Precision: [tensor(0.8248, device='cuda:0'), tensor(0.8235, device='cuda:0'), tensor(0.8258, device='cuda:0'), tensor(0.8258, device='cuda:0'), tensor(0.8238, device='cuda:0'), tensor(0.8263, device='cuda:0'), tensor(0.8267, device='cuda:0'), tensor(0.8255, device='cuda:0'), tensor(0.8258, device='cuda:0'), tensor(0.8256, device='cuda:0')]

Output distance: [tensor(13746., device='cuda:0'), tensor(13855.7861, device='cuda:0'), tensor(13699.0088, device='cuda:0'), tensor(13690.8145, device='cuda:0'), tensor(13830.8574, device='cuda:0'), tensor(13628.9102, device='cuda:0'), tensor(13644.9609, device='cuda:0'), tensor(13714.1318, device='cuda:0'), tensor(13710.5479, device='cuda:0'), tensor(13700.2158, device='cuda:0')]

Prediction loss: [tensor(10473.9443, device='cuda:0'), tensor(10293.4697, device='cuda:0'), tensor(10213.9258, device='cuda:0'), tensor(10666.5410, device='cuda:0'), tensor(10381.4209, device='cuda:0'), tensor(10608.5107, device='cuda:0'), tensor(10651.7969, device='cuda:0'), tensor(10546.0186, device='cuda:0'), tensor(10333.7168, device='cuda:0'), tensor(10400.8789, device='cuda:0')]

Others: [{'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(1.9179e+08, device='cuda:0'), tensor(1.8840e+08, device='cuda:0'), tensor(1.8731e+08, device='cuda:0'), tensor(1.9504e+08, device='cuda:0'), tensor(1.9088e+08, device='cuda:0'), tensor(1.9375e+08, device='cuda:0'), tensor(1.9551e+08, device='cuda:0'), tensor(1.9365e+08, device='cuda:0'), tensor(1.8952e+08, device='cuda:0'), tensor(1.9027e+08, device='cuda:0')]

Training loss: 192243888.0

Prediction time: [datetime.timedelta(microseconds=784699), datetime.timedelta(microseconds=892247), datetime.timedelta(microseconds=894238), datetime.timedelta(microseconds=789678), datetime.timedelta(microseconds=879302), datetime.timedelta(microseconds=814573), datetime.timedelta(microseconds=887268), datetime.timedelta(microseconds=817562), datetime.timedelta(microseconds=812582), datetime.timedelta(microseconds=903201)]

Phi time: [datetime.timedelta(seconds=1, microseconds=501489), datetime.timedelta(microseconds=927496), datetime.timedelta(microseconds=867257), datetime.timedelta(microseconds=875692), datetime.timedelta(microseconds=866440), datetime.timedelta(microseconds=869624), datetime.timedelta(microseconds=870769), datetime.timedelta(microseconds=869206), datetime.timedelta(microseconds=871399), datetime.timedelta(microseconds=881293)]

