def named_collate(batch):
r"""Puts each data field into a tensor with outer dimension batch size.
If named tensor adds batch to the first dimension."""
elem = batch[0]
elem_type = type(elem)
if isinstance(elem, torch.Tensor):
out = None
# TODO: Named tensor: once stack supports named tensors, drop the rename.
names = elem.names
elem = elem.rename(None)
if torch.utils.data.get_worker_info() is not None:
# If we're in a background process, concatenate directly into a
# shared memory tensor to avoid an extra copy
numel = sum([x.rename(None).numel() for x in batch])
storage = elem.storage()._new_shared(numel)
out = elem.new(storage)
out_batch = torch.stack([_.rename(None) for _ in batch], 0, out=out)
if any(names):
new_names = tuple(['batch'] + list(names))
out_batch = out_batch.refine_names(*new_names)
return out_batch
elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
and elem_type.__name__ != 'string_':
elem = batch[0]
if elem_type.__name__ == 'ndarray':
# array of string classes and object
if np_str_obj_array_pattern.search(elem.dtype.str) is not None:
raise TypeError(
default_collate_err_msg_format.format(elem.dtype))
return named_collate([torch.as_tensor(b) for b in batch])
elif elem.shape == (): # scalars
return torch.as_tensor(batch)
elif isinstance(elem, float):
return torch.tensor(batch, dtype=torch.float64)
elif isinstance(elem, int_classes):
return torch.tensor(batch)
elif isinstance(elem, string_classes):
return batch
elif isinstance(elem, container_abcs.Mapping):
return {key: named_collate([d[key] for d in batch]) for key in elem}
elif isinstance(elem, tuple) and hasattr(elem, '_fields'): # namedtuple
return elem_type(*(named_collate(samples) for samples in zip(*batch)))
elif isinstance(elem, container_abcs.Sequence):
transposed = zip(*batch)
return [named_collate(samples) for samples in transposed]
raise TypeError(default_collate_err_msg_format.format(elem_type))
def __call__(self, filename):
"""
Args:
filename (str, list, tuple, file): path file or file-like object or a list of files.
"""
filename = ensure_tuple(filename)
img_array = list()
compatible_meta = dict()
for name in filename:
img = nib.load(name)
img = correct_nifti_header_if_necessary(img)
header = dict(img.header)
header["filename_or_obj"] = name
header["affine"] = img.affine
header["original_affine"] = img.affine.copy()
header["as_closest_canonical"] = self.as_closest_canonical
ndim = img.header["dim"][0]
spatial_rank = min(ndim, 3)
header["spatial_shape"] = img.header["dim"][1:spatial_rank + 1]
if self.as_closest_canonical:
img = nib.as_closest_canonical(img)
header["affine"] = img.affine
img_array.append(np.array(img.get_fdata(dtype=self.dtype)))
img.uncache()
if self.image_only:
continue
if not compatible_meta:
for meta_key in header:
meta_datum = header[meta_key]
# pytype: disable=attribute-error
if (type(meta_datum).__name__ == "ndarray"
and np_str_obj_array_pattern.search(
meta_datum.dtype.str) is not None):
continue
# pytype: enable=attribute-error
compatible_meta[meta_key] = meta_datum
else:
assert np.allclose(
header["affine"], compatible_meta["affine"]
), "affine data of all images should be same."
img_array = np.stack(img_array,
axis=0) if len(img_array) > 1 else img_array[0]
if self.image_only:
return img_array
return img_array, compatible_meta
def collate_fn(batch, ignore_lists=True, pad_before_stack=True, cat_tensors=False):
r"""Puts each data field into a tensor with outer dimension batch size"""
# taken from pytorch source code - pytorch data collater, but does not do anything with lists (avoids zip behavior)
f = partial(collate_fn, ignore_lists=ignore_lists, pad_before_stack=pad_before_stack, cat_tensors=cat_tensors)
elem = batch[0]
elem_type = type(elem)
if isinstance(elem, torch.Tensor):
out = None
if torch.utils.data.get_worker_info() is not None:
# If we're in a background process, concatenate directly into a
# shared memory tensor to avoid an extra copy
numel = sum([x.numel() for x in batch])
storage = elem.storage()._new_shared(numel)
out = elem.new(storage)
if cat_tensors:
return torch.cat(batch, 0, out=out)
else:
if pad_before_stack:
return pad_sequence(batch, batch_first=True)
else:
return torch.stack(batch, 0, out=out)
elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
and elem_type.__name__ != 'string_':
elem = batch[0]
if elem_type.__name__ == 'ndarray':
# array of string classes and object
if np_str_obj_array_pattern.search(elem.dtype.str) is not None:
raise TypeError(default_collate_err_msg_format.format(elem.dtype))
return f([torch.as_tensor(b) for b in batch])
elif elem.shape == (): # scalars
return torch.as_tensor(batch)
elif isinstance(elem, float):
return torch.tensor(batch, dtype=torch.float64)
elif isinstance(elem, int_classes):
return torch.tensor(batch)
elif isinstance(elem, string_classes):
return batch
elif isinstance(elem, container_abcs.Mapping):
return {key: f([d[key] for d in batch]) for key in elem}
elif isinstance(elem, tuple) and hasattr(elem, '_fields'): # namedtuple
return elem_type(*(f(samples) for samples in zip(*batch)))
elif isinstance(elem, container_abcs.Sequence):
if ignore_lists:
return batch
else:
transposed = zip(*batch)
return [default_collate(samples) for samples in transposed]
raise TypeError(default_collate_err_msg_format.format(elem_type))
def dict_collate_fn(batch):
# To keep each image's label as separate dictionaries, default pytorch behaviour will stack each key
# Only modified one line of the pytorch 1.6.0 default collate function
elem = batch[0]
elem_type = type(elem)
if isinstance(elem, torch.Tensor):
out = None
if torch.utils.data.get_worker_info() is not None:
# If we're in a background process, concatenate directly into a
# shared memory tensor to avoid an extra copy
numel = sum([x.numel() for x in batch])
storage = elem.storage()._new_shared(numel)
out = elem.new(storage)
return torch.stack(batch, 0, out=out)
elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
and elem_type.__name__ != 'string_':
elem = batch[0]
if elem_type.__name__ == 'ndarray':
# array of string classes and object
if np_str_obj_array_pattern.search(elem.dtype.str) is not None:
raise TypeError(
default_collate_err_msg_format.format(elem.dtype))
return dict_collate_fn([torch.as_tensor(b) for b in batch])
elif elem.shape == (): # scalars
return torch.as_tensor(batch)
elif isinstance(elem, float):
return torch.tensor(batch, dtype=torch.float64)
elif isinstance(elem, int_classes):
return torch.tensor(batch)
elif isinstance(elem, string_classes):
return batch
elif isinstance(elem, container_abcs.Mapping):
return batch # !Only modified this line
elif isinstance(elem, tuple) and hasattr(elem, '_fields'): # namedtuple
return elem_type(*(dict_collate_fn(samples)
for samples in zip(*batch)))
elif isinstance(elem, container_abcs.Sequence):
# check to make sure that the elements in batch have consistent size
it = iter(batch)
elem_size = len(next(it))
if not all(len(elem) == elem_size for elem in it):
raise RuntimeError(
'each element in list of batch should be of equal size')
transposed = zip(*batch)
return [dict_collate_fn(samples) for samples in transposed]
raise TypeError(default_collate_err_msg_format.format(elem_type))
def collate_any(batch, key=None):
r"""Puts each data field into a tensor with outer dimension batch size"""
elem = batch[0]
elem_type = type(elem)
if isinstance(elem, torch.Tensor):
out = None
if torch.utils.data.get_worker_info() is not None:
# If we're in a background process, concatenate directly into a
# shared memory tensor to avoid an extra copy
numel = sum([x.numel() for x in batch])
storage = elem.storage()._new_shared(numel)
out = elem.new(storage)
return torch.stack(batch, 0, out=out)
elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
and elem_type.__name__ != 'string_':
elem = batch[0]
if elem_type.__name__ == 'ndarray':
# array of string classes and object
if np_str_obj_array_pattern.search(elem.dtype.str) is not None:
raise TypeError(
default_collate_err_msg_format.format(elem.dtype))
return collate_any([torch.as_tensor(b) for b in batch])
elif elem.shape == (): # scalars
return torch.as_tensor(batch)
elif isinstance(elem, float):
return torch.tensor(batch, dtype=torch.float64)
elif isinstance(elem, int_classes):
return torch.tensor(batch)
elif isinstance(elem, string_classes):
return batch
elif isinstance(elem, container_abcs.Mapping):
return {
key: collate_any([d[key] for d in batch], key=key)
for key in elem
}
elif isinstance(elem, tuple) and hasattr(elem, '_fields'): # namedtuple
return elem_type(*(collate_any(samples) for samples in zip(*batch)))
elif key is not None:
# we are not at top level, so NO TRANSPOSITION!!!
# unchanged batches for example for keypoints, object_infos
return batch
elif isinstance(elem, container_abcs.Sequence):
assert key is None, "You are transposing something at no-top level, which is WRONG"
transposed = zip(*batch)
return [collate_any(samples) for samples in transposed]
raise TypeError(default_collate_err_msg_format.format(elem_type))
def default_collate1(batch):
r"""Puts each data field into a tensor with outer dimension batch size"""
elem = batch[0]
elem_type = type(elem)
if isinstance(elem, torch.Tensor):
out = None
if torch.utils.data.get_worker_info() is not None:
# If we're in a background process, concatenate directly into a
# shared memory tensor to avoid an extra copy
numel = sum([x.numel() for x in batch])
storage = elem.storage()._new_shared(numel)
out = elem.new(storage)
return torch.stack(batch, 0, out=out)
elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
and elem_type.__name__ != 'string_':
elem = batch[0]
if elem_type.__name__ == 'ndarray':
# array of string classes and object
if np_str_obj_array_pattern.search(elem.dtype.str) is not None:
raise TypeError(
default_collate_err_msg_format.format(elem.dtype))
return default_collate([torch.as_tensor(b) for b in batch])
elif elem.shape == (): # scalars
return torch.as_tensor(batch)
elif isinstance(elem, float):
return torch.tensor(batch, dtype=torch.float64)
elif isinstance(elem, int_classes):
return torch.tensor(batch)
elif isinstance(elem, string_classes):
return batch
elif isinstance(elem, container_abcs.Mapping):
return {key: default_collate([d[key] for d in batch]) for key in elem}
elif isinstance(elem, tuple) and hasattr(elem, '_fields'): # namedtuple
return elem_type(*(default_collate(samples)
for samples in zip(*batch)))
elif isinstance(elem, container_abcs.Sequence):
if elem_type.__name__ == 'tuple':
targets = []
imgs = []
for sample in batch:
imgs.append(sample[0])
targets.append(torch.FloatTensor(
sample[1])) # torch.FloatTensor(sample[1])
return torch.stack(imgs, 0), targets
else:
transposed = zip(*batch)
return [default_collate(samples) for samples in transposed]
raise TypeError(default_collate_err_msg_format.format(elem_type))
def __getitem__(self, index):
meta_data = None
if self.image_only:
img = load_nifti(
self.image_files[index],
as_closest_canonical=self.as_closest_canonical,
image_only=self.image_only,
dtype=self.dtype,
)
else:
img, meta_data = load_nifti(
self.image_files[index],
as_closest_canonical=self.as_closest_canonical,
image_only=self.image_only,
dtype=self.dtype,
)
target = None
if self.seg_files is not None:
target = load_nifti(self.seg_files[index])
elif self.labels is not None:
target = self.labels[index]
seed = np.random.randint(2147483647)
if self.transform is not None:
if isinstance(self.transform, Randomizable):
self.transform.set_random_state(seed=seed)
img = self.transform(img)
if self.seg_transform is not None:
if isinstance(self.seg_transform, Randomizable):
self.seg_transform.set_random_state(seed=seed)
target = self.seg_transform(target)
if self.image_only or meta_data is None:
return img, target
compatible_meta = {}
for meta_key in meta_data:
meta_datum = meta_data[meta_key]
if (
type(meta_datum).__name__ == "ndarray"
and np_str_obj_array_pattern.search(meta_datum.dtype.str) is not None
):
continue
compatible_meta[meta_key] = meta_datum
return img, target, compatible_meta
def _collate(self, batch: Sequence[Any]) -> Any:
elem = batch[0]
elem_type = type(elem)
if isinstance(elem, Tensor):
out = None
if torch.utils.data.get_worker_info() is not None: # type: ignore
# If we're in a background process, concatenate directly into a
# shared memory tensor to avoid an extra copy
numel = sum(x.numel() for x in batch)
storage = elem.storage()._new_shared(numel)
out = elem.new(storage).resize_(len(batch), *list(elem.size()))
ndims = elem.dim()
if (ndims > 0) and ((ndims % 2) == 0):
return torch.cat(batch, dim=0, out=out) # type: ignore
return torch.stack(batch, dim=0, out=out) # type: ignore
elif (
elem_type.__module__ == "numpy"
and elem_type.__name__ != "str_"
and elem_type.__name__ != "string_"
):
elem = batch[0]
if elem_type.__name__ == "ndarray":
# array of string classes and object
if np_str_obj_array_pattern.search(elem.dtype.str) is not None:
raise TypeError(default_collate_err_msg_format.format(elem.dtype))
return self._collate([torch.as_tensor(b) for b in batch])
elif isinstance(elem, float):
return torch.tensor(batch, dtype=torch.float64)
elif isinstance(elem, int):
return torch.tensor(batch)
elif isinstance(elem, string_classes):
return batch
elif isinstance(elem, Mapping):
return {key: self._collate([d[key] for d in batch]) for key in elem}
elif isinstance(elem, tuple) and hasattr(elem, "_fields"): # namedtuple
return elem_type(**(self._collate(samples) for samples in zip(*batch)))
elif is_dataclass(elem): # dataclass
return elem_type(
**{
field.name: self._collate([getattr(d, field.name) for d in batch])
for field in fields(elem)
}
)
elif isinstance(elem, (tuple, list)):
transposed = zip(*batch)
return [self._collate(samples) for samples in transposed]
raise TypeError(default_collate_err_msg_format.format(elem_type))
def _convert_to_tensor(data: Any) -> Any:
"""
Maps all kind of collections and numbers to tensors.
Args:
data: the data to convert to tensor
Return:
the converted data
"""
if isinstance(data, numbers.Number):
return torch.tensor([data])
# is not array of object
elif isinstance(data, np.ndarray) and np_str_obj_array_pattern.search(data.dtype.str) is None:
return torch.from_numpy(data)
elif isinstance(data, torch.Tensor):
return data
raise TypeError(f"The given type ('{type(data).__name__}') cannot be converted to a tensor!")
def __getitem__(self, index):
meta_data = None
if self.image_only:
img = load_nifti(self.image_files[index],
as_closest_canonical=self.as_closest_canonical,
image_only=self.image_only,
dtype=self.dtype)
else:
img, meta_data = load_nifti(
self.image_files[index],
as_closest_canonical=self.as_closest_canonical,
image_only=self.image_only,
dtype=self.dtype)
seg = load_nifti(self.seg_files[index])
# https://github.com/pytorch/vision/issues/9#issuecomment-304224800
seed = np.random.randint(2147483647)
if self.transform is not None:
np.random.seed(seed)
img = self.transform(img)
random_sync_test = np.random.randint(2147483647)
if self.seg_transform is not None:
np.random.seed(
seed
) # ensure randomized transforms roll the same values for segmentations as images
seg = self.seg_transform(seg)
seg_seed = np.random.randint(2147483647)
assert (random_sync_test == seg_seed)
if self.image_only or meta_data is None:
return img, seg
compatible_meta = {}
for meta_key in meta_data:
meta_datum = meta_data[meta_key]
if type(meta_datum).__name__ == 'ndarray' \
and np_str_obj_array_pattern.search(meta_datum.dtype.str) is not None:
continue
compatible_meta[meta_key] = meta_datum
return img, seg, compatible_meta
def _copy_compatible_dict(from_dict: Dict, to_dict: Dict):
if not isinstance(to_dict, dict):
raise ValueError(f"to_dict must be a Dict, got {type(to_dict)}.")
if not to_dict:
for key in from_dict:
datum = from_dict[key]
if isinstance(datum, np.ndarray) and np_str_obj_array_pattern.search(datum.dtype.str) is not None:
continue
to_dict[key] = datum
else:
affine_key, shape_key = "affine", "spatial_shape"
if affine_key in from_dict and not np.allclose(from_dict[affine_key], to_dict[affine_key]):
raise RuntimeError(
"affine matrix of all images should be the same for channel-wise concatenation. "
f"Got {from_dict[affine_key]} and {to_dict[affine_key]}."
)
if shape_key in from_dict and not np.allclose(from_dict[shape_key], to_dict[shape_key]):
raise RuntimeError(
"spatial_shape of all images should be the same for channel-wise concatenation. "
f"Got {from_dict[shape_key]} and {to_dict[shape_key]}."
)
def adaptive_collate(batch: Any) -> Any:
elem = batch[0]
elem_type = type(elem)
if isinstance(elem, Tensor):
out = None
if torch.utils.data.get_worker_info() is not None:
# If we're in a background process, concatenate directly into a
# shared memory tensor to avoid an extra copy
numel = sum([x.numel() for x in batch])
storage = elem.storage()._new_shared(numel)
out = elem.new(storage)
ndims = elem.dim()
if ndims > 0 and ndims % 2 == 0:
return torch.cat(batch, dim=0, out=out)
else:
return torch.stack(batch, dim=0, out=out)
elif (elem_type.__module__ == "numpy" and elem_type.__name__ != "str_"
and elem_type.__name__ != "string_"):
elem = batch[0]
if elem_type.__name__ == "ndarray":
# array of string classes and object
if np_str_obj_array_pattern.search(elem.dtype.str) is not None:
raise TypeError(
default_collate_err_msg_format.format(elem.dtype))
return adaptive_collate([torch.as_tensor(b) for b in batch])
elif isinstance(elem, float):
return torch.tensor(batch, dtype=torch.float64)
elif isinstance(elem, int):
return torch.tensor(batch)
elif isinstance(elem, tuple) and hasattr(elem, "_fields"): # namedtuple
return elem_type(*(adaptive_collate(samples)
for samples in zip(*batch)))
elif isinstance(elem, (tuple, list)):
transposed = zip(*batch)
return [adaptive_collate(samples) for samples in transposed]
raise TypeError(default_collate_err_msg_format.format(elem_type))
