PyTorch 数据集(Dataset)

from torch.utils.data import Dataset
import pandas as pd

class myDataset(Dataset):
    def __init__(self,csv_file,txt_file,root_dir, other_file):
        self.csv_data = pd.read_csv(csv_file)
        with open(txt_file,'r') as f:
            data_list = f.readlines()
        self.txt_data = data_list
        self.root_dir = root_dir

    def __len__(self):
        return len(self.csv_data)

    def __gettime__(self,idx):
        data = (self.csv_data[idx],self.txt_data[idx])
        return data

# 原版使用方法
train_dataset = Data.TensorDataset(data_tensor=x, target_tensor=y)

# 新版使用方法
train_dataset = Data.TensorDataset(x,y)

import torch
import torch.utils.data as Data

BATCH_SIZE = 5

x = torch.linspace(1, 10, 10)
y = torch.linspace(10, 1, 10)

torch_dataset = Data.TensorDataset(x, y)
loader = Data.DataLoader(
    dataset=torch_dataset,
    batch_size=BATCH_SIZE,
    shuffle=True,
    num_workers=0,
)

for epoch in range(3):
    for step, (batch_x, batch_y) in enumerate(loader):
        print('Epoch: ', epoch, '| Step: ', step, '| batch x: ', batch_x.numpy(), '| batch y: ', batch_y.numpy())

from torch.utils.data import DataLoader

dataiter = DataLoader(myDataset,batch_size=32,shuffle=True,collate_fn=defaulf_collate)

root/dog/xxx.png
root/dog/xxy.png
root/dog/xxz.png

root/cat/123.png
root/cat/asd/png
root/cat/zxc.png

from torchvision.datasets import ImageFolder

dset = ImageFolder(root='root_path', transform=None, loader=default_loader)