Precision: [tensor(0.6626, device='cuda:0'), tensor(0.6595, device='cuda:0'), tensor(0.6629, device='cuda:0'), tensor(0.6532, device='cuda:0'), tensor(0.6529, device='cuda:0'), tensor(0.6556, device='cuda:0'), tensor(0.6561, device='cuda:0'), tensor(0.6624, device='cuda:0'), tensor(0.6624, device='cuda:0'), tensor(0.6626, device='cuda:0')]

Output distance: [tensor(4.9808, device='cuda:0'), tensor(4.9871, device='cuda:0'), tensor(4.9803, device='cuda:0'), tensor(4.9997, device='cuda:0'), tensor(5.0003, device='cuda:0'), tensor(4.9950, device='cuda:0'), tensor(4.9940, device='cuda:0'), tensor(4.9814, device='cuda:0'), tensor(4.9814, device='cuda:0'), tensor(4.9808, device='cuda:0')]

Prediction loss: [tensor(16355800., device='cuda:0'), tensor(17630680., device='cuda:0'), tensor(20604376., device='cuda:0'), tensor(19557364., device='cuda:0'), tensor(19216578., device='cuda:0'), tensor(16090462., device='cuda:0'), tensor(15645870., device='cuda:0'), tensor(19890652., device='cuda:0'), tensor(18764598., device='cuda:0'), tensor(17777478., device='cuda:0')]

Others: [{'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40692.8008, device='cuda:0'), tensor(41198.8672, device='cuda:0'), tensor(40989.4805, device='cuda:0'), tensor(40767.1641, device='cuda:0'), tensor(40861.6016, device='cuda:0'), tensor(40736.8047, device='cuda:0'), tensor(40729.2227, device='cuda:0'), tensor(40969.3516, device='cuda:0'), tensor(40632.2891, device='cuda:0'), tensor(40838.8633, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(microseconds=984723), datetime.timedelta(microseconds=987083), datetime.timedelta(microseconds=970144), datetime.timedelta(microseconds=966785), datetime.timedelta(microseconds=964743), datetime.timedelta(microseconds=980965), datetime.timedelta(microseconds=953718), datetime.timedelta(seconds=1, microseconds=1238), datetime.timedelta(microseconds=977344), datetime.timedelta(microseconds=997801)]

Phi time: [datetime.timedelta(microseconds=185309), datetime.timedelta(microseconds=198783), datetime.timedelta(microseconds=185615), datetime.timedelta(microseconds=182547), datetime.timedelta(microseconds=184309), datetime.timedelta(microseconds=201670), datetime.timedelta(microseconds=192577), datetime.timedelta(microseconds=188736), datetime.timedelta(microseconds=199881), datetime.timedelta(microseconds=186640)]

