Precision: [tensor(0.2455, device='cuda:0'), tensor(0.2427, device='cuda:0'), tensor(0.2458, device='cuda:0'), tensor(0.2423, device='cuda:0'), tensor(0.2415, device='cuda:0'), tensor(0.2412, device='cuda:0'), tensor(0.2435, device='cuda:0'), tensor(0.2445, device='cuda:0'), tensor(0.2452, device='cuda:0'), tensor(0.2400, device='cuda:0')]
Output distance: [tensor(19606016., device='cuda:0'), tensor(19619540., device='cuda:0'), tensor(19604636., device='cuda:0'), tensor(19627876., device='cuda:0'), tensor(19632534., device='cuda:0'), tensor(19645764., device='cuda:0'), tensor(19626270., device='cuda:0'), tensor(19602338., device='cuda:0'), tensor(19597956., device='cuda:0'), tensor(19637572., device='cuda:0')]
Prediction loss: [tensor(13761757., device='cuda:0'), tensor(13705796., device='cuda:0'), tensor(13652536., device='cuda:0'), tensor(13658543., device='cuda:0'), tensor(13712957., device='cuda:0'), tensor(13800533., device='cuda:0'), tensor(13754039., device='cuda:0'), tensor(13690052., device='cuda:0'), tensor(13757845., device='cuda:0'), tensor(13640128., 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': 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')}]
Compressed training loss: [tensor(2.5173e+11, device='cuda:0'), tensor(2.5061e+11, device='cuda:0'), tensor(2.4955e+11, device='cuda:0'), tensor(2.4956e+11, device='cuda:0'), tensor(2.5028e+11, device='cuda:0'), tensor(2.5204e+11, device='cuda:0'), tensor(2.5189e+11, device='cuda:0'), tensor(2.5034e+11, device='cuda:0'), tensor(2.5121e+11, device='cuda:0'), tensor(2.4965e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=566597), datetime.timedelta(microseconds=577548), datetime.timedelta(microseconds=583526), datetime.timedelta(microseconds=573570), datetime.timedelta(microseconds=573564), datetime.timedelta(microseconds=487931), datetime.timedelta(microseconds=630331), datetime.timedelta(microseconds=587515), datetime.timedelta(microseconds=583525), datetime.timedelta(microseconds=565612)]
Phi time: [datetime.timedelta(microseconds=887822), datetime.timedelta(microseconds=853305), datetime.timedelta(microseconds=861334), datetime.timedelta(microseconds=859354), datetime.timedelta(microseconds=869980), datetime.timedelta(microseconds=862756), datetime.timedelta(microseconds=920945), datetime.timedelta(microseconds=869724), datetime.timedelta(microseconds=878839), datetime.timedelta(microseconds=862135)]
