Precision: [tensor(0.3934, device='cuda:0'), tensor(0.3659, device='cuda:0'), tensor(0.3954, device='cuda:0'), tensor(0.3653, device='cuda:0'), tensor(0.3910, device='cuda:0'), tensor(0.3937, device='cuda:0'), tensor(0.3567, device='cuda:0'), tensor(0.4055, device='cuda:0'), tensor(0.3631, device='cuda:0'), tensor(0.3847, device='cuda:0')]

Output distance: [tensor(19.2385, device='cuda:0'), tensor(19.2935, device='cuda:0'), tensor(19.2346, device='cuda:0'), tensor(19.2947, device='cuda:0'), tensor(19.2434, device='cuda:0'), tensor(19.2379, device='cuda:0'), tensor(19.3120, device='cuda:0'), tensor(19.2143, device='cuda:0'), tensor(19.2993, device='cuda:0'), tensor(19.2560, device='cuda:0')]

Prediction loss: [tensor(107.7339, device='cuda:0'), tensor(108.9830, device='cuda:0'), tensor(106.7993, device='cuda:0'), tensor(107.8690, device='cuda:0'), tensor(108.4012, device='cuda:0'), tensor(107.7637, device='cuda:0'), tensor(107.3692, device='cuda:0'), tensor(108.1203, device='cuda:0'), tensor(108.2812, device='cuda:0'), tensor(107.2158, device='cuda:0')]

Others: [{'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, '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=2, microseconds=520310), datetime.timedelta(seconds=2, microseconds=539234), datetime.timedelta(seconds=2, microseconds=543213), datetime.timedelta(seconds=2, microseconds=526286), datetime.timedelta(seconds=2, microseconds=548191), datetime.timedelta(seconds=2, microseconds=527279), datetime.timedelta(seconds=2, microseconds=620835), datetime.timedelta(seconds=2, microseconds=541222), datetime.timedelta(seconds=2, microseconds=673660), datetime.timedelta(seconds=2, microseconds=654741)]

Phi time: [datetime.timedelta(seconds=4, microseconds=485444), datetime.timedelta(seconds=4, microseconds=435445), datetime.timedelta(seconds=4, microseconds=499564), datetime.timedelta(seconds=4, microseconds=437084), datetime.timedelta(seconds=4, microseconds=473904), datetime.timedelta(seconds=4, microseconds=430310), datetime.timedelta(seconds=4, microseconds=478879), datetime.timedelta(seconds=4, microseconds=505163), datetime.timedelta(seconds=4, microseconds=456107), datetime.timedelta(seconds=4, microseconds=465131)]

