Precision: [tensor(0.6902, device='cuda:0'), tensor(0.6923, device='cuda:0'), tensor(0.6876, device='cuda:0'), tensor(0.6810, device='cuda:0'), tensor(0.6952, device='cuda:0'), tensor(0.6913, device='cuda:0'), tensor(0.6855, device='cuda:0'), tensor(0.6913, device='cuda:0'), tensor(0.6871, device='cuda:0'), tensor(0.6808, device='cuda:0')]
Output distance: [tensor(354232.8438, device='cuda:0'), tensor(366525.1250, device='cuda:0'), tensor(456000.1562, device='cuda:0'), tensor(382449.1562, device='cuda:0'), tensor(365157.6562, device='cuda:0'), tensor(384068., device='cuda:0'), tensor(366964.3750, device='cuda:0'), tensor(328378.8125, device='cuda:0'), tensor(376558.0625, device='cuda:0'), tensor(327039., device='cuda:0')]
Prediction loss: [tensor(18597936., device='cuda:0'), tensor(19669886., device='cuda:0'), tensor(18268930., device='cuda:0'), tensor(18969442., device='cuda:0'), tensor(17843522., device='cuda:0'), tensor(18712048., device='cuda:0'), tensor(17966856., device='cuda:0'), tensor(17645378., device='cuda:0'), tensor(17161630., device='cuda:0'), tensor(17927400., 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': 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': 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': 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': 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(41002.7656, device='cuda:0'), tensor(40993.9727, device='cuda:0'), tensor(40838.1953, device='cuda:0'), tensor(40850.3750, device='cuda:0'), tensor(40780.7266, device='cuda:0'), tensor(40751.2344, device='cuda:0'), tensor(40905.1992, device='cuda:0'), tensor(40679.8438, device='cuda:0'), tensor(40987.4297, device='cuda:0'), tensor(41011.6133, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(microseconds=93600), datetime.timedelta(microseconds=89619), datetime.timedelta(microseconds=96588), datetime.timedelta(microseconds=95593), datetime.timedelta(microseconds=106546), datetime.timedelta(microseconds=89618), datetime.timedelta(microseconds=99576), datetime.timedelta(microseconds=96588), datetime.timedelta(microseconds=85635), datetime.timedelta(microseconds=82648)]
Phi time: [datetime.timedelta(microseconds=259901), datetime.timedelta(microseconds=259899), datetime.timedelta(microseconds=236998), datetime.timedelta(microseconds=261892), datetime.timedelta(microseconds=261891), datetime.timedelta(microseconds=259901), datetime.timedelta(microseconds=245958), datetime.timedelta(microseconds=240981), datetime.timedelta(microseconds=241975), datetime.timedelta(microseconds=235004)]
