Precision: [tensor(0.9990, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9992, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9988, device='cuda:0'), tensor(0.9988, device='cuda:0'), tensor(0.9990, device='cuda:0'), tensor(0.9990, device='cuda:0'), tensor(0.9992, device='cuda:0')]
Output distance: [tensor(72198.6953, device='cuda:0'), tensor(72097.5000, device='cuda:0'), tensor(72056.5000, device='cuda:0'), tensor(72523.0469, device='cuda:0'), tensor(72720.2266, device='cuda:0'), tensor(72114.6797, device='cuda:0'), tensor(72853.1641, device='cuda:0'), tensor(72567., device='cuda:0'), tensor(72042.9297, device='cuda:0'), tensor(72061.5781, device='cuda:0')]
Prediction loss: [tensor(75127.8125, device='cuda:0'), tensor(74430.0781, device='cuda:0'), tensor(74687.5547, device='cuda:0'), tensor(73220.2891, device='cuda:0'), tensor(75303.5859, device='cuda:0'), tensor(75267.6797, device='cuda:0'), tensor(75858.2422, device='cuda:0'), tensor(75930.3594, device='cuda:0'), tensor(75501.0391, device='cuda:0'), tensor(73707.2188, device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]
Compressed training loss: [tensor(64474008., device='cuda:0'), tensor(64540480., device='cuda:0'), tensor(65178880., device='cuda:0'), tensor(63456004., device='cuda:0'), tensor(65793376., device='cuda:0'), tensor(65019404., device='cuda:0'), tensor(63913208., device='cuda:0'), tensor(64191776., device='cuda:0'), tensor(64866092., device='cuda:0'), tensor(64031604., device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=586464), datetime.timedelta(microseconds=587510), datetime.timedelta(microseconds=588504), datetime.timedelta(microseconds=568591), datetime.timedelta(microseconds=570580), datetime.timedelta(microseconds=492910), datetime.timedelta(microseconds=610409), datetime.timedelta(microseconds=571578), datetime.timedelta(microseconds=569641), datetime.timedelta(microseconds=568593)]
Phi time: [datetime.timedelta(microseconds=868669), datetime.timedelta(microseconds=871239), datetime.timedelta(microseconds=858549), datetime.timedelta(microseconds=857432), datetime.timedelta(microseconds=859226), datetime.timedelta(microseconds=857054), datetime.timedelta(microseconds=887942), datetime.timedelta(microseconds=867116), datetime.timedelta(microseconds=865979), datetime.timedelta(microseconds=855226)]
