Precision: [tensor(0.1379, device='cuda:0'), tensor(0.1353, device='cuda:0'), tensor(0.1378, device='cuda:0'), tensor(0.1371, device='cuda:0'), tensor(0.1364, device='cuda:0'), tensor(0.1357, device='cuda:0'), tensor(0.1366, device='cuda:0'), tensor(0.1364, device='cuda:0'), tensor(0.1369, device='cuda:0'), tensor(0.1368, device='cuda:0')]
Output distance: [tensor(19787330., device='cuda:0'), tensor(19829982., device='cuda:0'), tensor(19809080., device='cuda:0'), tensor(19795314., device='cuda:0'), tensor(19831312., device='cuda:0'), tensor(19820516., device='cuda:0'), tensor(19829818., device='cuda:0'), tensor(19819096., device='cuda:0'), tensor(19807206., device='cuda:0'), tensor(19807672., device='cuda:0')]
Prediction loss: [tensor(12270571., device='cuda:0'), tensor(12221058., device='cuda:0'), tensor(12201182., device='cuda:0'), tensor(12258192., device='cuda:0'), tensor(12132245., device='cuda:0'), tensor(12156090., device='cuda:0'), tensor(12162209., device='cuda:0'), tensor(12155296., device='cuda:0'), tensor(12171241., device='cuda:0'), tensor(12112802., device='cuda:0')]
Others: [{'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': 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': 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(2.4840e+11, device='cuda:0'), tensor(2.4757e+11, device='cuda:0'), tensor(2.4781e+11, device='cuda:0'), tensor(2.4909e+11, device='cuda:0'), tensor(2.4587e+11, device='cuda:0'), tensor(2.4589e+11, device='cuda:0'), tensor(2.4622e+11, device='cuda:0'), tensor(2.4606e+11, device='cuda:0'), tensor(2.4644e+11, device='cuda:0'), tensor(2.4494e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=694120), datetime.timedelta(microseconds=678123), datetime.timedelta(microseconds=607409), datetime.timedelta(microseconds=604437), datetime.timedelta(microseconds=693059), datetime.timedelta(microseconds=677129), datetime.timedelta(microseconds=693059), datetime.timedelta(microseconds=589499), datetime.timedelta(microseconds=699980), datetime.timedelta(microseconds=602445)]
Phi time: [datetime.timedelta(microseconds=894609), datetime.timedelta(microseconds=854602), datetime.timedelta(microseconds=876515), datetime.timedelta(microseconds=850295), datetime.timedelta(microseconds=859570), datetime.timedelta(microseconds=867044), datetime.timedelta(microseconds=901747), datetime.timedelta(microseconds=867306), datetime.timedelta(microseconds=867316), datetime.timedelta(microseconds=858754)]
