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	A Large-Scale Video Benchmark for Human Activity Understanding with 19,994 videos.
THUMOS14	Consists of 413 videos with temporal annotations.
EPIC-KITCHENS	Large-scale dataset in first-person (egocentric) vision. Latest version is EPIC-KITCHENS-100.
Ego4D-MQ	Ego4D is the world's largest egocentric video dataset. MQ refers to its moment query task.
HACS	The same action taxonomy with ActivityNet, but consists of around 50K videos.
FineAction	Contains 103K temporal instances of 106 action categories, annotated in 17K untrimmed videos.
Multi-THUMOS	Dense, multilabel action annotations of THUMOS14.
Charades	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.