pytorch代码 deeplabv2_resnet101
时间: 2023-07-30 10:07:46 浏览: 97
Sure! Here's an example of PyTorch code for training DeepLabV2 with ResNet101 as the backbone:
```python
import torch
import torch.nn as nn
import torchvision.models as models
# Define DeepLabV2 model
class DeepLabV2(nn.Module):
def __init__(self, num_classes):
super(DeepLabV2, self).__init__()
self.resnet = models.resnet101(pretrained=True)
# Remove the fully connected layer and average pooling layer
del self.resnet.fc
del self.resnet.avgpool
self.conv1 = nn.Conv2d(2048, 256, kernel_size=1)
self.conv2 = nn.Conv2d(256, num_classes, kernel_size=1)
def forward(self, x):
x = self.resnet.conv1(x)
x = self.resnet.bn1(x)
x = self.resnet.relu(x)
x = self.resnet.maxpool(x)
x = self.resnet.layer1(x)
x = self.resnet.layer2(x)
x = self.resnet.layer3(x)
x = self.resnet.layer4(x)
x = self.conv1(x)
x = self.conv2(x)
return x
# Create an instance of the model
num_classes = 21 # Number of classes in the dataset
model = DeepLabV2(num_classes)
# Define loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
# Training loop
for epoch in range(num_epochs):
for images, labels in dataloader:
optimizer.zero_grad()
# Forward pass
outputs = model(images)
loss = criterion(outputs, labels)
# Backward pass and optimization
loss.backward()
optimizer.step()
```
This code defines the DeepLabV2 model with ResNet101 as the backbone. It removes the fully connected layer and average pooling layer from the ResNet101 model and replaces them with convolutional layers. The model is trained using the CrossEntropyLoss and optimized using stochastic gradient descent (SGD).
Please note that this code is a simplified example and may need modification based on your specific dataset and requirements.
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