torch vision中的数据集使用

1.数据集的下载与说明

去pytorch官网找到torchvision进入datasets寻找数据集即可，相关说明如下

2.数据集使用

import torchvision

# 训练数据集
# download 设置为true自动下载，想看下载链接可以跳转目标数据集的函数寻找
train_set = torchvision.datasets.CIFAR10(root = "./dataset", train = True, download=True)
# 测试数据集
test_set = torchvision.datasets.CIFAR10(root = "./dataset", train = True, download=True)

# 查看训练集
print(test_set[0])
print(test_set.classes)

img, target = test_set[0]
print(img)
print(target)
print(test_set[target])
img.show()

可以看出图片确实是frog

3. tensorboard 可视化

import torchvision
from torch.utils.tensorboard import SummaryWriter

dataset_transform = torchvision.transforms.Compose([
    torchvision.transforms.ToTensor()
])
# 训练数据集
train_set = torchvision.datasets.CIFAR10(root = "../dataset1", train = True, transform=dataset_transform, download=True)
# 测试数据集
test_set = torchvision.datasets.CIFAR10(root = "../dataset1", train = True, transform=dataset_transform, download=True)

# 查看训练集
# print(test_set[0])
# print(test_set.classes)
#
# img, target = test_set[0]
# print(img)
# print(target)
# print(test_set[target])
# img.show()

print(test_set[0]) #可以看到是tensor数据类型

#使用tensorboard可视化
writer = SummaryWriter("p10")

# 展示测试集前十张图片
for i in range(10):
    img, target = test_set[i]
    writer.add_image("test_set", img, i)

writer.close()