Precision: [tensor(0.9261, device='cuda:0'), tensor(0.9240, device='cuda:0'), tensor(0.9230, device='cuda:0'), tensor(0.9241, device='cuda:0'), tensor(0.9208, device='cuda:0'), tensor(0.9231, device='cuda:0'), tensor(0.9223, device='cuda:0'), tensor(0.9206, device='cuda:0'), tensor(0.9242, device='cuda:0'), tensor(0.9164, device='cuda:0')]
Output distance: [tensor(9616.1064, device='cuda:0'), tensor(9637.2432, device='cuda:0'), tensor(9724.2432, device='cuda:0'), tensor(9746.6143, device='cuda:0'), tensor(10243.6201, device='cuda:0'), tensor(9800.4189, device='cuda:0'), tensor(9960.6758, device='cuda:0'), tensor(10263.0049, device='cuda:0'), tensor(9903.0674, device='cuda:0'), tensor(10817.6055, device='cuda:0')]
Prediction loss: [tensor(20998.3359, device='cuda:0'), tensor(21325.5859, device='cuda:0'), tensor(21211.4453, device='cuda:0'), tensor(21389.4258, device='cuda:0'), tensor(21237.2402, device='cuda:0'), tensor(20777.0254, device='cuda:0'), tensor(21137.9688, device='cuda:0'), tensor(21137.2812, device='cuda:0'), tensor(21271.5957, device='cuda:0'), tensor(20752.6895, device='cuda:0')]
Others: [{'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]
Compressed training loss: [tensor(2.1228e+08, device='cuda:0'), tensor(2.1619e+08, device='cuda:0'), tensor(2.1460e+08, device='cuda:0'), tensor(2.1771e+08, device='cuda:0'), tensor(2.1562e+08, device='cuda:0'), tensor(2.1128e+08, device='cuda:0'), tensor(2.1430e+08, device='cuda:0'), tensor(2.1440e+08, device='cuda:0'), tensor(2.1665e+08, device='cuda:0'), tensor(2.1072e+08, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=660226), datetime.timedelta(microseconds=582551), datetime.timedelta(microseconds=593505), datetime.timedelta(microseconds=667202), datetime.timedelta(microseconds=592512), datetime.timedelta(microseconds=661233), datetime.timedelta(microseconds=670172), datetime.timedelta(microseconds=598462), datetime.timedelta(microseconds=671153), datetime.timedelta(microseconds=604439)]
Phi time: [datetime.timedelta(microseconds=863371), datetime.timedelta(microseconds=862743), datetime.timedelta(microseconds=856905), datetime.timedelta(microseconds=864017), datetime.timedelta(microseconds=897997), datetime.timedelta(microseconds=867096), datetime.timedelta(microseconds=863225), datetime.timedelta(microseconds=860163), datetime.timedelta(microseconds=866496), datetime.timedelta(microseconds=902232)]
