From 7cb0b5499e84258552f9f06da84cb1f8e9d2dd5c Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Fri, 21 May 2021 09:47:17 +0800 Subject: [PATCH] fix doc of slim --- deploy/slim/prune/README.md | 4 ++-- deploy/slim/prune/README_en.md | 4 ++-- deploy/slim/quantization/README.md | 10 +++++----- deploy/slim/quantization/README_en.md | 8 ++++---- 4 files changed, 13 insertions(+), 13 deletions(-) diff --git a/deploy/slim/prune/README.md b/deploy/slim/prune/README.md index e4407455..bc121467 100644 --- a/deploy/slim/prune/README.md +++ b/deploy/slim/prune/README.md @@ -24,12 +24,12 @@ ```bash git clone https://github.com/PaddlePaddle/PaddleSlim.git git checkout develop -cd Paddleslim +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 diff --git a/deploy/slim/prune/README_en.md b/deploy/slim/prune/README_en.md index c2bb9d53..0d04c7ea 100644 --- a/deploy/slim/prune/README_en.md +++ b/deploy/slim/prune/README_en.md @@ -23,14 +23,14 @@ Five steps for OCR model prune: ```bash git clone https://github.com/PaddlePaddle/PaddleSlim.git git checkout develop -cd Paddleslim +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 diff --git a/deploy/slim/quantization/README.md b/deploy/slim/quantization/README.md index 59ea08b1..62bc408f 100644 --- a/deploy/slim/quantization/README.md +++ b/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) diff --git a/deploy/slim/quantization/README_en.md b/deploy/slim/quantization/README_en.md index 231cdb64..bf3e91d6 100644 --- a/deploy/slim/quantization/README_en.md +++ b/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 -- GitLab