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I3D (RGB + Flow)

The Inflated 3D (I3D) features are extracted using a pre-trained model on Kinetics 400. Here, the features are extracted from the second-to-the-last layer of I3D, before summing them up. Therefore, it outputs two tensors with 1024-d features: for RGB and flow streams. By default, it expects to input 64 RGB and flow frames (224x224) which spans 2.56 seconds of the video recorded at 25 fps. In the default case, the features will be of size Tv x 1024 where Tv = duration / 2.56.

Please note, this implementation uses either PWC-Net (the default) and RAFT optical flow extraction instead of the TV-L1 algorithm, which was used in the original I3D paper as it hampers speed. Yet, it might possibly lead to worse peformance. Our tests show that the performance is reasonable. You may test it yourself by providing --show_pred flag.

CUDA 11 and GPUs like RTX 3090 and newer

PWC optical flow back-end is not supported on CUDA 11 and, therefore, GPUs like RTX 3090 and newer. RGB-only model should still work. For details please check this issue #13 If you were able to fix it, please share your workaround. Feel free to use flow_type=raft RAFT during extraction.



Set up the Environment for I3D

Depending on whether you would like to use PWC-Net or RAFT for optical flow extraction, you will need to install separate conda environments – conda_env_pwc.yml and conda_env_torch_zoo, respectively

# it will create a new conda environment called 'pwc' on your machine
conda env create -f conda_env_pwc.yml
# or/and if you would like to extract optical flow with RAFT
conda env create -f conda_env_torch_zoo.yml

Minimal Working Example

Open In Colab

Activate the environment

conda activate pwc

if you would like to use RAFT as optical flow extractor use torch_zoo instead of pwc:

and extract features from ./sample/v_ZNVhz7ctTq0.mp4 video and show the predicted classes

python main.py \
    feature_type=i3d \
    video_paths="[./sample/v_ZNVhz7ctTq0.mp4]" \
    show_pred=true

Examples

Activate the environment

conda activate pwc

The following will extract I3D features for sample videos using 0th and 2nd devices in parallel. The features are going to be extracted with the default parameters.

python main.py \
    feature_type=i3d \
    device_ids="[0, 2]" \
    video_paths="[./sample/v_ZNVhz7ctTq0.mp4, ./sample/v_GGSY1Qvo990.mp4]"

The video paths can be specified as a .txt file with paths

python main.py \
    feature_type=i3d \
    device_ids="[0, 2]" \
    file_with_video_paths=./sample/sample_video_paths.txt

It is also possible to extract features from either rgb or flow modalities individually (--streams) and, therefore, increasing the speed

python main.py \
    feature_type=i3d \
    streams=flow \
    device_ids="[0, 2]" \
    file_with_video_paths=./sample/sample_video_paths.txt

To extract optical flow frames using RAFT approach, specify --flow_type raft. Note that using RAFT will make the extraction slower than with PWC-Net yet visual inspection of extracted flow frames suggests that RAFT has a better quality of the estimated flow

# make sure to activate the correct environment (`torch_zoo`)
# conda activate torch_zoo
python main.py \
    feature_type=i3d \
    flow_type=raft \
    device_ids="[0, 2]" \
    file_with_video_paths=./sample/sample_video_paths.txt

The features can be saved as numpy arrays by specifying --on_extraction save_numpy or save_pickle. By default, it will create a folder ./output and will store features there

python main.py \
    feature_type=i3d \
    device_ids="[0, 2]" \
    on_extraction=save_numpy \
    file_with_video_paths=./sample/sample_video_paths.txt

You can change the output folder using --output_path argument.

Also, you may want to try to change I3D window and step sizes

python main.py \
    feature_type=i3d \
    device_ids="[0, 2]" \
    stack_size=24 \
    step_size=24 \
    file_with_video_paths=./sample/sample_video_paths.txt

By default, the frames are extracted according to the original fps of a video. If you would like to extract frames at a certain fps, specify --extraction_fps argument.

python main.py \
    feature_type=i3d \
    device_ids="[0, 2]" \
    extraction_fps=25 \
    stack_size=24 \
    step_size=24 \
    file_with_video_paths=./sample/sample_video_paths.txt

A fun note, the time span of the I3D features in the last example will match the time span of VGGish features with default parameters (24/25 = 0.96).

If --keep_tmp_files is specified, it keeps them in --tmp_path which is ./tmp by default. Be careful with the --keep_tmp_files argument when playing with --extraction_fps as it may mess up the frames you extracted before in the same folder.


Credits

  1. An implementation of PWC-Net in PyTorch
  2. The Official RAFT implementation (esp. ./demo.py).
  3. A port of I3D weights from TensorFlow to PyTorch
  4. The I3D paper: Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset.

License

The wrapping code is MIT and the port of I3D weights from TensorFlow to PyTorch. However, PWC Net (default flow extractor) has GPL-3.0 and RAFT BSD 3-Clause.