From 1b58da9a47accb40ce7b9414a6758d7c3fa1e2e7 Mon Sep 17 00:00:00 2001
From: wonder <hezhaohui0807@163.com>
Date: Sun, 2 Nov 2025 19:44:48 +0800
Subject: [PATCH] =?UTF-8?q?=20=F0=9F=94=84Update:=20[Lab-2]=20=E6=95=B0?=
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---
 Lab-2/src/data_transform.py | 88 +++++++++++++++++++++++++++++++++++++
 1 file changed, 88 insertions(+)
 create mode 100644 Lab-2/src/data_transform.py

diff --git a/Lab-2/src/data_transform.py b/Lab-2/src/data_transform.py
new file mode 100644
index 0000000..8a7eeb7
--- /dev/null
+++ b/Lab-2/src/data_transform.py
@@ -0,0 +1,88 @@
+import torch
+import torchvision
+import torchvision.transforms as transforms
+import matplotlib.pyplot as plt
+from torch.utils.data import DataLoader, random_split
+
+
+def get_transform():
+    """
+    返回 torchvision.transforms.Compose 对象，包含数据预处理变换操作。
+
+    Returns:
+    - transform (torchvision.transforms.Compose): 包含数据预处理变换的 Compose 对象。
+    """
+    return transforms.Compose([
+        transforms.ToTensor(),  # 将输入数据类型转换为 Tensor
+        transforms.Normalize((0.5,), (0.5,))  # 对数据进行标准化处理，均值和标准差均为 0.5
+    ])
+
+
+def load_mnist(data_size=100, batch_size=16, seed=42, root='../data'):
+    """
+    加载 MNIST 数据集。
+
+    Parameters:
+    - data_size (int): 加载的数据条目数。
+    - batch_size (int): 每批次处理的数据数量。
+    - seed (int): 随机种子，用于保证数据切割的可重复性。
+    - root (str): 数据集保存的路径。
+
+    Returns:
+    - train_loader (DataLoader): 训练数据集加载器。
+    """
+    transform = get_transform()
+
+    train_dataset = torchvision.datasets.MNIST(
+        root=root,
+        train=True,
+        download=True,
+        transform=transform
+    )
+
+    data_train, _ = random_split(
+        dataset=train_dataset,
+        lengths=[data_size, len(train_dataset) - data_size],
+        generator=torch.Generator().manual_seed(seed)
+    )
+
+    train_loader = DataLoader(
+        dataset=data_train,
+        batch_size=batch_size,
+        shuffle=True
+    )
+
+    return train_loader
+
+
+def load_and_preview_mnist(data_size=100, batch_size=16, seed=42, root='../data'):
+    """
+    加载并预览 MNIST 数据集。
+
+    Parameters:
+    - data_size (int): 加载的数据条目数。
+    - batch_size (int): 每批次处理的数据数量。
+    - seed (int): 随机种子，用于保证数据切割的可重复性。
+    - root (str): 数据集保存的路径。
+    """
+    train_loader = load_mnist(data_size=data_size, batch_size=batch_size, seed=seed, root=root)
+
+    images, labels = next(iter(train_loader))
+
+    img_grid = torchvision.utils.make_grid(images, nrow=batch_size // 4)
+    img = img_grid.numpy().transpose(1, 2, 0)
+    mean = [0.5, 0.5, 0.5]
+    std = [0.5, 0.5, 0.5]
+    img = img * std + mean
+
+    print([labels[i] for i in range(batch_size)])
+    plt.imshow(img, cmap='gray')
+    plt.axis('off')
+    plt.show()
+
+
+if __name__ == '__main__':
+    load_and_preview_mnist()
+
+    # Or use custom parameters:
+    # load_and_preview_mnist(data_size=200, batch_size=8, seed=123, root='../data')