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janus/models/projector.py

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+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+from typing import Tuple, Union
+
+import torch
+import torch.nn as nn
+from attrdict import AttrDict
+
+
+class MlpProjector(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+
+        self.cfg = cfg
+
+        if cfg.projector_type == "identity":
+            modules = nn.Identity()
+
+        elif cfg.projector_type == "linear":
+            modules = nn.Linear(cfg.input_dim, cfg.n_embed)
+
+        elif cfg.projector_type == "mlp_gelu":
+            mlp_depth = cfg.get("depth", 1)
+            modules = [nn.Linear(cfg.input_dim, cfg.n_embed)]
+            for _ in range(1, mlp_depth):
+                modules.append(nn.GELU())
+                modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
+            modules = nn.Sequential(*modules)
+
+        elif cfg.projector_type == "low_high_hybrid_split_mlp_gelu":
+            mlp_depth = cfg.get("depth", 1)
+            self.high_up_proj = nn.Linear(cfg.input_dim, cfg.n_embed // 2)
+            self.low_up_proj = nn.Linear(cfg.input_dim, cfg.n_embed // 2)
+
+            modules = []
+            for _ in range(1, mlp_depth):
+                modules.append(nn.GELU())
+                modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
+            modules = nn.Sequential(*modules)
+
+        else:
+            raise ValueError(f"Unknown projector type: {cfg.projector_type}")
+
+        self.layers = modules
+
+    def forward(
+        self, x_or_tuple: Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor]
+    ):
+        """
+
+        Args:
+            x_or_tuple (Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor]:  if it is a tuple of torch.Tensor,
+                then it comes from the hybrid vision encoder, and x = high_res_x, low_res_x);
+                otherwise it is the feature from the single vision encoder.
+
+        Returns:
+            x (torch.Tensor): [b, s, c]
+        """
+
+        if isinstance(x_or_tuple, tuple):
+            # self.cfg.projector_type == "low_high_hybrid_split_mlp_gelu":
+            high_x, low_x = x_or_tuple
+            high_x = self.high_up_proj(high_x)
+            low_x = self.low_up_proj(low_x)
+            x = torch.concat([high_x, low_x], dim=-1)
+        else:
+            x = x_or_tuple
+
+        return self.layers(x)
+
+
+if __name__ == "__main__":
+    cfg = AttrDict(
+        input_dim=1024,
+        n_embed=2048,
+        depth=2,
+        projector_type="low_high_hybrid_split_mlp_gelu",
+    )
+    inputs = (torch.rand(4, 576, 1024), torch.rand(4, 576, 1024))
+
+    m = MlpProjector(cfg)
+    out = m(inputs)
+    print(out.shape)