modulus output in forward

neural_filters/neural_filter.py

@@ -125,7 +125,7 @@ class NeuralFilter(torch.nn.Module):
         if is_packed:
             output = PackedSequence(output, batch_sizes)
 
-        return output, nexth
+        return output, nexth, a
 
     @property
     def gradients(self):
@@ -138,7 +138,7 @@ class NeuralFilter(torch.nn.Module):
 
     @property
     def denominator(self):
-        forgetgate = F.sigmoid(self.bias_forget).data.numpy()
+        forgetgate = F.sigmoid(self.bias_forget).detach().cpu().numpy()
         forgetgate = forgetgate.reshape((forgetgate.size, 1))
         one = np.ones(forgetgate.shape)
         denom = np.concatenate((one, -forgetgate), axis=1)

neural_filters/neural_filter_2CC.py

@@ -151,7 +151,7 @@ class NeuralFilter2CC(torch.nn.Module):
         if is_packed:
             output = PackedSequence(output, batch_sizes)
 
-        return output, nexth
+        return output, nexth, modulus
 
     def print_param(self):
         modulus = F.sigmoid(self.bias_modulus)
@@ -166,8 +166,8 @@ class NeuralFilter2CC(torch.nn.Module):
         cosangle = F.tanh(self.bias_theta)
         p1 = -2 * cosangle * modulus
         p2 = modulus.pow(2)
-        p1 = p1.data.numpy()
-        p2 = p2.data.numpy()
+        p1 = p1.detach().cpu().numpy()
+        p2 = p2.detach().cpu().numpy()
         p1 = p1.reshape(p1.size, 1)
         p2 = p2.reshape(p2.size, 1)
         one = np.ones(p1.shape)

neural_filters/neural_filter_2CD.py

@@ -53,10 +53,10 @@ class NeuralFilter2CD(torch.nn.Module):
         return s.format(name=self.__class__.__name__, **self.__dict__)
 
     def forward(self, input_var, hx=(None, None)):
-        inter, inter_hidden = self.cell(input_var, hx[0])
-        output, hidden = self.cell(inter, hx[1])
+        inter, inter_hidden, modulus = self.cell(input_var, hx[0])
+        output, hidden, modulus = self.cell(inter, hx[1])
 
-        return output, (inter_hidden, hidden)
+        return output, (inter_hidden, hidden), modulus
 
     @property
     def denominator(self):

neural_filters/neural_filter_2R.py

@@ -65,10 +65,10 @@ class NeuralFilter2R(torch.nn.Module):
         return s.format(name=self.__class__.__name__, **self.__dict__)
 
     def forward(self, input_var, hx=(None, None)):
-        interm, interm_hidden = self.first_cell(input_var, hx[0])
-        output, hidden = self.second_cell(interm, hx[1])
+        interm, interm_hidden, first_modulus = self.first_cell(input_var, hx[0])
+        output, hidden, second_modulus = self.second_cell(interm, hx[1])
 
-        return output, (interm_hidden, hidden)
+        return output, (interm_hidden, hidden), (first_modulus, second_modulus)
 
     @property
     def denominator(self):

setup.py

@@ -2,7 +2,7 @@ from setuptools import setup, find_packages
 
 setup(
     name='neural-filters',
-    version='1.0',
+    version='1.1',
     description='Linear filters for neural networks in pyTorch',
     author='Idiap research institute - Francois Marelli',
     author_email='francois.marelli@idiap.ch',