Precision: [tensor(0.6647, device='cuda:0'), tensor(0.6802, device='cuda:0'), tensor(0.6682, device='cuda:0'), tensor(0.6697, device='cuda:0'), tensor(0.6710, device='cuda:0'), tensor(0.6679, device='cuda:0'), tensor(0.6763, device='cuda:0'), tensor(0.6710, device='cuda:0'), tensor(0.6663, device='cuda:0'), tensor(0.6600, device='cuda:0')]

Output distance: [tensor(4.9766, device='cuda:0'), tensor(4.9457, device='cuda:0'), tensor(4.9698, device='cuda:0'), tensor(4.9667, device='cuda:0'), tensor(4.9640, device='cuda:0'), tensor(4.9703, device='cuda:0'), tensor(4.9535, device='cuda:0'), tensor(4.9640, device='cuda:0'), tensor(4.9735, device='cuda:0'), tensor(4.9861, device='cuda:0')]

Prediction loss: [tensor(18834890., device='cuda:0'), tensor(18865938., device='cuda:0'), tensor(17199690., device='cuda:0'), tensor(16522454., device='cuda:0'), tensor(17256878., device='cuda:0'), tensor(17751222., device='cuda:0'), tensor(20536790., device='cuda:0'), tensor(17765706., device='cuda:0'), tensor(18395174., device='cuda:0'), tensor(20448376., 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(40977.8281, device='cuda:0'), tensor(40687.5859, device='cuda:0'), tensor(41011.8750, device='cuda:0'), tensor(41017.4688, device='cuda:0'), tensor(40670.0508, device='cuda:0'), tensor(40845.7305, device='cuda:0'), tensor(40680.3906, device='cuda:0'), tensor(40817.7656, device='cuda:0'), tensor(40698.5234, device='cuda:0'), tensor(41031.5430, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(microseconds=975341), datetime.timedelta(microseconds=979246), datetime.timedelta(microseconds=982542), datetime.timedelta(microseconds=953135), datetime.timedelta(microseconds=964736), datetime.timedelta(microseconds=985369), datetime.timedelta(seconds=1, microseconds=9098), datetime.timedelta(microseconds=948722), datetime.timedelta(microseconds=959899), datetime.timedelta(microseconds=966797)]

Phi time: [datetime.timedelta(microseconds=194494), datetime.timedelta(microseconds=200216), datetime.timedelta(microseconds=208401), datetime.timedelta(microseconds=212467), datetime.timedelta(microseconds=209956), datetime.timedelta(microseconds=200012), datetime.timedelta(microseconds=190869), datetime.timedelta(microseconds=192607), datetime.timedelta(microseconds=191766), datetime.timedelta(microseconds=183087)]

