Precision: [tensor(0.9995, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9997, device='cuda:0')]

Output distance: [tensor(142094.0312, device='cuda:0'), tensor(141156.0781, device='cuda:0'), tensor(141254.1719, device='cuda:0'), tensor(140941.6094, device='cuda:0'), tensor(142386.3594, device='cuda:0'), tensor(142636.0312, device='cuda:0'), tensor(141892.8125, device='cuda:0'), tensor(141197.4219, device='cuda:0'), tensor(141391.4844, device='cuda:0'), tensor(140905.7188, device='cuda:0')]

Prediction loss: [tensor(130900.2422, device='cuda:0'), tensor(137166.6250, device='cuda:0'), tensor(135646.4688, device='cuda:0'), tensor(132351.9062, device='cuda:0'), tensor(136148.1875, device='cuda:0'), tensor(134896.5156, device='cuda:0'), tensor(130200.9453, device='cuda:0'), tensor(139094.6875, device='cuda:0'), tensor(132824.5938, device='cuda:0'), tensor(133298.4688, device='cuda:0')]

Others: [{'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(1.8944e+08, device='cuda:0'), tensor(1.9000e+08, device='cuda:0'), tensor(1.9012e+08, device='cuda:0'), tensor(1.8732e+08, device='cuda:0'), tensor(1.9031e+08, device='cuda:0'), tensor(1.8545e+08, device='cuda:0'), tensor(1.8653e+08, device='cuda:0'), tensor(1.9122e+08, device='cuda:0'), tensor(1.8541e+08, device='cuda:0'), tensor(1.9049e+08, device='cuda:0')]

Training loss: 191726560.0

Prediction time: [datetime.timedelta(microseconds=629341), datetime.timedelta(microseconds=591492), datetime.timedelta(microseconds=649244), datetime.timedelta(microseconds=583525), datetime.timedelta(microseconds=647255), datetime.timedelta(microseconds=650242), datetime.timedelta(microseconds=655226), datetime.timedelta(microseconds=638295), datetime.timedelta(microseconds=637298), datetime.timedelta(microseconds=648253)]

Phi time: [datetime.timedelta(seconds=1, microseconds=406200), datetime.timedelta(microseconds=853582), datetime.timedelta(microseconds=787638), datetime.timedelta(microseconds=788737), datetime.timedelta(microseconds=790748), datetime.timedelta(microseconds=814007), datetime.timedelta(microseconds=786789), datetime.timedelta(microseconds=812718), datetime.timedelta(microseconds=779624), datetime.timedelta(microseconds=786544)]

