# 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
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
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
# 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
├── 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
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
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
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
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
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
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
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
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
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
Optimizer:
name: MyOptim
args1: args1
args2: args2
lr:
name: Cosine
learning_rate: 0.001
regularizer:
name: 'L2'
factor: 0
```