Precision: [tensor(0.5204, device='cuda:0'), tensor(0.5131, device='cuda:0'), tensor(0.5131, device='cuda:0'), tensor(0.5263, device='cuda:0'), tensor(0.5237, device='cuda:0'), tensor(0.5230, device='cuda:0'), tensor(0.5136, device='cuda:0'), tensor(0.5215, device='cuda:0'), tensor(0.5316, device='cuda:0'), tensor(0.5145, device='cuda:0')]

Output distance: [tensor(18.9846, device='cuda:0'), tensor(18.9991, device='cuda:0'), tensor(18.9991, device='cuda:0'), tensor(18.9728, device='cuda:0'), tensor(18.9779, device='cuda:0'), tensor(18.9794, device='cuda:0'), tensor(18.9982, device='cuda:0'), tensor(18.9825, device='cuda:0'), tensor(18.9622, device='cuda:0'), tensor(18.9964, device='cuda:0')]

Prediction loss: [tensor(108.8119, device='cuda:0'), tensor(108.3778, device='cuda:0'), tensor(109.8383, device='cuda:0'), tensor(108.6087, device='cuda:0'), tensor(109.5964, device='cuda:0'), tensor(109.4997, device='cuda:0'), tensor(108.6495, device='cuda:0'), tensor(108.4008, device='cuda:0'), tensor(108.5222, device='cuda:0'), tensor(108.1069, device='cuda:0')]

Others: [{'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}]

Compressed training loss: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=5, microseconds=954102), datetime.timedelta(seconds=5, microseconds=989464), datetime.timedelta(seconds=5, microseconds=982039), datetime.timedelta(seconds=5, microseconds=981105), datetime.timedelta(seconds=6, microseconds=9360), datetime.timedelta(seconds=5, microseconds=976239), datetime.timedelta(seconds=5, microseconds=996294), datetime.timedelta(seconds=5, microseconds=994253), datetime.timedelta(seconds=6, microseconds=4747), datetime.timedelta(seconds=5, microseconds=986331)]

Phi time: [datetime.timedelta(seconds=4, microseconds=576112), datetime.timedelta(seconds=4, microseconds=679561), datetime.timedelta(seconds=4, microseconds=710952), datetime.timedelta(seconds=4, microseconds=631013), datetime.timedelta(seconds=4, microseconds=639893), datetime.timedelta(seconds=4, microseconds=625787), datetime.timedelta(seconds=4, microseconds=672337), datetime.timedelta(seconds=4, microseconds=635637), datetime.timedelta(seconds=4, microseconds=634952), datetime.timedelta(seconds=4, microseconds=667514)]

