e2e_r50_vd_pg.yml 3.3 KB
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Global:
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  use_gpu: True
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  epoch_num: 600
  log_smooth_window: 20
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  print_batch_step: 10
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  save_model_dir: ./output/pgnet_r50_vd_totaltext/
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  save_epoch_step: 10
  # evaluation is run every 0 iterationss after the 1000th iteration
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  eval_batch_step: [ 0, 1000 ]
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  # 1. If pretrained_model is saved in static mode, such as classification pretrained model
  #    from static branch, load_static_weights must be set as True.
  # 2. If you want to finetune the pretrained models we provide in the docs,
  #    you should set load_static_weights as False.
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  load_static_weights: False
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  cal_metric_during_train: False
  pretrained_model:
  checkpoints:
  save_inference_dir:
  use_visualdl: False
  infer_img:
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  valid_set: totaltext # two mode: totaltext valid curved words, partvgg valid non-curved words
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  save_res_path: ./output/pgnet_r50_vd_totaltext/predicts_pgnet.txt
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  character_dict_path: ppocr/utils/ic15_dict.txt
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  character_type: EN
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  max_text_length: 50 # the max length in seq
  max_text_nums: 30 # the max seq nums in a pic
  tcl_len: 64
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Architecture:
  model_type: e2e
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  algorithm: PGNet
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  Transform:
  Backbone:
    name: ResNet
    layers: 50
  Neck:
    name: PGFPN
  Head:
    name: PGHead

Loss:
  name: PGLoss
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  tcl_bs: 64
  max_text_length: 50 # the same as Global: max_text_length
  max_text_nums: 30 # the same as Global:max_text_nums
  pad_num: 36 # the length of dict for pad
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Optimizer:
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  name: Adam
  beta1: 0.9
  beta2: 0.999
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  lr:
    learning_rate: 0.001
  regularizer:
    name: 'L2'
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    factor: 0

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PostProcess:
  name: PGPostProcess
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  score_thresh: 0.5
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  mode: fast   # fast or slow two ways
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Metric:
  name: E2EMetric
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  mode: A   # two ways for eval, A: label from txt,  B: label from gt_mat
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  gt_mat_dir:  ./train_data/total_text/gt  # the dir of gt_mat
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  character_dict_path: ppocr/utils/ic15_dict.txt
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  main_indicator: f_score_e2e
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Train:
  dataset:
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    name: PGDataSet
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    data_dir: ./train_data/total_text/train
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    label_file_list: [./train_data/total_text/train/total_text.txt]
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    ratio_list: [1.0]
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    transforms:
      - DecodeImage: # load image
          img_mode: BGR
          channel_first: False
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      - E2ELabelEncodeTrain:
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      - PGProcessTrain:
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          batch_size: 14  # same as loader: batch_size_per_card
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          min_crop_size: 24
          min_text_size: 4
          max_text_size: 512
      - KeepKeys:
          keep_keys: [ 'images', 'tcl_maps', 'tcl_label_maps', 'border_maps','direction_maps', 'training_masks', 'label_list', 'pos_list', 'pos_mask' ] # dataloader will return list in this order
  loader:
    shuffle: True
    drop_last: True
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    batch_size_per_card: 14
    num_workers: 16
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Eval:
  dataset:
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    name: PGDataSet
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    data_dir: ./train_data/total_text/test
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    label_file_list: [./train_data/total_text/test/total_text.txt]
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    transforms:
      - DecodeImage: # load image
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          img_mode: RGB
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          channel_first: False
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      - E2ELabelEncodeTest:
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      - E2EResizeForTest:
          max_side_len: 768
      - NormalizeImage:
          scale: 1./255.
          mean: [ 0.485, 0.456, 0.406 ]
          std: [ 0.229, 0.224, 0.225 ]
          order: 'hwc'
      - ToCHWImage:
      - KeepKeys:
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          keep_keys: [ 'image', 'shape', 'polys', 'texts', 'ignore_tags', 'img_id']
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  loader:
    shuffle: False
    drop_last: False
    batch_size_per_card: 1 # must be 1
    num_workers: 2