Added test for uno horizontal_skips_map when None, and make validate_scaling_factor to return None if all scalings are 1 for eff. #273
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… skips_map are the same
… the swap of for loops for skip
…n fno block. This error was blocking the uno to use the scaling factor, as the validate_scaling_factor was giving None
…uno, and modify validate_scaling_factor to accept list of list
…aling_factto None when all scaling are 1 for efficiency
JeanKossaifi
reviewed
Feb 6, 2024
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| def apply_complex(fr, fi, x, dtype=torch.cfloat): |
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Could we make the variable names clearer? e.g. expand f, use real and imag etc.
JeanKossaifi
reviewed
Feb 6, 2024
neuralop/layers/complex_utils.py
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| into a module that operates on complex-valued spatial data. | ||
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| def __init__(self, mod): |
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Suggested change
| def __init__(self, mod): | |
| def __init__(self, module): |
JeanKossaifi
reviewed
Feb 6, 2024
neuralop/layers/complex_utils.py
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| def __init__(self, mod): | ||
| super(ComplexValued, self).__init__() | ||
| self.fr = deepcopy(mod) | ||
| self.fi = deepcopy(mod) |
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Is the deep copy needed? Are we expecting the module to be used somewhere else before conversion?
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This line of code is from @dhpitt. I am not sure if deepcopy is necessary.
JeanKossaifi
reviewed
Feb 6, 2024
Co-authored-by: Jean Kossaifi <jean.kossaifi@gmail.com>
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The latest update here is about complex FNO (main changes are in the spectral convolution). I have also added pytest when the data is complex. |
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