Precision: [tensor(0.9990, device='cuda:0'), tensor(0.9987, device='cuda:0'), tensor(0.9990, device='cuda:0'), tensor(0.9990, device='cuda:0'), tensor(0.9988, device='cuda:0'), tensor(0.9992, device='cuda:0'), tensor(0.9990, device='cuda:0'), tensor(0.9988, device='cuda:0'), tensor(0.9990, device='cuda:0'), tensor(0.9990, device='cuda:0')]
Output distance: [tensor(72892.4922, device='cuda:0'), tensor(72507.6719, device='cuda:0'), tensor(72675.2500, device='cuda:0'), tensor(72837.5547, device='cuda:0'), tensor(72534.1875, device='cuda:0'), tensor(72568.2422, device='cuda:0'), tensor(72568.6875, device='cuda:0'), tensor(72486.2344, device='cuda:0'), tensor(72445.3594, device='cuda:0'), tensor(72443.6172, device='cuda:0')]
Prediction loss: [tensor(75389.3672, device='cuda:0'), tensor(75430.6328, device='cuda:0'), tensor(73816.1875, device='cuda:0'), tensor(73856.6641, device='cuda:0'), tensor(74517.2891, device='cuda:0'), tensor(75880.4141, device='cuda:0'), tensor(75890.1406, device='cuda:0'), tensor(76631.4453, device='cuda:0'), tensor(76640.1250, device='cuda:0'), tensor(77316.7500, device='cuda:0')]
Others: [{'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': 5, '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': 5, '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': 5, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]
Compressed training loss: [tensor(64645988., device='cuda:0'), tensor(65017844., device='cuda:0'), tensor(63949352., device='cuda:0'), tensor(62984344., device='cuda:0'), tensor(63271880., device='cuda:0'), tensor(64632412., device='cuda:0'), tensor(64587224., device='cuda:0'), tensor(65234828., device='cuda:0'), tensor(65376004., device='cuda:0'), tensor(65960728., device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=576562), datetime.timedelta(microseconds=472994), datetime.timedelta(microseconds=556643), datetime.timedelta(microseconds=571577), datetime.timedelta(microseconds=559627), datetime.timedelta(microseconds=483946), datetime.timedelta(microseconds=478968), datetime.timedelta(microseconds=493903), datetime.timedelta(microseconds=531747), datetime.timedelta(microseconds=507795)]
Phi time: [datetime.timedelta(microseconds=903348), datetime.timedelta(microseconds=879241), datetime.timedelta(microseconds=857925), datetime.timedelta(microseconds=863887), datetime.timedelta(microseconds=859367), datetime.timedelta(microseconds=857494), datetime.timedelta(microseconds=860337), datetime.timedelta(microseconds=861586), datetime.timedelta(microseconds=912331), datetime.timedelta(microseconds=888895)]
