Precision: [tensor(0.6834, device='cuda:0'), tensor(0.6868, device='cuda:0'), tensor(0.6897, device='cuda:0'), tensor(0.6871, device='cuda:0'), tensor(0.6836, device='cuda:0'), tensor(0.6839, device='cuda:0'), tensor(0.6873, device='cuda:0'), tensor(0.6868, device='cuda:0'), tensor(0.6826, device='cuda:0'), tensor(0.6886, device='cuda:0')]
Output distance: [tensor(4.9394, device='cuda:0'), tensor(4.9325, device='cuda:0'), tensor(4.9268, device='cuda:0'), tensor(4.9320, device='cuda:0'), tensor(4.9388, device='cuda:0'), tensor(4.9383, device='cuda:0'), tensor(4.9315, device='cuda:0'), tensor(4.9325, device='cuda:0'), tensor(4.9409, device='cuda:0'), tensor(4.9289, device='cuda:0')]
Prediction loss: [tensor(17868630., device='cuda:0'), tensor(21097050., device='cuda:0'), tensor(17517328., device='cuda:0'), tensor(18881418., device='cuda:0'), tensor(17720050., device='cuda:0'), tensor(18542652., device='cuda:0'), tensor(19684098., device='cuda:0'), tensor(19382764., device='cuda:0'), tensor(18694082., device='cuda:0'), tensor(19912284., device='cuda:0')]
Others: [{'iter_num': 7, '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': 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': 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(40790.8750, device='cuda:0'), tensor(40939.3086, device='cuda:0'), tensor(40808.5938, device='cuda:0'), tensor(41006.6016, device='cuda:0'), tensor(40840.3164, device='cuda:0'), tensor(40770.7188, device='cuda:0'), tensor(40838.4922, device='cuda:0'), tensor(40770.1250, device='cuda:0'), tensor(40830.4922, device='cuda:0'), tensor(40880.1797, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=54528), datetime.timedelta(seconds=1, microseconds=60502), datetime.timedelta(seconds=1, microseconds=43574), datetime.timedelta(seconds=1, microseconds=49549), datetime.timedelta(seconds=1, microseconds=55524), datetime.timedelta(seconds=1, microseconds=30630), datetime.timedelta(seconds=1, microseconds=39592), datetime.timedelta(seconds=1, microseconds=27642), datetime.timedelta(seconds=1, microseconds=10712), datetime.timedelta(seconds=1, microseconds=33615)]
Phi time: [datetime.timedelta(microseconds=238986), datetime.timedelta(microseconds=241975), datetime.timedelta(microseconds=257906), datetime.timedelta(microseconds=234008), datetime.timedelta(microseconds=259898), datetime.timedelta(microseconds=253923), datetime.timedelta(microseconds=238986), datetime.timedelta(microseconds=235004), datetime.timedelta(microseconds=236000), datetime.timedelta(microseconds=236997)]
