Precision: [tensor(0.3014, device='cuda:0'), tensor(0.3053, device='cuda:0'), tensor(0.2967, device='cuda:0'), tensor(0.3016, device='cuda:0'), tensor(0.2989, device='cuda:0'), tensor(0.3083, device='cuda:0'), tensor(0.2954, device='cuda:0'), tensor(0.2989, device='cuda:0'), tensor(0.3050, device='cuda:0'), tensor(0.3008, device='cuda:0')]

Output distance: [tensor(20.2171, device='cuda:0'), tensor(20.1938, device='cuda:0'), tensor(20.2455, device='cuda:0'), tensor(20.2158, device='cuda:0'), tensor(20.2319, device='cuda:0'), tensor(20.1756, device='cuda:0'), tensor(20.2530, device='cuda:0'), tensor(20.2319, device='cuda:0'), tensor(20.1953, device='cuda:0'), tensor(20.2207, device='cuda:0')]

Prediction loss: [tensor(105.7879, device='cuda:0'), tensor(106.6837, device='cuda:0'), tensor(105.6031, device='cuda:0'), tensor(105.9460, device='cuda:0'), tensor(105.1121, device='cuda:0'), tensor(105.6280, device='cuda:0'), tensor(105.8162, device='cuda:0'), tensor(106.1476, device='cuda:0'), tensor(105.1965, device='cuda:0'), tensor(105.0937, device='cuda:0')]

Others: [{'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, '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=5, microseconds=769908), datetime.timedelta(seconds=5, microseconds=765575), datetime.timedelta(seconds=5, microseconds=755951), datetime.timedelta(seconds=5, microseconds=774014), datetime.timedelta(seconds=5, microseconds=763392), datetime.timedelta(seconds=5, microseconds=787826), datetime.timedelta(seconds=5, microseconds=778783), datetime.timedelta(seconds=5, microseconds=774069), datetime.timedelta(seconds=5, microseconds=778037), datetime.timedelta(seconds=5, microseconds=770451)]

Phi time: [datetime.timedelta(seconds=4, microseconds=398185), datetime.timedelta(seconds=4, microseconds=425063), datetime.timedelta(seconds=4, microseconds=459126), datetime.timedelta(seconds=4, microseconds=488393), datetime.timedelta(seconds=4, microseconds=418832), datetime.timedelta(seconds=4, microseconds=453181), datetime.timedelta(seconds=4, microseconds=439710), datetime.timedelta(seconds=4, microseconds=452923), datetime.timedelta(seconds=4, microseconds=483194), datetime.timedelta(seconds=4, microseconds=456045)]

