present_state = states[:, :1].contiguous()中present_state的形状

假设`states`是一个形状为`(b, s, c, h, w)`的5维张量，其中`b`表示batch size，`s`表示时间步，`c`表示通道数，`h`和`w`表示空间维度。那么，`present_state`的形状是`(b, 1, c, h, w)`。

具体来说，`present_state`是由`states`沿时间步的维度取第一个时间步的数据得到的，因此其时间步的维度为1，而其他维度与`states`的相同。这里使用`contiguous()`函数是为了保证`present_state`在内存中是连续存储的，以便后续的计算。

相关问题

if self.n_future > 0: present_state = states[:, :1].contiguous() if self.cfg.PROBABILISTIC.ENABLED: # Do probabilistic computation sample, output_distribution = self.distribution_forward( present_state, future_distribution_inputs, noise ) output = {**output, **output_distribution} # Prepare future prediction input b, _, _, h, w = present_state.shape hidden_state = present_state[:, 0] if self.cfg.PROBABILISTIC.ENABLED: future_prediction_input = sample.expand(-1, self.n_future, -1, -1, -1) else: future_prediction_input = hidden_state.new_zeros(b, self.n_future, self.latent_dim, h, w) # Recursively predict future states future_states = self.future_prediction(future_prediction_input, hidden_state) # Concatenate present state future_states = torch.cat([present_state, future_states], dim=1)这段代码得到什么？

这段代码的作用是进行未来状态的预测，输出一个字典 `output`，其中包含模型的预测结果。具体来说，这段代码做了以下几件事情：

1. 从输入的 `states` 中取出当前时刻的状态 `present_state`，即第一个时间步的状态。
2. 如果在模型的配置中启用了概率计算（`self.cfg.PROBABILISTIC.ENABLED` 为 True），则进行概率计算，并得到预测结果的采样值 `sample` 和输出分布 `output_distribution`。
3. 根据当前状态，准备好未来预测的输入数据 `future_prediction_input`，其形状为 `(batch_size, n_future, latent_dim, height, width)`，其中 `batch_size` 为批大小，`n_future` 为未来状态的时间步数，`latent_dim` 为隐藏状态的维度，`height` 和 `width` 分别为输入数据的高度和宽度。
4. 使用 `future_prediction` 函数递归地进行未来状态预测，其中 `future_prediction_input` 为输入数据，`hidden_state` 为隐藏状态，输出为 `future_states`，其形状为 `(batch_size, n_future, latent_dim, height, width)`。
5. 将当前状态 `present_state` 和预测的未来状态 `future_states` 进行拼接，得到完整的预测结果 `future_states`，其形状为 `(batch_size, n_future+1, latent_dim, height, width)`。
6. 将预测结果 `future_states` 加入到输出字典 `output` 中，返回该字典。

这段代码中加一个test loss功能 class LSTM(nn.Module): def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size, device): super().__init__() self.device = device self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.output_size = output_size self.num_directions = 1 # 单向LSTM self.batch_size = batch_size self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.num_layers, batch_first=True) self.linear = nn.Linear(65536, self.output_size) def forward(self, input_seq): h_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device) c_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device) output, _ = self.lstm(input_seq, (h_0, c_0)) pred = self.linear(output.contiguous().view(self.batch_size, -1)) return pred if __name__ == '__main__': # 加载已保存的模型参数 saved_model_path = '/content/drive/MyDrive/危急值/model/dangerous.pth' device = 'cuda:0' lstm_model = LSTM(input_size=1, hidden_size=64, num_layers=1, output_size=3, batch_size=256, device='cuda:0').to(device) state_dict = torch.load(saved_model_path) lstm_model.load_state_dict(state_dict) dataset = ECGDataset(X_train_df.to_numpy()) dataloader = DataLoader(dataset, batch_size=256, shuffle=True, num_workers=0, drop_last=True) loss_fn = nn.CrossEntropyLoss() optimizer = optim.SGD(lstm_model.parameters(), lr=1e-4) for epoch in range(200000): print(f'epoch:{epoch}') lstm_model.train() epoch_bar = tqdm(dataloader) for x, y in epoch_bar: optimizer.zero_grad() x_out = lstm_model(x.to(device).type(torch.cuda.FloatTensor)) loss = loss_fn(x_out, y.long().to(device)) loss.backward() epoch_bar.set_description(f'loss:{loss.item():.4f}') optimizer.step() if epoch % 100 == 0 or epoch == epoch - 1: torch.save(lstm_model.state_dict(), "/content/drive/MyDrive/危急值/model/dangerous.pth") print("权重成功保存一次")

以下是加入 Test Loss 功能后的代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader

class LSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size, device):
        super().__init__()
        self.device = device
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.output_size = output_size
        self.num_directions = 1 # 单向LSTM
        self.batch_size = batch_size
        self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.num_layers, batch_first=True)
        self.linear = nn.Linear(65536, self.output_size)

    def forward(self, input_seq):
        h_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device)
        c_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device)
        output, _ = self.lstm(input_seq, (h_0, c_0))
        pred = self.linear(output.contiguous().view(self.batch_size, -1))
        return pred

if __name__ == '__main__':
    # 加载已保存的模型参数
    saved_model_path = '/content/drive/MyDrive/危急值/model/dangerous.pth'
    device = 'cuda:0'
    lstm_model = LSTM(input_size=1, hidden_size=64, num_layers=1, output_size=3, batch_size=256, device=device).to(device)
    state_dict = torch.load(saved_model_path)
    lstm_model.load_state_dict(state_dict)
    
    # 加载测试数据集
    test_dataset = ECGDataset(X_test_df.to_numpy())
    test_loader = DataLoader(test_dataset, batch_size=256, shuffle=False, num_workers=0, drop_last=False)
    
    # 定义损失函数和优化器
    loss_fn = nn.CrossEntropyLoss()
    optimizer = optim.SGD(lstm_model.parameters(), lr=1e-4)
    
    # 开始训练
    for epoch in range(200000):
        print(f'epoch:{epoch}')
        lstm_model.train()
        epoch_bar = tqdm(dataloader)
        for x, y in epoch_bar:
            optimizer.zero_grad()
            x_out = lstm_model(x.to(device).type(torch.cuda.FloatTensor))
            loss = loss_fn(x_out, y.long().to(device))
            loss.backward()
            epoch_bar.set_description(f'loss:{loss.item():.4f}')
            optimizer.step()
        
        # 计算测试集上的平均损失
        test_loss = 0.0
        lstm_model.eval()
        with torch.no_grad():
            for x, y in test_loader:
                x_out = lstm_model(x.to(device).type(torch.cuda.FloatTensor))
                loss = loss_fn(x_out, y.long().to(device))
                test_loss += loss.item() * x.size(0)
        test_loss /= len(test_dataset)
        print(f'Test Loss: {test_loss:.4f}')
        
        if epoch % 100 == 0 or epoch == epoch - 1:
            torch.save(lstm_model.state_dict(), "/content/drive/MyDrive/危急值/model/dangerous.pth")
            print("权重成功保存一次")

在这个代码中，我们首先加载了测试数据集 `test_dataset` 和测试数据加载器 `test_loader`。在每个 epoch 完成后，我们计算测试集上的平均损失值。我们使用 `lstm_model.eval()` 将模型设为评估模式，并利用 `torch.no_grad()` 避免计算梯度，以加速计算。最后，我们输出测试集上的平均损失值。