import warnings
from typing import Optional, Tuple
import torch.nn as nn
import torch.nn.functional as F
[docs]
def resize(
input,
size=None,
scale_factor=None,
mode="nearest",
align_corners=None,
warning=True,
):
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`"
)
return F.interpolate(input, size, scale_factor, mode, align_corners)
[docs]
class Upsample(nn.Module):
def __init__(
self, size=None, scale_factor=None, mode="nearest", align_corners=None
):
super().__init__()
self.size = size
if isinstance(scale_factor, tuple):
self.scale_factor = tuple(float(factor) for factor in scale_factor)
else:
self.scale_factor = float(scale_factor) if scale_factor else None
self.mode = mode
self.align_corners = align_corners
[docs]
def forward(self, x):
if not self.size:
size = [int(t * self.scale_factor) for t in x.shape[-2:]]
else:
size = self.size
return resize(x, size, None, self.mode, self.align_corners)