写一个基于跨阶段特征融合及多尺度金字塔的单图超分辨率的pytorch代码

我们可以使用Pytorch实现一个基于跨阶段特征融合及多尺度金字塔的单图超分辨率的代码。下面是一个示例代码：import torch
import torch.nn as nn# 定义网络结构
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 8, 3, padding=1)
self.conv2 = nn.Conv2d(8, 8, 3, padding=1)
self.conv3 = nn.Conv2d(8, 16, 3, padding=1)
self.conv4 = nn.Conv2d(16, 16, 3, padding=1)
self.conv5 = nn.Conv2d(16, 32, 3, padding=1)
self.conv6 = nn.Conv2d(32, 32, 3, padding=1)
self.conv7 = nn.Conv2d(32, 32, 3, padding=1)
self.conv8 = nn.Conv2d(32, 64, 3, padding=1)
self.conv9 = nn.Conv2d(64, 64, 3, padding=1)
self.conv10 = nn.Conv2d(64, 64, 3, padding=1)
self.conv11 = nn.Conv2d(64, 64, 3, padding=1)
self.conv12 = nn.Conv2d(64, 64, 3, padding=1)
self.conv13 = nn.Conv2d(64, 1, 3, padding=1)

def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
x = self.conv5(x)
x = self.conv6(x)
x = self.conv7(x)
x = self.conv8(x)
x = self.conv9(x)
x = self.conv10(x)
x = self.conv11(x)
x = self.conv12(x)
x = self.conv13(x)
return x# 实例化网络
net = Net()# 定义损失函数
criterion = nn.MSELoss()# 定义优化器
optimizer = torch.optim.Adam(net.parameters(), lr=0.001)# 训练网络
for epoch in range(num_epochs):
running_loss = 0.0
for i, data in enumerate(dataloader):
inputs, labels = data
# 将张量按照指定维度拆分
inputs = torch.split(inputs, [int(inputs.shape[2]/2), int(inputs.shape[2]/2)], dim=2)
# 反向传播
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# 统计
running_loss += loss.item()
if i % 2000 == 1999:
print('[%d, %5d] loss: %.3f' % (epoch+1, i+1, running_loss/2000))
running_loss = 0.0print('Finished Training')