Keras implementation of Non-local blocks from the paper "Non-local Neural Networks"
- Support for
"Gaussian"
,"Embedded Gaussian"
and"Dot"
instantiations of the Non-Local block. - Support for variable shielded computation mode (reduces computation by N**2 x, where N is default to 2)
- Support for
"Concatenation"
instantiation will be supported when authors release their code.
The script non_local.py
contains a single function : non_local_block
which takes in an input tensor and wraps a non-local block around it.
from non_local import non_local_block
ip = Input(shape=(##)) # this can be a rank 3, 4 or 5 tensor shape
x = ConvND(...) # as againm can be Conv1D, Conv2D or Conv3D
x = non_local_block(x, computation_compression=2, mode='embedded')
...
From the paper, a basic Non-Local block looks like below (with the Embedded Gaussian instantiation)