Precision: [tensor(0.4516, device='cuda:0'), tensor(0.4586, device='cuda:0'), tensor(0.4580, device='cuda:0'), tensor(0.4623, device='cuda:0'), tensor(0.4429, device='cuda:0'), tensor(0.4576, device='cuda:0'), tensor(0.4583, device='cuda:0'), tensor(0.4596, device='cuda:0'), tensor(0.4567, device='cuda:0'), tensor(0.4558, device='cuda:0')]
Output distance: [tensor(19.2636, device='cuda:0'), tensor(19.2304, device='cuda:0'), tensor(19.2328, device='cuda:0'), tensor(19.2134, device='cuda:0'), tensor(19.3067, device='cuda:0'), tensor(19.2344, device='cuda:0'), tensor(19.2325, device='cuda:0'), tensor(19.2251, device='cuda:0'), tensor(19.2381, device='cuda:0'), tensor(19.2441, device='cuda:0')]
Prediction loss: [tensor(104.2799, device='cuda:0'), tensor(105.1389, device='cuda:0'), tensor(104.3668, device='cuda:0'), tensor(105.0635, device='cuda:0'), tensor(104.9536, device='cuda:0'), tensor(104.4379, device='cuda:0'), tensor(105.4601, device='cuda:0'), tensor(104.4053, device='cuda:0'), tensor(103.9911, device='cuda:0'), tensor(104.4936, device='cuda:0')]
Others: [{'iter_num': 9, 'num_positive': tensor(16282, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(16394, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(16340, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(16502, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(16283, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(16319, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(16446, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(16357, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(16259, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(16373, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}]
Compressed training loss: [tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=2, microseconds=916764), datetime.timedelta(seconds=2, microseconds=883340), datetime.timedelta(seconds=2, microseconds=766520), datetime.timedelta(seconds=2, microseconds=916559), datetime.timedelta(seconds=2, microseconds=899691), datetime.timedelta(seconds=2, microseconds=899883), datetime.timedelta(seconds=2, microseconds=933297), datetime.timedelta(seconds=2, microseconds=899746), datetime.timedelta(seconds=2, microseconds=933364), datetime.timedelta(seconds=2, microseconds=900270)]
Phi time: [datetime.timedelta(seconds=99, microseconds=433777), datetime.timedelta(seconds=99, microseconds=633663), datetime.timedelta(seconds=99, microseconds=432698), datetime.timedelta(seconds=99, microseconds=516263), datetime.timedelta(seconds=99, microseconds=416028), datetime.timedelta(seconds=99, microseconds=365989), datetime.timedelta(seconds=99, microseconds=549791), datetime.timedelta(seconds=99, microseconds=532887), datetime.timedelta(seconds=99, microseconds=468384), datetime.timedelta(seconds=99, microseconds=419887)]
