【深度学习】torchvision.transforms中的ToTensor和Normalize

transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))

一、transforms.ToTensor()

• 能够把灰度范围从0-255变换到0-1之间
• 从源码的角度看，调用的ToTensor的时候是调用这个class的__call__方法，然后ToTensor的call是调用了F的to_tensor方法，F是functional.py

class ToTensor(object):
    """Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.

    Converts a PIL Image or numpy.ndarray (H x W x C) in the range
    [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]
    if the PIL Image belongs to one of the modes (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1)
    or if the numpy.ndarray has dtype = np.uint8

    In the other cases, tensors are returned without scaling.
    """

    def __call__(self, pic):
        """
        Args:
            pic (PIL Image or numpy.ndarray): Image to be converted to tensor.

        Returns:
            Tensor: Converted image.
        """
        # 这里会调用functional中的to_tensor方法
        return F.to_tensor(pic)

    def __repr__(self):
        return self.__class__.__name__ + '()'

我们接下来看一下to_tensor的源码是怎么定义的：

def 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