Precision: [tensor(0.9990, device='cuda:0'), tensor(0.9990, device='cuda:0'), tensor(0.9985, device='cuda:0'), tensor(0.9990, device='cuda:0'), tensor(0.9987, device='cuda:0'), tensor(0.9990, device='cuda:0'), tensor(0.9988, device='cuda:0'), tensor(0.9992, device='cuda:0'), tensor(0.9992, device='cuda:0'), tensor(0.9995, device='cuda:0')]
Output distance: [tensor(353029.2500, device='cuda:0'), tensor(354146.2500, device='cuda:0'), tensor(354820.9375, device='cuda:0'), tensor(352613.4688, device='cuda:0'), tensor(352503.0312, device='cuda:0'), tensor(354512.1875, device='cuda:0'), tensor(353184.9688, device='cuda:0'), tensor(352384.5000, device='cuda:0'), tensor(352316.5938, device='cuda:0'), tensor(352646.1875, device='cuda:0')]
Prediction loss: [tensor(359779.5312, device='cuda:0'), tensor(382134.2188, device='cuda:0'), tensor(373165.8125, device='cuda:0'), tensor(366787.0625, device='cuda:0'), tensor(371209.2188, device='cuda:0'), tensor(356631.5312, device='cuda:0'), tensor(353549.7188, device='cuda:0'), tensor(352777.8438, device='cuda:0'), tensor(357842.5625, device='cuda:0'), tensor(353198.9062, device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]
Compressed training loss: [tensor(2.1264e+08, device='cuda:0'), tensor(2.2204e+08, device='cuda:0'), tensor(2.2194e+08, device='cuda:0'), tensor(2.1409e+08, device='cuda:0'), tensor(2.1869e+08, device='cuda:0'), tensor(2.0892e+08, device='cuda:0'), tensor(2.1358e+08, device='cuda:0'), tensor(2.1369e+08, device='cuda:0'), tensor(2.1572e+08, device='cuda:0'), tensor(2.1489e+08, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=613416), datetime.timedelta(microseconds=590519), datetime.timedelta(microseconds=489892), datetime.timedelta(microseconds=517825), datetime.timedelta(microseconds=522802), datetime.timedelta(microseconds=610435), datetime.timedelta(microseconds=576577), datetime.timedelta(microseconds=570602), datetime.timedelta(microseconds=580561), datetime.timedelta(microseconds=613422)]
Phi time: [datetime.timedelta(microseconds=859864), datetime.timedelta(microseconds=860466), datetime.timedelta(microseconds=862084), datetime.timedelta(microseconds=893564), datetime.timedelta(microseconds=883871), datetime.timedelta(microseconds=886795), datetime.timedelta(microseconds=858883), datetime.timedelta(microseconds=862330), datetime.timedelta(microseconds=857885), datetime.timedelta(microseconds=859631)]
