Clean up

examples/network_compression/mnist_compression.py

@@ -64,11 +64,9 @@ def forward(self, x):
 
     def wavelet_loss(self):
         if self.wavelet is None:
-            return torch.tensor(0.0), torch.tensor(0.0)
+            return torch.tensor(0.0)
         else:
-            acl, _, _ = self.fc1.wavelet.alias_cancellation_loss()
-            prl, _, _ = self.fc1.wavelet.perfect_reconstruction_loss()
-            return acl, prl
+            return self.fc1.get_wavelet_loss()
 
 
 def train(args, model, device, train_loader, optimizer, epoch):
@@ -79,8 +77,7 @@ def train(args, model, device, train_loader, optimizer, epoch):
         output = model(data)
         nll_loss = F.nll_loss(output, target)
         if args.compression == "Wavelet":
-            acl, prl = model.wavelet_loss()
-            wvl = acl + prl
+            wvl = model.wavelet_loss()
             loss = nll_loss + wvl * args.wave_loss_weight
         else:
             wvl = torch.tensor(0.0)
@@ -117,8 +114,7 @@ def test(args, model, device, test_loader, test_writer, epoch):
             correct += pred.eq(target.view_as(pred)).sum().item()
 
     test_loss /= len(test_loader.dataset)
-    acl, prl = model.wavelet_loss()
-    wvl_loss = acl + prl
+    wvl_loss = model.wavelet_loss()
 
     print(
         "\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(

src/ptwt/nn.py

@@ -1,3 +1,5 @@
+"""Neural network modules."""
+
 import numpy as np
 import pywt
 import torch
@@ -20,11 +22,14 @@ class WaveletLayer(torch.nn.Module):
 
     .. note::
 
-        Originally created by moritz (wolter@cs.uni-bonn.de)
-        at https://github.com/v0lta/Wavelet-network-compression/blob/master/wavelet_learning/wavelet_linear.py
+        Originally created by moritz (wolter@cs.uni-bonn.de) at
+        https://github.com/v0lta/Wavelet-network-compression/blob/master/wavelet_learning/wavelet_linear.py
     """
 
-    def __init__(self, depth: int, init_wavelet, scales, p_drop=0.5):
+    def __init__(
+        self, depth: int, init_wavelet: pywt.Wavelet, scales: int, p_drop: float = 0.5
+    ):
+        """Initialize the wavelet layer."""
         super().__init__()
         self.scales = scales
         self.wavelet = init_wavelet
@@ -98,7 +103,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         step6 = self._mul_s(step5)
         return step6
 
-    def extra_repr(self) -> str:
+    def extra_repr(self) -> str:  # noqa:D102
         return "depth={}".format(self.depth)
 
     def get_wavelet_loss(self) -> torch.Tensor: