Precision: [tensor(0.6863, device='cuda:0'), tensor(0.6815, device='cuda:0'), tensor(0.6936, device='cuda:0'), tensor(0.6826, device='cuda:0'), tensor(0.6813, device='cuda:0'), tensor(0.6831, device='cuda:0'), tensor(0.6839, device='cuda:0'), tensor(0.6881, device='cuda:0'), tensor(0.6829, device='cuda:0'), tensor(0.6855, device='cuda:0')]
Output distance: [tensor(327903.2812, device='cuda:0'), tensor(384038.0938, device='cuda:0'), tensor(409177.2500, device='cuda:0'), tensor(379411., device='cuda:0'), tensor(236273.4844, device='cuda:0'), tensor(364313.0938, device='cuda:0'), tensor(294029.9688, device='cuda:0'), tensor(334568.8125, device='cuda:0'), tensor(458874.0938, device='cuda:0'), tensor(440748.2812, device='cuda:0')]
Prediction loss: [tensor(18207642., device='cuda:0'), tensor(17657054., device='cuda:0'), tensor(17454176., device='cuda:0'), tensor(18993054., device='cuda:0'), tensor(18279078., device='cuda:0'), tensor(18340318., device='cuda:0'), tensor(18536052., device='cuda:0'), tensor(19175516., device='cuda:0'), tensor(19398348., device='cuda:0'), tensor(19817678., device='cuda:0')]
Others: [{'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': 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')}, {'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': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40776.3555, device='cuda:0'), tensor(40907.7891, device='cuda:0'), tensor(40939.1719, device='cuda:0'), tensor(40905.1562, device='cuda:0'), tensor(40877.5156, device='cuda:0'), tensor(40961.3047, device='cuda:0'), tensor(40843.6211, device='cuda:0'), tensor(40862.7461, device='cuda:0'), tensor(40833.8359, device='cuda:0'), tensor(40787.1797, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(microseconds=90612), datetime.timedelta(microseconds=104558), datetime.timedelta(microseconds=102564), datetime.timedelta(microseconds=84639), datetime.timedelta(microseconds=84638), datetime.timedelta(microseconds=85634), datetime.timedelta(microseconds=97583), datetime.timedelta(microseconds=86631), datetime.timedelta(microseconds=90614), datetime.timedelta(microseconds=96589)]
Phi time: [datetime.timedelta(microseconds=528758), datetime.timedelta(microseconds=270852), datetime.timedelta(microseconds=262887), datetime.timedelta(microseconds=236998), datetime.timedelta(microseconds=239983), datetime.timedelta(microseconds=250938), datetime.timedelta(microseconds=245960), datetime.timedelta(microseconds=241976), datetime.timedelta(microseconds=238989), datetime.timedelta(microseconds=257907)]
