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- Global:
- use_gpu: True
- epoch_num: 72
- log_smooth_window: 20
- print_batch_step: 5
- save_model_dir: ./output/rec/srn_new
- save_epoch_step: 3
- # evaluation is run every 5000 iterations after the 4000th iteration
- eval_batch_step: [0, 5000]
- cal_metric_during_train: True
- pretrained_model:
- checkpoints:
- save_inference_dir:
- use_visualdl: False
- infer_img: doc/imgs_words/ch/word_1.jpg
- # for data or label process
- character_dict_path:
- max_text_length: 25
- num_heads: 8
- infer_mode: False
- use_space_char: False
- save_res_path: ./output/rec/predicts_srn.txt
- Optimizer:
- name: Adam
- beta1: 0.9
- beta2: 0.999
- clip_norm: 10.0
- lr:
- learning_rate: 0.0001
- Architecture:
- model_type: rec
- algorithm: SRN
- in_channels: 1
- Transform:
- Backbone:
- name: ResNetFPN
- Head:
- name: SRNHead
- max_text_length: 25
- num_heads: 8
- num_encoder_TUs: 2
- num_decoder_TUs: 4
- hidden_dims: 512
- Loss:
- name: SRNLoss
- PostProcess:
- name: SRNLabelDecode
- Metric:
- name: RecMetric
- main_indicator: acc
- Train:
- dataset:
- name: LMDBDataSet
- data_dir: ./train_data/data_lmdb_release/training/
- transforms:
- - DecodeImage: # load image
- img_mode: BGR
- channel_first: False
- - SRNLabelEncode: # Class handling label
- - SRNRecResizeImg:
- image_shape: [1, 64, 256]
- - KeepKeys:
- keep_keys: ['image',
- 'label',
- 'length',
- 'encoder_word_pos',
- 'gsrm_word_pos',
- 'gsrm_slf_attn_bias1',
- 'gsrm_slf_attn_bias2'] # dataloader will return list in this order
- loader:
- shuffle: False
- batch_size_per_card: 64
- drop_last: False
- num_workers: 4
- Eval:
- dataset:
- name: LMDBDataSet
- data_dir: ./train_data/data_lmdb_release/validation/
- transforms:
- - DecodeImage: # load image
- img_mode: BGR
- channel_first: False
- - SRNLabelEncode: # Class handling label
- - SRNRecResizeImg:
- image_shape: [1, 64, 256]
- - KeepKeys:
- keep_keys: ['image',
- 'label',
- 'length',
- 'encoder_word_pos',
- 'gsrm_word_pos',
- 'gsrm_slf_attn_bias1',
- 'gsrm_slf_attn_bias2']
- loader:
- shuffle: False
- drop_last: False
- batch_size_per_card: 32
- num_workers: 4
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