Don't run profile if model is torchscripted

benchmark.py

@@ -205,8 +205,10 @@ class BenchmarkRunner:
         self.num_classes = self.model.num_classes
         self.param_count = count_params(self.model)
         _logger.info('Model %s created, param count: %d' % (model_name, self.param_count))
+        self.scripted = False
         if torchscript:
             self.model = torch.jit.script(self.model)
+            self.scripted = True
 
         data_config = resolve_data_config(kwargs, model=self.model, use_test_size=not use_train_size)
         self.input_size = data_config['input_size']
@@ -275,14 +277,14 @@ class InferenceBenchmarkRunner(BenchmarkRunner):
             img_size=self.input_size[-1],
             param_count=round(self.param_count / 1e6, 2),
         )
-
-        if has_deepspeed_profiling:
-            macs, _ = profile_deepspeed(self.model, self.input_size)
-            results['gmacs'] = round(macs / 1e9, 2)
-        elif has_fvcore_profiling:
-            macs, activations = profile_fvcore(self.model, self.input_size)
-            results['gmacs'] = round(macs / 1e9, 2)
-            results['macts'] = round(activations / 1e6, 2)
+        if not self.scripted:
+            if has_deepspeed_profiling:
+                macs, _ = profile_deepspeed(self.model, self.input_size)
+                results['gmacs'] = round(macs / 1e9, 2)
+            elif has_fvcore_profiling:
+                macs, activations = profile_fvcore(self.model, self.input_size)
+                results['gmacs'] = round(macs / 1e9, 2)
+                results['macts'] = round(activations / 1e6, 2)
 
         _logger.info(
             f"Inference benchmark of {self.model_name} done. "