Precision: [tensor(0.6884, device='cuda:0'), tensor(0.6844, device='cuda:0'), tensor(0.6771, device='cuda:0'), tensor(0.6865, device='cuda:0'), tensor(0.6850, device='cuda:0'), tensor(0.6905, device='cuda:0'), tensor(0.6818, device='cuda:0'), tensor(0.6844, device='cuda:0'), tensor(0.6797, device='cuda:0'), tensor(0.6852, device='cuda:0')]
Output distance: [tensor(4.9294, device='cuda:0'), tensor(4.9373, device='cuda:0'), tensor(4.9520, device='cuda:0'), tensor(4.9331, device='cuda:0'), tensor(4.9362, device='cuda:0'), tensor(4.9252, device='cuda:0'), tensor(4.9425, device='cuda:0'), tensor(4.9373, device='cuda:0'), tensor(4.9467, device='cuda:0'), tensor(4.9357, device='cuda:0')]
Prediction loss: [tensor(18588668., device='cuda:0'), tensor(18013448., device='cuda:0'), tensor(18171246., device='cuda:0'), tensor(17216396., device='cuda:0'), tensor(17479008., device='cuda:0'), tensor(18209790., device='cuda:0'), tensor(17758546., device='cuda:0'), tensor(18215660., device='cuda:0'), tensor(19658052., device='cuda:0'), tensor(18144988., 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(40914.5391, device='cuda:0'), tensor(40885.3867, device='cuda:0'), tensor(40866.2109, device='cuda:0'), tensor(40849.2422, device='cuda:0'), tensor(40773.4922, device='cuda:0'), tensor(40811.5039, device='cuda:0'), tensor(40983.2969, device='cuda:0'), tensor(40887.5469, device='cuda:0'), tensor(40649.6328, device='cuda:0'), tensor(40667.9688, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=45566), datetime.timedelta(seconds=1, microseconds=33615), datetime.timedelta(seconds=1, microseconds=37599), datetime.timedelta(seconds=1, microseconds=46565), datetime.timedelta(seconds=1, microseconds=56519), datetime.timedelta(seconds=1, microseconds=31625), datetime.timedelta(seconds=1, microseconds=31624), datetime.timedelta(seconds=1, microseconds=58511), datetime.timedelta(seconds=1, microseconds=31625), datetime.timedelta(seconds=1, microseconds=25650)]
Phi time: [datetime.timedelta(microseconds=238986), datetime.timedelta(microseconds=241975), datetime.timedelta(microseconds=235004), datetime.timedelta(microseconds=232016), datetime.timedelta(microseconds=233011), datetime.timedelta(microseconds=234007), datetime.timedelta(microseconds=231022), datetime.timedelta(microseconds=254919), datetime.timedelta(microseconds=253922), datetime.timedelta(microseconds=255915)]
