def average_abs_values_to_float(input_tensor: torch.Tensor) -> float: """Compute average of the tensor. Args: input_tensor: tensor on 'cpu' or 'cuda' Returns: Sum of absolute values divided by number of elements. """ total_delta = input_tensor.abs_().sum().to('cpu').item() return total_delta / input_tensor.numel()
def update_priorities(self, idc: List[int], td_errors: Tensor) -> None: td_errors.abs_() for idx, td_error in zip(idc, td_errors): # error = td_error.abs_().detach().cpu() self.priorities[idx] = td_error.item()