init
This commit is contained in:
@@ -0,0 +1,98 @@
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Global:
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||||
debug: false
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use_gpu: true
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||||
epoch_num: 100
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||||
log_smooth_window: 20
|
||||
print_batch_step: 10
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save_model_dir: ./output/rec_ppocr_v3_rotnet
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||||
save_epoch_step: 3
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||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model: null
|
||||
checkpoints: null
|
||||
save_inference_dir: null
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
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||||
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
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max_text_length: 25
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||||
infer_mode: false
|
||||
use_space_char: true
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||||
save_res_path: ./output/rec/predicts_chinese_lite_v2.0.txt
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||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
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||||
lr:
|
||||
name: Cosine
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||||
learning_rate: 0.001
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||||
regularizer:
|
||||
name: L2
|
||||
factor: 1.0e-05
|
||||
Architecture:
|
||||
model_type: cls
|
||||
algorithm: CLS
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||||
Transform: null
|
||||
Backbone:
|
||||
name: MobileNetV1Enhance
|
||||
scale: 0.5
|
||||
last_conv_stride: [1, 2]
|
||||
last_pool_type: avg
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||||
Neck:
|
||||
Head:
|
||||
name: ClsHead
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||||
class_dim: 4
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||||
|
||||
Loss:
|
||||
name: ClsLoss
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main_indicator: acc
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|
||||
PostProcess:
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||||
name: ClsPostProcess
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Metric:
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name: ClsMetric
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main_indicator: acc
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Train:
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dataset:
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name: SimpleDataSet
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data_dir: ./train_data
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label_file_list:
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- ./train_data/train_list.txt
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transforms:
|
||||
- DecodeImage:
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img_mode: BGR
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||||
channel_first: false
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||||
- BaseDataAugmentation:
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- RandAugment:
|
||||
- SSLRotateResize:
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||||
image_shape: [3, 48, 320]
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||||
- KeepKeys:
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||||
keep_keys: ["image", "label"]
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||||
loader:
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||||
collate_fn: "SSLRotateCollate"
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||||
shuffle: true
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batch_size_per_card: 32
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drop_last: true
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num_workers: 8
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Eval:
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dataset:
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name: SimpleDataSet
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data_dir: ./train_data
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label_file_list:
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- ./train_data/val_list.txt
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transforms:
|
||||
- DecodeImage:
|
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img_mode: BGR
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||||
channel_first: false
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||||
- SSLRotateResize:
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image_shape: [3, 48, 320]
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- KeepKeys:
|
||||
keep_keys: ["image", "label"]
|
||||
loader:
|
||||
collate_fn: "SSLRotateCollate"
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||||
shuffle: false
|
||||
drop_last: false
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||||
batch_size_per_card: 64
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num_workers: 8
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profiler_options: null
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@@ -0,0 +1,95 @@
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Global:
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||||
# use_gpu: true
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use_gpu: false
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||||
epoch_num: 100
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: ./output/cls/mv3/
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save_epoch_step: 3
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||||
# evaluation is run every 5000 iterations after the 4000th iteration
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eval_batch_step: [0, 1000]
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cal_metric_during_train: True
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_words_en/word_10.png
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label_list: ['0','180']
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||||
|
||||
Architecture:
|
||||
model_type: cls
|
||||
algorithm: CLS
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV3
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||||
scale: 0.35
|
||||
model_name: small
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||||
Neck:
|
||||
Head:
|
||||
name: ClsHead
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||||
class_dim: 2
|
||||
|
||||
Loss:
|
||||
name: ClsLoss
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
PostProcess:
|
||||
name: ClsPostProcess
|
||||
|
||||
Metric:
|
||||
name: ClsMetric
|
||||
main_indicator: acc
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/cls
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||||
label_file_list:
|
||||
- ./train_data/cls/train.txt
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||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- ClsLabelEncode: # Class handling label
|
||||
- BaseDataAugmentation:
|
||||
- RandAugment:
|
||||
- ClsResizeImg:
|
||||
image_shape: [3, 48, 192]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label'] # dataloader will return list in this order
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||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 512
|
||||
drop_last: True
|
||||
num_workers: 8
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||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
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||||
data_dir: ./train_data/cls
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||||
label_file_list:
|
||||
- ./train_data/cls/test.txt
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||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- ClsLabelEncode: # Class handling label
|
||||
- ClsResizeImg:
|
||||
image_shape: [3, 48, 192]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label'] # dataloader will return list in this order
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||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 512
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||||
num_workers: 4
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@@ -0,0 +1,206 @@
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||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 1200
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 2
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||||
save_model_dir: ./output/ch_db_mv3/
|
||||
save_epoch_step: 1200
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [3000, 2000]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
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||||
infer_img: doc/imgs_en/img_10.jpg
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||||
save_res_path: ./output/det_db/predicts_db.txt
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||||
use_amp: False
|
||||
amp_level: O2
|
||||
amp_dtype: bfloat16
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||||
|
||||
Architecture:
|
||||
name: DistillationModel
|
||||
algorithm: Distillation
|
||||
model_type: det
|
||||
Models:
|
||||
Teacher:
|
||||
pretrained: ./pretrain_models/ch_ppocr_server_v2.0_det_train/best_accuracy
|
||||
freeze_params: true
|
||||
return_all_feats: false
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Transform:
|
||||
Backbone:
|
||||
name: ResNet_vd
|
||||
layers: 18
|
||||
Neck:
|
||||
name: DBFPN
|
||||
out_channels: 256
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
Student:
|
||||
pretrained:
|
||||
freeze_params: false
|
||||
return_all_feats: false
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: large
|
||||
disable_se: True
|
||||
Neck:
|
||||
name: DBFPN
|
||||
out_channels: 96
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
Student2:
|
||||
pretrained:
|
||||
freeze_params: false
|
||||
return_all_feats: false
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: large
|
||||
disable_se: True
|
||||
Neck:
|
||||
name: DBFPN
|
||||
out_channels: 96
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
|
||||
Loss:
|
||||
name: CombinedLoss
|
||||
loss_config_list:
|
||||
- DistillationDilaDBLoss:
|
||||
weight: 1.0
|
||||
model_name_pairs:
|
||||
- ["Student", "Teacher"]
|
||||
- ["Student2", "Teacher"]
|
||||
key: maps
|
||||
balance_loss: true
|
||||
main_loss_type: DiceLoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
- DistillationDMLLoss:
|
||||
model_name_pairs:
|
||||
- ["Student", "Student2"]
|
||||
maps_name: "thrink_maps"
|
||||
weight: 1.0
|
||||
# act: None
|
||||
model_name_pairs: ["Student", "Student2"]
|
||||
key: maps
|
||||
- DistillationDBLoss:
|
||||
weight: 1.0
|
||||
model_name_list: ["Student", "Student2"]
|
||||
# key: maps
|
||||
# name: DBLoss
|
||||
balance_loss: true
|
||||
main_loss_type: DiceLoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 2
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
PostProcess:
|
||||
name: DistillationDBPostProcess
|
||||
model_name: ["Student", "Student2", "Teacher"]
|
||||
# key: maps
|
||||
thresh: 0.3
|
||||
box_thresh: 0.6
|
||||
max_candidates: 1000
|
||||
unclip_ratio: 1.5
|
||||
|
||||
Metric:
|
||||
name: DistillationMetric
|
||||
base_metric_name: DetMetric
|
||||
main_indicator: hmean
|
||||
key: "Student"
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
|
||||
ratio_list: [1.0]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- CopyPaste:
|
||||
- IaaAugment:
|
||||
augmenter_args:
|
||||
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
|
||||
- { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
|
||||
- { 'type': Resize, 'args': { 'size': [0.5, 3] } }
|
||||
- EastRandomCropData:
|
||||
size: [960, 960]
|
||||
max_tries: 50
|
||||
keep_ratio: true
|
||||
- MakeBorderMap:
|
||||
shrink_ratio: 0.4
|
||||
thresh_min: 0.3
|
||||
thresh_max: 0.7
|
||||
- MakeShrinkMap:
|
||||
shrink_ratio: 0.4
|
||||
min_text_size: 8
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- DetResizeForTest:
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 2
|
||||
@@ -0,0 +1,175 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 1200
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 2
|
||||
save_model_dir: ./output/ch_db_mv3/
|
||||
save_epoch_step: 1200
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [3000, 2000]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_en/img_10.jpg
|
||||
save_res_path: ./output/det_db/predicts_db.txt
|
||||
|
||||
Architecture:
|
||||
name: DistillationModel
|
||||
algorithm: Distillation
|
||||
model_type: det
|
||||
Models:
|
||||
Student:
|
||||
pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
|
||||
freeze_params: false
|
||||
return_all_feats: false
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: large
|
||||
disable_se: True
|
||||
Neck:
|
||||
name: DBFPN
|
||||
out_channels: 96
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
Teacher:
|
||||
pretrained: ./pretrain_models/ch_ppocr_server_v2.0_det_train/best_accuracy
|
||||
freeze_params: true
|
||||
return_all_feats: false
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Transform:
|
||||
Backbone:
|
||||
name: ResNet_vd
|
||||
layers: 18
|
||||
Neck:
|
||||
name: DBFPN
|
||||
out_channels: 256
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
|
||||
Loss:
|
||||
name: CombinedLoss
|
||||
loss_config_list:
|
||||
- DistillationDilaDBLoss:
|
||||
weight: 1.0
|
||||
model_name_pairs:
|
||||
- ["Student", "Teacher"]
|
||||
key: maps
|
||||
balance_loss: true
|
||||
main_loss_type: DiceLoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
- DistillationDBLoss:
|
||||
weight: 1.0
|
||||
model_name_list: ["Student"]
|
||||
name: DBLoss
|
||||
balance_loss: true
|
||||
main_loss_type: DiceLoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 2
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
PostProcess:
|
||||
name: DistillationDBPostProcess
|
||||
model_name: ["Student"]
|
||||
key: head_out
|
||||
thresh: 0.3
|
||||
box_thresh: 0.6
|
||||
max_candidates: 1000
|
||||
unclip_ratio: 1.5
|
||||
|
||||
Metric:
|
||||
name: DistillationMetric
|
||||
base_metric_name: DetMetric
|
||||
main_indicator: hmean
|
||||
key: "Student"
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
|
||||
ratio_list: [1.0]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- CopyPaste:
|
||||
- IaaAugment:
|
||||
augmenter_args:
|
||||
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
|
||||
- { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
|
||||
- { 'type': Resize, 'args': { 'size': [0.5, 3] } }
|
||||
- EastRandomCropData:
|
||||
size: [960, 960]
|
||||
max_tries: 50
|
||||
keep_ratio: true
|
||||
- MakeBorderMap:
|
||||
shrink_ratio: 0.4
|
||||
thresh_min: 0.3
|
||||
thresh_max: 0.7
|
||||
- MakeShrinkMap:
|
||||
shrink_ratio: 0.4
|
||||
min_text_size: 8
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- DetResizeForTest:
|
||||
# image_shape: [736, 1280]
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 2
|
||||
@@ -0,0 +1,178 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 1200
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 2
|
||||
save_model_dir: ./output/ch_db_mv3/
|
||||
save_epoch_step: 1200
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [3000, 2000]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_en/img_10.jpg
|
||||
save_res_path: ./output/det_db/predicts_db.txt
|
||||
|
||||
Architecture:
|
||||
name: DistillationModel
|
||||
algorithm: Distillation
|
||||
model_type: det
|
||||
Models:
|
||||
Student:
|
||||
pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
|
||||
freeze_params: false
|
||||
return_all_feats: false
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: large
|
||||
disable_se: True
|
||||
Neck:
|
||||
name: DBFPN
|
||||
out_channels: 96
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
Teacher:
|
||||
pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
|
||||
freeze_params: false
|
||||
return_all_feats: false
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: large
|
||||
disable_se: True
|
||||
Neck:
|
||||
name: DBFPN
|
||||
out_channels: 96
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
|
||||
|
||||
Loss:
|
||||
name: CombinedLoss
|
||||
loss_config_list:
|
||||
- DistillationDMLLoss:
|
||||
model_name_pairs:
|
||||
- ["Student", "Teacher"]
|
||||
maps_name: "thrink_maps"
|
||||
weight: 1.0
|
||||
# act: None
|
||||
model_name_pairs: ["Student", "Teacher"]
|
||||
key: maps
|
||||
- DistillationDBLoss:
|
||||
weight: 1.0
|
||||
model_name_list: ["Student", "Teacher"]
|
||||
# key: maps
|
||||
name: DBLoss
|
||||
balance_loss: true
|
||||
main_loss_type: DiceLoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 2
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
PostProcess:
|
||||
name: DistillationDBPostProcess
|
||||
model_name: ["Student", "Teacher"]
|
||||
key: head_out
|
||||
thresh: 0.3
|
||||
box_thresh: 0.6
|
||||
max_candidates: 1000
|
||||
unclip_ratio: 1.5
|
||||
|
||||
Metric:
|
||||
name: DistillationMetric
|
||||
base_metric_name: DetMetric
|
||||
main_indicator: hmean
|
||||
key: "Student"
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
|
||||
ratio_list: [1.0]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- CopyPaste:
|
||||
- IaaAugment:
|
||||
augmenter_args:
|
||||
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
|
||||
- { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
|
||||
- { 'type': Resize, 'args': { 'size': [0.5, 3] } }
|
||||
- EastRandomCropData:
|
||||
size: [960, 960]
|
||||
max_tries: 50
|
||||
keep_ratio: true
|
||||
- MakeBorderMap:
|
||||
shrink_ratio: 0.4
|
||||
thresh_min: 0.3
|
||||
thresh_max: 0.7
|
||||
- MakeShrinkMap:
|
||||
shrink_ratio: 0.4
|
||||
min_text_size: 8
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- DetResizeForTest:
|
||||
# image_shape: [736, 1280]
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 2
|
||||
@@ -0,0 +1,132 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 1200
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/ch_db_mv3/
|
||||
save_epoch_step: 1200
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [0, 400]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model: ./pretrain_models/student.pdparams
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_en/img_10.jpg
|
||||
save_res_path: ./output/det_db/predicts_db.txt
|
||||
|
||||
Architecture:
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: large
|
||||
disable_se: True
|
||||
Neck:
|
||||
name: DBFPN
|
||||
out_channels: 96
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
|
||||
Loss:
|
||||
name: DBLoss
|
||||
balance_loss: true
|
||||
main_loss_type: DiceLoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 2
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
PostProcess:
|
||||
name: DBPostProcess
|
||||
thresh: 0.3
|
||||
box_thresh: 0.6
|
||||
max_candidates: 1000
|
||||
unclip_ratio: 1.5
|
||||
|
||||
Metric:
|
||||
name: DetMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
|
||||
ratio_list: [1.0]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- IaaAugment:
|
||||
augmenter_args:
|
||||
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
|
||||
- { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
|
||||
- { 'type': Resize, 'args': { 'size': [0.5, 3] } }
|
||||
- EastRandomCropData:
|
||||
size: [960, 960]
|
||||
max_tries: 50
|
||||
keep_ratio: true
|
||||
- MakeBorderMap:
|
||||
shrink_ratio: 0.4
|
||||
thresh_min: 0.3
|
||||
thresh_max: 0.7
|
||||
- MakeShrinkMap:
|
||||
shrink_ratio: 0.4
|
||||
min_text_size: 8
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- DetResizeForTest:
|
||||
# image_shape: [736, 1280]
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 2
|
||||
@@ -0,0 +1,226 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/ch_PP-OCR_v3_det/
|
||||
save_epoch_step: 100
|
||||
eval_batch_step:
|
||||
- 0
|
||||
- 400
|
||||
cal_metric_during_train: false
|
||||
pretrained_model: null
|
||||
checkpoints: null
|
||||
save_inference_dir: null
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_en/img_10.jpg
|
||||
save_res_path: ./checkpoints/det_db/predicts_db.txt
|
||||
distributed: true
|
||||
d2s_train_image_shape: [3, -1, -1]
|
||||
amp_dtype: bfloat16
|
||||
|
||||
Architecture:
|
||||
name: DistillationModel
|
||||
algorithm: Distillation
|
||||
model_type: det
|
||||
Models:
|
||||
Student:
|
||||
pretrained:
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Transform: null
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: large
|
||||
disable_se: true
|
||||
Neck:
|
||||
name: RSEFPN
|
||||
out_channels: 96
|
||||
shortcut: True
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
Student2:
|
||||
pretrained:
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Transform: null
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: large
|
||||
disable_se: true
|
||||
Neck:
|
||||
name: RSEFPN
|
||||
out_channels: 96
|
||||
shortcut: True
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
Teacher:
|
||||
freeze_params: true
|
||||
return_all_feats: false
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Backbone:
|
||||
name: ResNet_vd
|
||||
in_channels: 3
|
||||
layers: 50
|
||||
Neck:
|
||||
name: LKPAN
|
||||
out_channels: 256
|
||||
Head:
|
||||
name: DBHead
|
||||
kernel_list: [7,2,2]
|
||||
k: 50
|
||||
|
||||
Loss:
|
||||
name: CombinedLoss
|
||||
loss_config_list:
|
||||
- DistillationDilaDBLoss:
|
||||
weight: 1.0
|
||||
model_name_pairs:
|
||||
- ["Student", "Teacher"]
|
||||
- ["Student2", "Teacher"]
|
||||
key: maps
|
||||
balance_loss: true
|
||||
main_loss_type: DiceLoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
- DistillationDMLLoss:
|
||||
model_name_pairs:
|
||||
- ["Student", "Student2"]
|
||||
maps_name: "thrink_maps"
|
||||
weight: 1.0
|
||||
model_name_pairs: ["Student", "Student2"]
|
||||
key: maps
|
||||
- DistillationDBLoss:
|
||||
weight: 1.0
|
||||
model_name_list: ["Student", "Student2"]
|
||||
balance_loss: true
|
||||
main_loss_type: DiceLoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 2
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 5.0e-05
|
||||
|
||||
PostProcess:
|
||||
name: DistillationDBPostProcess
|
||||
model_name: ["Student"]
|
||||
key: head_out
|
||||
thresh: 0.3
|
||||
box_thresh: 0.6
|
||||
max_candidates: 1000
|
||||
unclip_ratio: 1.5
|
||||
|
||||
Metric:
|
||||
name: DistillationMetric
|
||||
base_metric_name: DetMetric
|
||||
main_indicator: hmean
|
||||
key: "Student"
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
|
||||
ratio_list: [1.0]
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- DetLabelEncode: null
|
||||
- CopyPaste:
|
||||
- IaaAugment:
|
||||
augmenter_args:
|
||||
- type: Fliplr
|
||||
args:
|
||||
p: 0.5
|
||||
- type: Affine
|
||||
args:
|
||||
rotate:
|
||||
- -10
|
||||
- 10
|
||||
- type: Resize
|
||||
args:
|
||||
size:
|
||||
- 0.5
|
||||
- 3
|
||||
- EastRandomCropData:
|
||||
size:
|
||||
- 960
|
||||
- 960
|
||||
max_tries: 50
|
||||
keep_ratio: true
|
||||
- MakeBorderMap:
|
||||
shrink_ratio: 0.4
|
||||
thresh_min: 0.3
|
||||
thresh_max: 0.7
|
||||
- MakeShrinkMap:
|
||||
shrink_ratio: 0.4
|
||||
min_text_size: 8
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean:
|
||||
- 0.485
|
||||
- 0.456
|
||||
- 0.406
|
||||
std:
|
||||
- 0.229
|
||||
- 0.224
|
||||
- 0.225
|
||||
order: hwc
|
||||
- ToCHWImage: null
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- threshold_map
|
||||
- threshold_mask
|
||||
- shrink_map
|
||||
- shrink_mask
|
||||
loader:
|
||||
shuffle: true
|
||||
drop_last: false
|
||||
batch_size_per_card: 8
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- DetResizeForTest:
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 2
|
||||
@@ -0,0 +1,173 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 1200
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 2
|
||||
save_model_dir: ./output/ch_db_mv3/
|
||||
save_epoch_step: 1200
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [3000, 2000]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_en/img_10.jpg
|
||||
save_res_path: ./output/det_db/predicts_db.txt
|
||||
|
||||
Architecture:
|
||||
name: DistillationModel
|
||||
algorithm: Distillation
|
||||
model_type: det
|
||||
Models:
|
||||
Student:
|
||||
return_all_feats: false
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Backbone:
|
||||
name: ResNet_vd
|
||||
in_channels: 3
|
||||
layers: 50
|
||||
Neck:
|
||||
name: LKPAN
|
||||
out_channels: 256
|
||||
Head:
|
||||
name: DBHead
|
||||
kernel_list: [7,2,2]
|
||||
k: 50
|
||||
Student2:
|
||||
return_all_feats: false
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Backbone:
|
||||
name: ResNet_vd
|
||||
in_channels: 3
|
||||
layers: 50
|
||||
Neck:
|
||||
name: LKPAN
|
||||
out_channels: 256
|
||||
Head:
|
||||
name: DBHead
|
||||
kernel_list: [7,2,2]
|
||||
k: 50
|
||||
|
||||
|
||||
Loss:
|
||||
name: CombinedLoss
|
||||
loss_config_list:
|
||||
- DistillationDMLLoss:
|
||||
model_name_pairs:
|
||||
- ["Student", "Student2"]
|
||||
maps_name: "thrink_maps"
|
||||
weight: 1.0
|
||||
# act: None
|
||||
model_name_pairs: ["Student", "Student2"]
|
||||
key: maps
|
||||
- DistillationDBLoss:
|
||||
weight: 1.0
|
||||
model_name_list: ["Student", "Student2"]
|
||||
# key: maps
|
||||
name: DBLoss
|
||||
balance_loss: true
|
||||
main_loss_type: DiceLoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 2
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
PostProcess:
|
||||
name: DistillationDBPostProcess
|
||||
model_name: ["Student", "Student2"]
|
||||
key: head_out
|
||||
thresh: 0.3
|
||||
box_thresh: 0.6
|
||||
max_candidates: 1000
|
||||
unclip_ratio: 1.5
|
||||
|
||||
Metric:
|
||||
name: DistillationMetric
|
||||
base_metric_name: DetMetric
|
||||
main_indicator: hmean
|
||||
key: "Student"
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
|
||||
ratio_list: [1.0]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- CopyPaste:
|
||||
- IaaAugment:
|
||||
augmenter_args:
|
||||
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
|
||||
- { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
|
||||
- { 'type': Resize, 'args': { 'size': [0.5, 3] } }
|
||||
- EastRandomCropData:
|
||||
size: [960, 960]
|
||||
max_tries: 50
|
||||
keep_ratio: true
|
||||
- MakeBorderMap:
|
||||
shrink_ratio: 0.4
|
||||
thresh_min: 0.3
|
||||
thresh_max: 0.7
|
||||
- MakeShrinkMap:
|
||||
shrink_ratio: 0.4
|
||||
min_text_size: 8
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- DetResizeForTest:
|
||||
# image_shape: [736, 1280]
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 2
|
||||
@@ -0,0 +1,163 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/ch_PP-OCR_V3_det/
|
||||
save_epoch_step: 100
|
||||
eval_batch_step:
|
||||
- 0
|
||||
- 400
|
||||
cal_metric_during_train: false
|
||||
pretrained_model: https://paddleocr.bj.bcebos.com/pretrained/MobileNetV3_large_x0_5_pretrained.pdparams
|
||||
checkpoints: null
|
||||
save_inference_dir: null
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_en/img_10.jpg
|
||||
save_res_path: ./checkpoints/det_db/predicts_db.txt
|
||||
distributed: true
|
||||
|
||||
Architecture:
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: large
|
||||
disable_se: True
|
||||
Neck:
|
||||
name: RSEFPN
|
||||
out_channels: 96
|
||||
shortcut: True
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
|
||||
Loss:
|
||||
name: DBLoss
|
||||
balance_loss: true
|
||||
main_loss_type: DiceLoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 2
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 5.0e-05
|
||||
PostProcess:
|
||||
name: DBPostProcess
|
||||
thresh: 0.3
|
||||
box_thresh: 0.6
|
||||
max_candidates: 1000
|
||||
unclip_ratio: 1.5
|
||||
Metric:
|
||||
name: DetMetric
|
||||
main_indicator: hmean
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
|
||||
ratio_list: [1.0]
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- DetLabelEncode: null
|
||||
- IaaAugment:
|
||||
augmenter_args:
|
||||
- type: Fliplr
|
||||
args:
|
||||
p: 0.5
|
||||
- type: Affine
|
||||
args:
|
||||
rotate:
|
||||
- -10
|
||||
- 10
|
||||
- type: Resize
|
||||
args:
|
||||
size:
|
||||
- 0.5
|
||||
- 3
|
||||
- EastRandomCropData:
|
||||
size:
|
||||
- 960
|
||||
- 960
|
||||
max_tries: 50
|
||||
keep_ratio: true
|
||||
- MakeBorderMap:
|
||||
shrink_ratio: 0.4
|
||||
thresh_min: 0.3
|
||||
thresh_max: 0.7
|
||||
- MakeShrinkMap:
|
||||
shrink_ratio: 0.4
|
||||
min_text_size: 8
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean:
|
||||
- 0.485
|
||||
- 0.456
|
||||
- 0.406
|
||||
std:
|
||||
- 0.229
|
||||
- 0.224
|
||||
- 0.225
|
||||
order: hwc
|
||||
- ToCHWImage: null
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- threshold_map
|
||||
- threshold_mask
|
||||
- shrink_map
|
||||
- shrink_mask
|
||||
loader:
|
||||
shuffle: true
|
||||
drop_last: false
|
||||
batch_size_per_card: 8
|
||||
num_workers: 4
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- DetLabelEncode: null
|
||||
- DetResizeForTest: null
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean:
|
||||
- 0.485
|
||||
- 0.456
|
||||
- 0.406
|
||||
std:
|
||||
- 0.229
|
||||
- 0.224
|
||||
- 0.225
|
||||
order: hwc
|
||||
- ToCHWImage: null
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- shape
|
||||
- polys
|
||||
- ignore_tags
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 1
|
||||
num_workers: 2
|
||||
@@ -0,0 +1,235 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 20
|
||||
save_model_dir: ./output/ch_PP-OCRv4
|
||||
save_epoch_step: 50
|
||||
eval_batch_step:
|
||||
- 0
|
||||
- 1000
|
||||
cal_metric_during_train: true
|
||||
checkpoints: null
|
||||
pretrained_model: null
|
||||
save_inference_dir: null
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_en/img_10.jpg
|
||||
save_res_path: ./checkpoints/det_db/predicts_db.txt
|
||||
distributed: true
|
||||
Architecture:
|
||||
name: DistillationModel
|
||||
algorithm: Distillation
|
||||
model_type: det
|
||||
Models:
|
||||
Student:
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Transform: null
|
||||
Backbone:
|
||||
name: PPLCNetNew
|
||||
scale: 0.75
|
||||
pretrained: false
|
||||
Neck:
|
||||
name: RSEFPN
|
||||
out_channels: 96
|
||||
shortcut: true
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
Student2:
|
||||
pretrained: null
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Transform: null
|
||||
Backbone:
|
||||
name: PPLCNetNew
|
||||
scale: 0.75
|
||||
pretrained: true
|
||||
Neck:
|
||||
name: RSEFPN
|
||||
out_channels: 96
|
||||
shortcut: true
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
Teacher:
|
||||
pretrained: https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_det_cml_teacher_pretrained/teacher.pdparams
|
||||
freeze_params: true
|
||||
return_all_feats: false
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Backbone:
|
||||
name: ResNet_vd
|
||||
in_channels: 3
|
||||
layers: 50
|
||||
Neck:
|
||||
name: LKPAN
|
||||
out_channels: 256
|
||||
Head:
|
||||
name: DBHead
|
||||
kernel_list:
|
||||
- 7
|
||||
- 2
|
||||
- 2
|
||||
k: 50
|
||||
Loss:
|
||||
name: CombinedLoss
|
||||
loss_config_list:
|
||||
- DistillationDilaDBLoss:
|
||||
weight: 1.0
|
||||
model_name_pairs:
|
||||
- - Student
|
||||
- Teacher
|
||||
- - Student2
|
||||
- Teacher
|
||||
key: maps
|
||||
balance_loss: true
|
||||
main_loss_type: DiceLoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
- DistillationDMLLoss:
|
||||
model_name_pairs:
|
||||
- Student
|
||||
- Student2
|
||||
maps_name: thrink_maps
|
||||
weight: 1.0
|
||||
key: maps
|
||||
- DistillationDBLoss:
|
||||
weight: 1.0
|
||||
model_name_list:
|
||||
- Student
|
||||
- Student2
|
||||
balance_loss: true
|
||||
main_loss_type: DiceLoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 2
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 5.0e-05
|
||||
PostProcess:
|
||||
name: DistillationDBPostProcess
|
||||
model_name:
|
||||
- Student
|
||||
key: head_out
|
||||
thresh: 0.3
|
||||
box_thresh: 0.6
|
||||
max_candidates: 1000
|
||||
unclip_ratio: 1.5
|
||||
Metric:
|
||||
name: DistillationMetric
|
||||
base_metric_name: DetMetric
|
||||
main_indicator: hmean
|
||||
key: Student
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
|
||||
ratio_list: [1.0]
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- DetLabelEncode: null
|
||||
- IaaAugment:
|
||||
augmenter_args:
|
||||
- type: Fliplr
|
||||
args:
|
||||
p: 0.5
|
||||
- type: Affine
|
||||
args:
|
||||
rotate:
|
||||
- -10
|
||||
- 10
|
||||
- type: Resize
|
||||
args:
|
||||
size:
|
||||
- 0.5
|
||||
- 3
|
||||
- EastRandomCropData:
|
||||
size:
|
||||
- 640
|
||||
- 640
|
||||
max_tries: 50
|
||||
keep_ratio: true
|
||||
- MakeBorderMap:
|
||||
shrink_ratio: 0.4
|
||||
thresh_min: 0.3
|
||||
thresh_max: 0.7
|
||||
total_epoch: 500
|
||||
- MakeShrinkMap:
|
||||
shrink_ratio: 0.4
|
||||
min_text_size: 8
|
||||
total_epoch: 500
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean:
|
||||
- 0.485
|
||||
- 0.456
|
||||
- 0.406
|
||||
std:
|
||||
- 0.229
|
||||
- 0.224
|
||||
- 0.225
|
||||
order: hwc
|
||||
- ToCHWImage: null
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- threshold_map
|
||||
- threshold_mask
|
||||
- shrink_map
|
||||
- shrink_mask
|
||||
loader:
|
||||
shuffle: true
|
||||
drop_last: false
|
||||
batch_size_per_card: 16
|
||||
num_workers: 8
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- DetLabelEncode: null
|
||||
- DetResizeForTest: null
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean:
|
||||
- 0.485
|
||||
- 0.456
|
||||
- 0.406
|
||||
std:
|
||||
- 0.229
|
||||
- 0.224
|
||||
- 0.225
|
||||
order: hwc
|
||||
- ToCHWImage: null
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- shape
|
||||
- polys
|
||||
- ignore_tags
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 1
|
||||
num_workers: 2
|
||||
profiler_options: null
|
||||
@@ -0,0 +1,171 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: &epoch_num 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 100
|
||||
save_model_dir: ./output/ch_PP-OCRv4
|
||||
save_epoch_step: 10
|
||||
eval_batch_step:
|
||||
- 0
|
||||
- 1500
|
||||
cal_metric_during_train: false
|
||||
checkpoints:
|
||||
pretrained_model: https://paddleocr.bj.bcebos.com/pretrained/PPLCNetV3_x0_75_ocr_det.pdparams
|
||||
save_inference_dir: null
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_en/img_10.jpg
|
||||
save_res_path: ./checkpoints/det_db/predicts_db.txt
|
||||
distributed: true
|
||||
|
||||
Architecture:
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Transform: null
|
||||
Backbone:
|
||||
name: PPLCNetV3
|
||||
scale: 0.75
|
||||
det: True
|
||||
Neck:
|
||||
name: RSEFPN
|
||||
out_channels: 96
|
||||
shortcut: True
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
|
||||
Loss:
|
||||
name: DBLoss
|
||||
balance_loss: true
|
||||
main_loss_type: DiceLoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001 #(8*8c)
|
||||
warmup_epoch: 2
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 5.0e-05
|
||||
|
||||
PostProcess:
|
||||
name: DBPostProcess
|
||||
thresh: 0.3
|
||||
box_thresh: 0.6
|
||||
max_candidates: 1000
|
||||
unclip_ratio: 1.5
|
||||
|
||||
Metric:
|
||||
name: DetMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
|
||||
ratio_list: [1.0]
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- DetLabelEncode: null
|
||||
- CopyPaste: null
|
||||
- IaaAugment:
|
||||
augmenter_args:
|
||||
- type: Fliplr
|
||||
args:
|
||||
p: 0.5
|
||||
- type: Affine
|
||||
args:
|
||||
rotate:
|
||||
- -10
|
||||
- 10
|
||||
- type: Resize
|
||||
args:
|
||||
size:
|
||||
- 0.5
|
||||
- 3
|
||||
- EastRandomCropData:
|
||||
size:
|
||||
- 640
|
||||
- 640
|
||||
max_tries: 50
|
||||
keep_ratio: true
|
||||
- MakeBorderMap:
|
||||
shrink_ratio: 0.4
|
||||
thresh_min: 0.3
|
||||
thresh_max: 0.7
|
||||
total_epoch: *epoch_num
|
||||
- MakeShrinkMap:
|
||||
shrink_ratio: 0.4
|
||||
min_text_size: 8
|
||||
total_epoch: *epoch_num
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean:
|
||||
- 0.485
|
||||
- 0.456
|
||||
- 0.406
|
||||
std:
|
||||
- 0.229
|
||||
- 0.224
|
||||
- 0.225
|
||||
order: hwc
|
||||
- ToCHWImage: null
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- threshold_map
|
||||
- threshold_mask
|
||||
- shrink_map
|
||||
- shrink_mask
|
||||
loader:
|
||||
shuffle: true
|
||||
drop_last: false
|
||||
batch_size_per_card: 8
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- DetLabelEncode: null
|
||||
- DetResizeForTest:
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean:
|
||||
- 0.485
|
||||
- 0.456
|
||||
- 0.406
|
||||
std:
|
||||
- 0.229
|
||||
- 0.224
|
||||
- 0.225
|
||||
order: hwc
|
||||
- ToCHWImage: null
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- shape
|
||||
- polys
|
||||
- ignore_tags
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 1
|
||||
num_workers: 2
|
||||
profiler_options: null
|
||||
@@ -0,0 +1,172 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: &epoch_num 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 100
|
||||
save_model_dir: ./output/ch_PP-OCRv4
|
||||
save_epoch_step: 10
|
||||
eval_batch_step:
|
||||
- 0
|
||||
- 1500
|
||||
cal_metric_during_train: false
|
||||
checkpoints:
|
||||
pretrained_model: https://paddleocr.bj.bcebos.com/pretrained/PPHGNet_small_ocr_det.pdparams
|
||||
save_inference_dir: null
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_en/img_10.jpg
|
||||
save_res_path: ./checkpoints/det_db/predicts_db.txt
|
||||
distributed: true
|
||||
|
||||
Architecture:
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Transform: null
|
||||
Backbone:
|
||||
name: PPHGNet_small
|
||||
det: True
|
||||
Neck:
|
||||
name: LKPAN
|
||||
out_channels: 256
|
||||
intracl: true
|
||||
Head:
|
||||
name: PFHeadLocal
|
||||
k: 50
|
||||
mode: "large"
|
||||
|
||||
|
||||
Loss:
|
||||
name: DBLoss
|
||||
balance_loss: true
|
||||
main_loss_type: DiceLoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001 #(8*8c)
|
||||
warmup_epoch: 2
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 1e-6
|
||||
|
||||
PostProcess:
|
||||
name: DBPostProcess
|
||||
thresh: 0.3
|
||||
box_thresh: 0.6
|
||||
max_candidates: 1000
|
||||
unclip_ratio: 1.5
|
||||
|
||||
Metric:
|
||||
name: DetMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
|
||||
ratio_list: [1.0]
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- DetLabelEncode: null
|
||||
- CopyPaste: null
|
||||
- IaaAugment:
|
||||
augmenter_args:
|
||||
- type: Fliplr
|
||||
args:
|
||||
p: 0.5
|
||||
- type: Affine
|
||||
args:
|
||||
rotate:
|
||||
- -10
|
||||
- 10
|
||||
- type: Resize
|
||||
args:
|
||||
size:
|
||||
- 0.5
|
||||
- 3
|
||||
- EastRandomCropData:
|
||||
size:
|
||||
- 640
|
||||
- 640
|
||||
max_tries: 50
|
||||
keep_ratio: true
|
||||
- MakeBorderMap:
|
||||
shrink_ratio: 0.4
|
||||
thresh_min: 0.3
|
||||
thresh_max: 0.7
|
||||
total_epoch: *epoch_num
|
||||
- MakeShrinkMap:
|
||||
shrink_ratio: 0.4
|
||||
min_text_size: 8
|
||||
total_epoch: *epoch_num
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean:
|
||||
- 0.485
|
||||
- 0.456
|
||||
- 0.406
|
||||
std:
|
||||
- 0.229
|
||||
- 0.224
|
||||
- 0.225
|
||||
order: hwc
|
||||
- ToCHWImage: null
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- threshold_map
|
||||
- threshold_mask
|
||||
- shrink_map
|
||||
- shrink_mask
|
||||
loader:
|
||||
shuffle: true
|
||||
drop_last: false
|
||||
batch_size_per_card: 8
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- DetLabelEncode: null
|
||||
- DetResizeForTest:
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean:
|
||||
- 0.485
|
||||
- 0.456
|
||||
- 0.406
|
||||
std:
|
||||
- 0.229
|
||||
- 0.224
|
||||
- 0.225
|
||||
order: hwc
|
||||
- ToCHWImage: null
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- shape
|
||||
- polys
|
||||
- ignore_tags
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 1
|
||||
num_workers: 2
|
||||
profiler_options: null
|
||||
@@ -0,0 +1,132 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 1200
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 2
|
||||
save_model_dir: ./output/ch_db_mv3/
|
||||
save_epoch_step: 1200
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [3000, 2000]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_en/img_10.jpg
|
||||
save_res_path: ./output/det_db/predicts_db.txt
|
||||
|
||||
Architecture:
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: large
|
||||
disable_se: True
|
||||
Neck:
|
||||
name: DBFPN
|
||||
out_channels: 96
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
|
||||
Loss:
|
||||
name: DBLoss
|
||||
balance_loss: true
|
||||
main_loss_type: DiceLoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 2
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
PostProcess:
|
||||
name: DBPostProcess
|
||||
thresh: 0.3
|
||||
box_thresh: 0.6
|
||||
max_candidates: 1000
|
||||
unclip_ratio: 1.5
|
||||
|
||||
Metric:
|
||||
name: DetMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
|
||||
ratio_list: [1.0]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- IaaAugment:
|
||||
augmenter_args:
|
||||
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
|
||||
- { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
|
||||
- { 'type': Resize, 'args': { 'size': [0.5, 3] } }
|
||||
- EastRandomCropData:
|
||||
size: [960, 960]
|
||||
max_tries: 50
|
||||
keep_ratio: true
|
||||
- MakeBorderMap:
|
||||
shrink_ratio: 0.4
|
||||
thresh_min: 0.3
|
||||
thresh_max: 0.7
|
||||
- MakeShrinkMap:
|
||||
shrink_ratio: 0.4
|
||||
min_text_size: 8
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- DetResizeForTest:
|
||||
# image_shape: [736, 1280]
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 2
|
||||
@@ -0,0 +1,131 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 1200
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 2
|
||||
save_model_dir: ./output/ch_db_res18/
|
||||
save_epoch_step: 1200
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [3000, 2000]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model: ./pretrain_models/ResNet18_vd_pretrained
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_en/img_10.jpg
|
||||
save_res_path: ./output/det_db/predicts_db.txt
|
||||
|
||||
Architecture:
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Transform:
|
||||
Backbone:
|
||||
name: ResNet_vd
|
||||
layers: 18
|
||||
disable_se: True
|
||||
Neck:
|
||||
name: DBFPN
|
||||
out_channels: 256
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
|
||||
Loss:
|
||||
name: DBLoss
|
||||
balance_loss: true
|
||||
main_loss_type: DiceLoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 2
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
PostProcess:
|
||||
name: DBPostProcess
|
||||
thresh: 0.3
|
||||
box_thresh: 0.6
|
||||
max_candidates: 1000
|
||||
unclip_ratio: 1.5
|
||||
|
||||
Metric:
|
||||
name: DetMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
|
||||
ratio_list: [1.0]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- IaaAugment:
|
||||
augmenter_args:
|
||||
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
|
||||
- { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
|
||||
- { 'type': Resize, 'args': { 'size': [0.5, 3] } }
|
||||
- EastRandomCropData:
|
||||
size: [960, 960]
|
||||
max_tries: 50
|
||||
keep_ratio: true
|
||||
- MakeBorderMap:
|
||||
shrink_ratio: 0.4
|
||||
thresh_min: 0.3
|
||||
thresh_max: 0.7
|
||||
- MakeShrinkMap:
|
||||
shrink_ratio: 0.4
|
||||
min_text_size: 8
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- DetResizeForTest:
|
||||
# image_shape: [736, 1280]
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 2
|
||||
@@ -0,0 +1,133 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
use_mlu: false
|
||||
epoch_num: 1200
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/db_mv3/
|
||||
save_epoch_step: 1200
|
||||
# evaluation is run every 2000 iterations
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_en/img_10.jpg
|
||||
save_res_path: ./output/det_db/predicts_db.txt
|
||||
|
||||
Architecture:
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: large
|
||||
Neck:
|
||||
name: DBFPN
|
||||
out_channels: 256
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
|
||||
Loss:
|
||||
name: DBLoss
|
||||
balance_loss: true
|
||||
main_loss_type: DiceLoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
learning_rate: 0.001
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
PostProcess:
|
||||
name: DBPostProcess
|
||||
thresh: 0.3
|
||||
box_thresh: 0.6
|
||||
max_candidates: 1000
|
||||
unclip_ratio: 1.5
|
||||
|
||||
Metric:
|
||||
name: DetMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
|
||||
ratio_list: [1.0]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- IaaAugment:
|
||||
augmenter_args:
|
||||
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
|
||||
- { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
|
||||
- { 'type': Resize, 'args': { 'size': [0.5, 3] } }
|
||||
- EastRandomCropData:
|
||||
size: [640, 640]
|
||||
max_tries: 50
|
||||
keep_ratio: true
|
||||
- MakeBorderMap:
|
||||
shrink_ratio: 0.4
|
||||
thresh_min: 0.3
|
||||
thresh_max: 0.7
|
||||
- MakeShrinkMap:
|
||||
shrink_ratio: 0.4
|
||||
min_text_size: 8
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 16
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- DetResizeForTest:
|
||||
image_shape: [736, 1280]
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
@@ -0,0 +1,109 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 10000
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 2
|
||||
save_model_dir: ./output/east_mv3/
|
||||
save_epoch_step: 1000
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [4000, 5000]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img:
|
||||
save_res_path: ./output/det_east/predicts_east.txt
|
||||
|
||||
Architecture:
|
||||
model_type: det
|
||||
algorithm: EAST
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: large
|
||||
Neck:
|
||||
name: EASTFPN
|
||||
model_name: small
|
||||
Head:
|
||||
name: EASTHead
|
||||
model_name: small
|
||||
|
||||
Loss:
|
||||
name: EASTLoss
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
# name: Cosine
|
||||
learning_rate: 0.001
|
||||
# warmup_epoch: 0
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
PostProcess:
|
||||
name: EASTPostProcess
|
||||
score_thresh: 0.8
|
||||
cover_thresh: 0.1
|
||||
nms_thresh: 0.2
|
||||
|
||||
Metric:
|
||||
name: DetMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
|
||||
ratio_list: [1.0]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- EASTProcessTrain:
|
||||
image_shape: [512, 512]
|
||||
background_ratio: 0.125
|
||||
min_crop_side_ratio: 0.1
|
||||
min_text_size: 10
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'score_map', 'geo_map', 'training_mask'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 16
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- DetResizeForTest:
|
||||
limit_side_len: 2400
|
||||
limit_type: max
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 2
|
||||
@@ -0,0 +1,135 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 600
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/det_mv3_pse/
|
||||
save_epoch_step: 600
|
||||
# evaluation is run every 63 iterations
|
||||
eval_batch_step: [ 0,63 ]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
|
||||
checkpoints: #./output/det_r50_vd_pse_batch8_ColorJitter/best_accuracy
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_en/img_10.jpg
|
||||
save_res_path: ./output/det_pse/predicts_pse.txt
|
||||
|
||||
Architecture:
|
||||
model_type: det
|
||||
algorithm: PSE
|
||||
Transform: null
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: large
|
||||
Neck:
|
||||
name: FPN
|
||||
out_channels: 96
|
||||
Head:
|
||||
name: PSEHead
|
||||
hidden_dim: 96
|
||||
out_channels: 7
|
||||
|
||||
Loss:
|
||||
name: PSELoss
|
||||
alpha: 0.7
|
||||
ohem_ratio: 3
|
||||
kernel_sample_mask: pred
|
||||
reduction: none
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Step
|
||||
learning_rate: 0.001
|
||||
step_size: 200
|
||||
gamma: 0.1
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0.0005
|
||||
|
||||
PostProcess:
|
||||
name: PSEPostProcess
|
||||
thresh: 0
|
||||
box_thresh: 0.85
|
||||
min_area: 16
|
||||
box_type: quad # 'quad' or 'poly'
|
||||
scale: 1
|
||||
|
||||
Metric:
|
||||
name: DetMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
|
||||
ratio_list: [ 1.0 ]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- ColorJitter:
|
||||
brightness: 0.12549019607843137
|
||||
saturation: 0.5
|
||||
- IaaAugment:
|
||||
augmenter_args:
|
||||
- { 'type': Resize, 'args': { 'size': [ 0.5, 3 ] } }
|
||||
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
|
||||
- { 'type': Affine, 'args': { 'rotate': [ -10, 10 ] } }
|
||||
- MakePseGt:
|
||||
kernel_num: 7
|
||||
min_shrink_ratio: 0.4
|
||||
size: 640
|
||||
- RandomCropImgMask:
|
||||
size: [ 640,640 ]
|
||||
main_key: gt_text
|
||||
crop_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ]
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [ 0.485, 0.456, 0.406 ]
|
||||
std: [ 0.229, 0.224, 0.225 ]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ] # the order of the dataloader list
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 16
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
ratio_list: [ 1.0 ]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- DetResizeForTest:
|
||||
limit_side_len: 736
|
||||
limit_type: min
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [ 0.485, 0.456, 0.406 ]
|
||||
std: [ 0.229, 0.224, 0.225 ]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'image', 'shape', 'polys', 'ignore_tags' ]
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,107 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 600
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/det_ct/
|
||||
save_epoch_step: 10
|
||||
# evaluation is run every 2000 iterations
|
||||
eval_batch_step: [0,1000]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model: ./pretrain_models/ResNet18_vd_pretrained.pdparams
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_en/img623.jpg
|
||||
save_res_path: ./output/det_ct/predicts_ct.txt
|
||||
|
||||
Architecture:
|
||||
model_type: det
|
||||
algorithm: CT
|
||||
Transform:
|
||||
Backbone:
|
||||
name: ResNet_vd
|
||||
layers: 18
|
||||
Neck:
|
||||
name: CTFPN
|
||||
Head:
|
||||
name: CT_Head
|
||||
in_channels: 512
|
||||
hidden_dim: 128
|
||||
num_classes: 3
|
||||
|
||||
Loss:
|
||||
name: CTLoss
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
lr: #PolynomialDecay
|
||||
name: Linear
|
||||
learning_rate: 0.001
|
||||
end_lr: 0.
|
||||
epochs: 600
|
||||
step_each_epoch: 1254
|
||||
power: 0.9
|
||||
|
||||
PostProcess:
|
||||
name: CTPostProcess
|
||||
box_type: poly
|
||||
|
||||
Metric:
|
||||
name: CTMetric
|
||||
main_indicator: f_score
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/total_text/train
|
||||
label_file_list:
|
||||
- ./train_data/total_text/train/train.txt
|
||||
ratio_list: [1.0]
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- CTLabelEncode: # Class handling label
|
||||
- RandomScale:
|
||||
- MakeShrink:
|
||||
- GroupRandomHorizontalFlip:
|
||||
- GroupRandomRotate:
|
||||
- GroupRandomCropPadding:
|
||||
- MakeCentripetalShift:
|
||||
- ColorJitter:
|
||||
brightness: 0.125
|
||||
saturation: 0.5
|
||||
- ToCHWImage:
|
||||
- NormalizeImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'gt_kernel', 'training_mask', 'gt_instance', 'gt_kernel_instance', 'training_mask_distance', 'gt_distance'] # the order of the dataloader list
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: True
|
||||
batch_size_per_card: 4
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/total_text/test
|
||||
label_file_list:
|
||||
- ./train_data/total_text/test/test.txt
|
||||
ratio_list: [1.0]
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- CTLabelEncode: # Class handling label
|
||||
- ScaleAlignedShort:
|
||||
- NormalizeImage:
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'shape', 'polys', 'texts'] # the order of the dataloader list
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1
|
||||
num_workers: 2
|
||||
@@ -0,0 +1,164 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 1000
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/det_r50_icdar15/
|
||||
save_epoch_step: 200
|
||||
eval_batch_step:
|
||||
- 0
|
||||
- 2000
|
||||
cal_metric_during_train: false
|
||||
pretrained_model: ./pretrain_models/ResNet50_dcn_asf_synthtext_pretrained
|
||||
checkpoints: null
|
||||
save_inference_dir: null
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_en/img_10.jpg
|
||||
save_res_path: ./checkpoints/det_db/predicts_db.txt
|
||||
Architecture:
|
||||
model_type: det
|
||||
algorithm: DB++
|
||||
Transform: null
|
||||
Backbone:
|
||||
name: ResNet
|
||||
layers: 50
|
||||
dcn_stage: [False, True, True, True]
|
||||
Neck:
|
||||
name: DBFPN
|
||||
out_channels: 256
|
||||
use_asf: True
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
Loss:
|
||||
name: DBLoss
|
||||
balance_loss: true
|
||||
main_loss_type: BCELoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
Optimizer:
|
||||
name: Momentum
|
||||
momentum: 0.9
|
||||
lr:
|
||||
name: DecayLearningRate
|
||||
learning_rate: 0.007
|
||||
epochs: 1000
|
||||
factor: 0.9
|
||||
end_lr: 0
|
||||
weight_decay: 0.0001
|
||||
PostProcess:
|
||||
name: DBPostProcess
|
||||
thresh: 0.3
|
||||
box_thresh: 0.6
|
||||
max_candidates: 1000
|
||||
unclip_ratio: 1.5
|
||||
det_box_type: 'quad' # 'quad' or 'poly'
|
||||
Metric:
|
||||
name: DetMetric
|
||||
main_indicator: hmean
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
|
||||
ratio_list:
|
||||
- 1.0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- DetLabelEncode: null
|
||||
- IaaAugment:
|
||||
augmenter_args:
|
||||
- type: Fliplr
|
||||
args:
|
||||
p: 0.5
|
||||
- type: Affine
|
||||
args:
|
||||
rotate:
|
||||
- -10
|
||||
- 10
|
||||
- type: Resize
|
||||
args:
|
||||
size:
|
||||
- 0.5
|
||||
- 3
|
||||
- EastRandomCropData:
|
||||
size:
|
||||
- 640
|
||||
- 640
|
||||
max_tries: 10
|
||||
keep_ratio: true
|
||||
- MakeShrinkMap:
|
||||
shrink_ratio: 0.4
|
||||
min_text_size: 8
|
||||
- MakeBorderMap:
|
||||
shrink_ratio: 0.4
|
||||
thresh_min: 0.3
|
||||
thresh_max: 0.7
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean:
|
||||
- 0.48109378172549
|
||||
- 0.45752457890196
|
||||
- 0.40787054090196
|
||||
std:
|
||||
- 1.0
|
||||
- 1.0
|
||||
- 1.0
|
||||
order: hwc
|
||||
- ToCHWImage: null
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- threshold_map
|
||||
- threshold_mask
|
||||
- shrink_map
|
||||
- shrink_mask
|
||||
loader:
|
||||
shuffle: true
|
||||
drop_last: false
|
||||
batch_size_per_card: 4
|
||||
num_workers: 8
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- DetLabelEncode: null
|
||||
- DetResizeForTest:
|
||||
image_shape:
|
||||
- 1152
|
||||
- 2048
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean:
|
||||
- 0.48109378172549
|
||||
- 0.45752457890196
|
||||
- 0.40787054090196
|
||||
std:
|
||||
- 1.0
|
||||
- 1.0
|
||||
- 1.0
|
||||
order: hwc
|
||||
- ToCHWImage: null
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- shape
|
||||
- polys
|
||||
- ignore_tags
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 1
|
||||
num_workers: 2
|
||||
profiler_options: null
|
||||
@@ -0,0 +1,167 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 1000
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/det_r50_td_tr/
|
||||
save_epoch_step: 200
|
||||
eval_batch_step:
|
||||
- 0
|
||||
- 2000
|
||||
cal_metric_during_train: false
|
||||
pretrained_model: ./pretrain_models/ResNet50_dcn_asf_synthtext_pretrained
|
||||
checkpoints: null
|
||||
save_inference_dir: null
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_en/img_10.jpg
|
||||
save_res_path: ./checkpoints/det_db/predicts_db.txt
|
||||
Architecture:
|
||||
model_type: det
|
||||
algorithm: DB++
|
||||
Transform: null
|
||||
Backbone:
|
||||
name: ResNet
|
||||
layers: 50
|
||||
dcn_stage: [False, True, True, True]
|
||||
Neck:
|
||||
name: DBFPN
|
||||
out_channels: 256
|
||||
use_asf: True
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
Loss:
|
||||
name: DBLoss
|
||||
balance_loss: true
|
||||
main_loss_type: BCELoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
Optimizer:
|
||||
name: Momentum
|
||||
momentum: 0.9
|
||||
lr:
|
||||
name: DecayLearningRate
|
||||
learning_rate: 0.007
|
||||
epochs: 1000
|
||||
factor: 0.9
|
||||
end_lr: 0
|
||||
weight_decay: 0.0001
|
||||
PostProcess:
|
||||
name: DBPostProcess
|
||||
thresh: 0.3
|
||||
box_thresh: 0.5
|
||||
max_candidates: 1000
|
||||
unclip_ratio: 1.5
|
||||
det_box_type: 'quad' # 'quad' or 'poly'
|
||||
Metric:
|
||||
name: DetMetric
|
||||
main_indicator: hmean
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list:
|
||||
- ./train_data/TD_TR/TD500/train_gt_labels.txt
|
||||
- ./train_data/TD_TR/TR400/gt_labels.txt
|
||||
ratio_list:
|
||||
- 1.0
|
||||
- 1.0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- DetLabelEncode: null
|
||||
- IaaAugment:
|
||||
augmenter_args:
|
||||
- type: Fliplr
|
||||
args:
|
||||
p: 0.5
|
||||
- type: Affine
|
||||
args:
|
||||
rotate:
|
||||
- -10
|
||||
- 10
|
||||
- type: Resize
|
||||
args:
|
||||
size:
|
||||
- 0.5
|
||||
- 3
|
||||
- EastRandomCropData:
|
||||
size:
|
||||
- 640
|
||||
- 640
|
||||
max_tries: 10
|
||||
keep_ratio: true
|
||||
- MakeShrinkMap:
|
||||
shrink_ratio: 0.4
|
||||
min_text_size: 8
|
||||
- MakeBorderMap:
|
||||
shrink_ratio: 0.4
|
||||
thresh_min: 0.3
|
||||
thresh_max: 0.7
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean:
|
||||
- 0.48109378172549
|
||||
- 0.45752457890196
|
||||
- 0.40787054090196
|
||||
std:
|
||||
- 1.0
|
||||
- 1.0
|
||||
- 1.0
|
||||
order: hwc
|
||||
- ToCHWImage: null
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- threshold_map
|
||||
- threshold_mask
|
||||
- shrink_map
|
||||
- shrink_mask
|
||||
loader:
|
||||
shuffle: true
|
||||
drop_last: false
|
||||
batch_size_per_card: 4
|
||||
num_workers: 8
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list:
|
||||
- ./train_data/TD_TR/TD500/test_gt_labels.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- DetLabelEncode: null
|
||||
- DetResizeForTest:
|
||||
image_shape:
|
||||
- 736
|
||||
- 736
|
||||
keep_ratio: True
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean:
|
||||
- 0.48109378172549
|
||||
- 0.45752457890196
|
||||
- 0.40787054090196
|
||||
std:
|
||||
- 1.0
|
||||
- 1.0
|
||||
- 1.0
|
||||
order: hwc
|
||||
- ToCHWImage: null
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- shape
|
||||
- polys
|
||||
- ignore_tags
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 1
|
||||
num_workers: 2
|
||||
profiler_options: null
|
||||
@@ -0,0 +1,133 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 1200
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 5
|
||||
save_model_dir: ./output/det_r50_drrg_ctw/
|
||||
save_epoch_step: 100
|
||||
# evaluation is run every 1260 iterations
|
||||
eval_batch_step: [37800, 1260]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained.pdparams
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_en/img_10.jpg
|
||||
save_res_path: ./output/det_drrg/predicts_drrg.txt
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: det
|
||||
algorithm: DRRG
|
||||
Transform:
|
||||
Backbone:
|
||||
name: ResNet_vd
|
||||
layers: 50
|
||||
Neck:
|
||||
name: FPN_UNet
|
||||
in_channels: [256, 512, 1024, 2048]
|
||||
out_channels: 32
|
||||
Head:
|
||||
name: DRRGHead
|
||||
in_channels: 32
|
||||
text_region_thr: 0.3
|
||||
center_region_thr: 0.4
|
||||
Loss:
|
||||
name: DRRGLoss
|
||||
|
||||
Optimizer:
|
||||
name: Momentum
|
||||
momentum: 0.9
|
||||
lr:
|
||||
name: DecayLearningRate
|
||||
learning_rate: 0.028
|
||||
epochs: 1200
|
||||
factor: 0.9
|
||||
end_lr: 0.0000001
|
||||
weight_decay: 0.0001
|
||||
|
||||
PostProcess:
|
||||
name: DRRGPostprocess
|
||||
link_thr: 0.8
|
||||
|
||||
Metric:
|
||||
name: DetFCEMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/ctw1500/imgs/
|
||||
label_file_list:
|
||||
- ./train_data/ctw1500/imgs/training.txt
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
ignore_orientation: True
|
||||
- DetLabelEncode: # Class handling label
|
||||
- ColorJitter:
|
||||
brightness: 0.12549019607843137
|
||||
saturation: 0.5
|
||||
- RandomScaling:
|
||||
- RandomCropFlip:
|
||||
crop_ratio: 0.5
|
||||
- RandomCropPolyInstances:
|
||||
crop_ratio: 0.8
|
||||
min_side_ratio: 0.3
|
||||
- RandomRotatePolyInstances:
|
||||
rotate_ratio: 0.5
|
||||
max_angle: 60
|
||||
pad_with_fixed_color: False
|
||||
- SquareResizePad:
|
||||
target_size: 800
|
||||
pad_ratio: 0.6
|
||||
- IaaAugment:
|
||||
augmenter_args:
|
||||
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
|
||||
- DRRGTargets:
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'gt_text_mask', 'gt_center_region_mask', 'gt_mask',
|
||||
'gt_top_height_map', 'gt_bot_height_map', 'gt_sin_map',
|
||||
'gt_cos_map', 'gt_comp_attribs'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 4
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/ctw1500/imgs/
|
||||
label_file_list:
|
||||
- ./train_data/ctw1500/imgs/test.txt
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
ignore_orientation: True
|
||||
- DetLabelEncode: # Class handling label
|
||||
- DetResizeForTest:
|
||||
limit_type: 'min'
|
||||
limit_side_len: 640
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- Pad:
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 2
|
||||
@@ -0,0 +1,128 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 1200
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/det_r50_vd/
|
||||
save_epoch_step: 1200
|
||||
# evaluation is run every 2000 iterations
|
||||
eval_batch_step: [0,2000]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_en/img_10.jpg
|
||||
save_res_path: ./output/det_db/predicts_db.txt
|
||||
|
||||
Architecture:
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Transform:
|
||||
Backbone:
|
||||
name: ResNet_vd
|
||||
layers: 50
|
||||
Neck:
|
||||
name: DBFPN
|
||||
out_channels: 256
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
|
||||
Loss:
|
||||
name: DBLoss
|
||||
balance_loss: true
|
||||
main_loss_type: DiceLoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
learning_rate: 0.001
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
PostProcess:
|
||||
name: DBPostProcess
|
||||
thresh: 0.3
|
||||
box_thresh: 0.7
|
||||
max_candidates: 1000
|
||||
unclip_ratio: 1.5
|
||||
|
||||
Metric:
|
||||
name: DetMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
|
||||
ratio_list: [1.0]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- IaaAugment:
|
||||
augmenter_args:
|
||||
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
|
||||
- { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
|
||||
- { 'type': Resize, 'args': { 'size': [0.5, 3] } }
|
||||
- EastRandomCropData:
|
||||
size: [640, 640]
|
||||
max_tries: 50
|
||||
keep_ratio: true
|
||||
- MakeBorderMap:
|
||||
shrink_ratio: 0.4
|
||||
thresh_min: 0.3
|
||||
thresh_max: 0.7
|
||||
- MakeShrinkMap:
|
||||
shrink_ratio: 0.4
|
||||
min_text_size: 8
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 16
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- DetResizeForTest:
|
||||
image_shape: [736, 1280]
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,139 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 1500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 20
|
||||
save_model_dir: ./output/det_r50_dcn_fce_ctw/
|
||||
save_epoch_step: 100
|
||||
# evaluation is run every 835 iterations
|
||||
eval_batch_step: [0, 835]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_en/img_10.jpg
|
||||
save_res_path: ./output/det_fce/predicts_fce.txt
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: det
|
||||
algorithm: FCE
|
||||
Transform:
|
||||
Backbone:
|
||||
name: ResNet_vd
|
||||
layers: 50
|
||||
dcn_stage: [False, True, True, True]
|
||||
out_indices: [1,2,3]
|
||||
Neck:
|
||||
name: FCEFPN
|
||||
out_channels: 256
|
||||
has_extra_convs: False
|
||||
extra_stage: 0
|
||||
Head:
|
||||
name: FCEHead
|
||||
fourier_degree: 5
|
||||
Loss:
|
||||
name: FCELoss
|
||||
fourier_degree: 5
|
||||
num_sample: 50
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
learning_rate: 0.0001
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
PostProcess:
|
||||
name: FCEPostProcess
|
||||
scales: [8, 16, 32]
|
||||
alpha: 1.0
|
||||
beta: 1.0
|
||||
fourier_degree: 5
|
||||
box_type: 'poly'
|
||||
|
||||
Metric:
|
||||
name: DetFCEMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/ctw1500/imgs/
|
||||
label_file_list:
|
||||
- ./train_data/ctw1500/imgs/training.txt
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
ignore_orientation: True
|
||||
- DetLabelEncode: # Class handling label
|
||||
- ColorJitter:
|
||||
brightness: 0.142
|
||||
saturation: 0.5
|
||||
contrast: 0.5
|
||||
- RandomScaling:
|
||||
- RandomCropFlip:
|
||||
crop_ratio: 0.5
|
||||
- RandomCropPolyInstances:
|
||||
crop_ratio: 0.8
|
||||
min_side_ratio: 0.3
|
||||
- RandomRotatePolyInstances:
|
||||
rotate_ratio: 0.5
|
||||
max_angle: 30
|
||||
pad_with_fixed_color: False
|
||||
- SquareResizePad:
|
||||
target_size: 800
|
||||
pad_ratio: 0.6
|
||||
- IaaAugment:
|
||||
augmenter_args:
|
||||
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
|
||||
- FCENetTargets:
|
||||
fourier_degree: 5
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'p3_maps', 'p4_maps', 'p5_maps'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 6
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/ctw1500/imgs/
|
||||
label_file_list:
|
||||
- ./train_data/ctw1500/imgs/test.txt
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
ignore_orientation: True
|
||||
- DetLabelEncode: # Class handling label
|
||||
- DetResizeForTest:
|
||||
limit_type: 'min'
|
||||
limit_side_len: 736
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- Pad:
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 2
|
||||
@@ -0,0 +1,108 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 10000
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 2
|
||||
save_model_dir: ./output/east_r50_vd/
|
||||
save_epoch_step: 1000
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [4000, 5000]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model: ./pretrain_models/ResNet50_vd_pretrained
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img:
|
||||
save_res_path: ./output/det_east/predicts_east.txt
|
||||
|
||||
Architecture:
|
||||
model_type: det
|
||||
algorithm: EAST
|
||||
Transform:
|
||||
Backbone:
|
||||
name: ResNet_vd
|
||||
layers: 50
|
||||
Neck:
|
||||
name: EASTFPN
|
||||
model_name: large
|
||||
Head:
|
||||
name: EASTHead
|
||||
model_name: large
|
||||
|
||||
Loss:
|
||||
name: EASTLoss
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
# name: Cosine
|
||||
learning_rate: 0.001
|
||||
# warmup_epoch: 0
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
PostProcess:
|
||||
name: EASTPostProcess
|
||||
score_thresh: 0.8
|
||||
cover_thresh: 0.1
|
||||
nms_thresh: 0.2
|
||||
|
||||
Metric:
|
||||
name: DetMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
|
||||
ratio_list: [1.0]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- EASTProcessTrain:
|
||||
image_shape: [512, 512]
|
||||
background_ratio: 0.125
|
||||
min_crop_side_ratio: 0.1
|
||||
min_text_size: 10
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'score_map', 'geo_map', 'training_mask'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- DetResizeForTest:
|
||||
limit_side_len: 2400
|
||||
limit_type: max
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 2
|
||||
@@ -0,0 +1,134 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 600
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/det_r50_vd_pse/
|
||||
save_epoch_step: 600
|
||||
# evaluation is run every 125 iterations
|
||||
eval_batch_step: [ 0,125 ]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
|
||||
checkpoints: #./output/det_r50_vd_pse_batch8_ColorJitter/best_accuracy
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_en/img_10.jpg
|
||||
save_res_path: ./output/det_pse/predicts_pse.txt
|
||||
|
||||
Architecture:
|
||||
model_type: det
|
||||
algorithm: PSE
|
||||
Transform:
|
||||
Backbone:
|
||||
name: ResNet_vd
|
||||
layers: 50
|
||||
Neck:
|
||||
name: FPN
|
||||
out_channels: 256
|
||||
Head:
|
||||
name: PSEHead
|
||||
hidden_dim: 256
|
||||
out_channels: 7
|
||||
|
||||
Loss:
|
||||
name: PSELoss
|
||||
alpha: 0.7
|
||||
ohem_ratio: 3
|
||||
kernel_sample_mask: pred
|
||||
reduction: none
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Step
|
||||
learning_rate: 0.0001
|
||||
step_size: 200
|
||||
gamma: 0.1
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0.0005
|
||||
|
||||
PostProcess:
|
||||
name: PSEPostProcess
|
||||
thresh: 0
|
||||
box_thresh: 0.85
|
||||
min_area: 16
|
||||
box_type: quad # 'quad' or 'poly'
|
||||
scale: 1
|
||||
|
||||
Metric:
|
||||
name: DetMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
|
||||
ratio_list: [ 1.0 ]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- ColorJitter:
|
||||
brightness: 0.12549019607843137
|
||||
saturation: 0.5
|
||||
- IaaAugment:
|
||||
augmenter_args:
|
||||
- { 'type': Resize, 'args': { 'size': [ 0.5, 3 ] } }
|
||||
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
|
||||
- { 'type': Affine, 'args': { 'rotate': [ -10, 10 ] } }
|
||||
- MakePseGt:
|
||||
kernel_num: 7
|
||||
min_shrink_ratio: 0.4
|
||||
size: 640
|
||||
- RandomCropImgMask:
|
||||
size: [ 640,640 ]
|
||||
main_key: gt_text
|
||||
crop_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ]
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [ 0.485, 0.456, 0.406 ]
|
||||
std: [ 0.229, 0.224, 0.225 ]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ] # the order of the dataloader list
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
ratio_list: [ 1.0 ]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- DetResizeForTest:
|
||||
limit_side_len: 736
|
||||
limit_type: min
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [ 0.485, 0.456, 0.406 ]
|
||||
std: [ 0.229, 0.224, 0.225 ]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'image', 'shape', 'polys', 'ignore_tags' ]
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,109 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 5000
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 2
|
||||
save_model_dir: ./output/sast_r50_vd_ic15/
|
||||
save_epoch_step: 1000
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [4000, 5000]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img:
|
||||
save_res_path: ./output/sast_r50_vd_ic15/predicts_sast.txt
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: det
|
||||
algorithm: SAST
|
||||
Transform:
|
||||
Backbone:
|
||||
name: ResNet_SAST
|
||||
layers: 50
|
||||
Neck:
|
||||
name: SASTFPN
|
||||
with_cab: True
|
||||
Head:
|
||||
name: SASTHead
|
||||
|
||||
Loss:
|
||||
name: SASTLoss
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
# name: Cosine
|
||||
learning_rate: 0.001
|
||||
# warmup_epoch: 0
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
PostProcess:
|
||||
name: SASTPostProcess
|
||||
score_thresh: 0.5
|
||||
sample_pts_num: 2
|
||||
nms_thresh: 0.2
|
||||
expand_scale: 1.0
|
||||
shrink_ratio_of_width: 0.3
|
||||
|
||||
Metric:
|
||||
name: DetMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list: [./train_data/icdar2013/train_label_json.txt, ./train_data/icdar2015/train_label_json.txt, ./train_data/icdar17_mlt_latin/train_label_json.txt, ./train_data/coco_text_icdar_4pts/train_label_json.txt]
|
||||
ratio_list: [0.1, 0.45, 0.3, 0.15]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- SASTProcessTrain:
|
||||
image_shape: [512, 512]
|
||||
min_crop_side_ratio: 0.3
|
||||
min_crop_size: 24
|
||||
min_text_size: 4
|
||||
max_text_size: 512
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'score_map', 'border_map', 'training_mask', 'tvo_map', 'tco_map'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 4
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- DetResizeForTest:
|
||||
resize_long: 1536
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 2
|
||||
@@ -0,0 +1,108 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 5000
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 2
|
||||
save_model_dir: ./output/sast_r50_vd_tt/
|
||||
save_epoch_step: 1000
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [4000, 5000]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img:
|
||||
save_res_path: ./output/sast_r50_vd_tt/predicts_sast.txt
|
||||
|
||||
Architecture:
|
||||
model_type: det
|
||||
algorithm: SAST
|
||||
Transform:
|
||||
Backbone:
|
||||
name: ResNet_SAST
|
||||
layers: 50
|
||||
Neck:
|
||||
name: SASTFPN
|
||||
with_cab: True
|
||||
Head:
|
||||
name: SASTHead
|
||||
|
||||
Loss:
|
||||
name: SASTLoss
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
# name: Cosine
|
||||
learning_rate: 0.001
|
||||
# warmup_epoch: 0
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
PostProcess:
|
||||
name: SASTPostProcess
|
||||
score_thresh: 0.5
|
||||
sample_pts_num: 6
|
||||
nms_thresh: 0.2
|
||||
expand_scale: 1.2
|
||||
shrink_ratio_of_width: 0.2
|
||||
|
||||
Metric:
|
||||
name: DetMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list: [./train_data/art_latin_icdar_14pt/train_no_tt_test/train_label_json.txt, ./train_data/total_text_icdar_14pt/train_label_json.txt]
|
||||
ratio_list: [0.5, 0.5]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- SASTProcessTrain:
|
||||
image_shape: [512, 512]
|
||||
min_crop_side_ratio: 0.3
|
||||
min_crop_size: 24
|
||||
min_text_size: 4
|
||||
max_text_size: 512
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'score_map', 'border_map', 'training_mask', 'tvo_map', 'tco_map'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 4
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list:
|
||||
- ./train_data/total_text_icdar_14pt/test_label_json.txt
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- DetResizeForTest:
|
||||
resize_long: 768
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 2
|
||||
@@ -0,0 +1,131 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 1200
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 2
|
||||
save_model_dir: ./output/ch_db_res18/
|
||||
save_epoch_step: 1200
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [3000, 2000]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model: ./pretrain_models/ResNet18_vd_pretrained
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_en/img_10.jpg
|
||||
save_res_path: ./output/det_db/predicts_db.txt
|
||||
|
||||
Architecture:
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Transform:
|
||||
Backbone:
|
||||
name: ResNet_vd
|
||||
layers: 18
|
||||
disable_se: True
|
||||
Neck:
|
||||
name: DBFPN
|
||||
out_channels: 256
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
|
||||
Loss:
|
||||
name: DBLoss
|
||||
balance_loss: true
|
||||
main_loss_type: DiceLoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 2
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
PostProcess:
|
||||
name: DBPostProcess
|
||||
thresh: 0.3
|
||||
box_thresh: 0.6
|
||||
max_candidates: 1000
|
||||
unclip_ratio: 1.5
|
||||
|
||||
Metric:
|
||||
name: DetMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
|
||||
ratio_list: [1.0]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- IaaAugment:
|
||||
augmenter_args:
|
||||
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
|
||||
- { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
|
||||
- { 'type': Resize, 'args': { 'size': [0.5, 3] } }
|
||||
- EastRandomCropData:
|
||||
size: [960, 960]
|
||||
max_tries: 50
|
||||
keep_ratio: true
|
||||
- MakeBorderMap:
|
||||
shrink_ratio: 0.4
|
||||
thresh_min: 0.3
|
||||
thresh_max: 0.7
|
||||
- MakeShrinkMap:
|
||||
shrink_ratio: 0.4
|
||||
min_text_size: 8
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- DetResizeForTest:
|
||||
# image_shape: [736, 1280]
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 2
|
||||
@@ -0,0 +1,121 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: 600
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/pgnet_r50_vd_totaltext/
|
||||
save_epoch_step: 10
|
||||
# evaluation is run every 0 iterationss after the 1000th iteration
|
||||
eval_batch_step: [ 0, 1000 ]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img:
|
||||
infer_visual_type: EN # two mode: EN is for english datasets, CN is for chinese datasets
|
||||
valid_set: totaltext # two mode: totaltext valid curved words, partvgg valid non-curved words
|
||||
save_res_path: ./output/pgnet_r50_vd_totaltext/predicts_pgnet.txt
|
||||
character_dict_path: ppocr/utils/ic15_dict.txt
|
||||
character_type: EN
|
||||
max_text_length: 50 # the max length in seq
|
||||
max_text_nums: 30 # the max seq nums in a pic
|
||||
tcl_len: 64
|
||||
|
||||
Architecture:
|
||||
model_type: e2e
|
||||
algorithm: PGNet
|
||||
Transform:
|
||||
Backbone:
|
||||
name: ResNet
|
||||
layers: 50
|
||||
Neck:
|
||||
name: PGFPN
|
||||
Head:
|
||||
name: PGHead
|
||||
character_dict_path: ppocr/utils/ic15_dict.txt # the same as Global:character_dict_path
|
||||
|
||||
Loss:
|
||||
name: PGLoss
|
||||
tcl_bs: 64
|
||||
max_text_length: 50 # the same as Global: max_text_length
|
||||
max_text_nums: 30 # the same as Global：max_text_nums
|
||||
pad_num: 36 # the length of dict for pad
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 50
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0.0001
|
||||
|
||||
PostProcess:
|
||||
name: PGPostProcess
|
||||
score_thresh: 0.5
|
||||
mode: fast # fast or slow two ways
|
||||
point_gather_mode: align # same as PGProcessTrain: point_gather_mode
|
||||
|
||||
Metric:
|
||||
name: E2EMetric
|
||||
mode: A # two ways for eval, A: label from txt, B: label from gt_mat
|
||||
gt_mat_dir: ./train_data/total_text/gt # the dir of gt_mat
|
||||
character_dict_path: ppocr/utils/ic15_dict.txt
|
||||
main_indicator: f_score_e2e
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: PGDataSet
|
||||
data_dir: ./train_data/total_text/train
|
||||
label_file_list: [./train_data/total_text/train/train.txt]
|
||||
ratio_list: [1.0]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- E2ELabelEncodeTrain:
|
||||
- PGProcessTrain:
|
||||
batch_size: 14 # same as loader: batch_size_per_card
|
||||
use_resize: True
|
||||
use_random_crop: False
|
||||
min_crop_size: 24
|
||||
min_text_size: 4
|
||||
max_text_size: 512
|
||||
point_gather_mode: align # two mode: align and none, align mode is better than none mode
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'images', 'tcl_maps', 'tcl_label_maps', 'border_maps','direction_maps', 'training_masks', 'label_list', 'pos_list', 'pos_mask' ] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: True
|
||||
batch_size_per_card: 14
|
||||
num_workers: 16
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: PGDataSet
|
||||
data_dir: ./train_data/total_text/test
|
||||
label_file_list: [./train_data/total_text/test/test.txt]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- E2ELabelEncodeTest:
|
||||
- E2EResizeForTest:
|
||||
max_side_len: 768
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [ 0.485, 0.456, 0.406 ]
|
||||
std: [ 0.229, 0.224, 0.225 ]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'image', 'shape', 'polys', 'texts', 'ignore_tags', 'img_id']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 2
|
||||
@@ -0,0 +1,123 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: &epoch_num 200
|
||||
log_smooth_window: 10
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/re_layoutlmv2_xfund_zh
|
||||
save_epoch_step: 2000
|
||||
# evaluation is run every 10 iterations after the 0th iteration
|
||||
eval_batch_step: [ 0, 19 ]
|
||||
cal_metric_during_train: False
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
seed: 2022
|
||||
infer_img: ppstructure/docs/kie/input/zh_val_21.jpg
|
||||
save_res_path: ./output/re_layoutlmv2_xfund_zh/res/
|
||||
|
||||
Architecture:
|
||||
model_type: kie
|
||||
algorithm: &algorithm "LayoutLMv2"
|
||||
Transform:
|
||||
Backbone:
|
||||
name: LayoutLMv2ForRe
|
||||
pretrained: True
|
||||
checkpoints:
|
||||
|
||||
Loss:
|
||||
name: LossFromOutput
|
||||
key: loss
|
||||
reduction: mean
|
||||
|
||||
Optimizer:
|
||||
name: AdamW
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
clip_norm: 10
|
||||
lr:
|
||||
learning_rate: 0.00005
|
||||
warmup_epoch: 10
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 0.00000
|
||||
|
||||
PostProcess:
|
||||
name: VQAReTokenLayoutLMPostProcess
|
||||
|
||||
Metric:
|
||||
name: VQAReTokenMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/XFUND/zh_train/image
|
||||
label_file_list:
|
||||
- train_data/XFUND/zh_train/train.json
|
||||
ratio_list: [ 1.0 ]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- VQATokenLabelEncode: # Class handling label
|
||||
contains_re: True
|
||||
algorithm: *algorithm
|
||||
class_path: &class_path train_data/XFUND/class_list_xfun.txt
|
||||
- VQATokenPad:
|
||||
max_seq_len: &max_seq_len 512
|
||||
return_attention_mask: True
|
||||
- VQAReTokenRelation:
|
||||
- VQAReTokenChunk:
|
||||
max_seq_len: *max_seq_len
|
||||
- Resize:
|
||||
size: [224,224]
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids','image', 'entities', 'relations'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 8
|
||||
collate_fn: ListCollator
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/XFUND/zh_val/image
|
||||
label_file_list:
|
||||
- train_data/XFUND/zh_val/val.json
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- VQATokenLabelEncode: # Class handling label
|
||||
contains_re: True
|
||||
algorithm: *algorithm
|
||||
class_path: *class_path
|
||||
- VQATokenPad:
|
||||
max_seq_len: *max_seq_len
|
||||
return_attention_mask: True
|
||||
- VQAReTokenRelation:
|
||||
- VQAReTokenChunk:
|
||||
max_seq_len: *max_seq_len
|
||||
- Resize:
|
||||
size: [224,224]
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image','entities', 'relations'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 8
|
||||
collate_fn: ListCollator
|
||||
@@ -0,0 +1,123 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: &epoch_num 130
|
||||
log_smooth_window: 10
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/re_layoutxlm_xfund_zh
|
||||
save_epoch_step: 2000
|
||||
# evaluation is run every 10 iterations after the 0th iteration
|
||||
eval_batch_step: [ 0, 19 ]
|
||||
cal_metric_during_train: False
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
seed: 2022
|
||||
infer_img: ppstructure/docs/kie/input/zh_val_21.jpg
|
||||
save_res_path: ./output/re_layoutxlm_xfund_zh/res/
|
||||
|
||||
Architecture:
|
||||
model_type: kie
|
||||
algorithm: &algorithm "LayoutXLM"
|
||||
Transform:
|
||||
Backbone:
|
||||
name: LayoutXLMForRe
|
||||
pretrained: True
|
||||
checkpoints:
|
||||
|
||||
Loss:
|
||||
name: LossFromOutput
|
||||
key: loss
|
||||
reduction: mean
|
||||
|
||||
Optimizer:
|
||||
name: AdamW
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
clip_norm: 10
|
||||
lr:
|
||||
learning_rate: 0.00005
|
||||
warmup_epoch: 10
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 0.00000
|
||||
|
||||
PostProcess:
|
||||
name: VQAReTokenLayoutLMPostProcess
|
||||
|
||||
Metric:
|
||||
name: VQAReTokenMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/XFUND/zh_train/image
|
||||
label_file_list:
|
||||
- train_data/XFUND/zh_train/train.json
|
||||
ratio_list: [ 1.0 ]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- VQATokenLabelEncode: # Class handling label
|
||||
contains_re: True
|
||||
algorithm: *algorithm
|
||||
class_path: &class_path train_data/XFUND/class_list_xfun.txt
|
||||
- VQATokenPad:
|
||||
max_seq_len: &max_seq_len 512
|
||||
return_attention_mask: True
|
||||
- VQAReTokenRelation:
|
||||
- VQAReTokenChunk:
|
||||
max_seq_len: *max_seq_len
|
||||
- TensorizeEntitiesRelations:
|
||||
- Resize:
|
||||
size: [224,224]
|
||||
- NormalizeImage:
|
||||
scale: 1
|
||||
mean: [ 123.675, 116.28, 103.53 ]
|
||||
std: [ 58.395, 57.12, 57.375 ]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'input_ids', 'bbox','attention_mask', 'token_type_ids', 'image', 'entities', 'relations'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 2
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/XFUND/zh_val/image
|
||||
label_file_list:
|
||||
- train_data/XFUND/zh_val/val.json
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- VQATokenLabelEncode: # Class handling label
|
||||
contains_re: True
|
||||
algorithm: *algorithm
|
||||
class_path: *class_path
|
||||
- VQATokenPad:
|
||||
max_seq_len: *max_seq_len
|
||||
return_attention_mask: True
|
||||
- VQAReTokenRelation:
|
||||
- VQAReTokenChunk:
|
||||
max_seq_len: *max_seq_len
|
||||
- TensorizeEntitiesRelations:
|
||||
- Resize:
|
||||
size: [224,224]
|
||||
- NormalizeImage:
|
||||
scale: 1
|
||||
mean: [ 123.675, 116.28, 103.53 ]
|
||||
std: [ 58.395, 57.12, 57.375 ]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'entities', 'relations'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,121 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: &epoch_num 200
|
||||
log_smooth_window: 10
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/ser_layoutlm_xfund_zh
|
||||
save_epoch_step: 2000
|
||||
# evaluation is run every 10 iterations after the 0th iteration
|
||||
eval_batch_step: [ 0, 19 ]
|
||||
cal_metric_during_train: False
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
seed: 2022
|
||||
infer_img: ppstructure/docs/kie/input/zh_val_42.jpg
|
||||
save_res_path: ./output/re_layoutlm_xfund_zh/res
|
||||
|
||||
Architecture:
|
||||
model_type: kie
|
||||
algorithm: &algorithm "LayoutLM"
|
||||
Transform:
|
||||
Backbone:
|
||||
name: LayoutLMForSer
|
||||
pretrained: True
|
||||
checkpoints:
|
||||
num_classes: &num_classes 7
|
||||
|
||||
Loss:
|
||||
name: VQASerTokenLayoutLMLoss
|
||||
num_classes: *num_classes
|
||||
|
||||
Optimizer:
|
||||
name: AdamW
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Linear
|
||||
learning_rate: 0.00005
|
||||
epochs: *epoch_num
|
||||
warmup_epoch: 2
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 0.00000
|
||||
|
||||
PostProcess:
|
||||
name: VQASerTokenLayoutLMPostProcess
|
||||
class_path: &class_path train_data/XFUND/class_list_xfun.txt
|
||||
|
||||
Metric:
|
||||
name: VQASerTokenMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/XFUND/zh_train/image
|
||||
label_file_list:
|
||||
- train_data/XFUND/zh_train/train.json
|
||||
ratio_list: [ 1.0 ]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- VQATokenLabelEncode: # Class handling label
|
||||
contains_re: False
|
||||
algorithm: *algorithm
|
||||
class_path: *class_path
|
||||
- VQATokenPad:
|
||||
max_seq_len: &max_seq_len 512
|
||||
return_attention_mask: True
|
||||
- VQASerTokenChunk:
|
||||
max_seq_len: *max_seq_len
|
||||
- Resize:
|
||||
size: [224,224]
|
||||
- NormalizeImage:
|
||||
scale: 1
|
||||
mean: [ 123.675, 116.28, 103.53 ]
|
||||
std: [ 58.395, 57.12, 57.375 ]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 16
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/XFUND/zh_val/image
|
||||
label_file_list:
|
||||
- train_data/XFUND/zh_val/val.json
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- VQATokenLabelEncode: # Class handling label
|
||||
contains_re: False
|
||||
algorithm: *algorithm
|
||||
class_path: *class_path
|
||||
- VQATokenPad:
|
||||
max_seq_len: *max_seq_len
|
||||
return_attention_mask: True
|
||||
- VQASerTokenChunk:
|
||||
max_seq_len: *max_seq_len
|
||||
- Resize:
|
||||
size: [224,224]
|
||||
- NormalizeImage:
|
||||
scale: 1
|
||||
mean: [ 123.675, 116.28, 103.53 ]
|
||||
std: [ 58.395, 57.12, 57.375 ]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,122 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: &epoch_num 200
|
||||
log_smooth_window: 10
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/ser_layoutlmv2_xfund_zh/
|
||||
save_epoch_step: 2000
|
||||
# evaluation is run every 10 iterations after the 0th iteration
|
||||
eval_batch_step: [ 0, 19 ]
|
||||
cal_metric_during_train: False
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
seed: 2022
|
||||
infer_img: ppstructure/docs/kie/input/zh_val_42.jpg
|
||||
save_res_path: ./output/ser_layoutlmv2_xfund_zh/res/
|
||||
|
||||
Architecture:
|
||||
model_type: kie
|
||||
algorithm: &algorithm "LayoutLMv2"
|
||||
Transform:
|
||||
Backbone:
|
||||
name: LayoutLMv2ForSer
|
||||
pretrained: True
|
||||
checkpoints:
|
||||
num_classes: &num_classes 7
|
||||
|
||||
Loss:
|
||||
name: VQASerTokenLayoutLMLoss
|
||||
num_classes: *num_classes
|
||||
key: "backbone_out"
|
||||
|
||||
Optimizer:
|
||||
name: AdamW
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Linear
|
||||
learning_rate: 0.00005
|
||||
epochs: *epoch_num
|
||||
warmup_epoch: 2
|
||||
regularizer:
|
||||
|
||||
name: L2
|
||||
factor: 0.00000
|
||||
|
||||
PostProcess:
|
||||
name: VQASerTokenLayoutLMPostProcess
|
||||
class_path: &class_path train_data/XFUND/class_list_xfun.txt
|
||||
|
||||
Metric:
|
||||
name: VQASerTokenMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/XFUND/zh_train/image
|
||||
label_file_list:
|
||||
- train_data/XFUND/zh_train/train.json
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- VQATokenLabelEncode: # Class handling label
|
||||
contains_re: False
|
||||
algorithm: *algorithm
|
||||
class_path: *class_path
|
||||
- VQATokenPad:
|
||||
max_seq_len: &max_seq_len 512
|
||||
return_attention_mask: True
|
||||
- VQASerTokenChunk:
|
||||
max_seq_len: *max_seq_len
|
||||
- Resize:
|
||||
size: [224,224]
|
||||
- NormalizeImage:
|
||||
scale: 1
|
||||
mean: [ 123.675, 116.28, 103.53 ]
|
||||
std: [ 58.395, 57.12, 57.375 ]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/XFUND/zh_val/image
|
||||
label_file_list:
|
||||
- train_data/XFUND/zh_val/val.json
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- VQATokenLabelEncode: # Class handling label
|
||||
contains_re: False
|
||||
algorithm: *algorithm
|
||||
class_path: *class_path
|
||||
- VQATokenPad:
|
||||
max_seq_len: *max_seq_len
|
||||
return_attention_mask: True
|
||||
- VQASerTokenChunk:
|
||||
max_seq_len: *max_seq_len
|
||||
- Resize:
|
||||
size: [224,224]
|
||||
- NormalizeImage:
|
||||
scale: 1
|
||||
mean: [ 123.675, 116.28, 103.53 ]
|
||||
std: [ 58.395, 57.12, 57.375 ]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,122 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: &epoch_num 200
|
||||
log_smooth_window: 10
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/ser_layoutxlm_xfund_zh
|
||||
save_epoch_step: 2000
|
||||
# evaluation is run every 10 iterations after the 0th iteration
|
||||
eval_batch_step: [ 0, 19 ]
|
||||
cal_metric_during_train: False
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
seed: 2022
|
||||
infer_img: ppstructure/docs/kie/input/zh_val_42.jpg
|
||||
save_res_path: ./output/ser_layoutxlm_xfund_zh/res
|
||||
|
||||
Architecture:
|
||||
model_type: kie
|
||||
algorithm: &algorithm "LayoutXLM"
|
||||
Transform:
|
||||
Backbone:
|
||||
name: LayoutXLMForSer
|
||||
pretrained: True
|
||||
checkpoints:
|
||||
num_classes: &num_classes 7
|
||||
|
||||
Loss:
|
||||
name: VQASerTokenLayoutLMLoss
|
||||
num_classes: *num_classes
|
||||
key: "backbone_out"
|
||||
|
||||
Optimizer:
|
||||
name: AdamW
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Linear
|
||||
learning_rate: 0.00005
|
||||
epochs: *epoch_num
|
||||
warmup_epoch: 2
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 0.00000
|
||||
|
||||
PostProcess:
|
||||
name: VQASerTokenLayoutLMPostProcess
|
||||
class_path: &class_path train_data/XFUND/class_list_xfun.txt
|
||||
|
||||
Metric:
|
||||
name: VQASerTokenMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/XFUND/zh_train/image
|
||||
label_file_list:
|
||||
- train_data/XFUND/zh_train/train.json
|
||||
ratio_list: [ 1.0 ]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- VQATokenLabelEncode: # Class handling label
|
||||
contains_re: False
|
||||
algorithm: *algorithm
|
||||
class_path: *class_path
|
||||
- VQATokenPad:
|
||||
max_seq_len: &max_seq_len 512
|
||||
return_attention_mask: True
|
||||
- VQASerTokenChunk:
|
||||
max_seq_len: *max_seq_len
|
||||
- Resize:
|
||||
size: [224,224]
|
||||
- NormalizeImage:
|
||||
scale: 1
|
||||
mean: [ 123.675, 116.28, 103.53 ]
|
||||
std: [ 58.395, 57.12, 57.375 ]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/XFUND/zh_val/image
|
||||
label_file_list:
|
||||
- train_data/XFUND/zh_val/val.json
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- VQATokenLabelEncode: # Class handling label
|
||||
contains_re: False
|
||||
algorithm: *algorithm
|
||||
class_path: *class_path
|
||||
- VQATokenPad:
|
||||
max_seq_len: *max_seq_len
|
||||
return_attention_mask: True
|
||||
- VQASerTokenChunk:
|
||||
max_seq_len: *max_seq_len
|
||||
- Resize:
|
||||
size: [224,224]
|
||||
- NormalizeImage:
|
||||
scale: 1
|
||||
mean: [ 123.675, 116.28, 103.53 ]
|
||||
std: [ 58.395, 57.12, 57.375 ]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,111 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: 60
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 50
|
||||
save_model_dir: ./output/kie_5/
|
||||
save_epoch_step: 50
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [ 0, 80 ]
|
||||
# 1. If pretrained_model is saved in static mode, such as classification pretrained model
|
||||
# from static branch, load_static_weights must be set as True.
|
||||
# 2. If you want to finetune the pretrained models we provide in the docs,
|
||||
# you should set load_static_weights as False.
|
||||
load_static_weights: False
|
||||
cal_metric_during_train: False
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
class_path: &class_path ./train_data/wildreceipt/class_list.txt
|
||||
infer_img: ./train_data/wildreceipt/1.txt
|
||||
save_res_path: ./output/sdmgr_kie/predicts_kie.txt
|
||||
img_scale: [ 1024, 512 ]
|
||||
|
||||
Architecture:
|
||||
model_type: kie
|
||||
algorithm: SDMGR
|
||||
Transform:
|
||||
Backbone:
|
||||
name: Kie_backbone
|
||||
Head:
|
||||
name: SDMGRHead
|
||||
|
||||
Loss:
|
||||
name: SDMGRLoss
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Piecewise
|
||||
learning_rate: 0.001
|
||||
decay_epochs: [ 60, 80, 100]
|
||||
values: [ 0.001, 0.0001, 0.00001]
|
||||
warmup_epoch: 2
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0.00005
|
||||
|
||||
PostProcess:
|
||||
name: None
|
||||
|
||||
Metric:
|
||||
name: KIEMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/wildreceipt/
|
||||
label_file_list: [ './train_data/wildreceipt/wildreceipt_train.txt' ]
|
||||
ratio_list: [ 1.0 ]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- NormalizeImage:
|
||||
scale: 1
|
||||
mean: [ 123.675, 116.28, 103.53 ]
|
||||
std: [ 58.395, 57.12, 57.375 ]
|
||||
order: 'hwc'
|
||||
- KieLabelEncode: # Class handling label
|
||||
character_dict_path: ./train_data/wildreceipt/dict.txt
|
||||
class_path: *class_path
|
||||
- KieResize:
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'image', 'relations', 'texts', 'points', 'labels', 'tag', 'shape'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 4
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/wildreceipt
|
||||
label_file_list:
|
||||
- ./train_data/wildreceipt/wildreceipt_test.txt
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- KieLabelEncode: # Class handling label
|
||||
character_dict_path: ./train_data/wildreceipt/dict.txt
|
||||
- KieResize:
|
||||
- NormalizeImage:
|
||||
scale: 1
|
||||
mean: [ 123.675, 116.28, 103.53 ]
|
||||
std: [ 58.395, 57.12, 57.375 ]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'image', 'relations', 'texts', 'points', 'labels', 'tag', 'ori_image', 'ori_boxes', 'shape']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,130 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: &epoch_num 130
|
||||
log_smooth_window: 10
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/re_vi_layoutxlm_xfund_zh
|
||||
save_epoch_step: 2000
|
||||
# evaluation is run every 10 iterations after the 0th iteration
|
||||
eval_batch_step: [ 0, 19 ]
|
||||
cal_metric_during_train: False
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
seed: 2022
|
||||
infer_img: ppstructure/docs/kie/input/zh_val_21.jpg
|
||||
save_res_path: ./output/re/xfund_zh/with_gt
|
||||
kie_rec_model_dir:
|
||||
kie_det_model_dir:
|
||||
|
||||
Architecture:
|
||||
model_type: kie
|
||||
algorithm: &algorithm "LayoutXLM"
|
||||
Transform:
|
||||
Backbone:
|
||||
name: LayoutXLMForRe
|
||||
pretrained: True
|
||||
mode: vi
|
||||
checkpoints:
|
||||
|
||||
Loss:
|
||||
name: LossFromOutput
|
||||
key: loss
|
||||
reduction: mean
|
||||
|
||||
Optimizer:
|
||||
name: AdamW
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
clip_norm: 10
|
||||
lr:
|
||||
learning_rate: 0.00005
|
||||
warmup_epoch: 10
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 0.00000
|
||||
|
||||
PostProcess:
|
||||
name: VQAReTokenLayoutLMPostProcess
|
||||
|
||||
Metric:
|
||||
name: VQAReTokenMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/XFUND/zh_train/image
|
||||
label_file_list:
|
||||
- train_data/XFUND/zh_train/train.json
|
||||
ratio_list: [ 1.0 ]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- VQATokenLabelEncode: # Class handling label
|
||||
contains_re: True
|
||||
algorithm: *algorithm
|
||||
class_path: &class_path train_data/XFUND/class_list_xfun.txt
|
||||
use_textline_bbox_info: &use_textline_bbox_info True
|
||||
order_method: &order_method "tb-yx"
|
||||
- VQATokenPad:
|
||||
max_seq_len: &max_seq_len 512
|
||||
return_attention_mask: True
|
||||
- VQAReTokenRelation:
|
||||
- VQAReTokenChunk:
|
||||
max_seq_len: *max_seq_len
|
||||
- TensorizeEntitiesRelations:
|
||||
- Resize:
|
||||
size: [224,224]
|
||||
- NormalizeImage:
|
||||
scale: 1
|
||||
mean: [ 123.675, 116.28, 103.53 ]
|
||||
std: [ 58.395, 57.12, 57.375 ]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'input_ids', 'bbox','attention_mask', 'token_type_ids', 'entities', 'relations'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 2
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/XFUND/zh_val/image
|
||||
label_file_list:
|
||||
- train_data/XFUND/zh_val/val.json
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- VQATokenLabelEncode: # Class handling label
|
||||
contains_re: True
|
||||
algorithm: *algorithm
|
||||
class_path: *class_path
|
||||
use_textline_bbox_info: *use_textline_bbox_info
|
||||
order_method: *order_method
|
||||
- VQATokenPad:
|
||||
max_seq_len: *max_seq_len
|
||||
return_attention_mask: True
|
||||
- VQAReTokenRelation:
|
||||
- VQAReTokenChunk:
|
||||
max_seq_len: *max_seq_len
|
||||
- TensorizeEntitiesRelations:
|
||||
- Resize:
|
||||
size: [224,224]
|
||||
- NormalizeImage:
|
||||
scale: 1
|
||||
mean: [ 123.675, 116.28, 103.53 ]
|
||||
std: [ 58.395, 57.12, 57.375 ]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'entities', 'relations'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,177 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: &epoch_num 130
|
||||
log_smooth_window: 10
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/re_vi_layoutxlm_xfund_zh_udml
|
||||
save_epoch_step: 2000
|
||||
# evaluation is run every 10 iterations after the 0th iteration
|
||||
eval_batch_step: [ 0, 19 ]
|
||||
cal_metric_during_train: False
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
seed: 2022
|
||||
infer_img: ppstructure/docs/kie/input/zh_val_21.jpg
|
||||
save_res_path: ./output/re/xfund_zh/with_gt
|
||||
|
||||
Architecture:
|
||||
model_type: &model_type "kie"
|
||||
name: DistillationModel
|
||||
algorithm: Distillation
|
||||
Models:
|
||||
Teacher:
|
||||
pretrained:
|
||||
freeze_params: false
|
||||
return_all_feats: true
|
||||
model_type: *model_type
|
||||
algorithm: &algorithm "LayoutXLM"
|
||||
Transform:
|
||||
Backbone:
|
||||
name: LayoutXLMForRe
|
||||
pretrained: True
|
||||
mode: vi
|
||||
checkpoints:
|
||||
Student:
|
||||
pretrained:
|
||||
freeze_params: false
|
||||
return_all_feats: true
|
||||
model_type: *model_type
|
||||
algorithm: *algorithm
|
||||
Transform:
|
||||
Backbone:
|
||||
name: LayoutXLMForRe
|
||||
pretrained: True
|
||||
mode: vi
|
||||
checkpoints:
|
||||
|
||||
Loss:
|
||||
name: CombinedLoss
|
||||
loss_config_list:
|
||||
- DistillationLossFromOutput:
|
||||
weight: 1.0
|
||||
model_name_list: ["Student", "Teacher"]
|
||||
key: loss
|
||||
reduction: mean
|
||||
- DistillationVQADistanceLoss:
|
||||
weight: 0.5
|
||||
mode: "l2"
|
||||
model_name_pairs:
|
||||
- ["Student", "Teacher"]
|
||||
key: hidden_states
|
||||
index: 5
|
||||
name: "loss_5"
|
||||
- DistillationVQADistanceLoss:
|
||||
weight: 0.5
|
||||
mode: "l2"
|
||||
model_name_pairs:
|
||||
- ["Student", "Teacher"]
|
||||
key: hidden_states
|
||||
index: 8
|
||||
name: "loss_8"
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: AdamW
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
clip_norm: 10
|
||||
lr:
|
||||
learning_rate: 0.00005
|
||||
warmup_epoch: 10
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 0.00000
|
||||
|
||||
PostProcess:
|
||||
name: DistillationRePostProcess
|
||||
model_name: ["Student", "Teacher"]
|
||||
key: null
|
||||
|
||||
|
||||
Metric:
|
||||
name: DistillationMetric
|
||||
base_metric_name: VQAReTokenMetric
|
||||
main_indicator: hmean
|
||||
key: "Student"
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/XFUND/zh_train/image
|
||||
label_file_list:
|
||||
- train_data/XFUND/zh_train/train.json
|
||||
ratio_list: [ 1.0 ]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- VQATokenLabelEncode: # Class handling label
|
||||
contains_re: True
|
||||
algorithm: *algorithm
|
||||
class_path: &class_path train_data/XFUND/class_list_xfun.txt
|
||||
use_textline_bbox_info: &use_textline_bbox_info True
|
||||
# [None, "tb-yx"]
|
||||
order_method: &order_method "tb-yx"
|
||||
- VQATokenPad:
|
||||
max_seq_len: &max_seq_len 512
|
||||
return_attention_mask: True
|
||||
- VQAReTokenRelation:
|
||||
- VQAReTokenChunk:
|
||||
max_seq_len: *max_seq_len
|
||||
- TensorizeEntitiesRelations:
|
||||
- Resize:
|
||||
size: [224,224]
|
||||
- NormalizeImage:
|
||||
scale: 1
|
||||
mean: [ 123.675, 116.28, 103.53 ]
|
||||
std: [ 58.395, 57.12, 57.375 ]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'input_ids', 'bbox','attention_mask', 'token_type_ids', 'entities', 'relations'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 2
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/XFUND/zh_val/image
|
||||
label_file_list:
|
||||
- train_data/XFUND/zh_val/val.json
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- VQATokenLabelEncode: # Class handling label
|
||||
contains_re: True
|
||||
algorithm: *algorithm
|
||||
class_path: *class_path
|
||||
use_textline_bbox_info: *use_textline_bbox_info
|
||||
order_method: *order_method
|
||||
- VQATokenPad:
|
||||
max_seq_len: *max_seq_len
|
||||
return_attention_mask: True
|
||||
- VQAReTokenRelation:
|
||||
- VQAReTokenChunk:
|
||||
max_seq_len: *max_seq_len
|
||||
- TensorizeEntitiesRelations:
|
||||
- Resize:
|
||||
size: [224,224]
|
||||
- NormalizeImage:
|
||||
scale: 1
|
||||
mean: [ 123.675, 116.28, 103.53 ]
|
||||
std: [ 58.395, 57.12, 57.375 ]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'entities', 'relations'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 8
|
||||
|
||||
|
||||
@@ -0,0 +1,138 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: &epoch_num 200
|
||||
log_smooth_window: 10
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/ser_vi_layoutxlm_xfund_zh
|
||||
save_epoch_step: 2000
|
||||
# evaluation is run every 10 iterations after the 0th iteration
|
||||
eval_batch_step: [ 0, 19 ]
|
||||
cal_metric_during_train: False
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
seed: 2022
|
||||
infer_img: ppstructure/docs/kie/input/zh_val_42.jpg
|
||||
d2s_train_image_shape: [3, 224, 224]
|
||||
# if you want to predict using the groundtruth ocr info,
|
||||
# you can use the following config
|
||||
# infer_img: train_data/XFUND/zh_val/val.json
|
||||
# infer_mode: False
|
||||
|
||||
save_res_path: ./output/ser/xfund_zh/res
|
||||
kie_rec_model_dir:
|
||||
kie_det_model_dir:
|
||||
amp_custom_white_list: ['scale', 'concat', 'elementwise_add']
|
||||
|
||||
Architecture:
|
||||
model_type: kie
|
||||
algorithm: &algorithm "LayoutXLM"
|
||||
Transform:
|
||||
Backbone:
|
||||
name: LayoutXLMForSer
|
||||
pretrained: True
|
||||
checkpoints:
|
||||
# one of base or vi
|
||||
mode: vi
|
||||
num_classes: &num_classes 7
|
||||
|
||||
Loss:
|
||||
name: VQASerTokenLayoutLMLoss
|
||||
num_classes: *num_classes
|
||||
key: "backbone_out"
|
||||
|
||||
Optimizer:
|
||||
name: AdamW
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Linear
|
||||
learning_rate: 0.00005
|
||||
epochs: *epoch_num
|
||||
warmup_epoch: 2
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 0.00000
|
||||
|
||||
PostProcess:
|
||||
name: VQASerTokenLayoutLMPostProcess
|
||||
class_path: &class_path train_data/XFUND/class_list_xfun.txt
|
||||
|
||||
Metric:
|
||||
name: VQASerTokenMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/XFUND/zh_train/image
|
||||
label_file_list:
|
||||
- train_data/XFUND/zh_train/train.json
|
||||
ratio_list: [ 1.0 ]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- VQATokenLabelEncode: # Class handling label
|
||||
contains_re: False
|
||||
algorithm: *algorithm
|
||||
class_path: *class_path
|
||||
use_textline_bbox_info: &use_textline_bbox_info True
|
||||
# one of [None, "tb-yx"]
|
||||
order_method: &order_method "tb-yx"
|
||||
- VQATokenPad:
|
||||
max_seq_len: &max_seq_len 512
|
||||
return_attention_mask: True
|
||||
- VQASerTokenChunk:
|
||||
max_seq_len: *max_seq_len
|
||||
- Resize:
|
||||
size: [224,224]
|
||||
- NormalizeImage:
|
||||
scale: 1
|
||||
mean: [ 123.675, 116.28, 103.53 ]
|
||||
std: [ 58.395, 57.12, 57.375 ]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/XFUND/zh_val/image
|
||||
label_file_list:
|
||||
- train_data/XFUND/zh_val/val.json
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- VQATokenLabelEncode: # Class handling label
|
||||
contains_re: False
|
||||
algorithm: *algorithm
|
||||
class_path: *class_path
|
||||
use_textline_bbox_info: *use_textline_bbox_info
|
||||
order_method: *order_method
|
||||
- VQATokenPad:
|
||||
max_seq_len: *max_seq_len
|
||||
return_attention_mask: True
|
||||
- VQASerTokenChunk:
|
||||
max_seq_len: *max_seq_len
|
||||
- Resize:
|
||||
size: [224,224]
|
||||
- NormalizeImage:
|
||||
scale: 1
|
||||
mean: [ 123.675, 116.28, 103.53 ]
|
||||
std: [ 58.395, 57.12, 57.375 ]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,182 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: &epoch_num 200
|
||||
log_smooth_window: 10
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/ser_vi_layoutxlm_xfund_zh_udml
|
||||
save_epoch_step: 2000
|
||||
# evaluation is run every 10 iterations after the 0th iteration
|
||||
eval_batch_step: [ 0, 19 ]
|
||||
cal_metric_during_train: False
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
seed: 2022
|
||||
infer_img: ppstructure/docs/kie/input/zh_val_42.jpg
|
||||
save_res_path: ./output/ser_layoutxlm_xfund_zh/res
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: &model_type "kie"
|
||||
name: DistillationModel
|
||||
algorithm: Distillation
|
||||
Models:
|
||||
Teacher:
|
||||
pretrained:
|
||||
freeze_params: false
|
||||
return_all_feats: true
|
||||
model_type: *model_type
|
||||
algorithm: &algorithm "LayoutXLM"
|
||||
Transform:
|
||||
Backbone:
|
||||
name: LayoutXLMForSer
|
||||
pretrained: True
|
||||
# one of base or vi
|
||||
mode: vi
|
||||
checkpoints:
|
||||
num_classes: &num_classes 7
|
||||
Student:
|
||||
pretrained:
|
||||
freeze_params: false
|
||||
return_all_feats: true
|
||||
model_type: *model_type
|
||||
algorithm: *algorithm
|
||||
Transform:
|
||||
Backbone:
|
||||
name: LayoutXLMForSer
|
||||
pretrained: True
|
||||
# one of base or vi
|
||||
mode: vi
|
||||
checkpoints:
|
||||
num_classes: *num_classes
|
||||
|
||||
|
||||
Loss:
|
||||
name: CombinedLoss
|
||||
loss_config_list:
|
||||
- DistillationVQASerTokenLayoutLMLoss:
|
||||
weight: 1.0
|
||||
model_name_list: ["Student", "Teacher"]
|
||||
key: backbone_out
|
||||
num_classes: *num_classes
|
||||
- DistillationSERDMLLoss:
|
||||
weight: 1.0
|
||||
act: "softmax"
|
||||
use_log: true
|
||||
model_name_pairs:
|
||||
- ["Student", "Teacher"]
|
||||
key: backbone_out
|
||||
- DistillationVQADistanceLoss:
|
||||
weight: 0.5
|
||||
mode: "l2"
|
||||
model_name_pairs:
|
||||
- ["Student", "Teacher"]
|
||||
key: hidden_states_5
|
||||
name: "loss_5"
|
||||
- DistillationVQADistanceLoss:
|
||||
weight: 0.5
|
||||
mode: "l2"
|
||||
model_name_pairs:
|
||||
- ["Student", "Teacher"]
|
||||
key: hidden_states_8
|
||||
name: "loss_8"
|
||||
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: AdamW
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Linear
|
||||
learning_rate: 0.00005
|
||||
epochs: *epoch_num
|
||||
warmup_epoch: 10
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 0.00000
|
||||
|
||||
PostProcess:
|
||||
name: DistillationSerPostProcess
|
||||
model_name: ["Student", "Teacher"]
|
||||
key: backbone_out
|
||||
class_path: &class_path train_data/XFUND/class_list_xfun.txt
|
||||
|
||||
Metric:
|
||||
name: DistillationMetric
|
||||
base_metric_name: VQASerTokenMetric
|
||||
main_indicator: hmean
|
||||
key: "Student"
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/XFUND/zh_train/image
|
||||
label_file_list:
|
||||
- train_data/XFUND/zh_train/train.json
|
||||
ratio_list: [ 1.0 ]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- VQATokenLabelEncode: # Class handling label
|
||||
contains_re: False
|
||||
algorithm: *algorithm
|
||||
class_path: *class_path
|
||||
# one of [None, "tb-yx"]
|
||||
order_method: &order_method "tb-yx"
|
||||
- VQATokenPad:
|
||||
max_seq_len: &max_seq_len 512
|
||||
return_attention_mask: True
|
||||
- VQASerTokenChunk:
|
||||
max_seq_len: *max_seq_len
|
||||
- Resize:
|
||||
size: [224,224]
|
||||
- NormalizeImage:
|
||||
scale: 1
|
||||
mean: [ 123.675, 116.28, 103.53 ]
|
||||
std: [ 58.395, 57.12, 57.375 ]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 4
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/XFUND/zh_val/image
|
||||
label_file_list:
|
||||
- train_data/XFUND/zh_val/val.json
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- VQATokenLabelEncode: # Class handling label
|
||||
contains_re: False
|
||||
algorithm: *algorithm
|
||||
class_path: *class_path
|
||||
order_method: *order_method
|
||||
- VQATokenPad:
|
||||
max_seq_len: *max_seq_len
|
||||
return_attention_mask: True
|
||||
- VQASerTokenChunk:
|
||||
max_seq_len: *max_seq_len
|
||||
- Resize:
|
||||
size: [224,224]
|
||||
- NormalizeImage:
|
||||
scale: 1
|
||||
mean: [ 123.675, 116.28, 103.53 ]
|
||||
std: [ 58.395, 57.12, 57.375 ]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,133 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_ppocr_v3
|
||||
save_epoch_step: 3
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
|
||||
max_text_length: &max_text_length 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_ppocrv3.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 3.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SVTR_LCNet
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV1Enhance
|
||||
scale: 0.5
|
||||
last_conv_stride: [1, 2]
|
||||
last_pool_type: avg
|
||||
last_pool_kernel_size: [2, 2]
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 64
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- SARHead:
|
||||
enc_dim: 512
|
||||
max_text_length: *max_text_length
|
||||
|
||||
Loss:
|
||||
name: MultiLoss
|
||||
loss_config_list:
|
||||
- CTCLoss:
|
||||
- SARLoss:
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
ignore_space: False
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
ext_op_transform_idx: 1
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecConAug:
|
||||
prob: 0.5
|
||||
ext_data_num: 2
|
||||
image_shape: [48, 320, 3]
|
||||
max_text_length: *max_text_length
|
||||
- RecAug:
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 128
|
||||
drop_last: true
|
||||
num_workers: 4
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,209 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 800
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_ppocr_v3_distillation
|
||||
save_epoch_step: 3
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
|
||||
max_text_length: &max_text_length 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_ppocrv3_distillation.txt
|
||||
d2s_train_image_shape: [3, 48, -1]
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Piecewise
|
||||
decay_epochs : [700]
|
||||
values : [0.0005, 0.00005]
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 3.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: &model_type "rec"
|
||||
name: DistillationModel
|
||||
algorithm: Distillation
|
||||
Models:
|
||||
Teacher:
|
||||
pretrained:
|
||||
freeze_params: false
|
||||
return_all_feats: true
|
||||
model_type: *model_type
|
||||
algorithm: SVTR_LCNet
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV1Enhance
|
||||
scale: 0.5
|
||||
last_conv_stride: [1, 2]
|
||||
last_pool_type: avg
|
||||
last_pool_kernel_size: [2, 2]
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 64
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- SARHead:
|
||||
enc_dim: 512
|
||||
max_text_length: *max_text_length
|
||||
Student:
|
||||
pretrained:
|
||||
freeze_params: false
|
||||
return_all_feats: true
|
||||
model_type: *model_type
|
||||
algorithm: SVTR_LCNet
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV1Enhance
|
||||
scale: 0.5
|
||||
last_conv_stride: [1, 2]
|
||||
last_pool_type: avg
|
||||
last_pool_kernel_size: [2, 2]
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 64
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- SARHead:
|
||||
enc_dim: 512
|
||||
max_text_length: *max_text_length
|
||||
Loss:
|
||||
name: CombinedLoss
|
||||
loss_config_list:
|
||||
- DistillationDMLLoss:
|
||||
weight: 1.0
|
||||
act: "softmax"
|
||||
use_log: true
|
||||
model_name_pairs:
|
||||
- ["Student", "Teacher"]
|
||||
key: head_out
|
||||
multi_head: True
|
||||
dis_head: ctc
|
||||
name: dml_ctc
|
||||
- DistillationDMLLoss:
|
||||
weight: 0.5
|
||||
act: "softmax"
|
||||
use_log: true
|
||||
model_name_pairs:
|
||||
- ["Student", "Teacher"]
|
||||
key: head_out
|
||||
multi_head: True
|
||||
dis_head: sar
|
||||
name: dml_sar
|
||||
- DistillationDistanceLoss:
|
||||
weight: 1.0
|
||||
mode: "l2"
|
||||
model_name_pairs:
|
||||
- ["Student", "Teacher"]
|
||||
key: backbone_out
|
||||
- DistillationCTCLoss:
|
||||
weight: 1.0
|
||||
model_name_list: ["Student", "Teacher"]
|
||||
key: head_out
|
||||
multi_head: True
|
||||
- DistillationSARLoss:
|
||||
weight: 1.0
|
||||
model_name_list: ["Student", "Teacher"]
|
||||
key: head_out
|
||||
multi_head: True
|
||||
|
||||
PostProcess:
|
||||
name: DistillationCTCLabelDecode
|
||||
model_name: ["Student", "Teacher"]
|
||||
key: head_out
|
||||
multi_head: True
|
||||
|
||||
Metric:
|
||||
name: DistillationMetric
|
||||
base_metric_name: RecMetric
|
||||
main_indicator: acc
|
||||
key: "Student"
|
||||
ignore_space: False
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
ext_op_transform_idx: 1
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecConAug:
|
||||
prob: 0.5
|
||||
ext_data_num: 2
|
||||
image_shape: [48, 320, 3]
|
||||
max_text_length: *max_text_length
|
||||
- RecAug:
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 128
|
||||
drop_last: true
|
||||
num_workers: 4
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,133 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/v3_en_mobile
|
||||
save_epoch_step: 3
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/en_dict.txt
|
||||
max_text_length: &max_text_length 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_ppocrv3_en.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 3.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SVTR_LCNet
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV1Enhance
|
||||
scale: 0.5
|
||||
last_conv_stride: [1, 2]
|
||||
last_pool_type: avg
|
||||
last_pool_kernel_size: [2, 2]
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 64
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- SARHead:
|
||||
enc_dim: 512
|
||||
max_text_length: *max_text_length
|
||||
|
||||
Loss:
|
||||
name: MultiLoss
|
||||
loss_config_list:
|
||||
- CTCLoss:
|
||||
- SARLoss:
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
ignore_space: False
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
ext_op_transform_idx: 1
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecConAug:
|
||||
prob: 0.5
|
||||
ext_data_num: 2
|
||||
image_shape: [48, 320, 3]
|
||||
max_text_length: *max_text_length
|
||||
- RecAug:
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 128
|
||||
drop_last: true
|
||||
num_workers: 4
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,132 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/v3_arabic_mobile
|
||||
save_epoch_step: 3
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: ./doc/imgs_words/arabic/ar_2.jpg
|
||||
character_dict_path: ppocr/utils/dict/arabic_dict.txt
|
||||
max_text_length: &max_text_length 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_ppocrv3_arabic.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 3.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SVTR_LCNet
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV1Enhance
|
||||
scale: 0.5
|
||||
last_conv_stride: [1, 2]
|
||||
last_pool_type: avg
|
||||
last_pool_kernel_size: [2, 2]
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 64
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- SARHead:
|
||||
enc_dim: 512
|
||||
max_text_length: *max_text_length
|
||||
|
||||
Loss:
|
||||
name: MultiLoss
|
||||
loss_config_list:
|
||||
- CTCLoss:
|
||||
- SARLoss:
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
ignore_space: False
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
ext_op_transform_idx: 1
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecConAug:
|
||||
prob: 0.5
|
||||
ext_data_num: 2
|
||||
image_shape: [48, 320, 3]
|
||||
- RecAug:
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 128
|
||||
drop_last: true
|
||||
num_workers: 4
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,132 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/v3_chinese_cht_mobile
|
||||
save_epoch_step: 3
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/dict/chinese_cht_dict.txt
|
||||
max_text_length: &max_text_length 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_ppocrv3_chinese_cht.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 3.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SVTR_LCNet
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV1Enhance
|
||||
scale: 0.5
|
||||
last_conv_stride: [1, 2]
|
||||
last_pool_type: avg
|
||||
last_pool_kernel_size: [2, 2]
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 64
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- SARHead:
|
||||
enc_dim: 512
|
||||
max_text_length: *max_text_length
|
||||
|
||||
Loss:
|
||||
name: MultiLoss
|
||||
loss_config_list:
|
||||
- CTCLoss:
|
||||
- SARLoss:
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
ignore_space: False
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
ext_op_transform_idx: 1
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecConAug:
|
||||
prob: 0.5
|
||||
ext_data_num: 2
|
||||
image_shape: [48, 320, 3]
|
||||
- RecAug:
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 128
|
||||
drop_last: true
|
||||
num_workers: 4
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,132 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/v3_cyrillic_mobile
|
||||
save_epoch_step: 3
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/dict/cyrillic_dict.txt
|
||||
max_text_length: &max_text_length 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_ppocrv3_cyrillic.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 3.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SVTR_LCNet
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV1Enhance
|
||||
scale: 0.5
|
||||
last_conv_stride: [1, 2]
|
||||
last_pool_type: avg
|
||||
last_pool_kernel_size: [2, 2]
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 64
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- SARHead:
|
||||
enc_dim: 512
|
||||
max_text_length: *max_text_length
|
||||
|
||||
Loss:
|
||||
name: MultiLoss
|
||||
loss_config_list:
|
||||
- CTCLoss:
|
||||
- SARLoss:
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
ignore_space: False
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
ext_op_transform_idx: 1
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecConAug:
|
||||
prob: 0.5
|
||||
ext_data_num: 2
|
||||
image_shape: [48, 320, 3]
|
||||
- RecAug:
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 128
|
||||
drop_last: true
|
||||
num_workers: 4
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,132 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/v3_devanagari_mobile
|
||||
save_epoch_step: 3
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/dict/devanagari_dict.txt
|
||||
max_text_length: &max_text_length 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_ppocrv3_devanagari.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 3.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SVTR_LCNet
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV1Enhance
|
||||
scale: 0.5
|
||||
last_conv_stride: [1, 2]
|
||||
last_pool_type: avg
|
||||
last_pool_kernel_size: [2, 2]
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 64
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- SARHead:
|
||||
enc_dim: 512
|
||||
max_text_length: *max_text_length
|
||||
|
||||
Loss:
|
||||
name: MultiLoss
|
||||
loss_config_list:
|
||||
- CTCLoss:
|
||||
- SARLoss:
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
ignore_space: False
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
ext_op_transform_idx: 1
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecConAug:
|
||||
prob: 0.5
|
||||
ext_data_num: 2
|
||||
image_shape: [48, 320, 3]
|
||||
- RecAug:
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 128
|
||||
drop_last: true
|
||||
num_workers: 4
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,132 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/v3_japan_mobile
|
||||
save_epoch_step: 3
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/dict/japan_dict.txt
|
||||
max_text_length: &max_text_length 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_ppocrv3_japan.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 3.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SVTR_LCNet
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV1Enhance
|
||||
scale: 0.5
|
||||
last_conv_stride: [1, 2]
|
||||
last_pool_type: avg
|
||||
last_pool_kernel_size: [2, 2]
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 64
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- SARHead:
|
||||
enc_dim: 512
|
||||
max_text_length: *max_text_length
|
||||
|
||||
Loss:
|
||||
name: MultiLoss
|
||||
loss_config_list:
|
||||
- CTCLoss:
|
||||
- SARLoss:
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
ignore_space: False
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
ext_op_transform_idx: 1
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecConAug:
|
||||
prob: 0.5
|
||||
ext_data_num: 2
|
||||
image_shape: [48, 320, 3]
|
||||
- RecAug:
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 128
|
||||
drop_last: true
|
||||
num_workers: 4
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,132 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/v3_ka_mobile
|
||||
save_epoch_step: 3
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/dict/ka_dict.txt
|
||||
max_text_length: &max_text_length 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_ppocrv3_ka.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 3.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SVTR_LCNet
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV1Enhance
|
||||
scale: 0.5
|
||||
last_conv_stride: [1, 2]
|
||||
last_pool_type: avg
|
||||
last_pool_kernel_size: [2, 2]
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 64
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- SARHead:
|
||||
enc_dim: 512
|
||||
max_text_length: *max_text_length
|
||||
|
||||
Loss:
|
||||
name: MultiLoss
|
||||
loss_config_list:
|
||||
- CTCLoss:
|
||||
- SARLoss:
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
ignore_space: False
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
ext_op_transform_idx: 1
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecConAug:
|
||||
prob: 0.5
|
||||
ext_data_num: 2
|
||||
image_shape: [48, 320, 3]
|
||||
- RecAug:
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 128
|
||||
drop_last: true
|
||||
num_workers: 4
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,132 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/v3_korean_mobile
|
||||
save_epoch_step: 3
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/dict/korean_dict.txt
|
||||
max_text_length: &max_text_length 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_ppocrv3_korean.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 3.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SVTR_LCNet
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV1Enhance
|
||||
scale: 0.5
|
||||
last_conv_stride: [1, 2]
|
||||
last_pool_type: avg
|
||||
last_pool_kernel_size: [2, 2]
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 64
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- SARHead:
|
||||
enc_dim: 512
|
||||
max_text_length: *max_text_length
|
||||
|
||||
Loss:
|
||||
name: MultiLoss
|
||||
loss_config_list:
|
||||
- CTCLoss:
|
||||
- SARLoss:
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
ignore_space: False
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
ext_op_transform_idx: 1
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecConAug:
|
||||
prob: 0.5
|
||||
ext_data_num: 2
|
||||
image_shape: [48, 320, 3]
|
||||
- RecAug:
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 128
|
||||
drop_last: true
|
||||
num_workers: 4
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,132 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/v3_latin_mobile
|
||||
save_epoch_step: 3
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/dict/latin_dict.txt
|
||||
max_text_length: &max_text_length 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_ppocrv3_latin.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 3.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SVTR_LCNet
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV1Enhance
|
||||
scale: 0.5
|
||||
last_conv_stride: [1, 2]
|
||||
last_pool_type: avg
|
||||
last_pool_kernel_size: [2, 2]
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 64
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- SARHead:
|
||||
enc_dim: 512
|
||||
max_text_length: *max_text_length
|
||||
|
||||
Loss:
|
||||
name: MultiLoss
|
||||
loss_config_list:
|
||||
- CTCLoss:
|
||||
- SARLoss:
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
ignore_space: False
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
ext_op_transform_idx: 1
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecConAug:
|
||||
prob: 0.5
|
||||
ext_data_num: 2
|
||||
image_shape: [48, 320, 3]
|
||||
- RecAug:
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 128
|
||||
drop_last: true
|
||||
num_workers: 4
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,132 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/v3_ta_mobile
|
||||
save_epoch_step: 3
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/dict/ta_dict.txt
|
||||
max_text_length: &max_text_length 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_ppocrv3_ta.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 3.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SVTR_LCNet
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV1Enhance
|
||||
scale: 0.5
|
||||
last_conv_stride: [1, 2]
|
||||
last_pool_type: avg
|
||||
last_pool_kernel_size: [2, 2]
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 64
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- SARHead:
|
||||
enc_dim: 512
|
||||
max_text_length: *max_text_length
|
||||
|
||||
Loss:
|
||||
name: MultiLoss
|
||||
loss_config_list:
|
||||
- CTCLoss:
|
||||
- SARLoss:
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
ignore_space: False
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
ext_op_transform_idx: 1
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecConAug:
|
||||
prob: 0.5
|
||||
ext_data_num: 2
|
||||
image_shape: [48, 320, 3]
|
||||
- RecAug:
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 128
|
||||
drop_last: true
|
||||
num_workers: 4
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,132 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/v3_te_mobile
|
||||
save_epoch_step: 3
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/dict/te_dict.txt
|
||||
max_text_length: &max_text_length 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_ppocrv3_te.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 3.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SVTR_LCNet
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV1Enhance
|
||||
scale: 0.5
|
||||
last_conv_stride: [1, 2]
|
||||
last_pool_type: avg
|
||||
last_pool_kernel_size: [2, 2]
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 64
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- SARHead:
|
||||
enc_dim: 512
|
||||
max_text_length: *max_text_length
|
||||
|
||||
Loss:
|
||||
name: MultiLoss
|
||||
loss_config_list:
|
||||
- CTCLoss:
|
||||
- SARLoss:
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
ignore_space: False
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
ext_op_transform_idx: 1
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecConAug:
|
||||
prob: 0.5
|
||||
ext_data_num: 2
|
||||
image_shape: [48, 320, 3]
|
||||
- RecAug:
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 128
|
||||
drop_last: true
|
||||
num_workers: 4
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,138 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 200
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_ppocr_v4
|
||||
save_epoch_step: 10
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
|
||||
max_text_length: &max_text_length 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_ppocrv3.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 3.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SVTR_LCNet
|
||||
Transform:
|
||||
Backbone:
|
||||
name: PPLCNetV3
|
||||
scale: 0.95
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 120
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
kernel_size: [1, 3]
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- NRTRHead:
|
||||
nrtr_dim: 384
|
||||
max_text_length: *max_text_length
|
||||
|
||||
Loss:
|
||||
name: MultiLoss
|
||||
loss_config_list:
|
||||
- CTCLoss:
|
||||
- NRTRLoss:
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: MultiScaleDataSet
|
||||
ds_width: false
|
||||
data_dir: ./train_data/
|
||||
ext_op_transform_idx: 1
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecConAug:
|
||||
prob: 0.5
|
||||
ext_data_num: 2
|
||||
image_shape: [48, 320, 3]
|
||||
max_text_length: *max_text_length
|
||||
- RecAug:
|
||||
- MultiLabelEncode:
|
||||
gtc_encode: NRTRLabelEncode
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_gtc
|
||||
- length
|
||||
- valid_ratio
|
||||
sampler:
|
||||
name: MultiScaleSampler
|
||||
scales: [[320, 32], [320, 48], [320, 64]]
|
||||
first_bs: &bs 192
|
||||
fix_bs: false
|
||||
divided_factor: [8, 16] # w, h
|
||||
is_training: True
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: *bs
|
||||
drop_last: true
|
||||
num_workers: 8
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
gtc_encode: NRTRLabelEncode
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_gtc
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,140 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 200
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_ppocr_v4
|
||||
save_epoch_step: 10
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
|
||||
max_text_length: &max_text_length 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_ppocrv3.txt
|
||||
use_amp: True
|
||||
amp_level: O2
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 3.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SVTR_LCNet
|
||||
Transform:
|
||||
Backbone:
|
||||
name: PPLCNetV3
|
||||
scale: 0.95
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 120
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
kernel_size: [1, 3]
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- NRTRHead:
|
||||
nrtr_dim: 384
|
||||
max_text_length: *max_text_length
|
||||
|
||||
Loss:
|
||||
name: MultiLoss
|
||||
loss_config_list:
|
||||
- CTCLoss:
|
||||
- NRTRLoss:
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: MultiScaleDataSet
|
||||
ds_width: false
|
||||
data_dir: ./train_data/
|
||||
ext_op_transform_idx: 1
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecConAug:
|
||||
prob: 0.5
|
||||
ext_data_num: 2
|
||||
image_shape: [48, 320, 3]
|
||||
max_text_length: *max_text_length
|
||||
- RecAug:
|
||||
- MultiLabelEncode:
|
||||
gtc_encode: NRTRLabelEncode
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_gtc
|
||||
- length
|
||||
- valid_ratio
|
||||
sampler:
|
||||
name: MultiScaleSampler
|
||||
scales: [[320, 32], [320, 48], [320, 64]]
|
||||
first_bs: &bs 384
|
||||
fix_bs: false
|
||||
divided_factor: [8, 16] # w, h
|
||||
is_training: True
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: *bs
|
||||
drop_last: true
|
||||
num_workers: 16
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
gtc_encode: NRTRLabelEncode
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_gtc
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 16
|
||||
@@ -0,0 +1,231 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 200
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_dkd_400w_svtr_ctc_lcnet_blank_dkd0.1/
|
||||
save_epoch_step: 40
|
||||
eval_batch_step:
|
||||
- 0
|
||||
- 2000
|
||||
cal_metric_during_train: true
|
||||
pretrained_model: null
|
||||
checkpoints: ./output/rec_dkd_400w_svtr_ctc_lcnet_blank_dkd0.1/latest
|
||||
save_inference_dir: null
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
|
||||
max_text_length: 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_ppocrv3.txt
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 2
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 3.0e-05
|
||||
Architecture:
|
||||
model_type: rec
|
||||
name: DistillationModel
|
||||
algorithm: Distillation
|
||||
Models:
|
||||
Teacher:
|
||||
pretrained:
|
||||
freeze_params: true
|
||||
return_all_feats: true
|
||||
model_type: rec
|
||||
algorithm: SVTR
|
||||
Transform: null
|
||||
Backbone:
|
||||
name: SVTRNet
|
||||
img_size:
|
||||
- 48
|
||||
- 320
|
||||
out_char_num: 40
|
||||
out_channels: 192
|
||||
patch_merging: Conv
|
||||
embed_dim:
|
||||
- 64
|
||||
- 128
|
||||
- 256
|
||||
depth:
|
||||
- 3
|
||||
- 6
|
||||
- 3
|
||||
num_heads:
|
||||
- 2
|
||||
- 4
|
||||
- 8
|
||||
mixer:
|
||||
- Conv
|
||||
- Conv
|
||||
- Conv
|
||||
- Conv
|
||||
- Conv
|
||||
- Conv
|
||||
- Global
|
||||
- Global
|
||||
- Global
|
||||
- Global
|
||||
- Global
|
||||
- Global
|
||||
local_mixer:
|
||||
- - 5
|
||||
- 5
|
||||
- - 5
|
||||
- 5
|
||||
- - 5
|
||||
- 5
|
||||
last_stage: false
|
||||
prenorm: true
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 120
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
kernel_size: [1, 3]
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- NRTRHead:
|
||||
nrtr_dim: 384
|
||||
max_text_length: *max_text_length
|
||||
Student:
|
||||
pretrained:
|
||||
freeze_params: false
|
||||
return_all_feats: true
|
||||
model_type: rec
|
||||
algorithm: SVTR
|
||||
Transform: null
|
||||
Backbone:
|
||||
name: PPLCNetV3
|
||||
scale: 0.95
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 120
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
kernel_size: [1, 3]
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- NRTRHead:
|
||||
nrtr_dim: 384
|
||||
max_text_length: *max_text_length
|
||||
Loss:
|
||||
name: CombinedLoss
|
||||
loss_config_list:
|
||||
- DistillationDKDLoss:
|
||||
weight: 0.1
|
||||
model_name_pairs:
|
||||
- - Student
|
||||
- Teacher
|
||||
key: head_out
|
||||
multi_head: true
|
||||
alpha: 1.0
|
||||
beta: 2.0
|
||||
dis_head: gtc
|
||||
name: dkd
|
||||
- DistillationCTCLoss:
|
||||
weight: 1.0
|
||||
model_name_list:
|
||||
- Student
|
||||
key: head_out
|
||||
multi_head: true
|
||||
- DistillationNRTRLoss:
|
||||
weight: 1.0
|
||||
smoothing: false
|
||||
model_name_list:
|
||||
- Student
|
||||
key: head_out
|
||||
multi_head: true
|
||||
- DistillCTCLogits:
|
||||
weight: 1.0
|
||||
reduction: mean
|
||||
model_name_pairs:
|
||||
- - Student
|
||||
- Teacher
|
||||
key: head_out
|
||||
PostProcess:
|
||||
name: DistillationCTCLabelDecode
|
||||
model_name:
|
||||
- Student
|
||||
key: head_out
|
||||
multi_head: true
|
||||
Metric:
|
||||
name: DistillationMetric
|
||||
base_metric_name: RecMetric
|
||||
main_indicator: acc
|
||||
key: Student
|
||||
ignore_space: false
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
ratio_list:
|
||||
- 1.0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecAug:
|
||||
- MultiLabelEncode:
|
||||
gtc_encode: NRTRLabelEncode
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_gtc
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 128
|
||||
drop_last: true
|
||||
num_workers: 8
|
||||
use_shared_memory: true
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
gtc_encode: NRTRLabelEncode
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_gtc
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 4
|
||||
profiler_options: null
|
||||
@@ -0,0 +1,138 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 200
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_ppocr_v4
|
||||
save_epoch_step: 10
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
|
||||
max_text_length: &max_text_length 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_ppocrv3.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 3.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SVTR_LCNet
|
||||
Transform:
|
||||
Backbone:
|
||||
name: PPLCNetV3
|
||||
scale: 0.95
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 120
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
kernel_size: [1, 3]
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- NRTRHead:
|
||||
nrtr_dim: 384
|
||||
max_text_length: *max_text_length
|
||||
|
||||
Loss:
|
||||
name: MultiLoss
|
||||
loss_config_list:
|
||||
- CTCLoss:
|
||||
- NRTRLoss:
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: MultiScaleDataSet
|
||||
ds_width: false
|
||||
data_dir: ./train_data/
|
||||
ext_op_transform_idx: 1
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecConAug:
|
||||
prob: 0.5
|
||||
ext_data_num: 2
|
||||
image_shape: [48, 320, 3]
|
||||
max_text_length: *max_text_length
|
||||
- RecAug:
|
||||
- MultiLabelEncode:
|
||||
gtc_encode: NRTRLabelEncode
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_gtc
|
||||
- length
|
||||
- valid_ratio
|
||||
sampler:
|
||||
name: MultiScaleSampler
|
||||
scales: [[320, 32], [320, 48], [320, 64]]
|
||||
first_bs: &bs 192
|
||||
fix_bs: false
|
||||
divided_factor: [8, 16] # w, h
|
||||
is_training: True
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: *bs
|
||||
drop_last: true
|
||||
num_workers: 16
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
gtc_encode: NRTRLabelEncode
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_gtc
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 16
|
||||
@@ -0,0 +1,137 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 200
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_ppocr_v4_hgnet
|
||||
save_epoch_step: 10
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
|
||||
max_text_length: &max_text_length 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_ppocrv3.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 3.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SVTR_HGNet
|
||||
Transform:
|
||||
Backbone:
|
||||
name: PPHGNet_small
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 120
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
kernel_size: [1, 3]
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- NRTRHead:
|
||||
nrtr_dim: 384
|
||||
max_text_length: *max_text_length
|
||||
|
||||
Loss:
|
||||
name: MultiLoss
|
||||
loss_config_list:
|
||||
- CTCLoss:
|
||||
- NRTRLoss:
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: MultiScaleDataSet
|
||||
ds_width: false
|
||||
data_dir: ./train_data/
|
||||
ext_op_transform_idx: 1
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecConAug:
|
||||
prob: 0.5
|
||||
ext_data_num: 2
|
||||
image_shape: [48, 320, 3]
|
||||
max_text_length: *max_text_length
|
||||
- RecAug:
|
||||
- MultiLabelEncode:
|
||||
gtc_encode: NRTRLabelEncode
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_gtc
|
||||
- length
|
||||
- valid_ratio
|
||||
sampler:
|
||||
name: MultiScaleSampler
|
||||
scales: [[320, 32], [320, 48], [320, 64]]
|
||||
first_bs: &bs 128
|
||||
fix_bs: false
|
||||
divided_factor: [8, 16] # w, h
|
||||
is_training: True
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: *bs
|
||||
drop_last: true
|
||||
num_workers: 8
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
gtc_encode: NRTRLabelEncode
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_gtc
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,139 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 200
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_ppocr_v4_hgnet
|
||||
save_epoch_step: 10
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
|
||||
max_text_length: &max_text_length 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_ppocrv3.txt
|
||||
use_amp: True
|
||||
amp_level: O2
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 3.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SVTR_HGNet
|
||||
Transform:
|
||||
Backbone:
|
||||
name: PPHGNet_small
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 120
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
kernel_size: [1, 3]
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- NRTRHead:
|
||||
nrtr_dim: 384
|
||||
max_text_length: *max_text_length
|
||||
|
||||
Loss:
|
||||
name: MultiLoss
|
||||
loss_config_list:
|
||||
- CTCLoss:
|
||||
- NRTRLoss:
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: MultiScaleDataSet
|
||||
ds_width: false
|
||||
data_dir: ./train_data/
|
||||
ext_op_transform_idx: 1
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecConAug:
|
||||
prob: 0.5
|
||||
ext_data_num: 2
|
||||
image_shape: [48, 320, 3]
|
||||
max_text_length: *max_text_length
|
||||
- RecAug:
|
||||
- MultiLabelEncode:
|
||||
gtc_encode: NRTRLabelEncode
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_gtc
|
||||
- length
|
||||
- valid_ratio
|
||||
sampler:
|
||||
name: MultiScaleSampler
|
||||
scales: [[320, 32], [320, 48], [320, 64]]
|
||||
first_bs: &bs 256
|
||||
fix_bs: false
|
||||
divided_factor: [8, 16] # w, h
|
||||
is_training: True
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: *bs
|
||||
drop_last: true
|
||||
num_workers: 16
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
gtc_encode: NRTRLabelEncode
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_gtc
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 16
|
||||
@@ -0,0 +1,137 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 200
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_ppocr_v4_hgnet
|
||||
save_epoch_step: 10
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
|
||||
max_text_length: &max_text_length 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_ppocrv3.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 3.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SVTR_HGNet
|
||||
Transform:
|
||||
Backbone:
|
||||
name: PPHGNet_small
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 120
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
kernel_size: [1, 3]
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- NRTRHead:
|
||||
nrtr_dim: 384
|
||||
max_text_length: *max_text_length
|
||||
|
||||
Loss:
|
||||
name: MultiLoss
|
||||
loss_config_list:
|
||||
- CTCLoss:
|
||||
- NRTRLoss:
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: MultiScaleDataSet
|
||||
ds_width: false
|
||||
data_dir: ./train_data/
|
||||
ext_op_transform_idx: 1
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecConAug:
|
||||
prob: 0.5
|
||||
ext_data_num: 2
|
||||
image_shape: [48, 320, 3]
|
||||
max_text_length: *max_text_length
|
||||
- RecAug:
|
||||
- MultiLabelEncode:
|
||||
gtc_encode: NRTRLabelEncode
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_gtc
|
||||
- length
|
||||
- valid_ratio
|
||||
sampler:
|
||||
name: MultiScaleSampler
|
||||
scales: [[320, 32], [320, 48], [320, 64]]
|
||||
first_bs: &bs 256
|
||||
fix_bs: false
|
||||
divided_factor: [8, 16] # w, h
|
||||
is_training: True
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: *bs
|
||||
drop_last: true
|
||||
num_workers: 16
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
gtc_encode: NRTRLabelEncode
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_gtc
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 16
|
||||
@@ -0,0 +1,144 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 200
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec/svtr_large/
|
||||
save_epoch_step: 10
|
||||
# evaluation is run every 2000 iterations after the 0th iteration
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
|
||||
max_text_length: &max_text_length 40
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_svtr_large.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: AdamW
|
||||
beta1: 0.9
|
||||
beta2: 0.99
|
||||
epsilon: 1.0e-08
|
||||
weight_decay: 0.05
|
||||
no_weight_decay_name: norm pos_embed char_node_embed pos_node_embed char_pos_embed vis_pos_embed
|
||||
one_dim_param_no_weight_decay: true
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.00025 # 8gpus 64bs
|
||||
warmup_epoch: 5
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SVTR_LCNet
|
||||
Transform: null
|
||||
Backbone:
|
||||
name: SVTRNet
|
||||
img_size:
|
||||
- 48
|
||||
- 320
|
||||
out_char_num: 40
|
||||
out_channels: 512
|
||||
patch_merging: Conv
|
||||
embed_dim: [192, 256, 512]
|
||||
depth: [6, 6, 9]
|
||||
num_heads: [6, 8, 16]
|
||||
mixer: ['Conv','Conv','Conv','Conv','Conv','Conv','Conv','Conv','Conv','Global','Global','Global','Global','Global','Global','Global','Global','Global','Global','Global','Global']
|
||||
local_mixer: [[5, 5], [5, 5], [5, 5]]
|
||||
last_stage: False
|
||||
prenorm: True
|
||||
Head:
|
||||
name: MultiHead
|
||||
use_pool: true
|
||||
use_pos: true
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 256
|
||||
depth: 2
|
||||
hidden_dims: 256
|
||||
kernel_size: [1, 3]
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- NRTRHead:
|
||||
nrtr_dim: 512
|
||||
max_text_length: *max_text_length
|
||||
|
||||
Loss:
|
||||
name: MultiLoss
|
||||
loss_config_list:
|
||||
- CTCLoss:
|
||||
- NRTRLoss:
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
ignore_space: true
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
ext_op_transform_idx: 1
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecAug:
|
||||
- MultiLabelEncode:
|
||||
gtc_encode: NRTRLabelEncode
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_gtc
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 64
|
||||
drop_last: true
|
||||
num_workers: 8
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
gtc_encode: NRTRLabelEncode
|
||||
- SVTRRecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_gtc
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,150 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 50
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_ppocr_v4
|
||||
save_epoch_step: 10
|
||||
eval_batch_step:
|
||||
- 0
|
||||
- 2000
|
||||
cal_metric_during_train: true
|
||||
pretrained_model: refactor
|
||||
checkpoints: null
|
||||
save_inference_dir: null
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/en_dict.txt
|
||||
max_text_length: 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_ppocrv3.txt
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.0005
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 3.0e-05
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SVTR_LCNet
|
||||
Transform: null
|
||||
Backbone:
|
||||
name: PPLCNetV3
|
||||
scale: 0.95
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 120
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
kernel_size:
|
||||
- 1
|
||||
- 3
|
||||
use_guide: true
|
||||
Head:
|
||||
fc_decay: 1.0e-05
|
||||
- NRTRHead:
|
||||
nrtr_dim: 384
|
||||
max_text_length: 25
|
||||
Loss:
|
||||
name: MultiLoss
|
||||
loss_config_list:
|
||||
- CTCLoss: null
|
||||
- NRTRLoss: null
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
ignore_space: false
|
||||
Train:
|
||||
dataset:
|
||||
name: MultiScaleDataSet
|
||||
ds_width: false
|
||||
data_dir: ./train_data/
|
||||
ext_op_transform_idx: 1
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecConAug:
|
||||
prob: 0.5
|
||||
ext_data_num: 2
|
||||
image_shape:
|
||||
- 48
|
||||
- 320
|
||||
- 3
|
||||
max_text_length: 25
|
||||
- RecAug: null
|
||||
- MultiLabelEncode:
|
||||
gtc_encode: NRTRLabelEncode
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_gtc
|
||||
- length
|
||||
- valid_ratio
|
||||
sampler:
|
||||
name: MultiScaleSampler
|
||||
scales:
|
||||
- - 320
|
||||
- 32
|
||||
- - 320
|
||||
- 48
|
||||
- - 320
|
||||
- 64
|
||||
first_bs: 96
|
||||
fix_bs: false
|
||||
divided_factor:
|
||||
- 8
|
||||
- 16
|
||||
is_training: true
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 96
|
||||
drop_last: true
|
||||
num_workers: 8
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
gtc_encode: NRTRLabelEncode
|
||||
- RecResizeImg:
|
||||
image_shape:
|
||||
- 3
|
||||
- 48
|
||||
- 320
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_gtc
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 4
|
||||
profiler_options: null
|
||||
@@ -0,0 +1,110 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 800
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_mobile_pp-OCRv2
|
||||
save_epoch_step: 3
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
|
||||
max_text_length: 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_mobile_pp-OCRv2.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Piecewise
|
||||
decay_epochs : [700]
|
||||
values : [0.001, 0.0001]
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 2.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: CRNN
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV1Enhance
|
||||
scale: 0.5
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 64
|
||||
Head:
|
||||
name: CTCHead
|
||||
mid_channels: 96
|
||||
fc_decay: 0.00002
|
||||
|
||||
Loss:
|
||||
name: CTCLoss
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecAug:
|
||||
- CTCLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label
|
||||
- length
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 128
|
||||
drop_last: true
|
||||
num_workers: 8
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- CTCLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label
|
||||
- length
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,160 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 800
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_pp-OCRv2_distillation
|
||||
save_epoch_step: 3
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
|
||||
max_text_length: 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_pp-OCRv2_distillation.txt
|
||||
amp_custom_black_list: ['matmul','matmul_v2','elementwise_add']
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Piecewise
|
||||
decay_epochs : [700]
|
||||
values : [0.001, 0.0001]
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 2.0e-05
|
||||
|
||||
Architecture:
|
||||
model_type: &model_type "rec"
|
||||
name: DistillationModel
|
||||
algorithm: Distillation
|
||||
Models:
|
||||
Teacher:
|
||||
pretrained:
|
||||
freeze_params: false
|
||||
return_all_feats: true
|
||||
model_type: *model_type
|
||||
algorithm: CRNN
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV1Enhance
|
||||
scale: 0.5
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 64
|
||||
Head:
|
||||
name: CTCHead
|
||||
mid_channels: 96
|
||||
fc_decay: 0.00002
|
||||
Student:
|
||||
pretrained:
|
||||
freeze_params: false
|
||||
return_all_feats: true
|
||||
model_type: *model_type
|
||||
algorithm: CRNN
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV1Enhance
|
||||
scale: 0.5
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 64
|
||||
Head:
|
||||
name: CTCHead
|
||||
mid_channels: 96
|
||||
fc_decay: 0.00002
|
||||
|
||||
|
||||
Loss:
|
||||
name: CombinedLoss
|
||||
loss_config_list:
|
||||
- DistillationCTCLoss:
|
||||
weight: 1.0
|
||||
model_name_list: ["Student", "Teacher"]
|
||||
key: head_out
|
||||
- DistillationDMLLoss:
|
||||
weight: 1.0
|
||||
act: "softmax"
|
||||
use_log: true
|
||||
model_name_pairs:
|
||||
- ["Student", "Teacher"]
|
||||
key: head_out
|
||||
- DistillationDistanceLoss:
|
||||
weight: 1.0
|
||||
mode: "l2"
|
||||
model_name_pairs:
|
||||
- ["Student", "Teacher"]
|
||||
key: backbone_out
|
||||
|
||||
PostProcess:
|
||||
name: DistillationCTCLabelDecode
|
||||
model_name: ["Student", "Teacher"]
|
||||
key: head_out
|
||||
|
||||
Metric:
|
||||
name: DistillationMetric
|
||||
base_metric_name: RecMetric
|
||||
main_indicator: acc
|
||||
key: "Student"
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecAug:
|
||||
- CTCLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label
|
||||
- length
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 128
|
||||
drop_last: true
|
||||
num_sections: 1
|
||||
num_workers: 8
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- CTCLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label
|
||||
- length
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,124 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 800
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_mobile_pp-OCRv2_enhanced_ctc_loss
|
||||
save_epoch_step: 3
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
|
||||
max_text_length: 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_mobile_pp-OCRv2_enhanced_ctc_loss.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Piecewise
|
||||
decay_epochs : [700]
|
||||
values : [0.001, 0.0001]
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 2.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: CRNN
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV1Enhance
|
||||
scale: 0.5
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 64
|
||||
Head:
|
||||
name: CTCHead
|
||||
mid_channels: 96
|
||||
fc_decay: 0.00002
|
||||
return_feats: true
|
||||
|
||||
Loss:
|
||||
name: CombinedLoss
|
||||
loss_config_list:
|
||||
- CTCLoss:
|
||||
use_focal_loss: false
|
||||
weight: 1.0
|
||||
- CenterLoss:
|
||||
weight: 0.05
|
||||
num_classes: 6625
|
||||
feat_dim: 96
|
||||
center_file_path:
|
||||
# you can also try to add ace loss on your own dataset
|
||||
# - ACELoss:
|
||||
# weight: 0.1
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecAug:
|
||||
- CTCLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label
|
||||
- length
|
||||
- label_ace
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 128
|
||||
drop_last: true
|
||||
num_workers: 8
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- CTCLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label
|
||||
- length
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,100 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_chinese_common_v2.0
|
||||
save_epoch_step: 3
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [0, 2000]
|
||||
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: ppocr/utils/ppocr_keys_v1.txt
|
||||
max_text_length: 25
|
||||
infer_mode: False
|
||||
use_space_char: True
|
||||
save_res_path: ./output/rec/predicts_chinese_common_v2.0.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0.00004
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: CRNN
|
||||
Transform:
|
||||
Backbone:
|
||||
name: ResNet
|
||||
layers: 34
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 256
|
||||
Head:
|
||||
name: CTCHead
|
||||
fc_decay: 0.00004
|
||||
|
||||
Loss:
|
||||
name: CTCLoss
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list: ["./train_data/train_list.txt"]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- RecAug:
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 320]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 256
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list: ["./train_data/val_list.txt"]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 320]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 256
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,102 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_chinese_lite_v2.0
|
||||
save_epoch_step: 3
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [0, 2000]
|
||||
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: ppocr/utils/ppocr_keys_v1.txt
|
||||
max_text_length: 25
|
||||
infer_mode: False
|
||||
use_space_char: True
|
||||
save_res_path: ./output/rec/predicts_chinese_lite_v2.0.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0.00001
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: CRNN
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: small
|
||||
small_stride: [1, 2, 2, 2]
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 48
|
||||
Head:
|
||||
name: CTCHead
|
||||
fc_decay: 0.00001
|
||||
|
||||
Loss:
|
||||
name: CTCLoss
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list: ["./train_data/train_list.txt"]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- RecAug:
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 320]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 256
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list: ["./train_data/val_list.txt"]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 320]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 256
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,226 @@
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import yaml
|
||||
from argparse import ArgumentParser, RawDescriptionHelpFormatter
|
||||
import os.path
|
||||
import logging
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
|
||||
support_list = {
|
||||
'it': 'italian',
|
||||
'xi': 'spanish',
|
||||
'pu': 'portuguese',
|
||||
'ru': 'russian',
|
||||
'ar': 'arabic',
|
||||
'ta': 'tamil',
|
||||
'ug': 'uyghur',
|
||||
'fa': 'persian',
|
||||
'ur': 'urdu',
|
||||
'rs': 'serbian latin',
|
||||
'oc': 'occitan',
|
||||
'rsc': 'serbian cyrillic',
|
||||
'bg': 'bulgarian',
|
||||
'uk': 'ukranian',
|
||||
'be': 'belarusian',
|
||||
'te': 'telugu',
|
||||
'ka': 'kannada',
|
||||
'chinese_cht': 'chinese tradition',
|
||||
'hi': 'hindi',
|
||||
'mr': 'marathi',
|
||||
'ne': 'nepali',
|
||||
}
|
||||
|
||||
latin_lang = [
|
||||
'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'es', 'et', 'fr', 'ga', 'hr',
|
||||
'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi', 'ms', 'mt', 'nl',
|
||||
'no', 'oc', 'pi', 'pl', 'pt', 'ro', 'rs_latin', 'sk', 'sl', 'sq', 'sv',
|
||||
'sw', 'tl', 'tr', 'uz', 'vi', 'latin'
|
||||
]
|
||||
arabic_lang = ['ar', 'fa', 'ug', 'ur']
|
||||
cyrillic_lang = [
|
||||
'ru', 'rs_cyrillic', 'be', 'bg', 'uk', 'mn', 'abq', 'ady', 'kbd', 'ava',
|
||||
'dar', 'inh', 'che', 'lbe', 'lez', 'tab', 'cyrillic'
|
||||
]
|
||||
devanagari_lang = [
|
||||
'hi', 'mr', 'ne', 'bh', 'mai', 'ang', 'bho', 'mah', 'sck', 'new', 'gom',
|
||||
'sa', 'bgc', 'devanagari'
|
||||
]
|
||||
multi_lang = latin_lang + arabic_lang + cyrillic_lang + devanagari_lang
|
||||
|
||||
assert (os.path.isfile("./rec_multi_language_lite_train.yml")
|
||||
), "Loss basic configuration file rec_multi_language_lite_train.yml.\
|
||||
You can download it from \
|
||||
https://github.com/PaddlePaddle/PaddleOCR/tree/dygraph/configs/rec/multi_language/"
|
||||
|
||||
global_config = yaml.load(
|
||||
open("./rec_multi_language_lite_train.yml", 'rb'), Loader=yaml.Loader)
|
||||
project_path = os.path.abspath(os.path.join(os.getcwd(), "../../../"))
|
||||
|
||||
|
||||
class ArgsParser(ArgumentParser):
|
||||
def __init__(self):
|
||||
super(ArgsParser, self).__init__(
|
||||
formatter_class=RawDescriptionHelpFormatter)
|
||||
self.add_argument(
|
||||
"-o", "--opt", nargs='+', help="set configuration options")
|
||||
self.add_argument(
|
||||
"-l",
|
||||
"--language",
|
||||
nargs='+',
|
||||
help="set language type, support {}".format(support_list))
|
||||
self.add_argument(
|
||||
"--train",
|
||||
type=str,
|
||||
help="you can use this command to change the train dataset default path"
|
||||
)
|
||||
self.add_argument(
|
||||
"--val",
|
||||
type=str,
|
||||
help="you can use this command to change the eval dataset default path"
|
||||
)
|
||||
self.add_argument(
|
||||
"--dict",
|
||||
type=str,
|
||||
help="you can use this command to change the dictionary default path"
|
||||
)
|
||||
self.add_argument(
|
||||
"--data_dir",
|
||||
type=str,
|
||||
help="you can use this command to change the dataset default root path"
|
||||
)
|
||||
|
||||
def parse_args(self, argv=None):
|
||||
args = super(ArgsParser, self).parse_args(argv)
|
||||
args.opt = self._parse_opt(args.opt)
|
||||
args.language = self._set_language(args.language)
|
||||
return args
|
||||
|
||||
def _parse_opt(self, opts):
|
||||
config = {}
|
||||
if not opts:
|
||||
return config
|
||||
for s in opts:
|
||||
s = s.strip()
|
||||
k, v = s.split('=')
|
||||
config[k] = yaml.load(v, Loader=yaml.Loader)
|
||||
return config
|
||||
|
||||
def _set_language(self, type):
|
||||
lang = type[0]
|
||||
assert (type), "please use -l or --language to choose language type"
|
||||
assert(
|
||||
lang in support_list.keys() or lang in multi_lang
|
||||
),"the sub_keys(-l or --language) can only be one of support list: \n{},\nbut get: {}, " \
|
||||
"please check your running command".format(multi_lang, type)
|
||||
if lang in latin_lang:
|
||||
lang = "latin"
|
||||
elif lang in arabic_lang:
|
||||
lang = "arabic"
|
||||
elif lang in cyrillic_lang:
|
||||
lang = "cyrillic"
|
||||
elif lang in devanagari_lang:
|
||||
lang = "devanagari"
|
||||
global_config['Global'][
|
||||
'character_dict_path'] = 'ppocr/utils/dict/{}_dict.txt'.format(lang)
|
||||
global_config['Global'][
|
||||
'save_model_dir'] = './output/rec_{}_lite'.format(lang)
|
||||
global_config['Train']['dataset'][
|
||||
'label_file_list'] = ["train_data/{}_train.txt".format(lang)]
|
||||
global_config['Eval']['dataset'][
|
||||
'label_file_list'] = ["train_data/{}_val.txt".format(lang)]
|
||||
global_config['Global']['character_type'] = lang
|
||||
assert (
|
||||
os.path.isfile(
|
||||
os.path.join(project_path, global_config['Global'][
|
||||
'character_dict_path']))
|
||||
), "Loss default dictionary file {}_dict.txt.You can download it from \
|
||||
https://github.com/PaddlePaddle/PaddleOCR/tree/dygraph/ppocr/utils/dict/".format(
|
||||
lang)
|
||||
return lang
|
||||
|
||||
|
||||
def merge_config(config):
|
||||
"""
|
||||
Merge config into global config.
|
||||
Args:
|
||||
config (dict): Config to be merged.
|
||||
Returns: global config
|
||||
"""
|
||||
for key, value in config.items():
|
||||
if "." not in key:
|
||||
if isinstance(value, dict) and key in global_config:
|
||||
global_config[key].update(value)
|
||||
else:
|
||||
global_config[key] = value
|
||||
else:
|
||||
sub_keys = key.split('.')
|
||||
assert (
|
||||
sub_keys[0] in global_config
|
||||
), "the sub_keys can only be one of global_config: {}, but get: {}, please check your running command".format(
|
||||
global_config.keys(), sub_keys[0])
|
||||
cur = global_config[sub_keys[0]]
|
||||
for idx, sub_key in enumerate(sub_keys[1:]):
|
||||
if idx == len(sub_keys) - 2:
|
||||
cur[sub_key] = value
|
||||
else:
|
||||
cur = cur[sub_key]
|
||||
|
||||
|
||||
def loss_file(path):
|
||||
assert (
|
||||
os.path.exists(path)
|
||||
), "There is no such file:{},Please do not forget to put in the specified file".format(
|
||||
path)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
FLAGS = ArgsParser().parse_args()
|
||||
merge_config(FLAGS.opt)
|
||||
save_file_path = 'rec_{}_lite_train.yml'.format(FLAGS.language)
|
||||
if os.path.isfile(save_file_path):
|
||||
os.remove(save_file_path)
|
||||
|
||||
if FLAGS.train:
|
||||
global_config['Train']['dataset']['label_file_list'] = [FLAGS.train]
|
||||
train_label_path = os.path.join(project_path, FLAGS.train)
|
||||
loss_file(train_label_path)
|
||||
if FLAGS.val:
|
||||
global_config['Eval']['dataset']['label_file_list'] = [FLAGS.val]
|
||||
eval_label_path = os.path.join(project_path, FLAGS.val)
|
||||
loss_file(eval_label_path)
|
||||
if FLAGS.dict:
|
||||
global_config['Global']['character_dict_path'] = FLAGS.dict
|
||||
dict_path = os.path.join(project_path, FLAGS.dict)
|
||||
loss_file(dict_path)
|
||||
if FLAGS.data_dir:
|
||||
global_config['Eval']['dataset']['data_dir'] = FLAGS.data_dir
|
||||
global_config['Train']['dataset']['data_dir'] = FLAGS.data_dir
|
||||
data_dir = os.path.join(project_path, FLAGS.data_dir)
|
||||
loss_file(data_dir)
|
||||
|
||||
with open(save_file_path, 'w') as f:
|
||||
yaml.dump(
|
||||
dict(global_config), f, default_flow_style=False, sort_keys=False)
|
||||
logging.info("Project path is :{}".format(project_path))
|
||||
logging.info("Train list path set to :{}".format(global_config['Train'][
|
||||
'dataset']['label_file_list'][0]))
|
||||
logging.info("Eval list path set to :{}".format(global_config['Eval'][
|
||||
'dataset']['label_file_list'][0]))
|
||||
logging.info("Dataset root path set to :{}".format(global_config['Eval'][
|
||||
'dataset']['data_dir']))
|
||||
logging.info("Dict path set to :{}".format(global_config['Global'][
|
||||
'character_dict_path']))
|
||||
logging.info("Config file set to :configs/rec/multi_language/{}".
|
||||
format(save_file_path))
|
||||
@@ -0,0 +1,110 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_arabic_lite
|
||||
save_epoch_step: 3
|
||||
eval_batch_step:
|
||||
- 0
|
||||
- 2000
|
||||
cal_metric_during_train: true
|
||||
pretrained_model: null
|
||||
checkpoints: null
|
||||
save_inference_dir: null
|
||||
use_visualdl: false
|
||||
infer_img: null
|
||||
character_dict_path: ppocr/utils/dict/arabic_dict.txt
|
||||
max_text_length: 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 1.0e-05
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: CRNN
|
||||
Transform: null
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: small
|
||||
small_stride:
|
||||
- 1
|
||||
- 2
|
||||
- 2
|
||||
- 2
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 48
|
||||
Head:
|
||||
name: CTCHead
|
||||
fc_decay: 1.0e-05
|
||||
Loss:
|
||||
name: CTCLoss
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/
|
||||
label_file_list:
|
||||
- train_data/arabic_train.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecAug: null
|
||||
- CTCLabelEncode: null
|
||||
- RecResizeImg:
|
||||
image_shape:
|
||||
- 3
|
||||
- 32
|
||||
- 320
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label
|
||||
- length
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 256
|
||||
drop_last: true
|
||||
num_workers: 8
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/
|
||||
label_file_list:
|
||||
- train_data/arabic_val.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- CTCLabelEncode: null
|
||||
- RecResizeImg:
|
||||
image_shape:
|
||||
- 3
|
||||
- 32
|
||||
- 320
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label
|
||||
- length
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 256
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,110 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_cyrillic_lite
|
||||
save_epoch_step: 3
|
||||
eval_batch_step:
|
||||
- 0
|
||||
- 2000
|
||||
cal_metric_during_train: true
|
||||
pretrained_model: null
|
||||
checkpoints: null
|
||||
save_inference_dir: null
|
||||
use_visualdl: false
|
||||
infer_img: null
|
||||
character_dict_path: ppocr/utils/dict/cyrillic_dict.txt
|
||||
max_text_length: 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 1.0e-05
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: CRNN
|
||||
Transform: null
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: small
|
||||
small_stride:
|
||||
- 1
|
||||
- 2
|
||||
- 2
|
||||
- 2
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 48
|
||||
Head:
|
||||
name: CTCHead
|
||||
fc_decay: 1.0e-05
|
||||
Loss:
|
||||
name: CTCLoss
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/
|
||||
label_file_list:
|
||||
- train_data/cyrillic_train.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecAug: null
|
||||
- CTCLabelEncode: null
|
||||
- RecResizeImg:
|
||||
image_shape:
|
||||
- 3
|
||||
- 32
|
||||
- 320
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label
|
||||
- length
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 256
|
||||
drop_last: true
|
||||
num_workers: 8
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/
|
||||
label_file_list:
|
||||
- train_data/cyrillic_val.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- CTCLabelEncode: null
|
||||
- RecResizeImg:
|
||||
image_shape:
|
||||
- 3
|
||||
- 32
|
||||
- 320
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label
|
||||
- length
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 256
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,110 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_devanagari_lite
|
||||
save_epoch_step: 3
|
||||
eval_batch_step:
|
||||
- 0
|
||||
- 2000
|
||||
cal_metric_during_train: true
|
||||
pretrained_model: null
|
||||
checkpoints: null
|
||||
save_inference_dir: null
|
||||
use_visualdl: false
|
||||
infer_img: null
|
||||
character_dict_path: ppocr/utils/dict/devanagari_dict.txt
|
||||
max_text_length: 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 1.0e-05
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: CRNN
|
||||
Transform: null
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: small
|
||||
small_stride:
|
||||
- 1
|
||||
- 2
|
||||
- 2
|
||||
- 2
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 48
|
||||
Head:
|
||||
name: CTCHead
|
||||
fc_decay: 1.0e-05
|
||||
Loss:
|
||||
name: CTCLoss
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/
|
||||
label_file_list:
|
||||
- train_data/devanagari_train.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecAug: null
|
||||
- CTCLabelEncode: null
|
||||
- RecResizeImg:
|
||||
image_shape:
|
||||
- 3
|
||||
- 32
|
||||
- 320
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label
|
||||
- length
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 256
|
||||
drop_last: true
|
||||
num_workers: 8
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/
|
||||
label_file_list:
|
||||
- train_data/devanagari_val.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- CTCLabelEncode: null
|
||||
- RecResizeImg:
|
||||
image_shape:
|
||||
- 3
|
||||
- 32
|
||||
- 320
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label
|
||||
- length
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 256
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,101 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_en_number_lite
|
||||
save_epoch_step: 3
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [0, 2000]
|
||||
# if pretrained_model is saved in static mode, load_static_weights must set to True
|
||||
cal_metric_during_train: True
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img:
|
||||
# for data or label process
|
||||
character_dict_path: ppocr/utils/en_dict.txt
|
||||
max_text_length: 25
|
||||
infer_mode: False
|
||||
use_space_char: True
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0.00001
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: CRNN
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: small
|
||||
small_stride: [1, 2, 2, 2]
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 48
|
||||
Head:
|
||||
name: CTCHead
|
||||
fc_decay: 0.00001
|
||||
|
||||
Loss:
|
||||
name: CTCLoss
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list: ["./train_data/train_list.txt"]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- RecAug:
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 320]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 256
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list: ["./train_data/eval_list.txt"]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 320]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 256
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,101 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_french_lite
|
||||
save_epoch_step: 3
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [0, 2000]
|
||||
# if pretrained_model is saved in static mode, load_static_weights must set to True
|
||||
cal_metric_during_train: True
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img:
|
||||
# for data or label process
|
||||
character_dict_path: ppocr/utils/dict/french_dict.txt
|
||||
max_text_length: 25
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0.00001
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: CRNN
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: small
|
||||
small_stride: [1, 2, 2, 2]
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 48
|
||||
Head:
|
||||
name: CTCHead
|
||||
fc_decay: 0.00001
|
||||
|
||||
Loss:
|
||||
name: CTCLoss
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list: ["./train_data/train_list.txt"]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- RecAug:
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 320]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 256
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list: ["./train_data/eval_list.txt"]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 320]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 256
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,101 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_german_lite
|
||||
save_epoch_step: 3
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [0, 2000]
|
||||
# if pretrained_model is saved in static mode, load_static_weights must set to True
|
||||
cal_metric_during_train: True
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img:
|
||||
# for data or label process
|
||||
character_dict_path: ppocr/utils/dict/german_dict.txt
|
||||
max_text_length: 25
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0.00001
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: CRNN
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: small
|
||||
small_stride: [1, 2, 2, 2]
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 48
|
||||
Head:
|
||||
name: CTCHead
|
||||
fc_decay: 0.00001
|
||||
|
||||
Loss:
|
||||
name: CTCLoss
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list: ["./train_data/train_list.txt"]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- RecAug:
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 320]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 256
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list: ["./train_data/eval_list.txt"]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 320]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 256
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,101 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_japan_lite
|
||||
save_epoch_step: 3
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [0, 2000]
|
||||
# if pretrained_model is saved in static mode, load_static_weights must set to True
|
||||
cal_metric_during_train: True
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img:
|
||||
# for data or label process
|
||||
character_dict_path: ppocr/utils/dict/japan_dict.txt
|
||||
max_text_length: 25
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0.00001
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: CRNN
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: small
|
||||
small_stride: [1, 2, 2, 2]
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 48
|
||||
Head:
|
||||
name: CTCHead
|
||||
fc_decay: 0.00001
|
||||
|
||||
Loss:
|
||||
name: CTCLoss
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list: ["./train_data/train_list.txt"]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- RecAug:
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 320]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 256
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list: ["./train_data/eval_list.txt"]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 320]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 256
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,101 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_korean_lite
|
||||
save_epoch_step: 3
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [0, 2000]
|
||||
# if pretrained_model is saved in static mode, load_static_weights must set to True
|
||||
cal_metric_during_train: True
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img:
|
||||
# for data or label process
|
||||
character_dict_path: ppocr/utils/dict/korean_dict.txt
|
||||
max_text_length: 25
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0.00001
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: CRNN
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: small
|
||||
small_stride: [1, 2, 2, 2]
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 48
|
||||
Head:
|
||||
name: CTCHead
|
||||
fc_decay: 0.00001
|
||||
|
||||
Loss:
|
||||
name: CTCLoss
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list: ["./train_data/train_list.txt"]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- RecAug:
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 320]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 256
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list: ["./train_data/eval_list.txt"]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 320]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 256
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,110 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_latin_lite
|
||||
save_epoch_step: 3
|
||||
eval_batch_step:
|
||||
- 0
|
||||
- 2000
|
||||
cal_metric_during_train: true
|
||||
pretrained_model: null
|
||||
checkpoints: null
|
||||
save_inference_dir: null
|
||||
use_visualdl: false
|
||||
infer_img: null
|
||||
character_dict_path: ppocr/utils/dict/latin_dict.txt
|
||||
max_text_length: 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 1.0e-05
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: CRNN
|
||||
Transform: null
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: small
|
||||
small_stride:
|
||||
- 1
|
||||
- 2
|
||||
- 2
|
||||
- 2
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 48
|
||||
Head:
|
||||
name: CTCHead
|
||||
fc_decay: 1.0e-05
|
||||
Loss:
|
||||
name: CTCLoss
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/
|
||||
label_file_list:
|
||||
- train_data/latin_train.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecAug: null
|
||||
- CTCLabelEncode: null
|
||||
- RecResizeImg:
|
||||
image_shape:
|
||||
- 3
|
||||
- 32
|
||||
- 320
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label
|
||||
- length
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 256
|
||||
drop_last: true
|
||||
num_workers: 8
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/
|
||||
label_file_list:
|
||||
- train_data/latin_val.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- CTCLabelEncode: null
|
||||
- RecResizeImg:
|
||||
image_shape:
|
||||
- 3
|
||||
- 32
|
||||
- 320
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label
|
||||
- length
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 256
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,103 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_multi_language_lite
|
||||
save_epoch_step: 3
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [0, 2000]
|
||||
# if pretrained_model is saved in static mode, load_static_weights must set to True
|
||||
cal_metric_during_train: True
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img:
|
||||
# for data or label process
|
||||
character_dict_path:
|
||||
# Set the language of training, if set, select the default dictionary file
|
||||
character_type:
|
||||
max_text_length: 25
|
||||
infer_mode: False
|
||||
use_space_char: True
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0.00001
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: CRNN
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: small
|
||||
small_stride: [1, 2, 2, 2]
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 48
|
||||
Head:
|
||||
name: CTCHead
|
||||
fc_decay: 0.00001
|
||||
|
||||
Loss:
|
||||
name: CTCLoss
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/
|
||||
label_file_list: ["./train_data/train_list.txt"]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- RecAug:
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 320]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 256
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: train_data/
|
||||
label_file_list: ["./train_data/val_list.txt"]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 320]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 256
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,122 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: 240
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec/can/
|
||||
save_epoch_step: 1
|
||||
# evaluation is run every 1105 iterations (1 epoch)(batch_size = 8)
|
||||
eval_batch_step: [0, 1105]
|
||||
cal_metric_during_train: True
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/datasets/crohme_demo/hme_00.jpg
|
||||
# for data or label process
|
||||
character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt
|
||||
max_text_length: 36
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
save_res_path: ./output/rec/predicts_can.txt
|
||||
|
||||
Optimizer:
|
||||
name: Momentum
|
||||
momentum: 0.9
|
||||
clip_norm_global: 100.0
|
||||
lr:
|
||||
name: TwoStepCosine
|
||||
learning_rate: 0.01
|
||||
warmup_epoch: 1
|
||||
weight_decay: 0.0001
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: CAN
|
||||
in_channels: 1
|
||||
Transform:
|
||||
Backbone:
|
||||
name: DenseNet
|
||||
growthRate: 24
|
||||
reduction: 0.5
|
||||
bottleneck: True
|
||||
use_dropout: True
|
||||
input_channel: 1
|
||||
Head:
|
||||
name: CANHead
|
||||
in_channel: 684
|
||||
out_channel: 111
|
||||
max_text_length: 36
|
||||
ratio: 16
|
||||
attdecoder:
|
||||
is_train: True
|
||||
input_size: 256
|
||||
hidden_size: 256
|
||||
encoder_out_channel: 684
|
||||
dropout: True
|
||||
dropout_ratio: 0.5
|
||||
word_num: 111
|
||||
counting_decoder_out_channel: 111
|
||||
attention:
|
||||
attention_dim: 512
|
||||
word_conv_kernel: 1
|
||||
|
||||
Loss:
|
||||
name: CANLoss
|
||||
|
||||
PostProcess:
|
||||
name: CANLabelDecode
|
||||
|
||||
Metric:
|
||||
name: CANMetric
|
||||
main_indicator: exp_rate
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/CROHME/training/images/
|
||||
label_file_list: ["./train_data/CROHME/training/labels.txt"]
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
channel_first: False
|
||||
- NormalizeImage:
|
||||
mean: [0,0,0]
|
||||
std: [1,1,1]
|
||||
order: 'hwc'
|
||||
- GrayImageChannelFormat:
|
||||
inverse: True
|
||||
- CANLabelEncode:
|
||||
lower: False
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label']
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 8
|
||||
drop_last: False
|
||||
num_workers: 4
|
||||
collate_fn: DyMaskCollator
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/CROHME/evaluation/images/
|
||||
label_file_list: ["./train_data/CROHME/evaluation/labels.txt"]
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
channel_first: False
|
||||
- NormalizeImage:
|
||||
mean: [0,0,0]
|
||||
std: [1,1,1]
|
||||
order: 'hwc'
|
||||
- GrayImageChannelFormat:
|
||||
inverse: True
|
||||
- CANLabelEncode:
|
||||
lower: False
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1
|
||||
num_workers: 4
|
||||
collate_fn: DyMaskCollator
|
||||
@@ -0,0 +1,92 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: 8
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 5
|
||||
save_model_dir: ./output/rec/pren_new
|
||||
save_epoch_step: 3
|
||||
# evaluation is run every 2000 iterations after the 4000th iteration
|
||||
eval_batch_step: [4000, 2000]
|
||||
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: &max_text_length 25
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
save_res_path: ./output/rec/predicts_pren.txt
|
||||
|
||||
Optimizer:
|
||||
name: Adadelta
|
||||
lr:
|
||||
name: Piecewise
|
||||
decay_epochs: [2, 5, 7]
|
||||
values: [0.5, 0.1, 0.01, 0.001]
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: PREN
|
||||
in_channels: 3
|
||||
Backbone:
|
||||
name: EfficientNetb3_PREN
|
||||
Neck:
|
||||
name: PRENFPN
|
||||
n_r: 5
|
||||
d_model: 384
|
||||
max_len: *max_text_length
|
||||
dropout: 0.1
|
||||
Head:
|
||||
name: PRENHead
|
||||
|
||||
Loss:
|
||||
name: PRENLoss
|
||||
|
||||
PostProcess:
|
||||
name: PRENLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/training/
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- PRENLabelEncode:
|
||||
- RecAug:
|
||||
- PRENResizeImg:
|
||||
image_shape: [64, 256] # h,w
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label']
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 128
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/validation/
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- PRENLabelEncode:
|
||||
- PRENResizeImg:
|
||||
image_shape: [64, 256] # h,w
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 64
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,99 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 72
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec/ic15/
|
||||
save_epoch_step: 3
|
||||
# evaluation is run every 2000 iterations
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: True
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir: ./
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_words_en/word_10.png
|
||||
# for data or label process
|
||||
character_dict_path: ppocr/utils/en_dict.txt
|
||||
max_text_length: 25
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
save_res_path: ./output/rec/predicts_ic15.txt
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
learning_rate: 0.0005
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: CRNN
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: large
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 96
|
||||
Head:
|
||||
name: CTCHead
|
||||
fc_decay: 0
|
||||
|
||||
Loss:
|
||||
name: CTCLoss
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/ic15_data/
|
||||
label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 100]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 256
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
use_shared_memory: False
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/ic15_data
|
||||
label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 100]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 256
|
||||
num_workers: 4
|
||||
use_shared_memory: False
|
||||
@@ -0,0 +1,101 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: 21
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec/nrtr/
|
||||
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: False
|
||||
infer_img: doc/imgs_words_en/word_10.png
|
||||
# for data or label process
|
||||
character_dict_path: ppocr/utils/EN_symbol_dict.txt
|
||||
max_text_length: 25
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
save_res_path: ./output/rec/predicts_nrtr.txt
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.99
|
||||
clip_norm: 5.0
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.0005
|
||||
warmup_epoch: 2
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0.
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: NRTR
|
||||
in_channels: 1
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MTB
|
||||
cnn_num: 2
|
||||
Head:
|
||||
name: Transformer
|
||||
d_model: 512
|
||||
num_encoder_layers: 6
|
||||
beam_size: -1 # When Beam size is greater than 0, it means to use beam search when evaluation.
|
||||
|
||||
|
||||
Loss:
|
||||
name: CELoss
|
||||
smoothing: True
|
||||
|
||||
PostProcess:
|
||||
name: NRTRLabelDecode
|
||||
|
||||
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
|
||||
- NRTRLabelEncode: # Class handling label
|
||||
- GrayRecResizeImg:
|
||||
image_shape: [100, 32] # W H
|
||||
resize_type: PIL # PIL or OpenCV
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 512
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/evaluation/
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- NRTRLabelEncode: # Class handling label
|
||||
- GrayRecResizeImg:
|
||||
image_shape: [100, 32] # W H
|
||||
resize_type: PIL # PIL or OpenCV
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 256
|
||||
num_workers: 4
|
||||
use_shared_memory: False
|
||||
@@ -0,0 +1,95 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: 72
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec/mv3_none_bilstm_ctc/
|
||||
save_epoch_step: 3
|
||||
# evaluation is run every 2000 iterations
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: True
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_words_en/word_10.png
|
||||
# for data or label process
|
||||
character_dict_path:
|
||||
max_text_length: 25
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
save_res_path: ./output/rec/predicts_mv3_none_bilstm_ctc.txt
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
learning_rate: 0.0005
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: CRNN
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: large
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 96
|
||||
Head:
|
||||
name: CTCHead
|
||||
fc_decay: 0
|
||||
|
||||
Loss:
|
||||
name: CTCLoss
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
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
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 100]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
batch_size_per_card: 256
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/validation/
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 100]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 256
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,94 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: 72
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec/mv3_none_none_ctc/
|
||||
save_epoch_step: 3
|
||||
# evaluation is run every 2000 iterations
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: True
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_words_en/word_10.png
|
||||
# for data or label process
|
||||
character_dict_path:
|
||||
max_text_length: 25
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
save_res_path: ./output/rec/predicts_mv3_none_none_ctc.txt
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
learning_rate: 0.0005
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: Rosetta
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: large
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: reshape
|
||||
Head:
|
||||
name: CTCHead
|
||||
fc_decay: 0.0004
|
||||
|
||||
Loss:
|
||||
name: CTCLoss
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
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
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 100]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
batch_size_per_card: 256
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/validation/
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 100]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 256
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,101 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: 72
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec/rec_mv3_tps_bilstm_att/
|
||||
save_epoch_step: 3
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [0, 2000]
|
||||
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
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
save_res_path: ./output/rec/predicts_mv3_tps_bilstm_att.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
learning_rate: 0.0005
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0.00001
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: RARE
|
||||
Transform:
|
||||
name: TPS
|
||||
num_fiducial: 20
|
||||
loc_lr: 0.1
|
||||
model_name: small
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: large
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 96
|
||||
Head:
|
||||
name: AttentionHead
|
||||
hidden_size: 96
|
||||
|
||||
|
||||
Loss:
|
||||
name: AttentionLoss
|
||||
|
||||
PostProcess:
|
||||
name: AttnLabelDecode
|
||||
|
||||
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
|
||||
- AttnLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 100]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 256
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/validation/
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- AttnLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 100]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 256
|
||||
num_workers: 1
|
||||
@@ -0,0 +1,99 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: 72
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec/mv3_tps_bilstm_ctc/
|
||||
save_epoch_step: 3
|
||||
# evaluation is run every 2000 iterations
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: True
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_words_en/word_10.png
|
||||
# for data or label process
|
||||
character_dict_path:
|
||||
max_text_length: 25
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
save_res_path: ./output/rec/predicts_mv3_tps_bilstm_ctc.txt
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
learning_rate: 0.0005
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: STARNet
|
||||
Transform:
|
||||
name: TPS
|
||||
num_fiducial: 20
|
||||
loc_lr: 0.1
|
||||
model_name: small
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: large
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 96
|
||||
Head:
|
||||
name: CTCHead
|
||||
fc_decay: 0.0004
|
||||
|
||||
Loss:
|
||||
name: CTCLoss
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
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
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 100]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
batch_size_per_card: 256
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/validation/
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 100]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 256
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,109 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 5
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 20
|
||||
save_model_dir: ./output/rec/rec_r31_robustscanner/
|
||||
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: False
|
||||
infer_img: doc/imgs_words_en/word_10.png
|
||||
# for data or label process
|
||||
character_dict_path: ppocr/utils/dict90.txt
|
||||
max_text_length: &max_text_length 40
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
rm_symbol: True
|
||||
save_res_path: ./output/rec/predicts_robustscanner.txt
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Piecewise
|
||||
decay_epochs: [3, 4]
|
||||
values: [0.001, 0.0001, 0.00001]
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: RobustScanner
|
||||
Transform:
|
||||
Backbone:
|
||||
name: ResNet31
|
||||
init_type: KaimingNormal
|
||||
Head:
|
||||
name: RobustScannerHead
|
||||
enc_outchannles: 128
|
||||
hybrid_dec_rnn_layers: 2
|
||||
hybrid_dec_dropout: 0
|
||||
position_dec_rnn_layers: 2
|
||||
start_idx: 91
|
||||
mask: True
|
||||
padding_idx: 92
|
||||
encode_value: False
|
||||
max_text_length: *max_text_length
|
||||
|
||||
Loss:
|
||||
name: SARLoss
|
||||
|
||||
PostProcess:
|
||||
name: SARLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
is_filter: True
|
||||
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/training/
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- SARLabelEncode: # Class handling label
|
||||
- RobustScannerRecResizeImg:
|
||||
image_shape: [3, 48, 48, 160] # h:48 w:[48,160]
|
||||
width_downsample_ratio: 0.25
|
||||
max_text_length: *max_text_length
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'valid_ratio', 'word_positons'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 64
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
use_shared_memory: False
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/evaluation/
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- SARLabelEncode: # Class handling label
|
||||
- RobustScannerRecResizeImg:
|
||||
image_shape: [3, 48, 48, 160]
|
||||
max_text_length: *max_text_length
|
||||
width_downsample_ratio: 0.25
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'valid_ratio', 'word_positons'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 64
|
||||
num_workers: 4
|
||||
use_shared_memory: False
|
||||
|
||||
@@ -0,0 +1,98 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 5
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 20
|
||||
save_model_dir: ./sar_rec
|
||||
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: False
|
||||
infer_img:
|
||||
# for data or label process
|
||||
character_dict_path: ppocr/utils/dict90.txt
|
||||
max_text_length: 30
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
rm_symbol: True
|
||||
save_res_path: ./output/rec/predicts_sar.txt
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Piecewise
|
||||
decay_epochs: [3, 4]
|
||||
values: [0.001, 0.0001, 0.00001]
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SAR
|
||||
Transform:
|
||||
Backbone:
|
||||
name: ResNet31
|
||||
Head:
|
||||
name: SARHead
|
||||
|
||||
Loss:
|
||||
name: SARLoss
|
||||
|
||||
PostProcess:
|
||||
name: SARLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
label_file_list: ['./train_data/train_list.txt']
|
||||
data_dir: ./train_data/
|
||||
ratio_list: 1.0
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- SARLabelEncode: # Class handling label
|
||||
- SARRecResizeImg:
|
||||
image_shape: [3, 48, 48, 160] # h:48 w:[48,160]
|
||||
width_downsample_ratio: 0.25
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'valid_ratio'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 64
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
use_shared_memory: False
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/evaluation/
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- SARLabelEncode: # Class handling label
|
||||
- SARRecResizeImg:
|
||||
image_shape: [3, 48, 48, 160]
|
||||
width_downsample_ratio: 0.25
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'valid_ratio'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 64
|
||||
num_workers: 4
|
||||
use_shared_memory: False
|
||||
|
||||
@@ -0,0 +1,116 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: 6
|
||||
log_smooth_window: 50
|
||||
print_batch_step: 50
|
||||
save_model_dir: ./output/rec/rec_r32_gaspin_bilstm_att/
|
||||
save_epoch_step: 3
|
||||
# evaluation is run every 2000 iterations after the 4000th iteration
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: True
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_words_en/word_10.png
|
||||
# for data or label process
|
||||
character_dict_path: ./ppocr/utils/dict/spin_dict.txt
|
||||
max_text_length: 25
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
save_res_path: ./output/rec/predicts_r32_gaspin_bilstm_att.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: AdamW
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Piecewise
|
||||
decay_epochs: [3, 4, 5]
|
||||
values: [0.001, 0.0003, 0.00009, 0.000027]
|
||||
clip_norm: 5
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SPIN
|
||||
in_channels: 1
|
||||
Transform:
|
||||
name: GA_SPIN
|
||||
offsets: True
|
||||
default_type: 6
|
||||
loc_lr: 0.1
|
||||
stn: True
|
||||
Backbone:
|
||||
name: ResNet32
|
||||
out_channels: 512
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: cascadernn
|
||||
hidden_size: 256
|
||||
out_channels: [256, 512]
|
||||
with_linear: True
|
||||
Head:
|
||||
name: SPINAttentionHead
|
||||
hidden_size: 256
|
||||
|
||||
|
||||
Loss:
|
||||
name: SPINAttentionLoss
|
||||
ignore_index: 0
|
||||
|
||||
PostProcess:
|
||||
name: SPINLabelDecode
|
||||
use_space_char: False
|
||||
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
is_filter: True
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/ic15_data/
|
||||
label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- SPINLabelEncode: # Class handling label
|
||||
- SPINRecResizeImg:
|
||||
image_shape: [100, 32]
|
||||
interpolation : 2
|
||||
mean: [127.5]
|
||||
std: [127.5]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 8
|
||||
drop_last: True
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/ic15_data
|
||||
label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- SPINLabelEncode: # Class handling label
|
||||
- SPINRecResizeImg:
|
||||
image_shape: [100, 32]
|
||||
interpolation : 2
|
||||
mean: [127.5]
|
||||
std: [127.5]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 2
|
||||
@@ -0,0 +1,94 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 72
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec/r34_vd_none_bilstm_ctc/
|
||||
save_epoch_step: 3
|
||||
# evaluation is run every 2000 iterations
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: True
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_words_en/word_10.png
|
||||
# for data or label process
|
||||
character_dict_path:
|
||||
max_text_length: 25
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
save_res_path: ./output/rec/predicts_r34_vd_none_bilstm_ctc.txt
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
learning_rate: 0.0005
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: CRNN
|
||||
Transform:
|
||||
Backbone:
|
||||
name: ResNet
|
||||
layers: 34
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 256
|
||||
Head:
|
||||
name: CTCHead
|
||||
fc_decay: 0
|
||||
|
||||
Loss:
|
||||
name: CTCLoss
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
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
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 100]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 256
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/validation/
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 100]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 256
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,92 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 72
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec/r34_vd_none_none_ctc/
|
||||
save_epoch_step: 3
|
||||
# evaluation is run every 2000 iterations
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: True
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_words_en/word_10.png
|
||||
# for data or label process
|
||||
character_dict_path:
|
||||
max_text_length: 25
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
save_res_path: ./output/rec/predicts_r34_vd_none_none_ctc.txt
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
learning_rate: 0.0005
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: Rosetta
|
||||
Backbone:
|
||||
name: ResNet
|
||||
layers: 34
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: reshape
|
||||
Head:
|
||||
name: CTCHead
|
||||
fc_decay: 0.0004
|
||||
|
||||
Loss:
|
||||
name: CTCLoss
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
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
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 100]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 256
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/validation/
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 100]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 256
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,100 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: 400
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec/b3_rare_r34_none_gru/
|
||||
save_epoch_step: 3
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [0, 2000]
|
||||
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
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
save_res_path: ./output/rec/predicts_b3_rare_r34_none_gru.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
learning_rate: 0.0005
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0.00000
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: RARE
|
||||
Transform:
|
||||
name: TPS
|
||||
num_fiducial: 20
|
||||
loc_lr: 0.1
|
||||
model_name: large
|
||||
Backbone:
|
||||
name: ResNet
|
||||
layers: 34
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 256 #96
|
||||
Head:
|
||||
name: AttentionHead # AttentionHead
|
||||
hidden_size: 256 #
|
||||
l2_decay: 0.00001
|
||||
|
||||
Loss:
|
||||
name: AttentionLoss
|
||||
|
||||
PostProcess:
|
||||
name: AttnLabelDecode
|
||||
|
||||
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
|
||||
- AttnLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 100]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 256
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/validation/
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- AttnLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 100]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 256
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,98 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 72
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec/r34_vd_tps_bilstm_ctc/
|
||||
save_epoch_step: 3
|
||||
# evaluation is run every 2000 iterations
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: True
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_words_en/word_10.png
|
||||
# for data or label process
|
||||
character_dict_path:
|
||||
max_text_length: 25
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
save_res_path: ./output/rec/predicts_r34_vd_tps_bilstm_ctc.txt
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
learning_rate: 0.0005
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: STARNet
|
||||
Transform:
|
||||
name: TPS
|
||||
num_fiducial: 20
|
||||
loc_lr: 0.1
|
||||
model_name: large
|
||||
Backbone:
|
||||
name: ResNet
|
||||
layers: 34
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: rnn
|
||||
hidden_size: 256
|
||||
Head:
|
||||
name: CTCHead
|
||||
fc_decay: 0
|
||||
|
||||
Loss:
|
||||
name: CTCLoss
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
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
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 100]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 256
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/validation/
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 32, 100]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 256
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,103 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: 10
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec/r45_abinet/
|
||||
save_epoch_step: 1
|
||||
# evaluation is run every 2000 iterations
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: True
|
||||
pretrained_model: ./pretrain_models/abinet_vl_pretrained
|
||||
checkpoints:
|
||||
save_inference_dir: ./output/rec/r45_abinet/infer
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_words_en/word_10.png
|
||||
# for data or label process
|
||||
character_dict_path:
|
||||
character_type: en
|
||||
max_text_length: &max_text_length 25
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
save_res_path: ./output/rec/predicts_abinet.txt
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.99
|
||||
clip_norm: 20.0
|
||||
lr:
|
||||
name: Piecewise
|
||||
decay_epochs: [6]
|
||||
values: [0.0001, 0.00001]
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0.
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: ABINet
|
||||
in_channels: 3
|
||||
Transform:
|
||||
Backbone:
|
||||
name: ResNet45
|
||||
Head:
|
||||
name: ABINetHead
|
||||
use_lang: True
|
||||
iter_size: 3
|
||||
max_length: *max_text_length
|
||||
image_size: [ &h 32, &w 128 ] # [ h, w ]
|
||||
|
||||
|
||||
Loss:
|
||||
name: CELoss
|
||||
ignore_index: &ignore_index 100 # Must be greater than the number of character classes
|
||||
|
||||
PostProcess:
|
||||
name: ABINetLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/training/
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- ABINetRecAug:
|
||||
- ABINetLabelEncode: # Class handling label
|
||||
ignore_index: *ignore_index
|
||||
- ABINetRecResizeImg:
|
||||
image_shape: [3, *h, *w]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 96
|
||||
drop_last: True
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/evaluation/
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: RGB
|
||||
channel_first: False
|
||||
- ABINetLabelEncode: # Class handling label
|
||||
ignore_index: *ignore_index
|
||||
- ABINetRecResizeImg:
|
||||
image_shape: [3, *h, *w]
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 256
|
||||
num_workers: 4
|
||||
use_shared_memory: False
|
||||
@@ -0,0 +1,106 @@
|
||||
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
|
||||
|
||||
@@ -0,0 +1,106 @@
|
||||
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
|
||||
@@ -0,0 +1,112 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: 6
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 50
|
||||
save_model_dir: ./output/rec/rec_resnet_rfl_att/
|
||||
save_epoch_step: 1
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [0, 5000]
|
||||
cal_metric_during_train: True
|
||||
pretrained_model: ./pretrain_models/rec_resnet_rfl_visual/best_accuracy.pdparams
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_words_en/word_10.png
|
||||
# for data or label process
|
||||
character_dict_path:
|
||||
max_text_length: 25
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
save_res_path: ./output/rec/rec_resnet_rfl.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: AdamW
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
weight_decay: 0.0
|
||||
clip_norm_global: 5.0
|
||||
lr:
|
||||
name: Piecewise
|
||||
decay_epochs : [3, 4, 5]
|
||||
values : [0.001, 0.0003, 0.00009, 0.000027]
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: RFL
|
||||
in_channels: 1
|
||||
Transform:
|
||||
name: TPS
|
||||
num_fiducial: 20
|
||||
loc_lr: 1.0
|
||||
model_name: large
|
||||
Backbone:
|
||||
name: ResNetRFL
|
||||
use_cnt: True
|
||||
use_seq: True
|
||||
Neck:
|
||||
name: RFAdaptor
|
||||
use_v2s: True
|
||||
use_s2v: True
|
||||
Head:
|
||||
name: RFLHead
|
||||
in_channels: 512
|
||||
hidden_size: 256
|
||||
batch_max_legnth: 25
|
||||
out_channels: 38
|
||||
use_cnt: True
|
||||
use_seq: True
|
||||
|
||||
Loss:
|
||||
name: RFLLoss
|
||||
# ignore_index: 0
|
||||
|
||||
PostProcess:
|
||||
name: RFLLabelDecode
|
||||
|
||||
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
|
||||
- RFLLabelEncode: # Class handling label
|
||||
- RFLRecResizeImg:
|
||||
image_shape: [1, 32, 100]
|
||||
padding: false
|
||||
interpolation: 2
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length', 'cnt_label'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 64
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/validation/
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- RFLLabelEncode: # Class handling label
|
||||
- RFLRecResizeImg:
|
||||
image_shape: [1, 32, 100]
|
||||
padding: false
|
||||
interpolation: 2
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length', 'cnt_label'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 256
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,110 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: 6
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 50
|
||||
save_model_dir: ./output/rec/rec_resnet_rfl_visual/
|
||||
save_epoch_step: 1
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [0, 5000]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_words_en/word_10.png
|
||||
# for data or label process
|
||||
character_dict_path:
|
||||
max_text_length: 25
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
save_res_path: ./output/rec/rec_resnet_rfl_visual.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: AdamW
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
weight_decay: 0.0
|
||||
clip_norm_global: 5.0
|
||||
lr:
|
||||
name: Piecewise
|
||||
decay_epochs : [3, 4, 5]
|
||||
values : [0.001, 0.0003, 0.00009, 0.000027]
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: RFL
|
||||
in_channels: 1
|
||||
Transform:
|
||||
name: TPS
|
||||
num_fiducial: 20
|
||||
loc_lr: 1.0
|
||||
model_name: large
|
||||
Backbone:
|
||||
name: ResNetRFL
|
||||
use_cnt: True
|
||||
use_seq: False
|
||||
Neck:
|
||||
name: RFAdaptor
|
||||
use_v2s: False
|
||||
use_s2v: False
|
||||
Head:
|
||||
name: RFLHead
|
||||
in_channels: 512
|
||||
hidden_size: 256
|
||||
batch_max_legnth: 25
|
||||
out_channels: 38
|
||||
use_cnt: True
|
||||
use_seq: False
|
||||
Loss:
|
||||
name: RFLLoss
|
||||
|
||||
PostProcess:
|
||||
name: RFLLabelDecode
|
||||
|
||||
Metric:
|
||||
name: CNTMetric
|
||||
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
|
||||
- RFLLabelEncode: # Class handling label
|
||||
- RFLRecResizeImg:
|
||||
image_shape: [1, 32, 100]
|
||||
padding: false
|
||||
interpolation: 2
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length', 'cnt_label'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 64
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/evaluation
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- RFLLabelEncode: # Class handling label
|
||||
- RFLRecResizeImg:
|
||||
image_shape: [1, 32, 100]
|
||||
padding: false
|
||||
interpolation: 2
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length', 'cnt_label'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 256
|
||||
num_workers: 8
|
||||
@@ -0,0 +1,108 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: 6
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec/seed
|
||||
save_epoch_step: 3
|
||||
# evaluation is run every 5000 iterations after the 4000th iteration
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: True
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_words_en/word_10.png
|
||||
# for data or label process
|
||||
character_dict_path: ppocr/utils/EN_symbol_dict.txt
|
||||
max_text_length: 100
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
save_res_path: ./output/rec/predicts_seed.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adadelta
|
||||
weight_deacy: 0.0
|
||||
momentum: 0.9
|
||||
lr:
|
||||
name: Piecewise
|
||||
decay_epochs: [4, 5]
|
||||
values: [1.0, 0.1, 0.01]
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 2.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SEED
|
||||
Transform:
|
||||
name: STN_ON
|
||||
tps_inputsize: [32, 64]
|
||||
tps_outputsize: [32, 100]
|
||||
num_control_points: 20
|
||||
tps_margins: [0.05,0.05]
|
||||
stn_activation: none
|
||||
Backbone:
|
||||
name: ResNet_ASTER
|
||||
Head:
|
||||
name: AsterHead # AttentionHead
|
||||
sDim: 512
|
||||
attDim: 512
|
||||
max_len_labels: 100
|
||||
|
||||
Loss:
|
||||
name: AsterLoss
|
||||
|
||||
PostProcess:
|
||||
name: SEEDLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
is_filter: True
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/training/
|
||||
transforms:
|
||||
- Fasttext:
|
||||
path: "./cc.en.300.bin"
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- SEEDLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
character_dict_path:
|
||||
image_shape: [3, 64, 256]
|
||||
padding: False
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length', 'fast_label'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 256
|
||||
drop_last: True
|
||||
num_workers: 6
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/evaluation/
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- SEEDLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
character_dict_path:
|
||||
image_shape: [3, 64, 256]
|
||||
padding: False
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: True
|
||||
batch_size_per_card: 256
|
||||
num_workers: 4
|
||||
@@ -0,0 +1,117 @@
|
||||
Global:
|
||||
use_gpu: true
|
||||
epoch_num: 5
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 50
|
||||
save_model_dir: ./output/rec/rec_satrn/
|
||||
save_epoch_step: 1
|
||||
# evaluation is run every 5000 iterations
|
||||
eval_batch_step: [0, 5000]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img:
|
||||
# for data or label process
|
||||
character_dict_path: ppocr/utils/dict90.txt
|
||||
max_text_length: 25
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
rm_symbol: True
|
||||
save_res_path: ./output/rec/predicts_satrn.txt
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Piecewise
|
||||
decay_epochs: [3, 4]
|
||||
values: [0.0003, 0.00003, 0.000003]
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SATRN
|
||||
Backbone:
|
||||
name: ShallowCNN
|
||||
in_channels: 3
|
||||
hidden_dim: 256
|
||||
Head:
|
||||
name: SATRNHead
|
||||
enc_cfg:
|
||||
n_layers: 6
|
||||
n_head: 8
|
||||
d_k: 32
|
||||
d_v: 32
|
||||
d_model: 256
|
||||
n_position: 100
|
||||
d_inner: 1024
|
||||
dropout: 0.1
|
||||
dec_cfg:
|
||||
n_layers: 6
|
||||
d_embedding: 256
|
||||
n_head: 8
|
||||
d_model: 256
|
||||
d_inner: 1024
|
||||
d_k: 32
|
||||
d_v: 32
|
||||
max_seq_len: 25
|
||||
start_idx: 91
|
||||
|
||||
Loss:
|
||||
name: SATRNLoss
|
||||
|
||||
PostProcess:
|
||||
name: SATRNLabelDecode
|
||||
|
||||
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
|
||||
- SATRNLabelEncode: # Class handling label
|
||||
- SVTRRecResizeImg:
|
||||
image_shape: [3, 32, 100]
|
||||
padding: False
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'valid_ratio'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 128
|
||||
drop_last: True
|
||||
num_workers: 8
|
||||
use_shared_memory: False
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/evaluation/
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- SATRNLabelEncode: # Class handling label
|
||||
- SVTRRecResizeImg:
|
||||
image_shape: [3, 32, 100]
|
||||
padding: False
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'valid_ratio'] # dataloader will return list in this order
|
||||
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 128
|
||||
num_workers: 4
|
||||
use_shared_memory: False
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user