Precision: [tensor(0.5012, device='cuda:0'), tensor(0.4953, device='cuda:0'), tensor(0.4920, device='cuda:0'), tensor(0.4888, device='cuda:0'), tensor(0.4905, device='cuda:0'), tensor(0.4881, device='cuda:0'), tensor(0.4973, device='cuda:0'), tensor(0.4976, device='cuda:0'), tensor(0.4921, device='cuda:0'), tensor(0.5160, device='cuda:0')]
Output distance: [tensor(19.0230, device='cuda:0'), tensor(19.0348, device='cuda:0'), tensor(19.0414, device='cuda:0'), tensor(19.0478, device='cuda:0'), tensor(19.0444, device='cuda:0'), tensor(19.0493, device='cuda:0'), tensor(19.0308, device='cuda:0'), tensor(19.0302, device='cuda:0'), tensor(19.0411, device='cuda:0'), tensor(18.9933, device='cuda:0')]
Prediction loss: [tensor(109.1393, device='cuda:0'), tensor(108.1775, device='cuda:0'), tensor(109.0369, device='cuda:0'), tensor(108.1559, device='cuda:0'), tensor(108.5934, device='cuda:0'), tensor(108.6731, device='cuda:0'), tensor(108.4178, device='cuda:0'), tensor(108.6164, device='cuda:0'), tensor(108.6470, device='cuda:0'), tensor(109.8451, device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, 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=626586), datetime.timedelta(seconds=2, microseconds=634008), datetime.timedelta(seconds=2, microseconds=643289), datetime.timedelta(seconds=2, microseconds=644408), datetime.timedelta(seconds=2, microseconds=696135), datetime.timedelta(seconds=2, microseconds=664311), datetime.timedelta(seconds=2, microseconds=667369), datetime.timedelta(seconds=2, microseconds=650766), datetime.timedelta(seconds=2, microseconds=648867), datetime.timedelta(seconds=2, microseconds=635859)]
Phi time: [datetime.timedelta(seconds=97, microseconds=156207), datetime.timedelta(seconds=97, microseconds=286191), datetime.timedelta(seconds=97, microseconds=193614), datetime.timedelta(seconds=97, microseconds=21756), datetime.timedelta(seconds=97, microseconds=109609), datetime.timedelta(seconds=98, microseconds=377710), datetime.timedelta(seconds=97, microseconds=534111), datetime.timedelta(seconds=97, microseconds=398452), datetime.timedelta(seconds=97, microseconds=550331), datetime.timedelta(seconds=97, microseconds=360155)]
