Source code for akida.separable_convolutional

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

[docs]class SeparableConvolutional(Layer): """This represents a separable convolution layer. This layer accepts 1-bit, 2-bit or 4-bit 3D input tensors with an arbitrary number of channels. 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 integer representing the spatial dimensions of the convolutional kernel. filters (int): number of pointwise filters. name (str, optional): name of the layer. padding (:obj:`Padding`, optional): type of convolution. kernel_stride (list, optional): list of 2 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. """ 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