Precision: [tensor(0.8482, device='cuda:0'), tensor(0.8497, device='cuda:0'), tensor(0.8457, device='cuda:0'), tensor(0.8497, device='cuda:0'), tensor(0.8456, device='cuda:0'), tensor(0.8515, device='cuda:0'), tensor(0.8508, device='cuda:0'), tensor(0.8521, device='cuda:0'), tensor(0.8469, device='cuda:0'), tensor(0.8476, device='cuda:0')]

Output distance: [tensor(602.6806, device='cuda:0'), tensor(585.7496, device='cuda:0'), tensor(609.2462, device='cuda:0'), tensor(590.6132, device='cuda:0'), tensor(625.3476, device='cuda:0'), tensor(574.7292, device='cuda:0'), tensor(577.1158, device='cuda:0'), tensor(593.8986, device='cuda:0'), tensor(654.2486, device='cuda:0'), tensor(595.0381, device='cuda:0')]

Prediction loss: [tensor(608.7629, device='cuda:0'), tensor(572.2164, device='cuda:0'), tensor(605.6822, device='cuda:0'), tensor(528.5496, device='cuda:0'), tensor(604.0356, device='cuda:0'), tensor(581.6606, device='cuda:0'), tensor(584.7509, device='cuda:0'), tensor(609.2137, device='cuda:0'), tensor(714.0231, device='cuda:0'), tensor(545.2573, device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(8929003., device='cuda:0'), tensor(8698099., device='cuda:0'), tensor(8858374., device='cuda:0'), tensor(8060187., device='cuda:0'), tensor(9144826., device='cuda:0'), tensor(8690409., device='cuda:0'), tensor(8836002., device='cuda:0'), tensor(9267923., device='cuda:0'), tensor(9533630., device='cuda:0'), tensor(8466266., device='cuda:0')]

Training loss: 8901508.0

Prediction time: [datetime.timedelta(seconds=1, microseconds=50595), datetime.timedelta(seconds=1, microseconds=79421), datetime.timedelta(seconds=1, microseconds=77432), datetime.timedelta(seconds=1, microseconds=77431), datetime.timedelta(seconds=1, microseconds=59507), datetime.timedelta(seconds=1, microseconds=73448), datetime.timedelta(seconds=1, microseconds=69465), datetime.timedelta(seconds=1, microseconds=74443), datetime.timedelta(seconds=1, microseconds=58512), datetime.timedelta(seconds=1, microseconds=73445)]

Phi time: [datetime.timedelta(seconds=1, microseconds=247142), datetime.timedelta(microseconds=735974), datetime.timedelta(microseconds=652382), datetime.timedelta(microseconds=650521), datetime.timedelta(microseconds=651408), datetime.timedelta(microseconds=653158), datetime.timedelta(microseconds=653709), datetime.timedelta(microseconds=653405), datetime.timedelta(microseconds=652355), datetime.timedelta(microseconds=653282)]

