Precision: [tensor(0.8230, device='cuda:0'), tensor(0.8215, device='cuda:0'), tensor(0.8223, device='cuda:0'), tensor(0.8229, device='cuda:0'), tensor(0.8234, device='cuda:0'), tensor(0.8241, device='cuda:0'), tensor(0.8233, device='cuda:0'), tensor(0.8228, device='cuda:0'), tensor(0.8242, device='cuda:0'), tensor(0.8229, device='cuda:0')]

Output distance: [tensor(13725.2129, device='cuda:0'), tensor(13852.0820, device='cuda:0'), tensor(13776.3896, device='cuda:0'), tensor(13767.6602, device='cuda:0'), tensor(13740.2598, device='cuda:0'), tensor(13715.3369, device='cuda:0'), tensor(13733.7783, device='cuda:0'), tensor(13721.1816, device='cuda:0'), tensor(13674.7373, device='cuda:0'), tensor(13746.0479, device='cuda:0')]

Prediction loss: [tensor(10436.3682, device='cuda:0'), tensor(10360.3262, device='cuda:0'), tensor(10251.5703, device='cuda:0'), tensor(10460.1885, device='cuda:0'), tensor(10430.5176, device='cuda:0'), tensor(10511.5430, device='cuda:0'), tensor(10420.6289, device='cuda:0'), tensor(10334.7295, device='cuda:0'), tensor(10337.5801, device='cuda:0'), tensor(10565.9756, device='cuda:0')]

Others: [{'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(1.9270e+08, device='cuda:0'), tensor(1.9108e+08, device='cuda:0'), tensor(1.8931e+08, device='cuda:0'), tensor(1.9297e+08, device='cuda:0'), tensor(1.9229e+08, device='cuda:0'), tensor(1.9385e+08, device='cuda:0'), tensor(1.9257e+08, device='cuda:0'), tensor(1.9107e+08, device='cuda:0'), tensor(1.9087e+08, device='cuda:0'), tensor(1.9484e+08, device='cuda:0')]

Training loss: 191906416.0

Prediction time: [datetime.timedelta(microseconds=820549), datetime.timedelta(microseconds=904197), datetime.timedelta(microseconds=852414), datetime.timedelta(microseconds=933078), datetime.timedelta(microseconds=861376), datetime.timedelta(microseconds=939051), datetime.timedelta(microseconds=839468), datetime.timedelta(microseconds=853411), datetime.timedelta(microseconds=937058), datetime.timedelta(microseconds=854406)]

Phi time: [datetime.timedelta(seconds=1, microseconds=587432), datetime.timedelta(seconds=1, microseconds=6807), datetime.timedelta(microseconds=961699), datetime.timedelta(microseconds=965179), datetime.timedelta(microseconds=965147), datetime.timedelta(microseconds=958562), datetime.timedelta(microseconds=961593), datetime.timedelta(microseconds=975121), datetime.timedelta(microseconds=974453), datetime.timedelta(microseconds=964525)]

