Precision: [tensor(0.3211, device='cuda:0'), tensor(0.3139, device='cuda:0'), tensor(0.3226, device='cuda:0'), tensor(0.3203, device='cuda:0'), tensor(0.3098, device='cuda:0'), tensor(0.3227, device='cuda:0'), tensor(0.3206, device='cuda:0'), tensor(0.3159, device='cuda:0'), tensor(0.2988, device='cuda:0'), tensor(0.2949, device='cuda:0')]

Output distance: [tensor(20.0989, device='cuda:0'), tensor(20.1421, device='cuda:0'), tensor(20.0901, device='cuda:0'), tensor(20.1034, device='cuda:0'), tensor(20.1666, device='cuda:0'), tensor(20.0892, device='cuda:0'), tensor(20.1019, device='cuda:0'), tensor(20.1303, device='cuda:0'), tensor(20.2328, device='cuda:0'), tensor(20.2557, device='cuda:0')]

Prediction loss: [tensor(103.4694, device='cuda:0'), tensor(103.9455, device='cuda:0'), tensor(102.7731, device='cuda:0'), tensor(104.0823, device='cuda:0'), tensor(102.1281, device='cuda:0'), tensor(104.0260, device='cuda:0'), tensor(103.6840, device='cuda:0'), tensor(103.6709, device='cuda:0'), tensor(102.4806, device='cuda:0'), tensor(103.3174, device='cuda:0')]

Others: [{'iter_num': 13, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(19848, 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=628251), datetime.timedelta(seconds=2, microseconds=721128), datetime.timedelta(seconds=2, microseconds=719419), datetime.timedelta(seconds=2, microseconds=717188), datetime.timedelta(seconds=2, microseconds=729968), datetime.timedelta(seconds=2, microseconds=637705), datetime.timedelta(seconds=2, microseconds=727841), datetime.timedelta(seconds=2, microseconds=733617), datetime.timedelta(seconds=2, microseconds=723006), datetime.timedelta(seconds=2, microseconds=727454)]

Phi time: [datetime.timedelta(seconds=4, microseconds=389132), datetime.timedelta(seconds=4, microseconds=416182), datetime.timedelta(seconds=4, microseconds=429156), datetime.timedelta(seconds=4, microseconds=384470), datetime.timedelta(seconds=4, microseconds=384254), datetime.timedelta(seconds=4, microseconds=383685), datetime.timedelta(seconds=4, microseconds=437749), datetime.timedelta(seconds=4, microseconds=399879), datetime.timedelta(seconds=4, microseconds=370952), datetime.timedelta(seconds=4, microseconds=392420)]

