Precision: [tensor(0.4462, device='cuda:0'), tensor(0.4516, device='cuda:0'), tensor(0.4377, device='cuda:0'), tensor(0.4448, device='cuda:0'), tensor(0.4441, device='cuda:0'), tensor(0.4482, device='cuda:0'), tensor(0.4335, device='cuda:0'), tensor(0.4179, device='cuda:0'), tensor(0.4420, device='cuda:0'), tensor(0.4211, device='cuda:0')]

Output distance: [tensor(19.1330, device='cuda:0'), tensor(19.1221, device='cuda:0'), tensor(19.1499, device='cuda:0'), tensor(19.1357, device='cuda:0'), tensor(19.1372, device='cuda:0'), tensor(19.1291, device='cuda:0'), tensor(19.1584, device='cuda:0'), tensor(19.1895, device='cuda:0'), tensor(19.1415, device='cuda:0'), tensor(19.1832, device='cuda:0')]

Prediction loss: [tensor(108.3150, device='cuda:0'), tensor(109.3631, device='cuda:0'), tensor(108.5810, device='cuda:0'), tensor(107.6942, device='cuda:0'), tensor(108.6138, device='cuda:0'), tensor(108.1207, device='cuda:0'), tensor(107.6316, device='cuda:0'), tensor(107.4557, device='cuda:0'), tensor(108.6856, device='cuda:0'), tensor(107.4486, device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, 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=724501), datetime.timedelta(seconds=3, microseconds=744309), datetime.timedelta(seconds=3, microseconds=744884), datetime.timedelta(seconds=3, microseconds=721986), datetime.timedelta(seconds=3, microseconds=718557), datetime.timedelta(seconds=3, microseconds=745338), datetime.timedelta(seconds=3, microseconds=749094), datetime.timedelta(seconds=3, microseconds=741883), datetime.timedelta(seconds=3, microseconds=741409), datetime.timedelta(seconds=3, microseconds=735264)]

Phi time: [datetime.timedelta(seconds=4, microseconds=308855), datetime.timedelta(seconds=4, microseconds=450023), datetime.timedelta(seconds=4, microseconds=377198), datetime.timedelta(seconds=4, microseconds=386640), datetime.timedelta(seconds=4, microseconds=355697), datetime.timedelta(seconds=4, microseconds=422700), datetime.timedelta(seconds=4, microseconds=367203), datetime.timedelta(seconds=4, microseconds=378093), datetime.timedelta(seconds=4, microseconds=417784), datetime.timedelta(seconds=4, microseconds=418236)]

