def get_transform(mode, args):
seq_len = args.seq_len * 2 # for both rgb and flow
null_transform = transforms.Compose([
A.RandomSizedCrop(size=args.img_dim, consistent=False, seq_len=seq_len, bottom_area=0.2),
A.RandomHorizontalFlip(consistent=False, seq_len=seq_len),
A.ToTensor(),
])
base_transform = transforms.Compose([
A.RandomSizedCrop(size=args.img_dim, consistent=False, seq_len=seq_len, bottom_area=0.2),
transforms.RandomApply([
A.ColorJitter(0.4, 0.4, 0.4, 0.1, p=1.0, consistent=False, seq_len=seq_len)
], p=0.8),
A.RandomGray(p=0.2, seq_len=seq_len),
transforms.RandomApply([A.GaussianBlur([.1, 2.], seq_len=seq_len)], p=0.5),
A.RandomHorizontalFlip(consistent=False, seq_len=seq_len),
A.ToTensor(),
])
# oneclip: temporally take one clip, random augment twice
# twoclip: temporally take two clips, random augment for each
# merge oneclip & twoclip transforms with 50%/50% probability
transform = A.TransformController(
[A.TwoClipTransform(base_transform, null_transform, seq_len=seq_len, p=0.3),
A.OneClipTransform(base_transform, null_transform, seq_len=seq_len)],
weights=[0.5,0.5])
print(transform)
return transform
def get_transform(mode, args):
seq_len = args.seq_len * 2 # for both rgb and flow
null_transform = transforms.Compose([
A.RandomSizedCrop(size=args.img_dim,
consistent=False,
seq_len=seq_len,
bottom_area=0.2),
A.RandomHorizontalFlip(consistent=False, seq_len=seq_len),
A.ToTensor(),
])
base_transform = transforms.Compose([
A.RandomSizedCrop(size=args.img_dim,
consistent=False,
seq_len=seq_len,
bottom_area=0.2),
transforms.RandomApply([
A.ColorJitter(
0.4, 0.4, 0.4, 0.1, p=1.0, consistent=False, seq_len=seq_len)
],
p=0.8),
A.RandomGray(p=0.2, seq_len=seq_len),
transforms.RandomApply([A.GaussianBlur([.1, 2.], seq_len=seq_len)],
p=0.5),
A.RandomHorizontalFlip(consistent=False, seq_len=seq_len),
A.ToTensor(),
])
transform = A.TransformController([
A.TwoClipTransform(
base_transform, null_transform, seq_len=seq_len, p=0.3),
A.OneClipTransform(base_transform, null_transform, seq_len=seq_len)
],
weights=[0.5, 0.5])
return transform