Precision: [tensor(0.0358, device='cuda:0'), tensor(0.0423, device='cuda:0'), tensor(0.0324, device='cuda:0'), tensor(0.0213, device='cuda:0'), tensor(0.0501, device='cuda:0'), tensor(0.0514, device='cuda:0'), tensor(0.0330, device='cuda:0'), tensor(0.0215, device='cuda:0'), tensor(0.0310, device='cuda:0'), tensor(0.0333, device='cuda:0')]

Output distance: [tensor(21.8105, device='cuda:0'), tensor(21.7718, device='cuda:0'), tensor(21.8307, device='cuda:0'), tensor(21.8975, device='cuda:0'), tensor(21.7249, device='cuda:0'), tensor(21.7171, device='cuda:0'), tensor(21.8274, device='cuda:0'), tensor(21.8966, device='cuda:0'), tensor(21.8392, device='cuda:0'), tensor(21.8259, device='cuda:0')]

Prediction loss: [tensor(105.3318, device='cuda:0'), tensor(106.9613, device='cuda:0'), tensor(105.3986, device='cuda:0'), tensor(104.1271, device='cuda:0'), tensor(105.6724, device='cuda:0'), tensor(105.0719, device='cuda:0'), tensor(103.5026, device='cuda:0'), tensor(102.3756, device='cuda:0'), tensor(103.8214, device='cuda:0'), tensor(106.0505, device='cuda:0')]

Others: [{'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}]

Compressed training loss: [tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=82, microseconds=253583), datetime.timedelta(seconds=82, microseconds=275165), datetime.timedelta(seconds=82, microseconds=335814), datetime.timedelta(seconds=82, microseconds=496342), datetime.timedelta(seconds=82, microseconds=127493), datetime.timedelta(seconds=82, microseconds=346799), datetime.timedelta(seconds=82, microseconds=668334), datetime.timedelta(seconds=82, microseconds=248879), datetime.timedelta(seconds=82, microseconds=603116), datetime.timedelta(seconds=82, microseconds=4432)]

Phi time: [datetime.timedelta(seconds=170, microseconds=509047), datetime.timedelta(seconds=170, microseconds=392154), datetime.timedelta(seconds=170, microseconds=299073), datetime.timedelta(seconds=170, microseconds=297197), datetime.timedelta(seconds=170, microseconds=371573), datetime.timedelta(seconds=170, microseconds=252759), datetime.timedelta(seconds=170, microseconds=392290), datetime.timedelta(seconds=170, microseconds=204872), datetime.timedelta(seconds=170, microseconds=146407), datetime.timedelta(seconds=170, microseconds=234543)]

