from akida.core import (Layer, SeparableConvolutionalParams, Padding, PoolType,
ConvolutionalParams, DataProcessingParams,
NumNeuronsParams, WeightBitsParams, LearningParams,
ActivationsParams, ConvolutionKernelParams,
PoolingParams, StrideParams)
[docs]class SeparableConvolutional(Layer):
"""This represents a separable convolution layer.
This layer accepts 1-bit, 2-bit or 4-bit 3D input tensors with an arbitrary
number of channels.
It can be configured with 1-bit, 2-bit or 4-bit weights.
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.
Note: this layer applies a real convolution, and not a cross-correlation.
It can optionally apply a step-wise ReLU activation to its outputs.
The layer expects a 4D tensor whose first dimension is the sample index
as input.
It returns a 4D tensor whose first dimension is the sample index and the
last dimension is the number of convolution filters.
The order of the input spatial dimensions is preserved, but their value may
change according to the convolution and pooling parameters.
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 BaseException:
self = None
raise