Precision: [tensor(0.0668, device='cuda:0'), tensor(0.0639, device='cuda:0'), tensor(0.0652, device='cuda:0'), tensor(0.0649, device='cuda:0'), tensor(0.0664, device='cuda:0'), tensor(0.0673, device='cuda:0'), tensor(0.0654, device='cuda:0'), tensor(0.0630, device='cuda:0'), tensor(0.0654, device='cuda:0'), tensor(0.0671, device='cuda:0')]

Output distance: [tensor(23.3576, device='cuda:0'), tensor(23.3863, device='cuda:0'), tensor(23.3736, device='cuda:0'), tensor(23.3767, device='cuda:0'), tensor(23.3609, device='cuda:0'), tensor(23.3522, device='cuda:0'), tensor(23.3715, device='cuda:0'), tensor(23.3954, device='cuda:0'), tensor(23.3715, device='cuda:0'), tensor(23.3543, device='cuda:0')]

Prediction loss: [tensor(114.2049, device='cuda:0'), tensor(113.4834, device='cuda:0'), tensor(113.6990, device='cuda:0'), tensor(114.0685, device='cuda:0'), tensor(114.8847, device='cuda:0'), tensor(113.2563, device='cuda:0'), tensor(114.6027, device='cuda:0'), tensor(114.0857, device='cuda:0'), tensor(114.4685, device='cuda:0'), tensor(115.2593, device='cuda:0')]

Others: [{'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, 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=6, microseconds=835011), datetime.timedelta(seconds=6, microseconds=811113), datetime.timedelta(seconds=6, microseconds=867869), datetime.timedelta(seconds=6, microseconds=843971), datetime.timedelta(seconds=6, microseconds=836007), datetime.timedelta(seconds=6, microseconds=829038), datetime.timedelta(seconds=6, microseconds=791198), datetime.timedelta(seconds=6, microseconds=864946), datetime.timedelta(seconds=6, microseconds=836009), datetime.timedelta(seconds=6, microseconds=857916)]

Phi time: [datetime.timedelta(seconds=5, microseconds=2225), datetime.timedelta(seconds=5, microseconds=75063), datetime.timedelta(seconds=5, microseconds=64416), datetime.timedelta(seconds=5, microseconds=70338), datetime.timedelta(seconds=5, microseconds=69033), datetime.timedelta(seconds=5, microseconds=112325), datetime.timedelta(seconds=5, microseconds=69223), datetime.timedelta(seconds=5, microseconds=78132), datetime.timedelta(seconds=5, microseconds=69147), datetime.timedelta(seconds=5, microseconds=79518)]

