How to input image in a model without normalizing it?

vision
1

I’m struggling on how to train my network on images without normalizing them first ? Because when I use ToTensor as a transform, it normalizes the images automatically.

Can anyone PLEASE help me out?

2

Hi,

I think it can help:

try:
    import accimage
except ImportError:
    accimage = None

def _is_pil_image(img):
    if accimage is not None:
        return isinstance(img, (Image.Image, accimage.Image))
    else:
        return isinstance(img, Image.Image)


def _is_tensor_image(img):
    return torch.is_tensor(img) and img.ndimension() == 3


def _is_numpy_image(img):
    return isinstance(img, np.ndarray) and (img.ndim in {2, 3})

def my_to_tensor(pic):
    """Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
    See ``ToTensor`` for more details.
    Args:
        pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
    Returns:
        Tensor: Converted image.
    """
    if not(_is_pil_image(pic) or _is_numpy_image(pic)):
        raise TypeError('pic should be PIL Image or ndarray. Got {}'.format(type(pic)))

    if isinstance(pic, np.ndarray):
        # handle numpy array
        if pic.ndim == 2:
            pic = pic[:, :, None]

        img = torch.from_numpy(pic.transpose((2, 0, 1)))
        # backward compatibility
        if isinstance(img, torch.ByteTensor):
            return img.float()  ###### removed .div(255)
        else:
            return img

    if accimage is not None and isinstance(pic, accimage.Image):
        nppic = np.zeros([pic.channels, pic.height, pic.width], dtype=np.float32)
        pic.copyto(nppic)
        return torch.from_numpy(nppic)

    # handle PIL Image
    if pic.mode == 'I':
        img = torch.from_numpy(np.array(pic, np.int32, copy=False))
    elif pic.mode == 'I;16':
        img = torch.from_numpy(np.array(pic, np.int16, copy=False))
    elif pic.mode == 'F':
        img = torch.from_numpy(np.array(pic, np.float32, copy=False))
    elif pic.mode == '1':
        img = 255 * torch.from_numpy(np.array(pic, np.uint8, copy=False))
    else:
        img = torch.ByteTensor(torch.ByteStorage.from_buffer(pic.tobytes()))
    # PIL image mode: L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK
    if pic.mode == 'YCbCr':
        nchannel = 3
    elif pic.mode == 'I;16':
        nchannel = 1
    else:
        nchannel = len(pic.mode)
    img = img.view(pic.size[1], pic.size[0], nchannel)
    # put it from HWC to CHW format
    # yikes, this transpose takes 80% of the loading time/CPU
    img = img.transpose(0, 1).transpose(0, 2).contiguous()
    if isinstance(img, torch.ByteTensor):
        return img.float()  ###### removed .div(255)
    else:
        return img

### test
class MyToTensor(object):
    def __call__(self, pic):
        return my_to_tensor(pic)

Note that, I have just copy pasted source code of ToTensor and removed the lines where images have been normalized using .div(255).
You can use this MyToTensor instead of default ToTensor in transofrms.

Or another way will be to unnormalize tensors after using ToTensor in transforms. Here it is:

def unnormalize(tensor):
    return tensor.mul(255)

class Unnormalize(object):
    def __call__(self, tensor):
        return unnormalize(tensor)

img = Image.open('img.jpg')
img_tensor_normalized = transforms.ToTensor()(img)
img_tensor = Unnormalize()(img_tensor_normalized)

As before, you can add Unnormalize in your transforms pipepline after ToTensor().

Bests