small tweaks to try to retrieve each step of inference

Author: GeorgePearse

mmdet/models/backbones/swin.py

@@ -485,7 +485,9 @@ class SwinTransformer(BaseModule):
         mlp_ratio (int): Ratio of mlp hidden dim to embedding dim.
             Default: 4.
         depths (tuple[int]): Depths of each Swin Transformer stage.
-            Default: (2, 2, 6, 2).
+            Default: (2, 2, 6, 2). 
+            This means that the model has 4 "stages. <-- George Comment. The number of stages is retrieved by running stages = len(depths)
+
         num_heads (tuple[int]): Parallel attention heads of each Swin
             Transformer stage. Default: (3, 6, 12, 24).
         strides (tuple[int]): The patch merging or patch embedding stride of
@@ -571,6 +573,10 @@ def __init__(self,
 
         super(SwinTransformer, self).__init__(init_cfg=init_cfg)
 
+        # George comment
+        # default value of depths --> (2, 2, 6, 2)
+        # therefore num_layers = 4, which then ends up being 
+        # the number of stages
         num_layers = len(depths)
         self.out_indices = out_indices
         self.use_abs_pos_embed = use_abs_pos_embed
@@ -603,6 +609,7 @@ def __init__(self,
 
         self.stages = ModuleList()
         in_channels = embed_dims
+
         for i in range(num_layers):
             if i < num_layers - 1:
                 downsample = PatchMerging(
@@ -614,6 +621,11 @@ def __init__(self,
             else:
                 downsample = None
 
+            # George comment: 
+            # one stage for every layer 
+            # very annoying terminology switch 
+            # don't see why it wouldn't just be num_stages
+            # instead of num_layers
             stage = SwinBlockSequence(
                 embed_dims=in_channels,
                 num_heads=num_heads[i],
@@ -817,3 +829,59 @@ def correct_unfold_norm_order(x):
         new_ckpt['backbone.' + new_k] = new_v
 
     return new_ckpt
+
+
+@MODELS.register_module()
+class SwinTransformerFirst3Stages(SwinTransformer):
+
+    def forward(self, x):
+        x, hw_shape = self.patch_embed(x)
+
+        if self.use_abs_pos_embed:
+            x = x + self.absolute_pos_embed
+        x = self.drop_after_pos(x)
+
+        outs = []
+
+        # Switch this so that it only runs for the number of 
+        # frozen stages.
+        for i, stage in enumerate(self.stages):
+            x, hw_shape, out, out_hw_shape = stage(x, hw_shape)
+
+            if i >= self.frozen_stages: 
+                break
+
+            if i in self.out_indices:
+                norm_layer = getattr(self, f'norm{i}')
+                out = norm_layer(out)
+                out = out.view(-1, *out_hw_shape,
+                               self.num_features[i]).permute(0, 3, 1,
+                                                             2).contiguous()
+                outs.append(out)
+
+        return outs
+
+
+
+@MODELS.register_module()
+class SwinTransformerLastStage(SwinTransformer):
+
+    def forward(self, x):
+        stage = self.stages[:-1]
+
+        # Not yet sure if this is right
+        i = len(self.stages)
+        x, hw_shape, out, out_hw_shape = stage(x, hw_shape)
+
+        # Switch this so that it only runs for the number of 
+        # frozen stages.
+        norm_layer = getattr(self, f'norm{i}')
+        out = norm_layer(out)
+        out = out.view(-1, *out_hw_shape,
+                    self.num_features[i]).permute(0, 3, 1,
+                            2).contiguous()
+
+        # Return as list to keep the output format consistent
+        # You'd then feed this through to the rpn and roi_heads
+        return [out]
+