be applied on the images. Used only if train_transform or test_transform is None. train_transform - The set of transformations to be applied on the train images. If None, the 'transform' argument is used instead. test_transform - The set of transformations to be applied on the test images. If None, the 'transform' argument is used instead. joint_transform - The set of transformations to be applied 'jointly' on the train and test images. For example, it can be used to convert both test and train images to grayscale. """ self.transform = {'train': transform if train_transform is None else train_transform, 'test': transform if test_transform is None else test_transform, 'joint': joint_transform} def __call__(self, data: TensorDict): raise NotImplementedError class ATOMProcessing(BaseProcessing): """ The processing class used for training ATOM. The images are processed in the following way. First, the target bounding box is jittered by adding some noise. Next, a square region (called search region ) centered at the jittered target center, and of area search_area_factor^2 times the area of the jittered box is cropped from the image. The reason for jittering the target box is to avoid learning the bias that the target is always at the center of the search region. The search region is then resized to a fixed size given by the argument output_sz. A set of proposals are then generated for the test images by jittering the ground truth box. """ def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor, proposal_params, mode='pair', *args, **kwargs): """ args: search_area_factor - The size of the search region relative to the target size. output_sz - An integer, denoting the size to which the search region is resized. The search region is always square. center_jitter_factor - A dict containing the amount of jittering to be applied to the target center before extracting the search region. See _get_jittered_box for how the jittering is done. scale_jitter_factor - A dict containing the amount of jittering to be applied to the target size before extracting the search region. See _get_jittered_box for how the jittering is done. proposal_params - Arguments for the proposal generation process. See _generate_proposals for details. mode - Either 'pair' or 'sequence'. If mode='sequence', then output has an extra dimension for frames """ super().__init__(*args, **kwargs) self.search_area_factor = search_area_factor self.output_sz = output_sz self.center_jitter_factor = center_jitter_factor self.scale_jitter_factor = scale_jitter_factor self.proposal_params = proposal_params self.mode = mode def _get_jittered_box(self, box, mode): """ Jitter the input box args: box - input bounding box mode - string 'train' or 'test' indicating train or test data returns: torch.Tensor - jittered box """ jittered_size = box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_factor[mode]) max_offset = (jittered_size.prod().sqrt() * torch.tensor(self.center_jitter_factor[mode]).float()) jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (torch.rand(2) - 0.5) return torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0) def _generate_proposals(self, box): """ Generates proposals by adding noise to the input box args: box - input box returns: torch.Tensor - Array of shape (num_proposals, 4) containing proposals torch.Tensor - Array of shape (num_proposals,) containing IoU overlap of each proposal with the input box. The IoU is mapped to [-1, 1] """ # Generate proposals num_proposals = self.proposal_params['boxes_per_frame'] proposal_method = self.proposal_params.get('proposal_method', 'default') if proposal_method == 'default': proposals = torch.zeros((num_proposals, 4)) gt_iou = torch.zeros(num_proposals) for i in range(num_proposals): proposals[i, :], gt_iou[i] = prutils.perturb_box(box, min_iou=self.proposal_params['min_iou'], sigma_factor=self.proposal_params['sigma_factor']) elif proposal_method == 'gmm': proposals, _, _ = prutils.sample_box_gmm(box, self.proposal_params['proposal_sigma'], num_samples=num_proposals) gt_iou = prutils.iou(box.view(1,4), proposals.view(-1,4)) # Map to [-1, 1] gt_iou = gt_iou * 2 - 1 return proposals, gt_iou def __call__(self, data: TensorDict): """ args: data - The input data, should contain the following fields: 'train_images', test_images', 'train_anno', 'test_anno' returns: TensorDict - output data block with following fields: 'train_images', 'test_images', 'train_anno', 'test_anno', 'test_proposals', 'proposal_iou' """ # Apply joint transforms if self.transform['joint'] is not None: data['train_images'], data['train_anno'] = self.transform['joint'](image=data['train_images'], bbox=data['train_anno']) data['test_images'], data['test_anno'] = self.transform['joint'](image=data['test_images'], bbox=data['test_anno'], new_roll=False) for s in ['train', 'test']: assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \ "In pair mode, num train/test frames must be 1" # Add a uniform noise to the center pos jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']] # Crop image region centered at jittered_anno box crops, boxes = prutils.jittered_center_crop(data[s + '_images'], jittered_anno, data[s + '_anno'], self.search_area_factor, self.output_sz) # Apply transforms data[s + '_images'], data[s + '_anno'] = self.transform[s](image=crops, bbox=boxes, joint=False) # Generate proposals frame2_proposals, gt_iou = zip(*[self._generate_proposals(a) for a in data['test_anno']]) data['test_proposals'] = list(frame2_proposals) data['proposal_iou'] = list(gt_iou) # Prepare output if self.mode == 'sequence': data = data.apply(stack_tensors) else: data = data.apply(lambda x: x[0] if isinstance(x, list) else x) return data class KLBBregProcessing(BaseProcessing): """ Based on ATOMProcessing. It supports training ATOM using the Maximum Likelihood or KL-divergence based learning introduced in [https://arxiv.org/abs/1909.12297] and in PrDiMP [https://arxiv.org/abs/2003.12565]. """ def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor, proposal_params, mode='pair', *args, **kwargs): """ args: search_area_factor - The size of the search region relative to the target size. output_sz - An integer, denoting the size to which the search region is resized. The search region is always square. center_jitter_factor - A dict containing the amount of jittering to be applied to the target center before extracting the search region. See _get_jittered_box for how the jittering is done. scale_jitter_factor - A dict containing the amount of jittering to be applied to the target size before extracting the search region. See _get_jittered_box for how the jittering is done. proposal_params - Arguments for the proposal generation process. See _generate_proposals for details. mode - Either 'pair' or 'sequence'. If mode='sequence', then output has an extra dimension for frames """ super().__init__(*args, **kwargs) self.search_area_factor = search_area_factor self.output_sz = output_sz self.center_jitter_factor = center_jitter_factor self.scale_jitter_factor = scale_jitter_factor self.proposal_params = proposal_params self.mode = mode def _get_jittered_box(self, box, mode): """ Jitter the input box args: box - input bounding box mode - string 'train' or 'test' indicating train or test data returns: torch.Tensor - jittered box """ jittered_size = box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_factor[mode]) max_offset = (jittered_size.prod().sqrt() * torch.tensor(self.center_jitter_factor[mode]).float()) jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (torch.rand(2) - 0.5) return torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0) def _generate_proposals(self, box): """ """ # Generate proposals proposals, proposal_density, gt_density = prutils.sample_box_gmm(box, self.proposal_params['proposal_sigma'], gt_sigma=self.proposal_params['gt_sigma'], num_samples=self.proposal_params[ 'boxes_per_frame'], add_mean_box=self.proposal_params.get( 'add_mean_box', False)) return proposals, proposal_density, gt_density def __call__(self, data: TensorDict): """ args: data - The input data, should contain the following fields: 'train_images', test_images', 'train_anno', 'test_anno' returns: TensorDict - output data block with following fields: 'train_images', 'test_images', 'train_anno', 'test_anno', 'test_proposals', 'proposal_density', 'gt_density' """ # Apply joint transforms if self.transform['joint'] is not None: data['train_images'], data['train_anno'] = self.transform['joint'](image=data['train_images'], bbox=data['train_anno']) data['test_images'], data['test_anno'] = self.transform['joint'](image=data['test_images'], bbox=data['test_anno'], new_roll=False) for s in ['train', 'test']: assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \ "In pair mode, num train/test frames must be 1" # Add a uniform noise to the center pos jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']] # Crop image region centered at jittered_anno box crops, boxes, _ = prutils.jittered_center_crop(data[s + '_images'], jittered_anno, data[s + '_anno'], self.search_area_factor, self.output_sz) # Apply transforms data[s + '_images'], data[s + '_anno'] = self.transform[s](image=crops, bbox=boxes, joint=False) # Generate proposals proposals, proposal_density, gt_density = zip(*[self._generate_proposals(a) for a in data['test_anno']]) data['test_proposals'] = proposals data['proposal_density'] = proposal_density data['gt_density'] = gt_density # Prepare output if self.mode == 'sequence': data = data.apply(stack_tensors) else: data = data.apply(lambda x: x[0] if isinstance(x, list) else x) return data class ATOMwKLProcessing(BaseProcessing): """Same as ATOMProcessing but using the GMM-based sampling of proposal boxes used in KLBBregProcessing.""" def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor, proposal_params, mode='pair', *args, **kwargs): super().__init__(*args, **kwargs) self.search_area_factor = search_area_factor self.output_sz = output_sz self.center_jitter_factor = center_jitter_factor self.scale_jitter_factor = scale_jitter_factor self.proposal_params = proposal_params self.mode = mode def _get_jittered_box(self, box, mode): """ Jitter the input box args: box - input bounding box mode - string 'train' or 'test' indicating train or test data returns: torch.Tensor - jittered box """ jittered_size = box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_factor[mode]) max_offset = (jittered_size.prod().sqrt() * torch.tensor(self.center_jitter_factor[mode]).float()) jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (torch.rand(2) - 0.5) return torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0) def _generate_proposals(self, box): """ """ # Generate proposals proposals, proposal_density, gt_density = prutils.sample_box_gmm(box, self.proposal_params['proposal_sigma'], self.proposal_params['gt_sigma'], self.proposal_params['boxes_per_frame']) iou = prutils.iou_gen(proposals, box.view(1, 4)) return proposals, proposal_density, gt_density, iou def __call__(self, data: TensorDict): # Apply joint transforms if self.transform['joint'] is not None: data['train_images'], data['train_anno'] = self.transform['joint'](image=data['train_images'], bbox=data['train_anno']) data['test_images'], data['test_anno'] = self.transform['joint'](image=data['test_images'], bbox=data['test_anno'], new_roll=False) for s in ['train', 'test']: assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \ "In pair mode, num train/test frames must be 1" # Add a uniform noise to the center pos jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']] # Crop image region centered at jittered_anno box crops, boxes = prutils.jittered_center_crop(data[s + '_images'], jittered_anno, data[s + '_anno'], self.search_area_factor, self.output_sz) # Apply transforms data[s + '_images'], data[s + '_anno'] = self.transform[s](image=crops, bbox=boxes, joint=False) # Generate proposals proposals, proposal_density, gt_density, proposal_iou = zip( *[self._generate_proposals(a) for a in data['test_anno']]) data['test_proposals'] = proposals data['proposal_density'] = proposal_density data['gt_density'] = gt_density data['proposal_iou'] = proposal_iou # Prepare output if self.mode == 'sequence': data = data.apply(stack_tensors) else: data = data.apply(lambda x: x[0] if isinstance(x, list) else x) return data class DiMPProcessing(BaseProcessing): """ The processing class used for training DiMP. The images are processed in the following way. First, the target bounding box is jittered by adding some noise. Next, a square region (called search region ) centered at the jittered target center, and of area search_area_factor^2 times the area of the jittered box is cropped from the image. The reason for jittering the target box is to avoid learning the bias that the target is always at the center of the search region. The search region is then resized to a fixed size given by the argument output_sz. A Gaussian label centered at the target is generated for each image. These label functions are used for computing the loss of the predicted classification model on the test images. A set of proposals are also generated for the test images by jittering the ground truth box. These proposals are used to train the bounding box estimating branch. """ def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor, crop_type='replicate', max_scale_change=None, mode='pair', proposal_params=None, label_function_params=None, *args, **kwargs): """ args: search_area_factor - The size of the search region relative to the target size. output_sz - An integer, denoting the size to which the search region is resized. The search region is always square. center_jitter_factor - A dict containing the amount of jittering to be applied to the target center before extracting the search region. See _get_jittered_box for how the jittering is done. scale_jitter_factor - A dict containing the amount of jittering to be applied to the target size before extracting the search region. See _get_jittered_box for how the jittering is done. crop_type - If 'replicate', the boundary pixels are replicated in case the search region crop goes out of image. If 'inside', the search region crop is shifted/shrunk to fit completely inside the image. If 'inside_major', the search region crop is shifted/shrunk to fit completely inside one axis of the image. max_scale_change - Maximum allowed scale change when performing the crop (only applicable for 'inside' and 'inside_major') mode - Either 'pair' or 'sequence'. If mode='sequence', then output has an extra dimension for frames proposal_params - Arguments for the proposal generation process. See _generate_proposals for details. label_function_params - Arguments for the label generation process. See _generate_label_function for details. """ super().__init__(*args, **kwargs) self.search_area_factor = search_area_factor self.output_sz = output_sz self.center_jitter_factor = center_jitter_factor self.scale_jitter_factor = scale_jitter_factor self.crop_type = crop_type self.mode = mode self.max_scale_change = max_scale_change self.proposal_params = proposal_params self.label_function_params = label_function_params def _get_jittered_box(self, box, mode): """ Jitter the input box args: box - input bounding box mode - string 'train' or 'test' indicating train or test data returns: torch.Tensor - jittered box """ jittered_size = box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_factor[mode]) max_offset = (jittered_size.prod().sqrt() * torch.tensor(self.center_jitter_factor[mode]).float()) jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (torch.rand(2) - 0.5) return torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0) def _generate_proposals(self, box): """ Generates proposals by adding noise to the input box args: box - input box returns: torch.Tensor - Array of shape (num_proposals, 4) containing proposals torch.Tensor - Array of shape (num_proposals,) containing IoU overlap of each proposal with the input box. The IoU is mapped to [-1, 1] """ # Generate proposals num_proposals = self.proposal_params['boxes_per_frame'] proposal_method = self.proposal_params.get('proposal_method', 'default') if proposal_method == 'default': proposals = torch.zeros((num_proposals, 4)) gt_iou = torch.zeros(num_proposals) for i in range(num_proposals): proposals[i, :], gt_iou[i] = prutils.perturb_box(box, min_iou=self.proposal_params['min_iou'], sigma_factor=self.proposal_params['sigma_factor']) elif proposal_method == 'gmm': proposals, _, _ = prutils.sample_box_gmm(box, self.proposal_params['proposal_sigma'], num_samples=num_proposals) gt_iou = prutils.iou(box.view(1, 4), proposals.view(-1, 4)) else: raise ValueError('Unknown proposal method.') # Map to [-1, 1] gt_iou = gt_iou * 2 - 1 return proposals, gt_iou def _generate_label_function(self, target_bb): """ Generates the gaussian label function centered at target_bb args: target_bb - target bounding box (num_images, 4) returns: torch.Tensor - Tensor of shape (num_images, label_sz, label_sz) containing the label for each sample """ gauss_label = prutils.gaussian_label_function(target_bb.view(-1, 4), self.label_function_params['sigma_factor'], self.label_function_params['kernel_sz'], self.label_function_params['feature_sz'], self.output_sz, end_pad_if_even=self.label_function_params.get('end_pad_if_even', True)) return gauss_label def __call__(self, data: TensorDict): """ args: data - The input data, should contain the following fields: 'train_images', test_images', 'train_anno', 'test_anno' returns: TensorDict - output data block with following fields: 'train_images', 'test_images', 'train_anno', 'test_anno', 'test_proposals', 'proposal_iou', 'test_label' (optional), 'train_label' (optional), 'test_label_density' (optional), 'train_label_density' (optional) """ if self.transform['joint'] is not None: data['train_images'], data['train_anno'] = self.transform['joint'](image=data['train_images'], bbox=data['train_anno']) data['test_images'], data['test_anno'] = self.transform['joint'](image=data['test_images'], bbox=data['test_anno'], new_roll=False) for s in ['train', 'test']: assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \ "In pair mode, num train/test frames must be 1" # Add a uniform noise to the center pos jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']] crops, boxes = prutils.target_image_crop(data[s + '_images'], jittered_anno, data[s + '_anno'], self.search_area_factor, self.output_sz, mode=self.crop_type, max_scale_change=self.max_scale_change) data[s + '_images'], data[s + '_anno'] = self.transform[s](image=crops, bbox=boxes, joint=False) # Generate proposals if self.proposal_params: frame2_proposals, gt_iou = zip(*[self._generate_proposals(a) for a in data['test_anno']]) data['test_proposals'] = list(frame2_proposals) data['proposal_iou'] = list(gt_iou) # Prepare output if self.mode == 'sequence': data = data.apply(stack_tensors) else: data = data.apply(lambda x: x[0] if isinstance(x, list) else x) # Generate label functions if self.label_function_params is not None: data['train_label'] = self._generate_label_function(data['train_anno']) data['test_label'] = self._generate_label_function(data['test_anno']) return data class KLDiMPProcessing(BaseProcessing): """ The processing class used for training PrDiMP that additionally supports the probabilistic classifier and bounding box regressor. See DiMPProcessing for details. """ def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor, crop_type='replicate', max_scale_change=None, mode='pair', proposal_params=None, label_function_params=None, label_density_params=None, *args, **kwargs): """ args: search_area_factor - The size of the search region relative to the target size. output_sz - An integer, denoting the size to which the search region is resized. The search region is always square. center_jitter_factor - A dict containing the amount of jittering to be applied to the target center before extracting the search region. See _get_jittered_box for how the jittering is done. scale_jitter_factor - A dict containing the amount of jittering to be applied to the target size before extracting the search region. See _get_jittered_box for how the jittering is done. crop_type - If 'replicate', the boundary pixels are replicated in case the search region crop goes out of image. If 'inside', the search region crop is shifted/shrunk to fit completely inside the image. If 'inside_major', the search region crop is shifted/shrunk to fit completely inside one axis of the image. max_scale_change - Maximum allowed scale change when performing the crop (only applicable for 'inside' and 'inside_major') mode - Either 'pair' or 'sequence'. If mode='sequence', then output has an extra dimension for frames proposal_params - Arguments for the proposal generation process. See _generate_proposals for details. label_function_params - Arguments for the label generation process. See _generate_label_function for details. label_density_params - Arguments for the label density generation process. See _generate_label_function for details. """ super().__init__(*args, **kwargs) self.search_area_factor = search_area_factor self.output_sz = output_sz self.center_jitter_factor = center_jitter_factor self.scale_jitter_factor = scale_jitter_factor self.crop_type = crop_type self.mode = mode self.max_scale_change = max_scale_change self.proposal_params = proposal_params self.label_function_params = label_function_params self.label_density_params = label_density_params def _get_jittered_box(self, box, mode): """ Jitter the input box args: box - input bounding box mode - string 'train' or 'test' indicating train or test data returns: torch.Tensor - jittered box """ jittered_size = box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_factor[mode]) max_offset = (jittered_size.prod().sqrt() * torch.tensor(self.center_jitter_factor[mode]).float()) jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (torch.rand(2) - 0.5) return torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0) def _generate_proposals(self, box): """ Generate proposal sample boxes from a GMM proposal distribution and compute their ground-truth density. This is used for ML and KL based regression learning of the bounding box regressor. args: box - input bounding box """ # Generate proposals proposals, proposal_density, gt_density = prutils.sample_box_gmm(box, self.proposal_params['proposal_sigma'], gt_sigma=self.proposal_params['gt_sigma'], num_samples=self.proposal_params['boxes_per_frame'], add_mean_box=self.proposal_params.get('add_mean_box', False)) return proposals, proposal_density, gt_density def _generate_label_function(self, target_bb): """ Generates the gaussian label function centered at target_bb args: target_bb - target bounding box (num_images, 4) returns: torch.Tensor - Tensor of shape (num_images, label_sz, label_sz) containing the label for each sample """ gauss_label = prutils.gaussian_label_function(target_bb.view(-1, 4), self.label_function_params['sigma_factor'], self.label_function_params['kernel_sz'], self.label_function_params['feature_sz'], self.output_sz, end_pad_if_even=self.label_function_params.get('end_pad_if_even', True)) return gauss_label def _generate_label_density(self, target_bb): """ Generates the gaussian label density centered at target_bb args: target_bb - target bounding box (num_images, 4) returns: torch.Tensor - Tensor of shape (num_images, label_sz, label_sz) containing the label for each sample """ feat_sz = self.label_density_params['feature_sz'] * self.label_density_params.get('interp_factor', 1) gauss_label = prutils.gaussian_label_function(target_bb.view(-1, 4), self.label_density_params['sigma_factor'], self.label_density_params['kernel_sz'], feat_sz, self.output_sz, end_pad_if_even=self.label_density_params.get('end_pad_if_even', True), density=True, uni_bias=self.label_density_params.get('uni_weight', 0.0)) gauss_label *= (gauss_label > self.label_density_params.get('threshold', 0.0)).float() if self.label_density_params.get('normalize', False): g_sum = gauss_label.sum(dim=(-2,-1)) valid = g_sum>0.01 gauss_label[valid, :, :] /= g_sum[valid].view(-1, 1, 1) gauss_label[~valid, :, :] = 1.0 / (gauss_label.shape[-2] * gauss_label.shape[-1]) gauss_label *= 1.0 - self.label_density_params.get('shrink', 0.0) return gauss_label def __call__(self, data: TensorDict): """ args: data - The input data, should contain the following fields: 'train_images', test_images', 'train_anno', 'test_anno' returns: TensorDict - output data block with following fields: 'train_images', 'test_images', 'train_anno', 'test_anno', 'test_proposals', 'proposal_density', 'gt_density', 'test_label' (optional), 'train_label' (optional), 'test_label_density' (optional), 'train_label_density' (optional) """ if self.transform['joint'] is not None: data['train_images'], data['train_anno'] = self.transform['joint'](image=data['train_images'], bbox=data['train_anno']) data['test_images'], data['test_anno'] = self.transform['joint'](image=data['test_images'], bbox=data['test_anno'], new_roll=False) for s in ['train', 'test']: assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \ "In pair mode, num train/test frames must be 1" # Add a uniform noise to the center pos jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']] crops, boxes = prutils.target_image_crop(data[s + '_images'], jittered_anno, data[s + '_anno'], self.search_area_factor, self.output_sz, mode=self.crop_type, max_scale_change=self.max_scale_change) data[s + '_images'], data[s + '_anno'] = self.transform[s](image=crops, bbox=boxes, joint=False) # Generate proposals proposals, proposal_density, gt_density = zip(*[self._generate_proposals(a) for a in data['test_anno']]) data['test_proposals'] = proposals data['proposal_density'] = proposal_density data['gt_density'] = gt_density for s in ['train', 'test']: is_distractor = data.get('is_distractor_{}_frame'.format(s), None) if is_distractor is not None: for is_dist, box in zip(is_distractor, data[s+'_anno']): if is_dist: box[0] = 99999999.9 box[1] = 99999999.9 # Prepare output if self.mode == 'sequence': data = data.apply(stack_tensors) else: data = data.apply(lambda x: x[0] if isinstance(x, list) else x) # Generate label functions if self.label_function_params is not None: data['train_label'] = self._generate_label_function(data['train_anno']) data['test_label'] = self._generate_label_function(data['test_anno']) if self.label_density_params is not None: data['train_label_density'] = self._generate_label_density(data['train_anno']) data['test_label_density'] = self._generate_label_density(data['test_anno']) return data ================================================ FILE: external/AR/ltr/data/processing_utils.py ================================================ import torch import math import cv2 as cv import random import torch.nn.functional as F from .bounding_box_utils import rect_to_rel, rel_to_rect def sample_target(im, target_bb, search_area_factor, output_sz=None, mask=None): """ Extracts a square crop centered at target_bb box, of area search_area_factor^2 times target_bb area args: im - cv image target_bb - target box [x, y, w, h] search_area_factor - Ratio of crop size to target size output_sz - (float) Size to which the extracted crop is resized (always square). If None, no resizing is done. returns: cv image - extracted crop float - the factor by which the crop has been resized to make the crop size equal output_size """ x, y, w, h = target_bb.tolist() # Crop image crop_sz = math.ceil(math.sqrt(w * h) * search_area_factor) if crop_sz < 1: raise Exception('Too small bounding box.') x1 = round(x + 0.5 * w - crop_sz * 0.5) x2 = x1 + crop_sz y1 = round(y + 0.5 * h - crop_sz * 0.5) y2 = y1 + crop_sz x1_pad = max(0, -x1) x2_pad = max(x2 - im.shape[1] + 1, 0) y1_pad = max(0, -y1) y2_pad = max(y2 - im.shape[0] + 1, 0) # Crop target im_crop = im[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad, :] if mask is not None: mask_crop = mask[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad] # Pad im_crop_padded = cv.copyMakeBorder(im_crop, y1_pad, y2_pad, x1_pad, x2_pad, cv.BORDER_REPLICATE) if mask is not None: mask_crop_padded = F.pad(mask_crop, pad=(x1_pad, x2_pad, y1_pad, y2_pad), mode='constant', value=0) if output_sz is not None: resize_factor = output_sz / crop_sz im_crop_padded = cv.resize(im_crop_padded, (output_sz, output_sz)) if mask is None: return im_crop_padded, resize_factor mask_crop_padded = \ F.interpolate(mask_crop_padded[None, None], (output_sz, output_sz), mode='bilinear', align_corners=False)[0, 0] return im_crop_padded, resize_factor, mask_crop_padded else: if mask is None: return im_crop_padded, 1.0 return im_crop_padded, 1.0, mask_crop_padded def transform_image_to_crop(box_in: torch.Tensor, box_extract: torch.Tensor, resize_factor: float, crop_sz: torch.Tensor) -> torch.Tensor: """ Transform the box co-ordinates from the original image co-ordinates to the co-ordinates of the cropped image args: box_in - the box for which the co-ordinates are to be transformed box_extract - the box about which the image crop has been extracted. resize_factor - the ratio between the original image scale and the scale of the image crop crop_sz - size of the cropped image returns: torch.Tensor - transformed co-ordinates of box_in """ box_extract_center = box_extract[0:2] + 0.5 * box_extract[2:4] box_in_center = box_in[0:2] + 0.5 * box_in[2:4] box_out_center = (crop_sz - 1) / 2 + (box_in_center - box_extract_center) * resize_factor box_out_wh = box_in[2:4] * resize_factor box_out = torch.cat((box_out_center - 0.5 * box_out_wh, box_out_wh)) return box_out def jittered_center_crop(frames, box_extract, box_gt, search_area_factor, output_sz, masks=None): """ For each frame in frames, extracts a square crop centered at box_extract, of area search_area_factor^2 times box_extract area. The extracted crops are then resized to output_sz. Further, the co-ordinates of the box box_gt are transformed to the image crop co-ordinates args: frames - list of frames box_extract - list of boxes of same length as frames. The crops are extracted using anno_extract box_gt - list of boxes of same length as frames. The co-ordinates of these boxes are transformed from image co-ordinates to the crop co-ordinates search_area_factor - The area of the extracted crop is search_area_factor^2 times box_extract area output_sz - The size to which the extracted crops are resized returns: list - list of image crops list - box_gt location in the crop co-ordinates """ if masks is None: crops_resize_factors = [sample_target(f, a, search_area_factor, output_sz) for f, a in zip(frames, box_extract)] frames_crop, resize_factors = zip(*crops_resize_factors) masks_crop = None else: crops_resize_factors = [sample_target(f, a, search_area_factor, output_sz, m) for f, a, m in zip(frames, box_extract, masks)] frames_crop, resize_factors, masks_crop = zip(*crops_resize_factors) crop_sz = torch.Tensor([output_sz, output_sz]) # find the bb location in the crop box_crop = [transform_image_to_crop(a_gt, a_ex, rf, crop_sz) for a_gt, a_ex, rf in zip(box_gt, box_extract, resize_factors)] return frames_crop, box_crop, masks_crop def sample_target_adaptive(im, target_bb, search_area_factor, output_sz, mode: str = 'replicate', max_scale_change=None, mask=None): """ Extracts a crop centered at target_bb box, of area search_area_factor^2. If the crop area contains regions outside the image, it is shifted so that the it is inside the image. Further, if the crop area exceeds the image size, a smaller crop which fits the image is returned instead. args: im - Input numpy image to crop. target_bb - target box [x, y, w, h] search_area_factor - Ratio of crop size to target size output_sz - (float) Size to which the extracted crop is resized (always square). If None, no resizing is done. mode - If 'replicate', the boundary pixels are replicated in case the search region crop goes out of image. If 'inside', the search region crop is shifted/shrunk to fit completely inside the image. If 'inside_major', the search region crop is shifted/shrunk to fit completely inside one axis of the image. max_scale_change - Maximum allowed scale change when performing the crop (only applicable for 'inside' and 'inside_major') mask - Optional mask to apply the same crop. returns: numpy image - Extracted crop. torch.Tensor - A bounding box denoting the cropped region in the image. numpy mask - Cropped mask returned only if mask is not None. """ if max_scale_change is None: max_scale_change = float('inf') if isinstance(output_sz, (float, int)): output_sz = (output_sz, output_sz) output_sz = torch.Tensor(output_sz) im_h = im.shape[0] im_w = im.shape[1] bbx, bby, bbw, bbh = target_bb.tolist() # Crop image crop_sz_x, crop_sz_y = (output_sz * ( target_bb[2:].prod() / output_sz.prod()).sqrt() * search_area_factor).ceil().long().tolist() # Get new sample size if forced inside the image if mode == 'inside' or mode == 'inside_major': # Calculate rescaling factor if outside the image rescale_factor = [crop_sz_x / im_w, crop_sz_y / im_h] if mode == 'inside': rescale_factor = max(rescale_factor) elif mode == 'inside_major': rescale_factor = min(rescale_factor) rescale_factor = min(max(1, rescale_factor), max_scale_change) crop_sz_x = math.floor(crop_sz_x / rescale_factor) crop_sz_y = math.floor(crop_sz_y / rescale_factor) if crop_sz_x < 1 or crop_sz_y < 1: raise Exception('Too small bounding box.') x1 = round(bbx + 0.5 * bbw - crop_sz_x * 0.5) x2 = x1 + crop_sz_x y1 = round(bby + 0.5 * bbh - crop_sz_y * 0.5) y2 = y1 + crop_sz_y # Move box inside image shift_x = max(0, -x1) + min(0, im_w - x2) x1 += shift_x x2 += shift_x shift_y = max(0, -y1) + min(0, im_h - y2) y1 += shift_y y2 += shift_y out_x = (max(0, -x1) + max(0, x2 - im_w)) // 2 out_y = (max(0, -y1) + max(0, y2 - im_h)) // 2 shift_x = (-x1 - out_x) * (out_x > 0) shift_y = (-y1 - out_y) * (out_y > 0) x1 += shift_x x2 += shift_x y1 += shift_y y2 += shift_y x1_pad = max(0, -x1) x2_pad = max(x2 - im.shape[1] + 1, 0) y1_pad = max(0, -y1) y2_pad = max(y2 - im.shape[0] + 1, 0) # Crop target im_crop = im[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad, :] if mask is not None: mask_crop = mask[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad] # Pad im_crop_padded = cv.copyMakeBorder(im_crop, y1_pad, y2_pad, x1_pad, x2_pad, cv.BORDER_REPLICATE) if mask is not None: mask_crop_padded = F.pad(mask_crop, pad=(x1_pad, x2_pad, y1_pad, y2_pad), mode='constant', value=0) # Resize image im_out = cv.resize(im_crop_padded, tuple(output_sz.long().tolist())) if mask is not None: mask_out = \ F.interpolate(mask_crop_padded[None, None], tuple(output_sz.flip(0).long().tolist()), mode='nearest')[0, 0] crop_box = torch.Tensor([x1, y1, x2 - x1, y2 - y1]) if mask is None: return im_out, crop_box else: return im_out, crop_box, mask_out def crop_and_resize(im, box, crop_bb, output_sz, mask=None): if isinstance(output_sz, (float, int)): output_sz = (output_sz, output_sz) im_h = im.shape[0] im_w = im.shape[1] if crop_bb[2] < 1 or crop_bb[3] < 1: raise Exception('Too small bounding box.') x1 = crop_bb[0] x2 = crop_bb[0] + crop_bb[2] y1 = crop_bb[1] y2 = crop_bb[1] + crop_bb[3] x1_pad = max(0, -x1) x2_pad = max(x2 - im.shape[1] + 1, 0) y1_pad = max(0, -y1) y2_pad = max(y2 - im.shape[0] + 1, 0) # Crop target im_crop = im[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad, :] if mask is not None: mask_crop = mask[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad] # Pad im_crop_padded = cv.copyMakeBorder(im_crop, y1_pad, y2_pad, x1_pad, x2_pad, cv.BORDER_REPLICATE) if mask is not None: mask_crop_padded = F.pad(mask_crop, pad=(x1_pad, x2_pad, y1_pad, y2_pad), mode='constant', value=0) # Resize image im_out = cv.resize(im_crop_padded, output_sz) if mask is not None: mask_out = F.interpolate(mask_crop_padded[None, None], (output_sz[1], output_sz[0]), mode='nearest')[0, 0] rescale_factor = output_sz[0] / crop_bb[2] # Hack if box is not None: box_crop = box.clone() box_crop[0] -= crop_bb[0] box_crop[1] -= crop_bb[1] box_crop *= rescale_factor else: box_crop = None if mask is None: return im_out, box_crop else: return im_out, box_crop, mask_out def transform_box_to_crop(box: torch.Tensor, crop_box: torch.Tensor, crop_sz: torch.Tensor) -> torch.Tensor: """ Transform the box co-ordinates from the original image co-ordinates to the co-ordinates of the cropped image args: box - the box for which the co-ordinates are to be transformed crop_box - bounding box defining the crop in the original image crop_sz - size of the cropped image returns: torch.Tensor - transformed co-ordinates of box_in """ box_out = box.clone() box_out[:2] -= crop_box[:2] scale_factor = crop_sz / crop_box[2:] box_out[:2] *= scale_factor box_out[2:] *= scale_factor return box_out def target_image_crop(frames, box_extract, box_gt, search_area_factor, output_sz, mode: str = 'replicate', max_scale_change=None, masks=None): """ For each frame in frames, extracts a square crop centered at box_extract, of area search_area_factor^2 times box_extract area. If the crop area contains regions outside the image, it is shifted / shrunk so that it completely fits inside the image. The extracted crops are then resized to output_sz. Further, the co-ordinates of the box box_gt are transformed to the image crop co-ordinates args: frames - list of frames box_extract - list of boxes of same length as frames. The crops are extracted using anno_extract box_gt - list of boxes of same length as frames. The co-ordinates of these boxes are transformed from image co-ordinates to the crop co-ordinates search_area_factor - The area of the extracted crop is search_area_factor^2 times box_extract area output_sz - The size to which the extracted crops are resized mode - If 'replicate', the boundary pixels are replicated in case the search region crop goes out of image. If 'inside', the search region crop is shifted/shrunk to fit completely inside the image. If 'inside_major', the search region crop is shifted/shrunk to fit completely inside one axis of the image. max_scale_change - Maximum allowed scale change when performing the crop (only applicable for 'inside' and 'inside_major') masks - Optional masks to apply the same crop. returns: list - list of image crops list - box_gt location in the crop co-ordinates """ if isinstance(output_sz, (float, int)): output_sz = (output_sz, output_sz) if masks is None: frame_crops_boxes = [sample_target_adaptive(f, a, search_area_factor, output_sz, mode, max_scale_change) for f, a in zip(frames, box_extract)] frames_crop, crop_boxes = zip(*frame_crops_boxes) else: frame_crops_boxes_masks = [ sample_target_adaptive(f, a, search_area_factor, output_sz, mode, max_scale_change, mask=m) for f, a, m in zip(frames, box_extract, masks)] frames_crop, crop_boxes, masks_crop = zip(*frame_crops_boxes_masks) crop_sz = torch.Tensor(output_sz) # find the bb location in the crop box_crop = [transform_box_to_crop(bb_gt, crop_bb, crop_sz) for bb_gt, crop_bb in zip(box_gt, crop_boxes)] if masks is None: return frames_crop, box_crop else: return frames_crop, box_crop, masks_crop def iou(reference, proposals): """Compute the IoU between a reference box with multiple proposal boxes. args: reference - Tensor of shape (1, 4). proposals - Tensor of shape (num_proposals, 4) returns: torch.Tensor - Tensor of shape (num_proposals,) containing IoU of reference box with each proposal box. """ # Intersection box tl = torch.max(reference[:, :2], proposals[:, :2]) br = torch.min(reference[:, :2] + reference[:, 2:], proposals[:, :2] + proposals[:, 2:]) sz = (br - tl).clamp(0) # Area intersection = sz.prod(dim=1) union = reference[:, 2:].prod(dim=1) + proposals[:, 2:].prod(dim=1) - intersection return intersection / union def rand_uniform(a, b, shape=1): """ sample numbers uniformly between a and b. args: a - lower bound b - upper bound shape - shape of the output tensor returns: torch.Tensor - tensor of shape=shape """ return (b - a) * torch.rand(shape) + a def perturb_box(box, min_iou=0.5, sigma_factor=0.1): """ Perturb the input box by adding gaussian noise to the co-ordinates args: box - input box min_iou - minimum IoU overlap between input box and the perturbed box sigma_factor - amount of perturbation, relative to the box size. Can be either a single element, or a list of sigma_factors, in which case one of them will be uniformly sampled. Further, each of the sigma_factor element can be either a float, or a tensor of shape (4,) specifying the sigma_factor per co-ordinate returns: torch.Tensor - the perturbed box """ if isinstance(sigma_factor, list): # If list, sample one sigma_factor as current sigma factor c_sigma_factor = random.choice(sigma_factor) else: c_sigma_factor = sigma_factor if not isinstance(c_sigma_factor, torch.Tensor): c_sigma_factor = c_sigma_factor * torch.ones(4) perturb_factor = torch.sqrt(box[2] * box[3]) * c_sigma_factor # multiple tries to ensure that the perturbed box has iou > min_iou with the input box for i_ in range(100): c_x = box[0] + 0.5 * box[2] c_y = box[1] + 0.5 * box[3] c_x_per = random.gauss(c_x, perturb_factor[0]) c_y_per = random.gauss(c_y, perturb_factor[1]) w_per = random.gauss(box[2], perturb_factor[2]) h_per = random.gauss(box[3], perturb_factor[3]) if w_per <= 1: w_per = box[2] * rand_uniform(0.15, 0.5) if h_per <= 1: h_per = box[3] * rand_uniform(0.15, 0.5) box_per = torch.Tensor([c_x_per - 0.5 * w_per, c_y_per - 0.5 * h_per, w_per, h_per]).round() if box_per[2] <= 1: box_per[2] = box[2] * rand_uniform(0.15, 0.5) if box_per[3] <= 1: box_per[3] = box[3] * rand_uniform(0.15, 0.5) box_iou = iou(box.view(1, 4), box_per.view(1, 4)) # if there is sufficient overlap, return if box_iou > min_iou: return box_per, box_iou # else reduce the perturb factor perturb_factor *= 0.9 return box_per, box_iou def gauss_1d(sz, sigma, center, end_pad=0, density=False): k = torch.arange(-(sz - 1) / 2, (sz + 1) / 2 + end_pad).reshape(1, -1) gauss = torch.exp(-1.0 / (2 * sigma ** 2) * (k - center.reshape(-1, 1)) ** 2) if density: gauss /= math.sqrt(2 * math.pi) * sigma return gauss def gauss_2d(sz, sigma, center, end_pad=(0, 0), density=False): if isinstance(sigma, (float, int)): sigma = (sigma, sigma) return gauss_1d(sz[0].item(), sigma[0], center[:, 0], end_pad[0], density).reshape(center.shape[0], 1, -1) * \ gauss_1d(sz[1].item(), sigma[1], center[:, 1], end_pad[1], density).reshape(center.shape[0], -1, 1) def gaussian_label_function(target_bb, sigma_factor, kernel_sz, feat_sz, image_sz, end_pad_if_even=True, density=False, uni_bias=0): """Construct Gaussian label function.""" if isinstance(kernel_sz, (float, int)): kernel_sz = (kernel_sz, kernel_sz) if isinstance(feat_sz, (float, int)): feat_sz = (feat_sz, feat_sz) if isinstance(image_sz, (float, int)): image_sz = (image_sz, image_sz) image_sz = torch.Tensor(image_sz) feat_sz = torch.Tensor(feat_sz) target_center = target_bb[:, 0:2] + 0.5 * target_bb[:, 2:4] target_center_norm = (target_center - image_sz / 2) / image_sz center = feat_sz * target_center_norm + 0.5 * \ torch.Tensor([(kernel_sz[0] + 1) % 2, (kernel_sz[1] + 1) % 2]) sigma = sigma_factor * feat_sz.prod().sqrt().item() if end_pad_if_even: end_pad = (int(kernel_sz[0] % 2 == 0), int(kernel_sz[1] % 2 == 0)) else: end_pad = (0, 0) gauss_label = gauss_2d(feat_sz, sigma, center, end_pad, density=density) if density: sz = (feat_sz + torch.Tensor(end_pad)).prod() label = (1.0 - uni_bias) * gauss_label + uni_bias / sz else: label = gauss_label + uni_bias return label def gauss_density_centered(x, std): """Evaluate the probability density of a Gaussian centered at zero. args: x - Samples. std - List of standard deviations """ return torch.exp(-0.5 * (x / std) ** 2) / (math.sqrt(2 * math.pi) * std) def gmm_density_centered(x, std): """Evaluate the probability density of a GMM centered at zero. args: x - Samples. Assumes dim=-1 is the component dimension and dim=-2 is feature dimension. Rest are sample dimension. std - Tensor of standard deviations """ if x.dim() == std.dim() - 1: x = x.unsqueeze(-1) elif not (x.dim() == std.dim() and x.shape[-1] == 1): raise ValueError('Last dimension must be the gmm stds.') return gauss_density_centered(x, std).prod(-2).mean(-1) def sample_gmm_centered(std, num_samples=1): """Sample from a GMM distribution centered at zero: args: std - Tensor of standard deviations num_samples - number of samples """ num_components = std.shape[-1] num_dims = std.numel() // num_components std = std.view(1, num_dims, num_components) # Sample component ids k = torch.randint(num_components, (num_samples,), dtype=torch.int64) std_samp = std[0, :, k].t() # Sample x_centered = std_samp * torch.randn(num_samples, num_dims) prob_dens = gmm_density_centered(x_centered, std) return x_centered, prob_dens def sample_gmm(mean, std, num_samples=1): """Sample from a GMM distribution: args: mean - a single mean vector std - Tensor of standard deviations num_samples - number of samples """ num_dims = mean.numel() num_components = std.shape[-1] mean = mean.view(1, num_dims) std = std.view(1, -1, num_components) # Sample component ids k = torch.randint(num_components, (num_samples,), dtype=torch.int64) std_samp = std[0, :, k].t() # Sample x_centered = std_samp * torch.randn(num_samples, num_dims) x = x_centered + mean prob_dens = gmm_density_centered(x_centered, std) return x, prob_dens def sample_box_gmm(mean_box, proposal_sigma, gt_sigma=None, num_samples=1, add_mean_box=False): """Sample boxes from a Gaussian mixture model. args: mean_box - Center (or mean) bounding box proposal_sigma - List of standard deviations for each Gaussian gt_sigma - Standard deviation of the ground truth distribution num_samples - Number of sampled boxes add_mean_box - Also add mean box as first element returns: proposals, proposal density and ground truth density for all samples """ center_std = torch.Tensor([s[0] for s in proposal_sigma]) sz_std = torch.Tensor([s[1] for s in proposal_sigma]) std = torch.stack([center_std, center_std, sz_std, sz_std]) mean_box = mean_box.view(1, 4) sz_norm = mean_box[:, 2:].clone() # Sample boxes proposals_rel_centered, proposal_density = sample_gmm_centered(std, num_samples) # Add mean and map back mean_box_rel = rect_to_rel(mean_box, sz_norm) proposals_rel = proposals_rel_centered + mean_box_rel proposals = rel_to_rect(proposals_rel, sz_norm) if gt_sigma is None or gt_sigma[0] == 0 and gt_sigma[1] == 0: gt_density = torch.zeros_like(proposal_density) else: std_gt = torch.Tensor([gt_sigma[0], gt_sigma[0], gt_sigma[1], gt_sigma[1]]).view(1, 4) gt_density = gauss_density_centered(proposals_rel_centered, std_gt).prod(-1) if add_mean_box: proposals = torch.cat((mean_box, proposals)) proposal_density = torch.cat((torch.Tensor([-1]), proposal_density)) gt_density = torch.cat((torch.Tensor([1]), gt_density)) return proposals, proposal_density, gt_density ================================================ FILE: external/AR/ltr/data/processing_utils_SE.py ================================================ import torch import math import cv2 as cv import random import numpy as np def stack_tensors(x): if isinstance(x, list) and isinstance(x[0], torch.Tensor): return torch.stack(x) return x '''Added on 2019.12.23''' def sample_target_SE(im, target_bb, search_area_factor, output_sz=None, mode=cv.BORDER_REPLICATE): """ Extracts a crop centered at target_bb box, of size search_area_factor times target_bb(Both height and width) args: im - cv image target_bb - target box [x, y, w, h] search_area_factor - Ratio of crop size to target size output_sz - (float) Size to which the extracted crop is resized (always square). If None, no resizing is done. returns: cv image - extracted crop float - the factor by which the crop has been resized to make the crop size equal output_size """ x, y, w, h = target_bb.tolist() # Crop image ws = math.ceil(search_area_factor * w) hs = math.ceil(search_area_factor * h) if ws < 1 or hs < 1: raise Exception('Too small bounding box.') x1 = round(x + 0.5*w - ws*0.5) x2 = x1 + ws y1 = round(y + 0.5 * h - hs * 0.5) y2 = y1 + hs x1_pad = max(0, -x1) x2_pad = max(x2-im.shape[1]+1, 0) y1_pad = max(0, -y1) y2_pad = max(y2-im.shape[0]+1, 0) # Crop target im_crop = im[y1+y1_pad:y2-y2_pad, x1+x1_pad:x2-x2_pad, :] # Pad im_crop_padded = cv.copyMakeBorder(im_crop, y1_pad, y2_pad, x1_pad, x2_pad, mode) if output_sz is not None: w_rsz_f = output_sz / ws h_rsz_f = output_sz / hs im_crop_padded_rsz = cv.resize(im_crop_padded, (output_sz, output_sz)) if len(im_crop_padded_rsz.shape)==2: im_crop_padded_rsz = im_crop_padded_rsz[...,np.newaxis] return im_crop_padded_rsz, h_rsz_f, w_rsz_f else: return im_crop_padded, 1.0, 1.0 '''把mask映射到原图上''' def map_mask_back(im, target_bb, search_area_factor, mask, mode=cv.BORDER_REPLICATE): """ Extracts a crop centered at target_bb box, of size search_area_factor times target_bb(Both height and width) args: im - cv image target_bb - target box [x, y, w, h] search_area_factor - Ratio of crop size to target size output_sz - (float) Size to which the extracted crop is resized (always square). If None, no resizing is done. returns: cv image - extracted crop float - the factor by which the crop has been resized to make the crop size equal output_size """ H,W = (im.shape[0],im.shape[1]) base = np.zeros((H,W)) x, y, w, h = target_bb.tolist() # Crop image ws = math.ceil(search_area_factor * w) hs = math.ceil(search_area_factor * h) if ws < 1 or hs < 1: raise Exception('Too small bounding box.') x1 = round(x + 0.5*w - ws*0.5) x2 = x1 + ws y1 = round(y + 0.5 * h - hs * 0.5) y2 = y1 + hs x1_pad = max(0, -x1) x2_pad = max(x2-im.shape[1]+1, 0) y1_pad = max(0, -y1) y2_pad = max(y2-im.shape[0]+1, 0) '''pad base''' base_padded = cv.copyMakeBorder(base, y1_pad, y2_pad, x1_pad, x2_pad, mode) '''Resize mask''' mask_rsz = cv.resize(mask,(ws,hs)) '''fill region with mask''' base_padded[y1+y1_pad:y2+y1_pad, x1+x1_pad:x2+x1_pad] = mask_rsz.copy() '''crop base_padded to get final mask''' final_mask = base_padded[y1_pad:y1_pad+H,x1_pad:x1_pad+W] assert (final_mask.shape == (H,W)) return final_mask '''Added on 2019.12.23''' def transform_image_to_crop_SE(box_in: torch.Tensor, box_extract: torch.Tensor, resize_factor_h: float, resize_factor_w: float, crop_sz: torch.Tensor) -> torch.Tensor: """ Transform the box co-ordinates from the original image co-ordinates to the co-ordinates of the cropped image args: box_in - the box for which the co-ordinates are to be transformed box_extract - the box about which the image crop has been extracted. resize_factor - the ratio between the original image scale and the scale of the image crop crop_sz - size of the cropped image returns: torch.Tensor - transformed co-ordinates of box_in """ box_extract_center = box_extract[0:2] + 0.5*box_extract[2:4] box_in_center = box_in[0:2] + 0.5*box_in[2:4] box_out_xc = (crop_sz[0] -1)/2 + (box_in_center[0] - box_extract_center[0])*resize_factor_w box_out_yc = (crop_sz[0] -1)/2 + (box_in_center[1] - box_extract_center[1])*resize_factor_h box_out_w = box_in[2] * resize_factor_w box_out_h = box_in[3] * resize_factor_h '''2019.12.28 为了避免出现(x1,y1)小于0,或者(x2,y2)大于256的情况,这里我对它们加上了一些限制''' max_sz = crop_sz[0].item() box_out_x1 = torch.clamp(box_out_xc - 0.5 * box_out_w,0,max_sz) box_out_y1 = torch.clamp(box_out_yc - 0.5 * box_out_h,0,max_sz) box_out_x2 = torch.clamp(box_out_xc + 0.5 * box_out_w,0,max_sz) box_out_y2 = torch.clamp(box_out_yc + 0.5 * box_out_h,0,max_sz) box_out_w_new = box_out_x2 - box_out_x1 box_out_h_new = box_out_y2 - box_out_y1 box_out = torch.stack((box_out_x1, box_out_y1, box_out_w_new, box_out_h_new)) return box_out def centered_crop(frames, anno, area_factor, output_sz): crops_resize_factors = [sample_target(f, a, area_factor, output_sz) for f, a in zip(frames, anno)] frames_crop, resize_factors = zip(*crops_resize_factors) crop_sz = torch.Tensor([output_sz, output_sz]) # find the bb location in the crop anno_crop = [transform_image_to_crop(a, a, rf, crop_sz) for a, rf in zip(anno, resize_factors)] return frames_crop, anno_crop '''Added by Bin Yan 2019.12.23, changed on 2020.1.4(add a new args: "get_bbox_coord")''' def jittered_center_crop_SE(frames, box_extract, box_gt, search_area_factor, output_sz, get_bbox_coord=True, mode=cv.BORDER_REPLICATE): """ Crop a patch centered at box_extract. The height and width of cropped region is search_area_factor times that of box_extract. The extracted crops are then resized to output_sz. Further, the co-ordinates of the box box_gt are transformed to the image crop co-ordinates args: frames - list of frames box_extract - list of boxes of same length as frames. The crops are extracted using anno_extract box_gt - list of boxes of same length as frames. The co-ordinates of these boxes are transformed from image co-ordinates to the crop co-ordinates search_area_factor - The area of the extracted crop is search_area_factor^2 times box_extract area output_sz - The size to which the extracted crops are resized returns: list - list of image crops list - box_gt location in the crop co-ordinates """ '''call function "sample_target_SE" and function "transform_image_to_crop_SE"''' '''get cropped patch(fixed size)''' crops_resize_factors = [sample_target_SE(f, a, search_area_factor, output_sz, mode=mode) for f, a in zip(frames, box_extract)] frames_crop, resize_factors_h, resize_factors_w = zip(*crops_resize_factors) if get_bbox_coord: crop_sz = torch.Tensor([output_sz, output_sz]) # find the bb location in the crop '''get GT's cooridinate on the cropped patch''' box_crop = [transform_image_to_crop_SE(a_gt, a_ex, h_rsf, w_rsf, crop_sz) for a_gt, a_ex, h_rsf, w_rsf in zip(box_gt, box_extract, resize_factors_h, resize_factors_w)] return frames_crop, box_crop else: return frames_crop def sample_target_nopad(im, target_bb, search_area_factor, output_sz): """ Extracts a crop centered at target_bb box, of area search_area_factor^2. If the crop area contains regions outside the image, it is shifted so that the it is inside the image. Further, if the crop area exceeds the image size, a smaller crop which fits the image is returned instead. args: im - cv image target_bb - target box [x, y, w, h] search_area_factor - Ratio of crop size to target size output_sz - (float) Size to which the extracted crop is resized (always square). If None, no resizing is done. returns: cv image - extracted crop torch.Tensor - a bounding box denoting the cropped region in the image. """ if isinstance(output_sz, (float, int)): output_sz = (output_sz, output_sz) output_sz = torch.Tensor(output_sz) im_h = im.shape[0] im_w = im.shape[1] bbx, bby, bbw, bbh = target_bb.tolist() # Crop image crop_sz_x, crop_sz_y = (output_sz * (target_bb[2:].prod()/output_sz.prod()).sqrt() * search_area_factor).ceil() # Calculate rescaling factor if outside the image rescale_factor = max(1, crop_sz_x/im_w, crop_sz_y/im_h) crop_sz_x = math.floor(crop_sz_x / rescale_factor) crop_sz_y = math.floor(crop_sz_y / rescale_factor) if crop_sz_x < 1 or crop_sz_y < 1: raise Exception('Too small bounding box.') x1 = round(bbx + 0.5*bbw - crop_sz_x*0.5) x2 = x1 + crop_sz_x y1 = round(bby + 0.5*bbh - crop_sz_y*0.5) y2 = y1 + crop_sz_y # Move box inside image shift_x = max(0, -x1) + min(0, im_w - x2) x1 += shift_x x2 += shift_x shift_y = max(0, -y1) + min(0, im_h - y2) y1 += shift_y y2 += shift_y # Crop and resize image im_crop = im[y1:y2, x1:x2, :] im_out = cv.resize(im_crop, tuple(output_sz.long().tolist())) crop_box = torch.Tensor([x1, y1, x2-x1, y2-y1]) return im_out, crop_box def transform_box_to_crop(box: torch.Tensor, crop_box: torch.Tensor, crop_sz: torch.Tensor) -> torch.Tensor: """ Transform the box co-ordinates from the original image co-ordinates to the co-ordinates of the cropped image args: box - the box for which the co-ordinates are to be transformed crop_box - bounding box defining the crop in the original image crop_sz - size of the cropped image returns: torch.Tensor - transformed co-ordinates of box_in """ box_out = box.clone() box_out[:2] -= crop_box[:2] scale_factor = crop_sz / crop_box[2:] box_out[:2] *= scale_factor box_out[2:] *= scale_factor return box_out def jittered_center_crop_nopad(frames, box_extract, box_gt, search_area_factor, output_sz): """ For each frame in frames, extracts a square crop centered at box_extract, of area search_area_factor^2 times box_extract area. If the crop area contains regions outside the image, it is shifted / shrunk so that it completely fits inside the image. The extracted crops are then resized to output_sz. Further, the co-ordinates of the box box_gt are transformed to the image crop co-ordinates args: frames - list of frames box_extract - list of boxes of same length as frames. The crops are extracted using anno_extract box_gt - list of boxes of same length as frames. The co-ordinates of these boxes are transformed from image co-ordinates to the crop co-ordinates search_area_factor - The area of the extracted crop is search_area_factor^2 times box_extract area output_sz - The size to which the extracted crops are resized returns: list - list of image crops list - box_gt location in the crop co-ordinates """ if isinstance(output_sz, (float, int)): output_sz = (output_sz, output_sz) frame_crops_boxes = [sample_target_nopad(f, a, search_area_factor, output_sz) for f, a in zip(frames, box_extract)] frames_crop, crop_boxes = zip(*frame_crops_boxes) crop_sz = torch.Tensor(output_sz) # find the bb location in the crop box_crop = [transform_box_to_crop(bb_gt, crop_bb, crop_sz) for bb_gt, crop_bb in zip(box_gt, crop_boxes)] return frames_crop, box_crop def iou(reference, proposals): """Compute the IoU between a reference box with multiple proposal boxes. args: reference - Tensor of shape (1, 4). proposals - Tensor of shape (num_proposals, 4) returns: torch.Tensor - Tensor of shape (num_proposals,) containing IoU of reference box with each proposal box. """ # Intersection box tl = torch.max(reference[:,:2], proposals[:,:2]) br = torch.min(reference[:,:2] + reference[:,2:], proposals[:,:2] + proposals[:,2:]) sz = (br - tl).clamp(0) # Area intersection = sz.prod(dim=1) union = reference[:,2:].prod(dim=1) + proposals[:,2:].prod(dim=1) - intersection return intersection / union def rand_uniform(a, b, shape=1): """ sample numbers uniformly between a and b. args: a - lower bound b - upper bound shape - shape of the output tensor returns: torch.Tensor - tensor of shape=shape """ return (b - a) * torch.rand(shape) + a def perturb_box(box, min_iou=0.5, sigma_factor=0.1): """ Perturb the input box by adding gaussian noise to the co-ordinates args: box - input box min_iou - minimum IoU overlap between input box and the perturbed box sigma_factor - amount of perturbation, relative to the box size. Can be either a single element, or a list of sigma_factors, in which case one of them will be uniformly sampled. Further, each of the sigma_factor element can be either a float, or a tensor of shape (4,) specifying the sigma_factor per co-ordinate returns: torch.Tensor - the perturbed box """ if isinstance(sigma_factor, list): # If list, sample one sigma_factor as current sigma factor c_sigma_factor = random.choice(sigma_factor) else: c_sigma_factor = sigma_factor if not isinstance(c_sigma_factor, torch.Tensor): c_sigma_factor = c_sigma_factor * torch.ones(4) perturb_factor = torch.sqrt(box[2]*box[3])*c_sigma_factor # multiple tries to ensure that the perturbed box has iou > min_iou with the input box for i_ in range(100): c_x = box[0] + 0.5*box[2] c_y = box[1] + 0.5 * box[3] c_x_per = random.gauss(c_x, perturb_factor[0]) c_y_per = random.gauss(c_y, perturb_factor[1]) w_per = random.gauss(box[2], perturb_factor[2]) h_per = random.gauss(box[3], perturb_factor[3]) if w_per <= 1: w_per = box[2]*rand_uniform(0.15, 0.5) if h_per <= 1: h_per = box[3]*rand_uniform(0.15, 0.5) box_per = torch.Tensor([c_x_per - 0.5*w_per, c_y_per - 0.5*h_per, w_per, h_per]).round() if box_per[2] <= 1: box_per[2] = box[2]*rand_uniform(0.15, 0.5) if box_per[3] <= 1: box_per[3] = box[3]*rand_uniform(0.15, 0.5) box_iou = iou(box.view(1, 4), box_per.view(1, 4)) # if there is sufficient overlap, return if box_iou > min_iou: return box_per, box_iou # else reduce the perturb factor perturb_factor *= 0.9 return box_per, box_iou def gauss_1d(sz, sigma, center, end_pad=0): k = torch.arange(-(sz-1)/2, (sz+1)/2 + end_pad).reshape(1, -1) return torch.exp(-1.0/(2*sigma**2) * (k - center.reshape(-1, 1))**2) def gauss_2d(sz, sigma, center, end_pad=(0, 0)): if isinstance(sigma, (float, int)): sigma = (sigma, sigma) return gauss_1d(sz[0].item(), sigma[0], center[:, 0], end_pad[0]).reshape(center.shape[0], 1, -1) * \ gauss_1d(sz[1].item(), sigma[1], center[:, 1], end_pad[1]).reshape(center.shape[0], -1, 1) def gaussian_label_function(target_bb, sigma_factor, kernel_sz, feat_sz, image_sz, end_pad_if_even=True): """Construct Gaussian label function.""" if isinstance(kernel_sz, (float, int)): kernel_sz = (kernel_sz, kernel_sz) if isinstance(feat_sz, (float, int)): feat_sz = (feat_sz, feat_sz) if isinstance(image_sz, (float, int)): image_sz = (image_sz, image_sz) image_sz = torch.Tensor(image_sz) feat_sz = torch.Tensor(feat_sz) target_center = target_bb[:, 0:2] + 0.5 * target_bb[:, 2:4] target_center_norm = (target_center - image_sz / 2) / image_sz center = feat_sz * target_center_norm + 0.5 * \ torch.Tensor([(kernel_sz[0] + 1) % 2, (kernel_sz[1] + 1) % 2]) sigma = sigma_factor * feat_sz.prod().sqrt().item() if end_pad_if_even: end_pad = (int(kernel_sz[0]%2 == 0), int(kernel_sz[1]%2 == 0)) else: end_pad = (0, 0) gauss_label = gauss_2d(feat_sz, sigma, center, end_pad) return gauss_label ================================================ FILE: external/AR/ltr/data/sampler.py ================================================ import random import torch.utils.data from pytracking import TensorDict def no_processing(data): return data class TrackingSampler(torch.utils.data.Dataset): """ Class responsible for sampling frames from training sequences to form batches. Each training sample is a tuple consisting of i) a set of train frames, used to learn the DiMP classification model and obtain the modulation vector for IoU-Net, and ii) a set of test frames on which target classification loss for the predicted DiMP model, and the IoU prediction loss for the IoU-Net is calculated. The sampling is done in the following ways. First a dataset is selected at random. Next, a sequence is selected from that dataset. A base frame is then sampled randomly from the sequence. Next, a set of 'train frames' and 'test frames' are sampled from the sequence from the range [base_frame_id - max_gap, base_frame_id] and (base_frame_id, base_frame_id + max_gap] respectively. Only the frames in which the target is visible are sampled. If enough visible frames are not found, the 'max_gap' is increased gradually till enough frames are found. The sampled frames are then passed through the input 'processing' function for the necessary processing- """ def __init__(self, datasets, p_datasets, samples_per_epoch, max_gap, num_test_frames, num_train_frames=1, processing=no_processing, frame_sample_mode='causal'): """ args: datasets - List of datasets to be used for training p_datasets - List containing the probabilities by which each dataset will be sampled samples_per_epoch - Number of training samples per epoch max_gap - Maximum gap, in frame numbers, between the train frames and the test frames. num_test_frames - Number of test frames to sample. num_train_frames - Number of train frames to sample. processing - An instance of Processing class which performs the necessary processing of the data. frame_sample_mode - Either 'causal' or 'interval'. If 'causal', then the test frames are sampled in a causally, otherwise randomly within the interval. """ self.datasets = datasets # If p not provided, sample uniformly from all videos if p_datasets is None: p_datasets = [len(d) for d in self.datasets] # Normalize p_total = sum(p_datasets) self.p_datasets = [x / p_total for x in p_datasets] self.samples_per_epoch = samples_per_epoch self.max_gap = max_gap self.num_test_frames = num_test_frames self.num_train_frames = num_train_frames self.processing = processing self.frame_sample_mode = frame_sample_mode def __len__(self): return self.samples_per_epoch def _sample_visible_ids(self, visible, num_ids=1, min_id=None, max_id=None): """ Samples num_ids frames between min_id and max_id for which target is visible args: visible - 1d Tensor indicating whether target is visible for each frame num_ids - number of frames to be samples min_id - Minimum allowed frame number max_id - Maximum allowed frame number returns: list - List of sampled frame numbers. None if not sufficient visible frames could be found. """ if num_ids == 0: return [] if min_id is None or min_id < 0: min_id = 0 if max_id is None or max_id > len(visible): max_id = len(visible) valid_ids = [i for i in range(min_id, max_id) if visible[i]] # No visible ids if len(valid_ids) == 0: return None return random.choices(valid_ids, k=num_ids) def __getitem__(self, index): """ args: index (int): Index (Ignored since we sample randomly) returns: TensorDict - dict containing all the data blocks """ # Select a dataset dataset = random.choices(self.datasets, self.p_datasets)[0] is_video_dataset = dataset.is_video_sequence() # Sample a sequence with enough visible frames enough_visible_frames = False while not enough_visible_frames: # Sample a sequence seq_id = random.randint(0, dataset.get_num_sequences() - 1) # Sample frames seq_info_dict = dataset.get_sequence_info(seq_id) visible = seq_info_dict['visible'] enough_visible_frames = visible.type(torch.int64).sum().item() > 2 * ( self.num_test_frames + self.num_train_frames) and len(visible) >= 20 enough_visible_frames = enough_visible_frames or not is_video_dataset if is_video_dataset: train_frame_ids = None test_frame_ids = None gap_increase = 0 if self.frame_sample_mode == 'interval': # Sample frame numbers within interval defined by the first frame while test_frame_ids is None: base_frame_id = self._sample_visible_ids(visible, num_ids=1) extra_train_frame_ids = self._sample_visible_ids(visible, num_ids=self.num_train_frames - 1, min_id=base_frame_id[ 0] - self.max_gap - gap_increase, max_id=base_frame_id[ 0] + self.max_gap + gap_increase) if extra_train_frame_ids is None: gap_increase += 5 continue train_frame_ids = base_frame_id + extra_train_frame_ids test_frame_ids = self._sample_visible_ids(visible, num_ids=self.num_test_frames, min_id=train_frame_ids[0] - self.max_gap - gap_increase, max_id=train_frame_ids[0] + self.max_gap + gap_increase) gap_increase += 5 # Increase gap until a frame is found elif self.frame_sample_mode == 'causal': # Sample test and train frames in a causal manner, i.e. test_frame_ids > train_frame_ids while test_frame_ids is None: base_frame_id = self._sample_visible_ids(visible, num_ids=1, min_id=self.num_train_frames - 1, max_id=len(visible) - self.num_test_frames) prev_frame_ids = self._sample_visible_ids(visible, num_ids=self.num_train_frames - 1, min_id=base_frame_id[0] - self.max_gap - gap_increase, max_id=base_frame_id[0]) if prev_frame_ids is None: gap_increase += 5 continue train_frame_ids = base_frame_id + prev_frame_ids test_frame_ids = self._sample_visible_ids(visible, min_id=train_frame_ids[0] + 1, max_id=train_frame_ids[0] + self.max_gap + gap_increase, num_ids=self.num_test_frames) # Increase gap until a frame is found gap_increase += 5 else: # In case of image dataset, just repeat the image to generate synthetic video train_frame_ids = [1] * self.num_train_frames test_frame_ids = [1] * self.num_test_frames train_frames, train_anno, meta_obj_train = dataset.get_frames(seq_id, train_frame_ids, seq_info_dict) test_frames, test_anno, meta_obj_test = dataset.get_frames(seq_id, test_frame_ids, seq_info_dict) data = TensorDict({'train_images': train_frames, 'train_anno': train_anno['bbox'], 'test_images': test_frames, 'test_anno': test_anno['bbox'], 'dataset': dataset.get_name(), 'test_class': meta_obj_test.get('object_class_name')}) return self.processing(data) class DiMPSampler(TrackingSampler): """ See TrackingSampler.""" def __init__(self, datasets, p_datasets, samples_per_epoch, max_gap, num_test_frames, num_train_frames=1, processing=no_processing, frame_sample_mode='causal'): super().__init__(datasets=datasets, p_datasets=p_datasets, samples_per_epoch=samples_per_epoch, max_gap=max_gap, num_test_frames=num_test_frames, num_train_frames=num_train_frames, processing=processing, frame_sample_mode=frame_sample_mode) class ATOMSampler(TrackingSampler): """ See TrackingSampler.""" def __init__(self, datasets, p_datasets, samples_per_epoch, max_gap, num_test_frames=1, num_train_frames=1, processing=no_processing, frame_sample_mode='interval'): super().__init__(datasets=datasets, p_datasets=p_datasets, samples_per_epoch=samples_per_epoch, max_gap=max_gap, num_test_frames=num_test_frames, num_train_frames=num_train_frames, processing=processing, frame_sample_mode=frame_sample_mode) ================================================ FILE: external/AR/ltr/data/transforms.py ================================================ import random import numpy as np import math import cv2 as cv import torch import torch.nn.functional as F import torchvision.transforms.functional as tvisf class Transform: """A set of transformations, used for e.g. data augmentation. Args of constructor: transforms: An arbitrary number of transformations, derived from the TransformBase class. They are applied in the order they are given. The Transform object can jointly transform images, bounding boxes and segmentation masks. This is done by calling the object with the following key-word arguments (all are optional). The following arguments are inputs to be transformed. They are either supplied as a single instance, or a list of instances. image - Image coords - 2xN dimensional Tensor of 2D image coordinates [y, x] bbox - Bounding box on the form [x, y, w, h] mask - Segmentation mask with discrete classes The following parameters can be supplied with calling the transform object: joint [Bool] - If True then transform all images/coords/bbox/mask in the list jointly using the same transformation. Otherwise each tuple (images, coords, bbox, mask) will be transformed independently using different random rolls. Default: True. new_roll [Bool] - If False, then no new random roll is performed, and the saved result from the previous roll is used instead. Default: True. Check the DiMPProcessing class for examples. """ def __init__(self, *transforms): if len(transforms) == 1 and isinstance(transforms[0], (list, tuple)): transforms = transforms[0] self.transforms = transforms self._valid_inputs = ['image', 'coords', 'bbox', 'mask'] self._valid_args = ['joint', 'new_roll'] self._valid_all = self._valid_inputs + self._valid_args def __call__(self, **inputs): var_names = [k for k in inputs.keys() if k in self._valid_inputs] for v in inputs.keys(): if v not in self._valid_all: raise ValueError('Incorrect input \"{}\" to transform. Only supports inputs {} and arguments {}.'.format(v, self._valid_inputs, self._valid_args)) joint_mode = inputs.get('joint', True) new_roll = inputs.get('new_roll', True) if not joint_mode: out = zip(*[self(**inp) for inp in self._split_inputs(inputs)]) return tuple(list(o) for o in out) out = {k: v for k, v in inputs.items() if k in self._valid_inputs} for t in self.transforms: out = t(**out, joint=joint_mode, new_roll=new_roll) if len(var_names) == 1: return out[var_names[0]] # Make sure order is correct return tuple(out[v] for v in var_names) def _split_inputs(self, inputs): var_names = [k for k in inputs.keys() if k in self._valid_inputs] split_inputs = [{k: v for k, v in zip(var_names, vals)} for vals in zip(*[inputs[vn] for vn in var_names])] for arg_name, arg_val in filter(lambda it: it[0]!='joint' and it[0] in self._valid_args, inputs.items()): if isinstance(arg_val, list): for inp, av in zip(split_inputs, arg_val): inp[arg_name] = av else: for inp in split_inputs: inp[arg_name] = arg_val return split_inputs def __repr__(self): format_string = self.__class__.__name__ + '(' for t in self.transforms: format_string += '\n' format_string += ' {0}'.format(t) format_string += '\n)' return format_string class TransformBase: """Base class for transformation objects. See the Transform class for details.""" def __init__(self): self._valid_inputs = ['image', 'coords', 'bbox', 'mask'] self._valid_args = ['new_roll'] self._valid_all = self._valid_inputs + self._valid_args self._rand_params = None def __call__(self, **inputs): # Split input input_vars = {k: v for k, v in inputs.items() if k in self._valid_inputs} input_args = {k: v for k, v in inputs.items() if k in self._valid_args} # Roll random parameters for the transform if input_args.get('new_roll', True): rand_params = self.roll() if rand_params is None: rand_params = () elif not isinstance(rand_params, tuple): rand_params = (rand_params,) self._rand_params = rand_params outputs = dict() for var_name, var in input_vars.items(): if var is not None: transform_func = getattr(self, 'transform_' + var_name) if var_name in ['coords', 'bbox']: params = (self._get_image_size(input_vars),) + self._rand_params else: params = self._rand_params if isinstance(var, (list, tuple)): outputs[var_name] = [transform_func(x, *params) for x in var] else: outputs[var_name] = transform_func(var, *params) return outputs def _get_image_size(self, inputs): im = None for var_name in ['image', 'mask']: if inputs.get(var_name) is not None: im = inputs[var_name] break if im is None: return None if isinstance(im, (list, tuple)): im = im[0] if isinstance(im, np.ndarray): return im.shape[:2] if torch.is_tensor(im): return (im.shape[-2], im.shape[-1]) raise Exception('Unknown image type') def roll(self): return None def transform_image(self, image, *rand_params): """Must be deterministic""" return image def transform_coords(self, coords, image_shape, *rand_params): """Must be deterministic""" return coords def transform_bbox(self, bbox, image_shape, *rand_params): """Assumes [x, y, w, h]""" # Check if not overloaded if self.transform_coords.__code__ == TransformBase.transform_coords.__code__: return bbox coord = bbox.clone().view(-1,2).t().flip(0) x1 = coord[1, 0] x2 = coord[1, 0] + coord[1, 1] y1 = coord[0, 0] y2 = coord[0, 0] + coord[0, 1] coord_all = torch.tensor([[y1, y1, y2, y2], [x1, x2, x2, x1]]) coord_transf = self.transform_coords(coord_all, image_shape, *rand_params).flip(0) tl = torch.min(coord_transf, dim=1)[0] sz = torch.max(coord_transf, dim=1)[0] - tl bbox_out = torch.cat((tl, sz), dim=-1).reshape(bbox.shape) return bbox_out def transform_mask(self, mask, *rand_params): """Must be deterministic""" return mask class ToTensor(TransformBase): """Convert to a Tensor""" def transform_image(self, image): # handle numpy array if image.ndim == 2: image = image[:, :, None] image = torch.from_numpy(image.transpose((2, 0, 1))) # backward compatibility if isinstance(image, torch.ByteTensor): return image.float().div(255) else: return image def transfrom_mask(self, mask): if isinstance(mask, np.ndarray): return torch.from_numpy(mask) class ToTensorAndJitter(TransformBase): """Convert to a Tensor and jitter brightness""" def __init__(self, brightness_jitter=0.0, normalize=True): super().__init__() self.brightness_jitter = brightness_jitter self.normalize = normalize def roll(self): return np.random.uniform(max(0, 1 - self.brightness_jitter), 1 + self.brightness_jitter) def transform_image(self, image, brightness_factor): # handle numpy array image = torch.from_numpy(image.transpose((2, 0, 1))) # backward compatibility if self.normalize: return image.float().mul(brightness_factor/255.0).clamp(0.0, 1.0) else: return image.float().mul(brightness_factor).clamp(0.0, 255.0) def transform_mask(self, mask, brightness_factor): if isinstance(mask, np.ndarray): return torch.from_numpy(mask) else: return mask class Normalize(TransformBase): """Normalize image""" def __init__(self, mean, std, inplace=False): super().__init__() self.mean = mean self.std = std self.inplace = inplace def transform_image(self, image): return tvisf.normalize(image, self.mean, self.std, self.inplace) class ToGrayscale(TransformBase): """Converts image to grayscale with probability""" def __init__(self, probability = 0.5): super().__init__() self.probability = probability self.color_weights = np.array([0.2989, 0.5870, 0.1140], dtype=np.float32) def roll(self): return random.random() < self.probability def transform_image(self, image, do_grayscale): if do_grayscale: if torch.is_tensor(image): raise NotImplementedError('Implement torch variant.') img_gray = cv.cvtColor(image, cv.COLOR_RGB2GRAY) return np.stack([img_gray, img_gray, img_gray], axis=2) # return np.repeat(np.sum(img * self.color_weights, axis=2, keepdims=True).astype(np.uint8), 3, axis=2) return image class ToBGR(TransformBase): """Converts image to BGR""" def transform_image(self, image): if torch.is_tensor(image): raise NotImplementedError('Implement torch variant.') img_bgr = cv.cvtColor(image, cv.COLOR_RGB2BGR) return img_bgr class RandomHorizontalFlip(TransformBase): """Horizontally flip image randomly with a probability p.""" def __init__(self, probability = 0.5): super().__init__() self.probability = probability def roll(self): return random.random() < self.probability def transform_image(self, image, do_flip): if do_flip: if torch.is_tensor(image): return image.flip((2,)) return np.fliplr(image).copy() return image def transform_coords(self, coords, image_shape, do_flip): if do_flip: coords = coords.clone() coords[1,:] = (image_shape[1] - 1) - coords[1,:] return coords def transform_mask(self, mask, do_flip): if do_flip: if torch.is_tensor(mask): return mask.flip((-1,)) return np.fliplr(mask).copy() return mask class Blur(TransformBase): """ Blur the image by applying a gaussian kernel with given sigma""" def __init__(self, sigma): super().__init__() if isinstance(sigma, (float, int)): sigma = (sigma, sigma) self.sigma = sigma self.filter_size = [math.ceil(2*s) for s in self.sigma] x_coord = [torch.arange(-sz, sz+1, dtype=torch.float32) for sz in self.filter_size] self.filter = [torch.exp(-(x**2)/(2*s**2)) for x, s in zip(x_coord, self.sigma)] self.filter[0] = self.filter[0].view(1,1,-1,1) / self.filter[0].sum() self.filter[1] = self.filter[1].view(1,1,1,-1) / self.filter[1].sum() def transform_image(self, image): if torch.is_tensor(image): sz = image.shape[2:] im1 = F.conv2d(image.view(-1, 1, sz[0], sz[1]), self.filter[0], padding=(self.filter_size[0], 0)) return F.conv2d(im1, self.filter[1], padding=(0,self.filter_size[1])).view(-1,sz[0],sz[1]) else: raise NotImplementedError class RandomBlur(TransformBase): """ Blur the image, with a given probability, by applying a gaussian kernel with given sigma""" def __init__(self, sigma, probability=0.1): super().__init__() self.probability = probability if isinstance(sigma, (float, int)): sigma = (sigma, sigma) self.sigma = sigma self.filter_size = [math.ceil(2*s) for s in self.sigma] x_coord = [torch.arange(-sz, sz+1, dtype=torch.float32) for sz in self.filter_size] self.filter = [torch.exp(-(x**2)/(2*s**2)) for x, s in zip(x_coord, self.sigma)] self.filter[0] = self.filter[0].view(1,1,-1,1) / self.filter[0].sum() self.filter[1] = self.filter[1].view(1,1,1,-1) / self.filter[1].sum() def roll(self): return random.random() < self.probability def transform(self, image, do_blur=None): if do_blur is None: do_blur = False if do_blur: if torch.is_tensor(image): sz = image.shape[1:] im1 = F.conv2d(image.view(-1, 1, sz[0], sz[1]), self.filter[0], padding=(self.filter_size[0], 0)) return F.conv2d(im1, self.filter[1], padding=(0,self.filter_size[1])).view(-1,sz[0],sz[1]) else: raise NotImplementedError else: return image class RandomAffine(TransformBase): """Apply random affine transformation.""" def __init__(self, p_flip=0.0, max_rotation=0.0, max_shear=0.0, max_scale=0.0, max_ar_factor=0.0, border_mode='constant', pad_amount=0): super().__init__() self.p_flip = p_flip self.max_rotation = max_rotation self.max_shear = max_shear self.max_scale = max_scale self.max_ar_factor = max_ar_factor if border_mode == 'constant': self.border_flag = cv.BORDER_CONSTANT elif border_mode == 'replicate': self.border_flag == cv.BORDER_REPLICATE else: raise Exception self.pad_amount = pad_amount def roll(self): do_flip = random.random() < self.p_flip theta = random.uniform(-self.max_rotation, self.max_rotation) shear_x = random.uniform(-self.max_shear, self.max_shear) shear_y = random.uniform(-self.max_shear, self.max_shear) ar_factor = np.exp(random.uniform(-self.max_ar_factor, self.max_ar_factor)) scale_factor = np.exp(random.uniform(-self.max_scale, self.max_scale)) return do_flip, theta, (shear_x, shear_y), (scale_factor, scale_factor * ar_factor) def _construct_t_mat(self, image_shape, do_flip, theta, shear_values, scale_factors): im_h, im_w = image_shape t_mat = np.identity(3) if do_flip: if do_flip: t_mat[0, 0] = -1.0 t_mat[0, 2] = im_w t_rot = cv.getRotationMatrix2D((im_w * 0.5, im_h * 0.5), theta, 1.0) t_rot = np.concatenate((t_rot, np.array([0.0, 0.0, 1.0]).reshape(1, 3))) t_shear = np.array([[1.0, shear_values[0], -shear_values[0] * 0.5 * im_w], [shear_values[1], 1.0, -shear_values[1] * 0.5 * im_h], [0.0, 0.0, 1.0]]) t_scale = np.array([[scale_factors[0], 0.0, (1.0 - scale_factors[0]) * 0.5 * im_w], [0.0, scale_factors[1], (1.0 - scale_factors[1]) * 0.5 * im_h], [0.0, 0.0, 1.0]]) t_mat = t_scale @ t_rot @ t_shear @ t_mat t_mat[0, 2] += self.pad_amount t_mat[1, 2] += self.pad_amount t_mat = t_mat[:2, :] return t_mat def transform_image(self, image, do_flip, theta, shear_values, scale_factors): if torch.is_tensor(image): raise Exception('Only supported for numpy input') t_mat = self._construct_t_mat(image.shape[:2], do_flip, theta, shear_values, scale_factors) output_sz = (image.shape[1] + 2*self.pad_amount, image.shape[0] + 2*self.pad_amount) image_t = cv.warpAffine(image, t_mat, output_sz, flags=cv.INTER_LINEAR, borderMode=self.border_flag) return image_t def transform_coords(self, coords, image_shape, do_flip, theta, shear_values, scale_factors): t_mat = self._construct_t_mat(image_shape, do_flip, theta, shear_values, scale_factors) t_mat_tensor = torch.from_numpy(t_mat).float() coords_xy1 = torch.stack((coords[1, :], coords[0, :], torch.ones_like(coords[1, :]))) coords_xy_t = torch.mm(t_mat_tensor, coords_xy1) return coords_xy_t[[1, 0], :] def transform_mask(self, mask, do_flip, theta, shear_values, scale_factors): t_mat = self._construct_t_mat(mask.shape[:2], do_flip, theta, shear_values, scale_factors) output_sz = (mask.shape[1] + 2*self.pad_amount, mask.shape[0] + 2*self.pad_amount) mask_t = cv.warpAffine(mask.numpy(), t_mat, output_sz, flags=cv.INTER_NEAREST, borderMode=self.border_flag) return torch.from_numpy(mask_t) ================================================ FILE: external/AR/ltr/dataset/__init__.py ================================================ from .lasot import Lasot from .got10k import Got10k from .tracking_net import TrackingNet from .imagenetvid import ImagenetVID from .coco import MSCOCO from .coco_seq import MSCOCOSeq from .youtubevos import YouTubeVOS from .davis import Davis from .lvis import LVIS from .ecssd import ECSSD from .msra10k import MSRA10k from .hku_is import HKUIS from .sbd import SBD from .synthetic_video import SyntheticVideo from .synthetic_video_blend import SyntheticVideoBlend ================================================ FILE: external/AR/ltr/dataset/base_image_dataset.py ================================================ import torch.utils.data from ltr.data.image_loader import jpeg4py_loader class BaseImageDataset(torch.utils.data.Dataset): """ Base class for image datasets """ def __init__(self, name, root, image_loader=jpeg4py_loader): """ args: root - The root path to the dataset image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. """ self.name = name self.root = root self.image_loader = image_loader self.image_list = [] # Contains the list of sequences. self.class_list = [] def __len__(self): """ Returns size of the dataset returns: int - number of samples in the dataset """ return self.get_num_images() def __getitem__(self, index): """ Not to be used! Check get_frames() instead. """ return None def get_name(self): """ Name of the dataset returns: string - Name of the dataset """ raise NotImplementedError def get_num_images(self): """ Number of sequences in a dataset returns: int - number of sequences in the dataset.""" return len(self.image_list) def has_class_info(self): return False def get_class_name(self, image_id): return None def get_num_classes(self): return len(self.class_list) def get_class_list(self): return self.class_list def get_images_in_class(self, class_name): raise NotImplementedError def has_segmentation_info(self): return False def get_image_info(self, seq_id): """ Returns information about a particular image, args: seq_id - index of the image returns: Dict """ raise NotImplementedError def get_image(self, image_id, anno=None): """ Get a image args: image_id - index of image anno(None) - The annotation for the sequence (see get_sequence_info). If None, they will be loaded. returns: image - anno - dict - A dict containing meta information about the sequence, e.g. class of the target object. """ raise NotImplementedError ================================================ FILE: external/AR/ltr/dataset/base_video_dataset.py ================================================ import torch.utils.data from ltr.data.image_loader import jpeg4py_loader class BaseVideoDataset(torch.utils.data.Dataset): """ Base class for video datasets """ def __init__(self, name, root, image_loader=jpeg4py_loader): """ args: root - The root path to the dataset image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. """ self.name = name self.root = root self.image_loader = image_loader self.sequence_list = [] # Contains the list of sequences. self.class_list = [] def __len__(self): """ Returns size of the dataset returns: int - number of samples in the dataset """ return self.get_num_sequences() def __getitem__(self, index): """ Not to be used! Check get_frames() instead. """ return None def is_video_sequence(self): """ Returns whether the dataset is a video dataset or an image dataset returns: bool - True if a video dataset """ return True def is_synthetic_video_dataset(self): """ Returns whether the dataset contains real videos or synthetic returns: bool - True if a video dataset """ return False def get_name(self): """ Name of the dataset returns: string - Name of the dataset """ raise NotImplementedError def get_num_sequences(self): """ Number of sequences in a dataset returns: int - number of sequences in the dataset.""" return len(self.sequence_list) def has_class_info(self): return False def has_occlusion_info(self): return False def get_num_classes(self): return len(self.class_list) def get_class_list(self): return self.class_list def get_sequences_in_class(self, class_name): raise NotImplementedError def has_segmentation_info(self): return False def get_sequence_info(self, seq_id): """ Returns information about a particular sequences, args: seq_id - index of the sequence returns: Dict """ raise NotImplementedError def get_frames(self, seq_id, frame_ids, anno=None): """ Get a set of frames from a particular sequence args: seq_id - index of sequence frame_ids - a list of frame numbers anno(None) - The annotation for the sequence (see get_sequence_info). If None, they will be loaded. returns: list - List of frames corresponding to frame_ids list - List of dicts for each frame dict - A dict containing meta information about the sequence, e.g. class of the target object. """ raise NotImplementedError ================================================ FILE: external/AR/ltr/dataset/coco.py ================================================ import os from .base_image_dataset import BaseImageDataset from ltr.data.image_loader import jpeg4py_loader import torch from pycocotools.coco import COCO import random from collections import OrderedDict from ltr.admin.environment import env_settings class MSCOCO(BaseImageDataset): """ The COCO object detection dataset. Publication: Microsoft COCO: Common Objects in Context. Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar and C. Lawrence Zitnick ECCV, 2014 https://arxiv.org/pdf/1405.0312.pdf Download the images along with annotations from http://cocodataset.org/#download. The root folder should be organized as follows. - coco_root - annotations - instances_train2014.json - instances_train2017.json - images - train2014 - train2017 Note: You also have to install the coco pythonAPI from https://github.com/cocodataset/cocoapi. """ def __init__(self, root=None, image_loader=jpeg4py_loader, data_fraction=None, min_area=None, split="train", version="2014"): """ args: root - path to coco root folder image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. data_fraction - Fraction of dataset to be used. The complete dataset is used by default min_area - Objects with area less than min_area are filtered out. Default is 0.0 split - 'train' or 'val'. version - version of coco dataset (2014 or 2017) """ root = env_settings().coco_dir if root is None else root super().__init__('COCO', root, image_loader) self.img_pth = os.path.join(root, 'images/{}{}/'.format(split, version)) self.anno_path = os.path.join(root, 'annotations/instances_{}{}.json'.format(split, version)) self.coco_set = COCO(self.anno_path) self.cats = self.coco_set.cats self.class_list = self.get_class_list() # the parent class thing would happen in the sampler self.image_list = self._get_image_list(min_area=min_area) if data_fraction is not None: self.image_list = random.sample(self.image_list, int(len(self.image_list) * data_fraction)) self.im_per_class = self._build_im_per_class() def _get_image_list(self, min_area=None): ann_list = list(self.coco_set.anns.keys()) image_list = [a for a in ann_list if self.coco_set.anns[a]['iscrowd'] == 0] if min_area is not None: image_list = [a for a in image_list if self.coco_set.anns[a]['area'] > min_area] return image_list def get_num_classes(self): return len(self.class_list) def get_name(self): return 'coco' def has_class_info(self): return True def has_segmentation_info(self): return True def get_class_list(self): class_list = [] for cat_id in self.cats.keys(): class_list.append(self.cats[cat_id]['name']) return class_list def _build_im_per_class(self): im_per_class = {} for i, im in enumerate(self.image_list): class_name = self.cats[self.coco_set.anns[im]['category_id']]['name'] if class_name not in im_per_class: im_per_class[class_name] = [i] else: im_per_class[class_name].append(i) return im_per_class def get_images_in_class(self, class_name): return self.im_per_class[class_name] def get_image_info(self, im_id): anno = self._get_anno(im_id) bbox = torch.Tensor(anno['bbox']).view(4,) mask = torch.Tensor(self.coco_set.annToMask(anno)) valid = (bbox[2] > 0) & (bbox[3] > 0) visible = valid.clone().byte() return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible} def _get_anno(self, im_id): anno = self.coco_set.anns[self.image_list[im_id]] return anno def _get_image(self, im_id): path = self.coco_set.loadImgs([self.coco_set.anns[self.image_list[im_id]]['image_id']])[0]['file_name'] img = self.image_loader(os.path.join(self.img_pth, path)) return img def get_meta_info(self, im_id): try: cat_dict_current = self.cats[self.coco_set.anns[self.image_list[im_id]]['category_id']] object_meta = OrderedDict({'object_class_name': cat_dict_current['name'], 'motion_class': None, 'major_class': cat_dict_current['supercategory'], 'root_class': None, 'motion_adverb': None}) except: object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def get_class_name(self, im_id): cat_dict_current = self.cats[self.coco_set.anns[self.image_list[im_id]]['category_id']] return cat_dict_current['name'] def get_image(self, image_id, anno=None): frame = self._get_image(image_id) if anno is None: anno = self.get_image_info(image_id) object_meta = self.get_meta_info(image_id) return frame, anno, object_meta ================================================ FILE: external/AR/ltr/dataset/coco_seq.py ================================================ import os from .base_video_dataset import BaseVideoDataset from ltr.data.image_loader import jpeg4py_loader import torch import random from pycocotools.coco import COCO from collections import OrderedDict from ltr.admin.environment import env_settings class MSCOCOSeq(BaseVideoDataset): """ The COCO dataset. COCO is an image dataset. Thus, we treat each image as a sequence of length 1. Publication: Microsoft COCO: Common Objects in Context. Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar and C. Lawrence Zitnick ECCV, 2014 https://arxiv.org/pdf/1405.0312.pdf Download the images along with annotations from http://cocodataset.org/#download. The root folder should be organized as follows. - coco_root - annotations - instances_train2014.json - instances_train2017.json - images - train2014 - train2017 Note: You also have to install the coco pythonAPI from https://github.com/cocodataset/cocoapi. """ def __init__(self, root=None, image_loader=jpeg4py_loader, data_fraction=None, split="train", version="2014"): """ args: root - path to the coco dataset. image_loader (default_image_loader) - The function to read the images. If installed, jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else, opencv's imread is used. data_fraction (None) - Fraction of images to be used. The images are selected randomly. If None, all the images will be used split - 'train' or 'val'. version - version of coco dataset (2014 or 2017) """ root = env_settings().coco_dir if root is None else root super().__init__('COCO', root, image_loader) self.img_pth = os.path.join(root, 'images/{}{}/'.format(split, version)) self.anno_path = os.path.join(root, 'annotations/instances_{}{}.json'.format(split, version)) # Load the COCO set. self.coco_set = COCO(self.anno_path) self.cats = self.coco_set.cats self.class_list = self.get_class_list() self.sequence_list = self._get_sequence_list() if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction)) self.seq_per_class = self._build_seq_per_class() def _get_sequence_list(self): ann_list = list(self.coco_set.anns.keys()) seq_list = [a for a in ann_list if self.coco_set.anns[a]['iscrowd'] == 0] return seq_list def is_video_sequence(self): return False def get_num_classes(self): return len(self.class_list) def get_name(self): return 'coco' def has_class_info(self): return True def get_class_list(self): class_list = [] for cat_id in self.cats.keys(): class_list.append(self.cats[cat_id]['name']) return class_list def has_segmentation_info(self): return True def get_num_sequences(self): return len(self.sequence_list) def _build_seq_per_class(self): seq_per_class = {} for i, seq in enumerate(self.sequence_list): class_name = self.cats[self.coco_set.anns[seq]['category_id']]['name'] if class_name not in seq_per_class: seq_per_class[class_name] = [i] else: seq_per_class[class_name].append(i) return seq_per_class def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def get_sequence_info(self, seq_id): anno = self._get_anno(seq_id) bbox = torch.Tensor(anno['bbox']).view(1, 4) mask = torch.Tensor(self.coco_set.annToMask(anno)).unsqueeze(dim=0) valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0) visible = valid.clone().byte() return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible} def _get_anno(self, seq_id): anno = self.coco_set.anns[self.sequence_list[seq_id]] return anno def _get_frames(self, seq_id): path = self.coco_set.loadImgs([self.coco_set.anns[self.sequence_list[seq_id]]['image_id']])[0]['file_name'] img = self.image_loader(os.path.join(self.img_pth, path)) return img def get_meta_info(self, seq_id): try: cat_dict_current = self.cats[self.coco_set.anns[self.sequence_list[seq_id]]['category_id']] object_meta = OrderedDict({'object_class_name': cat_dict_current['name'], 'motion_class': None, 'major_class': cat_dict_current['supercategory'], 'root_class': None, 'motion_adverb': None}) except: object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def get_class_name(self, seq_id): cat_dict_current = self.cats[self.coco_set.anns[self.sequence_list[seq_id]]['category_id']] return cat_dict_current['name'] def get_frames(self, seq_id=None, frame_ids=None, anno=None): # COCO is an image dataset. Thus we replicate the image denoted by seq_id len(frame_ids) times, and return a # list containing these replicated images. frame = self._get_frames(seq_id) frame_list = [frame.copy() for _ in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[0, ...] for _ in frame_ids] object_meta = self.get_meta_info(seq_id) return frame_list, anno_frames, object_meta ================================================ FILE: external/AR/ltr/dataset/davis.py ================================================ from pathlib import Path from ltr.dataset.vos_base import VOSDatasetBase, VOSMeta from pytracking.evaluation import Sequence from ltr.admin.environment import env_settings from ltr.data.image_loader import jpeg4py_loader class Davis(VOSDatasetBase): """ The Davis VOS dataset Publication: A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung CVPR, 2016 http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Perazzi_A_Benchmark_Dataset_CVPR_2016_paper.pdf Download the dataset from https://davischallenge.org/davis2017/code.html """ def __init__(self, root=None, sequences=None, version='2017', split='train', multiobj=True, vis_threshold=10, image_loader=jpeg4py_loader): """ args: root - Dataset root path. If unset, it uses the path in your local.py config. sequences - List of sequence names. Limit to a subset of sequences if not None. version - '2016' or '2017 split - Any name in DAVIS/ImageSets/ multiobj - Whether the dataset will return all objects in a sequence or multiple sequences with one object in each. vis_threshold - Minimum number of pixels required to consider a target object "visible". image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. """ if version == '2017': if split in ['train', 'val']: root = env_settings().davis_dir if root is None else root elif split in ['test-dev']: root = env_settings().davis_testdev_dir if root is None else root else: raise Exception('Unknown split {}'.format(split)) else: root = env_settings().davis16_dir if root is None else root super().__init__(name='DAVIS', root=Path(root), version=version, split=split, multiobj=multiobj, vis_threshold=vis_threshold, image_loader=image_loader) dset_path = self.root self._jpeg_path = dset_path / 'JPEGImages' / '480p' self._anno_path = dset_path / 'Annotations' / '480p' meta_path = dset_path / "generated_meta.json" if meta_path.exists(): self.gmeta = VOSMeta(filename=meta_path) else: self.gmeta = VOSMeta.generate('DAVIS', self._jpeg_path, self._anno_path) self.gmeta.save(meta_path) if sequences is None: if self.split != 'all': fname = dset_path / 'ImageSets' / self.version / (self.split + '.txt') sequences = open(fname).read().splitlines() else: sequences = [p for p in sorted(self._jpeg_path.glob("*")) if p.is_dir()] self.sequence_names = sequences self._samples = [] for seq in sequences: obj_ids = self.gmeta.get_obj_ids(seq) if self.multiobj: # Multiple objects per sample self._samples.append((seq, obj_ids)) else: # One object per sample self._samples.extend([(seq, [obj_id]) for obj_id in obj_ids]) print("%s loaded." % self.get_name()) def _construct_sequence(self, sequence_info): seq_name = sequence_info['sequence'] images, gt_labels, gt_bboxes = self.get_paths_and_bboxes(sequence_info) return Sequence(name=seq_name, frames=images, dataset='DAVIS', ground_truth_rect=gt_bboxes, ground_truth_seg=gt_labels, object_ids=sequence_info['object_ids'], multiobj_mode=self.multiobj) ================================================ FILE: external/AR/ltr/dataset/ecssd.py ================================================ import os from .base_image_dataset import BaseImageDataset from ltr.data.image_loader import jpeg4py_loader, opencv_loader, imread_indexed import torch from collections import OrderedDict from ltr.admin.environment import env_settings from ltr.data.bounding_box_utils import masks_to_bboxes class ECSSD(BaseImageDataset): """ Extended Complex Scene Saliency Dataset (ECSSD) Publication: Hierarchical Image Saliency Detection on Extended CSSD Jianping Shi, Qiong Yan, Li Xu, Jiaya Jia TPAMI, 2016 https://arxiv.org/pdf/1408.5418.pdf Download the dataset from http://www.cse.cuhk.edu.hk/leojia/projects/hsaliency/dataset.html """ def __init__(self, root=None, image_loader=jpeg4py_loader, data_fraction=None, min_area=None): """ args: root - path to ECSSD root folder image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. data_fraction - Fraction of dataset to be used. The complete dataset is used by default min_area - Objects with area less than min_area are filtered out. Default is 0.0 """ root = env_settings().ecssd_dir if root is None else root super().__init__('ECSSD', root, image_loader) self.image_list = self._load_dataset(min_area=min_area) if data_fraction is not None: raise NotImplementedError def _load_dataset(self, min_area=None): images = [] for i in range(1, 1001): a = imread_indexed(os.path.join(self.root, 'ground_truth_mask', '{:04d}.png'.format(i))) if min_area is None or (a > 0).sum() > min_area: images.append(i) return images def get_name(self): return 'ecssd' def has_segmentation_info(self): return True def get_image_info(self, im_id): mask = imread_indexed(os.path.join(self.root, 'ground_truth_mask', '{:04d}.png'.format(self.image_list[im_id]))) mask = torch.Tensor(mask == 255) bbox = masks_to_bboxes(mask, fmt='t').view(4,) valid = (bbox[2] > 0) & (bbox[3] > 0) visible = valid.clone().byte() return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible} def get_meta_info(self, im_id): object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def get_image(self, image_id, anno=None): frame = self.image_loader(os.path.join(self.root, 'images', '{:04d}.jpg'.format(self.image_list[image_id]))) if anno is None: anno = self.get_image_info(image_id) object_meta = self.get_meta_info(image_id) return frame, anno, object_meta ================================================ FILE: external/AR/ltr/dataset/got10k.py ================================================ import os import os.path import numpy as np import torch import csv import pandas import random from collections import OrderedDict from .base_video_dataset import BaseVideoDataset from ltr.data.image_loader import jpeg4py_loader from ltr.admin.environment import env_settings class Got10k(BaseVideoDataset): """ GOT-10k dataset. Publication: GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild Lianghua Huang, Xin Zhao, and Kaiqi Huang arXiv:1810.11981, 2018 https://arxiv.org/pdf/1810.11981.pdf Download dataset from http://got-10k.aitestunion.com/downloads """ def __init__(self, root=None, image_loader=jpeg4py_loader, split=None, seq_ids=None, data_fraction=None): """ args: root - path to the got-10k training data. Note: This should point to the 'train' folder inside GOT-10k image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. split - 'train' or 'val'. Note: The validation split here is a subset of the official got-10k train split, not NOT the official got-10k validation split. To use the official validation split, provide that as the root folder instead. seq_ids - List containing the ids of the videos to be used for training. Note: Only one of 'split' or 'seq_ids' options can be used at the same time. data_fraction - Fraction of dataset to be used. The complete dataset is used by default """ root = env_settings().got10k_dir if root is None else root super().__init__('GOT10k', root, image_loader) # all folders inside the root self.sequence_list = self._get_sequence_list() # seq_id is the index of the folder inside the got10k root path if split is not None: if seq_ids is not None: raise ValueError('Cannot set both split_name and seq_ids.') ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') if split == 'train': file_path = os.path.join(ltr_path, 'data_specs', 'got10k_train_split.txt') elif split == 'val': file_path = os.path.join(ltr_path, 'data_specs', 'got10k_val_split.txt') elif split == 'vottrain': file_path = os.path.join(ltr_path, 'data_specs', 'got10k_vot_train_split.txt') elif split == 'votval': file_path = os.path.join(ltr_path, 'data_specs', 'got10k_vot_val_split.txt') else: raise ValueError('Unknown split name.') seq_ids = pandas.read_csv(file_path, header=None, squeeze=True, dtype=np.int64).values.tolist() elif seq_ids is None: seq_ids = list(range(0, len(self.sequence_list))) self.sequence_list = [self.sequence_list[i] for i in seq_ids] if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction)) self.sequence_meta_info = self._load_meta_info() self.seq_per_class = self._build_seq_per_class() self.class_list = list(self.seq_per_class.keys()) self.class_list.sort() def get_name(self): return 'got10k' def has_class_info(self): return True def has_occlusion_info(self): return True def _load_meta_info(self): sequence_meta_info = {s: self._read_meta(os.path.join(self.root, s)) for s in self.sequence_list} return sequence_meta_info def _read_meta(self, seq_path): try: with open(os.path.join(seq_path, 'meta_info.ini')) as f: meta_info = f.readlines() object_meta = OrderedDict({'object_class_name': meta_info[5].split(': ')[-1][:-1], 'motion_class': meta_info[6].split(': ')[-1][:-1], 'major_class': meta_info[7].split(': ')[-1][:-1], 'root_class': meta_info[8].split(': ')[-1][:-1], 'motion_adverb': meta_info[9].split(': ')[-1][:-1]}) except: object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def _build_seq_per_class(self): seq_per_class = {} for i, s in enumerate(self.sequence_list): object_class = self.sequence_meta_info[s]['object_class_name'] if object_class in seq_per_class: seq_per_class[object_class].append(i) else: seq_per_class[object_class] = [i] return seq_per_class def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def _get_sequence_list(self): with open(os.path.join(self.root, 'list.txt')) as f: dir_list = list(csv.reader(f)) dir_list = [dir_name[0] for dir_name in dir_list] return dir_list def _read_bb_anno(self, seq_path): bb_anno_file = os.path.join(seq_path, "groundtruth.txt") gt = pandas.read_csv(bb_anno_file, delimiter=',', header=None, dtype=np.float32, na_filter=False, low_memory=False).values return torch.tensor(gt) def _read_target_visible(self, seq_path): # Read full occlusion and out_of_view occlusion_file = os.path.join(seq_path, "absence.label") cover_file = os.path.join(seq_path, "cover.label") with open(occlusion_file, 'r', newline='') as f: occlusion = torch.ByteTensor([int(v[0]) for v in csv.reader(f)]) with open(cover_file, 'r', newline='') as f: cover = torch.ByteTensor([int(v[0]) for v in csv.reader(f)]) target_visible = ~occlusion & (cover>0).byte() visible_ratio = cover.float() / 8 return target_visible, visible_ratio def _get_sequence_path(self, seq_id): return os.path.join(self.root, self.sequence_list[seq_id]) def get_sequence_info(self, seq_id): seq_path = self._get_sequence_path(seq_id) bbox = self._read_bb_anno(seq_path) valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0) visible, visible_ratio = self._read_target_visible(seq_path) visible = visible & valid.byte() return {'bbox': bbox, 'valid': valid, 'visible': visible, 'visible_ratio': visible_ratio} def _get_frame_path(self, seq_path, frame_id): return os.path.join(seq_path, '{:08}.jpg'.format(frame_id+1)) # frames start from 1 def _get_frame(self, seq_path, frame_id): return self.image_loader(self._get_frame_path(seq_path, frame_id)) def get_class_name(self, seq_id): obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]] return obj_meta['object_class_name'] def get_frames(self, seq_id, frame_ids, anno=None): seq_path = self._get_sequence_path(seq_id) obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]] frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] return frame_list, anno_frames, obj_meta ================================================ FILE: external/AR/ltr/dataset/hku_is.py ================================================ import os from .base_image_dataset import BaseImageDataset from ltr.data.image_loader import jpeg4py_loader, opencv_loader, imread_indexed import torch from collections import OrderedDict from ltr.admin.environment import env_settings from ltr.data.bounding_box_utils import masks_to_bboxes class HKUIS(BaseImageDataset): """ HKU-IS salient object detection dataset Publication: Visual saliency based on multiscale deep features Guanbin Li and Yizhou Yu CVPR, 2015 https://arxiv.org/pdf/1503.08663.pdf Dowload dataset from https://sites.google.com/site/ligb86/hkuis """ def __init__(self, root=None, image_loader=jpeg4py_loader, data_fraction=None, min_area=None): """ args: root - path to HKU-IS root folder image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. data_fraction - Fraction of dataset to be used. The complete dataset is used by default min_area - Objects with area less than min_area are filtered out. Default is 0.0 """ root = env_settings().hkuis_dir if root is None else root super().__init__('HKUIS', root, image_loader) self.image_list, self.anno_list = self._load_dataset(min_area=min_area) if data_fraction is not None: raise NotImplementedError def _load_dataset(self, min_area=None): files_list = os.listdir(os.path.join(self.root, 'imgs')) image_list = [f[:-4] for f in files_list] images = [] annos = [] for f in image_list: a = imread_indexed(os.path.join(self.root, 'gt', '{}.png'.format(f))) if min_area is None or (a > 0).sum() > min_area: im = opencv_loader(os.path.join(self.root, 'imgs', '{}.png'.format(f))) images.append(im) annos.append(a) return images, annos def get_name(self): return 'hku-is' def has_segmentation_info(self): return True def get_image_info(self, im_id): mask = self.anno_list[im_id] mask = torch.Tensor(mask == 255) bbox = masks_to_bboxes(mask, fmt='t').view(4,) valid = (bbox[2] > 0) & (bbox[3] > 0) visible = valid.clone().byte() return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible} def get_meta_info(self, im_id): object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def get_image(self, image_id, anno=None): frame = self.image_list[image_id] if anno is None: anno = self.get_image_info(image_id) object_meta = self.get_meta_info(image_id) return frame, anno, object_meta ================================================ FILE: external/AR/ltr/dataset/imagenetvid.py ================================================ import os from .base_video_dataset import BaseVideoDataset from ltr.data.image_loader import default_image_loader import xml.etree.ElementTree as ET import json import torch import random from collections import OrderedDict from ltr.admin.environment import env_settings def get_target_to_image_ratio(seq): anno = torch.Tensor(seq['anno']) img_sz = torch.Tensor(seq['image_size']) return (anno[0, 2:4].prod() / (img_sz.prod())).sqrt() class ImagenetVID(BaseVideoDataset): """ Imagenet VID dataset. Publication: ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei IJCV, 2015 https://arxiv.org/pdf/1409.0575.pdf Download the dataset from http://image-net.org/ """ def __init__(self, root=None, image_loader=default_image_loader, min_length=0, max_target_area=1): """ args: root - path to the imagenet vid dataset. image_loader (default_image_loader) - The function to read the images. If installed, jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else, opencv's imread is used. min_length - Minimum allowed sequence length. max_target_area - max allowed ratio between target area and image area. Can be used to filter out targets which cover complete image. """ root = env_settings().imagenet_dir if root is None else root super().__init__(root, image_loader) cache_file = os.path.join(root, 'cache.json') if os.path.isfile(cache_file): # If available, load the pre-processed cache file containing meta-info for each sequence with open(cache_file, 'r') as f: sequence_list_dict = json.load(f) self.sequence_list = sequence_list_dict else: # Else process the imagenet annotations and generate the cache file self.sequence_list = self._process_anno(root) with open(cache_file, 'w') as f: json.dump(self.sequence_list, f) # Filter the sequences based on min_length and max_target_area in the first frame self.sequence_list = [x for x in self.sequence_list if len(x['anno']) >= min_length and get_target_to_image_ratio(x) < max_target_area] def get_name(self): return 'imagenetvid' def get_num_sequences(self): return len(self.sequence_list) def get_sequence_info(self, seq_id): bb_anno = torch.Tensor(self.sequence_list[seq_id]['anno']) valid = (bb_anno[:, 2] > 0) & (bb_anno[:, 3] > 0) visible = torch.ByteTensor(self.sequence_list[seq_id]['target_visible']) & valid.byte() return {'bbox': bb_anno, 'valid': valid, 'visible': visible} def _get_frame(self, sequence, frame_id): set_name = 'ILSVRC2015_VID_train_{:04d}'.format(sequence['set_id']) vid_name = 'ILSVRC2015_train_{:08d}'.format(sequence['vid_id']) frame_number = frame_id + sequence['start_frame'] frame_path = os.path.join(self.root, 'Data', 'VID', 'train', set_name, vid_name, '{:06d}.JPEG'.format(frame_number)) return self.image_loader(frame_path) def get_frames(self, seq_id, frame_ids, anno=None): sequence = self.sequence_list[seq_id] frame_list = [self._get_frame(sequence, f) for f in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) # Create anno dict anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] # added the class info to the meta info object_meta = OrderedDict({'object_class': sequence['class_name'], 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return frame_list, anno_frames, object_meta def _process_anno(self, root): # Builds individual tracklets base_vid_anno_path = os.path.join(root, 'Annotations', 'VID', 'train') all_sequences = [] for set in sorted(os.listdir(base_vid_anno_path)): set_id = int(set.split('_')[-1]) for vid in sorted(os.listdir(os.path.join(base_vid_anno_path, set))): vid_id = int(vid.split('_')[-1]) anno_files = sorted(os.listdir(os.path.join(base_vid_anno_path, set, vid))) frame1_anno = ET.parse(os.path.join(base_vid_anno_path, set, vid, anno_files[0])) image_size = [int(frame1_anno.find('size/width').text), int(frame1_anno.find('size/height').text)] objects = [ET.ElementTree(file=os.path.join(base_vid_anno_path, set, vid, f)).findall('object') for f in anno_files] tracklets = {} # Find all tracklets along with start frame for f_id, all_targets in enumerate(objects): for target in all_targets: tracklet_id = target.find('trackid').text if tracklet_id not in tracklets: tracklets[tracklet_id] = f_id for tracklet_id, tracklet_start in tracklets.items(): tracklet_anno = [] target_visible = [] class_name_id = None for f_id in range(tracklet_start, len(objects)): found = False for target in objects[f_id]: if target.find('trackid').text == tracklet_id: if not class_name_id: class_name_id = target.find('name').text x1 = int(target.find('bndbox/xmin').text) y1 = int(target.find('bndbox/ymin').text) x2 = int(target.find('bndbox/xmax').text) y2 = int(target.find('bndbox/ymax').text) tracklet_anno.append([x1, y1, x2 - x1, y2 - y1]) target_visible.append(target.find('occluded').text == '0') found = True break if not found: break new_sequence = {'set_id': set_id, 'vid_id': vid_id, 'class_name': class_name_id, 'start_frame': tracklet_start, 'anno': tracklet_anno, 'target_visible': target_visible, 'image_size': image_size} all_sequences.append(new_sequence) return all_sequences ================================================ FILE: external/AR/ltr/dataset/lasot.py ================================================ import os import os.path import torch import numpy as np import pandas import csv import random from collections import OrderedDict from .base_video_dataset import BaseVideoDataset from ltr.data.image_loader import jpeg4py_loader from ltr.admin.environment import env_settings class Lasot(BaseVideoDataset): """ LaSOT dataset. Publication: LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Hexin Bai, Yong Xu, Chunyuan Liao and Haibin Ling CVPR, 2019 https://arxiv.org/pdf/1809.07845.pdf Download the dataset from https://cis.temple.edu/lasot/download.html """ def __init__(self, root=None, image_loader=jpeg4py_loader, vid_ids=None, split=None, data_fraction=None): """ args: root - path to the lasot dataset. image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. vid_ids - List containing the ids of the videos (1 - 20) used for training. If vid_ids = [1, 3, 5], then the videos with subscripts -1, -3, and -5 from each class will be used for training. split - If split='train', the official train split (protocol-II) is used for training. Note: Only one of vid_ids or split option can be used at a time. data_fraction - Fraction of dataset to be used. The complete dataset is used by default """ root = env_settings().lasot_dir if root is None else root super().__init__('LaSOT', root, image_loader) # Keep a list of all classes self.class_list = [f for f in os.listdir(self.root)] self.class_to_id = {cls_name: cls_id for cls_id, cls_name in enumerate(self.class_list)} self.sequence_list = self._build_sequence_list(vid_ids, split) if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction)) self.seq_per_class = self._build_class_list() def _build_sequence_list(self, vid_ids=None, split=None): if split is not None: if vid_ids is not None: raise ValueError('Cannot set both split_name and vid_ids.') ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') if split == 'train': file_path = os.path.join(ltr_path, 'data_specs', 'lasot_train_split.txt') else: raise ValueError('Unknown split name.') sequence_list = pandas.read_csv(file_path, header=None, squeeze=True).values.tolist() elif vid_ids is not None: sequence_list = [c+'-'+str(v) for c in self.class_list for v in vid_ids] else: raise ValueError('Set either split_name or vid_ids.') return sequence_list def _build_class_list(self): seq_per_class = {} for seq_id, seq_name in enumerate(self.sequence_list): class_name = seq_name.split('-')[0] if class_name in seq_per_class: seq_per_class[class_name].append(seq_id) else: seq_per_class[class_name] = [seq_id] return seq_per_class def get_name(self): return 'lasot' def has_class_info(self): return True def has_occlusion_info(self): return True def get_num_sequences(self): return len(self.sequence_list) def get_num_classes(self): return len(self.class_list) def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def _read_bb_anno(self, seq_path): bb_anno_file = os.path.join(seq_path, "groundtruth.txt") gt = pandas.read_csv(bb_anno_file, delimiter=',', header=None, dtype=np.float32, na_filter=False, low_memory=False).values return torch.tensor(gt) def _read_target_visible(self, seq_path): # Read full occlusion and out_of_view occlusion_file = os.path.join(seq_path, "full_occlusion.txt") out_of_view_file = os.path.join(seq_path, "out_of_view.txt") with open(occlusion_file, 'r', newline='') as f: occlusion = torch.ByteTensor([int(v) for v in list(csv.reader(f))[0]]) with open(out_of_view_file, 'r') as f: out_of_view = torch.ByteTensor([int(v) for v in list(csv.reader(f))[0]]) target_visible = ~occlusion & ~out_of_view return target_visible def _get_sequence_path(self, seq_id): seq_name = self.sequence_list[seq_id] class_name = seq_name.split('-')[0] vid_id = seq_name.split('-')[1] return os.path.join(self.root, class_name, class_name + '-' + vid_id) def get_sequence_info(self, seq_id): seq_path = self._get_sequence_path(seq_id) bbox = self._read_bb_anno(seq_path) valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0) visible = self._read_target_visible(seq_path) & valid.byte() return {'bbox': bbox, 'valid': valid, 'visible': visible} def _get_frame_path(self, seq_path, frame_id): return os.path.join(seq_path, 'img', '{:08}.jpg'.format(frame_id+1)) # frames start from 1 def _get_frame(self, seq_path, frame_id): return self.image_loader(self._get_frame_path(seq_path, frame_id)) def _get_class(self, seq_path): raw_class = seq_path.split('/')[-2] return raw_class def get_class_name(self, seq_id): seq_path = self._get_sequence_path(seq_id) obj_class = self._get_class(seq_path) return obj_class def get_frames(self, seq_id, frame_ids, anno=None): seq_path = self._get_sequence_path(seq_id) obj_class = self._get_class(seq_path) frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] object_meta = OrderedDict({'object_class_name': obj_class, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return frame_list, anno_frames, object_meta ================================================ FILE: external/AR/ltr/dataset/lvis.py ================================================ import os from .base_image_dataset import BaseImageDataset from ltr.data.image_loader import jpeg4py_loader_w_failsafe import torch import random import lvis.lvis as lvis_pk from collections import OrderedDict from ltr.admin.environment import env_settings class LVIS(BaseImageDataset): """ The LVIS object detection dataset Publication: LVIS: A Dataset for Large Vocabulary Instance Segmentation Agrim Gupta, Piotr Dollár, and Ross Girshick CVPR, 2019 https://arxiv.org/pdf/1908.03195.pdf Download the images along with annotations from https://www.lvisdataset.org/dataset. The root folder should be organized as follows. - lvis_root - annotations - lvis_v0.5_train.json - lvis_v0.5_val.json - images - val2017 - train2017 Note: You also have to install the lvis Python API from https://github.com/lvis-dataset/lvis-api """ def __init__(self, root=None, image_loader=jpeg4py_loader_w_failsafe, data_fraction=None, min_area=None, split="train"): """ args: root - path to lvis root folder image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. data_fraction - Fraction of dataset to be used. The complete dataset is used by default min_area - Objects with area less than min_area are filtered out. Default is 0.0 split - 'train' or 'val'. """ root = env_settings().lvis_dir if root is None else root super().__init__('LVIS', root, image_loader) self.img_pth = os.path.join(root, 'images', f'{split}2017/') self.anno_path = os.path.join(root, 'annotations', f'lvis_v0.5_{split}.json') # Load the LVIS set. self.lvis_set = lvis_pk.LVIS(self.anno_path) self.cats = self.lvis_set.cats self.class_list = self.get_class_list() # the parent class thing would happen in the sampler self.image_list = self._get_image_list(min_area=min_area) if data_fraction is not None: self.image_list = random.sample(self.image_list, int(len(self.image_list) * data_fraction)) self.im_per_class = self._build_im_per_class() def _get_image_list(self, min_area=None): im_list = list(self.lvis_set.anns.keys()) # No 'iscrowd' information in LVIS if min_area is not None: im_list = [s for s in im_list if self.lvis_set.anns[s]['area'] > min_area] return im_list def get_num_classes(self): return len(self.class_list) def get_name(self): return 'lvis' def has_class_info(self): return True def get_class_list(self): class_list = [] for cat_id in self.cats.keys(): class_list.append(self.cats[cat_id]['name']) return class_list def has_segmentation_info(self): return True def _build_im_per_class(self): im_per_class = {} for i, im in enumerate(self.image_list): class_name = self.cats[self.lvis_set.anns[im]['category_id']]['name'] if class_name not in im_per_class: im_per_class[class_name] = [i] else: im_per_class[class_name].append(i) return im_per_class def get_images_in_class(self, class_name): return self.im_per_class[class_name] def get_image_info(self, im_id): anno = self._get_anno(im_id) bbox = torch.Tensor(anno['bbox']).view(4,) mask = torch.Tensor(self.lvis_set.ann_to_mask(anno)) valid = (bbox[2] > 0) & (bbox[3] > 0) visible = valid.clone().byte() return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible} def _get_anno(self, im_id): anno = self.lvis_set.anns[self.image_list[im_id]] return anno def _get_image(self, im_id): path = self.lvis_set.load_imgs([self.lvis_set.anns[self.image_list[im_id]]['image_id']])[0]['file_name'] img = self.image_loader(os.path.join(self.img_pth, path)) return img def get_meta_info(self, im_id): try: cat_dict_current = self.cats[self.lvis_set.anns[self.image_list[im_id]]['category_id']] object_meta = OrderedDict({'object_class_name': cat_dict_current['name'], 'motion_class': None, 'major_class': None, # No 'supercategory' information available in LVIS 'root_class': None, 'motion_adverb': None}) except: object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def get_class_name(self, im_id): cat_dict_current = self.cats[self.lvis_set.anns[self.image_list[im_id]]['category_id']] return cat_dict_current['name'] def get_image(self, image_id, anno=None): frame = self._get_image(image_id) if anno is None: anno = self.get_image_info(image_id) object_meta = self.get_meta_info(image_id) return frame, anno, object_meta ================================================ FILE: external/AR/ltr/dataset/msra10k.py ================================================ import os from .base_image_dataset import BaseImageDataset from ltr.data.image_loader import jpeg4py_loader, imread_indexed import torch from collections import OrderedDict from ltr.admin.environment import env_settings from ltr.data.bounding_box_utils import masks_to_bboxes class MSRA10k(BaseImageDataset): """ MSRA10k salient object detection dataset Publication: Global contrast based salient region detection Ming-Ming Cheng, Niloy J. Mitra, Xiaolei Huang, Philip H. S. Torr, and Shi-Min Hu TPAMI, 2015 https://mmcheng.net/mftp/Papers/SaliencyTPAMI.pdf Download dataset from https://mmcheng.net/msra10k/ """ def __init__(self, root=None, image_loader=jpeg4py_loader, data_fraction=None, min_area=None): """ args: root - path to MSRA10k root folder image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. data_fraction - Fraction of dataset to be used. The complete dataset is used by default min_area - Objects with area less than min_area are filtered out. Default is 0.0 """ root = env_settings().msra10k_dir if root is None else root super().__init__('MSRA10k', root, image_loader) self.image_list = self._load_dataset(min_area=min_area) if data_fraction is not None: raise NotImplementedError def _load_dataset(self, min_area=None): files_list = os.listdir(os.path.join(self.root, 'Imgs')) image_list = [f[:-4] for f in files_list if f[-3:] == 'jpg'] images = [] for f in image_list: a = imread_indexed(os.path.join(self.root, 'Imgs', '{}.png'.format(f))) if min_area is None or (a > 0).sum() > min_area: images.append(f) return images def get_name(self): return 'msra10k' def has_segmentation_info(self): return True def get_image_info(self, im_id): mask = imread_indexed(os.path.join(self.root, 'Imgs', '{}.png'.format(self.image_list[im_id]))) mask = torch.Tensor(mask == 255) bbox = masks_to_bboxes(mask, fmt='t').view(4,) valid = (bbox[2] > 0) & (bbox[3] > 0) visible = valid.clone().byte() return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible} def get_meta_info(self, im_id): object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def get_image(self, image_id, anno=None): frame = self.image_loader(os.path.join(self.root, 'Imgs', '{}.jpg'.format(self.image_list[image_id]))) if anno is None: anno = self.get_image_info(image_id) object_meta = self.get_meta_info(image_id) return frame, anno, object_meta ================================================ FILE: external/AR/ltr/dataset/sbd.py ================================================ from .base_image_dataset import BaseImageDataset from ltr.data.image_loader import jpeg4py_loader_w_failsafe import torch from collections import OrderedDict import os from scipy.io import loadmat from ltr.data.bounding_box_utils import masks_to_bboxes from ltr.admin.environment import env_settings class SBD(BaseImageDataset): """ Semantic Boundaries Dataset and Benchmark (SBD) Publication: Semantic contours from inverse detectors Bharath Hariharan, Pablo Arbelaez, Lubomir Bourdev, Subhransu Maji and Jitendra Malik ICCV, 2011 http://home.bharathh.info/pubs/pdfs/BharathICCV2011.pdf Download dataset from: http://home.bharathh.info/pubs/codes/SBD/download.html """ def __init__(self, root=None, image_loader=jpeg4py_loader_w_failsafe, data_fraction=None, split="train"): """ args: root - path to SBD root folder image_loader - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. data_fraction - Fraction of dataset to be used. The complete dataset is used by default split - dataset split ("train", "train_noval", "val") """ root = env_settings().sbd_dir if root is None else root super().__init__('SBD', root, image_loader) assert split in ["train", "train_noval", "val"] self.root = root self.image_path_list, self.anno_file_list = self._load_dataset(split) # Load mat fine anno_list = [loadmat(a) for a in self.anno_file_list] self.image_list = self._construct_image_list(anno_list) if data_fraction is not None: raise NotImplementedError def _load_dataset(self, split): split_f = os.path.join(self.root, split.rstrip('\n') + '.txt') with open(os.path.join(split_f), "r") as f: file_names = [x.strip() for x in f.readlines()] image_list = [os.path.join(self.root, 'img', x + ".jpg") for x in file_names] anno_list = [os.path.join(self.root, 'inst', x + ".mat") for x in file_names] assert (len(image_list) == len(anno_list)) return image_list, anno_list def _get_mask_from_mat(self, mat): return torch.tensor(mat['GTinst'][0]['Segmentation'][0]) def _construct_image_list(self, anno_list): image_list = [] for im_id, a in enumerate(anno_list): mask = self._get_mask_from_mat(a) for instance_id in range(1, mask.max().item() + 1): image_list.append((im_id, instance_id)) return image_list def get_name(self): return 'sbd' def has_segmentation_info(self): return True def get_image_info(self, im_id): image_id, instance_id = self.image_list[im_id] anno_mat = loadmat(self.anno_file_list[image_id]) mask = self._get_mask_from_mat(anno_mat) mask = (mask == instance_id).float() bbox = masks_to_bboxes(mask, fmt='t') valid = (bbox[2] > 0) & (bbox[3] > 0) visible = valid.clone().byte() return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible} def _get_image(self, im_id): image_id, _ = self.image_list[im_id] img = self.image_loader(self.image_path_list[image_id]) return img def get_meta_info(self, im_id): object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def get_image(self, image_id, anno=None): image = self._get_image(image_id) if anno is None: anno = self.get_image_info(image_id) object_meta = self.get_meta_info(image_id) return image, anno, object_meta ================================================ FILE: external/AR/ltr/dataset/synthetic_video.py ================================================ from collections import OrderedDict from .base_video_dataset import BaseVideoDataset from ltr.data.bounding_box_utils import masks_to_bboxes class SyntheticVideo(BaseVideoDataset): """ Create a synthetic video dataset from an image dataset by applying a random transformation to images. """ def __init__(self, base_image_dataset, transform=None): """ args: base_image_dataset - Image dataset used for generating synthetic videos transform - Set of transforms to be applied to the images to generate synthetic video. """ super().__init__(base_image_dataset.get_name() + '_syn_vid', base_image_dataset.root, base_image_dataset.image_loader) self.base_image_dataset = base_image_dataset self.transform = transform def get_name(self): return self.name def is_video_sequence(self): return False def has_class_info(self): return self.base_image_dataset.has_class_info() def has_occlusion_info(self): return True def get_num_sequences(self): return self.base_image_dataset.get_num_images() def get_num_classes(self): return len(self.class_list) def get_sequences_in_class(self, class_name): return self.get_images_in_class[class_name] def get_sequence_info(self, seq_id): image_info = self.base_image_dataset.get_image_info(seq_id) image_info = {k: v.unsqueeze(0) for k, v in image_info.items()} return image_info def get_class_name(self, seq_id): return self.base_image_dataset.get_class_name(seq_id) def get_frames(self, seq_id, frame_ids, anno=None): frame, anno, object_meta = self.base_image_dataset.get_image(seq_id, anno=anno) frame_list = [frame.copy() for _ in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[0].clone() for f_id in frame_ids] if self.transform is not None: if 'mask' in anno_frames.keys(): frame_list, anno_frames['bbox'], anno_frames['mask'] = self.transform(image=frame_list, bbox=anno_frames['bbox'], mask=anno_frames['mask'], joint=False) anno_frames['bbox'] = [masks_to_bboxes(m, fmt='t') for m in anno_frames['mask']] else: frame_list, anno_frames['bbox'] = self.transform(image=frame_list, bbox=anno_frames['bbox'], joint=False) object_meta = OrderedDict({'object_class_name': self.get_class_name(seq_id), 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return frame_list, anno_frames, object_meta ================================================ FILE: external/AR/ltr/dataset/synthetic_video_blend.py ================================================ from collections import OrderedDict from .base_video_dataset import BaseVideoDataset from ltr.data.bounding_box_utils import masks_to_bboxes import random import torch class SyntheticVideoBlend(BaseVideoDataset): """ Create a synthetic video by applying random transformations to an object (foreground) and pasting it in a background image. Currently, the foreground object is pasted at random locations in different frames. """ def __init__(self, foreground_image_dataset, background_image_dataset, foreground_transform=None, background_transform=None): """ args: foreground_image_dataset - A segmentation dataset from which foreground objects are cropped using the segmentation mask background_image_dataset - Dataset used to sample background image for the synthetic video foreground_transform - Random transformations to be applied to the foreground object in every frame background_transform - Random transformations to be applied to the background image in every frame """ assert foreground_image_dataset.has_segmentation_info() super().__init__(foreground_image_dataset.get_name() + '_syn_vid_blend', foreground_image_dataset.root, foreground_image_dataset.image_loader) self.foreground_image_dataset = foreground_image_dataset self.background_image_dataset = background_image_dataset self.foreground_transform = foreground_transform self.background_transform = background_transform def get_name(self): return self.name def is_video_sequence(self): return False def has_class_info(self): return self.foreground_image_dataset.has_class_info() def has_occlusion_info(self): return True def get_num_sequences(self): return self.foreground_image_dataset.get_num_images() def get_num_classes(self): return len(self.class_list) def get_sequences_in_class(self, class_name): return self.get_images_in_class[class_name] def get_sequence_info(self, seq_id): image_info = self.foreground_image_dataset.get_image_info(seq_id) image_info = {k: v.unsqueeze(0) for k, v in image_info.items()} return image_info def get_class_name(self, seq_id): return self.foreground_image_dataset.get_class_name(seq_id) def _paste_target(self, fg_image, fg_box, fg_mask, bg_image, paste_loc): fg_mask = fg_mask.view(fg_mask.shape[0], fg_mask.shape[1], 1) fg_box = fg_box.long().tolist() x1 = int(paste_loc[0] - 0.5 * fg_box[2]) x2 = x1 + fg_box[2] y1 = int(paste_loc[1] - 0.5 * fg_box[3]) y2 = y1 + fg_box[3] x1_pad = max(-x1, 0) y1_pad = max(-y1, 0) x2_pad = max(x2 - bg_image.shape[1], 0) y2_pad = max(y2 - bg_image.shape[0], 0) bg_mask = torch.zeros((bg_image.shape[0], bg_image.shape[1], 1), dtype=fg_mask.dtype, device=fg_mask.device) if x1_pad >= fg_mask.shape[1] or x2_pad >= fg_mask.shape[1] or y1_pad >= fg_mask.shape[0] or y2_pad >= \ fg_mask.shape[0]: return bg_image, bg_mask.squeeze(-1) fg_mask_patch = fg_mask[fg_box[1] + y1_pad:fg_box[1] + fg_box[3] - y2_pad, fg_box[0] + x1_pad:fg_box[0] + fg_box[2] - x2_pad, :] fg_image_patch = fg_image[fg_box[1] + y1_pad:fg_box[1] + fg_box[3] - y2_pad, fg_box[0] + x1_pad:fg_box[0] + fg_box[2] - x2_pad, :] bg_image[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad, :] = \ bg_image[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad, :] * (1 - fg_mask_patch.numpy()) \ + fg_mask_patch.numpy() * fg_image_patch bg_mask[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad, :] = fg_mask_patch return bg_image, bg_mask.squeeze(-1) def get_frames(self, seq_id, frame_ids, anno=None): # Handle foreground fg_frame, fg_anno, fg_object_meta = self.foreground_image_dataset.get_image(seq_id, anno=anno) fg_frame_list = [fg_frame.copy() for _ in frame_ids] fg_anno_frames = {} for key, value in fg_anno.items(): fg_anno_frames[key] = [value[0].clone() for f_id in frame_ids] if self.foreground_transform is not None: fg_frame_list, fg_anno_frames['bbox'], fg_anno_frames['mask'] = self.foreground_transform( image=fg_frame_list, bbox=fg_anno_frames['bbox'], mask=fg_anno_frames['mask'], joint=False) # Sample a random background bg_seq_id = random.randint(0, self.background_image_dataset.get_num_images() - 1) bg_frame, bg_anno, _ = self.background_image_dataset.get_image(bg_seq_id) bg_frame_list = [bg_frame.copy() for _ in frame_ids] bg_anno_frames = {} for key, value in bg_anno.items(): # Note: Since we get bg anno from image dataset, it does not has frame dimension bg_anno_frames[key] = [value.clone() for f_id in frame_ids] if self.background_transform is not None: if 'mask' in bg_anno_frames.keys(): bg_frame_list, bg_anno_frames['bbox'], bg_anno_frames['mask'] = self.background_transform( image=bg_frame_list, bbox=bg_anno_frames['bbox'], mask=bg_anno_frames['mask'], joint=False) else: bg_frame_list, bg_anno_frames['bbox'] = self.background_transform( image=bg_frame_list, bbox=bg_anno_frames['bbox'], joint=False) for i in range(len(frame_ids)): # To be safe, get target bb for the mask bbox = masks_to_bboxes(fg_anno_frames['mask'][i], fmt='t') loc_y = random.randint(0, bg_frame_list[i].shape[0] - 1) loc_x = random.randint(0, bg_frame_list[i].shape[1] - 1) paste_loc = (loc_x, loc_y) fg_frame_list[i], fg_anno_frames['mask'][i] = self._paste_target(fg_frame_list[i], bbox, fg_anno_frames['mask'][i], bg_frame_list[i], paste_loc) fg_anno_frames['bbox'][i] = masks_to_bboxes(fg_anno_frames['mask'][i], fmt='t') object_meta = OrderedDict({'object_class_name': self.get_class_name(seq_id), 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return fg_frame_list, fg_anno_frames, object_meta ================================================ FILE: external/AR/ltr/dataset/tracking_net.py ================================================ import torch import os import os.path import numpy as np import pandas import random from collections import OrderedDict from ltr.data.image_loader import jpeg4py_loader from .base_video_dataset import BaseVideoDataset from ltr.admin.environment import env_settings def list_sequences(root, set_ids): """ Lists all the videos in the input set_ids. Returns a list of tuples (set_id, video_name) args: root: Root directory to TrackingNet set_ids: Sets (0-11) which are to be used returns: list - list of tuples (set_id, video_name) containing the set_id and video_name for each sequence """ sequence_list = [] for s in set_ids: anno_dir = os.path.join(root, "TRAIN_" + str(s), "anno") sequences_cur_set = [(s, os.path.splitext(f)[0]) for f in os.listdir(anno_dir) if f.endswith('.txt')] sequence_list += sequences_cur_set return sequence_list class TrackingNet(BaseVideoDataset): """ TrackingNet dataset. Publication: TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild. Matthias Mueller,Adel Bibi, Silvio Giancola, Salman Al-Subaihi and Bernard Ghanem ECCV, 2018 https://ivul.kaust.edu.sa/Documents/Publications/2018/TrackingNet%20A%20Large%20Scale%20Dataset%20and%20Benchmark%20for%20Object%20Tracking%20in%20the%20Wild.pdf Download the dataset using the toolkit https://github.com/SilvioGiancola/TrackingNet-devkit. """ def __init__(self, root=None, image_loader=jpeg4py_loader, set_ids=None, data_fraction=None): """ args: root - The path to the TrackingNet folder, containing the training sets. image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. set_ids (None) - List containing the ids of the TrackingNet sets to be used for training. If None, all the sets (0 - 11) will be used. data_fraction - Fraction of dataset to be used. The complete dataset is used by default """ root = env_settings().trackingnet_dir if root is None else root super().__init__('TrackingNet', root, image_loader) if set_ids is None: set_ids = [i for i in range(12)] self.set_ids = set_ids # Keep a list of all videos. Sequence list is a list of tuples (set_id, video_name) containing the set_id and # video_name for each sequence self.sequence_list = list_sequences(self.root, self.set_ids) if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list) * data_fraction)) self.seq_to_class_map, self.seq_per_class = self._load_class_info() # we do not have the class_lists for the tracking net self.class_list = list(self.seq_per_class.keys()) self.class_list.sort() def _load_class_info(self): ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') class_map_path = os.path.join(ltr_path, 'data_specs', 'trackingnet_classmap.txt') with open(class_map_path, 'r') as f: seq_to_class_map = {seq_class.split('\t')[0]: seq_class.rstrip().split('\t')[1] for seq_class in f} seq_per_class = {} for i, seq in enumerate(self.sequence_list): class_name = seq_to_class_map[seq[1]] if class_name not in seq_per_class: seq_per_class[class_name] = [i] else: seq_per_class[class_name].append(i) return seq_to_class_map, seq_per_class def get_name(self): return 'trackingnet' def has_class_info(self): return True def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def _read_bb_anno(self, seq_id): set_id = self.sequence_list[seq_id][0] vid_name = self.sequence_list[seq_id][1] bb_anno_file = os.path.join(self.root, "TRAIN_" + str(set_id), "anno", vid_name + ".txt") gt = pandas.read_csv(bb_anno_file, delimiter=',', header=None, dtype=np.float32, na_filter=False, low_memory=False).values return torch.tensor(gt) def get_sequence_info(self, seq_id): bbox = self._read_bb_anno(seq_id) valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0) visible = valid.clone().byte() return {'bbox': bbox, 'valid': valid, 'visible': visible} def _get_frame(self, seq_id, frame_id): set_id = self.sequence_list[seq_id][0] vid_name = self.sequence_list[seq_id][1] frame_path = os.path.join(self.root, "TRAIN_" + str(set_id), "frames", vid_name, str(frame_id) + ".jpg") return self.image_loader(frame_path) def _get_class(self, seq_id): seq_name = self.sequence_list[seq_id][1] return self.seq_to_class_map[seq_name] def get_class_name(self, seq_id): obj_class = self._get_class(seq_id) return obj_class def get_frames(self, seq_id, frame_ids, anno=None): frame_list = [self._get_frame(seq_id, f) for f in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] obj_class = self._get_class(seq_id) object_meta = OrderedDict({'object_class_name': obj_class, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return frame_list, anno_frames, object_meta ================================================ FILE: external/AR/ltr/dataset/vos_base.py ================================================ import torch from pathlib import Path from collections import OrderedDict, defaultdict import json import numpy as np import os from .base_video_dataset import BaseVideoDataset from ltr.data.image_loader import jpeg4py_loader, imread_indexed from ltr.data.bounding_box_utils import masks_to_bboxes class VOSMeta: def __init__(self, data=None, filename=None): if filename is not None: self.load(filename) elif data is not None: self._data = data else: raise ValueError("Must set either data or filename parameter") def save(self, gen_meta: Path): gen_meta.parent.mkdir(exist_ok=True, parents=True) json.dump(self._data, open(gen_meta, "w")) def load(self, gen_meta: Path): if not gen_meta.exists(): print("Generated metadata file %s is not found." % gen_meta) print("Find and run VOSMeta.generate() to create it.") raise FileNotFoundError(gen_meta) self._data = json.load(open(gen_meta), object_pairs_hook=OrderedDict) @classmethod def generate(cls, dset_name: str, dset_images_path: Path, dset_annos_path: Path): """ Count the annotation mask pixels per object, per frame, in all sequences in a dataset :param dset_name: Dataset name, for printing the progress bar. :param dset_annos_path: Path to annotations directory, containing sequence directories, with annotation frames in them. :return: Dataset meta dict: {'sequence0': { 'shape': (height, width) 'obj_sizes': # Object pixels per frame {'frame0': {'object0': px_count, 'object1': px_count, ...}, 'frame1': {'object0': px_count, 'object1': px_count, ...}, ... }, 'bboxes': # Bounding boxes per frame {'frame0': {'object0': bbox, 'object1': bbox, ...}, 'frame1': {'object0': bbox, 'object1': bbox, ...}, ... }, ... } """ assert(dset_annos_path.exists()) dset_meta = OrderedDict() sequences = [p.stem for p in sorted(dset_annos_path.glob("*")) if p.is_dir()] try: from tqdm import tqdm except: def tqdm(x, *args, **kwargs): return x for seq in tqdm(sequences, desc=dset_name, unit="seq"): obj_sizes2 = defaultdict(OrderedDict) bboxes = defaultdict(OrderedDict) shape = None frame_names = [file.stem for file in sorted((dset_images_path / seq).glob("*.jpg"))] anno_paths = list(sorted((dset_annos_path / seq).glob("*.png"))) # Extract information from the given label frames for path in anno_paths: f_id = path.stem # Count label-pixels per frame labels = imread_indexed(path) # labels = np.array(Image.open(path)) obj_ids, obj_sizes = np.unique(labels, return_counts=True) obj_ids = [str(oid) for oid in obj_ids] obj_sizes = obj_sizes.tolist() if '0' in obj_ids: # Remove background id obj_ids = obj_ids[1:] obj_sizes = obj_sizes[1:] obj_sizes2[f_id] = OrderedDict(zip(obj_ids, obj_sizes)) # Generate per-label bounding boxes for obj_id in obj_ids: bboxes[f_id][obj_id] = cls._mask_to_bbox(labels == int(obj_id)) if shape is None: shape = labels.shape[:2] # Format result dset_meta[seq] = dict(shape=shape, obj_sizes=obj_sizes2, bboxes=bboxes, frame_names=frame_names) return VOSMeta(dset_meta) @staticmethod def _mask_to_bbox(mask: np.ndarray): mask = mask.astype(int) xs = mask.sum(axis=-2).nonzero()[0].tolist() ys = mask.sum(axis=-1).nonzero()[0].tolist() if len(ys) > 0 and len(xs) > 0: x, y, w, h = xs[0], ys[0], xs[-1] - xs[0], ys[-1] - ys[0] else: x, y, w, h = 0, 0, 0, 0 return [x, y, w, h] @staticmethod def _transpose_nested_dict(d): """ Permute a 2-level nested dict such that the inner and outer keys swap places. """ d2 = defaultdict(OrderedDict) for key1, inner in d.items(): for key2, value in inner.items(): d2[key2][key1] = value return d2 def select_split(self, dataset_name, split): ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') sequences = set([s.strip() for s in open(os.path.join(ltr_path, 'data_specs', dataset_name + '_' + split + '.txt')).readlines()]) all_sequences = set(self._data.keys()) to_remove = all_sequences.difference(sequences) for seq_name in to_remove: self._data.pop(seq_name) def get_sequence_names(self): return list(self._data.keys()) def get_shape(self, seq_name): """ Sequence image shape (h,w) """ h, w = self._data[seq_name]['shape'] return h, w def get_obj_ids(self, seq_name): """ All objects in the sequence """ return list(self.get_obj_sizes_per_object(seq_name).keys()) def get_frame_names(self, seq_name): """ All filename stems of the frames in the sequence """ return self._data[seq_name]['frame_names'] def enable_all_frames(self, dset_images_path): """ For YouTubeVOS: Update the frame names with (jpeg) files from the _all_frames set :param dset_images_path: /path/to/train_all_frames/JPEGImages (or valid or test) :param seq: Sequence name :return: """ # Try load the cached index idx_file = dset_images_path.parent / "frame_names.json" if idx_file.exists(): print('Loading cached frame names from %s' % idx_file) all_frame_names = json.load(open(idx_file)) else: # Cache the data to the user's home directory (guaranteed to be writable) all_frame_names = dict() user_idx_file = Path.home() / (dset_images_path.parent.stem + "_frame_names.json") print('Indexing YouTubeVOS "all_frames" frame names to %s' % user_idx_file) for seq in self._data: all_frame_names[seq] = [file.stem for file in sorted((dset_images_path / seq).glob("*.jpg"))] json.dump(all_frame_names, open(user_idx_file, "w")) print('Done. Move %s to %s to load faster next time.' % (user_idx_file, idx_file)) for seq, frame_names in all_frame_names.items(): self._data[seq]['frame_names'] = frame_names def get_aspect_ratio(self, seq_name): """ Sequence aspect ratio """ h, w = self._data[seq_name]['shape'] return w / h def get_obj_sizes_per_frame(self, seq_name): """ Get object pixel counts, grouped by frame names """ return self._data[seq_name]['obj_sizes'] def get_bboxes_per_frame(self, seq_name): """ Object bounding boxes, grouped by frame names """ return self._data[seq_name]['bboxes'] def get_obj_sizes_per_object(self, seq_name): """ Object pixel counts, grouped by object """ return self._transpose_nested_dict(self.get_obj_sizes_per_frame(seq_name)) def get_bboxes_per_object(self, seq_name): """ Object bounding boxes, grouped by object """ return self._transpose_nested_dict(self.get_bboxes_per_frame(seq_name)) @staticmethod def generate_datasets_meta(src, dst=Path("~/vosdataset_meta").expanduser()): VOSMeta.generate("SyntheticCoco", src / "JPEGImages", src / "Annotations").save(src / "generated_meta.json") class VOSDatasetBase(BaseVideoDataset): """ Generic VOS dataset reader base class, for both DAVIS and YouTubeVOS """ def __init__(self, name: str, root: Path, version=None, split='train', multiobj=True, vis_threshold=10, image_loader=jpeg4py_loader): """ :param root: Dataset root path, eg /path/to/DAVIS or /path/to/YouTubeVOS/ Note: YouTubeVOS 2018 and 2019 are expected to be in /path/to/YouTubeVOS/2018 and /path/to/YouTubeVOS/2019, respectively :param name: 'DAVIS' or 'YouTubeVOS' (case sensitive) :param version: DAVIS: '2016', '2017, YouTubeVOS: '2018' or '2019' :param split: DAVIS: Any name in DAVIS/ImageSets/, YouTubeVOS: 'test', 'train', 'valid' or 'jjtrain', 'jjvalid' :param multiobj: Whether the dataset will return all objects in a sequence or multiple sequences with one object in each. :param vis_threshold: Minimum number of pixels required to consider a target object "visible". :param image_loader: Image loader. """ assert root.exists() and root.is_dir() super().__init__(name, root, image_loader) self.version = version self.split = split self.vis_threshold = vis_threshold self.multiobj = multiobj def _load_image(self, path): im = self.image_loader(str(path)) assert im is not None im = np.atleast_3d(im) return im @staticmethod def _load_anno(path): if not path.exists(): return None # im = np.atleast_3d(np.array(Image.open(path))) im = imread_indexed(path) return im def get_num_sequences(self): return len(self._samples) def get_sequence_info(self, sample_id): """ Get sample meta data. :param sample_id: Sample to query. :return: dict of metadata: sequence: Sequence name frame_shape: (height, width) of the images frame_names: List of frame filename stems in the sequence object_ids: Id numbers of all objects occurring in the sequence obj_sizes: Matrix shape=(frames, object) of the number of pixels for each object in each frame Coordinates in this matrix relate to the frame_names and object_ids visible: Boolean matrix of the same shape as obj_sizes. Entries with more pixels than self.visible_threshold are True. """ m = self.gmeta seq_name, obj_ids = self._samples[sample_id] f_names = m.get_frame_names(seq_name) # All frames f2i = {f: i for i, f in enumerate(f_names)} # Frame name to matrix index o2i = {o: i for i, o in enumerate(obj_ids)} # Object id to matrix index # Get a matrix of object sizes: shape=(frames, objects) obj_sizes = torch.zeros((len(f_names), len(obj_ids)), dtype=torch.int) sizes_per_object = m.get_obj_sizes_per_object(seq_name) for obj_id in obj_ids: frames = sizes_per_object[obj_id] oid = o2i[obj_id] for f, sz in frames.items(): obj_sizes[f2i[f], oid] = sz visible = (obj_sizes > self.vis_threshold).byte() return dict(sequence=seq_name, frame_shape=m.get_shape(seq_name), frame_names=f_names, object_ids=obj_ids, object_sizes=obj_sizes, visible=visible, valid=visible) def get_paths_and_bboxes(self, sequence_info): seq_name = sequence_info['sequence'] annos_root = self._anno_path / seq_name images_root = self._jpeg_path / seq_name frame_names = sequence_info['frame_names'] f2i = {f: i for i, f in enumerate(frame_names)} images = [str(images_root / (f + ".jpg")) for f in frame_names] # Find the frames where ground truth is available and # get the bounding boxes and segmentation labels of those frames all_bboxes = self.gmeta.get_bboxes_per_frame(seq_name) gt_labels = [str(annos_root / (f + ".png")) if f in all_bboxes.keys() else None for f in frame_names] gt_bboxes = OrderedDict() for obj_id in sequence_info['object_ids']: gt_bboxes[obj_id] = np.array([all_bboxes.get(frame, {}).get(obj_id, [-1, -1, -1, -1]) for frame in frame_names]) return images, gt_labels, gt_bboxes def _construct_sequence(self, sequence_info): raise NotImplementedError def get_sequence_list(self): if len(self.sequence_list) > 0: return self.sequence_list self.sequence_list = [self._construct_sequence(self.get_sequence_info(i)) for i in range(len(self._samples))] return self.sequence_list def __len__(self): return len(self._samples) def _get_image_path(self, meta, frame_id): return self._jpeg_path / meta['sequence'] / (meta['frame_names'][frame_id] + ".jpg") def _get_anno_path(self, meta, frame_id): return self._anno_path / meta['sequence'] / (meta['frame_names'][frame_id] + ".png") def get_frames(self, sample_id, frame_ids, anno=None): """ Fetch frames with the given ids. :param sample_id: Sample to get. :param frame_ids: List of frame indices in the sequence belonging to the sample_id :return: dict of metadata and data: sequence: Sequence name images: List of images. No entries may be None labels: List of label/mask images. Entries may be None if the data is missing bboxes: List of bounding boxes. Entries may be None if the data is missing """ seq_name, obj_ids = self._samples[sample_id] meta = self.get_sequence_info(sample_id) if anno is None else anno frame_names = meta['frame_names'] images = [self._load_image(self._jpeg_path / seq_name / (frame_names[f] + ".jpg")) for f in frame_ids] labels = [self._load_anno(self._anno_path / seq_name / (frame_names[f] + ".png")) for f in frame_ids] # Generate bounding boxes for the requested objects bboxes = [] for lb in labels: lb = torch.from_numpy(lb.squeeze()) frame_bbs = {} for obj_id in obj_ids: bbox = masks_to_bboxes(lb == int(obj_id), fmt='t') if bbox[3] == 0 or bbox[2] == 0: print("!") frame_bbs[obj_id] = bbox bboxes.append(frame_bbs) # Insert empty bboxes for missing object ids for bbox in bboxes: for obj_id in obj_ids: if obj_id not in bbox: bbox[obj_id] = torch.zeros(4, dtype=torch.float32) # Remap to object id 1, if requested - for training if not self.multiobj: assert len(obj_ids) == 1 obj_id = obj_ids[0] labels = [torch.Tensor(lb == int(obj_id)) for lb in labels] bboxes = [bbox[obj_id] for bbox in bboxes] else: labels = [torch.Tensor(lb) for lb in labels] object_meta = {key: meta[key] for key in ['sequence', 'frame_shape', 'frame_names', 'object_ids']} anno_frames = dict(bbox=bboxes, mask=labels) for key in ['object_sizes', 'visible', 'valid']: value = meta[key] anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] return images, anno_frames, object_meta def get_name(self): return "%s/%s/%s" % (self.name, self.version, self.split) def has_class_info(self): return False def has_occlusion_info(self): return True def get_num_classes(self): return 0 def get_class_list(self): return [] def get_sequences_in_class(self, class_name): raise [] def has_segmentation_info(self): return True ================================================ FILE: external/AR/ltr/dataset/youtubevos.py ================================================ from pathlib import Path import os from ltr.dataset.vos_base import VOSDatasetBase, VOSMeta from pytracking.evaluation import Sequence import json from ltr.admin.environment import env_settings from ltr.data.image_loader import jpeg4py_loader class YouTubeVOSMeta: """ Thin wrapper for YouTubeVOS meta data meta.json { "videos": { "": { "objects": { "": { "category": "", "frames": [ "", "", ] } } } } } # is the same as the pixel values of object in annotated segmentation PNG files. # is the 5-digit index of frame in video, and not necessary to start from 0. """ def __init__(self, dset_split_path): self._data = json.load(open(dset_split_path / 'meta.json'))['videos'] def sequences(self): return list(self._data.keys()) def seq_frames(self, seq_name): """ All filename stems of the frames in the sequence """ frames = set() for obj_id in self.object_ids(seq_name): for f in self.object_frames(seq_name, obj_id): frames.add(f) return list(sorted(frames)) def object_ids(self, seq_name): """ All objects in the sequence """ return list(self._data[seq_name]['objects'].keys()) def object_category(self, seq_name, obj_id): return self._data[seq_name]['objects'][str(obj_id)]['category'] def object_frames(self, seq_name, obj_id): return self._data[seq_name]['objects'][str(obj_id)]['frames'] def object_first_frame(self, seq_name, obj_id): return self.object_frames(seq_name, obj_id)[0] class YouTubeVOS(VOSDatasetBase): """ YoutubeVOS video object segmentation dataset. Publication: YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark Ning Xu, Linjie Yang, Yuchen Fan, Dingcheng Yue, Yuchen Liang, Jianchao Yang, and Thomas Huang ECCV, 2018 https://arxiv.org/pdf/1809.03327.pdf Download dataset from: https://youtube-vos.org/dataset/ """ def __init__(self, root=None, version='2019', split='train', cleanup=None, all_frames=False, sequences=None, multiobj=True, vis_threshold=10, image_loader=jpeg4py_loader): """ args: root - Dataset root path. If unset, it uses the path in your local.py config. version - '2018' or '2019' split - 'test', 'train', 'valid', or 'jjtrain', 'jjvalid'. 'jjvalid' corresponds to a custom validation dataset consisting of 300 videos randomly sampled from the train set. 'jjtrain' contains the remaining videos used for training. cleanup - List of actions to take to to clean up known problems in the dataset. 'aspects': remove frames with weird aspect ratios, 'starts': fix up start frames from original meta data all_frames - Whether to use an "all_frames" split. sequences - List of sequence names. Limit to a subset of sequences if not None. multiobj - Whether the dataset will return all objects in a sequence or multiple sequences with one object in each. vis_threshold - Minimum number of pixels required to consider a target object "visible". image_loader - Image loader. """ root = env_settings().youtubevos_dir if root is None else root super().__init__(name="YouTubeVOS", root=Path(root), version=version, split=split, multiobj=multiobj, vis_threshold=vis_threshold, image_loader=image_loader) split_folder = self.split if self.split.startswith("jj"): split_folder = "train" dset_path = self.root / self.version / split_folder self._anno_path = dset_path / 'Annotations' if all_frames: self._jpeg_path = self.root / self.version / (split_folder + "_all_frames") / 'JPEGImages' else: self._jpeg_path = dset_path / 'JPEGImages' self.meta = YouTubeVOSMeta(dset_path) meta_path = dset_path / "generated_meta.json" if meta_path.exists(): self.gmeta = VOSMeta(filename=meta_path) else: self.gmeta = VOSMeta.generate('YouTubeVOS', self._jpeg_path, self._anno_path) self.gmeta.save(meta_path) if all_frames: self.gmeta.enable_all_frames(self._jpeg_path) if self.split not in ['train', 'valid', 'test']: self.gmeta.select_split('youtubevos', self.split) if sequences is None: sequences = self.gmeta.get_sequence_names() to_remove = set() cleanup = {} if cleanup is None else set(cleanup) if 'aspect' in cleanup: # Remove sequences with unusual aspect ratios for seq_name in sequences: a = self.gmeta.get_aspect_ratio(seq_name) if a < 1.45 or a > 1.9: to_remove.add(seq_name) if 'starts' in cleanup: # Fix incorrect start frames for some objects found with ytvos_start_frames_test() bad_start_frames = [("0e27472bea", '2', ['00055', '00060'], '00065'), ("5937b08d69", '4', ['00000'], '00005'), ("5e1ce354fd", '5', ['00010', '00015'], '00020'), ("7053e4f41e", '2', ['00000', '00005', '00010', '00015'], '00020'), ("720e3fa04c", '2', ['00050'], '00055'), ("c73c8e747f", '2', ['00035'], '00040')] for seq_name, obj_id, bad_frames, good_frame in bad_start_frames: # bad_frames is from meta.json included with the dataset # good_frame is from the generated meta - and the first actual frame where the object was seen. if seq_name in self.meta._data: frames = self.meta.object_frames(seq_name, obj_id) for f in bad_frames: frames.remove(f) assert frames[0] == good_frame sequences = [seq for seq in sequences if seq not in to_remove] self.sequence_names = sequences self._samples = [] for seq in sequences: obj_ids = self.meta.object_ids(seq) if self.multiobj: # Multiple objects per sample self._samples.append((seq, obj_ids)) else: # One object per sample self._samples.extend([(seq, [obj_id]) for obj_id in obj_ids]) print("%s loaded." % self.get_name()) if len(to_remove) > 0: print(" %d sequences were removed, (%d remaining)." % (len(to_remove), len(sequences))) def _construct_sequence(self, sequence_info): seq_name = sequence_info['sequence'] frame_names = sequence_info['frame_names'] fname_to_fid = {f: i for i, f in enumerate(frame_names)} images, gt_segs, gt_bboxes = self.get_paths_and_bboxes(sequence_info) init_data = dict() for obj_id in sequence_info['object_ids']: if obj_id == '0': print("!") f_name = self.meta.object_first_frame(seq_name, obj_id) f_id = fname_to_fid[f_name] if f_id not in init_data: init_data[f_id] = {'object_ids': [obj_id], 'bbox': {obj_id: gt_bboxes[obj_id][f_id,:]}, 'mask': os.path.join(os.path.dirname(gt_segs[f_id]), (f_name + ".png"))} assert init_data[f_id]['mask'] in gt_segs # If this fails, some file is missing else: init_data[f_id]['object_ids'].append(obj_id) init_data[f_id]['bbox'][obj_id] = gt_bboxes[obj_id][f_id,:] return Sequence(name=seq_name, frames=images, dataset='YouTubeVOS', ground_truth_rect=gt_bboxes, init_data=init_data, ground_truth_seg=gt_segs, object_ids=sequence_info['object_ids'], multiobj_mode=self.multiobj) ================================================ FILE: external/AR/ltr/external/PreciseRoIPooling/.gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class .vim-template* # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ .pytest_cache/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ ================================================ FILE: external/AR/ltr/external/PreciseRoIPooling/LICENSE ================================================ MIT License Copyright (c) 2018 Jiayuan Mao Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: external/AR/ltr/external/PreciseRoIPooling/README.md ================================================ # PreciseRoIPooling This repo implements the **Precise RoI Pooling** (PrRoI Pooling), proposed in the paper **Acquisition of Localization Confidence for Accurate Object Detection** published at ECCV 2018 (Oral Presentation). **Acquisition of Localization Confidence for Accurate Object Detection** _Borui Jiang*, Ruixuan Luo*, Jiayuan Mao*, Tete Xiao, Yuning Jiang_ (* indicates equal contribution.) https://arxiv.org/abs/1807.11590 ## Brief In short, Precise RoI Pooling is an integration-based (bilinear interpolation) average pooling method for RoI Pooling. It avoids any quantization and has a continuous gradient on bounding box coordinates. It is: - different from the original RoI Pooling proposed in [Fast R-CNN](https://arxiv.org/abs/1504.08083). PrRoI Pooling uses average pooling instead of max pooling for each bin and has a continuous gradient on bounding box coordinates. That is, one can take the derivatives of some loss function w.r.t the coordinates of each RoI and optimize the RoI coordinates. - different from the RoI Align proposed in [Mask R-CNN](https://arxiv.org/abs/1703.06870). PrRoI Pooling uses a full integration-based average pooling instead of sampling a constant number of points. This makes the gradient w.r.t. the coordinates continuous. For a better illustration, we illustrate RoI Pooling, RoI Align and PrRoI Pooing in the following figure. More details including the gradient computation can be found in our paper.
## Implementation PrRoI Pooling was originally implemented by [Tete Xiao](http://tetexiao.com/) based on MegBrain, an (internal) deep learning framework built by Megvii Inc. It was later adapted into open-source deep learning frameworks. Currently, we only support PyTorch. Unfortunately, we don't have any specific plan for the adaptation into other frameworks such as TensorFlow, but any contributions (pull requests) will be more than welcome. ## Usage (PyTorch 1.0) In the directory `pytorch/`, we provide a PyTorch-based implementation of PrRoI Pooling. It requires PyTorch 1.0+ and only supports CUDA (CPU mode is not implemented). Since we use PyTorch JIT for cxx/cuda code compilation, to use the module in your code, simply do: ``` from prroi_pool import PrRoIPool2D avg_pool = PrRoIPool2D(window_height, window_width, spatial_scale) roi_features = avg_pool(features, rois) # for those who want to use the "functional" from prroi_pool.functional import prroi_pool2d roi_features = prroi_pool2d(features, rois, window_height, window_width, spatial_scale) ``` ## Usage (PyTorch 0.4) **!!! Please first checkout to the branch pytorch0.4.** In the directory `pytorch/`, we provide a PyTorch-based implementation of PrRoI Pooling. It requires PyTorch 0.4 and only supports CUDA (CPU mode is not implemented). To use the PrRoI Pooling module, first goto `pytorch/prroi_pool` and execute `./travis.sh` to compile the essential components (you may need `nvcc` for this step). To use the module in your code, simply do: ``` from prroi_pool import PrRoIPool2D avg_pool = PrRoIPool2D(window_height, window_width, spatial_scale) roi_features = avg_pool(features, rois) # for those who want to use the "functional" from prroi_pool.functional import prroi_pool2d roi_features = prroi_pool2d(features, rois, window_height, window_width, spatial_scale) ``` Here, - RoI is an `m * 5` float tensor of format `(batch_index, x0, y0, x1, y1)`, following the convention in the original Caffe implementation of RoI Pooling, although in some frameworks the batch indices are provided by an integer tensor. - `spatial_scale` is multiplied to the RoIs. For example, if your feature maps are down-sampled by a factor of 16 (w.r.t. the input image), you should use a spatial scale of `1/16`. - The coordinates for RoI follows the [L, R) convension. That is, `(0, 0, 4, 4)` denotes a box of size `4x4`. ================================================ FILE: external/AR/ltr/external/PreciseRoIPooling/pytorch/prroi_pool/.gitignore ================================================ *.o /_prroi_pooling ================================================ FILE: external/AR/ltr/external/PreciseRoIPooling/pytorch/prroi_pool/__init__.py ================================================ #! /usr/bin/env python3 # -*- coding: utf-8 -*- # File : __init__.py # Author : Jiayuan Mao, Tete Xiao # Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com # Date : 07/13/2018 # # This file is part of PreciseRoIPooling. # Distributed under terms of the MIT license. # Copyright (c) 2017 Megvii Technology Limited. from .prroi_pool import * ================================================ FILE: external/AR/ltr/external/PreciseRoIPooling/pytorch/prroi_pool/functional.py ================================================ #! /usr/bin/env python3 # -*- coding: utf-8 -*- # File : functional.py # Author : Jiayuan Mao, Tete Xiao # Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com # Date : 07/13/2018 # # This file is part of PreciseRoIPooling. # Distributed under terms of the MIT license. # Copyright (c) 2017 Megvii Technology Limited. import torch import torch.autograd as ag __all__ = ['prroi_pool2d'] _prroi_pooling = None def _import_prroi_pooling(): global _prroi_pooling if _prroi_pooling is None: try: from os.path import join as pjoin, dirname from torch.utils.cpp_extension import load as load_extension root_dir = pjoin(dirname(__file__), 'src') _prroi_pooling = load_extension( '_prroi_pooling', [pjoin(root_dir, 'prroi_pooling_gpu.c'), pjoin(root_dir, 'prroi_pooling_gpu_impl.cu')], verbose=True ) except ImportError: raise ImportError('Can not compile Precise RoI Pooling library.') return _prroi_pooling class PrRoIPool2DFunction(ag.Function): @staticmethod def forward(ctx, features, rois, pooled_height, pooled_width, spatial_scale): _prroi_pooling = _import_prroi_pooling() assert 'FloatTensor' in features.type() and 'FloatTensor' in rois.type(), \ 'Precise RoI Pooling only takes float input, got {} for features and {} for rois.'.format(features.type(), rois.type()) pooled_height = int(pooled_height) pooled_width = int(pooled_width) spatial_scale = float(spatial_scale) features = features.contiguous() rois = rois.contiguous() params = (pooled_height, pooled_width, spatial_scale) if features.is_cuda: output = _prroi_pooling.prroi_pooling_forward_cuda(features, rois, *params) ctx.params = params # everything here is contiguous. ctx.save_for_backward(features, rois, output) else: raise NotImplementedError('Precise RoI Pooling only supports GPU (cuda) implememtations.') return output @staticmethod def backward(ctx, grad_output): _prroi_pooling = _import_prroi_pooling() features, rois, output = ctx.saved_tensors grad_input = grad_coor = None if features.requires_grad: grad_output = grad_output.contiguous() grad_input = _prroi_pooling.prroi_pooling_backward_cuda(features, rois, output, grad_output, *ctx.params) if rois.requires_grad: grad_output = grad_output.contiguous() grad_coor = _prroi_pooling.prroi_pooling_coor_backward_cuda(features, rois, output, grad_output, *ctx.params) return grad_input, grad_coor, None, None, None prroi_pool2d = PrRoIPool2DFunction.apply ================================================ FILE: external/AR/ltr/external/PreciseRoIPooling/pytorch/prroi_pool/prroi_pool.py ================================================ #! /usr/bin/env python3 # -*- coding: utf-8 -*- # File : prroi_pool.py # Author : Jiayuan Mao, Tete Xiao # Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com # Date : 07/13/2018 # # This file is part of PreciseRoIPooling. # Distributed under terms of the MIT license. # Copyright (c) 2017 Megvii Technology Limited. import torch.nn as nn from .functional import prroi_pool2d __all__ = ['PrRoIPool2D'] class PrRoIPool2D(nn.Module): def __init__(self, pooled_height, pooled_width, spatial_scale): super().__init__() self.pooled_height = int(pooled_height) self.pooled_width = int(pooled_width) self.spatial_scale = float(spatial_scale) def forward(self, features, rois): return prroi_pool2d(features, rois, self.pooled_height, self.pooled_width, self.spatial_scale) def extra_repr(self): return 'kernel_size=({pooled_height}, {pooled_width}), spatial_scale={spatial_scale}'.format(**self.__dict__) ================================================ FILE: external/AR/ltr/external/PreciseRoIPooling/pytorch/prroi_pool/src/prroi_pooling_gpu.c ================================================ /* * File : prroi_pooling_gpu.c * Author : Jiayuan Mao, Tete Xiao * Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com * Date : 07/13/2018 * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ #include #include #include #include #include #include "prroi_pooling_gpu_impl.cuh" at::Tensor prroi_pooling_forward_cuda(const at::Tensor &features, const at::Tensor &rois, int pooled_height, int pooled_width, float spatial_scale) { int nr_rois = rois.size(0); int nr_channels = features.size(1); int height = features.size(2); int width = features.size(3); int top_count = nr_rois * nr_channels * pooled_height * pooled_width; auto output = at::zeros({nr_rois, nr_channels, pooled_height, pooled_width}, features.options()); if (output.numel() == 0) { THCudaCheck(cudaGetLastError()); return output; } cudaStream_t stream = at::cuda::getCurrentCUDAStream(); PrRoIPoolingForwardGpu( stream, features.data(), rois.data(), output.data(), nr_channels, height, width, pooled_height, pooled_width, spatial_scale, top_count ); THCudaCheck(cudaGetLastError()); return output; } at::Tensor prroi_pooling_backward_cuda( const at::Tensor &features, const at::Tensor &rois, const at::Tensor &output, const at::Tensor &output_diff, int pooled_height, int pooled_width, float spatial_scale) { auto features_diff = at::zeros_like(features); int nr_rois = rois.size(0); int batch_size = features.size(0); int nr_channels = features.size(1); int height = features.size(2); int width = features.size(3); int top_count = nr_rois * nr_channels * pooled_height * pooled_width; int bottom_count = batch_size * nr_channels * height * width; if (output.numel() == 0) { THCudaCheck(cudaGetLastError()); return features_diff; } cudaStream_t stream = at::cuda::getCurrentCUDAStream(); PrRoIPoolingBackwardGpu( stream, features.data(), rois.data(), output.data(), output_diff.data(), features_diff.data(), nr_channels, height, width, pooled_height, pooled_width, spatial_scale, top_count, bottom_count ); THCudaCheck(cudaGetLastError()); return features_diff; } at::Tensor prroi_pooling_coor_backward_cuda( const at::Tensor &features, const at::Tensor &rois, const at::Tensor &output, const at::Tensor &output_diff, int pooled_height, int pooled_width, float spatial_scale) { auto coor_diff = at::zeros_like(rois); int nr_rois = rois.size(0); int nr_channels = features.size(1); int height = features.size(2); int width = features.size(3); int top_count = nr_rois * nr_channels * pooled_height * pooled_width; int bottom_count = nr_rois * 5; if (output.numel() == 0) { THCudaCheck(cudaGetLastError()); return coor_diff; } cudaStream_t stream = at::cuda::getCurrentCUDAStream(); PrRoIPoolingCoorBackwardGpu( stream, features.data(), rois.data(), output.data(), output_diff.data(), coor_diff.data(), nr_channels, height, width, pooled_height, pooled_width, spatial_scale, top_count, bottom_count ); THCudaCheck(cudaGetLastError()); return coor_diff; } PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("prroi_pooling_forward_cuda", &prroi_pooling_forward_cuda, "PRRoIPooling_forward"); m.def("prroi_pooling_backward_cuda", &prroi_pooling_backward_cuda, "PRRoIPooling_backward"); m.def("prroi_pooling_coor_backward_cuda", &prroi_pooling_coor_backward_cuda, "PRRoIPooling_backward_coor"); } ================================================ FILE: external/AR/ltr/external/PreciseRoIPooling/pytorch/prroi_pool/src/prroi_pooling_gpu.h ================================================ /* * File : prroi_pooling_gpu.h * Author : Jiayuan Mao, Tete Xiao * Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com * Date : 07/13/2018 * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ int prroi_pooling_forward_cuda(THCudaTensor *features, THCudaTensor *rois, THCudaTensor *output, int pooled_height, int pooled_width, float spatial_scale); int prroi_pooling_backward_cuda( THCudaTensor *features, THCudaTensor *rois, THCudaTensor *output, THCudaTensor *output_diff, THCudaTensor *features_diff, int pooled_height, int pooled_width, float spatial_scale ); int prroi_pooling_coor_backward_cuda( THCudaTensor *features, THCudaTensor *rois, THCudaTensor *output, THCudaTensor *output_diff, THCudaTensor *features_diff, int pooled_height, int pooled_width, float spatial_scal ); ================================================ FILE: external/AR/ltr/external/PreciseRoIPooling/pytorch/prroi_pool/src/prroi_pooling_gpu_impl.cu ================================================ /* * File : prroi_pooling_gpu_impl.cu * Author : Tete Xiao, Jiayuan Mao * Email : jasonhsiao97@gmail.com * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ #include "prroi_pooling_gpu_impl.cuh" #include #include #define CUDA_KERNEL_LOOP(i, n) \ for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ i < (n); \ i += blockDim.x * gridDim.x) #define CUDA_POST_KERNEL_CHECK \ do { \ cudaError_t err = cudaGetLastError(); \ if (cudaSuccess != err) { \ fprintf(stderr, "cudaCheckError() failed : %s\n", cudaGetErrorString(err)); \ exit(-1); \ } \ } while(0) #define CUDA_NUM_THREADS 512 namespace { static int CUDA_NUM_BLOCKS(const int N) { return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; } __device__ static float PrRoIPoolingGetData(F_DEVPTR_IN data, const int h, const int w, const int height, const int width) { bool overflow = (h < 0) || (w < 0) || (h >= height) || (w >= width); float retVal = overflow ? 0.0f : data[h * width + w]; return retVal; } __device__ static float PrRoIPoolingGetCoeff(float dh, float dw){ dw = dw > 0 ? dw : -dw; dh = dh > 0 ? dh : -dh; return (1.0f - dh) * (1.0f - dw); } __device__ static float PrRoIPoolingSingleCoorIntegral(float s, float t, float c1, float c2) { return 0.5 * (t * t - s * s) * c2 + (t - 0.5 * t * t - s + 0.5 * s * s) * c1; } __device__ static float PrRoIPoolingInterpolation(F_DEVPTR_IN data, const float h, const float w, const int height, const int width){ float retVal = 0.0f; int h1 = floorf(h); int w1 = floorf(w); retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h)+1; w1 = floorf(w); retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h); w1 = floorf(w)+1; retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h)+1; w1 = floorf(w)+1; retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); return retVal; } __device__ static float PrRoIPoolingMatCalculation(F_DEVPTR_IN this_data, const int s_h, const int s_w, const int e_h, const int e_w, const float y0, const float x0, const float y1, const float x1, const int h0, const int w0) { float alpha, beta, lim_alpha, lim_beta, tmp; float sum_out = 0; alpha = x0 - float(s_w); beta = y0 - float(s_h); lim_alpha = x1 - float(s_w); lim_beta = y1 - float(s_h); tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, s_h, s_w, h0, w0) * tmp; alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, s_h, e_w, h0, w0) * tmp; alpha = x0 - float(s_w); beta = float(e_h) - y1; lim_alpha = x1 - float(s_w); lim_beta = float(e_h) - y0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, e_h, s_w, h0, w0) * tmp; alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, e_h, e_w, h0, w0) * tmp; return sum_out; } __device__ static void PrRoIPoolingDistributeDiff(F_DEVPTR_OUT diff, const float top_diff, const int h, const int w, const int height, const int width, const float coeff) { bool overflow = (h < 0) || (w < 0) || (h >= height) || (w >= width); if (!overflow) atomicAdd(diff + h * width + w, top_diff * coeff); } __device__ static void PrRoIPoolingMatDistributeDiff(F_DEVPTR_OUT diff, const float top_diff, const int s_h, const int s_w, const int e_h, const int e_w, const float y0, const float x0, const float y1, const float x1, const int h0, const int w0) { float alpha, beta, lim_alpha, lim_beta, tmp; alpha = x0 - float(s_w); beta = y0 - float(s_h); lim_alpha = x1 - float(s_w); lim_beta = y1 - float(s_h); tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, s_h, s_w, h0, w0, tmp); alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, s_h, e_w, h0, w0, tmp); alpha = x0 - float(s_w); beta = float(e_h) - y1; lim_alpha = x1 - float(s_w); lim_beta = float(e_h) - y0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, e_h, s_w, h0, w0, tmp); alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, e_h, e_w, h0, w0, tmp); } __global__ void PrRoIPoolingForward( const int nthreads, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, ((float)0.0)); float roi_height = max(roi_end_h - roi_start_h, ((float)0.0)); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_data = bottom_data + (roi_batch_ind * channels + c) * height * width; float *this_out = top_data + index; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); if (win_size == 0) { *this_out = 0; return; } float sum_out = 0; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); for (int w_iter = s_w; w_iter < e_w; ++w_iter) for (int h_iter = s_h; h_iter < e_h; ++h_iter) sum_out += PrRoIPoolingMatCalculation(this_data, h_iter, w_iter, h_iter + 1, w_iter + 1, max(win_start_h, float(h_iter)), max(win_start_w, float(w_iter)), min(win_end_h, float(h_iter) + 1.0), min(win_end_w, float(w_iter + 1.0)), height, width); *this_out = sum_out / win_size; } } __global__ void PrRoIPoolingBackward( const int nthreads, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, (float)0); float roi_height = max(roi_end_h - roi_start_h, (float)0); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_out_grad = top_diff + index; float *this_data_grad = bottom_diff + (roi_batch_ind * channels + c) * height * width; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); float sum_out = win_size == float(0) ? float(0) : *this_out_grad / win_size; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); for (int w_iter = s_w; w_iter < e_w; ++w_iter) for (int h_iter = s_h; h_iter < e_h; ++h_iter) PrRoIPoolingMatDistributeDiff(this_data_grad, sum_out, h_iter, w_iter, h_iter + 1, w_iter + 1, max(win_start_h, float(h_iter)), max(win_start_w, float(w_iter)), min(win_end_h, float(h_iter) + 1.0), min(win_end_w, float(w_iter + 1.0)), height, width); } } __global__ void PrRoIPoolingCoorBackward( const int nthreads, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, (float)0); float roi_height = max(roi_end_h - roi_start_h, (float)0); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_out_grad = top_diff + index; const float *this_bottom_data = bottom_data + (roi_batch_ind * channels + c) * height * width; const float *this_top_data = top_data + index; float *this_data_grad = bottom_diff + n * 5; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); float sum_out = win_size == float(0) ? float(0) : *this_out_grad / win_size; // WARNING: to be discussed if (sum_out == 0) return; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); float g_x1_y = 0, g_x2_y = 0, g_x_y1 = 0, g_x_y2 = 0; for (int h_iter = s_h; h_iter < e_h; ++h_iter) { g_x1_y += PrRoIPoolingSingleCoorIntegral(max(win_start_h, float(h_iter)) - h_iter, min(win_end_h, float(h_iter + 1)) - h_iter, PrRoIPoolingInterpolation(this_bottom_data, h_iter, win_start_w, height, width), PrRoIPoolingInterpolation(this_bottom_data, h_iter + 1, win_start_w, height, width)); g_x2_y += PrRoIPoolingSingleCoorIntegral(max(win_start_h, float(h_iter)) - h_iter, min(win_end_h, float(h_iter + 1)) - h_iter, PrRoIPoolingInterpolation(this_bottom_data, h_iter, win_end_w, height, width), PrRoIPoolingInterpolation(this_bottom_data, h_iter + 1, win_end_w, height, width)); } for (int w_iter = s_w; w_iter < e_w; ++w_iter) { g_x_y1 += PrRoIPoolingSingleCoorIntegral(max(win_start_w, float(w_iter)) - w_iter, min(win_end_w, float(w_iter + 1)) - w_iter, PrRoIPoolingInterpolation(this_bottom_data, win_start_h, w_iter, height, width), PrRoIPoolingInterpolation(this_bottom_data, win_start_h, w_iter + 1, height, width)); g_x_y2 += PrRoIPoolingSingleCoorIntegral(max(win_start_w, float(w_iter)) - w_iter, min(win_end_w, float(w_iter + 1)) - w_iter, PrRoIPoolingInterpolation(this_bottom_data, win_end_h, w_iter, height, width), PrRoIPoolingInterpolation(this_bottom_data, win_end_h, w_iter + 1, height, width)); } float partial_x1 = -g_x1_y + (win_end_h - win_start_h) * (*this_top_data); float partial_y1 = -g_x_y1 + (win_end_w - win_start_w) * (*this_top_data); float partial_x2 = g_x2_y - (win_end_h - win_start_h) * (*this_top_data); float partial_y2 = g_x_y2 - (win_end_w - win_start_w) * (*this_top_data); partial_x1 = partial_x1 / win_size * spatial_scale; partial_x2 = partial_x2 / win_size * spatial_scale; partial_y1 = partial_y1 / win_size * spatial_scale; partial_y2 = partial_y2 / win_size * spatial_scale; // (b, x1, y1, x2, y2) this_data_grad[0] = 0; atomicAdd(this_data_grad + 1, (partial_x1 * (1.0 - float(pw) / pooled_width) + partial_x2 * (1.0 - float(pw + 1) / pooled_width)) * (*this_out_grad)); atomicAdd(this_data_grad + 2, (partial_y1 * (1.0 - float(ph) / pooled_height) + partial_y2 * (1.0 - float(ph + 1) / pooled_height)) * (*this_out_grad)); atomicAdd(this_data_grad + 3, (partial_x2 * float(pw + 1) / pooled_width + partial_x1 * float(pw) / pooled_width) * (*this_out_grad)); atomicAdd(this_data_grad + 4, (partial_y2 * float(ph + 1) / pooled_height + partial_y1 * float(ph) / pooled_height) * (*this_out_grad)); } } } /* !anonymous namespace */ #ifdef __cplusplus extern "C" { #endif void PrRoIPoolingForwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count) { PrRoIPoolingForward<<>>( top_count, bottom_data, bottom_rois, top_data, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } void PrRoIPoolingBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count) { cudaMemsetAsync(bottom_diff, 0, sizeof(float) * bottom_count, stream); PrRoIPoolingBackward<<>>( top_count, bottom_rois, top_diff, bottom_diff, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } void PrRoIPoolingCoorBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count) { cudaMemsetAsync(bottom_diff, 0, sizeof(float) * bottom_count, stream); PrRoIPoolingCoorBackward<<>>( top_count, bottom_data, bottom_rois, top_data, top_diff, bottom_diff, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } } /* !extern "C" */ ================================================ FILE: external/AR/ltr/external/PreciseRoIPooling/pytorch/prroi_pool/src/prroi_pooling_gpu_impl.cuh ================================================ /* * File : prroi_pooling_gpu_impl.cuh * Author : Tete Xiao, Jiayuan Mao * Email : jasonhsiao97@gmail.com * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ #ifndef PRROI_POOLING_GPU_IMPL_CUH #define PRROI_POOLING_GPU_IMPL_CUH #ifdef __cplusplus extern "C" { #endif #define F_DEVPTR_IN const float * #define F_DEVPTR_OUT float * void PrRoIPoolingForwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count); void PrRoIPoolingBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count); void PrRoIPoolingCoorBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count); #ifdef __cplusplus } /* !extern "C" */ #endif #endif /* !PRROI_POOLING_GPU_IMPL_CUH */ ================================================ FILE: external/AR/ltr/external/PreciseRoIPooling/pytorch/tests/test_prroi_pooling2d.py ================================================ # -*- coding: utf-8 -*- # File : test_prroi_pooling2d.py # Author : Jiayuan Mao # Email : maojiayuan@gmail.com # Date : 18/02/2018 # # This file is part of Jacinle. import unittest import torch import torch.nn as nn import torch.nn.functional as F from jactorch.utils.unittest import TorchTestCase from prroi_pool import PrRoIPool2D class TestPrRoIPool2D(TorchTestCase): def test_forward(self): pool = PrRoIPool2D(7, 7, spatial_scale=0.5) features = torch.rand((4, 16, 24, 32)).cuda() rois = torch.tensor([ [0, 0, 0, 14, 14], [1, 14, 14, 28, 28], ]).float().cuda() out = pool(features, rois) out_gold = F.avg_pool2d(features, kernel_size=2, stride=1) self.assertTensorClose(out, torch.stack(( out_gold[0, :, :7, :7], out_gold[1, :, 7:14, 7:14], ), dim=0)) def test_backward_shapeonly(self): pool = PrRoIPool2D(2, 2, spatial_scale=0.5) features = torch.rand((4, 2, 24, 32)).cuda() rois = torch.tensor([ [0, 0, 0, 4, 4], [1, 14, 14, 18, 18], ]).float().cuda() features.requires_grad = rois.requires_grad = True out = pool(features, rois) loss = out.sum() loss.backward() self.assertTupleEqual(features.size(), features.grad.size()) self.assertTupleEqual(rois.size(), rois.grad.size()) if __name__ == '__main__': unittest.main() ================================================ FILE: external/AR/ltr/external/PreciseRoIPooling/src/prroi_pooling_gpu_impl.cu ================================================ /* * File : prroi_pooling_gpu_impl.cu * Author : Tete Xiao, Jiayuan Mao * Email : jasonhsiao97@gmail.com * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ #include "prroi_pooling_gpu_impl.cuh" #include #include #define CUDA_KERNEL_LOOP(i, n) \ for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ i < (n); \ i += blockDim.x * gridDim.x) #define CUDA_POST_KERNEL_CHECK \ do { \ cudaError_t err = cudaGetLastError(); \ if (cudaSuccess != err) { \ fprintf(stderr, "cudaCheckError() failed : %s\n", cudaGetErrorString(err)); \ exit(-1); \ } \ } while(0) #define CUDA_NUM_THREADS 512 namespace { static int CUDA_NUM_BLOCKS(const int N) { return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; } __device__ static float PrRoIPoolingGetData(F_DEVPTR_IN data, const int h, const int w, const int height, const int width) { bool overflow = (h < 0) || (w < 0) || (h >= height) || (w >= width); float retVal = overflow ? 0.0f : data[h * width + w]; return retVal; } __device__ static float PrRoIPoolingGetCoeff(float dh, float dw){ dw = dw > 0 ? dw : -dw; dh = dh > 0 ? dh : -dh; return (1.0f - dh) * (1.0f - dw); } __device__ static float PrRoIPoolingSingleCoorIntegral(float s, float t, float c1, float c2) { return 0.5 * (t * t - s * s) * c2 + (t - 0.5 * t * t - s + 0.5 * s * s) * c1; } __device__ static float PrRoIPoolingInterpolation(F_DEVPTR_IN data, const float h, const float w, const int height, const int width){ float retVal = 0.0f; int h1 = floorf(h); int w1 = floorf(w); retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h)+1; w1 = floorf(w); retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h); w1 = floorf(w)+1; retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h)+1; w1 = floorf(w)+1; retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); return retVal; } __device__ static float PrRoIPoolingMatCalculation(F_DEVPTR_IN this_data, const int s_h, const int s_w, const int e_h, const int e_w, const float y0, const float x0, const float y1, const float x1, const int h0, const int w0) { float alpha, beta, lim_alpha, lim_beta, tmp; float sum_out = 0; alpha = x0 - float(s_w); beta = y0 - float(s_h); lim_alpha = x1 - float(s_w); lim_beta = y1 - float(s_h); tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, s_h, s_w, h0, w0) * tmp; alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, s_h, e_w, h0, w0) * tmp; alpha = x0 - float(s_w); beta = float(e_h) - y1; lim_alpha = x1 - float(s_w); lim_beta = float(e_h) - y0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, e_h, s_w, h0, w0) * tmp; alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, e_h, e_w, h0, w0) * tmp; return sum_out; } __device__ static void PrRoIPoolingDistributeDiff(F_DEVPTR_OUT diff, const float top_diff, const int h, const int w, const int height, const int width, const float coeff) { bool overflow = (h < 0) || (w < 0) || (h >= height) || (w >= width); if (!overflow) atomicAdd(diff + h * width + w, top_diff * coeff); } __device__ static void PrRoIPoolingMatDistributeDiff(F_DEVPTR_OUT diff, const float top_diff, const int s_h, const int s_w, const int e_h, const int e_w, const float y0, const float x0, const float y1, const float x1, const int h0, const int w0) { float alpha, beta, lim_alpha, lim_beta, tmp; alpha = x0 - float(s_w); beta = y0 - float(s_h); lim_alpha = x1 - float(s_w); lim_beta = y1 - float(s_h); tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, s_h, s_w, h0, w0, tmp); alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, s_h, e_w, h0, w0, tmp); alpha = x0 - float(s_w); beta = float(e_h) - y1; lim_alpha = x1 - float(s_w); lim_beta = float(e_h) - y0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, e_h, s_w, h0, w0, tmp); alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, e_h, e_w, h0, w0, tmp); } __global__ void PrRoIPoolingForward( const int nthreads, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, ((float)0.0)); float roi_height = max(roi_end_h - roi_start_h, ((float)0.0)); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_data = bottom_data + (roi_batch_ind * channels + c) * height * width; float *this_out = top_data + index; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); if (win_size == 0) { *this_out = 0; return; } float sum_out = 0; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); for (int w_iter = s_w; w_iter < e_w; ++w_iter) for (int h_iter = s_h; h_iter < e_h; ++h_iter) sum_out += PrRoIPoolingMatCalculation(this_data, h_iter, w_iter, h_iter + 1, w_iter + 1, max(win_start_h, float(h_iter)), max(win_start_w, float(w_iter)), min(win_end_h, float(h_iter) + 1.0), min(win_end_w, float(w_iter + 1.0)), height, width); *this_out = sum_out / win_size; } } __global__ void PrRoIPoolingBackward( const int nthreads, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, (float)0); float roi_height = max(roi_end_h - roi_start_h, (float)0); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_out_grad = top_diff + index; float *this_data_grad = bottom_diff + (roi_batch_ind * channels + c) * height * width; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); float sum_out = win_size == float(0) ? float(0) : *this_out_grad / win_size; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); for (int w_iter = s_w; w_iter < e_w; ++w_iter) for (int h_iter = s_h; h_iter < e_h; ++h_iter) PrRoIPoolingMatDistributeDiff(this_data_grad, sum_out, h_iter, w_iter, h_iter + 1, w_iter + 1, max(win_start_h, float(h_iter)), max(win_start_w, float(w_iter)), min(win_end_h, float(h_iter) + 1.0), min(win_end_w, float(w_iter + 1.0)), height, width); } } __global__ void PrRoIPoolingCoorBackward( const int nthreads, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, (float)0); float roi_height = max(roi_end_h - roi_start_h, (float)0); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_out_grad = top_diff + index; const float *this_bottom_data = bottom_data + (roi_batch_ind * channels + c) * height * width; const float *this_top_data = top_data + index; float *this_data_grad = bottom_diff + n * 5; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); float sum_out = win_size == float(0) ? float(0) : *this_out_grad / win_size; // WARNING: to be discussed if (sum_out == 0) return; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); float g_x1_y = 0, g_x2_y = 0, g_x_y1 = 0, g_x_y2 = 0; for (int h_iter = s_h; h_iter < e_h; ++h_iter) { g_x1_y += PrRoIPoolingSingleCoorIntegral(max(win_start_h, float(h_iter)) - h_iter, min(win_end_h, float(h_iter + 1)) - h_iter, PrRoIPoolingInterpolation(this_bottom_data, h_iter, win_start_w, height, width), PrRoIPoolingInterpolation(this_bottom_data, h_iter + 1, win_start_w, height, width)); g_x2_y += PrRoIPoolingSingleCoorIntegral(max(win_start_h, float(h_iter)) - h_iter, min(win_end_h, float(h_iter + 1)) - h_iter, PrRoIPoolingInterpolation(this_bottom_data, h_iter, win_end_w, height, width), PrRoIPoolingInterpolation(this_bottom_data, h_iter + 1, win_end_w, height, width)); } for (int w_iter = s_w; w_iter < e_w; ++w_iter) { g_x_y1 += PrRoIPoolingSingleCoorIntegral(max(win_start_w, float(w_iter)) - w_iter, min(win_end_w, float(w_iter + 1)) - w_iter, PrRoIPoolingInterpolation(this_bottom_data, win_start_h, w_iter, height, width), PrRoIPoolingInterpolation(this_bottom_data, win_start_h, w_iter + 1, height, width)); g_x_y2 += PrRoIPoolingSingleCoorIntegral(max(win_start_w, float(w_iter)) - w_iter, min(win_end_w, float(w_iter + 1)) - w_iter, PrRoIPoolingInterpolation(this_bottom_data, win_end_h, w_iter, height, width), PrRoIPoolingInterpolation(this_bottom_data, win_end_h, w_iter + 1, height, width)); } float partial_x1 = -g_x1_y + (win_end_h - win_start_h) * (*this_top_data); float partial_y1 = -g_x_y1 + (win_end_w - win_start_w) * (*this_top_data); float partial_x2 = g_x2_y - (win_end_h - win_start_h) * (*this_top_data); float partial_y2 = g_x_y2 - (win_end_w - win_start_w) * (*this_top_data); partial_x1 = partial_x1 / win_size * spatial_scale; partial_x2 = partial_x2 / win_size * spatial_scale; partial_y1 = partial_y1 / win_size * spatial_scale; partial_y2 = partial_y2 / win_size * spatial_scale; // (b, x1, y1, x2, y2) this_data_grad[0] = 0; atomicAdd(this_data_grad + 1, (partial_x1 * (1.0 - float(pw) / pooled_width) + partial_x2 * (1.0 - float(pw + 1) / pooled_width)) * (*this_out_grad)); atomicAdd(this_data_grad + 2, (partial_y1 * (1.0 - float(ph) / pooled_height) + partial_y2 * (1.0 - float(ph + 1) / pooled_height)) * (*this_out_grad)); atomicAdd(this_data_grad + 3, (partial_x2 * float(pw + 1) / pooled_width + partial_x1 * float(pw) / pooled_width) * (*this_out_grad)); atomicAdd(this_data_grad + 4, (partial_y2 * float(ph + 1) / pooled_height + partial_y1 * float(ph) / pooled_height) * (*this_out_grad)); } } } /* !anonymous namespace */ #ifdef __cplusplus extern "C" { #endif void PrRoIPoolingForwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count) { PrRoIPoolingForward<<>>( top_count, bottom_data, bottom_rois, top_data, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } void PrRoIPoolingBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count) { cudaMemsetAsync(bottom_diff, 0, sizeof(float) * bottom_count, stream); PrRoIPoolingBackward<<>>( top_count, bottom_rois, top_diff, bottom_diff, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } void PrRoIPoolingCoorBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count) { cudaMemsetAsync(bottom_diff, 0, sizeof(float) * bottom_count, stream); PrRoIPoolingCoorBackward<<>>( top_count, bottom_data, bottom_rois, top_data, top_diff, bottom_diff, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } } /* !extern "C" */ ================================================ FILE: external/AR/ltr/external/PreciseRoIPooling/src/prroi_pooling_gpu_impl.cuh ================================================ /* * File : prroi_pooling_gpu_impl.cuh * Author : Tete Xiao, Jiayuan Mao * Email : jasonhsiao97@gmail.com * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ #ifndef PRROI_POOLING_GPU_IMPL_CUH #define PRROI_POOLING_GPU_IMPL_CUH #ifdef __cplusplus extern "C" { #endif #define F_DEVPTR_IN const float * #define F_DEVPTR_OUT float * void PrRoIPoolingForwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count); void PrRoIPoolingBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count); void PrRoIPoolingCoorBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count); #ifdef __cplusplus } /* !extern "C" */ #endif #endif /* !PRROI_POOLING_GPU_IMPL_CUH */ ================================================ FILE: external/AR/ltr/models/AR_seg_mask/AR_seg_mask.py ================================================ import torch.nn as nn from ltr.models.neck import CorrNL from ltr import model_constructor import torch import ltr.models.backbone.resnet_seg as resnet_seg from ltr.models.head import seg_network from easydict import EasyDict as edict '''2020.4.14 replace mask head with frtm for higher-quality mask''' '''2020.4.22 Only use the mask branch''' class ARnet_seg_mask(nn.Module): """ Scale Estimation network module with three branches: bbox, coner and mask. """ def __init__(self, feature_extractor, neck_module, head_module, used_layers, extractor_grad=True,output_size=(256,256)): """ args: feature_extractor - backbone feature extractor bb_regressor - IoU prediction module bb_regressor_layer - List containing the name of the layers from feature_extractor, which are input to bb_regressor extractor_grad - Bool indicating whether backbone feature extractor requires gradients """ super(ARnet_seg_mask, self).__init__() self.feature_extractor = feature_extractor self.neck = neck_module self.refiner = head_module self.used_layers = used_layers self.output_size = output_size if not extractor_grad: for p in self.feature_extractor.parameters(): p.requires_grad_(False) def forward(self, train_imgs, test_imgs, train_bb, mode='train'): """ Forward pass Note: If the training is done in sequence mode, that is, test_imgs.dim() == 5, then the batch dimension corresponds to the first dimensions. test_imgs is thus of the form [sequence, batch, feature, row, col] """ self.forward_ref(train_imgs, train_bb) pred_dict = self.forward_test(test_imgs, mode) return pred_dict def forward_ref(self, train_imgs, train_bb): """ Forward pass of reference branch. size of train_imgs is (1,batch,3,H,W), train_bb is (1,batch,4)""" num_sequences = train_imgs.shape[-4] # batch num_train_images = train_imgs.shape[0] if train_imgs.dim() == 5 else 1 # 1 # Extract backbone features '''train_feat OrderedDict, key:'layer4' ''' train_feat_dict = self.extract_backbone_features(train_imgs.view(-1, *train_imgs.shape[-3:])) # 输入size是(batch,3,256,256) train_feat_list = [feat for feat in train_feat_dict.values()] #list,其中每个元素对应一层输出的特征(tensor) # get reference feature self.neck.get_ref_kernel(train_feat_list, train_bb.view(num_train_images, num_sequences, 4)) def forward_test(self, test_imgs, mode='train'): """ Forward pass of test branch. size of test_imgs is (1,batch,3,256,256)""" output = {} # Extract backbone features test_feat_dict = self.extract_backbone_features(test_imgs.view(-1, *test_imgs.shape[-3:]), layers=['layer1','layer2','layer3','layer4','layer5'])# 输入size是(batch,3,256,256) '''list,tensor''' # Save low-level feature list # Lfeat_list = [feat for name, feat in test_feat_dict.items() if name != 'layer3'] # fuse feature from two branches fusion_feat = self.neck.fuse_feat([test_feat_dict['layer4']]) # Obtain bbox prediction if mode=='train': output['mask'] = torch.sigmoid(self.refiner(fusion_feat, test_feat_dict, self.output_size)) elif mode == 'mask': output = torch.sigmoid(self.refiner(fusion_feat, test_feat_dict, self.output_size)) else: raise ValueError("mode should be train or test") return output def extract_backbone_features(self, im, layers=None): if layers is None: layers = self.used_layers return self.feature_extractor(im, layers) def extract_features(self, im, layers): return self.feature_extractor(im, layers) @model_constructor def ARnet_seg_mask_resnet50(backbone_pretrained=True,used_layers=('layer4',),pool_size=None): # backbone backbone_net = resnet_seg.resnet50(pretrained=backbone_pretrained) # neck neck_net = CorrNL.CorrNL(pool_size=pool_size) # multiple heads '''create segnet''' in_channels = 1024 # disc_params = edict(layer="layer4", in_channels=in_channels, c_channels=96, out_channels=64) # non-local feat (64 channels rather than 1) '''2020.4.22 change "out_channels" to pool_size * pool_size''' disc_params = edict(layer="layer4", in_channels=in_channels, c_channels=96, out_channels=pool_size*pool_size) # non-local feat (64 channels rather than 1) refnet_params = edict( layers=("layer5", "layer4", "layer3", "layer2"), nchannels=64, use_batch_norm=True) disc_params.in_channels = backbone_net.get_out_channels()[disc_params.layer] p = refnet_params refinement_layers_channels = {L: nch for L, nch in backbone_net.get_out_channels().items() if L in p.layers} refiner = seg_network.SegNetwork(disc_params.out_channels, p.nchannels, refinement_layers_channels, p.use_batch_norm) '''create Alpha-Refine''' net = ARnet_seg_mask(feature_extractor=backbone_net, neck_module=neck_net, head_module=refiner, used_layers=used_layers, extractor_grad=True, output_size=(int(pool_size*2*16),int(pool_size*2*16))) return net ================================================ FILE: external/AR/ltr/models/AR_seg_mask/__init__.py ================================================ ================================================ FILE: external/AR/ltr/models/__init__.py ================================================ ================================================ FILE: external/AR/ltr/models/backbone/__init__.py ================================================ from .resnet import resnet18, resnet50, resnet_baby from .resnet18_vggm import resnet18_vggmconv1 ================================================ FILE: external/AR/ltr/models/backbone/base.py ================================================ import torch import torch.nn as nn class Backbone(nn.Module): """Base class for backbone networks. Handles freezing layers etc. args: frozen_layers - Name of layers to freeze. Either list of strings, 'none' or 'all'. Default: 'none'. """ def __init__(self, frozen_layers=()): super().__init__() if isinstance(frozen_layers, str): if frozen_layers.lower() == 'none': frozen_layers = () elif frozen_layers.lower() != 'all': raise ValueError('Unknown option for frozen layers: \"{}\". Should be \"all\", \"none\" or list of layer names.'.format(frozen_layers)) self.frozen_layers = frozen_layers self._is_frozen_nograd = False def train(self, mode=True): super().train(mode) if mode == True: self._set_frozen_to_eval() if not self._is_frozen_nograd: self._set_frozen_to_nograd() self._is_frozen_nograd = True def _set_frozen_to_eval(self): if isinstance(self.frozen_layers, str) and self.frozen_layers.lower() == 'all': self.eval() else: for layer in self.frozen_layers: getattr(self, layer).eval() def _set_frozen_to_nograd(self): if isinstance(self.frozen_layers, str) and self.frozen_layers.lower() == 'all': for p in self.parameters(): p.requires_grad_(False) else: for layer in self.frozen_layers: for p in getattr(self, layer).parameters(): p.requires_grad_(False) ================================================ FILE: external/AR/ltr/models/backbone/resnet.py ================================================ import math import torch.nn as nn from collections import OrderedDict import torch.utils.model_zoo as model_zoo from torchvision.models.resnet import model_urls from .base import Backbone def conv3x3(in_planes, out_planes, stride=1, dilation=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, bias=False, dilation=dilation) class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1, use_bn=True): super(BasicBlock, self).__init__() self.use_bn = use_bn self.conv1 = conv3x3(inplanes, planes, stride, dilation=dilation) if use_bn: self.bn1 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes, dilation=dilation) if use_bn: self.bn2 = nn.BatchNorm2d(planes) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) if self.use_bn: out = self.bn1(out) out = self.relu(out) out = self.conv2(out) if self.use_bn: out = self.bn2(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out class Bottleneck(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=dilation, bias=False, dilation=dilation) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes * 4) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out class ResNet(Backbone): """ ResNet network module. Allows extracting specific feature blocks.""" def __init__(self, block, layers, output_layers, num_classes=1000, inplanes=64, dilation_factor=1, frozen_layers=()): self.inplanes = inplanes super(ResNet, self).__init__(frozen_layers=frozen_layers) self.output_layers = output_layers self.conv1 = nn.Conv2d(3, inplanes, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(inplanes) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) stride = [1 + (dilation_factor < l) for l in (8, 4, 2)] self.layer1 = self._make_layer(block, inplanes, layers[0], dilation=max(dilation_factor//8, 1)) self.layer2 = self._make_layer(block, inplanes*2, layers[1], stride=stride[0], dilation=max(dilation_factor//4, 1)) self.layer3 = self._make_layer(block, inplanes*4, layers[2], stride=stride[1], dilation=max(dilation_factor//2, 1)) self.layer4 = self._make_layer(block, inplanes*8, layers[3], stride=stride[2], dilation=dilation_factor) out_feature_strides = {'conv1': 4, 'layer1': 4, 'layer2': 4*stride[0], 'layer3': 4*stride[0]*stride[1], 'layer4': 4*stride[0]*stride[1]*stride[2]} # TODO better way? if isinstance(self.layer1[0], BasicBlock): out_feature_channels = {'conv1': inplanes, 'layer1': inplanes, 'layer2': inplanes*2, 'layer3': inplanes*4, 'layer4': inplanes*8} elif isinstance(self.layer1[0], Bottleneck): base_num_channels = 4 * inplanes out_feature_channels = {'conv1': inplanes, 'layer1': base_num_channels, 'layer2': base_num_channels * 2, 'layer3': base_num_channels * 4, 'layer4': base_num_channels * 8} else: raise Exception('block not supported') self._out_feature_strides = out_feature_strides self._out_feature_channels = out_feature_channels # self.avgpool = nn.AvgPool2d(7, stride=1) self.avgpool = nn.AdaptiveAvgPool2d((1,1)) self.fc = nn.Linear(inplanes*8 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def out_feature_strides(self, layer=None): if layer is None: return self._out_feature_strides else: return self._out_feature_strides[layer] def out_feature_channels(self, layer=None): if layer is None: return self._out_feature_channels else: return self._out_feature_channels[layer] def _make_layer(self, block, planes, blocks, stride=1, dilation=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample, dilation=dilation)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def _add_output_and_check(self, name, x, outputs, output_layers): if name in output_layers: outputs[name] = x return len(output_layers) == len(outputs) def forward(self, x, output_layers=None): """ Forward pass with input x. The output_layers specify the feature blocks which must be returned """ outputs = OrderedDict() if output_layers is None: output_layers = self.output_layers x = self.conv1(x) x = self.bn1(x) x = self.relu(x) if self._add_output_and_check('conv1', x, outputs, output_layers): return outputs x = self.maxpool(x) x = self.layer1(x) if self._add_output_and_check('layer1', x, outputs, output_layers): return outputs x = self.layer2(x) if self._add_output_and_check('layer2', x, outputs, output_layers): return outputs x = self.layer3(x) if self._add_output_and_check('layer3', x, outputs, output_layers): return outputs x = self.layer4(x) if self._add_output_and_check('layer4', x, outputs, output_layers): return outputs x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc(x) if self._add_output_and_check('fc', x, outputs, output_layers): return outputs if len(output_layers) == 1 and output_layers[0] == 'default': return x raise ValueError('output_layer is wrong.') def resnet_baby(output_layers=None, pretrained=False, inplanes=16, **kwargs): """Constructs a ResNet-18 model. """ if output_layers is None: output_layers = ['default'] else: for l in output_layers: if l not in ['conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']: raise ValueError('Unknown layer: {}'.format(l)) model = ResNet(BasicBlock, [2, 2, 2, 2], output_layers, inplanes=inplanes, **kwargs) if pretrained: raise NotImplementedError return model def resnet18(output_layers=None, pretrained=False, **kwargs): """Constructs a ResNet-18 model. """ if output_layers is None: output_layers = ['default'] else: for l in output_layers: if l not in ['conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']: raise ValueError('Unknown layer: {}'.format(l)) model = ResNet(BasicBlock, [2, 2, 2, 2], output_layers, **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet18'])) return model def resnet50(output_layers=None, pretrained=False, **kwargs): """Constructs a ResNet-50 model. """ if output_layers is None: output_layers = ['default'] else: for l in output_layers: if l not in ['conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']: raise ValueError('Unknown layer: {}'.format(l)) model = ResNet(Bottleneck, [3, 4, 6, 3], output_layers, **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet50'])) return model ================================================ FILE: external/AR/ltr/models/backbone/resnet18_vggm.py ================================================ import math import torch import torch.nn as nn from collections import OrderedDict from torchvision.models.resnet import BasicBlock from .base import Backbone class SpatialCrossMapLRN(nn.Module): def __init__(self, local_size=1, alpha=1.0, beta=0.75, k=1, ACROSS_CHANNELS=True): super(SpatialCrossMapLRN, self).__init__() self.ACROSS_CHANNELS = ACROSS_CHANNELS if ACROSS_CHANNELS: self.average=nn.AvgPool3d(kernel_size=(local_size, 1, 1), stride=1, padding=(int((local_size-1.0)/2), 0, 0)) else: self.average=nn.AvgPool2d(kernel_size=local_size, stride=1, padding=int((local_size-1.0)/2)) self.alpha = alpha self.beta = beta self.k = k def forward(self, x): if self.ACROSS_CHANNELS: div = x.pow(2).unsqueeze(1) div = self.average(div).squeeze(1) div = div.mul(self.alpha).add(self.k).pow(self.beta) else: div = x.pow(2) div = self.average(div) div = div.mul(self.alpha).add(self.k).pow(self.beta) x = x.div(div) return x class ResNetVGGm1(Backbone): def __init__(self, block, layers, output_layers, num_classes=1000, frozen_layers=()): self.inplanes = 64 super(ResNetVGGm1, self).__init__(frozen_layers=frozen_layers) self.output_layers = output_layers self.vggmconv1 = nn.Conv2d(3,96,(7, 7),(2, 2), padding=3) self.vgglrn = SpatialCrossMapLRN(5, 0.0005, 0.75, 2) self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer(block, 128, layers[1], stride=2) self.layer3 = self._make_layer(block, 256, layers[2], stride=2) self.layer4 = self._make_layer(block, 512, layers[3], stride=2) # self.avgpool = nn.AvgPool2d(7, stride=1) self.avgpool = nn.AdaptiveAvgPool2d((1,1)) self.fc = nn.Linear(512 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def _add_output_and_check(self, name, x, outputs, output_layers): if name in output_layers: outputs[name] = x return len(output_layers) == len(outputs) def forward(self, x, output_layers=None): outputs = OrderedDict() if output_layers is None: output_layers = self.output_layers if 'vggconv1' in output_layers: c1 = self.vgglrn(self.relu(self.vggmconv1(x))) if self._add_output_and_check('vggconv1', c1, outputs, output_layers): return outputs x = self.conv1(x) x = self.bn1(x) x = self.relu(x) if self._add_output_and_check('conv1', x, outputs, output_layers): return outputs x = self.maxpool(x) x = self.layer1(x) if self._add_output_and_check('layer1', x, outputs, output_layers): return outputs x = self.layer2(x) if self._add_output_and_check('layer2', x, outputs, output_layers): return outputs x = self.layer3(x) if self._add_output_and_check('layer3', x, outputs, output_layers): return outputs x = self.layer4(x) if self._add_output_and_check('layer4', x, outputs, output_layers): return outputs x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc(x) if self._add_output_and_check('fc', x, outputs, output_layers): return outputs if len(output_layers) == 1 and output_layers[0] == 'default': return x raise ValueError('output_layer is wrong.') def resnet18_vggmconv1(output_layers=None, path=None, **kwargs): """Constructs a ResNet-18 model with first-layer VGGm features. """ if output_layers is None: output_layers = ['default'] else: for l in output_layers: if l not in ['vggconv1', 'conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']: raise ValueError('Unknown layer: {}'.format(l)) model = ResNetVGGm1(BasicBlock, [2, 2, 2, 2], output_layers, **kwargs) if path is not None: model.load_state_dict(torch.load(path), strict=False) return model ================================================ FILE: external/AR/ltr/models/backbone/resnet_seg.py ================================================ import math import torch.nn as nn from collections import OrderedDict import torch.utils.model_zoo as model_zoo from torchvision.models.resnet import model_urls '''2020.4.14 newly added''' from collections import OrderedDict as odict from ltr.models.head.utils import get_out_channels def conv3x3(in_planes, out_planes, stride=1, dilation=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, bias=False, dilation=dilation) class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1): super(BasicBlock, self).__init__() self.conv1 = conv3x3(inplanes, planes, stride, dilation=dilation) self.bn1 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes, dilation=dilation) self.bn2 = nn.BatchNorm2d(planes) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out class Bottleneck(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=dilation, bias=False, dilation=dilation) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes * 4) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out class ResNet(nn.Module): """ ResNet network module. Allows extracting specific feature blocks.""" def __init__(self, block, layers, output_layers, num_classes=1000, inplanes=64, dilation_factor=1): self.inplanes = inplanes super(ResNet, self).__init__() self.output_layers = output_layers self.conv1 = nn.Conv2d(3, inplanes, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(inplanes) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) stride = [1 + (dilation_factor < l) for l in (8, 4, 2)] self.layer1 = self._make_layer(block, inplanes, layers[0], dilation=max(dilation_factor//8, 1)) self.layer2 = self._make_layer(block, inplanes*2, layers[1], stride=stride[0], dilation=max(dilation_factor//4, 1)) self.layer3 = self._make_layer(block, inplanes*4, layers[2], stride=stride[1], dilation=max(dilation_factor//2, 1)) self.layer4 = self._make_layer(block, inplanes*8, layers[3], stride=stride[2], dilation=dilation_factor) # self.avgpool = nn.AvgPool2d(7, stride=1) self.avgpool = nn.AdaptiveAvgPool2d((1,1)) self.fc = nn.Linear(inplanes*8 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() '''2020.4.14 newly added''' self._out_channels = odict( # Deep-to-shallow order is required by SegNetwork layer5=get_out_channels(self.layer4), layer4=get_out_channels(self.layer3), layer3=get_out_channels(self.layer2), layer2=get_out_channels(self.layer1), layer1=get_out_channels(self.conv1)) def _make_layer(self, block, planes, blocks, stride=1, dilation=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample, dilation=dilation)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def _add_output_and_check(self, name, x, outputs, output_layers): if name in output_layers: outputs[name] = x return len(output_layers) == len(outputs) def forward(self, x, output_layers=None): """ Forward pass with input x. The output_layers specify the feature blocks which must be returned """ outputs = OrderedDict() if output_layers is None: output_layers = self.output_layers x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x)# conv1: (batch,64,128,128) '''2020.4.14 change names for every layers''' if self._add_output_and_check('layer1', x, outputs, output_layers): return outputs x = self.layer1(x) # (batch,256,64,64) if self._add_output_and_check('layer2', x, outputs, output_layers): return outputs x = self.layer2(x) # (batch,512,32,32) if self._add_output_and_check('layer3', x, outputs, output_layers): return outputs x = self.layer3(x) # (batch,1024,16,16) if self._add_output_and_check('layer4', x, outputs, output_layers): return outputs x = self.layer4(x) if self._add_output_and_check('layer5', x, outputs, output_layers): return outputs x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc(x) if self._add_output_and_check('fc', x, outputs, output_layers): return outputs if len(output_layers) == 1 and output_layers[0] == 'default': return x raise ValueError('output_layer is wrong.') '''2020.4.14 newly added''' def get_out_channels(self): return self._out_channels def resnet18(output_layers=None, pretrained=False, dilation_factor=1): """Constructs a ResNet-18 model. """ if output_layers is None: output_layers = ['default'] else: for l in output_layers: if l not in ['conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']: raise ValueError('Unknown layer: {}'.format(l)) model = ResNet(BasicBlock, [2, 2, 2, 2], output_layers, dilation_factor=dilation_factor) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet18'])) return model def resnet50(output_layers=None, pretrained=False, dilation_factor=1): """Constructs a ResNet-50 model. """ if output_layers is None: output_layers = ['default'] else: for l in output_layers: if l not in ['conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']: raise ValueError('Unknown layer: {}'.format(l)) model = ResNet(Bottleneck, [3, 4, 6, 3], output_layers, dilation_factor=dilation_factor) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet50'])) return model '''newly added''' def resnet101(output_layers=None, pretrained=False, dilation_factor=1): """Constructs a ResNet-101 model. """ if output_layers is None: output_layers = ['default'] else: for l in output_layers: if l not in ['conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']: raise ValueError('Unknown layer: {}'.format(l)) model = ResNet(Bottleneck, [3, 4, 23, 3], output_layers, dilation_factor=dilation_factor) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet101'])) return model ================================================ FILE: external/AR/ltr/models/bbreg/__init__.py ================================================ from .atom_iou_net import AtomIoUNet ================================================ FILE: external/AR/ltr/models/bbreg/atom.py ================================================ import torch.nn as nn import ltr.models.backbone as backbones import ltr.models.bbreg as bbmodels from ltr import model_constructor class ATOMnet(nn.Module): """ ATOM network module""" def __init__(self, feature_extractor, bb_regressor, bb_regressor_layer, extractor_grad=True): """ args: feature_extractor - backbone feature extractor bb_regressor - IoU prediction module bb_regressor_layer - List containing the name of the layers from feature_extractor, which are input to bb_regressor extractor_grad - Bool indicating whether backbone feature extractor requires gradients """ super(ATOMnet, self).__init__() self.feature_extractor = feature_extractor self.bb_regressor = bb_regressor self.bb_regressor_layer = bb_regressor_layer if not extractor_grad: for p in self.feature_extractor.parameters(): p.requires_grad_(False) def forward(self, train_imgs, test_imgs, train_bb, test_proposals): """ Forward pass Note: If the training is done in sequence mode, that is, test_imgs.dim() == 5, then the batch dimension corresponds to the first dimensions. test_imgs is thus of the form [sequence, batch, feature, row, col] """ num_sequences = train_imgs.shape[-4] num_train_images = train_imgs.shape[0] if train_imgs.dim() == 5 else 1 num_test_images = test_imgs.shape[0] if test_imgs.dim() == 5 else 1 # Extract backbone features train_feat = self.extract_backbone_features(train_imgs.reshape(-1, *train_imgs.shape[-3:])) test_feat = self.extract_backbone_features(test_imgs.reshape(-1, *test_imgs.shape[-3:])) train_feat_iou = [feat for feat in train_feat.values()] test_feat_iou = [feat for feat in test_feat.values()] # Obtain iou prediction iou_pred = self.bb_regressor(train_feat_iou, test_feat_iou, train_bb.reshape(num_train_images, num_sequences, 4), test_proposals.reshape(num_train_images, num_sequences, -1, 4)) return iou_pred def extract_backbone_features(self, im, layers=None): if layers is None: layers = self.bb_regressor_layer return self.feature_extractor(im, layers) def extract_features(self, im, layers): return self.feature_extractor(im, layers) @model_constructor def atom_resnet18(iou_input_dim=(256,256), iou_inter_dim=(256,256), backbone_pretrained=True): # backbone backbone_net = backbones.resnet18(pretrained=backbone_pretrained) # Bounding box regressor iou_predictor = bbmodels.AtomIoUNet(pred_input_dim=iou_input_dim, pred_inter_dim=iou_inter_dim) net = ATOMnet(feature_extractor=backbone_net, bb_regressor=iou_predictor, bb_regressor_layer=['layer2', 'layer3'], extractor_grad=False) return net @model_constructor def atom_resnet50(iou_input_dim=(256,256), iou_inter_dim=(256,256), backbone_pretrained=True): # backbone backbone_net = backbones.resnet50(pretrained=backbone_pretrained) # Bounding box regressor iou_predictor = bbmodels.AtomIoUNet(input_dim=(4*128,4*256), pred_input_dim=iou_input_dim, pred_inter_dim=iou_inter_dim) net = ATOMnet(feature_extractor=backbone_net, bb_regressor=iou_predictor, bb_regressor_layer=['layer2', 'layer3'], extractor_grad=False) return net ================================================ FILE: external/AR/ltr/models/bbreg/atom_iou_net.py ================================================ import torch.nn as nn import torch from ltr.models.layers.blocks import LinearBlock from ltr.external.PreciseRoIPooling.pytorch.prroi_pool import PrRoIPool2D def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1): return nn.Sequential( nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=True), nn.BatchNorm2d(out_planes), nn.ReLU(inplace=True)) class AtomIoUNet(nn.Module): """Network module for IoU prediction. Refer to the ATOM paper for an illustration of the architecture. It uses two backbone feature layers as input. args: input_dim: Feature dimensionality of the two input backbone layers. pred_input_dim: Dimensionality input the the prediction network. pred_inter_dim: Intermediate dimensionality in the prediction network.""" def __init__(self, input_dim=(128,256), pred_input_dim=(256,256), pred_inter_dim=(256,256)): super().__init__() # _r for reference, _t for test self.conv3_1r = conv(input_dim[0], 128, kernel_size=3, stride=1) self.conv3_1t = conv(input_dim[0], 256, kernel_size=3, stride=1) self.conv3_2t = conv(256, pred_input_dim[0], kernel_size=3, stride=1) self.prroi_pool3r = PrRoIPool2D(3, 3, 1/8) self.prroi_pool3t = PrRoIPool2D(5, 5, 1/8) self.fc3_1r = conv(128, 256, kernel_size=3, stride=1, padding=0) self.conv4_1r = conv(input_dim[1], 256, kernel_size=3, stride=1) self.conv4_1t = conv(input_dim[1], 256, kernel_size=3, stride=1) self.conv4_2t = conv(256, pred_input_dim[1], kernel_size=3, stride=1) self.prroi_pool4r = PrRoIPool2D(1, 1, 1/16) self.prroi_pool4t = PrRoIPool2D(3, 3, 1 / 16) self.fc34_3r = conv(256 + 256, pred_input_dim[0], kernel_size=1, stride=1, padding=0) self.fc34_4r = conv(256 + 256, pred_input_dim[1], kernel_size=1, stride=1, padding=0) self.fc3_rt = LinearBlock(pred_input_dim[0], pred_inter_dim[0], 5) self.fc4_rt = LinearBlock(pred_input_dim[1], pred_inter_dim[1], 3) self.iou_predictor = nn.Linear(pred_inter_dim[0]+pred_inter_dim[1], 1, bias=True) # Init weights for m in self.modules(): if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d) or isinstance(m, nn.Linear): nn.init.kaiming_normal_(m.weight.data, mode='fan_in') if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): # In earlier versions batch norm parameters was initialized with default initialization, # which changed in pytorch 1.2. In 1.1 and earlier the weight was set to U(0,1). # So we use the same initialization here. # m.weight.data.fill_(1) m.weight.data.uniform_() m.bias.data.zero_() def forward(self, feat1, feat2, bb1, proposals2): """Runs the ATOM IoUNet during training operation. This forward pass is mainly used for training. Call the individual functions during tracking instead. args: feat1: Features from the reference frames (4 or 5 dims). feat2: Features from the test frames (4 or 5 dims). bb1: Target boxes (x,y,w,h) in image coords in the reference samples. Dims (images, sequences, 4). proposals2: Proposal boxes for which the IoU will be predicted (images, sequences, num_proposals, 4).""" assert bb1.dim() == 3 assert proposals2.dim() == 4 num_images = proposals2.shape[0] num_sequences = proposals2.shape[1] # Extract first train sample feat1 = [f[0,...] if f.dim()==5 else f.reshape(-1, num_sequences, *f.shape[-3:])[0,...] for f in feat1] bb1 = bb1[0,...] # Get modulation vector modulation = self.get_modulation(feat1, bb1) iou_feat = self.get_iou_feat(feat2) modulation = [f.reshape(1, num_sequences, -1).repeat(num_images, 1, 1).reshape(num_sequences*num_images, -1) for f in modulation] proposals2 = proposals2.reshape(num_sequences*num_images, -1, 4) pred_iou = self.predict_iou(modulation, iou_feat, proposals2) return pred_iou.reshape(num_images, num_sequences, -1) def predict_iou(self, modulation, feat, proposals): """Predicts IoU for the give proposals. args: modulation: Modulation vectors for the targets. Dims (batch, feature_dim). feat: IoU features (from get_iou_feat) for test images. Dims (batch, feature_dim, H, W). proposals: Proposal boxes for which the IoU will be predicted (batch, num_proposals, 4).""" fc34_3_r, fc34_4_r = modulation c3_t, c4_t = feat batch_size = c3_t.size()[0] # Modulation c3_t_att = c3_t * fc34_3_r.reshape(batch_size, -1, 1, 1) c4_t_att = c4_t * fc34_4_r.reshape(batch_size, -1, 1, 1) # Add batch_index to rois batch_index = torch.arange(batch_size, dtype=torch.float32).reshape(-1, 1).to(c3_t.device) # Push the different rois for the same image along the batch dimension num_proposals_per_batch = proposals.shape[1] # input proposals2 is in format xywh, convert it to x0y0x1y1 format proposals_xyxy = torch.cat((proposals[:, :, 0:2], proposals[:, :, 0:2] + proposals[:, :, 2:4]), dim=2) # Add batch index roi2 = torch.cat((batch_index.reshape(batch_size, -1, 1).expand(-1, num_proposals_per_batch, -1), proposals_xyxy), dim=2) roi2 = roi2.reshape(-1, 5).to(proposals_xyxy.device) roi3t = self.prroi_pool3t(c3_t_att, roi2) roi4t = self.prroi_pool4t(c4_t_att, roi2) fc3_rt = self.fc3_rt(roi3t) fc4_rt = self.fc4_rt(roi4t) fc34_rt_cat = torch.cat((fc3_rt, fc4_rt), dim=1) iou_pred = self.iou_predictor(fc34_rt_cat).reshape(batch_size, num_proposals_per_batch) return iou_pred def get_modulation(self, feat, bb): """Get modulation vectors for the targets. args: feat: Backbone features from reference images. Dims (batch, feature_dim, H, W). bb: Target boxes (x,y,w,h) in image coords in the reference samples. Dims (batch, 4).""" feat3_r, feat4_r = feat c3_r = self.conv3_1r(feat3_r) # Add batch_index to rois batch_size = bb.shape[0] batch_index = torch.arange(batch_size, dtype=torch.float32).reshape(-1, 1).to(bb.device) # input bb is in format xywh, convert it to x0y0x1y1 format bb = bb.clone() bb[:, 2:4] = bb[:, 0:2] + bb[:, 2:4] roi1 = torch.cat((batch_index, bb), dim=1) roi3r = self.prroi_pool3r(c3_r, roi1) c4_r = self.conv4_1r(feat4_r) roi4r = self.prroi_pool4r(c4_r, roi1) fc3_r = self.fc3_1r(roi3r) # Concatenate from block 3 and 4 fc34_r = torch.cat((fc3_r, roi4r), dim=1) fc34_3_r = self.fc34_3r(fc34_r) fc34_4_r = self.fc34_4r(fc34_r) return fc34_3_r, fc34_4_r def get_iou_feat(self, feat2): """Get IoU prediction features from a 4 or 5 dimensional backbone input.""" feat2 = [f.reshape(-1, *f.shape[-3:]) if f.dim()==5 else f for f in feat2] feat3_t, feat4_t = feat2 c3_t = self.conv3_2t(self.conv3_1t(feat3_t)) c4_t = self.conv4_2t(self.conv4_1t(feat4_t)) return c3_t, c4_t ================================================ FILE: external/AR/ltr/models/head/__init__.py ================================================ ================================================ FILE: external/AR/ltr/models/head/seg_network.py ================================================ import torch from torch import nn as nn from torch.nn import functional as F from ltr.models.head.utils import conv, relu, interpolate, adaptive_cat class TSE(nn.Module): def __init__(self, fc, ic, oc): super().__init__() nc = ic + oc self.reduce = nn.Sequential(conv(fc, oc, 1), relu(), conv(oc, oc, 1)) self.transform = nn.Sequential(conv(nc, nc, 3), relu(), conv(nc, nc, 3), relu(), conv(nc, oc, 3), relu()) def forward(self, ft, score, x=None): h = self.reduce(ft) hpool = F.adaptive_avg_pool2d(h, (1, 1)) if x is None else x h = adaptive_cat((h, score), dim=1, ref_tensor=0) h = self.transform(h) return h, hpool class CAB(nn.Module): def __init__(self, oc, deepest): super().__init__() self.convreluconv = nn.Sequential(conv(2 * oc, oc, 1), relu(), conv(oc, oc, 1)) self.deepest = deepest def forward(self, deeper, shallower, att_vec=None): shallow_pool = F.adaptive_avg_pool2d(shallower, (1, 1)) deeper_pool = deeper if self.deepest else F.adaptive_avg_pool2d(deeper, (1, 1)) if att_vec is not None: global_pool = torch.cat([shallow_pool, deeper_pool, att_vec], dim=1) else: global_pool = torch.cat((shallow_pool, deeper_pool), dim=1) conv_1x1 = self.convreluconv(global_pool) inputs = shallower * torch.sigmoid(conv_1x1) out = inputs + interpolate(deeper, inputs.shape[-2:]) return out class RRB(nn.Module): def __init__(self, oc, use_bn=False): super().__init__() self.conv1x1 = conv(oc, oc, 1) if use_bn: self.bblock = nn.Sequential(conv(oc, oc, 3), nn.BatchNorm2d(oc), relu(), conv(oc, oc, 3, bias=False)) else: self.bblock = nn.Sequential(conv(oc, oc, 3), relu(), conv(oc, oc, 3, bias=False)) # Basic block def forward(self, x): h = self.conv1x1(x) return F.relu(h + self.bblock(h)) class Upsampler(nn.Module): def __init__(self, in_channels=64): super().__init__() self.conv1 = conv(in_channels, in_channels // 2, 3) self.conv2 = conv(in_channels // 2, 1, 3) def forward(self, x, image_size): print(x.shape) x = F.interpolate(x, (2 * x.shape[-2], 2 * x.shape[-1]), mode='bicubic', align_corners=False) x = F.relu(self.conv1(x)) x = F.interpolate(x, image_size[-2:], mode='bicubic', align_corners=False) x = self.conv2(x) return x class PyrUpBicubic2d(nn.Module): def __init__(self, channels): super().__init__() self.channels = channels def kernel(d): x = d + torch.arange(-1, 3, dtype=torch.float32) x = torch.abs(x) a = -0.75 f = (x < 1).float() * ((a + 2) * x * x * x - (a + 3) * x * x + 1) + \ ((x >= 1) * (x < 2)).float() * (a * x * x * x - 5 * a * x * x + 8 * a * x - 4 * a) W = f.reshape(1, 1, 1, len(x)).float() Wt = W.permute(0, 1, 3, 2) return W, Wt We, We_t = kernel(-0.25) Wo, Wo_t = kernel(-0.25 - 0.5) # Building non-separable filters for now. It would make sense to # have separable filters if it proves to be faster. # .contiguous() is needed until a bug is fixed in nn.Conv2d. self.W00 = (We_t @ We).expand(channels, 1, 4, 4).contiguous() self.W01 = (We_t @ Wo).expand(channels, 1, 4, 4).contiguous() self.W10 = (Wo_t @ We).expand(channels, 1, 4, 4).contiguous() self.W11 = (Wo_t @ Wo).expand(channels, 1, 4, 4).contiguous() def forward(self, input): if input.device != self.W00.device: self.W00 = self.W00.to(input.device) self.W01 = self.W01.to(input.device) self.W10 = self.W10.to(input.device) self.W11 = self.W11.to(input.device) a = F.pad(input, (2, 2, 2, 2), 'replicate') I00 = F.conv2d(a, self.W00, groups=self.channels) I01 = F.conv2d(a, self.W01, groups=self.channels) I10 = F.conv2d(a, self.W10, groups=self.channels) I11 = F.conv2d(a, self.W11, groups=self.channels) n, c, h, w = I11.shape J0 = torch.stack((I00, I01), dim=-1).view(n, c, h, 2 * w) J1 = torch.stack((I10, I11), dim=-1).view(n, c, h, 2 * w) out = torch.stack((J0, J1), dim=-2).view(n, c, 2 * h, 2 * w) out = F.pad(out, (-1, -1, -1, -1)) return out class BackwardCompatibleUpsampler(nn.Module): """ Upsampler with bicubic interpolation that works with Pytorch 1.0.1 """ def __init__(self, in_channels=64): super().__init__() self.conv1 = conv(in_channels, in_channels // 2, 3) self.up1 = PyrUpBicubic2d(in_channels) self.conv2 = conv(in_channels // 2, 1, 3) self.up2 = PyrUpBicubic2d(in_channels // 2) def forward(self, x, image_size): x = self.up1(x) x = F.relu(self.conv1(x)) x = self.up2(x) x = F.interpolate(x, image_size[-2:], mode='bilinear', align_corners=False) x = self.conv2(x) return x class SegNetwork(nn.Module): def __init__(self, in_channels=1, out_channels=32, ft_channels=None, use_bn=False): super().__init__() assert ft_channels is not None self.ft_channels = ft_channels self.TSE = nn.ModuleDict() self.RRB1 = nn.ModuleDict() self.CAB = nn.ModuleDict() self.RRB2 = nn.ModuleDict() ic = in_channels oc = out_channels for L, fc in self.ft_channels.items(): self.TSE[L] = TSE(fc, ic, oc) self.RRB1[L] = RRB(oc, use_bn=use_bn) self.CAB[L] = CAB(oc, L == 'layer5') self.RRB2[L] = RRB(oc, use_bn=use_bn) #if torch.__version__ == '1.0.1' self.project = BackwardCompatibleUpsampler(out_channels) #self.project = Upsampler(out_channels) def forward(self, scores, features, image_size): num_targets = scores.shape[0] num_fmaps = features[next(iter(self.ft_channels))].shape[0] if num_targets > num_fmaps: multi_targets = True else: multi_targets = False x = None for i, L in enumerate(self.ft_channels): ft = features[L] s = interpolate(scores, ft.shape[-2:]) # Resample scores to match features size if multi_targets: h, hpool = self.TSE[L](ft.repeat(num_targets, 1, 1, 1), s, x) else: h, hpool = self.TSE[L](ft, s, x) h = self.RRB1[L](h) h = self.CAB[L](hpool, h) x = self.RRB2[L](h) x = self.project(x, image_size) return x ================================================ FILE: external/AR/ltr/models/head/utils.py ================================================ from collections import OrderedDict as odict import numpy as np import torch from torch import nn as nn from torch.nn import functional as F def text_bargraph(values): blocks = np.array(('u', ' ', '▁', '▂', '▃', '▄', '▅', '▆', '▇', '█', 'o')) nsteps = len(blocks) - 2 - 1 hstep = 1 / (2 * nsteps) values = np.array(values) nans = np.isnan(values) values[nans] = 0 # '░' indices = ((values + hstep) * nsteps + 1).astype(np.int) indices[values < 0] = 0 indices[values > 1] = len(blocks) - 1 graph = blocks[indices] graph[nans] = '░' graph = str.join('', graph) return graph class ModuleWrapper: """ A wrapper for hiding modules from PyTorch, so that the same module can be used in multiple places. and yet saved only once in a checkpoint, or not at all. """ # https://stackoverflow.com/questions/1466676/create-a-wrapper-class-to-call-a-pre-and-post-function-around-existing-functions def __init__(self, wrapped_module): self.__wrapped_module__ = wrapped_module def __getattr__(self, attr): orig_attr = self.__wrapped_module__.__getattribute__(attr) if callable(orig_attr): def hooked(*args, **kwargs): result = orig_attr(*args, **kwargs) # prevent wrapped_class from becoming unwrapped if result == self.__wrapped_module__: return self return result return hooked else: return orig_attr def __call__(self, *args, **kwargs): return self.__wrapped_module__(*args, **kwargs) def conv(ic, oc, ksize, bias=True, dilation=1, stride=1): return nn.Conv2d(ic, oc, ksize, padding=ksize // 2, bias=bias, dilation=dilation, stride=stride) def relu(negative_slope=0.0, inplace=False): return nn.LeakyReLU(negative_slope, inplace=inplace) def interpolate(t, sz): sz = sz.tolist() if torch.is_tensor(sz) else sz return F.interpolate(t, sz, mode='bilinear', align_corners=False) if t.shape[-2:] != sz else t def adaptive_cat(seq, dim=0, ref_tensor=0): sz = seq[ref_tensor].shape[-2:] t = torch.cat([interpolate(t, sz) for t in seq], dim=dim) return t def get_out_channels(layer): if hasattr(layer, 'out_channels'): oc = layer.out_channels elif hasattr(layer, '_modules'): oc = get_out_channels(layer._modules) else: ocs = [] for key in reversed(layer): ocs.append(get_out_channels(layer[key])) oc = 0 for elem in ocs: if elem: return elem return oc def is_finite(t): return (torch.isnan(t) + torch.isinf(t)) == 0 class AverageMeter: """Computes and stores the average and current value""" def __init__(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 self.seq_avg = [] def reset(self): self.__init__() def update(self, val, n=1): if not np.isnan(val): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count def update_multi(self, val): val = np.array(val) v = val[~np.isnan(val)] n = len(v) self.val = val self.sum += np.nansum(v) self.count += n self.avg = self.sum / self.count ================================================ FILE: external/AR/ltr/models/layers/__init__.py ================================================ ================================================ FILE: external/AR/ltr/models/layers/activation.py ================================================ import math import torch import torch.nn as nn import torch.nn.functional as F def softmax_reg(x: torch.Tensor, dim, reg=None): """Softmax with optinal denominator regularization.""" if reg is None: return torch.softmax(x, dim=dim) dim %= x.dim() if isinstance(reg, (float, int)): reg = x.new_tensor([reg]) reg = reg.expand([1 if d==dim else x.shape[d] for d in range(x.dim())]) x = torch.cat((x, reg), dim=dim) return torch.softmax(x, dim=dim)[[slice(-1) if d==dim else slice(None) for d in range(x.dim())]] class MLU(nn.Module): r"""MLU activation """ def __init__(self, min_val, inplace=False): super().__init__() self.min_val = min_val self.inplace = inplace def forward(self, input): return F.elu(F.leaky_relu(input, 1/self.min_val, inplace=self.inplace), self.min_val, inplace=self.inplace) class LeakyReluPar(nn.Module): r"""LeakyRelu parametric activation """ def forward(self, x, a): return (1.0 - a)/2.0 * torch.abs(x) + (1.0 + a)/2.0 * x class LeakyReluParDeriv(nn.Module): r"""Derivative of the LeakyRelu parametric activation, wrt x. """ def forward(self, x, a): return (1.0 - a)/2.0 * torch.sign(x.detach()) + (1.0 + a)/2.0 class BentIdentPar(nn.Module): r"""BentIdent parametric activation """ def __init__(self, b=1.0): super().__init__() self.b = b def forward(self, x, a): return (1.0 - a)/2.0 * (torch.sqrt(x*x + 4.0*self.b*self.b) - 2.0*self.b) + (1.0 + a)/2.0 * x class BentIdentParDeriv(nn.Module): r"""BentIdent parametric activation deriv """ def __init__(self, b=1.0): super().__init__() self.b = b def forward(self, x, a): return (1.0 - a)/2.0 * (x / torch.sqrt(x*x + 4.0*self.b*self.b)) + (1.0 + a)/2.0 ================================================ FILE: external/AR/ltr/models/layers/blocks.py ================================================ from torch import nn def conv_block(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1, bias=True, batch_norm=True, relu=True, padding_mode='zeros'): layers = [] assert padding_mode == 'zeros' or padding_mode == 'replicate' if padding_mode == 'replicate' and padding > 0: assert isinstance(padding, int) layers.append(nn.ReflectionPad2d(padding)) padding = 0 layers.append(nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias)) if batch_norm: layers.append(nn.BatchNorm2d(out_planes)) if relu: layers.append(nn.ReLU(inplace=True)) return nn.Sequential(*layers) class LinearBlock(nn.Module): def __init__(self, in_planes, out_planes, input_sz, bias=True, batch_norm=True, relu=True): super().__init__() self.linear = nn.Linear(in_planes*input_sz*input_sz, out_planes, bias=bias) self.bn = nn.BatchNorm2d(out_planes) if batch_norm else None self.relu = nn.ReLU(inplace=True) if relu else None def forward(self, x): x = self.linear(x.reshape(x.shape[0], -1)) if self.bn is not None: x = self.bn(x.reshape(x.shape[0], x.shape[1], 1, 1)) if self.relu is not None: x = self.relu(x) return x.reshape(x.shape[0], -1) ================================================ FILE: external/AR/ltr/models/layers/distance.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F class DistanceMap(nn.Module): """Generate a distance map from a origin center location. args: num_bins: Number of bins in the map. bin_displacement: Displacement of the bins. """ def __init__(self, num_bins, bin_displacement=1.0): super().__init__() self.num_bins = num_bins self.bin_displacement = bin_displacement def forward(self, center, output_sz): """Create the distance map. args: center: Torch tensor with (y,x) center position. Dims (batch, 2) output_sz: Size of output distance map. 2-dimensional tuple.""" center = center.view(-1,2) bin_centers = torch.arange(self.num_bins, dtype=torch.float32, device=center.device).view(1, -1, 1, 1) k0 = torch.arange(output_sz[0], dtype=torch.float32, device=center.device).view(1,1,-1,1) k1 = torch.arange(output_sz[1], dtype=torch.float32, device=center.device).view(1,1,1,-1) d0 = k0 - center[:,0].view(-1,1,1,1) d1 = k1 - center[:,1].view(-1,1,1,1) dist = torch.sqrt(d0*d0 + d1*d1) bin_diff = dist / self.bin_displacement - bin_centers bin_val = torch.cat((F.relu(1.0 - torch.abs(bin_diff[:,:-1,:,:]), inplace=True), (1.0 + bin_diff[:,-1:,:,:]).clamp(0, 1)), dim=1) return bin_val ================================================ FILE: external/AR/ltr/models/layers/filter.py ================================================ import torch import torch.nn.functional as F def apply_filter(feat, filter, dilation_factors=None): """Applies the filter on the input features (feat). The number of groups is automatically calculated. args: feat: These are the input features. Must have dimensions (images_in_sequence, sequences, feat_dim, H, W) filter: The filter to apply. Must have dimensions (sequences, feat_dim, fH, fW) or (sequences, filters, feat_dim/groups, fH, fW) output: scores: Output of filtering. Dimensions (images_in_sequence, sequences, yH, yW) or (images_in_sequence, sequences, filters, yH, yW) """ multiple_filters = (filter.dim() == 5) padding = (filter.shape[-2] // 2, filter.shape[-1] // 2) num_images = feat.shape[0] num_sequences = feat.shape[1] if feat.dim() == 5 else 1 num_filters = filter.shape[1] if multiple_filters else 1 num_channels = feat.shape[-3] groups = num_channels // filter.shape[-3] assert num_filters % groups == 0 and num_channels % groups == 0 if multiple_filters: if dilation_factors is None: scores = F.conv2d(feat.reshape(num_images, -1, feat.shape[-2], feat.shape[-1]), filter.view(-1, *filter.shape[-3:]), padding=padding, groups=num_sequences*groups) return scores.view(num_images, num_sequences, -1, scores.shape[-2], scores.shape[-1]) else: scores_all = [] start_id = 0 for d_factor, num_filters_with_d in dilation_factors.items(): f_d = filter[:, start_id:start_id+num_filters_with_d, ...].contiguous() padding_d = [p+d_factor-1 for p in padding] scores_d = F.conv2d(feat.reshape(num_images, -1, feat.shape[-2], feat.shape[-1]), f_d.view(-1, *f_d.shape[-3:]), padding=padding_d, groups=num_sequences * groups, dilation=d_factor) scores_d = scores_d.view(num_images, num_sequences, -1, scores_d.shape[-2], scores_d.shape[-1]) scores_all.append(scores_d) start_id += num_filters_with_d scores = torch.cat(scores_all, dim=2) return scores scores = F.conv2d(feat.reshape(num_images, -1, feat.shape[-2], feat.shape[-1]), filter, padding=padding, groups=num_sequences) return scores.view(num_images, num_sequences, scores.shape[-2], scores.shape[-1]) def apply_feat_transpose(feat, input, filter_ksz, training=True, groups=1): """Applies the transposed operation off apply_filter w.r.t. filter itself. Can be used to compute the filter gradient. args: feat: These are the input features. Must have dimensions (images_in_sequence, sequences, feat_dim, H, W) input: Input activation (e.g. residuals). Must have dimensions (images_in_sequence, sequences, yH, yW) or (images_in_sequence, sequences, filters, yH, yW) training: Choose the faster implementation whether training or not. output: Output of transposed operation. Dimensions (sequences, feat_dim, fH, fW) """ if groups != 1: raise NotImplementedError('Not implemented other values of group.') if training or input.dim() == 5: return _apply_feat_transpose_v3(feat, input, filter_ksz) return _apply_feat_transpose_v2(feat, input, filter_ksz) def _apply_feat_transpose_v1(feat, input, filter_ksz): """This one is slow as hell!!!!""" num_images = feat.shape[0] num_sequences = feat.shape[1] if feat.dim() == 5 else 1 feat_sz = (feat.shape[-2], feat.shape[-1]) if isinstance(filter_ksz, int): filter_ksz = (filter_ksz, filter_ksz) # trans_pad = sz + padding - filter_ksz trans_pad = [sz + ksz//2 - ksz for sz, ksz in zip(feat_sz, filter_ksz)] filter_grad = F.conv_transpose2d(input.flip((2, 3)).view(1, -1, input.shape[-2], input.shape[-1]), feat.reshape(-1, feat.shape[-3], feat.shape[-2], feat.shape[-1]), padding=trans_pad, groups=num_images * num_sequences) return filter_grad.view(num_images, num_sequences, -1, filter_grad.shape[-2], filter_grad.shape[-1]).sum(dim=0) def _apply_feat_transpose_v2(feat, input, filter_ksz): """Fast forward and slow backward""" multiple_filters = (input.dim() == 5) num_images = feat.shape[0] num_sequences = feat.shape[1] if feat.dim() == 5 else 1 num_filters = input.shape[2] if multiple_filters else 1 if isinstance(filter_ksz, int): filter_ksz = (filter_ksz, filter_ksz) trans_pad = [(ksz-1)//2 for ksz in filter_ksz] if multiple_filters: filter_grad = F.conv2d(input.reshape(-1, num_filters, input.shape[-2], input.shape[-1]).permute(1,0,2,3), feat.reshape(-1, 1, feat.shape[-2], feat.shape[-1]), padding=trans_pad, groups=num_images * num_sequences) if num_images == 1: return filter_grad.view(num_filters, num_sequences, -1, filter_grad.shape[-2], filter_grad.shape[-1]).flip((3,4)).permute(1,0,2,3,4) return filter_grad.view(num_filters, num_images, num_sequences, -1, filter_grad.shape[-2], filter_grad.shape[-1]).sum(dim=1).flip((3,4)).permute(1,0,2,3,4) filter_grad = F.conv2d(input.reshape(1, -1, input.shape[-2], input.shape[-1]), feat.reshape(-1, 1, feat.shape[-2], feat.shape[-1]), padding=trans_pad, groups=num_images * num_sequences) return filter_grad.view(num_images, num_sequences, -1, filter_grad.shape[-2], filter_grad.shape[-1]).sum(dim=0).flip((2,3)) def _apply_feat_transpose_v3(feat, input, filter_ksz): """Slow forward fast backward""" multiple_filters = (input.dim() == 5) num_images = feat.shape[0] num_sequences = feat.shape[1] if feat.dim() == 5 else 1 num_filters = input.shape[2] if multiple_filters else 1 if isinstance(filter_ksz, int): filter_ksz = (filter_ksz, filter_ksz) trans_pad = [ksz//2 for ksz in filter_ksz] filter_grad = F.conv2d(feat.reshape(-1, feat.shape[-3], feat.shape[-2], feat.shape[-1]).permute(1,0,2,3), input.reshape(-1, 1, input.shape[-2], input.shape[-1]), padding=trans_pad, groups=num_images * num_sequences) if multiple_filters: if num_images == 1: return filter_grad.view(-1, num_sequences, num_filters, filter_grad.shape[-2], filter_grad.shape[-1]).permute(1,2,0,3,4) return filter_grad.view(-1, num_images, num_sequences, num_filters, filter_grad.shape[-2], filter_grad.shape[-1]).sum(dim=1).permute(1,2,0,3,4) if num_images == 1: return filter_grad.permute(1,0,2,3) return filter_grad.view(-1, num_images, num_sequences, filter_grad.shape[-2], filter_grad.shape[-1]).sum(dim=1).permute(1,0,2,3) def _apply_feat_transpose_v4(feat, input, filter_ksz): """Slow forward fast backward""" num_images = feat.shape[0] num_sequences = feat.shape[1] if feat.dim() == 5 else 1 if isinstance(filter_ksz, int): filter_ksz = (filter_ksz, filter_ksz) trans_pad = [ksz//2 for ksz in filter_ksz] filter_grad = F.conv2d(feat.permute(2,1,0,3,4).reshape(feat.shape[-3], -1, feat.shape[-2], feat.shape[-1]), input.permute(1,0,2,3), padding=trans_pad, groups=num_sequences) return filter_grad.permute(1,0,2,3) def filter_gradient(feat, filter, label=None, training=True): """Computes gradient of the filter when applied on the input features and ground truth label. args: feat: These are the input features. Must have dimensions (images_in_sequence, sequences, feat_dim, H, W) filter: The filter to apply. Must have dimensions (sequences, feat_dim, fH, fW) label: Ground truth label in the L2 loss. Dimensions (images_in_sequence, sequences, yH, yW) output: filter_gradient: Dimensions same as input filter (sequences, feat_dim, fH, fW) """ residuals = apply_filter(feat, filter) if label is not None: residuals = residuals - label filter_ksz = (filter.shape[-2], filter.shape[-1]) return apply_feat_transpose(feat, residuals, filter_ksz, training=training) ================================================ FILE: external/AR/ltr/models/layers/normalization.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F class InstanceL2Norm(nn.Module): """Instance L2 normalization. """ def __init__(self, size_average=True, eps=1e-5, scale=1.0): super().__init__() self.size_average = size_average self.eps = eps self.scale = scale def forward(self, input): if self.size_average: return input * (self.scale * ((input.shape[1] * input.shape[2] * input.shape[3]) / ( torch.sum((input * input).view(input.shape[0], 1, 1, -1), dim=3, keepdim=True) + self.eps)).sqrt()) else: return input * (self.scale / (torch.sum((input * input).view(input.shape[0], 1, 1, -1), dim=3, keepdim=True) + self.eps).sqrt()) ================================================ FILE: external/AR/ltr/models/layers/transform.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from collections import OrderedDict def interpolate(x, sz): """Interpolate 4D tensor x to size sz.""" sz = sz.tolist() if torch.is_tensor(sz) else sz return F.interpolate(x, sz, mode='bilinear', align_corners=False) if x.shape[-2:] != sz else x class InterpCat(nn.Module): """Interpolate and concatenate features of different resolutions.""" def forward(self, input): if isinstance(input, (dict, OrderedDict)): input = list(input.values()) output_shape = None for x in input: if output_shape is None or output_shape[0] > x.shape[-2]: output_shape = x.shape[-2:] return torch.cat([interpolate(x, output_shape) for x in input], dim=-3) ================================================ FILE: external/AR/ltr/models/loss/__init__.py ================================================ from .target_classification import LBHinge ================================================ FILE: external/AR/ltr/models/loss/kl_regression.py ================================================ import math import torch import torch.nn as nn from torch.nn import functional as F class KLRegression(nn.Module): """KL-divergence loss for probabilistic regression. It is computed using Monte Carlo (MC) samples from an arbitrary distribution.""" def __init__(self, eps=0.0): super().__init__() self.eps = eps def forward(self, scores, sample_density, gt_density, mc_dim=-1): """Args: scores: predicted score values sample_density: probability density of the sample distribution gt_density: probability density of the ground truth distribution mc_dim: dimension of the MC samples""" exp_val = scores - torch.log(sample_density + self.eps) L = torch.logsumexp(exp_val, dim=mc_dim) - math.log(scores.shape[mc_dim]) - \ torch.mean(scores * (gt_density / (sample_density + self.eps)), dim=mc_dim) return L.mean() class MLRegression(nn.Module): """Maximum likelihood loss for probabilistic regression. It is computed using Monte Carlo (MC) samples from an arbitrary distribution.""" def __init__(self, eps=0.0): super().__init__() self.eps = eps def forward(self, scores, sample_density, gt_density=None, mc_dim=-1): """Args: scores: predicted score values. First sample must be ground-truth sample_density: probability density of the sample distribution gt_density: not used mc_dim: dimension of the MC samples. Only mc_dim=1 supported""" assert mc_dim == 1 assert (sample_density[:,0,...] == -1).all() exp_val = scores[:, 1:, ...] - torch.log(sample_density[:, 1:, ...] + self.eps) L = torch.logsumexp(exp_val, dim=mc_dim) - math.log(scores.shape[mc_dim] - 1) - scores[:, 0, ...] loss = L.mean() return loss class KLRegressionGrid(nn.Module): """KL-divergence loss for probabilistic regression. It is computed using the grid integration strategy.""" def forward(self, scores, gt_density, grid_dim=-1, grid_scale=1.0): """Args: scores: predicted score values gt_density: probability density of the ground truth distribution grid_dim: dimension(s) of the grid grid_scale: area of one grid cell""" score_corr = grid_scale * torch.sum(scores * gt_density, dim=grid_dim) L = torch.logsumexp(scores, dim=grid_dim) + math.log(grid_scale) - score_corr return L.mean() ================================================ FILE: external/AR/ltr/models/loss/target_classification.py ================================================ import torch.nn as nn import torch from torch.nn import functional as F class LBHinge(nn.Module): """Loss that uses a 'hinge' on the lower bound. This means that for samples with a label value smaller than the threshold, the loss is zero if the prediction is also smaller than that threshold. args: error_matric: What base loss to use (MSE by default). threshold: Threshold to use for the hinge. clip: Clip the loss if it is above this value. """ def __init__(self, error_metric=nn.MSELoss(), threshold=None, clip=None): super().__init__() self.error_metric = error_metric self.threshold = threshold if threshold is not None else -100 self.clip = clip def forward(self, prediction, label, target_bb=None): negative_mask = (label < self.threshold).float() positive_mask = (1.0 - negative_mask) prediction = negative_mask * F.relu(prediction) + positive_mask * prediction loss = self.error_metric(prediction, positive_mask * label) if self.clip is not None: loss = torch.min(loss, torch.tensor([self.clip], device=loss.device)) return loss ================================================ FILE: external/AR/ltr/models/meta/__init__.py ================================================ ================================================ FILE: external/AR/ltr/models/meta/steepestdescent.py ================================================ import math import torch import torch.nn as nn from pytracking import TensorList from ltr.models.layers import activation class GNSteepestDescent(nn.Module): """General module for steepest descent based meta learning.""" def __init__(self, residual_module, num_iter=1, compute_losses=False, detach_length=float('Inf'), parameter_batch_dim=0, residual_batch_dim=0, steplength_reg=0.0, filter_dilation_factors=None): super().__init__() self.residual_module = residual_module self.num_iter = num_iter self.compute_losses = compute_losses self.detach_length = detach_length self.steplength_reg = steplength_reg self._parameter_batch_dim = parameter_batch_dim self._residual_batch_dim = residual_batch_dim self.filter_dilation_factors = filter_dilation_factors def _sqr_norm(self, x: TensorList, batch_dim=0): sum_keep_batch_dim = lambda e: e.sum(dim=[d for d in range(e.dim()) if d != batch_dim]) return sum((x * x).apply(sum_keep_batch_dim)) def _compute_loss(self, res): return sum((res * res).sum()) / sum(res.numel()) def forward(self, meta_parameter: TensorList, num_iter=None, *args, **kwargs): # Make sure grad is enabled torch_grad_enabled = torch.is_grad_enabled() torch.set_grad_enabled(True) num_iter = self.num_iter if num_iter is None else num_iter meta_parameter_iterates = [meta_parameter] losses = [] for i in range(num_iter): if i > 0 and i % self.detach_length == 0: meta_parameter = meta_parameter.detach() meta_parameter.requires_grad_(True) # Compute residual vector r = self.residual_module(meta_parameter, filter_dilation_factors=self.filter_dilation_factors, **kwargs) if self.compute_losses: losses.append(self._compute_loss(r)) # Compute gradient of loss u = r.clone() g = TensorList(torch.autograd.grad(r, meta_parameter, u, create_graph=True)) # Multiply gradient with Jacobian h = TensorList(torch.autograd.grad(g, u, g, create_graph=True)) # Compute squared norms ip_gg = self._sqr_norm(g, batch_dim=self._parameter_batch_dim) ip_hh = self._sqr_norm(h, batch_dim=self._residual_batch_dim) # Compute step length alpha = ip_gg / (ip_hh + self.steplength_reg * ip_gg).clamp(1e-8) # Compute optimization step step = g.apply(lambda e: alpha.reshape([-1 if d==self._parameter_batch_dim else 1 for d in range(e.dim())]) * e) # Add step to parameter meta_parameter = meta_parameter - step meta_parameter_iterates.append(meta_parameter) if self.compute_losses: losses.append(self._compute_loss(self.residual_module(meta_parameter, filter_dilation_factors=self.filter_dilation_factors, **kwargs))) # Reset the grad enabled flag torch.set_grad_enabled(torch_grad_enabled) if not torch_grad_enabled: meta_parameter.detach_() for w in meta_parameter_iterates: w.detach_() for l in losses: l.detach_() return meta_parameter, meta_parameter_iterates, losses ================================================ FILE: external/AR/ltr/models/neck/CorrNL.py ================================================ import torch.nn as nn import torch from ltr.external.PreciseRoIPooling.pytorch.prroi_pool import PrRoIPool2D from torch.nn import functional as F from ltr.models.neck.neck_utils import * class CorrNL(nn.Module): """Network module for IoU prediction. Refer to the ATOM paper for an illustration of the architecture. It uses two backbone feature layers as input. args: input_dim: Feature dimensionality of the two input backbone layers. pred_input_dim: Dimensionality input the the prediction network. pred_inter_dim: Intermediate dimensionality in the prediction network.""" def __init__(self, pool_size=8, use_NL=True): super().__init__() self.prroi_pool = PrRoIPool2D(pool_size, pool_size, 1/16) num_corr_channel = pool_size*pool_size self.channel_attention = SEModule(num_corr_channel,reduction=4) self.spatial_attention = NONLocalBlock2D(in_channels=num_corr_channel) self.use_NL = use_NL def forward(self, feat1, feat2, bb1): """Runs the ATOM IoUNet during training operation. This forward pass is mainly used for training. Call the individual functions during tracking instead. args: feat1: Features from the reference frames (4 or 5 dims). feat2: Features from the test frames (4 or 5 dims). bb1: Target boxes (x,y,w,h) in image coords in the reference samples. Dims (images, sequences, 4). proposals2: Proposal boxes for which the IoU will be predicted (images, sequences, num_proposals, 4).""" assert bb1.dim() == 3 # num_images, num_sequences = bb1.size()[:2] # 1, 64 # Extract first train sample if len(feat1)==1: feat1 = feat1[0] # size为(64,C,H,W) feat2 = feat2[0] # size为(64,C,H,W) bb1 = bb1[0,...] # (64,4) else: raise ValueError("Only support single-layer feature map") '''get PrRoIPool feature ''' # Add batch_index to rois batch_size = bb1.shape[0] batch_index = torch.arange(batch_size, dtype=torch.float32).view(-1, 1).to(bb1.device) # (64,1) # input bb is in format xywh, convert it to x0y0x1y1 format bb1 = bb1.clone() bb1[:, 2:4] = bb1[:, 0:2] + bb1[:, 2:4] roi1 = torch.cat((batch_index, bb1), dim=1) #(64,1),(64,4) ---> (64,5) feat_roi1 = self.prroi_pool(feat1, roi1) # (64,C,H,W) feat_corr,_ = self.corr_fun(feat_roi1, feat2) # print('相关后的特征维度是:',feat_corr.size())#(batch,StxSt,Sr,Sr) '''channel attention: Squeeze and Excitation''' feat_ca = self.channel_attention(feat_corr) # 计算通道注意力特征 '''spatial attention: Non-local 2D''' feat_sa = self.spatial_attention(feat_ca) return feat_sa def get_ref_kernel(self, feat1, bb1): assert bb1.dim() == 3 # num_images, num_sequences = bb1.size()[:2] # 1, 64 # Extract first train sample if len(feat1) == 1: feat1 = feat1[0] # size为(64,C,H,W) bb1 = bb1[0, ...] # (64,4) else: raise ValueError("Only support single-layer feature map") '''get PrRoIPool feature ''' # Add batch_index to rois batch_size = bb1.shape[0] batch_index = torch.arange(batch_size, dtype=torch.float32).view(-1, 1).to(bb1.device) # (64,1) # input bb is in format xywh, convert it to x0y0x1y1 format bb1 = bb1.clone() bb1[:, 2:4] = bb1[:, 0:2] + bb1[:, 2:4] roi1 = torch.cat((batch_index, bb1), dim=1) # (64,1),(64,4) ---> (64,5) '''注意: feat1 and roi1 must be cuda tensor''' self.ref_kernel = self.prroi_pool(feat1.float(), roi1) # (64,C,H,W) # self.ref_kernel.half() def fuse_feat(self, feat2): '''fuse features from reference and test branch''' if len(feat2) == 1: feat2 = feat2[0] '''Step1: pixel-wise correlation''' feat_corr,_ = self.corr_fun(self.ref_kernel, feat2) # print('相关后的特征维度是:',feat_corr.size())#(batch,StxSt,Sr,Sr) (batch,64,16,16) '''Step2: channel attention: Squeeze and Excitation''' feat_ca = self.channel_attention(feat_corr) # 计算通道注意力特征 if not self.use_NL: # print('not use non-local') return feat_ca else: '''Step3: spatial attention: Non-local 2D''' feat_sa = self.spatial_attention(feat_ca) return feat_sa def corr_fun(self, Kernel_tmp, Feature, KERs=None): size = Kernel_tmp.size() CORR = [] Kernel = [] for i in range(len(Feature)): ker = Kernel_tmp[i:i + 1] fea = Feature[i:i + 1] ker = ker.view(size[1], size[2] * size[3]).transpose(0, 1) ker = ker.unsqueeze(2).unsqueeze(3) if not (type(KERs) == type(None)): ker = torch.cat([ker, KERs[i]], 0) co = F.conv2d(fea, ker.contiguous()) CORR.append(co) ker = ker.unsqueeze(0) Kernel.append(ker) corr = torch.cat(CORR, 0) Kernel = torch.cat(Kernel, 0) return corr, Kernel ================================================ FILE: external/AR/ltr/models/neck/neck_utils.py ================================================ import torch.nn as nn import torch from torch.nn import functional as F def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1): return nn.Sequential( nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=True), nn.BatchNorm2d(out_planes), nn.ReLU(inplace=True)) '''Channel attention module''' class SEModule(nn.Module): def __init__(self, channels, reduction=4): super(SEModule, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1, padding=0) self.relu = nn.ReLU(inplace=True) self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1, padding=0) self.sigmoid = nn.Sigmoid() def forward(self, x): module_input = x x = self.avg_pool(x) x = self.fc1(x) x = self.relu(x) x = self.fc2(x) x = self.sigmoid(x) return module_input * x '''Non-local module''' class _NonLocalBlockND(nn.Module): def __init__(self, in_channels, inter_channels=None, dimension=3, sub_sample=True, bn_layer=True): """ :param in_channels: :param inter_channels: :param dimension: :param sub_sample: :param bn_layer: """ super(_NonLocalBlockND, self).__init__() assert dimension in [1, 2, 3] self.dimension = dimension self.sub_sample = sub_sample self.in_channels = in_channels self.inter_channels = inter_channels if self.inter_channels is None: self.inter_channels = in_channels // 2 if self.inter_channels == 0: self.inter_channels = 1 if dimension == 3: conv_nd = nn.Conv3d max_pool_layer = nn.MaxPool3d(kernel_size=(1, 2, 2)) bn = nn.BatchNorm3d elif dimension == 2: conv_nd = nn.Conv2d max_pool_layer = nn.MaxPool2d(kernel_size=(2, 2)) bn = nn.BatchNorm2d else: conv_nd = nn.Conv1d max_pool_layer = nn.MaxPool1d(kernel_size=(2)) bn = nn.BatchNorm1d self.g = conv_nd(in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1, padding=0) if bn_layer: self.W = nn.Sequential( conv_nd(in_channels=self.inter_channels, out_channels=self.in_channels, kernel_size=1, stride=1, padding=0), bn(self.in_channels) ) nn.init.constant_(self.W[1].weight, 0) nn.init.constant_(self.W[1].bias, 0) else: self.W = conv_nd(in_channels=self.inter_channels, out_channels=self.in_channels, kernel_size=1, stride=1, padding=0) nn.init.constant_(self.W.weight, 0) nn.init.constant_(self.W.bias, 0) self.theta = conv_nd(in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1, padding=0) self.phi = conv_nd(in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1, padding=0) if sub_sample: self.g = nn.Sequential(self.g, max_pool_layer) self.phi = nn.Sequential(self.phi, max_pool_layer) def forward(self, x, return_nl_map=False): """ :param x: (b, c, t, h, w) :param return_nl_map: if True return z, nl_map, else only return z. :return: """ batch_size = x.size(0) g_x = self.g(x).view(batch_size, self.inter_channels, -1) g_x = g_x.permute(0, 2, 1) theta_x = self.theta(x).view(batch_size, self.inter_channels, -1) theta_x = theta_x.permute(0, 2, 1) phi_x = self.phi(x).view(batch_size, self.inter_channels, -1) f = torch.matmul(theta_x, phi_x) f_div_C = F.softmax(f, -1) y = torch.matmul(f_div_C, g_x) y = y.permute(0, 2, 1).contiguous() y = y.view(batch_size, self.inter_channels, *x.size()[2:]) W_y = self.W(y) z = W_y + x if return_nl_map: return z, f_div_C return z class NONLocalBlock2D(_NonLocalBlockND): def __init__(self, in_channels, inter_channels=None, sub_sample=True, bn_layer=True): super(NONLocalBlock2D, self).__init__(in_channels, inter_channels=inter_channels, dimension=2, sub_sample=sub_sample, bn_layer=bn_layer,) ================================================ FILE: external/AR/ltr/models/target_classifier/__init__.py ================================================ from .linear_filter import LinearFilter ================================================ FILE: external/AR/ltr/models/target_classifier/features.py ================================================ import torch from torch import nn import torch.nn.functional as F from torchvision.models.resnet import BasicBlock, Bottleneck from ltr.models.layers.normalization import InstanceL2Norm from ltr.models.layers.transform import InterpCat def residual_basic_block(feature_dim=256, num_blocks=1, l2norm=True, final_conv=False, norm_scale=1.0, out_dim=None, interp_cat=False, final_relu=False, init_pool=False): """Construct a network block based on the BasicBlock used in ResNet 18 and 34.""" if out_dim is None: out_dim = feature_dim feat_layers = [] if interp_cat: feat_layers.append(InterpCat()) if init_pool: feat_layers.append(nn.MaxPool2d(kernel_size=3, stride=2, padding=1)) for i in range(num_blocks): odim = feature_dim if i < num_blocks - 1 + int(final_conv) else out_dim feat_layers.append(BasicBlock(feature_dim, odim)) if final_conv: feat_layers.append(nn.Conv2d(feature_dim, out_dim, kernel_size=3, padding=1, bias=False)) if final_relu: feat_layers.append(nn.ReLU(inplace=True)) if l2norm: feat_layers.append(InstanceL2Norm(scale=norm_scale)) return nn.Sequential(*feat_layers) def residual_basic_block_pool(feature_dim=256, num_blocks=1, l2norm=True, final_conv=False, norm_scale=1.0, out_dim=None, pool=True): """Construct a network block based on the BasicBlock used in ResNet.""" if out_dim is None: out_dim = feature_dim feat_layers = [] for i in range(num_blocks): odim = feature_dim if i < num_blocks - 1 + int(final_conv) else out_dim feat_layers.append(BasicBlock(feature_dim, odim)) if final_conv: feat_layers.append(nn.Conv2d(feature_dim, out_dim, kernel_size=3, padding=1, bias=False)) if pool: feat_layers.append(nn.MaxPool2d(kernel_size=3, stride=2, padding=1)) if l2norm: feat_layers.append(InstanceL2Norm(scale=norm_scale)) return nn.Sequential(*feat_layers) def residual_bottleneck(feature_dim=256, num_blocks=1, l2norm=True, final_conv=False, norm_scale=1.0, out_dim=None, interp_cat=False, final_relu=False, final_pool=False): """Construct a network block based on the Bottleneck block used in ResNet.""" if out_dim is None: out_dim = feature_dim feat_layers = [] if interp_cat: feat_layers.append(InterpCat()) for i in range(num_blocks): planes = feature_dim if i < num_blocks - 1 + int(final_conv) else out_dim // 4 feat_layers.append(Bottleneck(4*feature_dim, planes)) if final_conv: feat_layers.append(nn.Conv2d(4*feature_dim, out_dim, kernel_size=3, padding=1, bias=False)) if final_relu: feat_layers.append(nn.ReLU(inplace=True)) if final_pool: feat_layers.append(nn.MaxPool2d(kernel_size=3, stride=2, padding=1)) if l2norm: feat_layers.append(InstanceL2Norm(scale=norm_scale)) return nn.Sequential(*feat_layers) ================================================ FILE: external/AR/ltr/models/target_classifier/initializer.py ================================================ import torch.nn as nn import torch import torch.nn.functional as F from ltr.external.PreciseRoIPooling.pytorch.prroi_pool import PrRoIPool2D from ltr.models.layers.blocks import conv_block import math class FilterPool(nn.Module): """Pool the target region in a feature map. args: filter_size: Size of the filter. feature_stride: Input feature stride. pool_square: Do a square pooling instead of pooling the exact target region.""" def __init__(self, filter_size=1, feature_stride=16, pool_square=False): super().__init__() self.prroi_pool = PrRoIPool2D(filter_size, filter_size, 1/feature_stride) self.pool_square = pool_square def forward(self, feat, bb): """Pool the regions in bb. args: feat: Input feature maps. Dims (num_samples, feat_dim, H, W). bb: Target bounding boxes (x, y, w, h) in the image coords. Dims (num_samples, 4). returns: pooled_feat: Pooled features. Dims (num_samples, feat_dim, wH, wW).""" # Add batch_index to rois bb = bb.reshape(-1,4) num_images_total = bb.shape[0] batch_index = torch.arange(num_images_total, dtype=torch.float32).reshape(-1, 1).to(bb.device) # input bb is in format xywh, convert it to x0y0x1y1 format pool_bb = bb.clone() if self.pool_square: bb_sz = pool_bb[:, 2:4].prod(dim=1, keepdim=True).sqrt() pool_bb[:, :2] += pool_bb[:, 2:]/2 - bb_sz/2 pool_bb[:, 2:] = bb_sz pool_bb[:, 2:4] = pool_bb[:, 0:2] + pool_bb[:, 2:4] roi1 = torch.cat((batch_index, pool_bb), dim=1) return self.prroi_pool(feat, roi1) class FilterInitializer(nn.Module): """Initializes a target classification filter by applying a number of conv layers before and after pooling the target region. args: filter_size: Size of the filter. feature_dim: Input feature dimentionality. feature_stride: Input feature stride. pool_square: Do a square pooling instead of pooling the exact target region. filter_norm: Normalize the output filter with its size in the end. num_filter_pre_convs: Conv layers before pooling. num_filter_post_convs: Conv layers after pooling.""" def __init__(self, filter_size=1, feature_dim=256, feature_stride=16, pool_square=False, filter_norm=True, num_filter_pre_convs=1, num_filter_post_convs=0): super().__init__() self.filter_pool = FilterPool(filter_size=filter_size, feature_stride=feature_stride, pool_square=pool_square) self.filter_norm = filter_norm # Make pre conv pre_conv_layers = [] for i in range(num_filter_pre_convs): pre_conv_layers.append(conv_block(feature_dim, feature_dim, kernel_size=3, padding=1)) self.filter_pre_layers = nn.Sequential(*pre_conv_layers) if pre_conv_layers else None # Make post conv post_conv_layers = [] for i in range(num_filter_post_convs): post_conv_layers.append(conv_block(feature_dim, feature_dim, kernel_size=1, padding=0)) post_conv_layers.append(nn.Conv2d(feature_dim, feature_dim, kernel_size=1, padding=0)) self.filter_post_layers = nn.Sequential(*post_conv_layers) # Init weights for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def forward(self, feat, bb): """Runs the initializer module. Note that [] denotes an optional dimension. args: feat: Input feature maps. Dims (images_in_sequence, [sequences], feat_dim, H, W). bb: Target bounding boxes (x, y, w, h) in the image coords. Dims (images_in_sequence, [sequences], 4). returns: weights: The output weights. Dims (sequences, feat_dim, wH, wW).""" num_images = bb.shape[0] if bb.dim() == 3 else 1 if self.filter_pre_layers is not None: feat = self.filter_pre_layers(feat.reshape(-1, feat.shape[-3], feat.shape[-2], feat.shape[-1])) feat_post = self.filter_pool(feat, bb) weights = self.filter_post_layers(feat_post) if num_images > 1: weights = torch.mean(weights.reshape(num_images, -1, weights.shape[-3], weights.shape[-2], weights.shape[-1]), dim=0) if self.filter_norm: weights = weights / (weights.shape[1] * weights.shape[2] * weights.shape[3]) return weights class FilterInitializerLinear(nn.Module): """Initializes a target classification filter by applying a linear conv layer and then pooling the target region. args: filter_size: Size of the filter. feature_dim: Input feature dimentionality. feature_stride: Input feature stride. pool_square: Do a square pooling instead of pooling the exact target region. filter_norm: Normalize the output filter with its size in the end. conv_ksz: Kernel size of the conv layer before pooling.""" def __init__(self, filter_size=1, feature_dim=256, feature_stride=16, pool_square=False, filter_norm=True, conv_ksz=3, init_weights='default'): super().__init__() self.filter_conv = nn.Conv2d(feature_dim, feature_dim, kernel_size=conv_ksz, padding=conv_ksz // 2) self.filter_pool = FilterPool(filter_size=filter_size, feature_stride=feature_stride, pool_square=pool_square) self.filter_norm = filter_norm # Init weights for m in self.modules(): if isinstance(m, nn.Conv2d): if init_weights == 'default': n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif init_weights == 'zero': m.weight.data.zero_() if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def forward(self, feat, bb): """Runs the initializer module. Note that [] denotes an optional dimension. args: feat: Input feature maps. Dims (images_in_sequence, [sequences], feat_dim, H, W). bb: Target bounding boxes (x, y, w, h) in the image coords. Dims (images_in_sequence, [sequences], 4). returns: weights: The output weights. Dims (sequences, feat_dim, wH, wW).""" num_images = feat.shape[0] feat = self.filter_conv(feat.reshape(-1, feat.shape[-3], feat.shape[-2], feat.shape[-1])) weights = self.filter_pool(feat, bb) # If multiple input images, compute the initial filter as the average filter. if num_images > 1: weights = torch.mean(weights.reshape(num_images, -1, weights.shape[-3], weights.shape[-2], weights.shape[-1]), dim=0) if self.filter_norm: weights = weights / (weights.shape[1] * weights.shape[2] * weights.shape[3]) return weights class FilterInitializerZero(nn.Module): """Initializes a target classification filter with zeros. args: filter_size: Size of the filter. feature_dim: Input feature dimentionality.""" def __init__(self, filter_size=1, feature_dim=256): super().__init__() self.filter_size = (feature_dim, filter_size, filter_size) def forward(self, feat, bb): """Runs the initializer module. Note that [] denotes an optional dimension. args: feat: Input feature maps. Dims (images_in_sequence, [sequences], feat_dim, H, W). bb: Target bounding boxes (x, y, w, h) in the image coords. Dims (images_in_sequence, [sequences], 4). returns: weights: The output weights. Dims (sequences, feat_dim, wH, wW).""" num_sequences = feat.shape[1] if feat.dim() == 5 else 1 return feat.new_zeros(num_sequences, self.filter_size[0], self.filter_size[1], self.filter_size[2]) class FilterInitializerSiamese(nn.Module): """Initializes a target classification filter by only pooling the target region (similar to Siamese trackers). args: filter_size: Size of the filter. feature_stride: Input feature stride. pool_square: Do a square pooling instead of pooling the exact target region. filter_norm: Normalize the output filter with its size in the end.""" def __init__(self, filter_size=1, feature_stride=16, pool_square=False, filter_norm=True): super().__init__() self.filter_pool = FilterPool(filter_size=filter_size, feature_stride=feature_stride, pool_square=pool_square) self.filter_norm = filter_norm # Init weights for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def forward(self, feat, bb): """Runs the initializer module. Note that [] denotes an optional dimension. args: feat: Input feature maps. Dims (images_in_sequence, [sequences], feat_dim, H, W). bb: Target bounding boxes (x, y, w, h) in the image coords. Dims (images_in_sequence, [sequences], 4). returns: weights: The output weights. Dims (sequences, feat_dim, wH, wW).""" num_images = feat.shape[0] feat = feat.reshape(-1, feat.shape[-3], feat.shape[-2], feat.shape[-1]) weights = self.filter_pool(feat, bb) if num_images > 1: weights = torch.mean(weights.reshape(num_images, -1, weights.shape[-3], weights.shape[-2], weights.shape[-1]), dim=0) if self.filter_norm: weights = weights / (weights.shape[1] * weights.shape[2] * weights.shape[3]) return weights ================================================ FILE: external/AR/ltr/models/target_classifier/linear_filter.py ================================================ import torch.nn as nn import ltr.models.layers.filter as filter_layer import math class LinearFilter(nn.Module): """Target classification filter module. args: filter_size: Size of filter (int). filter_initialize: Filter initializer module. filter_optimizer: Filter optimizer module. feature_extractor: Feature extractor module applied to the input backbone features.""" def __init__(self, filter_size, filter_initializer, filter_optimizer=None, feature_extractor=None): super().__init__() self.filter_size = filter_size # Modules self.filter_initializer = filter_initializer self.filter_optimizer = filter_optimizer self.feature_extractor = feature_extractor # Init weights for m in self.feature_extractor.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def forward(self, train_feat, test_feat, train_bb, *args, **kwargs): """Learns a target classification filter based on the train samples and return the resulting classification scores on the test samples. The forward function is ONLY used for training. Call the individual functions during tracking. args: train_feat: Backbone features for the train samples (4 or 5 dims). test_feat: Backbone features for the test samples (4 or 5 dims). trian_bb: Target boxes (x,y,w,h) for the train samples in image coordinates. Dims (images, sequences, 4). *args, **kwargs: These are passed to the optimizer module. returns: test_scores: Classification scores on the test samples.""" assert train_bb.dim() == 3 num_sequences = train_bb.shape[1] if train_feat.dim() == 5: train_feat = train_feat.reshape(-1, *train_feat.shape[-3:]) if test_feat.dim() == 5: test_feat = test_feat.reshape(-1, *test_feat.shape[-3:]) # Extract features train_feat = self.extract_classification_feat(train_feat, num_sequences) test_feat = self.extract_classification_feat(test_feat, num_sequences) # Train filter filter, filter_iter, losses = self.get_filter(train_feat, train_bb, *args, **kwargs) # Classify samples using all return filters test_scores = [self.classify(f, test_feat) for f in filter_iter] return test_scores def extract_classification_feat(self, feat, num_sequences=None): """Extract classification features based on the input backbone features.""" if self.feature_extractor is None: return feat if num_sequences is None: return self.feature_extractor(feat) output = self.feature_extractor(feat) return output.reshape(-1, num_sequences, *output.shape[-3:]) def classify(self, weights, feat): """Run classifier (filter) on the features (feat).""" scores = filter_layer.apply_filter(feat, weights) return scores def get_filter(self, feat, bb, *args, **kwargs): """Outputs the learned filter based on the input features (feat) and target boxes (bb) by running the filter initializer and optimizer. Note that [] denotes an optional dimension. args: feat: Input feature maps. Dims (images_in_sequence, [sequences], feat_dim, H, W). bb: Target bounding boxes (x, y, w, h) in the image coords. Dims (images_in_sequence, [sequences], 4). *args, **kwargs: These are passed to the optimizer module. returns: weights: The final oprimized weights. Dims (sequences, feat_dim, wH, wW). weight_iterates: The weights computed in each iteration (including initial input and final output). losses: Train losses.""" weights = self.filter_initializer(feat, bb) if self.filter_optimizer is not None: weights, weights_iter, losses = self.filter_optimizer(weights, feat=feat, bb=bb, *args, **kwargs) else: weights_iter = [weights] losses = None return weights, weights_iter, losses def train_classifier(self, backbone_feat, bb): num_sequences = bb.shape[1] if backbone_feat.dim() == 5: backbone_feat = backbone_feat.reshape(-1, *backbone_feat.shape[-3:]) # Extract features train_feat = self.extract_classification_feat(backbone_feat, num_sequences) # Get filters from each iteration final_filter, _, train_losses = self.get_filter(train_feat, bb) return final_filter, train_losses def track_frame(self, filter_weights, backbone_feat): if backbone_feat.dim() == 5: num_sequences = backbone_feat.shape[1] backbone_feat = backbone_feat.reshape(-1, *backbone_feat.shape[-3:]) else: num_sequences = None test_feat = self.extract_classification_feat(backbone_feat, num_sequences) scores = filter_layer.apply_filter(test_feat, filter_weights) return scores ================================================ FILE: external/AR/ltr/models/target_classifier/optimizer.py ================================================ import torch.nn as nn import torch import torch.nn.functional as F import ltr.models.layers.filter as filter_layer import ltr.models.layers.activation as activation from ltr.models.layers.distance import DistanceMap import math class DiMPSteepestDescentGN(nn.Module): """Optimizer module for DiMP. It unrolls the steepest descent with Gauss-Newton iterations to optimize the target filter. Moreover it learns parameters in the loss itself, as described in the DiMP paper. args: num_iter: Number of default optimization iterations. feat_stride: The stride of the input feature. init_step_length: Initial scaling of the step length (which is then learned). init_filter_reg: Initial filter regularization weight (which is then learned). init_gauss_sigma: The standard deviation to use for the initialization of the label function. num_dist_bins: Number of distance bins used for learning the loss label, mask and weight. bin_displacement: The displacement of the bins (level of discritization). mask_init_factor: Parameter controlling the initialization of the target mask. score_act: Type of score activation (target mask computation) to use. The default 'relu' is what is described in the paper. act_param: Parameter for the score_act. min_filter_reg: Enforce a minimum value on the regularization (helps stability sometimes). mask_act: What activation to do on the output of the mask computation ('sigmoid' or 'linear'). detach_length: Detach the filter every n-th iteration. Default is to never detech, i.e. 'Inf'. alpha_eps: Term in the denominator of the steepest descent that stabalizes learning. """ def __init__(self, num_iter=1, feat_stride=16, init_step_length=1.0, init_filter_reg=1e-2, init_gauss_sigma=1.0, num_dist_bins=5, bin_displacement=1.0, mask_init_factor=4.0, score_act='relu', act_param=None, min_filter_reg=1e-3, mask_act='sigmoid', detach_length=float('Inf'), alpha_eps=0): super().__init__() self.num_iter = num_iter self.feat_stride = feat_stride self.log_step_length = nn.Parameter(math.log(init_step_length) * torch.ones(1)) self.filter_reg = nn.Parameter(init_filter_reg * torch.ones(1)) self.distance_map = DistanceMap(num_dist_bins, bin_displacement) self.min_filter_reg = min_filter_reg self.detach_length = detach_length self.alpha_eps = alpha_eps # Distance coordinates d = torch.arange(num_dist_bins, dtype=torch.float32).reshape(1,-1,1,1) * bin_displacement if init_gauss_sigma == 0: init_gauss = torch.zeros_like(d) init_gauss[0,0,0,0] = 1 else: init_gauss = torch.exp(-1/2 * (d / init_gauss_sigma)**2) # Module that predicts the target label function (y in the paper) self.label_map_predictor = nn.Conv2d(num_dist_bins, 1, kernel_size=1, bias=False) self.label_map_predictor.weight.data = init_gauss - init_gauss.min() # Module that predicts the target mask (m in the paper) mask_layers = [nn.Conv2d(num_dist_bins, 1, kernel_size=1, bias=False)] if mask_act == 'sigmoid': mask_layers.append(nn.Sigmoid()) init_bias = 0.0 elif mask_act == 'linear': init_bias = 0.5 else: raise ValueError('Unknown activation') self.target_mask_predictor = nn.Sequential(*mask_layers) self.target_mask_predictor[0].weight.data = mask_init_factor * torch.tanh(2.0 - d) + init_bias # Module that predicts the residual weights (v in the paper) self.spatial_weight_predictor = nn.Conv2d(num_dist_bins, 1, kernel_size=1, bias=False) self.spatial_weight_predictor.weight.data.fill_(1.0) # The score actvation and its derivative if score_act == 'bentpar': self.score_activation = activation.BentIdentPar(act_param) self.score_activation_deriv = activation.BentIdentParDeriv(act_param) elif score_act == 'relu': self.score_activation = activation.LeakyReluPar() self.score_activation_deriv = activation.LeakyReluParDeriv() else: raise ValueError('Unknown score activation') def forward(self, weights, feat, bb, sample_weight=None, num_iter=None, compute_losses=True): """Runs the optimizer module. Note that [] denotes an optional dimension. args: weights: Initial weights. Dims (sequences, feat_dim, wH, wW). feat: Input feature maps. Dims (images_in_sequence, [sequences], feat_dim, H, W). bb: Target bounding boxes (x, y, w, h) in the image coords. Dims (images_in_sequence, [sequences], 4). sample_weight: Optional weight for each sample. Dims: (images_in_sequence, [sequences]). num_iter: Number of iterations to run. compute_losses: Whether to compute the (train) loss in each iteration. returns: weights: The final oprimized weights. weight_iterates: The weights computed in each iteration (including initial input and final output). losses: Train losses.""" # Sizes num_iter = self.num_iter if num_iter is None else num_iter num_images = feat.shape[0] num_sequences = feat.shape[1] if feat.dim() == 5 else 1 filter_sz = (weights.shape[-2], weights.shape[-1]) output_sz = (feat.shape[-2] + (weights.shape[-2] + 1) % 2, feat.shape[-1] + (weights.shape[-1] + 1) % 2) # Get learnable scalars step_length_factor = torch.exp(self.log_step_length) reg_weight = (self.filter_reg*self.filter_reg).clamp(min=self.min_filter_reg**2) # Compute distance map dmap_offset = (torch.Tensor(filter_sz).to(bb.device) % 2) / 2.0 center = ((bb[..., :2] + bb[..., 2:] / 2) / self.feat_stride).reshape(-1, 2).flip((1,)) - dmap_offset dist_map = self.distance_map(center, output_sz) # Compute label map masks and weight label_map = self.label_map_predictor(dist_map).reshape(num_images, num_sequences, *dist_map.shape[-2:]) target_mask = self.target_mask_predictor(dist_map).reshape(num_images, num_sequences, *dist_map.shape[-2:]) spatial_weight = self.spatial_weight_predictor(dist_map).reshape(num_images, num_sequences, *dist_map.shape[-2:]) # Get total sample weights if sample_weight is None: sample_weight = math.sqrt(1.0 / num_images) * spatial_weight elif isinstance(sample_weight, torch.Tensor): sample_weight = sample_weight.sqrt().reshape(num_images, num_sequences, 1, 1) * spatial_weight backprop_through_learning = (self.detach_length > 0) weight_iterates = [weights] losses = [] for i in range(num_iter): if not backprop_through_learning or (i > 0 and i % self.detach_length == 0): weights = weights.detach() # Compute residuals scores = filter_layer.apply_filter(feat, weights) scores_act = self.score_activation(scores, target_mask) score_mask = self.score_activation_deriv(scores, target_mask) residuals = sample_weight * (scores_act - label_map) if compute_losses: losses.append(((residuals**2).sum() + reg_weight * (weights**2).sum())/num_sequences) # Compute gradient residuals_mapped = score_mask * (sample_weight * residuals) weights_grad = filter_layer.apply_feat_transpose(feat, residuals_mapped, filter_sz, training=self.training) + \ reg_weight * weights # Map the gradient with the Jacobian scores_grad = filter_layer.apply_filter(feat, weights_grad) scores_grad = sample_weight * (score_mask * scores_grad) # Compute optimal step length alpha_num = (weights_grad * weights_grad).sum(dim=(1,2,3)) alpha_den = ((scores_grad * scores_grad).reshape(num_images, num_sequences, -1).sum(dim=(0,2)) + (reg_weight + self.alpha_eps) * alpha_num).clamp(1e-8) alpha = alpha_num / alpha_den # Update filter weights = weights - (step_length_factor * alpha.reshape(-1, 1, 1, 1)) * weights_grad # Add the weight iterate weight_iterates.append(weights) if compute_losses: scores = filter_layer.apply_filter(feat, weights) scores = self.score_activation(scores, target_mask) losses.append((((sample_weight * (scores - label_map))**2).sum() + reg_weight * (weights**2).sum())/num_sequences) return weights, weight_iterates, losses class DiMPL2SteepestDescentGN(nn.Module): """A simpler optimizer module that uses L2 loss. args: num_iter: Number of default optimization iterations. feat_stride: The stride of the input feature. init_step_length: Initial scaling of the step length (which is then learned). gauss_sigma: The standard deviation of the label function. hinge_threshold: Threshold for the hinge-based loss (see DiMP paper). init_filter_reg: Initial filter regularization weight (which is then learned). min_filter_reg: Enforce a minimum value on the regularization (helps stability sometimes). detach_length: Detach the filter every n-th iteration. Default is to never detech, i.e. 'Inf'. alpha_eps: Term in the denominator of the steepest descent that stabalizes learning. """ def __init__(self, num_iter=1, feat_stride=16, init_step_length=1.0, gauss_sigma=1.0, hinge_threshold=-999, init_filter_reg=1e-2, min_filter_reg=1e-3, detach_length=float('Inf'), alpha_eps=0.0): super().__init__() self.num_iter = num_iter self.feat_stride = feat_stride self.log_step_length = nn.Parameter(math.log(init_step_length) * torch.ones(1)) self.filter_reg = nn.Parameter(init_filter_reg * torch.ones(1)) self.min_filter_reg = min_filter_reg self.detach_length = detach_length self.hinge_threshold = hinge_threshold self.gauss_sigma = gauss_sigma self.alpha_eps = alpha_eps def get_label(self, center, output_sz): center = center.reshape(center.shape[0], -1, center.shape[-1]) k0 = torch.arange(output_sz[0], dtype=torch.float32).reshape(1, 1, -1, 1).to(center.device) k1 = torch.arange(output_sz[1], dtype=torch.float32).reshape(1, 1, 1, -1).to(center.device) g0 = torch.exp(-1.0 / (2 * self.gauss_sigma ** 2) * (k0 - center[:,:,0].reshape(*center.shape[:2], 1, 1)) ** 2) g1 = torch.exp(-1.0 / (2 * self.gauss_sigma ** 2) * (k1 - center[:,:,1].reshape(*center.shape[:2], 1, 1)) ** 2) gauss = g0 * g1 return gauss def forward(self, weights, feat, bb, sample_weight=None, num_iter=None, compute_losses=True): """Runs the optimizer module. Note that [] denotes an optional dimension. args: weights: Initial weights. Dims (sequences, feat_dim, wH, wW). feat: Input feature maps. Dims (images_in_sequence, [sequences], feat_dim, H, W). bb: Target bounding boxes (x, y, w, h) in the image coords. Dims (images_in_sequence, [sequences], 4). sample_weight: Optional weight for each sample. Dims: (images_in_sequence, [sequences]). num_iter: Number of iterations to run. compute_losses: Whether to compute the (train) loss in each iteration. returns: weights: The final oprimized weights. weight_iterates: The weights computed in each iteration (including initial input and final output). losses: Train losses.""" # Sizes num_iter = self.num_iter if num_iter is None else num_iter num_images = feat.shape[0] num_sequences = feat.shape[1] if feat.dim() == 5 else 1 filter_sz = (weights.shape[-2], weights.shape[-1]) output_sz = (feat.shape[-2] + (weights.shape[-2] + 1) % 2, feat.shape[-1] + (weights.shape[-1] + 1) % 2) # Get learnable scalars step_length_factor = torch.exp(self.log_step_length) reg_weight = (self.filter_reg*self.filter_reg).clamp(min=self.min_filter_reg**2) # Compute distance map dmap_offset = (torch.Tensor(filter_sz).to(bb.device) % 2) / 2.0 center = ((bb[..., :2] + bb[..., 2:] / 2) / self.feat_stride).flip((-1,)) - dmap_offset label_map = self.get_label(center, output_sz) target_mask = (label_map > self.hinge_threshold).float() label_map *= target_mask # Get total sample weights if sample_weight is None: sample_weight = math.sqrt(1.0 / num_images) elif isinstance(sample_weight, torch.Tensor): sample_weight = sample_weight.sqrt().reshape(num_images, num_sequences, 1, 1) weight_iterates = [weights] losses = [] for i in range(num_iter): if i > 0 and i % self.detach_length == 0: weights = weights.detach() # Compute residuals scores = filter_layer.apply_filter(feat, weights) scores_act = target_mask * scores + (1.0 - target_mask) * F.relu(scores) score_mask = target_mask + (1.0 - target_mask) * (scores.detach() > 0).float() residuals = sample_weight * (scores_act - label_map) if compute_losses: losses.append(((residuals**2).sum() + reg_weight * (weights**2).sum())/num_sequences) # Compute gradient residuals_mapped = score_mask * (sample_weight * residuals) weights_grad = filter_layer.apply_feat_transpose(feat, residuals_mapped, filter_sz, training=self.training) + \ reg_weight * weights # Map the gradient with the Jacobian scores_grad = filter_layer.apply_filter(feat, weights_grad) scores_grad = sample_weight * (score_mask * scores_grad) # Compute optimal step length alpha_num = (weights_grad * weights_grad).sum(dim=(1,2,3)) alpha_den = ((scores_grad * scores_grad).reshape(num_images, num_sequences, -1).sum(dim=(0,2)) + (reg_weight + self.alpha_eps) * alpha_num).clamp(1e-8) alpha = alpha_num / alpha_den # Update filter weights = weights - (step_length_factor * alpha.reshape(-1, 1, 1, 1)) * weights_grad # Add the weight iterate weight_iterates.append(weights) if compute_losses: scores = filter_layer.apply_filter(feat, weights) scores = target_mask * scores + (1.0 - target_mask) * F.relu(scores) losses.append((((sample_weight * (scores - label_map))**2).sum() + reg_weight * (weights**2).sum())/num_sequences) return weights, weight_iterates, losses class PrDiMPSteepestDescentNewton(nn.Module): """Optimizer module for PrDiMP. It unrolls the steepest descent with Newton iterations to optimize the target filter. See the PrDiMP paper. args: num_iter: Number of default optimization iterations. feat_stride: The stride of the input feature. init_step_length: Initial scaling of the step length (which is then learned). init_filter_reg: Initial filter regularization weight (which is then learned). gauss_sigma: The standard deviation to use for the label density function. min_filter_reg: Enforce a minimum value on the regularization (helps stability sometimes). detach_length: Detach the filter every n-th iteration. Default is to never detech, i.e. 'Inf'. alpha_eps: Term in the denominator of the steepest descent that stabalizes learning. init_uni_weight: Weight of uniform label distribution. normalize_label: Wheter to normalize the label distribution. label_shrink: How much to shrink to label distribution. softmax_reg: Regularization in the denominator of the SoftMax. label_threshold: Threshold probabilities smaller than this. """ def __init__(self, num_iter=1, feat_stride=16, init_step_length=1.0, init_filter_reg=1e-2, gauss_sigma=1.0, min_filter_reg=1e-3, detach_length=float('Inf'), alpha_eps=0.0, init_uni_weight=None, normalize_label=False, label_shrink=0, softmax_reg=None, label_threshold=0.0): super().__init__() self.num_iter = num_iter self.feat_stride = feat_stride self.log_step_length = nn.Parameter(math.log(init_step_length) * torch.ones(1)) self.filter_reg = nn.Parameter(init_filter_reg * torch.ones(1)) self.gauss_sigma = gauss_sigma self.min_filter_reg = min_filter_reg self.detach_length = detach_length self.alpha_eps = alpha_eps self.uni_weight = 0 if init_uni_weight is None else init_uni_weight self.normalize_label = normalize_label self.label_shrink = label_shrink self.softmax_reg = softmax_reg self.label_threshold = label_threshold def get_label_density(self, center, output_sz): center = center.reshape(center.shape[0], -1, center.shape[-1]) k0 = torch.arange(output_sz[0], dtype=torch.float32).reshape(1, 1, -1, 1).to(center.device) k1 = torch.arange(output_sz[1], dtype=torch.float32).reshape(1, 1, 1, -1).to(center.device) dist0 = (k0 - center[:,:,0].reshape(*center.shape[:2], 1, 1)) ** 2 dist1 = (k1 - center[:,:,1].reshape(*center.shape[:2], 1, 1)) ** 2 if self.gauss_sigma == 0: dist0_view = dist0.reshape(-1, dist0.shape[-2]) dist1_view = dist1.reshape(-1, dist1.shape[-1]) one_hot0 = torch.zeros_like(dist0_view) one_hot1 = torch.zeros_like(dist1_view) one_hot0[torch.arange(one_hot0.shape[0]), dist0_view.argmin(dim=-1)] = 1.0 one_hot1[torch.arange(one_hot1.shape[0]), dist1_view.argmin(dim=-1)] = 1.0 gauss = one_hot0.reshape(dist0.shape) * one_hot1.reshape(dist1.shape) else: g0 = torch.exp(-1.0 / (2 * self.gauss_sigma ** 2) * dist0) g1 = torch.exp(-1.0 / (2 * self.gauss_sigma ** 2) * dist1) gauss = (g0 / (2*math.pi*self.gauss_sigma**2)) * g1 gauss = gauss * (gauss > self.label_threshold).float() if self.normalize_label: gauss /= (gauss.sum(dim=(-2,-1), keepdim=True) + 1e-8) label_dens = (1.0 - self.label_shrink)*((1.0 - self.uni_weight) * gauss + self.uni_weight / (output_sz[0]*output_sz[1])) return label_dens def forward(self, weights, feat, bb, sample_weight=None, num_iter=None, compute_losses=True): """Runs the optimizer module. Note that [] denotes an optional dimension. args: weights: Initial weights. Dims (sequences, feat_dim, wH, wW). feat: Input feature maps. Dims (images_in_sequence, [sequences], feat_dim, H, W). bb: Target bounding boxes (x, y, w, h) in the image coords. Dims (images_in_sequence, [sequences], 4). sample_weight: Optional weight for each sample. Dims: (images_in_sequence, [sequences]). num_iter: Number of iterations to run. compute_losses: Whether to compute the (train) loss in each iteration. returns: weights: The final oprimized weights. weight_iterates: The weights computed in each iteration (including initial input and final output). losses: Train losses.""" # Sizes num_iter = self.num_iter if num_iter is None else num_iter num_images = feat.shape[0] num_sequences = feat.shape[1] if feat.dim() == 5 else 1 filter_sz = (weights.shape[-2], weights.shape[-1]) output_sz = (feat.shape[-2] + (weights.shape[-2] + 1) % 2, feat.shape[-1] + (weights.shape[-1] + 1) % 2) # Get learnable scalars step_length_factor = torch.exp(self.log_step_length) reg_weight = (self.filter_reg*self.filter_reg).clamp(min=self.min_filter_reg**2) # Compute label density offset = (torch.Tensor(filter_sz).to(bb.device) % 2) / 2.0 center = ((bb[..., :2] + bb[..., 2:] / 2) / self.feat_stride).flip((-1,)) - offset label_density = self.get_label_density(center, output_sz) # Get total sample weights if sample_weight is None: sample_weight = torch.Tensor([1.0 / num_images]).to(feat.device) elif isinstance(sample_weight, torch.Tensor): sample_weight = sample_weight.reshape(num_images, num_sequences, 1, 1) exp_reg = 0 if self.softmax_reg is None else math.exp(self.softmax_reg) def _compute_loss(scores, weights): return torch.sum(sample_weight.reshape(sample_weight.shape[0], -1) * (torch.log(scores.exp().sum(dim=(-2, -1)) + exp_reg) - (label_density * scores).sum(dim=(-2, -1)))) / num_sequences +\ reg_weight * (weights ** 2).sum() / num_sequences weight_iterates = [weights] losses = [] for i in range(num_iter): if i > 0 and i % self.detach_length == 0: weights = weights.detach() # Compute "residuals" scores = filter_layer.apply_filter(feat, weights) scores_softmax = activation.softmax_reg(scores.reshape(num_images, num_sequences, -1), dim=2, reg=self.softmax_reg).reshape(scores.shape) res = sample_weight*(scores_softmax - label_density) if compute_losses: losses.append(_compute_loss(scores, weights)) # Compute gradient weights_grad = filter_layer.apply_feat_transpose(feat, res, filter_sz, training=self.training) + \ reg_weight * weights # Map the gradient with the Hessian scores_grad = filter_layer.apply_filter(feat, weights_grad) sm_scores_grad = scores_softmax * scores_grad hes_scores_grad = sm_scores_grad - scores_softmax * torch.sum(sm_scores_grad, dim=(-2,-1), keepdim=True) grad_hes_grad = (scores_grad * hes_scores_grad).reshape(num_images, num_sequences, -1).sum(dim=2).clamp(min=0) grad_hes_grad = (sample_weight.reshape(sample_weight.shape[0], -1) * grad_hes_grad).sum(dim=0) # Compute optimal step length alpha_num = (weights_grad * weights_grad).sum(dim=(1,2,3)) alpha_den = (grad_hes_grad + (reg_weight + self.alpha_eps) * alpha_num).clamp(1e-8) alpha = alpha_num / alpha_den # Update filter weights = weights - (step_length_factor * alpha.reshape(-1, 1, 1, 1)) * weights_grad # Add the weight iterate weight_iterates.append(weights) if compute_losses: scores = filter_layer.apply_filter(feat, weights) losses.append(_compute_loss(scores, weights)) return weights, weight_iterates, losses ================================================ FILE: external/AR/ltr/models/target_classifier/residual_modules.py ================================================ import torch import torch.nn as nn import math import ltr.models.layers.filter as filter_layer import ltr.models.layers.activation as activation from ltr.models.layers.distance import DistanceMap from pytracking import TensorList class LinearFilterLearnGen(nn.Module): def __init__(self, feat_stride=16, init_filter_reg=1e-2, init_gauss_sigma=1.0, num_dist_bins=5, bin_displacement=1.0, mask_init_factor=4.0, score_act='bentpar', act_param=None, mask_act='sigmoid'): super().__init__() self.filter_reg = nn.Parameter(init_filter_reg * torch.ones(1)) self.feat_stride = feat_stride self.distance_map = DistanceMap(num_dist_bins, bin_displacement) # Distance coordinates d = torch.arange(num_dist_bins, dtype=torch.float32).reshape(1,-1,1,1) * bin_displacement if init_gauss_sigma == 0: init_gauss = torch.zeros_like(d) init_gauss[0,0,0,0] = 1 else: init_gauss = torch.exp(-1/2 * (d / init_gauss_sigma)**2) self.label_map_predictor = nn.Conv2d(num_dist_bins, 1, kernel_size=1, bias=False) self.label_map_predictor.weight.data = init_gauss - init_gauss.min() mask_layers = [nn.Conv2d(num_dist_bins, 1, kernel_size=1, bias=False)] if mask_act == 'sigmoid': mask_layers.append(nn.Sigmoid()) init_bias = 0.0 elif mask_act == 'linear': init_bias = 0.5 else: raise ValueError('Unknown activation') self.target_mask_predictor = nn.Sequential(*mask_layers) self.target_mask_predictor[0].weight.data = mask_init_factor * torch.tanh(2.0 - d) + init_bias self.spatial_weight_predictor = nn.Conv2d(num_dist_bins, 1, kernel_size=1, bias=False) self.spatial_weight_predictor.weight.data.fill_(1.0) if score_act == 'bentpar': self.score_activation = activation.BentIdentPar(act_param) elif score_act == 'relu': self.score_activation = activation.LeakyReluPar() else: raise ValueError('Unknown activation') def forward(self, meta_parameter: TensorList, feat, bb, sample_weight=None, is_distractor=None): filter = meta_parameter[0] num_images = feat.shape[0] num_sequences = feat.shape[1] if feat.dim() == 5 else 1 filter_sz = (filter.shape[-2], filter.shape[-1]) # Compute scores scores = filter_layer.apply_filter(feat, filter) # Compute distance map center = ((bb[..., :2] + bb[..., 2:] / 2) / self.feat_stride).reshape(-1, 2).flip((1,)) if is_distractor is not None: center[is_distractor.reshape(-1), :] = 99999 dist_map = self.distance_map(center, scores.shape[-2:]) # Compute label map masks and weight label_map = self.label_map_predictor(dist_map).reshape(num_images, num_sequences, dist_map.shape[-2], dist_map.shape[-1]) target_mask = self.target_mask_predictor(dist_map).reshape(num_images, num_sequences, dist_map.shape[-2], dist_map.shape[-1]) spatial_weight = self.spatial_weight_predictor(dist_map).reshape(num_images, num_sequences, dist_map.shape[-2], dist_map.shape[-1]) if sample_weight is None: sample_weight = math.sqrt(1.0 / num_images) * spatial_weight elif isinstance(sample_weight, torch.Tensor): sample_weight = sample_weight.sqrt().reshape(-1, 1, 1, 1) * spatial_weight # Compute data residual scores_act = self.score_activation(scores, target_mask) data_residual = sample_weight * (scores_act - label_map) # Compute regularization residual. Put batch in second dimension reg_residual = self.filter_reg*filter.reshape(1, num_sequences, -1) return TensorList([data_residual, reg_residual]) ================================================ FILE: external/AR/ltr/models/tracking/__init__.py ================================================ ================================================ FILE: external/AR/ltr/models/tracking/dimpnet.py ================================================ import math import torch import torch.nn as nn from collections import OrderedDict from ltr.models.meta import steepestdescent import ltr.models.target_classifier.linear_filter as target_clf import ltr.models.target_classifier.features as clf_features import ltr.models.target_classifier.initializer as clf_initializer import ltr.models.target_classifier.optimizer as clf_optimizer import ltr.models.bbreg as bbmodels import ltr.models.backbone as backbones from ltr import model_constructor class DiMPnet(nn.Module): """The DiMP network. args: feature_extractor: Backbone feature extractor network. Must return a dict of feature maps classifier: Target classification module. bb_regressor: Bounding box regression module. classification_layer: Name of the backbone feature layer to use for classification. bb_regressor_layer: Names of the backbone layers to use for bounding box regression.""" def __init__(self, feature_extractor, classifier, bb_regressor, classification_layer, bb_regressor_layer): super().__init__() self.feature_extractor = feature_extractor self.classifier = classifier self.bb_regressor = bb_regressor self.classification_layer = [classification_layer] if isinstance(classification_layer, str) else classification_layer self.bb_regressor_layer = bb_regressor_layer self.output_layers = sorted(list(set(self.classification_layer + self.bb_regressor_layer))) def forward(self, train_imgs, test_imgs, train_bb, test_proposals, *args, **kwargs): """Runs the DiMP network the way it is applied during training. The forward function is ONLY used for training. Call the individual functions during tracking. args: train_imgs: Train image samples (images, sequences, 3, H, W). test_imgs: Test image samples (images, sequences, 3, H, W). trian_bb: Target boxes (x,y,w,h) for the train images. Dims (images, sequences, 4). test_proposals: Proposal boxes to use for the IoUNet (bb_regressor) module. *args, **kwargs: These are passed to the classifier module. returns: test_scores: Classification scores on the test samples. iou_pred: Predicted IoU scores for the test_proposals.""" assert train_imgs.dim() == 5 and test_imgs.dim() == 5, 'Expect 5 dimensional inputs' # Extract backbone features train_feat = self.extract_backbone_features(train_imgs.reshape(-1, *train_imgs.shape[-3:])) test_feat = self.extract_backbone_features(test_imgs.reshape(-1, *test_imgs.shape[-3:])) # Classification features train_feat_clf = self.get_backbone_clf_feat(train_feat) test_feat_clf = self.get_backbone_clf_feat(test_feat) # Run classifier module target_scores = self.classifier(train_feat_clf, test_feat_clf, train_bb, *args, **kwargs) # Get bb_regressor features train_feat_iou = self.get_backbone_bbreg_feat(train_feat) test_feat_iou = self.get_backbone_bbreg_feat(test_feat) # Run the IoUNet module iou_pred = self.bb_regressor(train_feat_iou, test_feat_iou, train_bb, test_proposals) return target_scores, iou_pred def get_backbone_clf_feat(self, backbone_feat): feat = OrderedDict({l: backbone_feat[l] for l in self.classification_layer}) if len(self.classification_layer) == 1: return feat[self.classification_layer[0]] return feat def get_backbone_bbreg_feat(self, backbone_feat): return [backbone_feat[l] for l in self.bb_regressor_layer] def extract_classification_feat(self, backbone_feat): return self.classifier.extract_classification_feat(self.get_backbone_clf_feat(backbone_feat)) def extract_backbone_features(self, im, layers=None): if layers is None: layers = self.output_layers return self.feature_extractor(im, layers) def extract_features(self, im, layers=None): if layers is None: layers = self.bb_regressor_layer + ['classification'] if 'classification' not in layers: return self.feature_extractor(im, layers) backbone_layers = sorted(list(set([l for l in layers + self.classification_layer if l != 'classification']))) all_feat = self.feature_extractor(im, backbone_layers) all_feat['classification'] = self.extract_classification_feat(all_feat) return OrderedDict({l: all_feat[l] for l in layers}) @model_constructor def dimpnet18(filter_size=1, optim_iter=5, optim_init_step=1.0, optim_init_reg=0.01, classification_layer='layer3', feat_stride=16, backbone_pretrained=True, clf_feat_blocks=1, clf_feat_norm=True, init_filter_norm=False, final_conv=True, out_feature_dim=256, init_gauss_sigma=1.0, num_dist_bins=5, bin_displacement=1.0, mask_init_factor=4.0, iou_input_dim=(256, 256), iou_inter_dim=(256, 256), score_act='relu', act_param=None, target_mask_act='sigmoid', detach_length=float('Inf'), frozen_backbone_layers=()): # Backbone backbone_net = backbones.resnet18(pretrained=backbone_pretrained, frozen_layers=frozen_backbone_layers) # Feature normalization norm_scale = math.sqrt(1.0 / (out_feature_dim * filter_size * filter_size)) # Classifier features clf_feature_extractor = clf_features.residual_basic_block(num_blocks=clf_feat_blocks, l2norm=clf_feat_norm, final_conv=final_conv, norm_scale=norm_scale, out_dim=out_feature_dim) # Initializer for the DiMP classifier initializer = clf_initializer.FilterInitializerLinear(filter_size=filter_size, filter_norm=init_filter_norm, feature_dim=out_feature_dim) # Optimizer for the DiMP classifier optimizer = clf_optimizer.DiMPSteepestDescentGN(num_iter=optim_iter, feat_stride=feat_stride, init_step_length=optim_init_step, init_filter_reg=optim_init_reg, init_gauss_sigma=init_gauss_sigma, num_dist_bins=num_dist_bins, bin_displacement=bin_displacement, mask_init_factor=mask_init_factor, score_act=score_act, act_param=act_param, mask_act=target_mask_act, detach_length=detach_length) # The classifier module classifier = target_clf.LinearFilter(filter_size=filter_size, filter_initializer=initializer, filter_optimizer=optimizer, feature_extractor=clf_feature_extractor) # Bounding box regressor bb_regressor = bbmodels.AtomIoUNet(pred_input_dim=iou_input_dim, pred_inter_dim=iou_inter_dim) # DiMP network net = DiMPnet(feature_extractor=backbone_net, classifier=classifier, bb_regressor=bb_regressor, classification_layer=classification_layer, bb_regressor_layer=['layer2', 'layer3']) return net @model_constructor def dimpnet50(filter_size=1, optim_iter=5, optim_init_step=1.0, optim_init_reg=0.01, classification_layer='layer3', feat_stride=16, backbone_pretrained=True, clf_feat_blocks=0, clf_feat_norm=True, init_filter_norm=False, final_conv=True, out_feature_dim=512, init_gauss_sigma=1.0, num_dist_bins=5, bin_displacement=1.0, mask_init_factor=4.0, iou_input_dim=(256, 256), iou_inter_dim=(256, 256), score_act='relu', act_param=None, target_mask_act='sigmoid', detach_length=float('Inf'), frozen_backbone_layers=()): # Backbone backbone_net = backbones.resnet50(pretrained=backbone_pretrained, frozen_layers=frozen_backbone_layers) # Feature normalization norm_scale = math.sqrt(1.0 / (out_feature_dim * filter_size * filter_size)) # Classifier features if classification_layer == 'layer3': feature_dim = 256 elif classification_layer == 'layer4': feature_dim = 512 else: raise Exception clf_feature_extractor = clf_features.residual_bottleneck(feature_dim=feature_dim, num_blocks=clf_feat_blocks, l2norm=clf_feat_norm, final_conv=final_conv, norm_scale=norm_scale, out_dim=out_feature_dim) # Initializer for the DiMP classifier initializer = clf_initializer.FilterInitializerLinear(filter_size=filter_size, filter_norm=init_filter_norm, feature_dim=out_feature_dim) # Optimizer for the DiMP classifier optimizer = clf_optimizer.DiMPSteepestDescentGN(num_iter=optim_iter, feat_stride=feat_stride, init_step_length=optim_init_step, init_filter_reg=optim_init_reg, init_gauss_sigma=init_gauss_sigma, num_dist_bins=num_dist_bins, bin_displacement=bin_displacement, mask_init_factor=mask_init_factor, score_act=score_act, act_param=act_param, mask_act=target_mask_act, detach_length=detach_length) # The classifier module classifier = target_clf.LinearFilter(filter_size=filter_size, filter_initializer=initializer, filter_optimizer=optimizer, feature_extractor=clf_feature_extractor) # Bounding box regressor bb_regressor = bbmodels.AtomIoUNet(input_dim=(4*128,4*256), pred_input_dim=iou_input_dim, pred_inter_dim=iou_inter_dim) # DiMP network net = DiMPnet(feature_extractor=backbone_net, classifier=classifier, bb_regressor=bb_regressor, classification_layer=classification_layer, bb_regressor_layer=['layer2', 'layer3']) return net @model_constructor def L2dimpnet18(filter_size=1, optim_iter=5, optim_init_step=1.0, optim_init_reg=0.01, classification_layer='layer3', feat_stride=16, backbone_pretrained=True, clf_feat_blocks=1, clf_feat_norm=True, init_filter_norm=False, final_conv=True, out_feature_dim=256, iou_input_dim=(256, 256), iou_inter_dim=(256, 256), detach_length=float('Inf'), hinge_threshold=-999, gauss_sigma=1.0, alpha_eps=0): # Backbone backbone_net = backbones.resnet18(pretrained=backbone_pretrained) # Feature normalization norm_scale = math.sqrt(1.0 / (out_feature_dim * filter_size * filter_size)) # Classifier features clf_feature_extractor = clf_features.residual_basic_block(num_blocks=clf_feat_blocks, l2norm=clf_feat_norm, final_conv=final_conv, norm_scale=norm_scale, out_dim=out_feature_dim) # Initializer for the DiMP classifier initializer = clf_initializer.FilterInitializerLinear(filter_size=filter_size, filter_norm=init_filter_norm, feature_dim=out_feature_dim) # Optimizer for the DiMP classifier optimizer = clf_optimizer.DiMPL2SteepestDescentGN(num_iter=optim_iter, feat_stride=feat_stride, init_step_length=optim_init_step, hinge_threshold=hinge_threshold, init_filter_reg=optim_init_reg, gauss_sigma=gauss_sigma, detach_length=detach_length, alpha_eps=alpha_eps) # The classifier module classifier = target_clf.LinearFilter(filter_size=filter_size, filter_initializer=initializer, filter_optimizer=optimizer, feature_extractor=clf_feature_extractor) # Bounding box regressor bb_regressor = bbmodels.AtomIoUNet(pred_input_dim=iou_input_dim, pred_inter_dim=iou_inter_dim) # DiMP network net = DiMPnet(feature_extractor=backbone_net, classifier=classifier, bb_regressor=bb_regressor, classification_layer=classification_layer, bb_regressor_layer=['layer2', 'layer3']) return net @model_constructor def klcedimpnet18(filter_size=1, optim_iter=5, optim_init_step=1.0, optim_init_reg=0.01, classification_layer='layer3', feat_stride=16, backbone_pretrained=True, clf_feat_blocks=1, clf_feat_norm=True, init_filter_norm=False, final_conv=True, out_feature_dim=256, gauss_sigma=1.0, iou_input_dim=(256, 256), iou_inter_dim=(256, 256), detach_length=float('Inf'), alpha_eps=0.0, train_feature_extractor=True, init_uni_weight=None, optim_min_reg=1e-3, init_initializer='default', normalize_label=False, label_shrink=0, softmax_reg=None, label_threshold=0, final_relu=False, init_pool_square=False, frozen_backbone_layers=()): if not train_feature_extractor: frozen_backbone_layers = 'all' # Backbone backbone_net = backbones.resnet18(pretrained=backbone_pretrained, frozen_layers=frozen_backbone_layers) # Feature normalization norm_scale = math.sqrt(1.0 / (out_feature_dim * filter_size * filter_size)) # Classifier features clf_feature_extractor = clf_features.residual_basic_block(num_blocks=clf_feat_blocks, l2norm=clf_feat_norm, final_conv=final_conv, norm_scale=norm_scale, out_dim=out_feature_dim, final_relu=final_relu) # Initializer for the DiMP classifier initializer = clf_initializer.FilterInitializerLinear(filter_size=filter_size, filter_norm=init_filter_norm, feature_dim=out_feature_dim, init_weights=init_initializer, pool_square=init_pool_square) # Optimizer for the DiMP classifier optimizer = clf_optimizer.PrDiMPSteepestDescentNewton(num_iter=optim_iter, feat_stride=feat_stride, init_step_length=optim_init_step, init_filter_reg=optim_init_reg, gauss_sigma=gauss_sigma, detach_length=detach_length, alpha_eps=alpha_eps, init_uni_weight=init_uni_weight, min_filter_reg=optim_min_reg, normalize_label=normalize_label, label_shrink=label_shrink, softmax_reg=softmax_reg, label_threshold=label_threshold) # The classifier module classifier = target_clf.LinearFilter(filter_size=filter_size, filter_initializer=initializer, filter_optimizer=optimizer, feature_extractor=clf_feature_extractor) # Bounding box regressor bb_regressor = bbmodels.AtomIoUNet(pred_input_dim=iou_input_dim, pred_inter_dim=iou_inter_dim) # DiMP network net = DiMPnet(feature_extractor=backbone_net, classifier=classifier, bb_regressor=bb_regressor, classification_layer=classification_layer, bb_regressor_layer=['layer2', 'layer3']) return net @model_constructor def klcedimpnet50(filter_size=1, optim_iter=5, optim_init_step=1.0, optim_init_reg=0.01, classification_layer='layer3', feat_stride=16, backbone_pretrained=True, clf_feat_blocks=0, clf_feat_norm=True, init_filter_norm=False, final_conv=True, out_feature_dim=512, gauss_sigma=1.0, iou_input_dim=(256, 256), iou_inter_dim=(256, 256), detach_length=float('Inf'), alpha_eps=0.0, train_feature_extractor=True, init_uni_weight=None, optim_min_reg=1e-3, init_initializer='default', normalize_label=False, label_shrink=0, softmax_reg=None, label_threshold=0, final_relu=False, frozen_backbone_layers=()): if not train_feature_extractor: frozen_backbone_layers = 'all' # Backbone backbone_net = backbones.resnet50(pretrained=backbone_pretrained, frozen_layers=frozen_backbone_layers) # Feature normalization norm_scale = math.sqrt(1.0 / (out_feature_dim * filter_size * filter_size)) # Classifier features clf_feature_extractor = clf_features.residual_bottleneck(num_blocks=clf_feat_blocks, l2norm=clf_feat_norm, final_conv=final_conv, norm_scale=norm_scale, out_dim=out_feature_dim, final_relu=final_relu) # Initializer for the DiMP classifier initializer = clf_initializer.FilterInitializerLinear(filter_size=filter_size, filter_norm=init_filter_norm, feature_dim=out_feature_dim, init_weights=init_initializer) # Optimizer for the DiMP classifier optimizer = clf_optimizer.PrDiMPSteepestDescentNewton(num_iter=optim_iter, feat_stride=feat_stride, init_step_length=optim_init_step, init_filter_reg=optim_init_reg, gauss_sigma=gauss_sigma, detach_length=detach_length, alpha_eps=alpha_eps, init_uni_weight=init_uni_weight, min_filter_reg=optim_min_reg, normalize_label=normalize_label, label_shrink=label_shrink, softmax_reg=softmax_reg, label_threshold=label_threshold) # The classifier module classifier = target_clf.LinearFilter(filter_size=filter_size, filter_initializer=initializer, filter_optimizer=optimizer, feature_extractor=clf_feature_extractor) # Bounding box regressor bb_regressor = bbmodels.AtomIoUNet(input_dim=(4*128,4*256), pred_input_dim=iou_input_dim, pred_inter_dim=iou_inter_dim) # DiMP network net = DiMPnet(feature_extractor=backbone_net, classifier=classifier, bb_regressor=bb_regressor, classification_layer=classification_layer, bb_regressor_layer=['layer2', 'layer3']) return net ================================================ FILE: external/AR/ltr/run_training.py ================================================ import os import sys import argparse import importlib import multiprocessing import cv2 as cv import torch.backends.cudnn env_path = os.path.join(os.path.dirname(__file__), '..') if env_path not in sys.path: sys.path.append(env_path) import ltr.admin.settings as ws_settings def run_training(train_module, train_name, cudnn_benchmark=True): """Run a train scripts in train_settings. args: train_module: Name of module in the "train_settings/" folder. train_name: Name of the train settings file. cudnn_benchmark: Use cudnn benchmark or not (default is True). """ # This is needed to avoid strange crashes related to opencv cv.setNumThreads(0) torch.backends.cudnn.benchmark = cudnn_benchmark print('Training: {} {}'.format(train_module, train_name)) settings = ws_settings.Settings() settings.module_name = train_module settings.script_name = train_name settings.project_path = 'ltr/{}/{}'.format(train_module, train_name) expr_module = importlib.import_module('ltr.train_settings.{}.{}'.format(train_module, train_name)) expr_func = getattr(expr_module, 'run') expr_func(settings) def main(): parser = argparse.ArgumentParser(description='Run a train scripts in train_settings.') parser.add_argument('train_module', type=str, help='Name of module in the "train_settings/" folder.') parser.add_argument('train_name', type=str, help='Name of the train settings file.') parser.add_argument('--cudnn_benchmark', type=bool, default=True, help='Set cudnn benchmark on (1) or off (0) (default is on).') args = parser.parse_args() run_training(args.train_module, args.train_name, args.cudnn_benchmark) if __name__ == '__main__': multiprocessing.set_start_method('spawn', force=True) main() ================================================ FILE: external/AR/ltr/train_settings/__init__.py ================================================ ================================================ FILE: external/AR/ltr/train_settings/bbreg/__init__.py ================================================ ================================================ FILE: external/AR/ltr/train_settings/bbreg/atom.py ================================================ import torch.nn as nn import torch.optim as optim from ltr.dataset import Lasot, TrackingNet, MSCOCOSeq, Got10k from ltr.data import processing, sampler, LTRLoader import ltr.models.bbreg.atom as atom_models from ltr import actors from ltr.trainers import LTRTrainer import ltr.data.transforms as tfm def run(settings): # Most common settings are assigned in the settings struct settings.description = 'ATOM IoUNet with default settings, but additionally using GOT10k for training.' settings.batch_size = 64 settings.num_workers = 8 settings.print_interval = 1 settings.normalize_mean = [0.485, 0.456, 0.406] settings.normalize_std = [0.229, 0.224, 0.225] settings.search_area_factor = 5.0 settings.feature_sz = 18 settings.output_sz = settings.feature_sz * 16 settings.center_jitter_factor = {'train': 0, 'test': 4.5} settings.scale_jitter_factor = {'train': 0, 'test': 0.5} # Train datasets lasot_train = Lasot(settings.env.lasot_dir, split='train') got10k_train = Got10k(settings.env.got10k_dir, split='vottrain') trackingnet_train = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(4))) coco_train = MSCOCOSeq(settings.env.coco_dir) # Validation datasets got10k_val = Got10k(settings.env.got10k_dir, split='votval') # The joint augmentation transform, that is applied to the pairs jointly transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05)) # The augmentation transform applied to the training set (individually to each image in the pair) transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # The augmentation transform applied to the validation set (individually to each image in the pair) transform_val = tfm.Transform(tfm.ToTensor(), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # Data processing to do on the training pairs proposal_params = {'min_iou': 0.1, 'boxes_per_frame': 16, 'sigma_factor': [0.01, 0.05, 0.1, 0.2, 0.3]} data_processing_train = processing.ATOMProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, transform=transform_train, joint_transform=transform_joint) # Data processing to do on the validation pairs data_processing_val = processing.ATOMProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, transform=transform_val, joint_transform=transform_joint) # The sampler for training dataset_train = sampler.ATOMSampler([lasot_train, got10k_train, trackingnet_train, coco_train], [1,1,1,1], samples_per_epoch=1000*settings.batch_size, max_gap=50, processing=data_processing_train) # The loader for training loader_train = LTRLoader('train', dataset_train, training=True, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=True, drop_last=True, stack_dim=1) # The sampler for validation dataset_val = sampler.ATOMSampler([got10k_val], [1], samples_per_epoch=500*settings.batch_size, max_gap=50, processing=data_processing_val) # The loader for validation loader_val = LTRLoader('val', dataset_val, training=False, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=False, drop_last=True, epoch_interval=5, stack_dim=1) # Create network and actor net = atom_models.atom_resnet18(backbone_pretrained=True) objective = nn.MSELoss() actor = actors.AtomActor(net=net, objective=objective) # Optimizer optimizer = optim.Adam(actor.net.bb_regressor.parameters(), lr=1e-3) lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.2) # Create trainer trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler) # Run training (set fail_safe=False if you are debugging) trainer.train(50, load_latest=True, fail_safe=True) ================================================ FILE: external/AR/ltr/train_settings/bbreg/atom_gmm_sampl.py ================================================ import torch.nn as nn import torch.optim as optim from ltr.dataset import Lasot, TrackingNet, MSCOCOSeq, Got10k from ltr.data import processing, sampler, LTRLoader import ltr.models.bbreg.atom as atom_models from ltr import actors from ltr.trainers import LTRTrainer import ltr.data.transforms as tfm def run(settings): # Most common settings are assigned in the settings struct settings.description = 'ATOM IoUNet using the baseline ATOM* settings in [https://arxiv.org/abs/1909.12297].' \ 'Unlike standard ATOM, it employs the GMM-based proposal sampling and minor parameter changes.' settings.batch_size = 64 settings.num_workers = 8 settings.print_interval = 1 settings.normalize_mean = [0.485, 0.456, 0.406] settings.normalize_std = [0.229, 0.224, 0.225] settings.search_area_factor = 5.0 settings.feature_sz = 18 settings.output_sz = settings.feature_sz * 16 settings.center_jitter_factor = {'train': 0, 'test': 4.5} settings.scale_jitter_factor = {'train': 0, 'test': 0.5} # Train datasets lasot_train = Lasot(settings.env.lasot_dir, split='train') got10k_train = Got10k(settings.env.got10k_dir, split='vottrain') trackingnet_train = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(4))) coco_train = MSCOCOSeq(settings.env.coco_dir) # Validation datasets got10k_val = Got10k(settings.env.got10k_dir, split='votval') # The joint augmentation transform, that is applied to the pairs jointly transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05)) # The augmentation transform applied to the training set (individually to each image in the pair) transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # The augmentation transform applied to the validation set (individually to each image in the pair) transform_val = tfm.Transform(tfm.ToTensor(), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # Data processing to do on the training pairs proposal_params = {'proposal_method': 'gmm', 'boxes_per_frame': 128, 'proposal_sigma': [(0.05, 0.05), (0.5, 0.5)]} data_processing_train = processing.ATOMProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, transform=transform_train, joint_transform=transform_joint) # Data processing to do on the validation pairs data_processing_val = processing.ATOMProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, transform=transform_val, joint_transform=transform_joint) # The sampler for training dataset_train = sampler.ATOMSampler([lasot_train, got10k_train, trackingnet_train, coco_train], [1,1,1,1], samples_per_epoch=1000*settings.batch_size, max_gap=200, processing=data_processing_train) # The loader for training loader_train = LTRLoader('train', dataset_train, training=True, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=True, drop_last=True, stack_dim=1) # The sampler for validation dataset_val = sampler.ATOMSampler([got10k_val], [1], samples_per_epoch=500*settings.batch_size, max_gap=200, processing=data_processing_val) # The loader for validation loader_val = LTRLoader('val', dataset_val, training=False, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=False, drop_last=True, epoch_interval=5, stack_dim=1) # Create network and actor net = atom_models.atom_resnet18(backbone_pretrained=True) objective = nn.MSELoss() actor = actors.AtomActor(net=net, objective=objective) # Optimizer optimizer = optim.Adam(actor.net.bb_regressor.parameters(), lr=1e-3) lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.2) # Create trainer trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler) # Run training (set fail_safe=False if you are debugging) trainer.train(50, load_latest=True, fail_safe=True) ================================================ FILE: external/AR/ltr/train_settings/bbreg/atom_paper.py ================================================ import torch.nn as nn import torch.optim as optim from ltr.dataset import Lasot, TrackingNet, MSCOCOSeq from ltr.data import processing, sampler, LTRLoader import ltr.models.bbreg.atom as atom_models from ltr import actors from ltr.trainers import LTRTrainer import ltr.data.transforms as tfm def run(settings): # Most common settings are assigned in the settings struct settings.description = 'ATOM IoUNet with default settings according to the paper.' settings.batch_size = 64 settings.num_workers = 8 settings.print_interval = 1 settings.normalize_mean = [0.485, 0.456, 0.406] settings.normalize_std = [0.229, 0.224, 0.225] settings.search_area_factor = 5.0 settings.feature_sz = 18 settings.output_sz = settings.feature_sz * 16 settings.center_jitter_factor = {'train': 0, 'test': 4.5} settings.scale_jitter_factor = {'train': 0, 'test': 0.5} # Train datasets lasot_train = Lasot(settings.env.lasot_dir, split='train') trackingnet_train = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(11))) coco_train = MSCOCOSeq(settings.env.coco_dir) # Validation datasets trackingnet_val = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(11,12))) # The joint augmentation transform, that is applied to the pairs jointly transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05)) # The augmentation transform applied to the training set (individually to each image in the pair) transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # The augmentation transform applied to the validation set (individually to each image in the pair) transform_val = tfm.Transform(tfm.ToTensor(), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # Data processing to do on the training pairs proposal_params = {'min_iou': 0.1, 'boxes_per_frame': 16, 'sigma_factor': [0.01, 0.05, 0.1, 0.2, 0.3]} data_processing_train = processing.ATOMProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, transform=transform_train, joint_transform=transform_joint) # Data processing to do on the validation pairs data_processing_val = processing.ATOMProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, transform=transform_val, joint_transform=transform_joint) # The sampler for training dataset_train = sampler.ATOMSampler([lasot_train, trackingnet_train, coco_train], [1,1,1], samples_per_epoch=1000*settings.batch_size, max_gap=50, processing=data_processing_train) # The loader for training loader_train = LTRLoader('train', dataset_train, training=True, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=True, drop_last=True, stack_dim=1) # The sampler for validation dataset_val = sampler.ATOMSampler([trackingnet_val], [1], samples_per_epoch=500*settings.batch_size, max_gap=50, processing=data_processing_val) # The loader for validation loader_val = LTRLoader('val', dataset_val, training=False, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=False, drop_last=True, epoch_interval=5, stack_dim=1) # Create network and actor net = atom_models.atom_resnet18(backbone_pretrained=True) objective = nn.MSELoss() actor = actors.AtomActor(net=net, objective=objective) # Optimizer optimizer = optim.Adam(actor.net.bb_regressor.parameters(), lr=1e-3) lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.2) # Create trainer trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler) # Run training (set fail_safe=False if you are debugging) trainer.train(50, load_latest=True, fail_safe=True) ================================================ FILE: external/AR/ltr/train_settings/bbreg/atom_prob_ml.py ================================================ import torch.optim as optim from ltr.dataset import Lasot, TrackingNet, MSCOCOSeq, Got10k from ltr.data import processing, sampler, LTRLoader import ltr.models.bbreg.atom as atom_models import ltr.models.loss.kl_regression as klreg_losses import ltr.actors.bbreg as bbreg_actors from ltr.trainers import LTRTrainer import ltr.data.transforms as tfm def run(settings): # Most common settings are assigned in the settings struct settings.description = 'ATOM using the probabilistic maximum likelihood trained regression model for bounding-box' \ 'regression presented in [https://arxiv.org/abs/1909.12297].' settings.batch_size = 64 settings.num_workers = 8 settings.print_interval = 1 settings.normalize_mean = [0.485, 0.456, 0.406] settings.normalize_std = [0.229, 0.224, 0.225] settings.search_area_factor = 5.0 settings.feature_sz = 18 settings.output_sz = settings.feature_sz * 16 settings.center_jitter_factor = {'train': 0, 'test': 4.5} settings.scale_jitter_factor = {'train': 0, 'test': 0.5} # Train datasets lasot_train = Lasot(settings.env.lasot_dir, split='train') got10k_train = Got10k(settings.env.got10k_dir, split='vottrain') trackingnet_train = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(4))) coco_train = MSCOCOSeq(settings.env.coco_dir) # Validation datasets got10k_val = Got10k(settings.env.got10k_dir, split='votval') # The joint augmentation transform, that is applied to the pairs jointly transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05)) # The augmentation transform applied to the training set (individually to each image in the pair) transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # The augmentation transform applied to the validation set (individually to each image in the pair) transform_val = tfm.Transform(tfm.ToTensor(), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # Data processing to do on the training pairs proposal_params = {'boxes_per_frame': 128, 'gt_sigma': (0, 0), 'proposal_sigma': [(0.05, 0.05), (0.5, 0.5)], 'add_mean_box': True} data_processing_train = processing.KLBBregProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, transform=transform_train, joint_transform=transform_joint) # Data processing to do on the validation pairs data_processing_val = processing.KLBBregProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, transform=transform_val, joint_transform=transform_joint) # The sampler for training dataset_train = sampler.ATOMSampler([lasot_train, got10k_train, trackingnet_train, coco_train], [1,1,1,1], samples_per_epoch=1000*settings.batch_size, max_gap=200, processing=data_processing_train) # The loader for training loader_train = LTRLoader('train', dataset_train, training=True, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=True, drop_last=True, stack_dim=1) # The sampler for validation dataset_val = sampler.ATOMSampler([got10k_val], [1], samples_per_epoch=500*settings.batch_size, max_gap=200, processing=data_processing_val) # The loader for validation loader_val = LTRLoader('val', dataset_val, training=False, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=False, drop_last=True, epoch_interval=5, stack_dim=1) # Create network and actor net = atom_models.atom_resnet18(backbone_pretrained=True) objective = klreg_losses.MLRegression() actor = bbreg_actors.AtomBBKLActor(net=net, objective=objective) # Optimizer optimizer = optim.Adam(actor.net.bb_regressor.parameters(), lr=1e-3) lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.2) # Create trainer trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler) # Run training (set fail_safe=False if you are debugging) trainer.train(50, load_latest=True, fail_safe=True) ================================================ FILE: external/AR/ltr/train_settings/dimp/__init__.py ================================================ ================================================ FILE: external/AR/ltr/train_settings/dimp/dimp18.py ================================================ import torch.nn as nn import torch.optim as optim from ltr.dataset import Lasot, Got10k, TrackingNet, MSCOCOSeq from ltr.data import processing, sampler, LTRLoader from ltr.models.tracking import dimpnet import ltr.models.loss as ltr_losses from ltr import actors from ltr.trainers import LTRTrainer import ltr.data.transforms as tfm from ltr import MultiGPU def run(settings): settings.description = 'Default train settings for DiMP with ResNet18 as backbone.' settings.batch_size = 26 settings.num_workers = 8 settings.multi_gpu = False settings.print_interval = 1 settings.normalize_mean = [0.485, 0.456, 0.406] settings.normalize_std = [0.229, 0.224, 0.225] settings.search_area_factor = 5.0 settings.output_sigma_factor = 1/4 settings.target_filter_sz = 4 settings.feature_sz = 18 settings.output_sz = settings.feature_sz * 16 settings.center_jitter_factor = {'train': 3, 'test': 4.5} settings.scale_jitter_factor = {'train': 0.25, 'test': 0.5} settings.hinge_threshold = 0.05 # settings.print_stats = ['Loss/total', 'Loss/iou', 'ClfTrain/init_loss', 'ClfTrain/test_loss'] # Train datasets lasot_train = Lasot(settings.env.lasot_dir, split='train') got10k_train = Got10k(settings.env.got10k_dir, split='vottrain') trackingnet_train = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(4))) coco_train = MSCOCOSeq(settings.env.coco_dir) # Validation datasets got10k_val = Got10k(settings.env.got10k_dir, split='votval') # Data transform transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05)) transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) transform_val = tfm.Transform(tfm.ToTensor(), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # The tracking pairs processing module output_sigma = settings.output_sigma_factor / settings.search_area_factor proposal_params = {'min_iou': 0.1, 'boxes_per_frame': 8, 'sigma_factor': [0.01, 0.05, 0.1, 0.2, 0.3]} label_params = {'feature_sz': settings.feature_sz, 'sigma_factor': output_sigma, 'kernel_sz': settings.target_filter_sz} data_processing_train = processing.DiMPProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, label_function_params=label_params, transform=transform_train, joint_transform=transform_joint) data_processing_val = processing.DiMPProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, label_function_params=label_params, transform=transform_val, joint_transform=transform_joint) # Train sampler and loader dataset_train = sampler.DiMPSampler([lasot_train, got10k_train, trackingnet_train, coco_train], [0.25,1,1,1], samples_per_epoch=26000, max_gap=30, num_test_frames=3, num_train_frames=3, processing=data_processing_train) loader_train = LTRLoader('train', dataset_train, training=True, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=True, drop_last=True, stack_dim=1) # Validation samplers and loaders dataset_val = sampler.DiMPSampler([got10k_val], [1], samples_per_epoch=5000, max_gap=30, num_test_frames=3, num_train_frames=3, processing=data_processing_val) loader_val = LTRLoader('val', dataset_val, training=False, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=False, drop_last=True, epoch_interval=5, stack_dim=1) # Create network and actor net = dimpnet.dimpnet18(filter_size=settings.target_filter_sz, backbone_pretrained=True, optim_iter=5, clf_feat_norm=True, final_conv=True, optim_init_step=0.9, optim_init_reg=0.1, init_gauss_sigma=output_sigma * settings.feature_sz, num_dist_bins=100, bin_displacement=0.1, mask_init_factor=3.0, target_mask_act='sigmoid', score_act='relu') # Wrap the network for multi GPU training if settings.multi_gpu: net = MultiGPU(net, dim=1) objective = {'iou': nn.MSELoss(), 'test_clf': ltr_losses.LBHinge(threshold=settings.hinge_threshold)} loss_weight = {'iou': 1, 'test_clf': 100, 'test_init_clf': 100, 'test_iter_clf': 400} actor = actors.DiMPActor(net=net, objective=objective, loss_weight=loss_weight) # Optimizer optimizer = optim.Adam([{'params': actor.net.classifier.filter_initializer.parameters(), 'lr': 5e-5}, {'params': actor.net.classifier.filter_optimizer.parameters(), 'lr': 5e-4}, {'params': actor.net.classifier.feature_extractor.parameters(), 'lr': 5e-5}, {'params': actor.net.bb_regressor.parameters(), 'lr': 1e-3}, {'params': actor.net.feature_extractor.parameters()}], lr=2e-4) lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.2) trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler) trainer.train(50, load_latest=True, fail_safe=True) ================================================ FILE: external/AR/ltr/train_settings/dimp/dimp50.py ================================================ import torch.nn as nn import torch.optim as optim from ltr.dataset import Lasot, Got10k, TrackingNet, MSCOCOSeq from ltr.data import processing, sampler, LTRLoader from ltr.models.tracking import dimpnet import ltr.models.loss as ltr_losses from ltr import actors from ltr.trainers import LTRTrainer import ltr.data.transforms as tfm from ltr import MultiGPU def run(settings): settings.description = 'Default train settings for DiMP with ResNet50 as backbone.' settings.batch_size = 10 settings.num_workers = 8 settings.multi_gpu = False settings.print_interval = 1 settings.normalize_mean = [0.485, 0.456, 0.406] settings.normalize_std = [0.229, 0.224, 0.225] settings.search_area_factor = 5.0 settings.output_sigma_factor = 1/4 settings.target_filter_sz = 4 settings.feature_sz = 18 settings.output_sz = settings.feature_sz * 16 settings.center_jitter_factor = {'train': 3, 'test': 4.5} settings.scale_jitter_factor = {'train': 0.25, 'test': 0.5} settings.hinge_threshold = 0.05 # settings.print_stats = ['Loss/total', 'Loss/iou', 'ClfTrain/clf_ce', 'ClfTrain/test_loss'] # Train datasets lasot_train = Lasot(settings.env.lasot_dir, split='train') got10k_train = Got10k(settings.env.got10k_dir, split='vottrain') trackingnet_train = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(4))) coco_train = MSCOCOSeq(settings.env.coco_dir) # Validation datasets got10k_val = Got10k(settings.env.got10k_dir, split='votval') # Data transform transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05)) transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) transform_val = tfm.Transform(tfm.ToTensor(), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # The tracking pairs processing module output_sigma = settings.output_sigma_factor / settings.search_area_factor proposal_params = {'min_iou': 0.1, 'boxes_per_frame': 8, 'sigma_factor': [0.01, 0.05, 0.1, 0.2, 0.3]} label_params = {'feature_sz': settings.feature_sz, 'sigma_factor': output_sigma, 'kernel_sz': settings.target_filter_sz} data_processing_train = processing.DiMPProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, label_function_params=label_params, transform=transform_train, joint_transform=transform_joint) data_processing_val = processing.DiMPProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, label_function_params=label_params, transform=transform_val, joint_transform=transform_joint) # Train sampler and loader dataset_train = sampler.DiMPSampler([lasot_train, got10k_train, trackingnet_train, coco_train], [0.25,1,1,1], samples_per_epoch=26000, max_gap=30, num_test_frames=3, num_train_frames=3, processing=data_processing_train) loader_train = LTRLoader('train', dataset_train, training=True, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=True, drop_last=True, stack_dim=1) # Validation samplers and loaders dataset_val = sampler.DiMPSampler([got10k_val], [1], samples_per_epoch=5000, max_gap=30, num_test_frames=3, num_train_frames=3, processing=data_processing_val) loader_val = LTRLoader('val', dataset_val, training=False, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=False, drop_last=True, epoch_interval=5, stack_dim=1) # Create network and actor net = dimpnet.dimpnet50(filter_size=settings.target_filter_sz, backbone_pretrained=True, optim_iter=5, clf_feat_norm=True, clf_feat_blocks=0, final_conv=True, out_feature_dim=512, optim_init_step=0.9, optim_init_reg=0.1, init_gauss_sigma=output_sigma * settings.feature_sz, num_dist_bins=100, bin_displacement=0.1, mask_init_factor=3.0, target_mask_act='sigmoid', score_act='relu') # Wrap the network for multi GPU training if settings.multi_gpu: net = MultiGPU(net, dim=1) objective = {'iou': nn.MSELoss(), 'test_clf': ltr_losses.LBHinge(threshold=settings.hinge_threshold)} loss_weight = {'iou': 1, 'test_clf': 100, 'test_init_clf': 100, 'test_iter_clf': 400} actor = actors.DiMPActor(net=net, objective=objective, loss_weight=loss_weight) # Optimizer optimizer = optim.Adam([{'params': actor.net.classifier.filter_initializer.parameters(), 'lr': 5e-5}, {'params': actor.net.classifier.filter_optimizer.parameters(), 'lr': 5e-4}, {'params': actor.net.classifier.feature_extractor.parameters(), 'lr': 5e-5}, {'params': actor.net.bb_regressor.parameters()}, {'params': actor.net.feature_extractor.parameters(), 'lr': 2e-5}], lr=2e-4) lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.2) trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler) trainer.train(50, load_latest=True, fail_safe=True) ================================================ FILE: external/AR/ltr/train_settings/dimp/prdimp18.py ================================================ import torch.optim as optim from ltr.dataset import Lasot, Got10k, TrackingNet, MSCOCOSeq from ltr.data import processing, sampler, LTRLoader from ltr.models.tracking import dimpnet import ltr.models.loss as ltr_losses import ltr.models.loss.kl_regression as klreg_losses import ltr.actors.tracking as tracking_actors from ltr.trainers import LTRTrainer import ltr.data.transforms as tfm from ltr import MultiGPU def run(settings): settings.description = 'Default train settings for PrDiMP with ResNet18 as backbone.' settings.batch_size = 26 settings.num_workers = 8 settings.multi_gpu = False settings.print_interval = 1 settings.normalize_mean = [0.485, 0.456, 0.406] settings.normalize_std = [0.229, 0.224, 0.225] settings.search_area_factor = 5.0 settings.output_sigma_factor = 1/4 settings.target_filter_sz = 4 settings.feature_sz = 18 settings.output_sz = settings.feature_sz * 16 settings.center_jitter_factor = {'train': 3, 'test': 4.5} settings.scale_jitter_factor = {'train': 0.25, 'test': 0.5} settings.hinge_threshold = 0.05 settings.print_stats = ['Loss/total', 'Loss/bb_ce', 'ClfTrain/clf_ce'] # Train datasets lasot_train = Lasot(settings.env.lasot_dir, split='train') got10k_train = Got10k(settings.env.got10k_dir, split='vottrain') trackingnet_train = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(4))) coco_train = MSCOCOSeq(settings.env.coco_dir) # Validation datasets got10k_val = Got10k(settings.env.got10k_dir, split='votval') # Data transform transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05)) transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) transform_val = tfm.Transform(tfm.ToTensor(), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # The tracking pairs processing module output_sigma = settings.output_sigma_factor / settings.search_area_factor proposal_params = {'boxes_per_frame': 128, 'gt_sigma': (0.05, 0.05), 'proposal_sigma': [(0.05, 0.05), (0.5, 0.5)]} label_params = {'feature_sz': settings.feature_sz, 'sigma_factor': output_sigma, 'kernel_sz': settings.target_filter_sz} label_density_params = {'feature_sz': settings.feature_sz, 'sigma_factor': output_sigma, 'kernel_sz': settings.target_filter_sz, 'normalize': True} data_processing_train = processing.KLDiMPProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, label_function_params=label_params, label_density_params=label_density_params, transform=transform_train, joint_transform=transform_joint) data_processing_val = processing.KLDiMPProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, label_function_params=label_params, label_density_params=label_density_params, transform=transform_val, joint_transform=transform_joint) # Train sampler and loader dataset_train = sampler.DiMPSampler([lasot_train, got10k_train, trackingnet_train, coco_train], [0.25,1,1,1], samples_per_epoch=26000, max_gap=200, num_test_frames=3, num_train_frames=3, processing=data_processing_train) loader_train = LTRLoader('train', dataset_train, training=True, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=True, drop_last=True, stack_dim=1) # Validation samplers and loaders dataset_val = sampler.DiMPSampler([got10k_val], [1], samples_per_epoch=5000, max_gap=200, num_test_frames=3, num_train_frames=3, processing=data_processing_val) loader_val = LTRLoader('val', dataset_val, training=False, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=False, drop_last=True, epoch_interval=5, stack_dim=1) # Create network and actor net = dimpnet.klcedimpnet18(filter_size=settings.target_filter_sz, backbone_pretrained=True, optim_iter=5, clf_feat_norm=True, final_conv=True, optim_init_step=1.0, optim_init_reg=0.05, optim_min_reg=0.05, gauss_sigma=output_sigma * settings.feature_sz, alpha_eps=0.05, normalize_label=True, init_initializer='zero') # Wrap the network for multi GPU training if settings.multi_gpu: net = MultiGPU(net, dim=1) objective = {'bb_ce': klreg_losses.KLRegression(), 'clf_ce': klreg_losses.KLRegressionGrid()} loss_weight = {'bb_ce': 0.0025, 'clf_ce': 0.25, 'clf_ce_init': 0.25, 'clf_ce_iter': 1.0} actor = tracking_actors.KLDiMPActor(net=net, objective=objective, loss_weight=loss_weight) # Optimizer optimizer = optim.Adam([{'params': actor.net.classifier.parameters(), 'lr': 1e-3}, {'params': actor.net.bb_regressor.parameters(), 'lr': 1e-3}, {'params': actor.net.feature_extractor.parameters()}], lr=2e-4) lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.2) trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler) trainer.train(50, load_latest=True, fail_safe=True) ================================================ FILE: external/AR/ltr/train_settings/dimp/prdimp50.py ================================================ import torch.optim as optim from ltr.dataset import Lasot, Got10k, TrackingNet, MSCOCOSeq from ltr.data import processing, sampler, LTRLoader from ltr.models.tracking import dimpnet import ltr.models.loss as ltr_losses import ltr.models.loss.kl_regression as klreg_losses import ltr.actors.tracking as tracking_actors from ltr.trainers import LTRTrainer import ltr.data.transforms as tfm from ltr import MultiGPU def run(settings): settings.description = 'Default train settings for PrDiMP with ResNet50 as backbone.' settings.batch_size = 10 settings.num_workers = 8 settings.multi_gpu = False settings.print_interval = 1 settings.normalize_mean = [0.485, 0.456, 0.406] settings.normalize_std = [0.229, 0.224, 0.225] settings.search_area_factor = 5.0 settings.output_sigma_factor = 1/4 settings.target_filter_sz = 4 settings.feature_sz = 18 settings.output_sz = settings.feature_sz * 16 settings.center_jitter_factor = {'train': 3, 'test': 4.5} settings.scale_jitter_factor = {'train': 0.25, 'test': 0.5} settings.hinge_threshold = 0.05 settings.print_stats = ['Loss/total', 'Loss/bb_ce', 'ClfTrain/clf_ce'] # Train datasets lasot_train = Lasot(settings.env.lasot_dir, split='train') got10k_train = Got10k(settings.env.got10k_dir, split='vottrain') trackingnet_train = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(4))) coco_train = MSCOCOSeq(settings.env.coco_dir) # Validation datasets got10k_val = Got10k(settings.env.got10k_dir, split='votval') # Data transform transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05)) transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) transform_val = tfm.Transform(tfm.ToTensor(), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # The tracking pairs processing module output_sigma = settings.output_sigma_factor / settings.search_area_factor proposal_params = {'boxes_per_frame': 128, 'gt_sigma': (0.05, 0.05), 'proposal_sigma': [(0.05, 0.05), (0.5, 0.5)]} label_params = {'feature_sz': settings.feature_sz, 'sigma_factor': output_sigma, 'kernel_sz': settings.target_filter_sz} label_density_params = {'feature_sz': settings.feature_sz, 'sigma_factor': output_sigma, 'kernel_sz': settings.target_filter_sz, 'normalize': True} data_processing_train = processing.KLDiMPProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, label_function_params=label_params, label_density_params=label_density_params, transform=transform_train, joint_transform=transform_joint) data_processing_val = processing.KLDiMPProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', proposal_params=proposal_params, label_function_params=label_params, label_density_params=label_density_params, transform=transform_val, joint_transform=transform_joint) # Train sampler and loader dataset_train = sampler.DiMPSampler([lasot_train, got10k_train, trackingnet_train, coco_train], [0.25,1,1,1], samples_per_epoch=26000, max_gap=200, num_test_frames=3, num_train_frames=3, processing=data_processing_train) loader_train = LTRLoader('train', dataset_train, training=True, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=True, drop_last=True, stack_dim=1) # Validation samplers and loaders dataset_val = sampler.DiMPSampler([got10k_val], [1], samples_per_epoch=5000, max_gap=200, num_test_frames=3, num_train_frames=3, processing=data_processing_val) loader_val = LTRLoader('val', dataset_val, training=False, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=False, drop_last=True, epoch_interval=5, stack_dim=1) # Create network and actor net = dimpnet.klcedimpnet50(filter_size=settings.target_filter_sz, backbone_pretrained=True, optim_iter=5, clf_feat_norm=True, clf_feat_blocks=0, final_conv=True, out_feature_dim=512, optim_init_step=1.0, optim_init_reg=0.05, optim_min_reg=0.05, gauss_sigma=output_sigma * settings.feature_sz, alpha_eps=0.05, normalize_label=True, init_initializer='zero') # Wrap the network for multi GPU training if settings.multi_gpu: net = MultiGPU(net, dim=1) objective = {'bb_ce': klreg_losses.KLRegression(), 'clf_ce': klreg_losses.KLRegressionGrid()} loss_weight = {'bb_ce': 0.0025, 'clf_ce': 0.25, 'clf_ce_init': 0.25, 'clf_ce_iter': 1.0} actor = tracking_actors.KLDiMPActor(net=net, objective=objective, loss_weight=loss_weight) # Optimizer optimizer = optim.Adam([{'params': actor.net.classifier.parameters(), 'lr': 1e-3}, {'params': actor.net.bb_regressor.parameters(), 'lr': 1e-3}, {'params': actor.net.feature_extractor.parameters(), 'lr': 2e-5}], lr=2e-4) lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.2) trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler) trainer.train(50, load_latest=True, fail_safe=True) ================================================ FILE: external/AR/ltr/train_settings/dimp/super_dimp.py ================================================ import torch.optim as optim from ltr.dataset import Lasot, Got10k, TrackingNet, MSCOCOSeq from ltr.data import processing, sampler, LTRLoader from ltr.models.tracking import dimpnet import ltr.models.loss as ltr_losses import ltr.models.loss.kl_regression as klreg_losses import ltr.actors.tracking as tracking_actors from ltr.trainers import LTRTrainer import ltr.data.transforms as tfm from ltr import MultiGPU def run(settings): settings.description = 'SuperDiMP: Combines the DiMP classifier with the PrDiMP bounding box regressor and better' \ 'training settings (larger batch size, inside_major cropping, and flipping augmentation.' \ 'Gives results significantly better than both DiMP-50 and PrDiMP-50.' settings.batch_size = 20 settings.num_workers = 8 settings.multi_gpu = False settings.print_interval = 1 settings.normalize_mean = [0.485, 0.456, 0.406] settings.normalize_std = [0.229, 0.224, 0.225] settings.search_area_factor = 6.0 settings.output_sigma_factor = 1/4 settings.target_filter_sz = 4 settings.feature_sz = 22 settings.output_sz = settings.feature_sz * 16 settings.center_jitter_factor = {'train': 3, 'test': 5.5} settings.scale_jitter_factor = {'train': 0.25, 'test': 0.5} settings.hinge_threshold = 0.05 # settings.print_stats = ['Loss/total', 'Loss/iou', 'ClfTrain/init_loss', 'ClfTrain/test_loss'] # Train datasets lasot_train = Lasot(settings.env.lasot_dir, split='train') got10k_train = Got10k(settings.env.got10k_dir, split='vottrain') trackingnet_train = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(4))) coco_train = MSCOCOSeq(settings.env.coco_dir) # Validation datasets got10k_val = Got10k(settings.env.got10k_dir, split='votval') # Data transform transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05), tfm.RandomHorizontalFlip(probability=0.5)) transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2), tfm.RandomHorizontalFlip(probability=0.5), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) transform_val = tfm.Transform(tfm.ToTensor(), tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)) # The tracking pairs processing module output_sigma = settings.output_sigma_factor / settings.search_area_factor proposal_params = {'boxes_per_frame': 128, 'gt_sigma': (0.05, 0.05), 'proposal_sigma': [(0.05, 0.05), (0.5, 0.5)]} label_params = {'feature_sz': settings.feature_sz, 'sigma_factor': output_sigma, 'kernel_sz': settings.target_filter_sz} label_density_params = {'feature_sz': settings.feature_sz, 'sigma_factor': output_sigma, 'kernel_sz': settings.target_filter_sz} data_processing_train = processing.KLDiMPProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, crop_type='inside_major', max_scale_change=1.5, mode='sequence', proposal_params=proposal_params, label_function_params=label_params, label_density_params=label_density_params, transform=transform_train, joint_transform=transform_joint) data_processing_val = processing.KLDiMPProcessing(search_area_factor=settings.search_area_factor, output_sz=settings.output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, crop_type='inside_major', max_scale_change=1.5, mode='sequence', proposal_params=proposal_params, label_function_params=label_params, label_density_params=label_density_params, transform=transform_val, joint_transform=transform_joint) # Train sampler and loader dataset_train = sampler.DiMPSampler([lasot_train, got10k_train, trackingnet_train, coco_train], [1,1,1,1], samples_per_epoch=40000, max_gap=200, num_test_frames=3, num_train_frames=3, processing=data_processing_train) loader_train = LTRLoader('train', dataset_train, training=True, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=True, drop_last=True, stack_dim=1) # Validation samplers and loaders dataset_val = sampler.DiMPSampler([got10k_val], [1], samples_per_epoch=10000, max_gap=200, num_test_frames=3, num_train_frames=3, processing=data_processing_val) loader_val = LTRLoader('val', dataset_val, training=False, batch_size=settings.batch_size, num_workers=settings.num_workers, shuffle=False, drop_last=True, epoch_interval=5, stack_dim=1) # Create network and actor net = dimpnet.dimpnet50(filter_size=settings.target_filter_sz, backbone_pretrained=True, optim_iter=5, clf_feat_norm=True, clf_feat_blocks=0, final_conv=True, out_feature_dim=512, optim_init_step=0.9, optim_init_reg=0.1, init_gauss_sigma=output_sigma * settings.feature_sz, num_dist_bins=100, bin_displacement=0.1, mask_init_factor=3.0, target_mask_act='sigmoid', score_act='relu', frozen_backbone_layers=['conv1', 'bn1', 'layer1', 'layer2']) # Wrap the network for multi GPU training if settings.multi_gpu: net = MultiGPU(net, dim=1) objective = {'bb_ce': klreg_losses.KLRegression(), 'test_clf': ltr_losses.LBHinge(threshold=settings.hinge_threshold)} loss_weight = {'bb_ce': 0.01, 'test_clf': 100, 'test_init_clf': 100, 'test_iter_clf': 400} actor = tracking_actors.KLDiMPActor(net=net, objective=objective, loss_weight=loss_weight) # Optimizer optimizer = optim.Adam([{'params': actor.net.classifier.filter_initializer.parameters(), 'lr': 5e-5}, {'params': actor.net.classifier.filter_optimizer.parameters(), 'lr': 5e-4}, {'params': actor.net.classifier.feature_extractor.parameters(), 'lr': 5e-5}, {'params': actor.net.bb_regressor.parameters(), 'lr': 1e-3}, {'params': actor.net.feature_extractor.layer3.parameters(), 'lr': 2e-5}], lr=2e-4) lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.2) trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler) trainer.train(50, load_latest=True, fail_safe=True) ================================================ FILE: external/AR/ltr/trainers/__init__.py ================================================ from .base_trainer import BaseTrainer from .ltr_trainer import LTRTrainer ================================================ FILE: external/AR/ltr/trainers/base_trainer.py ================================================ import os import glob import torch import traceback from ltr.admin import loading, multigpu class BaseTrainer: """Base trainer class. Contains functions for training and saving/loading chackpoints. Trainer classes should inherit from this one and overload the train_epoch function.""" def __init__(self, actor, loaders, optimizer, settings, lr_scheduler=None): """ args: actor - The actor for training the network loaders - list of dataset loaders, e.g. [train_loader, val_loader]. In each epoch, the trainer runs one epoch for each loader. optimizer - The optimizer used for training, e.g. Adam settings - Training settings lr_scheduler - Learning rate scheduler """ self.actor = actor self.optimizer = optimizer self.lr_scheduler = lr_scheduler self.loaders = loaders self.update_settings(settings) self.epoch = 0 self.stats = {} self.device = getattr(settings, 'device', None) if self.device is None: self.device = torch.device("cuda:0" if torch.cuda.is_available() and settings.use_gpu else "cpu") self.actor.to(self.device) def update_settings(self, settings=None): """Updates the trainer settings. Must be called to update internal settings.""" if settings is not None: self.settings = settings if self.settings.env.workspace_dir is not None: self.settings.env.workspace_dir = os.path.expanduser(self.settings.env.workspace_dir) self._checkpoint_dir = os.path.join(self.settings.env.workspace_dir, 'checkpoints') if not os.path.exists(self._checkpoint_dir): os.makedirs(self._checkpoint_dir) else: self._checkpoint_dir = None def train(self, max_epochs, load_latest=False, fail_safe=True): """Do training for the given number of epochs. args: max_epochs - Max number of training epochs, load_latest - Bool indicating whether to resume from latest epoch. fail_safe - Bool indicating whether the training to automatically restart in case of any crashes. """ epoch = -1 num_tries = 10 for i in range(num_tries): try: if load_latest: self.load_checkpoint() for epoch in range(self.epoch+1, max_epochs+1): self.epoch = epoch self.train_epoch() if self.lr_scheduler is not None: self.lr_scheduler.step() if self._checkpoint_dir: self.save_checkpoint() except: print('Training crashed at epoch {}'.format(epoch)) if fail_safe: self.epoch -= 1 load_latest = True print('Traceback for the error!') print(traceback.format_exc()) print('Restarting training from last epoch ...') else: raise print('Finished training!') def train_epoch(self): raise NotImplementedError def save_checkpoint(self): """Saves a checkpoint of the network and other variables.""" net = self.actor.net.module if multigpu.is_multi_gpu(self.actor.net) else self.actor.net actor_type = type(self.actor).__name__ net_type = type(net).__name__ state = { 'epoch': self.epoch, 'actor_type': actor_type, 'net_type': net_type, 'net': net.state_dict(), 'net_info': getattr(net, 'info', None), 'constructor': getattr(net, 'constructor', None), 'optimizer': self.optimizer.state_dict(), 'stats': self.stats, 'settings': self.settings } directory = '{}/{}'.format(self._checkpoint_dir, self.settings.project_path) if not os.path.exists(directory): os.makedirs(directory) # First save as a tmp file tmp_file_path = '{}/{}_ep{:04d}.tmp'.format(directory, net_type, self.epoch) torch.save(state, tmp_file_path) file_path = '{}/{}_ep{:04d}.pth.tar'.format(directory, net_type, self.epoch) # Now rename to actual checkpoint. os.rename seems to be atomic if files are on same filesystem. Not 100% sure os.rename(tmp_file_path, file_path) def load_checkpoint(self, checkpoint = None, fields = None, ignore_fields = None, load_constructor = False): """Loads a network checkpoint file. Can be called in three different ways: load_checkpoint(): Loads the latest epoch from the workspace. Use this to continue training. load_checkpoint(epoch_num): Loads the network at the given epoch number (int). load_checkpoint(path_to_checkpoint): Loads the file from the given absolute path (str). """ net = self.actor.net.module if multigpu.is_multi_gpu(self.actor.net) else self.actor.net actor_type = type(self.actor).__name__ net_type = type(net).__name__ if checkpoint is None: # Load most recent checkpoint checkpoint_list = sorted(glob.glob('{}/{}/{}_ep*.pth.tar'.format(self._checkpoint_dir, self.settings.project_path, net_type))) if checkpoint_list: checkpoint_path = checkpoint_list[-1] else: print('No matching checkpoint file found') return elif isinstance(checkpoint, int): # Checkpoint is the epoch number checkpoint_path = '{}/{}/{}_ep{:04d}.pth.tar'.format(self._checkpoint_dir, self.settings.project_path, net_type, checkpoint) elif isinstance(checkpoint, str): # checkpoint is the path if os.path.isdir(checkpoint): checkpoint_list = sorted(glob.glob('{}/*_ep*.pth.tar'.format(checkpoint))) if checkpoint_list: checkpoint_path = checkpoint_list[-1] else: raise Exception('No checkpoint found') else: checkpoint_path = os.path.expanduser(checkpoint) else: raise TypeError # Load network checkpoint_dict = loading.torch_load_legacy(checkpoint_path) assert net_type == checkpoint_dict['net_type'], 'Network is not of correct type.' if fields is None: fields = checkpoint_dict.keys() if ignore_fields is None: ignore_fields = ['settings'] # Never load the scheduler. It exists in older checkpoints. ignore_fields.extend(['lr_scheduler', 'constructor', 'net_type', 'actor_type', 'net_info']) # Load all fields for key in fields: if key in ignore_fields: continue if key == 'net': net.load_state_dict(checkpoint_dict[key]) elif key == 'optimizer': self.optimizer.load_state_dict(checkpoint_dict[key]) else: setattr(self, key, checkpoint_dict[key]) # Set the net info if load_constructor and 'constructor' in checkpoint_dict and checkpoint_dict['constructor'] is not None: net.constructor = checkpoint_dict['constructor'] if 'net_info' in checkpoint_dict and checkpoint_dict['net_info'] is not None: net.info = checkpoint_dict['net_info'] # Update the epoch in lr scheduler if 'epoch' in fields: self.lr_scheduler.last_epoch = self.epoch return True ================================================ FILE: external/AR/ltr/trainers/ltr_trainer.py ================================================ import os from collections import OrderedDict from ltr.trainers import BaseTrainer from ltr.admin.stats import AverageMeter, StatValue from ltr.admin.tensorboard import TensorboardWriter import torch import time class LTRTrainer(BaseTrainer): def __init__(self, actor, loaders, optimizer, settings, lr_scheduler=None): """ args: actor - The actor for training the network loaders - list of dataset loaders, e.g. [train_loader, val_loader]. In each epoch, the trainer runs one epoch for each loader. optimizer - The optimizer used for training, e.g. Adam settings - Training settings lr_scheduler - Learning rate scheduler """ super().__init__(actor, loaders, optimizer, settings, lr_scheduler) self._set_default_settings() # Initialize statistics variables self.stats = OrderedDict({loader.name: None for loader in self.loaders}) # Initialize tensorboard tensorboard_writer_dir = os.path.join(self.settings.env.tensorboard_dir, self.settings.project_path) self.tensorboard_writer = TensorboardWriter(tensorboard_writer_dir, [l.name for l in loaders]) self.move_data_to_gpu = getattr(settings, 'move_data_to_gpu', True) def _set_default_settings(self): # Dict of all default values default = {'print_interval': 10, 'print_stats': None, 'description': ''} for param, default_value in default.items(): if getattr(self.settings, param, None) is None: setattr(self.settings, param, default_value) def cycle_dataset(self, loader): """Do a cycle of training or validation.""" self.actor.train(loader.training) torch.set_grad_enabled(loader.training) self._init_timing() for i, data in enumerate(loader, 1): # get inputs if self.move_data_to_gpu: data = data.to(self.device) data['epoch'] = self.epoch data['settings'] = self.settings # forward pass loss, stats = self.actor(data) # backward pass and update weights if loader.training: self.optimizer.zero_grad() loss.backward() self.optimizer.step() # update statistics batch_size = data['train_images'].shape[loader.stack_dim] self._update_stats(stats, batch_size, loader) # print statistics self._print_stats(i, loader, batch_size) def train_epoch(self): """Do one epoch for each loader.""" for loader in self.loaders: if self.epoch % loader.epoch_interval == 0: self.cycle_dataset(loader) self._stats_new_epoch() self._write_tensorboard() def _init_timing(self): self.num_frames = 0 self.start_time = time.time() self.prev_time = self.start_time def _update_stats(self, new_stats: OrderedDict, batch_size, loader): # Initialize stats if not initialized yet if loader.name not in self.stats.keys() or self.stats[loader.name] is None: self.stats[loader.name] = OrderedDict({name: AverageMeter() for name in new_stats.keys()}) for name, val in new_stats.items(): if name not in self.stats[loader.name].keys(): self.stats[loader.name][name] = AverageMeter() self.stats[loader.name][name].update(val, batch_size) def _print_stats(self, i, loader, batch_size): self.num_frames += batch_size current_time = time.time() batch_fps = batch_size / (current_time - self.prev_time) average_fps = self.num_frames / (current_time - self.start_time) self.prev_time = current_time if i % self.settings.print_interval == 0 or i == loader.__len__(): print_str = '[%s: %d, %d / %d] ' % (loader.name, self.epoch, i, loader.__len__()) print_str += 'FPS: %.1f (%.1f) , ' % (average_fps, batch_fps) for name, val in self.stats[loader.name].items(): if (self.settings.print_stats is None or name in self.settings.print_stats) and hasattr(val, 'avg'): print_str += '%s: %.5f , ' % (name, val.avg) print(print_str[:-5]) def _stats_new_epoch(self): # Record learning rate for loader in self.loaders: if loader.training: lr_list = self.lr_scheduler.get_lr() for i, lr in enumerate(lr_list): var_name = 'LearningRate/group{}'.format(i) if var_name not in self.stats[loader.name].keys(): self.stats[loader.name][var_name] = StatValue() self.stats[loader.name][var_name].update(lr) for loader_stats in self.stats.values(): if loader_stats is None: continue for stat_value in loader_stats.values(): if hasattr(stat_value, 'new_epoch'): stat_value.new_epoch() def _write_tensorboard(self): if self.epoch == 1: self.tensorboard_writer.write_info(self.settings.module_name, self.settings.script_name, self.settings.description) self.tensorboard_writer.write_epoch(self.stats, self.epoch) ================================================ FILE: external/AR/pytracking/ARcm_seg.py ================================================ import os import sys import torch import numpy as np import cv2 import torch.nn as nn from external.AR.pytracking.utils.loading import load_network from external.AR.ltr.data.processing_utils_SE import sample_target_SE, transform_image_to_crop_SE, map_mask_back env_path = os.path.join(os.path.dirname(__file__), '..') if env_path not in sys.path: sys.path.append(env_path) def mask_torch2numpy(Pmask): Pmask_arr = np.array(Pmask.squeeze().cpu()) # (H,W) (0,1) return Pmask_arr class ARcm_seg(object): def __init__(self, refine_net_dir, search_factor=2.0, input_sz=256): self.refine_network = self.get_network(refine_net_dir) self.search_factor = search_factor self.input_sz = input_sz self.mean = np.array([0.485, 0.456, 0.406]).reshape((1,1,3)) self.std = np.array([0.229, 0.224, 0.225]).reshape((1,1,3)) def initialize(self, frame1, bbox1): ''' :param frame1: cv array (H,W,3) :param bbox1: ndarray (4,) :return: ''' '''Step1: get cropped patch(tensor)''' patch1, h_f, w_f = sample_target_SE(frame1, bbox1, self.search_factor, self.input_sz, mode=cv2.BORDER_CONSTANT) patch1_tensor = self.img_preprocess(patch1) '''Step2: get GT's cooridinate on the cropped patch(tensor)''' crop_sz = torch.Tensor((self.input_sz, self.input_sz)) bbox1_tensor = self.gt_preprocess(bbox1) # (4,) bbox1_crop_tensor = transform_image_to_crop_SE(bbox1_tensor, bbox1_tensor, h_f, w_f, crop_sz).cuda() '''Step3: forward prop (reference branch)''' with torch.no_grad(): self.refine_network.forward_ref(patch1_tensor, bbox1_crop_tensor) '''refine''' def get_mask(self, Cframe, Cbbox, dtm=None, vis=False): ''' :param Cframe: Current frame(cv2 array) :param Cbbox: Current bbox (ndarray) (x1,y1,w,h) :return: mask ''' '''Step1: get cropped patch(tensor)''' Cpatch, h_f, w_f = sample_target_SE(Cframe, Cbbox, self.search_factor, self.input_sz, mode=cv2.BORDER_CONSTANT) Cpatch_tensor = self.img_preprocess(Cpatch) '''Step2: forward prop (test branch)''' with torch.no_grad(): if dtm is not None: '''2020.4.26 support input dtm''' pred = self.refine_network.forward_test(Cpatch_tensor, dtm, mode='mask') else: pred = self.refine_network.forward_test(Cpatch_tensor,mode='mask') Pmask_arr = mask_torch2numpy(pred) mask_arr = map_mask_back(Cframe, Cbbox, self.search_factor, Pmask_arr, mode=cv2.BORDER_CONSTANT) if vis: return mask_arr, Cpatch, Pmask_arr else: return mask_arr def get_network(self,checkpoint_dir): network = load_network(checkpoint_dir) network.cuda() network.eval() return network def img_preprocess(self,img_arr): '''---> Pytorch tensor(RGB),Normal(-1 to 1,subtract mean, divide std) input img_arr (H,W,3) output (1,1,3,H,W) ''' norm_img = ((img_arr/255.0) - self.mean)/(self.std) img_f32 = norm_img.astype(np.float32) img_tensor = torch.from_numpy(img_f32).cuda() img_tensor = img_tensor.permute((2,0,1)) return img_tensor.unsqueeze(dim=0).unsqueeze(dim=0) def gt_preprocess(self,gt_arr): ''' :param gt: ndarray (4,) :return: torch tensor (4,) ''' return torch.from_numpy(gt_arr.astype(np.float32)) def add_frame_mask(frame, mask, threshold=0.5): mask_new = (mask>threshold)*255 #(H,W) frame_new = frame.copy().astype(np.float) frame_new[...,1] += 0.3*mask_new frame_new = frame_new.clip(0,255).astype(np.uint8) return frame_new def add_frame_bbox(frame, refined_box, color): x1, y1, w, h = refined_box.tolist() cv2.rectangle(frame, (int(x1), int(y1)), (int(x1 + w), int(y1 + h)), color, 2) return frame ================================================ FILE: external/AR/pytracking/VOT/tracker_DiMP.m ================================================ % Set path to the python in the pytracking conda environment python_path = 'PATH_TO_CONDA_INSTALLATION/envs/pytracking/bin/python'; % Set path to pytracking pytracking_path = 'PATH_TO_VISIONML/pytracking'; % Set path to trax installation. Check % https://trax.readthedocs.io/en/latest/tutorial_compiling.html for % compilation information trax_path = 'PATH_TO_VOT_TOOLKIT/native/trax'; tracker_name = 'dimp'; % Name of the tracker to evaluate runfile_name = 'dimp18_vot'; % Name of the parameter file to use debug = 0; %% tracker_label = [tracker_name, '_', runfile_name]; % Generate python command tracker_command = sprintf(['%s -c "import sys; sys.path.append(''%s'');', ... 'sys.path.append(''%s/support/python'');', ... 'import run_vot;', ... 'run_vot.run_vot(''%s'', ''%s'', debug=%d)"'],... python_path, pytracking_path, trax_path, ... tracker_name, runfile_name, debug); tracker_interpreter = python_path; tracker_linkpath = {[trax_path, '/build'],... [trax_path, '/build/support/client'],... [trax_path, '/build/support/opencv']}; ================================================ FILE: external/AR/pytracking/VOT/trackers.ini ================================================ [DiMP] # label = DiMP protocol = traxpython command = run_vot; run_vot.run_vot2020('dimp', 'dimp50') # Set the tracker name and the parameter name # Specify a path to trax python wrapper if it is not visible (separate by ; if using multiple paths) paths = PATH_TO_PYTRACKING # Additional environment paths #env_PATH = ;${PATH} ================================================ FILE: external/AR/pytracking/VOT/vot.py ================================================ """ \file vot.py @brief Python utility functions for VOT integration @author Luka Cehovin, Alessio Dore @date 2016, 2019 """ import sys import copy import collections try: import trax except ImportError: raise Exception('TraX support not found. Please add trax module to Python path.') Rectangle = collections.namedtuple('Rectangle', ['x', 'y', 'width', 'height']) Point = collections.namedtuple('Point', ['x', 'y']) Polygon = collections.namedtuple('Polygon', ['points']) class VOT(object): """ Base class for Python VOT integration """ def __init__(self, region_format, channels=None): """ Constructor Args: region_format: Region format options """ assert(region_format in [trax.Region.RECTANGLE, trax.Region.POLYGON]) if channels is None: channels = ['color'] elif channels == 'rgbd': channels = ['color', 'depth'] elif channels == 'rgbt': channels = ['color', 'ir'] elif channels == 'ir': channels = ['ir'] else: raise Exception('Illegal configuration {}.'.format(channels)) self._trax = trax.Server([region_format], [trax.Image.PATH], channels) request = self._trax.wait() assert(request.type == 'initialize') if isinstance(request.region, trax.Polygon): self._region = Polygon([Point(x[0], x[1]) for x in request.region]) else: self._region = Rectangle(*request.region.bounds()) self._image = [str(x) for k, x in request.image.items()] if len(self._image) == 1: self._image = self._image[0] self._trax.status(request.region) def region(self): """ Send configuration message to the client and receive the initialization region and the path of the first image Returns: initialization region """ return self._region def report(self, region, confidence = None): """ Report the tracking results to the client Arguments: region: region for the frame """ assert(isinstance(region, Rectangle) or isinstance(region, Polygon)) if isinstance(region, Polygon): tregion = trax.Polygon.create([(x.x, x.y) for x in region.points]) else: tregion = trax.Rectangle.create(region.x, region.y, region.width, region.height) properties = {} if not confidence is None: properties['confidence'] = confidence self._trax.status(tregion, properties) def frame(self): """ Get a frame (image path) from client Returns: absolute path of the image """ if hasattr(self, "_image"): image = self._image del self._image return tuple(image) request = self._trax.wait() if request.type == 'frame': image = [str(x) for k, x in request.image.items()] if len(image) == 1: image = image[0] return tuple(image) else: return None def quit(self): if hasattr(self, '_trax'): self._trax.quit() def __del__(self): self.quit() ================================================ FILE: external/AR/pytracking/VOT2020_super_only_mask_384_HP/dimp_alpha_065.py ================================================ from pytracking.VOT2020_super_only_mask_384_HP.dimp_alpha_seg_class import run_vot_exp import os os.environ['CUDA_VISIBLE_DEVICES'] = '1' # run_vot_exp('dimp','dimp50_vot19','SEbcm',0.60,VIS=False) run_vot_exp('dimp','super_dimp','ARcm_coco_seg_only_mask_384',0.65,VIS=False) # run_vot_exp('dimp','super_dimp','ARcm_coco_seg_only_mask_384',0.65,VIS=True) ================================================ FILE: external/AR/pytracking/VOT2020_super_only_mask_384_HP/dimp_alpha_seg_class.py ================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import cv2 import torch import vot import sys import time '''Refine module & Pytracking base trackers''' import os from pytracking.evaluation import Tracker from pytracking.ARcm_seg import ARcm_seg from pytracking.vot20_utils import * '''''' '''DiMP-alpha class''' class DIMP_ALPHA(object): def __init__(self, tracker_name='dimp', para_name='dimp50_vot19', refine_model_name='ARcm_coco_seg', threshold=0.15): self.THRES = threshold '''create tracker''' '''DIMP''' tracker_info = Tracker(tracker_name, para_name, None) params = tracker_info.get_parameters() params.visualization = False params.debug = False params.visdom_info = {'use_visdom': False, 'server': '127.0.0.1', 'port': 8097} self.dimp = tracker_info.tracker_class(params) '''Alpha-Refine''' project_path = os.path.join(os.path.dirname(__file__), '..', '..') refine_root = os.path.join(project_path, 'ltr/checkpoints/ltr/ARcm_seg/') refine_path = os.path.join(refine_root, refine_model_name) '''2020.4.25 input size: 384x384''' self.alpha = ARcm_seg(refine_path, input_sz=384) def initialize(self, img_RGB, mask): region = rect_from_mask(mask) self.H, self.W, _ = img_RGB.shape gt_bbox_np = np.array(region).astype(np.float32) '''Initialize dimp for specific video''' gt_bbox_torch = torch.from_numpy(gt_bbox_np) init_info = {} init_info['init_bbox'] = gt_bbox_torch _ = self.dimp.initialize(img_RGB, init_info) '''initilize refinement module for specific video''' self.alpha.initialize(img_RGB, np.array(gt_bbox_np)) def track(self, img_RGB): '''TRACK''' '''base tracker''' outputs = self.dimp.track(img_RGB) pred_bbox = outputs['target_bbox'] '''Step1: Post-Process''' x1, y1, w, h = pred_bbox # add boundary and min size limit x1, y1, x2, y2 = bbox_clip(x1, y1, x1 + w, y1 + h, (self.H, self.W)) w = x2 - x1 h = y2 - y1 new_pos = torch.from_numpy(np.array([y1 + h / 2, x1 + w / 2]).astype(np.float32)) new_target_sz = torch.from_numpy(np.array([h, w]).astype(np.float32)) new_scale = torch.sqrt(new_target_sz.prod() / self.dimp.base_target_sz.prod()) ##### update self.dimp.pos = new_pos.clone() self.dimp.target_sz = new_target_sz self.dimp.target_scale = new_scale bbox_new = [x1, y1, w, h] '''Step2: Mask report''' pred_mask, search, search_mask = self.alpha.get_mask(img_RGB, np.array(bbox_new), vis=True) final_mask = (pred_mask > self.THRES).astype(np.uint8) search_region = search.astype(np.uint8) search_mask = (search_mask > self.THRES).astype(np.uint8) return bbox_new, final_mask, search_region, search_mask def run_vot_exp(tracker_name, para_name, refine_model_name, threshold, VIS=False): torch.set_num_threads(1) # torch.cuda.set_device(CUDA_ID) # set GPU id save_root = os.path.join('', para_name) if VIS and (not os.path.exists(save_root)): os.mkdir(save_root) tracker = DIMP_ALPHA(tracker_name=tracker_name, para_name=para_name, refine_model_name=refine_model_name, threshold=threshold) handle = vot.VOT("mask") selection = handle.region() imagefile = handle.frame() if not imagefile: sys.exit(0) if VIS: '''for vis''' seq_name = imagefile.split('/')[-3] save_v_dir = os.path.join(save_root, seq_name) if not os.path.exists(save_v_dir): os.mkdir(save_v_dir) cur_time = int(time.time() % 10000) save_dir = os.path.join(save_v_dir, str(cur_time)) if not os.path.exists(save_dir): os.makedirs(save_dir) image = cv2.cvtColor(cv2.imread(imagefile), cv2.COLOR_BGR2RGB) # Right # mask given by the toolkit ends with the target (zero-padding to the right and down is needed) mask = make_full_size(selection, (image.shape[1], image.shape[0])) tracker.initialize(image, mask) while True: imagefile = handle.frame() if not imagefile: break image = cv2.cvtColor(cv2.imread(imagefile), cv2.COLOR_BGR2RGB) # Right b1, m, search, search_m = tracker.track(image) handle.report(m) if VIS: '''Visualization''' # original image image_ori = image[:, :, ::-1].copy() # RGB --> BGR image_name = imagefile.split('/')[-1] save_path = os.path.join(save_dir, image_name) cv2.imwrite(save_path, image_ori) # dimp box image_b = image_ori.copy() cv2.rectangle(image_b, (int(b1[0]), int(b1[1])), (int(b1[0] + b1[2]), int(b1[1] + b1[3])), (0, 0, 255), 2) image_b_name = image_name.replace('.jpg', '_bbox.jpg') save_path = os.path.join(save_dir, image_b_name) cv2.imwrite(save_path, image_b) # search region search_bgr = search[:, :, ::-1].copy() search_name = image_name.replace('.jpg', '_search.jpg') save_path = os.path.join(save_dir, search_name) cv2.imwrite(save_path, search_bgr) # search region mask search_bgr_m = search_bgr.astype(np.float32) search_bgr_m[:, :, 1] += 127.0 * search_m search_bgr_m[:, :, 2] += 127.0 * search_m contours, _ = cv2.findContours(search_m, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) search_bgr_m = cv2.drawContours(search_bgr_m, contours, -1, (0, 255, 255), 4) search_bgr_m = search_bgr_m.clip(0, 255).astype(np.uint8) search_name_m = image_name.replace('.jpg', '_search_mask.jpg') save_path = os.path.join(save_dir, search_name_m) cv2.imwrite(save_path, search_bgr_m) # original image + mask image_m = image_ori.copy().astype(np.float32) image_m[:, :, 1] += 127.0 * m image_m[:, :, 2] += 127.0 * m contours, _ = cv2.findContours(m, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) image_m = cv2.drawContours(image_m, contours, -1, (0, 255, 255), 2) image_m = image_m.clip(0, 255).astype(np.uint8) image_mask_name_m = image_name.replace('.jpg', '_mask.jpg') save_path = os.path.join(save_dir, image_mask_name_m) cv2.imwrite(save_path, image_m) ================================================ FILE: external/AR/pytracking/VOT2020_super_only_mask_384_HP/mixformer_alpha_seg_class.py ================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import cv2 import torch import vot import sys import time import os import numpy as np from lib.test.tracker.mixformer_online import MixFormerOnline from pytracking.ARcm_seg import ARcm_seg from pytracking.vot20_utils import * import lib.test.parameter.mixformer_online as vot_params class MIXFORMER_ALPHA_SEG(object): def __init__(self, tracker, refine_model_name='ARcm_coco_seg', threshold=0.6): self.THRES = threshold self.tracker = tracker '''create tracker''' '''Alpha-Refine''' project_path = os.path.join(os.path.dirname(__file__), '..', '..') refine_root = os.path.join(project_path, 'ltr/checkpoints/ltr/ARcm_seg/') refine_path = os.path.join(refine_root, refine_model_name) '''2020.4.25 input size: 384x384''' self.alpha = ARcm_seg(refine_path, input_sz=384) def initialize(self, image, mask): region = rect_from_mask(mask) # init_info = {'init_bbox': region} # self.tracker.initialize(image, init_info) self.H, self.W, _ = image.shape gt_bbox_np = np.array(region).astype(np.float32) '''Initialize STARK for specific video''' init_info = {'init_bbox': list(gt_bbox_np)} self.tracker.initialize(image, init_info) '''initilize refinement module for specific video''' self.alpha.initialize(image, np.array(gt_bbox_np)) def track(self, img_RGB): '''TRACK''' '''base tracker''' outputs = self.tracker.track(img_RGB) pred_bbox = outputs['target_bbox'] '''Step2: Mask report''' pred_mask, search, search_mask = self.alpha.get_mask(img_RGB, np.array(pred_bbox), vis=True) final_mask = (pred_mask > self.THRES).astype(np.uint8) return final_mask, 1 def make_full_size(x, output_sz): ''' zero-pad input x (right and down) to match output_sz x: numpy array e.g., binary mask output_sz: size of the output [width, height] ''' if x.shape[0] == output_sz[1] and x.shape[1] == output_sz[0]: return x pad_x = output_sz[0] - x.shape[1] if pad_x < 0: x = x[:, :x.shape[1] + pad_x] # padding has to be set to zero, otherwise pad function fails pad_x = 0 pad_y = output_sz[1] - x.shape[0] if pad_y < 0: x = x[:x.shape[0] + pad_y, :] # padding has to be set to zero, otherwise pad function fails pad_y = 0 return np.pad(x, ((0, pad_y), (0, pad_x)), 'constant', constant_values=0) refine_model_name = 'ARcm_coco_seg_only_mask_384' params = vot_params.parameters("baseline", model="mixformer_online_22k.pth.tar") # params = vot_params.parameters("baseline") mixformer = MixFormerOnline(params, "VOT20") tracker = MIXFORMER_ALPHA_SEG(tracker=mixformer, refine_model_name=refine_model_name) handle = vot.VOT("mask") selection = handle.region() imagefile = handle.frame() if not imagefile: sys.exit(0) image = cv2.cvtColor(cv2.imread(imagefile), cv2.COLOR_BGR2RGB) # Right # mask given by the toolkit ends with the target (zero-padding to the right and down is needed) mask = make_full_size(selection, (image.shape[1], image.shape[0])) tracker.H = image.shape[0] tracker.W = image.shape[1] tracker.initialize(image, mask) while True: imagefile = handle.frame() if not imagefile: break image = cv2.cvtColor(cv2.imread(imagefile), cv2.COLOR_BGR2RGB) # Right region, confidence = tracker.track(image) handle.report(region, confidence) ================================================ FILE: external/AR/pytracking/VOT2020_super_only_mask_384_HP/mixformer_large_alpha_seg_class.py ================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import cv2 import torch import vot import sys import time import os import numpy as np from lib.test.tracker.mixformer_online import MixFormerOnline from pytracking.ARcm_seg import ARcm_seg from pytracking.vot20_utils import * import lib.test.parameter.mixformer_online as vot_params class MIXFORMER_ALPHA_SEG(object): def __init__(self, tracker, refine_model_name='ARcm_coco_seg', threshold=0.6): self.THRES = threshold self.tracker = tracker '''create tracker''' '''Alpha-Refine''' project_path = os.path.join(os.path.dirname(__file__), '..', '..') refine_root = os.path.join(project_path, 'ltr/checkpoints/ltr/ARcm_seg/') refine_path = os.path.join(refine_root, refine_model_name) '''2020.4.25 input size: 384x384''' self.alpha = ARcm_seg(refine_path, input_sz=384) def initialize(self, image, mask): region = rect_from_mask(mask) # init_info = {'init_bbox': region} # self.tracker.initialize(image, init_info) self.H, self.W, _ = image.shape gt_bbox_np = np.array(region).astype(np.float32) '''Initialize STARK for specific video''' init_info = {'init_bbox': list(gt_bbox_np)} self.tracker.initialize(image, init_info) '''initilize refinement module for specific video''' self.alpha.initialize(image, np.array(gt_bbox_np)) def track(self, img_RGB): '''TRACK''' '''base tracker''' outputs = self.tracker.track(img_RGB) pred_bbox = outputs['target_bbox'] '''Step2: Mask report''' pred_mask, search, search_mask = self.alpha.get_mask(img_RGB, np.array(pred_bbox), vis=True) final_mask = (pred_mask > self.THRES).astype(np.uint8) return final_mask, 1 def make_full_size(x, output_sz): ''' zero-pad input x (right and down) to match output_sz x: numpy array e.g., binary mask output_sz: size of the output [width, height] ''' if x.shape[0] == output_sz[1] and x.shape[1] == output_sz[0]: return x pad_x = output_sz[0] - x.shape[1] if pad_x < 0: x = x[:, :x.shape[1] + pad_x] # padding has to be set to zero, otherwise pad function fails pad_x = 0 pad_y = output_sz[1] - x.shape[0] if pad_y < 0: x = x[:x.shape[0] + pad_y, :] # padding has to be set to zero, otherwise pad function fails pad_y = 0 return np.pad(x, ((0, pad_y), (0, pad_x)), 'constant', constant_values=0) refine_model_name = 'ARcm_coco_seg_only_mask_384' # params = vot_params.parameters("baseline_large") params = vot_params.parameters("baseline_large", model="mixformerL_online_22k.pth.tar") mixformer = MixFormerOnline(params, "VOT20") tracker = MIXFORMER_ALPHA_SEG(tracker=mixformer, refine_model_name=refine_model_name) handle = vot.VOT("mask") selection = handle.region() imagefile = handle.frame() if not imagefile: sys.exit(0) image = cv2.cvtColor(cv2.imread(imagefile), cv2.COLOR_BGR2RGB) # Right # mask given by the toolkit ends with the target (zero-padding to the right and down is needed) mask = make_full_size(selection, (image.shape[1], image.shape[0])) tracker.H = image.shape[0] tracker.W = image.shape[1] tracker.initialize(image, mask) while True: imagefile = handle.frame() if not imagefile: break image = cv2.cvtColor(cv2.imread(imagefile), cv2.COLOR_BGR2RGB) # Right region, confidence = tracker.track(image) handle.report(region, confidence) ================================================ FILE: external/AR/pytracking/VOT2020_super_only_mask_384_HP/vot.py ================================================ """ \file vot.py @brief Python utility functions for VOT integration @author Luka Cehovin, Alessio Dore @date 2016 """ import sys import copy import collections import numpy as np try: import trax except ImportError: raise Exception('TraX support not found. Please add trax module to Python path.') Rectangle = collections.namedtuple('Rectangle', ['x', 'y', 'width', 'height']) Point = collections.namedtuple('Point', ['x', 'y']) Polygon = collections.namedtuple('Polygon', ['points']) class VOT(object): """ Base class for Python VOT integration """ def __init__(self, region_format, channels=None): """ Constructor Args: region_format: Region format options """ assert(region_format in [trax.Region.RECTANGLE, trax.Region.POLYGON, trax.Region.MASK]) if channels is None: channels = ['color'] elif channels == 'rgbd': channels = ['color', 'depth'] elif channels == 'rgbt': channels = ['color', 'ir'] elif channels == 'ir': channels = ['ir'] else: raise Exception('Illegal configuration {}.'.format(channels)) self._trax = trax.Server([region_format], [trax.Image.PATH], channels, customMetadata=dict(vot="python")) request = self._trax.wait() assert(request.type == 'initialize') if isinstance(request.region, trax.Polygon): self._region = Polygon([Point(x[0], x[1]) for x in request.region]) elif isinstance(request.region, trax.Mask): self._region = request.region.array(True) else: self._region = Rectangle(*request.region.bounds()) self._image = [x.path() for k, x in request.image.items()] if len(self._image) == 1: self._image = self._image[0] self._trax.status(request.region) def region(self): """ Send configuration message to the client and receive the initialization region and the path of the first image Returns: initialization region """ return self._region def report(self, region, confidence = None): """ Report the tracking results to the client Arguments: region: region for the frame """ assert(isinstance(region, (Rectangle, Polygon, np.ndarray))) if isinstance(region, Polygon): tregion = trax.Polygon.create([(x.x, x.y) for x in region.points]) elif isinstance(region, np.ndarray): tregion = trax.Mask.create(region) else: tregion = trax.Rectangle.create(region.x, region.y, region.width, region.height) properties = {} if not confidence is None: properties['confidence'] = confidence self._trax.status(tregion, properties) def frame(self): """ Get a frame (image path) from client Returns: absolute path of the image """ if hasattr(self, "_image"): image = self._image del self._image return image request = self._trax.wait() if request.type == 'frame': image = [x.path() for k, x in request.image.items()] if len(image) == 1: return image[0] return image else: return None def quit(self): if hasattr(self, '_trax'): self._trax.quit() def __del__(self): self.quit() ================================================ FILE: external/AR/pytracking/__init__.py ================================================ from pytracking.libs import TensorList, TensorDict ================================================ FILE: external/AR/pytracking/analysis/__init__.py ================================================ ================================================ FILE: external/AR/pytracking/analysis/evaluate_vos.py ================================================ import os import numpy as np import torch import pandas as pd from collections import OrderedDict from ltr.data.image_loader import imread_indexed from pytracking.evaluation import get_dataset from pathlib import Path from pytracking.analysis.plot_results import generate_formatted_report import pytracking.analysis.vos_utils as utils # Originally db_eval_sequence() in the davis challenge toolkit: def evaluate_sequence(seq_name, segmentations, annotations, object_info, measure='J'): """ Evaluate video sequence results. Arguments: segmentations (dict of ndarray): segmentation labels. annotations (dict of ndarray): ground-truth labels. object_info dict: {object_id: first_frame_index} measure evaluation metric (J,F) """ results = dict(raw=OrderedDict()) _measures = {'J': utils.davis_jaccard_measure, 'F': utils.davis_f_measure} _statistics = {'decay': utils.decay, 'mean': utils.mean, 'recall': utils.recall, 'std': utils.std} for obj_id, first_frame in object_info.items(): r = np.ones((len(annotations))) * np.nan for i, (an, sg) in enumerate(zip(annotations, segmentations)): if list(annotations.keys()).index(first_frame) < i < len(annotations) - 1: r[i] = _measures[measure](annotations[an] == obj_id, segmentations[sg] == obj_id) results['raw'][obj_id] = r for stat, stat_fn in _statistics.items(): results[stat] = [float(stat_fn(r)) for r in results['raw'].values()] return results def evaluate_dataset(results_path, dset_name, measure='J', to_file=True, scores=False, sequences=None, quiet=False): dset = get_dataset(dset_name) results = OrderedDict() dset_scores = [] dset_decay = [] dset_recall = [] if to_file: f = open(results_path / ("evaluation-%s.txt" % measure), "w") def _print(msg): if not quiet: print(msg) if to_file: print(msg, file=f) if sequences is not None: sequences = [sequences] if not isinstance(sequences, (list, tuple)) else sequences target_names = [] for j, sequence in enumerate(dset): if (sequences is not None) and (sequence.name not in sequences): continue # Load all frames frames = sequence.ground_truth_seg annotations = OrderedDict() segmentations = OrderedDict() for f in frames: if f is None: continue file = Path(f) annotations[file.name] = imread_indexed(file) if not scores: segmentations[file.name] = imread_indexed(os.path.join(results_path, sequence.name, file.name)) else: raise NotImplementedError # Find object ids and starting frames object_info = dict() for f_id, d in sequence.init_data.items(): for obj_id in d['object_ids']: object_info[int(obj_id)] = Path(d['mask']).name if 0 in object_info: # Remove background object_info.pop(0) # Evaluate n_seqs = len(dset) n_objs = len(object_info) seq_name = sequence.name _print("%d/%d: %s: %d object%s" % (j + 1, n_seqs, seq_name, n_objs, "s" if n_objs > 1 else "")) r = evaluate_sequence(seq_name, segmentations, annotations, object_info, measure=measure) results[seq_name] = r # Print scores, per frame and object, ignoring NaNs per_obj_score = [] # Per-object accuracies, averaged over the sequence per_frame_score = [] # Per-frame accuracies, averaged over the objects for obj_id, score in r['raw'].items(): target_names.append('{}_{}'.format(seq_name, obj_id)) per_frame_score.append(score) s = utils.mean(score) # Sequence average for one object per_obj_score.append(s) if n_objs > 1: _print("joint {obj}: acc {score:.3f} ┊{apf}┊".format(obj=obj_id, score=s, apf=utils.text_bargraph(score))) # Print mean object score per frame and final score dset_decay.extend(r['decay']) dset_recall.extend(r['recall']) dset_scores.extend(per_obj_score) seq_score = utils.mean(per_obj_score) # Final score seq_mean_score = utils.nanmean(np.array(per_frame_score), axis=0) # Mean object score per frame # Print sequence results _print("final : acc {seq:.3f} ({dset:.3f}) ┊{apf}┊".format( seq=seq_score, dset=np.mean(dset_scores), apf=utils.text_bargraph(seq_mean_score))) _print("%s: %.3f, recall: %.3f, decay: %.3f" % (measure, utils.mean(dset_scores), utils.mean(dset_recall), utils.mean(dset_decay))) if to_file: f.close() return target_names, dset_scores, dset_recall, dset_decay def evaluate_vos(trackers, dataset='yt2019_jjval', force=False): """ evaluate a list of trackers on a vos dataset. args: trackers - list of trackers to evaluate dataset - name of the dataset force - Force re-evaluation. If False, the pre-computed results are loaded if available """ csv_name_global = f'{dataset}_global_results.csv' csv_name_per_sequence = f'{dataset}_per-sequence_results.csv' table_g_all = [] table_seq_all = [] scores = {'J-Mean': [], 'J-Recall': [], 'J-Decay': []} display_names = [] for t in trackers: if t.display_name is not None: disp_name = t.display_name elif t.run_id is not None: disp_name = '{} {}_{:03d}'.format(t.name, t.parameter_name, t.run_id) else: disp_name = '{} {}'.format(t.name, t.parameter_name) display_names.append(disp_name) results_path = t.segmentation_dir csv_name_global_path = os.path.join(results_path, csv_name_global) csv_name_per_sequence_path = os.path.join(results_path, csv_name_per_sequence) if os.path.exists(csv_name_global_path) and os.path.exists(csv_name_per_sequence_path) and not force: table_g = pd.read_csv(csv_name_global_path) table_seq = pd.read_csv(csv_name_per_sequence_path) else: seq_names, dset_scores, dset_recall, dset_decay = evaluate_dataset(results_path, dataset, measure='J', to_file=False, scores=False, sequences=None) g_measures = ['J-Mean', 'J-Recall', 'J-Decay'] g_res = np.array([utils.mean(dset_scores), utils.mean(dset_recall), utils.mean(dset_decay)]) g_res = np.reshape(g_res, [1, len(g_res)]) table_g = pd.DataFrame(data=g_res, columns=g_measures) with open(csv_name_global_path, 'w') as f: table_g.to_csv(f, index=False, float_format="%.3f") seq_measures = ['Sequence', 'J-Mean', 'J-Recall', 'J-Decay'] table_seq = pd.DataFrame(data=list(zip(seq_names, dset_scores, dset_recall, dset_decay)), columns=seq_measures) with open(csv_name_per_sequence_path, 'w') as f: table_seq.to_csv(f, index=False, float_format="%.3f") scores['J-Mean'].append(table_g['J-Mean'].values[0]*100) scores['J-Recall'].append(table_g['J-Recall'].values[0]*100) scores['J-Decay'].append(table_g['J-Decay'].values[0]*100) table_g_all.append(table_g) table_seq_all.append(table_seq) report = generate_formatted_report(display_names, scores) print(report) return table_g_all, table_seq_all ================================================ FILE: external/AR/pytracking/analysis/extract_results.py ================================================ import os import sys import importlib import numpy as np from pytracking.utils.load_text import load_text import torch import pickle from tqdm import tqdm env_path = os.path.join(os.path.dirname(__file__), '../..') if env_path not in sys.path: sys.path.append(env_path) from pytracking.evaluation.environment import env_settings def calc_err_center(pred_bb, anno_bb, normalized=False): pred_center = pred_bb[:, :2] + 0.5 * (pred_bb[:, 2:] - 1.0) anno_center = anno_bb[:, :2] + 0.5 * (anno_bb[:, 2:] - 1.0) if normalized: pred_center = pred_center / anno_bb[:, 2:] anno_center = anno_center / anno_bb[:, 2:] err_center = ((pred_center - anno_center)**2).sum(1).sqrt() return err_center def calc_iou_overlap(pred_bb, anno_bb): tl = torch.max(pred_bb[:, :2], anno_bb[:, :2]) br = torch.min(pred_bb[:, :2] + pred_bb[:, 2:] - 1.0, anno_bb[:, :2] + anno_bb[:, 2:] - 1.0) sz = (br - tl + 1.0).clamp(0) # Area intersection = sz.prod(dim=1) union = pred_bb[:, 2:].prod(dim=1) + anno_bb[:, 2:].prod(dim=1) - intersection return intersection / union def calc_seq_err_robust(pred_bb, anno_bb, dataset, target_visible=None): pred_bb = pred_bb.clone() # Check if invalid values are present if torch.isnan(pred_bb).any() or (pred_bb[:, 2:] < 0.0).any(): raise Exception('Error: Invalid results') if torch.isnan(anno_bb).any(): if dataset == 'uav': pass else: raise Exception('Warning: NaNs in annotation') if (pred_bb[:, 2:] == 0.0).any(): for i in range(1, pred_bb.shape[0]): if (pred_bb[i, 2:] == 0.0).any() and not torch.isnan(anno_bb[i, :]).any(): pred_bb[i, :] = pred_bb[i-1, :] if pred_bb.shape[0] != anno_bb.shape[0]: if dataset == 'lasot': if pred_bb.shape[0] > anno_bb.shape[0]: # For monkey-17, there is a mismatch for some trackers. pred_bb = pred_bb[:anno_bb.shape[0], :] else: raise Exception('Mis-match in tracker prediction and GT lengths') else: # print('Warning: Mis-match in tracker prediction and GT lengths') if pred_bb.shape[0] > anno_bb.shape[0]: pred_bb = pred_bb[:anno_bb.shape[0], :] else: pad = torch.zeros((anno_bb.shape[0] - pred_bb.shape[0], 4)).type_as(pred_bb) pred_bb = torch.cat((pred_bb, pad), dim=0) pred_bb[0, :] = anno_bb[0, :] if target_visible is not None: target_visible = target_visible.bool() valid = ((anno_bb[:, 2:] > 0.0).sum(1) == 2) & target_visible else: valid = ((anno_bb[:, 2:] > 0.0).sum(1) == 2) err_center = calc_err_center(pred_bb, anno_bb) err_center_normalized = calc_err_center(pred_bb, anno_bb, normalized=True) err_overlap = calc_iou_overlap(pred_bb, anno_bb) # handle invalid anno cases if dataset in ['uav']: err_center[~valid] = -1.0 else: err_center[~valid] = float("Inf") err_center_normalized[~valid] = -1.0 err_overlap[~valid] = -1.0 if dataset == 'lasot': err_center_normalized[~target_visible] = float("Inf") err_center[~target_visible] = float("Inf") if torch.isnan(err_overlap).any(): raise Exception('Nans in calculated overlap') return err_overlap, err_center, err_center_normalized, valid def extract_results(trackers, dataset, report_name, skip_missing_seq=False, plot_bin_gap=0.05, exclude_invalid_frames=False): settings = env_settings() eps = 1e-16 result_plot_path = os.path.join(settings.result_plot_path, report_name) if not os.path.exists(result_plot_path): os.makedirs(result_plot_path) threshold_set_overlap = torch.arange(0.0, 1.0 + plot_bin_gap, plot_bin_gap, dtype=torch.float64) threshold_set_center = torch.arange(0, 51, dtype=torch.float64) threshold_set_center_norm = torch.arange(0, 51, dtype=torch.float64) / 100.0 avg_overlap_all = torch.zeros((len(dataset), len(trackers)), dtype=torch.float64) ave_success_rate_plot_overlap = torch.zeros((len(dataset), len(trackers), threshold_set_overlap.numel()), dtype=torch.float32) ave_success_rate_plot_center = torch.zeros((len(dataset), len(trackers), threshold_set_center.numel()), dtype=torch.float32) ave_success_rate_plot_center_norm = torch.zeros((len(dataset), len(trackers), threshold_set_center.numel()), dtype=torch.float32) valid_sequence = torch.ones(len(dataset), dtype=torch.uint8) for seq_id, seq in enumerate(tqdm(dataset)): # Load anno anno_bb = torch.tensor(seq.ground_truth_rect) target_visible = torch.tensor(seq.target_visible, dtype=torch.uint8) if seq.target_visible is not None else None for trk_id, trk in enumerate(trackers): # Load results base_results_path = '{}/{}'.format(trk.results_dir, seq.name) results_path = '{}.txt'.format(base_results_path) if os.path.isfile(results_path): pred_bb = torch.tensor(load_text(str(results_path), delimiter=('\t', ','), dtype=np.float64)) else: if skip_missing_seq: valid_sequence[seq_id] = 0 break else: raise Exception('Result not found. {}'.format(results_path)) # Calculate measures err_overlap, err_center, err_center_normalized, valid_frame = calc_seq_err_robust( pred_bb, anno_bb, seq.dataset, target_visible) avg_overlap_all[seq_id, trk_id] = err_overlap[valid_frame].mean() if exclude_invalid_frames: seq_length = valid_frame.long().sum() else: seq_length = anno_bb.shape[0] if seq_length <= 0: raise Exception('Seq length zero') ave_success_rate_plot_overlap[seq_id, trk_id, :] = (err_overlap.view(-1, 1) > threshold_set_overlap.view(1, -1)).sum(0).float() / seq_length ave_success_rate_plot_center[seq_id, trk_id, :] = (err_center.view(-1, 1) <= threshold_set_center.view(1, -1)).sum(0).float() / seq_length ave_success_rate_plot_center_norm[seq_id, trk_id, :] = (err_center_normalized.view(-1, 1) <= threshold_set_center_norm.view(1, -1)).sum(0).float() / seq_length print('\n\nComputed results over {} / {} sequences'.format(valid_sequence.long().sum().item(), valid_sequence.shape[0])) # Prepare dictionary for saving data seq_names = [s.name for s in dataset] tracker_names = [{'name': t.name, 'param': t.parameter_name, 'run_id': t.run_id, 'disp_name': t.display_name} for t in trackers] eval_data = {'sequences': seq_names, 'trackers': tracker_names, 'valid_sequence': valid_sequence.tolist(), 'ave_success_rate_plot_overlap': ave_success_rate_plot_overlap.tolist(), 'ave_success_rate_plot_center': ave_success_rate_plot_center.tolist(), 'ave_success_rate_plot_center_norm': ave_success_rate_plot_center_norm.tolist(), 'avg_overlap_all': avg_overlap_all.tolist(), 'threshold_set_overlap': threshold_set_overlap.tolist(), 'threshold_set_center': threshold_set_center.tolist(), 'threshold_set_center_norm': threshold_set_center_norm.tolist()} with open(result_plot_path + '/eval_data.pkl', 'wb') as fh: pickle.dump(eval_data, fh) return eval_data ================================================ FILE: external/AR/pytracking/analysis/playback_results.py ================================================ import os import sys import importlib import numpy as np import torch import time import matplotlib.patches as patches import cv2 as cv import matplotlib.pyplot as plt from pytracking.analysis.plot_results import get_plot_draw_styles from pytracking.utils.plotting import draw_figure from pytracking.evaluation import get_dataset, trackerlist env_path = os.path.join(os.path.dirname(__file__), '../..') if env_path not in sys.path: sys.path.append(env_path) class Display: def __init__(self, sequence_length, plot_draw_styles, sequence_name): self.active = True self.frame_number = 0 self.pause_mode = True self.step_size = 0 self.step_direction = 'forward' self.fig, self.ax = plt.subplots(1) self.fig.canvas.mpl_connect('key_press_event', self.key_callback_fn) plt.tight_layout() self.sequence_length = sequence_length self.sequence_name = sequence_name self.plot_draw_styles = plot_draw_styles def key_callback_fn(self, event): if event.key == ' ': self.pause_mode = not self.pause_mode self.step_size = 0 self.step_direction = 'forward' elif event.key == 'right': if self.pause_mode: self.frame_number += 1 if self.frame_number >= self.sequence_length: self.frame_number = self.sequence_length - 1 elif self.step_direction == 'stop': self.step_direction = 'forward' self.step_size = 0 elif self.step_direction == 'backward' and self.step_size == 0: self.step_direction = 'stop' else: self.step_size += 1 elif event.key == 'left': if self.pause_mode: self.frame_number -= 1 if self.frame_number < 0: self.frame_number = 0 elif self.step_direction == 'stop': self.step_direction = 'backward' self.step_size = 0 elif self.step_direction == 'forward' and self.step_size == 0: self.step_direction = 'stop' else: self.step_size -= 1 elif event.key == 'escape' or event.key == 'q': self.active = False def _get_speed(self): delta = 0 if self.step_direction == 'forward': delta = 2 ** abs(self.step_size) elif self.step_direction == 'backward': delta = -1 * 2 ** abs(self.step_size) return delta def step(self): delta = self._get_speed() self.frame_number += delta if self.frame_number < 0: self.frame_number = 0 elif self.frame_number >= self.sequence_length: self.frame_number = self.sequence_length - 1 def show(self, image, bb_list, trackers, gt=None): self.ax.cla() self.ax.imshow(image) # Draw rects rect_handles = [] for i, bb in enumerate(bb_list): rect = patches.Rectangle((bb[0], bb[1]), bb[2], bb[3], linewidth=1, edgecolor=self.plot_draw_styles[i]['color'], facecolor='none') self.ax.add_patch(rect) rect_handles.append(patches.Rectangle((bb[0], bb[1]), bb[2], bb[3], linewidth=1, edgecolor=self.plot_draw_styles[i]['color'], facecolor=self.plot_draw_styles[i]['color'], label=trackers[i])) if gt is not None: rect = patches.Rectangle((gt[0], gt[1]), gt[2], gt[3], linewidth=2, edgecolor='g', facecolor='none') self.ax.add_patch(rect) rect_handles.append(rect) self.ax.set_axis_off() self.ax.axis('equal') plt.legend(handles=rect_handles, loc=4, borderaxespad=0.) mode = 'manual' if self.pause_mode else 'auto ' speed = self._get_speed() self.fig.suptitle('Sequence: {} Mode: {} Speed: {:d}x'.format(self.sequence_name, mode, speed), fontsize=14) draw_figure(self.fig) def read_image(image_file: str): im = cv.imread(image_file) return cv.cvtColor(im, cv.COLOR_BGR2RGB) def _get_display_name(tracker): if tracker.display_name is None: if tracker.run_id is not None: return '{}_{}_{:03d}'.format(tracker.name, tracker.parameter_name, tracker.run_id) else: return '{}_{}'.format(tracker.name, tracker.parameter_name) else: return tracker.display_name def playback_results(trackers, sequence): """ Playback saved results of input trackers for a particular sequence. You can navigate the sequence using left/right arrow keys. You can also change to 'auto' mode by pressing space bar, in which case the sequence will be replayed at a particular speed. The speed for playback in 'auto' mode can be controlled using the left/right arrow keys. You can exit the application using escape or q keys. """ plot_draw_styles = get_plot_draw_styles() tracker_results = [] # Load results for trk_id, trk in enumerate(trackers): # Load results base_results_path = '{}/{}'.format(trk.results_dir, sequence.name) results_path = '{}.txt'.format(base_results_path) if os.path.isfile(results_path): try: pred_bb = torch.tensor(np.loadtxt(str(results_path), dtype=np.float64)) except: pred_bb = torch.tensor(np.loadtxt(str(results_path), delimiter=',', dtype=np.float64)) else: raise Exception('Result not found. {}'.format(results_path)) tracker_results.append(pred_bb) # Convert to list of shape seq_length * num_trackers * 4 tracker_results = torch.stack(tracker_results, dim=1).tolist() tracker_names = [_get_display_name(t) for t in trackers] display = Display(len(tracker_results), plot_draw_styles, sequence.name) while display.active: frame_number = display.frame_number image = read_image(sequence.frames[frame_number]) display.show(image, tracker_results[frame_number], tracker_names) time.sleep(0.01) if display.pause_mode and display.frame_number == frame_number: time.sleep(0.1) elif not display.pause_mode: display.step() ================================================ FILE: external/AR/pytracking/analysis/plot_results.py ================================================ import tikzplotlib import matplotlib import matplotlib.pyplot as plt import os import torch import pickle import json from pytracking.evaluation.environment import env_settings from pytracking.analysis.extract_results import extract_results def get_plot_draw_styles(): plot_draw_style = [{'color': (1.0, 0.0, 0.0), 'line_style': '-'}, {'color': (0.0, 1.0, 0.0), 'line_style': '-'}, {'color': (0.0, 0.0, 1.0), 'line_style': '-'}, {'color': (0.0, 0.0, 0.0), 'line_style': '-'}, {'color': (1.0, 0.0, 1.0), 'line_style': '-'}, {'color': (0.0, 1.0, 1.0), 'line_style': '-'}, {'color': (0.5, 0.5, 0.5), 'line_style': '-'}, {'color': (136.0 / 255.0, 0.0, 21.0 / 255.0), 'line_style': '-'}, {'color': (1.0, 127.0 / 255.0, 39.0 / 255.0), 'line_style': '-'}, {'color': (0.0, 162.0 / 255.0, 232.0 / 255.0), 'line_style': '-'}, {'color': (0.0, 0.5, 0.0), 'line_style': '-'}, {'color': (1.0, 0.5, 0.2), 'line_style': '-'}, {'color': (0.1, 0.4, 0.0), 'line_style': '-'}, {'color': (0.6, 0.3, 0.9), 'line_style': '-'}, {'color': (0.4, 0.7, 0.1), 'line_style': '-'}, {'color': (0.2, 0.1, 0.7), 'line_style': '-'}, {'color': (0.7, 0.6, 0.2), 'line_style': '-'}] return plot_draw_style def check_eval_data_is_valid(eval_data, trackers, dataset): """ Checks if the pre-computed results are valid""" seq_names = [s.name for s in dataset] seq_names_saved = eval_data['sequences'] tracker_names_f = [(t.name, t.parameter_name, t.run_id) for t in trackers] tracker_names_f_saved = [(t['name'], t['param'], t['run_id']) for t in eval_data['trackers']] return seq_names == seq_names_saved and tracker_names_f == tracker_names_f_saved def merge_multiple_runs(eval_data): new_tracker_names = [] ave_success_rate_plot_overlap_merged = [] ave_success_rate_plot_center_merged = [] ave_success_rate_plot_center_norm_merged = [] avg_overlap_all_merged = [] ave_success_rate_plot_overlap = torch.tensor(eval_data['ave_success_rate_plot_overlap']) ave_success_rate_plot_center = torch.tensor(eval_data['ave_success_rate_plot_center']) ave_success_rate_plot_center_norm = torch.tensor(eval_data['ave_success_rate_plot_center_norm']) avg_overlap_all = torch.tensor(eval_data['avg_overlap_all']) trackers = eval_data['trackers'] merged = torch.zeros(len(trackers), dtype=torch.uint8) for i in range(len(trackers)): if merged[i]: continue base_tracker = trackers[i] new_tracker_names.append(base_tracker) match = [t['name'] == base_tracker['name'] and t['param'] == base_tracker['param'] for t in trackers] match = torch.tensor(match) ave_success_rate_plot_overlap_merged.append(ave_success_rate_plot_overlap[:, match, :].mean(1)) ave_success_rate_plot_center_merged.append(ave_success_rate_plot_center[:, match, :].mean(1)) ave_success_rate_plot_center_norm_merged.append(ave_success_rate_plot_center_norm[:, match, :].mean(1)) avg_overlap_all_merged.append(avg_overlap_all[:, match].mean(1)) merged[match] = 1 ave_success_rate_plot_overlap_merged = torch.stack(ave_success_rate_plot_overlap_merged, dim=1) ave_success_rate_plot_center_merged = torch.stack(ave_success_rate_plot_center_merged, dim=1) ave_success_rate_plot_center_norm_merged = torch.stack(ave_success_rate_plot_center_norm_merged, dim=1) avg_overlap_all_merged = torch.stack(avg_overlap_all_merged, dim=1) eval_data['trackers'] = new_tracker_names eval_data['ave_success_rate_plot_overlap'] = ave_success_rate_plot_overlap_merged.tolist() eval_data['ave_success_rate_plot_center'] = ave_success_rate_plot_center_merged.tolist() eval_data['ave_success_rate_plot_center_norm'] = ave_success_rate_plot_center_norm_merged.tolist() eval_data['avg_overlap_all'] = avg_overlap_all_merged.tolist() return eval_data def get_tracker_display_name(tracker): if tracker['disp_name'] is None: if tracker['run_id'] is None: disp_name = '{}_{}'.format(tracker['name'], tracker['param']) else: disp_name = '{}_{}_{:03d}'.format(tracker['name'], tracker['param'], tracker['run_id']) else: disp_name = tracker['disp_name'] return disp_name def plot_draw_save(y, x, scores, trackers, plot_draw_styles, result_plot_path, plot_opts): # Plot settings font_size = plot_opts.get('font_size', 12) font_size_axis = plot_opts.get('font_size_axis', 13) line_width = plot_opts.get('line_width', 2) font_size_legend = plot_opts.get('font_size_legend', 13) plot_type = plot_opts['plot_type'] legend_loc = plot_opts['legend_loc'] xlabel = plot_opts['xlabel'] ylabel = plot_opts['ylabel'] xlim = plot_opts['xlim'] ylim = plot_opts['ylim'] title = plot_opts['title'] matplotlib.rcParams.update({'font.size': font_size}) matplotlib.rcParams.update({'axes.titlesize': font_size_axis}) matplotlib.rcParams.update({'axes.titleweight': 'black'}) matplotlib.rcParams.update({'axes.labelsize': font_size_axis}) fig, ax = plt.subplots() index_sort = scores.argsort(descending=False) plotted_lines = [] legend_text = [] for id, id_sort in enumerate(index_sort): line = ax.plot(x.tolist(), y[id_sort, :].tolist(), linewidth=line_width, color=plot_draw_styles[index_sort.numel() - id - 1]['color'], linestyle=plot_draw_styles[index_sort.numel() - id - 1]['line_style']) plotted_lines.append(line[0]) tracker = trackers[id_sort] disp_name = get_tracker_display_name(tracker) legend_text.append('{} [{:.1f}]'.format(disp_name, scores[id_sort])) ax.legend(plotted_lines[::-1], legend_text[::-1], loc=legend_loc, fancybox=False, edgecolor='black', fontsize=font_size_legend, framealpha=1.0) ax.set(xlabel=xlabel, ylabel=ylabel, xlim=xlim, ylim=ylim, title=title) ax.grid(True, linestyle='-.') fig.tight_layout() tikzplotlib.save('{}/{}_plot.tex'.format(result_plot_path, plot_type)) fig.savefig('{}/{}_plot.pdf'.format(result_plot_path, plot_type), dpi=300, format='pdf', transparent=True) plt.draw() def check_and_load_precomputed_results(trackers, dataset, report_name, force_evaluation=False, **kwargs): # Load data settings = env_settings() # Load pre-computed results result_plot_path = os.path.join(settings.result_plot_path, report_name) eval_data_path = os.path.join(result_plot_path, 'eval_data.pkl') if os.path.isfile(eval_data_path) and not force_evaluation: with open(eval_data_path, 'rb') as fh: eval_data = pickle.load(fh) else: # print('Pre-computed evaluation data not found. Computing results!') eval_data = extract_results(trackers, dataset, report_name, **kwargs) if not check_eval_data_is_valid(eval_data, trackers, dataset): # print('Pre-computed evaluation data invalid. Re-computing results!') eval_data = extract_results(trackers, dataset, report_name, **kwargs) else: # Update display names tracker_names = [{'name': t.name, 'param': t.parameter_name, 'run_id': t.run_id, 'disp_name': t.display_name} for t in trackers] eval_data['trackers'] = tracker_names return eval_data def get_auc_curve(ave_success_rate_plot_overlap, valid_sequence): ave_success_rate_plot_overlap = ave_success_rate_plot_overlap[valid_sequence, :, :] auc_curve = ave_success_rate_plot_overlap.mean(0) * 100.0 auc = auc_curve.mean(-1) return auc_curve, auc def get_prec_curve(ave_success_rate_plot_center, valid_sequence): ave_success_rate_plot_center = ave_success_rate_plot_center[valid_sequence, :, :] prec_curve = ave_success_rate_plot_center.mean(0) * 100.0 prec_score = prec_curve[:, 20] return prec_curve, prec_score def plot_results(trackers, dataset, report_name, merge_results=False, plot_types=('success'), force_evaluation=False, **kwargs): """ Plot results for the given trackers args: trackers - List of trackers to evaluate dataset - List of sequences to evaluate report_name - Name of the folder in env_settings.perm_mat_path where the computed results and plots are saved merge_results - If True, multiple random runs for a non-deterministic trackers are averaged plot_types - List of scores to display. Can contain 'success', 'prec' (precision), and 'norm_prec' (normalized precision) """ # Load data settings = env_settings() plot_draw_styles = get_plot_draw_styles() # Load pre-computed results result_plot_path = os.path.join(settings.result_plot_path, report_name) eval_data = check_and_load_precomputed_results(trackers, dataset, report_name, force_evaluation, **kwargs) # Merge results from multiple runs if merge_results: eval_data = merge_multiple_runs(eval_data) tracker_names = eval_data['trackers'] valid_sequence = torch.tensor(eval_data['valid_sequence'], dtype=torch.bool) print('\nPlotting results over {} / {} sequences'.format(valid_sequence.long().sum().item(), valid_sequence.shape[0])) print('\nGenerating plots for: {}'.format(report_name)) # ******************************** Success Plot ************************************** if 'success' in plot_types: ave_success_rate_plot_overlap = torch.tensor(eval_data['ave_success_rate_plot_overlap']) # Index out valid sequences auc_curve, auc = get_auc_curve(ave_success_rate_plot_overlap, valid_sequence) threshold_set_overlap = torch.tensor(eval_data['threshold_set_overlap']) success_plot_opts = {'plot_type': 'success', 'legend_loc': 'lower left', 'xlabel': 'Overlap threshold', 'ylabel': 'Overlap Precision [%]', 'xlim': (0, 1.0), 'ylim': (0, 100), 'title': 'Success plot'} plot_draw_save(auc_curve, threshold_set_overlap, auc, tracker_names, plot_draw_styles, result_plot_path, success_plot_opts) # ******************************** Precision Plot ************************************** if 'prec' in plot_types: ave_success_rate_plot_center = torch.tensor(eval_data['ave_success_rate_plot_center']) # Index out valid sequences prec_curve, prec_score = get_prec_curve(ave_success_rate_plot_center, valid_sequence) threshold_set_center = torch.tensor(eval_data['threshold_set_center']) precision_plot_opts = {'plot_type': 'precision', 'legend_loc': 'lower right', 'xlabel': 'Location error threshold [pixels]', 'ylabel': 'Distance Precision [%]', 'xlim': (0, 50), 'ylim': (0, 100), 'title': 'Precision plot'} plot_draw_save(prec_curve, threshold_set_center, prec_score, tracker_names, plot_draw_styles, result_plot_path, precision_plot_opts) # ******************************** Norm Precision Plot ************************************** if 'norm_prec' in plot_types: ave_success_rate_plot_center_norm = torch.tensor(eval_data['ave_success_rate_plot_center_norm']) # Index out valid sequences prec_curve, prec_score = get_prec_curve(ave_success_rate_plot_center_norm, valid_sequence) threshold_set_center_norm = torch.tensor(eval_data['threshold_set_center_norm']) norm_precision_plot_opts = {'plot_type': 'norm_precision', 'legend_loc': 'lower right', 'xlabel': 'Location error threshold', 'ylabel': 'Distance Precision [%]', 'xlim': (0, 0.5), 'ylim': (0, 100), 'title': 'Normalized Precision plot'} plot_draw_save(prec_curve, threshold_set_center_norm, prec_score, tracker_names, plot_draw_styles, result_plot_path, norm_precision_plot_opts) plt.show() def generate_formatted_report(row_labels, scores, table_name=''): name_width = max([len(d) for d in row_labels] + [len(table_name)]) + 5 min_score_width = 10 report_text = '\n{label: <{width}} |'.format(label=table_name, width=name_width) score_widths = [max(min_score_width, len(k) + 3) for k in scores.keys()] for s, s_w in zip(scores.keys(), score_widths): report_text = '{prev} {s: <{width}} |'.format(prev=report_text, s=s, width=s_w) report_text = '{prev}\n'.format(prev=report_text) for trk_id, d_name in enumerate(row_labels): # display name report_text = '{prev}{tracker: <{width}} |'.format(prev=report_text, tracker=d_name, width=name_width) for (score_type, score_value), s_w in zip(scores.items(), score_widths): report_text = '{prev} {score: <{width}} |'.format(prev=report_text, score='{:0.2f}'.format(score_value[trk_id].item()), width=s_w) report_text = '{prev}\n'.format(prev=report_text) return report_text def print_results(trackers, dataset, report_name, merge_results=False, plot_types=('success'), **kwargs): """ Print the results for the given trackers in a formatted table args: trackers - List of trackers to evaluate dataset - List of sequences to evaluate report_name - Name of the folder in env_settings.perm_mat_path where the computed results and plots are saved merge_results - If True, multiple random runs for a non-deterministic trackers are averaged plot_types - List of scores to display. Can contain 'success' (prints AUC, OP50, and OP75 scores), 'prec' (prints precision score), and 'norm_prec' (prints normalized precision score) """ # Load pre-computed results eval_data = check_and_load_precomputed_results(trackers, dataset, report_name, **kwargs) # Merge results from multiple runs if merge_results: eval_data = merge_multiple_runs(eval_data) tracker_names = eval_data['trackers'] valid_sequence = torch.tensor(eval_data['valid_sequence'], dtype=torch.bool) print('\nReporting results over {} / {} sequences'.format(valid_sequence.long().sum().item(), valid_sequence.shape[0])) scores = {} # ******************************** Success Plot ************************************** if 'success' in plot_types: threshold_set_overlap = torch.tensor(eval_data['threshold_set_overlap']) ave_success_rate_plot_overlap = torch.tensor(eval_data['ave_success_rate_plot_overlap']) # Index out valid sequences auc_curve, auc = get_auc_curve(ave_success_rate_plot_overlap, valid_sequence) scores['AUC'] = auc scores['OP50'] = auc_curve[:, threshold_set_overlap == 0.50] scores['OP75'] = auc_curve[:, threshold_set_overlap == 0.75] # ******************************** Precision Plot ************************************** if 'prec' in plot_types: ave_success_rate_plot_center = torch.tensor(eval_data['ave_success_rate_plot_center']) # Index out valid sequences prec_curve, prec_score = get_prec_curve(ave_success_rate_plot_center, valid_sequence) scores['Precision'] = prec_score # ******************************** Norm Precision Plot ********************************* if 'norm_prec' in plot_types: ave_success_rate_plot_center_norm = torch.tensor(eval_data['ave_success_rate_plot_center_norm']) # Index out valid sequences norm_prec_curve, norm_prec_score = get_prec_curve(ave_success_rate_plot_center_norm, valid_sequence) scores['Norm Precision'] = norm_prec_score # Print tracker_disp_names = [get_tracker_display_name(trk) for trk in tracker_names] report_text = generate_formatted_report(tracker_disp_names, scores, table_name=report_name) print(report_text) def plot_got_success(trackers, report_name): """ Plot success plot for GOT-10k dataset using the json reports. Save the json reports from http://got-10k.aitestunion.com/leaderboard in the directory set to env_settings.got_reports_path The tracker name in the experiment file should be set to the name of the report file for that tracker, e.g. DiMP50_report_2019_09_02_15_44_25 if the report is name DiMP50_report_2019_09_02_15_44_25.json args: trackers - List of trackers to evaluate report_name - Name of the folder in env_settings.perm_mat_path where the computed results and plots are saved """ # Load data settings = env_settings() plot_draw_styles = get_plot_draw_styles() result_plot_path = os.path.join(settings.result_plot_path, report_name) auc_curve = torch.zeros((len(trackers), 101)) scores = torch.zeros(len(trackers)) # Load results tracker_names = [] for trk_id, trk in enumerate(trackers): json_path = '{}/{}.json'.format(settings.got_reports_path, trk.name) if os.path.isfile(json_path): with open(json_path, 'r') as f: eval_data = json.load(f) else: raise Exception('Report not found {}'.format(json_path)) if len(eval_data.keys()) > 1: raise Exception # First field is the tracker name. Index it out eval_data = eval_data[list(eval_data.keys())[0]] if 'succ_curve' in eval_data.keys(): curve = eval_data['succ_curve'] ao = eval_data['ao'] elif 'overall' in eval_data.keys() and 'succ_curve' in eval_data['overall'].keys(): curve = eval_data['overall']['succ_curve'] ao = eval_data['overall']['ao'] else: raise Exception('Invalid JSON file {}'.format(json_path)) auc_curve[trk_id, :] = torch.tensor(curve) * 100.0 scores[trk_id] = ao * 100.0 tracker_names.append({'name': trk.name, 'param': trk.parameter_name, 'run_id': trk.run_id, 'disp_name': trk.display_name}) threshold_set_overlap = torch.arange(0.0, 1.01, 0.01, dtype=torch.float64) success_plot_opts = {'plot_type': 'success', 'legend_loc': 'lower left', 'xlabel': 'Overlap threshold', 'ylabel': 'Overlap Precision [%]', 'xlim': (0, 1.0), 'ylim': (0, 100), 'title': 'Success plot'} plot_draw_save(auc_curve, threshold_set_overlap, scores, tracker_names, plot_draw_styles, result_plot_path, success_plot_opts) plt.show() def print_per_sequence_results(trackers, dataset, report_name, merge_results=False, filter_criteria=None, **kwargs): """ Print per-sequence results for the given trackers. Additionally, the sequences to list can be filtered using the filter criteria. args: trackers - List of trackers to evaluate dataset - List of sequences to evaluate report_name - Name of the folder in env_settings.perm_mat_path where the computed results and plots are saved merge_results - If True, multiple random runs for a non-deterministic trackers are averaged filter_criteria - Filter sequence results which are reported. Following modes are supported None: No filtering. Display results for all sequences in dataset 'ao_min': Only display sequences for which the minimum average overlap (AO) score over the trackers is less than a threshold filter_criteria['threshold']. This mode can be used to select sequences where at least one tracker performs poorly. 'ao_max': Only display sequences for which the maximum average overlap (AO) score over the trackers is less than a threshold filter_criteria['threshold']. This mode can be used to select sequences all tracker performs poorly. 'delta_ao': Only display sequences for which the performance of different trackers vary by at least filter_criteria['threshold'] in average overlap (AO) score. This mode can be used to select sequences where the behaviour of the trackers greatly differ between each other. """ # Load pre-computed results eval_data = check_and_load_precomputed_results(trackers, dataset, report_name, **kwargs) # Merge results from multiple runs if merge_results: eval_data = merge_multiple_runs(eval_data) tracker_names = eval_data['trackers'] valid_sequence = torch.tensor(eval_data['valid_sequence'], dtype=torch.bool) sequence_names = eval_data['sequences'] avg_overlap_all = torch.tensor(eval_data['avg_overlap_all']) * 100.0 # Filter sequences if filter_criteria is not None: if filter_criteria['mode'] == 'ao_min': min_ao = avg_overlap_all.min(dim=1)[0] valid_sequence = valid_sequence & (min_ao < filter_criteria['threshold']) elif filter_criteria['mode'] == 'ao_max': max_ao = avg_overlap_all.max(dim=1)[0] valid_sequence = valid_sequence & (max_ao < filter_criteria['threshold']) elif filter_criteria['mode'] == 'delta_ao': min_ao = avg_overlap_all.min(dim=1)[0] max_ao = avg_overlap_all.max(dim=1)[0] valid_sequence = valid_sequence & ((max_ao - min_ao) > filter_criteria['threshold']) else: raise Exception avg_overlap_all = avg_overlap_all[valid_sequence, :] sequence_names = [s + ' (ID={})'.format(i) for i, (s, v) in enumerate(zip(sequence_names, valid_sequence.tolist())) if v] tracker_disp_names = [get_tracker_display_name(trk) for trk in tracker_names] scores_per_tracker = {k: avg_overlap_all[:, i] for i, k in enumerate(tracker_disp_names)} report_text = generate_formatted_report(sequence_names, scores_per_tracker) print(report_text) ================================================ FILE: external/AR/pytracking/analysis/vos_utils.py ================================================ import warnings import numpy as np from skimage.morphology import binary_dilation, disk from math import floor def text_bargraph(values): blocks = np.array(('u', ' ', '▁', '▂', '▃', '▄', '▅', '▆', '▇', '█', 'o')) nsteps = len(blocks)-2-1 hstep = 1 / (2*nsteps) values = np.array(values) nans = np.isnan(values) values[nans] = 0 # '░' indices = ((values + hstep) * nsteps + 1).astype(np.int) indices[values < 0] = 0 indices[values > 1] = len(blocks)-1 graph = blocks[indices] graph[nans] = '░' graph = str.join('', graph) return graph # ---------------------------------------------------------------------------- # The 2017 DAVIS Challenge on Video Object Segmentation # ----------------------------------------------------------------------------- # Copyright (c) 2017 Federico Perazzi # Licensed under the BSD License [see LICENSE for details] # Written by Federico Perazzi (federico@disneyresearch.com) # Adapted from DAVIS 2016 (Federico Perazzi) # ---------------------------------------------------------------------------- # Originally db_eval_iou() in the davis challenge toolkit: def davis_jaccard_measure(fg_mask, gt_mask): """ Compute region similarity as the Jaccard Index. :param fg_mask: (ndarray): binary segmentation map. :param gt_mask: (ndarray): binary annotation map. :return: jaccard (float): region similarity """ gt_mask = gt_mask.astype(np.bool) fg_mask = fg_mask.astype(np.bool) if np.isclose(np.sum(gt_mask), 0) and np.isclose(np.sum(fg_mask), 0): return 1 else: return np.sum((gt_mask & fg_mask)) / \ np.sum((gt_mask | fg_mask), dtype=np.float32) def davis_jaccard_measure_torch(fg_mask, gt_mask): """ Compute region similarity as the Jaccard Index. :param fg_mask: (ndarray): binary segmentation map. :param gt_mask: (ndarray): binary annotation map. :return: jaccard (float): region similarity """ #gt_mask = gt_mask.astype(np.bool) #fg_mask = fg_mask.astype(np.bool) if gt_mask.sum() == 0 and fg_mask.sum() == 0: return 1 else: return (gt_mask & fg_mask).sum() / \ (gt_mask | fg_mask).sum().float() # Originally db_eval_boundary() in the davis challenge toolkit: def davis_f_measure(foreground_mask, gt_mask, bound_th=0.008): """ Compute mean,recall and decay from per-frame evaluation. Calculates precision/recall for boundaries between foreground_mask and gt_mask using morphological operators to speed it up. Arguments: foreground_mask (ndarray): binary segmentation image. gt_mask (ndarray): binary annotated image. Returns: F (float): boundaries F-measure P (float): boundaries precision R (float): boundaries recall """ assert np.atleast_3d(foreground_mask).shape[2] == 1 bound_pix = bound_th if bound_th >= 1 else \ np.ceil(bound_th * np.linalg.norm(foreground_mask.shape)) # Get the pixel boundaries of both masks fg_boundary = seg2bmap(foreground_mask) gt_boundary = seg2bmap(gt_mask) fg_dil = binary_dilation(fg_boundary, disk(bound_pix)) gt_dil = binary_dilation(gt_boundary, disk(bound_pix)) # Get the intersection gt_match = gt_boundary * fg_dil fg_match = fg_boundary * gt_dil # Area of the intersection n_fg = np.sum(fg_boundary) n_gt = np.sum(gt_boundary) # % Compute precision and recall if n_fg == 0 and n_gt > 0: precision = 1 recall = 0 elif n_fg > 0 and n_gt == 0: precision = 0 recall = 1 elif n_fg == 0 and n_gt == 0: precision = 1 recall = 1 else: precision = np.sum(fg_match) / float(n_fg) recall = np.sum(gt_match) / float(n_gt) # Compute F measure if precision + recall == 0: F = 0 else: F = 2 * precision * recall / (precision + recall) return F def seg2bmap(seg, width=None, height=None): """ From a segmentation, compute a binary boundary map with 1 pixel wide boundaries. The boundary pixels are offset by 1/2 pixel towards the origin from the actual segment boundary. Arguments: seg : Segments labeled from 1..k. width : Width of desired bmap <= seg.shape[1] height : Height of desired bmap <= seg.shape[0] Returns: bmap (ndarray): Binary boundary map. David Martin January 2003 """ seg = seg.astype(np.bool) seg[seg > 0] = 1 assert np.atleast_3d(seg).shape[2] == 1 width = seg.shape[1] if width is None else width height = seg.shape[0] if height is None else height h, w = seg.shape[:2] ar1 = float(width) / float(height) ar2 = float(w) / float(h) assert not (width > w | height > h | abs(ar1 - ar2) > 0.01), \ 'Can''t convert %dx%d seg to %dx%d bmap.' % (w, h, width, height) e = np.zeros_like(seg) s = np.zeros_like(seg) se = np.zeros_like(seg) e[:, :-1] = seg[:, 1:] s[:-1, :] = seg[1:, :] se[:-1, :-1] = seg[1:, 1:] b = seg ^ e | seg ^ s | seg ^ se b[-1, :] = seg[-1, :] ^ e[-1, :] b[:, -1] = seg[:, -1] ^ s[:, -1] b[-1, -1] = 0 if w == width and h == height: bmap = b else: bmap = np.zeros((height, width)) for x in range(w): for y in range(h): if b[y, x]: j = 1 + floor((y - 1) + height / h) i = 1 + floor((x - 1) + width / h) bmap[j, i] = 1 return bmap def nanmean(*args, **kwargs): with warnings.catch_warnings(): warnings.simplefilter("ignore", category=RuntimeWarning) return np.nanmean(*args, **kwargs) def mean(X): """ Compute average ignoring NaN values. """ return np.nanmean(X) def recall(X, threshold=0.5): """ Fraction of values of X scoring higher than 'threshold' """ with warnings.catch_warnings(): warnings.simplefilter("ignore", category=RuntimeWarning) x = X[~np.isnan(X)] x = mean(x > threshold) return x def decay(X, n_bins=4): """ Performance loss over time. """ X = X[~np.isnan(X)] ids = np.round(np.linspace(1, len(X), n_bins + 1) + 1e-10) - 1 ids = ids.astype(np.uint8) D_bins = [X[ids[i]:ids[i + 1] + 1] for i in range(0, 4)] with warnings.catch_warnings(): warnings.simplefilter("ignore", category=RuntimeWarning) D = np.nanmean(D_bins[0]) - np.nanmean(D_bins[3]) return D def std(X): """ Compute standard deviation. """ return np.nanstd(X) ================================================ FILE: external/AR/pytracking/evaluation/__init__.py ================================================ from .data import Sequence from .tracker import Tracker, trackerlist from .datasets import get_dataset ================================================ FILE: external/AR/pytracking/evaluation/data.py ================================================ import numpy as np from pytracking.evaluation.environment import env_settings from ltr.data.image_loader import imread_indexed from collections import OrderedDict class BaseDataset: """Base class for all datasets.""" def __init__(self): self.env_settings = env_settings() def __len__(self): """Overload this function in your dataset. This should return number of sequences in the dataset.""" raise NotImplementedError def get_sequence_list(self): """Overload this in your dataset. Should return the list of sequences in the dataset.""" raise NotImplementedError class Sequence: """Class for the sequence in an evaluation.""" def __init__(self, name, frames, dataset, ground_truth_rect, ground_truth_seg=None, init_data=None, object_class=None, target_visible=None, object_ids=None, multiobj_mode=False): self.name = name self.frames = frames self.dataset = dataset self.ground_truth_rect = ground_truth_rect self.ground_truth_seg = ground_truth_seg self.object_class = object_class self.target_visible = target_visible self.object_ids = object_ids self.multiobj_mode = multiobj_mode self.init_data = self._construct_init_data(init_data) self._ensure_start_frame() def _ensure_start_frame(self): # Ensure start frame is 0 start_frame = min(list(self.init_data.keys())) if start_frame > 0: self.frames = self.frames[start_frame:] if self.ground_truth_rect is not None: if isinstance(self.ground_truth_rect, (dict, OrderedDict)): for obj_id, gt in self.ground_truth_rect.items(): self.ground_truth_rect[obj_id] = gt[start_frame:,:] else: self.ground_truth_rect = self.ground_truth_rect[start_frame:,:] if self.ground_truth_seg is not None: self.ground_truth_seg = self.ground_truth_seg[start_frame:] assert len(self.frames) == len(self.ground_truth_seg) if self.target_visible is not None: self.target_visible = self.target_visible[start_frame:] self.init_data = {frame-start_frame: val for frame, val in self.init_data.items()} def _construct_init_data(self, init_data): if init_data is not None: if not self.multiobj_mode: assert self.object_ids is None or len(self.object_ids) == 1 for frame, init_val in init_data.items(): if 'bbox' in init_val and isinstance(init_val['bbox'], (dict, OrderedDict)): init_val['bbox'] = init_val['bbox'][self.object_ids[0]] # convert to list for frame, init_val in init_data.items(): if 'bbox' in init_val: if isinstance(init_val['bbox'], (dict, OrderedDict)): init_val['bbox'] = OrderedDict({obj_id: list(init) for obj_id, init in init_val['bbox'].items()}) else: init_val['bbox'] = list(init_val['bbox']) else: init_data = {0: dict()} # Assume start from frame 0 if self.object_ids is not None: init_data[0]['object_ids'] = self.object_ids if self.ground_truth_rect is not None: if self.multiobj_mode: assert isinstance(self.ground_truth_rect, (dict, OrderedDict)) init_data[0]['bbox'] = OrderedDict({obj_id: list(gt[0,:]) for obj_id, gt in self.ground_truth_rect.items()}) else: assert self.object_ids is None or len(self.object_ids) == 1 if isinstance(self.ground_truth_rect, (dict, OrderedDict)): init_data[0]['bbox'] = list(self.ground_truth_rect[self.object_ids[0]][0, :]) else: init_data[0]['bbox'] = list(self.ground_truth_rect[0,:]) if self.ground_truth_seg is not None: init_data[0]['mask'] = self.ground_truth_seg[0] return init_data def init_info(self): info = self.frame_info(frame_num=0) return info def frame_info(self, frame_num): info = self.object_init_data(frame_num=frame_num) return info def init_bbox(self, frame_num=0): return self.object_init_data(frame_num=frame_num).get('init_bbox') def init_mask(self, frame_num=0): return self.object_init_data(frame_num=frame_num).get('init_mask') def get_info(self, keys, frame_num=None): info = dict() for k in keys: val = self.get(k, frame_num=frame_num) if val is not None: info[k] = val return info def object_init_data(self, frame_num=None) -> dict: if frame_num is None: frame_num = 0 if frame_num not in self.init_data: return dict() init_data = dict() for key, val in self.init_data[frame_num].items(): if val is None: continue init_data['init_'+key] = val if 'init_mask' in init_data and init_data['init_mask'] is not None: anno = imread_indexed(init_data['init_mask']) if not self.multiobj_mode and self.object_ids is not None: assert len(self.object_ids) == 1 anno = (anno == int(self.object_ids[0])).astype(np.uint8) init_data['init_mask'] = anno if self.object_ids is not None: init_data['object_ids'] = self.object_ids init_data['sequence_object_ids'] = self.object_ids return init_data def target_class(self, frame_num=None): return self.object_class def get(self, name, frame_num=None): return getattr(self, name)(frame_num) def __repr__(self): return "{self.__class__.__name__} {self.name}, length={len} frames".format(self=self, len=len(self.frames)) class SequenceList(list): """List of sequences. Supports the addition operator to concatenate sequence lists.""" def __getitem__(self, item): if isinstance(item, str): for seq in self: if seq.name == item: return seq raise IndexError('Sequence name not in the dataset.') elif isinstance(item, int): return super(SequenceList, self).__getitem__(item) elif isinstance(item, (tuple, list)): return SequenceList([super(SequenceList, self).__getitem__(i) for i in item]) else: return SequenceList(super(SequenceList, self).__getitem__(item)) def __add__(self, other): return SequenceList(super(SequenceList, self).__add__(other)) def copy(self): return SequenceList(super(SequenceList, self).copy()) ================================================ FILE: external/AR/pytracking/evaluation/datasets.py ================================================ from collections import namedtuple import importlib from pytracking.evaluation.data import SequenceList DatasetInfo = namedtuple('DatasetInfo', ['module', 'class_name', 'kwargs']) pt = "pytracking.evaluation.%sdataset" # Useful abbreviations to reduce the clutter dataset_dict = dict( otb=DatasetInfo(module=pt % "otb", class_name="OTBDataset", kwargs=dict()), nfs=DatasetInfo(module=pt % "nfs", class_name="NFSDataset", kwargs=dict()), uav=DatasetInfo(module=pt % "uav", class_name="UAVDataset", kwargs=dict()), tpl=DatasetInfo(module=pt % "tpl", class_name="TPLDataset", kwargs=dict()), tpl_nootb=DatasetInfo(module=pt % "tpl", class_name="TPLDataset", kwargs=dict(exclude_otb=True)), vot=DatasetInfo(module=pt % "vot", class_name="VOTDataset", kwargs=dict()), trackingnet=DatasetInfo(module=pt % "trackingnet", class_name="TrackingNetDataset", kwargs=dict()), got10k_test=DatasetInfo(module=pt % "got10k", class_name="GOT10KDataset", kwargs=dict(split='test')), got10k_val=DatasetInfo(module=pt % "got10k", class_name="GOT10KDataset", kwargs=dict(split='val')), got10k_ltrval=DatasetInfo(module=pt % "got10k", class_name="GOT10KDataset", kwargs=dict(split='ltrval')), lasot=DatasetInfo(module=pt % "lasot", class_name="LaSOTDataset", kwargs=dict()), dv2017_val=DatasetInfo(module="ltr.dataset.davis", class_name="Davis", kwargs=dict(version='2017', split='val')), dv2016_val=DatasetInfo(module="ltr.dataset.davis", class_name="Davis", kwargs=dict(version='2016', split='val')), dv2017_test_dev=DatasetInfo(module="ltr.dataset.davis", class_name="Davis", kwargs=dict(version='2017', split='test-dev')), dv2017_test_chal=DatasetInfo(module="ltr.dataset.davis", class_name="Davis", kwargs=dict(version='2017', split='test-challenge')), yt2019_test=DatasetInfo(module="ltr.dataset.youtubevos", class_name="YouTubeVOS", kwargs=dict(version='2019', split='test')), yt2019_valid=DatasetInfo(module="ltr.dataset.youtubevos", class_name="YouTubeVOS", kwargs=dict(version='2019', split='valid')), yt2019_valid_all=DatasetInfo(module="ltr.dataset.youtubevos", class_name="YouTubeVOS", kwargs=dict(version='2019', split='valid', all_frames=True)), yt2018_valid_all=DatasetInfo(module="ltr.dataset.youtubevos", class_name="YouTubeVOS", kwargs=dict(version='2018', split='valid', all_frames=True)), yt2018_jjval=DatasetInfo(module="ltr.dataset.youtubevos", class_name="YouTubeVOS", kwargs=dict(version='2018', split='jjvalid')), yt2019_jjval=DatasetInfo(module="ltr.dataset.youtubevos", class_name="YouTubeVOS", kwargs=dict(version='2019', split='jjvalid', cleanup=['starts'])), yt2019_jjval_all=DatasetInfo(module="ltr.dataset.youtubevos", class_name="YouTubeVOS", kwargs=dict(version='2019', split='jjvalid', all_frames=True, cleanup=['starts'])), ) def load_dataset(name: str): """ Import and load a single dataset.""" name = name.lower() dset_info = dataset_dict.get(name) if dset_info is None: raise ValueError('Unknown dataset \'%s\'' % name) m = importlib.import_module(dset_info.module) dataset = getattr(m, dset_info.class_name)(**dset_info.kwargs) # Call the constructor return dataset.get_sequence_list() def get_dataset(*args): """ Get a single or set of datasets.""" dset = SequenceList() for name in args: dset.extend(load_dataset(name)) return dset ================================================ FILE: external/AR/pytracking/evaluation/environment.py ================================================ import importlib import os class EnvSettings: def __init__(self): pytracking_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..')) self.results_path = '{}/tracking_results/'.format(pytracking_path) self.segmentation_path = '{}/segmentation_results/'.format(pytracking_path) self.network_path = '{}/networks/'.format(pytracking_path) self.result_plot_path = '{}/result_plots/'.format(pytracking_path) self.otb_path = '' self.nfs_path = '' self.uav_path = '' self.tpl_path = '' self.vot_path = '' self.got10k_path = '' self.lasot_path = '' self.trackingnet_path = '' self.davis_dir = '' self.youtubevos_dir = '' self.got_packed_results_path = '' self.got_reports_path = '' self.tn_packed_results_path = '' def create_default_local_file(): comment = {'results_path': 'Where to store tracking results', 'network_path': 'Where tracking networks are stored.'} path = os.path.join(os.path.dirname(__file__), 'local.py') with open(path, 'w') as f: settings = EnvSettings() f.write('from pytracking.evaluation.environment import EnvSettings\n\n') f.write('def local_env_settings():\n') f.write(' settings = EnvSettings()\n\n') f.write(' # Set your local paths here.\n\n') for attr in dir(settings): comment_str = None if attr in comment: comment_str = comment[attr] attr_val = getattr(settings, attr) if not attr.startswith('__') and not callable(attr_val): if comment_str is None: f.write(' settings.{} = \'{}\'\n'.format(attr, attr_val)) else: f.write(' settings.{} = \'{}\' # {}\n'.format(attr, attr_val, comment_str)) f.write('\n return settings\n\n') def env_settings(): env_module_name = 'pytracking.evaluation.local' try: env_module = importlib.import_module(env_module_name) return env_module.local_env_settings() except: env_file = os.path.join(os.path.dirname(__file__), 'local.py') # Create a default file create_default_local_file() raise RuntimeError('YOU HAVE NOT SETUP YOUR local.py!!!\n Go to "{}" and set all the paths you need. ' 'Then try to run again.'.format(env_file)) ================================================ FILE: external/AR/pytracking/evaluation/got10kdataset.py ================================================ import numpy as np from pytracking.evaluation.data import Sequence, BaseDataset, SequenceList from pytracking.utils.load_text import load_text import os class GOT10KDataset(BaseDataset): """ GOT-10k dataset. Publication: GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild Lianghua Huang, Xin Zhao, and Kaiqi Huang arXiv:1810.11981, 2018 https://arxiv.org/pdf/1810.11981.pdf Download dataset from http://got-10k.aitestunion.com/downloads """ def __init__(self, split): super().__init__() # Split can be test, val, or ltrval (a validation split consisting of videos from the official train set) if split == 'test' or split == 'val': self.base_path = os.path.join(self.env_settings.got10k_path, split) else: self.base_path = os.path.join(self.env_settings.got10k_path, 'train') self.sequence_list = self._get_sequence_list(split) self.split = split def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_list]) def _construct_sequence(self, sequence_name): anno_path = '{}/{}/groundtruth.txt'.format(self.base_path, sequence_name) ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64) frames_path = '{}/{}'.format(self.base_path, sequence_name) frame_list = [frame for frame in os.listdir(frames_path) if frame.endswith(".jpg")] frame_list.sort(key=lambda f: int(f[:-4])) frames_list = [os.path.join(frames_path, frame) for frame in frame_list] return Sequence(sequence_name, frames_list, 'got10k', ground_truth_rect.reshape(-1, 4)) def __len__(self): return len(self.sequence_list) def _get_sequence_list(self, split): with open('{}/list.txt'.format(self.base_path)) as f: sequence_list = f.read().splitlines() if split == 'ltrval': with open('{}/got10k_val_split.txt'.format(self.env_settings.dataspec_path)) as f: seq_ids = f.read().splitlines() sequence_list = [sequence_list[int(x)] for x in seq_ids] return sequence_list ================================================ FILE: external/AR/pytracking/evaluation/lasotdataset.py ================================================ import numpy as np from pytracking.evaluation.data import Sequence, BaseDataset, SequenceList from pytracking.utils.load_text import load_text class LaSOTDataset(BaseDataset): """ LaSOT test set consisting of 280 videos (see Protocol-II in the LaSOT paper) Publication: LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Hexin Bai, Yong Xu, Chunyuan Liao and Haibin Ling CVPR, 2019 https://arxiv.org/pdf/1809.07845.pdf Download the dataset from https://cis.temple.edu/lasot/download.html """ def __init__(self): super().__init__() self.base_path = self.env_settings.lasot_path self.sequence_list = self._get_sequence_list() self.clean_list = self.clean_seq_list() def clean_seq_list(self): clean_lst = [] for i in range(len(self.sequence_list)): cls, _ = self.sequence_list[i].split('-') clean_lst.append(cls) return clean_lst def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_list]) def _construct_sequence(self, sequence_name): class_name = sequence_name.split('-')[0] anno_path = '{}/{}/{}/groundtruth.txt'.format(self.base_path, class_name, sequence_name) ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64) occlusion_label_path = '{}/{}/{}/full_occlusion.txt'.format(self.base_path, class_name, sequence_name) # NOTE: pandas backed seems super super slow for loading occlusion/oov masks full_occlusion = load_text(str(occlusion_label_path), delimiter=',', dtype=np.float64, backend='numpy') out_of_view_label_path = '{}/{}/{}/out_of_view.txt'.format(self.base_path, class_name, sequence_name) out_of_view = load_text(str(out_of_view_label_path), delimiter=',', dtype=np.float64, backend='numpy') target_visible = np.logical_and(full_occlusion == 0, out_of_view == 0) frames_path = '{}/{}/{}/img'.format(self.base_path, class_name, sequence_name) frames_list = ['{}/{:08d}.jpg'.format(frames_path, frame_number) for frame_number in range(1, ground_truth_rect.shape[0] + 1)] target_class = class_name return Sequence(sequence_name, frames_list, 'lasot', ground_truth_rect.reshape(-1, 4), object_class=target_class, target_visible=target_visible) def __len__(self): return len(self.sequence_list) def _get_sequence_list(self): sequence_list = ['airplane-1', 'airplane-9', 'airplane-13', 'airplane-15', 'basketball-1', 'basketball-6', 'basketball-7', 'basketball-11', 'bear-2', 'bear-4', 'bear-6', 'bear-17', 'bicycle-2', 'bicycle-7', 'bicycle-9', 'bicycle-18', 'bird-2', 'bird-3', 'bird-15', 'bird-17', 'boat-3', 'boat-4', 'boat-12', 'boat-17', 'book-3', 'book-10', 'book-11', 'book-19', 'bottle-1', 'bottle-12', 'bottle-14', 'bottle-18', 'bus-2', 'bus-5', 'bus-17', 'bus-19', 'car-2', 'car-6', 'car-9', 'car-17', 'cat-1', 'cat-3', 'cat-18', 'cat-20', 'cattle-2', 'cattle-7', 'cattle-12', 'cattle-13', 'spider-14', 'spider-16', 'spider-18', 'spider-20', 'coin-3', 'coin-6', 'coin-7', 'coin-18', 'crab-3', 'crab-6', 'crab-12', 'crab-18', 'surfboard-12', 'surfboard-4', 'surfboard-5', 'surfboard-8', 'cup-1', 'cup-4', 'cup-7', 'cup-17', 'deer-4', 'deer-8', 'deer-10', 'deer-14', 'dog-1', 'dog-7', 'dog-15', 'dog-19', 'guitar-3', 'guitar-8', 'guitar-10', 'guitar-16', 'person-1', 'person-5', 'person-10', 'person-12', 'pig-2', 'pig-10', 'pig-13', 'pig-18', 'rubicCube-1', 'rubicCube-6', 'rubicCube-14', 'rubicCube-19', 'swing-10', 'swing-14', 'swing-17', 'swing-20', 'drone-13', 'drone-15', 'drone-2', 'drone-7', 'pool-12', 'pool-15', 'pool-3', 'pool-7', 'rabbit-10', 'rabbit-13', 'rabbit-17', 'rabbit-19', 'racing-10', 'racing-15', 'racing-16', 'racing-20', 'robot-1', 'robot-19', 'robot-5', 'robot-8', 'sepia-13', 'sepia-16', 'sepia-6', 'sepia-8', 'sheep-3', 'sheep-5', 'sheep-7', 'sheep-9', 'skateboard-16', 'skateboard-19', 'skateboard-3', 'skateboard-8', 'tank-14', 'tank-16', 'tank-6', 'tank-9', 'tiger-12', 'tiger-18', 'tiger-4', 'tiger-6', 'train-1', 'train-11', 'train-20', 'train-7', 'truck-16', 'truck-3', 'truck-6', 'truck-7', 'turtle-16', 'turtle-5', 'turtle-8', 'turtle-9', 'umbrella-17', 'umbrella-19', 'umbrella-2', 'umbrella-9', 'yoyo-15', 'yoyo-17', 'yoyo-19', 'yoyo-7', 'zebra-10', 'zebra-14', 'zebra-16', 'zebra-17', 'elephant-1', 'elephant-12', 'elephant-16', 'elephant-18', 'goldfish-3', 'goldfish-7', 'goldfish-8', 'goldfish-10', 'hat-1', 'hat-2', 'hat-5', 'hat-18', 'kite-4', 'kite-6', 'kite-10', 'kite-15', 'motorcycle-1', 'motorcycle-3', 'motorcycle-9', 'motorcycle-18', 'mouse-1', 'mouse-8', 'mouse-9', 'mouse-17', 'flag-3', 'flag-9', 'flag-5', 'flag-2', 'frog-3', 'frog-4', 'frog-20', 'frog-9', 'gametarget-1', 'gametarget-2', 'gametarget-7', 'gametarget-13', 'hand-2', 'hand-3', 'hand-9', 'hand-16', 'helmet-5', 'helmet-11', 'helmet-19', 'helmet-13', 'licenseplate-6', 'licenseplate-12', 'licenseplate-13', 'licenseplate-15', 'electricfan-1', 'electricfan-10', 'electricfan-18', 'electricfan-20', 'chameleon-3', 'chameleon-6', 'chameleon-11', 'chameleon-20', 'crocodile-3', 'crocodile-4', 'crocodile-10', 'crocodile-14', 'gecko-1', 'gecko-5', 'gecko-16', 'gecko-19', 'fox-2', 'fox-3', 'fox-5', 'fox-20', 'giraffe-2', 'giraffe-10', 'giraffe-13', 'giraffe-15', 'gorilla-4', 'gorilla-6', 'gorilla-9', 'gorilla-13', 'hippo-1', 'hippo-7', 'hippo-9', 'hippo-20', 'horse-1', 'horse-4', 'horse-12', 'horse-15', 'kangaroo-2', 'kangaroo-5', 'kangaroo-11', 'kangaroo-14', 'leopard-1', 'leopard-7', 'leopard-16', 'leopard-20', 'lion-1', 'lion-5', 'lion-12', 'lion-20', 'lizard-1', 'lizard-3', 'lizard-6', 'lizard-13', 'microphone-2', 'microphone-6', 'microphone-14', 'microphone-16', 'monkey-3', 'monkey-4', 'monkey-9', 'monkey-17', 'shark-2', 'shark-3', 'shark-5', 'shark-6', 'squirrel-8', 'squirrel-11', 'squirrel-13', 'squirrel-19', 'volleyball-1', 'volleyball-13', 'volleyball-18', 'volleyball-19'] return sequence_list ================================================ FILE: external/AR/pytracking/evaluation/mobifacedataset.py ================================================ from pytracking.evaluation.data import Sequence, BaseDataset, SequenceList import glob import numpy as np import os.path as osp from collections import OrderedDict import pandas as pd class MobifaceDataset(BaseDataset): """ Mobiface dataset. Publication: MobiFace: A Novel Dataset for Mobile Face Tracking in the Wild Yiming Lin, Shiyang Cheng, Jie Shen, Maja Pantic arXiv:1805.09749, 2018 https://arxiv.org/pdf/1805.09749v2 Download dataset from https://mobiface.github.io/ """ def __init__(self, split): """ args: split - Split to use. Can be i) 'train': official training set, ii) 'test': official test set, iii) 'all': whole dataset. """ super().__init__() self.base_path = self.env_settings.mobiface_path self.sequence_list = self._get_sequence_list(split) self.split = split def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_list]) def _get_sequence_list(self, split): self.train_meta_fn = osp.join(self.base_path, 'train.meta.csv') self.test_meta_fn = osp.join(self.base_path, 'test.meta.csv') self.train_meta = pd.read_csv(self.train_meta_fn,index_col=0).transpose().to_dict() self.test_meta = pd.read_csv(self.test_meta_fn,index_col=0).transpose().to_dict() if split == 'train': self.meta = self.train_meta elif split == 'test': self.meta = self.test_meta else: self.meta = {**self.train_meta, **self.test_meta} # In Python 3.5 or greater self.meta = OrderedDict(sorted(self.meta.items(), key=lambda t: t[0])) self.anno_files = [] for k,v in self.meta.items(): if k in self.train_meta.keys(): self.anno_files.append(osp.abspath(osp.join(self.base_path,'train', k+'.annot.csv'))) else: self.anno_files.append(osp.abspath(osp.join(self.base_path,'test', k+'.annot.csv'))) self.seq_names = sorted(list(self.meta.keys())) self.seq_dirs = [fn[:-len('.annot.csv')] for fn in self.anno_files] return self.seq_names def _construct_sequence(self, sequence_name): index = self.seq_names.index(sequence_name) img_files = sorted(glob.glob(self.seq_dirs[index]+'/*.jpg')) if len(img_files) == 0: img_files = sorted(glob.glob(self.seq_dirs[index]+'.png')) with open(self.anno_files[index], 'r') as f: anno = np.loadtxt(f, delimiter=',', skiprows=1, dtype=int) anno = anno[:,1:] assert anno.shape[1] == 4 return Sequence(sequence_name, img_files, anno.reshape(-1, 4)) def __len__(self): return len(self.sequence_list) ================================================ FILE: external/AR/pytracking/evaluation/multi_object_wrapper.py ================================================ import numpy as np from collections import OrderedDict import time import copy class MultiObjectWrapper: def __init__(self, base_tracker_class, params, visdom=None, fast_load=False): self.base_tracker_class = base_tracker_class self.params = params self.visdom = visdom self.initialized_ids = [] self.trackers = OrderedDict() self.fast_load = fast_load if self.fast_load: self.tracker_copy = self.base_tracker_class(self.params) if hasattr(self.tracker_copy, 'initialize_features'): self.tracker_copy.initialize_features() def create_tracker(self): tracker = None if self.fast_load: try: tracker = copy.deepcopy(self.tracker_copy) except: pass if tracker is None: tracker = self.base_tracker_class(self.params) tracker.visdom = self.visdom return tracker def _split_info(self, info): info_split = OrderedDict() init_other = OrderedDict() # Init other contains init info for all other objects for obj_id in info['init_object_ids']: info_split[obj_id] = dict() init_other[obj_id] = dict() info_split[obj_id]['object_ids'] = [obj_id] info_split[obj_id]['sequence_object_ids'] = info['sequence_object_ids'] if 'init_bbox' in info: info_split[obj_id]['init_bbox'] = info['init_bbox'][obj_id] init_other[obj_id]['init_bbox'] = info['init_bbox'][obj_id] if 'init_mask' in info: info_split[obj_id]['init_mask'] = (info['init_mask'] == int(obj_id)).astype(np.uint8) init_other[obj_id]['init_mask'] = info_split[obj_id]['init_mask'] for obj_info in info_split.values(): obj_info['init_other'] = init_other return info_split def _set_defaults(self, tracker_out: dict, defaults=None): defaults = {} if defaults is None else defaults for key, val in defaults.items(): if tracker_out.get(key) is None: tracker_out[key] = val return tracker_out def default_merge(self, out_all): out_merged = OrderedDict() out_first = list(out_all.values())[0] out_types = out_first.keys() # Merge segmentation mask if 'segmentation' in out_types and out_first['segmentation'] is not None: # Stack all masks # If a tracker outputs soft segmentation mask, use that. Else use the binary segmentation segmentation_maps = [out.get('segmentation_soft', out['segmentation']) for out in out_all.values()] segmentation_maps = np.stack(segmentation_maps) obj_ids = np.array([0, *map(int, out_all.keys())], dtype=np.uint8) segm_threshold = getattr(self.params, 'segmentation_threshold', 0.5) merged_segmentation = obj_ids[np.where(segmentation_maps.max(axis=0) > segm_threshold, segmentation_maps.argmax(axis=0) + 1, 0)] out_merged['segmentation'] = merged_segmentation # Merge other fields for key in out_types: if key == 'segmentation': pass else: out_merged[key] = {obj_id: out[key] for obj_id, out in out_all.items()} return out_merged def merge_outputs(self, out_all): if hasattr(self.base_tracker_class, 'merge_results'): out_merged = self.base_tracker_class.merge_results(out_all) else: out_merged = self.default_merge(out_all) return out_merged def initialize(self, image, info: dict) -> dict: self.initialized_ids = [] self.trackers = OrderedDict() if len(info['init_object_ids']) == 0: return None object_ids = info['object_ids'] init_info_split = self._split_info(info) self.trackers = OrderedDict({obj_id: self.create_tracker() for obj_id in object_ids}) out_all = OrderedDict() # Run individual trackers for each object for obj_id in info['init_object_ids']: start_time = time.time() out = self.trackers[obj_id].initialize(image, init_info_split[obj_id]) if out is None: out = {} init_default = {'target_bbox': init_info_split[obj_id].get('init_bbox'), 'time': time.time() - start_time, 'segmentation': init_info_split[obj_id].get('init_mask')} out = self._set_defaults(out, init_default) out_all[obj_id] = out # Merge results out_merged = self.merge_outputs(out_all) self.initialized_ids = info['init_object_ids'].copy() return out_merged def track(self, image, info: dict = None) -> dict: if info is None: info = {} prev_output = info.get('previous_output', OrderedDict()) if info.get('init_object_ids', False): init_info_split = self._split_info(info) for obj_init_info in init_info_split.values(): obj_init_info['previous_output'] = prev_output info['init_other'] = list(init_info_split.values())[0]['init_other'] out_all = OrderedDict() for obj_id in self.initialized_ids: start_time = time.time() out = self.trackers[obj_id].track(image, info) default = {'time': time.time() - start_time} out = self._set_defaults(out, default) out_all[obj_id] = out # Initialize new if info.get('init_object_ids', False): for obj_id in info['init_object_ids']: if not obj_id in self.trackers: self.trackers[obj_id] = self.create_tracker() start_time = time.time() out = self.trackers[obj_id].initialize(image, init_info_split[obj_id]) if out is None: out = {} init_default = {'target_bbox': init_info_split[obj_id].get('init_bbox'), 'time': time.time() - start_time, 'segmentation': init_info_split[obj_id].get('init_mask')} out = self._set_defaults(out, init_default) out_all[obj_id] = out self.initialized_ids.extend(info['init_object_ids']) # Merge results out_merged = self.merge_outputs(out_all) return out_merged def visdom_draw_tracking(self, image, box, segmentation): if isinstance(box, (OrderedDict, dict)): box = [v for k, v in box.items()] else: box = (box,) if segmentation is None: self.visdom.register((image, *box), 'Tracking', 1, 'Tracking') else: self.visdom.register((image, *box, segmentation), 'Tracking', 1, 'Tracking') ================================================ FILE: external/AR/pytracking/evaluation/nfsdataset.py ================================================ import numpy as np from pytracking.evaluation.data import Sequence, BaseDataset, SequenceList from pytracking.utils.load_text import load_text class NFSDataset(BaseDataset): """ NFS dataset. Publication: Need for Speed: A Benchmark for Higher Frame Rate Object Tracking H. Kiani Galoogahi, A. Fagg, C. Huang, D. Ramanan, and S.Lucey ICCV, 2017 http://openaccess.thecvf.com/content_ICCV_2017/papers/Galoogahi_Need_for_Speed_ICCV_2017_paper.pdf Download the dataset from http://ci2cv.net/nfs/index.html """ def __init__(self): super().__init__() self.base_path = self.env_settings.nfs_path self.sequence_info_list = self._get_sequence_info_list() def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_info_list]) def _construct_sequence(self, sequence_info): sequence_path = sequence_info['path'] nz = sequence_info['nz'] ext = sequence_info['ext'] start_frame = sequence_info['startFrame'] end_frame = sequence_info['endFrame'] init_omit = 0 if 'initOmit' in sequence_info: init_omit = sequence_info['initOmit'] frames = ['{base_path}/{sequence_path}/{frame:0{nz}}.{ext}'.format(base_path=self.base_path, sequence_path=sequence_path, frame=frame_num, nz=nz, ext=ext) for frame_num in range(start_frame+init_omit, end_frame+1)] anno_path = '{}/{}'.format(self.base_path, sequence_info['anno_path']) ground_truth_rect = load_text(str(anno_path), delimiter='\t', dtype=np.float64) return Sequence(sequence_info['name'], frames, 'nfs', ground_truth_rect[init_omit:,:], object_class=sequence_info['object_class']) def __len__(self): return len(self.sequence_info_list) def _get_sequence_info_list(self): sequence_info_list = [ {"name": "nfs_Gymnastics", "path": "sequences/Gymnastics", "startFrame": 1, "endFrame": 368, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_Gymnastics.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_MachLoop_jet", "path": "sequences/MachLoop_jet", "startFrame": 1, "endFrame": 99, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_MachLoop_jet.txt", "object_class": "aircraft", 'occlusion': False}, {"name": "nfs_Skiing_red", "path": "sequences/Skiing_red", "startFrame": 1, "endFrame": 69, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_Skiing_red.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_Skydiving", "path": "sequences/Skydiving", "startFrame": 1, "endFrame": 196, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_Skydiving.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_airboard_1", "path": "sequences/airboard_1", "startFrame": 1, "endFrame": 425, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_airboard_1.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_airplane_landing", "path": "sequences/airplane_landing", "startFrame": 1, "endFrame": 81, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_airplane_landing.txt", "object_class": "aircraft", 'occlusion': False}, {"name": "nfs_airtable_3", "path": "sequences/airtable_3", "startFrame": 1, "endFrame": 482, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_airtable_3.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_basketball_1", "path": "sequences/basketball_1", "startFrame": 1, "endFrame": 282, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_1.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_basketball_2", "path": "sequences/basketball_2", "startFrame": 1, "endFrame": 102, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_2.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_basketball_3", "path": "sequences/basketball_3", "startFrame": 1, "endFrame": 421, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_3.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_basketball_6", "path": "sequences/basketball_6", "startFrame": 1, "endFrame": 224, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_6.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_basketball_7", "path": "sequences/basketball_7", "startFrame": 1, "endFrame": 240, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_7.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_basketball_player", "path": "sequences/basketball_player", "startFrame": 1, "endFrame": 369, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_player.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_basketball_player_2", "path": "sequences/basketball_player_2", "startFrame": 1, "endFrame": 437, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_player_2.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_beach_flipback_person", "path": "sequences/beach_flipback_person", "startFrame": 1, "endFrame": 61, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_beach_flipback_person.txt", "object_class": "person head", 'occlusion': False}, {"name": "nfs_bee", "path": "sequences/bee", "startFrame": 1, "endFrame": 45, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bee.txt", "object_class": "insect", 'occlusion': False}, {"name": "nfs_biker_acrobat", "path": "sequences/biker_acrobat", "startFrame": 1, "endFrame": 128, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_acrobat.txt", "object_class": "bicycle", 'occlusion': False}, {"name": "nfs_biker_all_1", "path": "sequences/biker_all_1", "startFrame": 1, "endFrame": 113, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_all_1.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_biker_head_2", "path": "sequences/biker_head_2", "startFrame": 1, "endFrame": 132, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_head_2.txt", "object_class": "person head", 'occlusion': False}, {"name": "nfs_biker_head_3", "path": "sequences/biker_head_3", "startFrame": 1, "endFrame": 254, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_head_3.txt", "object_class": "person head", 'occlusion': False}, {"name": "nfs_biker_upper_body", "path": "sequences/biker_upper_body", "startFrame": 1, "endFrame": 194, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_upper_body.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_biker_whole_body", "path": "sequences/biker_whole_body", "startFrame": 1, "endFrame": 572, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_whole_body.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_billiard_2", "path": "sequences/billiard_2", "startFrame": 1, "endFrame": 604, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_billiard_2.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_billiard_3", "path": "sequences/billiard_3", "startFrame": 1, "endFrame": 698, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_billiard_3.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_billiard_6", "path": "sequences/billiard_6", "startFrame": 1, "endFrame": 771, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_billiard_6.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_billiard_7", "path": "sequences/billiard_7", "startFrame": 1, "endFrame": 724, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_billiard_7.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_billiard_8", "path": "sequences/billiard_8", "startFrame": 1, "endFrame": 778, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_billiard_8.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_bird_2", "path": "sequences/bird_2", "startFrame": 1, "endFrame": 476, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bird_2.txt", "object_class": "bird", 'occlusion': False}, {"name": "nfs_book", "path": "sequences/book", "startFrame": 1, "endFrame": 288, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_book.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_bottle", "path": "sequences/bottle", "startFrame": 1, "endFrame": 2103, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bottle.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_bowling_1", "path": "sequences/bowling_1", "startFrame": 1, "endFrame": 303, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bowling_1.txt", "object_class": "ball", 'occlusion': True}, {"name": "nfs_bowling_2", "path": "sequences/bowling_2", "startFrame": 1, "endFrame": 710, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bowling_2.txt", "object_class": "ball", 'occlusion': True}, {"name": "nfs_bowling_3", "path": "sequences/bowling_3", "startFrame": 1, "endFrame": 271, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bowling_3.txt", "object_class": "ball", 'occlusion': True}, {"name": "nfs_bowling_6", "path": "sequences/bowling_6", "startFrame": 1, "endFrame": 260, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bowling_6.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_bowling_ball", "path": "sequences/bowling_ball", "startFrame": 1, "endFrame": 275, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bowling_ball.txt", "object_class": "ball", 'occlusion': True}, {"name": "nfs_bunny", "path": "sequences/bunny", "startFrame": 1, "endFrame": 705, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bunny.txt", "object_class": "mammal", 'occlusion': False}, {"name": "nfs_car", "path": "sequences/car", "startFrame": 1, "endFrame": 2020, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car.txt", "object_class": "car", 'occlusion': True}, {"name": "nfs_car_camaro", "path": "sequences/car_camaro", "startFrame": 1, "endFrame": 36, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_camaro.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_car_drifting", "path": "sequences/car_drifting", "startFrame": 1, "endFrame": 173, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_drifting.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_car_jumping", "path": "sequences/car_jumping", "startFrame": 1, "endFrame": 22, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_jumping.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_car_rc_rolling", "path": "sequences/car_rc_rolling", "startFrame": 1, "endFrame": 62, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_rc_rolling.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_car_rc_rotating", "path": "sequences/car_rc_rotating", "startFrame": 1, "endFrame": 80, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_rc_rotating.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_car_side", "path": "sequences/car_side", "startFrame": 1, "endFrame": 108, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_side.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_car_white", "path": "sequences/car_white", "startFrame": 1, "endFrame": 2063, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_white.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_cheetah", "path": "sequences/cheetah", "startFrame": 1, "endFrame": 167, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_cheetah.txt", "object_class": "mammal", 'occlusion': True}, {"name": "nfs_cup", "path": "sequences/cup", "startFrame": 1, "endFrame": 1281, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_cup.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_cup_2", "path": "sequences/cup_2", "startFrame": 1, "endFrame": 182, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_cup_2.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_dog", "path": "sequences/dog", "startFrame": 1, "endFrame": 1030, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dog.txt", "object_class": "dog", 'occlusion': True}, {"name": "nfs_dog_1", "path": "sequences/dog_1", "startFrame": 1, "endFrame": 168, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dog_1.txt", "object_class": "dog", 'occlusion': False}, {"name": "nfs_dog_2", "path": "sequences/dog_2", "startFrame": 1, "endFrame": 594, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dog_2.txt", "object_class": "dog", 'occlusion': True}, {"name": "nfs_dog_3", "path": "sequences/dog_3", "startFrame": 1, "endFrame": 200, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dog_3.txt", "object_class": "dog", 'occlusion': False}, {"name": "nfs_dogs", "path": "sequences/dogs", "startFrame": 1, "endFrame": 198, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dogs.txt", "object_class": "dog", 'occlusion': True}, {"name": "nfs_dollar", "path": "sequences/dollar", "startFrame": 1, "endFrame": 1426, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dollar.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_drone", "path": "sequences/drone", "startFrame": 1, "endFrame": 70, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_drone.txt", "object_class": "aircraft", 'occlusion': False}, {"name": "nfs_ducks_lake", "path": "sequences/ducks_lake", "startFrame": 1, "endFrame": 107, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_ducks_lake.txt", "object_class": "bird", 'occlusion': False}, {"name": "nfs_exit", "path": "sequences/exit", "startFrame": 1, "endFrame": 359, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_exit.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_first", "path": "sequences/first", "startFrame": 1, "endFrame": 435, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_first.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_flower", "path": "sequences/flower", "startFrame": 1, "endFrame": 448, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_flower.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_footbal_skill", "path": "sequences/footbal_skill", "startFrame": 1, "endFrame": 131, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_footbal_skill.txt", "object_class": "ball", 'occlusion': True}, {"name": "nfs_helicopter", "path": "sequences/helicopter", "startFrame": 1, "endFrame": 310, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_helicopter.txt", "object_class": "aircraft", 'occlusion': False}, {"name": "nfs_horse_jumping", "path": "sequences/horse_jumping", "startFrame": 1, "endFrame": 117, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_horse_jumping.txt", "object_class": "horse", 'occlusion': True}, {"name": "nfs_horse_running", "path": "sequences/horse_running", "startFrame": 1, "endFrame": 139, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_horse_running.txt", "object_class": "horse", 'occlusion': False}, {"name": "nfs_iceskating_6", "path": "sequences/iceskating_6", "startFrame": 1, "endFrame": 603, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_iceskating_6.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_jellyfish_5", "path": "sequences/jellyfish_5", "startFrame": 1, "endFrame": 746, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_jellyfish_5.txt", "object_class": "invertebrate", 'occlusion': False}, {"name": "nfs_kid_swing", "path": "sequences/kid_swing", "startFrame": 1, "endFrame": 169, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_kid_swing.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_motorcross", "path": "sequences/motorcross", "startFrame": 1, "endFrame": 39, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_motorcross.txt", "object_class": "vehicle", 'occlusion': True}, {"name": "nfs_motorcross_kawasaki", "path": "sequences/motorcross_kawasaki", "startFrame": 1, "endFrame": 65, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_motorcross_kawasaki.txt", "object_class": "vehicle", 'occlusion': False}, {"name": "nfs_parkour", "path": "sequences/parkour", "startFrame": 1, "endFrame": 58, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_parkour.txt", "object_class": "person head", 'occlusion': False}, {"name": "nfs_person_scooter", "path": "sequences/person_scooter", "startFrame": 1, "endFrame": 413, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_person_scooter.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_pingpong_2", "path": "sequences/pingpong_2", "startFrame": 1, "endFrame": 1277, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_pingpong_2.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_pingpong_7", "path": "sequences/pingpong_7", "startFrame": 1, "endFrame": 1290, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_pingpong_7.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_pingpong_8", "path": "sequences/pingpong_8", "startFrame": 1, "endFrame": 296, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_pingpong_8.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_purse", "path": "sequences/purse", "startFrame": 1, "endFrame": 968, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_purse.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_rubber", "path": "sequences/rubber", "startFrame": 1, "endFrame": 1328, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_rubber.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_running", "path": "sequences/running", "startFrame": 1, "endFrame": 677, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_running.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_running_100_m", "path": "sequences/running_100_m", "startFrame": 1, "endFrame": 313, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_running_100_m.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_running_100_m_2", "path": "sequences/running_100_m_2", "startFrame": 1, "endFrame": 337, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_running_100_m_2.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_running_2", "path": "sequences/running_2", "startFrame": 1, "endFrame": 363, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_running_2.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_shuffleboard_1", "path": "sequences/shuffleboard_1", "startFrame": 1, "endFrame": 42, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffleboard_1.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_shuffleboard_2", "path": "sequences/shuffleboard_2", "startFrame": 1, "endFrame": 41, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffleboard_2.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_shuffleboard_4", "path": "sequences/shuffleboard_4", "startFrame": 1, "endFrame": 62, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffleboard_4.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_shuffleboard_5", "path": "sequences/shuffleboard_5", "startFrame": 1, "endFrame": 32, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffleboard_5.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_shuffleboard_6", "path": "sequences/shuffleboard_6", "startFrame": 1, "endFrame": 52, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffleboard_6.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_shuffletable_2", "path": "sequences/shuffletable_2", "startFrame": 1, "endFrame": 372, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffletable_2.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_shuffletable_3", "path": "sequences/shuffletable_3", "startFrame": 1, "endFrame": 368, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffletable_3.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_shuffletable_4", "path": "sequences/shuffletable_4", "startFrame": 1, "endFrame": 101, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffletable_4.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_ski_long", "path": "sequences/ski_long", "startFrame": 1, "endFrame": 274, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_ski_long.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_soccer_ball", "path": "sequences/soccer_ball", "startFrame": 1, "endFrame": 163, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_soccer_ball.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_soccer_ball_2", "path": "sequences/soccer_ball_2", "startFrame": 1, "endFrame": 1934, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_soccer_ball_2.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_soccer_ball_3", "path": "sequences/soccer_ball_3", "startFrame": 1, "endFrame": 1381, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_soccer_ball_3.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_soccer_player_2", "path": "sequences/soccer_player_2", "startFrame": 1, "endFrame": 475, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_soccer_player_2.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_soccer_player_3", "path": "sequences/soccer_player_3", "startFrame": 1, "endFrame": 319, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_soccer_player_3.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_stop_sign", "path": "sequences/stop_sign", "startFrame": 1, "endFrame": 302, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_stop_sign.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_suv", "path": "sequences/suv", "startFrame": 1, "endFrame": 2584, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_suv.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_tiger", "path": "sequences/tiger", "startFrame": 1, "endFrame": 1556, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_tiger.txt", "object_class": "mammal", 'occlusion': False}, {"name": "nfs_walking", "path": "sequences/walking", "startFrame": 1, "endFrame": 555, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_walking.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_walking_3", "path": "sequences/walking_3", "startFrame": 1, "endFrame": 1427, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_walking_3.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_water_ski_2", "path": "sequences/water_ski_2", "startFrame": 1, "endFrame": 47, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_water_ski_2.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_yoyo", "path": "sequences/yoyo", "startFrame": 1, "endFrame": 67, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_yoyo.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_zebra_fish", "path": "sequences/zebra_fish", "startFrame": 1, "endFrame": 671, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_zebra_fish.txt", "object_class": "fish", 'occlusion': False}, ] return sequence_info_list ================================================ FILE: external/AR/pytracking/evaluation/otbdataset.py ================================================ import numpy as np from pytracking.evaluation.data import Sequence, BaseDataset, SequenceList from pytracking.utils.load_text import load_text class OTBDataset(BaseDataset): """ OTB-2015 dataset Publication: Object Tracking Benchmark Wu, Yi, Jongwoo Lim, and Ming-hsuan Yan TPAMI, 2015 http://faculty.ucmerced.edu/mhyang/papers/pami15_tracking_benchmark.pdf Download the dataset from http://cvlab.hanyang.ac.kr/tracker_benchmark/index.html """ def __init__(self): super().__init__() self.base_path = self.env_settings.otb_path self.sequence_info_list = self._get_sequence_info_list() def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_info_list]) def _construct_sequence(self, sequence_info): sequence_path = sequence_info['path'] nz = sequence_info['nz'] ext = sequence_info['ext'] start_frame = sequence_info['startFrame'] end_frame = sequence_info['endFrame'] init_omit = 0 if 'initOmit' in sequence_info: init_omit = sequence_info['initOmit'] frames = ['{base_path}/{sequence_path}/{frame:0{nz}}.{ext}'.format(base_path=self.base_path, sequence_path=sequence_path, frame=frame_num, nz=nz, ext=ext) for frame_num in range(start_frame+init_omit, end_frame+1)] anno_path = '{}/{}'.format(self.base_path, sequence_info['anno_path']) # NOTE: OTB has some weird annos which panda cannot handle ground_truth_rect = load_text(str(anno_path), delimiter=(',', None), dtype=np.float64, backend='numpy') return Sequence(sequence_info['name'], frames, 'otb', ground_truth_rect[init_omit:,:], object_class=sequence_info['object_class']) def __len__(self): return len(self.sequence_info_list) def _get_sequence_info_list(self): sequence_info_list = [ {"name": "Basketball", "path": "Basketball/img", "startFrame": 1, "endFrame": 725, "nz": 4, "ext": "jpg", "anno_path": "Basketball/groundtruth_rect.txt", "object_class": "person"}, {"name": "Biker", "path": "Biker/img", "startFrame": 1, "endFrame": 142, "nz": 4, "ext": "jpg", "anno_path": "Biker/groundtruth_rect.txt", "object_class": "person head"}, {"name": "Bird1", "path": "Bird1/img", "startFrame": 1, "endFrame": 408, "nz": 4, "ext": "jpg", "anno_path": "Bird1/groundtruth_rect.txt", "object_class": "bird"}, {"name": "Bird2", "path": "Bird2/img", "startFrame": 1, "endFrame": 99, "nz": 4, "ext": "jpg", "anno_path": "Bird2/groundtruth_rect.txt", "object_class": "bird"}, {"name": "BlurBody", "path": "BlurBody/img", "startFrame": 1, "endFrame": 334, "nz": 4, "ext": "jpg", "anno_path": "BlurBody/groundtruth_rect.txt", "object_class": "person"}, {"name": "BlurCar1", "path": "BlurCar1/img", "startFrame": 247, "endFrame": 988, "nz": 4, "ext": "jpg", "anno_path": "BlurCar1/groundtruth_rect.txt", "object_class": "car"}, {"name": "BlurCar2", "path": "BlurCar2/img", "startFrame": 1, "endFrame": 585, "nz": 4, "ext": "jpg", "anno_path": "BlurCar2/groundtruth_rect.txt", "object_class": "car"}, {"name": "BlurCar3", "path": "BlurCar3/img", "startFrame": 3, "endFrame": 359, "nz": 4, "ext": "jpg", "anno_path": "BlurCar3/groundtruth_rect.txt", "object_class": "car"}, {"name": "BlurCar4", "path": "BlurCar4/img", "startFrame": 18, "endFrame": 397, "nz": 4, "ext": "jpg", "anno_path": "BlurCar4/groundtruth_rect.txt", "object_class": "car"}, {"name": "BlurFace", "path": "BlurFace/img", "startFrame": 1, "endFrame": 493, "nz": 4, "ext": "jpg", "anno_path": "BlurFace/groundtruth_rect.txt", "object_class": "face"}, {"name": "BlurOwl", "path": "BlurOwl/img", "startFrame": 1, "endFrame": 631, "nz": 4, "ext": "jpg", "anno_path": "BlurOwl/groundtruth_rect.txt", "object_class": "other"}, {"name": "Board", "path": "Board/img", "startFrame": 1, "endFrame": 698, "nz": 5, "ext": "jpg", "anno_path": "Board/groundtruth_rect.txt", "object_class": "other"}, {"name": "Bolt", "path": "Bolt/img", "startFrame": 1, "endFrame": 350, "nz": 4, "ext": "jpg", "anno_path": "Bolt/groundtruth_rect.txt", "object_class": "person"}, {"name": "Bolt2", "path": "Bolt2/img", "startFrame": 1, "endFrame": 293, "nz": 4, "ext": "jpg", "anno_path": "Bolt2/groundtruth_rect.txt", "object_class": "person"}, {"name": "Box", "path": "Box/img", "startFrame": 1, "endFrame": 1161, "nz": 4, "ext": "jpg", "anno_path": "Box/groundtruth_rect.txt", "object_class": "other"}, {"name": "Boy", "path": "Boy/img", "startFrame": 1, "endFrame": 602, "nz": 4, "ext": "jpg", "anno_path": "Boy/groundtruth_rect.txt", "object_class": "face"}, {"name": "Car1", "path": "Car1/img", "startFrame": 1, "endFrame": 1020, "nz": 4, "ext": "jpg", "anno_path": "Car1/groundtruth_rect.txt", "object_class": "car"}, {"name": "Car2", "path": "Car2/img", "startFrame": 1, "endFrame": 913, "nz": 4, "ext": "jpg", "anno_path": "Car2/groundtruth_rect.txt", "object_class": "car"}, {"name": "Car24", "path": "Car24/img", "startFrame": 1, "endFrame": 3059, "nz": 4, "ext": "jpg", "anno_path": "Car24/groundtruth_rect.txt", "object_class": "car"}, {"name": "Car4", "path": "Car4/img", "startFrame": 1, "endFrame": 659, "nz": 4, "ext": "jpg", "anno_path": "Car4/groundtruth_rect.txt", "object_class": "car"}, {"name": "CarDark", "path": "CarDark/img", "startFrame": 1, "endFrame": 393, "nz": 4, "ext": "jpg", "anno_path": "CarDark/groundtruth_rect.txt", "object_class": "car"}, {"name": "CarScale", "path": "CarScale/img", "startFrame": 1, "endFrame": 252, "nz": 4, "ext": "jpg", "anno_path": "CarScale/groundtruth_rect.txt", "object_class": "car"}, {"name": "ClifBar", "path": "ClifBar/img", "startFrame": 1, "endFrame": 472, "nz": 4, "ext": "jpg", "anno_path": "ClifBar/groundtruth_rect.txt", "object_class": "other"}, {"name": "Coke", "path": "Coke/img", "startFrame": 1, "endFrame": 291, "nz": 4, "ext": "jpg", "anno_path": "Coke/groundtruth_rect.txt", "object_class": "other"}, {"name": "Couple", "path": "Couple/img", "startFrame": 1, "endFrame": 140, "nz": 4, "ext": "jpg", "anno_path": "Couple/groundtruth_rect.txt", "object_class": "person"}, {"name": "Coupon", "path": "Coupon/img", "startFrame": 1, "endFrame": 327, "nz": 4, "ext": "jpg", "anno_path": "Coupon/groundtruth_rect.txt", "object_class": "other"}, {"name": "Crossing", "path": "Crossing/img", "startFrame": 1, "endFrame": 120, "nz": 4, "ext": "jpg", "anno_path": "Crossing/groundtruth_rect.txt", "object_class": "person"}, {"name": "Crowds", "path": "Crowds/img", "startFrame": 1, "endFrame": 347, "nz": 4, "ext": "jpg", "anno_path": "Crowds/groundtruth_rect.txt", "object_class": "person"}, {"name": "Dancer", "path": "Dancer/img", "startFrame": 1, "endFrame": 225, "nz": 4, "ext": "jpg", "anno_path": "Dancer/groundtruth_rect.txt", "object_class": "person"}, {"name": "Dancer2", "path": "Dancer2/img", "startFrame": 1, "endFrame": 150, "nz": 4, "ext": "jpg", "anno_path": "Dancer2/groundtruth_rect.txt", "object_class": "person"}, {"name": "David", "path": "David/img", "startFrame": 300, "endFrame": 770, "nz": 4, "ext": "jpg", "anno_path": "David/groundtruth_rect.txt", "object_class": "face"}, {"name": "David2", "path": "David2/img", "startFrame": 1, "endFrame": 537, "nz": 4, "ext": "jpg", "anno_path": "David2/groundtruth_rect.txt", "object_class": "face"}, {"name": "David3", "path": "David3/img", "startFrame": 1, "endFrame": 252, "nz": 4, "ext": "jpg", "anno_path": "David3/groundtruth_rect.txt", "object_class": "person"}, {"name": "Deer", "path": "Deer/img", "startFrame": 1, "endFrame": 71, "nz": 4, "ext": "jpg", "anno_path": "Deer/groundtruth_rect.txt", "object_class": "mammal"}, {"name": "Diving", "path": "Diving/img", "startFrame": 1, "endFrame": 215, "nz": 4, "ext": "jpg", "anno_path": "Diving/groundtruth_rect.txt", "object_class": "person"}, {"name": "Dog", "path": "Dog/img", "startFrame": 1, "endFrame": 127, "nz": 4, "ext": "jpg", "anno_path": "Dog/groundtruth_rect.txt", "object_class": "dog"}, {"name": "Dog1", "path": "Dog1/img", "startFrame": 1, "endFrame": 1350, "nz": 4, "ext": "jpg", "anno_path": "Dog1/groundtruth_rect.txt", "object_class": "dog"}, {"name": "Doll", "path": "Doll/img", "startFrame": 1, "endFrame": 3872, "nz": 4, "ext": "jpg", "anno_path": "Doll/groundtruth_rect.txt", "object_class": "other"}, {"name": "DragonBaby", "path": "DragonBaby/img", "startFrame": 1, "endFrame": 113, "nz": 4, "ext": "jpg", "anno_path": "DragonBaby/groundtruth_rect.txt", "object_class": "face"}, {"name": "Dudek", "path": "Dudek/img", "startFrame": 1, "endFrame": 1145, "nz": 4, "ext": "jpg", "anno_path": "Dudek/groundtruth_rect.txt", "object_class": "face"}, {"name": "FaceOcc1", "path": "FaceOcc1/img", "startFrame": 1, "endFrame": 892, "nz": 4, "ext": "jpg", "anno_path": "FaceOcc1/groundtruth_rect.txt", "object_class": "face"}, {"name": "FaceOcc2", "path": "FaceOcc2/img", "startFrame": 1, "endFrame": 812, "nz": 4, "ext": "jpg", "anno_path": "FaceOcc2/groundtruth_rect.txt", "object_class": "face"}, {"name": "Fish", "path": "Fish/img", "startFrame": 1, "endFrame": 476, "nz": 4, "ext": "jpg", "anno_path": "Fish/groundtruth_rect.txt", "object_class": "other"}, {"name": "FleetFace", "path": "FleetFace/img", "startFrame": 1, "endFrame": 707, "nz": 4, "ext": "jpg", "anno_path": "FleetFace/groundtruth_rect.txt", "object_class": "face"}, {"name": "Football", "path": "Football/img", "startFrame": 1, "endFrame": 362, "nz": 4, "ext": "jpg", "anno_path": "Football/groundtruth_rect.txt", "object_class": "person head"}, {"name": "Football1", "path": "Football1/img", "startFrame": 1, "endFrame": 74, "nz": 4, "ext": "jpg", "anno_path": "Football1/groundtruth_rect.txt", "object_class": "face"}, {"name": "Freeman1", "path": "Freeman1/img", "startFrame": 1, "endFrame": 326, "nz": 4, "ext": "jpg", "anno_path": "Freeman1/groundtruth_rect.txt", "object_class": "face"}, {"name": "Freeman3", "path": "Freeman3/img", "startFrame": 1, "endFrame": 460, "nz": 4, "ext": "jpg", "anno_path": "Freeman3/groundtruth_rect.txt", "object_class": "face"}, {"name": "Freeman4", "path": "Freeman4/img", "startFrame": 1, "endFrame": 283, "nz": 4, "ext": "jpg", "anno_path": "Freeman4/groundtruth_rect.txt", "object_class": "face"}, {"name": "Girl", "path": "Girl/img", "startFrame": 1, "endFrame": 500, "nz": 4, "ext": "jpg", "anno_path": "Girl/groundtruth_rect.txt", "object_class": "face"}, {"name": "Girl2", "path": "Girl2/img", "startFrame": 1, "endFrame": 1500, "nz": 4, "ext": "jpg", "anno_path": "Girl2/groundtruth_rect.txt", "object_class": "person"}, {"name": "Gym", "path": "Gym/img", "startFrame": 1, "endFrame": 767, "nz": 4, "ext": "jpg", "anno_path": "Gym/groundtruth_rect.txt", "object_class": "person"}, {"name": "Human2", "path": "Human2/img", "startFrame": 1, "endFrame": 1128, "nz": 4, "ext": "jpg", "anno_path": "Human2/groundtruth_rect.txt", "object_class": "person"}, {"name": "Human3", "path": "Human3/img", "startFrame": 1, "endFrame": 1698, "nz": 4, "ext": "jpg", "anno_path": "Human3/groundtruth_rect.txt", "object_class": "person"}, {"name": "Human4_2", "path": "Human4/img", "startFrame": 1, "endFrame": 667, "nz": 4, "ext": "jpg", "anno_path": "Human4/groundtruth_rect.2.txt", "object_class": "person"}, {"name": "Human5", "path": "Human5/img", "startFrame": 1, "endFrame": 713, "nz": 4, "ext": "jpg", "anno_path": "Human5/groundtruth_rect.txt", "object_class": "person"}, {"name": "Human6", "path": "Human6/img", "startFrame": 1, "endFrame": 792, "nz": 4, "ext": "jpg", "anno_path": "Human6/groundtruth_rect.txt", "object_class": "person"}, {"name": "Human7", "path": "Human7/img", "startFrame": 1, "endFrame": 250, "nz": 4, "ext": "jpg", "anno_path": "Human7/groundtruth_rect.txt", "object_class": "person"}, {"name": "Human8", "path": "Human8/img", "startFrame": 1, "endFrame": 128, "nz": 4, "ext": "jpg", "anno_path": "Human8/groundtruth_rect.txt", "object_class": "person"}, {"name": "Human9", "path": "Human9/img", "startFrame": 1, "endFrame": 305, "nz": 4, "ext": "jpg", "anno_path": "Human9/groundtruth_rect.txt", "object_class": "person"}, {"name": "Ironman", "path": "Ironman/img", "startFrame": 1, "endFrame": 166, "nz": 4, "ext": "jpg", "anno_path": "Ironman/groundtruth_rect.txt", "object_class": "person head"}, {"name": "Jogging_1", "path": "Jogging/img", "startFrame": 1, "endFrame": 307, "nz": 4, "ext": "jpg", "anno_path": "Jogging/groundtruth_rect.1.txt", "object_class": "person"}, {"name": "Jogging_2", "path": "Jogging/img", "startFrame": 1, "endFrame": 307, "nz": 4, "ext": "jpg", "anno_path": "Jogging/groundtruth_rect.2.txt", "object_class": "person"}, {"name": "Jump", "path": "Jump/img", "startFrame": 1, "endFrame": 122, "nz": 4, "ext": "jpg", "anno_path": "Jump/groundtruth_rect.txt", "object_class": "person"}, {"name": "Jumping", "path": "Jumping/img", "startFrame": 1, "endFrame": 313, "nz": 4, "ext": "jpg", "anno_path": "Jumping/groundtruth_rect.txt", "object_class": "face"}, {"name": "KiteSurf", "path": "KiteSurf/img", "startFrame": 1, "endFrame": 84, "nz": 4, "ext": "png", "anno_path": "KiteSurf/groundtruth_rect.txt", "object_class": "face"}, {"name": "Lemming", "path": "Lemming/img", "startFrame": 1, "endFrame": 1336, "nz": 4, "ext": "jpg", "anno_path": "Lemming/groundtruth_rect.txt", "object_class": "other"}, {"name": "Liquor", "path": "Liquor/img", "startFrame": 1, "endFrame": 1741, "nz": 4, "ext": "jpg", "anno_path": "Liquor/groundtruth_rect.txt", "object_class": "other"}, {"name": "Man", "path": "Man/img", "startFrame": 1, "endFrame": 134, "nz": 4, "ext": "jpg", "anno_path": "Man/groundtruth_rect.txt", "object_class": "face"}, {"name": "Matrix", "path": "Matrix/img", "startFrame": 1, "endFrame": 100, "nz": 4, "ext": "jpg", "anno_path": "Matrix/groundtruth_rect.txt", "object_class": "person head"}, {"name": "Mhyang", "path": "Mhyang/img", "startFrame": 1, "endFrame": 1490, "nz": 4, "ext": "jpg", "anno_path": "Mhyang/groundtruth_rect.txt", "object_class": "face"}, {"name": "MotorRolling", "path": "MotorRolling/img", "startFrame": 1, "endFrame": 164, "nz": 4, "ext": "jpg", "anno_path": "MotorRolling/groundtruth_rect.txt", "object_class": "vehicle"}, {"name": "MountainBike", "path": "MountainBike/img", "startFrame": 1, "endFrame": 228, "nz": 4, "ext": "jpg", "anno_path": "MountainBike/groundtruth_rect.txt", "object_class": "bicycle"}, {"name": "Panda", "path": "Panda/img", "startFrame": 1, "endFrame": 1000, "nz": 4, "ext": "jpg", "anno_path": "Panda/groundtruth_rect.txt", "object_class": "mammal"}, {"name": "RedTeam", "path": "RedTeam/img", "startFrame": 1, "endFrame": 1918, "nz": 4, "ext": "jpg", "anno_path": "RedTeam/groundtruth_rect.txt", "object_class": "vehicle"}, {"name": "Rubik", "path": "Rubik/img", "startFrame": 1, "endFrame": 1997, "nz": 4, "ext": "jpg", "anno_path": "Rubik/groundtruth_rect.txt", "object_class": "other"}, {"name": "Shaking", "path": "Shaking/img", "startFrame": 1, "endFrame": 365, "nz": 4, "ext": "jpg", "anno_path": "Shaking/groundtruth_rect.txt", "object_class": "face"}, {"name": "Singer1", "path": "Singer1/img", "startFrame": 1, "endFrame": 351, "nz": 4, "ext": "jpg", "anno_path": "Singer1/groundtruth_rect.txt", "object_class": "person"}, {"name": "Singer2", "path": "Singer2/img", "startFrame": 1, "endFrame": 366, "nz": 4, "ext": "jpg", "anno_path": "Singer2/groundtruth_rect.txt", "object_class": "person"}, {"name": "Skater", "path": "Skater/img", "startFrame": 1, "endFrame": 160, "nz": 4, "ext": "jpg", "anno_path": "Skater/groundtruth_rect.txt", "object_class": "person"}, {"name": "Skater2", "path": "Skater2/img", "startFrame": 1, "endFrame": 435, "nz": 4, "ext": "jpg", "anno_path": "Skater2/groundtruth_rect.txt", "object_class": "person"}, {"name": "Skating1", "path": "Skating1/img", "startFrame": 1, "endFrame": 400, "nz": 4, "ext": "jpg", "anno_path": "Skating1/groundtruth_rect.txt", "object_class": "person"}, {"name": "Skating2_1", "path": "Skating2/img", "startFrame": 1, "endFrame": 473, "nz": 4, "ext": "jpg", "anno_path": "Skating2/groundtruth_rect.1.txt", "object_class": "person"}, {"name": "Skating2_2", "path": "Skating2/img", "startFrame": 1, "endFrame": 473, "nz": 4, "ext": "jpg", "anno_path": "Skating2/groundtruth_rect.2.txt", "object_class": "person"}, {"name": "Skiing", "path": "Skiing/img", "startFrame": 1, "endFrame": 81, "nz": 4, "ext": "jpg", "anno_path": "Skiing/groundtruth_rect.txt", "object_class": "person"}, {"name": "Soccer", "path": "Soccer/img", "startFrame": 1, "endFrame": 392, "nz": 4, "ext": "jpg", "anno_path": "Soccer/groundtruth_rect.txt", "object_class": "face"}, {"name": "Subway", "path": "Subway/img", "startFrame": 1, "endFrame": 175, "nz": 4, "ext": "jpg", "anno_path": "Subway/groundtruth_rect.txt", "object_class": "person"}, {"name": "Surfer", "path": "Surfer/img", "startFrame": 1, "endFrame": 376, "nz": 4, "ext": "jpg", "anno_path": "Surfer/groundtruth_rect.txt", "object_class": "person head"}, {"name": "Suv", "path": "Suv/img", "startFrame": 1, "endFrame": 945, "nz": 4, "ext": "jpg", "anno_path": "Suv/groundtruth_rect.txt", "object_class": "car"}, {"name": "Sylvester", "path": "Sylvester/img", "startFrame": 1, "endFrame": 1345, "nz": 4, "ext": "jpg", "anno_path": "Sylvester/groundtruth_rect.txt", "object_class": "other"}, {"name": "Tiger1", "path": "Tiger1/img", "startFrame": 1, "endFrame": 354, "nz": 4, "ext": "jpg", "anno_path": "Tiger1/groundtruth_rect.txt", "initOmit": 5, "object_class": "other"}, {"name": "Tiger2", "path": "Tiger2/img", "startFrame": 1, "endFrame": 365, "nz": 4, "ext": "jpg", "anno_path": "Tiger2/groundtruth_rect.txt", "object_class": "other"}, {"name": "Toy", "path": "Toy/img", "startFrame": 1, "endFrame": 271, "nz": 4, "ext": "jpg", "anno_path": "Toy/groundtruth_rect.txt", "object_class": "other"}, {"name": "Trans", "path": "Trans/img", "startFrame": 1, "endFrame": 124, "nz": 4, "ext": "jpg", "anno_path": "Trans/groundtruth_rect.txt", "object_class": "other"}, {"name": "Trellis", "path": "Trellis/img", "startFrame": 1, "endFrame": 569, "nz": 4, "ext": "jpg", "anno_path": "Trellis/groundtruth_rect.txt", "object_class": "face"}, {"name": "Twinnings", "path": "Twinnings/img", "startFrame": 1, "endFrame": 472, "nz": 4, "ext": "jpg", "anno_path": "Twinnings/groundtruth_rect.txt", "object_class": "other"}, {"name": "Vase", "path": "Vase/img", "startFrame": 1, "endFrame": 271, "nz": 4, "ext": "jpg", "anno_path": "Vase/groundtruth_rect.txt", "object_class": "other"}, {"name": "Walking", "path": "Walking/img", "startFrame": 1, "endFrame": 412, "nz": 4, "ext": "jpg", "anno_path": "Walking/groundtruth_rect.txt", "object_class": "person"}, {"name": "Walking2", "path": "Walking2/img", "startFrame": 1, "endFrame": 500, "nz": 4, "ext": "jpg", "anno_path": "Walking2/groundtruth_rect.txt", "object_class": "person"}, {"name": "Woman", "path": "Woman/img", "startFrame": 1, "endFrame": 597, "nz": 4, "ext": "jpg", "anno_path": "Woman/groundtruth_rect.txt", "object_class": "person"} ] return sequence_info_list ================================================ FILE: external/AR/pytracking/evaluation/running.py ================================================ import numpy as np import multiprocessing import os import sys from itertools import product from collections import OrderedDict from pytracking.evaluation import Sequence, Tracker from ltr.data.image_loader import imwrite_indexed def _save_tracker_output(seq: Sequence, tracker: Tracker, output: dict): """Saves the output of the tracker.""" if not os.path.exists(tracker.results_dir): os.makedirs(tracker.results_dir) base_results_path = os.path.join(tracker.results_dir, seq.name) segmentation_path = os.path.join(tracker.segmentation_dir, seq.name) frame_names = [os.path.splitext(os.path.basename(f))[0] for f in seq.frames] def save_bb(file, data): tracked_bb = np.array(data).astype(int) np.savetxt(file, tracked_bb, delimiter='\t', fmt='%d') def save_time(file, data): exec_times = np.array(data).astype(float) np.savetxt(file, exec_times, delimiter='\t', fmt='%f') def _convert_dict(input_dict): data_dict = {} for elem in input_dict: for k, v in elem.items(): if k in data_dict.keys(): data_dict[k].append(v) else: data_dict[k] = [v, ] return data_dict for key, data in output.items(): # If data is empty if not data: continue if key == 'target_bbox': if isinstance(data[0], (dict, OrderedDict)): data_dict = _convert_dict(data) for obj_id, d in data_dict.items(): bbox_file = '{}_{}.txt'.format(base_results_path, obj_id) save_bb(bbox_file, d) else: # Single-object mode bbox_file = '{}.txt'.format(base_results_path) save_bb(bbox_file, data) elif key == 'time': if isinstance(data[0], dict): data_dict = _convert_dict(data) for obj_id, d in data_dict.items(): timings_file = '{}_{}_time.txt'.format(base_results_path, obj_id) save_time(timings_file, d) else: timings_file = '{}_time.txt'.format(base_results_path) save_time(timings_file, data) elif key == 'segmentation': assert len(frame_names) == len(data) if not os.path.exists(segmentation_path): os.makedirs(segmentation_path) for frame_name, frame_seg in zip(frame_names, data): imwrite_indexed(os.path.join(segmentation_path, '{}.png'.format(frame_name)), frame_seg) def run_sequence(seq: Sequence, tracker: Tracker, debug=False, visdom_info=None): """Runs a tracker on a sequence.""" def _results_exist(): if seq.object_ids is None: bbox_file = '{}/{}.txt'.format(tracker.results_dir, seq.name) return os.path.isfile(bbox_file) else: bbox_files = ['{}/{}_{}.txt'.format(tracker.results_dir, seq.name, obj_id) for obj_id in seq.object_ids] missing = [not os.path.isfile(f) for f in bbox_files] return sum(missing) == 0 visdom_info = {} if visdom_info is None else visdom_info if _results_exist() and not debug: print('FPS: {}'.format(-1)) return print('Tracker: {} {} {} , Sequence: {}'.format(tracker.name, tracker.parameter_name, tracker.run_id, seq.name)) if debug: output = tracker.run_sequence(seq, debug=debug, visdom_info=visdom_info) else: try: output = tracker.run_sequence(seq, debug=debug, visdom_info=visdom_info) except Exception as e: print(e) return sys.stdout.flush() if isinstance(output['time'][0], (dict, OrderedDict)): exec_time = sum([sum(times.values()) for times in output['time']]) num_frames = len(output['time']) else: exec_time = sum(output['time']) num_frames = len(output['time']) print('FPS: {}'.format(num_frames / exec_time)) if not debug: _save_tracker_output(seq, tracker, output) def run_dataset(dataset, trackers, debug=False, threads=0, visdom_info=None): """Runs a list of trackers on a dataset. args: dataset: List of Sequence instances, forming a dataset. trackers: List of Tracker instances. debug: Debug level. threads: Number of threads to use (default 0). visdom_info: Dict containing information about the server for visdom """ multiprocessing.set_start_method('spawn', force=True) print('Evaluating {:4d} trackers on {:5d} sequences'.format(len(trackers), len(dataset))) multiprocessing.set_start_method('spawn', force=True) visdom_info = {} if visdom_info is None else visdom_info if threads == 0: mode = 'sequential' else: mode = 'parallel' if mode == 'sequential': for seq in dataset: for tracker_info in trackers: run_sequence(seq, tracker_info, debug=debug, visdom_info=visdom_info) elif mode == 'parallel': param_list = [(seq, tracker_info, debug, visdom_info) for seq, tracker_info in product(dataset, trackers)] with multiprocessing.Pool(processes=threads) as pool: pool.starmap(run_sequence, param_list) print('Done') ================================================ FILE: external/AR/pytracking/evaluation/tpldataset.py ================================================ import numpy as np from pytracking.evaluation.data import Sequence, BaseDataset, SequenceList from pytracking.utils.load_text import load_text class TPLDataset(BaseDataset): """ Temple Color 128 dataset Publication: Encoding Color Information for Visual Tracking: Algorithms and Benchmark P. Liang, E. Blasch, and H. Ling TIP, 2015 http://www.dabi.temple.edu/~hbling/publication/TColor-128.pdf Download the dataset from http://www.dabi.temple.edu/~hbling/data/TColor-128/TColor-128.html """ def __init__(self, exclude_otb=False): """ args: exclude_otb (bool) - If True, sequences overlapping with the OTB dataset are excluded """ super().__init__() self.base_path = self.env_settings.tpl_path self.sequence_info_list = self._get_sequence_info_list(exclude_otb) def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_info_list]) def _construct_sequence(self, sequence_info): sequence_path = sequence_info['path'] nz = sequence_info['nz'] ext = sequence_info['ext'] start_frame = sequence_info['startFrame'] end_frame = sequence_info['endFrame'] init_omit = 0 if 'initOmit' in sequence_info: init_omit = sequence_info['initOmit'] frames = ['{base_path}/{sequence_path}/{frame:0{nz}}.{ext}'.format(base_path=self.base_path, sequence_path=sequence_path, frame=frame_num, nz=nz, ext=ext) for frame_num in range(start_frame+init_omit, end_frame+1)] anno_path = '{}/{}'.format(self.base_path, sequence_info['anno_path']) ground_truth_rect = load_text(str(anno_path), delimiter=(',', None), dtype=np.float64, backend='numpy') return Sequence(sequence_info['name'], frames, 'tpl', ground_truth_rect[init_omit:,:]) def __len__(self): return len(self.sequence_info_list) def _get_sequence_info_list(self, exclude_otb=False): sequence_info_list = [ {"name": "tpl_Skating2", "path": "tpl_Skating2/img", "startFrame": 1, "endFrame": 707, "nz": 4, "ext": "jpg", "anno_path": "tpl_Skating2/Skating2_gt.txt"}, {"name": "tpl_Pool_ce3", "path": "tpl_Pool_ce3/img", "startFrame": 1, "endFrame": 124, "nz": 4, "ext": "jpg", "anno_path": "tpl_Pool_ce3/Pool_ce3_gt.txt"}, {"name": "tpl_Microphone_ce1", "path": "tpl_Microphone_ce1/img", "startFrame": 1, "endFrame": 204, "nz": 4, "ext": "jpg", "anno_path": "tpl_Microphone_ce1/Microphone_ce1_gt.txt"}, {"name": "tpl_Torus", "path": "tpl_Torus/img", "startFrame": 1, "endFrame": 264, "nz": 4, "ext": "jpg", "anno_path": "tpl_Torus/Torus_gt.txt"}, {"name": "tpl_Lemming", "path": "tpl_Lemming/img", "startFrame": 1, "endFrame": 1336, "nz": 4, "ext": "jpg", "anno_path": "tpl_Lemming/Lemming_gt.txt"}, {"name": "tpl_Eagle_ce", "path": "tpl_Eagle_ce/img", "startFrame": 1, "endFrame": 112, "nz": 4, "ext": "jpg", "anno_path": "tpl_Eagle_ce/Eagle_ce_gt.txt"}, {"name": "tpl_Skating_ce2", "path": "tpl_Skating_ce2/img", "startFrame": 1, "endFrame": 497, "nz": 4, "ext": "jpg", "anno_path": "tpl_Skating_ce2/Skating_ce2_gt.txt"}, {"name": "tpl_Yo_yos_ce3", "path": "tpl_Yo_yos_ce3/img", "startFrame": 1, "endFrame": 201, "nz": 4, "ext": "jpg", "anno_path": "tpl_Yo_yos_ce3/Yo-yos_ce3_gt.txt"}, {"name": "tpl_Board", "path": "tpl_Board/img", "startFrame": 1, "endFrame": 598, "nz": 4, "ext": "jpg", "anno_path": "tpl_Board/Board_gt.txt"}, {"name": "tpl_Tennis_ce3", "path": "tpl_Tennis_ce3/img", "startFrame": 1, "endFrame": 204, "nz": 4, "ext": "jpg", "anno_path": "tpl_Tennis_ce3/Tennis_ce3_gt.txt"}, {"name": "tpl_SuperMario_ce", "path": "tpl_SuperMario_ce/img", "startFrame": 1, "endFrame": 146, "nz": 4, "ext": "jpg", "anno_path": "tpl_SuperMario_ce/SuperMario_ce_gt.txt"}, {"name": "tpl_Yo_yos_ce1", "path": "tpl_Yo_yos_ce1/img", "startFrame": 1, "endFrame": 235, "nz": 4, "ext": "jpg", "anno_path": "tpl_Yo_yos_ce1/Yo-yos_ce1_gt.txt"}, {"name": "tpl_Soccer", "path": "tpl_Soccer/img", "startFrame": 1, "endFrame": 392, "nz": 4, "ext": "jpg", "anno_path": "tpl_Soccer/Soccer_gt.txt"}, {"name": "tpl_Fish_ce2", "path": "tpl_Fish_ce2/img", "startFrame": 1, "endFrame": 573, "nz": 4, "ext": "jpg", "anno_path": "tpl_Fish_ce2/Fish_ce2_gt.txt"}, {"name": "tpl_Liquor", "path": "tpl_Liquor/img", "startFrame": 1, "endFrame": 1741, "nz": 4, "ext": "jpg", "anno_path": "tpl_Liquor/Liquor_gt.txt"}, {"name": "tpl_Plane_ce2", "path": "tpl_Plane_ce2/img", "startFrame": 1, "endFrame": 653, "nz": 4, "ext": "jpg", "anno_path": "tpl_Plane_ce2/Plane_ce2_gt.txt"}, {"name": "tpl_Couple", "path": "tpl_Couple/img", "startFrame": 1, "endFrame": 140, "nz": 4, "ext": "jpg", "anno_path": "tpl_Couple/Couple_gt.txt"}, {"name": "tpl_Logo_ce", "path": "tpl_Logo_ce/img", "startFrame": 1, "endFrame": 610, "nz": 4, "ext": "jpg", "anno_path": "tpl_Logo_ce/Logo_ce_gt.txt"}, {"name": "tpl_Hand_ce2", "path": "tpl_Hand_ce2/img", "startFrame": 1, "endFrame": 251, "nz": 4, "ext": "jpg", "anno_path": "tpl_Hand_ce2/Hand_ce2_gt.txt"}, {"name": "tpl_Kite_ce2", "path": "tpl_Kite_ce2/img", "startFrame": 1, "endFrame": 658, "nz": 4, "ext": "jpg", "anno_path": "tpl_Kite_ce2/Kite_ce2_gt.txt"}, {"name": "tpl_Walking", "path": "tpl_Walking/img", "startFrame": 1, "endFrame": 412, "nz": 4, "ext": "jpg", "anno_path": "tpl_Walking/Walking_gt.txt"}, {"name": "tpl_David", "path": "tpl_David/img", "startFrame": 300, "endFrame": 770, "nz": 4, "ext": "jpg", "anno_path": "tpl_David/David_gt.txt"}, {"name": "tpl_Boat_ce1", "path": "tpl_Boat_ce1/img", "startFrame": 1, "endFrame": 377, "nz": 4, "ext": "jpg", "anno_path": "tpl_Boat_ce1/Boat_ce1_gt.txt"}, {"name": "tpl_Airport_ce", "path": "tpl_Airport_ce/img", "startFrame": 1, "endFrame": 148, "nz": 4, "ext": "jpg", "anno_path": "tpl_Airport_ce/Airport_ce_gt.txt"}, {"name": "tpl_Tiger2", "path": "tpl_Tiger2/img", "startFrame": 1, "endFrame": 365, "nz": 4, "ext": "jpg", "anno_path": "tpl_Tiger2/Tiger2_gt.txt"}, {"name": "tpl_Suitcase_ce", "path": "tpl_Suitcase_ce/img", "startFrame": 1, "endFrame": 184, "nz": 4, "ext": "jpg", "anno_path": "tpl_Suitcase_ce/Suitcase_ce_gt.txt"}, {"name": "tpl_TennisBall_ce", "path": "tpl_TennisBall_ce/img", "startFrame": 1, "endFrame": 288, "nz": 4, "ext": "jpg", "anno_path": "tpl_TennisBall_ce/TennisBall_ce_gt.txt"}, {"name": "tpl_Singer_ce1", "path": "tpl_Singer_ce1/img", "startFrame": 1, "endFrame": 214, "nz": 4, "ext": "jpg", "anno_path": "tpl_Singer_ce1/Singer_ce1_gt.txt"}, {"name": "tpl_Pool_ce2", "path": "tpl_Pool_ce2/img", "startFrame": 1, "endFrame": 133, "nz": 4, "ext": "jpg", "anno_path": "tpl_Pool_ce2/Pool_ce2_gt.txt"}, {"name": "tpl_Surf_ce3", "path": "tpl_Surf_ce3/img", "startFrame": 1, "endFrame": 279, "nz": 4, "ext": "jpg", "anno_path": "tpl_Surf_ce3/Surf_ce3_gt.txt"}, {"name": "tpl_Bird", "path": "tpl_Bird/img", "startFrame": 1, "endFrame": 99, "nz": 4, "ext": "jpg", "anno_path": "tpl_Bird/Bird_gt.txt"}, {"name": "tpl_Crossing", "path": "tpl_Crossing/img", "startFrame": 1, "endFrame": 120, "nz": 4, "ext": "jpg", "anno_path": "tpl_Crossing/Crossing_gt.txt"}, {"name": "tpl_Plate_ce1", "path": "tpl_Plate_ce1/img", "startFrame": 1, "endFrame": 142, "nz": 4, "ext": "jpg", "anno_path": "tpl_Plate_ce1/Plate_ce1_gt.txt"}, {"name": "tpl_Cup", "path": "tpl_Cup/img", "startFrame": 1, "endFrame": 303, "nz": 4, "ext": "jpg", "anno_path": "tpl_Cup/Cup_gt.txt"}, {"name": "tpl_Surf_ce2", "path": "tpl_Surf_ce2/img", "startFrame": 1, "endFrame": 391, "nz": 4, "ext": "jpg", "anno_path": "tpl_Surf_ce2/Surf_ce2_gt.txt"}, {"name": "tpl_Busstation_ce2", "path": "tpl_Busstation_ce2/img", "startFrame": 6, "endFrame": 400, "nz": 4, "ext": "jpg", "anno_path": "tpl_Busstation_ce2/Busstation_ce2_gt.txt"}, {"name": "tpl_Charger_ce", "path": "tpl_Charger_ce/img", "startFrame": 1, "endFrame": 298, "nz": 4, "ext": "jpg", "anno_path": "tpl_Charger_ce/Charger_ce_gt.txt"}, {"name": "tpl_Pool_ce1", "path": "tpl_Pool_ce1/img", "startFrame": 1, "endFrame": 166, "nz": 4, "ext": "jpg", "anno_path": "tpl_Pool_ce1/Pool_ce1_gt.txt"}, {"name": "tpl_MountainBike", "path": "tpl_MountainBike/img", "startFrame": 1, "endFrame": 228, "nz": 4, "ext": "jpg", "anno_path": "tpl_MountainBike/MountainBike_gt.txt"}, {"name": "tpl_Guitar_ce1", "path": "tpl_Guitar_ce1/img", "startFrame": 1, "endFrame": 268, "nz": 4, "ext": "jpg", "anno_path": "tpl_Guitar_ce1/Guitar_ce1_gt.txt"}, {"name": "tpl_Busstation_ce1", "path": "tpl_Busstation_ce1/img", "startFrame": 1, "endFrame": 363, "nz": 4, "ext": "jpg", "anno_path": "tpl_Busstation_ce1/Busstation_ce1_gt.txt"}, {"name": "tpl_Diving", "path": "tpl_Diving/img", "startFrame": 1, "endFrame": 231, "nz": 4, "ext": "jpg", "anno_path": "tpl_Diving/Diving_gt.txt"}, {"name": "tpl_Skating_ce1", "path": "tpl_Skating_ce1/img", "startFrame": 1, "endFrame": 409, "nz": 4, "ext": "jpg", "anno_path": "tpl_Skating_ce1/Skating_ce1_gt.txt"}, {"name": "tpl_Hurdle_ce2", "path": "tpl_Hurdle_ce2/img", "startFrame": 27, "endFrame": 330, "nz": 4, "ext": "jpg", "anno_path": "tpl_Hurdle_ce2/Hurdle_ce2_gt.txt"}, {"name": "tpl_Plate_ce2", "path": "tpl_Plate_ce2/img", "startFrame": 1, "endFrame": 181, "nz": 4, "ext": "jpg", "anno_path": "tpl_Plate_ce2/Plate_ce2_gt.txt"}, {"name": "tpl_CarDark", "path": "tpl_CarDark/img", "startFrame": 1, "endFrame": 393, "nz": 4, "ext": "jpg", "anno_path": "tpl_CarDark/CarDark_gt.txt"}, {"name": "tpl_Singer_ce2", "path": "tpl_Singer_ce2/img", "startFrame": 1, "endFrame": 999, "nz": 4, "ext": "jpg", "anno_path": "tpl_Singer_ce2/Singer_ce2_gt.txt"}, {"name": "tpl_Shaking", "path": "tpl_Shaking/img", "startFrame": 1, "endFrame": 365, "nz": 4, "ext": "jpg", "anno_path": "tpl_Shaking/Shaking_gt.txt"}, {"name": "tpl_Iceskater", "path": "tpl_Iceskater/img", "startFrame": 1, "endFrame": 500, "nz": 4, "ext": "jpg", "anno_path": "tpl_Iceskater/Iceskater_gt.txt"}, {"name": "tpl_Badminton_ce2", "path": "tpl_Badminton_ce2/img", "startFrame": 1, "endFrame": 705, "nz": 4, "ext": "jpg", "anno_path": "tpl_Badminton_ce2/Badminton_ce2_gt.txt"}, {"name": "tpl_Spiderman_ce", "path": "tpl_Spiderman_ce/img", "startFrame": 1, "endFrame": 351, "nz": 4, "ext": "jpg", "anno_path": "tpl_Spiderman_ce/Spiderman_ce_gt.txt"}, {"name": "tpl_Kite_ce1", "path": "tpl_Kite_ce1/img", "startFrame": 1, "endFrame": 484, "nz": 4, "ext": "jpg", "anno_path": "tpl_Kite_ce1/Kite_ce1_gt.txt"}, {"name": "tpl_Skyjumping_ce", "path": "tpl_Skyjumping_ce/img", "startFrame": 1, "endFrame": 938, "nz": 4, "ext": "jpg", "anno_path": "tpl_Skyjumping_ce/Skyjumping_ce_gt.txt"}, {"name": "tpl_Ball_ce1", "path": "tpl_Ball_ce1/img", "startFrame": 1, "endFrame": 391, "nz": 4, "ext": "jpg", "anno_path": "tpl_Ball_ce1/Ball_ce1_gt.txt"}, {"name": "tpl_Yo_yos_ce2", "path": "tpl_Yo_yos_ce2/img", "startFrame": 1, "endFrame": 454, "nz": 4, "ext": "jpg", "anno_path": "tpl_Yo_yos_ce2/Yo-yos_ce2_gt.txt"}, {"name": "tpl_Ironman", "path": "tpl_Ironman/img", "startFrame": 1, "endFrame": 166, "nz": 4, "ext": "jpg", "anno_path": "tpl_Ironman/Ironman_gt.txt"}, {"name": "tpl_FaceOcc1", "path": "tpl_FaceOcc1/img", "startFrame": 1, "endFrame": 892, "nz": 4, "ext": "jpg", "anno_path": "tpl_FaceOcc1/FaceOcc1_gt.txt"}, {"name": "tpl_Surf_ce1", "path": "tpl_Surf_ce1/img", "startFrame": 1, "endFrame": 404, "nz": 4, "ext": "jpg", "anno_path": "tpl_Surf_ce1/Surf_ce1_gt.txt"}, {"name": "tpl_Ring_ce", "path": "tpl_Ring_ce/img", "startFrame": 1, "endFrame": 201, "nz": 4, "ext": "jpg", "anno_path": "tpl_Ring_ce/Ring_ce_gt.txt"}, {"name": "tpl_Surf_ce4", "path": "tpl_Surf_ce4/img", "startFrame": 1, "endFrame": 135, "nz": 4, "ext": "jpg", "anno_path": "tpl_Surf_ce4/Surf_ce4_gt.txt"}, {"name": "tpl_Ball_ce4", "path": "tpl_Ball_ce4/img", "startFrame": 1, "endFrame": 538, "nz": 4, "ext": "jpg", "anno_path": "tpl_Ball_ce4/Ball_ce4_gt.txt"}, {"name": "tpl_Bikeshow_ce", "path": "tpl_Bikeshow_ce/img", "startFrame": 1, "endFrame": 361, "nz": 4, "ext": "jpg", "anno_path": "tpl_Bikeshow_ce/Bikeshow_ce_gt.txt"}, {"name": "tpl_Kobe_ce", "path": "tpl_Kobe_ce/img", "startFrame": 1, "endFrame": 582, "nz": 4, "ext": "jpg", "anno_path": "tpl_Kobe_ce/Kobe_ce_gt.txt"}, {"name": "tpl_Tiger1", "path": "tpl_Tiger1/img", "startFrame": 1, "endFrame": 354, "nz": 4, "ext": "jpg", "anno_path": "tpl_Tiger1/Tiger1_gt.txt"}, {"name": "tpl_Skiing", "path": "tpl_Skiing/img", "startFrame": 1, "endFrame": 81, "nz": 4, "ext": "jpg", "anno_path": "tpl_Skiing/Skiing_gt.txt"}, {"name": "tpl_Tennis_ce1", "path": "tpl_Tennis_ce1/img", "startFrame": 1, "endFrame": 454, "nz": 4, "ext": "jpg", "anno_path": "tpl_Tennis_ce1/Tennis_ce1_gt.txt"}, {"name": "tpl_Carchasing_ce4", "path": "tpl_Carchasing_ce4/img", "startFrame": 1, "endFrame": 442, "nz": 4, "ext": "jpg", "anno_path": "tpl_Carchasing_ce4/Carchasing_ce4_gt.txt"}, {"name": "tpl_Walking2", "path": "tpl_Walking2/img", "startFrame": 1, "endFrame": 500, "nz": 4, "ext": "jpg", "anno_path": "tpl_Walking2/Walking2_gt.txt"}, {"name": "tpl_Sailor_ce", "path": "tpl_Sailor_ce/img", "startFrame": 1, "endFrame": 402, "nz": 4, "ext": "jpg", "anno_path": "tpl_Sailor_ce/Sailor_ce_gt.txt"}, {"name": "tpl_Railwaystation_ce", "path": "tpl_Railwaystation_ce/img", "startFrame": 1, "endFrame": 413, "nz": 4, "ext": "jpg", "anno_path": "tpl_Railwaystation_ce/Railwaystation_ce_gt.txt"}, {"name": "tpl_Bee_ce", "path": "tpl_Bee_ce/img", "startFrame": 1, "endFrame": 90, "nz": 4, "ext": "jpg", "anno_path": "tpl_Bee_ce/Bee_ce_gt.txt"}, {"name": "tpl_Girl", "path": "tpl_Girl/img", "startFrame": 1, "endFrame": 500, "nz": 4, "ext": "jpg", "anno_path": "tpl_Girl/Girl_gt.txt"}, {"name": "tpl_Subway", "path": "tpl_Subway/img", "startFrame": 1, "endFrame": 175, "nz": 4, "ext": "jpg", "anno_path": "tpl_Subway/Subway_gt.txt"}, {"name": "tpl_David3", "path": "tpl_David3/img", "startFrame": 1, "endFrame": 252, "nz": 4, "ext": "jpg", "anno_path": "tpl_David3/David3_gt.txt"}, {"name": "tpl_Electricalbike_ce", "path": "tpl_Electricalbike_ce/img", "startFrame": 1, "endFrame": 818, "nz": 4, "ext": "jpg", "anno_path": "tpl_Electricalbike_ce/Electricalbike_ce_gt.txt"}, {"name": "tpl_Michaeljackson_ce", "path": "tpl_Michaeljackson_ce/img", "startFrame": 1, "endFrame": 393, "nz": 4, "ext": "jpg", "anno_path": "tpl_Michaeljackson_ce/Michaeljackson_ce_gt.txt"}, {"name": "tpl_Woman", "path": "tpl_Woman/img", "startFrame": 1, "endFrame": 597, "nz": 4, "ext": "jpg", "anno_path": "tpl_Woman/Woman_gt.txt"}, {"name": "tpl_TableTennis_ce", "path": "tpl_TableTennis_ce/img", "startFrame": 1, "endFrame": 198, "nz": 4, "ext": "jpg", "anno_path": "tpl_TableTennis_ce/TableTennis_ce_gt.txt"}, {"name": "tpl_Motorbike_ce", "path": "tpl_Motorbike_ce/img", "startFrame": 1, "endFrame": 563, "nz": 4, "ext": "jpg", "anno_path": "tpl_Motorbike_ce/Motorbike_ce_gt.txt"}, {"name": "tpl_Baby_ce", "path": "tpl_Baby_ce/img", "startFrame": 1, "endFrame": 296, "nz": 4, "ext": "jpg", "anno_path": "tpl_Baby_ce/Baby_ce_gt.txt"}, {"name": "tpl_Gym", "path": "tpl_Gym/img", "startFrame": 1, "endFrame": 766, "nz": 4, "ext": "jpg", "anno_path": "tpl_Gym/Gym_gt.txt"}, {"name": "tpl_Matrix", "path": "tpl_Matrix/img", "startFrame": 1, "endFrame": 100, "nz": 4, "ext": "jpg", "anno_path": "tpl_Matrix/Matrix_gt.txt"}, {"name": "tpl_Kite_ce3", "path": "tpl_Kite_ce3/img", "startFrame": 1, "endFrame": 528, "nz": 4, "ext": "jpg", "anno_path": "tpl_Kite_ce3/Kite_ce3_gt.txt"}, {"name": "tpl_Fish_ce1", "path": "tpl_Fish_ce1/img", "startFrame": 1, "endFrame": 401, "nz": 4, "ext": "jpg", "anno_path": "tpl_Fish_ce1/Fish_ce1_gt.txt"}, {"name": "tpl_Hand_ce1", "path": "tpl_Hand_ce1/img", "startFrame": 1, "endFrame": 401, "nz": 4, "ext": "jpg", "anno_path": "tpl_Hand_ce1/Hand_ce1_gt.txt"}, {"name": "tpl_Doll", "path": "tpl_Doll/img", "startFrame": 1, "endFrame": 3872, "nz": 4, "ext": "jpg", "anno_path": "tpl_Doll/Doll_gt.txt"}, {"name": "tpl_Carchasing_ce3", "path": "tpl_Carchasing_ce3/img", "startFrame": 1, "endFrame": 572, "nz": 4, "ext": "jpg", "anno_path": "tpl_Carchasing_ce3/Carchasing_ce3_gt.txt"}, {"name": "tpl_Thunder_ce", "path": "tpl_Thunder_ce/img", "startFrame": 1, "endFrame": 375, "nz": 4, "ext": "jpg", "anno_path": "tpl_Thunder_ce/Thunder_ce_gt.txt"}, {"name": "tpl_Singer2", "path": "tpl_Singer2/img", "startFrame": 1, "endFrame": 366, "nz": 4, "ext": "jpg", "anno_path": "tpl_Singer2/Singer2_gt.txt"}, {"name": "tpl_Basketball", "path": "tpl_Basketball/img", "startFrame": 1, "endFrame": 725, "nz": 4, "ext": "jpg", "anno_path": "tpl_Basketball/Basketball_gt.txt"}, {"name": "tpl_Hand", "path": "tpl_Hand/img", "startFrame": 1, "endFrame": 244, "nz": 4, "ext": "jpg", "anno_path": "tpl_Hand/Hand_gt.txt"}, {"name": "tpl_Cup_ce", "path": "tpl_Cup_ce/img", "startFrame": 1, "endFrame": 338, "nz": 4, "ext": "jpg", "anno_path": "tpl_Cup_ce/Cup_ce_gt.txt"}, {"name": "tpl_MotorRolling", "path": "tpl_MotorRolling/img", "startFrame": 1, "endFrame": 164, "nz": 4, "ext": "jpg", "anno_path": "tpl_MotorRolling/MotorRolling_gt.txt"}, {"name": "tpl_Boat_ce2", "path": "tpl_Boat_ce2/img", "startFrame": 1, "endFrame": 412, "nz": 4, "ext": "jpg", "anno_path": "tpl_Boat_ce2/Boat_ce2_gt.txt"}, {"name": "tpl_CarScale", "path": "tpl_CarScale/img", "startFrame": 1, "endFrame": 252, "nz": 4, "ext": "jpg", "anno_path": "tpl_CarScale/CarScale_gt.txt"}, {"name": "tpl_Sunshade", "path": "tpl_Sunshade/img", "startFrame": 1, "endFrame": 172, "nz": 4, "ext": "jpg", "anno_path": "tpl_Sunshade/Sunshade_gt.txt"}, {"name": "tpl_Football1", "path": "tpl_Football1/img", "startFrame": 1, "endFrame": 74, "nz": 4, "ext": "jpg", "anno_path": "tpl_Football1/Football1_gt.txt"}, {"name": "tpl_Singer1", "path": "tpl_Singer1/img", "startFrame": 1, "endFrame": 351, "nz": 4, "ext": "jpg", "anno_path": "tpl_Singer1/Singer1_gt.txt"}, {"name": "tpl_Hurdle_ce1", "path": "tpl_Hurdle_ce1/img", "startFrame": 1, "endFrame": 300, "nz": 4, "ext": "jpg", "anno_path": "tpl_Hurdle_ce1/Hurdle_ce1_gt.txt"}, {"name": "tpl_Basketball_ce3", "path": "tpl_Basketball_ce3/img", "startFrame": 1, "endFrame": 441, "nz": 4, "ext": "jpg", "anno_path": "tpl_Basketball_ce3/Basketball_ce3_gt.txt"}, {"name": "tpl_Toyplane_ce", "path": "tpl_Toyplane_ce/img", "startFrame": 1, "endFrame": 405, "nz": 4, "ext": "jpg", "anno_path": "tpl_Toyplane_ce/Toyplane_ce_gt.txt"}, {"name": "tpl_Skating1", "path": "tpl_Skating1/img", "startFrame": 1, "endFrame": 400, "nz": 4, "ext": "jpg", "anno_path": "tpl_Skating1/Skating1_gt.txt"}, {"name": "tpl_Juice", "path": "tpl_Juice/img", "startFrame": 1, "endFrame": 404, "nz": 4, "ext": "jpg", "anno_path": "tpl_Juice/Juice_gt.txt"}, {"name": "tpl_Biker", "path": "tpl_Biker/img", "startFrame": 1, "endFrame": 180, "nz": 4, "ext": "jpg", "anno_path": "tpl_Biker/Biker_gt.txt"}, {"name": "tpl_Boy", "path": "tpl_Boy/img", "startFrame": 1, "endFrame": 602, "nz": 4, "ext": "jpg", "anno_path": "tpl_Boy/Boy_gt.txt"}, {"name": "tpl_Jogging1", "path": "tpl_Jogging1/img", "startFrame": 1, "endFrame": 307, "nz": 4, "ext": "jpg", "anno_path": "tpl_Jogging1/Jogging1_gt.txt"}, {"name": "tpl_Deer", "path": "tpl_Deer/img", "startFrame": 1, "endFrame": 71, "nz": 4, "ext": "jpg", "anno_path": "tpl_Deer/Deer_gt.txt"}, {"name": "tpl_Panda", "path": "tpl_Panda/img", "startFrame": 1, "endFrame": 241, "nz": 4, "ext": "jpg", "anno_path": "tpl_Panda/Panda_gt.txt"}, {"name": "tpl_Coke", "path": "tpl_Coke/img", "startFrame": 1, "endFrame": 291, "nz": 4, "ext": "jpg", "anno_path": "tpl_Coke/Coke_gt.txt"}, {"name": "tpl_Carchasing_ce1", "path": "tpl_Carchasing_ce1/img", "startFrame": 1, "endFrame": 501, "nz": 4, "ext": "jpg", "anno_path": "tpl_Carchasing_ce1/Carchasing_ce1_gt.txt"}, {"name": "tpl_Badminton_ce1", "path": "tpl_Badminton_ce1/img", "startFrame": 1, "endFrame": 579, "nz": 4, "ext": "jpg", "anno_path": "tpl_Badminton_ce1/Badminton_ce1_gt.txt"}, {"name": "tpl_Trellis", "path": "tpl_Trellis/img", "startFrame": 1, "endFrame": 569, "nz": 4, "ext": "jpg", "anno_path": "tpl_Trellis/Trellis_gt.txt"}, {"name": "tpl_Face_ce2", "path": "tpl_Face_ce2/img", "startFrame": 1, "endFrame": 148, "nz": 4, "ext": "jpg", "anno_path": "tpl_Face_ce2/Face_ce2_gt.txt"}, {"name": "tpl_Ball_ce2", "path": "tpl_Ball_ce2/img", "startFrame": 1, "endFrame": 603, "nz": 4, "ext": "jpg", "anno_path": "tpl_Ball_ce2/Ball_ce2_gt.txt"}, {"name": "tpl_Skiing_ce", "path": "tpl_Skiing_ce/img", "startFrame": 1, "endFrame": 511, "nz": 4, "ext": "jpg", "anno_path": "tpl_Skiing_ce/Skiing_ce_gt.txt"}, {"name": "tpl_Jogging2", "path": "tpl_Jogging2/img", "startFrame": 1, "endFrame": 307, "nz": 4, "ext": "jpg", "anno_path": "tpl_Jogging2/Jogging2_gt.txt"}, {"name": "tpl_Bike_ce1", "path": "tpl_Bike_ce1/img", "startFrame": 1, "endFrame": 801, "nz": 4, "ext": "jpg", "anno_path": "tpl_Bike_ce1/Bike_ce1_gt.txt"}, {"name": "tpl_Bike_ce2", "path": "tpl_Bike_ce2/img", "startFrame": 1, "endFrame": 812, "nz": 4, "ext": "jpg", "anno_path": "tpl_Bike_ce2/Bike_ce2_gt.txt"}, {"name": "tpl_Ball_ce3", "path": "tpl_Ball_ce3/img", "startFrame": 1, "endFrame": 273, "nz": 4, "ext": "jpg", "anno_path": "tpl_Ball_ce3/Ball_ce3_gt.txt"}, {"name": "tpl_Girlmov", "path": "tpl_Girlmov/img", "startFrame": 1, "endFrame": 1500, "nz": 4, "ext": "jpg", "anno_path": "tpl_Girlmov/Girlmov_gt.txt"}, {"name": "tpl_Bolt", "path": "tpl_Bolt/img", "startFrame": 1, "endFrame": 350, "nz": 4, "ext": "jpg", "anno_path": "tpl_Bolt/Bolt_gt.txt"}, {"name": "tpl_Basketball_ce2", "path": "tpl_Basketball_ce2/img", "startFrame": 1, "endFrame": 455, "nz": 4, "ext": "jpg", "anno_path": "tpl_Basketball_ce2/Basketball_ce2_gt.txt"}, {"name": "tpl_Bicycle", "path": "tpl_Bicycle/img", "startFrame": 1, "endFrame": 271, "nz": 4, "ext": "jpg", "anno_path": "tpl_Bicycle/Bicycle_gt.txt"}, {"name": "tpl_Face_ce", "path": "tpl_Face_ce/img", "startFrame": 1, "endFrame": 620, "nz": 4, "ext": "jpg", "anno_path": "tpl_Face_ce/Face_ce_gt.txt"}, {"name": "tpl_Basketball_ce1", "path": "tpl_Basketball_ce1/img", "startFrame": 1, "endFrame": 496, "nz": 4, "ext": "jpg", "anno_path": "tpl_Basketball_ce1/Basketball_ce1_gt.txt"}, {"name": "tpl_Messi_ce", "path": "tpl_Messi_ce/img", "startFrame": 1, "endFrame": 272, "nz": 4, "ext": "jpg", "anno_path": "tpl_Messi_ce/Messi_ce_gt.txt"}, {"name": "tpl_Tennis_ce2", "path": "tpl_Tennis_ce2/img", "startFrame": 1, "endFrame": 305, "nz": 4, "ext": "jpg", "anno_path": "tpl_Tennis_ce2/Tennis_ce2_gt.txt"}, {"name": "tpl_Microphone_ce2", "path": "tpl_Microphone_ce2/img", "startFrame": 1, "endFrame": 103, "nz": 4, "ext": "jpg", "anno_path": "tpl_Microphone_ce2/Microphone_ce2_gt.txt"}, {"name": "tpl_Guitar_ce2", "path": "tpl_Guitar_ce2/img", "startFrame": 1, "endFrame": 313, "nz": 4, "ext": "jpg", "anno_path": "tpl_Guitar_ce2/Guitar_ce2_gt.txt"} ] otb_sequences = ['tpl_Skating2', 'tpl_Lemming', 'tpl_Board', 'tpl_Soccer', 'tpl_Liquor', 'tpl_Couple', 'tpl_Walking', 'tpl_David', 'tpl_Tiger2', 'tpl_Bird', 'tpl_Crossing', 'tpl_MountainBike', 'tpl_Diving', 'tpl_CarDark', 'tpl_Shaking', 'tpl_Ironman', 'tpl_FaceOcc1', 'tpl_Tiger1', 'tpl_Skiing', 'tpl_Walking2', 'tpl_Girl', 'tpl_Girlmov', 'tpl_Subway', 'tpl_David3', 'tpl_Woman', 'tpl_Gym', 'tpl_Matrix', 'tpl_Doll', 'tpl_Singer2', 'tpl_Basketball', 'tpl_MotorRolling', 'tpl_CarScale', 'tpl_Football1', 'tpl_Singer1', 'tpl_Skating1', 'tpl_Biker', 'tpl_Boy', 'tpl_Jogging1', 'tpl_Deer', 'tpl_Panda', 'tpl_Coke', 'tpl_Trellis', 'tpl_Jogging2', 'tpl_Bolt', ] if exclude_otb: sequence_info_list_nootb = [] for seq in sequence_info_list: if seq['name'] not in otb_sequences: sequence_info_list_nootb.append(seq) sequence_info_list = sequence_info_list_nootb return sequence_info_list ================================================ FILE: external/AR/pytracking/evaluation/tracker.py ================================================ import importlib import os import numpy as np from collections import OrderedDict from pytracking.evaluation.environment import env_settings import time import cv2 as cv from pytracking.utils.visdom import Visdom import matplotlib.pyplot as plt import matplotlib.patches as patches from pytracking.utils.plotting import draw_figure, overlay_mask from pytracking.utils.convert_vot_anno_to_rect import convert_vot_anno_to_rect from ltr.data.bounding_box_utils import masks_to_bboxes from pytracking.evaluation.multi_object_wrapper import MultiObjectWrapper import torch _tracker_disp_colors = {1: (0, 255, 0), 2: (0, 0, 255), 3: (255, 0, 0), 4: (255, 255, 255), 5: (0, 0, 0), 6: (0, 255, 128), 7: (123, 123, 123), 8: (255, 128, 0), 9: (128, 0, 255)} def trackerlist(name: str, parameter_name: str, run_ids = None, display_name: str = None): """Generate list of trackers. args: name: Name of tracking method. parameter_name: Name of parameter file. run_ids: A single or list of run_ids. display_name: Name to be displayed in the result plots. """ if run_ids is None or isinstance(run_ids, int): run_ids = [run_ids] return [Tracker(name, parameter_name, run_id, display_name) for run_id in run_ids] class Tracker: """Wraps the tracker for evaluation and running purposes. args: name: Name of tracking method. parameter_name: Name of parameter file. run_id: The run id. display_name: Name to be displayed in the result plots. """ def __init__(self, name: str, parameter_name: str, run_id: int = None, display_name: str = None): assert run_id is None or isinstance(run_id, int) self.name = name self.parameter_name = parameter_name self.run_id = run_id self.display_name = display_name env = env_settings() if self.run_id is None: self.results_dir = '{}/{}/{}'.format(env.results_path, self.name, self.parameter_name) self.segmentation_dir = '{}/{}/{}'.format(env.segmentation_path, self.name, self.parameter_name) else: self.results_dir = '{}/{}/{}_{:03d}'.format(env.results_path, self.name, self.parameter_name, self.run_id) self.segmentation_dir = '{}/{}/{}_{:03d}'.format(env.segmentation_path, self.name, self.parameter_name, self.run_id) tracker_module_abspath = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'tracker', self.name)) if os.path.isdir(tracker_module_abspath): tracker_module = importlib.import_module('pytracking.tracker.{}'.format(self.name)) self.tracker_class = tracker_module.get_tracker_class() else: self.tracker_class = None self.visdom = None def _init_visdom(self, visdom_info, debug): visdom_info = {} if visdom_info is None else visdom_info self.pause_mode = False self.step = False if debug > 0 and visdom_info.get('use_visdom', True): try: self.visdom = Visdom(debug, {'handler': self._visdom_ui_handler, 'win_id': 'Tracking'}, visdom_info=visdom_info) # Show help help_text = 'You can pause/unpause the tracker by pressing ''space'' with the ''Tracking'' window ' \ 'selected. During paused mode, you can track for one frame by pressing the right arrow key.' \ 'To enable/disable plotting of a data block, tick/untick the corresponding entry in ' \ 'block list.' self.visdom.register(help_text, 'text', 1, 'Help') except: time.sleep(0.5) print('!!! WARNING: Visdom could not start, so using matplotlib visualization instead !!!\n' '!!! Start Visdom in a separate terminal window by typing \'visdom\' !!!') def _visdom_ui_handler(self, data): if data['event_type'] == 'KeyPress': if data['key'] == ' ': self.pause_mode = not self.pause_mode elif data['key'] == 'ArrowRight' and self.pause_mode: self.step = True def create_tracker(self, params): tracker = self.tracker_class(params) tracker.visdom = self.visdom return tracker def run_sequence(self, seq, visualization=None, debug=None, visdom_info=None, multiobj_mode=None): """Run tracker on sequence. args: seq: Sequence to run the tracker on. visualization: Set visualization flag (None means default value specified in the parameters). debug: Set debug level (None means default value specified in the parameters). visdom_info: Visdom info. multiobj_mode: Which mode to use for multiple objects. """ params = self.get_parameters() visualization_ = visualization debug_ = debug if debug is None: debug_ = getattr(params, 'debug', 0) if visualization is None: if debug is None: visualization_ = getattr(params, 'visualization', False) else: visualization_ = True if debug else False params.visualization = visualization_ params.debug = debug_ self._init_visdom(visdom_info, debug_) if visualization_ and self.visdom is None: self.init_visualization() # Get init information init_info = seq.init_info() is_single_object = not seq.multiobj_mode if multiobj_mode is None: multiobj_mode = getattr(params, 'multiobj_mode', getattr(self.tracker_class, 'multiobj_mode', 'default')) if multiobj_mode == 'default' or is_single_object: tracker = self.create_tracker(params) elif multiobj_mode == 'parallel': tracker = MultiObjectWrapper(self.tracker_class, params, self.visdom) else: raise ValueError('Unknown multi object mode {}'.format(multiobj_mode)) output = self._track_sequence(tracker, seq, init_info) return output def _track_sequence(self, tracker, seq, init_info): # Define outputs # Each field in output is a list containing tracker prediction for each frame. # In case of single object tracking mode: # target_bbox[i] is the predicted bounding box for frame i # time[i] is the processing time for frame i # segmentation[i] is the segmentation mask for frame i (numpy array) # In case of multi object tracking mode: # target_bbox[i] is an OrderedDict, where target_bbox[i][obj_id] is the predicted box for target obj_id in # frame i # time[i] is either the processing time for frame i, or an OrderedDict containing processing times for each # object in frame i # segmentation[i] is the multi-label segmentation mask for frame i (numpy array) output = {'target_bbox': [], 'time': [], 'segmentation': []} def _store_outputs(tracker_out: dict, defaults=None): defaults = {} if defaults is None else defaults for key in output.keys(): val = tracker_out.get(key, defaults.get(key, None)) if key in tracker_out or val is not None: output[key].append(val) # Initialize image = self._read_image(seq.frames[0]) if tracker.params.visualization and self.visdom is None: self.visualize(image, init_info.get('init_bbox')) start_time = time.time() out = tracker.initialize(image, init_info) if out is None: out = {} prev_output = OrderedDict(out) init_default = {'target_bbox': init_info.get('init_bbox'), 'time': time.time() - start_time, 'segmentation': init_info.get('init_mask')} _store_outputs(out, init_default) for frame_num, frame_path in enumerate(seq.frames[1:], start=1): while True: if not self.pause_mode: break elif self.step: self.step = False break else: time.sleep(0.1) image = self._read_image(frame_path) start_time = time.time() info = seq.frame_info(frame_num) info['previous_output'] = prev_output out = tracker.track(image, info) prev_output = OrderedDict(out) _store_outputs(out, {'time': time.time() - start_time}) segmentation = out['segmentation'] if 'segmentation' in out else None if self.visdom is not None: tracker.visdom_draw_tracking(image, out['target_bbox'], segmentation) elif tracker.params.visualization: self.visualize(image, out['target_bbox'], segmentation) for key in ['target_bbox', 'segmentation']: if key in output and len(output[key]) <= 1: output.pop(key) return output def run_video(self, videofilepath, optional_box=None, debug=None, visdom_info=None): """Run the tracker with the vieofile. args: debug: Debug level. """ params = self.get_parameters() debug_ = debug if debug is None: debug_ = getattr(params, 'debug', 0) params.debug = debug_ params.tracker_name = self.name params.param_name = self.parameter_name self._init_visdom(visdom_info, debug_) multiobj_mode = getattr(params, 'multiobj_mode', getattr(self.tracker_class, 'multiobj_mode', 'default')) if multiobj_mode == 'default': tracker = self.create_tracker(params) if hasattr(tracker, 'initialize_features'): tracker.initialize_features() elif multiobj_mode == 'parallel': tracker = MultiObjectWrapper(self.tracker_class, params, self.visdom, fast_load=True) else: raise ValueError('Unknown multi object mode {}'.format(multiobj_mode)) assert os.path.isfile(videofilepath), "Invalid param {}".format(videofilepath) ", videofilepath must be a valid videofile" cap = cv.VideoCapture(videofilepath) display_name = 'Display: ' + tracker.params.tracker_name cv.namedWindow(display_name, cv.WINDOW_NORMAL | cv.WINDOW_KEEPRATIO) cv.resizeWindow(display_name, 960, 720) success, frame = cap.read() cv.imshow(display_name, frame) def _build_init_info(box): return {'init_bbox': OrderedDict({1: box}), 'init_object_ids': [1, ], 'object_ids': [1, ], 'sequence_object_ids': [1, ]} if success is not True: print("Read frame from {} failed.".format(videofilepath)) exit(-1) if optional_box is not None: assert isinstance(optional_box, list, tuple) assert len(optional_box) == 4, "valid box's foramt is [x,y,w,h]" tracker.initialize(frame, _build_init_info(optional_box)) else: while True: # cv.waitKey() frame_disp = frame.copy() cv.putText(frame_disp, 'Select target ROI and press ENTER', (20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1.5, (0, 0, 0), 1) x, y, w, h = cv.selectROI(display_name, frame_disp, fromCenter=False) init_state = [x, y, w, h] tracker.initialize(frame, _build_init_info(init_state)) break while True: ret, frame = cap.read() if frame is None: return frame_disp = frame.copy() # Draw box out = tracker.track(frame) state = [int(s) for s in out['target_bbox'][1]] cv.rectangle(frame_disp, (state[0], state[1]), (state[2] + state[0], state[3] + state[1]), (0, 255, 0), 5) font_color = (0, 0, 0) cv.putText(frame_disp, 'Tracking!', (20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1) cv.putText(frame_disp, 'Press r to reset', (20, 55), cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1) cv.putText(frame_disp, 'Press q to quit', (20, 80), cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1) # Display the resulting frame cv.imshow(display_name, frame_disp) key = cv.waitKey(1) if key == ord('q'): break elif key == ord('r'): ret, frame = cap.read() frame_disp = frame.copy() cv.putText(frame_disp, 'Select target ROI and press ENTER', (20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1.5, (0, 0, 0), 1) cv.imshow(display_name, frame_disp) x, y, w, h = cv.selectROI(display_name, frame_disp, fromCenter=False) init_state = [x, y, w, h] tracker.initialize(frame, _build_init_info(init_state)) # When everything done, release the capture cap.release() cv.destroyAllWindows() def run_webcam(self, debug=None, visdom_info=None): """Run the tracker with the webcam. args: debug: Debug level. """ params = self.get_parameters() debug_ = debug if debug is None: debug_ = getattr(params, 'debug', 0) params.debug = debug_ params.tracker_name = self.name params.param_name = self.parameter_name self._init_visdom(visdom_info, debug_) multiobj_mode = getattr(params, 'multiobj_mode', getattr(self.tracker_class, 'multiobj_mode', 'default')) if multiobj_mode == 'default': tracker = self.create_tracker(params) elif multiobj_mode == 'parallel': tracker = MultiObjectWrapper(self.tracker_class, params, self.visdom, fast_load=True) else: raise ValueError('Unknown multi object mode {}'.format(multiobj_mode)) class UIControl: def __init__(self): self.mode = 'init' # init, select, track self.target_tl = (-1, -1) self.target_br = (-1, -1) self.new_init = False def mouse_callback(self, event, x, y, flags, param): if event == cv.EVENT_LBUTTONDOWN and self.mode == 'init': self.target_tl = (x, y) self.target_br = (x, y) self.mode = 'select' elif event == cv.EVENT_MOUSEMOVE and self.mode == 'select': self.target_br = (x, y) elif event == cv.EVENT_LBUTTONDOWN and self.mode == 'select': self.target_br = (x, y) self.mode = 'init' self.new_init = True def get_tl(self): return self.target_tl if self.target_tl[0] < self.target_br[0] else self.target_br def get_br(self): return self.target_br if self.target_tl[0] < self.target_br[0] else self.target_tl def get_bb(self): tl = self.get_tl() br = self.get_br() bb = [min(tl[0], br[0]), min(tl[1], br[1]), abs(br[0] - tl[0]), abs(br[1] - tl[1])] return bb ui_control = UIControl() cap = cv.VideoCapture(0) display_name = 'Display: ' + self.name cv.namedWindow(display_name, cv.WINDOW_NORMAL | cv.WINDOW_KEEPRATIO) cv.resizeWindow(display_name, 960, 720) cv.setMouseCallback(display_name, ui_control.mouse_callback) next_object_id = 1 sequence_object_ids = [] prev_output = OrderedDict() while True: # Capture frame-by-frame ret, frame = cap.read() frame_disp = frame.copy() info = OrderedDict() info['previous_output'] = prev_output if ui_control.new_init: ui_control.new_init = False init_state = ui_control.get_bb() info['init_object_ids'] = [next_object_id, ] info['init_bbox'] = OrderedDict({next_object_id: init_state}) sequence_object_ids.append(next_object_id) next_object_id += 1 # Draw box if ui_control.mode == 'select': cv.rectangle(frame_disp, ui_control.get_tl(), ui_control.get_br(), (255, 0, 0), 2) if len(sequence_object_ids) > 0: info['sequence_object_ids'] = sequence_object_ids out = tracker.track(frame, info) prev_output = OrderedDict(out) if 'segmentation' in out: frame_disp = overlay_mask(frame_disp, out['segmentation']) if 'target_bbox' in out: for obj_id, state in out['target_bbox'].items(): state = [int(s) for s in state] cv.rectangle(frame_disp, (state[0], state[1]), (state[2] + state[0], state[3] + state[1]), _tracker_disp_colors[obj_id], 5) # Put text font_color = (0, 0, 0) cv.putText(frame_disp, 'Select target', (20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1) cv.putText(frame_disp, 'Press r to reset', (20, 55), cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1) cv.putText(frame_disp, 'Press q to quit', (20, 85), cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1) # Display the resulting frame cv.imshow(display_name, frame_disp) key = cv.waitKey(1) if key == ord('q'): break elif key == ord('r'): next_object_id = 1 sequence_object_ids = [] prev_output = OrderedDict() info = OrderedDict() info['object_ids'] = [] info['init_object_ids'] = [] info['init_bbox'] = OrderedDict() tracker.initialize(frame, info) ui_control.mode = 'init' # When everything done, release the capture cap.release() cv.destroyAllWindows() def run_vot2020(self, debug=None, visdom_info=None): params = self.get_parameters() params.tracker_name = self.name params.param_name = self.parameter_name params.run_id = self.run_id debug_ = debug if debug is None: debug_ = getattr(params, 'debug', 0) if debug is None: visualization_ = getattr(params, 'visualization', False) else: visualization_ = True if debug else False params.visualization = visualization_ params.debug = debug_ self._init_visdom(visdom_info, debug_) tracker = self.create_tracker(params) tracker.initialize_features() output_segmentation = tracker.predicts_segmentation_mask() import pytracking.evaluation.vot2020 as vot def _convert_anno_to_list(vot_anno): vot_anno = [vot_anno[0], vot_anno[1], vot_anno[2], vot_anno[3]] return vot_anno def _convert_image_path(image_path): return image_path """Run tracker on VOT.""" if output_segmentation: handle = vot.VOT("mask") else: handle = vot.VOT("rectangle") vot_anno = handle.region() image_path = handle.frame() if not image_path: return image_path = _convert_image_path(image_path) image = self._read_image(image_path) if output_segmentation: vot_anno_mask = vot.make_full_size(vot_anno, (image.shape[1], image.shape[0])) bbox = masks_to_bboxes(torch.from_numpy(vot_anno_mask), fmt='t').squeeze().tolist() else: bbox = _convert_anno_to_list(vot_anno) vot_anno_mask = None out = tracker.initialize(image, {'init_mask': vot_anno_mask, 'init_bbox': bbox}) if out is None: out = {} prev_output = OrderedDict(out) # Track while True: image_path = handle.frame() if not image_path: break image_path = _convert_image_path(image_path) image = self._read_image(image_path) info = OrderedDict() info['previous_output'] = prev_output out = tracker.track(image, info) prev_output = OrderedDict(out) if output_segmentation: pred = out['segmentation'].astype(np.uint8) else: state = out['target_bbox'] pred = vot.Rectangle(*state) handle.report(pred, 1.0) segmentation = out['segmentation'] if 'segmentation' in out else None if self.visdom is not None: tracker.visdom_draw_tracking(image, out['target_bbox'], segmentation) elif tracker.params.visualization: self.visualize(image, out['target_bbox'], segmentation) def run_vot(self, debug=None, visdom_info=None): params = self.get_parameters() params.tracker_name = self.name params.param_name = self.parameter_name params.run_id = self.run_id debug_ = debug if debug is None: debug_ = getattr(params, 'debug', 0) if debug is None: visualization_ = getattr(params, 'visualization', False) else: visualization_ = True if debug else False params.visualization = visualization_ params.debug = debug_ self._init_visdom(visdom_info, debug_) tracker = self.create_tracker(params) tracker.initialize_features() import pytracking.evaluation.vot as vot def _convert_anno_to_list(vot_anno): vot_anno = [vot_anno[0][0][0], vot_anno[0][0][1], vot_anno[0][1][0], vot_anno[0][1][1], vot_anno[0][2][0], vot_anno[0][2][1], vot_anno[0][3][0], vot_anno[0][3][1]] return vot_anno def _convert_image_path(image_path): image_path_new = image_path[20:- 2] return "".join(image_path_new) """Run tracker on VOT.""" handle = vot.VOT("polygon") vot_anno_polygon = handle.region() vot_anno_polygon = _convert_anno_to_list(vot_anno_polygon) init_state = convert_vot_anno_to_rect(vot_anno_polygon, tracker.params.vot_anno_conversion_type) image_path = handle.frame() if not image_path: return image_path = _convert_image_path(image_path) image = self._read_image(image_path) tracker.initialize(image, {'init_bbox': init_state}) # Track while True: image_path = handle.frame() if not image_path: break image_path = _convert_image_path(image_path) image = self._read_image(image_path) out = tracker.track(image) state = out['target_bbox'] handle.report(vot.Rectangle(state[0], state[1], state[2], state[3])) segmentation = out['segmentation'] if 'segmentation' in out else None if self.visdom is not None: tracker.visdom_draw_tracking(image, out['target_bbox'], segmentation) elif tracker.params.visualization: self.visualize(image, out['target_bbox'], segmentation) def get_parameters(self): """Get parameters.""" param_module = importlib.import_module('pytracking.parameter.{}.{}'.format(self.name, self.parameter_name)) params = param_module.parameters() return params def init_visualization(self): self.pause_mode = False self.fig, self.ax = plt.subplots(1) self.fig.canvas.mpl_connect('key_press_event', self.press) plt.tight_layout() def visualize(self, image, state, segmentation=None): self.ax.cla() self.ax.imshow(image) if segmentation is not None: self.ax.imshow(segmentation, alpha=0.5) if isinstance(state, (OrderedDict, dict)): boxes = [v for k, v in state.items()] else: boxes = (state,) for i, box in enumerate(boxes, start=1): col = _tracker_disp_colors[i] col = [float(c) / 255.0 for c in col] rect = patches.Rectangle((box[0], box[1]), box[2], box[3], linewidth=1, edgecolor=col, facecolor='none') self.ax.add_patch(rect) if getattr(self, 'gt_state', None) is not None: gt_state = self.gt_state rect = patches.Rectangle((gt_state[0], gt_state[1]), gt_state[2], gt_state[3], linewidth=1, edgecolor='g', facecolor='none') self.ax.add_patch(rect) self.ax.set_axis_off() self.ax.axis('equal') draw_figure(self.fig) if self.pause_mode: keypress = False while not keypress: keypress = plt.waitforbuttonpress() def reset_tracker(self): pass def press(self, event): if event.key == 'p': self.pause_mode = not self.pause_mode print("Switching pause mode!") elif event.key == 'r': self.reset_tracker() print("Resetting target pos to gt!") def _read_image(self, image_file: str): im = cv.imread(image_file) return cv.cvtColor(im, cv.COLOR_BGR2RGB) ================================================ FILE: external/AR/pytracking/evaluation/trackingnetdataset.py ================================================ import numpy as np from pytracking.evaluation.data import Sequence, BaseDataset, SequenceList import os from pytracking.utils.load_text import load_text class TrackingNetDataset(BaseDataset): """ TrackingNet test set. Publication: TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild. Matthias Mueller,Adel Bibi, Silvio Giancola, Salman Al-Subaihi and Bernard Ghanem ECCV, 2018 https://ivul.kaust.edu.sa/Documents/Publications/2018/TrackingNet%20A%20Large%20Scale%20Dataset%20and%20Benchmark%20for%20Object%20Tracking%20in%20the%20Wild.pdf Download the dataset using the toolkit https://github.com/SilvioGiancola/TrackingNet-devkit. """ def __init__(self): super().__init__() self.base_path = self.env_settings.trackingnet_path sets = 'TEST' if not isinstance(sets, (list, tuple)): if sets == 'TEST': sets = ['TEST'] elif sets == 'TRAIN': sets = ['TRAIN_{}'.format(i) for i in range(5)] self.sequence_list = self._list_sequences(self.base_path, sets) def get_sequence_list(self): return SequenceList([self._construct_sequence(set, seq_name) for set, seq_name in self.sequence_list]) def _construct_sequence(self, set, sequence_name): anno_path = '{}/{}/anno/{}.txt'.format(self.base_path, set, sequence_name) ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64, backend='numpy') frames_path = '{}/{}/frames/{}'.format(self.base_path, set, sequence_name) frame_list = [frame for frame in os.listdir(frames_path) if frame.endswith(".jpg")] frame_list.sort(key=lambda f: int(f[:-4])) frames_list = [os.path.join(frames_path, frame) for frame in frame_list] return Sequence(sequence_name, frames_list, 'trackingnet', ground_truth_rect.reshape(-1, 4)) def __len__(self): return len(self.sequence_list) def _list_sequences(self, root, set_ids): sequence_list = [] for s in set_ids: anno_dir = os.path.join(root, s, "anno") sequences_cur_set = [(s, os.path.splitext(f)[0]) for f in os.listdir(anno_dir) if f.endswith('.txt')] sequence_list += sequences_cur_set return sequence_list ================================================ FILE: external/AR/pytracking/evaluation/uavdataset.py ================================================ import numpy as np from pytracking.evaluation.data import Sequence, BaseDataset, SequenceList from pytracking.utils.load_text import load_text class UAVDataset(BaseDataset): """ UAV123 dataset. Publication: A Benchmark and Simulator for UAV Tracking. Matthias Mueller, Neil Smith and Bernard Ghanem ECCV, 2016 https://ivul.kaust.edu.sa/Documents/Publications/2016/A%20Benchmark%20and%20Simulator%20for%20UAV%20Tracking.pdf Download the dataset from https://ivul.kaust.edu.sa/Pages/pub-benchmark-simulator-uav.aspx """ def __init__(self): super().__init__() self.base_path = self.env_settings.uav_path self.sequence_info_list = self._get_sequence_info_list() def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_info_list]) def _construct_sequence(self, sequence_info): sequence_path = sequence_info['path'] nz = sequence_info['nz'] ext = sequence_info['ext'] start_frame = sequence_info['startFrame'] end_frame = sequence_info['endFrame'] init_omit = 0 if 'initOmit' in sequence_info: init_omit = sequence_info['initOmit'] frames = ['{base_path}/{sequence_path}/{frame:0{nz}}.{ext}'.format(base_path=self.base_path, sequence_path=sequence_path, frame=frame_num, nz=nz, ext=ext) for frame_num in range(start_frame+init_omit, end_frame+1)] anno_path = '{}/{}'.format(self.base_path, sequence_info['anno_path']) ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64, backend='numpy') return Sequence(sequence_info['name'], frames, 'uav', ground_truth_rect[init_omit:,:], object_class=sequence_info['object_class']) def __len__(self): return len(self.sequence_info_list) def _get_sequence_info_list(self): sequence_info_list = [ {"name": "uav_bike1", "path": "data_seq/UAV123/bike1", "startFrame": 1, "endFrame": 3085, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/bike1.txt", "object_class": "vehicle"}, {"name": "uav_bike2", "path": "data_seq/UAV123/bike2", "startFrame": 1, "endFrame": 553, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/bike2.txt", "object_class": "vehicle"}, {"name": "uav_bike3", "path": "data_seq/UAV123/bike3", "startFrame": 1, "endFrame": 433, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/bike3.txt", "object_class": "vehicle"}, {"name": "uav_bird1_1", "path": "data_seq/UAV123/bird1", "startFrame": 1, "endFrame": 253, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/bird1_1.txt", "object_class": "bird"}, {"name": "uav_bird1_2", "path": "data_seq/UAV123/bird1", "startFrame": 775, "endFrame": 1477, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/bird1_2.txt", "object_class": "bird"}, {"name": "uav_bird1_3", "path": "data_seq/UAV123/bird1", "startFrame": 1573, "endFrame": 2437, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/bird1_3.txt", "object_class": "bird"}, {"name": "uav_boat1", "path": "data_seq/UAV123/boat1", "startFrame": 1, "endFrame": 901, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat1.txt", "object_class": "vessel"}, {"name": "uav_boat2", "path": "data_seq/UAV123/boat2", "startFrame": 1, "endFrame": 799, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat2.txt", "object_class": "vessel"}, {"name": "uav_boat3", "path": "data_seq/UAV123/boat3", "startFrame": 1, "endFrame": 901, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat3.txt", "object_class": "vessel"}, {"name": "uav_boat4", "path": "data_seq/UAV123/boat4", "startFrame": 1, "endFrame": 553, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat4.txt", "object_class": "vessel"}, {"name": "uav_boat5", "path": "data_seq/UAV123/boat5", "startFrame": 1, "endFrame": 505, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat5.txt", "object_class": "vessel"}, {"name": "uav_boat6", "path": "data_seq/UAV123/boat6", "startFrame": 1, "endFrame": 805, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat6.txt", "object_class": "vessel"}, {"name": "uav_boat7", "path": "data_seq/UAV123/boat7", "startFrame": 1, "endFrame": 535, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat7.txt", "object_class": "vessel"}, {"name": "uav_boat8", "path": "data_seq/UAV123/boat8", "startFrame": 1, "endFrame": 685, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat8.txt", "object_class": "vessel"}, {"name": "uav_boat9", "path": "data_seq/UAV123/boat9", "startFrame": 1, "endFrame": 1399, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat9.txt", "object_class": "vessel"}, {"name": "uav_building1", "path": "data_seq/UAV123/building1", "startFrame": 1, "endFrame": 469, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/building1.txt", "object_class": "other"}, {"name": "uav_building2", "path": "data_seq/UAV123/building2", "startFrame": 1, "endFrame": 577, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/building2.txt", "object_class": "other"}, {"name": "uav_building3", "path": "data_seq/UAV123/building3", "startFrame": 1, "endFrame": 829, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/building3.txt", "object_class": "other"}, {"name": "uav_building4", "path": "data_seq/UAV123/building4", "startFrame": 1, "endFrame": 787, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/building4.txt", "object_class": "other"}, {"name": "uav_building5", "path": "data_seq/UAV123/building5", "startFrame": 1, "endFrame": 481, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/building5.txt", "object_class": "other"}, {"name": "uav_car1_1", "path": "data_seq/UAV123/car1", "startFrame": 1, "endFrame": 751, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car1_1.txt", "object_class": "car"}, {"name": "uav_car1_2", "path": "data_seq/UAV123/car1", "startFrame": 751, "endFrame": 1627, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car1_2.txt", "object_class": "car"}, {"name": "uav_car1_3", "path": "data_seq/UAV123/car1", "startFrame": 1627, "endFrame": 2629, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car1_3.txt", "object_class": "car"}, {"name": "uav_car10", "path": "data_seq/UAV123/car10", "startFrame": 1, "endFrame": 1405, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car10.txt", "object_class": "car"}, {"name": "uav_car11", "path": "data_seq/UAV123/car11", "startFrame": 1, "endFrame": 337, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car11.txt", "object_class": "car"}, {"name": "uav_car12", "path": "data_seq/UAV123/car12", "startFrame": 1, "endFrame": 499, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car12.txt", "object_class": "car"}, {"name": "uav_car13", "path": "data_seq/UAV123/car13", "startFrame": 1, "endFrame": 415, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car13.txt", "object_class": "car"}, {"name": "uav_car14", "path": "data_seq/UAV123/car14", "startFrame": 1, "endFrame": 1327, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car14.txt", "object_class": "car"}, {"name": "uav_car15", "path": "data_seq/UAV123/car15", "startFrame": 1, "endFrame": 469, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car15.txt", "object_class": "car"}, {"name": "uav_car16_1", "path": "data_seq/UAV123/car16", "startFrame": 1, "endFrame": 415, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car16_1.txt", "object_class": "car"}, {"name": "uav_car16_2", "path": "data_seq/UAV123/car16", "startFrame": 415, "endFrame": 1993, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car16_2.txt", "object_class": "car"}, {"name": "uav_car17", "path": "data_seq/UAV123/car17", "startFrame": 1, "endFrame": 1057, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car17.txt", "object_class": "car"}, {"name": "uav_car18", "path": "data_seq/UAV123/car18", "startFrame": 1, "endFrame": 1207, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car18.txt", "object_class": "car"}, {"name": "uav_car1_s", "path": "data_seq/UAV123/car1_s", "startFrame": 1, "endFrame": 1475, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car1_s.txt", "object_class": "car"}, {"name": "uav_car2", "path": "data_seq/UAV123/car2", "startFrame": 1, "endFrame": 1321, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car2.txt", "object_class": "car"}, {"name": "uav_car2_s", "path": "data_seq/UAV123/car2_s", "startFrame": 1, "endFrame": 320, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car2_s.txt", "object_class": "car"}, {"name": "uav_car3", "path": "data_seq/UAV123/car3", "startFrame": 1, "endFrame": 1717, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car3.txt", "object_class": "car"}, {"name": "uav_car3_s", "path": "data_seq/UAV123/car3_s", "startFrame": 1, "endFrame": 1300, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car3_s.txt", "object_class": "car"}, {"name": "uav_car4", "path": "data_seq/UAV123/car4", "startFrame": 1, "endFrame": 1345, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car4.txt", "object_class": "car"}, {"name": "uav_car4_s", "path": "data_seq/UAV123/car4_s", "startFrame": 1, "endFrame": 830, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car4_s.txt", "object_class": "car"}, {"name": "uav_car5", "path": "data_seq/UAV123/car5", "startFrame": 1, "endFrame": 745, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car5.txt", "object_class": "car"}, {"name": "uav_car6_1", "path": "data_seq/UAV123/car6", "startFrame": 1, "endFrame": 487, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car6_1.txt", "object_class": "car"}, {"name": "uav_car6_2", "path": "data_seq/UAV123/car6", "startFrame": 487, "endFrame": 1807, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car6_2.txt", "object_class": "car"}, {"name": "uav_car6_3", "path": "data_seq/UAV123/car6", "startFrame": 1807, "endFrame": 2953, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car6_3.txt", "object_class": "car"}, {"name": "uav_car6_4", "path": "data_seq/UAV123/car6", "startFrame": 2953, "endFrame": 3925, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car6_4.txt", "object_class": "car"}, {"name": "uav_car6_5", "path": "data_seq/UAV123/car6", "startFrame": 3925, "endFrame": 4861, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car6_5.txt", "object_class": "car"}, {"name": "uav_car7", "path": "data_seq/UAV123/car7", "startFrame": 1, "endFrame": 1033, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car7.txt", "object_class": "car"}, {"name": "uav_car8_1", "path": "data_seq/UAV123/car8", "startFrame": 1, "endFrame": 1357, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car8_1.txt", "object_class": "car"}, {"name": "uav_car8_2", "path": "data_seq/UAV123/car8", "startFrame": 1357, "endFrame": 2575, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car8_2.txt", "object_class": "car"}, {"name": "uav_car9", "path": "data_seq/UAV123/car9", "startFrame": 1, "endFrame": 1879, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car9.txt", "object_class": "car"}, {"name": "uav_group1_1", "path": "data_seq/UAV123/group1", "startFrame": 1, "endFrame": 1333, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group1_1.txt", "object_class": "person"}, {"name": "uav_group1_2", "path": "data_seq/UAV123/group1", "startFrame": 1333, "endFrame": 2515, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group1_2.txt", "object_class": "person"}, {"name": "uav_group1_3", "path": "data_seq/UAV123/group1", "startFrame": 2515, "endFrame": 3925, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group1_3.txt", "object_class": "person"}, {"name": "uav_group1_4", "path": "data_seq/UAV123/group1", "startFrame": 3925, "endFrame": 4873, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group1_4.txt", "object_class": "person"}, {"name": "uav_group2_1", "path": "data_seq/UAV123/group2", "startFrame": 1, "endFrame": 907, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group2_1.txt", "object_class": "person"}, {"name": "uav_group2_2", "path": "data_seq/UAV123/group2", "startFrame": 907, "endFrame": 1771, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group2_2.txt", "object_class": "person"}, {"name": "uav_group2_3", "path": "data_seq/UAV123/group2", "startFrame": 1771, "endFrame": 2683, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group2_3.txt", "object_class": "person"}, {"name": "uav_group3_1", "path": "data_seq/UAV123/group3", "startFrame": 1, "endFrame": 1567, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group3_1.txt", "object_class": "person"}, {"name": "uav_group3_2", "path": "data_seq/UAV123/group3", "startFrame": 1567, "endFrame": 2827, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group3_2.txt", "object_class": "person"}, {"name": "uav_group3_3", "path": "data_seq/UAV123/group3", "startFrame": 2827, "endFrame": 4369, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group3_3.txt", "object_class": "person"}, {"name": "uav_group3_4", "path": "data_seq/UAV123/group3", "startFrame": 4369, "endFrame": 5527, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group3_4.txt", "object_class": "person"}, {"name": "uav_person1", "path": "data_seq/UAV123/person1", "startFrame": 1, "endFrame": 799, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person1.txt", "object_class": "person"}, {"name": "uav_person10", "path": "data_seq/UAV123/person10", "startFrame": 1, "endFrame": 1021, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person10.txt", "object_class": "person"}, {"name": "uav_person11", "path": "data_seq/UAV123/person11", "startFrame": 1, "endFrame": 721, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person11.txt", "object_class": "person"}, {"name": "uav_person12_1", "path": "data_seq/UAV123/person12", "startFrame": 1, "endFrame": 601, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person12_1.txt", "object_class": "person"}, {"name": "uav_person12_2", "path": "data_seq/UAV123/person12", "startFrame": 601, "endFrame": 1621, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person12_2.txt", "object_class": "person"}, {"name": "uav_person13", "path": "data_seq/UAV123/person13", "startFrame": 1, "endFrame": 883, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person13.txt", "object_class": "person"}, {"name": "uav_person14_1", "path": "data_seq/UAV123/person14", "startFrame": 1, "endFrame": 847, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person14_1.txt", "object_class": "person"}, {"name": "uav_person14_2", "path": "data_seq/UAV123/person14", "startFrame": 847, "endFrame": 1813, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person14_2.txt", "object_class": "person"}, {"name": "uav_person14_3", "path": "data_seq/UAV123/person14", "startFrame": 1813, "endFrame": 2923, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person14_3.txt", "object_class": "person"}, {"name": "uav_person15", "path": "data_seq/UAV123/person15", "startFrame": 1, "endFrame": 1339, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person15.txt", "object_class": "person"}, {"name": "uav_person16", "path": "data_seq/UAV123/person16", "startFrame": 1, "endFrame": 1147, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person16.txt", "object_class": "person"}, {"name": "uav_person17_1", "path": "data_seq/UAV123/person17", "startFrame": 1, "endFrame": 1501, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person17_1.txt", "object_class": "person"}, {"name": "uav_person17_2", "path": "data_seq/UAV123/person17", "startFrame": 1501, "endFrame": 2347, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person17_2.txt", "object_class": "person"}, {"name": "uav_person18", "path": "data_seq/UAV123/person18", "startFrame": 1, "endFrame": 1393, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person18.txt", "object_class": "person"}, {"name": "uav_person19_1", "path": "data_seq/UAV123/person19", "startFrame": 1, "endFrame": 1243, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person19_1.txt", "object_class": "person"}, {"name": "uav_person19_2", "path": "data_seq/UAV123/person19", "startFrame": 1243, "endFrame": 2791, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person19_2.txt", "object_class": "person"}, {"name": "uav_person19_3", "path": "data_seq/UAV123/person19", "startFrame": 2791, "endFrame": 4357, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person19_3.txt", "object_class": "person"}, {"name": "uav_person1_s", "path": "data_seq/UAV123/person1_s", "startFrame": 1, "endFrame": 1600, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person1_s.txt", "object_class": "person"}, {"name": "uav_person2_1", "path": "data_seq/UAV123/person2", "startFrame": 1, "endFrame": 1189, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person2_1.txt", "object_class": "person"}, {"name": "uav_person2_2", "path": "data_seq/UAV123/person2", "startFrame": 1189, "endFrame": 2623, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person2_2.txt", "object_class": "person"}, {"name": "uav_person20", "path": "data_seq/UAV123/person20", "startFrame": 1, "endFrame": 1783, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person20.txt", "object_class": "person"}, {"name": "uav_person21", "path": "data_seq/UAV123/person21", "startFrame": 1, "endFrame": 487, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person21.txt", "object_class": "person"}, {"name": "uav_person22", "path": "data_seq/UAV123/person22", "startFrame": 1, "endFrame": 199, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person22.txt", "object_class": "person"}, {"name": "uav_person23", "path": "data_seq/UAV123/person23", "startFrame": 1, "endFrame": 397, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person23.txt", "object_class": "person"}, {"name": "uav_person2_s", "path": "data_seq/UAV123/person2_s", "startFrame": 1, "endFrame": 250, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person2_s.txt", "object_class": "person"}, {"name": "uav_person3", "path": "data_seq/UAV123/person3", "startFrame": 1, "endFrame": 643, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person3.txt", "object_class": "person"}, {"name": "uav_person3_s", "path": "data_seq/UAV123/person3_s", "startFrame": 1, "endFrame": 505, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person3_s.txt", "object_class": "person"}, {"name": "uav_person4_1", "path": "data_seq/UAV123/person4", "startFrame": 1, "endFrame": 1501, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person4_1.txt", "object_class": "person"}, {"name": "uav_person4_2", "path": "data_seq/UAV123/person4", "startFrame": 1501, "endFrame": 2743, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person4_2.txt", "object_class": "person"}, {"name": "uav_person5_1", "path": "data_seq/UAV123/person5", "startFrame": 1, "endFrame": 877, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person5_1.txt", "object_class": "person"}, {"name": "uav_person5_2", "path": "data_seq/UAV123/person5", "startFrame": 877, "endFrame": 2101, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person5_2.txt", "object_class": "person"}, {"name": "uav_person6", "path": "data_seq/UAV123/person6", "startFrame": 1, "endFrame": 901, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person6.txt", "object_class": "person"}, {"name": "uav_person7_1", "path": "data_seq/UAV123/person7", "startFrame": 1, "endFrame": 1249, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person7_1.txt", "object_class": "person"}, {"name": "uav_person7_2", "path": "data_seq/UAV123/person7", "startFrame": 1249, "endFrame": 2065, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person7_2.txt", "object_class": "person"}, {"name": "uav_person8_1", "path": "data_seq/UAV123/person8", "startFrame": 1, "endFrame": 1075, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person8_1.txt", "object_class": "person"}, {"name": "uav_person8_2", "path": "data_seq/UAV123/person8", "startFrame": 1075, "endFrame": 1525, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person8_2.txt", "object_class": "person"}, {"name": "uav_person9", "path": "data_seq/UAV123/person9", "startFrame": 1, "endFrame": 661, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person9.txt", "object_class": "person"}, {"name": "uav_truck1", "path": "data_seq/UAV123/truck1", "startFrame": 1, "endFrame": 463, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/truck1.txt", "object_class": "truck"}, {"name": "uav_truck2", "path": "data_seq/UAV123/truck2", "startFrame": 1, "endFrame": 385, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/truck2.txt", "object_class": "truck"}, {"name": "uav_truck3", "path": "data_seq/UAV123/truck3", "startFrame": 1, "endFrame": 535, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/truck3.txt", "object_class": "truck"}, {"name": "uav_truck4_1", "path": "data_seq/UAV123/truck4", "startFrame": 1, "endFrame": 577, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/truck4_1.txt", "object_class": "truck"}, {"name": "uav_truck4_2", "path": "data_seq/UAV123/truck4", "startFrame": 577, "endFrame": 1261, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/truck4_2.txt", "object_class": "truck"}, {"name": "uav_uav1_1", "path": "data_seq/UAV123/uav1", "startFrame": 1, "endFrame": 1555, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav1_1.txt", "object_class": "aircraft"}, {"name": "uav_uav1_2", "path": "data_seq/UAV123/uav1", "startFrame": 1555, "endFrame": 2377, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav1_2.txt", "object_class": "aircraft"}, {"name": "uav_uav1_3", "path": "data_seq/UAV123/uav1", "startFrame": 2473, "endFrame": 3469, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav1_3.txt", "object_class": "aircraft"}, {"name": "uav_uav2", "path": "data_seq/UAV123/uav2", "startFrame": 1, "endFrame": 133, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav2.txt", "object_class": "aircraft"}, {"name": "uav_uav3", "path": "data_seq/UAV123/uav3", "startFrame": 1, "endFrame": 265, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav3.txt", "object_class": "aircraft"}, {"name": "uav_uav4", "path": "data_seq/UAV123/uav4", "startFrame": 1, "endFrame": 157, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav4.txt", "object_class": "aircraft"}, {"name": "uav_uav5", "path": "data_seq/UAV123/uav5", "startFrame": 1, "endFrame": 139, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav5.txt", "object_class": "aircraft"}, {"name": "uav_uav6", "path": "data_seq/UAV123/uav6", "startFrame": 1, "endFrame": 109, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav6.txt", "object_class": "aircraft"}, {"name": "uav_uav7", "path": "data_seq/UAV123/uav7", "startFrame": 1, "endFrame": 373, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav7.txt", "object_class": "aircraft"}, {"name": "uav_uav8", "path": "data_seq/UAV123/uav8", "startFrame": 1, "endFrame": 301, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav8.txt", "object_class": "aircraft"}, {"name": "uav_wakeboard1", "path": "data_seq/UAV123/wakeboard1", "startFrame": 1, "endFrame": 421, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard1.txt", "object_class": "person"}, {"name": "uav_wakeboard10", "path": "data_seq/UAV123/wakeboard10", "startFrame": 1, "endFrame": 469, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard10.txt", "object_class": "person"}, {"name": "uav_wakeboard2", "path": "data_seq/UAV123/wakeboard2", "startFrame": 1, "endFrame": 733, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard2.txt", "object_class": "person"}, {"name": "uav_wakeboard3", "path": "data_seq/UAV123/wakeboard3", "startFrame": 1, "endFrame": 823, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard3.txt", "object_class": "person"}, {"name": "uav_wakeboard4", "path": "data_seq/UAV123/wakeboard4", "startFrame": 1, "endFrame": 697, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard4.txt", "object_class": "person"}, {"name": "uav_wakeboard5", "path": "data_seq/UAV123/wakeboard5", "startFrame": 1, "endFrame": 1675, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard5.txt", "object_class": "person"}, {"name": "uav_wakeboard6", "path": "data_seq/UAV123/wakeboard6", "startFrame": 1, "endFrame": 1165, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard6.txt", "object_class": "person"}, {"name": "uav_wakeboard7", "path": "data_seq/UAV123/wakeboard7", "startFrame": 1, "endFrame": 199, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard7.txt", "object_class": "person"}, {"name": "uav_wakeboard8", "path": "data_seq/UAV123/wakeboard8", "startFrame": 1, "endFrame": 1543, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard8.txt", "object_class": "person"}, {"name": "uav_wakeboard9", "path": "data_seq/UAV123/wakeboard9", "startFrame": 1, "endFrame": 355, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard9.txt", "object_class": "person"} ] return sequence_info_list ================================================ FILE: external/AR/pytracking/evaluation/vot.py ================================================ """ \file vot.py @brief Python utility functions for VOT integration @author Luka Cehovin, Alessio Dore @date 2016 """ import sys import copy import collections try: import trax import trax.server TRAX = True except ImportError: TRAX = False Rectangle = collections.namedtuple('Rectangle', ['x', 'y', 'width', 'height']) Point = collections.namedtuple('Point', ['x', 'y']) Polygon = collections.namedtuple('Polygon', ['points']) def parse_region(string): tokens = map(float, string.split(',')) if len(tokens) == 4: return Rectangle(tokens[0], tokens[1], tokens[2], tokens[3]) elif len(tokens) % 2 == 0 and len(tokens) > 4: return Polygon([Point(tokens[i],tokens[i+1]) for i in xrange(0,len(tokens),2)]) return None def encode_region(region): if isinstance(region, Polygon): return ','.join(['{},{}'.format(p.x,p.y) for p in region.points]) elif isinstance(region, Rectangle): return '{},{},{},{}'.format(region.x, region.y, region.width, region.height) else: return "" def convert_region(region, to): if to == 'rectangle': if isinstance(region, Rectangle): return copy.copy(region) elif isinstance(region, Polygon): top = sys.float_info.max bottom = sys.float_info.min left = sys.float_info.max right = sys.float_info.min for point in region.points: top = min(top, point.y) bottom = max(bottom, point.y) left = min(left, point.x) right = max(right, point.x) return Rectangle(left, top, right - left, bottom - top) else: return None if to == 'polygon': if isinstance(region, Rectangle): points = [] points.append((region.x, region.y)) points.append((region.x + region.width, region.y)) points.append((region.x + region.width, region.y + region.height)) points.append((region.x, region.y + region.height)) return Polygon(points) elif isinstance(region, Polygon): return copy.copy(region) else: return None return None class VOT(object): """ Base class for Python VOT integration """ def __init__(self, region_format): """ Constructor Args: region_format: Region format options """ assert(region_format in ['rectangle', 'polygon']) if TRAX: options = trax.server.ServerOptions(region_format, trax.image.PATH) self._trax = trax.server.Server(options) request = self._trax.wait() assert(request.type == 'initialize') if request.region.type == 'polygon': self._region = Polygon([Point(x[0], x[1]) for x in request.region.points]) else: self._region = Rectangle(request.region.x, request.region.y, request.region.width, request.region.height) self._image = str(request.image) self._trax.status(request.region) else: self._files = [x.strip('\n') for x in open('images.txt', 'r').readlines()] self._frame = 0 self._region = convert_region(parse_region(open('region.txt', 'r').readline()), region_format) self._result = [] def region(self): """ Send configuration message to the client and receive the initialization region and the path of the first image Returns: initialization region """ return self._region def report(self, region, confidence = 0): """ Report the tracking results to the client Arguments: region: region for the frame """ assert(isinstance(region, Rectangle) or isinstance(region, Polygon)) if TRAX: if isinstance(region, Polygon): tregion = trax.region.Polygon([(x.x, x.y) for x in region.points]) else: tregion = trax.region.Rectangle(region.x, region.y, region.width, region.height) self._trax.status(tregion, {"confidence" : confidence}) else: self._result.append(region) self._frame += 1 def frame(self): """ Get a frame (image path) from client Returns: absolute path of the image """ if TRAX: if hasattr(self, "_image"): image = str(self._image) del self._image return image request = self._trax.wait() if request.type == 'frame': return str(request.image) else: return None else: if self._frame >= len(self._files): return None return self._files[self._frame] def quit(self): if TRAX: self._trax.quit() elif hasattr(self, '_result'): with open('output.txt', 'w') as f: for r in self._result: f.write(encode_region(r)) f.write('\n') def __del__(self): self.quit() ================================================ FILE: external/AR/pytracking/evaluation/vot2020.py ================================================ """ \file vot.py @brief Python utility functions for VOT integration @author Luka Cehovin, Alessio Dore @date 2016 """ import sys import copy import collections import numpy as np try: import trax except ImportError: raise Exception('TraX support not found. Please add trax module to Python path.') def make_full_size(x, output_sz): ''' zero-pad input x (right and down) to match output_sz x: numpy array e.g., binary mask output_sz: size of the output [width, height] ''' if x.shape[0] == output_sz[1] and x.shape[1] == output_sz[0]: return x pad_x = output_sz[0] - x.shape[1] if pad_x < 0: x = x[:, :x.shape[1] + pad_x] # padding has to be set to zero, otherwise pad function fails pad_x = 0 pad_y = output_sz[1] - x.shape[0] if pad_y < 0: x = x[:x.shape[0] + pad_y, :] # padding has to be set to zero, otherwise pad function fails pad_y = 0 return np.pad(x, ((0, pad_y), (0, pad_x)), 'constant', constant_values=0) Rectangle = collections.namedtuple('Rectangle', ['x', 'y', 'width', 'height']) Point = collections.namedtuple('Point', ['x', 'y']) Polygon = collections.namedtuple('Polygon', ['points']) class VOT(object): """ Base class for Python VOT integration """ def __init__(self, region_format, channels=None): """ Constructor Args: region_format: Region format options """ assert(region_format in [trax.Region.RECTANGLE, trax.Region.POLYGON, trax.Region.MASK]) if channels is None: channels = ['color'] elif channels == 'rgbd': channels = ['color', 'depth'] elif channels == 'rgbt': channels = ['color', 'ir'] elif channels == 'ir': channels = ['ir'] else: raise Exception('Illegal configuration {}.'.format(channels)) self._trax = trax.Server([region_format], [trax.Image.PATH], channels, customMetadata=dict(vot="python")) request = self._trax.wait() assert(request.type == 'initialize') if isinstance(request.region, trax.Polygon): self._region = Polygon([Point(x[0], x[1]) for x in request.region]) if isinstance(request.region, trax.Mask): self._region = request.region.array(True) else: self._region = Rectangle(*request.region.bounds()) self._image = [x.path() for k, x in request.image.items()] if len(self._image) == 1: self._image = self._image[0] self._trax.status(request.region) def region(self): """ Send configuration message to the client and receive the initialization region and the path of the first image Returns: initialization region """ return self._region def report(self, region, confidence = None): """ Report the tracking results to the client Arguments: region: region for the frame """ assert(isinstance(region, (Rectangle, Polygon, np.ndarray))) if isinstance(region, Polygon): tregion = trax.Polygon.create([(x.x, x.y) for x in region.points]) if isinstance(region, np.ndarray): tregion = trax.Mask.create(region) else: tregion = trax.Rectangle.create(region.x, region.y, region.width, region.height) properties = {} if not confidence is None: properties['confidence'] = confidence self._trax.status(tregion, properties) def frame(self): """ Get a frame (image path) from client Returns: absolute path of the image """ if hasattr(self, "_image"): image = self._image del self._image return image request = self._trax.wait() if request.type == 'frame': image = [x.path() for k, x in request.image.items()] if len(image) == 1: return image[0] return image else: return None def quit(self): if hasattr(self, '_trax'): self._trax.quit() def __del__(self): self.quit() ================================================ FILE: external/AR/pytracking/evaluation/votdataset.py ================================================ import numpy as np from pytracking.evaluation.data import Sequence, BaseDataset, SequenceList class VOTDataset(BaseDataset): """ VOT2018 dataset Publication: The sixth Visual Object Tracking VOT2018 challenge results. Matej Kristan, Ales Leonardis, Jiri Matas, Michael Felsberg, Roman Pfugfelder, Luka Cehovin Zajc, Tomas Vojir, Goutam Bhat, Alan Lukezic et al. ECCV, 2018 https://prints.vicos.si/publications/365 Download the dataset from http://www.votchallenge.net/vot2018/dataset.html """ def __init__(self): super().__init__() self.base_path = self.env_settings.vot_path self.sequence_list = self._get_sequence_list() def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_list]) def _construct_sequence(self, sequence_name): sequence_path = sequence_name nz = 8 ext = 'jpg' start_frame = 1 anno_path = '{}/{}/groundtruth.txt'.format(self.base_path, sequence_name) try: ground_truth_rect = np.loadtxt(str(anno_path), dtype=np.float64) except: ground_truth_rect = np.loadtxt(str(anno_path), delimiter=',', dtype=np.float64) end_frame = ground_truth_rect.shape[0] frames = ['{base_path}/{sequence_path}/color/{frame:0{nz}}.{ext}'.format(base_path=self.base_path, sequence_path=sequence_path, frame=frame_num, nz=nz, ext=ext) for frame_num in range(start_frame, end_frame+1)] # Convert gt if ground_truth_rect.shape[1] > 4: gt_x_all = ground_truth_rect[:, [0, 2, 4, 6]] gt_y_all = ground_truth_rect[:, [1, 3, 5, 7]] x1 = np.amin(gt_x_all, 1).reshape(-1,1) y1 = np.amin(gt_y_all, 1).reshape(-1,1) x2 = np.amax(gt_x_all, 1).reshape(-1,1) y2 = np.amax(gt_y_all, 1).reshape(-1,1) ground_truth_rect = np.concatenate((x1, y1, x2-x1, y2-y1), 1) return Sequence(sequence_name, frames, 'vot', ground_truth_rect) def __len__(self): return len(self.sequence_list) def _get_sequence_list(self): sequence_list= ['ants1', 'ants3', 'bag', 'ball1', 'ball2', 'basketball', 'birds1', 'blanket', 'bmx', 'bolt1', 'bolt2', 'book', 'butterfly', 'car1', 'conduction1', 'crabs1', 'crossing', 'dinosaur', 'drone_across', 'drone_flip', 'drone1', 'fernando', 'fish1', 'fish2', 'fish3', 'flamingo1', 'frisbee', 'girl', 'glove', 'godfather', 'graduate', 'gymnastics1', 'gymnastics2', 'gymnastics3', 'hand', 'handball1', 'handball2', 'helicopter', 'iceskater1', 'iceskater2', 'leaves', 'matrix', 'motocross1', 'motocross2', 'nature', 'pedestrian1', 'rabbit', 'racing', 'road', 'shaking', 'sheep', 'singer2', 'singer3', 'soccer1', 'soccer2', 'soldier', 'tiger', 'traffic', 'wiper', 'zebrafish1'] return sequence_list ================================================ FILE: external/AR/pytracking/experiments/__init__.py ================================================ ================================================ FILE: external/AR/pytracking/experiments/myexperiments.py ================================================ from pytracking.evaluation import Tracker, get_dataset, trackerlist def atom_nfs_uav(): # Run three runs of ATOM on NFS and UAV datasets trackers = trackerlist('atom', 'default', range(3)) dataset = get_dataset('nfs', 'uav') return trackers, dataset def uav_test(): # Run DiMP18, ATOM and ECO on the UAV dataset trackers = trackerlist('dimp', 'dimp18', range(1)) + \ trackerlist('atom', 'default', range(1)) + \ trackerlist('eco', 'default', range(1)) dataset = get_dataset('uav') return trackers, dataset ================================================ FILE: external/AR/pytracking/features/__init__.py ================================================ ================================================ FILE: external/AR/pytracking/features/augmentation.py ================================================ import numpy as np import math import torch import torch.nn.functional as F import cv2 as cv import random from pytracking.features.preprocessing import numpy_to_torch, torch_to_numpy class Transform: """Base data augmentation transform class.""" def __init__(self, output_sz = None, shift = None): self.output_sz = output_sz self.shift = (0,0) if shift is None else shift def __call__(self, image, is_mask=False): raise NotImplementedError def crop_to_output(self, image): if isinstance(image, torch.Tensor): imsz = image.shape[2:] if self.output_sz is None: pad_h = 0 pad_w = 0 else: pad_h = (self.output_sz[0] - imsz[0]) / 2 pad_w = (self.output_sz[1] - imsz[1]) / 2 pad_left = math.floor(pad_w) + self.shift[1] pad_right = math.ceil(pad_w) - self.shift[1] pad_top = math.floor(pad_h) + self.shift[0] pad_bottom = math.ceil(pad_h) - self.shift[0] return F.pad(image, (pad_left, pad_right, pad_top, pad_bottom), 'replicate') else: raise NotImplementedError class Identity(Transform): """Identity transformation.""" def __call__(self, image, is_mask=False): return self.crop_to_output(image) class FlipHorizontal(Transform): """Flip along horizontal axis.""" def __call__(self, image, is_mask=False): if isinstance(image, torch.Tensor): return self.crop_to_output(image.flip((3,))) else: return np.fliplr(image) class FlipVertical(Transform): """Flip along vertical axis.""" def __call__(self, image: torch.Tensor, is_mask=False): if isinstance(image, torch.Tensor): return self.crop_to_output(image.flip((2,))) else: return np.flipud(image) class Translation(Transform): """Translate.""" def __init__(self, translation, output_sz = None, shift = None): super().__init__(output_sz, shift) self.shift = (self.shift[0] + translation[0], self.shift[1] + translation[1]) def __call__(self, image, is_mask=False): if isinstance(image, torch.Tensor): return self.crop_to_output(image) else: raise NotImplementedError class Scale(Transform): """Scale.""" def __init__(self, scale_factor, output_sz = None, shift = None): super().__init__(output_sz, shift) self.scale_factor = scale_factor def __call__(self, image, is_mask=False): if isinstance(image, torch.Tensor): # Calculate new size. Ensure that it is even so that crop/pad becomes easier h_orig, w_orig = image.shape[2:] if h_orig != w_orig: raise NotImplementedError h_new = round(h_orig /self.scale_factor) h_new += (h_new - h_orig) % 2 w_new = round(w_orig /self.scale_factor) w_new += (w_new - w_orig) % 2 image_resized = F.interpolate(image, [h_new, w_new], mode='bilinear') return self.crop_to_output(image_resized) else: raise NotImplementedError class Affine(Transform): """Affine transformation.""" def __init__(self, transform_matrix, output_sz = None, shift = None): super().__init__(output_sz, shift) self.transform_matrix = transform_matrix def __call__(self, image, is_mask=False): if isinstance(image, torch.Tensor): return self.crop_to_output(numpy_to_torch(self(torch_to_numpy(image)))) else: return cv.warpAffine(image, self.transform_matrix, image.shape[1::-1], borderMode=cv.BORDER_REPLICATE) class Rotate(Transform): """Rotate with given angle.""" def __init__(self, angle, output_sz = None, shift = None): super().__init__(output_sz, shift) self.angle = math.pi * angle/180 def __call__(self, image, is_mask=False): if isinstance(image, torch.Tensor): return self.crop_to_output(numpy_to_torch(self(torch_to_numpy(image)))) else: c = (np.expand_dims(np.array(image.shape[:2]),1)-1)/2 R = np.array([[math.cos(self.angle), math.sin(self.angle)], [-math.sin(self.angle), math.cos(self.angle)]]) H =np.concatenate([R, c - R @ c], 1) return cv.warpAffine(image, H, image.shape[1::-1], borderMode=cv.BORDER_REPLICATE) class Blur(Transform): """Blur with given sigma (can be axis dependent).""" def __init__(self, sigma, output_sz = None, shift = None): super().__init__(output_sz, shift) if isinstance(sigma, (float, int)): sigma = (sigma, sigma) self.sigma = sigma self.filter_size = [math.ceil(2*s) for s in self.sigma] x_coord = [torch.arange(-sz, sz+1, dtype=torch.float32) for sz in self.filter_size] self.filter = [torch.exp(-(x**2)/(2*s**2)) for x, s in zip(x_coord, self.sigma)] self.filter[0] = self.filter[0].view(1,1,-1,1) / self.filter[0].sum() self.filter[1] = self.filter[1].view(1,1,1,-1) / self.filter[1].sum() def __call__(self, image, is_mask=False): if isinstance(image, torch.Tensor): sz = image.shape[2:] im1 = F.conv2d(image.view(-1,1,sz[0],sz[1]), self.filter[0], padding=(self.filter_size[0],0)) return self.crop_to_output(F.conv2d(im1, self.filter[1], padding=(0,self.filter_size[1])).view(1,-1,sz[0],sz[1])) else: raise NotImplementedError class RandomAffine(Transform): """Affine transformation.""" def __init__(self, p_flip=0.0, max_rotation=0.0, max_shear=0.0, max_scale=0.0, max_ar_factor=0.0, border_mode='constant', output_sz = None, shift = None): super().__init__(output_sz, shift) self.p_flip = p_flip self.max_rotation = max_rotation self.max_shear = max_shear self.max_scale = max_scale self.max_ar_factor = max_ar_factor self.pad_amount = 0 if border_mode == 'constant': self.border_flag = cv.BORDER_CONSTANT elif border_mode == 'replicate': self.border_flag == cv.BORDER_REPLICATE else: raise Exception self.roll_values = self.roll() def roll(self): do_flip = random.random() < self.p_flip theta = random.uniform(-self.max_rotation, self.max_rotation) shear_x = random.uniform(-self.max_shear, self.max_shear) shear_y = random.uniform(-self.max_shear, self.max_shear) ar_factor = np.exp(random.uniform(-self.max_ar_factor, self.max_ar_factor)) scale_factor = np.exp(random.uniform(-self.max_scale, self.max_scale)) return do_flip, theta, (shear_x, shear_y), (scale_factor, scale_factor * ar_factor) def _construct_t_mat(self, image_shape, do_flip, theta, shear_values, scale_factors): im_h, im_w = image_shape t_mat = np.identity(3) if do_flip: if do_flip: t_mat[0, 0] = -1.0 t_mat[0, 2] = im_w t_rot = cv.getRotationMatrix2D((im_w * 0.5, im_h * 0.5), theta, 1.0) t_rot = np.concatenate((t_rot, np.array([0.0, 0.0, 1.0]).reshape(1, 3))) t_shear = np.array([[1.0, shear_values[0], -shear_values[0] * 0.5 * im_w], [shear_values[1], 1.0, -shear_values[1] * 0.5 * im_h], [0.0, 0.0, 1.0]]) t_scale = np.array([[scale_factors[0], 0.0, (1.0 - scale_factors[0]) * 0.5 * im_w], [0.0, scale_factors[1], (1.0 - scale_factors[1]) * 0.5 * im_h], [0.0, 0.0, 1.0]]) t_mat = t_scale @ t_rot @ t_shear @ t_mat t_mat[0, 2] += self.pad_amount t_mat[1, 2] += self.pad_amount t_mat = t_mat[:2, :] return t_mat def __call__(self, image, is_mask=False): input_tensor = torch.is_tensor(image) if input_tensor: image = torch_to_numpy(image) do_flip, theta, shear_values, scale_factors = self.roll_values t_mat = self._construct_t_mat(image.shape[:2], do_flip, theta, shear_values, scale_factors) output_sz = (image.shape[1] + 2*self.pad_amount, image.shape[0] + 2*self.pad_amount) if not is_mask: image_t = cv.warpAffine(image, t_mat, output_sz, flags=cv.INTER_LINEAR, borderMode=self.border_flag) else: image_t = cv.warpAffine(image, t_mat, output_sz, flags=cv.INTER_NEAREST, borderMode=self.border_flag) image_t = image_t.reshape(image.shape) if input_tensor: image_t = numpy_to_torch(image_t) return self.crop_to_output(image_t) ================================================ FILE: external/AR/pytracking/features/color.py ================================================ import torch from pytracking.features.featurebase import FeatureBase class RGB(FeatureBase): """RGB feature normalized to [-0.5, 0.5].""" def dim(self): return 3 def stride(self): return self.pool_stride def extract(self, im: torch.Tensor): return im/255 - 0.5 class Grayscale(FeatureBase): """Grayscale feature normalized to [-0.5, 0.5].""" def dim(self): return 1 def stride(self): return self.pool_stride def extract(self, im: torch.Tensor): return torch.mean(im/255 - 0.5, 1, keepdim=True) ================================================ FILE: external/AR/pytracking/features/deep.py ================================================ from pytracking.features.featurebase import FeatureBase, MultiFeatureBase import torch import torchvision from pytracking import TensorList from pytracking.evaluation.environment import env_settings import os from pytracking.utils.loading import load_network from ltr.models.backbone.resnet18_vggm import resnet18_vggmconv1 normalize = torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) class ResNet18m1(MultiFeatureBase): """ResNet18 feature together with the VGG-m conv1 layer. args: output_layers: List of layers to output. net_path: Relative or absolute net path (default should be fine). use_gpu: Use GPU or CPU. """ def __init__(self, output_layers, net_path=None, use_gpu=True, *args, **kwargs): super(ResNet18m1, self).__init__(*args, **kwargs) for l in output_layers: if l not in ['vggconv1', 'conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']: raise ValueError('Unknown layer') self.output_layers = list(output_layers) self.use_gpu = use_gpu self.net_path = 'resnet18_vggmconv1/resnet18_vggmconv1.pth' if net_path is None else net_path def initialize(self): if isinstance(self.pool_stride, int) and self.pool_stride == 1: self.pool_stride = [1] * len(self.output_layers) self.layer_stride = {'vggconv1': 2, 'conv1': 2, 'layer1': 4, 'layer2': 8, 'layer3': 16, 'layer4': 32, 'fc': None} self.layer_dim = {'vggconv1': 96, 'conv1': 64, 'layer1': 64, 'layer2': 128, 'layer3': 256, 'layer4': 512, 'fc': None} self.mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, -1, 1, 1) self.std = torch.Tensor([0.229, 0.224, 0.225]).view(1, -1, 1, 1) if os.path.isabs(self.net_path): net_path_full = [self.net_path] else: root_paths = env_settings().network_path if isinstance(root_paths, str): root_paths = [root_paths] net_path_full = [os.path.join(root, self.net_path) for root in root_paths] self.net = None for net_path in net_path_full: try: self.net = resnet18_vggmconv1(self.output_layers, path=net_path) break except: pass if self.net is None: raise Exception('Did not find network file {}'.format(self.net_path)) if self.use_gpu: self.net.cuda() self.net.eval() def dim(self): return TensorList([self.layer_dim[l] for l in self.output_layers]) def stride(self): return TensorList([s * self.layer_stride[l] for l, s in zip(self.output_layers, self.pool_stride)]) def extract(self, im: torch.Tensor): im = im / 255 im -= self.mean im /= self.std if self.use_gpu: im = im.cuda() with torch.no_grad(): return TensorList(self.net(im).values()) class ATOMResNet18(MultiFeatureBase): """ResNet18 feature with the ATOM IoUNet. args: output_layers: List of layers to output. net_path: Relative or absolute net path (default should be fine). use_gpu: Use GPU or CPU. """ def __init__(self, output_layers=('layer3',), net_path='atom_iou', use_gpu=True, *args, **kwargs): super().__init__(*args, **kwargs) self.output_layers = list(output_layers) self.use_gpu = use_gpu self.net_path = net_path def initialize(self): self.net = load_network(self.net_path) if self.use_gpu: self.net.cuda() self.net.eval() self.iou_predictor = self.net.bb_regressor self.layer_stride = {'conv1': 2, 'layer1': 4, 'layer2': 8, 'layer3': 16, 'layer4': 32, 'classification': 16, 'fc': None} self.layer_dim = {'conv1': 64, 'layer1': 64, 'layer2': 128, 'layer3': 256, 'layer4': 512, 'classification': 256, 'fc': None} self.iounet_feature_layers = self.net.bb_regressor_layer if isinstance(self.pool_stride, int) and self.pool_stride == 1: self.pool_stride = [1] * len(self.output_layers) self.feature_layers = sorted(list(set(self.output_layers + self.iounet_feature_layers))) self.mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, -1, 1, 1) self.std = torch.Tensor([0.229, 0.224, 0.225]).view(1, -1, 1, 1) def dim(self): return TensorList([self.layer_dim[l] for l in self.output_layers]) def stride(self): return TensorList([s * self.layer_stride[l] for l, s in zip(self.output_layers, self.pool_stride)]) def extract(self, im: torch.Tensor): im = im / 255 im -= self.mean im /= self.std if self.use_gpu: im = im.cuda() with torch.no_grad(): output_features = self.net.extract_features(im, self.feature_layers) # Store the raw resnet features which are input to iounet self.iounet_backbone_features = TensorList( [output_features[layer].clone() for layer in self.iounet_feature_layers]) # Store the processed features from iounet, just before pooling with torch.no_grad(): self.iounet_features = TensorList(self.iou_predictor.get_iou_feat(self.iounet_backbone_features)) return TensorList([output_features[layer] for layer in self.output_layers]) ================================================ FILE: external/AR/pytracking/features/extractor.py ================================================ import torch from pytracking.features.preprocessing import sample_patch from pytracking import TensorList class ExtractorBase: """Base feature extractor class. args: features: List of features. """ def __init__(self, features): self.features = features def initialize(self): for f in self.features: f.initialize() class SingleResolutionExtractor(ExtractorBase): """Single resolution feature extractor. args: features: List of features. """ def __init__(self, features): super().__init__(features) self.feature_stride = self.features[0].stride() if isinstance(self.feature_stride, (list, TensorList)): self.feature_stride = self.feature_stride[0] def stride(self): return self.feature_stride def size(self, input_sz): return input_sz // self.stride() def extract(self, im, pos, scales, image_sz): if isinstance(scales, (int, float)): scales = [scales] # Get image patches im_patches = torch.cat([sample_patch(im, pos, s*image_sz, image_sz) for s in scales]) # Compute features feature_map = torch.cat(TensorList([f.get_feature(im_patches) for f in self.features]).unroll(), dim=1) return feature_map class MultiResolutionExtractor(ExtractorBase): """Multi-resolution feature extractor. args: features: List of features. """ def __init__(self, features, patch_mode='replicate', max_scale_change=None): super().__init__(features) self.patch_mode = patch_mode self.max_scale_change = max_scale_change self.is_color = None def stride(self): return torch.Tensor(TensorList([f.stride() for f in self.features if self._return_feature(f)]).unroll().list()) def size(self, input_sz): return TensorList([f.size(input_sz) for f in self.features if self._return_feature(f)]).unroll() def dim(self): return TensorList([f.dim() for f in self.features if self._return_feature(f)]).unroll() def get_fparams(self, name: str = None): if name is None: return [f.fparams for f in self.features if self._return_feature(f)] return TensorList([getattr(f.fparams, name) for f in self.features if self._return_feature(f)]).unroll() def get_attribute(self, name: str, ignore_missing: bool = False): if ignore_missing: return TensorList([getattr(f, name) for f in self.features if self._return_feature(f) and hasattr(f, name)]) else: return TensorList([getattr(f, name, None) for f in self.features if self._return_feature(f)]) def get_unique_attribute(self, name: str): feat = None for f in self.features: if self._return_feature(f) and hasattr(f, name): if feat is not None: raise RuntimeError('The attribute was not unique.') feat = f if feat is None: raise RuntimeError('The attribute did not exist') return getattr(feat, name) def _return_feature(self, f): return self.is_color is None or self.is_color and f.use_for_color or not self.is_color and f.use_for_gray def set_is_color(self, is_color: bool): self.is_color = is_color def extract(self, im, pos, scales, image_sz, return_patches=False): """Extract features. args: im: Image. pos: Center position for extraction. scales: Image scales to extract features from. image_sz: Size to resize the image samples to before extraction. """ if isinstance(scales, (int, float)): scales = [scales] # Get image patches patch_iter, coord_iter = zip(*(sample_patch(im, pos, s*image_sz, image_sz, mode=self.patch_mode, max_scale_change=self.max_scale_change) for s in scales)) im_patches = torch.cat(list(patch_iter)) patch_coords = torch.cat(list(coord_iter)) # im_patches = torch.cat([sample_patch(im, pos, s*image_sz, image_sz) for s in scales]) # Compute features feature_map = TensorList([f.get_feature(im_patches) for f in self.features]).unroll() if return_patches: return feature_map, patch_coords, im_patches else: return feature_map, patch_coords def extract_transformed(self, im, pos, scale, image_sz, transforms): """Extract features from a set of transformed image samples. args: im: Image. pos: Center position for extraction. scale: Image scale to extract features from. image_sz: Size to resize the image samples to before extraction. transforms: A set of image transforms to apply. """ # Get image patche im_patch, _ = sample_patch(im, pos, scale*image_sz, image_sz) # Apply transforms im_patches = torch.cat([T(im_patch) for T in transforms]) # Compute features feature_map = TensorList([f.get_feature(im_patches) for f in self.features]).unroll() return feature_map ================================================ FILE: external/AR/pytracking/features/featurebase.py ================================================ import torch import torch.nn.functional as F from pytracking import TensorList class FeatureBase: """Base feature class. args: fparams: Feature specific parameters. pool_stride: Amount of average pooling to apply do downsample the feature map. output_size: Alternatively, specify the output size of the feature map. Adaptive average pooling will be applied. normalize_power: The power exponent for the normalization. None means no normalization (default). use_for_color: Use this feature for color images. use_for_gray: Use this feature for grayscale images. """ def __init__(self, fparams = None, pool_stride = None, output_size = None, normalize_power = None, use_for_color = True, use_for_gray = True): self.fparams = fparams self.pool_stride = 1 if pool_stride is None else pool_stride self.output_size = output_size self.normalize_power = normalize_power self.use_for_color = use_for_color self.use_for_gray = use_for_gray def initialize(self): pass def dim(self): raise NotImplementedError def stride(self): raise NotImplementedError def size(self, im_sz): if self.output_size is None: return im_sz // self.stride() if isinstance(im_sz, torch.Tensor): return torch.Tensor([self.output_size[0], self.output_size[1]]) return self.output_size def extract(self, im): """Performs feature extraction.""" raise NotImplementedError def get_feature(self, im: torch.Tensor): """Get the feature. Generally, call this function. args: im: image patch as a torch.Tensor. """ # Return empty tensor if it should not be used is_color = im.shape[1] == 3 if is_color and not self.use_for_color or not is_color and not self.use_for_gray: return torch.Tensor([]) # Extract feature feat = self.extract(im) # Pool/downsample if self.output_size is not None: feat = F.adaptive_avg_pool2d(feat, self.output_size) elif self.pool_stride != 1: feat = F.avg_pool2d(feat, self.pool_stride, self.pool_stride) # Normalize if self.normalize_power is not None: feat /= (torch.sum(feat.abs().view(feat.shape[0],1,1,-1)**self.normalize_power, dim=3, keepdim=True) / (feat.shape[1]*feat.shape[2]*feat.shape[3]) + 1e-10)**(1/self.normalize_power) return feat class MultiFeatureBase(FeatureBase): """Base class for features potentially having multiple feature blocks as output (like CNNs). See FeatureBase for more info. """ def size(self, im_sz): if self.output_size is None: return TensorList([im_sz // s for s in self.stride()]) if isinstance(im_sz, torch.Tensor): return TensorList([im_sz // s if sz is None else torch.Tensor([sz[0], sz[1]]) for sz, s in zip(self.output_size, self.stride())]) def get_feature(self, im: torch.Tensor): """Get the feature. Generally, call this function. args: im: image patch as a torch.Tensor. """ # Return empty tensor if it should not be used is_color = im.shape[1] == 3 if is_color and not self.use_for_color or not is_color and not self.use_for_gray: return torch.Tensor([]) feat_list = self.extract(im) output_sz = [None]*len(feat_list) if self.output_size is None else self.output_size # Pool/downsample for i, (sz, s) in enumerate(zip(output_sz, self.pool_stride)): if sz is not None: feat_list[i] = F.adaptive_avg_pool2d(feat_list[i], sz) elif s != 1: feat_list[i] = F.avg_pool2d(feat_list[i], s, s) # Normalize if self.normalize_power is not None: for feat in feat_list: feat /= (torch.sum(feat.abs().view(feat.shape[0],1,1,-1)**self.normalize_power, dim=3, keepdim=True) / (feat.shape[1]*feat.shape[2]*feat.shape[3]) + 1e-10)**(1/self.normalize_power) return feat_list ================================================ FILE: external/AR/pytracking/features/net_wrappers.py ================================================ import torch from pytracking.utils.loading import load_network class NetWrapper: """Used for wrapping networks in pytracking. Network modules and functions can be accessed directly as if they were members of this class.""" _rec_iter=0 def __init__(self, net_path, use_gpu=True, initialize=False, **kwargs): self.net_path = net_path self.use_gpu = use_gpu self.net = None self.net_kwargs = kwargs if initialize: self.initialize() def __getattr__(self, name): if self._rec_iter > 0: self._rec_iter = 0 return None self._rec_iter += 1 try: ret_val = getattr(self.net, name) except Exception as e: self._rec_iter = 0 raise e self._rec_iter = 0 return ret_val def load_network(self): self.net = load_network(self.net_path, **self.net_kwargs) if self.use_gpu: self.cuda() self.eval() def initialize(self): self.load_network() class NetWithBackbone(NetWrapper): """Wraps a network with a common backbone. Assumes the network have a 'extract_backbone_features(image)' function.""" def __init__(self, net_path, use_gpu=True, initialize=False, image_format='rgb', mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), **kwargs): super().__init__(net_path, use_gpu, initialize, **kwargs) self.image_format = image_format self._mean = torch.Tensor(mean).view(1, -1, 1, 1) self._std = torch.Tensor(std).view(1, -1, 1, 1) def initialize(self, image_format='rgb', mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)): super().initialize() def preprocess_image(self, im: torch.Tensor): """Normalize the image with the mean and standard deviation used by the network.""" if self.image_format in ['rgb', 'bgr']: im = im/255 if self.image_format in ['bgr', 'bgr255']: im = im[:, [2, 1, 0], :, :] im -= self._mean im /= self._std if self.use_gpu: im = im.cuda() return im def extract_backbone(self, im: torch.Tensor): """Extract backbone features from the network. Expects a float tensor image with pixel range [0, 255].""" im = self.preprocess_image(im) return self.net.extract_backbone_features(im) ================================================ FILE: external/AR/pytracking/features/preprocessing.py ================================================ import torch import torch.nn.functional as F import numpy as np def numpy_to_torch(a: np.ndarray): return torch.from_numpy(a).float().permute(2, 0, 1).unsqueeze(0) def torch_to_numpy(a: torch.Tensor): return a.squeeze(0).permute(1,2,0).numpy() def sample_patch_transformed(im, pos, scale, image_sz, transforms, is_mask=False): """Extract transformed image samples. args: im: Image. pos: Center position for extraction. scale: Image scale to extract features from. image_sz: Size to resize the image samples to before extraction. transforms: A set of image transforms to apply. """ # Get image patche im_patch, _ = sample_patch(im, pos, scale*image_sz, image_sz, is_mask=is_mask) # Apply transforms im_patches = torch.cat([T(im_patch, is_mask=is_mask) for T in transforms]) return im_patches def sample_patch_multiscale(im, pos, scales, image_sz, mode: str='replicate', max_scale_change=None): """Extract image patches at multiple scales. args: im: Image. pos: Center position for extraction. scales: Image scales to extract image patches from. image_sz: Size to resize the image samples to mode: how to treat image borders: 'replicate' (default), 'inside' or 'inside_major' max_scale_change: maximum allowed scale change when using 'inside' and 'inside_major' mode """ if isinstance(scales, (int, float)): scales = [scales] # Get image patches patch_iter, coord_iter = zip(*(sample_patch(im, pos, s*image_sz, image_sz, mode=mode, max_scale_change=max_scale_change) for s in scales)) im_patches = torch.cat(list(patch_iter)) patch_coords = torch.cat(list(coord_iter)) return im_patches, patch_coords def sample_patch(im: torch.Tensor, pos: torch.Tensor, sample_sz: torch.Tensor, output_sz: torch.Tensor = None, mode: str = 'replicate', max_scale_change=None, is_mask=False): """Sample an image patch. args: im: Image pos: center position of crop sample_sz: size to crop output_sz: size to resize to mode: how to treat image borders: 'replicate' (default), 'inside' or 'inside_major' max_scale_change: maximum allowed scale change when using 'inside' and 'inside_major' mode """ # if mode not in ['replicate', 'inside']: # raise ValueError('Unknown border mode \'{}\'.'.format(mode)) # copy and convert posl = pos.long().clone() pad_mode = mode # Get new sample size if forced inside the image if mode == 'inside' or mode == 'inside_major': pad_mode = 'replicate' im_sz = torch.Tensor([im.shape[2], im.shape[3]]) shrink_factor = (sample_sz.float() / im_sz) if mode == 'inside': shrink_factor = shrink_factor.max() elif mode == 'inside_major': shrink_factor = shrink_factor.min() shrink_factor.clamp_(min=1, max=max_scale_change) sample_sz = (sample_sz.float() / shrink_factor).long() # Compute pre-downsampling factor if output_sz is not None: resize_factor = torch.min(sample_sz.float() / output_sz.float()).item() df = int(max(int(resize_factor - 0.1), 1)) else: df = int(1) sz = sample_sz.float() / df # new size # Do downsampling if df > 1: os = posl % df # offset posl = (posl - os) / df # new position im2 = im[..., os[0].item()::df, os[1].item()::df] # downsample else: im2 = im # compute size to crop szl = torch.max(sz.round(), torch.Tensor([2])).long() # Extract top and bottom coordinates tl = posl - (szl - 1)/2 br = posl + szl/2 + 1 # Shift the crop to inside if mode == 'inside' or mode == 'inside_major': im2_sz = torch.LongTensor([im2.shape[2], im2.shape[3]]) shift = (-tl).clamp(0) - (br - im2_sz).clamp(0) tl += shift br += shift outside = ((-tl).clamp(0) + (br - im2_sz).clamp(0)) // 2 shift = (-tl - outside) * (outside > 0).long() tl += shift br += shift # Get image patch # im_patch = im2[...,tl[0].item():br[0].item(),tl[1].item():br[1].item()] # Get image patch if not is_mask: im_patch = F.pad(im2, (-tl[1].item(), br[1].item() - im2.shape[3], -tl[0].item(), br[0].item() - im2.shape[2]), pad_mode) else: im_patch = F.pad(im2, (-tl[1].item(), br[1].item() - im2.shape[3], -tl[0].item(), br[0].item() - im2.shape[2])) # Get image coordinates patch_coord = df * torch.cat((tl, br)).view(1,4) if output_sz is None or (im_patch.shape[-2] == output_sz[0] and im_patch.shape[-1] == output_sz[1]): return im_patch.clone(), patch_coord # Resample if not is_mask: im_patch = F.interpolate(im_patch, output_sz.long().tolist(), mode='bilinear') else: im_patch = F.interpolate(im_patch, output_sz.long().tolist(), mode='nearest') return im_patch, patch_coord ================================================ FILE: external/AR/pytracking/features/util.py ================================================ import torch from pytracking.features.featurebase import FeatureBase class Concatenate(FeatureBase): """A feature that concatenates other features. args: features: List of features to concatenate. """ def __init__(self, features, pool_stride = None, normalize_power = None, use_for_color = True, use_for_gray = True): super(Concatenate, self).__init__(pool_stride, normalize_power, use_for_color, use_for_gray) self.features = features self.input_stride = self.features[0].stride() for feat in self.features: if self.input_stride != feat.stride(): raise ValueError('Strides for the features must be the same for a bultiresolution feature.') def dim(self): return sum([f.dim() for f in self.features]) def stride(self): return self.pool_stride * self.input_stride def extract(self, im: torch.Tensor): return torch.cat([f.get_feature(im) for f in self.features], 1) ================================================ FILE: external/AR/pytracking/libs/__init__.py ================================================ from .tensorlist import TensorList from .tensordict import TensorDict ================================================ FILE: external/AR/pytracking/libs/complex.py ================================================ import torch from pytracking.libs.tensorlist import tensor_operation def is_complex(a: torch.Tensor) -> bool: return a.dim() >= 4 and a.shape[-1] == 2 def is_real(a: torch.Tensor) -> bool: return not is_complex(a) @tensor_operation def mult(a: torch.Tensor, b: torch.Tensor): """Pointwise complex multiplication of complex tensors.""" if is_real(a): if a.dim() >= b.dim(): raise ValueError('Incorrect dimensions.') # a is real return mult_real_cplx(a, b) if is_real(b): if b.dim() >= a.dim(): raise ValueError('Incorrect dimensions.') # b is real return mult_real_cplx(b, a) # Both complex c = mult_real_cplx(a[..., 0], b) c[..., 0] -= a[..., 1] * b[..., 1] c[..., 1] += a[..., 1] * b[..., 0] return c @tensor_operation def mult_conj(a: torch.Tensor, b: torch.Tensor): """Pointwise complex multiplication of complex tensors, with conjugate on b: a*conj(b).""" if is_real(a): if a.dim() >= b.dim(): raise ValueError('Incorrect dimensions.') # a is real return mult_real_cplx(a, conj(b)) if is_real(b): if b.dim() >= a.dim(): raise ValueError('Incorrect dimensions.') # b is real return mult_real_cplx(b, a) # Both complex c = mult_real_cplx(b[...,0], a) c[..., 0] += a[..., 1] * b[..., 1] c[..., 1] -= a[..., 0] * b[..., 1] return c @tensor_operation def mult_real_cplx(a: torch.Tensor, b: torch.Tensor): """Pointwise complex multiplication of real tensor a with complex tensor b.""" if is_real(b): raise ValueError('Last dimension must have length 2.') return a.unsqueeze(-1) * b @tensor_operation def div(a: torch.Tensor, b: torch.Tensor): """Pointwise complex division of complex tensors.""" if is_real(b): if b.dim() >= a.dim(): raise ValueError('Incorrect dimensions.') # b is real return div_cplx_real(a, b) return div_cplx_real(mult_conj(a, b), abs_sqr(b)) @tensor_operation def div_cplx_real(a: torch.Tensor, b: torch.Tensor): """Pointwise complex division of complex tensor a with real tensor b.""" if is_real(a): raise ValueError('Last dimension must have length 2.') return a / b.unsqueeze(-1) @tensor_operation def abs_sqr(a: torch.Tensor): """Squared absolute value.""" if is_real(a): raise ValueError('Last dimension must have length 2.') return torch.sum(a*a, -1) @tensor_operation def abs(a: torch.Tensor): """Absolute value.""" if is_real(a): raise ValueError('Last dimension must have length 2.') return torch.sqrt(abs_sqr(a)) @tensor_operation def conj(a: torch.Tensor): """Complex conjugate.""" if is_real(a): raise ValueError('Last dimension must have length 2.') # return a * torch.Tensor([1, -1], device=a.device) return complex(a[...,0], -a[...,1]) @tensor_operation def real(a: torch.Tensor): """Real part.""" if is_real(a): raise ValueError('Last dimension must have length 2.') return a[..., 0] @tensor_operation def imag(a: torch.Tensor): """Imaginary part.""" if is_real(a): raise ValueError('Last dimension must have length 2.') return a[..., 1] @tensor_operation def complex(a: torch.Tensor, b: torch.Tensor = None): """Create complex tensor from real and imaginary part.""" if b is None: b = a.new_zeros(a.shape) elif a is None: a = b.new_zeros(b.shape) return torch.cat((a.unsqueeze(-1), b.unsqueeze(-1)), -1) @tensor_operation def mtimes(a: torch.Tensor, b: torch.Tensor, conj_a=False, conj_b=False): """Complex matrix multiplication of complex tensors. The dimensions (-3, -2) are matrix multiplied. -1 is the complex dimension.""" if is_real(a): if a.dim() >= b.dim(): raise ValueError('Incorrect dimensions.') return mtimes_real_complex(a, b, conj_b=conj_b) if is_real(b): if b.dim() >= a.dim(): raise ValueError('Incorrect dimensions.') return mtimes_complex_real(a, b, conj_a=conj_a) if not conj_a and not conj_b: return complex(torch.matmul(a[..., 0], b[..., 0]) - torch.matmul(a[..., 1], b[..., 1]), torch.matmul(a[..., 0], b[..., 1]) + torch.matmul(a[..., 1], b[..., 0])) if conj_a and not conj_b: return complex(torch.matmul(a[..., 0], b[..., 0]) + torch.matmul(a[..., 1], b[..., 1]), torch.matmul(a[..., 0], b[..., 1]) - torch.matmul(a[..., 1], b[..., 0])) if not conj_a and conj_b: return complex(torch.matmul(a[..., 0], b[..., 0]) + torch.matmul(a[..., 1], b[..., 1]), torch.matmul(a[..., 1], b[..., 0]) - torch.matmul(a[..., 0], b[..., 1])) if conj_a and conj_b: return complex(torch.matmul(a[..., 0], b[..., 0]) - torch.matmul(a[..., 1], b[..., 1]), -torch.matmul(a[..., 0], b[..., 1]) - torch.matmul(a[..., 1], b[..., 0])) @tensor_operation def mtimes_real_complex(a: torch.Tensor, b: torch.Tensor, conj_b=False): if is_real(b): raise ValueError('Incorrect dimensions.') if not conj_b: return complex(torch.matmul(a, b[..., 0]), torch.matmul(a, b[..., 1])) if conj_b: return complex(torch.matmul(a, b[..., 0]), -torch.matmul(a, b[..., 1])) @tensor_operation def mtimes_complex_real(a: torch.Tensor, b: torch.Tensor, conj_a=False): if is_real(a): raise ValueError('Incorrect dimensions.') if not conj_a: return complex(torch.matmul(a[..., 0], b), torch.matmul(a[..., 1], b)) if conj_a: return complex(torch.matmul(a[..., 0], b), -torch.matmul(a[..., 1], b)) @tensor_operation def exp_imag(a: torch.Tensor): """Complex exponential with imaginary input: e^(i*a)""" a = a.unsqueeze(-1) return torch.cat((torch.cos(a), torch.sin(a)), -1) ================================================ FILE: external/AR/pytracking/libs/dcf.py ================================================ import torch import math from pytracking import fourier from pytracking import complex import torch.nn.functional as F def hann1d(sz: int, centered = True) -> torch.Tensor: """1D cosine window.""" if centered: return 0.5 * (1 - torch.cos((2 * math.pi / (sz + 1)) * torch.arange(1, sz + 1).float())) w = 0.5 * (1 + torch.cos((2 * math.pi / (sz + 2)) * torch.arange(0, sz//2 + 1).float())) return torch.cat([w, w[1:sz-sz//2].flip((0,))]) def hann2d(sz: torch.Tensor, centered = True) -> torch.Tensor: """2D cosine window.""" return hann1d(sz[0].item(), centered).reshape(1, 1, -1, 1) * hann1d(sz[1].item(), centered).reshape(1, 1, 1, -1) def hann2d_clipped(sz: torch.Tensor, effective_sz: torch.Tensor, centered = True) -> torch.Tensor: """1D clipped cosine window.""" # Ensure that the difference is even effective_sz += (effective_sz - sz) % 2 effective_window = hann1d(effective_sz[0].item(), True).reshape(1, 1, -1, 1) * hann1d(effective_sz[1].item(), True).reshape(1, 1, 1, -1) pad = (sz - effective_sz) / 2 window = F.pad(effective_window, (pad[1].item(), pad[1].item(), pad[0].item(), pad[0].item()), 'replicate') if centered: return window else: mid = (sz / 2).int() window_shift_lr = torch.cat((window[:, :, :, mid[1]:], window[:, :, :, :mid[1]]), 3) return torch.cat((window_shift_lr[:, :, mid[0]:, :], window_shift_lr[:, :, :mid[0], :]), 2) def gauss_fourier(sz: int, sigma: float, half: bool = False) -> torch.Tensor: if half: k = torch.arange(0, int(sz/2+1)) else: k = torch.arange(-int((sz-1)/2), int(sz/2+1)) return (math.sqrt(2*math.pi) * sigma / sz) * torch.exp(-2 * (math.pi * sigma * k.float() / sz)**2) def gauss_spatial(sz, sigma, center=0, end_pad=0): k = torch.arange(-(sz-1)/2, (sz+1)/2+end_pad) return torch.exp(-1.0/(2*sigma**2) * (k - center)**2) def label_function(sz: torch.Tensor, sigma: torch.Tensor): return gauss_fourier(sz[0].item(), sigma[0].item()).reshape(1, 1, -1, 1) * gauss_fourier(sz[1].item(), sigma[1].item(), True).reshape(1, 1, 1, -1) def label_function_spatial(sz: torch.Tensor, sigma: torch.Tensor, center: torch.Tensor = torch.zeros(2), end_pad: torch.Tensor = torch.zeros(2)): """The origin is in the middle of the image.""" return gauss_spatial(sz[0].item(), sigma[0].item(), center[0], end_pad[0].item()).reshape(1, 1, -1, 1) * \ gauss_spatial(sz[1].item(), sigma[1].item(), center[1], end_pad[1].item()).reshape(1, 1, 1, -1) def cubic_spline_fourier(f, a): """The continuous Fourier transform of a cubic spline kernel.""" bf = (6*(1 - torch.cos(2 * math.pi * f)) + 3*a*(1 - torch.cos(4 * math.pi * f)) - (6 + 8*a)*math.pi*f*torch.sin(2 * math.pi * f) - 2*a*math.pi*f*torch.sin(4 * math.pi * f)) \ / (4 * math.pi**4 * f**4) bf[f == 0] = 1 return bf def get_interp_fourier(sz: torch.Tensor, method='ideal', bicubic_param=0.5, centering=True, windowing=False, device='cpu'): ky, kx = fourier.get_frequency_coord(sz) if method=='ideal': interp_y = torch.ones(ky.shape) / sz[0] interp_x = torch.ones(kx.shape) / sz[1] elif method=='bicubic': interp_y = cubic_spline_fourier(ky / sz[0], bicubic_param) / sz[0] interp_x = cubic_spline_fourier(kx / sz[1], bicubic_param) / sz[1] else: raise ValueError('Unknown method.') if centering: interp_y = complex.mult(interp_y, complex.exp_imag((-math.pi/sz[0]) * ky)) interp_x = complex.mult(interp_x, complex.exp_imag((-math.pi/sz[1]) * kx)) if windowing: raise NotImplementedError return interp_y.to(device), interp_x.to(device) def interpolate_dft(a: torch.Tensor, interp_fs) -> torch.Tensor: if isinstance(interp_fs, torch.Tensor): return complex.mult(a, interp_fs) if isinstance(interp_fs, (tuple, list)): return complex.mult(complex.mult(a, interp_fs[0]), interp_fs[1]) raise ValueError('"interp_fs" must be tensor or tuple of tensors.') def get_reg_filter(sz: torch.Tensor, target_sz: torch.Tensor, params): """Computes regularization filter in CCOT and ECO.""" if not params.use_reg_window: return params.reg_window_min * torch.ones(1,1,1,1) if getattr(params, 'reg_window_square', False): target_sz = target_sz.prod().sqrt() * torch.ones(2) # Normalization factor reg_scale = 0.5 * target_sz # Construct grid if getattr(params, 'reg_window_centered', True): wrg = torch.arange(-int((sz[0]-1)/2), int(sz[0]/2+1), dtype=torch.float32).view(1,1,-1,1) wcg = torch.arange(-int((sz[1]-1)/2), int(sz[1]/2+1), dtype=torch.float32).view(1,1,1,-1) else: wrg = torch.cat([torch.arange(0, int(sz[0]/2+1), dtype=torch.float32), torch.arange(-int((sz[0] - 1) / 2), 0, dtype=torch.float32)]).view(1,1,-1,1) wcg = torch.cat([torch.arange(0, int(sz[1]/2+1), dtype=torch.float32), torch.arange(-int((sz[1] - 1) / 2), 0, dtype=torch.float32)]).view(1,1,1,-1) # Construct regularization window reg_window = (params.reg_window_edge - params.reg_window_min) * \ (torch.abs(wrg/reg_scale[0])**params.reg_window_power + torch.abs(wcg/reg_scale[1])**params.reg_window_power) + params.reg_window_min # Compute DFT and enforce sparsity reg_window_dft = torch.rfft(reg_window, 2) / sz.prod() reg_window_dft_abs = complex.abs(reg_window_dft) reg_window_dft[reg_window_dft_abs < params.reg_sparsity_threshold * reg_window_dft_abs.max(), :] = 0 # Do the inverse transform to correct for the window minimum reg_window_sparse = torch.irfft(reg_window_dft, 2, signal_sizes=sz.long().tolist()) reg_window_dft[0,0,0,0,0] += params.reg_window_min - sz.prod() * reg_window_sparse.min() reg_window_dft = complex.real(fourier.rfftshift2(reg_window_dft)) # Remove zeros max_inds,_ = reg_window_dft.nonzero().max(dim=0) mid_ind = int((reg_window_dft.shape[2]-1)/2) top = max_inds[-2].item() + 1 bottom = 2*mid_ind - max_inds[-2].item() right = max_inds[-1].item() + 1 reg_window_dft = reg_window_dft[..., bottom:top, :right] if reg_window_dft.shape[-1] > 1: reg_window_dft = torch.cat([reg_window_dft[..., 1:].flip((2, 3)), reg_window_dft], -1) return reg_window_dft def max2d(a: torch.Tensor) -> (torch.Tensor, torch.Tensor): """Computes maximum and argmax in the last two dimensions.""" max_val_row, argmax_row = torch.max(a, dim=-2) max_val, argmax_col = torch.max(max_val_row, dim=-1) argmax_row = argmax_row.view(argmax_col.numel(),-1)[torch.arange(argmax_col.numel()), argmax_col.view(-1)] argmax_row = argmax_row.reshape(argmax_col.shape) argmax = torch.cat((argmax_row.unsqueeze(-1), argmax_col.unsqueeze(-1)), -1) return max_val, argmax ================================================ FILE: external/AR/pytracking/libs/fourier.py ================================================ import torch import torch.nn.functional as F from pytracking import complex, TensorList from pytracking.libs.tensorlist import tensor_operation @tensor_operation def rfftshift2(a: torch.Tensor): h = a.shape[2] + 2 return torch.cat((a[:,:,(h-1)//2:,...], a[:,:,:h//2,...]), 2) @tensor_operation def irfftshift2(a: torch.Tensor): mid = int((a.shape[2]-1)/2) return torch.cat((a[:,:,mid:,...], a[:,:,:mid,...]), 2) @tensor_operation def cfft2(a): """Do FFT and center the low frequency component. Always produces odd (full) output sizes.""" return rfftshift2(torch.rfft(a, 2)) @tensor_operation def cifft2(a, signal_sizes=None): """Do inverse FFT corresponding to cfft2.""" return torch.irfft(irfftshift2(a), 2, signal_sizes=signal_sizes) @tensor_operation def sample_fs(a: torch.Tensor, grid_sz: torch.Tensor = None, rescale = True): """Samples the Fourier series.""" # Size of the fourier series sz = torch.Tensor([a.shape[2], 2*a.shape[3]-1]).float() # Default grid if grid_sz is None or sz[0] == grid_sz[0] and sz[1] == grid_sz[1]: if rescale: return sz.prod().item() * cifft2(a) return cifft2(a) if sz[0] > grid_sz[0] or sz[1] > grid_sz[1]: raise ValueError("Only grid sizes that are smaller than the Fourier series size are supported.") tot_pad = (grid_sz - sz).tolist() is_even = [s.item() % 2 == 0 for s in sz] # Compute paddings pad_top = int((tot_pad[0]+1)/2) if is_even[0] else int(tot_pad[0]/2) pad_bottom = int(tot_pad[0] - pad_top) pad_right = int((tot_pad[1]+1)/2) if rescale: return grid_sz.prod().item() * cifft2(F.pad(a, (0, 0, 0, pad_right, pad_top, pad_bottom)), signal_sizes=grid_sz.long().tolist()) else: return cifft2(F.pad(a, (0, 0, 0, pad_right, pad_top, pad_bottom)), signal_sizes=grid_sz.long().tolist()) def get_frequency_coord(sz, add_complex_dim = False, device='cpu'): """Frequency coordinates.""" ky = torch.arange(-int((sz[0]-1)/2), int(sz[0]/2+1), dtype=torch.float32, device=device).view(1,1,-1,1) kx = torch.arange(0, int(sz[1]/2+1), dtype=torch.float32, device=device).view(1,1,1,-1) if add_complex_dim: ky = ky.unsqueeze(-1) kx = kx.unsqueeze(-1) return ky, kx @tensor_operation def shift_fs(a: torch.Tensor, shift: torch.Tensor): """Shift a sample a in the Fourier domain. Params: a : The fourier coefficiens of the sample. shift : The shift to be performed normalized to the range [-pi, pi].""" if a.dim() != 5: raise ValueError('a must be the Fourier coefficients, a 5-dimensional tensor.') if shift[0] == 0 and shift[1] == 0: return a ky, kx = get_frequency_coord((a.shape[2], 2*a.shape[3]-1), device=a.device) return complex.mult(complex.mult(a, complex.exp_imag(shift[0].item()*ky)), complex.exp_imag(shift[1].item()*kx)) def sum_fs(a: TensorList) -> torch.Tensor: """Sum a list of Fourier series expansions.""" s = None mid = None for e in sorted(a, key=lambda elem: elem.shape[-3], reverse=True): if s is None: s = e.clone() mid = int((s.shape[-3] - 1) / 2) else: # Compute coordinates top = mid - int((e.shape[-3] - 1) / 2) bottom = mid + int(e.shape[-3] / 2) + 1 right = e.shape[-2] # Add the data s[..., top:bottom, :right, :] += e return s def sum_fs12(a: TensorList) -> torch.Tensor: """Sum a list of Fourier series expansions.""" s = None mid = None for e in sorted(a, key=lambda elem: elem.shape[0], reverse=True): if s is None: s = e.clone() mid = int((s.shape[0] - 1) / 2) else: # Compute coordinates top = mid - int((e.shape[0] - 1) / 2) bottom = mid + int(e.shape[0] / 2) + 1 right = e.shape[1] # Add the data s[top:bottom, :right, ...] += e return s @tensor_operation def inner_prod_fs(a: torch.Tensor, b: torch.Tensor): if complex.is_complex(a) and complex.is_complex(b): return 2 * (a.reshape(-1) @ b.reshape(-1)) - a[:, :, :, 0, :].reshape(-1) @ b[:, :, :, 0, :].reshape(-1) elif complex.is_real(a) and complex.is_real(b): return 2 * (a.reshape(-1) @ b.reshape(-1)) - a[:, :, :, 0].reshape(-1) @ b[:, :, :, 0].reshape(-1) else: raise NotImplementedError('Not implemented for mixed real and complex.') ================================================ FILE: external/AR/pytracking/libs/operation.py ================================================ import torch import torch.nn.functional as F from pytracking.libs.tensorlist import tensor_operation, TensorList @tensor_operation def conv2d(input: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor = None, stride=1, padding=0, dilation=1, groups=1, mode=None): """Standard conv2d. Returns the input if weight=None.""" if weight is None: return input ind = None if mode is not None: if padding != 0: raise ValueError('Cannot input both padding and mode.') if mode == 'same': padding = (weight.shape[2]//2, weight.shape[3]//2) if weight.shape[2] % 2 == 0 or weight.shape[3] % 2 == 0: ind = (slice(-1) if weight.shape[2] % 2 == 0 else slice(None), slice(-1) if weight.shape[3] % 2 == 0 else slice(None)) elif mode == 'valid': padding = (0, 0) elif mode == 'full': padding = (weight.shape[2]-1, weight.shape[3]-1) else: raise ValueError('Unknown mode for padding.') out = F.conv2d(input, weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups) if ind is None: return out return out[:,:,ind[0],ind[1]] @tensor_operation def conv1x1(input: torch.Tensor, weight: torch.Tensor): """Do a convolution with a 1x1 kernel weights. Implemented with matmul, which can be faster than using conv.""" if weight is None: return input return torch.conv2d(input, weight) ================================================ FILE: external/AR/pytracking/libs/optimization.py ================================================ import torch import torch.autograd import math from pytracking.libs import TensorList from pytracking.utils.plotting import plot_graph from ltr.models.layers.activation import softmax_reg class L2Problem: """Base class for representing an L2 optimization problem.""" def __call__(self, x: TensorList) -> TensorList: """Shall compute the residuals of the problem.""" raise NotImplementedError def ip_input(self, a, b): """Inner product of the input space.""" return sum(a.view(-1) @ b.view(-1)) def ip_output(self, a, b): """Inner product of the output space.""" return sum(a.view(-1) @ b.view(-1)) def M1(self, x): """M1 preconditioner.""" return x def M2(self, x): """M2 preconditioner.""" return x class MinimizationProblem: """General minimization problem.""" def __call__(self, x: TensorList) -> TensorList: """Shall compute the loss.""" raise NotImplementedError def ip_input(self, a, b): """Inner product of the input space.""" return sum(a.view(-1) @ b.view(-1)) def M1(self, x): return x def M2(self, x): return x class ConjugateGradientBase: """Conjugate Gradient optimizer base class. Implements the CG loop.""" def __init__(self, fletcher_reeves = True, standard_alpha = True, direction_forget_factor = 0, debug = False): self.fletcher_reeves = fletcher_reeves self.standard_alpha = standard_alpha self.direction_forget_factor = direction_forget_factor self.debug = debug # State self.p = None self.rho = torch.ones(1) self.r_prev = None # Right hand side self.b = None def reset_state(self): self.p = None self.rho = torch.ones(1) self.r_prev = None def run_CG(self, num_iter, x=None, eps=0.0): """Main conjugate gradient method. args: num_iter: Number of iterations. x: Initial guess. Assumed zero if None. eps: Stop if the residual norm gets smaller than this. """ # Apply forgetting factor if self.direction_forget_factor == 0: self.reset_state() elif self.p is not None: self.rho /= self.direction_forget_factor if x is None: r = self.b.clone() else: r = self.b - self.A(x) # Norms of residuals etc for debugging resvec = None if self.debug: normr = self.residual_norm(r) resvec = torch.zeros(num_iter+1) resvec[0] = normr # Loop over iterations for ii in range(num_iter): # Preconditioners y = self.M1(r) z = self.M2(y) rho1 = self.rho self.rho = self.ip(r, z) if self.check_zero(self.rho): if self.debug: print('Stopped CG since rho = 0') if resvec is not None: resvec = resvec[:ii+1] return x, resvec if self.p is None: self.p = z.clone() else: if self.fletcher_reeves: beta = self.rho / rho1 else: rho2 = self.ip(self.r_prev, z) beta = (self.rho - rho2) / rho1 beta = beta.clamp(0) self.p = z + self.p * beta q = self.A(self.p) pq = self.ip(self.p, q) if self.standard_alpha: alpha = self.rho / pq else: alpha = self.ip(self.p, r) / pq # Save old r for PR formula if not self.fletcher_reeves: self.r_prev = r.clone() # Form new iterate if x is None: x = self.p * alpha else: x += self.p * alpha if ii < num_iter - 1 or self.debug: r -= q * alpha if eps > 0.0 or self.debug: normr = self.residual_norm(r) if self.debug: self.evaluate_CG_iteration(x) resvec[ii+1] = normr if eps > 0 and normr <= eps: if self.debug: print('Stopped CG since norm smaller than eps') break if resvec is not None: resvec = resvec[:ii+2] return x, resvec def A(self, x): # Implements the left hand operation raise NotImplementedError def ip(self, a, b): # Implements the inner product return a.view(-1) @ b.view(-1) def residual_norm(self, r): res = self.ip(r, r).sum() if isinstance(res, (TensorList, list, tuple)): res = sum(res) return res.sqrt() def check_zero(self, s, eps = 0.0): ss = s.abs() <= eps if isinstance(ss, (TensorList, list, tuple)): ss = sum(ss) return ss.item() > 0 def M1(self, x): # M1 preconditioner return x def M2(self, x): # M2 preconditioner return x def evaluate_CG_iteration(self, x): pass class ConjugateGradient(ConjugateGradientBase): """Conjugate Gradient optimizer, performing single linearization of the residuals in the start.""" def __init__(self, problem: L2Problem, variable: TensorList, cg_eps = 0.0, fletcher_reeves = True, standard_alpha = True, direction_forget_factor = 0, debug = False, plotting = False, visdom=None): super().__init__(fletcher_reeves, standard_alpha, direction_forget_factor, debug or plotting) self.problem = problem self.x = variable self.plotting = plotting self.fig_num = (10,11) self.visdom = visdom self.cg_eps = cg_eps self.f0 = None self.g = None self.dfdxt_g = None self.residuals = torch.zeros(0) self.losses = torch.zeros(0) def clear_temp(self): self.f0 = None self.g = None self.dfdxt_g = None def run(self, num_cg_iter): """Run the oprimizer with the provided number of iterations.""" if num_cg_iter == 0: return lossvec = None if self.debug: lossvec = torch.zeros(2) self.x.requires_grad_(True) # Evaluate function at current estimate self.f0 = self.problem(self.x) # Create copy with graph detached self.g = self.f0.detach() if self.debug: lossvec[0] = self.problem.ip_output(self.g, self.g) self.g.requires_grad_(True) # Get df/dx^t @ f0 self.dfdxt_g = TensorList(torch.autograd.grad(self.f0, self.x, self.g, create_graph=True)) # Get the right hand side self.b = - self.dfdxt_g.detach() # Run CG delta_x, res = self.run_CG(num_cg_iter, eps=self.cg_eps) self.x.detach_() self.x += delta_x if self.debug: self.f0 = self.problem(self.x) lossvec[-1] = self.problem.ip_output(self.f0, self.f0) self.residuals = torch.cat((self.residuals, res)) self.losses = torch.cat((self.losses, lossvec)) if self.visdom is not None: self.visdom.register(self.losses, 'lineplot', 3, 'Loss') self.visdom.register(self.residuals, 'lineplot', 3, 'CG residuals') elif self.plotting: plot_graph(self.losses, self.fig_num[0], title='Loss') plot_graph(self.residuals, self.fig_num[1], title='CG residuals') self.x.detach_() self.clear_temp() def A(self, x): dfdx_x = torch.autograd.grad(self.dfdxt_g, self.g, x, retain_graph=True) return TensorList(torch.autograd.grad(self.f0, self.x, dfdx_x, retain_graph=True)) def ip(self, a, b): return self.problem.ip_input(a, b) def M1(self, x): return self.problem.M1(x) def M2(self, x): return self.problem.M2(x) class GaussNewtonCG(ConjugateGradientBase): """Gauss-Newton with Conjugate Gradient optimizer.""" def __init__(self, problem: L2Problem, variable: TensorList, cg_eps = 0.0, fletcher_reeves = True, standard_alpha = True, direction_forget_factor = 0, debug = False, analyze = False, plotting = False, visdom=None): super().__init__(fletcher_reeves, standard_alpha, direction_forget_factor, debug or analyze or plotting) self.problem = problem self.x = variable self.analyze_convergence = analyze self.plotting = plotting self.fig_num = (10,11,12) self.visdom = visdom self.cg_eps = cg_eps self.f0 = None self.g = None self.dfdxt_g = None self.residuals = torch.zeros(0) self.losses = torch.zeros(0) self.gradient_mags = torch.zeros(0) def clear_temp(self): self.f0 = None self.g = None self.dfdxt_g = None def run_GN(self, *args, **kwargs): return self.run(*args, **kwargs) def run(self, num_cg_iter, num_gn_iter=None): """Run the optimizer. args: num_cg_iter: Number of CG iterations per GN iter. If list, then each entry specifies number of CG iterations and number of GN iterations is given by the length of the list. num_gn_iter: Number of GN iterations. Shall only be given if num_cg_iter is an integer. """ if isinstance(num_cg_iter, int): if num_gn_iter is None: raise ValueError('Must specify number of GN iter if CG iter is constant') num_cg_iter = [num_cg_iter]*num_gn_iter num_gn_iter = len(num_cg_iter) if num_gn_iter == 0: return if self.analyze_convergence: self.evaluate_CG_iteration(0) # Outer loop for running the GN iterations. for cg_iter in num_cg_iter: self.run_GN_iter(cg_iter) if self.debug: if not self.analyze_convergence: self.f0 = self.problem(self.x) loss = self.problem.ip_output(self.f0, self.f0) self.losses = torch.cat((self.losses, loss.detach().cpu().view(-1))) if self.visdom is not None: self.visdom.register(self.losses, 'lineplot', 3, 'Loss') self.visdom.register(self.residuals, 'lineplot', 3, 'CG residuals') if self.analyze_convergence: self.visdom.register(self.gradient_mags, 'lineplot', 4, 'Gradient magnitude') elif self.plotting: plot_graph(self.losses, self.fig_num[0], title='Loss') plot_graph(self.residuals, self.fig_num[1], title='CG residuals') if self.analyze_convergence: plot_graph(self.gradient_mags, self.fig_num[2], 'Gradient magnitude') self.x.detach_() self.clear_temp() return self.losses, self.residuals def run_GN_iter(self, num_cg_iter): """Runs a single GN iteration.""" self.x.requires_grad_(True) # Evaluate function at current estimate self.f0 = self.problem(self.x) # Create copy with graph detached self.g = self.f0.detach() if self.debug and not self.analyze_convergence: loss = self.problem.ip_output(self.g, self.g) self.losses = torch.cat((self.losses, loss.detach().cpu().view(-1))) self.g.requires_grad_(True) # Get df/dx^t @ f0 self.dfdxt_g = TensorList(torch.autograd.grad(self.f0, self.x, self.g, create_graph=True)) # Get the right hand side self.b = - self.dfdxt_g.detach() # Run CG delta_x, res = self.run_CG(num_cg_iter, eps=self.cg_eps) self.x.detach_() self.x += delta_x if self.debug: self.residuals = torch.cat((self.residuals, res)) def A(self, x): dfdx_x = torch.autograd.grad(self.dfdxt_g, self.g, x, retain_graph=True) return TensorList(torch.autograd.grad(self.f0, self.x, dfdx_x, retain_graph=True)) def ip(self, a, b): return self.problem.ip_input(a, b) def M1(self, x): return self.problem.M1(x) def M2(self, x): return self.problem.M2(x) def evaluate_CG_iteration(self, delta_x): if self.analyze_convergence: x = (self.x + delta_x).detach() x.requires_grad_(True) # compute loss and gradient f = self.problem(x) loss = self.problem.ip_output(f, f) grad = TensorList(torch.autograd.grad(loss, x)) # store in the vectors self.losses = torch.cat((self.losses, loss.detach().cpu().view(-1))) self.gradient_mags = torch.cat((self.gradient_mags, sum(grad.view(-1) @ grad.view(-1)).cpu().sqrt().detach().view(-1))) class GradientDescentL2: """Gradient descent with momentum for L2 problems.""" def __init__(self, problem: L2Problem, variable: TensorList, step_length: float, momentum: float = 0.0, debug = False, plotting = False, visdom=None): self.problem = problem self.x = variable self.step_legnth = step_length self.momentum = momentum self.debug = debug or plotting self.plotting = plotting self.fig_num = (10,11) self.visdom = visdom self.losses = torch.zeros(0) self.gradient_mags = torch.zeros(0) self.residuals = None self.clear_temp() def clear_temp(self): self.f0 = None self.dir = None def run(self, num_iter, dummy = None): if num_iter == 0: return lossvec = None if self.debug: lossvec = torch.zeros(num_iter+1) grad_mags = torch.zeros(num_iter+1) for i in range(num_iter): self.x.requires_grad_(True) # Evaluate function at current estimate self.f0 = self.problem(self.x) # Compute loss loss = self.problem.ip_output(self.f0, self.f0) # Compute grad grad = TensorList(torch.autograd.grad(loss, self.x)) # Update direction if self.dir is None: self.dir = grad else: self.dir = grad + self.momentum * self.dir self.x.detach_() self.x -= self.step_legnth * self.dir if self.debug: lossvec[i] = loss.item() grad_mags[i] = sum(grad.view(-1) @ grad.view(-1)).sqrt().item() if self.debug: self.x.requires_grad_(True) self.f0 = self.problem(self.x) loss = self.problem.ip_output(self.f0, self.f0) grad = TensorList(torch.autograd.grad(loss, self.x)) lossvec[-1] = self.problem.ip_output(self.f0, self.f0).item() grad_mags[-1] = sum(grad.view(-1) @ grad.view(-1)).cpu().sqrt().item() self.losses = torch.cat((self.losses, lossvec)) self.gradient_mags = torch.cat((self.gradient_mags, grad_mags)) if self.visdom is not None: self.visdom.register(self.losses, 'lineplot', 3, 'Loss') self.visdom.register(self.gradient_mags, 'lineplot', 4, 'Gradient magnitude') elif self.plotting: plot_graph(self.losses, self.fig_num[0], title='Loss') plot_graph(self.gradient_mags, self.fig_num[1], title='Gradient magnitude') self.x.detach_() self.clear_temp() class NewtonCG(ConjugateGradientBase): """Newton with Conjugate Gradient. Handels general minimization problems.""" def __init__(self, problem: MinimizationProblem, variable: TensorList, init_hessian_reg = 0.0, hessian_reg_factor = 1.0, cg_eps = 0.0, fletcher_reeves = True, standard_alpha = True, direction_forget_factor = 0, debug = False, analyze = False, plotting = False, fig_num=(10, 11, 12)): super().__init__(fletcher_reeves, standard_alpha, direction_forget_factor, debug or analyze or plotting) self.problem = problem self.x = variable self.analyze_convergence = analyze self.plotting = plotting self.fig_num = fig_num self.hessian_reg = init_hessian_reg self.hessian_reg_factor = hessian_reg_factor self.cg_eps = cg_eps self.f0 = None self.g = None self.residuals = torch.zeros(0) self.losses = torch.zeros(0) self.gradient_mags = torch.zeros(0) def clear_temp(self): self.f0 = None self.g = None def run(self, num_cg_iter, num_newton_iter=None): if isinstance(num_cg_iter, int): if num_cg_iter == 0: return if num_newton_iter is None: num_newton_iter = 1 num_cg_iter = [num_cg_iter] * num_newton_iter num_newton_iter = len(num_cg_iter) if num_newton_iter == 0: return if self.analyze_convergence: self.evaluate_CG_iteration(0) for cg_iter in num_cg_iter: self.run_newton_iter(cg_iter) self.hessian_reg *= self.hessian_reg_factor if self.debug: if not self.analyze_convergence: loss = self.problem(self.x) self.losses = torch.cat((self.losses, loss.detach().cpu().view(-1))) if self.plotting: plot_graph(self.losses, self.fig_num[0], title='Loss') plot_graph(self.residuals, self.fig_num[1], title='CG residuals') if self.analyze_convergence: plot_graph(self.gradient_mags, self.fig_num[2], 'Gradient magnitude') self.x.detach_() self.clear_temp() return self.losses, self.residuals def run_newton_iter(self, num_cg_iter): self.x.requires_grad_(True) # Evaluate function at current estimate self.f0 = self.problem(self.x) if self.debug and not self.analyze_convergence: self.losses = torch.cat((self.losses, self.f0.detach().cpu().view(-1))) # Gradient of loss self.g = TensorList(torch.autograd.grad(self.f0, self.x, create_graph=True)) # Get the right hand side self.b = - self.g.detach() # Run CG delta_x, res = self.run_CG(num_cg_iter, eps=self.cg_eps) self.x.detach_() self.x += delta_x if self.debug: self.residuals = torch.cat((self.residuals, res)) def A(self, x): return TensorList(torch.autograd.grad(self.g, self.x, x, retain_graph=True)) + self.hessian_reg * x def ip(self, a, b): # Implements the inner product return self.problem.ip_input(a, b) def M1(self, x): return self.problem.M1(x) def M2(self, x): return self.problem.M2(x) def evaluate_CG_iteration(self, delta_x): if self.analyze_convergence: x = (self.x + delta_x).detach() x.requires_grad_(True) # compute loss and gradient loss = self.problem(x) grad = TensorList(torch.autograd.grad(loss, x)) # store in the vectors self.losses = torch.cat((self.losses, loss.detach().cpu().view(-1))) self.gradient_mags = torch.cat((self.gradient_mags, sum(grad.view(-1) @ grad.view(-1)).cpu().sqrt().detach().view(-1))) class GradientDescent: """Gradient descent for general minimization problems.""" def __init__(self, problem: MinimizationProblem, variable: TensorList, step_length: float, momentum: float = 0.0, debug = False, plotting = False, fig_num=(10,11)): self.problem = problem self.x = variable self.step_legnth = step_length self.momentum = momentum self.debug = debug or plotting self.plotting = plotting self.fig_num = fig_num self.losses = torch.zeros(0) self.gradient_mags = torch.zeros(0) self.residuals = None self.clear_temp() def clear_temp(self): self.dir = None def run(self, num_iter, dummy = None): if num_iter == 0: return lossvec = None if self.debug: lossvec = torch.zeros(num_iter+1) grad_mags = torch.zeros(num_iter+1) for i in range(num_iter): self.x.requires_grad_(True) # Evaluate function at current estimate loss = self.problem(self.x) # Compute grad grad = TensorList(torch.autograd.grad(loss, self.x)) # Update direction if self.dir is None: self.dir = grad else: self.dir = grad + self.momentum * self.dir self.x.detach_() self.x -= self.step_legnth * self.dir if self.debug: lossvec[i] = loss.item() grad_mags[i] = sum(grad.view(-1) @ grad.view(-1)).sqrt().item() if self.debug: self.x.requires_grad_(True) loss = self.problem(self.x) grad = TensorList(torch.autograd.grad(loss, self.x)) lossvec[-1] = loss.item() grad_mags[-1] = sum(grad.view(-1) @ grad.view(-1)).cpu().sqrt().item() self.losses = torch.cat((self.losses, lossvec)) self.gradient_mags = torch.cat((self.gradient_mags, grad_mags)) if self.plotting: plot_graph(self.losses, self.fig_num[0], title='Loss') plot_graph(self.gradient_mags, self.fig_num[1], title='Gradient magnitude') self.x.detach_() self.clear_temp() ================================================ FILE: external/AR/pytracking/libs/tensordict.py ================================================ from collections import OrderedDict import torch import copy class TensorDict(OrderedDict): """Container mainly used for dicts of torch tensors. Extends OrderedDict with pytorch functionality.""" def concat(self, other): """Concatenates two dicts without copying internal data.""" return TensorDict(self, **other) def copy(self): return TensorDict(super(TensorDict, self).copy()) def __deepcopy__(self, memodict={}): return TensorDict(copy.deepcopy(list(self), memodict)) def __getattr__(self, name): if not hasattr(torch.Tensor, name): raise AttributeError('\'TensorDict\' object has not attribute \'{}\''.format(name)) def apply_attr(*args, **kwargs): return TensorDict({n: getattr(e, name)(*args, **kwargs) if hasattr(e, name) else e for n, e in self.items()}) return apply_attr def attribute(self, attr: str, *args): return TensorDict({n: getattr(e, attr, *args) for n, e in self.items()}) def apply(self, fn, *args, **kwargs): return TensorDict({n: fn(e, *args, **kwargs) for n, e in self.items()}) @staticmethod def _iterable(a): return isinstance(a, (TensorDict, list)) ================================================ FILE: external/AR/pytracking/libs/tensorlist.py ================================================ import functools import torch import copy class TensorList(list): """Container mainly used for lists of torch tensors. Extends lists with pytorch functionality.""" def __init__(self, list_of_tensors = None): if list_of_tensors is None: list_of_tensors = list() super(TensorList, self).__init__(list_of_tensors) def __deepcopy__(self, memodict={}): return TensorList(copy.deepcopy(list(self), memodict)) def __getitem__(self, item): if isinstance(item, int): return super(TensorList, self).__getitem__(item) elif isinstance(item, (tuple, list)): return TensorList([super(TensorList, self).__getitem__(i) for i in item]) else: return TensorList(super(TensorList, self).__getitem__(item)) def __add__(self, other): if TensorList._iterable(other): return TensorList([e1 + e2 for e1, e2 in zip(self, other)]) return TensorList([e + other for e in self]) def __radd__(self, other): if TensorList._iterable(other): return TensorList([e2 + e1 for e1, e2 in zip(self, other)]) return TensorList([other + e for e in self]) def __iadd__(self, other): if TensorList._iterable(other): for i, e2 in enumerate(other): self[i] += e2 else: for i in range(len(self)): self[i] += other return self def __sub__(self, other): if TensorList._iterable(other): return TensorList([e1 - e2 for e1, e2 in zip(self, other)]) return TensorList([e - other for e in self]) def __rsub__(self, other): if TensorList._iterable(other): return TensorList([e2 - e1 for e1, e2 in zip(self, other)]) return TensorList([other - e for e in self]) def __isub__(self, other): if TensorList._iterable(other): for i, e2 in enumerate(other): self[i] -= e2 else: for i in range(len(self)): self[i] -= other return self def __mul__(self, other): if TensorList._iterable(other): return TensorList([e1 * e2 for e1, e2 in zip(self, other)]) return TensorList([e * other for e in self]) def __rmul__(self, other): if TensorList._iterable(other): return TensorList([e2 * e1 for e1, e2 in zip(self, other)]) return TensorList([other * e for e in self]) def __imul__(self, other): if TensorList._iterable(other): for i, e2 in enumerate(other): self[i] *= e2 else: for i in range(len(self)): self[i] *= other return self def __truediv__(self, other): if TensorList._iterable(other): return TensorList([e1 / e2 for e1, e2 in zip(self, other)]) return TensorList([e / other for e in self]) def __rtruediv__(self, other): if TensorList._iterable(other): return TensorList([e2 / e1 for e1, e2 in zip(self, other)]) return TensorList([other / e for e in self]) def __itruediv__(self, other): if TensorList._iterable(other): for i, e2 in enumerate(other): self[i] /= e2 else: for i in range(len(self)): self[i] /= other return self def __matmul__(self, other): if TensorList._iterable(other): return TensorList([e1 @ e2 for e1, e2 in zip(self, other)]) return TensorList([e @ other for e in self]) def __rmatmul__(self, other): if TensorList._iterable(other): return TensorList([e2 @ e1 for e1, e2 in zip(self, other)]) return TensorList([other @ e for e in self]) def __imatmul__(self, other): if TensorList._iterable(other): for i, e2 in enumerate(other): self[i] @= e2 else: for i in range(len(self)): self[i] @= other return self def __mod__(self, other): if TensorList._iterable(other): return TensorList([e1 % e2 for e1, e2 in zip(self, other)]) return TensorList([e % other for e in self]) def __rmod__(self, other): if TensorList._iterable(other): return TensorList([e2 % e1 for e1, e2 in zip(self, other)]) return TensorList([other % e for e in self]) def __pos__(self): return TensorList([+e for e in self]) def __neg__(self): return TensorList([-e for e in self]) def __le__(self, other): if TensorList._iterable(other): return TensorList([e1 <= e2 for e1, e2 in zip(self, other)]) return TensorList([e <= other for e in self]) def __ge__(self, other): if TensorList._iterable(other): return TensorList([e1 >= e2 for e1, e2 in zip(self, other)]) return TensorList([e >= other for e in self]) def concat(self, other): return TensorList(super(TensorList, self).__add__(other)) def copy(self): return TensorList(super(TensorList, self).copy()) def unroll(self): if not any(isinstance(t, TensorList) for t in self): return self new_list = TensorList() for t in self: if isinstance(t, TensorList): new_list.extend(t.unroll()) else: new_list.append(t) return new_list def list(self): return list(self) def attribute(self, attr: str, *args): return TensorList([getattr(e, attr, *args) for e in self]) def apply(self, fn): return TensorList([fn(e) for e in self]) def __getattr__(self, name): if not hasattr(torch.Tensor, name): raise AttributeError('\'TensorList\' object has not attribute \'{}\''.format(name)) def apply_attr(*args, **kwargs): return TensorList([getattr(e, name)(*args, **kwargs) for e in self]) return apply_attr @staticmethod def _iterable(a): return isinstance(a, (TensorList, list)) def tensor_operation(op): def islist(a): return isinstance(a, TensorList) @functools.wraps(op) def oplist(*args, **kwargs): if len(args) == 0: raise ValueError('Must be at least one argument without keyword (i.e. operand).') if len(args) == 1: if islist(args[0]): return TensorList([op(a, **kwargs) for a in args[0]]) else: # Multiple operands, assume max two if islist(args[0]) and islist(args[1]): return TensorList([op(a, b, *args[2:], **kwargs) for a, b in zip(*args[:2])]) if islist(args[0]): return TensorList([op(a, *args[1:], **kwargs) for a in args[0]]) if islist(args[1]): return TensorList([op(args[0], b, *args[2:], **kwargs) for b in args[1]]) # None of the operands are lists return op(*args, **kwargs) return oplist ================================================ FILE: external/AR/pytracking/parameter/__init__.py ================================================ ================================================ FILE: external/AR/pytracking/parameter/atom/__init__.py ================================================ ================================================ FILE: external/AR/pytracking/parameter/atom/atom_gmm_sampl.py ================================================ from pytracking.utils import TrackerParams, FeatureParams, Choice from pytracking.features.extractor import MultiResolutionExtractor from pytracking.features import deep import torch def parameters(): params = TrackerParams() # These are usually set from outside params.debug = 0 # Debug level params.visualization = False # Do visualization # Use GPU or not (IoUNet requires this to be True) params.use_gpu = True # Feature specific parameters deep_params = TrackerParams() # Patch sampling parameters params.max_image_sample_size = (18*16)**2 # Maximum image sample size params.min_image_sample_size = (18*16)**2 # Minimum image sample size params.search_area_scale = 5 # Scale relative to target size params.feature_size_odd = False # Good to use False for even-sized kernels and vice versa # Optimization parameters params.CG_iter = 5 # The number of Conjugate Gradient iterations in each update after the first frame params.init_CG_iter = 60 # The total number of Conjugate Gradient iterations used in the first frame params.init_GN_iter = 6 # The number of Gauss-Newton iterations used in the first frame (only if the projection matrix is updated) params.post_init_CG_iter = 0 # CG iterations to run after GN params.fletcher_reeves = False # Use the Fletcher-Reeves (true) or Polak-Ribiere (false) formula in the Conjugate Gradient params.standard_alpha = True # Use the standard formula for computing the step length in Conjugate Gradient params.CG_forgetting_rate = None # Forgetting rate of the last conjugate direction # Learning parameters for each feature type deep_params.learning_rate = 0.01 # Learning rate deep_params.init_samples_minimum_weight = 0.25 # Minimum weight of initial samples in memory deep_params.output_sigma_factor = 1/4 # Standard deviation of Gaussian label relative to target size # Training parameters params.sample_memory_size = 250 # Memory size params.train_skipping = 10 # How often to run training (every n-th frame) # Online model parameters deep_params.kernel_size = (4,4) # Kernel size of filter deep_params.compressed_dim = 64 # Dimension output of projection matrix deep_params.filter_reg = 1e-1 # Filter regularization factor deep_params.projection_reg = 1e-4 # Projection regularization factor # Windowing params.feature_window = False # Perform windowing of features params.window_output = False # Perform windowing of output scores # Detection parameters params.scale_factors = torch.ones(1) # What scales to use for localization (only one scale if IoUNet is used) params.score_upsample_factor = 1 # How much Fourier upsampling to use # Init data augmentation parameters params.augmentation = {'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45,-45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3,1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6,-0.6)], 'dropout': (7, 0.2)} params.augmentation_expansion_factor = 2 # How much to expand sample when doing augmentation params.random_shift_factor = 1/3 # How much random shift to do on each augmented sample deep_params.use_augmentation = True # Whether to use augmentation for this feature # Factorized convolution parameters # params.use_projection_matrix = True # Use projection matrix, i.e. use the factorized convolution formulation params.update_projection_matrix = True # Whether the projection matrix should be optimized or not params.proj_init_method = 'randn' # Method for initializing the projection matrix params.filter_init_method = 'randn' # Method for initializing the spatial filter params.projection_activation = 'none' # Activation function after projection ('none', 'relu', 'elu' or 'mlu') params.response_activation = ('mlu', 0.05) # Activation function on the output scores ('none', 'relu', 'elu' or 'mlu') # Advanced localization parameters params.advanced_localization = True # Use this or not params.target_not_found_threshold = 0.25 # Absolute score threshold to detect target missing params.distractor_threshold = 0.8 # Relative threshold to find distractors params.hard_negative_threshold = 0.5 # Relative threshold to find hard negative samples params.target_neighborhood_scale = 2.2 # Target neighborhood to remove params.dispalcement_scale = 0.8 # Dispacement to consider for distractors params.hard_negative_learning_rate = 0.02 # Learning rate if hard negative detected params.hard_negative_CG_iter = 5 # Number of optimization iterations to use if hard negative detected params.update_scale_when_uncertain = True # Update scale or not if distractor is close # IoUNet parameters params.use_iou_net = True # Use IoU net or not params.box_refinement_space = 'relative' params.iounet_augmentation = False # Use the augmented samples to compute the modulation vector params.iounet_k = 3 # Top-k average to estimate final box params.num_init_random_boxes = 9 # Num extra random boxes in addition to the classifier prediction params.box_jitter_pos = 0.1 # How much to jitter the translation for random boxes params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes params.maximal_aspect_ratio = 6 # Limit on the aspect ratio params.box_refinement_iter = 10 # Number of iterations for refining the boxes params.box_refinement_step_length = (1e-2, 5e-2) # 1 # Gradient step length in the bounding box refinement 5e-3 2e-2 params.box_refinement_step_decay = 1 # Multiplicative step length decay (1 means no decay) # Setup the feature extractor (which includes the IoUNet) deep_fparams = FeatureParams(feature_params=[deep_params]) deep_feat = deep.ATOMResNet18(net_path='atom_gmm_sampl', output_layers=['layer3'], fparams=deep_fparams, normalize_power=2) params.features = MultiResolutionExtractor([deep_feat]) return params ================================================ FILE: external/AR/pytracking/parameter/atom/atom_prob_ml.py ================================================ from pytracking.utils import TrackerParams, FeatureParams, Choice from pytracking.features.extractor import MultiResolutionExtractor from pytracking.features import deep import torch def parameters(): params = TrackerParams() # These are usually set from outside params.debug = 0 # Debug level params.visualization = False # Do visualization # Use GPU or not (IoUNet requires this to be True) params.use_gpu = True # Feature specific parameters deep_params = TrackerParams() # Patch sampling parameters params.max_image_sample_size = (18*16)**2 # Maximum image sample size params.min_image_sample_size = (18*16)**2 # Minimum image sample size params.search_area_scale = 5 # Scale relative to target size params.feature_size_odd = False # Good to use False for even-sized kernels and vice versa # Optimization parameters params.CG_iter = 5 # The number of Conjugate Gradient iterations in each update after the first frame params.init_CG_iter = 60 # The total number of Conjugate Gradient iterations used in the first frame params.init_GN_iter = 6 # The number of Gauss-Newton iterations used in the first frame (only if the projection matrix is updated) params.post_init_CG_iter = 0 # CG iterations to run after GN params.fletcher_reeves = False # Use the Fletcher-Reeves (true) or Polak-Ribiere (false) formula in the Conjugate Gradient params.standard_alpha = True # Use the standard formula for computing the step length in Conjugate Gradient params.CG_forgetting_rate = None # Forgetting rate of the last conjugate direction # Learning parameters for each feature type deep_params.learning_rate = 0.01 # Learning rate deep_params.init_samples_minimum_weight = 0.25 # Minimum weight of initial samples in memory deep_params.output_sigma_factor = 1/4 # Standard deviation of Gaussian label relative to target size # Training parameters params.sample_memory_size = 250 # Memory size params.train_skipping = 10 # How often to run training (every n-th frame) # Online model parameters deep_params.kernel_size = (4,4) # Kernel size of filter deep_params.compressed_dim = 64 # Dimension output of projection matrix deep_params.filter_reg = 1e-1 # Filter regularization factor deep_params.projection_reg = 1e-4 # Projection regularization factor # Windowing params.feature_window = False # Perform windowing of features params.window_output = False # Perform windowing of output scores # Detection parameters params.scale_factors = torch.ones(1) # What scales to use for localization (only one scale if IoUNet is used) params.score_upsample_factor = 1 # How much Fourier upsampling to use # Init data augmentation parameters params.augmentation = {'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45,-45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3,1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6,-0.6)], 'dropout': (7, 0.2)} params.augmentation_expansion_factor = 2 # How much to expand sample when doing augmentation params.random_shift_factor = 1/3 # How much random shift to do on each augmented sample deep_params.use_augmentation = True # Whether to use augmentation for this feature # Factorized convolution parameters # params.use_projection_matrix = True # Use projection matrix, i.e. use the factorized convolution formulation params.update_projection_matrix = True # Whether the projection matrix should be optimized or not params.proj_init_method = 'randn' # Method for initializing the projection matrix params.filter_init_method = 'randn' # Method for initializing the spatial filter params.projection_activation = 'none' # Activation function after projection ('none', 'relu', 'elu' or 'mlu') params.response_activation = ('mlu', 0.05) # Activation function on the output scores ('none', 'relu', 'elu' or 'mlu') # Advanced localization parameters params.advanced_localization = True # Use this or not params.target_not_found_threshold = 0.25 # Absolute score threshold to detect target missing params.distractor_threshold = 0.8 # Relative threshold to find distractors params.hard_negative_threshold = 0.5 # Relative threshold to find hard negative samples params.target_neighborhood_scale = 2.2 # Target neighborhood to remove params.dispalcement_scale = 0.8 # Dispacement to consider for distractors params.hard_negative_learning_rate = 0.02 # Learning rate if hard negative detected params.hard_negative_CG_iter = 5 # Number of optimization iterations to use if hard negative detected params.update_scale_when_uncertain = True # Update scale or not if distractor is close # IoUNet parameters params.use_iou_net = True # Use IoU net or not params.box_refinement_space = 'relative' params.iounet_augmentation = False # Use the augmented samples to compute the modulation vector params.iounet_k = 3 # Top-k average to estimate final box params.num_init_random_boxes = 9 # Num extra random boxes in addition to the classifier prediction params.box_jitter_pos = 0.1 # How much to jitter the translation for random boxes params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes params.maximal_aspect_ratio = 6 # Limit on the aspect ratio params.box_refinement_iter = 10 # Number of iterations for refining the boxes params.box_refinement_step_length = (2e-4, 10e-4) # 1 # Gradient step length in the bounding box refinement params.box_refinement_step_decay = 1 # Multiplicative step length decay (1 means no decay) # Setup the feature extractor (which includes the IoUNet) deep_fparams = FeatureParams(feature_params=[deep_params]) deep_feat = deep.ATOMResNet18(net_path='atom_prob_ml', output_layers=['layer3'], fparams=deep_fparams, normalize_power=2) params.features = MultiResolutionExtractor([deep_feat]) return params ================================================ FILE: external/AR/pytracking/parameter/atom/default.py ================================================ from pytracking.utils import TrackerParams, FeatureParams, Choice from pytracking.features.extractor import MultiResolutionExtractor from pytracking.features import deep import torch def parameters(): params = TrackerParams() # These are usually set from outside params.debug = 0 # Debug level params.visualization = False # Do visualization # Use GPU or not (IoUNet requires this to be True) params.use_gpu = True # Feature specific parameters deep_params = TrackerParams() # Patch sampling parameters params.max_image_sample_size = (18*16)**2 # Maximum image sample size params.min_image_sample_size = (18*16)**2 # Minimum image sample size params.search_area_scale = 5 # Scale relative to target size params.feature_size_odd = False # Good to use False for even-sized kernels and vice versa # Optimization parameters params.CG_iter = 5 # The number of Conjugate Gradient iterations in each update after the first frame params.init_CG_iter = 60 # The total number of Conjugate Gradient iterations used in the first frame params.init_GN_iter = 6 # The number of Gauss-Newton iterations used in the first frame (only if the projection matrix is updated) params.post_init_CG_iter = 0 # CG iterations to run after GN params.fletcher_reeves = False # Use the Fletcher-Reeves (true) or Polak-Ribiere (false) formula in the Conjugate Gradient params.standard_alpha = True # Use the standard formula for computing the step length in Conjugate Gradient params.CG_forgetting_rate = None # Forgetting rate of the last conjugate direction # Learning parameters for each feature type deep_params.learning_rate = 0.01 # Learning rate deep_params.init_samples_minimum_weight = 0.25 # Minimum weight of initial samples in memory deep_params.output_sigma_factor = 1/4 # Standard deviation of Gaussian label relative to target size # Training parameters params.sample_memory_size = 250 # Memory size params.train_skipping = 10 # How often to run training (every n-th frame) # Online model parameters deep_params.kernel_size = (4,4) # Kernel size of filter deep_params.compressed_dim = 64 # Dimension output of projection matrix deep_params.filter_reg = 1e-1 # Filter regularization factor deep_params.projection_reg = 1e-4 # Projection regularization factor # Windowing params.feature_window = False # Perform windowing of features params.window_output = False # Perform windowing of output scores # Detection parameters params.scale_factors = torch.ones(1) # What scales to use for localization (only one scale if IoUNet is used) params.score_upsample_factor = 1 # How much Fourier upsampling to use # Init data augmentation parameters params.augmentation = {'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45,-45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3,1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6,-0.6)], 'dropout': (7, 0.2)} params.augmentation_expansion_factor = 2 # How much to expand sample when doing augmentation params.random_shift_factor = 1/3 # How much random shift to do on each augmented sample deep_params.use_augmentation = True # Whether to use augmentation for this feature # Factorized convolution parameters # params.use_projection_matrix = True # Use projection matrix, i.e. use the factorized convolution formulation params.update_projection_matrix = True # Whether the projection matrix should be optimized or not params.proj_init_method = 'randn' # Method for initializing the projection matrix params.filter_init_method = 'randn' # Method for initializing the spatial filter params.projection_activation = 'none' # Activation function after projection ('none', 'relu', 'elu' or 'mlu') params.response_activation = ('mlu', 0.05) # Activation function on the output scores ('none', 'relu', 'elu' or 'mlu') # Advanced localization parameters params.advanced_localization = True # Use this or not params.target_not_found_threshold = 0.25 # Absolute score threshold to detect target missing params.distractor_threshold = 0.8 # Relative threshold to find distractors params.hard_negative_threshold = 0.5 # Relative threshold to find hard negative samples params.target_neighborhood_scale = 2.2 # Target neighborhood to remove params.dispalcement_scale = 0.8 # Dispacement to consider for distractors params.hard_negative_learning_rate = 0.02 # Learning rate if hard negative detected params.hard_negative_CG_iter = 5 # Number of optimization iterations to use if hard negative detected params.update_scale_when_uncertain = True # Update scale or not if distractor is close # IoUNet parameters params.use_iou_net = True # Use IoU net or not params.iounet_augmentation = False # Use the augmented samples to compute the modulation vector params.iounet_k = 3 # Top-k average to estimate final box params.num_init_random_boxes = 9 # Num extra random boxes in addition to the classifier prediction params.box_jitter_pos = 0.1 # How much to jitter the translation for random boxes params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes params.maximal_aspect_ratio = 6 # Limit on the aspect ratio params.box_refinement_iter = 5 # Number of iterations for refining the boxes params.box_refinement_step_length = 1 # Gradient step length in the bounding box refinement params.box_refinement_step_decay = 1 # Multiplicative step length decay (1 means no decay) # Setup the feature extractor (which includes the IoUNet) deep_fparams = FeatureParams(feature_params=[deep_params]) deep_feat = deep.ATOMResNet18(net_path='atom_default.pth', output_layers=['layer3'], fparams=deep_fparams, normalize_power=2) params.features = MultiResolutionExtractor([deep_feat]) return params ================================================ FILE: external/AR/pytracking/parameter/atom/default_vot.py ================================================ from pytracking.utils import TrackerParams, FeatureParams, Choice from pytracking.features.extractor import MultiResolutionExtractor from pytracking.features import deep import torch def parameters(): params = TrackerParams() # These are usually set from outside params.debug = 0 # Debug level params.visualization = False # Do visualization # Use GPU or not (IoUNet requires this to be True) params.use_gpu = True # Feature specific parameters deep_params = TrackerParams() # Patch sampling parameters params.max_image_sample_size = (14 * 16) ** 2 # Maximum image sample size params.min_image_sample_size = (14 * 16) ** 2 # Minimum image sample size params.search_area_scale = 4 # Scale relative to target size params.feature_size_odd = False # Good to use False for even-sized kernels and vice versa # Optimization parameters params.CG_iter = 5 # The number of Conjugate Gradient iterations in each update after the first frame params.init_CG_iter = 60 # The total number of Conjugate Gradient iterations used in the first frame params.init_GN_iter = 6 # The number of Gauss-Newton iterations used in the first frame (only if the projection matrix is updated) params.post_init_CG_iter = 0 # CG iterations to run after GN params.fletcher_reeves = False # Use the Fletcher-Reeves (true) or Polak-Ribiere (false) formula in the Conjugate Gradient params.standard_alpha = True # Use the standard formula for computing the step length in Conjugate Gradient params.CG_forgetting_rate = None # Forgetting rate of the last conjugate direction # Learning parameters for each feature type deep_params.learning_rate = 0.0075 # Learning rate deep_params.output_sigma_factor = 1/4 # Standard deviation of Gaussian label relative to target size # Training parameters params.sample_memory_size = 250 # Memory size params.train_skipping = 10 # How often to run training (every n-th frame) # Online model parameters deep_params.kernel_size = (4, 4) # Kernel size of filter deep_params.compressed_dim = 64 # Dimension output of projection matrix deep_params.filter_reg = 1e-1 # Filter regularization factor deep_params.projection_reg = 1e-4 # Projection regularization factor # Windowing params.feature_window = False # Perform windowing of features params.window_output = True # Perform windowing of output scores # Detection parameters params.scale_factors = torch.ones(1) # What scales to use for localization (only one scale if IoUNet is used) params.score_upsample_factor = 1 # How much Fourier upsampling to use # Init data augmentation parameters params.augmentation = {'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45,-45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3,1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6,-0.6)], 'dropout': (7, 0.2)} params.augmentation_expansion_factor = 2 # How much to expand sample when doing augmentation params.random_shift_factor = 1 / 3 # How much random shift to do on each augmented sample deep_params.use_augmentation = True # Whether to use augmentation for this feature # Factorized convolution parameters # params.use_projection_matrix = True # Use projection matrix, i.e. use the factorized convolution formulation params.update_projection_matrix = True # Whether the projection matrix should be optimized or not params.proj_init_method = 'randn' # Method for initializing the projection matrix params.filter_init_method = 'randn' # Method for initializing the spatial filter params.projection_activation = 'none' # Activation function after projection ('none', 'relu', 'elu' or 'mlu') params.response_activation = ('mlu', 0.05) # Activation function on the output scores ('none', 'relu', 'elu' or 'mlu') # Advanced localization parameters params.advanced_localization = True # Use this or not params.target_not_found_threshold = -1 # Absolute score threshold to detect target missing params.distractor_threshold = 100 # Relative threshold to find distractors params.hard_negative_threshold = 0.3 # Relative threshold to find hard negative samples params.target_neighborhood_scale = 2.2 # Target neighborhood to remove params.dispalcement_scale = 0.7 # Dispacement to consider for distractors params.hard_negative_learning_rate = 0.02 # Learning rate if hard negative detected params.hard_negative_CG_iter = 5 # Number of optimization iterations to use if hard negative detected params.update_scale_when_uncertain = True # Update scale or not if distractor is close # IoUNet parameters params.iounet_augmentation = False # Use the augmented samples to compute the modulation vector params.iounet_k = 3 # Top-k average to estimate final box params.num_init_random_boxes = 9 # Num extra random boxes in addition to the classifier prediction params.box_jitter_pos = 0.1 # How much to jitter the translation for random boxes params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes params.maximal_aspect_ratio = 6 # Limit on the aspect ratio params.box_refinement_iter = 5 # Number of iterations for refining the boxes params.box_refinement_step_length = 1 # Gradient step length in the bounding box refinement params.box_refinement_step_decay = 1 # Multiplicative step length decay (1 means no decay) # Setup the feature extractor (which includes the IoUNet) deep_fparams = FeatureParams(feature_params=[deep_params]) deep_feat = deep.ATOMResNet18(net_path='atom_default.pth', output_layers=['layer3'], fparams=deep_fparams, normalize_power=2) params.features = MultiResolutionExtractor([deep_feat]) params.vot_anno_conversion_type = 'preserve_area' return params ================================================ FILE: external/AR/pytracking/parameter/atom/multiscale_no_iounet.py ================================================ from pytracking.utils import TrackerParams, FeatureParams, Choice from pytracking.features.extractor import MultiResolutionExtractor from pytracking.features import deep import torch def parameters(): params = TrackerParams() # These are usually set from outside params.debug = 0 # Debug level params.visualization = False # Do visualization # Use GPU or not (IoUNet requires this to be True) params.use_gpu = True # Feature specific parameters deep_params = TrackerParams() # Patch sampling parameters params.max_image_sample_size = (18*16)**2 # Maximum image sample size params.min_image_sample_size = (18*16)**2 # Minimum image sample size params.search_area_scale = 5 # Scale relative to target size params.feature_size_odd = False # Good to use False for even-sized kernels and vice versa # Optimization parameters params.CG_iter = 5 # The number of Conjugate Gradient iterations in each update after the first frame params.init_CG_iter = 60 # The total number of Conjugate Gradient iterations used in the first frame params.init_GN_iter = 6 # The number of Gauss-Newton iterations used in the first frame (only if the projection matrix is updated) params.post_init_CG_iter = 0 # CG iterations to run after GN params.fletcher_reeves = False # Use the Fletcher-Reeves (true) or Polak-Ribiere (false) formula in the Conjugate Gradient params.standard_alpha = True # Use the standard formula for computing the step length in Conjugate Gradient params.CG_forgetting_rate = None # Forgetting rate of the last conjugate direction # Learning parameters for each feature type deep_params.learning_rate = 0.01 # Learning rate deep_params.init_samples_minimum_weight = 0.25 # Minimum weight of initial samples in memory deep_params.output_sigma_factor = 1/4 # Standard deviation of Gaussian label relative to target size # Training parameters params.sample_memory_size = 250 # Memory size params.train_skipping = 10 # How often to run training (every n-th frame) # Online model parameters deep_params.kernel_size = (4,4) # Kernel size of filter deep_params.compressed_dim = 64 # Dimension output of projection matrix deep_params.filter_reg = 1e-1 # Filter regularization factor deep_params.projection_reg = 1e-4 # Projection regularization factor # Windowing params.feature_window = False # Perform windowing of features params.window_output = False # Perform windowing of output scores # Detection parameters params.scale_factors = 1.02**torch.arange(-2, 3).float() # What scales to use for localization (only one scale if IoUNet is used) params.score_upsample_factor = 1 # How much Fourier upsampling to use # Init data augmentation parameters params.augmentation = {'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45,-45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3,1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6,-0.6)], 'dropout': (7, 0.2)} params.augmentation_expansion_factor = 2 # How much to expand sample when doing augmentation params.random_shift_factor = 1/3 # How much random shift to do on each augmented sample deep_params.use_augmentation = True # Whether to use augmentation for this feature # Factorized convolution parameters # params.use_projection_matrix = True # Use projection matrix, i.e. use the factorized convolution formulation params.update_projection_matrix = True # Whether the projection matrix should be optimized or not params.proj_init_method = 'randn' # Method for initializing the projection matrix params.filter_init_method = 'randn' # Method for initializing the spatial filter params.projection_activation = 'none' # Activation function after projection ('none', 'relu', 'elu' or 'mlu') params.response_activation = ('mlu', 0.05) # Activation function on the output scores ('none', 'relu', 'elu' or 'mlu') # Advanced localization parameters params.advanced_localization = True # Use this or not params.target_not_found_threshold = 0.25 # Absolute score threshold to detect target missing params.distractor_threshold = 0.8 # Relative threshold to find distractors params.hard_negative_threshold = 0.5 # Relative threshold to find hard negative samples params.target_neighborhood_scale = 2.2 # Target neighborhood to remove params.dispalcement_scale = 0.8 # Dispacement to consider for distractors params.hard_negative_learning_rate = 0.02 # Learning rate if hard negative detected params.hard_negative_CG_iter = 5 # Number of optimization iterations to use if hard negative detected params.update_scale_when_uncertain = True # Update scale or not if distractor is close # IoUNet parameters params.use_iou_net = False # Use IoU net or not params.iounet_augmentation = False # Use the augmented samples to compute the modulation vector params.iounet_k = 3 # Top-k average to estimate final box params.num_init_random_boxes = 9 # Num extra random boxes in addition to the classifier prediction params.box_jitter_pos = 0.1 # How much to jitter the translation for random boxes params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes params.maximal_aspect_ratio = 6 # Limit on the aspect ratio params.box_refinement_iter = 5 # Number of iterations for refining the boxes params.box_refinement_step_length = 1 # Gradient step length in the bounding box refinement params.box_refinement_step_decay = 1 # Multiplicative step length decay (1 means no decay) # Setup the feature extractor (which includes the IoUNet) deep_fparams = FeatureParams(feature_params=[deep_params]) deep_feat = deep.ATOMResNet18(net_path='atom_default.pth', output_layers=['layer3'], fparams=deep_fparams, normalize_power=2) params.features = MultiResolutionExtractor([deep_feat]) return params ================================================ FILE: external/AR/pytracking/parameter/dimp/__init__.py ================================================ ================================================ FILE: external/AR/pytracking/parameter/dimp/dimp18.py ================================================ from pytracking.utils import TrackerParams from pytracking.features.net_wrappers import NetWithBackbone def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.image_sample_size = 18*16 params.search_area_scale = 5 # Learning parameters params.sample_memory_size = 50 params.learning_rate = 0.01 params.init_samples_minimum_weight = 0.25 params.train_skipping = 20 # Net optimization params params.update_classifier = True params.net_opt_iter = 10 params.net_opt_update_iter = 2 params.net_opt_hn_iter = 1 # Detection parameters params.window_output = False # Init augmentation parameters params.use_augmentation = True params.augmentation = {'fliplr': True, 'rotate': [10, -10, 45, -45], 'blur': [(3,1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6,-0.6)], 'dropout': (2, 0.2)} params.augmentation_expansion_factor = 2 params.random_shift_factor = 1/3 # Advanced localization parameters params.advanced_localization = True params.target_not_found_threshold = 0.25 params.distractor_threshold = 0.8 params.hard_negative_threshold = 0.5 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.8 params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True # IoUnet parameters params.iounet_augmentation = False params.iounet_use_log_scale = True params.iounet_k = 3 params.num_init_random_boxes = 9 params.box_jitter_pos = 0.1 params.box_jitter_sz = 0.5 params.maximal_aspect_ratio = 6 params.box_refinement_iter = 5 params.box_refinement_step_length = 1 params.box_refinement_step_decay = 1 params.net = NetWithBackbone(net_path='dimp18.pth', use_gpu=params.use_gpu) params.vot_anno_conversion_type = 'preserve_area' return params ================================================ FILE: external/AR/pytracking/parameter/dimp/dimp18_vot.py ================================================ from pytracking.utils import TrackerParams from pytracking.features.net_wrappers import NetWithBackbone def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.image_sample_size = 14 * 16 params.search_area_scale = 4 params.feature_size_odd = False # Learning parameters params.sample_memory_size = 250 params.learning_rate = 0.0075 params.init_samples_minimum_weight = 0.0 params.train_skipping = 10 # Net optimization params params.update_classifier = True params.net_opt_iter = 25 params.net_opt_update_iter = 3 params.net_opt_hn_iter = 3 # Detection parameters params.window_output = True # Init augmentation parameters params.use_augmentation = True params.augmentation = {'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45, -45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3, 1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6, -0.6)], 'dropout': (7, 0.2)} params.augmentation_expansion_factor = 2 params.random_shift_factor = 1/3 # Advanced localization parameters params.advanced_localization = True params.target_not_found_threshold = 0.0 params.distractor_threshold = 100 params.hard_negative_threshold = 0.45 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.7 params.perform_hn_without_windowing = True params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True # IoUnet parameters params.iounet_augmentation = False params.iounet_use_log_scale = True params.iounet_k = 3 params.num_init_random_boxes = 9 params.box_jitter_pos = 0.1 params.box_jitter_sz = 0.5 params.maximal_aspect_ratio = 6 params.box_refinement_iter = 5 params.box_refinement_step_length = 1 params.box_refinement_step_decay = 1 params.net = NetWithBackbone(net_path='dimp18.pth', use_gpu=params.use_gpu) params.vot_anno_conversion_type = 'preserve_area' return params ================================================ FILE: external/AR/pytracking/parameter/dimp/dimp50.py ================================================ from pytracking.utils import TrackerParams from pytracking.features.net_wrappers import NetWithBackbone def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.image_sample_size = 18*16 params.search_area_scale = 5 # Learning parameters params.sample_memory_size = 50 params.learning_rate = 0.01 params.init_samples_minimum_weight = 0.25 params.train_skipping = 20 # Net optimization params params.update_classifier = True params.net_opt_iter = 10 params.net_opt_update_iter = 2 params.net_opt_hn_iter = 1 # Detection parameters params.window_output = False # Init augmentation parameters params.use_augmentation = True params.augmentation = {'fliplr': True, 'rotate': [10, -10, 45, -45], 'blur': [(3,1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6,-0.6)], 'dropout': (2, 0.2)} params.augmentation_expansion_factor = 2 params.random_shift_factor = 1/3 # Advanced localization parameters params.advanced_localization = True params.target_not_found_threshold = 0.25 params.distractor_threshold = 0.8 params.hard_negative_threshold = 0.5 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.8 params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True # IoUnet parameters params.iounet_augmentation = False params.iounet_use_log_scale = True params.iounet_k = 3 params.num_init_random_boxes = 9 params.box_jitter_pos = 0.1 params.box_jitter_sz = 0.5 params.maximal_aspect_ratio = 6 params.box_refinement_iter = 5 params.box_refinement_step_length = 1 params.box_refinement_step_decay = 1 params.net = NetWithBackbone(net_path='dimp50.pth', use_gpu=params.use_gpu) params.vot_anno_conversion_type = 'preserve_area' return params ================================================ FILE: external/AR/pytracking/parameter/dimp/dimp50_vot.py ================================================ from pytracking.utils import TrackerParams from pytracking.features.net_wrappers import NetWithBackbone def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.image_sample_size = 14 * 16 params.search_area_scale = 4 # Learning parameters params.sample_memory_size = 250 params.learning_rate = 0.0075 params.init_samples_minimum_weight = 0.0 params.train_skipping = 10 # Net optimization params params.update_classifier = True params.net_opt_iter = 25 params.net_opt_update_iter = 3 params.net_opt_hn_iter = 3 # Detection parameters params.window_output = True # Init augmentation parameters params.use_augmentation = True params.augmentation = {'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45, -45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3, 1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6, -0.6)], 'dropout': (7, 0.2)} params.augmentation_expansion_factor = 2 params.random_shift_factor = 1/3 # Advanced localization parameters params.advanced_localization = True params.target_not_found_threshold = 0.0 params.distractor_threshold = 100 params.hard_negative_threshold = 0.45 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.7 params.perform_hn_without_windowing = True params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True # IoUnet parameters params.iounet_augmentation = False params.iounet_use_log_scale = True params.iounet_k = 3 params.num_init_random_boxes = 9 params.box_jitter_pos = 0.1 params.box_jitter_sz = 0.5 params.maximal_aspect_ratio = 6 params.box_refinement_iter = 5 params.box_refinement_step_length = 1 params.box_refinement_step_decay = 1 params.net = NetWithBackbone(net_path='dimp50.pth', use_gpu=params.use_gpu) params.vot_anno_conversion_type = 'preserve_area' return params ================================================ FILE: external/AR/pytracking/parameter/dimp/dimp50_vot19.py ================================================ from pytracking.utils import TrackerParams from pytracking.features.net_wrappers import NetWithBackbone def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.image_sample_size = 16 * 16 params.search_area_scale = 4.5 # Learning parameters params.sample_memory_size = 100 params.learning_rate = 0.0075 params.init_samples_minimum_weight = 0.0 params.train_skipping = 10 # Net optimization params params.update_classifier = True params.net_opt_iter = 15 params.net_opt_update_iter = 2 params.net_opt_hn_iter = 2 # Detection parameters params.window_output = True # Init augmentation parameters params.use_augmentation = True params.augmentation = {'fliplr': True, 'rotate': [-5, 10, -30, 60], 'blur': [(2, 0.2), (1, 3)], 'relativeshift': [(0.6, 0.6), (-0.6, -0.6)], 'dropout': (3, 0.2)} params.augmentation_expansion_factor = 1.4 params.random_shift_factor = 1/3 # Advanced localization parameters params.advanced_localization = True params.target_not_found_threshold = 0.0 params.distractor_threshold = 100 params.hard_negative_threshold = 0.45 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.7 params.perform_hn_without_windowing = True params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True # IoUnet parameters params.iounet_augmentation = False params.iounet_use_log_scale = True params.iounet_k = 3 params.num_init_random_boxes = 9 params.box_jitter_pos = 0.1 params.box_jitter_sz = 0.5 params.maximal_aspect_ratio = 6 params.box_refinement_iter = 3 params.box_refinement_step_length = 1 params.box_refinement_step_decay = 1 params.net = NetWithBackbone(net_path='dimp50.pth', use_gpu=params.use_gpu) params.vot_anno_conversion_type = 'preserve_area' return params ================================================ FILE: external/AR/pytracking/parameter/dimp/prdimp18.py ================================================ from pytracking.utils import TrackerParams from pytracking.features.net_wrappers import NetWithBackbone def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.image_sample_size = 18*16 params.search_area_scale = 5 # Learning parameters params.sample_memory_size = 50 params.learning_rate = 0.01 params.init_samples_minimum_weight = 0.25 params.train_skipping = 20 # Net optimization params params.update_classifier = True params.net_opt_iter = 10 params.net_opt_update_iter = 2 params.net_opt_hn_iter = 1 # Detection parameters params.window_output = False # Init augmentation parameters params.use_augmentation = True params.augmentation = {'fliplr': True, 'rotate': [10, -10, 45, -45], 'blur': [(3,1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6,-0.6)], 'dropout': (2, 0.2)} params.augmentation_expansion_factor = 2 params.random_shift_factor = 1/3 # Advanced localization parameters params.advanced_localization = True params.score_preprocess = 'softmax' params.target_not_found_threshold = 0.04 params.distractor_threshold = 0.8 params.hard_negative_threshold = 0.5 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.8 params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True # IoUnet parameters params.box_refinement_space = 'relative' params.iounet_augmentation = False # Use the augmented samples to compute the modulation vector params.iounet_k = 3 # Top-k average to estimate final box params.num_init_random_boxes = 9 # Num extra random boxes in addition to the classifier prediction params.box_jitter_pos = 0.1 # How much to jitter the translation for random boxes params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes params.maximal_aspect_ratio = 6 # Limit on the aspect ratio params.box_refinement_iter = 10 # Number of iterations for refining the boxes params.box_refinement_step_length = 2.5e-3 # 1 # Gradient step length in the bounding box refinement params.box_refinement_step_decay = 1 # Multiplicative step length decay (1 means no decay) params.net = NetWithBackbone(net_path='prdimp18.pth.tar', use_gpu=params.use_gpu) params.vot_anno_conversion_type = 'preserve_area' return params ================================================ FILE: external/AR/pytracking/parameter/dimp/prdimp50.py ================================================ from pytracking.utils import TrackerParams from pytracking.features.net_wrappers import NetWithBackbone def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.image_sample_size = 22*16 params.search_area_scale = 6 params.border_mode = 'inside_major' params.patch_max_scale_change = 1.5 # Learning parameters params.sample_memory_size = 50 params.learning_rate = 0.01 params.init_samples_minimum_weight = 0.25 params.train_skipping = 20 # Net optimization params params.update_classifier = True params.net_opt_iter = 10 params.net_opt_update_iter = 2 params.net_opt_hn_iter = 1 # Detection parameters params.window_output = False # Init augmentation parameters params.use_augmentation = True params.augmentation = {'fliplr': True, 'rotate': [10, -10, 45, -45], 'blur': [(3,1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6,-0.6)], 'dropout': (2, 0.2)} params.augmentation_expansion_factor = 2 params.random_shift_factor = 1/3 # Advanced localization parameters params.advanced_localization = True params.score_preprocess = 'softmax' params.target_not_found_threshold = 0.04 params.distractor_threshold = 0.8 params.hard_negative_threshold = 0.5 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.8 params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True # IoUnet parameters params.box_refinement_space = 'relative' params.iounet_augmentation = False # Use the augmented samples to compute the modulation vector params.iounet_k = 3 # Top-k average to estimate final box params.num_init_random_boxes = 9 # Num extra random boxes in addition to the classifier prediction params.box_jitter_pos = 0.1 # How much to jitter the translation for random boxes params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes params.maximal_aspect_ratio = 6 # Limit on the aspect ratio params.box_refinement_iter = 10 # Number of iterations for refining the boxes params.box_refinement_step_length = 2.5e-3 # 1 # Gradient step length in the bounding box refinement params.box_refinement_step_decay = 1 # Multiplicative step length decay (1 means no decay) params.net = NetWithBackbone(net_path='prdimp50.pth.tar', use_gpu=params.use_gpu) params.vot_anno_conversion_type = 'preserve_area' return params ================================================ FILE: external/AR/pytracking/parameter/dimp/super_dimp.py ================================================ from pytracking.utils import TrackerParams from pytracking.features.net_wrappers import NetWithBackbone def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.image_sample_size = 22*16 params.search_area_scale = 6 params.border_mode = 'inside_major' params.patch_max_scale_change = 1.5 # Learning parameters params.sample_memory_size = 50 params.learning_rate = 0.01 params.init_samples_minimum_weight = 0.25 params.train_skipping = 20 # Net optimization params params.update_classifier = True params.net_opt_iter = 10 params.net_opt_update_iter = 2 params.net_opt_hn_iter = 1 # Detection parameters params.window_output = False # Init augmentation parameters params.use_augmentation = True params.augmentation = {'fliplr': True, 'rotate': [10, -10, 45, -45], 'blur': [(3,1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6,-0.6)], 'dropout': (2, 0.2)} params.augmentation_expansion_factor = 2 params.random_shift_factor = 1/3 # Advanced localization parameters params.advanced_localization = True params.target_not_found_threshold = 0.25 params.distractor_threshold = 0.8 params.hard_negative_threshold = 0.5 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.8 params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True # IoUnet parameters params.box_refinement_space = 'relative' params.iounet_augmentation = False # Use the augmented samples to compute the modulation vector params.iounet_k = 3 # Top-k average to estimate final box params.num_init_random_boxes = 9 # Num extra random boxes in addition to the classifier prediction params.box_jitter_pos = 0.1 # How much to jitter the translation for random boxes params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes params.maximal_aspect_ratio = 6 # Limit on the aspect ratio params.box_refinement_iter = 10 # Number of iterations for refining the boxes params.box_refinement_step_length = 2.5e-3 # 1 # Gradient step length in the bounding box refinement params.box_refinement_step_decay = 1 # Multiplicative step length decay (1 means no decay) params.net = NetWithBackbone(net_path='super_dimp.pth.tar', use_gpu=params.use_gpu) params.vot_anno_conversion_type = 'preserve_area' return params ================================================ FILE: external/AR/pytracking/parameter/eco/__init__.py ================================================ ================================================ FILE: external/AR/pytracking/parameter/eco/default.py ================================================ from pytracking.utils import TrackerParams, FeatureParams from pytracking.features.extractor import MultiResolutionExtractor from pytracking.features import deep import torch def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True # Feature specific parameters shallow_params = TrackerParams() deep_params = TrackerParams() # Patch sampling parameters params.max_image_sample_size = 250**2 # Maximum image sample size params.min_image_sample_size = 200**2 # Minimum image sample size params.search_area_scale = 4.5 # Scale relative to target size # Conjugate Gradient parameters params.CG_iter = 5 # The number of Conjugate Gradient iterations in each update after the first frame params.init_CG_iter = 100 # The total number of Conjugate Gradient iterations used in the first frame params.init_GN_iter = 10 # The number of Gauss-Newton iterations used in the first frame (only if the projection matrix is updated) params.post_init_CG_iter = 0 # CG iterations to run after GN params.fletcher_reeves = False # Use the Fletcher-Reeves (true) or Polak-Ribiere (false) formula in the Conjugate Gradient params.standard_alpha = True # Use the standard formula for computing the step length in Conjugate Gradient params.CG_forgetting_rate = 75 # Forgetting rate of the last conjugate direction params.precond_data_param = 0.3 # Weight of the data term in the preconditioner params.precond_reg_param = 0.15 # Weight of the regularization term in the preconditioner params.precond_proj_param = 35 # Weight of the projection matrix part in the preconditioner # Learning parameters shallow_params.learning_rate = 0.025 deep_params.learning_rate = 0.0075 shallow_params.output_sigma_factor = 1/16 deep_params.output_sigma_factor = 1/4 # Training parameters params.sample_memory_size = 200 # Memory size params.train_skipping = 10 # How often to run training (every n-th frame) # Detection parameters params.scale_factors = 1.02**torch.arange(-2, 3).float() # What scales to use for localization params.score_upsample_factor = 1 # How much Fourier upsampling to use params.score_fusion_strategy = 'weightedsum' # Fusion strategy shallow_params.translation_weight = 0.4 # Weight of this feature deep_params.translation_weight = 1 - shallow_params.translation_weight # Init augmentation parameters params.augmentation = {'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45,-45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3,1), (1, 3), (2, 2)], 'shift': [(6, 6), (-6, 6), (6, -6), (-6,-6)], 'dropout': (7, 0.2)} # Whether to use augmentation for this feature deep_params.use_augmentation = True shallow_params.use_augmentation = True # Factorized convolution parameters # params.use_projection_matrix = True # Use projection matrix, i.e. use the factorized convolution formulation params.update_projection_matrix = True # Whether the projection matrix should be optimized or not # params.proj_init_method = 'pca' # Method for initializing the projection matrix params.projection_reg = 5e-8 # Regularization paremeter of the projection matrix shallow_params.compressed_dim = 16 # Dimension output of projection matrix for shallow features deep_params.compressed_dim = 64 # Dimension output of projection matrix for deep features # Interpolation parameters params.interpolation_method = 'bicubic' # The kind of interpolation kernel params.interpolation_bicubic_a = -0.75 # The parameter for the bicubic interpolation kernel params.interpolation_centering = True # Center the kernel at the feature sample params.interpolation_windowing = False # Do additional windowing on the Fourier coefficients of the kernel # Regularization parameters shallow_params.use_reg_window = True # Use spatial regularization or not shallow_params.reg_window_min = 1e-4 # The minimum value of the regularization window shallow_params.reg_window_edge = 10e-3 # The impact of the spatial regularization shallow_params.reg_window_power = 2 # The degree of the polynomial to use (e.g. 2 is a quadratic window) shallow_params.reg_sparsity_threshold = 0.05 # A relative threshold of which DFT coefficients that should be set to zero deep_params.use_reg_window = True # Use spatial regularization or not deep_params.reg_window_min = 10e-4 # The minimum value of the regularization window deep_params.reg_window_edge = 50e-3 # The impact of the spatial regularization deep_params.reg_window_power = 2 # The degree of the polynomial to use (e.g. 2 is a quadratic window) deep_params.reg_sparsity_threshold = 0.1 # A relative threshold of which DFT coefficients that should be set to zero fparams = FeatureParams(feature_params=[shallow_params, deep_params]) features = deep.ResNet18m1(output_layers=['vggconv1', 'layer3'], use_gpu=params.use_gpu, fparams=fparams, pool_stride=[2, 1], normalize_power=2) params.features = MultiResolutionExtractor([features]) return params ================================================ FILE: external/AR/pytracking/tracker/__init__.py ================================================ ================================================ FILE: external/AR/pytracking/tracker/atom/__init__.py ================================================ from .atom import ATOM def get_tracker_class(): return ATOM ================================================ FILE: external/AR/pytracking/tracker/atom/atom.py ================================================ from pytracking.tracker.base import BaseTracker import torch import torch.nn.functional as F import torch.nn import math import time from pytracking import dcf, fourier, TensorList, operation from pytracking.features.preprocessing import numpy_to_torch from pytracking.utils.plotting import show_tensor from pytracking.libs.optimization import GaussNewtonCG, ConjugateGradient, GradientDescentL2 from .optim import ConvProblem, FactorizedConvProblem from pytracking.features import augmentation import ltr.data.bounding_box_utils as bbutils class ATOM(BaseTracker): multiobj_mode = 'parallel' def initialize_features(self): if not getattr(self, 'features_initialized', False): self.params.features.initialize() self.features_initialized = True def initialize(self, image, info: dict) -> dict: state = info['init_bbox'] # Initialize some stuff self.frame_num = 1 if not self.params.has('device'): self.params.device = 'cuda' if self.params.use_gpu else 'cpu' # Initialize features self.initialize_features() # Check if image is color self.params.features.set_is_color(image.shape[2] == 3) # Get feature specific params self.fparams = self.params.features.get_fparams('feature_params') tic = time.time() # Get position and size self.pos = torch.Tensor([state[1] + (state[3] - 1)/2, state[0] + (state[2] - 1)/2]) self.target_sz = torch.Tensor([state[3], state[2]]) # Set search area self.target_scale = 1.0 search_area = torch.prod(self.target_sz * self.params.search_area_scale).item() if search_area > self.params.max_image_sample_size: self.target_scale = math.sqrt(search_area / self.params.max_image_sample_size) elif search_area < self.params.min_image_sample_size: self.target_scale = math.sqrt(search_area / self.params.min_image_sample_size) # Check if IoUNet is used self.use_iou_net = self.params.get('use_iou_net', True) # Target size in base scale self.base_target_sz = self.target_sz / self.target_scale # Use odd square search area and set sizes feat_max_stride = max(self.params.features.stride()) if self.params.get('search_area_shape', 'square') == 'square': self.img_sample_sz = torch.round(torch.sqrt(torch.prod(self.base_target_sz * self.params.search_area_scale))) * torch.ones(2) elif self.params.search_area_shape == 'initrect': self.img_sample_sz = torch.round(self.base_target_sz * self.params.search_area_scale) else: raise ValueError('Unknown search area shape') if self.params.feature_size_odd: self.img_sample_sz += feat_max_stride - self.img_sample_sz % (2 * feat_max_stride) else: self.img_sample_sz += feat_max_stride - (self.img_sample_sz + feat_max_stride) % (2 * feat_max_stride) # Set sizes self.img_support_sz = self.img_sample_sz self.feature_sz = self.params.features.size(self.img_sample_sz) self.output_sz = self.params.score_upsample_factor * self.img_support_sz # Interpolated size of the output self.kernel_size = self.fparams.attribute('kernel_size') self.iou_img_sample_sz = self.img_sample_sz # Optimization options self.params.precond_learning_rate = self.fparams.attribute('learning_rate') if self.params.CG_forgetting_rate is None or max(self.params.precond_learning_rate) >= 1: self.params.direction_forget_factor = 0 else: self.params.direction_forget_factor = (1 - max(self.params.precond_learning_rate))**self.params.CG_forgetting_rate self.output_window = None if self.params.get('window_output', False): if self.params.get('use_clipped_window', False): self.output_window = dcf.hann2d_clipped(self.output_sz.long(), self.output_sz.long()*self.params.effective_search_area / self.params.search_area_scale, centered=False).to(self.params.device) else: self.output_window = dcf.hann2d(self.output_sz.long(), centered=False).to(self.params.device) # Initialize some learning things self.init_learning() # Convert image im = numpy_to_torch(image) self.im = im # For debugging only # Setup scale bounds self.image_sz = torch.Tensor([im.shape[2], im.shape[3]]) self.min_scale_factor = torch.max(10 / self.base_target_sz) self.max_scale_factor = torch.min(self.image_sz / self.base_target_sz) # Extract and transform sample x = self.generate_init_samples(im) # Initialize iounet if self.use_iou_net: self.init_iou_net() # Initialize projection matrix self.init_projection_matrix(x) # Transform to get the training sample train_x = self.preprocess_sample(x) # Generate label function init_y = self.init_label_function(train_x) # Init memory self.init_memory(train_x) # Init optimizer and do initial optimization self.init_optimization(train_x, init_y) self.pos_iounet = self.pos.clone() out = {'time': time.time() - tic} return out def init_optimization(self, train_x, init_y): # Initialize filter filter_init_method = self.params.get('filter_init_method', 'zeros') self.filter = TensorList( [x.new_zeros(1, cdim, sz[0], sz[1]) for x, cdim, sz in zip(train_x, self.compressed_dim, self.kernel_size)]) if filter_init_method == 'zeros': pass elif filter_init_method == 'randn': for f in self.filter: f.normal_(0, 1/f.numel()) else: raise ValueError('Unknown "filter_init_method"') # Get parameters self.params.update_projection_matrix = self.params.get('update_projection_matrix', True) and self.params.use_projection_matrix optimizer = self.params.get('optimizer', 'GaussNewtonCG') # Setup factorized joint optimization if self.params.update_projection_matrix: self.joint_problem = FactorizedConvProblem(self.init_training_samples, init_y, self.filter_reg, self.fparams.attribute('projection_reg'), self.params, self.init_sample_weights, self.projection_activation, self.response_activation) # Variable containing both filter and projection matrix joint_var = self.filter.concat(self.projection_matrix) # Initialize optimizer analyze_convergence = self.params.get('analyze_convergence', False) if optimizer == 'GaussNewtonCG': self.joint_optimizer = GaussNewtonCG(self.joint_problem, joint_var, debug=(self.params.debug >= 1), plotting=(self.params.debug >= 3), analyze=analyze_convergence, visdom=self.visdom) elif optimizer == 'GradientDescentL2': self.joint_optimizer = GradientDescentL2(self.joint_problem, joint_var, self.params.optimizer_step_length, self.params.optimizer_momentum, plotting=(self.params.debug >= 3), debug=(self.params.debug >= 1), visdom=self.visdom) # Do joint optimization if isinstance(self.params.init_CG_iter, (list, tuple)): self.joint_optimizer.run(self.params.init_CG_iter) else: self.joint_optimizer.run(self.params.init_CG_iter // self.params.init_GN_iter, self.params.init_GN_iter) if analyze_convergence: opt_name = 'CG' if self.params.get('CG_optimizer', True) else 'GD' for val_name, values in zip(['loss', 'gradient'], [self.joint_optimizer.losses, self.joint_optimizer.gradient_mags]): val_str = ' '.join(['{:.8e}'.format(v.item()) for v in values]) file_name = '{}_{}.txt'.format(opt_name, val_name) with open(file_name, 'a') as f: f.write(val_str + '\n') raise RuntimeError('Exiting') # Re-project samples with the new projection matrix compressed_samples = self.project_sample(self.init_training_samples, self.projection_matrix) for train_samp, init_samp in zip(self.training_samples, compressed_samples): train_samp[:init_samp.shape[0],...] = init_samp self.hinge_mask = None # Initialize optimizer self.conv_problem = ConvProblem(self.training_samples, self.y, self.filter_reg, self.sample_weights, self.response_activation) if optimizer == 'GaussNewtonCG': self.filter_optimizer = ConjugateGradient(self.conv_problem, self.filter, fletcher_reeves=self.params.fletcher_reeves, direction_forget_factor=self.params.direction_forget_factor, debug=(self.params.debug>=1), plotting=(self.params.debug>=3), visdom=self.visdom) elif optimizer == 'GradientDescentL2': self.filter_optimizer = GradientDescentL2(self.conv_problem, self.filter, self.params.optimizer_step_length, self.params.optimizer_momentum, debug=(self.params.debug >= 1), plotting=(self.params.debug>=3), visdom=self.visdom) # Transfer losses from previous optimization if self.params.update_projection_matrix: self.filter_optimizer.residuals = self.joint_optimizer.residuals self.filter_optimizer.losses = self.joint_optimizer.losses if not self.params.update_projection_matrix: self.filter_optimizer.run(self.params.init_CG_iter) # Post optimization self.filter_optimizer.run(self.params.post_init_CG_iter) # Free memory del self.init_training_samples if self.params.use_projection_matrix: del self.joint_problem, self.joint_optimizer def track(self, image, info: dict = None) -> dict: self.debug_info = {} self.frame_num += 1 self.debug_info['frame_num'] = self.frame_num # Convert image im = numpy_to_torch(image) self.im = im # For debugging only # ------- LOCALIZATION ------- # # Get sample sample_pos = self.pos.round() sample_scales = self.target_scale * self.params.scale_factors test_x = self.extract_processed_sample(im, self.pos, sample_scales, self.img_sample_sz) # Compute scores scores_raw = self.apply_filter(test_x) translation_vec, scale_ind, s, flag = self.localize_target(scores_raw) # Update position and scale if flag != 'not_found': if self.use_iou_net: update_scale_flag = self.params.get('update_scale_when_uncertain', True) or flag != 'uncertain' if self.params.get('use_classifier', True): self.update_state(sample_pos + translation_vec) self.refine_target_box(sample_pos, sample_scales[scale_ind], scale_ind, update_scale_flag) elif self.params.get('use_classifier', True): self.update_state(sample_pos + translation_vec, sample_scales[scale_ind]) score_map = s[scale_ind, ...] max_score = torch.max(score_map).item() self.debug_info['max_score'] = max_score self.debug_info['flag'] = flag if self.visdom is not None: self.visdom.register(score_map, 'heatmap', 2, 'Score Map') self.visdom.register(self.debug_info, 'info_dict', 1, 'Status') elif self.params.debug >= 2: show_tensor(score_map, 5, title='Max score = {:.2f}'.format(max_score)) # ------- UPDATE ------- # # Check flags and set learning rate if hard negative update_flag = flag not in ['not_found', 'uncertain'] hard_negative = (flag == 'hard_negative') learning_rate = self.params.hard_negative_learning_rate if hard_negative else None if update_flag: # Get train sample train_x = TensorList([x[scale_ind:scale_ind+1, ...] for x in test_x]) # Create label for sample train_y = self.get_label_function(sample_pos, sample_scales[scale_ind]) # Update memory self.update_memory(train_x, train_y, learning_rate) # Train filter if hard_negative: self.filter_optimizer.run(self.params.hard_negative_CG_iter) elif (self.frame_num-1) % self.params.train_skipping == 0: self.filter_optimizer.run(self.params.CG_iter) # Set the pos of the tracker to iounet pos if self.use_iou_net and flag != 'not_found': self.pos = self.pos_iounet.clone() # Return new state new_state = torch.cat((self.pos[[1,0]] - (self.target_sz[[1,0]]-1)/2, self.target_sz[[1,0]])) out = {'target_bbox': new_state.tolist()} return out def apply_filter(self, sample_x: TensorList): return operation.conv2d(sample_x, self.filter, mode='same') def localize_target(self, scores_raw): # Weighted sum (if multiple features) with interpolation in fourier domain weight = self.fparams.attribute('translation_weight', 1.0) scores_raw = weight * scores_raw sf_weighted = fourier.cfft2(scores_raw) / (scores_raw.size(2) * scores_raw.size(3)) for i, (sz, ksz) in enumerate(zip(self.feature_sz, self.kernel_size)): sf_weighted[i] = fourier.shift_fs(sf_weighted[i], math.pi * (1 - torch.Tensor([ksz[0]%2, ksz[1]%2]) / sz)) scores_fs = fourier.sum_fs(sf_weighted) scores = fourier.sample_fs(scores_fs, self.output_sz) if self.output_window is not None and not self.params.get('perform_hn_without_windowing', False): scores *= self.output_window if self.params.get('advanced_localization', False): return self.localize_advanced(scores) # Get maximum max_score, max_disp = dcf.max2d(scores) _, scale_ind = torch.max(max_score, dim=0) max_disp = max_disp.float().cpu() # Convert to displacements in the base scale disp = (max_disp + self.output_sz / 2) % self.output_sz - self.output_sz / 2 # Compute translation vector and scale change factor translation_vec = disp[scale_ind, ...].view(-1) * (self.img_support_sz / self.output_sz) * self.target_scale translation_vec *= self.params.scale_factors[scale_ind] # Shift the score output for visualization purposes if self.params.debug >= 2: sz = scores.shape[-2:] scores = torch.cat([scores[...,sz[0]//2:,:], scores[...,:sz[0]//2,:]], -2) scores = torch.cat([scores[...,:,sz[1]//2:], scores[...,:,:sz[1]//2]], -1) return translation_vec, scale_ind, scores, None def localize_advanced(self, scores): """Dows the advanced localization with hard negative detection and target not found.""" sz = scores.shape[-2:] if self.output_window is not None and self.params.get('perform_hn_without_windowing', False): scores_orig = scores.clone() scores_orig = torch.cat([scores_orig[..., (sz[0] + 1) // 2:, :], scores_orig[..., :(sz[0] + 1) // 2, :]], -2) scores_orig = torch.cat([scores_orig[..., :, (sz[1] + 1) // 2:], scores_orig[..., :, :(sz[1] + 1) // 2]], -1) scores *= self.output_window # Shift scores back scores = torch.cat([scores[...,(sz[0]+1)//2:,:], scores[...,:(sz[0]+1)//2,:]], -2) scores = torch.cat([scores[...,:,(sz[1]+1)//2:], scores[...,:,:(sz[1]+1)//2]], -1) # Find maximum max_score1, max_disp1 = dcf.max2d(scores) _, scale_ind = torch.max(max_score1, dim=0) max_score1 = max_score1[scale_ind] max_disp1 = max_disp1[scale_ind,...].float().cpu().view(-1) target_disp1 = max_disp1 - self.output_sz // 2 translation_vec1 = target_disp1 * (self.img_support_sz / self.output_sz) * self.target_scale if max_score1.item() < self.params.target_not_found_threshold: return translation_vec1, scale_ind, scores, 'not_found' if self.output_window is not None and self.params.get('perform_hn_without_windowing', False): scores = scores_orig # Mask out target neighborhood target_neigh_sz = self.params.target_neighborhood_scale * self.target_sz / self.target_scale tneigh_top = max(round(max_disp1[0].item() - target_neigh_sz[0].item() / 2), 0) tneigh_bottom = min(round(max_disp1[0].item() + target_neigh_sz[0].item() / 2 + 1), sz[0]) tneigh_left = max(round(max_disp1[1].item() - target_neigh_sz[1].item() / 2), 0) tneigh_right = min(round(max_disp1[1].item() + target_neigh_sz[1].item() / 2 + 1), sz[1]) scores_masked = scores[scale_ind:scale_ind+1,...].clone() scores_masked[...,tneigh_top:tneigh_bottom,tneigh_left:tneigh_right] = 0 # Find new maximum max_score2, max_disp2 = dcf.max2d(scores_masked) max_disp2 = max_disp2.float().cpu().view(-1) target_disp2 = max_disp2 - self.output_sz // 2 translation_vec2 = target_disp2 * (self.img_support_sz / self.output_sz) * self.target_scale # Handle the different cases if max_score2 > self.params.distractor_threshold * max_score1: disp_norm1 = torch.sqrt(torch.sum(target_disp1**2)) disp_norm2 = torch.sqrt(torch.sum(target_disp2**2)) disp_threshold = self.params.dispalcement_scale * math.sqrt(sz[0] * sz[1]) / 2 if disp_norm2 > disp_threshold and disp_norm1 < disp_threshold: return translation_vec1, scale_ind, scores, 'hard_negative' if disp_norm2 < disp_threshold and disp_norm1 > disp_threshold: return translation_vec2, scale_ind, scores, 'hard_negative' if disp_norm2 > disp_threshold and disp_norm1 > disp_threshold: return translation_vec1, scale_ind, scores, 'uncertain' # If also the distractor is close, return with highest score return translation_vec1, scale_ind, scores, 'uncertain' if max_score2 > self.params.hard_negative_threshold * max_score1 and max_score2 > self.params.target_not_found_threshold: return translation_vec1, scale_ind, scores, 'hard_negative' return translation_vec1, scale_ind, scores, None def extract_sample(self, im: torch.Tensor, pos: torch.Tensor, scales, sz: torch.Tensor): return self.params.features.extract(im, pos, scales, sz)[0] def get_iou_features(self): return self.params.features.get_unique_attribute('iounet_features') def get_iou_backbone_features(self): return self.params.features.get_unique_attribute('iounet_backbone_features') def extract_processed_sample(self, im: torch.Tensor, pos: torch.Tensor, scales, sz: torch.Tensor) -> (TensorList, TensorList): x = self.extract_sample(im, pos, scales, sz) return self.preprocess_sample(self.project_sample(x)) def preprocess_sample(self, x: TensorList) -> (TensorList, TensorList): if self.params.get('_feature_window', False): x = x * self.feature_window return x def project_sample(self, x: TensorList, proj_matrix = None): # Apply projection matrix if proj_matrix is None: proj_matrix = self.projection_matrix return operation.conv2d(x, proj_matrix).apply(self.projection_activation) def init_learning(self): # Get window function self.feature_window = TensorList([dcf.hann2d(sz).to(self.params.device) for sz in self.feature_sz]) # Filter regularization self.filter_reg = self.fparams.attribute('filter_reg') # Activation function after the projection matrix (phi_1 in the paper) projection_activation = self.params.get('projection_activation', 'none') if isinstance(projection_activation, tuple): projection_activation, act_param = projection_activation if projection_activation == 'none': self.projection_activation = lambda x: x elif projection_activation == 'relu': self.projection_activation = torch.nn.ReLU(inplace=True) elif projection_activation == 'elu': self.projection_activation = torch.nn.ELU(inplace=True) elif projection_activation == 'mlu': self.projection_activation = lambda x: F.elu(F.leaky_relu(x, 1 / act_param), act_param) else: raise ValueError('Unknown activation') # Activation function after the output scores (phi_2 in the paper) response_activation = self.params.get('response_activation', 'none') if isinstance(response_activation, tuple): response_activation, act_param = response_activation if response_activation == 'none': self.response_activation = lambda x: x elif response_activation == 'relu': self.response_activation = torch.nn.ReLU(inplace=True) elif response_activation == 'elu': self.response_activation = torch.nn.ELU(inplace=True) elif response_activation == 'mlu': self.response_activation = lambda x: F.elu(F.leaky_relu(x, 1 / act_param), act_param) else: raise ValueError('Unknown activation') def generate_init_samples(self, im: torch.Tensor) -> TensorList: """Generate augmented initial samples.""" # Compute augmentation size aug_expansion_factor = self.params.get('augmentation_expansion_factor', None) aug_expansion_sz = self.img_sample_sz.clone() aug_output_sz = None if aug_expansion_factor is not None and aug_expansion_factor != 1: aug_expansion_sz = (self.img_sample_sz * aug_expansion_factor).long() aug_expansion_sz += (aug_expansion_sz - self.img_sample_sz.long()) % 2 aug_expansion_sz = aug_expansion_sz.float() aug_output_sz = self.img_sample_sz.long().tolist() # Random shift operator get_rand_shift = lambda: None random_shift_factor = self.params.get('random_shift_factor', 0) if random_shift_factor > 0: get_rand_shift = lambda: ((torch.rand(2) - 0.5) * self.img_sample_sz * random_shift_factor).long().tolist() # Create transofmations self.transforms = [augmentation.Identity(aug_output_sz)] if 'shift' in self.params.augmentation: self.transforms.extend([augmentation.Translation(shift, aug_output_sz) for shift in self.params.augmentation['shift']]) if 'relativeshift' in self.params.augmentation: get_absolute = lambda shift: (torch.Tensor(shift) * self.img_sample_sz/2).long().tolist() self.transforms.extend([augmentation.Translation(get_absolute(shift), aug_output_sz) for shift in self.params.augmentation['relativeshift']]) if 'fliplr' in self.params.augmentation and self.params.augmentation['fliplr']: self.transforms.append(augmentation.FlipHorizontal(aug_output_sz, get_rand_shift())) if 'blur' in self.params.augmentation: self.transforms.extend([augmentation.Blur(sigma, aug_output_sz, get_rand_shift()) for sigma in self.params.augmentation['blur']]) if 'scale' in self.params.augmentation: self.transforms.extend([augmentation.Scale(scale_factor, aug_output_sz, get_rand_shift()) for scale_factor in self.params.augmentation['scale']]) if 'rotate' in self.params.augmentation: self.transforms.extend([augmentation.Rotate(angle, aug_output_sz, get_rand_shift()) for angle in self.params.augmentation['rotate']]) # Generate initial samples init_samples = self.params.features.extract_transformed(im, self.pos, self.target_scale, aug_expansion_sz, self.transforms) # Remove augmented samples for those that shall not have for i, use_aug in enumerate(self.fparams.attribute('use_augmentation')): if not use_aug: init_samples[i] = init_samples[i][0:1, ...] # Add dropout samples if 'dropout' in self.params.augmentation: num, prob = self.params.augmentation['dropout'] self.transforms.extend(self.transforms[:1]*num) for i, use_aug in enumerate(self.fparams.attribute('use_augmentation')): if use_aug: init_samples[i] = torch.cat([init_samples[i], F.dropout2d(init_samples[i][0:1,...].expand(num,-1,-1,-1), p=prob, training=True)]) return init_samples def init_projection_matrix(self, x): # Set if using projection matrix self.params.use_projection_matrix = self.params.get('use_projection_matrix', True) if self.params.use_projection_matrix: self.compressed_dim = self.fparams.attribute('compressed_dim', None) proj_init_method = self.params.get('proj_init_method', 'pca') if proj_init_method == 'pca': x_mat = TensorList([e.permute(1, 0, 2, 3).reshape(e.shape[1], -1).clone() for e in x]) x_mat -= x_mat.mean(dim=1, keepdim=True) cov_x = x_mat @ x_mat.t() self.projection_matrix = TensorList( [None if cdim is None else torch.svd(C)[0][:, :cdim].t().unsqueeze(-1).unsqueeze(-1).clone() for C, cdim in zip(cov_x, self.compressed_dim)]) elif proj_init_method == 'randn': self.projection_matrix = TensorList( [None if cdim is None else ex.new_zeros(cdim,ex.shape[1],1,1).normal_(0,1/math.sqrt(ex.shape[1])) for ex, cdim in zip(x, self.compressed_dim)]) else: self.compressed_dim = x.size(1) self.projection_matrix = TensorList([None]*len(x)) def init_label_function(self, train_x): # Allocate label function self.y = TensorList([x.new_zeros(self.params.sample_memory_size, 1, x.shape[2], x.shape[3]) for x in train_x]) # Output sigma factor output_sigma_factor = self.fparams.attribute('output_sigma_factor') self.sigma = (self.feature_sz / self.img_support_sz * self.base_target_sz).prod().sqrt() * output_sigma_factor * torch.ones(2) # Center pos in normalized coords target_center_norm = (self.pos - self.pos.round()) / (self.target_scale * self.img_support_sz) # Generate label functions for y, sig, sz, ksz, x in zip(self.y, self.sigma, self.feature_sz, self.kernel_size, train_x): center_pos = sz * target_center_norm + 0.5 * torch.Tensor([(ksz[0] + 1) % 2, (ksz[1] + 1) % 2]) for i, T in enumerate(self.transforms[:x.shape[0]]): sample_center = center_pos + torch.Tensor(T.shift) / self.img_support_sz * sz y[i, 0, ...] = dcf.label_function_spatial(sz, sig, sample_center) # Return only the ones to use for initial training return TensorList([y[:x.shape[0], ...] for y, x in zip(self.y, train_x)]) def init_memory(self, train_x): # Initialize first-frame training samples self.num_init_samples = train_x.size(0) self.init_sample_weights = TensorList([x.new_ones(1) / x.shape[0] for x in train_x]) self.init_training_samples = train_x # Sample counters and weights self.num_stored_samples = self.num_init_samples.copy() self.previous_replace_ind = [None] * len(self.num_stored_samples) self.sample_weights = TensorList([x.new_zeros(self.params.sample_memory_size) for x in train_x]) for sw, init_sw, num in zip(self.sample_weights, self.init_sample_weights, self.num_init_samples): sw[:num] = init_sw # Initialize memory self.training_samples = TensorList( [x.new_zeros(self.params.sample_memory_size, cdim, x.shape[2], x.shape[3]) for x, cdim in zip(train_x, self.compressed_dim)]) def update_memory(self, sample_x: TensorList, sample_y: TensorList, learning_rate = None): replace_ind = self.update_sample_weights(self.sample_weights, self.previous_replace_ind, self.num_stored_samples, self.num_init_samples, self.fparams, learning_rate) self.previous_replace_ind = replace_ind for train_samp, x, ind in zip(self.training_samples, sample_x, replace_ind): train_samp[ind:ind+1,...] = x for y_memory, y, ind in zip(self.y, sample_y, replace_ind): y_memory[ind:ind+1,...] = y if self.hinge_mask is not None: for m, y, ind in zip(self.hinge_mask, sample_y, replace_ind): m[ind:ind+1,...] = (y >= self.params.hinge_threshold).float() self.num_stored_samples += 1 def update_sample_weights(self, sample_weights, previous_replace_ind, num_stored_samples, num_init_samples, fparams, learning_rate = None): # Update weights and get index to replace in memory replace_ind = [] for sw, prev_ind, num_samp, num_init, fpar in zip(sample_weights, previous_replace_ind, num_stored_samples, num_init_samples, fparams): lr = learning_rate if lr is None: lr = fpar.learning_rate init_samp_weight = getattr(fpar, 'init_samples_minimum_weight', None) if init_samp_weight == 0: init_samp_weight = None s_ind = 0 if init_samp_weight is None else num_init if num_samp == 0 or lr == 1: sw[:] = 0 sw[0] = 1 r_ind = 0 else: # Get index to replace _, r_ind = torch.min(sw[s_ind:], 0) r_ind = r_ind.item() + s_ind # Update weights if prev_ind is None: sw /= 1 - lr sw[r_ind] = lr else: sw[r_ind] = sw[prev_ind] / (1 - lr) sw /= sw.sum() if init_samp_weight is not None and sw[:num_init].sum() < init_samp_weight: sw /= init_samp_weight + sw[num_init:].sum() sw[:num_init] = init_samp_weight / num_init replace_ind.append(r_ind) return replace_ind def get_label_function(self, sample_pos, sample_scale): # Generate label function train_y = TensorList() target_center_norm = (self.pos - sample_pos) / (sample_scale * self.img_support_sz) for sig, sz, ksz in zip(self.sigma, self.feature_sz, self.kernel_size): center = sz * target_center_norm + 0.5 * torch.Tensor([(ksz[0] + 1) % 2, (ksz[1] + 1) % 2]) train_y.append(dcf.label_function_spatial(sz, sig, center)) return train_y def update_state(self, new_pos, new_scale = None): # Update scale if new_scale is not None: self.target_scale = new_scale.clamp(self.min_scale_factor, self.max_scale_factor) self.target_sz = self.base_target_sz * self.target_scale # Update pos inside_ratio = 0.2 inside_offset = (inside_ratio - 0.5) * self.target_sz self.pos = torch.max(torch.min(new_pos, self.image_sz - inside_offset), inside_offset) def get_iounet_box(self, pos, sz, sample_pos, sample_scale): """All inputs in original image coordinates""" box_center = (pos - sample_pos) / sample_scale + (self.iou_img_sample_sz - 1) / 2 box_sz = sz / sample_scale target_ul = box_center - (box_sz - 1) / 2 return torch.cat([target_ul.flip((0,)), box_sz.flip((0,))]) def init_iou_net(self): # Setup IoU net self.iou_predictor = self.params.features.get_unique_attribute('iou_predictor') for p in self.iou_predictor.parameters(): p.requires_grad = False # Get target boxes for the different augmentations self.iou_target_box = self.get_iounet_box(self.pos, self.target_sz, self.pos.round(), self.target_scale) target_boxes = TensorList() if self.params.iounet_augmentation: for T in self.transforms: if not isinstance(T, (augmentation.Identity, augmentation.Translation, augmentation.FlipHorizontal, augmentation.FlipVertical, augmentation.Blur)): break target_boxes.append(self.iou_target_box + torch.Tensor([T.shift[1], T.shift[0], 0, 0])) else: target_boxes.append(self.iou_target_box.clone()) target_boxes = torch.cat(target_boxes.view(1,4), 0).to(self.params.device) # Get iou features iou_backbone_features = self.get_iou_backbone_features() # Remove other augmentations such as rotation iou_backbone_features = TensorList([x[:target_boxes.shape[0],...] for x in iou_backbone_features]) # Extract target feat with torch.no_grad(): target_feat = self.iou_predictor.get_modulation(iou_backbone_features, target_boxes) self.target_feat = TensorList([x.detach().mean(0) for x in target_feat]) if self.params.get('iounet_not_use_reference', False): self.target_feat = TensorList([torch.full_like(tf, tf.norm() / tf.numel()) for tf in self.target_feat]) def refine_target_box(self, sample_pos, sample_scale, scale_ind, update_scale = True): # Initial box for refinement init_box = self.get_iounet_box(self.pos, self.target_sz, sample_pos, sample_scale) # Extract features from the relevant scale iou_features = self.get_iou_features() iou_features = TensorList([x[scale_ind:scale_ind+1,...] for x in iou_features]) init_boxes = init_box.view(1,4).clone() if self.params.num_init_random_boxes > 0: # Get random initial boxes square_box_sz = init_box[2:].prod().sqrt() rand_factor = square_box_sz * torch.cat([self.params.box_jitter_pos * torch.ones(2), self.params.box_jitter_sz * torch.ones(2)]) minimal_edge_size = init_box[2:].min()/3 rand_bb = (torch.rand(self.params.num_init_random_boxes, 4) - 0.5) * rand_factor new_sz = (init_box[2:] + rand_bb[:,2:]).clamp(minimal_edge_size) new_center = (init_box[:2] + init_box[2:]/2) + rand_bb[:,:2] init_boxes = torch.cat([new_center - new_sz/2, new_sz], 1) init_boxes = torch.cat([init_box.view(1,4), init_boxes]) # Refine boxes by maximizing iou output_boxes, output_iou = self.optimize_boxes(iou_features, init_boxes) # Remove weird boxes with extreme aspect ratios output_boxes[:, 2:].clamp_(1) aspect_ratio = output_boxes[:,2] / output_boxes[:,3] keep_ind = (aspect_ratio < self.params.maximal_aspect_ratio) * (aspect_ratio > 1/self.params.maximal_aspect_ratio) output_boxes = output_boxes[keep_ind,:] output_iou = output_iou[keep_ind] # If no box found if output_boxes.shape[0] == 0: return # Take average of top k boxes k = self.params.get('iounet_k', 5) topk = min(k, output_boxes.shape[0]) _, inds = torch.topk(output_iou, topk) predicted_box = output_boxes[inds, :].mean(0) predicted_iou = output_iou.view(-1, 1)[inds, :].mean(0) # Update position new_pos = predicted_box[:2] + predicted_box[2:]/2 - (self.iou_img_sample_sz - 1) / 2 new_pos = new_pos.flip((0,)) * sample_scale + sample_pos new_target_sz = predicted_box[2:].flip((0,)) * sample_scale new_scale = torch.sqrt(new_target_sz.prod() / self.base_target_sz.prod()) self.pos_iounet = new_pos.clone() if self.params.get('use_iounet_pos_for_learning', True): self.pos = new_pos.clone() self.target_sz = new_target_sz if update_scale: self.target_scale = new_scale def optimize_boxes(self, iou_features, init_boxes): # Optimize iounet boxes output_boxes = init_boxes.view(1, -1, 4).to(self.params.device) step_length = self.params.box_refinement_step_length init_step_length = self.params.box_refinement_step_length if isinstance(step_length, (tuple, list)): init_step_length = torch.Tensor([step_length[0], step_length[0], step_length[1], step_length[1]]).to( self.params.device).view(1, 1, 4) box_refinement_space = self.params.get('box_refinement_space', 'default') step_length = init_step_length * output_boxes.new_ones(1, output_boxes.shape[1], 1) outputs_prev = -99999999 * output_boxes.new_ones(1, output_boxes.shape[1]) step = torch.zeros_like(output_boxes) if box_refinement_space == 'default': # Optimization using bounding box space used in original IoUNet for i_ in range(self.params.box_refinement_iter): # forward pass bb_init = output_boxes.clone().detach() bb_init.requires_grad = True outputs = self.iou_predictor.predict_iou(self.target_feat, iou_features, bb_init) if isinstance(outputs, (list, tuple)): outputs = outputs[0] outputs.backward(gradient=torch.ones_like(outputs)) # Update mask and step length update_mask = (outputs.detach() > outputs_prev) | (self.params.box_refinement_step_decay >= 1) update_mask_float = update_mask.view(1, -1, 1).float() step_length[~update_mask, :] *= self.params.box_refinement_step_decay outputs_prev = outputs.detach().clone() # Update proposal step = update_mask_float * step_length * bb_init.grad * bb_init[:, :, 2:].repeat(1, 1, 2) - ( 1.0 - update_mask_float) * step output_boxes = bb_init + step output_boxes.detach_() elif box_refinement_space == 'relative': # Optimization using relative bounding box space sz_norm = output_boxes[:, :1, 2:].clone() output_boxes_rel = bbutils.rect_to_rel(output_boxes, sz_norm) for i_ in range(self.params.box_refinement_iter): # forward pass bb_init_rel = output_boxes_rel.clone().detach() bb_init_rel.requires_grad = True bb_init = bbutils.rel_to_rect(bb_init_rel, sz_norm) outputs = self.iou_predictor.predict_iou(self.target_feat, iou_features, bb_init) if isinstance(outputs, (list, tuple)): outputs = outputs[0] outputs.backward(gradient=torch.ones_like(outputs)) # Update mask and step length update_mask = (outputs.detach() > outputs_prev) | (self.params.box_refinement_step_decay >= 1) update_mask_float = update_mask.view(1, -1, 1).float() step_length[~update_mask, :] *= self.params.box_refinement_step_decay outputs_prev = outputs.detach().clone() # Update proposal step = update_mask_float * step_length * bb_init_rel.grad - (1.0 - update_mask_float) * step output_boxes_rel = bb_init_rel + step output_boxes_rel.detach_() # for s in outputs.view(-1): # print('{:.2f} '.format(s.item()), end='') # print('') # print('') output_boxes = bbutils.rel_to_rect(output_boxes_rel, sz_norm) else: raise ValueError('Unknown box_refinement_space {}'.format(box_refinement_space)) return output_boxes.view(-1, 4).cpu(), outputs.detach().view(-1).cpu() ================================================ FILE: external/AR/pytracking/tracker/atom/optim.py ================================================ import torch from pytracking import optimization, TensorList, operation import math class FactorizedConvProblem(optimization.L2Problem): def __init__(self, training_samples: TensorList, y:TensorList, filter_reg: torch.Tensor, projection_reg, params, sample_weights: TensorList, projection_activation, response_activation): self.training_samples = training_samples self.y = y self.filter_reg = filter_reg self.sample_weights = sample_weights self.params = params self.projection_reg = projection_reg self.projection_activation = projection_activation self.response_activation = response_activation self.diag_M = self.filter_reg.concat(projection_reg) def __call__(self, x: TensorList): """ Compute residuals :param x: [filters, projection_matrices] :return: [data_terms, filter_regularizations, proj_mat_regularizations] """ filter = x[:len(x)//2] # w2 in paper P = x[len(x)//2:] # w1 in paper # Do first convolution compressed_samples = operation.conv1x1(self.training_samples, P).apply(self.projection_activation) # Do second convolution residuals = operation.conv2d(compressed_samples, filter, mode='same').apply(self.response_activation) # Compute data residuals residuals = residuals - self.y residuals = self.sample_weights.sqrt().view(-1, 1, 1, 1) * residuals # Add regularization for projection matrix residuals.extend(self.filter_reg.apply(math.sqrt) * filter) # Add regularization for projection matrix residuals.extend(self.projection_reg.apply(math.sqrt) * P) return residuals def ip_input(self, a: TensorList, b: TensorList): num = len(a) // 2 # Number of filters a_filter = a[:num] b_filter = b[:num] a_P = a[num:] b_P = b[num:] # Filter inner product # ip_out = a_filter.reshape(-1) @ b_filter.reshape(-1) ip_out = operation.conv2d(a_filter, b_filter).view(-1) # Add projection matrix part # ip_out += a_P.reshape(-1) @ b_P.reshape(-1) ip_out += operation.conv2d(a_P.view(1,-1,1,1), b_P.view(1,-1,1,1)).view(-1) # Have independent inner products for each filter return ip_out.concat(ip_out.clone()) def M1(self, x: TensorList): return x / self.diag_M class ConvProblem(optimization.L2Problem): def __init__(self, training_samples: TensorList, y:TensorList, filter_reg: torch.Tensor, sample_weights: TensorList, response_activation): self.training_samples = training_samples self.y = y self.filter_reg = filter_reg self.sample_weights = sample_weights self.response_activation = response_activation def __call__(self, x: TensorList): """ Compute residuals :param x: [filters] :return: [data_terms, filter_regularizations] """ # Do convolution and compute residuals residuals = operation.conv2d(self.training_samples, x, mode='same').apply(self.response_activation) residuals = residuals - self.y residuals = self.sample_weights.sqrt().view(-1, 1, 1, 1) * residuals # Add regularization for projection matrix residuals.extend(self.filter_reg.apply(math.sqrt) * x) return residuals def ip_input(self, a: TensorList, b: TensorList): # return a.reshape(-1) @ b.reshape(-1) # return (a * b).sum() return operation.conv2d(a, b).view(-1) ================================================ FILE: external/AR/pytracking/tracker/base/__init__.py ================================================ from .basetracker import BaseTracker ================================================ FILE: external/AR/pytracking/tracker/base/basetracker.py ================================================ from _collections import OrderedDict class BaseTracker: """Base class for all trackers.""" def __init__(self, params): self.params = params self.visdom = None def initialize(self, image, info: dict) -> dict: """Overload this function in your tracker. This should initialize the model.""" raise NotImplementedError def track(self, image, info: dict = None) -> dict: """Overload this function in your tracker. This should track in the frame and update the model.""" raise NotImplementedError def visdom_draw_tracking(self, image, box, segmentation=None): if isinstance(box, OrderedDict): box = [v for k, v in box.items()] else: box = (box,) if segmentation is None: self.visdom.register((image, *box), 'Tracking', 1, 'Tracking') else: self.visdom.register((image, *box, segmentation), 'Tracking', 1, 'Tracking') ================================================ FILE: external/AR/pytracking/tracker/dimp/__init__.py ================================================ from .dimp import DiMP def get_tracker_class(): return DiMP ================================================ FILE: external/AR/pytracking/tracker/dimp/dimp.py ================================================ from pytracking.tracker.base import BaseTracker import torch import torch.nn.functional as F import math import time from pytracking import dcf, TensorList from pytracking.features.preprocessing import numpy_to_torch from pytracking.utils.plotting import show_tensor, plot_graph from pytracking.features.preprocessing import sample_patch_multiscale, sample_patch_transformed from pytracking.features import augmentation import ltr.data.bounding_box_utils as bbutils from ltr.models.target_classifier.initializer import FilterInitializerZero from ltr.models.layers import activation class DiMP(BaseTracker): multiobj_mode = 'parallel' def initialize_features(self): if not getattr(self, 'features_initialized', False): self.params.net.initialize() self.features_initialized = True def initialize(self, image, info: dict) -> dict: # Initialize some stuff self.frame_num = 1 if not self.params.has('device'): self.params.device = 'cuda' if self.params.use_gpu else 'cpu' # Initialize network self.initialize_features() # The DiMP network self.net = self.params.net # Time initialization tic = time.time() # Convert image im = numpy_to_torch(image) # Get target position and size state = info['init_bbox'] self.pos = torch.Tensor([state[1] + (state[3] - 1)/2, state[0] + (state[2] - 1)/2]) self.target_sz = torch.Tensor([state[3], state[2]]) # Get object id self.object_id = info.get('object_ids', [None])[0] self.id_str = '' if self.object_id is None else ' {}'.format(self.object_id) # Set sizes self.image_sz = torch.Tensor([im.shape[2], im.shape[3]]) sz = self.params.image_sample_size sz = torch.Tensor([sz, sz] if isinstance(sz, int) else sz) if self.params.get('use_image_aspect_ratio', False): sz = self.image_sz * sz.prod().sqrt() / self.image_sz.prod().sqrt() stride = self.params.get('feature_stride', 32) sz = torch.round(sz / stride) * stride self.img_sample_sz = sz self.img_support_sz = self.img_sample_sz # Set search area search_area = torch.prod(self.target_sz * self.params.search_area_scale).item() self.target_scale = math.sqrt(search_area) / self.img_sample_sz.prod().sqrt() # Target size in base scale self.base_target_sz = self.target_sz / self.target_scale # Setup scale factors if not self.params.has('scale_factors'): self.params.scale_factors = torch.ones(1) elif isinstance(self.params.scale_factors, (list, tuple)): self.params.scale_factors = torch.Tensor(self.params.scale_factors) # Setup scale bounds self.min_scale_factor = torch.max(10 / self.base_target_sz) self.max_scale_factor = torch.min(self.image_sz / self.base_target_sz) # Extract and transform sample init_backbone_feat = self.generate_init_samples(im) # Initialize classifier self.init_classifier(init_backbone_feat) # Initialize IoUNet if self.params.get('use_iou_net', True): self.init_iou_net(init_backbone_feat) out = {'time': time.time() - tic} return out def track(self, image, info: dict = None) -> dict: self.debug_info = {} self.frame_num += 1 self.debug_info['frame_num'] = self.frame_num # Convert image im = numpy_to_torch(image) # ------- LOCALIZATION ------- # # Extract backbone features backbone_feat, sample_coords, im_patches = self.extract_backbone_features(im, self.get_centered_sample_pos(), self.target_scale * self.params.scale_factors, self.img_sample_sz) # Extract classification features test_x = self.get_classification_features(backbone_feat) # Location of sample sample_pos, sample_scales = self.get_sample_location(sample_coords) # Compute classification scores scores_raw = self.classify_target(test_x) # Localize the target translation_vec, scale_ind, s, flag = self.localize_target(scores_raw, sample_pos, sample_scales) new_pos = sample_pos[scale_ind,:] + translation_vec # Update position and scale if flag != 'not_found': if self.params.get('use_iou_net', True): update_scale_flag = self.params.get('update_scale_when_uncertain', True) or flag != 'uncertain' if self.params.get('use_classifier', True): self.update_state(new_pos) self.refine_target_box(backbone_feat, sample_pos[scale_ind,:], sample_scales[scale_ind], scale_ind, update_scale_flag) elif self.params.get('use_classifier', True): self.update_state(new_pos, sample_scales[scale_ind]) # ------- UPDATE ------- # update_flag = flag not in ['not_found', 'uncertain'] hard_negative = (flag == 'hard_negative') learning_rate = self.params.get('hard_negative_learning_rate', None) if hard_negative else None if update_flag and self.params.get('update_classifier', False): # Get train sample train_x = test_x[scale_ind:scale_ind+1, ...] # Create target_box and label for spatial sample target_box = self.get_iounet_box(self.pos, self.target_sz, sample_pos[scale_ind,:], sample_scales[scale_ind]) # Update the classifier model self.update_classifier(train_x, target_box, learning_rate, s[scale_ind,...]) # Set the pos of the tracker to iounet pos if self.params.get('use_iou_net', True) and flag != 'not_found' and hasattr(self, 'pos_iounet'): self.pos = self.pos_iounet.clone() score_map = s[scale_ind, ...] max_score = torch.max(score_map).item() # Visualize and set debug info self.search_area_box = torch.cat((sample_coords[scale_ind,[1,0]], sample_coords[scale_ind,[3,2]] - sample_coords[scale_ind,[1,0]] - 1)) self.debug_info['flag' + self.id_str] = flag self.debug_info['max_score' + self.id_str] = max_score if self.visdom is not None: self.visdom.register(score_map, 'heatmap', 2, 'Score Map' + self.id_str) self.visdom.register(self.debug_info, 'info_dict', 1, 'Status') elif self.params.debug >= 2: show_tensor(score_map, 5, title='Max score = {:.2f}'.format(max_score)) # Compute output bounding box new_state = torch.cat((self.pos[[1,0]] - (self.target_sz[[1,0]]-1)/2, self.target_sz[[1,0]])) if self.params.get('output_not_found_box', False) and flag == 'not_found': output_state = [-1, -1, -1, -1] else: output_state = new_state.tolist() '''2020.4.26 ''' out = {'target_bbox': output_state, 'dcf_center':new_pos[[1,0]]} return out def get_sample_location(self, sample_coord): """Get the location of the extracted sample.""" sample_coord = sample_coord.float() sample_pos = 0.5*(sample_coord[:,:2] + sample_coord[:,2:] - 1) sample_scales = ((sample_coord[:,2:] - sample_coord[:,:2]) / self.img_sample_sz).prod(dim=1).sqrt() return sample_pos, sample_scales def get_centered_sample_pos(self): """Get the center position for the new sample. Make sure the target is correctly centered.""" return self.pos + ((self.feature_sz + self.kernel_size) % 2) * self.target_scale * \ self.img_support_sz / (2*self.feature_sz) def classify_target(self, sample_x: TensorList): """Classify target by applying the DiMP filter.""" with torch.no_grad(): scores = self.net.classifier.classify(self.target_filter, sample_x) return scores def localize_target(self, scores, sample_pos, sample_scales): """Run the target localization.""" scores = scores.squeeze(1) preprocess_method = self.params.get('score_preprocess', 'none') if preprocess_method == 'none': pass elif preprocess_method == 'exp': scores = scores.exp() elif preprocess_method == 'softmax': reg_val = getattr(self.net.classifier.filter_optimizer, 'softmax_reg', None) scores_view = scores.view(scores.shape[0], -1) scores_softmax = activation.softmax_reg(scores_view, dim=-1, reg=reg_val) scores = scores_softmax.view(scores.shape) else: raise Exception('Unknown score_preprocess in params.') score_filter_ksz = self.params.get('score_filter_ksz', 1) if score_filter_ksz > 1: assert score_filter_ksz % 2 == 1 kernel = scores.new_ones(1,1,score_filter_ksz,score_filter_ksz) scores = F.conv2d(scores.view(-1,1,*scores.shape[-2:]), kernel, padding=score_filter_ksz//2).view(scores.shape) if self.params.get('advanced_localization', False): return self.localize_advanced(scores, sample_pos, sample_scales) # Get maximum score_sz = torch.Tensor(list(scores.shape[-2:])) score_center = (score_sz - 1)/2 max_score, max_disp = dcf.max2d(scores) _, scale_ind = torch.max(max_score, dim=0) max_disp = max_disp[scale_ind,...].float().cpu().view(-1) target_disp = max_disp - score_center # Compute translation vector and scale change factor output_sz = score_sz - (self.kernel_size + 1) % 2 translation_vec = target_disp * (self.img_support_sz / output_sz) * sample_scales[scale_ind] return translation_vec, scale_ind, scores, None def localize_advanced(self, scores, sample_pos, sample_scales): """Run the target advanced localization (as in ATOM).""" sz = scores.shape[-2:] score_sz = torch.Tensor(list(sz)) output_sz = score_sz - (self.kernel_size + 1) % 2 score_center = (score_sz - 1)/2 scores_hn = scores if self.output_window is not None and self.params.get('perform_hn_without_windowing', False): scores_hn = scores.clone() scores *= self.output_window max_score1, max_disp1 = dcf.max2d(scores) _, scale_ind = torch.max(max_score1, dim=0) sample_scale = sample_scales[scale_ind] max_score1 = max_score1[scale_ind] max_disp1 = max_disp1[scale_ind,...].float().cpu().view(-1) target_disp1 = max_disp1 - score_center translation_vec1 = target_disp1 * (self.img_support_sz / output_sz) * sample_scale if max_score1.item() < self.params.target_not_found_threshold: return translation_vec1, scale_ind, scores_hn, 'not_found' if max_score1.item() < self.params.get('uncertain_threshold', -float('inf')): return translation_vec1, scale_ind, scores_hn, 'uncertain' if max_score1.item() < self.params.get('hard_sample_threshold', -float('inf')): return translation_vec1, scale_ind, scores_hn, 'hard_negative' # Mask out target neighborhood target_neigh_sz = self.params.target_neighborhood_scale * (self.target_sz / sample_scale) * (output_sz / self.img_support_sz) tneigh_top = max(round(max_disp1[0].item() - target_neigh_sz[0].item() / 2), 0) tneigh_bottom = min(round(max_disp1[0].item() + target_neigh_sz[0].item() / 2 + 1), sz[0]) tneigh_left = max(round(max_disp1[1].item() - target_neigh_sz[1].item() / 2), 0) tneigh_right = min(round(max_disp1[1].item() + target_neigh_sz[1].item() / 2 + 1), sz[1]) scores_masked = scores_hn[scale_ind:scale_ind + 1, ...].clone() scores_masked[...,tneigh_top:tneigh_bottom,tneigh_left:tneigh_right] = 0 # Find new maximum max_score2, max_disp2 = dcf.max2d(scores_masked) max_disp2 = max_disp2.float().cpu().view(-1) target_disp2 = max_disp2 - score_center translation_vec2 = target_disp2 * (self.img_support_sz / output_sz) * sample_scale prev_target_vec = (self.pos - sample_pos[scale_ind,:]) / ((self.img_support_sz / output_sz) * sample_scale) # Handle the different cases if max_score2 > self.params.distractor_threshold * max_score1: disp_norm1 = torch.sqrt(torch.sum((target_disp1-prev_target_vec)**2)) disp_norm2 = torch.sqrt(torch.sum((target_disp2-prev_target_vec)**2)) disp_threshold = self.params.dispalcement_scale * math.sqrt(sz[0] * sz[1]) / 2 if disp_norm2 > disp_threshold and disp_norm1 < disp_threshold: return translation_vec1, scale_ind, scores_hn, 'hard_negative' if disp_norm2 < disp_threshold and disp_norm1 > disp_threshold: return translation_vec2, scale_ind, scores_hn, 'hard_negative' if disp_norm2 > disp_threshold and disp_norm1 > disp_threshold: return translation_vec1, scale_ind, scores_hn, 'uncertain' # If also the distractor is close, return with highest score return translation_vec1, scale_ind, scores_hn, 'uncertain' if max_score2 > self.params.hard_negative_threshold * max_score1 and max_score2 > self.params.target_not_found_threshold: return translation_vec1, scale_ind, scores_hn, 'hard_negative' return translation_vec1, scale_ind, scores_hn, 'normal' def extract_backbone_features(self, im: torch.Tensor, pos: torch.Tensor, scales, sz: torch.Tensor): im_patches, patch_coords = sample_patch_multiscale(im, pos, scales, sz, mode=self.params.get('border_mode', 'replicate'), max_scale_change=self.params.get('patch_max_scale_change', None)) with torch.no_grad(): backbone_feat = self.net.extract_backbone(im_patches) return backbone_feat, patch_coords, im_patches def get_classification_features(self, backbone_feat): with torch.no_grad(): return self.net.extract_classification_feat(backbone_feat) def get_iou_backbone_features(self, backbone_feat): return self.net.get_backbone_bbreg_feat(backbone_feat) def get_iou_features(self, backbone_feat): with torch.no_grad(): return self.net.bb_regressor.get_iou_feat(self.get_iou_backbone_features(backbone_feat)) def get_iou_modulation(self, iou_backbone_feat, target_boxes): with torch.no_grad(): return self.net.bb_regressor.get_modulation(iou_backbone_feat, target_boxes) def generate_init_samples(self, im: torch.Tensor) -> TensorList: """Perform data augmentation to generate initial training samples.""" mode = self.params.get('border_mode', 'replicate') if mode == 'inside': # Get new sample size if forced inside the image im_sz = torch.Tensor([im.shape[2], im.shape[3]]) sample_sz = self.target_scale * self.img_sample_sz shrink_factor = (sample_sz.float() / im_sz) if mode == 'inside': shrink_factor = shrink_factor.max() elif mode == 'inside_major': shrink_factor = shrink_factor.min() shrink_factor.clamp_(min=1, max=self.params.get('patch_max_scale_change', None)) sample_sz = (sample_sz.float() / shrink_factor) self.init_sample_scale = (sample_sz / self.img_sample_sz).prod().sqrt() tl = self.pos - (sample_sz - 1) / 2 br = self.pos + sample_sz / 2 + 1 global_shift = - ((-tl).clamp(0) - (br - im_sz).clamp(0)) / self.init_sample_scale else: self.init_sample_scale = self.target_scale global_shift = torch.zeros(2) self.init_sample_pos = self.pos.round() # Compute augmentation size aug_expansion_factor = self.params.get('augmentation_expansion_factor', None) aug_expansion_sz = self.img_sample_sz.clone() aug_output_sz = None if aug_expansion_factor is not None and aug_expansion_factor != 1: aug_expansion_sz = (self.img_sample_sz * aug_expansion_factor).long() aug_expansion_sz += (aug_expansion_sz - self.img_sample_sz.long()) % 2 aug_expansion_sz = aug_expansion_sz.float() aug_output_sz = self.img_sample_sz.long().tolist() # Random shift for each sample get_rand_shift = lambda: None random_shift_factor = self.params.get('random_shift_factor', 0) if random_shift_factor > 0: get_rand_shift = lambda: ((torch.rand(2) - 0.5) * self.img_sample_sz * random_shift_factor + global_shift).long().tolist() # Always put identity transformation first, since it is the unaugmented sample that is always used self.transforms = [augmentation.Identity(aug_output_sz, global_shift.long().tolist())] augs = self.params.augmentation if self.params.get('use_augmentation', True) else {} # Add all augmentations if 'shift' in augs: self.transforms.extend([augmentation.Translation(shift, aug_output_sz, global_shift.long().tolist()) for shift in augs['shift']]) if 'relativeshift' in augs: get_absolute = lambda shift: (torch.Tensor(shift) * self.img_sample_sz/2).long().tolist() self.transforms.extend([augmentation.Translation(get_absolute(shift), aug_output_sz, global_shift.long().tolist()) for shift in augs['relativeshift']]) if 'fliplr' in augs and augs['fliplr']: self.transforms.append(augmentation.FlipHorizontal(aug_output_sz, get_rand_shift())) if 'blur' in augs: self.transforms.extend([augmentation.Blur(sigma, aug_output_sz, get_rand_shift()) for sigma in augs['blur']]) if 'scale' in augs: self.transforms.extend([augmentation.Scale(scale_factor, aug_output_sz, get_rand_shift()) for scale_factor in augs['scale']]) if 'rotate' in augs: self.transforms.extend([augmentation.Rotate(angle, aug_output_sz, get_rand_shift()) for angle in augs['rotate']]) # Extract augmented image patches im_patches = sample_patch_transformed(im, self.init_sample_pos, self.init_sample_scale, aug_expansion_sz, self.transforms) # Extract initial backbone features with torch.no_grad(): init_backbone_feat = self.net.extract_backbone(im_patches) return init_backbone_feat def init_target_boxes(self): """Get the target bounding boxes for the initial augmented samples.""" self.classifier_target_box = self.get_iounet_box(self.pos, self.target_sz, self.init_sample_pos, self.init_sample_scale) init_target_boxes = TensorList() for T in self.transforms: init_target_boxes.append(self.classifier_target_box + torch.Tensor([T.shift[1], T.shift[0], 0, 0])) init_target_boxes = torch.cat(init_target_boxes.view(1, 4), 0).to(self.params.device) self.target_boxes = init_target_boxes.new_zeros(self.params.sample_memory_size, 4) self.target_boxes[:init_target_boxes.shape[0],:] = init_target_boxes return init_target_boxes def init_memory(self, train_x: TensorList): # Initialize first-frame spatial training samples self.num_init_samples = train_x.size(0) init_sample_weights = TensorList([x.new_ones(1) / x.shape[0] for x in train_x]) # Sample counters and weights for spatial self.num_stored_samples = self.num_init_samples.copy() self.previous_replace_ind = [None] * len(self.num_stored_samples) self.sample_weights = TensorList([x.new_zeros(self.params.sample_memory_size) for x in train_x]) for sw, init_sw, num in zip(self.sample_weights, init_sample_weights, self.num_init_samples): sw[:num] = init_sw # Initialize memory self.training_samples = TensorList( [x.new_zeros(self.params.sample_memory_size, x.shape[1], x.shape[2], x.shape[3]) for x in train_x]) for ts, x in zip(self.training_samples, train_x): ts[:x.shape[0],...] = x def update_memory(self, sample_x: TensorList, target_box, learning_rate = None): # Update weights and get replace ind replace_ind = self.update_sample_weights(self.sample_weights, self.previous_replace_ind, self.num_stored_samples, self.num_init_samples, learning_rate) self.previous_replace_ind = replace_ind # Update sample and label memory for train_samp, x, ind in zip(self.training_samples, sample_x, replace_ind): train_samp[ind:ind+1,...] = x # Update bb memory self.target_boxes[replace_ind[0],:] = target_box self.num_stored_samples += 1 def update_sample_weights(self, sample_weights, previous_replace_ind, num_stored_samples, num_init_samples, learning_rate = None): # Update weights and get index to replace replace_ind = [] for sw, prev_ind, num_samp, num_init in zip(sample_weights, previous_replace_ind, num_stored_samples, num_init_samples): lr = learning_rate if lr is None: lr = self.params.learning_rate init_samp_weight = self.params.get('init_samples_minimum_weight', None) if init_samp_weight == 0: init_samp_weight = None s_ind = 0 if init_samp_weight is None else num_init if num_samp == 0 or lr == 1: sw[:] = 0 sw[0] = 1 r_ind = 0 else: # Get index to replace if num_samp < sw.shape[0]: r_ind = num_samp else: _, r_ind = torch.min(sw[s_ind:], 0) r_ind = r_ind.item() + s_ind # Update weights if prev_ind is None: sw /= 1 - lr sw[r_ind] = lr else: sw[r_ind] = sw[prev_ind] / (1 - lr) sw /= sw.sum() if init_samp_weight is not None and sw[:num_init].sum() < init_samp_weight: sw /= init_samp_weight + sw[num_init:].sum() sw[:num_init] = init_samp_weight / num_init replace_ind.append(r_ind) return replace_ind def update_state(self, new_pos, new_scale = None): # Update scale if new_scale is not None: self.target_scale = new_scale.clamp(self.min_scale_factor, self.max_scale_factor) self.target_sz = self.base_target_sz * self.target_scale # Update pos inside_ratio = self.params.get('target_inside_ratio', 0.2) inside_offset = (inside_ratio - 0.5) * self.target_sz self.pos = torch.max(torch.min(new_pos, self.image_sz - inside_offset), inside_offset) def get_iounet_box(self, pos, sz, sample_pos, sample_scale): """All inputs in original image coordinates. Generates a box in the cropped image sample reference frame, in the format used by the IoUNet.""" box_center = (pos - sample_pos) / sample_scale + (self.img_sample_sz - 1) / 2 box_sz = sz / sample_scale target_ul = box_center - (box_sz - 1) / 2 return torch.cat([target_ul.flip((0,)), box_sz.flip((0,))]) def init_iou_net(self, backbone_feat): # Setup IoU net and objective for p in self.net.bb_regressor.parameters(): p.requires_grad = False # Get target boxes for the different augmentations self.classifier_target_box = self.get_iounet_box(self.pos, self.target_sz, self.init_sample_pos, self.init_sample_scale) target_boxes = TensorList() if self.params.iounet_augmentation: for T in self.transforms: if not isinstance(T, (augmentation.Identity, augmentation.Translation, augmentation.FlipHorizontal, augmentation.FlipVertical, augmentation.Blur)): break target_boxes.append(self.classifier_target_box + torch.Tensor([T.shift[1], T.shift[0], 0, 0])) else: target_boxes.append(self.classifier_target_box + torch.Tensor([self.transforms[0].shift[1], self.transforms[0].shift[0], 0, 0])) target_boxes = torch.cat(target_boxes.view(1,4), 0).to(self.params.device) # Get iou features iou_backbone_feat = self.get_iou_backbone_features(backbone_feat) # Remove other augmentations such as rotation iou_backbone_feat = TensorList([x[:target_boxes.shape[0],...] for x in iou_backbone_feat]) # Get modulation vector self.iou_modulation = self.get_iou_modulation(iou_backbone_feat, target_boxes) if torch.is_tensor(self.iou_modulation[0]): self.iou_modulation = TensorList([x.detach().mean(0) for x in self.iou_modulation]) def init_classifier(self, init_backbone_feat): # Get classification features x = self.get_classification_features(init_backbone_feat) # Overwrite some parameters in the classifier. (These are not generally changed) self._overwrite_classifier_params(feature_dim=x.shape[-3]) # Add the dropout augmentation here, since it requires extraction of the classification features if 'dropout' in self.params.augmentation and self.params.get('use_augmentation', True): num, prob = self.params.augmentation['dropout'] self.transforms.extend(self.transforms[:1]*num) x = torch.cat([x, F.dropout2d(x[0:1,...].expand(num,-1,-1,-1), p=prob, training=True)]) # Set feature size and other related sizes self.feature_sz = torch.Tensor(list(x.shape[-2:])) ksz = self.net.classifier.filter_size self.kernel_size = torch.Tensor([ksz, ksz] if isinstance(ksz, (int, float)) else ksz) self.output_sz = self.feature_sz + (self.kernel_size + 1)%2 # Construct output window self.output_window = None if self.params.get('window_output', False): if self.params.get('use_clipped_window', False): self.output_window = dcf.hann2d_clipped(self.output_sz.long(), (self.output_sz*self.params.effective_search_area / self.params.search_area_scale).long(), centered=True).to(self.params.device) else: self.output_window = dcf.hann2d(self.output_sz.long(), centered=True).to(self.params.device) self.output_window = self.output_window.squeeze(0) # Get target boxes for the different augmentations target_boxes = self.init_target_boxes() # Set number of iterations plot_loss = self.params.debug > 0 num_iter = self.params.get('net_opt_iter', None) # Get target filter by running the discriminative model prediction module with torch.no_grad(): self.target_filter, _, losses = self.net.classifier.get_filter(x, target_boxes, num_iter=num_iter, compute_losses=plot_loss) # Init memory if self.params.get('update_classifier', True): self.init_memory(TensorList([x])) if plot_loss: if isinstance(losses, dict): losses = losses['train'] self.losses = torch.cat(losses) if self.visdom is not None: self.visdom.register((self.losses, torch.arange(self.losses.numel())), 'lineplot', 3, 'Training Loss' + self.id_str) elif self.params.debug >= 3: plot_graph(self.losses, 10, title='Training Loss' + self.id_str) def _overwrite_classifier_params(self, feature_dim): # Overwrite some parameters in the classifier. (These are not generally changed) pred_module = getattr(self.net.classifier.filter_optimizer, 'score_predictor', self.net.classifier.filter_optimizer) if self.params.get('label_threshold', None) is not None: self.net.classifier.filter_optimizer.label_threshold = self.params.label_threshold if self.params.get('label_shrink', None) is not None: self.net.classifier.filter_optimizer.label_shrink = self.params.label_shrink if self.params.get('softmax_reg', None) is not None: self.net.classifier.filter_optimizer.softmax_reg = self.params.softmax_reg if self.params.get('filter_reg', None) is not None: pred_module.filter_reg[0] = self.params.filter_reg pred_module.min_filter_reg = self.params.filter_reg if self.params.get('filter_init_zero', False): self.net.classifier.filter_initializer = FilterInitializerZero(self.net.classifier.filter_size, feature_dim) def update_classifier(self, train_x, target_box, learning_rate=None, scores=None): # Set flags and learning rate hard_negative_flag = learning_rate is not None if learning_rate is None: learning_rate = self.params.learning_rate # Update the tracker memory if hard_negative_flag or self.frame_num % self.params.get('train_sample_interval', 1) == 0: self.update_memory(TensorList([train_x]), target_box, learning_rate) # Decide the number of iterations to run num_iter = 0 low_score_th = self.params.get('low_score_opt_threshold', None) if hard_negative_flag: num_iter = self.params.get('net_opt_hn_iter', None) elif low_score_th is not None and low_score_th > scores.max().item(): num_iter = self.params.get('net_opt_low_iter', None) elif (self.frame_num - 1) % self.params.train_skipping == 0: num_iter = self.params.get('net_opt_update_iter', None) plot_loss = self.params.debug > 0 if num_iter > 0: # Get inputs for the DiMP filter optimizer module samples = self.training_samples[0][:self.num_stored_samples[0],...] target_boxes = self.target_boxes[:self.num_stored_samples[0],:].clone() sample_weights = self.sample_weights[0][:self.num_stored_samples[0]] # Run the filter optimizer module with torch.no_grad(): self.target_filter, _, losses = self.net.classifier.filter_optimizer(self.target_filter, num_iter=num_iter, feat=samples, bb=target_boxes, sample_weight=sample_weights, compute_losses=plot_loss) if plot_loss: if isinstance(losses, dict): losses = losses['train'] self.losses = torch.cat((self.losses, torch.cat(losses))) if self.visdom is not None: self.visdom.register((self.losses, torch.arange(self.losses.numel())), 'lineplot', 3, 'Training Loss' + self.id_str) elif self.params.debug >= 3: plot_graph(self.losses, 10, title='Training Loss' + self.id_str) def refine_target_box(self, backbone_feat, sample_pos, sample_scale, scale_ind, update_scale = True): """Run the ATOM IoUNet to refine the target bounding box.""" if hasattr(self.net.bb_regressor, 'predict_bb'): return self.direct_box_regression(backbone_feat, sample_pos, sample_scale, scale_ind, update_scale) # Initial box for refinement init_box = self.get_iounet_box(self.pos, self.target_sz, sample_pos, sample_scale) # Extract features from the relevant scale iou_features = self.get_iou_features(backbone_feat) iou_features = TensorList([x[scale_ind:scale_ind+1,...] for x in iou_features]) # Generate random initial boxes init_boxes = init_box.view(1,4).clone() if self.params.num_init_random_boxes > 0: square_box_sz = init_box[2:].prod().sqrt() rand_factor = square_box_sz * torch.cat([self.params.box_jitter_pos * torch.ones(2), self.params.box_jitter_sz * torch.ones(2)]) minimal_edge_size = init_box[2:].min()/3 rand_bb = (torch.rand(self.params.num_init_random_boxes, 4) - 0.5) * rand_factor new_sz = (init_box[2:] + rand_bb[:,2:]).clamp(minimal_edge_size) new_center = (init_box[:2] + init_box[2:]/2) + rand_bb[:,:2] init_boxes = torch.cat([new_center - new_sz/2, new_sz], 1) init_boxes = torch.cat([init_box.view(1,4), init_boxes]) # Optimize the boxes output_boxes, output_iou = self.optimize_boxes(iou_features, init_boxes) # Remove weird boxes output_boxes[:, 2:].clamp_(1) aspect_ratio = output_boxes[:,2] / output_boxes[:,3] keep_ind = (aspect_ratio < self.params.maximal_aspect_ratio) * (aspect_ratio > 1/self.params.maximal_aspect_ratio) output_boxes = output_boxes[keep_ind,:] output_iou = output_iou[keep_ind] # If no box found if output_boxes.shape[0] == 0: return # Predict box k = self.params.get('iounet_k', 5) topk = min(k, output_boxes.shape[0]) _, inds = torch.topk(output_iou, topk) predicted_box = output_boxes[inds, :].mean(0) predicted_iou = output_iou.view(-1, 1)[inds, :].mean(0) # Get new position and size new_pos = predicted_box[:2] + predicted_box[2:] / 2 new_pos = (new_pos.flip((0,)) - (self.img_sample_sz - 1) / 2) * sample_scale + sample_pos new_target_sz = predicted_box[2:].flip((0,)) * sample_scale new_scale = torch.sqrt(new_target_sz.prod() / self.base_target_sz.prod()) self.pos_iounet = new_pos.clone() if self.params.get('use_iounet_pos_for_learning', True): self.pos = new_pos.clone() self.target_sz = new_target_sz if update_scale: self.target_scale = new_scale # self.visualize_iou_pred(iou_features, predicted_box) def optimize_boxes(self, iou_features, init_boxes): box_refinement_space = self.params.get('box_refinement_space', 'default') if box_refinement_space == 'default': return self.optimize_boxes_default(iou_features, init_boxes) if box_refinement_space == 'relative': return self.optimize_boxes_relative(iou_features, init_boxes) raise ValueError('Unknown box_refinement_space {}'.format(box_refinement_space)) def optimize_boxes_default(self, iou_features, init_boxes): """Optimize iounet boxes with the default parametrization""" output_boxes = init_boxes.view(1, -1, 4).to(self.params.device) step_length = self.params.box_refinement_step_length if isinstance(step_length, (tuple, list)): step_length = torch.Tensor([step_length[0], step_length[0], step_length[1], step_length[1]], device=self.params.device).view(1,1,4) for i_ in range(self.params.box_refinement_iter): # forward pass bb_init = output_boxes.clone().detach() bb_init.requires_grad = True outputs = self.net.bb_regressor.predict_iou(self.iou_modulation, iou_features, bb_init) if isinstance(outputs, (list, tuple)): outputs = outputs[0] outputs.backward(gradient = torch.ones_like(outputs)) # Update proposal output_boxes = bb_init + step_length * bb_init.grad * bb_init[:, :, 2:].repeat(1, 1, 2) output_boxes.detach_() step_length *= self.params.box_refinement_step_decay return output_boxes.view(-1,4).cpu(), outputs.detach().view(-1).cpu() def optimize_boxes_relative(self, iou_features, init_boxes): """Optimize iounet boxes with the relative parametrization ised in PrDiMP""" output_boxes = init_boxes.view(1, -1, 4).to(self.params.device) step_length = self.params.box_refinement_step_length if isinstance(step_length, (tuple, list)): step_length = torch.Tensor([step_length[0], step_length[0], step_length[1], step_length[1]]).to(self.params.device).view(1,1,4) sz_norm = output_boxes[:,:1,2:].clone() output_boxes_rel = bbutils.rect_to_rel(output_boxes, sz_norm) for i_ in range(self.params.box_refinement_iter): # forward pass bb_init_rel = output_boxes_rel.clone().detach() bb_init_rel.requires_grad = True bb_init = bbutils.rel_to_rect(bb_init_rel, sz_norm) outputs = self.net.bb_regressor.predict_iou(self.iou_modulation, iou_features, bb_init) if isinstance(outputs, (list, tuple)): outputs = outputs[0] outputs.backward(gradient = torch.ones_like(outputs)) # Update proposal output_boxes_rel = bb_init_rel + step_length * bb_init_rel.grad output_boxes_rel.detach_() step_length *= self.params.box_refinement_step_decay # for s in outputs.view(-1): # print('{:.2f} '.format(s.item()), end='') # print('') # print('') output_boxes = bbutils.rel_to_rect(output_boxes_rel, sz_norm) return output_boxes.view(-1,4).cpu(), outputs.detach().view(-1).cpu() def direct_box_regression(self, backbone_feat, sample_pos, sample_scale, scale_ind, update_scale = True): """Implementation of direct bounding box regression.""" # Initial box for refinement init_box = self.get_iounet_box(self.pos, self.target_sz, sample_pos, sample_scale) # Extract features from the relevant scale iou_features = self.get_iou_features(backbone_feat) iou_features = TensorList([x[scale_ind:scale_ind+1,...] for x in iou_features]) # Generate random initial boxes init_boxes = init_box.view(1, 1, 4).clone().to(self.params.device) # Optimize the boxes output_boxes = self.net.bb_regressor.predict_bb(self.iou_modulation, iou_features, init_boxes).view(-1,4).cpu() # Remove weird boxes output_boxes[:, 2:].clamp_(1) predicted_box = output_boxes[0, :] # Get new position and size new_pos = predicted_box[:2] + predicted_box[2:] / 2 new_pos = (new_pos.flip((0,)) - (self.img_sample_sz - 1) / 2) * sample_scale + sample_pos new_target_sz = predicted_box[2:].flip((0,)) * sample_scale new_scale_bbr = torch.sqrt(new_target_sz.prod() / self.base_target_sz.prod()) new_scale = new_scale_bbr self.pos_iounet = new_pos.clone() if self.params.get('use_iounet_pos_for_learning', True): self.pos = new_pos.clone() self.target_sz = new_target_sz if update_scale: self.target_scale = new_scale def visualize_iou_pred(self, iou_features, center_box): center_box = center_box.view(1,1,4) sz_norm = center_box[...,2:].clone() center_box_rel = bbutils.rect_to_rel(center_box, sz_norm) pos_dist = 1.0 sz_dist = math.log(3.0) pos_step = 0.01 sz_step = 0.01 pos_scale = torch.arange(-pos_dist, pos_dist+pos_step, step=pos_step) sz_scale = torch.arange(-sz_dist, sz_dist+sz_step, step=sz_step) bbx = torch.zeros(1, pos_scale.numel(), 4) bbx[0,:,0] = pos_scale.clone() bby = torch.zeros(pos_scale.numel(), 1, 4) bby[:,0,1] = pos_scale.clone() bbw = torch.zeros(1, sz_scale.numel(), 4) bbw[0,:,2] = sz_scale.clone() bbh = torch.zeros(sz_scale.numel(), 1, 4) bbh[:,0,3] = sz_scale.clone() pos_boxes = bbutils.rel_to_rect((center_box_rel + bbx) + bby, sz_norm).view(1,-1,4).to(self.params.device) sz_boxes = bbutils.rel_to_rect((center_box_rel + bbw) + bbh, sz_norm).view(1,-1,4).to(self.params.device) pos_scores = self.net.bb_regressor.predict_iou(self.iou_modulation, iou_features, pos_boxes).exp() sz_scores = self.net.bb_regressor.predict_iou(self.iou_modulation, iou_features, sz_boxes).exp() show_tensor(pos_scores.view(pos_scale.numel(),-1), title='Position scores', fig_num=21) show_tensor(sz_scores.view(sz_scale.numel(),-1), title='Size scores', fig_num=22) def visdom_draw_tracking(self, image, box, segmentation=None): if hasattr(self, 'search_area_box'): self.visdom.register((image, box, self.search_area_box), 'Tracking', 1, 'Tracking') else: self.visdom.register((image, box), 'Tracking', 1, 'Tracking') ================================================ FILE: external/AR/pytracking/tracker/eco/__init__.py ================================================ from .eco import ECO def get_tracker_class(): return ECO ================================================ FILE: external/AR/pytracking/tracker/eco/eco.py ================================================ from pytracking.tracker.base import BaseTracker import torch import torch.nn.functional as F import math from pytracking import complex, dcf, fourier, TensorList from pytracking.libs.tensorlist import tensor_operation from pytracking.features.preprocessing import numpy_to_torch from pytracking.utils.plotting import show_tensor from pytracking.libs.optimization import GaussNewtonCG from .optim import FilterOptim, FactorizedConvProblem from pytracking.features import augmentation class ECO(BaseTracker): multiobj_mode = 'parallel' def initialize_features(self): if not getattr(self, 'features_initialized', False): self.params.features.initialize() self.features_initialized = True def initialize(self, image, info: dict) -> dict: state = info['init_bbox'] # Initialize some stuff self.frame_num = 1 if not self.params.has('device'): self.params.device = 'cuda' if self.params.use_gpu else 'cpu' # Initialize features self.initialize_features() # Chack if image is color self.params.features.set_is_color(image.shape[2] == 3) # Get feature specific params self.fparams = self.params.features.get_fparams('feature_params') # Get position and size self.pos = torch.Tensor([state[1] + (state[3] - 1)/2, state[0] + (state[2] - 1)/2]) self.target_sz = torch.Tensor([state[3], state[2]]) # Set search area self.target_scale = 1.0 search_area = torch.prod(self.target_sz * self.params.search_area_scale).item() if search_area > self.params.max_image_sample_size: self.target_scale = math.sqrt(search_area / self.params.max_image_sample_size) elif search_area < self.params.min_image_sample_size: self.target_scale = math.sqrt(search_area / self.params.min_image_sample_size) # Target size in base scale self.base_target_sz = self.target_sz / self.target_scale # Use odd square search area and set sizes feat_max_stride = max(self.params.features.stride()) self.img_sample_sz = torch.round(torch.sqrt(torch.prod(self.base_target_sz * self.params.search_area_scale))) * torch.ones(2) self.img_sample_sz += feat_max_stride - self.img_sample_sz % (2 * feat_max_stride) # Set other sizes (corresponds to ECO code) self.img_support_sz = self.img_sample_sz self.feature_sz = self.params.features.size(self.img_sample_sz) self.filter_sz = self.feature_sz + (self.feature_sz + 1) % 2 self.output_sz = self.params.score_upsample_factor * self.img_support_sz # Interpolated size of the output self.compressed_dim = self.fparams.attribute('compressed_dim') # Number of filters self.num_filters = len(self.filter_sz) # Get window function self.window = TensorList([dcf.hann2d(sz).to(self.params.device) for sz in self.feature_sz]) # Get interpolation function self.interp_fs = TensorList([dcf.get_interp_fourier(sz, self.params.interpolation_method, self.params.interpolation_bicubic_a, self.params.interpolation_centering, self.params.interpolation_windowing, self.params.device) for sz in self.filter_sz]) # Get regularization filter self.reg_filter = TensorList([dcf.get_reg_filter(self.img_support_sz, self.base_target_sz, fparams).to(self.params.device) for fparams in self.fparams]) self.reg_energy = self.reg_filter.view(-1) @ self.reg_filter.view(-1) # Get label function output_sigma_factor = self.fparams.attribute('output_sigma_factor') sigma = (self.filter_sz / self.img_support_sz) * torch.sqrt(self.base_target_sz.prod()) * output_sigma_factor self.yf = TensorList([dcf.label_function(sz, sig).to(self.params.device) for sz, sig in zip(self.filter_sz, sigma)]) # Optimization options self.params.precond_learning_rate = self.fparams.attribute('learning_rate') if self.params.CG_forgetting_rate is None or max(self.params.precond_learning_rate) >= 1: self.params.direction_forget_factor = 0 else: self.params.direction_forget_factor = (1 - max(self.params.precond_learning_rate))**self.params.CG_forgetting_rate # Convert image im = numpy_to_torch(image) # Setup bounds self.image_sz = torch.Tensor([im.shape[2], im.shape[3]]) self.min_scale_factor = torch.max(10 / self.base_target_sz) self.max_scale_factor = torch.min(self.image_sz / self.base_target_sz) # Extract and transform sample x = self.generate_init_samples(im) # Initialize projection matrix x_mat = TensorList([e.permute(1,0,2,3).reshape(e.shape[1], -1).clone() for e in x]) x_mat -= x_mat.mean(dim=1, keepdim=True) cov_x = x_mat @ x_mat.t() self.projection_matrix = TensorList([torch.svd(C)[0][:,:cdim].clone() for C, cdim in zip(cov_x, self.compressed_dim)]) # Transform to get the training sample train_xf = self.preprocess_sample(x) # Shift the samples back if 'shift' in self.params.augmentation: for xf in train_xf: if xf.shape[0] == 1: continue for i, shift in enumerate(self.params.augmentation['shift']): shift_samp = 2 * math.pi * torch.Tensor(shift) / self.img_support_sz xf[1+i:2+i,...] = fourier.shift_fs(xf[1+i:2+i,...], shift=shift_samp) # Shift sample shift_samp = 2*math.pi * (self.pos - self.pos.round()) / (self.target_scale * self.img_support_sz) train_xf = fourier.shift_fs(train_xf, shift=shift_samp) # Initialize first-frame training samples num_init_samples = train_xf.size(0) self.init_sample_weights = TensorList([xf.new_ones(1) / xf.shape[0] for xf in train_xf]) self.init_training_samples = train_xf.permute(2, 3, 0, 1, 4) # Sample counters and weights self.num_stored_samples = num_init_samples self.previous_replace_ind = [None]*len(self.num_stored_samples) self.sample_weights = TensorList([xf.new_zeros(self.params.sample_memory_size) for xf in train_xf]) for sw, init_sw, num in zip(self.sample_weights, self.init_sample_weights, num_init_samples): sw[:num] = init_sw # Initialize memory self.training_samples = TensorList( [xf.new_zeros(xf.shape[2], xf.shape[3], self.params.sample_memory_size, cdim, 2) for xf, cdim in zip(train_xf, self.compressed_dim)]) # Initialize filter self.filter = TensorList( [xf.new_zeros(1, cdim, xf.shape[2], xf.shape[3], 2) for xf, cdim in zip(train_xf, self.compressed_dim)]) # Do joint optimization self.joint_problem = FactorizedConvProblem(self.init_training_samples, self.yf, self.reg_filter, self.projection_matrix, self.params, self.init_sample_weights) joint_var = self.filter.concat(self.projection_matrix) self.joint_optimizer = GaussNewtonCG(self.joint_problem, joint_var, debug=(self.params.debug>=1), visdom=self.visdom) if self.params.update_projection_matrix: self.joint_optimizer.run(self.params.init_CG_iter // self.params.init_GN_iter, self.params.init_GN_iter) # Re-project samples with the new projection matrix compressed_samples = complex.mtimes(self.init_training_samples, self.projection_matrix) for train_samp, init_samp in zip(self.training_samples, compressed_samples): train_samp[:,:,:init_samp.shape[2],:,:] = init_samp # Initialize optimizer self.filter_optimizer = FilterOptim(self.params, self.reg_energy) self.filter_optimizer.register(self.filter, self.training_samples, self.yf, self.sample_weights, self.reg_filter) self.filter_optimizer.sample_energy = self.joint_problem.sample_energy self.filter_optimizer.residuals = self.joint_optimizer.residuals.clone() if not self.params.update_projection_matrix: self.filter_optimizer.run(self.params.init_CG_iter) # Post optimization self.filter_optimizer.run(self.params.post_init_CG_iter) self.symmetrize_filter() def track(self, image, info: dict = None) -> dict: self.debug_info = {} self.frame_num += 1 self.debug_info['frame_num'] = self.frame_num # Convert image im = numpy_to_torch(image) # ------- LOCALIZATION ------- # # Get sample sample_pos = self.pos.round() sample_scales = self.target_scale * self.params.scale_factors test_xf = self.extract_fourier_sample(im, self.pos, sample_scales, self.img_sample_sz) # Compute scores sf = self.apply_filter(test_xf) translation_vec, scale_ind, s = self.localize_target(sf) scale_change_factor = self.params.scale_factors[scale_ind] # Update position and scale self.update_state(sample_pos + translation_vec, self.target_scale * scale_change_factor) score_map = s[scale_ind, ...] max_score = torch.max(score_map).item() self.debug_info['max_score'] = max_score if self.visdom is not None: self.visdom.register(score_map, 'heatmap', 2, 'Score Map') self.visdom.register(self.debug_info, 'info_dict', 1, 'Status') elif self.params.debug >= 2: show_tensor(score_map, 5, title='Max score = {:.2f}'.format(max_score)) # if self.params.debug >= 3: # for i, hf in enumerate(self.filter): # show_tensor(fourier.sample_fs(hf).abs().mean(1), 6+i) # ------- UPDATE ------- # # Get train sample train_xf = TensorList([xf[scale_ind:scale_ind+1, ...] for xf in test_xf]) # Shift the sample shift_samp = 2*math.pi * (self.pos - sample_pos) / (sample_scales[scale_ind] * self.img_support_sz) train_xf = fourier.shift_fs(train_xf, shift=shift_samp) # Update memory self.update_memory(train_xf) # Train filter if self.frame_num % self.params.train_skipping == 1: self.filter_optimizer.run(self.params.CG_iter, train_xf) self.symmetrize_filter() # Return new state new_state = torch.cat((self.pos[[1,0]] - (self.target_sz[[1,0]]-1)/2, self.target_sz[[1,0]])) out = {'target_bbox': new_state.tolist()} return out def apply_filter(self, sample_xf: TensorList) -> torch.Tensor: return complex.mult(self.filter, sample_xf).sum(1, keepdim=True) def localize_target(self, sf: TensorList): if self.params.score_fusion_strategy == 'sum': scores = fourier.sample_fs(fourier.sum_fs(sf), self.output_sz) elif self.params.score_fusion_strategy == 'weightedsum': weight = self.fparams.attribute('translation_weight') scores = fourier.sample_fs(fourier.sum_fs(weight * sf), self.output_sz) elif self.params.score_fusion_strategy == 'transcale': alpha = self.fparams.attribute('scale_weight') beta = self.fparams.attribute('translation_weight') sample_sz = torch.round(self.output_sz.view(1,-1) * self.params.scale_factors.view(-1,1)) scores = 0 for sfe, a, b in zip(sf, alpha, beta): sfe = fourier.shift_fs(sfe, math.pi*torch.ones(2)) scores_scales = [] for sind, sz in enumerate(sample_sz): pd = (self.output_sz-sz)/2 scores_scales.append(F.pad(fourier.sample_fs(sfe[sind:sind+1,...], sz), (math.floor(pd[1].item()), math.ceil(pd[1].item()), math.floor(pd[0].item()), math.ceil(pd[0].item())))) scores_cat = torch.cat(scores_scales) scores = scores + (b - a) * scores_cat.mean(dim=0, keepdim=True) + a * scores_cat else: raise ValueError('Unknown score fusion strategy.') # Get maximum max_score, max_disp = dcf.max2d(scores) _, scale_ind = torch.max(max_score, dim=0) max_disp = max_disp.float().cpu() # Convert to displacements in the base scale if self.params.score_fusion_strategy in ['sum', 'weightedsum']: disp = (max_disp + self.output_sz / 2) % self.output_sz - self.output_sz / 2 elif self.params.score_fusion_strategy == 'transcale': disp = max_disp - self.output_sz / 2 # Compute translation vector and scale change factor translation_vec = disp[scale_ind, ...].view(-1) * (self.img_support_sz / self.output_sz) * self.target_scale if self.params.score_fusion_strategy in ['sum', 'weightedsum']: translation_vec *= self.params.scale_factors[scale_ind] return translation_vec, scale_ind, scores def extract_sample(self, im: torch.Tensor, pos: torch.Tensor, scales, sz: torch.Tensor): return self.params.features.extract(im, pos, scales, sz)[0] def extract_fourier_sample(self, im: torch.Tensor, pos: torch.Tensor, scales, sz: torch.Tensor) -> TensorList: x = self.extract_sample(im, pos, scales, sz) return self.preprocess_sample(self.project_sample(x)) def preprocess_sample(self, x: TensorList) -> TensorList: x *= self.window sample_xf = fourier.cfft2(x) return TensorList([dcf.interpolate_dft(xf, bf) for xf, bf in zip(sample_xf, self.interp_fs)]) def project_sample(self, x: TensorList): @tensor_operation def _project_sample(x: torch.Tensor, P: torch.Tensor): if P is None: return x return torch.matmul(x.permute(2, 3, 0, 1), P).permute(2, 3, 0, 1) return _project_sample(x, self.projection_matrix) def generate_init_samples(self, im: torch.Tensor) -> TensorList: # Do data augmentation transforms = [augmentation.Identity()] if 'shift' in self.params.augmentation: transforms.extend([augmentation.Translation(shift) for shift in self.params.augmentation['shift']]) if 'fliplr' in self.params.augmentation and self.params.augmentation['fliplr']: transforms.append(augmentation.FlipHorizontal()) if 'rotate' in self.params.augmentation: transforms.extend([augmentation.Rotate(angle) for angle in self.params.augmentation['rotate']]) if 'blur' in self.params.augmentation: transforms.extend([augmentation.Blur(sigma) for sigma in self.params.augmentation['blur']]) init_samples = self.params.features.extract_transformed(im, self.pos, self.target_scale, self.img_sample_sz, transforms) # Remove augmented samples for those that shall not have for i, use_aug in enumerate(self.fparams.attribute('use_augmentation')): if not use_aug: init_samples[i] = init_samples[i][0:1, ...] if 'dropout' in self.params.augmentation: num, prob = self.params.augmentation['dropout'] for i, use_aug in enumerate(self.fparams.attribute('use_augmentation')): if use_aug: init_samples[i] = torch.cat([init_samples[i], F.dropout2d(init_samples[i][0:1,...].expand(num,-1,-1,-1), p=prob, training=True)]) return init_samples def update_memory(self, sample_xf: TensorList): # Update weights and get index to replace replace_ind = self.update_sample_weights() for train_samp, xf, ind in zip(self.training_samples, sample_xf, replace_ind): train_samp[:,:,ind:ind+1,:,:] = xf.permute(2, 3, 0, 1, 4) def update_sample_weights(self): replace_ind = [] for sw, prev_ind, num_samp, fparams in zip(self.sample_weights, self.previous_replace_ind, self.num_stored_samples, self.fparams): if num_samp == 0 or fparams.learning_rate == 1: sw[:] = 0 sw[0] = 1 r_ind = 0 else: # Get index to replace _, r_ind = torch.min(sw, 0) r_ind = r_ind.item() # Update weights if prev_ind is None: sw /= 1 - fparams.learning_rate sw[r_ind] = fparams.learning_rate else: sw[r_ind] = sw[prev_ind] / (1 - fparams.learning_rate) sw /= sw.sum() replace_ind.append(r_ind) self.previous_replace_ind = replace_ind.copy() self.num_stored_samples += 1 return replace_ind def update_state(self, new_pos, new_scale): # Update scale self.target_scale = new_scale.clamp(self.min_scale_factor, self.max_scale_factor) self.target_sz = self.base_target_sz * self.target_scale # Update pos inside_ratio = 0.2 inside_offset = (inside_ratio - 0.5) * self.target_sz self.pos = torch.max(torch.min(new_pos, self.image_sz - inside_offset), inside_offset) def symmetrize_filter(self): for hf in self.filter: hf[:,:,:,0,:] /= 2 hf[:,:,:,0,:] += complex.conj(hf[:,:,:,0,:].flip((2,))) ================================================ FILE: external/AR/pytracking/tracker/eco/optim.py ================================================ import torch import torch.nn.functional as F from pytracking import complex, optimization, fourier, TensorList from pytracking.utils.plotting import plot_graph import math class FactorizedConvProblem(optimization.L2Problem): def __init__(self, training_samples: TensorList, yf:TensorList, reg_filter: torch.Tensor, init_proj_mat: TensorList, params, sample_weights: torch.Tensor = None): self.training_samples = training_samples self.yf = complex.complex(yf).permute(2, 3, 0, 1, 4) self.reg_filter = reg_filter self.sample_weights_sqrt = None if sample_weights is None else sample_weights.sqrt() self.params = params # Sample energy for preconditioner compressed_samples = complex.mtimes(self.training_samples, init_proj_mat) self.sample_energy = complex.abs_sqr(compressed_samples).mean(dim=2, keepdim=True).permute(2, 3, 0, 1) self.reg_energy = self.reg_filter.view(-1) @ self.reg_filter.view(-1) # Projection energy for preconditioner self.proj_energy = 2 * fourier.inner_prod_fs(yf, yf) / self.training_samples.size(3) # Filter part of preconditioner self.diag_M = (1 - self.params.precond_reg_param) * (self.params.precond_data_param * self.sample_energy + (1 - self.params.precond_data_param) * self.sample_energy.mean(1, keepdim=True)) + \ self.params.precond_reg_param * self.reg_energy self.diag_M.unsqueeze_(-1) # Projection matrix part of preconditioner self.diag_M.extend(self.params.precond_proj_param * (self.proj_energy + self.params.projection_reg)) def __call__(self, x: TensorList): """ Compute residuals :param x: [filters, projection_matrices] :return: [data_terms, filter_regularizations, proj_mat_regularizations] """ hf = x[:len(x)//2] P = x[len(x)//2:] compressed_samples = complex.mtimes(self.training_samples, P) residuals = complex.mtimes(compressed_samples, hf.permute(2, 3, 1, 0, 4)) # (h, w, num_samp, num_filt, 2) residuals = residuals - self.yf if self.sample_weights_sqrt is not None: residuals = complex.mult(self.sample_weights_sqrt.view(1, 1, -1, 1), residuals) # Add spatial regularization for hfe, reg_filter in zip(hf, self.reg_filter): reg_pad1 = min(reg_filter.shape[-2] - 1, hfe.shape[-3] - 1) reg_pad2 = min(reg_filter.shape[-1] - 1, hfe.shape[-2] - 1) # Add part needed for convolution if reg_pad2 > 0: hfe_left_padd = complex.conj(hfe[...,1:reg_pad2+1,:].clone().detach().flip((2,3))) hfe_conv = torch.cat([hfe_left_padd, hfe], -2) else: hfe_conv = hfe.clone() # Shift data to batch dimension hfe_conv = hfe_conv.permute(0,1,4,2,3).reshape(-1, 1, hfe_conv.shape[-3], hfe_conv.shape[-2]) # Do first convolution hfe_conv = F.conv2d(hfe_conv, reg_filter, padding=(reg_pad1, reg_pad2)) residuals.append(hfe_conv) # Add regularization for projection matrix residuals.extend(math.sqrt(self.params.projection_reg) * P) return residuals def ip_input(self, a: TensorList, b: TensorList): num = len(a) // 2 # Number of filters a_filter = a[:num] b_filter = b[:num] a_P = a[num:] b_P = b[num:] # Filter inner product ip_out = fourier.inner_prod_fs(a_filter, b_filter) # Add projection matrix part ip_out += a_P.reshape(-1) @ b_P.reshape(-1) # Have independent inner products for each filter return ip_out.concat(ip_out.clone()) def ip_output(self, a: TensorList, b: TensorList): num = len(a) // 3 # Number of filters a_data = a[:num].permute(2,3,0,1,4) b_data = b[:num].permute(2,3,0,1,4) a_filt_reg = a[num:2*num] b_filt_reg = b[num:2*num] a_P_reg = a[2*num:] b_P_reg = b[2*num:] ip_data = sum(fourier.inner_prod_fs(a_data, b_data)) ip_filt_reg = ip_data.new_zeros(1) for ar, br, res_data, reg_filter in zip(a_filt_reg, b_filt_reg, a_data, self.reg_filter): reg_pad2 = min(reg_filter.shape[-1] - 1, res_data.shape[-2] - 1) arp = ar.reshape(1, -1, 2, ar.shape[2], ar.shape[3]).permute(0, 1, 3, 4, 2) brp = br.reshape(1, -1, 2, br.shape[2], br.shape[3]).permute(0, 1, 3, 4, 2) ip_filt_reg += fourier.inner_prod_fs(arp[:,:,:,2*reg_pad2:,:], brp[:,:,:,2*reg_pad2:,:]) ip_P_reg = sum(a_P_reg.view(-1) @ b_P_reg.view(-1)) return ip_data + ip_filt_reg + ip_P_reg def M1(self, x: TensorList): return x / self.diag_M class FilterOptim(optimization.ConjugateGradientBase): def __init__(self, params, reg_energy): super(FilterOptim, self).__init__(params.fletcher_reeves, params.standard_alpha, params.direction_forget_factor, (params.debug >= 3)) # Parameters self.params = params self.reg_energy = reg_energy self.sample_energy = None self.residuals = torch.zeros(0) def register(self, filter, training_samples, yf, sample_weights, reg_filter): self.filter = filter self.training_samples = training_samples # (h, w, num_samples, num_channels, 2) self.yf = yf self.sample_weights = sample_weights self.reg_filter = reg_filter def run(self, num_iter, new_xf: TensorList = None): if num_iter == 0: return if new_xf is not None: new_sample_energy = complex.abs_sqr(new_xf) if self.sample_energy is None: self.sample_energy = new_sample_energy else: self.sample_energy = (1 - self.params.precond_learning_rate) * self.sample_energy + self.params.precond_learning_rate * new_sample_energy # Compute right hand side self.b = complex.mtimes(self.sample_weights.view(1,1,1,-1), self.training_samples).permute(2,3,0,1,4) self.b = complex.mult_conj(self.yf, self.b) self.diag_M = (1 - self.params.precond_reg_param) * (self.params.precond_data_param * self.sample_energy + (1 - self.params.precond_data_param) * self.sample_energy.mean(1, keepdim=True)) + self.params.precond_reg_param * self.reg_energy _, res = self.run_CG(num_iter, self.filter) if self.debug: self.residuals = torch.cat((self.residuals, res)) plot_graph(self.residuals, 9) def A(self, hf: TensorList): # Classify sh = complex.mtimes(self.training_samples, hf.permute(2,3,1,0,4)) # (h, w, num_samp, num_filt, 2) sh = complex.mult(self.sample_weights.view(1,1,-1,1), sh) # Multiply with transpose hf_out = complex.mtimes(sh.permute(0,1,3,2,4), self.training_samples, conj_b=True).permute(2,3,0,1,4) # Add regularization for hfe, hfe_out, reg_filter in zip(hf, hf_out, self.reg_filter): reg_pad1 = min(reg_filter.shape[-2] - 1, hfe.shape[-3] - 1) reg_pad2 = min(reg_filter.shape[-1] - 1, 2*hfe.shape[-2]- 2) # Add part needed for convolution if reg_pad2 > 0: hfe_conv = torch.cat([complex.conj(hfe[...,1:reg_pad2+1,:].flip((2,3))), hfe], -2) else: hfe_conv = hfe.clone() # Shift data to batch dimension hfe_conv = hfe_conv.permute(0,1,4,2,3).reshape(-1, 1, hfe_conv.shape[-3], hfe_conv.shape[-2]) # Do first convolution hfe_conv = F.conv2d(hfe_conv, reg_filter, padding=(reg_pad1, reg_pad2)) # Do second convolution remove_size = min(reg_pad2, hfe.shape[-2]-1) hfe_conv = F.conv2d(hfe_conv[...,remove_size:], reg_filter) # Reshape back and add hfe_out += hfe_conv.reshape(hfe.shape[0], hfe.shape[1], 2, hfe.shape[2], hfe.shape[3]).permute(0,1,3,4,2) return hf_out def ip(self, a: torch.Tensor, b: torch.Tensor): return fourier.inner_prod_fs(a, b) def M1(self, hf): return complex.div(hf, self.diag_M) ================================================ FILE: external/AR/pytracking/util_scripts/__init__.py ================================================ ================================================ FILE: external/AR/pytracking/util_scripts/download_results.py ================================================ import os import sys import gdown import re import shutil import argparse import tempfile env_path = os.path.join(os.path.dirname(__file__), '../..') if env_path not in sys.path: sys.path.append(env_path) from pytracking.evaluation.environment import env_settings results_link_dict = { "dimp": { "prdimp50_003.zip": "1p13j3iwcOCubBi3ms0hLwqnP6-x0J8Mc", "prdimp50_002.zip": "1PPKgrAepbuyM2kjfzYAozQKTL6AjcQOz", "prdimp50_001.zip": "17NFBObEDeK6mW4Mk2vN5Ekk1SGbFvxRS", "prdimp50_000.zip": "1r3Efq7AumML2yGQ_KV4zmf4ATKVE1bo6", "prdimp18_004.zip": "1DF4ZJQAa4CwvN_OiT4te33AV0kpsO7JM", "prdimp18_003.zip": "1RgwJAN4TxnzgVgsfvrHIg1OUXD1EBZkO", "prdimp18_002.zip": "17lMllYhygCqgE81DoHX4BZar3xc3auzM", "prdimp18_001.zip": "1Yg7DmGYOnn2k0MYtSjjKlGyzO1Uimj4G", "prdimp18_000.zip": "1DuZJSBJ-23WJBQTOWSAaoPYSbGAJJN2Z", "prdimp50_004.zip": "1f9bx9-dtx3B5_IvIJhjjJyp-cnXciqLO", "dimp50_004.zip": "1Lj3p8mYCoIqxzdQXZkWFTw-MA8c6eeLa", "dimp50_000.zip": "1LCgf5sg453Z4bY37A_W5mbXeG68U1fET", "dimp18_000.zip": "17M7dJZ1oKrIY4-O5lL_mlQPEubUn034g", "dimp18_001.zip": "1AsiliVgISyDTouYOQYVOXA0srj3YskhJ", "dimp50_got_001.zip": "1EE5FcPXqMBkv_0ghfzytCMmbKxWxy04p", "dimp18_002.zip": "1I0GrBaPnySOyPWSvItHhXH8182tFCi_Y", "dimp50_got_002.zip": "1ALXzVkn58GZ1E0I22vrbXkEXwy5u0xOc", "dimp18_got_000.zip": "1BxowlgGEonnuaVXwiDwiYr7VV7BRWLvr", "dimp50_001.zip": "1XfPvwAcymW88J1rq7RlhyKmqsawJDK-K", "dimp18_got_002.zip": "1awqXQnFRr5NwjLfI-Ngtt3zT7XmQIwzs", "dimp18_got_001.zip": "1rr2J6NuuYJ5E4wDUw-PrxaNKjIsfgAyk", "dimp50_got_000.zip": "1ruP8XJOu0woq-bvKdHJ9_Y9RceHDrDjm", "dimp18_004.zip": "1EztF6bpROFwZ1PSJWgMB7bQ4G_Z08YIg", "dimp18_003.zip": "1iuiFLv04WE7GfBjm8UkZXFq4gheG2Ru8", "dimp50_003.zip": "1rLsgeQXyKpD6ryl9BjlIVdO3vd27ekwy", "dimp50_002.zip": "1wj2jUwlpHgsP1hAcuxXAVriUPuEspsu4", }, "atom": { "default_004.zip": "1BapnQh_8iRM44DXj862eOZV4q8zQLdmT", "default_003.zip": "1YpfOBLBEUQQiX0fWMPA5pnW3dm0NG3E5", "default_got_000.zip": "1uJnC0PPQhavwRbAL7VQ2Zow8YdLVzeCb", "default_got_001.zip": "1YzJm0H31veDW-lMxwy8MYNpMULgsYHKf", "default_000.zip": "1x6fKGZk3V839mX99Gl_pw7JUaiMaTxc5", "default_002.zip": "1QIlQFv3p6MBTwsYdIMYmzUDBDQGxGsUC", "default_001.zip": "1-K2--GNCURDKEgUuiEF18K4DcCLvDEVt", "default_got_002.zip": "1qGtArxdAy0uWSd-HqFT5zmXpR6TCm4Vc", }, } def _download_file(file_id, path): link = 'https://drive.google.com/uc?id=' + file_id gdown.download(link, path, quiet=True) def download_results(download_path, trackers='all'): """ Script to automatically download tracker results for PyTracking. args: download_path - Directory where the zipped results are downloaded trackers - Tracker results which are to be downloaded. If set to 'all', all available results are downloaded. If set to a name of a tracker (e.g. atom), all results for that tracker are downloaded. Otherwise, it can be set to a dict, where the keys are the names of the trackers for which results are downloaded. The value can be set to either 'all', in which case all available results for the tracker are downloaded. Else the value should be a list of parameter file names. """ print('Using download path ''{}'''.format(download_path)) os.makedirs(download_path, exist_ok=True) if isinstance(trackers, str): if trackers == 'all': trackers = {k: 'all' for k in results_link_dict.keys()} elif trackers in results_link_dict: trackers = {trackers: 'all'} else: raise Exception('tracker_list must be set to ''all'', a tracker name, or be a dict') elif isinstance(trackers, dict): pass else: raise Exception('tracker_list must be set to ''all'', or be a dict') for trk, runfiles in trackers.items(): trk_path = os.path.join(download_path, trk) if not os.path.exists(trk_path): os.makedirs(trk_path) if runfiles == 'all': for params, fileid in results_link_dict[trk].items(): print('Downloading: {}/{}'.format(trk, params)) _download_file(fileid, os.path.join(trk_path, params)) elif isinstance(runfiles, (list, tuple)): for p in runfiles: for params, fileid in results_link_dict[trk].items(): if re.match(r'{}(|_(\d\d\d)).zip'.format(p), params) is not None: print('Downloading: {}/{}'.format(trk, params)) _download_file(fileid, os.path.join(trk_path, params)) else: raise Exception('tracker_list values must either be set to ''all'', or be a list of param names') def unpack_tracking_results(download_path, output_path=None): """ Unpacks zipped benchmark results. The directory 'download_path' should have the following structure - root - tracker1 - param1.zip - param2.zip . . - tracker2 - param1.zip - param2.zip . . args: download_path - Path to the directory where the zipped results are stored output_path - Path to the directory where the results will be unpacked. Set to env_settings().results_path by default """ if output_path is None: output_path = env_settings().results_path if not os.path.exists(output_path): os.makedirs(output_path) trackers = os.listdir(download_path) for t in trackers: runfiles = os.listdir(os.path.join(download_path, t)) for r in runfiles: save_path = os.path.join(output_path, t) if not os.path.exists(save_path): os.makedirs(save_path) shutil.unpack_archive(os.path.join(download_path, t, r), os.path.join(save_path, r[:-4]), 'zip') def main(): parser = argparse.ArgumentParser(description='Download and unpack zipped results') parser.add_argument('--tracker', type=str, default='all', help='Name of tracker results to download, or ''all''.') parser.add_argument('--output_path', type=str, default=None, help='Path to the directory where the results will be unpacked.') parser.add_argument('--temp_download_path', type=str, default=None, help='Temporary path used for downloading the Zip files.') parser.add_argument('--download', type=bool, default=True, help='Whether to download results or unpack existing downloaded files.') args = parser.parse_args() download_path = args.temp_download_path if download_path is None: download_path = '{}/pytracking_results/'.format(tempfile.gettempdir()) if args.download: download_results(download_path, args.tracker) unpack_tracking_results(download_path, args.output_path) if __name__ == '__main__': main() ================================================ FILE: external/AR/pytracking/util_scripts/pack_got10k_results.py ================================================ import numpy as np import os import shutil from pytracking.evaluation.environment import env_settings def pack_got10k_results(tracker_name, param_name, output_name): """ Packs got10k results into a zip folder which can be directly uploaded to the evaluation server. The packed file is saved in the folder env_settings().got_packed_results_path args: tracker_name - name of the tracker param_name - name of the parameter file output_name - name of the packed zip file """ output_path = os.path.join(env_settings().got_packed_results_path, output_name) if not os.path.exists(output_path): os.makedirs(output_path) results_path = env_settings().results_path for i in range(1,181): seq_name = 'GOT-10k_Test_{:06d}'.format(i) seq_output_path = '{}/{}'.format(output_path, seq_name) if not os.path.exists(seq_output_path): os.makedirs(seq_output_path) for run_id in range(3): res = np.loadtxt('{}/{}/{}_{:03d}/{}.txt'.format(results_path, tracker_name, param_name, run_id, seq_name), dtype=np.float64) times = np.loadtxt( '{}/{}/{}_{:03d}/{}_time.txt'.format(results_path, tracker_name, param_name, run_id, seq_name), dtype=np.float64) np.savetxt('{}/{}_{:03d}.txt'.format(seq_output_path, seq_name, run_id+1), res, delimiter=',', fmt='%f') np.savetxt('{}/{}_time.txt'.format(seq_output_path, seq_name), times, fmt='%f') # Generate ZIP file shutil.make_archive(output_path, 'zip', output_path) # Remove raw text files shutil.rmtree(output_path) ================================================ FILE: external/AR/pytracking/util_scripts/pack_trackingnet_results.py ================================================ import numpy as np import os import shutil from pytracking.evaluation.environment import env_settings from pytracking.evaluation.datasets import get_dataset def pack_trackingnet_results(tracker_name, param_name, run_id=None, output_name=None): """ Packs trackingnet results into a zip folder which can be directly uploaded to the evaluation server. The packed file is saved in the folder env_settings().tn_packed_results_path args: tracker_name - name of the tracker param_name - name of the parameter file run_id - run id for the tracker output_name - name of the packed zip file """ if output_name is None: if run_id is None: output_name = '{}_{}'.format(tracker_name, param_name) else: output_name = '{}_{}_{:03d}'.format(tracker_name, param_name, run_id) output_path = os.path.join(env_settings().tn_packed_results_path, output_name) if not os.path.exists(output_path): os.makedirs(output_path) results_path = env_settings().results_path tn_dataset = get_dataset('trackingnet') for seq in tn_dataset: seq_name = seq.name if run_id is None: seq_results_path = '{}/{}/{}/{}.txt'.format(results_path, tracker_name, param_name, seq_name) else: seq_results_path = '{}/{}/{}_{:03d}/{}.txt'.format(results_path, tracker_name, param_name, run_id, seq_name) results = np.loadtxt(seq_results_path, dtype=np.float64) np.savetxt('{}/{}.txt'.format(output_path, seq_name), results, delimiter=',', fmt='%.2f') # Generate ZIP file shutil.make_archive(output_path, 'zip', output_path) # Remove raw text files shutil.rmtree(output_path) ================================================ FILE: external/AR/pytracking/utils/__init__.py ================================================ from .params import TrackerParams, FeatureParams, Choice ================================================ FILE: external/AR/pytracking/utils/convert_vot_anno_to_rect.py ================================================ import numpy as np def convert_vot_anno_to_rect(vot_anno, type): if len(vot_anno) == 4: return vot_anno if type == 'union': x1 = min(vot_anno[0::2]) x2 = max(vot_anno[0::2]) y1 = min(vot_anno[1::2]) y2 = max(vot_anno[1::2]) return [x1, y1, x2 - x1, y2 - y1] elif type == 'preserve_area': if len(vot_anno) != 8: raise ValueError vot_anno = np.array(vot_anno) cx = np.mean(vot_anno[0::2]) cy = np.mean(vot_anno[1::2]) x1 = min(vot_anno[0::2]) x2 = max(vot_anno[0::2]) y1 = min(vot_anno[1::2]) y2 = max(vot_anno[1::2]) A1 = np.linalg.norm(vot_anno[0:2] - vot_anno[2: 4]) * np.linalg.norm(vot_anno[2: 4] - vot_anno[4:6]) A2 = (x2 - x1) * (y2 - y1) s = np.sqrt(A1 / A2) w = s * (x2 - x1) + 1 h = s * (y2 - y1) + 1 x = cx - 0.5*w y = cy - 0.5*h return [x, y, w, h] else: raise ValueError ================================================ FILE: external/AR/pytracking/utils/load_text.py ================================================ import numpy as np import pandas as pd def load_text_numpy(path, delimiter, dtype): if isinstance(delimiter, (tuple, list)): for d in delimiter: try: ground_truth_rect = np.loadtxt(path, delimiter=d, dtype=dtype) return ground_truth_rect except: pass raise Exception('Could not read file {}'.format(path)) else: ground_truth_rect = np.loadtxt(path, delimiter=delimiter, dtype=dtype) return ground_truth_rect def load_text_pandas(path, delimiter, dtype): if isinstance(delimiter, (tuple, list)): for d in delimiter: try: ground_truth_rect = pd.read_csv(path, delimiter=d, header=None, dtype=dtype, na_filter=False, low_memory=False).values return ground_truth_rect except Exception as e: pass raise Exception('Could not read file {}'.format(path)) else: ground_truth_rect = pd.read_csv(path, delimiter=delimiter, header=None, dtype=dtype, na_filter=False, low_memory=False).values return ground_truth_rect def load_text(path, delimiter=' ', dtype=np.float32, backend='numpy'): if backend == 'numpy': return load_text_numpy(path, delimiter, dtype) elif backend == 'pandas': return load_text_pandas(path, delimiter, dtype) ================================================ FILE: external/AR/pytracking/utils/loading.py ================================================ import os import ltr.admin.loading as ltr_loading from pytracking.evaluation.environment import env_settings def load_network(net_path, **kwargs): """Load network for tracking. args: net_path - Path to network. If it is not an absolute path, it is relative to the network_path in the local.py. See ltr.admin.loading.load_network for further details. **kwargs - Additional key-word arguments that are sent to ltr.admin.loading.load_network. """ kwargs['backbone_pretrained'] = False if os.path.isabs(net_path): path_full = net_path net, _ = ltr_loading.load_network(path_full, **kwargs) elif isinstance(env_settings().network_path, (list, tuple)): net = None for p in env_settings().network_path: path_full = os.path.join(p, net_path) try: net, _ = ltr_loading.load_network(path_full, **kwargs) break except Exception as e: # print(e) pass assert net is not None, 'Failed to load network' else: path_full = os.path.join(env_settings().network_path, net_path) net, _ = ltr_loading.load_network(path_full, **kwargs) return net ================================================ FILE: external/AR/pytracking/utils/params.py ================================================ from pytracking import TensorList import random class TrackerParams: """Class for tracker parameters.""" def set_default_values(self, default_vals: dict): for name, val in default_vals.items(): if not hasattr(self, name): setattr(self, name, val) def get(self, name: str, *default): """Get a parameter value with the given name. If it does not exists, it return the default value given as a second argument or returns an error if no default value is given.""" if len(default) > 1: raise ValueError('Can only give one default value.') if not default: return getattr(self, name) return getattr(self, name, default[0]) def has(self, name: str): """Check if there exist a parameter with the given name.""" return hasattr(self, name) class FeatureParams: """Class for feature specific parameters""" def __init__(self, *args, **kwargs): if len(args) > 0: raise ValueError for name, val in kwargs.items(): if isinstance(val, list): setattr(self, name, TensorList(val)) else: setattr(self, name, val) def Choice(*args): """Can be used to sample random parameter values.""" return random.choice(args) ================================================ FILE: external/AR/pytracking/utils/plotting.py ================================================ import matplotlib.pyplot as plt import numpy as np import torch import cv2 def draw_figure(fig): fig.canvas.draw() fig.canvas.flush_events() plt.pause(0.001) def show_tensor(a: torch.Tensor, fig_num = None, title = None, range=(None, None), ax=None): """Display a 2D tensor. args: fig_num: Figure number. title: Title of figure. """ a_np = a.squeeze().cpu().clone().detach().numpy() if a_np.ndim == 3: a_np = np.transpose(a_np, (1, 2, 0)) if ax is None: fig = plt.figure(fig_num) plt.tight_layout() plt.cla() plt.imshow(a_np, vmin=range[0], vmax=range[1]) plt.axis('off') plt.axis('equal') if title is not None: plt.title(title) draw_figure(fig) else: ax.cla() ax.imshow(a_np, vmin=range[0], vmax=range[1]) ax.set_axis_off() ax.axis('equal') if title is not None: ax.set_title(title) draw_figure(plt.gcf()) def plot_graph(a: torch.Tensor, fig_num = None, title = None): """Plot graph. Data is a 1D tensor. args: fig_num: Figure number. title: Title of figure. """ a_np = a.squeeze().cpu().clone().detach().numpy() if a_np.ndim > 1: raise ValueError fig = plt.figure(fig_num) # plt.tight_layout() plt.cla() plt.plot(a_np) if title is not None: plt.title(title) draw_figure(fig) def show_image_with_boxes(im, boxes, iou_pred=None, disp_ids=None): im_np = im.clone().cpu().squeeze().numpy() im_np = np.ascontiguousarray(im_np.transpose(1, 2, 0).astype(np.uint8)) boxes = boxes.view(-1, 4).cpu().numpy().round().astype(int) # Draw proposals for i_ in range(boxes.shape[0]): if disp_ids is None or disp_ids[i_]: bb = boxes[i_, :] disp_color = (i_*38 % 256, (255 - i_*97) % 256, (123 + i_*66) % 256) cv2.rectangle(im_np, (bb[0], bb[1]), (bb[0] + bb[2], bb[1] + bb[3]), disp_color, 1) if iou_pred is not None: text_pos = (bb[0], bb[1] - 5) cv2.putText(im_np, 'ID={} IOU = {:3.2f}'.format(i_, iou_pred[i_]), text_pos, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1, bottomLeftOrigin=False) im_tensor = torch.from_numpy(im_np.transpose(2, 0, 1)).float() return im_tensor def _pascal_color_map(N=256, normalized=False): """ Python implementation of the color map function for the PASCAL VOC data set. Official Matlab version can be found in the PASCAL VOC devkit http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html#devkit """ def bitget(byteval, idx): return (byteval & (1 << idx)) != 0 dtype = 'float32' if normalized else 'uint8' cmap = np.zeros((N, 3), dtype=dtype) for i in range(N): r = g = b = 0 c = i for j in range(8): r = r | (bitget(c, 0) << 7 - j) g = g | (bitget(c, 1) << 7 - j) b = b | (bitget(c, 2) << 7 - j) c = c >> 3 cmap[i] = np.array([r, g, b]) cmap = cmap / 255 if normalized else cmap return cmap def overlay_mask(im, ann, alpha=0.5, colors=None, contour_thickness=None): """ Overlay mask over image. Source: https://github.com/albertomontesg/davis-interactive/blob/master/davisinteractive/utils/visualization.py This function allows you to overlay a mask over an image with some transparency. # Arguments im: Numpy Array. Array with the image. The shape must be (H, W, 3) and the pixels must be represented as `np.uint8` data type. ann: Numpy Array. Array with the mask. The shape must be (H, W) and the values must be intergers alpha: Float. Proportion of alpha to apply at the overlaid mask. colors: Numpy Array. Optional custom colormap. It must have shape (N, 3) being N the maximum number of colors to represent. contour_thickness: Integer. Thickness of each object index contour draw over the overlay. This function requires to have installed the package `opencv-python`. # Returns Numpy Array: Image of the overlay with shape (H, W, 3) and data type `np.uint8`. """ im, ann = np.asarray(im, dtype=np.uint8), np.asarray(ann, dtype=np.int) if im.shape[:-1] != ann.shape: raise ValueError('First two dimensions of `im` and `ann` must match') if im.shape[-1] != 3: raise ValueError('im must have three channels at the 3 dimension') colors = colors or _pascal_color_map() colors = np.asarray(colors, dtype=np.uint8) mask = colors[ann] fg = im * alpha + (1 - alpha) * mask img = im.copy() img[ann > 0] = fg[ann > 0] if contour_thickness: # pragma: no cover import cv2 for obj_id in np.unique(ann[ann > 0]): contours = cv2.findContours((ann == obj_id).astype( np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:] cv2.drawContours(img, contours[0], -1, colors[obj_id].tolist(), contour_thickness) return img ================================================ FILE: external/AR/pytracking/utils/visdom.py ================================================ import visdom import visdom.server from pytracking.features.preprocessing import numpy_to_torch from pytracking.utils.plotting import show_image_with_boxes, overlay_mask import cv2 import torch import copy import numpy as np from collections import OrderedDict class VisBase: def __init__(self, visdom, show_data, title): self.visdom = visdom self.show_data = show_data self.title = title self.raw_data = None def update(self, data, **kwargs): self.save_data(data, **kwargs) if self.show_data: self.draw_data() def save_data(self, data, **kwargs): raise NotImplementedError def draw_data(self): raise NotImplementedError def toggle_display(self, new_mode=None): if new_mode is not None: self.show_data = new_mode else: self.show_data = not self.show_data if self.show_data: self.draw_data() else: self.visdom.close(self.title) class VisImage(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) def save_data(self, data): data = data.float() self.raw_data = data def draw_data(self): self.visdom.image(self.raw_data.clone(), opts={'title': self.title}, win=self.title) class VisHeatmap(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) def save_data(self, data): data = data.squeeze().flip(0) self.raw_data = data def draw_data(self): self.visdom.heatmap(self.raw_data.clone(), opts={'title': self.title}, win=self.title) class VisFeaturemap(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) self.block_list = None def block_list_callback_handler(self, data): self.block_list[data['propertyId']]['value'] = data['value'] self.visdom.properties(self.block_list, opts={'title': 'Featuremap UI'}, win='featuremap_ui') self.draw_data() def save_data(self, data): data = data.view(-1, *data.shape[-2:]) data = data.flip(1) if self.block_list is None: self.block_list = [] self.draw_feat = [] for i in range(data.shape[0]): self.block_list.append({'type': 'checkbox', 'name': 'Channel {:04d}'.format(i), 'value': False}) self.visdom.properties(self.block_list, opts={'title': 'Featuremap UI'}, win='featuremap_ui') self.visdom.register_event_handler(self.block_list_callback_handler, 'featuremap_ui') self.raw_data = data def draw_data(self): if self.block_list is not None and self.show_data: for i, d in enumerate(self.block_list): if d['value']: fig_title = '{} ch: {:04d}'.format(self.title, i) self.visdom.heatmap(self.raw_data[i, :, :].clone(), opts={'title': fig_title}, win=fig_title) class VisCostVolume(VisBase): def __init__(self, visdom, show_data, title, flip=False): super().__init__(visdom, show_data, title) self.show_slice = False self.slice_pos = None self.flip = flip def show_cost_volume(self): data = self.raw_data.clone() # data_perm = data.permute(2, 0, 3, 1).contiguous() data_perm = data.permute(0, 2, 1, 3).contiguous() if self.flip: data_perm = data_perm.permute(2, 3, 0, 1).contiguous() data_perm = data_perm.view(data_perm.shape[0] * data_perm.shape[1], -1) self.visdom.heatmap(data_perm.flip(0), opts={'title': self.title}, win=self.title) def set_zoom_pos(self, slice_pos): self.slice_pos = slice_pos def toggle_show_slice(self, new_mode=None): if new_mode is not None: self.show_slice = new_mode else: self.show_slice = not self.show_slice def show_cost_volume_slice(self): slice_pos = self.slice_pos # slice_pos: [row, col] cost_volume_data = self.raw_data.clone() if self.flip: cost_volume_slice = cost_volume_data[:, :, slice_pos[0], slice_pos[1]] else: cost_volume_slice = cost_volume_data[slice_pos[0], slice_pos[1], :, :] self.visdom.heatmap(cost_volume_slice.flip(0), opts={'title': self.title}, win=self.title) def save_data(self, data): data = data.view(data.shape[-2], data.shape[-1], data.shape[-2], data.shape[-1]) self.raw_data = data def draw_data(self): if self.show_slice: self.show_cost_volume_slice() else: self.show_cost_volume() class VisCostVolumeUI(VisBase): def cv_ui_handler(self, data): zoom_toggled = False if data['event_type'] == 'KeyPress': if data['key'] == 'ArrowRight': self.zoom_pos[1] = min(self.zoom_pos[1] + 1, self.feat_shape[1]-1) elif data['key'] == 'ArrowLeft': self.zoom_pos[1] = max(self.zoom_pos[1] - 1, 0) elif data['key'] == 'ArrowUp': self.zoom_pos[0] = max(self.zoom_pos[0] - 1, 0) elif data['key'] == 'ArrowDown': self.zoom_pos[0] = min(self.zoom_pos[0] + 1, self.feat_shape[0]-1) elif data['key'] == 'Enter': self.zoom_mode = not self.zoom_mode zoom_toggled = True # Update image self.show_image() # Update cost volumes for block_title, block in self.registered_blocks.items(): if isinstance(block, VisCostVolume): block.set_zoom_pos(self.zoom_pos) block.toggle_show_slice(self.zoom_mode) if (self.zoom_mode or zoom_toggled) and block.show_data: block.draw_data() def __init__(self, visdom, show_data, title, feat_shape, registered_blocks): super().__init__(visdom, show_data, title) self.feat_shape = feat_shape self.zoom_mode = False self.zoom_pos = [int((feat_shape[0] - 1) / 2), int((feat_shape[1] - 1) / 2)] self.registered_blocks = registered_blocks self.visdom.register_event_handler(self.cv_ui_handler, title) def draw_grid(self, data): stride_r = int(data.shape[1] / self.feat_shape[0]) stride_c = int(data.shape[2] / self.feat_shape[1]) # Draw grid data[:, list(range(0, data.shape[1], stride_r)), :] = 0 data[:, :, list(range(0, data.shape[2], stride_c))] = 0 data[0, list(range(0, data.shape[1], stride_r)), :] = 255 data[0, :, list(range(0, data.shape[2], stride_c))] = 255 return data def shade_cell(self, data): stride_r = int(data.shape[1] / self.feat_shape[0]) stride_c = int(data.shape[2] / self.feat_shape[1]) r1 = self.zoom_pos[0]*stride_r r2 = min((self.zoom_pos[0] + 1)*stride_r, data.shape[1]) c1 = self.zoom_pos[1] * stride_c c2 = min((self.zoom_pos[1] + 1) * stride_c, data.shape[2]) factor = 0.8 if self.zoom_mode else 0.5 data[:, r1:r2, c1:c2] = data[:, r1:r2, c1:c2] * (1 - factor) + torch.tensor([255.0, 0.0, 0.0]).view(3, 1, 1).to(data.device) * factor return data def show_image(self, data=None): if data is None: data = self.raw_data.clone() data = self.draw_grid(data) data = self.shade_cell(data) self.visdom.image(data, opts={'title': self.title}, win=self.title) def save_data(self, data): # Ignore feat shape data = data[0] data = data.float() self.raw_data = data def draw_data(self): self.show_image(self.raw_data.clone()) class VisInfoDict(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) self.raw_data = OrderedDict() def generate_display_text(self, data): display_text = '' for key, value in data.items(): key = key.replace('_', ' ') if value is None: display_text += '{}: {}
'.format(key, 'None') elif isinstance(value, (str, int)): display_text += '{}: {}
'.format(key, value) else: display_text += '{}: {:.2f}
'.format(key, value) return display_text def save_data(self, data): for key, val in data.items(): self.raw_data[key] = val def draw_data(self): data = copy.deepcopy(self.raw_data) display_text = self.generate_display_text(data) self.visdom.text(display_text, opts={'title': self.title}, win=self.title) class VisText(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) def save_data(self, data): self.raw_data = data def draw_data(self): data = copy.deepcopy(self.raw_data) self.visdom.text(data, opts={'title': self.title}, win=self.title) class VisLinePlot(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) def save_data(self, data): self.raw_data = data def draw_data(self): if isinstance(self.raw_data, (list, tuple)): data_y = self.raw_data[0].clone() data_x = self.raw_data[1].clone() else: data_y = self.raw_data.clone() data_x = torch.arange(data_y.shape[0]) self.visdom.line(data_y, data_x, opts={'title': self.title}, win=self.title) class VisTracking(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) def save_data(self, data): image = data[0] boxes_masks = data[1:] boxes, masks = [], [] for bm in boxes_masks: if bm is None: continue if isinstance(bm, list): boxes.append(torch.Tensor(bm)); continue if len(bm.shape) > 1: # Binarize segmentation if a float tensor is provided if bm.dtype != np.uint8: bm = (bm > 0.5).astype(np.uint8) masks.append(bm); continue boxes.append(bm.float()) self.raw_data = [image, boxes, masks] def draw_data(self): disp_image = self.raw_data[0].copy() resize_factor = 1 if max(disp_image.shape) > 480: resize_factor = 480.0 / float(max(disp_image.shape)) disp_image = cv2.resize(disp_image, None, fx=resize_factor, fy=resize_factor) for i, mask in enumerate(self.raw_data[2]): self.raw_data[2][i] = cv2.resize(mask, None, fx=resize_factor, fy=resize_factor) boxes = [resize_factor * b.clone() for b in self.raw_data[1]] for i, disp_rect in enumerate(boxes): color = ((255*((i%3)>0)), 255*((i+1)%2), (255*(i%5))//4) cv2.rectangle(disp_image, (int(disp_rect[0]), int(disp_rect[1])), (int(disp_rect[0] + disp_rect[2]), int(disp_rect[1] + disp_rect[3])), color, 2) for i, mask in enumerate(self.raw_data[2], 1): disp_image = overlay_mask(disp_image, mask * i) disp_image = numpy_to_torch(disp_image).squeeze(0) disp_image = disp_image.float() self.visdom.image(disp_image, opts={'title': self.title}, win=self.title) class VisBBReg(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) self.block_list = [] def block_list_callback_handler(self, data): self.block_list[data['propertyId']]['value'] = data['value'] self.visdom.properties(self.block_list, opts={'title': 'BBReg Vis'}, win='bbreg_vis') self.draw_data() def save_data(self, data): self.image = data[0].float() self.init_boxes = data[1] self.final_boxes = data[2] self.final_ious = data[3] def draw_data(self): if len(self.block_list) == 0: self.block_list.append({'type': 'checkbox', 'name': 'ID 0', 'value': True}) self.block_list.append({'type': 'checkbox', 'name': 'ID 1', 'value': True}) self.visdom.properties(self.block_list, opts={'title': 'BBReg Vis'}, win='bbreg_vis') self.visdom.register_event_handler(self.block_list_callback_handler, 'bbreg_vis') disp_image = self.image ids = [x['value'] for x in self.block_list] init_box_image = show_image_with_boxes(disp_image.clone(), self.init_boxes.clone(), disp_ids=ids) final_box_image = show_image_with_boxes(disp_image.clone(), self.final_boxes.clone(), self.final_ious.clone(), disp_ids=ids) self.visdom.image(init_box_image, opts={'title': 'Init Boxes'}, win='Init Boxes') self.visdom.image(final_box_image, opts={'title': 'Final Boxes'}, win='Final Boxes') class Visdom: def __init__(self, debug=0, ui_info=None, visdom_info=None): self.debug = debug self.visdom = visdom.Visdom(server=visdom_info.get('server', '127.0.0.1'), port=visdom_info.get('port', 8097)) self.registered_blocks = {} self.blocks_list = [] self.visdom.properties(self.blocks_list, opts={'title': 'Block List'}, win='block_list') self.visdom.register_event_handler(self.block_list_callback_handler, 'block_list') if ui_info is not None: self.visdom.register_event_handler(ui_info['handler'], ui_info['win_id']) def block_list_callback_handler(self, data): field_name = self.blocks_list[data['propertyId']]['name'] self.registered_blocks[field_name].toggle_display(data['value']) self.blocks_list[data['propertyId']]['value'] = data['value'] self.visdom.properties(self.blocks_list, opts={'title': 'Block List'}, win='block_list') def register(self, data, mode, debug_level=0, title='Data', **kwargs): if title not in self.registered_blocks.keys(): show_data = self.debug >= debug_level if title != 'Tracking': self.blocks_list.append({'type': 'checkbox', 'name': title, 'value': show_data}) self.visdom.properties(self.blocks_list, opts={'title': 'Block List'}, win='block_list') if mode == 'image': self.registered_blocks[title] = VisImage(self.visdom, show_data, title) elif mode == 'heatmap': self.registered_blocks[title] = VisHeatmap(self.visdom, show_data, title) elif mode == 'cost_volume': self.registered_blocks[title] = VisCostVolume(self.visdom, show_data, title) elif mode == 'cost_volume_flip': self.registered_blocks[title] = VisCostVolume(self.visdom, show_data, title, flip=True) elif mode == 'cost_volume_ui': self.registered_blocks[title] = VisCostVolumeUI(self.visdom, show_data, title, data[1], self.registered_blocks) elif mode == 'info_dict': self.registered_blocks[title] = VisInfoDict(self.visdom, show_data, title) elif mode == 'text': self.registered_blocks[title] = VisText(self.visdom, show_data, title) elif mode == 'lineplot': self.registered_blocks[title] = VisLinePlot(self.visdom, show_data, title) elif mode == 'Tracking': self.registered_blocks[title] = VisTracking(self.visdom, show_data, title) elif mode == 'bbreg': self.registered_blocks[title] = VisBBReg(self.visdom, show_data, title) elif mode == 'featmap': self.registered_blocks[title] = VisFeaturemap(self.visdom, show_data, title) else: raise ValueError('Visdom Error: Unknown data mode {}'.format(mode)) # Update self.registered_blocks[title].update(data, **kwargs) ================================================ FILE: external/AR/pytracking/vot20_utils.py ================================================ import numpy as np def make_full_size(x, output_sz): ''' zero-pad input x (right and down) to match output_sz x: numpy array e.g., binary mask output_sz: size of the output [width, height] ''' if x.shape[0] == output_sz[1] and x.shape[1] == output_sz[0]: return x pad_x = output_sz[0] - x.shape[1] if pad_x < 0: x = x[:, :x.shape[1] + pad_x] # padding has to be set to zero, otherwise pad function fails pad_x = 0 pad_y = output_sz[1] - x.shape[0] if pad_y < 0: x = x[:x.shape[0] + pad_y, :] # padding has to be set to zero, otherwise pad function fails pad_y = 0 return np.pad(x, ((0, pad_y), (0, pad_x)), 'constant', constant_values=0) def rect_from_mask(mask): ''' create an axis-aligned rectangle from a given binary mask mask in created as a minimal rectangle containing all non-zero pixels ''' x_ = np.sum(mask, axis=0) y_ = np.sum(mask, axis=1) x0 = np.min(np.nonzero(x_)) x1 = np.max(np.nonzero(x_)) y0 = np.min(np.nonzero(y_)) y1 = np.max(np.nonzero(y_)) return [x0, y0, x1 - x0 + 1, y1 - y0 + 1] def mask_from_rect(rect, output_sz): ''' create a binary mask from a given rectangle rect: axis-aligned rectangle [x0, y0, width, height] output_sz: size of the output [width, height] ''' mask = np.zeros((output_sz[1], output_sz[0]), dtype=np.uint8) x0 = max(int(round(rect[0])), 0) y0 = max(int(round(rect[1])), 0) x1 = min(int(round(rect[0] + rect[2])), output_sz[0]) y1 = min(int(round(rect[1] + rect[3])), output_sz[1]) mask[y0:y1, x0:x1] = 1 return mask def bbox_clip(x1, y1, x2, y2, boundary, min_sz=10): '''boundary (H,W)''' x1_new = max(0, min(x1, boundary[1] - min_sz)) y1_new = max(0, min(y1, boundary[0] - min_sz)) x2_new = max(min_sz, min(x2, boundary[1])) y2_new = max(min_sz, min(y2, boundary[0])) return x1_new, y1_new, x2_new, y2_new ================================================ FILE: external/PreciseRoIPooling/.gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class .vim-template* # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ .pytest_cache/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ ================================================ FILE: external/PreciseRoIPooling/LICENSE ================================================ MIT License Copyright (c) 2018 Jiayuan Mao Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: external/PreciseRoIPooling/README.md ================================================ # PreciseRoIPooling This repo implements the **Precise RoI Pooling** (PrRoI Pooling), proposed in the paper **Acquisition of Localization Confidence for Accurate Object Detection** published at ECCV 2018 (Oral Presentation). **Acquisition of Localization Confidence for Accurate Object Detection** _Borui Jiang*, Ruixuan Luo*, Jiayuan Mao*, Tete Xiao, Yuning Jiang_ (* indicates equal contribution.) https://arxiv.org/abs/1807.11590 ## Brief In short, Precise RoI Pooling is an integration-based (bilinear interpolation) average pooling method for RoI Pooling. It avoids any quantization and has a continuous gradient on bounding box coordinates. It is: - different from the original RoI Pooling proposed in [Fast R-CNN](https://arxiv.org/abs/1504.08083). PrRoI Pooling uses average pooling instead of max pooling for each bin and has a continuous gradient on bounding box coordinates. That is, one can take the derivatives of some loss function w.r.t the coordinates of each RoI and optimize the RoI coordinates. - different from the RoI Align proposed in [Mask R-CNN](https://arxiv.org/abs/1703.06870). PrRoI Pooling uses a full integration-based average pooling instead of sampling a constant number of points. This makes the gradient w.r.t. the coordinates continuous. For a better illustration, we illustrate RoI Pooling, RoI Align and PrRoI Pooing in the following figure. More details including the gradient computation can be found in our paper.
## Implementation PrRoI Pooling was originally implemented by [Tete Xiao](http://tetexiao.com/) based on MegBrain, an (internal) deep learning framework built by Megvii Inc. It was later adapted into open-source deep learning frameworks. Currently, we only support PyTorch. Unfortunately, we don't have any specific plan for the adaptation into other frameworks such as TensorFlow, but any contributions (pull requests) will be more than welcome. ## Usage (PyTorch 1.0) In the directory `pytorch/`, we provide a PyTorch-based implementation of PrRoI Pooling. It requires PyTorch 1.0+ and only supports CUDA (CPU mode is not implemented). Since we use PyTorch JIT for cxx/cuda code compilation, to use the module in your code, simply do: ``` from prroi_pool import PrRoIPool2D avg_pool = PrRoIPool2D(window_height, window_width, spatial_scale) roi_features = avg_pool(features, rois) # for those who want to use the "functional" from prroi_pool.functional import prroi_pool2d roi_features = prroi_pool2d(features, rois, window_height, window_width, spatial_scale) ``` ## Usage (PyTorch 0.4) **!!! Please first checkout to the branch pytorch0.4.** In the directory `pytorch/`, we provide a PyTorch-based implementation of PrRoI Pooling. It requires PyTorch 0.4 and only supports CUDA (CPU mode is not implemented). To use the PrRoI Pooling module, first goto `pytorch/prroi_pool` and execute `./travis.sh` to compile the essential components (you may need `nvcc` for this step). To use the module in your code, simply do: ``` from prroi_pool import PrRoIPool2D avg_pool = PrRoIPool2D(window_height, window_width, spatial_scale) roi_features = avg_pool(features, rois) # for those who want to use the "functional" from prroi_pool.functional import prroi_pool2d roi_features = prroi_pool2d(features, rois, window_height, window_width, spatial_scale) ``` Here, - RoI is an `m * 5` float tensor of format `(batch_index, x0, y0, x1, y1)`, following the convention in the original Caffe implementation of RoI Pooling, although in some frameworks the batch indices are provided by an integer tensor. - `spatial_scale` is multiplied to the RoIs. For example, if your feature maps are down-sampled by a factor of 16 (w.r.t. the input image), you should use a spatial scale of `1/16`. - The coordinates for RoI follows the [L, R) convension. That is, `(0, 0, 4, 4)` denotes a box of size `4x4`. ================================================ FILE: external/PreciseRoIPooling/pytorch/prroi_pool/.gitignore ================================================ *.o /_prroi_pooling ================================================ FILE: external/PreciseRoIPooling/pytorch/prroi_pool/__init__.py ================================================ #! /usr/bin/env python3 # -*- coding: utf-8 -*- # File : __init__.py # Author : Jiayuan Mao, Tete Xiao # Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com # Date : 07/13/2018 # # This file is part of PreciseRoIPooling. # Distributed under terms of the MIT license. # Copyright (c) 2017 Megvii Technology Limited. from .prroi_pool import * ================================================ FILE: external/PreciseRoIPooling/pytorch/prroi_pool/functional.py ================================================ #! /usr/bin/env python3 # -*- coding: utf-8 -*- # File : functional.py # Author : Jiayuan Mao, Tete Xiao # Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com # Date : 07/13/2018 # # This file is part of PreciseRoIPooling. # Distributed under terms of the MIT license. # Copyright (c) 2017 Megvii Technology Limited. import torch import torch.autograd as ag __all__ = ['prroi_pool2d'] _prroi_pooling = None def _import_prroi_pooling(): global _prroi_pooling if _prroi_pooling is None: try: from os.path import join as pjoin, dirname from torch.utils.cpp_extension import load as load_extension root_dir = pjoin(dirname(__file__), 'src') _prroi_pooling = load_extension( '_prroi_pooling', [pjoin(root_dir, 'prroi_pooling_gpu.c'), pjoin(root_dir, 'prroi_pooling_gpu_impl.cu')], verbose=True ) except ImportError: raise ImportError('Can not compile Precise RoI Pooling library.') return _prroi_pooling class PrRoIPool2DFunction(ag.Function): @staticmethod def forward(ctx, features, rois, pooled_height, pooled_width, spatial_scale): _prroi_pooling = _import_prroi_pooling() assert 'FloatTensor' in features.type() and 'FloatTensor' in rois.type(), \ 'Precise RoI Pooling only takes float input, got {} for features and {} for rois.'.format(features.type(), rois.type()) pooled_height = int(pooled_height) pooled_width = int(pooled_width) spatial_scale = float(spatial_scale) features = features.contiguous() rois = rois.contiguous() params = (pooled_height, pooled_width, spatial_scale) if features.is_cuda: output = _prroi_pooling.prroi_pooling_forward_cuda(features, rois, *params) ctx.params = params # everything here is contiguous. ctx.save_for_backward(features, rois, output) else: raise NotImplementedError('Precise RoI Pooling only supports GPU (cuda) implememtations.') return output @staticmethod def backward(ctx, grad_output): _prroi_pooling = _import_prroi_pooling() features, rois, output = ctx.saved_tensors grad_input = grad_coor = None if features.requires_grad: grad_output = grad_output.contiguous() grad_input = _prroi_pooling.prroi_pooling_backward_cuda(features, rois, output, grad_output, *ctx.params) if rois.requires_grad: grad_output = grad_output.contiguous() grad_coor = _prroi_pooling.prroi_pooling_coor_backward_cuda(features, rois, output, grad_output, *ctx.params) return grad_input, grad_coor, None, None, None prroi_pool2d = PrRoIPool2DFunction.apply ================================================ FILE: external/PreciseRoIPooling/pytorch/prroi_pool/prroi_pool.py ================================================ #! /usr/bin/env python3 # -*- coding: utf-8 -*- # File : prroi_pool.py # Author : Jiayuan Mao, Tete Xiao # Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com # Date : 07/13/2018 # # This file is part of PreciseRoIPooling. # Distributed under terms of the MIT license. # Copyright (c) 2017 Megvii Technology Limited. import torch.nn as nn from .functional import prroi_pool2d __all__ = ['PrRoIPool2D'] class PrRoIPool2D(nn.Module): def __init__(self, pooled_height, pooled_width, spatial_scale): super().__init__() self.pooled_height = int(pooled_height) self.pooled_width = int(pooled_width) self.spatial_scale = float(spatial_scale) def forward(self, features, rois): return prroi_pool2d(features, rois, self.pooled_height, self.pooled_width, self.spatial_scale) def extra_repr(self): return 'kernel_size=({pooled_height}, {pooled_width}), spatial_scale={spatial_scale}'.format(**self.__dict__) ================================================ FILE: external/PreciseRoIPooling/pytorch/prroi_pool/src/prroi_pooling_gpu.c ================================================ /* * File : prroi_pooling_gpu.c * Author : Jiayuan Mao, Tete Xiao * Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com * Date : 07/13/2018 * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ #include #include #include #include #include "prroi_pooling_gpu_impl.cuh" at::Tensor prroi_pooling_forward_cuda(const at::Tensor &features, const at::Tensor &rois, int pooled_height, int pooled_width, float spatial_scale) { int nr_rois = rois.size(0); int nr_channels = features.size(1); int height = features.size(2); int width = features.size(3); int top_count = nr_rois * nr_channels * pooled_height * pooled_width; auto output = at::zeros({nr_rois, nr_channels, pooled_height, pooled_width}, features.options()); if (output.numel() == 0) { AT_CUDA_CHECK(cudaGetLastError()); return output; } cudaStream_t stream = at::cuda::getCurrentCUDAStream(); PrRoIPoolingForwardGpu( stream, features.data(), rois.data(), output.data(), nr_channels, height, width, pooled_height, pooled_width, spatial_scale, top_count ); AT_CUDA_CHECK(cudaGetLastError()); return output; } at::Tensor prroi_pooling_backward_cuda( const at::Tensor &features, const at::Tensor &rois, const at::Tensor &output, const at::Tensor &output_diff, int pooled_height, int pooled_width, float spatial_scale) { auto features_diff = at::zeros_like(features); int nr_rois = rois.size(0); int batch_size = features.size(0); int nr_channels = features.size(1); int height = features.size(2); int width = features.size(3); int top_count = nr_rois * nr_channels * pooled_height * pooled_width; int bottom_count = batch_size * nr_channels * height * width; if (output.numel() == 0) { AT_CUDA_CHECK(cudaGetLastError()); return features_diff; } cudaStream_t stream = at::cuda::getCurrentCUDAStream(); PrRoIPoolingBackwardGpu( stream, features.data(), rois.data(), output.data(), output_diff.data(), features_diff.data(), nr_channels, height, width, pooled_height, pooled_width, spatial_scale, top_count, bottom_count ); AT_CUDA_CHECK(cudaGetLastError()); return features_diff; } at::Tensor prroi_pooling_coor_backward_cuda( const at::Tensor &features, const at::Tensor &rois, const at::Tensor &output, const at::Tensor &output_diff, int pooled_height, int pooled_width, float spatial_scale) { auto coor_diff = at::zeros_like(rois); int nr_rois = rois.size(0); int nr_channels = features.size(1); int height = features.size(2); int width = features.size(3); int top_count = nr_rois * nr_channels * pooled_height * pooled_width; int bottom_count = nr_rois * 5; if (output.numel() == 0) { AT_CUDA_CHECK(cudaGetLastError()); return coor_diff; } cudaStream_t stream = at::cuda::getCurrentCUDAStream(); PrRoIPoolingCoorBackwardGpu( stream, features.data(), rois.data(), output.data(), output_diff.data(), coor_diff.data(), nr_channels, height, width, pooled_height, pooled_width, spatial_scale, top_count, bottom_count ); AT_CUDA_CHECK(cudaGetLastError()); return coor_diff; } PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("prroi_pooling_forward_cuda", &prroi_pooling_forward_cuda, "PRRoIPooling_forward"); m.def("prroi_pooling_backward_cuda", &prroi_pooling_backward_cuda, "PRRoIPooling_backward"); m.def("prroi_pooling_coor_backward_cuda", &prroi_pooling_coor_backward_cuda, "PRRoIPooling_backward_coor"); } ================================================ FILE: external/PreciseRoIPooling/pytorch/prroi_pool/src/prroi_pooling_gpu.h ================================================ /* * File : prroi_pooling_gpu.h * Author : Jiayuan Mao, Tete Xiao * Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com * Date : 07/13/2018 * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ int prroi_pooling_forward_cuda(THCudaTensor *features, THCudaTensor *rois, THCudaTensor *output, int pooled_height, int pooled_width, float spatial_scale); int prroi_pooling_backward_cuda( THCudaTensor *features, THCudaTensor *rois, THCudaTensor *output, THCudaTensor *output_diff, THCudaTensor *features_diff, int pooled_height, int pooled_width, float spatial_scale ); int prroi_pooling_coor_backward_cuda( THCudaTensor *features, THCudaTensor *rois, THCudaTensor *output, THCudaTensor *output_diff, THCudaTensor *features_diff, int pooled_height, int pooled_width, float spatial_scal ); ================================================ FILE: external/PreciseRoIPooling/pytorch/prroi_pool/src/prroi_pooling_gpu_impl.cu ================================================ /* * File : prroi_pooling_gpu_impl.cu * Author : Tete Xiao, Jiayuan Mao * Email : jasonhsiao97@gmail.com * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ #include "prroi_pooling_gpu_impl.cuh" #include #include #define CUDA_KERNEL_LOOP(i, n) \ for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ i < (n); \ i += blockDim.x * gridDim.x) #define CUDA_POST_KERNEL_CHECK \ do { \ cudaError_t err = cudaGetLastError(); \ if (cudaSuccess != err) { \ fprintf(stderr, "cudaCheckError() failed : %s\n", cudaGetErrorString(err)); \ exit(-1); \ } \ } while(0) #define CUDA_NUM_THREADS 512 namespace { static int CUDA_NUM_BLOCKS(const int N) { return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; } __device__ static float PrRoIPoolingGetData(F_DEVPTR_IN data, const int h, const int w, const int height, const int width) { bool overflow = (h < 0) || (w < 0) || (h >= height) || (w >= width); float retVal = overflow ? 0.0f : data[h * width + w]; return retVal; } __device__ static float PrRoIPoolingGetCoeff(float dh, float dw){ dw = dw > 0 ? dw : -dw; dh = dh > 0 ? dh : -dh; return (1.0f - dh) * (1.0f - dw); } __device__ static float PrRoIPoolingSingleCoorIntegral(float s, float t, float c1, float c2) { return 0.5 * (t * t - s * s) * c2 + (t - 0.5 * t * t - s + 0.5 * s * s) * c1; } __device__ static float PrRoIPoolingInterpolation(F_DEVPTR_IN data, const float h, const float w, const int height, const int width){ float retVal = 0.0f; int h1 = floorf(h); int w1 = floorf(w); retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h)+1; w1 = floorf(w); retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h); w1 = floorf(w)+1; retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h)+1; w1 = floorf(w)+1; retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); return retVal; } __device__ static float PrRoIPoolingMatCalculation(F_DEVPTR_IN this_data, const int s_h, const int s_w, const int e_h, const int e_w, const float y0, const float x0, const float y1, const float x1, const int h0, const int w0) { float alpha, beta, lim_alpha, lim_beta, tmp; float sum_out = 0; alpha = x0 - float(s_w); beta = y0 - float(s_h); lim_alpha = x1 - float(s_w); lim_beta = y1 - float(s_h); tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, s_h, s_w, h0, w0) * tmp; alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, s_h, e_w, h0, w0) * tmp; alpha = x0 - float(s_w); beta = float(e_h) - y1; lim_alpha = x1 - float(s_w); lim_beta = float(e_h) - y0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, e_h, s_w, h0, w0) * tmp; alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, e_h, e_w, h0, w0) * tmp; return sum_out; } __device__ static void PrRoIPoolingDistributeDiff(F_DEVPTR_OUT diff, const float top_diff, const int h, const int w, const int height, const int width, const float coeff) { bool overflow = (h < 0) || (w < 0) || (h >= height) || (w >= width); if (!overflow) atomicAdd(diff + h * width + w, top_diff * coeff); } __device__ static void PrRoIPoolingMatDistributeDiff(F_DEVPTR_OUT diff, const float top_diff, const int s_h, const int s_w, const int e_h, const int e_w, const float y0, const float x0, const float y1, const float x1, const int h0, const int w0) { float alpha, beta, lim_alpha, lim_beta, tmp; alpha = x0 - float(s_w); beta = y0 - float(s_h); lim_alpha = x1 - float(s_w); lim_beta = y1 - float(s_h); tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, s_h, s_w, h0, w0, tmp); alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, s_h, e_w, h0, w0, tmp); alpha = x0 - float(s_w); beta = float(e_h) - y1; lim_alpha = x1 - float(s_w); lim_beta = float(e_h) - y0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, e_h, s_w, h0, w0, tmp); alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, e_h, e_w, h0, w0, tmp); } __global__ void PrRoIPoolingForward( const int nthreads, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, ((float)0.0)); float roi_height = max(roi_end_h - roi_start_h, ((float)0.0)); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_data = bottom_data + (roi_batch_ind * channels + c) * height * width; float *this_out = top_data + index; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); if (win_size == 0) { *this_out = 0; return; } float sum_out = 0; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); for (int w_iter = s_w; w_iter < e_w; ++w_iter) for (int h_iter = s_h; h_iter < e_h; ++h_iter) sum_out += PrRoIPoolingMatCalculation(this_data, h_iter, w_iter, h_iter + 1, w_iter + 1, max(win_start_h, float(h_iter)), max(win_start_w, float(w_iter)), min(win_end_h, float(h_iter) + 1.0), min(win_end_w, float(w_iter + 1.0)), height, width); *this_out = sum_out / win_size; } } __global__ void PrRoIPoolingBackward( const int nthreads, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, (float)0); float roi_height = max(roi_end_h - roi_start_h, (float)0); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_out_grad = top_diff + index; float *this_data_grad = bottom_diff + (roi_batch_ind * channels + c) * height * width; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); float sum_out = win_size == float(0) ? float(0) : *this_out_grad / win_size; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); for (int w_iter = s_w; w_iter < e_w; ++w_iter) for (int h_iter = s_h; h_iter < e_h; ++h_iter) PrRoIPoolingMatDistributeDiff(this_data_grad, sum_out, h_iter, w_iter, h_iter + 1, w_iter + 1, max(win_start_h, float(h_iter)), max(win_start_w, float(w_iter)), min(win_end_h, float(h_iter) + 1.0), min(win_end_w, float(w_iter + 1.0)), height, width); } } __global__ void PrRoIPoolingCoorBackward( const int nthreads, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, (float)0); float roi_height = max(roi_end_h - roi_start_h, (float)0); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_out_grad = top_diff + index; const float *this_bottom_data = bottom_data + (roi_batch_ind * channels + c) * height * width; const float *this_top_data = top_data + index; float *this_data_grad = bottom_diff + n * 5; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); float sum_out = win_size == float(0) ? float(0) : *this_out_grad / win_size; // WARNING: to be discussed if (sum_out == 0) return; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); float g_x1_y = 0, g_x2_y = 0, g_x_y1 = 0, g_x_y2 = 0; for (int h_iter = s_h; h_iter < e_h; ++h_iter) { g_x1_y += PrRoIPoolingSingleCoorIntegral(max(win_start_h, float(h_iter)) - h_iter, min(win_end_h, float(h_iter + 1)) - h_iter, PrRoIPoolingInterpolation(this_bottom_data, h_iter, win_start_w, height, width), PrRoIPoolingInterpolation(this_bottom_data, h_iter + 1, win_start_w, height, width)); g_x2_y += PrRoIPoolingSingleCoorIntegral(max(win_start_h, float(h_iter)) - h_iter, min(win_end_h, float(h_iter + 1)) - h_iter, PrRoIPoolingInterpolation(this_bottom_data, h_iter, win_end_w, height, width), PrRoIPoolingInterpolation(this_bottom_data, h_iter + 1, win_end_w, height, width)); } for (int w_iter = s_w; w_iter < e_w; ++w_iter) { g_x_y1 += PrRoIPoolingSingleCoorIntegral(max(win_start_w, float(w_iter)) - w_iter, min(win_end_w, float(w_iter + 1)) - w_iter, PrRoIPoolingInterpolation(this_bottom_data, win_start_h, w_iter, height, width), PrRoIPoolingInterpolation(this_bottom_data, win_start_h, w_iter + 1, height, width)); g_x_y2 += PrRoIPoolingSingleCoorIntegral(max(win_start_w, float(w_iter)) - w_iter, min(win_end_w, float(w_iter + 1)) - w_iter, PrRoIPoolingInterpolation(this_bottom_data, win_end_h, w_iter, height, width), PrRoIPoolingInterpolation(this_bottom_data, win_end_h, w_iter + 1, height, width)); } float partial_x1 = -g_x1_y + (win_end_h - win_start_h) * (*this_top_data); float partial_y1 = -g_x_y1 + (win_end_w - win_start_w) * (*this_top_data); float partial_x2 = g_x2_y - (win_end_h - win_start_h) * (*this_top_data); float partial_y2 = g_x_y2 - (win_end_w - win_start_w) * (*this_top_data); partial_x1 = partial_x1 / win_size * spatial_scale; partial_x2 = partial_x2 / win_size * spatial_scale; partial_y1 = partial_y1 / win_size * spatial_scale; partial_y2 = partial_y2 / win_size * spatial_scale; // (b, x1, y1, x2, y2) this_data_grad[0] = 0; atomicAdd(this_data_grad + 1, (partial_x1 * (1.0 - float(pw) / pooled_width) + partial_x2 * (1.0 - float(pw + 1) / pooled_width)) * (*this_out_grad)); atomicAdd(this_data_grad + 2, (partial_y1 * (1.0 - float(ph) / pooled_height) + partial_y2 * (1.0 - float(ph + 1) / pooled_height)) * (*this_out_grad)); atomicAdd(this_data_grad + 3, (partial_x2 * float(pw + 1) / pooled_width + partial_x1 * float(pw) / pooled_width) * (*this_out_grad)); atomicAdd(this_data_grad + 4, (partial_y2 * float(ph + 1) / pooled_height + partial_y1 * float(ph) / pooled_height) * (*this_out_grad)); } } } /* !anonymous namespace */ #ifdef __cplusplus extern "C" { #endif void PrRoIPoolingForwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count) { PrRoIPoolingForward<<>>( top_count, bottom_data, bottom_rois, top_data, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } void PrRoIPoolingBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count) { cudaMemsetAsync(bottom_diff, 0, sizeof(float) * bottom_count, stream); PrRoIPoolingBackward<<>>( top_count, bottom_rois, top_diff, bottom_diff, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } void PrRoIPoolingCoorBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count) { cudaMemsetAsync(bottom_diff, 0, sizeof(float) * bottom_count, stream); PrRoIPoolingCoorBackward<<>>( top_count, bottom_data, bottom_rois, top_data, top_diff, bottom_diff, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } } /* !extern "C" */ ================================================ FILE: external/PreciseRoIPooling/pytorch/prroi_pool/src/prroi_pooling_gpu_impl.cuh ================================================ /* * File : prroi_pooling_gpu_impl.cuh * Author : Tete Xiao, Jiayuan Mao * Email : jasonhsiao97@gmail.com * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ #ifndef PRROI_POOLING_GPU_IMPL_CUH #define PRROI_POOLING_GPU_IMPL_CUH #ifdef __cplusplus extern "C" { #endif #define F_DEVPTR_IN const float * #define F_DEVPTR_OUT float * void PrRoIPoolingForwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count); void PrRoIPoolingBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count); void PrRoIPoolingCoorBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count); #ifdef __cplusplus } /* !extern "C" */ #endif #endif /* !PRROI_POOLING_GPU_IMPL_CUH */ ================================================ FILE: external/PreciseRoIPooling/pytorch/tests/test_prroi_pooling2d.py ================================================ # -*- coding: utf-8 -*- # File : test_prroi_pooling2d.py # Author : Jiayuan Mao # Email : maojiayuan@gmail.com # Date : 18/02/2018 # # This file is part of Jacinle. import unittest import torch import torch.nn as nn import torch.nn.functional as F from jactorch.utils.unittest import TorchTestCase from prroi_pool import PrRoIPool2D class TestPrRoIPool2D(TorchTestCase): def test_forward(self): pool = PrRoIPool2D(7, 7, spatial_scale=0.5) features = torch.rand((4, 16, 24, 32)).cuda() rois = torch.tensor([ [0, 0, 0, 14, 14], [1, 14, 14, 28, 28], ]).float().cuda() out = pool(features, rois) out_gold = F.avg_pool2d(features, kernel_size=2, stride=1) self.assertTensorClose(out, torch.stack(( out_gold[0, :, :7, :7], out_gold[1, :, 7:14, 7:14], ), dim=0)) def test_backward_shapeonly(self): pool = PrRoIPool2D(2, 2, spatial_scale=0.5) features = torch.rand((4, 2, 24, 32)).cuda() rois = torch.tensor([ [0, 0, 0, 4, 4], [1, 14, 14, 18, 18], ]).float().cuda() features.requires_grad = rois.requires_grad = True out = pool(features, rois) loss = out.sum() loss.backward() self.assertTupleEqual(features.size(), features.grad.size()) self.assertTupleEqual(rois.size(), rois.grad.size()) if __name__ == '__main__': unittest.main() ================================================ FILE: external/PreciseRoIPooling/src/prroi_pooling_gpu_impl.cu ================================================ /* * File : prroi_pooling_gpu_impl.cu * Author : Tete Xiao, Jiayuan Mao * Email : jasonhsiao97@gmail.com * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ #include "prroi_pooling_gpu_impl.cuh" #include #include #define CUDA_KERNEL_LOOP(i, n) \ for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ i < (n); \ i += blockDim.x * gridDim.x) #define CUDA_POST_KERNEL_CHECK \ do { \ cudaError_t err = cudaGetLastError(); \ if (cudaSuccess != err) { \ fprintf(stderr, "cudaCheckError() failed : %s\n", cudaGetErrorString(err)); \ exit(-1); \ } \ } while(0) #define CUDA_NUM_THREADS 512 namespace { static int CUDA_NUM_BLOCKS(const int N) { return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; } __device__ static float PrRoIPoolingGetData(F_DEVPTR_IN data, const int h, const int w, const int height, const int width) { bool overflow = (h < 0) || (w < 0) || (h >= height) || (w >= width); float retVal = overflow ? 0.0f : data[h * width + w]; return retVal; } __device__ static float PrRoIPoolingGetCoeff(float dh, float dw){ dw = dw > 0 ? dw : -dw; dh = dh > 0 ? dh : -dh; return (1.0f - dh) * (1.0f - dw); } __device__ static float PrRoIPoolingSingleCoorIntegral(float s, float t, float c1, float c2) { return 0.5 * (t * t - s * s) * c2 + (t - 0.5 * t * t - s + 0.5 * s * s) * c1; } __device__ static float PrRoIPoolingInterpolation(F_DEVPTR_IN data, const float h, const float w, const int height, const int width){ float retVal = 0.0f; int h1 = floorf(h); int w1 = floorf(w); retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h)+1; w1 = floorf(w); retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h); w1 = floorf(w)+1; retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); h1 = floorf(h)+1; w1 = floorf(w)+1; retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1)); return retVal; } __device__ static float PrRoIPoolingMatCalculation(F_DEVPTR_IN this_data, const int s_h, const int s_w, const int e_h, const int e_w, const float y0, const float x0, const float y1, const float x1, const int h0, const int w0) { float alpha, beta, lim_alpha, lim_beta, tmp; float sum_out = 0; alpha = x0 - float(s_w); beta = y0 - float(s_h); lim_alpha = x1 - float(s_w); lim_beta = y1 - float(s_h); tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, s_h, s_w, h0, w0) * tmp; alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, s_h, e_w, h0, w0) * tmp; alpha = x0 - float(s_w); beta = float(e_h) - y1; lim_alpha = x1 - float(s_w); lim_beta = float(e_h) - y0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, e_h, s_w, h0, w0) * tmp; alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); sum_out += PrRoIPoolingGetData(this_data, e_h, e_w, h0, w0) * tmp; return sum_out; } __device__ static void PrRoIPoolingDistributeDiff(F_DEVPTR_OUT diff, const float top_diff, const int h, const int w, const int height, const int width, const float coeff) { bool overflow = (h < 0) || (w < 0) || (h >= height) || (w >= width); if (!overflow) atomicAdd(diff + h * width + w, top_diff * coeff); } __device__ static void PrRoIPoolingMatDistributeDiff(F_DEVPTR_OUT diff, const float top_diff, const int s_h, const int s_w, const int e_h, const int e_w, const float y0, const float x0, const float y1, const float x1, const int h0, const int w0) { float alpha, beta, lim_alpha, lim_beta, tmp; alpha = x0 - float(s_w); beta = y0 - float(s_h); lim_alpha = x1 - float(s_w); lim_beta = y1 - float(s_h); tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, s_h, s_w, h0, w0, tmp); alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, s_h, e_w, h0, w0, tmp); alpha = x0 - float(s_w); beta = float(e_h) - y1; lim_alpha = x1 - float(s_w); lim_beta = float(e_h) - y0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, e_h, s_w, h0, w0, tmp); alpha = float(e_w) - x1; lim_alpha = float(e_w) - x0; tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha) * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta); PrRoIPoolingDistributeDiff(diff, top_diff, e_h, e_w, h0, w0, tmp); } __global__ void PrRoIPoolingForward( const int nthreads, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, ((float)0.0)); float roi_height = max(roi_end_h - roi_start_h, ((float)0.0)); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_data = bottom_data + (roi_batch_ind * channels + c) * height * width; float *this_out = top_data + index; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); if (win_size == 0) { *this_out = 0; return; } float sum_out = 0; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); for (int w_iter = s_w; w_iter < e_w; ++w_iter) for (int h_iter = s_h; h_iter < e_h; ++h_iter) sum_out += PrRoIPoolingMatCalculation(this_data, h_iter, w_iter, h_iter + 1, w_iter + 1, max(win_start_h, float(h_iter)), max(win_start_w, float(w_iter)), min(win_end_h, float(h_iter) + 1.0), min(win_end_w, float(w_iter + 1.0)), height, width); *this_out = sum_out / win_size; } } __global__ void PrRoIPoolingBackward( const int nthreads, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, (float)0); float roi_height = max(roi_end_h - roi_start_h, (float)0); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_out_grad = top_diff + index; float *this_data_grad = bottom_diff + (roi_batch_ind * channels + c) * height * width; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); float sum_out = win_size == float(0) ? float(0) : *this_out_grad / win_size; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); for (int w_iter = s_w; w_iter < e_w; ++w_iter) for (int h_iter = s_h; h_iter < e_h; ++h_iter) PrRoIPoolingMatDistributeDiff(this_data_grad, sum_out, h_iter, w_iter, h_iter + 1, w_iter + 1, max(win_start_h, float(h_iter)), max(win_start_w, float(w_iter)), min(win_end_h, float(h_iter) + 1.0), min(win_end_w, float(w_iter + 1.0)), height, width); } } __global__ void PrRoIPoolingCoorBackward( const int nthreads, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const float spatial_scale) { CUDA_KERNEL_LOOP(index, nthreads) { // (n, c, ph, pw) is an element in the pooled output int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int c = (index / pooled_width / pooled_height) % channels; int n = index / pooled_width / pooled_height / channels; bottom_rois += n * 5; int roi_batch_ind = bottom_rois[0]; float roi_start_w = bottom_rois[1] * spatial_scale; float roi_start_h = bottom_rois[2] * spatial_scale; float roi_end_w = bottom_rois[3] * spatial_scale; float roi_end_h = bottom_rois[4] * spatial_scale; float roi_width = max(roi_end_w - roi_start_w, (float)0); float roi_height = max(roi_end_h - roi_start_h, (float)0); float bin_size_h = roi_height / static_cast(pooled_height); float bin_size_w = roi_width / static_cast(pooled_width); const float *this_out_grad = top_diff + index; const float *this_bottom_data = bottom_data + (roi_batch_ind * channels + c) * height * width; const float *this_top_data = top_data + index; float *this_data_grad = bottom_diff + n * 5; float win_start_w = roi_start_w + bin_size_w * pw; float win_start_h = roi_start_h + bin_size_h * ph; float win_end_w = win_start_w + bin_size_w; float win_end_h = win_start_h + bin_size_h; float win_size = max(float(0.0), bin_size_w * bin_size_h); float sum_out = win_size == float(0) ? float(0) : *this_out_grad / win_size; // WARNING: to be discussed if (sum_out == 0) return; int s_w, s_h, e_w, e_h; s_w = floorf(win_start_w); e_w = ceilf(win_end_w); s_h = floorf(win_start_h); e_h = ceilf(win_end_h); float g_x1_y = 0, g_x2_y = 0, g_x_y1 = 0, g_x_y2 = 0; for (int h_iter = s_h; h_iter < e_h; ++h_iter) { g_x1_y += PrRoIPoolingSingleCoorIntegral(max(win_start_h, float(h_iter)) - h_iter, min(win_end_h, float(h_iter + 1)) - h_iter, PrRoIPoolingInterpolation(this_bottom_data, h_iter, win_start_w, height, width), PrRoIPoolingInterpolation(this_bottom_data, h_iter + 1, win_start_w, height, width)); g_x2_y += PrRoIPoolingSingleCoorIntegral(max(win_start_h, float(h_iter)) - h_iter, min(win_end_h, float(h_iter + 1)) - h_iter, PrRoIPoolingInterpolation(this_bottom_data, h_iter, win_end_w, height, width), PrRoIPoolingInterpolation(this_bottom_data, h_iter + 1, win_end_w, height, width)); } for (int w_iter = s_w; w_iter < e_w; ++w_iter) { g_x_y1 += PrRoIPoolingSingleCoorIntegral(max(win_start_w, float(w_iter)) - w_iter, min(win_end_w, float(w_iter + 1)) - w_iter, PrRoIPoolingInterpolation(this_bottom_data, win_start_h, w_iter, height, width), PrRoIPoolingInterpolation(this_bottom_data, win_start_h, w_iter + 1, height, width)); g_x_y2 += PrRoIPoolingSingleCoorIntegral(max(win_start_w, float(w_iter)) - w_iter, min(win_end_w, float(w_iter + 1)) - w_iter, PrRoIPoolingInterpolation(this_bottom_data, win_end_h, w_iter, height, width), PrRoIPoolingInterpolation(this_bottom_data, win_end_h, w_iter + 1, height, width)); } float partial_x1 = -g_x1_y + (win_end_h - win_start_h) * (*this_top_data); float partial_y1 = -g_x_y1 + (win_end_w - win_start_w) * (*this_top_data); float partial_x2 = g_x2_y - (win_end_h - win_start_h) * (*this_top_data); float partial_y2 = g_x_y2 - (win_end_w - win_start_w) * (*this_top_data); partial_x1 = partial_x1 / win_size * spatial_scale; partial_x2 = partial_x2 / win_size * spatial_scale; partial_y1 = partial_y1 / win_size * spatial_scale; partial_y2 = partial_y2 / win_size * spatial_scale; // (b, x1, y1, x2, y2) this_data_grad[0] = 0; atomicAdd(this_data_grad + 1, (partial_x1 * (1.0 - float(pw) / pooled_width) + partial_x2 * (1.0 - float(pw + 1) / pooled_width)) * (*this_out_grad)); atomicAdd(this_data_grad + 2, (partial_y1 * (1.0 - float(ph) / pooled_height) + partial_y2 * (1.0 - float(ph + 1) / pooled_height)) * (*this_out_grad)); atomicAdd(this_data_grad + 3, (partial_x2 * float(pw + 1) / pooled_width + partial_x1 * float(pw) / pooled_width) * (*this_out_grad)); atomicAdd(this_data_grad + 4, (partial_y2 * float(ph + 1) / pooled_height + partial_y1 * float(ph) / pooled_height) * (*this_out_grad)); } } } /* !anonymous namespace */ #ifdef __cplusplus extern "C" { #endif void PrRoIPoolingForwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count) { PrRoIPoolingForward<<>>( top_count, bottom_data, bottom_rois, top_data, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } void PrRoIPoolingBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count) { cudaMemsetAsync(bottom_diff, 0, sizeof(float) * bottom_count, stream); PrRoIPoolingBackward<<>>( top_count, bottom_rois, top_diff, bottom_diff, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } void PrRoIPoolingCoorBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count) { cudaMemsetAsync(bottom_diff, 0, sizeof(float) * bottom_count, stream); PrRoIPoolingCoorBackward<<>>( top_count, bottom_data, bottom_rois, top_data, top_diff, bottom_diff, channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_); CUDA_POST_KERNEL_CHECK; } } /* !extern "C" */ ================================================ FILE: external/PreciseRoIPooling/src/prroi_pooling_gpu_impl.cuh ================================================ /* * File : prroi_pooling_gpu_impl.cuh * Author : Tete Xiao, Jiayuan Mao * Email : jasonhsiao97@gmail.com * * Distributed under terms of the MIT license. * Copyright (c) 2017 Megvii Technology Limited. */ #ifndef PRROI_POOLING_GPU_IMPL_CUH #define PRROI_POOLING_GPU_IMPL_CUH #ifdef __cplusplus extern "C" { #endif #define F_DEVPTR_IN const float * #define F_DEVPTR_OUT float * void PrRoIPoolingForwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_OUT top_data, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count); void PrRoIPoolingBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count); void PrRoIPoolingCoorBackwardGpu( cudaStream_t stream, F_DEVPTR_IN bottom_data, F_DEVPTR_IN bottom_rois, F_DEVPTR_IN top_data, F_DEVPTR_IN top_diff, F_DEVPTR_OUT bottom_diff, const int channels_, const int height_, const int width_, const int pooled_height_, const int pooled_width_, const float spatial_scale_, const int top_count, const int bottom_count); #ifdef __cplusplus } /* !extern "C" */ #endif #endif /* !PRROI_POOLING_GPU_IMPL_CUH */ ================================================ FILE: external/vot20/cttrack/config.yaml ================================================ registry: - ./trackers.ini stack: vot2020 ================================================ FILE: external/vot20/cttrack/trackers.ini ================================================ [cttrack_large] # label = cttrack_large protocol = traxpython command = from cttrack_start import main;main() # Specify a path to trax python wrapper if it is not visible (separate by ; if using multiple paths) # paths = /home/lr/workspace/CTTrack: paths = # Additional environment paths env_PATH = : ================================================ FILE: lib/__init__.py ================================================ ================================================ FILE: lib/config/__init__.py ================================================ ================================================ FILE: lib/config/artrack/config.py ================================================ from easydict import EasyDict as edict import yaml """ Add default config for OSTrack. """ cfg = edict() # MODEL cfg.MODEL = edict() cfg.MODEL.PRETRAIN_FILE = "mae_pretrain_vit_base.pth" cfg.MODEL.PRETRAIN_PTH = "" cfg.MODEL.EXTRA_MERGER = False cfg.MODEL.RETURN_INTER = False cfg.MODEL.RETURN_STAGES = [2, 5, 8, 11] # MODEL.BACKBONE cfg.MODEL.BACKBONE = edict() cfg.MODEL.BACKBONE.TYPE = "vit_base_patch16_224" cfg.MODEL.BACKBONE.STRIDE = 16 cfg.MODEL.BACKBONE.MID_PE = False cfg.MODEL.BACKBONE.SEP_SEG = False cfg.MODEL.BACKBONE.CAT_MODE = 'direct' cfg.MODEL.BACKBONE.MERGE_LAYER = 0 cfg.MODEL.BACKBONE.ADD_CLS_TOKEN = False cfg.MODEL.BACKBONE.CLS_TOKEN_USE_MODE = 'ignore' cfg.MODEL.BACKBONE.CE_LOC = [] cfg.MODEL.BACKBONE.CE_KEEP_RATIO = [] cfg.MODEL.BACKBONE.CE_TEMPLATE_RANGE = 'ALL' # choose between ALL, CTR_POINT, CTR_REC, GT_BOX # MODEL.HEAD cfg.MODEL.BINS = 400 cfg.MODEL.RANGE = 2 cfg.MODEL.ENCODER_LAYER = 3 cfg.MODEL.NUM_HEADS = 12 cfg.MODEL.MLP_RATIO = 4 cfg.MODEL.QKV_BIAS = True cfg.MODEL.DROP_RATE = 0.1 cfg.MODEL.ATTN_DROP = 0.0 cfg.MODEL.DROP_PATH = 0.0 cfg.MODEL.DECODER_LAYER = 6 cfg.MODEL.HEAD = edict() cfg.MODEL.HEAD.TYPE = "PIX" cfg.MODEL.HEAD.NUM_CHANNELS = 1024 # TRAIN cfg.TRAIN = edict() cfg.TRAIN.LR = 0.0001 cfg.TRAIN.WEIGHT_DECAY = 0.0001 cfg.TRAIN.EPOCH = 500 cfg.TRAIN.LR_DROP_EPOCH = 400 cfg.TRAIN.BATCH_SIZE = 16 cfg.TRAIN.NUM_WORKER = 10 cfg.TRAIN.OPTIMIZER = "ADAMW" cfg.TRAIN.BACKBONE_MULTIPLIER = 0.1 cfg.TRAIN.GIOU_WEIGHT = 2.0 cfg.TRAIN.L1_WEIGHT = 5.0 cfg.TRAIN.FREEZE_LAYERS = [0, ] cfg.TRAIN.PRINT_INTERVAL = 50 cfg.TRAIN.VAL_EPOCH_INTERVAL = 20 cfg.TRAIN.GRAD_CLIP_NORM = 0.1 cfg.TRAIN.AMP = False cfg.TRAIN.CE_START_EPOCH = 20 # candidate elimination start epoch cfg.TRAIN.CE_WARM_EPOCH = 80 # candidate elimination warm up epoch cfg.TRAIN.DROP_PATH_RATE = 0.1 # drop path rate for ViT backbone # TRAIN.SCHEDULER cfg.TRAIN.SCHEDULER = edict() cfg.TRAIN.SCHEDULER.TYPE = "step" cfg.TRAIN.SCHEDULER.DECAY_RATE = 0.1 # DATA cfg.DATA = edict() cfg.DATA.SAMPLER_MODE = "causal" # sampling methods cfg.DATA.MEAN = [0.485, 0.456, 0.406] cfg.DATA.STD = [0.229, 0.224, 0.225] cfg.DATA.MAX_SAMPLE_INTERVAL = 200 # DATA.TRAIN cfg.DATA.TRAIN = edict() cfg.DATA.TRAIN.DATASETS_NAME = ["LASOT", "GOT10K_vottrain"] cfg.DATA.TRAIN.DATASETS_RATIO = [1, 1] cfg.DATA.TRAIN.SAMPLE_PER_EPOCH = 60000 # DATA.VAL cfg.DATA.VAL = edict() cfg.DATA.VAL.DATASETS_NAME = ["GOT10K_votval"] cfg.DATA.VAL.DATASETS_RATIO = [1] cfg.DATA.VAL.SAMPLE_PER_EPOCH = 10000 # DATA.SEARCH cfg.DATA.SEARCH = edict() cfg.DATA.SEARCH.SIZE = 256 cfg.DATA.SEARCH.FACTOR = 5.0 cfg.DATA.SEARCH.CENTER_JITTER = 4.5 cfg.DATA.SEARCH.SCALE_JITTER = 0.5 cfg.DATA.SEARCH.NUMBER = 1 # DATA.TEMPLATE cfg.DATA.TEMPLATE = edict() cfg.DATA.TEMPLATE.NUMBER = 1 cfg.DATA.TEMPLATE.SIZE = 128 cfg.DATA.TEMPLATE.FACTOR = 2.0 cfg.DATA.TEMPLATE.CENTER_JITTER = 0 cfg.DATA.TEMPLATE.SCALE_JITTER = 0 # TEST cfg.TEST = edict() cfg.TEST.TEMPLATE_FACTOR = 2.0 cfg.TEST.TEMPLATE_SIZE = 128 cfg.TEST.SEARCH_FACTOR = 5.0 cfg.TEST.SEARCH_SIZE = 256 cfg.TEST.EPOCH = 500 def _edict2dict(dest_dict, src_edict): if isinstance(dest_dict, dict) and isinstance(src_edict, dict): for k, v in src_edict.items(): if not isinstance(v, edict): dest_dict[k] = v else: dest_dict[k] = {} _edict2dict(dest_dict[k], v) else: return def gen_config(config_file): cfg_dict = {} _edict2dict(cfg_dict, cfg) with open(config_file, 'w') as f: yaml.dump(cfg_dict, f, default_flow_style=False) def _update_config(base_cfg, exp_cfg): if isinstance(base_cfg, dict) and isinstance(exp_cfg, edict): for k, v in exp_cfg.items(): if k in base_cfg: if not isinstance(v, dict): base_cfg[k] = v else: _update_config(base_cfg[k], v) else: raise ValueError("{} not exist in config.py".format(k)) else: return def update_config_from_file(filename, base_cfg=None): exp_config = None with open(filename) as f: exp_config = edict(yaml.safe_load(f)) if base_cfg is not None: _update_config(base_cfg, exp_config) else: _update_config(cfg, exp_config) ================================================ FILE: lib/config/artrack_seq/config.py ================================================ from easydict import EasyDict as edict import yaml """ Add default config for OSTrack. """ cfg = edict() # MODEL cfg.MODEL = edict() cfg.MODEL.PRETRAIN_FILE = "mae_pretrain_vit_base.pth" cfg.MODEL.PRETRAIN_PTH = "" cfg.MODEL.PRENUM = 7 cfg.MODEL.EXTRA_MERGER = False cfg.MODEL.RETURN_INTER = False cfg.MODEL.RETURN_STAGES = [2, 5, 8, 11] # MODEL.BACKBONE cfg.MODEL.BACKBONE = edict() cfg.MODEL.BACKBONE.TYPE = "vit_base_patch16_224" cfg.MODEL.BACKBONE.STRIDE = 16 cfg.MODEL.BACKBONE.MID_PE = False cfg.MODEL.BACKBONE.SEP_SEG = False cfg.MODEL.BACKBONE.CAT_MODE = 'direct' cfg.MODEL.BACKBONE.MERGE_LAYER = 0 cfg.MODEL.BACKBONE.ADD_CLS_TOKEN = False cfg.MODEL.BACKBONE.CLS_TOKEN_USE_MODE = 'ignore' cfg.MODEL.BACKBONE.CE_LOC = [] cfg.MODEL.BACKBONE.CE_KEEP_RATIO = [] cfg.MODEL.BACKBONE.CE_TEMPLATE_RANGE = 'ALL' # choose between ALL, CTR_POINT, CTR_REC, GT_BOX # MODEL.HEAD cfg.MODEL.BINS = 400 cfg.MODEL.RANGE = 2 cfg.MODEL.ENCODER_LAYER = 3 cfg.MODEL.NUM_HEADS = 12 cfg.MODEL.MLP_RATIO = 4 cfg.MODEL.QKV_BIAS = True cfg.MODEL.DROP_RATE = 0.1 cfg.MODEL.ATTN_DROP = 0.0 cfg.MODEL.DROP_PATH = 0.0 cfg.MODEL.DECODER_LAYER = 6 cfg.MODEL.HEAD = edict() cfg.MODEL.HEAD.TYPE = "PIX" cfg.MODEL.HEAD.NUM_CHANNELS = 1024 # TRAIN cfg.TRAIN = edict() cfg.TRAIN.LR = 0.0001 cfg.TRAIN.WEIGHT_DECAY = 0.0001 cfg.TRAIN.EPOCH = 500 cfg.TRAIN.LR_DROP_EPOCH = 400 cfg.TRAIN.BATCH_SIZE = 16 cfg.TRAIN.NUM_WORKER = 10 cfg.TRAIN.OPTIMIZER = "ADAMW" cfg.TRAIN.BACKBONE_MULTIPLIER = 0.1 cfg.TRAIN.GIOU_WEIGHT = 2.0 cfg.TRAIN.L1_WEIGHT = 5.0 cfg.TRAIN.FREEZE_LAYERS = [0, ] cfg.TRAIN.PRINT_INTERVAL = 50 cfg.TRAIN.VAL_EPOCH_INTERVAL = 20 cfg.TRAIN.GRAD_CLIP_NORM = 0.1 cfg.TRAIN.AMP = False cfg.TRAIN.CE_START_EPOCH = 20 # candidate elimination start epoch cfg.TRAIN.CE_WARM_EPOCH = 80 # candidate elimination warm up epoch cfg.TRAIN.DROP_PATH_RATE = 0.1 # drop path rate for ViT backbone # TRAIN.SCHEDULER cfg.TRAIN.SCHEDULER = edict() cfg.TRAIN.SCHEDULER.TYPE = "step" cfg.TRAIN.SCHEDULER.DECAY_RATE = 0.1 # DATA cfg.DATA = edict() cfg.DATA.SAMPLER_MODE = "causal" # sampling methods cfg.DATA.MEAN = [0.485, 0.456, 0.406] cfg.DATA.STD = [0.229, 0.224, 0.225] cfg.DATA.MAX_SAMPLE_INTERVAL = 200 cfg.DATA.MAX_GAP = 300 cfg.DATA.MAX_INTERVAL = 5 cfg.DATA.INTERVAL_PROB = 0.0 cfg.DATA.TEMP = 2 # DATA.TRAIN cfg.DATA.TRAIN = edict() cfg.DATA.TRAIN.DATASETS_NAME = ["LASOT", "GOT10K_vottrain"] cfg.DATA.TRAIN.DATASETS_RATIO = [1, 1] cfg.DATA.TRAIN.SAMPLE_PER_EPOCH = 60000 # DATA.VAL cfg.DATA.VAL = edict() cfg.DATA.VAL.DATASETS_NAME = ["GOT10K_votval"] cfg.DATA.VAL.DATASETS_RATIO = [1] cfg.DATA.VAL.SAMPLE_PER_EPOCH = 10000 # DATA.SEARCH cfg.DATA.SEARCH = edict() cfg.DATA.SEARCH.SIZE = 256 cfg.DATA.SEARCH.FACTOR = 5.0 cfg.DATA.SEARCH.CENTER_JITTER = 4.5 cfg.DATA.SEARCH.SCALE_JITTER = 0.5 cfg.DATA.SEARCH.NUMBER = 1 # DATA.TEMPLATE cfg.DATA.TEMPLATE = edict() cfg.DATA.TEMPLATE.NUMBER = 1 cfg.DATA.TEMPLATE.SIZE = 128 cfg.DATA.TEMPLATE.FACTOR = 2.0 cfg.DATA.TEMPLATE.CENTER_JITTER = 0 cfg.DATA.TEMPLATE.SCALE_JITTER = 0 # TEST cfg.TEST = edict() cfg.TEST.TEMPLATE_FACTOR = 2.0 cfg.TEST.TEMPLATE_SIZE = 128 cfg.TEST.SEARCH_FACTOR = 5.0 cfg.TEST.SEARCH_SIZE = 256 cfg.TEST.EPOCH = 500 def _edict2dict(dest_dict, src_edict): if isinstance(dest_dict, dict) and isinstance(src_edict, dict): for k, v in src_edict.items(): if not isinstance(v, edict): dest_dict[k] = v else: dest_dict[k] = {} _edict2dict(dest_dict[k], v) else: return def gen_config(config_file): cfg_dict = {} _edict2dict(cfg_dict, cfg) with open(config_file, 'w') as f: yaml.dump(cfg_dict, f, default_flow_style=False) def _update_config(base_cfg, exp_cfg): if isinstance(base_cfg, dict) and isinstance(exp_cfg, edict): for k, v in exp_cfg.items(): if k in base_cfg: if not isinstance(v, dict): base_cfg[k] = v else: _update_config(base_cfg[k], v) else: raise ValueError("{} not exist in config.py".format(k)) else: return def update_config_from_file(filename, base_cfg=None): exp_config = None with open(filename) as f: exp_config = edict(yaml.safe_load(f)) if base_cfg is not None: _update_config(base_cfg, exp_config) else: _update_config(cfg, exp_config) ================================================ FILE: lib/config/artrackv2/config.py ================================================ from easydict import EasyDict as edict import yaml """ Add default config for OSTrack. """ cfg = edict() # MODEL cfg.MODEL = edict() cfg.MODEL.PRETRAIN_FILE = "mae_pretrain_vit_base.pth" cfg.MODEL.PRETRAIN_PTH = "" cfg.MODEL.EXTRA_MERGER = False cfg.MODEL.RETURN_INTER = False cfg.MODEL.RETURN_STAGES = [2, 5, 8, 11] # MODEL.BACKBONE cfg.MODEL.BACKBONE = edict() cfg.MODEL.BACKBONE.TYPE = "vit_base_patch16_224" cfg.MODEL.BACKBONE.STRIDE = 16 cfg.MODEL.BACKBONE.MID_PE = False cfg.MODEL.BACKBONE.SEP_SEG = False cfg.MODEL.BACKBONE.CAT_MODE = 'direct' cfg.MODEL.BACKBONE.MERGE_LAYER = 0 cfg.MODEL.BACKBONE.ADD_CLS_TOKEN = False cfg.MODEL.BACKBONE.CLS_TOKEN_USE_MODE = 'ignore' cfg.MODEL.BACKBONE.CE_LOC = [] cfg.MODEL.BACKBONE.CE_KEEP_RATIO = [] cfg.MODEL.BACKBONE.CE_TEMPLATE_RANGE = 'ALL' # choose between ALL, CTR_POINT, CTR_REC, GT_BOX # MODEL.HEAD cfg.MODEL.BINS = 400 cfg.MODEL.RANGE = 2 cfg.MODEL.EXTENSION = 3 cfg.MODEL.ENCODER_LAYER = 3 cfg.MODEL.NUM_HEADS = 12 cfg.MODEL.MLP_RATIO = 4 cfg.MODEL.QKV_BIAS = True cfg.MODEL.DROP_RATE = 0.1 cfg.MODEL.ATTN_DROP = 0.0 cfg.MODEL.DROP_PATH = 0.0 cfg.MODEL.DECODER_LAYER = 6 cfg.MODEL.HEAD = edict() cfg.MODEL.HEAD.TYPE = "PIX" cfg.MODEL.HEAD.NUM_CHANNELS = 1024 # TRAIN cfg.TRAIN = edict() cfg.TRAIN.LR = 0.0001 cfg.TRAIN.WEIGHT_DECAY = 0.0001 cfg.TRAIN.EPOCH = 500 cfg.TRAIN.LR_DROP_EPOCH = 400 cfg.TRAIN.BATCH_SIZE = 16 cfg.TRAIN.NUM_WORKER = 10 cfg.TRAIN.OPTIMIZER = "ADAMW" cfg.TRAIN.BACKBONE_MULTIPLIER = 0.1 cfg.TRAIN.GIOU_WEIGHT = 2.0 cfg.TRAIN.L1_WEIGHT = 5.0 cfg.TRAIN.SCORE_WEIGHT = 0.0 cfg.TRAIN.FREEZE_LAYERS = [0, ] cfg.TRAIN.PRINT_INTERVAL = 50 cfg.TRAIN.VAL_EPOCH_INTERVAL = 20 cfg.TRAIN.GRAD_CLIP_NORM = 0.1 cfg.TRAIN.AMP = False cfg.TRAIN.CE_START_EPOCH = 20 # candidate elimination start epoch cfg.TRAIN.CE_WARM_EPOCH = 80 # candidate elimination warm up epoch cfg.TRAIN.DROP_PATH_RATE = 0.1 # drop path rate for ViT backbone # TRAIN.SCHEDULER cfg.TRAIN.SCHEDULER = edict() cfg.TRAIN.SCHEDULER.TYPE = "step" cfg.TRAIN.SCHEDULER.DECAY_RATE = 0.1 # DATA cfg.DATA = edict() cfg.DATA.SAMPLER_MODE = "causal" # sampling methods cfg.DATA.MEAN = [0.485, 0.456, 0.406] cfg.DATA.STD = [0.229, 0.224, 0.225] cfg.DATA.MAX_SAMPLE_INTERVAL = 200 # DATA.TRAIN cfg.DATA.TRAIN = edict() cfg.DATA.TRAIN.DATASETS_NAME = ["LASOT", "GOT10K_vottrain"] cfg.DATA.TRAIN.DATASETS_RATIO = [1, 1] cfg.DATA.TRAIN.SAMPLE_PER_EPOCH = 60000 # DATA.VAL cfg.DATA.VAL = edict() cfg.DATA.VAL.DATASETS_NAME = ["GOT10K_votval"] cfg.DATA.VAL.DATASETS_RATIO = [1] cfg.DATA.VAL.SAMPLE_PER_EPOCH = 10000 # DATA.SEARCH cfg.DATA.SEARCH = edict() cfg.DATA.SEARCH.SIZE = 256 cfg.DATA.SEARCH.FACTOR = 5.0 cfg.DATA.SEARCH.CENTER_JITTER = 4.5 cfg.DATA.SEARCH.SCALE_JITTER = 0.5 cfg.DATA.SEARCH.NUMBER = 1 # DATA.TEMPLATE cfg.DATA.TEMPLATE = edict() cfg.DATA.TEMPLATE.NUMBER = 1 cfg.DATA.TEMPLATE.SIZE = 128 cfg.DATA.TEMPLATE.FACTOR = 2.0 cfg.DATA.TEMPLATE.CENTER_JITTER = 0 cfg.DATA.TEMPLATE.SCALE_JITTER = 0 # TEST cfg.TEST = edict() cfg.TEST.TEMPLATE_FACTOR = 2.0 cfg.TEST.TEMPLATE_SIZE = 128 cfg.TEST.SEARCH_FACTOR = 5.0 cfg.TEST.SEARCH_SIZE = 256 cfg.TEST.EPOCH = 500 def _edict2dict(dest_dict, src_edict): if isinstance(dest_dict, dict) and isinstance(src_edict, dict): for k, v in src_edict.items(): if not isinstance(v, edict): dest_dict[k] = v else: dest_dict[k] = {} _edict2dict(dest_dict[k], v) else: return def gen_config(config_file): cfg_dict = {} _edict2dict(cfg_dict, cfg) with open(config_file, 'w') as f: yaml.dump(cfg_dict, f, default_flow_style=False) def _update_config(base_cfg, exp_cfg): if isinstance(base_cfg, dict) and isinstance(exp_cfg, edict): for k, v in exp_cfg.items(): if k in base_cfg: if not isinstance(v, dict): base_cfg[k] = v else: _update_config(base_cfg[k], v) else: raise ValueError("{} not exist in config.py".format(k)) else: return def update_config_from_file(filename, base_cfg=None): exp_config = None with open(filename) as f: exp_config = edict(yaml.safe_load(f)) if base_cfg is not None: _update_config(base_cfg, exp_config) else: _update_config(cfg, exp_config) ================================================ FILE: lib/config/artrackv2_seq/config.py ================================================ from easydict import EasyDict as edict import yaml """ Add default config for OSTrack. """ cfg = edict() # MODEL cfg.MODEL = edict() cfg.MODEL.PRETRAIN_FILE = "mae_pretrain_vit_base.pth" cfg.MODEL.PRETRAIN_PTH = "" cfg.MODEL.EXTRA_MERGER = False cfg.MODEL.RETURN_INTER = False cfg.MODEL.RETURN_STAGES = [2, 5, 8, 11] # MODEL.DECODER cfg.MODEL.DECODER = edict() cfg.MODEL.DECODER.TYPE = "mask" cfg.MODEL.DECODER.MASK_RATIO = 0.75 cfg.MODEL.DECODER.EMBEDDIM = 512 cfg.MODEL.DECODER.DEPTH = 8 cfg.MODEL.DECODER.NUMHEADS = 16 cfg.MODEL.DECODER.MLPRATIO = 4 # MODEL.BACKBONE cfg.MODEL.BACKBONE = edict() cfg.MODEL.BACKBONE.TYPE = "vit_base_patch16_224" cfg.MODEL.BACKBONE.STRIDE = 16 cfg.MODEL.BACKBONE.PATCHSIZE = 16 cfg.MODEL.BACKBONE.MID_PE = False cfg.MODEL.BACKBONE.SEP_SEG = False cfg.MODEL.BACKBONE.CAT_MODE = 'direct' cfg.MODEL.BACKBONE.MERGE_LAYER = 0 cfg.MODEL.BACKBONE.ADD_CLS_TOKEN = False cfg.MODEL.BACKBONE.CLS_TOKEN_USE_MODE = 'ignore' cfg.MODEL.BACKBONE.CE_LOC = [] cfg.MODEL.BACKBONE.CE_KEEP_RATIO = [] cfg.MODEL.BACKBONE.CE_TEMPLATE_RANGE = 'ALL' # choose between ALL, CTR_POINT, CTR_REC, GT_BOX # MODEL.HEAD cfg.MODEL.BINS = 400 cfg.MODEL.RANGE = 2 cfg.MODEL.EXTENSION = 3 cfg.MODEL.PRENUM = 7 cfg.MODEL.ENCODER_LAYER = 3 cfg.MODEL.NUM_HEADS = 12 cfg.MODEL.MLP_RATIO = 4 cfg.MODEL.QKV_BIAS = True cfg.MODEL.DROP_RATE = 0.1 cfg.MODEL.ATTN_DROP = 0.0 cfg.MODEL.DROP_PATH = 0.0 cfg.MODEL.DECODER_LAYER = 6 cfg.MODEL.HEAD = edict() cfg.MODEL.HEAD.TYPE = "PIX" cfg.MODEL.HEAD.NUM_CHANNELS = 1024 # TRAIN cfg.TRAIN = edict() cfg.TRAIN.LR = 0.0001 cfg.TRAIN.WEIGHT_DECAY = 0.0001 cfg.TRAIN.EPOCH = 500 cfg.TRAIN.LR_DROP_EPOCH = 400 cfg.TRAIN.BATCH_SIZE = 16 cfg.TRAIN.NUM_WORKER = 10 cfg.TRAIN.OPTIMIZER = "ADAMW" cfg.TRAIN.BACKBONE_MULTIPLIER = 0.1 cfg.TRAIN.GIOU_WEIGHT = 2.0 cfg.TRAIN.L1_WEIGHT = 5.0 cfg.TRAIN.SCORE_WEIGHT = 1.0 cfg.TRAIN.FREEZE_LAYERS = [0, ] cfg.TRAIN.PRINT_INTERVAL = 50 cfg.TRAIN.VAL_EPOCH_INTERVAL = 20 cfg.TRAIN.GRAD_CLIP_NORM = 0.1 cfg.TRAIN.AMP = False cfg.TRAIN.CE_START_EPOCH = 20 # candidate elimination start epoch cfg.TRAIN.CE_WARM_EPOCH = 80 # candidate elimination warm up epoch cfg.TRAIN.DROP_PATH_RATE = 0.1 # drop path rate for ViT backbone # TRAIN.SCHEDULER cfg.TRAIN.SCHEDULER = edict() cfg.TRAIN.SCHEDULER.TYPE = "step" cfg.TRAIN.SCHEDULER.DECAY_RATE = 0.1 # DATA cfg.DATA = edict() cfg.DATA.MAX_GAP = 300 cfg.DATA.SAMPLER_MODE = "causal" # sampling methods cfg.DATA.MEAN = [0.485, 0.456, 0.406] cfg.DATA.STD = [0.229, 0.224, 0.225] cfg.DATA.MAX_SAMPLE_INTERVAL = 200 cfg.DATA.MAX_GAP = 300 cfg.DATA.MAX_INTERVAL = 5 cfg.DATA.INTERVAL_PROB = 0.0 cfg.DATA.TEMP = 2 # DATA.TRAIN cfg.DATA.TRAIN = edict() cfg.DATA.TRAIN.DATASETS_NAME = ["LASOT", "GOT10K_vottrain"] cfg.DATA.TRAIN.DATASETS_RATIO = [1, 1] cfg.DATA.TRAIN.SAMPLE_PER_EPOCH = 60000 # DATA.VAL cfg.DATA.VAL = edict() cfg.DATA.VAL.DATASETS_NAME = ["GOT10K_votval"] cfg.DATA.VAL.DATASETS_RATIO = [1] cfg.DATA.VAL.SAMPLE_PER_EPOCH = 10000 # DATA.SEARCH cfg.DATA.SEARCH = edict() cfg.DATA.SEARCH.SIZE = 256 cfg.DATA.SEARCH.FACTOR = 5.0 cfg.DATA.SEARCH.CENTER_JITTER = 4.5 cfg.DATA.SEARCH.SCALE_JITTER = 0.5 cfg.DATA.SEARCH.NUMBER = 1 # DATA.TEMPLATE cfg.DATA.TEMPLATE = edict() cfg.DATA.TEMPLATE.NUMBER = 1 cfg.DATA.TEMPLATE.SIZE = 128 cfg.DATA.TEMPLATE.FACTOR = 2.0 cfg.DATA.TEMPLATE.CENTER_JITTER = 0 cfg.DATA.TEMPLATE.SCALE_JITTER = 0 # TEST cfg.TEST = edict() cfg.TEST.TEMPLATE_FACTOR = 2.0 cfg.TEST.TEMPLATE_SIZE = 128 cfg.TEST.SEARCH_FACTOR = 5.0 cfg.TEST.SEARCH_SIZE = 256 cfg.TEST.EPOCH = 500 def _edict2dict(dest_dict, src_edict): if isinstance(dest_dict, dict) and isinstance(src_edict, dict): for k, v in src_edict.items(): if not isinstance(v, edict): dest_dict[k] = v else: dest_dict[k] = {} _edict2dict(dest_dict[k], v) else: return def gen_config(config_file): cfg_dict = {} _edict2dict(cfg_dict, cfg) with open(config_file, 'w') as f: yaml.dump(cfg_dict, f, default_flow_style=False) def _update_config(base_cfg, exp_cfg): if isinstance(base_cfg, dict) and isinstance(exp_cfg, edict): for k, v in exp_cfg.items(): if k in base_cfg: if not isinstance(v, dict): base_cfg[k] = v else: _update_config(base_cfg[k], v) else: raise ValueError("{} not exist in config.py".format(k)) else: return def update_config_from_file(filename, base_cfg=None): exp_config = None with open(filename) as f: exp_config = edict(yaml.safe_load(f)) if base_cfg is not None: _update_config(base_cfg, exp_config) else: _update_config(cfg, exp_config) ================================================ FILE: lib/models/__init__.py ================================================ from .artrack.artrack import build_artrack ================================================ FILE: lib/models/artrack/__init__.py ================================================ from .artrack import build_artrack ================================================ FILE: lib/models/artrack/artrack.py ================================================ """ Basic OSTrack model. """ import math import os from typing import List import torch from torch import nn from torch.nn.modules.transformer import _get_clones from timm.models.layers import DropPath, to_2tuple, trunc_normal_ from lib.models.layers.head import build_pix_head from lib.models.artrack.vit import vit_base_patch16_224, vit_large_patch16_224 from lib.utils.box_ops import box_xyxy_to_cxcywh class ARTrack(nn.Module): """ This is the base class for ARTrack """ def __init__(self, transformer, pix_head, hidden_dim): """ Initializes the model. Parameters: transformer: torch module of the transformer architecture. """ super().__init__() self.identity = torch.nn.Parameter(torch.zeros(1, 2, hidden_dim)) self.identity = trunc_normal_(self.identity, std=.02) self.backbone = transformer self.pix_head = pix_head def forward(self, template: torch.Tensor, search: torch.Tensor, seq_input=None ): x = self.backbone(z=template, x=search, identity=self.identity,) # Forward head feat_last = x if isinstance(x, list): feat_last = x[-1] pos_z = self.backbone.pos_embed_z pos_x = self.backbone.pos_embed_x out = self.forward_head(feat_last, pos_z, pos_x, self.identity, seq_input) out['backbone_feat'] = x return out def forward_head(self, cat_feature, pos_z, pos_x, identity, seq_input=None,): output_dict = self.pix_head(cat_feature, pos_z, pos_x, identity, seq_input) return output_dict def build_artrack(cfg, training=True): current_dir = os.path.dirname(os.path.abspath(__file__)) # This is your Project Root pretrained_path = os.path.join(current_dir, '../../../pretrained_models') if cfg.MODEL.PRETRAIN_FILE and ('ARTrack' not in cfg.MODEL.PRETRAIN_FILE) and training: pretrained = os.path.join(pretrained_path, cfg.MODEL.PRETRAIN_FILE) else: pretrained = '' if cfg.MODEL.BACKBONE.TYPE == 'vit_base_patch16_224': backbone = vit_base_patch16_224(pretrained, drop_path_rate=cfg.TRAIN.DROP_PATH_RATE) hidden_dim = backbone.embed_dim patch_start_index = 1 elif cfg.MODEL.BACKBONE.TYPE == 'vit_large_patch16_224': print("i use vit_large") backbone = vit_large_patch16_224(pretrained, drop_path_rate=cfg.TRAIN.DROP_PATH_RATE) hidden_dim = backbone.embed_dim patch_start_index = 1 else: raise NotImplementedError backbone.finetune_track(cfg=cfg, patch_start_index=patch_start_index) pix_head = build_pix_head(cfg, hidden_dim) model = ARTrack( backbone, pix_head, hidden_dim, ) if cfg.MODEL.PRETRAIN_PTH != "": load_from = cfg.MODEL.PRETRAIN_PTH checkpoint = torch.load(load_from, map_location="cpu") missing_keys, unexpected_keys = model.load_state_dict(checkpoint["net"], strict=False) print('Load pretrained model from: ' + load_from) if 'ARTrack' in cfg.MODEL.PRETRAIN_FILE and training: checkpoint = torch.load(cfg.MODEL.PRETRAIN_FILE, map_location="cpu") missing_keys, unexpected_keys = model.load_state_dict(checkpoint["net"], strict=False) print('Load pretrained model from: ' + cfg.MODEL.PRETRAIN_FILE) return model ================================================ FILE: lib/models/artrack/base_backbone.py ================================================ from functools import partial import torch import torch.nn as nn import torch.nn.functional as F from timm.models.vision_transformer import resize_pos_embed from timm.models.layers import DropPath, to_2tuple, trunc_normal_ from lib.models.layers.patch_embed import PatchEmbed from lib.models.artrack.utils import combine_tokens, recover_tokens class BaseBackbone(nn.Module): def __init__(self): super().__init__() # for original ViT self.pos_embed = None self.img_size = [224, 224] self.patch_size = 16 self.embed_dim = 384 self.cat_mode = 'direct' self.pos_embed_z = None self.pos_embed_x = None self.template_segment_pos_embed = None self.search_segment_pos_embed = None self.return_inter = False self.return_stage = [2, 5, 8, 11] self.add_cls_token = False self.add_sep_seg = False def finetune_track(self, cfg, patch_start_index=1): search_size = to_2tuple(cfg.DATA.SEARCH.SIZE) template_size = to_2tuple(cfg.DATA.TEMPLATE.SIZE) new_patch_size = cfg.MODEL.BACKBONE.STRIDE self.cat_mode = cfg.MODEL.BACKBONE.CAT_MODE self.return_inter = cfg.MODEL.RETURN_INTER self.add_sep_seg = cfg.MODEL.BACKBONE.SEP_SEG # resize patch embedding if new_patch_size != self.patch_size: print('Inconsistent Patch Size With The Pretrained Weights, Interpolate The Weight!') old_patch_embed = {} for name, param in self.patch_embed.named_parameters(): if 'weight' in name: param = nn.functional.interpolate(param, size=(new_patch_size, new_patch_size), mode='bicubic', align_corners=False) param = nn.Parameter(param) old_patch_embed[name] = param self.patch_embed = PatchEmbed(img_size=self.img_size, patch_size=new_patch_size, in_chans=3, embed_dim=self.embed_dim) self.patch_embed.proj.bias = old_patch_embed['proj.bias'] self.patch_embed.proj.weight = old_patch_embed['proj.weight'] # for patch embedding patch_pos_embed = self.pos_embed[:, patch_start_index:, :] patch_pos_embed = patch_pos_embed.transpose(1, 2) B, E, Q = patch_pos_embed.shape P_H, P_W = self.img_size[0] // self.patch_size, self.img_size[1] // self.patch_size patch_pos_embed = patch_pos_embed.view(B, E, P_H, P_W) # for search region H, W = search_size new_P_H, new_P_W = H // new_patch_size, W // new_patch_size search_patch_pos_embed = nn.functional.interpolate(patch_pos_embed, size=(new_P_H, new_P_W), mode='bicubic', align_corners=False) search_patch_pos_embed = search_patch_pos_embed.flatten(2).transpose(1, 2) # for template region H, W = template_size new_P_H, new_P_W = H // new_patch_size, W // new_patch_size template_patch_pos_embed = nn.functional.interpolate(patch_pos_embed, size=(new_P_H, new_P_W), mode='bicubic', align_corners=False) template_patch_pos_embed = template_patch_pos_embed.flatten(2).transpose(1, 2) self.pos_embed_z = nn.Parameter(template_patch_pos_embed) self.pos_embed_x = nn.Parameter(search_patch_pos_embed) # for cls token (keep it but not used) if self.add_cls_token and patch_start_index > 0: cls_pos_embed = self.pos_embed[:, 0:1, :] self.cls_pos_embed = nn.Parameter(cls_pos_embed) # separate token and segment token if self.add_sep_seg: self.template_segment_pos_embed = nn.Parameter(torch.zeros(1, 1, self.embed_dim)) self.template_segment_pos_embed = trunc_normal_(self.template_segment_pos_embed, std=.02) self.search_segment_pos_embed = nn.Parameter(torch.zeros(1, 1, self.embed_dim)) self.search_segment_pos_embed = trunc_normal_(self.search_segment_pos_embed, std=.02) if self.return_inter: for i_layer in self.fpn_stage: if i_layer != 11: norm_layer = partial(nn.LayerNorm, eps=1e-6) layer = norm_layer(self.embed_dim) layer_name = f'norm{i_layer}' self.add_module(layer_name, layer) def forward_features(self, z, x, identity): B, H, W = x.shape[0], x.shape[2], x.shape[3] x = self.patch_embed(x) z = self.patch_embed(z) s_x = x.shape[1] s_z = z.shape[1] if self.add_cls_token: cls_tokens = self.cls_token.expand(B, -1, -1) cls_tokens = cls_tokens + self.cls_pos_embed z += self.pos_embed_z x += self.pos_embed_x z += identity[:, 0, :].repeat(B, self.pos_embed_z.shape[1], 1) x += identity[:, 1, :].repeat(B, self.pos_embed_x.shape[1], 1) if self.add_sep_seg: x += self.search_segment_pos_embed z += self.template_segment_pos_embed x = combine_tokens(z, x, mode=self.cat_mode) if self.add_cls_token: x = torch.cat([cls_tokens, x], dim=1) x = self.pos_drop(x) for i, blk in enumerate(self.blocks): x = blk(x) lens_z = self.pos_embed_z.shape[1] lens_x = self.pos_embed_x.shape[1] #x = recover_tokens(x, lens_z, lens_x, mode=self.cat_mode) return self.norm(x) def forward(self, z, x, identity, **kwargs): """ Joint feature extraction and relation modeling for the basic ViT backbone. Args: z (torch.Tensor): template feature, [B, C, H_z, W_z] x (torch.Tensor): search region feature, [B, C, H_x, W_x] Returns: x (torch.Tensor): merged template and search region feature, [B, L_z+L_x, C] attn : None """ x = self.forward_features(z, x, identity) return x ================================================ FILE: lib/models/artrack/utils.py ================================================ import math import torch import torch.nn.functional as F def combine_tokens(template_tokens, search_tokens, mode='direct', return_res=False): # [B, HW, C] len_t = template_tokens.shape[1] len_s = search_tokens.shape[1] if mode == 'direct': merged_feature = torch.cat((template_tokens, search_tokens), dim=1) elif mode == 'template_central': central_pivot = len_s // 2 first_half = search_tokens[:, :central_pivot, :] second_half = search_tokens[:, central_pivot:, :] merged_feature = torch.cat((first_half, template_tokens, second_half), dim=1) elif mode == 'partition': feat_size_s = int(math.sqrt(len_s)) feat_size_t = int(math.sqrt(len_t)) window_size = math.ceil(feat_size_t / 2.) # pad feature maps to multiples of window size B, _, C = template_tokens.shape H = W = feat_size_t template_tokens = template_tokens.view(B, H, W, C) pad_l = pad_b = pad_r = 0 # pad_r = (window_size - W % window_size) % window_size pad_t = (window_size - H % window_size) % window_size template_tokens = F.pad(template_tokens, (0, 0, pad_l, pad_r, pad_t, pad_b)) _, Hp, Wp, _ = template_tokens.shape template_tokens = template_tokens.view(B, Hp // window_size, window_size, W, C) template_tokens = torch.cat([template_tokens[:, 0, ...], template_tokens[:, 1, ...]], dim=2) _, Hc, Wc, _ = template_tokens.shape template_tokens = template_tokens.view(B, -1, C) merged_feature = torch.cat([template_tokens, search_tokens], dim=1) # calculate new h and w, which may be useful for SwinT or others merged_h, merged_w = feat_size_s + Hc, feat_size_s if return_res: return merged_feature, merged_h, merged_w else: raise NotImplementedError return merged_feature def recover_tokens(merged_tokens, len_template_token, len_search_token, mode='direct'): if mode == 'direct': recovered_tokens = merged_tokens elif mode == 'template_central': central_pivot = len_search_token // 2 len_remain = len_search_token - central_pivot len_half_and_t = central_pivot + len_template_token first_half = merged_tokens[:, :central_pivot, :] second_half = merged_tokens[:, -len_remain:, :] template_tokens = merged_tokens[:, central_pivot:len_half_and_t, :] recovered_tokens = torch.cat((template_tokens, first_half, second_half), dim=1) elif mode == 'partition': recovered_tokens = merged_tokens else: raise NotImplementedError return recovered_tokens def window_partition(x, window_size: int): """ Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C) """ B, H, W, C = x.shape x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) return windows def window_reverse(windows, window_size: int, H: int, W: int): """ Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image W (int): Width of image Returns: x: (B, H, W, C) """ B = int(windows.shape[0] / (H * W / window_size / window_size)) x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) return x ================================================ FILE: lib/models/artrack/vit.py ================================================ """ Vision Transformer (ViT) in PyTorch A PyTorch implement of Vision Transformers as described in: 'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929 `How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers` - https://arxiv.org/abs/2106.10270 The official jax code is released and available at https://github.com/google-research/vision_transformer DeiT model defs and weights from https://github.com/facebookresearch/deit, paper `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877 Acknowledgments: * The paper authors for releasing code and weights, thanks! * I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out for some einops/einsum fun * Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT * Bert reference code checks against Huggingface Transformers and Tensorflow Bert Hacked together by / Copyright 2021 Ross Wightman Modified by Botao Ye """ import math import logging from functools import partial from collections import OrderedDict from copy import deepcopy import torch import torch.nn as nn import torch.nn.functional as F from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD from timm.models.helpers import build_model_with_cfg, named_apply, adapt_input_conv from timm.models.layers import Mlp, DropPath, trunc_normal_, lecun_normal_ from timm.models.registry import register_model from lib.models.layers.patch_embed import PatchEmbed from lib.models.artrack.base_backbone import BaseBackbone class Attention(nn.Module): def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim ** -0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x, return_attention=False): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) attn = (q @ k.transpose(-2, -1)) * self.scale attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) if return_attention: return x, attn return x class Block(nn.Module): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): super().__init__() self.norm1 = norm_layer(dim) self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) def forward(self, x, return_attention=False): if return_attention: feat, attn = self.attn(self.norm1(x), True) x = x + self.drop_path(feat) x = x + self.drop_path(self.mlp(self.norm2(x))) return x, attn else: x = x + self.drop_path(self.attn(self.norm1(x))) x = x + self.drop_path(self.mlp(self.norm2(x))) return x class VisionTransformer(BaseBackbone): """ Vision Transformer A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - https://arxiv.org/abs/2010.11929 Includes distillation token & head support for `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877 """ def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None, distilled=False, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None, act_layer=None, weight_init=''): """ Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number of input channels num_classes (int): number of classes for classification head embed_dim (int): embedding dimension depth (int): depth of transformer num_heads (int): number of attention heads mlp_ratio (int): ratio of mlp hidden dim to embedding dim qkv_bias (bool): enable bias for qkv if True representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set distilled (bool): model includes a distillation token and head as in DeiT models drop_rate (float): dropout rate attn_drop_rate (float): attention dropout rate drop_path_rate (float): stochastic depth rate embed_layer (nn.Module): patch embedding layer norm_layer: (nn.Module): normalization layer weight_init: (str): weight init scheme """ super().__init__() self.num_classes = num_classes self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models self.num_tokens = 2 if distilled else 1 norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) act_layer = act_layer or nn.GELU self.patch_embed = embed_layer( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) num_patches = self.patch_embed.num_patches self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim)) self.pos_drop = nn.Dropout(p=drop_rate) dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule self.blocks = nn.Sequential(*[ Block( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer) for i in range(depth)]) # self.extension = nn.Sequential(*[ # Block( # dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, # attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer) # for i in range(6)]) self.norm = norm_layer(embed_dim) # # Representation layer # if representation_size and not distilled: # self.num_features = representation_size # self.pre_logits = nn.Sequential(OrderedDict([ # ('fc', nn.Linear(embed_dim, representation_size)), # ('act', nn.Tanh()) # ])) # else: # self.pre_logits = nn.Identity() # # # Classifier head(s) # self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity() # self.head_dist = None # if distilled: # self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity() self.init_weights(weight_init) def init_weights(self, mode=''): assert mode in ('jax', 'jax_nlhb', 'nlhb', '') head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0. trunc_normal_(self.pos_embed, std=.02) if self.dist_token is not None: trunc_normal_(self.dist_token, std=.02) if mode.startswith('jax'): # leave cls token as zeros to match jax impl named_apply(partial(_init_vit_weights, head_bias=head_bias, jax_impl=True), self) else: trunc_normal_(self.cls_token, std=.02) self.apply(_init_vit_weights) def _init_weights(self, m): # this fn left here for compat with downstream users _init_vit_weights(m) @torch.jit.ignore() def load_pretrained(self, checkpoint_path, prefix=''): _load_weights(self, checkpoint_path, prefix) @torch.jit.ignore def no_weight_decay(self): return {'pos_embed', 'cls_token', 'dist_token'} def get_classifier(self): if self.dist_token is None: return self.head else: return self.head, self.head_dist def reset_classifier(self, num_classes, global_pool=''): self.num_classes = num_classes self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() if self.num_tokens == 2: self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity() def _init_vit_weights(module: nn.Module, name: str = '', head_bias: float = 0., jax_impl: bool = False): """ ViT weight initialization * When called without n, head_bias, jax_impl args it will behave exactly the same as my original init for compatibility with prev hparam / downstream use cases (ie DeiT). * When called w/ valid n (module name) and jax_impl=True, will (hopefully) match JAX impl """ if isinstance(module, nn.Linear): if name.startswith('head'): nn.init.zeros_(module.weight) nn.init.constant_(module.bias, head_bias) elif name.startswith('pre_logits'): lecun_normal_(module.weight) nn.init.zeros_(module.bias) else: if jax_impl: nn.init.xavier_uniform_(module.weight) if module.bias is not None: if 'mlp' in name: nn.init.normal_(module.bias, std=1e-6) else: nn.init.zeros_(module.bias) else: trunc_normal_(module.weight, std=.02) if module.bias is not None: nn.init.zeros_(module.bias) elif jax_impl and isinstance(module, nn.Conv2d): # NOTE conv was left to pytorch default in my original init lecun_normal_(module.weight) if module.bias is not None: nn.init.zeros_(module.bias) elif isinstance(module, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm2d)): nn.init.zeros_(module.bias) nn.init.ones_(module.weight) @torch.no_grad() def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''): """ Load weights from .npz checkpoints for official Google Brain Flax implementation """ import numpy as np def _n2p(w, t=True): if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1: w = w.flatten() if t: if w.ndim == 4: w = w.transpose([3, 2, 0, 1]) elif w.ndim == 3: w = w.transpose([2, 0, 1]) elif w.ndim == 2: w = w.transpose([1, 0]) return torch.from_numpy(w) w = np.load(checkpoint_path) if not prefix and 'opt/target/embedding/kernel' in w: prefix = 'opt/target/' if hasattr(model.patch_embed, 'backbone'): # hybrid backbone = model.patch_embed.backbone stem_only = not hasattr(backbone, 'stem') stem = backbone if stem_only else backbone.stem stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel']))) stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale'])) stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias'])) if not stem_only: for i, stage in enumerate(backbone.stages): for j, block in enumerate(stage.blocks): bp = f'{prefix}block{i + 1}/unit{j + 1}/' for r in range(3): getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel'])) getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale'])) getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias'])) if block.downsample is not None: block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel'])) block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale'])) block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias'])) embed_conv_w = _n2p(w[f'{prefix}embedding/kernel']) else: embed_conv_w = adapt_input_conv( model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel'])) model.patch_embed.proj.weight.copy_(embed_conv_w) model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias'])) model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False)) pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False) if pos_embed_w.shape != model.pos_embed.shape: pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size) model.pos_embed.copy_(pos_embed_w) model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale'])) model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias'])) if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]: model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel'])) model.head.bias.copy_(_n2p(w[f'{prefix}head/bias'])) if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w: model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel'])) model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias'])) for i, block in enumerate(model.blocks.children()): block_prefix = f'{prefix}Transformer/encoderblock_{i}/' mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/' block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale'])) block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias'])) block.attn.qkv.weight.copy_(torch.cat([ _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')])) block.attn.qkv.bias.copy_(torch.cat([ _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')])) block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1)) block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias'])) for r in range(2): getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel'])) getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias'])) block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale'])) block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias'])) def resize_pos_embed(posemb, posemb_new, num_tokens=1, gs_new=()): # Rescale the grid of position embeddings when loading from state_dict. Adapted from # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224 print('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape) ntok_new = posemb_new.shape[1] if num_tokens: posemb_tok, posemb_grid = posemb[:, :num_tokens], posemb[0, num_tokens:] ntok_new -= num_tokens else: posemb_tok, posemb_grid = posemb[:, :0], posemb[0] gs_old = int(math.sqrt(len(posemb_grid))) if not len(gs_new): # backwards compatibility gs_new = [int(math.sqrt(ntok_new))] * 2 assert len(gs_new) >= 2 print('Position embedding grid-size from %s to %s', [gs_old, gs_old], gs_new) posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2) posemb_grid = F.interpolate(posemb_grid, size=gs_new, mode='bilinear') posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1) posemb = torch.cat([posemb_tok, posemb_grid], dim=1) return posemb def checkpoint_filter_fn(state_dict, model): """ convert patch embedding weight from manual patchify + linear proj to conv""" out_dict = {} if 'model' in state_dict: # For deit models state_dict = state_dict['model'] for k, v in state_dict.items(): if 'patch_embed.proj.weight' in k and len(v.shape) < 4: # For old models that I trained prior to conv based patchification O, I, H, W = model.patch_embed.proj.weight.shape v = v.reshape(O, -1, H, W) elif k == 'pos_embed' and v.shape != model.pos_embed.shape: # To resize pos embedding when using model at different size from pretrained weights v = resize_pos_embed( v, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size) out_dict[k] = v return out_dict def _create_vision_transformer(variant, pretrained=False, default_cfg=None, **kwargs): if kwargs.get('features_only', None): raise RuntimeError('features_only not implemented for Vision Transformer models.') model = VisionTransformer(**kwargs) if pretrained: if 'npz' in pretrained: model.load_pretrained(pretrained, prefix='') else: checkpoint = torch.load(pretrained, map_location="cpu") missing_keys, unexpected_keys = model.load_state_dict(checkpoint["model"], strict=False) print('Load pretrained model from: ' + pretrained) return model def vit_base_patch16_224(pretrained=False, **kwargs): """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929). """ model_kwargs = dict( patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs) model = _create_vision_transformer('vit_base_patch16_224_in21k', pretrained=pretrained, **model_kwargs) return model def vit_large_patch16_224(pretrained=False, **kwargs): model_kwargs = dict( patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs) model = _create_vision_transformer('vit_large_patch16_224_in21k', pretrained=pretrained, **model_kwargs) return model ================================================ FILE: lib/models/artrack/vit_ce.py ================================================ import math import logging from functools import partial from collections import OrderedDict from copy import deepcopy import torch import torch.nn as nn import torch.nn.functional as F from timm.models.layers import to_2tuple from lib.models.layers.patch_embed import PatchEmbed from .utils import combine_tokens, recover_tokens from .vit import VisionTransformer from ..layers.attn_blocks import CEBlock _logger = logging.getLogger(__name__) class VisionTransformerCE(VisionTransformer): """ Vision Transformer with candidate elimination (CE) module A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - https://arxiv.org/abs/2010.11929 Includes distillation token & head support for `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877 """ def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None, distilled=False, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None, act_layer=None, weight_init='', ce_loc=None, ce_keep_ratio=None): """ Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number of input channels num_classes (int): number of classes for classification head embed_dim (int): embedding dimension depth (int): depth of transformer num_heads (int): number of attention heads mlp_ratio (int): ratio of mlp hidden dim to embedding dim qkv_bias (bool): enable bias for qkv if True representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set distilled (bool): model includes a distillation token and head as in DeiT models drop_rate (float): dropout rate attn_drop_rate (float): attention dropout rate drop_path_rate (float): stochastic depth rate embed_layer (nn.Module): patch embedding layer norm_layer: (nn.Module): normalization layer weight_init: (str): weight init scheme """ # super().__init__() super().__init__() if isinstance(img_size, tuple): self.img_size = img_size else: self.img_size = to_2tuple(img_size) self.patch_size = patch_size self.in_chans = in_chans self.num_classes = num_classes self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models self.num_tokens = 2 if distilled else 1 norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) act_layer = act_layer or nn.GELU self.patch_embed = embed_layer( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) num_patches = self.patch_embed.num_patches self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim)) self.pos_drop = nn.Dropout(p=drop_rate) dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule blocks = [] ce_index = 0 self.ce_loc = ce_loc for i in range(depth): ce_keep_ratio_i = 1.0 if ce_loc is not None and i in ce_loc: ce_keep_ratio_i = ce_keep_ratio[ce_index] ce_index += 1 blocks.append( CEBlock( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer, keep_ratio_search=ce_keep_ratio_i) ) self.blocks = nn.Sequential(*blocks) self.norm = norm_layer(embed_dim) self.init_weights(weight_init) def forward_features(self, z, x, mask_z=None, mask_x=None, ce_template_mask=None, ce_keep_rate=None, return_last_attn=False ): B, H, W = x.shape[0], x.shape[2], x.shape[3] x = self.patch_embed(x) z = self.patch_embed(z) # attention mask handling # B, H, W if mask_z is not None and mask_x is not None: mask_z = F.interpolate(mask_z[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0] mask_z = mask_z.flatten(1).unsqueeze(-1) mask_x = F.interpolate(mask_x[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0] mask_x = mask_x.flatten(1).unsqueeze(-1) mask_x = combine_tokens(mask_z, mask_x, mode=self.cat_mode) mask_x = mask_x.squeeze(-1) if self.add_cls_token: cls_tokens = self.cls_token.expand(B, -1, -1) cls_tokens = cls_tokens + self.cls_pos_embed z += self.pos_embed_z x += self.pos_embed_x if self.add_sep_seg: x += self.search_segment_pos_embed z += self.template_segment_pos_embed x = combine_tokens(z, x, mode=self.cat_mode) if self.add_cls_token: x = torch.cat([cls_tokens, x], dim=1) x = self.pos_drop(x) lens_z = self.pos_embed_z.shape[1] lens_x = self.pos_embed_x.shape[1] global_index_t = torch.linspace(0, lens_z - 1, lens_z).to(x.device) global_index_t = global_index_t.repeat(B, 1) global_index_s = torch.linspace(0, lens_x - 1, lens_x).to(x.device) global_index_s = global_index_s.repeat(B, 1) removed_indexes_s = [] for i, blk in enumerate(self.blocks): x, global_index_t, global_index_s, removed_index_s, attn = \ blk(x, global_index_t, global_index_s, mask_x, ce_template_mask, ce_keep_rate) if self.ce_loc is not None and i in self.ce_loc: removed_indexes_s.append(removed_index_s) x = self.norm(x) lens_x_new = global_index_s.shape[1] lens_z_new = global_index_t.shape[1] z = x[:, :lens_z_new] x = x[:, lens_z_new:] if removed_indexes_s and removed_indexes_s[0] is not None: removed_indexes_cat = torch.cat(removed_indexes_s, dim=1) pruned_lens_x = lens_x - lens_x_new pad_x = torch.zeros([B, pruned_lens_x, x.shape[2]], device=x.device) x = torch.cat([x, pad_x], dim=1) index_all = torch.cat([global_index_s, removed_indexes_cat], dim=1) # recover original token order C = x.shape[-1] # x = x.gather(1, index_all.unsqueeze(-1).expand(B, -1, C).argsort(1)) x = torch.zeros_like(x).scatter_(dim=1, index=index_all.unsqueeze(-1).expand(B, -1, C).to(torch.int64), src=x) x = recover_tokens(x, lens_z_new, lens_x, mode=self.cat_mode) # re-concatenate with the template, which may be further used by other modules x = torch.cat([z, x], dim=1) aux_dict = { "attn": attn, "removed_indexes_s": removed_indexes_s, # used for visualization } return x, aux_dict def forward(self, z, x, ce_template_mask=None, ce_keep_rate=None, tnc_keep_rate=None, return_last_attn=False): x, aux_dict = self.forward_features(z, x, ce_template_mask=ce_template_mask, ce_keep_rate=ce_keep_rate,) return x, aux_dict def _create_vision_transformer(pretrained=False, **kwargs): model = VisionTransformerCE(**kwargs) if pretrained: if 'npz' in pretrained: model.load_pretrained(pretrained, prefix='') else: checkpoint = torch.load(pretrained, map_location="cpu") missing_keys, unexpected_keys = model.load_state_dict(checkpoint["model"], strict=False) print('Load pretrained model from: ' + pretrained) return model def vit_base_patch16_224_ce(pretrained=False, **kwargs): """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929). """ model_kwargs = dict( patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs) model = _create_vision_transformer(pretrained=pretrained, **model_kwargs) return model def vit_large_patch16_224_ce(pretrained=False, **kwargs): """ ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929). """ model_kwargs = dict( patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs) model = _create_vision_transformer(pretrained=pretrained, **model_kwargs) return model ================================================ FILE: lib/models/artrack_seq/__init__.py ================================================ from .artrack_seq import build_artrack_seq ================================================ FILE: lib/models/artrack_seq/artrack_seq.py ================================================ """ Basic OSTrack model. """ import math import os from typing import List import torch from torch import nn from torch.nn.modules.transformer import _get_clones from timm.models.layers import DropPath, to_2tuple, trunc_normal_ from lib.models.layers.head_seq import build_pix_head from lib.models.artrack_seq.vit import vit_base_patch16_224, vit_large_patch16_224 from lib.utils.box_ops import box_xyxy_to_cxcywh import time class ARTrackSeq(nn.Module): """ This is the base class for ARTrackSeq """ def __init__(self, transformer, pix_head, hidden_dim): """ Initializes the model. Parameters: transformer: torch module of the transformer architecture. aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used. """ super().__init__() self.backbone = transformer self.pix_head = pix_head self.identity = torch.nn.Parameter(torch.zeros(1, 2, hidden_dim)) self.identity = trunc_normal_(self.identity, std=.02) def forward(self, template: torch.Tensor, search: torch.Tensor, seq_input=None, head_type=None, stage=None, search_feature=None, update=None ): x, aux_dict = self.backbone(z=template, x=search, identity=self.identity) # Forward head feat_last = x if isinstance(x, list): feat_last = x[-1] pos_z = self.backbone.pos_embed_z pos_x = self.backbone.pos_embed_x out = self.forward_head(feat_last, pos_z, pos_x, self.identity, seq_input, stage) out.update(aux_dict) out['backbone_feat'] = x return out def forward_head(self, cat_feature, pos_z, pos_x, identity, seq_input=None, stage=None): """ cat_feature: output embeddings of the backbone, it can be (HW1+HW2, B, C) or (HW2, B, C) """ output_dict = self.pix_head(cat_feature, pos_z, pos_x, identity, seq_input, stage) return output_dict def build_artrack_seq(cfg, training=True): current_dir = os.path.dirname(os.path.abspath(__file__)) # This is your Project Root pretrained_path = os.path.join(current_dir, '../../../pretrained_models') if cfg.MODEL.PRETRAIN_FILE and ('ARTrack' not in cfg.MODEL.PRETRAIN_FILE) and training: pretrained = os.path.join(pretrained_path, cfg.MODEL.PRETRAIN_FILE) else: pretrained = '' if cfg.MODEL.BACKBONE.TYPE == 'vit_base_patch16_224': backbone = vit_base_patch16_224(pretrained, drop_path_rate=cfg.TRAIN.DROP_PATH_RATE) hidden_dim = backbone.embed_dim patch_start_index = 1 elif cfg.MODEL.BACKBONE.TYPE == 'vit_large_patch16_224': print("i use vit_large") backbone = vit_large_patch16_224(pretrained, drop_path_rate=cfg.TRAIN.DROP_PATH_RATE) hidden_dim = backbone.embed_dim patch_start_index = 1 else: raise NotImplementedError backbone.finetune_track(cfg=cfg, patch_start_index=patch_start_index) pix_head = build_pix_head(cfg, hidden_dim) model = ARTrackSeq( backbone, pix_head, hidden_dim, ) load_from = cfg.MODEL.PRETRAIN_PTH checkpoint = torch.load(load_from, map_location="cpu") missing_keys, unexpected_keys = model.load_state_dict(checkpoint["net"], strict=False) print('Load pretrained model from: ' + load_from) if 'sequence' in cfg.MODEL.PRETRAIN_FILE and training: print("i change myself") checkpoint = torch.load(cfg.MODEL.PRETRAIN_FILE, map_location="cpu") missing_keys, unexpected_keys = model.load_state_dict(checkpoint["net"], strict=False) print('Load pretrained model from: ' + cfg.MODEL.PRETRAIN_FILE) return model ================================================ FILE: lib/models/artrack_seq/base_backbone.py ================================================ from functools import partial import torch import torch.nn as nn import torch.nn.functional as F from timm.models.vision_transformer import resize_pos_embed from timm.models.layers import DropPath, to_2tuple, trunc_normal_ from lib.models.layers.patch_embed import PatchEmbed from lib.models.artrack_seq.utils import combine_tokens, recover_tokens class BaseBackbone(nn.Module): def __init__(self): super().__init__() # for original ViT self.pos_embed = None self.img_size = [224, 224] self.patch_size = 16 self.embed_dim = 384 self.cat_mode = 'direct' self.pos_embed_z = None self.pos_embed_x = None self.template_segment_pos_embed = None self.search_segment_pos_embed = None self.return_inter = False self.return_stage = [2, 5, 8, 11] self.add_cls_token = False self.add_sep_seg = False def finetune_track(self, cfg, patch_start_index=1): search_size = to_2tuple(cfg.DATA.SEARCH.SIZE) template_size = to_2tuple(cfg.DATA.TEMPLATE.SIZE) new_patch_size = cfg.MODEL.BACKBONE.STRIDE self.cat_mode = cfg.MODEL.BACKBONE.CAT_MODE self.return_inter = cfg.MODEL.RETURN_INTER self.add_sep_seg = cfg.MODEL.BACKBONE.SEP_SEG # resize patch embedding if new_patch_size != self.patch_size: print('Inconsistent Patch Size With The Pretrained Weights, Interpolate The Weight!') old_patch_embed = {} for name, param in self.patch_embed.named_parameters(): if 'weight' in name: param = nn.functional.interpolate(param, size=(new_patch_size, new_patch_size), mode='bicubic', align_corners=False) param = nn.Parameter(param) old_patch_embed[name] = param self.patch_embed = PatchEmbed(img_size=self.img_size, patch_size=new_patch_size, in_chans=3, embed_dim=self.embed_dim) self.patch_embed.proj.bias = old_patch_embed['proj.bias'] self.patch_embed.proj.weight = old_patch_embed['proj.weight'] # for patch embedding patch_pos_embed = self.pos_embed[:, patch_start_index:, :] patch_pos_embed = patch_pos_embed.transpose(1, 2) B, E, Q = patch_pos_embed.shape P_H, P_W = self.img_size[0] // self.patch_size, self.img_size[1] // self.patch_size patch_pos_embed = patch_pos_embed.view(B, E, P_H, P_W) # for search region H, W = search_size new_P_H, new_P_W = H // new_patch_size, W // new_patch_size search_patch_pos_embed = nn.functional.interpolate(patch_pos_embed, size=(new_P_H, new_P_W), mode='bicubic', align_corners=False) search_patch_pos_embed = search_patch_pos_embed.flatten(2).transpose(1, 2) # for template region H, W = template_size new_P_H, new_P_W = H // new_patch_size, W // new_patch_size template_patch_pos_embed = nn.functional.interpolate(patch_pos_embed, size=(new_P_H, new_P_W), mode='bicubic', align_corners=False) template_patch_pos_embed = template_patch_pos_embed.flatten(2).transpose(1, 2) self.pos_embed_z = nn.Parameter(template_patch_pos_embed) self.pos_embed_x = nn.Parameter(search_patch_pos_embed) # for cls token (keep it but not used) if self.add_cls_token and patch_start_index > 0: cls_pos_embed = self.pos_embed[:, 0:1, :] self.cls_pos_embed = nn.Parameter(cls_pos_embed) # separate token and segment token if self.add_sep_seg: self.template_segment_pos_embed = nn.Parameter(torch.zeros(1, 1, self.embed_dim)) self.template_segment_pos_embed = trunc_normal_(self.template_segment_pos_embed, std=.02) self.search_segment_pos_embed = nn.Parameter(torch.zeros(1, 1, self.embed_dim)) self.search_segment_pos_embed = trunc_normal_(self.search_segment_pos_embed, std=.02) # self.cls_token = None # self.pos_embed = None if self.return_inter: for i_layer in self.fpn_stage: if i_layer != 11: norm_layer = partial(nn.LayerNorm, eps=1e-6) layer = norm_layer(self.embed_dim) layer_name = f'norm{i_layer}' self.add_module(layer_name, layer) def forward_features(self, z, x, identity): B, H, W = x.shape[0], x.shape[2], x.shape[3] x = self.patch_embed(x) z = self.patch_embed(z) s_x = x.shape[1] s_z = z.shape[1] if self.add_cls_token: cls_tokens = self.cls_token.expand(B, -1, -1) cls_tokens = cls_tokens + self.cls_pos_embed z += self.pos_embed_z x += self.pos_embed_x z += identity[:, 0, :].repeat(B, self.pos_embed_z.shape[1], 1) x += identity[:, 1, :].repeat(B, self.pos_embed_x.shape[1], 1) if self.add_sep_seg: x += self.search_segment_pos_embed z += self.template_segment_pos_embed x = combine_tokens(z, x, mode=self.cat_mode) if self.add_cls_token: x = torch.cat([cls_tokens, x], dim=1) x = self.pos_drop(x) for i, blk in enumerate(self.blocks): x = blk(x) lens_z = self.pos_embed_z.shape[1] lens_x = self.pos_embed_x.shape[1] # x = recover_tokens(x, lens_z, lens_x, mode=self.cat_mode) aux_dict = {"attn": None} return self.norm(x), aux_dict def forward(self, z, x, identity, **kwargs): """ Joint feature extraction and relation modeling for the basic ViT backbone. Args: z (torch.Tensor): template feature, [B, C, H_z, W_z] x (torch.Tensor): search region feature, [B, C, H_x, W_x] Returns: x (torch.Tensor): merged template and search region feature, [B, L_z+L_x, C] attn : None """ x, aux_dict = self.forward_features(z, x, identity) return x, aux_dict ================================================ FILE: lib/models/artrack_seq/utils.py ================================================ import math import torch import torch.nn.functional as F def combine_tokens(template_tokens, search_tokens, mode='direct', return_res=False): # [B, HW, C] len_t = template_tokens.shape[1] len_s = search_tokens.shape[1] if mode == 'direct': merged_feature = torch.cat((template_tokens, search_tokens), dim=1) elif mode == 'template_central': central_pivot = len_s // 2 first_half = search_tokens[:, :central_pivot, :] second_half = search_tokens[:, central_pivot:, :] merged_feature = torch.cat((first_half, template_tokens, second_half), dim=1) elif mode == 'partition': feat_size_s = int(math.sqrt(len_s)) feat_size_t = int(math.sqrt(len_t)) window_size = math.ceil(feat_size_t / 2.) # pad feature maps to multiples of window size B, _, C = template_tokens.shape H = W = feat_size_t template_tokens = template_tokens.view(B, H, W, C) pad_l = pad_b = pad_r = 0 # pad_r = (window_size - W % window_size) % window_size pad_t = (window_size - H % window_size) % window_size template_tokens = F.pad(template_tokens, (0, 0, pad_l, pad_r, pad_t, pad_b)) _, Hp, Wp, _ = template_tokens.shape template_tokens = template_tokens.view(B, Hp // window_size, window_size, W, C) template_tokens = torch.cat([template_tokens[:, 0, ...], template_tokens[:, 1, ...]], dim=2) _, Hc, Wc, _ = template_tokens.shape template_tokens = template_tokens.view(B, -1, C) merged_feature = torch.cat([template_tokens, search_tokens], dim=1) # calculate new h and w, which may be useful for SwinT or others merged_h, merged_w = feat_size_s + Hc, feat_size_s if return_res: return merged_feature, merged_h, merged_w else: raise NotImplementedError return merged_feature def recover_tokens(merged_tokens, len_template_token, len_search_token, mode='direct'): if mode == 'direct': recovered_tokens = merged_tokens elif mode == 'template_central': central_pivot = len_search_token // 2 len_remain = len_search_token - central_pivot len_half_and_t = central_pivot + len_template_token first_half = merged_tokens[:, :central_pivot, :] second_half = merged_tokens[:, -len_remain:, :] template_tokens = merged_tokens[:, central_pivot:len_half_and_t, :] recovered_tokens = torch.cat((template_tokens, first_half, second_half), dim=1) elif mode == 'partition': recovered_tokens = merged_tokens else: raise NotImplementedError return recovered_tokens def window_partition(x, window_size: int): """ Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C) """ B, H, W, C = x.shape x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) return windows def window_reverse(windows, window_size: int, H: int, W: int): """ Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image W (int): Width of image Returns: x: (B, H, W, C) """ B = int(windows.shape[0] / (H * W / window_size / window_size)) x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) return x ================================================ FILE: lib/models/artrack_seq/vit.py ================================================ """ Vision Transformer (ViT) in PyTorch A PyTorch implement of Vision Transformers as described in: 'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929 `How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers` - https://arxiv.org/abs/2106.10270 The official jax code is released and available at https://github.com/google-research/vision_transformer DeiT model defs and weights from https://github.com/facebookresearch/deit, paper `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877 Acknowledgments: * The paper authors for releasing code and weights, thanks! * I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out for some einops/einsum fun * Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT * Bert reference code checks against Huggingface Transformers and Tensorflow Bert Hacked together by / Copyright 2021 Ross Wightman Modified by Botao Ye """ import math import logging from functools import partial from collections import OrderedDict from copy import deepcopy import torch import torch.nn as nn import torch.nn.functional as F from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD from timm.models.helpers import build_model_with_cfg, named_apply, adapt_input_conv from timm.models.layers import Mlp, DropPath, trunc_normal_, lecun_normal_ from timm.models.registry import register_model from lib.models.layers.patch_embed import PatchEmbed from lib.models.artrack_seq.base_backbone import BaseBackbone class Attention(nn.Module): def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim ** -0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x, return_attention=False): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) attn = (q @ k.transpose(-2, -1)) * self.scale attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) if return_attention: return x, attn return x class Block(nn.Module): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): super().__init__() self.norm1 = norm_layer(dim) self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) def forward(self, x, return_attention=False): if return_attention: feat, attn = self.attn(self.norm1(x), True) x = x + self.drop_path(feat) x = x + self.drop_path(self.mlp(self.norm2(x))) return x, attn else: x = x + self.drop_path(self.attn(self.norm1(x))) x = x + self.drop_path(self.mlp(self.norm2(x))) return x class VisionTransformer(BaseBackbone): """ Vision Transformer A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - https://arxiv.org/abs/2010.11929 Includes distillation token & head support for `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877 """ def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None, distilled=False, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None, act_layer=None, weight_init=''): """ Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number of input channels num_classes (int): number of classes for classification head embed_dim (int): embedding dimension depth (int): depth of transformer num_heads (int): number of attention heads mlp_ratio (int): ratio of mlp hidden dim to embedding dim qkv_bias (bool): enable bias for qkv if True representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set distilled (bool): model includes a distillation token and head as in DeiT models drop_rate (float): dropout rate attn_drop_rate (float): attention dropout rate drop_path_rate (float): stochastic depth rate embed_layer (nn.Module): patch embedding layer norm_layer: (nn.Module): normalization layer weight_init: (str): weight init scheme """ super().__init__() self.num_classes = num_classes self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models self.num_tokens = 2 if distilled else 1 norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) act_layer = act_layer or nn.GELU self.patch_embed = embed_layer( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) num_patches = self.patch_embed.num_patches self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim)) self.pos_drop = nn.Dropout(p=drop_rate) dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule self.blocks = nn.Sequential(*[ Block( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer) for i in range(depth)]) self.norm = norm_layer(embed_dim) # # Representation layer # if representation_size and not distilled: # self.num_features = representation_size # self.pre_logits = nn.Sequential(OrderedDict([ # ('fc', nn.Linear(embed_dim, representation_size)), # ('act', nn.Tanh()) # ])) # else: # self.pre_logits = nn.Identity() # # # Classifier head(s) # self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity() # self.head_dist = None # if distilled: # self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity() self.init_weights(weight_init) def init_weights(self, mode=''): assert mode in ('jax', 'jax_nlhb', 'nlhb', '') head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0. trunc_normal_(self.pos_embed, std=.02) if self.dist_token is not None: trunc_normal_(self.dist_token, std=.02) if mode.startswith('jax'): # leave cls token as zeros to match jax impl named_apply(partial(_init_vit_weights, head_bias=head_bias, jax_impl=True), self) else: trunc_normal_(self.cls_token, std=.02) self.apply(_init_vit_weights) def _init_weights(self, m): # this fn left here for compat with downstream users _init_vit_weights(m) @torch.jit.ignore() def load_pretrained(self, checkpoint_path, prefix=''): _load_weights(self, checkpoint_path, prefix) @torch.jit.ignore def no_weight_decay(self): return {'pos_embed', 'cls_token', 'dist_token'} def get_classifier(self): if self.dist_token is None: return self.head else: return self.head, self.head_dist def reset_classifier(self, num_classes, global_pool=''): self.num_classes = num_classes self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() if self.num_tokens == 2: self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity() def _init_vit_weights(module: nn.Module, name: str = '', head_bias: float = 0., jax_impl: bool = False): """ ViT weight initialization * When called without n, head_bias, jax_impl args it will behave exactly the same as my original init for compatibility with prev hparam / downstream use cases (ie DeiT). * When called w/ valid n (module name) and jax_impl=True, will (hopefully) match JAX impl """ if isinstance(module, nn.Linear): if name.startswith('head'): nn.init.zeros_(module.weight) nn.init.constant_(module.bias, head_bias) elif name.startswith('pre_logits'): lecun_normal_(module.weight) nn.init.zeros_(module.bias) else: if jax_impl: nn.init.xavier_uniform_(module.weight) if module.bias is not None: if 'mlp' in name: nn.init.normal_(module.bias, std=1e-6) else: nn.init.zeros_(module.bias) else: trunc_normal_(module.weight, std=.02) if module.bias is not None: nn.init.zeros_(module.bias) elif jax_impl and isinstance(module, nn.Conv2d): # NOTE conv was left to pytorch default in my original init lecun_normal_(module.weight) if module.bias is not None: nn.init.zeros_(module.bias) elif isinstance(module, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm2d)): nn.init.zeros_(module.bias) nn.init.ones_(module.weight) @torch.no_grad() def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''): """ Load weights from .npz checkpoints for official Google Brain Flax implementation """ import numpy as np def _n2p(w, t=True): if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1: w = w.flatten() if t: if w.ndim == 4: w = w.transpose([3, 2, 0, 1]) elif w.ndim == 3: w = w.transpose([2, 0, 1]) elif w.ndim == 2: w = w.transpose([1, 0]) return torch.from_numpy(w) w = np.load(checkpoint_path) if not prefix and 'opt/target/embedding/kernel' in w: prefix = 'opt/target/' if hasattr(model.patch_embed, 'backbone'): # hybrid backbone = model.patch_embed.backbone stem_only = not hasattr(backbone, 'stem') stem = backbone if stem_only else backbone.stem stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel']))) stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale'])) stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias'])) if not stem_only: for i, stage in enumerate(backbone.stages): for j, block in enumerate(stage.blocks): bp = f'{prefix}block{i + 1}/unit{j + 1}/' for r in range(3): getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel'])) getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale'])) getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias'])) if block.downsample is not None: block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel'])) block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale'])) block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias'])) embed_conv_w = _n2p(w[f'{prefix}embedding/kernel']) else: embed_conv_w = adapt_input_conv( model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel'])) model.patch_embed.proj.weight.copy_(embed_conv_w) model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias'])) model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False)) pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False) if pos_embed_w.shape != model.pos_embed.shape: pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size) model.pos_embed.copy_(pos_embed_w) model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale'])) model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias'])) if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]: model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel'])) model.head.bias.copy_(_n2p(w[f'{prefix}head/bias'])) if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w: model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel'])) model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias'])) for i, block in enumerate(model.blocks.children()): block_prefix = f'{prefix}Transformer/encoderblock_{i}/' mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/' block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale'])) block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias'])) block.attn.qkv.weight.copy_(torch.cat([ _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')])) block.attn.qkv.bias.copy_(torch.cat([ _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')])) block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1)) block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias'])) for r in range(2): getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel'])) getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias'])) block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale'])) block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias'])) def resize_pos_embed(posemb, posemb_new, num_tokens=1, gs_new=()): # Rescale the grid of position embeddings when loading from state_dict. Adapted from # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224 print('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape) ntok_new = posemb_new.shape[1] if num_tokens: posemb_tok, posemb_grid = posemb[:, :num_tokens], posemb[0, num_tokens:] ntok_new -= num_tokens else: posemb_tok, posemb_grid = posemb[:, :0], posemb[0] gs_old = int(math.sqrt(len(posemb_grid))) if not len(gs_new): # backwards compatibility gs_new = [int(math.sqrt(ntok_new))] * 2 assert len(gs_new) >= 2 print('Position embedding grid-size from %s to %s', [gs_old, gs_old], gs_new) posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2) posemb_grid = F.interpolate(posemb_grid, size=gs_new, mode='bilinear') posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1) posemb = torch.cat([posemb_tok, posemb_grid], dim=1) return posemb def checkpoint_filter_fn(state_dict, model): """ convert patch embedding weight from manual patchify + linear proj to conv""" out_dict = {} if 'model' in state_dict: # For deit models state_dict = state_dict['model'] for k, v in state_dict.items(): if 'patch_embed.proj.weight' in k and len(v.shape) < 4: # For old models that I trained prior to conv based patchification O, I, H, W = model.patch_embed.proj.weight.shape v = v.reshape(O, -1, H, W) elif k == 'pos_embed' and v.shape != model.pos_embed.shape: # To resize pos embedding when using model at different size from pretrained weights v = resize_pos_embed( v, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size) out_dict[k] = v return out_dict def _create_vision_transformer(variant, pretrained=False, default_cfg=None, **kwargs): if kwargs.get('features_only', None): raise RuntimeError('features_only not implemented for Vision Transformer models.') model = VisionTransformer(**kwargs) if pretrained: if 'npz' in pretrained: model.load_pretrained(pretrained, prefix='') else: checkpoint = torch.load(pretrained, map_location="cpu") missing_keys, unexpected_keys = model.load_state_dict(checkpoint["model"], strict=False) print('Load pretrained model from: ' + pretrained) return model def vit_base_patch16_224(pretrained=False, **kwargs): """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929). """ model_kwargs = dict( patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs) model = _create_vision_transformer('vit_base_patch16_224_in21k', pretrained=pretrained, **model_kwargs) return model def vit_large_patch16_224(pretrained=False, **kwargs): model_kwargs = dict( patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs) model = _create_vision_transformer('vit_large_patch16_224_in21k', pretrained=pretrained, **model_kwargs) return model ================================================ FILE: lib/models/artrack_seq/vit_ce.py ================================================ import math import logging from functools import partial from collections import OrderedDict from copy import deepcopy import torch import torch.nn as nn import torch.nn.functional as F from timm.models.layers import to_2tuple from lib.models.layers.patch_embed import PatchEmbed from .utils import combine_tokens, recover_tokens from .vit import VisionTransformer from ..layers.attn_blocks import CEBlock _logger = logging.getLogger(__name__) class VisionTransformerCE(VisionTransformer): """ Vision Transformer with candidate elimination (CE) module A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - https://arxiv.org/abs/2010.11929 Includes distillation token & head support for `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877 """ def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None, distilled=False, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None, act_layer=None, weight_init='', ce_loc=None, ce_keep_ratio=None): """ Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number of input channels num_classes (int): number of classes for classification head embed_dim (int): embedding dimension depth (int): depth of transformer num_heads (int): number of attention heads mlp_ratio (int): ratio of mlp hidden dim to embedding dim qkv_bias (bool): enable bias for qkv if True representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set distilled (bool): model includes a distillation token and head as in DeiT models drop_rate (float): dropout rate attn_drop_rate (float): attention dropout rate drop_path_rate (float): stochastic depth rate embed_layer (nn.Module): patch embedding layer norm_layer: (nn.Module): normalization layer weight_init: (str): weight init scheme """ # super().__init__() super().__init__() if isinstance(img_size, tuple): self.img_size = img_size else: self.img_size = to_2tuple(img_size) self.patch_size = patch_size self.in_chans = in_chans self.num_classes = num_classes self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models self.num_tokens = 2 if distilled else 1 norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) act_layer = act_layer or nn.GELU self.patch_embed = embed_layer( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) num_patches = self.patch_embed.num_patches self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim)) self.pos_drop = nn.Dropout(p=drop_rate) dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule blocks = [] ce_index = 0 self.ce_loc = ce_loc for i in range(depth): ce_keep_ratio_i = 1.0 if ce_loc is not None and i in ce_loc: ce_keep_ratio_i = ce_keep_ratio[ce_index] ce_index += 1 blocks.append( CEBlock( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer, keep_ratio_search=ce_keep_ratio_i) ) self.blocks = nn.Sequential(*blocks) self.norm = norm_layer(embed_dim) self.init_weights(weight_init) def forward_features(self, z, x, mask_z=None, mask_x=None, ce_template_mask=None, ce_keep_rate=None, return_last_attn=False ): B, H, W = x.shape[0], x.shape[2], x.shape[3] x = self.patch_embed(x) z = self.patch_embed(z) # attention mask handling # B, H, W if mask_z is not None and mask_x is not None: mask_z = F.interpolate(mask_z[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0] mask_z = mask_z.flatten(1).unsqueeze(-1) mask_x = F.interpolate(mask_x[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0] mask_x = mask_x.flatten(1).unsqueeze(-1) mask_x = combine_tokens(mask_z, mask_x, mode=self.cat_mode) mask_x = mask_x.squeeze(-1) if self.add_cls_token: cls_tokens = self.cls_token.expand(B, -1, -1) cls_tokens = cls_tokens + self.cls_pos_embed z += self.pos_embed_z x += self.pos_embed_x if self.add_sep_seg: x += self.search_segment_pos_embed z += self.template_segment_pos_embed x = combine_tokens(z, x, mode=self.cat_mode) if self.add_cls_token: x = torch.cat([cls_tokens, x], dim=1) x = self.pos_drop(x) lens_z = self.pos_embed_z.shape[1] lens_x = self.pos_embed_x.shape[1] global_index_t = torch.linspace(0, lens_z - 1, lens_z).to(x.device) global_index_t = global_index_t.repeat(B, 1) global_index_s = torch.linspace(0, lens_x - 1, lens_x).to(x.device) global_index_s = global_index_s.repeat(B, 1) removed_indexes_s = [] for i, blk in enumerate(self.blocks): x, global_index_t, global_index_s, removed_index_s, attn = \ blk(x, global_index_t, global_index_s, mask_x, ce_template_mask, ce_keep_rate) if self.ce_loc is not None and i in self.ce_loc: removed_indexes_s.append(removed_index_s) x = self.norm(x) lens_x_new = global_index_s.shape[1] lens_z_new = global_index_t.shape[1] z = x[:, :lens_z_new] x = x[:, lens_z_new:] if removed_indexes_s and removed_indexes_s[0] is not None: removed_indexes_cat = torch.cat(removed_indexes_s, dim=1) pruned_lens_x = lens_x - lens_x_new pad_x = torch.zeros([B, pruned_lens_x, x.shape[2]], device=x.device) x = torch.cat([x, pad_x], dim=1) index_all = torch.cat([global_index_s, removed_indexes_cat], dim=1) # recover original token order C = x.shape[-1] # x = x.gather(1, index_all.unsqueeze(-1).expand(B, -1, C).argsort(1)) x = torch.zeros_like(x).scatter_(dim=1, index=index_all.unsqueeze(-1).expand(B, -1, C).to(torch.int64), src=x) x = recover_tokens(x, lens_z_new, lens_x, mode=self.cat_mode) # re-concatenate with the template, which may be further used by other modules x = torch.cat([z, x], dim=1) aux_dict = { "attn": attn, "removed_indexes_s": removed_indexes_s, # used for visualization } return x, aux_dict def forward(self, z, x, ce_template_mask=None, ce_keep_rate=None, tnc_keep_rate=None, return_last_attn=False): x, aux_dict = self.forward_features(z, x, ce_template_mask=ce_template_mask, ce_keep_rate=ce_keep_rate,) return x, aux_dict def _create_vision_transformer(pretrained=False, **kwargs): model = VisionTransformerCE(**kwargs) if pretrained: if 'npz' in pretrained: model.load_pretrained(pretrained, prefix='') else: checkpoint = torch.load(pretrained, map_location="cpu") missing_keys, unexpected_keys = model.load_state_dict(checkpoint["model"], strict=False) print('Load pretrained model from: ' + pretrained) return model def vit_base_patch16_224_ce(pretrained=False, **kwargs): """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929). """ model_kwargs = dict( patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs) model = _create_vision_transformer(pretrained=pretrained, **model_kwargs) return model def vit_large_patch16_224_ce(pretrained=False, **kwargs): """ ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929). """ model_kwargs = dict( patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs) model = _create_vision_transformer(pretrained=pretrained, **model_kwargs) return model ================================================ FILE: lib/models/artrackv2/__init__.py ================================================ from .artrackv2 import build_artrackv2 ================================================ FILE: lib/models/artrackv2/artrackv2.py ================================================ from copy import deepcopy import math import os from typing import List import torch from torch import nn from torch.nn.modules.transformer import _get_clones from timm.models.layers import DropPath, to_2tuple, trunc_normal_ from lib.models.artrackv2.vit import vit_base_patch16_224, vit_large_patch16_224 from lib.utils.box_ops import box_xyxy_to_cxcywh class ARTrackV2(nn.Module): def __init__(self, transformer, score_mlp, hidden_dim, ): super().__init__() self.identity = torch.nn.Parameter(torch.zeros(1, 3, hidden_dim)) self.identity = trunc_normal_(self.identity, std=.02) self.backbone = transformer self.score_mlp = score_mlp def forward(self, template: torch.Tensor, search: torch.Tensor, ce_template_mask=None, ce_keep_rate=None, return_last_attn=False, seq_input=None, target_in_search_img=None, gt_bboxes=None, ): template_0 = template[0] template_1 = template[1] out, z_0_feat, z_1_feat, x_feat = self.backbone(z_0=template_0, z_1=template_1, x=search, identity=self.identity, seqs_input=seq_input, ce_template_mask=ce_template_mask, ce_keep_rate=ce_keep_rate, return_last_attn=return_last_attn,) score_feat = out["score_feat"] score = self.score_mlp(score_feat) out["score"] = score return out class MlpScoreDecoder(nn.Module): def __init__(self, in_dim, hidden_dim, num_layers, bn=False): super().__init__() self.num_layers = num_layers h = [hidden_dim] * (num_layers - 1) out_dim = 1 # score if bn: self.layers = nn.Sequential(*[nn.Sequential(nn.Linear(n, k), nn.BatchNorm1d(k), nn.ReLU()) if i < num_layers - 1 else nn.Sequential(nn.Linear(n, k), nn.BatchNorm1d(k)) for i, (n, k) in enumerate(zip([in_dim] + h, h + [out_dim]))]) else: self.layers = nn.Sequential(*[nn.Sequential(nn.Linear(n, k), nn.ReLU()) if i < num_layers - 1 else nn.Linear(n, k) for i, (n, k) in enumerate(zip([in_dim] + h, h + [out_dim]))]) def forward(self, reg_tokens): """ reg tokens shape: (b, 4, embed_dim) """ x = self.layers(reg_tokens) # (b, 4, 1) x = x.mean(dim=1) # (b, 1) return x def build_score_decoder(cfg, hidden_dim): return MlpScoreDecoder( in_dim=hidden_dim, hidden_dim=hidden_dim, num_layers=2, bn=False ) def build_artrackv2(cfg, training=True): current_dir = os.path.dirname(os.path.abspath(__file__)) # This is your Project Root pretrained_path = os.path.join(current_dir, '../../../pretrained_models') if cfg.MODEL.PRETRAIN_FILE and ('ARTrack' not in cfg.MODEL.PRETRAIN_FILE) and training: pretrained = os.path.join(pretrained_path, cfg.MODEL.PRETRAIN_FILE) else: pretrained = '' if cfg.MODEL.BACKBONE.TYPE == 'vit_base_patch16_224': backbone = vit_base_patch16_224(pretrained, drop_path_rate=cfg.TRAIN.DROP_PATH_RATE, bins=cfg.MODEL.BINS, range=cfg.MODEL.RANGE, extension=cfg.MODEL.EXTENSION) hidden_dim = backbone.embed_dim patch_start_index = 1 elif cfg.MODEL.BACKBONE.TYPE == 'vit_large_patch16_224': print("i use vit_large") backbone = vit_large_patch16_224(pretrained, drop_path_rate=cfg.TRAIN.DROP_PATH_RATE, bins=cfg.MODEL.BINS, range=cfg.MODEL.RANGE, extension=cfg.MODEL.EXTENSION) hidden_dim = backbone.embed_dim patch_start_index = 1 else: raise NotImplementedError backbone.finetune_track(cfg=cfg, patch_start_index=patch_start_index) score_decoder = build_score_decoder(cfg, hidden_dim) model = ARTrackV2( backbone, score_decoder, hidden_dim, ) return model ================================================ FILE: lib/models/artrackv2/base_backbone.py ================================================ from functools import partial import torch import torch.nn as nn import torch.nn.functional as F from timm.models.vision_transformer import resize_pos_embed from timm.models.layers import DropPath, to_2tuple, trunc_normal_ from lib.models.layers.patch_embed import PatchEmbed from lib.models.artrackv2.utils import combine_tokens, recover_tokens def generate_square_subsequent_mask(sz, sx, ss): r"""Generate a square mask for the sequence. The masked positions are filled with float('-inf'). Unmasked positions are filled with float(0.0). """ sum = sz + sx + ss mask = (torch.triu(torch.ones(sum, sum)) == 1).transpose(0, 1) mask[:, :] = 0 mask[:int(sz/2), :int(sz/2)] = 1 #template self mask[int(sz/2):sz, int(sz/2):sz] = 1 # dt self mask[int(sz/2):sz, sz:sz+sx] = 1 # dt search mask[int(sz / 2):sz, -1] = 1 # dt search mask[sz:sz+sx, :sz+sx] = 1 # sr dt-t-sr mask[sz+sx:, :] = 1 # co dt-t-sr-co return ~mask class BaseBackbone(nn.Module): def __init__(self): super().__init__() # for original ViT self.pos_embed = None self.img_size = [224, 224] self.patch_size = 16 self.embed_dim = 384 self.cat_mode = 'direct' self.pos_embed_z0 = None self.pos_embed_z1 = None self.pos_embed_x = None self.template_segment_pos_embed = None self.search_segment_pos_embed = None self.return_inter = False self.return_stage = [2, 5, 8, 11] self.add_cls_token = False self.add_sep_seg = False def finetune_track(self, cfg, patch_start_index=1): search_size = to_2tuple(cfg.DATA.SEARCH.SIZE) template_size = to_2tuple(cfg.DATA.TEMPLATE.SIZE) new_patch_size = cfg.MODEL.BACKBONE.STRIDE self.cat_mode = cfg.MODEL.BACKBONE.CAT_MODE self.return_inter = cfg.MODEL.RETURN_INTER self.add_sep_seg = cfg.MODEL.BACKBONE.SEP_SEG # resize patch embedding if new_patch_size != self.patch_size: print('Inconsistent Patch Size With The Pretrained Weights, Interpolate The Weight!') old_patch_embed = {} for name, param in self.patch_embed.named_parameters(): if 'weight' in name: param = nn.functional.interpolate(param, size=(new_patch_size, new_patch_size), mode='bicubic', align_corners=False) param = nn.Parameter(param) old_patch_embed[name] = param self.patch_embed = PatchEmbed(img_size=self.img_size, patch_size=new_patch_size, in_chans=3, embed_dim=self.embed_dim) self.patch_embed.proj.bias = old_patch_embed['proj.bias'] self.patch_embed.proj.weight = old_patch_embed['proj.weight'] # for patch embedding patch_pos_embed = self.pos_embed[:, patch_start_index:, :] patch_pos_embed = patch_pos_embed.transpose(1, 2) B, E, Q = patch_pos_embed.shape P_H, P_W = self.img_size[0] // self.patch_size, self.img_size[1] // self.patch_size patch_pos_embed = patch_pos_embed.view(B, E, P_H, P_W) # for search region H, W = search_size new_P_H, new_P_W = H // new_patch_size, W // new_patch_size search_patch_pos_embed = nn.functional.interpolate(patch_pos_embed, size=(new_P_H, new_P_W), mode='bicubic', align_corners=False) search_patch_pos_embed = search_patch_pos_embed.flatten(2).transpose(1, 2) # for template region H, W = template_size new_P_H, new_P_W = H // new_patch_size, W // new_patch_size template_patch_pos_embed = nn.functional.interpolate(patch_pos_embed, size=(new_P_H, new_P_W), mode='bicubic', align_corners=False) template_patch_pos_embed = template_patch_pos_embed.flatten(2).transpose(1, 2) self.pos_embed_z0 = nn.Parameter(template_patch_pos_embed) self.pos_embed_z1 = nn.Parameter(template_patch_pos_embed) self.pos_embed_x = nn.Parameter(search_patch_pos_embed) # for cls token (keep it but not used) if self.add_cls_token and patch_start_index > 0: cls_pos_embed = self.pos_embed[:, 0:1, :] self.cls_pos_embed = nn.Parameter(cls_pos_embed) # separate token and segment token if self.add_sep_seg: self.template_segment_pos_embed = nn.Parameter(torch.zeros(1, 1, self.embed_dim)) self.template_segment_pos_embed = trunc_normal_(self.template_segment_pos_embed, std=.02) self.search_segment_pos_embed = nn.Parameter(torch.zeros(1, 1, self.embed_dim)) self.search_segment_pos_embed = trunc_normal_(self.search_segment_pos_embed, std=.02) if self.return_inter: for i_layer in self.fpn_stage: if i_layer != 11: norm_layer = partial(nn.LayerNorm, eps=1e-6) layer = norm_layer(self.embed_dim) layer_name = f'norm{i_layer}' self.add_module(layer_name, layer) def forward_features(self, z_0, z_1, x, identity, seqs_input): share_weight = self.word_embeddings.weight.T B, H, W = x.shape[0], x.shape[2], x.shape[3] seqs_input = seqs_input.to(torch.int64).to(x.device) tgt = self.word_embeddings(seqs_input).permute(1, 0, 2) query_embed = self.position_embeddings.weight.unsqueeze(1) query_embed = query_embed.repeat(1, B, 1) tgt = tgt.transpose(0, 1) query_embed = query_embed.transpose(0, 1) x = self.patch_embed(x) z_0 = self.patch_embed(z_0) z_1 = self.patch_embed(z_1) len_x = x.shape[1] len_z = z_0.shape[1] + z_1.shape[1] len_seq = seqs_input.shape[1] mask = generate_square_subsequent_mask(len_z, len_x, len_seq).to(tgt.device) if self.add_cls_token: cls_tokens = self.cls_token.expand(B, -1, -1) cls_tokens = cls_tokens + self.cls_pos_embed z_0 += self.pos_embed_z0 z_1 += self.pos_embed_z1 x += self.pos_embed_x tgt += query_embed z_0 += identity[:, 0, :].repeat(B, self.pos_embed_z0.shape[1], 1) z_1 += identity[:, 1, :].repeat(B, self.pos_embed_z1.shape[1], 1) x += identity[:, 2, :].repeat(B, self.pos_embed_x.shape[1], 1) if self.add_sep_seg: x += self.search_segment_pos_embed z += self.template_segment_pos_embed z = torch.cat((z_0, z_1), dim=1) x = combine_tokens(z, x, mode=self.cat_mode) x = torch.cat((x, tgt), dim=1) if self.add_cls_token: x = torch.cat([cls_tokens, x], dim=1) x = self.pos_drop(x) for i, blk in enumerate(self.blocks): x = blk(x, padding_mask=mask) # for j, blk in enumerate(self.extension): x = blk(x, padding_mask=mask) x_out = self.norm(x[:, -5:-1]) score_feat = x[:, -1] lens_z = self.pos_embed_z0.shape[1] lens_x = self.pos_embed_x.shape[1] z_0_feat = x[:, :lens_z] z_1_feat = x[:, lens_z:lens_z*2] x_feat = x[:, lens_z*2:lens_z*2+lens_x] #x = recover_tokens(x, lens_z, lens_x, mode=self.cat_mode) at = torch.matmul(x_out, share_weight) at = at + self.output_bias at = at[:, -4:] at = at.transpose(0, 1) output = {'feat': at, 'score_feat':score_feat, "state": "train"} return output, z_0_feat, z_1_feat, x_feat def forward_track(self, z_0, z_1, x, identity): share_weight = self.word_embeddings.weight.T out_list = [] x0 = self.bins * self.range y0 = self.bins * self.range + 1 x1 = self.bins * self.range + 2 y1 = self.bins * self.range + 3 score = self.bins * self.range + 5 B, H, W = x.shape[0], x.shape[2], x.shape[3] seq = torch.cat([torch.ones((B, 1)).to(x) * x0, torch.ones((B, 1)).to(x) * y0, torch.ones((B, 1)).to(x) * x1, torch.ones((B, 1)).to(x) * y1, torch.ones((B, 1)).to(x) * score], dim=1) seq_all = torch.cat([seq], dim=1) seqs_input = seq_all.to(torch.int64).to(x.device) output_x_feat = x.clone() tgt = self.word_embeddings(seqs_input).permute(1, 0, 2) x = self.patch_embed(x) z_0 = self.patch_embed(z_0) z_1 = self.patch_embed(z_1) len_x = x.shape[1] len_z = z_0.shape[1] + z_1.shape[1] len_seq = seqs_input.shape[1] z_0 += identity[:, 0, :].repeat(B, self.pos_embed_z0.shape[1], 1) z_1 += identity[:, 1, :].repeat(B, self.pos_embed_z0.shape[1], 1) x += identity[:, 2, :].repeat(B, self.pos_embed_x.shape[1], 1) query_pos_embed = self.position_embeddings.weight.unsqueeze(1) query_pos_embed = query_pos_embed.repeat(1, B, 1) tgt = tgt.transpose(0, 1) query_pos_embed = query_pos_embed.transpose(0, 1) z_0 += self.pos_embed_z0 z_1 += self.pos_embed_z1 x += self.pos_embed_x mask = generate_square_subsequent_mask(len_z, len_x, len_seq).to(tgt.device) tgt += query_pos_embed[:, :tgt.shape[1]] z = torch.cat((z_0, z_1), dim=1) zx = combine_tokens(z, x, mode=self.cat_mode) zxs = torch.cat((zx, tgt), dim=1) zxs = self.pos_drop(zxs) for j, blk in enumerate(self.blocks): zxs = blk(zxs, padding_mask=mask) for j, blk in enumerate(self.extension): zxs = blk(zxs, padding_mask=mask) lens_z_single = self.pos_embed_z0.shape[1] lens_x = self.pos_embed_x.shape[1] z_0_feat = zxs[:, :lens_z_single] z_1_feat = zxs[:, lens_z_single:lens_z_single * 2] x_feat = zxs[:, lens_z_single * 2:lens_z_single * 2 + lens_x] x_out = self.norm(zxs[:, -5:-1]) score_feat = x[:, -1] possibility = torch.matmul(x_out, share_weight) out = possibility + self.output_bias temp = out.transpose(0, 1) out_list.append(out.unsqueeze(0)) out = out.softmax(-1) value, extra_seq = out.topk(dim=-1, k=1)[0], out.topk(dim=-1, k=1)[1] for i in range(4): value, extra_seq = out[:, i, :].topk(dim=-1, k=1)[0], out[:, i, :].topk(dim=-1, k=1)[1] if i == 0: seqs_output = extra_seq values = value else: seqs_output = torch.cat([seqs_output, extra_seq], dim=-1) values = torch.cat([values, value], dim=-1) output = {'seqs': seqs_output, 'class': values, 'feat': temp, "state": "val/test", "x_feat": output_x_feat.detach(), "score_feat": score_feat} return output, None, None, None def forward(self, z_0, z_1, x, identity, seqs_input, **kwargs): """ Joint feature extraction and relation modeling for the basic ViT backbone. Args: z (torch.Tensor): template feature, [B, C, H_z, W_z] x (torch.Tensor): search region feature, [B, C, H_x, W_x] Returns: x (torch.Tensor): merged template and search region feature, [B, L_z+L_x, C] attn : None """ if seqs_input == None: output = self.forward_track(z_0, z_1, x, identity) else: output = self.forward_features(z_0, z_1, x, identity, seqs_input) return output ================================================ FILE: lib/models/artrackv2/utils.py ================================================ import math import torch import torch.nn.functional as F def combine_tokens(template_tokens, search_tokens, mode='direct', return_res=False): # [B, HW, C] len_t = template_tokens.shape[1] len_s = search_tokens.shape[1] if mode == 'direct': merged_feature = torch.cat((template_tokens, search_tokens), dim=1) elif mode == 'template_central': central_pivot = len_s // 2 first_half = search_tokens[:, :central_pivot, :] second_half = search_tokens[:, central_pivot:, :] merged_feature = torch.cat((first_half, template_tokens, second_half), dim=1) elif mode == 'partition': feat_size_s = int(math.sqrt(len_s)) feat_size_t = int(math.sqrt(len_t)) window_size = math.ceil(feat_size_t / 2.) # pad feature maps to multiples of window size B, _, C = template_tokens.shape H = W = feat_size_t template_tokens = template_tokens.view(B, H, W, C) pad_l = pad_b = pad_r = 0 # pad_r = (window_size - W % window_size) % window_size pad_t = (window_size - H % window_size) % window_size template_tokens = F.pad(template_tokens, (0, 0, pad_l, pad_r, pad_t, pad_b)) _, Hp, Wp, _ = template_tokens.shape template_tokens = template_tokens.view(B, Hp // window_size, window_size, W, C) template_tokens = torch.cat([template_tokens[:, 0, ...], template_tokens[:, 1, ...]], dim=2) _, Hc, Wc, _ = template_tokens.shape template_tokens = template_tokens.view(B, -1, C) merged_feature = torch.cat([template_tokens, search_tokens], dim=1) # calculate new h and w, which may be useful for SwinT or others merged_h, merged_w = feat_size_s + Hc, feat_size_s if return_res: return merged_feature, merged_h, merged_w else: raise NotImplementedError return merged_feature def recover_tokens(merged_tokens, len_template_token, len_search_token, mode='direct'): if mode == 'direct': recovered_tokens = merged_tokens elif mode == 'template_central': central_pivot = len_search_token // 2 len_remain = len_search_token - central_pivot len_half_and_t = central_pivot + len_template_token first_half = merged_tokens[:, :central_pivot, :] second_half = merged_tokens[:, -len_remain:, :] template_tokens = merged_tokens[:, central_pivot:len_half_and_t, :] recovered_tokens = torch.cat((template_tokens, first_half, second_half), dim=1) elif mode == 'partition': recovered_tokens = merged_tokens else: raise NotImplementedError return recovered_tokens def window_partition(x, window_size: int): """ Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C) """ B, H, W, C = x.shape x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) return windows def window_reverse(windows, window_size: int, H: int, W: int): """ Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image W (int): Width of image Returns: x: (B, H, W, C) """ B = int(windows.shape[0] / (H * W / window_size / window_size)) x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) return x ================================================ FILE: lib/models/artrackv2/vit.py ================================================ """ Vision Transformer (ViT) in PyTorch A PyTorch implement of Vision Transformers as described in: 'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929 `How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers` - https://arxiv.org/abs/2106.10270 The official jax code is released and available at https://github.com/google-research/vision_transformer DeiT model defs and weights from https://github.com/facebookresearch/deit, paper `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877 Acknowledgments: * The paper authors for releasing code and weights, thanks! * I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out for some einops/einsum fun * Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT * Bert reference code checks against Huggingface Transformers and Tensorflow Bert Hacked together by / Copyright 2021 Ross Wightman Modified by Botao Ye """ import math import logging from functools import partial from collections import OrderedDict from copy import deepcopy import torch import torch.nn as nn import torch.nn.functional as F from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD from timm.models.helpers import build_model_with_cfg, named_apply, adapt_input_conv from timm.models.layers import Mlp, DropPath, trunc_normal_, lecun_normal_ from timm.models.registry import register_model from lib.models.layers.patch_embed import PatchEmbed from lib.models.artrackv2.base_backbone import BaseBackbone class Attention(nn.Module): def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim ** -0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x, return_attention=False, padding_mask=None): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) attn = (q @ k.transpose(-2, -1)) * self.scale if padding_mask != None: attn = attn.masked_fill(padding_mask, float("-inf")) attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) if return_attention: return x, attn return x class Block(nn.Module): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): super().__init__() self.norm1 = norm_layer(dim) self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) def forward(self, x, return_attention=False, padding_mask=None): if return_attention: feat, attn = self.attn(self.norm1(x), True, padding_mask) x = x + self.drop_path(feat) x = x + self.drop_path(self.mlp(self.norm2(x))) return x, attn else: x = x + self.drop_path(self.attn(self.norm1(x), padding_mask=padding_mask)) x = x + self.drop_path(self.mlp(self.norm2(x))) return x class VisionTransformer(BaseBackbone): """ Vision Transformer A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - https://arxiv.org/abs/2010.11929 Includes distillation token & head support for `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877 """ def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None, distilled=False, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None, act_layer=None, weight_init='', bins=400, range_time=2, extension=3): """ Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number of input channels num_classes (int): number of classes for classification head embed_dim (int): embedding dimension depth (int): depth of transformer num_heads (int): number of attention heads mlp_ratio (int): ratio of mlp hidden dim to embedding dim qkv_bias (bool): enable bias for qkv if True representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set distilled (bool): model includes a distillation token and head as in DeiT models drop_rate (float): dropout rate attn_drop_rate (float): attention dropout rate drop_path_rate (float): stochastic depth rate embed_layer (nn.Module): patch embedding layer norm_layer: (nn.Module): normalization layer weight_init: (str): weight init scheme """ super().__init__() self.num_classes = num_classes self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models self.num_tokens = 2 if distilled else 1 norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) act_layer = act_layer or nn.GELU self.bins = bins in_channel = embed_dim self.range = range_time self.word_embeddings = nn.Embedding(self.bins * self.range + 6, in_channel, padding_idx=self.bins * self.range + 4, max_norm=None, norm_type=2.0) nn.init.kaiming_normal_(self.word_embeddings.weight.data) self.position_embeddings = nn.Embedding( 5, in_channel) self.output_bias = torch.nn.Parameter(torch.zeros(self.bins * self.range + 6)) self.patch_embed = embed_layer( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) num_patches = self.patch_embed.num_patches self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim)) self.pos_drop = nn.Dropout(p=drop_rate) dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule self.blocks = nn.Sequential(*[ Block( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer) for i in range(depth)]) self.extension = nn.Sequential(*[ Block( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer) for i in range(extension)]) self.norm = norm_layer(embed_dim) self.apply(self.ext_init_weights) self.init_weights(weight_init) def ext_init_weights(self, m): if isinstance(m, nn.Linear): # we use xavier_uniform following official JAX ViT: torch.nn.init.xavier_uniform_(m.weight) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) def init_weights(self, mode=''): assert mode in ('jax', 'jax_nlhb', 'nlhb', '') head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0. trunc_normal_(self.pos_embed, std=.02) if self.dist_token is not None: trunc_normal_(self.dist_token, std=.02) if mode.startswith('jax'): # leave cls token as zeros to match jax impl named_apply(partial(_init_vit_weights, head_bias=head_bias, jax_impl=True), self) else: trunc_normal_(self.cls_token, std=.02) self.apply(_init_vit_weights) def _init_weights(self, m): # this fn left here for compat with downstream users _init_vit_weights(m) @torch.jit.ignore() def load_pretrained(self, checkpoint_path, prefix=''): _load_weights(self, checkpoint_path, prefix) @torch.jit.ignore def no_weight_decay(self): return {'pos_embed', 'cls_token', 'dist_token'} def get_classifier(self): if self.dist_token is None: return self.head else: return self.head, self.head_dist def reset_classifier(self, num_classes, global_pool=''): self.num_classes = num_classes self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() if self.num_tokens == 2: self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity() def _init_vit_weights(module: nn.Module, name: str = '', head_bias: float = 0., jax_impl: bool = False): """ ViT weight initialization * When called without n, head_bias, jax_impl args it will behave exactly the same as my original init for compatibility with prev hparam / downstream use cases (ie DeiT). * When called w/ valid n (module name) and jax_impl=True, will (hopefully) match JAX impl """ if isinstance(module, nn.Linear): if name.startswith('head'): nn.init.zeros_(module.weight) nn.init.constant_(module.bias, head_bias) elif name.startswith('pre_logits'): lecun_normal_(module.weight) nn.init.zeros_(module.bias) else: if jax_impl: nn.init.xavier_uniform_(module.weight) if module.bias is not None: if 'mlp' in name: nn.init.normal_(module.bias, std=1e-6) else: nn.init.zeros_(module.bias) else: trunc_normal_(module.weight, std=.02) if module.bias is not None: nn.init.zeros_(module.bias) elif jax_impl and isinstance(module, nn.Conv2d): # NOTE conv was left to pytorch default in my original init lecun_normal_(module.weight) if module.bias is not None: nn.init.zeros_(module.bias) elif isinstance(module, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm2d)): nn.init.zeros_(module.bias) nn.init.ones_(module.weight) @torch.no_grad() def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''): """ Load weights from .npz checkpoints for official Google Brain Flax implementation """ import numpy as np def _n2p(w, t=True): if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1: w = w.flatten() if t: if w.ndim == 4: w = w.transpose([3, 2, 0, 1]) elif w.ndim == 3: w = w.transpose([2, 0, 1]) elif w.ndim == 2: w = w.transpose([1, 0]) return torch.from_numpy(w) w = np.load(checkpoint_path) if not prefix and 'opt/target/embedding/kernel' in w: prefix = 'opt/target/' if hasattr(model.patch_embed, 'backbone'): # hybrid backbone = model.patch_embed.backbone stem_only = not hasattr(backbone, 'stem') stem = backbone if stem_only else backbone.stem stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel']))) stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale'])) stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias'])) if not stem_only: for i, stage in enumerate(backbone.stages): for j, block in enumerate(stage.blocks): bp = f'{prefix}block{i + 1}/unit{j + 1}/' for r in range(3): getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel'])) getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale'])) getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias'])) if block.downsample is not None: block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel'])) block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale'])) block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias'])) embed_conv_w = _n2p(w[f'{prefix}embedding/kernel']) else: embed_conv_w = adapt_input_conv( model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel'])) model.patch_embed.proj.weight.copy_(embed_conv_w) model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias'])) model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False)) pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False) if pos_embed_w.shape != model.pos_embed.shape: pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size) model.pos_embed.copy_(pos_embed_w) model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale'])) model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias'])) if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]: model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel'])) model.head.bias.copy_(_n2p(w[f'{prefix}head/bias'])) if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w: model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel'])) model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias'])) for i, block in enumerate(model.blocks.children()): block_prefix = f'{prefix}Transformer/encoderblock_{i}/' mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/' block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale'])) block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias'])) block.attn.qkv.weight.copy_(torch.cat([ _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')])) block.attn.qkv.bias.copy_(torch.cat([ _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')])) block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1)) block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias'])) for r in range(2): getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel'])) getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias'])) block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale'])) block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias'])) def resize_pos_embed(posemb, posemb_new, num_tokens=1, gs_new=()): # Rescale the grid of position embeddings when loading from state_dict. Adapted from # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224 print('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape) ntok_new = posemb_new.shape[1] if num_tokens: posemb_tok, posemb_grid = posemb[:, :num_tokens], posemb[0, num_tokens:] ntok_new -= num_tokens else: posemb_tok, posemb_grid = posemb[:, :0], posemb[0] gs_old = int(math.sqrt(len(posemb_grid))) if not len(gs_new): # backwards compatibility gs_new = [int(math.sqrt(ntok_new))] * 2 assert len(gs_new) >= 2 print('Position embedding grid-size from %s to %s', [gs_old, gs_old], gs_new) posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2) posemb_grid = F.interpolate(posemb_grid, size=gs_new, mode='bilinear') posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1) posemb = torch.cat([posemb_tok, posemb_grid], dim=1) return posemb def checkpoint_filter_fn(state_dict, model): """ convert patch embedding weight from manual patchify + linear proj to conv""" out_dict = {} if 'model' in state_dict: # For deit models state_dict = state_dict['model'] for k, v in state_dict.items(): if 'patch_embed.proj.weight' in k and len(v.shape) < 4: # For old models that I trained prior to conv based patchification O, I, H, W = model.patch_embed.proj.weight.shape v = v.reshape(O, -1, H, W) elif k == 'pos_embed' and v.shape != model.pos_embed.shape: # To resize pos embedding when using model at different size from pretrained weights v = resize_pos_embed( v, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size) out_dict[k] = v return out_dict def _create_vision_transformer(variant, pretrained=False, default_cfg=None, **kwargs): if kwargs.get('features_only', None): raise RuntimeError('features_only not implemented for Vision Transformer models.') model = VisionTransformer(**kwargs) if pretrained: if 'npz' in pretrained: model.load_pretrained(pretrained, prefix='') else: checkpoint = torch.load(pretrained, map_location="cpu") missing_keys, unexpected_keys = model.load_state_dict(checkpoint["model"], strict=False) print('Load pretrained model from: ' + pretrained) return model def vit_base_patch16_224(pretrained=False, bins=400, range=2, extension=3, **kwargs): """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929). """ model_kwargs = dict( patch_size=16, embed_dim=768, depth=12, num_heads=12, bins=bins, range_time=range, extension=extension, **kwargs) model = _create_vision_transformer('vit_base_patch16_224_in21k', pretrained=pretrained, **model_kwargs) return model def vit_large_patch16_224(pretrained=False, bins=400, range=2, extension=3, **kwargs): model_kwargs = dict( patch_size=16, embed_dim=1024, depth=24, num_heads=16, bins=bins, range_time=range, extension=extension, **kwargs) model = _create_vision_transformer('vit_large_patch16_224_in21k', pretrained=pretrained, **model_kwargs) return model ================================================ FILE: lib/models/artrackv2_seq/__init__.py ================================================ from .artrackv2_seq import build_artrackv2_seq ================================================ FILE: lib/models/artrackv2_seq/artrackv2_seq.py ================================================ """ Basic OSTrack model. """ from copy import deepcopy import math import os from typing import List import torch from torch import nn from torch.nn.modules.transformer import _get_clones from timm.models.layers import DropPath, to_2tuple, trunc_normal_ from lib.models.artrackv2_seq.vit import vit_base_patch16_224, vit_large_patch16_224 from lib.utils.box_ops import box_xyxy_to_cxcywh from lib.models.layers.mask_decoder import build_maskdecoder from lib.models.layers.head import build_decoder, MLP, DropPathAllocator import time class ARTrackV2Seq(nn.Module): """ This is the base class for OSTrack """ def __init__(self, transformer, cross_2_decoder, score_mlp, hidden_dim, ): """ Initializes the model. Parameters: transformer: torch module of the transformer architecture. aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used. """ super().__init__() self.backbone = transformer self.score_mlp = score_mlp self.identity = torch.nn.Parameter(torch.zeros(1, 3, hidden_dim)) self.identity = trunc_normal_(self.identity, std=.02) self.cross_2_decoder = cross_2_decoder def forward(self, template: torch.Tensor, dz_feat: torch.Tensor, search: torch.Tensor, ce_template_mask=None, ce_keep_rate=None, return_last_attn=False, seq_input=None, head_type=None, stage=None, search_feature=None, target_in_search_img=None, gt_bboxes=None, ): template_0 = template[:, 0] out, z_0_feat, z_1_feat, x_feat, score_feat = self.backbone(z_0=template_0, z_1_feat=dz_feat, x=search, identity=self.identity, seqs_input=seq_input, ce_template_mask=ce_template_mask, ce_keep_rate=ce_keep_rate, return_last_attn=return_last_attn,) seq_feat = out['seq_feat'].permute(1, 0 ,2) score = self.score_mlp(score_feat) out['score'] = score loss = torch.tensor(0.0, dtype=torch.float32).to(search.device) if target_in_search_img != None: target_in_search_gt = self.backbone.patch_embed(target_in_search_img) z_1_feat = z_1_feat.reshape(z_1_feat.shape[0], int(z_1_feat.shape[1] ** 0.5), int(z_1_feat.shape[1] ** 0.5), z_1_feat.shape[2]).permute(0, 3, 1, 2) target_in_search_gt = self.cross_2_decoder.unpatchify(target_in_search_gt) update_img, loss_temp = self.cross_2_decoder(z_1_feat, target_in_search_gt) update_feat = self.cross_2_decoder.patchify(update_img) out['dz_feat'] = update_feat loss += loss_temp out['renew_loss'] = loss else: z_1_feat = z_1_feat.reshape(z_1_feat.shape[0], int(z_1_feat.shape[1] ** 0.5), int(z_1_feat.shape[1] ** 0.5), z_1_feat.shape[2]).permute(0, 3, 1, 2) update_feat = self.cross_2_decoder(z_1_feat, eval=True) update_feat = self.cross_2_decoder.patchify(update_feat) out['dz_feat'] = update_feat return out class MlpScoreDecoder(nn.Module): def __init__(self, in_dim, hidden_dim, num_layers, bn=False): super().__init__() self.num_layers = num_layers h = [hidden_dim] * (num_layers - 1) out_dim = 1 # score if bn: self.layers = nn.Sequential(*[nn.Sequential(nn.Linear(n, k), nn.BatchNorm1d(k), nn.ReLU()) if i < num_layers - 1 else nn.Sequential(nn.Linear(n, k), nn.BatchNorm1d(k)) for i, (n, k) in enumerate(zip([in_dim] + h, h + [out_dim]))]) else: self.layers = nn.Sequential(*[nn.Sequential(nn.Linear(n, k), nn.ReLU()) if i < num_layers - 1 else nn.Linear(n, k) for i, (n, k) in enumerate(zip([in_dim] + h, h + [out_dim]))]) def forward(self, reg_tokens): """ reg tokens shape: (b, 4, embed_dim) """ x = self.layers(reg_tokens) # (b, 4, 1) x = x.mean(dim=1) # (b, 1) return x def build_score_decoder(cfg, hidden_dim): return MlpScoreDecoder( in_dim=hidden_dim, hidden_dim=hidden_dim, num_layers=2, bn=False ) def build_artrackv2_seq(cfg, training=True): current_dir = os.path.dirname(os.path.abspath(__file__)) # This is your Project Root pretrained_path = os.path.join(current_dir, '../../../pretrained_models') if cfg.MODEL.PRETRAIN_FILE and ('OSTrack' not in cfg.MODEL.PRETRAIN_FILE) and training: pretrained = os.path.join(pretrained_path, cfg.MODEL.PRETRAIN_FILE) else: pretrained = '' if cfg.MODEL.BACKBONE.TYPE == 'vit_base_patch16_224': backbone = vit_base_patch16_224(pretrained, drop_path_rate=cfg.TRAIN.DROP_PATH_RATE, bins=cfg.MODEL.BINS, range=cfg.MODEL.RANGE, extension=cfg.MODEL.EXTENSION, prenum=cfg.MODEL.PRENUM) hidden_dim = backbone.embed_dim patch_start_index = 1 elif cfg.MODEL.BACKBONE.TYPE == 'vit_large_patch16_224': print("i use vit_large") backbone = vit_large_patch16_224(pretrained, drop_path_rate=cfg.TRAIN.DROP_PATH_RATE, bins=cfg.MODEL.BINS, range=cfg.MODEL.RANGE, extension=cfg.MODEL.EXTENSION, prenum=cfg.MODEL.PRENUM) hidden_dim = backbone.embed_dim patch_start_index = 1 else: raise NotImplementedError backbone.finetune_track(cfg=cfg, patch_start_index=patch_start_index) cross_2_decoder = build_maskdecoder(cfg, hidden_dim) drop_path = cfg.MODEL.DROP_PATH drop_path_allocator = DropPathAllocator(drop_path) num_heads = cfg.MODEL.NUM_HEADS mlp_ratio = cfg.MODEL.MLP_RATIO qkv_bias = cfg.MODEL.QKV_BIAS drop_rate = cfg.MODEL.DROP_RATE attn_drop = cfg.MODEL.ATTN_DROP score_mlp = build_score_decoder(cfg, hidden_dim) model = ARTrackV2Seq( backbone, cross_2_decoder, score_mlp, hidden_dim, ) load_from = cfg.MODEL.PRETRAIN_PTH checkpoint = torch.load(load_from, map_location="cpu") missing_keys, unexpected_keys = model.load_state_dict(checkpoint["net"], strict=False) print('Load pretrained model from: ' + load_from) if 'sequence' in cfg.MODEL.PRETRAIN_FILE and training: print("i change myself") checkpoint = torch.load(cfg.MODEL.PRETRAIN_FILE, map_location="cpu") missing_keys, unexpected_keys = model.load_state_dict(checkpoint["net"], strict=False) print('Load pretrained model from: ' + cfg.MODEL.PRETRAIN_FILE) return model ================================================ FILE: lib/models/artrackv2_seq/base_backbone.py ================================================ from functools import partial import torch import torch.nn as nn import torch.nn.functional as F from timm.models.vision_transformer import resize_pos_embed from timm.models.layers import DropPath, to_2tuple, trunc_normal_ from lib.models.layers.patch_embed import PatchEmbed from lib.models.artrackv2_seq.utils import combine_tokens, recover_tokens import time def generate_square_subsequent_mask(sz, sx, ss): r"""Generate a square mask for the sequence. The masked positions are filled with float('-inf'). Unmasked positions are filled with float(0.0). """ # 0 means mask, 1 means visible sum = sz + sx + ss mask = (torch.triu(torch.ones(sum, sum)) == 1).transpose(0, 1) mask[:, :] = 0 mask[:int(sz/2), :int(sz/2)] = 1 #template self mask[int(sz/2):sz, int(sz/2):sz] = 1 # dt self mask[int(sz/2):sz, sz:sz+sx] = 1 # dt search mask[int(sz / 2):sz, -1] = 1 # dt search mask[sz:sz+sx, :sz+sx] = 1 # sr dt-t-sr mask[sz+sx:, :] = 1 # co dt-t-sr-co return ~mask class BaseBackbone(nn.Module): def __init__(self): super().__init__() # for original ViT self.pos_embed = None self.img_size = [224, 224] self.patch_size = 16 self.embed_dim = 384 self.cat_mode = 'direct' self.pos_embed_z = None self.pos_embed_x = None self.template_segment_pos_embed = None self.search_segment_pos_embed = None self.return_inter = False self.return_stage = [2, 5, 8, 11] self.add_cls_token = False self.add_sep_seg = False def finetune_track(self, cfg, patch_start_index=1): search_size = to_2tuple(cfg.DATA.SEARCH.SIZE) template_size = to_2tuple(cfg.DATA.TEMPLATE.SIZE) new_patch_size = cfg.MODEL.BACKBONE.STRIDE self.cat_mode = cfg.MODEL.BACKBONE.CAT_MODE self.return_inter = cfg.MODEL.RETURN_INTER self.add_sep_seg = cfg.MODEL.BACKBONE.SEP_SEG # resize patch embedding if new_patch_size != self.patch_size: print('Inconsistent Patch Size With The Pretrained Weights, Interpolate The Weight!') old_patch_embed = {} for name, param in self.patch_embed.named_parameters(): if 'weight' in name: param = nn.functional.interpolate(param, size=(new_patch_size, new_patch_size), mode='bicubic', align_corners=False) param = nn.Parameter(param) old_patch_embed[name] = param self.patch_embed = PatchEmbed(img_size=self.img_size, patch_size=new_patch_size, in_chans=3, embed_dim=self.embed_dim) self.patch_embed.proj.bias = old_patch_embed['proj.bias'] self.patch_embed.proj.weight = old_patch_embed['proj.weight'] # for patch embedding patch_pos_embed = self.pos_embed[:, patch_start_index:, :] patch_pos_embed = patch_pos_embed.transpose(1, 2) B, E, Q = patch_pos_embed.shape P_H, P_W = self.img_size[0] // self.patch_size, self.img_size[1] // self.patch_size patch_pos_embed = patch_pos_embed.view(B, E, P_H, P_W) # for search region H, W = search_size new_P_H, new_P_W = H // new_patch_size, W // new_patch_size search_patch_pos_embed = nn.functional.interpolate(patch_pos_embed, size=(new_P_H, new_P_W), mode='bicubic', align_corners=False) search_patch_pos_embed = search_patch_pos_embed.flatten(2).transpose(1, 2) # for template region H, W = template_size new_P_H, new_P_W = H // new_patch_size, W // new_patch_size template_patch_pos_embed = nn.functional.interpolate(patch_pos_embed, size=(new_P_H, new_P_W), mode='bicubic', align_corners=False) template_patch_pos_embed = template_patch_pos_embed.flatten(2).transpose(1, 2) self.pos_embed_z = nn.Parameter(template_patch_pos_embed) self.pos_embed_z0 = nn.Parameter(template_patch_pos_embed) self.pos_embed_z1 = nn.Parameter(template_patch_pos_embed) self.pos_embed_x = nn.Parameter(search_patch_pos_embed) # for cls token (keep it but not used) if self.add_cls_token and patch_start_index > 0: cls_pos_embed = self.pos_embed[:, 0:1, :] self.cls_pos_embed = nn.Parameter(cls_pos_embed) # separate token and segment token if self.add_sep_seg: self.template_segment_pos_embed = nn.Parameter(torch.zeros(1, 1, self.embed_dim)) self.template_segment_pos_embed = trunc_normal_(self.template_segment_pos_embed, std=.02) self.search_segment_pos_embed = nn.Parameter(torch.zeros(1, 1, self.embed_dim)) self.search_segment_pos_embed = trunc_normal_(self.search_segment_pos_embed, std=.02) # self.cls_token = None # self.pos_embed = None if self.return_inter: for i_layer in self.fpn_stage: if i_layer != 11: norm_layer = partial(nn.LayerNorm, eps=1e-6) layer = norm_layer(self.embed_dim) layer_name = f'norm{i_layer}' self.add_module(layer_name, layer) def forward_features(self, z_0, z_1_feat, x, identity, seqs_input): share_weight = self.word_embeddings.weight.T out_list = [] x0 = self.bins * self.range y0 = self.bins * self.range + 1 x1 = self.bins * self.range + 2 y1 = self.bins * self.range + 3 score = self.bins * self.range + 5 B, H, W = x.shape[0], x.shape[2], x.shape[3] command = torch.cat([torch.ones((B, 1)).to(x) * x0, torch.ones((B, 1)).to(x) * y0, torch.ones((B, 1)).to(x) * x1, torch.ones((B, 1)).to(x) * y1, torch.ones((B, 1)).to(x) * score], dim=1) trajectory = seqs_input command = command.to(trajectory) seqs_input_ = torch.cat([trajectory, command], dim=1) seqs_input_ = seqs_input_.to(torch.int64).to(x.device) output_x_feat = x.clone() tgt = self.word_embeddings(seqs_input_).permute(1, 0, 2) x = self.patch_embed(x) z_0 = self.patch_embed(z_0) z_1 = z_1_feat len_x = x.shape[1] len_z = z_0.shape[1] + z_1.shape[1] len_seq = seqs_input_.shape[1] z_0 += identity[:, 0, :].repeat(B, self.pos_embed_z.shape[1], 1) z_1 += identity[:, 1, :].repeat(B, self.pos_embed_z.shape[1], 1) x += identity[:, 2, :].repeat(B, self.pos_embed_x.shape[1], 1) query_command_embed_ = self.position_embeddings.weight.unsqueeze(1) prev_embed_ = self.prev_position_embeddings.weight.unsqueeze(1) query_seq_embed = torch.cat([prev_embed_, query_command_embed_], dim=0) query_seq_embed = query_seq_embed.repeat(1, B, 1) tgt = tgt.transpose(0, 1) query_seq_embed = query_seq_embed.transpose(0, 1) z_0 += self.pos_embed_z0 z_1 += self.pos_embed_z1 x += self.pos_embed_x mask = generate_square_subsequent_mask(len_z, len_x, len_seq).to(tgt.device) tgt += query_seq_embed[:, :tgt.shape[1]] z = torch.cat((z_0, z_1), dim=1) zx = combine_tokens(z, x, mode=self.cat_mode) zxs = torch.cat((zx, tgt), dim=1) zxs = self.pos_drop(zxs) for j, blk in enumerate(self.blocks): zxs = blk(zxs, padding_mask=mask) for j, blk in enumerate(self.extension): zxs = blk(zxs, padding_mask=mask) lens_z_single = self.pos_embed_z.shape[1] z_0_feat = zxs[:, :lens_z_single] z_1_feat = zxs[:, lens_z_single:lens_z_single*2] x_feat = zxs[:, lens_z_single*2:lens_z_single*2+len_x] x_out = self.norm(zxs[:, -5:-1]) score_feat = zxs[:, -1] seq_feat = x_out possibility = torch.matmul(x_out, share_weight) out = possibility + self.output_bias temp = out.transpose(0, 1) out_list.append(out.unsqueeze(0)) out = out.softmax(-1) value, extra_seq = out.topk(dim=-1, k=1)[0], out.topk(dim=-1, k=1)[1] for i in range(4): value, extra_seq = out[:, i, :].topk(dim=-1, k=1)[0], out[:, i, :].topk(dim=-1, k=1)[1] if i == 0: seqs_output = extra_seq values = value else: seqs_output = torch.cat([seqs_output, extra_seq], dim=-1) values = torch.cat([values, value], dim=-1) output = {'seqs': seqs_output, 'class': values, 'feat': temp, "state": "val/test", "x_feat": output_x_feat.detach(), "seq_feat": seq_feat} return output, z_0_feat, z_1_feat, x_feat, score_feat def forward(self, z_0, z_1_feat, x, identity, seqs_input, **kwargs): """ Joint feature extraction and relation modeling for the basic ViT backbone. Args: z (torch.Tensor): template feature, [B, C, H_z, W_z] x (torch.Tensor): search region feature, [B, C, H_x, W_x] Returns: x (torch.Tensor): merged template and search region feature, [B, L_z+L_x, C] attn : None """ output = self.forward_features(z_0, z_1_feat, x, identity, seqs_input) return output ================================================ FILE: lib/models/artrackv2_seq/utils.py ================================================ import math import torch import torch.nn.functional as F def combine_tokens(template_tokens, search_tokens, mode='direct', return_res=False): # [B, HW, C] len_t = template_tokens.shape[1] len_s = search_tokens.shape[1] if mode == 'direct': merged_feature = torch.cat((template_tokens, search_tokens), dim=1) elif mode == 'template_central': central_pivot = len_s // 2 first_half = search_tokens[:, :central_pivot, :] second_half = search_tokens[:, central_pivot:, :] merged_feature = torch.cat((first_half, template_tokens, second_half), dim=1) elif mode == 'partition': feat_size_s = int(math.sqrt(len_s)) feat_size_t = int(math.sqrt(len_t)) window_size = math.ceil(feat_size_t / 2.) # pad feature maps to multiples of window size B, _, C = template_tokens.shape H = W = feat_size_t template_tokens = template_tokens.view(B, H, W, C) pad_l = pad_b = pad_r = 0 # pad_r = (window_size - W % window_size) % window_size pad_t = (window_size - H % window_size) % window_size template_tokens = F.pad(template_tokens, (0, 0, pad_l, pad_r, pad_t, pad_b)) _, Hp, Wp, _ = template_tokens.shape template_tokens = template_tokens.view(B, Hp // window_size, window_size, W, C) template_tokens = torch.cat([template_tokens[:, 0, ...], template_tokens[:, 1, ...]], dim=2) _, Hc, Wc, _ = template_tokens.shape template_tokens = template_tokens.view(B, -1, C) merged_feature = torch.cat([template_tokens, search_tokens], dim=1) # calculate new h and w, which may be useful for SwinT or others merged_h, merged_w = feat_size_s + Hc, feat_size_s if return_res: return merged_feature, merged_h, merged_w else: raise NotImplementedError return merged_feature def recover_tokens(merged_tokens, len_template_token, len_search_token, mode='direct'): if mode == 'direct': recovered_tokens = merged_tokens elif mode == 'template_central': central_pivot = len_search_token // 2 len_remain = len_search_token - central_pivot len_half_and_t = central_pivot + len_template_token first_half = merged_tokens[:, :central_pivot, :] second_half = merged_tokens[:, -len_remain:, :] template_tokens = merged_tokens[:, central_pivot:len_half_and_t, :] recovered_tokens = torch.cat((template_tokens, first_half, second_half), dim=1) elif mode == 'partition': recovered_tokens = merged_tokens else: raise NotImplementedError return recovered_tokens def window_partition(x, window_size: int): """ Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C) """ B, H, W, C = x.shape x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) return windows def window_reverse(windows, window_size: int, H: int, W: int): """ Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image W (int): Width of image Returns: x: (B, H, W, C) """ B = int(windows.shape[0] / (H * W / window_size / window_size)) x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) return x ================================================ FILE: lib/models/artrackv2_seq/vit.py ================================================ """ Vision Transformer (ViT) in PyTorch A PyTorch implement of Vision Transformers as described in: 'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929 `How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers` - https://arxiv.org/abs/2106.10270 The official jax code is released and available at https://github.com/google-research/vision_transformer DeiT model defs and weights from https://github.com/facebookresearch/deit, paper `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877 Acknowledgments: * The paper authors for releasing code and weights, thanks! * I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out for some einops/einsum fun * Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT * Bert reference code checks against Huggingface Transformers and Tensorflow Bert Hacked together by / Copyright 2021 Ross Wightman Modified by Botao Ye """ import math import logging from functools import partial from collections import OrderedDict from copy import deepcopy import torch import torch.nn as nn import torch.nn.functional as F from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD from timm.models.helpers import build_model_with_cfg, named_apply, adapt_input_conv from timm.models.layers import Mlp, DropPath, trunc_normal_, lecun_normal_ from timm.models.registry import register_model from lib.models.layers.patch_embed import PatchEmbed from lib.models.artrackv2_seq.base_backbone import BaseBackbone class Attention(nn.Module): def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim ** -0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x, return_attention=False, padding_mask=None): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) attn = (q @ k.transpose(-2, -1)) * self.scale if padding_mask != None: attn = attn.masked_fill(padding_mask, float("-inf")) attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) if return_attention: return x, attn return x class Block(nn.Module): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): super().__init__() self.norm1 = norm_layer(dim) self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) def forward(self, x, return_attention=False, padding_mask=None): if return_attention: feat, attn = self.attn(self.norm1(x), True, padding_mask) x = x + self.drop_path(feat) x = x + self.drop_path(self.mlp(self.norm2(x))) return x, attn else: x = x + self.drop_path(self.attn(self.norm1(x), padding_mask=padding_mask)) x = x + self.drop_path(self.mlp(self.norm2(x))) return x class VisionTransformer(BaseBackbone): """ Vision Transformer A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - https://arxiv.org/abs/2010.11929 Includes distillation token & head support for `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877 """ def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None, distilled=False, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None, act_layer=None, weight_init='', bins=400, range_time=2, prenum=7, extension=3): """ Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number of input channels num_classes (int): number of classes for classification head embed_dim (int): embedding dimension depth (int): depth of transformer num_heads (int): number of attention heads mlp_ratio (int): ratio of mlp hidden dim to embedding dim qkv_bias (bool): enable bias for qkv if True representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set distilled (bool): model includes a distillation token and head as in DeiT models drop_rate (float): dropout rate attn_drop_rate (float): attention dropout rate drop_path_rate (float): stochastic depth rate embed_layer (nn.Module): patch embedding layer norm_layer: (nn.Module): normalization layer weight_init: (str): weight init scheme """ super().__init__() self.num_classes = num_classes self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models self.num_tokens = 2 if distilled else 1 norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) act_layer = act_layer or nn.GELU self.bins = bins in_channel = embed_dim self.range = range_time self.word_embeddings = nn.Embedding(self.bins * self.range + 6, in_channel, padding_idx=self.bins * self.range+4, max_norm=1, norm_type=2.0) self.position_embeddings = nn.Embedding( 5, in_channel) self.output_bias = torch.nn.Parameter(torch.zeros(self.bins * self.range + 6)) self.prev_position_embeddings = nn.Embedding(prenum * 4, in_channel) self.patch_embed = embed_layer( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) num_patches = self.patch_embed.num_patches self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim)) self.pos_drop = nn.Dropout(p=drop_rate) dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule self.blocks = nn.Sequential(*[ Block( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer) for i in range(depth)]) self.extension = nn.Sequential(*[ Block( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer) for i in range(extension)]) self.norm = norm_layer(embed_dim) self.init_weights(weight_init) def init_weights(self, mode=''): assert mode in ('jax', 'jax_nlhb', 'nlhb', '') head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0. trunc_normal_(self.pos_embed, std=.02) if self.dist_token is not None: trunc_normal_(self.dist_token, std=.02) if mode.startswith('jax'): # leave cls token as zeros to match jax impl named_apply(partial(_init_vit_weights, head_bias=head_bias, jax_impl=True), self) else: trunc_normal_(self.cls_token, std=.02) self.apply(_init_vit_weights) def _init_weights(self, m): # this fn left here for compat with downstream users _init_vit_weights(m) @torch.jit.ignore() def load_pretrained(self, checkpoint_path, prefix=''): _load_weights(self, checkpoint_path, prefix) @torch.jit.ignore def no_weight_decay(self): return {'pos_embed', 'cls_token', 'dist_token'} def get_classifier(self): if self.dist_token is None: return self.head else: return self.head, self.head_dist def reset_classifier(self, num_classes, global_pool=''): self.num_classes = num_classes self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() if self.num_tokens == 2: self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity() def _init_vit_weights(module: nn.Module, name: str = '', head_bias: float = 0., jax_impl: bool = False): """ ViT weight initialization * When called without n, head_bias, jax_impl args it will behave exactly the same as my original init for compatibility with prev hparam / downstream use cases (ie DeiT). * When called w/ valid n (module name) and jax_impl=True, will (hopefully) match JAX impl """ if isinstance(module, nn.Linear): if name.startswith('head'): nn.init.zeros_(module.weight) nn.init.constant_(module.bias, head_bias) elif name.startswith('pre_logits'): lecun_normal_(module.weight) nn.init.zeros_(module.bias) else: if jax_impl: nn.init.xavier_uniform_(module.weight) if module.bias is not None: if 'mlp' in name: nn.init.normal_(module.bias, std=1e-6) else: nn.init.zeros_(module.bias) else: trunc_normal_(module.weight, std=.02) if module.bias is not None: nn.init.zeros_(module.bias) elif jax_impl and isinstance(module, nn.Conv2d): # NOTE conv was left to pytorch default in my original init lecun_normal_(module.weight) if module.bias is not None: nn.init.zeros_(module.bias) elif isinstance(module, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm2d)): nn.init.zeros_(module.bias) nn.init.ones_(module.weight) @torch.no_grad() def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''): """ Load weights from .npz checkpoints for official Google Brain Flax implementation """ import numpy as np def _n2p(w, t=True): if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1: w = w.flatten() if t: if w.ndim == 4: w = w.transpose([3, 2, 0, 1]) elif w.ndim == 3: w = w.transpose([2, 0, 1]) elif w.ndim == 2: w = w.transpose([1, 0]) return torch.from_numpy(w) w = np.load(checkpoint_path) if not prefix and 'opt/target/embedding/kernel' in w: prefix = 'opt/target/' if hasattr(model.patch_embed, 'backbone'): # hybrid backbone = model.patch_embed.backbone stem_only = not hasattr(backbone, 'stem') stem = backbone if stem_only else backbone.stem stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel']))) stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale'])) stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias'])) if not stem_only: for i, stage in enumerate(backbone.stages): for j, block in enumerate(stage.blocks): bp = f'{prefix}block{i + 1}/unit{j + 1}/' for r in range(3): getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel'])) getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale'])) getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias'])) if block.downsample is not None: block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel'])) block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale'])) block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias'])) embed_conv_w = _n2p(w[f'{prefix}embedding/kernel']) else: embed_conv_w = adapt_input_conv( model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel'])) model.patch_embed.proj.weight.copy_(embed_conv_w) model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias'])) model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False)) pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False) if pos_embed_w.shape != model.pos_embed.shape: pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size) model.pos_embed.copy_(pos_embed_w) model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale'])) model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias'])) if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]: model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel'])) model.head.bias.copy_(_n2p(w[f'{prefix}head/bias'])) if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w: model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel'])) model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias'])) for i, block in enumerate(model.blocks.children()): block_prefix = f'{prefix}Transformer/encoderblock_{i}/' mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/' block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale'])) block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias'])) block.attn.qkv.weight.copy_(torch.cat([ _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')])) block.attn.qkv.bias.copy_(torch.cat([ _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')])) block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1)) block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias'])) for r in range(2): getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel'])) getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias'])) block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale'])) block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias'])) def resize_pos_embed(posemb, posemb_new, num_tokens=1, gs_new=()): # Rescale the grid of position embeddings when loading from state_dict. Adapted from # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224 print('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape) ntok_new = posemb_new.shape[1] if num_tokens: posemb_tok, posemb_grid = posemb[:, :num_tokens], posemb[0, num_tokens:] ntok_new -= num_tokens else: posemb_tok, posemb_grid = posemb[:, :0], posemb[0] gs_old = int(math.sqrt(len(posemb_grid))) if not len(gs_new): # backwards compatibility gs_new = [int(math.sqrt(ntok_new))] * 2 assert len(gs_new) >= 2 print('Position embedding grid-size from %s to %s', [gs_old, gs_old], gs_new) posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2) posemb_grid = F.interpolate(posemb_grid, size=gs_new, mode='bilinear') posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1) posemb = torch.cat([posemb_tok, posemb_grid], dim=1) return posemb def checkpoint_filter_fn(state_dict, model): """ convert patch embedding weight from manual patchify + linear proj to conv""" out_dict = {} if 'model' in state_dict: # For deit models state_dict = state_dict['model'] for k, v in state_dict.items(): if 'patch_embed.proj.weight' in k and len(v.shape) < 4: # For old models that I trained prior to conv based patchification O, I, H, W = model.patch_embed.proj.weight.shape v = v.reshape(O, -1, H, W) elif k == 'pos_embed' and v.shape != model.pos_embed.shape: # To resize pos embedding when using model at different size from pretrained weights v = resize_pos_embed( v, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size) out_dict[k] = v return out_dict def _create_vision_transformer(variant, pretrained=False, default_cfg=None, **kwargs): if kwargs.get('features_only', None): raise RuntimeError('features_only not implemented for Vision Transformer models.') model = VisionTransformer(**kwargs) if pretrained: if 'npz' in pretrained: model.load_pretrained(pretrained, prefix='') else: checkpoint = torch.load(pretrained, map_location="cpu") missing_keys, unexpected_keys = model.load_state_dict(checkpoint["model"], strict=False) print('Load pretrained model from: ' + pretrained) return model def vit_base_patch16_224(pretrained=False, bins=400, range=2, extension=3, prenum=7, **kwargs): """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929). """ model_kwargs = dict( patch_size=16, embed_dim=768, depth=12, num_heads=12, bins=bins, range_time=range, extension=extension, prenum=prenum, **kwargs) model = _create_vision_transformer('vit_base_patch16_224_in21k', pretrained=pretrained, **model_kwargs) return model def vit_large_patch16_224(pretrained=False, bins=400, range=2, extension=3, prenum=7, **kwargs): model_kwargs = dict( patch_size=16, embed_dim=1024, depth=24, num_heads=16, bins=bins, range_time=range, extension=extension, prenum=prenum, **kwargs) model = _create_vision_transformer('vit_large_patch16_224_in21k', pretrained=pretrained, **model_kwargs) return model ================================================ FILE: lib/models/layers/__init__.py ================================================ ================================================ FILE: lib/models/layers/attn.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from timm.models.layers import trunc_normal_ from lib.models.layers.rpe import generate_2d_concatenated_self_attention_relative_positional_encoding_index class Attention(nn.Module): def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0., rpe=False, z_size=7, x_size=14): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim ** -0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) self.rpe =rpe if self.rpe: relative_position_index = \ generate_2d_concatenated_self_attention_relative_positional_encoding_index([z_size, z_size], [x_size, x_size]) self.register_buffer("relative_position_index", relative_position_index) # define a parameter table of relative position bias self.relative_position_bias_table = nn.Parameter(torch.empty((num_heads, relative_position_index.max() + 1))) trunc_normal_(self.relative_position_bias_table, std=0.02) def forward(self, x, mask=None, return_attention=False): # x: B, N, C # mask: [B, N, ] torch.bool B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple) attn = (q @ k.transpose(-2, -1)) * self.scale if self.rpe: relative_position_bias = self.relative_position_bias_table[:, self.relative_position_index].unsqueeze(0) attn += relative_position_bias if mask is not None: attn = attn.masked_fill(mask.unsqueeze(1).unsqueeze(2), float('-inf'),) split_attn = False len_t = 49 if split_attn: attn_t = attn[..., :len_t].softmax(dim=-1) attn_s = attn[..., len_t:].softmax(dim=-1) attn = torch.cat([attn_t, attn_s], dim=-1) else: attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) if return_attention: return x, attn else: return x class Attention_talking_head(nn.Module): # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py # with slight modifications to add Talking Heads Attention (https://arxiv.org/pdf/2003.02436v1.pdf) def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., rpe=True, z_size=7, x_size=14): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = qk_scale or head_dim ** -0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_l = nn.Linear(num_heads, num_heads) self.proj_w = nn.Linear(num_heads, num_heads) self.proj_drop = nn.Dropout(proj_drop) self.rpe = rpe if self.rpe: relative_position_index = \ generate_2d_concatenated_self_attention_relative_positional_encoding_index([z_size, z_size], [x_size, x_size]) self.register_buffer("relative_position_index", relative_position_index) # define a parameter table of relative position bias self.relative_position_bias_table = nn.Parameter(torch.empty((num_heads, relative_position_index.max() + 1))) trunc_normal_(self.relative_position_bias_table, std=0.02) def forward(self, x, mask=None): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0] * self.scale, qkv[1], qkv[2] attn = (q @ k.transpose(-2, -1)) if self.rpe: relative_position_bias = self.relative_position_bias_table[:, self.relative_position_index].unsqueeze(0) attn += relative_position_bias if mask is not None: attn = attn.masked_fill(mask.unsqueeze(1).unsqueeze(2), float('-inf'),) attn = self.proj_l(attn.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) attn = attn.softmax(dim=-1) attn = self.proj_w(attn.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) return x ================================================ FILE: lib/models/layers/attn_blocks.py ================================================ import math import torch import torch.nn as nn from timm.models.layers import Mlp, DropPath, trunc_normal_, lecun_normal_ from lib.models.layers.attn import Attention def candidate_elimination(attn: torch.Tensor, tokens: torch.Tensor, lens_t: int, keep_ratio: float, global_index: torch.Tensor, box_mask_z: torch.Tensor): """ Eliminate potential background candidates for computation reduction and noise cancellation. Args: attn (torch.Tensor): [B, num_heads, L_t + L_s, L_t + L_s], attention weights tokens (torch.Tensor): [B, L_t + L_s, C], template and search region tokens lens_t (int): length of template keep_ratio (float): keep ratio of search region tokens (candidates) global_index (torch.Tensor): global index of search region tokens box_mask_z (torch.Tensor): template mask used to accumulate attention weights Returns: tokens_new (torch.Tensor): tokens after candidate elimination keep_index (torch.Tensor): indices of kept search region tokens removed_index (torch.Tensor): indices of removed search region tokens """ lens_s = attn.shape[-1] - lens_t bs, hn, _, _ = attn.shape lens_keep = math.ceil(keep_ratio * lens_s) if lens_keep == lens_s: return tokens, global_index, None attn_t = attn[:, :, :lens_t, lens_t:] if box_mask_z is not None: box_mask_z = box_mask_z.unsqueeze(1).unsqueeze(-1).expand(-1, attn_t.shape[1], -1, attn_t.shape[-1]) # attn_t = attn_t[:, :, box_mask_z, :] attn_t = attn_t[box_mask_z] attn_t = attn_t.view(bs, hn, -1, lens_s) attn_t = attn_t.mean(dim=2).mean(dim=1) # B, H, L-T, L_s --> B, L_s # attn_t = [attn_t[i, :, box_mask_z[i, :], :] for i in range(attn_t.size(0))] # attn_t = [attn_t[i].mean(dim=1).mean(dim=0) for i in range(len(attn_t))] # attn_t = torch.stack(attn_t, dim=0) else: attn_t = attn_t.mean(dim=2).mean(dim=1) # B, H, L-T, L_s --> B, L_s # use sort instead of topk, due to the speed issue # https://github.com/pytorch/pytorch/issues/22812 sorted_attn, indices = torch.sort(attn_t, dim=1, descending=True) topk_attn, topk_idx = sorted_attn[:, :lens_keep], indices[:, :lens_keep] non_topk_attn, non_topk_idx = sorted_attn[:, lens_keep:], indices[:, lens_keep:] keep_index = global_index.gather(dim=1, index=topk_idx) removed_index = global_index.gather(dim=1, index=non_topk_idx) # separate template and search tokens tokens_t = tokens[:, :lens_t] tokens_s = tokens[:, lens_t:] # obtain the attentive and inattentive tokens B, L, C = tokens_s.shape # topk_idx_ = topk_idx.unsqueeze(-1).expand(B, lens_keep, C) attentive_tokens = tokens_s.gather(dim=1, index=topk_idx.unsqueeze(-1).expand(B, -1, C)) # inattentive_tokens = tokens_s.gather(dim=1, index=non_topk_idx.unsqueeze(-1).expand(B, -1, C)) # compute the weighted combination of inattentive tokens # fused_token = non_topk_attn @ inattentive_tokens # concatenate these tokens # tokens_new = torch.cat([tokens_t, attentive_tokens, fused_token], dim=0) tokens_new = torch.cat([tokens_t, attentive_tokens], dim=1) return tokens_new, keep_index, removed_index class CEBlock(nn.Module): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, keep_ratio_search=1.0,): super().__init__() self.norm1 = norm_layer(dim) self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) self.keep_ratio_search = keep_ratio_search def forward(self, x, global_index_template, global_index_search, mask=None, ce_template_mask=None, keep_ratio_search=None): x_attn, attn = self.attn(self.norm1(x), mask, True) x = x + self.drop_path(x_attn) lens_t = global_index_template.shape[1] removed_index_search = None if self.keep_ratio_search < 1 and (keep_ratio_search is None or keep_ratio_search < 1): keep_ratio_search = self.keep_ratio_search if keep_ratio_search is None else keep_ratio_search x, global_index_search, removed_index_search = candidate_elimination(attn, x, lens_t, keep_ratio_search, global_index_search, ce_template_mask) x = x + self.drop_path(self.mlp(self.norm2(x))) return x, global_index_template, global_index_search, removed_index_search, attn class Block(nn.Module): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): super().__init__() self.norm1 = norm_layer(dim) self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) def forward(self, x, mask=None): x = x + self.drop_path(self.attn(self.norm1(x), mask)) x = x + self.drop_path(self.mlp(self.norm2(x))) return x ================================================ FILE: lib/models/layers/frozen_bn.py ================================================ import torch class FrozenBatchNorm2d(torch.nn.Module): """ BatchNorm2d where the batch statistics and the affine parameters are fixed. Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than torchvision.models.resnet[18,34,50,101] produce nans. """ def __init__(self, n): super(FrozenBatchNorm2d, self).__init__() self.register_buffer("weight", torch.ones(n)) self.register_buffer("bias", torch.zeros(n)) self.register_buffer("running_mean", torch.zeros(n)) self.register_buffer("running_var", torch.ones(n)) def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): num_batches_tracked_key = prefix + 'num_batches_tracked' if num_batches_tracked_key in state_dict: del state_dict[num_batches_tracked_key] super(FrozenBatchNorm2d, self)._load_from_state_dict( state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) def forward(self, x): # move reshapes to the beginning # to make it fuser-friendly w = self.weight.reshape(1, -1, 1, 1) b = self.bias.reshape(1, -1, 1, 1) rv = self.running_var.reshape(1, -1, 1, 1) rm = self.running_mean.reshape(1, -1, 1, 1) eps = 1e-5 scale = w * (rv + eps).rsqrt() # rsqrt(x): 1/sqrt(x), r: reciprocal bias = b - rm * scale return x * scale + bias ================================================ FILE: lib/models/layers/head.py ================================================ import torch.nn as nn import torch import torch.nn.functional as F from typing import Optional from torch import Tensor from torch.nn import Identity from timm.models.layers import trunc_normal_ from timm.models.layers import DropPath from lib.models.layers.frozen_bn import FrozenBatchNorm2d import copy def top_k_top_p_filtering_batch(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')): """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering Args: logits: logits distribution shape (vocabulary size) top_k > 0: keep only top k tokens with highest probability (top-k filtering). top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751) From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317 """ top_k = min(top_k, logits.size(-1)) # Safety check if top_k > 0: for i in range(logits.shape[0]): indices_to_remove = logits[i] < torch.topk(logits[i], top_k)[0][..., -1, None] logits[i][indices_to_remove] = filter_value if top_p > 0.0: for i in range(logits.shape[0]): sorted_logits, sorted_indices = torch.sort(logits[i], descending=True) cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) # Remove tokens with cumulative probability above the threshold sorted_indices_to_remove = cumulative_probs > top_p # Shift the indices to the right to keep also the first token above the threshold sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() sorted_indices_to_remove[..., 0] = 0 indices_to_remove = sorted_indices[sorted_indices_to_remove] logits[i][indices_to_remove] = filter_value return logits def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1, freeze_bn=False): if freeze_bn: return nn.Sequential( nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=True), FrozenBatchNorm2d(out_planes), nn.ReLU(inplace=True)) else: return nn.Sequential( nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=True), nn.BatchNorm2d(out_planes), nn.ReLU(inplace=True)) class Corner_Predictor(nn.Module): """ Corner Predictor module""" def __init__(self, inplanes=64, channel=256, feat_sz=20, stride=16, freeze_bn=False): super(Corner_Predictor, self).__init__() self.feat_sz = feat_sz self.stride = stride self.img_sz = self.feat_sz * self.stride '''top-left corner''' self.conv1_tl = conv(inplanes, channel, freeze_bn=freeze_bn) self.conv2_tl = conv(channel, channel // 2, freeze_bn=freeze_bn) self.conv3_tl = conv(channel // 2, channel // 4, freeze_bn=freeze_bn) self.conv4_tl = conv(channel // 4, channel // 8, freeze_bn=freeze_bn) self.conv5_tl = nn.Conv2d(channel // 8, 1, kernel_size=1) '''bottom-right corner''' self.conv1_br = conv(inplanes, channel, freeze_bn=freeze_bn) self.conv2_br = conv(channel, channel // 2, freeze_bn=freeze_bn) self.conv3_br = conv(channel // 2, channel // 4, freeze_bn=freeze_bn) self.conv4_br = conv(channel // 4, channel // 8, freeze_bn=freeze_bn) self.conv5_br = nn.Conv2d(channel // 8, 1, kernel_size=1) '''about coordinates and indexs''' with torch.no_grad(): self.indice = torch.arange(0, self.feat_sz).view(-1, 1) * self.stride # generate mesh-grid self.coord_x = self.indice.repeat((self.feat_sz, 1)) \ .view((self.feat_sz * self.feat_sz,)).float().cuda() self.coord_y = self.indice.repeat((1, self.feat_sz)) \ .view((self.feat_sz * self.feat_sz,)).float().cuda() def forward(self, x, return_dist=False, softmax=True): """ Forward pass with input x. """ score_map_tl, score_map_br = self.get_score_map(x) if return_dist: coorx_tl, coory_tl, prob_vec_tl = self.soft_argmax(score_map_tl, return_dist=True, softmax=softmax) coorx_br, coory_br, prob_vec_br = self.soft_argmax(score_map_br, return_dist=True, softmax=softmax) return torch.stack((coorx_tl, coory_tl, coorx_br, coory_br), dim=1) / self.img_sz, prob_vec_tl, prob_vec_br else: coorx_tl, coory_tl = self.soft_argmax(score_map_tl) coorx_br, coory_br = self.soft_argmax(score_map_br) return torch.stack((coorx_tl, coory_tl, coorx_br, coory_br), dim=1) / self.img_sz def get_score_map(self, x): # top-left branch x_tl1 = self.conv1_tl(x) x_tl2 = self.conv2_tl(x_tl1) x_tl3 = self.conv3_tl(x_tl2) x_tl4 = self.conv4_tl(x_tl3) score_map_tl = self.conv5_tl(x_tl4) # bottom-right branch x_br1 = self.conv1_br(x) x_br2 = self.conv2_br(x_br1) x_br3 = self.conv3_br(x_br2) x_br4 = self.conv4_br(x_br3) score_map_br = self.conv5_br(x_br4) return score_map_tl, score_map_br def soft_argmax(self, score_map, return_dist=False, softmax=True): """ get soft-argmax coordinate for a given heatmap """ score_vec = score_map.view((-1, self.feat_sz * self.feat_sz)) # (batch, feat_sz * feat_sz) prob_vec = nn.functional.softmax(score_vec, dim=1) exp_x = torch.sum((self.coord_x * prob_vec), dim=1) exp_y = torch.sum((self.coord_y * prob_vec), dim=1) if return_dist: if softmax: return exp_x, exp_y, prob_vec else: return exp_x, exp_y, score_vec else: return exp_x, exp_y class CenterPredictor(nn.Module, ): def __init__(self, inplanes=64, channel=256, feat_sz=20, stride=16, freeze_bn=False): super(CenterPredictor, self).__init__() self.feat_sz = feat_sz self.stride = stride self.img_sz = self.feat_sz * self.stride # corner predict self.conv1_ctr = conv(inplanes, channel, freeze_bn=freeze_bn) self.conv2_ctr = conv(channel, channel // 2, freeze_bn=freeze_bn) self.conv3_ctr = conv(channel // 2, channel // 4, freeze_bn=freeze_bn) self.conv4_ctr = conv(channel // 4, channel // 8, freeze_bn=freeze_bn) self.conv5_ctr = nn.Conv2d(channel // 8, 1, kernel_size=1) # size regress self.conv1_offset = conv(inplanes, channel, freeze_bn=freeze_bn) self.conv2_offset = conv(channel, channel // 2, freeze_bn=freeze_bn) self.conv3_offset = conv(channel // 2, channel // 4, freeze_bn=freeze_bn) self.conv4_offset = conv(channel // 4, channel // 8, freeze_bn=freeze_bn) self.conv5_offset = nn.Conv2d(channel // 8, 2, kernel_size=1) # size regress self.conv1_size = conv(inplanes, channel, freeze_bn=freeze_bn) self.conv2_size = conv(channel, channel // 2, freeze_bn=freeze_bn) self.conv3_size = conv(channel // 2, channel // 4, freeze_bn=freeze_bn) self.conv4_size = conv(channel // 4, channel // 8, freeze_bn=freeze_bn) self.conv5_size = nn.Conv2d(channel // 8, 2, kernel_size=1) for p in self.parameters(): if p.dim() > 1: nn.init.xavier_uniform_(p) def forward(self, x, gt_score_map=None): """ Forward pass with input x. """ score_map_ctr, size_map, offset_map = self.get_score_map(x) # assert gt_score_map is None if gt_score_map is None: bbox = self.cal_bbox(score_map_ctr, size_map, offset_map) else: bbox = self.cal_bbox(gt_score_map.unsqueeze(1), size_map, offset_map) return score_map_ctr, bbox, size_map, offset_map def cal_bbox(self, score_map_ctr, size_map, offset_map, return_score=False): max_score, idx = torch.max(score_map_ctr.flatten(1), dim=1, keepdim=True) idx_y = idx // self.feat_sz idx_x = idx % self.feat_sz idx = idx.unsqueeze(1).expand(idx.shape[0], 2, 1) size = size_map.flatten(2).gather(dim=2, index=idx) offset = offset_map.flatten(2).gather(dim=2, index=idx).squeeze(-1) # bbox = torch.cat([idx_x - size[:, 0] / 2, idx_y - size[:, 1] / 2, # idx_x + size[:, 0] / 2, idx_y + size[:, 1] / 2], dim=1) / self.feat_sz # cx, cy, w, h bbox = torch.cat([(idx_x.to(torch.float) + offset[:, :1]) / self.feat_sz, (idx_y.to(torch.float) + offset[:, 1:]) / self.feat_sz, size.squeeze(-1)], dim=1) if return_score: return bbox, max_score return bbox def get_pred(self, score_map_ctr, size_map, offset_map): max_score, idx = torch.max(score_map_ctr.flatten(1), dim=1, keepdim=True) idx_y = idx // self.feat_sz idx_x = idx % self.feat_sz idx = idx.unsqueeze(1).expand(idx.shape[0], 2, 1) size = size_map.flatten(2).gather(dim=2, index=idx) offset = offset_map.flatten(2).gather(dim=2, index=idx).squeeze(-1) # bbox = torch.cat([idx_x - size[:, 0] / 2, idx_y - size[:, 1] / 2, # idx_x + size[:, 0] / 2, idx_y + size[:, 1] / 2], dim=1) / self.feat_sz return size * self.feat_sz, offset def get_score_map(self, x): def _sigmoid(x): y = torch.clamp(x.sigmoid_(), min=1e-4, max=1 - 1e-4) return y # ctr branch x_ctr1 = self.conv1_ctr(x) x_ctr2 = self.conv2_ctr(x_ctr1) x_ctr3 = self.conv3_ctr(x_ctr2) x_ctr4 = self.conv4_ctr(x_ctr3) score_map_ctr = self.conv5_ctr(x_ctr4) # offset branch x_offset1 = self.conv1_offset(x) x_offset2 = self.conv2_offset(x_offset1) x_offset3 = self.conv3_offset(x_offset2) x_offset4 = self.conv4_offset(x_offset3) score_map_offset = self.conv5_offset(x_offset4) # size branch x_size1 = self.conv1_size(x) x_size2 = self.conv2_size(x_size1) x_size3 = self.conv3_size(x_size2) x_size4 = self.conv4_size(x_size3) score_map_size = self.conv5_size(x_size4) return _sigmoid(score_map_ctr), _sigmoid(score_map_size), score_map_offset class MLP(nn.Module): """ Very simple multi-layer perceptron (also called FFN)""" def __init__(self, input_dim, hidden_dim, output_dim, num_layers, BN=False): super().__init__() self.num_layers = num_layers h = [hidden_dim] * (num_layers - 1) if BN: self.layers = nn.ModuleList(nn.Sequential(nn.Linear(n, k), nn.BatchNorm1d(k)) for n, k in zip([input_dim] + h, h + [output_dim])) else: self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) def forward(self, x): for i, layer in enumerate(self.layers): x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) return x class SelfAttention(nn.Module): def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., attn_pos_encoding_only=False): super(SelfAttention, self).__init__() assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}." self.dim = dim self.num_heads = num_heads head_dim = dim // num_heads self.scale = qk_scale or head_dim ** -0.5 if attn_pos_encoding_only: self.qkv = nn.Linear(dim, 3 * dim, bias=qkv_bias) else: self.q = nn.Linear(dim, dim, bias=qkv_bias) self.k = nn.Linear(dim, dim, bias=qkv_bias) self.v = nn.Linear(dim, dim, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) self.attn_pos_encoding_only = attn_pos_encoding_only def forward(self, x, q_ape, k_ape, attn_pos): ''' Args: x (torch.Tensor): (B, L, C) q_ape (torch.Tensor | None): (1 or B, L, C), absolute positional encoding for q k_ape (torch.Tensor | None): (1 or B, L, C), absolute positional encoding for k attn_pos (torch.Tensor | None): (1 or B, num_heads, L, L), untied positional encoding Returns: torch.Tensor: (B, L, C) ''' B, N, C = x.shape if self.attn_pos_encoding_only: assert q_ape is None and k_ape is None qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] else: q = x + q_ape if q_ape is not None else x q = self.q(q).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) k = x + k_ape if k_ape is not None else x k = self.k(k).reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) v = self.v(x).reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) attn = q @ k.transpose(-2, -1) attn = attn * self.scale if attn_pos is not None: attn = attn + attn_pos attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = attn @ v x = x.transpose(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) return x class CrossAttention(nn.Module): def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., attn_pos_encoding_only=False): super(CrossAttention, self).__init__() assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}." self.dim = dim self.num_heads = num_heads head_dim = dim // num_heads self.scale = qk_scale or head_dim ** -0.5 if attn_pos_encoding_only: self.q = nn.Linear(dim, dim, bias=qkv_bias) self.kv = nn.Linear(dim, 2 * dim, bias=qkv_bias) else: self.q = nn.Linear(dim, dim, bias=qkv_bias) self.k = nn.Linear(dim, dim, bias=qkv_bias) self.v = nn.Linear(dim, dim, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) self.attn_pos_encoding_only = attn_pos_encoding_only def forward(self, q, kv, q_ape, k_ape, attn_pos): ''' Args: q (torch.Tensor): (B, L_q, C) kv (torch.Tensor): (B, L_kv, C) q_ape (torch.Tensor | None): (1 or B, L_q, C), absolute positional encoding for q k_ape (torch.Tensor | None): (1 or B, L_kv, C), absolute positional encoding for k attn_pos (torch.Tensor | None): (1 or B, num_heads, L_q, L_kv), untied positional encoding Returns: torch.Tensor: (B, L_q, C) ''' B, q_N, C = q.shape kv_N = kv.shape[1] if self.attn_pos_encoding_only: assert q_ape is None and k_ape is None q = self.q(q).reshape(B, q_N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) kv = self.kv(kv).reshape(B, kv_N, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) k, v = kv[0], kv[1] else: q = q + q_ape if q_ape is not None else q q = self.q(q).reshape(B, q_N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) k = kv + k_ape if k_ape is not None else kv k = self.k(k).reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) v = self.v(kv).reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) attn = q @ k.transpose(-2, -1) attn = attn * self.scale if attn_pos is not None: attn = attn + attn_pos attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = attn @ v x = x.transpose(1, 2).reshape(B, q_N, C) x = self.proj(x) x = self.proj_drop(x) return x class Mlp(nn.Module): """ Multilayer perceptron.""" def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def forward(self, x): ''' Args: x (torch.Tensor): (B, L, C), input tensor Returns: torch.Tensor: (B, L, C), output tensor ''' x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x class FeatureFusion(nn.Module): def __init__(self, dim, num_heads, mlp_ratio=2., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., drop_path=nn.Identity(), act_layer=nn.GELU, norm_layer=nn.LayerNorm, attn_pos_encoding_only=False): super(FeatureFusion, self).__init__() self.z_norm1 = norm_layer(dim) self.x_norm1 = norm_layer(dim) self.z_self_attn = SelfAttention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop, attn_pos_encoding_only) self.x_self_attn = SelfAttention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop, attn_pos_encoding_only) self.z_norm2_1 = norm_layer(dim) self.z_norm2_2 = norm_layer(dim) self.x_norm2_1 = norm_layer(dim) self.x_norm2_2 = norm_layer(dim) self.z_x_cross_attention = CrossAttention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop, attn_pos_encoding_only) self.x_z_cross_attention = CrossAttention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop, attn_pos_encoding_only) mlp_hidden_dim = int(dim * mlp_ratio) self.z_norm3 = norm_layer(dim) self.x_norm3 = norm_layer(dim) print(mlp_ratio) self.z_mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) self.x_mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) self.drop_path = drop_path def forward(self, z, x, z_self_attn_pos, x_self_attn_pos, z_x_cross_attn_pos, x_z_cross_attn_pos): z = z + self.drop_path(self.z_self_attn(self.z_norm1(z), None, None, z_self_attn_pos)) x = x + self.drop_path(self.x_self_attn(self.x_norm1(x), None, None, x_self_attn_pos)) z = z + self.drop_path(self.z_x_cross_attention(self.z_norm2_1(z), self.x_norm2_1(x), None, None, z_x_cross_attn_pos)) x = x + self.drop_path(self.x_z_cross_attention(self.x_norm2_2(x), self.z_norm2_2(z), None, None, x_z_cross_attn_pos)) z = z + self.drop_path(self.z_mlp(self.z_norm3(z))) x = x + self.drop_path(self.x_mlp(self.x_norm3(x))) return z, x class FeatureFusionEncoder(nn.Module): def __init__(self, feature_fusion_layers, z_pos_enc, x_pos_enc, z_rel_pos_index, x_rel_pos_index, z_x_rel_pos_index, x_z_rel_pos_index, z_rel_pos_bias_table, x_rel_pos_bias_table, z_x_rel_pos_bias_table, x_z_rel_pos_bias_table): super(FeatureFusionEncoder, self).__init__() self.layers = nn.ModuleList(feature_fusion_layers) self.z_pos_enc = z_pos_enc self.x_pos_enc = x_pos_enc self.register_buffer('z_rel_pos_index', z_rel_pos_index, False) self.register_buffer('x_rel_pos_index', x_rel_pos_index, False) self.register_buffer('z_x_rel_pos_index', z_x_rel_pos_index, False) self.register_buffer('x_z_rel_pos_index', x_z_rel_pos_index, False) self.z_rel_pos_bias_table = z_rel_pos_bias_table self.x_rel_pos_bias_table = x_rel_pos_bias_table self.z_x_rel_pos_bias_table = z_x_rel_pos_bias_table self.x_z_rel_pos_bias_table = x_z_rel_pos_bias_table def forward(self, z, x, z_pos, x_pos): ''' Args: z (torch.Tensor): (B, L_z, C), template image feature tokens x (torch.Tensor): (B, L_x, C), search image feature tokens z_pos (torch.Tensor | None): (1 or B, L_z, C), optional positional encoding for z x_pos (torch.Tensor | None): (1 or B, L_x, C), optional positional encoding for x Returns: Tuple[torch.Tensor, torch.Tensor]: (B, L_z, C): template image feature tokens (B, L_x, C): search image feature tokens ''' # Support untied positional encoding only for simplicity assert z_pos is None and x_pos is None # untied positional encoding z_q_pos, z_k_pos = self.z_pos_enc() x_q_pos, x_k_pos = self.x_pos_enc() z_self_attn_pos = (z_q_pos @ z_k_pos.transpose(-2, -1)).unsqueeze(0) x_self_attn_pos = (x_q_pos @ x_k_pos.transpose(-2, -1)).unsqueeze(0) z_x_cross_attn_pos = (z_q_pos @ x_k_pos.transpose(-2, -1)).unsqueeze(0) x_z_cross_attn_pos = (x_q_pos @ z_k_pos.transpose(-2, -1)).unsqueeze(0) # relative positional encoding z_self_attn_pos = z_self_attn_pos + self.z_rel_pos_bias_table(self.z_rel_pos_index) x_self_attn_pos = x_self_attn_pos + self.x_rel_pos_bias_table(self.x_rel_pos_index) z_x_cross_attn_pos = z_x_cross_attn_pos + self.z_x_rel_pos_bias_table(self.z_x_rel_pos_index) x_z_cross_attn_pos = x_z_cross_attn_pos + self.x_z_rel_pos_bias_table(self.x_z_rel_pos_index) for layer in self.layers: z, x = layer(z, x, z_self_attn_pos, x_self_attn_pos, z_x_cross_attn_pos, x_z_cross_attn_pos) return z, x class Learned2DPositionalEncoder(nn.Module): def __init__(self, dim, w, h): super(Learned2DPositionalEncoder, self).__init__() self.w_pos = nn.Parameter(torch.empty(w, dim)) self.h_pos = nn.Parameter(torch.empty(h, dim)) trunc_normal_(self.w_pos, std=0.02) trunc_normal_(self.h_pos, std=0.02) def forward(self): w = self.w_pos.shape[0] h = self.h_pos.shape[0] return (self.w_pos[None, :, :] + self.h_pos[:, None, :]).view(h * w, -1) class Untied2DPositionalEncoder(nn.Module): def __init__(self, dim, num_heads, w, h, scale=None, with_q=True, with_k=True): super(Untied2DPositionalEncoder, self).__init__() assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}." self.pos = Learned2DPositionalEncoder(dim, w, h) self.norm = nn.LayerNorm(dim) self.pos_q_linear = None self.pos_k_linear = None if with_q: self.pos_q_linear = nn.Linear(dim, dim) if with_k: self.pos_k_linear = nn.Linear(dim, dim) self.num_heads = num_heads head_dim = dim // num_heads self.scale = scale or head_dim ** -0.5 def forward(self): pos = self.norm(self.pos()) seq_len = pos.shape[0] if self.pos_q_linear is not None and self.pos_k_linear is not None: pos_q = self.pos_q_linear(pos).view(seq_len, self.num_heads, -1).transpose(0, 1) * self.scale pos_k = self.pos_k_linear(pos).view(seq_len, self.num_heads, -1).transpose(0, 1) return pos_q, pos_k elif self.pos_q_linear is not None: pos_q = self.pos_q_linear(pos).view(seq_len, self.num_heads, -1).transpose(0, 1) * self.scale return pos_q elif self.pos_k_linear is not None: pos_k = self.pos_k_linear(pos).view(seq_len, self.num_heads, -1).transpose(0, 1) return pos_k else: raise RuntimeError def generate_2d_relative_positional_encoding_index(z_shape, x_shape): ''' z_shape: (z_h, z_w) x_shape: (x_h, x_w) ''' z_2d_index_h, z_2d_index_w = torch.meshgrid(torch.arange(z_shape[0]), torch.arange(z_shape[1])) x_2d_index_h, x_2d_index_w = torch.meshgrid(torch.arange(x_shape[0]), torch.arange(x_shape[1])) z_2d_index_h = z_2d_index_h.flatten(0) z_2d_index_w = z_2d_index_w.flatten(0) x_2d_index_h = x_2d_index_h.flatten(0) x_2d_index_w = x_2d_index_w.flatten(0) diff_h = z_2d_index_h[:, None] - x_2d_index_h[None, :] diff_w = z_2d_index_w[:, None] - x_2d_index_w[None, :] diff = torch.stack((diff_h, diff_w), dim=-1) _, indices = torch.unique(diff.view(-1, 2), return_inverse=True, dim=0) return indices.view(z_shape[0] * z_shape[1], x_shape[0] * x_shape[1]) class RelativePosition2DEncoder(nn.Module): def __init__(self, num_heads, embed_size): super(RelativePosition2DEncoder, self).__init__() self.relative_position_bias_table = nn.Parameter(torch.empty((num_heads, embed_size))) trunc_normal_(self.relative_position_bias_table, std=0.02) def forward(self, attn_rpe_index): ''' Args: attn_rpe_index (torch.Tensor): (*), any shape containing indices, max(attn_rpe_index) < embed_size Returns: torch.Tensor: (1, num_heads, *) ''' return self.relative_position_bias_table[:, attn_rpe_index].unsqueeze(0) class DropPathAllocator: def __init__(self, max_drop_path_rate, stochastic_depth_decay = True): self.max_drop_path_rate = max_drop_path_rate self.stochastic_depth_decay = stochastic_depth_decay self.allocated = [] self.allocating = [] def __enter__(self): self.allocating = [] def __exit__(self, exc_type, exc_val, exc_tb): if len(self.allocating) != 0: self.allocated.append(self.allocating) self.allocating = None if not self.stochastic_depth_decay: for depth_module in self.allocated: for module in depth_module: if isinstance(module, DropPath): module.drop_prob = self.max_drop_path_rate else: depth = self.get_depth() dpr = [x.item() for x in torch.linspace(0, self.max_drop_path_rate, depth)] assert len(dpr) == len(self.allocated) for drop_path_rate, depth_modules in zip(dpr, self.allocated): for module in depth_modules: if isinstance(module, DropPath): module.drop_prob = drop_path_rate def __len__(self): length = 0 for depth_modules in self.allocated: length += len(depth_modules) return length def increase_depth(self): self.allocated.append(self.allocating) self.allocating = [] def get_depth(self): return len(self.allocated) def allocate(self): if self.max_drop_path_rate == 0 or (self.stochastic_depth_decay and self.get_depth() == 0): drop_path_module = Identity() else: drop_path_module = DropPath() self.allocating.append(drop_path_module) return drop_path_module def get_all_allocated(self): allocated = [] for depth_module in self.allocated: for module in depth_module: allocated.append(module) return allocated def build_encoder(encoder_layer, num_heads, mlp_ratio, qkv_bias, drop_rate, attn_drop, dim, z_size, x_size, drop_path): z_shape = [z_size, z_size] x_shape = [x_size, x_size] encoder_layers = [] for i in range(encoder_layer): encoder_layers.append( FeatureFusion(dim, num_heads, mlp_ratio, qkv_bias, drop=drop_rate, attn_drop=attn_drop, drop_path=drop_path.allocate(), attn_pos_encoding_only=True) ) z_abs_encoder = Untied2DPositionalEncoder(dim, num_heads, z_shape[0], z_shape[1]) x_abs_encoder = Untied2DPositionalEncoder(dim, num_heads, x_shape[0], x_shape[1]) z_self_attn_rel_pos_index = generate_2d_relative_positional_encoding_index(z_shape, z_shape) x_self_attn_rel_pos_index = generate_2d_relative_positional_encoding_index(x_shape, x_shape) z_x_cross_attn_rel_pos_index = generate_2d_relative_positional_encoding_index(z_shape, x_shape) x_z_cross_attn_rel_pos_index = generate_2d_relative_positional_encoding_index(x_shape, z_shape) z_self_attn_rel_pos_bias_table = RelativePosition2DEncoder(num_heads, z_self_attn_rel_pos_index.max() + 1) x_self_attn_rel_pos_bias_table = RelativePosition2DEncoder(num_heads, x_self_attn_rel_pos_index.max() + 1) z_x_cross_attn_rel_pos_bias_table = RelativePosition2DEncoder(num_heads, z_x_cross_attn_rel_pos_index.max() + 1) x_z_cross_attn_rel_pos_bias_table = RelativePosition2DEncoder(num_heads, x_z_cross_attn_rel_pos_index.max() + 1) return FeatureFusionEncoder(encoder_layers, z_abs_encoder, x_abs_encoder, z_self_attn_rel_pos_index, x_self_attn_rel_pos_index, z_x_cross_attn_rel_pos_index, x_z_cross_attn_rel_pos_index, z_self_attn_rel_pos_bias_table, x_self_attn_rel_pos_bias_table, z_x_cross_attn_rel_pos_bias_table, x_z_cross_attn_rel_pos_bias_table) class TargetQueryDecoderLayer(nn.Module): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., drop_path=nn.Identity(), act_layer=nn.GELU, norm_layer=nn.LayerNorm): super(TargetQueryDecoderLayer, self).__init__() self.norm_1 = norm_layer(dim) self.self_attn1 = nn.MultiheadAttention(dim, num_heads, dropout=drop) self.norm_2_query = norm_layer(dim) self.norm_2_memory = norm_layer(dim) self.multihead_attn = nn.MultiheadAttention(dim, num_heads, dropout=drop) self.norm_3 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlpz = Mlp(dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) self.drop_path = drop_path def forward(self, query, memoryz, memoryx, query_pos, pos_z, pos_x, identity, tgt_mask: Optional[Tensor] = None, memory_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] = None, memory_key_padding_mask: Optional[Tensor] = None, ): ''' Args: query (torch.Tensor): (B, num_queries, C) memory (torch.Tensor): (B, L, C) query_pos (torch.Tensor): (1 or B, num_queries, C) memory_pos (torch.Tensor): (1 or B, L, C) Returns: torch.Tensor: (B, num_queries, C) ''' tgt = query q = k = self.norm_1(query) + query_pos query = query + self.drop_path(self.self_attn1(q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask)[0]) q2 = self.norm_2_query(query) + query_pos memory = torch.cat((memoryz,memoryx),dim=1) pos = torch.cat((pos_z, pos_x), dim=1) ide = torch.cat((identity[: ,0, :].repeat(1, pos_z.shape[1], 1), identity[:, 1, :].repeat(1, pos_x.shape[1], 1)), dim=1) k2 = (self.norm_2_memory(memory) + pos + ide).permute(1, 0, 2) memory_in = memory.permute(1, 0, 2) query = query + self.drop_path( self.multihead_attn(query=q2, key=k2, value=memory_in, attn_mask=memory_mask, key_padding_mask=memory_key_padding_mask)[0]) query = query + self.drop_path(self.mlpz(self.norm_3(query))) return query def _get_clones(module, N): return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) class TargetQueryDecoderBlock(nn.Module): def __init__(self, dim, decoder_layers, num_layer): super(TargetQueryDecoderBlock, self).__init__() self.layers = nn.ModuleList(decoder_layers) self.num_layers = num_layer self.norm = nn.LayerNorm(dim) def forward(self, tgt, z, x, pos_z, pos_x, identity, query_pos: Optional[Tensor] = None, tgt_mask: Optional[Tensor] = None, memory_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] = None, memory_key_padding_mask: Optional[Tensor] = None): ''' Args: z (torch.Tensor): (B, L_z, C) x (torch.Tensor): (B, L_x, C) Returns: torch.Tensor: (B, num_queries, C) ''' output = tgt for layer in self.layers: output = layer(output, z, x, query_pos, pos_z, pos_x, identity, tgt_mask=tgt_mask, memory_mask=memory_mask, tgt_key_padding_mask=tgt_key_padding_mask, memory_key_padding_mask=memory_key_padding_mask) output = self.norm(output) return output def build_decoder(decoder_layer, drop_path, dim, num_heads, mlp_ratio, qkv_bias, drop_rate, attn_drop_rate, z_size, x_size): z_shape = [z_size, z_size] x_shape = [x_size, x_size] num_layers = decoder_layer decoder_layers = [] for _ in range(num_layers): decoder_layers.append( TargetQueryDecoderLayer(dim, num_heads, mlp_ratio, qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=drop_path.allocate())) drop_path.increase_depth() decoder = TargetQueryDecoderBlock(dim, decoder_layers, num_layers) return decoder def generate_square_subsequent_mask(sz): r"""Generate a square mask for the sequence. The masked positions are filled with float('-inf'). Unmasked positions are filled with float(0.0). """ mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1) mask = mask.float().masked_fill(mask == 0, float( '-inf')).masked_fill(mask == 1, float(0.0)) return mask class Pix2Track(nn.Module): def __init__(self, in_channel=64, feat_sz=20, feat_tz=10, range=2, stride=16, encoder_layer=3, decoder_layer=3, bins=400,num_heads=12, mlp_ratio=2, qkv_bias=True, drop_rate=0.0,attn_drop=0.0, drop_path=nn.Identity): super(Pix2Track, self).__init__() self.bins = bins self.range = range self.word_embeddings = nn.Embedding(self.bins * self.range + 2, in_channel, padding_idx=self.bins * self.range, max_norm=1, norm_type=2.0) print(self.bins) self.position_embeddings = nn.Embedding( 5, in_channel) self.output_bias = torch.nn.Parameter(torch.zeros(self.bins * self.range + 2)) self.encoder_layer = encoder_layer self.drop_path = drop_path self.tz = feat_tz * feat_tz self.sz = feat_sz * feat_sz trunc_normal_(self.word_embeddings.weight, std=.02) if self.encoder_layer > 0 : self.encoder = build_encoder(encoder_layer, num_heads, mlp_ratio, qkv_bias, drop_rate, attn_drop, in_channel, feat_tz, feat_sz, self.drop_path) else: self.encoder = None self.decoder = build_decoder(decoder_layer, self.drop_path, in_channel, num_heads, mlp_ratio, qkv_bias, drop_rate, attn_drop, feat_tz, feat_sz) def forward(self, zx_feat, pos_z, pos_x, identity, seqs_input=None): share_weight = self.word_embeddings.weight.T z_feat = zx_feat[:, :self.tz] x_feat = zx_feat[:, self.tz:] bs = zx_feat.shape[0] if self.encoder != None: z_feat, x_feat = self.encoder(z_feat, x_feat, None, None) if seqs_input != None: seqs_input = seqs_input.to(torch.int64).to(zx_feat.device) tgt = self.word_embeddings(seqs_input).permute(1, 0, 2) query_embed = self.position_embeddings.weight.unsqueeze(1) query_embed = query_embed.repeat(1, bs, 1) decoder_feat = self.decoder(tgt, z_feat, x_feat, pos_z, pos_x, identity, query_embed, tgt_mask=generate_square_subsequent_mask(len(tgt)).to(tgt.device)) at = torch.matmul(decoder_feat, share_weight) at = at + self.output_bias output = {'feat': at, "state": "train"} else: origin_seq = torch.ones(bs, 1) * self.bins * self.range seqs_input = origin_seq.to(zx_feat.device).to(torch.int64) for i in range(4): tgt = self.word_embeddings(seqs_input).permute(1, 0, 2) query_embed = self.position_embeddings.weight.unsqueeze(1) query_embed = query_embed.repeat(1, bs, 1) decoder_feat_cls = self.decoder(tgt, z_feat, x_feat, pos_z, pos_x, identity, query_embed[:len(tgt)], tgt_mask=generate_square_subsequent_mask(len(tgt)).to(tgt.device)) out = torch.matmul(decoder_feat_cls.transpose(0, 1)[:, -1, :], share_weight) + self.output_bias out = out.softmax(-1) value, extra_seq = out.topk(dim=-1, k=1)[0], out.topk(dim=-1, k=1)[1] seqs_input = torch.cat([seqs_input, extra_seq], dim=-1) if i == 0: seqs_output = extra_seq values = value else: seqs_output = torch.cat([seqs_output, extra_seq], dim=-1) values = torch.cat([values, value], dim=-1) output = {'seqs': seqs_output, 'class': values, "state": "val/test"} return output def build_pix_head(cfg, hidden_dim): stride = cfg.MODEL.BACKBONE.STRIDE if cfg.MODEL.HEAD.TYPE == "MLP": mlp_head = MLP(hidden_dim, hidden_dim, 4, 3) # dim_in, dim_hidden, dim_out, 3 layers return mlp_head elif "CORNER" in cfg.MODEL.HEAD.TYPE: feat_sz = int(cfg.DATA.SEARCH.SIZE / stride) channel = getattr(cfg.MODEL, "NUM_CHANNELS", 256) print("head channel: %d" % channel) if cfg.MODEL.HEAD.TYPE == "CORNER": corner_head = Corner_Predictor(inplanes=cfg.MODEL.HIDDEN_DIM, channel=channel, feat_sz=feat_sz, stride=stride) else: raise ValueError() return corner_head elif cfg.MODEL.HEAD.TYPE == "CENTER": in_channel = hidden_dim out_channel = cfg.MODEL.HEAD.NUM_CHANNELS feat_sz = int(cfg.DATA.SEARCH.SIZE / stride) center_head = CenterPredictor(inplanes=in_channel, channel=out_channel, feat_sz=feat_sz, stride=stride) return center_head elif cfg.MODEL.HEAD.TYPE == "PIX": in_channel = hidden_dim feat_sz = int(cfg.DATA.SEARCH.SIZE / stride) feat_tz = int(cfg.DATA.TEMPLATE.SIZE / stride) decoder_layer = cfg.MODEL.DECODER_LAYER encoder_layer = cfg.MODEL.ENCODER_LAYER bins = cfg.MODEL.BINS num_heads = cfg.MODEL.NUM_HEADS mlp_ratio = cfg.MODEL.MLP_RATIO qkv_bias = cfg.MODEL.QKV_BIAS drop_rate = cfg.MODEL.DROP_RATE attn_drop = cfg.MODEL.ATTN_DROP drop_path = cfg.MODEL.DROP_PATH drop_path_allocator = DropPathAllocator(drop_path) range = cfg.MODEL.RANGE pix_head = Pix2Track(in_channel=in_channel, feat_sz=feat_sz, feat_tz=feat_tz, range=range, stride=stride, encoder_layer=encoder_layer, decoder_layer=decoder_layer, bins=bins, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop_rate=drop_rate, attn_drop=attn_drop, drop_path=drop_path_allocator) return pix_head else: raise ValueError("HEAD TYPE %s is not supported." % cfg.MODEL.HEAD_TYPE) ================================================ FILE: lib/models/layers/head_seq.py ================================================ import torch.nn as nn import torch import torch.nn.functional as F from typing import Optional from torch import Tensor from torch.nn import Identity from timm.models.layers import trunc_normal_ from timm.models.layers import DropPath from lib.models.layers.frozen_bn import FrozenBatchNorm2d import copy def top_k_top_p_filtering_batch(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')): """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering Args: logits: logits distribution shape (vocabulary size) top_k > 0: keep only top k tokens with highest probability (top-k filtering). top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751) From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317 """ top_k = min(top_k, logits.size(-1)) # Safety check if top_k > 0: # Remove all tokens with a probability less than the last token of the top-k # torch.topk()返回最后一维最大的top_k个元素，返回值为二维(values,indices) # ...表示其他维度由计算机自行推断 for i in range(logits.shape[0]): indices_to_remove = logits[i] < torch.topk(logits[i], top_k)[0][..., -1, None] logits[i][indices_to_remove] = filter_value # 对于topk之外的其他元素的logits值设为负无穷 if top_p > 0.0: for i in range(logits.shape[0]): sorted_logits, sorted_indices = torch.sort(logits[i], descending=True) # 对logits进行递减排序 cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) # Remove tokens with cumulative probability above the threshold sorted_indices_to_remove = cumulative_probs > top_p # Shift the indices to the right to keep also the first token above the threshold sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() sorted_indices_to_remove[..., 0] = 0 indices_to_remove = sorted_indices[sorted_indices_to_remove] logits[i][indices_to_remove] = filter_value return logits def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1, freeze_bn=False): if freeze_bn: return nn.Sequential( nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=True), FrozenBatchNorm2d(out_planes), nn.ReLU(inplace=True)) else: return nn.Sequential( nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=True), nn.BatchNorm2d(out_planes), nn.ReLU(inplace=True)) class Corner_Predictor(nn.Module): """ Corner Predictor module""" def __init__(self, inplanes=64, channel=256, feat_sz=20, stride=16, freeze_bn=False): super(Corner_Predictor, self).__init__() self.feat_sz = feat_sz self.stride = stride self.img_sz = self.feat_sz * self.stride '''top-left corner''' self.conv1_tl = conv(inplanes, channel, freeze_bn=freeze_bn) self.conv2_tl = conv(channel, channel // 2, freeze_bn=freeze_bn) self.conv3_tl = conv(channel // 2, channel // 4, freeze_bn=freeze_bn) self.conv4_tl = conv(channel // 4, channel // 8, freeze_bn=freeze_bn) self.conv5_tl = nn.Conv2d(channel // 8, 1, kernel_size=1) '''bottom-right corner''' self.conv1_br = conv(inplanes, channel, freeze_bn=freeze_bn) self.conv2_br = conv(channel, channel // 2, freeze_bn=freeze_bn) self.conv3_br = conv(channel // 2, channel // 4, freeze_bn=freeze_bn) self.conv4_br = conv(channel // 4, channel // 8, freeze_bn=freeze_bn) self.conv5_br = nn.Conv2d(channel // 8, 1, kernel_size=1) '''about coordinates and indexs''' with torch.no_grad(): self.indice = torch.arange(0, self.feat_sz).view(-1, 1) * self.stride # generate mesh-grid self.coord_x = self.indice.repeat((self.feat_sz, 1)) \ .view((self.feat_sz * self.feat_sz,)).float().cuda() self.coord_y = self.indice.repeat((1, self.feat_sz)) \ .view((self.feat_sz * self.feat_sz,)).float().cuda() def forward(self, x, return_dist=False, softmax=True): """ Forward pass with input x. """ score_map_tl, score_map_br = self.get_score_map(x) if return_dist: coorx_tl, coory_tl, prob_vec_tl = self.soft_argmax(score_map_tl, return_dist=True, softmax=softmax) coorx_br, coory_br, prob_vec_br = self.soft_argmax(score_map_br, return_dist=True, softmax=softmax) return torch.stack((coorx_tl, coory_tl, coorx_br, coory_br), dim=1) / self.img_sz, prob_vec_tl, prob_vec_br else: coorx_tl, coory_tl = self.soft_argmax(score_map_tl) coorx_br, coory_br = self.soft_argmax(score_map_br) return torch.stack((coorx_tl, coory_tl, coorx_br, coory_br), dim=1) / self.img_sz def get_score_map(self, x): # top-left branch x_tl1 = self.conv1_tl(x) x_tl2 = self.conv2_tl(x_tl1) x_tl3 = self.conv3_tl(x_tl2) x_tl4 = self.conv4_tl(x_tl3) score_map_tl = self.conv5_tl(x_tl4) # bottom-right branch x_br1 = self.conv1_br(x) x_br2 = self.conv2_br(x_br1) x_br3 = self.conv3_br(x_br2) x_br4 = self.conv4_br(x_br3) score_map_br = self.conv5_br(x_br4) return score_map_tl, score_map_br def soft_argmax(self, score_map, return_dist=False, softmax=True): """ get soft-argmax coordinate for a given heatmap """ score_vec = score_map.view((-1, self.feat_sz * self.feat_sz)) # (batch, feat_sz * feat_sz) prob_vec = nn.functional.softmax(score_vec, dim=1) exp_x = torch.sum((self.coord_x * prob_vec), dim=1) exp_y = torch.sum((self.coord_y * prob_vec), dim=1) if return_dist: if softmax: return exp_x, exp_y, prob_vec else: return exp_x, exp_y, score_vec else: return exp_x, exp_y class CenterPredictor(nn.Module, ): def __init__(self, inplanes=64, channel=256, feat_sz=20, stride=16, freeze_bn=False): super(CenterPredictor, self).__init__() self.feat_sz = feat_sz self.stride = stride self.img_sz = self.feat_sz * self.stride # corner predict self.conv1_ctr = conv(inplanes, channel, freeze_bn=freeze_bn) self.conv2_ctr = conv(channel, channel // 2, freeze_bn=freeze_bn) self.conv3_ctr = conv(channel // 2, channel // 4, freeze_bn=freeze_bn) self.conv4_ctr = conv(channel // 4, channel // 8, freeze_bn=freeze_bn) self.conv5_ctr = nn.Conv2d(channel // 8, 1, kernel_size=1) # size regress self.conv1_offset = conv(inplanes, channel, freeze_bn=freeze_bn) self.conv2_offset = conv(channel, channel // 2, freeze_bn=freeze_bn) self.conv3_offset = conv(channel // 2, channel // 4, freeze_bn=freeze_bn) self.conv4_offset = conv(channel // 4, channel // 8, freeze_bn=freeze_bn) self.conv5_offset = nn.Conv2d(channel // 8, 2, kernel_size=1) # size regress self.conv1_size = conv(inplanes, channel, freeze_bn=freeze_bn) self.conv2_size = conv(channel, channel // 2, freeze_bn=freeze_bn) self.conv3_size = conv(channel // 2, channel // 4, freeze_bn=freeze_bn) self.conv4_size = conv(channel // 4, channel // 8, freeze_bn=freeze_bn) self.conv5_size = nn.Conv2d(channel // 8, 2, kernel_size=1) for p in self.parameters(): if p.dim() > 1: nn.init.xavier_uniform_(p) def forward(self, x, gt_score_map=None): """ Forward pass with input x. """ score_map_ctr, size_map, offset_map = self.get_score_map(x) # assert gt_score_map is None if gt_score_map is None: bbox = self.cal_bbox(score_map_ctr, size_map, offset_map) else: bbox = self.cal_bbox(gt_score_map.unsqueeze(1), size_map, offset_map) return score_map_ctr, bbox, size_map, offset_map def cal_bbox(self, score_map_ctr, size_map, offset_map, return_score=False): max_score, idx = torch.max(score_map_ctr.flatten(1), dim=1, keepdim=True) idx_y = idx // self.feat_sz idx_x = idx % self.feat_sz idx = idx.unsqueeze(1).expand(idx.shape[0], 2, 1) size = size_map.flatten(2).gather(dim=2, index=idx) offset = offset_map.flatten(2).gather(dim=2, index=idx).squeeze(-1) # bbox = torch.cat([idx_x - size[:, 0] / 2, idx_y - size[:, 1] / 2, # idx_x + size[:, 0] / 2, idx_y + size[:, 1] / 2], dim=1) / self.feat_sz # cx, cy, w, h bbox = torch.cat([(idx_x.to(torch.float) + offset[:, :1]) / self.feat_sz, (idx_y.to(torch.float) + offset[:, 1:]) / self.feat_sz, size.squeeze(-1)], dim=1) if return_score: return bbox, max_score return bbox def get_pred(self, score_map_ctr, size_map, offset_map): max_score, idx = torch.max(score_map_ctr.flatten(1), dim=1, keepdim=True) idx_y = idx // self.feat_sz idx_x = idx % self.feat_sz idx = idx.unsqueeze(1).expand(idx.shape[0], 2, 1) size = size_map.flatten(2).gather(dim=2, index=idx) offset = offset_map.flatten(2).gather(dim=2, index=idx).squeeze(-1) # bbox = torch.cat([idx_x - size[:, 0] / 2, idx_y - size[:, 1] / 2, # idx_x + size[:, 0] / 2, idx_y + size[:, 1] / 2], dim=1) / self.feat_sz return size * self.feat_sz, offset def get_score_map(self, x): def _sigmoid(x): y = torch.clamp(x.sigmoid_(), min=1e-4, max=1 - 1e-4) return y # ctr branch x_ctr1 = self.conv1_ctr(x) x_ctr2 = self.conv2_ctr(x_ctr1) x_ctr3 = self.conv3_ctr(x_ctr2) x_ctr4 = self.conv4_ctr(x_ctr3) score_map_ctr = self.conv5_ctr(x_ctr4) # offset branch x_offset1 = self.conv1_offset(x) x_offset2 = self.conv2_offset(x_offset1) x_offset3 = self.conv3_offset(x_offset2) x_offset4 = self.conv4_offset(x_offset3) score_map_offset = self.conv5_offset(x_offset4) # size branch x_size1 = self.conv1_size(x) x_size2 = self.conv2_size(x_size1) x_size3 = self.conv3_size(x_size2) x_size4 = self.conv4_size(x_size3) score_map_size = self.conv5_size(x_size4) return _sigmoid(score_map_ctr), _sigmoid(score_map_size), score_map_offset class MLP(nn.Module): """ Very simple multi-layer perceptron (also called FFN)""" def __init__(self, input_dim, hidden_dim, output_dim, num_layers, BN=False): super().__init__() self.num_layers = num_layers h = [hidden_dim] * (num_layers - 1) if BN: self.layers = nn.ModuleList(nn.Sequential(nn.Linear(n, k), nn.BatchNorm1d(k)) for n, k in zip([input_dim] + h, h + [output_dim])) else: self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) def forward(self, x): for i, layer in enumerate(self.layers): x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) return x class SelfAttention(nn.Module): def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., attn_pos_encoding_only=False): super(SelfAttention, self).__init__() assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}." self.dim = dim self.num_heads = num_heads head_dim = dim // num_heads self.scale = qk_scale or head_dim ** -0.5 if attn_pos_encoding_only: self.qkv = nn.Linear(dim, 3 * dim, bias=qkv_bias) else: self.q = nn.Linear(dim, dim, bias=qkv_bias) self.k = nn.Linear(dim, dim, bias=qkv_bias) self.v = nn.Linear(dim, dim, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) self.attn_pos_encoding_only = attn_pos_encoding_only def forward(self, x, q_ape, k_ape, attn_pos): ''' Args: x (torch.Tensor): (B, L, C) q_ape (torch.Tensor | None): (1 or B, L, C), absolute positional encoding for q k_ape (torch.Tensor | None): (1 or B, L, C), absolute positional encoding for k attn_pos (torch.Tensor | None): (1 or B, num_heads, L, L), untied positional encoding Returns: torch.Tensor: (B, L, C) ''' B, N, C = x.shape if self.attn_pos_encoding_only: assert q_ape is None and k_ape is None qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] else: q = x + q_ape if q_ape is not None else x q = self.q(q).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) k = x + k_ape if k_ape is not None else x k = self.k(k).reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) v = self.v(x).reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) attn = q @ k.transpose(-2, -1) attn = attn * self.scale if attn_pos is not None: attn = attn + attn_pos attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = attn @ v x = x.transpose(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) return x class CrossAttention(nn.Module): def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., attn_pos_encoding_only=False): super(CrossAttention, self).__init__() assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}." self.dim = dim self.num_heads = num_heads head_dim = dim // num_heads self.scale = qk_scale or head_dim ** -0.5 if attn_pos_encoding_only: self.q = nn.Linear(dim, dim, bias=qkv_bias) self.kv = nn.Linear(dim, 2 * dim, bias=qkv_bias) else: self.q = nn.Linear(dim, dim, bias=qkv_bias) self.k = nn.Linear(dim, dim, bias=qkv_bias) self.v = nn.Linear(dim, dim, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) self.attn_pos_encoding_only = attn_pos_encoding_only def forward(self, q, kv, q_ape, k_ape, attn_pos): ''' Args: q (torch.Tensor): (B, L_q, C) kv (torch.Tensor): (B, L_kv, C) q_ape (torch.Tensor | None): (1 or B, L_q, C), absolute positional encoding for q k_ape (torch.Tensor | None): (1 or B, L_kv, C), absolute positional encoding for k attn_pos (torch.Tensor | None): (1 or B, num_heads, L_q, L_kv), untied positional encoding Returns: torch.Tensor: (B, L_q, C) ''' B, q_N, C = q.shape kv_N = kv.shape[1] if self.attn_pos_encoding_only: assert q_ape is None and k_ape is None q = self.q(q).reshape(B, q_N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) kv = self.kv(kv).reshape(B, kv_N, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) k, v = kv[0], kv[1] else: q = q + q_ape if q_ape is not None else q q = self.q(q).reshape(B, q_N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) k = kv + k_ape if k_ape is not None else kv k = self.k(k).reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) v = self.v(kv).reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) attn = q @ k.transpose(-2, -1) attn = attn * self.scale if attn_pos is not None: attn = attn + attn_pos attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = attn @ v x = x.transpose(1, 2).reshape(B, q_N, C) x = self.proj(x) x = self.proj_drop(x) return x class Mlp(nn.Module): """ Multilayer perceptron.""" def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def forward(self, x): ''' Args: x (torch.Tensor): (B, L, C), input tensor Returns: torch.Tensor: (B, L, C), output tensor ''' x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x class FeatureFusion(nn.Module): def __init__(self, dim, num_heads, mlp_ratio=2., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., drop_path=nn.Identity(), act_layer=nn.GELU, norm_layer=nn.LayerNorm, attn_pos_encoding_only=False): super(FeatureFusion, self).__init__() self.z_norm1 = norm_layer(dim) self.x_norm1 = norm_layer(dim) self.z_self_attn = SelfAttention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop, attn_pos_encoding_only) self.x_self_attn = SelfAttention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop, attn_pos_encoding_only) self.z_norm2_1 = norm_layer(dim) self.z_norm2_2 = norm_layer(dim) self.x_norm2_1 = norm_layer(dim) self.x_norm2_2 = norm_layer(dim) self.z_x_cross_attention = CrossAttention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop, attn_pos_encoding_only) self.x_z_cross_attention = CrossAttention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop, attn_pos_encoding_only) mlp_hidden_dim = int(dim * mlp_ratio) self.z_norm3 = norm_layer(dim) self.x_norm3 = norm_layer(dim) print(mlp_ratio) self.z_mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) self.x_mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) self.drop_path = drop_path def forward(self, z, x, z_self_attn_pos, x_self_attn_pos, z_x_cross_attn_pos, x_z_cross_attn_pos): z = z + self.drop_path(self.z_self_attn(self.z_norm1(z), None, None, z_self_attn_pos)) x = x + self.drop_path(self.x_self_attn(self.x_norm1(x), None, None, x_self_attn_pos)) z = z + self.drop_path( self.z_x_cross_attention(self.z_norm2_1(z), self.x_norm2_1(x), None, None, z_x_cross_attn_pos)) x = x + self.drop_path( self.x_z_cross_attention(self.x_norm2_2(x), self.z_norm2_2(z), None, None, x_z_cross_attn_pos)) z = z + self.drop_path(self.z_mlp(self.z_norm3(z))) x = x + self.drop_path(self.x_mlp(self.x_norm3(x))) return z, x class FeatureFusionEncoder(nn.Module): def __init__(self, feature_fusion_layers, z_pos_enc, x_pos_enc, z_rel_pos_index, x_rel_pos_index, z_x_rel_pos_index, x_z_rel_pos_index, z_rel_pos_bias_table, x_rel_pos_bias_table, z_x_rel_pos_bias_table, x_z_rel_pos_bias_table): super(FeatureFusionEncoder, self).__init__() self.layers = nn.ModuleList(feature_fusion_layers) self.z_pos_enc = z_pos_enc self.x_pos_enc = x_pos_enc self.register_buffer('z_rel_pos_index', z_rel_pos_index, False) self.register_buffer('x_rel_pos_index', x_rel_pos_index, False) self.register_buffer('z_x_rel_pos_index', z_x_rel_pos_index, False) self.register_buffer('x_z_rel_pos_index', x_z_rel_pos_index, False) self.z_rel_pos_bias_table = z_rel_pos_bias_table self.x_rel_pos_bias_table = x_rel_pos_bias_table self.z_x_rel_pos_bias_table = z_x_rel_pos_bias_table self.x_z_rel_pos_bias_table = x_z_rel_pos_bias_table def forward(self, z, x, z_pos, x_pos): ''' Args: z (torch.Tensor): (B, L_z, C), template image feature tokens x (torch.Tensor): (B, L_x, C), search image feature tokens z_pos (torch.Tensor | None): (1 or B, L_z, C), optional positional encoding for z x_pos (torch.Tensor | None): (1 or B, L_x, C), optional positional encoding for x Returns: Tuple[torch.Tensor, torch.Tensor]: (B, L_z, C): template image feature tokens (B, L_x, C): search image feature tokens ''' # Support untied positional encoding only for simplicity assert z_pos is None and x_pos is None # untied positional encoding z_q_pos, z_k_pos = self.z_pos_enc() x_q_pos, x_k_pos = self.x_pos_enc() z_self_attn_pos = (z_q_pos @ z_k_pos.transpose(-2, -1)).unsqueeze(0) x_self_attn_pos = (x_q_pos @ x_k_pos.transpose(-2, -1)).unsqueeze(0) z_x_cross_attn_pos = (z_q_pos @ x_k_pos.transpose(-2, -1)).unsqueeze(0) x_z_cross_attn_pos = (x_q_pos @ z_k_pos.transpose(-2, -1)).unsqueeze(0) # relative positional encoding z_self_attn_pos = z_self_attn_pos + self.z_rel_pos_bias_table(self.z_rel_pos_index) x_self_attn_pos = x_self_attn_pos + self.x_rel_pos_bias_table(self.x_rel_pos_index) z_x_cross_attn_pos = z_x_cross_attn_pos + self.z_x_rel_pos_bias_table(self.z_x_rel_pos_index) x_z_cross_attn_pos = x_z_cross_attn_pos + self.x_z_rel_pos_bias_table(self.x_z_rel_pos_index) for layer in self.layers: z, x = layer(z, x, z_self_attn_pos, x_self_attn_pos, z_x_cross_attn_pos, x_z_cross_attn_pos) return z, x class Learned2DPositionalEncoder(nn.Module): def __init__(self, dim, w, h): super(Learned2DPositionalEncoder, self).__init__() self.w_pos = nn.Parameter(torch.empty(w, dim)) self.h_pos = nn.Parameter(torch.empty(h, dim)) trunc_normal_(self.w_pos, std=0.02) trunc_normal_(self.h_pos, std=0.02) def forward(self): w = self.w_pos.shape[0] h = self.h_pos.shape[0] return (self.w_pos[None, :, :] + self.h_pos[:, None, :]).view(h * w, -1) class Untied2DPositionalEncoder(nn.Module): def __init__(self, dim, num_heads, w, h, scale=None, with_q=True, with_k=True): super(Untied2DPositionalEncoder, self).__init__() assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}." self.pos = Learned2DPositionalEncoder(dim, w, h) self.norm = nn.LayerNorm(dim) self.pos_q_linear = None self.pos_k_linear = None if with_q: self.pos_q_linear = nn.Linear(dim, dim) if with_k: self.pos_k_linear = nn.Linear(dim, dim) self.num_heads = num_heads head_dim = dim // num_heads self.scale = scale or head_dim ** -0.5 def forward(self): pos = self.norm(self.pos()) seq_len = pos.shape[0] if self.pos_q_linear is not None and self.pos_k_linear is not None: pos_q = self.pos_q_linear(pos).view(seq_len, self.num_heads, -1).transpose(0, 1) * self.scale pos_k = self.pos_k_linear(pos).view(seq_len, self.num_heads, -1).transpose(0, 1) return pos_q, pos_k elif self.pos_q_linear is not None: pos_q = self.pos_q_linear(pos).view(seq_len, self.num_heads, -1).transpose(0, 1) * self.scale return pos_q elif self.pos_k_linear is not None: pos_k = self.pos_k_linear(pos).view(seq_len, self.num_heads, -1).transpose(0, 1) return pos_k else: raise RuntimeError def generate_2d_relative_positional_encoding_index(z_shape, x_shape): ''' z_shape: (z_h, z_w) x_shape: (x_h, x_w) ''' z_2d_index_h, z_2d_index_w = torch.meshgrid(torch.arange(z_shape[0]), torch.arange(z_shape[1])) x_2d_index_h, x_2d_index_w = torch.meshgrid(torch.arange(x_shape[0]), torch.arange(x_shape[1])) z_2d_index_h = z_2d_index_h.flatten(0) z_2d_index_w = z_2d_index_w.flatten(0) x_2d_index_h = x_2d_index_h.flatten(0) x_2d_index_w = x_2d_index_w.flatten(0) diff_h = z_2d_index_h[:, None] - x_2d_index_h[None, :] diff_w = z_2d_index_w[:, None] - x_2d_index_w[None, :] diff = torch.stack((diff_h, diff_w), dim=-1) _, indices = torch.unique(diff.view(-1, 2), return_inverse=True, dim=0) return indices.view(z_shape[0] * z_shape[1], x_shape[0] * x_shape[1]) class RelativePosition2DEncoder(nn.Module): def __init__(self, num_heads, embed_size): super(RelativePosition2DEncoder, self).__init__() self.relative_position_bias_table = nn.Parameter(torch.empty((num_heads, embed_size))) trunc_normal_(self.relative_position_bias_table, std=0.02) def forward(self, attn_rpe_index): ''' Args: attn_rpe_index (torch.Tensor): (*), any shape containing indices, max(attn_rpe_index) < embed_size Returns: torch.Tensor: (1, num_heads, *) ''' return self.relative_position_bias_table[:, attn_rpe_index].unsqueeze(0) class DropPathAllocator: def __init__(self, max_drop_path_rate, stochastic_depth_decay=True): self.max_drop_path_rate = max_drop_path_rate self.stochastic_depth_decay = stochastic_depth_decay self.allocated = [] self.allocating = [] def __enter__(self): self.allocating = [] def __exit__(self, exc_type, exc_val, exc_tb): if len(self.allocating) != 0: self.allocated.append(self.allocating) self.allocating = None if not self.stochastic_depth_decay: for depth_module in self.allocated: for module in depth_module: if isinstance(module, DropPath): module.drop_prob = self.max_drop_path_rate else: depth = self.get_depth() dpr = [x.item() for x in torch.linspace(0, self.max_drop_path_rate, depth)] assert len(dpr) == len(self.allocated) for drop_path_rate, depth_modules in zip(dpr, self.allocated): for module in depth_modules: if isinstance(module, DropPath): module.drop_prob = drop_path_rate def __len__(self): length = 0 for depth_modules in self.allocated: length += len(depth_modules) return length def increase_depth(self): self.allocated.append(self.allocating) self.allocating = [] def get_depth(self): return len(self.allocated) def allocate(self): if self.max_drop_path_rate == 0 or (self.stochastic_depth_decay and self.get_depth() == 0): drop_path_module = Identity() else: drop_path_module = DropPath() self.allocating.append(drop_path_module) return drop_path_module def get_all_allocated(self): allocated = [] for depth_module in self.allocated: for module in depth_module: allocated.append(module) return allocated def build_encoder(encoder_layer, num_heads, mlp_ratio, qkv_bias, drop_rate, attn_drop, dim, z_size, x_size, drop_path): z_shape = [z_size, z_size] x_shape = [x_size, x_size] encoder_layers = [] for i in range(encoder_layer): encoder_layers.append( FeatureFusion(dim, num_heads, mlp_ratio, qkv_bias, drop=drop_rate, attn_drop=attn_drop, drop_path=drop_path.allocate(), attn_pos_encoding_only=True) ) z_abs_encoder = Untied2DPositionalEncoder(dim, num_heads, z_shape[0], z_shape[1]) x_abs_encoder = Untied2DPositionalEncoder(dim, num_heads, x_shape[0], x_shape[1]) z_self_attn_rel_pos_index = generate_2d_relative_positional_encoding_index(z_shape, z_shape) x_self_attn_rel_pos_index = generate_2d_relative_positional_encoding_index(x_shape, x_shape) z_x_cross_attn_rel_pos_index = generate_2d_relative_positional_encoding_index(z_shape, x_shape) x_z_cross_attn_rel_pos_index = generate_2d_relative_positional_encoding_index(x_shape, z_shape) z_self_attn_rel_pos_bias_table = RelativePosition2DEncoder(num_heads, z_self_attn_rel_pos_index.max() + 1) x_self_attn_rel_pos_bias_table = RelativePosition2DEncoder(num_heads, x_self_attn_rel_pos_index.max() + 1) z_x_cross_attn_rel_pos_bias_table = RelativePosition2DEncoder(num_heads, z_x_cross_attn_rel_pos_index.max() + 1) x_z_cross_attn_rel_pos_bias_table = RelativePosition2DEncoder(num_heads, x_z_cross_attn_rel_pos_index.max() + 1) return FeatureFusionEncoder(encoder_layers, z_abs_encoder, x_abs_encoder, z_self_attn_rel_pos_index, x_self_attn_rel_pos_index, z_x_cross_attn_rel_pos_index, x_z_cross_attn_rel_pos_index, z_self_attn_rel_pos_bias_table, x_self_attn_rel_pos_bias_table, z_x_cross_attn_rel_pos_bias_table, x_z_cross_attn_rel_pos_bias_table) class TargetQueryDecoderLayer(nn.Module): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., drop_path=nn.Identity(), act_layer=nn.GELU, norm_layer=nn.LayerNorm): super(TargetQueryDecoderLayer, self).__init__() self.norm_1 = norm_layer(dim) self.self_attn1 = nn.MultiheadAttention(dim, num_heads, dropout=drop) self.norm_2_query = norm_layer(dim) self.norm_2_memory = norm_layer(dim) self.multihead_attn = nn.MultiheadAttention(dim, num_heads, dropout=drop) self.norm_3 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlpz = Mlp(dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) self.drop_path = drop_path def forward(self, query, memoryz, memoryx, query_pos, pos_z, pos_x, identity, identity_search, tgt_mask: Optional[Tensor] = None, memory_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] = None, memory_key_padding_mask: Optional[Tensor] = None, ): ''' Args: query (torch.Tensor): (B, num_queries, C) memory (torch.Tensor): (B, L, C) query_pos (torch.Tensor): (1 or B, num_queries, C) memory_pos (torch.Tensor): (1 or B, L, C) Returns: torch.Tensor: (B, num_queries, C) ''' tgt = query q = k = self.norm_1(query) + query_pos query = query + self.drop_path(self.self_attn1(q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask)[0]) q2 = self.norm_2_query(query) + query_pos memory = torch.cat((memoryz, memoryx), dim=1) pos = torch.cat((pos_z, pos_x), dim=1) ide = torch.cat( (identity[:, 0, :].repeat(1, pos_z.shape[1], 1), identity[:, 1, :].repeat(1, pos_x.shape[1], 1)), dim=1) k2 = (self.norm_2_memory(memory) + pos + ide).permute(1, 0, 2) memory_in = memory.permute(1, 0, 2) query = query + self.drop_path( self.multihead_attn(query=q2, key=k2, value=memory_in, attn_mask=memory_mask, key_padding_mask=memory_key_padding_mask)[0]) query = query + self.drop_path(self.mlpz(self.norm_3(query))) return query def _get_clones(module, N): return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) class TargetQueryDecoderBlock(nn.Module): def __init__(self, dim, decoder_layers, num_layer): super(TargetQueryDecoderBlock, self).__init__() self.layers = nn.ModuleList(decoder_layers) self.num_layers = num_layer self.norm = nn.LayerNorm(dim) def forward(self, tgt, z, x, pos_z, pos_x, identity, identity_search, query_pos: Optional[Tensor] = None, tgt_mask: Optional[Tensor] = None, memory_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] = None, memory_key_padding_mask: Optional[Tensor] = None): ''' Args: z (torch.Tensor): (B, L_z, C) x (torch.Tensor): (B, L_x, C) Returns: torch.Tensor: (B, num_queries, C) ''' output = tgt for layer in self.layers: output = layer(output, z, x, query_pos, pos_z, pos_x, identity, identity_search, tgt_mask=tgt_mask, memory_mask=memory_mask, tgt_key_padding_mask=tgt_key_padding_mask, memory_key_padding_mask=memory_key_padding_mask) output = self.norm(output) return output def build_decoder(decoder_layer, drop_path, dim, num_heads, mlp_ratio, qkv_bias, drop_rate, attn_drop_rate, z_size, x_size): z_shape = [z_size, z_size] x_shape = [x_size, x_size] num_layers = decoder_layer decoder_layers = [] for _ in range(num_layers): decoder_layers.append( TargetQueryDecoderLayer(dim, num_heads, mlp_ratio, qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=drop_path.allocate())) drop_path.increase_depth() decoder = TargetQueryDecoderBlock(dim, decoder_layers, num_layers) return decoder def generate_square_subsequent_mask(sz): r"""Generate a square mask for the sequence. The masked positions are filled with float('-inf'). Unmasked positions are filled with float(0.0). """ mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1) mask = mask.float().masked_fill(mask == 0, float( '-inf')).masked_fill(mask == 1, float(0.0)) return mask class Pix2Track(nn.Module): def __init__(self, in_channel=64, feat_sz=20, feat_tz=10, range=2, pre_num=7, stride=16, encoder_layer=3, decoder_layer=3, bins=400, num_heads=12, mlp_ratio=2, qkv_bias=True, drop_rate=0.0, attn_drop=0.0, drop_path=nn.Identity): super(Pix2Track, self).__init__() self.bins = bins self.range = range self.pre_num = pre_num self.word_embeddings = nn.Embedding(self.bins * self.range + 2, in_channel, padding_idx=self.bins * self.range, max_norm=1, norm_type=2.0) self.position_embeddings = nn.Embedding( 5, in_channel) self.prev_position_embeddings = nn.Embedding(self.pre_num * 4, in_channel) self.output_bias = torch.nn.Parameter(torch.zeros(self.bins * self.range + 2)) self.momentum_param = 0.25 self.identity_search = torch.nn.Parameter(torch.zeros(1, 1, 768)) self.identity_search = trunc_normal_(self.identity_search, std=.02) self.encoder_layer = encoder_layer self.drop_path = drop_path self.tz = feat_tz * feat_tz self.sz = feat_sz * feat_sz trunc_normal_(self.word_embeddings.weight, std=.02) if self.encoder_layer > 0: self.encoder = build_encoder(encoder_layer, num_heads, mlp_ratio, qkv_bias, drop_rate, attn_drop, in_channel, feat_tz, feat_sz, self.drop_path) else: self.encoder = None self.decoder = build_decoder(decoder_layer, self.drop_path, in_channel, num_heads, mlp_ratio, qkv_bias, drop_rate, attn_drop, feat_tz, feat_sz) self.magic_num = (self.range-1) * 0.5 def forward(self, zx_feat, pos_z, pos_x, identity, seqs_input=None, stage=None): emb_weight = self.word_embeddings.weight.clone() share_weight = emb_weight.T z_feat = zx_feat[:, :self.tz] x_feat = zx_feat[:, self.tz:] out_list = [] bs = zx_feat.shape[0] if self.encoder != None: z_feat, x_feat = self.encoder(z_feat, x_feat, None, None) output_x_feat = x_feat.clone() if stage == None: seqs_input = seqs_input.to(torch.int64).to(zx_feat.device) tgt = self.word_embeddings(seqs_input).permute(1, 0, 2) query_embed_ = self.position_embeddings.weight.unsqueeze(1) prev_embed = self.prev_position_embeddings.weight.unsqueeze(1) query_embed = torch.cat([prev_embed, query_embed_], dim=0) query_embed = query_embed.repeat(1, bs, 1) decoder_feat_cls = self.decoder(tgt, z_feat, x_feat, pos_z, pos_x, identity, self.identity_search, query_embed[:len(tgt)], tgt_mask=generate_square_subsequent_mask(len(tgt)).to(tgt.device)) at = torch.matmul(decoder_feat_cls, share_weight) at = at + self.output_bias output = {'feat': at, "state": "train"} else: seqs_origin = seqs_input start_token = torch.ones(bs, 1) * self.bins * self.range start_token = start_token.to(seqs_origin) real_seq = torch.cat([seqs_origin, start_token], dim=1) seqs_input = real_seq.to(zx_feat.device).to(torch.int32) for i in range(4): tgt = self.word_embeddings(seqs_input).permute(1, 0, 2) query_embed_ = self.position_embeddings.weight.unsqueeze(1) prev_embed = self.prev_position_embeddings.weight.unsqueeze(1) query_embed = torch.cat([prev_embed, query_embed_], dim=0) query_embed = query_embed.repeat(1, bs, 1) decoder_feat_cls = self.decoder(tgt, z_feat, x_feat, pos_z, pos_x, identity, self.identity_search, query_embed[:len(tgt)], tgt_mask=generate_square_subsequent_mask(len(seqs_input[0])).to( tgt.device)) out = torch.matmul(decoder_feat_cls.transpose(0, 1)[:, -1, :], share_weight) + self.output_bias out_list.append(out.unsqueeze(0)) out = out.softmax(-1) value, extra_seq = out.topk(dim=-1, k=1)[0], out.topk(dim=-1, k=1)[1] seqs_input = torch.cat([seqs_input, extra_seq], dim=-1) if i == 0: seqs_output = extra_seq values = value else: seqs_output = torch.cat([seqs_output, extra_seq], dim=-1) values = torch.cat([values, value], dim=-1) if not (not out_list): feat = torch.cat(out_list) output = {'seqs': seqs_output, 'class': values, 'feat': feat, "state": "val/test", "x_feat": output_x_feat.detach()} return output def build_pix_head(cfg, hidden_dim): stride = cfg.MODEL.BACKBONE.STRIDE in_channel = hidden_dim feat_sz = int(cfg.DATA.SEARCH.SIZE / stride) feat_tz = int(cfg.DATA.TEMPLATE.SIZE / stride) decoder_layer = cfg.MODEL.DECODER_LAYER encoder_layer = cfg.MODEL.ENCODER_LAYER pre_num = cfg.MODEL.PRENUM bins = cfg.MODEL.BINS range = cfg.MODEL.RANGE num_heads = cfg.MODEL.NUM_HEADS mlp_ratio = cfg.MODEL.MLP_RATIO qkv_bias = cfg.MODEL.QKV_BIAS drop_rate = cfg.MODEL.DROP_RATE attn_drop = cfg.MODEL.ATTN_DROP drop_path = cfg.MODEL.DROP_PATH drop_path_allocator = DropPathAllocator(drop_path) pix_head = Pix2Track(in_channel=in_channel, feat_sz=feat_sz, feat_tz=feat_tz, range=range, pre_num=pre_num, stride=stride, encoder_layer=encoder_layer, decoder_layer=decoder_layer, bins=bins, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop_rate=drop_rate, attn_drop=attn_drop, drop_path=drop_path_allocator) return pix_head ================================================ FILE: lib/models/layers/mask_decoder.py ================================================ # -*- coding:utf-8 -*- # author : Skye Song # file : vit_decoder.py # Copyright (c) Skye-Song. All Rights Reserved import torch import torch.nn as nn from einops import rearrange from lib.utils.box_ops import box_xywh_to_cxywh, box_cxcywh_to_xyxy from ..mask_decoder.block import Block from ..mask_decoder.pos_embed import get_2d_sincos_pos_embed from external.PreciseRoIPooling.pytorch.prroi_pool import PrRoIPool2D from lib.utils.image import * class MaskDecoder(nn.Module): def __init__(self, mask_ratio=0.75, patch_size=16, num_patches=8 ** 2, embed_dim=1024, decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16, pool_size=8, mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False): super().__init__() self.mask_ratio = mask_ratio print(self.mask_ratio) #self.mask_ratio = 0.75 self.num_patches = num_patches self.patch_size = patch_size self.search_prroipool = PrRoIPool2D(pool_size, pool_size, spatial_scale=1.0) self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True) self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim)) self.decoder_pos_embed = nn.Parameter(torch.zeros(1, num_patches, decoder_embed_dim), requires_grad=False) # fixed sin-cos embedding self.decoder_blocks = nn.ModuleList([ Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer) for i in range(decoder_depth)]) self.decoder_norm = norm_layer(decoder_embed_dim) self.decoder_pred = nn.Linear(decoder_embed_dim, patch_size ** 2 * 3, bias=True) # decoder to patch self.norm_pix_loss = norm_pix_loss self.initialize_weights() def initialize_weights(self): # initialize (and freeze) pos_embed by sin-cos embedding decoder_pos_embed = get_2d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], int(self.num_patches ** .5), cls_token=False) self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0)) # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.) torch.nn.init.normal_(self.mask_token, std=.02) # initialize nn.Linear and nn.LayerNorm self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): # we use xavier_uniform following official JAX ViT: torch.nn.init.xavier_uniform_(m.weight) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) def random_masking(self, x): """ Perform per-sample random masking by per-sample shuffling. Per-sample shuffling is done by argsort random noise. x: [N, L, D], sequence """ N, L, D = x.shape # batch, length, dim len_keep = int(L * (1 - self.mask_ratio)) noise = torch.rand(N, L, device=x.device) # noise in [0, 1] # sort noise for each sample ids_shuffle = torch.argsort(noise, dim=1) # ascend: small is keep, large is remove ids_restore = torch.argsort(ids_shuffle, dim=1) # keep the first subset ids_keep = ids_shuffle[:, :len_keep] x_keep = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D)) # generate the binary mask: 0 is keep, 1 is remove mask = torch.ones([N, L], device=x.device) mask[:, :len_keep] = 0 # unshuffle to get the binary mask mask = torch.gather(mask, dim=1, index=ids_restore) # get the masked x mask_tokens = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] - x_keep.shape[1], 1) x_ = torch.cat([x_keep, mask_tokens], dim=1) # no cls token x_masked = torch.gather(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2])) # unshuffle return x_masked, mask def forward_decoder(self, x, eval=False): # embed tokens x = self.decoder_embed(x) mask = None # append mask tokens to sequence if not eval: x, mask = self.random_masking(x) # add pos embed x = x + self.decoder_pos_embed # apply Transformer blocks for blk in self.decoder_blocks: x = blk(x) x = self.decoder_norm(x) # predictor projection x = self.decoder_pred(x) return x, mask def unpatchify(self, x): """ x: (N, L, patch_size**2 *3) imgs: (N, 3, H, W) """ p = self.patch_size h = w = int(x.shape[1] ** .5) assert h * w == x.shape[1] x = x.reshape(shape=(x.shape[0], h, w, p, p, 3)) x = torch.einsum('nhwpqc->nchpwq', x) imgs = x.reshape(shape=(x.shape[0], 3, h * p, h * p)) return imgs def patchify(self, imgs): """ imgs: (N, 3, H, W) x: (N, L, patch_size**2 *3) """ p = self.patch_size assert imgs.shape[2] == imgs.shape[3] and imgs.shape[2] % p == 0 h = w = imgs.shape[2] // p x = imgs.reshape(shape=(imgs.shape[0], 3, h, p, w, p)) x = torch.einsum('nchpwq->nhwpqc', x) x = x.reshape(shape=(imgs.shape[0], h * w, p ** 2 * 3)) return x def forward_loss(self, imgs, pred, mask=None): """ imgs: [N, 3, H, W] pred: [N, L, p*p*3] mask: [N, L], 0 is keep, 1 is remove, """ target = self.patchify(imgs) if self.norm_pix_loss: mean = target.mean(dim=-1, keepdim=True) var = target.var(dim=-1, keepdim=True) target = (target - mean) / (var + 1.e-6) ** .5 loss = (pred - target) ** 2 loss = loss.mean(dim=-1) # [N, L], mean loss per patc if mask == None: loss = loss.sum() / pred.shape[1] / pred.shape[0] # mean loss on removed patches else: loss = loss.sum() / pred.shape[1] / pred.shape[0] return loss def crop_search_feat(self, search_feat, gt_bboxes): crop_bboxes = box_xywh_to_cxywh(gt_bboxes) crop_sz = torch.sqrt(gt_bboxes[:, 2] * gt_bboxes[:, 3]) * 2.0 crop_sz = torch.clamp(crop_sz, min=0., max=1.) crop_bboxes[:, 2] = crop_bboxes[:, 3] = crop_sz crop_bboxes = crop_bboxes * search_feat.shape[-1] crop_bboxes = box_cxcywh_to_xyxy(crop_bboxes.clone().view(-1, 4)) batch_size = crop_bboxes.shape[0] batch_index = torch.arange(batch_size, dtype=torch.float32).view(-1, 1).to(crop_bboxes.device) target_roi = torch.cat((batch_index, crop_bboxes), dim=1) search_box_feat = self.search_prroipool(search_feat, target_roi) return search_box_feat # gt_bboxes = gt_bboxes * search_feat.shape[-1] # gt_bboxes = box_xywh_to_xyxy(gt_bboxes.clone().view(-1, 4)) # batch_size = gt_bboxes.shape[0] # batch_index = torch.arange(batch_size, dtype=torch.float32).view(-1, 1).to(gt_bboxes.device) # # target_roi = torch.cat((batch_index, gt_bboxes), dim=1) # search_box_feat = self.search_prroipool(search_feat, target_roi) # return search_box_feat def forward(self, x, images=None, gt_bboxes=None, eval=False,): # input x = [B,C,H,W] # input images = [b,3 h,w] if gt_bboxes is not None: x = self.crop_search_feat(x, gt_bboxes) x = rearrange(x, 'b c h w -> b (h w) c').contiguous() pred, mask = self.forward_decoder(x, eval) # [N, L, p*p*3] if eval: return self.unpatchify(pred) if mask != None: loss = self.forward_loss(imgs=images, pred=pred, mask=mask) else: loss = self.forward_loss(imgs=images, pred=pred) pred = self.unpatchify(pred) return pred, loss def mask_decoder(): model = MaskDecoder( mask_ratio=0.75, patch_size=16, num_patches=8 ** 2, embed_dim=1024, decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16, mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False) return model def build_maskdecoder(cfg, hidden_dim): pool_size = int(cfg.DATA.TEMPLATE.SIZE / cfg.MODEL.BACKBONE.PATCHSIZE) num_patches = (cfg.DATA.TEMPLATE.SIZE // cfg.MODEL.BACKBONE.PATCHSIZE) ** 2 model = MaskDecoder( mask_ratio=cfg.MODEL.DECODER.MASK_RATIO, patch_size=cfg.MODEL.BACKBONE.PATCHSIZE, num_patches=num_patches, embed_dim=hidden_dim, decoder_embed_dim=cfg.MODEL.DECODER.EMBEDDIM, decoder_depth=cfg.MODEL.DECODER.DEPTH, decoder_num_heads=cfg.MODEL.DECODER.NUMHEADS, pool_size=pool_size, mlp_ratio=cfg.MODEL.DECODER.MLPRATIO, norm_layer=nn.LayerNorm, norm_pix_loss=False) return model ================================================ FILE: lib/models/layers/patch_embed.py ================================================ import torch.nn as nn from timm.models.layers import to_2tuple class PatchEmbed(nn.Module): """ 2D Image to Patch Embedding """ def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True): super().__init__() img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) self.img_size = img_size self.patch_size = patch_size self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) self.num_patches = self.grid_size[0] * self.grid_size[1] self.flatten = flatten self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() def forward(self, x): # allow different input size # B, C, H, W = x.shape # _assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).") # _assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).") x = self.proj(x) if self.flatten: x = x.flatten(2).transpose(1, 2) # BCHW -> BNC x = self.norm(x) return x ================================================ FILE: lib/models/layers/rpe.py ================================================ import torch import torch.nn as nn from timm.models.layers import trunc_normal_ def generate_2d_relative_positional_encoding_index(z_shape, x_shape): ''' z_shape: (z_h, z_w) x_shape: (x_h, x_w) ''' z_2d_index_h, z_2d_index_w = torch.meshgrid(torch.arange(z_shape[0]), torch.arange(z_shape[1])) x_2d_index_h, x_2d_index_w = torch.meshgrid(torch.arange(x_shape[0]), torch.arange(x_shape[1])) z_2d_index_h = z_2d_index_h.flatten(0) z_2d_index_w = z_2d_index_w.flatten(0) x_2d_index_h = x_2d_index_h.flatten(0) x_2d_index_w = x_2d_index_w.flatten(0) diff_h = z_2d_index_h[:, None] - x_2d_index_h[None, :] diff_w = z_2d_index_w[:, None] - x_2d_index_w[None, :] diff = torch.stack((diff_h, diff_w), dim=-1) _, indices = torch.unique(diff.view(-1, 2), return_inverse=True, dim=0) return indices.view(z_shape[0] * z_shape[1], x_shape[0] * x_shape[1]) def generate_2d_concatenated_self_attention_relative_positional_encoding_index(z_shape, x_shape): ''' z_shape: (z_h, z_w) x_shape: (x_h, x_w) ''' z_2d_index_h, z_2d_index_w = torch.meshgrid(torch.arange(z_shape[0]), torch.arange(z_shape[1])) x_2d_index_h, x_2d_index_w = torch.meshgrid(torch.arange(x_shape[0]), torch.arange(x_shape[1])) z_2d_index_h = z_2d_index_h.flatten(0) z_2d_index_w = z_2d_index_w.flatten(0) x_2d_index_h = x_2d_index_h.flatten(0) x_2d_index_w = x_2d_index_w.flatten(0) concatenated_2d_index_h = torch.cat((z_2d_index_h, x_2d_index_h)) concatenated_2d_index_w = torch.cat((z_2d_index_w, x_2d_index_w)) diff_h = concatenated_2d_index_h[:, None] - concatenated_2d_index_h[None, :] diff_w = concatenated_2d_index_w[:, None] - concatenated_2d_index_w[None, :] z_len = z_shape[0] * z_shape[1] x_len = x_shape[0] * x_shape[1] a = torch.empty((z_len + x_len), dtype=torch.int64) a[:z_len] = 0 a[z_len:] = 1 b=a[:, None].repeat(1, z_len + x_len) c=a[None, :].repeat(z_len + x_len, 1) diff = torch.stack((diff_h, diff_w, b, c), dim=-1) _, indices = torch.unique(diff.view((z_len + x_len) * (z_len + x_len), 4), return_inverse=True, dim=0) return indices.view((z_len + x_len), (z_len + x_len)) def generate_2d_concatenated_cross_attention_relative_positional_encoding_index(z_shape, x_shape): ''' z_shape: (z_h, z_w) x_shape: (x_h, x_w) ''' z_2d_index_h, z_2d_index_w = torch.meshgrid(torch.arange(z_shape[0]), torch.arange(z_shape[1])) x_2d_index_h, x_2d_index_w = torch.meshgrid(torch.arange(x_shape[0]), torch.arange(x_shape[1])) z_2d_index_h = z_2d_index_h.flatten(0) z_2d_index_w = z_2d_index_w.flatten(0) x_2d_index_h = x_2d_index_h.flatten(0) x_2d_index_w = x_2d_index_w.flatten(0) concatenated_2d_index_h = torch.cat((z_2d_index_h, x_2d_index_h)) concatenated_2d_index_w = torch.cat((z_2d_index_w, x_2d_index_w)) diff_h = x_2d_index_h[:, None] - concatenated_2d_index_h[None, :] diff_w = x_2d_index_w[:, None] - concatenated_2d_index_w[None, :] z_len = z_shape[0] * z_shape[1] x_len = x_shape[0] * x_shape[1] a = torch.empty(z_len + x_len, dtype=torch.int64) a[: z_len] = 0 a[z_len:] = 1 c = a[None, :].repeat(x_len, 1) diff = torch.stack((diff_h, diff_w, c), dim=-1) _, indices = torch.unique(diff.view(x_len * (z_len + x_len), 3), return_inverse=True, dim=0) return indices.view(x_len, (z_len + x_len)) class RelativePosition2DEncoder(nn.Module): def __init__(self, num_heads, embed_size): super(RelativePosition2DEncoder, self).__init__() self.relative_position_bias_table = nn.Parameter(torch.empty((num_heads, embed_size))) trunc_normal_(self.relative_position_bias_table, std=0.02) def forward(self, attn_rpe_index): ''' Args: attn_rpe_index (torch.Tensor): (*), any shape containing indices, max(attn_rpe_index) < embed_size Returns: torch.Tensor: (1, num_heads, *) ''' return self.relative_position_bias_table[:, attn_rpe_index].unsqueeze(0) ================================================ FILE: lib/models/mask_decoder/__init__.py ================================================ # -*- coding:utf-8 -*- # author : Skye Song # file : __init__.py.py # Copyright (c) Skye-Song. All Rights Reserved ================================================ FILE: lib/models/mask_decoder/attention.py ================================================ # -*- coding:utf-8 -*- # author : Skye Song # file : attention.py # Copyright (c) Skye-Song. All Rights Reserved import torch import torch.nn as nn from einops import rearrange from lib.utils.image import * class Attention(nn.Module): def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim ** -0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x, padding_mask=None, **kwargs): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # (B, head, N, C//head) attn = (q @ k.transpose(-2, -1)) * self.scale # (B, head, N, N) if padding_mask is not None: assert padding_mask.size()[0] == B assert padding_mask.size()[1] == N attn = attn.masked_fill(padding_mask.unsqueeze(1).unsqueeze(2), float("-inf")) attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) return x class ClsMixAttention(nn.Module): def __init__(self, dim, num_heads, qkv_bias=False, attn_drop=0., proj_drop=0., ): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim ** -0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x, t_h, t_w, s_h, s_w, online_size=1, padding_mask=None): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # (B, head, N, C) q_cls, q_t, q_s = torch.split(q, [1, t_h * t_w * (1 + online_size), s_h * s_w], dim=2) k_cls, k_t, k_s = torch.split(k, [1, t_h * t_w * (1 + online_size), s_h * s_w], dim=2) v_cls, v_t, v_s = torch.split(v, [1, t_h * t_w * (1 + online_size), s_h * s_w], dim=2) # cls token attention attn = (q_cls @ k.transpose(-2, -1)) * self.scale # (B, head, N_q, N) if padding_mask is not None: assert padding_mask.size()[0] == B assert padding_mask.size()[1] == N attn = attn.masked_fill(padding_mask.unsqueeze(1).unsqueeze(2), float("-inf")) attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x_cls = rearrange(attn @ v, 'b h t d -> b t (h d)') # template attention attn = (q_t @ k_t.transpose(-2, -1)) * self.scale # (B, head, N_q, N) if padding_mask is not None: assert padding_mask.size()[0] == B assert padding_mask.size()[1] == N attn = attn.masked_fill(padding_mask.unsqueeze(1).unsqueeze(2), float("-inf")) attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x_t = rearrange(attn @ v_t, 'b h t d -> b t (h d)') # search region attention attn = (q_s @ k.transpose(-2, -1)) * self.scale # (B, head, N_s, N) if padding_mask is not None: assert padding_mask.size()[0] == B assert padding_mask.size()[1] == N attn = attn.masked_fill(padding_mask.unsqueeze(1).unsqueeze(2), float("-inf")) attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x_s = rearrange(attn @ v, 'b h t d -> b t (h d)') x = torch.cat([x_cls, x_t, x_s], dim=1) x = self.proj(x) x = self.proj_drop(x) return x class MixAttention(nn.Module): def __init__(self, dim, num_heads, qkv_bias=False, attn_drop=0., proj_drop=0., ): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim ** -0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x, t_h, t_w, s_h, s_w, padding_mask=None): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # (B, head, N, C) q_t, q_s = torch.split(q, [t_h * t_w * 2, s_h * s_w], dim=2) k_t, k_s = torch.split(k, [t_h * t_w * 2, s_h * s_w], dim=2) v_t, v_s = torch.split(v, [t_h * t_w * 2, s_h * s_w], dim=2) # template attention attn = (q_t @ k_t.transpose(-2, -1)) * self.scale # (B, head, N_q, N) if padding_mask is not None: assert padding_mask.size()[0] == B assert padding_mask.size()[1] == N attn = attn.masked_fill(padding_mask.unsqueeze(1).unsqueeze(2), float("-inf")) attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x_t = rearrange(attn @ v_t, 'b h t d -> b t (h d)') # search region attention attn = (q_s @ k.transpose(-2, -1)) * self.scale # (B, head, N_s, N) if padding_mask is not None: assert padding_mask.size()[0] == B assert padding_mask.size()[1] == N attn = attn.masked_fill(padding_mask.unsqueeze(1).unsqueeze(2), float("-inf")) attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x_s = rearrange(attn @ v, 'b h t d -> b t (h d)') x = torch.cat([x_t, x_s], dim=1) x = self.proj(x) x = self.proj_drop(x) return x class NottAttention(nn.Module): def __init__(self, dim, num_heads, qkv_bias=False, attn_drop=0., proj_drop=0., ): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim ** -0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x, t_h, t_w, s_h, s_w, padding_mask=None): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # (B, head, N, C) q_t, q_s = torch.split(q, [t_h * t_w * 2, s_h * s_w], dim=2) k_t, k_s = torch.split(k, [t_h * t_w * 2, s_h * s_w], dim=2) v_t, v_s = torch.split(v, [t_h * t_w * 2, s_h * s_w], dim=2) # template attention attn = (q_t @ k_s.transpose(-2, -1)) * self.scale # (B, head, N_q, N) if padding_mask is not None: assert padding_mask.size()[0] == B assert padding_mask.size()[1] == N attn = attn.masked_fill(padding_mask.unsqueeze(1).unsqueeze(2), float("-inf")) attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x_t = rearrange(attn @ v_s, 'b h t d -> b t (h d)') # search region attention attn = (q_s @ k.transpose(-2, -1)) * self.scale # (B, head, N_s, N) if padding_mask is not None: assert padding_mask.size()[0] == B assert padding_mask.size()[1] == N attn = attn.masked_fill(padding_mask.unsqueeze(1).unsqueeze(2), float("-inf")) attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x_s = rearrange(attn @ v, 'b h t d -> b t (h d)') x = torch.cat([x_t, x_s], dim=1) x = self.proj(x) x = self.proj_drop(x) return x class NossAttention(nn.Module): def __init__(self, dim, num_heads, qkv_bias=False, attn_drop=0., proj_drop=0., ): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim ** -0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x, t_h, t_w, s_h, s_w, padding_mask=None): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # (B, head, N, C) q_t, q_s = torch.split(q, [t_h * t_w * 2, s_h * s_w], dim=2) k_t, k_s = torch.split(k, [t_h * t_w * 2, s_h * s_w], dim=2) v_t, v_s = torch.split(v, [t_h * t_w * 2, s_h * s_w], dim=2) # template attention attn = (q_t @ k.transpose(-2, -1)) * self.scale # (B, head, N_q, N) if padding_mask is not None: assert padding_mask.size()[0] == B assert padding_mask.size()[1] == N attn = attn.masked_fill(padding_mask.unsqueeze(1).unsqueeze(2), float("-inf")) attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x_t = rearrange(attn @ v, 'b h t d -> b t (h d)') # search region attention attn = (q_s @ k_t.transpose(-2, -1)) * self.scale # (B, head, N_s, N) if padding_mask is not None: assert padding_mask.size()[0] == B assert padding_mask.size()[1] == N attn = attn.masked_fill(padding_mask.unsqueeze(1).unsqueeze(2), float("-inf")) attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x_s = rearrange(attn @ v_t, 'b h t d -> b t (h d)') x = torch.cat([x_t, x_s], dim=1) x = self.proj(x) x = self.proj_drop(x) return x class CrossAttention(nn.Module): def __init__(self, dim, num_heads, qkv_bias=False, attn_drop=0., proj_drop=0., ): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim ** -0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x, t_h, t_w, s_h, s_w): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # (B, head, N, C) q_t, q_s = torch.split(q, [t_h * t_w * 2, s_h * s_w], dim=2) k_t, k_s = torch.split(k, [((t_h + 1) // 2) ** 2 * 2, s_h * s_w // 4], dim=4) v_t, v_s = torch.split(v, [((t_h + 1) // 2) ** 2 * 2, s_h * s_w // 4], dim=4) # template attention attn = (q_t @ k_s.transpose(-2, -1)) * self.scale attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x_t = rearrange(attn @ v_s, 'b h t d -> b t (h d)') # search region attention attn = (q_s @ k_t.transpose(-2, -1)) * self.scale attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x_s = rearrange(attn @ v_t, 'b h t d -> b t (h d)') x = torch.cat([x_t, x_s], dim=1) x = self.proj(x) x = self.proj_drop(x) return x ================================================ FILE: lib/models/mask_decoder/block.py ================================================ # -*- coding:utf-8 -*- # author : Skye Song # file : block.py # Copyright (c) Skye-Song. All Rights Reserved from .attention import * from .drop import DropPath from .mlp import Mlp class Block(nn.Module): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0., attention = "Attention", act_layer=nn.GELU, norm_layer=nn.LayerNorm): super().__init__() if norm_layer is None: norm_layer = nn.LayerNorm self.norm1 = norm_layer(dim) self.attn = globals()[attention](dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) def forward(self, x, **kwargs): x = x + self.drop_path(self.attn(self.norm1(x), **kwargs)) x = x + self.drop_path(self.mlp(self.norm2(x))) return x ================================================ FILE: lib/models/mask_decoder/drop.py ================================================ """ DropBlock, DropPath PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers. Papers: DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890) Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382) Code: DropBlock impl inspired by two Tensorflow impl that I liked: - https://github.com/tensorflow/tpu/blob/master/models/official/resnet/resnet_model.py#L74 - https://github.com/clovaai/assembled-cnn/blob/master/nets/blocks.py Hacked together by / Copyright 2020 Ross Wightman """ import torch import torch.nn as nn import torch.nn.functional as F def drop_block_2d( x, drop_prob: float = 0.1, block_size: int = 7, gamma_scale: float = 1.0, with_noise: bool = False, inplace: bool = False, batchwise: bool = False): """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf DropBlock with an experimental gaussian noise option. This layer has been tested on a few training runs with success, but needs further validation and possibly optimization for lower runtime impact. """ B, C, H, W = x.shape total_size = W * H clipped_block_size = min(block_size, min(W, H)) # seed_drop_rate, the gamma parameter gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / ( (W - block_size + 1) * (H - block_size + 1)) # Forces the block to be inside the feature map. w_i, h_i = torch.meshgrid(torch.arange(W).to(x.device), torch.arange(H).to(x.device)) valid_block = ((w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2)) & \ ((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2)) valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype) if batchwise: # one mask for whole batch, quite a bit faster uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device) else: uniform_noise = torch.rand_like(x) block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype) block_mask = -F.max_pool2d( -block_mask, kernel_size=clipped_block_size, # block_size, stride=1, padding=clipped_block_size // 2) if with_noise: normal_noise = torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x) if inplace: x.mul_(block_mask).add_(normal_noise * (1 - block_mask)) else: x = x * block_mask + normal_noise * (1 - block_mask) else: normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)).to(x.dtype) if inplace: x.mul_(block_mask * normalize_scale) else: x = x * block_mask * normalize_scale return x def drop_block_fast_2d( x: torch.Tensor, drop_prob: float = 0.1, block_size: int = 7, gamma_scale: float = 1.0, with_noise: bool = False, inplace: bool = False, batchwise: bool = False): """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid block mask at edges. """ B, C, H, W = x.shape total_size = W * H clipped_block_size = min(block_size, min(W, H)) gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / ( (W - block_size + 1) * (H - block_size + 1)) if batchwise: # one mask for whole batch, quite a bit faster block_mask = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device) < gamma else: # mask per batch element block_mask = torch.rand_like(x) < gamma block_mask = F.max_pool2d( block_mask.to(x.dtype), kernel_size=clipped_block_size, stride=1, padding=clipped_block_size // 2) if with_noise: normal_noise = torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x) if inplace: x.mul_(1. - block_mask).add_(normal_noise * block_mask) else: x = x * (1. - block_mask) + normal_noise * block_mask else: block_mask = 1 - block_mask normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)).to(dtype=x.dtype) if inplace: x.mul_(block_mask * normalize_scale) else: x = x * block_mask * normalize_scale return x class DropBlock2d(nn.Module): """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf """ def __init__(self, drop_prob=0.1, block_size=7, gamma_scale=1.0, with_noise=False, inplace=False, batchwise=False, fast=True): super(DropBlock2d, self).__init__() self.drop_prob = drop_prob self.gamma_scale = gamma_scale self.block_size = block_size self.with_noise = with_noise self.inplace = inplace self.batchwise = batchwise self.fast = fast # FIXME finish comparisons of fast vs not def forward(self, x): if not self.training or not self.drop_prob: return x if self.fast: return drop_block_fast_2d( x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise) else: return drop_block_2d( x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise) def drop_path(x, drop_prob: float = 0., training: bool = False): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the argument. """ if drop_prob == 0. or not training: return x keep_prob = 1 - drop_prob shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) random_tensor.floor_() # binarize output = x.div(keep_prob) * random_tensor return output class DropPath(nn.Module): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). """ def __init__(self, drop_prob=None): super(DropPath, self).__init__() self.drop_prob = drop_prob def forward(self, x): return drop_path(x, self.drop_prob, self.training) ================================================ FILE: lib/models/mask_decoder/mlp.py ================================================ """ MLP module w/ dropout and configurable activation layer Hacked together by / Copyright 2020 Ross Wightman """ from torch import nn as nn import torch.nn.functional as F class Mlp(nn.Module): """ MLP as used in Vision Transformer, MLP-Mixer and related networks """ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x class MultiLayerMlp(nn.Module): """ Very simple multi-layer perceptron (also called FFN)""" def __init__(self, input_dim, hidden_dim, output_dim, num_layers, BN=False): super().__init__() self.num_layers = num_layers h = [hidden_dim] * (num_layers - 1) if BN: self.layers = nn.ModuleList(nn.Sequential(nn.Linear(n, k), nn.BatchNorm1d(k)) for n, k in zip([input_dim] + h, h + [output_dim])) else: self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) def forward(self, x): for i, layer in enumerate(self.layers): x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) return x class GluMlp(nn.Module): """ MLP w/ GLU style gating See: https://arxiv.org/abs/1612.08083, https://arxiv.org/abs/2002.05202 """ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.Sigmoid, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features assert hidden_features % 2 == 0 self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features // 2, out_features) self.drop = nn.Dropout(drop) def init_weights(self): # override init of fc1 w/ gate portion set to weight near zero, bias=1 fc1_mid = self.fc1.bias.shape[0] // 2 nn.init.ones_(self.fc1.bias[fc1_mid:]) nn.init.normal_(self.fc1.weight[fc1_mid:], std=1e-6) def forward(self, x): x = self.fc1(x) x, gates = x.chunk(2, dim=-1) x = x * self.act(gates) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x class GatedMlp(nn.Module): """ MLP as used in gMLP """ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, gate_layer=None, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() if gate_layer is not None: assert hidden_features % 2 == 0 self.gate = gate_layer(hidden_features) hidden_features = hidden_features // 2 # FIXME base reduction on gate property? else: self.gate = nn.Identity() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.gate(x) x = self.fc2(x) x = self.drop(x) return x class ConvMlp(nn.Module): """ MLP using 1x1 convs that keeps spatial dims """ def __init__( self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU, norm_layer=None, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Conv2d(in_features, hidden_features, kernel_size=1, bias=True) self.norm = norm_layer(hidden_features) if norm_layer else nn.Identity() self.act = act_layer() self.fc2 = nn.Conv2d(hidden_features, out_features, kernel_size=1, bias=True) self.drop = nn.Dropout(drop) def forward(self, x): x = self.fc1(x) x = self.norm(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) return x ================================================ FILE: lib/models/mask_decoder/norm.py ================================================ # -*- coding:utf-8 -*- # author : Skye Song # file : norm.py # Copyright (c) Skye-Song. All Rights Reserved import torch class FrozenBatchNorm2d(torch.nn.Module): """ BatchNorm2d where the batch statistics and the affine parameters are fixed. Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than torchvision.models.resnet[18,34,50,101] produce nans. """ def __init__(self, n): super(FrozenBatchNorm2d, self).__init__() self.register_buffer("weight", torch.ones(n)) self.register_buffer("bias", torch.zeros(n)) self.register_buffer("running_mean", torch.zeros(n)) self.register_buffer("running_var", torch.ones(n)) def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): num_batches_tracked_key = prefix + 'num_batches_tracked' if num_batches_tracked_key in state_dict: del state_dict[num_batches_tracked_key] super(FrozenBatchNorm2d, self)._load_from_state_dict( state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) def forward(self, x): # move reshapes to the beginning # to make it fuser-friendly w = self.weight.reshape(1, -1, 1, 1) b = self.bias.reshape(1, -1, 1, 1) rv = self.running_var.reshape(1, -1, 1, 1) rm = self.running_mean.reshape(1, -1, 1, 1) eps = 1e-5 scale = w * (rv + eps).rsqrt() # rsqrt(x): 1/sqrt(x), r: reciprocal bias = b - rm * scale return x * scale + bias ================================================ FILE: lib/models/mask_decoder/patch_embed.py ================================================ """ Image to Patch Embedding using Conv2d A convolution based approach to patchifying a 2D image w/ embedding projection. Based on the impl in https://github.com/google-research/vision_transformer Hacked together by / Copyright 2020 Ross Wightman """ from torch import nn as nn from itertools import repeat import collections.abc # From PyTorch internals def _ntuple(n): def parse(x): if isinstance(x, collections.abc.Iterable): return x return tuple(repeat(x, n)) return parse to_1tuple = _ntuple(1) to_2tuple = _ntuple(2) to_3tuple = _ntuple(3) to_4tuple = _ntuple(4) to_ntuple = _ntuple class PatchEmbed(nn.Module): """ 2D Image to Patch Embedding """ def __init__(self, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True): super().__init__() self.flatten = flatten self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() def forward(self, x): x = self.proj(x) if self.flatten: x = x.flatten(2).transpose(1, 2) # BCHW -> BNC x = self.norm(x) return x ================================================ FILE: lib/models/mask_decoder/pos_embed.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # -------------------------------------------------------- # Position embedding utils # -------------------------------------------------------- import numpy as np import torch # -------------------------------------------------------- # 2D sine-cosine position embedding # References: # Transformer: https://github.com/tensorflow/models/blob/master/official/nlp/transformer/model_utils.py # MoCo v3: https://github.com/facebookresearch/moco-v3 # -------------------------------------------------------- def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False): """ grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) """ grid_h = np.arange(grid_size, dtype=np.float32) grid_w = np.arange(grid_size, dtype=np.float32) grid = np.meshgrid(grid_w, grid_h) # here w goes first grid = np.stack(grid, axis=0) grid = grid.reshape([2, 1, grid_size, grid_size]) pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) if cls_token: pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0) return pos_embed def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): assert embed_dim % 2 == 0 # use half of dimensions to encode grid_h emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) return emb def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): """ embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D) """ assert embed_dim % 2 == 0 omega = np.arange(embed_dim // 2, dtype=np.float) omega /= embed_dim / 2. omega = 1. / 10000**omega # (D/2,) pos = pos.reshape(-1) # (M,) out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product emb_sin = np.sin(out) # (M, D/2) emb_cos = np.cos(out) # (M, D/2) emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) return emb # -------------------------------------------------------- # Interpolate position embeddings for high-resolution # References: # DeiT: https://github.com/facebookresearch/deit # -------------------------------------------------------- def interpolate_pos_embed(model, checkpoint_model): if 'pos_embed' in checkpoint_model: pos_embed_checkpoint = checkpoint_model['pos_embed'] embedding_size = pos_embed_checkpoint.shape[-1] num_patches = model.patch_embed.num_patches num_extra_tokens = model.pos_embed.shape[-2] - num_patches # height (== width) for the checkpoint position embedding orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) # height (== width) for the new position embedding new_size = int(num_patches ** 0.5) # class_token and dist_token are kept unchanged if orig_size != new_size: print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] # only the position tokens are interpolated pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) pos_tokens = torch.nn.functional.interpolate( pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) checkpoint_model['pos_embed'] = new_pos_embed ================================================ FILE: lib/models/mask_decoder/weight_init.py ================================================ import torch import math import warnings from torch.nn.init import _calculate_fan_in_and_fan_out def _no_grad_trunc_normal_(tensor, mean, std, a, b): # Cut & paste from PyTorch official master until it's in a few official releases - RW # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf def norm_cdf(x): # Computes standard normal cumulative distribution function return (1. + math.erf(x / math.sqrt(2.))) / 2. if (mean < a - 2 * std) or (mean > b + 2 * std): warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " "The distribution of values may be incorrect.", stacklevel=2) with torch.no_grad(): # Values are generated by using a truncated uniform distribution and # then using the inverse CDF for the normal distribution. # Get upper and lower cdf values l = norm_cdf((a - mean) / std) u = norm_cdf((b - mean) / std) # Uniformly fill tensor with values from [l, u], then translate to # [2l-1, 2u-1]. tensor.uniform_(2 * l - 1, 2 * u - 1) # Use inverse cdf transform for normal distribution to get truncated # standard normal tensor.erfinv_() # Transform to proper mean, std tensor.mul_(std * math.sqrt(2.)) tensor.add_(mean) # Clamp to ensure it's in the proper range tensor.clamp_(min=a, max=b) return tensor def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): # type: (Tensor, float, float, float, float) -> Tensor r"""Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn from the normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` with values outside :math:`[a, b]` redrawn until they are within the bounds. The method used for generating the random values works best when :math:`a \leq \text{mean} \leq b`. Args: tensor: an n-dimensional `torch.Tensor` mean: the mean of the normal distribution std: the standard deviation of the normal distribution a: the minimum cutoff value b: the maximum cutoff value Examples: >>> w = torch.empty(3, 5) >>> nn.init.trunc_normal_(w) """ return _no_grad_trunc_normal_(tensor, mean, std, a, b) def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'): fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) if mode == 'fan_in': denom = fan_in elif mode == 'fan_out': denom = fan_out elif mode == 'fan_avg': denom = (fan_in + fan_out) / 2 variance = scale / denom if distribution == "truncated_normal": # constant is stddev of standard normal truncated to (-2, 2) trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978) elif distribution == "normal": tensor.normal_(std=math.sqrt(variance)) elif distribution == "uniform": bound = math.sqrt(3 * variance) tensor.uniform_(-bound, bound) else: raise ValueError(f"invalid distribution {distribution}") def lecun_normal_(tensor): variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal') ================================================ FILE: lib/test/__init__.py ================================================ ================================================ FILE: lib/test/analysis/__init__.py ================================================ ================================================ FILE: lib/test/analysis/extract_results.py ================================================ import os import sys import numpy as np from lib.test.utils.load_text import load_text import torch import pickle from tqdm import tqdm env_path = os.path.join(os.path.dirname(__file__), '../../..') if env_path not in sys.path: sys.path.append(env_path) from lib.test.evaluation.environment import env_settings def calc_err_center(pred_bb, anno_bb, normalized=False): pred_center = pred_bb[:, :2] + 0.5 * (pred_bb[:, 2:] - 1.0) anno_center = anno_bb[:, :2] + 0.5 * (anno_bb[:, 2:] - 1.0) if normalized: pred_center = pred_center / anno_bb[:, 2:] anno_center = anno_center / anno_bb[:, 2:] err_center = ((pred_center - anno_center)**2).sum(1).sqrt() return err_center def calc_iou_overlap(pred_bb, anno_bb): tl = torch.max(pred_bb[:, :2], anno_bb[:, :2]) br = torch.min(pred_bb[:, :2] + pred_bb[:, 2:] - 1.0, anno_bb[:, :2] + anno_bb[:, 2:] - 1.0) sz = (br - tl + 1.0).clamp(0) # Area intersection = sz.prod(dim=1) union = pred_bb[:, 2:].prod(dim=1) + anno_bb[:, 2:].prod(dim=1) - intersection return intersection / union def calc_seq_err_robust(pred_bb, anno_bb, dataset, target_visible=None): pred_bb = pred_bb.clone() # Check if invalid values are present if torch.isnan(pred_bb).any() or (pred_bb[:, 2:] < 0.0).any(): raise Exception('Error: Invalid results') if torch.isnan(anno_bb).any(): if dataset == 'uav': pass else: raise Exception('Warning: NaNs in annotation') if (pred_bb[:, 2:] == 0.0).any(): for i in range(1, pred_bb.shape[0]): if (pred_bb[i, 2:] == 0.0).any() and not torch.isnan(anno_bb[i, :]).any(): pred_bb[i, :] = pred_bb[i-1, :] if pred_bb.shape[0] != anno_bb.shape[0]: if dataset == 'lasot': if pred_bb.shape[0] > anno_bb.shape[0]: # For monkey-17, there is a mismatch for some trackers. pred_bb = pred_bb[:anno_bb.shape[0], :] else: raise Exception('Mis-match in tracker prediction and GT lengths') else: # print('Warning: Mis-match in tracker prediction and GT lengths') if pred_bb.shape[0] > anno_bb.shape[0]: pred_bb = pred_bb[:anno_bb.shape[0], :] else: pad = torch.zeros((anno_bb.shape[0] - pred_bb.shape[0], 4)).type_as(pred_bb) pred_bb = torch.cat((pred_bb, pad), dim=0) pred_bb[0, :] = anno_bb[0, :] if target_visible is not None: target_visible = target_visible.bool() valid = ((anno_bb[:, 2:] > 0.0).sum(1) == 2) & target_visible else: valid = ((anno_bb[:, 2:] > 0.0).sum(1) == 2) err_center = calc_err_center(pred_bb, anno_bb) err_center_normalized = calc_err_center(pred_bb, anno_bb, normalized=True) err_overlap = calc_iou_overlap(pred_bb, anno_bb) # handle invalid anno cases if dataset in ['uav']: err_center[~valid] = -1.0 else: err_center[~valid] = float("Inf") err_center_normalized[~valid] = -1.0 err_overlap[~valid] = -1.0 if dataset == 'lasot': err_center_normalized[~target_visible] = float("Inf") err_center[~target_visible] = float("Inf") if torch.isnan(err_overlap).any(): raise Exception('Nans in calculated overlap') return err_overlap, err_center, err_center_normalized, valid def extract_results(trackers, dataset, report_name, skip_missing_seq=False, plot_bin_gap=0.05, exclude_invalid_frames=False): settings = env_settings() eps = 1e-16 result_plot_path = os.path.join(settings.result_plot_path, report_name) if not os.path.exists(result_plot_path): os.makedirs(result_plot_path) threshold_set_overlap = torch.arange(0.0, 1.0 + plot_bin_gap, plot_bin_gap, dtype=torch.float64) threshold_set_center = torch.arange(0, 51, dtype=torch.float64) threshold_set_center_norm = torch.arange(0, 51, dtype=torch.float64) / 100.0 avg_overlap_all = torch.zeros((len(dataset), len(trackers)), dtype=torch.float64) ave_success_rate_plot_overlap = torch.zeros((len(dataset), len(trackers), threshold_set_overlap.numel()), dtype=torch.float32) ave_success_rate_plot_center = torch.zeros((len(dataset), len(trackers), threshold_set_center.numel()), dtype=torch.float32) ave_success_rate_plot_center_norm = torch.zeros((len(dataset), len(trackers), threshold_set_center.numel()), dtype=torch.float32) valid_sequence = torch.ones(len(dataset), dtype=torch.uint8) for seq_id, seq in enumerate(tqdm(dataset)): # Load anno anno_bb = torch.tensor(seq.ground_truth_rect) target_visible = torch.tensor(seq.target_visible, dtype=torch.uint8) if seq.target_visible is not None else None for trk_id, trk in enumerate(trackers): # Load results base_results_path = '{}/{}'.format(trk.results_dir, seq.name) results_path = '{}.txt'.format(base_results_path) if os.path.isfile(results_path): pred_bb = torch.tensor(load_text(str(results_path), delimiter=('\t', ','), dtype=np.float64)) else: if skip_missing_seq: valid_sequence[seq_id] = 0 break else: raise Exception('Result not found. {}'.format(results_path)) # Calculate measures err_overlap, err_center, err_center_normalized, valid_frame = calc_seq_err_robust( pred_bb, anno_bb, seq.dataset, target_visible) avg_overlap_all[seq_id, trk_id] = err_overlap[valid_frame].mean() if exclude_invalid_frames: seq_length = valid_frame.long().sum() else: seq_length = anno_bb.shape[0] if seq_length <= 0: raise Exception('Seq length zero') ave_success_rate_plot_overlap[seq_id, trk_id, :] = (err_overlap.view(-1, 1) > threshold_set_overlap.view(1, -1)).sum(0).float() / seq_length ave_success_rate_plot_center[seq_id, trk_id, :] = (err_center.view(-1, 1) <= threshold_set_center.view(1, -1)).sum(0).float() / seq_length ave_success_rate_plot_center_norm[seq_id, trk_id, :] = (err_center_normalized.view(-1, 1) <= threshold_set_center_norm.view(1, -1)).sum(0).float() / seq_length print('\n\nComputed results over {} / {} sequences'.format(valid_sequence.long().sum().item(), valid_sequence.shape[0])) # Prepare dictionary for saving data seq_names = [s.name for s in dataset] tracker_names = [{'name': t.name, 'param': t.parameter_name, 'run_id': t.run_id, 'disp_name': t.display_name} for t in trackers] eval_data = {'sequences': seq_names, 'trackers': tracker_names, 'valid_sequence': valid_sequence.tolist(), 'ave_success_rate_plot_overlap': ave_success_rate_plot_overlap.tolist(), 'ave_success_rate_plot_center': ave_success_rate_plot_center.tolist(), 'ave_success_rate_plot_center_norm': ave_success_rate_plot_center_norm.tolist(), 'avg_overlap_all': avg_overlap_all.tolist(), 'threshold_set_overlap': threshold_set_overlap.tolist(), 'threshold_set_center': threshold_set_center.tolist(), 'threshold_set_center_norm': threshold_set_center_norm.tolist()} with open(result_plot_path + '/eval_data.pkl', 'wb') as fh: pickle.dump(eval_data, fh) return eval_data ================================================ FILE: lib/test/analysis/plot_results.py ================================================ import tikzplotlib import matplotlib import matplotlib.pyplot as plt import os import torch import pickle import json from lib.test.evaluation.environment import env_settings from lib.test.analysis.extract_results import extract_results def get_plot_draw_styles(): plot_draw_style = [{'color': (1.0, 0.0, 0.0), 'line_style': '-'}, {'color': (0.0, 1.0, 0.0), 'line_style': '-'}, {'color': (0.0, 0.0, 1.0), 'line_style': '-'}, {'color': (0.0, 0.0, 0.0), 'line_style': '-'}, {'color': (1.0, 0.0, 1.0), 'line_style': '-'}, {'color': (0.0, 1.0, 1.0), 'line_style': '-'}, {'color': (0.5, 0.5, 0.5), 'line_style': '-'}, {'color': (136.0 / 255.0, 0.0, 21.0 / 255.0), 'line_style': '-'}, {'color': (1.0, 127.0 / 255.0, 39.0 / 255.0), 'line_style': '-'}, {'color': (0.0, 162.0 / 255.0, 232.0 / 255.0), 'line_style': '-'}, {'color': (0.0, 0.5, 0.0), 'line_style': '-'}, {'color': (1.0, 0.5, 0.2), 'line_style': '-'}, {'color': (0.1, 0.4, 0.0), 'line_style': '-'}, {'color': (0.6, 0.3, 0.9), 'line_style': '-'}, {'color': (0.4, 0.7, 0.1), 'line_style': '-'}, {'color': (0.2, 0.1, 0.7), 'line_style': '-'}, {'color': (0.7, 0.6, 0.2), 'line_style': '-'}] return plot_draw_style def check_eval_data_is_valid(eval_data, trackers, dataset): """ Checks if the pre-computed results are valid""" seq_names = [s.name for s in dataset] seq_names_saved = eval_data['sequences'] tracker_names_f = [(t.name, t.parameter_name, t.run_id) for t in trackers] tracker_names_f_saved = [(t['name'], t['param'], t['run_id']) for t in eval_data['trackers']] return seq_names == seq_names_saved and tracker_names_f == tracker_names_f_saved def merge_multiple_runs(eval_data): new_tracker_names = [] ave_success_rate_plot_overlap_merged = [] ave_success_rate_plot_center_merged = [] ave_success_rate_plot_center_norm_merged = [] avg_overlap_all_merged = [] ave_success_rate_plot_overlap = torch.tensor(eval_data['ave_success_rate_plot_overlap']) ave_success_rate_plot_center = torch.tensor(eval_data['ave_success_rate_plot_center']) ave_success_rate_plot_center_norm = torch.tensor(eval_data['ave_success_rate_plot_center_norm']) avg_overlap_all = torch.tensor(eval_data['avg_overlap_all']) trackers = eval_data['trackers'] merged = torch.zeros(len(trackers), dtype=torch.uint8) for i in range(len(trackers)): if merged[i]: continue base_tracker = trackers[i] new_tracker_names.append(base_tracker) match = [t['name'] == base_tracker['name'] and t['param'] == base_tracker['param'] for t in trackers] match = torch.tensor(match) ave_success_rate_plot_overlap_merged.append(ave_success_rate_plot_overlap[:, match, :].mean(1)) ave_success_rate_plot_center_merged.append(ave_success_rate_plot_center[:, match, :].mean(1)) ave_success_rate_plot_center_norm_merged.append(ave_success_rate_plot_center_norm[:, match, :].mean(1)) avg_overlap_all_merged.append(avg_overlap_all[:, match].mean(1)) merged[match] = 1 ave_success_rate_plot_overlap_merged = torch.stack(ave_success_rate_plot_overlap_merged, dim=1) ave_success_rate_plot_center_merged = torch.stack(ave_success_rate_plot_center_merged, dim=1) ave_success_rate_plot_center_norm_merged = torch.stack(ave_success_rate_plot_center_norm_merged, dim=1) avg_overlap_all_merged = torch.stack(avg_overlap_all_merged, dim=1) eval_data['trackers'] = new_tracker_names eval_data['ave_success_rate_plot_overlap'] = ave_success_rate_plot_overlap_merged.tolist() eval_data['ave_success_rate_plot_center'] = ave_success_rate_plot_center_merged.tolist() eval_data['ave_success_rate_plot_center_norm'] = ave_success_rate_plot_center_norm_merged.tolist() eval_data['avg_overlap_all'] = avg_overlap_all_merged.tolist() return eval_data def get_tracker_display_name(tracker): if tracker['disp_name'] is None: if tracker['run_id'] is None: disp_name = '{}_{}'.format(tracker['name'], tracker['param']) else: disp_name = '{}_{}_{:03d}'.format(tracker['name'], tracker['param'], tracker['run_id']) else: disp_name = tracker['disp_name'] return disp_name def plot_draw_save(y, x, scores, trackers, plot_draw_styles, result_plot_path, plot_opts): plt.rcParams['text.usetex']=True plt.rcParams["font.family"] = "Times New Roman" # Plot settings font_size = plot_opts.get('font_size', 20) font_size_axis = plot_opts.get('font_size_axis', 20) line_width = plot_opts.get('line_width', 2) font_size_legend = plot_opts.get('font_size_legend', 20) plot_type = plot_opts['plot_type'] legend_loc = plot_opts['legend_loc'] xlabel = plot_opts['xlabel'] ylabel = plot_opts['ylabel'] ylabel = "%s"%(ylabel.replace('%','\%')) xlim = plot_opts['xlim'] ylim = plot_opts['ylim'] title = r"$\bf{%s}$" %(plot_opts['title']) matplotlib.rcParams.update({'font.size': font_size}) matplotlib.rcParams.update({'axes.titlesize': font_size_axis}) matplotlib.rcParams.update({'axes.titleweight': 'black'}) matplotlib.rcParams.update({'axes.labelsize': font_size_axis}) fig, ax = plt.subplots() index_sort = scores.argsort(descending=False) plotted_lines = [] legend_text = [] for id, id_sort in enumerate(index_sort): line = ax.plot(x.tolist(), y[id_sort, :].tolist(), linewidth=line_width, color=plot_draw_styles[index_sort.numel() - id - 1]['color'], linestyle=plot_draw_styles[index_sort.numel() - id - 1]['line_style']) plotted_lines.append(line[0]) tracker = trackers[id_sort] disp_name = get_tracker_display_name(tracker) legend_text.append('{} [{:.1f}]'.format(disp_name, scores[id_sort])) try: # add bold to our method for i in range(1,2): legend_text[-i] = r'\textbf{%s}'%(legend_text[-i]) ax.legend(plotted_lines[::-1], legend_text[::-1], loc=legend_loc, fancybox=False, edgecolor='black', fontsize=font_size_legend, framealpha=1.0) except: pass ax.set(xlabel=xlabel, ylabel=ylabel, xlim=xlim, ylim=ylim, title=title) ax.grid(True, linestyle='-.') fig.tight_layout() tikzplotlib.save('{}/{}_plot.tex'.format(result_plot_path, plot_type)) fig.savefig('{}/{}_plot.pdf'.format(result_plot_path, plot_type), dpi=300, format='pdf', transparent=True) plt.draw() def check_and_load_precomputed_results(trackers, dataset, report_name, force_evaluation=False, **kwargs): # Load data settings = env_settings() # Load pre-computed results result_plot_path = os.path.join(settings.result_plot_path, report_name) eval_data_path = os.path.join(result_plot_path, 'eval_data.pkl') if os.path.isfile(eval_data_path) and not force_evaluation: with open(eval_data_path, 'rb') as fh: eval_data = pickle.load(fh) else: # print('Pre-computed evaluation data not found. Computing results!') eval_data = extract_results(trackers, dataset, report_name, **kwargs) if not check_eval_data_is_valid(eval_data, trackers, dataset): # print('Pre-computed evaluation data invalid. Re-computing results!') eval_data = extract_results(trackers, dataset, report_name, **kwargs) # pass else: # Update display names tracker_names = [{'name': t.name, 'param': t.parameter_name, 'run_id': t.run_id, 'disp_name': t.display_name} for t in trackers] eval_data['trackers'] = tracker_names with open(eval_data_path, 'wb') as fh: pickle.dump(eval_data, fh) return eval_data def get_auc_curve(ave_success_rate_plot_overlap, valid_sequence): ave_success_rate_plot_overlap = ave_success_rate_plot_overlap[valid_sequence, :, :] auc_curve = ave_success_rate_plot_overlap.mean(0) * 100.0 auc = auc_curve.mean(-1) return auc_curve, auc def get_prec_curve(ave_success_rate_plot_center, valid_sequence): ave_success_rate_plot_center = ave_success_rate_plot_center[valid_sequence, :, :] prec_curve = ave_success_rate_plot_center.mean(0) * 100.0 prec_score = prec_curve[:, 20] return prec_curve, prec_score def plot_results(trackers, dataset, report_name, merge_results=False, plot_types=('success'), force_evaluation=False, **kwargs): """ Plot results for the given trackers args: trackers - List of trackers to evaluate dataset - List of sequences to evaluate report_name - Name of the folder in env_settings.perm_mat_path where the computed results and plots are saved merge_results - If True, multiple random runs for a non-deterministic trackers are averaged plot_types - List of scores to display. Can contain 'success', 'prec' (precision), and 'norm_prec' (normalized precision) """ # Load data settings = env_settings() plot_draw_styles = get_plot_draw_styles() # Load pre-computed results result_plot_path = os.path.join(settings.result_plot_path, report_name) eval_data = check_and_load_precomputed_results(trackers, dataset, report_name, force_evaluation, **kwargs) # Merge results from multiple runs if merge_results: eval_data = merge_multiple_runs(eval_data) tracker_names = eval_data['trackers'] valid_sequence = torch.tensor(eval_data['valid_sequence'], dtype=torch.bool) print('\nPlotting results over {} / {} sequences'.format(valid_sequence.long().sum().item(), valid_sequence.shape[0])) print('\nGenerating plots for: {}'.format(report_name)) # ******************************** Success Plot ************************************** if 'success' in plot_types: ave_success_rate_plot_overlap = torch.tensor(eval_data['ave_success_rate_plot_overlap']) # Index out valid sequences auc_curve, auc = get_auc_curve(ave_success_rate_plot_overlap, valid_sequence) threshold_set_overlap = torch.tensor(eval_data['threshold_set_overlap']) success_plot_opts = {'plot_type': 'success', 'legend_loc': 'lower left', 'xlabel': 'Overlap threshold', 'ylabel': 'Overlap Precision [%]', 'xlim': (0, 1.0), 'ylim': (0, 88), 'title': 'Success'} plot_draw_save(auc_curve, threshold_set_overlap, auc, tracker_names, plot_draw_styles, result_plot_path, success_plot_opts) # ******************************** Precision Plot ************************************** if 'prec' in plot_types: ave_success_rate_plot_center = torch.tensor(eval_data['ave_success_rate_plot_center']) # Index out valid sequences prec_curve, prec_score = get_prec_curve(ave_success_rate_plot_center, valid_sequence) threshold_set_center = torch.tensor(eval_data['threshold_set_center']) precision_plot_opts = {'plot_type': 'precision', 'legend_loc': 'lower right', 'xlabel': 'Location error threshold [pixels]', 'ylabel': 'Distance Precision [%]', 'xlim': (0, 50), 'ylim': (0, 100), 'title': 'Precision plot'} plot_draw_save(prec_curve, threshold_set_center, prec_score, tracker_names, plot_draw_styles, result_plot_path, precision_plot_opts) # ******************************** Norm Precision Plot ************************************** if 'norm_prec' in plot_types: ave_success_rate_plot_center_norm = torch.tensor(eval_data['ave_success_rate_plot_center_norm']) # Index out valid sequences prec_curve, prec_score = get_prec_curve(ave_success_rate_plot_center_norm, valid_sequence) threshold_set_center_norm = torch.tensor(eval_data['threshold_set_center_norm']) norm_precision_plot_opts = {'plot_type': 'norm_precision', 'legend_loc': 'lower right', 'xlabel': 'Location error threshold', 'ylabel': 'Distance Precision [%]', 'xlim': (0, 0.5), 'ylim': (0, 85), 'title': 'Normalized Precision'} plot_draw_save(prec_curve, threshold_set_center_norm, prec_score, tracker_names, plot_draw_styles, result_plot_path, norm_precision_plot_opts) plt.show() def generate_formatted_report(row_labels, scores, table_name=''): name_width = max([len(d) for d in row_labels] + [len(table_name)]) + 5 min_score_width = 10 report_text = '\n{label: <{width}} |'.format(label=table_name, width=name_width) score_widths = [max(min_score_width, len(k) + 3) for k in scores.keys()] for s, s_w in zip(scores.keys(), score_widths): report_text = '{prev} {s: <{width}} |'.format(prev=report_text, s=s, width=s_w) report_text = '{prev}\n'.format(prev=report_text) for trk_id, d_name in enumerate(row_labels): # display name report_text = '{prev}{tracker: <{width}} |'.format(prev=report_text, tracker=d_name, width=name_width) for (score_type, score_value), s_w in zip(scores.items(), score_widths): report_text = '{prev} {score: <{width}} |'.format(prev=report_text, score='{:0.2f}'.format(score_value[trk_id].item()), width=s_w) report_text = '{prev}\n'.format(prev=report_text) return report_text def print_results(trackers, dataset, report_name, merge_results=False, plot_types=('success'), **kwargs): """ Print the results for the given trackers in a formatted table args: trackers - List of trackers to evaluate dataset - List of sequences to evaluate report_name - Name of the folder in env_settings.perm_mat_path where the computed results and plots are saved merge_results - If True, multiple random runs for a non-deterministic trackers are averaged plot_types - List of scores to display. Can contain 'success' (prints AUC, OP50, and OP75 scores), 'prec' (prints precision score), and 'norm_prec' (prints normalized precision score) """ # Load pre-computed results eval_data = check_and_load_precomputed_results(trackers, dataset, report_name, **kwargs) # Merge results from multiple runs if merge_results: eval_data = merge_multiple_runs(eval_data) tracker_names = eval_data['trackers'] valid_sequence = torch.tensor(eval_data['valid_sequence'], dtype=torch.bool) print('\nReporting results over {} / {} sequences'.format(valid_sequence.long().sum().item(), valid_sequence.shape[0])) scores = {} # ******************************** Success Plot ************************************** if 'success' in plot_types: threshold_set_overlap = torch.tensor(eval_data['threshold_set_overlap']) ave_success_rate_plot_overlap = torch.tensor(eval_data['ave_success_rate_plot_overlap']) # Index out valid sequences auc_curve, auc = get_auc_curve(ave_success_rate_plot_overlap, valid_sequence) scores['AUC'] = auc scores['OP50'] = auc_curve[:, threshold_set_overlap == 0.50] scores['OP75'] = auc_curve[:, threshold_set_overlap == 0.75] # ******************************** Precision Plot ************************************** if 'prec' in plot_types: ave_success_rate_plot_center = torch.tensor(eval_data['ave_success_rate_plot_center']) # Index out valid sequences prec_curve, prec_score = get_prec_curve(ave_success_rate_plot_center, valid_sequence) scores['Precision'] = prec_score # ******************************** Norm Precision Plot ********************************* if 'norm_prec' in plot_types: ave_success_rate_plot_center_norm = torch.tensor(eval_data['ave_success_rate_plot_center_norm']) # Index out valid sequences norm_prec_curve, norm_prec_score = get_prec_curve(ave_success_rate_plot_center_norm, valid_sequence) scores['Norm Precision'] = norm_prec_score # Print tracker_disp_names = [get_tracker_display_name(trk) for trk in tracker_names] report_text = generate_formatted_report(tracker_disp_names, scores, table_name=report_name) print(report_text) def plot_got_success(trackers, report_name): """ Plot success plot for GOT-10k dataset using the json reports. Save the json reports from http://got-10k.aitestunion.com/leaderboard in the directory set to env_settings.got_reports_path The tracker name in the experiment file should be set to the name of the report file for that tracker, e.g. DiMP50_report_2019_09_02_15_44_25 if the report is name DiMP50_report_2019_09_02_15_44_25.json args: trackers - List of trackers to evaluate report_name - Name of the folder in env_settings.perm_mat_path where the computed results and plots are saved """ # Load data settings = env_settings() plot_draw_styles = get_plot_draw_styles() result_plot_path = os.path.join(settings.result_plot_path, report_name) auc_curve = torch.zeros((len(trackers), 101)) scores = torch.zeros(len(trackers)) # Load results tracker_names = [] for trk_id, trk in enumerate(trackers): json_path = '{}/{}.json'.format(settings.got_reports_path, trk.name) if os.path.isfile(json_path): with open(json_path, 'r') as f: eval_data = json.load(f) else: raise Exception('Report not found {}'.format(json_path)) if len(eval_data.keys()) > 1: raise Exception # First field is the tracker name. Index it out eval_data = eval_data[list(eval_data.keys())[0]] if 'succ_curve' in eval_data.keys(): curve = eval_data['succ_curve'] ao = eval_data['ao'] elif 'overall' in eval_data.keys() and 'succ_curve' in eval_data['overall'].keys(): curve = eval_data['overall']['succ_curve'] ao = eval_data['overall']['ao'] else: raise Exception('Invalid JSON file {}'.format(json_path)) auc_curve[trk_id, :] = torch.tensor(curve) * 100.0 scores[trk_id] = ao * 100.0 tracker_names.append({'name': trk.name, 'param': trk.parameter_name, 'run_id': trk.run_id, 'disp_name': trk.display_name}) threshold_set_overlap = torch.arange(0.0, 1.01, 0.01, dtype=torch.float64) success_plot_opts = {'plot_type': 'success', 'legend_loc': 'lower left', 'xlabel': 'Overlap threshold', 'ylabel': 'Overlap Precision [%]', 'xlim': (0, 1.0), 'ylim': (0, 100), 'title': 'Success plot'} plot_draw_save(auc_curve, threshold_set_overlap, scores, tracker_names, plot_draw_styles, result_plot_path, success_plot_opts) plt.show() def print_per_sequence_results(trackers, dataset, report_name, merge_results=False, filter_criteria=None, **kwargs): """ Print per-sequence results for the given trackers. Additionally, the sequences to list can be filtered using the filter criteria. args: trackers - List of trackers to evaluate dataset - List of sequences to evaluate report_name - Name of the folder in env_settings.perm_mat_path where the computed results and plots are saved merge_results - If True, multiple random runs for a non-deterministic trackers are averaged filter_criteria - Filter sequence results which are reported. Following modes are supported None: No filtering. Display results for all sequences in dataset 'ao_min': Only display sequences for which the minimum average overlap (AO) score over the trackers is less than a threshold filter_criteria['threshold']. This mode can be used to select sequences where at least one tracker performs poorly. 'ao_max': Only display sequences for which the maximum average overlap (AO) score over the trackers is less than a threshold filter_criteria['threshold']. This mode can be used to select sequences all tracker performs poorly. 'delta_ao': Only display sequences for which the performance of different trackers vary by at least filter_criteria['threshold'] in average overlap (AO) score. This mode can be used to select sequences where the behaviour of the trackers greatly differ between each other. """ # Load pre-computed results eval_data = check_and_load_precomputed_results(trackers, dataset, report_name, **kwargs) # Merge results from multiple runs if merge_results: eval_data = merge_multiple_runs(eval_data) tracker_names = eval_data['trackers'] valid_sequence = torch.tensor(eval_data['valid_sequence'], dtype=torch.bool) sequence_names = eval_data['sequences'] avg_overlap_all = torch.tensor(eval_data['avg_overlap_all']) * 100.0 # Filter sequences if filter_criteria is not None: if filter_criteria['mode'] == 'ao_min': min_ao = avg_overlap_all.min(dim=1)[0] valid_sequence = valid_sequence & (min_ao < filter_criteria['threshold']) elif filter_criteria['mode'] == 'ao_max': max_ao = avg_overlap_all.max(dim=1)[0] valid_sequence = valid_sequence & (max_ao < filter_criteria['threshold']) elif filter_criteria['mode'] == 'delta_ao': min_ao = avg_overlap_all.min(dim=1)[0] max_ao = avg_overlap_all.max(dim=1)[0] valid_sequence = valid_sequence & ((max_ao - min_ao) > filter_criteria['threshold']) else: raise Exception avg_overlap_all = avg_overlap_all[valid_sequence, :] sequence_names = [s + ' (ID={})'.format(i) for i, (s, v) in enumerate(zip(sequence_names, valid_sequence.tolist())) if v] tracker_disp_names = [get_tracker_display_name(trk) for trk in tracker_names] scores_per_tracker = {k: avg_overlap_all[:, i] for i, k in enumerate(tracker_disp_names)} report_text = generate_formatted_report(sequence_names, scores_per_tracker) print(report_text) def print_results_per_video(trackers, dataset, report_name, merge_results=False, plot_types=('success'), per_video=False, **kwargs): """ Print the results for the given trackers in a formatted table args: trackers - List of trackers to evaluate dataset - List of sequences to evaluate report_name - Name of the folder in env_settings.perm_mat_path where the computed results and plots are saved merge_results - If True, multiple random runs for a non-deterministic trackers are averaged plot_types - List of scores to display. Can contain 'success' (prints AUC, OP50, and OP75 scores), 'prec' (prints precision score), and 'norm_prec' (prints normalized precision score) """ # Load pre-computed results eval_data = check_and_load_precomputed_results(trackers, dataset, report_name, **kwargs) # Merge results from multiple runs if merge_results: eval_data = merge_multiple_runs(eval_data) seq_lens = len(eval_data['sequences']) eval_datas = [{} for _ in range(seq_lens)] if per_video: for key, value in eval_data.items(): if len(value) == seq_lens: for i in range(seq_lens): eval_datas[i][key] = [value[i]] else: for i in range(seq_lens): eval_datas[i][key] = value tracker_names = eval_data['trackers'] valid_sequence = torch.tensor(eval_data['valid_sequence'], dtype=torch.bool) print('\nReporting results over {} / {} sequences'.format(valid_sequence.long().sum().item(), valid_sequence.shape[0])) scores = {} # ******************************** Success Plot ************************************** if 'success' in plot_types: threshold_set_overlap = torch.tensor(eval_data['threshold_set_overlap']) ave_success_rate_plot_overlap = torch.tensor(eval_data['ave_success_rate_plot_overlap']) # Index out valid sequences auc_curve, auc = get_auc_curve(ave_success_rate_plot_overlap, valid_sequence) scores['AUC'] = auc scores['OP50'] = auc_curve[:, threshold_set_overlap == 0.50] scores['OP75'] = auc_curve[:, threshold_set_overlap == 0.75] # ******************************** Precision Plot ************************************** if 'prec' in plot_types: ave_success_rate_plot_center = torch.tensor(eval_data['ave_success_rate_plot_center']) # Index out valid sequences prec_curve, prec_score = get_prec_curve(ave_success_rate_plot_center, valid_sequence) scores['Precision'] = prec_score # ******************************** Norm Precision Plot ********************************* if 'norm_prec' in plot_types: ave_success_rate_plot_center_norm = torch.tensor(eval_data['ave_success_rate_plot_center_norm']) # Index out valid sequences norm_prec_curve, norm_prec_score = get_prec_curve(ave_success_rate_plot_center_norm, valid_sequence) scores['Norm Precision'] = norm_prec_score # Print tracker_disp_names = [get_tracker_display_name(trk) for trk in tracker_names] report_text = generate_formatted_report(tracker_disp_names, scores, table_name=report_name) print(report_text) if per_video: for i in range(seq_lens): eval_data = eval_datas[i] print('\n{} sequences'.format(eval_data['sequences'][0])) scores = {} valid_sequence = torch.tensor(eval_data['valid_sequence'], dtype=torch.bool) # ******************************** Success Plot ************************************** if 'success' in plot_types: threshold_set_overlap = torch.tensor(eval_data['threshold_set_overlap']) ave_success_rate_plot_overlap = torch.tensor(eval_data['ave_success_rate_plot_overlap']) # Index out valid sequences auc_curve, auc = get_auc_curve(ave_success_rate_plot_overlap, valid_sequence) scores['AUC'] = auc scores['OP50'] = auc_curve[:, threshold_set_overlap == 0.50] scores['OP75'] = auc_curve[:, threshold_set_overlap == 0.75] # ******************************** Precision Plot ************************************** if 'prec' in plot_types: ave_success_rate_plot_center = torch.tensor(eval_data['ave_success_rate_plot_center']) # Index out valid sequences prec_curve, prec_score = get_prec_curve(ave_success_rate_plot_center, valid_sequence) scores['Precision'] = prec_score # ******************************** Norm Precision Plot ********************************* if 'norm_prec' in plot_types: ave_success_rate_plot_center_norm = torch.tensor(eval_data['ave_success_rate_plot_center_norm']) # Index out valid sequences norm_prec_curve, norm_prec_score = get_prec_curve(ave_success_rate_plot_center_norm, valid_sequence) scores['Norm Precision'] = norm_prec_score # Print tracker_disp_names = [get_tracker_display_name(trk) for trk in tracker_names] report_text = generate_formatted_report(tracker_disp_names, scores, table_name=report_name) print(report_text) ================================================ FILE: lib/test/evaluation/__init__.py ================================================ from .data import Sequence from .tracker import Tracker, trackerlist from .datasets import get_dataset from .environment import create_default_local_file_ITP_test ================================================ FILE: lib/test/evaluation/data.py ================================================ import numpy as np from lib.test.evaluation.environment import env_settings from lib.train.data.image_loader import imread_indexed from collections import OrderedDict class BaseDataset: """Base class for all datasets.""" def __init__(self): self.env_settings = env_settings() def __len__(self): """Overload this function in your dataset. This should return number of sequences in the dataset.""" raise NotImplementedError def get_sequence_list(self): """Overload this in your dataset. Should return the list of sequences in the dataset.""" raise NotImplementedError class Sequence: """Class for the sequence in an evaluation.""" def __init__(self, name, frames, dataset, ground_truth_rect, ground_truth_seg=None, init_data=None, object_class=None, target_visible=None, object_ids=None, multiobj_mode=False): self.name = name self.frames = frames self.dataset = dataset self.ground_truth_rect = ground_truth_rect self.ground_truth_seg = ground_truth_seg self.object_class = object_class self.target_visible = target_visible self.object_ids = object_ids self.multiobj_mode = multiobj_mode self.init_data = self._construct_init_data(init_data) self._ensure_start_frame() def _ensure_start_frame(self): # Ensure start frame is 0 start_frame = min(list(self.init_data.keys())) if start_frame > 0: self.frames = self.frames[start_frame:] if self.ground_truth_rect is not None: if isinstance(self.ground_truth_rect, (dict, OrderedDict)): for obj_id, gt in self.ground_truth_rect.items(): self.ground_truth_rect[obj_id] = gt[start_frame:,:] else: self.ground_truth_rect = self.ground_truth_rect[start_frame:,:] if self.ground_truth_seg is not None: self.ground_truth_seg = self.ground_truth_seg[start_frame:] assert len(self.frames) == len(self.ground_truth_seg) if self.target_visible is not None: self.target_visible = self.target_visible[start_frame:] self.init_data = {frame-start_frame: val for frame, val in self.init_data.items()} def _construct_init_data(self, init_data): if init_data is not None: if not self.multiobj_mode: assert self.object_ids is None or len(self.object_ids) == 1 for frame, init_val in init_data.items(): if 'bbox' in init_val and isinstance(init_val['bbox'], (dict, OrderedDict)): init_val['bbox'] = init_val['bbox'][self.object_ids[0]] # convert to list for frame, init_val in init_data.items(): if 'bbox' in init_val: if isinstance(init_val['bbox'], (dict, OrderedDict)): init_val['bbox'] = OrderedDict({obj_id: list(init) for obj_id, init in init_val['bbox'].items()}) else: init_val['bbox'] = list(init_val['bbox']) else: init_data = {0: dict()} # Assume start from frame 0 if self.object_ids is not None: init_data[0]['object_ids'] = self.object_ids if self.ground_truth_rect is not None: if self.multiobj_mode: assert isinstance(self.ground_truth_rect, (dict, OrderedDict)) init_data[0]['bbox'] = OrderedDict({obj_id: list(gt[0,:]) for obj_id, gt in self.ground_truth_rect.items()}) else: assert self.object_ids is None or len(self.object_ids) == 1 if isinstance(self.ground_truth_rect, (dict, OrderedDict)): init_data[0]['bbox'] = list(self.ground_truth_rect[self.object_ids[0]][0, :]) else: init_data[0]['bbox'] = list(self.ground_truth_rect[0,:]) if self.ground_truth_seg is not None: init_data[0]['mask'] = self.ground_truth_seg[0] return init_data def init_info(self): info = self.frame_info(frame_num=0) return info def frame_info(self, frame_num): info = self.object_init_data(frame_num=frame_num) return info def init_bbox(self, frame_num=0): return self.object_init_data(frame_num=frame_num).get('init_bbox') def init_mask(self, frame_num=0): return self.object_init_data(frame_num=frame_num).get('init_mask') def get_info(self, keys, frame_num=None): info = dict() for k in keys: val = self.get(k, frame_num=frame_num) if val is not None: info[k] = val return info def object_init_data(self, frame_num=None) -> dict: if frame_num is None: frame_num = 0 if frame_num not in self.init_data: return dict() init_data = dict() for key, val in self.init_data[frame_num].items(): if val is None: continue init_data['init_'+key] = val if 'init_mask' in init_data and init_data['init_mask'] is not None: anno = imread_indexed(init_data['init_mask']) if not self.multiobj_mode and self.object_ids is not None: assert len(self.object_ids) == 1 anno = (anno == int(self.object_ids[0])).astype(np.uint8) init_data['init_mask'] = anno if self.object_ids is not None: init_data['object_ids'] = self.object_ids init_data['sequence_object_ids'] = self.object_ids return init_data def target_class(self, frame_num=None): return self.object_class def get(self, name, frame_num=None): return getattr(self, name)(frame_num) def __repr__(self): return "{self.__class__.__name__} {self.name}, length={len} frames".format(self=self, len=len(self.frames)) class SequenceList(list): """List of sequences. Supports the addition operator to concatenate sequence lists.""" def __getitem__(self, item): if isinstance(item, str): for seq in self: if seq.name == item: return seq raise IndexError('Sequence name not in the dataset.') elif isinstance(item, int): return super(SequenceList, self).__getitem__(item) elif isinstance(item, (tuple, list)): return SequenceList([super(SequenceList, self).__getitem__(i) for i in item]) else: return SequenceList(super(SequenceList, self).__getitem__(item)) def __add__(self, other): return SequenceList(super(SequenceList, self).__add__(other)) def copy(self): return SequenceList(super(SequenceList, self).copy()) ================================================ FILE: lib/test/evaluation/datasets.py ================================================ from collections import namedtuple import importlib from lib.test.evaluation.data import SequenceList DatasetInfo = namedtuple('DatasetInfo', ['module', 'class_name', 'kwargs']) pt = "lib.test.evaluation.%sdataset" # Useful abbreviations to reduce the clutter dataset_dict = dict( otb=DatasetInfo(module=pt % "otb", class_name="OTBDataset", kwargs=dict()), nfs=DatasetInfo(module=pt % "nfs", class_name="NFSDataset", kwargs=dict()), uav=DatasetInfo(module=pt % "uav", class_name="UAVDataset", kwargs=dict()), tc128=DatasetInfo(module=pt % "tc128", class_name="TC128Dataset", kwargs=dict()), tc128ce=DatasetInfo(module=pt % "tc128ce", class_name="TC128CEDataset", kwargs=dict()), trackingnet=DatasetInfo(module=pt % "trackingnet", class_name="TrackingNetDataset", kwargs=dict()), got10k_test=DatasetInfo(module=pt % "got10k", class_name="GOT10KDataset", kwargs=dict(split='test')), got10k_val=DatasetInfo(module=pt % "got10k", class_name="GOT10KDataset", kwargs=dict(split='val')), got10k_ltrval=DatasetInfo(module=pt % "got10k", class_name="GOT10KDataset", kwargs=dict(split='ltrval')), lasot=DatasetInfo(module=pt % "lasot", class_name="LaSOTDataset", kwargs=dict()), lasot_lmdb=DatasetInfo(module=pt % "lasot_lmdb", class_name="LaSOTlmdbDataset", kwargs=dict()), vot18=DatasetInfo(module=pt % "vot", class_name="VOTDataset", kwargs=dict()), vot22=DatasetInfo(module=pt % "vot", class_name="VOTDataset", kwargs=dict(year=22)), itb=DatasetInfo(module=pt % "itb", class_name="ITBDataset", kwargs=dict()), tnl2k=DatasetInfo(module=pt % "tnl2k", class_name="TNL2kDataset", kwargs=dict()), lasot_extension_subset=DatasetInfo(module=pt % "lasotextensionsubset", class_name="LaSOTExtensionSubsetDataset", kwargs=dict()), ) def load_dataset(name: str): """ Import and load a single dataset.""" name = name.lower() dset_info = dataset_dict.get(name) if dset_info is None: raise ValueError('Unknown dataset \'%s\'' % name) m = importlib.import_module(dset_info.module) dataset = getattr(m, dset_info.class_name)(**dset_info.kwargs) # Call the constructor return dataset.get_sequence_list() def get_dataset(*args): """ Get a single or set of datasets.""" dset = SequenceList() for name in args: dset.extend(load_dataset(name)) return dset ================================================ FILE: lib/test/evaluation/environment.py ================================================ import importlib import os class EnvSettings: def __init__(self): test_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..')) self.results_path = '{}/tracking_results/'.format(test_path) self.segmentation_path = '{}/segmentation_results/'.format(test_path) self.network_path = '{}/networks/'.format(test_path) self.result_plot_path = '{}/result_plots/'.format(test_path) self.otb_path = '' self.nfs_path = '' self.uav_path = '' self.tpl_path = '' self.vot_path = '' self.got10k_path = '' self.lasot_path = '' self.trackingnet_path = '' self.davis_dir = '' self.youtubevos_dir = '' self.got_packed_results_path = '' self.got_reports_path = '' self.tn_packed_results_path = '' def create_default_local_file(): comment = {'results_path': 'Where to store tracking results', 'network_path': 'Where tracking networks are stored.'} path = os.path.join(os.path.dirname(__file__), 'local.py') with open(path, 'w') as f: settings = EnvSettings() f.write('from test.evaluation.environment import EnvSettings\n\n') f.write('def local_env_settings():\n') f.write(' settings = EnvSettings()\n\n') f.write(' # Set your local paths here.\n\n') for attr in dir(settings): comment_str = None if attr in comment: comment_str = comment[attr] attr_val = getattr(settings, attr) if not attr.startswith('__') and not callable(attr_val): if comment_str is None: f.write(' settings.{} = \'{}\'\n'.format(attr, attr_val)) else: f.write(' settings.{} = \'{}\' # {}\n'.format(attr, attr_val, comment_str)) f.write('\n return settings\n\n') class EnvSettings_ITP: def __init__(self, workspace_dir, data_dir, save_dir): self.prj_dir = workspace_dir self.save_dir = save_dir self.results_path = os.path.join(save_dir, 'test/tracking_results') self.segmentation_path = os.path.join(save_dir, 'test/segmentation_results') self.network_path = os.path.join(save_dir, 'test/networks') self.result_plot_path = os.path.join(save_dir, 'test/result_plots') self.otb_path = os.path.join(data_dir, 'otb') self.nfs_path = os.path.join(data_dir, 'nfs') self.uav_path = os.path.join(data_dir, 'uav') self.tc128_path = os.path.join(data_dir, 'TC128') self.tpl_path = '' self.vot_path = os.path.join(data_dir, 'VOT2019') self.got10k_path = os.path.join(data_dir, 'got10k') self.got10k_lmdb_path = os.path.join(data_dir, 'got10k_lmdb') self.lasot_path = os.path.join(data_dir, 'lasot') self.lasot_lmdb_path = os.path.join(data_dir, 'lasot_lmdb') self.trackingnet_path = os.path.join(data_dir, 'trackingnet') self.vot18_path = os.path.join(data_dir, 'vot2018') self.vot22_path = os.path.join(data_dir, 'vot2022') self.itb_path = os.path.join(data_dir, 'itb') self.tnl2k_path = os.path.join(data_dir, 'tnl2k') self.lasot_extension_subset_path_path = os.path.join(data_dir, 'lasot_extension_subset') self.davis_dir = '' self.youtubevos_dir = '' self.got_packed_results_path = '' self.got_reports_path = '' self.tn_packed_results_path = '' def create_default_local_file_ITP_test(workspace_dir, data_dir, save_dir): comment = {'results_path': 'Where to store tracking results', 'network_path': 'Where tracking networks are stored.'} path = os.path.join(os.path.dirname(__file__), 'local.py') with open(path, 'w') as f: settings = EnvSettings_ITP(workspace_dir, data_dir, save_dir) f.write('from lib.test.evaluation.environment import EnvSettings\n\n') f.write('def local_env_settings():\n') f.write(' settings = EnvSettings()\n\n') f.write(' # Set your local paths here.\n\n') for attr in dir(settings): comment_str = None if attr in comment: comment_str = comment[attr] attr_val = getattr(settings, attr) if not attr.startswith('__') and not callable(attr_val): if comment_str is None: f.write(' settings.{} = \'{}\'\n'.format(attr, attr_val)) else: f.write(' settings.{} = \'{}\' # {}\n'.format(attr, attr_val, comment_str)) f.write('\n return settings\n\n') def env_settings(): env_module_name = 'lib.test.evaluation.local' try: env_module = importlib.import_module(env_module_name) return env_module.local_env_settings() except: env_file = os.path.join(os.path.dirname(__file__), 'local.py') # Create a default file create_default_local_file() raise RuntimeError('YOU HAVE NOT SETUP YOUR local.py!!!\n Go to "{}" and set all the paths you need. ' 'Then try to run again.'.format(env_file)) ================================================ FILE: lib/test/evaluation/got10kdataset.py ================================================ import numpy as np from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList from lib.test.utils.load_text import load_text import os class GOT10KDataset(BaseDataset): """ GOT-10k dataset. Publication: GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild Lianghua Huang, Xin Zhao, and Kaiqi Huang arXiv:1810.11981, 2018 https://arxiv.org/pdf/1810.11981.pdf Download dataset from http://got-10k.aitestunion.com/downloads """ def __init__(self, split): super().__init__() # Split can be test, val, or ltrval (a validation split consisting of videos from the official train set) if split == 'test' or split == 'val': self.base_path = os.path.join(self.env_settings.got10k_path, split) else: self.base_path = os.path.join(self.env_settings.got10k_path, 'train') self.sequence_list = self._get_sequence_list(split) self.split = split def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_list]) def _construct_sequence(self, sequence_name): anno_path = '{}/{}/groundtruth.txt'.format(self.base_path, sequence_name) ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64) frames_path = '{}/{}'.format(self.base_path, sequence_name) frame_list = [frame for frame in os.listdir(frames_path) if frame.endswith(".jpg")] frame_list.sort(key=lambda f: int(f[:-4])) frames_list = [os.path.join(frames_path, frame) for frame in frame_list] return Sequence(sequence_name, frames_list, 'got10k', ground_truth_rect.reshape(-1, 4)) def __len__(self): return len(self.sequence_list) def _get_sequence_list(self, split): with open('{}/list.txt'.format(self.base_path)) as f: sequence_list = f.read().splitlines() if split == 'ltrval': with open('{}/got10k_val_split.txt'.format(self.env_settings.dataspec_path)) as f: seq_ids = f.read().splitlines() sequence_list = [sequence_list[int(x)] for x in seq_ids] return sequence_list ================================================ FILE: lib/test/evaluation/itbdataset.py ================================================ import numpy as np from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList from lib.test.utils.load_text import load_text import os class ITBDataset(BaseDataset): """ NUS-PRO dataset """ def __init__(self): super().__init__() self.base_path = self.env_settings.itb_path self.sequence_info_list = self._get_sequence_info_list(self.base_path) def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_info_list]) def _construct_sequence(self, sequence_info): sequence_path = sequence_info['path'] nz = sequence_info['nz'] ext = sequence_info['ext'] start_frame = sequence_info['startFrame'] end_frame = sequence_info['endFrame'] init_omit = 0 if 'initOmit' in sequence_info: init_omit = sequence_info['initOmit'] frames = ['{base_path}/{sequence_path}/{frame:0{nz}}.{ext}'.format(base_path=self.base_path, sequence_path=sequence_path, frame=frame_num, nz=nz, ext=ext) for frame_num in range(start_frame + init_omit, end_frame + 1)] anno_path = '{}/{}'.format(self.base_path, sequence_info['anno_path']) # NOTE: NUS has some weird annos which panda cannot handle ground_truth_rect = load_text(str(anno_path), delimiter=(',', None), dtype=np.float64, backend='numpy') return Sequence(sequence_info['name'], frames, 'otb', ground_truth_rect[init_omit:, :], object_class=sequence_info['object_class']) def __len__(self): return len(self.sequence_info_list) def get_fileNames(self, rootdir): fs = [] fs_all = [] for root, dirs, files in os.walk(rootdir, topdown=True): files.sort() files.sort(key=len) if files is not None: for name in files: _, ending = os.path.splitext(name) if ending == ".jpg": _, root_ = os.path.split(root) fs.append(os.path.join(root_, name)) fs_all.append(os.path.join(root, name)) return fs_all, fs def _get_sequence_info_list(self, base_path): sequence_info_list = [] for scene in os.listdir(base_path): if '.' in scene: continue videos = os.listdir(os.path.join(base_path, scene)) for video in videos: _, fs = self.get_fileNames(os.path.join(base_path, scene, video)) video_tmp = {"name": video, "path": scene + '/' + video, "startFrame": 1, "endFrame": len(fs), "nz": len(fs[0].split('/')[-1].split('.')[0]), "ext": "jpg", "anno_path": scene + '/' + video + "/groundtruth.txt", "object_class": "unknown"} sequence_info_list.append(video_tmp) return sequence_info_list # sequence_info_list_50 # ================================================ FILE: lib/test/evaluation/lasot_lmdbdataset.py ================================================ from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList from lib.utils.lmdb_utils import * '''2021.1.27 LaSOT dataset using lmdb data''' class LaSOTlmdbDataset(BaseDataset): """ LaSOT test set consisting of 280 videos (see Protocol-II in the LaSOT paper) Publication: LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Hexin Bai, Yong Xu, Chunyuan Liao and Haibin Ling CVPR, 2019 https://arxiv.org/pdf/1809.07845.pdf Download the dataset from https://cis.temple.edu/lasot/download.html """ def __init__(self): super().__init__() self.base_path = self.env_settings.lasot_lmdb_path self.sequence_list = self._get_sequence_list() self.clean_list = self.clean_seq_list() def clean_seq_list(self): clean_lst = [] for i in range(len(self.sequence_list)): cls, _ = self.sequence_list[i].split('-') clean_lst.append(cls) return clean_lst def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_list]) def _construct_sequence(self, sequence_name): class_name = sequence_name.split('-')[0] anno_path = str('{}/{}/groundtruth.txt'.format(class_name, sequence_name)) # decode the groundtruth gt_str_list = decode_str(self.base_path, anno_path).split('\n')[:-1] # the last line is empty gt_list = [list(map(float, line.split(','))) for line in gt_str_list] ground_truth_rect = np.array(gt_list).astype(np.float64) # decode occlusion file occlusion_label_path = str('{}/{}/full_occlusion.txt'.format(class_name, sequence_name)) occ_list = list(map(int, decode_str(self.base_path, occlusion_label_path).split(','))) full_occlusion = np.array(occ_list).astype(np.float64) # decode out of view file out_of_view_label_path = str('{}/{}/out_of_view.txt'.format(class_name, sequence_name)) out_of_view_list = list(map(int, decode_str(self.base_path, out_of_view_label_path).split(','))) out_of_view = np.array(out_of_view_list).astype(np.float64) target_visible = np.logical_and(full_occlusion == 0, out_of_view == 0) frames_path = '{}/{}/img'.format(class_name, sequence_name) frames_list = [[self.base_path, '{}/{:08d}.jpg'.format(frames_path, frame_number)] for frame_number in range(1, ground_truth_rect.shape[0] + 1)] target_class = class_name return Sequence(sequence_name, frames_list, 'lasot', ground_truth_rect.reshape(-1, 4), object_class=target_class, target_visible=target_visible) def __len__(self): return len(self.sequence_list) def _get_sequence_list(self): sequence_list = ['airplane-1', 'airplane-9', 'airplane-13', 'airplane-15', 'basketball-1', 'basketball-6', 'basketball-7', 'basketball-11', 'bear-2', 'bear-4', 'bear-6', 'bear-17', 'bicycle-2', 'bicycle-7', 'bicycle-9', 'bicycle-18', 'bird-2', 'bird-3', 'bird-15', 'bird-17', 'boat-3', 'boat-4', 'boat-12', 'boat-17', 'book-3', 'book-10', 'book-11', 'book-19', 'bottle-1', 'bottle-12', 'bottle-14', 'bottle-18', 'bus-2', 'bus-5', 'bus-17', 'bus-19', 'car-2', 'car-6', 'car-9', 'car-17', 'cat-1', 'cat-3', 'cat-18', 'cat-20', 'cattle-2', 'cattle-7', 'cattle-12', 'cattle-13', 'spider-14', 'spider-16', 'spider-18', 'spider-20', 'coin-3', 'coin-6', 'coin-7', 'coin-18', 'crab-3', 'crab-6', 'crab-12', 'crab-18', 'surfboard-12', 'surfboard-4', 'surfboard-5', 'surfboard-8', 'cup-1', 'cup-4', 'cup-7', 'cup-17', 'deer-4', 'deer-8', 'deer-10', 'deer-14', 'dog-1', 'dog-7', 'dog-15', 'dog-19', 'guitar-3', 'guitar-8', 'guitar-10', 'guitar-16', 'person-1', 'person-5', 'person-10', 'person-12', 'pig-2', 'pig-10', 'pig-13', 'pig-18', 'rubicCube-1', 'rubicCube-6', 'rubicCube-14', 'rubicCube-19', 'swing-10', 'swing-14', 'swing-17', 'swing-20', 'drone-13', 'drone-15', 'drone-2', 'drone-7', 'pool-12', 'pool-15', 'pool-3', 'pool-7', 'rabbit-10', 'rabbit-13', 'rabbit-17', 'rabbit-19', 'racing-10', 'racing-15', 'racing-16', 'racing-20', 'robot-1', 'robot-19', 'robot-5', 'robot-8', 'sepia-13', 'sepia-16', 'sepia-6', 'sepia-8', 'sheep-3', 'sheep-5', 'sheep-7', 'sheep-9', 'skateboard-16', 'skateboard-19', 'skateboard-3', 'skateboard-8', 'tank-14', 'tank-16', 'tank-6', 'tank-9', 'tiger-12', 'tiger-18', 'tiger-4', 'tiger-6', 'train-1', 'train-11', 'train-20', 'train-7', 'truck-16', 'truck-3', 'truck-6', 'truck-7', 'turtle-16', 'turtle-5', 'turtle-8', 'turtle-9', 'umbrella-17', 'umbrella-19', 'umbrella-2', 'umbrella-9', 'yoyo-15', 'yoyo-17', 'yoyo-19', 'yoyo-7', 'zebra-10', 'zebra-14', 'zebra-16', 'zebra-17', 'elephant-1', 'elephant-12', 'elephant-16', 'elephant-18', 'goldfish-3', 'goldfish-7', 'goldfish-8', 'goldfish-10', 'hat-1', 'hat-2', 'hat-5', 'hat-18', 'kite-4', 'kite-6', 'kite-10', 'kite-15', 'motorcycle-1', 'motorcycle-3', 'motorcycle-9', 'motorcycle-18', 'mouse-1', 'mouse-8', 'mouse-9', 'mouse-17', 'flag-3', 'flag-9', 'flag-5', 'flag-2', 'frog-3', 'frog-4', 'frog-20', 'frog-9', 'gametarget-1', 'gametarget-2', 'gametarget-7', 'gametarget-13', 'hand-2', 'hand-3', 'hand-9', 'hand-16', 'helmet-5', 'helmet-11', 'helmet-19', 'helmet-13', 'licenseplate-6', 'licenseplate-12', 'licenseplate-13', 'licenseplate-15', 'electricfan-1', 'electricfan-10', 'electricfan-18', 'electricfan-20', 'chameleon-3', 'chameleon-6', 'chameleon-11', 'chameleon-20', 'crocodile-3', 'crocodile-4', 'crocodile-10', 'crocodile-14', 'gecko-1', 'gecko-5', 'gecko-16', 'gecko-19', 'fox-2', 'fox-3', 'fox-5', 'fox-20', 'giraffe-2', 'giraffe-10', 'giraffe-13', 'giraffe-15', 'gorilla-4', 'gorilla-6', 'gorilla-9', 'gorilla-13', 'hippo-1', 'hippo-7', 'hippo-9', 'hippo-20', 'horse-1', 'horse-4', 'horse-12', 'horse-15', 'kangaroo-2', 'kangaroo-5', 'kangaroo-11', 'kangaroo-14', 'leopard-1', 'leopard-7', 'leopard-16', 'leopard-20', 'lion-1', 'lion-5', 'lion-12', 'lion-20', 'lizard-1', 'lizard-3', 'lizard-6', 'lizard-13', 'microphone-2', 'microphone-6', 'microphone-14', 'microphone-16', 'monkey-3', 'monkey-4', 'monkey-9', 'monkey-17', 'shark-2', 'shark-3', 'shark-5', 'shark-6', 'squirrel-8', 'squirrel-11', 'squirrel-13', 'squirrel-19', 'volleyball-1', 'volleyball-13', 'volleyball-18', 'volleyball-19'] return sequence_list ================================================ FILE: lib/test/evaluation/lasotdataset.py ================================================ import numpy as np from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList from lib.test.utils.load_text import load_text class LaSOTDataset(BaseDataset): """ LaSOT test set consisting of 280 videos (see Protocol-II in the LaSOT paper) Publication: LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Hexin Bai, Yong Xu, Chunyuan Liao and Haibin Ling CVPR, 2019 https://arxiv.org/pdf/1809.07845.pdf Download the dataset from https://cis.temple.edu/lasot/download.html """ def __init__(self): super().__init__() self.base_path = self.env_settings.lasot_path self.sequence_list = self._get_sequence_list() self.clean_list = self.clean_seq_list() def clean_seq_list(self): clean_lst = [] for i in range(len(self.sequence_list)): cls, _ = self.sequence_list[i].split('-') clean_lst.append(cls) return clean_lst def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_list]) def _construct_sequence(self, sequence_name): class_name = sequence_name.split('-')[0] anno_path = '{}/{}/{}/groundtruth.txt'.format(self.base_path, class_name, sequence_name) ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64) occlusion_label_path = '{}/{}/{}/full_occlusion.txt'.format(self.base_path, class_name, sequence_name) # NOTE: pandas backed seems super super slow for loading occlusion/oov masks full_occlusion = load_text(str(occlusion_label_path), delimiter=',', dtype=np.float64, backend='numpy') out_of_view_label_path = '{}/{}/{}/out_of_view.txt'.format(self.base_path, class_name, sequence_name) out_of_view = load_text(str(out_of_view_label_path), delimiter=',', dtype=np.float64, backend='numpy') target_visible = np.logical_and(full_occlusion == 0, out_of_view == 0) frames_path = '{}/{}/{}/img'.format(self.base_path, class_name, sequence_name) frames_list = ['{}/{:08d}.jpg'.format(frames_path, frame_number) for frame_number in range(1, ground_truth_rect.shape[0] + 1)] target_class = class_name return Sequence(sequence_name, frames_list, 'lasot', ground_truth_rect.reshape(-1, 4), object_class=target_class, target_visible=target_visible) def __len__(self): return len(self.sequence_list) def _get_sequence_list(self): sequence_list = ['airplane-1', 'airplane-9', 'airplane-13', 'airplane-15', 'basketball-1', 'basketball-6', 'basketball-7', 'basketball-11', 'bear-2', 'bear-4', 'bear-6', 'bear-17', 'bicycle-2', 'bicycle-7', 'bicycle-9', 'bicycle-18', 'bird-2', 'bird-3', 'bird-15', 'bird-17', 'boat-3', 'boat-4', 'boat-12', 'boat-17', 'book-3', 'book-10', 'book-11', 'book-19', 'bottle-1', 'bottle-12', 'bottle-14', 'bottle-18', 'bus-2', 'bus-5', 'bus-17', 'bus-19', 'car-2', 'car-6', 'car-9', 'car-17', 'cat-1', 'cat-3', 'cat-18', 'cat-20', 'cattle-2', 'cattle-7', 'cattle-12', 'cattle-13', 'spider-14', 'spider-16', 'spider-18', 'spider-20', 'coin-3', 'coin-6', 'coin-7', 'coin-18', 'crab-3', 'crab-6', 'crab-12', 'crab-18', 'surfboard-12', 'surfboard-4', 'surfboard-5', 'surfboard-8', 'cup-1', 'cup-4', 'cup-7', 'cup-17', 'deer-4', 'deer-8', 'deer-10', 'deer-14', 'dog-1', 'dog-7', 'dog-15', 'dog-19', 'guitar-3', 'guitar-8', 'guitar-10', 'guitar-16', 'person-1', 'person-5', 'person-10', 'person-12', 'pig-2', 'pig-10', 'pig-13', 'pig-18', 'rubicCube-1', 'rubicCube-6', 'rubicCube-14', 'rubicCube-19', 'swing-10', 'swing-14', 'swing-17', 'swing-20', 'drone-13', 'drone-15', 'drone-2', 'drone-7', 'pool-12', 'pool-15', 'pool-3', 'pool-7', 'rabbit-10', 'rabbit-13', 'rabbit-17', 'rabbit-19', 'racing-10', 'racing-15', 'racing-16', 'racing-20', 'robot-1', 'robot-19', 'robot-5', 'robot-8', 'sepia-13', 'sepia-16', 'sepia-6', 'sepia-8', 'sheep-3', 'sheep-5', 'sheep-7', 'sheep-9', 'skateboard-16', 'skateboard-19', 'skateboard-3', 'skateboard-8', 'tank-14', 'tank-16', 'tank-6', 'tank-9', 'tiger-12', 'tiger-18', 'tiger-4', 'tiger-6', 'train-1', 'train-11', 'train-20', 'train-7', 'truck-16', 'truck-3', 'truck-6', 'truck-7', 'turtle-16', 'turtle-5', 'turtle-8', 'turtle-9', 'umbrella-17', 'umbrella-19', 'umbrella-2', 'umbrella-9', 'yoyo-15', 'yoyo-17', 'yoyo-19', 'yoyo-7', 'zebra-10', 'zebra-14', 'zebra-16', 'zebra-17', 'elephant-1', 'elephant-12', 'elephant-16', 'elephant-18', 'goldfish-3', 'goldfish-7', 'goldfish-8', 'goldfish-10', 'hat-1', 'hat-2', 'hat-5', 'hat-18', 'kite-4', 'kite-6', 'kite-10', 'kite-15', 'motorcycle-1', 'motorcycle-3', 'motorcycle-9', 'motorcycle-18', 'mouse-1', 'mouse-8', 'mouse-9', 'mouse-17', 'flag-3', 'flag-9', 'flag-5', 'flag-2', 'frog-3', 'frog-4', 'frog-20', 'frog-9', 'gametarget-1', 'gametarget-2', 'gametarget-7', 'gametarget-13', 'hand-2', 'hand-3', 'hand-9', 'hand-16', 'helmet-5', 'helmet-11', 'helmet-19', 'helmet-13', 'licenseplate-6', 'licenseplate-12', 'licenseplate-13', 'licenseplate-15', 'electricfan-1', 'electricfan-10', 'electricfan-18', 'electricfan-20', 'chameleon-3', 'chameleon-6', 'chameleon-11', 'chameleon-20', 'crocodile-3', 'crocodile-4', 'crocodile-10', 'crocodile-14', 'gecko-1', 'gecko-5', 'gecko-16', 'gecko-19', 'fox-2', 'fox-3', 'fox-5', 'fox-20', 'giraffe-2', 'giraffe-10', 'giraffe-13', 'giraffe-15', 'gorilla-4', 'gorilla-6', 'gorilla-9', 'gorilla-13', 'hippo-1', 'hippo-7', 'hippo-9', 'hippo-20', 'horse-1', 'horse-4', 'horse-12', 'horse-15', 'kangaroo-2', 'kangaroo-5', 'kangaroo-11', 'kangaroo-14', 'leopard-1', 'leopard-7', 'leopard-16', 'leopard-20', 'lion-1', 'lion-5', 'lion-12', 'lion-20', 'lizard-1', 'lizard-3', 'lizard-6', 'lizard-13', 'microphone-2', 'microphone-6', 'microphone-14', 'microphone-16', 'monkey-3', 'monkey-4', 'monkey-9', 'monkey-17', 'shark-2', 'shark-3', 'shark-5', 'shark-6', 'squirrel-8', 'squirrel-11', 'squirrel-13', 'squirrel-19', 'volleyball-1', 'volleyball-13', 'volleyball-18', 'volleyball-19'] return sequence_list ================================================ FILE: lib/test/evaluation/lasotextensionsubsetdataset.py ================================================ import numpy as np from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList from lib.test.utils.load_text import load_text class LaSOTExtensionSubsetDataset(BaseDataset): """ LaSOT test set consisting of 280 videos (see Protocol-II in the LaSOT paper) Publication: LaSOT: A High-quality Large-scale Single Object Tracking Benchmark Heng Fan, Hexin Bai, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Harshit, Mingzhen Huang, Juehuan Liu, Yong Xu, Chunyuan Liao, Lin Yuan, Haibin Ling IJCV, 2020 https://arxiv.org/pdf/2009.03465.pdf Download the dataset from http://vision.cs.stonybrook.edu/~lasot/download.html """ def __init__(self): super().__init__() self.base_path = self.env_settings.lasot_extension_subset_path self.sequence_list = self._get_sequence_list() self.clean_list = self.clean_seq_list() def clean_seq_list(self): clean_lst = [] for i in range(len(self.sequence_list)): cls, _ = self.sequence_list[i].split('-') clean_lst.append(cls) return clean_lst def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_list]) def _construct_sequence(self, sequence_name): class_name = sequence_name.split('-')[0] anno_path = '{}/{}/{}/groundtruth.txt'.format(self.base_path, class_name, sequence_name) ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64) occlusion_label_path = '{}/{}/{}/full_occlusion.txt'.format(self.base_path, class_name, sequence_name) # NOTE: pandas backed seems super super slow for loading occlusion/oov masks full_occlusion = load_text(str(occlusion_label_path), delimiter=',', dtype=np.float64, backend='numpy') out_of_view_label_path = '{}/{}/{}/out_of_view.txt'.format(self.base_path, class_name, sequence_name) out_of_view = load_text(str(out_of_view_label_path), delimiter=',', dtype=np.float64, backend='numpy') target_visible = np.logical_and(full_occlusion == 0, out_of_view == 0) frames_path = '{}/{}/{}/img'.format(self.base_path, class_name, sequence_name) frames_list = ['{}/{:08d}.jpg'.format(frames_path, frame_number) for frame_number in range(1, ground_truth_rect.shape[0] + 1)] target_class = class_name return Sequence(sequence_name, frames_list, 'lasot_extension_subset', ground_truth_rect.reshape(-1, 4), object_class=target_class, target_visible=target_visible) def __len__(self): return len(self.sequence_list) def _get_sequence_list(self): sequence_list = ['atv-1', 'atv-2', 'atv-3', 'atv-4', 'atv-5', 'atv-6', 'atv-7', 'atv-8', 'atv-9', 'atv-10', 'badminton-1', 'badminton-2', 'badminton-3', 'badminton-4', 'badminton-5', 'badminton-6', 'badminton-7', 'badminton-8', 'badminton-9', 'badminton-10', 'cosplay-1', 'cosplay-10', 'cosplay-2', 'cosplay-3', 'cosplay-4', 'cosplay-5', 'cosplay-6', 'cosplay-7', 'cosplay-8', 'cosplay-9', 'dancingshoe-1', 'dancingshoe-2', 'dancingshoe-3', 'dancingshoe-4', 'dancingshoe-5', 'dancingshoe-6', 'dancingshoe-7', 'dancingshoe-8', 'dancingshoe-9', 'dancingshoe-10', 'footbag-1', 'footbag-2', 'footbag-3', 'footbag-4', 'footbag-5', 'footbag-6', 'footbag-7', 'footbag-8', 'footbag-9', 'footbag-10', 'frisbee-1', 'frisbee-2', 'frisbee-3', 'frisbee-4', 'frisbee-5', 'frisbee-6', 'frisbee-7', 'frisbee-8', 'frisbee-9', 'frisbee-10', 'jianzi-1', 'jianzi-2', 'jianzi-3', 'jianzi-4', 'jianzi-5', 'jianzi-6', 'jianzi-7', 'jianzi-8', 'jianzi-9', 'jianzi-10', 'lantern-1', 'lantern-2', 'lantern-3', 'lantern-4', 'lantern-5', 'lantern-6', 'lantern-7', 'lantern-8', 'lantern-9', 'lantern-10', 'misc-1', 'misc-2', 'misc-3', 'misc-4', 'misc-5', 'misc-6', 'misc-7', 'misc-8', 'misc-9', 'misc-10', 'opossum-1', 'opossum-2', 'opossum-3', 'opossum-4', 'opossum-5', 'opossum-6', 'opossum-7', 'opossum-8', 'opossum-9', 'opossum-10', 'paddle-1', 'paddle-2', 'paddle-3', 'paddle-4', 'paddle-5', 'paddle-6', 'paddle-7', 'paddle-8', 'paddle-9', 'paddle-10', 'raccoon-1', 'raccoon-2', 'raccoon-3', 'raccoon-4', 'raccoon-5', 'raccoon-6', 'raccoon-7', 'raccoon-8', 'raccoon-9', 'raccoon-10', 'rhino-1', 'rhino-2', 'rhino-3', 'rhino-4', 'rhino-5', 'rhino-6', 'rhino-7', 'rhino-8', 'rhino-9', 'rhino-10', 'skatingshoe-1', 'skatingshoe-2', 'skatingshoe-3', 'skatingshoe-4', 'skatingshoe-5', 'skatingshoe-6', 'skatingshoe-7', 'skatingshoe-8', 'skatingshoe-9', 'skatingshoe-10', 'wingsuit-1', 'wingsuit-2', 'wingsuit-3', 'wingsuit-4', 'wingsuit-5', 'wingsuit-6', 'wingsuit-7', 'wingsuit-8', 'wingsuit-9', 'wingsuit-10'] return sequence_list ================================================ FILE: lib/test/evaluation/local.py ================================================ from lib.test.evaluation.environment import EnvSettings def local_env_settings(): settings = EnvSettings() # Set your local paths here. settings.davis_dir = '' settings.got10k_lmdb_path = '/home/wangguijie/code/Siamese-ResNet-track/data/got10k_lmdb' settings.got10k_path = '/home/baiyifan/GOT-10k/' settings.got_packed_results_path = '' settings.got_reports_path = '' settings.itb_path = '/home/wangguijie/code/Siamese-ResNet-track/data/itb' settings.lasot_extension_subset_path_path = '/home/wangguijie/code/Siamese-ResNet-track/data/lasot_extension_subset' settings.lasot_lmdb_path = '/home/wangguijie/code/Siamese-ResNet-track/data/lasot_lmdb' settings.lasot_path = '/home/wangguijie/code/Siamese-ResNet-track/data/lasot' settings.network_path = '/data1/baiyifan/artrackv2_256_got/' # Where tracking networks are stored. settings.nfs_path = '/home/wangguijie/code/Siamese-ResNet-track/data/nfs' settings.otb_path = '/home/wangguijie/code/Siamese-ResNet-track/data/otb' settings.prj_dir = '/home/baiyifan/code/AR2_github/ARTrack-main/' settings.result_plot_path = '/data1/baiyifan/artrackv2_256_got/' settings.results_path = '/data1/baiyifan/artrackv2_256_got/' # Where to store tracking results settings.save_dir = '/data1/baiyifan/artrackv2_256_got/' settings.segmentation_path = '/home/wangguijie/code/Siamese-ResNet-track/output/test/segmentation_results' settings.tc128_path = '/home/wangguijie/code/Siamese-ResNet-track/data/TC128' settings.tn_packed_results_path = '' settings.tnl2k_path = '/home/wangguijie/code/Siamese-ResNet-track/data/tnl2k' settings.tpl_path = '' settings.trackingnet_path = '/home/wangguijie/code/Siamese-ResNet-track/data/trackingnet' settings.uav_path = '/home/wangguijie/code/Siamese-ResNet-track/data/uav' settings.vot18_path = '/home/wangguijie/code/Siamese-ResNet-track/data/vot2018' settings.vot22_path = '/home/wangguijie/code/Siamese-ResNet-track/data/vot2022' settings.vot_path = '/home/wangguijie/code/Siamese-ResNet-track/data/VOT2019' settings.youtubevos_dir = '' return settings ================================================ FILE: lib/test/evaluation/nfsdataset.py ================================================ import numpy as np from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList from lib.test.utils.load_text import load_text class NFSDataset(BaseDataset): """ NFS dataset. Publication: Need for Speed: A Benchmark for Higher Frame Rate Object Tracking H. Kiani Galoogahi, A. Fagg, C. Huang, D. Ramanan, and S.Lucey ICCV, 2017 http://openaccess.thecvf.com/content_ICCV_2017/papers/Galoogahi_Need_for_Speed_ICCV_2017_paper.pdf Download the dataset from http://ci2cv.net/nfs/index.html """ def __init__(self): super().__init__() self.base_path = self.env_settings.nfs_path self.sequence_info_list = self._get_sequence_info_list() def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_info_list]) def _construct_sequence(self, sequence_info): sequence_path = sequence_info['path'] nz = sequence_info['nz'] ext = sequence_info['ext'] start_frame = sequence_info['startFrame'] end_frame = sequence_info['endFrame'] init_omit = 0 if 'initOmit' in sequence_info: init_omit = sequence_info['initOmit'] frames = ['{base_path}/{sequence_path}/{frame:0{nz}}.{ext}'.format(base_path=self.base_path, sequence_path=sequence_path, frame=frame_num, nz=nz, ext=ext) for frame_num in range(start_frame+init_omit, end_frame+1)] anno_path = '{}/{}'.format(self.base_path, sequence_info['anno_path']) ground_truth_rect = load_text(str(anno_path), delimiter='\t', dtype=np.float64) return Sequence(sequence_info['name'], frames, 'nfs', ground_truth_rect[init_omit:,:], object_class=sequence_info['object_class']) def __len__(self): return len(self.sequence_info_list) def _get_sequence_info_list(self): sequence_info_list = [ {"name": "nfs_Gymnastics", "path": "sequences/Gymnastics", "startFrame": 1, "endFrame": 368, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_Gymnastics.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_MachLoop_jet", "path": "sequences/MachLoop_jet", "startFrame": 1, "endFrame": 99, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_MachLoop_jet.txt", "object_class": "aircraft", 'occlusion': False}, {"name": "nfs_Skiing_red", "path": "sequences/Skiing_red", "startFrame": 1, "endFrame": 69, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_Skiing_red.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_Skydiving", "path": "sequences/Skydiving", "startFrame": 1, "endFrame": 196, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_Skydiving.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_airboard_1", "path": "sequences/airboard_1", "startFrame": 1, "endFrame": 425, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_airboard_1.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_airplane_landing", "path": "sequences/airplane_landing", "startFrame": 1, "endFrame": 81, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_airplane_landing.txt", "object_class": "aircraft", 'occlusion': False}, {"name": "nfs_airtable_3", "path": "sequences/airtable_3", "startFrame": 1, "endFrame": 482, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_airtable_3.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_basketball_1", "path": "sequences/basketball_1", "startFrame": 1, "endFrame": 282, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_1.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_basketball_2", "path": "sequences/basketball_2", "startFrame": 1, "endFrame": 102, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_2.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_basketball_3", "path": "sequences/basketball_3", "startFrame": 1, "endFrame": 421, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_3.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_basketball_6", "path": "sequences/basketball_6", "startFrame": 1, "endFrame": 224, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_6.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_basketball_7", "path": "sequences/basketball_7", "startFrame": 1, "endFrame": 240, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_7.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_basketball_player", "path": "sequences/basketball_player", "startFrame": 1, "endFrame": 369, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_player.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_basketball_player_2", "path": "sequences/basketball_player_2", "startFrame": 1, "endFrame": 437, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_player_2.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_beach_flipback_person", "path": "sequences/beach_flipback_person", "startFrame": 1, "endFrame": 61, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_beach_flipback_person.txt", "object_class": "person head", 'occlusion': False}, {"name": "nfs_bee", "path": "sequences/bee", "startFrame": 1, "endFrame": 45, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bee.txt", "object_class": "insect", 'occlusion': False}, {"name": "nfs_biker_acrobat", "path": "sequences/biker_acrobat", "startFrame": 1, "endFrame": 128, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_acrobat.txt", "object_class": "bicycle", 'occlusion': False}, {"name": "nfs_biker_all_1", "path": "sequences/biker_all_1", "startFrame": 1, "endFrame": 113, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_all_1.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_biker_head_2", "path": "sequences/biker_head_2", "startFrame": 1, "endFrame": 132, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_head_2.txt", "object_class": "person head", 'occlusion': False}, {"name": "nfs_biker_head_3", "path": "sequences/biker_head_3", "startFrame": 1, "endFrame": 254, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_head_3.txt", "object_class": "person head", 'occlusion': False}, {"name": "nfs_biker_upper_body", "path": "sequences/biker_upper_body", "startFrame": 1, "endFrame": 194, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_upper_body.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_biker_whole_body", "path": "sequences/biker_whole_body", "startFrame": 1, "endFrame": 572, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_whole_body.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_billiard_2", "path": "sequences/billiard_2", "startFrame": 1, "endFrame": 604, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_billiard_2.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_billiard_3", "path": "sequences/billiard_3", "startFrame": 1, "endFrame": 698, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_billiard_3.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_billiard_6", "path": "sequences/billiard_6", "startFrame": 1, "endFrame": 771, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_billiard_6.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_billiard_7", "path": "sequences/billiard_7", "startFrame": 1, "endFrame": 724, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_billiard_7.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_billiard_8", "path": "sequences/billiard_8", "startFrame": 1, "endFrame": 778, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_billiard_8.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_bird_2", "path": "sequences/bird_2", "startFrame": 1, "endFrame": 476, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bird_2.txt", "object_class": "bird", 'occlusion': False}, {"name": "nfs_book", "path": "sequences/book", "startFrame": 1, "endFrame": 288, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_book.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_bottle", "path": "sequences/bottle", "startFrame": 1, "endFrame": 2103, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bottle.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_bowling_1", "path": "sequences/bowling_1", "startFrame": 1, "endFrame": 303, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bowling_1.txt", "object_class": "ball", 'occlusion': True}, {"name": "nfs_bowling_2", "path": "sequences/bowling_2", "startFrame": 1, "endFrame": 710, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bowling_2.txt", "object_class": "ball", 'occlusion': True}, {"name": "nfs_bowling_3", "path": "sequences/bowling_3", "startFrame": 1, "endFrame": 271, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bowling_3.txt", "object_class": "ball", 'occlusion': True}, {"name": "nfs_bowling_6", "path": "sequences/bowling_6", "startFrame": 1, "endFrame": 260, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bowling_6.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_bowling_ball", "path": "sequences/bowling_ball", "startFrame": 1, "endFrame": 275, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bowling_ball.txt", "object_class": "ball", 'occlusion': True}, {"name": "nfs_bunny", "path": "sequences/bunny", "startFrame": 1, "endFrame": 705, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bunny.txt", "object_class": "mammal", 'occlusion': False}, {"name": "nfs_car", "path": "sequences/car", "startFrame": 1, "endFrame": 2020, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car.txt", "object_class": "car", 'occlusion': True}, {"name": "nfs_car_camaro", "path": "sequences/car_camaro", "startFrame": 1, "endFrame": 36, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_camaro.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_car_drifting", "path": "sequences/car_drifting", "startFrame": 1, "endFrame": 173, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_drifting.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_car_jumping", "path": "sequences/car_jumping", "startFrame": 1, "endFrame": 22, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_jumping.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_car_rc_rolling", "path": "sequences/car_rc_rolling", "startFrame": 1, "endFrame": 62, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_rc_rolling.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_car_rc_rotating", "path": "sequences/car_rc_rotating", "startFrame": 1, "endFrame": 80, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_rc_rotating.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_car_side", "path": "sequences/car_side", "startFrame": 1, "endFrame": 108, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_side.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_car_white", "path": "sequences/car_white", "startFrame": 1, "endFrame": 2063, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_white.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_cheetah", "path": "sequences/cheetah", "startFrame": 1, "endFrame": 167, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_cheetah.txt", "object_class": "mammal", 'occlusion': True}, {"name": "nfs_cup", "path": "sequences/cup", "startFrame": 1, "endFrame": 1281, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_cup.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_cup_2", "path": "sequences/cup_2", "startFrame": 1, "endFrame": 182, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_cup_2.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_dog", "path": "sequences/dog", "startFrame": 1, "endFrame": 1030, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dog.txt", "object_class": "dog", 'occlusion': True}, {"name": "nfs_dog_1", "path": "sequences/dog_1", "startFrame": 1, "endFrame": 168, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dog_1.txt", "object_class": "dog", 'occlusion': False}, {"name": "nfs_dog_2", "path": "sequences/dog_2", "startFrame": 1, "endFrame": 594, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dog_2.txt", "object_class": "dog", 'occlusion': True}, {"name": "nfs_dog_3", "path": "sequences/dog_3", "startFrame": 1, "endFrame": 200, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dog_3.txt", "object_class": "dog", 'occlusion': False}, {"name": "nfs_dogs", "path": "sequences/dogs", "startFrame": 1, "endFrame": 198, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dogs.txt", "object_class": "dog", 'occlusion': True}, {"name": "nfs_dollar", "path": "sequences/dollar", "startFrame": 1, "endFrame": 1426, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dollar.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_drone", "path": "sequences/drone", "startFrame": 1, "endFrame": 70, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_drone.txt", "object_class": "aircraft", 'occlusion': False}, {"name": "nfs_ducks_lake", "path": "sequences/ducks_lake", "startFrame": 1, "endFrame": 107, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_ducks_lake.txt", "object_class": "bird", 'occlusion': False}, {"name": "nfs_exit", "path": "sequences/exit", "startFrame": 1, "endFrame": 359, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_exit.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_first", "path": "sequences/first", "startFrame": 1, "endFrame": 435, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_first.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_flower", "path": "sequences/flower", "startFrame": 1, "endFrame": 448, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_flower.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_footbal_skill", "path": "sequences/footbal_skill", "startFrame": 1, "endFrame": 131, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_footbal_skill.txt", "object_class": "ball", 'occlusion': True}, {"name": "nfs_helicopter", "path": "sequences/helicopter", "startFrame": 1, "endFrame": 310, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_helicopter.txt", "object_class": "aircraft", 'occlusion': False}, {"name": "nfs_horse_jumping", "path": "sequences/horse_jumping", "startFrame": 1, "endFrame": 117, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_horse_jumping.txt", "object_class": "horse", 'occlusion': True}, {"name": "nfs_horse_running", "path": "sequences/horse_running", "startFrame": 1, "endFrame": 139, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_horse_running.txt", "object_class": "horse", 'occlusion': False}, {"name": "nfs_iceskating_6", "path": "sequences/iceskating_6", "startFrame": 1, "endFrame": 603, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_iceskating_6.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_jellyfish_5", "path": "sequences/jellyfish_5", "startFrame": 1, "endFrame": 746, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_jellyfish_5.txt", "object_class": "invertebrate", 'occlusion': False}, {"name": "nfs_kid_swing", "path": "sequences/kid_swing", "startFrame": 1, "endFrame": 169, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_kid_swing.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_motorcross", "path": "sequences/motorcross", "startFrame": 1, "endFrame": 39, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_motorcross.txt", "object_class": "vehicle", 'occlusion': True}, {"name": "nfs_motorcross_kawasaki", "path": "sequences/motorcross_kawasaki", "startFrame": 1, "endFrame": 65, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_motorcross_kawasaki.txt", "object_class": "vehicle", 'occlusion': False}, {"name": "nfs_parkour", "path": "sequences/parkour", "startFrame": 1, "endFrame": 58, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_parkour.txt", "object_class": "person head", 'occlusion': False}, {"name": "nfs_person_scooter", "path": "sequences/person_scooter", "startFrame": 1, "endFrame": 413, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_person_scooter.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_pingpong_2", "path": "sequences/pingpong_2", "startFrame": 1, "endFrame": 1277, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_pingpong_2.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_pingpong_7", "path": "sequences/pingpong_7", "startFrame": 1, "endFrame": 1290, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_pingpong_7.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_pingpong_8", "path": "sequences/pingpong_8", "startFrame": 1, "endFrame": 296, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_pingpong_8.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_purse", "path": "sequences/purse", "startFrame": 1, "endFrame": 968, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_purse.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_rubber", "path": "sequences/rubber", "startFrame": 1, "endFrame": 1328, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_rubber.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_running", "path": "sequences/running", "startFrame": 1, "endFrame": 677, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_running.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_running_100_m", "path": "sequences/running_100_m", "startFrame": 1, "endFrame": 313, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_running_100_m.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_running_100_m_2", "path": "sequences/running_100_m_2", "startFrame": 1, "endFrame": 337, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_running_100_m_2.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_running_2", "path": "sequences/running_2", "startFrame": 1, "endFrame": 363, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_running_2.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_shuffleboard_1", "path": "sequences/shuffleboard_1", "startFrame": 1, "endFrame": 42, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffleboard_1.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_shuffleboard_2", "path": "sequences/shuffleboard_2", "startFrame": 1, "endFrame": 41, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffleboard_2.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_shuffleboard_4", "path": "sequences/shuffleboard_4", "startFrame": 1, "endFrame": 62, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffleboard_4.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_shuffleboard_5", "path": "sequences/shuffleboard_5", "startFrame": 1, "endFrame": 32, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffleboard_5.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_shuffleboard_6", "path": "sequences/shuffleboard_6", "startFrame": 1, "endFrame": 52, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffleboard_6.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_shuffletable_2", "path": "sequences/shuffletable_2", "startFrame": 1, "endFrame": 372, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffletable_2.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_shuffletable_3", "path": "sequences/shuffletable_3", "startFrame": 1, "endFrame": 368, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffletable_3.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_shuffletable_4", "path": "sequences/shuffletable_4", "startFrame": 1, "endFrame": 101, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffletable_4.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_ski_long", "path": "sequences/ski_long", "startFrame": 1, "endFrame": 274, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_ski_long.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_soccer_ball", "path": "sequences/soccer_ball", "startFrame": 1, "endFrame": 163, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_soccer_ball.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_soccer_ball_2", "path": "sequences/soccer_ball_2", "startFrame": 1, "endFrame": 1934, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_soccer_ball_2.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_soccer_ball_3", "path": "sequences/soccer_ball_3", "startFrame": 1, "endFrame": 1381, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_soccer_ball_3.txt", "object_class": "ball", 'occlusion': False}, {"name": "nfs_soccer_player_2", "path": "sequences/soccer_player_2", "startFrame": 1, "endFrame": 475, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_soccer_player_2.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_soccer_player_3", "path": "sequences/soccer_player_3", "startFrame": 1, "endFrame": 319, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_soccer_player_3.txt", "object_class": "person", 'occlusion': True}, {"name": "nfs_stop_sign", "path": "sequences/stop_sign", "startFrame": 1, "endFrame": 302, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_stop_sign.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_suv", "path": "sequences/suv", "startFrame": 1, "endFrame": 2584, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_suv.txt", "object_class": "car", 'occlusion': False}, {"name": "nfs_tiger", "path": "sequences/tiger", "startFrame": 1, "endFrame": 1556, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_tiger.txt", "object_class": "mammal", 'occlusion': False}, {"name": "nfs_walking", "path": "sequences/walking", "startFrame": 1, "endFrame": 555, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_walking.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_walking_3", "path": "sequences/walking_3", "startFrame": 1, "endFrame": 1427, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_walking_3.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_water_ski_2", "path": "sequences/water_ski_2", "startFrame": 1, "endFrame": 47, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_water_ski_2.txt", "object_class": "person", 'occlusion': False}, {"name": "nfs_yoyo", "path": "sequences/yoyo", "startFrame": 1, "endFrame": 67, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_yoyo.txt", "object_class": "other", 'occlusion': False}, {"name": "nfs_zebra_fish", "path": "sequences/zebra_fish", "startFrame": 1, "endFrame": 671, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_zebra_fish.txt", "object_class": "fish", 'occlusion': False}, ] return sequence_info_list ================================================ FILE: lib/test/evaluation/otbdataset.py ================================================ import numpy as np from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList from lib.test.utils.load_text import load_text class OTBDataset(BaseDataset): """ OTB-2015 dataset Publication: Object Tracking Benchmark Wu, Yi, Jongwoo Lim, and Ming-hsuan Yan TPAMI, 2015 http://faculty.ucmerced.edu/mhyang/papers/pami15_tracking_benchmark.pdf Download the dataset from http://cvlab.hanyang.ac.kr/tracker_benchmark/index.html """ def __init__(self): super().__init__() self.base_path = self.env_settings.otb_path self.sequence_info_list = self._get_sequence_info_list() def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_info_list]) def _construct_sequence(self, sequence_info): sequence_path = sequence_info['path'] nz = sequence_info['nz'] ext = sequence_info['ext'] start_frame = sequence_info['startFrame'] end_frame = sequence_info['endFrame'] init_omit = 0 if 'initOmit' in sequence_info: init_omit = sequence_info['initOmit'] frames = ['{base_path}/{sequence_path}/{frame:0{nz}}.{ext}'.format(base_path=self.base_path, sequence_path=sequence_path, frame=frame_num, nz=nz, ext=ext) for frame_num in range(start_frame+init_omit, end_frame+1)] anno_path = '{}/{}'.format(self.base_path, sequence_info['anno_path']) # NOTE: OTB has some weird annos which panda cannot handle ground_truth_rect = load_text(str(anno_path), delimiter=(',', None), dtype=np.float64, backend='numpy') return Sequence(sequence_info['name'], frames, 'otb', ground_truth_rect[init_omit:,:], object_class=sequence_info['object_class']) def __len__(self): return len(self.sequence_info_list) def _get_sequence_info_list(self): sequence_info_list = [ {"name": "Basketball", "path": "Basketball/img", "startFrame": 1, "endFrame": 725, "nz": 4, "ext": "jpg", "anno_path": "Basketball/groundtruth_rect.txt", "object_class": "person"}, {"name": "Biker", "path": "Biker/img", "startFrame": 1, "endFrame": 142, "nz": 4, "ext": "jpg", "anno_path": "Biker/groundtruth_rect.txt", "object_class": "person head"}, {"name": "Bird1", "path": "Bird1/img", "startFrame": 1, "endFrame": 408, "nz": 4, "ext": "jpg", "anno_path": "Bird1/groundtruth_rect.txt", "object_class": "bird"}, {"name": "Bird2", "path": "Bird2/img", "startFrame": 1, "endFrame": 99, "nz": 4, "ext": "jpg", "anno_path": "Bird2/groundtruth_rect.txt", "object_class": "bird"}, {"name": "BlurBody", "path": "BlurBody/img", "startFrame": 1, "endFrame": 334, "nz": 4, "ext": "jpg", "anno_path": "BlurBody/groundtruth_rect.txt", "object_class": "person"}, {"name": "BlurCar1", "path": "BlurCar1/img", "startFrame": 247, "endFrame": 988, "nz": 4, "ext": "jpg", "anno_path": "BlurCar1/groundtruth_rect.txt", "object_class": "car"}, {"name": "BlurCar2", "path": "BlurCar2/img", "startFrame": 1, "endFrame": 585, "nz": 4, "ext": "jpg", "anno_path": "BlurCar2/groundtruth_rect.txt", "object_class": "car"}, {"name": "BlurCar3", "path": "BlurCar3/img", "startFrame": 3, "endFrame": 359, "nz": 4, "ext": "jpg", "anno_path": "BlurCar3/groundtruth_rect.txt", "object_class": "car"}, {"name": "BlurCar4", "path": "BlurCar4/img", "startFrame": 18, "endFrame": 397, "nz": 4, "ext": "jpg", "anno_path": "BlurCar4/groundtruth_rect.txt", "object_class": "car"}, {"name": "BlurFace", "path": "BlurFace/img", "startFrame": 1, "endFrame": 493, "nz": 4, "ext": "jpg", "anno_path": "BlurFace/groundtruth_rect.txt", "object_class": "face"}, {"name": "BlurOwl", "path": "BlurOwl/img", "startFrame": 1, "endFrame": 631, "nz": 4, "ext": "jpg", "anno_path": "BlurOwl/groundtruth_rect.txt", "object_class": "other"}, {"name": "Board", "path": "Board/img", "startFrame": 1, "endFrame": 698, "nz": 5, "ext": "jpg", "anno_path": "Board/groundtruth_rect.txt", "object_class": "other"}, {"name": "Bolt", "path": "Bolt/img", "startFrame": 1, "endFrame": 350, "nz": 4, "ext": "jpg", "anno_path": "Bolt/groundtruth_rect.txt", "object_class": "person"}, {"name": "Bolt2", "path": "Bolt2/img", "startFrame": 1, "endFrame": 293, "nz": 4, "ext": "jpg", "anno_path": "Bolt2/groundtruth_rect.txt", "object_class": "person"}, {"name": "Box", "path": "Box/img", "startFrame": 1, "endFrame": 1161, "nz": 4, "ext": "jpg", "anno_path": "Box/groundtruth_rect.txt", "object_class": "other"}, {"name": "Boy", "path": "Boy/img", "startFrame": 1, "endFrame": 602, "nz": 4, "ext": "jpg", "anno_path": "Boy/groundtruth_rect.txt", "object_class": "face"}, {"name": "Car1", "path": "Car1/img", "startFrame": 1, "endFrame": 1020, "nz": 4, "ext": "jpg", "anno_path": "Car1/groundtruth_rect.txt", "object_class": "car"}, {"name": "Car2", "path": "Car2/img", "startFrame": 1, "endFrame": 913, "nz": 4, "ext": "jpg", "anno_path": "Car2/groundtruth_rect.txt", "object_class": "car"}, {"name": "Car24", "path": "Car24/img", "startFrame": 1, "endFrame": 3059, "nz": 4, "ext": "jpg", "anno_path": "Car24/groundtruth_rect.txt", "object_class": "car"}, {"name": "Car4", "path": "Car4/img", "startFrame": 1, "endFrame": 659, "nz": 4, "ext": "jpg", "anno_path": "Car4/groundtruth_rect.txt", "object_class": "car"}, {"name": "CarDark", "path": "CarDark/img", "startFrame": 1, "endFrame": 393, "nz": 4, "ext": "jpg", "anno_path": "CarDark/groundtruth_rect.txt", "object_class": "car"}, {"name": "CarScale", "path": "CarScale/img", "startFrame": 1, "endFrame": 252, "nz": 4, "ext": "jpg", "anno_path": "CarScale/groundtruth_rect.txt", "object_class": "car"}, {"name": "ClifBar", "path": "ClifBar/img", "startFrame": 1, "endFrame": 472, "nz": 4, "ext": "jpg", "anno_path": "ClifBar/groundtruth_rect.txt", "object_class": "other"}, {"name": "Coke", "path": "Coke/img", "startFrame": 1, "endFrame": 291, "nz": 4, "ext": "jpg", "anno_path": "Coke/groundtruth_rect.txt", "object_class": "other"}, {"name": "Couple", "path": "Couple/img", "startFrame": 1, "endFrame": 140, "nz": 4, "ext": "jpg", "anno_path": "Couple/groundtruth_rect.txt", "object_class": "person"}, {"name": "Coupon", "path": "Coupon/img", "startFrame": 1, "endFrame": 327, "nz": 4, "ext": "jpg", "anno_path": "Coupon/groundtruth_rect.txt", "object_class": "other"}, {"name": "Crossing", "path": "Crossing/img", "startFrame": 1, "endFrame": 120, "nz": 4, "ext": "jpg", "anno_path": "Crossing/groundtruth_rect.txt", "object_class": "person"}, {"name": "Crowds", "path": "Crowds/img", "startFrame": 1, "endFrame": 347, "nz": 4, "ext": "jpg", "anno_path": "Crowds/groundtruth_rect.txt", "object_class": "person"}, {"name": "Dancer", "path": "Dancer/img", "startFrame": 1, "endFrame": 225, "nz": 4, "ext": "jpg", "anno_path": "Dancer/groundtruth_rect.txt", "object_class": "person"}, {"name": "Dancer2", "path": "Dancer2/img", "startFrame": 1, "endFrame": 150, "nz": 4, "ext": "jpg", "anno_path": "Dancer2/groundtruth_rect.txt", "object_class": "person"}, {"name": "David", "path": "David/img", "startFrame": 300, "endFrame": 770, "nz": 4, "ext": "jpg", "anno_path": "David/groundtruth_rect.txt", "object_class": "face"}, {"name": "David2", "path": "David2/img", "startFrame": 1, "endFrame": 537, "nz": 4, "ext": "jpg", "anno_path": "David2/groundtruth_rect.txt", "object_class": "face"}, {"name": "David3", "path": "David3/img", "startFrame": 1, "endFrame": 252, "nz": 4, "ext": "jpg", "anno_path": "David3/groundtruth_rect.txt", "object_class": "person"}, {"name": "Deer", "path": "Deer/img", "startFrame": 1, "endFrame": 71, "nz": 4, "ext": "jpg", "anno_path": "Deer/groundtruth_rect.txt", "object_class": "mammal"}, {"name": "Diving", "path": "Diving/img", "startFrame": 1, "endFrame": 215, "nz": 4, "ext": "jpg", "anno_path": "Diving/groundtruth_rect.txt", "object_class": "person"}, {"name": "Dog", "path": "Dog/img", "startFrame": 1, "endFrame": 127, "nz": 4, "ext": "jpg", "anno_path": "Dog/groundtruth_rect.txt", "object_class": "dog"}, {"name": "Dog1", "path": "Dog1/img", "startFrame": 1, "endFrame": 1350, "nz": 4, "ext": "jpg", "anno_path": "Dog1/groundtruth_rect.txt", "object_class": "dog"}, {"name": "Doll", "path": "Doll/img", "startFrame": 1, "endFrame": 3872, "nz": 4, "ext": "jpg", "anno_path": "Doll/groundtruth_rect.txt", "object_class": "other"}, {"name": "DragonBaby", "path": "DragonBaby/img", "startFrame": 1, "endFrame": 113, "nz": 4, "ext": "jpg", "anno_path": "DragonBaby/groundtruth_rect.txt", "object_class": "face"}, {"name": "Dudek", "path": "Dudek/img", "startFrame": 1, "endFrame": 1145, "nz": 4, "ext": "jpg", "anno_path": "Dudek/groundtruth_rect.txt", "object_class": "face"}, {"name": "FaceOcc1", "path": "FaceOcc1/img", "startFrame": 1, "endFrame": 892, "nz": 4, "ext": "jpg", "anno_path": "FaceOcc1/groundtruth_rect.txt", "object_class": "face"}, {"name": "FaceOcc2", "path": "FaceOcc2/img", "startFrame": 1, "endFrame": 812, "nz": 4, "ext": "jpg", "anno_path": "FaceOcc2/groundtruth_rect.txt", "object_class": "face"}, {"name": "Fish", "path": "Fish/img", "startFrame": 1, "endFrame": 476, "nz": 4, "ext": "jpg", "anno_path": "Fish/groundtruth_rect.txt", "object_class": "other"}, {"name": "FleetFace", "path": "FleetFace/img", "startFrame": 1, "endFrame": 707, "nz": 4, "ext": "jpg", "anno_path": "FleetFace/groundtruth_rect.txt", "object_class": "face"}, {"name": "Football", "path": "Football/img", "startFrame": 1, "endFrame": 362, "nz": 4, "ext": "jpg", "anno_path": "Football/groundtruth_rect.txt", "object_class": "person head"}, {"name": "Football1", "path": "Football1/img", "startFrame": 1, "endFrame": 74, "nz": 4, "ext": "jpg", "anno_path": "Football1/groundtruth_rect.txt", "object_class": "face"}, {"name": "Freeman1", "path": "Freeman1/img", "startFrame": 1, "endFrame": 326, "nz": 4, "ext": "jpg", "anno_path": "Freeman1/groundtruth_rect.txt", "object_class": "face"}, {"name": "Freeman3", "path": "Freeman3/img", "startFrame": 1, "endFrame": 460, "nz": 4, "ext": "jpg", "anno_path": "Freeman3/groundtruth_rect.txt", "object_class": "face"}, {"name": "Freeman4", "path": "Freeman4/img", "startFrame": 1, "endFrame": 283, "nz": 4, "ext": "jpg", "anno_path": "Freeman4/groundtruth_rect.txt", "object_class": "face"}, {"name": "Girl", "path": "Girl/img", "startFrame": 1, "endFrame": 500, "nz": 4, "ext": "jpg", "anno_path": "Girl/groundtruth_rect.txt", "object_class": "face"}, {"name": "Girl2", "path": "Girl2/img", "startFrame": 1, "endFrame": 1500, "nz": 4, "ext": "jpg", "anno_path": "Girl2/groundtruth_rect.txt", "object_class": "person"}, {"name": "Gym", "path": "Gym/img", "startFrame": 1, "endFrame": 767, "nz": 4, "ext": "jpg", "anno_path": "Gym/groundtruth_rect.txt", "object_class": "person"}, {"name": "Human2", "path": "Human2/img", "startFrame": 1, "endFrame": 1128, "nz": 4, "ext": "jpg", "anno_path": "Human2/groundtruth_rect.txt", "object_class": "person"}, {"name": "Human3", "path": "Human3/img", "startFrame": 1, "endFrame": 1698, "nz": 4, "ext": "jpg", "anno_path": "Human3/groundtruth_rect.txt", "object_class": "person"}, {"name": "Human4_2", "path": "Human4/img", "startFrame": 1, "endFrame": 667, "nz": 4, "ext": "jpg", "anno_path": "Human4/groundtruth_rect.2.txt", "object_class": "person"}, {"name": "Human5", "path": "Human5/img", "startFrame": 1, "endFrame": 713, "nz": 4, "ext": "jpg", "anno_path": "Human5/groundtruth_rect.txt", "object_class": "person"}, {"name": "Human6", "path": "Human6/img", "startFrame": 1, "endFrame": 792, "nz": 4, "ext": "jpg", "anno_path": "Human6/groundtruth_rect.txt", "object_class": "person"}, {"name": "Human7", "path": "Human7/img", "startFrame": 1, "endFrame": 250, "nz": 4, "ext": "jpg", "anno_path": "Human7/groundtruth_rect.txt", "object_class": "person"}, {"name": "Human8", "path": "Human8/img", "startFrame": 1, "endFrame": 128, "nz": 4, "ext": "jpg", "anno_path": "Human8/groundtruth_rect.txt", "object_class": "person"}, {"name": "Human9", "path": "Human9/img", "startFrame": 1, "endFrame": 305, "nz": 4, "ext": "jpg", "anno_path": "Human9/groundtruth_rect.txt", "object_class": "person"}, {"name": "Ironman", "path": "Ironman/img", "startFrame": 1, "endFrame": 166, "nz": 4, "ext": "jpg", "anno_path": "Ironman/groundtruth_rect.txt", "object_class": "person head"}, {"name": "Jogging_1", "path": "Jogging/img", "startFrame": 1, "endFrame": 307, "nz": 4, "ext": "jpg", "anno_path": "Jogging/groundtruth_rect.1.txt", "object_class": "person"}, {"name": "Jogging_2", "path": "Jogging/img", "startFrame": 1, "endFrame": 307, "nz": 4, "ext": "jpg", "anno_path": "Jogging/groundtruth_rect.2.txt", "object_class": "person"}, {"name": "Jump", "path": "Jump/img", "startFrame": 1, "endFrame": 122, "nz": 4, "ext": "jpg", "anno_path": "Jump/groundtruth_rect.txt", "object_class": "person"}, {"name": "Jumping", "path": "Jumping/img", "startFrame": 1, "endFrame": 313, "nz": 4, "ext": "jpg", "anno_path": "Jumping/groundtruth_rect.txt", "object_class": "face"}, {"name": "KiteSurf", "path": "KiteSurf/img", "startFrame": 1, "endFrame": 84, "nz": 4, "ext": "jpg", "anno_path": "KiteSurf/groundtruth_rect.txt", "object_class": "face"}, {"name": "Lemming", "path": "Lemming/img", "startFrame": 1, "endFrame": 1336, "nz": 4, "ext": "jpg", "anno_path": "Lemming/groundtruth_rect.txt", "object_class": "other"}, {"name": "Liquor", "path": "Liquor/img", "startFrame": 1, "endFrame": 1741, "nz": 4, "ext": "jpg", "anno_path": "Liquor/groundtruth_rect.txt", "object_class": "other"}, {"name": "Man", "path": "Man/img", "startFrame": 1, "endFrame": 134, "nz": 4, "ext": "jpg", "anno_path": "Man/groundtruth_rect.txt", "object_class": "face"}, {"name": "Matrix", "path": "Matrix/img", "startFrame": 1, "endFrame": 100, "nz": 4, "ext": "jpg", "anno_path": "Matrix/groundtruth_rect.txt", "object_class": "person head"}, {"name": "Mhyang", "path": "Mhyang/img", "startFrame": 1, "endFrame": 1490, "nz": 4, "ext": "jpg", "anno_path": "Mhyang/groundtruth_rect.txt", "object_class": "face"}, {"name": "MotorRolling", "path": "MotorRolling/img", "startFrame": 1, "endFrame": 164, "nz": 4, "ext": "jpg", "anno_path": "MotorRolling/groundtruth_rect.txt", "object_class": "vehicle"}, {"name": "MountainBike", "path": "MountainBike/img", "startFrame": 1, "endFrame": 228, "nz": 4, "ext": "jpg", "anno_path": "MountainBike/groundtruth_rect.txt", "object_class": "bicycle"}, {"name": "Panda", "path": "Panda/img", "startFrame": 1, "endFrame": 1000, "nz": 4, "ext": "jpg", "anno_path": "Panda/groundtruth_rect.txt", "object_class": "mammal"}, {"name": "RedTeam", "path": "RedTeam/img", "startFrame": 1, "endFrame": 1918, "nz": 4, "ext": "jpg", "anno_path": "RedTeam/groundtruth_rect.txt", "object_class": "vehicle"}, {"name": "Rubik", "path": "Rubik/img", "startFrame": 1, "endFrame": 1997, "nz": 4, "ext": "jpg", "anno_path": "Rubik/groundtruth_rect.txt", "object_class": "other"}, {"name": "Shaking", "path": "Shaking/img", "startFrame": 1, "endFrame": 365, "nz": 4, "ext": "jpg", "anno_path": "Shaking/groundtruth_rect.txt", "object_class": "face"}, {"name": "Singer1", "path": "Singer1/img", "startFrame": 1, "endFrame": 351, "nz": 4, "ext": "jpg", "anno_path": "Singer1/groundtruth_rect.txt", "object_class": "person"}, {"name": "Singer2", "path": "Singer2/img", "startFrame": 1, "endFrame": 366, "nz": 4, "ext": "jpg", "anno_path": "Singer2/groundtruth_rect.txt", "object_class": "person"}, {"name": "Skater", "path": "Skater/img", "startFrame": 1, "endFrame": 160, "nz": 4, "ext": "jpg", "anno_path": "Skater/groundtruth_rect.txt", "object_class": "person"}, {"name": "Skater2", "path": "Skater2/img", "startFrame": 1, "endFrame": 435, "nz": 4, "ext": "jpg", "anno_path": "Skater2/groundtruth_rect.txt", "object_class": "person"}, {"name": "Skating1", "path": "Skating1/img", "startFrame": 1, "endFrame": 400, "nz": 4, "ext": "jpg", "anno_path": "Skating1/groundtruth_rect.txt", "object_class": "person"}, {"name": "Skating2_1", "path": "Skating2/img", "startFrame": 1, "endFrame": 473, "nz": 4, "ext": "jpg", "anno_path": "Skating2/groundtruth_rect.1.txt", "object_class": "person"}, {"name": "Skating2_2", "path": "Skating2/img", "startFrame": 1, "endFrame": 473, "nz": 4, "ext": "jpg", "anno_path": "Skating2/groundtruth_rect.2.txt", "object_class": "person"}, {"name": "Skiing", "path": "Skiing/img", "startFrame": 1, "endFrame": 81, "nz": 4, "ext": "jpg", "anno_path": "Skiing/groundtruth_rect.txt", "object_class": "person"}, {"name": "Soccer", "path": "Soccer/img", "startFrame": 1, "endFrame": 392, "nz": 4, "ext": "jpg", "anno_path": "Soccer/groundtruth_rect.txt", "object_class": "face"}, {"name": "Subway", "path": "Subway/img", "startFrame": 1, "endFrame": 175, "nz": 4, "ext": "jpg", "anno_path": "Subway/groundtruth_rect.txt", "object_class": "person"}, {"name": "Surfer", "path": "Surfer/img", "startFrame": 1, "endFrame": 376, "nz": 4, "ext": "jpg", "anno_path": "Surfer/groundtruth_rect.txt", "object_class": "person head"}, {"name": "Suv", "path": "Suv/img", "startFrame": 1, "endFrame": 945, "nz": 4, "ext": "jpg", "anno_path": "Suv/groundtruth_rect.txt", "object_class": "car"}, {"name": "Sylvester", "path": "Sylvester/img", "startFrame": 1, "endFrame": 1345, "nz": 4, "ext": "jpg", "anno_path": "Sylvester/groundtruth_rect.txt", "object_class": "other"}, {"name": "Tiger1", "path": "Tiger1/img", "startFrame": 1, "endFrame": 354, "nz": 4, "ext": "jpg", "anno_path": "Tiger1/groundtruth_rect.txt", "initOmit": 5, "object_class": "other"}, {"name": "Tiger2", "path": "Tiger2/img", "startFrame": 1, "endFrame": 365, "nz": 4, "ext": "jpg", "anno_path": "Tiger2/groundtruth_rect.txt", "object_class": "other"}, {"name": "Toy", "path": "Toy/img", "startFrame": 1, "endFrame": 271, "nz": 4, "ext": "jpg", "anno_path": "Toy/groundtruth_rect.txt", "object_class": "other"}, {"name": "Trans", "path": "Trans/img", "startFrame": 1, "endFrame": 124, "nz": 4, "ext": "jpg", "anno_path": "Trans/groundtruth_rect.txt", "object_class": "other"}, {"name": "Trellis", "path": "Trellis/img", "startFrame": 1, "endFrame": 569, "nz": 4, "ext": "jpg", "anno_path": "Trellis/groundtruth_rect.txt", "object_class": "face"}, {"name": "Twinnings", "path": "Twinnings/img", "startFrame": 1, "endFrame": 472, "nz": 4, "ext": "jpg", "anno_path": "Twinnings/groundtruth_rect.txt", "object_class": "other"}, {"name": "Vase", "path": "Vase/img", "startFrame": 1, "endFrame": 271, "nz": 4, "ext": "jpg", "anno_path": "Vase/groundtruth_rect.txt", "object_class": "other"}, {"name": "Walking", "path": "Walking/img", "startFrame": 1, "endFrame": 412, "nz": 4, "ext": "jpg", "anno_path": "Walking/groundtruth_rect.txt", "object_class": "person"}, {"name": "Walking2", "path": "Walking2/img", "startFrame": 1, "endFrame": 500, "nz": 4, "ext": "jpg", "anno_path": "Walking2/groundtruth_rect.txt", "object_class": "person"}, {"name": "Woman", "path": "Woman/img", "startFrame": 1, "endFrame": 597, "nz": 4, "ext": "jpg", "anno_path": "Woman/groundtruth_rect.txt", "object_class": "person"} ] return sequence_info_list ================================================ FILE: lib/test/evaluation/running.py ================================================ import numpy as np import multiprocessing import os import sys from itertools import product from collections import OrderedDict from lib.test.evaluation import Sequence, Tracker import torch def _save_tracker_output(seq: Sequence, tracker: Tracker, output: dict): """Saves the output of the tracker.""" if not os.path.exists(tracker.results_dir): print("create tracking result dir:", tracker.results_dir) os.makedirs(tracker.results_dir) if seq.dataset in ['trackingnet', 'got10k']: if not os.path.exists(os.path.join(tracker.results_dir, seq.dataset)): os.makedirs(os.path.join(tracker.results_dir, seq.dataset)) '''2021.1.5 create new folder for these two datasets''' if seq.dataset in ['trackingnet', 'got10k']: base_results_path = os.path.join(tracker.results_dir, seq.dataset, seq.name) else: base_results_path = os.path.join(tracker.results_dir, seq.name) def save_bb(file, data): tracked_bb = np.array(data).astype(int) np.savetxt(file, tracked_bb, delimiter='\t', fmt='%d') def save_time(file, data): exec_times = np.array(data).astype(float) np.savetxt(file, exec_times, delimiter='\t', fmt='%f') def save_score(file, data): scores = np.array(data).astype(float) np.savetxt(file, scores, delimiter='\t', fmt='%.2f') def _convert_dict(input_dict): data_dict = {} for elem in input_dict: for k, v in elem.items(): if k in data_dict.keys(): data_dict[k].append(v) else: data_dict[k] = [v, ] return data_dict for key, data in output.items(): # If data is empty if not data: continue if key == 'target_bbox': if isinstance(data[0], (dict, OrderedDict)): data_dict = _convert_dict(data) for obj_id, d in data_dict.items(): bbox_file = '{}_{}.txt'.format(base_results_path, obj_id) save_bb(bbox_file, d) else: # Single-object mode bbox_file = '{}.txt'.format(base_results_path) save_bb(bbox_file, data) if key == 'all_boxes': if isinstance(data[0], (dict, OrderedDict)): data_dict = _convert_dict(data) for obj_id, d in data_dict.items(): bbox_file = '{}_{}_all_boxes.txt'.format(base_results_path, obj_id) save_bb(bbox_file, d) else: # Single-object mode bbox_file = '{}_all_boxes.txt'.format(base_results_path) save_bb(bbox_file, data) if key == 'all_scores': if isinstance(data[0], (dict, OrderedDict)): data_dict = _convert_dict(data) for obj_id, d in data_dict.items(): bbox_file = '{}_{}_all_scores.txt'.format(base_results_path, obj_id) save_score(bbox_file, d) else: # Single-object mode print("saving scores...") bbox_file = '{}_all_scores.txt'.format(base_results_path) save_score(bbox_file, data) elif key == 'time': if isinstance(data[0], dict): data_dict = _convert_dict(data) for obj_id, d in data_dict.items(): timings_file = '{}_{}_time.txt'.format(base_results_path, obj_id) save_time(timings_file, d) else: timings_file = '{}_time.txt'.format(base_results_path) save_time(timings_file, data) def run_sequence(seq: Sequence, tracker: Tracker, debug=False, num_gpu=8): """Runs a tracker on a sequence.""" '''2021.1.2 Add multiple gpu support''' try: worker_name = multiprocessing.current_process().name worker_id = int(worker_name[worker_name.find('-') + 1:]) - 1 gpu_id = worker_id % num_gpu torch.cuda.set_device(gpu_id) except: pass def _results_exist(): if seq.object_ids is None: if seq.dataset in ['trackingnet', 'got10k']: base_results_path = os.path.join(tracker.results_dir, seq.dataset, seq.name) bbox_file = '{}.txt'.format(base_results_path) else: bbox_file = '{}/{}.txt'.format(tracker.results_dir, seq.name) return os.path.isfile(bbox_file) else: bbox_files = ['{}/{}_{}.txt'.format(tracker.results_dir, seq.name, obj_id) for obj_id in seq.object_ids] missing = [not os.path.isfile(f) for f in bbox_files] return sum(missing) == 0 if _results_exist() and not debug: print('FPS: {}'.format(-1)) return print('Tracker: {} {} {} , Sequence: {}'.format(tracker.name, tracker.parameter_name, tracker.run_id, seq.name)) if debug: output = tracker.run_sequence(seq, debug=debug) else: try: output = tracker.run_sequence(seq, debug=debug) except Exception as e: print(e) return sys.stdout.flush() if isinstance(output['time'][0], (dict, OrderedDict)): exec_time = sum([sum(times.values()) for times in output['time']]) num_frames = len(output['time']) else: exec_time = sum(output['time']) num_frames = len(output['time']) print('FPS: {}'.format(num_frames / exec_time)) if not debug: _save_tracker_output(seq, tracker, output) def run_dataset(dataset, trackers, debug=False, threads=0, num_gpus=8): """Runs a list of trackers on a dataset. args: dataset: List of Sequence instances, forming a dataset. trackers: List of Tracker instances. debug: Debug level. threads: Number of threads to use (default 0). """ multiprocessing.set_start_method('spawn', force=True) print('Evaluating {:4d} trackers on {:5d} sequences'.format(len(trackers), len(dataset))) multiprocessing.set_start_method('spawn', force=True) if threads == 0: mode = 'sequential' else: mode = 'parallel' if mode == 'sequential': for seq in dataset: for tracker_info in trackers: run_sequence(seq, tracker_info, debug=debug) elif mode == 'parallel': param_list = [(seq, tracker_info, debug, num_gpus) for seq, tracker_info in product(dataset, trackers)] with multiprocessing.Pool(processes=threads) as pool: pool.starmap(run_sequence, param_list) print('Done') ================================================ FILE: lib/test/evaluation/tc128cedataset.py ================================================ import numpy as np from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList import os import glob import six class TC128CEDataset(BaseDataset): """ TC-128 Dataset (78 newly added sequences) modified from the implementation in got10k-toolkit (https://github.com/got-10k/toolkit) """ def __init__(self): super().__init__() self.base_path = self.env_settings.tc128_path self.anno_files = sorted(glob.glob( os.path.join(self.base_path, '*/*_gt.txt'))) """filter the newly added sequences (_ce)""" self.anno_files = [s for s in self.anno_files if "_ce" in s] self.seq_dirs = [os.path.dirname(f) for f in self.anno_files] self.seq_names = [os.path.basename(d) for d in self.seq_dirs] # valid frame range for each sequence self.range_files = [glob.glob(os.path.join(d, '*_frames.txt'))[0] for d in self.seq_dirs] def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.seq_names]) def _construct_sequence(self, sequence_name): if isinstance(sequence_name, six.string_types): if not sequence_name in self.seq_names: raise Exception('Sequence {} not found.'.format(sequence_name)) index = self.seq_names.index(sequence_name) # load valid frame range frames = np.loadtxt(self.range_files[index], dtype=int, delimiter=',') img_files = [os.path.join(self.seq_dirs[index], 'img/%04d.jpg' % f) for f in range(frames[0], frames[1] + 1)] # load annotations anno = np.loadtxt(self.anno_files[index], delimiter=',') assert len(img_files) == len(anno) assert anno.shape[1] == 4 # return img_files, anno return Sequence(sequence_name, img_files, 'tc128', anno.reshape(-1, 4)) def __len__(self): return len(self.seq_names) ================================================ FILE: lib/test/evaluation/tc128dataset.py ================================================ import numpy as np from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList import os import glob import six class TC128Dataset(BaseDataset): """ TC-128 Dataset modified from the implementation in got10k-toolkit (https://github.com/got-10k/toolkit) """ def __init__(self): super().__init__() self.base_path = self.env_settings.tc128_path self.anno_files = sorted(glob.glob( os.path.join(self.base_path, '*/*_gt.txt'))) self.seq_dirs = [os.path.dirname(f) for f in self.anno_files] self.seq_names = [os.path.basename(d) for d in self.seq_dirs] # valid frame range for each sequence self.range_files = [glob.glob(os.path.join(d, '*_frames.txt'))[0] for d in self.seq_dirs] def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.seq_names]) def _construct_sequence(self, sequence_name): if isinstance(sequence_name, six.string_types): if not sequence_name in self.seq_names: raise Exception('Sequence {} not found.'.format(sequence_name)) index = self.seq_names.index(sequence_name) # load valid frame range frames = np.loadtxt(self.range_files[index], dtype=int, delimiter=',') img_files = [os.path.join(self.seq_dirs[index], 'img/%04d.jpg' % f) for f in range(frames[0], frames[1] + 1)] # load annotations anno = np.loadtxt(self.anno_files[index], delimiter=',') assert len(img_files) == len(anno) assert anno.shape[1] == 4 # return img_files, anno return Sequence(sequence_name, img_files, 'tc128', anno.reshape(-1, 4)) def __len__(self): return len(self.seq_names) ================================================ FILE: lib/test/evaluation/tnl2kdataset.py ================================================ import os import numpy as np from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList from lib.test.utils.load_text import load_text, load_str ############ # current 00000492.png of test_015_Sord_video_Q01_done is damaged and replaced by a copy of 00000491.png ############ class TNL2kDataset(BaseDataset): """ TNL2k test set """ def __init__(self): super().__init__() self.base_path = self.env_settings.tnl2k_path self.sequence_list = self._get_sequence_list() def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_list]) def _construct_sequence(self, sequence_name): # class_name = sequence_name.split('-')[0] anno_path = '{}/{}/groundtruth.txt'.format(self.base_path, sequence_name) ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64) text_dsp_path = '{}/{}/language.txt'.format(self.base_path, sequence_name) text_dsp = load_str(text_dsp_path) frames_path = '{}/{}/imgs'.format(self.base_path, sequence_name) frames_list = [f for f in os.listdir(frames_path)] frames_list = sorted(frames_list) frames_list = ['{}/{}'.format(frames_path, frame_i) for frame_i in frames_list] # target_class = class_name return Sequence(sequence_name, frames_list, 'tnl2k', ground_truth_rect.reshape(-1, 4), text_dsp=text_dsp) def __len__(self): return len(self.sequence_list) def _get_sequence_list(self): sequence_list = [] for seq in os.listdir(self.base_path): if os.path.isdir(os.path.join(self.base_path, seq)): sequence_list.append(seq) return sequence_list ================================================ FILE: lib/test/evaluation/tracker.py ================================================ import importlib import os from collections import OrderedDict from lib.test.evaluation.environment import env_settings import time import cv2 as cv from lib.utils.lmdb_utils import decode_img from pathlib import Path import numpy as np def trackerlist(name: str, parameter_name: str, dataset_name: str, run_ids = None, display_name: str = None, result_only=False): """Generate list of trackers. args: name: Name of tracking method. parameter_name: Name of parameter file. run_ids: A single or list of run_ids. display_name: Name to be displayed in the result plots. """ if run_ids is None or isinstance(run_ids, int): run_ids = [run_ids] return [Tracker(name, parameter_name, dataset_name, run_id, display_name, result_only) for run_id in run_ids] class Tracker: """Wraps the tracker for evaluation and running purposes. args: name: Name of tracking method. parameter_name: Name of parameter file. run_id: The run id. display_name: Name to be displayed in the result plots. """ def __init__(self, name: str, parameter_name: str, dataset_name: str, run_id: int = None, display_name: str = None, result_only=False): assert run_id is None or isinstance(run_id, int) self.name = name self.parameter_name = parameter_name self.dataset_name = dataset_name self.run_id = run_id self.display_name = display_name env = env_settings() if self.run_id is None: self.results_dir = '{}/{}/{}'.format(env.results_path, self.name, self.parameter_name) else: self.results_dir = '{}/{}/{}_{:03d}'.format(env.results_path, self.name, self.parameter_name, self.run_id) if result_only: self.results_dir = '{}/{}'.format(env.results_path, self.name) tracker_module_abspath = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'tracker', '%s.py' % self.name)) if os.path.isfile(tracker_module_abspath): tracker_module = importlib.import_module('lib.test.tracker.{}'.format(self.name)) self.tracker_class = tracker_module.get_tracker_class() else: self.tracker_class = None def create_tracker(self, params): tracker = self.tracker_class(params, self.dataset_name) return tracker def run_sequence(self, seq, debug=None): """Run tracker on sequence. args: seq: Sequence to run the tracker on. visualization: Set visualization flag (None means default value specified in the parameters). debug: Set debug level (None means default value specified in the parameters). multiobj_mode: Which mode to use for multiple objects. """ params = self.get_parameters() debug_ = debug if debug is None: debug_ = getattr(params, 'debug', 0) params.debug = debug_ # Get init information init_info = seq.init_info() tracker = self.create_tracker(params) output = self._track_sequence(tracker, seq, init_info) return output def _track_sequence(self, tracker, seq, init_info): # Define outputs # Each field in output is a list containing tracker prediction for each frame. # In case of single object tracking mode: # target_bbox[i] is the predicted bounding box for frame i # time[i] is the processing time for frame i # In case of multi object tracking mode: # target_bbox[i] is an OrderedDict, where target_bbox[i][obj_id] is the predicted box for target obj_id in # frame i # time[i] is either the processing time for frame i, or an OrderedDict containing processing times for each # object in frame i output = {'target_bbox': [], 'time': []} if tracker.params.save_all_boxes: output['all_boxes'] = [] output['all_scores'] = [] def _store_outputs(tracker_out: dict, defaults=None): defaults = {} if defaults is None else defaults for key in output.keys(): val = tracker_out.get(key, defaults.get(key, None)) if key in tracker_out or val is not None: output[key].append(val) # Initialize image = self._read_image(seq.frames[0]) start_time = time.time() out = tracker.initialize(image, init_info) if out is None: out = {} prev_output = OrderedDict(out) init_default = {'target_bbox': init_info.get('init_bbox'), 'time': time.time() - start_time} if tracker.params.save_all_boxes: init_default['all_boxes'] = out['all_boxes'] init_default['all_scores'] = out['all_scores'] _store_outputs(out, init_default) for frame_num, frame_path in enumerate(seq.frames[1:], start=1): image = self._read_image(frame_path) start_time = time.time() info = seq.frame_info(frame_num) info['previous_output'] = prev_output if len(seq.ground_truth_rect) > 1: info['gt_bbox'] = seq.ground_truth_rect[frame_num] out = tracker.track(image, info) prev_output = OrderedDict(out) _store_outputs(out, {'time': time.time() - start_time}) for key in ['target_bbox', 'all_boxes', 'all_scores']: if key in output and len(output[key]) <= 1: output.pop(key) return output def run_video(self, videofilepath, optional_box=None, debug=None, visdom_info=None, save_results=False): """Run the tracker with the vieofile. args: debug: Debug level. """ params = self.get_parameters() debug_ = debug if debug is None: debug_ = getattr(params, 'debug', 0) params.debug = debug_ params.tracker_name = self.name params.param_name = self.parameter_name # self._init_visdom(visdom_info, debug_) multiobj_mode = getattr(params, 'multiobj_mode', getattr(self.tracker_class, 'multiobj_mode', 'default')) if multiobj_mode == 'default': tracker = self.create_tracker(params) elif multiobj_mode == 'parallel': tracker = MultiObjectWrapper(self.tracker_class, params, self.visdom, fast_load=True) else: raise ValueError('Unknown multi object mode {}'.format(multiobj_mode)) assert os.path.isfile(videofilepath), "Invalid param {}".format(videofilepath) ", videofilepath must be a valid videofile" output_boxes = [] cap = cv.VideoCapture(videofilepath) display_name = 'Display: ' + tracker.params.tracker_name cv.namedWindow(display_name, cv.WINDOW_NORMAL | cv.WINDOW_KEEPRATIO) cv.resizeWindow(display_name, 960, 720) success, frame = cap.read() cv.imshow(display_name, frame) def _build_init_info(box): return {'init_bbox': box} if success is not True: print("Read frame from {} failed.".format(videofilepath)) exit(-1) if optional_box is not None: assert isinstance(optional_box, (list, tuple)) assert len(optional_box) == 4, "valid box's foramt is [x,y,w,h]" tracker.initialize(frame, _build_init_info(optional_box)) output_boxes.append(optional_box) else: while True: # cv.waitKey() frame_disp = frame.copy() cv.putText(frame_disp, 'Select target ROI and press ENTER', (20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1.5, (0, 0, 0), 1) x, y, w, h = cv.selectROI(display_name, frame_disp, fromCenter=False) init_state = [x, y, w, h] tracker.initialize(frame, _build_init_info(init_state)) output_boxes.append(init_state) break while True: ret, frame = cap.read() if frame is None: break frame_disp = frame.copy() # Draw box out = tracker.track(frame) state = [int(s) for s in out['target_bbox']] output_boxes.append(state) cv.rectangle(frame_disp, (state[0], state[1]), (state[2] + state[0], state[3] + state[1]), (0, 255, 0), 5) font_color = (0, 0, 0) cv.putText(frame_disp, 'Tracking!', (20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1) cv.putText(frame_disp, 'Press r to reset', (20, 55), cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1) cv.putText(frame_disp, 'Press q to quit', (20, 80), cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1) # Display the resulting frame cv.imshow(display_name, frame_disp) key = cv.waitKey(1) if key == ord('q'): break elif key == ord('r'): ret, frame = cap.read() frame_disp = frame.copy() cv.putText(frame_disp, 'Select target ROI and press ENTER', (20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1.5, (0, 0, 0), 1) cv.imshow(display_name, frame_disp) x, y, w, h = cv.selectROI(display_name, frame_disp, fromCenter=False) init_state = [x, y, w, h] tracker.initialize(frame, _build_init_info(init_state)) output_boxes.append(init_state) # When everything done, release the capture cap.release() cv.destroyAllWindows() if save_results: if not os.path.exists(self.results_dir): os.makedirs(self.results_dir) video_name = Path(videofilepath).stem base_results_path = os.path.join(self.results_dir, 'video_{}'.format(video_name)) tracked_bb = np.array(output_boxes).astype(int) bbox_file = '{}.txt'.format(base_results_path) np.savetxt(bbox_file, tracked_bb, delimiter='\t', fmt='%d') def get_parameters(self): """Get parameters.""" param_module = importlib.import_module('lib.test.parameter.{}'.format(self.name)) params = param_module.parameters(self.parameter_name) return params def _read_image(self, image_file: str): if isinstance(image_file, str): im = cv.imread(image_file) return cv.cvtColor(im, cv.COLOR_BGR2RGB) elif isinstance(image_file, list) and len(image_file) == 2: return decode_img(image_file[0], image_file[1]) else: raise ValueError("type of image_file should be str or list") ================================================ FILE: lib/test/evaluation/trackingnetdataset.py ================================================ import numpy as np from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList import os from lib.test.utils.load_text import load_text class TrackingNetDataset(BaseDataset): """ TrackingNet test set. Publication: TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild. Matthias Mueller,Adel Bibi, Silvio Giancola, Salman Al-Subaihi and Bernard Ghanem ECCV, 2018 https://ivul.kaust.edu.sa/Documents/Publications/2018/TrackingNet%20A%20Large%20Scale%20Dataset%20and%20Benchmark%20for%20Object%20Tracking%20in%20the%20Wild.pdf Download the dataset using the toolkit https://github.com/SilvioGiancola/TrackingNet-devkit. """ def __init__(self): super().__init__() self.base_path = self.env_settings.trackingnet_path sets = 'TEST' if not isinstance(sets, (list, tuple)): if sets == 'TEST': sets = ['TEST'] elif sets == 'TRAIN': sets = ['TRAIN_{}'.format(i) for i in range(5)] self.sequence_list = self._list_sequences(self.base_path, sets) def get_sequence_list(self): return SequenceList([self._construct_sequence(set, seq_name) for set, seq_name in self.sequence_list]) def _construct_sequence(self, set, sequence_name): anno_path = '{}/{}/anno/{}.txt'.format(self.base_path, set, sequence_name) ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64, backend='numpy') frames_path = '{}/{}/frames/{}'.format(self.base_path, set, sequence_name) frame_list = [frame for frame in os.listdir(frames_path) if frame.endswith(".jpg")] frame_list.sort(key=lambda f: int(f[:-4])) frames_list = [os.path.join(frames_path, frame) for frame in frame_list] return Sequence(sequence_name, frames_list, 'trackingnet', ground_truth_rect.reshape(-1, 4)) def __len__(self): return len(self.sequence_list) def _list_sequences(self, root, set_ids): sequence_list = [] for s in set_ids: anno_dir = os.path.join(root, s, "anno") sequences_cur_set = [(s, os.path.splitext(f)[0]) for f in os.listdir(anno_dir) if f.endswith('.txt')] sequence_list += sequences_cur_set return sequence_list ================================================ FILE: lib/test/evaluation/uavdataset.py ================================================ import numpy as np from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList from lib.test.utils.load_text import load_text class UAVDataset(BaseDataset): """ UAV123 dataset. Publication: A Benchmark and Simulator for UAV Tracking. Matthias Mueller, Neil Smith and Bernard Ghanem ECCV, 2016 https://ivul.kaust.edu.sa/Documents/Publications/2016/A%20Benchmark%20and%20Simulator%20for%20UAV%20Tracking.pdf Download the dataset from https://ivul.kaust.edu.sa/Pages/pub-benchmark-simulator-uav.aspx """ def __init__(self): super().__init__() self.base_path = self.env_settings.uav_path self.sequence_info_list = self._get_sequence_info_list() def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_info_list]) def _construct_sequence(self, sequence_info): sequence_path = sequence_info['path'] nz = sequence_info['nz'] ext = sequence_info['ext'] start_frame = sequence_info['startFrame'] end_frame = sequence_info['endFrame'] init_omit = 0 if 'initOmit' in sequence_info: init_omit = sequence_info['initOmit'] frames = ['{base_path}/{sequence_path}/{frame:0{nz}}.{ext}'.format(base_path=self.base_path, sequence_path=sequence_path, frame=frame_num, nz=nz, ext=ext) for frame_num in range(start_frame+init_omit, end_frame+1)] anno_path = '{}/{}'.format(self.base_path, sequence_info['anno_path']) ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64, backend='numpy') return Sequence(sequence_info['name'], frames, 'uav', ground_truth_rect[init_omit:,:], object_class=sequence_info['object_class']) def __len__(self): return len(self.sequence_info_list) def _get_sequence_info_list(self): sequence_info_list = [ {"name": "uav_bike1", "path": "data_seq/UAV123/bike1", "startFrame": 1, "endFrame": 3085, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/bike1.txt", "object_class": "vehicle"}, {"name": "uav_bike2", "path": "data_seq/UAV123/bike2", "startFrame": 1, "endFrame": 553, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/bike2.txt", "object_class": "vehicle"}, {"name": "uav_bike3", "path": "data_seq/UAV123/bike3", "startFrame": 1, "endFrame": 433, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/bike3.txt", "object_class": "vehicle"}, {"name": "uav_bird1_1", "path": "data_seq/UAV123/bird1", "startFrame": 1, "endFrame": 253, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/bird1_1.txt", "object_class": "bird"}, {"name": "uav_bird1_2", "path": "data_seq/UAV123/bird1", "startFrame": 775, "endFrame": 1477, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/bird1_2.txt", "object_class": "bird"}, {"name": "uav_bird1_3", "path": "data_seq/UAV123/bird1", "startFrame": 1573, "endFrame": 2437, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/bird1_3.txt", "object_class": "bird"}, {"name": "uav_boat1", "path": "data_seq/UAV123/boat1", "startFrame": 1, "endFrame": 901, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat1.txt", "object_class": "vessel"}, {"name": "uav_boat2", "path": "data_seq/UAV123/boat2", "startFrame": 1, "endFrame": 799, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat2.txt", "object_class": "vessel"}, {"name": "uav_boat3", "path": "data_seq/UAV123/boat3", "startFrame": 1, "endFrame": 901, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat3.txt", "object_class": "vessel"}, {"name": "uav_boat4", "path": "data_seq/UAV123/boat4", "startFrame": 1, "endFrame": 553, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat4.txt", "object_class": "vessel"}, {"name": "uav_boat5", "path": "data_seq/UAV123/boat5", "startFrame": 1, "endFrame": 505, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat5.txt", "object_class": "vessel"}, {"name": "uav_boat6", "path": "data_seq/UAV123/boat6", "startFrame": 1, "endFrame": 805, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat6.txt", "object_class": "vessel"}, {"name": "uav_boat7", "path": "data_seq/UAV123/boat7", "startFrame": 1, "endFrame": 535, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat7.txt", "object_class": "vessel"}, {"name": "uav_boat8", "path": "data_seq/UAV123/boat8", "startFrame": 1, "endFrame": 685, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat8.txt", "object_class": "vessel"}, {"name": "uav_boat9", "path": "data_seq/UAV123/boat9", "startFrame": 1, "endFrame": 1399, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/boat9.txt", "object_class": "vessel"}, {"name": "uav_building1", "path": "data_seq/UAV123/building1", "startFrame": 1, "endFrame": 469, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/building1.txt", "object_class": "other"}, {"name": "uav_building2", "path": "data_seq/UAV123/building2", "startFrame": 1, "endFrame": 577, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/building2.txt", "object_class": "other"}, {"name": "uav_building3", "path": "data_seq/UAV123/building3", "startFrame": 1, "endFrame": 829, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/building3.txt", "object_class": "other"}, {"name": "uav_building4", "path": "data_seq/UAV123/building4", "startFrame": 1, "endFrame": 787, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/building4.txt", "object_class": "other"}, {"name": "uav_building5", "path": "data_seq/UAV123/building5", "startFrame": 1, "endFrame": 481, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/building5.txt", "object_class": "other"}, {"name": "uav_car1_1", "path": "data_seq/UAV123/car1", "startFrame": 1, "endFrame": 751, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car1_1.txt", "object_class": "car"}, {"name": "uav_car1_2", "path": "data_seq/UAV123/car1", "startFrame": 751, "endFrame": 1627, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car1_2.txt", "object_class": "car"}, {"name": "uav_car1_3", "path": "data_seq/UAV123/car1", "startFrame": 1627, "endFrame": 2629, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car1_3.txt", "object_class": "car"}, {"name": "uav_car10", "path": "data_seq/UAV123/car10", "startFrame": 1, "endFrame": 1405, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car10.txt", "object_class": "car"}, {"name": "uav_car11", "path": "data_seq/UAV123/car11", "startFrame": 1, "endFrame": 337, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car11.txt", "object_class": "car"}, {"name": "uav_car12", "path": "data_seq/UAV123/car12", "startFrame": 1, "endFrame": 499, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car12.txt", "object_class": "car"}, {"name": "uav_car13", "path": "data_seq/UAV123/car13", "startFrame": 1, "endFrame": 415, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car13.txt", "object_class": "car"}, {"name": "uav_car14", "path": "data_seq/UAV123/car14", "startFrame": 1, "endFrame": 1327, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car14.txt", "object_class": "car"}, {"name": "uav_car15", "path": "data_seq/UAV123/car15", "startFrame": 1, "endFrame": 469, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car15.txt", "object_class": "car"}, {"name": "uav_car16_1", "path": "data_seq/UAV123/car16", "startFrame": 1, "endFrame": 415, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car16_1.txt", "object_class": "car"}, {"name": "uav_car16_2", "path": "data_seq/UAV123/car16", "startFrame": 415, "endFrame": 1993, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car16_2.txt", "object_class": "car"}, {"name": "uav_car17", "path": "data_seq/UAV123/car17", "startFrame": 1, "endFrame": 1057, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car17.txt", "object_class": "car"}, {"name": "uav_car18", "path": "data_seq/UAV123/car18", "startFrame": 1, "endFrame": 1207, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car18.txt", "object_class": "car"}, {"name": "uav_car1_s", "path": "data_seq/UAV123/car1_s", "startFrame": 1, "endFrame": 1475, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car1_s.txt", "object_class": "car"}, {"name": "uav_car2", "path": "data_seq/UAV123/car2", "startFrame": 1, "endFrame": 1321, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car2.txt", "object_class": "car"}, {"name": "uav_car2_s", "path": "data_seq/UAV123/car2_s", "startFrame": 1, "endFrame": 320, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car2_s.txt", "object_class": "car"}, {"name": "uav_car3", "path": "data_seq/UAV123/car3", "startFrame": 1, "endFrame": 1717, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car3.txt", "object_class": "car"}, {"name": "uav_car3_s", "path": "data_seq/UAV123/car3_s", "startFrame": 1, "endFrame": 1300, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car3_s.txt", "object_class": "car"}, {"name": "uav_car4", "path": "data_seq/UAV123/car4", "startFrame": 1, "endFrame": 1345, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car4.txt", "object_class": "car"}, {"name": "uav_car4_s", "path": "data_seq/UAV123/car4_s", "startFrame": 1, "endFrame": 830, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car4_s.txt", "object_class": "car"}, {"name": "uav_car5", "path": "data_seq/UAV123/car5", "startFrame": 1, "endFrame": 745, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car5.txt", "object_class": "car"}, {"name": "uav_car6_1", "path": "data_seq/UAV123/car6", "startFrame": 1, "endFrame": 487, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car6_1.txt", "object_class": "car"}, {"name": "uav_car6_2", "path": "data_seq/UAV123/car6", "startFrame": 487, "endFrame": 1807, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car6_2.txt", "object_class": "car"}, {"name": "uav_car6_3", "path": "data_seq/UAV123/car6", "startFrame": 1807, "endFrame": 2953, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car6_3.txt", "object_class": "car"}, {"name": "uav_car6_4", "path": "data_seq/UAV123/car6", "startFrame": 2953, "endFrame": 3925, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car6_4.txt", "object_class": "car"}, {"name": "uav_car6_5", "path": "data_seq/UAV123/car6", "startFrame": 3925, "endFrame": 4861, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car6_5.txt", "object_class": "car"}, {"name": "uav_car7", "path": "data_seq/UAV123/car7", "startFrame": 1, "endFrame": 1033, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car7.txt", "object_class": "car"}, {"name": "uav_car8_1", "path": "data_seq/UAV123/car8", "startFrame": 1, "endFrame": 1357, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car8_1.txt", "object_class": "car"}, {"name": "uav_car8_2", "path": "data_seq/UAV123/car8", "startFrame": 1357, "endFrame": 2575, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car8_2.txt", "object_class": "car"}, {"name": "uav_car9", "path": "data_seq/UAV123/car9", "startFrame": 1, "endFrame": 1879, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/car9.txt", "object_class": "car"}, {"name": "uav_group1_1", "path": "data_seq/UAV123/group1", "startFrame": 1, "endFrame": 1333, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group1_1.txt", "object_class": "person"}, {"name": "uav_group1_2", "path": "data_seq/UAV123/group1", "startFrame": 1333, "endFrame": 2515, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group1_2.txt", "object_class": "person"}, {"name": "uav_group1_3", "path": "data_seq/UAV123/group1", "startFrame": 2515, "endFrame": 3925, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group1_3.txt", "object_class": "person"}, {"name": "uav_group1_4", "path": "data_seq/UAV123/group1", "startFrame": 3925, "endFrame": 4873, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group1_4.txt", "object_class": "person"}, {"name": "uav_group2_1", "path": "data_seq/UAV123/group2", "startFrame": 1, "endFrame": 907, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group2_1.txt", "object_class": "person"}, {"name": "uav_group2_2", "path": "data_seq/UAV123/group2", "startFrame": 907, "endFrame": 1771, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group2_2.txt", "object_class": "person"}, {"name": "uav_group2_3", "path": "data_seq/UAV123/group2", "startFrame": 1771, "endFrame": 2683, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group2_3.txt", "object_class": "person"}, {"name": "uav_group3_1", "path": "data_seq/UAV123/group3", "startFrame": 1, "endFrame": 1567, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group3_1.txt", "object_class": "person"}, {"name": "uav_group3_2", "path": "data_seq/UAV123/group3", "startFrame": 1567, "endFrame": 2827, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group3_2.txt", "object_class": "person"}, {"name": "uav_group3_3", "path": "data_seq/UAV123/group3", "startFrame": 2827, "endFrame": 4369, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group3_3.txt", "object_class": "person"}, {"name": "uav_group3_4", "path": "data_seq/UAV123/group3", "startFrame": 4369, "endFrame": 5527, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/group3_4.txt", "object_class": "person"}, {"name": "uav_person1", "path": "data_seq/UAV123/person1", "startFrame": 1, "endFrame": 799, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person1.txt", "object_class": "person"}, {"name": "uav_person10", "path": "data_seq/UAV123/person10", "startFrame": 1, "endFrame": 1021, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person10.txt", "object_class": "person"}, {"name": "uav_person11", "path": "data_seq/UAV123/person11", "startFrame": 1, "endFrame": 721, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person11.txt", "object_class": "person"}, {"name": "uav_person12_1", "path": "data_seq/UAV123/person12", "startFrame": 1, "endFrame": 601, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person12_1.txt", "object_class": "person"}, {"name": "uav_person12_2", "path": "data_seq/UAV123/person12", "startFrame": 601, "endFrame": 1621, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person12_2.txt", "object_class": "person"}, {"name": "uav_person13", "path": "data_seq/UAV123/person13", "startFrame": 1, "endFrame": 883, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person13.txt", "object_class": "person"}, {"name": "uav_person14_1", "path": "data_seq/UAV123/person14", "startFrame": 1, "endFrame": 847, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person14_1.txt", "object_class": "person"}, {"name": "uav_person14_2", "path": "data_seq/UAV123/person14", "startFrame": 847, "endFrame": 1813, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person14_2.txt", "object_class": "person"}, {"name": "uav_person14_3", "path": "data_seq/UAV123/person14", "startFrame": 1813, "endFrame": 2923, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person14_3.txt", "object_class": "person"}, {"name": "uav_person15", "path": "data_seq/UAV123/person15", "startFrame": 1, "endFrame": 1339, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person15.txt", "object_class": "person"}, {"name": "uav_person16", "path": "data_seq/UAV123/person16", "startFrame": 1, "endFrame": 1147, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person16.txt", "object_class": "person"}, {"name": "uav_person17_1", "path": "data_seq/UAV123/person17", "startFrame": 1, "endFrame": 1501, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person17_1.txt", "object_class": "person"}, {"name": "uav_person17_2", "path": "data_seq/UAV123/person17", "startFrame": 1501, "endFrame": 2347, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person17_2.txt", "object_class": "person"}, {"name": "uav_person18", "path": "data_seq/UAV123/person18", "startFrame": 1, "endFrame": 1393, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person18.txt", "object_class": "person"}, {"name": "uav_person19_1", "path": "data_seq/UAV123/person19", "startFrame": 1, "endFrame": 1243, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person19_1.txt", "object_class": "person"}, {"name": "uav_person19_2", "path": "data_seq/UAV123/person19", "startFrame": 1243, "endFrame": 2791, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person19_2.txt", "object_class": "person"}, {"name": "uav_person19_3", "path": "data_seq/UAV123/person19", "startFrame": 2791, "endFrame": 4357, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person19_3.txt", "object_class": "person"}, {"name": "uav_person1_s", "path": "data_seq/UAV123/person1_s", "startFrame": 1, "endFrame": 1600, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person1_s.txt", "object_class": "person"}, {"name": "uav_person2_1", "path": "data_seq/UAV123/person2", "startFrame": 1, "endFrame": 1189, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person2_1.txt", "object_class": "person"}, {"name": "uav_person2_2", "path": "data_seq/UAV123/person2", "startFrame": 1189, "endFrame": 2623, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person2_2.txt", "object_class": "person"}, {"name": "uav_person20", "path": "data_seq/UAV123/person20", "startFrame": 1, "endFrame": 1783, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person20.txt", "object_class": "person"}, {"name": "uav_person21", "path": "data_seq/UAV123/person21", "startFrame": 1, "endFrame": 487, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person21.txt", "object_class": "person"}, {"name": "uav_person22", "path": "data_seq/UAV123/person22", "startFrame": 1, "endFrame": 199, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person22.txt", "object_class": "person"}, {"name": "uav_person23", "path": "data_seq/UAV123/person23", "startFrame": 1, "endFrame": 397, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person23.txt", "object_class": "person"}, {"name": "uav_person2_s", "path": "data_seq/UAV123/person2_s", "startFrame": 1, "endFrame": 250, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person2_s.txt", "object_class": "person"}, {"name": "uav_person3", "path": "data_seq/UAV123/person3", "startFrame": 1, "endFrame": 643, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person3.txt", "object_class": "person"}, {"name": "uav_person3_s", "path": "data_seq/UAV123/person3_s", "startFrame": 1, "endFrame": 505, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person3_s.txt", "object_class": "person"}, {"name": "uav_person4_1", "path": "data_seq/UAV123/person4", "startFrame": 1, "endFrame": 1501, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person4_1.txt", "object_class": "person"}, {"name": "uav_person4_2", "path": "data_seq/UAV123/person4", "startFrame": 1501, "endFrame": 2743, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person4_2.txt", "object_class": "person"}, {"name": "uav_person5_1", "path": "data_seq/UAV123/person5", "startFrame": 1, "endFrame": 877, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person5_1.txt", "object_class": "person"}, {"name": "uav_person5_2", "path": "data_seq/UAV123/person5", "startFrame": 877, "endFrame": 2101, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person5_2.txt", "object_class": "person"}, {"name": "uav_person6", "path": "data_seq/UAV123/person6", "startFrame": 1, "endFrame": 901, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person6.txt", "object_class": "person"}, {"name": "uav_person7_1", "path": "data_seq/UAV123/person7", "startFrame": 1, "endFrame": 1249, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person7_1.txt", "object_class": "person"}, {"name": "uav_person7_2", "path": "data_seq/UAV123/person7", "startFrame": 1249, "endFrame": 2065, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person7_2.txt", "object_class": "person"}, {"name": "uav_person8_1", "path": "data_seq/UAV123/person8", "startFrame": 1, "endFrame": 1075, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person8_1.txt", "object_class": "person"}, {"name": "uav_person8_2", "path": "data_seq/UAV123/person8", "startFrame": 1075, "endFrame": 1525, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person8_2.txt", "object_class": "person"}, {"name": "uav_person9", "path": "data_seq/UAV123/person9", "startFrame": 1, "endFrame": 661, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person9.txt", "object_class": "person"}, {"name": "uav_truck1", "path": "data_seq/UAV123/truck1", "startFrame": 1, "endFrame": 463, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/truck1.txt", "object_class": "truck"}, {"name": "uav_truck2", "path": "data_seq/UAV123/truck2", "startFrame": 1, "endFrame": 385, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/truck2.txt", "object_class": "truck"}, {"name": "uav_truck3", "path": "data_seq/UAV123/truck3", "startFrame": 1, "endFrame": 535, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/truck3.txt", "object_class": "truck"}, {"name": "uav_truck4_1", "path": "data_seq/UAV123/truck4", "startFrame": 1, "endFrame": 577, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/truck4_1.txt", "object_class": "truck"}, {"name": "uav_truck4_2", "path": "data_seq/UAV123/truck4", "startFrame": 577, "endFrame": 1261, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/truck4_2.txt", "object_class": "truck"}, {"name": "uav_uav1_1", "path": "data_seq/UAV123/uav1", "startFrame": 1, "endFrame": 1555, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav1_1.txt", "object_class": "aircraft"}, {"name": "uav_uav1_2", "path": "data_seq/UAV123/uav1", "startFrame": 1555, "endFrame": 2377, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav1_2.txt", "object_class": "aircraft"}, {"name": "uav_uav1_3", "path": "data_seq/UAV123/uav1", "startFrame": 2473, "endFrame": 3469, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav1_3.txt", "object_class": "aircraft"}, {"name": "uav_uav2", "path": "data_seq/UAV123/uav2", "startFrame": 1, "endFrame": 133, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav2.txt", "object_class": "aircraft"}, {"name": "uav_uav3", "path": "data_seq/UAV123/uav3", "startFrame": 1, "endFrame": 265, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav3.txt", "object_class": "aircraft"}, {"name": "uav_uav4", "path": "data_seq/UAV123/uav4", "startFrame": 1, "endFrame": 157, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav4.txt", "object_class": "aircraft"}, {"name": "uav_uav5", "path": "data_seq/UAV123/uav5", "startFrame": 1, "endFrame": 139, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav5.txt", "object_class": "aircraft"}, {"name": "uav_uav6", "path": "data_seq/UAV123/uav6", "startFrame": 1, "endFrame": 109, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav6.txt", "object_class": "aircraft"}, {"name": "uav_uav7", "path": "data_seq/UAV123/uav7", "startFrame": 1, "endFrame": 373, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav7.txt", "object_class": "aircraft"}, {"name": "uav_uav8", "path": "data_seq/UAV123/uav8", "startFrame": 1, "endFrame": 301, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/uav8.txt", "object_class": "aircraft"}, {"name": "uav_wakeboard1", "path": "data_seq/UAV123/wakeboard1", "startFrame": 1, "endFrame": 421, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard1.txt", "object_class": "person"}, {"name": "uav_wakeboard10", "path": "data_seq/UAV123/wakeboard10", "startFrame": 1, "endFrame": 469, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard10.txt", "object_class": "person"}, {"name": "uav_wakeboard2", "path": "data_seq/UAV123/wakeboard2", "startFrame": 1, "endFrame": 733, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard2.txt", "object_class": "person"}, {"name": "uav_wakeboard3", "path": "data_seq/UAV123/wakeboard3", "startFrame": 1, "endFrame": 823, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard3.txt", "object_class": "person"}, {"name": "uav_wakeboard4", "path": "data_seq/UAV123/wakeboard4", "startFrame": 1, "endFrame": 697, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard4.txt", "object_class": "person"}, {"name": "uav_wakeboard5", "path": "data_seq/UAV123/wakeboard5", "startFrame": 1, "endFrame": 1675, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard5.txt", "object_class": "person"}, {"name": "uav_wakeboard6", "path": "data_seq/UAV123/wakeboard6", "startFrame": 1, "endFrame": 1165, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard6.txt", "object_class": "person"}, {"name": "uav_wakeboard7", "path": "data_seq/UAV123/wakeboard7", "startFrame": 1, "endFrame": 199, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard7.txt", "object_class": "person"}, {"name": "uav_wakeboard8", "path": "data_seq/UAV123/wakeboard8", "startFrame": 1, "endFrame": 1543, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard8.txt", "object_class": "person"}, {"name": "uav_wakeboard9", "path": "data_seq/UAV123/wakeboard9", "startFrame": 1, "endFrame": 355, "nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard9.txt", "object_class": "person"} ] return sequence_info_list ================================================ FILE: lib/test/evaluation/votdataset.py ================================================ from typing import Union, TextIO import numpy as np from numba import jit from lib.test.evaluation.data import SequenceList, BaseDataset, Sequence class VOTDataset(BaseDataset): """ VOT2018 dataset Publication: The sixth Visual Object Tracking VOT2018 challenge results. Matej Kristan, Ales Leonardis, Jiri Matas, Michael Felsberg, Roman Pfugfelder, Luka Cehovin Zajc, Tomas Vojir, Goutam Bhat, Alan Lukezic et al. ECCV, 2018 https://prints.vicos.si/publications/365 Download the dataset from http://www.votchallenge.net/vot2018/dataset.html """ def __init__(self, year=18): super().__init__() self.year = year if year == 18: self.base_path = self.env_settings.vot18_path elif year == 20: self.base_path = self.env_settings.vot20_path elif year == 22: self.base_path = self.env_settings.vot22_path self.sequence_list = self._get_sequence_list(year) def get_sequence_list(self): return SequenceList([self._construct_sequence(s) for s in self.sequence_list]) def _construct_sequence(self, sequence_name): sequence_path = sequence_name nz = 8 ext = 'jpg' start_frame = 1 anno_path = '{}/{}/groundtruth.txt'.format(self.base_path, sequence_name) if self.year == 18 or self.year == 22: try: ground_truth_rect = np.loadtxt(str(anno_path), dtype=np.float64) except: ground_truth_rect = np.loadtxt(str(anno_path), delimiter=',', dtype=np.float64) end_frame = ground_truth_rect.shape[0] frames = ['{base_path}/{sequence_path}/color/{frame:0{nz}}.{ext}'.format(base_path=self.base_path, sequence_path=sequence_path, frame=frame_num, nz=nz, ext=ext) for frame_num in range(start_frame, end_frame+1)] # Convert gt if ground_truth_rect.shape[1] > 4: gt_x_all = ground_truth_rect[:, [0, 2, 4, 6]] gt_y_all = ground_truth_rect[:, [1, 3, 5, 7]] x1 = np.amin(gt_x_all, 1).reshape(-1,1) y1 = np.amin(gt_y_all, 1).reshape(-1,1) x2 = np.amax(gt_x_all, 1).reshape(-1,1) y2 = np.amax(gt_y_all, 1).reshape(-1,1) ground_truth_rect = np.concatenate((x1, y1, x2-x1, y2-y1), 1) elif self.year == 20: ground_truth_rect = read_file(str(anno_path)) ground_truth_rect = np.array(ground_truth_rect, dtype=np.float64) end_frame = ground_truth_rect.shape[0] frames = ['{base_path}/{sequence_path}/color/{frame:0{nz}}.{ext}'.format(base_path=self.base_path, sequence_path=sequence_path, frame=frame_num, nz=nz, ext=ext) for frame_num in range(start_frame, end_frame + 1)] else: raise NotImplementedError return Sequence(sequence_name, frames, 'vot', ground_truth_rect) def __len__(self): return len(self.sequence_list) def _get_sequence_list(self, year): if year == 18: sequence_list= ['ants1', 'ants3', 'bag', 'ball1', 'ball2', 'basketball', 'birds1', 'blanket', 'bmx', 'bolt1', 'bolt2', 'book', 'butterfly', 'car1', 'conduction1', 'crabs1', 'crossing', 'dinosaur', 'drone_across', 'drone_flip', 'drone1', 'fernando', 'fish1', 'fish2', 'fish3', 'flamingo1', 'frisbee', 'girl', 'glove', 'godfather', 'graduate', 'gymnastics1', 'gymnastics2', 'gymnastics3', 'hand', 'handball1', 'handball2', 'helicopter', 'iceskater1', 'iceskater2', 'leaves', 'matrix', 'motocross1', 'motocross2', 'nature', 'pedestrian1', 'rabbit', 'racing', 'road', 'shaking', 'sheep', 'singer2', 'singer3', 'soccer1', 'soccer2', 'soldier', 'tiger', 'traffic', 'wiper', 'zebrafish1'] elif year == 20: sequence_list= ['agility', 'ants1', 'ball2', 'ball3', 'basketball', 'birds1', 'bolt1', 'book', 'butterfly', 'car1', 'conduction1', 'crabs1', 'dinosaur', 'dribble', 'drone1', 'drone_across', 'drone_flip', 'fernando', 'fish1', 'fish2', 'flamingo1', 'frisbee', 'girl', 'glove', 'godfather', 'graduate', 'gymnastics1', 'gymnastics2', 'gymnastics3', 'hand', 'hand02', 'hand2', 'handball1', 'handball2', 'helicopter', 'iceskater1', 'iceskater2', 'lamb', 'leaves', 'marathon', 'matrix', 'monkey', 'motocross1', 'nature', 'polo', 'rabbit', 'rabbit2', 'road', 'rowing', 'shaking', 'singer2', 'singer3', 'soccer1', 'soccer2', 'soldier', 'surfing', 'tiger', 'wheel', 'wiper', 'zebrafish1'] elif year == 22: sequence_list= ['agility', 'animal', 'ants1', 'bag', 'ball2', 'ball3', 'basketball', 'birds1', 'birds2', 'bolt1', 'book', 'bubble', 'butterfly', 'car1', 'conduction1', 'crabs1', 'dinosaur', 'diver', 'drone1', 'drone_across', 'fernando', 'fish1', 'fish2', 'flamingo1', 'frisbee', 'girl', 'graduate', 'gymnastics1', 'gymnastics2', 'gymnastics3', 'hand', 'hand2', 'handball1', 'handball2', 'helicopter', 'iceskater1', 'iceskater2', 'kangaroo', 'lamb', 'leaves', 'marathon', 'matrix', 'monkey', 'motocross1', 'nature', 'polo', 'rabbit', 'rabbit2', 'rowing', 'shaking', 'singer2', 'singer3', 'snake', 'soccer1', 'soccer2', 'soldier', 'surfing', 'tennis', 'tiger', 'wheel', 'wiper', 'zebrafish1'] else: raise NotImplementedError return sequence_list def parse(string): """ parse string to the appropriate region format and return region object """ from vot.region.shapes import Rectangle, Polygon, Mask if string[0] == 'm': # input is a mask - decode it m_, offset_, region = create_mask_from_string(string[1:].split(',')) # return Mask(m_, offset=offset_) return region else: # input is not a mask - check if special, rectangle or polygon raise NotImplementedError print('Unknown region format.') return None def read_file(fp: Union[str, TextIO]): if isinstance(fp, str): with open(fp) as file: lines = file.readlines() else: lines = fp.readlines() regions = [] # iterate over all lines in the file for i, line in enumerate(lines): regions.append(parse(line.strip())) return regions def create_mask_from_string(mask_encoding): """ mask_encoding: a string in the following format: x0, y0, w, h, RLE output: mask, offset mask: 2-D binary mask, size defined in the mask encoding offset: (x, y) offset of the mask in the image coordinates """ elements = [int(el) for el in mask_encoding] tl_x, tl_y, region_w, region_h = elements[:4] rle = np.array([el for el in elements[4:]], dtype=np.int32) # create mask from RLE within target region mask = rle_to_mask(rle, region_w, region_h) region = [tl_x, tl_y, region_w, region_h] return mask, (tl_x, tl_y), region @jit(nopython=True) def rle_to_mask(rle, width, height): """ rle: input rle mask encoding each evenly-indexed element represents number of consecutive 0s each oddly indexed element represents number of consecutive 1s width and height are dimensions of the mask output: 2-D binary mask """ # allocate list of zeros v = [0] * (width * height) # set id of the last different element to the beginning of the vector idx_ = 0 for i in range(len(rle)): if i % 2 != 0: # write as many 1s as RLE says (zeros are already in the vector) for j in range(rle[i]): v[idx_+j] = 1 idx_ += rle[i] ================================================ FILE: lib/test/parameter/__init__.py ================================================ ================================================ FILE: lib/test/parameter/artrack.py ================================================ from lib.test.utils import TrackerParams import os from lib.test.evaluation.environment import env_settings from lib.config.artrack.config import cfg, update_config_from_file def parameters(yaml_name: str): params = TrackerParams() prj_dir = env_settings().prj_dir save_dir = env_settings().save_dir # update default config from yaml file yaml_file = os.path.join(prj_dir, 'experiments/artrack/%s.yaml' % yaml_name) update_config_from_file(yaml_file) params.cfg = cfg print("test config: ", cfg) # template and search region params.template_factor = cfg.TEST.TEMPLATE_FACTOR params.template_size = cfg.TEST.TEMPLATE_SIZE params.search_factor = cfg.TEST.SEARCH_FACTOR params.search_size = cfg.TEST.SEARCH_SIZE # Network checkpoint path params.checkpoint = os.path.join(save_dir, "checkpoints/train/artrack/%s/ARTrack_ep%04d.pth.tar" % (yaml_name, cfg.TEST.EPOCH)) # whether to save boxes from all queries params.save_all_boxes = False return params ================================================ FILE: lib/test/parameter/artrack_seq.py ================================================ from lib.test.utils import TrackerParams import os from lib.test.evaluation.environment import env_settings from lib.config.artrack_seq.config import cfg, update_config_from_file def parameters(yaml_name: str): params = TrackerParams() prj_dir = env_settings().prj_dir save_dir = env_settings().save_dir # update default config from yaml file yaml_file = os.path.join(prj_dir, 'experiments/artrack_seq/%s.yaml' % yaml_name) update_config_from_file(yaml_file) params.cfg = cfg print("test config: ", cfg) # template and search region params.template_factor = cfg.TEST.TEMPLATE_FACTOR params.template_size = cfg.TEST.TEMPLATE_SIZE params.search_factor = cfg.TEST.SEARCH_FACTOR params.search_size = cfg.TEST.SEARCH_SIZE # Network checkpoint path params.checkpoint = os.path.join(save_dir, "checkpoints/train/artrack_seq/%s/ARTrackSeq_ep%04d.pth.tar" % (yaml_name, cfg.TEST.EPOCH)) # whether to save boxes from all queries params.save_all_boxes = False return params ================================================ FILE: lib/test/parameter/artrackv2.py ================================================ from lib.test.utils import TrackerParams import os from lib.test.evaluation.environment import env_settings from lib.config.artrackv2.config import cfg, update_config_from_file def parameters(yaml_name: str): params = TrackerParams() prj_dir = env_settings().prj_dir save_dir = env_settings().save_dir # update default config from yaml file yaml_file = os.path.join(prj_dir, 'experiments/artrackv2/%s.yaml' % yaml_name) update_config_from_file(yaml_file) params.cfg = cfg print("test config: ", cfg) # template and search region params.template_factor = cfg.TEST.TEMPLATE_FACTOR params.template_size = cfg.TEST.TEMPLATE_SIZE params.search_factor = cfg.TEST.SEARCH_FACTOR params.search_size = cfg.TEST.SEARCH_SIZE # Network checkpoint path params.checkpoint = os.path.join(save_dir, "checkpoints/train/artrackv2/%s/ARTrackV2_ep%04d.pth.tar" % (yaml_name, cfg.TEST.EPOCH)) # whether to save boxes from all queries params.save_all_boxes = False return params ================================================ FILE: lib/test/parameter/artrackv2_seq.py ================================================ from lib.test.utils import TrackerParams import os from lib.test.evaluation.environment import env_settings from lib.config.artrackv2_seq.config import cfg, update_config_from_file def parameters(yaml_name: str): params = TrackerParams() prj_dir = env_settings().prj_dir save_dir = env_settings().save_dir # update default config from yaml file yaml_file = os.path.join(prj_dir, 'experiments/artrackv2_seq/%s.yaml' % yaml_name) update_config_from_file(yaml_file) params.cfg = cfg print("test config: ", cfg) # template and search region params.template_factor = cfg.TEST.TEMPLATE_FACTOR params.template_size = cfg.TEST.TEMPLATE_SIZE params.search_factor = cfg.TEST.SEARCH_FACTOR params.search_size = cfg.TEST.SEARCH_SIZE # Network checkpoint path params.checkpoint = os.path.join(save_dir, "checkpoints/train/artrackv2_seq/%s/ARTrackV2Seq_ep%04d.pth.tar" % (yaml_name, cfg.TEST.EPOCH)) # whether to save boxes from all queries params.save_all_boxes = False return params ================================================ FILE: lib/test/tracker/__init__.py ================================================ ================================================ FILE: lib/test/tracker/artrack.py ================================================ import math from lib.models.artrack import build_artrack from lib.test.tracker.basetracker import BaseTracker import torch from lib.test.tracker.vis_utils import gen_visualization from lib.test.utils.hann import hann2d from lib.train.data.processing_utils import sample_target # for debug import cv2 import os from lib.test.tracker.data_utils import Preprocessor from lib.utils.box_ops import clip_box from lib.utils.ce_utils import generate_mask_cond import random class RandomErasing(object): def __init__(self, EPSILON=0.5, sl=0.02, sh=0.33, r1=0.3, mean=[0.4914, 0.4822, 0.4465]): self.EPSILON = EPSILON self.mean = mean self.sl = sl self.sh = sh self.r1 = r1 def __call__(self, img): if random.uniform(0, 1) > self.EPSILON: return img for attempt in range(100): print(img.size()) area = img.size()[1] * img.size()[2] target_area = random.uniform(self.sl, self.sh) * area aspect_ratio = random.uniform(self.r1, 1 / self.r1) h = int(round(math.sqrt(target_area * aspect_ratio))) w = int(round(math.sqrt(target_area / aspect_ratio))) if w < img.size()[2] and h < img.size()[1]: x1 = random.randint(0, img.size()[1] - h) y1 = random.randint(0, img.size()[2] - w) if img.size()[0] == 3: # img[0, x1:x1+h, y1:y1+w] = random.uniform(0, 1) # img[1, x1:x1+h, y1:y1+w] = random.uniform(0, 1) # img[2, x1:x1+h, y1:y1+w] = random.uniform(0, 1) img[0, x1:x1 + h, y1:y1 + w] = self.mean[0] img[1, x1:x1 + h, y1:y1 + w] = self.mean[1] img[2, x1:x1 + h, y1:y1 + w] = self.mean[2] # img[:, x1:x1+h, y1:y1+w] = torch.from_numpy(np.random.rand(3, h, w)) else: img[0, x1:x1 + h, y1:y1 + w] = self.mean[1] # img[0, x1:x1+h, y1:y1+w] = torch.from_numpy(np.random.rand(1, h, w)) return img return img class ARTrack(BaseTracker): def __init__(self, params, dataset_name): super(ARTrack, self).__init__(params) network = build_artrack(params.cfg, training=False) print(self.params.checkpoint) network.load_state_dict(torch.load(self.params.checkpoint, map_location='cpu')['net'], strict=True) self.cfg = params.cfg self.bins = self.cfg.MODEL.BINS self.network = network.cuda() self.network.eval() self.preprocessor = Preprocessor() self.state = None self.range = self.cfg.MODEL.RANGE self.feat_sz = self.cfg.TEST.SEARCH_SIZE // self.cfg.MODEL.BACKBONE.STRIDE # motion constrain self.output_window = hann2d(torch.tensor([self.feat_sz, self.feat_sz]).long(), centered=True).cuda() # for debug self.debug = params.debug self.use_visdom = params.debug self.frame_id = 0 self.erase = RandomErasing() if self.debug: if not self.use_visdom: self.save_dir = "debug" if not os.path.exists(self.save_dir): os.makedirs(self.save_dir) else: # self.add_hook() self._init_visdom(None, 1) # for save boxes from all queries self.save_all_boxes = params.save_all_boxes self.z_dict1 = {} def initialize(self, image, info: dict): # forward the template once z_patch_arr, resize_factor, z_amask_arr = sample_target(image, info['init_bbox'], self.params.template_factor, output_sz=self.params.template_size)#output_sz=self.params.template_size self.z_patch_arr = z_patch_arr template = self.preprocessor.process(z_patch_arr, z_amask_arr) with torch.no_grad(): self.z_dict1 = template self.box_mask_z = None #if self.cfg.MODEL.BACKBONE.CE_LOC: # template_bbox = self.transform_bbox_to_crop(info['init_bbox'], resize_factor, # template.tensors.device).squeeze(1) # self.box_mask_z = generate_mask_cond(self.cfg, 1, template.tensors.device, template_bbox) # save states self.state = info['init_bbox'] self.frame_id = 0 if self.save_all_boxes: '''save all predicted boxes''' all_boxes_save = info['init_bbox'] * self.cfg.MODEL.NUM_OBJECT_QUERIES return {"all_boxes": all_boxes_save} def track(self, image, info: dict = None): magic_num = (self.range - 1) * 0.5 H, W, _ = image.shape self.frame_id += 1 x_patch_arr, resize_factor, x_amask_arr = sample_target(image, self.state, self.params.search_factor, output_sz=self.params.search_size) # (x1, y1, w, h) search = self.preprocessor.process(x_patch_arr, x_amask_arr) with torch.no_grad(): x_dict = search # merge the template and the search # run the transformer out_dict = self.network.forward( template=self.z_dict1.tensors, search=x_dict.tensors) # add hann windows # pred_score_map = out_dict['score_map'] # response = self.output_window * pred_score_map # pred_boxes = self.network.box_head.cal_bbox(response, out_dict['size_map'], out_dict['offset_map']) # pred_boxes = pred_boxes.view(-1, 4) pred_boxes = out_dict['seqs'][:, 0:4] / (self.bins - 1) - magic_num pred_boxes = pred_boxes.view(-1, 4).mean(dim=0) pred_new = pred_boxes pred_new[2] = pred_boxes[2] - pred_boxes[0] pred_new[3] = pred_boxes[3] - pred_boxes[1] pred_new[0] = pred_boxes[0] + pred_boxes[2]/2 pred_new[1] = pred_boxes[1] + pred_boxes[3]/2 pred_boxes = (pred_new * self.params.search_size / resize_factor).tolist() # Baseline: Take the mean of all pred boxes as the final result #pred_box = (pred_boxes.mean( # dim=0) * self.params.search_size / resize_factor).tolist() # (cx, cy, w, h) [0,1] # get the final box result self.state = clip_box(self.map_box_back(pred_boxes, resize_factor), H, W, margin=10) # for debug if self.debug: if not self.use_visdom: x1, y1, w, h = self.state image_BGR = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) cv2.rectangle(image_BGR, (int(x1),int(y1)), (int(x1+w),int(y1+h)), color=(0,0,255), thickness=2) save_path = os.path.join(self.save_dir, "%04d.jpg" % self.frame_id) cv2.imwrite(save_path, image_BGR) else: self.visdom.register((image, info['gt_bbox'].tolist(), self.state), 'Tracking', 1, 'Tracking') self.visdom.register(torch.from_numpy(x_patch_arr).permute(2, 0, 1), 'image', 1, 'search_region') self.visdom.register(torch.from_numpy(self.z_patch_arr).permute(2, 0, 1), 'image', 1, 'template') self.visdom.register(pred_score_map.view(self.feat_sz, self.feat_sz), 'heatmap', 1, 'score_map') self.visdom.register((pred_score_map * self.output_window).view(self.feat_sz, self.feat_sz), 'heatmap', 1, 'score_map_hann') if 'removed_indexes_s' in out_dict and out_dict['removed_indexes_s']: removed_indexes_s = out_dict['removed_indexes_s'] removed_indexes_s = [removed_indexes_s_i.cpu().numpy() for removed_indexes_s_i in removed_indexes_s] masked_search = gen_visualization(x_patch_arr, removed_indexes_s) self.visdom.register(torch.from_numpy(masked_search).permute(2, 0, 1), 'image', 1, 'masked_search') while self.pause_mode: if self.step: self.step = False break if self.save_all_boxes: '''save all predictions''' all_boxes = self.map_box_back_batch(pred_boxes * self.params.search_size / resize_factor, resize_factor) all_boxes_save = all_boxes.view(-1).tolist() # (4N, ) return {"target_bbox": self.state, "all_boxes": all_boxes_save} else: return {"target_bbox": self.state} def map_box_back(self, pred_box: list, resize_factor: float): cx_prev, cy_prev = self.state[0] + 0.5 * self.state[2], self.state[1] + 0.5 * self.state[3] cx, cy, w, h = pred_box half_side = 0.5 * self.params.search_size / resize_factor cx_real = cx + (cx_prev - half_side) cy_real = cy + (cy_prev - half_side) #cx_real = cx + cx_prev #cy_real = cy + cy_prev return [cx_real - 0.5 * w, cy_real - 0.5 * h, w, h] def map_box_back_batch(self, pred_box: torch.Tensor, resize_factor: float): cx_prev, cy_prev = self.state[0] + 0.5 * self.state[2], self.state[1] + 0.5 * self.state[3] cx, cy, w, h = pred_box.unbind(-1) # (N,4) --> (N,) half_side = 0.5 * self.params.search_size / resize_factor cx_real = cx + (cx_prev - half_side) cy_real = cy + (cy_prev - half_side) return torch.stack([cx_real - 0.5 * w, cy_real - 0.5 * h, w, h], dim=-1) def add_hook(self): conv_features, enc_attn_weights, dec_attn_weights = [], [], [] for i in range(12): self.network.backbone.blocks[i].attn.register_forward_hook( # lambda self, input, output: enc_attn_weights.append(output[1]) lambda self, input, output: enc_attn_weights.append(output[1]) ) self.enc_attn_weights = enc_attn_weights def get_tracker_class(): return ARTrack ================================================ FILE: lib/test/tracker/artrack_seq.py ================================================ import math from lib.models.artrack_seq import build_artrack_seq from lib.test.tracker.basetracker import BaseTracker import torch from lib.test.tracker.vis_utils import gen_visualization from lib.test.utils.hann import hann2d from lib.train.data.processing_utils import sample_target, transform_image_to_crop # for debug import cv2 import os from lib.test.tracker.data_utils import Preprocessor from lib.utils.box_ops import clip_box from lib.utils.ce_utils import generate_mask_cond class ARTrackSeq(BaseTracker): def __init__(self, params, dataset_name): super(ARTrackSeq, self).__init__(params) network = build_artrack_seq(params.cfg, training=False) print(self.params.checkpoint) network.load_state_dict(torch.load(self.params.checkpoint, map_location='cpu')['net'], strict=True) self.cfg = params.cfg self.bins = self.cfg.MODEL.BINS self.network = network.cuda() self.network.eval() self.preprocessor = Preprocessor() self.state = None self.feat_sz = self.cfg.TEST.SEARCH_SIZE // self.cfg.MODEL.BACKBONE.STRIDE # motion constrain self.output_window = hann2d(torch.tensor([self.feat_sz, self.feat_sz]).long(), centered=True).cuda() # for debug self.debug = params.debug self.use_visdom = params.debug self.frame_id = 0 if self.debug: if not self.use_visdom: self.save_dir = "debug" if not os.path.exists(self.save_dir): os.makedirs(self.save_dir) else: # self.add_hook() self._init_visdom(None, 1) # for save boxes from all queries self.save_all_boxes = params.save_all_boxes self.z_dict1 = {} self.store_result = None self.save_all = 7 self.x_feat = None self.update = None self.update_threshold = 5.0 self.update_intervals = 1 def initialize(self, image, info: dict): # forward the template once self.x_feat = None z_patch_arr, resize_factor, z_amask_arr = sample_target(image, info['init_bbox'], self.params.template_factor, output_sz=self.params.template_size) # output_sz=self.params.template_size self.z_patch_arr = z_patch_arr template = self.preprocessor.process(z_patch_arr, z_amask_arr) with torch.no_grad(): self.z_dict1 = template self.box_mask_z = None # if self.cfg.MODEL.BACKBONE.CE_LOC: # template_bbox = self.transform_bbox_to_crop(info['init_bbox'], resize_factor, # template.tensors.device).squeeze(1) # self.box_mask_z = generate_mask_cond(self.cfg, 1, template.tensors.device, template_bbox) # save states self.state = info['init_bbox'] self.store_result = [info['init_bbox'].copy()] for i in range(self.save_all - 1): self.store_result.append(info['init_bbox'].copy()) self.frame_id = 0 self.update = None if self.save_all_boxes: '''save all predicted boxes''' all_boxes_save = info['init_bbox'] * self.cfg.MODEL.NUM_OBJECT_QUERIES return {"all_boxes": all_boxes_save} def track(self, image, info: dict = None): H, W, _ = image.shape self.frame_id += 1 x_patch_arr, resize_factor, x_amask_arr = sample_target(image, self.state, self.params.search_factor, output_sz=self.params.search_size) # (x1, y1, w, h) for i in range(len(self.store_result)): box_temp = self.store_result[i].copy() box_out_i = transform_image_to_crop(torch.Tensor(self.store_result[i]), torch.Tensor(self.state), resize_factor, torch.Tensor([self.cfg.TEST.SEARCH_SIZE, self.cfg.TEST.SEARCH_SIZE]), normalize=True) box_out_i[2] = box_out_i[2] + box_out_i[0] box_out_i[3] = box_out_i[3] + box_out_i[1] box_out_i = box_out_i.clamp(min=-0.5, max=1.5) box_out_i = (box_out_i + 0.5) * (self.bins - 1) if i == 0: seqs_out = box_out_i else: seqs_out = torch.cat((seqs_out, box_out_i), dim=-1) seqs_out = seqs_out.unsqueeze(0) search = self.preprocessor.process(x_patch_arr, x_amask_arr) with torch.no_grad(): x_dict = search # merge the template and the search # run the transformer out_dict = self.network.forward( template=self.z_dict1.tensors, search=x_dict.tensors, seq_input=seqs_out, stage="sequence", search_feature=self.x_feat, update=None) self.x_feat = out_dict['x_feat'] pred_boxes = out_dict['seqs'][:, 0:4] / (self.bins - 1) - 0.5 pred_boxes = pred_boxes.view(-1, 4).mean(dim=0) pred_new = pred_boxes pred_new[2] = pred_boxes[2] - pred_boxes[0] pred_new[3] = pred_boxes[3] - pred_boxes[1] pred_new[0] = pred_boxes[0] + pred_new[2] / 2 pred_new[1] = pred_boxes[1] + pred_new[3] / 2 pred_boxes = (pred_new * self.params.search_size / resize_factor).tolist() # Baseline: Take the mean of all pred boxes as the final result # pred_box = (pred_boxes.mean( # dim=0) * self.params.search_size / resize_factor).tolist() # (cx, cy, w, h) [0,1] # get the final box result self.state = clip_box(self.map_box_back(pred_boxes, resize_factor), H, W, margin=10) if len(self.store_result) < self.save_all: self.store_result.append(self.state.copy()) else: for i in range(self.save_all): if i != self.save_all - 1: self.store_result[i] = self.store_result[i + 1] else: self.store_result[i] = self.state.copy() # for debug if self.debug: if not self.use_visdom: x1, y1, w, h = self.state image_BGR = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) cv2.rectangle(image_BGR, (int(x1), int(y1)), (int(x1 + w), int(y1 + h)), color=(0, 0, 255), thickness=2) save_path = os.path.join(self.save_dir, "%04d.jpg" % self.frame_id) cv2.imwrite(save_path, image_BGR) else: self.visdom.register((image, info['gt_bbox'].tolist(), self.state), 'Tracking', 1, 'Tracking') self.visdom.register(torch.from_numpy(x_patch_arr).permute(2, 0, 1), 'image', 1, 'search_region') self.visdom.register(torch.from_numpy(self.z_patch_arr).permute(2, 0, 1), 'image', 1, 'template') self.visdom.register(pred_score_map.view(self.feat_sz, self.feat_sz), 'heatmap', 1, 'score_map') self.visdom.register((pred_score_map * self.output_window).view(self.feat_sz, self.feat_sz), 'heatmap', 1, 'score_map_hann') if 'removed_indexes_s' in out_dict and out_dict['removed_indexes_s']: removed_indexes_s = out_dict['removed_indexes_s'] removed_indexes_s = [removed_indexes_s_i.cpu().numpy() for removed_indexes_s_i in removed_indexes_s] masked_search = gen_visualization(x_patch_arr, removed_indexes_s) self.visdom.register(torch.from_numpy(masked_search).permute(2, 0, 1), 'image', 1, 'masked_search') while self.pause_mode: if self.step: self.step = False break if self.save_all_boxes: '''save all predictions''' all_boxes = self.map_box_back_batch(pred_boxes * self.params.search_size / resize_factor, resize_factor) all_boxes_save = all_boxes.view(-1).tolist() # (4N, ) return {"target_bbox": self.state, "all_boxes": all_boxes_save} else: return {"target_bbox": self.state} def map_box_back(self, pred_box: list, resize_factor: float): cx_prev, cy_prev = self.state[0] + 0.5 * self.state[2], self.state[1] + 0.5 * self.state[3] cx, cy, w, h = pred_box half_side = 0.5 * self.params.search_size / resize_factor cx_real = cx + (cx_prev - half_side) cy_real = cy + (cy_prev - half_side) # cx_real = cx + cx_prev # cy_real = cy + cy_prev return [cx_real - 0.5 * w, cy_real - 0.5 * h, w, h] def map_box_back_batch(self, pred_box: torch.Tensor, resize_factor: float): cx_prev, cy_prev = self.state[0] + 0.5 * self.state[2], self.state[1] + 0.5 * self.state[3] cx, cy, w, h = pred_box.unbind(-1) # (N,4) --> (N,) half_side = 0.5 * self.params.search_size / resize_factor cx_real = cx + (cx_prev - half_side) cy_real = cy + (cy_prev - half_side) return torch.stack([cx_real - 0.5 * w, cy_real - 0.5 * h, w, h], dim=-1) def add_hook(self): conv_features, enc_attn_weights, dec_attn_weights = [], [], [] for i in range(12): self.network.backbone.blocks[i].attn.register_forward_hook( # lambda self, input, output: enc_attn_weights.append(output[1]) lambda self, input, output: enc_attn_weights.append(output[1]) ) self.enc_attn_weights = enc_attn_weights def get_tracker_class(): return ARTrackSeq ================================================ FILE: lib/test/tracker/artrackv2.py ================================================ import math from lib.models.artrackv2 import build_artrackv2 from lib.test.tracker.basetracker import BaseTracker import torch from lib.test.tracker.vis_utils import gen_visualization from lib.test.utils.hann import hann2d from lib.train.data.processing_utils import sample_target # for debug import cv2 import os from lib.test.tracker.data_utils import Preprocessor from lib.utils.box_ops import clip_box from lib.utils.ce_utils import generate_mask_cond import random class RandomErasing(object): def __init__(self, EPSILON=0.5, sl=0.02, sh=0.33, r1=0.3, mean=[0.4914, 0.4822, 0.4465]): self.EPSILON = EPSILON self.mean = mean self.sl = sl self.sh = sh self.r1 = r1 def __call__(self, img): if random.uniform(0, 1) > self.EPSILON: return img for attempt in range(100): print(img.size()) area = img.size()[1] * img.size()[2] target_area = random.uniform(self.sl, self.sh) * area aspect_ratio = random.uniform(self.r1, 1 / self.r1) h = int(round(math.sqrt(target_area * aspect_ratio))) w = int(round(math.sqrt(target_area / aspect_ratio))) if w < img.size()[2] and h < img.size()[1]: x1 = random.randint(0, img.size()[1] - h) y1 = random.randint(0, img.size()[2] - w) if img.size()[0] == 3: img[0, x1:x1 + h, y1:y1 + w] = self.mean[0] img[1, x1:x1 + h, y1:y1 + w] = self.mean[1] img[2, x1:x1 + h, y1:y1 + w] = self.mean[2] else: img[0, x1:x1 + h, y1:y1 + w] = self.mean[1] return img return img class ARTrackV2(BaseTracker): def __init__(self, params, dataset_name): super(ARTrackV2, self).__init__(params) network = build_artrackv2(params.cfg, training=False) network.load_state_dict(torch.load(self.params.checkpoint, map_location='cpu')['net'], strict=True) self.cfg = params.cfg self.bins = self.cfg.MODEL.BINS self.network = network.cuda() self.network.eval() self.preprocessor = Preprocessor() self.state = None self.update_ = False self.feat_sz = self.cfg.TEST.SEARCH_SIZE // self.cfg.MODEL.BACKBONE.STRIDE # motion constrain self.output_window = hann2d(torch.tensor([self.feat_sz, self.feat_sz]).long(), centered=True).cuda() # for debug self.debug = params.debug self.use_visdom = params.debug self.frame_id = 0 self.erase = RandomErasing() if self.debug: if not self.use_visdom: self.save_dir = "debug" if not os.path.exists(self.save_dir): os.makedirs(self.save_dir) else: # self.add_hook() self._init_visdom(None, 1) # for save boxes from all queries self.save_all_boxes = params.save_all_boxes self.z_dict1 = {} def initialize(self, image, info: dict): # forward the template once z_patch_arr, resize_factor, z_amask_arr = sample_target(image, info['init_bbox'], self.params.template_factor, output_sz=self.params.template_size) # output_sz=self.params.template_size self.z_patch_arr = z_patch_arr template = self.preprocessor.process(z_patch_arr, z_amask_arr) with torch.no_grad(): # initialize dynamic template as template in first frame self.z_dict1 = template self.z_dict2 = template self.box_mask_z = None self.state = info['init_bbox'] self.frame_id = 0 if self.save_all_boxes: '''save all predicted boxes''' all_boxes_save = info['init_bbox'] * self.cfg.MODEL.NUM_OBJECT_QUERIES return {"all_boxes": all_boxes_save} def track(self, image, info: dict = None): H, W, _ = image.shape self.frame_id += 1 x_patch_arr, resize_factor, x_amask_arr = sample_target(image, self.state, self.params.search_factor, output_sz=self.params.search_size) # (x1, y1, w, h) search = self.preprocessor.process(x_patch_arr, x_amask_arr) with torch.no_grad(): x_dict = search # merge the template and the search # run the transformer if self.update_: template = torch.concat([self.z_dict1.tensors.unsqueeze(0), self.z_dict2.unsqueeze(0)], dim=0) else: template = torch.concat([self.z_dict1.tensors.unsqueeze(0), self.z_dict2.tensors.unsqueeze(0)], dim=0) # merge the template and the search # run the transformer out_dict = self.network.forward( template=template, search=x_dict.tensors, ce_template_mask=self.box_mask_z) pred_boxes = out_dict['seqs'][:, 0:4] / (self.bins - 1) - 0.5 pred_boxes = pred_boxes.view(-1, 4).mean(dim=0) pred_new = pred_boxes pred_new[2] = pred_boxes[2] - pred_boxes[0] pred_new[3] = pred_boxes[3] - pred_boxes[1] pred_new[0] = pred_boxes[0] + pred_boxes[2] / 2 pred_new[1] = pred_boxes[1] + pred_boxes[3] / 2 pred_boxes = (pred_new * self.params.search_size / resize_factor).tolist() # Baseline: Take the mean of all pred boxes as the final result # pred_box = (pred_boxes.mean( # dim=0) * self.params.search_size / resize_factor).tolist() # (cx, cy, w, h) [0,1] # get the final box result self.state = clip_box(self.map_box_back(pred_boxes, resize_factor), H, W, margin=10) # for debug if self.debug: if not self.use_visdom: x1, y1, w, h = self.state image_BGR = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) cv2.rectangle(image_BGR, (int(x1), int(y1)), (int(x1 + w), int(y1 + h)), color=(0, 0, 255), thickness=2) save_path = os.path.join(self.save_dir, "%04d.jpg" % self.frame_id) cv2.imwrite(save_path, image_BGR) else: self.visdom.register((image, info['gt_bbox'].tolist(), self.state), 'Tracking', 1, 'Tracking') self.visdom.register(torch.from_numpy(x_patch_arr).permute(2, 0, 1), 'image', 1, 'search_region') self.visdom.register(torch.from_numpy(self.z_patch_arr).permute(2, 0, 1), 'image', 1, 'template') self.visdom.register(pred_score_map.view(self.feat_sz, self.feat_sz), 'heatmap', 1, 'score_map') self.visdom.register((pred_score_map * self.output_window).view(self.feat_sz, self.feat_sz), 'heatmap', 1, 'score_map_hann') if 'removed_indexes_s' in out_dict and out_dict['removed_indexes_s']: removed_indexes_s = out_dict['removed_indexes_s'] removed_indexes_s = [removed_indexes_s_i.cpu().numpy() for removed_indexes_s_i in removed_indexes_s] masked_search = gen_visualization(x_patch_arr, removed_indexes_s) self.visdom.register(torch.from_numpy(masked_search).permute(2, 0, 1), 'image', 1, 'masked_search') while self.pause_mode: if self.step: self.step = False break if self.save_all_boxes: '''save all predictions''' all_boxes = self.map_box_back_batch(pred_boxes * self.params.search_size / resize_factor, resize_factor) all_boxes_save = all_boxes.view(-1).tolist() # (4N, ) return {"target_bbox": self.state, "all_boxes": all_boxes_save} else: return {"target_bbox": self.state} def map_box_back(self, pred_box: list, resize_factor: float): cx_prev, cy_prev = self.state[0] + 0.5 * self.state[2], self.state[1] + 0.5 * self.state[3] cx, cy, w, h = pred_box half_side = 0.5 * self.params.search_size / resize_factor cx_real = cx + (cx_prev - half_side) cy_real = cy + (cy_prev - half_side) # cx_real = cx + cx_prev # cy_real = cy + cy_prev return [cx_real - 0.5 * w, cy_real - 0.5 * h, w, h] def map_box_back_batch(self, pred_box: torch.Tensor, resize_factor: float): cx_prev, cy_prev = self.state[0] + 0.5 * self.state[2], self.state[1] + 0.5 * self.state[3] cx, cy, w, h = pred_box.unbind(-1) # (N,4) --> (N,) half_side = 0.5 * self.params.search_size / resize_factor cx_real = cx + (cx_prev - half_side) cy_real = cy + (cy_prev - half_side) return torch.stack([cx_real - 0.5 * w, cy_real - 0.5 * h, w, h], dim=-1) def add_hook(self): conv_features, enc_attn_weights, dec_attn_weights = [], [], [] for i in range(12): self.network.backbone.blocks[i].attn.register_forward_hook( # lambda self, input, output: enc_attn_weights.append(output[1]) lambda self, input, output: enc_attn_weights.append(output[1]) ) self.enc_attn_weights = enc_attn_weights def get_tracker_class(): return ARTrackV2 ================================================ FILE: lib/test/tracker/artrackv2_seq.py ================================================ import math from lib.models.artrackv2_seq import build_artrackv2_seq from lib.test.tracker.basetracker import BaseTracker import torch from lib.test.tracker.vis_utils import gen_visualization from lib.test.utils.hann import hann2d from lib.train.data.processing_utils import sample_target, transform_image_to_crop # for debug import cv2 import os from lib.test.tracker.data_utils import Preprocessor from lib.utils.box_ops import clip_box from lib.utils.ce_utils import generate_mask_cond class ARTrackV2Seq(BaseTracker): def __init__(self, params, dataset_name): super(ARTrackV2Seq, self).__init__(params) network = build_artrackv2_seq(params.cfg, training=False) network.load_state_dict(torch.load(self.params.checkpoint, map_location='cpu')['net'], strict=True) self.cfg = params.cfg self.bins = params.cfg.MODEL.BINS self.network = network.cuda() self.network.eval() self.preprocessor = Preprocessor() self.state = None self.dz_feat = None self.feat_sz = self.cfg.TEST.SEARCH_SIZE // self.cfg.MODEL.BACKBONE.STRIDE # motion constrain self.output_window = hann2d(torch.tensor([self.feat_sz, self.feat_sz]).long(), centered=True).cuda() # for debug self.debug = params.debug self.use_visdom = params.debug self.frame_id = 0 if self.debug: if not self.use_visdom: self.save_dir = "debug" if not os.path.exists(self.save_dir): os.makedirs(self.save_dir) else: # self.add_hook() self._init_visdom(None, 1) # for save boxes from all queries self.save_all_boxes = params.save_all_boxes self.z_dict1 = {} self.store_result = None self.prenum = params.cfg.MODEL.PRENUM self.range = params.cfg.MODEL.RANGE self.x_feat = None def initialize(self, image, info: dict): # forward the template once self.x_feat = None self.update_ = False z_patch_arr, resize_factor, z_amask_arr = sample_target(image, info['init_bbox'], self.params.template_factor, output_sz=self.params.template_size) # output_sz=self.params.template_size self.z_patch_arr = z_patch_arr template = self.preprocessor.process(z_patch_arr, z_amask_arr) with torch.no_grad(): self.z_dict1 = template self.z_dict2 = template self.dz_feat = None self.box_mask_z = None # save states self.state = info['init_bbox'] self.store_result = [info['init_bbox'].copy()] for i in range(self.prenum - 1): self.store_result.append(info['init_bbox'].copy()) self.frame_id = 0 if self.save_all_boxes: '''save all predicted boxes''' all_boxes_save = info['init_bbox'] * self.cfg.MODEL.NUM_OBJECT_QUERIES return {"all_boxes": all_boxes_save} def track(self, image, info: dict = None): H, W, _ = image.shape self.frame_id += 1 x_patch_arr, resize_factor, x_amask_arr = sample_target(image, self.state, self.params.search_factor, output_sz=self.params.search_size) # (x1, y1, w, h) if self.dz_feat == None: self.dz_feat = self.network.backbone.patch_embed(self.z_dict2.tensors) for i in range(len(self.store_result)): box_temp = self.store_result[i].copy() box_out_i = transform_image_to_crop(torch.Tensor(self.store_result[i]), torch.Tensor(self.state), resize_factor, torch.Tensor([self.cfg.TEST.SEARCH_SIZE, self.cfg.TEST.SEARCH_SIZE]), normalize=True) box_out_i[2] = box_out_i[2] + box_out_i[0] box_out_i[3] = box_out_i[3] + box_out_i[1] box_out_i = box_out_i.clamp(min=-0.5, max=1.5) box_out_i = (box_out_i + 0.5) * (self.bins - 1) if i == 0: seqs_out = box_out_i else: seqs_out = torch.cat((seqs_out, box_out_i), dim=-1) seqs_out = seqs_out.unsqueeze(0) search = self.preprocessor.process(x_patch_arr, x_amask_arr) with torch.no_grad(): x_dict = search # merge the template and the search # run the transformer if self.update_: template = torch.concat([self.z_dict1.tensors.unsqueeze(1), self.z_dict2.unsqueeze(1)], dim=1) else: template = torch.concat([self.z_dict1.tensors.unsqueeze(1), self.z_dict2.tensors.unsqueeze(1)], dim=1) out_dict = self.network.forward( template=template, dz_feat=self.dz_feat, search=x_dict.tensors, ce_template_mask=self.box_mask_z, seq_input=seqs_out, stage="sequence", search_feature=self.x_feat) self.dz_feat = out_dict['dz_feat'] self.x_feat = out_dict['x_feat'] pred_boxes = (out_dict['seqs'][:, 0:4] + 0.5) / (self.bins - 1) - 0.5 pred_feat = out_dict['feat'] pred = pred_feat.permute(1, 0, 2).reshape(-1, self.bins * self.range + 6) pred = pred_feat[0:4, :, 0:self.bins * self.range] out = pred.softmax(-1).to(pred) mul = torch.range((-1 * self.range * 0.5 + 0.5) + 1 / (self.bins * self.range), (self.range * 0.5 + 0.5) - 1 / (self.bins * self.range), 2 / (self.bins * self.range)).to(pred) ans = out * mul ans = ans.sum(dim=-1) ans = ans.permute(1, 0).to(pred) pred_boxes = (ans + pred_boxes) / 2 pred_boxes = pred_boxes.view(-1, 4).mean(dim=0) pred_new = pred_boxes pred_new[2] = pred_boxes[2] - pred_boxes[0] pred_new[3] = pred_boxes[3] - pred_boxes[1] pred_new[0] = pred_boxes[0] + pred_new[2] / 2 pred_new[1] = pred_boxes[1] + pred_new[3] / 2 pred_boxes = (pred_new * self.params.search_size / resize_factor).tolist() self.state = clip_box(self.map_box_back(pred_boxes, resize_factor), H, W, margin=10) if len(self.store_result) < self.prenum: self.store_result.append(self.state.copy()) else: for i in range(self.prenum): if i != self.prenum - 1: self.store_result[i] = self.store_result[i + 1] else: self.store_result[i] = self.state.copy() # for debug if self.debug: if not self.use_visdom: x1, y1, w, h = self.state image_BGR = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) cv2.rectangle(image_BGR, (int(x1), int(y1)), (int(x1 + w), int(y1 + h)), color=(0, 0, 255), thickness=2) save_path = os.path.join(self.save_dir, "%04d.jpg" % self.frame_id) cv2.imwrite(save_path, image_BGR) else: self.visdom.register((image, info['gt_bbox'].tolist(), self.state), 'Tracking', 1, 'Tracking') self.visdom.register(torch.from_numpy(x_patch_arr).permute(2, 0, 1), 'image', 1, 'search_region') self.visdom.register(torch.from_numpy(self.z_patch_arr).permute(2, 0, 1), 'image', 1, 'template') self.visdom.register(pred_score_map.view(self.feat_sz, self.feat_sz), 'heatmap', 1, 'score_map') self.visdom.register((pred_score_map * self.output_window).view(self.feat_sz, self.feat_sz), 'heatmap', 1, 'score_map_hann') if 'removed_indexes_s' in out_dict and out_dict['removed_indexes_s']: removed_indexes_s = out_dict['removed_indexes_s'] removed_indexes_s = [removed_indexes_s_i.cpu().numpy() for removed_indexes_s_i in removed_indexes_s] masked_search = gen_visualization(x_patch_arr, removed_indexes_s) self.visdom.register(torch.from_numpy(masked_search).permute(2, 0, 1), 'image', 1, 'masked_search') while self.pause_mode: if self.step: self.step = False break if self.save_all_boxes: '''save all predictions''' all_boxes = self.map_box_back_batch(pred_boxes * self.params.search_size / resize_factor, resize_factor) all_boxes_save = all_boxes.view(-1).tolist() # (4N, ) return {"target_bbox": self.state, "all_boxes": all_boxes_save} else: return {"target_bbox": self.state} def map_box_back(self, pred_box: list, resize_factor: float): cx_prev, cy_prev = self.state[0] + 0.5 * self.state[2], self.state[1] + 0.5 * self.state[3] cx, cy, w, h = pred_box half_side = 0.5 * self.params.search_size / resize_factor cx_real = cx + (cx_prev - half_side) cy_real = cy + (cy_prev - half_side) # cx_real = cx + cx_prev # cy_real = cy + cy_prev return [cx_real - 0.5 * w, cy_real - 0.5 * h, w, h] def map_box_back_batch(self, pred_box: torch.Tensor, resize_factor: float): cx_prev, cy_prev = self.state[0] + 0.5 * self.state[2], self.state[1] + 0.5 * self.state[3] cx, cy, w, h = pred_box.unbind(-1) # (N,4) --> (N,) half_side = 0.5 * self.params.search_size / resize_factor cx_real = cx + (cx_prev - half_side) cy_real = cy + (cy_prev - half_side) return torch.stack([cx_real - 0.5 * w, cy_real - 0.5 * h, w, h], dim=-1) def add_hook(self): conv_features, enc_attn_weights, dec_attn_weights = [], [], [] for i in range(12): self.network.backbone.blocks[i].attn.register_forward_hook( # lambda self, input, output: enc_attn_weights.append(output[1]) lambda self, input, output: enc_attn_weights.append(output[1]) ) self.enc_attn_weights = enc_attn_weights def get_tracker_class(): return ARTrackV2Seq ================================================ FILE: lib/test/tracker/basetracker.py ================================================ import time import torch from _collections import OrderedDict from lib.train.data.processing_utils import transform_image_to_crop from lib.vis.visdom_cus import Visdom class BaseTracker: """Base class for all trackers.""" def __init__(self, params): self.params = params self.visdom = None def predicts_segmentation_mask(self): return False def initialize(self, image, info: dict) -> dict: """Overload this function in your tracker. This should initialize the model.""" raise NotImplementedError def track(self, image, info: dict = None) -> dict: """Overload this function in your tracker. This should track in the frame and update the model.""" raise NotImplementedError def visdom_draw_tracking(self, image, box, segmentation=None): if isinstance(box, OrderedDict): box = [v for k, v in box.items()] else: box = (box,) if segmentation is None: self.visdom.register((image, *box), 'Tracking', 1, 'Tracking') else: self.visdom.register((image, *box, segmentation), 'Tracking', 1, 'Tracking') def transform_bbox_to_crop(self, box_in, resize_factor, device, box_extract=None, crop_type='template'): # box_in: list [x1, y1, w, h], not normalized # box_extract: same as box_in # out bbox: Torch.tensor [1, 1, 4], x1y1wh, normalized if crop_type == 'template': crop_sz = torch.Tensor([self.params.template_size, self.params.template_size]) elif crop_type == 'search': crop_sz = torch.Tensor([self.params.search_size, self.params.search_size]) else: raise NotImplementedError box_in = torch.tensor(box_in) if box_extract is None: box_extract = box_in else: box_extract = torch.tensor(box_extract) template_bbox = transform_image_to_crop(box_in, box_extract, resize_factor, crop_sz, normalize=True) template_bbox = template_bbox.view(1, 1, 4).to(device) return template_bbox def _init_visdom(self, visdom_info, debug): visdom_info = {} if visdom_info is None else visdom_info self.pause_mode = False self.step = False self.next_seq = False if debug > 0 and visdom_info.get('use_visdom', True): try: self.visdom = Visdom(debug, {'handler': self._visdom_ui_handler, 'win_id': 'Tracking'}, visdom_info=visdom_info) # # Show help # help_text = 'You can pause/unpause the tracker by pressing ''space'' with the ''Tracking'' window ' \ # 'selected. During paused mode, you can track for one frame by pressing the right arrow key.' \ # 'To enable/disable plotting of a data block, tick/untick the corresponding entry in ' \ # 'block list.' # self.visdom.register(help_text, 'text', 1, 'Help') except: time.sleep(0.5) print('!!! WARNING: Visdom could not start, so using matplotlib visualization instead !!!\n' '!!! Start Visdom in a separate terminal window by typing \'visdom\' !!!') def _visdom_ui_handler(self, data): if data['event_type'] == 'KeyPress': if data['key'] == ' ': self.pause_mode = not self.pause_mode elif data['key'] == 'ArrowRight' and self.pause_mode: self.step = True elif data['key'] == 'n': self.next_seq = True ================================================ FILE: lib/test/tracker/data_utils.py ================================================ import torch import numpy as np from lib.utils.misc import NestedTensor class Preprocessor(object): def __init__(self): self.mean = torch.tensor([0.485, 0.456, 0.406]).view((1, 3, 1, 1)).cuda() self.std = torch.tensor([0.229, 0.224, 0.225]).view((1, 3, 1, 1)).cuda() def process(self, img_arr: np.ndarray, amask_arr: np.ndarray): # Deal with the image patch img_tensor = torch.tensor(img_arr).cuda().float().permute((2,0,1)).unsqueeze(dim=0) img_tensor_norm = ((img_tensor / 255.0) - self.mean) / self.std # (1,3,H,W) # Deal with the attention mask amask_tensor = torch.from_numpy(amask_arr).to(torch.bool).cuda().unsqueeze(dim=0) # (1,H,W) return NestedTensor(img_tensor_norm, amask_tensor) class PreprocessorX(object): def __init__(self): self.mean = torch.tensor([0.485, 0.456, 0.406]).view((1, 3, 1, 1)).cuda() self.std = torch.tensor([0.229, 0.224, 0.225]).view((1, 3, 1, 1)).cuda() def process(self, img_arr: np.ndarray, amask_arr: np.ndarray): # Deal with the image patch img_tensor = torch.tensor(img_arr).cuda().float().permute((2,0,1)).unsqueeze(dim=0) img_tensor_norm = ((img_tensor / 255.0) - self.mean) / self.std # (1,3,H,W) # Deal with the attention mask amask_tensor = torch.from_numpy(amask_arr).to(torch.bool).cuda().unsqueeze(dim=0) # (1,H,W) return img_tensor_norm, amask_tensor class PreprocessorX_onnx(object): def __init__(self): self.mean = np.array([0.485, 0.456, 0.406]).reshape((1, 3, 1, 1)) self.std = np.array([0.229, 0.224, 0.225]).reshape((1, 3, 1, 1)) def process(self, img_arr: np.ndarray, amask_arr: np.ndarray): """img_arr: (H,W,3), amask_arr: (H,W)""" # Deal with the image patch img_arr_4d = img_arr[np.newaxis, :, :, :].transpose(0, 3, 1, 2) img_arr_4d = (img_arr_4d / 255.0 - self.mean) / self.std # (1, 3, H, W) # Deal with the attention mask amask_arr_3d = amask_arr[np.newaxis, :, :] # (1,H,W) return img_arr_4d.astype(np.float32), amask_arr_3d.astype(np.bool) ================================================ FILE: lib/test/tracker/vis_utils.py ================================================ import numpy as np ############## used for visulize eliminated tokens ################# def get_keep_indices(decisions): keep_indices = [] for i in range(3): if i == 0: keep_indices.append(decisions[i]) else: keep_indices.append(keep_indices[-1][decisions[i]]) return keep_indices def gen_masked_tokens(tokens, indices, alpha=0.2): # indices = [i for i in range(196) if i not in indices] indices = indices[0].astype(int) tokens = tokens.copy() tokens[indices] = alpha * tokens[indices] + (1 - alpha) * 255 return tokens def recover_image(tokens, H, W, Hp, Wp, patch_size): # image: (C, 196, 16, 16) image = tokens.reshape(Hp, Wp, patch_size, patch_size, 3).swapaxes(1, 2).reshape(H, W, 3) return image def pad_img(img): height, width, channels = img.shape im_bg = np.ones((height, width + 8, channels)) * 255 im_bg[0:height, 0:width, :] = img return im_bg def gen_visualization(image, mask_indices, patch_size=16): # image [224, 224, 3] # mask_indices, list of masked token indices # mask mask_indices need to cat # mask_indices = mask_indices[::-1] num_stages = len(mask_indices) for i in range(1, num_stages): mask_indices[i] = np.concatenate([mask_indices[i-1], mask_indices[i]], axis=1) # keep_indices = get_keep_indices(decisions) image = np.asarray(image) H, W, C = image.shape Hp, Wp = H // patch_size, W // patch_size image_tokens = image.reshape(Hp, patch_size, Wp, patch_size, 3).swapaxes(1, 2).reshape(Hp * Wp, patch_size, patch_size, 3) stages = [ recover_image(gen_masked_tokens(image_tokens, mask_indices[i]), H, W, Hp, Wp, patch_size) for i in range(num_stages) ] imgs = [image] + stages imgs = [pad_img(img) for img in imgs] viz = np.concatenate(imgs, axis=1) return viz ================================================ FILE: lib/test/utils/__init__.py ================================================ from .params import TrackerParams, FeatureParams, Choice ================================================ FILE: lib/test/utils/_init_paths.py ================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os.path as osp import sys def add_path(path): if path not in sys.path: sys.path.insert(0, path) this_dir = osp.dirname(__file__) prj_path = osp.join(this_dir, '..', '..', '..') add_path(prj_path) ================================================ FILE: lib/test/utils/hann.py ================================================ import torch import math import torch.nn.functional as F def hann1d(sz: int, centered = True) -> torch.Tensor: """1D cosine window.""" if centered: return 0.5 * (1 - torch.cos((2 * math.pi / (sz + 1)) * torch.arange(1, sz + 1).float())) w = 0.5 * (1 + torch.cos((2 * math.pi / (sz + 2)) * torch.arange(0, sz//2 + 1).float())) return torch.cat([w, w[1:sz-sz//2].flip((0,))]) def hann2d(sz: torch.Tensor, centered = True) -> torch.Tensor: """2D cosine window.""" return hann1d(sz[0].item(), centered).reshape(1, 1, -1, 1) * hann1d(sz[1].item(), centered).reshape(1, 1, 1, -1) def hann2d_bias(sz: torch.Tensor, ctr_point: torch.Tensor, centered = True) -> torch.Tensor: """2D cosine window.""" distance = torch.stack([ctr_point, sz-ctr_point], dim=0) max_distance, _ = distance.max(dim=0) hann1d_x = hann1d(max_distance[0].item() * 2, centered) hann1d_x = hann1d_x[max_distance[0] - distance[0, 0]: max_distance[0] + distance[1, 0]] hann1d_y = hann1d(max_distance[1].item() * 2, centered) hann1d_y = hann1d_y[max_distance[1] - distance[0, 1]: max_distance[1] + distance[1, 1]] return hann1d_y.reshape(1, 1, -1, 1) * hann1d_x.reshape(1, 1, 1, -1) def hann2d_clipped(sz: torch.Tensor, effective_sz: torch.Tensor, centered = True) -> torch.Tensor: """1D clipped cosine window.""" # Ensure that the difference is even effective_sz += (effective_sz - sz) % 2 effective_window = hann1d(effective_sz[0].item(), True).reshape(1, 1, -1, 1) * hann1d(effective_sz[1].item(), True).reshape(1, 1, 1, -1) pad = (sz - effective_sz) // 2 window = F.pad(effective_window, (pad[1].item(), pad[1].item(), pad[0].item(), pad[0].item()), 'replicate') if centered: return window else: mid = (sz / 2).int() window_shift_lr = torch.cat((window[:, :, :, mid[1]:], window[:, :, :, :mid[1]]), 3) return torch.cat((window_shift_lr[:, :, mid[0]:, :], window_shift_lr[:, :, :mid[0], :]), 2) def gauss_fourier(sz: int, sigma: float, half: bool = False) -> torch.Tensor: if half: k = torch.arange(0, int(sz/2+1)) else: k = torch.arange(-int((sz-1)/2), int(sz/2+1)) return (math.sqrt(2*math.pi) * sigma / sz) * torch.exp(-2 * (math.pi * sigma * k.float() / sz)**2) def gauss_spatial(sz, sigma, center=0, end_pad=0): k = torch.arange(-(sz-1)/2, (sz+1)/2+end_pad) return torch.exp(-1.0/(2*sigma**2) * (k - center)**2) def label_function(sz: torch.Tensor, sigma: torch.Tensor): return gauss_fourier(sz[0].item(), sigma[0].item()).reshape(1, 1, -1, 1) * gauss_fourier(sz[1].item(), sigma[1].item(), True).reshape(1, 1, 1, -1) def label_function_spatial(sz: torch.Tensor, sigma: torch.Tensor, center: torch.Tensor = torch.zeros(2), end_pad: torch.Tensor = torch.zeros(2)): """The origin is in the middle of the image.""" return gauss_spatial(sz[0].item(), sigma[0].item(), center[0], end_pad[0].item()).reshape(1, 1, -1, 1) * \ gauss_spatial(sz[1].item(), sigma[1].item(), center[1], end_pad[1].item()).reshape(1, 1, 1, -1) def cubic_spline_fourier(f, a): """The continuous Fourier transform of a cubic spline kernel.""" bf = (6*(1 - torch.cos(2 * math.pi * f)) + 3*a*(1 - torch.cos(4 * math.pi * f)) - (6 + 8*a)*math.pi*f*torch.sin(2 * math.pi * f) - 2*a*math.pi*f*torch.sin(4 * math.pi * f)) \ / (4 * math.pi**4 * f**4) bf[f == 0] = 1 return bf def max2d(a: torch.Tensor) -> (torch.Tensor, torch.Tensor): """Computes maximum and argmax in the last two dimensions.""" max_val_row, argmax_row = torch.max(a, dim=-2) max_val, argmax_col = torch.max(max_val_row, dim=-1) argmax_row = argmax_row.view(argmax_col.numel(),-1)[torch.arange(argmax_col.numel()), argmax_col.view(-1)] argmax_row = argmax_row.reshape(argmax_col.shape) argmax = torch.cat((argmax_row.unsqueeze(-1), argmax_col.unsqueeze(-1)), -1) return max_val, argmax ================================================ FILE: lib/test/utils/load_text.py ================================================ import numpy as np import pandas as pd def load_text_numpy(path, delimiter, dtype): if isinstance(delimiter, (tuple, list)): for d in delimiter: try: ground_truth_rect = np.loadtxt(path, delimiter=d, dtype=dtype) return ground_truth_rect except: pass raise Exception('Could not read file {}'.format(path)) else: ground_truth_rect = np.loadtxt(path, delimiter=delimiter, dtype=dtype) return ground_truth_rect def load_text_pandas(path, delimiter, dtype): if isinstance(delimiter, (tuple, list)): for d in delimiter: try: ground_truth_rect = pd.read_csv(path, delimiter=d, header=None, dtype=dtype, na_filter=False, low_memory=False).values return ground_truth_rect except Exception as e: pass raise Exception('Could not read file {}'.format(path)) else: ground_truth_rect = pd.read_csv(path, delimiter=delimiter, header=None, dtype=dtype, na_filter=False, low_memory=False).values return ground_truth_rect def load_text(path, delimiter=' ', dtype=np.float32, backend='numpy'): if backend == 'numpy': return load_text_numpy(path, delimiter, dtype) elif backend == 'pandas': return load_text_pandas(path, delimiter, dtype) def load_str(path): with open(path, "r") as f: text_str = f.readline().strip().lower() return text_str ================================================ FILE: lib/test/utils/params.py ================================================ from lib.utils import TensorList import random class TrackerParams: """Class for tracker parameters.""" def set_default_values(self, default_vals: dict): for name, val in default_vals.items(): if not hasattr(self, name): setattr(self, name, val) def get(self, name: str, *default): """Get a parameter value with the given name. If it does not exists, it return the default value given as a second argument or returns an error if no default value is given.""" if len(default) > 1: raise ValueError('Can only give one default value.') if not default: return getattr(self, name) return getattr(self, name, default[0]) def has(self, name: str): """Check if there exist a parameter with the given name.""" return hasattr(self, name) class FeatureParams: """Class for feature specific parameters""" def __init__(self, *args, **kwargs): if len(args) > 0: raise ValueError for name, val in kwargs.items(): if isinstance(val, list): setattr(self, name, TensorList(val)) else: setattr(self, name, val) def Choice(*args): """Can be used to sample random parameter values.""" return random.choice(args) ================================================ FILE: lib/test/utils/transform_got10k.py ================================================ import numpy as np import os import shutil import argparse import _init_paths from lib.test.evaluation.environment import env_settings def transform_got10k(tracker_name, cfg_name): env = env_settings() result_dir = env.results_path src_dir = os.path.join(result_dir, "%s/%s/got10k/" % (tracker_name, cfg_name)) dest_dir = os.path.join(result_dir, "%s/%s/got10k_submit/" % (tracker_name, cfg_name)) if not os.path.exists(dest_dir): os.makedirs(dest_dir) items = os.listdir(src_dir) for item in items: if "all" in item: continue src_path = os.path.join(src_dir, item) if "time" not in item: seq_name = item.replace(".txt", '') seq_dir = os.path.join(dest_dir, seq_name) if not os.path.exists(seq_dir): os.makedirs(seq_dir) new_item = item.replace(".txt", '_001.txt') dest_path = os.path.join(seq_dir, new_item) bbox_arr = np.loadtxt(src_path, dtype=np.int, delimiter='\t') np.savetxt(dest_path, bbox_arr, fmt='%d', delimiter=',') else: seq_name = item.replace("_time.txt", '') seq_dir = os.path.join(dest_dir, seq_name) if not os.path.exists(seq_dir): os.makedirs(seq_dir) dest_path = os.path.join(seq_dir, item) os.system("cp %s %s" % (src_path, dest_path)) # make zip archive shutil.make_archive(src_dir, "zip", src_dir) shutil.make_archive(dest_dir, "zip", dest_dir) # Remove the original files shutil.rmtree(src_dir) shutil.rmtree(dest_dir) if __name__ == "__main__": parser = argparse.ArgumentParser(description='transform got10k results.') parser.add_argument('--tracker_name', type=str, help='Name of tracking method.') parser.add_argument('--cfg_name', type=str, help='Name of config file.') args = parser.parse_args() transform_got10k(args.tracker_name, args.cfg_name) ================================================ FILE: lib/test/utils/transform_trackingnet.py ================================================ import numpy as np import os import shutil import argparse import _init_paths from lib.test.evaluation.environment import env_settings def transform_trackingnet(tracker_name, cfg_name): env = env_settings() result_dir = env.results_path src_dir = os.path.join(result_dir, "%s/%s/trackingnet/" % (tracker_name, cfg_name)) dest_dir = os.path.join(result_dir, "%s/%s/trackingnet_submit/" % (tracker_name, cfg_name)) if not os.path.exists(dest_dir): os.makedirs(dest_dir) items = os.listdir(src_dir) for item in items: if "all" in item: continue if "time" not in item: src_path = os.path.join(src_dir, item) dest_path = os.path.join(dest_dir, item) bbox_arr = np.loadtxt(src_path, dtype=np.int, delimiter='\t') np.savetxt(dest_path, bbox_arr, fmt='%d', delimiter=',') # make zip archive shutil.make_archive(src_dir, "zip", src_dir) shutil.make_archive(dest_dir, "zip", dest_dir) # Remove the original files shutil.rmtree(src_dir) shutil.rmtree(dest_dir) if __name__ == "__main__": parser = argparse.ArgumentParser(description='transform trackingnet results.') parser.add_argument('--tracker_name', type=str, help='Name of tracking method.') parser.add_argument('--cfg_name', type=str, help='Name of config file.') args = parser.parse_args() transform_trackingnet(args.tracker_name, args.cfg_name) ================================================ FILE: lib/train/__init__.py ================================================ from .admin.multigpu import MultiGPU ================================================ FILE: lib/train/_init_paths.py ================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os.path as osp import sys def add_path(path): if path not in sys.path: sys.path.insert(0, path) this_dir = osp.dirname(__file__) prj_path = osp.join(this_dir, '../..') add_path(prj_path) ================================================ FILE: lib/train/actors/__init__.py ================================================ from .base_actor import BaseActor from .artrack import ARTrackActor from .artrack_seq import ARTrackSeqActor from .artrackv2 import ARTrackV2Actor from .artrackv2_seq import ARTrackV2SeqActor ================================================ FILE: lib/train/actors/artrack.py ================================================ from . import BaseActor from lib.utils.misc import NestedTensor from lib.utils.box_ops import box_cxcywh_to_xyxy, box_xywh_to_xyxy import torch import math import numpy as np from lib.utils.merge import merge_template_search from ...utils.heapmap_utils import generate_heatmap from ...utils.ce_utils import generate_mask_cond, adjust_keep_rate def fp16_clamp(x, min=None, max=None): if not x.is_cuda and x.dtype == torch.float16: # clamp for cpu float16, tensor fp16 has no clamp implementation return x.float().clamp(min, max).half() return x.clamp(min, max) def generate_sa_simdr(joints): ''' :param joints: [num_joints, 3] :param joints_vis: [num_joints, 3] :return: target, target_weight(1: visible, 0: invisible) ''' num_joints = 48 image_size = [256, 256] simdr_split_ratio = 1.5625 sigma = 6 target_x1 = np.zeros((num_joints, int(image_size[0] * simdr_split_ratio)), dtype=np.float32) target_y1 = np.zeros((num_joints, int(image_size[1] * simdr_split_ratio)), dtype=np.float32) target_x2 = np.zeros((num_joints, int(image_size[0] * simdr_split_ratio)), dtype=np.float32) target_y2 = np.zeros((num_joints, int(image_size[1] * simdr_split_ratio)), dtype=np.float32) zero_4_begin = np.zeros((num_joints, 1), dtype=np.float32) tmp_size = sigma * 3 for joint_id in range(num_joints): mu_x1 = joints[joint_id][0] mu_y1 = joints[joint_id][1] mu_x2 = joints[joint_id][2] mu_y2 = joints[joint_id][3] x1 = np.arange(0, int(image_size[0] * simdr_split_ratio), 1, np.float32) y1 = np.arange(0, int(image_size[1] * simdr_split_ratio), 1, np.float32) x2 = np.arange(0, int(image_size[0] * simdr_split_ratio), 1, np.float32) y2 = np.arange(0, int(image_size[1] * simdr_split_ratio), 1, np.float32) target_x1[joint_id] = (np.exp(- ((x1 - mu_x1) ** 2) / (2 * sigma ** 2))) / ( sigma * np.sqrt(np.pi * 2)) target_y1[joint_id] = (np.exp(- ((y1 - mu_y1) ** 2) / (2 * sigma ** 2))) / ( sigma * np.sqrt(np.pi * 2)) target_x2[joint_id] = (np.exp(- ((x2 - mu_x2) ** 2) / (2 * sigma ** 2))) / ( sigma * np.sqrt(np.pi * 2)) target_y2[joint_id] = (np.exp(- ((y2 - mu_y2) ** 2) / (2 * sigma ** 2))) / ( sigma * np.sqrt(np.pi * 2)) return target_x1, target_y1, target_x2, target_y2 # angle cost def SIoU_loss(test1, test2, theta=4): eps = 1e-7 cx_pred = (test1[:, 0] + test1[:, 2]) / 2 cy_pred = (test1[:, 1] + test1[:, 3]) / 2 cx_gt = (test2[:, 0] + test2[:, 2]) / 2 cy_gt = (test2[:, 1] + test2[:, 3]) / 2 dist = ((cx_pred - cx_gt)**2 + (cy_pred - cy_gt)**2) ** 0.5 ch = torch.max(cy_gt, cy_pred) - torch.min(cy_gt, cy_pred) x = ch / (dist + eps) angle = 1 - 2*torch.sin(torch.arcsin(x)-torch.pi/4)**2 # distance cost xmin = torch.min(test1[:, 0], test2[:, 0]) xmax = torch.max(test1[:, 2], test2[:, 2]) ymin = torch.min(test1[:, 1], test2[:, 1]) ymax = torch.max(test1[:, 3], test2[:, 3]) cw = xmax - xmin ch = ymax - ymin px = ((cx_gt - cx_pred) / (cw+eps))**2 py = ((cy_gt - cy_pred) / (ch+eps))**2 gama = 2 - angle dis = (1 - torch.exp(-1 * gama * px)) + (1 - torch.exp(-1 * gama * py)) #shape cost w_pred = test1[:, 2] - test1[:, 0] h_pred = test1[:, 3] - test1[:, 1] w_gt = test2[:, 2] - test2[:, 0] h_gt = test2[:, 3] - test2[:, 1] ww = torch.abs(w_pred - w_gt) / (torch.max(w_pred, w_gt) + eps) wh = torch.abs(h_gt - h_pred) / (torch.max(h_gt, h_pred) + eps) omega = (1 - torch.exp(-1 * wh)) ** theta + (1 - torch.exp(-1 * ww)) ** theta #IoU loss lt = torch.max(test1[..., :2], test2[..., :2]) # [B, rows, 2] rb = torch.min(test1[..., 2:], test2[..., 2:]) # [B, rows, 2] wh = fp16_clamp(rb - lt, min=0) overlap = wh[..., 0] * wh[..., 1] area1 = (test1[..., 2] - test1[..., 0]) * ( test1[..., 3] - test1[..., 1]) area2 = (test2[..., 2] - test2[..., 0]) * ( test2[..., 3] - test2[..., 1]) iou = overlap / (area1 + area2 - overlap) SIoU = 1 - iou + (omega + dis) / 2 return SIoU, iou def ciou(pred, target, eps=1e-7): # overlap lt = torch.max(pred[:, :2], target[:, :2]) rb = torch.min(pred[:, 2:], target[:, 2:]) wh = (rb - lt).clamp(min=0) overlap = wh[:, 0] * wh[:, 1] # union ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1]) ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1]) union = ap + ag - overlap + eps # IoU ious = overlap / union # enclose area enclose_x1y1 = torch.min(pred[:, :2], target[:, :2]) enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:]) enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0) cw = enclose_wh[:, 0] ch = enclose_wh[:, 1] c2 = cw**2 + ch**2 + eps b1_x1, b1_y1 = pred[:, 0], pred[:, 1] b1_x2, b1_y2 = pred[:, 2], pred[:, 3] b2_x1, b2_y1 = target[:, 0], target[:, 1] b2_x2, b2_y2 = target[:, 2], target[:, 3] w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2))**2 / 4 right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2))**2 / 4 rho2 = left + right factor = 4 / math.pi**2 v = factor * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2) # CIoU cious = ious - (rho2 / c2 + v**2 / (1 - ious + v)) return cious, ious class ARTrackActor(BaseActor): """ Actor for training ARTrack models """ def __init__(self, net, objective, loss_weight, settings, bins, search_size, cfg=None): super().__init__(net, objective) self.loss_weight = loss_weight self.settings = settings self.bs = self.settings.batchsize # batch size self.cfg = cfg self.bins = bins self.range = self.cfg.MODEL.RANGE self.search_size = search_size self.logsoftmax = torch.nn.LogSoftmax(dim=1) self.focal = None self.loss_weight['KL'] = 100 self.loss_weight['focal'] = 2 def __call__(self, data): """ args: data - The input data, should contain the fields 'template', 'search', 'gt_bbox'. template_images: (N_t, batch, 3, H, W) search_images: (N_s, batch, 3, H, W) returns: loss - the training loss status - dict containing detailed losses """ # forward pass out_dict = self.forward_pass(data) # compute losses loss, status = self.compute_losses(out_dict, data) return loss, status def forward_pass(self, data): # currently only support 1 template and 1 search region assert len(data['template_images']) == 1 assert len(data['search_images']) == 1 template_list = [] for i in range(self.settings.num_template): template_img_i = data['template_images'][i].view(-1, *data['template_images'].shape[2:]) # (batch, 3, 128, 128) template_list.append(template_img_i) search_img = data['search_images'][0].view(-1, *data['search_images'].shape[2:]) # (batch, 3, 320, 320) if len(template_list) == 1: template_list = template_list[0] gt_bbox = data['search_anno'][-1] begin = self.bins * self.range end = self.bins * self.range + 1 magic_num = (self.range - 1) * 0.5 gt_bbox[:, 2] = gt_bbox[:, 0] + gt_bbox[:, 2] gt_bbox[:, 3] = gt_bbox[:, 1] + gt_bbox[:, 3] gt_bbox = gt_bbox.clamp(min=(-1*magic_num), max=(1+magic_num)) data['real_bbox'] = gt_bbox seq_ori = (gt_bbox + magic_num) * (self.bins - 1) seq_ori = seq_ori.int().to(search_img) B = seq_ori.shape[0] seq_input = torch.cat([torch.ones((B, 1)).to(search_img) * begin, seq_ori], dim=1) seq_output = torch.cat([seq_ori, torch.ones((B, 1)).to(search_img) * end], dim=1) data['seq_input'] = seq_input data['seq_output'] = seq_output out_dict = self.net(template=template_list, search=search_img, seq_input=seq_input) return out_dict def compute_losses(self, pred_dict, gt_dict, return_status=True): bins = self.bins magic_num = (self.range - 1) * 0.5 seq_output = gt_dict['seq_output'] pred_feat = pred_dict["feat"] if self.focal == None: weight = torch.ones(bins*self.range+2) * 1 weight[bins*self.range+1] = 0.1 weight[bins*self.range] = 0.1 weight.to(pred_feat) self.klloss = torch.nn.KLDivLoss(reduction='none').to(pred_feat) self.focal = torch.nn.CrossEntropyLoss(weight=weight, size_average=True).to(pred_feat) # compute varfifocal loss pred = pred_feat.permute(1, 0, 2).reshape(-1, bins*2+2) target = seq_output.reshape(-1).to(torch.int64) varifocal_loss = self.focal(pred, target) # compute giou and L1 loss beta = 1 pred = pred_feat[0:4, :, 0:bins*self.range] * beta target = seq_output[:, 0:4].to(pred_feat) out = pred.softmax(-1).to(pred) mul = torch.range((-1*magic_num+1/(self.bins*self.range)), (1+magic_num-1/(self.bins*self.range)), 2/(self.bins*self.range)).to(pred) ans = out * mul ans = ans.sum(dim=-1) ans = ans.permute(1, 0).to(pred) target = target / (bins - 1) - magic_num extra_seq = ans extra_seq = extra_seq.to(pred) sious, iou = SIoU_loss(extra_seq, target, 4) sious = sious.mean() siou_loss = sious l1_loss = self.objective['l1'](extra_seq, target) loss = self.loss_weight['giou'] * siou_loss + self.loss_weight['l1'] * l1_loss + self.loss_weight['focal'] * varifocal_loss if return_status: # status for log mean_iou = iou.detach().mean() status = {"Loss/total": loss.item(), "Loss/giou": siou_loss.item(), "Loss/l1": l1_loss.item(), "Loss/location": varifocal_loss.item(), "IoU": mean_iou.item()} return loss, status else: return loss ================================================ FILE: lib/train/actors/artrack_seq.py ================================================ from . import BaseActor from lib.utils.misc import NestedTensor from lib.utils.box_ops import box_cxcywh_to_xyxy, box_xywh_to_xyxy import torch import math import numpy as np import numpy import cv2 import torch.nn.functional as F import torchvision.transforms.functional as tvisf import lib.train.data.bounding_box_utils as bbutils from lib.utils.merge import merge_template_search from torch.distributions.categorical import Categorical from ...utils.heapmap_utils import generate_heatmap from ...utils.ce_utils import generate_mask_cond, adjust_keep_rate def IoU(rect1, rect2): """ caculate interection over union Args: rect1: (x1, y1, x2, y2) rect2: (x1, y1, x2, y2) Returns: iou """ # overlap x1, y1, x2, y2 = rect1[0], rect1[1], rect1[2], rect1[3] tx1, ty1, tx2, ty2 = rect2[0], rect2[1], rect2[2], rect2[3] xx1 = np.maximum(tx1, x1) yy1 = np.maximum(ty1, y1) xx2 = np.minimum(tx2, x2) yy2 = np.minimum(ty2, y2) ww = np.maximum(0, xx2 - xx1) hh = np.maximum(0, yy2 - yy1) area = (x2 - x1) * (y2 - y1) target_a = (tx2 - tx1) * (ty2 - ty1) inter = ww * hh iou = inter / (area + target_a - inter) return iou def fp16_clamp(x, min=None, max=None): if not x.is_cuda and x.dtype == torch.float16: # clamp for cpu float16, tensor fp16 has no clamp implementation return x.float().clamp(min, max).half() return x.clamp(min, max) def generate_sa_simdr(joints): ''' :param joints: [num_joints, 3] :param joints_vis: [num_joints, 3] :return: target, target_weight(1: visible, 0: invisible) ''' num_joints = 48 image_size = [256, 256] simdr_split_ratio = 1.5625 sigma = 6 target_x1 = np.zeros((num_joints, int(image_size[0] * simdr_split_ratio)), dtype=np.float32) target_y1 = np.zeros((num_joints, int(image_size[1] * simdr_split_ratio)), dtype=np.float32) target_x2 = np.zeros((num_joints, int(image_size[0] * simdr_split_ratio)), dtype=np.float32) target_y2 = np.zeros((num_joints, int(image_size[1] * simdr_split_ratio)), dtype=np.float32) zero_4_begin = np.zeros((num_joints, 1), dtype=np.float32) tmp_size = sigma * 3 for joint_id in range(num_joints): mu_x1 = joints[joint_id][0] mu_y1 = joints[joint_id][1] mu_x2 = joints[joint_id][2] mu_y2 = joints[joint_id][3] x1 = np.arange(0, int(image_size[0] * simdr_split_ratio), 1, np.float32) y1 = np.arange(0, int(image_size[1] * simdr_split_ratio), 1, np.float32) x2 = np.arange(0, int(image_size[0] * simdr_split_ratio), 1, np.float32) y2 = np.arange(0, int(image_size[1] * simdr_split_ratio), 1, np.float32) target_x1[joint_id] = (np.exp(- ((x1 - mu_x1) ** 2) / (2 * sigma ** 2))) / ( sigma * np.sqrt(np.pi * 2)) target_y1[joint_id] = (np.exp(- ((y1 - mu_y1) ** 2) / (2 * sigma ** 2))) / ( sigma * np.sqrt(np.pi * 2)) target_x2[joint_id] = (np.exp(- ((x2 - mu_x2) ** 2) / (2 * sigma ** 2))) / ( sigma * np.sqrt(np.pi * 2)) target_y2[joint_id] = (np.exp(- ((y2 - mu_y2) ** 2) / (2 * sigma ** 2))) / ( sigma * np.sqrt(np.pi * 2)) return target_x1, target_y1, target_x2, target_y2 # angle cost def SIoU_loss(test1, test2, theta=4): eps = 1e-7 cx_pred = (test1[:, 0] + test1[:, 2]) / 2 cy_pred = (test1[:, 1] + test1[:, 3]) / 2 cx_gt = (test2[:, 0] + test2[:, 2]) / 2 cy_gt = (test2[:, 1] + test2[:, 3]) / 2 dist = ((cx_pred - cx_gt) ** 2 + (cy_pred - cy_gt) ** 2) ** 0.5 ch = torch.max(cy_gt, cy_pred) - torch.min(cy_gt, cy_pred) x = ch / (dist + eps) angle = 1 - 2 * torch.sin(torch.arcsin(x) - torch.pi / 4) ** 2 # distance cost xmin = torch.min(test1[:, 0], test2[:, 0]) xmax = torch.max(test1[:, 2], test2[:, 2]) ymin = torch.min(test1[:, 1], test2[:, 1]) ymax = torch.max(test1[:, 3], test2[:, 3]) cw = xmax - xmin ch = ymax - ymin px = ((cx_gt - cx_pred) / (cw + eps)) ** 2 py = ((cy_gt - cy_pred) / (ch + eps)) ** 2 gama = 2 - angle dis = (1 - torch.exp(-1 * gama * px)) + (1 - torch.exp(-1 * gama * py)) # shape cost w_pred = test1[:, 2] - test1[:, 0] h_pred = test1[:, 3] - test1[:, 1] w_gt = test2[:, 2] - test2[:, 0] h_gt = test2[:, 3] - test2[:, 1] ww = torch.abs(w_pred - w_gt) / (torch.max(w_pred, w_gt) + eps) wh = torch.abs(h_gt - h_pred) / (torch.max(h_gt, h_pred) + eps) omega = (1 - torch.exp(-1 * wh)) ** theta + (1 - torch.exp(-1 * ww)) ** theta # IoU loss lt = torch.max(test1[..., :2], test2[..., :2]) # [B, rows, 2] rb = torch.min(test1[..., 2:], test2[..., 2:]) # [B, rows, 2] wh = fp16_clamp(rb - lt, min=0) overlap = wh[..., 0] * wh[..., 1] area1 = (test1[..., 2] - test1[..., 0]) * ( test1[..., 3] - test1[..., 1]) area2 = (test2[..., 2] - test2[..., 0]) * ( test2[..., 3] - test2[..., 1]) iou = overlap / (area1 + area2 - overlap) SIoU = 1 - iou + (omega + dis) / 2 return SIoU, iou def ciou(pred, target, eps=1e-7): # overlap lt = torch.max(pred[:, :2], target[:, :2]) rb = torch.min(pred[:, 2:], target[:, 2:]) wh = (rb - lt).clamp(min=0) overlap = wh[:, 0] * wh[:, 1] # union ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1]) ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1]) union = ap + ag - overlap + eps # IoU ious = overlap / union # enclose area enclose_x1y1 = torch.min(pred[:, :2], target[:, :2]) enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:]) enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0) cw = enclose_wh[:, 0] ch = enclose_wh[:, 1] c2 = cw ** 2 + ch ** 2 + eps b1_x1, b1_y1 = pred[:, 0], pred[:, 1] b1_x2, b1_y2 = pred[:, 2], pred[:, 3] b2_x1, b2_y1 = target[:, 0], target[:, 1] b2_x2, b2_y2 = target[:, 2], target[:, 3] w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2)) ** 2 / 4 right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2)) ** 2 / 4 rho2 = left + right factor = 4 / math.pi ** 2 v = factor * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2) # CIoU cious = ious - (rho2 / c2 + v ** 2 / (1 - ious + v)) return cious, ious class ARTrackSeqActor(BaseActor): """ Actor for training OSTrack models """ def __init__(self, net, objective, loss_weight, settings, bins, search_size, cfg=None): super().__init__(net, objective) self.loss_weight = loss_weight self.settings = settings self.bs = self.settings.batchsize # batch size self.cfg = cfg self.bins = bins self.search_size = search_size self.logsoftmax = torch.nn.LogSoftmax(dim=1) self.focal = None self.range = cfg.MODEL.RANGE self.pre_num = cfg.MODEL.PRENUM self.loss_weight['KL'] = 0 self.loss_weight['focal'] = 0 self.pre_bbox = None self.x_feat_rem = None self.update_rem = None def __call__(self, data): """ args: data - The input data, should contain the fields 'template', 'search', 'gt_bbox'. template_images: (N_t, batch, 3, H, W) search_images: (N_s, batch, 3, H, W) returns: loss - the training loss status - dict containing detailed losses """ # forward pass out_dict = self.forward_pass(data) # compute losses loss, status = self.compute_losses(out_dict, data) return loss, status def _bbox_clip(self, cx, cy, width, height, boundary): cx = max(0, min(cx, boundary[1])) cy = max(0, min(cy, boundary[0])) width = max(10, min(width, boundary[1])) height = max(10, min(height, boundary[0])) return cx, cy, width, height def get_subwindow(self, im, pos, model_sz, original_sz, avg_chans): """ args: im: bgr based image pos: center position model_sz: exemplar size s_z: original size avg_chans: channel average """ if isinstance(pos, float): pos = [pos, pos] sz = original_sz im_sz = im.shape c = (original_sz + 1) / 2 # context_xmin = round(pos[0] - c) # py2 and py3 round context_xmin = np.floor(pos[0] - c + 0.5) context_xmax = context_xmin + sz - 1 # context_ymin = round(pos[1] - c) context_ymin = np.floor(pos[1] - c + 0.5) context_ymax = context_ymin + sz - 1 left_pad = int(max(0., -context_xmin)) top_pad = int(max(0., -context_ymin)) right_pad = int(max(0., context_xmax - im_sz[1] + 1)) bottom_pad = int(max(0., context_ymax - im_sz[0] + 1)) context_xmin = context_xmin + left_pad context_xmax = context_xmax + left_pad context_ymin = context_ymin + top_pad context_ymax = context_ymax + top_pad r, c, k = im.shape if any([top_pad, bottom_pad, left_pad, right_pad]): size = (r + top_pad + bottom_pad, c + left_pad + right_pad, k) te_im = np.zeros(size, np.uint8) te_im[top_pad:top_pad + r, left_pad:left_pad + c, :] = im if top_pad: te_im[0:top_pad, left_pad:left_pad + c, :] = avg_chans if bottom_pad: te_im[r + top_pad:, left_pad:left_pad + c, :] = avg_chans if left_pad: te_im[:, 0:left_pad, :] = avg_chans if right_pad: te_im[:, c + left_pad:, :] = avg_chans im_patch = te_im[int(context_ymin):int(context_ymax + 1), int(context_xmin):int(context_xmax + 1), :] else: im_patch = im[int(context_ymin):int(context_ymax + 1), int(context_xmin):int(context_xmax + 1), :] if not np.array_equal(model_sz, original_sz): try: im_patch = cv2.resize(im_patch, (model_sz, model_sz)) except: return None im_patch = im_patch.transpose(2, 0, 1) im_patch = im_patch[np.newaxis, :, :, :] im_patch = im_patch.astype(np.float32) im_patch = torch.from_numpy(im_patch) im_patch = im_patch.cuda() return im_patch def batch_init(self, images, template_bbox, initial_bbox) -> dict: self.frame_num = 1 self.device = 'cuda' # Convert bbox (x1, y1, w, h) -> (cx, cy, w, h) template_bbox = bbutils.batch_xywh2center2(template_bbox) # ndarray:(2*num_seq,4) initial_bbox = bbutils.batch_xywh2center2(initial_bbox) # ndarray:(2*num_seq,4) self.center_pos = initial_bbox[:, :2] # ndarray:(2*num_seq,2) self.size = initial_bbox[:, 2:] # ndarray:(2*num_seq,2) self.pre_bbox = initial_bbox for i in range(self.pre_num - 1): self.pre_bbox = numpy.concatenate((self.pre_bbox, initial_bbox), axis=1) # print(self.pre_bbox.shape) template_factor = self.cfg.DATA.TEMPLATE.FACTOR w_z = template_bbox[:, 2] * template_factor # ndarray:(2*num_seq) h_z = template_bbox[:, 3] * template_factor # ndarray:(2*num_seq) s_z = np.ceil(np.sqrt(w_z * h_z)) # ndarray:(2*num_seq) self.channel_average = [] for img in images: self.channel_average.append(np.mean(img, axis=(0, 1))) self.channel_average = np.array(self.channel_average) # ndarray:(2*num_seq,3) # get crop z_crop_list = [] for i in range(len(images)): here_crop = self.get_subwindow(images[i], template_bbox[i, :2], self.cfg.DATA.TEMPLATE.SIZE, s_z[i], self.channel_average[i]) z_crop = here_crop.float().mul(1.0 / 255.0).clamp(0.0, 1.0) self.mean = [0.485, 0.456, 0.406] self.std = [0.229, 0.224, 0.225] self.inplace = False z_crop[0] = tvisf.normalize(z_crop[0], self.mean, self.std, self.inplace) z_crop_list.append(z_crop.clone()) z_crop = torch.cat(z_crop_list, dim=0) # Tensor(2*num_seq,3,128,128) self.update_rem = None out = {'template_images': z_crop} return out def batch_track(self, img, gt_boxes, template, action_mode='max') -> dict: search_factor = self.cfg.DATA.SEARCH.FACTOR w_x = self.size[:, 0] * search_factor h_x = self.size[:, 1] * search_factor s_x = np.ceil(np.sqrt(w_x * h_x)) gt_boxes_corner = bbutils.batch_xywh2corner(gt_boxes) # ndarray:(2*num_seq,4) x_crop_list = [] gt_in_crop_list = [] pre_seq_list = [] pre_seq_in_list = [] x_feat_list = [] magic_num = (self.range - 1) * 0.5 for i in range(len(img)): channel_avg = np.mean(img[i], axis=(0, 1)) x_crop = self.get_subwindow(img[i], self.center_pos[i], self.cfg.DATA.SEARCH.SIZE, round(s_x[i]), channel_avg) if x_crop == None: return None for q in range(self.pre_num): pre_seq_temp = bbutils.batch_center2corner(self.pre_bbox[:, 0 + 4 * q:4 + 4 * q]) if q == 0: pre_seq = pre_seq_temp else: pre_seq = numpy.concatenate((pre_seq, pre_seq_temp), axis=1) if gt_boxes_corner is not None and np.sum(np.abs(gt_boxes_corner[i] - np.zeros(4))) > 10: pre_in = np.zeros(4 * self.pre_num) for w in range(self.pre_num): pre_in[0 + w * 4:2 + w * 4] = pre_seq[i, 0 + w * 4:2 + w * 4] - self.center_pos[i] pre_in[2 + w * 4:4 + w * 4] = pre_seq[i, 2 + w * 4:4 + w * 4] - self.center_pos[i] pre_in[0 + w * 4:4 + w * 4] = pre_in[0 + w * 4:4 + w * 4] * ( self.cfg.DATA.SEARCH.SIZE / s_x[i]) + self.cfg.DATA.SEARCH.SIZE / 2 pre_in[0 + w * 4:4 + w * 4] = pre_in[0 + w * 4:4 + w * 4] / self.cfg.DATA.SEARCH.SIZE pre_seq_list.append(pre_in) gt_in_crop = np.zeros(4) gt_in_crop[:2] = gt_boxes_corner[i, :2] - self.center_pos[i] gt_in_crop[2:] = gt_boxes_corner[i, 2:] - self.center_pos[i] gt_in_crop = gt_in_crop * (self.cfg.DATA.SEARCH.SIZE / s_x[i]) + self.cfg.DATA.SEARCH.SIZE / 2 gt_in_crop[2:] = gt_in_crop[2:] - gt_in_crop[:2] # (x1,y1,x2,y2) to (x1,y1,w,h) gt_in_crop_list.append(gt_in_crop) else: pre_in = np.zeros(4 * self.pre_num) pre_seq_list.append(pre_in) gt_in_crop_list.append(np.zeros(4)) pre_seq_input = torch.from_numpy(pre_in).clamp(-1 * magic_num, 1 + magic_num) pre_seq_input = (pre_seq_input + 0.5) * (self.bins - 1) pre_seq_in_list.append(pre_seq_input.clone()) x_crop = x_crop.float().mul(1.0 / 255.0).clamp(0.0, 1.0) x_crop[0] = tvisf.normalize(x_crop[0], self.mean, self.std, self.inplace) x_crop_list.append(x_crop.clone()) x_crop = torch.cat(x_crop_list, dim=0) pre_seq_output = torch.cat(pre_seq_in_list, dim=0).reshape(-1, 4 * self.pre_num) outputs = self.net(template, x_crop, seq_input=pre_seq_output, head_type=None, stage="batch_track", search_feature=self.x_feat_rem, update=None) selected_indices = outputs['seqs'].detach() x_feat = outputs['x_feat'].detach().cpu() self.x_feat_rem = x_feat.clone() x_feat_list.append(x_feat.clone()) pred_bbox = selected_indices[:, 0:4].data.cpu().numpy() bbox = (pred_bbox / (self.bins - 1) - magic_num) * s_x.reshape(-1, 1) cx = bbox[:, 0] + self.center_pos[:, 0] - s_x / 2 cy = bbox[:, 1] + self.center_pos[:, 1] - s_x / 2 width = bbox[:, 2] - bbox[:, 0] height = bbox[:, 3] - bbox[:, 1] cx = cx + width / 2 cy = cy + height / 2 for i in range(len(img)): cx[i], cy[i], width[i], height[i] = self._bbox_clip(cx[i], cy[i], width[i], height[i], img[i].shape[:2]) self.center_pos = np.stack([cx, cy], 1) self.size = np.stack([width, height], 1) for e in range(self.pre_num): if e != self.pre_num - 1: self.pre_bbox[:, 0 + e * 4:4 + e * 4] = self.pre_bbox[:, 4 + e * 4:8 + e * 4] else: self.pre_bbox[:, 0 + e * 4:4 + e * 4] = numpy.stack([cx, cy, width, height], 1) bbox = np.stack([cx - width / 2, cy - height / 2, width, height], 1) out = { 'search_images': x_crop, 'pred_bboxes': bbox, 'selected_indices': selected_indices.cpu(), 'gt_in_crop': torch.tensor(np.stack(gt_in_crop_list, axis=0), dtype=torch.float), 'pre_seq': torch.tensor(np.stack(pre_seq_list, axis=0), dtype=torch.float), 'x_feat': torch.tensor([item.cpu().detach().numpy() for item in x_feat_list], dtype=torch.float), } return out def explore(self, data): results = {} search_images_list = [] search_anno_list = [] iou_list = [] pre_seq_list = [] x_feat_list = [] num_frames = data['num_frames'] images = data['search_images'] gt_bbox = data['search_annos'] template = data['template_images'] template_bbox = data['template_annos'] template = template template_bbox = template_bbox template_bbox = np.array(template_bbox) num_seq = len(num_frames) for idx in range(np.max(num_frames)): here_images = [img[idx] for img in images] # S, N here_gt_bbox = np.array([gt[idx] for gt in gt_bbox]) here_images = here_images here_gt_bbox = np.concatenate([here_gt_bbox], 0) if idx == 0: outputs_template = self.batch_init(template, template_bbox, here_gt_bbox) results['template_images'] = outputs_template['template_images'] else: outputs = self.batch_track(here_images, here_gt_bbox, outputs_template['template_images'], action_mode='half') if outputs == None: return None x_feat = outputs['x_feat'] pred_bbox = outputs['pred_bboxes'] search_images_list.append(outputs['search_images']) search_anno_list.append(outputs['gt_in_crop']) if len(outputs['pre_seq']) != 8: print(outputs['pre_seq']) print(len(outputs['pre_seq'])) print(idx) print(data['num_frames']) print(data['search_annos']) return None pre_seq_list.append(outputs['pre_seq']) pred_bbox_corner = bbutils.batch_xywh2corner(pred_bbox) gt_bbox_corner = bbutils.batch_xywh2corner(here_gt_bbox) here_iou = [] for i in range(num_seq): bbox_iou = IoU(pred_bbox_corner[i], gt_bbox_corner[i]) here_iou.append(bbox_iou) iou_list.append(here_iou) x_feat_list.append(x_feat.clone()) results['x_feat'] = torch.cat([torch.stack(x_feat_list)], dim=2) results['search_images'] = torch.cat([torch.stack(search_images_list)], dim=1) results['search_anno'] = torch.cat([torch.stack(search_anno_list)], dim=1) results['pre_seq'] = torch.cat([torch.stack(pre_seq_list)], dim=1) iou_tensor = torch.tensor(iou_list, dtype=torch.float) results['baseline_iou'] = torch.cat([iou_tensor[:, :num_seq]], dim=1) return results def forward_pass(self, data): # currently only support 1 template and 1 search region assert len(data['template_images']) == 1 assert len(data['search_images']) == 1 template_list = [] for i in range(self.settings.num_template): template_img_i = data['template_images'][i].view(-1, *data['template_images'].shape[2:]) # (batch, 3, 128, 128) template_list.append(template_img_i) search_img = data['search_images'][0].view(-1, *data['search_images'].shape[2:]) # (batch, 3, 320, 320) box_mask_z = None ce_keep_rate = None if self.cfg.MODEL.BACKBONE.CE_LOC: box_mask_z = generate_mask_cond(self.cfg, template_list[0].shape[0], template_list[0].device, data['template_anno'][0]) ce_start_epoch = self.cfg.TRAIN.CE_START_EPOCH ce_warm_epoch = self.cfg.TRAIN.CE_WARM_EPOCH ce_keep_rate = adjust_keep_rate(data['epoch'], warmup_epochs=ce_start_epoch, total_epochs=ce_start_epoch + ce_warm_epoch, ITERS_PER_EPOCH=1, base_keep_rate=self.cfg.MODEL.BACKBONE.CE_KEEP_RATIO[0]) if len(template_list) == 1: template_list = template_list[0] gt_bbox = data['search_anno'][-1] begin = self.bins end = self.bins + 1 gt_bbox[:, 2] = gt_bbox[:, 0] + gt_bbox[:, 2] gt_bbox[:, 3] = gt_bbox[:, 1] + gt_bbox[:, 3] gt_bbox = gt_bbox.clamp(min=0.5, max=1.5) data['real_bbox'] = gt_bbox seq_ori = gt_bbox * (self.bins - 1) seq_ori = seq_ori.int().to(search_img) B = seq_ori.shape[0] seq_input = torch.cat([torch.ones((B, 1)).to(search_img) * begin, seq_ori], dim=1) seq_output = torch.cat([seq_ori, torch.ones((B, 1)).to(search_img) * end], dim=1) data['seq_input'] = seq_input data['seq_output'] = seq_output out_dict = self.net(template=template_list, search=search_img, ce_template_mask=box_mask_z, ce_keep_rate=ce_keep_rate, return_last_attn=False, seq_input=seq_input) return out_dict def compute_sequence_losses(self, data): num_frames = data['search_images'].shape[0] template_images = data['template_images'].repeat(num_frames, 1, 1, 1, 1) template_images = template_images.view(-1, *template_images.size()[2:]) search_images = data['search_images'].reshape(-1, *data['search_images'].size()[2:]) search_anno = data['search_anno'].reshape(-1, *data['search_anno'].size()[2:]) magic_num = (self.range - 1) * 0.5 self.loss_weight['focal'] = 0 pre_seq = data['pre_seq'].reshape(-1, 4 * self.pre_num) x_feat = data['x_feat'].reshape(-1, *data['x_feat'].size()[2:]) pre_seq = pre_seq.clamp(-1 * magic_num, 1 + magic_num) pre_seq = (pre_seq + magic_num) * (self.bins - 1) outputs = self.net(template_images, search_images, seq_input=pre_seq, stage="forward_pass", search_feature=x_feat, update=None) pred_feat = outputs["feat"] # generate labels if self.focal == None: weight = torch.ones(self.bins * self.range + 2) * 1 weight[self.bins * self.range + 1] = 0.1 weight[self.bins * self.range] = 0.1 weight.to(pred_feat) self.focal = torch.nn.CrossEntropyLoss(weight=weight, size_average=True).to(pred_feat) search_anno[:, 2] = search_anno[:, 2] + search_anno[:, 0] search_anno[:, 3] = search_anno[:, 3] + search_anno[:, 1] target = (search_anno / self.cfg.DATA.SEARCH.SIZE + 0.5) * (self.bins - 1) target = target.clamp(min=0.0, max=(self.bins * self.range - 0.0001)) target_iou = target target = torch.cat([target], dim=1) target = target.reshape(-1).to(torch.int64) pred = pred_feat.permute(1, 0, 2).reshape(-1, self.bins * self.range + 2) varifocal_loss = self.focal(pred, target) pred = pred_feat[0:4, :, 0:self.bins * self.range] target = target_iou[:, 0:4].to(pred_feat) / (self.bins - 1) - magic_num out = pred.softmax(-1).to(pred) mul = torch.range(-1 * magic_num + 1 / (self.bins * self.range), 1 + magic_num - 1 / (self.bins * self.range), 2 / (self.bins * self.range)).to(pred) ans = out * mul ans = ans.sum(dim=-1) ans = ans.permute(1, 0).to(pred) extra_seq = ans extra_seq = extra_seq.to(pred) cious, iou = SIoU_loss(extra_seq, target, 4) cious = cious.mean() giou_loss = cious loss_bb = self.loss_weight['giou'] * giou_loss + self.loss_weight[ 'focal'] * varifocal_loss total_losses = loss_bb mean_iou = iou.detach().mean() status = {"Loss/total": total_losses.item(), "Loss/giou": giou_loss.item(), "Loss/location": varifocal_loss.item(), "IoU": mean_iou.item()} return total_losses, status ================================================ FILE: lib/train/actors/artrackv2.py ================================================ from . import BaseActor from copy import deepcopy from lib.utils.misc import NestedTensor from lib.utils.box_ops import box_cxcywh_to_xyxy, box_xywh_to_xyxy import torch import math import numpy as np from lib.utils.merge import merge_template_search from ...utils.heapmap_utils import generate_heatmap from ...utils.ce_utils import generate_mask_cond, adjust_keep_rate def fp16_clamp(x, min=None, max=None): if not x.is_cuda and x.dtype == torch.float16: # clamp for cpu float16, tensor fp16 has no clamp implementation return x.float().clamp(min, max).half() return x.clamp(min, max) def generate_sa_simdr(joints): ''' :param joints: [num_joints, 3] :param joints_vis: [num_joints, 3] :return: target, target_weight(1: visible, 0: invisible) ''' num_joints = 48 image_size = [256, 256] simdr_split_ratio = 1.5625 sigma = 6 target_x1 = np.zeros((num_joints, int(image_size[0] * simdr_split_ratio)), dtype=np.float32) target_y1 = np.zeros((num_joints, int(image_size[1] * simdr_split_ratio)), dtype=np.float32) target_x2 = np.zeros((num_joints, int(image_size[0] * simdr_split_ratio)), dtype=np.float32) target_y2 = np.zeros((num_joints, int(image_size[1] * simdr_split_ratio)), dtype=np.float32) zero_4_begin = np.zeros((num_joints, 1), dtype=np.float32) tmp_size = sigma * 3 for joint_id in range(num_joints): mu_x1 = joints[joint_id][0] mu_y1 = joints[joint_id][1] mu_x2 = joints[joint_id][2] mu_y2 = joints[joint_id][3] x1 = np.arange(0, int(image_size[0] * simdr_split_ratio), 1, np.float32) y1 = np.arange(0, int(image_size[1] * simdr_split_ratio), 1, np.float32) x2 = np.arange(0, int(image_size[0] * simdr_split_ratio), 1, np.float32) y2 = np.arange(0, int(image_size[1] * simdr_split_ratio), 1, np.float32) target_x1[joint_id] = (np.exp(- ((x1 - mu_x1) ** 2) / (2 * sigma ** 2))) / ( sigma * np.sqrt(np.pi * 2)) target_y1[joint_id] = (np.exp(- ((y1 - mu_y1) ** 2) / (2 * sigma ** 2))) / ( sigma * np.sqrt(np.pi * 2)) target_x2[joint_id] = (np.exp(- ((x2 - mu_x2) ** 2) / (2 * sigma ** 2))) / ( sigma * np.sqrt(np.pi * 2)) target_y2[joint_id] = (np.exp(- ((y2 - mu_y2) ** 2) / (2 * sigma ** 2))) / ( sigma * np.sqrt(np.pi * 2)) return target_x1, target_y1, target_x2, target_y2 # angle cost def SIoU_loss(test1, test2, theta=4): eps = 1e-7 cx_pred = (test1[:, 0] + test1[:, 2]) / 2 cy_pred = (test1[:, 1] + test1[:, 3]) / 2 cx_gt = (test2[:, 0] + test2[:, 2]) / 2 cy_gt = (test2[:, 1] + test2[:, 3]) / 2 dist = ((cx_pred - cx_gt) ** 2 + (cy_pred - cy_gt) ** 2) ** 0.5 ch = torch.max(cy_gt, cy_pred) - torch.min(cy_gt, cy_pred) x = ch / (dist + eps) angle = 1 - 2 * torch.sin(torch.arcsin(x) - torch.pi / 4) ** 2 # distance cost xmin = torch.min(test1[:, 0], test2[:, 0]) xmax = torch.max(test1[:, 2], test2[:, 2]) ymin = torch.min(test1[:, 1], test2[:, 1]) ymax = torch.max(test1[:, 3], test2[:, 3]) cw = xmax - xmin ch = ymax - ymin px = ((cx_gt - cx_pred) / (cw + eps)) ** 2 py = ((cy_gt - cy_pred) / (ch + eps)) ** 2 gama = 2 - angle dis = (1 - torch.exp(-1 * gama * px)) + (1 - torch.exp(-1 * gama * py)) # shape cost w_pred = test1[:, 2] - test1[:, 0] h_pred = test1[:, 3] - test1[:, 1] w_gt = test2[:, 2] - test2[:, 0] h_gt = test2[:, 3] - test2[:, 1] ww = torch.abs(w_pred - w_gt) / (torch.max(w_pred, w_gt) + eps) wh = torch.abs(h_gt - h_pred) / (torch.max(h_gt, h_pred) + eps) omega = (1 - torch.exp(-1 * wh)) ** theta + (1 - torch.exp(-1 * ww)) ** theta # IoU loss lt = torch.max(test1[..., :2], test2[..., :2]) # [B, rows, 2] rb = torch.min(test1[..., 2:], test2[..., 2:]) # [B, rows, 2] wh = fp16_clamp(rb - lt, min=0) overlap = wh[..., 0] * wh[..., 1] area1 = (test1[..., 2] - test1[..., 0]) * ( test1[..., 3] - test1[..., 1]) area2 = (test2[..., 2] - test2[..., 0]) * ( test2[..., 3] - test2[..., 1]) iou = overlap / (area1 + area2 - overlap) SIoU = 1 - iou + (omega + dis) / 2 return SIoU, iou def ciou(pred, target, eps=1e-7): # overlap lt = torch.max(pred[:, :2], target[:, :2]) rb = torch.min(pred[:, 2:], target[:, 2:]) wh = (rb - lt).clamp(min=0) overlap = wh[:, 0] * wh[:, 1] # union ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1]) ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1]) union = ap + ag - overlap + eps # IoU ious = overlap / union # enclose area enclose_x1y1 = torch.min(pred[:, :2], target[:, :2]) enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:]) enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0) cw = enclose_wh[:, 0] ch = enclose_wh[:, 1] c2 = cw ** 2 + ch ** 2 + eps b1_x1, b1_y1 = pred[:, 0], pred[:, 1] b1_x2, b1_y2 = pred[:, 2], pred[:, 3] b2_x1, b2_y1 = target[:, 0], target[:, 1] b2_x2, b2_y2 = target[:, 2], target[:, 3] w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2)) ** 2 / 4 right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2)) ** 2 / 4 rho2 = left + right factor = 4 / math.pi ** 2 v = factor * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2) # CIoU cious = ious - (rho2 / c2 + v ** 2 / (1 - ious + v)) return cious, ious class ARTrackV2Actor(BaseActor): """ Actor for training OSTrack models """ def __init__(self, net, objective, loss_weight, settings, bins, search_size, cfg=None): super().__init__(net, objective) self.loss_weight = loss_weight self.settings = settings self.bs = self.settings.batchsize # batch size self.cfg = cfg self.bins = bins self.search_size = search_size self.logsoftmax = torch.nn.LogSoftmax(dim=1) self.focal = None self.range = self.cfg.MODEL.RANGE self.loss_weight['KL'] = 100 self.loss_weight['focal'] = 2 self.loss_weight['renew'] = 0.3 def __call__(self, data): """ args: data - The input data, should contain the fields 'template', 'search', 'gt_bbox'. template_images: (N_t, batch, 3, H, W) search_images: (N_s, batch, 3, H, W) returns: loss - the training loss status - dict containing detailed losses """ # forward pass out_dict = self.forward_pass(data) # compute losses loss, status = self.compute_losses(out_dict, data) return loss, status def forward_pass(self, data): # currently only support 1 template and 1 search region assert len(data['template_images']) == 2 assert len(data['search_images']) == 1 # print(data['dataset']) template_list = [] for i in range(self.settings.num_template): template_img_i = data['template_images'][i].view(-1, *data['template_images'].shape[2:]) # (batch, 3, 128, 128) # template_att_i = data['template_att'][i].view(-1, *data['template_att'].shape[2:]) # (batch, 128, 128) template_list.append(template_img_i) search_img = data['search_images'][0].view(-1, *data['search_images'].shape[2:]) # (batch, 3, 320, 320) target_in_search_img = data['target_in_search_images'][0].view(-1, *data['target_in_search_images'].shape[ 2:]) # (batch, 3, 320, 320) gt_bboxes = deepcopy(data['search_anno']) # search_att = data['search_att'][0].view(-1, *data['search_att'].shape[2:]) # (batch, 320, 320) box_mask_z = None ce_keep_rate = None if self.cfg.MODEL.BACKBONE.CE_LOC: box_mask_z = generate_mask_cond(self.cfg, template_list[0].shape[0], template_list[0].device, data['template_anno'][0]) ce_start_epoch = self.cfg.TRAIN.CE_START_EPOCH ce_warm_epoch = self.cfg.TRAIN.CE_WARM_EPOCH ce_keep_rate = adjust_keep_rate(data['epoch'], warmup_epochs=ce_start_epoch, total_epochs=ce_start_epoch + ce_warm_epoch, ITERS_PER_EPOCH=1, base_keep_rate=self.cfg.MODEL.BACKBONE.CE_KEEP_RATIO[0]) if len(template_list) == 1: template_list = template_list[0] gt_bbox = data['search_anno'][-1] x0 = self.bins * self.range y0 = self.bins * self.range + 1 x1 = self.bins * self.range + 2 y1 = self.bins * self.range + 3 score = self.bins * self.range + 5 end = self.bins * self.range + 4 gt_bbox[:, 2] = gt_bbox[:, 0] + gt_bbox[:, 2] gt_bbox[:, 3] = gt_bbox[:, 1] + gt_bbox[:, 3] gt_bbox = gt_bbox.clamp(min=(-0.5 * self.range + 0.5), max=(0.5 + self.range * 0.5)) data['real_bbox'] = gt_bbox seq_ori = (gt_bbox + (self.range * 0.5 - 0.5)) * (self.bins - 1) seq_ori = seq_ori.int().to(search_img) B = seq_ori.shape[0] seq_ori_4_4 = seq_ori[:, 0:3] seq_input = torch.cat([torch.ones((B, 1)).to(search_img) * x0, torch.ones((B, 1)).to(search_img) * y0, torch.ones((B, 1)).to(search_img) * x1, torch.ones((B, 1)).to(search_img) * y1, torch.ones((B, 1)).to(search_img) * score], dim=1) seq_output = torch.cat([seq_ori], dim=1) data['seq_input'] = seq_input data['seq_output'] = seq_output out_dict = self.net(template=template_list, search=search_img, ce_template_mask=box_mask_z, ce_keep_rate=ce_keep_rate, return_last_attn=False, seq_input=seq_input, target_in_search_img=target_in_search_img, gt_bboxes=gt_bboxes[-1]) return out_dict def compute_losses(self, pred_dict, gt_dict, return_status=True): # gt gaussian map bins = self.bins gt_bbox = gt_dict['search_anno'][-1] # (Ns, batch, 4) (x1,y1,w,h) -> (batch, 4) real_bbox = gt_dict['real_bbox'] seq_output = gt_dict['seq_output'] pred_feat = pred_dict["feat"] if self.focal == None: weight = torch.ones(bins * self.range + 6) * 1 weight[bins * self.range + 4] = 0.1 weight[bins * self.range + 3] = 0.1 weight[bins * self.range + 2] = 0.1 weight[bins * self.range + 1] = 0.1 weight[bins * self.range] = 0.1 weight.to(pred_feat) self.focal = torch.nn.CrossEntropyLoss(weight=weight, size_average=True).to(pred_feat) pred = pred_feat.permute(1, 0, 2).reshape(-1, bins * self.range + 6) target = seq_output.reshape(-1).to(torch.int64) varifocal_loss = self.focal(pred, target) beta = 1 pred = pred_feat[0:4, :, 0:bins * self.range] * beta target = seq_output[:, 0:4].to(pred_feat) target_box = seq_output[:, 0:4].cpu().numpy() out = pred.softmax(-1).to(pred) mul = torch.range((self.range * 0.5 * -1 + 0.5) + 1 / (self.bins * self.range), (0.5 + self.range * 0.5) - 1 / (self.bins * self.range), 2 / (self.bins * self.range)).to(pred) ans = out * mul ans = ans.sum(dim=-1) ans = ans.permute(1, 0).to(pred) target = target / (bins - 1) - (self.range * 0.5 - 0.5) extra_seq = ans extra_seq = extra_seq.to(pred) cious, iou = SIoU_loss(extra_seq, target, 4) cious = cious.mean() giou_loss = cious l1_loss = self.objective['l1'](extra_seq, target) score = pred_dict["score"] score_loss = self.objective['l1'](score, iou) loss = self.loss_weight['giou'] * giou_loss + self.loss_weight[ 'focal'] * varifocal_loss + self.loss_weight['score'] * score_loss if return_status: # status for log mean_iou = iou.detach().mean() status = {"Loss/total": loss.item(), "Loss/score": score_loss.item(), "Loss/giou": giou_loss.item(), "Loss/l1": l1_loss.item(), "Loss/location": varifocal_loss.item(), "IoU": mean_iou.item()} return loss, status else: return loss ================================================ FILE: lib/train/actors/artrackv2_seq.py ================================================ from . import BaseActor from lib.utils.misc import NestedTensor from lib.utils.box_ops import box_cxcywh_to_xyxy, box_xywh_to_xyxy import torch import math import numpy as np import numpy import cv2 import torch.nn.functional as F import torchvision.transforms.functional as tvisf import lib.train.data.bounding_box_utils as bbutils from lib.utils.merge import merge_template_search from torch.distributions.categorical import Categorical from ...utils.heapmap_utils import generate_heatmap from ...utils.ce_utils import generate_mask_cond, adjust_keep_rate def IoU(rect1, rect2): """ caculate interection over union Args: rect1: (x1, y1, x2, y2) rect2: (x1, y1, x2, y2) Returns: iou """ # overlap x1, y1, x2, y2 = rect1[0], rect1[1], rect1[2], rect1[3] tx1, ty1, tx2, ty2 = rect2[0], rect2[1], rect2[2], rect2[3] xx1 = np.maximum(tx1, x1) yy1 = np.maximum(ty1, y1) xx2 = np.minimum(tx2, x2) yy2 = np.minimum(ty2, y2) ww = np.maximum(0, xx2 - xx1) hh = np.maximum(0, yy2 - yy1) area = (x2 - x1) * (y2 - y1) target_a = (tx2 - tx1) * (ty2 - ty1) inter = ww * hh iou = inter / (area + target_a - inter) return iou def fp16_clamp(x, min=None, max=None): if not x.is_cuda and x.dtype == torch.float16: # clamp for cpu float16, tensor fp16 has no clamp implementation return x.float().clamp(min, max).half() return x.clamp(min, max) def generate_sa_simdr(joints): ''' :param joints: [num_joints, 3] :param joints_vis: [num_joints, 3] :return: target, target_weight(1: visible, 0: invisible) ''' num_joints = 48 image_size = [256, 256] simdr_split_ratio = 1.5625 sigma = 6 target_x1 = np.zeros((num_joints, int(image_size[0] * simdr_split_ratio)), dtype=np.float32) target_y1 = np.zeros((num_joints, int(image_size[1] * simdr_split_ratio)), dtype=np.float32) target_x2 = np.zeros((num_joints, int(image_size[0] * simdr_split_ratio)), dtype=np.float32) target_y2 = np.zeros((num_joints, int(image_size[1] * simdr_split_ratio)), dtype=np.float32) zero_4_begin = np.zeros((num_joints, 1), dtype=np.float32) tmp_size = sigma * 3 for joint_id in range(num_joints): mu_x1 = joints[joint_id][0] mu_y1 = joints[joint_id][1] mu_x2 = joints[joint_id][2] mu_y2 = joints[joint_id][3] x1 = np.arange(0, int(image_size[0] * simdr_split_ratio), 1, np.float32) y1 = np.arange(0, int(image_size[1] * simdr_split_ratio), 1, np.float32) x2 = np.arange(0, int(image_size[0] * simdr_split_ratio), 1, np.float32) y2 = np.arange(0, int(image_size[1] * simdr_split_ratio), 1, np.float32) target_x1[joint_id] = (np.exp(- ((x1 - mu_x1) ** 2) / (2 * sigma ** 2))) / ( sigma * np.sqrt(np.pi * 2)) target_y1[joint_id] = (np.exp(- ((y1 - mu_y1) ** 2) / (2 * sigma ** 2))) / ( sigma * np.sqrt(np.pi * 2)) target_x2[joint_id] = (np.exp(- ((x2 - mu_x2) ** 2) / (2 * sigma ** 2))) / ( sigma * np.sqrt(np.pi * 2)) target_y2[joint_id] = (np.exp(- ((y2 - mu_y2) ** 2) / (2 * sigma ** 2))) / ( sigma * np.sqrt(np.pi * 2)) return target_x1, target_y1, target_x2, target_y2 # angle cost def SIoU_loss(test1, test2, theta=4): eps = 1e-7 cx_pred = (test1[:, 0] + test1[:, 2]) / 2 cy_pred = (test1[:, 1] + test1[:, 3]) / 2 cx_gt = (test2[:, 0] + test2[:, 2]) / 2 cy_gt = (test2[:, 1] + test2[:, 3]) / 2 dist = ((cx_pred - cx_gt) ** 2 + (cy_pred - cy_gt) ** 2) ** 0.5 ch = torch.max(cy_gt, cy_pred) - torch.min(cy_gt, cy_pred) x = ch / (dist + eps) angle = 1 - 2 * torch.sin(torch.arcsin(x) - torch.pi / 4) ** 2 # distance cost xmin = torch.min(test1[:, 0], test2[:, 0]) xmax = torch.max(test1[:, 2], test2[:, 2]) ymin = torch.min(test1[:, 1], test2[:, 1]) ymax = torch.max(test1[:, 3], test2[:, 3]) cw = xmax - xmin ch = ymax - ymin px = ((cx_gt - cx_pred) / (cw + eps)) ** 2 py = ((cy_gt - cy_pred) / (ch + eps)) ** 2 gama = 2 - angle dis = (1 - torch.exp(-1 * gama * px)) + (1 - torch.exp(-1 * gama * py)) # shape cost w_pred = test1[:, 2] - test1[:, 0] h_pred = test1[:, 3] - test1[:, 1] w_gt = test2[:, 2] - test2[:, 0] h_gt = test2[:, 3] - test2[:, 1] ww = torch.abs(w_pred - w_gt) / (torch.max(w_pred, w_gt) + eps) wh = torch.abs(h_gt - h_pred) / (torch.max(h_gt, h_pred) + eps) omega = (1 - torch.exp(-1 * wh)) ** theta + (1 - torch.exp(-1 * ww)) ** theta # IoU loss lt = torch.max(test1[..., :2], test2[..., :2]) # [B, rows, 2] rb = torch.min(test1[..., 2:], test2[..., 2:]) # [B, rows, 2] wh = fp16_clamp(rb - lt, min=0) overlap = wh[..., 0] * wh[..., 1] area1 = (test1[..., 2] - test1[..., 0]) * ( test1[..., 3] - test1[..., 1]) area2 = (test2[..., 2] - test2[..., 0]) * ( test2[..., 3] - test2[..., 1]) iou = overlap / (area1 + area2 - overlap) SIoU = 1 - iou + (omega + dis) / 2 return SIoU, iou def ciou(pred, target, eps=1e-7): # overlap lt = torch.max(pred[:, :2], target[:, :2]) rb = torch.min(pred[:, 2:], target[:, 2:]) wh = (rb - lt).clamp(min=0) overlap = wh[:, 0] * wh[:, 1] # union ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1]) ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1]) union = ap + ag - overlap + eps # IoU ious = overlap / union # enclose area enclose_x1y1 = torch.min(pred[:, :2], target[:, :2]) enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:]) enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0) cw = enclose_wh[:, 0] ch = enclose_wh[:, 1] c2 = cw ** 2 + ch ** 2 + eps b1_x1, b1_y1 = pred[:, 0], pred[:, 1] b1_x2, b1_y2 = pred[:, 2], pred[:, 3] b2_x1, b2_y1 = target[:, 0], target[:, 1] b2_x2, b2_y2 = target[:, 2], target[:, 3] w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2)) ** 2 / 4 right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2)) ** 2 / 4 rho2 = left + right factor = 4 / math.pi ** 2 v = factor * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2) # CIoU cious = ious - (rho2 / c2 + v ** 2 / (1 - ious + v)) return cious, ious class ARTrackV2SeqActor(BaseActor): """ Actor for training OSTrack models """ def __init__(self, net, objective, loss_weight, settings, bins, search_size, cfg=None): super().__init__(net, objective) self.loss_weight = loss_weight self.settings = settings self.bs = self.settings.batchsize # batch size self.cfg = cfg self.bins = bins self.search_size = search_size self.logsoftmax = torch.nn.LogSoftmax(dim=1) self.focal = None self.range = cfg.MODEL.RANGE self.loss_weight['KL'] = 0 self.loss_weight['focal'] = 0 self.pre_num = cfg.MODEL.PRENUM self.pre_bbox = None self.x_feat_rem = None def __call__(self, data): """ args: data - The input data, should contain the fields 'template', 'search', 'gt_bbox'. template_images: (N_t, batch, 3, H, W) search_images: (N_s, batch, 3, H, W) returns: loss - the training loss status - dict containing detailed losses """ # forward pass out_dict = self.forward_pass(data) # compute losses loss, status = self.compute_losses(out_dict, data) return loss, status def _bbox_clip(self, cx, cy, width, height, boundary): cx = max(0, min(cx, boundary[1])) cy = max(0, min(cy, boundary[0])) width = max(10, min(width, boundary[1])) height = max(10, min(height, boundary[0])) return cx, cy, width, height def get_subwindow(self, im, pos, model_sz, original_sz, avg_chans): """ args: im: bgr based image pos: center position model_sz: exemplar size s_z: original size avg_chans: channel average """ if isinstance(pos, float): pos = [pos, pos] sz = original_sz im_sz = im.shape c = (original_sz + 1) / 2 context_xmin = np.floor(pos[0] - c + 0.5) context_xmax = context_xmin + sz - 1 context_ymin = np.floor(pos[1] - c + 0.5) context_ymax = context_ymin + sz - 1 left_pad = int(max(0., -context_xmin)) top_pad = int(max(0., -context_ymin)) right_pad = int(max(0., context_xmax - im_sz[1] + 1)) bottom_pad = int(max(0., context_ymax - im_sz[0] + 1)) context_xmin = context_xmin + left_pad context_xmax = context_xmax + left_pad context_ymin = context_ymin + top_pad context_ymax = context_ymax + top_pad r, c, k = im.shape if any([top_pad, bottom_pad, left_pad, right_pad]): size = (r + top_pad + bottom_pad, c + left_pad + right_pad, k) te_im = np.zeros(size, np.uint8) te_im[top_pad:top_pad + r, left_pad:left_pad + c, :] = im if top_pad: te_im[0:top_pad, left_pad:left_pad + c, :] = avg_chans if bottom_pad: te_im[r + top_pad:, left_pad:left_pad + c, :] = avg_chans if left_pad: te_im[:, 0:left_pad, :] = avg_chans if right_pad: te_im[:, c + left_pad:, :] = avg_chans im_patch = te_im[int(context_ymin):int(context_ymax + 1), int(context_xmin):int(context_xmax + 1), :] else: im_patch = im[int(context_ymin):int(context_ymax + 1), int(context_xmin):int(context_xmax + 1), :] if not np.array_equal(model_sz, original_sz): try: im_patch = cv2.resize(im_patch, (model_sz, model_sz)) except: return None im_patch = im_patch.transpose(2, 0, 1) im_patch = im_patch[np.newaxis, :, :, :] im_patch = im_patch.astype(np.float32) im_patch = torch.from_numpy(im_patch) im_patch = im_patch.cuda() return im_patch def batch_init(self, images, template_bbox, initial_bbox) -> dict: self.frame_num = 1 self.device = 'cuda' # Convert bbox (x1, y1, w, h) -> (cx, cy, w, h) template_bbox_1 = template_bbox[:, 0] template_bbox_2 = template_bbox[:, 1] template_bbox_1 = bbutils.batch_xywh2center2(template_bbox_1) # ndarray:(2*num_seq,4) template_bbox_2 = bbutils.batch_xywh2center2(template_bbox_2) # ndarray:(2*num_seq,4) initial_bbox = bbutils.batch_xywh2center2(initial_bbox) # ndarray:(2*num_seq,4) self.center_pos = initial_bbox[:, :2] # ndarray:(2*num_seq,2) self.size = initial_bbox[:, 2:] # ndarray:(2*num_seq,2) self.pre_bbox = initial_bbox for i in range(self.pre_num - 1): self.pre_bbox = numpy.concatenate((self.pre_bbox, initial_bbox), axis=1) template_factor = self.cfg.DATA.TEMPLATE.FACTOR w_z_1 = template_bbox_1[:, 2] * template_factor # ndarray:(2*num_seq) h_z_1 = template_bbox_1[:, 3] * template_factor # ndarray:(2*num_seq) s_z_1 = np.ceil(np.sqrt(w_z_1 * h_z_1)) # ndarray:(2*num_seq) w_z_2 = template_bbox_2[:, 2] * template_factor # ndarray:(2*num_seq) h_z_2 = template_bbox_2[:, 3] * template_factor # ndarray:(2*num_seq) s_z_2 = np.ceil(np.sqrt(w_z_2 * h_z_2)) # ndarray:(2*num_seq) self.channel_average = [] for img in images: self.channel_average.append(np.mean(img[0], axis=(0, 1))) self.channel_average.append(np.mean(img[1], axis=(0, 1))) self.channel_average = np.array(self.channel_average) # ndarray:(2*num_seq,3) # get crop z_crop_list = [] z_1_list = [] z_2_list = [] for i in range(len(images)): here_crop_1 = self.get_subwindow(images[i][0], template_bbox_1[i, :2], self.cfg.DATA.TEMPLATE.SIZE, s_z_1[i], self.channel_average[2 * i]) here_crop_2 = self.get_subwindow(images[i][1], template_bbox_2[i, :2], self.cfg.DATA.TEMPLATE.SIZE, s_z_2[i], self.channel_average[2 * i + 1]) z_crop_1 = here_crop_1.float().mul(1.0 / 255.0).clamp(0.0, 1.0) z_crop_2 = here_crop_2.float().mul(1.0 / 255.0).clamp(0.0, 1.0) self.mean = [0.485, 0.456, 0.406] self.std = [0.229, 0.224, 0.225] self.inplace = False z_crop_1[0] = tvisf.normalize(z_crop_1[0], self.mean, self.std, self.inplace) z_crop_2[0] = tvisf.normalize(z_crop_2[0], self.mean, self.std, self.inplace) z_1_list.append(z_crop_1.unsqueeze(1).clone()) z_2_list.append(z_crop_2.unsqueeze(1).clone()) z_crop = torch.concat([z_crop_1.unsqueeze(1), z_crop_2.unsqueeze(1)], dim=1) z_crop_list.append(z_crop.clone()) z_crop = torch.cat(z_crop_list, dim=0) # Tensor(2*num_seq,3,128,128) z_1_crop = torch.cat(z_1_list, dim=0) z_2_crop = torch.cat(z_2_list, dim=0) model_to_access = getattr(self.net, 'module', self.net) z_2_crop = z_2_crop.squeeze(1).to(model_to_access.backbone.word_embeddings.weight) z_2_feat = model_to_access.backbone.patch_embed(z_2_crop) out = {'template_images': z_crop, "z_1": z_1_crop, "z_2": z_2_crop, "z_2_feat": z_2_feat} return out def batch_track(self, img, gt_boxes, template, dz_feat, action_mode='max') -> dict: search_factor = self.cfg.DATA.SEARCH.FACTOR w_x = self.size[:, 0] * search_factor h_x = self.size[:, 1] * search_factor s_x = np.ceil(np.sqrt(w_x * h_x)) gt_boxes_corner = bbutils.batch_xywh2corner(gt_boxes) # ndarray:(2*num_seq,4) initial_bbox = bbutils.batch_xywh2center2(gt_boxes) x_crop_list = [] gt_in_crop_list = [] pre_seq_list = [] pre_seq_in_list = [] x_feat_list = [] target_in_search_list = [] update_feat_list = [] for i in range(len(img)): template_factor = self.cfg.DATA.TEMPLATE.FACTOR w_z_1 = initial_bbox[:, 2] * template_factor # ndarray:(2*num_seq) h_z_1 = initial_bbox[:, 3] * template_factor # ndarray:(2*num_seq) s_z_1 = np.ceil(np.sqrt(w_z_1 * h_z_1)) # ndarray:(2*num_seq) channel_avg = np.mean(img[i], axis=(0, 1)) target_in_search = self.get_subwindow(img[i], initial_bbox[i, :2], self.cfg.DATA.TEMPLATE.SIZE, round(s_z_1[i]), channel_avg) x_crop = self.get_subwindow(img[i], self.center_pos[i], self.cfg.DATA.SEARCH.SIZE, round(s_x[i]), channel_avg) if x_crop == None: return None if target_in_search == None: return None for q in range(self.pre_num): pre_seq_temp = bbutils.batch_center2corner(self.pre_bbox[:, 0 + 4 * q:4 + 4 * q]) if q == 0: pre_seq = pre_seq_temp else: pre_seq = numpy.concatenate((pre_seq, pre_seq_temp), axis=1) if gt_boxes_corner is not None and np.sum(np.abs(gt_boxes_corner[i] - np.zeros(4))) > 10: pre_in = np.zeros(4 * self.pre_num) for w in range(self.pre_num): pre_in[0 + w * 4:2 + w * 4] = pre_seq[i, 0 + w * 4:2 + w * 4] - self.center_pos[i] pre_in[2 + w * 4:4 + w * 4] = pre_seq[i, 2 + w * 4:4 + w * 4] - self.center_pos[i] pre_in[0 + w * 4:4 + w * 4] = pre_in[0 + w * 4:4 + w * 4] * ( self.cfg.DATA.SEARCH.SIZE / s_x[i]) + self.cfg.DATA.SEARCH.SIZE / 2 pre_in[0 + w * 4:4 + w * 4] = pre_in[0 + w * 4:4 + w * 4] / self.cfg.DATA.SEARCH.SIZE pre_seq_list.append(pre_in) gt_in_crop = np.zeros(4) gt_in_crop[:2] = gt_boxes_corner[i, :2] - self.center_pos[i] gt_in_crop[2:] = gt_boxes_corner[i, 2:] - self.center_pos[i] gt_in_crop = gt_in_crop * (self.cfg.DATA.SEARCH.SIZE / s_x[i]) + self.cfg.DATA.SEARCH.SIZE / 2 gt_in_crop[2:] = gt_in_crop[2:] - gt_in_crop[:2] # (x1,y1,x2,y2) to (x1,y1,w,h) gt_in_crop_list.append(gt_in_crop) else: pre_in = np.zeros(4 * self.pre_num) pre_seq_list.append(pre_in) gt_in_crop_list.append(np.zeros(4)) pre_seq_input = torch.from_numpy(pre_in).clamp(-0.5 * self.range + 0.5, 0.5 + self.range * 0.5) pre_seq_input = (pre_seq_input + (0.5 * self.range - 0.5)) * (self.bins - 1) pre_seq_in_list.append(pre_seq_input.clone()) x_crop = x_crop.float().mul(1.0 / 255.0).clamp(0.0, 1.0) target_in_search = target_in_search.float().mul(1.0 / 255.0).clamp(0.0, 1.0) rem_x = x_crop x_crop[0] = tvisf.normalize(x_crop[0], self.mean, self.std, self.inplace) target_in_search[0] = tvisf.normalize(target_in_search[0], self.mean, self.std, self.inplace) x_crop_list.append(x_crop.clone()) target_in_search_list.append(target_in_search.clone()) x_crop = torch.cat(x_crop_list, dim=0) target_in_search = torch.cat(target_in_search_list, dim=0) pre_seq_output = torch.cat(pre_seq_in_list, dim=0).reshape(-1, 4 * self.pre_num) pre = torch.zeros_like(pre_seq_output) outputs = self.net(template, dz_feat.cuda(), x_crop, seq_input=pre_seq_output, head_type=None, stage="batch_track", search_feature=self.x_feat_rem, target_in_search_img=target_in_search, gt_bboxes=gt_boxes[-1]) selected_indices = outputs['seqs'].detach() x_feat = outputs['x_feat'].detach().cpu() self.x_feat_rem = x_feat.clone() x_feat_list.append(x_feat.clone()) update_feat = outputs['dz_feat'].detach().cpu() update_feat_list.append(update_feat.clone()) pred_bbox = selected_indices[:, 0:4].data.cpu().numpy() bbox = (pred_bbox / (self.bins - 1) - (self.range * 0.5 - 0.5)) * s_x.reshape(-1, 1) cx = bbox[:, 0] + self.center_pos[:, 0] - s_x / 2 cy = bbox[:, 1] + self.center_pos[:, 1] - s_x / 2 width = bbox[:, 2] - bbox[:, 0] height = bbox[:, 3] - bbox[:, 1] cx = cx + width / 2 cy = cy + height / 2 for i in range(len(img)): cx[i], cy[i], width[i], height[i] = self._bbox_clip(cx[i], cy[i], width[i], height[i], img[i].shape[:2]) self.center_pos = np.stack([cx, cy], 1) self.size = np.stack([width, height], 1) for e in range(self.pre_num): if e != self.pre_num - 1: self.pre_bbox[:, 0 + e * 4:4 + e * 4] = self.pre_bbox[:, 4 + e * 4:8 + e * 4] else: self.pre_bbox[:, 0 + e * 4:4 + e * 4] = numpy.stack([cx, cy, width, height], 1) bbox = np.stack([cx - width / 2, cy - height / 2, width, height], 1) out = { 'dz_feat': update_feat, 'search_images': x_crop, 'target_in_search': target_in_search, 'pred_bboxes': bbox, 'selected_indices': selected_indices.cpu(), 'gt_in_crop': torch.tensor(np.stack(gt_in_crop_list, axis=0), dtype=torch.float), 'pre_seq': torch.tensor(np.stack(pre_seq_list, axis=0), dtype=torch.float), 'x_feat': torch.tensor([item.cpu().detach().numpy() for item in x_feat_list], dtype=torch.float), } return out def explore(self, data): results = {} search_images_list = [] search_anno_list = [] action_tensor_list = [] iou_list = [] # cover_list = [] pre_seq_list = [] x_feat_list = [] target_in_search_list = [] template_all_list = [] dz_feat_udpate_list = [] num_frames = data['num_frames'] images = data['search_images'] gt_bbox = data['search_annos'] template = data['template_images'] template_bbox = data['template_annos'] template = template template_bbox = template_bbox template_bbox = np.array(template_bbox) num_seq = len(num_frames) for idx in range(np.max(num_frames)): here_images = [img[idx] for img in images] # S, N here_gt_bbox = np.array([gt[idx] for gt in gt_bbox]) here_images = here_images here_gt_bbox = np.concatenate([here_gt_bbox], 0) if idx == 0: outputs_template = self.batch_init(template, template_bbox, here_gt_bbox) results['template_images'] = outputs_template['z_1'] self.template_temp = outputs_template['z_1'].clone() z_all = [outputs_template['z_1'], outputs_template['z_2']] results['z_all'] = z_all self.dz_feat_update = outputs_template['z_2_feat'] else: outputs = self.batch_track(here_images, here_gt_bbox, self.template_temp, self.dz_feat_update, action_mode='half') if outputs == None: return None template_all_list.append(self.template_temp.clone()) dz_feat_udpate_list.append(self.dz_feat_update.clone().to(outputs['dz_feat'])) x_feat = outputs['x_feat'] self.dz_feat_update = outputs['dz_feat'] pred_bbox = outputs['pred_bboxes'] search_images_list.append(outputs['search_images']) target_in_search_list.append(outputs['target_in_search']) search_anno_list.append(outputs['gt_in_crop']) if len(outputs['pre_seq']) != 8: print(outputs['pre_seq']) print(len(outputs['pre_seq'])) print(idx) print(data['num_frames']) print(data['search_annos']) return None pre_seq_list.append(outputs['pre_seq']) pred_bbox_corner = bbutils.batch_xywh2corner(pred_bbox) gt_bbox_corner = bbutils.batch_xywh2corner(here_gt_bbox) here_iou = [] for i in range(num_seq): bbox_iou = IoU(pred_bbox_corner[i], gt_bbox_corner[i]) here_iou.append(bbox_iou) iou_list.append(here_iou) x_feat_list.append(x_feat.clone()) search_images_reverse_list = [] search_anno_reverse_list = [] action_tensor_reverse_list = [] iou_reverse_list = [] pre_seq_reverse_list = [] x_feat_reverse_list = [] target_in_search_reverse_list = [] dz_feat_update_reverse_list = [] template_all_reverse_list = [] for idx in range(np.max(num_frames)): real_idx = np.max(num_frames) - 1 - idx here_images = [img[real_idx] for img in images] # S, N here_gt_bbox = np.array([gt[real_idx] for gt in gt_bbox]) here_images = here_images here_gt_bbox = np.concatenate([here_gt_bbox], 0) if idx == 0: outputs_template = self.batch_init(template, template_bbox, here_gt_bbox) results['template_images'] = outputs_template['z_1'] self.template_temp = outputs_template['z_1'].clone() z_all = [outputs_template['z_1'], outputs_template['z_2']] results['z_all'] = z_all self.dz_feat_update = outputs_template['z_2_feat'].clone() else: outputs = self.batch_track(here_images, here_gt_bbox, self.template_temp, self.dz_feat_update, action_mode='half') if outputs == None: return None template_all_reverse_list.append(self.template_temp.clone()) dz_feat_update_reverse_list.append(self.dz_feat_update.clone().to(outputs['dz_feat'])) x_feat = outputs['x_feat'] self.dz_feat_update = outputs['dz_feat'] pred_bbox = outputs['pred_bboxes'] search_images_reverse_list.append(outputs['search_images']) target_in_search_reverse_list.append(outputs['target_in_search']) search_anno_reverse_list.append(outputs['gt_in_crop']) if len(outputs['pre_seq']) != 8: print(outputs['pre_seq']) print(len(outputs['pre_seq'])) print(idx) print(data['num_frames']) print(data['search_annos']) return None pre_seq_reverse_list.append(outputs['pre_seq']) pred_bbox_corner = bbutils.batch_xywh2corner(pred_bbox) gt_bbox_corner = bbutils.batch_xywh2corner(here_gt_bbox) here_iou = [] for i in range(num_seq): bbox_iou = IoU(pred_bbox_corner[i], gt_bbox_corner[i]) here_iou.append(bbox_iou) iou_reverse_list.append(here_iou) x_feat_reverse_list.append(x_feat.clone()) results['x_feat'] = torch.cat([torch.stack(x_feat_list), torch.stack(x_feat_reverse_list)], dim=2) results['search_images'] = torch.cat([torch.stack(search_images_list), torch.stack(search_images_reverse_list)], dim=1) results['template_images_z0'] = torch.cat( [torch.stack(template_all_list), torch.stack(template_all_reverse_list)], dim=1) results['dz_feat_update'] = torch.cat( [torch.stack(dz_feat_udpate_list), torch.stack(dz_feat_update_reverse_list)], dim=1) results['search_anno'] = torch.cat([torch.stack(search_anno_list), torch.stack(search_anno_reverse_list)], dim=1) results['pre_seq'] = torch.cat([torch.stack(pre_seq_list), torch.stack(pre_seq_reverse_list)], dim=1) results['target_in_search'] = torch.cat( [torch.stack(target_in_search_list), torch.stack(target_in_search_reverse_list)], dim=1) iou_tensor = torch.tensor(iou_list, dtype=torch.float) iou_tensor_reverse = torch.tensor(iou_reverse_list, dtype=torch.float) results['baseline_iou'] = torch.cat([iou_tensor[:, :num_seq], iou_tensor_reverse[:, :num_seq]], dim=1) # results['explore_iou'] = iou_tensor[:, num_seq:] # results['action_tensor'] = torch.stack(action_tensor_list) return results def forward_pass(self, data): # currently only support 1 template and 1 search region assert len(data['template_images']) == 1 assert len(data['search_images']) == 1 template_list = [] for i in range(self.settings.num_template): template_img_i = data['template_images'][i].view(-1, *data['template_images'].shape[2:]) # (batch, 3, 128, 128) template_list.append(template_img_i) search_img = data['search_images'][0].view(-1, *data['search_images'].shape[2:]) # (batch, 3, 320, 320) box_mask_z = None ce_keep_rate = None if self.cfg.MODEL.BACKBONE.CE_LOC: box_mask_z = generate_mask_cond(self.cfg, template_list[0].shape[0], template_list[0].device, data['template_anno'][0]) ce_start_epoch = self.cfg.TRAIN.CE_START_EPOCH ce_warm_epoch = self.cfg.TRAIN.CE_WARM_EPOCH ce_keep_rate = adjust_keep_rate(data['epoch'], warmup_epochs=ce_start_epoch, total_epochs=ce_start_epoch + ce_warm_epoch, ITERS_PER_EPOCH=1, base_keep_rate=self.cfg.MODEL.BACKBONE.CE_KEEP_RATIO[0]) if len(template_list) == 1: template_list = template_list[0] gt_bbox = data['search_anno'][-1] begin = self.bins end = self.bins + 1 gt_bbox[:, 2] = gt_bbox[:, 0] + gt_bbox[:, 2] gt_bbox[:, 3] = gt_bbox[:, 1] + gt_bbox[:, 3] gt_bbox = gt_bbox.clamp(min=0.0, max=1.0) data['real_bbox'] = gt_bbox seq_ori = gt_bbox * (self.bins - 1) seq_ori = seq_ori.int().to(search_img) B = seq_ori.shape[0] seq_ori_4_4 = seq_ori[:, 0:3] seq_input = torch.cat([torch.ones((B, 1)).to(search_img) * begin, seq_ori], dim=1) seq_output = torch.cat([seq_ori, torch.ones((B, 1)).to(search_img) * end], dim=1) data['seq_input'] = seq_input data['seq_output'] = seq_output out_dict = self.net(template=template_list, search=search_img, ce_template_mask=box_mask_z, ce_keep_rate=ce_keep_rate, return_last_attn=False, seq_input=seq_input) return out_dict def compute_sequence_losses(self, data): num_frames = data['search_images'].shape[0] template_images_for = data['template_images_z0'].reshape(-1, *data['template_images_z0'].size()[2:]) dz_feat = data['dz_feat_update'].reshape(-1, *data['dz_feat_update'].size()[2:]) target_in_search = data['target_in_search'].reshape(-1, *data['target_in_search'].size()[2:]) search_images = data['search_images'].reshape(-1, *data['search_images'].size()[2:]) search_anno = data['search_anno'].reshape(-1, *data['search_anno'].size()[2:]) pre_seq = data['pre_seq'].reshape(-1, 4 * self.pre_num) x_feat = data['x_feat'].reshape(-1, *data['x_feat'].size()[2:]) epoch = data['epoch'] if epoch < 11: self.loss_weight['focal'] = 2 self.loss_weight['score_update'] = 1 elif epoch < 31: self.loss_weight['focal'] = 0 self.loss_weight['score_update'] = 0.1 else: self.loss_weight['focal'] = 0 self.loss_weight['score_update'] = 0.0 pre_seq = pre_seq.clamp(-0.5 * self.range + 0.5, 0.5 + self.range * 0.5) pre_seq = (pre_seq + (self.range * 0.5 - 0.5)) * (self.bins - 1) outputs = self.net(template_images_for, dz_feat, search_images, seq_input=pre_seq, stage="forward_pass", search_feature=x_feat, target_in_search_img=target_in_search) score = outputs['score'] renew_loss = outputs['renew_loss'] pred_feat = outputs["feat"] if self.focal == None: weight = torch.ones(self.bins * self.range + 6) * 1 weight[self.bins * self.range + 4] = 0.1 weight[self.bins * self.range + 3] = 0.1 weight[self.bins * self.range + 2] = 0.1 weight[self.bins * self.range + 1] = 0.1 weight[self.bins * self.range] = 0.1 weight.to(pred_feat) self.focal = torch.nn.CrossEntropyLoss(weight=weight, size_average=True).to(pred_feat) search_anno[:, 2] = search_anno[:, 2] + search_anno[:, 0] search_anno[:, 3] = search_anno[:, 3] + search_anno[:, 1] target = (search_anno / self.cfg.DATA.SEARCH.SIZE + (self.range * 0.5 - 0.5)) * (self.bins - 1) target = target.clamp(min=0.0, max=(self.bins * self.range - 0.0001)) target_iou = target end_flag = torch.ones((target.shape[0], 1)) * (self.bins * self.range + 1) end_flag = end_flag.to(target) target = torch.cat([target], dim=1) target = target.reshape(-1).to(torch.int64) pred = pred_feat.permute(1, 0, 2).reshape(-1, self.bins * self.range + 6) varifocal_loss = self.focal(pred, target) pred = pred_feat[0:4, :, 0:self.bins * self.range] target = target_iou[:, 0:4].to(pred_feat) / (self.bins - 1) - (self.range * 0.5 - 0.5) out = pred.softmax(-1).to(pred) mul = torch.range((-1 * self.range * 0.5 + 0.5) + 1 / (self.bins * self.range), (self.range * 0.5 + 0.5) - 1 / (self.bins * self.range), 2 / (self.bins * self.range)).to(pred) ans = out * mul ans = ans.sum(dim=-1) ans = ans.permute(1, 0).to(pred) extra_seq = ans extra_seq = extra_seq.to(pred) cious, iou = SIoU_loss(extra_seq, target, 4) cious = cious.mean() score_real = score score_loss = self.objective['l1'](score_real, iou) giou_loss = cious l1_loss = self.objective['l1'](extra_seq, target) loss_bb = (self.loss_weight['giou'] * giou_loss + self.loss_weight['l1'] * l1_loss + self.loss_weight[ 'focal'] * varifocal_loss) total_losses = loss_bb + renew_loss * self.loss_weight['score_update'] + score_loss * self.loss_weight['score_update'] mean_iou = iou.detach().mean() status = {"Loss/total": total_losses.item() / 2, "Loss/score": score_loss.item() / 2, "Loss/giou": giou_loss.item() / 2, "Loss/l1": l1_loss.item() / 2, "Loss/location": varifocal_loss.item() / 2, "Loss/renew": renew_loss.item() / 2, "IoU": mean_iou.item() / 2} return total_losses, status ================================================ FILE: lib/train/actors/base_actor.py ================================================ from lib.utils import TensorDict class BaseActor: """ Base class for actor. The actor class handles the passing of the data through the network and calculation the loss""" def __init__(self, net, objective): """ args: net - The network to train objective - The loss function """ self.net = net self.objective = objective def __call__(self, data: TensorDict): """ Called in each training iteration. Should pass in input data through the network, calculate the loss, and return the training stats for the input data args: data - A TensorDict containing all the necessary data blocks. returns: loss - loss for the input data stats - a dict containing detailed losses """ raise NotImplementedError def to(self, device): """ Move the network to device args: device - device to use. 'cpu' or 'cuda' """ self.net.to(device) def train(self, mode=True): """ Set whether the network is in train mode. args: mode (True) - Bool specifying whether in training mode. """ self.net.train(mode) def eval(self): """ Set network to eval mode""" self.train(False) ================================================ FILE: lib/train/admin/__init__.py ================================================ from .environment import env_settings, create_default_local_file_ITP_train from .stats import AverageMeter, StatValue #from .tensorboard import TensorboardWriter ================================================ FILE: lib/train/admin/environment.py ================================================ import importlib import os from collections import OrderedDict def create_default_local_file(): path = os.path.join(os.path.dirname(__file__), 'local.py') empty_str = '\'\'' default_settings = OrderedDict({ 'workspace_dir': empty_str, 'tensorboard_dir': 'self.workspace_dir + \'/tensorboard/\'', 'pretrained_networks': 'self.workspace_dir + \'/pretrained_networks/\'', 'lasot_dir': empty_str, 'got10k_dir': empty_str, 'trackingnet_dir': empty_str, 'coco_dir': empty_str, 'lvis_dir': empty_str, 'sbd_dir': empty_str, 'imagenet_dir': empty_str, 'imagenetdet_dir': empty_str, 'ecssd_dir': empty_str, 'hkuis_dir': empty_str, 'msra10k_dir': empty_str, 'davis_dir': empty_str, 'youtubevos_dir': empty_str}) comment = {'workspace_dir': 'Base directory for saving network checkpoints.', 'tensorboard_dir': 'Directory for tensorboard files.'} with open(path, 'w') as f: f.write('class EnvironmentSettings:\n') f.write(' def __init__(self):\n') for attr, attr_val in default_settings.items(): comment_str = None if attr in comment: comment_str = comment[attr] if comment_str is None: f.write(' self.{} = {}\n'.format(attr, attr_val)) else: f.write(' self.{} = {} # {}\n'.format(attr, attr_val, comment_str)) def create_default_local_file_ITP_train(workspace_dir, data_dir): path = os.path.join(os.path.dirname(__file__), 'local.py') empty_str = '\'\'' default_settings = OrderedDict({ 'workspace_dir': workspace_dir, 'tensorboard_dir': os.path.join(workspace_dir, 'tensorboard'), # Directory for tensorboard files. 'pretrained_networks': os.path.join(workspace_dir, 'pretrained_networks'), 'lasot_dir': os.path.join(data_dir, 'lasot'), 'got10k_dir': os.path.join(data_dir, 'got10k/train'), 'got10k_val_dir': os.path.join(data_dir, 'got10k/val'), 'lasot_lmdb_dir': os.path.join(data_dir, 'lasot_lmdb'), 'got10k_lmdb_dir': os.path.join(data_dir, 'got10k_lmdb'), 'trackingnet_dir': os.path.join(data_dir, 'trackingnet'), 'trackingnet_lmdb_dir': os.path.join(data_dir, 'trackingnet_lmdb'), 'coco_dir': os.path.join(data_dir, 'coco'), 'coco_lmdb_dir': os.path.join(data_dir, 'coco_lmdb'), 'lvis_dir': empty_str, 'sbd_dir': empty_str, 'imagenet_dir': os.path.join(data_dir, 'vid'), 'imagenet_lmdb_dir': os.path.join(data_dir, 'vid_lmdb'), 'imagenetdet_dir': empty_str, 'ecssd_dir': empty_str, 'hkuis_dir': empty_str, 'msra10k_dir': empty_str, 'davis_dir': empty_str, 'youtubevos_dir': empty_str}) comment = {'workspace_dir': 'Base directory for saving network checkpoints.', 'tensorboard_dir': 'Directory for tensorboard files.'} with open(path, 'w') as f: f.write('class EnvironmentSettings:\n') f.write(' def __init__(self):\n') for attr, attr_val in default_settings.items(): comment_str = None if attr in comment: comment_str = comment[attr] if comment_str is None: if attr_val == empty_str: f.write(' self.{} = {}\n'.format(attr, attr_val)) else: f.write(' self.{} = \'{}\'\n'.format(attr, attr_val)) else: f.write(' self.{} = \'{}\' # {}\n'.format(attr, attr_val, comment_str)) def env_settings(): env_module_name = 'lib.train.admin.local' try: env_module = importlib.import_module(env_module_name) return env_module.EnvironmentSettings() except: env_file = os.path.join(os.path.dirname(__file__), 'local.py') create_default_local_file() raise RuntimeError('YOU HAVE NOT SETUP YOUR local.py!!!\n Go to "{}" and set all the paths you need. Then try to run again.'.format(env_file)) ================================================ FILE: lib/train/admin/local.py ================================================ class EnvironmentSettings: def __init__(self): self.workspace_dir = '/home/baiyifan/code/2stage_update_intrain' # Base directory for saving network checkpoints. self.tensorboard_dir = '/home/baiyifan/code/2stage/tensorboard' # Directory for tensorboard files. self.pretrained_networks = '/home/baiyifan/code/2stage/pretrained_networks' self.lasot_dir = '/home/baiyifan/LaSOT/LaSOTBenchmark' self.got10k_dir = '/home/baiyifan/GOT-10k/train' self.got10k_val_dir = '/home/baiyifan/GOT-10k/val' self.lasot_lmdb_dir = '/home/baiyifan/code/2stage/data/lasot_lmdb' self.got10k_lmdb_dir = '/home/baiyifan/code/2stage/data/got10k_lmdb' self.trackingnet_dir = '/ssddata/TrackingNet/all_zip' self.trackingnet_lmdb_dir = '/home/baiyifan/code/2stage/data/trackingnet_lmdb' self.coco_dir = '/home/baiyifan/coco' self.coco_lmdb_dir = '/home/baiyifan/code/2stage/data/coco_lmdb' self.lvis_dir = '' self.sbd_dir = '' self.imagenet_dir = '/home/baiyifan/code/2stage/data/vid' self.imagenet_lmdb_dir = '/home/baiyifan/code/2stage/data/vid_lmdb' self.imagenetdet_dir = '' self.ecssd_dir = '' self.hkuis_dir = '' self.msra10k_dir = '' self.davis_dir = '' self.youtubevos_dir = '' ================================================ FILE: lib/train/admin/multigpu.py ================================================ import torch.nn as nn # Here we use DistributedDataParallel(DDP) rather than DataParallel(DP) for multiple GPUs training def is_multi_gpu(net): return isinstance(net, (MultiGPU, nn.parallel.distributed.DistributedDataParallel)) class MultiGPU(nn.parallel.distributed.DistributedDataParallel): def __getattr__(self, item): try: return super().__getattr__(item) except: pass return getattr(self.module, item) ================================================ FILE: lib/train/admin/settings.py ================================================ from lib.train.admin.environment import env_settings class Settings: """ Training settings, e.g. the paths to datasets and networks.""" def __init__(self): self.set_default() def set_default(self): self.env = env_settings() self.use_gpu = True ================================================ FILE: lib/train/admin/stats.py ================================================ class StatValue: def __init__(self): self.clear() def reset(self): self.val = 0 def clear(self): self.reset() self.history = [] def update(self, val): self.val = val self.history.append(self.val) class AverageMeter(object): """Computes and stores the average and current value""" def __init__(self): self.clear() self.has_new_data = False def reset(self): self.avg = 0 self.val = 0 self.sum = 0 self.count = 0 def clear(self): self.reset() self.history = [] def update(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count def new_epoch(self): if self.count > 0: self.history.append(self.avg) self.reset() self.has_new_data = True else: self.has_new_data = False def topk_accuracy(output, target, topk=(1,)): """Computes the precision@k for the specified values of k""" single_input = not isinstance(topk, (tuple, list)) if single_input: topk = (topk,) maxk = max(topk) batch_size = target.size(0) _, pred = output.topk(maxk, 1, True, True) pred = pred.t() correct = pred.eq(target.view(1, -1).expand_as(pred)) res = [] for k in topk: correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)[0] res.append(correct_k * 100.0 / batch_size) if single_input: return res[0] return res ================================================ FILE: lib/train/admin/tensorboard.py ================================================ #import os #from collections import OrderedDict #try: # from torch.utils.tensorboard import SummaryWriter #except: # print('WARNING: You are using tensorboardX instead sis you have a too old pytorch version.') # from tensorboardX import SummaryWriter #class TensorboardWriter: # def __init__(self, directory, loader_names): # self.directory = directory # self.writer = OrderedDict({name: SummaryWriter(os.path.join(self.directory, name)) for name in loader_names}) # def write_info(self, script_name, description): # tb_info_writer = SummaryWriter(os.path.join(self.directory, 'info')) # tb_info_writer.add_text('Script_name', script_name) # tb_info_writer.add_text('Description', description) # tb_info_writer.close() # def write_epoch(self, stats: OrderedDict, epoch: int, ind=-1): # for loader_name, loader_stats in stats.items(): # if loader_stats is None: # continue # for var_name, val in loader_stats.items(): # if hasattr(val, 'history') and getattr(val, 'has_new_data', True): # self.writer[loader_name].add_scalar(var_name, val.history[ind], epoch) ================================================ FILE: lib/train/base_functions.py ================================================ import torch from torch.utils.data.distributed import DistributedSampler # datasets related from lib.train.dataset import Lasot, Got10k, MSCOCOSeq, ImagenetVID, TrackingNet from lib.train.dataset import Lasot_lmdb, Got10k_lmdb, MSCOCOSeq_lmdb, ImagenetVID_lmdb, TrackingNet_lmdb from lib.train.data import sampler, opencv_loader, processing, LTRLoader import lib.train.data.transforms as tfm from lib.utils.misc import is_main_process def update_settings(settings, cfg): settings.print_interval = cfg.TRAIN.PRINT_INTERVAL settings.search_area_factor = {'template': cfg.DATA.TEMPLATE.FACTOR, 'search': cfg.DATA.SEARCH.FACTOR} settings.output_sz = {'template': cfg.DATA.TEMPLATE.SIZE, 'search': cfg.DATA.SEARCH.SIZE} settings.center_jitter_factor = {'template': cfg.DATA.TEMPLATE.CENTER_JITTER, 'search': cfg.DATA.SEARCH.CENTER_JITTER} settings.scale_jitter_factor = {'template': cfg.DATA.TEMPLATE.SCALE_JITTER, 'search': cfg.DATA.SEARCH.SCALE_JITTER} settings.grad_clip_norm = cfg.TRAIN.GRAD_CLIP_NORM settings.print_stats = None settings.batchsize = cfg.TRAIN.BATCH_SIZE settings.scheduler_type = cfg.TRAIN.SCHEDULER.TYPE def names2datasets(name_list: list, settings, image_loader): assert isinstance(name_list, list) datasets = [] #settings.use_lmdb = True for name in name_list: assert name in ["LASOT", "GOT10K_vottrain", "GOT10K_votval", "GOT10K_train_full", "GOT10K_official_val", "COCO17", "VID", "TRACKINGNET"] if name == "LASOT": if settings.use_lmdb: print("Building lasot dataset from lmdb") datasets.append(Lasot_lmdb(settings.env.lasot_lmdb_dir, split='train', image_loader=image_loader)) else: datasets.append(Lasot(settings.env.lasot_dir, split='train', image_loader=image_loader)) if name == "GOT10K_vottrain": if settings.use_lmdb: print("Building got10k from lmdb") datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='vottrain', image_loader=image_loader)) else: datasets.append(Got10k(settings.env.got10k_dir, split='vottrain', image_loader=image_loader)) if name == "GOT10K_train_full": if settings.use_lmdb: print("Building got10k_train_full from lmdb") datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='train_full', image_loader=image_loader)) else: datasets.append(Got10k(settings.env.got10k_dir, split='train_full', image_loader=image_loader)) if name == "GOT10K_votval": if settings.use_lmdb: print("Building got10k from lmdb") datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='votval', image_loader=image_loader)) else: datasets.append(Got10k(settings.env.got10k_dir, split='votval', image_loader=image_loader)) if name == "GOT10K_official_val": if settings.use_lmdb: raise ValueError("Not implement") else: datasets.append(Got10k(settings.env.got10k_val_dir, split=None, image_loader=image_loader)) if name == "COCO17": if settings.use_lmdb: print("Building COCO2017 from lmdb") datasets.append(MSCOCOSeq_lmdb(settings.env.coco_lmdb_dir, version="2017", image_loader=image_loader)) else: datasets.append(MSCOCOSeq(settings.env.coco_dir, version="2017", image_loader=image_loader)) if name == "VID": if settings.use_lmdb: print("Building VID from lmdb") datasets.append(ImagenetVID_lmdb(settings.env.imagenet_lmdb_dir, image_loader=image_loader)) else: datasets.append(ImagenetVID(settings.env.imagenet_dir, image_loader=image_loader)) if name == "TRACKINGNET": if settings.use_lmdb: print("Building TrackingNet from lmdb") datasets.append(TrackingNet_lmdb(settings.env.trackingnet_lmdb_dir, image_loader=image_loader)) else: # raise ValueError("NOW WE CAN ONLY USE TRACKINGNET FROM LMDB") datasets.append(TrackingNet(settings.env.trackingnet_dir, image_loader=image_loader)) return datasets def build_dataloaders(cfg, settings): # Data transform transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05), tfm.RandomHorizontalFlip(probability=0.5)) transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2), tfm.RandomHorizontalFlip_Norm(probability=0.5), tfm.Normalize(mean=cfg.DATA.MEAN, std=cfg.DATA.STD)) transform_val = tfm.Transform(tfm.ToTensor(), tfm.Normalize(mean=cfg.DATA.MEAN, std=cfg.DATA.STD)) # The tracking pairs processing module output_sz = settings.output_sz search_area_factor = settings.search_area_factor data_processing_train = processing.STARKProcessing(search_area_factor=search_area_factor, output_sz=output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', transform=transform_train, joint_transform=transform_joint, settings=settings) data_processing_val = processing.STARKProcessing(search_area_factor=search_area_factor, output_sz=output_sz, center_jitter_factor=settings.center_jitter_factor, scale_jitter_factor=settings.scale_jitter_factor, mode='sequence', transform=transform_val, joint_transform=transform_joint, settings=settings) # Train sampler and loader settings.num_template = getattr(cfg.DATA.TEMPLATE, "NUMBER", 1) settings.num_search = getattr(cfg.DATA.SEARCH, "NUMBER", 1) sampler_mode = getattr(cfg.DATA, "SAMPLER_MODE", "causal") train_cls = getattr(cfg.TRAIN, "TRAIN_CLS", False) print("sampler_mode", sampler_mode) dataset_train = sampler.TrackingSampler(datasets=names2datasets(cfg.DATA.TRAIN.DATASETS_NAME, settings, opencv_loader), p_datasets=cfg.DATA.TRAIN.DATASETS_RATIO, samples_per_epoch=cfg.DATA.TRAIN.SAMPLE_PER_EPOCH, max_gap=cfg.DATA.MAX_SAMPLE_INTERVAL, num_search_frames=settings.num_search, num_template_frames=settings.num_template, processing=data_processing_train, frame_sample_mode=sampler_mode, train_cls=train_cls) train_sampler = DistributedSampler(dataset_train) if settings.local_rank != -1 else None shuffle = False if settings.local_rank != -1 else True loader_train = LTRLoader('train', dataset_train, training=True, batch_size=cfg.TRAIN.BATCH_SIZE, shuffle=shuffle, num_workers=cfg.TRAIN.NUM_WORKER, drop_last=True, stack_dim=1, sampler=train_sampler) # Validation samplers and loaders dataset_val = sampler.TrackingSampler(datasets=names2datasets(cfg.DATA.VAL.DATASETS_NAME, settings, opencv_loader), p_datasets=cfg.DATA.VAL.DATASETS_RATIO, samples_per_epoch=cfg.DATA.VAL.SAMPLE_PER_EPOCH, max_gap=cfg.DATA.MAX_SAMPLE_INTERVAL, num_search_frames=settings.num_search, num_template_frames=settings.num_template, processing=data_processing_val, frame_sample_mode=sampler_mode, train_cls=train_cls) val_sampler = DistributedSampler(dataset_val) if settings.local_rank != -1 else None loader_val = LTRLoader('val', dataset_val, training=False, batch_size=cfg.TRAIN.BATCH_SIZE, num_workers=cfg.TRAIN.NUM_WORKER, drop_last=True, stack_dim=1, sampler=val_sampler, epoch_interval=cfg.TRAIN.VAL_EPOCH_INTERVAL) return loader_train, loader_val def get_optimizer_scheduler(net, cfg): train_cls = getattr(cfg.TRAIN, "TRAIN_CLS", False) if train_cls: print("Only training classification head. Learnable parameters are shown below.") param_dicts = [ {"params": [p for n, p in net.named_parameters() if "cls" in n and p.requires_grad]} ] for n, p in net.named_parameters(): if "cls" not in n: p.requires_grad = False else: print(n) else: param_dicts = [ {"params": [p for n, p in net.named_parameters() if "backbone" not in n and p.requires_grad]}, { "params": [p for n, p in net.named_parameters() if "backbone" in n and p.requires_grad], "lr": cfg.TRAIN.LR * cfg.TRAIN.BACKBONE_MULTIPLIER, }, ] if is_main_process(): print("Learnable parameters are shown below.") for n, p in net.named_parameters(): if p.requires_grad: print(n) if cfg.TRAIN.OPTIMIZER == "ADAMW": optimizer = torch.optim.AdamW(param_dicts, lr=cfg.TRAIN.LR, weight_decay=cfg.TRAIN.WEIGHT_DECAY) else: raise ValueError("Unsupported Optimizer") if cfg.TRAIN.SCHEDULER.TYPE == 'step': lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, cfg.TRAIN.LR_DROP_EPOCH) elif cfg.TRAIN.SCHEDULER.TYPE == "Mstep": lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=cfg.TRAIN.SCHEDULER.MILESTONES, gamma=cfg.TRAIN.SCHEDULER.GAMMA) else: raise ValueError("Unsupported scheduler") return optimizer, lr_scheduler def get_optimizer_scheduler_v2(net, cfg): train_cls = getattr(cfg.TRAIN, "TRAIN_CLS", False) if train_cls: print("Only training classification head. Learnable parameters are shown below.") param_dicts = [ {"params": [p for n, p in net.named_parameters() if "cls" in n and p.requires_grad]} ] for n, p in net.named_parameters(): if "cls" not in n: p.requires_grad = False else: print(n) else: param_dicts = [ {"params": [p for n, p in net.named_parameters() if "backbone" not in n and p.requires_grad ]}, { "params": [p for n, p in net.named_parameters() if "backbone" in n and p.requires_grad and "output_bias" not in n and "embeddings" not in n and "extension" not in n], "lr": cfg.TRAIN.LR * cfg.TRAIN.BACKBONE_MULTIPLIER, }, { "params": [p for n, p in net.named_parameters() if "backbone" in n and p.requires_grad and ("output_bias" in n or "embeddings" in n or "extension" in n)], "lr": cfg.TRAIN.LR, }, ] if is_main_process(): print("Learnable parameters are shown below.") for n, p in net.named_parameters(): if p.requires_grad: print(n) if cfg.TRAIN.OPTIMIZER == "ADAMW": optimizer = torch.optim.AdamW(param_dicts, lr=cfg.TRAIN.LR, weight_decay=cfg.TRAIN.WEIGHT_DECAY) else: raise ValueError("Unsupported Optimizer") if cfg.TRAIN.SCHEDULER.TYPE == 'step': lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, cfg.TRAIN.LR_DROP_EPOCH) elif cfg.TRAIN.SCHEDULER.TYPE == "Mstep": lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=cfg.TRAIN.SCHEDULER.MILESTONES, gamma=cfg.TRAIN.SCHEDULER.GAMMA) else: raise ValueError("Unsupported scheduler") return optimizer, lr_scheduler ================================================ FILE: lib/train/data/__init__.py ================================================ from .loader import LTRLoader from .image_loader import jpeg4py_loader, opencv_loader, jpeg4py_loader_w_failsafe, default_image_loader ================================================ FILE: lib/train/data/bounding_box_utils.py ================================================ import torch import numpy as np def batch_center2corner(boxes): xmin = boxes[:, 0] - boxes[:, 2] * 0.5 ymin = boxes[:, 1] - boxes[:, 3] * 0.5 xmax = boxes[:, 0] + boxes[:, 2] * 0.5 ymax = boxes[:, 1] + boxes[:, 3] * 0.5 if isinstance(boxes, np.ndarray): return np.stack([xmin, ymin, xmax, ymax], 1) else: return torch.stack([xmin, ymin, xmax, ymax], 1) def batch_corner2center(boxes): cx = (boxes[:, 0] + boxes[:, 2]) * 0.5 cy = (boxes[:, 1] + boxes[:, 3]) * 0.5 w = (boxes[:, 2] - boxes[:, 0]) h = (boxes[:, 3] - boxes[:, 1]) if isinstance(boxes, np.ndarray): return np.stack([cx, cy, w, h], 1) else: return torch.stack([cx, cy, w, h], 1) def batch_xywh2center(boxes): cx = boxes[:, 0] + (boxes[:, 2] - 1) / 2 cy = boxes[:, 1] + (boxes[:, 3] - 1) / 2 w = boxes[:, 2] h = boxes[:, 3] if isinstance(boxes, np.ndarray): return np.stack([cx, cy, w, h], 1) else: return torch.stack([cx, cy, w, h], 1) def batch_xywh2center2(boxes): cx = boxes[:, 0] + boxes[:, 2] / 2 cy = boxes[:, 1] + boxes[:, 3] / 2 w = boxes[:, 2] h = boxes[:, 3] if isinstance(boxes, np.ndarray): return np.stack([cx, cy, w, h], 1) else: return torch.stack([cx, cy, w, h], 1) def batch_xywh2corner(boxes): xmin = boxes[:, 0] ymin = boxes[:, 1] xmax = boxes[:, 0] + boxes[:, 2] ymax = boxes[:, 1] + boxes[:, 3] if isinstance(boxes, np.ndarray): return np.stack([xmin, ymin, xmax, ymax], 1) else: return torch.stack([xmin, ymin, xmax, ymax], 1) def rect_to_rel(bb, sz_norm=None): """Convert standard rectangular parametrization of the bounding box [x, y, w, h] to relative parametrization [cx/sw, cy/sh, log(w), log(h)], where [cx, cy] is the center coordinate. args: bb - N x 4 tensor of boxes. sz_norm - [N] x 2 tensor of value of [sw, sh] (optional). sw=w and sh=h if not given. """ c = bb[...,:2] + 0.5 * bb[...,2:] if sz_norm is None: c_rel = c / bb[...,2:] else: c_rel = c / sz_norm sz_rel = torch.log(bb[...,2:]) return torch.cat((c_rel, sz_rel), dim=-1) def rel_to_rect(bb, sz_norm=None): """Inverts the effect of rect_to_rel. See above.""" sz = torch.exp(bb[...,2:]) if sz_norm is None: c = bb[...,:2] * sz else: c = bb[...,:2] * sz_norm tl = c - 0.5 * sz return torch.cat((tl, sz), dim=-1) def masks_to_bboxes(mask, fmt='c'): """ Convert a mask tensor to one or more bounding boxes. Note: This function is a bit new, make sure it does what it says. /Andreas :param mask: Tensor of masks, shape = (..., H, W) :param fmt: bbox layout. 'c' => "center + size" or (x_center, y_center, width, height) 't' => "top left + size" or (x_left, y_top, width, height) 'v' => "vertices" or (x_left, y_top, x_right, y_bottom) :return: tensor containing a batch of bounding boxes, shape = (..., 4) """ batch_shape = mask.shape[:-2] mask = mask.reshape((-1, *mask.shape[-2:])) bboxes = [] for m in mask: mx = m.sum(dim=-2).nonzero() my = m.sum(dim=-1).nonzero() bb = [mx.min(), my.min(), mx.max(), my.max()] if (len(mx) > 0 and len(my) > 0) else [0, 0, 0, 0] bboxes.append(bb) bboxes = torch.tensor(bboxes, dtype=torch.float32, device=mask.device) bboxes = bboxes.reshape(batch_shape + (4,)) if fmt == 'v': return bboxes x1 = bboxes[..., :2] s = bboxes[..., 2:] - x1 + 1 if fmt == 'c': return torch.cat((x1 + 0.5 * s, s), dim=-1) elif fmt == 't': return torch.cat((x1, s), dim=-1) raise ValueError("Undefined bounding box layout '%s'" % fmt) def masks_to_bboxes_multi(mask, ids, fmt='c'): assert mask.dim() == 2 bboxes = [] for id in ids: mx = (mask == id).sum(dim=-2).nonzero() my = (mask == id).float().sum(dim=-1).nonzero() bb = [mx.min(), my.min(), mx.max(), my.max()] if (len(mx) > 0 and len(my) > 0) else [0, 0, 0, 0] bb = torch.tensor(bb, dtype=torch.float32, device=mask.device) x1 = bb[:2] s = bb[2:] - x1 + 1 if fmt == 'v': pass elif fmt == 'c': bb = torch.cat((x1 + 0.5 * s, s), dim=-1) elif fmt == 't': bb = torch.cat((x1, s), dim=-1) else: raise ValueError("Undefined bounding box layout '%s'" % fmt) bboxes.append(bb) return bboxes ================================================ FILE: lib/train/data/image_loader.py ================================================ import jpeg4py import cv2 as cv from PIL import Image import numpy as np davis_palette = np.repeat(np.expand_dims(np.arange(0,256), 1), 3, 1).astype(np.uint8) davis_palette[:22, :] = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0], [191, 0, 0], [64, 128, 0], [191, 128, 0], [64, 0, 128], [191, 0, 128], [64, 128, 128], [191, 128, 128], [0, 64, 0], [128, 64, 0], [0, 191, 0], [128, 191, 0], [0, 64, 128], [128, 64, 128]] def default_image_loader(path): """The default image loader, reads the image from the given path. It first tries to use the jpeg4py_loader, but reverts to the opencv_loader if the former is not available.""" if default_image_loader.use_jpeg4py is None: # Try using jpeg4py im = jpeg4py_loader(path) if im is None: default_image_loader.use_jpeg4py = False print('Using opencv_loader instead.') else: default_image_loader.use_jpeg4py = True return im if default_image_loader.use_jpeg4py: return jpeg4py_loader(path) return opencv_loader(path) default_image_loader.use_jpeg4py = None def jpeg4py_loader(path): """ Image reading using jpeg4py https://github.com/ajkxyz/jpeg4py""" try: return jpeg4py.JPEG(path).decode() except Exception as e: print('ERROR: Could not read image "{}"'.format(path)) print(e) return None def opencv_loader(path): """ Read image using opencv's imread function and returns it in rgb format""" try: im = cv.imread(path, cv.IMREAD_COLOR) # convert to rgb and return return cv.cvtColor(im, cv.COLOR_BGR2RGB) except Exception as e: print('ERROR: Could not read image "{}"'.format(path)) print(e) return None def jpeg4py_loader_w_failsafe(path): """ Image reading using jpeg4py https://github.com/ajkxyz/jpeg4py""" try: return jpeg4py.JPEG(path).decode() except: try: im = cv.imread(path, cv.IMREAD_COLOR) # convert to rgb and return return cv.cvtColor(im, cv.COLOR_BGR2RGB) except Exception as e: print('ERROR: Could not read image "{}"'.format(path)) print(e) return None def opencv_seg_loader(path): """ Read segmentation annotation using opencv's imread function""" try: return cv.imread(path) except Exception as e: print('ERROR: Could not read image "{}"'.format(path)) print(e) return None def imread_indexed(filename): """ Load indexed image with given filename. Used to read segmentation annotations.""" im = Image.open(filename) annotation = np.atleast_3d(im)[...,0] return annotation def imwrite_indexed(filename, array, color_palette=None): """ Save indexed image as png. Used to save segmentation annotation.""" if color_palette is None: color_palette = davis_palette if np.atleast_3d(array).shape[2] != 1: raise Exception("Saving indexed PNGs requires 2D array.") im = Image.fromarray(array) im.putpalette(color_palette.ravel()) im.save(filename, format='PNG') ================================================ FILE: lib/train/data/loader.py ================================================ import torch import torch.utils.data.dataloader import importlib import collections from torch._six import string_classes from lib.utils import TensorDict, TensorList if float(torch.__version__[:3]) >= 1.9 or len('.'.join((torch.__version__).split('.')[0:2])) > 3: int_classes = int else: from torch._six import int_classes import warnings warnings.filterwarnings("ignore") def _check_use_shared_memory(): if hasattr(torch.utils.data.dataloader, '_use_shared_memory'): return getattr(torch.utils.data.dataloader, '_use_shared_memory') collate_lib = importlib.import_module('torch.utils.data._utils.collate') if hasattr(collate_lib, '_use_shared_memory'): return getattr(collate_lib, '_use_shared_memory') return torch.utils.data.get_worker_info() is not None def ltr_collate(batch): """Puts each data field into a tensor with outer dimension batch size""" error_msg = "batch must contain tensors, numbers, dicts or lists; found {}" elem_type = type(batch[0]) if isinstance(batch[0], torch.Tensor): out = None if _check_use_shared_memory(): # If we're in a background process, concatenate directly into a # shared memory tensor to avoid an extra copy numel = sum([x.numel() for x in batch]) storage = batch[0].storage()._new_shared(numel) out = batch[0].new(storage) return torch.stack(batch, 0, out=out) # if batch[0].dim() < 4: # return torch.stack(batch, 0, out=out) # return torch.cat(batch, 0, out=out) elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ and elem_type.__name__ != 'string_': elem = batch[0] if elem_type.__name__ == 'ndarray': # array of string classes and object if torch.utils.data.dataloader.re.search('[SaUO]', elem.dtype.str) is not None: raise TypeError(error_msg.format(elem.dtype)) return torch.stack([torch.from_numpy(b) for b in batch], 0) if elem.shape == (): # scalars py_type = float if elem.dtype.name.startswith('float') else int return torch.utils.data.dataloader.numpy_type_map[elem.dtype.name](list(map(py_type, batch))) elif isinstance(batch[0], int_classes): return torch.LongTensor(batch) elif isinstance(batch[0], float): return torch.DoubleTensor(batch) elif isinstance(batch[0], string_classes): return batch elif isinstance(batch[0], TensorDict): return TensorDict({key: ltr_collate([d[key] for d in batch]) for key in batch[0]}) elif isinstance(batch[0], collections.Mapping): return {key: ltr_collate([d[key] for d in batch]) for key in batch[0]} elif isinstance(batch[0], TensorList): transposed = zip(*batch) return TensorList([ltr_collate(samples) for samples in transposed]) elif isinstance(batch[0], collections.Sequence): transposed = zip(*batch) return [ltr_collate(samples) for samples in transposed] elif batch[0] is None: return batch raise TypeError((error_msg.format(type(batch[0])))) def ltr_collate_stack1(batch): """Puts each data field into a tensor. The tensors are stacked at dim=1 to form the batch""" error_msg = "batch must contain tensors, numbers, dicts or lists; found {}" elem_type = type(batch[0]) if isinstance(batch[0], torch.Tensor): out = None if _check_use_shared_memory(): # If we're in a background process, concatenate directly into a # shared memory tensor to avoid an extra copy numel = sum([x.numel() for x in batch]) storage = batch[0].storage()._new_shared(numel) out = batch[0].new(storage) return torch.stack(batch, 1, out=out) # if batch[0].dim() < 4: # return torch.stack(batch, 0, out=out) # return torch.cat(batch, 0, out=out) elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ and elem_type.__name__ != 'string_': elem = batch[0] if elem_type.__name__ == 'ndarray': # array of string classes and object if torch.utils.data.dataloader.re.search('[SaUO]', elem.dtype.str) is not None: raise TypeError(error_msg.format(elem.dtype)) return torch.stack([torch.from_numpy(b) for b in batch], 1) if elem.shape == (): # scalars py_type = float if elem.dtype.name.startswith('float') else int return torch.utils.data.dataloader.numpy_type_map[elem.dtype.name](list(map(py_type, batch))) elif isinstance(batch[0], int_classes): return torch.LongTensor(batch) elif isinstance(batch[0], float): return torch.DoubleTensor(batch) elif isinstance(batch[0], string_classes): return batch elif isinstance(batch[0], TensorDict): return TensorDict({key: ltr_collate_stack1([d[key] for d in batch]) for key in batch[0]}) elif isinstance(batch[0], collections.Mapping): return {key: ltr_collate_stack1([d[key] for d in batch]) for key in batch[0]} elif isinstance(batch[0], TensorList): transposed = zip(*batch) return TensorList([ltr_collate_stack1(samples) for samples in transposed]) elif isinstance(batch[0], collections.Sequence): transposed = zip(*batch) return [ltr_collate_stack1(samples) for samples in transposed] elif batch[0] is None: return batch raise TypeError((error_msg.format(type(batch[0])))) class LTRLoader(torch.utils.data.dataloader.DataLoader): """ Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset. Note: The only difference with default pytorch DataLoader is that an additional option stack_dim is available to select along which dimension the data should be stacked to form a batch. Arguments: dataset (Dataset): dataset from which to load the data. batch_size (int, optional): how many samples per batch to load (default: 1). shuffle (bool, optional): set to ``True`` to have the data reshuffled at every epoch (default: False). sampler (Sampler, optional): defines the strategy to draw samples from the dataset. If specified, ``shuffle`` must be False. batch_sampler (Sampler, optional): like sampler, but returns a batch of indices at a time. Mutually exclusive with batch_size, shuffle, sampler, and drop_last. num_workers (int, optional): how many subprocesses to use for data loading. 0 means that the data will be loaded in the main process. (default: 0) collate_fn (callable, optional): merges a list of samples to form a mini-batch. stack_dim (int): Dimension along which to stack to form the batch. (default: 0) pin_memory (bool, optional): If ``True``, the data loader will copy tensors into CUDA pinned memory before returning them. drop_last (bool, optional): set to ``True`` to drop the last incomplete batch, if the dataset size is not divisible by the batch size. If ``False`` and the size of dataset is not divisible by the batch size, then the last batch will be smaller. (default: False) timeout (numeric, optional): if positive, the timeout value for collecting a batch from workers. Should always be non-negative. (default: 0) worker_init_fn (callable, optional): If not None, this will be called on each worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as input, after seeding and before data loading. (default: None) .. note:: By default, each worker will have its PyTorch seed set to ``base_seed + worker_id``, where ``base_seed`` is a long generated by main process using its RNG. However, seeds for other libraries may be duplicated upon initializing workers (w.g., NumPy), causing each worker to return identical random numbers. (See :ref:`dataloader-workers-random-seed` section in FAQ.) You may use ``torch.initial_seed()`` to access the PyTorch seed for each worker in :attr:`worker_init_fn`, and use it to set other seeds before data loading. .. warning:: If ``spawn`` start method is used, :attr:`worker_init_fn` cannot be an unpicklable object, e.g., a lambda function. """ __initialized = False def __init__(self, name, dataset, training=True, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, epoch_interval=1, collate_fn=None, stack_dim=0, pin_memory=False, drop_last=False, timeout=0, worker_init_fn=None): print("pin_memory is", pin_memory) if collate_fn is None: if stack_dim == 0: collate_fn = ltr_collate elif stack_dim == 1: collate_fn = ltr_collate_stack1 else: raise ValueError('Stack dim no supported. Must be 0 or 1.') super(LTRLoader, self).__init__(dataset, batch_size, shuffle, sampler, batch_sampler, num_workers, collate_fn, pin_memory, drop_last, timeout, worker_init_fn) self.name = name self.training = training self.epoch_interval = epoch_interval self.stack_dim = stack_dim ================================================ FILE: lib/train/data/processing.py ================================================ import torch import torchvision.transforms as transforms from lib.utils import TensorDict import lib.train.data.processing_utils as prutils import torch.nn.functional as F def stack_tensors(x): if isinstance(x, (list, tuple)) and isinstance(x[0], torch.Tensor): return torch.stack(x) return x class BaseProcessing: """ Base class for Processing. Processing class is used to process the data returned by a dataset, before passing it through the network. For example, it can be used to crop a search region around the object, apply various data augmentations, etc.""" def __init__(self, transform=transforms.ToTensor(), template_transform=None, search_transform=None, joint_transform=None): """ args: transform - The set of transformations to be applied on the images. Used only if template_transform or search_transform is None. template_transform - The set of transformations to be applied on the template images. If None, the 'transform' argument is used instead. search_transform - The set of transformations to be applied on the search images. If None, the 'transform' argument is used instead. joint_transform - The set of transformations to be applied 'jointly' on the template and search images. For example, it can be used to convert both template and search images to grayscale. """ self.transform = {'template': transform if template_transform is None else template_transform, 'search': transform if search_transform is None else search_transform, 'joint': joint_transform} def __call__(self, data: TensorDict): raise NotImplementedError class STARKProcessing(BaseProcessing): """ The processing class used for training LittleBoy. The images are processed in the following way. First, the target bounding box is jittered by adding some noise. Next, a square region (called search region ) centered at the jittered target center, and of area search_area_factor^2 times the area of the jittered box is cropped from the image. The reason for jittering the target box is to avoid learning the bias that the target is always at the center of the search region. The search region is then resized to a fixed size given by the argument output_sz. """ def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor, mode='pair', settings=None, *args, **kwargs): """ args: search_area_factor - The size of the search region relative to the target size. output_sz - An integer, denoting the size to which the search region is resized. The search region is always square. center_jitter_factor - A dict containing the amount of jittering to be applied to the target center before extracting the search region. See _get_jittered_box for how the jittering is done. scale_jitter_factor - A dict containing the amount of jittering to be applied to the target size before extracting the search region. See _get_jittered_box for how the jittering is done. mode - Either 'pair' or 'sequence'. If mode='sequence', then output has an extra dimension for frames """ super().__init__(*args, **kwargs) self.search_area_factor = search_area_factor self.output_sz = output_sz self.center_jitter_factor = center_jitter_factor self.scale_jitter_factor = scale_jitter_factor self.mode = mode self.settings = settings def _get_jittered_box(self, box, mode): """ Jitter the input box args: box - input bounding box mode - string 'template' or 'search' indicating template or search data returns: torch.Tensor - jittered box """ jittered_size = box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_factor[mode]) max_offset = (jittered_size.prod().sqrt() * torch.tensor(self.center_jitter_factor[mode]).float()) jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (torch.rand(2) - 0.5) return torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0) def __call__(self, data: TensorDict): """ args: data - The input data, should contain the following fields: 'template_images', search_images', 'template_anno', 'search_anno' returns: TensorDict - output data block with following fields: 'template_images', 'search_images', 'template_anno', 'search_anno', 'test_proposals', 'proposal_iou' """ # Apply joint transforms if self.transform['joint'] is not None: data['template_images'], data['template_anno'], data['template_masks'] = self.transform['joint']( image=data['template_images'], bbox=data['template_anno'], mask=data['template_masks']) data['search_images'], data['search_anno'], data['search_masks'] = self.transform['joint']( image=data['search_images'], bbox=data['search_anno'], mask=data['search_masks'], new_roll=False) data["target_in_search_images"] = data["search_images"] data["target_in_search_anno"] = data["search_anno"] data["target_in_search_masks"] = data["search_masks"] self.scale_jitter_factor["target_in_search"] = self.scale_jitter_factor["template"] self.center_jitter_factor["target_in_search"] = self.center_jitter_factor["template"] self.search_area_factor["target_in_search"] = self.search_area_factor["template"] self.output_sz["target_in_search"] = self.output_sz["template"] self.transform["target_in_search"] = self.transform["search"] for s in ['template', 'search', 'target_in_search']: assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \ "In pair mode, num train/test frames must be 1" # Add a uniform noise to the center pos jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']] # 2021.1.9 Check whether data is valid. Avoid too small bounding boxes w, h = torch.stack(jittered_anno, dim=0)[:, 2], torch.stack(jittered_anno, dim=0)[:, 3] crop_sz = torch.ceil(torch.sqrt(w * h) * self.search_area_factor[s]) if (crop_sz < 1).any(): data['valid'] = False # print("Too small box is found. Replace it with new data.") return data # Crop image region centered at jittered_anno box and get the attention mask crops, boxes, att_mask, mask_crops = prutils.jittered_center_crop(data[s + '_images'], jittered_anno, data[s + '_anno'], self.search_area_factor[s], self.output_sz[s], masks=data[s + '_masks']) # Apply transforms data[s + '_images'], data[s + '_anno'], data[s + '_att'], data[s + '_masks'] = self.transform[s]( image=crops, bbox=boxes, att=att_mask, mask=mask_crops, joint=False) # 2021.1.9 Check whether elements in data[s + '_att'] is all 1 # Note that type of data[s + '_att'] is tuple, type of ele is torch.tensor for ele in data[s + '_att']: if (ele == 1).all(): data['valid'] = False # print("Values of original attention mask are all one. Replace it with new data.") return data # 2021.1.10 more strict conditions: require the donwsampled masks not to be all 1 for ele in data[s + '_att']: feat_size = self.output_sz[s] // 16 # 16 is the backbone stride # (1,1,128,128) (1,1,256,256) --> (1,1,8,8) (1,1,16,16) mask_down = F.interpolate(ele[None, None].float(), size=feat_size).to(torch.bool)[0] if (mask_down == 1).all(): data['valid'] = False # print("Values of down-sampled attention mask are all one. " # "Replace it with new data.") return data data['valid'] = True # if we use copy-and-paste augmentation if data["template_masks"] is None or data["search_masks"] is None: data["template_masks"] = torch.zeros((1, self.output_sz["template"], self.output_sz["template"])) data["search_masks"] = torch.zeros((1, self.output_sz["search"], self.output_sz["search"])) # Prepare output if self.mode == 'sequence': data = data.apply(stack_tensors) else: data = data.apply(lambda x: x[0] if isinstance(x, list) else x) return data ================================================ FILE: lib/train/data/processing_utils.py ================================================ import torch import math import cv2 as cv import torch.nn.functional as F import numpy as np '''modified from the original test implementation Replace cv.BORDER_REPLICATE with cv.BORDER_CONSTANT Add a variable called att_mask for computing attention and positional encoding later''' def sample_target(im, target_bb, search_area_factor, output_sz=None, mask=None): """ Extracts a square crop centered at target_bb box, of area search_area_factor^2 times target_bb area args: im - cv image target_bb - target box [x, y, w, h] search_area_factor - Ratio of crop size to target size output_sz - (float) Size to which the extracted crop is resized (always square). If None, no resizing is done. returns: cv image - extracted crop float - the factor by which the crop has been resized to make the crop size equal output_size """ if not isinstance(target_bb, list): x, y, w, h = target_bb.tolist() else: x, y, w, h = target_bb # Crop image crop_sz = math.ceil(math.sqrt(w * h) * search_area_factor) if crop_sz < 1: raise Exception('Too small bounding box.') x1 = round(x + 0.5 * w - crop_sz * 0.5) x2 = x1 + crop_sz y1 = round(y + 0.5 * h - crop_sz * 0.5) y2 = y1 + crop_sz x1_pad = max(0, -x1) x2_pad = max(x2 - im.shape[1] + 1, 0) y1_pad = max(0, -y1) y2_pad = max(y2 - im.shape[0] + 1, 0) # Crop target im_crop = im[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad, :] if mask is not None: mask_crop = mask[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad] # Pad im_crop_padded = cv.copyMakeBorder(im_crop, y1_pad, y2_pad, x1_pad, x2_pad, cv.BORDER_CONSTANT) # deal with attention mask H, W, _ = im_crop_padded.shape att_mask = np.ones((H,W)) end_x, end_y = -x2_pad, -y2_pad if y2_pad == 0: end_y = None if x2_pad == 0: end_x = None att_mask[y1_pad:end_y, x1_pad:end_x] = 0 if mask is not None: mask_crop_padded = F.pad(mask_crop, pad=(x1_pad, x2_pad, y1_pad, y2_pad), mode='constant', value=0) if output_sz is not None: resize_factor = output_sz / crop_sz im_crop_padded = cv.resize(im_crop_padded, (output_sz, output_sz)) att_mask = cv.resize(att_mask, (output_sz, output_sz)).astype(np.bool_) if mask is None: return im_crop_padded, resize_factor, att_mask mask_crop_padded = \ F.interpolate(mask_crop_padded[None, None], (output_sz, output_sz), mode='bilinear', align_corners=False)[0, 0] return im_crop_padded, resize_factor, att_mask, mask_crop_padded else: if mask is None: return im_crop_padded, att_mask.astype(np.bool_), 1.0 return im_crop_padded, 1.0, att_mask.astype(np.bool_), mask_crop_padded def transform_image_to_crop(box_in: torch.Tensor, box_extract: torch.Tensor, resize_factor: float, crop_sz: torch.Tensor, normalize=False) -> torch.Tensor: """ Transform the box co-ordinates from the original image co-ordinates to the co-ordinates of the cropped image args: box_in - the box for which the co-ordinates are to be transformed box_extract - the box about which the image crop has been extracted. resize_factor - the ratio between the original image scale and the scale of the image crop crop_sz - size of the cropped image returns: torch.Tensor - transformed co-ordinates of box_in """ box_extract_center = box_extract[0:2] + 0.5 * box_extract[2:4] box_in_center = box_in[0:2] + 0.5 * box_in[2:4] box_out_center = (crop_sz - 1) / 2 + (box_in_center - box_extract_center) * resize_factor box_out_wh = box_in[2:4] * resize_factor box_out = torch.cat((box_out_center - 0.5 * box_out_wh, box_out_wh)) if normalize: return box_out / crop_sz[0] else: return box_out def jittered_center_crop(frames, box_extract, box_gt, search_area_factor, output_sz, masks=None): """ For each frame in frames, extracts a square crop centered at box_extract, of area search_area_factor^2 times box_extract area. The extracted crops are then resized to output_sz. Further, the co-ordinates of the box box_gt are transformed to the image crop co-ordinates args: frames - list of frames box_extract - list of boxes of same length as frames. The crops are extracted using anno_extract box_gt - list of boxes of same length as frames. The co-ordinates of these boxes are transformed from image co-ordinates to the crop co-ordinates search_area_factor - The area of the extracted crop is search_area_factor^2 times box_extract area output_sz - The size to which the extracted crops are resized returns: list - list of image crops list - box_gt location in the crop co-ordinates """ if masks is None: crops_resize_factors = [sample_target(f, a, search_area_factor, output_sz) for f, a in zip(frames, box_extract)] frames_crop, resize_factors, att_mask = zip(*crops_resize_factors) masks_crop = None else: crops_resize_factors = [sample_target(f, a, search_area_factor, output_sz, m) for f, a, m in zip(frames, box_extract, masks)] frames_crop, resize_factors, att_mask, masks_crop = zip(*crops_resize_factors) # frames_crop: tuple of ndarray (128,128,3), att_mask: tuple of ndarray (128,128) crop_sz = torch.Tensor([output_sz, output_sz]) # find the bb location in the crop '''Note that here we use normalized coord''' box_crop = [transform_image_to_crop(a_gt, a_ex, rf, crop_sz, normalize=True) for a_gt, a_ex, rf in zip(box_gt, box_extract, resize_factors)] # (x1,y1,w,h) list of tensors return frames_crop, box_crop, att_mask, masks_crop def transform_box_to_crop(box: torch.Tensor, crop_box: torch.Tensor, crop_sz: torch.Tensor, normalize=False) -> torch.Tensor: """ Transform the box co-ordinates from the original image co-ordinates to the co-ordinates of the cropped image args: box - the box for which the co-ordinates are to be transformed crop_box - bounding box defining the crop in the original image crop_sz - size of the cropped image returns: torch.Tensor - transformed co-ordinates of box_in """ box_out = box.clone() box_out[:2] -= crop_box[:2] scale_factor = crop_sz / crop_box[2:] box_out[:2] *= scale_factor box_out[2:] *= scale_factor if normalize: return box_out / crop_sz[0] else: return box_out ================================================ FILE: lib/train/data/sampler.py ================================================ import random import torch.utils.data from lib.utils import TensorDict import numpy as np def no_processing(data): return data class TrackingSampler(torch.utils.data.Dataset): """ Class responsible for sampling frames from training sequences to form batches. The sampling is done in the following ways. First a dataset is selected at random. Next, a sequence is selected from that dataset. A base frame is then sampled randomly from the sequence. Next, a set of 'train frames' and 'test frames' are sampled from the sequence from the range [base_frame_id - max_gap, base_frame_id] and (base_frame_id, base_frame_id + max_gap] respectively. Only the frames in which the target is visible are sampled. If enough visible frames are not found, the 'max_gap' is increased gradually till enough frames are found. The sampled frames are then passed through the input 'processing' function for the necessary processing- """ def __init__(self, datasets, p_datasets, samples_per_epoch, max_gap, num_search_frames, num_template_frames=1, processing=no_processing, frame_sample_mode='causal', train_cls=False, pos_prob=0.5): """ args: datasets - List of datasets to be used for training p_datasets - List containing the probabilities by which each dataset will be sampled samples_per_epoch - Number of training samples per epoch max_gap - Maximum gap, in frame numbers, between the train frames and the test frames. num_search_frames - Number of search frames to sample. num_template_frames - Number of template frames to sample. processing - An instance of Processing class which performs the necessary processing of the data. frame_sample_mode - Either 'causal' or 'interval'. If 'causal', then the test frames are sampled in a causally, otherwise randomly within the interval. """ self.datasets = datasets self.train_cls = train_cls # whether we are training classification self.pos_prob = pos_prob # probability of sampling positive class when making classification # If p not provided, sample uniformly from all videos if p_datasets is None: p_datasets = [len(d) for d in self.datasets] # Normalize p_total = sum(p_datasets) self.p_datasets = [x / p_total for x in p_datasets] self.samples_per_epoch = samples_per_epoch self.max_gap = max_gap self.num_search_frames = num_search_frames self.num_template_frames = num_template_frames self.processing = processing self.frame_sample_mode = frame_sample_mode def __len__(self): return self.samples_per_epoch def _sample_visible_ids(self, visible, num_ids=1, min_id=None, max_id=None, allow_invisible=False, force_invisible=False): """ Samples num_ids frames between min_id and max_id for which target is visible args: visible - 1d Tensor indicating whether target is visible for each frame num_ids - number of frames to be samples min_id - Minimum allowed frame number max_id - Maximum allowed frame number returns: list - List of sampled frame numbers. None if not sufficient visible frames could be found. """ if num_ids == 0: return [] if min_id is None or min_id < 0: min_id = 0 if max_id is None or max_id > len(visible): max_id = len(visible) # get valid ids if force_invisible: valid_ids = [i for i in range(min_id, max_id) if not visible[i]] else: if allow_invisible: valid_ids = [i for i in range(min_id, max_id)] else: valid_ids = [i for i in range(min_id, max_id) if visible[i]] # No visible ids if len(valid_ids) == 0: return None return random.choices(valid_ids, k=num_ids) def __getitem__(self, index): if self.train_cls: return self.getitem_cls() else: return self.getitem() def getitem(self): """ returns: TensorDict - dict containing all the data blocks """ valid = False while not valid: # Select a dataset dataset = random.choices(self.datasets, self.p_datasets)[0] is_video_dataset = dataset.is_video_sequence() # sample a sequence from the given dataset seq_id, visible, seq_info_dict = self.sample_seq_from_dataset(dataset, is_video_dataset) if is_video_dataset: template_frame_ids = None search_frame_ids = None gap_increase = 0 if self.frame_sample_mode == 'causal': # Sample test and train frames in a causal manner, i.e. search_frame_ids > template_frame_ids while search_frame_ids is None: base_frame_id = self._sample_visible_ids(visible, num_ids=1, min_id=self.num_template_frames - 1, max_id=len(visible) - self.num_search_frames) prev_frame_ids = self._sample_visible_ids(visible, num_ids=self.num_template_frames - 1, min_id=base_frame_id[0] - self.max_gap - gap_increase, max_id=base_frame_id[0]) if prev_frame_ids is None: gap_increase += 5 continue template_frame_ids = base_frame_id + prev_frame_ids search_frame_ids = self._sample_visible_ids(visible, min_id=template_frame_ids[0] + 1, max_id=template_frame_ids[0] + self.max_gap + gap_increase, num_ids=self.num_search_frames) # Increase gap until a frame is found gap_increase += 5 elif self.frame_sample_mode == "trident" or self.frame_sample_mode == "trident_pro": template_frame_ids, search_frame_ids = self.get_frame_ids_trident(visible) elif self.frame_sample_mode == "stark": template_frame_ids, search_frame_ids = self.get_frame_ids_stark(visible, seq_info_dict["valid"]) else: raise ValueError("Illegal frame sample mode") else: # In case of image dataset, just repeat the image to generate synthetic video template_frame_ids = [1] * self.num_template_frames search_frame_ids = [1] * self.num_search_frames try: template_frames, template_anno, meta_obj_train = dataset.get_frames(seq_id, template_frame_ids, seq_info_dict) search_frames, search_anno, meta_obj_test = dataset.get_frames(seq_id, search_frame_ids, seq_info_dict) H, W, _ = template_frames[0].shape template_masks = template_anno['mask'] if 'mask' in template_anno else [torch.zeros((H, W))] * self.num_template_frames search_masks = search_anno['mask'] if 'mask' in search_anno else [torch.zeros((H, W))] * self.num_search_frames data = TensorDict({'template_images': template_frames, 'template_anno': template_anno['bbox'], 'template_masks': template_masks, 'search_images': search_frames, 'search_anno': search_anno['bbox'], 'search_masks': search_masks, 'dataset': dataset.get_name(), 'test_class': meta_obj_test.get('object_class_name')}) # make data augmentation data = self.processing(data) # check whether data is valid valid = data['valid'] except: valid = False return data def getitem_cls(self): # get data for classification """ args: index (int): Index (Ignored since we sample randomly) aux (bool): whether the current data is for auxiliary use (e.g. copy-and-paste) returns: TensorDict - dict containing all the data blocks """ valid = False label = None while not valid: # Select a dataset dataset = random.choices(self.datasets, self.p_datasets)[0] is_video_dataset = dataset.is_video_sequence() # sample a sequence from the given dataset seq_id, visible, seq_info_dict = self.sample_seq_from_dataset(dataset, is_video_dataset) # sample template and search frame ids if is_video_dataset: if self.frame_sample_mode in ["trident", "trident_pro"]: template_frame_ids, search_frame_ids = self.get_frame_ids_trident(visible) elif self.frame_sample_mode == "stark": template_frame_ids, search_frame_ids = self.get_frame_ids_stark(visible, seq_info_dict["valid"]) else: raise ValueError("illegal frame sample mode") else: # In case of image dataset, just repeat the image to generate synthetic video template_frame_ids = [1] * self.num_template_frames search_frame_ids = [1] * self.num_search_frames try: # "try" is used to handle trackingnet data failure # get images and bounding boxes (for templates) template_frames, template_anno, meta_obj_train = dataset.get_frames(seq_id, template_frame_ids, seq_info_dict) H, W, _ = template_frames[0].shape template_masks = template_anno['mask'] if 'mask' in template_anno else [torch.zeros( (H, W))] * self.num_template_frames # get images and bounding boxes (for searches) # positive samples if random.random() < self.pos_prob: label = torch.ones(1,) search_frames, search_anno, meta_obj_test = dataset.get_frames(seq_id, search_frame_ids, seq_info_dict) search_masks = search_anno['mask'] if 'mask' in search_anno else [torch.zeros( (H, W))] * self.num_search_frames # negative samples else: label = torch.zeros(1,) if is_video_dataset: search_frame_ids = self._sample_visible_ids(visible, num_ids=1, force_invisible=True) if search_frame_ids is None: search_frames, search_anno, meta_obj_test = self.get_one_search() else: search_frames, search_anno, meta_obj_test = dataset.get_frames(seq_id, search_frame_ids, seq_info_dict) search_anno["bbox"] = [self.get_center_box(H, W)] else: search_frames, search_anno, meta_obj_test = self.get_one_search() H, W, _ = search_frames[0].shape search_masks = search_anno['mask'] if 'mask' in search_anno else [torch.zeros( (H, W))] * self.num_search_frames data = TensorDict({'template_images': template_frames, 'template_anno': template_anno['bbox'], 'template_masks': template_masks, 'search_images': search_frames, 'search_anno': search_anno['bbox'], 'search_masks': search_masks, 'dataset': dataset.get_name(), 'test_class': meta_obj_test.get('object_class_name')}) # make data augmentation data = self.processing(data) # add classification label data["label"] = label # check whether data is valid valid = data['valid'] except: valid = False return data def get_center_box(self, H, W, ratio=1/8): cx, cy, w, h = W/2, H/2, W * ratio, H * ratio return torch.tensor([int(cx-w/2), int(cy-h/2), int(w), int(h)]) def sample_seq_from_dataset(self, dataset, is_video_dataset): # Sample a sequence with enough visible frames enough_visible_frames = False while not enough_visible_frames: # Sample a sequence seq_id = random.randint(0, dataset.get_num_sequences() - 1) # Sample frames seq_info_dict = dataset.get_sequence_info(seq_id) visible = seq_info_dict['visible'] enough_visible_frames = visible.type(torch.int64).sum().item() > 2 * ( self.num_search_frames + self.num_template_frames) and len(visible) >= 20 enough_visible_frames = enough_visible_frames or not is_video_dataset return seq_id, visible, seq_info_dict def get_one_search(self): # Select a dataset dataset = random.choices(self.datasets, self.p_datasets)[0] is_video_dataset = dataset.is_video_sequence() # sample a sequence seq_id, visible, seq_info_dict = self.sample_seq_from_dataset(dataset, is_video_dataset) # sample a frame if is_video_dataset: if self.frame_sample_mode == "stark": search_frame_ids = self._sample_visible_ids(seq_info_dict["valid"], num_ids=1) else: search_frame_ids = self._sample_visible_ids(visible, num_ids=1, allow_invisible=True) else: search_frame_ids = [1] # get the image, bounding box and other info search_frames, search_anno, meta_obj_test = dataset.get_frames(seq_id, search_frame_ids, seq_info_dict) return search_frames, search_anno, meta_obj_test def get_frame_ids_trident(self, visible): # get template and search ids in a 'trident' manner template_frame_ids_extra = [] while None in template_frame_ids_extra or len(template_frame_ids_extra) == 0: template_frame_ids_extra = [] # first randomly sample two frames from a video template_frame_id1 = self._sample_visible_ids(visible, num_ids=1) # the initial template id search_frame_ids = self._sample_visible_ids(visible, num_ids=1) # the search region id # get the dynamic template id for max_gap in self.max_gap: if template_frame_id1[0] >= search_frame_ids[0]: min_id, max_id = search_frame_ids[0], search_frame_ids[0] + max_gap else: min_id, max_id = search_frame_ids[0] - max_gap, search_frame_ids[0] if self.frame_sample_mode == "trident_pro": f_id = self._sample_visible_ids(visible, num_ids=1, min_id=min_id, max_id=max_id, allow_invisible=True) else: f_id = self._sample_visible_ids(visible, num_ids=1, min_id=min_id, max_id=max_id) if f_id is None: template_frame_ids_extra += [None] else: template_frame_ids_extra += f_id template_frame_ids = template_frame_id1 + template_frame_ids_extra return template_frame_ids, search_frame_ids def get_frame_ids_stark(self, visible, valid): # get template and search ids in a 'stark' manner template_frame_ids_extra = [] while None in template_frame_ids_extra or len(template_frame_ids_extra) == 0: template_frame_ids_extra = [] # first randomly sample two frames from a video template_frame_id1 = self._sample_visible_ids(visible, num_ids=1) # the initial template id search_frame_ids = self._sample_visible_ids(visible, num_ids=1) # the search region id # get the dynamic template id for max_gap in self.max_gap: if template_frame_id1[0] >= search_frame_ids[0]: min_id, max_id = search_frame_ids[0], search_frame_ids[0] + max_gap else: min_id, max_id = search_frame_ids[0] - max_gap, search_frame_ids[0] """we require the frame to be valid but not necessary visible""" f_id = self._sample_visible_ids(valid, num_ids=1, min_id=min_id, max_id=max_id) if f_id is None: template_frame_ids_extra += [None] else: template_frame_ids_extra += f_id template_frame_ids = template_frame_id1 + template_frame_ids_extra return template_frame_ids, search_frame_ids ================================================ FILE: lib/train/data/sequence_sampler.py ================================================ import random import torch.utils.data import numpy as np from lib.utils import TensorDict class SequenceSampler(torch.utils.data.Dataset): """ Sample sequence for sequence-level training """ def __init__(self, datasets, p_datasets, samples_per_epoch, max_gap, num_search_frames, num_template_frames=1, frame_sample_mode='sequential', max_interval=10, prob=0.7): """ args: datasets - List of datasets to be used for training p_datasets - List containing the probabilities by which each dataset will be sampled samples_per_epoch - Number of training samples per epoch max_gap - Maximum gap, in frame numbers, between the train frames and the search frames.\ max_interval - Maximum interval between sampled frames num_search_frames - Number of search frames to sample. num_template_frames - Number of template frames to sample. processing - An instance of Processing class which performs the necessary processing of the data. frame_sample_mode - Either 'causal' or 'interval'. If 'causal', then the search frames are sampled in a causally, otherwise randomly within the interval. prob - sequential sampling by prob / interval sampling by 1-prob """ self.datasets = datasets # If p not provided, sample uniformly from all videos if p_datasets is None: p_datasets = [len(d) for d in self.datasets] # Normalize p_total = sum(p_datasets) self.p_datasets = [x / p_total for x in p_datasets] self.samples_per_epoch = samples_per_epoch self.max_gap = max_gap self.max_interval = max_interval self.num_search_frames = num_search_frames self.num_template_frames = num_template_frames self.frame_sample_mode = frame_sample_mode self.prob=prob self.extra=1 def __len__(self): return self.samples_per_epoch def _sample_visible_ids(self, visible, num_ids=1, min_id=None, max_id=None): """ Samples num_ids frames between min_id and max_id for which target is visible args: visible - 1d Tensor indicating whether target is visible for each frame num_ids - number of frames to be samples min_id - Minimum allowed frame number max_id - Maximum allowed frame number returns: list - List of sampled frame numbers. None if not sufficient visible frames could be found. """ if num_ids == 0: return [] if min_id is None or min_id < 0: min_id = 0 if max_id is None or max_id > len(visible): max_id = len(visible) valid_ids = [i for i in range(min_id, max_id) if visible[i]] # No visible ids if len(valid_ids) == 0: return None return random.choices(valid_ids, k=num_ids) def _sequential_sample(self, visible): # Sample frames in sequential manner template_frame_ids = self._sample_visible_ids(visible, num_ids=1, min_id=0, max_id=len(visible) - self.num_search_frames) if self.max_gap == -1: left = template_frame_ids[0] else: # template frame (1) ->(max_gap) -> search frame (num_search_frames) left_max = min(len(visible) - self.num_search_frames, template_frame_ids[0] + self.max_gap) left = self._sample_visible_ids(visible, num_ids=1, min_id=template_frame_ids[0], max_id=left_max)[0] valid_ids = [i for i in range(left, len(visible)) if visible[i]] search_frame_ids = valid_ids[:self.num_search_frames] # if length is not enough last = search_frame_ids[-1] while len(search_frame_ids) < self.num_search_frames: if last >= len(visible) - 1: search_frame_ids.append(last) else: last += 1 if visible[last]: search_frame_ids.append(last) return template_frame_ids, search_frame_ids def _random_interval_sample(self, visible): # Get valid ids valid_ids = [i for i in range(len(visible)) if visible[i]] # Sample template frame avg_interval = self.max_interval while avg_interval * (self.num_search_frames - 1) > len(visible): avg_interval = max(avg_interval - 1, 1) while True: template_frame_ids = self._sample_visible_ids(visible, num_ids=1, min_id=0, max_id=len(visible) - avg_interval * (self.num_search_frames - 1)) if template_frame_ids == None: avg_interval = avg_interval - 1 else: break if avg_interval == 0: template_frame_ids = [valid_ids[0]] break # Sample first search frame if self.max_gap == -1: search_frame_ids = template_frame_ids else: avg_interval = self.max_interval while avg_interval * (self.num_search_frames - 1) > len(visible): avg_interval = max(avg_interval - 1, 1) while True: left_max = min(max(len(visible) - avg_interval * (self.num_search_frames - 1), template_frame_ids[0] + 1), template_frame_ids[0] + self.max_gap) search_frame_ids = self._sample_visible_ids(visible, num_ids=1, min_id=template_frame_ids[0], max_id=left_max) if search_frame_ids == None: avg_interval = avg_interval - 1 else: break if avg_interval == -1: search_frame_ids = template_frame_ids break # Sample rest of the search frames with random interval last = search_frame_ids[0] while last <= len(visible) - 1 and len(search_frame_ids) < self.num_search_frames: # sample id with interval max_id = min(last + self.max_interval + 1, len(visible)) id = self._sample_visible_ids(visible, num_ids=1, min_id=last, max_id=max_id) if id is None: # If not found in current range, find from previous range last = last + self.max_interval else: search_frame_ids.append(id[0]) last = search_frame_ids[-1] # if length is not enough, randomly sample new ids if len(search_frame_ids) < self.num_search_frames: valid_ids = [x for x in valid_ids if x > search_frame_ids[0] and x not in search_frame_ids] if len(valid_ids) > 0: new_ids = random.choices(valid_ids, k=min(len(valid_ids), self.num_search_frames - len(search_frame_ids))) search_frame_ids = search_frame_ids + new_ids search_frame_ids = sorted(search_frame_ids, key=int) # if length is still not enough, duplicate last frame while len(search_frame_ids) < self.num_search_frames: search_frame_ids.append(search_frame_ids[-1]) for i in range(1, self.num_search_frames): if search_frame_ids[i] - search_frame_ids[i - 1] > self.max_interval: print(search_frame_ids[i] - search_frame_ids[i - 1]) return template_frame_ids, search_frame_ids def __getitem__(self, index): """ args: index (int): Index (Ignored since we sample randomly) returns: TensorDict - dict containing all the data blocks """ # Select a dataset dataset = random.choices(self.datasets, self.p_datasets)[0] if dataset.get_name() == 'got10k' : max_gap = self.max_gap max_interval = self.max_interval else: max_gap = self.max_gap max_interval = self.max_interval self.max_gap = max_gap * self.extra self.max_interval = max_interval * self.extra is_video_dataset = dataset.is_video_sequence() # Sample a sequence with enough visible frames enough_visible_frames = False while not enough_visible_frames: # Sample a sequence seq_id = random.randint(0, dataset.get_num_sequences() - 1) # Sample frames seq_info_dict = dataset.get_sequence_info(seq_id) visible = seq_info_dict['visible'] enough_visible_frames = visible.type(torch.int64).sum().item() > 2 * ( self.num_search_frames + self.num_template_frames) and len(visible) >= (self.num_search_frames + self.num_template_frames) enough_visible_frames = enough_visible_frames or not is_video_dataset if is_video_dataset: if self.frame_sample_mode == 'sequential': template_frame_ids, search_frame_ids = self._sequential_sample(visible) elif self.frame_sample_mode == 'random_interval': if random.random() < self.prob: template_frame_ids, search_frame_ids = self._random_interval_sample(visible) else: template_frame_ids, search_frame_ids = self._sequential_sample(visible) else: self.max_gap = max_gap self.max_interval = max_interval raise NotImplementedError else: # In case of image dataset, just repeat the image to generate synthetic video template_frame_ids = [1] * self.num_template_frames search_frame_ids = [1] * self.num_search_frames #print(dataset.get_name(), search_frame_ids, self.max_gap, self.max_interval) self.max_gap = max_gap self.max_interval = max_interval #print(self.max_gap, self.max_interval) template_frames, template_anno, meta_obj_template = dataset.get_frames(seq_id, template_frame_ids, seq_info_dict) search_frames, search_anno, meta_obj_search = dataset.get_frames(seq_id, search_frame_ids, seq_info_dict) template_bbox = [bbox.numpy() for bbox in template_anno['bbox']] # tensor -> numpy array search_bbox = [bbox.numpy() for bbox in search_anno['bbox']] # tensor -> numpy array return TensorDict({'template_images': np.array(template_frames).squeeze(), # 1 template images 'template_annos': np.array(template_bbox).squeeze(), 'search_images': np.array(search_frames), # (num_frames) search images 'search_annos': np.array(search_bbox), 'seq_id': seq_id, 'dataset': dataset.get_name(), 'search_class': meta_obj_search.get('object_class_name'), 'num_frames': len(search_frames) }) ================================================ FILE: lib/train/data/sequence_sampler_v2.py ================================================ import random import torch.utils.data import numpy as np from lib.utils import TensorDict class SequenceSampler(torch.utils.data.Dataset): """ Sample sequence for sequence-level training """ def __init__(self, datasets, p_datasets, samples_per_epoch, max_gap, num_search_frames, num_template_frames=1, frame_sample_mode='sequential', max_interval=10, prob=0.7): """ args: datasets - List of datasets to be used for training p_datasets - List containing the probabilities by which each dataset will be sampled samples_per_epoch - Number of training samples per epoch max_gap - Maximum gap, in frame numbers, between the train frames and the search frames.\ max_interval - Maximum interval between sampled frames num_search_frames - Number of search frames to sample. num_template_frames - Number of template frames to sample. processing - An instance of Processing class which performs the necessary processing of the data. frame_sample_mode - Either 'causal' or 'interval'. If 'causal', then the search frames are sampled in a causally, otherwise randomly within the interval. prob - sequential sampling by prob / interval sampling by 1-prob """ self.datasets = datasets # If p not provided, sample uniformly from all videos if p_datasets is None: p_datasets = [len(d) for d in self.datasets] # Normalize p_total = sum(p_datasets) self.p_datasets = [x / p_total for x in p_datasets] self.samples_per_epoch = samples_per_epoch self.max_gap = max_gap self.max_interval = max_interval self.num_search_frames = num_search_frames self.num_template_frames = num_template_frames self.frame_sample_mode = frame_sample_mode self.prob = prob self.extra = 1 def __len__(self): return self.samples_per_epoch def _sample_visible_ids(self, visible, num_ids=1, min_id=None, max_id=None): """ Samples num_ids frames between min_id and max_id for which target is visible args: visible - 1d Tensor indicating whether target is visible for each frame num_ids - number of frames to be samples min_id - Minimum allowed frame number max_id - Maximum allowed frame number returns: list - List of sampled frame numbers. None if not sufficient visible frames could be found. """ if num_ids == 0: return [] if min_id is None or min_id < 0: min_id = 0 if max_id is None or max_id > len(visible): max_id = len(visible) valid_ids = [i for i in range(min_id, max_id) if visible[i]] # No visible ids if len(valid_ids) == 0: return None return random.choices(valid_ids, k=num_ids) def _sequential_sample(self, visible): # Sample frames in sequential manner template_frame_ids = self._sample_visible_ids(visible, num_ids=1, min_id=0, max_id=len(visible) - self.num_search_frames) template_another = self._sample_visible_ids(visible, num_ids=1, min_id=template_frame_ids[0], max_id=min(len(visible) - self.num_search_frames, template_frame_ids[0] + self.max_gap)) template_frame_ids.append(template_another[0]) template_frame_ids.sort() if self.max_gap == -1: left = template_frame_ids[1] else: # template frame (1) ->(max_gap) -> search frame (num_search_frames) left_max = min(len(visible) - self.num_search_frames, template_frame_ids[1] + self.max_gap) left = self._sample_visible_ids(visible, num_ids=1, min_id=template_frame_ids[1], max_id=left_max)[0] valid_ids = [i for i in range(left, len(visible)) if visible[i]] search_frame_ids = valid_ids[:self.num_search_frames] # if length is not enough last = search_frame_ids[-1] while len(search_frame_ids) < self.num_search_frames: if last >= len(visible) - 1: search_frame_ids.append(last) else: last += 1 if visible[last]: search_frame_ids.append(last) return template_frame_ids, search_frame_ids def _random_interval_sample(self, visible): # Get valid ids valid_ids = [i for i in range(len(visible)) if visible[i]] # Sample template frame avg_interval = self.max_interval while avg_interval * (self.num_search_frames - 1) > len(visible): avg_interval = max(avg_interval - 1, 1) while True: template_frame_ids = self._sample_visible_ids(visible, num_ids=1, min_id=0, max_id=len(visible) - avg_interval * ( self.num_search_frames - 1)) if template_frame_ids == None: avg_interval = avg_interval - 1 else: break if avg_interval == 0: template_frame_ids = [valid_ids[0]] break # Sample first search frame if self.max_gap == -1: search_frame_ids = template_frame_ids else: avg_interval = self.max_interval while avg_interval * (self.num_search_frames - 1) > len(visible): avg_interval = max(avg_interval - 1, 1) while True: left_max = min( max(len(visible) - avg_interval * (self.num_search_frames - 1), template_frame_ids[0] + 1), template_frame_ids[0] + self.max_gap) search_frame_ids = self._sample_visible_ids(visible, num_ids=1, min_id=template_frame_ids[0], max_id=left_max) if search_frame_ids == None: avg_interval = avg_interval - 1 else: break if avg_interval == -1: search_frame_ids = template_frame_ids break # Sample rest of the search frames with random interval last = search_frame_ids[0] while last <= len(visible) - 1 and len(search_frame_ids) < self.num_search_frames: # sample id with interval max_id = min(last + self.max_interval + 1, len(visible)) id = self._sample_visible_ids(visible, num_ids=1, min_id=last, max_id=max_id) if id is None: # If not found in current range, find from previous range last = last + self.max_interval else: search_frame_ids.append(id[0]) last = search_frame_ids[-1] # if length is not enough, randomly sample new ids if len(search_frame_ids) < self.num_search_frames: valid_ids = [x for x in valid_ids if x > search_frame_ids[0] and x not in search_frame_ids] if len(valid_ids) > 0: new_ids = random.choices(valid_ids, k=min(len(valid_ids), self.num_search_frames - len(search_frame_ids))) search_frame_ids = search_frame_ids + new_ids search_frame_ids = sorted(search_frame_ids, key=int) # if length is still not enough, duplicate last frame while len(search_frame_ids) < self.num_search_frames: search_frame_ids.append(search_frame_ids[-1]) for i in range(1, self.num_search_frames): if search_frame_ids[i] - search_frame_ids[i - 1] > self.max_interval: print(search_frame_ids[i] - search_frame_ids[i - 1]) return template_frame_ids, search_frame_ids def __getitem__(self, index): """ args: index (int): Index (Ignored since we sample randomly) returns: TensorDict - dict containing all the data blocks """ # Select a dataset dataset = random.choices(self.datasets, self.p_datasets)[0] if dataset.get_name() == 'got10k': max_gap = self.max_gap max_interval = self.max_interval else: max_gap = self.max_gap max_interval = self.max_interval self.max_gap = max_gap * self.extra self.max_interval = max_interval * self.extra is_video_dataset = dataset.is_video_sequence() # Sample a sequence with enough visible frames enough_visible_frames = False while not enough_visible_frames: # Sample a sequence seq_id = random.randint(0, dataset.get_num_sequences() - 1) # Sample frames seq_info_dict = dataset.get_sequence_info(seq_id) visible = seq_info_dict['visible'] enough_visible_frames = visible.type(torch.int64).sum().item() > 2 * ( self.num_search_frames + self.num_template_frames) and len(visible) >= ( self.num_search_frames + self.num_template_frames) enough_visible_frames = enough_visible_frames or not is_video_dataset if is_video_dataset: if self.frame_sample_mode == 'sequential': template_frame_ids, search_frame_ids = self._sequential_sample(visible) elif self.frame_sample_mode == 'random_interval': if random.random() < self.prob: template_frame_ids, search_frame_ids = self._random_interval_sample(visible) else: template_frame_ids, search_frame_ids = self._sequential_sample(visible) else: self.max_gap = max_gap self.max_interval = max_interval raise NotImplementedError else: # In case of image dataset, just repeat the image to generate synthetic video template_frame_ids = [1] * self.num_template_frames search_frame_ids = [1] * self.num_search_frames self.max_gap = max_gap self.max_interval = max_interval # print("this is template_frame_ids", template_frame_ids) # print("this is search_frame_ids", search_frame_ids) template_frames, template_anno, meta_obj_template = dataset.get_frames(seq_id, template_frame_ids, seq_info_dict) search_frames, search_anno, meta_obj_search = dataset.get_frames(seq_id, search_frame_ids, seq_info_dict) # visible_ratio = search_anno['visible_ratio'] template_bbox = [bbox.numpy() for bbox in template_anno['bbox']] # tensor -> numpy array search_bbox = [bbox.numpy() for bbox in search_anno['bbox']] # tensor -> numpy array return TensorDict({'template_images': np.array(template_frames).squeeze(), # 1 template images 'template_annos': np.array(template_bbox).squeeze(), 'search_images': np.array(search_frames), # (num_frames) search images 'search_annos': np.array(search_bbox), 'seq_id': seq_id, 'dataset': dataset.get_name(), 'search_class': meta_obj_search.get('object_class_name'), 'num_frames': len(search_frames), # 'visible_ratio': visible_ratio }) ================================================ FILE: lib/train/data/transforms.py ================================================ import random import numpy as np import math import cv2 as cv import torch import torch.nn.functional as F import torchvision.transforms.functional as tvisf class Transform: """A set of transformations, used for e.g. data augmentation. Args of constructor: transforms: An arbitrary number of transformations, derived from the TransformBase class. They are applied in the order they are given. The Transform object can jointly transform images, bounding boxes and segmentation masks. This is done by calling the object with the following key-word arguments (all are optional). The following arguments are inputs to be transformed. They are either supplied as a single instance, or a list of instances. image - Image coords - 2xN dimensional Tensor of 2D image coordinates [y, x] bbox - Bounding box on the form [x, y, w, h] mask - Segmentation mask with discrete classes The following parameters can be supplied with calling the transform object: joint [Bool] - If True then transform all images/coords/bbox/mask in the list jointly using the same transformation. Otherwise each tuple (images, coords, bbox, mask) will be transformed independently using different random rolls. Default: True. new_roll [Bool] - If False, then no new random roll is performed, and the saved result from the previous roll is used instead. Default: True. Check the DiMPProcessing class for examples. """ def __init__(self, *transforms): if len(transforms) == 1 and isinstance(transforms[0], (list, tuple)): transforms = transforms[0] self.transforms = transforms self._valid_inputs = ['image', 'coords', 'bbox', 'mask', 'att'] self._valid_args = ['joint', 'new_roll'] self._valid_all = self._valid_inputs + self._valid_args def __call__(self, **inputs): var_names = [k for k in inputs.keys() if k in self._valid_inputs] for v in inputs.keys(): if v not in self._valid_all: raise ValueError('Incorrect input \"{}\" to transform. Only supports inputs {} and arguments {}.'.format(v, self._valid_inputs, self._valid_args)) joint_mode = inputs.get('joint', True) new_roll = inputs.get('new_roll', True) if not joint_mode: out = zip(*[self(**inp) for inp in self._split_inputs(inputs)]) return tuple(list(o) for o in out) out = {k: v for k, v in inputs.items() if k in self._valid_inputs} for t in self.transforms: out = t(**out, joint=joint_mode, new_roll=new_roll) if len(var_names) == 1: return out[var_names[0]] # Make sure order is correct return tuple(out[v] for v in var_names) def _split_inputs(self, inputs): var_names = [k for k in inputs.keys() if k in self._valid_inputs] split_inputs = [{k: v for k, v in zip(var_names, vals)} for vals in zip(*[inputs[vn] for vn in var_names])] for arg_name, arg_val in filter(lambda it: it[0]!='joint' and it[0] in self._valid_args, inputs.items()): if isinstance(arg_val, list): for inp, av in zip(split_inputs, arg_val): inp[arg_name] = av else: for inp in split_inputs: inp[arg_name] = arg_val return split_inputs def __repr__(self): format_string = self.__class__.__name__ + '(' for t in self.transforms: format_string += '\n' format_string += ' {0}'.format(t) format_string += '\n)' return format_string class TransformBase: """Base class for transformation objects. See the Transform class for details.""" def __init__(self): """2020.12.24 Add 'att' to valid inputs""" self._valid_inputs = ['image', 'coords', 'bbox', 'mask', 'att'] self._valid_args = ['new_roll'] self._valid_all = self._valid_inputs + self._valid_args self._rand_params = None def __call__(self, **inputs): # Split input input_vars = {k: v for k, v in inputs.items() if k in self._valid_inputs} input_args = {k: v for k, v in inputs.items() if k in self._valid_args} # Roll random parameters for the transform if input_args.get('new_roll', True): rand_params = self.roll() if rand_params is None: rand_params = () elif not isinstance(rand_params, tuple): rand_params = (rand_params,) self._rand_params = rand_params outputs = dict() for var_name, var in input_vars.items(): if var is not None: transform_func = getattr(self, 'transform_' + var_name) if var_name in ['coords', 'bbox']: params = (self._get_image_size(input_vars),) + self._rand_params else: params = self._rand_params if isinstance(var, (list, tuple)): outputs[var_name] = [transform_func(x, *params) for x in var] else: outputs[var_name] = transform_func(var, *params) return outputs def _get_image_size(self, inputs): im = None for var_name in ['image', 'mask']: if inputs.get(var_name) is not None: im = inputs[var_name] break if im is None: return None if isinstance(im, (list, tuple)): im = im[0] if isinstance(im, np.ndarray): return im.shape[:2] if torch.is_tensor(im): return (im.shape[-2], im.shape[-1]) raise Exception('Unknown image type') def roll(self): return None def transform_image(self, image, *rand_params): """Must be deterministic""" return image def transform_coords(self, coords, image_shape, *rand_params): """Must be deterministic""" return coords def transform_bbox(self, bbox, image_shape, *rand_params): """Assumes [x, y, w, h]""" # Check if not overloaded if self.transform_coords.__code__ == TransformBase.transform_coords.__code__: return bbox coord = bbox.clone().view(-1,2).t().flip(0) x1 = coord[1, 0] x2 = coord[1, 0] + coord[1, 1] y1 = coord[0, 0] y2 = coord[0, 0] + coord[0, 1] coord_all = torch.tensor([[y1, y1, y2, y2], [x1, x2, x2, x1]]) coord_transf = self.transform_coords(coord_all, image_shape, *rand_params).flip(0) tl = torch.min(coord_transf, dim=1)[0] sz = torch.max(coord_transf, dim=1)[0] - tl bbox_out = torch.cat((tl, sz), dim=-1).reshape(bbox.shape) return bbox_out def transform_mask(self, mask, *rand_params): """Must be deterministic""" return mask def transform_att(self, att, *rand_params): """2020.12.24 Added to deal with attention masks""" return att class ToTensor(TransformBase): """Convert to a Tensor""" def transform_image(self, image): # handle numpy array if image.ndim == 2: image = image[:, :, None] image = torch.from_numpy(image.transpose((2, 0, 1))) # backward compatibility if isinstance(image, torch.ByteTensor): return image.float().div(255) else: return image def transfrom_mask(self, mask): if isinstance(mask, np.ndarray): return torch.from_numpy(mask) def transform_att(self, att): if isinstance(att, np.ndarray): return torch.from_numpy(att).to(torch.bool) elif isinstance(att, torch.Tensor): return att.to(torch.bool) else: raise ValueError ("dtype must be np.ndarray or torch.Tensor") class ToTensorAndJitter(TransformBase): """Convert to a Tensor and jitter brightness""" def __init__(self, brightness_jitter=0.0, normalize=True): super().__init__() self.brightness_jitter = brightness_jitter self.normalize = normalize def roll(self): return np.random.uniform(max(0, 1 - self.brightness_jitter), 1 + self.brightness_jitter) def transform_image(self, image, brightness_factor): # handle numpy array image = torch.from_numpy(image.transpose((2, 0, 1))) # backward compatibility if self.normalize: return image.float().mul(brightness_factor/255.0).clamp(0.0, 1.0) else: return image.float().mul(brightness_factor).clamp(0.0, 255.0) def transform_mask(self, mask, brightness_factor): if isinstance(mask, np.ndarray): return torch.from_numpy(mask) else: return mask def transform_att(self, att, brightness_factor): if isinstance(att, np.ndarray): return torch.from_numpy(att).to(torch.bool) elif isinstance(att, torch.Tensor): return att.to(torch.bool) else: raise ValueError ("dtype must be np.ndarray or torch.Tensor") class Normalize(TransformBase): """Normalize image""" def __init__(self, mean, std, inplace=False): super().__init__() self.mean = mean self.std = std self.inplace = inplace def transform_image(self, image): return tvisf.normalize(image, self.mean, self.std, self.inplace) class ToGrayscale(TransformBase): """Converts image to grayscale with probability""" def __init__(self, probability = 0.5): super().__init__() self.probability = probability self.color_weights = np.array([0.2989, 0.5870, 0.1140], dtype=np.float32) def roll(self): return random.random() < self.probability def transform_image(self, image, do_grayscale): if do_grayscale: if torch.is_tensor(image): raise NotImplementedError('Implement torch variant.') img_gray = cv.cvtColor(image, cv.COLOR_RGB2GRAY) return np.stack([img_gray, img_gray, img_gray], axis=2) # return np.repeat(np.sum(img * self.color_weights, axis=2, keepdims=True).astype(np.uint8), 3, axis=2) return image class ToBGR(TransformBase): """Converts image to BGR""" def transform_image(self, image): if torch.is_tensor(image): raise NotImplementedError('Implement torch variant.') img_bgr = cv.cvtColor(image, cv.COLOR_RGB2BGR) return img_bgr class RandomHorizontalFlip(TransformBase): """Horizontally flip image randomly with a probability p.""" def __init__(self, probability = 0.5): super().__init__() self.probability = probability def roll(self): return random.random() < self.probability def transform_image(self, image, do_flip): if do_flip: if torch.is_tensor(image): return image.flip((2,)) return np.fliplr(image).copy() return image def transform_coords(self, coords, image_shape, do_flip): if do_flip: coords_flip = coords.clone() coords_flip[1,:] = (image_shape[1] - 1) - coords[1,:] return coords_flip return coords def transform_mask(self, mask, do_flip): if do_flip: if torch.is_tensor(mask): return mask.flip((-1,)) return np.fliplr(mask).copy() return mask def transform_att(self, att, do_flip): if do_flip: if torch.is_tensor(att): return att.flip((-1,)) return np.fliplr(att).copy() return att class RandomHorizontalFlip_Norm(RandomHorizontalFlip): """Horizontally flip image randomly with a probability p. The difference is that the coord is normalized to [0,1]""" def __init__(self, probability = 0.5): super().__init__() self.probability = probability def transform_coords(self, coords, image_shape, do_flip): """we should use 1 rather than image_shape""" if do_flip: coords_flip = coords.clone() coords_flip[1,:] = 1 - coords[1,:] return coords_flip return coords ================================================ FILE: lib/train/data/wandb_logger.py ================================================ from collections import OrderedDict try: import wandb except ImportError: raise ImportError( 'Please run "pip install wandb" to install wandb') class WandbWriter: def __init__(self, exp_name, cfg, output_dir, cur_step=0, step_interval=0): self.wandb = wandb self.step = cur_step self.interval = step_interval wandb.init(project="tracking", name=exp_name, config=cfg, dir=output_dir) def write_log(self, stats: OrderedDict, epoch=-1): self.step += 1 for loader_name, loader_stats in stats.items(): if loader_stats is None: continue log_dict = {} for var_name, val in loader_stats.items(): if hasattr(val, 'avg'): log_dict.update({loader_name + '/' + var_name: val.avg}) else: log_dict.update({loader_name + '/' + var_name: val.val}) if epoch >= 0: log_dict.update({loader_name + '/epoch': epoch}) self.wandb.log(log_dict, step=self.step*self.interval) ================================================ FILE: lib/train/data_specs/README.md ================================================ # README ## Description for different text files GOT10K - got10k_train_full_split.txt: the complete GOT-10K training set. (9335 videos) - got10k_train_split.txt: part of videos from the GOT-10K training set - got10k_val_split.txt: another part of videos from the GOT-10K training set - got10k_vot_exclude.txt: 1k videos that are forbidden from "using to train models then testing on VOT" (as required by [VOT Challenge](https://www.votchallenge.net/vot2020/participation.html)) - got10k_vot_train_split.txt: part of videos from the "VOT-permitted" GOT-10K training set - got10k_vot_val_split.txt: another part of videos from the "VOT-permitted" GOT-10K training set LaSOT - lasot_train_split.txt: the complete LaSOT training set TrackingNnet - trackingnet_classmap.txt: The map from the sequence name to the target class for the TrackingNet ================================================ FILE: lib/train/data_specs/got10k_train_full_split.txt ================================================ 3784 8998 3906 1631 8277 8358 2338 7938 2988 8302 2662 2663 2825 7447 4781 2218 6348 5860 4517 2819 8075 5391 116 3606 7976 7941 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6995 999 5163 6023 3786 6536 5850 3524 3528 4508 6674 2939 8227 4598 7550 8495 8622 1152 4538 4003 1318 739 3296 8202 1552 6204 5236 3576 4699 9238 1879 488 2274 433 5587 1678 9282 7914 8552 6445 7971 8331 6880 7476 7282 1570 7271 3827 6489 8091 9287 7351 1765 5286 6921 542 1762 8553 4987 894 3622 7855 92 3131 4811 3590 6517 4510 733 4954 1360 5669 2842 8107 5646 5968 1618 1827 7709 8521 5807 5321 9239 5501 3745 4437 1586 7273 5265 6605 7917 1607 6074 4668 7061 1580 8694 8461 4573 618 9173 5243 435 8770 2421 7450 3870 8308 2605 2934 9240 6887 4512 1198 7585 7691 7738 2843 8423 7929 6971 7854 86 9128 4298 622 790 9155 6579 2203 7716 1265 8645 3834 1174 7380 623 8936 4306 8082 4312 8661 5753 7243 2768 8155 85 4143 3047 8479 7809 2833 5555 7578 1637 1936 8130 5549 8062 7143 5522 8966 5614 8105 8719 7655 7502 8268 5760 6695 5565 7615 9226 4870 4507 3160 4835 1598 2465 4422 5248 7867 1078 5015 6660 1676 5354 6391 5351 7184 6280 5936 6124 1327 2906 269 8292 2466 8809 5167 8142 8204 2713 1910 2930 2494 5592 7384 7726 5727 625 1735 5710 5518 2491 1410 4989 5183 8777 6562 4947 3692 6129 384 1097 2084 5209 3723 7272 6895 2459 543 8621 5394 6211 2074 1511 2524 7776 5055 7191 6207 7922 281 8436 2918 3141 4800 6323 7631 8903 2716 3735 3012 5301 3975 2800 7963 105 1920 7391 4909 1754 4816 5488 5145 5098 5139 5268 9317 8631 4346 7318 136 3993 1220 2151 308 7483 7582 3071 1339 3777 8191 5378 7087 1056 7465 5608 6564 512 2754 2687 1596 5376 1512 566 6382 7360 1757 8035 2296 4264 3551 1053 4716 1537 8518 254 6253 7132 8557 3490 9267 5473 2412 7539 7136 6670 3974 891 1323 5958 1217 2879 9118 1259 2317 7033 2467 6665 6244 2180 2140 7098 5126 6395 4150 547 4120 4307 1725 2737 8549 8195 1245 6286 935 1756 1701 1626 7379 3492 3717 5802 2817 1234 1005 4101 21 2576 4650 3381 1030 2844 1641 936 2729 6469 8913 8369 5994 341 81 4083 1685 5152 3380 8739 6615 3829 164 7927 4779 829 4216 8528 3641 4606 2769 6970 1545 8850 4971 5489 2008 4564 8682 7784 5768 9252 901 438 3577 2765 5904 664 3348 6298 3602 2502 8617 7684 4293 5166 5805 4126 2451 6906 7234 9243 3778 2940 1087 9053 5026 2504 5283 2820 4242 797 3925 1383 8750 7861 1403 6973 7617 968 3065 5395 4347 8144 2688 6527 8597 8673 7327 6331 1422 7115 244 7013 2092 54 7970 5742 3464 4823 8588 2938 3060 6406 4149 2375 6616 8803 1555 4369 1380 3011 6144 3367 4990 7370 7131 1995 2602 985 8785 8480 9125 1927 3269 3771 1032 7378 6900 5726 2731 2020 4503 3313 6727 8793 2304 523 6036 58 7993 5512 5049 2721 8482 673 7937 1168 4472 8247 7287 9017 6421 9190 3584 1819 1792 2810 6033 638 6749 7677 981 7160 4726 1886 7845 7911 6975 568 7422 4613 4501 2569 4263 3206 4133 2420 3706 8894 2263 5774 4925 9180 8888 2945 2091 1873 6303 729 6728 2156 3267 1860 6597 1374 4930 5253 938 580 5825 4839 166 8198 6892 8701 74 7094 7284 8954 3156 6140 4279 5594 2229 7535 5466 8413 7105 8192 2632 7638 9308 8530 832 4643 2201 3268 4322 6510 2967 262 403 7973 1258 8828 4036 5838 9263 8529 2788 4202 237 3838 1291 2305 4056 5628 7281 1430 6476 7935 2850 6041 2013 4016 4576 5312 6827 6321 8669 8439 830 1942 1519 2750 6106 6993 6235 5899 7313 5331 4371 7086 4399 8600 2660 5409 3465 5499 6231 5745 1801 5337 4468 1451 4192 1275 8230 2302 1114 4960 8860 3900 6468 5058 1505 8868 5588 3858 1947 2565 1472 8499 243 8442 6583 7085 5374 2250 4291 4426 492 2311 8305 3662 5338 8780 7488 3890 5005 2442 4680 7358 9116 4397 5999 587 7902 83 3566 2134 8942 4767 6601 2456 1745 5736 5254 8017 4015 7690 3798 8947 1067 2116 7945 590 2547 2535 64 2053 5359 2493 6669 4351 6412 7473 6147 7175 6983 5196 745 2657 3497 697 3161 7528 2239 5991 3201 7681 2440 5189 2959 2044 8917 2046 6313 6333 5318 2763 4301 2555 2213 2933 4121 1340 3903 4392 7889 5323 1055 707 3857 518 6078 5134 6645 9138 1592 680 4446 7943 3461 3887 5601 2321 6621 558 4914 913 5637 6453 8511 4531 1218 5508 2603 6802 8426 8297 2947 5971 6552 5262 5935 782 7435 8357 6139 1136 1473 5008 3585 3627 2914 5356 2997 2347 881 5652 4849 8808 8351 4017 2010 6836 7616 4391 3630 3712 6099 2969 5238 4333 2301 4406 1236 1050 1864 1104 8408 8251 8795 5879 3365 7481 8206 2452 1767 8859 124 3948 4444 8962 4438 5003 1740 8428 3105 5117 1095 1480 8755 7881 3097 4877 155 1917 2455 6042 337 6724 6045 8483 7135 2242 4566 1679 834 1746 795 3548 2314 2036 4046 9129 6979 7084 5091 2413 8170 5775 1817 529 7220 813 2916 5130 8972 126 1243 2370 4831 9122 3010 5104 2613 6761 7482 909 2146 4595 5340 3512 6283 2346 653 6121 2615 7421 1869 1002 8834 2991 8992 632 1093 4543 645 2352 4115 373 1483 6966 8598 3896 3434 5987 8318 1815 1223 1548 6885 5073 6330 2573 1369 4095 1431 2185 5766 1301 7258 8048 7598 2847 1996 2378 8561 743 6381 271 1956 7439 7596 7134 6636 5804 1858 6214 4730 8536 1203 3118 9202 1875 5885 8975 168 5898 4014 4186 3346 3041 5558 9296 8157 4339 3234 1738 2604 6803 5387 5590 125 2173 8012 8005 4858 3069 651 372 378 8366 6299 1449 7793 8541 3235 8043 3086 3983 6949 4690 2176 6494 7637 8406 3856 7408 350 7021 8224 7044 7662 6697 7679 169 528 7029 2790 7138 7432 7602 8333 1582 1378 519 482 9279 8015 6592 4514 3542 2612 628 5053 6699 6227 2094 1621 847 3598 2728 8490 7276 6620 8345 9216 4278 4059 9058 5063 5816 4173 8134 1997 3182 3224 8129 5109 4494 189 7640 8243 180 2963 1123 5593 3263 4185 7140 8990 6320 9275 4601 4854 5907 1135 8083 5964 7788 1992 8069 9174 6160 35 8572 2865 46 3952 6418 2510 5783 20 3816 2715 3930 2548 5204 4122 4103 708 7756 3825 777 3550 8502 3929 5440 6751 7764 4070 7331 3743 9131 9206 3828 23 41 4197 234 5723 7622 8832 4626 2169 5599 2976 5266 1967 1150 5334 90 822 2538 3169 6771 7442 498 4967 5580 7581 7680 4728 1115 4040 1064 3106 6266 4415 9294 5597 7059 197 7218 6948 5690 4234 1653 4485 4019 3370 919 1330 6085 2078 3768 5427 4545 2435 8862 3633 8145 5221 1388 5913 8140 7471 7156 6989 1190 6832 2830 4387 3454 7469 2910 4526 5187 2410 9223 6247 6912 4681 1300 7407 8612 6523 3616 6894 7253 4515 5874 5448 7137 7957 1130 3092 7054 3516 5797 1000 2727 4336 9090 6403 7255 8919 6522 6760 8898 4803 1938 374 8686 9150 3985 7045 3475 6065 7991 1409 7851 6671 6090 5826 7857 1155 8964 1117 7072 6064 2497 4899 2397 3189 2369 15 5027 5754 8950 5617 8391 914 6264 279 6174 5184 3733 7392 5278 2924 567 7994 352 8084 2148 2723 3359 70 1870 7708 220 3994 9013 3191 9220 4155 5717 1110 2198 9179 785 5325 4770 4250 52 4634 5072 9037 601 8036 7996 2483 7232 8675 8836 1279 5346 7676 6104 1515 4603 5607 7894 5144 2628 68 440 3586 3083 4830 4378 7762 1134 4542 7850 6296 2866 4011 8751 4776 7954 7102 5697 2032 5729 5017 6962 2051 1092 764 9019 2759 8581 1484 8618 912 2382 4892 8447 8176 5491 5695 5504 1060 7064 709 578 4320 2379 7649 8416 1613 5344 7512 7865 3037 6689 6557 1569 5955 3707 9168 8566 1775 5950 6943 7804 434 6179 9300 1142 7947 6456 6291 5789 6538 9134 3049 5075 5399 5161 1623 948 6302 6063 7516 117 506 3302 7146 355 3854 1081 2827 1496 2574 6167 3183 4287 5482 1722 7319 7277 3860 3443 3298 8364 3826 7254 2360 5093 7039 6325 4230 2567 6241 4443 559 2625 4228 8967 6405 1674 3936 4475 8556 8585 896 3713 6259 4297 6718 2392 2279 4927 1283 2374 2860 7665 663 596 6293 6805 2811 7383 8306 8330 3153 2153 2618 2441 3615 8092 552 5285 5255 8124 9247 5530 8175 6242 5660 3433 1610 1832 3892 3862 640 2127 2474 4196 3495 7217 5206 4836 7759 4376 800 4227 3699 9055 5665 6826 7463 9065 4720 5069 3245 3453 3358 6532 5970 7921 4087 1547 3424 8040 7995 6787 9069 8716 2561 8199 1479 2767 7818 7145 604 7597 4896 9281 4666 185 8171 7978 3059 9196 9221 2135 1800 2974 1529 5948 446 4436 8672 3508 6208 5673 6998 5203 278 7041 9110 5853 8121 1764 3046 2400 6575 4738 2228 7761 9322 7019 6931 6383 6762 283 3935 2534 7717 6785 471 8214 231 4241 5310 3844 5746 2011 7209 336 6433 756 9167 6741 3345 7685 4018 6682 9147 4790 5836 5906 8747 676 3964 6362 3510 7510 2308 1806 5917 1189 4012 3387 1331 5319 5423 8900 147 3780 1696 9111 6783 6497 4104 1898 3987 260 4616 2121 9283 1400 2437 4670 2735 1163 2096 6521 1423 4523 2243 6667 6990 3944 6915 6763 5611 404 2691 1015 7092 7562 8624 2291 4193 5934 5503 2326 4408 2960 842 1963 3354 5568 9050 3806 439 9154 6055 6451 2190 7633 688 4354 8890 2813 2872 8102 8317 6609 1497 8389 6449 1682 3594 5103 5812 863 268 3054 8079 2260 2027 3091 7687 6703 3557 2019 8427 2799 8182 6641 3168 2284 1934 4865 1077 6507 1658 3811 1774 7897 2238 2943 191 3869 3246 4057 3188 414 8072 7838 1382 4962 6010 5363 4042 1983 4077 7429 1833 3583 4044 1109 1295 386 5481 3927 311 ================================================ FILE: lib/train/data_specs/got10k_val_split.txt ================================================ 1349 5651 5878 562 2202 8904 765 1501 8654 2975 2689 3680 5180 1900 7707 4723 8912 4029 3579 869 2888 8657 6599 741 4288 2244 7357 5704 8791 208 8587 7969 4805 8526 4887 8871 7468 3343 886 7794 5764 2646 6454 6101 7885 7744 1297 4119 4856 122 2286 2925 5131 3570 5843 3027 5320 5626 540 1862 5401 7335 699 7760 9198 3259 7345 8698 1280 6479 3100 3988 1322 5737 1268 3257 6791 3326 4815 7644 1082 2826 6821 8984 2553 5290 5909 4762 9242 8096 8066 4325 6666 7193 7114 8060 2376 7872 6788 3544 5460 3507 2509 6626 3429 5542 4220 2968 5271 4249 3863 1868 5581 2012 6270 8038 4050 121 2845 1565 1998 2275 5524 6068 7624 4913 9277 1506 803 8848 5925 2450 2072 8190 4753 9162 1923 825 7303 9028 2088 8516 1556 5937 7847 2367 7549 1049 1521 4739 3931 8958 4130 7877 7876 897 5985 7346 7537 111 3700 1126 7896 1288 3419 4673 1051 5720 1068 3458 146 291 6256 5514 2857 4580 6239 6525 8717 391 4841 6676 4360 1453 4211 73 1675 1987 4025 1321 662 8265 6424 2758 7765 7656 3209 7497 7600 9039 7697 5177 2983 5622 9295 1200 3284 964 2024 1269 4551 8088 5659 2212 5199 5551 8607 5573 2247 5200 6341 7951 8429 7720 5919 1273 3529 6707 9176 7552 3255 5649 6110 9235 1137 9272 775 788 5786 5186 6746 2667 9145 7630 3953 1828 8827 6471 4702 7815 467 6387 3195 6238 6508 2373 5983 4931 2948 921 2438 517 3949 2137 3216 5683 3695 1719 4837 9159 6981 860 7410 5497 1770 5557 8810 5194 4857 9100 6329 2609 1925 3686 9041 4924 349 9187 3393 3661 7120 6858 4587 3831 3130 5396 5060 6486 3937 8023 824 5398 1354 8861 5534 7292 4389 6029 6226 3505 4326 7445 581 6089 3450 7324 6516 6775 1207 4575 5135 9265 3918 9020 3473 3898 7812 6571 6757 6639 2557 1206 6148 7325 8790 4938 7026 4383 8041 1250 7267 1952 7561 8811 4941 8373 4848 6602 8355 8104 5214 6654 4330 995 3181 3422 456 1782 3408 6530 719 7587 5910 3058 740 2009 4207 5336 2798 9229 8668 2473 4221 1493 3281 171 9157 9139 7766 6220 9127 3324 5308 3708 2431 8080 2093 2585 406 7040 5064 5247 4758 6512 2953 4257 4935 2705 2572 3436 8513 5884 1385 4852 2637 7091 2761 6007 8332 6694 2422 4917 2186 6898 1390 6965 3132 7698 475 2002 2692 5024 7365 7373 4091 1731 947 3962 8692 1788 8734 8656 6862 6856 1950 1914 5658 3635 1620 4780 2580 1454 2786 687 7238 3648 6452 1197 3190 5900 9043 4958 1935 1821 1187 1153 7737 7223 3820 7169 7350 5674 6254 3025 6680 1690 2899 3893 1577 5728 9189 5077 34 3560 2179 5462 1402 3654 1376 7936 4246 5506 1179 5647 4686 8644 1352 2855 6079 2254 2668 2287 2457 3418 7264 677 3074 2655 1042 2210 4504 7089 8309 4209 4280 3258 2977 84 4705 1244 3511 6355 8813 3228 9266 1122 613 732 5202 8425 2638 6470 2886 3541 8132 2063 8201 5129 2818 7949 6936 8090 4465 7295 5239 7009 9271 8563 2832 952 8136 6776 3565 5188 7288 6999 285 5487 7763 7608 8584 2071 7868 2804 3655 7048 6847 3276 4082 4272 3910 3709 1574 4559 7580 7081 5014 7769 8183 6386 7574 356 4937 2487 9315 7572 3040 671 2682 8626 3868 8623 387 8679 4074 1481 3527 3595 4754 2453 1579 4638 9123 1829 316 3009 3691 763 4875 3572 4642 3128 4273 2777 6032 4793 233 7147 996 3199 8835 3517 7210 6125 6037 3684 8589 3915 3095 8310 3180 7043 4458 2889 57 4483 7667 8375 1434 7493 6986 4733 8471 5827 2111 1313 7986 3075 2614 7547 4977 8527 3212 7300 5842 5244 3291 597 1007 2030 227 3830 5540 247 5643 9333 1958 3096 1371 5220 7926 2927 1516 7130 193 1522 6165 6923 3794 4223 5535 2472 8630 3971 9101 2946 222 4609 7291 8542 6501 7548 4557 6274 1010 5226 7309 1317 9056 6275 1624 1099 4191 4030 7270 5392 2316 3819 1670 8154 8045 4807 8864 2391 5908 8338 8218 6400 9193 3165 843 6613 6941 4380 9332 5629 7557 4321 3702 681 734 1159 4665 5959 1697 5509 8774 7389 3832 3751 8637 3079 1680 6841 703 684 8293 3682 5733 4818 3231 3078 5562 9001 3889 7024 2519 1713 3287 219 6021 8776 2289 7212 4832 4684 4617 4237 2649 8185 6326 3568 551 1426 4181 8869 312 2905 4165 8248 2558 900 1044 8613 7743 5437 7604 3122 5708 8649 2878 4695 4491 1929 7533 5223 7711 915 1844 5751 3008 8055 961 6142 4636 61 198 2271 5698 4596 4500 5709 5819 7972 2992 1643 1048 6281 8886 360 4198 1841 6814 3960 2606 7001 5888 450 7133 7015 7034 5153 8920 5066 469 1302 8816 463 8651 5869 8193 6582 5578 1231 9274 7260 7751 8052 6799 2089 2342 8451 3260 5550 7795 2288 1205 40 496 8367 7836 5973 3908 5242 5062 2706 997 6514 5419 9201 1965 6062 3050 5302 8735 358 2398 7470 1644 8179 7047 1549 5414 2539 7381 589 8166 8505 6035 3956 4540 6721 8074 1062 2384 2531 7159 3502 3902 4584 2554 264 8720 2849 4916 5218 7202 883 4560 1677 4317 7863 4509 6577 2903 1452 1416 5369 473 6233 6359 5992 4934 8059 6834 4907 3320 8267 8280 2066 2402 1485 3772 3732 4764 9126 3575 5564 4768 5641 1884 2330 1804 344 698 3089 1532 4454 761 7289 8094 3432 1747 6811 8722 8826 4646 3222 8614 2901 7003 652 8663 4266 413 810 75 3334 4905 6438 4756 5137 6528 6534 6988 6177 8533 889 5384 7201 5132 7802 6864 3973 873 4840 1482 8376 3769 5858 6675 4286 2593 5863 4353 7817 7540 4999 4838 2303 6002 7913 1508 5317 7755 2784 4964 3431 6209 3755 6022 6399 6232 3954 455 5416 6448 1558 7591 245 140 9210 6585 4084 967 7798 6795 7095 6733 3861 9264 361 1045 755 8042 7074 7778 6415 4724 6450 2049 1563 1307 3485 1790 7869 3282 6907 3920 2868 5801 5632 1079 5009 3955 7517 5128 3417 3019 2725 1784 2312 2753 6976 342 8266 1849 2273 5037 7880 3793 7401 5412 8279 1257 3670 9049 3266 8955 6519 8916 2858 694 5650 1019 4669 1785 3533 5877 2704 8603 3726 6668 497 1085 6815 6157 6646 6964 186 8097 5645 8481 8215 3775 2542 7514 5699 4072 3518 5767 3239 3740 1404 8981 4086 6397 6984 4204 6899 682 6589 3317 2944 3456 4340 7424 9208 6504 4409 1 145 1882 4620 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2991 8992 632 1093 4543 645 2352 4115 373 1483 6966 8598 3896 3434 5987 8318 1815 1223 1548 6885 5073 6330 2573 1369 4095 1431 2185 5766 1301 7258 8048 7598 2847 1996 2378 8561 743 6381 271 1956 7439 7134 6636 5804 1858 6214 4730 8536 1203 3118 9202 1875 5885 168 5898 4014 4186 3346 3041 5558 9296 8157 4339 3234 2604 6803 5387 5590 125 2173 8012 8005 4858 651 372 378 8366 6299 1449 7793 8541 3235 8043 3086 3983 6949 4690 6494 8406 7408 350 7021 8224 7044 7662 6697 7679 169 528 7029 2790 7432 7602 8333 1582 1378 482 9279 8015 4514 3542 628 5053 6699 6227 2094 1621 847 3598 2728 7276 6620 8345 4278 4059 9058 4173 8134 1997 3182 3224 8129 5109 4494 189 7640 180 2963 1123 5593 3263 4185 7140 8990 6320 9275 4601 4854 5907 1135 8083 5964 7788 1992 8069 9174 6160 35 8572 2865 46 3952 6418 2510 5783 3816 2715 3930 2548 5204 708 7756 3825 777 3550 3929 5440 6751 7764 4070 7331 3743 9131 9206 3828 23 41 4197 234 5723 7622 8832 2169 5599 2976 5266 1967 90 822 2538 3169 6771 7442 498 4967 5580 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8566 1775 5950 6943 7804 434 6179 1142 7947 6456 6291 5789 6538 9134 3049 5075 5161 1623 948 6302 6063 7516 117 506 3302 7146 355 1081 2827 1496 2574 6167 3183 4287 5482 7319 7277 3860 3443 3298 8364 3826 7254 2360 5093 7039 6325 2567 4443 559 2625 4228 8967 6405 1674 3936 4475 8556 8585 896 3713 6259 4297 6718 2392 2279 4927 1283 2860 7665 663 596 6293 6805 2811 7383 8306 8330 3153 2153 2618 2441 3615 8092 552 5285 8124 9247 5530 8175 6242 5660 3433 1610 1832 3892 3862 640 2127 4196 3495 7217 5206 4836 7759 800 4227 3699 9055 5665 6826 7463 9065 4720 5069 3453 3358 6532 5970 7921 4087 1547 3424 8040 7995 6787 9069 8716 2561 8199 1479 2767 7818 7145 604 7597 4896 9281 4666 185 7978 3059 9221 2135 1800 2974 1529 5948 446 4436 8672 3508 6208 5673 6998 5203 278 7041 9110 5853 8121 1764 3046 6575 4738 2228 7761 9322 7019 6931 6383 6762 283 3935 6785 471 8214 231 3844 5746 2011 7209 336 6433 756 9167 6741 3345 7685 4018 6682 9147 4790 5836 5906 676 3964 6362 3510 7510 2308 1806 5917 3387 5423 8900 147 3780 1696 9111 6783 6497 4104 3987 260 4616 2121 9283 1400 4670 2735 2096 6521 1423 4523 2243 6667 6990 3944 6915 6763 404 2691 1015 7092 7562 8624 2291 5934 5503 2326 2960 842 1963 5568 9050 3806 439 9154 6055 6451 7633 688 4354 8890 2813 2872 8102 6609 1497 8389 6449 1682 3594 5103 5812 863 3054 8079 2260 2027 3091 7687 6703 3557 2019 8427 2799 8182 6641 3168 2284 1934 6507 1658 3811 1774 7897 2238 2943 191 3869 3188 414 8072 7838 1382 4962 5363 4042 1983 4077 7429 4044 1109 1295 386 5481 3927 311 ================================================ FILE: lib/train/data_specs/got10k_vot_val_split.txt ================================================ 1349 5878 562 2202 8904 1501 8654 2975 2689 3680 5180 1900 7707 4723 8912 4029 3579 869 2888 8657 6599 741 4288 2244 7357 5704 8791 208 4805 8526 4887 8871 7468 3343 886 7794 2646 6454 6101 7885 7744 1297 4119 4856 122 2286 2925 5131 5843 5320 5626 540 1862 7335 699 7760 9198 3259 7345 8698 1280 6479 3100 3988 1322 5737 1268 3257 6791 3326 4815 7644 1082 2826 6821 8984 2553 5290 5909 4762 8096 8066 4325 6666 7193 7114 8060 7872 6788 3544 5460 3507 2509 6626 3429 5542 4220 2968 5271 3863 1868 5581 2012 6270 8038 4050 121 2845 1565 1998 2275 5524 6068 7624 4913 9277 1506 803 8848 5925 2450 2072 8190 4753 9162 825 7303 9028 2088 8516 1556 5937 7847 2367 7549 1049 1521 4739 3931 8958 4130 7876 897 5985 7346 7537 111 3700 1126 7896 3419 1051 5720 1068 3458 146 291 6256 5514 2857 4580 6239 6525 8717 391 4841 6676 4360 4211 73 1675 1987 4025 1321 662 8265 6424 2758 7765 7656 3209 7497 7600 9039 7697 5177 2983 5622 9295 3284 964 2024 1269 4551 8088 5659 2212 5199 5551 8607 5573 5200 7951 8429 7720 5919 1273 3529 6707 9176 7552 3255 5649 6110 1137 9272 788 5786 5186 2667 7630 3953 1828 8827 6471 7815 467 6387 3195 6238 6508 2373 5983 4931 2948 921 2438 517 3949 2137 3216 5683 3695 1719 4837 9159 6981 860 7410 5497 1770 5557 8810 5194 4857 9100 6329 2609 1925 3686 9041 4924 349 9187 3393 3661 7120 6858 4587 3831 3130 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8869 312 2905 4165 8248 2558 900 1044 8613 7743 5437 7604 3122 5708 8649 2878 4695 4491 7533 5223 7711 1844 5751 3008 8055 4636 61 198 2271 5698 4596 4500 5709 5819 7972 2992 1643 1048 6281 8886 360 4198 6814 3960 2606 7001 5888 450 7133 7015 7034 5153 8920 5066 469 1302 8816 463 8651 5869 6582 5578 1231 9274 7260 7751 8052 6799 2089 2342 8451 3260 5550 7795 2288 1205 40 496 8367 7836 5973 3908 5242 5062 2706 997 5419 9201 1965 6062 3050 5302 8735 358 2398 7470 1644 8179 7047 1549 5414 2539 7381 589 8166 8505 6035 3956 4540 6721 8074 1062 2384 2531 7159 3902 4584 2554 264 8720 2849 4916 5218 7202 883 4560 1677 4317 7863 4509 6577 2903 1452 1416 5369 473 6233 6359 5992 4934 8059 6834 4907 3320 8267 8280 2066 2402 1485 3772 3732 4764 9126 3575 5564 5641 1884 2330 1804 344 698 3089 1532 4454 761 8094 3432 6811 8722 8826 3222 8614 2901 7003 652 8663 4266 413 810 75 3334 4905 6438 4756 5137 6528 6534 6988 6177 8533 889 5384 7201 5132 7802 6864 3973 873 4840 1482 8376 3769 5858 6675 4286 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715 9060 3872 1750 5889 4047 5960 3120 3449 1421 1102 3333 9197 8796 8123 8007 2028 8404 1945 1985 8109 5380 3504 6739 4180 5835 4243 25 4002 1976 158 5181 4885 8985 11 6425 5926 7062 5083 8394 4259 5844 1990 3942 5532 2220 28 5957 149 6748 3559 7647 2566 1359 5259 7010 554 6005 8172 8125 1350 9051 1973 1386 159 7007 3220 1846 3093 4445 2056 8370 3211 4384 2231 273 642 5311 265 226 9012 7879 118 7109 7251 1760 8667 2876 7162 3552 6901 6779 5021 6524 4957 3114 4544 441 1848 2136 2458 8662 1127 5541 3026 1080 6780 2224 8259 1073 9000 7244 7977 500 4435 7376 7979 1435 9291 7704 3521 210 6269 8570 3285 8039 3546 6203 1183 6107 4147 2234 7185 3192 7155 2001 7777 876 944 908 7791 6784 65 9172 5675 3886 7891 2978 1008 5630 591 5067 1139 577 9015 574 8137 7786 5765 4900 4090 7842 ================================================ FILE: lib/train/data_specs/lasot_train_split.txt ================================================ airplane-10 airplane-11 airplane-12 airplane-14 airplane-16 airplane-17 airplane-18 airplane-19 airplane-2 airplane-20 airplane-3 airplane-4 airplane-5 airplane-6 airplane-7 airplane-8 basketball-10 basketball-12 basketball-13 basketball-14 basketball-15 basketball-16 basketball-17 basketball-18 basketball-19 basketball-2 basketball-20 basketball-3 basketball-4 basketball-5 basketball-8 basketball-9 bear-1 bear-10 bear-11 bear-12 bear-13 bear-14 bear-15 bear-16 bear-18 bear-19 bear-20 bear-3 bear-5 bear-7 bear-8 bear-9 bicycle-1 bicycle-10 bicycle-11 bicycle-12 bicycle-13 bicycle-14 bicycle-15 bicycle-16 bicycle-17 bicycle-19 bicycle-20 bicycle-3 bicycle-4 bicycle-5 bicycle-6 bicycle-8 bird-1 bird-10 bird-11 bird-12 bird-13 bird-14 bird-16 bird-18 bird-19 bird-20 bird-4 bird-5 bird-6 bird-7 bird-8 bird-9 boat-1 boat-10 boat-11 boat-13 boat-14 boat-15 boat-16 boat-18 boat-19 boat-2 boat-20 boat-5 boat-6 boat-7 boat-8 boat-9 book-1 book-12 book-13 book-14 book-15 book-16 book-17 book-18 book-2 book-20 book-4 book-5 book-6 book-7 book-8 book-9 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motorcycle _yQQjARqD1s_0 boat _yfoe4GCA0Q_4 airplane _yfoe4GCA0Q_2 airplane _yv5Cwbm9EA_0 person _zIDofZkgS4_1 truck _zQt1CSSKyA_1 bicycle _0eR2vQAEqE_0 elephant _0eR2vQAEqE_1 elephant _17u-cPTYt0_0 car _17u-cPTYt0_1 car _2mIWIhbDPY_0 bus _37U5Elgnck_0 person _5fE6dP48FM_0 cow _5sIT4l5izM_0 knife _6qUuUUYvUQ_0 person _7zbbqEa3nw_1 train _7zbbqEa3nw_4 train _8VTthFkvS0_0 bird _8iyumFI4sQ_1 elephant _8iyumFI4sQ_2 elephant _8iyumFI4sQ_3 elephant _81FImml2gk_0 dog _9bypka_Q4c_0 bus _-CvwC7H730_0 person _-XcxnQLKPM_0 dog __Q5A7gExpI_0 person ================================================ FILE: lib/train/dataset/COCO_tool.py ================================================ __author__ = 'tylin' __version__ = '2.0' # Interface for accessing the Microsoft COCO dataset. # Microsoft COCO is a large image dataset designed for object detection, # segmentation, and caption generation. pycocotools is a Python API that # assists in loading, parsing and visualizing the annotations in COCO. # Please visit http://mscoco.org/ for more information on COCO, including # for the data, paper, and tutorials. The exact format of the annotations # is also described on the COCO website. For example usage of the pycocotools # please see pycocotools_demo.ipynb. In addition to this API, please download both # the COCO images and annotations in order to run the demo. # An alternative to using the API is to load the annotations directly # into Python dictionary # Using the API provides additional utility functions. Note that this API # supports both *instance* and *caption* annotations. In the case of # captions not all functions are defined (e.g. categories are undefined). # The following API functions are defined: # COCO - COCO api class that loads COCO annotation file and prepare data structures. # decodeMask - Decode binary mask M encoded via run-length encoding. # encodeMask - Encode binary mask M using run-length encoding. # getAnnIds - Get ann ids that satisfy given filter conditions. # getCatIds - Get cat ids that satisfy given filter conditions. # getImgIds - Get img ids that satisfy given filter conditions. # loadAnns - Load anns with the specified ids. # loadCats - Load cats with the specified ids. # loadImgs - Load imgs with the specified ids. # annToMask - Convert segmentation in an annotation to binary mask. # showAnns - Display the specified annotations. # loadRes - Load algorithm results and create API for accessing them. # download - Download COCO images from mscoco.org server. # Throughout the API "ann"=annotation, "cat"=category, and "img"=image. # Help on each functions can be accessed by: "help COCO>function". # See also COCO>decodeMask, # COCO>encodeMask, COCO>getAnnIds, COCO>getCatIds, # COCO>getImgIds, COCO>loadAnns, COCO>loadCats, # COCO>loadImgs, COCO>annToMask, COCO>showAnns # Microsoft COCO Toolbox. version 2.0 # Data, paper, and tutorials available at: http://mscoco.org/ # Code written by Piotr Dollar and Tsung-Yi Lin, 2014. # Licensed under the Simplified BSD License [see bsd.txt] import json import time import matplotlib.pyplot as plt from matplotlib.collections import PatchCollection from matplotlib.patches import Polygon import numpy as np import copy import itertools from pycocotools import mask as maskUtils import os from collections import defaultdict import sys PYTHON_VERSION = sys.version_info[0] if PYTHON_VERSION == 2: from urllib import urlretrieve elif PYTHON_VERSION == 3: from urllib.request import urlretrieve def _isArrayLike(obj): return hasattr(obj, '__iter__') and hasattr(obj, '__len__') class COCO: def __init__(self, dataset): """ Constructor of Microsoft COCO helper class for reading and visualizing annotations. :param annotation_file (str): location of annotation file :param image_folder (str): location to the folder that hosts images. :return: """ # load dataset self.dataset,self.anns,self.cats,self.imgs = dict(),dict(),dict(),dict() self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list) assert type(dataset)==dict, 'annotation file format {} not supported'.format(type(dataset)) self.dataset = dataset self.createIndex() def createIndex(self): # create index print('creating index...') anns, cats, imgs = {}, {}, {} imgToAnns,catToImgs = defaultdict(list),defaultdict(list) if 'annotations' in self.dataset: for ann in self.dataset['annotations']: imgToAnns[ann['image_id']].append(ann) anns[ann['id']] = ann if 'images' in self.dataset: for img in self.dataset['images']: imgs[img['id']] = img if 'categories' in self.dataset: for cat in self.dataset['categories']: cats[cat['id']] = cat if 'annotations' in self.dataset and 'categories' in self.dataset: for ann in self.dataset['annotations']: catToImgs[ann['category_id']].append(ann['image_id']) print('index created!') # create class members self.anns = anns self.imgToAnns = imgToAnns self.catToImgs = catToImgs self.imgs = imgs self.cats = cats def info(self): """ Print information about the annotation file. :return: """ for key, value in self.dataset['info'].items(): print('{}: {}'.format(key, value)) def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None): """ Get ann ids that satisfy given filter conditions. default skips that filter :param imgIds (int array) : get anns for given imgs catIds (int array) : get anns for given cats areaRng (float array) : get anns for given area range (e.g. [0 inf]) iscrowd (boolean) : get anns for given crowd label (False or True) :return: ids (int array) : integer array of ann ids """ imgIds = imgIds if _isArrayLike(imgIds) else [imgIds] catIds = catIds if _isArrayLike(catIds) else [catIds] if len(imgIds) == len(catIds) == len(areaRng) == 0: anns = self.dataset['annotations'] else: if not len(imgIds) == 0: lists = [self.imgToAnns[imgId] for imgId in imgIds if imgId in self.imgToAnns] anns = list(itertools.chain.from_iterable(lists)) else: anns = self.dataset['annotations'] anns = anns if len(catIds) == 0 else [ann for ann in anns if ann['category_id'] in catIds] anns = anns if len(areaRng) == 0 else [ann for ann in anns if ann['area'] > areaRng[0] and ann['area'] < areaRng[1]] if not iscrowd == None: ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd] else: ids = [ann['id'] for ann in anns] return ids def getCatIds(self, catNms=[], supNms=[], catIds=[]): """ filtering parameters. default skips that filter. :param catNms (str array) : get cats for given cat names :param supNms (str array) : get cats for given supercategory names :param catIds (int array) : get cats for given cat ids :return: ids (int array) : integer array of cat ids """ catNms = catNms if _isArrayLike(catNms) else [catNms] supNms = supNms if _isArrayLike(supNms) else [supNms] catIds = catIds if _isArrayLike(catIds) else [catIds] if len(catNms) == len(supNms) == len(catIds) == 0: cats = self.dataset['categories'] else: cats = self.dataset['categories'] cats = cats if len(catNms) == 0 else [cat for cat in cats if cat['name'] in catNms] cats = cats if len(supNms) == 0 else [cat for cat in cats if cat['supercategory'] in supNms] cats = cats if len(catIds) == 0 else [cat for cat in cats if cat['id'] in catIds] ids = [cat['id'] for cat in cats] return ids def getImgIds(self, imgIds=[], catIds=[]): ''' Get img ids that satisfy given filter conditions. :param imgIds (int array) : get imgs for given ids :param catIds (int array) : get imgs with all given cats :return: ids (int array) : integer array of img ids ''' imgIds = imgIds if _isArrayLike(imgIds) else [imgIds] catIds = catIds if _isArrayLike(catIds) else [catIds] if len(imgIds) == len(catIds) == 0: ids = self.imgs.keys() else: ids = set(imgIds) for i, catId in enumerate(catIds): if i == 0 and len(ids) == 0: ids = set(self.catToImgs[catId]) else: ids &= set(self.catToImgs[catId]) return list(ids) def loadAnns(self, ids=[]): """ Load anns with the specified ids. :param ids (int array) : integer ids specifying anns :return: anns (object array) : loaded ann objects """ if _isArrayLike(ids): return [self.anns[id] for id in ids] elif type(ids) == int: return [self.anns[ids]] def loadCats(self, ids=[]): """ Load cats with the specified ids. :param ids (int array) : integer ids specifying cats :return: cats (object array) : loaded cat objects """ if _isArrayLike(ids): return [self.cats[id] for id in ids] elif type(ids) == int: return [self.cats[ids]] def loadImgs(self, ids=[]): """ Load anns with the specified ids. :param ids (int array) : integer ids specifying img :return: imgs (object array) : loaded img objects """ if _isArrayLike(ids): return [self.imgs[id] for id in ids] elif type(ids) == int: return [self.imgs[ids]] def showAnns(self, anns, draw_bbox=False): """ Display the specified annotations. :param anns (array of object): annotations to display :return: None """ if len(anns) == 0: return 0 if 'segmentation' in anns[0] or 'keypoints' in anns[0]: datasetType = 'instances' elif 'caption' in anns[0]: datasetType = 'captions' else: raise Exception('datasetType not supported') if datasetType == 'instances': ax = plt.gca() ax.set_autoscale_on(False) polygons = [] color = [] for ann in anns: c = (np.random.random((1, 3))*0.6+0.4).tolist()[0] if 'segmentation' in ann: if type(ann['segmentation']) == list: # polygon for seg in ann['segmentation']: poly = np.array(seg).reshape((int(len(seg)/2), 2)) polygons.append(Polygon(poly)) color.append(c) else: # mask t = self.imgs[ann['image_id']] if type(ann['segmentation']['counts']) == list: rle = maskUtils.frPyObjects([ann['segmentation']], t['height'], t['width']) else: rle = [ann['segmentation']] m = maskUtils.decode(rle) img = np.ones( (m.shape[0], m.shape[1], 3) ) if ann['iscrowd'] == 1: color_mask = np.array([2.0,166.0,101.0])/255 if ann['iscrowd'] == 0: color_mask = np.random.random((1, 3)).tolist()[0] for i in range(3): img[:,:,i] = color_mask[i] ax.imshow(np.dstack( (img, m*0.5) )) if 'keypoints' in ann and type(ann['keypoints']) == list: # turn skeleton into zero-based index sks = np.array(self.loadCats(ann['category_id'])[0]['skeleton'])-1 kp = np.array(ann['keypoints']) x = kp[0::3] y = kp[1::3] v = kp[2::3] for sk in sks: if np.all(v[sk]>0): plt.plot(x[sk],y[sk], linewidth=3, color=c) plt.plot(x[v>0], y[v>0],'o',markersize=8, markerfacecolor=c, markeredgecolor='k',markeredgewidth=2) plt.plot(x[v>1], y[v>1],'o',markersize=8, markerfacecolor=c, markeredgecolor=c, markeredgewidth=2) if draw_bbox: [bbox_x, bbox_y, bbox_w, bbox_h] = ann['bbox'] poly = [[bbox_x, bbox_y], [bbox_x, bbox_y+bbox_h], [bbox_x+bbox_w, bbox_y+bbox_h], [bbox_x+bbox_w, bbox_y]] np_poly = np.array(poly).reshape((4,2)) polygons.append(Polygon(np_poly)) color.append(c) p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4) ax.add_collection(p) p = PatchCollection(polygons, facecolor='none', edgecolors=color, linewidths=2) ax.add_collection(p) elif datasetType == 'captions': for ann in anns: print(ann['caption']) def loadRes(self, resFile): """ Load result file and return a result api object. :param resFile (str) : file name of result file :return: res (obj) : result api object """ res = COCO() res.dataset['images'] = [img for img in self.dataset['images']] print('Loading and preparing results...') tic = time.time() if type(resFile) == str or (PYTHON_VERSION == 2 and type(resFile) == unicode): with open(resFile) as f: anns = json.load(f) elif type(resFile) == np.ndarray: anns = self.loadNumpyAnnotations(resFile) else: anns = resFile assert type(anns) == list, 'results in not an array of objects' annsImgIds = [ann['image_id'] for ann in anns] assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), \ 'Results do not correspond to current coco set' if 'caption' in anns[0]: imgIds = set([img['id'] for img in res.dataset['images']]) & set([ann['image_id'] for ann in anns]) res.dataset['images'] = [img for img in res.dataset['images'] if img['id'] in imgIds] for id, ann in enumerate(anns): ann['id'] = id+1 elif 'bbox' in anns[0] and not anns[0]['bbox'] == []: res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) for id, ann in enumerate(anns): bb = ann['bbox'] x1, x2, y1, y2 = [bb[0], bb[0]+bb[2], bb[1], bb[1]+bb[3]] if not 'segmentation' in ann: ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]] ann['area'] = bb[2]*bb[3] ann['id'] = id+1 ann['iscrowd'] = 0 elif 'segmentation' in anns[0]: res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) for id, ann in enumerate(anns): # now only support compressed RLE format as segmentation results ann['area'] = maskUtils.area(ann['segmentation']) if not 'bbox' in ann: ann['bbox'] = maskUtils.toBbox(ann['segmentation']) ann['id'] = id+1 ann['iscrowd'] = 0 elif 'keypoints' in anns[0]: res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) for id, ann in enumerate(anns): s = ann['keypoints'] x = s[0::3] y = s[1::3] x0,x1,y0,y1 = np.min(x), np.max(x), np.min(y), np.max(y) ann['area'] = (x1-x0)*(y1-y0) ann['id'] = id + 1 ann['bbox'] = [x0,y0,x1-x0,y1-y0] print('DONE (t={:0.2f}s)'.format(time.time()- tic)) res.dataset['annotations'] = anns res.createIndex() return res def download(self, tarDir = None, imgIds = [] ): ''' Download COCO images from mscoco.org server. :param tarDir (str): COCO results directory name imgIds (list): images to be downloaded :return: ''' if tarDir is None: print('Please specify target directory') return -1 if len(imgIds) == 0: imgs = self.imgs.values() else: imgs = self.loadImgs(imgIds) N = len(imgs) if not os.path.exists(tarDir): os.makedirs(tarDir) for i, img in enumerate(imgs): tic = time.time() fname = os.path.join(tarDir, img['file_name']) if not os.path.exists(fname): urlretrieve(img['coco_url'], fname) print('downloaded {}/{} images (t={:0.1f}s)'.format(i, N, time.time()- tic)) def loadNumpyAnnotations(self, data): """ Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class} :param data (numpy.ndarray) :return: annotations (python nested list) """ print('Converting ndarray to lists...') assert(type(data) == np.ndarray) print(data.shape) assert(data.shape[1] == 7) N = data.shape[0] ann = [] for i in range(N): if i % 1000000 == 0: print('{}/{}'.format(i,N)) ann += [{ 'image_id' : int(data[i, 0]), 'bbox' : [ data[i, 1], data[i, 2], data[i, 3], data[i, 4] ], 'score' : data[i, 5], 'category_id': int(data[i, 6]), }] return ann def annToRLE(self, ann): """ Convert annotation which can be polygons, uncompressed RLE to RLE. :return: binary mask (numpy 2D array) """ t = self.imgs[ann['image_id']] h, w = t['height'], t['width'] segm = ann['segmentation'] if type(segm) == list: # polygon -- a single object might consist of multiple parts # we merge all parts into one mask rle code rles = maskUtils.frPyObjects(segm, h, w) rle = maskUtils.merge(rles) elif type(segm['counts']) == list: # uncompressed RLE rle = maskUtils.frPyObjects(segm, h, w) else: # rle rle = ann['segmentation'] return rle def annToMask(self, ann): """ Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask. :return: binary mask (numpy 2D array) """ rle = self.annToRLE(ann) m = maskUtils.decode(rle) return m ================================================ FILE: lib/train/dataset/__init__.py ================================================ from .lasot import Lasot from .got10k import Got10k from .tracking_net import TrackingNet from .imagenetvid import ImagenetVID from .coco import MSCOCO from .coco_seq import MSCOCOSeq from .got10k_lmdb import Got10k_lmdb from .lasot_lmdb import Lasot_lmdb from .imagenetvid_lmdb import ImagenetVID_lmdb from .coco_seq_lmdb import MSCOCOSeq_lmdb from .tracking_net_lmdb import TrackingNet_lmdb ================================================ FILE: lib/train/dataset/base_image_dataset.py ================================================ import torch.utils.data from lib.train.data.image_loader import jpeg4py_loader class BaseImageDataset(torch.utils.data.Dataset): """ Base class for image datasets """ def __init__(self, name, root, image_loader=jpeg4py_loader): """ args: root - The root path to the dataset image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. """ self.name = name self.root = root self.image_loader = image_loader self.image_list = [] # Contains the list of sequences. self.class_list = [] def __len__(self): """ Returns size of the dataset returns: int - number of samples in the dataset """ return self.get_num_images() def __getitem__(self, index): """ Not to be used! Check get_frames() instead. """ return None def get_name(self): """ Name of the dataset returns: string - Name of the dataset """ raise NotImplementedError def get_num_images(self): """ Number of sequences in a dataset returns: int - number of sequences in the dataset.""" return len(self.image_list) def has_class_info(self): return False def get_class_name(self, image_id): return None def get_num_classes(self): return len(self.class_list) def get_class_list(self): return self.class_list def get_images_in_class(self, class_name): raise NotImplementedError def has_segmentation_info(self): return False def get_image_info(self, seq_id): """ Returns information about a particular image, args: seq_id - index of the image returns: Dict """ raise NotImplementedError def get_image(self, image_id, anno=None): """ Get a image args: image_id - index of image anno(None) - The annotation for the sequence (see get_sequence_info). If None, they will be loaded. returns: image - anno - dict - A dict containing meta information about the sequence, e.g. class of the target object. """ raise NotImplementedError ================================================ FILE: lib/train/dataset/base_video_dataset.py ================================================ import torch.utils.data # 2021.1.5 use jpeg4py_loader_w_failsafe as default from lib.train.data.image_loader import jpeg4py_loader_w_failsafe class BaseVideoDataset(torch.utils.data.Dataset): """ Base class for video datasets """ def __init__(self, name, root, image_loader=jpeg4py_loader_w_failsafe): """ args: root - The root path to the dataset image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. """ self.name = name self.root = root self.image_loader = image_loader self.sequence_list = [] # Contains the list of sequences. self.class_list = [] def __len__(self): """ Returns size of the dataset returns: int - number of samples in the dataset """ return self.get_num_sequences() def __getitem__(self, index): """ Not to be used! Check get_frames() instead. """ return None def is_video_sequence(self): """ Returns whether the dataset is a video dataset or an image dataset returns: bool - True if a video dataset """ return True def is_synthetic_video_dataset(self): """ Returns whether the dataset contains real videos or synthetic returns: bool - True if a video dataset """ return False def get_name(self): """ Name of the dataset returns: string - Name of the dataset """ raise NotImplementedError def get_num_sequences(self): """ Number of sequences in a dataset returns: int - number of sequences in the dataset.""" return len(self.sequence_list) def has_class_info(self): return False def has_occlusion_info(self): return False def get_num_classes(self): return len(self.class_list) def get_class_list(self): return self.class_list def get_sequences_in_class(self, class_name): raise NotImplementedError def has_segmentation_info(self): return False def get_sequence_info(self, seq_id): """ Returns information about a particular sequences, args: seq_id - index of the sequence returns: Dict """ raise NotImplementedError def get_frames(self, seq_id, frame_ids, anno=None): """ Get a set of frames from a particular sequence args: seq_id - index of sequence frame_ids - a list of frame numbers anno(None) - The annotation for the sequence (see get_sequence_info). If None, they will be loaded. returns: list - List of frames corresponding to frame_ids list - List of dicts for each frame dict - A dict containing meta information about the sequence, e.g. class of the target object. """ raise NotImplementedError ================================================ FILE: lib/train/dataset/coco.py ================================================ import os from .base_image_dataset import BaseImageDataset import torch import random from collections import OrderedDict from lib.train.data import jpeg4py_loader from lib.train.admin import env_settings from pycocotools.coco import COCO class MSCOCO(BaseImageDataset): """ The COCO object detection dataset. Publication: Microsoft COCO: Common Objects in Context. Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar and C. Lawrence Zitnick ECCV, 2014 https://arxiv.org/pdf/1405.0312.pdf Download the images along with annotations from http://cocodataset.org/#download. The root folder should be organized as follows. - coco_root - annotations - instances_train2014.json - instances_train2017.json - images - train2014 - train2017 Note: You also have to install the coco pythonAPI from https://github.com/cocodataset/cocoapi. """ def __init__(self, root=None, image_loader=jpeg4py_loader, data_fraction=None, min_area=None, split="train", version="2014"): """ args: root - path to coco root folder image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. data_fraction - Fraction of dataset to be used. The complete dataset is used by default min_area - Objects with area less than min_area are filtered out. Default is 0.0 split - 'train' or 'val'. version - version of coco dataset (2014 or 2017) """ root = env_settings().coco_dir if root is None else root super().__init__('COCO', root, image_loader) self.img_pth = os.path.join(root, 'images/{}{}/'.format(split, version)) self.anno_path = os.path.join(root, 'annotations/instances_{}{}.json'.format(split, version)) self.coco_set = COCO(self.anno_path) self.cats = self.coco_set.cats self.class_list = self.get_class_list() # the parent class thing would happen in the sampler self.image_list = self._get_image_list(min_area=min_area) if data_fraction is not None: self.image_list = random.sample(self.image_list, int(len(self.image_list) * data_fraction)) self.im_per_class = self._build_im_per_class() def _get_image_list(self, min_area=None): ann_list = list(self.coco_set.anns.keys()) image_list = [a for a in ann_list if self.coco_set.anns[a]['iscrowd'] == 0] if min_area is not None: image_list = [a for a in image_list if self.coco_set.anns[a]['area'] > min_area] return image_list def get_num_classes(self): return len(self.class_list) def get_name(self): return 'coco' def has_class_info(self): return True def has_segmentation_info(self): return True def get_class_list(self): class_list = [] for cat_id in self.cats.keys(): class_list.append(self.cats[cat_id]['name']) return class_list def _build_im_per_class(self): im_per_class = {} for i, im in enumerate(self.image_list): class_name = self.cats[self.coco_set.anns[im]['category_id']]['name'] if class_name not in im_per_class: im_per_class[class_name] = [i] else: im_per_class[class_name].append(i) return im_per_class def get_images_in_class(self, class_name): return self.im_per_class[class_name] def get_image_info(self, im_id): anno = self._get_anno(im_id) bbox = torch.Tensor(anno['bbox']).view(4,) mask = torch.Tensor(self.coco_set.annToMask(anno)) valid = (bbox[2] > 0) & (bbox[3] > 0) visible = valid.clone().byte() return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible} def _get_anno(self, im_id): anno = self.coco_set.anns[self.image_list[im_id]] return anno def _get_image(self, im_id): path = self.coco_set.loadImgs([self.coco_set.anns[self.image_list[im_id]]['image_id']])[0]['file_name'] img = self.image_loader(os.path.join(self.img_pth, path)) return img def get_meta_info(self, im_id): try: cat_dict_current = self.cats[self.coco_set.anns[self.image_list[im_id]]['category_id']] object_meta = OrderedDict({'object_class_name': cat_dict_current['name'], 'motion_class': None, 'major_class': cat_dict_current['supercategory'], 'root_class': None, 'motion_adverb': None}) except: object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def get_class_name(self, im_id): cat_dict_current = self.cats[self.coco_set.anns[self.image_list[im_id]]['category_id']] return cat_dict_current['name'] def get_image(self, image_id, anno=None): frame = self._get_image(image_id) if anno is None: anno = self.get_image_info(image_id) object_meta = self.get_meta_info(image_id) return frame, anno, object_meta ================================================ FILE: lib/train/dataset/coco_seq.py ================================================ import os from .base_video_dataset import BaseVideoDataset from lib.train.data import jpeg4py_loader import torch import random from pycocotools.coco import COCO from collections import OrderedDict from lib.train.admin import env_settings class MSCOCOSeq(BaseVideoDataset): """ The COCO dataset. COCO is an image dataset. Thus, we treat each image as a sequence of length 1. Publication: Microsoft COCO: Common Objects in Context. Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar and C. Lawrence Zitnick ECCV, 2014 https://arxiv.org/pdf/1405.0312.pdf Download the images along with annotations from http://cocodataset.org/#download. The root folder should be organized as follows. - coco_root - annotations - instances_train2014.json - instances_train2017.json - images - train2014 - train2017 Note: You also have to install the coco pythonAPI from https://github.com/cocodataset/cocoapi. """ def __init__(self, root=None, image_loader=jpeg4py_loader, data_fraction=None, split="train", version="2014"): """ args: root - path to the coco dataset. image_loader (default_image_loader) - The function to read the images. If installed, jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else, opencv's imread is used. data_fraction (None) - Fraction of images to be used. The images are selected randomly. If None, all the images will be used split - 'train' or 'val'. version - version of coco dataset (2014 or 2017) """ root = env_settings().coco_dir if root is None else root super().__init__('COCO', root, image_loader) self.img_pth = os.path.join(root, 'images/{}{}/'.format(split, version)) self.anno_path = os.path.join(root, 'annotations/instances_{}{}.json'.format(split, version)) # Load the COCO set. self.coco_set = COCO(self.anno_path) self.cats = self.coco_set.cats self.class_list = self.get_class_list() self.sequence_list = self._get_sequence_list() if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction)) self.seq_per_class = self._build_seq_per_class() def _get_sequence_list(self): ann_list = list(self.coco_set.anns.keys()) seq_list = [a for a in ann_list if self.coco_set.anns[a]['iscrowd'] == 0] return seq_list def is_video_sequence(self): return False def get_num_classes(self): return len(self.class_list) def get_name(self): return 'coco' def has_class_info(self): return True def get_class_list(self): class_list = [] for cat_id in self.cats.keys(): class_list.append(self.cats[cat_id]['name']) return class_list def has_segmentation_info(self): return True def get_num_sequences(self): return len(self.sequence_list) def _build_seq_per_class(self): seq_per_class = {} for i, seq in enumerate(self.sequence_list): class_name = self.cats[self.coco_set.anns[seq]['category_id']]['name'] if class_name not in seq_per_class: seq_per_class[class_name] = [i] else: seq_per_class[class_name].append(i) return seq_per_class def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def get_sequence_info(self, seq_id): anno = self._get_anno(seq_id) bbox = torch.Tensor(anno['bbox']).view(1, 4) mask = torch.Tensor(self.coco_set.annToMask(anno)).unsqueeze(dim=0) '''2021.1.3 To avoid too small bounding boxes. Here we change the threshold to 50 pixels''' valid = (bbox[:, 2] > 50) & (bbox[:, 3] > 50) visible = valid.clone().byte() return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible} def _get_anno(self, seq_id): anno = self.coco_set.anns[self.sequence_list[seq_id]] return anno def _get_frames(self, seq_id): path = self.coco_set.loadImgs([self.coco_set.anns[self.sequence_list[seq_id]]['image_id']])[0]['file_name'] img = self.image_loader(os.path.join(self.img_pth, path)) return img def get_meta_info(self, seq_id): try: cat_dict_current = self.cats[self.coco_set.anns[self.sequence_list[seq_id]]['category_id']] object_meta = OrderedDict({'object_class_name': cat_dict_current['name'], 'motion_class': None, 'major_class': cat_dict_current['supercategory'], 'root_class': None, 'motion_adverb': None}) except: object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def get_class_name(self, seq_id): cat_dict_current = self.cats[self.coco_set.anns[self.sequence_list[seq_id]]['category_id']] return cat_dict_current['name'] def get_frames(self, seq_id=None, frame_ids=None, anno=None): # COCO is an image dataset. Thus we replicate the image denoted by seq_id len(frame_ids) times, and return a # list containing these replicated images. frame = self._get_frames(seq_id) frame_list = [frame.copy() for _ in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[0, ...] for _ in frame_ids] object_meta = self.get_meta_info(seq_id) return frame_list, anno_frames, object_meta ================================================ FILE: lib/train/dataset/coco_seq_lmdb.py ================================================ import os from .base_video_dataset import BaseVideoDataset from lib.train.data import jpeg4py_loader import torch import random from collections import OrderedDict from lib.train.admin import env_settings from lib.train.dataset.COCO_tool import COCO from lib.utils.lmdb_utils import decode_img, decode_json import time class MSCOCOSeq_lmdb(BaseVideoDataset): """ The COCO dataset. COCO is an image dataset. Thus, we treat each image as a sequence of length 1. Publication: Microsoft COCO: Common Objects in Context. Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar and C. Lawrence Zitnick ECCV, 2014 https://arxiv.org/pdf/1405.0312.pdf Download the images along with annotations from http://cocodataset.org/#download. The root folder should be organized as follows. - coco_root - annotations - instances_train2014.json - instances_train2017.json - images - train2014 - train2017 Note: You also have to install the coco pythonAPI from https://github.com/cocodataset/cocoapi. """ def __init__(self, root=None, image_loader=jpeg4py_loader, data_fraction=None, split="train", version="2014"): """ args: root - path to the coco dataset. image_loader (default_image_loader) - The function to read the images. If installed, jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else, opencv's imread is used. data_fraction (None) - Fraction of images to be used. The images are selected randomly. If None, all the images will be used split - 'train' or 'val'. version - version of coco dataset (2014 or 2017) """ root = env_settings().coco_dir if root is None else root super().__init__('COCO_lmdb', root, image_loader) self.root = root self.img_pth = 'images/{}{}/'.format(split, version) self.anno_path = 'annotations/instances_{}{}.json'.format(split, version) # Load the COCO set. print('loading annotations into memory...') tic = time.time() coco_json = decode_json(root, self.anno_path) print('Done (t={:0.2f}s)'.format(time.time() - tic)) self.coco_set = COCO(coco_json) self.cats = self.coco_set.cats self.class_list = self.get_class_list() self.sequence_list = self._get_sequence_list() if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction)) self.seq_per_class = self._build_seq_per_class() def _get_sequence_list(self): ann_list = list(self.coco_set.anns.keys()) seq_list = [a for a in ann_list if self.coco_set.anns[a]['iscrowd'] == 0] return seq_list def is_video_sequence(self): return False def get_num_classes(self): return len(self.class_list) def get_name(self): return 'coco_lmdb' def has_class_info(self): return True def get_class_list(self): class_list = [] for cat_id in self.cats.keys(): class_list.append(self.cats[cat_id]['name']) return class_list def has_segmentation_info(self): return True def get_num_sequences(self): return len(self.sequence_list) def _build_seq_per_class(self): seq_per_class = {} for i, seq in enumerate(self.sequence_list): class_name = self.cats[self.coco_set.anns[seq]['category_id']]['name'] if class_name not in seq_per_class: seq_per_class[class_name] = [i] else: seq_per_class[class_name].append(i) return seq_per_class def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def get_sequence_info(self, seq_id): anno = self._get_anno(seq_id) bbox = torch.Tensor(anno['bbox']).view(1, 4) mask = torch.Tensor(self.coco_set.annToMask(anno)).unsqueeze(dim=0) '''2021.1.3 To avoid too small bounding boxes. Here we change the threshold to 50 pixels''' valid = (bbox[:, 2] > 50) & (bbox[:, 3] > 50) visible = valid.clone().byte() return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible} def _get_anno(self, seq_id): anno = self.coco_set.anns[self.sequence_list[seq_id]] return anno def _get_frames(self, seq_id): path = self.coco_set.loadImgs([self.coco_set.anns[self.sequence_list[seq_id]]['image_id']])[0]['file_name'] # img = self.image_loader(os.path.join(self.img_pth, path)) img = decode_img(self.root, os.path.join(self.img_pth, path)) return img def get_meta_info(self, seq_id): try: cat_dict_current = self.cats[self.coco_set.anns[self.sequence_list[seq_id]]['category_id']] object_meta = OrderedDict({'object_class_name': cat_dict_current['name'], 'motion_class': None, 'major_class': cat_dict_current['supercategory'], 'root_class': None, 'motion_adverb': None}) except: object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def get_class_name(self, seq_id): cat_dict_current = self.cats[self.coco_set.anns[self.sequence_list[seq_id]]['category_id']] return cat_dict_current['name'] def get_frames(self, seq_id=None, frame_ids=None, anno=None): # COCO is an image dataset. Thus we replicate the image denoted by seq_id len(frame_ids) times, and return a # list containing these replicated images. frame = self._get_frames(seq_id) frame_list = [frame.copy() for _ in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[0, ...] for _ in frame_ids] object_meta = self.get_meta_info(seq_id) return frame_list, anno_frames, object_meta ================================================ FILE: lib/train/dataset/got10k.py ================================================ import os import os.path import numpy as np import torch import csv import pandas import random from collections import OrderedDict from .base_video_dataset import BaseVideoDataset from lib.train.data import jpeg4py_loader from lib.train.admin import env_settings class Got10k(BaseVideoDataset): """ GOT-10k dataset. Publication: GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild Lianghua Huang, Xin Zhao, and Kaiqi Huang arXiv:1810.11981, 2018 https://arxiv.org/pdf/1810.11981.pdf Download dataset from http://got-10k.aitestunion.com/downloads """ def __init__(self, root=None, image_loader=jpeg4py_loader, split=None, seq_ids=None, data_fraction=None): """ args: root - path to the got-10k training data. Note: This should point to the 'train' folder inside GOT-10k image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. split - 'train' or 'val'. Note: The validation split here is a subset of the official got-10k train split, not NOT the official got-10k validation split. To use the official validation split, provide that as the root folder instead. seq_ids - List containing the ids of the videos to be used for training. Note: Only one of 'split' or 'seq_ids' options can be used at the same time. data_fraction - Fraction of dataset to be used. The complete dataset is used by default """ root = env_settings().got10k_dir if root is None else root super().__init__('GOT10k', root, image_loader) # all folders inside the root self.sequence_list = self._get_sequence_list() # seq_id is the index of the folder inside the got10k root path if split is not None: if seq_ids is not None: raise ValueError('Cannot set both split_name and seq_ids.') ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') if split == 'train': file_path = os.path.join(ltr_path, 'data_specs', 'got10k_train_split.txt') elif split == 'val': file_path = os.path.join(ltr_path, 'data_specs', 'got10k_val_split.txt') elif split == 'train_full': file_path = os.path.join(ltr_path, 'data_specs', 'got10k_train_full_split.txt') elif split == 'vottrain': file_path = os.path.join(ltr_path, 'data_specs', 'got10k_vot_train_split.txt') elif split == 'votval': file_path = os.path.join(ltr_path, 'data_specs', 'got10k_vot_val_split.txt') else: raise ValueError('Unknown split name.') # seq_ids = pandas.read_csv(file_path, header=None, squeeze=True, dtype=np.int64).values.tolist() seq_ids = pandas.read_csv(file_path, header=None, dtype=np.int64).squeeze("columns").values.tolist() elif seq_ids is None: seq_ids = list(range(0, len(self.sequence_list))) self.sequence_list = [self.sequence_list[i] for i in seq_ids] if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction)) self.sequence_meta_info = self._load_meta_info() self.seq_per_class = self._build_seq_per_class() self.class_list = list(self.seq_per_class.keys()) self.class_list.sort() def get_name(self): return 'got10k' def has_class_info(self): return True def has_occlusion_info(self): return True def _load_meta_info(self): sequence_meta_info = {s: self._read_meta(os.path.join(self.root, s)) for s in self.sequence_list} return sequence_meta_info def _read_meta(self, seq_path): try: with open(os.path.join(seq_path, 'meta_info.ini')) as f: meta_info = f.readlines() object_meta = OrderedDict({'object_class_name': meta_info[5].split(': ')[-1][:-1], 'motion_class': meta_info[6].split(': ')[-1][:-1], 'major_class': meta_info[7].split(': ')[-1][:-1], 'root_class': meta_info[8].split(': ')[-1][:-1], 'motion_adverb': meta_info[9].split(': ')[-1][:-1]}) except: object_meta = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return object_meta def _build_seq_per_class(self): seq_per_class = {} for i, s in enumerate(self.sequence_list): object_class = self.sequence_meta_info[s]['object_class_name'] if object_class in seq_per_class: seq_per_class[object_class].append(i) else: seq_per_class[object_class] = [i] return seq_per_class def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def _get_sequence_list(self): with open(os.path.join(self.root, 'list.txt')) as f: dir_list = list(csv.reader(f)) dir_list = [dir_name[0] for dir_name in dir_list] return dir_list def _read_bb_anno(self, seq_path): bb_anno_file = os.path.join(seq_path, "groundtruth.txt") gt = pandas.read_csv(bb_anno_file, delimiter=',', header=None, dtype=np.float32, na_filter=False, low_memory=False).values return torch.tensor(gt) def _read_target_visible(self, seq_path): # Read full occlusion and out_of_view occlusion_file = os.path.join(seq_path, "absence.label") cover_file = os.path.join(seq_path, "cover.label") with open(occlusion_file, 'r', newline='') as f: occlusion = torch.ByteTensor([int(v[0]) for v in csv.reader(f)]) with open(cover_file, 'r', newline='') as f: cover = torch.ByteTensor([int(v[0]) for v in csv.reader(f)]) target_visible = ~occlusion & (cover>0).byte() visible_ratio = cover.float() / 8 return target_visible, visible_ratio def _get_sequence_path(self, seq_id): return os.path.join(self.root, self.sequence_list[seq_id]) def get_sequence_info(self, seq_id): seq_path = self._get_sequence_path(seq_id) bbox = self._read_bb_anno(seq_path) valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0) visible, visible_ratio = self._read_target_visible(seq_path) visible = visible & valid.byte() return {'bbox': bbox, 'valid': valid, 'visible': visible, 'visible_ratio': visible_ratio} def _get_frame_path(self, seq_path, frame_id): return os.path.join(seq_path, '{:08}.jpg'.format(frame_id+1)) # frames start from 1 def _get_frame(self, seq_path, frame_id): return self.image_loader(self._get_frame_path(seq_path, frame_id)) def get_class_name(self, seq_id): obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]] return obj_meta['object_class_name'] def get_frames(self, seq_id, frame_ids, anno=None): seq_path = self._get_sequence_path(seq_id) obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]] frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] return frame_list, anno_frames, obj_meta ================================================ FILE: lib/train/dataset/got10k_lmdb.py ================================================ import os import os.path import numpy as np import torch import csv import pandas import random from collections import OrderedDict from .base_video_dataset import BaseVideoDataset from lib.train.data import jpeg4py_loader from lib.train.admin import env_settings '''2021.1.16 Gok10k for loading lmdb dataset''' from lib.utils.lmdb_utils import * class Got10k_lmdb(BaseVideoDataset): def __init__(self, root=None, image_loader=jpeg4py_loader, split=None, seq_ids=None, data_fraction=None): """ args: root - path to the got-10k training data. Note: This should point to the 'train' folder inside GOT-10k image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. split - 'train' or 'val'. Note: The validation split here is a subset of the official got-10k train split, not NOT the official got-10k validation split. To use the official validation split, provide that as the root folder instead. seq_ids - List containing the ids of the videos to be used for training. Note: Only one of 'split' or 'seq_ids' options can be used at the same time. data_fraction - Fraction of dataset to be used. The complete dataset is used by default use_lmdb - whether the dataset is stored in lmdb format """ root = env_settings().got10k_lmdb_dir if root is None else root super().__init__('GOT10k_lmdb', root, image_loader) # all folders inside the root self.sequence_list = self._get_sequence_list() # seq_id is the index of the folder inside the got10k root path if split is not None: if seq_ids is not None: raise ValueError('Cannot set both split_name and seq_ids.') train_lib_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') if split == 'train': file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_train_split.txt') elif split == 'val': file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_val_split.txt') elif split == 'train_full': file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_train_full_split.txt') elif split == 'vottrain': file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_vot_train_split.txt') elif split == 'votval': file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_vot_val_split.txt') else: raise ValueError('Unknown split name.') seq_ids = pandas.read_csv(file_path, header=None, squeeze=True, dtype=np.int64).values.tolist() elif seq_ids is None: seq_ids = list(range(0, len(self.sequence_list))) self.sequence_list = [self.sequence_list[i] for i in seq_ids] if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction)) self.sequence_meta_info = self._load_meta_info() self.seq_per_class = self._build_seq_per_class() self.class_list = list(self.seq_per_class.keys()) self.class_list.sort() def get_name(self): return 'got10k_lmdb' def has_class_info(self): return True def has_occlusion_info(self): return True def _load_meta_info(self): def _read_meta(meta_info): object_meta = OrderedDict({'object_class_name': meta_info[5].split(': ')[-1], 'motion_class': meta_info[6].split(': ')[-1], 'major_class': meta_info[7].split(': ')[-1], 'root_class': meta_info[8].split(': ')[-1], 'motion_adverb': meta_info[9].split(': ')[-1]}) return object_meta sequence_meta_info = {} for s in self.sequence_list: try: meta_str = decode_str(self.root, "train/%s/meta_info.ini" %s) sequence_meta_info[s] = _read_meta(meta_str.split('\n')) except: sequence_meta_info[s] = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return sequence_meta_info def _build_seq_per_class(self): seq_per_class = {} for i, s in enumerate(self.sequence_list): object_class = self.sequence_meta_info[s]['object_class_name'] if object_class in seq_per_class: seq_per_class[object_class].append(i) else: seq_per_class[object_class] = [i] return seq_per_class def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def _get_sequence_list(self): dir_str = decode_str(self.root, 'train/list.txt') dir_list = dir_str.split('\n') return dir_list def _read_bb_anno(self, seq_path): bb_anno_file = os.path.join(seq_path, "groundtruth.txt") gt_str_list = decode_str(self.root, bb_anno_file).split('\n')[:-1] # the last line in got10k is empty gt_list = [list(map(float, line.split(','))) for line in gt_str_list] gt_arr = np.array(gt_list).astype(np.float32) return torch.tensor(gt_arr) def _read_target_visible(self, seq_path): # full occlusion and out_of_view files occlusion_file = os.path.join(seq_path, "absence.label") cover_file = os.path.join(seq_path, "cover.label") # Read these files occ_list = list(map(int, decode_str(self.root, occlusion_file).split('\n')[:-1])) # the last line in got10k is empty occlusion = torch.ByteTensor(occ_list) cover_list = list(map(int, decode_str(self.root, cover_file).split('\n')[:-1])) # the last line in got10k is empty cover = torch.ByteTensor(cover_list) target_visible = ~occlusion & (cover>0).byte() visible_ratio = cover.float() / 8 return target_visible, visible_ratio def _get_sequence_path(self, seq_id): return os.path.join("train", self.sequence_list[seq_id]) def get_sequence_info(self, seq_id): seq_path = self._get_sequence_path(seq_id) bbox = self._read_bb_anno(seq_path) valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0) visible, visible_ratio = self._read_target_visible(seq_path) visible = visible & valid.byte() return {'bbox': bbox, 'valid': valid, 'visible': visible, 'visible_ratio': visible_ratio} def _get_frame_path(self, seq_path, frame_id): return os.path.join(seq_path, '{:08}.jpg'.format(frame_id+1)) # frames start from 1 def _get_frame(self, seq_path, frame_id): return decode_img(self.root, self._get_frame_path(seq_path, frame_id)) def get_class_name(self, seq_id): obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]] return obj_meta['object_class_name'] def get_frames(self, seq_id, frame_ids, anno=None): seq_path = self._get_sequence_path(seq_id) obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]] frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] return frame_list, anno_frames, obj_meta ================================================ FILE: lib/train/dataset/imagenetvid.py ================================================ import os from .base_video_dataset import BaseVideoDataset from lib.train.data import jpeg4py_loader import xml.etree.ElementTree as ET import json import torch from collections import OrderedDict from lib.train.admin import env_settings def get_target_to_image_ratio(seq): anno = torch.Tensor(seq['anno']) img_sz = torch.Tensor(seq['image_size']) return (anno[0, 2:4].prod() / (img_sz.prod())).sqrt() class ImagenetVID(BaseVideoDataset): """ Imagenet VID dataset. Publication: ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei IJCV, 2015 https://arxiv.org/pdf/1409.0575.pdf Download the dataset from http://image-net.org/ """ def __init__(self, root=None, image_loader=jpeg4py_loader, min_length=0, max_target_area=1): """ args: root - path to the imagenet vid dataset. image_loader (default_image_loader) - The function to read the images. If installed, jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else, opencv's imread is used. min_length - Minimum allowed sequence length. max_target_area - max allowed ratio between target area and image area. Can be used to filter out targets which cover complete image. """ root = env_settings().imagenet_dir if root is None else root super().__init__("imagenetvid", root, image_loader) cache_file = os.path.join(root, 'cache.json') if os.path.isfile(cache_file): # If available, load the pre-processed cache file containing meta-info for each sequence with open(cache_file, 'r') as f: sequence_list_dict = json.load(f) self.sequence_list = sequence_list_dict else: # Else process the imagenet annotations and generate the cache file self.sequence_list = self._process_anno(root) with open(cache_file, 'w') as f: json.dump(self.sequence_list, f) # Filter the sequences based on min_length and max_target_area in the first frame self.sequence_list = [x for x in self.sequence_list if len(x['anno']) >= min_length and get_target_to_image_ratio(x) < max_target_area] def get_name(self): return 'imagenetvid' def get_num_sequences(self): return len(self.sequence_list) def get_sequence_info(self, seq_id): bb_anno = torch.Tensor(self.sequence_list[seq_id]['anno']) valid = (bb_anno[:, 2] > 0) & (bb_anno[:, 3] > 0) visible = torch.ByteTensor(self.sequence_list[seq_id]['target_visible']) & valid.byte() return {'bbox': bb_anno, 'valid': valid, 'visible': visible} def _get_frame(self, sequence, frame_id): set_name = 'ILSVRC2015_VID_train_{:04d}'.format(sequence['set_id']) vid_name = 'ILSVRC2015_train_{:08d}'.format(sequence['vid_id']) frame_number = frame_id + sequence['start_frame'] frame_path = os.path.join(self.root, 'Data', 'VID', 'train', set_name, vid_name, '{:06d}.JPEG'.format(frame_number)) return self.image_loader(frame_path) def get_frames(self, seq_id, frame_ids, anno=None): sequence = self.sequence_list[seq_id] frame_list = [self._get_frame(sequence, f) for f in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) # Create anno dict anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] # added the class info to the meta info object_meta = OrderedDict({'object_class': sequence['class_name'], 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return frame_list, anno_frames, object_meta def _process_anno(self, root): # Builds individual tracklets base_vid_anno_path = os.path.join(root, 'Annotations', 'VID', 'train') all_sequences = [] for set in sorted(os.listdir(base_vid_anno_path)): set_id = int(set.split('_')[-1]) for vid in sorted(os.listdir(os.path.join(base_vid_anno_path, set))): vid_id = int(vid.split('_')[-1]) anno_files = sorted(os.listdir(os.path.join(base_vid_anno_path, set, vid))) frame1_anno = ET.parse(os.path.join(base_vid_anno_path, set, vid, anno_files[0])) image_size = [int(frame1_anno.find('size/width').text), int(frame1_anno.find('size/height').text)] objects = [ET.ElementTree(file=os.path.join(base_vid_anno_path, set, vid, f)).findall('object') for f in anno_files] tracklets = {} # Find all tracklets along with start frame for f_id, all_targets in enumerate(objects): for target in all_targets: tracklet_id = target.find('trackid').text if tracklet_id not in tracklets: tracklets[tracklet_id] = f_id for tracklet_id, tracklet_start in tracklets.items(): tracklet_anno = [] target_visible = [] class_name_id = None for f_id in range(tracklet_start, len(objects)): found = False for target in objects[f_id]: if target.find('trackid').text == tracklet_id: if not class_name_id: class_name_id = target.find('name').text x1 = int(target.find('bndbox/xmin').text) y1 = int(target.find('bndbox/ymin').text) x2 = int(target.find('bndbox/xmax').text) y2 = int(target.find('bndbox/ymax').text) tracklet_anno.append([x1, y1, x2 - x1, y2 - y1]) target_visible.append(target.find('occluded').text == '0') found = True break if not found: break new_sequence = {'set_id': set_id, 'vid_id': vid_id, 'class_name': class_name_id, 'start_frame': tracklet_start, 'anno': tracklet_anno, 'target_visible': target_visible, 'image_size': image_size} all_sequences.append(new_sequence) return all_sequences ================================================ FILE: lib/train/dataset/imagenetvid_lmdb.py ================================================ import os from .base_video_dataset import BaseVideoDataset from lib.train.data import jpeg4py_loader import torch from collections import OrderedDict from lib.train.admin import env_settings from lib.utils.lmdb_utils import decode_img, decode_json def get_target_to_image_ratio(seq): anno = torch.Tensor(seq['anno']) img_sz = torch.Tensor(seq['image_size']) return (anno[0, 2:4].prod() / (img_sz.prod())).sqrt() class ImagenetVID_lmdb(BaseVideoDataset): """ Imagenet VID dataset. Publication: ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei IJCV, 2015 https://arxiv.org/pdf/1409.0575.pdf Download the dataset from http://image-net.org/ """ def __init__(self, root=None, image_loader=jpeg4py_loader, min_length=0, max_target_area=1): """ args: root - path to the imagenet vid dataset. image_loader (default_image_loader) - The function to read the images. If installed, jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else, opencv's imread is used. min_length - Minimum allowed sequence length. max_target_area - max allowed ratio between target area and image area. Can be used to filter out targets which cover complete image. """ root = env_settings().imagenet_dir if root is None else root super().__init__("imagenetvid_lmdb", root, image_loader) sequence_list_dict = decode_json(root, "cache.json") self.sequence_list = sequence_list_dict # Filter the sequences based on min_length and max_target_area in the first frame self.sequence_list = [x for x in self.sequence_list if len(x['anno']) >= min_length and get_target_to_image_ratio(x) < max_target_area] def get_name(self): return 'imagenetvid_lmdb' def get_num_sequences(self): return len(self.sequence_list) def get_sequence_info(self, seq_id): bb_anno = torch.Tensor(self.sequence_list[seq_id]['anno']) valid = (bb_anno[:, 2] > 0) & (bb_anno[:, 3] > 0) visible = torch.ByteTensor(self.sequence_list[seq_id]['target_visible']) & valid.byte() return {'bbox': bb_anno, 'valid': valid, 'visible': visible} def _get_frame(self, sequence, frame_id): set_name = 'ILSVRC2015_VID_train_{:04d}'.format(sequence['set_id']) vid_name = 'ILSVRC2015_train_{:08d}'.format(sequence['vid_id']) frame_number = frame_id + sequence['start_frame'] frame_path = os.path.join('Data', 'VID', 'train', set_name, vid_name, '{:06d}.JPEG'.format(frame_number)) return decode_img(self.root, frame_path) def get_frames(self, seq_id, frame_ids, anno=None): sequence = self.sequence_list[seq_id] frame_list = [self._get_frame(sequence, f) for f in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) # Create anno dict anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] # added the class info to the meta info object_meta = OrderedDict({'object_class': sequence['class_name'], 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return frame_list, anno_frames, object_meta ================================================ FILE: lib/train/dataset/lasot.py ================================================ import os import os.path import torch import numpy as np import pandas import csv import random from collections import OrderedDict from .base_video_dataset import BaseVideoDataset from lib.train.data import jpeg4py_loader from lib.train.admin import env_settings class Lasot(BaseVideoDataset): """ LaSOT dataset. Publication: LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Hexin Bai, Yong Xu, Chunyuan Liao and Haibin Ling CVPR, 2019 https://arxiv.org/pdf/1809.07845.pdf Download the dataset from https://cis.temple.edu/lasot/download.html """ def __init__(self, root=None, image_loader=jpeg4py_loader, vid_ids=None, split=None, data_fraction=None): """ args: root - path to the lasot dataset. image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. vid_ids - List containing the ids of the videos (1 - 20) used for training. If vid_ids = [1, 3, 5], then the videos with subscripts -1, -3, and -5 from each class will be used for training. split - If split='train', the official train split (protocol-II) is used for training. Note: Only one of vid_ids or split option can be used at a time. data_fraction - Fraction of dataset to be used. The complete dataset is used by default """ root = env_settings().lasot_dir if root is None else root super().__init__('LaSOT', root, image_loader) # Keep a list of all classes self.class_list = [f for f in os.listdir(self.root)] self.class_to_id = {cls_name: cls_id for cls_id, cls_name in enumerate(self.class_list)} self.sequence_list = self._build_sequence_list(vid_ids, split) if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction)) self.seq_per_class = self._build_class_list() def _build_sequence_list(self, vid_ids=None, split=None): if split is not None: if vid_ids is not None: raise ValueError('Cannot set both split_name and vid_ids.') ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') if split == 'train': file_path = os.path.join(ltr_path, 'data_specs', 'lasot_train_split.txt') else: raise ValueError('Unknown split name.') # sequence_list = pandas.read_csv(file_path, header=None, squeeze=True).values.tolist() sequence_list = pandas.read_csv(file_path, header=None).squeeze("columns").values.tolist() elif vid_ids is not None: sequence_list = [c+'-'+str(v) for c in self.class_list for v in vid_ids] else: raise ValueError('Set either split_name or vid_ids.') return sequence_list def _build_class_list(self): seq_per_class = {} for seq_id, seq_name in enumerate(self.sequence_list): class_name = seq_name.split('-')[0] if class_name in seq_per_class: seq_per_class[class_name].append(seq_id) else: seq_per_class[class_name] = [seq_id] return seq_per_class def get_name(self): return 'lasot' def has_class_info(self): return True def has_occlusion_info(self): return True def get_num_sequences(self): return len(self.sequence_list) def get_num_classes(self): return len(self.class_list) def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def _read_bb_anno(self, seq_path): bb_anno_file = os.path.join(seq_path, "groundtruth.txt") gt = pandas.read_csv(bb_anno_file, delimiter=',', header=None, dtype=np.float32, na_filter=False, low_memory=False).values return torch.tensor(gt) def _read_target_visible(self, seq_path): # Read full occlusion and out_of_view occlusion_file = os.path.join(seq_path, "full_occlusion.txt") out_of_view_file = os.path.join(seq_path, "out_of_view.txt") with open(occlusion_file, 'r', newline='') as f: occlusion = torch.ByteTensor([int(v) for v in list(csv.reader(f))[0]]) with open(out_of_view_file, 'r') as f: out_of_view = torch.ByteTensor([int(v) for v in list(csv.reader(f))[0]]) target_visible = ~occlusion & ~out_of_view return target_visible def _get_sequence_path(self, seq_id): seq_name = self.sequence_list[seq_id] class_name = seq_name.split('-')[0] vid_id = seq_name.split('-')[1] return os.path.join(self.root, class_name, class_name + '-' + vid_id) def get_sequence_info(self, seq_id): seq_path = self._get_sequence_path(seq_id) bbox = self._read_bb_anno(seq_path) valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0) visible = self._read_target_visible(seq_path) & valid.byte() return {'bbox': bbox, 'valid': valid, 'visible': visible} def _get_frame_path(self, seq_path, frame_id): return os.path.join(seq_path, 'img', '{:08}.jpg'.format(frame_id+1)) # frames start from 1 def _get_frame(self, seq_path, frame_id): return self.image_loader(self._get_frame_path(seq_path, frame_id)) def _get_class(self, seq_path): raw_class = seq_path.split('/')[-2] return raw_class def get_class_name(self, seq_id): seq_path = self._get_sequence_path(seq_id) obj_class = self._get_class(seq_path) return obj_class def get_frames(self, seq_id, frame_ids, anno=None): seq_path = self._get_sequence_path(seq_id) obj_class = self._get_class(seq_path) frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] object_meta = OrderedDict({'object_class_name': obj_class, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return frame_list, anno_frames, object_meta ================================================ FILE: lib/train/dataset/lasot_lmdb.py ================================================ import os import os.path import torch import numpy as np import pandas import csv import random from collections import OrderedDict from .base_video_dataset import BaseVideoDataset from lib.train.data import jpeg4py_loader from lib.train.admin import env_settings '''2021.1.16 Lasot for loading lmdb dataset''' from lib.utils.lmdb_utils import * class Lasot_lmdb(BaseVideoDataset): def __init__(self, root=None, image_loader=jpeg4py_loader, vid_ids=None, split=None, data_fraction=None): """ args: root - path to the lasot dataset. image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. vid_ids - List containing the ids of the videos (1 - 20) used for training. If vid_ids = [1, 3, 5], then the videos with subscripts -1, -3, and -5 from each class will be used for training. split - If split='train', the official train split (protocol-II) is used for training. Note: Only one of vid_ids or split option can be used at a time. data_fraction - Fraction of dataset to be used. The complete dataset is used by default """ root = env_settings().lasot_lmdb_dir if root is None else root super().__init__('LaSOT_lmdb', root, image_loader) self.sequence_list = self._build_sequence_list(vid_ids, split) class_list = [seq_name.split('-')[0] for seq_name in self.sequence_list] self.class_list = [] for ele in class_list: if ele not in self.class_list: self.class_list.append(ele) # Keep a list of all classes self.class_to_id = {cls_name: cls_id for cls_id, cls_name in enumerate(self.class_list)} if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction)) self.seq_per_class = self._build_class_list() def _build_sequence_list(self, vid_ids=None, split=None): if split is not None: if vid_ids is not None: raise ValueError('Cannot set both split_name and vid_ids.') ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') if split == 'train': file_path = os.path.join(ltr_path, 'data_specs', 'lasot_train_split.txt') else: raise ValueError('Unknown split name.') sequence_list = pandas.read_csv(file_path, header=None, squeeze=True).values.tolist() elif vid_ids is not None: sequence_list = [c+'-'+str(v) for c in self.class_list for v in vid_ids] else: raise ValueError('Set either split_name or vid_ids.') return sequence_list def _build_class_list(self): seq_per_class = {} for seq_id, seq_name in enumerate(self.sequence_list): class_name = seq_name.split('-')[0] if class_name in seq_per_class: seq_per_class[class_name].append(seq_id) else: seq_per_class[class_name] = [seq_id] return seq_per_class def get_name(self): return 'lasot_lmdb' def has_class_info(self): return True def has_occlusion_info(self): return True def get_num_sequences(self): return len(self.sequence_list) def get_num_classes(self): return len(self.class_list) def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def _read_bb_anno(self, seq_path): bb_anno_file = os.path.join(seq_path, "groundtruth.txt") gt_str_list = decode_str(self.root, bb_anno_file).split('\n')[:-1] # the last line is empty gt_list = [list(map(float, line.split(','))) for line in gt_str_list] gt_arr = np.array(gt_list).astype(np.float32) return torch.tensor(gt_arr) def _read_target_visible(self, seq_path): # Read full occlusion and out_of_view occlusion_file = os.path.join(seq_path, "full_occlusion.txt") out_of_view_file = os.path.join(seq_path, "out_of_view.txt") occ_list = list(map(int, decode_str(self.root, occlusion_file).split(','))) occlusion = torch.ByteTensor(occ_list) out_view_list = list(map(int, decode_str(self.root, out_of_view_file).split(','))) out_of_view = torch.ByteTensor(out_view_list) target_visible = ~occlusion & ~out_of_view return target_visible def _get_sequence_path(self, seq_id): seq_name = self.sequence_list[seq_id] class_name = seq_name.split('-')[0] vid_id = seq_name.split('-')[1] return os.path.join(class_name, class_name + '-' + vid_id) def get_sequence_info(self, seq_id): seq_path = self._get_sequence_path(seq_id) bbox = self._read_bb_anno(seq_path) valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0) visible = self._read_target_visible(seq_path) & valid.byte() return {'bbox': bbox, 'valid': valid, 'visible': visible} def _get_frame_path(self, seq_path, frame_id): return os.path.join(seq_path, 'img', '{:08}.jpg'.format(frame_id+1)) # frames start from 1 def _get_frame(self, seq_path, frame_id): return decode_img(self.root, self._get_frame_path(seq_path, frame_id)) def _get_class(self, seq_path): raw_class = seq_path.split('/')[-2] return raw_class def get_class_name(self, seq_id): seq_path = self._get_sequence_path(seq_id) obj_class = self._get_class(seq_path) return obj_class def get_frames(self, seq_id, frame_ids, anno=None): seq_path = self._get_sequence_path(seq_id) obj_class = self._get_class(seq_path) frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] object_meta = OrderedDict({'object_class_name': obj_class, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return frame_list, anno_frames, object_meta ================================================ FILE: lib/train/dataset/tracking_net.py ================================================ import torch import os import os.path import numpy as np import pandas import random from collections import OrderedDict from lib.train.data import jpeg4py_loader from .base_video_dataset import BaseVideoDataset from lib.train.admin import env_settings def list_sequences(root, set_ids): """ Lists all the videos in the input set_ids. Returns a list of tuples (set_id, video_name) args: root: Root directory to TrackingNet set_ids: Sets (0-11) which are to be used returns: list - list of tuples (set_id, video_name) containing the set_id and video_name for each sequence """ sequence_list = [] for s in set_ids: anno_dir = os.path.join(root, "TRAIN_" + str(s), "anno") sequences_cur_set = [(s, os.path.splitext(f)[0]) for f in os.listdir(anno_dir) if f.endswith('.txt')] sequence_list += sequences_cur_set return sequence_list class TrackingNet(BaseVideoDataset): """ TrackingNet dataset. Publication: TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild. Matthias Mueller,Adel Bibi, Silvio Giancola, Salman Al-Subaihi and Bernard Ghanem ECCV, 2018 https://ivul.kaust.edu.sa/Documents/Publications/2018/TrackingNet%20A%20Large%20Scale%20Dataset%20and%20Benchmark%20for%20Object%20Tracking%20in%20the%20Wild.pdf Download the dataset using the toolkit https://github.com/SilvioGiancola/TrackingNet-devkit. """ def __init__(self, root=None, image_loader=jpeg4py_loader, set_ids=None, data_fraction=None): """ args: root - The path to the TrackingNet folder, containing the training sets. image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. set_ids (None) - List containing the ids of the TrackingNet sets to be used for training. If None, all the sets (0 - 11) will be used. data_fraction - Fraction of dataset to be used. The complete dataset is used by default """ root = env_settings().trackingnet_dir if root is None else root super().__init__('TrackingNet', root, image_loader) if set_ids is None: set_ids = [i for i in range(12)] self.set_ids = set_ids # Keep a list of all videos. Sequence list is a list of tuples (set_id, video_name) containing the set_id and # video_name for each sequence self.sequence_list = list_sequences(self.root, self.set_ids) if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list) * data_fraction)) self.seq_to_class_map, self.seq_per_class = self._load_class_info() # we do not have the class_lists for the tracking net self.class_list = list(self.seq_per_class.keys()) self.class_list.sort() def _load_class_info(self): ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') class_map_path = os.path.join(ltr_path, 'data_specs', 'trackingnet_classmap.txt') with open(class_map_path, 'r') as f: seq_to_class_map = {seq_class.split('\t')[0]: seq_class.rstrip().split('\t')[1] for seq_class in f} seq_per_class = {} for i, seq in enumerate(self.sequence_list): class_name = seq_to_class_map.get(seq[1], 'Unknown') if class_name not in seq_per_class: seq_per_class[class_name] = [i] else: seq_per_class[class_name].append(i) return seq_to_class_map, seq_per_class def get_name(self): return 'trackingnet' def has_class_info(self): return True def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def _read_bb_anno(self, seq_id): set_id = self.sequence_list[seq_id][0] vid_name = self.sequence_list[seq_id][1] bb_anno_file = os.path.join(self.root, "TRAIN_" + str(set_id), "anno", vid_name + ".txt") gt = pandas.read_csv(bb_anno_file, delimiter=',', header=None, dtype=np.float32, na_filter=False, low_memory=False).values return torch.tensor(gt) def get_sequence_info(self, seq_id): bbox = self._read_bb_anno(seq_id) valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0) visible = valid.clone().byte() return {'bbox': bbox, 'valid': valid, 'visible': visible} def _get_frame(self, seq_id, frame_id): set_id = self.sequence_list[seq_id][0] vid_name = self.sequence_list[seq_id][1] frame_path = os.path.join(self.root, "TRAIN_" + str(set_id), "frames", vid_name, str(frame_id) + ".jpg") return self.image_loader(frame_path) def _get_class(self, seq_id): seq_name = self.sequence_list[seq_id][1] return self.seq_to_class_map[seq_name] def get_class_name(self, seq_id): obj_class = self._get_class(seq_id) return obj_class def get_frames(self, seq_id, frame_ids, anno=None): frame_list = [self._get_frame(seq_id, f) for f in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] obj_class = self._get_class(seq_id) object_meta = OrderedDict({'object_class_name': obj_class, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return frame_list, anno_frames, object_meta ================================================ FILE: lib/train/dataset/tracking_net_lmdb.py ================================================ import torch import os import os.path import numpy as np import random from collections import OrderedDict from lib.train.data import jpeg4py_loader from .base_video_dataset import BaseVideoDataset from lib.train.admin import env_settings import json from lib.utils.lmdb_utils import decode_img, decode_str def list_sequences(root): """ Lists all the videos in the input set_ids. Returns a list of tuples (set_id, video_name) args: root: Root directory to TrackingNet returns: list - list of tuples (set_id, video_name) containing the set_id and video_name for each sequence """ fname = os.path.join(root, "seq_list.json") with open(fname, "r") as f: sequence_list = json.loads(f.read()) return sequence_list class TrackingNet_lmdb(BaseVideoDataset): """ TrackingNet dataset. Publication: TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild. Matthias Mueller,Adel Bibi, Silvio Giancola, Salman Al-Subaihi and Bernard Ghanem ECCV, 2018 https://ivul.kaust.edu.sa/Documents/Publications/2018/TrackingNet%20A%20Large%20Scale%20Dataset%20and%20Benchmark%20for%20Object%20Tracking%20in%20the%20Wild.pdf Download the dataset using the toolkit https://github.com/SilvioGiancola/TrackingNet-devkit. """ def __init__(self, root=None, image_loader=jpeg4py_loader, set_ids=None, data_fraction=None): """ args: root - The path to the TrackingNet folder, containing the training sets. image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. set_ids (None) - List containing the ids of the TrackingNet sets to be used for training. If None, all the sets (0 - 11) will be used. data_fraction - Fraction of dataset to be used. The complete dataset is used by default """ root = env_settings().trackingnet_lmdb_dir if root is None else root super().__init__('TrackingNet_lmdb', root, image_loader) if set_ids is None: set_ids = [i for i in range(12)] self.set_ids = set_ids # Keep a list of all videos. Sequence list is a list of tuples (set_id, video_name) containing the set_id and # video_name for each sequence self.sequence_list = list_sequences(self.root) if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list) * data_fraction)) self.seq_to_class_map, self.seq_per_class = self._load_class_info() # we do not have the class_lists for the tracking net self.class_list = list(self.seq_per_class.keys()) self.class_list.sort() def _load_class_info(self): ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') class_map_path = os.path.join(ltr_path, 'data_specs', 'trackingnet_classmap.txt') with open(class_map_path, 'r') as f: seq_to_class_map = {seq_class.split('\t')[0]: seq_class.rstrip().split('\t')[1] for seq_class in f} seq_per_class = {} for i, seq in enumerate(self.sequence_list): class_name = seq_to_class_map.get(seq[1], 'Unknown') if class_name not in seq_per_class: seq_per_class[class_name] = [i] else: seq_per_class[class_name].append(i) return seq_to_class_map, seq_per_class def get_name(self): return 'trackingnet_lmdb' def has_class_info(self): return True def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def _read_bb_anno(self, seq_id): set_id = self.sequence_list[seq_id][0] vid_name = self.sequence_list[seq_id][1] gt_str_list = decode_str(os.path.join(self.root, "TRAIN_%d_lmdb" % set_id), os.path.join("anno", vid_name + ".txt")).split('\n')[:-1] gt_list = [list(map(float, line.split(','))) for line in gt_str_list] gt_arr = np.array(gt_list).astype(np.float32) return torch.tensor(gt_arr) def get_sequence_info(self, seq_id): bbox = self._read_bb_anno(seq_id) valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0) visible = valid.clone().byte() return {'bbox': bbox, 'valid': valid, 'visible': visible} def _get_frame(self, seq_id, frame_id): set_id = self.sequence_list[seq_id][0] vid_name = self.sequence_list[seq_id][1] return decode_img(os.path.join(self.root, "TRAIN_%d_lmdb" % set_id), os.path.join("frames", vid_name, str(frame_id) + ".jpg")) def _get_class(self, seq_id): seq_name = self.sequence_list[seq_id][1] return self.seq_to_class_map[seq_name] def get_class_name(self, seq_id): obj_class = self._get_class(seq_id) return obj_class def get_frames(self, seq_id, frame_ids, anno=None): frame_list = [self._get_frame(seq_id, f) for f in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] obj_class = self._get_class(seq_id) object_meta = OrderedDict({'object_class_name': obj_class, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return frame_list, anno_frames, object_meta ================================================ FILE: lib/train/run_training.py ================================================ import os import sys import argparse import importlib import cv2 as cv import torch.backends.cudnn import torch.distributed as dist import torch import random import numpy as np torch.backends.cudnn.benchmark = False import _init_paths import lib.train.admin.settings as ws_settings def init_seeds(seed): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False torch.set_num_threads(4) cv.setNumThreads(1) cv.ocl.setUseOpenCL(False) def run_training(script_name, config_name, cudnn_benchmark=True, local_rank=-1, save_dir=None, base_seed=None, use_lmdb=False, script_name_prv=None, config_name_prv=None, use_wandb=False, distill=None, script_teacher=None, config_teacher=None): """Run the train script. args: script_name: Name of emperiment in the "experiments/" folder. config_name: Name of the yaml file in the "experiments/". cudnn_benchmark: Use cudnn benchmark or not (default is True). """ if save_dir is None: print("save_dir dir is not given. Use the default dir instead.") # This is needed to avoid strange crashes related to opencv torch.set_num_threads(4) cv.setNumThreads(4) torch.backends.cudnn.benchmark = cudnn_benchmark print('script_name: {}.py config_name: {}.yaml'.format(script_name, config_name)) '''2021.1.5 set seed for different process''' if base_seed is not None: if local_rank != -1: init_seeds(base_seed + local_rank) else: init_seeds(base_seed) settings = ws_settings.Settings() settings.script_name = script_name settings.config_name = config_name settings.project_path = 'train/{}/{}'.format(script_name, config_name) if script_name_prv is not None and config_name_prv is not None: settings.project_path_prv = 'train/{}/{}'.format(script_name_prv, config_name_prv) settings.local_rank = local_rank settings.save_dir = os.path.abspath(save_dir) settings.use_lmdb = use_lmdb prj_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")) settings.cfg_file = os.path.join(prj_dir, 'experiments/%s/%s.yaml' % (script_name, config_name)) settings.use_wandb = use_wandb if distill: settings.distill = distill settings.script_teacher = script_teacher settings.config_teacher = config_teacher if script_teacher is not None and config_teacher is not None: settings.project_path_teacher = 'train/{}/{}'.format(script_teacher, config_teacher) settings.cfg_file_teacher = os.path.join(prj_dir, 'experiments/%s/%s.yaml' % (script_teacher, config_teacher)) expr_module = importlib.import_module('lib.train.train_script_distill') else: expr_module = importlib.import_module('lib.train.train_script') expr_func = getattr(expr_module, 'run') expr_func(settings) def main(): parser = argparse.ArgumentParser(description='Run a train scripts in train_settings.') parser.add_argument('--script', type=str, required=True, help='Name of the train script.') parser.add_argument('--config', type=str, required=True, help="Name of the config file.") parser.add_argument('--cudnn_benchmark', type=bool, default=False, help='Set cudnn benchmark on (1) or off (0) (default is on).') parser.add_argument('--local_rank', default=-1, type=int, help='node rank for distributed training') parser.add_argument('--save_dir', type=str, help='the directory to save checkpoints and logs') parser.add_argument('--seed', type=int, default=42, help='seed for random numbers') parser.add_argument('--use_lmdb', type=int, choices=[0, 1], default=0) # whether datasets are in lmdb format parser.add_argument('--script_prv', type=str, default=None, help='Name of the train script of previous model.') parser.add_argument('--config_prv', type=str, default=None, help="Name of the config file of previous model.") parser.add_argument('--use_wandb', type=int, choices=[0, 1], default=0) # whether to use wandb # for knowledge distillation parser.add_argument('--distill', type=int, choices=[0, 1], default=0) # whether to use knowledge distillation parser.add_argument('--script_teacher', type=str, help='teacher script name') parser.add_argument('--config_teacher', type=str, help='teacher yaml configure file name') args = parser.parse_args() if args.local_rank != -1: dist.init_process_group(backend='nccl') torch.cuda.set_device(args.local_rank) else: torch.cuda.set_device(0) run_training(args.script, args.config, cudnn_benchmark=args.cudnn_benchmark, local_rank=args.local_rank, save_dir=args.save_dir, base_seed=args.seed, use_lmdb=args.use_lmdb, script_name_prv=args.script_prv, config_name_prv=args.config_prv, use_wandb=args.use_wandb, distill=args.distill, script_teacher=args.script_teacher, config_teacher=args.config_teacher) if __name__ == '__main__': main() ================================================ FILE: lib/train/train_script.py ================================================ import os # loss function related from lib.utils.box_ops import giou_loss from torch.nn.functional import l1_loss from torch.nn import BCEWithLogitsLoss # train pipeline related from lib.train.trainers import LTRTrainer, LTRSeqTrainer, LTRSeqTrainerV2 from lib.train.dataset import Lasot, Got10k, MSCOCOSeq, ImagenetVID, TrackingNet from lib.train.dataset import Lasot_lmdb, Got10k_lmdb, MSCOCOSeq_lmdb, ImagenetVID_lmdb, TrackingNet_lmdb from lib.train.data import sampler, opencv_loader, processing, LTRLoader, sequence_sampler, sequence_sampler_v2 # distributed training related from torch.nn.parallel import DistributedDataParallel as DDP # some more advanced functions from .base_functions import * # network related from lib.models.artrack import build_artrack from lib.models.artrackv2 import build_artrackv2 from lib.models.artrack_seq import build_artrack_seq from lib.models.artrackv2_seq import build_artrackv2_seq # forward propagation related from lib.train.actors import ARTrackActor, ARTrackSeqActor, ARTrackV2Actor, ARTrackV2SeqActor # for import modules import importlib from ..utils.focal_loss import FocalLoss def names2datasets(name_list: list, settings, image_loader): assert isinstance(name_list, list) datasets = [] #settings.use_lmdb = True for name in name_list: assert name in ["LASOT", "GOT10K_vottrain", "GOT10K_votval", "GOT10K_train_full", "GOT10K_official_val", "COCO17", "VID", "TRACKINGNET"] if name == "LASOT": if settings.use_lmdb: print("Building lasot dataset from lmdb") datasets.append(Lasot_lmdb(settings.env.lasot_lmdb_dir, split='train', image_loader=image_loader)) else: datasets.append(Lasot(settings.env.lasot_dir, split='train', image_loader=image_loader)) if name == "GOT10K_vottrain": if settings.use_lmdb: print("Building got10k from lmdb") datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='vottrain', image_loader=image_loader)) else: datasets.append(Got10k(settings.env.got10k_dir, split='vottrain', image_loader=image_loader)) if name == "GOT10K_train_full": if settings.use_lmdb: print("Building got10k_train_full from lmdb") datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='train_full', image_loader=image_loader)) else: datasets.append(Got10k(settings.env.got10k_dir, split='train_full', image_loader=image_loader)) if name == "GOT10K_votval": if settings.use_lmdb: print("Building got10k from lmdb") datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='votval', image_loader=image_loader)) else: datasets.append(Got10k(settings.env.got10k_dir, split='votval', image_loader=image_loader)) if name == "GOT10K_official_val": if settings.use_lmdb: raise ValueError("Not implement") else: datasets.append(Got10k(settings.env.got10k_val_dir, split=None, image_loader=image_loader)) if name == "COCO17": if settings.use_lmdb: print("Building COCO2017 from lmdb") datasets.append(MSCOCOSeq_lmdb(settings.env.coco_lmdb_dir, version="2017", image_loader=image_loader)) else: datasets.append(MSCOCOSeq(settings.env.coco_dir, version="2017", image_loader=image_loader)) if name == "VID": if settings.use_lmdb: print("Building VID from lmdb") datasets.append(ImagenetVID_lmdb(settings.env.imagenet_lmdb_dir, image_loader=image_loader)) else: datasets.append(ImagenetVID(settings.env.imagenet_dir, image_loader=image_loader)) if name == "TRACKINGNET": if settings.use_lmdb: print("Building TrackingNet from lmdb") datasets.append(TrackingNet_lmdb(settings.env.trackingnet_lmdb_dir, image_loader=image_loader)) else: # raise ValueError("NOW WE CAN ONLY USE TRACKINGNET FROM LMDB") datasets.append(TrackingNet(settings.env.trackingnet_dir, image_loader=image_loader)) return datasets def slt_collate(batch): ret = {} for k in batch[0].keys(): here_list = [] for ex in batch: here_list.append(ex[k]) ret[k] = here_list return ret class SLTLoader(torch.utils.data.dataloader.DataLoader): """ Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset. """ __initialized = False def __init__(self, name, dataset, training=True, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, epoch_interval=1, collate_fn=None, stack_dim=0, pin_memory=False, drop_last=False, timeout=0, worker_init_fn=None): if collate_fn is None: collate_fn = slt_collate super(SLTLoader, self).__init__(dataset, batch_size, shuffle, sampler, batch_sampler, num_workers, collate_fn, pin_memory, drop_last, timeout, worker_init_fn) self.name = name self.training = training self.epoch_interval = epoch_interval self.stack_dim = stack_dim def run(settings): settings.description = 'Training script for STARK-S, STARK-ST stage1, and STARK-ST stage2' # update the default configs with config file if not os.path.exists(settings.cfg_file): raise ValueError("%s doesn't exist." % settings.cfg_file) config_module = importlib.import_module("lib.config.%s.config" % settings.script_name) cfg = config_module.cfg config_module.update_config_from_file(settings.cfg_file) if settings.local_rank in [-1, 0]: print("New configuration is shown below.") for key in cfg.keys(): print("%s configuration:" % key, cfg[key]) print('\n') # update settings based on cfg update_settings(settings, cfg) # Record the training log log_dir = os.path.join(settings.save_dir, 'logs') if settings.local_rank in [-1, 0]: if not os.path.exists(log_dir): os.makedirs(log_dir) settings.log_file = os.path.join(log_dir, "%s-%s.log" % (settings.script_name, settings.config_name)) # Build dataloaders if "RepVGG" in cfg.MODEL.BACKBONE.TYPE or "swin" in cfg.MODEL.BACKBONE.TYPE or "LightTrack" in cfg.MODEL.BACKBONE.TYPE: cfg.ckpt_dir = settings.save_dir bins = cfg.MODEL.BINS search_size = cfg.DATA.SEARCH.SIZE # Create network if settings.script_name == "artrack": net = build_artrack(cfg) loader_train, loader_val = build_dataloaders(cfg, settings) elif settings.script_name == "artrack_seq": net = build_artrack_seq(cfg) dataset_train = sequence_sampler.SequenceSampler( datasets=names2datasets(cfg.DATA.TRAIN.DATASETS_NAME, settings, opencv_loader), p_datasets=cfg.DATA.TRAIN.DATASETS_RATIO, samples_per_epoch=cfg.DATA.TRAIN.SAMPLE_PER_EPOCH, max_gap=cfg.DATA.MAX_GAP, max_interval=cfg.DATA.MAX_INTERVAL, num_search_frames=cfg.DATA.SEARCH.NUMBER, num_template_frames=1, frame_sample_mode='random_interval', prob=cfg.DATA.INTERVAL_PROB) loader_train = SLTLoader('train', dataset_train, training=True, batch_size=cfg.TRAIN.BATCH_SIZE, num_workers=cfg.TRAIN.NUM_WORKER, shuffle=False, drop_last=True) elif settings.script_name == "artrackv2": net = build_artrackv2(cfg) loader_train, loader_val = build_dataloaders(cfg, settings) elif settings.script_name == "artrackv2_seq": net = build_artrackv2_seq(cfg) dataset_train = sequence_sampler_v2.SequenceSampler( datasets=names2datasets(cfg.DATA.TRAIN.DATASETS_NAME, settings, opencv_loader), p_datasets=cfg.DATA.TRAIN.DATASETS_RATIO, samples_per_epoch=cfg.DATA.TRAIN.SAMPLE_PER_EPOCH, max_gap=cfg.DATA.MAX_GAP, max_interval=cfg.DATA.MAX_INTERVAL, num_search_frames=cfg.DATA.SEARCH.NUMBER, num_template_frames=1, frame_sample_mode='random_interval', prob=cfg.DATA.INTERVAL_PROB) loader_train = SLTLoader('train', dataset_train, training=True, batch_size=cfg.TRAIN.BATCH_SIZE, num_workers=cfg.TRAIN.NUM_WORKER, shuffle=False, drop_last=True) else: raise ValueError("illegal script name") # wrap networks to distributed one net.cuda() if settings.local_rank != -1: # net = torch.nn.SyncBatchNorm.convert_sync_batchnorm(net) # add syncBN converter net = DDP(net, device_ids=[settings.local_rank], find_unused_parameters=True) settings.device = torch.device("cuda:%d" % settings.local_rank) else: settings.device = torch.device("cuda:0") settings.deep_sup = getattr(cfg.TRAIN, "DEEP_SUPERVISION", False) settings.distill = getattr(cfg.TRAIN, "DISTILL", False) settings.distill_loss_type = getattr(cfg.TRAIN, "DISTILL_LOSS_TYPE", "KL") # Loss functions and Actors if settings.script_name == "artrack": focal_loss = FocalLoss() objective = {'giou': giou_loss, 'l1': l1_loss, 'focal': focal_loss} loss_weight = {'giou': cfg.TRAIN.GIOU_WEIGHT, 'l1': cfg.TRAIN.L1_WEIGHT, 'focal': 2.} actor = ARTrackActor(net=net, objective=objective, loss_weight=loss_weight, settings=settings, cfg=cfg, bins=bins, search_size=search_size) elif settings.script_name == "artrack_seq": focal_loss = FocalLoss() objective = {'giou': giou_loss, 'l1': l1_loss, 'focal': focal_loss} loss_weight = {'giou': cfg.TRAIN.GIOU_WEIGHT, 'l1': cfg.TRAIN.L1_WEIGHT, 'focal': 2.} actor = ARTrackSeqActor(net=net, objective=objective, loss_weight=loss_weight, settings=settings, cfg=cfg, bins=bins, search_size=search_size) elif settings.script_name == "artrackv2": focal_loss = FocalLoss() objective = {'giou': giou_loss, 'l1': l1_loss, 'focal': focal_loss} loss_weight = {'giou': cfg.TRAIN.GIOU_WEIGHT, 'l1': cfg.TRAIN.L1_WEIGHT, 'focal': 2., 'score': cfg.TRAIN.SCORE_WEIGHT} actor = ARTrackV2Actor(net=net, objective=objective, loss_weight=loss_weight, settings=settings, cfg=cfg, bins=bins, search_size=search_size) elif settings.script_name == "artrackv2_seq": focal_loss = FocalLoss() objective = {'giou': giou_loss, 'l1': l1_loss, 'focal': focal_loss} loss_weight = {'giou': cfg.TRAIN.GIOU_WEIGHT, 'l1': cfg.TRAIN.L1_WEIGHT, 'focal': 2., 'score_update': cfg.TRAIN.SCORE_WEIGHT} actor = ARTrackV2SeqActor(net=net, objective=objective, loss_weight=loss_weight, settings=settings, cfg=cfg, bins=bins, search_size=search_size) else: raise ValueError("illegal script name") # if cfg.TRAIN.DEEP_SUPERVISION: # raise ValueError("Deep supervision is not supported now.") # Optimizer, parameters, and learning rates if settings.script_name == 'artrack' or settings.script_name == 'artrack_seq': optimizer, lr_scheduler = get_optimizer_scheduler(net, cfg) elif settings.script_name == 'artrackv2' or settings.script_name == 'artrackv2_seq': optimizer, lr_scheduler = get_optimizer_scheduler_v2(net, cfg) use_amp = getattr(cfg.TRAIN, "AMP", False) if settings.script_name == "artrack": trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler, use_amp=use_amp) elif settings.script_name == "artrack_seq": trainer = LTRSeqTrainer(actor, [loader_train], optimizer, settings, lr_scheduler, use_amp=use_amp) elif settings.script_name == "artrackv2": trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler, use_amp=use_amp) elif settings.script_name == "artrackv2_seq": trainer = LTRSeqTrainerV2(actor, [loader_train], optimizer, settings, lr_scheduler, use_amp=use_amp) # train process trainer.train(cfg.TRAIN.EPOCH, load_latest=True, fail_safe=True) ================================================ FILE: lib/train/train_script_distill.py ================================================ import os # loss function related from lib.utils.box_ops import giou_loss from torch.nn.functional import l1_loss from torch.nn import BCEWithLogitsLoss # train pipeline related from lib.train.trainers import LTRTrainer # distributed training related from torch.nn.parallel import DistributedDataParallel as DDP # some more advanced functions from .base_functions import * # network related from lib.models.stark import build_starks, build_starkst from lib.models.stark import build_stark_lightning_x_trt # forward propagation related from lib.train.actors import STARKLightningXtrtdistillActor # for import modules import importlib def build_network(script_name, cfg): # Create network if script_name == "stark_s": net = build_starks(cfg) elif script_name == "stark_st1" or script_name == "stark_st2": net = build_starkst(cfg) elif script_name == "stark_lightning_X_trt": net = build_stark_lightning_x_trt(cfg, phase="train") else: raise ValueError("illegal script name") return net def run(settings): settings.description = 'Training script for STARK-S, STARK-ST stage1, and STARK-ST stage2' # update the default configs with config file if not os.path.exists(settings.cfg_file): raise ValueError("%s doesn't exist." % settings.cfg_file) config_module = importlib.import_module("lib.config.%s.config" % settings.script_name) cfg = config_module.cfg config_module.update_config_from_file(settings.cfg_file) if settings.local_rank in [-1, 0]: print("New configuration is shown below.") for key in cfg.keys(): print("%s configuration:" % key, cfg[key]) print('\n') # update the default teacher configs with teacher config file if not os.path.exists(settings.cfg_file_teacher): raise ValueError("%s doesn't exist." % settings.cfg_file_teacher) config_module_teacher = importlib.import_module("lib.config.%s.config" % settings.script_teacher) cfg_teacher = config_module_teacher.cfg config_module_teacher.update_config_from_file(settings.cfg_file_teacher) if settings.local_rank in [-1, 0]: print("New teacher configuration is shown below.") for key in cfg_teacher.keys(): print("%s configuration:" % key, cfg_teacher[key]) print('\n') # update settings based on cfg update_settings(settings, cfg) # Record the training log log_dir = os.path.join(settings.save_dir, 'logs') if settings.local_rank in [-1, 0]: if not os.path.exists(log_dir): os.makedirs(log_dir) settings.log_file = os.path.join(log_dir, "%s-%s.log" % (settings.script_name, settings.config_name)) # Build dataloaders loader_train, loader_val = build_dataloaders(cfg, settings) if "RepVGG" in cfg.MODEL.BACKBONE.TYPE or "swin" in cfg.MODEL.BACKBONE.TYPE: cfg.ckpt_dir = settings.save_dir """turn on the distillation mode""" cfg.TRAIN.DISTILL = True cfg_teacher.TRAIN.DISTILL = True net = build_network(settings.script_name, cfg) net_teacher = build_network(settings.script_teacher, cfg_teacher) # wrap networks to distributed one net.cuda() net_teacher.cuda() net_teacher.eval() if settings.local_rank != -1: net = DDP(net, device_ids=[settings.local_rank], find_unused_parameters=True) net_teacher = DDP(net_teacher, device_ids=[settings.local_rank], find_unused_parameters=True) settings.device = torch.device("cuda:%d" % settings.local_rank) else: settings.device = torch.device("cuda:0") # settings.deep_sup = getattr(cfg.TRAIN, "DEEP_SUPERVISION", False) # settings.distill = getattr(cfg.TRAIN, "DISTILL", False) settings.distill_loss_type = getattr(cfg.TRAIN, "DISTILL_LOSS_TYPE", "L1") # Loss functions and Actors if settings.script_name == "stark_lightning_X_trt": objective = {'giou': giou_loss, 'l1': l1_loss} loss_weight = {'giou': cfg.TRAIN.GIOU_WEIGHT, 'l1': cfg.TRAIN.L1_WEIGHT} actor = STARKLightningXtrtdistillActor(net=net, objective=objective, loss_weight=loss_weight, settings=settings, net_teacher=net_teacher) else: raise ValueError("illegal script name") # Optimizer, parameters, and learning rates optimizer, lr_scheduler = get_optimizer_scheduler(net, cfg) use_amp = getattr(cfg.TRAIN, "AMP", False) trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler, use_amp=use_amp) # train process trainer.train(cfg.TRAIN.EPOCH, load_latest=True, fail_safe=True, distill=True) ================================================ FILE: lib/train/trainers/__init__.py ================================================ from .base_trainer import BaseTrainer from .ltr_trainer import LTRTrainer from .ltr_seq_trainer import LTRSeqTrainer from .ltr_seq_trainer_v2 import LTRSeqTrainerV2 ================================================ FILE: lib/train/trainers/base_trainer.py ================================================ import os import glob import torch import traceback from lib.train.admin import multigpu from torch.utils.data.distributed import DistributedSampler class BaseTrainer: """Base trainer class. Contains functions for training and saving/loading checkpoints. Trainer classes should inherit from this one and overload the train_epoch function.""" def __init__(self, actor, loaders, optimizer, settings, lr_scheduler=None): """ args: actor - The actor for training the network loaders - list of dataset loaders, e.g. [train_loader, val_loader]. In each epoch, the trainer runs one epoch for each loader. optimizer - The optimizer used for training, e.g. Adam settings - Training settings lr_scheduler - Learning rate scheduler """ self.actor = actor self.optimizer = optimizer self.lr_scheduler = lr_scheduler self.loaders = loaders self.update_settings(settings) self.epoch = 0 self.stats = {} self.device = getattr(settings, 'device', None) if self.device is None: self.device = torch.device("cuda:0" if torch.cuda.is_available() and settings.use_gpu else "cpu") self.actor.to(self.device) self.settings = settings def update_settings(self, settings=None): """Updates the trainer settings. Must be called to update internal settings.""" if settings is not None: self.settings = settings if self.settings.env.workspace_dir is not None: self.settings.env.workspace_dir = os.path.expanduser(self.settings.env.workspace_dir) '''2021.1.4 New function: specify checkpoint dir''' if self.settings.save_dir is None: self._checkpoint_dir = os.path.join(self.settings.env.workspace_dir, 'checkpoints') else: self._checkpoint_dir = os.path.join(self.settings.save_dir, 'checkpoints') print("checkpoints will be saved to %s" % self._checkpoint_dir) if self.settings.local_rank in [-1, 0]: if not os.path.exists(self._checkpoint_dir): print("Training with multiple GPUs. checkpoints directory doesn't exist. " "Create checkpoints directory") os.makedirs(self._checkpoint_dir) else: self._checkpoint_dir = None def train(self, max_epochs, load_latest=False, fail_safe=True, load_previous_ckpt=False, distill=False): """Do training for the given number of epochs. args: max_epochs - Max number of training epochs, load_latest - Bool indicating whether to resume from latest epoch. fail_safe - Bool indicating whether the training to automatically restart in case of any crashes. """ epoch = -1 num_tries = 1 for i in range(num_tries): try: if load_latest: self.load_checkpoint() if load_previous_ckpt: directory = '{}/{}'.format(self._checkpoint_dir, self.settings.project_path_prv) self.load_state_dict(directory) if distill: directory_teacher = '{}/{}'.format(self._checkpoint_dir, self.settings.project_path_teacher) self.load_state_dict(directory_teacher, distill=True) for epoch in range(self.epoch+1, max_epochs+1): self.epoch = epoch self.train_epoch() if self.lr_scheduler is not None: if self.settings.scheduler_type != 'cosine': self.lr_scheduler.step() else: self.lr_scheduler.step(epoch - 1) # only save the last 10 checkpoints save_every_epoch = getattr(self.settings, "save_every_epoch", False) save_epochs = [] if epoch > (max_epochs - 1) or save_every_epoch or epoch % 5 == 0 or epoch in save_epochs or epoch > (max_epochs - 5): # if epoch > (max_epochs - 10) or save_every_epoch or epoch % 100 == 0: if self._checkpoint_dir: if self.settings.local_rank in [-1, 0]: self.save_checkpoint() except: print('Training crashed at epoch {}'.format(epoch)) if fail_safe: self.epoch -= 1 load_latest = True print('Traceback for the error!') print(traceback.format_exc()) print('Restarting training from last epoch ...') else: raise print('Finished training!') def train_epoch(self): raise NotImplementedError def save_checkpoint(self): """Saves a checkpoint of the network and other variables.""" net = self.actor.net.module if multigpu.is_multi_gpu(self.actor.net) else self.actor.net actor_type = type(self.actor).__name__ net_type = type(net).__name__ state = { 'epoch': self.epoch, 'actor_type': actor_type, 'net_type': net_type, 'net': net.state_dict(), 'net_info': getattr(net, 'info', None), 'constructor': getattr(net, 'constructor', None), 'optimizer': self.optimizer.state_dict(), 'stats': self.stats, 'settings': self.settings } directory = '{}/{}'.format(self._checkpoint_dir, self.settings.project_path) print(directory) if not os.path.exists(directory): print("directory doesn't exist. creating...") os.makedirs(directory) # First save as a tmp file tmp_file_path = '{}/{}_ep{:04d}.tmp'.format(directory, net_type, self.epoch) torch.save(state, tmp_file_path) file_path = '{}/{}_ep{:04d}.pth.tar'.format(directory, net_type, self.epoch) # Now rename to actual checkpoint. os.rename seems to be atomic if files are on same filesystem. Not 100% sure os.rename(tmp_file_path, file_path) def load_checkpoint(self, checkpoint = None, fields = None, ignore_fields = None, load_constructor = False): """Loads a network checkpoint file. Can be called in three different ways: load_checkpoint(): Loads the latest epoch from the workspace. Use this to continue training. load_checkpoint(epoch_num): Loads the network at the given epoch number (int). load_checkpoint(path_to_checkpoint): Loads the file from the given absolute path (str). """ net = self.actor.net.module if multigpu.is_multi_gpu(self.actor.net) else self.actor.net actor_type = type(self.actor).__name__ net_type = type(net).__name__ if checkpoint is None: # Load most recent checkpoint checkpoint_list = sorted(glob.glob('{}/{}/{}_ep*.pth.tar'.format(self._checkpoint_dir, self.settings.project_path, net_type))) if checkpoint_list: checkpoint_path = checkpoint_list[-1] else: print('No matching checkpoint file found') return elif isinstance(checkpoint, int): # Checkpoint is the epoch number checkpoint_path = '{}/{}/{}_ep{:04d}.pth.tar'.format(self._checkpoint_dir, self.settings.project_path, net_type, checkpoint) elif isinstance(checkpoint, str): # checkpoint is the path if os.path.isdir(checkpoint): checkpoint_list = sorted(glob.glob('{}/*_ep*.pth.tar'.format(checkpoint))) if checkpoint_list: checkpoint_path = checkpoint_list[-1] else: raise Exception('No checkpoint found') else: checkpoint_path = os.path.expanduser(checkpoint) else: raise TypeError # Load network print(checkpoint_path) checkpoint_dict = torch.load(checkpoint_path, map_location='cpu') print(checkpoint_dict['net_type']) assert net_type == checkpoint_dict['net_type'], 'Network is not of correct type.' if fields is None: fields = checkpoint_dict.keys() if ignore_fields is None: ignore_fields = ['settings'] # Never load the scheduler. It exists in older checkpoints. ignore_fields.extend(['lr_scheduler', 'constructor', 'net_type', 'actor_type', 'net_info']) # Load all fields for key in fields: if key in ignore_fields: continue if key == 'net': net.load_state_dict(checkpoint_dict[key]) elif key == 'optimizer': self.optimizer.load_state_dict(checkpoint_dict[key]) else: setattr(self, key, checkpoint_dict[key]) # Set the net info if load_constructor and 'constructor' in checkpoint_dict and checkpoint_dict['constructor'] is not None: net.constructor = checkpoint_dict['constructor'] if 'net_info' in checkpoint_dict and checkpoint_dict['net_info'] is not None: net.info = checkpoint_dict['net_info'] # Update the epoch in lr scheduler if 'epoch' in fields: self.lr_scheduler.last_epoch = self.epoch # 2021.1.10 Update the epoch in data_samplers for loader in self.loaders: if isinstance(loader.sampler, DistributedSampler): loader.sampler.set_epoch(self.epoch) return True def load_state_dict(self, checkpoint=None, distill=False): """Loads a network checkpoint file. Can be called in three different ways: load_checkpoint(): Loads the latest epoch from the workspace. Use this to continue training. load_checkpoint(epoch_num): Loads the network at the given epoch number (int). load_checkpoint(path_to_checkpoint): Loads the file from the given absolute path (str). """ if distill: net = self.actor.net_teacher.module if multigpu.is_multi_gpu(self.actor.net_teacher) \ else self.actor.net_teacher else: net = self.actor.net.module if multigpu.is_multi_gpu(self.actor.net) else self.actor.net net_type = type(net).__name__ if isinstance(checkpoint, str): # checkpoint is the path if os.path.isdir(checkpoint): checkpoint_list = sorted(glob.glob('{}/*_ep*.pth.tar'.format(checkpoint))) if checkpoint_list: checkpoint_path = checkpoint_list[-1] else: raise Exception('No checkpoint found') else: checkpoint_path = os.path.expanduser(checkpoint) else: raise TypeError # Load network print("Loading pretrained model from ", checkpoint_path) checkpoint_dict = torch.load(checkpoint_path, map_location='cpu') assert net_type == checkpoint_dict['net_type'], 'Network is not of correct type.' missing_k, unexpected_k = net.load_state_dict(checkpoint_dict["net"], strict=False) print("previous checkpoint is loaded.") print("missing keys: ", missing_k) print("unexpected keys:", unexpected_k) return True ================================================ FILE: lib/train/trainers/ltr_seq_trainer.py ================================================ import os import datetime from collections import OrderedDict from torch.nn.utils import clip_grad_norm_ # from lib.train.data.wandb_logger import WandbWriter from lib.train.trainers import BaseTrainer from lib.train.admin import AverageMeter, StatValue from memory_profiler import profile # from lib.train.admin import TensorboardWriter import torch import time import numpy as np from torch.utils.data.distributed import DistributedSampler from torch.cuda.amp import autocast from torch.cuda.amp import GradScaler from lib.utils.misc import get_world_size class LTRSeqTrainer(BaseTrainer): def __init__(self, actor, loaders, optimizer, settings, lr_scheduler=None, use_amp=False): """ args: actor - The actor for training the network loaders - list of dataset loaders, e.g. [train_loader, val_loader]. In each epoch, the trainer runs one epoch for each loader. optimizer - The optimizer used for training, e.g. Adam settings - Training settings lr_scheduler - Learning rate scheduler """ super().__init__(actor, loaders, optimizer, settings, lr_scheduler) self._set_default_settings() # Initialize statistics variables self.stats = OrderedDict({loader.name: None for loader in self.loaders}) # Initialize tensorboard and wandb # self.wandb_writer = None # if settings.local_rank in [-1, 0]: # tensorboard_writer_dir = os.path.join(self.settings.env.tensorboard_dir, self.settings.project_path) # if not os.path.exists(tensorboard_writer_dir): # os.makedirs(tensorboard_writer_dir) # self.tensorboard_writer = TensorboardWriter(tensorboard_writer_dir, [l.name for l in loaders]) # if settings.use_wandb: # world_size = get_world_size() # cur_train_samples = self.loaders[0].dataset.samples_per_epoch * max(0, self.epoch - 1) # interval = (world_size * settings.batchsize) # * interval # self.wandb_writer = WandbWriter(settings.project_path[6:], {}, tensorboard_writer_dir, cur_train_samples, interval) self.move_data_to_gpu = getattr(settings, 'move_data_to_gpu', True) print("move_data", self.move_data_to_gpu) self.settings = settings self.use_amp = use_amp if use_amp: self.scaler = GradScaler() def _set_default_settings(self): # Dict of all default values default = {'print_interval': 10, 'print_stats': None, 'description': ''} for param, default_value in default.items(): if getattr(self.settings, param, None) is None: setattr(self.settings, param, default_value) self.miou_list = [] def cycle_dataset(self, loader): """Do a cycle of training or validation.""" torch.autograd.set_detect_anomaly(True) self.actor.train(loader.training) torch.set_grad_enabled(loader.training) self._init_timing() for i, data in enumerate(loader, 1): self.actor.eval() self.data_read_done_time = time.time() with torch.no_grad(): explore_result = self.actor.explore(data) if explore_result == None: print("this time i skip") # self._update_stats(stats, batch_size, loader) continue # get inputs # print(data) self.data_to_gpu_time = time.time() data['epoch'] = self.epoch data['settings'] = self.settings stats = {} reward_record = [] miou_record = [] e_miou_record = [] num_seq = len(data['num_frames']) # Calculate reward tensor # reward_tensor = torch.zeros(explore_result['baseline_iou'].size()) baseline_iou = explore_result['baseline_iou'] # explore_iou = explore_result['explore_iou'] for seq_idx in range(num_seq): num_frames = data['num_frames'][seq_idx] - 1 b_miou = torch.mean(baseline_iou[:num_frames, seq_idx]) # e_miou = torch.mean(explore_iou[:num_frames, seq_idx]) miou_record.append(b_miou.item()) # e_miou_record.append(e_miou.item()) b_reward = b_miou.item() # e_reward = e_miou.item() # iou_gap = e_reward - b_reward # reward_record.append(iou_gap) # reward_tensor[:num_frames, seq_idx] = iou_gap # Training mode cursor = 0 bs_backward = 1 # print(self.actor.net.module.box_head.decoder.layers[2].mlpx.fc1.weight) self.optimizer.zero_grad() while cursor < num_seq: # print("now is ", cursor , "and all is ", num_seq) model_inputs = {} model_inputs['slt_loss_weight'] = 15 if cursor < num_seq: model_inputs['template_images'] = explore_result['template_images'][ cursor:cursor + bs_backward].cuda() else: model_inputs['template_images'] = explore_result['template_images_reverse'][ cursor - num_seq:cursor - num_seq + bs_backward].cuda() model_inputs['search_images'] = explore_result['search_images'][:, cursor:cursor + bs_backward].cuda() model_inputs['search_anno'] = explore_result['search_anno'][:, cursor:cursor + bs_backward].cuda() model_inputs['pre_seq'] = explore_result['pre_seq'][:, cursor:cursor + bs_backward].cuda() model_inputs['x_feat'] = explore_result['x_feat'].squeeze(1)[:, cursor:cursor + bs_backward].cuda() model_inputs['epoch'] = data['epoch'] # model_inputs['template_update'] = explore_result['template_update'].squeeze(1)[:, # cursor:cursor + bs_backward].cuda() # print("this is cursor") # print(explore_result['pre_seq'].shape) # print(explore_result['x_feat'].squeeze(1).shape) # model_inputs['action_tensor'] = explore_result['action_tensor'][:, cursor:cursor + bs_backward].cuda() # model_inputs['reward_tensor'] = reward_tensor[:, cursor:cursor + bs_backward].cuda() loss, stats_cur = self.actor.compute_sequence_losses(model_inputs) # for name, param in self.actor.net.named_parameters(): # shape, c = (param.grad.shape, param.grad.sum()) if param.grad is not None else (None, None) # print(f'{name}: {param.shape} \n\t grad: {shape} \n\t {c}') # print("i make this!") loss.backward() # print("i made that?") for key, val in stats_cur.items(): if key in stats: stats[key] += val * (bs_backward / num_seq) else: stats[key] = val * (bs_backward / num_seq) cursor += bs_backward grad_norm = clip_grad_norm_(self.actor.net.parameters(), 100) stats['grad_norm'] = grad_norm # print(self.actor.net.module.backbone.blocks[8].mlp.fc1.weight) self.optimizer.step() # print(self.optimizer) miou = np.mean(miou_record) self.miou_list.append(miou) # stats['reward'] = np.mean(reward_record) # stats['e_mIoU'] = np.mean(e_miou_record) stats['mIoU'] = miou stats['mIoU10'] = np.mean(self.miou_list[-10:]) stats['mIoU100'] = np.mean(self.miou_list[-100:]) batch_size = num_seq * np.max(data['num_frames']) self._update_stats(stats, batch_size, loader) self._print_stats(i, loader, batch_size) torch.cuda.empty_cache() # # forward pass # if not self.use_amp: # loss, stats = self.actor(data) # else: # with autocast(): # loss, stats = self.actor(data) # # # backward pass and update weights # if loader.training: # self.optimizer.zero_grad() # if not self.use_amp: # loss.backward() # if self.settings.grad_clip_norm > 0: # torch.nn.utils.clip_grad_norm_(self.actor.net.parameters(), self.settings.grad_clip_norm) # self.optimizer.step() # else: # self.scaler.scale(loss).backward() # self.scaler.step(self.optimizer) # self.scaler.update() # update statistics # batch_size = data['template_images'].shape[loader.stack_dim] # self._update_stats(stats, batch_size, loader) # print statistics # self._print_stats(i, loader, batch_size) # update wandb status # if self.wandb_writer is not None and i % self.settings.print_interval == 0: # if self.settings.local_rank in [-1, 0]: # self.wandb_writer.write_log(self.stats, self.epoch) # calculate ETA after every epoch # epoch_time = self.prev_time - self.start_time # print("Epoch Time: " + str(datetime.timedelta(seconds=epoch_time))) # print("Avg Data Time: %.5f" % (self.avg_date_time / self.num_frames * batch_size)) # print("Avg GPU Trans Time: %.5f" % (self.avg_gpu_trans_time / self.num_frames * batch_size)) # print("Avg Forward Time: %.5f" % (self.avg_forward_time / self.num_frames * batch_size)) def train_epoch(self): """Do one epoch for each loader.""" for loader in self.loaders: if self.epoch % loader.epoch_interval == 0: # 2021.1.10 Set epoch if isinstance(loader.sampler, DistributedSampler): loader.sampler.set_epoch(self.epoch) self.cycle_dataset(loader) self._stats_new_epoch() # if self.settings.local_rank in [-1, 0]: # self._write_tensorboard() def _init_timing(self): self.num_frames = 0 self.start_time = time.time() self.prev_time = self.start_time self.avg_date_time = 0 self.avg_gpu_trans_time = 0 self.avg_forward_time = 0 def _update_stats(self, new_stats: OrderedDict, batch_size, loader): # Initialize stats if not initialized yet if loader.name not in self.stats.keys() or self.stats[loader.name] is None: self.stats[loader.name] = OrderedDict({name: AverageMeter() for name in new_stats.keys()}) # add lr state if loader.training: lr_list = self.lr_scheduler.get_last_lr() for i, lr in enumerate(lr_list): var_name = 'LearningRate/group{}'.format(i) if var_name not in self.stats[loader.name].keys(): self.stats[loader.name][var_name] = StatValue() self.stats[loader.name][var_name].update(lr) for name, val in new_stats.items(): if name not in self.stats[loader.name].keys(): self.stats[loader.name][name] = AverageMeter() self.stats[loader.name][name].update(val, batch_size) def _print_stats(self, i, loader, batch_size): self.num_frames += batch_size current_time = time.time() batch_fps = batch_size / (current_time - self.prev_time) average_fps = self.num_frames / (current_time - self.start_time) prev_frame_time_backup = self.prev_time self.prev_time = current_time self.avg_date_time += (self.data_read_done_time - prev_frame_time_backup) self.avg_gpu_trans_time += (self.data_to_gpu_time - self.data_read_done_time) self.avg_forward_time += current_time - self.data_to_gpu_time if i % self.settings.print_interval == 0 or i == loader.__len__(): print_str = '[%s: %d, %d / %d] ' % (loader.name, self.epoch, i, loader.__len__()) print_str += 'FPS: %.1f (%.1f) , ' % (average_fps, batch_fps) # 2021.12.14 add data time print print_str += 'DataTime: %.3f (%.3f) , ' % ( self.avg_date_time / self.num_frames * batch_size, self.avg_gpu_trans_time / self.num_frames * batch_size) print_str += 'ForwardTime: %.3f , ' % (self.avg_forward_time / self.num_frames * batch_size) print_str += 'TotalTime: %.3f , ' % ((current_time - self.start_time) / self.num_frames * batch_size) # print_str += 'DataTime: %.3f (%.3f) , ' % (self.data_read_done_time - prev_frame_time_backup, self.data_to_gpu_time - self.data_read_done_time) # print_str += 'ForwardTime: %.3f , ' % (current_time - self.data_to_gpu_time) # print_str += 'TotalTime: %.3f , ' % (current_time - prev_frame_time_backup) for name, val in self.stats[loader.name].items(): if (self.settings.print_stats is None or name in self.settings.print_stats): if hasattr(val, 'avg'): print_str += '%s: %.5f , ' % (name, val.avg) # else: # print_str += '%s: %r , ' % (name, val) print(print_str[:-5]) log_str = print_str[:-5] + '\n' with open(self.settings.log_file, 'a') as f: f.write(log_str) def _stats_new_epoch(self): # Record learning rate for loader in self.loaders: if loader.training: try: lr_list = self.lr_scheduler.get_last_lr() except: lr_list = self.lr_scheduler._get_lr(self.epoch) for i, lr in enumerate(lr_list): var_name = 'LearningRate/group{}'.format(i) if var_name not in self.stats[loader.name].keys(): self.stats[loader.name][var_name] = StatValue() self.stats[loader.name][var_name].update(lr) for loader_stats in self.stats.values(): if loader_stats is None: continue for stat_value in loader_stats.values(): if hasattr(stat_value, 'new_epoch'): stat_value.new_epoch() # def _write_tensorboard(self): # if self.epoch == 1: # self.tensorboard_writer.write_info(self.settings.script_name, self.settings.description) # self.tensorboard_writer.write_epoch(self.stats, self.epoch) ================================================ FILE: lib/train/trainers/ltr_seq_trainer_v2.py ================================================ import os import datetime from collections import OrderedDict from torch.nn.utils import clip_grad_norm_ # from lib.train.data.wandb_logger import WandbWriter from lib.train.trainers import BaseTrainer from lib.train.admin import AverageMeter, StatValue from memory_profiler import profile # from lib.train.admin import TensorboardWriter import torch import time import numpy as np from torch.utils.data.distributed import DistributedSampler from torch.cuda.amp import autocast from torch.cuda.amp import GradScaler from lib.utils.misc import get_world_size class LTRSeqTrainerV2(BaseTrainer): def __init__(self, actor, loaders, optimizer, settings, lr_scheduler=None, use_amp=False): """ args: actor - The actor for training the network loaders - list of dataset loaders, e.g. [train_loader, val_loader]. In each epoch, the trainer runs one epoch for each loader. optimizer - The optimizer used for training, e.g. Adam settings - Training settings lr_scheduler - Learning rate scheduler """ super().__init__(actor, loaders, optimizer, settings, lr_scheduler) self._set_default_settings() # Initialize statistics variables self.stats = OrderedDict({loader.name: None for loader in self.loaders}) # Initialize tensorboard and wandb # self.wandb_writer = None # if settings.local_rank in [-1, 0]: # tensorboard_writer_dir = os.path.join(self.settings.env.tensorboard_dir, self.settings.project_path) # if not os.path.exists(tensorboard_writer_dir): # os.makedirs(tensorboard_writer_dir) # self.tensorboard_writer = TensorboardWriter(tensorboard_writer_dir, [l.name for l in loaders]) # if settings.use_wandb: # world_size = get_world_size() # cur_train_samples = self.loaders[0].dataset.samples_per_epoch * max(0, self.epoch - 1) # interval = (world_size * settings.batchsize) # * interval # self.wandb_writer = WandbWriter(settings.project_path[6:], {}, tensorboard_writer_dir, cur_train_samples, interval) self.move_data_to_gpu = getattr(settings, 'move_data_to_gpu', True) print("move_data", self.move_data_to_gpu) self.settings = settings self.use_amp = use_amp if use_amp: self.scaler = GradScaler() def _set_default_settings(self): # Dict of all default values default = {'print_interval': 10, 'print_stats': None, 'description': ''} for param, default_value in default.items(): if getattr(self.settings, param, None) is None: setattr(self.settings, param, default_value) self.miou_list = [] def cycle_dataset(self, loader): """Do a cycle of training or validation.""" torch.autograd.set_detect_anomaly(True) self.actor.train(loader.training) torch.set_grad_enabled(loader.training) self._init_timing() for i, data in enumerate(loader, 1): self.actor.eval() self.data_read_done_time = time.time() with torch.no_grad(): explore_result = self.actor.explore(data) if explore_result == None: print("this time i skip") continue # get inputs # print(data) self.data_to_gpu_time = time.time() data['epoch'] = self.epoch data['settings'] = self.settings stats = {} reward_record = [] miou_record = [] e_miou_record = [] num_seq = len(data['num_frames']) # Calculate reward tensor # reward_tensor = torch.zeros(explore_result['baseline_iou'].size()) baseline_iou = explore_result['baseline_iou'] # explore_iou = explore_result['explore_iou'] for seq_idx in range(num_seq): num_frames = data['num_frames'][seq_idx] - 1 b_miou = torch.mean(baseline_iou[:num_frames, seq_idx]) # e_miou = torch.mean(explore_iou[:num_frames, seq_idx]) miou_record.append(b_miou.item()) # e_miou_record.append(e_miou.item()) b_reward = b_miou.item() # e_reward = e_miou.item() # iou_gap = e_reward - b_reward # reward_record.append(iou_gap) # reward_tensor[:num_frames, seq_idx] = iou_gap # Training mode cursor = 0 bs_backward = 1 # print(self.actor.net.module.box_head.decoder.layers[2].mlpx.fc1.weight) self.optimizer.zero_grad() self.actor.train() while cursor < num_seq * 2: model_inputs = {} model_inputs['slt_loss_weight'] = 15 model_inputs['search_images'] = explore_result['search_images'][:, cursor:cursor + bs_backward].cuda() model_inputs['search_anno'] = explore_result['search_anno'][:, cursor:cursor + bs_backward].cuda() model_inputs['pre_seq'] = explore_result['pre_seq'][:, cursor:cursor + bs_backward].cuda() model_inputs['x_feat'] = explore_result['x_feat'].squeeze(1)[:, cursor:cursor + bs_backward].cuda() model_inputs['template_images_z0'] = explore_result['template_images_z0'][:, cursor:cursor + bs_backward].cuda() model_inputs['dz_feat_update'] = explore_result['dz_feat_update'][:, cursor:cursor + bs_backward].cuda() model_inputs['target_in_search'] = explore_result['target_in_search'][:, cursor:cursor + bs_backward].cuda() model_inputs['epoch'] = self.epoch loss, stats_cur = self.actor.compute_sequence_losses(model_inputs) loss.backward() for key, val in stats_cur.items(): if key in stats: stats[key] += val * (bs_backward / num_seq) else: stats[key] = val * (bs_backward / num_seq) cursor += bs_backward grad_norm = clip_grad_norm_(self.actor.net.parameters(), 100) stats['grad_norm'] = grad_norm self.optimizer.step() miou = np.mean(miou_record) self.miou_list.append(miou) stats['mIoU'] = miou stats['mIoU10'] = np.mean(self.miou_list[-10:]) stats['mIoU100'] = np.mean(self.miou_list[-100:]) batch_size = num_seq * np.max(data['num_frames']) self._update_stats(stats, batch_size, loader) self._print_stats(i, loader, batch_size) torch.cuda.empty_cache() def train_epoch(self): """Do one epoch for each loader.""" for loader in self.loaders: if self.epoch % loader.epoch_interval == 0: # 2021.1.10 Set epoch if isinstance(loader.sampler, DistributedSampler): loader.sampler.set_epoch(self.epoch) self.cycle_dataset(loader) self._stats_new_epoch() # if self.settings.local_rank in [-1, 0]: # self._write_tensorboard() def _init_timing(self): self.num_frames = 0 self.start_time = time.time() self.prev_time = self.start_time self.avg_date_time = 0 self.avg_gpu_trans_time = 0 self.avg_forward_time = 0 def _update_stats(self, new_stats: OrderedDict, batch_size, loader): # Initialize stats if not initialized yet if loader.name not in self.stats.keys() or self.stats[loader.name] is None: self.stats[loader.name] = OrderedDict({name: AverageMeter() for name in new_stats.keys()}) # add lr state if loader.training: lr_list = self.lr_scheduler.get_last_lr() for i, lr in enumerate(lr_list): var_name = 'LearningRate/group{}'.format(i) if var_name not in self.stats[loader.name].keys(): self.stats[loader.name][var_name] = StatValue() self.stats[loader.name][var_name].update(lr) for name, val in new_stats.items(): if name not in self.stats[loader.name].keys(): self.stats[loader.name][name] = AverageMeter() self.stats[loader.name][name].update(val, batch_size) def _print_stats(self, i, loader, batch_size): self.num_frames += batch_size current_time = time.time() batch_fps = batch_size / (current_time - self.prev_time) average_fps = self.num_frames / (current_time - self.start_time) prev_frame_time_backup = self.prev_time self.prev_time = current_time self.avg_date_time += (self.data_read_done_time - prev_frame_time_backup) self.avg_gpu_trans_time += (self.data_to_gpu_time - self.data_read_done_time) self.avg_forward_time += current_time - self.data_to_gpu_time if i % self.settings.print_interval == 0 or i == loader.__len__(): print_str = '[%s: %d, %d / %d] ' % (loader.name, self.epoch, i, loader.__len__()) print_str += 'FPS: %.1f (%.1f) , ' % (average_fps, batch_fps) # 2021.12.14 add data time print print_str += 'DataTime: %.3f (%.3f) , ' % ( self.avg_date_time / self.num_frames * batch_size, self.avg_gpu_trans_time / self.num_frames * batch_size) print_str += 'ForwardTime: %.3f , ' % (self.avg_forward_time / self.num_frames * batch_size) print_str += 'TotalTime: %.3f , ' % ((current_time - self.start_time) / self.num_frames * batch_size) # print_str += 'DataTime: %.3f (%.3f) , ' % (self.data_read_done_time - prev_frame_time_backup, self.data_to_gpu_time - self.data_read_done_time) # print_str += 'ForwardTime: %.3f , ' % (current_time - self.data_to_gpu_time) # print_str += 'TotalTime: %.3f , ' % (current_time - prev_frame_time_backup) for name, val in self.stats[loader.name].items(): if (self.settings.print_stats is None or name in self.settings.print_stats): if hasattr(val, 'avg'): print_str += '%s: %.5f , ' % (name, val.avg) # else: # print_str += '%s: %r , ' % (name, val) print(print_str[:-5]) log_str = print_str[:-5] + '\n' with open(self.settings.log_file, 'a') as f: f.write(log_str) def _stats_new_epoch(self): # Record learning rate for loader in self.loaders: if loader.training: try: lr_list = self.lr_scheduler.get_last_lr() except: lr_list = self.lr_scheduler._get_lr(self.epoch) for i, lr in enumerate(lr_list): var_name = 'LearningRate/group{}'.format(i) if var_name not in self.stats[loader.name].keys(): self.stats[loader.name][var_name] = StatValue() self.stats[loader.name][var_name].update(lr) for loader_stats in self.stats.values(): if loader_stats is None: continue for stat_value in loader_stats.values(): if hasattr(stat_value, 'new_epoch'): stat_value.new_epoch() # def _write_tensorboard(self): # if self.epoch == 1: # self.tensorboard_writer.write_info(self.settings.script_name, self.settings.description) # self.tensorboard_writer.write_epoch(self.stats, self.epoch) ================================================ FILE: lib/train/trainers/ltr_trainer.py ================================================ import os import datetime from collections import OrderedDict #from lib.train.data.wandb_logger import WandbWriter from lib.train.trainers import BaseTrainer from lib.train.admin import AverageMeter, StatValue #from lib.train.admin import TensorboardWriter import torch import time from torch.utils.data.distributed import DistributedSampler from torch.cuda.amp import autocast from torch.cuda.amp import GradScaler from lib.utils.misc import get_world_size class LTRTrainer(BaseTrainer): def __init__(self, actor, loaders, optimizer, settings, lr_scheduler=None, use_amp=False): """ args: actor - The actor for training the network loaders - list of dataset loaders, e.g. [train_loader, val_loader]. In each epoch, the trainer runs one epoch for each loader. optimizer - The optimizer used for training, e.g. Adam settings - Training settings lr_scheduler - Learning rate scheduler """ super().__init__(actor, loaders, optimizer, settings, lr_scheduler) self._set_default_settings() # Initialize statistics variables self.stats = OrderedDict({loader.name: None for loader in self.loaders}) # Initialize tensorboard and wandb #self.wandb_writer = None #if settings.local_rank in [-1, 0]: # tensorboard_writer_dir = os.path.join(self.settings.env.tensorboard_dir, self.settings.project_path) # if not os.path.exists(tensorboard_writer_dir): # os.makedirs(tensorboard_writer_dir) # self.tensorboard_writer = TensorboardWriter(tensorboard_writer_dir, [l.name for l in loaders]) # if settings.use_wandb: # world_size = get_world_size() # cur_train_samples = self.loaders[0].dataset.samples_per_epoch * max(0, self.epoch - 1) # interval = (world_size * settings.batchsize) # * interval # self.wandb_writer = WandbWriter(settings.project_path[6:], {}, tensorboard_writer_dir, cur_train_samples, interval) self.move_data_to_gpu = getattr(settings, 'move_data_to_gpu', True) print("move_data", self.move_data_to_gpu) self.settings = settings self.use_amp = use_amp if use_amp: self.scaler = GradScaler() def _set_default_settings(self): # Dict of all default values default = {'print_interval': 10, 'print_stats': None, 'description': ''} for param, default_value in default.items(): if getattr(self.settings, param, None) is None: setattr(self.settings, param, default_value) def cycle_dataset(self, loader): """Do a cycle of training or validation.""" self.actor.train(loader.training) torch.set_grad_enabled(loader.training) self._init_timing() for i, data in enumerate(loader, 1): self.data_read_done_time = time.time() # get inputs if self.move_data_to_gpu: data = data.to(self.device) self.data_to_gpu_time = time.time() data['epoch'] = self.epoch data['settings'] = self.settings # forward pass if not self.use_amp: loss, stats = self.actor(data) else: with autocast(): loss, stats = self.actor(data) # backward pass and update weights if loader.training: self.optimizer.zero_grad() if not self.use_amp: loss.backward() if self.settings.grad_clip_norm > 0: torch.nn.utils.clip_grad_norm_(self.actor.net.parameters(), self.settings.grad_clip_norm) self.optimizer.step() else: self.scaler.scale(loss).backward() self.scaler.step(self.optimizer) self.scaler.update() # update statistics batch_size = data['template_images'].shape[loader.stack_dim] self._update_stats(stats, batch_size, loader) # print statistics self._print_stats(i, loader, batch_size) # update wandb status #if self.wandb_writer is not None and i % self.settings.print_interval == 0: # if self.settings.local_rank in [-1, 0]: # self.wandb_writer.write_log(self.stats, self.epoch) # calculate ETA after every epoch epoch_time = self.prev_time - self.start_time print("Epoch Time: " + str(datetime.timedelta(seconds=epoch_time))) print("Avg Data Time: %.5f" % (self.avg_date_time / self.num_frames * batch_size)) print("Avg GPU Trans Time: %.5f" % (self.avg_gpu_trans_time / self.num_frames * batch_size)) print("Avg Forward Time: %.5f" % (self.avg_forward_time / self.num_frames * batch_size)) def train_epoch(self): """Do one epoch for each loader.""" for loader in self.loaders: if self.epoch % loader.epoch_interval == 0: # 2021.1.10 Set epoch if isinstance(loader.sampler, DistributedSampler): loader.sampler.set_epoch(self.epoch) self.cycle_dataset(loader) self._stats_new_epoch() #if self.settings.local_rank in [-1, 0]: # self._write_tensorboard() def _init_timing(self): self.num_frames = 0 self.start_time = time.time() self.prev_time = self.start_time self.avg_date_time = 0 self.avg_gpu_trans_time = 0 self.avg_forward_time = 0 def _update_stats(self, new_stats: OrderedDict, batch_size, loader): # Initialize stats if not initialized yet if loader.name not in self.stats.keys() or self.stats[loader.name] is None: self.stats[loader.name] = OrderedDict({name: AverageMeter() for name in new_stats.keys()}) # add lr state if loader.training: lr_list = self.lr_scheduler.get_last_lr() for i, lr in enumerate(lr_list): var_name = 'LearningRate/group{}'.format(i) if var_name not in self.stats[loader.name].keys(): self.stats[loader.name][var_name] = StatValue() self.stats[loader.name][var_name].update(lr) for name, val in new_stats.items(): if name not in self.stats[loader.name].keys(): self.stats[loader.name][name] = AverageMeter() self.stats[loader.name][name].update(val, batch_size) def _print_stats(self, i, loader, batch_size): self.num_frames += batch_size current_time = time.time() batch_fps = batch_size / (current_time - self.prev_time) average_fps = self.num_frames / (current_time - self.start_time) prev_frame_time_backup = self.prev_time self.prev_time = current_time self.avg_date_time += (self.data_read_done_time - prev_frame_time_backup) self.avg_gpu_trans_time += (self.data_to_gpu_time - self.data_read_done_time) self.avg_forward_time += current_time - self.data_to_gpu_time if i % self.settings.print_interval == 0 or i == loader.__len__(): print_str = '[%s: %d, %d / %d] ' % (loader.name, self.epoch, i, loader.__len__()) print_str += 'FPS: %.1f (%.1f) , ' % (average_fps, batch_fps) # 2021.12.14 add data time print print_str += 'DataTime: %.3f (%.3f) , ' % (self.avg_date_time / self.num_frames * batch_size, self.avg_gpu_trans_time / self.num_frames * batch_size) print_str += 'ForwardTime: %.3f , ' % (self.avg_forward_time / self.num_frames * batch_size) print_str += 'TotalTime: %.3f , ' % ((current_time - self.start_time) / self.num_frames * batch_size) # print_str += 'DataTime: %.3f (%.3f) , ' % (self.data_read_done_time - prev_frame_time_backup, self.data_to_gpu_time - self.data_read_done_time) # print_str += 'ForwardTime: %.3f , ' % (current_time - self.data_to_gpu_time) # print_str += 'TotalTime: %.3f , ' % (current_time - prev_frame_time_backup) for name, val in self.stats[loader.name].items(): if (self.settings.print_stats is None or name in self.settings.print_stats): if hasattr(val, 'avg'): print_str += '%s: %.5f , ' % (name, val.avg) # else: # print_str += '%s: %r , ' % (name, val) print(print_str[:-5]) log_str = print_str[:-5] + '\n' with open(self.settings.log_file, 'a') as f: f.write(log_str) def _stats_new_epoch(self): # Record learning rate for loader in self.loaders: if loader.training: try: lr_list = self.lr_scheduler.get_last_lr() except: lr_list = self.lr_scheduler._get_lr(self.epoch) for i, lr in enumerate(lr_list): var_name = 'LearningRate/group{}'.format(i) if var_name not in self.stats[loader.name].keys(): self.stats[loader.name][var_name] = StatValue() self.stats[loader.name][var_name].update(lr) for loader_stats in self.stats.values(): if loader_stats is None: continue for stat_value in loader_stats.values(): if hasattr(stat_value, 'new_epoch'): stat_value.new_epoch() #def _write_tensorboard(self): # if self.epoch == 1: # self.tensorboard_writer.write_info(self.settings.script_name, self.settings.description) # self.tensorboard_writer.write_epoch(self.stats, self.epoch) ================================================ FILE: lib/utils/__init__.py ================================================ from .tensor import TensorDict, TensorList ================================================ FILE: lib/utils/box_ops.py ================================================ import torch from torchvision.ops.boxes import box_area import numpy as np def box_xywh_to_cxywh(x): x1, y1, w, h = x.unbind(-1) b = [x1+0.5*w, y1+0.5*h, w, h] return torch.stack(b, dim=-1) def box_cxcywh_to_xyxy(x): x_c, y_c, w, h = x.unbind(-1) b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)] return torch.stack(b, dim=-1) def box_xywh_to_xyxy(x): x1, y1, w, h = x.unbind(-1) b = [x1, y1, x1 + w, y1 + h] return torch.stack(b, dim=-1) def box_xyxy_to_xywh(x): x1, y1, x2, y2 = x.unbind(-1) b = [x1, y1, x2 - x1, y2 - y1] return torch.stack(b, dim=-1) def box_xyxy_to_cxcywh(x): x0, y0, x1, y1 = x.unbind(-1) b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)] return torch.stack(b, dim=-1) # modified from torchvision to also return the union '''Note that this function only supports shape (N,4)''' def box_iou(boxes1, boxes2): """ :param boxes1: (N, 4) (x1,y1,x2,y2) :param boxes2: (N, 4) (x1,y1,x2,y2) :return: """ area1 = box_area(boxes1) # (N,) area2 = box_area(boxes2) # (N,) lt = torch.max(boxes1[:, :2], boxes2[:, :2]) # (N,2) rb = torch.min(boxes1[:, 2:], boxes2[:, 2:]) # (N,2) wh = (rb - lt).clamp(min=0) # (N,2) inter = wh[:, 0] * wh[:, 1] # (N,) union = area1 + area2 - inter iou = inter / union return iou, union '''Note that this implementation is different from DETR's''' def generalized_box_iou(boxes1, boxes2): """ Generalized IoU from https://giou.stanford.edu/ The boxes should be in [x0, y0, x1, y1] format boxes1: (N, 4) boxes2: (N, 4) """ # degenerate boxes gives inf / nan results # so do an early check # try: #assert (boxes1[:, 2:] >= boxes1[:, :2]).all() # assert (boxes2[:, 2:] >= boxes2[:, :2]).all() iou, union = box_iou(boxes1, boxes2) # (N,) lt = torch.min(boxes1[:, :2], boxes2[:, :2]) rb = torch.max(boxes1[:, 2:], boxes2[:, 2:]) wh = (rb - lt).clamp(min=0) # (N,2) area = wh[:, 0] * wh[:, 1] # (N,) return iou - (area - union) / area, iou def giou_loss(boxes1, boxes2): """ :param boxes1: (N, 4) (x1,y1,x2,y2) :param boxes2: (N, 4) (x1,y1,x2,y2) :return: """ giou, iou = generalized_box_iou(boxes1, boxes2) return (1 - giou).mean(), iou def clip_box(box: list, H, W, margin=0): x1, y1, w, h = box x2, y2 = x1 + w, y1 + h x1 = min(max(0, x1), W-margin) x2 = min(max(margin, x2), W) y1 = min(max(0, y1), H-margin) y2 = min(max(margin, y2), H) w = max(margin, x2-x1) h = max(margin, y2-y1) return [x1, y1, w, h] ================================================ FILE: lib/utils/ce_utils.py ================================================ import math import torch import torch.nn.functional as F def generate_bbox_mask(bbox_mask, bbox): b, h, w = bbox_mask.shape for i in range(b): bbox_i = bbox[i].cpu().tolist() bbox_mask[i, int(bbox_i[1]):int(bbox_i[1] + bbox_i[3] - 1), int(bbox_i[0]):int(bbox_i[0] + bbox_i[2] - 1)] = 1 return bbox_mask def generate_mask_cond(cfg, bs, device, gt_bbox): template_size = cfg.DATA.TEMPLATE.SIZE stride = cfg.MODEL.BACKBONE.STRIDE template_feat_size = template_size // stride if cfg.MODEL.BACKBONE.CE_TEMPLATE_RANGE == 'ALL': box_mask_z = None elif cfg.MODEL.BACKBONE.CE_TEMPLATE_RANGE == 'CTR_POINT': if template_feat_size == 8: index = slice(3, 4) elif template_feat_size == 12: index = slice(5, 6) elif template_feat_size == 7: index = slice(3, 4) elif template_feat_size == 14: index = slice(6, 7) else: raise NotImplementedError box_mask_z = torch.zeros([bs, template_feat_size, template_feat_size], device=device) box_mask_z[:, index, index] = 1 box_mask_z = box_mask_z.flatten(1).to(torch.bool) elif cfg.MODEL.BACKBONE.CE_TEMPLATE_RANGE == 'CTR_REC': # use fixed 4x4 region, 3:5 for 8x8 # use fixed 4x4 region 5:6 for 12x12 if template_feat_size == 8: index = slice(3, 5) elif template_feat_size == 12: index = slice(5, 7) elif template_feat_size == 7: index = slice(3, 4) else: raise NotImplementedError box_mask_z = torch.zeros([bs, template_feat_size, template_feat_size], device=device) box_mask_z[:, index, index] = 1 box_mask_z = box_mask_z.flatten(1).to(torch.bool) elif cfg.MODEL.BACKBONE.CE_TEMPLATE_RANGE == 'GT_BOX': box_mask_z = torch.zeros([bs, template_size, template_size], device=device) # box_mask_z_ori = data['template_seg'][0].view(-1, 1, *data['template_seg'].shape[2:]) # (batch, 1, 128, 128) box_mask_z = generate_bbox_mask(box_mask_z, gt_bbox * template_size).unsqueeze(1).to( torch.float) # (batch, 1, 128, 128) # box_mask_z_vis = box_mask_z.cpu().numpy() box_mask_z = F.interpolate(box_mask_z, scale_factor=1. / cfg.MODEL.BACKBONE.STRIDE, mode='bilinear', align_corners=False) box_mask_z = box_mask_z.flatten(1).to(torch.bool) # box_mask_z_vis = box_mask_z[:, 0, ...].cpu().numpy() # gaussian_maps_vis = generate_heatmap(data['template_anno'], self.cfg.DATA.TEMPLATE.SIZE, self.cfg.MODEL.STRIDE)[0].cpu().numpy() else: raise NotImplementedError return box_mask_z def adjust_keep_rate(epoch, warmup_epochs, total_epochs, ITERS_PER_EPOCH, base_keep_rate=0.5, max_keep_rate=1, iters=-1): if epoch < warmup_epochs: return 1 if epoch >= total_epochs: return base_keep_rate if iters == -1: iters = epoch * ITERS_PER_EPOCH total_iters = ITERS_PER_EPOCH * (total_epochs - warmup_epochs) iters = iters - ITERS_PER_EPOCH * warmup_epochs keep_rate = base_keep_rate + (max_keep_rate - base_keep_rate) \ * (math.cos(iters / total_iters * math.pi) + 1) * 0.5 return keep_rate ================================================ FILE: lib/utils/focal_loss.py ================================================ from abc import ABC import torch import torch.nn as nn import torch.nn.functional as F class FocalLoss(nn.Module, ABC): def __init__(self, alpha=2, beta=4): super(FocalLoss, self).__init__() self.alpha = alpha self.beta = beta def forward(self, prediction, target): positive_index = target.eq(1).float() negative_index = target.lt(1).float() negative_weights = torch.pow(1 - target, self.beta) # clamp min value is set to 1e-12 to maintain the numerical stability prediction = torch.clamp(prediction, 1e-12) positive_loss = torch.log(prediction) * torch.pow(1 - prediction, self.alpha) * positive_index negative_loss = torch.log(1 - prediction) * torch.pow(prediction, self.alpha) * negative_weights * negative_index num_positive = positive_index.float().sum() positive_loss = positive_loss.sum() negative_loss = negative_loss.sum() if num_positive == 0: loss = -negative_loss else: loss = -(positive_loss + negative_loss) / num_positive return loss class LBHinge(nn.Module): """Loss that uses a 'hinge' on the lower bound. This means that for samples with a label value smaller than the threshold, the loss is zero if the prediction is also smaller than that threshold. args: error_matric: What base loss to use (MSE by default). threshold: Threshold to use for the hinge. clip: Clip the loss if it is above this value. """ def __init__(self, error_metric=nn.MSELoss(), threshold=None, clip=None): super().__init__() self.error_metric = error_metric self.threshold = threshold if threshold is not None else -100 self.clip = clip def forward(self, prediction, label, target_bb=None): negative_mask = (label < self.threshold).float() positive_mask = (1.0 - negative_mask) prediction = negative_mask * F.relu(prediction) + positive_mask * prediction loss = self.error_metric(prediction, positive_mask * label) if self.clip is not None: loss = torch.min(loss, torch.tensor([self.clip], device=loss.device)) return loss ================================================ FILE: lib/utils/heapmap_utils.py ================================================ import numpy as np import torch def generate_heatmap(bboxes, patch_size=320, stride=16): """ Generate ground truth heatmap same as CenterNet Args: bboxes (torch.Tensor): shape of [num_search, bs, 4] Returns: gaussian_maps: list of generated heatmap """ gaussian_maps = [] heatmap_size = patch_size // stride for single_patch_bboxes in bboxes: bs = single_patch_bboxes.shape[0] gt_scoremap = torch.zeros(bs, heatmap_size, heatmap_size) classes = torch.arange(bs).to(torch.long) bbox = single_patch_bboxes * heatmap_size wh = bbox[:, 2:] centers_int = (bbox[:, :2] + wh / 2).round() CenterNetHeatMap.generate_score_map(gt_scoremap, classes, wh, centers_int, 0.7) gaussian_maps.append(gt_scoremap.to(bbox.device)) return gaussian_maps class CenterNetHeatMap(object): @staticmethod def generate_score_map(fmap, gt_class, gt_wh, centers_int, min_overlap): radius = CenterNetHeatMap.get_gaussian_radius(gt_wh, min_overlap) radius = torch.clamp_min(radius, 0) radius = radius.type(torch.int).cpu().numpy() for i in range(gt_class.shape[0]): channel_index = gt_class[i] CenterNetHeatMap.draw_gaussian(fmap[channel_index], centers_int[i], radius[i]) @staticmethod def get_gaussian_radius(box_size, min_overlap): """ copyed from CornerNet box_size (w, h), it could be a torch.Tensor, numpy.ndarray, list or tuple notice: we are using a bug-version, please refer to fix bug version in CornerNet """ # box_tensor = torch.Tensor(box_size) box_tensor = box_size width, height = box_tensor[..., 0], box_tensor[..., 1] a1 = 1 b1 = height + width c1 = width * height * (1 - min_overlap) / (1 + min_overlap) sq1 = torch.sqrt(b1 ** 2 - 4 * a1 * c1) r1 = (b1 + sq1) / 2 a2 = 4 b2 = 2 * (height + width) c2 = (1 - min_overlap) * width * height sq2 = torch.sqrt(b2 ** 2 - 4 * a2 * c2) r2 = (b2 + sq2) / 2 a3 = 4 * min_overlap b3 = -2 * min_overlap * (height + width) c3 = (min_overlap - 1) * width * height sq3 = torch.sqrt(b3 ** 2 - 4 * a3 * c3) r3 = (b3 + sq3) / 2 return torch.min(r1, torch.min(r2, r3)) @staticmethod def gaussian2D(radius, sigma=1): # m, n = [(s - 1.) / 2. for s in shape] m, n = radius y, x = np.ogrid[-m: m + 1, -n: n + 1] gauss = np.exp(-(x * x + y * y) / (2 * sigma * sigma)) gauss[gauss < np.finfo(gauss.dtype).eps * gauss.max()] = 0 return gauss @staticmethod def draw_gaussian(fmap, center, radius, k=1): diameter = 2 * radius + 1 gaussian = CenterNetHeatMap.gaussian2D((radius, radius), sigma=diameter / 6) gaussian = torch.Tensor(gaussian) x, y = int(center[0]), int(center[1]) height, width = fmap.shape[:2] left, right = min(x, radius), min(width - x, radius + 1) top, bottom = min(y, radius), min(height - y, radius + 1) masked_fmap = fmap[y - top: y + bottom, x - left: x + right] masked_gaussian = gaussian[radius - top: radius + bottom, radius - left: radius + right] if min(masked_gaussian.shape) > 0 and min(masked_fmap.shape) > 0: masked_fmap = torch.max(masked_fmap, masked_gaussian * k) fmap[y - top: y + bottom, x - left: x + right] = masked_fmap # return fmap def compute_grids(features, strides): """ grids regret to the input image size """ grids = [] for level, feature in enumerate(features): h, w = feature.size()[-2:] shifts_x = torch.arange( 0, w * strides[level], step=strides[level], dtype=torch.float32, device=feature.device) shifts_y = torch.arange( 0, h * strides[level], step=strides[level], dtype=torch.float32, device=feature.device) shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) shift_x = shift_x.reshape(-1) shift_y = shift_y.reshape(-1) grids_per_level = torch.stack((shift_x, shift_y), dim=1) + \ strides[level] // 2 grids.append(grids_per_level) return grids def get_center3x3(locations, centers, strides, range=3): ''' Inputs: locations: M x 2 centers: N x 2 strides: M ''' range = (range - 1) / 2 M, N = locations.shape[0], centers.shape[0] locations_expanded = locations.view(M, 1, 2).expand(M, N, 2) # M x N x 2 centers_expanded = centers.view(1, N, 2).expand(M, N, 2) # M x N x 2 strides_expanded = strides.view(M, 1, 1).expand(M, N, 2) # M x N centers_discret = ((centers_expanded / strides_expanded).int() * strides_expanded).float() + \ strides_expanded / 2 # M x N x 2 dist_x = (locations_expanded[:, :, 0] - centers_discret[:, :, 0]).abs() dist_y = (locations_expanded[:, :, 1] - centers_discret[:, :, 1]).abs() return (dist_x <= strides_expanded[:, :, 0] * range) & \ (dist_y <= strides_expanded[:, :, 0] * range) def get_pred(score_map_ctr, size_map, offset_map, feat_size): max_score, idx = torch.max(score_map_ctr.flatten(1), dim=1, keepdim=True) idx = idx.unsqueeze(1).expand(idx.shape[0], 2, 1) size = size_map.flatten(2).gather(dim=2, index=idx).squeeze(-1) offset = offset_map.flatten(2).gather(dim=2, index=idx).squeeze(-1) return size * feat_size, offset ================================================ FILE: lib/utils/image.py ================================================ ================================================ FILE: lib/utils/lmdb_utils.py ================================================ import lmdb import numpy as np import cv2 import json LMDB_ENVS = dict() LMDB_HANDLES = dict() LMDB_FILELISTS = dict() def get_lmdb_handle(name): global LMDB_HANDLES, LMDB_FILELISTS item = LMDB_HANDLES.get(name, None) if item is None: env = lmdb.open(name, readonly=True, lock=False, readahead=False, meminit=False) LMDB_ENVS[name] = env item = env.begin(write=False) LMDB_HANDLES[name] = item return item def decode_img(lmdb_fname, key_name): handle = get_lmdb_handle(lmdb_fname) binfile = handle.get(key_name.encode()) if binfile is None: print("Illegal data detected. %s %s" % (lmdb_fname, key_name)) s = np.frombuffer(binfile, np.uint8) x = cv2.cvtColor(cv2.imdecode(s, cv2.IMREAD_COLOR), cv2.COLOR_BGR2RGB) return x def decode_str(lmdb_fname, key_name): handle = get_lmdb_handle(lmdb_fname) binfile = handle.get(key_name.encode()) string = binfile.decode() return string def decode_json(lmdb_fname, key_name): return json.loads(decode_str(lmdb_fname, key_name)) if __name__ == "__main__": lmdb_fname = "/data/sda/v-yanbi/iccv21/LittleBoy_clean/data/got10k_lmdb" '''Decode image''' # key_name = "test/GOT-10k_Test_000001/00000001.jpg" # img = decode_img(lmdb_fname, key_name) # cv2.imwrite("001.jpg", img) '''Decode str''' # key_name = "test/list.txt" # key_name = "train/GOT-10k_Train_000001/groundtruth.txt" key_name = "train/GOT-10k_Train_000001/absence.label" str_ = decode_str(lmdb_fname, key_name) print(str_) ================================================ FILE: lib/utils/merge.py ================================================ import torch def merge_template_search(inp_list, return_search=False, return_template=False): """NOTICE: search region related features must be in the last place""" seq_dict = {"feat": torch.cat([x["feat"] for x in inp_list], dim=0), "mask": torch.cat([x["mask"] for x in inp_list], dim=1), "pos": torch.cat([x["pos"] for x in inp_list], dim=0)} if return_search: x = inp_list[-1] seq_dict.update({"feat_x": x["feat"], "mask_x": x["mask"], "pos_x": x["pos"]}) if return_template: z = inp_list[0] seq_dict.update({"feat_z": z["feat"], "mask_z": z["mask"], "pos_z": z["pos"]}) return seq_dict def get_qkv(inp_list): """The 1st element of the inp_list is about the template, the 2nd (the last) element is about the search region""" dict_x = inp_list[-1] dict_c = {"feat": torch.cat([x["feat"] for x in inp_list], dim=0), "mask": torch.cat([x["mask"] for x in inp_list], dim=1), "pos": torch.cat([x["pos"] for x in inp_list], dim=0)} # concatenated dict q = dict_x["feat"] + dict_x["pos"] k = dict_c["feat"] + dict_c["pos"] v = dict_c["feat"] key_padding_mask = dict_c["mask"] return q, k, v, key_padding_mask ================================================ FILE: lib/utils/misc.py ================================================ # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved """ Misc functions, including distributed helpers. Mostly copy-paste from torchvision references. """ import os import subprocess import time from collections import defaultdict, deque import datetime import pickle from typing import Optional, List import torch import torch.distributed as dist from torch import Tensor # needed due to empty tensor bug in pytorch and torchvision 0.5 import torchvision vers = torchvision.__version__.split('.') if int(vers[0]) <= 0 and int(vers[1]) < 7: from torchvision.ops import _new_empty_tensor from torchvision.ops.misc import _output_size class SmoothedValue(object): """Track a series of values and provide access to smoothed values over a window or the global series average. """ def __init__(self, window_size=20, fmt=None): if fmt is None: fmt = "{median:.4f} ({global_avg:.4f})" self.deque = deque(maxlen=window_size) self.total = 0.0 self.count = 0 self.fmt = fmt def update(self, value, n=1): self.deque.append(value) self.count += n self.total += value * n def synchronize_between_processes(self): """ Warning: does not synchronize the deque! """ if not is_dist_avail_and_initialized(): return t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda') dist.barrier() dist.all_reduce(t) t = t.tolist() self.count = int(t[0]) self.total = t[1] @property def median(self): d = torch.tensor(list(self.deque)) return d.median().item() @property def avg(self): d = torch.tensor(list(self.deque), dtype=torch.float32) return d.mean().item() @property def global_avg(self): return self.total / self.count @property def max(self): return max(self.deque) @property def value(self): return self.deque[-1] def __str__(self): return self.fmt.format( median=self.median, avg=self.avg, global_avg=self.global_avg, max=self.max, value=self.value) def all_gather(data): """ Run all_gather on arbitrary picklable data (not necessarily tensors) Args: data: any picklable object Returns: list[data]: list of data gathered from each rank """ world_size = get_world_size() if world_size == 1: return [data] # serialized to a Tensor buffer = pickle.dumps(data) storage = torch.ByteStorage.from_buffer(buffer) tensor = torch.ByteTensor(storage).to("cuda") # obtain Tensor size of each rank local_size = torch.tensor([tensor.numel()], device="cuda") size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)] dist.all_gather(size_list, local_size) size_list = [int(size.item()) for size in size_list] max_size = max(size_list) # receiving Tensor from all ranks # we pad the tensor because torch all_gather does not support # gathering tensors of different shapes tensor_list = [] for _ in size_list: tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda")) if local_size != max_size: padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda") tensor = torch.cat((tensor, padding), dim=0) dist.all_gather(tensor_list, tensor) data_list = [] for size, tensor in zip(size_list, tensor_list): buffer = tensor.cpu().numpy().tobytes()[:size] data_list.append(pickle.loads(buffer)) return data_list def reduce_dict(input_dict, average=True): """ Args: input_dict (dict): all the values will be reduced average (bool): whether to do average or sum Reduce the values in the dictionary from all processes so that all processes have the averaged results. Returns a dict with the same fields as input_dict, after reduction. """ world_size = get_world_size() if world_size < 2: return input_dict with torch.no_grad(): names = [] values = [] # sort the keys so that they are consistent across processes for k in sorted(input_dict.keys()): names.append(k) values.append(input_dict[k]) values = torch.stack(values, dim=0) dist.all_reduce(values) if average: values /= world_size reduced_dict = {k: v for k, v in zip(names, values)} return reduced_dict class MetricLogger(object): def __init__(self, delimiter="\t"): self.meters = defaultdict(SmoothedValue) self.delimiter = delimiter def update(self, **kwargs): for k, v in kwargs.items(): if isinstance(v, torch.Tensor): v = v.item() assert isinstance(v, (float, int)) self.meters[k].update(v) def __getattr__(self, attr): if attr in self.meters: return self.meters[attr] if attr in self.__dict__: return self.__dict__[attr] raise AttributeError("'{}' object has no attribute '{}'".format( type(self).__name__, attr)) def __str__(self): loss_str = [] for name, meter in self.meters.items(): loss_str.append( "{}: {}".format(name, str(meter)) ) return self.delimiter.join(loss_str) def synchronize_between_processes(self): for meter in self.meters.values(): meter.synchronize_between_processes() def add_meter(self, name, meter): self.meters[name] = meter def log_every(self, iterable, print_freq, header=None): i = 0 if not header: header = '' start_time = time.time() end = time.time() iter_time = SmoothedValue(fmt='{avg:.4f}') data_time = SmoothedValue(fmt='{avg:.4f}') space_fmt = ':' + str(len(str(len(iterable)))) + 'd' if torch.cuda.is_available(): log_msg = self.delimiter.join([ header, '[{0' + space_fmt + '}/{1}]', 'eta: {eta}', '{meters}', 'time: {time}', 'data: {data}', 'max mem: {memory:.0f}' ]) else: log_msg = self.delimiter.join([ header, '[{0' + space_fmt + '}/{1}]', 'eta: {eta}', '{meters}', 'time: {time}', 'data: {data}' ]) MB = 1024.0 * 1024.0 for obj in iterable: data_time.update(time.time() - end) yield obj iter_time.update(time.time() - end) if i % print_freq == 0 or i == len(iterable) - 1: eta_seconds = iter_time.global_avg * (len(iterable) - i) eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) if torch.cuda.is_available(): print(log_msg.format( i, len(iterable), eta=eta_string, meters=str(self), time=str(iter_time), data=str(data_time), memory=torch.cuda.max_memory_allocated() / MB)) else: print(log_msg.format( i, len(iterable), eta=eta_string, meters=str(self), time=str(iter_time), data=str(data_time))) i += 1 end = time.time() total_time = time.time() - start_time total_time_str = str(datetime.timedelta(seconds=int(total_time))) print('{} Total time: {} ({:.4f} s / it)'.format( header, total_time_str, total_time / len(iterable))) def get_sha(): cwd = os.path.dirname(os.path.abspath(__file__)) def _run(command): return subprocess.check_output(command, cwd=cwd).decode('ascii').strip() sha = 'N/A' diff = "clean" branch = 'N/A' try: sha = _run(['git', 'rev-parse', 'HEAD']) subprocess.check_output(['git', 'diff'], cwd=cwd) diff = _run(['git', 'diff-index', 'HEAD']) diff = "has uncommited changes" if diff else "clean" branch = _run(['git', 'rev-parse', '--abbrev-ref', 'HEAD']) except Exception: pass message = f"sha: {sha}, status: {diff}, branch: {branch}" return message def collate_fn(batch): batch = list(zip(*batch)) batch[0] = nested_tensor_from_tensor_list(batch[0]) return tuple(batch) def _max_by_axis(the_list): # type: (List[List[int]]) -> List[int] maxes = the_list[0] # get the first one for sublist in the_list[1:]: # [h,w,3] for index, item in enumerate(sublist): # index: 0,1,2 maxes[index] = max(maxes[index], item) # compare current max with the other elements in the whole return maxes class NestedTensor(object): def __init__(self, tensors, mask: Optional[Tensor]): self.tensors = tensors self.mask = mask def to(self, device): # type: (Device) -> NestedTensor # noqa cast_tensor = self.tensors.to(device) mask = self.mask if mask is not None: assert mask is not None cast_mask = mask.to(device) else: cast_mask = None return NestedTensor(cast_tensor, cast_mask) def decompose(self): return self.tensors, self.mask def __repr__(self): return str(self.tensors) def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): # TODO make this more general if tensor_list[0].ndim == 3: if torchvision._is_tracing(): # nested_tensor_from_tensor_list() does not export well to ONNX # call _onnx_nested_tensor_from_tensor_list() instead return _onnx_nested_tensor_from_tensor_list(tensor_list) # TODO make it support different-sized images max_size = _max_by_axis([list(img.shape) for img in tensor_list]) # [[3,h1,w1], [3,h2,w2], [3,h3,w3], ...] # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list])) batch_shape = [len(tensor_list)] + max_size # () b, c, h, w = batch_shape dtype = tensor_list[0].dtype device = tensor_list[0].device tensor = torch.zeros(batch_shape, dtype=dtype, device=device) mask = torch.ones((b, h, w), dtype=torch.bool, device=device) for img, pad_img, m in zip(tensor_list, tensor, mask): pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) # copy valid regions of the images to the largest padded base. m[: img.shape[1], :img.shape[2]] = False else: raise ValueError('not supported') return NestedTensor(tensor, mask) # _onnx_nested_tensor_from_tensor_list() is an implementation of # nested_tensor_from_tensor_list() that is supported by ONNX tracing. @torch.jit.unused def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor: max_size = [] for i in range(tensor_list[0].dim()): max_size_i = torch.max(torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)).to(torch.int64) max_size.append(max_size_i) max_size = tuple(max_size) # work around for # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) # m[: img.shape[1], :img.shape[2]] = False # which is not yet supported in onnx padded_imgs = [] padded_masks = [] for img in tensor_list: padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))] padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0])) padded_imgs.append(padded_img) m = torch.zeros_like(img[0], dtype=torch.int, device=img.device) padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1) padded_masks.append(padded_mask.to(torch.bool)) tensor = torch.stack(padded_imgs) mask = torch.stack(padded_masks) return NestedTensor(tensor, mask=mask) def setup_for_distributed(is_master): """ This function disables printing when not in master process """ import builtins as __builtin__ builtin_print = __builtin__.print def print(*args, **kwargs): force = kwargs.pop('force', False) if is_master or force: builtin_print(*args, **kwargs) __builtin__.print = print def is_dist_avail_and_initialized(): if not dist.is_available(): return False if not dist.is_initialized(): return False return True def get_world_size(): if not is_dist_avail_and_initialized(): return 1 return dist.get_world_size() def get_rank(): if not is_dist_avail_and_initialized(): return 0 return dist.get_rank() def is_main_process(): return get_rank() == 0 def save_on_master(*args, **kwargs): if is_main_process(): torch.save(*args, **kwargs) def init_distributed_mode(args): if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ: args.rank = int(os.environ["RANK"]) args.world_size = int(os.environ['WORLD_SIZE']) args.gpu = int(os.environ['LOCAL_RANK']) elif 'SLURM_PROCID' in os.environ: args.rank = int(os.environ['SLURM_PROCID']) args.gpu = args.rank % torch.cuda.device_count() else: print('Not using distributed mode') args.distributed = False return args.distributed = True torch.cuda.set_device(args.gpu) args.dist_backend = 'nccl' print('| distributed init (rank {}): {}'.format( args.rank, args.dist_url), flush=True) torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank) torch.distributed.barrier() setup_for_distributed(args.rank == 0) @torch.no_grad() def accuracy(output, target, topk=(1,)): """Computes the precision@k for the specified values of k""" if target.numel() == 0: return [torch.zeros([], device=output.device)] maxk = max(topk) batch_size = target.size(0) _, pred = output.topk(maxk, 1, True, True) pred = pred.t() correct = pred.eq(target.view(1, -1).expand_as(pred)) res = [] for k in topk: correct_k = correct[:k].view(-1).float().sum(0) res.append(correct_k.mul_(100.0 / batch_size)) return res def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None): # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor """ Equivalent to nn.functional.interpolate, but with support for empty batch sizes. This will eventually be supported natively by PyTorch, and this class can go away. """ if float(torchvision.__version__[:3]) < 0.7: if input.numel() > 0: return torch.nn.functional.interpolate( input, size, scale_factor, mode, align_corners ) output_shape = _output_size(2, input, size, scale_factor) output_shape = list(input.shape[:-2]) + list(output_shape) return _new_empty_tensor(input, output_shape) else: return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners) ================================================ FILE: lib/utils/tensor.py ================================================ import functools import torch import copy from collections import OrderedDict class TensorDict(OrderedDict): """Container mainly used for dicts of torch tensors. Extends OrderedDict with pytorch functionality.""" def concat(self, other): """Concatenates two dicts without copying internal data.""" return TensorDict(self, **other) def copy(self): return TensorDict(super(TensorDict, self).copy()) def __deepcopy__(self, memodict={}): return TensorDict(copy.deepcopy(list(self), memodict)) def __getattr__(self, name): if not hasattr(torch.Tensor, name): raise AttributeError('\'TensorDict\' object has not attribute \'{}\''.format(name)) def apply_attr(*args, **kwargs): return TensorDict({n: getattr(e, name)(*args, **kwargs) if hasattr(e, name) else e for n, e in self.items()}) return apply_attr def attribute(self, attr: str, *args): return TensorDict({n: getattr(e, attr, *args) for n, e in self.items()}) def apply(self, fn, *args, **kwargs): return TensorDict({n: fn(e, *args, **kwargs) for n, e in self.items()}) @staticmethod def _iterable(a): return isinstance(a, (TensorDict, list)) class TensorList(list): """Container mainly used for lists of torch tensors. Extends lists with pytorch functionality.""" def __init__(self, list_of_tensors = None): if list_of_tensors is None: list_of_tensors = list() super(TensorList, self).__init__(list_of_tensors) def __deepcopy__(self, memodict={}): return TensorList(copy.deepcopy(list(self), memodict)) def __getitem__(self, item): if isinstance(item, int): return super(TensorList, self).__getitem__(item) elif isinstance(item, (tuple, list)): return TensorList([super(TensorList, self).__getitem__(i) for i in item]) else: return TensorList(super(TensorList, self).__getitem__(item)) def __add__(self, other): if TensorList._iterable(other): return TensorList([e1 + e2 for e1, e2 in zip(self, other)]) return TensorList([e + other for e in self]) def __radd__(self, other): if TensorList._iterable(other): return TensorList([e2 + e1 for e1, e2 in zip(self, other)]) return TensorList([other + e for e in self]) def __iadd__(self, other): if TensorList._iterable(other): for i, e2 in enumerate(other): self[i] += e2 else: for i in range(len(self)): self[i] += other return self def __sub__(self, other): if TensorList._iterable(other): return TensorList([e1 - e2 for e1, e2 in zip(self, other)]) return TensorList([e - other for e in self]) def __rsub__(self, other): if TensorList._iterable(other): return TensorList([e2 - e1 for e1, e2 in zip(self, other)]) return TensorList([other - e for e in self]) def __isub__(self, other): if TensorList._iterable(other): for i, e2 in enumerate(other): self[i] -= e2 else: for i in range(len(self)): self[i] -= other return self def __mul__(self, other): if TensorList._iterable(other): return TensorList([e1 * e2 for e1, e2 in zip(self, other)]) return TensorList([e * other for e in self]) def __rmul__(self, other): if TensorList._iterable(other): return TensorList([e2 * e1 for e1, e2 in zip(self, other)]) return TensorList([other * e for e in self]) def __imul__(self, other): if TensorList._iterable(other): for i, e2 in enumerate(other): self[i] *= e2 else: for i in range(len(self)): self[i] *= other return self def __truediv__(self, other): if TensorList._iterable(other): return TensorList([e1 / e2 for e1, e2 in zip(self, other)]) return TensorList([e / other for e in self]) def __rtruediv__(self, other): if TensorList._iterable(other): return TensorList([e2 / e1 for e1, e2 in zip(self, other)]) return TensorList([other / e for e in self]) def __itruediv__(self, other): if TensorList._iterable(other): for i, e2 in enumerate(other): self[i] /= e2 else: for i in range(len(self)): self[i] /= other return self def __matmul__(self, other): if TensorList._iterable(other): return TensorList([e1 @ e2 for e1, e2 in zip(self, other)]) return TensorList([e @ other for e in self]) def __rmatmul__(self, other): if TensorList._iterable(other): return TensorList([e2 @ e1 for e1, e2 in zip(self, other)]) return TensorList([other @ e for e in self]) def __imatmul__(self, other): if TensorList._iterable(other): for i, e2 in enumerate(other): self[i] @= e2 else: for i in range(len(self)): self[i] @= other return self def __mod__(self, other): if TensorList._iterable(other): return TensorList([e1 % e2 for e1, e2 in zip(self, other)]) return TensorList([e % other for e in self]) def __rmod__(self, other): if TensorList._iterable(other): return TensorList([e2 % e1 for e1, e2 in zip(self, other)]) return TensorList([other % e for e in self]) def __pos__(self): return TensorList([+e for e in self]) def __neg__(self): return TensorList([-e for e in self]) def __le__(self, other): if TensorList._iterable(other): return TensorList([e1 <= e2 for e1, e2 in zip(self, other)]) return TensorList([e <= other for e in self]) def __ge__(self, other): if TensorList._iterable(other): return TensorList([e1 >= e2 for e1, e2 in zip(self, other)]) return TensorList([e >= other for e in self]) def concat(self, other): return TensorList(super(TensorList, self).__add__(other)) def copy(self): return TensorList(super(TensorList, self).copy()) def unroll(self): if not any(isinstance(t, TensorList) for t in self): return self new_list = TensorList() for t in self: if isinstance(t, TensorList): new_list.extend(t.unroll()) else: new_list.append(t) return new_list def list(self): return list(self) def attribute(self, attr: str, *args): return TensorList([getattr(e, attr, *args) for e in self]) def apply(self, fn): return TensorList([fn(e) for e in self]) def __getattr__(self, name): if not hasattr(torch.Tensor, name): raise AttributeError('\'TensorList\' object has not attribute \'{}\''.format(name)) def apply_attr(*args, **kwargs): return TensorList([getattr(e, name)(*args, **kwargs) for e in self]) return apply_attr @staticmethod def _iterable(a): return isinstance(a, (TensorList, list)) def tensor_operation(op): def islist(a): return isinstance(a, TensorList) @functools.wraps(op) def oplist(*args, **kwargs): if len(args) == 0: raise ValueError('Must be at least one argument without keyword (i.e. operand).') if len(args) == 1: if islist(args[0]): return TensorList([op(a, **kwargs) for a in args[0]]) else: # Multiple operands, assume max two if islist(args[0]) and islist(args[1]): return TensorList([op(a, b, *args[2:], **kwargs) for a, b in zip(*args[:2])]) if islist(args[0]): return TensorList([op(a, *args[1:], **kwargs) for a in args[0]]) if islist(args[1]): return TensorList([op(args[0], b, *args[2:], **kwargs) for b in args[1]]) # None of the operands are lists return op(*args, **kwargs) return oplist ================================================ FILE: lib/utils/variable_hook.py ================================================ import torch from bytecode import Bytecode, Instr class get_local(object): cache = {} is_activate = False def __init__(self, varname): self.varname = varname def __call__(self, func): if not type(self).is_activate: return func type(self).cache[func.__qualname__] = [] c = Bytecode.from_code(func.__code__) extra_code = [ Instr('STORE_FAST', '_res'), Instr('LOAD_FAST', self.varname), Instr('STORE_FAST', '_value'), Instr('LOAD_FAST', '_res'), Instr('LOAD_FAST', '_value'), Instr('BUILD_TUPLE', 2), Instr('STORE_FAST', '_result_tuple'), Instr('LOAD_FAST', '_result_tuple'), ] c[-1:-1] = extra_code func.__code__ = c.to_code() def wrapper(*args, **kwargs): res, values = func(*args, **kwargs) if isinstance(values, torch.Tensor): type(self).cache[func.__qualname__].append(values.detach().cpu().numpy()) elif isinstance(values, list): # list of Tensor type(self).cache[func.__qualname__].append([value.detach().cpu().numpy() for value in values]) else: raise NotImplementedError return res return wrapper @classmethod def clear(cls): for key in cls.cache.keys(): cls.cache[key] = [] @classmethod def activate(cls): cls.is_activate = True ================================================ FILE: lib/vis/__init__.py ================================================ ================================================ FILE: lib/vis/plotting.py ================================================ import matplotlib.pyplot as plt import numpy as np import torch import cv2 def draw_figure(fig): fig.canvas.draw() fig.canvas.flush_events() plt.pause(0.001) def show_tensor(a: torch.Tensor, fig_num = None, title = None, range=(None, None), ax=None): """Display a 2D tensor. args: fig_num: Figure number. title: Title of figure. """ a_np = a.squeeze().cpu().clone().detach().numpy() if a_np.ndim == 3: a_np = np.transpose(a_np, (1, 2, 0)) if ax is None: fig = plt.figure(fig_num) plt.tight_layout() plt.cla() plt.imshow(a_np, vmin=range[0], vmax=range[1]) plt.axis('off') plt.axis('equal') if title is not None: plt.title(title) draw_figure(fig) else: ax.cla() ax.imshow(a_np, vmin=range[0], vmax=range[1]) ax.set_axis_off() ax.axis('equal') if title is not None: ax.set_title(title) draw_figure(plt.gcf()) def plot_graph(a: torch.Tensor, fig_num = None, title = None): """Plot graph. Data is a 1D tensor. args: fig_num: Figure number. title: Title of figure. """ a_np = a.squeeze().cpu().clone().detach().numpy() if a_np.ndim > 1: raise ValueError fig = plt.figure(fig_num) # plt.tight_layout() plt.cla() plt.plot(a_np) if title is not None: plt.title(title) draw_figure(fig) def show_image_with_boxes(im, boxes, iou_pred=None, disp_ids=None): im_np = im.clone().cpu().squeeze().numpy() im_np = np.ascontiguousarray(im_np.transpose(1, 2, 0).astype(np.uint8)) boxes = boxes.view(-1, 4).cpu().numpy().round().astype(int) # Draw proposals for i_ in range(boxes.shape[0]): if disp_ids is None or disp_ids[i_]: bb = boxes[i_, :] disp_color = (i_*38 % 256, (255 - i_*97) % 256, (123 + i_*66) % 256) cv2.rectangle(im_np, (bb[0], bb[1]), (bb[0] + bb[2], bb[1] + bb[3]), disp_color, 1) if iou_pred is not None: text_pos = (bb[0], bb[1] - 5) cv2.putText(im_np, 'ID={} IOU = {:3.2f}'.format(i_, iou_pred[i_]), text_pos, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1, bottomLeftOrigin=False) im_tensor = torch.from_numpy(im_np.transpose(2, 0, 1)).float() return im_tensor def _pascal_color_map(N=256, normalized=False): """ Python implementation of the color map function for the PASCAL VOC data set. Official Matlab version can be found in the PASCAL VOC devkit http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html#devkit """ def bitget(byteval, idx): return (byteval & (1 << idx)) != 0 dtype = 'float32' if normalized else 'uint8' cmap = np.zeros((N, 3), dtype=dtype) for i in range(N): r = g = b = 0 c = i for j in range(8): r = r | (bitget(c, 0) << 7 - j) g = g | (bitget(c, 1) << 7 - j) b = b | (bitget(c, 2) << 7 - j) c = c >> 3 cmap[i] = np.array([r, g, b]) cmap = cmap / 255 if normalized else cmap return cmap def overlay_mask(im, ann, alpha=0.5, colors=None, contour_thickness=None): """ Overlay mask over image. Source: https://github.com/albertomontesg/davis-interactive/blob/master/davisinteractive/utils/visualization.py This function allows you to overlay a mask over an image with some transparency. # Arguments im: Numpy Array. Array with the image. The shape must be (H, W, 3) and the pixels must be represented as `np.uint8` data type. ann: Numpy Array. Array with the mask. The shape must be (H, W) and the values must be intergers alpha: Float. Proportion of alpha to apply at the overlaid mask. colors: Numpy Array. Optional custom colormap. It must have shape (N, 3) being N the maximum number of colors to represent. contour_thickness: Integer. Thickness of each object index contour draw over the overlay. This function requires to have installed the package `opencv-python`. # Returns Numpy Array: Image of the overlay with shape (H, W, 3) and data type `np.uint8`. """ im, ann = np.asarray(im, dtype=np.uint8), np.asarray(ann, dtype=np.int) if im.shape[:-1] != ann.shape: raise ValueError('First two dimensions of `im` and `ann` must match') if im.shape[-1] != 3: raise ValueError('im must have three channels at the 3 dimension') colors = colors or _pascal_color_map() colors = np.asarray(colors, dtype=np.uint8) mask = colors[ann] fg = im * alpha + (1 - alpha) * mask img = im.copy() img[ann > 0] = fg[ann > 0] if contour_thickness: # pragma: no cover import cv2 for obj_id in np.unique(ann[ann > 0]): contours = cv2.findContours((ann == obj_id).astype( np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:] cv2.drawContours(img, contours[0], -1, colors[obj_id].tolist(), contour_thickness) return img ================================================ FILE: lib/vis/utils.py ================================================ import torch import numpy as np def numpy_to_torch(a: np.ndarray): return torch.from_numpy(a).float().permute(2, 0, 1).unsqueeze(0) ================================================ FILE: lib/vis/visdom_cus.py ================================================ import visdom import visdom.server import cv2 import torch import copy import numpy as np from collections import OrderedDict from enum import Enum from lib.vis.plotting import overlay_mask, show_image_with_boxes from lib.vis.utils import numpy_to_torch class cv_colors(Enum): WHITE = (255, 255, 255)[::-1] RED = (0, 0, 255)[::-1] GREEN = (0, 255, 0)[::-1] BLUE = (255, 0, 0)[::-1] PURPLE = (247, 44, 200)[::-1] ORANGE = (44, 162, 247)[::-1] MINT = (239, 255, 66)[::-1] YELLOW = (2, 255, 250)[::-1] BLACK = (0, 0, 0)[::-1] def index_to_color(idx): return { 0: cv_colors.GREEN.value, 1: cv_colors.BLUE.value, 2: cv_colors.RED.value, 3: cv_colors.MINT.value, 4: cv_colors.YELLOW.value, 5: cv_colors.WHITE.value, 6: cv_colors.BLACK.value, }[idx] class VisBase: def __init__(self, visdom, show_data, title): self.visdom = visdom self.show_data = show_data self.title = title self.raw_data = None def update(self, data, **kwargs): self.save_data(data, **kwargs) if self.show_data: self.draw_data() def save_data(self, data, **kwargs): raise NotImplementedError def draw_data(self): raise NotImplementedError def toggle_display(self, new_mode=None): if new_mode is not None: self.show_data = new_mode else: self.show_data = not self.show_data if self.show_data: self.draw_data() else: self.visdom.close(self.title) class VisImage(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) def save_data(self, data): data = data.float() self.raw_data = data def draw_data(self): self.visdom.image(self.raw_data.clone(), opts={'title': self.title}, win=self.title) class VisHeatmap(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) def save_data(self, data, **kwargs): data = data.squeeze().flip(0) if kwargs: self.raw_data = [data, kwargs] else: self.raw_data = [data] # self.raw_data = data def draw_data(self): if len(self.raw_data) == 2: self.visdom.heatmap(self.raw_data[0].clone(), opts={'title': self.title + ' ' + self.raw_data[1]['caption'], **self.raw_data[1]}, win=self.title) else: self.visdom.heatmap(self.raw_data[0].clone(), opts={'title': self.title}, win=self.title) # self.visdom.heatmap(self.raw_data.clone(), opts={'title': self.title}, win=self.title) class VisFeaturemap(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) self.block_list = None def block_list_callback_handler(self, data): self.block_list[data['propertyId']]['value'] = data['value'] self.visdom.properties(self.block_list, opts={'title': 'Featuremap UI'}, win='featuremap_ui') self.draw_data() def save_data(self, data): data = data.view(-1, *data.shape[-2:]) data = data.flip(1) if self.block_list is None: self.block_list = [] self.draw_feat = [] for i in range(data.shape[0]): self.block_list.append({'type': 'checkbox', 'name': 'Channel {:04d}'.format(i), 'value': False}) self.visdom.properties(self.block_list, opts={'title': 'Featuremap UI'}, win='featuremap_ui') self.visdom.register_event_handler(self.block_list_callback_handler, 'featuremap_ui') self.raw_data = data def draw_data(self): if self.block_list is not None and self.show_data: for i, d in enumerate(self.block_list): if d['value']: fig_title = '{} ch: {:04d}'.format(self.title, i) self.visdom.heatmap(self.raw_data[i, :, :].clone(), opts={'title': fig_title}, win=fig_title) class VisCostVolume(VisBase): def __init__(self, visdom, show_data, title, flip=False): super().__init__(visdom, show_data, title) self.show_slice = False self.slice_pos = None self.flip = flip def show_cost_volume(self): data = self.raw_data.clone() # data_perm = data.permute(2, 0, 3, 1).contiguous() data_perm = data.permute(0, 2, 1, 3).contiguous() if self.flip: data_perm = data_perm.permute(2, 3, 0, 1).contiguous() data_perm = data_perm.view(data_perm.shape[0] * data_perm.shape[1], -1) self.visdom.heatmap(data_perm.flip(0), opts={'title': self.title}, win=self.title) def set_zoom_pos(self, slice_pos): self.slice_pos = slice_pos def toggle_show_slice(self, new_mode=None): if new_mode is not None: self.show_slice = new_mode else: self.show_slice = not self.show_slice def show_cost_volume_slice(self): slice_pos = self.slice_pos # slice_pos: [row, col] cost_volume_data = self.raw_data.clone() if self.flip: cost_volume_slice = cost_volume_data[:, :, slice_pos[0], slice_pos[1]] else: cost_volume_slice = cost_volume_data[slice_pos[0], slice_pos[1], :, :] self.visdom.heatmap(cost_volume_slice.flip(0), opts={'title': self.title}, win=self.title) def save_data(self, data): data = data.view(data.shape[-2], data.shape[-1], data.shape[-2], data.shape[-1]) self.raw_data = data def draw_data(self): if self.show_slice: self.show_cost_volume_slice() else: self.show_cost_volume() class VisCostVolumeUI(VisBase): def cv_ui_handler(self, data): zoom_toggled = False if data['event_type'] == 'KeyPress': if data['key'] == 'ArrowRight': self.zoom_pos[1] = min(self.zoom_pos[1] + 1, self.feat_shape[1] - 1) elif data['key'] == 'ArrowLeft': self.zoom_pos[1] = max(self.zoom_pos[1] - 1, 0) elif data['key'] == 'ArrowUp': self.zoom_pos[0] = max(self.zoom_pos[0] - 1, 0) elif data['key'] == 'ArrowDown': self.zoom_pos[0] = min(self.zoom_pos[0] + 1, self.feat_shape[0] - 1) elif data['key'] == 'Enter': self.zoom_mode = not self.zoom_mode zoom_toggled = True # Update image self.show_image() # Update cost volumes for block_title, block in self.registered_blocks.items(): if isinstance(block, VisCostVolume): block.set_zoom_pos(self.zoom_pos) block.toggle_show_slice(self.zoom_mode) if (self.zoom_mode or zoom_toggled) and block.show_data: block.draw_data() def __init__(self, visdom, show_data, title, feat_shape, registered_blocks): super().__init__(visdom, show_data, title) self.feat_shape = feat_shape self.zoom_mode = False self.zoom_pos = [int((feat_shape[0] - 1) / 2), int((feat_shape[1] - 1) / 2)] self.registered_blocks = registered_blocks self.visdom.register_event_handler(self.cv_ui_handler, title) def draw_grid(self, data): stride_r = int(data.shape[1] / self.feat_shape[0]) stride_c = int(data.shape[2] / self.feat_shape[1]) # Draw grid data[:, list(range(0, data.shape[1], stride_r)), :] = 0 data[:, :, list(range(0, data.shape[2], stride_c))] = 0 data[0, list(range(0, data.shape[1], stride_r)), :] = 255 data[0, :, list(range(0, data.shape[2], stride_c))] = 255 return data def shade_cell(self, data): stride_r = int(data.shape[1] / self.feat_shape[0]) stride_c = int(data.shape[2] / self.feat_shape[1]) r1 = self.zoom_pos[0] * stride_r r2 = min((self.zoom_pos[0] + 1) * stride_r, data.shape[1]) c1 = self.zoom_pos[1] * stride_c c2 = min((self.zoom_pos[1] + 1) * stride_c, data.shape[2]) factor = 0.8 if self.zoom_mode else 0.5 data[:, r1:r2, c1:c2] = data[:, r1:r2, c1:c2] * (1 - factor) + torch.tensor([255.0, 0.0, 0.0]).view(3, 1, 1).to( data.device) * factor return data def show_image(self, data=None): if data is None: data = self.raw_data.clone() data = self.draw_grid(data) data = self.shade_cell(data) self.visdom.image(data, opts={'title': self.title}, win=self.title) def save_data(self, data): # Ignore feat shape data = data[0] data = data.float() self.raw_data = data def draw_data(self): self.show_image(self.raw_data.clone()) class VisInfoDict(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) self.raw_data = OrderedDict() def generate_display_text(self, data): display_text = '' for key, value in data.items(): key = key.replace('_', ' ') if value is None: display_text += '{}: {}
'.format(key, 'None') elif isinstance(value, (str, int)): display_text += '{}: {}
'.format(key, value) else: display_text += '{}: {:.2f}
'.format(key, value) return display_text def save_data(self, data): for key, val in data.items(): self.raw_data[key] = val def draw_data(self): data = copy.deepcopy(self.raw_data) display_text = self.generate_display_text(data) self.visdom.text(display_text, opts={'title': self.title}, win=self.title) class VisText(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) def save_data(self, data): self.raw_data = data def draw_data(self): data = copy.deepcopy(self.raw_data) self.visdom.text(data, opts={'title': self.title}, win=self.title) class VisLinePlot(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) def save_data(self, data): self.raw_data = data def draw_data(self): if isinstance(self.raw_data, (list, tuple)): data_y = self.raw_data[0].clone() data_x = self.raw_data[1].clone() else: data_y = self.raw_data.clone() data_x = torch.arange(data_y.shape[0]) self.visdom.line(data_y, data_x, opts={'title': self.title}, win=self.title) class VisTracking(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) self.count = 0 def save_data(self, data, **kwargs): image = data[0] boxes_masks = data[1:] boxes, masks = [], [] for bm in boxes_masks: if bm is None: continue if isinstance(bm, list): boxes.append(torch.Tensor(bm)) continue if len(bm.shape) > 1: # Binarize segmentation if a float tensor is provided if bm.dtype != np.uint8: bm = (bm > 0.5).astype(np.uint8) masks.append(bm) continue boxes.append(bm.float()) if kwargs: self.raw_data = [image, boxes, masks, kwargs] else: self.raw_data = [image, boxes, masks] def draw_data(self): disp_image = self.raw_data[0].copy() resize_factor = 1 if max(disp_image.shape) > 480: resize_factor = 480.0 / float(max(disp_image.shape)) disp_image = cv2.resize(disp_image, None, fx=resize_factor, fy=resize_factor) for i, mask in enumerate(self.raw_data[2]): self.raw_data[2][i] = cv2.resize(mask, None, fx=resize_factor, fy=resize_factor) # if box has score scores = None if self.raw_data[1][0].shape[0] == 5: scores = [box[4].item() for box in self.raw_data[1]] self.raw_data[1] = [box[:4] for box in self.raw_data[1]] boxes = [resize_factor * b.clone() for b in self.raw_data[1]] for i, disp_rect in enumerate(boxes): # color = ((255 * ((i % 3) > 0)), 255 * ((i + 1) % 2), (255 * (i % 5)) // 4) color = index_to_color(i % 7) cv2.rectangle(disp_image, (int(disp_rect[0]), int(disp_rect[1])), (int(disp_rect[0] + disp_rect[2]), int(disp_rect[1] + disp_rect[3])), color, 2) if scores is not None: cv2.putText(disp_image, "{:.3f}".format(scores[i]), (int(disp_rect[0]), int(disp_rect[1])), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1) for i, mask in enumerate(self.raw_data[2], 1): disp_image = overlay_mask(disp_image, mask * i) # import os # write_img = disp_image.copy() # write_img = write_img[:, :, ::-1] # # cv2.imwrite(os.path.join('/home/yebotao/test', str(self.count).zfill(3) + '.jpg'), write_img) # cv2.imwrite(os.path.join('/home/yebotao/test', self.raw_data[3]['caption'].split('_')[-1] + '.jpg'), write_img) # self.count += 1 disp_image = numpy_to_torch(disp_image).squeeze(0) disp_image = disp_image.float() if len(self.raw_data) > 3: self.visdom.image(disp_image, opts={'title': self.title, **self.raw_data[3]}, win=self.title) else: self.visdom.image(disp_image, opts={'title': self.title}, win=self.title) class VisBBReg(VisBase): def __init__(self, visdom, show_data, title): super().__init__(visdom, show_data, title) self.block_list = [] def block_list_callback_handler(self, data): self.block_list[data['propertyId']]['value'] = data['value'] self.visdom.properties(self.block_list, opts={'title': 'BBReg Vis'}, win='bbreg_vis') self.draw_data() def save_data(self, data): self.image = data[0].float() self.init_boxes = data[1] self.final_boxes = data[2] self.final_ious = data[3] def draw_data(self): if len(self.block_list) == 0: self.block_list.append({'type': 'checkbox', 'name': 'ID 0', 'value': True}) self.block_list.append({'type': 'checkbox', 'name': 'ID 1', 'value': True}) self.visdom.properties(self.block_list, opts={'title': 'BBReg Vis'}, win='bbreg_vis') self.visdom.register_event_handler(self.block_list_callback_handler, 'bbreg_vis') disp_image = self.image ids = [x['value'] for x in self.block_list] init_box_image = show_image_with_boxes(disp_image.clone(), self.init_boxes.clone(), disp_ids=ids) final_box_image = show_image_with_boxes(disp_image.clone(), self.final_boxes.clone(), self.final_ious.clone(), disp_ids=ids) self.visdom.image(init_box_image, opts={'title': 'Init Boxes'}, win='Init Boxes') self.visdom.image(final_box_image, opts={'title': 'Final Boxes'}, win='Final Boxes') class Visdom: def __init__(self, debug=0, ui_info=None, visdom_info=None, env=None): self.debug = debug if env is not None: self.visdom = visdom.Visdom(server=visdom_info.get('server', '127.0.0.1'), port=visdom_info.get('port', 8097), env=env) else: self.visdom = visdom.Visdom(server=visdom_info.get('server', '127.0.0.1'), port=visdom_info.get('port', 8097)) self.registered_blocks = {} self.blocks_list = [] self.visdom.properties(self.blocks_list, opts={'title': 'Block List'}, win='block_list') self.visdom.register_event_handler(self.block_list_callback_handler, 'block_list') if ui_info is not None: self.visdom.register_event_handler(ui_info['handler'], ui_info['win_id']) def block_list_callback_handler(self, data): field_name = self.blocks_list[data['propertyId']]['name'] self.registered_blocks[field_name].toggle_display(data['value']) self.blocks_list[data['propertyId']]['value'] = data['value'] self.visdom.properties(self.blocks_list, opts={'title': 'Block List'}, win='block_list') def register(self, data, mode, debug_level=0, title='Data', **kwargs): if title not in self.registered_blocks.keys(): show_data = self.debug >= debug_level if title != 'Tracking': self.blocks_list.append({'type': 'checkbox', 'name': title, 'value': show_data}) self.visdom.properties(self.blocks_list, opts={'title': 'Block List'}, win='block_list') if mode == 'image': self.registered_blocks[title] = VisImage(self.visdom, show_data, title) elif mode == 'heatmap': self.registered_blocks[title] = VisHeatmap(self.visdom, show_data, title) elif mode == 'cost_volume': self.registered_blocks[title] = VisCostVolume(self.visdom, show_data, title) elif mode == 'cost_volume_flip': self.registered_blocks[title] = VisCostVolume(self.visdom, show_data, title, flip=True) elif mode == 'cost_volume_ui': self.registered_blocks[title] = VisCostVolumeUI(self.visdom, show_data, title, data[1], self.registered_blocks) elif mode == 'info_dict': self.registered_blocks[title] = VisInfoDict(self.visdom, show_data, title) elif mode == 'text': self.registered_blocks[title] = VisText(self.visdom, show_data, title) elif mode == 'lineplot': self.registered_blocks[title] = VisLinePlot(self.visdom, show_data, title) elif mode == 'Tracking': self.registered_blocks[title] = VisTracking(self.visdom, show_data, title) elif mode == 'bbreg': self.registered_blocks[title] = VisBBReg(self.visdom, show_data, title) elif mode == 'featmap': self.registered_blocks[title] = VisFeaturemap(self.visdom, show_data, title) else: raise ValueError('Visdom Error: Unknown data mode {}'.format(mode)) # Update self.registered_blocks[title].update(data, **kwargs) ================================================ FILE: tracking/_init_paths.py ================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os.path as osp import sys def add_path(path): if path not in sys.path: sys.path.insert(0, path) this_dir = osp.dirname(__file__) prj_path = osp.join(this_dir, '..') add_path(prj_path) ================================================ FILE: tracking/analysis_results.py ================================================ import _init_paths import matplotlib.pyplot as plt plt.rcParams['figure.figsize'] = [8, 8] from lib.test.analysis.plot_results import plot_results, print_results, print_per_sequence_results from lib.test.evaluation import get_dataset, trackerlist trackers = [] dataset_name = 'lasot_extension_subset' #dataset_name = 'lasot' """stark""" # trackers.extend(trackerlist(name='stark_s', parameter_name='baseline', dataset_name=dataset_name, # run_ids=None, display_name='STARK-S50')) # trackers.extend(trackerlist(name='stark_st', parameter_name='baseline', dataset_name=dataset_name, # run_ids=None, display_name='STARK-ST50')) # trackers.extend(trackerlist(name='stark_st', parameter_name='baseline_R101', dataset_name=dataset_name, # run_ids=None, display_name='STARK-ST101')) """TransT""" # trackers.extend(trackerlist(name='TransT_N2', parameter_name=None, dataset_name=None, # run_ids=None, display_name='TransT_N2', result_only=True)) # trackers.extend(trackerlist(name='TransT_N4', parameter_name=None, dataset_name=None, # run_ids=None, display_name='TransT_N4', result_only=True)) """pytracking""" # trackers.extend(trackerlist('atom', 'default', None, range(0,5), 'ATOM')) # trackers.extend(trackerlist('dimp', 'dimp18', None, range(0,5), 'DiMP18')) # trackers.extend(trackerlist('dimp', 'dimp50', None, range(0,5), 'DiMP50')) # trackers.extend(trackerlist('dimp', 'prdimp18', None, range(0,5), 'PrDiMP18')) # trackers.extend(trackerlist('dimp', 'prdimp50', None, range(0,5), 'PrDiMP50')) """ostrack""" trackers.extend(trackerlist(name='artrack_seq', parameter_name='artrack_seq_256_full', dataset_name=dataset_name, run_ids=None, display_name='ARTrackSeq_256')) #trackers.extend(trackerlist(name='ostrack', parameter_name='vitb_384_mae_ce_32x4_ep300', dataset_name=dataset_name, #. run_ids=None, display_name='OSTrack384')) dataset = get_dataset(dataset_name) # dataset = get_dataset('otb', 'nfs', 'uav', 'tc128ce') # plot_results(trackers, dataset, 'OTB2015', merge_results=True, plot_types=('success', 'norm_prec'), # skip_missing_seq=False, force_evaluation=True, plot_bin_gap=0.05) print_results(trackers, dataset, dataset_name, merge_results=True, plot_types=('success', 'norm_prec', 'prec')) # print_results(trackers, dataset, 'UNO', merge_results=True, plot_types=('success', 'prec')) ================================================ FILE: tracking/analysis_results_ITP.py ================================================ import _init_paths import argparse from lib.test.analysis.plot_results import print_results from lib.test.evaluation import get_dataset, trackerlist def parse_args(): """ args for evaluation. """ parser = argparse.ArgumentParser(description='Parse args for training') # for train parser.add_argument('--script', type=str, help='training script name') parser.add_argument('--config', type=str, default='baseline', help='yaml configure file name') args = parser.parse_args() return args if __name__ == "__main__": args = parse_args() trackers = [] trackers.extend(trackerlist(args.script, args.config, "None", None, args.config)) dataset = get_dataset('lasot') print_results(trackers, dataset, 'LaSOT', merge_results=True, plot_types=('success', 'prec', 'norm_prec')) ================================================ FILE: tracking/convert_transt.py ================================================ import _init_paths import os from lib.test.evaluation import get_dataset import shutil trackers = [] # dataset_name = 'uav' dataset_name = 'nfs' root_dir = "/data/sda/v-yanbi/iccv21/STARK_Latest/Stark" base_dir = os.path.join(root_dir, "test/tracking_results/TransT_N2") dataset = get_dataset(dataset_name) for x in dataset: seq_name = x.name file_name = "%s.txt" % (seq_name.replace("nfs_", "")) file_path = os.path.join(base_dir, file_name) file_path_new = os.path.join(base_dir, "%s.txt" % seq_name) if os.path.exists(file_path): shutil.move(file_path, file_path_new) ================================================ FILE: tracking/create_default_local_file.py ================================================ import argparse import os import _init_paths from lib.train.admin import create_default_local_file_ITP_train from lib.test.evaluation import create_default_local_file_ITP_test def parse_args(): parser = argparse.ArgumentParser(description='Create default local file on ITP or PAI') parser.add_argument("--workspace_dir", type=str, required=True) # workspace dir parser.add_argument("--data_dir", type=str, required=True) parser.add_argument("--save_dir", type=str, required=True) args = parser.parse_args() return args if __name__ == "__main__": args = parse_args() workspace_dir = os.path.realpath(args.workspace_dir) data_dir = os.path.realpath(args.data_dir) save_dir = os.path.realpath(args.save_dir) create_default_local_file_ITP_train(workspace_dir, data_dir) create_default_local_file_ITP_test(workspace_dir, data_dir, save_dir) ================================================ FILE: tracking/download_pytracking_results.py ================================================ import os import sys import gdown import re import shutil import argparse import tempfile import _init_paths from lib.test.evaluation.environment import env_settings pytracking_results_link_dict = { "dimp": { "prdimp50_003.zip": "1p13j3iwcOCubBi3ms0hLwqnP6-x0J8Mc", "prdimp50_002.zip": "1PPKgrAepbuyM2kjfzYAozQKTL6AjcQOz", "prdimp50_001.zip": "17NFBObEDeK6mW4Mk2vN5Ekk1SGbFvxRS", "prdimp50_000.zip": "1r3Efq7AumML2yGQ_KV4zmf4ATKVE1bo6", "prdimp18_004.zip": "1DF4ZJQAa4CwvN_OiT4te33AV0kpsO7JM", "prdimp18_003.zip": "1RgwJAN4TxnzgVgsfvrHIg1OUXD1EBZkO", "prdimp18_002.zip": "17lMllYhygCqgE81DoHX4BZar3xc3auzM", "prdimp18_001.zip": "1Yg7DmGYOnn2k0MYtSjjKlGyzO1Uimj4G", "prdimp18_000.zip": "1DuZJSBJ-23WJBQTOWSAaoPYSbGAJJN2Z", "prdimp50_004.zip": "1f9bx9-dtx3B5_IvIJhjjJyp-cnXciqLO", "dimp50_004.zip": "1Lj3p8mYCoIqxzdQXZkWFTw-MA8c6eeLa", "dimp50_000.zip": "1LCgf5sg453Z4bY37A_W5mbXeG68U1fET", "dimp18_000.zip": "17M7dJZ1oKrIY4-O5lL_mlQPEubUn034g", "dimp18_001.zip": "1AsiliVgISyDTouYOQYVOXA0srj3YskhJ", "dimp18_002.zip": "1I0GrBaPnySOyPWSvItHhXH8182tFCi_Y", "dimp50_001.zip": "1XfPvwAcymW88J1rq7RlhyKmqsawJDK-K", "dimp18_004.zip": "1EztF6bpROFwZ1PSJWgMB7bQ4G_Z08YIg", "dimp18_003.zip": "1iuiFLv04WE7GfBjm8UkZXFq4gheG2Ru8", "dimp50_003.zip": "1rLsgeQXyKpD6ryl9BjlIVdO3vd27ekwy", "dimp50_002.zip": "1wj2jUwlpHgsP1hAcuxXAVriUPuEspsu4", }, "atom": { "default_004.zip": "1BapnQh_8iRM44DXj862eOZV4q8zQLdmT", "default_003.zip": "1YpfOBLBEUQQiX0fWMPA5pnW3dm0NG3E5", "default_000.zip": "1x6fKGZk3V839mX99Gl_pw7JUaiMaTxc5", "default_002.zip": "1QIlQFv3p6MBTwsYdIMYmzUDBDQGxGsUC", "default_001.zip": "1-K2--GNCURDKEgUuiEF18K4DcCLvDEVt", }, "kys": { "default_004.zip": "1QdfkA3d4MzKwdDiBOM1ZhDJWk9NmALxD", "default_000.zip": "1SCs79_ePTc8zxPDzRAgAmbbRlnmE89SN", "default_003.zip": "1TCzq38QW4YiMrgU5VR6NAEefJ85gwzfT", "default_002.zip": "1_9u1ybCFxHu0yJmW5ZzDR4-isJMEUsDf", "default_001.zip": "1utJhdosNj6vlI75dfzUxGM3Vy8OjWslT", }, } def _download_file(file_id, path): link = 'https://drive.google.com/uc?id=' + file_id gdown.download(link, path, quiet=True) def download_results(download_path, trackers='pytracking'): """ Script to automatically download tracker results for PyTracking. args: download_path - Directory where the zipped results are downloaded trackers - Tracker results which are to be downloaded. If set to 'pytracking', results for all pytracking based trackers will be downloaded. If set to 'external', results for available external trackers will be downloaded. If set to 'all', all available results are downloaded. If set to a name of a tracker (e.g. atom), all results for that tracker are downloaded. Otherwise, it can be set to a dict, where the keys are the names of the trackers for which results are downloaded. The value can be set to either 'all', in which case all available results for the tracker are downloaded. Else the value should be a list of parameter file names. """ print('Using download path ''{}'''.format(download_path)) os.makedirs(download_path, exist_ok=True) if isinstance(trackers, str): if trackers == 'all': all_trackers = list(pytracking_results_link_dict.keys()) + list(external_results_link_dict.keys()) trackers = {k: 'all' for k in all_trackers} elif trackers == 'pytracking': trackers = {k: 'all' for k in pytracking_results_link_dict.keys()} elif trackers == 'external': trackers = {k: 'all' for k in external_results_link_dict.keys()} elif trackers in pytracking_results_link_dict or trackers in external_results_link_dict: trackers = {trackers: 'all'} else: raise Exception('tracker_list must be set to ''all'', a tracker name, or be a dict') elif isinstance(trackers, dict): pass else: raise Exception('tracker_list must be set to ''all'', or be a dict') common_link_dict = pytracking_results_link_dict # for k, v in external_results_link_dict.items(): # common_link_dict[k] = v for trk, runfiles in trackers.items(): trk_path = os.path.join(download_path, trk) if not os.path.exists(trk_path): os.makedirs(trk_path) if runfiles == 'all': for params, fileid in common_link_dict[trk].items(): print('Downloading: {}/{}'.format(trk, params)) _download_file(fileid, os.path.join(trk_path, params)) elif isinstance(runfiles, (list, tuple)): for p in runfiles: for params, fileid in common_link_dict[trk].items(): if re.match(r'{}(|_(\d\d\d)).zip'.format(p), params) is not None: print('Downloading: {}/{}'.format(trk, params)) _download_file(fileid, os.path.join(trk_path, params)) else: raise Exception('tracker_list values must either be set to ''all'', or be a list of param names') def unpack_tracking_results(download_path, output_path=None): """ Unpacks zipped benchmark results. The directory 'download_path' should have the following structure - root - tracker1 - param1.zip - param2.zip . . - tracker2 - param1.zip - param2.zip . . args: download_path - Path to the directory where the zipped results are stored output_path - Path to the directory where the results will be unpacked. Set to env_settings().results_path by default """ if output_path is None: output_path = env_settings().results_path if not os.path.exists(output_path): os.makedirs(output_path) trackers = os.listdir(download_path) for t in trackers: runfiles = os.listdir(os.path.join(download_path, t)) for r in runfiles: save_path = os.path.join(output_path, t) if not os.path.exists(save_path): os.makedirs(save_path) shutil.unpack_archive(os.path.join(download_path, t, r), os.path.join(save_path, r[:-4]), 'zip') def main(): parser = argparse.ArgumentParser(description='Download and unpack zipped results') parser.add_argument('--tracker', type=str, default='pytracking', help='Name of tracker results to download, or "pytracking" (downloads results for PyTracking' ' based trackers, or "external" (downloads results for external trackers) or "all"') parser.add_argument('--output_path', type=str, default=None, help='Path to the directory where the results will be unpacked.') parser.add_argument('--temp_download_path', type=str, default=None, help='Temporary path used for downloading the Zip files.') parser.add_argument('--download', type=bool, default=True, help='Whether to download results or unpack existing downloaded files.') args = parser.parse_args() download_path = args.temp_download_path if download_path is None: download_path = '{}/pytracking_results/'.format(tempfile.gettempdir()) if args.download: download_results(download_path, args.tracker) unpack_tracking_results(download_path, args.output_path) if __name__ == '__main__': main() ================================================ FILE: tracking/pre_read_datasets.py ================================================ import _init_paths import multiprocessing as mp import argparse import os from lib.utils.lmdb_utils import decode_str import time import json def parse_args(): """ args for training. """ parser = argparse.ArgumentParser(description='Parse args for training') parser.add_argument('--data_dir', type=str, help='directory where lmdb data is located') parser.add_argument('--dataset_str', type=str, help="which datasets to use") args = parser.parse_args() return args def get_trknet_dict(trknet_dir): with open(os.path.join(trknet_dir, "seq_list.json"), "r") as f: seq_list = json.loads(f.read()) res_dict = {} set_idx_pre = -1 for set_idx, seq_name in seq_list: if set_idx != set_idx_pre: res_dict[set_idx] = "anno/%s.txt" % seq_name set_idx_pre = set_idx return res_dict def target(lmdb_dir, key_name): _ = decode_str(lmdb_dir, key_name) if __name__ == "__main__": args = parse_args() data_dir = args.data_dir dataset_str = args.dataset_str key_dict = {"got10k_lmdb": "train/list.txt", "lasot_lmdb": "LaSOTBenchmark.json", "coco_lmdb": "annotations/instances_train2017.json", "vid_lmdb": "cache.json"} print("Ready to pre load datasets") start = time.time() ps = [] datasets = [] if 'g' in dataset_str: datasets.append("got10k_lmdb") if 'l' in dataset_str: datasets.append("lasot_lmdb") if 'c' in dataset_str: datasets.append("coco_lmdb") if 'v' in dataset_str: datasets.append("vid_lmdb") for dataset in datasets: lmdb_dir = os.path.join(data_dir, dataset) p = mp.Process(target=target, args=(lmdb_dir, key_dict[dataset])) print("add %s %s to job queue" % (lmdb_dir, key_dict[dataset])) ps.append(p) # deal with trackingnet if 't' in dataset_str: trknet_dict = get_trknet_dict(os.path.join(data_dir, "trackingnet_lmdb")) for set_idx, seq_path in trknet_dict.items(): lmdb_dir = os.path.join(data_dir, "trackingnet_lmdb", "TRAIN_%d_lmdb" % set_idx) p = mp.Process(target=target, args=(lmdb_dir, seq_path)) print("add %s %s to job queue" % (lmdb_dir, seq_path)) ps.append(p) for p in ps: p.start() for p in ps: p.join() print("Pre read over") end = time.time() hour = (end - start) / 3600 print("it takes %.2f hours to pre-read data" % hour) ================================================ FILE: tracking/test.py ================================================ import os import sys import argparse prj_path = os.path.join(os.path.dirname(__file__), '..') if prj_path not in sys.path: sys.path.append(prj_path) from lib.test.evaluation import get_dataset from lib.test.evaluation.running import run_dataset from lib.test.evaluation.tracker import Tracker def run_tracker(tracker_name, tracker_param, run_id=None, dataset_name='otb', sequence=None, debug=0, threads=0, num_gpus=8): """Run tracker on sequence or dataset. args: tracker_name: Name of tracking method. tracker_param: Name of parameter file. run_id: The run id. dataset_name: Name of dataset (otb, nfs, uav, tpl, vot, tn, gott, gotv, lasot). sequence: Sequence number or name. debug: Debug level. threads: Number of threads. """ dataset = get_dataset(dataset_name) if sequence is not None: dataset = [dataset[sequence]] trackers = [Tracker(tracker_name, tracker_param, dataset_name, run_id)] run_dataset(dataset, trackers, debug, threads, num_gpus=num_gpus) def main(): parser = argparse.ArgumentParser(description='Run tracker on sequence or dataset.') parser.add_argument('tracker_name', type=str, help='Name of tracking method.') parser.add_argument('tracker_param', type=str, help='Name of config file.') parser.add_argument('--runid', type=int, default=None, help='The run id.') parser.add_argument('--dataset_name', type=str, default='otb', help='Name of dataset (otb, nfs, uav, tpl, vot, tn, gott, gotv, lasot).') parser.add_argument('--sequence', type=str, default=None, help='Sequence number or name.') parser.add_argument('--debug', type=int, default=0, help='Debug level.') parser.add_argument('--threads', type=int, default=0, help='Number of threads.') parser.add_argument('--num_gpus', type=int, default=8) args = parser.parse_args() try: seq_name = int(args.sequence) except: seq_name = args.sequence run_tracker(args.tracker_name, args.tracker_param, args.runid, args.dataset_name, seq_name, args.debug, args.threads, num_gpus=args.num_gpus) if __name__ == '__main__': main() ================================================ FILE: tracking/test_exp.py ================================================ import os import sys import argparse prj_path = os.path.join(os.path.dirname(__file__), '..') if prj_path not in sys.path: sys.path.append(prj_path) from lib.test.evaluation import get_dataset from lib.test.evaluation.running import run_dataset from lib.test.evaluation.tracker import Tracker def run_tracker(tracker_name, tracker_param, run_id=None, dataset_name='otb', sequence=None, debug=0, threads=0, num_gpus=8): """Run tracker on sequence or dataset. args: tracker_name: Name of tracking method. tracker_param: Name of parameter file. run_id: The run id. dataset_name: Name of dataset (otb, nfs, uav, tpl, vot, tn, gott, gotv, lasot). sequence: Sequence number or name. debug: Debug level. threads: Number of threads. """ dataset = get_dataset(*dataset_name) if sequence is not None: dataset = [dataset[sequence]] trackers = [Tracker(tracker_name, tracker_param, dataset_name, run_id)] run_dataset(dataset, trackers, debug, threads, num_gpus=num_gpus) def main(): parser = argparse.ArgumentParser(description='Run tracker on sequence or dataset.') parser.add_argument('tracker_name', type=str, help='Name of tracking method.') parser.add_argument('tracker_param', type=str, help='Name of config file.') parser.add_argument('--runid', type=int, default=None, help='The run id.') parser.add_argument('--dataset_name', type=str, default='otb', help='Name of dataset (otb, nfs, uav, tpl, vot, tn, gott, gotv, lasot).') parser.add_argument('--sequence', type=str, default=None, help='Sequence number or name.') parser.add_argument('--debug', type=int, default=0, help='Debug level.') parser.add_argument('--threads', type=int, default=0, help='Number of threads.') parser.add_argument('--num_gpus', type=int, default=8) args = parser.parse_args() try: seq_name = int(args.sequence) except: seq_name = args.sequence args.dataset_name = ['trackingnet', 'got10k_test', 'lasot'] run_tracker(args.tracker_name, args.tracker_param, args.runid, args.dataset_name, seq_name, args.debug, args.threads, num_gpus=args.num_gpus) if __name__ == '__main__': main() ================================================ FILE: tracking/train.py ================================================ import os import argparse import random import torch def parse_args(): """ args for training. """ parser = argparse.ArgumentParser(description='Parse args for training') # for train parser.add_argument('--script', type=str, help='training script name') parser.add_argument('--config', type=str, default='baseline', help='yaml configure file name') parser.add_argument('--save_dir', type=str, help='root directory to save checkpoints, logs, and tensorboard') parser.add_argument('--mode', type=str, choices=["single", "multiple", "multi_node"], default="multiple", help="train on single gpu or multiple gpus") parser.add_argument('--nproc_per_node', type=int, help="number of GPUs per node") # specify when mode is multiple parser.add_argument('--use_lmdb', type=int, choices=[0, 1], default=0) # whether datasets are in lmdb format parser.add_argument('--script_prv', type=str, help='training script name') parser.add_argument('--config_prv', type=str, default='baseline', help='yaml configure file name') parser.add_argument('--use_wandb', type=int, choices=[0, 1], default=0) # whether to use wandb # for knowledge distillation parser.add_argument('--distill', type=int, choices=[0, 1], default=0) # whether to use knowledge distillation parser.add_argument('--script_teacher', type=str, help='teacher script name') parser.add_argument('--config_teacher', type=str, help='teacher yaml configure file name') # for multiple machines parser.add_argument('--rank', type=int, help='Rank of the current process.') parser.add_argument('--world-size', type=int, help='Number of processes participating in the job.') parser.add_argument('--ip', type=str, default='127.0.0.1', help='IP of the current rank 0.') parser.add_argument('--port', type=int, default='20000', help='Port of the current rank 0.') args = parser.parse_args() return args def main(): torch.set_num_threads(8) args = parse_args() if args.mode == "single": train_cmd = "python lib/train/run_training.py --script %s --config %s --save_dir %s --use_lmdb %d " \ "--script_prv %s --config_prv %s --distill %d --script_teacher %s --config_teacher %s --use_wandb %d"\ % (args.script, args.config, args.save_dir, args.use_lmdb, args.script_prv, args.config_prv, args.distill, args.script_teacher, args.config_teacher, args.use_wandb) elif args.mode == "multiple": train_cmd = "python -m torch.distributed.launch --nproc_per_node %d --master_port %d lib/train/run_training.py " \ "--script %s --config %s --save_dir %s --use_lmdb %d --script_prv %s --config_prv %s --use_wandb %d " \ "--distill %d --script_teacher %s --config_teacher %s" \ % (args.nproc_per_node, random.randint(10000, 50000), args.script, args.config, args.save_dir, args.use_lmdb, args.script_prv, args.config_prv, args.use_wandb, args.distill, args.script_teacher, args.config_teacher) elif args.mode == "multi_node": train_cmd = "python -m torch.distributed.launch --nproc_per_node %d --master_addr %s --master_port %d --nnodes %d --node_rank %d lib/train/run_training.py " \ "--script %s --config %s --save_dir %s --use_lmdb %d --script_prv %s --config_prv %s --use_wandb %d " \ "--distill %d --script_teacher %s --config_teacher %s" \ % (args.nproc_per_node, args.ip, args.port, args.world_size, args.rank, args.script, args.config, args.save_dir, args.use_lmdb, args.script_prv, args.config_prv, args.use_wandb, args.distill, args.script_teacher, args.config_teacher) else: raise ValueError("mode should be 'single' or 'multiple'.") os.system(train_cmd) if __name__ == "__main__": main()