Question

我正在尝试通过加载程序进行迭代以检查其是否正常运行，但是给出了以下错误：

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

我尝试同时添加transforms.ToTensor()和transforms.ToPILImage()，这给我一个错误，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))

如果plt.imshow(images[0])运行正常，它应该可以让我简单地看到图像。

Answer 1

transforms.RandomHorizontalFlip()适用于PIL.Images，而不适用于torch.Tensor。在上面的代码中，您要在transforms.ToTensor()之前应用transforms.RandomHorizontalFlip()，这会导致张量。

但是，根据官方pytorch文档here

transforms.RandomHorizontalFlip（）水平翻转给定的PIL 以给定的概率随机拍摄图像。

因此，只需在上面的代码中更改转换的顺序，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()即可转换为pil映像，然后它将起作用，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])
])

img应该是PIL图片。得到了<class'torch.Tensor'>

2 个答案: