Precision: [tensor(0.8481, device='cuda:0'), tensor(0.8462, device='cuda:0'), tensor(0.8414, device='cuda:0'), tensor(0.8500, device='cuda:0'), tensor(0.8582, device='cuda:0'), tensor(0.8576, device='cuda:0'), tensor(0.8003, device='cuda:0'), tensor(0.8471, device='cuda:0'), tensor(0.8399, device='cuda:0'), tensor(0.8532, device='cuda:0')]

Output distance: [tensor(605.7637, device='cuda:0'), tensor(631.2932, device='cuda:0'), tensor(740.3105, device='cuda:0'), tensor(588.9625, device='cuda:0'), tensor(581.5358, device='cuda:0'), tensor(566.4851, device='cuda:0'), tensor(9389.3115, device='cuda:0'), tensor(658.8632, device='cuda:0'), tensor(641.3888, device='cuda:0'), tensor(578.0683, device='cuda:0')]

Prediction loss: [tensor(606.8458, device='cuda:0'), tensor(664.9415, device='cuda:0'), tensor(803.5231, device='cuda:0'), tensor(607.5394, device='cuda:0'), tensor(681.6657, device='cuda:0'), tensor(603.6100, device='cuda:0'), tensor(16628.9180, device='cuda:0'), tensor(704.6506, device='cuda:0'), tensor(589.5212, device='cuda:0'), tensor(586.5919, 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(9027141., device='cuda:0'), tensor(9225473., device='cuda:0'), tensor(9189180., device='cuda:0'), tensor(8909418., device='cuda:0'), tensor(9375712., device='cuda:0'), tensor(8906367., device='cuda:0'), tensor(9481850., device='cuda:0'), tensor(9184803., device='cuda:0'), tensor(8622844., device='cuda:0'), tensor(8664991., device='cuda:0')]

Training loss: 8878621.0

Prediction time: [datetime.timedelta(seconds=1, microseconds=75475), datetime.timedelta(seconds=1, microseconds=101366), datetime.timedelta(seconds=1, microseconds=81450), datetime.timedelta(seconds=1, microseconds=88423), datetime.timedelta(seconds=1, microseconds=94360), datetime.timedelta(seconds=1, microseconds=90373), datetime.timedelta(seconds=1, microseconds=103323), datetime.timedelta(seconds=1, microseconds=102322), datetime.timedelta(seconds=1, microseconds=87389), datetime.timedelta(seconds=1, microseconds=117263)]

Phi time: [datetime.timedelta(seconds=1, microseconds=295913), datetime.timedelta(microseconds=744151), datetime.timedelta(microseconds=672520), datetime.timedelta(microseconds=667082), datetime.timedelta(microseconds=668796), datetime.timedelta(microseconds=667942), datetime.timedelta(microseconds=673455), datetime.timedelta(microseconds=671228), datetime.timedelta(microseconds=672677), datetime.timedelta(microseconds=674679)]

