def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
# type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor
"""
Equivalent to nn.functional.interpolate, but with support for empty batch sizes.
This will eventually be supported natively by PyTorch, and this
class can go away.
"""
if float(torchvision.__version__[:3]) < 0.7:
if input.numel() > 0:
return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners)
output_shape = _output_size(2, input, size, scale_factor)
output_shape = list(input.shape[:-2]) + list(output_shape)
return _new_empty_tensor(input, output_shape)
else:
return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)
def interpolate(
input: "Tensor",
size: "Optional[List[int]]" = None,
scale_factor: "Optional[float]" = None,
mode: str = "nearest",
align_corners: "Optional[bool]" = None,
) -> "Tensor":
"""
Equivalent to nn.functional.interpolate, but with support for empty batch sizes.
This will eventually be supported natively by PyTorch, and this
class can go away.
"""
# if float(torchvision.__version__[:3]) < 0.7:
if LooseVersion(torchvision.__version__) < LooseVersion("0.7.0"):
if input.numel() > 0:
return torch.nn.functional.interpolate(input, size, scale_factor,
mode, align_corners)
output_shape = _output_size(2, input, size, scale_factor)
output_shape = list(input.shape[:-2]) + list(output_shape)
return _new_empty_tensor(input, output_shape)
else:
return torchvision.ops.misc.interpolate(input, size, scale_factor,
mode, align_corners)
