pep8 cleanup

torchxrayvision/autoencoders.py

@@ -15,6 +15,7 @@
  "class": "ResNetAE101"
 }
 
+
 class Bottleneck(nn.Module):
  expansion = 4
 
@@ -171,13 +172,12 @@ def _make_up_block(self, block, init_channels, num_layer, stride=1):
  return nn.Sequential(*layers)
 
  def encode(self, x, check_resolution=True):
- 
+
  if check_resolution and hasattr(self, 'weights_metadata'):
  resolution = self.weights_metadata['resolution']
  if (x.shape[2] != resolution) | (x.shape[3] != resolution):
  raise ValueError("Input size ({}x{}) is not the native resolution ({}x{}) for this model. Set check_resolution=False on the encode function to override this error.".format(x.shape[2], x.shape[3], resolution, resolution))
-
-
+
  x = self.conv1(x)
  x = self.bn1(x)
  x = self.relu(x)

torchxrayvision/baseline_models/chestx_det/__init__.py

@@ -41,7 +41,7 @@ class PSPNet(nn.Module):
  def __init__(self):
 
  super(PSPNet, self).__init__()
- 
+
  self.transform = torchvision.transforms.Compose([
  torchvision.transforms.Normalize(
  [0.485, 0.456, 0.406],
@@ -50,9 +50,9 @@ def __init__(self):
  ])
 
  self._targets = ['Left Clavicle', 'Right Clavicle', 'Left Scapula', 'Right Scapula',
- 'Left Lung', 'Right Lung', 'Left Hilus Pulmonis', 'Right Hilus Pulmonis',
- 'Heart', 'Aorta', 'Facies Diaphragmatica', 'Mediastinum',  'Weasand', 'Spine']
- 
+  'Left Lung', 'Right Lung', 'Left Hilus Pulmonis', 'Right Hilus Pulmonis',
+  'Heart', 'Aorta', 'Facies Diaphragmatica', 'Mediastinum', 'Weasand', 'Spine']
+
  model = pspnet(len(self.targets))
 
  url = "https://github.com/mlmed/torchxrayvision/releases/download/v1/pspnet_chestxray_best_model_4.pth"
@@ -74,7 +74,7 @@ def __init__(self):
  except Exception as e:
  print("Loading failure. Check weights file:", self.weights_filename_local)
  raise (e)
- 
+
  model.eval()
  self.model = model
  self.upsample = nn.Upsample(size=(512, 512), mode='bilinear', align_corners=False)
@@ -90,7 +90,7 @@ def forward(self, x):
 
  # expecting values between [-1024,1024]
  x = (x + 1024) / 2048
- 
+
  # now between [0,1] for this model preprocessing
  x = self.transform(x)
  y = self.model(x)