add layer_norm for convnext outputs (#6201)

ppdet/modeling/backbones/convnext.py

@@ -141,6 +141,7 @@ class ConvNeXt(nn.Layer):
             layer_scale_init_value=1e-6,
             head_init_scale=1.,
             return_idx=[1, 2, 3],
+            norm_output=True,
             pretrained=None, ):
         super().__init__()
 
@@ -178,6 +179,14 @@ class ConvNeXt(nn.Layer):
         self.return_idx = return_idx
         self.dims = [dims[i] for i in return_idx]  # [::-1]
 
+        self.norm_output = norm_output
+        if norm_output:
+            self.norms = nn.LayerList([
+                LayerNorm(
+                    c, eps=1e-6, data_format="channels_first")
+                for c in self.dims
+            ])
+
         self.apply(self._init_weights)
         # self.head.weight.set_value(self.head.weight.numpy() * head_init_scale) 
         # self.head.bias.set_value(self.head.weight.numpy() * head_init_scale) 
@@ -202,9 +211,11 @@ class ConvNeXt(nn.Layer):
             x = self.stages[i](x)
             output.append(x)
 
-        output = [output[i] for i in self.return_idx]
+        outputs = [output[i] for i in self.return_idx]
+        if self.norm_output:
+            outputs = [self.norms[i](out) for i, out in enumerate(outputs)]
 
-        return output
+        return outputs
 
     def forward(self, x):
         x = self.forward_features(x['image'])