使用pytorch实现这串代码from sklearn.model_selection import train_test_split ts = 0.3 # Percentage of images that we want to use for testing. X_train, X_test1, y_train, y_test1 = train_test_split(X, y, test_size=ts, random_state=42) X_test, X_cal, y_test, y_cal = train_test_split(X_test1, y_test1, test_size=ts, random_state=42

以下是使用PyTorch实现上述代码的示例：

import torch
from sklearn.model_selection import train_test_split

# 定义ts和随机种子
ts = 0.3
random_state = 42

# 将数据集和标签分成训练集和测试集
X_train, X_test1, y_train, y_test1 = train_test_split(X, y, test_size=ts, random_state=random_state)

# 将测试集分成测试集和验证集
X_test, X_cal, y_test, y_cal = train_test_split(X_test1, y_test1, test_size=ts, random_state=random_state)

# 将数据转换为张量
X_train = torch.from_numpy(X_train)
y_train = torch.from_numpy(y_train)
X_test = torch.from_numpy(X_test)
y_test = torch.from_numpy(y_test)
X_cal = torch.from_numpy(X_cal)
y_cal = torch.from_numpy(y_cal)

首先，导入必要的库。然后，定义`ts`和`random_state`变量。接着，使用`train_test_split`函数将数据集`X`和标签`y`分成训练集和测试集，并将它们存储在`X_train`、`X_test1`、`y_train`和`y_test1`变量中。

接下来，使用`train_test_split`函数将测试集分成测试集和验证集，并将它们存储在`X_test`、`X_cal`、`y_test`和`y_cal`变量中。

最后，将数据转换为PyTorch张量，以便在PyTorch中使用它们。