Function _output_size

util/misc.py:35–47

(dim, input, size, scale_factor)

                "Input is {}D, scale_factor size is {}".format(dim, len(scale_factor))
            )
    def _output_size(dim, input, size, scale_factor):
        # type: (int, Tensor, Optional[List[int]], Optional[float]) -> List[int]
        assert dim == 2
        _check_size_scale_factor(dim, size, scale_factor)
        if size is not None:
            return size
        # if dim is not 2 or scale_factor is iterable use _ntuple instead of concat
        assert scale_factor is not None and isinstance(scale_factor, (int, float))
        scale_factors = [scale_factor, scale_factor]
        # math.floor might return float in py2.7
        return [
            int(math.floor(input.size(i + 2) * scale_factors[i])) for i in range(dim)
        ]
elif float(major_version) < 1 and float(minor_version) < 7:
    from torchvision.ops import _new_empty_tensor
    from torchvision.ops.misc import _output_size