def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
    """Convert torch Tensors into image numpy arrays.

    After clamping to [min, max], values will be normalized to [0, 1].

    Args:
        tensor (Tensor or list[Tensor]): Accept shapes:
            1) 4D mini-batch Tensor of shape (B x 3/1 x H x W);
            2) 3D Tensor of shape (3/1 x H x W);
            3) 2D Tensor of shape (H x W).
            Tensor channel should be in RGB order.
        rgb2bgr (bool): Whether to change rgb to bgr.
        out_type (numpy type): output types. If ``np.uint8``, transform outputs
            to uint8 type with range [0, 255]; otherwise, float type with
            range [0, 1]. Default: ``np.uint8``.
        min_max (tuple[int]): min and max values for clamp.

    Returns:
        (Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of
        shape (H x W). The channel order is BGR.
    """
    if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):
        raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}')

    if torch.is_tensor(tensor):
        tensor = [tensor]
    result = []
    for _tensor in tensor:
        _tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
        _tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0])

        n_dim = _tensor.dim()
        if n_dim == 4:
            img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy()
            img_np = img_np.transpose(1, 2, 0)
            if rgb2bgr:
                img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
        elif n_dim == 3:
            img_np = _tensor.numpy()
            img_np = img_np.transpose(1, 2, 0)
            if img_np.shape[2] == 1:  # gray image
                img_np = np.squeeze(img_np, axis=2)
            else:
                if rgb2bgr:
                    img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
        elif n_dim == 2:
            img_np = _tensor.numpy()
        else:
            raise TypeError(f'Only support 4D, 3D or 2D tensor. But received with dimension: {n_dim}')
        if out_type == np.uint8:
            # Unlike MATLAB, numpy.unit8() WILL NOT round by default.
            img_np = (img_np * 255.0).round()
        img_np = img_np.astype(out_type)
        result.append(img_np)
    if len(result) == 1:
        result = result[0]
    return result
Convert torch Tensors into image numpy arrays.

After clamping to [min, max], values will be normalized to [0, 1].

Args:
tensor (Tensor or list[Tensor]): Accept shapes:
1) 4D mini-batch Tensor of shape (B x 3/1 x H x W);
2) 3D Tensor of shape (3/1 x H x W);
3) 2D Tensor of shape (H x W).
Tensor channel should be in RGB order.
rgb2bgr (bool): Whether to change rgb to bgr.
out_type (numpy type): output types. If ``np.uint8``, transform outputs
to uint8 type with range [0, 255]; otherwise, float type with
range [0, 1]. Default: ``np.uint8``.
min_max (tuple[int]): min and max values for clamp.

Returns:
(Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of
shape (H x W). The channel order is BGR.
opensora/models/super_resolution/basicsr/utils/img_util.py