Precision: [tensor(0.5065, device='cuda:0'), tensor(0.5115, device='cuda:0'), tensor(0.5192, device='cuda:0'), tensor(0.5014, device='cuda:0'), tensor(0.5066, device='cuda:0'), tensor(0.5062, device='cuda:0'), tensor(0.5042, device='cuda:0'), tensor(0.5054, device='cuda:0'), tensor(0.5021, device='cuda:0'), tensor(0.5056, device='cuda:0')]

Output distance: [tensor(5.2670, device='cuda:0'), tensor(5.2371, device='cuda:0'), tensor(5.1909, device='cuda:0'), tensor(5.2975, device='cuda:0'), tensor(5.2665, device='cuda:0'), tensor(5.2691, device='cuda:0'), tensor(5.2812, device='cuda:0'), tensor(5.2738, device='cuda:0'), tensor(5.2933, device='cuda:0'), tensor(5.2728, device='cuda:0')]

Prediction loss: [tensor(18214260., device='cuda:0'), tensor(17695670., device='cuda:0'), tensor(18793896., device='cuda:0'), tensor(16565990., device='cuda:0'), tensor(17999648., device='cuda:0'), tensor(18728350., device='cuda:0'), tensor(17080112., device='cuda:0'), tensor(19851612., device='cuda:0'), tensor(18721308., device='cuda:0'), tensor(17554768., device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40762.3672, device='cuda:0'), tensor(40672.8633, device='cuda:0'), tensor(40667.5430, device='cuda:0'), tensor(40951.9180, device='cuda:0'), tensor(40673.1836, device='cuda:0'), tensor(41003.9258, device='cuda:0'), tensor(40732.3516, device='cuda:0'), tensor(40963.3750, device='cuda:0'), tensor(40889.7148, device='cuda:0'), tensor(40692.9023, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=206249), datetime.timedelta(seconds=1, microseconds=168473), datetime.timedelta(microseconds=993447), datetime.timedelta(seconds=1, microseconds=159943), datetime.timedelta(seconds=1, microseconds=159564), datetime.timedelta(seconds=1, microseconds=165751), datetime.timedelta(seconds=1, microseconds=158143), datetime.timedelta(seconds=1, microseconds=155312), datetime.timedelta(seconds=1, microseconds=134450), datetime.timedelta(seconds=1, microseconds=178001)]

Phi time: [datetime.timedelta(microseconds=182185), datetime.timedelta(microseconds=189467), datetime.timedelta(microseconds=186563), datetime.timedelta(microseconds=196078), datetime.timedelta(microseconds=187449), datetime.timedelta(microseconds=199894), datetime.timedelta(microseconds=202502), datetime.timedelta(microseconds=186530), datetime.timedelta(microseconds=187678), datetime.timedelta(microseconds=183440)]

