def __getitem__(self, index):
if self.image_source == 'memory':
load_func = self.load_from_memory
elif self.image_source == 'disk':
load_func = self.load_from_disk
else:
raise NotImplementedError(
"Possible loading options: 'memory' and 'disk'!")
Xi = load_func(self.X, index, filetype='png', grayscale=False)
if self.y is not None:
Mi = self.load_target(self.y, index, load_func)
Xi, *Mi = from_pil(Xi, *Mi)
Xi, *Mi = self.image_augment_with_target(Xi, *Mi)
Xi = self.image_augment(Xi)
Xi, *Mi = to_pil(Xi, *Mi)
if self.mask_transform is not None:
Mi = [self.mask_transform(m) for m in Mi]
if self.image_transform is not None:
Xi = self.image_transform(Xi)
Mi = torch.cat(Mi, dim=0)
return Xi, Mi
else:
Xi = from_pil(Xi)
Xi = self.image_augment(Xi)
Xi = to_pil(Xi)
if self.image_transform is not None:
Xi = self.image_transform(Xi)
return Xi
def __getitem__(self, index):
if self.image_source == 'memory':
load_func = self.load_from_memory
elif self.image_source == 'disk':
load_func = self.load_from_disk
else:
raise NotImplementedError(
"Possible loading options: 'memory' and 'disk'!")
Xi = load_func(self.X, index, filetype='png', grayscale=False)
Xi = from_pil(Xi)
if self.image_augment is not None:
Xi = self.image_augment(Xi)
if self.tta_params is not None:
tta_transform_specs = self.tta_params[index]
Xi = self.tta_transform(Xi, tta_transform_specs)
Xi = to_pil(Xi)
if self.image_transform is not None:
Xi = self.image_transform(Xi)
return Xi
def read_json(self, path):
with open(path, 'r') as file:
data = json.load(file)
masks = [to_pil(binary_from_rle(rle)) for rle in data]
return masks
def load_target(self, data_source, index, load_func):
Mi = load_func(data_source, index, filetype='png', grayscale=True)
Mi = from_pil(Mi)
target = [to_pil(Mi == class_nr) for class_nr in [0, 1]]
return target