|
|
|
|
|
|
|
|
dataset_type = 'CVRPDataset' |
|
|
data_root = 'CVRPDataset/' |
|
|
|
|
|
crop_size = (512, 512) |
|
|
|
|
|
train_pipeline = [ |
|
|
dict(type='LoadImageFromFile'), |
|
|
dict(type='LoadAnnotations'), |
|
|
dict( |
|
|
type='RandomResize', |
|
|
scale=(2048, 1024), |
|
|
ratio_range=(0.5, 2.0), |
|
|
keep_ratio=True), |
|
|
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), |
|
|
dict(type='RandomFlip', prob=0.5), |
|
|
dict(type='PhotoMetricDistortion'), |
|
|
dict(type='PackSegInputs') |
|
|
] |
|
|
|
|
|
test_pipeline = [ |
|
|
dict(type='LoadImageFromFile'), |
|
|
dict(type='Resize', scale=(2048, 1024), keep_ratio=True), |
|
|
dict(type='LoadAnnotations'), |
|
|
dict(type='PackSegInputs') |
|
|
] |
|
|
|
|
|
img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] |
|
|
tta_pipeline = [ |
|
|
dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')), |
|
|
dict( |
|
|
type='TestTimeAug', |
|
|
transforms=[ |
|
|
[ |
|
|
dict(type='Resize', scale_factor=r, keep_ratio=True) |
|
|
for r in img_ratios |
|
|
], |
|
|
[ |
|
|
dict(type='RandomFlip', prob=0., direction='horizontal'), |
|
|
dict(type='RandomFlip', prob=1., direction='horizontal') |
|
|
], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')] |
|
|
]) |
|
|
] |
|
|
|
|
|
train_dataloader = dict( |
|
|
batch_size=2, |
|
|
num_workers=2, |
|
|
persistent_workers=True, |
|
|
sampler=dict(type='InfiniteSampler', shuffle=True), |
|
|
dataset=dict( |
|
|
type=dataset_type, |
|
|
data_root=data_root, |
|
|
data_prefix=dict( |
|
|
img_path='img_dir/train', seg_map_path='ann_dir/train'), |
|
|
pipeline=train_pipeline)) |
|
|
|
|
|
val_dataloader = dict( |
|
|
batch_size=1, |
|
|
num_workers=4, |
|
|
persistent_workers=True, |
|
|
sampler=dict(type='DefaultSampler', shuffle=False), |
|
|
dataset=dict( |
|
|
type=dataset_type, |
|
|
data_root=data_root, |
|
|
data_prefix=dict( |
|
|
img_path='img_dir/val', seg_map_path='ann_dir/val'), |
|
|
pipeline=test_pipeline)) |
|
|
|
|
|
test_dataloader = val_dataloader |
|
|
|
|
|
val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU', 'mDice', 'mFscore']) |
|
|
|
|
|
test_evaluator = val_evaluator |
