resnet-image-embedding/README.md

# Image Embedding Operator with Resnet50

Authors: Kyle, shiyu22

## Overview

This Operator generates feature vectors from the pytorch pretrained **Resnet50** model[1], which is trained on [imagenet dataset](https://image-net.org/download.php).

**Resnet** models were proposed in “[Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)”[2], this model was the winner of ImageNet challenge in 2015. "The fundamental breakthrough with ResNet was it allowed us to train extremely deep neural networks with 150+layers successfully. Prior to ResNet training very deep neural networks were difficult due to the problem of vanishing gradients"[3].

## Interface

`Class Resnet50ImageEmbedding(Operator)` [\[source\]](./resnet50_image_embedding.py)

`__init__(self, model_name: str)`

**params:**

- model_name(str): the model name for embedding, like 'resnet50'.

`__call__(self, img_tensor: torch.Tensor)`

**params:**

- img_tensor(torch.Tensor): the normalized image tensor.

**return:**

- cnn(numpy.ndarray): the embedding of image.

## How to use

### Requirements

You can get the required python package by [requirements.txt](./requirements.txt) and [pytorch/requirements.txt](./pytorch/requirements.txt). Towhee will automatically install these packages when you first load the Operator Repo, so you don't need to install them manually, here is just a list.

- towhee
- torch
- torchvision
- numpy

### How it works

The `towhee/resnet50-image-embedding` Operator implements the function of image embedding, which can add to the pipeline, for example, it's the key Operator named embedding_model within [image_embedding_resnet50](https://hub.towhee.io/towhee/image-embedding-resnet50) pipeline, and it is the red box in the picture below.

![img](./pic/operator.png)

When using this Operator to write Pipeline's Yaml file, you need to declare the following content according to the interface of Resnet50ImageEmbedding class:

```yaml
operators:
    -
        name: 'embedding_model'
        function: 'towhee/resnet50-image-embedding'
        tag: 'main'
        init_args:
            model_name: 'resnet50'
        inputs:
            -
                df: 'image_preproc'
                name: 'img_tensor'
                col: 0
        outputs:
            -
                df: 'embedding'
        iter_info:
            type: map
dataframes:
    -
        name: 'image_preproc'
        columns:
            -
                name: 'img_transformed'
                vtype: 'torch.Tensor'
    -
        name: 'embedding'
        columns:
            -
                name: 'cnn'
                vtype: 'numpy.ndarray'
```

We can see that in yaml, the **operator** part declares the `init_args` of the class and the `input` and `output` dataframe, and the **dataframe** declares the parameter `name` and `vtype`.

### File Structure

Here is the main file structure of the `resnet50-image-embedding` Operator. If you want to learn more about the source code or modify it yourself, you can learn from it.

```bash
├── .gitattributes
├── .gitignore
├── README.md
├── __init__.py
├── requirements.txt              #General python dependency package
├── resnet50_image_embedding.py   #The python file for Towhee, it defines the interface of the system and usually does not need to be modified.
├── resnet50_image_embedding.yaml #The YAML file contains Operator information, such as model frame, input, and output.
├── pytorch               #The directory of the pytorh
│   ├── __init__.py
│   ├── model             #The directory of the pytorch model, which can store data such as weights.
│   ├── requirements.txt  #The python dependency package for the pytorch model.
│   └── model.py          #The code of the pytorch model, including the initialization model and prediction.
├── test_data/   #The directory of test data, including test.jpg
└── test_resnet50_image_embedding.py  #The unittest file of this Operator.
```

## Reference

[1].https://pytorch.org/hub/pytorch_vision_resnet/

[2].https://arxiv.org/abs/1512.03385

[3].https://towardsdatascience.com/understanding-and-coding-a-resnet-in-keras-446d7ff84d33