Precision: [tensor(0.6860, device='cuda:0'), tensor(0.6839, device='cuda:0'), tensor(0.6881, device='cuda:0'), tensor(0.6831, device='cuda:0'), tensor(0.6836, device='cuda:0'), tensor(0.6934, device='cuda:0'), tensor(0.6852, device='cuda:0'), tensor(0.6831, device='cuda:0'), tensor(0.6852, device='cuda:0'), tensor(0.6857, device='cuda:0')]
Output distance: [tensor(4.9341, device='cuda:0'), tensor(4.9383, device='cuda:0'), tensor(4.9299, device='cuda:0'), tensor(4.9399, device='cuda:0'), tensor(4.9388, device='cuda:0'), tensor(4.9194, device='cuda:0'), tensor(4.9357, device='cuda:0'), tensor(4.9399, device='cuda:0'), tensor(4.9357, device='cuda:0'), tensor(4.9346, device='cuda:0')]
Prediction loss: [tensor(19349066., device='cuda:0'), tensor(18725822., device='cuda:0'), tensor(17624420., device='cuda:0'), tensor(20427700., device='cuda:0'), tensor(17727262., device='cuda:0'), tensor(18843406., device='cuda:0'), tensor(19832400., device='cuda:0'), tensor(18742408., device='cuda:0'), tensor(18907306., device='cuda:0'), tensor(18352894., device='cuda:0')]
Others: [{'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': 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': 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': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40925.9531, device='cuda:0'), tensor(40713.3711, device='cuda:0'), tensor(40773.4297, device='cuda:0'), tensor(40743.3867, device='cuda:0'), tensor(40719.9883, device='cuda:0'), tensor(40876.4766, device='cuda:0'), tensor(40860.2891, device='cuda:0'), tensor(40980.2812, device='cuda:0'), tensor(40773.2773, device='cuda:0'), tensor(40654.1719, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=6, microseconds=507402), datetime.timedelta(seconds=6, microseconds=439690), datetime.timedelta(seconds=6, microseconds=526321), datetime.timedelta(seconds=6, microseconds=498440), datetime.timedelta(seconds=6, microseconds=261444), datetime.timedelta(seconds=6, microseconds=420768), datetime.timedelta(seconds=6, microseconds=541257), datetime.timedelta(seconds=6, microseconds=424752), datetime.timedelta(seconds=6, microseconds=481512), datetime.timedelta(seconds=6, microseconds=444668)]
Phi time: [datetime.timedelta(microseconds=362463), datetime.timedelta(microseconds=400303), datetime.timedelta(microseconds=329603), datetime.timedelta(microseconds=380387), datetime.timedelta(microseconds=406276), datetime.timedelta(microseconds=393333), datetime.timedelta(microseconds=410261), datetime.timedelta(microseconds=385366), datetime.timedelta(microseconds=336573), datetime.timedelta(microseconds=407273)]
