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- Global:
- use_gpu: true
- epoch_num: 8
- log_smooth_window: 200
- print_batch_step: 200
- save_model_dir: ./output/rec/r45_visionlan
- save_epoch_step: 1
- # evaluation is run every 2000 iterations
- eval_batch_step: [0, 2000]
- cal_metric_during_train: True
- pretrained_model:
- checkpoints:
- save_inference_dir:
- use_visualdl: True
- infer_img: doc/imgs_words/en/word_2.png
- # for data or label process
- character_dict_path:
- max_text_length: &max_text_length 25
- training_step: &training_step LA
- infer_mode: False
- use_space_char: False
- save_res_path: ./output/rec/predicts_visionlan.txt
- Optimizer:
- name: Adam
- beta1: 0.9
- beta2: 0.999
- clip_norm: 20.0
- group_lr: true
- training_step: *training_step
- lr:
- name: Piecewise
- decay_epochs: [6]
- values: [0.0001, 0.00001]
- regularizer:
- name: 'L2'
- factor: 0
- Architecture:
- model_type: rec
- algorithm: VisionLAN
- Transform:
- Backbone:
- name: ResNet45
- strides: [2, 2, 2, 1, 1]
- Head:
- name: VLHead
- n_layers: 3
- n_position: 256
- n_dim: 512
- max_text_length: *max_text_length
- training_step: *training_step
- Loss:
- name: VLLoss
- mode: *training_step
- weight_res: 0.5
- weight_mas: 0.5
- PostProcess:
- name: VLLabelDecode
- Metric:
- name: RecMetric
- is_filter: true
- Train:
- dataset:
- name: LMDBDataSet
- data_dir: ./train_data/data_lmdb_release/training/
- transforms:
- - DecodeImage: # load image
- img_mode: RGB
- channel_first: False
- - ABINetRecAug:
- - VLLabelEncode: # Class handling label
- - VLRecResizeImg:
- image_shape: [3, 64, 256]
- - KeepKeys:
- keep_keys: ['image', 'label', 'label_res', 'label_sub', 'label_id', 'length'] # dataloader will return list in this order
- loader:
- shuffle: True
- batch_size_per_card: 220
- drop_last: True
- num_workers: 4
- Eval:
- dataset:
- name: LMDBDataSet
- data_dir: ./train_data/data_lmdb_release/validation/
- transforms:
- - DecodeImage: # load image
- img_mode: RGB
- channel_first: False
- - VLLabelEncode: # Class handling label
- - VLRecResizeImg:
- image_shape: [3, 64, 256]
- - KeepKeys:
- keep_keys: ['image', 'label', 'label_res', 'label_sub', 'label_id', 'length'] # dataloader will return list in this order
- loader:
- shuffle: False
- drop_last: False
- batch_size_per_card: 64
- num_workers: 4
-
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