Update `get_flops_with_torch_profiler()` (#12635)

ultralytics/utils/torch_utils.py

@@ -331,19 +331,28 @@ def get_flops(model, imgsz=640):
 
 
 def get_flops_with_torch_profiler(model, imgsz=640):
-    """Compute model FLOPs (thop alternative)."""
-    if TORCH_2_0:
-        model = de_parallel(model)
-        p = next(model.parameters())
+    """Compute model FLOPs (thop package alternative, but 2-10x slower unfortunately)."""
+    if not TORCH_2_0:  # torch profiler implemented in torch>=2.0
+        return 0.0
+    model = de_parallel(model)
+    p = next(model.parameters())
+    if not isinstance(imgsz, list):
+        imgsz = [imgsz, imgsz]  # expand if int/float
+    try:
+        # Use stride size for input tensor
         stride = (max(int(model.stride.max()), 32) if hasattr(model, "stride") else 32) * 2  # max stride
-        im = torch.zeros((1, p.shape[1], stride, stride), device=p.device)  # input image in BCHW format
+        im = torch.empty((1, p.shape[1], stride, stride), device=p.device)  # input image in BCHW format
         with torch.profiler.profile(with_flops=True) as prof:
             model(im)
         flops = sum(x.flops for x in prof.key_averages()) / 1e9
-        imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz]  # expand if int/float
         flops = flops * imgsz[0] / stride * imgsz[1] / stride  # 640x640 GFLOPs
-        return flops
-    return 0
+    except Exception:
+        # Use actual image size for input tensor (i.e. required for RTDETR models)
+        im = torch.empty((1, p.shape[1], *imgsz), device=p.device)  # input image in BCHW format
+        with torch.profiler.profile(with_flops=True) as prof:
+            model(im)
+        flops = sum(x.flops for x in prof.key_averages()) / 1e9
+    return flops
 
 
 def initialize_weights(model):