Source code for akida.layers.depthwise_conv2d_transpose

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

[docs]class DepthwiseConv2DTranspose(Layer): """This represents the Akida V2 DepthwiseConv2DTranspose layer. It applies a transposed depthwise convolution (also called deconvolution) optionally followed by a bias addition and a ReLU activation. This is like a standard transposed convolution, except it acts on each input channel separately. Inputs shape must be in the form (X, Y, C). Being the result of a quantized operation, it is possible to apply some shifts to adjust the inputs/outputs scales to the equivalent operation performed on floats, while maintaining a limited usage of bits and performing the operations on integer values. The order of the input spatial dimensions is preserved, but their values may change according to the layer parameters. Note that the layer performs only transpose depthwise convolution with a "Same" padding and a kernel stride equal to 2. The DepthwiseConv2DTranspose operation can be described as follows: >>> inputs = inputs << input_shift >>> prod = depthwise_conv2d_transpose(inputs, weights) >>> output = prod + (bias << bias_shift) #optional >>> output = ReLU(output) #optional >>> output = output * output_scale >> output_shift Note that output values will be saturated on the range that can be represented with output_bits. Args: kernel_size (int): Integer representing the spatial dimensions of the depthwise kernel. activation (bool, optional): enable or disable activation function. Defaults to True. output_bits (int, optional): output bitwidth. Defaults to 8. buffer_bits (int, optional): buffer bitwidth. Defaults to 28. post_op_buffer_bits (int, optional): internal bitwidth for post operations. Defaults to 32. name (str, optional): name of the layer. Defaults to empty string. """ def __init__(self, kernel_size, activation=True, output_bits=8, buffer_bits=28, post_op_buffer_bits=32, name=""): try: params = LayerParams( LayerType.DepthwiseConv2DTranspose, { "kernel_size": kernel_size, "activation": activation, "output_bits": output_bits, "buffer_bits": buffer_bits, "post_op_buffer_bits": post_op_buffer_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