Precision: [tensor(0.3589, device='cuda:0'), tensor(0.3560, device='cuda:0'), tensor(0.3567, device='cuda:0'), tensor(0.3593, device='cuda:0'), tensor(0.3537, device='cuda:0'), tensor(0.3557, device='cuda:0'), tensor(0.3560, device='cuda:0'), tensor(0.3606, device='cuda:0'), tensor(0.3579, device='cuda:0'), tensor(0.3544, device='cuda:0')]

Output distance: [tensor(20.4362, device='cuda:0'), tensor(20.4649, device='cuda:0'), tensor(20.4586, device='cuda:0'), tensor(20.4329, device='cuda:0'), tensor(20.4879, device='cuda:0'), tensor(20.4680, device='cuda:0'), tensor(20.4649, device='cuda:0'), tensor(20.4196, device='cuda:0'), tensor(20.4468, device='cuda:0'), tensor(20.4810, device='cuda:0')]

Prediction loss: [tensor(101.7914, device='cuda:0'), tensor(102.1555, device='cuda:0'), tensor(101.8019, device='cuda:0'), tensor(101.8588, device='cuda:0'), tensor(101.7511, device='cuda:0'), tensor(101.1086, device='cuda:0'), tensor(101.4589, device='cuda:0'), tensor(102.3162, device='cuda:0'), tensor(101.9035, device='cuda:0'), tensor(101.5148, device='cuda:0')]

Others: [{'iter_num': 11, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(33080, 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=3, microseconds=224374), datetime.timedelta(seconds=3, microseconds=245330), datetime.timedelta(seconds=3, microseconds=382700), datetime.timedelta(seconds=3, microseconds=365821), datetime.timedelta(seconds=3, microseconds=236372), datetime.timedelta(seconds=3, microseconds=394701), datetime.timedelta(seconds=3, microseconds=221435), datetime.timedelta(seconds=3, microseconds=224414), datetime.timedelta(seconds=3, microseconds=226406), datetime.timedelta(seconds=3, microseconds=209482)]

Phi time: [datetime.timedelta(seconds=5, microseconds=716338), datetime.timedelta(seconds=5, microseconds=667317), datetime.timedelta(seconds=5, microseconds=671116), datetime.timedelta(seconds=5, microseconds=661048), datetime.timedelta(seconds=5, microseconds=661910), datetime.timedelta(seconds=5, microseconds=625034), datetime.timedelta(seconds=5, microseconds=640854), datetime.timedelta(seconds=5, microseconds=637061), datetime.timedelta(seconds=5, microseconds=628631), datetime.timedelta(seconds=5, microseconds=630987)]

