详解Pytorch中Dataset的使用

此案例教我们加载并处理TorchVision的FashionMNIST Dataset。

root 目录是 train/test data 存储的地方

download=True 如果root目录没有，则从网上下载

transform and target_transform specify the feature and label transformations

import torch
from torch.utils.data import Dataset
from torchvision import datasets
from torchvision.transforms import ToTensor
import matplotlib.pyplot as plt
 
 
training_data = datasets.FashionMNIST(
    root="data",
    train=True,
    download=True,
    http://www.devze.comtransform=ToTensor()
)
 
test_data = datasets.FashionMNIST(
    root="data",
    train=False,
    download=True,
    transform=ToTensor()
)

运行得到的结果是这样的：

遍历并可视化数据集

给数据集手动加上序号sample_idx，并用matplotlib进行绘制：

labels_map = {
    0: "T-Shirt",
    1: "Trouser",
    2: "Pullover",
    3: "Dress",
    4: "Coat",
编程客栈    5: "Sandal",
    6: "Shirt",
    7: "Sneaker",
 php   8: "Bag",
    9: "Ankle Boot",
}
figure = plt.figure(figsize=(8, 8))
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=android"gray")
plt.show()

traning_data

torch.randint(len(training_data), size=(1,)).item()

为我的文件自定义一个Dataset

一个自定义的Dataset必须有三个函数：__init__, __len__, and __getitem__

图片存储在img_dir

import os
import pandas as pd
from torchvision.io import read_image
 
class CustomImageDataset(Dataset):
    def __init__(self, annotations_file, img_dir, transform=None, target_transform=No开发者_JAVA入门ne):
        self.img_labels = pd.read_csv(annotations_file)
        self.img_dir = img_dir
        self.transform = transform
        self.target_transform = target_transfohttp://www.devze.comrm
 
    def __len__(self):
        return len(self.img_labels)
 
    def __getitem__(self, idx):
        img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0])
        image = read_image(img_path)
        label = self.img_labels.iloc[idx, 1]
        if self.transform:
            image = self.transform(image)
        if self.target_transform:
            label = self.target_transform(label)
        return image, label

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