Precision: [tensor(0.5056, device='cuda:0'), tensor(0.5345, device='cuda:0'), tensor(0.5284, device='cuda:0'), tensor(0.5196, device='cuda:0'), tensor(0.5218, device='cuda:0'), tensor(0.5358, device='cuda:0'), tensor(0.5092, device='cuda:0'), tensor(0.5319, device='cuda:0'), tensor(0.5228, device='cuda:0'), tensor(0.5020, device='cuda:0')]

Output distance: [tensor(19.0142, device='cuda:0'), tensor(18.9565, device='cuda:0'), tensor(18.9686, device='cuda:0'), tensor(18.9861, device='cuda:0'), tensor(18.9819, device='cuda:0'), tensor(18.9538, device='cuda:0'), tensor(19.0070, device='cuda:0'), tensor(18.9616, device='cuda:0'), tensor(18.9797, device='cuda:0'), tensor(19.0215, device='cuda:0')]

Prediction loss: [tensor(108.1270, device='cuda:0'), tensor(108.9998, device='cuda:0'), tensor(108.6677, device='cuda:0'), tensor(109.0935, device='cuda:0'), tensor(108.7357, device='cuda:0'), tensor(108.8159, device='cuda:0'), tensor(109.0150, device='cuda:0'), tensor(108.9742, device='cuda:0'), tensor(108.5124, device='cuda:0'), tensor(109.5931, device='cuda:0')]

Others: [{'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, '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=3, microseconds=46107), datetime.timedelta(seconds=3, microseconds=7246), datetime.timedelta(seconds=3, microseconds=37118), datetime.timedelta(seconds=3, microseconds=11228), datetime.timedelta(seconds=3, microseconds=18196), datetime.timedelta(seconds=3, microseconds=33133), datetime.timedelta(seconds=3, microseconds=4260), datetime.timedelta(seconds=3, microseconds=23174), datetime.timedelta(seconds=3, microseconds=29153), datetime.timedelta(seconds=3, microseconds=23177)]

Phi time: [datetime.timedelta(seconds=5, microseconds=390803), datetime.timedelta(seconds=5, microseconds=389318), datetime.timedelta(seconds=5, microseconds=405999), datetime.timedelta(seconds=5, microseconds=383822), datetime.timedelta(seconds=5, microseconds=406750), datetime.timedelta(seconds=5, microseconds=380063), datetime.timedelta(seconds=5, microseconds=401316), datetime.timedelta(seconds=5, microseconds=418874), datetime.timedelta(seconds=5, microseconds=386843), datetime.timedelta(seconds=5, microseconds=409650)]

