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