Precision: [tensor(0.6577, device='cuda:0'), tensor(0.6637, device='cuda:0'), tensor(0.6658, device='cuda:0'), tensor(0.6482, device='cuda:0'), tensor(0.6584, device='cuda:0'), tensor(0.6671, device='cuda:0'), tensor(0.6619, device='cuda:0'), tensor(0.6642, device='cuda:0'), tensor(0.6550, device='cuda:0'), tensor(0.6666, device='cuda:0')]

Output distance: [tensor(4.9908, device='cuda:0'), tensor(4.9787, device='cuda:0'), tensor(4.9745, device='cuda:0'), tensor(5.0097, device='cuda:0'), tensor(4.9892, device='cuda:0'), tensor(4.9719, device='cuda:0'), tensor(4.9824, device='cuda:0'), tensor(4.9777, device='cuda:0'), tensor(4.9961, device='cuda:0'), tensor(4.9730, device='cuda:0')]

Prediction loss: [tensor(18228154., device='cuda:0'), tensor(17716460., device='cuda:0'), tensor(20897138., device='cuda:0'), tensor(18912288., device='cuda:0'), tensor(19059904., device='cuda:0'), tensor(17349402., device='cuda:0'), tensor(18162596., device='cuda:0'), tensor(20245536., device='cuda:0'), tensor(19181122., device='cuda:0'), tensor(17887588., device='cuda:0')]

Others: [{'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40905.5625, device='cuda:0'), tensor(40443.9609, device='cuda:0'), tensor(40615.1016, device='cuda:0'), tensor(41101.3867, device='cuda:0'), tensor(40973.8203, device='cuda:0'), tensor(40657.3125, device='cuda:0'), tensor(40747.9023, device='cuda:0'), tensor(40890.8164, device='cuda:0'), tensor(41154.6875, device='cuda:0'), tensor(40850.8320, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(microseconds=985254), datetime.timedelta(microseconds=980643), datetime.timedelta(microseconds=975506), datetime.timedelta(microseconds=961363), datetime.timedelta(microseconds=971368), datetime.timedelta(microseconds=963081), datetime.timedelta(microseconds=971390), datetime.timedelta(microseconds=967133), datetime.timedelta(microseconds=973431), datetime.timedelta(microseconds=968714)]

Phi time: [datetime.timedelta(microseconds=188146), datetime.timedelta(microseconds=190180), datetime.timedelta(microseconds=195492), datetime.timedelta(microseconds=189572), datetime.timedelta(microseconds=182752), datetime.timedelta(microseconds=188542), datetime.timedelta(microseconds=184286), datetime.timedelta(microseconds=172354), datetime.timedelta(microseconds=176242), datetime.timedelta(microseconds=185318)]

