利用Resnet50对灰度图片进行图像去噪,灰度图片形状为[16,1,50,50]。
时间: 2024-05-14 10:19:05 浏览: 7
可以按照以下步骤进行:
1. 导入必要的库和模块:
```python
import torch
import torch.nn as nn
import torchvision
```
2. 定义Resnet50模型,这里可以使用预训练的模型:
```python
model = torchvision.models.resnet50(pretrained=True)
```
3. 修改模型的输入和输出,将输入通道数改为1,输出通道数改为1:
```python
model.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
model.fc = nn.Linear(2048, 1)
```
4. 定义损失函数和优化器:
```python
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
```
5. 循环迭代训练模型:
```python
num_epochs = 10
for epoch in range(num_epochs):
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
inputs, labels = data[0].to(device), data[1].to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
print('Epoch [%d], Loss: %.4f' % (epoch+1, running_loss/len(train_loader)))
```
6. 对测试集进行预测并输出结果:
```python
test_loss = 0.0
with torch.no_grad():
for data in test_loader:
inputs, labels = data[0].to(device), data[1].to(device)
outputs = model(inputs)
loss = criterion(outputs, labels)
test_loss += loss.item()
print('Test Loss: %.4f' % (test_loss/len(test_loader)))
```
完整代码如下:
```python
import torch
import torch.nn as nn
import torchvision
# 定义Resnet50模型
model = torchvision.models.resnet50(pretrained=True)
# 修改模型的输入和输出
model.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
model.fc = nn.Linear(2048, 1)
# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# 循环迭代训练模型
num_epochs = 10
for epoch in range(num_epochs):
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
inputs, labels = data[0].to(device), data[1].to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
print('Epoch [%d], Loss: %.4f' % (epoch+1, running_loss/len(train_loader)))
# 对测试集进行预测并输出结果
test_loss = 0.0
with torch.no_grad():
for data in test_loader:
inputs, labels = data[0].to(device), data[1].to(device)
outputs = model(inputs)
loss = criterion(outputs, labels)
test_loss += loss.item()
print('Test Loss: %.4f' % (test_loss/len(test_loader)))
```
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