deprecated torch functions

v 1.2

neural_filters/neural_filter.py

@@ -28,7 +28,6 @@ along with neural_filters. If not, see <http://www.gnu.org/licenses/>.
 import numpy as np
 import torch
 from torch.nn import Parameter
-from torch.nn import functional as F
 from torch.nn._functions.rnn import Recurrent, VariableRecurrent
 from torch.nn.utils.rnn import PackedSequence
 
@@ -97,7 +96,7 @@ class NeuralFilter(torch.nn.Module):
 
     def step(self, input_var, hidden, a=None):
         if a is None:
-            a = F.sigmoid(self.bias_forget)
+            a = torch.sigmoid(self.bias_forget)
 
         next_state = (a * hidden) + input_var
         return next_state
@@ -117,7 +116,7 @@ class NeuralFilter(torch.nn.Module):
         self.check_forward_args(input_var, hidden, batch_sizes)
 
         # compute this once for all steps for efficiency
-        a = F.sigmoid(self.bias_forget)
+        a = torch.sigmoid(self.bias_forget)
 
         func = Recurrent(self.step) if batch_sizes is None else VariableRecurrent(self.step)
         nexth, output = func(input_var, hidden, (a,), batch_sizes)
@@ -138,7 +137,7 @@ class NeuralFilter(torch.nn.Module):
 
     @property
     def denominator(self):
-        forgetgate = F.sigmoid(self.bias_forget).detach().cpu().numpy()
+        forgetgate = torch.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

@@ -114,7 +114,7 @@ class NeuralFilter2CC(torch.nn.Module):
 
     def step(self, input_var, hidden, a=None, b=None):
         if a is None or b is None:
-            modulus = F.sigmoid(self.bias_modulus)
+            modulus = torch.sigmoid(self.bias_modulus)
             cosangle = F.tanh(self.bias_theta)
             a = 2 * cosangle * modulus
             b = - modulus.pow(2)
@@ -140,7 +140,7 @@ class NeuralFilter2CC(torch.nn.Module):
         self.check_forward_args(input_var, hidden, batch_sizes)
 
         # do not recompute this at each step to gain efficiency
-        modulus = F.sigmoid(self.bias_modulus)
+        modulus = torch.sigmoid(self.bias_modulus)
         cosangle = F.tanh(self.bias_theta)
         a = 2 * cosangle * modulus
         b = - modulus.pow(2)
@@ -154,7 +154,7 @@ class NeuralFilter2CC(torch.nn.Module):
         return output, nexth, modulus
 
     def print_param(self):
-        modulus = F.sigmoid(self.bias_modulus)
+        modulus = torch.sigmoid(self.bias_modulus)
         cosangle = F.tanh(self.bias_theta)
         p1 = -2 * cosangle * modulus
         p2 = modulus.pow(2)
@@ -162,7 +162,7 @@ class NeuralFilter2CC(torch.nn.Module):
 
     @property
     def denominator(self):
-        modulus = F.sigmoid(self.bias_modulus)
+        modulus = torch.sigmoid(self.bias_modulus)
         cosangle = F.tanh(self.bias_theta)
         p1 = -2 * cosangle * modulus
         p2 = modulus.pow(2)

neural_filters/neural_filter_2R.py

@@ -25,10 +25,10 @@ along with neural_filters. If not, see <http://www.gnu.org/licenses/>.
 
 """
 
-from . import NeuralFilter
-
-import torch
 import numpy as np
+import torch
+
+from . import NeuralFilter
 
 
 class NeuralFilter2R(torch.nn.Module):

setup.py

@@ -2,7 +2,7 @@ from setuptools import setup, find_packages
 
 setup(
     name='neural-filters',
-    version='1.1',
+    version='1.2',
     description='Linear filters for neural networks in pyTorch',
     author='Idiap research institute - Francois Marelli',
     author_email='francois.marelli@idiap.ch',