Spaces:
Runtime error
Runtime error
File size: 2,289 Bytes
378b1f2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
_base_ = ['../../_base_/default_runtime.py']
model = dict(
type='SDMGR',
backbone=dict(type='UNet', base_channels=16),
bbox_head=dict(
type='SDMGRHead', visual_dim=16, num_chars=92, num_classes=4),
visual_modality=False,
train_cfg=None,
test_cfg=None,
class_list=None,
openset=True)
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
train_pipeline = [
dict(type='LoadAnnotations'),
dict(type='ResizeNoImg', img_scale=(1024, 512), keep_ratio=True),
dict(type='KIEFormatBundle'),
dict(
type='Collect',
keys=['img', 'relations', 'texts', 'gt_bboxes', 'gt_labels'],
meta_keys=('filename', 'ori_filename', 'ori_texts'))
]
test_pipeline = [
dict(type='LoadAnnotations'),
dict(type='ResizeNoImg', img_scale=(1024, 512), keep_ratio=True),
dict(type='KIEFormatBundle'),
dict(
type='Collect',
keys=['img', 'relations', 'texts', 'gt_bboxes'],
meta_keys=('filename', 'ori_filename', 'ori_texts', 'ori_bboxes',
'img_norm_cfg', 'ori_filename', 'img_shape'))
]
dataset_type = 'OpensetKIEDataset'
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}/openset_train.txt',
pipeline=train_pipeline,
img_prefix=data_root,
link_type='one-to-many',
loader=loader,
dict_file=f'{data_root}/dict.txt',
test_mode=False)
test = dict(
type=dataset_type,
ann_file=f'{data_root}/openset_test.txt',
pipeline=test_pipeline,
img_prefix=data_root,
link_type='one-to-many',
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='openset_f1', metric_options=None)
find_unused_parameters = True
|