Precision: [tensor(0.6860, device='cuda:0'), tensor(0.6905, device='cuda:0'), tensor(0.6857, device='cuda:0'), tensor(0.6876, device='cuda:0'), tensor(0.6829, device='cuda:0'), tensor(0.6873, device='cuda:0'), tensor(0.6889, device='cuda:0'), tensor(0.6902, device='cuda:0'), tensor(0.6913, device='cuda:0'), tensor(0.6878, device='cuda:0')]
Output distance: [tensor(384223.0938, device='cuda:0'), tensor(315112.0312, device='cuda:0'), tensor(611200., device='cuda:0'), tensor(294679.4062, device='cuda:0'), tensor(418153.2500, device='cuda:0'), tensor(294922.3750, device='cuda:0'), tensor(419602.3438, device='cuda:0'), tensor(624453.1250, device='cuda:0'), tensor(615551.3750, device='cuda:0'), tensor(593022.4375, device='cuda:0')]
Prediction loss: [tensor(18717834., device='cuda:0'), tensor(19447188., device='cuda:0'), tensor(19077638., device='cuda:0'), tensor(18730050., device='cuda:0'), tensor(17882666., device='cuda:0'), tensor(17461716., device='cuda:0'), tensor(20356284., device='cuda:0'), tensor(16679615., device='cuda:0'), tensor(18114128., device='cuda:0'), tensor(18040176., device='cuda:0')]
Others: [{'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40786.2656, device='cuda:0'), tensor(40896.9531, device='cuda:0'), tensor(40903.6797, device='cuda:0'), tensor(40974.5586, device='cuda:0'), tensor(40798.3242, device='cuda:0'), tensor(40923.9531, device='cuda:0'), tensor(40913.6836, device='cuda:0'), tensor(40975.6523, device='cuda:0'), tensor(40873.7773, device='cuda:0'), tensor(40711.9609, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(microseconds=86632), datetime.timedelta(microseconds=88622), datetime.timedelta(microseconds=90613), datetime.timedelta(microseconds=87626), datetime.timedelta(microseconds=95592), datetime.timedelta(microseconds=84638), datetime.timedelta(microseconds=94597), datetime.timedelta(microseconds=83644), datetime.timedelta(microseconds=90614), datetime.timedelta(microseconds=88622)]
Phi time: [datetime.timedelta(microseconds=234009), datetime.timedelta(microseconds=244963), datetime.timedelta(microseconds=243968), datetime.timedelta(microseconds=246954), datetime.timedelta(microseconds=242972), datetime.timedelta(microseconds=237994), datetime.timedelta(microseconds=242972), datetime.timedelta(microseconds=236996), datetime.timedelta(microseconds=246955), datetime.timedelta(microseconds=244964)]
