Precision: [tensor(0.6868, device='cuda:0'), tensor(0.6868, device='cuda:0'), tensor(0.6797, device='cuda:0'), tensor(0.6863, device='cuda:0'), tensor(0.6813, device='cuda:0'), tensor(0.6847, device='cuda:0'), tensor(0.6865, device='cuda:0'), tensor(0.6826, device='cuda:0'), tensor(0.6913, device='cuda:0'), tensor(0.6847, device='cuda:0')]

Output distance: [tensor(4.9325, device='cuda:0'), tensor(4.9325, device='cuda:0'), tensor(4.9467, device='cuda:0'), tensor(4.9336, device='cuda:0'), tensor(4.9436, device='cuda:0'), tensor(4.9367, device='cuda:0'), tensor(4.9331, device='cuda:0'), tensor(4.9409, device='cuda:0'), tensor(4.9236, device='cuda:0'), tensor(4.9367, device='cuda:0')]

Prediction loss: [tensor(17426852., device='cuda:0'), tensor(19948740., device='cuda:0'), tensor(18931598., device='cuda:0'), tensor(19633458., device='cuda:0'), tensor(18863210., device='cuda:0'), tensor(19936368., device='cuda:0'), tensor(18746686., device='cuda:0'), tensor(17537274., device='cuda:0'), tensor(18706820., device='cuda:0'), tensor(17352444., 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(40791.5781, device='cuda:0'), tensor(41024.1289, device='cuda:0'), tensor(40793.4531, device='cuda:0'), tensor(40937.6758, device='cuda:0'), tensor(40750.1289, device='cuda:0'), tensor(40896.5078, device='cuda:0'), tensor(40815.8359, device='cuda:0'), tensor(41103.5859, device='cuda:0'), tensor(40851.4297, device='cuda:0'), tensor(40976.8594, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(microseconds=980447), datetime.timedelta(microseconds=982229), datetime.timedelta(microseconds=994047), datetime.timedelta(microseconds=990179), datetime.timedelta(microseconds=972890), datetime.timedelta(microseconds=985940), datetime.timedelta(microseconds=968854), datetime.timedelta(microseconds=987147), datetime.timedelta(microseconds=966845), datetime.timedelta(microseconds=977407)]

Phi time: [datetime.timedelta(microseconds=225509), datetime.timedelta(microseconds=229361), datetime.timedelta(microseconds=222811), datetime.timedelta(microseconds=243255), datetime.timedelta(microseconds=213566), datetime.timedelta(microseconds=223020), datetime.timedelta(microseconds=230211), datetime.timedelta(microseconds=236592), datetime.timedelta(microseconds=225045), datetime.timedelta(microseconds=223581)]

