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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: ./sar_rec
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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:
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# for data or label process
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character_dict_path: ppocr/utils/dict90.txt
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character_type: EN_symbol
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max_text_length: 30
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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_sar.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: SAR
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Transform:
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Backbone:
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name: ResNet31
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Head:
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name: SARHead
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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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Train:
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dataset:
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name: SimpleDataSet
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2021-09-02 15:18:13 +08:00
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label_file_list: ['./train_data/train_list.txt']
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data_dir: ./train_data/
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ratio_list: 1.0
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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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- SARRecResizeImg:
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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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- KeepKeys:
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keep_keys: ['image', 'label', 'valid_ratio'] # dataloader will return list in this order
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loader:
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shuffle: True
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2021-08-31 19:33:41 +08:00
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batch_size_per_card: 64
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drop_last: True
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num_workers: 8
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use_shared_memory: False
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Eval:
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dataset:
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name: LMDBDataSet
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2021-09-14 11:31:45 +08:00
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data_dir: ./train_data/data_lmdb_release/evaluation/
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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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- SARRecResizeImg:
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image_shape: [3, 48, 48, 160]
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width_downsample_ratio: 0.25
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- KeepKeys:
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keep_keys: ['image', 'label', 'valid_ratio'] # 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: 64
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num_workers: 4
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use_shared_memory: False
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2021-08-24 12:23:03 +08:00
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