make the function signature consistent

src/lib/models/model.py

@@ -21,11 +21,11 @@ _model_factory = {
   'hourglass': get_large_hourglass_net,
 }
 
-def create_model(arch, head, head_conv):
+def create_model(arch, heads, head_conv):
   num_layers = int(arch[arch.find('_') + 1:]) if '_' in arch else 0
   arch = arch[:arch.find('_')] if '_' in arch else arch
   get_model = _model_factory[arch]
-  model = get_model(num_layers, head, head_conv)
+  model = get_model(num_layers=num_layers, heads=heads, head_conv=head_conv)
   return model
 
 def load_model(model, model_path, optimizer=None, resume=False, 

src/lib/models/networks/dlav0.py

@@ -532,7 +532,7 @@ def fill_fc_weights(layers):
 
 class DLASeg(nn.Module):
     def __init__(self, base_name, heads,
-                 pretrained=True, down_ratio=4, add_conv=256):
+                 pretrained=True, down_ratio=4, head_conv=256):
         super(DLASeg, self).__init__()
         assert down_ratio in [2, 4, 8, 16]
         self.heads = heads
@@ -551,12 +551,12 @@ class DLASeg(nn.Module):
 
         for head in self.heads:
             classes = self.heads[head]
-            if add_conv > 0:
+            if head_conv > 0:
                 fc = nn.Sequential(
-                  nn.Conv2d(channels[self.first_level], add_conv,
+                  nn.Conv2d(channels[self.first_level], head_conv,
                     kernel_size=3, padding=1, bias=True),
                   nn.ReLU(inplace=True),
-                  nn.Conv2d(add_conv, classes, 
+                  nn.Conv2d(head_conv, classes, 
                     kernel_size=1, stride=1, 
                     padding=0, bias=True))
                 if 'hm' in head:
@@ -639,9 +639,9 @@ def dla169up(classes, pretrained_base=None, **kwargs):
     return model
 '''
 
-def get_pose_net(heads, down_ratio=4, add_conv=256):
+def get_pose_net(heads, down_ratio=4, head_conv=256):
   model = DLASeg('dla34', heads,
                  pretrained=True,
                  down_ratio=down_ratio,
-                 add_conv=add_conv)
+                 head_conv=head_conv)
   return model

src/lib/models/networks/large_hourglass.py

@@ -295,6 +295,6 @@ class HourglassNet(exkp):
             kp_layer=residual, cnv_dim=256
         )
 
-def get_large_hourglass_net(_, heads, __):
+def get_large_hourglass_net(num_layers, heads, head_conv):
   model = HourglassNet(heads, 2)
   return model

src/lib/models/networks/pose_dla_dcn.py

@@ -482,7 +482,7 @@ class DLASeg(nn.Module):
         return [z]
     
 
-def get_pose_net(num_layers, heads, version, down_ratio=4, head_conv=256):
+def get_pose_net(num_layers, heads, head_conv=256, down_ratio=4):
   model = DLASeg('dla{}'.format(num_layers), heads,
                  pretrained=True,
                  down_ratio=down_ratio,