Precision: [tensor(0.9998, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9997, device='cuda:0')]

Output distance: [tensor(23546.9941, device='cuda:0'), tensor(23542.9707, device='cuda:0'), tensor(23514.8379, device='cuda:0'), tensor(23527.4336, device='cuda:0'), tensor(23512.9551, device='cuda:0'), tensor(23522.8965, device='cuda:0'), tensor(23504.1543, device='cuda:0'), tensor(23496.1953, device='cuda:0'), tensor(23523.1484, device='cuda:0'), tensor(23534.4316, device='cuda:0')]

Prediction loss: [tensor(23491.0234, device='cuda:0'), tensor(22901.0527, device='cuda:0'), tensor(22835.2695, device='cuda:0'), tensor(23026.8906, device='cuda:0'), tensor(24082.2441, device='cuda:0'), tensor(24250.8164, device='cuda:0'), tensor(22661.9766, device='cuda:0'), tensor(23713.1895, device='cuda:0'), tensor(23280.8398, device='cuda:0'), tensor(25047.2441, device='cuda:0')]

Others: [{'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': 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': 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(8817134., device='cuda:0'), tensor(8713650., device='cuda:0'), tensor(8641583., device='cuda:0'), tensor(8798425., device='cuda:0'), tensor(8940669., device='cuda:0'), tensor(8925651., device='cuda:0'), tensor(8740518., device='cuda:0'), tensor(8910579., device='cuda:0'), tensor(8755524., device='cuda:0'), tensor(9182053., device='cuda:0')]

Training loss: 8871483.0

Prediction time: [datetime.timedelta(microseconds=706001), datetime.timedelta(microseconds=822515), datetime.timedelta(microseconds=713971), datetime.timedelta(microseconds=806579), datetime.timedelta(microseconds=719949), datetime.timedelta(microseconds=734883), datetime.timedelta(microseconds=807573), datetime.timedelta(microseconds=727917), datetime.timedelta(microseconds=722936), datetime.timedelta(microseconds=716960)]

Phi time: [datetime.timedelta(seconds=1, microseconds=557733), datetime.timedelta(seconds=1, microseconds=12256), datetime.timedelta(microseconds=944407), datetime.timedelta(microseconds=972248), datetime.timedelta(microseconds=946991), datetime.timedelta(microseconds=970331), datetime.timedelta(microseconds=961372), datetime.timedelta(microseconds=949897), datetime.timedelta(microseconds=970742), datetime.timedelta(microseconds=952953)]

