Update head.py

ultralytics/nn/modules/head.py

@@ -91,13 +91,14 @@ class Detect(nn.Module):
         # Inference path
         shape = x[0].shape  # BCHW
         x_cat = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2)
-        # if self.dynamic or self.shape != shape:
-        self.anchors, self.strides = (
-            x.transpose(0, 1) for x in make_anchors(torch.Tensor([80, 40, 20]).cuda(), self.stride, 0.5)
-        )
-        self.shape = shape
         if self.export and self.format == "sony":
+            self.anchors, self.strides = (
+                x.transpose(0, 1) for x in make_anchors(torch.Tensor([80, 40, 20]).cuda(), self.stride, 0.5)
+            )
             self.strides /= 640  # NOTE: the relu could be removed in the future.
+        elif self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
+            self.shape = shape
 
         if self.export and self.format in {"saved_model", "pb", "tflite", "edgetpu", "tfjs"}:  # avoid TF FlexSplitV ops
             box = x_cat[:, : self.reg_max * 4]
@@ -114,7 +115,7 @@ class Detect(nn.Module):
             norm = self.strides / (self.stride[0] * grid_size)
             dbox = self.decode_bboxes(self.dfl(box) * norm, self.anchors.unsqueeze(0) * norm[:, :2])
         elif self.export and self.format == "sony":
-            dbox = self.decode_bboxes(self.dfl(box) * self.strides, self.anchors.unsqueeze(0) * self.strides)
+            dbox = self.decode_bboxes(self.dfl(box) * self.strides, self.anchors.unsqueeze(0) * self.strides, xywh=False)
             # NOTE: the relu could be removed in the future.
             y1 = self.relu(dbox[:, 0, :])
             x1 = self.relu(dbox[:, 1, :])
@@ -140,9 +141,9 @@ class Detect(nn.Module):
                 a[-1].bias.data[:] = 1.0  # box
                 b[-1].bias.data[: m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (.01 objects, 80 classes, 640 img)
 
-    def decode_bboxes(self, bboxes, anchors):
+    def decode_bboxes(self, bboxes, anchors, xywh=True):
         """Decode bounding boxes."""
-        return dist2bbox(bboxes, anchors, xywh=False, dim=1)
+        return dist2bbox(bboxes, anchors, xywh=xywh and (not self.end2end), dim=1)
 
     @staticmethod
     def postprocess(preds: torch.Tensor, max_det: int, nc: int = 80):