# Video-Text Retrieval Embedding with DRL

*author: Chen Zhang*


<br />



## Description

This operator extracts features for video or text with [DRL(Disentangled Representation Learning for Text-Video Retrieval)](https://arxiv.org/pdf/2203.07111v1.pdf), and then it can get the similarity by  Weighted Token-wise Interaction (WTI) module.


<br />

![](WTI.png)

## Code Example

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

```python
from towhee import pipe, ops, DataCollection

p = (
    pipe.input('text') \
        .map('text', 'vec', ops.video_text_embedding.drl(base_encoder='clip_vit_b32', modality='text', device='cuda:0')) \
        .output('text', 'vec')
)

DataCollection(p('kids feeding and playing with the horse')).show()
```

![](text_emb_result.png)    

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

```python
from towhee import pipe, ops, DataCollection

p = (
    pipe.input('video_path') \
        .map('video_path', 'flame_gen', ops.video_decode.ffmpeg(sample_type='uniform_temporal_subsample', args={'num_samples': 12})) \
        .map('flame_gen', 'flame_list', lambda x: [y for y in x]) \
        .map('flame_list', 'vec', ops.video_text_embedding.drl(base_encoder='clip_vit_b32', modality='video', device='cuda:0')) \
        .output('video_path', 'flame_list', 'vec')
)

DataCollection(p('./demo_video.mp4')).show()
```

![](video_emb_result.png)    
<br />

Note: For this model, cpu is not support, and you must specify device='cuda...'

## Factory Constructor

Create the operator via the following factory method

***drl(base_encoder, modality)***

**Parameters:**

​   ***base_encoder:*** *str*

​   The base CLIP encode name in DRL model. Supported model names: 
- clip_vit_b32


​   ***modality:*** *str*

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


<br />



## Interface

An video-text embedding operator takes a list of [towhee VideoFrame](link/to/towhee/image/api/doc) or string as input and generate an embedding in ndarray.


**Parameters:**

​	***data:*** *List[towhee.types.VideoFrame]*  or *str*

​  The data (list of VideoFrame(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. When text, the shape is (text_token_num, model_dim), when video, the shape is (video_token_num, model_dim)