├── EVMamba ├── requirements │ ├── mminstall.txt │ ├── readthedocs.txt │ ├── build.txt │ ├── tests.txt │ ├── optional.txt │ └── docs.txt ├── mmaction │ ├── evaluation │ │ ├── functional │ │ │ ├── ava_evaluation │ │ │ │ ├── __init__.py │ │ │ │ ├── __pycache__ │ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ │ ├── metrics.cpython-310.pyc │ │ │ │ │ ├── metrics.cpython-38.pyc │ │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ │ ├── np_box_ops.cpython-38.pyc │ │ │ │ │ ├── np_box_list.cpython-310.pyc │ │ │ │ │ ├── np_box_list.cpython-38.pyc │ │ │ │ │ └── np_box_ops.cpython-310.pyc │ │ │ │ └── README.md │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── accuracy.cpython-38.pyc │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── accuracy.cpython-310.pyc │ │ │ │ ├── ava_utils.cpython-310.pyc │ │ │ │ ├── ava_utils.cpython-38.pyc │ │ │ │ ├── eval_detection.cpython-310.pyc │ │ │ │ └── eval_detection.cpython-38.pyc │ │ │ └── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-310.pyc │ │ │ └── __init__.cpython-38.pyc │ │ ├── __init__.py │ │ └── metrics │ │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── acc_metric.cpython-310.pyc │ │ │ ├── acc_metric.cpython-38.pyc │ │ │ ├── anet_metric.cpython-38.pyc │ │ │ ├── ava_metric.cpython-310.pyc │ │ │ ├── ava_metric.cpython-38.pyc │ │ │ └── anet_metric.cpython-310.pyc │ │ │ └── __init__.py │ ├── models │ │ ├── necks │ │ │ ├── __init__.py │ │ │ └── __pycache__ │ │ │ │ ├── tpn.cpython-310.pyc │ │ │ │ ├── tpn.cpython-38.pyc │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ └── __init__.cpython-38.pyc │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-38.pyc │ │ │ └── __init__.cpython-310.pyc │ │ ├── common │ │ │ ├── __pycache__ │ │ │ │ ├── tam.cpython-38.pyc │ │ │ │ ├── tam.cpython-310.pyc │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── conv2plus1d.cpython-38.pyc │ │ │ │ ├── conv_audio.cpython-310.pyc │ │ │ │ ├── conv_audio.cpython-38.pyc │ │ │ │ ├── transformer.cpython-38.pyc │ │ │ │ ├── conv2plus1d.cpython-310.pyc │ │ │ │ ├── transformer.cpython-310.pyc │ │ │ │ ├── sub_batchnorm3d.cpython-310.pyc │ │ │ │ └── sub_batchnorm3d.cpython-38.pyc │ │ │ └── __init__.py │ │ ├── heads │ │ │ ├── __pycache__ │ │ │ │ ├── base.cpython-38.pyc │ │ │ │ ├── base.cpython-310.pyc │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── gcn_head.cpython-310.pyc │ │ │ │ ├── gcn_head.cpython-38.pyc │ │ │ │ ├── i3d_head.cpython-310.pyc │ │ │ │ ├── i3d_head.cpython-38.pyc │ │ │ │ ├── mvit_head.cpython-38.pyc │ │ │ │ ├── omni_head.cpython-38.pyc │ │ │ │ ├── tpn_head.cpython-310.pyc │ │ │ │ ├── tpn_head.cpython-38.pyc │ │ │ │ ├── trn_head.cpython-310.pyc │ │ │ │ ├── trn_head.cpython-38.pyc │ │ │ │ ├── tsm_head.cpython-310.pyc │ │ │ │ ├── tsm_head.cpython-38.pyc │ │ │ │ ├── tsn_head.cpython-310.pyc │ │ │ │ ├── tsn_head.cpython-38.pyc │ │ │ │ ├── x3d_head.cpython-310.pyc │ │ │ │ ├── x3d_head.cpython-38.pyc │ │ │ │ ├── mvit_head.cpython-310.pyc │ │ │ │ ├── omni_head.cpython-310.pyc │ │ │ │ ├── rgbpose_head.cpython-38.pyc │ │ │ │ ├── rgbpose_head.cpython-310.pyc │ │ │ │ ├── slowfast_head.cpython-310.pyc │ │ │ │ ├── slowfast_head.cpython-38.pyc │ │ │ │ ├── tsn_audio_head.cpython-38.pyc │ │ │ │ ├── uniformer_head.cpython-38.pyc │ │ │ │ ├── timesformer_head.cpython-310.pyc │ │ │ │ ├── timesformer_head.cpython-38.pyc │ │ │ │ ├── tsn_audio_head.cpython-310.pyc │ │ │ │ └── uniformer_head.cpython-310.pyc │ │ │ └── __init__.py │ │ ├── backbones │ │ │ ├── __pycache__ │ │ │ │ ├── c2d.cpython-38.pyc │ │ │ │ ├── c3d.cpython-38.pyc │ │ │ │ ├── x3d.cpython-38.pyc │ │ │ │ ├── aagcn.cpython-38.pyc │ │ │ │ ├── c2d.cpython-310.pyc │ │ │ │ ├── c3d.cpython-310.pyc │ │ │ │ ├── mvit.cpython-310.pyc │ │ │ │ ├── mvit.cpython-38.pyc │ │ │ │ ├── rope.cpython-310.pyc │ │ │ │ ├── stgcn.cpython-38.pyc │ │ │ │ ├── swin.cpython-310.pyc │ │ │ │ ├── swin.cpython-38.pyc │ │ │ │ ├── tanet.cpython-38.pyc │ │ │ │ ├── x3d.cpython-310.pyc │ │ │ │ ├── Vmamba.cpython-310.pyc │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── aagcn.cpython-310.pyc │ │ │ │ ├── mamba.cpython-310.pyc │ │ │ │ ├── resnet.cpython-310.pyc │ │ │ │ ├── resnet.cpython-38.pyc │ │ │ │ ├── resnet3d.cpython-38.pyc │ │ │ │ ├── stgcn.cpython-310.pyc │ │ │ │ ├── tanet.cpython-310.pyc │ │ │ │ ├── vit_mae.cpython-310.pyc │ │ │ │ ├── vit_mae.cpython-38.pyc │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── resnet3d.cpython-310.pyc │ │ │ │ ├── resnet_tin.cpython-38.pyc │ │ │ │ ├── resnet_tsm.cpython-38.pyc │ │ │ │ ├── uniformer.cpython-310.pyc │ │ │ │ ├── uniformer.cpython-38.pyc │ │ │ │ ├── mobilenet_v2.cpython-310.pyc │ │ │ │ ├── mobilenet_v2.cpython-38.pyc │ │ │ │ ├── resnet2plus1d.cpython-38.pyc │ │ │ │ ├── resnet3d_csn.cpython-310.pyc │ │ │ │ ├── resnet3d_csn.cpython-38.pyc │ │ │ │ ├── resnet_audio.cpython-310.pyc │ │ │ │ ├── resnet_audio.cpython-38.pyc │ │ │ │ ├── resnet_omni.cpython-310.pyc │ │ │ │ ├── resnet_omni.cpython-38.pyc │ │ │ │ ├── resnet_tin.cpython-310.pyc │ │ │ │ ├── resnet_tsm.cpython-310.pyc │ │ │ │ ├── rgbposeconv3d.cpython-38.pyc │ │ │ │ ├── timesformer.cpython-310.pyc │ │ │ │ ├── timesformer.cpython-38.pyc │ │ │ │ ├── uniformerv2.cpython-310.pyc │ │ │ │ ├── uniformerv2.cpython-38.pyc │ │ │ │ ├── visionmamba.cpython-310.pyc │ │ │ │ ├── resnet2plus1d.cpython-310.pyc │ │ │ │ ├── rgbposeconv3d.cpython-310.pyc │ │ │ │ ├── mobilenet_v2_tsm.cpython-310.pyc │ │ │ │ ├── mobilenet_v2_tsm.cpython-38.pyc │ │ │ │ ├── resnet3d_slowfast.cpython-310.pyc │ │ │ │ ├── resnet3d_slowfast.cpython-38.pyc │ │ │ │ ├── resnet3d_slowonly.cpython-310.pyc │ │ │ │ └── resnet3d_slowonly.cpython-38.pyc │ │ │ └── classification │ │ │ │ ├── __pycache__ │ │ │ │ ├── config.cpython-310.pyc │ │ │ │ ├── config_b.cpython-310.pyc │ │ │ │ ├── config_s.cpython-310.pyc │ │ │ │ └── config_bv2.cpython-310.pyc │ │ │ │ └── models │ │ │ │ └── __pycache__ │ │ │ │ ├── csms6s.cpython-310.pyc │ │ │ │ ├── vmamba.cpython-310.pyc │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── csm_triton.cpython-310.pyc │ │ │ │ ├── vmambav2.cpython-310.pyc │ │ │ │ └── patchembed_event.cpython-310.pyc │ │ ├── losses │ │ │ ├── __pycache__ │ │ │ │ ├── base.cpython-310.pyc │ │ │ │ ├── base.cpython-38.pyc │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── bmn_loss.cpython-38.pyc │ │ │ │ ├── hvu_loss.cpython-38.pyc │ │ │ │ ├── nll_loss.cpython-38.pyc │ │ │ │ ├── ssn_loss.cpython-38.pyc │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── bmn_loss.cpython-310.pyc │ │ │ │ ├── hvu_loss.cpython-310.pyc │ │ │ │ ├── nll_loss.cpython-310.pyc │ │ │ │ ├── ssn_loss.cpython-310.pyc │ │ │ │ ├── ohem_hinge_loss.cpython-310.pyc │ │ │ │ ├── ohem_hinge_loss.cpython-38.pyc │ │ │ │ ├── cross_entropy_loss.cpython-38.pyc │ │ │ │ ├── cross_entropy_loss.cpython-310.pyc │ │ │ │ ├── binary_logistic_regression_loss.cpython-38.pyc │ │ │ │ └── binary_logistic_regression_loss.cpython-310.pyc │ │ │ ├── __init__.py │ │ │ ├── nll_loss.py │ │ │ └── base.py │ │ ├── roi_heads │ │ │ ├── bbox_heads │ │ │ │ ├── __init__.py │ │ │ │ └── __pycache__ │ │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ │ ├── bbox_head.cpython-38.pyc │ │ │ │ │ └── bbox_head.cpython-310.pyc │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── roi_head.cpython-38.pyc │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ └── roi_head.cpython-310.pyc │ │ │ ├── roi_extractors │ │ │ │ ├── __init__.py │ │ │ │ └── __pycache__ │ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ │ ├── single_straight3d.cpython-310.pyc │ │ │ │ │ └── single_straight3d.cpython-38.pyc │ │ │ ├── shared_heads │ │ │ │ ├── __pycache__ │ │ │ │ │ ├── lfb.cpython-310.pyc │ │ │ │ │ ├── lfb.cpython-38.pyc │ │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ │ ├── acrn_head.cpython-310.pyc │ │ │ │ │ ├── acrn_head.cpython-38.pyc │ │ │ │ │ ├── fbo_head.cpython-310.pyc │ │ │ │ │ ├── fbo_head.cpython-38.pyc │ │ │ │ │ ├── lfb_infer_head.cpython-310.pyc │ │ │ │ │ └── lfb_infer_head.cpython-38.pyc │ │ │ │ └── __init__.py │ │ │ └── __init__.py │ │ ├── utils │ │ │ ├── __pycache__ │ │ │ │ ├── embed.cpython-310.pyc │ │ │ │ ├── embed.cpython-38.pyc │ │ │ │ ├── graph.cpython-310.pyc │ │ │ │ ├── graph.cpython-38.pyc │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── gcn_utils.cpython-38.pyc │ │ │ │ ├── gcn_utils.cpython-310.pyc │ │ │ │ ├── blending_utils.cpython-38.pyc │ │ │ │ └── blending_utils.cpython-310.pyc │ │ │ └── __init__.py │ │ ├── localizers │ │ │ ├── __pycache__ │ │ │ │ ├── bmn.cpython-310.pyc │ │ │ │ ├── bmn.cpython-38.pyc │ │ │ │ ├── bsn.cpython-310.pyc │ │ │ │ ├── bsn.cpython-38.pyc │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ └── __init__.cpython-38.pyc │ │ │ ├── __init__.py │ │ │ └── utils │ │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── bsn_utils.cpython-310.pyc │ │ │ │ ├── bsn_utils.cpython-38.pyc │ │ │ │ ├── proposal_utils.cpython-310.pyc │ │ │ │ └── proposal_utils.cpython-38.pyc │ │ │ │ └── __init__.py │ │ ├── task_modules │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ └── __init__.cpython-38.pyc │ │ │ └── assigners │ │ │ │ ├── __init__.py │ │ │ │ └── __pycache__ │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── max_iou_assigner_ava.cpython-38.pyc │ │ │ │ └── max_iou_assigner_ava.cpython-310.pyc │ │ ├── recognizers │ │ │ ├── __pycache__ │ │ │ │ ├── base.cpython-310.pyc │ │ │ │ ├── base.cpython-38.pyc │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── recognizer2d.cpython-310.pyc │ │ │ │ ├── recognizer2d.cpython-38.pyc │ │ │ │ ├── recognizer3d.cpython-310.pyc │ │ │ │ ├── recognizer3d.cpython-38.pyc │ │ │ │ ├── recognizer3d_mm.cpython-310.pyc │ │ │ │ ├── recognizer3d_mm.cpython-38.pyc │ │ │ │ ├── recognizer_audio.cpython-38.pyc │ │ │ │ ├── recognizer_gcn.cpython-310.pyc │ │ │ │ ├── recognizer_gcn.cpython-38.pyc │ │ │ │ ├── recognizer_omni.cpython-310.pyc │ │ │ │ ├── recognizer_omni.cpython-38.pyc │ │ │ │ └── recognizer_audio.cpython-310.pyc │ │ │ ├── __init__.py │ │ │ └── recognizer_audio.py │ │ ├── data_preprocessors │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── data_preprocessor.cpython-310.pyc │ │ │ │ ├── data_preprocessor.cpython-38.pyc │ │ │ │ ├── multimodal_data_preprocessor.cpython-310.pyc │ │ │ │ └── multimodal_data_preprocessor.cpython-38.pyc │ │ │ └── __init__.py │ │ └── __init__.py │ ├── __pycache__ │ │ ├── version.cpython-38.pyc │ │ ├── __init__.cpython-310.pyc │ │ ├── __init__.cpython-38.pyc │ │ ├── registry.cpython-310.pyc │ │ ├── registry.cpython-38.pyc │ │ └── version.cpython-310.pyc │ ├── utils │ │ ├── __pycache__ │ │ │ ├── misc.cpython-38.pyc │ │ │ ├── misc.cpython-310.pyc │ │ │ ├── typing.cpython-38.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── setup_env.cpython-38.pyc │ │ │ ├── typing.cpython-310.pyc │ │ │ ├── collect_env.cpython-38.pyc │ │ │ ├── setup_env.cpython-310.pyc │ │ │ ├── collect_env.cpython-310.pyc │ │ │ ├── gradcam_utils.cpython-310.pyc │ │ │ └── gradcam_utils.cpython-38.pyc │ │ ├── __init__.py │ │ ├── collect_env.py │ │ └── typing.py │ ├── datasets │ │ ├── __pycache__ │ │ │ ├── base.cpython-310.pyc │ │ │ ├── base.cpython-38.pyc │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── ava_dataset.cpython-38.pyc │ │ │ ├── audio_dataset.cpython-38.pyc │ │ │ ├── ava_dataset.cpython-310.pyc │ │ │ ├── pose_dataset.cpython-310.pyc │ │ │ ├── pose_dataset.cpython-38.pyc │ │ │ ├── video_dataset.cpython-38.pyc │ │ │ ├── audio_dataset.cpython-310.pyc │ │ │ ├── rawframe_dataset.cpython-38.pyc │ │ │ ├── video_dataset.cpython-310.pyc │ │ │ ├── rawframe_dataset.cpython-310.pyc │ │ │ ├── repeat_aug_dataset.cpython-38.pyc │ │ │ ├── activitynet_dataset.cpython-310.pyc │ │ │ ├── activitynet_dataset.cpython-38.pyc │ │ │ └── repeat_aug_dataset.cpython-310.pyc │ │ ├── transforms │ │ │ └── __pycache__ │ │ │ │ ├── loading.cpython-38.pyc │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── formatting.cpython-38.pyc │ │ │ │ ├── loading.cpython-310.pyc │ │ │ │ ├── processing.cpython-38.pyc │ │ │ │ ├── wrappers.cpython-310.pyc │ │ │ │ ├── wrappers.cpython-38.pyc │ │ │ │ ├── formatting.cpython-310.pyc │ │ │ │ ├── processing.cpython-310.pyc │ │ │ │ ├── pose_transforms.cpython-38.pyc │ │ │ │ ├── trajectories.cpython-310.pyc │ │ │ │ ├── hr_preprocessor.cpython-310.pyc │ │ │ │ └── pose_transforms.cpython-310.pyc │ │ └── __init__.py │ ├── engine │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-38.pyc │ │ │ └── __init__.cpython-310.pyc │ │ ├── model │ │ │ ├── __init__.py │ │ │ └── __pycache__ │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── weight_init.cpython-310.pyc │ │ │ │ └── weight_init.cpython-38.pyc │ │ ├── hooks │ │ │ ├── __pycache__ │ │ │ │ ├── output.cpython-310.pyc │ │ │ │ ├── output.cpython-38.pyc │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── visualization_hook.cpython-310.pyc │ │ │ │ └── visualization_hook.cpython-38.pyc │ │ │ └── __init__.py │ │ ├── runner │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── multi_loop.cpython-310.pyc │ │ │ │ └── multi_loop.cpython-38.pyc │ │ │ └── __init__.py │ │ ├── optimizers │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── tsm_optim_wrapper_constructor.cpython-38.pyc │ │ │ │ ├── swin_optim_wrapper_constructor.cpython-310.pyc │ │ │ │ ├── swin_optim_wrapper_constructor.cpython-38.pyc │ │ │ │ ├── tsm_optim_wrapper_constructor.cpython-310.pyc │ │ │ │ ├── layer_decay_optim_wrapper_constructor.cpython-38.pyc │ │ │ │ └── layer_decay_optim_wrapper_constructor.cpython-310.pyc │ │ │ └── __init__.py │ │ └── __init__.py │ ├── structures │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── action_data_sample.cpython-310.pyc │ │ │ └── action_data_sample.cpython-38.pyc │ │ ├── bbox │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── bbox_target.cpython-38.pyc │ │ │ │ ├── transforms.cpython-310.pyc │ │ │ │ ├── transforms.cpython-38.pyc │ │ │ │ └── bbox_target.cpython-310.pyc │ │ │ └── __init__.py │ │ └── __init__.py │ ├── visualization │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── video_backend.cpython-38.pyc │ │ │ ├── video_backend.cpython-310.pyc │ │ │ ├── action_visualizer.cpython-310.pyc │ │ │ └── action_visualizer.cpython-38.pyc │ │ └── __init__.py │ ├── apis │ │ ├── inferencers │ │ │ └── __init__.py │ │ └── __init__.py │ ├── testing │ │ └── __init__.py │ ├── version.py │ └── __init__.py ├── train.sh ├── MANIFEST.in ├── dist_train.sh ├── tools │ ├── data │ │ ├── skeleton │ │ │ ├── S001C001P001R001A001_rgb.avi │ │ │ ├── babel2mma2.py │ │ │ ├── label_map_ntu60.txt │ │ │ └── compress_nturgbd.py │ │ ├── ava │ │ │ ├── extract_rgb_frames.sh │ │ │ ├── extract_frames.sh │ │ │ ├── download_annotations.sh │ │ │ ├── fetch_ava_proposals.sh │ │ │ ├── download_videos_parallel.sh │ │ │ ├── download_videos.sh │ │ │ ├── download_videos_gnu_parallel.sh │ │ │ ├── cut_videos.sh │ │ │ ├── label_map.txt │ │ │ └── extract_rgb_frames_ffmpeg.sh │ │ ├── mmit │ │ │ ├── extract_rgb_frames.sh │ │ │ ├── extract_frames.sh │ │ │ ├── extract_rgb_frames_opencv.sh │ │ │ ├── generate_videos_filelist.sh │ │ │ ├── generate_rawframes_filelist.sh │ │ │ └── preprocess_data.sh │ │ ├── sthv1 │ │ │ ├── encode_videos.sh │ │ │ ├── extract_flow.sh │ │ │ ├── generate_videos_filelist.sh │ │ │ └── generate_rawframes_filelist.sh │ │ ├── sthv2 │ │ │ ├── extract_rgb_frames.sh │ │ │ ├── extract_frames.sh │ │ │ ├── extract_rgb_frames_opencv.sh │ │ │ ├── generate_videos_filelist.sh │ │ │ └── generate_rawframes_filelist.sh │ │ ├── diving48 │ │ │ ├── extract_rgb_frames.sh │ │ │ ├── extract_frames.sh │ │ │ ├── extract_rgb_frames_opencv.sh │ │ │ ├── download_videos.sh │ │ │ ├── generate_videos_filelist.sh │ │ │ ├── generate_rawframes_filelist.sh │ │ │ ├── download_annotations.sh │ │ │ └── label_map.txt │ │ ├── hmdb51 │ │ │ ├── extract_rgb_frames.sh │ │ │ ├── extract_frames.sh │ │ │ ├── extract_rgb_frames_opencv.sh │ │ │ ├── generate_videos_filelist.sh │ │ │ ├── generate_rawframes_filelist.sh │ │ │ ├── download_annotations.sh │ │ │ ├── label_map.txt │ │ │ └── download_videos.sh │ │ ├── jester │ │ │ ├── encode_videos.sh │ │ │ ├── extract_flow.sh │ │ │ ├── generate_videos_filelist.sh │ │ │ ├── generate_rawframes_filelist.sh │ │ │ └── label_map.txt │ │ ├── ucf101 │ │ │ ├── extract_rgb_frames.sh │ │ │ ├── extract_frames.sh │ │ │ ├── extract_rgb_frames_opencv.sh │ │ │ ├── generate_videos_filelist.sh │ │ │ ├── generate_rawframes_filelist.sh │ │ │ ├── download_videos.sh │ │ │ └── download_annotations.sh │ │ ├── gym │ │ │ ├── extract_frames.sh │ │ │ ├── download_videos.sh │ │ │ ├── download_annotations.sh │ │ │ └── environment.yml │ │ ├── activitynet │ │ │ ├── extract_frames.sh │ │ │ ├── download_annotations.sh │ │ │ ├── download_videos.sh │ │ │ ├── download_bsn_videos.sh │ │ │ ├── download_feature_annotations.sh │ │ │ ├── download_features.sh │ │ │ └── environment.yml │ │ ├── hvu │ │ │ ├── generate_rawframes_filelist.sh │ │ │ ├── generate_videos_filelist.sh │ │ │ ├── download_videos.sh │ │ │ ├── extract_frames.sh │ │ │ ├── parse_tag_list.py │ │ │ ├── download_annotations.sh │ │ │ ├── environment.yml │ │ │ └── generate_sub_file_list.py │ │ ├── mit │ │ │ ├── extract_rgb_frames.sh │ │ │ ├── extract_rgb_frames_opencv.sh │ │ │ ├── generate_videos_filelist.sh │ │ │ ├── extract_frames.sh │ │ │ ├── generate_rawframes_filelist.sh │ │ │ └── preprocess_data.sh │ │ ├── thumos14 │ │ │ ├── extract_rgb_frames.sh │ │ │ ├── fetch_tag_proposals.sh │ │ │ ├── extract_rgb_frames_opencv.sh │ │ │ ├── extract_frames.sh │ │ │ ├── denormalize_proposal_file.sh │ │ │ ├── download_videos.sh │ │ │ └── download_annotations.sh │ │ ├── kinetics │ │ │ ├── download_videos.sh │ │ │ ├── extract_rgb_frames.sh │ │ │ ├── generate_videos_filelist.sh │ │ │ ├── environment.yml │ │ │ ├── extract_rgb_frames_opencv.sh │ │ │ ├── generate_rawframes_filelist.sh │ │ │ ├── extract_frames.sh │ │ │ ├── rename_classnames.sh │ │ │ ├── download_annotations.sh │ │ │ └── download_backup_annotations.sh │ │ ├── ava_kinetics │ │ │ └── softlink_ava.py │ │ └── omnisource │ │ │ └── trim_raw_video.py │ ├── misc │ │ └── dist_clip_feature_extraction.sh │ ├── dist_train.sh │ ├── dist_test.sh │ ├── slurm_test.sh │ ├── slurm_train.sh │ └── analysis_tools │ │ └── print_config.py ├── requirements.txt ├── configs │ └── _base_ │ │ ├── schedules │ │ ├── sgd_50e.py │ │ ├── sgd_100e.py │ │ ├── adam_20e.py │ │ ├── sgd_150e_warmup.py │ │ ├── sgd_tsm_100e.py │ │ ├── sgd_tsm_50e.py │ │ ├── sgd_tsm_mobilenet_v2_50e.py │ │ └── sgd_tsm_mobilenet_v2_100e.py │ │ ├── models │ │ └── CeleX-HAR.py │ │ └── default_runtime.py ├── README.md ├── setup.cfg └── model-index.yml ├── CeleXHAR_samples.jpg └── figures ├── celexHAR.png ├── framework.png ├── resultsVIS.png ├── celexhar_logo.png ├── CeleXHAR_youtube.png └── benchmarkresults.png /EVMamba/requirements/mminstall.txt: -------------------------------------------------------------------------------- 1 | mmcv>=2.0.0rc0,<2.1.0 2 | mmengine>=0.5.0,<1.0.0 3 | -------------------------------------------------------------------------------- /EVMamba/requirements/readthedocs.txt: -------------------------------------------------------------------------------- 1 | mmcv 2 | titlecase 3 | torch 4 | torchvision 5 | -------------------------------------------------------------------------------- /CeleXHAR_samples.jpg: 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All rights reserved. 2 | from .tpn import TPN 3 | 4 | __all__ = ['TPN'] 5 | -------------------------------------------------------------------------------- /EVMamba/requirements/build.txt: -------------------------------------------------------------------------------- 1 | decord >= 0.4.1 2 | einops 3 | matplotlib 4 | numpy 5 | opencv-contrib-python 6 | Pillow 7 | scipy 8 | torch>=1.3 9 | -------------------------------------------------------------------------------- /EVMamba/mmaction/__pycache__/version.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Event-AHU/CeleX-HAR/HEAD/EVMamba/mmaction/__pycache__/version.cpython-38.pyc -------------------------------------------------------------------------------- /EVMamba/mmaction/__pycache__/__init__.cpython-310.pyc: -------------------------------------------------------------------------------- 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All rights reserved. 2 | from .weight_init import ConvBranchInit 3 | 4 | __all__ = ['ConvBranchInit'] 5 | -------------------------------------------------------------------------------- /EVMamba/mmaction/models/__pycache__/__init__.cpython-310.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Event-AHU/CeleX-HAR/HEAD/EVMamba/mmaction/models/__pycache__/__init__.cpython-310.pyc -------------------------------------------------------------------------------- /EVMamba/mmaction/models/common/__pycache__/tam.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Event-AHU/CeleX-HAR/HEAD/EVMamba/mmaction/models/common/__pycache__/tam.cpython-38.pyc -------------------------------------------------------------------------------- /EVMamba/mmaction/models/heads/__pycache__/base.cpython-38.pyc: 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All rights reserved. 2 | from .max_iou_assigner_ava import MaxIoUAssignerAVA 3 | 4 | __all__ = ['MaxIoUAssignerAVA'] 5 | -------------------------------------------------------------------------------- /EVMamba/mmaction/visualization/__pycache__/action_visualizer.cpython-310.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Event-AHU/CeleX-HAR/HEAD/EVMamba/mmaction/visualization/__pycache__/action_visualizer.cpython-310.pyc -------------------------------------------------------------------------------- /EVMamba/mmaction/visualization/__pycache__/action_visualizer.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Event-AHU/CeleX-HAR/HEAD/EVMamba/mmaction/visualization/__pycache__/action_visualizer.cpython-38.pyc -------------------------------------------------------------------------------- /EVMamba/requirements/optional.txt: 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https://raw.githubusercontent.com/Event-AHU/CeleX-HAR/HEAD/EVMamba/mmaction/models/backbones/classification/models/__pycache__/patchembed_event.cpython-310.pyc -------------------------------------------------------------------------------- /EVMamba/tools/data/ava/extract_rgb_frames.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/ava/videos_15min/ ../../data/ava/rawframes/ --task rgb --level 1 --mixed-ext 5 | echo "Genearte raw frames (RGB only)" 6 | 7 | cd ava/ 8 | -------------------------------------------------------------------------------- /EVMamba/tools/data/mmit/extract_rgb_frames.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/mmit/videos/ ../../data/mmit/rawframes/ --task rgb --level 2 --ext mp4 5 | 6 | echo "Genearte raw frames (RGB only)" 7 | 8 | cd mmit/ 9 | -------------------------------------------------------------------------------- /EVMamba/tools/data/sthv1/encode_videos.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_videos.py ../../data/sthv1/rawframes/ ../../data/sthv1/videos/ --fps 12 --level 1 --start-idx 1 --filename-tmpl '%05d' 5 | echo "Encode videos" 6 | 7 | cd sthv1/ 8 | -------------------------------------------------------------------------------- /EVMamba/tools/data/sthv2/extract_rgb_frames.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/sthv2/videos/ ../../data/sthv2/rawframes/ --task rgb --level 1 --ext webm 5 | echo "Genearte raw frames (RGB only)" 6 | 7 | cd sthv2/ 8 | -------------------------------------------------------------------------------- /EVMamba/tools/data/diving48/extract_rgb_frames.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/diving48/videos/ ../../data/diving48/rawframes/ --task rgb --level 1 --ext mp4 5 | echo "Genearte raw frames (RGB only)" 6 | 7 | cd - 8 | -------------------------------------------------------------------------------- /EVMamba/tools/data/hmdb51/extract_rgb_frames.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/hmdb51/videos/ ../../data/hmdb51/rawframes/ --task rgb --level 2 --ext avi 5 | echo "Genearte raw frames (RGB only)" 6 | 7 | cd hmdb51/ 8 | -------------------------------------------------------------------------------- /EVMamba/tools/data/jester/encode_videos.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_videos.py ../../data/jester/rawframes/ ../../data/jester/videos/ --fps 12 --level 1 --start-idx 1 --filename-tmpl '%05d' 5 | echo "Encode videos" 6 | 7 | cd jester/ 8 | -------------------------------------------------------------------------------- /EVMamba/tools/data/sthv1/extract_flow.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/sthv1/rawframes/ ../../data/sthv1/rawframes/ --task flow --level 1 --flow-type tvl1 --input-frames 5 | echo "Flow (tv-l1) Generated" 6 | cd sthv1/ 7 | -------------------------------------------------------------------------------- /EVMamba/tools/data/ucf101/extract_rgb_frames.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/ucf101/videos/ ../../data/ucf101/rawframes/ --task rgb --level 2 --ext avi 5 | echo "Genearte raw frames (RGB only)" 6 | 7 | cd ucf101/ 8 | -------------------------------------------------------------------------------- /EVMamba/mmaction/engine/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .hooks import * # noqa: F401, F403 3 | from .model import * # noqa: F401, F403 4 | from .optimizers import * # noqa: F401, F403 5 | from .runner import * # noqa: F401, F403 6 | -------------------------------------------------------------------------------- /EVMamba/tools/data/hmdb51/extract_frames.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/hmdb51/videos/ ../../data/hmdb51/rawframes/ --task both --level 2 --flow-type tvl1 5 | echo "Raw frames (RGB and Flow) Generated" 6 | cd hmdb51/ 7 | -------------------------------------------------------------------------------- /EVMamba/tools/data/jester/extract_flow.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/jester/rawframes/ ../../data/jester/rawframes/ --task flow --level 1 --flow-type tvl1 --input-frames 5 | echo "Flow (tv-l1) Generated" 6 | cd jester/ 7 | -------------------------------------------------------------------------------- /EVMamba/tools/data/ucf101/extract_frames.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/ucf101/videos/ ../../data/ucf101/rawframes/ --task both --level 2 --flow-type tvl1 5 | echo "Raw frames (RGB and Flow) Generated" 6 | cd ucf101/ 7 | -------------------------------------------------------------------------------- /EVMamba/tools/data/ava/extract_frames.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/ava/videos_15min/ ../../data/ava/rawframes/ --task both --level 1 --flow-type tvl1 --mixed-ext 5 | echo "Raw frames (RGB and Flow) Generated" 6 | cd ava/ 7 | -------------------------------------------------------------------------------- /EVMamba/tools/data/mmit/extract_frames.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/mmit/videos/ ../../../data/mmit/rawframes/ --task both --level 2 --flow-type tvl1 --ext mp4 5 | echo "Raw frames (RGB and Flow) Generated" 6 | cd mmit/ 7 | -------------------------------------------------------------------------------- /EVMamba/tools/data/sthv2/extract_frames.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/sthv2/videos/ ../../data/sthv2/rawframes/ --task both --level 1 --flow-type tvl1 --ext webm 5 | echo "Raw frames (RGB and tv-l1) Generated" 6 | cd sthv2/ 7 | -------------------------------------------------------------------------------- /EVMamba/tools/data/diving48/extract_frames.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/diving48/videos/ ../../data/diving48/rawframes/ --task both --level 1 --flow-type tvl1 --ext mp4 5 | echo "Raw frames (RGB and tv-l1) Generated" 6 | cd - 7 | -------------------------------------------------------------------------------- /EVMamba/tools/data/mmit/extract_rgb_frames_opencv.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/mmit/videos/ ../../data/mmit/rawframes/ --task rgb --level 2 --ext mp4 --use-opencv 5 | 6 | echo "Genearte raw frames (RGB only)" 7 | 8 | cd mmit/ 9 | -------------------------------------------------------------------------------- /EVMamba/tools/data/sthv2/extract_rgb_frames_opencv.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/sthv2/videos/ ../../data/sthv2/rawframes/ --task rgb --level 1 --ext webm --use-opencv 5 | echo "Genearte raw frames (RGB only)" 6 | 7 | cd sthv2/ 8 | -------------------------------------------------------------------------------- /EVMamba/mmaction/apis/inferencers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .actionrecog_inferencer import ActionRecogInferencer 3 | from .mmaction2_inferencer import MMAction2Inferencer 4 | 5 | __all__ = ['ActionRecogInferencer', 'MMAction2Inferencer'] 6 | -------------------------------------------------------------------------------- /EVMamba/mmaction/models/roi_heads/shared_heads/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .acrn_head import ACRNHead 3 | from .fbo_head import FBOHead 4 | from .lfb_infer_head import LFBInferHead 5 | 6 | __all__ = ['ACRNHead', 'LFBInferHead', 'FBOHead'] 7 | -------------------------------------------------------------------------------- /EVMamba/tools/data/diving48/extract_rgb_frames_opencv.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/diving48/videos/ ../../data/diving48/rawframes/ --task rgb --level 1 --ext mp4 --use-opencv 5 | echo "Genearte raw frames (RGB only)" 6 | 7 | cd - 8 | -------------------------------------------------------------------------------- /EVMamba/tools/data/hmdb51/extract_rgb_frames_opencv.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/hmdb51/videos/ ../../data/hmdb51/rawframes/ --task rgb --level 2 --ext avi --use-opencv 5 | echo "Genearte raw frames (RGB only)" 6 | 7 | cd hmdb51/ 8 | -------------------------------------------------------------------------------- /EVMamba/tools/data/ucf101/extract_rgb_frames_opencv.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/ucf101/videos/ ../../data/ucf101/rawframes/ --task rgb --level 2 --ext avi --use-opencv 5 | echo "Genearte raw frames (RGB only)" 6 | 7 | cd ucf101/ 8 | -------------------------------------------------------------------------------- /EVMamba/tools/data/hmdb51/generate_videos_filelist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../../../ 4 | 5 | PYTHONPATH=. python tools/data/build_file_list.py hmdb51 data/hmdb51/videos/ --level 2 --format videos --shuffle 6 | echo "Filelist for videos generated." 7 | 8 | cd tools/data/hmdb51/ 9 | -------------------------------------------------------------------------------- /EVMamba/tools/data/ucf101/generate_videos_filelist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../../../ 4 | 5 | PYTHONPATH=. python tools/data/build_file_list.py ucf101 data/ucf101/videos/ --level 2 --format videos --shuffle 6 | echo "Filelist for videos generated." 7 | 8 | cd tools/data/ucf101/ 9 | -------------------------------------------------------------------------------- /EVMamba/mmaction/structures/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .action_data_sample import ActionDataSample 3 | from .bbox import bbox2result, bbox_target 4 | 5 | __all__ = [ 6 | 'ActionDataSample', 7 | 'bbox2result', 8 | 'bbox_target', 9 | ] 10 | -------------------------------------------------------------------------------- /EVMamba/requirements/docs.txt: -------------------------------------------------------------------------------- 1 | docutils==0.16.0 2 | einops 3 | myst-parser 4 | opencv-python 5 | -e git+https://github.com/open-mmlab/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme 6 | scipy 7 | sphinx==4.0.2 8 | sphinx_copybutton 9 | sphinx_markdown_tables 10 | sphinx_rtd_theme==0.5.2 11 | -------------------------------------------------------------------------------- /EVMamba/tools/data/gym/extract_frames.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/gym/subactions/ ../../data/gym/subaction_frames/ --level 1 --flow-type tvl1 --ext mp4 --task both --new-short 256 5 | echo "Raw frames (RGB and tv-l1) Generated" 6 | 7 | cd gym/ 8 | -------------------------------------------------------------------------------- /EVMamba/tools/data/hmdb51/generate_rawframes_filelist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../../../ 4 | 5 | PYTHONPATH=. python tools/data/build_file_list.py hmdb51 data/hmdb51/rawframes/ --level 2 --format rawframes --shuffle 6 | echo "Filelist for rawframes generated." 7 | 8 | cd tools/data/hmdb51/ 9 | -------------------------------------------------------------------------------- /EVMamba/tools/data/ucf101/generate_rawframes_filelist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../../../ 4 | 5 | PYTHONPATH=. python tools/data/build_file_list.py ucf101 data/ucf101/rawframes/ --level 2 --format rawframes --shuffle 6 | echo "Filelist for rawframes generated." 7 | 8 | cd tools/data/ucf101/ 9 | -------------------------------------------------------------------------------- /EVMamba/mmaction/evaluation/metrics/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .acc_metric import AccMetric, ConfusionMatrix 3 | from .anet_metric import ANetMetric 4 | from .ava_metric import AVAMetric 5 | 6 | __all__ = ['AccMetric', 'AVAMetric', 'ANetMetric', 'ConfusionMatrix'] 7 | -------------------------------------------------------------------------------- /EVMamba/mmaction/models/data_preprocessors/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .data_preprocessor import ActionDataPreprocessor 3 | from .multimodal_data_preprocessor import MultiModalDataPreprocessor 4 | 5 | __all__ = ['ActionDataPreprocessor', 'MultiModalDataPreprocessor'] 6 | -------------------------------------------------------------------------------- /EVMamba/tools/data/activitynet/extract_frames.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | cd ../ 3 | python build_rawframes.py ../../data/ActivityNet/videos/ ../../data/ActivityNet/rawframes/ --level 1 --flow-type tvl1 --ext mp4 --task both --new-short 256 4 | echo "Raw frames (RGB and tv-l1) Generated for train set" 5 | 6 | cd activitynet/ 7 | -------------------------------------------------------------------------------- /EVMamba/tools/data/activitynet/download_annotations.sh: -------------------------------------------------------------------------------- 1 | DATA_DIR="../../../data/ActivityNet/" 2 | 3 | if [[ ! -d "${DATA_DIR}" ]]; then 4 | echo "${DATA_DIR} does not exist. Creating"; 5 | mkdir -p ${DATA_DIR} 6 | fi 7 | 8 | cd ${DATA_DIR} 9 | 10 | wget http://ec2-52-25-205-214.us-west-2.compute.amazonaws.com/files/activity_net.v1-3.min.json 11 | 12 | cd - 13 | -------------------------------------------------------------------------------- /EVMamba/mmaction/apis/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .inference import (detection_inference, inference_recognizer, 3 | init_recognizer, pose_inference) 4 | from .inferencers import * # NOQA 5 | 6 | __all__ = [ 7 | 'init_recognizer', 'inference_recognizer', 'detection_inference', 8 | 'pose_inference' 9 | ] 10 | -------------------------------------------------------------------------------- /EVMamba/tools/data/activitynet/download_videos.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | # set up environment 4 | conda env create -f environment.yml 5 | source activate activitynet 6 | pip install --upgrade youtube-dl 7 | pip install mmcv 8 | 9 | DATA_DIR="../../../data/ActivityNet" 10 | python download.py 11 | 12 | source deactivate activitynet 13 | conda remove -n activitynet --all 14 | -------------------------------------------------------------------------------- /EVMamba/mmaction/visualization/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .action_visualizer import ActionVisualizer 3 | from .video_backend import (LocalVisBackend, TensorboardVisBackend, 4 | WandbVisBackend) 5 | 6 | __all__ = [ 7 | 'ActionVisualizer', 'LocalVisBackend', 'WandbVisBackend', 8 | 'TensorboardVisBackend' 9 | ] 10 | -------------------------------------------------------------------------------- /EVMamba/tools/data/activitynet/download_bsn_videos.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | # set up environment 4 | conda env create -f environment.yml 5 | source activate activitynet 6 | pip install --upgrade youtube-dl 7 | pip install mmcv 8 | 9 | DATA_DIR="../../../data/ActivityNet" 10 | python download.py --bsn 11 | 12 | source deactivate activitynet 13 | conda remove -n activitynet --all 14 | -------------------------------------------------------------------------------- /EVMamba/mmaction/models/roi_heads/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .bbox_heads import BBoxHeadAVA 3 | from .roi_extractors import SingleRoIExtractor3D 4 | from .roi_head import AVARoIHead 5 | from .shared_heads import ACRNHead, FBOHead, LFBInferHead 6 | 7 | __all__ = [ 8 | 'AVARoIHead', 'BBoxHeadAVA', 'SingleRoIExtractor3D', 'ACRNHead', 'FBOHead', 9 | 'LFBInferHead' 10 | ] 11 | -------------------------------------------------------------------------------- /EVMamba/tools/data/diving48/download_videos.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | DATA_DIR="../../../data/diving48/" 4 | 5 | if [[ ! -d "${DATA_DIR}" ]]; then 6 | echo "${DATA_DIR} does not exist. Creating"; 7 | mkdir -p ${DATA_DIR} 8 | fi 9 | 10 | cd ${DATA_DIR} 11 | 12 | wget http://www.svcl.ucsd.edu/projects/resound/Diving48_rgb.tar.gz --no-check-certificate 13 | tar -zxvf Diving48_rgb.tar.gz 14 | mv ./rgb ./videos 15 | 16 | cd - 17 | -------------------------------------------------------------------------------- /EVMamba/tools/data/gym/download_videos.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | # set up environment 4 | conda env create -f environment.yml 5 | source activate gym 6 | pip install mmengine 7 | pip install --upgrade youtube-dl 8 | 9 | DATA_DIR="../../../data/gym" 10 | ANNO_DIR="../../../data/gym/annotations" 11 | python download.py ${ANNO_DIR}/annotation.json ${DATA_DIR}/videos 12 | 13 | source deactivate gym 14 | conda remove -n gym --all 15 | -------------------------------------------------------------------------------- /EVMamba/mmaction/models/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .blending_utils import (BaseMiniBatchBlending, CutmixBlending, 3 | MixupBlending, RandomBatchAugment) 4 | from .gcn_utils import * # noqa: F401,F403 5 | from .graph import Graph 6 | 7 | __all__ = [ 8 | 'BaseMiniBatchBlending', 'CutmixBlending', 'MixupBlending', 'Graph', 9 | 'RandomBatchAugment' 10 | ] 11 | -------------------------------------------------------------------------------- /EVMamba/tools/data/sthv1/generate_videos_filelist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../../../ 4 | PYTHONPATH=. python tools/data/build_file_list.py sthv1 data/sthv1/videos/ --num-split 1 --level 1 --subset train --format videos --shuffle 5 | PYTHONPATH=. python tools/data/build_file_list.py sthv1 data/sthv1/videos/ --num-split 1 --level 1 --subset val --format videos --shuffle 6 | echo "Filelist for videos generated." 7 | 8 | cd tools/data/sthv1/ 9 | -------------------------------------------------------------------------------- /EVMamba/tools/data/sthv2/generate_videos_filelist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../../../ 4 | PYTHONPATH=. python tools/data/build_file_list.py sthv2 data/sthv2/videos/ --num-split 1 --level 1 --subset train --format videos --shuffle 5 | PYTHONPATH=. python tools/data/build_file_list.py sthv2 data/sthv2/videos/ --num-split 1 --level 1 --subset val --format videos --shuffle 6 | echo "Filelist for videos generated." 7 | 8 | cd tools/data/sthv2/ 9 | -------------------------------------------------------------------------------- /EVMamba/tools/data/ava/download_annotations.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -e 4 | 5 | VERSION=${VERSION:-"2.1"} 6 | DATA_DIR="../../../data/ava/annotations" 7 | 8 | if [[ ! -d "${DATA_DIR}" ]]; then 9 | echo "${DATA_DIR} does not exist. Creating"; 10 | mkdir -p ${DATA_DIR} 11 | fi 12 | 13 | wget https://research.google.com/ava/download/ava_v${VERSION}.zip 14 | unzip -j ava_v${VERSION}.zip -d ${DATA_DIR}/ 15 | rm ava_v${VERSION}.zip 16 | -------------------------------------------------------------------------------- /EVMamba/tools/data/jester/generate_videos_filelist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../../../ 4 | PYTHONPATH=. python tools/data/build_file_list.py jester data/jester/videos/ --num-split 1 --level 1 --subset train --format videos --shuffle 5 | PYTHONPATH=. python tools/data/build_file_list.py jester data/jester/videos/ --num-split 1 --level 1 --subset val --format videos --shuffle 6 | echo "Filelist for videos generated." 7 | 8 | cd tools/data/jester/ 9 | -------------------------------------------------------------------------------- /EVMamba/tools/data/ucf101/download_videos.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | DATA_DIR="../../../data/ucf101/" 4 | 5 | if [[ ! -d "${DATA_DIR}" ]]; then 6 | echo "${DATA_DIR} does not exist. Creating"; 7 | mkdir -p ${DATA_DIR} 8 | fi 9 | 10 | cd ${DATA_DIR} 11 | 12 | wget https://www.crcv.ucf.edu/datasets/human-actions/ucf101/UCF101.rar --no-check-certificate 13 | unrar x UCF101.rar 14 | mv ./UCF-101 ./videos 15 | 16 | cd "../../tools/data/ucf101" 17 | -------------------------------------------------------------------------------- /EVMamba/mmaction/models/localizers/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .bsn_utils import generate_bsp_feature, generate_candidate_proposals 3 | from .proposal_utils import (post_processing, soft_nms, temporal_iop, 4 | temporal_iou) 5 | 6 | __all__ = [ 7 | 'generate_bsp_feature', 'generate_candidate_proposals', 'soft_nms', 8 | 'temporal_iop', 'temporal_iou', 'post_processing' 9 | ] 10 | -------------------------------------------------------------------------------- /EVMamba/tools/data/diving48/generate_videos_filelist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../../../ 4 | PYTHONPATH=. python tools/data/build_file_list.py diving48 data/diving48/videos/ --num-split 1 --level 1 --subset train --format videos --shuffle 5 | PYTHONPATH=. python tools/data/build_file_list.py diving48 data/diving48/videos/ --num-split 1 --level 1 --subset val --format videos --shuffle 6 | echo "Filelist for videos generated." 7 | 8 | cd tools/data/diving48/ 9 | -------------------------------------------------------------------------------- /EVMamba/tools/data/sthv2/generate_rawframes_filelist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../../../ 4 | PYTHONPATH=. python tools/data/build_file_list.py sthv2 data/sthv2/rawframes/ --num-split 1 --level 1 --subset train --format rawframes --shuffle 5 | PYTHONPATH=. python tools/data/build_file_list.py sthv2 data/sthv2/rawframes/ --num-split 1 --level 1 --subset val --format rawframes --shuffle 6 | echo "Filelist for rawframes generated." 7 | 8 | cd tools/data/sthv2/ 9 | -------------------------------------------------------------------------------- /EVMamba/tools/misc/dist_clip_feature_extraction.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | CONFIG=$1 4 | CHECKPOINT=$2 5 | GPUS=$3 6 | PORT=${PORT:-29500} 7 | 8 | PYTHONPATH="$(dirname $0)/../..":$PYTHONPATH \ 9 | # Arguments starting from the forth one are captured by ${@:4} 10 | python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \ 11 | $(dirname "$0")/clip_feature_extraction.py $CONFIG $CHECKPOINT \ 12 | --launcher pytorch ${@:4} 13 | -------------------------------------------------------------------------------- /EVMamba/tools/data/hvu/generate_rawframes_filelist.sh: -------------------------------------------------------------------------------- 1 | # to generate file list of frames 2 | python generate_file_list.py --input_csv ../../../data/hvu/annotations/hvu_train.csv --src_dir ../../../data/hvu/rawframes_train \ 3 | --output ../../../data/hvu/hvu_train.json --mode frames 4 | python generate_file_list.py --input_csv ../../../data/hvu/annotations/hvu_val.csv --src_dir ../../../data/hvu/rawframes_val \ 5 | --output ../../../data/hvu/hvu_val.json --mode frames 6 | -------------------------------------------------------------------------------- /EVMamba/tools/data/hvu/generate_videos_filelist.sh: -------------------------------------------------------------------------------- 1 | # to generate file lists of videos 2 | python generate_file_list.py --input_csv ../../../data/hvu/annotations/hvu_train.csv --src_dir ../../../data/hvu/videos_train \ 3 | --output ../../../data/hvu/hvu_train_video.json --mode videos 4 | python generate_file_list.py --input_csv ../../../data/hvu/annotations/hvu_val.csv --src_dir ../../../data/hvu/videos_val \ 5 | --output ../../../data/hvu/hvu_val_video.json --mode videos 6 | -------------------------------------------------------------------------------- /EVMamba/tools/data/mit/extract_rgb_frames.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/mit/videos/training ../../data/mit/rawframes/training/ --level 2 --ext mp4 --task rgb 5 | echo "Raw frames (RGB only) generated for train set" 6 | 7 | python build_rawframes.py ../../data/mit/videos/validation ../../data/mit/rawframes/validation/ --level 2 --ext mp4 --task rgb 8 | echo "Raw frames (RGB only) generated for val set" 9 | 10 | cd mit/ 11 | -------------------------------------------------------------------------------- /EVMamba/tools/data/diving48/generate_rawframes_filelist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../../../ 4 | PYTHONPATH=. python tools/data/build_file_list.py diving48 data/diving48/rawframes/ --num-split 1 --level 1 --subset train --format rawframes --shuffle 5 | PYTHONPATH=. python tools/data/build_file_list.py diving48 data/diving48/rawframes/ --num-split 1 --level 1 --subset val --format rawframes --shuffle 6 | echo "Filelist for rawframes generated." 7 | 8 | cd tools/data/diving48/ 9 | -------------------------------------------------------------------------------- /EVMamba/tools/data/thumos14/extract_rgb_frames.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/thumos14/videos/val/ ../../data/thumos14/rawframes/val/ --level 1 --ext mp4 --task rgb 5 | echo "Raw frames (RGB only) generated for val set" 6 | 7 | python build_rawframes.py ../../data/thumos14/videos/test/ ../../data/thumos14/rawframes/test/ --level 1 --ext mp4 --task rgb 8 | echo "Raw frames (RGB only) generated for test set" 9 | 10 | cd thumos14/ 11 | -------------------------------------------------------------------------------- /EVMamba/mmaction/engine/optimizers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .layer_decay_optim_wrapper_constructor import \ 3 | LearningRateDecayOptimizerConstructor 4 | from .swin_optim_wrapper_constructor import SwinOptimWrapperConstructor 5 | from .tsm_optim_wrapper_constructor import TSMOptimWrapperConstructor 6 | 7 | __all__ = [ 8 | 'TSMOptimWrapperConstructor', 'SwinOptimWrapperConstructor', 9 | 'LearningRateDecayOptimizerConstructor' 10 | ] 11 | -------------------------------------------------------------------------------- /EVMamba/tools/data/sthv1/generate_rawframes_filelist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../../../ 4 | PYTHONPATH=. python tools/data/build_file_list.py sthv1 data/sthv1/rawframes/ --rgb-prefix '0' --num-split 1 --level 1 --subset train --format rawframes --shuffle 5 | PYTHONPATH=. python tools/data/build_file_list.py sthv1 data/sthv1/rawframes/ --rgb-prefix '0' --num-split 1 --level 1 --subset val --format rawframes --shuffle 6 | echo "Filelist for rawframes generated." 7 | 8 | cd tools/data/sthv1/ 9 | -------------------------------------------------------------------------------- /EVMamba/tools/data/mmit/generate_videos_filelist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../../../ 4 | PYTHONPATH=. python tools/data/build_file_list.py mmit data/mmit/videos/ --level 2 --format videos --num-split 1 --subset train --shuffle 5 | echo "Train filelist for videos generated." 6 | 7 | PYTHONPATH=. python tools/data/build_file_list.py mmit data/mmit/videos/ --level 2 --format videos --num-split 1 --subset val --shuffle 8 | echo "Val filelist for videos generated." 9 | cd tools/data/mmit/ 10 | -------------------------------------------------------------------------------- /EVMamba/mmaction/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .collect_env import collect_env 3 | from .gradcam_utils import GradCAM 4 | from .misc import frame_extract, get_random_string, get_shm_dir, get_thread_id 5 | from .setup_env import register_all_modules 6 | from .typing import * # noqa: F401,F403 7 | 8 | __all__ = [ 9 | 'collect_env', 'get_random_string', 'get_thread_id', 'get_shm_dir', 10 | 'frame_extract', 'GradCAM', 'register_all_modules' 11 | ] 12 | -------------------------------------------------------------------------------- /EVMamba/tools/data/ava/fetch_ava_proposals.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -e 4 | 5 | DATA_DIR="../../../data/ava/annotations" 6 | 7 | wget https://download.openmmlab.com/mmaction/dataset/ava/ava_dense_proposals_train.FAIR.recall_93.9.pkl -P ${DATA_DIR} 8 | wget https://download.openmmlab.com/mmaction/dataset/ava/ava_dense_proposals_val.FAIR.recall_93.9.pkl -P ${DATA_DIR} 9 | wget https://download.openmmlab.com/mmaction/dataset/ava/ava_dense_proposals_test.FAIR.recall_93.9.pkl -P ${DATA_DIR} 10 | -------------------------------------------------------------------------------- /EVMamba/tools/data/jester/generate_rawframes_filelist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../../../ 4 | PYTHONPATH=. python tools/data/build_file_list.py jester data/jester/rawframes/ --rgb-prefix '0' --num-split 1 --level 1 --subset train --format rawframes --shuffle 5 | PYTHONPATH=. python tools/data/build_file_list.py jester data/jester/rawframes/ --rgb-prefix '0' --num-split 1 --level 1 --subset val --format rawframes --shuffle 6 | echo "Filelist for rawframes generated." 7 | 8 | cd tools/data/jester/ 9 | -------------------------------------------------------------------------------- /EVMamba/tools/data/hvu/download_videos.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | # set up environment 4 | conda env create -f environment.yml 5 | source activate hvu 6 | pip install mmcv 7 | pip install --upgrade youtube-dl 8 | 9 | DATA_DIR="../../../data/hvu" 10 | ANNO_DIR="../../../data/hvu/annotations" 11 | python download.py ${ANNO_DIR}/hvu_train.csv ${DATA_DIR}/videos_train 12 | python download.py ${ANNO_DIR}/hvu_val.csv ${DATA_DIR}/videos_val 13 | 14 | source deactivate hvu 15 | conda remove -n hvu --all 16 | -------------------------------------------------------------------------------- /EVMamba/tools/data/mit/extract_rgb_frames_opencv.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/mit/videos/training ../../data/mit/rawframes/training/ --level 2 --ext mp4 --task rgb --use-opencv 5 | echo "Raw frames (RGB only) generated for train set" 6 | 7 | python build_rawframes.py ../../data/mit/videos/validation ../../data/mit/rawframes/validation/ --level 2 --ext mp4 --task rgb --use-opencv 8 | echo "Raw frames (RGB only) generated for val set" 9 | 10 | cd mit/ 11 | -------------------------------------------------------------------------------- /EVMamba/tools/data/mit/generate_videos_filelist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../../../ 4 | PYTHONPATH=. python tools/data/build_file_list.py mit data/mit/videos/training/ --level 2 --format videos --num-split 1 --subset train --shuffle 5 | echo "Train filelist for videos generated." 6 | 7 | PYTHONPATH=. python tools/data/build_file_list.py mit data/mit/videos/validation/ --level 2 --format videos --num-split 1 --subset val --shuffle 8 | echo "Val filelist for videos generated." 9 | cd tools/data/mit/ 10 | -------------------------------------------------------------------------------- /EVMamba/tools/data/thumos14/fetch_tag_proposals.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | PROP_DIR="../../../data/thumos14/proposals" 4 | 5 | if [[ ! -d "${PROP_DIR}" ]]; then 6 | echo "${PROP_DIR} does not exist. Creating"; 7 | mkdir -p ${PROP_DIR} 8 | fi 9 | 10 | wget https://download.openmmlab.com/mmaction/dataset/thumos14/thumos14_tag_val_normalized_proposal_list.txt -P ${PROP_DIR} 11 | wget https://download.openmmlab.com/mmaction/dataset/thumos14/thumos14_tag_test_normalized_proposal_list.txt -P ${PROP_DIR} 12 | -------------------------------------------------------------------------------- /EVMamba/tools/data/ucf101/download_annotations.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | DATA_DIR="../../../data/ucf101/annotations" 4 | 5 | if [[ ! -d "${DATA_DIR}" ]]; then 6 | echo "${DATA_DIR} does not exist. Creating"; 7 | mkdir -p ${DATA_DIR} 8 | fi 9 | 10 | wget https://www.crcv.ucf.edu/wp-content/uploads/2019/03/UCF101TrainTestSplits-RecognitionTask.zip --no-check-certificate 11 | 12 | unzip -j UCF101TrainTestSplits-RecognitionTask.zip -d ${DATA_DIR}/ 13 | rm UCF101TrainTestSplits-RecognitionTask.zip 14 | -------------------------------------------------------------------------------- /EVMamba/tools/data/mit/extract_frames.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/mit/videos/training ../../data/mit/rawframes/training/ --level 2 --flow-type tvl1 --ext mp4 --task both 5 | echo "Raw frames (RGB and tv-l1) Generated for train set" 6 | 7 | python build_rawframes.py ../../data/mit/vides/validation/ ../../data/mit/rawframes/validation/ --level 2 --flow-type tvl1 --ext mp4 --task both 8 | echo "Raw frames (RGB and tv-l1) Generated for val set" 9 | 10 | cd mit/ 11 | -------------------------------------------------------------------------------- /EVMamba/tools/data/mmit/generate_rawframes_filelist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../../../ 4 | PYTHONPATH=. python tools/data/build_file_list.py mmit data/mmit/rawframes/ --level 2 --format rawframes --num-split 1 --subset train --shuffle 5 | echo "Train filelist for rawframes generated." 6 | 7 | PYTHONPATH=. python tools/data/build_file_list.py mmit data/mmit/rawframes/ --level 2 --format rawframes --num-split 1 --subset val --shuffle 8 | echo "Val filelist for rawframes generated." 9 | cd tools/data/mmit/ 10 | -------------------------------------------------------------------------------- /EVMamba/tools/data/thumos14/extract_rgb_frames_opencv.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/thumos14/videos/val/ ../../data/thumos14/rawframes/val/ --level 1 --ext mp4 --task rgb --use-opencv 5 | echo "Raw frames (RGB only) generated for val set" 6 | 7 | python build_rawframes.py ../../data/thumos14/videos/test/ ../../data/thumos14/rawframes/test/ --level 1 --ext mp4 --task rgb --use-opencv 8 | echo "Raw frames (RGB only) generated for test set" 9 | 10 | cd thumos14/ 11 | -------------------------------------------------------------------------------- /EVMamba/tools/data/ava/download_videos_parallel.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -e 4 | 5 | DATA_DIR="../../../data/ava/videos" 6 | ANNO_DIR="../../../data/ava/annotations" 7 | 8 | if [[ ! -d "${DATA_DIR}" ]]; then 9 | echo "${DATA_DIR} does not exist. Creating"; 10 | mkdir -p ${DATA_DIR} 11 | fi 12 | 13 | wget https://s3.amazonaws.com/ava-dataset/annotations/ava_file_names_trainval_v2.1.txt -P ${ANNO_DIR} 14 | 15 | python download_videos_parallel.py ${ANNO_DIR}/ava_file_names_trainval_v2.1.txt ${DATA_DIR} 16 | -------------------------------------------------------------------------------- /EVMamba/tools/data/diving48/download_annotations.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | DATA_DIR="../../../data/diving48/annotations" 4 | 5 | if [[ ! -d "${DATA_DIR}" ]]; then 6 | echo "${DATA_DIR} does not exist. Creating"; 7 | mkdir -p ${DATA_DIR} 8 | fi 9 | 10 | cd ${DATA_DIR} 11 | 12 | wget http://www.svcl.ucsd.edu/projects/resound/Diving48_vocab.json 13 | wget http://www.svcl.ucsd.edu/projects/resound/Diving48_V2_train.json 14 | wget http://www.svcl.ucsd.edu/projects/resound/Diving48_V2_test.json 15 | 16 | cd - 17 | -------------------------------------------------------------------------------- /EVMamba/tools/data/mmit/preprocess_data.sh: -------------------------------------------------------------------------------- 1 | DATA_DIR="../../../data/mmit/" 2 | 3 | if [[ ! -d "${DATA_DIR}" ]]; then 4 | echo "${DATA_DIR} does not exist. Creating"; 5 | mkdir -p ${DATA_DIR} 6 | fi 7 | 8 | cd ${DATA_DIR} 9 | 10 | # Download the Multi_Moments_in_Time_Raw.zip here manually 11 | unzip Multi_Moments_in_Time_Raw.zip 12 | rm Multi_Moments_in_Time.zip 13 | 14 | if [ ! -d "./annotations" ]; then 15 | mkdir ./annotations 16 | fi 17 | 18 | mv *.txt annotations && mv *.csv annotations 19 | 20 | cd - 21 | -------------------------------------------------------------------------------- /EVMamba/tools/data/thumos14/extract_frames.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/thumos14/videos/val/ ../../data/thumos14/rawframes/val/ --level 1 --flow-type tvl1 --ext mp4 --task both 5 | echo "Raw frames (RGB and tv-l1) Generated for val set" 6 | 7 | python build_rawframes.py ../../data/thumos14/videos/test/ ../../data/thumos14/rawframes/test/ --level 1 --flow-type tvl1 --ext mp4 --task both 8 | echo "Raw frames (RGB and tv-l1) Generated for test set" 9 | 10 | cd thumos14/ 11 | -------------------------------------------------------------------------------- /EVMamba/tools/data/hvu/extract_frames.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../ 4 | python build_rawframes.py ../../data/hvu/videos_train/ ../../data/hvu/rawframes_train/ --level 1 --flow-type tvl1 --ext mp4 --task both --new-short 256 5 | echo "Raw frames (RGB and tv-l1) Generated for train set" 6 | 7 | python build_rawframes.py ../../data/hvu/videos_val/ ../../data/hvu/rawframes_val/ --level 1 --flow-type tvl1 --ext mp4 --task both --new-short 256 8 | echo "Raw frames (RGB and tv-l1) Generated for val set" 9 | 10 | cd hvu/ 11 | -------------------------------------------------------------------------------- /EVMamba/tools/data/mit/generate_rawframes_filelist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../../../ 4 | PYTHONPATH=. python tools/data/build_file_list.py mit data/mit/rawframes/training/ --level 2 --format rawframes --num-split 1 --subset train --shuffle 5 | echo "Train filelist for rawframes generated." 6 | 7 | PYTHONPATH=. python tools/data/build_file_list.py mit data/mit/rawframes/validation/ --level 2 --format rawframes --num-split 1 --subset val --shuffle 8 | echo "Val filelist for rawframes generated." 9 | cd tools/data/mit/ 10 | -------------------------------------------------------------------------------- /EVMamba/tools/data/hvu/parse_tag_list.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import mmcv 3 | 4 | tag_list = '../../../data/hvu/annotations/hvu_categories.csv' 5 | 6 | lines = open(tag_list).readlines() 7 | lines = [x.strip().split(',') for x in lines[1:]] 8 | tag_categories = {} 9 | for line in lines: 10 | tag, category = line 11 | tag_categories.setdefault(category, []).append(tag) 12 | 13 | for k in tag_categories: 14 | tag_categories[k].sort() 15 | 16 | mmcv.dump(tag_categories, 'hvu_tags.json') 17 | -------------------------------------------------------------------------------- /EVMamba/tools/dist_train.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -x 4 | 5 | CONFIG=$1 6 | GPUS=$2 7 | NNODES=${NNODES:-1} 8 | NODE_RANK=${NODE_RANK:-0} 9 | PORT=${PORT:-29600} 10 | MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"} 11 | 12 | PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ 13 | python -m torch.distributed.launch --nnodes=$NNODES --node_rank=$NODE_RANK --master_addr=$MASTER_ADDR \ 14 | --nproc_per_node=$GPUS --master_port=$PORT $(dirname "$0")/train.py $CONFIG --launcher pytorch ${@:3} 15 | # Any arguments from the third one are captured by ${@:3} 16 | -------------------------------------------------------------------------------- /EVMamba/mmaction/models/common/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .conv2plus1d import Conv2plus1d 3 | from .conv_audio import ConvAudio 4 | from .sub_batchnorm3d import SubBatchNorm3D 5 | from .tam import TAM 6 | from .transformer import (DividedSpatialAttentionWithNorm, 7 | DividedTemporalAttentionWithNorm, FFNWithNorm) 8 | 9 | __all__ = [ 10 | 'Conv2plus1d', 'TAM', 'DividedSpatialAttentionWithNorm', 11 | 'DividedTemporalAttentionWithNorm', 'FFNWithNorm', 'SubBatchNorm3D', 12 | 'ConvAudio' 13 | ] 14 | -------------------------------------------------------------------------------- /EVMamba/mmaction/models/recognizers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .base import BaseRecognizer 3 | from .recognizer2d import Recognizer2D 4 | from .recognizer3d import Recognizer3D 5 | from .recognizer3d_mm import MMRecognizer3D 6 | from .recognizer_audio import RecognizerAudio 7 | from .recognizer_gcn import RecognizerGCN 8 | from .recognizer_omni import RecognizerOmni 9 | 10 | __all__ = [ 11 | 'BaseRecognizer', 'RecognizerGCN', 'Recognizer2D', 'Recognizer3D', 12 | 'RecognizerAudio', 'RecognizerOmni', 'MMRecognizer3D' 13 | ] 14 | -------------------------------------------------------------------------------- /EVMamba/configs/_base_/schedules/sgd_50e.py: -------------------------------------------------------------------------------- 1 | train_cfg = dict( 2 | type='EpochBasedTrainLoop', max_epochs=50, val_begin=1, val_interval=1) 3 | val_cfg = dict(type='ValLoop') 4 | test_cfg = dict(type='TestLoop') 5 | 6 | param_scheduler = [ 7 | dict( 8 | type='MultiStepLR', 9 | begin=0, 10 | end=50, 11 | by_epoch=True, 12 | milestones=[20, 40], 13 | gamma=0.1) 14 | ] 15 | 16 | optim_wrapper = dict( 17 | optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001), 18 | clip_grad=dict(max_norm=40, norm_type=2)) 19 | -------------------------------------------------------------------------------- /EVMamba/tools/dist_test.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -x 4 | 5 | CONFIG=$1 6 | CHECKPOINT=$2 7 | GPUS=$3 8 | NNODES=${NNODES:-1} 9 | NODE_RANK=${NODE_RANK:-0} 10 | PORT=${PORT:-29500} 11 | MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"} 12 | 13 | PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ 14 | # Arguments starting from the forth one are captured by ${@:4} 15 | python -m torch.distributed.launch --nnodes=$NNODES --node_rank=$NODE_RANK --master_addr=$MASTER_ADDR \ 16 | --nproc_per_node=$GPUS --master_port=$PORT $(dirname "$0")/test.py $CONFIG $CHECKPOINT --launcher pytorch ${@:4} 17 | -------------------------------------------------------------------------------- /EVMamba/configs/_base_/schedules/sgd_100e.py: -------------------------------------------------------------------------------- 1 | train_cfg = dict( 2 | type='EpochBasedTrainLoop', max_epochs=100, val_begin=1, val_interval=1) 3 | val_cfg = dict(type='ValLoop') 4 | test_cfg = dict(type='TestLoop') 5 | 6 | param_scheduler = [ 7 | dict( 8 | type='MultiStepLR', 9 | begin=0, 10 | end=100, 11 | by_epoch=True, 12 | milestones=[40, 80], 13 | gamma=0.1) 14 | ] 15 | 16 | optim_wrapper = dict( 17 | optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001), 18 | clip_grad=dict(max_norm=40, norm_type=2)) 19 | -------------------------------------------------------------------------------- /EVMamba/README.md: -------------------------------------------------------------------------------- 1 | ```shell 2 | conda create -n evmamba python=3.10.13 3 | conda activate evmamba 4 | pip install torch==2.0.0 torchvision==0.15.1 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu118 5 | 6 | #### Please refer to Vmamba to install the following packages(https://github.com/MzeroMiko/VMamba)： 7 | pip install -r requirements.txt 8 | cd kernels/selective_scan && pip install . 9 | #### 10 | 11 | pip install -U openmim 12 | mim install mmengine 13 | pip install mmcv==2.0.0 -f https://download.openmmlab.com/mmcv/dist/cu118/torch2.0/index.html 14 | pip install -v -e . 15 | ``` -------------------------------------------------------------------------------- /EVMamba/mmaction/models/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .backbones import * # noqa: F401,F403 3 | from .common import * # noqa: F401,F403 4 | from .data_preprocessors import * # noqa: F401,F403 5 | from .heads import * # noqa: F401,F403 6 | from .localizers import * # noqa: F401,F403 7 | from .losses import * # noqa: F401,F403 8 | from .necks import * # noqa: F401,F403 9 | from .recognizers import * # noqa: F401,F403 10 | from .roi_heads import * # noqa: F401,F403 11 | from .task_modules import * # noqa: F401,F403 12 | from .utils import * # noqa: F401,F403 13 | -------------------------------------------------------------------------------- /EVMamba/tools/data/hmdb51/download_annotations.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -e 4 | 5 | DATA_DIR="../../../data/hmdb51/annotations" 6 | 7 | if [[ ! -d "${DATA_DIR}" ]]; then 8 | echo "${DATA_DIR} does not exist. Creating"; 9 | mkdir -p ${DATA_DIR} 10 | fi 11 | 12 | cd ${DATA_DIR} 13 | wget http://serre-lab.clps.brown.edu/wp-content/uploads/2013/10/test_train_splits.rar --no-check-certificate 14 | 15 | # sudo apt-get install unrar 16 | unrar x test_train_splits.rar 17 | rm test_train_splits.rar 18 | 19 | mv testTrainMulti_7030_splits/*.txt ./ 20 | rmdir testTrainMulti_7030_splits 21 | 22 | cd - 23 | -------------------------------------------------------------------------------- /EVMamba/mmaction/utils/collect_env.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmengine.utils import get_git_hash 3 | from mmengine.utils.dl_utils import collect_env as collect_basic_env 4 | 5 | import mmaction 6 | 7 | 8 | def collect_env(): 9 | """Collect the information of the running environments.""" 10 | env_info = collect_basic_env() 11 | env_info['MMAction2'] = ( 12 | mmaction.__version__ + '+' + get_git_hash(digits=7)) 13 | return env_info 14 | 15 | 16 | if __name__ == '__main__': 17 | for name, val in collect_env().items(): 18 | print(f'{name}: {val}') 19 | -------------------------------------------------------------------------------- /EVMamba/tools/data/ava/download_videos.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -e 4 | 5 | DATA_DIR="../../../data/ava/videos" 6 | ANNO_DIR="../../../data/ava/annotations" 7 | 8 | if [[ ! -d "${DATA_DIR}" ]]; then 9 | echo "${DATA_DIR} does not exist. Creating"; 10 | mkdir -p ${DATA_DIR} 11 | fi 12 | 13 | wget https://s3.amazonaws.com/ava-dataset/annotations/ava_file_names_trainval_v2.1.txt -P ${ANNO_DIR} 14 | 15 | cat ${ANNO_DIR}/ava_file_names_trainval_v2.1.txt | 16 | while read vid; 17 | do wget -c "https://s3.amazonaws.com/ava-dataset/trainval/${vid}" -P ${DATA_DIR}; done 18 | 19 | echo "Downloading finished." 20 | -------------------------------------------------------------------------------- /EVMamba/tools/data/gym/download_annotations.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -e 4 | 5 | DATA_DIR="../../../data/gym/annotations" 6 | 7 | if [[ ! -d "${DATA_DIR}" ]]; then 8 | echo "${DATA_DIR} does not exist. Creating"; 9 | mkdir -p ${DATA_DIR} 10 | fi 11 | 12 | wget https://sdolivia.github.io/FineGym/resources/dataset/finegym_annotation_info_v1.0.json -O $DATA_DIR/annotation.json 13 | wget https://sdolivia.github.io/FineGym/resources/dataset/gym99_train_element_v1.0.txt -O $DATA_DIR/gym99_train_org.txt 14 | wget https://sdolivia.github.io/FineGym/resources/dataset/gym99_val_element.txt -O $DATA_DIR/gym99_val_org.txt 15 | -------------------------------------------------------------------------------- /EVMamba/configs/_base_/schedules/adam_20e.py: -------------------------------------------------------------------------------- 1 | train_cfg = dict( 2 | type='EpochBasedTrainLoop', max_epochs=20, val_begin=1, val_interval=1) 3 | val_cfg = dict(type='ValLoop') 4 | test_cfg = dict(type='TestLoop') 5 | 6 | param_scheduler = [ 7 | dict( 8 | type='MultiStepLR', 9 | begin=0, 10 | end=20, 11 | by_epoch=True, 12 | milestones=[10], 13 | gamma=0.1) 14 | ] 15 | 16 | optimizer = dict( 17 | type='Adam', lr=0.01, weight_decay=0.00001) # this lr is used for 1 gpus 18 | 19 | optim_wrapper = dict( 20 | optimizer=optimizer, clip_grad=dict(max_norm=40, norm_type=2)) 21 | -------------------------------------------------------------------------------- /EVMamba/tools/data/thumos14/denormalize_proposal_file.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd ../../../ 4 | PYTHONPATH=. python tools/data/denormalize_proposal_file.py thumos14 --norm-proposal-file data/thumos14/proposals/thumos14_tag_val_normalized_proposal_list.txt --data-prefix data/thumos14/rawframes/val/ 5 | echo "Proposal file denormalized for val set" 6 | 7 | PYTHONPATH=. python tools/data/denormalize_proposal_file.py thumos14 --norm-proposal-file data/thumos14/proposals/thumos14_tag_test_normalized_proposal_list.txt --data-prefix data/thumos14/rawframes/test/ 8 | echo "Proposal file denormalized for test set" 9 | 10 | cd tools/data/thumos14/ 11 | -------------------------------------------------------------------------------- /EVMamba/tools/data/activitynet/download_feature_annotations.sh: -------------------------------------------------------------------------------- 1 | DATA_DIR="../../../data/ActivityNet/" 2 | 3 | if [[ ! -d "${DATA_DIR}" ]]; then 4 | echo "${DATA_DIR} does not exist. Creating"; 5 | mkdir -p ${DATA_DIR} 6 | fi 7 | 8 | cd ${DATA_DIR} 9 | 10 | wget https://raw.githubusercontent.com/wzmsltw/BSN-boundary-sensitive-network/master/data/activitynet_annotations/anet_anno_action.json 11 | 12 | wget https://raw.githubusercontent.com/wzmsltw/BSN-boundary-sensitive-network/master/data/activitynet_annotations/video_info_new.csv 13 | 14 | wget https://download.openmmlab.com/mmaction/localization/anet_activity_indexes_val.txt 15 | 16 | cd - 17 | -------------------------------------------------------------------------------- /EVMamba/tools/data/hmdb51/label_map.txt: -------------------------------------------------------------------------------- 1 | brush_hair 2 | cartwheel 3 | catch 4 | chew 5 | clap 6 | climb 7 | climb_stairs 8 | dive 9 | draw_sword 10 | dribble 11 | drink 12 | eat 13 | fall_floor 14 | fencing 15 | flic_flac 16 | golf 17 | handstand 18 | hit 19 | hug 20 | jump 21 | kick 22 | kick_ball 23 | kiss 24 | laugh 25 | pick 26 | pour 27 | pullup 28 | punch 29 | push 30 | pushup 31 | ride_bike 32 | ride_horse 33 | run 34 | shake_hands 35 | shoot_ball 36 | shoot_bow 37 | shoot_gun 38 | sit 39 | situp 40 | smile 41 | smoke 42 | somersault 43 | stand 44 | swing_baseball 45 | sword 46 | sword_exercise 47 | talk 48 | throw 49 | turn 50 | walk 51 | wave 52 | -------------------------------------------------------------------------------- /EVMamba/configs/_base_/schedules/sgd_150e_warmup.py: -------------------------------------------------------------------------------- 1 | train_cfg = dict( 2 | type='EpochBasedTrainLoop', max_epochs=150, val_begin=1, val_interval=1) 3 | val_cfg = dict(type='ValLoop') 4 | test_cfg = dict(type='TestLoop') 5 | 6 | param_scheduler = [ 7 | dict(type='LinearLR', start_factor=0.1, by_epoch=True, begin=0, end=10), 8 | dict( 9 | type='MultiStepLR', 10 | begin=0, 11 | end=150, 12 | by_epoch=True, 13 | milestones=[90, 130], 14 | gamma=0.1) 15 | ] 16 | 17 | optim_wrapper = dict( 18 | optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001), 19 | clip_grad=dict(max_norm=40, norm_type=2)) 20 | -------------------------------------------------------------------------------- /EVMamba/tools/data/hvu/download_annotations.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -e 4 | 5 | DATA_DIR="../../../data/hvu/annotations" 6 | 7 | if [[ ! -d "${DATA_DIR}" ]]; then 8 | echo "${DATA_DIR} does not exist. Creating"; 9 | mkdir -p ${DATA_DIR} 10 | fi 11 | 12 | git clone https://github.com/holistic-video-understanding/HVU-Dataset.git 13 | 14 | cd HVU-Dataset 15 | unzip -o HVU_Train_V1.0.zip 16 | unzip -o HVU_Val_V1.0.zip 17 | cd .. 18 | mv HVU-Dataset/HVU_Train_V1.0.csv ${DATA_DIR}/hvu_train.csv 19 | mv HVU-Dataset/HVU_Val_V1.0.csv ${DATA_DIR}/hvu_val.csv 20 | mv HVU-Dataset/HVU_Tags_Categories_V1.0.csv ${DATA_DIR}/hvu_categories.csv 21 | 22 | rm -rf HVU-Dataset 23 | -------------------------------------------------------------------------------- /EVMamba/tools/data/mit/preprocess_data.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | DATA_DIR="../../../data/mit/" 4 | 5 | if [[ ! -d "${DATA_DIR}" ]]; then 6 | echo "${DATA_DIR} does not exist. Creating"; 7 | mkdir -p ${DATA_DIR} 8 | fi 9 | 10 | cd ${DATA_DIR} 11 | 12 | # Download the Moments_in_Time_Raw.zip here manually 13 | unzip Moments_in_Time_Raw.zip 14 | rm Moments_in_Time_Raw.zip 15 | 16 | if [ ! -d "./videos" ]; then 17 | mkdir ./videos 18 | fi 19 | mv ./training ./videos && mv ./validation ./video 20 | 21 | if [ ! -d "./annotations" ]; then 22 | mkdir ./annotations 23 | fi 24 | 25 | mv *.txt annotations && mv *.csv annotations 26 | 27 | cd "../../tools/data/mit" 28 | -------------------------------------------------------------------------------- /EVMamba/configs/_base_/schedules/sgd_tsm_100e.py: -------------------------------------------------------------------------------- 1 | train_cfg = dict( 2 | type='EpochBasedTrainLoop', max_epochs=100, val_begin=1, val_interval=1) 3 | val_cfg = dict(type='ValLoop') 4 | test_cfg = dict(type='TestLoop') 5 | 6 | param_scheduler = [ 7 | dict( 8 | type='MultiStepLR', 9 | begin=0, 10 | end=100, 11 | by_epoch=True, 12 | milestones=[40, 80], 13 | gamma=0.1) 14 | ] 15 | 16 | optim_wrapper = dict( 17 | constructor='TSMOptimWrapperConstructor', 18 | paramwise_cfg=dict(fc_lr5=True), 19 | optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001), 20 | clip_grad=dict(max_norm=20, norm_type=2)) 21 | -------------------------------------------------------------------------------- /EVMamba/configs/_base_/schedules/sgd_tsm_50e.py: -------------------------------------------------------------------------------- 1 | train_cfg = dict( 2 | type='EpochBasedTrainLoop', max_epochs=50, val_begin=1, val_interval=1) 3 | val_cfg = dict(type='ValLoop') 4 | test_cfg = dict(type='TestLoop') 5 | 6 | param_scheduler = [ 7 | dict( 8 | type='MultiStepLR', 9 | begin=0, 10 | end=50, 11 | by_epoch=True, 12 | milestones=[20, 40], 13 | gamma=0.1) 14 | ] 15 | 16 | optim_wrapper = dict( 17 | constructor='TSMOptimWrapperConstructor', 18 | paramwise_cfg=dict(fc_lr5=True), 19 | optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001), 20 | clip_grad=dict(max_norm=20, norm_type=2)) 21 | -------------------------------------------------------------------------------- /EVMamba/setup.cfg: -------------------------------------------------------------------------------- 1 | [bdist_wheel] 2 | universal=1 3 | 4 | [aliases] 5 | test=pytest 6 | 7 | [yapf] 8 | based_on_style = pep8 9 | blank_line_before_nested_class_or_def = true 10 | split_before_expression_after_opening_paren = true 11 | split_penalty_import_names=0 12 | SPLIT_PENALTY_AFTER_OPENING_BRACKET=800 13 | 14 | [isort] 15 | line_length = 79 16 | multi_line_output = 0 17 | extra_standard_library = pkg_resources,setuptools 18 | known_first_party = mmaction 19 | known_third_party = cv2,decord,einops,joblib,matplotlib,mmcv,numpy,pandas,pytest,pytorch_sphinx_theme,scipy,seaborn,titlecase,torch,webcolors 20 | no_lines_before = STDLIB,LOCALFOLDER 21 | default_section = THIRDPARTY 22 | -------------------------------------------------------------------------------- /EVMamba/tools/data/hmdb51/download_videos.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -e 4 | 5 | DATA_DIR="../../../data/hmdb51/" 6 | 7 | if [[ ! -d "${DATA_DIR}" ]]; then 8 | echo "${DATA_DIR} does not exist. Creating"; 9 | mkdir -p ${DATA_DIR} 10 | fi 11 | 12 | cd ${DATA_DIR} 13 | 14 | mkdir -p ./videos 15 | cd ./videos 16 | 17 | wget http://serre-lab.clps.brown.edu/wp-content/uploads/2013/10/hmdb51_org.rar --no-check-certificate 18 | 19 | # sudo apt-get install unrar 20 | unrar x ./hmdb51_org.rar 21 | rm ./hmdb51_org.rar 22 | 23 | # extract all rar files with full path 24 | for file in *.rar; do unrar x $file; done 25 | 26 | rm ./*.rar 27 | cd "../../../tools/data/hmdb51" 28 | -------------------------------------------------------------------------------- /EVMamba/tools/data/jester/label_map.txt: -------------------------------------------------------------------------------- 1 | Swiping Left 2 | Swiping Right 3 | Swiping Down 4 | Swiping Up 5 | Pushing Hand Away 6 | Pulling Hand In 7 | Sliding Two Fingers Left 8 | Sliding Two Fingers Right 9 | Sliding Two Fingers Down 10 | Sliding Two Fingers Up 11 | Pushing Two Fingers Away 12 | Pulling Two Fingers In 13 | Rolling Hand Forward 14 | Rolling Hand Backward 15 | Turning Hand Clockwise 16 | Turning Hand Counterclockwise 17 | Zooming In With Full Hand 18 | Zooming Out With Full Hand 19 | Zooming In With Two Fingers 20 | Zooming Out With Two Fingers 21 | Thumb Up 22 | Thumb Down 23 | Shaking Hand 24 | Stop Sign 25 | Drumming Fingers 26 | No gesture 27 | Doing other things 28 | -------------------------------------------------------------------------------- /EVMamba/configs/_base_/schedules/sgd_tsm_mobilenet_v2_50e.py: -------------------------------------------------------------------------------- 1 | train_cfg = dict( 2 | type='EpochBasedTrainLoop', max_epochs=50, val_begin=1, val_interval=1) 3 | val_cfg = dict(type='ValLoop') 4 | test_cfg = dict(type='TestLoop') 5 | 6 | param_scheduler = [ 7 | dict( 8 | type='MultiStepLR', 9 | begin=0, 10 | end=50, 11 | by_epoch=True, 12 | milestones=[20, 40], 13 | gamma=0.1) 14 | ] 15 | 16 | optim_wrapper = dict( 17 | constructor='TSMOptimWrapperConstructor', 18 | paramwise_cfg=dict(fc_lr5=True), 19 | optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.00002), 20 | clip_grad=dict(max_norm=20, norm_type=2)) 21 | -------------------------------------------------------------------------------- /EVMamba/configs/_base_/schedules/sgd_tsm_mobilenet_v2_100e.py: -------------------------------------------------------------------------------- 1 | train_cfg = dict( 2 | type='EpochBasedTrainLoop', max_epochs=100, val_begin=1, val_interval=1) 3 | val_cfg = dict(type='ValLoop') 4 | test_cfg = dict(type='TestLoop') 5 | 6 | param_scheduler = [ 7 | dict( 8 | type='MultiStepLR', 9 | begin=0, 10 | end=100, 11 | by_epoch=True, 12 | milestones=[40, 80], 13 | gamma=0.1) 14 | ] 15 | 16 | optim_wrapper = dict( 17 | constructor='TSMOptimWrapperConstructor', 18 | paramwise_cfg=dict(fc_lr5=True), 19 | optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.00002), 20 | clip_grad=dict(max_norm=20, norm_type=2)) 21 | -------------------------------------------------------------------------------- /EVMamba/tools/data/ava/download_videos_gnu_parallel.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -e 4 | 5 | DATA_DIR="../../../data/ava/videos" 6 | ANNO_DIR="../../../data/ava/annotations" 7 | 8 | if [[ ! -d "${DATA_DIR}" ]]; then 9 | echo "${DATA_DIR} does not exist. Creating"; 10 | mkdir -p ${DATA_DIR} 11 | fi 12 | 13 | wget https://s3.amazonaws.com/ava-dataset/annotations/ava_file_names_trainval_v2.1.txt -P ${ANNO_DIR} 14 | 15 | # sudo apt-get install parallel 16 | # parallel downloading to speed up 17 | awk '{print "https://s3.amazonaws.com/ava-dataset/trainval/"$0}' ${ANNO_DIR}/ava_file_names_trainval_v2.1.txt | 18 | parallel -j 8 wget -c -q {} -P ${DATA_DIR} 19 | 20 | echo "Downloading finished." 21 | -------------------------------------------------------------------------------- /EVMamba/tools/data/activitynet/download_features.sh: -------------------------------------------------------------------------------- 1 | DATA_DIR="../../../data/ActivityNet/activitynet_feature_cuhk/" 2 | 3 | if [[ ! -d "${DATA_DIR}" ]]; then 4 | echo "${DATA_DIR} does not exist. Creating"; 5 | mkdir -p ${DATA_DIR} 6 | fi 7 | 8 | wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1ISemndlSDS2FtqQOKL0t3Cjj9yk2yznF' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1ISemndlSDS2FtqQOKL0t3Cjj9yk2yznF" -O "csv_mean_100.zip" && rm -rf /tmp/cookies.txt 9 | 10 | unzip csv_mean_100.zip -d ${DATA_DIR}/ 11 | rm csv_mean_100.zip 12 | -------------------------------------------------------------------------------- /EVMamba/mmaction/models/losses/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .base import BaseWeightedLoss 3 | from .binary_logistic_regression_loss import BinaryLogisticRegressionLoss 4 | from .bmn_loss import BMNLoss 5 | from .cross_entropy_loss import (BCELossWithLogits, CBFocalLoss, 6 | CrossEntropyLoss) 7 | from .hvu_loss import HVULoss 8 | from .nll_loss import NLLLoss 9 | from .ohem_hinge_loss import OHEMHingeLoss 10 | from .ssn_loss import SSNLoss 11 | 12 | __all__ = [ 13 | 'BaseWeightedLoss', 'CrossEntropyLoss', 'NLLLoss', 'BCELossWithLogits', 14 | 'BinaryLogisticRegressionLoss', 'BMNLoss', 'OHEMHingeLoss', 'SSNLoss', 15 | 'HVULoss', 'CBFocalLoss' 16 | ] 17 | -------------------------------------------------------------------------------- /EVMamba/mmaction/testing/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from ._utils import (check_norm_state, generate_backbone_demo_inputs, 3 | generate_detector_demo_inputs, 4 | generate_recognizer_demo_inputs, get_audio_recognizer_cfg, 5 | get_cfg, get_detector_cfg, get_localizer_cfg, 6 | get_recognizer_cfg, get_skeletongcn_cfg) 7 | 8 | __all__ = [ 9 | 'check_norm_state', 'generate_backbone_demo_inputs', 10 | 'generate_recognizer_demo_inputs', 'get_cfg', 'get_recognizer_cfg', 11 | 'get_audio_recognizer_cfg', 'get_localizer_cfg', 'get_detector_cfg', 12 | 'generate_detector_demo_inputs', 'get_skeletongcn_cfg' 13 | ] 14 | -------------------------------------------------------------------------------- /EVMamba/tools/slurm_test.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -x 4 | 5 | PARTITION=$1 6 | JOB_NAME=$2 7 | CONFIG=$3 8 | CHECKPOINT=$4 9 | GPUS=${GPUS:-8} 10 | GPUS_PER_NODE=${GPUS_PER_NODE:-8} 11 | CPUS_PER_TASK=${CPUS_PER_TASK:-5} 12 | PY_ARGS=${@:5} # Arguments starting from the fifth one are captured 13 | SRUN_ARGS=${SRUN_ARGS:-""} 14 | 15 | PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ 16 | srun -p ${PARTITION} \ 17 | --job-name=${JOB_NAME} \ 18 | --gres=gpu:${GPUS_PER_NODE} \ 19 | --ntasks=${GPUS} \ 20 | --ntasks-per-node=${GPUS_PER_NODE} \ 21 | --cpus-per-task=${CPUS_PER_TASK} \ 22 | --kill-on-bad-exit=1 \ 23 | ${SRUN_ARGS} \ 24 | python -u tools/test.py ${CONFIG} ${CHECKPOINT} --launcher="slurm" ${PY_ARGS} 25 | -------------------------------------------------------------------------------- /EVMamba/tools/data/thumos14/download_videos.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | DATA_DIR="../../../data/thumos14/" 4 | 5 | if [[ ! -d "${DATA_DIR}" ]]; then 6 | echo "${DATA_DIR} does not exist. Creating"; 7 | mkdir -p ${DATA_DIR} 8 | fi 9 | 10 | cd ${DATA_DIR} 11 | 12 | wget https://storage.googleapis.com/thumos14_files/TH14_validation_set_mp4.zip 13 | wget https://storage.googleapis.com/thumos14_files/TH14_Test_set_mp4.zip 14 | 15 | if [ ! -d "./videos/val" ]; then 16 | mkdir -p ./videos/val 17 | fi 18 | unzip -j TH14_validation_set_mp4.zip -d videos/val 19 | 20 | if [ ! -d "./videos/test" ]; then 21 | mkdir -p ./videos/test 22 | fi 23 | unzip -P "THUMOS14_REGISTERED" -j TH14_Test_set_mp4.zip -d videos/test 24 | 25 | cd "../../tools/data/thumos14/" 26 | -------------------------------------------------------------------------------- /EVMamba/tools/slurm_train.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | export MASTER_PORT=$((12000 + $RANDOM % 20000)) 4 | set -x 5 | 6 | PARTITION=$1 7 | JOB_NAME=$2 8 | CONFIG=$3 9 | GPUS=${GPUS:-8} 10 | GPUS_PER_NODE=${GPUS_PER_NODE:-8} 11 | CPUS_PER_TASK=${CPUS_PER_TASK:-5} 12 | SRUN_ARGS=${SRUN_ARGS:-""} 13 | PY_ARGS=${@:4} # Any arguments from the forth one are captured by this 14 | 15 | PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ 16 | srun -p ${PARTITION} \ 17 | --job-name=${JOB_NAME} \ 18 | --gres=gpu:${GPUS_PER_NODE} \ 19 | --ntasks=${GPUS} \ 20 | --ntasks-per-node=${GPUS_PER_NODE} \ 21 | --cpus-per-task=${CPUS_PER_TASK} \ 22 | --kill-on-bad-exit=1 \ 23 | ${SRUN_ARGS} \ 24 | python -u tools/train.py ${CONFIG} --launcher="slurm" ${PY_ARGS} 25 | -------------------------------------------------------------------------------- /EVMamba/mmaction/datasets/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .activitynet_dataset import ActivityNetDataset 3 | from .audio_dataset import AudioDataset 4 | from .ava_dataset import AVADataset, AVAKineticsDataset 5 | from .base import BaseActionDataset 6 | from .pose_dataset import PoseDataset 7 | from .rawframe_dataset import RawframeDataset 8 | from .repeat_aug_dataset import RepeatAugDataset, repeat_pseudo_collate 9 | from .transforms import * # noqa: F401, F403 10 | from .video_dataset import VideoDataset 11 | 12 | __all__ = [ 13 | 'AVADataset', 'AVAKineticsDataset', 'ActivityNetDataset', 'AudioDataset', 14 | 'BaseActionDataset', 'PoseDataset', 'RawframeDataset', 'RepeatAugDataset', 15 | 'VideoDataset', 'repeat_pseudo_collate' 16 | ] 17 | -------------------------------------------------------------------------------- /EVMamba/configs/_base_/models/CeleX-HAR.py: -------------------------------------------------------------------------------- 1 | preprocess_cfg = dict( 2 | mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375]) 3 | 4 | model = dict( 5 | type='Recognizer2D', 6 | backbone=dict( 7 | type='', 8 | pretrained='', 9 | depth='', 10 | norm_eval=False, 11 | shift_div=8), 12 | cls_head=dict( 13 | type='TSMHead', 14 | num_classes=150, 15 | in_channels=1000, 16 | spatial_type='avg', 17 | consensus=dict(type='AvgConsensus', dim=1), 18 | dropout_ratio=0.5, 19 | init_std=0.001, 20 | is_shift=True, 21 | average_clips='prob'), 22 | data_preprocessor=dict(type='ActionDataPreprocessor', **preprocess_cfg), 23 | train_cfg=None, 24 | test_cfg=None) 25 | -------------------------------------------------------------------------------- /EVMamba/tools/analysis_tools/print_config.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import argparse 3 | 4 | from mmengine import Config, DictAction 5 | 6 | 7 | def parse_args(): 8 | parser = argparse.ArgumentParser(description='Print the whole config') 9 | parser.add_argument('config', help='config file path') 10 | parser.add_argument( 11 | '--options', nargs='+', action=DictAction, help='arguments in dict') 12 | args = parser.parse_args() 13 | 14 | return args 15 | 16 | 17 | def main(): 18 | args = parse_args() 19 | 20 | cfg = Config.fromfile(args.config) 21 | if args.options is not None: 22 | cfg.merge_from_dict(args.options) 23 | print(f'Config:\n{cfg.pretty_text}') 24 | 25 | 26 | if __name__ == '__main__': 27 | main() 28 | -------------------------------------------------------------------------------- /EVMamba/tools/data/kinetics/download_videos.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | # set up environment 4 | conda env create -f environment.yml 5 | source activate kinetics 6 | pip install --upgrade youtube-dl 7 | 8 | DATASET=$1 9 | if [ "$DATASET" == "kinetics400" ] || [ "$1" == "kinetics600" ] || [ "$1" == "kinetics700" ]; then 10 | echo "We are processing $DATASET" 11 | else 12 | echo "Bad Argument, we only support kinetics400, kinetics600 or kinetics700" 13 | exit 0 14 | fi 15 | 16 | DATA_DIR="../../../data/${DATASET}" 17 | ANNO_DIR="../../../data/${DATASET}/annotations" 18 | python download.py ${ANNO_DIR}/kinetics_train.csv ${DATA_DIR}/videos_train 19 | python download.py ${ANNO_DIR}/kinetics_val.csv ${DATA_DIR}/videos_val 20 | 21 | source deactivate kinetics 22 | conda remove -n kinetics --all 23 | -------------------------------------------------------------------------------- /EVMamba/tools/data/kinetics/extract_rgb_frames.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | DATASET=$1 4 | if [ "$DATASET" == "kinetics400" ] || [ "$1" == "kinetics600" ] || [ "$1" == "kinetics700" ]; then 5 | echo "We are processing $DATASET" 6 | else 7 | echo "Bad Argument, we only support kinetics400, kinetics600 or kinetics700" 8 | exit 0 9 | fi 10 | 11 | cd ../ 12 | python build_rawframes.py ../../data/${DATASET}/videos_train/ ../../data/${DATASET}/rawframes_train/ --level 2 --ext mp4 --task rgb --new-short 256 13 | echo "Raw frames (RGB only) generated for train set" 14 | 15 | python build_rawframes.py ../../data/${DATASET}/videos_val/ ../../data/${DATASET}/rawframes_val/ --level 2 --ext mp4 --task rgb --new-short 256 16 | echo "Raw frames (RGB only) generated for val set" 17 | 18 | cd ${DATASET}/ 19 | -------------------------------------------------------------------------------- /EVMamba/tools/data/kinetics/generate_videos_filelist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | DATASET=$1 4 | if [ "$DATASET" == "kinetics400" ] || [ "$1" == "kinetics600" ] || [ "$1" == "kinetics700" ]; then 5 | echo "We are processing $DATASET" 6 | else 7 | echo "Bad Argument, we only support kinetics400, kinetics600 or kinetics700" 8 | exit 0 9 | fi 10 | 11 | cd ../../../ 12 | PYTHONPATH=. python tools/data/build_file_list.py ${DATASET} data/${DATASET}/videos_train/ --level 2 --format videos --num-split 1 --subset train --shuffle 13 | echo "Train filelist for video generated." 14 | 15 | PYTHONPATH=. python tools/data/build_file_list.py ${DATASET} data/${DATASET}/videos_val/ --level 2 --format videos --num-split 1 --subset val --shuffle 16 | echo "Val filelist for video generated." 17 | cd tools/data/kinetics/ 18 | -------------------------------------------------------------------------------- /EVMamba/tools/data/gym/environment.yml: -------------------------------------------------------------------------------- 1 | name: gym 2 | channels: 3 | - anaconda 4 | - menpo 5 | - conda-forge 6 | - defaults 7 | dependencies: 8 | - ca-certificates=2020.1.1 9 | - certifi=2020.4.5.1 10 | - ffmpeg=2.8.6 11 | - libcxx=10.0.0 12 | - libedit=3.1.20181209 13 | - libffi=3.3 14 | - ncurses=6.2 15 | - openssl=1.1.1g 16 | - pip=20.0.2 17 | - python=3.7.7 18 | - readline=8.0 19 | - setuptools=46.4.0 20 | - sqlite=3.31.1 21 | - tk=8.6.8 22 | - wheel=0.34.2 23 | - xz=5.2.5 24 | - zlib=1.2.11 25 | - pip: 26 | - decorator==4.4.2 27 | - intel-openmp==2019.0 28 | - joblib==0.15.1 29 | - mkl==2019.0 30 | - numpy==1.18.4 31 | - olefile==0.46 32 | - pandas==1.0.3 33 | - python-dateutil==2.8.1 34 | - pytz==2020.1 35 | - six==1.14.0 36 | - youtube-dl 37 | -------------------------------------------------------------------------------- /EVMamba/tools/data/ava_kinetics/softlink_ava.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import argparse 3 | import os 4 | 5 | p = argparse.ArgumentParser() 6 | p.add_argument( 7 | '--ava_root', 8 | type=str, 9 | default='../../../data/ava', 10 | help='the path to save ava dataset') 11 | p.add_argument( 12 | '--avakinetics_root', 13 | type=str, 14 | default='../../../data/ava_kinetics', 15 | help='the path to save ava-kinetics dataset') 16 | args = p.parse_args() 17 | 18 | ava_frames = os.path.abspath(args.ava_root + '/rawframes/') 19 | kinetics_frames = os.path.abspath(args.avakinetics_root + '/rawframes/') 20 | 21 | ava_folders = os.listdir(ava_frames) 22 | for folder in ava_folders: 23 | cmd = 'ln -s %s/%s %s/%s' % (ava_frames, folder, kinetics_frames, folder) 24 | os.system(cmd) 25 | -------------------------------------------------------------------------------- /EVMamba/tools/data/hvu/environment.yml: -------------------------------------------------------------------------------- 1 | name: kinetics 2 | channels: 3 | - anaconda 4 | - menpo 5 | - conda-forge 6 | - defaults 7 | dependencies: 8 | - ca-certificates=2020.1.1 9 | - certifi=2020.4.5.1 10 | - ffmpeg=2.8.6 11 | - libcxx=10.0.0 12 | - libedit=3.1.20181209 13 | - libffi=3.3 14 | - ncurses=6.2 15 | - openssl=1.1.1g 16 | - pip=20.0.2 17 | - python=3.7.7 18 | - readline=8.0 19 | - setuptools=46.4.0 20 | - sqlite=3.31.1 21 | - tk=8.6.8 22 | - wheel=0.34.2 23 | - xz=5.2.5 24 | - zlib=1.2.11 25 | - pip: 26 | - decorator==4.4.2 27 | - intel-openmp==2019.0 28 | - joblib==0.15.1 29 | - mkl==2019.0 30 | - numpy==1.18.4 31 | - olefile==0.46 32 | - pandas==1.0.3 33 | - python-dateutil==2.8.1 34 | - pytz==2020.1 35 | - six==1.14.0 36 | - youtube-dl 37 | -------------------------------------------------------------------------------- /EVMamba/tools/data/kinetics/environment.yml: -------------------------------------------------------------------------------- 1 | name: kinetics 2 | channels: 3 | - anaconda 4 | - menpo 5 | - conda-forge 6 | - defaults 7 | dependencies: 8 | - ca-certificates=2020.1.1 9 | - certifi=2020.4.5.1 10 | - ffmpeg=2.8.6 11 | - libcxx=10.0.0 12 | - libedit=3.1.20181209 13 | - libffi=3.3 14 | - ncurses=6.2 15 | - openssl=1.1.1g 16 | - pip=20.0.2 17 | - python=3.7.7 18 | - readline=8.0 19 | - setuptools=46.4.0 20 | - sqlite=3.31.1 21 | - tk=8.6.8 22 | - wheel=0.34.2 23 | - xz=5.2.5 24 | - zlib=1.2.11 25 | - pip: 26 | - decorator==4.4.2 27 | - intel-openmp==2019.0 28 | - joblib==0.15.1 29 | - mkl==2019.0 30 | - numpy==1.18.4 31 | - olefile==0.46 32 | - pandas==1.0.3 33 | - python-dateutil==2.8.1 34 | - pytz==2020.1 35 | - six==1.14.0 36 | - youtube-dl 37 | -------------------------------------------------------------------------------- /EVMamba/tools/data/activitynet/environment.yml: -------------------------------------------------------------------------------- 1 | name: activitynet 2 | channels: 3 | - anaconda 4 | - menpo 5 | - conda-forge 6 | - defaults 7 | dependencies: 8 | - ca-certificates=2020.1.1 9 | - certifi=2020.4.5.1 10 | - ffmpeg=2.8.6 11 | - libcxx=10.0.0 12 | - libedit=3.1.20181209 13 | - libffi=3.3 14 | - ncurses=6.2 15 | - openssl=1.1.1g 16 | - pip=20.0.2 17 | - python=3.7.7 18 | - readline=8.0 19 | - setuptools=46.4.0 20 | - sqlite=3.31.1 21 | - tk=8.6.8 22 | - wheel=0.34.2 23 | - xz=5.2.5 24 | - zlib=1.2.11 25 | - pip: 26 | - decorator==4.4.2 27 | - intel-openmp==2019.0 28 | - joblib==0.15.1 29 | - mkl==2019.0 30 | - numpy==1.18.4 31 | - olefile==0.46 32 | - pandas==1.0.3 33 | - python-dateutil==2.8.1 34 | - pytz==2020.1 35 | - six==1.14.0 36 | - youtube-dl 37 | -------------------------------------------------------------------------------- /EVMamba/tools/data/kinetics/extract_rgb_frames_opencv.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | DATASET=$1 4 | if [ "$DATASET" == "kinetics400" ] || [ "$1" == "kinetics600" ] || [ "$1" == "kinetics700" ]; then 5 | echo "We are processing $DATASET" 6 | else 7 | echo "Bad Argument, we only support kinetics400, kinetics600 or kinetics700" 8 | exit 0 9 | fi 10 | 11 | cd ../ 12 | python build_rawframes.py ../../data/${DATASET}/videos_train/ ../../data/${DATASET}/rawframes_train/ --level 2 --ext mp4 --task rgb --new-short 256 --use-opencv 13 | echo "Raw frames (RGB only) generated for train set" 14 | 15 | python build_rawframes.py ../../data/${DATASET}/videos_val/ ../../data/${DATASET}/rawframes_val/ --level 2 --ext mp4 --task rgb --new-short 256 --use-opencv 16 | echo "Raw frames (RGB only) generated for val set" 17 | 18 | cd ${DATASET}/ 19 | -------------------------------------------------------------------------------- /EVMamba/tools/data/kinetics/generate_rawframes_filelist.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | DATASET=$1 4 | if [ "$DATASET" == "kinetics400" ] || [ "$1" == "kinetics600" ] || [ "$1" == "kinetics700" ]; then 5 | echo "We are processing $DATASET" 6 | else 7 | echo "Bad Argument, we only support kinetics400, kinetics600 or kinetics700" 8 | exit 0 9 | fi 10 | 11 | cd ../../../ 12 | PYTHONPATH=. python tools/data/build_file_list.py ${DATASET} data/${DATASET}/rawframes_train/ --level 2 --format rawframes --num-split 1 --subset train --shuffle 13 | echo "Train filelist for rawframes generated." 14 | 15 | PYTHONPATH=. python tools/data/build_file_list.py ${DATASET} data/${DATASET}/rawframes_val/ --level 2 --format rawframes --num-split 1 --subset val --shuffle 16 | echo "Val filelist for rawframes generated." 17 | cd tools/data/${DATASET}/ 18 | -------------------------------------------------------------------------------- /EVMamba/tools/data/kinetics/extract_frames.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | DATASET=$1 4 | if [ "$DATASET" == "kinetics400" ] || [ "$1" == "kinetics600" ] || [ "$1" == "kinetics700" ]; then 5 | echo "We are processing $DATASET" 6 | else 7 | echo "Bad Argument, we only support kinetics400, kinetics600 or kinetics700" 8 | exit 0 9 | fi 10 | 11 | cd ../ 12 | python build_rawframes.py ../../data/${DATASET}/videos_train/ ../../data/${DATASET}/rawframes_train/ --level 2 --flow-type tvl1 --ext mp4 --task both --new-short 256 13 | echo "Raw frames (RGB and tv-l1) Generated for train set" 14 | 15 | python build_rawframes.py ../../data/${DATASET}/videos_val/ ../../data/${DATASET}/rawframes_val/ --level 2 --flow-type tvl1 --ext mp4 --task both --new-short 256 16 | echo "Raw frames (RGB and tv-l1) Generated for val set" 17 | 18 | cd ${DATASET}/ 19 | -------------------------------------------------------------------------------- /EVMamba/mmaction/models/losses/nll_loss.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import torch.nn.functional as F 3 | 4 | from mmaction.registry import MODELS 5 | from .base import BaseWeightedLoss 6 | 7 | 8 | @MODELS.register_module() 9 | class NLLLoss(BaseWeightedLoss): 10 | """NLL Loss. 11 | 12 | It will calculate NLL loss given cls_score and label. 13 | """ 14 | 15 | def _forward(self, cls_score, label, **kwargs): 16 | """Forward function. 17 | 18 | Args: 19 | cls_score (torch.Tensor): The class score. 20 | label (torch.Tensor): The ground truth label. 21 | kwargs: Any keyword argument to be used to calculate nll loss. 22 | 23 | Returns: 24 | torch.Tensor: The returned nll loss. 25 | """ 26 | loss_cls = F.nll_loss(cls_score, label, **kwargs) 27 | return loss_cls 28 | -------------------------------------------------------------------------------- /EVMamba/mmaction/models/heads/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .base import BaseHead 3 | from .gcn_head import GCNHead 4 | from .i3d_head import I3DHead 5 | from .mvit_head import MViTHead 6 | from .omni_head import OmniHead 7 | from .rgbpose_head import RGBPoseHead 8 | from .slowfast_head import SlowFastHead 9 | from .timesformer_head import TimeSformerHead 10 | from .tpn_head import TPNHead 11 | from .trn_head import TRNHead 12 | from .tsm_head import TSMHead 13 | from .tsn_audio_head import TSNAudioHead 14 | from .tsn_head import TSNHead 15 | from .uniformer_head import UniFormerHead 16 | from .x3d_head import X3DHead 17 | 18 | __all__ = [ 19 | 'BaseHead', 'GCNHead', 'I3DHead', 'MViTHead', 'OmniHead', 'SlowFastHead', 20 | 'TPNHead', 'TRNHead', 'TSMHead', 'TSNAudioHead', 'TSNHead', 21 | 'TimeSformerHead', 'UniFormerHead', 'RGBPoseHead', 'X3DHead' 22 | ] 23 | -------------------------------------------------------------------------------- /EVMamba/mmaction/version.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Open-MMLab. All rights reserved. 2 | 3 | __version__ = '1.0.0' 4 | 5 | 6 | def parse_version_info(version_str: str): 7 | """Parse a version string into a tuple. 8 | 9 | Args: 10 | version_str (str): The version string. 11 | Returns: 12 | tuple[int or str]: The version info, e.g., "1.3.0" is parsed into 13 | (1, 3, 0), and "2.0.0rc1" is parsed into (2, 0, 0, 'rc1'). 14 | """ 15 | version_info = [] 16 | for x in version_str.split('.'): 17 | if x.isdigit(): 18 | version_info.append(int(x)) 19 | elif x.find('rc') != -1: 20 | patch_version = x.split('rc') 21 | version_info.append(int(patch_version[0])) 22 | version_info.append(f'rc{patch_version[1]}') 23 | return tuple(version_info) 24 | 25 | 26 | version_info = parse_version_info(__version__) 27 | -------------------------------------------------------------------------------- /EVMamba/tools/data/skeleton/babel2mma2.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | # In this example, we convert babel120_train to MMAction2 format 3 | # The required files can be downloaded from the homepage of BABEL project 4 | import numpy as np 5 | from mmcv import dump, load 6 | 7 | 8 | def gen_babel(x, y): 9 | data = [] 10 | for i, xx in enumerate(x): 11 | sample = dict() 12 | sample['keypoint'] = xx.transpose(3, 1, 2, 0).astype(np.float16) 13 | sample['label'] = y[1][0][i] 14 | names = [y[0][i], y[1][1][i], y[1][2][i], y[1][3][i]] 15 | sample['frame_dir'] = '_'.join([str(k) for k in names]) 16 | sample['total_frames'] = 150 17 | data.append(sample) 18 | return data 19 | 20 | 21 | x = np.load('train_ntu_sk_120.npy') 22 | y = load('train_label_120.pkl') 23 | 24 | data = gen_babel(x, y) 25 | dump(data, 'babel120_train.pkl') 26 | -------------------------------------------------------------------------------- /EVMamba/tools/data/kinetics/rename_classnames.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | # Rename classname for convenience 4 | DATASET=$1 5 | if [ "$DATASET" == "kinetics400" ] || [ "$1" == "kinetics600" ] || [ "$1" == "kinetics700" ]; then 6 | echo "We are processing $DATASET" 7 | else 8 | echo "Bad Argument, we only support kinetics400, kinetics600 or kinetics700" 9 | exit 0 10 | fi 11 | 12 | cd ../../../data/${DATASET}/ 13 | ls ./videos_train | while read class; do \ 14 | newclass=`echo $class | tr " " "_" `; 15 | if [ "${class}" != "${newclass}" ] 16 | then 17 | mv "videos_train/${class}" "videos_train/${newclass}"; 18 | fi 19 | done 20 | 21 | ls ./videos_val | while read class; do \ 22 | newclass=`echo $class | tr " " "_" `; 23 | if [ "${class}" != "${newclass}" ] 24 | then 25 | mv "videos_val/${class}" "videos_val/${newclass}"; 26 | fi 27 | done 28 | 29 | cd ../../tools/data/kinetics/ 30 | -------------------------------------------------------------------------------- /EVMamba/tools/data/thumos14/download_annotations.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | DATA_DIR="../../../data/thumos14/" 4 | 5 | if [[ ! -d "${DATA_DIR}" ]]; then 6 | echo "${DATA_DIR} does not exist. Creating"; 7 | mkdir -p ${DATA_DIR} 8 | fi 9 | cd ${DATA_DIR} 10 | 11 | wget http://crcv.ucf.edu/THUMOS14/Validation_set/TH14_Temporal_annotations_validation.zip --no-check-certificate 12 | wget http://crcv.ucf.edu/THUMOS14/test_set/TH14_Temporal_annotations_test.zip --no-check-certificate 13 | 14 | if [ ! -d "./annotations_val" ]; then 15 | mkdir ./annotations_val 16 | fi 17 | unzip -j TH14_Temporal_annotations_validation.zip -d annotations_val 18 | 19 | if [ ! -d "./annotations_test" ]; then 20 | mkdir ./annotations_test 21 | fi 22 | unzip -j TH14_Temporal_annotations_test.zip -d annotations_test 23 | 24 | rm TH14_Temporal_annotations_validation.zip 25 | rm TH14_Temporal_annotations_test.zip 26 | 27 | cd "../../tools/data/thumos14/" 28 | -------------------------------------------------------------------------------- /EVMamba/configs/_base_/default_runtime.py: -------------------------------------------------------------------------------- 1 | default_scope = 'mmaction' 2 | 3 | default_hooks = dict( 4 | runtime_info=dict(type='RuntimeInfoHook'), 5 | timer=dict(type='IterTimerHook'), 6 | logger=dict(type='LoggerHook', interval=20, ignore_last=False), 7 | param_scheduler=dict(type='ParamSchedulerHook'), 8 | checkpoint=dict(type='CheckpointHook', interval=1, save_best='auto'), 9 | sampler_seed=dict(type='DistSamplerSeedHook'), 10 | sync_buffers=dict(type='SyncBuffersHook')) 11 | 12 | env_cfg = dict( 13 | cudnn_benchmark=False, 14 | mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), 15 | dist_cfg=dict(backend='nccl')) 16 | 17 | log_processor = dict(type='LogProcessor', window_size=20, by_epoch=True) 18 | 19 | vis_backends = [dict(type='LocalVisBackend')] 20 | visualizer = dict(type='ActionVisualizer', vis_backends=vis_backends) 21 | 22 | log_level = 'INFO' 23 | load_from = None 24 | resume = False 25 | -------------------------------------------------------------------------------- /EVMamba/tools/data/kinetics/download_annotations.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | DATASET=$1 4 | if [ "$DATASET" == "kinetics400" ] || [ "$1" == "kinetics600" ] || [ "$1" == "kinetics700" ]; then 5 | echo "We are processing $DATASET" 6 | else 7 | echo "Bad Argument, we only support kinetics400, kinetics600 or kinetics700" 8 | exit 0 9 | fi 10 | 11 | DATA_DIR="../../../data/${DATASET}/annotations" 12 | 13 | if [[ ! -d "${DATA_DIR}" ]]; then 14 | echo "${DATA_DIR} does not exist. Creating"; 15 | mkdir -p ${DATA_DIR} 16 | fi 17 | 18 | wget https://storage.googleapis.com/deepmind-media/Datasets/${DATASET}.tar.gz 19 | 20 | tar -zxvf ${DATASET}.tar.gz --strip-components 1 -C ${DATA_DIR}/ 21 | mv ${DATA_DIR}/train.csv ${DATA_DIR}/kinetics_train.csv 22 | mv ${DATA_DIR}/validate.csv ${DATA_DIR}/kinetics_val.csv 23 | mv ${DATA_DIR}/test.csv ${DATA_DIR}/kinetics_test.csv 24 | 25 | rm ${DATASET}.tar.gz 26 | rm ${DATA_DIR}/*.json 27 | -------------------------------------------------------------------------------- /EVMamba/tools/data/kinetics/download_backup_annotations.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | DATASET=$1 4 | if [ "$DATASET" == "kinetics400" ] || [ "$1" == "kinetics600" ] || [ "$1" == "kinetics700" ]; then 5 | echo "We are processing $DATASET" 6 | else 7 | echo "Bad Argument, we only support kinetics400, kinetics600 or kinetics700" 8 | exit 0 9 | fi 10 | 11 | DATA_DIR="../../../data/${DATASET}/annotations" 12 | 13 | if [[ ! -d "${DATA_DIR}" ]]; then 14 | echo "${DATA_DIR} does not exist. Creating"; 15 | mkdir -p ${DATA_DIR} 16 | fi 17 | 18 | 19 | wget https://download.openmmlab.com/mmaction/dataset/${DATASET}/annotations/kinetics_train.csv 20 | wget https://download.openmmlab.com/mmaction/dataset/${DATASET}/annotations/kinetics_val.csv 21 | wget https://download.openmmlab.com/mmaction/dataset/${DATASET}/annotations/kinetics_test.csv 22 | 23 | mv kinetics_train.csv ${DATA_DIR}/kinetics_train.csv 24 | mv kinetics_val.csv ${DATA_DIR}/kinetics_val.csv 25 | mv kinetics_test.csv ${DATA_DIR}/kinetics_test.csv 26 | -------------------------------------------------------------------------------- /EVMamba/mmaction/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import mmcv 3 | import mmengine 4 | from mmengine.utils import digit_version 5 | 6 | from .version import __version__ 7 | 8 | mmcv_minimum_version = '2.0.0rc4' 9 | mmcv_maximum_version = '2.1.0' 10 | mmcv_version = digit_version(mmcv.__version__) 11 | 12 | mmengine_minimum_version = '0.7.1' 13 | mmengine_maximum_version = '1.0.0' 14 | mmengine_version = digit_version(mmengine.__version__) 15 | 16 | assert (digit_version(mmcv_minimum_version) <= mmcv_version 17 | < digit_version(mmcv_maximum_version)), \ 18 | f'MMCV=={mmcv.__version__} is used but incompatible. ' \ 19 | f'Please install mmcv>={mmcv_minimum_version}, <{mmcv_maximum_version}.' 20 | 21 | assert (digit_version(mmengine_minimum_version) <= mmengine_version 22 | < digit_version(mmengine_maximum_version)), \ 23 | f'MMEngine=={mmengine.__version__} is used but incompatible. ' \ 24 | f'Please install mmengine>={mmengine_minimum_version}, ' \ 25 | f'<{mmengine_maximum_version}.' 26 | 27 | __all__ = ['__version__'] 28 | -------------------------------------------------------------------------------- /EVMamba/mmaction/evaluation/functional/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .accuracy import (average_precision_at_temporal_iou, 3 | average_recall_at_avg_proposals, confusion_matrix, 4 | get_weighted_score, interpolated_precision_recall, 5 | mean_average_precision, mean_class_accuracy, 6 | mmit_mean_average_precision, pairwise_temporal_iou, 7 | softmax, top_k_accuracy, top_k_classes) 8 | from .ava_utils import ava_eval, read_labelmap, results2csv 9 | from .eval_detection import ActivityNetLocalization 10 | 11 | __all__ = [ 12 | 'top_k_accuracy', 'mean_class_accuracy', 'confusion_matrix', 13 | 'mean_average_precision', 'get_weighted_score', 14 | 'average_recall_at_avg_proposals', 'pairwise_temporal_iou', 15 | 'average_precision_at_temporal_iou', 'ActivityNetLocalization', 'softmax', 16 | 'interpolated_precision_recall', 'mmit_mean_average_precision', 17 | 'top_k_classes', 'read_labelmap', 'ava_eval', 'results2csv' 18 | ] 19 | -------------------------------------------------------------------------------- /EVMamba/mmaction/utils/typing.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | """Collecting some commonly used type hint in mmaction.""" 3 | from typing import Dict, List, Optional, Tuple, Union 4 | 5 | import torch 6 | from mmengine.config import ConfigDict 7 | from mmengine.structures import InstanceData, LabelData 8 | 9 | from mmaction.structures import ActionDataSample 10 | 11 | # Type hint of config data 12 | ConfigType = Union[ConfigDict, dict] 13 | OptConfigType = Optional[ConfigType] 14 | # Type hint of one or more config data 15 | MultiConfig = Union[ConfigType, List[ConfigType]] 16 | OptMultiConfig = Optional[MultiConfig] 17 | 18 | InstanceList = List[InstanceData] 19 | OptInstanceList = Optional[InstanceList] 20 | 21 | LabelList = List[LabelData] 22 | OptLabelList = Optional[LabelList] 23 | 24 | SampleList = List[ActionDataSample] 25 | OptSampleList = Optional[SampleList] 26 | 27 | ForwardResults = Union[Dict[str, torch.Tensor], List[ActionDataSample], 28 | Tuple[torch.Tensor], torch.Tensor] 29 | 30 | 31 | class SamplingResult: 32 | """Dummy :class:`SamplingResult` in mmdet.""" 33 | 34 | def __init__(self, *args, **kwargs): 35 | pass 36 | -------------------------------------------------------------------------------- /EVMamba/model-index.yml: -------------------------------------------------------------------------------- 1 | Import: 2 | - configs/recognition/csn/metafile.yml 3 | - configs/recognition/i3d/metafile.yml 4 | - configs/recognition/r2plus1d/metafile.yml 5 | - configs/recognition/slowfast/metafile.yml 6 | - configs/recognition/slowonly/metafile.yml 7 | - configs/recognition/timesformer/metafile.yml 8 | - configs/recognition/tin/metafile.yml 9 | - configs/recognition/tpn/metafile.yml 10 | - configs/recognition/tsm/metafile.yml 11 | - configs/recognition/tsn/metafile.yml 12 | - configs/recognition/c3d/metafile.yml 13 | - configs/recognition/tanet/metafile.yml 14 | - configs/recognition/x3d/metafile.yml 15 | - configs/recognition/trn/metafile.yml 16 | - configs/recognition/swin/metafile.yml 17 | - configs/recognition/c2d/metafile.yml 18 | - configs/recognition/omnisource/metafile.yml 19 | - configs/recognition/mvit/metafile.yml 20 | - configs/recognition/uniformer/metafile.yml 21 | - configs/recognition/uniformerv2/metafile.yml 22 | - configs/recognition/videomae/metafile.yml 23 | - configs/detection/slowfast/metafile.yml 24 | - configs/detection/slowonly/metafile.yml 25 | - configs/detection/acrn/metafile.yml 26 | - configs/skeleton/stgcn/metafile.yml 27 | - configs/skeleton/2s-agcn/metafile.yml 28 | - configs/skeleton/stgcnpp/metafile.yml 29 | - configs/skeleton/posec3d/metafile.yml 30 | -------------------------------------------------------------------------------- /EVMamba/tools/data/ava/cut_videos.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | # Copyright (c) Facebook, Inc. and its affiliates. 4 | # 5 | # Licensed under the Apache License, Version 2.0 (the "License"); 6 | # you may not use this file except in compliance with the License. 7 | # You may obtain a copy of the License at 8 | # 9 | # http://www.apache.org/licenses/LICENSE-2.0 10 | # 11 | # Unless required by applicable law or agreed to in writing, software 12 | # distributed under the License is distributed on an "AS IS" BASIS, 13 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 14 | # See the License for the specific language governing permissions and 15 | # limitations under the License. 16 | ############################################################################## 17 | 18 | # Cut each video from its 15th to 30th minute. 19 | 20 | IN_DATA_DIR="../../../data/ava/videos" 21 | OUT_DATA_DIR="../../../data/ava/videos_15min" 22 | 23 | if [[ ! -d "${OUT_DATA_DIR}" ]]; then 24 | echo "${OUT_DATA_DIR} doesn't exist. Creating it."; 25 | mkdir -p ${OUT_DATA_DIR} 26 | fi 27 | 28 | for video in $(ls -A1 -U ${IN_DATA_DIR}/*) 29 | do 30 | out_name="${OUT_DATA_DIR}/${video##*/}" 31 | if [ ! -f "${out_name}" ]; then 32 | ffmpeg -ss 900 -t 901 -i "${video}" -r 30 -strict experimental "${out_name}" 33 | fi 34 | done 35 | -------------------------------------------------------------------------------- /EVMamba/tools/data/diving48/label_map.txt: -------------------------------------------------------------------------------- 1 | Back+15som+05Twis+FREE 2 | Back+15som+15Twis+FREE 3 | Back+15som+25Twis+FREE 4 | Back+15som+NoTwis+PIKE 5 | Back+15som+NoTwis+TUCK 6 | Back+25som+15Twis+PIKE 7 | Back+25som+25Twis+PIKE 8 | Back+25som+NoTwis+PIKE 9 | Back+25som+NoTwis+TUCK 10 | Back+2som+15Twis+FREE 11 | Back+2som+25Twis+FREE 12 | Back+35som+NoTwis+PIKE 13 | Back+35som+NoTwis+TUCK 14 | Back+3som+NoTwis+PIKE 15 | Back+3som+NoTwis+TUCK 16 | Back+Dive+NoTwis+PIKE 17 | Back+Dive+NoTwis+TUCK 18 | Forward+15som+1Twis+FREE 19 | Forward+15som+2Twis+FREE 20 | Forward+15som+NoTwis+PIKE 21 | Forward+1som+NoTwis+PIKE 22 | Forward+25som+1Twis+PIKE 23 | Forward+25som+2Twis+PIKE 24 | Forward+25som+3Twis+PIKE 25 | Forward+25som+NoTwis+PIKE 26 | Forward+25som+NoTwis+TUCK 27 | Forward+35som+NoTwis+PIKE 28 | Forward+35som+NoTwis+TUCK 29 | Forward+45som+NoTwis+TUCK 30 | Forward+Dive+NoTwis+PIKE 31 | Forward+Dive+NoTwis+STR 32 | Inward+15som+NoTwis+PIKE 33 | Inward+15som+NoTwis+TUCK 34 | Inward+25som+NoTwis+PIKE 35 | Inward+25som+NoTwis+TUCK 36 | Inward+35som+NoTwis+TUCK 37 | Inward+Dive+NoTwis+PIKE 38 | Reverse+15som+05Twis+FREE 39 | Reverse+15som+15Twis+FREE 40 | Reverse+15som+25Twis+FREE 41 | Reverse+15som+35Twis+FREE 42 | Reverse+15som+NoTwis+PIKE 43 | Reverse+25som+15Twis+PIKE 44 | Reverse+25som+NoTwis+PIKE 45 | Reverse+25som+NoTwis+TUCK 46 | Reverse+35som+NoTwis+TUCK 47 | Reverse+Dive+NoTwis+PIKE 48 | Reverse+Dive+NoTwis+TUCK 49 | -------------------------------------------------------------------------------- /EVMamba/tools/data/omnisource/trim_raw_video.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import os 3 | import os.path as osp 4 | import sys 5 | from subprocess import check_output 6 | 7 | import mmcv 8 | 9 | 10 | def get_duration(vid_name): 11 | command = f'ffprobe -i {vid_name} 2>&1 | grep "Duration"' 12 | output = str(check_output(command, shell=True)) 13 | output = output.split(',')[0].split('Duration:')[1].strip() 14 | h, m, s = output.split(':') 15 | duration = int(h) * 3600 + int(m) * 60 + float(s) 16 | return duration 17 | 18 | 19 | def trim(vid_name): 20 | try: 21 | lt = get_duration(vid_name) 22 | except Exception: 23 | print(f'get_duration failed for video {vid_name}', flush=True) 24 | return 25 | 26 | i = 0 27 | name, _ = osp.splitext(vid_name) 28 | 29 | # We output 10-second clips into the folder `name` 30 | dest = name 31 | mmcv.mkdir_or_exist(dest) 32 | 33 | command_tmpl = ('ffmpeg -y loglevel error -i {} -ss {} -t {} -crf 18 ' 34 | '-c:v libx264 {}/part_{}.mp4') 35 | while i * 10 < lt: 36 | os.system(command_tmpl.format(vid_name, i * 10, 10, dest, i)) 37 | i += 1 38 | 39 | # remove a raw video after decomposing it into 10-second clip to save space 40 | os.remove(vid_name) 41 | 42 | 43 | if __name__ == '__main__': 44 | vid_name = sys.argv[1] 45 | trim(vid_name) 46 | -------------------------------------------------------------------------------- /EVMamba/tools/data/hvu/generate_sub_file_list.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import argparse 3 | import os.path as osp 4 | 5 | import mmcv 6 | 7 | 8 | def main(annotation_file, category): 9 | assert category in [ 10 | 'action', 'attribute', 'concept', 'event', 'object', 'scene' 11 | ] 12 | 13 | data = mmcv.load(annotation_file) 14 | basename = osp.basename(annotation_file) 15 | dirname = osp.dirname(annotation_file) 16 | basename = basename.replace('hvu', f'hvu_{category}') 17 | 18 | target_file = osp.join(dirname, basename) 19 | 20 | result = [] 21 | for item in data: 22 | label = item['label'] 23 | if category in label: 24 | item['label'] = label[category] 25 | result.append(item) 26 | 27 | mmcv.dump(data, target_file) 28 | 29 | 30 | if __name__ == '__main__': 31 | description = 'Helper script for generating HVU per-category file list.' 32 | p = argparse.ArgumentParser(description=description) 33 | p.add_argument( 34 | 'annotation_file', 35 | type=str, 36 | help=('The annotation file which contains tags of all categories.')) 37 | p.add_argument( 38 | 'category', 39 | type=str, 40 | choices=['action', 'attribute', 'concept', 'event', 'object', 'scene'], 41 | help='The tag category that you want to generate file list for.') 42 | main(**vars(p.parse_args())) 43 | -------------------------------------------------------------------------------- /EVMamba/tools/data/skeleton/label_map_ntu60.txt: -------------------------------------------------------------------------------- 1 | drink water 2 | eat meal/snack 3 | brushing teeth 4 | brushing hair 5 | drop 6 | pickup 7 | throw 8 | sitting down 9 | standing up (from sitting position) 10 | clapping 11 | reading 12 | writing 13 | tear up paper 14 | wear jacket 15 | take off jacket 16 | wear a shoe 17 | take off a shoe 18 | wear on glasses 19 | take off glasses 20 | put on a hat/cap 21 | take off a hat/cap 22 | cheer up 23 | hand waving 24 | kicking something 25 | reach into pocket 26 | hopping (one foot jumping) 27 | jump up 28 | make a phone call/answer phone 29 | playing with phone/tablet 30 | typing on a keyboard 31 | pointing to something with finger 32 | taking a selfie 33 | check time (from watch) 34 | rub two hands together 35 | nod head/bow 36 | shake head 37 | wipe face 38 | salute 39 | put the palms together 40 | cross hands in front (say stop) 41 | sneeze/cough 42 | staggering 43 | falling 44 | touch head (headache) 45 | touch chest (stomachache/heart pain) 46 | touch back (backache) 47 | touch neck (neckache) 48 | nausea or vomiting condition 49 | use a fan (with hand or paper)/feeling warm 50 | punching/slapping other person 51 | kicking other person 52 | pushing other person 53 | pat on back of other person 54 | point finger at the other person 55 | hugging other person 56 | giving something to other person 57 | touch other person's pocket 58 | handshaking 59 | walking towards each other 60 | walking apart from each other 61 | -------------------------------------------------------------------------------- /EVMamba/mmaction/models/losses/base.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from abc import ABCMeta, abstractmethod 3 | 4 | import torch.nn as nn 5 | 6 | 7 | class BaseWeightedLoss(nn.Module, metaclass=ABCMeta): 8 | """Base class for loss. 9 | 10 | All subclass should overwrite the ``_forward()`` method which returns the 11 | normal loss without loss weights. 12 | 13 | Args: 14 | loss_weight (float): Factor scalar multiplied on the loss. 15 | Default: 1.0. 16 | """ 17 | 18 | def __init__(self, loss_weight=1.0): 19 | super().__init__() 20 | self.loss_weight = loss_weight 21 | 22 | @abstractmethod 23 | def _forward(self, *args, **kwargs): 24 | """Forward function.""" 25 | pass 26 | 27 | def forward(self, *args, **kwargs): 28 | """Defines the computation performed at every call. 29 | 30 | Args: 31 | *args: The positional arguments for the corresponding 32 | loss. 33 | **kwargs: The keyword arguments for the corresponding 34 | loss. 35 | 36 | Returns: 37 | torch.Tensor: The calculated loss. 38 | """ 39 | ret = self._forward(*args, **kwargs) 40 | if isinstance(ret, dict): 41 | for k in ret: 42 | if 'loss' in k: 43 | ret[k] *= self.loss_weight 44 | else: 45 | ret *= self.loss_weight 46 | return ret 47 | -------------------------------------------------------------------------------- /EVMamba/mmaction/models/recognizers/recognizer_audio.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from torch import Tensor 3 | 4 | from mmaction.registry import MODELS 5 | from .base import BaseRecognizer 6 | 7 | 8 | @MODELS.register_module() 9 | class RecognizerAudio(BaseRecognizer): 10 | """Audio recognizer model framework.""" 11 | 12 | def extract_feat(self, 13 | batch_inputs: Tensor, 14 | stage: str = 'backbone', 15 | **kwargs) -> tuple: 16 | """Extract features of different stages. 17 | 18 | Args: 19 | batch_inputs (Tensor): The input data. 20 | stage (str): Which stage to output the feature. 21 | Defaults to ``backbone``. 22 | 23 | Returns: 24 | Tensor: The extracted features. 25 | dict: A dict recording the kwargs for downstream 26 | pipeline. This will be an empty dict in audio recognizer. 27 | """ 28 | 29 | # Record the kwargs required by `loss` and `predict` 30 | loss_predict_kwargs = dict() 31 | batch_inputs = batch_inputs.view((-1, ) + batch_inputs.shape[2:]) 32 | 33 | x = self.backbone(batch_inputs) 34 | 35 | if stage == 'backbone': 36 | return x, loss_predict_kwargs 37 | 38 | if self.with_cls_head and stage == 'head': 39 | x = self.cls_head(x, **loss_predict_kwargs) 40 | return x, loss_predict_kwargs 41 | -------------------------------------------------------------------------------- /EVMamba/tools/data/ava/label_map.txt: -------------------------------------------------------------------------------- 1 | 1: bend/bow (at the waist) 2 | 3: crouch/kneel 3 | 4: dance 4 | 5: fall down 5 | 6: get up 6 | 7: jump/leap 7 | 8: lie/sleep 8 | 9: martial art 9 | 10: run/jog 10 | 11: sit 11 | 12: stand 12 | 13: swim 13 | 14: walk 14 | 15: answer phone 15 | 17: carry/hold (an object) 16 | 20: climb (e.g., a mountain) 17 | 22: close (e.g., a door, a box) 18 | 24: cut 19 | 26: dress/put on clothing 20 | 27: drink 21 | 28: drive (e.g., a car, a truck) 22 | 29: eat 23 | 30: enter 24 | 34: hit (an object) 25 | 36: lift/pick up 26 | 37: listen (e.g., to music) 27 | 38: open (e.g., a window, a car door) 28 | 41: play musical instrument 29 | 43: point to (an object) 30 | 45: pull (an object) 31 | 46: push (an object) 32 | 47: put down 33 | 48: read 34 | 49: ride (e.g., a bike, a car, a horse) 35 | 51: sail boat 36 | 52: shoot 37 | 54: smoke 38 | 56: take a photo 39 | 57: text on/look at a cellphone 40 | 58: throw 41 | 59: touch (an object) 42 | 60: turn (e.g., a screwdriver) 43 | 61: watch (e.g., TV) 44 | 62: work on a computer 45 | 63: write 46 | 64: fight/hit (a person) 47 | 65: give/serve (an object) to (a person) 48 | 66: grab (a person) 49 | 67: hand clap 50 | 68: hand shake 51 | 69: hand wave 52 | 70: hug (a person) 53 | 72: kiss (a person) 54 | 73: lift (a person) 55 | 74: listen to (a person) 56 | 76: push (another person) 57 | 77: sing to (e.g., self, a person, a group) 58 | 78: take (an object) from (a person) 59 | 79: talk to (e.g., self, a person, a group) 60 | 80: watch (a person) 61 | -------------------------------------------------------------------------------- /EVMamba/tools/data/skeleton/compress_nturgbd.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import multiprocessing as mp 3 | import os 4 | import os.path as osp 5 | import subprocess 6 | 7 | 8 | def get_shape(vid): 9 | cmd = 'ffprobe -v error -select_streams v:0 -show_entries ' \ 10 | 'stream=width,height -of csv=s=x:p=0 \"{}\"'.format(vid) 11 | w, h = subprocess.check_output(cmd, shell=True).decode('utf-8').split('x') 12 | return int(w), int(h) 13 | 14 | 15 | def compress(src, dest, shape=None, target_size=540, fps=-1): 16 | if shape is None: 17 | shape = get_shape(src) 18 | w, h = shape 19 | scale_str = f'-vf scale=-2:{target_size}' if w >= h else \ 20 | f'-vf scale={target_size}:-2' 21 | fps_str = f'-r {fps}' if fps > 0 else '' 22 | quality_str = '-q:v 1' 23 | vcodec_str = '-c:v libx264' 24 | cmd = f'ffmpeg -y -loglevel error -i {src} -threads 1 ' \ 25 | f'{quality_str} {scale_str} {fps_str} {vcodec_str} {dest}' 26 | os.system(cmd) 27 | 28 | 29 | def compress_nturgbd(name): 30 | src = name 31 | dest = src.replace('nturgbd_raw', 32 | 'nturgbd_videos').replace('_rgb.avi', '.mp4') 33 | shape = (1920, 1080) 34 | compress(src, dest, shape) 35 | 36 | 37 | src_dir = 'data/nturgbd_raw' 38 | tgt_dir = 'data/nturgbd_videos' 39 | os.makedirs(tgt_dir, exist_ok=True) 40 | files = [osp.join(src_dir, x) for x in os.listdir(src_dir) if '.avi' in x] 41 | pool = mp.Pool(32) 42 | pool.map(compress_nturgbd, files) 43 | -------------------------------------------------------------------------------- /EVMamba/tools/data/ava/extract_rgb_frames_ffmpeg.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | # Copyright (c) Facebook, Inc. and its affiliates. 4 | # 5 | # Licensed under the Apache License, Version 2.0 (the "License"); 6 | # you may not use this file except in compliance with the License. 7 | # You may obtain a copy of the License at 8 | # 9 | # http://www.apache.org/licenses/LICENSE-2.0 10 | # 11 | # Unless required by applicable law or agreed to in writing, software 12 | # distributed under the License is distributed on an "AS IS" BASIS, 13 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 14 | # See the License for the specific language governing permissions and 15 | # limitations under the License. 16 | ############################################################################## 17 | 18 | # Extract frames from videos. 19 | 20 | IN_DATA_DIR="../../../data/ava/videos_15min" 21 | OUT_DATA_DIR="../../../data/ava/rawframes" 22 | 23 | if [[ ! -d "${OUT_DATA_DIR}" ]]; then 24 | echo "${OUT_DATA_DIR} doesn't exist. Creating it."; 25 | mkdir -p ${OUT_DATA_DIR} 26 | fi 27 | 28 | for video in $(ls -A1 -U ${IN_DATA_DIR}/*) 29 | do 30 | video_name=${video##*/} 31 | 32 | if [[ $video_name = *".webm" ]]; then 33 | video_name=${video_name::-5} 34 | else 35 | video_name=${video_name::-4} 36 | fi 37 | 38 | out_video_dir=${OUT_DATA_DIR}/${video_name} 39 | mkdir -p "${out_video_dir}" 40 | 41 | out_name="${out_video_dir}/img_%05d.jpg" 42 | 43 | ffmpeg -i "${video}" -r 30 -q:v 1 "${out_name}" 44 | done 45 | --------------------------------------------------------------------------------