def stack_tables_measurements()

in uimnet/evaluation/oodomain.py [0:0]

• 19 lines of code
• 9 McCabe index (conditional complexity)

def stack_tables_measurements(ood_results:List[List[Dict]]):
  # Collect records
  stacked = []
  for workers_results in ood_results:

    # Getting train and eval_cfg
    train_cfg = [el.pop('train_cfg') for el in workers_results][0]
    eval_cfg = [el.pop('eval_cfg') for el in workers_results][0]

    # measurements are dict of tensors
    op = functools.partial(torch.cat, dim=0)
    valid_tables = utils.map_dict([el['valid_tables'] for el in workers_results], op=op)
    test_tables = utils.map_dict([el['test_tables'] for el in workers_results], op=op)
    valid_measurements = utils.map_dict([el['valid_measurements'] for el in workers_results], op=op)
    test_measurements = utils.map_dict([el['test_measurements'] for el in workers_results], op=op)

    stacked += [dict(
      train_cfg=train_cfg,
      eval_cfg=eval_cfg,
      valid_tables=valid_tables,
      test_tables=test_tables,
      valid_measurements=valid_measurements,
      test_measurements=test_measurements,
    )]

  return stacked