def _load_cache_item(self, item, transforms):
for _transform in transforms:
# execute all the deterministic transforms
if isinstance(
_transform,
Randomizable) or not isinstance(_transform, Transform):
break
item = apply_transform(_transform, item)
return item
def __getitem__(self, index: int):
def to_list(x):
return list(x) if isinstance(x, (tuple, list)) else [x]
data = list()
for dataset in self.data:
data.extend(to_list(dataset[index]))
if self.transform is not None:
data = apply_transform(self.transform, data,
map_items=False) # transform the list data
return data
def __getitem__(self, index: int):
self.randomize()
meta_data = None
img_loader = LoadNifti(as_closest_canonical=self.as_closest_canonical,
image_only=self.image_only,
dtype=self.dtype)
if self.image_only:
img = img_loader(self.image_files[index])
else:
img, meta_data = img_loader(self.image_files[index])
seg = None
if self.seg_files is not None:
seg_loader = LoadNifti(image_only=True)
seg = seg_loader(self.seg_files[index])
label = None
if self.labels is not None:
label = self.labels[index]
if self.transform is not None:
if isinstance(self.transform, Randomizable):
self.transform.set_random_state(seed=self._seed)
img = apply_transform(self.transform, img)
data = [img]
if self.seg_transform is not None:
if isinstance(self.seg_transform, Randomizable):
self.seg_transform.set_random_state(seed=self._seed)
seg = apply_transform(self.seg_transform, seg)
if seg is not None:
data.append(seg)
if label is not None:
data.append(label)
if not self.image_only and meta_data is not None:
data.append(meta_data)
if len(data) == 1:
return data[0]
return data
def __getitem__(self, index):
if index < self.cache_num:
# load data from cache and execute from the first random transform
start_run = False
data = self._cache[index]
for _transform in self.transform.transforms: # pytype: disable=attribute-error
if not start_run and not isinstance(
_transform, Randomizable) and isinstance(
_transform, Transform):
continue
else:
start_run = True
data = apply_transform(_transform, data)
else:
# no cache for this data, execute all the transforms directly
data = super(CacheDataset, self).__getitem__(index)
return data
def _first_random_and_beyond_transform(self, item_transformed):
"""
Process the data from before the first random transform to the final state ready for evaluation.
Args:
item_transformed: The data to be transformed (already processed up to the first random transform)
Returns:
the transformed element through the random transforms
"""
start_post_randomize_run = False
for _transform in self.transform.transforms: # pytype: disable=attribute-error
if (start_post_randomize_run
or isinstance(_transform, Randomizable)
or not isinstance(_transform, Transform)):
start_post_randomize_run = True
item_transformed = apply_transform(_transform,
item_transformed)
return item_transformed
def _pre_first_random_transform(self, item_transformed):
"""
Process the data from original state up to the first random element.
Args:
item_transformed: The data to be transformed
Returns:
the transformed element up to the first identified
random transform object
"""
for _transform in self.transform.transforms: # pytype: disable=attribute-error
# execute all the deterministic transforms
if isinstance(
_transform,
Randomizable) or not isinstance(_transform, Transform):
break
item_transformed = apply_transform(_transform, item_transformed)
return item_transformed
def __getitem__(self, index: int):
data = self.data[index]
if self.transform is not None:
data = apply_transform(self.transform, data)
return data
def run_post_transform(engine):
engine.state.output = apply_transform(post_transform,
engine.state.output)