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_base_ = [ | |
'../_base_/datasets/ctw1500.py', | |
'../_base_/default_runtime.py', | |
'../_base_/schedules/schedule_adam_600e.py', | |
'_base_panet_resnet18_fpem-ffm.py', | |
] | |
model = dict(det_head=dict(module_loss=dict(shrink_ratio=(1, 0.7)))) | |
default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=20), ) | |
train_pipeline = [ | |
dict(type='LoadImageFromFile', color_type='color_ignore_orientation'), | |
dict( | |
type='LoadOCRAnnotations', | |
with_polygon=True, | |
with_bbox=True, | |
with_label=True, | |
), | |
dict(type='ShortScaleAspectJitter', short_size=640, scale_divisor=32), | |
dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
dict(type='RandomRotate', max_angle=10), | |
dict(type='TextDetRandomCrop', target_size=(640, 640)), | |
dict(type='Pad', size=(640, 640)), | |
dict( | |
type='TorchVisionWrapper', | |
op='ColorJitter', | |
brightness=32.0 / 255, | |
saturation=0.5), | |
dict( | |
type='PackTextDetInputs', | |
meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor')) | |
] | |
test_pipeline = [ | |
dict(type='LoadImageFromFile', color_type='color_ignore_orientation'), | |
# TODO Replace with mmcv.RescaleToShort when it's ready | |
dict( | |
type='ShortScaleAspectJitter', | |
short_size=640, | |
scale_divisor=1, | |
ratio_range=(1.0, 1.0), | |
aspect_ratio_range=(1.0, 1.0)), | |
dict( | |
type='LoadOCRAnnotations', | |
with_polygon=True, | |
with_bbox=True, | |
with_label=True), | |
dict( | |
type='PackTextDetInputs', | |
meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor')) | |
] | |
# dataset settings | |
ctw1500_textdet_train = _base_.ctw1500_textdet_train | |
ctw1500_textdet_test = _base_.ctw1500_textdet_test | |
# pipeline settings | |
ctw1500_textdet_train.pipeline = train_pipeline | |
ctw1500_textdet_test.pipeline = test_pipeline | |
train_dataloader = dict( | |
batch_size=16, | |
num_workers=4, | |
persistent_workers=True, | |
sampler=dict(type='DefaultSampler', shuffle=True), | |
dataset=ctw1500_textdet_train) | |
val_dataloader = dict( | |
batch_size=1, | |
num_workers=4, | |
persistent_workers=True, | |
sampler=dict(type='DefaultSampler', shuffle=False), | |
dataset=ctw1500_textdet_test) | |
test_dataloader = val_dataloader | |
val_evaluator = dict( | |
type='HmeanIOUMetric', pred_score_thrs=dict(start=0.3, stop=1, step=0.05)) | |
test_evaluator = val_evaluator | |
auto_scale_lr = dict(base_batch_size=16) | |