upload

OpenTAD/.gitignore

@@ -0,0 +1,123 @@
+# ignore folder
+.vscode
+.idea
+dataset
+/pretrained/
+/logs/
+/exps/
+/trash/
+
+# ignore annotation
+!/data/
+/data/*
+!/data/*.sh
+
+dcgm
+log
+*.err
+*.out
+/wandb/
+build/
+dist/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+parts/
+sdist/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/

OpenTAD/LICENSE

@@ -0,0 +1,201 @@
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OpenTAD/README.md

@@ -0,0 +1,121 @@
+# OpenTAD: An Open-Source Temporal Action Detection Toolbox.
+
+<p align="left">
+<!-- <a href="https://arxiv.org/abs/xxx.xxx" alt="arXiv"> -->
+    <!-- <img src="https://img.shields.io/badge/arXiv-xxx.xxx-b31b1b.svg?style=flat" /></a> -->
+<a href="https://github.com/sming256/opentad/blob/main/LICENSE" alt="license">
+    <img src="https://img.shields.io/badge/License-Apache_2.0-blue.svg" /></a>
+<a href="https://github.com/sming256/OpenTAD/issues" alt="docs">
+    <img src="https://img.shields.io/github/issues-raw/sming256/OpenTAD?color=%23FF9600" /></a>
+<a href="https://img.shields.io/github/stars/sming256/opentad" alt="arXiv">
+    <img src="https://img.shields.io/github/stars/sming256/opentad" /></a>
+</p>
+
+OpenTAD is an open-source temporal action detection (TAD) toolbox based on PyTorch.
+
+
+## 🥳 What's New
+
+- A technical report of this library will be provided soon.
+- 2024/03/28: The beta version v0.1.0 of OpenTAD is released. Any feedbacks and suggestions are welcome!
+
+
+## 📖 Major Features
+
+- **Support SoTA TAD methods with modular design.** We decompose the TAD pipeline into different components, and implement them in a modular way. This design makes it easy to implement new methods and reproduce existing methods.
+- **Support multiple TAD datasets.** We support 8 TAD datasets, including ActivityNet-1.3, THUMOS-14, HACS, Ego4D-MQ, Epic-Kitchens-100, FineAction, Multi-THUMOS, Charades datasets.
+- **Support feature-based training and end-to-end training.** The feature-based training can easily be extended to end-to-end training with raw video input, and the video backbone can be easily replaced.
+- **Release various pre-extracted features.** We release the feature extraction code, as well as many pre-extracted features on each dataset.
+
+## 🌟 Model Zoo
+
+<table align="center">
+  <tbody>
+    <tr align="center" valign="bottom">
+      <td>
+        <b>One Stage</b>
+      </td>
+      <td>
+        <b>Two Stage</b>
+      </td>
+      <td>
+        <b>DETR</b>
+      </td>
+      <td>
+        <b>End-to-End Training</b>
+      </td>
+    </tr>
+    <tr valign="top">
+      <td>
+        <ul>
+            <li><a href="configs/actionformer">ActionFormer (ECCV'22)</a></li>
+            <li><a href="configs/tridet">TriDet (CVPR'23)</a></li>
+            <li><a href="configs/temporalmaxer">TemporalMaxer (arXiv'23)</a></li>
+            <li><a href="configs/videomambasuite">VideoMambaSuite (arXiv'24)</a></li>
+      </ul>
+      </td>
+      <td>
+        <ul>
+            <li><a href="configs/bmn">BMN (ICCV'19)</a></li>
+            <li><a href="configs/gtad">GTAD (CVPR'20)</a></li>
+            <li><a href="configs/tsi">TSI (ACCV'20)</a></li>
+            <li><a href="configs/vsgn">VSGN (ICCV'21)</a></li>
+        </ul>
+      </td>
+      <td>
+        <ul>
+          <li><a href="configs/tadtr">TadTR (TIP'22)</a></li>
+        </ul>
+      </td>
+      <td>
+        <ul>
+          <li><a href="configs/afsd">AFSD (CVPR'21)</a></li>
+          <li><a href="configs/tadtr">E2E-TAD (CVPR'22)</a></li>
+          <li><a href="configs/etad">ETAD (CVPRW'23)</a></li>
+          <li><a href="configs/re2tal">Re2TAL (CVPR'23)</a></li>
+          <li><a href="configs/adatad">AdaTAD (CVPR'24)</a></li>
+        </ul>
+      </td>
+    </tr>
+</td>
+    </tr>
+  </tbody>
+</table>
+
+The detailed configs, results, and pretrained models of each method can be found in above folders.
+
+## 🛠️ Installation
+
+Please refer to [install.md](docs/en/install.md) for installation and data preparation.
+
+
+## 🚀 Usage
+
+Please refer to [usage.md](docs/en/usage.md) for details of training and evaluation scripts.
+
+
+## 📄 Updates
+Please refer to [changelog.md](docs/en/changelog.md) for update details.
+
+
+## 🤝 Roadmap
+
+All the things that need to be done in the future is in [roadmap.md](docs/en/roadmap.md).
+
+
+## 🖊️ Citation
+
+**[Acknowledgement]** This repo is inspired by [OpenMMLab](https://github.com/open-mmlab) project, and we give our thanks to their contributors.
+
+If you think this repo is helpful, please cite us:
+
+```bibtex
+@misc{2024opentad,
+    title={OpenTAD: An Open-Source Toolbox for Temporal Action Detection},
+    author={Shuming Liu, Chen Zhao, Fatimah Zohra, Mattia Soldan, Carlos Hinojosa, Alejandro Pardo, Anthony Cioppa, Lama Alssum, Mengmeng Xu, Merey Ramazanova, Juan León Alcázar, Silvio Giancola, Bernard Ghanem},
+    howpublished = {\url{https://github.com/sming256/opentad}},
+    year={2024}
+}
+```
+
+If you have any questions, please contact: `shuming.liu@kaust.edu.sa`.

OpenTAD/configs/_base_/datasets/activitynet-1.3/e2e_resize_768_1x224x224.py

@@ -0,0 +1,85 @@
+dataset_type = "AnetResizeDataset"
+annotation_path = "data/activitynet-1.3/annotations/activity_net.v1-3.min.json"
+class_map = "data/activitynet-1.3/annotations/category_idx.txt"
+data_path = "data/activitynet-1.3/raw_data/Anet_videos_15fps_short256"
+block_list = "data/activitynet-1.3/raw_data/Anet_videos_15fps_short256/missing_files.txt"
+
+resize_length = 768
+
+dataset = dict(
+    train=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="training",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        resize_length=resize_length,
+        class_agnostic=True,
+        pipeline=[
+            dict(type="PrepareVideoInfo", format="mp4", prefix="v_"),
+            dict(type="mmaction.DecordInit", num_threads=4),
+            dict(type="LoadFrames", num_clips=1, method="resize"),
+            dict(type="mmaction.DecordDecode"),
+            dict(type="mmaction.Resize", scale=(-1, 224)),
+            dict(type="mmaction.CenterCrop", crop_size=224),
+            dict(type="mmaction.FormatShape", input_format="NCTHW"),
+            dict(type="ConvertToTensor", keys=["imgs", "gt_segments", "gt_labels"]),
+            dict(type="Collect", inputs="imgs", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        resize_length=resize_length,
+        class_agnostic=True,
+        pipeline=[
+            dict(type="PrepareVideoInfo", format="mp4", prefix="v_"),
+            dict(type="mmaction.DecordInit", num_threads=4),
+            dict(type="LoadFrames", num_clips=1, method="resize"),
+            dict(type="mmaction.DecordDecode"),
+            dict(type="mmaction.Resize", scale=(-1, 224)),
+            dict(type="mmaction.CenterCrop", crop_size=224),
+            dict(type="mmaction.FormatShape", input_format="NCTHW"),
+            dict(type="ConvertToTensor", keys=["imgs", "gt_segments", "gt_labels"]),
+            dict(type="Collect", inputs="imgs", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        resize_length=resize_length,
+        class_agnostic=True,
+        pipeline=[
+            dict(type="PrepareVideoInfo", format="mp4", prefix="v_"),
+            dict(type="mmaction.DecordInit", num_threads=4),
+            dict(type="LoadFrames", num_clips=1, method="resize"),
+            dict(type="mmaction.DecordDecode"),
+            dict(type="mmaction.Resize", scale=(-1, 224)),
+            dict(type="mmaction.CenterCrop", crop_size=224),
+            dict(type="mmaction.FormatShape", input_format="NCTHW"),
+            dict(type="ConvertToTensor", keys=["imgs"]),
+            dict(type="Collect", inputs="imgs", keys=["masks"]),
+        ],
+    ),
+)
+
+evaluation = dict(
+    type="mAP",
+    subset="validation",
+    tiou_thresholds=[0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95],
+    ground_truth_filename=annotation_path,
+    blocked_videos="data/activitynet-1.3/annotations/blocked.json",
+)

OpenTAD/configs/_base_/datasets/activitynet-1.3/features_tsp_pad.py

@@ -0,0 +1,82 @@
+dataset_type = "AnetPaddingDataset"
+annotation_path = "data/activitynet-1.3/annotations/activity_net.v1-3.min.json"
+class_map = "data/activitynet-1.3/annotations/category_idx.txt"
+data_path = "data/activitynet-1.3/features/anet_tsp_npy_unresize/"
+block_list = "data/activitynet-1.3/features/anet_tsp_npy_unresize/missing_files.txt"
+
+pad_len = 768
+
+dataset = dict(
+    train=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="training",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        feature_stride=16,
+        sample_stride=1,  # 1x16=16
+        offset_frames=8,
+        fps=15,
+        class_agnostic=True,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy", prefix="v_"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="RandomTrunc", trunc_len=pad_len, trunc_thresh=0.5, crop_ratio=[0.9, 1.0]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        feature_stride=16,
+        sample_stride=1,  # 1x16=16
+        offset_frames=8,
+        fps=15,
+        class_agnostic=True,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy", prefix="v_"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="Padding", length=pad_len),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        feature_stride=16,
+        sample_stride=1,  # 1x16=16
+        offset_frames=8,
+        fps=15,
+        class_agnostic=True,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy", prefix="v_"),
+            dict(type="ConvertToTensor", keys=["feats"]),
+            dict(type="Padding", length=pad_len),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+evaluation = dict(
+    type="mAP",
+    subset="validation",
+    tiou_thresholds=[0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95],
+    ground_truth_filename=annotation_path,
+    blocked_videos="data/activitynet-1.3/annotations/blocked.json",
+)

OpenTAD/configs/_base_/datasets/activitynet-1.3/features_tsp_resize.py

@@ -0,0 +1,73 @@
+dataset_type = "AnetResizeDataset"
+annotation_path = "data/activitynet-1.3/annotations/activity_net.v1-3.min.json"
+class_map = "data/activitynet-1.3/annotations/category_idx.txt"
+data_path = "data/activitynet-1.3/features/anet_tsp_npy_unresize/"
+block_list = "data/activitynet-1.3/features/anet_tsp_npy_unresize/missing_files.txt"
+
+resize_length = 128
+
+dataset = dict(
+    train=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="training",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        resize_length=resize_length,
+        class_agnostic=True,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy", prefix="v_"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="ResizeFeat", tool="torchvision_align"),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        resize_length=resize_length,
+        class_agnostic=True,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy", prefix="v_"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="ResizeFeat", tool="torchvision_align"),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        resize_length=resize_length,
+        class_agnostic=True,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy", prefix="v_"),
+            dict(type="ConvertToTensor", keys=["feats"]),
+            dict(type="ResizeFeat", tool="torchvision_align"),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+evaluation = dict(
+    type="mAP",
+    subset="validation",
+    tiou_thresholds=[0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95],
+    ground_truth_filename=annotation_path,
+    blocked_videos="data/activitynet-1.3/annotations/blocked.json",
+)

OpenTAD/configs/_base_/datasets/activitynet-1.3/features_tsp_resize_trunc.py

@@ -0,0 +1,74 @@
+dataset_type = "AnetResizeDataset"
+annotation_path = "data/activitynet-1.3/annotations/activity_net.v1-3.min.json"
+class_map = "data/activitynet-1.3/annotations/category_idx.txt"
+data_path = "data/activitynet-1.3/features/anet_tsp_npy_unresize/"
+block_list = "data/activitynet-1.3/features/anet_tsp_npy_unresize/missing_files.txt"
+
+resize_length = 192
+
+dataset = dict(
+    train=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="training",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        resize_length=resize_length,
+        class_agnostic=True,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy", prefix="v_"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="ResizeFeat", tool="torchvision_align"),
+            dict(type="RandomTrunc", trunc_len=resize_length, trunc_thresh=0.5, crop_ratio=[0.9, 1.0]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        resize_length=resize_length,
+        class_agnostic=True,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy", prefix="v_"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="ResizeFeat", tool="torchvision_align"),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="testing", # validation
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        resize_length=resize_length,
+        class_agnostic=True,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy", prefix="v_"),
+            dict(type="ConvertToTensor", keys=["feats"]),
+            dict(type="ResizeFeat", tool="torchvision_align"),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+evaluation = dict(
+    type="mAP",
+    subset="validation",
+    tiou_thresholds=[0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95],
+    ground_truth_filename=annotation_path,
+    blocked_videos="data/activitynet-1.3/annotations/blocked.json",
+)

OpenTAD/configs/_base_/datasets/charades/features_i3d_pad.py

@@ -0,0 +1,82 @@
+dataset_type = "EpicKitchensPaddingDataset"
+annotation_path = "data/charades/annotations/charades.json"
+class_map = "data/charades/annotations/category_idx.txt"
+data_path = "data/charades/features/i3d_charades_finetuned_stride8/"
+block_list = data_path + "missing_files.txt"
+
+trunc_len = 768
+
+dataset = dict(
+    train=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="training",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # dataloader setting
+        fps=24,
+        feature_stride=8,
+        sample_stride=1,
+        offset_frames=0,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="RandomTrunc", trunc_len=trunc_len, trunc_thresh=0.5, crop_ratio=[0.9, 1.0]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="testing",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # dataloader setting
+        fps=24,
+        feature_stride=8,
+        sample_stride=1,
+        offset_frames=0,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="Padding", length=trunc_len),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="testing",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        # dataloader setting
+        fps=24,
+        feature_stride=8,
+        sample_stride=1,
+        offset_frames=0,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats"]),
+            dict(type="Padding", length=trunc_len),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+
+evaluation = dict(
+    type="mAP",
+    subset="testing",
+    tiou_thresholds=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/datasets/charades/features_vgg_rgb_pad.py

@@ -0,0 +1,82 @@
+dataset_type = "EpicKitchensPaddingDataset"
+annotation_path = "data/charades/annotations/charades.json"
+class_map = "data/charades/annotations/category_idx.txt"
+data_path = "data/charades/features/Charades_v1_features_vgg_rgb_stride4/"
+block_list = data_path + "missing_files.txt"
+
+trunc_len = 1200
+
+dataset = dict(
+    train=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="training",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # dataloader setting
+        fps=24,
+        feature_stride=4,
+        sample_stride=1,
+        offset_frames=0,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="RandomTrunc", trunc_len=trunc_len, trunc_thresh=0.5, crop_ratio=[0.9, 1.0]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="testing",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # dataloader setting
+        fps=24,
+        feature_stride=4,
+        sample_stride=1,
+        offset_frames=0,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="Padding", length=trunc_len),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="testing",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        # dataloader setting
+        fps=24,
+        feature_stride=4,
+        sample_stride=1,
+        offset_frames=0,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats"]),
+            dict(type="Padding", length=trunc_len),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+
+evaluation = dict(
+    type="mAP",
+    subset="testing",
+    tiou_thresholds=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/datasets/ego4d_mq/e2e_train_trunc_test_sw.py

@@ -0,0 +1,101 @@
+annotation_path = "data/ego4d/annotations/ego4d_v2_220429.json"
+class_map = "data/ego4d/annotations/category_idx.txt"
+data_path = "data/ego4d/raw_data/MQ_data/mq_videos_short320/"
+block_list = None
+
+window_size = 900
+# size 900 is enough for all ego4d mq videos
+dataset = dict(
+    train=dict(
+        type="Ego4DPaddingDataset",
+        ann_file=annotation_path,
+        subset_name="train",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        # dataloader setting
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=8,  # after resizing, the offset is 8 frames
+        pipeline=[
+            dict(type="PrepareVideoInfo", format="mp4"),
+            dict(type="mmaction.DecordInit", num_threads=4),
+            dict(
+                type="LoadFrames",
+                num_clips=1,
+                method="random_trunc",
+                trunc_len=window_size,
+                trunc_thresh=0.3,
+                crop_ratio=[0.9, 1.0],
+            ),
+            dict(type="mmaction.DecordDecode"),
+            dict(type="mmaction.Resize", scale=(-1, 256)),
+            dict(type="mmaction.RandomResizedCrop"),
+            dict(type="mmaction.Resize", scale=(224, 224), keep_ratio=False),
+            dict(type="mmaction.Flip", flip_ratio=0.5),
+            dict(type="mmaction.FormatShape", input_format="NCTHW"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type="Ego4DSlidingDataset",
+        ann_file=annotation_path,
+        subset_name="val",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # dataloader setting
+        window_size=window_size,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=8,  # after resizing, the offset is 8 frames
+        window_overlap_ratio=0,
+        pipeline=[
+            dict(type="PrepareVideoInfo", format="mp4"),
+            dict(type="mmaction.DecordInit", num_threads=4),
+            dict(type="LoadFrames", num_clips=1, method="sliding_window"),
+            dict(type="mmaction.DecordDecode"),
+            dict(type="mmaction.Resize", scale=(-1, 224)),
+            dict(type="mmaction.CenterCrop", crop_size=224),
+            dict(type="mmaction.FormatShape", input_format="NCTHW"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type="Ego4DSlidingDataset",
+        ann_file=annotation_path,
+        subset_name="val",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        # dataloader setting
+        window_size=window_size,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=8,  # after resizing, the offset is 8 frames
+        window_overlap_ratio=0,
+        pipeline=[
+            dict(type="PrepareVideoInfo", format="mp4"),
+            dict(type="mmaction.DecordInit", num_threads=4),
+            dict(type="LoadFrames", num_clips=1, method="sliding_window"),
+            dict(type="mmaction.DecordDecode"),
+            dict(type="mmaction.Resize", scale=(-1, 224)),
+            dict(type="mmaction.CenterCrop", crop_size=224),
+            dict(type="mmaction.FormatShape", input_format="NCTHW"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+
+evaluation = dict(
+    type="mAP",
+    subset="val",
+    tiou_thresholds=[0.1, 0.2, 0.3, 0.4, 0.5],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/datasets/ego4d_mq/features_internvideo_train_trunc_test_sw.py

@@ -0,0 +1,82 @@
+annotation_path = "data/ego4d/annotations/ego4d_v2_220429.json"
+class_map = "data/ego4d/annotations/category_idx.txt"
+data_path = "data/ego4d/features/zoo_project/videomae_large_internvideo_img224_stride16_len16_interval1_ego4d/"
+block_list = None
+
+window_size = 900
+# size 900 is enough for all ego4d mq videos
+dataset = dict(
+    train=dict(
+        type="Ego4DPaddingDataset",
+        ann_file=annotation_path,
+        subset_name="train",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        # dataloader setting
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=8,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="RandomTrunc", trunc_len=window_size, trunc_thresh=0.3, crop_ratio=[0.9, 1.0]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type="Ego4DSlidingDataset",
+        ann_file=annotation_path,
+        subset_name="val",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # dataloader setting
+        window_size=window_size,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=8,
+        window_overlap_ratio=0,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="SlidingWindowTrunc", with_mask=True),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type="Ego4DSlidingDataset",
+        ann_file=annotation_path,
+        subset_name="val",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        # dataloader setting
+        window_size=window_size,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=8,
+        window_overlap_ratio=0,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats"]),
+            dict(type="SlidingWindowTrunc", with_mask=True),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+
+evaluation = dict(
+    type="mAP",
+    subset="val",
+    tiou_thresholds=[0.1, 0.2, 0.3, 0.4, 0.5],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/datasets/ego4d_mq/features_slowfast_trunc.py

@@ -0,0 +1,77 @@
+dataset_type = "Ego4DPaddingDataset"
+annotation_path = "data/ego4d/annotations/ego4d_v2_220429_feature.json"
+class_map = "data/ego4d/annotations/category_idx.txt"
+data_path = "data/ego4d/features/mq_slowfast/"
+block_list = None
+
+trunc_len = 1024
+
+dataset = dict(
+    train=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="train",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        # thumos dataloader setting
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=16,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="RandomTrunc", trunc_len=trunc_len, trunc_thresh=0.3, crop_ratio=[0.9, 1.0]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="val",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # thumos dataloader setting
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=16,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="val",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        # thumos dataloader setting
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=16,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats"]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+
+evaluation = dict(
+    type="mAP",
+    subset="val",
+    tiou_thresholds=[0.1, 0.2, 0.3, 0.4, 0.5],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/datasets/epic_kitchens-100/e2e_noun_train_trunc_test_sw.py

@@ -0,0 +1,104 @@
+annotation_path = "data/epic_kitchens-100/annotations/epic_kitchens_noun.json"
+class_map = "data/epic_kitchens-100/annotations/category_idx_noun.txt"
+data_path = "data/epic_kitchens-100/raw_data/epic_kitchens_100_30fps_512x288/"
+block_list = None
+
+window_size = 768
+
+dataset = dict(
+    train=dict(
+        type="EpicKitchensPaddingDataset",
+        ann_file=annotation_path,
+        subset_name="train",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        # epic-kitchens dataloader setting
+        fps=30,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=8,
+        pipeline=[
+            dict(type="PrepareVideoInfo", format="mp4"),
+            dict(type="mmaction.DecordInit", num_threads=4),
+            dict(
+                type="LoadFrames",
+                num_clips=1,
+                method="random_trunc",
+                trunc_len=window_size,
+                trunc_thresh=0.3,
+                crop_ratio=[0.9, 1.0],
+            ),
+            dict(type="mmaction.DecordDecode"),
+            dict(type="mmaction.Resize", scale=(-1, 256)),
+            dict(type="mmaction.RandomResizedCrop"),
+            dict(type="mmaction.Resize", scale=(224, 224), keep_ratio=False),
+            dict(type="mmaction.Flip", flip_ratio=0.5),
+            dict(type="mmaction.FormatShape", input_format="NCTHW"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type="EpicKitchensSlidingDataset",
+        ann_file=annotation_path,
+        subset_name="val",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # dataloader setting
+        window_size=window_size,
+        fps=30,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=8,
+        window_overlap_ratio=0.25,
+        pipeline=[
+            dict(type="PrepareVideoInfo", format="mp4"),
+            dict(type="mmaction.DecordInit", num_threads=4),
+            dict(type="LoadFrames", num_clips=1, method="sliding_window"),
+            dict(type="mmaction.DecordDecode"),
+            dict(type="mmaction.Resize", scale=(-1, 224)),
+            dict(type="mmaction.CenterCrop", crop_size=224),
+            dict(type="mmaction.FormatShape", input_format="NCTHW"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type="EpicKitchensSlidingDataset",
+        ann_file=annotation_path,
+        subset_name="val",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        # epic-kitchens dataloader setting
+        window_size=window_size,
+        fps=30,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=8,
+        window_overlap_ratio=0.5,
+        pipeline=[
+            dict(type="PrepareVideoInfo", format="mp4"),
+            dict(type="mmaction.DecordInit", num_threads=4),
+            dict(type="LoadFrames", num_clips=1, method="sliding_window"),
+            dict(type="mmaction.DecordDecode"),
+            dict(type="mmaction.Resize", scale=(-1, 224)),
+            dict(type="mmaction.CenterCrop", crop_size=224),
+            dict(type="mmaction.FormatShape", input_format="NCTHW"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+
+evaluation = dict(
+    type="mAP",
+    subset="val",
+    tiou_thresholds=[0.1, 0.2, 0.3, 0.4, 0.5],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/datasets/epic_kitchens-100/e2e_verb_train_trunc_test_sw.py

@@ -0,0 +1,104 @@
+annotation_path = "data/epic_kitchens-100/annotations/epic_kitchens_verb.json"
+class_map = "data/epic_kitchens-100/annotations/category_idx_verb.txt"
+data_path = "data/epic_kitchens-100/raw_data/epic_kitchens_100_30fps_512x288/"
+block_list = None
+
+window_size = 768
+
+dataset = dict(
+    train=dict(
+        type="EpicKitchensPaddingDataset",
+        ann_file=annotation_path,
+        subset_name="train",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        # epic-kitchens dataloader setting
+        fps=30,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=8,
+        pipeline=[
+            dict(type="PrepareVideoInfo", format="mp4"),
+            dict(type="mmaction.DecordInit", num_threads=4),
+            dict(
+                type="LoadFrames",
+                num_clips=1,
+                method="random_trunc",
+                trunc_len=window_size,
+                trunc_thresh=0.3,
+                crop_ratio=[0.9, 1.0],
+            ),
+            dict(type="mmaction.DecordDecode"),
+            dict(type="mmaction.Resize", scale=(-1, 256)),
+            dict(type="mmaction.RandomResizedCrop"),
+            dict(type="mmaction.Resize", scale=(224, 224), keep_ratio=False),
+            dict(type="mmaction.Flip", flip_ratio=0.5),
+            dict(type="mmaction.FormatShape", input_format="NCTHW"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type="EpicKitchensSlidingDataset",
+        ann_file=annotation_path,
+        subset_name="val",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # dataloader setting
+        window_size=window_size,
+        fps=30,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=8,
+        window_overlap_ratio=0.25,
+        pipeline=[
+            dict(type="PrepareVideoInfo", format="mp4"),
+            dict(type="mmaction.DecordInit", num_threads=4),
+            dict(type="LoadFrames", num_clips=1, method="sliding_window"),
+            dict(type="mmaction.DecordDecode"),
+            dict(type="mmaction.Resize", scale=(-1, 224)),
+            dict(type="mmaction.CenterCrop", crop_size=224),
+            dict(type="mmaction.FormatShape", input_format="NCTHW"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type="EpicKitchensSlidingDataset",
+        ann_file=annotation_path,
+        subset_name="val",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        # epic-kitchens dataloader setting
+        window_size=window_size,
+        fps=30,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=8,
+        window_overlap_ratio=0.5,
+        pipeline=[
+            dict(type="PrepareVideoInfo", format="mp4"),
+            dict(type="mmaction.DecordInit", num_threads=4),
+            dict(type="LoadFrames", num_clips=1, method="sliding_window"),
+            dict(type="mmaction.DecordDecode"),
+            dict(type="mmaction.Resize", scale=(-1, 224)),
+            dict(type="mmaction.CenterCrop", crop_size=224),
+            dict(type="mmaction.FormatShape", input_format="NCTHW"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+
+evaluation = dict(
+    type="mAP",
+    subset="val",
+    tiou_thresholds=[0.1, 0.2, 0.3, 0.4, 0.5],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/datasets/epic_kitchens-100/features_slowfast_noun.py

@@ -0,0 +1,80 @@
+dataset_type = "EpicKitchensPaddingDataset"
+annotation_path = "data/epic_kitchens-100/annotations/epic_kitchens_noun.json"
+class_map = "data/epic_kitchens-100/annotations/category_idx_noun.txt"
+data_path = "data/epic_kitchens-100/features/slowfast_fps30_stride16_clip32/"
+block_list = data_path + "missing_files.txt"
+
+trunc_len = 2304
+
+dataset = dict(
+    train=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="train",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        # epic-kitchens dataloader setting
+        fps=30,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=16,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="RandomTrunc", trunc_len=trunc_len, trunc_thresh=0.3, crop_ratio=[0.9, 1.0]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="val",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # epic-kitchens dataloader setting
+        fps=30,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=16,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="val",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        # epic-kitchens dataloader setting
+        fps=30,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=16,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats"]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+
+evaluation = dict(
+    type="mAP",
+    subset="val",
+    tiou_thresholds=[0.1, 0.2, 0.3, 0.4, 0.5],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/datasets/epic_kitchens-100/features_slowfast_noun_train_trunc_test_sw.py

@@ -0,0 +1,85 @@
+annotation_path = "data/epic_kitchens-100/annotations/epic_kitchens_noun.json"
+class_map = "data/epic_kitchens-100/annotations/category_idx_noun.txt"
+data_path = "data/epic_kitchens-100/features/slowfast_fps30_stride16_clip32/"
+block_list = data_path + "missing_files.txt"
+
+window_size = 2304
+
+dataset = dict(
+    train=dict(
+        type="EpicKitchensPaddingDataset",
+        ann_file=annotation_path,
+        subset_name="train",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        # epic-kitchens dataloader setting
+        fps=30,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=16,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="RandomTrunc", trunc_len=window_size, trunc_thresh=0.3, crop_ratio=[0.9, 1.0]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type="EpicKitchensSlidingDataset",
+        ann_file=annotation_path,
+        subset_name="val",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # dataloader setting
+        window_size=window_size,
+        fps=30,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=16,
+        window_overlap_ratio=0.25,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="SlidingWindowTrunc", with_mask=True),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type="EpicKitchensSlidingDataset",
+        ann_file=annotation_path,
+        subset_name="val",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        # epic-kitchens dataloader setting
+        window_size=window_size,
+        fps=30,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=16,
+        window_overlap_ratio=0.5,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats"]),
+            dict(type="SlidingWindowTrunc", with_mask=True),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+
+evaluation = dict(
+    type="mAP",
+    subset="val",
+    tiou_thresholds=[0.1, 0.2, 0.3, 0.4, 0.5],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/datasets/epic_kitchens-100/features_slowfast_verb.py

@@ -0,0 +1,80 @@
+dataset_type = "EpicKitchensPaddingDataset"
+annotation_path = "data/epic_kitchens-100/annotations/epic_kitchens_verb.json"
+class_map = "data/epic_kitchens-100/annotations/category_idx_verb.txt"
+data_path = "data/epic_kitchens-100/features/slowfast_fps30_stride16_clip32/"
+block_list = data_path + "missing_files.txt"
+
+trunc_len = 2304
+
+dataset = dict(
+    train=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="train",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        # epic-kitchens dataloader setting
+        fps=30,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=16,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="RandomTrunc", trunc_len=trunc_len, trunc_thresh=0.3, crop_ratio=[0.9, 1.0]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="val",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # epic-kitchens dataloader setting
+        fps=30,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=16,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="val",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        # epic-kitchens dataloader setting
+        fps=30,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=16,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats"]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+
+evaluation = dict(
+    type="mAP",
+    subset="val",
+    tiou_thresholds=[0.1, 0.2, 0.3, 0.4, 0.5],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/datasets/epic_kitchens-100/features_slowfast_verb_train_trunc_test_sw.py

@@ -0,0 +1,85 @@
+annotation_path = "data/epic_kitchens-100/annotations/epic_kitchens_verb.json"
+class_map = "data/epic_kitchens-100/annotations/category_idx_verb.txt"
+data_path = "data/epic_kitchens-100/features/slowfast_fps30_stride16_clip32/"
+block_list = data_path + "missing_files.txt"
+
+window_size = 2304
+
+dataset = dict(
+    train=dict(
+        type="EpicKitchensPaddingDataset",
+        ann_file=annotation_path,
+        subset_name="train",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        # epic-kitchens dataloader setting
+        fps=30,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=16,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="RandomTrunc", trunc_len=window_size, trunc_thresh=0.3, crop_ratio=[0.9, 1.0]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type="EpicKitchensSlidingDataset",
+        ann_file=annotation_path,
+        subset_name="val",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # dataloader setting
+        window_size=window_size,
+        fps=30,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=16,
+        window_overlap_ratio=0.25,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="SlidingWindowTrunc", with_mask=True),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type="EpicKitchensSlidingDataset",
+        ann_file=annotation_path,
+        subset_name="val",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        # epic-kitchens dataloader setting
+        window_size=window_size,
+        fps=30,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=16,
+        window_overlap_ratio=0.5,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats"]),
+            dict(type="SlidingWindowTrunc", with_mask=True),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+
+evaluation = dict(
+    type="mAP",
+    subset="val",
+    tiou_thresholds=[0.1, 0.2, 0.3, 0.4, 0.5],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/datasets/fineaction/features_internvideo_pad.py

@@ -0,0 +1,79 @@
+dataset_type = "AnetPaddingDataset"
+annotation_path = "data/fineaction/annotations/annotations_gt.json"
+class_map = "data/fineaction/annotations/category_idx.txt"
+data_path = "data/fineaction/features/intervideo_mae_huge_k700_stride16_len16_fineaction/"
+block_list = data_path + "missing_files.txt"
+
+pad_len = 2304
+
+dataset = dict(
+    train=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="training",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=8,
+        fps=30,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="RandomTrunc", trunc_len=pad_len, trunc_thresh=0.5, crop_ratio=[0.9, 1.0]),
+            dict(type="Rearrange", keys=["feats"], ops="t c-> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=8,
+        fps=30,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="Padding", length=pad_len),
+            dict(type="Rearrange", keys=["feats"], ops="t c-> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        feature_stride=16,
+        sample_stride=1,
+        offset_frames=8,
+        fps=30,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats"]),
+            dict(type="Padding", length=pad_len),
+            dict(type="Rearrange", keys=["feats"], ops="t c-> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+
+evaluation = dict(
+    type="mAP",
+    subset="validation",
+    tiou_thresholds=[0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/datasets/fineaction/features_internvideo_resize_trunc.py

@@ -0,0 +1,73 @@
+dataset_type = "AnetResizeDataset"
+annotation_path = "data/fineaction/annotations/annotations_gt.json"
+class_map = "data/fineaction/annotations/category_idx.txt"
+data_path = "data/fineaction/features/intervideo_mae_huge_k700_stride16_len16_fineaction/"
+block_list = "data/fineaction/features/intervideo_mae_huge_k700_stride16_len16_fineaction/missing_files.txt"
+
+resize_length = 192
+
+dataset = dict(
+    train=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="training",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        resize_length=resize_length,
+        class_agnostic=True,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="ResizeFeat", tool="torchvision_align"),
+            dict(type="RandomTrunc", trunc_len=resize_length, trunc_thresh=0.5, crop_ratio=[0.9, 1.0]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        resize_length=resize_length,
+        class_agnostic=True,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="ResizeFeat", tool="torchvision_align"),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        resize_length=resize_length,
+        class_agnostic=True,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats"]),
+            dict(type="ResizeFeat", tool="torchvision_align"),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+evaluation = dict(
+    type="mAP",
+    subset="validation",
+    tiou_thresholds=[0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/datasets/hacs-1.1.1/features_slowfast_pad.py

@@ -0,0 +1,79 @@
+dataset_type = "AnetPaddingDataset"
+annotation_path = "data/hacs-1.1.1/annotations/HACS_segments_v1.1.1.json"
+class_map = "data/hacs-1.1.1/annotations/category_idx.txt"
+data_path = "data/hacs-1.1.1/features/slowfast101_15fps_stride8_len32_hacs/"
+block_list = data_path + "missing_files.txt"
+
+pad_len = 960
+
+dataset = dict(
+    train=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="training",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        feature_stride=8,
+        sample_stride=1,  # 1x8=8
+        offset_frames=16,
+        fps=15,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="RandomTrunc", trunc_len=pad_len, trunc_thresh=0.5, crop_ratio=[0.9, 1.0]),
+            dict(type="Rearrange", keys=["feats"], ops="t c-> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        feature_stride=8,
+        sample_stride=1,  # 1x8=8
+        offset_frames=16,
+        fps=15,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="Padding", length=pad_len),
+            dict(type="Rearrange", keys=["feats"], ops="t c-> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        feature_stride=8,
+        sample_stride=1,  # 1x8=8
+        offset_frames=16,
+        fps=15,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats"]),
+            dict(type="Padding", length=pad_len),
+            dict(type="Rearrange", keys=["feats"], ops="t c-> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+
+evaluation = dict(
+    type="mAP",
+    subset="validation",
+    tiou_thresholds=[0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/datasets/hacs-1.1.1/features_slowfast_resize.py

@@ -0,0 +1,73 @@
+dataset_type = "AnetResizeDataset"
+annotation_path = "data/hacs-1.1.1/annotations/HACS_segments_v1.1.1.json"
+class_map = "data/hacs-1.1.1/annotations/category_idx.txt"
+data_path = "data/hacs-1.1.1/features/slowfast101_15fps_stride8_len32_hacs/"
+block_list = data_path + "missing_files.txt"
+
+resize_length = 224
+
+dataset = dict(
+    train=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="training",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=True,
+        resize_length=resize_length,
+        class_agnostic=True,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="ResizeFeat", tool="torchvision_align"),
+            dict(type="Rearrange", keys=["feats"], ops="t c-> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        resize_length=resize_length,
+        class_agnostic=True,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="ResizeFeat", tool="torchvision_align"),
+            dict(type="Rearrange", keys=["feats"], ops="t c-> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        resize_length=resize_length,
+        class_agnostic=True,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats"]),
+            dict(type="ResizeFeat", tool="torchvision_align"),
+            dict(type="Rearrange", keys=["feats"], ops="t c-> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+
+evaluation = dict(
+    type="mAP",
+    subset="validation",
+    tiou_thresholds=[0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/datasets/multithumos/features_i3d_pad.py

@@ -0,0 +1,77 @@
+dataset_type = "ThumosPaddingDataset"
+annotation_path = "data/multithumos/annotations/multithumos_anno.json"
+class_map = "data/multithumos/annotations/category_idx.txt"
+data_path = "data/thumos-14/features/i3d_actionformer_stride4_thumos/"
+block_list = data_path + "missing_files.txt"
+
+trunc_len = 2304
+
+dataset = dict(
+    train=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="training",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # thumos dataloader setting
+        feature_stride=4,
+        sample_stride=1,
+        offset_frames=8,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="RandomTrunc", trunc_len=trunc_len, trunc_thresh=0.5, crop_ratio=[0.9, 1.0]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # thumos dataloader setting
+        feature_stride=4,
+        sample_stride=1,
+        offset_frames=8,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        # thumos dataloader setting
+        feature_stride=4,
+        sample_stride=1,
+        offset_frames=8,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats"]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+
+evaluation = dict(
+    type="mAP",
+    subset="validation",
+    tiou_thresholds=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/datasets/multithumos/features_i3d_rgb_pad.py

@@ -0,0 +1,80 @@
+dataset_type = "ThumosPaddingDataset"
+annotation_path = "data/multithumos/annotations/multithumos_anno.json"
+class_map = "data/multithumos/annotations/category_idx.txt"
+data_path = "data/thumos-14/features/i3d_actionformer_stride4_thumos/"
+block_list = data_path + "missing_files.txt"
+
+trunc_len = 2304
+
+dataset = dict(
+    train=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="training",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # thumos dataloader setting
+        feature_stride=4,
+        sample_stride=1,
+        offset_frames=8,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="ChannelReduction", in_channels=2048, index=[0, 1024]),  # only use rgb features
+            dict(type="RandomTrunc", trunc_len=trunc_len, trunc_thresh=0.5, crop_ratio=[0.9, 1.0]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # thumos dataloader setting
+        feature_stride=4,
+        sample_stride=1,
+        offset_frames=8,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="ChannelReduction", in_channels=2048, index=[0, 1024]),  # only use rgb features
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        # thumos dataloader setting
+        feature_stride=4,
+        sample_stride=1,
+        offset_frames=8,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats"]),
+            dict(type="ChannelReduction", in_channels=2048, index=[0, 1024]),  # only use rgb features
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+
+evaluation = dict(
+    type="mAP",
+    subset="validation",
+    tiou_thresholds=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/datasets/thumos-14/e2e_sw_256x224x224.py

@@ -0,0 +1,96 @@
+dataset_type = "ThumosSlidingDataset"
+annotation_path = "data/thumos-14/annotations/thumos_14_anno.json"
+class_map = "data/thumos-14/annotations/category_idx.txt"
+data_path = "data/thumos-14/raw_data/video"
+block_list = None
+
+window_size = 256
+
+dataset = dict(
+    train=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="training",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # thumos dataloader setting
+        feature_stride=4,
+        sample_stride=1,  # 1x4=4
+        window_size=window_size,
+        window_overlap_ratio=0.25,
+        pipeline=[
+            dict(type="PrepareVideoInfo", format="mp4"),
+            dict(type="mmaction.DecordInit", num_threads=4),
+            dict(type="LoadFrames", num_clips=1, method="sliding_window"),
+            dict(type="mmaction.DecordDecode"),
+            dict(type="mmaction.Resize", scale=(-1, 256)),
+            dict(type="mmaction.RandomResizedCrop"),
+            dict(type="mmaction.Resize", scale=(224, 224), keep_ratio=False),
+            dict(type="mmaction.Flip", flip_ratio=0.5),
+            dict(type="mmaction.FormatShape", input_format="NCTHW"),
+            dict(type="ConvertToTensor", keys=["imgs", "gt_segments", "gt_labels"]),
+            dict(type="Collect", inputs="imgs", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # thumos dataloader setting
+        feature_stride=4,
+        sample_stride=1,  # 1x4=4
+        window_size=window_size,
+        window_overlap_ratio=0.25,
+        pipeline=[
+            dict(type="PrepareVideoInfo", format="mp4"),
+            dict(type="mmaction.DecordInit", num_threads=4),
+            dict(type="LoadFrames", num_clips=1, method="sliding_window"),
+            dict(type="mmaction.DecordDecode"),
+            dict(type="mmaction.Resize", scale=(-1, 224)),
+            dict(type="mmaction.CenterCrop", crop_size=224),
+            dict(type="mmaction.FormatShape", input_format="NCTHW"),
+            dict(type="ConvertToTensor", keys=["imgs", "gt_segments", "gt_labels"]),
+            dict(type="Collect", inputs="imgs", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        # thumos dataloader setting
+        feature_stride=4,
+        sample_stride=1,  # 1x4=4
+        window_size=window_size,
+        window_overlap_ratio=0.5,
+        pipeline=[
+            dict(type="PrepareVideoInfo", format="mp4"),
+            dict(type="mmaction.DecordInit", num_threads=4),
+            dict(type="LoadFrames", num_clips=1, method="sliding_window"),
+            dict(type="mmaction.DecordDecode"),
+            dict(type="mmaction.Resize", scale=(-1, 224)),
+            dict(type="mmaction.CenterCrop", crop_size=224),
+            dict(type="mmaction.FormatShape", input_format="NCTHW"),
+            dict(type="ConvertToTensor", keys=["imgs"]),
+            dict(type="Collect", inputs="imgs", keys=["masks"]),
+        ],
+    ),
+)
+
+
+evaluation = dict(
+    type="mAP",
+    subset="validation",
+    tiou_thresholds=[0.3, 0.4, 0.5, 0.6, 0.7],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/datasets/thumos-14/e2e_train_trunc_test_sw_256x224x224.py

@@ -0,0 +1,100 @@
+annotation_path = "data/thumos-14/annotations/thumos_14_anno.json"
+class_map = "data/thumos-14/annotations/category_idx.txt"
+data_path = "data/thumos-14/raw_data/video"
+block_list = None
+
+window_size = 256
+
+dataset = dict(
+    train=dict(
+        type="ThumosPaddingDataset",
+        ann_file=annotation_path,
+        subset_name="training",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # thumos dataloader setting
+        feature_stride=4,
+        sample_stride=1,  # 1x4=4
+        pipeline=[
+            dict(type="PrepareVideoInfo", format="mp4"),
+            dict(type="mmaction.DecordInit", num_threads=4),
+            dict(
+                type="LoadFrames",
+                num_clips=1,
+                method="random_trunc",
+                trunc_len=window_size,
+                trunc_thresh=0.5,
+                crop_ratio=[0.9, 1.0],
+            ),
+            dict(type="mmaction.DecordDecode"),
+            dict(type="mmaction.Resize", scale=(-1, 256)),
+            dict(type="mmaction.RandomResizedCrop"),
+            dict(type="mmaction.Resize", scale=(224, 224), keep_ratio=False),
+            dict(type="mmaction.Flip", flip_ratio=0.5),
+            dict(type="mmaction.FormatShape", input_format="NCTHW"),
+            dict(type="ConvertToTensor", keys=["imgs", "gt_segments", "gt_labels"]),
+            dict(type="Collect", inputs="imgs", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type="ThumosSlidingDataset",
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # thumos dataloader setting
+        feature_stride=4,
+        sample_stride=1,  # 1x4=4
+        window_size=window_size,
+        window_overlap_ratio=0.25,
+        pipeline=[
+            dict(type="PrepareVideoInfo", format="mp4"),
+            dict(type="mmaction.DecordInit", num_threads=4),
+            dict(type="LoadFrames", num_clips=1, method="sliding_window"),
+            dict(type="mmaction.DecordDecode"),
+            dict(type="mmaction.Resize", scale=(-1, 224)),
+            dict(type="mmaction.CenterCrop", crop_size=224),
+            dict(type="mmaction.FormatShape", input_format="NCTHW"),
+            dict(type="ConvertToTensor", keys=["imgs", "gt_segments", "gt_labels"]),
+            dict(type="Collect", inputs="imgs", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type="ThumosSlidingDataset",
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        # thumos dataloader setting
+        feature_stride=4,
+        sample_stride=1,  # 1x4=4
+        window_size=window_size,
+        window_overlap_ratio=0.5,
+        pipeline=[
+            dict(type="PrepareVideoInfo", format="mp4"),
+            dict(type="mmaction.DecordInit", num_threads=4),
+            dict(type="LoadFrames", num_clips=1, method="sliding_window"),
+            dict(type="mmaction.DecordDecode"),
+            dict(type="mmaction.Resize", scale=(-1, 224)),
+            dict(type="mmaction.CenterCrop", crop_size=224),
+            dict(type="mmaction.FormatShape", input_format="NCTHW"),
+            dict(type="ConvertToTensor", keys=["imgs"]),
+            dict(type="Collect", inputs="imgs", keys=["masks"]),
+        ],
+    ),
+)
+
+
+evaluation = dict(
+    type="mAP",
+    subset="validation",
+    tiou_thresholds=[0.3, 0.4, 0.5, 0.6, 0.7],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/datasets/thumos-14/features_i3d_pad.py

@@ -0,0 +1,77 @@
+dataset_type = "ThumosPaddingDataset"
+annotation_path = "data/thumos-14/annotations/thumos_14_anno.json"
+class_map = "data/thumos-14/annotations/category_idx.txt"
+data_path = "data/thumos-14/features/i3d_actionformer_stride4_thumos/"
+block_list = data_path + "missing_files.txt"
+
+trunc_len = 2304
+
+dataset = dict(
+    train=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="training",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # thumos dataloader setting
+        feature_stride=4,
+        sample_stride=1,  # 1x4=4
+        offset_frames=8,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="RandomTrunc", trunc_len=trunc_len, trunc_thresh=0.5, crop_ratio=[0.9, 1.0]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # thumos dataloader setting
+        feature_stride=4,
+        sample_stride=1,  # 1x4=4
+        offset_frames=8,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        # thumos dataloader setting
+        feature_stride=4,
+        sample_stride=1,  # 1x4=4
+        offset_frames=8,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats"]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+
+evaluation = dict(
+    type="mAP",
+    subset="validation",
+    tiou_thresholds=[0.3, 0.4, 0.5, 0.6, 0.7],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/datasets/thumos-14/features_i3d_sw.py

@@ -0,0 +1,85 @@
+dataset_type = "ThumosSlidingDataset"
+annotation_path = "data/thumos-14/annotations/thumos_14_anno.json"
+class_map = "data/thumos-14/annotations/category_idx.txt"
+data_path = "data/thumos-14/features/i3d_actionformer_stride4_thumos/"
+block_list = data_path + "missing_files.txt"
+
+window_size = 256
+
+dataset = dict(
+    train=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="training",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # thumos dataloader setting
+        feature_stride=4,
+        sample_stride=1,  # 1x4=4
+        window_size=window_size,
+        window_overlap_ratio=0.25,
+        offset_frames=8,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="SlidingWindowTrunc", with_mask=True),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # thumos dataloader setting
+        feature_stride=4,
+        sample_stride=1,  # 1x4=4
+        window_size=window_size,
+        window_overlap_ratio=0.25,
+        offset_frames=8,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="SlidingWindowTrunc", with_mask=True),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        # thumos dataloader setting
+        feature_stride=4,
+        sample_stride=1,  # 1x4=4
+        window_size=window_size,
+        window_overlap_ratio=0.5,
+        offset_frames=8,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats"]),
+            dict(type="SlidingWindowTrunc", with_mask=True),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+
+evaluation = dict(
+    type="mAP",
+    subset="validation",
+    tiou_thresholds=[0.3, 0.4, 0.5, 0.6, 0.7],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/datasets/thumos-14/features_i3d_train_trunc_test_sw.py

@@ -0,0 +1,82 @@
+annotation_path = "data/thumos-14/annotations/thumos_14_anno.json"
+class_map = "data/thumos-14/annotations/category_idx.txt"
+data_path = "data/thumos-14/features/i3d_actionformer_stride4_thumos/"
+block_list = data_path + "missing_files.txt"
+
+window_size = 2304
+
+dataset = dict(
+    train=dict(
+        type="ThumosPaddingDataset",
+        ann_file=annotation_path,
+        subset_name="training",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # thumos dataloader setting
+        feature_stride=4,
+        sample_stride=1,  # 1x4=4
+        offset_frames=8,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="RandomTrunc", trunc_len=window_size, trunc_thresh=0.75, crop_ratio=[0.9, 1.0]),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type="ThumosSlidingDataset",
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # thumos dataloader setting
+        feature_stride=4,
+        sample_stride=1,  # 1x4=4
+        window_size=window_size,
+        offset_frames=8,
+        window_overlap_ratio=0.25,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="SlidingWindowTrunc", with_mask=True),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type="ThumosSlidingDataset",
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        # thumos dataloader setting
+        feature_stride=4,
+        sample_stride=1,  # 1x4=4
+        window_size=window_size,
+        offset_frames=8,
+        window_overlap_ratio=0.5,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats"]),
+            dict(type="SlidingWindowTrunc", with_mask=True),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+
+evaluation = dict(
+    type="mAP",
+    subset="validation",
+    tiou_thresholds=[0.3, 0.4, 0.5, 0.6, 0.7],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/datasets/thumos-14/features_tsn_sw.py

@@ -0,0 +1,81 @@
+dataset_type = "ThumosSlidingDataset"
+annotation_path = "data/thumos-14/annotations/thumos_14_anno.json"
+class_map = "data/thumos-14/annotations/category_idx.txt"
+data_path = "data/thumos-14/features/tsn_gtad_stride1_thumos"
+block_list = "data/thumos-14/features/tsn_gtad_stride1_thumos/missing_files.txt"
+
+window_size = 128
+dataset = dict(
+    train=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="training",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # thumos dataloader setting
+        feature_stride=1,
+        sample_stride=5,  # 1x4=4
+        window_size=window_size,
+        window_overlap_ratio=0.25,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="SlidingWindowTrunc", with_mask=True),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    val=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        # thumos dataloader setting
+        feature_stride=1,
+        sample_stride=5,  # 1x4=4
+        window_size=window_size,
+        window_overlap_ratio=0.25,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats", "gt_segments", "gt_labels"]),
+            dict(type="SlidingWindowTrunc", with_mask=True),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks", "gt_segments", "gt_labels"]),
+        ],
+    ),
+    test=dict(
+        type=dataset_type,
+        ann_file=annotation_path,
+        subset_name="validation",
+        block_list=block_list,
+        class_map=class_map,
+        data_path=data_path,
+        filter_gt=False,
+        test_mode=True,
+        # thumos dataloader setting
+        feature_stride=1,
+        sample_stride=5,  # 1x4=4
+        window_size=window_size,
+        window_overlap_ratio=0.5,
+        pipeline=[
+            dict(type="LoadFeats", feat_format="npy"),
+            dict(type="ConvertToTensor", keys=["feats"]),
+            dict(type="SlidingWindowTrunc", with_mask=True),
+            dict(type="Rearrange", keys=["feats"], ops="t c -> c t"),
+            dict(type="Collect", inputs="feats", keys=["masks"]),
+        ],
+    ),
+)
+
+
+evaluation = dict(
+    type="mAP",
+    subset="validation",
+    tiou_thresholds=[0.3, 0.4, 0.5, 0.6, 0.7],
+    ground_truth_filename=annotation_path,
+)

OpenTAD/configs/_base_/models/actionformer.py

@@ -0,0 +1,43 @@
+model = dict(
+    type="ActionFormer",
+    projection=dict(
+        type="Conv1DTransformerProj",
+        in_channels=2048,
+        out_channels=512,
+        arch=(2, 2, 5),  # layers in embed / stem / branch
+        conv_cfg=dict(kernel_size=3, proj_pdrop=0.0),
+        norm_cfg=dict(type="LN"),
+        attn_cfg=dict(n_head=4, n_mha_win_size=19),
+        path_pdrop=0.1,
+        use_abs_pe=False,
+        max_seq_len=2304,
+    ),
+    neck=dict(
+        type="FPNIdentity",
+        in_channels=512,
+        out_channels=512,
+        num_levels=6,
+    ),
+    rpn_head=dict(
+        type="ActionFormerHead",
+        num_classes=20,
+        in_channels=512,
+        feat_channels=512,
+        num_convs=2,
+        cls_prior_prob=0.01,
+        prior_generator=dict(
+            type="PointGenerator",
+            strides=[1, 2, 4, 8, 16, 32],
+            regression_range=[(0, 4), (4, 8), (8, 16), (16, 32), (32, 64), (64, 10000)],
+        ),
+        loss_normalizer=100,
+        loss_normalizer_momentum=0.9,
+        center_sample="radius",
+        center_sample_radius=1.5,
+        label_smoothing=0.0,
+        loss=dict(
+            cls_loss=dict(type="FocalLoss"),
+            reg_loss=dict(type="DIOULoss"),
+        ),
+    ),
+)

OpenTAD/configs/_base_/models/afsd.py

@@ -0,0 +1,30 @@
+model = dict(
+    type="AFSD",
+    neck=dict(
+        type="AFSDNeck",
+        in_channels=2048,
+        out_channels=512,
+        frame_num=768,  # 96*8
+        layer_num=6,
+    ),
+    rpn_head=dict(
+        type="AFSDCoarseHead",
+        in_channels=512,
+        out_channels=512,
+        frame_num=768,  # 96*8
+        fpn_strides=[4, 8, 16, 32, 64, 128],
+        num_classes=2,
+        layer_num=6,
+        feat_t=768 // 8,
+    ),
+    roi_head=dict(
+        type="AFSDRefineHead",
+        in_channels=512,
+        num_classes=2,
+        # for loss
+        overlap_thresh=0.6,
+        loc_weight=1.0,
+        loc_bounded=True,
+        use_smooth_l1=True,
+    ),
+)

OpenTAD/configs/_base_/models/bmn.py

@@ -0,0 +1,41 @@
+model = dict(
+    type="BMN",
+    projection=dict(
+        type="ConvSingleProj",
+        in_channels=400,
+        out_channels=256,
+        num_convs=2,
+        conv_cfg=dict(groups=4),
+    ),
+    rpn_head=dict(
+        type="TemporalEvaluationHead",  # tem
+        in_channels=256,
+        num_classes=2,
+        conv_cfg=dict(groups=4),
+        loss=dict(pos_thresh=0.5, gt_type=["startness", "endness"]),
+    ),
+    roi_head=dict(
+        type="StandardProposalMapHead",
+        proposal_generator=dict(type="DenseProposalMap", tscale=128, dscale=128),
+        proposal_roi_extractor=dict(
+            type="BMNExtractor",
+            in_channels=256,
+            roi_channels=512,
+            out_channels=128,
+            tscale=128,
+            dscale=128,
+            prop_extend_ratio=0.5,
+        ),
+        proposal_head=dict(
+            type="PEMHead",  # FC_head
+            in_channels=128,
+            feat_channels=128,
+            num_convs=2,
+            num_classes=2,
+            loss=dict(
+                cls_loss=dict(type="BalancedBCELoss", pos_thresh=0.9),
+                reg_loss=dict(type="BalancedL2Loss", high_thresh=0.7, low_thresh=0.3, weight=5.0),
+            ),
+        ),
+    ),
+)

OpenTAD/configs/_base_/models/etad.py

@@ -0,0 +1,60 @@
+model = dict(
+    type="ETAD",
+    projection=dict(
+        type="ConvSingleProj",
+        in_channels=400,
+        out_channels=256,
+        num_convs=1,
+        conv_cfg=dict(groups=4),
+        norm_cfg=dict(type="GN", num_groups=16),
+    ),
+    neck=dict(
+        type="LSTMNeck",
+        in_channels=256,
+        out_channels=256,
+        conv_cfg=dict(groups=4),
+        norm_cfg=dict(type="GN", num_groups=16),
+    ),
+    rpn_head=dict(
+        type="TemporalEvaluationHead",  # tem
+        in_channels=256,
+        num_classes=2,
+        shared=True,
+        conv_cfg=dict(groups=4),
+        loss=dict(pos_thresh=0.5, gt_type=["startness", "endness"]),
+    ),
+    roi_head=dict(
+        type="ETADRoIHead",
+        stages=dict(
+            number=3,
+            loss_weight=[1, 1, 1],
+            pos_iou_thresh=[0.7, 0.8, 0.9],
+        ),
+        proposal_generator=dict(
+            type="ProposalMapSampling",
+            tscale=128,
+            dscale=128,
+            sampling_ratio=0.06,
+            strategy="random",
+        ),
+        proposal_roi_extractor=dict(
+            type="ROIAlignExtractor",
+            roi_size=16,
+            extend_ratio=0.5,
+            base_stride=1,
+        ),
+        proposal_head=dict(
+            type="ETADHead",
+            in_channels=256,
+            roi_size=16,
+            feat_channels=512,
+            fcs_num=3,
+            fcs_channels=128,
+            loss=dict(
+                cls_weight=1.0,
+                reg_weight=5.0,
+                boundary_weight=10.0,
+            ),
+        ),
+    ),
+)

OpenTAD/configs/_base_/models/gtad.py

@@ -0,0 +1,46 @@
+model = dict(
+    type="GTAD",
+    projection=dict(
+        type="ConvSingleProj",
+        num_convs=1,
+        in_channels=400,
+        out_channels=256,
+        conv_cfg=dict(groups=4),
+    ),
+    neck=dict(
+        type="GCNeXt",
+        in_channels=256,
+        out_channels=256,
+        k=3,
+        groups=32,
+    ),
+    rpn_head=dict(
+        type="GCNextTemporalEvaluationHead",
+        in_channels=256,
+        num_classes=2,
+        loss=dict(pos_thresh=0.5, gt_type=["startness", "endness"]),
+    ),
+    roi_head=dict(
+        type="StandardProposalMapHead",
+        proposal_generator=dict(type="DenseProposalMap", tscale=100, dscale=100),
+        proposal_roi_extractor=dict(
+            type="GTADExtractor",
+            in_channels=256,
+            out_channels=512,
+            tscale=100,
+            dscale=100,
+        ),
+        proposal_head=dict(
+            type="PEMHead",  # FC_head
+            in_channels=512,
+            feat_channels=128,
+            num_convs=3,
+            num_classes=2,
+            kernel_size=1,
+            loss=dict(
+                cls_loss=dict(type="BalancedBCELoss", pos_thresh=0.9),
+                reg_loss=dict(type="BalancedL2Loss", high_thresh=0.7, low_thresh=0.3, weight=5.0),
+            ),
+        ),
+    ),
+)

OpenTAD/configs/_base_/models/tadtr.py

@@ -0,0 +1,73 @@
+model = dict(
+    type="TadTR",  # Done
+    projection=dict(
+        type="ConvSingleProj",
+        in_channels=2048,
+        out_channels=256,
+        num_convs=1,
+        conv_cfg=dict(kernel_size=1, padding=0),
+        norm_cfg=dict(type="GN", num_groups=32),
+        act_cfg=None,
+    ),
+    transformer=dict(
+        type="TadTRTransformer",
+        num_proposals=40,
+        num_classes=20,
+        with_act_reg=True,
+        roi_size=16,
+        roi_extend_ratio=0.25,
+        aux_loss=True,
+        position_embedding=dict(
+            type="PositionEmbeddingSine",
+            num_pos_feats=256,
+            temperature=10000,
+            offset=-0.5,
+            normalize=True,
+        ),
+        encoder=dict(
+            type="DeformableDETREncoder",
+            embed_dim=256,
+            num_heads=8,
+            num_points=4,
+            attn_dropout=0.1,
+            ffn_dim=1024,
+            ffn_dropout=0.1,
+            num_layers=4,
+            num_feature_levels=1,
+            post_norm=False,
+        ),
+        decoder=dict(
+            type="DeformableDETRDecoder",
+            embed_dim=256,
+            num_heads=8,
+            num_points=4,
+            attn_dropout=0.1,
+            ffn_dim=1024,
+            ffn_dropout=0.1,
+            num_layers=4,
+            num_feature_levels=1,
+            return_intermediate=True,
+        ),
+        loss=dict(
+            type="TadTRSetCriterion",
+            num_classes=20,
+            matcher=dict(
+                type="HungarianMatcher",
+                cost_class=6.0,
+                cost_bbox=5.0,
+                cost_giou=2.0,
+                cost_class_type="focal_loss_cost",  # ce_cost, focal_loss_cost
+                iou_type="iou",
+                use_multi_class=False,
+            ),
+            loss_class_type="focal_loss",  # ce_loss, focal_loss
+            weight_dict=dict(
+                loss_class=2.0,
+                loss_bbox=5.0,
+                loss_iou=2.0,
+                loss_actionness=4.0,
+            ),
+            use_multi_class=False,
+        ),
+    ),
+)

OpenTAD/configs/_base_/models/temporalmaxer.py

@@ -0,0 +1,44 @@
+model = dict(
+    type="TemporalMaxer",
+    projection=dict(
+        type="TemporalMaxerProj",
+        in_channels=2048,
+        out_channels=512,
+        arch=(2, 0, 5),  # feature projection layers, downsampling layer
+        conv_cfg=dict(kernel_size=3),
+        norm_cfg=dict(type="LN"),
+    ),
+    neck=dict(
+        type="FPNIdentity",
+        in_channels=512,
+        out_channels=512,
+        num_levels=6,
+    ),
+    rpn_head=dict(
+        type="TemporalMaxerHead",
+        num_classes=20,
+        in_channels=512,
+        feat_channels=512,
+        num_convs=2,
+        cls_prior_prob=0.01,
+        prior_generator=dict(
+            type="PointGenerator",
+            strides=[1, 2, 4, 8, 16, 32],
+            regression_range=[(0, 4), (4, 8), (8, 16), (16, 32), (32, 64), (64, 10000)],
+        ),
+        loss_normalizer=100,
+        loss_normalizer_momentum=0.9,
+        loss=dict(
+            cls_loss=dict(type="FocalLoss"),
+            reg_loss=dict(type="DIOULoss"),
+        ),
+        assigner=dict(
+            type="AnchorFreeSimOTAAssigner",
+            iou_weight=2,
+            cls_weight=1.0,
+            center_radius=1.5,
+            keep_percent=1.0,
+            confuse_weight=0.0,
+        ),
+    ),
+)

OpenTAD/configs/_base_/models/tridet.py

@@ -0,0 +1,52 @@
+model = dict(
+    type="TriDet",
+    projection=dict(
+        type="TriDetProj",
+        in_channels=2048,
+        out_channels=512,
+        sgp_mlp_dim=768,
+        arch=(2, 2, 5),  # layers in embed / stem / branch
+        downsample_type="max",
+        sgp_win_size=[1, 1, 1, 1, 1, 1],
+        k=5,
+        init_conv_vars=0,
+        conv_cfg=dict(kernel_size=3),
+        norm_cfg=dict(type="LN"),
+        path_pdrop=0.1,
+        use_abs_pe=False,
+        max_seq_len=2304,
+        input_noise=0.0,
+    ),
+    neck=dict(
+        type="FPNIdentity",
+        in_channels=512,
+        out_channels=512,
+        num_levels=6,
+    ),
+    rpn_head=dict(
+        type="TriDetHead",
+        num_classes=20,
+        in_channels=512,
+        feat_channels=512,
+        num_convs=2,
+        cls_prior_prob=0.01,
+        prior_generator=dict(
+            type="PointGenerator",
+            strides=[1, 2, 4, 8, 16, 32],
+            regression_range=[(0, 4), (4, 8), (8, 16), (16, 32), (32, 64), (64, 10000)],
+        ),
+        loss_normalizer=100,
+        loss_normalizer_momentum=0.9,
+        center_sample="radius",
+        center_sample_radius=1.5,
+        label_smoothing=0.0,
+        boundary_kernel_size=3,
+        iou_weight_power=0.2,
+        num_bins=16,
+        loss=dict(
+            cls_loss=dict(type="FocalLoss"),
+            reg_loss=dict(type="DIOULoss"),
+            iou_rate=dict(type="GIOULoss"),
+        ),
+    ),
+)

OpenTAD/configs/_base_/models/tsi.py

@@ -0,0 +1,39 @@
+model = dict(
+    type="TSI",
+    projection=dict(
+        type="ConvSingleProj",
+        in_channels=400,
+        out_channels=256,
+        num_convs=2,
+        conv_cfg=dict(groups=4),
+    ),
+    rpn_head=dict(
+        type="LocalGlobalTemporalEvaluationHead",
+        in_channels=256,
+        loss=dict(pos_thresh=0.5),
+    ),
+    roi_head=dict(
+        type="StandardProposalMapHead",
+        proposal_generator=dict(type="DenseProposalMap", tscale=128, dscale=128),
+        proposal_roi_extractor=dict(
+            type="BMNExtractor",
+            in_channels=256,
+            roi_channels=512,
+            out_channels=128,
+            tscale=128,
+            dscale=128,
+            prop_extend_ratio=0.5,
+        ),
+        proposal_head=dict(
+            type="TSIHead",  # modified on PEMHead
+            in_channels=128,
+            feat_channels=128,
+            num_convs=2,
+            num_classes=2,
+            loss=dict(
+                cls_loss=dict(type="ScaleInvariantLoss", pos_thresh=0.9),
+                reg_loss=dict(type="BalancedL2Loss", high_thresh=0.7, low_thresh=0.3, weight=5.0),
+            ),
+        ),
+    ),
+)

OpenTAD/configs/_base_/models/videomambasuite.py

@@ -0,0 +1,42 @@
+model = dict(
+    type="VideoMambaSuite",
+    projection=dict(
+        type="MambaProj",
+        in_channels=2048,
+        out_channels=512,
+        arch=(2, 2, 5),  # layers in embed / stem / branch
+        conv_cfg=dict(kernel_size=3),
+        norm_cfg=dict(type="LN"),
+        use_abs_pe=False,
+        max_seq_len=2304,
+        mamba_cfg=dict(kernel_size=4, drop_path_rate=0.3, use_mamba_type="dbm"),
+    ),
+    neck=dict(
+        type="FPNIdentity",
+        in_channels=512,
+        out_channels=512,
+        num_levels=6,
+    ),
+    rpn_head=dict(
+        type="ActionFormerHead",
+        num_classes=20,
+        in_channels=512,
+        feat_channels=512,
+        num_convs=2,
+        cls_prior_prob=0.01,
+        prior_generator=dict(
+            type="PointGenerator",
+            strides=[1, 2, 4, 8, 16, 32],
+            regression_range=[(0, 4), (4, 8), (8, 16), (16, 32), (32, 64), (64, 10000)],
+        ),
+        loss_normalizer=100,
+        loss_normalizer_momentum=0.9,
+        center_sample="radius",
+        center_sample_radius=1.5,
+        label_smoothing=0.0,
+        loss=dict(
+            cls_loss=dict(type="FocalLoss"),
+            reg_loss=dict(type="DIOULoss"),
+        ),
+    ),
+)

OpenTAD/configs/_base_/models/vsgn.py

@@ -0,0 +1,43 @@
+tscale = 256
+model = dict(
+    type="VSGN",
+    projection=dict(
+        type="VSGNPyramidProj",
+        in_channels=2048,
+        out_channels=256,
+        pyramid_levels=[2, 4, 8, 16, 32],
+    ),
+    neck=dict(
+        type="VSGNFPN",
+        in_channels=256,
+        out_channels=256,
+        num_levels=5,
+    ),
+    rpn_head=dict(
+        type="VSGNRPNHead",
+        in_channels=256,
+        num_layers=4,
+        num_classes=1,
+        iou_thr=0.6,
+        anchor_generator=dict(
+            pyramid_levels=[2, 4, 8, 16, 32],
+            tscale=tscale,
+            anchor_scale=[7, 7.5],
+        ),
+        tem_head=dict(
+            type="TemporalEvaluationHead",
+            in_channels=256,
+            num_classes=3,
+            loss=dict(pos_thresh=0.5, gt_type=["startness", "endness", "actionness"]),
+        ),
+        loss_cls=dict(type="BalancedCEloss"),  # CE for THUMOS, Sigmoid for ANET
+        loss_loc=dict(type="GIoULoss"),  # this is only a placeholder
+    ),
+    roi_head=dict(
+        type="VSGNRoIHead",
+        in_channels=256,
+        iou_thr=0.7,
+        roi_extractor=dict(type="CornerExtractor", beta=8.0, base_stride=2, tscale=tscale),
+        loss_loc=dict(type="GIoULoss"),  # this is only a placeholder
+    ),
+)

OpenTAD/configs/actionformer/README.md

@@ -0,0 +1,104 @@
+# ActionFormer
+
+> [ActionFormer: Localizing Moments of Actions with Transformers](https://arxiv.org/abs/2202.07925)  
+> Chen-Lin Zhang, Jianxin Wu, Yin Li
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Self-attention based Transformer models have demonstrated impressive results for image classification and object detection, and more recently for video understanding. Inspired by this success, we investigate the application of Transformer networks for temporal action localization in videos. To this end, we present ActionFormer -- a simple yet powerful model to identify actions in time and recognize their categories in a single shot, without using action proposals or relying on pre-defined anchor windows. ActionFormer combines a multiscale feature representation with local self-attention, and uses a light-weighted decoder to classify every moment in time and estimate the corresponding action boundaries. We show that this orchestrated design results in major improvements upon prior works. Without bells and whistles, ActionFormer achieves 71.0% mAP at tIoU=0.5 on THUMOS14, outperforming the best prior model by 14.1 absolute percentage points. Further, ActionFormer demonstrates strong results on ActivityNet 1.3 (36.6% average mAP) and EPIC-Kitchens 100 (+13.5% average mAP over prior works).
+
+## Results and Models
+
+**ActivityNet-1.3** with CUHK classifier.
+
+| Features | mAP@0.5 | mAP@0.75 | mAP@0.95 | ave. mAP |        Config         |                                                                                          Download                                                                                          |
+| :------: | :-----: | :------: | :------: | :------: | :-------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   TSP    |  55.08  |  38.27   |   8.91   |  37.07   | [config](anet_tsp.py) | [model](https://drive.google.com/file/d/1loC72F4U79jWfoRL9SB2rdk3xykBKqHN/view?usp=sharing)   \| [log](https://drive.google.com/file/d/1YveGerbI1es51t2Ii7WZDgPlJy3lGBLf/view?usp=sharing) |
+
+**THUMOS-14**
+
+| Features | mAP@0.3 | mAP@0.4 | mAP@0.5 | mAP@0.6 | mAP@0.7 | ave. mAP |         Config          |                                                                                          Download                                                                                          |
+| :------: | :-----: | :-----: | :-----: | :-----: | :-----: | :------: | :---------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   I3D    |  83.78  |  80.06  |  73.16  |  60.46  |  44.72  |  68.44   | [config](thumos_i3d.py) | [model](https://drive.google.com/file/d/17oP-fMOjw6wwnaQWTlikWwoZoSkiIFkt/view?usp=sharing)   \| [log](https://drive.google.com/file/d/1WJe98mKoXaP2X9Th-gKC8rw0JeKxfJkq/view?usp=sharing) |
+
+**HACS**
+
+| Features | mAP@0.5 | mAP@0.75 | mAP@0.95 | ave. mAP |           Config           |                                                                                          Download                                                                                          |
+| :------: | :-----: | :------: | :------: | :------: | :------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| SlowFast |  56.18  |  37.97   |  11.05   |  37.71   | [config](hacs_slowfast.py) | [model](https://drive.google.com/file/d/1IdxR5lyfXzk5wjl-8YDcH0Nw2BEDwzWz/view?usp=sharing)   \| [log](https://drive.google.com/file/d/1Eu2O9IKuR8XLeZ37OxCq7NjSUKPE-3Zw/view?usp=sharing) |
+
+**Epic-Kitchens-100**
+
+| Subset | Features | mAP@0.1 | mAP@0.2 | mAP@0.3 | mAP@0.4 | mAP@0.5 | ave. mAP |                  Config                  |                                                                                          Download                                                                                          |
+| :----: | :------: | :-----: | :-----: | :-----: | :-----: | :-----: | :------: | :--------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|  Noun  | SlowFast |  25.78  |  24.73  |  22.83  |  20.84  |  17.45  |  22.33   | [config](epic_kitchens_slowfast_noun.py) | [model](https://drive.google.com/file/d/1RckzXf5W8oD_ARZw5dyYo03ZKVrU1n9-/view?usp=sharing)   \| [log](https://drive.google.com/file/d/18dVA27hWRBjM8lp4S12DscCkNJBqFrWp/view?usp=sharing) |
+|  Verb  | SlowFast |  27.68  |  26.79  |  25.62  |  24.06  |  20.48  |  24.93   | [config](epic_kitchens_slowfast_verb.py) | [model](https://drive.google.com/file/d/1-RLtnku727Fh39rihyGVxLCU5klTIvbn/view?usp=sharing)   \| [log](https://drive.google.com/file/d/1w18Ccyi22ZHgM0ECx6rAKOXqFoO9L0Iq/view?usp=sharing) |
+
+
+**MultiTHUMOS**
+
+|    Features    | mAP@0.2 | mAP@0.5 | mAP@0.7 | ave. mAP (0.1:0.9:0.1) |              Config              |                                                                                          Download                                                                                          |
+| :------------: | :-----: | :-----: | :-----: | :--------------------: | :------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   I3D (rgb)    |  53.50  |  39.04  |  19.69  |         34.01          | [config](multithumos_i3d_rgb.py) | [model](https://drive.google.com/file/d/1ufrZRt9uVu6_IJXPa-JdRK05vsc0UQXO/view?usp=sharing)   \| [log](https://drive.google.com/file/d/1PGrIRfWOHPIofDdSNRQX4UglzBWvlVhO/view?usp=sharing) |
+| I3D (rgb+flow) |  60.16  |  44.99  |  24.55  |         39.18          |   [config](multithumos_i3d.py)   | [model](https://drive.google.com/file/d/1rBOoxuUR3tlzyQmWZGUtPXmsKUT7yLFm/view?usp=sharing)   \| [log](https://drive.google.com/file/d/1-h_9g0HppsqnrvirVNoiZSTTuSWmmRxC/view?usp=sharing) |
+
+**Charades**
+
+| Features  | mAP@0.2 | mAP@0.5 | mAP@0.7 | ave. mAP (0.1:0.9:0.1) |            Config             |                                                                                          Download                                                                                          |
+| :-------: | :-----: | :-----: | :-----: | :--------------------: | :---------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| I3D (rgb) |  29.42  |  21.76  |  12.78  |         19.39          | [config](charades_i3d_rgb.py) | [model](https://drive.google.com/file/d/1EFCNke077m4JC_6OMJZXEnaKW2UAgFpA/view?usp=sharing)   \| [log](https://drive.google.com/file/d/1CuwGJ9m2YtvnKHsq9slkgtANoNEmbqVP/view?usp=sharing) |
+
+**FineAction** with InternVideo classifier
+
+|     Features      | mAP@0.5 | mAP@0.75 | mAP@0.95 | ave. mAP |                Config                |                                                                                         Download                                                                                          |
+| :---------------: | :-----: | :------: | :------: | :------: | :----------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|  VideoMAE_H_K700  |  29.44  |  19.46   |   5.06   |  19.32   |  [config](fineaction_videomae_h.py)  | [model](https://drive.google.com/file/d/1uNQufJMf9U6Igv6w4J70xiEVqYUKteTE/view?usp=sharing)  \| [log](https://drive.google.com/file/d/1VAQbtZuvRiTk8oFS7EIF9ilOKm1165u-/view?usp=sharing) |
+| VideoMAEv2_g_K710 |  29.85  |  19.72   |   5.17   |  19.62   | [config](fineaction_videomaev2_g.py) | [model](https://drive.google.com/file/d/1o7HdsZIR-JufAGHD6cq-xRRIEX3IMlyY/view?usp=sharing)  \| [log](https://drive.google.com/file/d/1QenPC5OV9gI62wKkgbrdYyJpLSxP5awp/view?usp=sharing) |
+
+
+## Train
+
+You can use the following command to train a model.
+
+```shell
+torchrun --nnodes=1 --nproc_per_node=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/train.py ${CONFIG_FILE} [optional arguments]
+```
+
+Example: train ActionFormer on ActivityNet dataset.
+
+```shell
+torchrun --nnodes=1 --nproc_per_node=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/train.py configs/actionformer/anet_tsp.py
+```
+
+For more details, you can refer to the Training part in the [Usage](../../docs/en/usage.md).
+
+## Test
+
+You can use the following command to test a model.
+
+```shell
+torchrun --nnodes=1 --nproc_per_node=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/test.py ${CONFIG_FILE} --checkpoint ${CHECKPOINT_FILE} [optional arguments]
+```
+
+Example: test ActionFormer on ActivityNet dataset.
+
+```shell
+torchrun --nnodes=1 --nproc_per_node=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/test.py configs/actionformer/anet_tsp.py --checkpoint exps/anet/actionformer_tsp/gpu1_id0/checkpoint/epoch_14.pth
+```
+
+For more details, you can refer to the Test part in the [Usage](../../docs/en/usage.md).
+
+## Citation
+
+```latex
+@inproceedings{zhang2022actionformer,
+  title={Actionformer: Localizing moments of actions with transformers},
+  author={Zhang, Chen-Lin and Wu, Jianxin and Li, Yin},
+  booktitle={Computer Vision--ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part IV},
+  pages={492--510},
+  year={2022},
+  organization={Springer}
+}
+```

OpenTAD/configs/actionformer/anet_tsp.py

@@ -0,0 +1,64 @@
+_base_ = [
+    "../_base_/datasets/activitynet-1.3/features_tsp_resize_trunc.py",  # dataset config
+    "../_base_/models/actionformer.py",  # model config
+]
+
+model = dict(
+    projection=dict(
+        in_channels=512,
+        out_channels=256,
+        attn_cfg=dict(n_mha_win_size=[7, 7, 7, 7, 7, -1]),
+        use_abs_pe=True,
+        max_seq_len=192,
+        input_pdrop=0.2,
+    ),
+    neck=dict(in_channels=256, out_channels=256),
+    rpn_head=dict(
+        in_channels=256,
+        feat_channels=256,
+        num_classes=1,
+        label_smoothing=0.1,
+        loss_weight=2.0,
+        loss_normalizer=200,
+    ),
+)
+
+solver = dict(
+    train=dict(batch_size=16, num_workers=4),
+    val=dict(batch_size=16, num_workers=4),
+    test=dict(batch_size=16, num_workers=4),
+    clip_grad_norm=1,
+    ema=True,
+)
+
+optimizer = dict(type="AdamW", lr=1e-3, weight_decay=0.05, paramwise=True)
+scheduler = dict(type="LinearWarmupCosineAnnealingLR", warmup_epoch=5, max_epoch=15)
+
+inference = dict(load_from_raw_predictions=False, save_raw_prediction=False)
+post_processing = dict(
+    nms=dict(
+        use_soft_nms=True,
+        sigma=0.75,
+        max_seg_num=100,
+        iou_threshold=0,  # does not matter when use soft nms
+        min_score=0.001,
+        multiclass=False,
+        voting_thresh=0.9,  #  set 0 to disable
+    ),
+    #external_cls=dict(
+    #    type="CUHKANETClassifier",
+    #    path="data/activitynet-1.3/classifiers/cuhk_val_simp_7.json",
+    #    topk=2,
+    #),
+    save_dict=True,
+)
+
+workflow = dict(
+    logging_interval=200,
+    checkpoint_interval=1,
+    val_loss_interval=1,
+    val_eval_interval=1,
+    val_start_epoch=7,
+)
+
+work_dir = "exps/anet/actionformer_tsp"

OpenTAD/configs/actionformer/charades_i3d_rgb.py

@@ -0,0 +1,46 @@
+_base_ = [
+    "../_base_/datasets/charades/features_i3d_pad.py",  # dataset config
+    "../_base_/models/actionformer.py",  # model config
+]
+
+model = dict(
+    projection=dict(in_channels=1024),
+    rpn_head=dict(num_classes=157),
+)
+
+solver = dict(
+    train=dict(batch_size=16, num_workers=4),
+    val=dict(batch_size=16, num_workers=4),
+    test=dict(batch_size=16, num_workers=4),
+    clip_grad_norm=1,
+    ema=True,
+)
+
+optimizer = dict(type="AdamW", lr=1e-4, weight_decay=0.05, paramwise=True)
+scheduler = dict(type="LinearWarmupCosineAnnealingLR", warmup_epoch=5, max_epoch=15)
+
+inference = dict(load_from_raw_predictions=False, save_raw_prediction=False)
+post_processing = dict(
+    pre_nms_topk=8000,
+    pre_nms_thresh=0.001,
+    nms=dict(
+        use_soft_nms=True,
+        sigma=0.5,
+        max_seg_num=8000,
+        min_score=0.001,
+        multiclass=True,
+        voting_thresh=0.7,  #  set 0 to disable
+    ),
+    save_dict=False,
+)
+
+workflow = dict(
+    logging_interval=200,
+    checkpoint_interval=1,
+    val_loss_interval=1,
+    val_eval_interval=1,
+    val_start_epoch=5,
+    end_epoch=10,
+)
+
+work_dir = "exps/charades/actionformer_i3d_rgb"

OpenTAD/configs/actionformer/ego4d_egovlp.py

@@ -0,0 +1,16 @@
+_base_ = ["ego4d_slowfast.py"]
+
+data_path = "data/ego4d/features/mq_egovlp/"
+dataset = dict(
+    train=dict(data_path=data_path, offset_frames=8),
+    val=dict(data_path=data_path, offset_frames=8),
+    test=dict(data_path=data_path, offset_frames=8),
+)
+
+model = dict(
+    projection=dict(in_channels=256, out_channels=384),
+    neck=dict(in_channels=384, out_channels=384),
+    rpn_head=dict(in_channels=384, feat_channels=384),
+)
+
+work_dir = "exps/ego4d/actionformer_egovlp"

OpenTAD/configs/actionformer/ego4d_internvideo.py

@@ -0,0 +1,5 @@
+_base_ = ["ego4d_slowfast.py"]
+
+model = dict(projection=dict(in_channels=1024))
+
+work_dir = "exps/ego4d/actionformer_internvideo"

OpenTAD/configs/actionformer/ego4d_slowfast.py

@@ -0,0 +1,58 @@
+_base_ = [
+    "../_base_/models/actionformer.py",  # model config
+    "../_base_/datasets/ego4d_mq/features_slowfast_trunc.py",  # dataset config
+]
+
+model = dict(
+    projection=dict(
+        in_channels=2304,
+        arch=(2, 2, 7),
+        use_abs_pe=True,
+        max_seq_len=1024,
+        conv_cfg=dict(proj_pdrop=0.1),
+        attn_cfg=dict(n_mha_win_size=9),
+    ),
+    neck=dict(type="FPNIdentity", num_levels=8),
+    rpn_head=dict(
+        num_classes=110,
+        prior_generator=dict(
+            strides=[1, 2, 4, 8, 16, 32, 64, 128],
+            regression_range=[(0, 4), (2, 8), (4, 16), (8, 32), (16, 64), (32, 128), (64, 256), (128, 10000)],
+        ),
+    ),
+)
+
+solver = dict(
+    train=dict(batch_size=2, num_workers=2),
+    val=dict(batch_size=1, num_workers=1),
+    test=dict(batch_size=1, num_workers=1),
+    clip_grad_norm=1,
+    ema=True,
+)
+
+optimizer = dict(type="AdamW", lr=1e-4, weight_decay=0.05, paramwise=True)
+scheduler = dict(type="LinearWarmupCosineAnnealingLR", warmup_epoch=5, max_epoch=15)
+
+inference = dict(load_from_raw_predictions=False, save_raw_prediction=False)
+post_processing = dict(
+    pre_nms_topk=5000,
+    nms=dict(
+        use_soft_nms=True,
+        sigma=0.9,
+        max_seg_num=2000,
+        min_score=0.001,
+        multiclass=True,
+        voting_thresh=0.95,  #  set 0 to disable
+    ),
+    save_dict=False,
+)
+
+workflow = dict(
+    logging_interval=200,
+    checkpoint_interval=1,
+    val_loss_interval=1,
+    val_eval_interval=1,
+    val_start_epoch=8,
+)
+
+work_dir = "exps/ego4d/actionformer_slowfast"

OpenTAD/configs/actionformer/epic_kitchens_slowfast_noun.py

@@ -0,0 +1,56 @@
+_base_ = [
+    "../_base_/datasets/epic_kitchens-100/features_slowfast_noun.py",  # dataset config
+    "../_base_/models/actionformer.py",  # model config
+]
+
+model = dict(
+    projection=dict(
+        in_channels=2304,
+        attn_cfg=dict(n_mha_win_size=9),
+    ),
+    rpn_head=dict(
+        num_classes=293,  # total 300, but 7 classes are empty
+        prior_generator=dict(
+            strides=[1, 2, 4, 8, 16, 32],
+            regression_range=[(0, 4), (2, 8), (4, 16), (8, 32), (16, 64), (32, 10000)],
+        ),
+        label_smoothing=0.1,
+        loss_normalizer=250,
+    ),
+)
+
+solver = dict(
+    train=dict(batch_size=2, num_workers=2),
+    val=dict(batch_size=1, num_workers=1),
+    test=dict(batch_size=1, num_workers=1),
+    clip_grad_norm=1,
+    ema=True,
+)
+
+optimizer = dict(type="AdamW", lr=1e-4, weight_decay=0.05, paramwise=True)
+scheduler = dict(type="LinearWarmupCosineAnnealingLR", warmup_epoch=5, max_epoch=20)
+
+inference = dict(load_from_raw_predictions=False, save_raw_prediction=False)
+post_processing = dict(
+    pre_nms_topk=5000,
+    nms=dict(
+        use_soft_nms=True,
+        sigma=0.4,
+        max_seg_num=2000,
+        iou_threshold=0,  # does not matter when use soft nms
+        min_score=0.001,
+        multiclass=True,
+        voting_thresh=0.75,  #  set 0 to disable
+    ),
+    save_dict=False,
+)
+
+workflow = dict(
+    logging_interval=200,
+    checkpoint_interval=1,
+    val_loss_interval=1,
+    val_eval_interval=1,
+    val_start_epoch=15,
+)
+
+work_dir = "exps/epic_kitchens/actionformer_slowfast_noun"

OpenTAD/configs/actionformer/epic_kitchens_slowfast_verb.py

@@ -0,0 +1,55 @@
+_base_ = [
+    "../_base_/datasets/epic_kitchens-100/features_slowfast_verb.py",  # dataset config
+    "../_base_/models/actionformer.py",  # model config
+]
+
+model = dict(
+    projection=dict(
+        in_channels=2304,
+        attn_cfg=dict(n_mha_win_size=9),
+    ),
+    rpn_head=dict(
+        num_classes=97,
+        prior_generator=dict(
+            strides=[1, 2, 4, 8, 16, 32],
+            regression_range=[(0, 4), (2, 8), (4, 16), (8, 32), (16, 64), (32, 10000)],
+        ),
+        loss_normalizer=250,
+    ),
+)
+
+solver = dict(
+    train=dict(batch_size=2, num_workers=2),
+    val=dict(batch_size=1, num_workers=1),
+    test=dict(batch_size=1, num_workers=1),
+    clip_grad_norm=1,
+    ema=True,
+)
+
+optimizer = dict(type="AdamW", lr=1e-4, weight_decay=0.05, paramwise=True)
+scheduler = dict(type="LinearWarmupCosineAnnealingLR", warmup_epoch=5, max_epoch=20)
+
+inference = dict(load_from_raw_predictions=False, save_raw_prediction=False)
+post_processing = dict(
+    pre_nms_topk=5000,
+    nms=dict(
+        use_soft_nms=True,
+        sigma=0.4,
+        max_seg_num=2000,
+        iou_threshold=0,  # does not matter when use soft nms
+        min_score=0.001,
+        multiclass=True,
+        voting_thresh=0.75,  #  set 0 to disable
+    ),
+    save_dict=False,
+)
+
+workflow = dict(
+    logging_interval=200,
+    checkpoint_interval=1,
+    val_loss_interval=1,
+    val_eval_interval=1,
+    val_start_epoch=15,
+)
+
+work_dir = "exps/epic_kitchens/actionformer_slowfast_verb"

OpenTAD/configs/actionformer/fineaction_videomae_h.py

@@ -0,0 +1,63 @@
+_base_ = [
+    "../_base_/datasets/fineaction/features_internvideo_resize_trunc.py",  # dataset config
+    "../_base_/models/actionformer.py",  # model config
+]
+
+model = dict(
+    projection=dict(
+        in_channels=1280,
+        out_channels=256,
+        attn_cfg=dict(n_mha_win_size=[7, 7, 7, 7, 7, -1]),
+        use_abs_pe=True,
+        max_seq_len=192,
+    ),
+    neck=dict(in_channels=256, out_channels=256),
+    rpn_head=dict(
+        in_channels=256,
+        feat_channels=256,
+        num_classes=1,
+        label_smoothing=0.1,
+        loss_weight=2.0,
+        loss_normalizer=200,
+    ),
+)
+
+solver = dict(
+    train=dict(batch_size=16, num_workers=4),
+    val=dict(batch_size=16, num_workers=4),
+    test=dict(batch_size=16, num_workers=4),
+    clip_grad_norm=1,
+    ema=True,
+)
+
+optimizer = dict(type="AdamW", lr=1e-3, weight_decay=0.05, paramwise=True)
+scheduler = dict(type="LinearWarmupCosineAnnealingLR", warmup_epoch=5, max_epoch=20)
+
+inference = dict(load_from_raw_predictions=False, save_raw_prediction=False)
+post_processing = dict(
+    nms=dict(
+        use_soft_nms=True,
+        sigma=0.75,
+        max_seg_num=100,
+        iou_threshold=0,  # does not matter when use soft nms
+        min_score=0.001,
+        multiclass=False,
+        voting_thresh=0.9,  #  set 0 to disable
+    ),
+    external_cls=dict(
+        type="StandardClassifier",
+        path="./data/fineaction/classifiers/new_swinB_1x1x256_views2x3_max_label_avg_prob.json",
+        topk=2,
+    ),
+    save_dict=False,
+)
+
+workflow = dict(
+    logging_interval=200,
+    checkpoint_interval=1,
+    val_loss_interval=1,
+    val_eval_interval=1,
+    val_start_epoch=10,
+)
+
+work_dir = "exps/fineaction/actionformer_videomae_h"