Precision: [tensor(0.5516, device='cuda:0'), tensor(0.5519, device='cuda:0'), tensor(0.5484, device='cuda:0'), tensor(0.5501, device='cuda:0'), tensor(0.5512, device='cuda:0'), tensor(0.5532, device='cuda:0'), tensor(0.5510, device='cuda:0'), tensor(0.5500, device='cuda:0'), tensor(0.5517, device='cuda:0'), tensor(0.5500, device='cuda:0')]
Output distance: [tensor(4.9966, device='cuda:0'), tensor(4.9950, device='cuda:0'), tensor(5.0160, device='cuda:0'), tensor(5.0055, device='cuda:0'), tensor(4.9992, device='cuda:0'), tensor(4.9871, device='cuda:0'), tensor(5.0003, device='cuda:0'), tensor(5.0060, device='cuda:0'), tensor(4.9961, device='cuda:0'), tensor(5.0060, device='cuda:0')]
Prediction loss: [tensor(17710844., device='cuda:0'), tensor(18920732., device='cuda:0'), tensor(19252360., device='cuda:0'), tensor(19903096., device='cuda:0'), tensor(17935860., device='cuda:0'), tensor(19813850., device='cuda:0'), tensor(17952034., device='cuda:0'), tensor(18678816., device='cuda:0'), tensor(18082618., device='cuda:0'), tensor(20490542., device='cuda:0')]
Others: [{'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40905.0938, device='cuda:0'), tensor(40817.2227, device='cuda:0'), tensor(40772.6719, device='cuda:0'), tensor(40848.0898, device='cuda:0'), tensor(41054.7344, device='cuda:0'), tensor(40789.3359, device='cuda:0'), tensor(40777.2500, device='cuda:0'), tensor(40842.7656, device='cuda:0'), tensor(40910.3633, device='cuda:0'), tensor(40838.2578, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=11, microseconds=326275), datetime.timedelta(seconds=11, microseconds=221716), datetime.timedelta(seconds=11, microseconds=300383), datetime.timedelta(seconds=11, microseconds=291421), datetime.timedelta(seconds=11, microseconds=287439), datetime.timedelta(seconds=11, microseconds=244620), datetime.timedelta(seconds=9, microseconds=30946), datetime.timedelta(seconds=11, microseconds=328266), datetime.timedelta(seconds=8, microseconds=970281), datetime.timedelta(seconds=11, microseconds=386022)]
Phi time: [datetime.timedelta(microseconds=357493), datetime.timedelta(microseconds=390357), datetime.timedelta(microseconds=336583), datetime.timedelta(microseconds=394339), datetime.timedelta(microseconds=425209), datetime.timedelta(microseconds=376413), datetime.timedelta(microseconds=389359), datetime.timedelta(microseconds=393343), datetime.timedelta(microseconds=334591), datetime.timedelta(microseconds=337577)]
