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benjijamorris
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May 23, 2025
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Looks great! My one suggestion would be to add an explicit spatial_dims argument. I think this would take the guesswork out of trying to find the spatial dimensions and make the code simpler but I can be convinced otherwise!
| raise TypeError(f"Input '{key}' must be a PyTorch tensor, got {type(x)}") | ||
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| # Determine expected tensor dimensions and spatial size length | ||
| input_dims = x.dim() |
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Monai transforms expect C[Z]YX images - would it be reasonable to enforce this based on the spatial size and then just add the batch dimension right before pooling? I often have an explicit spatial_dims argument to help with this kind of check
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| # Normalize spatial_size to match expected number of spatial dimensions | ||
| try: | ||
| spatial_size = ensure_tuple_rep(self.spatial_size, expected_spatial_dims) |
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Adding an explicit spatial_dims argument here would let you find the spatial size once in the init
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| orig_size = x.shape[-expected_spatial_dims:] | ||
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| # Replace non-positive spatial_size values with original dimensions |
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What does this PR do?
maxpool_resizetransform to obtain maxpool-like functionality when resizing inputs.MONAI provides resize functionality via
monai.transforms.Resizedwhich usestorch.nn.functional.interpolatealong with one of the available interpolation modes. None of the modes offer the maxpool-like functionality and thus we need a custom transform. No additional dependencies required. This is an optional transform to be used with config and does not introduce any breaking changes.Before submitting
pytestcommand?pre-commit run -acommand?Did you have fun?
Make sure you had fun coding 🙃