1 files changed, 45 insertions, 0 deletions
diff --git a/posrecon/models.py b/posrecon/models.py
index 62cf322..9e8cb22 100644
--- a/posrecon/models.py
+++ b/posrecon/models.py
@@ -105,3 +105,48 @@ class ShuffledVisionTransformer(VisionTransformer):
             features = self.forward_features(x, pos_embed, probe)
             x = self.forward_head(features)
             return x
+
+
+class MaskedShuffledVisionTransformer(ShuffledVisionTransformer):
+    def __init__(self, *args, **kwargs):
+        super(MaskedShuffledVisionTransformer, self).__init__(*args, **kwargs)
+        self.mask_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim) * 0.02)
+        trunc_normal_(self.mask_token, std=.02)
+
+    def generate_masks(self, mask_rate=0.75):
+        nmasks = int(self.patch_embed.num_patches * mask_rate)
+        shuffled_indices = torch.randperm(self.patch_embed.num_patches) + self.num_prefix_tokens
+        visible_indices, _ = shuffled_indices[:self.patch_embed.num_patches - nmasks].sort()
+        return visible_indices
+
+    def mask_embed(self, embed, visible_indices):
+        nmasks = self.patch_embed.num_patches - len(visible_indices)
+        mask_tokens = self.mask_token.expand(embed.size(0), nmasks, -1)
+        masked_features = torch.cat([embed[:, visible_indices, :], mask_tokens], dim=1)
+        return masked_features
+
+    def forward_features(self, x, pos_embed=None, visible_indices=None, probe=False):
+        patch_embed = self.patch_embed(x)
+        x = self._pos_embed(patch_embed, pos_embed)
+        x = self.mask_embed(x, visible_indices)
+        if self.grad_checkpointing and not torch.jit.is_scripting():
+            x = checkpoint_seq(self.blocks, x)
+        else:
+            x = self.blocks(x)
+        x = self.norm(x)
+        if probe:
+            return x, patch_embed
+        else:
+            return x
+
+    def forward(self, x, pos_embed=None, visible_indices=None, probe=False):
+        assert pos_embed is not None
+        assert visible_indices is not None
+        if probe:
+            features, patch_embed = self.forward_features(x, pos_embed, visible_indices, probe)
+            x = self.forward_head(features)
+            return x, features, patch_embed
+        else:
+            features = self.forward_features(x, pos_embed, visible_indices, probe)
+            x = self.forward_head(features)
+            return x