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_base_ = [
'_base_dbnetpp_resnet50-dcnv2_fpnc.py',
'../_base_/pretrain_runtime.py',
'../_base_/datasets/synthtext.py',
'../_base_/schedules/schedule_sgd_100k.py',
]
train_pipeline = [
dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
dict(
type='LoadOCRAnnotations',
with_bbox=True,
with_polygon=True,
with_label=True,
),
dict(type='FixInvalidPolygon'),
dict(
type='TorchVisionWrapper',
op='ColorJitter',
brightness=32.0 / 255,
saturation=0.5),
dict(
type='ImgAugWrapper',
args=[['Fliplr', 0.5],
dict(cls='Affine', rotate=[-10, 10]), ['Resize', [0.5, 3.0]]]),
dict(type='RandomCrop', min_side_ratio=0.1),
dict(type='Resize', scale=(640, 640), keep_ratio=True),
dict(type='Pad', size=(640, 640)),
dict(
type='PackTextDetInputs',
meta_keys=('img_path', 'ori_shape', 'img_shape'))
]
synthtext_textdet_train = _base_.synthtext_textdet_train
synthtext_textdet_train.pipeline = train_pipeline
train_dataloader = dict(
batch_size=16,
num_workers=8,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=synthtext_textdet_train)
auto_scale_lr = dict(base_batch_size=16)
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