def get_transform(train):
base_size = 520
crop_size = 480
min_size = int((0.5 if train else 1.0) * base_size)
max_size = int((2.0 if train else 1.0) * base_size)
transforms = []
transforms.append(T.RandomResize(min_size, max_size))
if train:
transforms.append(
T.RandomColorJitter(brightness=0.25,
contrast=0.25,
saturation=0.25,
hue=0.25))
transforms.append(T.RandomGaussianSmoothing(radius=[0, 5]))
transforms.append(T.RandomRotation(degrees=30, fill=0))
transforms.append(T.RandomHorizontalFlip(0.5))
transforms.append(T.RandomPerspective(fill=0))
transforms.append(T.RandomCrop(crop_size, fill=0))
transforms.append(T.RandomGrayscale(p=0.1))
transforms.append(T.ToTensor())
transforms.append(
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]))
return T.Compose(transforms)
def get_transform(train, net_w=416, net_h=416):
transforms = []
transforms.append(T.ToTensor())
if train == True:
transforms.append(T.RandomSpatialJitter(jitter=0.3,net_w=net_w,net_h=net_h))
transforms.append(T.RandomColorJitter(hue=0.1,saturation=1.5,exposure=1.5))
transforms.append(T.RandomHorizontalFlip(prob=0.5))
else:
transforms.append(T.MakeLetterBoxImage(width=net_w,height=net_h))
return T.Compose(transforms)