from akida.core import (Layer, SeparableConvolutionalParams, Padding, PoolType,
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_size (list): list of 2 integer representing the spatial
dimensions of the convolutional kernel.
filters (int): number of pointwise filters.
name (str, optional): name of the layer.
padding (:obj:`Padding`, optional): type of convolution.
kernel_stride (list, optional): list of 2 integer representing the
convolution stride (X, Y).
weights_bits (int, optional): number of bits used to quantize weights.
pool_size (list, optional): list of 2 integers, representing the window
size over which to take the maximum or the average (depending on
pool_type parameter).
pool_type (:obj:`PoolType`, optional): pooling type
(None, Max or Average).
pool_stride (list, optional): list of 2 integers representing
the stride dimensions.
activation (bool, optional): enable or disable activation
function.
threshold (int, optional): threshold for neurons to fire or
generate an event.
act_step (float, optional): length of the potential
quantization intervals.
act_bits (int, optional): number of bits used to quantize
the neuron response.
"""
def __init__(self,
kernel_size,
filters,
name="",
padding=Padding.Same,
kernel_stride=(1, 1),
weights_bits=2,
pool_size=(-1, -1),
pool_type=PoolType.NoPooling,
pool_stride=(-1, -1),
activation=True,
threshold=0,
act_step=1,
act_bits=1):
try:
params = SeparableConvolutionalParams(
ConvolutionalParams(
DataProcessingParams(
NumNeuronsParams(filters),
WeightBitsParams(weights_bits), LearningParams(),
ActivationsParams(activation, threshold, act_step,
act_bits)),
ConvolutionKernelParams(kernel_size, padding),
PoolingParams(pool_size, pool_type, pool_stride),
StrideParams(kernel_stride)))
# 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