diff --git a/configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml b/configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml
index c45a1a3c8b8edc8a542bc740f6abd958b9a1e701..6453934b7324b2b351aeb6fdf8e4e4de24b022bf 100644
--- a/configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml
+++ b/configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml
@@ -71,7 +71,7 @@ PostProcess:
Metric:
name: RecMetric
main_indicator: acc
- ignore_space: True
+ ignore_space: False
Train:
dataset:
diff --git a/configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml b/configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml
index 80ec7c6308aa006f46331503e00368444c425559..773a3649d8378cb39373b5b90837f17f9ecba335 100644
--- a/configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml
+++ b/configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml
@@ -129,7 +129,7 @@ Loss:
key: head_out
multi_head: True
- DistillationSARLoss:
- weight: 1.0
+ weight: 0.5
model_name_list: ["Student", "Teacher"]
key: head_out
multi_head: True
@@ -145,7 +145,7 @@ Metric:
base_metric_name: RecMetric
main_indicator: acc
key: "Student"
- ignore_space: True
+ ignore_space: False
Train:
dataset:
diff --git a/doc/doc_ch/knowledge_distillation.md b/doc/doc_ch/knowledge_distillation.md
index 7c79f885b516b7061e094ac115180560645d3f64..eba2ff90249a6d949abf2cacb132f42269c9760d 100644
--- a/doc/doc_ch/knowledge_distillation.md
+++ b/doc/doc_ch/knowledge_distillation.md
@@ -60,7 +60,7 @@ PaddleOCR中集成了知识蒸馏的算法，具体地，有以下几个主要
### 2.1 识别配置文件解析
-配置文件在[ch_PP-OCRv2_rec_distillation.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml)。
+配置文件在[ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml)。
#### 2.1.1 模型结构
@@ -69,7 +69,7 @@ PaddleOCR中集成了知识蒸馏的算法，具体地，有以下几个主要
```yaml
Architecture:
- model_type: &model_type "rec" # 模型类别，rec、det等，每个子网络的的模型类别都与
+ model_type: &model_type "rec" # 模型类别，rec、det等，每个子网络的的模型相同
name: DistillationModel # 结构名称，蒸馏任务中，为DistillationModel，用于构建对应的结构
algorithm: Distillation # 算法名称
Models: # 模型，包含子网络的配置信息
@@ -78,37 +78,55 @@ Architecture:
freeze_params: false # 是否需要固定参数
return_all_feats: true # 子网络的参数，表示是否需要返回所有的features，如果为False，则只返回最后的输出
model_type: *model_type # 模型类别
- algorithm: CRNN # 子网络的算法名称，该子网络剩余参与均为构造参数，与普通的模型训练配置一致
+ algorithm: SVTR # 子网络的算法名称，该子网络其余参数均为构造参数，与普通的模型训练配置一致
Transform:
Backbone:
name: MobileNetV1Enhance
scale: 0.5
- Neck:
- name: SequenceEncoder
- encoder_type: rnn
- hidden_size: 64
+ last_conv_stride: [1, 2]
+ last_pool_type: avg
Head:
- name: CTCHead
- mid_channels: 96
- fc_decay: 0.00002
- Student: # 另外一个子网络，这里给的是DML的蒸馏示例，两个子网络结构相同，均需要学习参数
- pretrained: # 下面的组网参数同上
+ name: MultiHead
+ head_list:
+ - CTCHead:
+ Neck:
+ name: svtr
+ dims: 64
+ depth: 2
+ hidden_dims: 120
+ use_guide: True
+ Head:
+ fc_decay: 0.00001
+ - SARHead:
+ enc_dim: 512
+ max_text_length: *max_text_length
+ Student:
+ pretrained:
freeze_params: false
return_all_feats: true
model_type: *model_type
- algorithm: CRNN
+ algorithm: SVTR
Transform:
Backbone:
name: MobileNetV1Enhance
scale: 0.5
- Neck:
- name: SequenceEncoder
- encoder_type: rnn
- hidden_size: 64
+ last_conv_stride: [1, 2]
+ last_pool_type: avg
Head:
- name: CTCHead
- mid_channels: 96
- fc_decay: 0.00002
+ name: MultiHead
+ head_list:
+ - CTCHead:
+ Neck:
+ name: svtr
+ dims: 64
+ depth: 2
+ hidden_dims: 120
+ use_guide: True
+ Head:
+ fc_decay: 0.00001
+ - SARHead:
+ enc_dim: 512
+ max_text_length: *max_text_length
```
当然，这里如果希望添加更多的子网络进行训练，也可以按照`Student`与`Teacher`的添加方式，在配置文件中添加相应的字段。比如说如果希望有3个模型互相监督，共同训练，那么`Architecture`可以写为如下格式。
@@ -124,55 +142,82 @@ Architecture:
freeze_params: false
return_all_feats: true
model_type: *model_type
- algorithm: CRNN
+ algorithm: SVTR
Transform:
Backbone:
name: MobileNetV1Enhance
scale: 0.5
- Neck:
- name: SequenceEncoder
- encoder_type: rnn
- hidden_size: 64
+ last_conv_stride: [1, 2]
+ last_pool_type: avg
Head:
- name: CTCHead
- mid_channels: 96
- fc_decay: 0.00002
+ name: MultiHead
+ head_list:
+ - CTCHead:
+ Neck:
+ name: svtr
+ dims: 64
+ depth: 2
+ hidden_dims: 120
+ use_guide: True
+ Head:
+ fc_decay: 0.00001
+ - SARHead:
+ enc_dim: 512
+ max_text_length: *max_text_length
Student:
pretrained:
freeze_params: false
return_all_feats: true
model_type: *model_type
- algorithm: CRNN
+ algorithm: SVTR
Transform:
Backbone:
name: MobileNetV1Enhance
scale: 0.5
- Neck:
- name: SequenceEncoder
- encoder_type: rnn
- hidden_size: 64
+ last_conv_stride: [1, 2]
+ last_pool_type: avg
Head:
- name: CTCHead
- mid_channels: 96
- fc_decay: 0.00002
- Student2: # 知识蒸馏任务中引入的新的子网络，其他部分与上述配置相同
+ name: MultiHead
+ head_list:
+ - CTCHead:
+ Neck:
+ name: svtr
+ dims: 64
+ depth: 2
+ hidden_dims: 120
+ use_guide: True
+ Head:
+ fc_decay: 0.00001
+ - SARHead:
+ enc_dim: 512
+ max_text_length: *max_text_length
+ Student2:
pretrained:
freeze_params: false
return_all_feats: true
model_type: *model_type
- algorithm: CRNN
+ algorithm: SVTR
Transform:
Backbone:
name: MobileNetV1Enhance
scale: 0.5
- Neck:
- name: SequenceEncoder
- encoder_type: rnn
- hidden_size: 64
+ last_conv_stride: [1, 2]
+ last_pool_type: avg
Head:
- name: CTCHead
- mid_channels: 96
- fc_decay: 0.00002
+ name: MultiHead
+ head_list:
+ - CTCHead:
+ Neck:
+ name: svtr
+ dims: 64
+ depth: 2
+ hidden_dims: 120
+ use_guide: True
+ Head:
+ fc_decay: 0.00001
+ - SARHead:
+ enc_dim: 512
+ max_text_length: *max_text_length
```
最终该模型训练时，包含3个子网络：`Teacher`, `Student`, `Student2`。
@@ -205,34 +250,56 @@ Architecture:
```yaml
Loss:
- name: CombinedLoss # 损失函数名称，基于改名称，构建用于损失函数的类
- loss_config_list: # 损失函数配置文件列表，为CombinedLoss的必备函数
- - DistillationCTCLoss: # 基于蒸馏的CTC损失函数，继承自标准的CTC loss
- weight: 1.0 # 损失函数的权重，loss_config_list中，每个损失函数的配置都必须包含该字段
- model_name_list: ["Student", "Teacher"] # 对于蒸馏模型的预测结果，提取这两个子网络的输出，与gt计算CTC loss
- key: head_out # 取子网络输出dict中，该key对应的tensor
+ name: CombinedLoss
+ loss_config_list:
- DistillationDMLLoss: # 蒸馏的DML损失函数，继承自标准的DMLLoss
weight: 1.0 # 权重
act: "softmax" # 激活函数，对输入使用激活函数处理，可以为softmax, sigmoid或者为None，默认为None
+ use_log: true # 对输入计算log，如果函数已经
model_name_pairs: # 用于计算DML loss的子网络名称对，如果希望计算其他子网络的DML loss，可以在列表下面继续填充
- ["Student", "Teacher"]
key: head_out # 取子网络输出dict中，该key对应的tensor
+ multi_head: True # 是否为多头结构
+ dis_head: ctc # 指定用于计算损失函数的head
+ name: dml_ctc # 蒸馏loss的前缀名称，避免不同loss之间的命名冲突
+ - DistillationDMLLoss: # 蒸馏的DML损失函数，继承自标准的DMLLoss
+ weight: 0.5 # 权重
+ act: "softmax" # 激活函数，对输入使用激活函数处理，可以为softmax, sigmoid或者为None，默认为None
+ use_log: true # 对输入计算log，如果函数已经
+ model_name_pairs: # 用于计算DML loss的子网络名称对，如果希望计算其他子网络的DML loss，可以在列表下面继续填充
+ - ["Student", "Teacher"]
+ key: head_out # 取子网络输出dict中，该key对应的tensor
+ multi_head: True # 是否为多头结构
+ dis_head: sar # 指定用于计算损失函数的head
+ name: dml_sar # 蒸馏loss的前缀名称，避免不同loss之间的命名冲突
- DistillationDistanceLoss: # 蒸馏的距离损失函数
weight: 1.0 # 权重
mode: "l2" # 距离计算方法，目前支持l1, l2, smooth_l1
model_name_pairs: # 用于计算distance loss的子网络名称对
- ["Student", "Teacher"]
key: backbone_out # 取子网络输出dict中，该key对应的tensor
+ - DistillationCTCLoss: # 基于蒸馏的CTC损失函数，继承自标准的CTC loss
+ weight: 1.0 # 损失函数的权重，loss_config_list中，每个损失函数的配置都必须包含该字段
+ model_name_list: ["Student", "Teacher"] # 对于蒸馏模型的预测结果，提取这两个子网络的输出，与gt计算CTC loss
+ key: head_out # 取子网络输出dict中，该key对应的tensor
+ - DistillationSARLoss: # 基于蒸馏的SAR损失函数，继承自标准的SARLoss
+ weight: 1.0 # 损失函数的权重，loss_config_list中，每个损失函数的配置都必须包含该字段
+ model_name_list: ["Student", "Teacher"] # 对于蒸馏模型的预测结果，提取这两个子网络的输出，与gt计算CTC loss
+ key: head_out # 取子网络输出dict中，该key对应的tensor
+ multi_head: True # 是否为多头结构，为true时，取出其中的SAR分支计算损失函数
```
上述损失函数中，所有的蒸馏损失函数均继承自标准的损失函数类，主要功能为: 对蒸馏模型的输出进行解析，找到用于计算损失的中间节点(tensor)，再使用标准的损失函数类去计算。
-以上述配置为例，最终蒸馏训练的损失函数包含下面3个部分。
+以上述配置为例，最终蒸馏训练的损失函数包含下面5个部分。
-- `Student`和`Teacher`的最终输出(`head_out`)与gt的CTC loss，权重为1。在这里因为2个子网络都需要更新参数，因此2者都需要计算与g的loss。
-- `Student`和`Teacher`的最终输出(`head_out`)之间的DML loss，权重为1。
+- `Student`和`Teacher`最终输出(`head_out`)的CTC分支与gt的CTC loss，权重为1。在这里因为2个子网络都需要更新参数，因此2者都需要计算与g的loss。
+- `Student`和`Teacher`最终输出(`head_out`)的SAR分支与gt的SAR loss，权重为1.0。在这里因为2个子网络都需要更新参数，因此2者都需要计算与g的loss。
+- `Student`和`Teacher`最终输出(`head_out`)的CTC分支之间的DML loss，权重为1。
+- `Student`和`Teacher`最终输出(`head_out`)的SAR分支之间的DML loss，权重为0.5。
- `Student`和`Teacher`的骨干网络输出(`backbone_out`)之间的l2 loss，权重为1。
+
关于`CombinedLoss`更加具体的实现可以参考: [combined_loss.py](../../ppocr/losses/combined_loss.py#L23)。关于`DistillationCTCLoss`等蒸馏损失函数更加具体的实现可以参考[distillation_loss.py](../../ppocr/losses/distillation_loss.py)。
@@ -245,6 +312,7 @@ PostProcess:
name: DistillationCTCLabelDecode # 蒸馏任务的CTC解码后处理，继承自标准的CTCLabelDecode类
model_name: ["Student", "Teacher"] # 对于蒸馏模型的预测结果，提取这两个子网络的输出，进行解码
key: head_out # 取子网络输出dict中，该key对应的tensor
+ multi_head: True # 多头结构时，会取出其中的CTC分支进行计算
```
以上述配置为例，最终会同时计算`Student`和`Teahcer` 2个子网络的CTC解码输出，返回一个`dict`，`key`为用于处理的子网络名称，`value`为用于处理的子网络列表。
@@ -262,6 +330,7 @@ Metric:
base_metric_name: RecMetric # 指标计算的基类，对于模型的输出，会基于该类，计算指标
main_indicator: acc # 指标的名称
key: "Student" # 选取该子网络的 main_indicator 作为作为保存保存best model的判断标准
+ ignore_space: False # 评估时是否忽略空格的影响
```
以上述配置为例，最终会使用`Student`子网络的acc指标作为保存best model的判断指标，同时，日志中也会打印出所有子网络的acc指标。
@@ -273,15 +342,15 @@ Metric:
对蒸馏得到的识别蒸馏进行微调有2种方式。
-（1）基于知识蒸馏的微调：这种情况比较简单，下载预训练模型，在[ch_PP-OCRv2_rec_distillation.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml)中配置好预训练模型路径以及自己的数据路径，即可进行模型微调训练。
+（1）基于知识蒸馏的微调：这种情况比较简单，下载预训练模型，在[ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml)中配置好预训练模型路径以及自己的数据路径，即可进行模型微调训练。
（2）微调时不使用知识蒸馏：这种情况，需要首先将预训练模型中的学生模型参数提取出来，具体步骤如下。
* 首先下载预训练模型并解压。
```shell
# 下面预训练模型并解压
-wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar
-tar -xf ch_PP-OCRv2_rec_train.tar
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_train.tar
+tar -xf ch_PP-OCRv3_rec_train.tar
```
* 然后使用python，对其中的学生模型参数进行提取
@@ -289,7 +358,7 @@ tar -xf ch_PP-OCRv2_rec_train.tar
```python
import paddle
# 加载预训练模型
-all_params = paddle.load("ch_PP-OCRv2_rec_train/best_accuracy.pdparams")
+all_params = paddle.load("ch_PP-OCRv3_rec_train/best_accuracy.pdparams")
# 查看权重参数的keys
print(all_params.keys())
# 学生模型的权重提取
@@ -297,10 +366,10 @@ s_params = {key[len("Student."):]: all_params[key] for key in all_params if "Stu
# 查看学生模型权重参数的keys
print(s_params.keys())
# 保存
-paddle.save(s_params, "ch_PP-OCRv2_rec_train/student.pdparams")
+paddle.save(s_params, "ch_PP-OCRv3_rec_train/student.pdparams")
```
-转化完成之后，使用[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)，修改预训练模型的路径（为导出的`student.pdparams`模型路径）以及自己的数据路径，即可进行模型微调。
+转化完成之后，使用[ch_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml)，修改预训练模型的路径（为导出的`student.pdparams`模型路径）以及自己的数据路径，即可进行模型微调。
### 2.2 检测配置文件解析
diff --git a/doc/doc_en/knowledge_distillation_en.md b/doc/doc_en/knowledge_distillation_en.md
index 8ba7bf9148850680adb9ae74ae1bce8f1f43bea7..bd36907c98c6d556fe1dea85712ece0e717fe426 100755
--- a/doc/doc_en/knowledge_distillation_en.md
+++ b/doc/doc_en/knowledge_distillation_en.md
@@ -74,6 +74,7 @@ The configuration file is in [ch_PP-OCRv2_rec_distillation.yml](../../configs/re
#### 2.1.1 Model Structure
In the knowledge distillation task, the model structure configuration is as follows.
+
```yaml
Architecture:
model_type: &model_type "rec" # Model category, recognition, detection, etc.
@@ -85,37 +86,55 @@ Architecture:
freeze_params: false # Do you need fixed parameters
return_all_feats: true # Do you need to return all features, if it is False, only the final output is returned
model_type: *model_type # Model category
- algorithm: CRNN # The algorithm name of the sub-network. The remaining parameters of the sub-network are consistent with the general model training configuration
+ algorithm: SVTR # The algorithm name of the sub-network. The remaining parameters of the sub-network are consistent with the general model training configuration
Transform:
Backbone:
name: MobileNetV1Enhance
scale: 0.5
- Neck:
- name: SequenceEncoder
- encoder_type: rnn
- hidden_size: 64
+ last_conv_stride: [1, 2]
+ last_pool_type: avg
Head:
- name: CTCHead
- mid_channels: 96
- fc_decay: 0.00002
+ name: MultiHead
+ head_list:
+ - CTCHead:
+ Neck:
+ name: svtr
+ dims: 64
+ depth: 2
+ hidden_dims: 120
+ use_guide: True
+ Head:
+ fc_decay: 0.00001
+ - SARHead:
+ enc_dim: 512
+ max_text_length: *max_text_length
Student: # Another sub-network, here is a distillation example of DML, the two sub-networks have the same structure, and both need to learn parameters
pretrained: # The following parameters are the same as above
freeze_params: false
return_all_feats: true
model_type: *model_type
- algorithm: CRNN
+ algorithm: SVTR
Transform:
Backbone:
name: MobileNetV1Enhance
scale: 0.5
- Neck:
- name: SequenceEncoder
- encoder_type: rnn
- hidden_size: 64
+ last_conv_stride: [1, 2]
+ last_pool_type: avg
Head:
- name: CTCHead
- mid_channels: 96
- fc_decay: 0.00002
+ name: MultiHead
+ head_list:
+ - CTCHead:
+ Neck:
+ name: svtr
+ dims: 64
+ depth: 2
+ hidden_dims: 120
+ use_guide: True
+ Head:
+ fc_decay: 0.00001
+ - SARHead:
+ enc_dim: 512
+ max_text_length: *max_text_length
```
If you want to add more sub-networks for training, you can also add the corresponding fields in the configuration file according to the way of adding `Student` and `Teacher`.
@@ -132,55 +151,83 @@ Architecture:
freeze_params: false
return_all_feats: true
model_type: *model_type
- algorithm: CRNN
+ algorithm: SVTR
Transform:
Backbone:
name: MobileNetV1Enhance
scale: 0.5
- Neck:
- name: SequenceEncoder
- encoder_type: rnn
- hidden_size: 64
+ last_conv_stride: [1, 2]
+ last_pool_type: avg
Head:
- name: CTCHead
- mid_channels: 96
- fc_decay: 0.00002
+ name: MultiHead
+ head_list:
+ - CTCHead:
+ Neck:
+ name: svtr
+ dims: 64
+ depth: 2
+ hidden_dims: 120
+ use_guide: True
+ Head:
+ fc_decay: 0.00001
+ - SARHead:
+ enc_dim: 512
+ max_text_length: *max_text_length
Student:
pretrained:
freeze_params: false
return_all_feats: true
model_type: *model_type
- algorithm: CRNN
+ algorithm: SVTR
Transform:
Backbone:
name: MobileNetV1Enhance
scale: 0.5
- Neck:
- name: SequenceEncoder
- encoder_type: rnn
- hidden_size: 64
+ last_conv_stride: [1, 2]
+ last_pool_type: avg
Head:
- name: CTCHead
- mid_channels: 96
- fc_decay: 0.00002
- Student2: # The new sub-network introduced in the knowledge distillation task, the configuration is the same as above
+ name: MultiHead
+ head_list:
+ - CTCHead:
+ Neck:
+ name: svtr
+ dims: 64
+ depth: 2
+ hidden_dims: 120
+ use_guide: True
+ Head:
+ fc_decay: 0.00001
+ - SARHead:
+ enc_dim: 512
+ max_text_length: *max_text_length
+ Student2:
pretrained:
freeze_params: false
return_all_feats: true
model_type: *model_type
- algorithm: CRNN
+ algorithm: SVTR
Transform:
Backbone:
name: MobileNetV1Enhance
scale: 0.5
- Neck:
- name: SequenceEncoder
- encoder_type: rnn
- hidden_size: 64
+ last_conv_stride: [1, 2]
+ last_pool_type: avg
Head:
- name: CTCHead
- mid_channels: 96
- fc_decay: 0.00002
+ name: MultiHead
+ head_list:
+ - CTCHead:
+ Neck:
+ name: svtr
+ dims: 64
+ depth: 2
+ hidden_dims: 120
+ use_guide: True
+ Head:
+ fc_decay: 0.00001
+ - SARHead:
+ enc_dim: 512
+ max_text_length: *max_text_length
+```
```
When the model is finally trained, it contains 3 sub-networks: `Teacher`, `Student`, `Student2`.
@@ -224,23 +271,42 @@ Loss:
act: "softmax" # Activation function, use it to process the input, can be softmax, sigmoid or None, the default is None
model_name_pairs: # The subnet name pair used to calculate DML loss. If you want to calculate the DML loss of other subnets, you can continue to add it below the list
- ["Student", "Teacher"]
- key: head_out
+ key: head_out
+ multi_head: True # whether to use mult_head
+ dis_head: ctc # assign the head name to calculate loss
+ name: dml_ctc # prefix name of the loss
+ - DistillationDMLLoss: # DML loss function, inherited from the standard DMLLoss
+ weight: 0.5
+ act: "softmax" # Activation function, use it to process the input, can be softmax, sigmoid or None, the default is None
+ model_name_pairs: # The subnet name pair used to calculate DML loss. If you want to calculate the DML loss of other subnets, you can continue to add it below the list
+ - ["Student", "Teacher"]
+ key: head_out
+ multi_head: True # whether to use mult_head
+ dis_head: sar # assign the head name to calculate loss
+ name: dml_sar # prefix name of the loss
- DistillationDistanceLoss: # Distilled distance loss function
weight: 1.0
mode: "l2" # Support l1, l2 or smooth_l1
model_name_pairs: # Calculate the distance loss of the subnet name pair
- ["Student", "Teacher"]
key: backbone_out
+ - DistillationSARLoss: # SAR loss function based on distillation, inherited from standard SAR loss
+ weight: 1.0 # The weight of the loss function. In loss_config_list, each loss function must include this field
+ model_name_list: ["Student", "Teacher"] # For the prediction results of the distillation model, extract the output of these two sub-networks and calculate the SAR loss with gt
+ key: head_out # In the sub-network output dict, take the corresponding tensor
+ multi_head: True # whether it is multi-head or not, if true, SAR branch is used to calculate the loss
```
Among the above loss functions, all distillation loss functions are inherited from the standard loss function class.
The main functions are: Analyze the output of the distillation model, find the intermediate node (tensor) used to calculate the loss,
and then use the standard loss function class to calculate.
-Taking the above configuration as an example, the final distillation training loss function contains the following three parts.
+Taking the above configuration as an example, the final distillation training loss function contains the following five parts.
-- The final output `head_out` of `Student` and `Teacher` calculates the CTC loss with gt (loss weight equals 1.0). Here, because both sub-networks need to update the parameters, both of them need to calculate the loss with gt.
-- DML loss between `Student` and `Teacher`'s final output `head_out` (loss weight equals 1.0).
+- CTC branch of the final output `head_out` for `Student` and `Teacher` calculates the CTC loss with gt (loss weight equals 1.0). Here, because both sub-networks need to update the parameters, both of them need to calculate the loss with gt.
+- SAR branch of the final output `head_out` for `Student` and `Teacher` calculates the SAR loss with gt (loss weight equals 1.0). Here, because both sub-networks need to update the parameters, both of them need to calculate the loss with gt.
+- DML loss between CTC branch of `Student` and `Teacher`'s final output `head_out` (loss weight equals 1.0).
+- DML loss between SAR branch of `Student` and `Teacher`'s final output `head_out` (loss weight equals 0.5).
- L2 loss between `Student` and `Teacher`'s backbone network output `backbone_out` (loss weight equals 1.0).
For more specific implementation of `CombinedLoss`, please refer to: [combined_loss.py](../../ppocr/losses/combined_loss.py#L23).
@@ -257,6 +323,7 @@ PostProcess:
name: DistillationCTCLabelDecode # CTC decoding post-processing of distillation tasks, inherited from the standard CTCLabelDecode class
model_name: ["Student", "Teacher"] # For the prediction results of the distillation model, extract the outputs of these two sub-networks and decode them
key: head_out # Take the corresponding tensor in the subnet output dict
+ multi_head: True # whether it is multi-head or not, if true, CTC branch is used to calculate the loss
```
Taking the above configuration as an example, the CTC decoding output of the two sub-networks `Student` and `Teahcer` will be calculated at the same time.
@@ -276,6 +343,7 @@ Metric:
base_metric_name: RecMetric # The base class of indicator calculation. For the output of the model, the indicator will be calculated based on this class
main_indicator: acc # The name of the indicator
key: "Student" # Select the main_indicator of this subnet as the criterion for saving the best model
+ ignore_space: False # whether to ignore space during evaulation
```
Taking the above configuration as an example, the accuracy metric of the `Student` subnet will be used as the judgment metric for saving the best model.
@@ -289,13 +357,13 @@ For more specific implementation of `DistillationMetric`, please refer to: [dist
There are two ways to fine-tune the recognition distillation task.
-1. Fine-tuning based on knowledge distillation: this situation is relatively simple, download the pre-trained model. Then configure the pre-training model path and your own data path in [ch_PP-OCRv2_rec_distillation.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml) to perform fine-tuning training of the model.
+1. Fine-tuning based on knowledge distillation: this situation is relatively simple, download the pre-trained model. Then configure the pre-training model path and your own data path in [ch_PP-OCRv2_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml) to perform fine-tuning training of the model.
2. Do not use knowledge distillation in fine-tuning: In this case, you need to first extract the student model parameters from the pre-training model. The specific steps are as follows.
- First download the pre-trained model and unzip it.
```shell
-wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar
-tar -xf ch_PP-OCRv2_rec_train.tar
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_train.tar
+tar -xf ch_PP-OCRv3_rec_train.tar
```
- Then use python to extract the student model parameters
@@ -303,7 +371,7 @@ tar -xf ch_PP-OCRv2_rec_train.tar
```python
import paddle
# Load the pre-trained model
-all_params = paddle.load("ch_PP-OCRv2_rec_train/best_accuracy.pdparams")
+all_params = paddle.load("ch_PP-OCRv3_rec_train/best_accuracy.pdparams")
# View the keys of the weight parameter
print(all_params.keys())
# Weight extraction of student model
@@ -311,10 +379,10 @@ s_params = {key[len("Student."):]: all_params[key] for key in all_params if "Stu
# View the keys of the weight parameters of the student model
print(s_params.keys())
# Save weight parameters
-paddle.save(s_params, "ch_PP-OCRv2_rec_train/student.pdparams")
+paddle.save(s_params, "ch_PP-OCRv3_rec_train/student.pdparams")
```
-After the extraction is complete, use [ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml) to modify the path of the pre-trained model (the path of the exported `student.pdparams` model) and your own data path to fine-tune the model.
+After the extraction is complete, use [ch_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml) to modify the path of the pre-trained model (the path of the exported `student.pdparams` model) and your own data path to fine-tune the model.
### 2.2 Detection Model Configuration File Analysis
diff --git a/ppocr/utils/utility.py b/ppocr/utils/utility.py
index dc2a6e7405e4d01ddffabf4a23c98bcf3ea3c205..48a84cfdf91555523da9ff882ee463d0e3d2d9b7 100755
--- a/ppocr/utils/utility.py
+++ b/ppocr/utils/utility.py
@@ -49,18 +49,23 @@ def get_check_global_params(mode):
return check_params
+def _check_image_file(path):
+ img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif'}
+ return any([path.lower().endswith(e) for e in img_end])
+
+
def get_image_file_list(img_file):
imgs_lists = []
if img_file is None or not os.path.exists(img_file):
raise Exception("not found any img file in {}".format(img_file))
- img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif', 'GIF'}
- if os.path.isfile(img_file) and imghdr.what(img_file) in img_end:
+ img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif'}
+ if os.path.isfile(img_file) and _check_image_file(file_path):
imgs_lists.append(img_file)
elif os.path.isdir(img_file):
for single_file in os.listdir(img_file):
file_path = os.path.join(img_file, single_file)
- if os.path.isfile(file_path) and imghdr.what(file_path) in img_end:
+ if os.path.isfile(file_path) and _check_image_file(file_path):
imgs_lists.append(file_path)
if len(imgs_lists) == 0:
raise Exception("not found any img file in {}".format(img_file))