Precision: [tensor(0.0340, device='cuda:0'), tensor(0.0336, device='cuda:0'), tensor(0.0348, device='cuda:0'), tensor(0.0440, device='cuda:0'), tensor(0.0492, device='cuda:0'), tensor(0.0334, device='cuda:0'), tensor(0.0367, device='cuda:0'), tensor(0.0362, device='cuda:0'), tensor(0.0233, device='cuda:0'), tensor(0.0399, device='cuda:0')]

Output distance: [tensor(23.6856, device='cuda:0'), tensor(23.6898, device='cuda:0'), tensor(23.6775, device='cuda:0'), tensor(23.5856, device='cuda:0'), tensor(23.5333, device='cuda:0'), tensor(23.6917, device='cuda:0'), tensor(23.6584, device='cuda:0'), tensor(23.6635, device='cuda:0'), tensor(23.7926, device='cuda:0'), tensor(23.6267, device='cuda:0')]

Prediction loss: [tensor(90.7571, device='cuda:0'), tensor(93.2794, device='cuda:0'), tensor(91.5024, device='cuda:0'), tensor(91.9842, device='cuda:0'), tensor(90.5961, device='cuda:0'), tensor(87.1281, device='cuda:0'), tensor(90.5890, device='cuda:0'), tensor(89.5431, device='cuda:0'), tensor(88.6051, device='cuda:0'), tensor(88.2292, device='cuda:0')]

Others: [{'iter_num': 19, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 17, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 17, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 17, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 17, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 17, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 19, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 17, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}]

Compressed training loss: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=3, microseconds=166570), datetime.timedelta(seconds=3, microseconds=122756), datetime.timedelta(seconds=3, microseconds=121760), datetime.timedelta(seconds=3, microseconds=131719), datetime.timedelta(seconds=3, microseconds=63007), datetime.timedelta(seconds=3, microseconds=129726), datetime.timedelta(seconds=3, microseconds=124744), datetime.timedelta(seconds=2, microseconds=999278), datetime.timedelta(seconds=3, microseconds=211380), datetime.timedelta(seconds=3, microseconds=138685)]

Phi time: [datetime.timedelta(seconds=4, microseconds=942039), datetime.timedelta(seconds=4, microseconds=685133), datetime.timedelta(seconds=4, microseconds=665214), datetime.timedelta(seconds=4, microseconds=683138), datetime.timedelta(seconds=4, microseconds=678200), datetime.timedelta(seconds=4, microseconds=681145), datetime.timedelta(seconds=4, microseconds=694094), datetime.timedelta(seconds=4, microseconds=712761), datetime.timedelta(seconds=4, microseconds=693097), datetime.timedelta(seconds=4, microseconds=720875)]

