def transform_tr(self, sample):
if self.table == {}:
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.RandomScaleCrop(base_size=400, crop_size=400, fill=0),
#tr.Remap(self.building_table, self.nonbuilding_table, self.channels)
tr.RandomGaussianBlur(),
#tr.ConvertFromInts(),
#tr.PhotometricDistort(),
tr.Normalize(mean=self.source_dist['mean'],
std=self.source_dist['std']),
tr.ToTensor(),
])
else:
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.RandomScaleCrop(base_size=400, crop_size=400, fill=0),
tr.Remap(self.table, self.channels),
tr.RandomGaussianBlur(),
#tr.ConvertFromInts(),
#tr.PhotometricDistort(),
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=400, crop_size=400, fill=0),
tr.Normalize(),
tr.ToTensor(),
])
return composed_transforms(sample)
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
# tr.RandomHorizontalFlip(),
tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255),
# tr.RandomGaussianBlur(),
tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
tr.ToTensor()])
return composed_transforms(sample)
def transform_tr(self, sample):
train_transforms = list()
train_transforms.append(tr.RandomHorizontalFlip())
train_transforms.append(tr.RandomScaleCrop(base_size=self.cfg.LOAD_SIZE, crop_size=self.cfg.FINE_SIZE))
train_transforms.append(tr.RandomGaussianBlur())
train_transforms.append(tr.ToTensor())
train_transforms.append(tr.Normalize(mean=self.cfg.MEAN, std=self.cfg.STD, ms_targets=self.ms_targets))
composed_transforms = transforms.Compose(train_transforms)
return composed_transforms(sample)
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.RandomScaleCrop(base_size=400, crop_size=400, fill=0),
tr.RandomGaussianBlur(),
tr.Normalize(mean=self.source_dist['mean'], std=self.source_dist['std']),
tr.ToTensor(),
])
return composed_transforms(sample)
def transform_finetune(self, sample):
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
# tr.RandomCrop(crop_size = 200),
tr.RandomScaleCrop(base_size=600, crop_size=400, fill=0),
tr.RandomGaussianBlur(),
tr.Normalize(),
tr.ToTensor()
])
return composed_transforms(sample)
def transform_pair_train(self, sample):
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.RandomScaleCrop(base_size=400, crop_size=400, fill=0),
#tr.RandomGaussianBlur(),
tr.HorizontalFlip(),
tr.GaussianBlur(),
tr.Normalize(if_pair=True),
tr.ToTensor(if_pair=True),
])
return composed_transforms(sample)
def transform_pair_train(self, sample):
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.RandomScaleCrop(base_size=400, crop_size=400, fill=0),
tr.RandomGaussianBlur(),
tr.ColorDistort(if_pair=True),
#tr.CropAndResize(if_pair=True),
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(),
tr.RandomVerticalFlip(),
tr.RandomRotate(180),
tr.RandomScaleCrop(base_size=400, crop_size=400, fill=0),
tr.RandomGaussianBlur(),
# tr.Normalize(),
tr.Normalize(mean=(0.2382, 0.2741, 0.3068),
std=(0.1586, 0.1593, 0.1618)),
tr.ToTensor(),
])
return composed_transforms(sample)
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.RandomVerticalFlip(),
tr.RandomRotate(180),
tr.RandomScaleCrop(base_size=400, crop_size=400, fill=0),
tr.RandomGaussianBlur(),
# tr.Normalize(),
tr.Normalize(mean=(0.1420, 0.2116, 0.2823),
std=(0.0899, 0.1083, 0.1310)),
tr.ToTensor(),
])
return composed_transforms(sample)
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
#tr.FixScaleCrop(400),
tr.RandomHorizontalFlip(),
tr.RandomVerticalFlip(),
tr.RandomRotate(180),
tr.RandomScaleCrop(base_size=400, crop_size=400, fill=0),
tr.RandomGaussianBlur(),
tr.Normalize(mean=self.mean_std[0], std=self.mean_std[1]),
tr.ToTensor(),
])
#print(self.mean_std)
return composed_transforms(sample)
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.RandomVerticalFlip(),
tr.RandomRotate(180),
tr.RandomScaleCrop(base_size=400, crop_size=400, fill=0),
tr.RandomGaussianBlur(),
# tr.Normalize(),
tr.Normalize(mean=(0.3441, 0.3809, 0.4014),
std=(0.1883, 0.2039, 0.2119)),
tr.ToTensor(),
])
return composed_transforms(sample)
