'DataLoader' object does not support indexing

user_8388965 0
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I have downloaded the ImageNet dataset via this pytorch api by setting download=True. But I cannot iterate through the dataloader.

The error says "'DataLoader' object does not support indexing"

trainset = torch.utils.data.DataLoader(
    datasets.ImageNet('/media/farshid/DataStore/temp/Imagenet/', split='train',
                      download=False))
trainloader = torch.utils.data.DataLoader(trainset, batch_size=1, shuffle=False, num_workers=1)

I tried a simple approach I just tried to run the following,

trainloader[0]

In the root directory, the pattern is 

root/  
    train/  
          n01440764/
          n01443537/ 
                   n01443537_2.jpg

The docs in the official website doesnt say anything else. https://pytorch.org/docs/stable/torchvision/datasets.html#imagenet

What am I doing wrong ?

imagenet pytorch computer-vision python
3 Answers

Well, the answer is pretty simple (besides error mentioned in the other answer).

DataLoader has no __getitem__ method (see in the source code for yourself).

It is used for iterating, not random access, over data (or batches of data). If you want to access specific element you should use torch.utils.data.Dataset, in your case:

trainset = torchvision.datasets.ImageNet('/media/farshid/DataStore/temp/Imagenet/', split='train', )
trainset[0]

Getting a batch

If you want to get a batch you may iterate over it and break afterwards:

for batch in dataloader:
    print(batch) # or anything else you want to do
    break

DataLoader creates random indices in default or specified way (see samplers), hence there is no __getitem__ as it wouldn't make sense for this object.

You may also inherit from the DataLoader and create your own __getitem__ function doing what you want (more complicated though).

Full example

# torch.utils.data.Dataset object
trainset = datasets.ImageNet('/media/farshid/DataStore/temp/Imagenet/', split='train', download=True)
# torch.utils.data.DataLoader object
trainloader =torch.utils.data.DataLoader(trainset, batch_size=1, shuffle=False)

for batch in trainloader:
    print(batch)
    break

Above should print the first batch whatever is inside.

user_108864200

Solution 

input_transform = standard_transforms.Compose([
    transforms.Resize((255,255)), # to Make sure all the 
    transforms.CenterCrop(224),   # imgs are at the same size 
    transforms.ToTensor()
])  


# torch.utils.data.Dataset object
trainset = datasets.ImageNet('/media/farshid/DataStore/temp/Imagenet/',
                             split='train', download=False, transform = input_transform)
# torch.utils.data.DataLoader object
trainloader =torch.utils.data.DataLoader(trainset, batch_size=2, shuffle=False)


for batch_idx, data in enumerate(trainloader, 0):
    x, y = data 
    break
user_83889650

The input dataset to torch.utils.data.DataLoader() should be of type torch.utils.data.Dataset, not torch.utils.data.DataLoader, which is what you are doing in above code.

So, your above code should be:

trainset = torchvision.datasets.ImageNet('/media/farshid/DataStore/temp/Imagenet/', 
                                          split='train', 
                                          download=False)

trainloader = torch.utils.data.DataLoader(trainset, 
                                          batch_size=1, 
                                          shuffle=False, 
                                          num_workers=1)

For more details, check the official torch documentation here.

user_58109500
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