2021-01-29 11:15:03 +08:00
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Global:
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2021-02-02 19:28:29 +08:00
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use_gpu: True
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2021-01-29 11:15:03 +08:00
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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/rec/rec_mv3_tps_bilstm_att/
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save_epoch_step: 3
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# evaluation is run every 5000 iterations after the 4000th iteration
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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: doc/imgs_words/ch/word_1.jpg
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# for data or label process
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character_dict_path:
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character_type: en
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max_text_length: 25
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infer_mode: False
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use_space_char: False
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2021-04-25 20:49:45 +08:00
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save_res_path: ./output/rec/predicts_mv3_tps_bilstm_att.txt
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2021-01-29 11:15:03 +08:00
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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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learning_rate: 0.0005
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regularizer:
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name: 'L2'
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factor: 0.00001
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Architecture:
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model_type: rec
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algorithm: RARE
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Transform:
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name: TPS
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num_fiducial: 20
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loc_lr: 0.1
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model_name: small
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Backbone:
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name: MobileNetV3
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scale: 0.5
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model_name: large
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Neck:
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name: SequenceEncoder
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encoder_type: rnn
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hidden_size: 96
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Head:
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name: AttentionHead
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hidden_size: 96
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Loss:
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name: AttentionLoss
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PostProcess:
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name: AttnLabelDecode
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Metric:
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name: RecMetric
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main_indicator: acc
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Train:
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dataset:
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2021-02-02 19:28:29 +08:00
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name: LMDBDataSet
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2021-02-05 16:22:32 +08:00
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data_dir: ./train_data/data_lmdb_release/training/
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2021-01-29 11:15:03 +08:00
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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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- AttnLabelEncode: # Class handling label
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- RecResizeImg:
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image_shape: [3, 32, 100]
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- KeepKeys:
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keep_keys: ['image', 'label', 'length'] # 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: 256
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drop_last: True
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num_workers: 8
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Eval:
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dataset:
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2021-02-02 19:28:29 +08:00
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name: LMDBDataSet
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2021-02-05 16:22:32 +08:00
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data_dir: ./train_data/data_lmdb_release/validation/
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2021-01-29 11:15:03 +08:00
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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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- AttnLabelEncode: # Class handling label
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- RecResizeImg:
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image_shape: [3, 32, 100]
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- KeepKeys:
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keep_keys: ['image', 'label', 'length'] # 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: 256
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num_workers: 1
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