transforms.ToTensor()

\(torchvision.transforms.functional.py\) 中to_tensor()函数源码：

def to_tensor(pic) -> Tensor:
    """Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
    This function does not support torchscript.

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

    Args:
        pic (PIL Image or numpy.ndarray): Image to be converted to tensor.

    Returns:
        Tensor: Converted image.
    """
    if not torch.jit.is_scripting() and not torch.jit.is_tracing():
        _log_api_usage_once(to_tensor)
    if not (F_pil._is_pil_image(pic) or _is_numpy(pic)):
        raise TypeError(f"pic should be PIL Image or ndarray. Got {type(pic)}")

    if _is_numpy(pic) and not _is_numpy_image(pic):
        raise ValueError(f"pic should be 2/3 dimensional. Got {pic.ndim} dimensions.")

    default_float_dtype = torch.get_default_dtype()

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

        img = torch.from_numpy(pic.transpose((2, 0, 1))).contiguous()
        # backward compatibility
        if isinstance(img, torch.ByteTensor):
            return img.to(dtype=default_float_dtype).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).to(dtype=default_float_dtype)

    # handle PIL Image
    mode_to_nptype = {"I": np.int32, "I;16": np.int16, "F": np.float32}
    img = torch.from_numpy(np.array(pic, mode_to_nptype.get(pic.mode, np.uint8), copy=True))

    if pic.mode == "1":
        img = 255 * img
    img = img.view(pic.size[1], pic.size[0], len(pic.getbands()))
    # put it from HWC to CHW format
    img = img.permute((2, 0, 1)).contiguous()
    if isinstance(img, torch.ByteTensor):
        return img.to(dtype=default_float_dtype).div(255)
    else:
        return img

从to_tensor()函数源码看出其接受 \(PIL \ Image\)、\(numpy.ndarray\) 格式，功能如下：

• 先由 \(HWC\)(高、宽、通道) 转置为 \(CHW\)(通道、高、宽) 格式；
• 再转为 \(float32\) 类型；
• 最后，每个像素除以 \(255\)。

来自：https://blog.csdn.net/wuqingshan2010/article/details/110133046