Precision: [tensor(0.7344, device='cuda:0'), tensor(0.7325, device='cuda:0'), tensor(0.7364, device='cuda:0'), tensor(0.7280, device='cuda:0'), tensor(0.7277, device='cuda:0'), tensor(0.7304, device='cuda:0'), tensor(0.7408, device='cuda:0'), tensor(0.7309, device='cuda:0'), tensor(0.7294, device='cuda:0'), tensor(0.7351, device='cuda:0')]
Output distance: [tensor(5.0118, device='cuda:0'), tensor(5.0150, device='cuda:0'), tensor(5.0100, device='cuda:0'), tensor(5.0192, device='cuda:0'), tensor(5.0189, device='cuda:0'), tensor(5.0163, device='cuda:0'), tensor(5.0037, device='cuda:0'), tensor(5.0150, device='cuda:0'), tensor(5.0181, device='cuda:0'), tensor(5.0102, device='cuda:0')]
Prediction loss: [tensor(17534088., device='cuda:0'), tensor(17341294., device='cuda:0'), tensor(18769972., device='cuda:0'), tensor(17621938., device='cuda:0'), tensor(18230920., device='cuda:0'), tensor(21115386., device='cuda:0'), tensor(18270606., device='cuda:0'), tensor(18889336., device='cuda:0'), tensor(18454308., device='cuda:0'), tensor(16672169., device='cuda:0')]
Others: [{'iter_num': 5, 'num_positive': tensor(2391, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2385, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2386, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2397, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2402, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2396, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2392, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2401, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2391, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2397, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40815.3203, device='cuda:0'), tensor(40864.0078, device='cuda:0'), tensor(40802.0352, device='cuda:0'), tensor(40869.1094, device='cuda:0'), tensor(40846.3750, device='cuda:0'), tensor(40798.9453, device='cuda:0'), tensor(40778.2969, device='cuda:0'), tensor(40972.0703, device='cuda:0'), tensor(40868.6602, device='cuda:0'), tensor(40769.0312, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=13701), datetime.timedelta(seconds=1, microseconds=31624), datetime.timedelta(seconds=1, microseconds=26646), datetime.timedelta(seconds=1, microseconds=19675), datetime.timedelta(seconds=1, microseconds=40587), datetime.timedelta(seconds=1, microseconds=28636), datetime.timedelta(seconds=1, microseconds=41583), datetime.timedelta(seconds=1, microseconds=15693), datetime.timedelta(seconds=1, microseconds=41583), datetime.timedelta(microseconds=995777)]
Phi time: [datetime.timedelta(microseconds=234007), datetime.timedelta(microseconds=248943), datetime.timedelta(microseconds=249941), datetime.timedelta(microseconds=254920), datetime.timedelta(microseconds=231021), datetime.timedelta(microseconds=235003), datetime.timedelta(microseconds=248944), datetime.timedelta(microseconds=230024), datetime.timedelta(microseconds=236995), datetime.timedelta(microseconds=231021)]
