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Global:
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use_gpu: true
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epoch_num: 5
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log_smooth_window: 20
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print_batch_step: 20
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save_model_dir: ./output/rec/rec_r31_robustscanner/
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save_epoch_step: 1
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# evaluation is run every 2000 iterations
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eval_batch_step: [0, 2000]
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cal_metric_during_train: True
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pretrained_model:
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checkpoints:
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save_inference_dir:
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use_visualdl: False
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infer_img: ./inference/rec_inference
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# for data or label process
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character_dict_path: ppocr/utils/dict90.txt
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max_text_length: &max_text_length 40
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infer_mode: False
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use_space_char: False
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rm_symbol: True
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save_res_path: ./output/rec/predicts_robustscanner.txt
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Optimizer:
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name: Adam
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beta1: 0.9
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beta2: 0.999
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lr:
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name: Piecewise
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decay_epochs: [3, 4]
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values: [0.001, 0.0001, 0.00001]
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regularizer:
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name: 'L2'
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factor: 0
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Architecture:
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model_type: rec
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algorithm: RobustScanner
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Transform:
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Backbone:
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name: ResNet31
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init_type: KaimingNormal
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Head:
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name: RobustScannerHead
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enc_outchannles: 128
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hybrid_dec_rnn_layers: 2
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hybrid_dec_dropout: 0
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position_dec_rnn_layers: 2
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start_idx: 91
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mask: True
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padding_idx: 92
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encode_value: False
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max_text_length: *max_text_length
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Loss:
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name: SARLoss
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PostProcess:
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name: SARLabelDecode
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Metric:
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name: RecMetric
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is_filter: True
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Train:
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dataset:
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name: SimpleDataSet
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data_dir: ./train_data/ic15_data/
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label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- SARLabelEncode: # Class handling label
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- RobustScannerRecResizeImg:
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image_shape: [3, 48, 48, 160] # h:48 w:[48,160]
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width_downsample_ratio: 0.25
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max_text_length: *max_text_length
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- KeepKeys:
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keep_keys: ['image', 'label', 'valid_ratio', 'word_positons'] # dataloader will return list in this order
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loader:
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shuffle: True
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batch_size_per_card: 16
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drop_last: True
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num_workers: 0
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use_shared_memory: False
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Eval:
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dataset:
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name: SimpleDataSet
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data_dir: ./train_data/ic15_data
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label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- SARLabelEncode: # Class handling label
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- RobustScannerRecResizeImg:
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image_shape: [3, 48, 48, 160]
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max_text_length: *max_text_length
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width_downsample_ratio: 0.25
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- KeepKeys:
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keep_keys: ['image', 'label', 'valid_ratio', 'word_positons'] # dataloader will return list in this order
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loader:
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shuffle: False
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drop_last: False
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batch_size_per_card: 16
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num_workers: 0
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use_shared_memory: False
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===========================train_params===========================
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model_name:rec_r31_robustscanner
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python:python3.7
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gpu_list:0|0,1
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Global.use_gpu:True|True
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Global.auto_cast:null
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Global.epoch_num:lite_train_lite_infer=2|whole_train_whole_infer=5
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Global.save_model_dir:./output/
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Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=64
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Global.pretrained_model:null
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train_model_name:latest
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train_infer_img_dir:./inference/rec_inference
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null:null
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##
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trainer:norm_train
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norm_train:tools/train.py -c test_tipc/configs/rec_r31_robustscanner/rec_r31_robustscanner.yml -o
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pact_train:null
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fpgm_train:null
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distill_train:null
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null:null
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null:null
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##
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===========================eval_params===========================
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eval:tools/eval.py -c test_tipc/configs/rec_r31_robustscanner/rec_r31_robustscanner.yml -o
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null:null
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##
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===========================infer_params===========================
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Global.save_inference_dir:./output/
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Global.checkpoints:
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norm_export:tools/export_model.py -c test_tipc/configs/rec_r31_robustscanner/rec_r31_robustscanner.yml -o
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quant_export:null
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fpgm_export:null
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distill_export:null
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export1:null
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export2:null
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##
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train_model:./inference/rec_r31_robustscanner/best_accuracy
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infer_export:tools/export_model.py -c test_tipc/configs/rec_r31_robustscanner/rec_r31_robustscanner.yml -o
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infer_quant:False
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inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/dict90.txt --rec_image_shape="3,48,48,160" --use_space_char=False --rec_algorithm="RobustScanner"
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--use_gpu:True|False
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--enable_mkldnn:False
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--cpu_threads:6
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--rec_batch_num:1
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--use_tensorrt:False
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--precision:fp32
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--rec_model_dir:
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--image_dir:./inference/rec_inference
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--save_log_path:./test/output/
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--benchmark:True
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null:null
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===========================infer_benchmark_params==========================
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random_infer_input:[{float32,[3,48,160]}]
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