thanks for your replying !!!
actually i have found some problems in C:\ProgramData\Miniconda3\envs\pytorch\lib\site-packages\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 channely.

    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.tensor(mean, dtype=torch.float32)
    std = torch.tensor(std, dtype=torch.float32)
    tensor.sub_(mean[:, None, None]).div_(std[:, None, None])
    return tensor

  > mean = torch.tensor(mean, dtype=torch.float32)
  >  std = torch.tensor(std, dtype=torch.float32)  

  this two lines code seem not to work, it should have set "mean, std" to torch.float32 but its is_cuda_type is true! .

my solution is：
the first step :comments  transforms.Normalize()
'val': transforms.Compose([
            transforms.Resize(input_size),
            transforms.CenterCrop(input_size),
            transforms.ToTensor(),
            #transforms.Normalize(mean, std, inplace=False)
        ]),

the second step:do Normalize
for inputs, labels in dataloader['val']:
        inputs = inputs.to(device)
       
        **inputs.sub_(mean[:, None, None]).div_(std[:, None, None])**

        labels = labels.to(device)

that’s ok!