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,
transform=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",
5: "Sandal",
6: "Shirt",
7: "Sneaker",
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="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=None):
self.img_labels = pd.read_csv(annotations_file)
self.img_dir = img_dir
self.transform = transform
self.target_transform = target_transform
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到此这篇关于详解Pytorch中Dataset的使用的文章就介绍到这了,更多相关Pytorch Dataset使用内容请搜索编程网以前的文章或继续浏览下面的相关文章希望大家以后多多支持编程网！
