# Video-Text Retrieval Embdding with CLIP4Clip

*author: Chen Zhang*


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## Description

This operator extracts features for video or text with [CLIP4Clip](https://arxiv.org/abs/2104.08860) which can generate embeddings for text and video by jointly training a video encoder and text encoder to maximize the cosine similarity.


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## 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*:

```python
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()
```
![](vect_simplified_video.png)   
![](vect_simplified_text.png)   

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

```python
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()
```

![](vect_explicit_video.png)    
![](vect_explicit_text.png)   

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## 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.  

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## Interface

An video-text embedding operator takes a list of [towhee image](link/to/towhee/image/api/doc) 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.