Precision: [tensor(0.6080, device='cuda:0'), tensor(0.5973, device='cuda:0'), tensor(0.6062, device='cuda:0'), tensor(0.5975, device='cuda:0'), tensor(0.6057, device='cuda:0'), tensor(0.5994, device='cuda:0'), tensor(0.5978, device='cuda:0'), tensor(0.5949, device='cuda:0'), tensor(0.5939, device='cuda:0'), tensor(0.6059, device='cuda:0')]

Output distance: [tensor(5.0901, device='cuda:0'), tensor(5.1116, device='cuda:0'), tensor(5.0937, device='cuda:0'), tensor(5.1111, device='cuda:0'), tensor(5.0948, device='cuda:0'), tensor(5.1074, device='cuda:0'), tensor(5.1105, device='cuda:0'), tensor(5.1163, device='cuda:0'), tensor(5.1184, device='cuda:0'), tensor(5.0943, device='cuda:0')]

Prediction loss: [tensor(16923938., device='cuda:0'), tensor(19071968., device='cuda:0'), tensor(17273892., device='cuda:0'), tensor(18368258., device='cuda:0'), tensor(18231166., device='cuda:0'), tensor(18320838., device='cuda:0'), tensor(18618228., device='cuda:0'), tensor(17263592., device='cuda:0'), tensor(18378694., device='cuda:0'), tensor(18577612., 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': 7, '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(40763.9727, device='cuda:0'), tensor(40815.2891, device='cuda:0'), tensor(40872.9531, device='cuda:0'), tensor(41146.8516, device='cuda:0'), tensor(41029.3398, device='cuda:0'), tensor(41015.1016, device='cuda:0'), tensor(40729.6289, device='cuda:0'), tensor(41060.1914, device='cuda:0'), tensor(40916.9805, device='cuda:0'), tensor(40670.4180, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=104316), datetime.timedelta(seconds=1, microseconds=130205), datetime.timedelta(seconds=1, microseconds=105311), datetime.timedelta(seconds=1, microseconds=110290), datetime.timedelta(seconds=1, microseconds=120250), datetime.timedelta(seconds=1, microseconds=122240), datetime.timedelta(seconds=1, microseconds=99338), datetime.timedelta(seconds=1, microseconds=111287), datetime.timedelta(seconds=1, microseconds=122240), datetime.timedelta(seconds=1, microseconds=115270)]

Phi time: [datetime.timedelta(microseconds=220066), datetime.timedelta(microseconds=216084), datetime.timedelta(microseconds=229029), datetime.timedelta(microseconds=216084), datetime.timedelta(microseconds=217079), datetime.timedelta(microseconds=225047), datetime.timedelta(microseconds=226042), datetime.timedelta(microseconds=220066), datetime.timedelta(microseconds=213097), datetime.timedelta(microseconds=225045)]

