Precision: [tensor(0.9993, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9988, device='cuda:0'), tensor(0.9992, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9990, device='cuda:0'), tensor(0.9992, device='cuda:0')]
Output distance: [tensor(327597.5312, device='cuda:0'), tensor(326281.3125, device='cuda:0'), tensor(327179.9688, device='cuda:0'), tensor(327064.2812, device='cuda:0'), tensor(327732.8750, device='cuda:0'), tensor(328548.0938, device='cuda:0'), tensor(326818.2812, device='cuda:0'), tensor(326762.1250, device='cuda:0'), tensor(326910.2188, device='cuda:0'), tensor(327037.3125, device='cuda:0')]
Prediction loss: [tensor(332266.9688, device='cuda:0'), tensor(324674., device='cuda:0'), tensor(331696.4375, device='cuda:0'), tensor(342253.8125, device='cuda:0'), tensor(332389.1250, device='cuda:0'), tensor(337043.2500, device='cuda:0'), tensor(339173.9375, device='cuda:0'), tensor(337941.1250, device='cuda:0'), tensor(350638.0312, device='cuda:0'), tensor(338308.5000, 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': 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': 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': 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')}]
Compressed training loss: [tensor(2.1007e+08, device='cuda:0'), tensor(2.0717e+08, device='cuda:0'), tensor(2.0921e+08, device='cuda:0'), tensor(2.1267e+08, device='cuda:0'), tensor(2.1335e+08, device='cuda:0'), tensor(2.1473e+08, device='cuda:0'), tensor(2.1314e+08, device='cuda:0'), tensor(2.1294e+08, device='cuda:0'), tensor(2.1817e+08, device='cuda:0'), tensor(2.1633e+08, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=570651), datetime.timedelta(microseconds=570655), datetime.timedelta(microseconds=579616), datetime.timedelta(microseconds=489942), datetime.timedelta(microseconds=627362), datetime.timedelta(microseconds=565623), datetime.timedelta(microseconds=582551), datetime.timedelta(microseconds=570680), datetime.timedelta(microseconds=541724), datetime.timedelta(microseconds=615413)]
Phi time: [datetime.timedelta(microseconds=912021), datetime.timedelta(microseconds=866469), datetime.timedelta(microseconds=883010), datetime.timedelta(microseconds=856260), datetime.timedelta(microseconds=872460), datetime.timedelta(microseconds=875328), datetime.timedelta(microseconds=851450), datetime.timedelta(microseconds=865710), datetime.timedelta(microseconds=863816), datetime.timedelta(microseconds=906109)]
