102 lines
2.3 KiB
YAML
102 lines
2.3 KiB
YAML
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Global:
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use_gpu: False
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epoch_num: 400
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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/b3_rare_r34_none_gru/
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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_symbol
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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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save_res_path: ./output/rec/predicts_b3_rare_r34_none_gru.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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learning_rate: 0.0005
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regularizer:
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name: 'L2'
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factor: 0.00000
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Architecture:
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model_type: rec
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algorithm: ASTER
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Transform:
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name: STN_ON
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tps_inputsize: [32, 64]
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tps_outputsize: [32, 100]
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num_control_points: 20
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tps_margins: [0.05,0.05]
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stn_activation: none
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Backbone:
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name: ResNet_ASTER
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Head:
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name: AsterHead # AttentionHead
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sDim: 512
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attDim: 512
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max_len_labels: 100
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Loss:
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name: AsterLoss
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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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name: SimpleDataSet
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data_dir: ./train_data/ic15_data/
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label_file_list: ["./train_data/ic15_data/1.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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- 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: 2
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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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name: SimpleDataSet
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data_dir: ./train_data/ic15_data/
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label_file_list: ["./train_data/ic15_data/1.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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- 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: 2
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num_workers: 8
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