Precision: [tensor(0.8231, device='cuda:0'), tensor(0.8241, device='cuda:0'), tensor(0.8236, device='cuda:0'), tensor(0.8237, device='cuda:0'), tensor(0.8242, device='cuda:0'), tensor(0.8239, device='cuda:0'), tensor(0.8222, device='cuda:0'), tensor(0.8242, device='cuda:0'), tensor(0.8241, device='cuda:0'), tensor(0.8203, device='cuda:0')]

Output distance: [tensor(13812.4189, device='cuda:0'), tensor(13743.7510, device='cuda:0'), tensor(13743.5635, device='cuda:0'), tensor(13736.1934, device='cuda:0'), tensor(13736.2783, device='cuda:0'), tensor(13730.3574, device='cuda:0'), tensor(13931.8301, device='cuda:0'), tensor(13715.1602, device='cuda:0'), tensor(13755.5068, device='cuda:0'), tensor(13989.0078, device='cuda:0')]

Prediction loss: [tensor(10609.8203, device='cuda:0'), tensor(10937.7070, device='cuda:0'), tensor(10094.9893, device='cuda:0'), tensor(10544.3350, device='cuda:0'), tensor(10600.0596, device='cuda:0'), tensor(10717.2920, device='cuda:0'), tensor(10505.2051, device='cuda:0'), tensor(10654.2744, device='cuda:0'), tensor(10624.0762, device='cuda:0'), tensor(10398.0039, device='cuda:0')]

Others: [{'iter_num': 15, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 15, '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': 15, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 15, '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': 15, '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')}]

Compressed training loss: [tensor(1.9431e+08, device='cuda:0'), tensor(1.9989e+08, device='cuda:0'), tensor(1.8516e+08, device='cuda:0'), tensor(1.9212e+08, device='cuda:0'), tensor(1.9321e+08, device='cuda:0'), tensor(1.9621e+08, device='cuda:0'), tensor(1.9336e+08, device='cuda:0'), tensor(1.9492e+08, device='cuda:0'), tensor(1.9484e+08, device='cuda:0'), tensor(1.9122e+08, device='cuda:0')]

Training loss: 191926768.0

Prediction time: [datetime.timedelta(microseconds=763759), datetime.timedelta(microseconds=803592), datetime.timedelta(microseconds=806582), datetime.timedelta(microseconds=715964), datetime.timedelta(microseconds=787660), datetime.timedelta(microseconds=787658), datetime.timedelta(microseconds=726919), datetime.timedelta(microseconds=799608), datetime.timedelta(microseconds=730901), datetime.timedelta(microseconds=722934)]

Phi time: [datetime.timedelta(seconds=1, microseconds=369129), datetime.timedelta(microseconds=819463), datetime.timedelta(microseconds=741205), datetime.timedelta(microseconds=740650), datetime.timedelta(microseconds=743387), datetime.timedelta(microseconds=743028), datetime.timedelta(microseconds=743820), datetime.timedelta(microseconds=738878), datetime.timedelta(microseconds=745609), datetime.timedelta(microseconds=749251)]

