Source code for akida.input_convolutional

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

[docs]class InputConvolutional(Layer): """The ``InputConvolutional`` layer is an image-specific input layer. The InputConvolutional layer accepts images in 8-bit pixels, either grayscale or RGB. It is the only akida 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. kernel_size (list): list of 2 integer representing the spatial dimensions of the convolutional kernel. filters (int): number of filters. name (str, optional): name of the layer. padding (:obj:`Padding`, optional): type of convolution. kernel_stride (tuple, optional): tuple of 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. act_bits (int, optional): number of bits used to quantize the neuron response. padding_value (int, optional): value used when padding. """ 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