Precision: [tensor(0.3395, device='cuda:0'), tensor(0.3355, device='cuda:0'), tensor(0.3371, device='cuda:0'), tensor(0.3337, device='cuda:0'), tensor(0.3379, device='cuda:0'), tensor(0.3334, device='cuda:0'), tensor(0.3358, device='cuda:0'), tensor(0.3397, device='cuda:0'), tensor(0.3337, device='cuda:0'), tensor(0.3358, device='cuda:0')]
Output distance: [tensor(5.6272, device='cuda:0'), tensor(5.6351, device='cuda:0'), tensor(5.6319, device='cuda:0'), tensor(5.6388, device='cuda:0'), tensor(5.6304, device='cuda:0'), tensor(5.6393, device='cuda:0'), tensor(5.6346, device='cuda:0'), tensor(5.6267, device='cuda:0'), tensor(5.6388, device='cuda:0'), tensor(5.6346, device='cuda:0')]
Prediction loss: [tensor(36.2568, device='cuda:0'), tensor(37.6134, device='cuda:0'), tensor(36.4763, device='cuda:0'), tensor(36.5217, device='cuda:0'), tensor(37.9846, device='cuda:0'), tensor(37.7310, device='cuda:0'), tensor(35.9053, device='cuda:0'), tensor(37.2525, device='cuda:0'), tensor(37.9967, device='cuda:0'), tensor(35.1803, 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': 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': 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': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(48868.8633, device='cuda:0'), tensor(48997.7695, device='cuda:0'), tensor(48958.7656, device='cuda:0'), tensor(48783.4414, device='cuda:0'), tensor(48975.2227, device='cuda:0'), tensor(48917.1680, device='cuda:0'), tensor(48866.7344, device='cuda:0'), tensor(48914., device='cuda:0'), tensor(48862.7070, device='cuda:0'), tensor(48776.6172, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=63487), datetime.timedelta(seconds=1, microseconds=55516), datetime.timedelta(seconds=1, microseconds=61547), datetime.timedelta(seconds=1, microseconds=63439), datetime.timedelta(seconds=1, microseconds=37545), datetime.timedelta(seconds=1, microseconds=58581), datetime.timedelta(seconds=1, microseconds=65478), datetime.timedelta(seconds=1, microseconds=62490), datetime.timedelta(seconds=1, microseconds=50596), datetime.timedelta(seconds=1, microseconds=70451)]
Phi time: [datetime.timedelta(seconds=5, microseconds=687858), datetime.timedelta(seconds=5, microseconds=651022), datetime.timedelta(seconds=5, microseconds=664912), datetime.timedelta(seconds=5, microseconds=647040), datetime.timedelta(seconds=5, microseconds=661978), datetime.timedelta(seconds=5, microseconds=672882), datetime.timedelta(seconds=5, microseconds=662976), datetime.timedelta(seconds=5, microseconds=660968), datetime.timedelta(seconds=5, microseconds=676913), datetime.timedelta(seconds=5, microseconds=655011)]
