Source code for akida.layers.separable_convolutional

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

[docs]class SeparableConvolutional(Layer): """This represents a separable convolution layer. A standard Separable Convolution Layer in a network having an input with arbitrary number of channels is converted to SeparableConvolutional Layer on Akida. This layer optionally executes Pooling and ReLU operation to the outputs of Separable Convolution. This layer accepts 1-bit, 2-bit or 4-bit 3D input tensors. It can be configured with 1-bit, 2-bit or 4-bit weights. 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. Note: this layer applies a real convolution, and not a cross-correlation. 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 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: kernel_size (list): list of 2 integers representing the spatial dimensions of the convolutional kernel. filters (int): number of pointwise filters. name (str, optional): name of the layer. Defaults to empty string. padding (:obj:`Padding`, optional): type of convolution. Defaults to Padding.Same. kernel_stride (list, optional): list of 2 integers representing the convolution stride (X, Y). Defaults to (1, 1). weights_bits (int, optional): number of bits used to quantize weights. Defaults to 2. 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. """ def __init__(self, kernel_size, filters, name="", padding=Padding.Same, kernel_stride=(1, 1), weights_bits=2, pool_size=(-1, -1), pool_type=PoolType.NoPooling, pool_stride=(-1, -1), activation=True, act_bits=1): 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.SeparableConvolutional, { "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 }) # 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