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import torch
import torch.nn as nn
import torch.nn.init as init
import torchvision.models as models
from torchvision.models import ResNet34_Weights
class ResNetEncoder(nn.Module):
def __init__(self, freeze=True):
super().__init__()
resnet = models.resnet34(weights=ResNet34_Weights.IMAGENET1K_V1)
self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
with torch.no_grad():
self.conv1.weight[:] = resnet.conv1.weight.mean(dim=1, keepdim=True)
self.bn1 = resnet.bn1
self.relu = resnet.relu
self.maxpool = resnet.maxpool
self.layer1 = resnet.layer1
self.layer2 = resnet.layer2
self.layer3 = resnet.layer3
self.layer4 = resnet.layer4
if freeze:
for param in self.parameters():
param.requires_grad = False
def forward(self, x):
# x = (x - 0.449) / 0.226
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x1 = self.maxpool(x)
x2 = self.layer1(x1)
del x1
x3 = self.layer2(x2)
x4 = self.layer3(x3)
x5 = self.layer4(x4)
return x, x2, x3, x4, x5
def icnr(tensor, scale=2, init_func=init.kaiming_normal_):
ni, nf, h, w = tensor.shape
ni2 = int(ni / (scale ** 2))
k = init_func(torch.zeros([ni2, nf, h, w]))
k = k.repeat_interleave(scale ** 2, 0)
with torch.no_grad():
tensor.copy_(k)
class PixelShuffleICNR(nn.Module):
def __init__(self, in_channels, out_channels, scale=2):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels * (scale ** 2), kernel_size=3, padding=1)
icnr(self.conv.weight, scale=scale)
self.pixel_shuffle = nn.PixelShuffle(scale)
self.bn = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv(x)
x = self.pixel_shuffle(x)
x = self.bn(x)
x = self.relu(x)
return x
class DecoderBlock(nn.Module):
def __init__(self, in_channels, skip_channels, out_channels):
super().__init__()
self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)
self.conv = nn.Sequential(
nn.Conv2d(in_channels + skip_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
)
def forward(self, x, skip):
x = self.upsample(x)
if skip is not None:
x = torch.cat([x, skip], dim=1)
return self.conv(x)
class Decoder(nn.Module):
def __init__(self):
super().__init__()
self.dec4 = DecoderBlock(512, 256, 256)
self.dec3 = DecoderBlock(256, 128, 128)
self.dec2 = DecoderBlock(128, 64, 64)
self.dec1 = DecoderBlock(64, 64, 64)
self.pixel_shuffle = PixelShuffleICNR(64, 16, scale=2)
self.final = nn.Conv2d(16, 2, kernel_size=3, padding=1)
def forward(self, x5, x4, x3, x2, x1):
d4 = self.dec4(x5, x4)
d3 = self.dec3(d4, x3)
del d4, x4, x3
d2 = self.dec2(d3, x2)
del d3, x2
d1 = self.dec1(d2, x1)
del d2, x1
out = self.pixel_shuffle(d1)
del d1
out = self.final(out)
return torch.tanh(out)
class UNet(nn.Module):
def __init__(self):
super().__init__()
self.encoder = ResNetEncoder()
self.decoder = Decoder()
def forward(self, x):
x, x2, x3, x4, x5 = self.encoder(x)
return self.decoder(x5, x4, x3, x2, x)
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