Precision: [tensor(0.5554, device='cuda:0'), tensor(0.5650, device='cuda:0'), tensor(0.5547, device='cuda:0'), tensor(0.5608, device='cuda:0'), tensor(0.5572, device='cuda:0'), tensor(0.5645, device='cuda:0'), tensor(0.5644, device='cuda:0'), tensor(0.5536, device='cuda:0'), tensor(0.5661, device='cuda:0'), tensor(0.5655, device='cuda:0')]
Output distance: [tensor(18.9255, device='cuda:0'), tensor(18.9084, device='cuda:0'), tensor(18.9268, device='cuda:0'), tensor(18.9160, device='cuda:0'), tensor(18.9220, device='cuda:0'), tensor(18.9095, device='cuda:0'), tensor(18.9090, device='cuda:0'), tensor(18.9288, device='cuda:0'), tensor(18.9058, device='cuda:0'), tensor(18.9070, device='cuda:0')]
Prediction loss: [tensor(109.1681, device='cuda:0'), tensor(107.8609, device='cuda:0'), tensor(108.8553, device='cuda:0'), tensor(107.9965, device='cuda:0'), tensor(108.9019, device='cuda:0'), tensor(109.2080, device='cuda:0'), tensor(109.0053, device='cuda:0'), tensor(109.2197, device='cuda:0'), tensor(108.7044, device='cuda:0'), tensor(108.9973, device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(5965, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5954, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5958, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5954, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5984, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5947, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5974, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5963, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5981, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5977, 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=766409), datetime.timedelta(seconds=2, microseconds=716379), datetime.timedelta(seconds=2, microseconds=716903), datetime.timedelta(seconds=2, microseconds=716277), datetime.timedelta(seconds=2, microseconds=716776), datetime.timedelta(seconds=2, microseconds=749717), datetime.timedelta(seconds=2, microseconds=716123), datetime.timedelta(seconds=2, microseconds=733487), datetime.timedelta(seconds=2, microseconds=715507), datetime.timedelta(seconds=2, microseconds=716625)]
Phi time: [datetime.timedelta(seconds=99, microseconds=66096), datetime.timedelta(seconds=99, microseconds=216096), datetime.timedelta(seconds=99, microseconds=181743), datetime.timedelta(seconds=99, microseconds=135081), datetime.timedelta(seconds=99, microseconds=2238), datetime.timedelta(seconds=99, microseconds=52197), datetime.timedelta(seconds=99, microseconds=283545), datetime.timedelta(seconds=99, microseconds=283111), datetime.timedelta(seconds=99, microseconds=182806), datetime.timedelta(seconds=99, microseconds=215927)]
