Precision: [tensor(0.1617, device='cuda:0'), tensor(0.1652, device='cuda:0'), tensor(0.1661, device='cuda:0'), tensor(0.1754, device='cuda:0'), tensor(0.1632, device='cuda:0'), tensor(0.1527, device='cuda:0'), tensor(0.1516, device='cuda:0'), tensor(0.1372, device='cuda:0'), tensor(0.1450, device='cuda:0'), tensor(0.1521, device='cuda:0')]

Output distance: [tensor(21.0550, device='cuda:0'), tensor(21.0345, device='cuda:0'), tensor(21.0290, device='cuda:0'), tensor(20.9728, device='cuda:0'), tensor(21.0459, device='cuda:0'), tensor(21.1094, device='cuda:0'), tensor(21.1161, device='cuda:0'), tensor(21.2022, device='cuda:0'), tensor(21.1554, device='cuda:0'), tensor(21.1128, device='cuda:0')]

Prediction loss: [tensor(100.9785, device='cuda:0'), tensor(100.6970, device='cuda:0'), tensor(102.1093, device='cuda:0'), tensor(102.0752, device='cuda:0'), tensor(100.2234, device='cuda:0'), tensor(101.1032, device='cuda:0'), tensor(101.0096, device='cuda:0'), tensor(100.7660, device='cuda:0'), tensor(99.6976, device='cuda:0'), tensor(101.1188, device='cuda:0')]

Others: [{'iter_num': 19, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 19, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 17, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 21, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 19, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 19, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 17, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 17, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 17, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 19, '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=3, microseconds=288055), datetime.timedelta(seconds=3, microseconds=259179), datetime.timedelta(seconds=3, microseconds=173540), datetime.timedelta(seconds=3, microseconds=365727), datetime.timedelta(seconds=3, microseconds=265152), datetime.timedelta(seconds=3, microseconds=261171), datetime.timedelta(seconds=3, microseconds=194449), datetime.timedelta(seconds=3, microseconds=172543), datetime.timedelta(seconds=3, microseconds=223328), datetime.timedelta(seconds=3, microseconds=190468)]

Phi time: [datetime.timedelta(seconds=4, microseconds=817838), datetime.timedelta(seconds=4, microseconds=808839), datetime.timedelta(seconds=4, microseconds=821062), datetime.timedelta(seconds=4, microseconds=787300), datetime.timedelta(seconds=4, microseconds=803553), datetime.timedelta(seconds=4, microseconds=805327), datetime.timedelta(seconds=4, microseconds=793964), datetime.timedelta(seconds=4, microseconds=801723), datetime.timedelta(seconds=4, microseconds=811911), datetime.timedelta(seconds=4, microseconds=702136)]

