# -*- coding: utf-8 -*-
# @Time    : 18-3-15 下午6:43
# @Author  : zhwzhong
# @File    : model.py
# @Contact : [email protected]
# @Function:
from torchvision import transforms, datasets as ds
import torchvision as tv
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import numpy as
 
 np

transform = transforms.Compose(
    [
        transforms.ToTensor()
    ]
)
train_set = tv.datasets.ImageFolder(root='./', transform=transform)
data_loader = DataLoader(dataset=train_set)

to_pil_image = transforms.ToPILImage()

for image, label in data_loader:

    # 方法1：Image.show()
    # transforms.ToPILImage()中有一句
 

    # npimg = np.transpose(pic.numpy(), (1, 2, 0))
    # 因此pic只能是3-D Tensor，所以要用image[0]消去batch那一維
    img = to_pil_image(image[0])
    img.show()

    # 方法2：plt.imshow(ndarray)
    img = image[0]  # plt.imshow()只能接受3-D Tensor，所以也要用image[0]消去batch那一維
    img = img.numpy()  # FloatTensor轉為ndarray
    img = np.transpose(img, (1
 
, 2, 0))  # 把channel那一維放到最後

    # 顯示圖片
    plt.imshow(img)
    plt.show()