I retrained my model without Batch normalization and AlphaDropout. During learning, I could achieve an accuracy on the validation set (through the DataLoader, shuffle=True) of ~0.48, and now:

validator obtains different values, but in the same range of ~0.48 w/ and w/o shuffle=True
the result of validator and simple validator always coincide to the same value when num_workers=1 (with both shuffle=True, and shuffle=False)

My naive conclusions: model.eval() does not completely fix the values of the batch normalization and dropout layers; there is something weird happening when using multiple processes to load the data.
Can anybody explain any of these results?