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| import warnings
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| import torch.nn.functional as F
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| def resize(input, size=None, scale_factor=None, mode="nearest", align_corners=None, warning=False):
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| if warning:
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| if size is not None and align_corners:
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| input_h, input_w = tuple(int(x) for x in input.shape[2:])
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| output_h, output_w = tuple(int(x) for x in size)
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| if output_h > input_h or output_w > output_h:
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| if (
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| (output_h > 1 and output_w > 1 and input_h > 1 and input_w > 1)
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| and (output_h - 1) % (input_h - 1)
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| and (output_w - 1) % (input_w - 1)
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| ):
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| warnings.warn(
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| f"When align_corners={align_corners}, "
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| "the output would more aligned if "
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| f"input size {(input_h, input_w)} is `x+1` and "
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| f"out size {(output_h, output_w)} is `nx+1`"
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| )
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| return F.interpolate(input, size, scale_factor, mode, align_corners)
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|
