PaddleOCR/docs/version2.x/algorithm/add_new_algorithm.en.md

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comments: true
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# Add New Algorithm

PaddleOCR decomposes an algorithm into the following parts, and modularizes each part to make it more convenient to develop new algorithms.

* Data loading and processing
* Network
* Post-processing
* Loss
* Metric
* Optimizer

The following will introduce each part separately, and introduce how to add the modules required for the new algorithm.

## Data loading and processing

Data loading and processing are composed of different modules, which complete the image reading, data augment and label production. This part is under [ppocr/data](../../../ppocr/data). The explanation of each file and folder are as follows:

```bash linenums="1"
ppocr/data/
├── imaug             # Scripts for image reading, data augment and label production
│   ├── label_ops.py  # Modules that transform the label
│   ├── operators.py  # Modules that transform the image
│   ├──.....
├── __init__.py
├── lmdb_dataset.py   # The dataset that reads the lmdb
└── simple_dataset.py # Read the dataset saved in the form of `image_path\tgt`
```

PaddleOCR has a large number of built-in image operation related modules. For modules that are not built-in, you can add them through the following steps:

1. Create a new file under the [ppocr/data/imaug](../../../ppocr/data/imaug) folder, such as my_module.py.
2. Add code in the my_module.py file, the sample code is as follows:

    ```python linenums="1"
    class MyModule:
        def __init__(self, *args, **kwargs):
            # your init code
            pass

        def __call__(self, data):
            img = data['image']
            label = data['label']
            # your process code

            data['image'] = img
            data['label'] = label
            return data
    ```

3. Import the added module in the [ppocr/data/imaug/\__init\__.py](../../../ppocr/data/imaug/__init__.py) file.

All different modules of data processing are executed by sequence, combined and executed in the form of a list in the config file. Such as:

```yaml linenums="1"
# angle class data process
transforms:
  - DecodeImage: # load image
      img_mode: BGR
      channel_first: False
  - MyModule:
      args1: args1
      args2: args2
  - KeepKeys:
      keep_keys: [ 'image', 'label' ] # dataloader will return list in this order
```

## Network

The network part completes the construction of the network, and PaddleOCR divides the network into four parts, which are under [ppocr/modeling](../../../ppocr/modeling). The data entering the network will pass through these four parts in sequence(transforms->backbones->
necks->heads).

```bash linenums="1"
├── architectures # Code for building network
├── transforms    # Image Transformation Module
├── backbones     # Feature extraction module
├── necks         # Feature enhancement module
└── heads         # Output module
```

PaddleOCR has built-in commonly used modules related to algorithms such as DB, EAST, SAST, CRNN and Attention. For modules that do not have built-in, you can add them through the following steps, the four parts are added in the same steps, take backbones as an example:

1. Create a new file under the [ppocr/modeling/backbones](../../../ppocr/modeling/backbones) folder, such as my_backbone.py.
2. Add code in the my_backbone.py file, the sample code is as follows:

    ```python linenums="1"
    import paddle
    import paddle.nn as nn
    import paddle.nn.functional as F


    class MyBackbone(nn.Layer):
        def __init__(self, *args, **kwargs):
            super(MyBackbone, self).__init__()
            # your init code
            self.conv = nn.xxxx

        def forward(self, inputs):
            # your network forward
            y = self.conv(inputs)
            return y
    ```

3. Import the added module in the [ppocr/modeling/backbones/\__init\__.py](../../../ppocr/modeling/backbones/__init__.py) file.

After adding the four-part modules of the network, you only need to configure them in the configuration file to use, such as:

```yaml linenums="1"
Architecture:
model_type: rec
algorithm: CRNN
Transform:
    name: MyTransform
    args1: args1
    args2: args2
Backbone:
    name: MyBackbone
    args1: args1
Neck:
    name: MyNeck
    args1: args1
Head:
    name: MyHead
    args1: args1
```

## Post-processing

Post-processing realizes decoding network output to obtain text box or recognized text. This part is under [ppocr/postprocess](../../../ppocr/postprocess).
PaddleOCR has built-in post-processing modules related to algorithms such as DB, EAST, SAST, CRNN and Attention. For components that are not built-in, they can be added through the following steps:

1. Create a new file under the [ppocr/postprocess](../../../ppocr/postprocess) folder, such as my_postprocess.py.
2. Add code in the my_postprocess.py file, the sample code is as follows:

    ```python linenums="1"
    import paddle


    class MyPostProcess:
        def __init__(self, *args, **kwargs):
            # your init code
            pass

        def __call__(self, preds, label=None, *args, **kwargs):
            if isinstance(preds, paddle.Tensor):
                preds = preds.numpy()
            # you preds decode code
            preds = self.decode_preds(preds)
            if label is None:
                return preds
            # you label decode code
            label = self.decode_label(label)
            return preds, label

        def decode_preds(self, preds):
            # you preds decode code
            pass

        def decode_label(self, preds):
            # you label decode code
            pass
    ```

3. Import the added module in the [ppocr/postprocess/\__init\__.py](../../../ppocr/postprocess/__init__.py) file.

After the post-processing module is added, you only need to configure it in the configuration file to use, such as:

```yaml linenums="1"
PostProcess:
name: MyPostProcess
args1: args1
args2: args2
```

## Loss

The loss function is used to calculate the distance between the network output and the label. This part is under [ppocr/losses](../../../ppocr/losses).
PaddleOCR has built-in loss function modules related to algorithms such as DB, EAST, SAST, CRNN and Attention. For modules that do not have built-in modules, you can add them through the following steps:

1. Create a new file in the [ppocr/losses](../../../ppocr/losses) folder, such as my_loss.py.
2. Add code in the my_loss.py file, the sample code is as follows:

    ```python linenums="1"
    import paddle
    from paddle import nn


    class MyLoss(nn.Layer):
        def __init__(self, **kwargs):
            super(MyLoss, self).__init__()
            # you init code
            pass

        def __call__(self, predicts, batch):
            label = batch[1]
            # your loss code
            loss = self.loss(input=predicts, label=label)
            return {'loss': loss}
    ```

3. Import the added module in the [ppocr/losses/\__init\__.py](../../../ppocr/losses/__init__.py) file.

After the loss function module is added, you only need to configure it in the configuration file to use it, such as:

```yaml linenums="1"
Loss:
  name: MyLoss
  args1: args1
  args2: args2
```

## Metric

Metric is used to calculate the performance of the network on the current batch. This part is under [ppocr/metrics](../../../ppocr/metrics). PaddleOCR has built-in evaluation modules related to algorithms such as detection, classification and recognition. For modules that do not have built-in modules, you can add them through the following steps:

1. Create a new file under the [ppocr/metrics](../../../ppocr/metrics) folder, such as my_metric.py.
2. Add code in the my_metric.py file, the sample code is as follows:

    ```python linenums="1"

    class MyMetric(object):
        def __init__(self, main_indicator='acc', **kwargs):
            # main_indicator is used for select best model
            self.main_indicator = main_indicator
            self.reset()

        def __call__(self, preds, batch, *args, **kwargs):
            # preds is out of postprocess
            # batch is out of dataloader
            labels = batch[1]
            cur_correct_num = 0
            cur_all_num = 0
            # you metric code
            self.correct_num += cur_correct_num
            self.all_num += cur_all_num
            return {'acc': cur_correct_num / cur_all_num, }

        def get_metric(self):
            """
            return metrics {
                    'acc': 0,
                    'norm_edit_dis': 0,
                }
            """
            acc = self.correct_num / self.all_num
            self.reset()
            return {'acc': acc}

        def reset(self):
            # reset metric
            self.correct_num = 0
            self.all_num = 0

    ```

3. Import the added module in the [ppocr/metrics/\__init\__.py](../../../ppocr/metrics/__init__.py) file.

After the metric module is added, you only need to configure it in the configuration file to use it, such as:

```yaml linenums="1"
Metric:
  name: MyMetric
  main_indicator: acc
```

## Optimizer

The optimizer is used to train the network. The optimizer also contains network regularization and learning rate decay modules. This part is under [ppocr/optimizer](../../../ppocr/optimizer). PaddleOCR has built-in
Commonly used optimizer modules such as `Momentum`, `Adam` and `RMSProp`, common regularization modules such as `Linear`, `Cosine`, `Step` and `Piecewise`, and common learning rate decay modules such as `L1Decay` and `L2Decay`.
Modules without built-in can be added through the following steps, take `optimizer` as an example:

1. Create your own optimizer in the [ppocr/optimizer/optimizer.py](../../../ppocr/optimizer/optimizer.py) file, the sample code is as follows:

    ```python linenums="1"
    from paddle import optimizer as optim


    class MyOptim(object):
        def __init__(self, learning_rate=0.001, *args, **kwargs):
            self.learning_rate = learning_rate

        def __call__(self, parameters):
            # It is recommended to wrap the built-in optimizer of paddle
            opt = optim.XXX(
                learning_rate=self.learning_rate,
                parameters=parameters)
            return opt

    ```

After the optimizer module is added, you only need to configure it in the configuration file to use, such as:

```yaml linenums="1"
Optimizer:
  name: MyOptim
  args1: args1
  args2: args2
  lr:
    name: Cosine
    learning_rate: 0.001
  regularizer:
    name: 'L2'
    factor: 0
```