roi-pooling | repo contains the implementation of Region of Interest | Machine Learning library

How can I perform data augmentation when I use ROI-Pooling in a CNN network which I developed using MXnet ?

For example suppose I have a resnet50 architecture which uses a roi-pooling layer and I want to use random-crops data augmentation in the ImageRecord Iterator.

Is there an automatic way that data coordinates in the rois passed to the roi pooling layer, transform so as to be applied in images generated by the data-augmentation process of the ImageRecord Iterator ?

I am currently reading the paper on 'CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection', it is using the skip-connection to fuse conv3-3, conv4-3 and conv5-3 together, the steps are shown below

Extract the feature maps of the face region (at multiple scales conv3-3, conv4-3, conv5-3) and apply RoI-Pooling to it (i.e. convert to a fixed height and width). L2-normalize each feature map. Concatenate the (RoI-pooled and normalized) feature maps of the face (at multiple scales) with each other (creates one tensor). Apply a 1x1 convolution to the face tensor. Apply two fully connected layers to the face tensor, creating a vector.

I used the caffe and made a prototxt based on faster-RCNN VGG16 , the following parts are added into the original prototxt