pytroch 数据集 datasets DataLoader示例

# 安装依赖包
! pip install torchvision

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import torch
from torch.utils.data import Dataset
from torchvision import datasets
from torchvision.transforms import ToTensor
import matplotlib.pyplot as plt

# pytorch自带的datasets类
training_data = datasets.FashionMNIST( # 下载FashionMNIST数据集
    root="data", # 存储训练/测试数据的路径
    train=True,  # 指定训练或测试数据集
    download=True, # 从互联网下载数据
    transform=ToTensor() # 指定特征和标签转换
)

test_data = datasets.FashionMNIST(
    root="data",
    train=False,
    download=True,
    transform=ToTensor()
)

labels_map = {  # 标签字典
    0: "T-Shirt",
    1: "Trouser",
    2: "Pullover",
    3: "Dress",
    4: "Coat",
    5: "Sandal",
    6: "Shirt",
    7: "Sneaker",
    8: "Bag",
    9: "Ankle Boot",
}
figure = plt.figure(figsize=(8, 8))  # 展示窗口大小
cols, rows = 3, 3
for i in range(1, cols * rows + 1):
    sample_idx = torch.randint(len(training_data), size=(1, )).item()
    img, label = training_data[sample_idx]
    figure.add_subplot(rows, cols, i)
    plt.title(labels_map[label])
    plt.axis("off")
    plt.imshow(img.squeeze(), cmap="gray")
plt.show() # 展示图片

# 创建数据集
import os
import pandas as pd
from torchvision.io import read_image

# 自定义dataset类
class CustomImageDataset(Dataset):  # 创建数据集类
    def __init__(
            self,
            annotations_file,
            img_dir,
            transform=None,  # 转换函数
            target_transform=None  # 转换函数
    ):
        self.img_labels = pd.read_csv(annotations_file)  # 标签
        self.img_dir = img_dir  # 图片文件夹
        self.transform = transform  # 数据转换
        self.target_transform = target_transform  # 标签转换

    def __len__(self):  # 数据集中的样本数
        return len(self.img_labels)

    def __getitem__(self, idx):  # 加载并返回给定索引处的数据集中的样本
        img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx,
                                                                   0])  # 图片路径
        image = read_image(img_path)  # 读取图片
        label = self.img_labels.iloc[idx, 1]  # 读取标签
        if self.transform:
            image = self.transform(image)
        if self.target_transform:
            label = self.target_transform(label)
        return image, label

from torch.utils.data import DataLoader

train_dataloader = DataLoader(
    training_data,  # 数据集
    batch_size=64,  # 训练批
    shuffle=True  # 洗牌打乱数据集
)
test_dataloader = DataLoader(test_data, batch_size=64, shuffle=True)

# Display image and label.
train_features, train_labels = next(iter(train_dataloader))
print(f"Feature batch shape: {train_features.size()}")
print(f"Labels batch shape: {train_labels.size()}")
img = train_features[0].squeeze()
label = train_labels[0]
plt.imshow(img, cmap="gray")
plt.show()
print(f"Label: {label}")