training/train_nav.py [862:887]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                    loss.backward()

                    ensure_shared_grads(model.cpu(), shared_model)
                    optim.step()

                    if t % args.print_every == 0:
                        print(metrics.get_stat_string())
                        logging.info("TRAIN: metrics: {}".format(metrics.get_stat_string()))
                        if args.log == True:
                            metrics.dump_log()

                    print('[CHECK][Cache:%d][Total:%d]' %
                          (len(train_loader.dataset.img_data_cache),
                           len(train_loader.dataset.env_list)))
                    logging.info('TRAIN: [CHECK][Cache:{}][Total:{}]'.format(
                        len(train_loader.dataset.img_data_cache), len(train_loader.dataset.env_list)))

                if all_envs_loaded == False:
                    train_loader.dataset._load_envs(in_order=True)
                    if len(train_loader.dataset.pruned_env_set) == 0:
                        done = True
                        if args.cache == False:
                            train_loader.dataset._load_envs(
                                start_idx=0, in_order=True)
                else:
                    done = True
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



training/train_nav.py [959:985]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                    loss.backward()

                    ensure_shared_grads(model.cpu(), shared_model)
                    optim.step()

                    if t % args.print_every == 0:
                        print(metrics.get_stat_string())
                        logging.info("TRAIN: metrics: {}".format(metrics.get_stat_string()))
                        if args.log == True:
                            metrics.dump_log()

                    print('[CHECK][Cache:%d][Total:%d]' %
                          (len(train_loader.dataset.img_data_cache),
                           len(train_loader.dataset.env_list)))
                    logging.info('TRAIN: [CHECK][Cache:{}][Total:{}]'.format(
                        len(train_loader.dataset.img_data_cache), len(train_loader.dataset.env_list)))


                if all_envs_loaded == False:
                    train_loader.dataset._load_envs(in_order=True)
                    if len(train_loader.dataset.pruned_env_set) == 0:
                        done = True
                        if args.cache == False:
                            train_loader.dataset._load_envs(
                                start_idx=0, in_order=True)
                else:
                    done = True
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -