# Video-Text Retrieval Embdding with MDMMT

*author: Chen Zhang*


<br />



## 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.


<br />


## Code Example

Read the text to generate a text embedding. 

```python
from towhee.dc2 import pipe, ops, DataCollection

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

DataCollection(p('Hello world.')).show()
```
![](text_emb_result.png)

Load a video embeddings extracted from different upstream expert networks, such as video, RGB, audio.

```python
import torch
from towhee.dc2 import pipe, ops, DataCollection


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}

p = (
    pipe.input('video_input_dict') \
        .map('video_input_dict', 'vec', ops.video_text_embedding.mdmmt(modality='video', device='cuda:0')) \
        .output('video_input_dict', 'vec')
)

DataCollection(p(video_input_dict)).show()
```
![](video_emb_result.png)


<br />



## 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.  


<br />



## 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.