Problem about using slim.batch_norm() of Tensorflow

After using resnet_v2_50 in tensorflow/models, I found that the inference result is totally incorrect, though the training accuracy looks very well.
Firstly, I suspected the regularization of samples:

Indeed I had extended the image to a too big size. But after I changing padding size to ’10’, the inference accuracy was still incorrect.
Then I checked the code about importing data:

and changed my inference code as the data importing routines. But the problem still existed.

About one week past. Finally, I found this issue in Github. It explains all my questions: the cause is the slim.batch_norm(). After I adding these code to my program (learning from slim.create_train_op()):

The inference accuracy is — still low. Without other choice, I removed all slim.batch_norm() in resnet_v2.py, and at this time inference accuracy becomes the same with training accuracy.
Looks problem partly been solved, but I still need to find out why sli.batch_norm() doesn’t work well in inference …

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