mindocr/configs/rec/crnn/crnn_resnet34_server.yaml at 81cd5ca6897ef918bf36983a949e0d0ac5a821c7 · mindspore-lab/mindocr · GitHub

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system:
  mode: 0 # 0 for graph mode, 1 for pynative mode in MindSpore
  distribute: True
  amp_level: 'O3'
  seed: 42
  log_interval: 100
  val_while_train: True
  drop_overflow_update: False

common:
  character_dict_path: &character_dict_path  mindocr/utils/dict/en_dict.txt
  num_classes: &num_classes 96 # num_chars_in_dict+1,  TODO: retreive it from dict or check correctness
  max_text_len: &max_text_len 24
  infer_mode: &infer_mode False
  use_space_char: &use_space_char True
  lower: &lower False
  batch_size: &batch_size 64

model:
  type: rec
  transform: null
  backbone:
    name: rec_resnet34
    pretrained: False
  neck:
    name: RNNEncoder
    hidden_size: 256
  head:
    name: CTCHead
    weight_init: crnn_customised
    bias_init: crnn_customised
    out_channels: *num_classes

postprocess:
  name: RecCTCLabelDecode
  character_dict_path: *character_dict_path
  use_space_char: *use_space_char

metric:
  name: RecMetric
  main_indicator: acc
  character_dict_path: *character_dict_path
  ignore_space: True
  print_flag: False

loss:
  name: CTCLoss
  pred_seq_len: 25 # TODO: retrieve from the network output shape.
  max_label_len: *max_text_len  # this value should be smaller than pre_seq_len
  batch_size: *batch_size

scheduler:
  scheduler: warmup_cosine_decay
  min_lr: 0.000001
  lr: 0.001
  num_epochs: 30
  warmup_epochs: 2
  decay_epochs: 28

optimizer:
  opt: adamw
  filter_bias_and_bn: True
  momentum: 0.95
  weight_decay: 0.0001
  nesterov: False

loss_scaler:
  type: dynamic
  loss_scale: 512
  scale_factor: 2.0
  scale_window: 1000

train:
  ckpt_save_dir: './crnn_resnet34_server'
  pred_cast_fp32: False # let CTCLoss cast internally
  ema: True # added
  dataset_sink_mode: False
  dataset:
    type: LMDBDataset
    dataset_root: /path/to/data_lmdb_release/
    data_dir: training/
    # label_file: # not required when using LMDBDataset
    sample_ratio: 1.0
    shuffle: True
    transform_pipeline:
      - DecodeImage:
          img_mode: RGB
          to_float32: False
      - RecCTCLabelEncode:
          max_text_len: *max_text_len
          character_dict_path: *character_dict_path
          use_space_char: *use_space_char
          lower: *lower
      - RecResizeNormImg:
          image_shape: [32, 100] # H, W
          infer_mode: *infer_mode
          character_dict_path: *character_dict_path
          padding: True # aspect ratio will be preserved if true.
          norm_before_pad: True
      - ToCHWImage:
    #  the order of the dataloader list, matching the network input and the input labels for the loss function, and optional data for debug/visaulize
    output_columns: ['image', 'text_seq'] #, 'length'] #'img_path']
    net_input_column_index: [0] # input indices for network forward func in output_columns
    label_column_index: [1] # input indices marked as label
    #keys_for_loss: 4 # num labels for loss func

  loader:
      shuffle: True
      batch_size: *batch_size
      drop_remainder: True
      max_rowsize: 12
      num_workers: 8

eval:
  ckpt_load_path: ./crnn_resnet34_server/best.ckpt
  dataset_sink_mode: False
  dataset:
    type: LMDBDataset
    dataset_root: /path/to/data_lmdb_release/
    data_dir: validation/
    # label_file: # not required when using LMDBDataset
    sample_ratio: 1.0
    shuffle: False
    transform_pipeline:
      - DecodeImage:
          img_mode: RGB
          to_float32: False
      - RecCTCLabelEncode:
          max_text_len: *max_text_len
          character_dict_path: *character_dict_path
          use_space_char: *use_space_char
          lower: *lower
      - RecResizeNormImg:
          image_shape: [32, 100] # H, W
          infer_mode: *infer_mode
          character_dict_path: *character_dict_path
          padding: True # aspect ratio will be preserved if true.
          norm_before_pad: True
      - ToCHWImage:
    #  the order of the dataloader list, matching the network input and the input labels for the loss function, and optional data for debug/visaulize
    output_columns: ['image', 'text_padded', 'text_length']  # TODO return text string padding w/ fixed length, and a scaler to indicate the length
    net_input_column_index: [0] # input indices for network forward func in output_columns
    label_column_index: [1, 2] # input indices marked as label

  loader:
      shuffle: False # TODO: tbc
      batch_size: 64
      drop_remainder: False
      max_rowsize: 12
      num_workers: 8