Precision: [tensor(0.8604, device='cuda:0'), tensor(0.8584, device='cuda:0'), tensor(0.8607, device='cuda:0'), tensor(0.8606, device='cuda:0'), tensor(0.8603, device='cuda:0'), tensor(0.8606, device='cuda:0'), tensor(0.8607, device='cuda:0'), tensor(0.8605, device='cuda:0'), tensor(0.8606, device='cuda:0'), tensor(0.8606, device='cuda:0')]

Output distance: [tensor(532.2445, device='cuda:0'), tensor(545.3950, device='cuda:0'), tensor(531.6309, device='cuda:0'), tensor(532.8753, device='cuda:0'), tensor(533.0673, device='cuda:0'), tensor(530.4278, device='cuda:0'), tensor(531.2637, device='cuda:0'), tensor(532.8768, device='cuda:0'), tensor(529.6003, device='cuda:0'), tensor(532.2771, device='cuda:0')]

Prediction loss: [tensor(608.3899, device='cuda:0'), tensor(603.3773, device='cuda:0'), tensor(600.6763, device='cuda:0'), tensor(609.0370, device='cuda:0'), tensor(624.5608, device='cuda:0'), tensor(616.1537, device='cuda:0'), tensor(603.0713, device='cuda:0'), tensor(619.9161, device='cuda:0'), tensor(609.0534, device='cuda:0'), tensor(590.9443, 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': 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': 9, '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': 9, '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')}]

Compressed training loss: [tensor(8874509., device='cuda:0'), tensor(8831834., device='cuda:0'), tensor(8781706., device='cuda:0'), tensor(8905301., device='cuda:0'), tensor(9104406., device='cuda:0'), tensor(8985584., device='cuda:0'), tensor(8808615., device='cuda:0'), tensor(9041283., device='cuda:0'), tensor(8896648., device='cuda:0'), tensor(8678500., device='cuda:0')]

Training loss: 8881052.0

Prediction time: [datetime.timedelta(seconds=1, microseconds=165059), datetime.timedelta(seconds=1, microseconds=171034), datetime.timedelta(seconds=1, microseconds=164063), datetime.timedelta(seconds=1, microseconds=161073), datetime.timedelta(seconds=1, microseconds=32620), datetime.timedelta(seconds=1, microseconds=168047), datetime.timedelta(seconds=1, microseconds=177010), datetime.timedelta(seconds=1, microseconds=22663), datetime.timedelta(seconds=1, microseconds=176995), datetime.timedelta(seconds=1, microseconds=173025)]

Phi time: [datetime.timedelta(seconds=1, microseconds=932971), datetime.timedelta(seconds=1, microseconds=286745), datetime.timedelta(seconds=1, microseconds=302132), datetime.timedelta(seconds=1, microseconds=338447), datetime.timedelta(seconds=1, microseconds=320391), datetime.timedelta(seconds=1, microseconds=351781), datetime.timedelta(seconds=1, microseconds=318229), datetime.timedelta(seconds=1, microseconds=325913), datetime.timedelta(seconds=1, microseconds=323787), datetime.timedelta(seconds=1, microseconds=314100)]

