小卷积多线程下的PyTorch与mkl-dnn后端性能

要使用小卷积多线程下的PyTorch与mkl-dnn后端来提高性能，可以按照以下步骤进行：

1. 确保已安装PyTorch和mkl-dnn库。可以使用如下命令进行安装：

pip install torch
pip install intel-numpy

2. 导入必要的库和设置环境变量：

import torch
import os

os.environ["OMP_NUM_THREADS"] = "1"  # 设置线程数为1
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["MKLDNN_VERBOSE"] = "1"  # 可选，用于打印mkl-dnn的详细信息

3. 定义一个小卷积神经网络模型：

class ConvNet(torch.nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = torch.nn.Conv2d(1, 20, 5)
        self.conv2 = torch.nn.Conv2d(20, 50, 5)
        self.fc1 = torch.nn.Linear(4 * 4 * 50, 500)
        self.fc2 = torch.nn.Linear(500, 10)

    def forward(self, x):
        x = torch.nn.functional.relu(self.conv1(x))
        x = torch.nn.functional.max_pool2d(x, 2, 2)
        x = torch.nn.functional.relu(self.conv2(x))
        x = torch.nn.functional.max_pool2d(x, 2, 2)
        x = x.view(-1, 4 * 4 * 50)
        x = torch.nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

4. 创建模型实例并设置为使用mkl-dnn后端：

model = ConvNet()
model.to(torch.device("cpu"))  # 将模型移动到CPU
torch.backends.mkldnn.enabled = True  # 启用mkl-dnn后端
torch.backends.mkldnn.benchmark = True  # 启用mkl-dnn的性能优化

5. 准备数据和优化器：

criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

6. 执行训练和测试循环：

for epoch in range(num_epochs):
    model.train()  # 设置为训练模式
    for i, (images, labels) in enumerate(train_loader):
        # 正向传播
        outputs = model(images)
        loss = criterion(outputs, labels)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # 打印训练信息
        if (i + 1) % 100 == 0:
            print(f"Epoch [{epoch + 1}/{num_epochs}], Step [{i + 1}/{total_step}], Loss: {loss.item():.4f}")

    # 在测试集上进行评估
    model.eval()  # 设置为评估模式
    with torch.no_grad():
        correct = 0
        total = 0
        for images, labels in test_loader:
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

        print(f"Test Accuracy of the model on the 10000 test images: {100 * correct / total} %")

通过上述步骤，您可以使用小卷积多线程下的PyTorch与mkl-dnn后端来提高性能。请根据您的具体需求进行适当的调整和优化。