Source code for minerva.utils.upsample

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)