Precision: [tensor(0.2155, device='cuda:0'), tensor(0.2188, device='cuda:0'), tensor(0.2175, device='cuda:0'), tensor(0.2172, device='cuda:0'), tensor(0.2182, device='cuda:0'), tensor(0.2173, device='cuda:0'), tensor(0.2183, device='cuda:0'), tensor(0.2163, device='cuda:0'), tensor(0.2185, device='cuda:0'), tensor(0.2185, device='cuda:0')]
Output distance: [tensor(19549536., device='cuda:0'), tensor(19512526., device='cuda:0'), tensor(19516684., device='cuda:0'), tensor(19533624., device='cuda:0'), tensor(19524824., device='cuda:0'), tensor(19530482., device='cuda:0'), tensor(19506710., device='cuda:0'), tensor(19534280., device='cuda:0'), tensor(19518290., device='cuda:0'), tensor(19514258., device='cuda:0')]
Prediction loss: [tensor(13529450., device='cuda:0'), tensor(13659591., device='cuda:0'), tensor(13546075., device='cuda:0'), tensor(13572174., device='cuda:0'), tensor(13555705., device='cuda:0'), tensor(13509217., device='cuda:0'), tensor(13665231., device='cuda:0'), tensor(13522672., device='cuda:0'), tensor(13554970., device='cuda:0'), tensor(13676293., device='cuda:0')]
Others: [{'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')}, {'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(2.4686e+11, device='cuda:0'), tensor(2.4880e+11, device='cuda:0'), tensor(2.4683e+11, device='cuda:0'), tensor(2.4748e+11, device='cuda:0'), tensor(2.4688e+11, device='cuda:0'), tensor(2.4636e+11, device='cuda:0'), tensor(2.4886e+11, device='cuda:0'), tensor(2.4675e+11, device='cuda:0'), tensor(2.4664e+11, device='cuda:0'), tensor(2.4918e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=580589), datetime.timedelta(microseconds=499881), datetime.timedelta(microseconds=629331), datetime.timedelta(microseconds=572572), datetime.timedelta(microseconds=577551), datetime.timedelta(microseconds=566647), datetime.timedelta(microseconds=504908), datetime.timedelta(microseconds=626343), datetime.timedelta(microseconds=574563), datetime.timedelta(microseconds=571577)]
Phi time: [datetime.timedelta(microseconds=859766), datetime.timedelta(microseconds=860867), datetime.timedelta(microseconds=899393), datetime.timedelta(microseconds=863207), datetime.timedelta(microseconds=857826), datetime.timedelta(microseconds=856979), datetime.timedelta(microseconds=854427), datetime.timedelta(microseconds=880198), datetime.timedelta(microseconds=858828), datetime.timedelta(microseconds=868308)]
