Precision: [tensor(0.4708, device='cuda:0'), tensor(0.4878, device='cuda:0'), tensor(0.4692, device='cuda:0'), tensor(0.4784, device='cuda:0'), tensor(0.4690, device='cuda:0'), tensor(0.4944, device='cuda:0'), tensor(0.4800, device='cuda:0'), tensor(0.4926, device='cuda:0'), tensor(0.4717, device='cuda:0'), tensor(0.4779, device='cuda:0')]

Output distance: [tensor(19.0837, device='cuda:0'), tensor(19.0499, device='cuda:0'), tensor(19.0871, device='cuda:0'), tensor(19.0686, device='cuda:0'), tensor(19.0874, device='cuda:0'), tensor(19.0366, device='cuda:0'), tensor(19.0653, device='cuda:0'), tensor(19.0402, device='cuda:0'), tensor(19.0819, device='cuda:0'), tensor(19.0695, device='cuda:0')]

Prediction loss: [tensor(107.9047, device='cuda:0'), tensor(108.7117, device='cuda:0'), tensor(107.9343, device='cuda:0'), tensor(108.1953, device='cuda:0'), tensor(107.5987, device='cuda:0'), tensor(108.5247, device='cuda:0'), tensor(108.6201, device='cuda:0'), tensor(109.0486, device='cuda:0'), tensor(107.7216, device='cuda:0'), tensor(108.0683, device='cuda:0')]

Others: [{'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, '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=242466), datetime.timedelta(seconds=2, microseconds=239235), datetime.timedelta(seconds=2, microseconds=229112), datetime.timedelta(seconds=2, microseconds=229018), datetime.timedelta(seconds=2, microseconds=205971), datetime.timedelta(seconds=2, microseconds=279229), datetime.timedelta(seconds=2, microseconds=237266), datetime.timedelta(seconds=2, microseconds=244457), datetime.timedelta(seconds=2, microseconds=215336), datetime.timedelta(seconds=2, microseconds=239837)]

Phi time: [datetime.timedelta(seconds=4, microseconds=493096), datetime.timedelta(seconds=4, microseconds=488361), datetime.timedelta(seconds=4, microseconds=472415), datetime.timedelta(seconds=4, microseconds=488409), datetime.timedelta(seconds=4, microseconds=491610), datetime.timedelta(seconds=4, microseconds=511684), datetime.timedelta(seconds=4, microseconds=544587), datetime.timedelta(seconds=4, microseconds=489308), datetime.timedelta(seconds=4, microseconds=467682), datetime.timedelta(seconds=4, microseconds=499093)]

