Reading the code on the link here: https://github.com/pytorch/examples/blob/42e5b996718797e45c46a25c55b031e6768f8440/imagenet/main.py#L89-L101 112
it seems that the problem was that before scaling or center cropping to 224, I needed to do a scaling on 256 and then a center crop. Now, the resnets are working as good as the reported accuracy.
The code for that:
val_loader = torch.utils.data.DataLoader(
    datasets.ImageFolder(valdir, transforms.Compose([
        transforms.Scale(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        normalize,
    ])),
    batch_size=args.batch_size, shuffle=False,
    num_workers=args.workers, pin_memory=True)

I used the same ratio between Scale and CenterCrop for Inception net (there the input size is supposed to be 299 pixels, rather than 224) and it seems to work quite well too.