make pep happy

Author: 1adrianb

bnn/layers/conv.py

@@ -35,7 +35,7 @@ def __init__(
     def forward(self, input: torch.Tensor) -> torch.Tensor:
         input_proc = self.activation_pre_process(input)
         input_proc = self._conv_forward(input_proc, self.weight_pre_process(self.weight), bias=self.bias)
-        
+
         return self.activation_post_process(
             input_proc,
             input
@@ -90,7 +90,7 @@ def __init__(
     def forward(self, input: torch.Tensor) -> torch.Tensor:
         input_proc = self.activation_pre_process(input)
         input_proc = self._conv_forward(input_proc, self.weight_pre_process(self.weight), bias=self.bias)
-        
+
         return self.activation_post_process(
             input_proc,
             input

bnn/version.py

@@ -1 +1 @@
-__version__ = '0.1.1'
+__version__ = '0.1.1'

test/smoke_test.py

@@ -1,2 +1,2 @@
 import torch
-import bnn
+import bnn

test/test_layers.py

@@ -11,17 +11,18 @@
     XNORWeightBinarizer
 )
 
+
 class BinaryLayersTestCase(unittest.TestCase):
     def setUp(self) -> None:
         self.test_bconfig = BConfig(
             activation_pre_process=BasicInputBinarizer,
             activation_post_process=BasicScaleBinarizer,
             weight_pre_process=XNORWeightBinarizer
         )
-        self.data = torch.tensor([-0.05263, -0.05068, -0.03849,  0.03104,  0.0772,  0.03038, -0.06640,  0.05894,
-         0.13059,  0.03433, -0.25811,  0.13785]).view(1, 3, 2, 2)
-        self.weights = torch.tensor([-0.0252,  0.0084, -0.0676,  0.0891, -0.0010,  0.0518,  0.0380,  0.2866,
-        -0.0050])
+        self.data = torch.tensor([-0.05263, -0.05068, -0.03849, 0.03104, 0.0772, 0.03038, -0.06640, 0.05894,
+                                  0.13059, 0.03433, -0.25811, 0.13785]).view(1, 3, 2, 2)
+        self.weights = torch.tensor([-0.0252, 0.0084, -0.0676, 0.0891, -0.0010, 0.0518, 0.0380, 0.2866,
+                                     -0.0050])
 
     def tearDown(self) -> None:
         pass
@@ -31,38 +32,38 @@ def test_linear_layer(self):
         layer.weight.data.copy_(self.weights.view(3, 3))
         x = self.data[:, :, 0, 0].view(1, 3)
         layer = prepare_binary_model(layer, bconfig=self.test_bconfig)
-        
+
         output = layer(x)
         expected = torch.tensor([[0.0337, -0.0473, -0.1099]])
         self.assertTrue(torch.allclose(expected, output, atol=1e-4))
-        
+
     def test_conv1d_layer(self):
         layer = nn.Conv1d(3, 3, 1, bias=False)
         layer.weight.data.copy_(self.weights.view(3, 3, 1))
-        x = self.data[:,:,:,0].view(1, 3, 2)
+        x = self.data[:, :, :, 0].view(1, 3, 2)
         layer = prepare_binary_model(layer, bconfig=self.test_bconfig)
-        
+
         output = layer(x)
-        expected = torch.tensor([[[ 0.0337,  0.0337],
-         [-0.0473, -0.0473],
-         [-0.1099, -0.1099]]])
+        expected = torch.tensor([[[0.0337, 0.0337],
+                                  [-0.0473, -0.0473],
+                                  [-0.1099, -0.1099]]])
         self.assertTrue(torch.allclose(expected, output, atol=1e-4))
-        
+
     def test_conv2d_layer(self):
         layer = nn.Conv2d(3, 3, 1, bias=False)
         layer.weight.data.copy_(self.weights.view(3, 3, 1, 1))
         x = self.data
         layer = prepare_binary_model(layer, bconfig=self.test_bconfig)
-        
+
         output = layer(x)
-        expected = torch.tensor([[[[ 0.0337,  0.0337],
-          [ 0.0337, -0.0337]],
+        expected = torch.tensor([[[[0.0337, 0.0337],
+                                   [0.0337, -0.0337]],
 
-         [[-0.0473, -0.0473],
-          [-0.0473,  0.0473]],
+                                  [[-0.0473, -0.0473],
+                                   [-0.0473, 0.0473]],
 
-         [[-0.1099, -0.1099],
-          [-0.1099,  0.1099]]]])
+                                  [[-0.1099, -0.1099],
+                                   [-0.1099, 0.1099]]]])
         self.assertTrue(torch.allclose(expected, output, atol=1e-4))