Precision: [tensor(0.5627, device='cuda:0'), tensor(0.5650, device='cuda:0'), tensor(0.5637, device='cuda:0'), tensor(0.5620, device='cuda:0'), tensor(0.5642, device='cuda:0'), tensor(0.5607, device='cuda:0'), tensor(0.5617, device='cuda:0'), tensor(0.5636, device='cuda:0'), tensor(0.5609, device='cuda:0'), tensor(0.5639, device='cuda:0')]

Output distance: [tensor(4.9299, device='cuda:0'), tensor(4.9163, device='cuda:0'), tensor(4.9241, device='cuda:0'), tensor(4.9341, device='cuda:0'), tensor(4.9210, device='cuda:0'), tensor(4.9420, device='cuda:0'), tensor(4.9357, device='cuda:0'), tensor(4.9247, device='cuda:0'), tensor(4.9409, device='cuda:0'), tensor(4.9226, device='cuda:0')]

Prediction loss: [tensor(18080298., device='cuda:0'), tensor(19186108., device='cuda:0'), tensor(19681730., device='cuda:0'), tensor(18143702., device='cuda:0'), tensor(17981666., device='cuda:0'), tensor(19270910., device='cuda:0'), tensor(18563694., device='cuda:0'), tensor(19017630., device='cuda:0'), tensor(17968546., device='cuda:0'), tensor(18174220., device='cuda:0')]

Others: [{'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40866.3711, device='cuda:0'), tensor(40800.6953, device='cuda:0'), tensor(40825.1523, device='cuda:0'), tensor(40763.5195, device='cuda:0'), tensor(40921.9648, device='cuda:0'), tensor(40922.3516, device='cuda:0'), tensor(40766.6875, device='cuda:0'), tensor(40803.3633, device='cuda:0'), tensor(40887.2188, device='cuda:0'), tensor(40754.1328, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=69578), datetime.timedelta(seconds=1, microseconds=72818), datetime.timedelta(seconds=1, microseconds=49990), datetime.timedelta(seconds=1, microseconds=98932), datetime.timedelta(seconds=1, microseconds=78942), datetime.timedelta(seconds=1, microseconds=51000), datetime.timedelta(seconds=1, microseconds=81361), datetime.timedelta(seconds=1, microseconds=62627), datetime.timedelta(seconds=1, microseconds=49996), datetime.timedelta(seconds=1, microseconds=51031)]

Phi time: [datetime.timedelta(microseconds=289063), datetime.timedelta(microseconds=311671), datetime.timedelta(microseconds=299998), datetime.timedelta(microseconds=275673), datetime.timedelta(microseconds=307586), datetime.timedelta(microseconds=280196), datetime.timedelta(microseconds=298953), datetime.timedelta(microseconds=306094), datetime.timedelta(microseconds=299962), datetime.timedelta(microseconds=298938)]

