commit
de8b5b2593
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@ -4,7 +4,7 @@ Global:
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epoch_num: 1200
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log_smooth_window: 20
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print_batch_step: 2
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save_model_dir: output
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save_model_dir: ./output/det_db/
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save_epoch_step: 200
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eval_batch_step: 5000
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train_batch_size_per_card: 16
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@ -13,7 +13,7 @@ Global:
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reader_yml: ./configs/det/det_db_icdar15_reader.yml
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pretrain_weights: ./pretrain_models/MobileNetV3_pretrained/MobileNetV3_large_x0_5_pretrained/
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checkpoints:
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save_res_path: ./output/predicts_db.txt
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save_res_path: ./output/det_db/predicts_db.txt
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save_inference_dir:
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Architecture:
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@ -4,7 +4,7 @@ Global:
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epoch_num: 1200
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log_smooth_window: 20
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print_batch_step: 2
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save_model_dir: output
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save_model_dir: ./output/det_db/
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save_epoch_step: 200
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eval_batch_step: 5000
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train_batch_size_per_card: 8
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@ -12,8 +12,10 @@ Global:
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image_shape: [3, 640, 640]
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reader_yml: ./configs/det/det_db_icdar15_reader.yml
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pretrain_weights: ./pretrain_models/ResNet50_vd_pretrained/
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save_res_path: ./output/predicts_db.txt
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save_res_path: ./output/det_db/predicts_db.txt
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checkpoints:
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save_inference_dir:
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Architecture:
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function: ppocr.modeling.architectures.det_model,DetModel
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@ -4,7 +4,7 @@ Global:
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epoch_num: 100000
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log_smooth_window: 20
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print_batch_step: 5
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save_model_dir: output
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save_model_dir: ./output/det_east/
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save_epoch_step: 200
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eval_batch_step: 5000
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train_batch_size_per_card: 16
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@ -12,7 +12,9 @@ Global:
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image_shape: [3, 512, 512]
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reader_yml: ./configs/det/det_east_icdar15_reader.yml
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pretrain_weights: ./pretrain_models/MobileNetV3_pretrained/MobileNetV3_large_x0_5_pretrained/
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save_res_path: ./output/predicts_east.txt
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checkpoints:
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save_res_path: ./output/det_east/predicts_east.txt
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save_inference_dir:
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Architecture:
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function: ppocr.modeling.architectures.det_model,DetModel
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@ -4,7 +4,7 @@ Global:
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epoch_num: 100000
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log_smooth_window: 20
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print_batch_step: 5
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save_model_dir: output
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save_model_dir: ./output/det_east/
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save_epoch_step: 200
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eval_batch_step: 5000
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train_batch_size_per_card: 8
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@ -12,8 +12,10 @@ Global:
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image_shape: [3, 512, 512]
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reader_yml: ./configs/det/det_east_icdar15_reader.yml
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pretrain_weights: ./pretrain_models/ResNet50_vd_pretrained/
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save_res_path: ./output/predicts_east.txt
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save_res_path: ./output/det_east/predicts_east.txt
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checkpoints:
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save_inference_dir:
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Architecture:
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function: ppocr.modeling.architectures.det_model,DetModel
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@ -4,7 +4,7 @@ Global:
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epoch_num: 72
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: output
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save_model_dir: output/rec_CRNN
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save_epoch_step: 3
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eval_batch_step: 2000
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train_batch_size_per_card: 256
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@ -14,7 +14,9 @@ Global:
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character_type: en
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loss_type: ctc
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reader_yml: ./configs/rec/rec_benchmark_reader.yml
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pretrain_weights:
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pretrain_weights:
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checkpoints:
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save_inference_dir:
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Architecture:
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function: ppocr.modeling.architectures.rec_model,RecModel
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@ -4,7 +4,7 @@ Global:
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epoch_num: 72
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: output
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save_model_dir: output/rec_Rosetta
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save_epoch_step: 3
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eval_batch_step: 2000
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train_batch_size_per_card: 256
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@ -15,7 +15,9 @@ Global:
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loss_type: ctc
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reader_yml: ./configs/rec/rec_benchmark_reader.yml
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pretrain_weights:
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checkpoints:
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save_inference_dir:
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Architecture:
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function: ppocr.modeling.architectures.rec_model,RecModel
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@ -4,7 +4,7 @@ Global:
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epoch_num: 72
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: output
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save_model_dir: output/rec_RARE
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save_epoch_step: 3
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eval_batch_step: 2000
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train_batch_size_per_card: 256
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@ -14,7 +14,9 @@ Global:
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character_type: en
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loss_type: attention
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reader_yml: ./configs/rec/rec_benchmark_reader.yml
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pretrain_weights:
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pretrain_weights:
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checkpoints:
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save_inference_dir:
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Architecture:
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function: ppocr.modeling.architectures.rec_model,RecModel
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@ -4,7 +4,7 @@ Global:
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epoch_num: 72
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: output
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save_model_dir: output/rec_STARNet
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save_epoch_step: 3
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eval_batch_step: 2000
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train_batch_size_per_card: 256
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@ -14,7 +14,10 @@ Global:
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character_type: en
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loss_type: ctc
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reader_yml: ./configs/rec/rec_benchmark_reader.yml
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pretrain_weights:
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pretrain_weights:
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checkpoints:
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save_inference_dir:
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Architecture:
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function: ppocr.modeling.architectures.rec_model,RecModel
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@ -4,7 +4,7 @@ Global:
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epoch_num: 72
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: output
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save_model_dir: output/rec_CRNN
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save_epoch_step: 3
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eval_batch_step: 2000
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train_batch_size_per_card: 256
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@ -14,7 +14,9 @@ Global:
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character_type: en
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loss_type: ctc
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reader_yml: ./configs/rec/rec_benchmark_reader.yml
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pretrain_weights:
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pretrain_weights:
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checkpoints:
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save_inference_dir:
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Architecture:
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function: ppocr.modeling.architectures.rec_model,RecModel
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@ -4,7 +4,7 @@ Global:
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epoch_num: 72
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: output
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save_model_dir: output/rec_Rosetta
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save_epoch_step: 3
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eval_batch_step: 2000
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train_batch_size_per_card: 256
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character_type: en
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loss_type: ctc
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reader_yml: ./configs/rec/rec_benchmark_reader.yml
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pretrain_weights:
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pretrain_weights:
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checkpoints:
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save_inference_dir:
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Architecture:
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function: ppocr.modeling.architectures.rec_model,RecModel
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@ -4,7 +4,7 @@ Global:
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epoch_num: 72
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: output
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save_model_dir: output/rec_RARE
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save_epoch_step: 3
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eval_batch_step: 2000
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train_batch_size_per_card: 256
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@ -14,7 +14,9 @@ Global:
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character_type: en
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loss_type: attention
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reader_yml: ./configs/rec/rec_benchmark_reader.yml
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pretrain_weights:
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pretrain_weights:
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checkpoints:
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save_inference_dir:
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Architecture:
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function: ppocr.modeling.architectures.rec_model,RecModel
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@ -4,7 +4,7 @@ Global:
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epoch_num: 72
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: output
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save_model_dir: output/rec_STARNet
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save_epoch_step: 3
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eval_batch_step: 2000
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train_batch_size_per_card: 256
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@ -15,6 +15,8 @@ Global:
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loss_type: ctc
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reader_yml: ./configs/rec/rec_benchmark_reader.yml
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pretrain_weights:
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checkpoints:
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save_inference_dir:
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Architecture:
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function: ppocr.modeling.architectures.rec_model,RecModel
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@ -196,7 +196,7 @@ class DBHead(object):
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fuse = fluid.layers.concat(input=[p5, p4, p3, p2], axis=1)
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shrink_maps = self.binarize(fuse)
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if mode != "train":
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return shrink_maps
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return {"maps", shrink_maps}
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threshold_maps = self.thresh(fuse)
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binary_maps = self.step_function(shrink_maps, threshold_maps)
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y = fluid.layers.concat(
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@ -128,6 +128,7 @@ class DBPostProcess(object):
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def __call__(self, outs_dict, ratio_list):
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pred = outs_dict['maps']
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pred = pred[:, 0, :, :]
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segmentation = pred > self.thresh
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@ -24,6 +24,7 @@ import copy
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import numpy as np
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import math
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import time
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import sys
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class TextDetector(object):
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@ -52,10 +53,10 @@ class TextDetector(object):
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utility.create_predictor(args, mode="det")
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def order_points_clockwise(self, pts):
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#######
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## https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
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########
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"""
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reference from: https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
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# sort the points based on their x-coordinates
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"""
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xSorted = pts[np.argsort(pts[:, 0]), :]
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# grab the left-most and right-most points from the sorted
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@ -141,7 +142,7 @@ class TextDetector(object):
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outs_dict['f_score'] = outputs[0]
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outs_dict['f_geo'] = outputs[1]
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else:
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outs_dict['maps'] = [outputs[0]]
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outs_dict['maps'] = outputs[0]
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dt_boxes_list = self.postprocess_op(outs_dict, [ratio_list])
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dt_boxes = dt_boxes_list[0]
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dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
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@ -219,6 +219,8 @@ def train_eval_det_run(config, exe, train_info_dict, eval_info_dict):
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eval_batch_step = config['Global']['eval_batch_step']
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save_epoch_step = config['Global']['save_epoch_step']
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save_model_dir = config['Global']['save_model_dir']
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if not os.path.exists(save_model_dir):
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os.makedirs(save_model_dir)
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train_stats = TrainingStats(log_smooth_window,
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train_info_dict['fetch_name_list'])
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best_eval_hmean = -1
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@ -282,6 +284,8 @@ def train_eval_rec_run(config, exe, train_info_dict, eval_info_dict):
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eval_batch_step = config['Global']['eval_batch_step']
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save_epoch_step = config['Global']['save_epoch_step']
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save_model_dir = config['Global']['save_model_dir']
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if not os.path.exists(save_model_dir):
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os.makedirs(save_model_dir)
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train_stats = TrainingStats(log_smooth_window, ['loss', 'acc'])
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best_eval_acc = -1
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best_batch_id = 0
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Loading…
Reference in New Issue