From c0009b3f2e1160b6e6ec109c11fe4391c4d72a39 Mon Sep 17 00:00:00 2001 From: monai-bot Date: Mon, 20 Nov 2023 06:16:07 +0000 Subject: [PATCH] [MONAI] code formatting Signed-off-by: monai-bot --- monai/apps/detection/transforms/box_ops.py | 2 +- monai/data/grid_dataset.py | 4 ++-- monai/data/image_writer.py | 2 +- monai/data/wsi_reader.py | 2 +- monai/metrics/utils.py | 2 +- monai/networks/nets/resnet.py | 6 +++--- monai/transforms/croppad/array.py | 8 +++----- monai/transforms/spatial/array.py | 2 +- monai/transforms/utility/array.py | 2 +- monai/transforms/utils.py | 8 ++++---- tests/test_inverse.py | 2 +- tests/utils.py | 4 ++-- 12 files changed, 21 insertions(+), 23 deletions(-) diff --git a/monai/apps/detection/transforms/box_ops.py b/monai/apps/detection/transforms/box_ops.py index fb870c952e..404854c4c0 100644 --- a/monai/apps/detection/transforms/box_ops.py +++ b/monai/apps/detection/transforms/box_ops.py @@ -407,7 +407,7 @@ def rot90_boxes( spatial_dims: int = get_spatial_dims(boxes=boxes) spatial_size_ = list(ensure_tuple_rep(spatial_size, spatial_dims)) - axes = ensure_tuple(axes) # type: ignore + axes = ensure_tuple(axes) if len(axes) != 2: raise ValueError("len(axes) must be 2.") diff --git a/monai/data/grid_dataset.py b/monai/data/grid_dataset.py index 9079032e6f..689138179a 100644 --- a/monai/data/grid_dataset.py +++ b/monai/data/grid_dataset.py @@ -352,8 +352,8 @@ def __iter__(self): raise RuntimeError( "Cache buffer is not initialized, please call `set_data()` before epoch begins." ) - data = self._cache[cache_index] # type: ignore - other = self._cache_other[cache_index] # type: ignore + data = self._cache[cache_index] + other = self._cache_other[cache_index] # load data from cache and execute from the first random transform data = deepcopy(data) if self.copy_cache else data diff --git a/monai/data/image_writer.py b/monai/data/image_writer.py index db0bfa96b8..b9e8b9e68e 100644 --- a/monai/data/image_writer.py +++ b/monai/data/image_writer.py @@ -276,7 +276,7 @@ def resample_if_needed( # convert back at the end if isinstance(output_array, MetaTensor): output_array.applied_operations = [] - data_array, *_ = convert_data_type(output_array, output_type=orig_type) # type: ignore + data_array, *_ = convert_data_type(output_array, output_type=orig_type) affine, *_ = convert_data_type(output_array.affine, output_type=orig_type) # type: ignore return data_array[0], affine diff --git a/monai/data/wsi_reader.py b/monai/data/wsi_reader.py index 54e12eb0cd..b31d4d9c3a 100644 --- a/monai/data/wsi_reader.py +++ b/monai/data/wsi_reader.py @@ -111,7 +111,7 @@ def __init__( self.set_device(device) self.mode = mode self.kwargs = kwargs - self.mpp: tuple[float, float] | None = ensure_tuple_rep(mpp, 2) if mpp is not None else None # type: ignore + self.mpp: tuple[float, float] | None = ensure_tuple_rep(mpp, 2) if mpp is not None else None self.power = power self.mpp_rtol = mpp_rtol self.mpp_atol = mpp_atol diff --git a/monai/metrics/utils.py b/monai/metrics/utils.py index 4d4e6570c5..62e6520b96 100644 --- a/monai/metrics/utils.py +++ b/monai/metrics/utils.py @@ -205,7 +205,7 @@ def get_mask_edges( or_vol = seg_pred | seg_gt if not or_vol.any(): pred, gt = lib.zeros(seg_pred.shape, dtype=bool), lib.zeros(seg_gt.shape, dtype=bool) - return (pred, gt) if spacing is None else (pred, gt, pred, gt) # type: ignore + return (pred, gt) if spacing is None else (pred, gt, pred, gt) channel_first = [seg_pred[None], seg_gt[None], or_vol[None]] if spacing is None and not use_cucim: # cpu only erosion seg_pred, seg_gt, or_vol = convert_to_tensor(channel_first, device="cpu", dtype=bool) diff --git a/monai/networks/nets/resnet.py b/monai/networks/nets/resnet.py index fca73f4de3..34a4b7057e 100644 --- a/monai/networks/nets/resnet.py +++ b/monai/networks/nets/resnet.py @@ -239,9 +239,9 @@ def __init__( self.conv1 = conv_type( n_input_channels, self.in_planes, - kernel_size=conv1_kernel_size, # type: ignore - stride=conv1_stride, # type: ignore - padding=tuple(k // 2 for k in conv1_kernel_size), # type: ignore + kernel_size=conv1_kernel_size, + stride=conv1_stride, + padding=tuple(k // 2 for k in conv1_kernel_size), bias=False, ) self.bn1 = norm_type(self.in_planes) diff --git a/monai/transforms/croppad/array.py b/monai/transforms/croppad/array.py index 6a3798e7ba..ce3701b263 100644 --- a/monai/transforms/croppad/array.py +++ b/monai/transforms/croppad/array.py @@ -386,7 +386,7 @@ def compute_slices( if roi_slices: if not all(s.step is None or s.step == 1 for s in roi_slices): raise ValueError(f"only slice steps of 1/None are currently supported, got {roi_slices}.") - return ensure_tuple(roi_slices) # type: ignore + return ensure_tuple(roi_slices) else: if roi_center is not None and roi_size is not None: roi_center_t = convert_to_tensor(data=roi_center, dtype=torch.int16, wrap_sequence=True, device="cpu") @@ -408,10 +408,8 @@ def compute_slices( roi_end_t = torch.maximum(roi_end_t, roi_start_t) # convert to slices (accounting for 1d) if roi_start_t.numel() == 1: - return ensure_tuple([slice(int(roi_start_t.item()), int(roi_end_t.item()))]) # type: ignore - return ensure_tuple( # type: ignore - [slice(int(s), int(e)) for s, e in zip(roi_start_t.tolist(), roi_end_t.tolist())] - ) + return ensure_tuple([slice(int(roi_start_t.item()), int(roi_end_t.item()))]) + return ensure_tuple([slice(int(s), int(e)) for s, e in zip(roi_start_t.tolist(), roi_end_t.tolist())]) def __call__( # type: ignore[override] self, img: torch.Tensor, slices: tuple[slice, ...], lazy: bool | None = None diff --git a/monai/transforms/spatial/array.py b/monai/transforms/spatial/array.py index 9d55aa013b..8ad86b72dd 100644 --- a/monai/transforms/spatial/array.py +++ b/monai/transforms/spatial/array.py @@ -1157,7 +1157,7 @@ def __init__(self, k: int = 1, spatial_axes: tuple[int, int] = (0, 1), lazy: boo """ LazyTransform.__init__(self, lazy=lazy) self.k = (4 + (k % 4)) % 4 # 0, 1, 2, 3 - spatial_axes_: tuple[int, int] = ensure_tuple(spatial_axes) # type: ignore + spatial_axes_: tuple[int, int] = ensure_tuple(spatial_axes) if len(spatial_axes_) != 2: raise ValueError(f"spatial_axes must be 2 numbers to define the plane to rotate, got {spatial_axes_}.") self.spatial_axes = spatial_axes_ diff --git a/monai/transforms/utility/array.py b/monai/transforms/utility/array.py index caf02d7b00..2322f2123f 100644 --- a/monai/transforms/utility/array.py +++ b/monai/transforms/utility/array.py @@ -372,7 +372,7 @@ def __call__(self, img: NdarrayOrTensor, dtype: DtypeLike | torch.dtype = None) TypeError: When ``img`` type is not in ``Union[numpy.ndarray, torch.Tensor]``. """ - return convert_data_type(img, output_type=type(img), dtype=dtype or self.dtype)[0] # type: ignore + return convert_data_type(img, output_type=type(img), dtype=dtype or self.dtype)[0] class ToTensor(Transform): diff --git a/monai/transforms/utils.py b/monai/transforms/utils.py index 678219991f..e282ecff24 100644 --- a/monai/transforms/utils.py +++ b/monai/transforms/utils.py @@ -521,7 +521,7 @@ def correct_crop_centers( for c, v_s, v_e in zip(centers, valid_start, valid_end): center_i = min(max(c, v_s), v_e - 1) valid_centers.append(int(center_i)) - return ensure_tuple(valid_centers) # type: ignore + return ensure_tuple(valid_centers) def generate_pos_neg_label_crop_centers( @@ -579,7 +579,7 @@ def generate_pos_neg_label_crop_centers( # shift center to range of valid centers centers.append(correct_crop_centers(center, spatial_size, label_spatial_shape, allow_smaller)) - return ensure_tuple(centers) # type: ignore + return ensure_tuple(centers) def generate_label_classes_crop_centers( @@ -639,7 +639,7 @@ def generate_label_classes_crop_centers( # shift center to range of valid centers centers.append(correct_crop_centers(center, spatial_size, label_spatial_shape, allow_smaller)) - return ensure_tuple(centers) # type: ignore + return ensure_tuple(centers) def create_grid( @@ -2218,7 +2218,7 @@ def distance_transform_edt( if not r_vals: return None device = img.device if isinstance(img, torch.Tensor) else None - return convert_data_type(r_vals[0] if len(r_vals) == 1 else r_vals, output_type=type(img), device=device)[0] # type: ignore + return convert_data_type(r_vals[0] if len(r_vals) == 1 else r_vals, output_type=type(img), device=device)[0] if __name__ == "__main__": diff --git a/tests/test_inverse.py b/tests/test_inverse.py index 3f07b43d6d..6bd14a19f1 100644 --- a/tests/test_inverse.py +++ b/tests/test_inverse.py @@ -310,7 +310,7 @@ TESTS_COMPOSE_X2 = [(t[0] + " Compose", t[1], t[2], t[3], Compose(Compose(t[4:]))) for t in TESTS] -TESTS = TESTS + TESTS_COMPOSE_X2 # type: ignore +TESTS = TESTS + TESTS_COMPOSE_X2 NUM_SAMPLES = 5 N_SAMPLES_TESTS = [ diff --git a/tests/utils.py b/tests/utils.py index cf1711292f..ee800598bb 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -832,9 +832,9 @@ def equal_state_dict(st_1, st_2): [[2.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 2.0, 0.0], [0.0, 0.0, 0.0, 1.0]] ) _metatensor_creator = partial(MetaTensor, meta={"a": "b", "affine": DEFAULT_TEST_AFFINE}) -TEST_NDARRAYS_NO_META_TENSOR: tuple[Callable] = (np.array,) + TEST_TORCH_TENSORS # type: ignore +TEST_NDARRAYS_NO_META_TENSOR: tuple[Callable] = (np.array,) + TEST_TORCH_TENSORS TEST_NDARRAYS: tuple[Callable] = TEST_NDARRAYS_NO_META_TENSOR + (_metatensor_creator,) # type: ignore -TEST_TORCH_AND_META_TENSORS: tuple[Callable] = TEST_TORCH_TENSORS + (_metatensor_creator,) # type: ignore +TEST_TORCH_AND_META_TENSORS: tuple[Callable] = TEST_TORCH_TENSORS + (_metatensor_creator,) # alias for branch tests TEST_NDARRAYS_ALL = TEST_NDARRAYS