def transform_tr(self, sample):
train_transforms = list()
train_transforms.append(tr.Resize(self.cfg.LOAD_SIZE))
train_transforms.append(tr.RandomScale(self.cfg.RANDOM_SCALE_SIZE))
train_transforms.append(
tr.RandomCrop(self.cfg.FINE_SIZE, pad_if_needed=True, fill=0))
train_transforms.append(tr.RandomRotate())
train_transforms.append(tr.RandomGaussianBlur())
train_transforms.append(tr.RandomHorizontalFlip())
# if self.cfg.TARGET_MODAL == 'lab':
# train_transforms.append(tr.RGB2Lab())
if self.cfg.MULTI_SCALE:
for item in self.cfg.MULTI_TARGETS:
self.ms_targets.append(item)
train_transforms.append(
tr.MultiScale(size=self.cfg.FINE_SIZE,
scale_times=self.cfg.MULTI_SCALE_NUM,
ms_targets=self.ms_targets))
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_val(self, sample):
val_transforms = list()
val_transforms.append(tr.Resize(self.cfg.LOAD_SIZE))
if self.cfg.MULTI_SCALE:
val_transforms.append(tr.MultiScale(size=self.cfg.FINE_SIZE,scale_times=self.cfg.MULTI_SCALE_NUM, ms_targets=self.ms_targets))
val_transforms.append(tr.ToTensor())
val_transforms.append(tr.Normalize(mean=self.cfg.MEAN, std=self.cfg.STD, ms_targets=self.ms_targets))
composed_transforms = transforms.Compose(val_transforms)
def transform_val(self, sample):
val_transforms = list()
if not self.cfg.SLIDE_WINDOWS:
val_transforms.append(tr.CenterCrop(self.cfg.FINE_SIZE))
if self.cfg.MULTI_SCALE:
val_transforms.append(
tr.MultiScale(size=self.cfg.FINE_SIZE,
scale_times=self.cfg.MULTI_SCALE_NUM,
ms_targets=self.ms_targets))
val_transforms.append(tr.ToTensor())
val_transforms.append(
tr.Normalize(mean=self.cfg.MEAN,
std=self.cfg.STD,
ms_targets=self.ms_targets))
composed_transforms = transforms.Compose(val_transforms)
return composed_transforms(sample)
