add doc of how to add new algorithm
This commit is contained in:
WenmuZhou
parent
28b2d43e57
commit
3aae17e0d5
|
|
@ -0,0 +1,303 @@
|
|||
# 添加新算法
|
||||
|
||||
PaddleOCR将一个算法分解为以下几个部分，并对各部分进行模块化处理，方便快速组合出新的算法。
|
||||
|
||||
* 数据加载和处理
|
||||
* 网络
|
||||
* 后处理
|
||||
* 损失函数
|
||||
* 指标评估
|
||||
* 优化器
|
||||
|
||||
下面将分别对每个部分进行介绍，并介绍如何在该部分里添加新算法所需模块。
|
||||
|
||||
## 数据加载和处理
|
||||
|
||||
数据加载和处理由不同的模块(module)组成,其完成了图片的读取、数据增强和label的制作。这一部分在[ppocr/data](../../ppocr/data)下。 各个文件及文件夹作用说明如下:
|
||||
|
||||
```bash
|
||||
ppocr/data/
|
||||
├── imaug # 图片的读取、数据增强和label制作相关的文件
|
||||
│ ├── label_ops.py # 对label进行变换的modules
|
||||
│ ├── operators.py # 对image进行变换的modules
|
||||
│ ├──.....
|
||||
├── __init__.py
|
||||
├── lmdb_dataset.py # 读取lmdb的数据集的dataset
|
||||
└── simple_dataset.py # 读取以`image_path\tgt`形式保存的数据集的dataset
|
||||
```
|
||||
|
||||
PaddleOCR内置了大量图像操作相关模块，对于没有没有内置的模块可通过如下步骤添加:
|
||||
|
||||
1. 在 [ppocr/data/imaug](../../ppocr/data/imaug) 文件夹下新建文件，如my_module.py。
|
||||
2. 在 my_module.py 文件内添加相关代码，示例代码如下:
|
||||
|
||||
```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. 在 [ppocr/data/imaug/\__init\__.py](../../ppocr/data/imaug/__init__.py) 文件内导入添加的模块。
|
||||
|
||||
数据处理的所有处理步骤由不同的模块顺序执行而成，在config文件中按照列表的形式组合并执行。如:
|
||||
|
||||
```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
|
||||
```
|
||||
|
||||
## 网络
|
||||
|
||||
网络部分完成了网络的组网操作，PaddleOCR将网络划分为四部分，这一部分在[ppocr/modeling](../../ppocr/modeling)下。 进入网络的数据将按照顺序(transforms->backbones->
|
||||
necks->heads)依次通过这四个部分。
|
||||
|
||||
```bash
|
||||
├── architectures # 网络的组网代码
|
||||
├── transforms # 网络的图像变换模块
|
||||
├── backbones # 网络的特征提取模块
|
||||
├── necks # 网络的特征增强模块
|
||||
└── heads # 网络的输出模块
|
||||
```
|
||||
|
||||
PaddleOCR内置了DB,EAST,SAST,CRNN和Attention等算法相关的常用模块，对于没有内置的模块可通过如下步骤添加，四个部分添加步骤一致，以backbones为例:
|
||||
|
||||
1. 在 [ppocr/modeling/backbones](../../ppocr/modeling/backbones) 文件夹下新建文件，如my_backbone.py。
|
||||
2. 在 my_backbone.py 文件内添加相关代码，示例代码如下:
|
||||
|
||||
```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 necwork forward
|
||||
y = self.conv(inputs)
|
||||
return y
|
||||
```
|
||||
|
||||
3. 在 [ppocr/modeling/backbones/\__init\__.py](../../ppocr/modeling/backbones/__init__.py)文件内导入添加的模块。
|
||||
|
||||
在完成网络的四部分模块添加之后，只需要配置文件中进行配置即可使用，如:
|
||||
|
||||
```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
|
||||
```
|
||||
|
||||
## 后处理
|
||||
|
||||
后处理主要完成从网络输出到人类友好结果的变换。这一部分在[ppocr/postprocess](../../ppocr/postprocess)下。
|
||||
PaddleOCR内置了DB,EAST,SAST,CRNN和Attention等算法相关的后处理模块，对于没有内置的组件可通过如下步骤添加:
|
||||
|
||||
1. 在 [ppocr/postprocess](../../ppocr/postprocess) 文件夹下新建文件，如 my_postprocess.py。
|
||||
2. 在 my_postprocess.py 文件内添加相关代码，示例代码如下:
|
||||
|
||||
```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. 在 [ppocr/postprocess/\__init\__.py](../../ppocr/postprocess/__init__.py)文件内导入添加的模块。
|
||||
|
||||
在后处理模块添加之后，只需要配置文件中进行配置即可使用，如:
|
||||
|
||||
```yaml
|
||||
PostProcess:
|
||||
name: MyPostProcess
|
||||
args1: args1
|
||||
args2: args2
|
||||
```
|
||||
|
||||
## 损失函数
|
||||
|
||||
损失函数用于计算网络输出和label之间的距离。这一部分在[ppocr/losses](../../ppocr/losses)下。
|
||||
PaddleOCR内置了DB,EAST,SAST,CRNN和Attention等算法相关的损失函数模块，对于没有内置的模块可通过如下步骤添加:
|
||||
|
||||
1. 在 [ppocr/losses](../../ppocr/losses) 文件夹下新建文件，如 my_loss.py。
|
||||
2. 在 my_loss.py 文件内添加相关代码，示例代码如下:
|
||||
|
||||
```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. 在 [ppocr/losses/\__init\__.py](../../ppocr/losses/__init__.py)文件内导入添加的模块。
|
||||
|
||||
在损失函数添加之后，只需要配置文件中进行配置即可使用，如:
|
||||
|
||||
```yaml
|
||||
Loss:
|
||||
name: MyLoss
|
||||
args1: args1
|
||||
args2: args2
|
||||
```
|
||||
|
||||
## 指标评估
|
||||
|
||||
指标评估用于计算网络在当前batch上的性能。这一部分在[ppocr/metrics](../../ppocr/metrics)下。 PaddleOCR内置了检测，分类和识别等算法相关的指标评估模块，对于没有内置的模块可通过如下步骤添加:
|
||||
|
||||
1. 在 [ppocr/metrics](../../ppocr/metrics) 文件夹下新建文件，如my_metric.py。
|
||||
2. 在 my_metric.py 文件内添加相关代码，示例代码如下:
|
||||
|
||||
```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 metircs {
|
||||
'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. 在 [ppocr/metrics/\__init\__.py](../../ppocr/metrics/__init__.py)文件内导入添加的模块。
|
||||
|
||||
在指标评估模块添加之后，只需要配置文件中进行配置即可使用，如:
|
||||
|
||||
```yaml
|
||||
Metric:
|
||||
name: MyMetric
|
||||
main_indicator: acc
|
||||
```
|
||||
|
||||
## 优化器
|
||||
|
||||
优化器用于训练网络。优化器内部还包含了网络正则化和学习率衰减模块。 这一部分在[ppocr/optimizer](../../ppocr/optimizer)下。 PaddleOCR内置了`Momentum`,`Adam`
|
||||
和`RMSProp`等常用的优化器模块，`Linear`,`Cosine`,`Step`和`Piecewise`等常用的正则化模块与`L1Decay`和`L2Decay`等常用的学习率衰减模块。
|
||||
对于没有内置的模块可通过如下步骤添加，以`optimizer`为例:
|
||||
|
||||
1. 在 [ppocr/optimizer/optimizer.py](../../ppocr/optimizer/optimizer.py) 文件内创建自己的优化器，示例代码如下:
|
||||
|
||||
```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
|
||||
|
||||
```
|
||||
|
||||
在优化器模块添加之后，只需要配置文件中进行配置即可使用，如:
|
||||
|
||||
```yaml
|
||||
Optimizer:
|
||||
name: MyOptim
|
||||
args1: args1
|
||||
args2: args2
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
```
|
||||
|
|
@ -0,0 +1,304 @@
|
|||
# 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 necwork 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 mainly completes the transformation from network output to human-friendly results. 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 metircs {
|
||||
'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
|
||||
```
|
||||
|
||||
## 优化器
|
||||
|
||||
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
|
||||
```
|
||||
Loading…
Reference in New Issue