Precision: [tensor(0.0032, device='cuda:0'), tensor(0.0037, device='cuda:0'), tensor(0.0027, device='cuda:0'), tensor(0.0031, device='cuda:0'), tensor(0.0030, device='cuda:0'), tensor(0.0041, device='cuda:0'), tensor(0.0030, device='cuda:0'), tensor(0.0031, device='cuda:0'), tensor(0.0163, device='cuda:0'), tensor(0.0029, device='cuda:0')]

Output distance: [tensor(23.9933, device='cuda:0'), tensor(23.9879, device='cuda:0'), tensor(23.9985, device='cuda:0'), tensor(23.9946, device='cuda:0'), tensor(23.9955, device='cuda:0'), tensor(23.9849, device='cuda:0'), tensor(23.9952, device='cuda:0'), tensor(23.9949, device='cuda:0'), tensor(23.8622, device='cuda:0'), tensor(23.9964, device='cuda:0')]

Prediction loss: [tensor(119.8860, device='cuda:0'), tensor(121.1923, device='cuda:0'), tensor(120.3838, device='cuda:0'), tensor(121.3489, device='cuda:0'), tensor(122.3975, device='cuda:0'), tensor(122.3921, device='cuda:0'), tensor(122.3581, device='cuda:0'), tensor(121.3931, device='cuda:0'), tensor(112.6287, device='cuda:0'), tensor(122.0708, 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': 53, '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=341106), datetime.timedelta(seconds=6, microseconds=316212), datetime.timedelta(seconds=6, microseconds=330153), datetime.timedelta(seconds=6, microseconds=309242), datetime.timedelta(seconds=6, microseconds=330153), datetime.timedelta(seconds=6, microseconds=298289), datetime.timedelta(seconds=6, microseconds=271402), datetime.timedelta(seconds=6, microseconds=262439), datetime.timedelta(seconds=5, microseconds=852180), datetime.timedelta(seconds=6, microseconds=312229)]

Phi time: [datetime.timedelta(seconds=4, microseconds=795660), datetime.timedelta(seconds=4, microseconds=822547), datetime.timedelta(seconds=4, microseconds=834497), datetime.timedelta(seconds=4, microseconds=865364), datetime.timedelta(seconds=4, microseconds=854411), datetime.timedelta(seconds=4, microseconds=879970), datetime.timedelta(seconds=4, microseconds=874877), datetime.timedelta(seconds=4, microseconds=876319), datetime.timedelta(seconds=4, microseconds=858735), datetime.timedelta(seconds=4, microseconds=835659)]

