def to_tensor()

in habitat_baselines/slambased/monodepth.py [0:0]
34 lines of code
11 McCabe index (conditional complexity)

    def to_tensor(self, pic):
        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):

            img = torch.from_numpy(pic.transpose((2, 0, 1)))
            return img.float().div(255)

        if accimage is not None and isinstance(pic, accimage.Image):  # type: ignore
            nppic = np.zeros(  # type: ignore[unreachable]
                [pic.channels, pic.height, pic.width], dtype=np.float32
            )
            pic.copyto(nppic)
            return torch.from_numpy(nppic)

        # handle PIL Image
        if pic.mode == "I":
            img = torch.from_numpy(np.array(pic, np.int32, copy=False))
        elif pic.mode == "I;16":
            img = torch.from_numpy(np.array(pic, np.int16, copy=False))
        else:
            img = torch.ByteTensor(
                torch.ByteStorage.from_buffer(pic.tobytes())  # type: ignore
            )
        # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK
        if pic.mode == "YCbCr":
            nchannel = 3
        elif pic.mode == "I;16":
            nchannel = 1
        else:
            nchannel = len(pic.mode)
        img = img.view(pic.size[1], pic.size[0], nchannel)
        # put it from HWC to CHW format
        # yikes, this transpose takes 80% of the loading time/CPU
        img = img.transpose(0, 1).transpose(0, 2).contiguous()
        if isinstance(img, torch.ByteTensor):
            return img.float().div(255)
        else:
            return img