Precision: [tensor(0.3242, device='cuda:0'), tensor(0.3282, device='cuda:0'), tensor(0.3200, device='cuda:0'), tensor(0.3284, device='cuda:0'), tensor(0.3243, device='cuda:0'), tensor(0.3203, device='cuda:0'), tensor(0.3196, device='cuda:0'), tensor(0.3223, device='cuda:0'), tensor(0.3136, device='cuda:0'), tensor(0.3204, device='cuda:0')]

Output distance: [tensor(20.7836, device='cuda:0'), tensor(20.7434, device='cuda:0'), tensor(20.8250, device='cuda:0'), tensor(20.7412, device='cuda:0'), tensor(20.7823, device='cuda:0'), tensor(20.8229, device='cuda:0'), tensor(20.8295, device='cuda:0'), tensor(20.8020, device='cuda:0'), tensor(20.8891, device='cuda:0'), tensor(20.8213, device='cuda:0')]

Prediction loss: [tensor(100.7933, device='cuda:0'), tensor(101.7222, device='cuda:0'), tensor(100.4262, device='cuda:0'), tensor(101.5398, device='cuda:0'), tensor(101.6768, device='cuda:0'), tensor(101.5757, device='cuda:0'), tensor(101.0784, device='cuda:0'), tensor(101.6777, device='cuda:0'), tensor(100.3121, device='cuda:0'), tensor(101.3741, 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': 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')}, {'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=94876), datetime.timedelta(seconds=3, microseconds=89896), datetime.timedelta(seconds=3, microseconds=91883), datetime.timedelta(seconds=3, microseconds=109806), datetime.timedelta(seconds=3, microseconds=96865), datetime.timedelta(seconds=3, microseconds=104833), datetime.timedelta(seconds=3, microseconds=111802), datetime.timedelta(seconds=3, microseconds=112797), datetime.timedelta(seconds=3, microseconds=96862), datetime.timedelta(seconds=3, microseconds=79935)]

Phi time: [datetime.timedelta(seconds=5, microseconds=358882), datetime.timedelta(seconds=5, microseconds=348207), datetime.timedelta(seconds=5, microseconds=337620), datetime.timedelta(seconds=5, microseconds=373550), datetime.timedelta(seconds=5, microseconds=329548), datetime.timedelta(seconds=5, microseconds=365755), datetime.timedelta(seconds=5, microseconds=365679), datetime.timedelta(seconds=5, microseconds=391876), datetime.timedelta(seconds=5, microseconds=371384), datetime.timedelta(seconds=5, microseconds=378936)]

