• This repository is for testing the idea of the following paper:

Wang, Xiaolong, and Abhinav Gupta. "Videos as space-time region graphs." Proceedings of the European conference on computer vision (ECCV). 2018.

• It means that it may contain several mismatch with the original implementation introduced on the paper.

• Also the performance is much lower than the publication (24 vs 43) and I never test Kinetics pre-trained ResNet-50-I3D.

• This repository is based on https://github.com/kenshohara/3D-ResNets-PyTorch.

• The architecture of ResNet-50-I3D in the paper is different from that in the above repository. I did not use Kinetics pre-trained model but use ImageNet pre-trained model.

• Currently, RPN is used on every iteration which requires approximately 3 times more training time.

• Kinetics pre-trained model can be found in here.

• PyTorch (ver. 1.2+ required)
• Torchvision (ver. 0.4+ required)

conda install pytorch torchvision cudatoolkit=10.1 -c soumith

pip install -r requirements.txt

• FFmpeg, FFprobe

• Python 3

• Download videos using the official crawler.
  • Locate test set in video_directory/test.
• Convert from avi to jpg files using util_scripts/generate_video_jpgs.py

python -m util_scripts.generate_video_jpgs mp4_video_dir_path jpg_video_dir_path kinetics

• Generate annotation file in json format similar to ActivityNet using util_scripts/kinetics_json.py
  • The CSV files (kinetics_{train, val, test}.csv) are included in the crawler.

python -m util_scripts.kinetics_json csv_dir_path 700 jpg_video_dir_path jpg dst_json_path

• Download videos from the official website.
• For Something-Something v2, please run util_scripts/vid2img_sthv1.[py

python util_scripts/sthv1_json.py 'data/something/v1' 'data/something/v1/img' 'data/sthv1.json'

python util_scripts/sthv2_json.py 'data/something/v2' 'data/something/v2/img' 'data/sthv2.json'

Assume the structure of data directories is the following:

~/
  data/
    something/
      v1/
        img/
          .../ (directories of video names)
            ... (jpg files)
      v2/
        img/
          .../ (directories of video names)
            ... (jpg files)
    kinetics_videos/
      jpg/
        .../ (directories of class names)
          .../ (directories of video names)
            ... (jpg files)
    results/
      save_100.pth
    kinetics.json

Confirm all options.

python main.py -h

Train ResNets-50 on the Kinetics-700 dataset (700 classes) with 4 CPU threads (for data loading).
Batch size is 128.
Save models at every 5 epochs. All GPUs is used for the training. If you want a part of GPUs, use CUDA_VISIBLE_DEVICES=....

python main.py --root_path ~/data --video_path kinetics_videos/jpg --annotation_path kinetics.json \
--result_path results --dataset kinetics --model resnet \
--model_depth 50 --n_classes 700 --batch_size 128 --n_threads 4 --checkpoint 5

Calculate top-5 class probabilities of each video using a trained model (~/data/results/save_200.pth.)
Note that inference_batch_size should be small because actual batch size is calculated by inference_batch_size * (n_video_frames / inference_stride).

python main.py --root_path ~/data --video_path kinetics_videos/jpg --annotation_path kinetics.json \
--result_path results --dataset kinetics --resume_path results/save_200.pth \
--model_depth 50 --n_classes 700 --n_threads 4 --no_train --no_val --inference --output_topk 5 --inference_batch_size 1

Evaluate top-1 video accuracy of a recognition result (data/results/val.json).

python -m util_scripts.eval_accuracy data/sthv2.json data/results/val.json --subset val -k 1 --ignore

First of all, we need to train backbone network (ResNet-50-I3D) for 100 epochs with learning rate as 0.00125 (decayed at 90 epoch to 0.000125) The original batchsize is 8 but in this implementation, we use 32 to reduce the training time.

python main.py --root_path data --video_path data/something/v1/img --annotation_path sthv1.json \
--result_path resnet_strg_imgnet_bs32 --dataset somethingv1 --n_classes 174 --n_pretrain_classes 700 \
--ft_begin_module fc --tensorboard --wandb --conv1_t_size 5 --learning_rate 0.00125 --sample_duration 32 \
--n_epochs 100 --multistep_milestones 90 --model resnet_strg --model_depth 50 --batch_size 32 \
--n_threads 8 --checkpoint 1

Then, we need to train with GCN module until 30 epochs with learning rate as 0.000125.

python main.py --root_path data --video_path data/something/v1/img --annotation_path sthv1.json \
--result_path resnet_strg_imgnet_32_gcn --dataset somethingv1 --n_classes 174 --n_pretrain_classes 174 \
--ft_begin_module fc --tensorboard --wandb --conv1_t_size 5  --learning_rate 0.000125 \
--sample_duration 32 --n_epochs 30 --model resnet_strg --model_depth 50 --batch_size 32 \
--nrois 10 --det_interval 2 --strg \
--n_threads 8 --checkpoint 1 --pretrain_path resnet_strg_imgnet_bs32/save_100.pth