Source code for akida.layers.input_convolutional

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


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