clip4clip/README.md

# 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
from towhee.dc2 import pipe, ops, DataCollection

p = (
    pipe.input('text') \
        .map('text', 'vec', ops.video_text_embedding.clip4clip(model_name='clip_vit_b32', modality='text', device='cuda:1')) \
        .output('text', 'vec')
)

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

```
![](text_emb_output.png)  

```python
from towhee.dc2 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.clip4clip(model_name='clip_vit_b32', modality='video', device='cuda:2')) \
        .output('video_path', 'flame_list', 'vec')
)

DataCollection(p('./demo_video.mp4')).show()
```
![](video_emb_ouput.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.