Pytorch学习(四)：训练分类器
以下内容为Pytorch官网教程的翻译简化和一些自己的总结：
数据加载
一般而言，处理如图片，文本，音频，视频等数据时，可使用标准Python库加载数据成numpy数组形式，然后转换为张量。
特别地：
- 对于图像数据，Pillow，OpenCV很有用
- 对于音频数据，scipy和librosa
- 对于文本数据，纯Python加载，或NLTK和SpaCy
对于视觉数据，有torchvision库提供了基本的数据加载和转换工具，见torchvision.datasets和torch.utils.data.DataLoader
分类器模型示例
加载CIFAR10数据集，并标准化：
import torch
import torchvision
import torchvision.transforms as transforms
# 加载数据集时的处理函数，此处为复合操作，标准化和转换张量
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
batch_size = 4
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
shuffle=False, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
定义卷积神经网络：
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = torch.flatten(x, 1) # flatten all dimensions except batch
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
定义优化器和损失函数：
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
训练模型：
for epoch in range(2): # 在数据集上循环多轮(此处为两轮)
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# 数据为样本，标签列表
inputs, labels = data
# 每一步梯度置零
optimizer.zero_grad()
# 前向传播，反向传播，优化
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# 损失张量转实数，损失累加
running_loss += loss.item()
if i % 2000 == 1999: # 每两千轮打印一次
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
PATH = './cifar_net.pth'
torch.save(net.state_dict(), PATH) # 模型保存
在测试集上检验模型：
net = Net()
net.load_state_dict(torch.load(PATH)) # 模型加载
correct = 0
total = 0
# 测试时不需要计算梯度
with torch.no_grad():
for data in testloader: # 注意数据是分批的
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs.data, 1) # 预测结果为张量
total += labels.size(0) # 每批数据的样本数
correct += (predicted == labels).sum().item() # 预测正确的样本统计
print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
除了测试模型的整体性能，还可以深入模型的具体表现，检验模型在哪些类别上表现良好，哪些类别上表现不佳：
# 分类别统计
correct_pred = {classname: 0 for classname in classes}
total_pred = {classname: 0 for classname in classes}
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predictions = torch.max(outputs, 1)
for label, prediction in zip(labels, predictions):
if label == prediction:
correct_pred[classes[label]] += 1
total_pred[classes[label]] += 1
for classname, correct_count in correct_pred.items():
accuracy = 100 * float(correct_count) / total_pred[classname]
print("Accuracy for class {:5s} is: {:.1f} %".format(classname,
accuracy))
可将模型迁移至GPU训练：
# 定义设备，第一台可见的cuda设备
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# 模型迁移至GPU
net.to(device) # 将递归地遍历所有方法并将参数和缓存转换为CUDA张量
# 注意每一步的样本和标签也要迁移至GPU
inputs, labels = data[0].to(device), data[1].to(device)