Precision: [tensor(0.9995, device='cuda:0'), tensor(0.9992, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9997, device='cuda:0')]

Output distance: [tensor(23746.0664, device='cuda:0'), tensor(23748.5352, device='cuda:0'), tensor(23726.6719, device='cuda:0'), tensor(23727.1523, device='cuda:0'), tensor(23739.3418, device='cuda:0'), tensor(23715.8770, device='cuda:0'), tensor(23728.7383, device='cuda:0'), tensor(23794.3867, device='cuda:0'), tensor(23748.0352, device='cuda:0'), tensor(23788.3516, device='cuda:0')]

Prediction loss: [tensor(23358.4434, device='cuda:0'), tensor(23826.7461, device='cuda:0'), tensor(23595.4023, device='cuda:0'), tensor(23354.4316, device='cuda:0'), tensor(23452.1738, device='cuda:0'), tensor(24314.7168, device='cuda:0'), tensor(23615.0098, device='cuda:0'), tensor(23869.5840, device='cuda:0'), tensor(24150.7520, device='cuda:0'), tensor(23152.5254, device='cuda:0')]

Others: [{'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(8791420., device='cuda:0'), tensor(8777704., device='cuda:0'), tensor(8810767., device='cuda:0'), tensor(8751730., device='cuda:0'), tensor(8711016., device='cuda:0'), tensor(8933048., device='cuda:0'), tensor(8758879., device='cuda:0'), tensor(8775039., device='cuda:0'), tensor(8913404., device='cuda:0'), tensor(8770377., device='cuda:0')]

Training loss: 8832518.0

Prediction time: [datetime.timedelta(microseconds=936031), datetime.timedelta(microseconds=968891), datetime.timedelta(microseconds=954950), datetime.timedelta(microseconds=965904), datetime.timedelta(microseconds=996772), datetime.timedelta(microseconds=956942), datetime.timedelta(microseconds=946983), datetime.timedelta(microseconds=953954), datetime.timedelta(microseconds=962867), datetime.timedelta(microseconds=958932)]

Phi time: [datetime.timedelta(seconds=1, microseconds=878058), datetime.timedelta(seconds=1, microseconds=277870), datetime.timedelta(seconds=1, microseconds=292312), datetime.timedelta(seconds=1, microseconds=283330), datetime.timedelta(seconds=1, microseconds=281500), datetime.timedelta(seconds=1, microseconds=297653), datetime.timedelta(seconds=1, microseconds=319391), datetime.timedelta(seconds=1, microseconds=283238), datetime.timedelta(seconds=1, microseconds=283779), datetime.timedelta(seconds=1, microseconds=292227)]

