Merge pull request #2852 from LDOUBLEV/fix_dyg

fix doc of slim

deploy/slim/prune/README.md

@@ -23,13 +23,13 @@
 
 ```bash
 git clone https://github.com/PaddlePaddle/PaddleSlim.git
+cd PaddleSlim
 git checkout develop
-cd Paddleslim
 python3 setup.py install
 ```
 
 ### 2. 获取预训练模型
-模型裁剪需要加载事先训练好的模型，PaddleOCR也提供了一系列(模型)[../../../doc/doc_ch/models_list.md]，开发者可根据需要自行选择模型或使用自己的模型。
+模型裁剪需要加载事先训练好的模型，PaddleOCR也提供了一系列[模型](../../../doc/doc_ch/models_list.md)，开发者可根据需要自行选择模型或使用自己的模型。
 
 ### 3. 敏感度分析训练
 
@@ -49,14 +49,14 @@ python3 setup.py install
 
 进入PaddleOCR根目录，通过以下命令对模型进行敏感度分析训练：
 ```bash
-python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model"
+python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model" Global.save_model_dir=./output/prune_model/
 ```
 
 ### 4. 导出模型、预测部署
 
 在得到裁剪训练保存的模型后，我们可以将其导出为inference_model：
 ```bash
-pytho3.7 deploy/slim/prune/export_prune_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./output/det_db/best_accuracy  Global.save_inference_dir=inference_model
+pytho3.7 deploy/slim/prune/export_prune_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./output/det_db/best_accuracy  Global.save_inference_dir=./prune/prune_inference_model
 ```
 
 inference model的预测和部署参考：

deploy/slim/prune/README_en.md

@@ -22,15 +22,15 @@ Five steps for OCR model prune:
 
 ```bash
 git clone https://github.com/PaddlePaddle/PaddleSlim.git
+cd PaddleSlim
 git checkout develop
-cd Paddleslim
 python3 setup.py install
 ```
 
 
 ### 2. Download Pretrain Model
 Model prune needs to load pre-trained models.
-PaddleOCR also provides a series of (models)[../../../doc/doc_en/models_list_en.md]. Developers can choose their own models or use their own models according to their needs.
+PaddleOCR also provides a series of [models](../../../doc/doc_en/models_list_en.md). Developers can choose their own models or use their own models according to their needs.
 
 
 ### 3. Pruning sensitivity analysis
@@ -54,7 +54,7 @@ Enter the PaddleOCR root directory，perform sensitivity analysis on the model w
 
 ```bash
 
-python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model"
+python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model"  Global.save_model_dir=./output/prune_model/
 
 ```
 
@@ -63,7 +63,7 @@ python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_
 
 We can export the pruned model as inference_model for deployment:
 ```bash
-python deploy/slim/prune/export_prune_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml  -o Global.pretrained_model=./output/det_db/best_accuracy Global.save_inference_dir=inference_model
+python deploy/slim/prune/export_prune_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml  -o Global.pretrained_model=./output/det_db/best_accuracy  Global.save_inference_dir=./prune/prune_inference_model
 ```
 
 Reference for prediction and deployment of inference model:

deploy/slim/quantization/README.md

@@ -23,7 +23,7 @@
 
 ```bash
 git clone https://github.com/PaddlePaddle/PaddleSlim.git
-cd Paddleslim
+cd PaddleSlim
 python setup.py install
 ```
 
@@ -37,12 +37,12 @@ PaddleOCR提供了一系列训练好的[模型](../../../doc/doc_ch/models_list.
 
 量化训练的代码位于slim/quantization/quant.py 中，比如训练检测模型，训练指令如下：
 ```bash
-python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model='your trained model'   Global.save_model_dir=./output/quant_model
+python deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model='your trained model'   Global.save_model_dir=./output/quant_model
 
 # 比如下载提供的训练模型
 wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
 tar -xf ch_ppocr_mobile_v2.0_det_train.tar
-python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./ch_ppocr_mobile_v2.0_det_train/best_accuracy   Global.save_inference_dir=./output/quant_inference_model
+python deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_ppocr_mobile_v2.0_det_train/best_accuracy   Global.save_model_dir=./output/quant_inference_model
 
 ```
 如果要训练识别模型的量化，修改配置文件和加载的模型参数即可。
@@ -52,10 +52,10 @@ python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global
 在得到量化训练保存的模型后，我们可以将其导出为inference_model，用于预测部署：
 
 ```bash
-python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_model_dir=./output/quant_inference_model
+python deploy/slim/quantization/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_inference_dir=./output/quant_inference_model
 ```
 
 ### 5. 量化模型部署
 
-上述步骤导出的量化模型，参数精度仍然是FP32，表现为量化后的模型大小不变，但是参数的数值范围是int8，导出的模型可以通过PaddleLite的opt模型转换工具完成模型转换。
+上述步骤导出的量化模型，参数精度仍然是FP32，但是参数的数值范围是int8，导出的模型可以通过PaddleLite的opt模型转换工具完成模型转换。
 量化模型部署的可参考 [移动端模型部署](../../lite/readme.md)

deploy/slim/quantization/README_en.md

@@ -26,7 +26,7 @@ After training, if you want to further compress the model size and accelerate th
 
 ```bash
 git clone https://github.com/PaddlePaddle/PaddleSlim.git
-cd Paddleslim
+cd PaddlSlim
 python setup.py install
 ```
 
@@ -43,12 +43,12 @@ After the quantization strategy is defined, the model can be quantified.
 
 The code for quantization training is located in `slim/quantization/quant.py`. For example, to train a detection model, the training instructions are as follows:
 ```bash
-python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model='your trained model'   Global.save_model_dir=./output/quant_model
+python deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model='your trained model'   Global.save_model_dir=./output/quant_model
 
 # download provided model
 wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
 tar -xf ch_ppocr_mobile_v2.0_det_train.tar
-python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./ch_ppocr_mobile_v2.0_det_train/best_accuracy   Global.save_model_dir=./output/quant_model
+python deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_ppocr_mobile_v2.0_det_train/best_accuracy   Global.save_model_dir=./output/quant_model
 ```
 
 
@@ -57,7 +57,7 @@ python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global
 After getting the model after pruning and finetuning we, can export it as inference_model for predictive deployment:
 
 ```bash
-python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_inference_dir=./output/quant_inference_model
+python deploy/slim/quantization/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_inference_dir=./output/quant_inference_model
 ```
 
 ### 5. Deploy