Show number of parameters when onnx files are being analyzed

src/turnkeyml/analyze/script.py

@@ -766,24 +766,12 @@ def forward_spy(*args, **kwargs):
             model_info = tracer_args.models_found[model_hash]
 
             if invocation_hash not in model_info.unique_invocations:
-                model_info.unique_invocations[invocation_hash] = (
-                    status.UniqueInvocationInfo(
-                        name=model_info.name,
-                        script_name=model_info.script_name,
-                        file=model_info.file,
-                        line=model_info.line,
-                        params=model_info.params,
-                        depth=model_info.depth,
-                        build_model=model_info.build_model,
-                        model_type=model_info.model_type,
-                        model_class=type(model_info.model),
-                        invocation_hash=invocation_hash,
-                        hash=model_info.hash,
-                        is_target=invocation_hash in tracer_args.targets
-                        or len(tracer_args.targets) == 0,
-                        input_shapes=input_shapes,
-                        parent_hash=parent_invocation_hash,
-                    )
+                model_info.add_unique_invocation(
+                    invocation_hash=invocation_hash,
+                    is_target=invocation_hash in tracer_args.targets
+                    or len(tracer_args.targets) == 0,
+                    input_shapes=input_shapes,
+                    parent_hash=parent_invocation_hash,
                 )
             model_info.last_unique_invocation_executed = invocation_hash
 

src/turnkeyml/analyze/status.py

@@ -327,6 +327,35 @@ class ModelInfo(BasicInfo):
     def __post_init__(self):
         self.params = analyze_model.count_parameters(self.model, self.model_type)
 
+    def add_unique_invocation(
+        self,
+        invocation_hash: int,
+        is_target: bool,
+        input_shapes: Dict,
+        parent_hash: Union[str, None] = None,
+        executed: int = 0,
+    ):
+        model_class = (
+            type(self.model) if self.model_type == build.ModelType.PYTORCH else None
+        )
+        self.unique_invocations[invocation_hash] = UniqueInvocationInfo(
+            name=self.name,
+            script_name=self.script_name,
+            file=self.file,
+            line=self.line,
+            params=self.params,
+            depth=self.depth,
+            build_model=self.build_model,
+            model_type=self.model_type,
+            model_class=model_class,
+            invocation_hash=invocation_hash,
+            hash=self.hash,
+            is_target=is_target,
+            input_shapes=input_shapes,
+            parent_hash=parent_hash,
+            executed=executed,
+        )
+
 
 def update(
     models_found: Dict[str, ModelInfo],

src/turnkeyml/files_api.py

@@ -20,7 +20,7 @@
     explore_invocation,
     get_model_hash,
 )
-from turnkeyml.analyze.status import ModelInfo, UniqueInvocationInfo, Verbosity
+from turnkeyml.analyze.status import ModelInfo, Verbosity
 import turnkeyml.common.build as build
 import turnkeyml.build.onnx_helpers as onnx_helpers
 
@@ -374,24 +374,6 @@ def benchmark_files(
                 onnx_inputs = onnx_helpers.dummy_inputs(file_path_absolute)
                 input_shapes = {key: value.shape for key, value in onnx_inputs.items()}
 
-                # Create the UniqueInvocationInfo
-                #  - execute=1 is required or else the ONNX model will be
-                #       skipped in later stages of evaluation
-                #  - is_target=True is required or else traceback wont be printed for
-                #       in the event of any errors
-                #  - Most other values can be left as default
-                invocation_info = UniqueInvocationInfo(
-                    name=onnx_name,
-                    script_name=onnx_name,
-                    file=file_path_absolute,
-                    build_model=not build_only,
-                    model_type=build.ModelType.ONNX_FILE,
-                    executed=1,
-                    input_shapes=input_shapes,
-                    hash=onnx_hash,
-                    is_target=True,
-                )
-
                 # Create the ModelInfo
                 model_info = ModelInfo(
                     model=file_path_absolute,
@@ -400,17 +382,28 @@ def benchmark_files(
                     file=file_path_absolute,
                     build_model=not build_only,
                     model_type=build.ModelType.ONNX_FILE,
-                    unique_invocations={onnx_hash: invocation_info},
                     hash=onnx_hash,
                 )
 
+                # Add UniqueInvocationInfo
+                #  - is_target=True is required or else traceback wont be printed for
+                #      in the event of any errors
+                #  - execute=1 is required or else the ONNX model will be
+                #       skipped in later stages of evaluation
+                model_info.add_unique_invocation(
+                    invocation_hash=onnx_hash,
+                    is_target=True,
+                    input_shapes=input_shapes,
+                    executed=1,
+                )
+
                 # Begin evaluating the ONNX model
                 tracer_args.script_name = onnx_name
                 tracer_args.models_found[tracer_args.script_name] = model_info
                 explore_invocation(
                     model_inputs=onnx_inputs,
                     model_info=model_info,
-                    invocation_info=invocation_info,
+                    invocation_info=model_info.unique_invocations[onnx_hash],
                     tracer_args=tracer_args,
                 )
                 models_found = tracer_args.models_found