Precision: [tensor(0.7406, device='cuda:0'), tensor(0.7337, device='cuda:0'), tensor(0.7436, device='cuda:0'), tensor(0.7305, device='cuda:0'), tensor(0.7380, device='cuda:0'), tensor(0.7311, device='cuda:0'), tensor(0.7298, device='cuda:0'), tensor(0.7350, device='cuda:0'), tensor(0.7332, device='cuda:0'), tensor(0.7349, device='cuda:0')]
Output distance: [tensor(5.0305, device='cuda:0'), tensor(5.0407, device='cuda:0'), tensor(5.0291, device='cuda:0'), tensor(5.0438, device='cuda:0'), tensor(5.0362, device='cuda:0'), tensor(5.0417, device='cuda:0'), tensor(5.0365, device='cuda:0'), tensor(5.0365, device='cuda:0'), tensor(5.0362, device='cuda:0'), tensor(5.0415, device='cuda:0')]
Prediction loss: [tensor(19155216., device='cuda:0'), tensor(19604992., device='cuda:0'), tensor(17926918., device='cuda:0'), tensor(19500488., device='cuda:0'), tensor(18454864., device='cuda:0'), tensor(18737516., device='cuda:0'), tensor(18394718., device='cuda:0'), tensor(20124476., device='cuda:0'), tensor(19212390., device='cuda:0'), tensor(18114416., device='cuda:0')]
Others: [{'iter_num': 5, 'num_positive': tensor(2182, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2163, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2165, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2167, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2160, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2179, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2235, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2185, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 3, 'num_positive': tensor(2204, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2146, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40908.2422, device='cuda:0'), tensor(40831.3750, device='cuda:0'), tensor(40738.2031, device='cuda:0'), tensor(40876.8242, device='cuda:0'), tensor(40753.9883, device='cuda:0'), tensor(40867.3086, device='cuda:0'), tensor(40950.4648, device='cuda:0'), tensor(40864.5664, device='cuda:0'), tensor(40805.2031, device='cuda:0'), tensor(40812.1016, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=6, microseconds=378943), datetime.timedelta(seconds=5, microseconds=986720), datetime.timedelta(seconds=5, microseconds=896989), datetime.timedelta(seconds=5, microseconds=835251), datetime.timedelta(seconds=6, microseconds=50402), datetime.timedelta(seconds=5, microseconds=869105), datetime.timedelta(seconds=5, microseconds=999554), datetime.timedelta(seconds=6, microseconds=2543), datetime.timedelta(seconds=3, microseconds=814822), datetime.timedelta(seconds=5, microseconds=684890)]
Phi time: [datetime.timedelta(microseconds=243002), datetime.timedelta(microseconds=313669), datetime.timedelta(microseconds=371618), datetime.timedelta(microseconds=431184), datetime.timedelta(microseconds=325608), datetime.timedelta(microseconds=430184), datetime.timedelta(microseconds=343549), datetime.timedelta(microseconds=345533), datetime.timedelta(microseconds=338541), datetime.timedelta(microseconds=385366)]
