Fix network_type docstring formatting in PerceptualLoss

monai/data/image_writer.py

@@ -116,7 +116,26 @@ def resolve_writer(ext_name, error_if_not_found=True) -> Sequence:
         except Exception:  # other writer init errors indicating it exists
             avail_writers.append(_writer)
     if not avail_writers and error_if_not_found:
-        raise OptionalImportError(f"No ImageWriter backend found for {fmt}.")
+        recommended_packages = {
+            "png": "Pillow",
+            "jpg": "Pillow",
+            "jpeg": "Pillow",
+            "nii": "nibabel or SimpleITK",
+            "nii.gz": "nibabel or SimpleITK",
+            "nrrd": "pynrrd",
+            "tif": "Pillow or tifffile",
+            "tiff": "Pillow or tifffile",
+        }
+
+        fmt_clean = fmt.lower()
+        package_hint = recommended_packages.get(fmt_clean, "")
+
+        msg = f"No ImageWriter backend found for {fmt}."
+        if package_hint:
+            msg += f" Please install '{package_hint}' (e.g., pip install {package_hint})."
+
+        raise OptionalImportError(msg)
+
     writer_tuple = ensure_tuple(avail_writers)
     SUPPORTED_WRITERS[fmt] = writer_tuple
     return writer_tuple

monai/losses/perceptual.py

@@ -23,7 +23,7 @@
 torchvision, _ = optional_import("torchvision")
 
 
-class PercetualNetworkType(StrEnum):
+class PerceptualNetworkType(StrEnum):
     alex = "alex"
     vgg = "vgg"
     squeeze = "squeeze"
@@ -49,9 +49,15 @@ class PerceptualLoss(nn.Module):
 
     Args:
         spatial_dims: number of spatial dimensions.
-        network_type: {``"alex"``, ``"vgg"``, ``"squeeze"``, ``"radimagenet_resnet50"``,
-        ``"medicalnet_resnet10_23datasets"``, ``"medicalnet_resnet50_23datasets"``, ``"resnet50"``}
-            Specifies the network architecture to use. Defaults to ``"alex"``.
+        network_type: str | PercetualNetworkType = PercetualNetworkType.alex,
+            One of:
+            - "alex"
+            - "vgg"
+            - "squeeze"
+            - "radimagenet_resnet50"
+            - "medicalnet_resnet10_23datasets"
+            - "medicalnet_resnet50_23datasets"
+            - "resnet50"
         is_fake_3d: if True use 2.5D approach for a 3D perceptual loss.
         fake_3d_ratio: ratio of how many slices per axis are used in the 2.5D approach.
         cache_dir: path to cache directory to save the pretrained network weights.
@@ -70,7 +76,7 @@ class PerceptualLoss(nn.Module):
     def __init__(
         self,
         spatial_dims: int,
-        network_type: str = PercetualNetworkType.alex,
+        network_type: str = PerceptualNetworkType.alex,
         is_fake_3d: bool = True,
         fake_3d_ratio: float = 0.5,
         cache_dir: str | None = None,
@@ -93,10 +99,10 @@ def __init__(
         if channel_wise and "medicalnet_" not in network_type:
             raise ValueError("Channel-wise loss is only compatible with MedicalNet networks.")
 
-        if network_type.lower() not in list(PercetualNetworkType):
+        if network_type.lower() not in list(PerceptualNetworkType):
             raise ValueError(
                 "Unrecognised criterion entered for Adversarial Loss. Must be one in: %s"
-                % ", ".join(PercetualNetworkType)
+                % ", ".join(PerceptualNetworkType)
             )
 
         if cache_dir: