Precision: [tensor(0.6889, device='cuda:0'), tensor(0.6952, device='cuda:0'), tensor(0.6836, device='cuda:0'), tensor(0.6881, device='cuda:0'), tensor(0.6899, device='cuda:0'), tensor(0.6855, device='cuda:0'), tensor(0.6852, device='cuda:0'), tensor(0.6918, device='cuda:0'), tensor(0.6842, device='cuda:0'), tensor(0.6815, device='cuda:0')]
Output distance: [tensor(4.9283, device='cuda:0'), tensor(4.9157, device='cuda:0'), tensor(4.9388, device='cuda:0'), tensor(4.9299, device='cuda:0'), tensor(4.9262, device='cuda:0'), tensor(4.9352, device='cuda:0'), tensor(4.9357, device='cuda:0'), tensor(4.9226, device='cuda:0'), tensor(4.9378, device='cuda:0'), tensor(4.9430, device='cuda:0')]
Prediction loss: [tensor(20817284., device='cuda:0'), tensor(18492006., device='cuda:0'), tensor(19166962., device='cuda:0'), tensor(19344004., device='cuda:0'), tensor(17964386., device='cuda:0'), tensor(18669902., device='cuda:0'), tensor(19375500., device='cuda:0'), tensor(19266826., device='cuda:0'), tensor(19344894., device='cuda:0'), tensor(17540754., 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(40751.2812, device='cuda:0'), tensor(40897.1562, device='cuda:0'), tensor(40798.7344, device='cuda:0'), tensor(40910.5547, device='cuda:0'), tensor(40985.5312, device='cuda:0'), tensor(40773.0586, device='cuda:0'), tensor(40896.8672, device='cuda:0'), tensor(40701.0625, device='cuda:0'), tensor(40793.1953, device='cuda:0'), tensor(40825.8633, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=6, microseconds=289327), datetime.timedelta(seconds=6, microseconds=422760), datetime.timedelta(seconds=6, microseconds=560177), datetime.timedelta(seconds=6, microseconds=462592), datetime.timedelta(seconds=6, microseconds=306256), datetime.timedelta(seconds=6, microseconds=468566), datetime.timedelta(seconds=6, microseconds=416786), datetime.timedelta(seconds=6, microseconds=416787), datetime.timedelta(seconds=6, microseconds=466575), datetime.timedelta(seconds=6, microseconds=339115)]
Phi time: [datetime.timedelta(microseconds=401298), datetime.timedelta(microseconds=430175), datetime.timedelta(microseconds=364455), datetime.timedelta(microseconds=374412), datetime.timedelta(microseconds=421213), datetime.timedelta(microseconds=435155), datetime.timedelta(microseconds=395323), datetime.timedelta(microseconds=394328), datetime.timedelta(microseconds=353502), datetime.timedelta(microseconds=418226)]
