Fix YoloNAS on cuda

src/super_gradients/examples/predict/detection_predict.py

@@ -1,9 +1,13 @@
+import torch
 from super_gradients.common.object_names import Models
 from super_gradients.training import models
 
 # Note that currently only YoloX, PPYoloE and YOLO-NAS are supported.
 model = models.get(Models.YOLO_NAS_L, pretrained_weights="coco")
 
+# We want to use cuda if available to speed up inference.
+model = model.to("cuda" if torch.cuda.is_available() else "cpu")
+
 IMAGES = [
     "../../../../documentation/source/images/examples/countryside.jpg",
     "../../../../documentation/source/images/examples/street_busy.jpg",

src/super_gradients/training/models/detection_models/pp_yolo_e/pp_yolo_head.py

@@ -270,22 +270,23 @@ def _generate_anchors(self, feats=None, dtype=None, device=None):
             else:
                 h = int(self.eval_size[0] / stride)
                 w = int(self.eval_size[1] / stride)
-            shift_x = torch.arange(end=w, dtype=dtype) + self.grid_cell_offset
-            shift_y = torch.arange(end=h, dtype=dtype) + self.grid_cell_offset
+
+            # ONNX export does not support arange with float16, so it is created as fp32 and then casted to fp16
+            # This produce correct fp16 weights in ONNX model when exported
+            shift_x = torch.arange(end=w, dtype=torch.float32, device=device) + self.grid_cell_offset
+            shift_y = torch.arange(end=h, dtype=torch.float32, device=device) + self.grid_cell_offset
+
             if torch_version_is_greater_or_equal(1, 10):
                 shift_y, shift_x = torch.meshgrid(shift_y, shift_x, indexing="ij")
             else:
                 shift_y, shift_x = torch.meshgrid(shift_y, shift_x)
 
             anchor_point = torch.stack([shift_x, shift_y], dim=-1).to(dtype=dtype)
             anchor_points.append(anchor_point.reshape([-1, 2]))
-            stride_tensor.append(torch.full([h * w, 1], stride, dtype=dtype))
+            stride_tensor.append(torch.full([h * w, 1], stride, dtype=dtype, device=device))
+
         anchor_points = torch.cat(anchor_points)
         stride_tensor = torch.cat(stride_tensor)
-
-        if device is not None:
-            anchor_points = anchor_points.to(device)
-            stride_tensor = stride_tensor.to(device)
         return anchor_points, stride_tensor
 
     def forward(self, feats: Tuple[Tensor]):

src/super_gradients/training/models/detection_models/yolo_nas/dfl_heads.py

@@ -283,8 +283,10 @@ def _generate_anchors(self, feats=None, dtype=None, device=None):
                 h = int(self.eval_size[0] / stride)
                 w = int(self.eval_size[1] / stride)
 
-            shift_x = torch.arange(end=w, dtype=dtype) + self.grid_cell_offset
-            shift_y = torch.arange(end=h, dtype=dtype) + self.grid_cell_offset
+            # ONNX export does not support arange with float16, so it is created as fp32 and then casted to fp16
+            # This produce correct fp16 weights in ONNX model when exported
+            shift_x = torch.arange(end=w, dtype=torch.float32, device=device) + self.grid_cell_offset
+            shift_y = torch.arange(end=h, dtype=torch.float32, device=device) + self.grid_cell_offset
 
             if torch_version_is_greater_or_equal(1, 10):
                 shift_y, shift_x = torch.meshgrid(shift_y, shift_x, indexing="ij")
@@ -293,11 +295,8 @@ def _generate_anchors(self, feats=None, dtype=None, device=None):
 
             anchor_point = torch.stack([shift_x, shift_y], dim=-1).to(dtype=dtype)
             anchor_points.append(anchor_point.reshape([-1, 2]))
-            stride_tensor.append(torch.full([h * w, 1], stride, dtype=dtype))
+            stride_tensor.append(torch.full([h * w, 1], stride, dtype=dtype, device=device))
+
         anchor_points = torch.cat(anchor_points)
         stride_tensor = torch.cat(stride_tensor)
-
-        if device is not None:
-            anchor_points = anchor_points.to(device)
-            stride_tensor = stride_tensor.to(device)
         return anchor_points, stride_tensor