Precision: [tensor(0.3325, device='cuda:0'), tensor(0.3724, device='cuda:0'), tensor(0.3318, device='cuda:0'), tensor(0.3708, device='cuda:0'), tensor(0.3522, device='cuda:0'), tensor(0.3458, device='cuda:0'), tensor(0.3402, device='cuda:0'), tensor(0.3726, device='cuda:0'), tensor(0.3547, device='cuda:0'), tensor(0.3485, device='cuda:0')]

Output distance: [tensor(19.3603, device='cuda:0'), tensor(19.2805, device='cuda:0'), tensor(19.3619, device='cuda:0'), tensor(19.2839, device='cuda:0'), tensor(19.3210, device='cuda:0'), tensor(19.3337, device='cuda:0'), tensor(19.3449, device='cuda:0'), tensor(19.2802, device='cuda:0'), tensor(19.3159, device='cuda:0'), tensor(19.3283, device='cuda:0')]

Prediction loss: [tensor(107.1336, device='cuda:0'), tensor(108.2040, device='cuda:0'), tensor(107.2461, device='cuda:0'), tensor(108.3579, device='cuda:0'), tensor(107.8675, device='cuda:0'), tensor(109.0886, device='cuda:0'), tensor(107.3900, device='cuda:0'), tensor(109.3979, device='cuda:0'), tensor(108.7175, device='cuda:0'), tensor(108.3045, device='cuda:0')]

Others: [{'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, '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=5, microseconds=503708), datetime.timedelta(seconds=5, microseconds=536476), datetime.timedelta(seconds=5, microseconds=525562), datetime.timedelta(seconds=5, microseconds=520584), datetime.timedelta(seconds=5, microseconds=525569), datetime.timedelta(seconds=5, microseconds=550460), datetime.timedelta(seconds=5, microseconds=525562), datetime.timedelta(seconds=5, microseconds=536519), datetime.timedelta(seconds=5, microseconds=564401), datetime.timedelta(seconds=5, microseconds=526561)]

Phi time: [datetime.timedelta(seconds=4, microseconds=400494), datetime.timedelta(seconds=4, microseconds=527247), datetime.timedelta(seconds=4, microseconds=491639), datetime.timedelta(seconds=4, microseconds=451604), datetime.timedelta(seconds=4, microseconds=487613), datetime.timedelta(seconds=4, microseconds=538910), datetime.timedelta(seconds=4, microseconds=516359), datetime.timedelta(seconds=4, microseconds=434917), datetime.timedelta(seconds=4, microseconds=465474), datetime.timedelta(seconds=4, microseconds=478821)]

