Method process

lib/test/tracker/data_utils.py:11–17

(self, img_arr: np.ndarray, amask_arr: np.ndarray)

        self.std = torch.tensor([0.229, 0.224, 0.225]).view((1, 3, 1, 1)).cuda()

    def process(self, img_arr: np.ndarray, amask_arr: np.ndarray):
        # Deal with the image patch
        img_tensor = torch.tensor(img_arr).cuda().float().permute((2,0,1)).unsqueeze(dim=0)
        img_tensor_norm = ((img_tensor / 255.0) - self.mean) / self.std  # (1,3,H,W)
        # Deal with the attention mask
        amask_tensor = torch.from_numpy(amask_arr).to(torch.bool).cuda().unsqueeze(dim=0)  # (1,H,W)
        return NestedTensor(img_tensor_norm, amask_tensor)


class PreprocessorX(object):