Precision: [tensor(0.6857, device='cuda:0'), tensor(0.6829, device='cuda:0'), tensor(0.6907, device='cuda:0'), tensor(0.6889, device='cuda:0'), tensor(0.6915, device='cuda:0'), tensor(0.6892, device='cuda:0'), tensor(0.6855, device='cuda:0'), tensor(0.6960, device='cuda:0'), tensor(0.6886, device='cuda:0'), tensor(0.6934, device='cuda:0')]
Output distance: [tensor(4.9346, device='cuda:0'), tensor(4.9404, device='cuda:0'), tensor(4.9247, device='cuda:0'), tensor(4.9283, device='cuda:0'), tensor(4.9231, device='cuda:0'), tensor(4.9278, device='cuda:0'), tensor(4.9352, device='cuda:0'), tensor(4.9142, device='cuda:0'), tensor(4.9289, device='cuda:0'), tensor(4.9194, device='cuda:0')]
Prediction loss: [tensor(17128526., device='cuda:0'), tensor(18087082., device='cuda:0'), tensor(17577466., device='cuda:0'), tensor(18505216., device='cuda:0'), tensor(18761918., device='cuda:0'), tensor(18451764., device='cuda:0'), tensor(18059048., device='cuda:0'), tensor(18808030., device='cuda:0'), tensor(19154622., device='cuda:0'), tensor(18901122., 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(40942.4922, device='cuda:0'), tensor(40838.3945, device='cuda:0'), tensor(41106.6016, device='cuda:0'), tensor(40798.1445, device='cuda:0'), tensor(40866.4727, device='cuda:0'), tensor(40851.4141, device='cuda:0'), tensor(40926.6172, device='cuda:0'), tensor(40678.3164, device='cuda:0'), tensor(40966.0898, device='cuda:0'), tensor(40787.3008, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=42578), datetime.timedelta(seconds=1, microseconds=14697), datetime.timedelta(seconds=1, microseconds=28636), datetime.timedelta(seconds=1, microseconds=36603), datetime.timedelta(seconds=1, microseconds=28638), datetime.timedelta(seconds=1, microseconds=29634), datetime.timedelta(seconds=1, microseconds=61498), datetime.timedelta(seconds=1, microseconds=32621), datetime.timedelta(seconds=1, microseconds=11710), datetime.timedelta(seconds=1, microseconds=23659)]
Phi time: [datetime.timedelta(microseconds=387358), datetime.timedelta(microseconds=252927), datetime.timedelta(microseconds=231021), datetime.timedelta(microseconds=235999), datetime.timedelta(microseconds=230025), datetime.timedelta(microseconds=252926), datetime.timedelta(microseconds=233011), datetime.timedelta(microseconds=250936), datetime.timedelta(microseconds=235002), datetime.timedelta(microseconds=234008)]
