Comments (5)
Could you please attach the full error messages and tell me the numpy version? We should have handled this condition at here.
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Test with best model: => Loaded checkpoint '/home/lz/logs/model_best.pth.tar' Extract Features: [1/13] Time 0.755 (0.755) Data 0.618 (0.618) Extract Features: [2/13] Time 0.136 (0.445) Data 0.001 (0.309) Extract Features: [3/13] Time 0.138 (0.343) Data 0.000 (0.206) Extract Features: [4/13] Time 0.136 (0.291) Data 0.000 (0.155) Extract Features: [5/13] Time 0.138 (0.261) Data 0.000 (0.124) Extract Features: [6/13] Time 0.137 (0.240) Data 0.000 (0.103) Extract Features: [7/13] Time 0.137 (0.225) Data 0.000 (0.089) Extract Features: [8/13] Time 0.137 (0.214) Data 0.000 (0.078) Extract Features: [9/13] Time 0.138 (0.206) Data 0.000 (0.069) Extract Features: [10/13] Time 0.135 (0.199) Data 0.000 (0.062) Extract Features: [11/13] Time 0.138 (0.193) Data 0.000 (0.056) Extract Features: [12/13] Time 0.136 (0.188) Data 0.000 (0.052) Extract Features: [13/13] Time 0.137 (0.184) Data 0.000 (0.048) Extract Features: [1/20] Time 0.357 (0.357) Data 0.222 (0.222) Extract Features: [2/20] Time 0.137 (0.247) Data 0.001 (0.111) Extract Features: [3/20] Time 0.138 (0.211) Data 0.000 (0.074) Extract Features: [4/20] Time 0.137 (0.192) Data 0.000 (0.056) Extract Features: [5/20] Time 0.139 (0.182) Data 0.000 (0.045) Extract Features: [6/20] Time 0.136 (0.174) Data 0.000 (0.037) Extract Features: [7/20] Time 0.139 (0.169) Data 0.000 (0.032) Extract Features: [8/20] Time 0.137 (0.165) Data 0.000 (0.028) Extract Features: [9/20] Time 0.137 (0.162) Data 0.000 (0.025) Extract Features: [10/20] Time 0.137 (0.159) Data 0.000 (0.023) Extract Features: [11/20] Time 0.139 (0.157) Data 0.000 (0.020) Extract Features: [12/20] Time 0.137 (0.156) Data 0.000 (0.019) Extract Features: [13/20] Time 0.138 (0.154) Data 0.000 (0.017) Extract Features: [14/20] Time 0.136 (0.153) Data 0.000 (0.016) Extract Features: [15/20] Time 0.139 (0.152) Data 0.000 (0.015) Extract Features: [16/20] Time 0.137 (0.151) Data 0.000 (0.014) Extract Features: [17/20] Time 0.137 (0.150) Data 0.000 (0.013) Extract Features: [18/20] Time 0.136 (0.150) Data 0.000 (0.013) Extract Features: [19/20] Time 0.138 (0.149) Data 0.000 (0.012) Extract Features: [20/20] Time 0.582 (0.171) Data 0.000 (0.011) Traceback (most recent call last): File "", line 1, in runfile('/home/lz/toolbox/open-reid/examples/softmax_loss.py', wdir='/home/lz/toolbox/open-reid/examples') File "/home/lz/anaconda3/lib/python3.6/site-packages/spyder/utils/site/sitecustomize.py", line 880, in runfile execfile(filename, namespace) File "/home/lz/anaconda3/lib/python3.6/site-packages/spyder/utils/site/sitecustomize.py", line 102, in execfile exec(compile(f.read(), filename, 'exec'), namespace) File "/home/lz/toolbox/open-reid/examples/softmax_loss.py", line 217, in main(parser.parse_args()) File "/home/lz/toolbox/open-reid/examples/softmax_loss.py", line 169, in main evaluator.evaluate(test_loader, dataset.query, dataset.gallery, metric) File "/home/lz/anaconda3/lib/python3.6/site-packages/open_reid-0.2.0-py3.6.egg/reid/evaluators.py", line 119, in evaluate distmat = pairwise_distance(features, query, gallery, metric=metric) File "/home/lz/anaconda3/lib/python3.6/site-packages/open_reid-0.2.0-py3.6.egg/reid/evaluators.py", line 60, in pairwise_distance x = metric.transform(x) File "/home/lz/anaconda3/lib/python3.6/site-packages/open_reid-0.2.0-py3.6.egg/reid/dist_metric.py", line 25, in transform X = self.metric.transform(X) File "/home/lz/anaconda3/lib/python3.6/site-packages/metric_learn-0.3.0-py3.6.egg/metric_learn/base_metric.py", line 46, in transform L = self.transformer() File "/home/lz/anaconda3/lib/python3.6/site-packages/metric_learn-0.3.0-py3.6.egg/metric_learn/base_metric.py", line 29, in transformer return inv(cholesky(self.metric())) File "/home/lz/anaconda3/lib/python3.6/site-packages/numpy/linalg/linalg.py", line 612, in cholesky r = gufunc(a, signature=signature, extobj=extobj) File "/home/lz/anaconda3/lib/python3.6/site-packages/numpy/linalg/linalg.py", line 93, in _raise_linalgerror_nonposdef raise LinAlgError("Matrix is not positive definite") LinAlgError: Matrix is not positive definite
Numpy version: 1.12.1
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Have you modified the code? Did you have metric.train(model, train_loader)
before the evaluation? Just like this line.
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Yes I have that line of code. I did not modify the code. The error occurred when training kissme on viper, but everything was fine on market1501.
from open-reid.
It was caused by numerical unstability. We have changed to an iterative algorithm to make the dist-metric matrix positive definite. See our recent commit.
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Related Issues (20)
- Dependencies - setup.py
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