fix doc of slim
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@ -24,12 +24,12 @@
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```bash
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git clone https://github.com/PaddlePaddle/PaddleSlim.git
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git checkout develop
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cd Paddleslim
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cd PaddleSlim
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python3 setup.py install
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```
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### 2. 获取预训练模型
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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。
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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。
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### 3. 敏感度分析训练
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@ -23,14 +23,14 @@ Five steps for OCR model prune:
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```bash
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git clone https://github.com/PaddlePaddle/PaddleSlim.git
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git checkout develop
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cd Paddleslim
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cd PaddleSlim
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python3 setup.py install
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```
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### 2. Download Pretrain Model
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Model prune needs to load pre-trained models.
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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.
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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.
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### 3. Pruning sensitivity analysis
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@ -23,7 +23,7 @@
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```bash
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git clone https://github.com/PaddlePaddle/PaddleSlim.git
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cd Paddleslim
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cd PaddleSlim
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python setup.py install
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```
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@ -37,12 +37,12 @@ PaddleOCR提供了一系列训练好的[模型](../../../doc/doc_ch/models_list.
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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：
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```bash
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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
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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
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# 比如下载提供的训练模型
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wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
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tar -xf ch_ppocr_mobile_v2.0_det_train.tar
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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
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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
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```
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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。
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@ -52,10 +52,10 @@ python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global
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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：
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```bash
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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
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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
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```
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### 5. 量化模型部署
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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。
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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。
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量化模型部署的可参考 [移动端模型部署](../../lite/readme.md)
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@ -26,7 +26,7 @@ After training, if you want to further compress the model size and accelerate th
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```bash
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git clone https://github.com/PaddlePaddle/PaddleSlim.git
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cd Paddleslim
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cd PaddlSlim
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python setup.py install
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```
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@ -43,12 +43,12 @@ After the quantization strategy is defined, the model can be quantified.
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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:
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```bash
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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
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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
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# download provided model
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wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
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tar -xf ch_ppocr_mobile_v2.0_det_train.tar
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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
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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
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```
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@ -57,7 +57,7 @@ python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global
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After getting the model after pruning and finetuning we, can export it as inference_model for predictive deployment:
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```bash
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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
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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
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```
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### 5. Deploy
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