Precision: [tensor(0.8138, device='cuda:0'), tensor(0.8158, device='cuda:0'), tensor(0.7835, device='cuda:0'), tensor(0.8097, device='cuda:0'), tensor(0.8157, device='cuda:0'), tensor(0.8018, device='cuda:0'), tensor(0.8044, device='cuda:0'), tensor(0.8112, device='cuda:0'), tensor(0.8118, device='cuda:0'), tensor(0.8175, device='cuda:0')]

Output distance: [tensor(57018696., device='cuda:0'), tensor(11938503., device='cuda:0'), tensor(1.3743e+10, device='cuda:0'), tensor(1.4067e+09, device='cuda:0'), tensor(5230359., device='cuda:0'), tensor(90583328., device='cuda:0'), tensor(8.9247e+08, device='cuda:0'), tensor(14893072., device='cuda:0'), tensor(1.6935e+08, device='cuda:0'), tensor(944788.5625, device='cuda:0')]

Prediction loss: [tensor(79431096., device='cuda:0'), tensor(16794936., device='cuda:0'), tensor(1.7840e+10, device='cuda:0'), tensor(1.8586e+09, device='cuda:0'), tensor(6576766., device='cuda:0'), tensor(1.1775e+08, device='cuda:0'), tensor(1.1535e+09, device='cuda:0'), tensor(19055048., device='cuda:0'), tensor(2.2354e+08, device='cuda:0'), tensor(1227771.2500, device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(17991, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17987, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17992, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17990, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17997, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17991, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17996, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17988, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17996, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17997, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(1.9630e+08, device='cuda:0'), tensor(1.9297e+08, device='cuda:0'), tensor(1.9282e+08, device='cuda:0'), tensor(1.9364e+08, device='cuda:0'), tensor(1.9123e+08, device='cuda:0'), tensor(1.9023e+08, device='cuda:0'), tensor(1.9205e+08, device='cuda:0'), tensor(1.9199e+08, device='cuda:0'), tensor(1.9524e+08, device='cuda:0'), tensor(1.8533e+08, device='cuda:0')]

Training loss: 191554896.0

Prediction time: [datetime.timedelta(seconds=1, microseconds=526521), datetime.timedelta(seconds=1, microseconds=561382), datetime.timedelta(seconds=1, microseconds=561378), datetime.timedelta(seconds=1, microseconds=551412), datetime.timedelta(seconds=1, microseconds=551420), datetime.timedelta(seconds=1, microseconds=554408), datetime.timedelta(seconds=1, microseconds=549429), datetime.timedelta(seconds=1, microseconds=553411), datetime.timedelta(seconds=1, microseconds=552417), datetime.timedelta(seconds=1, microseconds=554408)]

Phi time: [datetime.timedelta(seconds=1, microseconds=366458), datetime.timedelta(microseconds=818026), datetime.timedelta(microseconds=743855), datetime.timedelta(microseconds=738135), datetime.timedelta(microseconds=738697), datetime.timedelta(microseconds=738539), datetime.timedelta(microseconds=741568), datetime.timedelta(microseconds=738220), datetime.timedelta(microseconds=738984), datetime.timedelta(microseconds=739765)]

