From c64e235a1f88075168fa326661b8bbd2a8c91166 Mon Sep 17 00:00:00 2001 From: Double_V Date: Fri, 7 May 2021 10:48:31 +0800 Subject: [PATCH] delete load_static_weights for detection (#2725) * delete load_static_weights for detection * master to develop for PaddleClas referencee --- .../det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml | 5 ---- .../ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml | 5 ---- configs/det/det_mv3_db.yml | 5 ---- configs/det/det_mv3_east.yml | 5 ---- configs/det/det_r50_vd_db.yml | 5 ---- configs/det/det_r50_vd_east.yml | 5 ---- configs/det/det_r50_vd_sast_icdar15.yml | 5 ---- configs/det/det_r50_vd_sast_totaltext.yml | 5 ---- configs/e2e/e2e_r50_vd_pg.yml | 5 ---- doc/doc_ch/detection.md | 27 +++++++------------ doc/doc_ch/inference.md | 19 ++++++------- doc/doc_en/detection_en.md | 27 ++++++------------- doc/doc_en/inference_en.md | 19 ++++++------- 13 files changed, 33 insertions(+), 104 deletions(-) diff --git a/configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml b/configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml index e001c376..87718cad 100644 --- a/configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml +++ b/configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml @@ -7,11 +7,6 @@ Global: save_epoch_step: 1200 # evaluation is run every 5000 iterations after the 4000th iteration eval_batch_step: [3000, 2000] - # 1. If pretrained_model is saved in static mode, such as classification pretrained model - # from static branch, load_static_weights must be set as True. - # 2. If you want to finetune the pretrained models we provide in the docs, - # you should set load_static_weights as False. - load_static_weights: True cal_metric_during_train: False pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained checkpoints: diff --git a/configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml b/configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml index 4229248d..7b07ef99 100644 --- a/configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml +++ b/configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml @@ -7,11 +7,6 @@ Global: save_epoch_step: 1200 # evaluation is run every 5000 iterations after the 4000th iteration eval_batch_step: [3000, 2000] - # 1. If pretrained_model is saved in static mode, such as classification pretrained model - # from static branch, load_static_weights must be set as True. - # 2. If you want to finetune the pretrained models we provide in the docs, - # you should set load_static_weights as False. - load_static_weights: True cal_metric_during_train: False pretrained_model: ./pretrain_models/ResNet18_vd_pretrained checkpoints: diff --git a/configs/det/det_mv3_db.yml b/configs/det/det_mv3_db.yml index f8aab705..b69ed58c 100644 --- a/configs/det/det_mv3_db.yml +++ b/configs/det/det_mv3_db.yml @@ -7,11 +7,6 @@ Global: save_epoch_step: 1200 # evaluation is run every 2000 iterations eval_batch_step: [0, 2000] - # 1. If pretrained_model is saved in static mode, such as classification pretrained model - # from static branch, load_static_weights must be set as True. - # 2. If you want to finetune the pretrained models we provide in the docs, - # you should set load_static_weights as False. - load_static_weights: True cal_metric_during_train: False pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained checkpoints: diff --git a/configs/det/det_mv3_east.yml b/configs/det/det_mv3_east.yml index 187ac160..4ae32ab0 100644 --- a/configs/det/det_mv3_east.yml +++ b/configs/det/det_mv3_east.yml @@ -7,11 +7,6 @@ Global: save_epoch_step: 1000 # evaluation is run every 5000 iterations after the 4000th iteration eval_batch_step: [4000, 5000] - # 1. If pretrained_model is saved in static mode, such as classification pretrained model - # from static branch, load_static_weights must be set as True. - # 2. If you want to finetune the pretrained models we provide in the docs, - # you should set load_static_weights as False. - load_static_weights: True cal_metric_during_train: False pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained checkpoints: diff --git a/configs/det/det_r50_vd_db.yml b/configs/det/det_r50_vd_db.yml index 3fa8948d..42b3898e 100644 --- a/configs/det/det_r50_vd_db.yml +++ b/configs/det/det_r50_vd_db.yml @@ -7,11 +7,6 @@ Global: save_epoch_step: 1200 # evaluation is run every 2000 iterations eval_batch_step: [0,2000] - # 1. If pretrained_model is saved in static mode, such as classification pretrained model - # from static branch, load_static_weights must be set as True. - # 2. If you want to finetune the pretrained models we provide in the docs, - # you should set load_static_weights as False. - load_static_weights: True cal_metric_during_train: False pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained checkpoints: diff --git a/configs/det/det_r50_vd_east.yml b/configs/det/det_r50_vd_east.yml index abef0b61..0253c5bd 100644 --- a/configs/det/det_r50_vd_east.yml +++ b/configs/det/det_r50_vd_east.yml @@ -7,11 +7,6 @@ Global: save_epoch_step: 1000 # evaluation is run every 5000 iterations after the 4000th iteration eval_batch_step: [4000, 5000] - # 1. If pretrained_model is saved in static mode, such as classification pretrained model - # from static branch, load_static_weights must be set as True. - # 2. If you want to finetune the pretrained models we provide in the docs, - # you should set load_static_weights as False. - load_static_weights: True cal_metric_during_train: False pretrained_model: ./pretrain_models/ResNet50_vd_pretrained/ checkpoints: diff --git a/configs/det/det_r50_vd_sast_icdar15.yml b/configs/det/det_r50_vd_sast_icdar15.yml index c90327b2..dbfcefca 100755 --- a/configs/det/det_r50_vd_sast_icdar15.yml +++ b/configs/det/det_r50_vd_sast_icdar15.yml @@ -7,11 +7,6 @@ Global: save_epoch_step: 1000 # evaluation is run every 5000 iterations after the 4000th iteration eval_batch_step: [4000, 5000] - # 1. If pretrained_model is saved in static mode, such as classification pretrained model - # from static branch, load_static_weights must be set as True. - # 2. If you want to finetune the pretrained models we provide in the docs, - # you should set load_static_weights as False. - load_static_weights: True cal_metric_during_train: False pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/ checkpoints: diff --git a/configs/det/det_r50_vd_sast_totaltext.yml b/configs/det/det_r50_vd_sast_totaltext.yml index e6f467c6..88dd31f3 100755 --- a/configs/det/det_r50_vd_sast_totaltext.yml +++ b/configs/det/det_r50_vd_sast_totaltext.yml @@ -7,11 +7,6 @@ Global: save_epoch_step: 1000 # evaluation is run every 5000 iterations after the 4000th iteration eval_batch_step: [4000, 5000] - # 1. If pretrained_model is saved in static mode, such as classification pretrained model - # from static branch, load_static_weights must be set as True. - # 2. If you want to finetune the pretrained models we provide in the docs, - # you should set load_static_weights as False. - load_static_weights: True cal_metric_during_train: False pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/ checkpoints: diff --git a/configs/e2e/e2e_r50_vd_pg.yml b/configs/e2e/e2e_r50_vd_pg.yml index 8128bde1..4a6e19f4 100644 --- a/configs/e2e/e2e_r50_vd_pg.yml +++ b/configs/e2e/e2e_r50_vd_pg.yml @@ -7,11 +7,6 @@ Global: save_epoch_step: 10 # evaluation is run every 0 iterationss after the 1000th iteration eval_batch_step: [ 0, 1000 ] - # 1. If pretrained_model is saved in static mode, such as classification pretrained model - # from static branch, load_static_weights must be set as True. - # 2. If you want to finetune the pretrained models we provide in the docs, - # you should set load_static_weights as False. - load_static_weights: False cal_metric_during_train: False pretrained_model: checkpoints: diff --git a/doc/doc_ch/detection.md b/doc/doc_ch/detection.md index a8dee65a..08b309cb 100644 --- a/doc/doc_ch/detection.md +++ b/doc/doc_ch/detection.md @@ -45,26 +45,17 @@ json.dumps编码前的图像标注信息是包含多个字典的list，字典中 ## 快速启动训练 首先下载模型backbone的pretrain model，PaddleOCR的检测模型目前支持两种backbone，分别是MobileNetV3、ResNet_vd系列， -您可以根据需求使用[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures)中的模型更换backbone。 +您可以根据需求使用[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/develop/ppcls/modeling/architectures)中的模型更换backbone， +对应的backbone预训练模型可以从[PaddleClas repo 主页中找到下载链接](https://github.com/PaddlePaddle/PaddleClas#mobile-series)。 ```shell cd PaddleOCR/ +# 根据backbone的不同选择下载对应的预训练模型 # 下载MobileNetV3的预训练模型 -wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_5_pretrained.tar +wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams # 或，下载ResNet18_vd的预训练模型 -wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_vd_pretrained.tar +wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_vd_pretrained.pdparams # 或，下载ResNet50_vd的预训练模型 -wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar - -# 解压预训练模型文件，以MobileNetV3为例 -tar -xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models/ - -# 注：正确解压backbone预训练权重文件后，文件夹下包含众多以网络层命名的权重文件，格式如下： -./pretrain_models/MobileNetV3_large_x0_5_pretrained/ - └─ conv_last_bn_mean - └─ conv_last_bn_offset - └─ conv_last_bn_scale - └─ conv_last_bn_variance - └─ ...... +wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams ``` @@ -120,16 +111,16 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="{pat 测试单张图像的检测效果 ```shell -python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false +python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" ``` 测试DB模型时，调整后处理阈值， ```shell -python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5 +python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5 ``` 测试文件夹下所有图像的检测效果 ```shell -python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false +python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" ``` diff --git a/doc/doc_ch/inference.md b/doc/doc_ch/inference.md index 547e29be..97e3b92b 100755 --- a/doc/doc_ch/inference.md +++ b/doc/doc_ch/inference.md @@ -49,10 +49,9 @@ wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobi # -c 后面设置训练算法的yml配置文件 # -o 配置可选参数 # Global.pretrained_model 参数设置待转换的训练模型地址，不用添加文件后缀 .pdmodel，.pdopt或.pdparams。 -# Global.load_static_weights 参数需要设置为 False。 # Global.save_inference_dir参数设置转换的模型将保存的地址。 -python3 tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db/ +python3 tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_inference_dir=./inference/det_db/ ``` 转inference模型时，使用的配置文件和训练时使用的配置文件相同。另外，还需要设置配置文件中的`Global.pretrained_model`参数，其指向训练中保存的模型参数文件。 转换成功后，在模型保存目录下有三个文件： @@ -76,10 +75,9 @@ wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobi # -c 后面设置训练算法的yml配置文件 # -o 配置可选参数 # Global.pretrained_model 参数设置待转换的训练模型地址，不用添加文件后缀 .pdmodel，.pdopt或.pdparams。 -# Global.load_static_weights 参数需要设置为 False。 # Global.save_inference_dir参数设置转换的模型将保存的地址。 -python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn/ +python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.save_inference_dir=./inference/rec_crnn/ ``` **注意：**如果您是在自己的数据集上训练的模型，并且调整了中文字符的字典文件，请注意修改配置文件中的`character_dict_path`是否是所需要的字典文件。 @@ -105,10 +103,9 @@ wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobi # -c 后面设置训练算法的yml配置文件 # -o 配置可选参数 # Global.pretrained_model 参数设置待转换的训练模型地址，不用添加文件后缀 .pdmodel，.pdopt或.pdparams。 -# Global.load_static_weights 参数需要设置为 False。 # Global.save_inference_dir参数设置转换的模型将保存的地址。 -python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/cls/ +python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy Global.save_inference_dir=./inference/cls/ ``` 转换成功后，在目录下有三个文件： @@ -164,7 +161,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_di 首先将DB文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（ [模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar) )，可以使用如下命令进行转换： ``` -python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=./det_r50_vd_db_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db +python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=./det_r50_vd_db_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_db ``` DB文本检测模型推理，可以执行如下命令： @@ -185,7 +182,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_ 首先将EAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（ [模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar) )，可以使用如下命令进行转换： ``` -python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.pretrained_model=./det_r50_vd_east_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_east +python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.pretrained_model=./det_r50_vd_east_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_east ``` **EAST文本检测模型推理，需要设置参数`--det_algorithm="EAST"`**，可以执行如下命令： @@ -205,7 +202,7 @@ python3 tools/infer/predict_det.py --det_algorithm="EAST" --image_dir="./doc/img #### (1). 四边形文本检测模型（ICDAR2015） 首先将SAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例([模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar))，可以使用如下命令进行转换： ``` -python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.pretrained_model=./det_r50_vd_sast_icdar15_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_ic15 +python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.pretrained_model=./det_r50_vd_sast_icdar15_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_sast_ic15 ``` **SAST文本检测模型推理，需要设置参数`--det_algorithm="SAST"`**，可以执行如下命令： @@ -220,7 +217,7 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img 首先将SAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在Total-Text英文数据集训练的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar))，可以使用如下命令进行转换： ``` -python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_tt +python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_sast_tt ``` @@ -270,7 +267,7 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.98458153) 的模型为例（ [模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar) )，可以使用如下命令进行转换： ``` -python3 tools/export_model.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn +python3 tools/export_model.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_crnn ``` CRNN 文本识别模型推理，可以执行如下命令： diff --git a/doc/doc_en/detection_en.md b/doc/doc_en/detection_en.md index 3ee9092c..b736beb5 100644 --- a/doc/doc_en/detection_en.md +++ b/doc/doc_en/detection_en.md @@ -38,28 +38,17 @@ If you want to train PaddleOCR on other datasets, please build the annotation fi ## TRAINING -First download the pretrained model. The detection model of PaddleOCR currently supports 3 backbones, namely MobileNetV3, ResNet18_vd and ResNet50_vd. You can use the model in [PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures) to replace backbone according to your needs. +First download the pretrained model. The detection model of PaddleOCR currently supports 3 backbones, namely MobileNetV3, ResNet18_vd and ResNet50_vd. You can use the model in [PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/develop/ppcls/modeling/architectures) to replace backbone according to your needs. +And the responding download link of backbone pretrain weights can be found in [PaddleClas repo](https://github.com/PaddlePaddle/PaddleClas#mobile-series). ```shell cd PaddleOCR/ # Download the pre-trained model of MobileNetV3 -wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_5_pretrained.tar +wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams # or, download the pre-trained model of ResNet18_vd -wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_vd_pretrained.tar +wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_vd_pretrained.pdparams # or, download the pre-trained model of ResNet50_vd -wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar +wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams -# decompressing the pre-training model file, take MobileNetV3 as an example -tar -xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models/ - -# Note: After decompressing the backbone pre-training weight file correctly, the file list in the folder is as follows: -./pretrain_models/MobileNetV3_large_x0_5_pretrained/ - └─ conv_last_bn_mean - └─ conv_last_bn_offset - └─ conv_last_bn_scale - └─ conv_last_bn_variance - └─ ...... - -``` #### START TRAINING *If CPU version installed, please set the parameter `use_gpu` to `false` in the configuration.* @@ -113,16 +102,16 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="{pat Test the detection result on a single image: ```shell -python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false +python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" ``` When testing the DB model, adjust the post-processing threshold: ```shell -python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5 +python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5 ``` Test the detection result on all images in the folder: ```shell -python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false +python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" ``` diff --git a/doc/doc_en/inference_en.md b/doc/doc_en/inference_en.md index 2ada84a0..a78bb6b6 100755 --- a/doc/doc_en/inference_en.md +++ b/doc/doc_en/inference_en.md @@ -52,10 +52,9 @@ The above model is a DB algorithm trained with MobileNetV3 as the backbone. To c # -c Set the training algorithm yml configuration file # -o Set optional parameters # Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams. -# Global.load_static_weights needs to be set to False # Global.save_inference_dir Set the address where the converted model will be saved. -python3 tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db/ +python3 tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_inference_dir=./inference/det_db/ ``` When converting to an inference model, the configuration file used is the same as the configuration file used during training. In addition, you also need to set the `Global.pretrained_model` parameter in the configuration file. @@ -80,10 +79,9 @@ The recognition model is converted to the inference model in the same way as the # -c Set the training algorithm yml configuration file # -o Set optional parameters # Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams. -# Global.load_static_weights needs to be set to False # Global.save_inference_dir Set the address where the converted model will be saved. -python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn/ +python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.save_inference_dir=./inference/rec_crnn/ ``` If you have a model trained on your own dataset with a different dictionary file, please make sure that you modify the `character_dict_path` in the configuration file to your dictionary file path. @@ -109,10 +107,9 @@ The angle classification model is converted to the inference model in the same w # -c Set the training algorithm yml configuration file # -o Set optional parameters # Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams. -# Global.load_static_weights needs to be set to False # Global.save_inference_dir Set the address where the converted model will be saved. -python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/cls/ +python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy Global.save_inference_dir=./inference/cls/ ``` After the conversion is successful, there are two files in the directory: @@ -171,7 +168,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/22.jpg" --det_model_d First, convert the model saved in the DB text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)), you can use the following command to convert: ``` -python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=./det_r50_vd_db_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db +python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=./det_r50_vd_db_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_db ``` DB text detection model inference, you can execute the following command: @@ -192,7 +189,7 @@ The visualized text detection results are saved to the `./inference_results` fol First, convert the model saved in the EAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)), you can use the following command to convert: ``` -python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.pretrained_model=./det_r50_vd_east_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_east +python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.pretrained_model=./det_r50_vd_east_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_east ``` **For EAST text detection model inference, you need to set the parameter ``--det_algorithm="EAST"``**, run the following command: @@ -213,7 +210,7 @@ The visualized text detection results are saved to the `./inference_results` fol First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)), you can use the following command to convert: ``` -python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.pretrained_model=./det_r50_vd_sast_icdar15_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_ic15 +python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.pretrained_model=./det_r50_vd_sast_icdar15_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_sast_ic15 ``` **For SAST quadrangle text detection model inference, you need to set the parameter `--det_algorithm="SAST"`**, run the following command: @@ -230,7 +227,7 @@ The visualized text detection results are saved to the `./inference_results` fol First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the Total-Text English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar)), you can use the following command to convert: ``` -python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_tt +python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_sast_tt ``` **For SAST curved text detection model inference, you need to set the parameter `--det_algorithm="SAST"` and `--det_sast_polygon=True`**, run the following command: @@ -279,7 +276,7 @@ Taking CRNN as an example, we introduce the recognition model inference based on First, convert the model saved in the CRNN text recognition training process into an inference model. Taking the model based on Resnet34_vd backbone network, using MJSynth and SynthText (two English text recognition synthetic datasets) for training, as an example ([model download address](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar)). It can be converted as follow: ``` -python3 tools/export_model.py -c configs/det/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn +python3 tools/export_model.py -c configs/det/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_crnn ``` For CRNN text recognition model inference, execute the following commands: -- GitLab