Precision: [tensor(0.9587, device='cuda:0'), tensor(0.9570, device='cuda:0'), tensor(0.9594, device='cuda:0'), tensor(0.9614, device='cuda:0'), tensor(0.9598, device='cuda:0'), tensor(0.9591, device='cuda:0'), tensor(0.9612, device='cuda:0'), tensor(0.9616, device='cuda:0'), tensor(0.9619, device='cuda:0'), tensor(0.9584, device='cuda:0')]

Output distance: [tensor(106.7391, device='cuda:0'), tensor(109.0680, device='cuda:0'), tensor(106.5727, device='cuda:0'), tensor(99.3237, device='cuda:0'), tensor(100.4689, device='cuda:0'), tensor(106.5510, device='cuda:0'), tensor(100.0322, device='cuda:0'), tensor(101.6627, device='cuda:0'), tensor(95.3006, device='cuda:0'), tensor(110.1556, device='cuda:0')]

Prediction loss: [tensor(384.6583, device='cuda:0'), tensor(381.1825, device='cuda:0'), tensor(386.7249, device='cuda:0'), tensor(369.7780, device='cuda:0'), tensor(377.7440, device='cuda:0'), tensor(375.0317, device='cuda:0'), tensor(386.0698, device='cuda:0'), tensor(379.9598, device='cuda:0'), tensor(384.0517, device='cuda:0'), tensor(378.7520, device='cuda:0')]

Others: [{'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(3657649.7500, device='cuda:0'), tensor(3618486.7500, device='cuda:0'), tensor(3657342., device='cuda:0'), tensor(3491791.7500, device='cuda:0'), tensor(3573520.5000, device='cuda:0'), tensor(3547799.5000, device='cuda:0'), tensor(3649446.2500, device='cuda:0'), tensor(3610012.5000, device='cuda:0'), tensor(3635186.7500, device='cuda:0'), tensor(3612076.5000, device='cuda:0')]

Training loss: 3600704.5

Prediction time: [datetime.timedelta(microseconds=849401), datetime.timedelta(microseconds=902174), datetime.timedelta(microseconds=983824), datetime.timedelta(microseconds=881263), datetime.timedelta(microseconds=883254), datetime.timedelta(microseconds=867321), datetime.timedelta(microseconds=881263), datetime.timedelta(microseconds=976856), datetime.timedelta(microseconds=886242), datetime.timedelta(microseconds=986815)]

Phi time: [datetime.timedelta(seconds=1, microseconds=564381), datetime.timedelta(seconds=1, microseconds=11601), datetime.timedelta(microseconds=999006), datetime.timedelta(seconds=1, microseconds=2921), datetime.timedelta(microseconds=978228), datetime.timedelta(microseconds=969931), datetime.timedelta(microseconds=969939), datetime.timedelta(microseconds=970983), datetime.timedelta(microseconds=977267), datetime.timedelta(microseconds=988810)]

