def __call__(self, data: Mapping[Hashable, Any]) -> Dict[Hashable, Any]:
d = dict(data)
for (
key,
orig_key,
meta_key,
orig_meta_key,
meta_key_postfix,
nearest_interp,
to_tensor,
device,
post_func,
) in self.key_iterator(
d,
self.orig_keys,
self.meta_keys,
self.orig_meta_keys,
self.meta_key_postfix,
self.nearest_interp,
self.to_tensor,
self.device,
self.post_func,
):
transform_key = f"{orig_key}{InverseKeys.KEY_SUFFIX}"
if transform_key not in d:
warnings.warn(
f"transform info of `{orig_key}` is not available or no InvertibleTransform applied."
)
continue
transform_info = d[transform_key]
if nearest_interp:
transform_info = convert_inverse_interp_mode(
trans_info=deepcopy(transform_info),
mode="nearest",
align_corners=None)
input = d[key]
if isinstance(input, torch.Tensor):
input = input.detach()
# construct the input dict data for BatchInverseTransform
input_dict = {orig_key: input, transform_key: transform_info}
orig_meta_key = orig_meta_key or f"{orig_key}_{meta_key_postfix}"
meta_key = meta_key or f"{key}_{meta_key_postfix}"
if orig_meta_key in d:
input_dict[orig_meta_key] = d[orig_meta_key]
with allow_missing_keys_mode(self.transform): # type: ignore
inverted = self.transform.inverse(input_dict)
# save the inverted data
d[key] = post_func(
self._totensor(inverted[orig_key]).
to(device) if to_tensor else inverted[orig_key])
# save the inverted meta dict
if orig_meta_key in d:
d[meta_key] = inverted.get(orig_meta_key)
return d
def __call__(
self,
data: Dict[str, Any],
num_examples: int = 10
) -> Union[Tuple[np.ndarray, np.ndarray, np.ndarray, float], np.ndarray]:
"""
Args:
data: dictionary data to be processed.
num_examples: number of realisations to be processed and results combined.
Returns:
- if `return_full_data==False`: mode, mean, std, vvc. The mode, mean and standard deviation are calculated across
`num_examples` outputs at each voxel. The volume variation coefficient (VVC) is `std/mean` across the whole output,
including `num_examples`. See original paper for clarification.
- if `return_full_data==False`: data is returned as-is after applying the `inferrer_fn` and then concatenating across
the first dimension containing `num_examples`. This allows the user to perform their own analysis if desired.
"""
d = dict(data)
# check num examples is multiple of batch size
if num_examples % self.batch_size != 0:
raise ValueError("num_examples should be multiple of batch size.")
# generate batch of data of size == batch_size, dataset and dataloader
data_in = [deepcopy(d) for _ in range(num_examples)]
ds = Dataset(data_in, self.transform)
dl = DataLoader(ds,
self.num_workers,
batch_size=self.batch_size,
collate_fn=pad_list_data_collate)
transform_key = self.orig_key + InverseKeys.KEY_SUFFIX
# create inverter
inverter = BatchInverseTransform(self.transform,
dl,
collate_fn=list_data_collate)
outputs: List[np.ndarray] = []
for batch_data in tqdm(dl) if has_tqdm and self.progress else dl:
batch_images = batch_data[self.image_key].to(self.device)
# do model forward pass
batch_output = self.inferrer_fn(batch_images)
if isinstance(batch_output, torch.Tensor):
batch_output = batch_output.detach().cpu()
if isinstance(batch_output, np.ndarray):
batch_output = torch.Tensor(batch_output)
transform_info = batch_data[transform_key]
if self.nearest_interp:
transform_info = convert_inverse_interp_mode(
trans_info=deepcopy(transform_info),
mode="nearest",
align_corners=None)
# create a dictionary containing the inferred batch and their transforms
inferred_dict = {
self.orig_key: batch_output,
transform_key: transform_info
}
# if meta dict is present, add that too (required for some inverse transforms)
meta_dict_key = self.orig_meta_keys or f"{self.orig_key}_{self.meta_key_postfix}"
if meta_dict_key in batch_data:
inferred_dict[meta_dict_key] = batch_data[meta_dict_key]
# do inverse transformation (allow missing keys as only inverting the orig_key)
with allow_missing_keys_mode(self.transform): # type: ignore
inv_batch = inverter(inferred_dict)
# append
outputs.append(inv_batch[self.orig_key])
# output
output: np.ndarray = np.concatenate(outputs)
if self.return_full_data:
return output
# calculate metrics
mode = np.array(
torch.mode(torch.Tensor(output.astype(np.int64)), dim=0).values)
mean: np.ndarray = np.mean(output, axis=0) # type: ignore
std: np.ndarray = np.std(output, axis=0) # type: ignore
vvc: float = (np.std(output) / np.mean(output)).item()
return mode, mean, std, vvc