Merge pull request #718 from tink2123/save_log

update doc for saving log

doc/doc_ch/customize.md

@@ -6,7 +6,7 @@
 
 PaddleOCR提供了EAST、DB两种文本检测算法，均支持MobileNetV3、ResNet50_vd两种骨干网络，根据需要选择相应的配置文件，启动训练。例如，训练使用MobileNetV3作为骨干网络的DB检测模型（即超轻量模型使用的配置）：
 ```
-python3 tools/train.py -c configs/det/det_mv3_db.yml
+python3 tools/train.py -c configs/det/det_mv3_db.yml 2>&1 | tee det_db.log
 ```
 更详细的数据准备和训练教程参考文档教程中[文本检测模型训练/评估/预测](./detection.md)。
 
@@ -14,7 +14,7 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml
 
 PaddleOCR提供了CRNN、Rosetta、STAR-Net、RARE四种文本识别算法，均支持MobileNetV3、ResNet34_vd两种骨干网络，根据需要选择相应的配置文件，启动训练。例如，训练使用MobileNetV3作为骨干网络的CRNN识别模型（即超轻量模型使用的配置）：
 ```
-python3 tools/train.py -c configs/rec/rec_chinese_lite_train.yml
+python3 tools/train.py -c configs/rec/rec_chinese_lite_train.yml 2>&1 | tee rec_ch_lite.log
 ```
 更详细的数据准备和训练教程参考文档教程中[文本识别模型训练/评估/预测](./recognition.md)。
 

doc/doc_ch/detection.md

@@ -62,7 +62,10 @@ tar -xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_model
 *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*
 
 ```shell
-python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained/
+# 训练 mv3_db 模型，并将训练日志保存为 tain_det.log
+python3 tools/train.py -c configs/det/det_mv3_db.yml \ 
+     -o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained/ \
+     2>&1 | tee train_det.log
 ```
 
 上述指令中，通过-c 选择训练使用configs/det/det_db_mv3.yml配置文件。

doc/doc_ch/recognition.md

@@ -128,8 +128,8 @@ tar -xf rec_mv3_none_bilstm_ctc.tar && rm -rf rec_mv3_none_bilstm_ctc.tar
 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
-# 训练icdar15英文数据
-python3 tools/train.py -c configs/rec/rec_icdar15_train.yml
+# 训练icdar15英文数据 并将训练日志保存为 tain_rec.log
+python3 tools/train.py -c configs/rec/rec_icdar15_train.yml 2>&1 | tee train_rec.log
 ```
 
 - 数据增强

doc/doc_en/customize_en.md

@@ -6,7 +6,7 @@ The process of making a customized ultra-lightweight OCR models can be divided i
 
 PaddleOCR provides two text detection algorithms: EAST and DB. Both support MobileNetV3 and ResNet50_vd backbone networks, select the corresponding configuration file as needed and start training. For example, to train with MobileNetV3 as the backbone network for DB detection model :
 ```
-python3 tools/train.py -c configs/det/det_mv3_db.yml
+python3 tools/train.py -c configs/det/det_mv3_db.yml 2>&1 | tee det_db.log
 ```
 For more details about data preparation and training tutorials, refer to the documentation [Text detection model training/evaluation/prediction](./detection_en.md)
 
@@ -14,7 +14,7 @@ For more details about data preparation and training tutorials, refer to the doc
 
 PaddleOCR provides four text recognition algorithms: CRNN, Rosetta, STAR-Net, and RARE. They all support two backbone networks: MobileNetV3 and ResNet34_vd, select the corresponding configuration files as needed to start training. For example, to train a CRNN recognition model that uses MobileNetV3 as the backbone network:
 ```
-python3 tools/train.py -c configs/rec/rec_chinese_lite_train.yml
+python3 tools/train.py -c configs/rec/rec_chinese_lite_train.yml 2>&1 | tee rec_ch_lite.log
 ```
 For more details about data preparation and training tutorials, refer to the documentation [Text recognition model training/evaluation/prediction](./recognition_en.md)
 

doc/doc_en/detection_en.md

@@ -62,7 +62,7 @@ tar -xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_model
 #### START TRAINING
 *If CPU version installed, please set the parameter `use_gpu` to `false` in the configuration.*
 ```shell
-python3 tools/train.py -c configs/det/det_mv3_db.yml
+python3 tools/train.py -c configs/det/det_mv3_db.yml 2>&1 | tee train_det.log
 ```
 
 In the above instruction, use `-c` to select the training to use the `configs/det/det_db_mv3.yml` configuration file.

doc/doc_en/recognition_en.md

@@ -130,8 +130,8 @@ Start training:
 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/rec/rec_icdar15_train.yml
+# Training icdar15 English data and saving the log as train_rec.log
+python3 tools/train.py -c configs/rec/rec_icdar15_train.yml 2>&1 | tee train_rec.log
 ```
 
 - Data Augmentation