Precision: [tensor(0.3901, device='cuda:0'), tensor(0.3727, device='cuda:0'), tensor(0.3584, device='cuda:0'), tensor(0.3525, device='cuda:0'), tensor(0.4125, device='cuda:0'), tensor(0.3891, device='cuda:0'), tensor(0.3904, device='cuda:0'), tensor(0.3809, device='cuda:0'), tensor(0.3688, device='cuda:0'), tensor(0.3638, device='cuda:0')]

Output distance: [tensor(19.2452, device='cuda:0'), tensor(19.2799, device='cuda:0'), tensor(19.3086, device='cuda:0'), tensor(19.3204, device='cuda:0'), tensor(19.2004, device='cuda:0'), tensor(19.2473, device='cuda:0'), tensor(19.2446, device='cuda:0'), tensor(19.2636, device='cuda:0'), tensor(19.2878, device='cuda:0'), tensor(19.2978, device='cuda:0')]

Prediction loss: [tensor(108.4842, device='cuda:0'), tensor(107.5275, device='cuda:0'), tensor(108.0965, device='cuda:0'), tensor(107.5528, device='cuda:0'), tensor(109.0782, device='cuda:0'), tensor(108.1299, device='cuda:0'), tensor(107.3974, device='cuda:0'), tensor(108.0421, device='cuda:0'), tensor(107.2847, device='cuda:0'), tensor(108.1722, device='cuda:0')]

Others: [{'iter_num': 13, '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': 13, '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': 13, '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=2, microseconds=622876), datetime.timedelta(seconds=2, microseconds=519315), datetime.timedelta(seconds=2, microseconds=539228), datetime.timedelta(seconds=2, microseconds=538232), datetime.timedelta(seconds=2, microseconds=521305), datetime.timedelta(seconds=2, microseconds=875813), datetime.timedelta(seconds=2, microseconds=528276), datetime.timedelta(seconds=2, microseconds=523297), datetime.timedelta(seconds=2, microseconds=596983), datetime.timedelta(seconds=2, microseconds=699552)]

Phi time: [datetime.timedelta(seconds=4, microseconds=468549), datetime.timedelta(seconds=4, microseconds=464514), datetime.timedelta(seconds=4, microseconds=427449), datetime.timedelta(seconds=4, microseconds=466994), datetime.timedelta(seconds=4, microseconds=428350), datetime.timedelta(seconds=4, microseconds=416901), datetime.timedelta(seconds=4, microseconds=869684), datetime.timedelta(seconds=4, microseconds=446467), datetime.timedelta(seconds=4, microseconds=409454), datetime.timedelta(seconds=4, microseconds=470005)]

