Video-Text Retrieval Embdding with CLIP4Clip

author: Chen Zhang

Description

This operator extracts features for video or text with CLIP4Clip which can generate embeddings for text and video by jointly training a video encoder and text encoder to maximize the cosine similarity.

Code Example

Load an video from path './demo_video.mp4' to generate an video embedding.

Read the text 'kids feeding and playing with the horse' to generate an text embedding.

Write the pipeline in simplified style:

import towhee

towhee.dc(['./demo_video.mp4']) \
        .video_decode.ffmpeg(sample_type='uniform_temporal_subsample', args={'num_samples': 12}) \
        .runas_op(func=lambda x: [y[0] for y in x]) \
        .clip4clip(model_name='clip_vit_b32', modality='video', weight_path='./pytorch_model.bin.1') \
        .show()

towhee.dc(['kids feeding and playing with the horse']) \
      .clip4clip(model_name='clip_vit_b32', modality='text', weight_path='./pytorch_model.bin.1') \
      .show()

Write a same pipeline with explicit inputs/outputs name specifications:

import towhee

towhee.dc['path'](['./demo_video.mp4']) \
        .video_decode.ffmpeg['path', 'frames'](sample_type='uniform_temporal_subsample', args={'num_samples': 12}) \
        .runas_op['frames', 'frames'](func=lambda x: [y[0] for y in x]) \
        .clip4clip['frames', 'vec'](model_name='clip_vit_b32', modality='video', weight_path='./pytorch_model.bin.1') \
        .show()

towhee.dc['text'](["kids feeding and playing with the horse"]) \
      .clip4clip['text','vec'](model_name='clip_vit_b32', modality='text', weight_path='./pytorch_model.bin.1') \
      .select['text', 'vec']() \
      .show()

Factory Constructor

Create the operator via the following factory method

clip4clip(model_name, modality, weight_path)

Parameters:

​ model_name: str

​ The model name of CLIP. Supported model names:

• clip_vit_b32

​ modality: str

​ Which modality(video or text) is used to generate the embedding.

​ weight_path: str

​ pretrained model weights path.

Interface

An video-text embedding operator takes a list of towhee image or string as input and generate an embedding in ndarray.

Parameters:

​ data: List[towhee.types.Image] or str

​ The data (list of image(which is uniform subsampled from a video) or text based on specified modality) to generate embedding.

Returns: numpy.ndarray

​ The data embedding extracted by model.