Merge branch 'dev' into dev

Author: ericspod

monai/networks/layers/filtering.py

@@ -221,7 +221,8 @@ def __init__(self, spatial_sigma, color_sigma):
             self.len_spatial_sigma = 3
         else:
             raise ValueError(
-                f"len(spatial_sigma) {spatial_sigma} must match number of spatial dims {self.ken_spatial_sigma}."
+                f"Length of `spatial_sigma` must match number of spatial dims (1, 2 or 3)"
+                f"or be a single float value ({spatial_sigma=})."
             )
 
         # Register sigmas as trainable parameters.
@@ -231,6 +232,10 @@ def __init__(self, spatial_sigma, color_sigma):
         self.sigma_color = torch.nn.Parameter(torch.tensor(color_sigma))
 
     def forward(self, input_tensor):
+        if len(input_tensor.shape) < 3:
+            raise ValueError(
+                f"Input must have at least 3 dimensions (batch, channel, *spatial_dims), got {len(input_tensor.shape)}"
+            )
         if input_tensor.shape[1] != 1:
             raise ValueError(
                 f"Currently channel dimensions >1 ({input_tensor.shape[1]}) are not supported. "
@@ -239,24 +244,27 @@ def forward(self, input_tensor):
             )
 
         len_input = len(input_tensor.shape)
+        spatial_dims = len_input - 2
 
         # C++ extension so far only supports 5-dim inputs.
-        if len_input == 3:
+        if spatial_dims == 1:
             input_tensor = input_tensor.unsqueeze(3).unsqueeze(4)
-        elif len_input == 4:
+        elif spatial_dims == 2:
             input_tensor = input_tensor.unsqueeze(4)
 
-        if self.len_spatial_sigma != len_input:
-            raise ValueError(f"Spatial dimension ({len_input}) must match initialized len(spatial_sigma).")
+        if self.len_spatial_sigma != spatial_dims:
+            raise ValueError(
+                f"Number of spatial dimensions ({spatial_dims}) must match initialized `len(spatial_sigma)`."
+            )
 
         prediction = TrainableBilateralFilterFunction.apply(
             input_tensor, self.sigma_x, self.sigma_y, self.sigma_z, self.sigma_color
         )
 
         # Make sure to return tensor of the same shape as the input.
-        if len_input == 3:
+        if spatial_dims == 1:
             prediction = prediction.squeeze(4).squeeze(3)
-        elif len_input == 4:
+        elif spatial_dims == 2:
             prediction = prediction.squeeze(4)
 
         return prediction
@@ -389,7 +397,8 @@ def __init__(self, spatial_sigma, color_sigma):
             self.len_spatial_sigma = 3
         else:
             raise ValueError(
-                f"len(spatial_sigma) {spatial_sigma} must match number of spatial dims {self.ken_spatial_sigma}."
+                f"Length of `spatial_sigma` must match number of spatial dims (1, 2 or 3)\n"
+                f"or be a single float value ({spatial_sigma=})."
             )
 
         # Register sigmas as trainable parameters.
@@ -399,39 +408,45 @@ def __init__(self, spatial_sigma, color_sigma):
         self.sigma_color = torch.nn.Parameter(torch.tensor(color_sigma))
 
     def forward(self, input_tensor, guidance_tensor):
+        if len(input_tensor.shape) < 3:
+            raise ValueError(
+                f"Input must have at least 3 dimensions (batch, channel, *spatial_dims), got {len(input_tensor.shape)}"
+            )
         if input_tensor.shape[1] != 1:
             raise ValueError(
-                f"Currently channel dimensions >1 ({input_tensor.shape[1]}) are not supported. "
+                f"Currently channel dimensions > 1 ({input_tensor.shape[1]}) are not supported. "
                 "Please use multiple parallel filter layers if you want "
                 "to filter multiple channels."
             )
         if input_tensor.shape != guidance_tensor.shape:
             raise ValueError(
-                "Shape of input image must equal shape of guidance image."
-                f"Got {input_tensor.shape} and {guidance_tensor.shape}."
+                f"Shape of input image must equal shape of guidance image, got {input_tensor.shape} and {guidance_tensor.shape}."
             )
 
         len_input = len(input_tensor.shape)
+        spatial_dims = len_input - 2
 
         # C++ extension so far only supports 5-dim inputs.
-        if len_input == 3:
+        if spatial_dims == 1:
             input_tensor = input_tensor.unsqueeze(3).unsqueeze(4)
             guidance_tensor = guidance_tensor.unsqueeze(3).unsqueeze(4)
-        elif len_input == 4:
+        elif spatial_dims == 2:
             input_tensor = input_tensor.unsqueeze(4)
             guidance_tensor = guidance_tensor.unsqueeze(4)
 
-        if self.len_spatial_sigma != len_input:
-            raise ValueError(f"Spatial dimension ({len_input}) must match initialized len(spatial_sigma).")
+        if self.len_spatial_sigma != spatial_dims:
+            raise ValueError(
+                f"Number of spatial dimensions ({spatial_dims}) must match initialized `len(spatial_sigma)`."
+            )
 
         prediction = TrainableJointBilateralFilterFunction.apply(
             input_tensor, guidance_tensor, self.sigma_x, self.sigma_y, self.sigma_z, self.sigma_color
         )
 
         # Make sure to return tensor of the same shape as the input.
-        if len_input == 3:
+        if spatial_dims == 1:
             prediction = prediction.squeeze(4).squeeze(3)
-        elif len_input == 4:
+        elif spatial_dims == 2:
             prediction = prediction.squeeze(4)
 
         return prediction