Precision: [tensor(0.7323, device='cuda:0'), tensor(0.7341, device='cuda:0'), tensor(0.7320, device='cuda:0'), tensor(0.7295, device='cuda:0'), tensor(0.7338, device='cuda:0'), tensor(0.7332, device='cuda:0'), tensor(0.7334, device='cuda:0'), tensor(0.7394, device='cuda:0'), tensor(0.7366, device='cuda:0'), tensor(0.7340, device='cuda:0')]
Output distance: [tensor(5.0144, device='cuda:0'), tensor(5.0150, device='cuda:0'), tensor(5.0134, device='cuda:0'), tensor(5.0202, device='cuda:0'), tensor(5.0123, device='cuda:0'), tensor(5.0134, device='cuda:0'), tensor(5.0137, device='cuda:0'), tensor(5.0084, device='cuda:0'), tensor(5.0058, device='cuda:0'), tensor(5.0118, device='cuda:0')]
Prediction loss: [tensor(18898954., device='cuda:0'), tensor(18250314., device='cuda:0'), tensor(18780064., device='cuda:0'), tensor(18152366., device='cuda:0'), tensor(20057754., device='cuda:0'), tensor(18675400., device='cuda:0'), tensor(18886840., device='cuda:0'), tensor(17707688., device='cuda:0'), tensor(17203896., device='cuda:0'), tensor(18124476., device='cuda:0')]
Others: [{'iter_num': 5, 'num_positive': tensor(2391, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2369, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2403, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2373, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2393, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2391, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2386, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2368, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2418, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2395, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40833.7266, device='cuda:0'), tensor(40814.6055, device='cuda:0'), tensor(40914.2969, device='cuda:0'), tensor(40813.8008, device='cuda:0'), tensor(40731.1016, device='cuda:0'), tensor(40997.8203, device='cuda:0'), tensor(40680.0469, device='cuda:0'), tensor(40821.8281, device='cuda:0'), tensor(40819.8438, device='cuda:0'), tensor(40852.9805, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(microseconds=967890), datetime.timedelta(microseconds=984874), datetime.timedelta(microseconds=982830), datetime.timedelta(microseconds=982883), datetime.timedelta(microseconds=975859), datetime.timedelta(microseconds=983827), datetime.timedelta(microseconds=961924), datetime.timedelta(microseconds=999764), datetime.timedelta(microseconds=988802), datetime.timedelta(microseconds=980843)]
Phi time: [datetime.timedelta(microseconds=231028), datetime.timedelta(microseconds=234942), datetime.timedelta(microseconds=245909), datetime.timedelta(microseconds=234001), datetime.timedelta(microseconds=229028), datetime.timedelta(microseconds=223051), datetime.timedelta(microseconds=225041), datetime.timedelta(microseconds=232015), datetime.timedelta(microseconds=252928), datetime.timedelta(microseconds=224043)]
