def transform_val(self, sample):
composed_transforms = transforms.Compose([
tr.FixScaleCrop(crop_size=self.args.crop_size),
#tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
tr.ToTensor()
])
return composed_transforms(sample)
def transform_val(self, sample):
composed_transforms = transforms.Compose([
tr.FixScaleCrop(400),
tr.Normalize(mean=self.source_dist['mean'],
std=self.source_dist['std']),
tr.ToTensor(),
])
return composed_transforms(sample)
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
#tr.RandomHorizontalFlip(),
#tr.RandomScaleCrop(base_size=self.base_size, crop_size=self.crop_size),
##tr.RandomGaussianBlur(),
tr.FixScaleCrop(crop_size=self.crop_size),
tr.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225)),
tr.ToTensor()
])
return composed_transforms(sample)
def transform_pair_val(self, sample):
composed_transforms = transforms.Compose([
tr.FixScaleCrop(400),
tr.HorizontalFlip(),
tr.GaussianBlur(),
tr.Normalize(mean=self.source_dist['mean'],
std=self.source_dist['std'],
if_pair=True),
tr.ToTensor(if_pair=True),
])
return composed_transforms(sample)
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
#tr.RandomHorizontalFlip(),
# to make the image in the same batch has same shape
tr.FixScaleCrop(crop_size=self.args.crop_size),
#tr.RandomGaussianBlur(),
#tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
tr.ToTensor()
])
return composed_transforms(sample)