nohup: ignoring input

Namespace(annotation_label=[{'id': 1, 'properties': [{'name': '磨玻璃结节', 'id': 1}, {'name': '实性结节', 'id': 2}, {'name': '混合结节', 'id': 3}, {'name': '钙化结节', 'id': 4}]}, {'id': 2, 'properties': [{'name': '良性', 'id': 1}, {'name': '恶性', 'id': 2}]}], check_cancleflag=False, data={'data_loader': {'data_dir': '/data/job_521/job_data_preprocess/output/preprocess/preprocess_file_details/nii', 'train_db': '', 'validate_db': '', 'test_db': '/data/job_521/job_data_preprocess/output/preprocess/preprocess_file_details/json2mdb', 'rand_crop_ratio': 0.0, 'num_workers': 0, 'blacklist': [], 'balanced_sampling': True, 'pos_target_range': [1, 2, 3, 4, 5, 6, 7, 26], 'neg_target_range': [], 'irrelevant_target_range': [], 'data_mode': 'rib', 'mask_dir': ''}, 'data_process': {'crop_size': [128, 128, 128], 'spacing': [1.0, 1.0, 1.0], 'bbox_border': 0, 'pad_value': -1000, 'stride': 4, 'num_neg': 800, 'num_hard': 2, 'bound_size': 8, 'diameterlim': [12.0, 100.0], 'scalelim': [0.8, 1.2], 'augtype': {'flip': True, 'rotate': True, 'scale': True, 'swap': False}, 'fpn_diameter_range': '', '#fpn_diameter_range': [[20, 99999], [12, 32], [8, 16]], 'hu_min': -300.0, 'hu_max': 1000.0, 'max_objs': 20, 'downsampling_ratio': 2, 'min_overlap': 0.1}}, deploy={'torchscript_save_path': '', 'torchscript_compare_res_dir': ''}, in_channels=1, job_data_root='/data/job_521/job_data_preprocess', local_rank=-1, master_ip='127.0.0.1', model={'meta_architecture': 'NoduleNet', 'bbox_reg_weight': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 'anchor': {'bases': [3.0, 10.0, 30.0], '##bases': [5, 20, 40, 60, 90], '#bases': [[30, 55, 90], [20, 30, 40], [5, 10, 20]], 'aspect_ratios': [[1.0, 1.0, 1.0]]}, 'backbone': {'conv_body': 'ResUNet', 'bn_momentum': 0.1, 'fpn': False}, 'rpn': {'use_fpn': False, 'bg_thresh_high': 0.2, 'fg_thresh_low': 0.5, 'nms_iou_thresh': 0.1, 'train_pre_nms_score_thresh': 0.2, 'test_pre_nms_score_thresh': 0.05, 'deploy_pre_nms_top_k': 10000, 'balanced_cls_loss': True}, 'roi_box_head': {'roi_batch_size': 16, 'num_class': 2, 'pooler_resolution': 7, 'sampling_ratio': 2, 'bg_thresh_high': 0.02, 'fg_thresh_low': 0.7, 'fg_fraction': 0.5, 'test_pre_nms_score_thresh': 0}}, out_channels=3, port=29500, pred=True, preprocess_workers=8, pretrain_cp=[{'module_name': 'lungDetection3D', 'use_model': True, 'model_name': 'model.pth', 'train_args': {'annotation_label': {'name': '结节良恶性', 'id': 2, 'properties': [{'name': '良性', 'id': 1}, {'name': '恶性', 'id': 2}]}}}], pretrain_msg='/data/job_521/job_data_preprocess/input/model.pth', rank=0, split={'train': 0.7, 'test': 0.3}, test=False, testing={'preprocess_save_path': '/data/job_521/job_data_preprocess/output/preprocess/preprocess_file_details', 'test_slide_len': 96, 'test_margin': 16, 'test_pre_nms_top_k': 10000, 'test_nms_overlap_threshold': 0.1, 'saver_pred_result': '/data/job_521/job_data_preprocess/output/eval/eval_result.json', 'saver_pred_details': '/data/job_521/job_data_preprocess/output/eval/eval_file_details', 'use_rcnn': False, 'test_tmp_rpns': '/data/job_521/job_data_preprocess/output/tmp/rpns', 'test_tmp_dir': '/data/job_521/job_data_preprocess/output/tmp', 'test_nii_dir': '/data/job_521/job_data_preprocess/output/preprocess/preprocess_file_details/nii'}, train=False, training={'num_gpus': 2, 'amp': False, 'fp16_allreduce': False, 'batch_size': 4, 'solver': {'optimizer': 'SGD', 'base_lr': 0.005, 'momentum': 0.9, 'wright_decay': 0.0001}, 'saver': {'saver_root': '', 'saver_train_bestmodel': '', 'saver_eval_pmd': '', 'saver_eval_pjson': '', 'saver_train_metrics': '', 'saver_train_result': '', 'saver_eval_result': '', 'saver_frequency': 1}, 'sheduler': {'total_epochs': 3, 'warmup': 1, 'switch_roi_epoch': 201, 'switch_balanced_sampling_epoch': 201, 'lr_shedule': True, 'num_neg_shedule': False, 'rand_crop_ratio_shedule': False}}, use_amp=False, usr_ckpt=False, working_label=2, world_size=2)
[Loading model from /data/job_521/job_data_preprocess/input/model.pth]
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Total # of eval data 821

0it [00:00, ?it/s]
1it [00:09,  9.56s/it]
2it [00:17,  8.73s/it]
3it [00:40, 15.36s/it]
4it [00:54, 14.84s/it]
5it [01:14, 16.63s/it]
6it [01:40, 19.64s/it]
7it [01:53, 17.45s/it]
8it [02:07, 16.31s/it]
9it [02:23, 16.47s/it]