Precision: [tensor(0.7115, device='cuda:0'), tensor(0.7112, device='cuda:0'), tensor(0.7015, device='cuda:0'), tensor(0.7102, device='cuda:0'), tensor(0.7065, device='cuda:0'), tensor(0.7028, device='cuda:0'), tensor(0.7062, device='cuda:0'), tensor(0.7081, device='cuda:0'), tensor(0.7057, device='cuda:0'), tensor(0.7086, device='cuda:0')]
Output distance: [tensor(4.8832, device='cuda:0'), tensor(4.8837, device='cuda:0'), tensor(4.9031, device='cuda:0'), tensor(4.8858, device='cuda:0'), tensor(4.8931, device='cuda:0'), tensor(4.9005, device='cuda:0'), tensor(4.8937, device='cuda:0'), tensor(4.8900, device='cuda:0'), tensor(4.8947, device='cuda:0'), tensor(4.8889, device='cuda:0')]
Prediction loss: [tensor(35.2581, device='cuda:0'), tensor(37.0322, device='cuda:0'), tensor(36.8943, device='cuda:0'), tensor(36.1819, device='cuda:0'), tensor(36.1503, device='cuda:0'), tensor(36.5318, device='cuda:0'), tensor(34.8497, device='cuda:0'), tensor(38.3565, device='cuda:0'), tensor(36.9545, device='cuda:0'), tensor(38.0074, device='cuda:0')]
Others: [{'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': 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': 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': 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': 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')}]
Compressed training loss: [tensor(48702.5625, device='cuda:0'), tensor(48995.2969, device='cuda:0'), tensor(48849.0312, device='cuda:0'), tensor(48827.9297, device='cuda:0'), tensor(49025.3438, device='cuda:0'), tensor(48970.9766, device='cuda:0'), tensor(48971.8828, device='cuda:0'), tensor(48581.4180, device='cuda:0'), tensor(48770.6758, device='cuda:0'), tensor(48858.6094, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=48553), datetime.timedelta(seconds=1, microseconds=59452), datetime.timedelta(seconds=1, microseconds=58558), datetime.timedelta(seconds=1, microseconds=68465), datetime.timedelta(seconds=1, microseconds=61497), datetime.timedelta(seconds=1, microseconds=60446), datetime.timedelta(seconds=1, microseconds=39644), datetime.timedelta(seconds=1, microseconds=48604), datetime.timedelta(seconds=1, microseconds=44573), datetime.timedelta(seconds=1, microseconds=41581)]
Phi time: [datetime.timedelta(seconds=5, microseconds=666952), datetime.timedelta(seconds=5, microseconds=640070), datetime.timedelta(seconds=5, microseconds=674920), datetime.timedelta(seconds=5, microseconds=646044), datetime.timedelta(seconds=5, microseconds=654014), datetime.timedelta(seconds=5, microseconds=648031), datetime.timedelta(seconds=5, microseconds=676913), datetime.timedelta(seconds=5, microseconds=645990), datetime.timedelta(seconds=5, microseconds=663972), datetime.timedelta(seconds=5, microseconds=644052)]
