undo whitespace changes

src/modnet/models/backbones/wrapper.py

@@ -26,7 +26,7 @@ class BaseBackbone(nn.Module):
 
 
 class MobileNetV2Backbone(BaseBackbone):
-    """ MobileNetV2 Backbone
+    """ MobileNetV2 Backbone 
     """
 
     def __init__(self, in_channels):
@@ -72,11 +72,11 @@ class MobileNetV2Backbone(BaseBackbone):
         return [enc2x, enc4x, enc8x, enc16x, enc32x]
 
     def load_pretrained_ckpt(self):
-        # the pre-trained model is provided by https://github.com/thuyngch/Human-Segmentation-PyTorch
+        # the pre-trained model is provided by https://github.com/thuyngch/Human-Segmentation-PyTorch 
         ckpt_path = './pretrained/mobilenetv2_human_seg.ckpt'
         if not os.path.exists(ckpt_path):
             print('cannot find the pretrained mobilenetv2 backbone')
             exit()
-
+        
         ckpt = torch.load(ckpt_path)
         self.model.load_state_dict(ckpt)

src/modnet/models/modnet.py

@@ -21,7 +21,7 @@ class IBNorm(nn.Module):
 
         self.bnorm = nn.BatchNorm2d(self.bnorm_channels, affine=True)
         self.inorm = nn.InstanceNorm2d(self.inorm_channels, affine=False)
-
+        
     def forward(self, x):
         bn_x = self.bnorm(x[:, :self.bnorm_channels, ...].contiguous())
         in_x = self.inorm(x[:, self.bnorm_channels:, ...].contiguous())
@@ -33,18 +33,18 @@ class Conv2dIBNormRelu(nn.Module):
     """ Convolution + IBNorm + ReLu
     """
 
-    def __init__(self, in_channels, out_channels, kernel_size,
-                 stride=1, padding=0, dilation=1, groups=1, bias=True,
+    def __init__(self, in_channels, out_channels, kernel_size, 
+                 stride=1, padding=0, dilation=1, groups=1, bias=True, 
                  with_ibn=True, with_relu=True):
         super(Conv2dIBNormRelu, self).__init__()
 
         layers = [
-            nn.Conv2d(in_channels, out_channels, kernel_size,
-                      stride=stride, padding=padding, dilation=dilation,
+            nn.Conv2d(in_channels, out_channels, kernel_size, 
+                      stride=stride, padding=padding, dilation=dilation, 
                       groups=groups, bias=bias)
         ]
 
-        if with_ibn:
+        if with_ibn:       
             layers.append(IBNorm(out_channels))
         if with_relu:
             layers.append(nn.ReLU(inplace=True))
@@ -52,7 +52,7 @@ class Conv2dIBNormRelu(nn.Module):
         self.layers = nn.Sequential(*layers)
 
     def forward(self, x):
-        return self.layers(x)
+        return self.layers(x) 
 
 
 class SEBlock(nn.Module):
@@ -89,7 +89,7 @@ class LRBranch(nn.Module):
         super(LRBranch, self).__init__()
 
         enc_channels = backbone.enc_channels
-
+        
         self.backbone = backbone
         self.se_block = SEBlock(enc_channels[4], enc_channels[4], reduction=4)
         self.conv_lr16x = Conv2dIBNormRelu(enc_channels[4], enc_channels[3], 5, stride=1, padding=2)
@@ -111,7 +111,7 @@ class LRBranch(nn.Module):
             lr = self.conv_lr(lr8x)
             pred_semantic = torch.sigmoid(lr)
 
-        return pred_semantic, lr8x, [enc2x, enc4x]
+        return pred_semantic, lr8x, [enc2x, enc4x] 
 
 
 class HRBranch(nn.Module):
@@ -177,7 +177,7 @@ class FusionBranch(nn.Module):
     def __init__(self, hr_channels, enc_channels):
         super(FusionBranch, self).__init__()
         self.conv_lr4x = Conv2dIBNormRelu(enc_channels[2], hr_channels, 5, stride=1, padding=2)
-
+        
         self.conv_f2x = Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1)
         self.conv_f = nn.Sequential(
             Conv2dIBNormRelu(hr_channels + 3, int(hr_channels / 2), 3, stride=1, padding=1),
@@ -226,7 +226,7 @@ class MODNet(nn.Module):
                 self._init_norm(m)
 
         if self.backbone_pretrained:
-            self.backbone.load_pretrained_ckpt()
+            self.backbone.load_pretrained_ckpt()                
 
     def forward(self, img, inference):
         pred_semantic, lr8x, [enc2x, enc4x] = self.lr_branch(img, inference)
@@ -234,7 +234,7 @@ class MODNet(nn.Module):
         pred_matte = self.f_branch(img, lr8x, hr2x)
 
         return pred_semantic, pred_detail, pred_matte
-
+    
     def freeze_norm(self):
         norm_types = [nn.BatchNorm2d, nn.InstanceNorm2d]
         for m in self.modules():