Precision: [tensor(0.6873, device='cuda:0'), tensor(0.6936, device='cuda:0'), tensor(0.6934, device='cuda:0'), tensor(0.6794, device='cuda:0'), tensor(0.6884, device='cuda:0'), tensor(0.6844, device='cuda:0'), tensor(0.6915, device='cuda:0'), tensor(0.6902, device='cuda:0'), tensor(0.6886, device='cuda:0'), tensor(0.6839, device='cuda:0')]

Output distance: [tensor(4.9315, device='cuda:0'), tensor(4.9189, device='cuda:0'), tensor(4.9194, device='cuda:0'), tensor(4.9472, device='cuda:0'), tensor(4.9294, device='cuda:0'), tensor(4.9373, device='cuda:0'), tensor(4.9231, device='cuda:0'), tensor(4.9257, device='cuda:0'), tensor(4.9289, device='cuda:0'), tensor(4.9383, device='cuda:0')]

Prediction loss: [tensor(18986310., device='cuda:0'), tensor(18790072., device='cuda:0'), tensor(19558132., device='cuda:0'), tensor(19079948., device='cuda:0'), tensor(16943304., device='cuda:0'), tensor(19253998., device='cuda:0'), tensor(19271476., device='cuda:0'), tensor(18849632., device='cuda:0'), tensor(17865774., device='cuda:0'), tensor(19210640., device='cuda:0')]

Others: [{'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40909.5078, device='cuda:0'), tensor(40844.3867, device='cuda:0'), tensor(40790.2305, device='cuda:0'), tensor(41003.1367, device='cuda:0'), tensor(40889.2305, device='cuda:0'), tensor(40821.7852, device='cuda:0'), tensor(40941.3203, device='cuda:0'), tensor(40890.2266, device='cuda:0'), tensor(40876.1641, device='cuda:0'), tensor(40852.3750, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(microseconds=991212), datetime.timedelta(seconds=1, microseconds=27593), datetime.timedelta(seconds=1, microseconds=14046), datetime.timedelta(seconds=1, microseconds=12246), datetime.timedelta(microseconds=995288), datetime.timedelta(seconds=1, microseconds=67), datetime.timedelta(microseconds=992193), datetime.timedelta(microseconds=989116), datetime.timedelta(microseconds=988922), datetime.timedelta(seconds=1, microseconds=6086)]

Phi time: [datetime.timedelta(microseconds=223390), datetime.timedelta(microseconds=226814), datetime.timedelta(microseconds=240810), datetime.timedelta(microseconds=229070), datetime.timedelta(microseconds=233322), datetime.timedelta(microseconds=216602), datetime.timedelta(microseconds=238148), datetime.timedelta(microseconds=223351), datetime.timedelta(microseconds=227118), datetime.timedelta(microseconds=220938)]

