Precision: [tensor(0.8239, device='cuda:0'), tensor(0.8238, device='cuda:0'), tensor(0.8255, device='cuda:0'), tensor(0.8239, device='cuda:0'), tensor(0.8247, device='cuda:0'), tensor(0.8236, device='cuda:0'), tensor(0.8241, device='cuda:0'), tensor(0.8249, device='cuda:0'), tensor(0.8242, device='cuda:0'), tensor(0.8235, device='cuda:0')]

Output distance: [tensor(13923.5195, device='cuda:0'), tensor(14087.1172, device='cuda:0'), tensor(14076.3398, device='cuda:0'), tensor(13995.4775, device='cuda:0'), tensor(14161.5039, device='cuda:0'), tensor(14350.7344, device='cuda:0'), tensor(14072.0469, device='cuda:0'), tensor(13913.5449, device='cuda:0'), tensor(14277.4941, device='cuda:0'), tensor(13811.0029, device='cuda:0')]

Prediction loss: [tensor(10559.0166, device='cuda:0'), tensor(10682.7627, device='cuda:0'), tensor(10882.0830, device='cuda:0'), tensor(10651.0508, device='cuda:0'), tensor(10770.9551, device='cuda:0'), tensor(11156.3340, device='cuda:0'), tensor(10569.7871, device='cuda:0'), tensor(10566.8350, device='cuda:0'), tensor(10943.5732, device='cuda:0'), tensor(10297.3828, device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(1.9161e+08, device='cuda:0'), tensor(1.9244e+08, device='cuda:0'), tensor(1.9291e+08, device='cuda:0'), tensor(1.9283e+08, device='cuda:0'), tensor(1.9251e+08, device='cuda:0'), tensor(1.9704e+08, device='cuda:0'), tensor(1.9129e+08, device='cuda:0'), tensor(1.9102e+08, device='cuda:0'), tensor(1.9442e+08, device='cuda:0'), tensor(1.8801e+08, device='cuda:0')]

Training loss: 191652816.0

Prediction time: [datetime.timedelta(seconds=1, microseconds=800426), datetime.timedelta(seconds=1, microseconds=845189), datetime.timedelta(seconds=1, microseconds=820342), datetime.timedelta(seconds=1, microseconds=803414), datetime.timedelta(seconds=1, microseconds=845238), datetime.timedelta(seconds=1, microseconds=834285), datetime.timedelta(seconds=1, microseconds=822336), datetime.timedelta(seconds=1, microseconds=848226), datetime.timedelta(seconds=1, microseconds=834285), datetime.timedelta(seconds=1, microseconds=800427)]

Phi time: [datetime.timedelta(seconds=1, microseconds=497210), datetime.timedelta(microseconds=923892), datetime.timedelta(microseconds=865120), datetime.timedelta(microseconds=864760), datetime.timedelta(microseconds=888735), datetime.timedelta(microseconds=867243), datetime.timedelta(microseconds=861779), datetime.timedelta(microseconds=871973), datetime.timedelta(microseconds=867098), datetime.timedelta(microseconds=866145)]

