The torchvision transformations work an PIL.Images.
You could therefore store or load images in your Dataset and after the cropping transform it to a tensor. Alternatively, if you already have the tensors, you could transform them back to an image, apply the transformation, and transform it back to a tensor.
import torchvision.transforms.functional as TF

...
def __getitem__(self, index):
    out = self.tensor[index]
    out = TF.to_pil_image(out)
    out = self.center_crop(out)
    out = TF.to_tensor(out)
    return out
...