Precision: [tensor(0.6278, device='cuda:0'), tensor(0.6242, device='cuda:0'), tensor(0.6250, device='cuda:0'), tensor(0.6297, device='cuda:0'), tensor(0.6221, device='cuda:0'), tensor(0.6286, device='cuda:0'), tensor(0.6245, device='cuda:0'), tensor(0.6224, device='cuda:0'), tensor(0.6229, device='cuda:0'), tensor(0.6260, device='cuda:0')]
Output distance: [tensor(4.9543, device='cuda:0'), tensor(4.9677, device='cuda:0'), tensor(4.9643, device='cuda:0'), tensor(4.9535, device='cuda:0'), tensor(4.9766, device='cuda:0'), tensor(4.9535, device='cuda:0'), tensor(4.9672, device='cuda:0'), tensor(4.9714, device='cuda:0'), tensor(4.9685, device='cuda:0'), tensor(4.9661, device='cuda:0')]
Prediction loss: [tensor(17539266., device='cuda:0'), tensor(15666712., device='cuda:0'), tensor(19127492., device='cuda:0'), tensor(18963950., device='cuda:0'), tensor(18746740., device='cuda:0'), tensor(18491544., device='cuda:0'), tensor(18724366., device='cuda:0'), tensor(18915344., device='cuda:0'), tensor(18916084., device='cuda:0'), tensor(18157258., device='cuda:0')]
Others: [{'iter_num': 5, 'num_positive': tensor(5244, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5189, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5208, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5177, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5139, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5221, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5185, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5209, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5234, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5137, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40930.1797, device='cuda:0'), tensor(40796.1914, device='cuda:0'), tensor(40702.5742, device='cuda:0'), tensor(40786.6289, device='cuda:0'), tensor(40852., device='cuda:0'), tensor(40859.2578, device='cuda:0'), tensor(40722.8359, device='cuda:0'), tensor(40819.2383, device='cuda:0'), tensor(40933.7539, device='cuda:0'), tensor(40795.6836, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=6, microseconds=36399), datetime.timedelta(seconds=6, microseconds=21465), datetime.timedelta(seconds=5, microseconds=894055), datetime.timedelta(seconds=5, microseconds=898984), datetime.timedelta(seconds=5, microseconds=915911), datetime.timedelta(seconds=6, microseconds=43369), datetime.timedelta(seconds=5, microseconds=950761), datetime.timedelta(seconds=6, microseconds=42377), datetime.timedelta(seconds=5, microseconds=958728), datetime.timedelta(seconds=6, microseconds=8515)]
Phi time: [datetime.timedelta(microseconds=229029), datetime.timedelta(microseconds=535723), datetime.timedelta(microseconds=449094), datetime.timedelta(microseconds=460048), datetime.timedelta(microseconds=448098), datetime.timedelta(microseconds=373546), datetime.timedelta(microseconds=407277), datetime.timedelta(microseconds=370430), datetime.timedelta(microseconds=443119), datetime.timedelta(microseconds=421417)]
