Source code for akida.input_convolutional

from akida.core import (Layer, InputLayerParams, NumNeuronsParams,
                        ConvolutionKernelParams, StrideParams,
                        ActivationsParams, WeightBitsParams, PoolingParams,
                        InputParams, InputConvolutionalParams, ConvolutionMode,
                        PoolingType)


[docs]class InputConvolutional(Layer): """The ``InputConvolutional`` layer is an image-specific input layer. It is used if images are sent directly to AEE without using the event-generating method. If the User applies their own event-generating method, the resulting events should be sent to an InputData type layer instead. The InputConvolutional layer accepts images in 8-bit pixels, either grayscale or RGB. Images are converted to events using a combination of convolution kernels, activation thresholds and winner-take-all (WTA) policies. Note that since the layer input is dense, expect approximately one event per pixel – fewer if there are large contrast-free regions in the image, such as with the MNIST dataset. Note that this format is not appropriate for neuromorphic camera type input which data is natively event-based and should be sent to an InputData type input layer. Args: input_width (int): input width. input_height (int): input height. input_channels (int): number of channels of the input image. kernel_width (int): convolutional kernel width. kernel_height (int): convolutional kernel height. num_neurons (int): number of neurons (filters). 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. padding_value (int, optional): value used when padding. """ def __init__(self, input_width, input_height, input_channels, kernel_width, kernel_height, num_neurons, name="", convolution_mode=ConvolutionMode.Same, stride_x=1, stride_y=1, weights_bits=1, 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, padding_value=0): try: params = InputConvolutionalParams( InputLayerParams( InputParams(input_width, input_height, input_channels)), ConvolutionKernelParams(kernel_width, kernel_height, convolution_mode), NumNeuronsParams(num_neurons), StrideParams(stride_x, stride_y), WeightBitsParams(weights_bits), PoolingParams(pooling_width, pooling_height, pooling_type, pooling_stride_x, pooling_stride_y), ActivationsParams(activations_enabled, threshold_fire, threshold_fire_step, threshold_fire_bits), padding_value) # 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