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add more dataset yamls and fix re exceptions (#6791) 

* add more dataset yamls and fix re exceptions
pull/6818/head
littletomatodonkey 2022-07-06 13:58:46 +08:00 committed by GitHub
parent 5a0108b8ac
commit 7a99588dd8
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18 changed files with 1366 additions and 38 deletions
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125
configs/vqa/re/layoutlmv2_funsd.yml 100644
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Global:
use_gpu: True
epoch_num: &epoch_num 200
log_smooth_window: 10
print_batch_step: 10
save_model_dir: ./output/re_layoutlmv2_funsd
save_epoch_step: 2000
# evaluation is run every 10 iterations after the 0th iteration
eval_batch_step: [ 0, 57 ]
cal_metric_during_train: False
save_inference_dir:
use_visualdl: False
seed: 2022
infer_img: train_data/FUNSD/testing_data/images/83624198.png
save_res_path: ./output/re_layoutlmv2_funsd/res/
Architecture:
model_type: vqa
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/FUNSD/training_data/images/
label_file_list:
- ./train_data/FUNSD/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/FUNSD/class_list.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:
# dataloader will return list in this order
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'entities', 'relations']
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/FUNSD/testing_data/images/
label_file_list:
- ./train_data/FUNSD/test.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:
# dataloader will return list in this order
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'entities', 'relations']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 8
num_workers: 8
collate_fn: ListCollator

6
configs/vqa/re/layoutlmv2.yml → configs/vqa/re/layoutlmv2_xund_zh.yml
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@ -3,16 +3,16 @@ Global:
epoch_num: &epoch_num 200
log_smooth_window: 10
print_batch_step: 10
save_model_dir: ./output/re_layoutlmv2/
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 ]
eval_batch_step: [ 0, 57 ]
cal_metric_during_train: False
save_inference_dir:
use_visualdl: False
seed: 2048
infer_img: ppstructure/docs/vqa/input/zh_val_21.jpg
save_res_path: ./output/re/
save_res_path: ./output/re_layoutlmv2_xfund_zh/res/
Architecture:
model_type: vqa

129
configs/vqa/re/layoutxlm_funsd.yml 100644
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Global:
use_gpu: True
epoch_num: &epoch_num 200
log_smooth_window: 10
print_batch_step: 10
save_model_dir: ./output/re_layoutxlm_funsd
save_epoch_step: 2000
# evaluation is run every 10 iterations after the 0th iteration
eval_batch_step: [ 0, 57 ]
cal_metric_during_train: False
save_inference_dir:
use_visualdl: False
seed: 2022
infer_img: train_data/FUNSD/testing_data/images/83624198.png
save_res_path: ./output/re_layoutxlm_funsd/res/
Architecture:
model_type: vqa
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/FUNSD/training_data/images/
label_file_list:
- ./train_data/FUNSD/train_v4.json
# - ./train_data/FUNSD/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/FUNSD/class_list.txt
use_textline_bbox_info: &use_textline_bbox_info True
- 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
mean: [ 123.675, 116.28, 103.53 ]
std: [ 58.395, 57.12, 57.375 ]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
# dataloader will return list in this order
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'entities', 'relations']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 8
num_workers: 16
collate_fn: ListCollator
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/FUNSD/testing_data/images/
label_file_list:
- ./train_data/FUNSD/test_v4.json
# - ./train_data/FUNSD/test.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
- 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
mean: [ 123.675, 116.28, 103.53 ]
std: [ 58.395, 57.12, 57.375 ]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
# dataloader will return list in this order
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'entities', 'relations']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 8
num_workers: 8
collate_fn: ListCollator

0
configs/vqa/re/layoutxlm.yml → configs/vqa/re/layoutxlm_xfund_zh.yml
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124
configs/vqa/ser/layoutlm_funsd.yml 100644
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Global:
use_gpu: True
epoch_num: &epoch_num 200
log_smooth_window: 10
print_batch_step: 10
save_model_dir: ./output/ser_layoutlm_funsd
save_epoch_step: 2000
# evaluation is run every 10 iterations after the 0th iteration
eval_batch_step: [ 0, 57 ]
cal_metric_during_train: False
save_inference_dir:
use_visualdl: False
seed: 2022
infer_img: train_data/FUNSD/testing_data/images/83624198.png
save_res_path: ./output/ser_layoutlm_funsd/res/
Architecture:
model_type: vqa
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/FUNSD/class_list.txt
Metric:
name: VQASerTokenMetric
main_indicator: hmean
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/FUNSD/training_data/images/
label_file_list:
- ./train_data/FUNSD/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
use_textline_bbox_info: &use_textline_bbox_info True
- 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:
# dataloader will return list in this order
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels']
loader:
shuffle: True
drop_last: False
batch_size_per_card: 8
num_workers: 4
Eval:
dataset:
name: SimpleDataSet
data_dir: train_data/FUNSD/testing_data/images/
label_file_list:
- ./train_data/FUNSD/test.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
- 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:
# dataloader will return list in this order
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 8
num_workers: 4

124
configs/vqa/ser/layoutlm_sroie.yml 100644
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@ -0,0 +1,124 @@
Global:
use_gpu: True
epoch_num: &epoch_num 200
log_smooth_window: 10
print_batch_step: 10
save_model_dir: ./output/ser_layoutlm_sroie
save_epoch_step: 2000
# evaluation is run every 10 iterations after the 0th iteration
eval_batch_step: [ 0, 200 ]
cal_metric_during_train: False
save_inference_dir:
use_visualdl: False
seed: 2022
infer_img: train_data/SROIE/test/X00016469670.jpg
save_res_path: ./output/ser_layoutlm_sroie/res/
Architecture:
model_type: vqa
algorithm: &algorithm "LayoutLM"
Transform:
Backbone:
name: LayoutLMForSer
pretrained: True
checkpoints:
num_classes: &num_classes 9
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/SROIE/class_list.txt
Metric:
name: VQASerTokenMetric
main_indicator: hmean
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/SROIE/train
label_file_list:
- ./train_data/SROIE/train.txt
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
- 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:
# dataloader will return list in this order
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels']
loader:
shuffle: True
drop_last: False
batch_size_per_card: 8
num_workers: 4
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/SROIE/test
label_file_list:
- ./train_data/SROIE/test.txt
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
- 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:
# dataloader will return list in this order
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 8
num_workers: 4

6
configs/vqa/ser/layoutlm.yml → configs/vqa/ser/layoutlm_xfund_zh.yml
View File

@ -3,16 +3,16 @@ Global:
epoch_num: &epoch_num 200
log_smooth_window: 10
print_batch_step: 10
save_model_dir: ./output/ser_layoutlm/
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 ]
eval_batch_step: [ 0, 57 ]
cal_metric_during_train: False
save_inference_dir:
use_visualdl: False
seed: 2022
infer_img: ppstructure/docs/vqa/input/zh_val_42.jpg
save_res_path: ./output/ser/
save_res_path: ./output/ser_layoutlm_xfund_zh/res/
Architecture:
model_type: vqa

123
configs/vqa/ser/layoutlmv2_funsd.yml 100644
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@ -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/ser_layoutlmv2_funsd
save_epoch_step: 2000
# evaluation is run every 10 iterations after the 0th iteration
eval_batch_step: [ 0, 100 ]
cal_metric_during_train: False
save_inference_dir:
use_visualdl: False
seed: 2022
infer_img: train_data/FUNSD/testing_data/images/83624198.png
save_res_path: ./output/ser_layoutlmv2_funsd/res/
Architecture:
model_type: vqa
algorithm: &algorithm "LayoutLMv2"
Transform:
Backbone:
name: LayoutLMv2ForSer
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/FUNSD/class_list.txt
Metric:
name: VQASerTokenMetric
main_indicator: hmean
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/FUNSD/training_data/images/
label_file_list:
- ./train_data/FUNSD/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:
# dataloader will return list in this order
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels']
loader:
shuffle: True
drop_last: False
batch_size_per_card: 8
num_workers: 4
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/FUNSD/testing_data/images/
label_file_list:
- ./train_data/FUNSD/test.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:
# dataloader will return list in this order
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 8
num_workers: 4

123
configs/vqa/ser/layoutlmv2_sroie.yml 100644
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@ -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/ser_layoutlmv2_sroie
save_epoch_step: 2000
# evaluation is run every 10 iterations after the 0th iteration
eval_batch_step: [ 0, 200 ]
cal_metric_during_train: False
save_inference_dir:
use_visualdl: False
seed: 2022
infer_img: train_data/SROIE/test/X00016469670.jpg
save_res_path: ./output/ser_layoutlmv2_sroie/res/
Architecture:
model_type: vqa
algorithm: &algorithm "LayoutLMv2"
Transform:
Backbone:
name: LayoutLMv2ForSer
pretrained: True
checkpoints:
num_classes: &num_classes 9
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/SROIE/class_list.txt
Metric:
name: VQASerTokenMetric
main_indicator: hmean
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/SROIE/train
label_file_list:
- ./train_data/SROIE/train.txt
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:
# dataloader will return list in this order
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels']
loader:
shuffle: True
drop_last: False
batch_size_per_card: 8
num_workers: 4
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/SROIE/test
label_file_list:
- ./train_data/SROIE/test.txt
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:
# dataloader will return list in this order
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 8
num_workers: 4

4
configs/vqa/ser/layoutlmv2.yml → configs/vqa/ser/layoutlmv2_xfund_zh.yml
View File

@ -3,7 +3,7 @@ Global:
epoch_num: &epoch_num 200
log_smooth_window: 10
print_batch_step: 10
save_model_dir: ./output/ser_layoutlmv2/
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 ]
@ -12,7 +12,7 @@ Global:
use_visualdl: False
seed: 2022
infer_img: ppstructure/docs/vqa/input/zh_val_42.jpg
save_res_path: ./output/ser/
save_res_path: ./output/ser_layoutlmv2_xfund_zh/res/
Architecture:
model_type: vqa

123
configs/vqa/ser/layoutxlm_funsd.yml 100644
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@ -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/ser_layoutxlm_funsd
save_epoch_step: 2000
# evaluation is run every 10 iterations after the 0th iteration
eval_batch_step: [ 0, 57 ]
cal_metric_during_train: False
save_inference_dir:
use_visualdl: False
seed: 2022
infer_img: train_data/FUNSD/testing_data/images/83624198.png
save_res_path: output/ser_layoutxlm_funsd/res/
Architecture:
model_type: vqa
algorithm: &algorithm "LayoutXLM"
Transform:
Backbone:
name: LayoutXLMForSer
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/FUNSD/class_list.txt
Metric:
name: VQASerTokenMetric
main_indicator: hmean
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/FUNSD/training_data/images/
label_file_list:
- ./train_data/FUNSD/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:
# dataloader will return list in this order
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels']
loader:
shuffle: True
drop_last: False
batch_size_per_card: 8
num_workers: 4
Eval:
dataset:
name: SimpleDataSet
data_dir: train_data/FUNSD/testing_data/images/
label_file_list:
- ./train_data/FUNSD/test.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:
# dataloader will return list in this order
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 8
num_workers: 4

123
configs/vqa/ser/layoutxlm_sroie.yml 100644
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@ -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/ser_layoutxlm_sroie
save_epoch_step: 2000
# evaluation is run every 10 iterations after the 0th iteration
eval_batch_step: [ 0, 200 ]
cal_metric_during_train: False
save_inference_dir:
use_visualdl: False
seed: 2022
infer_img: train_data/SROIE/test/X00016469670.jpg
save_res_path: res_img_aug_with_gt
Architecture:
model_type: vqa
algorithm: &algorithm "LayoutXLM"
Transform:
Backbone:
name: LayoutXLMForSer
pretrained: True
checkpoints:
num_classes: &num_classes 9
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/SROIE/class_list.txt
Metric:
name: VQASerTokenMetric
main_indicator: hmean
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/SROIE/train
label_file_list:
- ./train_data/SROIE/train.txt
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:
# dataloader will return list in this order
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels']
loader:
shuffle: True
drop_last: False
batch_size_per_card: 8
num_workers: 4
Eval:
dataset:
name: SimpleDataSet
data_dir: train_data/SROIE/test
label_file_list:
- ./train_data/SROIE/test.txt
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:
# dataloader will return list in this order
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 8
num_workers: 4

123
configs/vqa/ser/layoutxlm_wildreceipt.yml 100644
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@ -0,0 +1,123 @@
Global:
use_gpu: True
epoch_num: &epoch_num 100
log_smooth_window: 10
print_batch_step: 10
save_model_dir: ./output/ser_layoutxlm_wildreceipt
save_epoch_step: 2000
# evaluation is run every 10 iterations after the 0th iteration
eval_batch_step: [ 0, 200 ]
cal_metric_during_train: False
save_inference_dir:
use_visualdl: False
seed: 2022
infer_img: train_data//wildreceipt/image_files/Image_12/10/845be0dd6f5b04866a2042abd28d558032ef2576.jpeg
save_res_path: ./output/ser_layoutxlm_wildreceipt/res
Architecture:
model_type: vqa
algorithm: &algorithm "LayoutXLM"
Transform:
Backbone:
name: LayoutXLMForSer
pretrained: True
checkpoints:
num_classes: &num_classes 51
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/wildreceipt/class_list.txt
Metric:
name: VQASerTokenMetric
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
- 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:
# dataloader will return list in this order
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels']
loader:
shuffle: True
drop_last: False
batch_size_per_card: 8
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
- 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:
# dataloader will return list in this order
keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 8
num_workers: 4

4
configs/vqa/ser/layoutxlm.yml → configs/vqa/ser/layoutxlm_xfund_zh.yml
View File

@ -3,7 +3,7 @@ Global:
epoch_num: &epoch_num 200
log_smooth_window: 10
print_batch_step: 10
save_model_dir: ./output/ser_layoutxlm/
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 ]
@ -12,7 +12,7 @@ Global:
use_visualdl: False
seed: 2022
infer_img: ppstructure/docs/vqa/input/zh_val_42.jpg
save_res_path: ./output/ser
save_res_path: ./output/ser_layoutxlm_xfund_zh/res
Architecture:
model_type: vqa

79
ppocr/data/imaug/label_ops.py
View File

@ -869,6 +869,7 @@ class VQATokenLabelEncode(object):
contains_re=False,
add_special_ids=False,
algorithm='LayoutXLM',
use_textline_bbox_info=True,
infer_mode=False,
ocr_engine=None,
**kwargs):
@ -897,11 +898,51 @@ class VQATokenLabelEncode(object):
self.add_special_ids = add_special_ids
self.infer_mode = infer_mode
self.ocr_engine = ocr_engine
self.use_textline_bbox_info = use_textline_bbox_info
def split_bbox(self, bbox, text, tokenizer):
words = text.split()
token_bboxes = []
curr_word_idx = 0
x1, y1, x2, y2 = bbox
unit_w = (x2 - x1) / len(text)
for idx, word in enumerate(words):
curr_w = len(word) * unit_w
word_bbox = [x1, y1, x1 + curr_w, y2]
token_bboxes.extend([word_bbox] * len(tokenizer.tokenize(word)))
x1 += (len(word) + 1) * unit_w
return token_bboxes
def filter_empty_contents(self, ocr_info):
"""
find out the empty texts and remove the links
"""
new_ocr_info = []
empty_index = []
for idx, info in enumerate(ocr_info):
if len(info["transcription"]) > 0:
new_ocr_info.append(copy.deepcopy(info))
else:
empty_index.append(info["id"])
for idx, info in enumerate(new_ocr_info):
new_link = []
for link in info["linking"]:
if link[0] in empty_index or link[1] in empty_index:
continue
new_link.append(link)
new_ocr_info[idx]["linking"] = new_link
return new_ocr_info
def __call__(self, data):
# load bbox and label info
ocr_info = self._load_ocr_info(data)
# for re
train_re = self.contains_re and not self.infer_mode
if train_re:
ocr_info = self.filter_empty_contents(ocr_info)
height, width, _ = data['image'].shape
words_list = []
@ -913,8 +954,6 @@ class VQATokenLabelEncode(object):
entities = []
# for re
train_re = self.contains_re and not self.infer_mode
if train_re:
relations = []
id2label = {}
@ -924,18 +963,19 @@ class VQATokenLabelEncode(object):
data['ocr_info'] = copy.deepcopy(ocr_info)
for info in ocr_info:
text = info["transcription"]
if len(text) <= 0:
continue
if train_re:
# for re
if len(info["transcription"]) == 0:
if len(text) == 0:
empty_entity.add(info["id"])
continue
id2label[info["id"]] = info["label"]
relations.extend([tuple(sorted(l)) for l in info["linking"]])
# smooth_box
info["bbox"] = self.trans_poly_to_bbox(info["points"])
bbox = self._smooth_box(info["bbox"], height, width)
text = info["transcription"]
encode_res = self.tokenizer.encode(
text, pad_to_max_seq_len=False, return_attention_mask=True)
@ -946,6 +986,19 @@ class VQATokenLabelEncode(object):
-1]
encode_res["attention_mask"] = encode_res["attention_mask"][1:
-1]
if self.use_textline_bbox_info:
bbox = [info["bbox"]] * len(encode_res["input_ids"])
else:
bbox = self.split_bbox(info["bbox"], info["transcription"],
self.tokenizer)
if len(bbox) <= 0:
continue
bbox = self._smooth_box(bbox, height, width)
if self.add_special_ids:
bbox.insert(0, [0, 0, 0, 0])
bbox.append([0, 0, 0, 0])
# parse label
if not self.infer_mode:
label = info['label']
@ -970,7 +1023,7 @@ class VQATokenLabelEncode(object):
})
input_ids_list.extend(encode_res["input_ids"])
token_type_ids_list.extend(encode_res["token_type_ids"])
bbox_list.extend([bbox] * len(encode_res["input_ids"]))
bbox_list.extend(bbox)
words_list.append(text)
segment_offset_id.append(len(input_ids_list))
if not self.infer_mode:
@ -1019,12 +1072,14 @@ class VQATokenLabelEncode(object):
info_dict = json.loads(info)
return info_dict
def _smooth_box(self, bbox, height, width):
bbox[0] = int(bbox[0] * 1000.0 / width)
bbox[2] = int(bbox[2] * 1000.0 / width)
bbox[1] = int(bbox[1] * 1000.0 / height)
bbox[3] = int(bbox[3] * 1000.0 / height)
return bbox
def _smooth_box(self, bboxes, height, width):
bboxes = np.array(bboxes)
bboxes[:, 0] = bboxes[:, 0] * 1000 / width
bboxes[:, 2] = bboxes[:, 2] * 1000 / width
bboxes[:, 1] = bboxes[:, 1] * 1000 / height
bboxes[:, 3] = bboxes[:, 3] * 1000 / height
bboxes = bboxes.astype("int64").tolist()
return bboxes
def _parse_label(self, label, encode_res):
gt_label = []

33
ppocr/metrics/vqa_token_re_metric.py
View File

@ -37,23 +37,26 @@ class VQAReTokenMetric(object):
gt_relations = []
for b in range(len(self.relations_list)):
rel_sent = []
for head, tail in zip(self.relations_list[b]["head"],
self.relations_list[b]["tail"]):
rel = {}
rel["head_id"] = head
rel["head"] = (self.entities_list[b]["start"][rel["head_id"]],
self.entities_list[b]["end"][rel["head_id"]])
rel["head_type"] = self.entities_list[b]["label"][rel[
"head_id"]]
if "head" in self.relations_list[b]:
for head, tail in zip(self.relations_list[b]["head"],
self.relations_list[b]["tail"]):
rel = {}
rel["head_id"] = head
rel["head"] = (
self.entities_list[b]["start"][rel["head_id"]],
self.entities_list[b]["end"][rel["head_id"]])
rel["head_type"] = self.entities_list[b]["label"][rel[
"head_id"]]
rel["tail_id"] = tail
rel["tail"] = (self.entities_list[b]["start"][rel["tail_id"]],
self.entities_list[b]["end"][rel["tail_id"]])
rel["tail_type"] = self.entities_list[b]["label"][rel[
"tail_id"]]
rel["tail_id"] = tail
rel["tail"] = (
self.entities_list[b]["start"][rel["tail_id"]],
self.entities_list[b]["end"][rel["tail_id"]])
rel["tail_type"] = self.entities_list[b]["label"][rel[
"tail_id"]]
rel["type"] = 1
rel_sent.append(rel)
rel["type"] = 1
rel_sent.append(rel)
gt_relations.append(rel_sent)
re_metrics = self.re_score(
self.pred_relations_list, gt_relations, mode="boundaries")

4
ppocr/modeling/backbones/vqa_layoutlm.py
View File

@ -43,9 +43,11 @@ class NLPBaseModel(nn.Layer):
super(NLPBaseModel, self).__init__()
if checkpoints is not None:
self.model = model_class.from_pretrained(checkpoints)
elif isinstance(pretrained, (str, )) and os.path.exists(pretrained):
self.model = model_class.from_pretrained(pretrained)
else:
pretrained_model_name = pretrained_model_dict[base_model_class]
if pretrained:
if pretrained is True:
base_model = base_model_class.from_pretrained(
pretrained_model_name)
else:

151
ppstructure/vqa/tools/trans_funsd_label.py 100644
View File

@ -0,0 +1,151 @@
# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
#
# 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 json
import os
import sys
import cv2
import numpy as np
from copy import deepcopy
def trans_poly_to_bbox(poly):
x1 = np.min([p[0] for p in poly])
x2 = np.max([p[0] for p in poly])
y1 = np.min([p[1] for p in poly])
y2 = np.max([p[1] for p in poly])
return [x1, y1, x2, y2]
def get_outer_poly(bbox_list):
x1 = min([bbox[0] for bbox in bbox_list])
y1 = min([bbox[1] for bbox in bbox_list])
x2 = max([bbox[2] for bbox in bbox_list])
y2 = max([bbox[3] for bbox in bbox_list])
return [[x1, y1], [x2, y1], [x2, y2], [x1, y2]]
def load_funsd_label(image_dir, anno_dir):
imgs = os.listdir(image_dir)
annos = os.listdir(anno_dir)
imgs = [img.replace(".png", "") for img in imgs]
annos = [anno.replace(".json", "") for anno in annos]
fn_info_map = dict()
for anno_fn in annos:
res = []
with open(os.path.join(anno_dir, anno_fn + ".json"), "r") as fin:
infos = json.load(fin)
infos = infos["form"]
old_id2new_id_map = dict()
global_new_id = 0
for info in infos:
if info["text"] is None:
continue
words = info["words"]
if len(words) <= 0:
continue
word_idx = 1
curr_bboxes = [words[0]["box"]]
curr_texts = [words[0]["text"]]
while word_idx < len(words):
# switch to a new link
if words[word_idx]["box"][0] + 10 <= words[word_idx - 1][
"box"][2]:
if len("".join(curr_texts[0])) > 0:
res.append({
"transcription": " ".join(curr_texts),
"label": info["label"],
"points": get_outer_poly(curr_bboxes),
"linking": info["linking"],
"id": global_new_id,
})
if info["id"] not in old_id2new_id_map:
old_id2new_id_map[info["id"]] = []
old_id2new_id_map[info["id"]].append(global_new_id)
global_new_id += 1
curr_bboxes = [words[word_idx]["box"]]
curr_texts = [words[word_idx]["text"]]
else:
curr_bboxes.append(words[word_idx]["box"])
curr_texts.append(words[word_idx]["text"])
word_idx += 1
if len("".join(curr_texts[0])) > 0:
res.append({
"transcription": " ".join(curr_texts),
"label": info["label"],
"points": get_outer_poly(curr_bboxes),
"linking": info["linking"],
"id": global_new_id,
})
if info["id"] not in old_id2new_id_map:
old_id2new_id_map[info["id"]] = []
old_id2new_id_map[info["id"]].append(global_new_id)
global_new_id += 1
res = sorted(
res, key=lambda r: (r["points"][0][1], r["points"][0][0]))
for i in range(len(res) - 1):
for j in range(i, 0, -1):
if abs(res[j + 1]["points"][0][1] - res[j]["points"][0][1]) < 20 and \
(res[j + 1]["points"][0][0] < res[j]["points"][0][0]):
tmp = deepcopy(res[j])
res[j] = deepcopy(res[j + 1])
res[j + 1] = deepcopy(tmp)
else:
break
# re-generate unique ids
for idx, r in enumerate(res):
new_links = []
for link in r["linking"]:
# illegal links will be removed
if link[0] not in old_id2new_id_map or link[
1] not in old_id2new_id_map:
continue
for src in old_id2new_id_map[link[0]]:
for dst in old_id2new_id_map[link[1]]:
new_links.append([src, dst])
res[idx]["linking"] = deepcopy(new_links)
fn_info_map[anno_fn] = res
return fn_info_map
def main():
test_image_dir = "train_data/FUNSD/testing_data/images/"
test_anno_dir = "train_data/FUNSD/testing_data/annotations/"
test_output_dir = "train_data/FUNSD/test.json"
fn_info_map = load_funsd_label(test_image_dir, test_anno_dir)
with open(test_output_dir, "w") as fout:
for fn in fn_info_map:
fout.write(fn + ".png" + "\t" + json.dumps(
fn_info_map[fn], ensure_ascii=False) + "\n")
train_image_dir = "train_data/FUNSD/training_data/images/"
train_anno_dir = "train_data/FUNSD/training_data/annotations/"
train_output_dir = "train_data/FUNSD/train.json"
fn_info_map = load_funsd_label(train_image_dir, train_anno_dir)
with open(train_output_dir, "w") as fout:
for fn in fn_info_map:
fout.write(fn + ".png" + "\t" + json.dumps(
fn_info_map[fn], ensure_ascii=False) + "\n")
print("====ok====")
return
if __name__ == "__main__":
main()