Get a dataloader of resized images from a single dataset

vision
1

Hi, I would like to train a net with images of different resolutions (from cifar10). My strategy was the following :

  • download the cifar dataset with resolution = 224, with
transform = transforms.Compose([transforms.Resize(size=(224, 224)), transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))])

trainset = torchvision.datasets.CIFAR10(root= path, train=True, transform=transform, download=True)
  • then from this trainset, get a dataloader with images of the desired resolution, by downsampling.

I don’t know how to do this… As I’m going to work with different resolutions, I do not want to download the cifar dataset every time.

2

If you are using torchvision.datasets.CIFAR10, the dataset will be downloaded once and stored in the location passed via root.
Note that you are not downloading the CIFAR10 dataset in a resolution of 224x224, but you are resizing each image to this resolution.
CIFAR10 contains RGB images with the resolution 32x32.
If you want to apply different resolutions, I would recommend to create different datasets passing the transformation with the desired shape.

1 Like
3

Finally I changed my method : every time I call net(image), I make sure to interpolate the image to the desired size with torch.nn.functionnal.interpolate. This way I only have one dataset.
Thank you !
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