Sorry I was wrong in stating on the quadratic relationship. But the Activation (eg Relu) and output regularization for the conv2d would introduce nonlinear effects. So it is not possible to model 2 consecutive conv into one. Even if there is no Relu or other nonlinear effects, when you try to force fuse of 2 pure Conv ops, you would have an expanded kernel size, defeating the purpose of fuse. If your intention is to reduce MAC counts, consider a new model architecture and retrain the network.
I see. Why does it result in a quadratic operation though?
The proposed fusion is not feasible though, mathematically speaking. Conceptually it results in a quadratic operations on the first level input which cannot be modeled as a conv.
Hello everyone,
While diving into kernel fusion in TensorFlow, I noticed that the main goal is to fuse convolution layers with following element wise operations, but never fuse two convolutions with each other. I was wondering if you plan on doing so. I suppose it will be a very interesting and useful feature, to which I would like to contribute to.
Thank you,
Dimitra Karatza
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