Create sam2_model_stub.py

sam2_model_stub.py

@@ -0,0 +1,45 @@
+# sam2_model_stub.py
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class SAM2Hierarchical(nn.Module):
+    def __init__(self, num_classes=6, in_channels=3, backbone="vit_b", freeze_backbone=True, use_cls_head=True):
+        super().__init__()
+        self.use_cls_head = use_cls_head
+
+        # Minimal vision backbone stub (fake transformer or CNN)
+        self.backbone = nn.Sequential(
+            nn.Conv2d(in_channels, 64, kernel_size=3, stride=2, padding=1),
+            nn.BatchNorm2d(64),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
+            nn.BatchNorm2d(128),
+            nn.ReLU(inplace=True)
+        )
+
+        # Segmentation head stub
+        self.segmentation_head = nn.Sequential(
+            nn.Conv2d(128, 64, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(64, num_classes, kernel_size=1)
+        )
+
+        # Optional classification head
+        if self.use_cls_head:
+            self.cls_head = nn.Linear(128, num_classes)
+
+        if freeze_backbone:
+            for param in self.backbone.parameters():
+                param.requires_grad = False
+
+    def forward(self, x):
+        features = self.backbone(x)
+        logits = self.segmentation_head(features)
+
+        if self.use_cls_head:
+            # Just return segmentation output; inference only cares about logits
+            return logits
+
+        return logits