I am trying to create visual explanations such as GradCAM from a trained object detection model. In order to implement the algorithm I need to access intermediate tensors and calculate gradients to the feature maps. The frozen inference graph seems to omit a lot of information such as tensor names, after using the exporter script (exporter_main_v2.py), therefore I am not able to access a specific tensor.
Using the "training model" (before converting to pb), I am also struggling to access specific tensors, because the model is not plain Keras/Tensorflow, but instead parts of the model are abstracted away in classes such as SSDMetaArch, SSDMobileNetFeatureExtractor etc. which hide the actual implementation of the model.
Any idea how to approach this problem?