Precision: [tensor(0.9621, device='cuda:0'), tensor(0.9634, device='cuda:0'), tensor(0.9621, device='cuda:0'), tensor(0.9625, device='cuda:0'), tensor(0.9617, device='cuda:0'), tensor(0.9633, device='cuda:0'), tensor(0.9618, device='cuda:0'), tensor(0.9648, device='cuda:0'), tensor(0.9627, device='cuda:0'), tensor(0.9630, device='cuda:0')]

Output distance: [tensor(101.5077, device='cuda:0'), tensor(97.1212, device='cuda:0'), tensor(99.6410, device='cuda:0'), tensor(99.5770, device='cuda:0'), tensor(102.3404, device='cuda:0'), tensor(96.8032, device='cuda:0'), tensor(102.0700, device='cuda:0'), tensor(94.0134, device='cuda:0'), tensor(99.4215, device='cuda:0'), tensor(99.6572, device='cuda:0')]

Prediction loss: [tensor(392.2100, device='cuda:0'), tensor(381.4651, device='cuda:0'), tensor(374.0802, device='cuda:0'), tensor(391.1285, device='cuda:0'), tensor(396.3525, device='cuda:0'), tensor(390.4313, device='cuda:0'), tensor(392.3589, device='cuda:0'), tensor(381.1290, device='cuda:0'), tensor(392.0654, device='cuda:0'), tensor(384.9816, device='cuda:0')]

Others: [{'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(3609302.7500, device='cuda:0'), tensor(3497750.5000, device='cuda:0'), tensor(3435592.5000, device='cuda:0'), tensor(3588591., device='cuda:0'), tensor(3641148.7500, device='cuda:0'), tensor(3586058.7500, device='cuda:0'), tensor(3607993.2500, device='cuda:0'), tensor(3495975.5000, device='cuda:0'), tensor(3595257.2500, device='cuda:0'), tensor(3538220.7500, device='cuda:0')]

Training loss: 3569047.5

Prediction time: [datetime.timedelta(seconds=1, microseconds=145139), datetime.timedelta(seconds=1, microseconds=149126), datetime.timedelta(seconds=1, microseconds=161075), datetime.timedelta(seconds=1, microseconds=151119), datetime.timedelta(seconds=1, microseconds=150122), datetime.timedelta(seconds=1, microseconds=161089), datetime.timedelta(seconds=1, microseconds=164064), datetime.timedelta(seconds=1, microseconds=185970), datetime.timedelta(seconds=1, microseconds=147134), datetime.timedelta(seconds=1, microseconds=163055)]

Phi time: [datetime.timedelta(seconds=1, microseconds=850656), datetime.timedelta(seconds=1, microseconds=275886), datetime.timedelta(seconds=1, microseconds=288711), datetime.timedelta(seconds=1, microseconds=292412), datetime.timedelta(seconds=1, microseconds=291858), datetime.timedelta(seconds=1, microseconds=318156), datetime.timedelta(seconds=1, microseconds=287084), datetime.timedelta(seconds=1, microseconds=285474), datetime.timedelta(seconds=1, microseconds=293422), datetime.timedelta(seconds=1, microseconds=290402)]

