Precision: [tensor(0.6290, device='cuda:0'), tensor(0.6279, device='cuda:0'), tensor(0.6282, device='cuda:0'), tensor(0.6285, device='cuda:0'), tensor(0.6316, device='cuda:0'), tensor(0.6302, device='cuda:0'), tensor(0.6249, device='cuda:0'), tensor(0.6289, device='cuda:0'), tensor(0.6325, device='cuda:0'), tensor(0.6285, device='cuda:0')]
Output distance: [tensor(4.9252, device='cuda:0'), tensor(4.9286, device='cuda:0'), tensor(4.9244, device='cuda:0'), tensor(4.9262, device='cuda:0'), tensor(4.9165, device='cuda:0'), tensor(4.9194, device='cuda:0'), tensor(4.9362, device='cuda:0'), tensor(4.9247, device='cuda:0'), tensor(4.9123, device='cuda:0'), tensor(4.9260, device='cuda:0')]
Prediction loss: [tensor(18516932., device='cuda:0'), tensor(18217752., device='cuda:0'), tensor(19083040., device='cuda:0'), tensor(18125780., device='cuda:0'), tensor(17934152., device='cuda:0'), tensor(18870622., device='cuda:0'), tensor(18466946., device='cuda:0'), tensor(18541274., device='cuda:0'), tensor(19305120., device='cuda:0'), tensor(18439068., device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(5625, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5622, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5672, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5631, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5640, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5655, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5639, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5635, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5660, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5636, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40928.1562, device='cuda:0'), tensor(40828.4414, device='cuda:0'), tensor(40786.7148, device='cuda:0'), tensor(40839.2188, device='cuda:0'), tensor(40811.3281, device='cuda:0'), tensor(40874.0156, device='cuda:0'), tensor(40751.7461, device='cuda:0'), tensor(40820.4844, device='cuda:0'), tensor(40790.9688, device='cuda:0'), tensor(40750.9570, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=89380), datetime.timedelta(seconds=1, microseconds=61498), datetime.timedelta(seconds=1, microseconds=61498), datetime.timedelta(seconds=1, microseconds=63490), datetime.timedelta(seconds=1, microseconds=60503), datetime.timedelta(seconds=1, microseconds=24656), datetime.timedelta(seconds=1, microseconds=51541), datetime.timedelta(seconds=1, microseconds=77431), datetime.timedelta(seconds=1, microseconds=69464), datetime.timedelta(seconds=1, microseconds=46563)]
Phi time: [datetime.timedelta(microseconds=237991), datetime.timedelta(microseconds=244961), datetime.timedelta(microseconds=252928), datetime.timedelta(microseconds=253923), datetime.timedelta(microseconds=251931), datetime.timedelta(microseconds=235999), datetime.timedelta(microseconds=235004), datetime.timedelta(microseconds=235999), datetime.timedelta(microseconds=250936), datetime.timedelta(microseconds=238987)]
