用DQN训练unet建筑物变化检测pytorch代码

下面是使用DQN训练UNet进行建筑物变化检测的PyTorch代码。请注意，这只是一个示例，你需要根据你的数据集和模型进行适当的修改。

首先，我们需要导入必要的库：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import random
from collections import deque

接下来，我们定义DQN模型。在这个示例中，我们将使用一个简单的全连接神经网络作为DQN，它将接受UNet的输出并输出一个Q值。

class DQN(nn.Module):
    def __init__(self, state_size, action_size):
        super(DQN, self).__init__()
        self.fc1 = nn.Linear(state_size, 64)
        self.fc2 = nn.Linear(64, 64)
        self.fc3 = nn.Linear(64, action_size)

    def forward(self, x):
        x = nn.functional.relu(self.fc1(x))
        x = nn.functional.relu(self.fc2(x))
        x = self.fc3(x)
        return x

现在我们定义一个ReplayBuffer类，用于存储先前的状态、动作、奖励和下一个状态，以便我们可以使用DQN从中随机选择一些样本进行训练。

class ReplayBuffer():
    def __init__(self, buffer_size):
        self.buffer_size = buffer_size
        self.buffer = deque(maxlen=buffer_size)

    def add(self, state, action, reward, next_state, done):
        experience = (state, action, reward, next_state, done)
        self.buffer.append(experience)

    def sample(self, batch_size):
        if len(self.buffer) < batch_size:
            batch = random.sample(self.buffer, len(self.buffer))
        else:
            batch = random.sample(self.buffer, batch_size)
        states, actions, rewards, next_states, dones = zip(*batch)
        return states, actions, rewards, next_states, dones

现在我们定义一个Agent类，它将使用DQN模型和ReplayBuffer类来实现DQN算法。

class Agent():
    def __init__(self, state_size, action_size, buffer_size, batch_size, gamma, epsilon, epsilon_decay):
        self.state_size = state_size
        self.action_size = action_size
        self.buffer_size = buffer_size
        self.batch_size = batch_size
        self.gamma = gamma
        self.epsilon = epsilon
        self.epsilon_decay = epsilon_decay
        self.epsilon_min = 0.01
        self.tau = 0.1

        self.model = DQN(state_size, action_size)
        self.target_model = DQN(state_size, action_size)
        self.optimizer = optim.Adam(self.model.parameters(), lr=0.001)

        self.memory = ReplayBuffer(buffer_size)

    def select_action(self, state):
        if random.uniform(0, 1) < self.epsilon:
            return random.randrange(self.action_size)
        else:
            state = torch.from_numpy(state).float().unsqueeze(0)
            q_values = self.model(state)
            return np.argmax(q_values.detach().numpy())

    def train(self):
        if len(self.memory.buffer) < self.batch_size:
            return

        states, actions, rewards, next_states, dones = self.memory.sample(self.batch_size)

        states = torch.from_numpy(np.array(states)).float()
        actions = torch.from_numpy(np.array(actions)).long()
        rewards = torch.from_numpy(np.array(rewards)).float()
        next_states = torch.from_numpy(np.array(next_states)).float()
        dones = torch.from_numpy(np.array(dones)).float()

        q_values = self.model(states).gather(1, actions.unsqueeze(1)).squeeze(1)
        next_q_values = self.target_model(next_states).max(1)[0].detach()
        expected_q_values = rewards + self.gamma * next_q_values * (1 - dones)

        loss = nn.functional.smooth_l1_loss(q_values, expected_q_values)

        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

        for param, target_param in zip(self.model.parameters(), self.target_model.parameters()):
            target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)

        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

最后，我们定义一个主函数，它将训练我们的DQN模型并输出结果。

def main():
    state_size = 64
    action_size = 2
    buffer_size = 100000
    batch_size = 32
    gamma = 0.99
    epsilon = 1.0
    epsilon_decay = 0.995
    num_episodes = 1000

    agent = Agent(state_size, action_size, buffer_size, batch_size, gamma, epsilon, epsilon_decay)

    # 训练循环
    for episode in range(num_episodes):
        # 初始化环境
        state = env.reset()

        # 记录总奖励
        total_reward = 0

        # 训练循环
        while True:
            # 选择动作
            action = agent.select_action(state)

            # 执行动作并观察结果
            next_state, reward, done, _ = env.step(action)

            # 将经验添加到回放缓冲区
            agent.memory.add(state, action, reward, next_state, done)

            # 训练DQN
            agent.train()

            # 更新状态
            state = next_state

            # 更新总奖励
            total_reward += reward

            # 如果游戏结束了，退出循环
            if done:
                break

        # 输出训练结果
        print("Episode: {}/{}, Total Reward: {}, Epsilon: {:.2f}".format(episode+1, num_episodes, total_reward, agent.epsilon))

if __name__ == "__main__":
    main()

请注意，上面的代码仅用作示例。你需要根据你的数据集和模型进行适当的修改。