Added an experimental clip fusion model that is showing promise for embedding concepts

toolkit/models/clip_fusion.py

@@ -0,0 +1,143 @@
+import torch
+import torch.nn as nn
+
+
+# Conv1d MLP
+# MLP that can alternately be used as a conv1d on dim 1
+class MLPC(nn.Module):
+    def __init__(
+            self,
+            in_dim,
+            out_dim,
+            hidden_dim,
+            do_conv=False,
+            use_residual=True
+    ):
+        super().__init__()
+        self.do_conv = do_conv
+        if use_residual:
+            assert in_dim == out_dim
+        # dont normalize if using conv
+        if not do_conv:
+            self.layernorm = nn.LayerNorm(in_dim)
+
+        if do_conv:
+            self.fc1 = nn.Conv1d(in_dim, hidden_dim, 1)
+            self.fc2 = nn.Conv1d(hidden_dim, out_dim, 1)
+        else:
+            self.fc1 = nn.Linear(in_dim, hidden_dim)
+            self.fc2 = nn.Linear(hidden_dim, out_dim)
+
+        self.use_residual = use_residual
+        self.act_fn = nn.GELU()
+
+    def forward(self, x):
+        residual = x
+        if not self.do_conv:
+            x = self.layernorm(x)
+        x = self.fc1(x)
+        x = self.act_fn(x)
+        x = self.fc2(x)
+        if self.use_residual:
+            x = x + residual
+        return x
+
+
+class ZipperBlock(nn.Module):
+    def __init__(
+            self,
+            in_size,
+            in_tokens,
+            out_size,
+            out_tokens,
+            hidden_size,
+            hidden_tokens,
+    ):
+        super().__init__()
+        self.in_size = in_size
+        self.in_tokens = in_tokens
+        self.out_size = out_size
+        self.out_tokens = out_tokens
+        self.hidden_size = hidden_size
+        self.hidden_tokens = hidden_tokens
+        # permute to (batch_size, out_size, in_tokens)
+
+        self.zip_token = MLPC(
+            in_dim=self.in_tokens,
+            out_dim=self.out_tokens,
+            hidden_dim=self.hidden_tokens,
+            do_conv=True,  # no need to permute
+            use_residual=False
+        )
+
+        # permute to (batch_size, out_tokens, out_size)
+
+        # in shpae: (batch_size, in_tokens, in_size)
+        self.zip_size = MLPC(
+            in_dim=self.in_size,
+            out_dim=self.out_size,
+            hidden_dim=self.hidden_size,
+            use_residual=False
+        )
+
+    def forward(self, x):
+        x = self.zip_token(x)
+        x = self.zip_size(x)
+        return x
+
+
+# CLIPFusionModule
+# Fuses any size of vision and text embeddings into a single embedding.
+# remaps tokens and vectors.
+class CLIPFusionModule(nn.Module):
+    def __init__(
+            self,
+            text_hidden_size: int = 768,
+            text_tokens: int = 77,
+            vision_hidden_size: int = 1024,
+            vision_tokens: int = 257,
+            num_blocks: int = 2,
+    ):
+        super(CLIPFusionModule, self).__init__()
+
+        self.text_hidden_size = text_hidden_size
+        self.text_tokens = text_tokens
+        self.vision_hidden_size = vision_hidden_size
+        self.vision_tokens = vision_tokens
+
+        self.resampler = ZipperBlock(
+            in_size=self.vision_hidden_size,
+            in_tokens=self.vision_tokens,
+            out_size=self.text_hidden_size,
+            out_tokens=self.text_tokens,
+            hidden_size=self.vision_hidden_size * 2,
+            hidden_tokens=self.vision_tokens * 2
+        )
+
+        self.zipper_blocks = torch.nn.ModuleList([
+            ZipperBlock(
+                in_size=self.text_hidden_size * 2,
+                in_tokens=self.text_tokens,
+                out_size=self.text_hidden_size,
+                out_tokens=self.text_tokens,
+                hidden_size=self.text_hidden_size * 2,
+                hidden_tokens=self.text_tokens * 2
+            ) for i in range(num_blocks)
+        ])
+
+    def forward(self, text_embeds, vision_embeds):
+        # text_embeds = (batch_size, 77, 768)
+        # vision_embeds = (batch_size, 257, 1024)
+        # output = (batch_size, 77, 768)
+
+        vision_embeds = self.resampler(vision_embeds)
+        x = vision_embeds
+        for i, block in enumerate(self.zipper_blocks):
+            res = x
+            x = torch.cat([text_embeds, x], dim=-1)
+            x = block(x)
+            x = x + res
+
+        x = text_embeds + x
+
+        return x