Fixes for mypy type checking

Signed-off-by: Eric Kerfoot <[email protected]>

Author: ericspod

monai/apps/detection/networks/retinanet_network.py

@@ -88,8 +88,8 @@ def __init__(
 
         for layer in self.conv.children():
             if isinstance(layer, conv_type):  # type: ignore
-                torch.nn.init.normal_(layer.weight, std=0.01)
-                torch.nn.init.constant_(layer.bias, 0)
+                torch.nn.init.normal_(layer.weight, std=0.01)  # type: ignore[arg-type]
+                torch.nn.init.constant_(layer.bias, 0)  # type: ignore[arg-type]
 
         self.cls_logits = conv_type(in_channels, num_anchors * num_classes, kernel_size=3, stride=1, padding=1)
         torch.nn.init.normal_(self.cls_logits.weight, std=0.01)
@@ -167,8 +167,8 @@ def __init__(self, in_channels: int, num_anchors: int, spatial_dims: int):
 
         for layer in self.conv.children():
             if isinstance(layer, conv_type):  # type: ignore
-                torch.nn.init.normal_(layer.weight, std=0.01)
-                torch.nn.init.zeros_(layer.bias)
+                torch.nn.init.normal_(layer.weight, std=0.01)  # type: ignore[arg-type]
+                torch.nn.init.zeros_(layer.bias)  # type: ignore[arg-type]
 
     def forward(self, x: list[Tensor]) -> list[Tensor]:
         """
@@ -297,7 +297,7 @@ def __init__(
             )
         self.feature_extractor = feature_extractor
 
-        self.feature_map_channels: int = self.feature_extractor.out_channels
+        self.feature_map_channels: int = self.feature_extractor.out_channels  # type: ignore[assignment]
         self.num_anchors = num_anchors
         self.classification_head = RetinaNetClassificationHead(
             self.feature_map_channels, self.num_anchors, self.num_classes, spatial_dims=self.spatial_dims

monai/apps/detection/utils/box_coder.py

@@ -221,15 +221,15 @@ def decode_single(self, rel_codes: Tensor, reference_boxes: Tensor) -> Tensor:
 
             pred_ctr_xyx_axis = dxyz_axis * whd_axis[:, None] + ctr_xyz_axis[:, None]
             pred_whd_axis = torch.exp(dwhd_axis) * whd_axis[:, None]
-            pred_whd_axis = pred_whd_axis.to(dxyz_axis.dtype)
+            pred_whd_axis = pred_whd_axis.to(dxyz_axis.dtype)  # type: ignore[union-attr]
 
             # When convert float32 to float16, Inf or Nan may occur
             if torch.isnan(pred_whd_axis).any() or torch.isinf(pred_whd_axis).any():
                 raise ValueError("pred_whd_axis is NaN or Inf.")
 
             # Distance from center to box's corner.
             c_to_c_whd_axis = (
-                torch.tensor(0.5, dtype=pred_ctr_xyx_axis.dtype, device=pred_whd_axis.device) * pred_whd_axis
+                torch.tensor(0.5, dtype=pred_ctr_xyx_axis.dtype, device=pred_whd_axis.device) * pred_whd_axis  # type: ignore[arg-type]
             )
 
             pred_boxes.append(pred_ctr_xyx_axis - c_to_c_whd_axis)

monai/apps/reconstruction/networks/blocks/varnetblock.py

@@ -55,7 +55,7 @@ def soft_dc(self, x: Tensor, ref_kspace: Tensor, mask: Tensor) -> Tensor:
         Returns:
             Output of DC block with the same shape as x
         """
-        return torch.where(mask, x - ref_kspace, self.zeros) * self.dc_weight
+        return torch.where(mask, x - ref_kspace, self.zeros) * self.dc_weight  # type: ignore
 
     def forward(self, current_kspace: Tensor, ref_kspace: Tensor, mask: Tensor, sens_maps: Tensor) -> Tensor:
         """

monai/data/utils.py

@@ -753,7 +753,7 @@ def affine_to_spacing(affine: NdarrayTensor, r: int = 3, dtype=float, suppress_z
     if isinstance(_affine, torch.Tensor):
         spacing = torch.sqrt(torch.sum(_affine * _affine, dim=0))
     else:
-        spacing = np.sqrt(np.sum(_affine * _affine, axis=0))
+        spacing = np.sqrt(np.sum(_affine * _affine, axis=0))  # type: ignore[operator]
     if suppress_zeros:
         spacing[spacing == 0] = 1.0
     spacing_, *_ = convert_to_dst_type(spacing, dst=affine, dtype=dtype)

monai/data/video_dataset.py

@@ -177,7 +177,7 @@ def get_available_codecs() -> dict[str, str]:
                 for codec, ext in all_codecs.items():
                     writer = cv2.VideoWriter()
                     fname = os.path.join(tmp_dir, f"test{ext}")
-                    fourcc = cv2.VideoWriter_fourcc(*codec)
+                    fourcc = cv2.VideoWriter_fourcc(*codec)  # type: ignore[attr-defined]
                     noviderr = writer.open(fname, fourcc, 1, (10, 10))
                     if noviderr:
                         codecs[codec] = ext

monai/engines/utils.py

@@ -309,7 +309,7 @@ def __init__(self, scheduler: nn.Module, num_train_timesteps: int, condition_nam
         self.scheduler = scheduler
 
     def get_target(self, images, noise, timesteps):
-        return self.scheduler.get_velocity(images, noise, timesteps)
+        return self.scheduler.get_velocity(images, noise, timesteps)  # type: ignore[operator]
 
 
 def default_make_latent(

monai/inferers/inferer.py

@@ -882,7 +882,7 @@ def sample(
                 )
 
             # 2. compute previous image: x_t -> x_t-1
-            image, _ = scheduler.step(model_output, t, image)
+            image, _ = scheduler.step(model_output, t, image)  # type: ignore[operator]
             if save_intermediates and t % intermediate_steps == 0:
                 intermediates.append(image)
         if save_intermediates:
@@ -986,8 +986,8 @@ def get_likelihood(
             predicted_mean = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * noisy_image
 
             # get the posterior mean and variance
-            posterior_mean = scheduler._get_mean(timestep=t, x_0=inputs, x_t=noisy_image)
-            posterior_variance = scheduler._get_variance(timestep=t, predicted_variance=predicted_variance)
+            posterior_mean = scheduler._get_mean(timestep=t, x_0=inputs, x_t=noisy_image)  # type: ignore[operator]
+            posterior_variance = scheduler._get_variance(timestep=t, predicted_variance=predicted_variance)  # type: ignore[operator]
 
             log_posterior_variance = torch.log(posterior_variance)
             log_predicted_variance = torch.log(predicted_variance) if predicted_variance else log_posterior_variance
@@ -1436,7 +1436,7 @@ def sample(  # type: ignore[override]
                 )
 
             # 3. compute previous image: x_t -> x_t-1
-            image, _ = scheduler.step(model_output, t, image)
+            image, _ = scheduler.step(model_output, t, image)  # type: ignore[operator]
             if save_intermediates and t % intermediate_steps == 0:
                 intermediates.append(image)
         if save_intermediates:
@@ -1562,8 +1562,8 @@ def get_likelihood(  # type: ignore[override]
             predicted_mean = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * noisy_image
 
             # get the posterior mean and variance
-            posterior_mean = scheduler._get_mean(timestep=t, x_0=inputs, x_t=noisy_image)
-            posterior_variance = scheduler._get_variance(timestep=t, predicted_variance=predicted_variance)
+            posterior_mean = scheduler._get_mean(timestep=t, x_0=inputs, x_t=noisy_image)  # type: ignore[operator]
+            posterior_variance = scheduler._get_variance(timestep=t, predicted_variance=predicted_variance)  # type: ignore[operator]
 
             log_posterior_variance = torch.log(posterior_variance)
             log_predicted_variance = torch.log(predicted_variance) if predicted_variance else log_posterior_variance

monai/inferers/merger.py

@@ -53,8 +53,8 @@ def __init__(
         cropped_shape: Sequence[int] | None = None,
         device: torch.device | str | None = None,
     ) -> None:
-        self.merged_shape = merged_shape
-        self.cropped_shape = self.merged_shape if cropped_shape is None else cropped_shape
+        self.merged_shape: tuple[int, ...] = tuple(merged_shape)
+        self.cropped_shape: tuple[int, ...] = tuple(self.merged_shape if cropped_shape is None else cropped_shape)
         self.device = device
         self.is_finalized = False
 
@@ -231,9 +231,9 @@ def __init__(
         dtype: np.dtype | str = "float32",
         value_dtype: np.dtype | str = "float32",
         count_dtype: np.dtype | str = "uint8",
-        store: zarr.storage.Store | str = "merged.zarr",
-        value_store: zarr.storage.Store | str | None = None,
-        count_store: zarr.storage.Store | str | None = None,
+        store: zarr.storage.Store | str = "merged.zarr",  # type: ignore
+        value_store: zarr.storage.Store | str | None = None,  # type: ignore
+        count_store: zarr.storage.Store | str | None = None,  # type: ignore
         compressor: str | None = None,
         value_compressor: str | None = None,
         count_compressor: str | None = None,
@@ -251,18 +251,18 @@ def __init__(
         if version_geq(get_package_version("zarr"), "3.0.0"):
             if value_store is None:
                 self.tmpdir = TemporaryDirectory()
-                self.value_store = zarr.storage.LocalStore(self.tmpdir.name)
+                self.value_store = zarr.storage.LocalStore(self.tmpdir.name)  # type: ignore
             else:
-                self.value_store = value_store
+                self.value_store = value_store  # type: ignore
             if count_store is None:
                 self.tmpdir = TemporaryDirectory()
-                self.count_store = zarr.storage.LocalStore(self.tmpdir.name)
+                self.count_store = zarr.storage.LocalStore(self.tmpdir.name)  # type: ignore
             else:
-                self.count_store = count_store
+                self.count_store = count_store  # type: ignore
         else:
             self.tmpdir = None
-            self.value_store = zarr.storage.TempStore() if value_store is None else value_store
-            self.count_store = zarr.storage.TempStore() if count_store is None else count_store
+            self.value_store = zarr.storage.TempStore() if value_store is None else value_store  # type: ignore
+            self.count_store = zarr.storage.TempStore() if count_store is None else count_store  # type: ignore
         self.chunks = chunks
         self.compressor = compressor
         self.value_compressor = value_compressor
@@ -314,7 +314,7 @@ def aggregate(self, values: torch.Tensor, location: Sequence[int]) -> None:
         map_slice = ensure_tuple_size(map_slice, values.ndim, pad_val=slice(None), pad_from_start=True)
         with self.lock:
             self.values[map_slice] += values.numpy()
-            self.counts[map_slice] += 1
+            self.counts[map_slice] += 1  # type: ignore[operator]
 
     def finalize(self) -> zarr.Array:
         """
@@ -332,7 +332,7 @@ def finalize(self) -> zarr.Array:
         if not self.is_finalized:
             # use chunks for division to fit into memory
             for chunk in iterate_over_chunks(self.values.chunks, self.values.cdata_shape):
-                self.output[chunk] = self.values[chunk] / self.counts[chunk]
+                self.output[chunk] = self.values[chunk] / self.counts[chunk]  # type: ignore[operator]
             # finalize the shape
             self.output.resize(self.cropped_shape)
             # set finalize flag to protect performing in-place division again

monai/losses/sure_loss.py

@@ -11,7 +11,7 @@
 
 from __future__ import annotations
 
-from typing import Callable, Optional
+from typing import Callable, Optional, cast
 
 import torch
 import torch.nn as nn
@@ -92,7 +92,7 @@ def sure_loss_function(
         y_ref = operator(x)
 
     # get perturbed output
-    x_perturbed = x + eps * perturb_noise
+    x_perturbed = x + eps * perturb_noise  # type: ignore
     y_perturbed = operator(x_perturbed)
     # divergence
     divergence = torch.sum(1.0 / eps * torch.matmul(perturb_noise.permute(0, 1, 3, 2), y_perturbed - y_ref))  # type: ignore

monai/networks/blocks/feature_pyramid_network.py

@@ -54,7 +54,9 @@
 
 from collections import OrderedDict
 from collections.abc import Callable
+from typing import cast
 
+import torch
 import torch.nn.functional as F
 from torch import Tensor, nn
 
@@ -194,8 +196,8 @@ def __init__(
         conv_type_: type[nn.Module] = Conv[Conv.CONV, spatial_dims]
         for m in self.modules():
             if isinstance(m, conv_type_):
-                nn.init.kaiming_uniform_(m.weight, a=1)
-                nn.init.constant_(m.bias, 0.0)
+                nn.init.kaiming_uniform_(cast(torch.Tensor, m.weight), a=1)
+                nn.init.constant_(cast(torch.Tensor, m.bias), 0.0)
 
         if extra_blocks is not None:
             if not isinstance(extra_blocks, ExtraFPNBlock):