Precision: [tensor(0.9995, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9997, device='cuda:0')]
Output distance: [tensor(69337.0781, device='cuda:0'), tensor(69127.0156, device='cuda:0'), tensor(69038.5234, device='cuda:0'), tensor(69225.3281, device='cuda:0'), tensor(69103.3672, device='cuda:0'), tensor(69187.1172, device='cuda:0'), tensor(68991.9375, device='cuda:0'), tensor(69035.6250, device='cuda:0'), tensor(69145.8203, device='cuda:0'), tensor(69083.6250, device='cuda:0')]
Prediction loss: [tensor(71276.0703, device='cuda:0'), tensor(73083.6562, device='cuda:0'), tensor(72133.4141, device='cuda:0'), tensor(70463.4688, device='cuda:0'), tensor(72993.6875, device='cuda:0'), tensor(70529.9219, device='cuda:0'), tensor(72245.8203, device='cuda:0'), tensor(70842.6484, device='cuda:0'), tensor(71544.6328, device='cuda:0'), tensor(72518.2812, device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]
Compressed training loss: [tensor(64959404., device='cuda:0'), tensor(65108692., device='cuda:0'), tensor(64704528., device='cuda:0'), tensor(64567904., device='cuda:0'), tensor(65037492., device='cuda:0'), tensor(63452988., device='cuda:0'), tensor(64916620., device='cuda:0'), tensor(63540124., device='cuda:0'), tensor(64009492., device='cuda:0'), tensor(65714620., device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=579542), datetime.timedelta(microseconds=483948), datetime.timedelta(microseconds=533737), datetime.timedelta(microseconds=613448), datetime.timedelta(microseconds=503880), datetime.timedelta(microseconds=629284), datetime.timedelta(microseconds=504859), datetime.timedelta(microseconds=618378), datetime.timedelta(microseconds=603441), datetime.timedelta(microseconds=479965)]
Phi time: [datetime.timedelta(microseconds=871379), datetime.timedelta(microseconds=858190), datetime.timedelta(microseconds=883383), datetime.timedelta(microseconds=887456), datetime.timedelta(microseconds=861757), datetime.timedelta(microseconds=885980), datetime.timedelta(microseconds=877741), datetime.timedelta(microseconds=892818), datetime.timedelta(microseconds=887441), datetime.timedelta(microseconds=859419)]
