Precision: [tensor(0.1821, device='cuda:0'), tensor(0.1905, device='cuda:0'), tensor(0.1794, device='cuda:0'), tensor(0.1834, device='cuda:0'), tensor(0.1735, device='cuda:0'), tensor(0.1886, device='cuda:0'), tensor(0.1841, device='cuda:0'), tensor(0.1779, device='cuda:0'), tensor(0.1800, device='cuda:0'), tensor(0.1765, device='cuda:0')]

Output distance: [tensor(22.2047, device='cuda:0'), tensor(22.1206, device='cuda:0'), tensor(22.2319, device='cuda:0'), tensor(22.1917, device='cuda:0'), tensor(22.2899, device='cuda:0'), tensor(22.1394, device='cuda:0'), tensor(22.1841, device='cuda:0'), tensor(22.2464, device='cuda:0'), tensor(22.2258, device='cuda:0'), tensor(22.2609, device='cuda:0')]

Prediction loss: [tensor(98.8126, device='cuda:0'), tensor(99.2971, device='cuda:0'), tensor(98.5347, device='cuda:0'), tensor(97.7628, device='cuda:0'), tensor(96.6903, device='cuda:0'), tensor(97.7114, device='cuda:0'), tensor(98.7339, device='cuda:0'), tensor(98.2030, device='cuda:0'), tensor(98.8025, device='cuda:0'), tensor(98.7328, device='cuda:0')]

Others: [{'iter_num': 13, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, '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': 15, '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': 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')}]

Compressed training loss: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=3, microseconds=30148), datetime.timedelta(seconds=3, microseconds=160595), datetime.timedelta(seconds=3, microseconds=34132), datetime.timedelta(seconds=3, microseconds=144663), datetime.timedelta(seconds=3, microseconds=45086), datetime.timedelta(seconds=2, microseconds=933509), datetime.timedelta(seconds=3, microseconds=33136), datetime.timedelta(seconds=3, microseconds=76950), datetime.timedelta(seconds=2, microseconds=936545), datetime.timedelta(seconds=3, microseconds=32140)]

Phi time: [datetime.timedelta(seconds=4, microseconds=961954), datetime.timedelta(seconds=4, microseconds=943034), datetime.timedelta(seconds=4, microseconds=969920), datetime.timedelta(seconds=4, microseconds=926781), datetime.timedelta(seconds=4, microseconds=974756), datetime.timedelta(seconds=4, microseconds=945027), datetime.timedelta(seconds=4, microseconds=961955), datetime.timedelta(seconds=4, microseconds=950005), datetime.timedelta(seconds=4, microseconds=974571), datetime.timedelta(seconds=4, microseconds=961956)]

