New filters, denoms, gradients

neural_filters/NeuralFilter1P.py

@@ -49,6 +49,10 @@ class NeuralFilter1P(NeuralFilterCell):
 
         self.reset_parameters()
 
+    def __repr__(self):
+        s = '{name}({input_size},{hidden_size})'
+        return s.format(name=self.__class__.__name__, **self.__dict__)
+
     def reset_parameters(self):
         stdv = 1.0 / math.sqrt(self.hidden_size)
         for weight in self.parameters():

neural_filters/NeuralFilter2CC.py

+"""
+NeuralFilter2CC
+***************
+
+This module implements a trainable all-pole second order filter with complex conjugate 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/>.
+
+"""
+
+import torch
+from torch.nn import Parameter
+from torch.nn import functional as F
+import numpy as np
+
+
+class NeuralFilter2CC(torch.nn.Module):
+    """
+    A trainable second-order all-pole filter :math:`\\frac{1}{1 - 2 P \\cos(\\theta) z^{-1} + P^{2} z^{-2}}`
+
+    * **hidden_size** (int) - the size of the data vector
+    """
+
+    def __init__(self, hidden_size):
+        super(NeuralFilter2CC, self).__init__()
+
+        self.hidden_size = hidden_size
+
+        self.bias_theta = Parameter(torch.Tensor(hidden_size))
+        self.bias_modulus = Parameter(torch.Tensor(hidden_size))
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        self.bias_modulus.data.zero_()
+        self.bias_theta.data.zero_()
+
+    def __repr__(self):
+        s = '{name}({hidden_size})'
+        return s.format(name=self.__class__.__name__, **self.__dict__)
+
+    def check_forward_input(self, input):
+        if input.size(-1) != self.hidden_size:
+            raise RuntimeError(
+                "input has inconsistent input_size(-1): got {}, expected {}".format(
+                    input.size(1), self.hidden_size))
+
+    def check_forward_hidden(self, input, hx):
+        if input.size(1) != hx.size(0):
+            raise RuntimeError(
+                "Input batch size {} doesn't match hidden batch size {}".format(
+                    input.size(1), hx.size(0)))
+
+        if hx.size(1) != self.hidden_size:
+            raise RuntimeError(
+                "hidden has inconsistent hidden_size: got {}, expected {}".format(
+                    hx.size(1), self.hidden_size))
+
+    def step(self, input, delayed, delayed2):
+
+        modulus = F.sigmoid(self.bias_modulus)
+        cosangle = F.tanh(self.bias_theta)
+
+        next = input + 2 * cosangle * modulus * delayed - modulus.pow(2) * delayed2
+
+        return next
+
+    def forward(self, input, delayed=None, delayed2=None):
+        if delayed is None:
+            delayed = torch.autograd.Variable(input.data.new(input.size(1),
+                                                             self.hidden_size
+                                                             ).zero_(), requires_grad=False)
+
+        if delayed2 is None:
+            delayed2 = torch.autograd.Variable(input.data.new(input.size(1),
+                                                              self.hidden_size
+                                                              ).zero_(), requires_grad=False)
+
+        self.check_forward_input(input)
+        self.check_forward_hidden(input, delayed)
+        self.check_forward_hidden(input, delayed2)
+
+        d1 = delayed
+        d2 = delayed2
+
+        output = []
+        steps = range(input.size(0))
+        for i in steps:
+            next = self.step(input[i], d1, d2)
+            output.append(next)
+            d2, d1 = d1, next
+
+        output = torch.cat(output, 0).view(input.size(0), *output[0].size())
+
+        return output, d1, d2
+
+    def print_param(self):
+        modulus = F.sigmoid(self.bias_modulus)
+        cosangle = F.tanh(self.bias_theta)
+        p1 = -2 * cosangle * modulus
+        p2 = modulus.pow(2)
+        print('{}\t{}'.format(p1.data[0], p2.data[0]))
+
+    @property
+    def denominator(self):
+        modulus = F.sigmoid(self.bias_modulus)
+        cosangle = F.tanh(self.bias_theta)
+        p1 = -2 * cosangle * modulus
+        p2 = modulus.pow(2)
+        p1 = p1.data.numpy()
+        p2 = p2.data.numpy()
+        p1 = p1.reshape(p1.size,1)
+        p2 = p2.reshape(p2.size, 1)
+        one = np.ones(p1.shape)
+
+        denom = np.concatenate((one, p1, p2), axis=1)
+        return denom
+
+    @property
+    def gradients(self):
+        mod_grad = self.bias_modulus.grad
+        if mod_grad is not None:
+            mod_grad = mod_grad.data.numpy()
+            mod_grad = mod_grad.reshape(mod_grad.size, 1)
+            cos_grad = self.bias_theta.grad.data.numpy()
+            cos_grad = cos_grad.reshape(cos_grad.size, 1)
+            return np.concatenate((mod_grad, cos_grad), axis=1)
+        else:
+            return np.zeros((self.hidden_size, 2))

neural_filters/NeuralFilter2CD.py

+"""
+NeuralFilter2CD
+***************
+
+This module implements a trainable critically damped  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/>.
+
+"""
+
+from . import NeuralFilterCell
+
+import torch
+import numpy as np
+
+class NeuralFilter2CD (torch.nn.Module):
+    """
+        A trainable second-order critically damped all-pole filter :math:`\\frac{1}{(1 - P z^{-1})^{2}}`
+
+        * **hidden_size** (int) - the size of data vector
+        """
+
+    def __init__(self, hidden_size):
+        super(NeuralFilter2CD, self).__init__()
+
+        self.hidden_size = hidden_size
+
+        self.cell = NeuralFilterCell(self.hidden_size)
+
+    def __repr__(self):
+        s = '{name}({hidden_size})'
+        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)
+
+        interm, interm_hidden = self.cell(input, hx)
+        output, hidden = self.cell(interm)
+
+        return output, hidden
+
+    @property
+    def denominator(self):
+        first = self.cell.denominator
+        denom = np.zeros((first.shape[0], 3))
+        for i in range(self.hidden_size):
+            denom[i] = np.polymul(first[i], first[i])
+        return denom
+
+    @property
+    def gradients(self):
+        return self.cell.gradients

neural_filters/NeuralFilter2R.py

@@ -28,6 +28,7 @@ along with neural_filters. If not, see <http://www.gnu.org/licenses/>.
 from . import NeuralFilterCell
 
 import torch
+import numpy as np
 
 class NeuralFilter2R (torch.nn.Module):
     """
@@ -45,7 +46,7 @@ class NeuralFilter2R (torch.nn.Module):
         self.second_cell = NeuralFilterCell(self.hidden_size)
 
     def __repr__(self):
-        s = '{name}({input_size}, {hidden_size}'
+        s = '{name}({hidden_size})'
         return s.format(name=self.__class__.__name__, **self.__dict__)
 
     def forward(self, input, hx=None):
@@ -54,7 +55,22 @@ class NeuralFilter2R (torch.nn.Module):
                                                          self.hidden_size
                                                          ).zero_(), requires_grad=False)
 
-        interm, interm_hidden = self.first_cell(input)
+        interm, interm_hidden = self.first_cell(input, hx)
         output, hidden = self.second_cell(interm)
 
         return output, hidden
+
+    @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)
\ No newline at end of file

neural_filters/NeuralFilterCell.py

@@ -28,6 +28,7 @@ along with neural_filters. If not, see <http://www.gnu.org/licenses/>.
 import torch
 from torch.nn import Parameter
 from torch.nn import functional as F
+import numpy as np
 
 
 class NeuralFilterCell(torch.nn.Module):
@@ -47,10 +48,10 @@ class NeuralFilterCell(torch.nn.Module):
         self.reset_parameters()
 
     def reset_parameters(self):
-        self.bias_forget.data[0]=0
+        self.bias_forget.data.zero_()
 
     def __repr__(self):
-        s = '{name}({input_size}, {hidden_size}'
+        s = '{name}({hidden_size})'
         return s.format(name=self.__class__.__name__, **self.__dict__)
 
     def check_forward_input(self, input):
@@ -95,3 +96,20 @@ class NeuralFilterCell(torch.nn.Module):
         output = torch.cat(output, 0).view(input.size(0), *output[0].size())
 
         return output, hidden
+
+    @property
+    def gradients(self):
+        grad = self.bias_forget.grad
+        if grad is not None:
+            gradient = grad.data.numpy()
+            return gradient.reshape((gradient.size, 1))
+        else:
+            return np.zeros((self.hidden_size, 1))
+
+    @property
+    def denominator(self):
+        forgetgate = F.sigmoid(self.bias_forget).data.numpy()
+        forgetgate = forgetgate.reshape((forgetgate.size, 1))
+        one = np.ones(forgetgate.shape)
+        denom = np.concatenate((one, -forgetgate), axis=1)
+        return denom

neural_filters/__init__.py

 from .NeuralFilterCell import *
 from .NeuralFilter1P import *
 from .NeuralFilter2R import *
+from .NeuralFilter2CC import *
+from .NeuralFilter2CD import *
\ No newline at end of file