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()
