[PyTorch小试牛刀]实战六·准备自己的数据集用于训练（基于猫狗大战数据集）

import torch as t
import torchvision as tv
import os

data_dir = "./datas"

BATCH_SIZE = 100

EPOCH = 10

transform = {
    x:tv.transforms.Compose(
        [tv.transforms.Resize([64,64]),tv.transforms.ToTensor()]#tv.transforms.Resize 用于重设图片大小
    ) 
    for x in ["train","valid"]
}

datasets = {
    x:tv.datasets.ImageFolder(root = os.path.join(data_dir,x),transform=transform[x])
    for x in ["train","valid"]
}

dataloader = {
    x:t.utils.data.DataLoader(dataset= datasets[x],
        batch_size=BATCH_SIZE,
        shuffle=True
    ) 
    for x in ["train","valid"]
}

b_x,b_y = next(iter(dataloader["train"]))

print(b_x.shape,b_y.shape)

index_classes = datasets["train"].class_to_idx

print(index_classes)

import torch as t
import torchvision as tv


data_dir = "./datas"

BATCH_SIZE = 100

EPOCH = 10

transform = {
    "train":tv.transforms.Compose(
        [tv.transforms.Resize([64,64]),tv.transforms.ToTensor()]
    ),
    "valid":tv.transforms.Compose(
        [tv.transforms.Resize([64,64]),tv.transforms.ToTensor()]
    ),
}

datasets = {
    "train":tv.datasets.ImageFolder(root = os.path.join(data_dir,"train"),transform=transform["train"]),
    "vaild":tv.datasets.ImageFolder(root = os.path.join(data_dir,"vaild"),transform=transform["vaild"]),
}
dataloader = {
    "train":t.utils.data.DataLoader(dataset= datasets["train"],
        batch_size=BATCH_SIZE,
        shuffle=True
    ),
    "valid":t.utils.data.DataLoader(dataset= datasets["valid"],
        batch_size=100,
        shuffle=True
    )
}

b_x,b_y = next(iter(dataloader["train"]))

print(b_x.shape,b_y.shape)

index_classes = datasets["train"].class_to_idx

print(index_classes)

torch.Size([100, 3, 64, 64]) torch.Size([100])
{'cats': 0, 'dogs': 1}