Precision: [tensor(0.1180, device='cuda:0'), tensor(0.1207, device='cuda:0'), tensor(0.0976, device='cuda:0'), tensor(0.0970, device='cuda:0'), tensor(0.1029, device='cuda:0'), tensor(0.1040, device='cuda:0'), tensor(0.1054, device='cuda:0'), tensor(0.0960, device='cuda:0'), tensor(0.1108, device='cuda:0'), tensor(0.1302, device='cuda:0')]

Output distance: [tensor(22.8458, device='cuda:0'), tensor(22.8180, device='cuda:0'), tensor(23.0499, device='cuda:0'), tensor(23.0553, device='cuda:0'), tensor(22.9961, device='cuda:0'), tensor(22.9852, device='cuda:0'), tensor(22.9719, device='cuda:0'), tensor(23.0650, device='cuda:0'), tensor(22.9175, device='cuda:0'), tensor(22.7231, device='cuda:0')]

Prediction loss: [tensor(96.0431, device='cuda:0'), tensor(96.5146, device='cuda:0'), tensor(97.4560, device='cuda:0'), tensor(94.7988, device='cuda:0'), tensor(96.4928, device='cuda:0'), tensor(95.8837, device='cuda:0'), tensor(96.2471, device='cuda:0'), tensor(93.2256, device='cuda:0'), tensor(94.5378, device='cuda:0'), tensor(96.6272, device='cuda:0')]

Others: [{'iter_num': 15, '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': 15, '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': 13, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, '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': 13, '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': 13, '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=109810), datetime.timedelta(seconds=3, microseconds=76950), datetime.timedelta(seconds=3, microseconds=175532), datetime.timedelta(seconds=3, microseconds=122756), datetime.timedelta(seconds=2, microseconds=997288), datetime.timedelta(seconds=2, microseconds=982352), datetime.timedelta(seconds=3, microseconds=139682), datetime.timedelta(seconds=3, microseconds=275), datetime.timedelta(seconds=3, microseconds=176527), datetime.timedelta(seconds=3, microseconds=43094)]

Phi time: [datetime.timedelta(seconds=4, microseconds=984858), datetime.timedelta(seconds=4, microseconds=864369), datetime.timedelta(seconds=4, microseconds=846444), datetime.timedelta(seconds=4, microseconds=858393), datetime.timedelta(seconds=4, microseconds=863373), datetime.timedelta(seconds=4, microseconds=859391), datetime.timedelta(seconds=4, microseconds=892250), datetime.timedelta(seconds=5, microseconds=15729), datetime.timedelta(seconds=4, microseconds=832503), datetime.timedelta(seconds=4, microseconds=858393)]

