Precision: [tensor(0.4864, device='cuda:0'), tensor(0.4988, device='cuda:0'), tensor(0.4661, device='cuda:0'), tensor(0.4977, device='cuda:0'), tensor(0.4601, device='cuda:0'), tensor(0.4615, device='cuda:0'), tensor(0.4856, device='cuda:0'), tensor(0.4732, device='cuda:0'), tensor(0.4977, device='cuda:0'), tensor(0.4799, device='cuda:0')]

Output distance: [tensor(19.0526, device='cuda:0'), tensor(19.0278, device='cuda:0'), tensor(19.0931, device='cuda:0'), tensor(19.0299, device='cuda:0'), tensor(19.1052, device='cuda:0'), tensor(19.1025, device='cuda:0'), tensor(19.0541, device='cuda:0'), tensor(19.0789, device='cuda:0'), tensor(19.0299, device='cuda:0'), tensor(19.0656, device='cuda:0')]

Prediction loss: [tensor(109.2512, device='cuda:0'), tensor(109.1447, device='cuda:0'), tensor(107.4414, device='cuda:0'), tensor(108.5375, device='cuda:0'), tensor(108.8163, device='cuda:0'), tensor(109.1546, device='cuda:0'), tensor(107.6532, device='cuda:0'), tensor(108.4147, device='cuda:0'), tensor(108.3253, device='cuda:0'), tensor(108.2642, device='cuda:0')]

Others: [{'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, 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=2, microseconds=503053), datetime.timedelta(seconds=2, microseconds=471974), datetime.timedelta(seconds=2, microseconds=483748), datetime.timedelta(seconds=2, microseconds=490530), datetime.timedelta(seconds=2, microseconds=481756), datetime.timedelta(seconds=2, microseconds=466250), datetime.timedelta(seconds=2, microseconds=502147), datetime.timedelta(seconds=2, microseconds=487984), datetime.timedelta(seconds=2, microseconds=468171), datetime.timedelta(seconds=2, microseconds=491343)]

Phi time: [datetime.timedelta(seconds=4, microseconds=449134), datetime.timedelta(seconds=4, microseconds=421121), datetime.timedelta(seconds=4, microseconds=453583), datetime.timedelta(seconds=4, microseconds=427734), datetime.timedelta(seconds=4, microseconds=437003), datetime.timedelta(seconds=4, microseconds=443945), datetime.timedelta(seconds=4, microseconds=461395), datetime.timedelta(seconds=4, microseconds=402278), datetime.timedelta(seconds=4, microseconds=408796), datetime.timedelta(seconds=4, microseconds=451849)]

