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svtrv2_smtr_bi.yml
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svtrv2_smtr_bi.yml
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Global:
device: gpu
epoch_num: 20
log_smooth_window: 20
print_batch_step: 10
output_dir: ./output/rec/u14m_filter/svtrv2_lnconv_smtr_bi
save_epoch_step: 1
# evaluation is run every 2000 iterations
eval_batch_step: [0, 500]
eval_epoch_step: [0, 1]
cal_metric_during_train: True
pretrained_model:
checkpoints:
use_tensorboard: false
infer_img: ../ltb/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/u14m_filter/predicts_svtrv2_lnconv_smtr_bi.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: SMTR
in_channels: 3
Transform:
Encoder:
name: SVTRv2LNConv
use_pos_embed: False
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
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
Loss:
name: SMTRLoss
PostProcess:
name: SMTRLabelDecode
next_mode: *next
character_dict_path: *character_dict_path
use_space_char: *use_space_char
Metric:
name: RecMetric
main_indicator: acc
is_filter: True
Train:
dataset:
name: RatioDataSet
ds_width: True
padding: false
padding_rand: true
padding_doub: true
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:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- PARSeqAug:
- 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 128
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
- ARLabelEncode: # Class handling label
max_text_length: 200
- SliceResize:
image_shape: [3, 32, 128]
padding: False
max_ratio: 12
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False
batch_size_per_card: 1
num_workers: 2