Precision: [tensor(0.5864, device='cuda:0'), tensor(0.5455, device='cuda:0'), tensor(0.5666, device='cuda:0'), tensor(0.5673, device='cuda:0'), tensor(0.5685, device='cuda:0'), tensor(0.5475, device='cuda:0'), tensor(0.5497, device='cuda:0'), tensor(0.5690, device='cuda:0'), tensor(0.5630, device='cuda:0'), tensor(0.5726, device='cuda:0')]
Output distance: [tensor(18.8735, device='cuda:0'), tensor(18.9459, device='cuda:0'), tensor(18.9083, device='cuda:0'), tensor(18.9073, device='cuda:0'), tensor(18.9054, device='cuda:0'), tensor(18.9423, device='cuda:0'), tensor(18.9385, device='cuda:0'), tensor(18.9040, device='cuda:0'), tensor(18.9148, device='cuda:0'), tensor(18.8978, device='cuda:0')]
Prediction loss: [tensor(109.1462, device='cuda:0'), tensor(107.8238, device='cuda:0'), tensor(108.7049, device='cuda:0'), tensor(108.6617, device='cuda:0'), tensor(108.1770, device='cuda:0'), tensor(108.5375, device='cuda:0'), tensor(108.8158, device='cuda:0'), tensor(108.4687, device='cuda:0'), tensor(108.4581, device='cuda:0'), tensor(107.9562, device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(5817, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5776, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5819, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5805, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5796, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5784, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5787, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5823, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5810, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5812, 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=2, microseconds=703564), datetime.timedelta(seconds=2, microseconds=716240), datetime.timedelta(seconds=2, microseconds=733357), datetime.timedelta(seconds=2, microseconds=734821), datetime.timedelta(seconds=2, microseconds=733412), datetime.timedelta(seconds=2, microseconds=733143), datetime.timedelta(seconds=2, microseconds=733114), datetime.timedelta(seconds=3, microseconds=192459), datetime.timedelta(seconds=3, microseconds=301000), datetime.timedelta(seconds=2, microseconds=715543)]
Phi time: [datetime.timedelta(seconds=99, microseconds=517609), datetime.timedelta(seconds=99, microseconds=500509), datetime.timedelta(seconds=99, microseconds=273322), datetime.timedelta(seconds=99, microseconds=402400), datetime.timedelta(seconds=99, microseconds=448457), datetime.timedelta(seconds=99, microseconds=482854), datetime.timedelta(seconds=99, microseconds=466206), datetime.timedelta(seconds=106, microseconds=948191), datetime.timedelta(seconds=116, microseconds=808234), datetime.timedelta(seconds=113, microseconds=949711)]
