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I have a model trained in Caffe that works perfectly on CPU, nevertheless, although the conversion to graph works correctly and I can run inference with no problems I always get very bad results at the output layer.
I use an input of dimensions (1x3x100x10) with kernels of (3x3). I am not sure if this could be the problem, I read in the documentation that only squared kernels could be used, but I didn't find information about the inputs.
I use a convolutional layer at the first step and when I compare both outputs (from my caffe model, and from the converted graph file) I find both very different.
Is this a bug or a not supported feature?.
PD: I tested other models (with square inputs, for example, yolo) and they all work fine.
Thanks for your answers!