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SLANet_ch.yml
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Global:
use_gpu: True
epoch_num: 400
log_smooth_window: 20
print_batch_step: 20
save_model_dir: ./output/SLANet_ch
save_epoch_step: 400
# evaluation is run every 331 iterations after the 0th iteration
eval_batch_step: [0, 331]
cal_metric_during_train: True
pretrained_model:
checkpoints:
save_inference_dir: ./output/SLANet_ch/infer
use_visualdl: False
infer_img: ppstructure/docs/table/table.jpg
# for data or label process
character_dict_path: ppocr/utils/dict/table_structure_dict_ch.txt
character_type: en
max_text_length: &max_text_length 500
box_format: &box_format xyxyxyxy # 'xywh', 'xyxy', 'xyxyxyxy'
infer_mode: False
use_sync_bn: True
save_res_path: output/infer
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
clip_norm: 5.0
lr:
learning_rate: 0.001
regularizer:
name: 'L2'
factor: 0.00000
Architecture:
model_type: table
algorithm: SLANet
Backbone:
name: PPLCNet
scale: 1.0
pretrained: True
use_ssld: True
Neck:
name: CSPPAN
out_channels: 96
Head:
name: SLAHead
hidden_size: 256
max_text_length: *max_text_length
loc_reg_num: &loc_reg_num 8
Loss:
name: SLALoss
structure_weight: 1.0
loc_weight: 2.0
loc_loss: smooth_l1
PostProcess:
name: TableLabelDecode
merge_no_span_structure: &merge_no_span_structure True
Metric:
name: TableMetric
main_indicator: acc
compute_bbox_metric: False
loc_reg_num: *loc_reg_num
box_format: *box_format
del_thead_tbody: True
Train:
dataset:
name: PubTabDataSet
data_dir: train_data/table/train/
label_file_list: [train_data/table/train.txt]
transforms:
- DecodeImage:
img_mode: BGR
channel_first: False
- TableLabelEncode:
learn_empty_box: False
merge_no_span_structure: *merge_no_span_structure
replace_empty_cell_token: False
loc_reg_num: *loc_reg_num
max_text_length: *max_text_length
- TableBoxEncode:
in_box_format: *box_format
out_box_format: *box_format
- ResizeTableImage:
max_len: 488
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- PaddingTableImage:
size: [488, 488]
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'structure', 'bboxes', 'bbox_masks', 'length', 'shape']
loader:
shuffle: True
batch_size_per_card: 48
drop_last: True
num_workers: 1
Eval:
dataset:
name: PubTabDataSet
data_dir: train_data/table/val/
label_file_list: [train_data/table/val.txt]
transforms:
- DecodeImage:
img_mode: BGR
channel_first: False
- TableLabelEncode:
learn_empty_box: False
merge_no_span_structure: *merge_no_span_structure
replace_empty_cell_token: False
loc_reg_num: *loc_reg_num
max_text_length: *max_text_length
- TableBoxEncode:
in_box_format: *box_format
out_box_format: *box_format
- ResizeTableImage:
max_len: 488
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- PaddingTableImage:
size: [488, 488]
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'structure', 'bboxes', 'bbox_masks', 'length', 'shape']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 48
num_workers: 1