Precision: [tensor(0.7353, device='cuda:0'), tensor(0.7368, device='cuda:0'), tensor(0.7342, device='cuda:0'), tensor(0.7359, device='cuda:0'), tensor(0.7390, device='cuda:0'), tensor(0.7358, device='cuda:0'), tensor(0.7287, device='cuda:0'), tensor(0.7306, device='cuda:0'), tensor(0.7287, device='cuda:0'), tensor(0.7351, device='cuda:0')]
Output distance: [tensor(5.0102, device='cuda:0'), tensor(5.0089, device='cuda:0'), tensor(5.0110, device='cuda:0'), tensor(5.0097, device='cuda:0'), tensor(5.0031, device='cuda:0'), tensor(5.0071, device='cuda:0'), tensor(5.0176, device='cuda:0'), tensor(5.0158, device='cuda:0'), tensor(5.0189, device='cuda:0'), tensor(5.0079, device='cuda:0')]
Prediction loss: [tensor(18864280., device='cuda:0'), tensor(19451680., device='cuda:0'), tensor(18132188., device='cuda:0'), tensor(19065172., device='cuda:0'), tensor(18108052., device='cuda:0'), tensor(18127258., device='cuda:0'), tensor(18620104., device='cuda:0'), tensor(19181290., device='cuda:0'), tensor(17447488., device='cuda:0'), tensor(18165690., device='cuda:0')]
Others: [{'iter_num': 5, 'num_positive': tensor(2395, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2390, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2400, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2393, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2414, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2415, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2403, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2398, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2392, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2416, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40826.7734, device='cuda:0'), tensor(40812.8086, device='cuda:0'), tensor(40793.0078, device='cuda:0'), tensor(40682.2305, device='cuda:0'), tensor(40802.3398, device='cuda:0'), tensor(40803.6484, device='cuda:0'), tensor(40992.7344, device='cuda:0'), tensor(40736.4688, device='cuda:0'), tensor(40868.4219, device='cuda:0'), tensor(40861.1055, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=116268), datetime.timedelta(seconds=1, microseconds=92369), datetime.timedelta(seconds=1, microseconds=45564), datetime.timedelta(seconds=1, microseconds=35608), datetime.timedelta(seconds=1, microseconds=25652), datetime.timedelta(seconds=1, microseconds=15690), datetime.timedelta(seconds=1, microseconds=99338), datetime.timedelta(seconds=1, microseconds=51540), datetime.timedelta(seconds=1, microseconds=4739), datetime.timedelta(seconds=1, microseconds=29635)]
Phi time: [datetime.timedelta(microseconds=490919), datetime.timedelta(microseconds=285789), datetime.timedelta(microseconds=278820), datetime.timedelta(microseconds=266870), datetime.timedelta(microseconds=253922), datetime.timedelta(microseconds=234007), datetime.timedelta(microseconds=240980), datetime.timedelta(microseconds=265874), datetime.timedelta(microseconds=252930), datetime.timedelta(microseconds=252928)]
