Precision: [tensor(0.2691, device='cuda:0'), tensor(0.2768, device='cuda:0'), tensor(0.2713, device='cuda:0'), tensor(0.2747, device='cuda:0'), tensor(0.2742, device='cuda:0'), tensor(0.2783, device='cuda:0'), tensor(0.2753, device='cuda:0'), tensor(0.2711, device='cuda:0'), tensor(0.2732, device='cuda:0'), tensor(0.2754, device='cuda:0')]

Output distance: [tensor(21.3343, device='cuda:0'), tensor(21.2570, device='cuda:0'), tensor(21.3126, device='cuda:0'), tensor(21.2784, device='cuda:0'), tensor(21.2833, device='cuda:0'), tensor(21.2424, device='cuda:0'), tensor(21.2724, device='cuda:0'), tensor(21.3141, device='cuda:0'), tensor(21.2929, device='cuda:0'), tensor(21.2712, device='cuda:0')]

Prediction loss: [tensor(100.1954, device='cuda:0'), tensor(101.0145, device='cuda:0'), tensor(100.8478, device='cuda:0'), tensor(100.5550, device='cuda:0'), tensor(100.5924, device='cuda:0'), tensor(101.5110, device='cuda:0'), tensor(101.1415, device='cuda:0'), tensor(100.7361, device='cuda:0'), tensor(100.4316, device='cuda:0'), tensor(100.9065, device='cuda:0')]

Others: [{'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': 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': 13, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, '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=98810), datetime.timedelta(seconds=3, microseconds=135701), datetime.timedelta(seconds=3, microseconds=123755), datetime.timedelta(seconds=3, microseconds=98860), datetime.timedelta(seconds=3, microseconds=226315), datetime.timedelta(seconds=3, microseconds=94874), datetime.timedelta(seconds=3, microseconds=85909), datetime.timedelta(seconds=3, microseconds=36123), datetime.timedelta(seconds=3, microseconds=218350), datetime.timedelta(seconds=3, microseconds=84917)]

Phi time: [datetime.timedelta(seconds=5, microseconds=186764), datetime.timedelta(seconds=5, microseconds=146587), datetime.timedelta(seconds=5, microseconds=176562), datetime.timedelta(seconds=5, microseconds=150438), datetime.timedelta(seconds=5, microseconds=151354), datetime.timedelta(seconds=5, microseconds=159922), datetime.timedelta(seconds=5, microseconds=151443), datetime.timedelta(seconds=5, microseconds=156632), datetime.timedelta(seconds=5, microseconds=176414), datetime.timedelta(seconds=5, microseconds=162711)]

