caffe-cp-decomposition

This is a heavy modification of the original cp-decomposition algorithm that implements the method from their paper Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition.

The most important modification is that now you can decompose more than one layer, according to given tensor rank (refer to cpd_example.py).

It also fixes some bugs and remove unnecessary complexity, making the usage and extensibility much simpler. In other words, it does what it is supposed to do: CP decomposition of convolutional layers. No more, no less.

Requirements

• pycaffe
• scikit-tensor

Limitations

With simplicity in mind, some limitations arises such as:

• Convolutional layer paramaters that are non-uniform (e.g kernel_h, kernel_w, pad_h, pad_w, stride_h and stride_w ) are not supported. However, you can easily modify the code to your needs.
• Multi branch networks are not supported.