fix path (zhanghang1989#72)

SuperMousse · Jun 15, 2018 · 2dd88e5 · 2dd88e5
1 parent 32e382b
commit 2dd88e5
Show file tree

Hide file tree

Showing 2 changed files with 3 additions and 10 deletions.
diff --git a/encoding/models/encnet.py b/encoding/models/encnet.py
@@ -166,7 +166,7 @@ def get_encnet_resnet50_pcontext(pretrained=False, root='~/.encoding/models', **
     >>> model = get_encnet_resnet50_pcontext(pretrained=True)
     >>> print(model)
     """
-    return get_encnet('pcontext', 'resnet50', pretrained, aux=False, **kwargs)
+    return get_encnet('pcontext', 'resnet50', pretrained, root=root, aux=False, **kwargs)
 
 def get_encnet_resnet101_pcontext(pretrained=False, root='~/.encoding/models', **kwargs):
     r"""EncNet-PSP model from the paper `"Context Encoding for Semantic Segmentation"
@@ -185,7 +185,7 @@ def get_encnet_resnet101_pcontext(pretrained=False, root='~/.encoding/models', *
     >>> model = get_encnet_resnet101_pcontext(pretrained=True)
     >>> print(model)
     """
-    return get_encnet('pcontext', 'resnet101', pretrained, aux=False, **kwargs)
+    return get_encnet('pcontext', 'resnet101', pretrained, root=root, aux=False, **kwargs)
 
 def get_encnet_resnet50_ade(pretrained=False, root='~/.encoding/models', **kwargs):
     r"""EncNet-PSP model from the paper `"Context Encoding for Semantic Segmentation"
@@ -204,4 +204,4 @@ def get_encnet_resnet50_ade(pretrained=False, root='~/.encoding/models', **kwarg
     >>> model = get_encnet_resnet50_ade(pretrained=True)
     >>> print(model)
     """
-    return get_encnet('ade20k', 'resnet50', pretrained, aux=True, **kwargs)
+    return get_encnet('ade20k', 'resnet50', pretrained, root=root, aux=True, **kwargs)
diff --git a/encoding/nn/customize.py b/encoding/nn/customize.py
@@ -177,13 +177,6 @@ def __init__(self, in_channels, norm_layer, up_kwargs):
         # bilinear upsample options
         self._up_kwargs = up_kwargs
 
-    def _cat_each(self, x, feat1, feat2, feat3, feat4):
-        assert(len(x) == len(feat1))
-        z = []
-        for i in range(len(x)):
-            z.append(torch.cat((x[i], feat1[i], feat2[i], feat3[i], feat4[i]), 1))
-        return z
-
     def forward(self, x):
         _, _, h, w = x.size()
         feat1 = F.upsample(self.conv1(self.pool1(x)), (h, w), **self._up_kwargs)