Glad it works!
About the visualization of other layers, I answered a similar question regarding to this here, you can take a look: GradCam and Guided Backprop intergration in Fastai library
In short, at least for resnet model, learn.model[0][-1][-1] (aka m[0][-1][-1] in the code) is the last pooling layer of the model’s body to perform the gradcam on, so the heatmap produced at this layer should highlight the best part of your image for classification task (though I am not sure if this is equivalent to having the higher res version). To visualize other layers, you can play with x and y in m[0][x][y]