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# Installation
**Step 1.** Install PyTorch=2.0.1, Python=3.10.12
```
conda create -n opentad python=3.10.12
source activate opentad
conda install pytorch=2.0.1 torchvision=0.15.2 pytorch-cuda=11.8 -c pytorch -c nvidia
```
**Step 2.** Install mmaction2 for end-to-end training
```
pip install openmim
mim install mmcv==2.0.1
mim install mmaction2==1.1.0
```
**Step 3.** Install OpenTAD
```
git clone git@github.com:sming256/OpenTAD.git
cd OpenTAD
pip install -r requirements.txt
```
The code is tested with Python 3.10.12, PyTorch 2.0.1, CUDA 11.8, and gcc 11.3.0 on Ubuntu 20.04, other versions might also work.
**Step 4.** Prepare the annotation and data.
| Dataset | Description |
| :--------------------------------------------------------- | :-------------------------------------------------------------------------------------------- |
| [ActivityNet](/tools/prepare_data/activitynet/README.md) | A Large-Scale Video Benchmark for Human Activity Understanding with 19,994 videos. |
| [THUMOS14](/tools/prepare_data/thumos/README.md) | Consists of 413 videos with temporal annotations. |
| [EPIC-KITCHENS](/tools/prepare_data/epic/README.md) | Large-scale dataset in first-person (egocentric) vision. Latest version is EPIC-KITCHENS-100. |
| [Ego4D-MQ](/tools/prepare_data/ego4d/README.md) | Ego4D is the world's largest egocentric video dataset. MQ refers to its moment query task. |
| [HACS](/tools/prepare_data/hacs/README.md) | The same action taxonomy with ActivityNet, but consists of around 50K videos. |
| [FineAction](/tools/prepare_data/fineaction/README.md) | Contains 103K temporal instances of 106 action categories, annotated in 17K untrimmed videos. |
| [Multi-THUMOS](/tools/prepare_data/multi-thumos/README.md) | Dense, multilabel action annotations of THUMOS14. |
| [Charades](/tools/prepare_data/charades/README.md) | Contains dense-labeled 9,848 annotated videos of daily activities. |
PS: If you meet `FileNotFoundError: [Errno 2] No such file or directory: 'xxx/missing_files.txt'`
- It means you may need to generate a `missing_files.txt`, which should record the missing features compared to all the videos in the annotation files. You can use `python tools/prepare_data/generate_missing_list.py annotation.json feature_folder` to generate the txt file.
- eg. `python tools/prepare_data/generate_missing_list.py data/fineaction/annotations/annotations_gt.json data/fineaction/features/fineaction_mae_g`
- In the provided feature from this codebase, we have already included this txt in the zip file. |