neural_filter_2R.py 2.72 KB
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"""
NeuralFilter2R
**************

This module implements a trainable all-pole second order filter with real poles using pyTorch


Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/

Written by Francois Marelli <Francois.Marelli@idiap.ch>

This file is part of neural_filters.

neural_filters is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.

neural_filters is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with neural_filters. If not, see <http://www.gnu.org/licenses/>.

"""

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from . import NeuralFilter
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import torch
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import numpy as np
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class NeuralFilter2R (torch.nn.Module):
    """
        A trainable second-order all-(real)pole filter :math:`\\frac{1}{1 - P_{1} z^{-1}} \\frac{1}{1 - P_{2} z^{-1}}`

        * **hidden_size** (int) - the size of data vector
        """

    def __init__(self, hidden_size):
        super(NeuralFilter2R, self).__init__()

        self.hidden_size = hidden_size

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        self.first_cell = NeuralFilter(self.hidden_size)
        self.second_cell = NeuralFilter(self.hidden_size)
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        self.reset_parameters((-0.5, 0.5))

    def reset_parameters(self, init=None):
        if isinstance(init, tuple):
            self.first_cell.reset_parameters(init[0])
            self.second_cell.reset_parameters(init[1])
        else:
            self.first_cell.reset_parameters(init)
            self.second_cell.reset_parameters(init)

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    def __repr__(self):
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        s = '{name}({hidden_size})'
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        return s.format(name=self.__class__.__name__, **self.__dict__)

    def forward(self, input, hx=None):
        if hx is None:
            hx = torch.autograd.Variable(input.data.new(input.size(1),
                                                         self.hidden_size
                                                         ).zero_(), requires_grad=False)

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        interm, interm_hidden = self.first_cell(input, hx)
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        output, hidden = self.second_cell(interm)

        return output, hidden
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    @property
    def denominator(self):
        first = self.first_cell.denominator
        second = self.second_cell.denominator
        denom = np.zeros((first.shape[0],3))
        for i in range(self.hidden_size):
            denom[i] = np.polymul(first[i], second[i])
        return denom

    @property
    def gradients(self):
        first = self.first_cell.gradients
        second = self.second_cell.gradients
        return np.concatenate((first, second), axis=1)