Precision: [tensor(0.8598, device='cuda:0'), tensor(0.8592, device='cuda:0'), tensor(0.8588, device='cuda:0'), tensor(0.8595, device='cuda:0'), tensor(0.8598, device='cuda:0'), tensor(0.8592, device='cuda:0'), tensor(0.8588, device='cuda:0'), tensor(0.8588, device='cuda:0'), tensor(0.8589, device='cuda:0'), tensor(0.8602, device='cuda:0')]

Output distance: [tensor(538.4249, device='cuda:0'), tensor(542.9960, device='cuda:0'), tensor(544.1670, device='cuda:0'), tensor(547.7831, device='cuda:0'), tensor(546.9241, device='cuda:0'), tensor(549.6989, device='cuda:0'), tensor(549.7003, device='cuda:0'), tensor(546.0491, device='cuda:0'), tensor(548.7081, device='cuda:0'), tensor(534.9995, device='cuda:0')]

Prediction loss: [tensor(595.6130, device='cuda:0'), tensor(607.9030, device='cuda:0'), tensor(606.1735, device='cuda:0'), tensor(612.4073, device='cuda:0'), tensor(607.2170, device='cuda:0'), tensor(605.3394, device='cuda:0'), tensor(601.5515, device='cuda:0'), tensor(595.7258, device='cuda:0'), tensor(614.8451, device='cuda:0'), tensor(598.0187, device='cuda:0')]

Others: [{'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(8763887., device='cuda:0'), tensor(8898768., device='cuda:0'), tensor(8857041., device='cuda:0'), tensor(8911471., device='cuda:0'), tensor(8889773., device='cuda:0'), tensor(8855153., device='cuda:0'), tensor(8837859., device='cuda:0'), tensor(8772194., device='cuda:0'), tensor(8965470., device='cuda:0'), tensor(8790788., device='cuda:0')]

Training loss: 8886636.0

Prediction time: [datetime.timedelta(seconds=1, microseconds=62493), datetime.timedelta(seconds=1, microseconds=86392), datetime.timedelta(seconds=1, microseconds=86392), datetime.timedelta(seconds=1, microseconds=247708), datetime.timedelta(seconds=1, microseconds=85398), datetime.timedelta(seconds=1, microseconds=86391), datetime.timedelta(seconds=1, microseconds=84401), datetime.timedelta(seconds=1, microseconds=86391), datetime.timedelta(seconds=1, microseconds=84400), datetime.timedelta(seconds=1, microseconds=89380)]

Phi time: [datetime.timedelta(seconds=1, microseconds=906236), datetime.timedelta(seconds=1, microseconds=283065), datetime.timedelta(seconds=1, microseconds=300640), datetime.timedelta(seconds=1, microseconds=323967), datetime.timedelta(seconds=1, microseconds=298768), datetime.timedelta(seconds=1, microseconds=321569), datetime.timedelta(seconds=1, microseconds=298574), datetime.timedelta(seconds=1, microseconds=296116), datetime.timedelta(seconds=1, microseconds=318101), datetime.timedelta(seconds=1, microseconds=294810)]

