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5ab5cab |
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import torch
import torch.nn as nn
class Upsample(nn.Module):
def __init__(self, in_channels : int, with_conv : bool):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size = 3, stride = 1, padding = 1)
def forward(self, x):
x = torch.nn.functional.interpolate(x, scale_factor = 2.0, mode = "nearest")
if self.with_conv:
x = self.conv(x)
return x
class Downsample(nn.Module):
def __init__(self, in_channels : int, with_conv : bool):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size = 3, stride = 2, padding = 0)
def forward(self, x):
if self.with_conv:
pad = (0, 1, 0, 1)
x = torch.nn.functional.pad(x, pad, mode = "constant", value = 0)
x = self.conv(x)
else:
x = torch.nn.functional.avg_pool2d(x, kernel_size = 2, stride = 2)
return x |