Precision: [tensor(0.4220, device='cuda:0'), tensor(0.4568, device='cuda:0'), tensor(0.4386, device='cuda:0'), tensor(0.4379, device='cuda:0'), tensor(0.4243, device='cuda:0'), tensor(0.4479, device='cuda:0'), tensor(0.4551, device='cuda:0'), tensor(0.4500, device='cuda:0'), tensor(0.4086, device='cuda:0'), tensor(0.4468, device='cuda:0')]

Output distance: [tensor(19.1814, device='cuda:0'), tensor(19.1119, device='cuda:0'), tensor(19.1481, device='cuda:0'), tensor(19.1496, device='cuda:0'), tensor(19.1768, device='cuda:0'), tensor(19.1297, device='cuda:0'), tensor(19.1152, device='cuda:0'), tensor(19.1255, device='cuda:0'), tensor(19.2083, device='cuda:0'), tensor(19.1318, device='cuda:0')]

Prediction loss: [tensor(108.4000, device='cuda:0'), tensor(108.0787, device='cuda:0'), tensor(107.1510, device='cuda:0'), tensor(106.7495, device='cuda:0'), tensor(107.7272, device='cuda:0'), tensor(108.0942, device='cuda:0'), tensor(108.1304, device='cuda:0'), tensor(108.8490, device='cuda:0'), tensor(107.1921, device='cuda:0'), tensor(108.9964, 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': 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')}, {'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=327192), datetime.timedelta(seconds=2, microseconds=318231), datetime.timedelta(seconds=2, microseconds=330180), datetime.timedelta(seconds=2, microseconds=323210), datetime.timedelta(seconds=2, microseconds=308273), datetime.timedelta(seconds=2, microseconds=344119), datetime.timedelta(seconds=2, microseconds=378974), datetime.timedelta(seconds=2, microseconds=312197), datetime.timedelta(seconds=2, microseconds=303345), datetime.timedelta(seconds=2, microseconds=326197)]

Phi time: [datetime.timedelta(seconds=4, microseconds=598024), datetime.timedelta(seconds=4, microseconds=583875), datetime.timedelta(seconds=4, microseconds=549629), datetime.timedelta(seconds=4, microseconds=569730), datetime.timedelta(seconds=4, microseconds=569099), datetime.timedelta(seconds=4, microseconds=564277), datetime.timedelta(seconds=4, microseconds=566601), datetime.timedelta(seconds=4, microseconds=554755), datetime.timedelta(seconds=4, microseconds=579292), datetime.timedelta(seconds=4, microseconds=583778)]

