ultralytics 8.2.35 add YOLOv9t/s/m models (#13504)

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/nn/modules/block.py

@@ -32,7 +32,9 @@ __all__ = (
     "RepC3",
     "ResNetLayer",
     "RepNCSPELAN4",
+    "ELAN1",
     "ADown",
+    "AConv",
     "SPPELAN",
     "CBFuse",
     "CBLinear",
@@ -603,6 +605,33 @@ class RepNCSPELAN4(nn.Module):
         return self.cv4(torch.cat(y, 1))
 
 
+class ELAN1(RepNCSPELAN4):
+    """ELAN1 module with 4 convolutions."""
+
+    def __init__(self, c1, c2, c3, c4):
+        """Initializes ELAN1 layer with specified channel sizes."""
+        super().__init__(c1, c2, c3, c4)
+        self.c = c3 // 2
+        self.cv1 = Conv(c1, c3, 1, 1)
+        self.cv2 = Conv(c3 // 2, c4, 3, 1)
+        self.cv3 = Conv(c4, c4, 3, 1)
+        self.cv4 = Conv(c3 + (2 * c4), c2, 1, 1)
+
+
+class AConv(nn.Module):
+    """AConv."""
+
+    def __init__(self, c1, c2):
+        """Initializes AConv module with convolution layers."""
+        super().__init__()
+        self.cv1 = Conv(c1, c2, 3, 2, 1)
+
+    def forward(self, x):
+        """Forward pass through AConv layer."""
+        x = torch.nn.functional.avg_pool2d(x, 2, 1, 0, False, True)
+        return self.cv1(x)
+
+
 class ADown(nn.Module):
     """ADown."""