Precision: [tensor(0.3044, device='cuda:0'), tensor(0.3089, device='cuda:0'), tensor(0.3047, device='cuda:0'), tensor(0.3128, device='cuda:0'), tensor(0.3089, device='cuda:0'), tensor(0.3175, device='cuda:0'), tensor(0.3069, device='cuda:0'), tensor(0.3075, device='cuda:0'), tensor(0.3093, device='cuda:0'), tensor(0.3162, device='cuda:0')]

Output distance: [tensor(20.1992, device='cuda:0'), tensor(20.1717, device='cuda:0'), tensor(20.1974, device='cuda:0'), tensor(20.1487, device='cuda:0'), tensor(20.1720, device='cuda:0'), tensor(20.1206, device='cuda:0'), tensor(20.1838, device='cuda:0'), tensor(20.1805, device='cuda:0'), tensor(20.1699, device='cuda:0'), tensor(20.1285, device='cuda:0')]

Prediction loss: [tensor(104.7511, device='cuda:0'), tensor(104.6004, device='cuda:0'), tensor(106.3786, device='cuda:0'), tensor(104.4064, device='cuda:0'), tensor(106.1690, device='cuda:0'), tensor(105.4242, device='cuda:0'), tensor(104.7706, device='cuda:0'), tensor(105.7775, device='cuda:0'), tensor(105.0809, device='cuda:0'), tensor(104.6670, device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, 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=4, microseconds=487964), datetime.timedelta(seconds=4, microseconds=509872), datetime.timedelta(seconds=4, microseconds=497923), datetime.timedelta(seconds=4, microseconds=511865), datetime.timedelta(seconds=4, microseconds=497924), datetime.timedelta(seconds=4, microseconds=494934), datetime.timedelta(seconds=4, microseconds=485974), datetime.timedelta(seconds=4, microseconds=495932), datetime.timedelta(seconds=4, microseconds=511865), datetime.timedelta(seconds=4, microseconds=511861)]

Phi time: [datetime.timedelta(seconds=4, microseconds=914203), datetime.timedelta(seconds=5, microseconds=28711), datetime.timedelta(seconds=5, microseconds=17392), datetime.timedelta(seconds=5, microseconds=26143), datetime.timedelta(seconds=5, microseconds=68598), datetime.timedelta(seconds=5, microseconds=27633), datetime.timedelta(seconds=5, microseconds=47921), datetime.timedelta(seconds=5, microseconds=24664), datetime.timedelta(seconds=5, microseconds=22044), datetime.timedelta(seconds=5, microseconds=57827)]

