def run_evaluation()

in run.py [0:0]

• 27 lines of code
• 6 McCabe index (conditional complexity)

    def run_evaluation(actions, action_filter=None):
        errors_p1 = []
        errors_p2 = []
        errors_p3 = []
        errors_vel = []

        for action_key in actions.keys():
            if action_filter is not None:
                found = False
                for a in action_filter:
                    if action_key.startswith(a):
                        found = True
                        break
                if not found:
                    continue

            poses_act, poses_2d_act = fetch_actions(actions[action_key])
            gen = UnchunkedGenerator(None, poses_act, poses_2d_act,
                                     pad=pad, causal_shift=causal_shift, augment=args.test_time_augmentation,
                                     kps_left=kps_left, kps_right=kps_right, joints_left=joints_left, joints_right=joints_right)
            e1, e2, e3, ev = evaluate(gen, action_key)
            errors_p1.append(e1)
            errors_p2.append(e2)
            errors_p3.append(e3)
            errors_vel.append(ev)

        print('Protocol #1   (MPJPE) action-wise average:', round(np.mean(errors_p1), 1), 'mm')
        print('Protocol #2 (P-MPJPE) action-wise average:', round(np.mean(errors_p2), 1), 'mm')
        print('Protocol #3 (N-MPJPE) action-wise average:', round(np.mean(errors_p3), 1), 'mm')
        print('Velocity      (MPJVE) action-wise average:', round(np.mean(errors_vel), 2), 'mm')