Precision: [tensor(0.5501, device='cuda:0'), tensor(0.5487, device='cuda:0'), tensor(0.5515, device='cuda:0'), tensor(0.5521, device='cuda:0'), tensor(0.5517, device='cuda:0'), tensor(0.5525, device='cuda:0'), tensor(0.5506, device='cuda:0'), tensor(0.5502, device='cuda:0'), tensor(0.5496, device='cuda:0'), tensor(0.5499, device='cuda:0')]
Output distance: [tensor(5.0055, device='cuda:0'), tensor(5.0139, device='cuda:0'), tensor(4.9971, device='cuda:0'), tensor(4.9934, device='cuda:0'), tensor(4.9961, device='cuda:0'), tensor(4.9913, device='cuda:0'), tensor(5.0024, device='cuda:0'), tensor(5.0050, device='cuda:0'), tensor(5.0087, device='cuda:0'), tensor(5.0066, device='cuda:0')]
Prediction loss: [tensor(18108676., device='cuda:0'), tensor(17185082., device='cuda:0'), tensor(18545984., device='cuda:0'), tensor(20357586., device='cuda:0'), tensor(19858454., device='cuda:0'), tensor(18235672., device='cuda:0'), tensor(17647712., device='cuda:0'), tensor(19231868., device='cuda:0'), tensor(18348448., device='cuda:0'), tensor(18721464., device='cuda:0')]
Others: [{'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40990.6953, device='cuda:0'), tensor(40701.8672, device='cuda:0'), tensor(40817.7188, device='cuda:0'), tensor(41130.4844, device='cuda:0'), tensor(39847.8750, device='cuda:0'), tensor(40542.1953, device='cuda:0'), tensor(39896.7109, device='cuda:0'), tensor(41000.1680, device='cuda:0'), tensor(39734.3047, device='cuda:0'), tensor(40963.3320, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=10, microseconds=277413), datetime.timedelta(seconds=10, microseconds=99168), datetime.timedelta(seconds=10, microseconds=75270), datetime.timedelta(seconds=10, microseconds=143979), datetime.timedelta(seconds=10, microseconds=179827), datetime.timedelta(seconds=10, microseconds=178831), datetime.timedelta(seconds=10, microseconds=125057), datetime.timedelta(seconds=7, microseconds=794942), datetime.timedelta(seconds=10, microseconds=152941), datetime.timedelta(seconds=7, microseconds=760087)]
Phi time: [datetime.timedelta(microseconds=358479), datetime.timedelta(microseconds=407273), datetime.timedelta(microseconds=365450), datetime.timedelta(microseconds=348521), datetime.timedelta(microseconds=375395), datetime.timedelta(microseconds=366446), datetime.timedelta(microseconds=358478), datetime.timedelta(microseconds=307695), datetime.timedelta(microseconds=394328), datetime.timedelta(microseconds=389351)]
