Precision: [tensor(0.0871, device='cuda:0'), tensor(0.0470, device='cuda:0'), tensor(0.0562, device='cuda:0'), tensor(0.0831, device='cuda:0'), tensor(0.0561, device='cuda:0'), tensor(0.0797, device='cuda:0'), tensor(0.0464, device='cuda:0'), tensor(0.0441, device='cuda:0'), tensor(0.0541, device='cuda:0'), tensor(0.0887, device='cuda:0')]

Output distance: [tensor(19.8513, device='cuda:0'), tensor(19.9314, device='cuda:0'), tensor(19.9129, device='cuda:0'), tensor(19.8591, device='cuda:0'), tensor(19.9132, device='cuda:0'), tensor(19.8661, device='cuda:0'), tensor(19.9326, device='cuda:0'), tensor(19.9371, device='cuda:0'), tensor(19.9172, device='cuda:0'), tensor(19.8479, device='cuda:0')]

Prediction loss: [tensor(111.0469, device='cuda:0'), tensor(109.9517, device='cuda:0'), tensor(106.8734, device='cuda:0'), tensor(110.6785, device='cuda:0'), tensor(108.8055, device='cuda:0'), tensor(110.5668, device='cuda:0'), tensor(106.2422, device='cuda:0'), tensor(106.9488, device='cuda:0'), tensor(108.4540, device='cuda:0'), tensor(110.6356, 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=61, microseconds=881203), datetime.timedelta(seconds=61, microseconds=872345), datetime.timedelta(seconds=61, microseconds=923597), datetime.timedelta(seconds=61, microseconds=816199), datetime.timedelta(seconds=55, microseconds=243656), datetime.timedelta(seconds=61, microseconds=967426), datetime.timedelta(seconds=62, microseconds=78124), datetime.timedelta(seconds=61, microseconds=824308), datetime.timedelta(seconds=61, microseconds=920386), datetime.timedelta(seconds=62, microseconds=496)]

Phi time: [datetime.timedelta(seconds=174, microseconds=623270), datetime.timedelta(seconds=173, microseconds=774078), datetime.timedelta(seconds=173, microseconds=740837), datetime.timedelta(seconds=174, microseconds=471501), datetime.timedelta(seconds=174, microseconds=461475), datetime.timedelta(seconds=172, microseconds=40448), datetime.timedelta(seconds=170, microseconds=212160), datetime.timedelta(seconds=170, microseconds=45909), datetime.timedelta(seconds=170, microseconds=281730), datetime.timedelta(seconds=170, microseconds=273168)]

