Precision: [tensor(0.2270, device='cuda:0'), tensor(0.2263, device='cuda:0'), tensor(0.2247, device='cuda:0'), tensor(0.2235, device='cuda:0'), tensor(0.2238, device='cuda:0'), tensor(0.2238, device='cuda:0'), tensor(0.2242, device='cuda:0'), tensor(0.2230, device='cuda:0'), tensor(0.2252, device='cuda:0'), tensor(0.2217, device='cuda:0')]
Output distance: [tensor(20380418., device='cuda:0'), tensor(20394124., device='cuda:0'), tensor(20406084., device='cuda:0'), tensor(20405946., device='cuda:0'), tensor(20403300., device='cuda:0'), tensor(20406080., device='cuda:0'), tensor(20400530., device='cuda:0'), tensor(20420228., device='cuda:0'), tensor(20392184., device='cuda:0'), tensor(20422734., device='cuda:0')]
Prediction loss: [tensor(14264351., device='cuda:0'), tensor(14135148., device='cuda:0'), tensor(14188504., device='cuda:0'), tensor(14054177., device='cuda:0'), tensor(14197286., device='cuda:0'), tensor(14153456., device='cuda:0'), tensor(14174267., device='cuda:0'), tensor(14171246., device='cuda:0'), tensor(14193377., device='cuda:0'), tensor(14136650., device='cuda:0')]
Others: [{'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': 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': 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': 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': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]
Compressed training loss: [tensor(2.6171e+11, device='cuda:0'), tensor(2.5922e+11, device='cuda:0'), tensor(2.6002e+11, device='cuda:0'), tensor(2.5773e+11, device='cuda:0'), tensor(2.6018e+11, device='cuda:0'), tensor(2.5925e+11, device='cuda:0'), tensor(2.5932e+11, device='cuda:0'), tensor(2.5968e+11, device='cuda:0'), tensor(2.6034e+11, device='cuda:0'), tensor(2.5907e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=498910), datetime.timedelta(microseconds=622389), datetime.timedelta(microseconds=567623), datetime.timedelta(microseconds=568600), datetime.timedelta(microseconds=566626), datetime.timedelta(microseconds=576585), datetime.timedelta(microseconds=572592), datetime.timedelta(microseconds=561646), datetime.timedelta(microseconds=564628), datetime.timedelta(microseconds=568670)]
Phi time: [datetime.timedelta(microseconds=874671), datetime.timedelta(microseconds=882863), datetime.timedelta(microseconds=851814), datetime.timedelta(microseconds=856779), datetime.timedelta(microseconds=890784), datetime.timedelta(microseconds=859087), datetime.timedelta(microseconds=852913), datetime.timedelta(microseconds=853552), datetime.timedelta(microseconds=853038), datetime.timedelta(microseconds=856080)]
