Precision: [tensor(0.5183, device='cuda:0'), tensor(0.5241, device='cuda:0'), tensor(0.5204, device='cuda:0'), tensor(0.5280, device='cuda:0'), tensor(0.5208, device='cuda:0'), tensor(0.5222, device='cuda:0'), tensor(0.5203, device='cuda:0'), tensor(0.5197, device='cuda:0'), tensor(0.5229, device='cuda:0'), tensor(0.5226, device='cuda:0')]

Output distance: [tensor(5.1961, device='cuda:0'), tensor(5.1615, device='cuda:0'), tensor(5.1835, device='cuda:0'), tensor(5.1378, device='cuda:0'), tensor(5.1814, device='cuda:0'), tensor(5.1730, device='cuda:0'), tensor(5.1846, device='cuda:0'), tensor(5.1877, device='cuda:0'), tensor(5.1688, device='cuda:0'), tensor(5.1704, device='cuda:0')]

Prediction loss: [tensor(18971122., device='cuda:0'), tensor(20095506., device='cuda:0'), tensor(20042758., device='cuda:0'), tensor(20825348., device='cuda:0'), tensor(17137800., device='cuda:0'), tensor(18172710., device='cuda:0'), tensor(18426060., device='cuda:0'), tensor(17695752., device='cuda:0'), tensor(18094732., device='cuda:0'), tensor(19502432., device='cuda:0')]

Others: [{'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, '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': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40653.6797, device='cuda:0'), tensor(40987.0039, device='cuda:0'), tensor(40889.1719, device='cuda:0'), tensor(40920.3711, device='cuda:0'), tensor(40922.0625, device='cuda:0'), tensor(40658.9922, device='cuda:0'), tensor(40688.1016, device='cuda:0'), tensor(40673.8281, device='cuda:0'), tensor(40826.4180, device='cuda:0'), tensor(40709.3672, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=46128), datetime.timedelta(seconds=1, microseconds=53903), datetime.timedelta(seconds=1, microseconds=43257), datetime.timedelta(seconds=1, microseconds=24492), datetime.timedelta(seconds=1, microseconds=33483), datetime.timedelta(seconds=1, microseconds=34338), datetime.timedelta(seconds=1, microseconds=24465), datetime.timedelta(seconds=1, microseconds=13017), datetime.timedelta(seconds=1, microseconds=30603), datetime.timedelta(seconds=1, microseconds=45530)]

Phi time: [datetime.timedelta(microseconds=182032), datetime.timedelta(microseconds=201848), datetime.timedelta(microseconds=201768), datetime.timedelta(microseconds=199847), datetime.timedelta(microseconds=202512), datetime.timedelta(microseconds=201614), datetime.timedelta(microseconds=174608), datetime.timedelta(microseconds=186938), datetime.timedelta(microseconds=182582), datetime.timedelta(microseconds=174347)]

