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import torch.nn as nn
from torch.nn import functional as F
import torch
import numpy as np
def tensor2array(tensors):
arrays = tensors.detach().to("cpu").numpy()
return np.transpose(arrays, (0, 2, 3, 1))
class ResidualBlock(nn.Module):
def __init__(self, channels):
super(ResidualBlock, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(channels, channels, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(channels, channels, kernel_size=3, padding=1)
)
def forward(self, x):
residual = self.conv(x)
return x + residual
class DownsampleBlock(nn.Module):
def __init__(self, in_channels, out_channels, withConvRelu=True):
super(DownsampleBlock, self).__init__()
if withConvRelu:
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, stride=2),
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
nn.ReLU(inplace=True)
)
else:
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, stride=2)
def forward(self, x):
return self.conv(x)
class ConvBlock(nn.Module):
def __init__(self, inChannels, outChannels, convNum):
super(ConvBlock, self).__init__()
self.inConv = nn.Sequential(
nn.Conv2d(inChannels, outChannels, kernel_size=3, padding=1),
nn.ReLU(inplace=True)
)
layers = []
for _ in range(convNum - 1):
layers.append(nn.Conv2d(outChannels, outChannels, kernel_size=3, padding=1))
layers.append(nn.ReLU(inplace=True))
self.conv = nn.Sequential(*layers)
def forward(self, x):
x = self.inConv(x)
x = self.conv(x)
return x
class UpsampleBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(UpsampleBlock, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, stride=1),
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
nn.ReLU(inplace=True)
)
def forward(self, x):
x = F.interpolate(x, scale_factor=2, mode='nearest')
return self.conv(x)
class SkipConnection(nn.Module):
def __init__(self, channels):
super(SkipConnection, self).__init__()
self.conv = nn.Conv2d(2 * channels, channels, 1, bias=False)
def forward(self, x, y):
x = torch.cat((x, y), 1)
return self.conv(x) |