使用pytorch写一个深度学习目标检测网络
时间: 2023-03-21 11:02:21 浏览: 86
非常感谢您的问题。以下是一个简单的使用PyTorch实现目标检测的代码示例:
```
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
from torchvision import transforms
from torch.utils.data import DataLoader
# 定义网络
class MyDetectionNet(nn.Module):
def __init__(self):
super(MyDetectionNet, self).__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(16)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(32)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
self.bn3 = nn.BatchNorm2d(64)
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(64 * 4 * 4, 512)
self.fc2 = nn.Linear(512, 2)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = nn.functional.relu(x)
x = self.pool1(x)
x = self.conv2(x)
x = self.bn2(x)
x = nn.functional.relu(x)
x = self.pool2(x)
x = self.conv3(x)
x = self.bn3(x)
x = nn.functional.relu(x)
x = self.pool3(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = nn.functional.relu(x)
x = self.fc2(x)
return x
# 准备数据集
transform = transforms.Compose([
transforms.Resize((64, 64)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
train_set = torchvision.datasets.ImageFolder(root='./train', transform=transform)
train_loader = DataLoader(train_set, batch_size=64, shuffle=True)
# 训练模型
net = MyDetectionNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01)
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 100 == 99:
print(f'[{epoch+1}, {i+1}] loss: {running_loss/100:.3f}')
running_loss = 0.0
print('Finished Training')
# 测试模型
test_set = torchvision.datasets.ImageFolder(root='./test', transform=transform)
test_loader = DataLoader(test_set, batch_size=64, shuffle=True)
correct = 0
total = 0
with torch.no_grad():
for data in test_loader:
inputs, labels = data
outputs = net(inputs)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(