Add --ort-logs to show more logging when creating inference session

onnx_diagnostic/_command_lines_parser.py

@@ -542,6 +542,12 @@ def get_parser_validate() -> ArgumentParser:
         "the onnx exporter should use.",
         default="",
     )
+    parser.add_argument(
+        "--ort-logs",
+        default=False,
+        action=BooleanOptionalAction,
+        help="Enables onnxruntime logging when the session is created",
+    )
     return parser
 
 
@@ -601,6 +607,7 @@ def _cmd_validate(argv: List[Any]):
             repeat=args.repeat,
             warmup=args.warmup,
             inputs2=args.inputs2,
+            ort_logs=args.ort_logs,
             output_names=(
                 None if len(args.outnames.strip()) < 2 else args.outnames.strip().split(",")
             ),

onnx_diagnostic/torch_models/validate.py

@@ -292,6 +292,7 @@ def validate_model(
     warmup: int = 0,
     inputs2: int = 1,
     output_names: Optional[List[str]] = None,
+    ort_logs: bool = False,
 ) -> Tuple[Dict[str, Union[int, float, str]], Dict[str, Any]]:
     """
     Validates a model.
@@ -344,6 +345,7 @@ def validate_model(
         this ensures that the model does support dynamism, the value is used
         as an increment to the first set of values (added to dimensions)
     :param output_names: output names the onnx exporter should use
+    :param ort_logs: increases onnxruntime verbosity when creating the session
     :return: two dictionaries, one with some metrics,
         another one with whatever the function produces
 
@@ -758,6 +760,7 @@ def validate_model(
             repeat=repeat,
             warmup=warmup,
             inputs2=inputs2,
+            ort_logs=ort_logs,
         )
         summary.update(summary_valid)
 
@@ -1158,6 +1161,7 @@ def validate_onnx_model(
     repeat: int = 1,
     warmup: int = 0,
     inputs2: int = 1,
+    ort_logs: bool = False,
 ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
     """
     Verifies that an onnx model produces the same
@@ -1176,6 +1180,7 @@ def validate_onnx_model(
     :param inputs2: to validate the model on the second input set
         to make sure the exported model supports dynamism, the value is
         used as an increment added to the first set of inputs (added to dimensions)
+    :param ort_logs: triggers the logs for onnxruntime
     :return: two dictionaries, one with some metrics,
         another one with whatever the function produces
     """
@@ -1232,8 +1237,13 @@ def _mk(key, flavour=flavour):
 
         if verbose:
             print("[validate_onnx_model] runtime is onnxruntime")
-        cls_runtime = lambda model, providers: onnxruntime.InferenceSession(
+        sess_opts = onnxruntime.SessionOptions()
+        if ort_logs:
+            sess_opts.log_severity_level = 0
+            sess_opts.log_verbosity_level = 4
+        cls_runtime = lambda model, providers, _o=sess_opts: onnxruntime.InferenceSession(
             (model.SerializeToString() if isinstance(model, onnx.ModelProto) else model),
+            _o,
             providers=providers,
         )
     elif runtime == "torch":