def get_dataset(opts):
""" Dataset And Augmentation
"""
if opts.dataset == 'voc':
train_transform = et.ExtCompose([
#et.ExtResize(size=opts.crop_size),
et.ExtRandomScale((0.5, 2.0)),
et.ExtRandomCrop(size=(opts.crop_size, opts.crop_size), pad_if_needed=True),
et.ExtRandomHorizontalFlip(),
et.ExtToTensor(),
et.ExtNormalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
if opts.crop_val:
val_transform = et.ExtCompose([
et.ExtResize(opts.crop_size),
et.ExtCenterCrop(opts.crop_size),
et.ExtToTensor(),
et.ExtNormalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
else:
val_transform = et.ExtCompose([
et.ExtToTensor(),
et.ExtNormalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
train_dst = VOCSegmentation(root=opts.data_root, year=opts.year,
image_set='train', download=opts.download, transform=train_transform)
val_dst = VOCSegmentation(root=opts.data_root, year=opts.year,
image_set='val', download=False, transform=val_transform)
if opts.dataset == 'cityscapes':
train_transform = et.ExtCompose([
#et.ExtResize( 512 ),
et.ExtRandomCrop(size=(opts.crop_size, opts.crop_size)),
et.ExtColorJitter( brightness=0.5, contrast=0.5, saturation=0.5 ),
et.ExtRandomHorizontalFlip(),
et.ExtToTensor(),
et.ExtNormalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
val_transform = et.ExtCompose([
et.ExtResize( 256 ),
et.ExtToTensor(),
et.ExtNormalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
train_dst = Cityscapes(root=opts.data_root,
split='train', transform=train_transform)
val_dst = Cityscapes(root=opts.data_root,
split='val', transform=val_transform)
return train_dst, val_dst
def get_dataset(opts):
""" Dataset And Augmentation
"""
if opts.dataset=='voc':
train_transform = ExtCompose( [
ExtRandomScale((0.5, 2.0)),
ExtRandomCrop(size=(opts.crop_size, opts.crop_size), pad_if_needed=True),
ExtRandomHorizontalFlip(),
ExtToTensor(),
ExtNormalize( mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225] ),
])
if opts.crop_val:
val_transform = ExtCompose([
ExtResize(size=opts.crop_size),
ExtCenterCrop(size=opts.crop_size),
ExtToTensor(),
ExtNormalize( mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225] ),
])
else:
# no crop, batch size = 1
val_transform = ExtCompose([
ExtToTensor(),
ExtNormalize( mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225] ),
])
train_dst = VOCSegmentation(root=opts.data_root, year=opts.year, image_set='train', download=opts.download, transform=train_transform)
val_dst = VOCSegmentation(root=opts.data_root, year=opts.year, image_set='val', download=False, transform=val_transform)
if opts.dataset=='cityscapes':
train_transform = ExtCompose( [
ExtScale(0.5),
ExtRandomCrop(size=(opts.crop_size, opts.crop_size)),
ExtRandomHorizontalFlip(),
ExtToTensor(),
ExtNormalize( mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225] ),
] )
val_transform = ExtCompose( [
ExtScale(0.5),
ExtToTensor(),
ExtNormalize( mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225] ),
] )
train_dst = Cityscapes(root=opts.data_root, split='train', download=opts.download, target_type='semantic', transform=train_transform)
val_dst = Cityscapes(root=opts.data_root, split='test', target_type='semantic', download=False, transform=val_transform)
return train_dst, val_dst
def get_dataset(opts):
""" Dataset And Augmentation
"""
if opts.crop_val:
val_transform = et.ExtCompose([
et.ExtResize(opts.crop_size),
et.ExtCenterCrop(opts.crop_size),
et.ExtToTensor(),
et.ExtNormalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
else:
val_transform = et.ExtCompose([
et.ExtToTensor(),
et.ExtNormalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
val_dst = VOCSegmentation(root=opts.data_root,
year=opts.year,
image_set='val',
download=False,
transform=val_transform,
ret_fname=True)
return val_dst
def get_dataset(opts):
""" Dataset And Augmentation
"""
if opts.dataset == 'voc':
train_transform = et.ExtCompose([
#et.ExtResize(size=opts.crop_size),
et.ExtRandomScale((0.5, 2.0)),
et.ExtRandomCrop(size=(opts.crop_size, opts.crop_size),
pad_if_needed=True),
et.ExtRandomHorizontalFlip(),
et.ExtToTensor(),
et.ExtNormalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
if opts.crop_val:
val_transform = et.ExtCompose([
et.ExtResize(opts.crop_size),
et.ExtCenterCrop(opts.crop_size),
et.ExtToTensor(),
et.ExtNormalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
else:
val_transform = et.ExtCompose([
et.ExtToTensor(),
et.ExtNormalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
train_dst = VOCSegmentation(root=opts.data_root,
year=opts.year,
image_set='train',
download=opts.download,
transform=train_transform)
val_dst = VOCSegmentation(root=opts.data_root,
year=opts.year,
image_set='val',
download=False,
transform=val_transform)
if opts.dataset == 'cityscapes':
train_transform = et.ExtCompose([
#et.ExtResize( 512 ),
et.ExtRandomCrop(size=(opts.crop_size, opts.crop_size)),
et.ExtColorJitter(brightness=0.5, contrast=0.5, saturation=0.5),
et.ExtRandomHorizontalFlip(),
et.ExtToTensor(),
et.ExtNormalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
val_transform = et.ExtCompose([
#et.ExtResize( 512 ),
et.ExtToTensor(),
et.ExtNormalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
train_dst = Cityscapes(root=opts.data_root,
split='train',
transform=train_transform)
val_dst = Cityscapes(root=opts.data_root,
split='val',
transform=val_transform)
if opts.dataset == 'weedcluster':
train_dst = WeedClusterDataset(root=opts.data_root, split='train')
val_dst = WeedClusterDataset(root=opts.data_root, split='val')
if opts.dataset == 'cloudshadow':
train_dst = CloudShadowDataset(root=opts.data_root, split='train')
val_dst = CloudShadowDataset(root=opts.data_root, split='val')
if opts.dataset == 'doubleplant':
train_dst = DoublePlantDataset(root=opts.data_root, split='train')
val_dst = DoublePlantDataset(root=opts.data_root, split='val')
if opts.dataset == 'planterskip':
train_dst = PlanterSkipDataset(root=opts.data_root, split='train')
val_dst = PlanterSkipDataset(root=opts.data_root, split='val')
if opts.dataset == 'standingwater':
train_dst = StandingWaterDataset(root=opts.data_root, split='train')
val_dst = StandingWaterDataset(root=opts.data_root, split='val')
if opts.dataset == 'waterway':
train_dst = WaterwayDataset(root=opts.data_root, split='train')
val_dst = WaterwayDataset(root=opts.data_root, split='val')
return train_dst, val_dst