Precision: [tensor(0.0231, device='cuda:0'), tensor(0.0235, device='cuda:0'), tensor(0.0226, device='cuda:0'), tensor(0.0226, device='cuda:0'), tensor(0.0198, device='cuda:0'), tensor(0.0231, device='cuda:0'), tensor(0.0239, device='cuda:0'), tensor(0.0219, device='cuda:0'), tensor(0.0216, device='cuda:0'), tensor(0.0255, device='cuda:0')]

Output distance: [tensor(23.7941, device='cuda:0'), tensor(23.7899, device='cuda:0'), tensor(23.7996, device='cuda:0'), tensor(23.7996, device='cuda:0'), tensor(23.8271, device='cuda:0'), tensor(23.7941, device='cuda:0'), tensor(23.7866, device='cuda:0'), tensor(23.8065, device='cuda:0'), tensor(23.8096, device='cuda:0'), tensor(23.7709, device='cuda:0')]

Prediction loss: [tensor(118.3105, device='cuda:0'), tensor(117.3930, device='cuda:0'), tensor(117.6859, device='cuda:0'), tensor(117.4349, device='cuda:0'), tensor(118.8429, device='cuda:0'), tensor(117.3706, device='cuda:0'), tensor(118.5019, device='cuda:0'), tensor(119.0932, device='cuda:0'), tensor(117.8895, device='cuda:0'), tensor(117.7977, device='cuda:0')]

Others: [{'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}]

Compressed training loss: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=6, microseconds=681940), datetime.timedelta(seconds=6, microseconds=684927), datetime.timedelta(seconds=6, microseconds=659035), datetime.timedelta(seconds=6, microseconds=633145), datetime.timedelta(seconds=6, microseconds=627168), datetime.timedelta(seconds=6, microseconds=653061), datetime.timedelta(seconds=6, microseconds=687915), datetime.timedelta(seconds=6, microseconds=650074), datetime.timedelta(seconds=6, microseconds=661027), datetime.timedelta(seconds=6, microseconds=677957)]

Phi time: [datetime.timedelta(seconds=4, microseconds=903407), datetime.timedelta(seconds=4, microseconds=946910), datetime.timedelta(seconds=4, microseconds=952205), datetime.timedelta(seconds=4, microseconds=946228), datetime.timedelta(seconds=4, microseconds=961164), datetime.timedelta(seconds=4, microseconds=970127), datetime.timedelta(seconds=4, microseconds=977286), datetime.timedelta(seconds=4, microseconds=991042), datetime.timedelta(seconds=4, microseconds=980903), datetime.timedelta(seconds=4, microseconds=975887)]

