Precision: [tensor(0.6283, device='cuda:0'), tensor(0.6222, device='cuda:0'), tensor(0.6221, device='cuda:0'), tensor(0.6187, device='cuda:0'), tensor(0.6248, device='cuda:0'), tensor(0.6296, device='cuda:0'), tensor(0.6271, device='cuda:0'), tensor(0.6270, device='cuda:0'), tensor(0.6172, device='cuda:0'), tensor(0.6257, device='cuda:0')]
Output distance: [tensor(4.9546, device='cuda:0'), tensor(4.9779, device='cuda:0'), tensor(4.9748, device='cuda:0'), tensor(4.9842, device='cuda:0'), tensor(4.9677, device='cuda:0'), tensor(4.9567, device='cuda:0'), tensor(4.9609, device='cuda:0'), tensor(4.9593, device='cuda:0'), tensor(4.9884, device='cuda:0'), tensor(4.9648, device='cuda:0')]
Prediction loss: [tensor(18771604., device='cuda:0'), tensor(18880640., device='cuda:0'), tensor(17765204., device='cuda:0'), tensor(19213970., device='cuda:0'), tensor(18553764., device='cuda:0'), tensor(18814242., device='cuda:0'), tensor(19607298., device='cuda:0'), tensor(19041464., device='cuda:0'), tensor(18947646., device='cuda:0'), tensor(18504542., device='cuda:0')]
Others: [{'iter_num': 5, 'num_positive': tensor(5217, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5116, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5170, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5166, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5163, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5135, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5175, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5201, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5162, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5170, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40904.3516, device='cuda:0'), tensor(40770.1953, device='cuda:0'), tensor(40729.2969, device='cuda:0'), tensor(40839.4492, device='cuda:0'), tensor(40800.1367, device='cuda:0'), tensor(40837.9102, device='cuda:0'), tensor(40796.9922, device='cuda:0'), tensor(40865.9531, device='cuda:0'), tensor(40872.1719, device='cuda:0'), tensor(40841.6875, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=43575), datetime.timedelta(seconds=1, microseconds=69464), datetime.timedelta(seconds=1, microseconds=49549), datetime.timedelta(seconds=1, microseconds=54527), datetime.timedelta(seconds=1, microseconds=64485), datetime.timedelta(seconds=1, microseconds=33616), datetime.timedelta(seconds=1, microseconds=41584), datetime.timedelta(seconds=1, microseconds=55524), datetime.timedelta(seconds=1, microseconds=64485), datetime.timedelta(seconds=1, microseconds=43574)]
Phi time: [datetime.timedelta(microseconds=235998), datetime.timedelta(microseconds=241973), datetime.timedelta(microseconds=232016), datetime.timedelta(microseconds=253924), datetime.timedelta(microseconds=233013), datetime.timedelta(microseconds=251931), datetime.timedelta(microseconds=235998), datetime.timedelta(microseconds=235999), datetime.timedelta(microseconds=235004), datetime.timedelta(microseconds=254919)]
