diff --git a/monai/data/utils.py b/monai/data/utils.py index 94c8582e9a..737b2f84b5 100644 --- a/monai/data/utils.py +++ b/monai/data/utils.py @@ -403,7 +403,7 @@ def _detect_batch_size(batch_data: Sequence): dict_batch[k] = v return dict_batch - elif isinstance(batch_data, list): + if isinstance(batch_data, list): batch_size = _detect_batch_size(batch_data) list_batch = [] for b in batch_data: diff --git a/monai/handlers/utils.py b/monai/handlers/utils.py index 793683f2c5..bedc340daa 100644 --- a/monai/handlers/utils.py +++ b/monai/handlers/utils.py @@ -259,7 +259,7 @@ def from_engine(keys: KeysCollection, first: bool = False): def _wrapper(data): if isinstance(data, dict): return tuple(data[k] for k in keys) - elif isinstance(data, list) and isinstance(data[0], dict): + if isinstance(data, list) and isinstance(data[0], dict): # if data is a list of dictionaries, extract expected keys and construct lists, # if `first=True`, only extract keys from the first item of the list ret = [data[0][k] if first else [i[k] for i in data] for k in keys] diff --git a/monai/networks/blocks/fcn.py b/monai/networks/blocks/fcn.py index aa6d69fad0..9fec264d9e 100644 --- a/monai/networks/blocks/fcn.py +++ b/monai/networks/blocks/fcn.py @@ -191,25 +191,24 @@ def forward(self, x: torch.Tensor): fs3 = self.refine8(self.up_conv(fs2) + gcfm4) fs4 = self.refine9(self.up_conv(fs3) + gcfm5) return self.refine10(self.up_conv(fs4)) - else: - fs1 = self.refine6( - F.interpolate(gcfm1, fm3.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm2 - ) - fs2 = self.refine7(F.interpolate(fs1, fm2.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm3) - fs3 = self.refine8( - F.interpolate(fs2, pool_x.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm4 - ) - fs4 = self.refine9( - F.interpolate(fs3, conv_x.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm5 - ) - return self.refine10( - F.interpolate( - fs4, - org_input.size()[2:], - mode=self.upsample_mode, - align_corners=True, - ) + fs1 = self.refine6( + F.interpolate(gcfm1, fm3.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm2 + ) + fs2 = self.refine7(F.interpolate(fs1, fm2.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm3) + fs3 = self.refine8( + F.interpolate(fs2, pool_x.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm4 + ) + fs4 = self.refine9( + F.interpolate(fs3, conv_x.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm5 + ) + return self.refine10( + F.interpolate( + fs4, + org_input.size()[2:], + mode=self.upsample_mode, + align_corners=True, ) + ) class MCFCN(FCN): diff --git a/monai/transforms/intensity/array.py b/monai/transforms/intensity/array.py index 14b3e54459..ca4f1ef388 100644 --- a/monai/transforms/intensity/array.py +++ b/monai/transforms/intensity/array.py @@ -1330,7 +1330,7 @@ def __init__( raise AssertionError( "If a sequence is passed to k_intensity, then a sequence of locations must be passed to loc" ) - elif len(k_intensity) != len(loc): + if len(k_intensity) != len(loc): raise AssertionError("There must be one intensity_factor value for each tuple of indices in loc.") if isinstance(self.loc[0], Sequence) and k_intensity is not None: if not isinstance(self.k_intensity, Sequence): @@ -1541,8 +1541,7 @@ def _make_sequence(self, x: torch.Tensor) -> Sequence[Sequence[float]]: if not isinstance(self.intensity_range[0], Sequence): intensity_range = (ensure_tuple(self.intensity_range),) * x.shape[0] return intensity_range - else: - return ensure_tuple(self.intensity_range) + return ensure_tuple(self.intensity_range) else: # set default range if one not provided return self._set_default_range(x) diff --git a/monai/transforms/post/array.py b/monai/transforms/post/array.py index a33fce785e..c7558eddc3 100644 --- a/monai/transforms/post/array.py +++ b/monai/transforms/post/array.py @@ -334,12 +334,11 @@ def __call__(self, img: NdarrayTensor) -> NdarrayTensor: """ if isinstance(img, np.ndarray): return np.asarray(np.where(np.isin(img, self.applied_labels), img, 0)) - elif isinstance(img, torch.Tensor): + if isinstance(img, torch.Tensor): img_arr = img.detach().cpu().numpy() img_arr = self(img_arr) return torch.as_tensor(img_arr, device=img.device) - else: - raise NotImplementedError(f"{self.__class__} can not handle data of type {type(img)}.") + raise NotImplementedError(f"{self.__class__} can not handle data of type {type(img)}.") class FillHoles(Transform): @@ -415,12 +414,11 @@ def __call__(self, img: NdarrayTensor) -> NdarrayTensor: """ if isinstance(img, np.ndarray): return fill_holes(img, self.applied_labels, self.connectivity) - elif isinstance(img, torch.Tensor): + if isinstance(img, torch.Tensor): img_arr = img.detach().cpu().numpy() img_arr = self(img_arr) return torch.as_tensor(img_arr, device=img.device) - else: - raise NotImplementedError(f"{self.__class__} can not handle data of type {type(img)}.") + raise NotImplementedError(f"{self.__class__} can not handle data of type {type(img)}.") class LabelToContour(Transform): diff --git a/monai/transforms/utility/array.py b/monai/transforms/utility/array.py index 3de2408abd..fe73c6189c 100644 --- a/monai/transforms/utility/array.py +++ b/monai/transforms/utility/array.py @@ -1001,8 +1001,7 @@ def __call__( def _compute(op: Callable, data: np.ndarray): if self.channel_wise: return [op(c) for c in data] - else: - return op(data) + return op(data) custom_index = 0 for o in self.ops: diff --git a/monai/transforms/utils.py b/monai/transforms/utils.py index 366e2d245e..c108d973dc 100644 --- a/monai/transforms/utils.py +++ b/monai/transforms/utils.py @@ -1043,7 +1043,7 @@ def convert_to_tensor(data): """ if isinstance(data, torch.Tensor): return data.contiguous() - elif isinstance(data, np.ndarray): + if isinstance(data, np.ndarray): # skip array of string classes and object, refer to: # https://github.com/pytorch/pytorch/blob/v1.9.0/torch/utils/data/_utils/collate.py#L13 if re.search(r"[SaUO]", data.dtype.str) is None: @@ -1107,11 +1107,11 @@ def tensor_to_numpy(data): if isinstance(data, torch.Tensor): # invert Tensor to numpy, if scalar data, convert to number return data.item() if data.ndim == 0 else np.ascontiguousarray(data.detach().cpu().numpy()) - elif isinstance(data, dict): + if isinstance(data, dict): return {k: tensor_to_numpy(v) for k, v in data.items()} - elif isinstance(data, list): + if isinstance(data, list): return [tensor_to_numpy(i) for i in data] - elif isinstance(data, tuple): + if isinstance(data, tuple): return tuple(tensor_to_numpy(i) for i in data) return data diff --git a/monai/utils/deprecated.py b/monai/utils/deprecated.py index 4cf99f4b67..4c6b2db108 100644 --- a/monai/utils/deprecated.py +++ b/monai/utils/deprecated.py @@ -100,9 +100,8 @@ def _wrapper(*args, **kwargs): if is_func: return _wrapper - else: - obj.__init__ = _wrapper - return obj + obj.__init__ = _wrapper + return obj return _decorator diff --git a/monai/utils/dist.py b/monai/utils/dist.py index 5cb365e088..beb958a5c8 100644 --- a/monai/utils/dist.py +++ b/monai/utils/dist.py @@ -34,7 +34,7 @@ def get_dist_device(): backend = dist.get_backend() if backend == "nccl" and torch.cuda.is_available(): return torch.device(f"cuda:{torch.cuda.current_device()}") - elif backend == "gloo": + if backend == "gloo": return torch.device("cpu") return None diff --git a/monai/utils/jupyter_utils.py b/monai/utils/jupyter_utils.py index b86f9f442c..26487083b1 100644 --- a/monai/utils/jupyter_utils.py +++ b/monai/utils/jupyter_utils.py @@ -224,8 +224,7 @@ def _get_loss(data): if isinstance(output, list): return _get_loss(output[0]) - else: - return _get_loss(output) + return _get_loss(output) class StatusMembers(Enum):