Your classifier is trained and frozen. You want one last point of accuracy without retraining anything, so you reach for test-time augmentation: instead of predicting once per image, predict on several versions of it and average the predictions.
The test set holds 1,250 images. For every test image, TTA builds:
runs the model on every one of those versions, and averages the results.
One forward pass is one run of the model on one version of one image. Plain prediction, with no TTA, is one forward pass per test image.
How many extra forward passes does TTA cost over plain prediction on this test set?
Your answer is a whole number of forward passes.