onnx validation

onnxconv.py

@@ -7,6 +7,8 @@
 from onnxconverter_common import float16
 import onnx
 import numpy as np
+from onnx import shape_inference
+import onnxruntime as ort
 
 class IMFDecoder(nn.Module):
     def __init__(self, model):
@@ -118,7 +120,6 @@ def convert_model_to_32bit(model, output_path):
     onnx.save(model, output_path)
     print(f"Converted model saved to {output_path}")
 
-
 def export_to_onnx(model, x_current, x_reference, file_name):
     try:
         print("Model structure before tracing:")
@@ -167,18 +168,57 @@ def export_to_onnx(model, x_current, x_reference, file_name):
         # Load the ONNX model
         onnx_model = onnx.load(file_name)
 
-    # Convert int64 to int32
-        print("Converting int64 to int32...")
+        # Check the model
+        print("\nChecking the model...")
+        onnx.checker.check_model(onnx_model)
+        print("Model checked successfully")
+
+        # Print model input and output shapes
+        print("\nModel Input and Output Shapes:")
+        for input in onnx_model.graph.input:
+            print(f"Input: {input.name}, Shape: {[dim.dim_value for dim in input.type.tensor_type.shape.dim]}")
+        for output in onnx_model.graph.output:
+            print(f"Output: {output.name}, Shape: {[dim.dim_value for dim in output.type.tensor_type.shape.dim]}")
+
+        # Perform shape inference
+        print("\nPerforming shape inference...")
+        inferred_model = shape_inference.infer_shapes(onnx_model)
+        onnx.save(inferred_model, file_name)
+        print("Shape inference completed and model saved")
+
+        # Convert int64 to int32
+        print("\nConverting int64 to int32...")
         web_compatible_file = file_name.replace('.onnx', '_web.onnx')
-        onnx_model = convert_model_to_32bit(onnx_model,web_compatible_file)
-
-
+        convert_model_to_32bit(onnx_model, web_compatible_file)
+
+        # Validate the converted model
+        print("\nValidating the converted model...")
+        onnx.checker.check_model(onnx.load(web_compatible_file))
+        print("Converted model validated successfully")
+
+        # Test the model with ONNX Runtime
+        print("\nTesting the model with ONNX Runtime...")
+        ort_session = ort.InferenceSession(web_compatible_file)
+
+        # Prepare inputs (assuming x_current and x_reference are PyTorch tensors)
+        ort_inputs = {
+            'x_current': x_current.numpy(),
+            'x_reference': x_reference.numpy()
+        }
+
+        # Run inference
+        ort_outputs = ort_session.run(None, ort_inputs)
+        print("ONNX Runtime inference successful")
+
+        print(f"\nConverted and validated model saved to {web_compatible_file}")
+        print("This model should now be compatible with WONNX")
 
     except Exception as e:
-        print(f"Error during ONNX export: {str(e)}")
+        print(f"Error during ONNX export and validation: {str(e)}")
         import traceback
         traceback.print_exc()
 
+
 # Load your model
 model = IMFModel()
 model.eval()