Fix formatting

DagnyT · Feb 9, 2018 · b5c06ef · b5c06ef
1 parent 42865a9
commit b5c06ef
Showing 1 changed file with 44 additions and 44 deletions.
diff --git a/pretrained/3rd_party/HardNetPS/README.md b/pretrained/3rd_party/HardNetPS/README.md
@@ -2,36 +2,36 @@ This version of HardNet is trained on [PS dataset](https://github.com/rmitra/PS-
 
 The structure of the network has minor changes, use this definition:
 
-class HardNetPS(nn.Module):
-   def __init__(self):
-        super(HardNetPS, self).__init__()
-        self.features = nn.Sequential(
-        nn.Conv2d(1, 32, kernel_size=3, padding=1, bias = True),
-        nn.BatchNorm2d(32, affine=True),
-        nn.ReLU(),
-        nn.Conv2d(32, 32, kernel_size=3, padding=1, bias = True),
-        nn.BatchNorm2d(32, affine=True),
-        nn.ReLU(),
-        nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1, bias = True),
-        nn.BatchNorm2d(64, affine=True),
-        nn.ReLU(),
-        nn.Conv2d(64, 64, kernel_size=3, padding=1, bias = True),
-        nn.BatchNorm2d(64, affine=True),
-        nn.ReLU(),
-        nn.Conv2d(64, 128, kernel_size=3, stride=2,padding=1, bias = True),
-        nn.BatchNorm2d(128, affine=True),
-        nn.ReLU(),
-        nn.Conv2d(128, 128, kernel_size=3, padding=1, bias = True),
-        nn.BatchNorm2d(128, affine=True),
-        nn.ReLU(),
-        nn.Conv2d(128, 128, kernel_size=8, bias = True)
-    )
-    return
-    def input_norm(self,x):
-        flat = x.view(x.size(0), -1)
-        mp = torch.mean(flat, dim=1)
-        sp = torch.std(flat, dim=1) + 1e-7
-        return (x - mp.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).expand_as(x)) / sp.unsqueeze(-1).unsqueeze(-1).unsqueeze(1).expand_as(x)
+    class HardNetPS(nn.Module):
+       def __init__(self):
+            super(HardNetPS, self).__init__()
+            self.features = nn.Sequential(
+            nn.Conv2d(1, 32, kernel_size=3, padding=1, bias = True),
+            nn.BatchNorm2d(32, affine=True),
+            nn.ReLU(),
+            nn.Conv2d(32, 32, kernel_size=3, padding=1, bias = True),
+            nn.BatchNorm2d(32, affine=True),
+            nn.ReLU(),
+            nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1, bias = True),
+            nn.BatchNorm2d(64, affine=True),
+            nn.ReLU(),
+            nn.Conv2d(64, 64, kernel_size=3, padding=1, bias = True),
+            nn.BatchNorm2d(64, affine=True),
+            nn.ReLU(),
+            nn.Conv2d(64, 128, kernel_size=3, stride=2,padding=1, bias = True),
+            nn.BatchNorm2d(128, affine=True),
+            nn.ReLU(),
+            nn.Conv2d(128, 128, kernel_size=3, padding=1, bias = True),
+            nn.BatchNorm2d(128, affine=True),
+            nn.ReLU(),
+            nn.Conv2d(128, 128, kernel_size=8, bias = True)
+        )
+        return
+        def input_norm(self,x):
+            flat = x.view(x.size(0), -1)
+            mp = torch.mean(flat, dim=1)
+            sp = torch.std(flat, dim=1) + 1e-7
+            return (x - mp.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).expand_as(x)) / sp.unsqueeze(-1).unsqueeze(-1).unsqueeze(1).expand_as(x)
 
     def forward(self, input):
         x_features = self.features(self.input_norm(input))
@@ -42,17 +42,17 @@ class HardNetPS(nn.Module):
 If you use this weights, please cite:
 
 
-@ARTICLE{2018arXiv180101466M,
-   author = {{Mitra}, R. and {Doiphode}, N. and {Gautam}, U. and {Narayan}, S. and 
-    {Ahmed}, S. and {Chandran}, S. and {Jain}, A.},
-    title = "{A Large Dataset for Improving Patch Matching}",
-  journal = {ArXiv e-prints},
-archivePrefix = "arXiv",
-   eprint = {1801.01466},
- primaryClass = "cs.CV",
- keywords = {Computer Science - Computer Vision and Pattern Recognition},
-     year = 2018,
-    month = jan,
-   adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180101466M},
-  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
-}
+    @ARTICLE{2018arXiv180101466M,
+       author = {{Mitra}, R. and {Doiphode}, N. and {Gautam}, U. and {Narayan}, S. and 
+        {Ahmed}, S. and {Chandran}, S. and {Jain}, A.},
+        title = "{A Large Dataset for Improving Patch Matching}",
+      journal = {ArXiv e-prints},
+    archivePrefix = "arXiv",
+       eprint = {1801.01466},
+     primaryClass = "cs.CV",
+     keywords = {Computer Science - Computer Vision and Pattern Recognition},
+         year = 2018,
+        month = jan,
+       adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180101466M},
+      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
+    }