Precision: [tensor(0.1528, device='cuda:0'), tensor(0.0742, device='cuda:0'), tensor(0.0780, device='cuda:0'), tensor(0.0606, device='cuda:0'), tensor(0.0889, device='cuda:0'), tensor(0.0922, device='cuda:0'), tensor(0.0963, device='cuda:0'), tensor(0.0535, device='cuda:0'), tensor(0.1632, device='cuda:0'), tensor(0.0937, device='cuda:0')]

Output distance: [tensor(19.7198, device='cuda:0'), tensor(19.8770, device='cuda:0'), tensor(19.8694, device='cuda:0'), tensor(19.9042, device='cuda:0'), tensor(19.8476, device='cuda:0'), tensor(19.8410, device='cuda:0'), tensor(19.8328, device='cuda:0'), tensor(19.9184, device='cuda:0'), tensor(19.6989, device='cuda:0'), tensor(19.8380, device='cuda:0')]

Prediction loss: [tensor(106.7103, device='cuda:0'), tensor(107.5949, device='cuda:0'), tensor(105.1903, device='cuda:0'), tensor(103.8133, device='cuda:0'), tensor(105.0351, device='cuda:0'), tensor(106.9539, device='cuda:0'), tensor(106.2930, device='cuda:0'), tensor(105.1046, device='cuda:0'), tensor(107.7361, device='cuda:0'), tensor(104.6620, device='cuda:0')]

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

Compressed training loss: [tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=567620), datetime.timedelta(seconds=1, microseconds=578584), datetime.timedelta(seconds=1, microseconds=566126), datetime.timedelta(seconds=1, microseconds=605897), datetime.timedelta(seconds=1, microseconds=615560), datetime.timedelta(seconds=1, microseconds=579411), datetime.timedelta(seconds=1, microseconds=591007), datetime.timedelta(seconds=1, microseconds=585427), datetime.timedelta(seconds=1, microseconds=594079), datetime.timedelta(seconds=1, microseconds=574688)]

Phi time: [datetime.timedelta(seconds=170, microseconds=281419), datetime.timedelta(seconds=170, microseconds=817655), datetime.timedelta(seconds=170, microseconds=535910), datetime.timedelta(seconds=170, microseconds=572009), datetime.timedelta(seconds=170, microseconds=606335), datetime.timedelta(seconds=170, microseconds=306323), datetime.timedelta(seconds=170, microseconds=377111), datetime.timedelta(seconds=170, microseconds=409826), datetime.timedelta(seconds=170, microseconds=678794), datetime.timedelta(seconds=170, microseconds=32946)]

