Precision: [tensor(0.3316, device='cuda:0'), tensor(0.3391, device='cuda:0'), tensor(0.3356, device='cuda:0'), tensor(0.3288, device='cuda:0'), tensor(0.3321, device='cuda:0'), tensor(0.3371, device='cuda:0'), tensor(0.3362, device='cuda:0'), tensor(0.3388, device='cuda:0'), tensor(0.3381, device='cuda:0'), tensor(0.3325, device='cuda:0')]

Output distance: [tensor(6.3166, device='cuda:0'), tensor(6.2715, device='cuda:0'), tensor(6.2925, device='cuda:0'), tensor(6.3334, device='cuda:0'), tensor(6.3135, device='cuda:0'), tensor(6.2835, device='cuda:0'), tensor(6.2888, device='cuda:0'), tensor(6.2730, device='cuda:0'), tensor(6.2778, device='cuda:0'), tensor(6.3114, device='cuda:0')]

Prediction loss: [tensor(16922790., device='cuda:0'), tensor(19543182., device='cuda:0'), tensor(14152298., device='cuda:0'), tensor(22031712., device='cuda:0'), tensor(16936440., device='cuda:0'), tensor(21085386., device='cuda:0'), tensor(15760582., device='cuda:0'), tensor(16428874., device='cuda:0'), tensor(15226351., device='cuda:0'), tensor(20215082., device='cuda:0')]

Others: [{'iter_num': 19, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 17, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 17, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 19, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 17, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 17, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 19, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 21, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 27, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 17, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40762.6602, device='cuda:0'), tensor(40879.5859, device='cuda:0'), tensor(40906.8828, device='cuda:0'), tensor(40925.1016, device='cuda:0'), tensor(40838.1953, device='cuda:0'), tensor(40489.4258, device='cuda:0'), tensor(40614.3281, device='cuda:0'), tensor(40730.9570, device='cuda:0'), tensor(40917.5078, device='cuda:0'), tensor(41462.4805, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=220861), datetime.timedelta(seconds=1, microseconds=226833), datetime.timedelta(seconds=1, microseconds=195972), datetime.timedelta(seconds=1, microseconds=198953), datetime.timedelta(seconds=1, microseconds=244762), datetime.timedelta(seconds=1, microseconds=214884), datetime.timedelta(seconds=1, microseconds=253722), datetime.timedelta(seconds=1, microseconds=242774), datetime.timedelta(seconds=1, microseconds=273638), datetime.timedelta(seconds=1, microseconds=183021)]

Phi time: [datetime.timedelta(microseconds=191194), datetime.timedelta(microseconds=197172), datetime.timedelta(microseconds=200158), datetime.timedelta(microseconds=197170), datetime.timedelta(microseconds=192190), datetime.timedelta(microseconds=203146), datetime.timedelta(microseconds=197170), datetime.timedelta(microseconds=202151), datetime.timedelta(microseconds=194183), datetime.timedelta(microseconds=197172)]

