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update angle_class doc

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WenmuZhou 2020-12-02 18:43:15 +08:00
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doc/doc_ch/angle_class.md
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@ -45,7 +45,7 @@ train_data/cls/word_002.jpg 180
``` ```
|-train_data |-train_data
|-cls |-cls
|- 和一个cls_gt_test.txt |- cls_gt_test.txt
|- test |- test
|- word_001.jpg |- word_001.jpg
|- word_002.jpg |- word_002.jpg
@ -62,29 +62,36 @@ PaddleOCR提供了训练脚本、评估脚本和预测脚本。
*如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false* *如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
``` ```
# 设置PYTHONPATH路径 # GPU训练 支持单卡，多卡训练，通过selected_gpus指定卡号
export PYTHONPATH=$PYTHONPATH:. # 启动训练，下面的命令已经写入train.sh文件中，只需修改文件里的配置文件路径即可
# GPU训练 支持单卡，多卡训练，通过CUDA_VISIBLE_DEVICES指定卡号 python3 -m paddle.distributed.launch --selected_gpus '0,1,2,3,4,5,6,7' tools/train.py -c configs/cls/cls_mv3.yml
export CUDA_VISIBLE_DEVICES=0,1,2,3
# 启动训练
python3 tools/train.py -c configs/cls/cls_mv3.yml
``` ```
- 数据增强 - 数据增强
PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入扰动，请在配置文件中设置 `distort: true`。 PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入扰动，请在配置文件中取消`Train.dataset.transforms`下的`RecAug`和`RandAugment`字段的注释。
默认的扰动方式有：颜色空间转换(cvtColor)、模糊(blur)、抖动(jitter)、噪声(Gasuss noise)、随机切割(random crop)、透视(perspective)、颜色反转(reverse),随机数据增强(RandAugment)。 默认的扰动方式有：颜色空间转换(cvtColor)、模糊(blur)、抖动(jitter)、噪声(Gasuss noise)、随机切割(random crop)、透视(perspective)、颜色反转(reverse),随机数据增强(RandAugment)。
训练过程中除随机数据增强外每种扰动方式以50%的概率被选择，具体代码实现请参考： 训练过程中除随机数据增强外每种扰动方式以50%的概率被选择，具体代码实现请参考：
[randaugment.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/cls/randaugment.py) [rec_img_aug.py](../../ppocr/data/imaug/rec_img_aug.py)
[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py) [randaugment.py](../../ppocr/data/imaug/randaugment.py)
*由于OpenCV的兼容性问题，扰动操作暂时只支持linux* *由于OpenCV的兼容性问题，扰动操作暂时只支持linux*
### 训练 ### 训练
PaddleOCR支持训练和评估交替进行, 可以在 `configs/cls/cls_mv3.yml` 中修改 `eval_batch_step` 设置评估频率，默认每500个iter评估一次。评估过程中默认将最佳acc模型，保存为 `output/cls_mv3/best_accuracy` 。 PaddleOCR支持训练和评估交替进行, 可以在 `configs/cls/cls_mv3.yml` 中修改 `eval_batch_step` 设置评估频率，默认每1000个iter评估一次。训练过程中将会保存如下内容：
```bash
├── best_accuracy.pdopt # 最佳模型的优化器参数
├── best_accuracy.pdparams # 最佳模型的参数
├── best_accuracy.states # 最佳模型的指标和epoch等信息
├── config.yml # 本次实验的配置文件
├── latest.pdopt # 最新模型的优化器参数
├── latest.pdparams # 最新模型的参数
├── latest.states # 最新模型的指标和epoch等信息
└── train.log # 训练日志
```
如果验证集很大，测试将会比较耗时，建议减少评估次数，或训练完再进行评估。 如果验证集很大，测试将会比较耗时，建议减少评估次数，或训练完再进行评估。
@ -92,9 +99,8 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/cls/cls_mv3.yml`
### 评估 ### 评估
评估数据集可以通过`configs/cls/cls_reader.yml` 修改EvalReader中的 `label_file_path` 设置。 评估数据集可以通过修改`configs/cls/cls_mv3.yml`文件里的`Eval.dataset.label_file_list` 字段设置。
*注意* 评估时必须确保配置文件中 infer_img 字段为空
``` ```
export CUDA_VISIBLE_DEVICES=0 export CUDA_VISIBLE_DEVICES=0
# GPU 评估， Global.checkpoints 为待测权重 # GPU 评估， Global.checkpoints 为待测权重
@ -107,21 +113,20 @@ python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/
使用 PaddleOCR 训练好的模型，可以通过以下脚本进行快速预测。 使用 PaddleOCR 训练好的模型，可以通过以下脚本进行快速预测。
默认预测图片存储在 `infer_img` 里，通过 `-o Global.checkpoints` 指定权重： 通过 `Global.infer_img` 指定预测图片或文件夹路径，通过 `Global.checkpoints` 指定权重：
``` ```
# 预测分类结果 # 预测分类结果
python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/ch/word_1.jpg
``` ```
预测图片： 预测图片：
![](../imgs_words/en/word_1.png) ![](../imgs_words/ch/word_1.jpg)
得到输入图像的预测结果： 得到输入图像的预测结果：
``` ```
infer_img: doc/imgs_words/en/word_1.png infer_img: doc/imgs_words/ch/word_1.jpg
scores: [[0.93161047 0.06838956]] result: ('0', 0.9998784)
label: [0]
``` ```

38
doc/doc_en/angle_class_en.md
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@ -65,26 +65,35 @@ Start training:
``` ```
# Set PYTHONPATH path # Set PYTHONPATH path
export PYTHONPATH=$PYTHONPATH:. export PYTHONPATH=$PYTHONPATH:.
# GPU training Support single card and multi-card training, specify the card number through CUDA_VISIBLE_DEVICES # GPU training Support single card and multi-card training, specify the card number through selected_gpus
export CUDA_VISIBLE_DEVICES=0,1,2,3 # Start training, the following command has been written into the train.sh file, just modify the configuration file path in the file
# Training icdar15 English data python3 -m paddle.distributed.launch --selected_gpus '0,1,2,3,4,5,6,7' tools/train.py -c configs/cls/cls_mv3.yml
python3 tools/train.py -c configs/cls/cls_mv3.yml
``` ```
- Data Augmentation - Data Augmentation
PaddleOCR provides a variety of data augmentation methods. If you want to add disturbance during training, please set `distort: true` in the configuration file. PaddleOCR provides a variety of data augmentation methods. If you want to add disturbance during training, Please uncomment the `RecAug` and `RandAugment` fields under `Train.dataset.transforms` in the configuration file.
The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, random crop, perspective, color reverse, RandAugment. The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, random crop, perspective, color reverse, RandAugment.
Except for RandAugment, each disturbance method is selected with a 50% probability during the training process. For specific code implementation, please refer to: Except for RandAugment, each disturbance method is selected with a 50% probability during the training process. For specific code implementation, please refer to:
[randaugment.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/cls/randaugment.py) [rec_img_aug.py](../../ppocr/data/imaug/rec_img_aug.py)
[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py) [randaugment.py](../../ppocr/data/imaug/randaugment.py)
- Training - Training
PaddleOCR supports alternating training and evaluation. You can modify `eval_batch_step` in `configs/cls/cls_mv3.yml` to set the evaluation frequency. By default, it is evaluated every 500 iter and the best acc model is saved under `output/cls_mv3/best_accuracy` during the evaluation process. PaddleOCR supports alternating training and evaluation. You can modify `eval_batch_step` in `configs/cls/cls_mv3.yml` to set the evaluation frequency. By default, it is evaluated every 1000 iter. The following content will be saved during training:
```bash
├── best_accuracy.pdopt # Optimizer parameters for the best model
├── best_accuracy.pdparams # Parameters of the best model
├── best_accuracy.states # Metric info and epochs of the best model
├── config.yml # Configuration file for this experiment
├── latest.pdopt # Optimizer parameters for the latest model
├── latest.pdparams # Parameters of the latest model
├── latest.states # Metric info and epochs of the latest model
└── train.log # Training log
```
If the evaluation set is large, the test will be time-consuming. It is recommended to reduce the number of evaluations, or evaluate after training. If the evaluation set is large, the test will be time-consuming. It is recommended to reduce the number of evaluations, or evaluate after training.
@ -92,7 +101,7 @@ If the evaluation set is large, the test will be time-consuming. It is recommend
### EVALUATION ### EVALUATION
The evaluation data set can be modified via `configs/cls/cls_reader.yml` setting of `label_file_path` in EvalReader. The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/cls/cls_mv3.yml` file.
``` ```
export CUDA_VISIBLE_DEVICES=0 export CUDA_VISIBLE_DEVICES=0
@ -106,21 +115,20 @@ python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/
Using the model trained by paddleocr, you can quickly get prediction through the following script. Using the model trained by paddleocr, you can quickly get prediction through the following script.
The default prediction picture is stored in `infer_img`, and the weight is specified via `-o Global.checkpoints`: Use `Global.infer_img` to specify the path of the predicted picture or folder, and use `Global.checkpoints` to specify the weight:
``` ```
# Predict English results # Predict English results
python3 tools/infer_rec.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/en/word_1.jpg python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words_en/word_10.png
``` ```
Input image: Input image:
![](../imgs_words/en/word_1.png) ![](../imgs_words_en/word_10.png)
Get the prediction result of the input image: Get the prediction result of the input image:
``` ```
infer_img: doc/imgs_words/en/word_1.png infer_img: doc/imgs_words_en/word_10.png
scores: [[0.93161047 0.06838956]] result: ('0', 0.9999995)
label: [0]
``` ```