pytorch qat量化

PyTorch QAT（Quantization Aware Training）是一种量化训练方法，可以将浮点模型转换为定点模型，从而提高模型的推理速度和减少存储空间。下面是一个简单的PyTorch QAT示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.quantization as quantization

# 定义一个简单的模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(784, 256)
        self.fc2 = nn.Linear(256, 128)
        self.fc3 = nn.Linear(128, 10)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        x = x.view(-1, 784)
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 加载MNIST数据集
train_loader = torch.utils.data.DataLoader(
    torchvision.datasets.MNIST('/mnist/', train=True, download=True,
                             transform=torchvision.transforms.Compose([
                               torchvision.transforms.ToTensor(),
                               torchvision.transforms.Normalize(
                                 (0.1307,), (0.3081,))
                             ])),
    batch_size=128, shuffle=True)

# 定义训练函数
def train(model, criterion, optimizer, train_loader, num_epochs):
    for epoch in range(num_epochs):
        model.train()
        for i, (inputs, targets) in enumerate(train_loader):
            inputs, targets = inputs.cuda(), targets.cuda()
            outputs = model(inputs)
            loss = criterion(outputs, targets)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

# 定义评估函数
def evaluate(model, data_loader):
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for inputs, targets in data_loader:
            inputs, targets = inputs.cuda(), targets.cuda()
            outputs = model(inputs)
            _, predicted = torch.max(outputs.data, 1)
            total += targets.size(0)
            correct += (predicted == targets).sum().item()
    return 100.0 * correct / total

# 定义量化模型函数
def quantize(model):
    model.qconfig = quantization.get_default_qat_qconfig('fbgemm')
    quantization.prepare_qat(model, inplace=True)
    return model

# 定义反量化模型函数
def dequantize(model):
    quantization.convert(model, inplace=True)
    return model

# 实例化模型、损失函数和优化器
model = Net().cuda()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)

# 训练模型
train(model, criterion, optimizer, train_loader, 5)

# 评估模型
test_loader = torch.utils.data.DataLoader(
    torchvision.datasets.MNIST('/mnist/', train=False, download=True,
                             transform=torchvision.transforms.Compose([
                               torchvision.transforms.ToTensor(),
                               torchvision.transforms.Normalize(
                                 (0.1307,), (0.3081,))
                             ])),
    batch_size=128, shuffle=True)
accuracy = evaluate(model, test_loader)
print('Accuracy before quantization: %.2f%%' % accuracy)

# 量化模型
quantized_model = quantize(model)

# 评估量化模型
accuracy = evaluate(quantized_model, test_loader)
print('Accuracy after quantization: %.2f%%' % accuracy)

# 反量化模型
dequantized_model = dequantize(quantized_model)

# 评估反量化模型
accuracy = evaluate(dequantized_model, test_loader)
print('Accuracy after dequantization: %.2f%%' % accuracy)

以上代码中的 `Net` 类定义了一个简单的神经网络模型。`train` 函数用于训练模型，`evaluate` 函数用于评估模型的准确性。`quantize` 函数用于将模型量化为定点模型，`dequantize` 函数用于反量化模型。在主程序中，首先使用浮点模型训练模型，然后量化模型，评估量化模型的准确性，反量化模型，再次评估反量化模型的准确性。