The adaptive pooling only happens right before the classifier part of the network and just serves to average the size down to a single feature vector. Issue with something like 28x28 is that since there are several layers with stride=2, you will be disproportionally losing values out of the right side of the image. Again, it probably won’t be an issue for MNIST, because the image is mostly uniform background near the sides anyway.