# Video-Text Retrieval Embdding with MDMMT

*author: Chen Zhang*


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## Description

This operator extracts features for video or text with [MDMMT: Multidomain Multimodal Transformer for Video Retrieval](https://arxiv.org/pdf/2103.10699.pdf), 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 a video embeddings extracted from different upstream expert networks, such as video, RGB, audio.

Read the text to generate a text embedding. 

 *Write the pipeline code*:

```python
import towhee
import torch

torch.manual_seed(42)

# features are embeddings extracted from the upstream models.
features = {
    "VIDEO": torch.rand(30, 2048),
    "CLIP": torch.rand(30, 512),
    "tf_vggish": torch.rand(30, 128),
}

# features_t is the time series of the features, usually uniformly sampled. 
features_t = {
    "VIDEO": torch.linspace(1, 30, steps=30),
    "CLIP": torch.linspace(1, 30, steps=30),
    "tf_vggish": torch.linspace(1, 30, steps=30),
}

# features_ind is the mask of the features.
features_ind = {
    "VIDEO": torch.as_tensor([1] * 25 + [0] * 5),
    "CLIP": torch.as_tensor([1] * 25 + [0] * 5),
    "tf_vggish": torch.as_tensor([1] * 25 + [0] * 5),
}

video_input_dict = {"features": features, "features_t": features_t, "features_ind": features_ind}

towhee.dc([video_input_dict]).video_text_embedding.mdmmt(modality='video', device='cpu').show()

towhee.dc(['Hello world.']).video_text_embedding.mdmmt(modality='text', device='cpu').show()
```
![](vect_simplified_video.png)
![](vect_simplified_text.png)

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

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## Factory Constructor

Create the operator via the following factory method

***mdmmt(modality: str)***

**Parameters:**

​   ***modality:*** *str*

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

​   ***weight_path:*** *Optional[str]*

​   pretrained model weights path.  

​   ***device:*** *Optional[str]*

​   cpu or cuda.  

​   ***mmtvid_params:*** *Optional[dict]*

​   mmtvid model params for custom model.  

​   ***mmttxt_params:*** *Optional[dict]*

​   mmttxt model params for custom model.  


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## Interface

When video modality, load a video embeddings extracted from different upstream expert networks, such as video, RGB, audio.  
When text modality, read the text to generate a text embedding. 


**Parameters:**

​	***data:*** *dict* or *str*

​  The embedding dict extracted from different upstream expert networks or text, based on specified modality).	



**Returns:** *numpy.ndarray*

​   The data embedding extracted by model.