Fix three malformed error messages

monai/inferers/inferer.py

@@ -1773,7 +1773,7 @@ def __init__(
         super().__init__(scheduler=scheduler)
         self.scale_factor = scale_factor
         if (ldm_latent_shape is None) ^ (autoencoder_latent_shape is None):
-            raise ValueError("If ldm_latent_shape is None, autoencoder_latent_shape must be None" "and vice versa.")
+            raise ValueError("If ldm_latent_shape is None, autoencoder_latent_shape must be None and vice versa.")
         self.ldm_latent_shape = ldm_latent_shape
         self.autoencoder_latent_shape = autoencoder_latent_shape
         if self.ldm_latent_shape is not None and self.autoencoder_latent_shape is not None:

monai/metrics/utils.py

@@ -101,7 +101,7 @@ def do_metric_reduction(
 
     Raises:
         ValueError: When ``reduction`` is not one of
-            ["mean", "sum", "mean_batch", "sum_batch", "mean_channel", "sum_channel" "none"].
+            ["mean", "sum", "mean_batch", "sum_batch", "mean_channel", "sum_channel", "none"].
     """
 
     # some elements might be Nan (if ground truth y was missing (zeros))
@@ -141,7 +141,7 @@ def do_metric_reduction(
     elif reduction != MetricReduction.NONE:
         raise ValueError(
             f"Unsupported reduction: {reduction}, available options are "
-            '["mean", "sum", "mean_batch", "sum_batch", "mean_channel", "sum_channel" "none"].'
+            '["mean", "sum", "mean_batch", "sum_batch", "mean_channel", "sum_channel", "none"].'
         )
     return f, not_nans
 

monai/networks/utils.py

@@ -440,7 +440,7 @@ def pixelunshuffle(x: torch.Tensor, spatial_dims: int, scale_factor: int) -> tor
 
     if any(d % factor != 0 for d in input_size[2:]):
         raise ValueError(
-            f"All spatial dimensions must be divisible by factor {factor}. " f", spatial shape is: {input_size[2:]}"
+            f"All spatial dimensions must be divisible by factor {factor}, spatial shape is: {input_size[2:]}"
         )
     output_size = [batch_size, new_channels] + [d // factor for d in input_size[2:]]
     reshaped_size = [batch_size, channels] + sum([[d // factor, factor] for d in input_size[2:]], [])

tests/networks/utils/test_pixelunshuffle.py

@@ -40,6 +40,11 @@ def test_different_scale_factor(self):
         out = pixelunshuffle(x, spatial_dims=2, scale_factor=3)
         torch.testing.assert_close(out, torch.pixel_unshuffle(x, 3))
 
+    def test_indivisible_spatial_dims(self):
+        x = torch.randn(1, 2, 7, 8)
+        with self.assertRaisesRegex(ValueError, r"divisible by factor 2, spatial shape is: \[7, 8\]"):
+            pixelunshuffle(x, spatial_dims=2, scale_factor=2)
+
     def test_inverse_operation(self):
         x = torch.arange(4096).reshape(1, 8, 8, 8, 8)
         shuffled = pixelshuffle(x, spatial_dims=3, scale_factor=2)