Precision: [tensor(0.5639, device='cuda:0'), tensor(0.5372, device='cuda:0'), tensor(0.5394, device='cuda:0'), tensor(0.5629, device='cuda:0'), tensor(0.5476, device='cuda:0'), tensor(0.5407, device='cuda:0'), tensor(0.5376, device='cuda:0'), tensor(0.5499, device='cuda:0'), tensor(0.5585, device='cuda:0'), tensor(0.5417, device='cuda:0')]

Output distance: [tensor(18.8975, device='cuda:0'), tensor(18.9510, device='cuda:0'), tensor(18.9465, device='cuda:0'), tensor(18.8996, device='cuda:0'), tensor(18.9302, device='cuda:0'), tensor(18.9441, device='cuda:0'), tensor(18.9501, device='cuda:0'), tensor(18.9256, device='cuda:0'), tensor(18.9084, device='cuda:0'), tensor(18.9420, device='cuda:0')]

Prediction loss: [tensor(108.7343, device='cuda:0'), tensor(108.7604, device='cuda:0'), tensor(108.8666, device='cuda:0'), tensor(109.2237, device='cuda:0'), tensor(108.7819, device='cuda:0'), tensor(108.5798, device='cuda:0'), tensor(108.4202, device='cuda:0'), tensor(107.9861, device='cuda:0'), tensor(108.7259, device='cuda:0'), tensor(109.1727, device='cuda:0')]

Others: [{'iter_num': 9, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}]

Compressed training loss: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=2, microseconds=713576), datetime.timedelta(seconds=2, microseconds=856972), datetime.timedelta(seconds=2, microseconds=861954), datetime.timedelta(seconds=2, microseconds=929666), datetime.timedelta(seconds=2, microseconds=721541), datetime.timedelta(seconds=2, microseconds=852988), datetime.timedelta(seconds=2, microseconds=725523), datetime.timedelta(seconds=2, microseconds=852989), datetime.timedelta(seconds=2, microseconds=838958), datetime.timedelta(seconds=2, microseconds=743364)]

Phi time: [datetime.timedelta(seconds=5, microseconds=239555), datetime.timedelta(seconds=5, microseconds=144255), datetime.timedelta(seconds=5, microseconds=125663), datetime.timedelta(seconds=5, microseconds=188077), datetime.timedelta(seconds=5, microseconds=125407), datetime.timedelta(seconds=5, microseconds=160152), datetime.timedelta(seconds=5, microseconds=152865), datetime.timedelta(seconds=5, microseconds=163542), datetime.timedelta(seconds=5, microseconds=81857), datetime.timedelta(seconds=5, microseconds=118175)]

