Precision: [tensor(0.2749, device='cuda:0'), tensor(0.2689, device='cuda:0'), tensor(0.2645, device='cuda:0'), tensor(0.2402, device='cuda:0'), tensor(0.2722, device='cuda:0'), tensor(0.2285, device='cuda:0'), tensor(0.3157, device='cuda:0'), tensor(0.2093, device='cuda:0'), tensor(0.2642, device='cuda:0'), tensor(0.2612, device='cuda:0')]

Output distance: [tensor(19.4755, device='cuda:0'), tensor(19.4876, device='cuda:0'), tensor(19.4964, device='cuda:0'), tensor(19.5450, device='cuda:0'), tensor(19.4810, device='cuda:0'), tensor(19.5683, device='cuda:0'), tensor(19.3939, device='cuda:0'), tensor(19.6067, device='cuda:0'), tensor(19.4970, device='cuda:0'), tensor(19.5030, device='cuda:0')]

Prediction loss: [tensor(106.9120, device='cuda:0'), tensor(108.0617, device='cuda:0'), tensor(106.5768, device='cuda:0'), tensor(107.6225, device='cuda:0'), tensor(107.8180, device='cuda:0'), tensor(107.7243, device='cuda:0'), tensor(110.0270, device='cuda:0'), tensor(105.7950, device='cuda:0'), tensor(107.0154, device='cuda:0'), tensor(106.6245, 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=924837), datetime.timedelta(seconds=1, microseconds=904923), datetime.timedelta(seconds=1, microseconds=873055), datetime.timedelta(seconds=1, microseconds=882018), datetime.timedelta(seconds=1, microseconds=875038), datetime.timedelta(seconds=1, microseconds=855132), datetime.timedelta(seconds=1, microseconds=861108), datetime.timedelta(seconds=1, microseconds=861156), datetime.timedelta(seconds=1, microseconds=874053), datetime.timedelta(seconds=1, microseconds=872062)]

Phi time: [datetime.timedelta(seconds=4, microseconds=571571), datetime.timedelta(seconds=4, microseconds=514973), datetime.timedelta(seconds=4, microseconds=568350), datetime.timedelta(seconds=4, microseconds=566406), datetime.timedelta(seconds=4, microseconds=592287), datetime.timedelta(seconds=4, microseconds=614890), datetime.timedelta(seconds=4, microseconds=612601), datetime.timedelta(seconds=4, microseconds=636722), datetime.timedelta(seconds=4, microseconds=649649), datetime.timedelta(seconds=4, microseconds=654893)]

