Precision: [tensor(0.6271, device='cuda:0'), tensor(0.6283, device='cuda:0'), tensor(0.6200, device='cuda:0'), tensor(0.6382, device='cuda:0'), tensor(0.6263, device='cuda:0'), tensor(0.6215, device='cuda:0'), tensor(0.6318, device='cuda:0'), tensor(0.6281, device='cuda:0'), tensor(0.6232, device='cuda:0'), tensor(0.6198, device='cuda:0')]
Output distance: [tensor(4.9611, device='cuda:0'), tensor(4.9533, device='cuda:0'), tensor(4.9772, device='cuda:0'), tensor(4.9252, device='cuda:0'), tensor(4.9625, device='cuda:0'), tensor(4.9730, device='cuda:0'), tensor(4.9438, device='cuda:0'), tensor(4.9567, device='cuda:0'), tensor(4.9703, device='cuda:0'), tensor(4.9814, device='cuda:0')]
Prediction loss: [tensor(18421192., device='cuda:0'), tensor(18742228., device='cuda:0'), tensor(20241358., device='cuda:0'), tensor(18174756., device='cuda:0'), tensor(18441730., device='cuda:0'), tensor(16939876., device='cuda:0'), tensor(19091440., device='cuda:0'), tensor(17476058., device='cuda:0'), tensor(19914132., device='cuda:0'), tensor(19464380., device='cuda:0')]
Others: [{'iter_num': 5, 'num_positive': tensor(5170, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5236, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5219, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5249, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5183, 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(5236, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5195, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5191, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5161, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40940.4102, device='cuda:0'), tensor(40842.4258, device='cuda:0'), tensor(40775.9883, device='cuda:0'), tensor(40856.6484, device='cuda:0'), tensor(40883.3828, device='cuda:0'), tensor(40882.9648, device='cuda:0'), tensor(40959.8516, device='cuda:0'), tensor(40751.1484, device='cuda:0'), tensor(41009.0195, device='cuda:0'), tensor(40927.0508, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=6, microseconds=427915), datetime.timedelta(seconds=6, microseconds=149090), datetime.timedelta(seconds=6, microseconds=242695), datetime.timedelta(seconds=6, microseconds=198880), datetime.timedelta(seconds=6, microseconds=300451), datetime.timedelta(seconds=6, microseconds=384099), datetime.timedelta(seconds=6, microseconds=284518), datetime.timedelta(seconds=6, microseconds=254644), datetime.timedelta(seconds=6, microseconds=337297), datetime.timedelta(seconds=8, microseconds=629638)]
Phi time: [datetime.timedelta(microseconds=386371), datetime.timedelta(microseconds=404296), datetime.timedelta(microseconds=401309), datetime.timedelta(microseconds=444129), datetime.timedelta(microseconds=381394), datetime.timedelta(microseconds=409276), datetime.timedelta(microseconds=361478), datetime.timedelta(microseconds=424213), datetime.timedelta(microseconds=397326), datetime.timedelta(microseconds=376414)]
