Precision: [tensor(0.8161, device='cuda:0'), tensor(0.8149, device='cuda:0'), tensor(0.8197, device='cuda:0'), tensor(0.8130, device='cuda:0'), tensor(0.8152, device='cuda:0'), tensor(0.8172, device='cuda:0'), tensor(0.8040, device='cuda:0'), tensor(0.8166, device='cuda:0'), tensor(0.7672, device='cuda:0'), tensor(0.8060, device='cuda:0')]

Output distance: [tensor(14524.4541, device='cuda:0'), tensor(15522.2295, device='cuda:0'), tensor(14227.4375, device='cuda:0'), tensor(14607.6543, device='cuda:0'), tensor(14872.2227, device='cuda:0'), tensor(14411.4941, device='cuda:0'), tensor(15591.9434, device='cuda:0'), tensor(14413.2109, device='cuda:0'), tensor(119290.0391, device='cuda:0'), tensor(16480.2578, device='cuda:0')]

Prediction loss: [tensor(10085.0986, device='cuda:0'), tensor(11707.5283, device='cuda:0'), tensor(9835.9688, device='cuda:0'), tensor(10109.2129, device='cuda:0'), tensor(9735.7939, device='cuda:0'), tensor(10294.2334, device='cuda:0'), tensor(10606.6777, device='cuda:0'), tensor(9812.4561, device='cuda:0'), tensor(203831.7031, device='cuda:0'), tensor(12546.7812, 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(1.8799e+08, device='cuda:0'), tensor(1.8733e+08, device='cuda:0'), tensor(1.8533e+08, device='cuda:0'), tensor(1.8651e+08, device='cuda:0'), tensor(1.8384e+08, device='cuda:0'), tensor(1.9120e+08, device='cuda:0'), tensor(1.8516e+08, device='cuda:0'), tensor(1.8411e+08, device='cuda:0'), tensor(2.0354e+08, device='cuda:0'), tensor(1.9399e+08, device='cuda:0')]

Training loss: 192421984.0

Prediction time: [datetime.timedelta(seconds=1, microseconds=54531), datetime.timedelta(seconds=1, microseconds=110294), datetime.timedelta(seconds=1, microseconds=92356), datetime.timedelta(seconds=1, microseconds=99339), datetime.timedelta(seconds=1, microseconds=87388), datetime.timedelta(seconds=1, microseconds=116265), datetime.timedelta(seconds=1, microseconds=89382), datetime.timedelta(seconds=1, microseconds=108297), datetime.timedelta(seconds=1, microseconds=87389), datetime.timedelta(seconds=1, microseconds=99338)]

Phi time: [datetime.timedelta(seconds=1, microseconds=303940), datetime.timedelta(microseconds=745844), datetime.timedelta(microseconds=669602), datetime.timedelta(microseconds=674363), datetime.timedelta(microseconds=671631), datetime.timedelta(microseconds=672752), datetime.timedelta(microseconds=669267), datetime.timedelta(microseconds=673794), datetime.timedelta(microseconds=669520), datetime.timedelta(microseconds=675634)]

