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# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from typing import Optional, Tuple, Union
import torch
from torch.nn import functional as F
def resize(input: torch.Tensor,
size: Optional[Union[Tuple[int, int], torch.Size]] = None,
scale_factor: Optional[float] = None,
mode: str = 'nearest',
align_corners: Optional[bool] = None,
warning: bool = True) -> torch.Tensor:
"""Resize a given input tensor using specified size or scale_factor.
Args:
input (torch.Tensor): The input tensor to be resized.
size (Optional[Union[Tuple[int, int], torch.Size]]): The desired
output size. Defaults to None.
scale_factor (Optional[float]): The scaling factor for resizing.
Defaults to None.
mode (str): The interpolation mode. Defaults to 'nearest'.
align_corners (Optional[bool]): Determines whether to align the
corners when using certain interpolation modes. Defaults to None.
warning (bool): Whether to display a warning when the input and
output sizes are not ideal for alignment. Defaults to True.
Returns:
torch.Tensor: The resized tensor.
"""
# Check if a warning should be displayed regarding input and output sizes
if warning:
if size is not None and align_corners:
input_h, input_w = tuple(int(x) for x in input.shape[2:])
output_h, output_w = tuple(int(x) for x in size)
if output_h > input_h or output_w > output_h:
if ((output_h > 1 and output_w > 1 and input_h > 1
and input_w > 1) and (output_h - 1) % (input_h - 1)
and (output_w - 1) % (input_w - 1)):
warnings.warn(
f'When align_corners={align_corners}, '
'the output would be more aligned if '
f'input size {(input_h, input_w)} is `x+1` and '
f'out size {(output_h, output_w)} is `nx+1`')
# Convert torch.Size to tuple if necessary
if isinstance(size, torch.Size):
size = tuple(int(x) for x in size)
# Perform the resizing operation
return F.interpolate(input, size, scale_factor, mode, align_corners)
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