Precision: [tensor(0.0964, device='cuda:0'), tensor(0.1153, device='cuda:0'), tensor(0.0937, device='cuda:0'), tensor(0.0964, device='cuda:0'), tensor(0.0985, device='cuda:0'), tensor(0.1015, device='cuda:0'), tensor(0.1031, device='cuda:0'), tensor(0.1054, device='cuda:0'), tensor(0.0929, device='cuda:0'), tensor(0.0970, device='cuda:0')]

Output distance: [tensor(7.7275, device='cuda:0'), tensor(7.6146, device='cuda:0'), tensor(7.7438, device='cuda:0'), tensor(7.7275, device='cuda:0'), tensor(7.7154, device='cuda:0'), tensor(7.6970, device='cuda:0'), tensor(7.6876, device='cuda:0'), tensor(7.6739, device='cuda:0'), tensor(7.7485, device='cuda:0'), tensor(7.7243, device='cuda:0')]

Prediction loss: [tensor(17790416., device='cuda:0'), tensor(13873722., device='cuda:0'), tensor(18934182., device='cuda:0'), tensor(16857580., device='cuda:0'), tensor(16523089., device='cuda:0'), tensor(18080886., device='cuda:0'), tensor(18655890., device='cuda:0'), tensor(20971398., device='cuda:0'), tensor(16581181., device='cuda:0'), tensor(15659117., device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40840.5625, device='cuda:0'), tensor(40777.8672, device='cuda:0'), tensor(41178.6484, device='cuda:0'), tensor(40925.9102, device='cuda:0'), tensor(41182.1797, device='cuda:0'), tensor(41360.3906, device='cuda:0'), tensor(40906.0391, device='cuda:0'), tensor(40712.2539, device='cuda:0'), tensor(41268.1641, device='cuda:0'), tensor(41215.3516, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=342308), datetime.timedelta(seconds=1, microseconds=290527), datetime.timedelta(seconds=1, microseconds=309446), datetime.timedelta(seconds=1, microseconds=280569), datetime.timedelta(seconds=1, microseconds=314425), datetime.timedelta(seconds=1, microseconds=294510), datetime.timedelta(seconds=1, microseconds=310442), datetime.timedelta(seconds=1, microseconds=274594), datetime.timedelta(seconds=1, microseconds=288535), datetime.timedelta(seconds=1, microseconds=289530)]

Phi time: [datetime.timedelta(microseconds=192185), datetime.timedelta(microseconds=194176), datetime.timedelta(microseconds=195173), datetime.timedelta(microseconds=193181), datetime.timedelta(microseconds=194177), datetime.timedelta(microseconds=198159), datetime.timedelta(microseconds=197163), datetime.timedelta(microseconds=193181), datetime.timedelta(microseconds=197164), datetime.timedelta(microseconds=196168)]

