img 应该是 PIL Image.得到了 <class 'torch.Tensor'> [英] img should be PIL Image. Got <class 'torch.Tensor'>

问题描述

我正在尝试遍历加载器以检查它是否正常工作，但是给出了以下错误:

I'm trying to iterate through a loader to check if it's working, however the below error is given:

TypeError: img 应该是 PIL Image.得到了

我已经尝试添加 transforms.ToTensor() 和 transforms.ToPILImage() 并且它给了我一个错误要求相反.即，使用 ToPILImage()，它将要求张量，反之亦然.

I've tried adding both transforms.ToTensor() and transforms.ToPILImage() and it gives me an error asking for the opposite. i.e, with ToPILImage(), it will ask for tensor, and vice versa.

# 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))

它应该允许我在运行 plt.imshow(images[0]) 后简单地看到图像，如果它工作正常.

It should allow me to simply see the image once I run plt.imshow(images[0]), if its working correctly.

推荐答案

transforms.RandomHorizo​​ntalFlip() 适用于 PIL.Images，而不是 torch.Tensor.在上面的代码中，您在 transforms.RandomHorizo​​ntalFlip() 之前应用 transforms.ToTensor()，这会产生张量.

transforms.RandomHorizontalFlip() works on PIL.Images, not torch.Tensor. In your code above, you are applying transforms.ToTensor() prior to transforms.RandomHorizontalFlip(), which results in tensor.

但是，根据官方 pytorch 文档这里、

But, as per the official pytorch documentation here,

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

transforms.RandomHorizontalFlip() horizontally flip the given PIL Image randomly with a given probability.

因此，只需更改上面代码中的转换顺序，如下所示:

So, just change the order of your transformation in above code, like below:

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])]) 

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