CLIP

The CLIP features are extracted at each frame of the provided video. CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pairs. We use CLIP's official augmentations and extract vision features from its image encoder. The implementation uses the OpenAI CLIP. The extracted features are going to be of size num_frames x 512. We additionally output timesteps in ms for each feature and fps of the video.

Quick Start

Ensure that the environment is properly set up before proceeding. See Setup Environment for detailed instructions.

Activate the environment

conda activate video_features

and extract features at 1 fps from ./sample/v_GGSY1Qvo990.mp4 video and output results.

python main.py \
    feature_type="clip" \
    model_name="ViT-B/32" \
    extraction_fps=1 \
    video_paths="[./sample/v_GGSY1Qvo990.mp4]" \
    on_extraction="print"

Supported Arguments

Argument	Default	Description
`model_name`	`"ViT-B/32"`	A variant of CLIP. `"ViT-B/16"`, `"RN50x16"`, `"RN50x4"`, `"RN101"`, `"RN50"`, and `"custom"` are supported.
`batch_size`	`1`	You may speed up extraction of features by increasing the batch size as much as your GPU permits.
`extraction_fps`	`null`	If specified (e.g. as `5`), the video will be re-encoded to the `extraction_fps` fps. Leave unspecified or `null` to skip re-encoding.
`device`	`"cuda:0"`	The device specification. It follows the PyTorch style. Use `"cuda:3"` for the 4th GPU on the machine or `"cpu"` for CPU-only.
`video_paths`	`null`	A list of videos for feature extraction. E.g. `"[./sample/v_ZNVhz7ctTq0.mp4, ./sample/v_GGSY1Qvo990.mp4]"` or just one path `"./sample/v_GGSY1Qvo990.mp4"`.
`file_with_video_paths`	`null`	A path to a text file with video paths (one path per line). Hint: given a folder `./dataset` with `.mp4` files one could use: `find ./dataset -name "*mp4" > ./video_paths.txt`.
`on_extraction`	`print`	If `print`, the features are printed to the terminal. If `save_numpy` or `save_pickle`, the features are saved to either `.npy` file or `.pkl`.
`output_path`	`"./output"`	A path to a folder for storing the extracted features (if `on_extraction` is either `save_numpy` or `save_pickle`).
`keep_tmp_files`	`false`	If `true`, the reencoded videos will be kept in `tmp_path`.
`tmp_path`	`"./tmp"`	A path to a folder for storing temporal files (e.g. reencoded videos).
`show_pred`	`false`	If `true`, the script will print the predictions of the model on a down-stream task. It is useful for debugging.
`pred_texts`	`null`	If `show_pred=true`, the texts specified in `pred_texts` are used for zero-shot classification (e.g. `pred_texts="['a dog smiles', 'a woman is lifting']"`). If `pred_texts` is unspecified, Kinetics 400 classes will be used.

Examples

Make sure the environment is set up correctly. For instructions, refer to Setup Environment.

Start by activating the environment

conda activate video_features

It is pretty much the same procedure as with other features. Here we take ViT/B-32 as an example, but we also support ViT-B/16, RN50x16, RN50x4, RN101, RN50 and others in OpenAI CLIP implementation. In addition, if you want to use your weights, you need to copy your weights to models/clip/checkpoints, rename it CLIP-custom.pth and specify model_name=custom.

python main.py \
    feature_type="clip" \
    model_name="ViT-B/32" \
    device="cuda:0" \
    video_paths="[./sample/v_ZNVhz7ctTq0.mp4, ./sample/v_GGSY1Qvo990.mp4]"

If you would like to save the features, use --on_extraction save_numpy (or save_pickle) – by default, the features are saved in ./output/ or where --output_path specifies. In the case of frame-wise features, besides features, it also saves timestamps in ms and the original fps of the video into the same folder with features.

python main.py \
    feature_type="clip" \
    model_name="ViT-B/32" \
    device="cuda:0" \
    on_extraction=save_numpy \
    file_with_video_paths=./sample/sample_video_paths.txt

We may increase the extraction speed with batching. Therefore, frame-wise features have --batch_size argument, which defaults to 1.

python main.py \
    feature_type="clip" \
    model_name="ViT-B/32" \
    device="cuda:0" \
    batch_size=128 \
    video_paths="[./sample/v_ZNVhz7ctTq0.mp4, ./sample/v_GGSY1Qvo990.mp4]"

If you would like to extract features at a certain fps, add --extraction_fps argument

python main.py \
    feature_type="clip" \
    model_name="ViT-B/32" \
    device="cuda:0" \
    extraction_fps=5 \
    video_paths="[./sample/v_ZNVhz7ctTq0.mp4, ./sample/v_GGSY1Qvo990.mp4]"

If you would like to verify the extracted features, you can set show_pred="true" and provide several sentences with pred_texts argument. The value of pred_texts should be a list of strings. The probability that each frame corresponds to all the sentences you provide will be output.

python main.py \
    feature_type="clip" \
    model_name="ViT-B/32" \
    device="cuda:0" \
    extraction_fps=1 \
    show_pred="true" \
    pred_texts="['a dog smiles', 'a woman is lifting']" \
    video_paths="[./sample/v_ZNVhz7ctTq0.mp4, ./sample/v_GGSY1Qvo990.mp4]"

You will get the output for each frame like:

  Logits | Prob. | Label
  23.061 | 0.962 | a dog smiles
  19.824 | 0.038 | a woman is lifting

  Logits | Prob. | Label
  22.770 | 0.963 | a dog smiles
  19.520 | 0.037 | a woman is lifting

  Logits | Prob. | Label
  24.619 | 0.929 | a dog smiles
  22.048 | 0.071 | a woman is lifting
...

  Logits | Prob. | Label
  30.966 | 1.000 | a woman is lifting
  15.272 | 0.000 | a dog smiles

  Logits | Prob. | Label
  32.671 | 1.000 | a woman is lifting
  15.413 | 0.000 | a dog smiles

  Logits | Prob. | Label
  32.555 | 1.000 | a woman is lifting
  16.151 | 0.000 | a dog smiles
...

You may also leave pred_texts unspecified or null (None) if you wish to apply CLIP for zero-shot prediction on Kinetics 400.

Credits

The OpenAI CLIP implementation.
The CLIP paper
Thanks to @Kamino666 who adapted this model for video_features

License

The OpenAI CLIP implementation code is under MIT.