Precision: [tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0')]

Output distance: [tensor(39196.5508, device='cuda:0'), tensor(39340.3516, device='cuda:0'), tensor(39153.3945, device='cuda:0'), tensor(39224.3242, device='cuda:0'), tensor(39327.8594, device='cuda:0'), tensor(39124.0820, device='cuda:0'), tensor(39165.0625, device='cuda:0'), tensor(39385.4219, device='cuda:0'), tensor(39441.9805, device='cuda:0'), tensor(39531.5156, device='cuda:0')]

Prediction loss: [tensor(39818.8828, device='cuda:0'), tensor(38937.8594, device='cuda:0'), tensor(37919.5352, device='cuda:0'), tensor(40612.8516, device='cuda:0'), tensor(39945.9492, device='cuda:0'), tensor(38882.3828, device='cuda:0'), tensor(38871.2227, device='cuda:0'), tensor(38708.0820, device='cuda:0'), tensor(41086.9648, device='cuda:0'), tensor(39855.8164, device='cuda:0')]

Others: [{'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(3570375.5000, device='cuda:0'), tensor(3583881.5000, device='cuda:0'), tensor(3451083.5000, device='cuda:0'), tensor(3684107.2500, device='cuda:0'), tensor(3684616.2500, device='cuda:0'), tensor(3589839.2500, device='cuda:0'), tensor(3499095.7500, device='cuda:0'), tensor(3542406., device='cuda:0'), tensor(3729895.7500, device='cuda:0'), tensor(3636687.7500, device='cuda:0')]

Training loss: 3577298.5

Prediction time: [datetime.timedelta(microseconds=584520), datetime.timedelta(microseconds=611456), datetime.timedelta(microseconds=679170), datetime.timedelta(microseconds=602449), datetime.timedelta(microseconds=658209), datetime.timedelta(microseconds=593483), datetime.timedelta(microseconds=677130), datetime.timedelta(microseconds=683103), datetime.timedelta(microseconds=623356), datetime.timedelta(microseconds=594478)]

Phi time: [datetime.timedelta(seconds=1, microseconds=345458), datetime.timedelta(microseconds=847543), datetime.timedelta(microseconds=777471), datetime.timedelta(microseconds=782347), datetime.timedelta(microseconds=782881), datetime.timedelta(microseconds=783734), datetime.timedelta(microseconds=787593), datetime.timedelta(microseconds=783385), datetime.timedelta(microseconds=801425), datetime.timedelta(microseconds=780252)]

