Precision: [tensor(0.6273, device='cuda:0'), tensor(0.6203, device='cuda:0'), tensor(0.6209, device='cuda:0'), tensor(0.6215, device='cuda:0'), tensor(0.6251, device='cuda:0'), tensor(0.6247, device='cuda:0'), tensor(0.6265, device='cuda:0'), tensor(0.6196, device='cuda:0'), tensor(0.6244, device='cuda:0'), tensor(0.6240, device='cuda:0')]
Output distance: [tensor(4.9614, device='cuda:0'), tensor(4.9732, device='cuda:0'), tensor(4.9793, device='cuda:0'), tensor(4.9716, device='cuda:0'), tensor(4.9640, device='cuda:0'), tensor(4.9640, device='cuda:0'), tensor(4.9590, device='cuda:0'), tensor(4.9842, device='cuda:0'), tensor(4.9656, device='cuda:0'), tensor(4.9640, device='cuda:0')]
Prediction loss: [tensor(19780364., device='cuda:0'), tensor(17574296., device='cuda:0'), tensor(19055648., device='cuda:0'), tensor(18474318., device='cuda:0'), tensor(22456278., device='cuda:0'), tensor(17892194., device='cuda:0'), tensor(19087800., device='cuda:0'), tensor(18776462., device='cuda:0'), tensor(18724656., device='cuda:0'), tensor(18904452., device='cuda:0')]
Others: [{'iter_num': 5, 'num_positive': tensor(5157, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5268, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5151, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5242, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5207, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5225, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5224, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5126, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5215, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5253, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40785.1875, device='cuda:0'), tensor(40903.0703, device='cuda:0'), tensor(40818.7109, device='cuda:0'), tensor(40951.5078, device='cuda:0'), tensor(39831.6602, device='cuda:0'), tensor(40962.7852, device='cuda:0'), tensor(40816.7969, device='cuda:0'), tensor(40773.1328, device='cuda:0'), tensor(40932.4531, device='cuda:0'), tensor(40875.6328, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=5, microseconds=113315), datetime.timedelta(seconds=5, microseconds=222849), datetime.timedelta(seconds=5, microseconds=105349), datetime.timedelta(seconds=5, microseconds=121279), datetime.timedelta(seconds=5, microseconds=217871), datetime.timedelta(seconds=5, microseconds=125263), datetime.timedelta(seconds=5, microseconds=166090), datetime.timedelta(seconds=5, microseconds=229820), datetime.timedelta(seconds=5, microseconds=64519), datetime.timedelta(seconds=5, microseconds=120284)]
Phi time: [datetime.timedelta(microseconds=235998), datetime.timedelta(microseconds=313670), datetime.timedelta(microseconds=360471), datetime.timedelta(microseconds=349519), datetime.timedelta(microseconds=382378), datetime.timedelta(microseconds=335578), datetime.timedelta(microseconds=387358), datetime.timedelta(microseconds=366445), datetime.timedelta(microseconds=317655), datetime.timedelta(microseconds=409264)]
