Precision: [tensor(0.4047, device='cuda:0'), tensor(0.4095, device='cuda:0'), tensor(0.4064, device='cuda:0'), tensor(0.3899, device='cuda:0'), tensor(0.4064, device='cuda:0'), tensor(0.3938, device='cuda:0'), tensor(0.3968, device='cuda:0'), tensor(0.4057, device='cuda:0'), tensor(0.4061, device='cuda:0'), tensor(0.4033, device='cuda:0')]

Output distance: [tensor(19.5973, device='cuda:0'), tensor(19.5683, device='cuda:0'), tensor(19.5868, device='cuda:0'), tensor(19.6859, device='cuda:0'), tensor(19.5871, device='cuda:0'), tensor(19.6623, device='cuda:0'), tensor(19.6445, device='cuda:0'), tensor(19.5910, device='cuda:0'), tensor(19.5889, device='cuda:0'), tensor(19.6058, device='cuda:0')]

Prediction loss: [tensor(103.8297, device='cuda:0'), tensor(104.4953, device='cuda:0'), tensor(104.5663, device='cuda:0'), tensor(104.1989, device='cuda:0'), tensor(104.9403, device='cuda:0'), tensor(103.8234, device='cuda:0'), tensor(104.2501, device='cuda:0'), tensor(105.1031, device='cuda:0'), tensor(104.5600, device='cuda:0'), tensor(104.5601, device='cuda:0')]

Others: [{'iter_num': 13, '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': 13, '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': 13, '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': 13, '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': 13, '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=780825), datetime.timedelta(seconds=2, microseconds=765148), datetime.timedelta(seconds=2, microseconds=783683), datetime.timedelta(seconds=2, microseconds=766671), datetime.timedelta(seconds=2, microseconds=766940), datetime.timedelta(seconds=2, microseconds=766988), datetime.timedelta(seconds=2, microseconds=899120), datetime.timedelta(seconds=2, microseconds=766652), datetime.timedelta(seconds=2, microseconds=783630), datetime.timedelta(seconds=2, microseconds=762986)]

Phi time: [datetime.timedelta(seconds=4, microseconds=649489), datetime.timedelta(seconds=4, microseconds=717602), datetime.timedelta(seconds=4, microseconds=648089), datetime.timedelta(seconds=4, microseconds=652148), datetime.timedelta(seconds=4, microseconds=714532), datetime.timedelta(seconds=4, microseconds=633565), datetime.timedelta(seconds=4, microseconds=666325), datetime.timedelta(seconds=4, microseconds=682364), datetime.timedelta(seconds=4, microseconds=682718), datetime.timedelta(seconds=4, microseconds=659886)]

