ultralytics 8.1.39 add YOLO-World training (#9268)

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

ultralytics/nn/modules/block.py

@@ -519,7 +519,8 @@ class ContrastiveHead(nn.Module):
     def __init__(self):
         """Initializes ContrastiveHead with specified region-text similarity parameters."""
         super().__init__()
-        self.bias = nn.Parameter(torch.zeros([]))
+        # NOTE: use -10.0 to keep the init cls loss consistency with other losses
+        self.bias = nn.Parameter(torch.tensor([-10.0]))
         self.logit_scale = nn.Parameter(torch.ones([]) * torch.tensor(1 / 0.07).log())
 
     def forward(self, x, w):
@@ -542,7 +543,8 @@ class BNContrastiveHead(nn.Module):
         """Initialize ContrastiveHead with region-text similarity parameters."""
         super().__init__()
         self.norm = nn.BatchNorm2d(embed_dims)
-        self.bias = nn.Parameter(torch.zeros([]))
+        # NOTE: use -10.0 to keep the init cls loss consistency with other losses
+        self.bias = nn.Parameter(torch.tensor([-10.0]))
         # use -1.0 is more stable
         self.logit_scale = nn.Parameter(-1.0 * torch.ones([]))
 

ultralytics/nn/modules/head.py

@@ -250,6 +250,15 @@ class WorldDetect(Detect):
         y = torch.cat((dbox, cls.sigmoid()), 1)
         return y if self.export else (y, x)
 
+    def bias_init(self):
+        """Initialize Detect() biases, WARNING: requires stride availability."""
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            # b[-1].bias.data[:] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (.01 objects, 80 classes, 640 img)
+
 
 class RTDETRDecoder(nn.Module):
     """