mirror of https://github.com/alibaba/EasyCV.git
49 lines
2.4 KiB
Python
49 lines
2.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
# Adapt from: https://github.com/open-mmlab/mmpose/blob/master/mmpose/models/utils/ops.py
|
|
import warnings
|
|
|
|
import torch
|
|
import torch.nn.functional as F
|
|
|
|
|
|
def resize_tensor(input,
|
|
size=None,
|
|
scale_factor=None,
|
|
mode='nearest',
|
|
align_corners=None,
|
|
warning=True):
|
|
"""Resize tensor with F.interpolate.
|
|
|
|
Args:
|
|
input (Tensor): the input tensor.
|
|
size (Tuple[int, int]): output spatial size.
|
|
scale_factor (float or Tuple[float]): multiplier for spatial size.
|
|
If scale_factor is a tuple, its length has to match input.dim().
|
|
mode (str): algorithm used for upsampling:
|
|
'nearest' | 'linear' | 'bilinear' | 'bicubic' | 'trilinear' | 'area'. Default: 'nearest'
|
|
align_corners (bool): Geometrically, we consider the pixels of the input and output as squares rather than points.
|
|
If set to True, the input and output tensors are aligned by the center points of their corner pixels,
|
|
preserving the values at the corner pixels.
|
|
|
|
If set to False, the input and output tensors are aligned by the corner points of their corner pixels,
|
|
and the interpolation uses edge value padding for out-of-boundary values,
|
|
making this operation independent of input size when scale_factor is kept the same.
|
|
This only has an effect when mode is 'linear', 'bilinear', 'bicubic' or 'trilinear'.
|
|
"""
|
|
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 more aligned if '
|
|
f'input size {(input_h, input_w)} is `x+1` and '
|
|
f'out size {(output_h, output_w)} is `nx+1`')
|
|
if isinstance(size, torch.Size):
|
|
size = tuple(int(x) for x in size)
|
|
return F.interpolate(input, size, scale_factor, mode, align_corners)
|