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Fix: swap input_amplitude and target_amplitude in JukeboxLoss.forward #8821

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Fix: swap input_amplitude and target_amplitude in JukeboxLoss.forward #8821

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d0efb62
Fix: swap input_amplitude and target_amplitude in JukeboxLoss.forward
Zeesejo Apr 11, 2026
4417d79
Fix: remove redundant `1-` from SSIMLoss docstring examples
Zeesejo Apr 11, 2026
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4 changes: 2 additions & 2 deletions monai/losses/spectral_loss.py
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Original file line number Diff line number Diff line change
Expand Up @@ -55,8 +55,8 @@ def __init__(
self.fft_norm = fft_norm

def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
input_amplitude = self._get_fft_amplitude(target)
target_amplitude = self._get_fft_amplitude(input)
input_amplitude = self._get_fft_amplitude(input)
target_amplitude = self._get_fft_amplitude(target)
Comment on lines 57 to +59
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Copilot AI Apr 11, 2026

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PR description indicates "In-line docstrings updated", but this diff only swaps the input_amplitude/target_amplitude assignments and doesn’t modify any docstrings/comments. Either update the PR checklist/description to match the actual change set or include the intended docstring update in this PR.

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Comment on lines 57 to 60
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Copilot AI Apr 11, 2026

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The existing unit tests for JukeboxLoss validate numeric output/shape, but they won’t fail if input/target amplitudes are swapped because MSE is symmetric. To prevent regressions of this specific semantics fix, consider adding a test that patches/spies on _get_fft_amplitude (e.g., via unittest.mock) and asserts it is called first with input and then with target inside forward().

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# Compute distance between amplitude of frequency components
# See Section 3.3 from https://arxiv.org/abs/2005.00341
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6 changes: 3 additions & 3 deletions monai/losses/ssim_loss.py
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Expand Up @@ -111,17 +111,17 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
# 2D data
x = torch.ones([1,1,10,10])/2
y = torch.ones([1,1,10,10])/2
print(1-SSIMLoss(spatial_dims=2)(x,y))
print(SSIMLoss(spatial_dims=2)(x,y))

# pseudo-3D data
x = torch.ones([1,5,10,10])/2 # 5 could represent number of slices
y = torch.ones([1,5,10,10])/2
print(1-SSIMLoss(spatial_dims=2)(x,y))
print(SSIMLoss(spatial_dims=2)(x,y))

# 3D data
x = torch.ones([1,1,10,10,10])/2
y = torch.ones([1,1,10,10,10])/2
print(1-SSIMLoss(spatial_dims=3)(x,y))
print(SSIMLoss(spatial_dims=3)(x,y))
"""
ssim_value = self.ssim_metric._compute_tensor(input, target).view(-1, 1)
loss: torch.Tensor = 1 - ssim_value
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