基于paddle2.0实现知识蒸馏的代码

以下是一个基于PaddlePaddle 2.0实现知识蒸馏的代码示例：

import paddle
import paddle.nn.functional as F
from paddle.vision.models import resnet18

# 加载预训练的教师模型和数据集
teacher_model = resnet18(pretrained=True)
train_dataset = paddle.vision.datasets.Cifar10(mode='train')
val_dataset = paddle.vision.datasets.Cifar10(mode='test')

# 定义学生模型
class StudentModel(paddle.nn.Layer):
    def __init__(self):
        super(StudentModel, self).__init__()
        self.conv1 = paddle.nn.Conv2D(in_channels=3, out_channels=32, kernel_size=3, padding=1)
        self.bn1 = paddle.nn.BatchNorm2D(num_features=32)
        self.conv2 = paddle.nn.Conv2D(in_channels=32, out_channels=64, kernel_size=3, padding=1)
        self.bn2 = paddle.nn.BatchNorm2D(num_features=64)
        self.pool = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
        self.fc1 = paddle.nn.Linear(in_features=64*8*8, out_features=512)
        self.fc2 = paddle.nn.Linear(in_features=512, out_features=10)

    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = self.pool(F.relu(self.bn2(self.conv2(x))))
        x = paddle.flatten(x, start_axis=1, stop_axis=-1)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

student_model = StudentModel()

# 定义损失函数
def loss_fn(logits, labels, soft_labels, temperature=3.0):
    hard_loss = F.cross_entropy(logits, labels)
    soft_loss = F.mse_loss(F.softmax(logits/temperature, axis=1), F.softmax(soft_labels/temperature, axis=1))
    loss = hard_loss + 0.7 * soft_loss
    return loss

# 定义优化器
optimizer = paddle.optimizer.Adam(learning_rate=0.001, parameters=student_model.parameters())

# 训练和测试
for epoch in range(10):
    for batch_id, (data, label) in enumerate(train_dataset()):
        soft_label = teacher_model(data)
        output = student_model(data)
        loss = loss_fn(output, label, soft_label)
        loss.backward()
        optimizer.step()
        optimizer.clear_grad()

    # 验证集上测试
    student_model.eval()
    accs = []
    for batch_id, (data, label) in enumerate(val_dataset()):
        output = student_model(data)
        acc = paddle.metric.accuracy(output, label)
        accs.append(acc.numpy())
    avg_acc = np.mean(accs)
    print("Epoch {} - Test Accuracy: {}".format(epoch+1, avg_acc))
    student_model.train()

在这个代码示例中，我们使用CIFAR-10数据集作为训练和测试数据，使用ResNet-18作为教师模型，使用一个简单的卷积神经网络作为学生模型。损失函数包括交叉熵损失和均方误差损失，其中软标签来自于教师模型的预测结果。我们使用Adam优化器进行参数更新，同时在每个epoch结束后，在验证集上测试学生模型的性能。