93 lines
1.9 KiB
YAML
93 lines
1.9 KiB
YAML
Global:
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use_gpu: True
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epoch_num: 8
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log_smooth_window: 20
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print_batch_step: 5
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save_model_dir: ./output/rec/pren_new
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save_epoch_step: 3
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# evaluation is run every 2000 iterations after the 4000th iteration
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eval_batch_step: [4000, 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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max_text_length: &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_pren.txt
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Optimizer:
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name: Adadelta
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lr:
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name: Piecewise
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decay_epochs: [2, 5, 7]
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values: [0.5, 0.1, 0.01, 0.001]
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Architecture:
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model_type: rec
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algorithm: PREN
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in_channels: 3
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Backbone:
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name: EfficientNetb3_PREN
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Neck:
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name: PRENFPN
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n_r: 5
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d_model: 384
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max_len: *max_text_length
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dropout: 0.1
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Head:
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name: PRENHead
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Loss:
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name: PRENLoss
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PostProcess:
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name: PRENLabelDecode
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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: LMDBDataSet
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data_dir: ./train_data/data_lmdb_release/training/
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transforms:
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- DecodeImage:
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img_mode: BGR
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channel_first: False
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- PRENLabelEncode:
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- RecAug:
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- PRENResizeImg:
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image_shape: [64, 256] # h,w
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- KeepKeys:
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keep_keys: ['image', 'label']
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loader:
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shuffle: True
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batch_size_per_card: 128
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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: LMDBDataSet
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data_dir: ./train_data/data_lmdb_release/validation/
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transforms:
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- DecodeImage:
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img_mode: BGR
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channel_first: False
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- PRENLabelEncode:
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- PRENResizeImg:
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image_shape: [64, 256] # h,w
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- KeepKeys:
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keep_keys: ['image', 'label']
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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: 8
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