Precision: [tensor(0.6944, device='cuda:0'), tensor(0.6850, device='cuda:0'), tensor(0.6873, device='cuda:0'), tensor(0.6863, device='cuda:0'), tensor(0.6892, device='cuda:0'), tensor(0.6923, device='cuda:0'), tensor(0.6794, device='cuda:0'), tensor(0.6918, device='cuda:0'), tensor(0.6823, device='cuda:0'), tensor(0.6852, device='cuda:0')]
Output distance: [tensor(4.9173, device='cuda:0'), tensor(4.9362, device='cuda:0'), tensor(4.9315, device='cuda:0'), tensor(4.9336, device='cuda:0'), tensor(4.9278, device='cuda:0'), tensor(4.9215, device='cuda:0'), tensor(4.9472, device='cuda:0'), tensor(4.9226, device='cuda:0'), tensor(4.9415, device='cuda:0'), tensor(4.9357, device='cuda:0')]
Prediction loss: [tensor(19604244., device='cuda:0'), tensor(18403924., device='cuda:0'), tensor(19105524., device='cuda:0'), tensor(20880370., device='cuda:0'), tensor(17731078., device='cuda:0'), tensor(19966286., device='cuda:0'), tensor(18542728., device='cuda:0'), tensor(17942848., device='cuda:0'), tensor(17576354., device='cuda:0'), tensor(18660764., device='cuda:0')]
Others: [{'iter_num': 3, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, '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': 7, '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': 7, '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(40619.0273, device='cuda:0'), tensor(40744.3867, device='cuda:0'), tensor(40785.6797, device='cuda:0'), tensor(40883.9883, device='cuda:0'), tensor(40844.0234, device='cuda:0'), tensor(40617.7773, device='cuda:0'), tensor(40804.2578, device='cuda:0'), tensor(40706.1055, device='cuda:0'), tensor(40987.6250, device='cuda:0'), tensor(40973.2148, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=81414), datetime.timedelta(seconds=1, microseconds=45565), datetime.timedelta(seconds=1, microseconds=31624), datetime.timedelta(seconds=1, microseconds=35607), datetime.timedelta(seconds=1, microseconds=36603), datetime.timedelta(seconds=1, microseconds=22664), datetime.timedelta(seconds=1, microseconds=54527), datetime.timedelta(seconds=1, microseconds=88384), datetime.timedelta(seconds=1, microseconds=46562), datetime.timedelta(seconds=1, microseconds=37599)]
Phi time: [datetime.timedelta(microseconds=234008), datetime.timedelta(microseconds=236995), datetime.timedelta(microseconds=232017), datetime.timedelta(microseconds=250936), datetime.timedelta(microseconds=235003), datetime.timedelta(microseconds=251931), datetime.timedelta(microseconds=229030), datetime.timedelta(microseconds=232016), datetime.timedelta(microseconds=231019), datetime.timedelta(microseconds=233011)]
