training/train_eqa.py [315:341]:
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                        metrics_slug['d_0_' + str(i)] = dists_to_target[0]
                        metrics_slug['d_T_' + str(i)] = dists_to_target[-1]
                        metrics_slug['d_D_' + str(
                            i)] = dists_to_target[0] - dists_to_target[-1]
                        metrics_slug['d_min_' + str(i)] = np.array(
                            dists_to_target).min()
                        metrics_slug['ep_len_' + str(i)] = episode_length
                        if action == 3:
                            metrics_slug['stop_' + str(i)] = 1
                        else:
                            metrics_slug['stop_' + str(i)] = 0
                        inside_room = []
                        for p in pos_queue:
                            inside_room.append(
                                h3d.is_inside_room(
                                    p, eval_loader.dataset.target_room))
                        if inside_room[-1] == True:
                            metrics_slug['r_T_' + str(i)] = 1
                        else:
                            metrics_slug['r_T_' + str(i)] = 0
                        if any([x == True for x in inside_room]) == True:
                            metrics_slug['r_e_' + str(i)] = 1
                        else:
                            metrics_slug['r_e_' + str(i)] = 0

                    # navigation metrics
                    metrics_list = []
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training/train_nav.py [439:465]:
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                        metrics_slug['d_0_' + str(i)] = dists_to_target[0]
                        metrics_slug['d_T_' + str(i)] = dists_to_target[-1]
                        metrics_slug['d_D_' + str(
                            i)] = dists_to_target[0] - dists_to_target[-1]
                        metrics_slug['d_min_' + str(i)] = np.array(
                            dists_to_target).min()
                        metrics_slug['ep_len_' + str(i)] = episode_length
                        if action == 3:
                            metrics_slug['stop_' + str(i)] = 1
                        else:
                            metrics_slug['stop_' + str(i)] = 0
                        inside_room = []
                        for p in pos_queue:
                            inside_room.append(
                                h3d.is_inside_room(
                                    p, eval_loader.dataset.target_room))
                        if inside_room[-1] == True:
                            metrics_slug['r_T_' + str(i)] = 1
                        else:
                            metrics_slug['r_T_' + str(i)] = 0
                        if any([x == True for x in inside_room]) == True:
                            metrics_slug['r_e_' + str(i)] = 1
                        else:
                            metrics_slug['r_e_' + str(i)] = 0

                    # collate and update metrics
                    metrics_list = []
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