from akida.core import (Layer, SeparableConvolutionalParams, ConvolutionMode,
PoolingType, ConvolutionalParams, DataProcessingParams,
NumNeuronsParams, WeightBitsParams, LearningParams,
ActivationsParams, ConvolutionKernelParams,
PoolingParams, StrideParams)
[docs]class SeparableConvolutional(Layer):
"""Separable convolutions consist in first performing a depthwise spatial
convolution (which acts on each input channel separately) followed by a
pointwise convolution which mixes together the resulting output channels.
Intuitively, separable convolutions can be understood as a way to factorize
a convolution kernel into two smaller kernels, thus decreasing the number of
computations required to evaluate the output potentials. The
``SeparableConvolutional`` layer can also integrate a final pooling
operation to reduce its spatial output dimensions.
Args:
kernel_width (int): convolutional kernel width.
kernel_height (int): convolutional kernel height.
num_neurons (int): number of pointwise neurons.
name (str, optional): name of the layer.
convolution_mode (:obj:`ConvolutionMode`, optional): type of convolution.
stride_x (int, optional): convolution stride X.
stride_y (int, optional): convolution stride Y.
weights_bits (int, optional): number of bits used to quantize weights.
pooling_width (int, optional): pooling window width. If set to -1 it
will be global.
pooling_height (int, optional): pooling window height. If set to -1
it will be global.
pooling_type (:obj:`PoolingType`, optional): pooling type
(None, Max or Average).
pooling_stride_x (int, optional): pooling stride on x dimension.
pooling_stride_y (int, optional): pooling stride on y dimension.
activations_enabled (bool, optional): enable or disable activation
function.
threshold_fire (int, optional): threshold for neurons to fire or
generate an event.
threshold_fire_step (float, optional): length of the potential
quantization intervals.
threshold_fire_bits (int, optional): number of bits used to quantize
the neuron response.
"""
def __init__(self,
kernel_width,
kernel_height,
num_neurons,
name="",
convolution_mode=ConvolutionMode.Same,
stride_x=1,
stride_y=1,
weights_bits=2,
pooling_width=-1,
pooling_height=-1,
pooling_type=PoolingType.NoPooling,
pooling_stride_x=-1,
pooling_stride_y=-1,
activations_enabled=True,
threshold_fire=0,
threshold_fire_step=1,
threshold_fire_bits=1):
try:
params = SeparableConvolutionalParams(
ConvolutionalParams(
DataProcessingParams(
NumNeuronsParams(num_neurons),
WeightBitsParams(weights_bits), LearningParams(),
ActivationsParams(activations_enabled, threshold_fire,
threshold_fire_step,
threshold_fire_bits)),
ConvolutionKernelParams(kernel_width, kernel_height,
convolution_mode),
PoolingParams(pooling_width, pooling_height, pooling_type,
pooling_stride_x, pooling_stride_y),
StrideParams(stride_x, stride_y)))
# Call parent constructor to initialize C++ bindings
# Note that we invoke directly __init__ instead of using super, as
# specified in pybind documentation
Layer.__init__(self, params, name)
except:
self = None
raise