Precision: [tensor(0.3115, device='cuda:0'), tensor(0.3231, device='cuda:0'), tensor(0.2958, device='cuda:0'), tensor(0.3086, device='cuda:0'), tensor(0.3124, device='cuda:0'), tensor(0.3009, device='cuda:0'), tensor(0.3111, device='cuda:0'), tensor(0.3097, device='cuda:0'), tensor(0.3009, device='cuda:0'), tensor(0.3038, device='cuda:0')]

Output distance: [tensor(20.1563, device='cuda:0'), tensor(20.0871, device='cuda:0'), tensor(20.2506, device='cuda:0'), tensor(20.1738, device='cuda:0'), tensor(20.1508, device='cuda:0'), tensor(20.2198, device='cuda:0'), tensor(20.1587, device='cuda:0'), tensor(20.1672, device='cuda:0'), tensor(20.2198, device='cuda:0'), tensor(20.2025, device='cuda:0')]

Prediction loss: [tensor(103.9074, device='cuda:0'), tensor(104.6197, device='cuda:0'), tensor(101.9886, device='cuda:0'), tensor(103.8015, device='cuda:0'), tensor(103.9543, device='cuda:0'), tensor(103.0726, device='cuda:0'), tensor(104.1110, device='cuda:0'), tensor(104.5566, device='cuda:0'), tensor(103.3608, device='cuda:0'), tensor(103.9839, device='cuda:0')]

Others: [{'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, '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=421109), datetime.timedelta(seconds=2, microseconds=436950), datetime.timedelta(seconds=2, microseconds=457944), datetime.timedelta(seconds=2, microseconds=431696), datetime.timedelta(seconds=2, microseconds=443456), datetime.timedelta(seconds=2, microseconds=421412), datetime.timedelta(seconds=2, microseconds=535390), datetime.timedelta(seconds=2, microseconds=433158), datetime.timedelta(seconds=2, microseconds=554232), datetime.timedelta(seconds=2, microseconds=431695)]

Phi time: [datetime.timedelta(seconds=4, microseconds=393701), datetime.timedelta(seconds=4, microseconds=373900), datetime.timedelta(seconds=4, microseconds=354015), datetime.timedelta(seconds=4, microseconds=355447), datetime.timedelta(seconds=4, microseconds=352854), datetime.timedelta(seconds=4, microseconds=350456), datetime.timedelta(seconds=4, microseconds=315434), datetime.timedelta(seconds=4, microseconds=358576), datetime.timedelta(seconds=4, microseconds=399965), datetime.timedelta(seconds=4, microseconds=375915)]

