Keras implementation of the Global Context block from the paper GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond.
Supports Conv1D, Conv2D and Conv3D directly with no modifications.
Import global_context_block from gc.py and provide it a tensor as input.
from gc import global_context_block
ip = Input(...)
x = ConvND(...)(ip)
# apply Global Context
x = global_context_block(x, reduction_ratio=16, transform_activation='linear')
...There are just two parameters to manage :
- reduction_ratio: The ratio to scale the transform block.
- transform_activation: The activation function prior to addition of the input with the context.
The paper uses no activation, but `sigmoid` may do better.