img 应该是 PIL Image。得到 <class 'torch.Tensor'>

Question

我正在尝试遍历加载程序以检查它是否正常工作，但是给出了以下错误：

TypeError: img should be PIL Image. Got <class 'torch.Tensor'>

我试着将两者transforms.ToTensor()并transforms.ToPILImage()和它给我一个错误，要求相反。即，使用ToPILImage()，它将要求张量，反之亦然。

# Imports here
%matplotlib inline
import matplotlib.pyplot as plt
from torch import nn, optim
import torch.nn.functional as F
import torch
from torchvision import transforms, datasets, models
import seaborn as sns
import pandas as pd
import numpy as np

data_dir = 'flowers'
train_dir = data_dir + '/train'
valid_dir = data_dir + '/valid'
test_dir = data_dir + '/test'

#Creating transform for training set
train_transforms = transforms.Compose(
[transforms.Resize(255), 
transforms.CenterCrop(224), 
transforms.ToTensor(), 
transforms.RandomHorizontalFlip(), 
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])

#Creating transform for test set
test_transforms = transforms.Compose(
[transforms.Resize(255),
transforms.CenterCrop(224), 
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])])

#transforming for all data
train_data = datasets.ImageFolder(train_dir, transform=train_transforms)
test_data = datasets.ImageFolder(test_dir, transform = test_transforms)
valid_data = datasets.ImageFolder(valid_dir, transform = test_transforms)

#Creating data loaders for test and training sets
trainloader = torch.utils.data.DataLoader(train_data, batch_size = 32, 
shuffle = True)
testloader = torch.utils.data.DataLoader(test_data, batch_size=32)
images, labels = next(iter(trainloader))

plt.imshow(images[0])如果运行正常，它应该允许我在运行后简单地查看图像。

Answer 1

transforms.RandomHorizontalFlip()工作PIL.Images，不是torch.Tensor。在上面的代码中，您在transforms.ToTensor()之前应用transforms.RandomHorizontalFlip()，这会导致张量。

但是，根据此处的官方 pytorch 文档，

transforms.RandomHorizo​​ntalFlip() 以给定的概率随机水平翻转给定的 PIL 图像。

因此，只需更改上面代码中转换的顺序，如下所示：

train_transforms = transforms.Compose([transforms.Resize(255), 
                                       transforms.CenterCrop(224),  
                                       transforms.RandomHorizontalFlip(),
                                       transforms.ToTensor(), 
                                       transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) 

Answer 2

只需添加transforms.ToPILImage()转换为 pil 图像，然后它就可以工作，例如：

transform = transforms.Compose([
    transforms.ToPILImage(),
    transforms.Resize(255),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.RandomHorizontalFlip(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])