Precision: [tensor(0.5920, device='cuda:0'), tensor(0.5923, device='cuda:0'), tensor(0.5891, device='cuda:0'), tensor(0.5920, device='cuda:0'), tensor(0.6059, device='cuda:0'), tensor(0.5941, device='cuda:0'), tensor(0.5944, device='cuda:0'), tensor(0.5839, device='cuda:0'), tensor(0.5889, device='cuda:0'), tensor(0.6078, device='cuda:0')]

Output distance: [tensor(5.1221, device='cuda:0'), tensor(5.1216, device='cuda:0'), tensor(5.1279, device='cuda:0'), tensor(5.1221, device='cuda:0'), tensor(5.0943, device='cuda:0'), tensor(5.1179, device='cuda:0'), tensor(5.1174, device='cuda:0'), tensor(5.1384, device='cuda:0'), tensor(5.1284, device='cuda:0'), tensor(5.0906, device='cuda:0')]

Prediction loss: [tensor(21281298., device='cuda:0'), tensor(16491758., device='cuda:0'), tensor(19120554., device='cuda:0'), tensor(19397648., device='cuda:0'), tensor(17029234., device='cuda:0'), tensor(18677048., device='cuda:0'), tensor(18878234., device='cuda:0'), tensor(17738032., device='cuda:0'), tensor(20761534., device='cuda:0'), tensor(18562616., device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 29, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40808.4492, device='cuda:0'), tensor(41144.8516, device='cuda:0'), tensor(40887.8125, device='cuda:0'), tensor(40728.5234, device='cuda:0'), tensor(41171.2930, device='cuda:0'), tensor(40724.1172, device='cuda:0'), tensor(41306.2266, device='cuda:0'), tensor(40898.9453, device='cuda:0'), tensor(40937.7773, device='cuda:0'), tensor(40827.5508, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=275590), datetime.timedelta(seconds=1, microseconds=284553), datetime.timedelta(seconds=1, microseconds=276585), datetime.timedelta(seconds=1, microseconds=271606), datetime.timedelta(seconds=1, microseconds=116266), datetime.timedelta(seconds=1, microseconds=222810), datetime.timedelta(seconds=1, microseconds=251690), datetime.timedelta(seconds=1, microseconds=254678), datetime.timedelta(seconds=1, microseconds=285547), datetime.timedelta(seconds=1, microseconds=133194)]

Phi time: [datetime.timedelta(microseconds=213096), datetime.timedelta(microseconds=224050), datetime.timedelta(microseconds=224050), datetime.timedelta(microseconds=227038), datetime.timedelta(microseconds=216083), datetime.timedelta(microseconds=225046), datetime.timedelta(microseconds=218076), datetime.timedelta(microseconds=223054), datetime.timedelta(microseconds=214091), datetime.timedelta(microseconds=229028)]

