Source code for akida.separable_convolutional

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