From c6ab13203cb87453d4dc037f90c9a08c11bc469d Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Wed, 2 Dec 2020 18:43:15 +0800 Subject: [PATCH] update angle_class doc --- doc/doc_ch/angle_class.md | 43 ++++++++++++++++++++---------------- doc/doc_en/angle_class_en.md | 38 ++++++++++++++++++------------- 2 files changed, 47 insertions(+), 34 deletions(-) diff --git a/doc/doc_ch/angle_class.md b/doc/doc_ch/angle_class.md index b2118661..d6a36b86 100644 --- a/doc/doc_ch/angle_class.md +++ b/doc/doc_ch/angle_class.md @@ -45,7 +45,7 @@ train_data/cls/word_002.jpg 180 ``` |-train_data |-cls - |- 和一个cls_gt_test.txt + |- cls_gt_test.txt |- test |- word_001.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* ``` -# 设置PYTHONPATH路径 -export PYTHONPATH=$PYTHONPATH:. -# GPU训练 支持单卡，多卡训练，通过CUDA_VISIBLE_DEVICES指定卡号 -export CUDA_VISIBLE_DEVICES=0,1,2,3 -# 启动训练 -python3 tools/train.py -c configs/cls/cls_mv3.yml +# GPU训练 支持单卡，多卡训练，通过selected_gpus指定卡号 +# 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 +python3 -m paddle.distributed.launch --selected_gpus '0,1,2,3,4,5,6,7' tools/train.py -c configs/cls/cls_mv3.yml ``` - 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)。 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) -[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py) +[rec_img_aug.py](../../ppocr/data/imaug/rec_img_aug.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* ### 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。 @@ -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 # 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。 -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 -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： -![](../imgs_words/en/word_1.png) +![](../imgs_words/ch/word_1.jpg) 得到输入图像的预测结果： ``` -infer_img: doc/imgs_words/en/word_1.png - scores: [[0.93161047 0.06838956]] - label: [0] +infer_img: doc/imgs_words/ch/word_1.jpg + result: ('0', 0.9998784) ``` diff --git a/doc/doc_en/angle_class_en.md b/doc/doc_en/angle_class_en.md index c7fff3a1..defdff3c 100644 --- a/doc/doc_en/angle_class_en.md +++ b/doc/doc_en/angle_class_en.md @@ -65,26 +65,35 @@ Start training: ``` # Set PYTHONPATH path export PYTHONPATH=$PYTHONPATH:. -# GPU training Support single card and multi-card training, specify the card number through CUDA_VISIBLE_DEVICES -export CUDA_VISIBLE_DEVICES=0,1,2,3 -# Training icdar15 English data -python3 tools/train.py -c configs/cls/cls_mv3.yml +# GPU training Support single card and multi-card training, specify the card number through selected_gpus +# Start training, the following command has been written into the train.sh file, just modify the configuration file path in the file +python3 -m paddle.distributed.launch --selected_gpus '0,1,2,3,4,5,6,7' tools/train.py -c configs/cls/cls_mv3.yml ``` - 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. 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) -[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py) +[rec_img_aug.py](../../ppocr/data/imaug/rec_img_aug.py) +[randaugment.py](../../ppocr/data/imaug/randaugment.py) - 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. @@ -92,7 +101,7 @@ If the evaluation set is large, the test will be time-consuming. It is recommend ### 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 @@ -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. -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 -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: -![](../imgs_words/en/word_1.png) +![](../imgs_words_en/word_10.png) Get the prediction result of the input image: ``` -infer_img: doc/imgs_words/en/word_1.png - scores: [[0.93161047 0.06838956]] - label: [0] +infer_img: doc/imgs_words_en/word_10.png + result: ('0', 0.9999995) ``` -- GitLab