Precision: [tensor(0.2657, device='cuda:0'), tensor(0.2409, device='cuda:0'), tensor(0.2763, device='cuda:0'), tensor(0.2793, device='cuda:0'), tensor(0.2748, device='cuda:0'), tensor(0.2582, device='cuda:0'), tensor(0.2242, device='cuda:0'), tensor(0.2533, device='cuda:0'), tensor(0.2650, device='cuda:0'), tensor(0.2728, device='cuda:0')]

Output distance: [tensor(19.4940, device='cuda:0'), tensor(19.5435, device='cuda:0'), tensor(19.4728, device='cuda:0'), tensor(19.4667, device='cuda:0'), tensor(19.4758, device='cuda:0'), tensor(19.5091, device='cuda:0'), tensor(19.5771, device='cuda:0'), tensor(19.5187, device='cuda:0'), tensor(19.4955, device='cuda:0'), tensor(19.4797, device='cuda:0')]

Prediction loss: [tensor(107.3228, device='cuda:0'), tensor(106.5257, device='cuda:0'), tensor(108.2544, device='cuda:0'), tensor(109.1551, device='cuda:0'), tensor(107.4933, device='cuda:0'), tensor(106.9003, device='cuda:0'), tensor(107.2106, device='cuda:0'), tensor(107.6624, device='cuda:0'), tensor(108.3141, device='cuda:0'), tensor(109.4353, device='cuda:0')]

Others: [{'iter_num': 13, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, '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': 13, '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': 13, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(6616, 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=561139), datetime.timedelta(seconds=2, microseconds=566119), datetime.timedelta(seconds=2, microseconds=847977), datetime.timedelta(seconds=2, microseconds=890742), datetime.timedelta(seconds=2, microseconds=835972), datetime.timedelta(seconds=2, microseconds=939535), datetime.timedelta(seconds=2, microseconds=916628), datetime.timedelta(seconds=2, microseconds=889742), datetime.timedelta(seconds=2, microseconds=945506), datetime.timedelta(seconds=2, microseconds=925594)]

Phi time: [datetime.timedelta(seconds=4, microseconds=164976), datetime.timedelta(seconds=4, microseconds=156664), datetime.timedelta(seconds=4, microseconds=493543), datetime.timedelta(seconds=4, microseconds=802084), datetime.timedelta(seconds=4, microseconds=774301), datetime.timedelta(seconds=4, microseconds=780125), datetime.timedelta(seconds=4, microseconds=789812), datetime.timedelta(seconds=4, microseconds=773559), datetime.timedelta(seconds=4, microseconds=796072), datetime.timedelta(seconds=4, microseconds=785538)]

