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img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
max_scale, min_scale = 1024, 512
train_pipeline = [
dict(type='LoadAnnotations'),
dict(
type='ResizeNoImg', img_scale=(max_scale, min_scale), keep_ratio=True),
dict(type='KIEFormatBundle'),
dict(
type='Collect',
keys=['img', 'relations', 'texts', 'gt_bboxes', 'gt_labels'],
meta_keys=('filename', 'ori_texts'))
]
test_pipeline = [
dict(type='LoadAnnotations'),
dict(
type='ResizeNoImg', img_scale=(max_scale, min_scale), keep_ratio=True),
dict(type='KIEFormatBundle'),
dict(
type='Collect',
keys=['img', 'relations', 'texts', 'gt_bboxes'],
meta_keys=('filename', 'ori_texts', 'img_norm_cfg', 'ori_filename',
'img_shape'))
]
dataset_type = 'KIEDataset'
data_root = 'data/wildreceipt'
loader = dict(
type='HardDiskLoader',
repeat=1,
parser=dict(
type='LineJsonParser',
keys=['file_name', 'height', 'width', 'annotations']))
train = dict(
type=dataset_type,
ann_file=f'{data_root}/train.txt',
pipeline=train_pipeline,
img_prefix=data_root,
loader=loader,
dict_file=f'{data_root}/dict.txt',
test_mode=False)
test = dict(
type=dataset_type,
ann_file=f'{data_root}/test.txt',
pipeline=test_pipeline,
img_prefix=data_root,
loader=loader,
dict_file=f'{data_root}/dict.txt',
test_mode=True)
data = dict(
samples_per_gpu=4,
workers_per_gpu=1,
val_dataloader=dict(samples_per_gpu=1),
test_dataloader=dict(samples_per_gpu=1),
train=train,
val=test,
test=test)
evaluation = dict(
interval=1,
metric='macro_f1',
metric_options=dict(
macro_f1=dict(
ignores=[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 25])))
model = dict(
type='SDMGR',
backbone=dict(type='UNet', base_channels=16),
bbox_head=dict(
type='SDMGRHead', visual_dim=16, num_chars=92, num_classes=26),
visual_modality=False,
train_cfg=None,
test_cfg=None,
class_list=f'{data_root}/class_list.txt')
optimizer = dict(type='Adam', weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=1,
warmup_ratio=1,
step=[40, 50])
total_epochs = 60
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
find_unused_parameters = True
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