You can.
If the training is not stable, then test/eval does not lead to better accuracy as compared to training.
In such a cases, you can do model.train() before testing/eval so that, batchnorm running mean is adjusted and hence can be used without hampering on test/eval accuracy due to batchnorm.
Just to ensure, @smth have I understood it correctly?