updat

ChatUniVi/model/arch.py

@@ -5,6 +5,7 @@
 from ChatUniVi.constants import *
 from .cluster import CTM, TCBlock
 from collections import OrderedDict
+from .multimodal_projector.builder import build_vision_projector
 
 
 class MetaModel:
@@ -58,8 +59,8 @@ def initialize_vision_modules(self, model_args, fsdp=None):
         else:
             self.vision_tower = vision_tower
 
-        if not hasattr(self, 'mm_projector') or not self.mm_projector.weight.size(0):
-            self.mm_projector = nn.Linear(self.config.mm_hidden_size, self.config.hidden_size)
+        if not hasattr(self, 'mm_projector'):
+            self.mm_projector = build_vision_projector(self.config)
 
         if pretrain_mm_mlp_adapter is not None:
             mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')

ChatUniVi/model/multimodal_projector/builder.py

@@ -0,0 +1,52 @@
+import torch
+import torch.nn as nn
+import re
+
+
+class IdentityMap(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x, *args, **kwargs):
+        return x
+
+    @property
+    def config(self):
+        return {"mm_projector_type": 'identity'}
+
+
+class SimpleResBlock(nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.pre_norm = nn.LayerNorm(channels)
+
+        self.proj = nn.Sequential(
+            nn.Linear(channels, channels),
+            nn.GELU(),
+            nn.Linear(channels, channels)
+        )
+    def forward(self, x):
+        x = self.pre_norm(x)
+        return x + self.proj(x)
+
+
+def build_vision_projector(config, delay_load=False, **kwargs):
+    projector_type = getattr(config, 'mm_projector_type', 'linear')
+
+    if projector_type == 'linear':
+        return nn.Linear(config.mm_hidden_size, config.hidden_size)
+
+    print("projector_type:", projector_type)
+    mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
+    if mlp_gelu_match:
+        mlp_depth = int(mlp_gelu_match.group(1))
+        modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
+        for _ in range(1, mlp_depth):
+            modules.append(nn.GELU())
+            modules.append(nn.Linear(config.hidden_size, config.hidden_size))
+        return nn.Sequential(*modules)
+
+    if projector_type == 'identity':
+        return IdentityMap()
+
+    raise ValueError(f'Unknown projector type: {projector_type}')

ChatUniVi/train/train.py

@@ -57,6 +57,7 @@ class ModelArguments:
     mm_use_im_patch_token: bool = field(default=True)
     mm_vision_select_feature: Optional[str] = field(default="patch")
 
+    mm_projector_type: Optional[str] = field(default='linear')
     model_use: str = field(default="BASE")
     mm_use_box_start_end: bool = field(default=False)