From 92671d73561be8a85ee9cfff937627b33c8a51c2 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Tue, 18 May 2021 09:41:55 +0800 Subject: [PATCH] pretrain_weights to pretrained_model --- deploy/slim/prune/README.md | 4 ++-- deploy/slim/prune/README_en.md | 4 ++-- deploy/slim/quantization/README.md | 4 ++-- deploy/slim/quantization/README_en.md | 5 ++--- 4 files changed, 8 insertions(+), 9 deletions(-) diff --git a/deploy/slim/prune/README.md b/deploy/slim/prune/README.md index d9675c5a..e4407455 100644 --- a/deploy/slim/prune/README.md +++ b/deploy/slim/prune/README.md @@ -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.pretrain_weights="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" ``` ### 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.pretrain_weights=./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=inference_model ``` inference model的预测和部署参考： diff --git a/deploy/slim/prune/README_en.md b/deploy/slim/prune/README_en.md index 70cfd580..c2bb9d53 100644 --- a/deploy/slim/prune/README_en.md +++ b/deploy/slim/prune/README_en.md @@ -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.pretrain_weights="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" ``` @@ -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.pretrain_weights=./output/det_db/best_accuracy Global.test_batch_size_per_card=1 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=inference_model ``` Reference for prediction and deployment of inference model: diff --git a/deploy/slim/quantization/README.md b/deploy/slim/quantization/README.md index 4ac3f7c3..41ed3ef5 100644 --- a/deploy/slim/quantization/README.md +++ b/deploy/slim/quantization/README.md @@ -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.pretrain_weights='your trained model' Global.save_model_dir=./output/quant_model +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 # 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.pretrain_weights=./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/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 ``` 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。 diff --git a/deploy/slim/quantization/README_en.md b/deploy/slim/quantization/README_en.md index 36407a2b..231cdb64 100644 --- a/deploy/slim/quantization/README_en.md +++ b/deploy/slim/quantization/README_en.md @@ -43,13 +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.pretrain_weights='your trained model' Global.save_model_dir=./output/quant_model +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 # 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.pretrain_weights=./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/det_mv3_db.yml -o Global.pretrained_model=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_model_dir=./output/quant_model ``` -- GitLab