torchvision/transforms/functional.py
def normalize(tensor, mean, std, inplace=False):
    """Normalize a tensor image with mean and standard deviation.

    .. note::
        This transform acts out of place by default, i.e., it does not mutates the input tensor.

    See :class:`~torchvision.transforms.Normalize` for more details.

    Args:
        tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
        mean (sequence): Sequence of means for each channel.
        std (sequence): Sequence of standard deviations for each channel.

    Returns:
        Tensor: Normalized Tensor image.
    """
    if not _is_tensor_image(tensor):
        raise TypeError('tensor is not a torch image.')

    if not inplace:
        tensor = tensor.clone()

    mean = torch.as_tensor(mean, dtype=torch.float32, device=tensor.device)
    std = torch.as_tensor(std, dtype=torch.float32, device=tensor.device)
    tensor.sub_(mean[:, None, None]).div_(std[:, None, None])
    return tensor
def normalize(tensor, mean, std, inplace=False):
    if not _is_tensor_image(tensor):
        raise TypeError('tensor is not a torch image.')

    if not inplace:
        tensor = tensor.clone()

    mean = torch.as_tensor(mean, dtype=torch.float32, device=tensor.device)
    std = torch.as_tensor(std, dtype=torch.float32, device=tensor.device)
    tensor.sub_(mean[:, None, None]).div_(std[:, None, None])
    return tensor
def normalize ( tensor , mean , std , inplace = False ) : if not _is_tensor_image ( tensor ) : raise TypeError ( 'tensor is not a torch image.' ) if not inplace : tensor = tensor . clone ( ) mean = torch . as_tensor ( mean , dtype = torch . float32 , device = tensor . device ) std = torch . as_tensor ( std , dtype = torch . float32 , device = tensor . device ) tensor . sub_ ( mean [ : , None , None ] ) . div_ ( std [ : , None , None ] ) return tensor
Normalize a tensor image with mean and standard deviation.

    .. note::
        This transform acts out of place by default, i.e., it does not mutates the input tensor.

    See :class:`~torchvision.transforms.Normalize` for more details.

    Args:
        tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
        mean (sequence): Sequence of means for each channel.
        std (sequence): Sequence of standard deviations for each channel.

    Returns:
        Tensor: Normalized Tensor image.
Normalize a tensor image with mean and standard deviation .