OpenOCR/configs/rec/gtc/svtrv2_lnconv_smtr_gtc_smtr_long.yml

Global:
  device: gpu
  epoch_num: 20
  log_smooth_window: 20
  print_batch_step: 10
  output_dir: ./output/rec/svtrv2_lnconv_smtr_gtc_nodetach_smtr_long_infer
  save_epoch_step: [15, 1]
  # evaluation is run every 2000 iterations
  eval_batch_step: [0, 1000]
  eval_epoch_step: [0, 1]
  cal_metric_during_train: True
  pretrained_model:
  checkpoints:
  use_tensorboard: false
  infer_img:
  # for data or label process
  character_dict_path: &character_dict_path ./tools/utils/EN_symbol_dict.txt # 96en
  # ./tools/utils/ppocr_keys_v1.txt  # ch
  max_text_length: &max_text_length 25
  use_space_char: &use_space_char False
  save_res_path: ./output/rec/predicts_smtr.txt
  use_amp: True
  distributed: true

Optimizer:
  name: AdamW
  lr: 0.000325
  weight_decay: 0.05
  filter_bias_and_bn: True

LRScheduler:
  name: OneCycleLR
  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
  cycle_momentum: False
Architecture:
  model_type: rec
  algorithm: BGPD
  in_channels: 3
  Transform:
  Encoder:
    name: SVTRv2LNConvTwo33
    use_pos_embed: False
    out_channels: 256
    dims: [128, 256, 384]
    depths: [6, 6, 6]
    num_heads: [4, 8, 12]
    mixer: [['Conv','Conv','Conv','Conv','Conv','Conv'],['Conv','Conv','FGlobal','Global','Global','Global'],['Global','Global','Global','Global','Global','Global']]
    local_k: [[5, 5], [5, 5], [-1, -1]]
    sub_k: [[1, 1], [2, 1], [-1, -1]]
    last_stage: false
    feat2d: True
  Decoder:
    name: GTCDecoder
    infer_gtc: True
    detach: False
    gtc_decoder:
      name: SMTRDecoder
      num_layer: 1
      ds: True
      max_len: *max_text_length
      next_mode: &next True
      sub_str_len: &subsl 5
      infer_aug: True
    ctc_decoder:
      name: RCTCDecoder

Loss:
  name: GTCLoss
  ctc_weight: 0.1
  gtc_loss:
    name: SMTRLoss

PostProcess:
  name: GTCLabelDecode
  gtc_label_decode:
    name: SMTRLabelDecode
    next_mode: *next
  character_dict_path: *character_dict_path
  use_space_char: *use_space_char
  only_gtc: True

Metric:
  name: RecGTCMetric
  main_indicator: acc
  is_filter: True

Train:
  dataset:
    name: RatioDataSetTVResize
    ds_width: True
    padding: false
    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_train_challenging',
    '../Union14M-L-LMDB-Filtered/filter_train_hard',
    '../Union14M-L-LMDB-Filtered/filter_train_medium',
    '../Union14M-L-LMDB-Filtered/filter_train_normal',
    '../Union14M-L-LMDB-Filtered/filter_train_easy',
    ]
    transforms:
      - DecodeImagePIL: # load image
          img_mode: RGB
      - PARSeqAugPIL:
      - SMTRLabelEncode: # Class handling label
          sub_str_len: *subsl
          character_dict_path: *character_dict_path
          use_space_char: *use_space_char
          max_text_length: *max_text_length
      - KeepKeys:
          keep_keys: ['image', 'label', 'label_subs', 'label_next', 'length_subs',
          'label_subs_pre', 'label_next_pre', 'length_subs_pre', 'length'] # dataloader will return list in this order
  sampler:
    name: RatioSampler
    scales: [[128, 32]] # w, h
    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
    first_bs: &bs 256
    fix_bs: false
    divided_factor: [4, 16] # w, h
    is_training: True
  loader:
    shuffle: True
    batch_size_per_card: *bs
    drop_last: True
    max_ratio: &max_ratio 12
    num_workers: 4

Eval:
  dataset:
    name: SimpleDataSet
    data_dir: ../ltb/
    label_file_list: ['../ltb/ultra_long_70_list.txt']
    transforms:
      - DecodeImage: # load image
          img_mode: BGR
          channel_first: False
      - GTCLabelEncode: # Class handling label
          gtc_label_encode:
            name: ARLabelEncode
          character_dict_path: *character_dict_path
          use_space_char: *use_space_char
          max_text_length: 200
      - SliceResize:
          image_shape: [3, 32, 128]
          padding: False
          max_ratio: 12
      - KeepKeys:
          keep_keys: ['image', 'label', 'length', 'ctc_label', 'ctc_length'] # dataloader will return list in this order
  loader:
    shuffle: False
    drop_last: False
    batch_size_per_card: 1
    num_workers: 2