Pytorch 在U-Net体系结构中,h和w don';不匹配,我不';我不知道我是否误解了它
我对我读到的一篇文章中的图像转换结构感到困惑。它们连接的图层的输出尺寸不匹配 它应该是第9层和第7层吗Pytorch 在U-Net体系结构中,h和w don';不匹配,我不';我不知道我是否误解了它,pytorch,image-generation,image-translation,Pytorch,Image Generation,Image Translation,我对我读到的一篇文章中的图像转换结构感到困惑。它们连接的图层的输出尺寸不匹配 它应该是第9层和第7层吗 class ImageTransformationNetwork(nn.Module): def __init__(self): super(ImageTransformationNetwork, self).__init__() self.layer1 = nn.Sequential(nn.Conv2d(3, 64, padding=1, kernel_size=4, st
class ImageTransformationNetwork(nn.Module):
def __init__(self):
super(ImageTransformationNetwork, self).__init__()
self.layer1 = nn.Sequential(nn.Conv2d(3, 64, padding=1, kernel_size=4, stride=2),
nn.LeakyReLU())
self.layer2 = nn.Sequential(nn.Conv2d(64, 128, padding=1, kernel_size=4, stride=2),
nn.LeakyReLU(),
nn.BatchNorm2d(128))
self.layer3 = nn.Sequential(nn.Conv2d(128, 256, padding=1, kernel_size=4, stride=2),
nn.LeakyReLU(),
nn.BatchNorm2d(256))
self.layer4 = nn.Sequential(nn.Conv2d(256, 512, padding=1, kernel_size=4, stride=2),
nn.LeakyReLU(),
nn.BatchNorm2d(512))
self.layer5 = nn.Sequential(nn.Conv2d(512, 512, padding=1, kernel_size=4, stride=2),
nn.LeakyReLU(),
nn.BatchNorm2d(512))
self.layer6 = nn.Sequential(nn.Conv2d(512, 512, padding=1, kernel_size=4, stride=2),
nn.LeakyReLU(),
nn.BatchNorm2d(512))
self.layer7 = nn.Sequential(nn.Conv2d(512, 512, padding=1, kernel_size=4, stride=2),
nn.LeakyReLU(),
nn.BatchNorm2d(512))
self.layer8 = nn.Sequential(nn.Conv2d(512, 512, padding=1, kernel_size=4, stride=2),
nn.LeakyReLU())
self.layer9 = nn.Sequential(nn.ConvTranspose2d(512, 1024, padding=1, kernel_size=4, stride=2),
nn.ReLU(),
nn.BatchNorm2d(1024))
self.layer10 = nn.Sequential(nn.ConvTranspose2d(1024+512, 1024, padding=1, kernel_size=4, stride=2),
nn.ReLU(),
nn.BatchNorm2d(1024))
self.layer11 = nn.Sequential(nn.ConvTranspose2d(1024+512, 1024, padding=1, kernel_size=4, stride=2),
nn.ReLU(),
nn.BatchNorm2d(1024))
self.layer12 = nn.Sequential(nn.ConvTranspose2d(1024+512, 1024, padding=1, kernel_size=4, stride=2),
nn.ReLU(),
nn.BatchNorm2d(1024))
self.layer13 = nn.Sequential(nn.ConvTranspose2d(1024+512, 512, padding=1, kernel_size=4, stride=2),
nn.ReLU(),
nn.BatchNorm2d(512))
self.layer14 = nn.Sequential(nn.ConvTranspose2d(512+256, 256, padding=1, kernel_size=4, stride=2),
nn.ReLU(),
nn.BatchNorm2d(256))
self.layer15 = nn.Sequential(nn.ConvTranspose2d(256+128, 128, padding=1, kernel_size=4, stride=2),
nn.Tanh(),
nn.BatchNorm2d(128))
self.layer16 = nn.Sequential(nn.ConvTranspose2d(128, 3, padding=1, kernel_size=4, stride=2))
def forward(self, x):
x1 = self.layer1(x)
x2 = self.layer2(x1)
x3 = self.layer3(x2)
x4 = self.layer4(x3)
x5 = self.layer5(x4)
x6 = self.layer6(x5)
x7 = self.layer7(x6)
x8 = self.layer8(x7)
x9 = self.layer9(x8)
x10 = self.layer10(torch.cat([x7, x9], dim=1))
x11 = self.layer11(torch.cat([x6, x10], dim=1))
x12 = self.layer12(torch.cat([x5, x11], dim=1))
x13 = self.layer13(torch.cat([x4, x12], dim=1))
x14 = self.layer14(torch.cat([x3, x13], dim=1))
x15 = self.layer15(torch.cat([x2, x14], dim=1))
x16 = self.layer16(x15)
return x16
我不知道我是否理解正确。你能提供一些帮助吗