[Refactor] Add build_pos_embed and build_layers for BEiT (#1517)

* [Refactor] Add build_pos_embed and build_layers for BEiT

* Update mmseg/models/backbones/beit.py

mmseg/models/backbones/beit.py

@@ -306,25 +306,31 @@ class BEiT(BaseModule):
         elif pretrained is not None:
             raise TypeError('pretrained must be a str or None')
 
+        self.in_channels = in_channels
         self.img_size = img_size
         self.patch_size = patch_size
         self.norm_eval = norm_eval
         self.pretrained = pretrained
-
-        self.patch_embed = PatchEmbed(
-            in_channels=in_channels,
-            embed_dims=embed_dims,
-            conv_type='Conv2d',
-            kernel_size=patch_size,
-            stride=patch_size,
-            padding=0,
-            norm_cfg=norm_cfg if patch_norm else None,
-            init_cfg=None)
-
-        window_size = (img_size[0] // patch_size, img_size[1] // patch_size)
-        self.patch_shape = window_size
+        self.num_layers = num_layers
+        self.embed_dims = embed_dims
+        self.num_heads = num_heads
+        self.mlp_ratio = mlp_ratio
+        self.attn_drop_rate = attn_drop_rate
+        self.drop_path_rate = drop_path_rate
+        self.num_fcs = num_fcs
+        self.qv_bias = qv_bias
+        self.act_cfg = act_cfg
+        self.norm_cfg = norm_cfg
+        self.patch_norm = patch_norm
+        self.init_values = init_values
+        self.window_size = (img_size[0] // patch_size,
+                            img_size[1] // patch_size)
+        self.patch_shape = self.window_size
         self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dims))
 
+        self._build_patch_embedding()
+        self._build_layers()
+
         if isinstance(out_indices, int):
             if out_indices == -1:
                 out_indices = num_layers - 1
@@ -334,29 +340,47 @@ class BEiT(BaseModule):
         else:
             raise TypeError('out_indices must be type of int, list or tuple')
 
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, num_layers)]
-        self.layers = ModuleList()
-        for i in range(num_layers):
-            self.layers.append(
-                BEiTTransformerEncoderLayer(
-                    embed_dims=embed_dims,
-                    num_heads=num_heads,
-                    feedforward_channels=mlp_ratio * embed_dims,
-                    attn_drop_rate=attn_drop_rate,
-                    drop_path_rate=dpr[i],
-                    num_fcs=num_fcs,
-                    bias='qv_bias' if qv_bias else False,
-                    act_cfg=act_cfg,
-                    norm_cfg=norm_cfg,
-                    window_size=window_size,
-                    init_values=init_values))
-
         self.final_norm = final_norm
         if final_norm:
             self.norm1_name, norm1 = build_norm_layer(
                 norm_cfg, embed_dims, postfix=1)
             self.add_module(self.norm1_name, norm1)
 
+    def _build_patch_embedding(self):
+        """Build patch embedding layer."""
+        self.patch_embed = PatchEmbed(
+            in_channels=self.in_channels,
+            embed_dims=self.embed_dims,
+            conv_type='Conv2d',
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+            padding=0,
+            norm_cfg=self.norm_cfg if self.patch_norm else None,
+            init_cfg=None)
+
+    def _build_layers(self):
+        """Build transformer encoding layers."""
+
+        dpr = [
+            x.item()
+            for x in torch.linspace(0, self.drop_path_rate, self.num_layers)
+        ]
+        self.layers = ModuleList()
+        for i in range(self.num_layers):
+            self.layers.append(
+                BEiTTransformerEncoderLayer(
+                    embed_dims=self.embed_dims,
+                    num_heads=self.num_heads,
+                    feedforward_channels=self.mlp_ratio * self.embed_dims,
+                    attn_drop_rate=self.attn_drop_rate,
+                    drop_path_rate=dpr[i],
+                    num_fcs=self.num_fcs,
+                    bias='qv_bias' if self.qv_bias else False,
+                    act_cfg=self.act_cfg,
+                    norm_cfg=self.norm_cfg,
+                    window_size=self.window_size,
+                    init_values=self.init_values))
+
     @property
     def norm1(self):
         return getattr(self, self.norm1_name)
@@ -419,7 +443,6 @@ class BEiT(BaseModule):
         https://github.com/microsoft/unilm/blob/master/beit/semantic_segmentation/mmcv_custom/checkpoint.py.  # noqa: E501
         Copyright (c) Microsoft Corporation
         Licensed under the MIT License
-
         Args:
             checkpoint (dict): Key and value of the pretrain model.
         Returns: