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rec_r45_visionlan.yml
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rec_r45_visionlan.yml
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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