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Fix TrainableBilateralFilter 3D input validation (#7444) #8729

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Fix TrainableBilateralFilter 3D input validation (#7444) #8729

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4924aa6
Fix TrainableBilateralFilter 3D input validation (#7444)
getrichthroughcode Feb 1, 2026
8264cac
fix: apply same dimension handling fixes to TrainableJointBilateralFi…
getrichthroughcode Mar 4, 2026
a8c7a0e
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Mar 4, 2026
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40 changes: 25 additions & 15 deletions monai/networks/layers/filtering.py
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Original file line number Diff line number Diff line change
Expand Up @@ -221,7 +221,7 @@ 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"len(spatial_sigma) {spatial_sigma} must match number of spatial dims (1, 2 or 3)."
)

# Register sigmas as trainable parameters.
Expand All @@ -231,6 +231,10 @@ 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. "
Expand All @@ -239,24 +243,25 @@ 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"Spatial dimension ({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
Expand Down Expand Up @@ -389,7 +394,7 @@ 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"len(spatial_sigma) {spatial_sigma} must match number of spatial dims (1, 2, or 3)."
)
Comment on lines 395 to 398
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@coderabbitai coderabbitai bot Feb 1, 2026

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⚠️ Potential issue | 🔴 Critical

Same bug: self.len_spatial_sigma undefined in else branch.

Identical issue as TrainableBilateralFilter.__init__.

Proposed fix 
         else:
             raise ValueError(
-                f"len(spatial_sigma) {spatial_sigma} must match number of spatial dims {self.len_spatial_sigma}."
+                f"len(spatial_sigma) must be 1, 2, or 3, got {len(spatial_sigma)}."
             )
🧰 Tools
🪛 Ruff (0.14.14)

[warning] 396-398: Avoid specifying long messages outside the exception class

(TRY003)

🤖 Prompt for AI Agents 
In `@monai/networks/layers/filtering.py` around lines 395 - 398, The else branch
references an undefined attribute self.len_spatial_sigma; fix it by using a
defined value (e.g., compute len_spatial = len(self.spatial_sigma) or use
self.spatial_ndim) when building the error message in the failing branch of the
initializer (same place as TrainableBilateralFilter.__init__). Replace
self.len_spatial_sigma with the actual computed length (len(self.spatial_sigma)
or self.spatial_ndim) so the ValueError message prints a valid
expected-dimension value.


# Register sigmas as trainable parameters.
Expand All @@ -399,9 +404,13 @@ 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."
)
Expand All @@ -412,26 +421,27 @@ def forward(self, input_tensor, guidance_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)
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"Spatial dimension ({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
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