Precision: [tensor(0.0866, device='cuda:0'), tensor(0.0748, device='cuda:0'), tensor(0.0783, device='cuda:0'), tensor(0.1622, device='cuda:0'), tensor(0.0638, device='cuda:0'), tensor(0.0718, device='cuda:0'), tensor(0.1418, device='cuda:0'), tensor(0.1019, device='cuda:0'), tensor(0.0765, device='cuda:0'), tensor(0.0931, device='cuda:0')]

Output distance: [tensor(19.8522, device='cuda:0'), tensor(19.8758, device='cuda:0'), tensor(19.8688, device='cuda:0'), tensor(19.7010, device='cuda:0'), tensor(19.8978, device='cuda:0'), tensor(19.8818, device='cuda:0'), tensor(19.7418, device='cuda:0'), tensor(19.8216, device='cuda:0'), tensor(19.8724, device='cuda:0'), tensor(19.8392, device='cuda:0')]

Prediction loss: [tensor(105.5925, device='cuda:0'), tensor(105.7216, device='cuda:0'), tensor(107.9013, device='cuda:0'), tensor(108.5735, device='cuda:0'), tensor(108.4406, device='cuda:0'), tensor(106.6197, device='cuda:0'), tensor(108.8806, device='cuda:0'), tensor(106.9604, device='cuda:0'), tensor(104.1519, device='cuda:0'), tensor(105.3160, device='cuda:0')]

Others: [{'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, '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': 7, '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: [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=2, microseconds=283270), datetime.timedelta(seconds=2, microseconds=317168), datetime.timedelta(seconds=2, microseconds=277747), datetime.timedelta(seconds=2, microseconds=269439), datetime.timedelta(seconds=2, microseconds=225313), datetime.timedelta(seconds=2, microseconds=219852), datetime.timedelta(seconds=2, microseconds=298112), datetime.timedelta(seconds=2, microseconds=209362), datetime.timedelta(seconds=2, microseconds=245486), datetime.timedelta(seconds=2, microseconds=209113)]

Phi time: [datetime.timedelta(seconds=184, microseconds=432153), datetime.timedelta(seconds=182, microseconds=248494), datetime.timedelta(seconds=174, microseconds=490968), datetime.timedelta(seconds=174, microseconds=805407), datetime.timedelta(seconds=174, microseconds=475817), datetime.timedelta(seconds=173, microseconds=832750), datetime.timedelta(seconds=174, microseconds=220395), datetime.timedelta(seconds=174, microseconds=285480), datetime.timedelta(seconds=174, microseconds=548078), datetime.timedelta(seconds=174, microseconds=395676)]

