Precision: [tensor(0.5206, device='cuda:0'), tensor(0.5065, device='cuda:0'), tensor(0.5337, device='cuda:0'), tensor(0.5268, device='cuda:0'), tensor(0.5372, device='cuda:0'), tensor(0.5304, device='cuda:0'), tensor(0.5204, device='cuda:0'), tensor(0.5358, device='cuda:0'), tensor(0.5231, device='cuda:0'), tensor(0.5206, device='cuda:0')]

Output distance: [tensor(18.9843, device='cuda:0'), tensor(19.0124, device='cuda:0'), tensor(18.9580, device='cuda:0'), tensor(18.9719, device='cuda:0'), tensor(18.9510, device='cuda:0'), tensor(18.9646, device='cuda:0'), tensor(18.9846, device='cuda:0'), tensor(18.9538, device='cuda:0'), tensor(18.9791, device='cuda:0'), tensor(18.9843, device='cuda:0')]

Prediction loss: [tensor(108.3161, device='cuda:0'), tensor(108.7022, device='cuda:0'), tensor(108.6390, device='cuda:0'), tensor(109.0571, device='cuda:0'), tensor(108.5728, device='cuda:0'), tensor(108.1079, device='cuda:0'), tensor(108.5641, device='cuda:0'), tensor(109.3603, device='cuda:0'), tensor(108.6428, device='cuda:0'), tensor(108.8777, device='cuda:0')]

Others: [{'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, '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=2, microseconds=249987), datetime.timedelta(seconds=2, microseconds=282863), datetime.timedelta(seconds=2, microseconds=279340), datetime.timedelta(seconds=2, microseconds=232276), datetime.timedelta(seconds=2, microseconds=368339), datetime.timedelta(seconds=2, microseconds=285854), datetime.timedelta(seconds=2, microseconds=327500), datetime.timedelta(seconds=2, microseconds=276447), datetime.timedelta(seconds=2, microseconds=288992), datetime.timedelta(seconds=2, microseconds=261369)]

Phi time: [datetime.timedelta(seconds=4, microseconds=631887), datetime.timedelta(seconds=4, microseconds=655746), datetime.timedelta(seconds=4, microseconds=634017), datetime.timedelta(seconds=4, microseconds=637041), datetime.timedelta(seconds=4, microseconds=658923), datetime.timedelta(seconds=4, microseconds=614514), datetime.timedelta(seconds=4, microseconds=636133), datetime.timedelta(seconds=4, microseconds=610874), datetime.timedelta(seconds=4, microseconds=618656), datetime.timedelta(seconds=4, microseconds=599805)]

