Precision: [tensor(0.0520, device='cuda:0'), tensor(0.1119, device='cuda:0'), tensor(0.0691, device='cuda:0'), tensor(0.0661, device='cuda:0'), tensor(0.0813, device='cuda:0'), tensor(0.0612, device='cuda:0'), tensor(0.1329, device='cuda:0'), tensor(0.0719, device='cuda:0'), tensor(0.1217, device='cuda:0'), tensor(0.0963, device='cuda:0')]

Output distance: [tensor(19.9214, device='cuda:0'), tensor(19.8017, device='cuda:0'), tensor(19.8872, device='cuda:0'), tensor(19.8933, device='cuda:0'), tensor(19.8628, device='cuda:0'), tensor(19.9030, device='cuda:0'), tensor(19.7597, device='cuda:0'), tensor(19.8815, device='cuda:0'), tensor(19.7820, device='cuda:0'), tensor(19.8328, device='cuda:0')]

Prediction loss: [tensor(104.7572, device='cuda:0'), tensor(107.3377, device='cuda:0'), tensor(104.2574, device='cuda:0'), tensor(105.8284, device='cuda:0'), tensor(108.9240, device='cuda:0'), tensor(104.5105, device='cuda:0'), tensor(105.7323, device='cuda:0'), tensor(103.3214, device='cuda:0'), tensor(109.7257, device='cuda:0'), tensor(105.7279, device='cuda:0')]

Others: [{'iter_num': 15, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, '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=2, microseconds=576148), datetime.timedelta(seconds=2, microseconds=550307), datetime.timedelta(seconds=2, microseconds=633903), datetime.timedelta(seconds=2, microseconds=573164), datetime.timedelta(seconds=2, microseconds=516398), datetime.timedelta(seconds=2, microseconds=613984), datetime.timedelta(seconds=2, microseconds=579132), datetime.timedelta(seconds=2, microseconds=576127), datetime.timedelta(seconds=2, microseconds=517394), datetime.timedelta(seconds=2, microseconds=522372)]

Phi time: [datetime.timedelta(seconds=4, microseconds=255518), datetime.timedelta(seconds=4, microseconds=258997), datetime.timedelta(seconds=4, microseconds=218211), datetime.timedelta(seconds=4, microseconds=242667), datetime.timedelta(seconds=4, microseconds=205807), datetime.timedelta(seconds=4, microseconds=213370), datetime.timedelta(seconds=4, microseconds=216972), datetime.timedelta(seconds=4, microseconds=243445), datetime.timedelta(seconds=4, microseconds=199003), datetime.timedelta(seconds=4, microseconds=278472)]

