Precision: [tensor(0.9937, device='cuda:0'), tensor(0.9313, device='cuda:0'), tensor(0.9915, device='cuda:0'), tensor(0.9985, device='cuda:0'), tensor(0.9973, device='cuda:0'), tensor(0.9555, device='cuda:0'), tensor(0.9932, device='cuda:0'), tensor(0.6268, device='cuda:0'), tensor(0.9978, device='cuda:0'), tensor(0.9803, device='cuda:0')]

Output distance: [tensor(213046.1562, device='cuda:0'), tensor(2614434., device='cuda:0'), tensor(203808.6562, device='cuda:0'), tensor(146795.7500, device='cuda:0'), tensor(153525.0469, device='cuda:0'), tensor(12665782., device='cuda:0'), tensor(170051.8750, device='cuda:0'), tensor(1.2040e+09, device='cuda:0'), tensor(153131.4219, device='cuda:0'), tensor(235403.0312, device='cuda:0')]

Prediction loss: [tensor(225977.6250, device='cuda:0'), tensor(3297390.5000, device='cuda:0'), tensor(204772.4062, device='cuda:0'), tensor(137486.2031, device='cuda:0'), tensor(139925.7656, device='cuda:0'), tensor(14276018., device='cuda:0'), tensor(168778.3594, device='cuda:0'), tensor(1.2516e+09, device='cuda:0'), tensor(131731.8281, device='cuda:0'), tensor(228701.9219, device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(1.9114e+08, device='cuda:0'), tensor(1.8673e+08, device='cuda:0'), tensor(1.9072e+08, device='cuda:0'), tensor(1.8944e+08, device='cuda:0'), tensor(1.9346e+08, device='cuda:0'), tensor(1.9084e+08, device='cuda:0'), tensor(1.9218e+08, device='cuda:0'), tensor(1.9259e+08, device='cuda:0'), tensor(1.8101e+08, device='cuda:0'), tensor(1.8874e+08, device='cuda:0')]

Training loss: 192045808.0

Prediction time: [datetime.timedelta(seconds=1, microseconds=240737), datetime.timedelta(seconds=1, microseconds=290572), datetime.timedelta(seconds=1, microseconds=264636), datetime.timedelta(seconds=1, microseconds=267624), datetime.timedelta(seconds=1, microseconds=272593), datetime.timedelta(seconds=1, microseconds=268620), datetime.timedelta(seconds=1, microseconds=268621), datetime.timedelta(seconds=1, microseconds=273599), datetime.timedelta(seconds=1, microseconds=271609), datetime.timedelta(seconds=1, microseconds=263642)]

Phi time: [datetime.timedelta(seconds=1, microseconds=271429), datetime.timedelta(microseconds=731328), datetime.timedelta(microseconds=658750), datetime.timedelta(microseconds=661193), datetime.timedelta(microseconds=659044), datetime.timedelta(microseconds=656606), datetime.timedelta(microseconds=657279), datetime.timedelta(microseconds=656818), datetime.timedelta(microseconds=655597), datetime.timedelta(microseconds=654987)]

