Precision: [tensor(0.7379, device='cuda:0'), tensor(0.7310, device='cuda:0'), tensor(0.7399, device='cuda:0'), tensor(0.7363, device='cuda:0'), tensor(0.7408, device='cuda:0'), tensor(0.7378, device='cuda:0'), tensor(0.7272, device='cuda:0'), tensor(0.7290, device='cuda:0'), tensor(0.7298, device='cuda:0'), tensor(0.7437, device='cuda:0')]
Output distance: [tensor(5.0407, device='cuda:0'), tensor(5.0402, device='cuda:0'), tensor(5.0291, device='cuda:0'), tensor(5.0417, device='cuda:0'), tensor(5.0276, device='cuda:0'), tensor(5.0333, device='cuda:0'), tensor(5.0454, device='cuda:0'), tensor(5.0391, device='cuda:0'), tensor(5.0454, device='cuda:0'), tensor(5.0226, device='cuda:0')]
Prediction loss: [tensor(17645580., device='cuda:0'), tensor(18523806., device='cuda:0'), tensor(18658654., device='cuda:0'), tensor(19187034., device='cuda:0'), tensor(18493262., device='cuda:0'), tensor(17570266., device='cuda:0'), tensor(19365480., device='cuda:0'), tensor(18826394., device='cuda:0'), tensor(17202682., device='cuda:0'), tensor(20592236., device='cuda:0')]
Others: [{'iter_num': 5, 'num_positive': tensor(2125, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2193, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2199, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2131, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2203, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2185, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2185, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2221, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2161, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2216, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40817.0586, device='cuda:0'), tensor(40914.2656, device='cuda:0'), tensor(40826.4805, device='cuda:0'), tensor(40806.8438, device='cuda:0'), tensor(40824.7539, device='cuda:0'), tensor(40635.7656, device='cuda:0'), tensor(40843.2578, device='cuda:0'), tensor(41029.0078, device='cuda:0'), tensor(40838.5117, device='cuda:0'), tensor(40942.2070, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=23659), datetime.timedelta(seconds=1, microseconds=38594), datetime.timedelta(seconds=1, microseconds=7727), datetime.timedelta(seconds=1, microseconds=42579), datetime.timedelta(seconds=1, microseconds=13701), datetime.timedelta(seconds=1, microseconds=19675), datetime.timedelta(seconds=1, microseconds=34612), datetime.timedelta(seconds=1, microseconds=10714), datetime.timedelta(seconds=1, microseconds=14696), datetime.timedelta(seconds=1, microseconds=8722)]
Phi time: [datetime.timedelta(microseconds=235003), datetime.timedelta(microseconds=251932), datetime.timedelta(microseconds=251931), datetime.timedelta(microseconds=235003), datetime.timedelta(microseconds=254919), datetime.timedelta(microseconds=252927), datetime.timedelta(microseconds=251932), datetime.timedelta(microseconds=257906), datetime.timedelta(microseconds=232016), datetime.timedelta(microseconds=235003)]
