[ghostnet] Fix in_features for linear layer in reset_classifier.
diff --git a/timm/models/ghostnet.py b/timm/models/ghostnet.py
index b7c0f5ddcc..d34b548521 100644
--- a/timm/models/ghostnet.py
+++ b/timm/models/ghostnet.py
@@ -276,7 +276,7 @@ def reset_classifier(self, num_classes, global_pool='avg'):
         # cannot meaningfully change pooling of efficient head after creation
         self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
         self.flatten = nn.Flatten(1) if global_pool else nn.Identity()  # don't flatten if pooling disabled
-        self.classifier = Linear(self.pool_dim, num_classes) if num_classes > 0 else nn.Identity()
+        self.classifier = Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
 
     def forward_features(self, x):
         x = self.conv_stem(x)

Resetting the classifier for the ghostnet model results in a mismatch:

> 
RuntimeError: mat1 and mat2 shapes cannot be multiplied (80x1280 and 960x6)
