Precision: [tensor(0.2338, device='cuda:0'), tensor(0.2154, device='cuda:0'), tensor(0.2700, device='cuda:0'), tensor(0.2095, device='cuda:0'), tensor(0.2121, device='cuda:0'), tensor(0.2627, device='cuda:0'), tensor(0.2344, device='cuda:0'), tensor(0.1799, device='cuda:0'), tensor(0.2390, device='cuda:0'), tensor(0.2116, device='cuda:0')]

Output distance: [tensor(19.5577, device='cuda:0'), tensor(19.5946, device='cuda:0'), tensor(19.4855, device='cuda:0'), tensor(19.6064, device='cuda:0'), tensor(19.6013, device='cuda:0'), tensor(19.5000, device='cuda:0'), tensor(19.5565, device='cuda:0'), tensor(19.6657, device='cuda:0'), tensor(19.5475, device='cuda:0'), tensor(19.6022, device='cuda:0')]

Prediction loss: [tensor(110.1555, device='cuda:0'), tensor(108.3403, device='cuda:0'), tensor(109.5477, device='cuda:0'), tensor(109.5478, device='cuda:0'), tensor(107.8886, device='cuda:0'), tensor(109.3761, device='cuda:0'), tensor(108.7463, device='cuda:0'), tensor(106.9124, device='cuda:0'), tensor(108.3622, device='cuda:0'), tensor(108.6767, 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=101, microseconds=424784), datetime.timedelta(seconds=101, microseconds=763346), datetime.timedelta(seconds=101, microseconds=844887), datetime.timedelta(seconds=102, microseconds=320386), datetime.timedelta(seconds=101, microseconds=562290), datetime.timedelta(seconds=101, microseconds=300853), datetime.timedelta(seconds=101, microseconds=882558), datetime.timedelta(seconds=102, microseconds=161028), datetime.timedelta(seconds=102, microseconds=350585), datetime.timedelta(seconds=101, microseconds=925704)]

Phi time: [datetime.timedelta(seconds=4, microseconds=784278), datetime.timedelta(seconds=4, microseconds=551131), datetime.timedelta(seconds=4, microseconds=534653), datetime.timedelta(seconds=4, microseconds=579161), datetime.timedelta(seconds=4, microseconds=549552), datetime.timedelta(seconds=4, microseconds=592609), datetime.timedelta(seconds=4, microseconds=668744), datetime.timedelta(seconds=4, microseconds=691959), datetime.timedelta(seconds=4, microseconds=645063), datetime.timedelta(seconds=4, microseconds=535244)]

