Source code for akida.layers.input_convolutional

from akida.core import (Layer, Padding, PoolType, LayerParams, LayerType)

[docs]class InputConvolutional(Layer): """This represents an image-specific input convolutional layer. The initial convolutional layer in a network, which receives image inputs in either RGB or grayscale format is converted into an InputConvolutional Layer on Akida. This layer optionally executes Pooling and ReLU operation to the outputs of Convolution. It is the only Akida V1 layer with 8-bit weights. It applies a 'convolution' (actually a cross-correlation) optionally followed by a pooling operation to the input images. 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 representing the 8-bit images 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: input_shape (tuple): the 3D input shape. filters (int): number of filters. kernel_size (list): list of 2 integers representing the spatial dimensions of the convolutional kernel. name (str, optional): name of the layer. Defaults to empty string. padding (:obj:`Padding`, optional): type of convolution. Defaults to Padding.Same. kernel_stride (tuple, optional): tuple of integer representing the convolution stride (X, Y). Defaults to (1, 1). weights_bits (int, optional): number of bits used to quantize weights. Defaults to 1. 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). Defaults to (-1, -1). pool_type (:obj:`PoolType`, optional): pooling type (NoPooling, Max or Average). Defaults to PoolType.NoPooling. pool_stride (list, optional): list of 2 integers representing the stride dimensions. Defaults to (-1, -1) activation (bool, optional): enable or disable activation function. Defaults to True. act_bits (int, optional): number of bits used to quantize the neuron response. Defaults to 1. padding_value (int, optional): value used when padding. Defaults to 0. """ def __init__(self, input_shape, kernel_size, filters, name="", padding=Padding.Same, kernel_stride=(1, 1), weights_bits=1, pool_size=(-1, -1), pool_type=PoolType.NoPooling, pool_stride=(-1, -1), activation=True, act_bits=1, padding_value=0): try: pooling_stride_x = pool_stride[0] if pool_stride[0] < 0: pooling_stride_x = pool_size[0] pooling_stride_y = pool_stride[1] if pool_stride[1] < 0: pooling_stride_y = pool_size[1] params = LayerParams( LayerType.InputConvolutional, { "input_width": input_shape[0], "input_height": input_shape[1], "input_channels": input_shape[2], "kernel_width": kernel_size[0], "kernel_height": kernel_size[1], "padding": padding, "filters": filters, "stride_x": kernel_stride[0], "stride_y": kernel_stride[1], "weights_bits": weights_bits, "pooling_width": pool_size[0], "pooling_height": pool_size[1], "pool_type": pool_type, "pooling_stride_x": pooling_stride_x, "pooling_stride_y": pooling_stride_y, "activation": activation, "act_bits": act_bits, "padding_value": 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 BaseException: self = None raise