How to deal with images with different input size?

[manmanCover]

It seems like collection and distribution operations require the training and testing images have the same input feature size.
So How to deal with images with different input size?

[EthanZhangYi]

The PSA module is designed to produce global information over the whole image. For the images with different input size, we do inference by sliding window mechanism.

[manmanCover]

@EthanZhangYi Thanks for your reply. You mean that the current implementation can use sliding window？

[EthanZhangYi]

@manmanCover
For training, we crop images randomly.
For evaluation, we test images by sliding-window.

Later we will release our pytorch version. It is nearly ready.

[manmanCover]

@EthanZhangYi Thank you！

[manmanCover]

@EthanZhangYi By the way, what's the size of the sliding window? I found that this detail hasn't been mentioned in the paper?
And how do you implement the sliding operation? Crop images into patches and then join them together after cnn？

[EthanZhangYi]

You can find the implementation of sliding-window processing here. https://github.com/hszhao/PSANet/blob/master/evaluation/scale_process.m
stride = 2/3
We crop images into patches and then join them together after CNN.