Precision: [tensor(0.6797, device='cuda:0'), tensor(0.6813, device='cuda:0'), tensor(0.6815, device='cuda:0'), tensor(0.6855, device='cuda:0'), tensor(0.6802, device='cuda:0'), tensor(0.6763, device='cuda:0'), tensor(0.6794, device='cuda:0'), tensor(0.6794, device='cuda:0'), tensor(0.6787, device='cuda:0'), tensor(0.6755, device='cuda:0')]

Output distance: [tensor(4.9467, device='cuda:0'), tensor(4.9436, device='cuda:0'), tensor(4.9430, device='cuda:0'), tensor(4.9352, device='cuda:0'), tensor(4.9457, device='cuda:0'), tensor(4.9535, device='cuda:0'), tensor(4.9472, device='cuda:0'), tensor(4.9472, device='cuda:0'), tensor(4.9488, device='cuda:0'), tensor(4.9551, device='cuda:0')]

Prediction loss: [tensor(18058700., device='cuda:0'), tensor(19126454., device='cuda:0'), tensor(17982714., device='cuda:0'), tensor(17057748., device='cuda:0'), tensor(17488982., device='cuda:0'), tensor(17951124., device='cuda:0'), tensor(20097564., device='cuda:0'), tensor(20670516., device='cuda:0'), tensor(19643040., device='cuda:0'), tensor(19295854., device='cuda:0')]

Others: [{'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(41048.4375, device='cuda:0'), tensor(40652.1133, device='cuda:0'), tensor(40738.0430, device='cuda:0'), tensor(40834.6875, device='cuda:0'), tensor(40784.2773, device='cuda:0'), tensor(40920.2031, device='cuda:0'), tensor(40830.2227, device='cuda:0'), tensor(40988.6875, device='cuda:0'), tensor(40907.0391, device='cuda:0'), tensor(40997.2109, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=3180), datetime.timedelta(microseconds=981339), datetime.timedelta(microseconds=982306), datetime.timedelta(microseconds=951234), datetime.timedelta(microseconds=965578), datetime.timedelta(microseconds=983582), datetime.timedelta(microseconds=991069), datetime.timedelta(seconds=1, microseconds=2238), datetime.timedelta(microseconds=967579), datetime.timedelta(microseconds=962402)]

Phi time: [datetime.timedelta(microseconds=222107), datetime.timedelta(microseconds=213701), datetime.timedelta(microseconds=212882), datetime.timedelta(microseconds=203836), datetime.timedelta(microseconds=221868), datetime.timedelta(microseconds=202502), datetime.timedelta(microseconds=215644), datetime.timedelta(microseconds=221367), datetime.timedelta(microseconds=214357), datetime.timedelta(microseconds=203693)]

