Onnx model testing v2

python/models/models_onnx/model_conv2d.onnx

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python/models/models_onnx/model_redundant.onnx

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python/scripts/export_wrong_dimension.py

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+print("Exporting Wrong Dimension model to ONNX format...")
+
+import torch
+import sys
+import os
+
+# Add the models directory to Python path - using relative path
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'models', 'models_onnx')))
+
+try:
+    from wrong_dimension_model import WrongDimensionModel
+
+    model = WrongDimensionModel()
+    model.eval()
+    dummy_input = torch.randn(1, 1, 28, 28)
+
+    print("Testing forward pass...")
+    with torch.no_grad():
+        output = model(dummy_input)
+        print(f"Model output shape: {output.shape}")
+
+    print("Exporting to ONNX...")
+    torch.onnx.export(
+        model, dummy_input, "model_wrong_dimension.onnx",
+        input_names=["input"], output_names=["output"],
+        opset_version=11
+    )
+    print("✅ Exported model_wrong_dimension.onnx successfully!")
+
+except Exception as e:
+    print(f"❌ Error: {e}")
+    import traceback
+    traceback.print_exc()

python/scripts/generate_io_conv2d.py

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+print("Generating input and output for Conv2D ONNX model...")
+
+import onnxruntime as ort
+import torch
+import json
+import numpy as np
+
+try:
+    input_tensor = torch.randn(1, 1, 28, 28)
+    input_numpy = input_tensor.numpy()
+
+    session = ort.InferenceSession("/Users/elenapashkova/GravyTesting-Internal/python/models/models_onnx/model_conv2d.onnx")
+    print("ONNX model loaded")
+
+    outputs = session.run(None, {"input": input_numpy})
+    print("Inference complete")
+
+    with open("input.json", "w") as f:
+        json.dump(input_numpy.tolist(), f)
+    print("Input saved")
+
+    with open("expected_output.json", "w") as f:
+        json.dump(outputs[0].tolist(), f)
+    print("Output saved")
+except Exception as e:
+    print("Error:", e)

python/scripts/generate_io_redundant.py

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+print("Generating input/output for Redundant model...")
+
+import torch
+import json
+import os
+import sys
+
+# Add the models directory to Python path - using relative path
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'models', 'models_onnx')))
+
+try:
+    from redundant_layers_model import RedundantModel
+
+    model = RedundantModel()
+    model.eval()
+    dummy_input = torch.randn(1, 1, 28, 28)
+
+    with torch.no_grad():
+        output = model(dummy_input)
+
+    input_data = dummy_input.numpy().tolist()
+    output_data = output.detach().numpy().tolist()
+
+    io_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'testing', 'core', 'input_output_data', 'model_redundant'))
+    os.makedirs(io_dir, exist_ok=True)
+
+    with open(os.path.join(io_dir, "input.json"), "w") as f:
+        json.dump({"input": input_data}, f)
+
+    with open(os.path.join(io_dir, "output.json"), "w") as f:
+        json.dump({"output": output_data}, f)
+
+    print("✅ Successfully saved input/output JSONs to:", io_dir)
+
+except Exception as e:
+    print(f"❌ Error: {e}")
+    import traceback
+    traceback.print_exc()

python/scripts/generate_io_wrong_dimension.py

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+print("Generating input/output for Wrong Dimension model...")
+
+import torch
+import json
+import os
+import sys
+
+# Add the models directory to Python path - using relative path
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'models', 'models_onnx')))
+
+try:
+    from wrong_dimension_model import WrongDimensionModel
+
+    model = WrongDimensionModel()
+    model.eval()
+    dummy_input = torch.randn(1, 1, 28, 28)
+
+    with torch.no_grad():
+        output = model(dummy_input)
+
+    input_data = dummy_input.numpy().tolist()
+    output_data = output.detach().numpy().tolist()
+
+    print("Creating output directory...")
+    io_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'testing', 'core', 'input_output_data', 'model_wrong_dimension'))
+    os.makedirs(io_dir, exist_ok=True)
+
+    with open(os.path.join(io_dir, "input.json"), "w") as f:
+        json.dump({"input": input_data}, f)
+
+    with open(os.path.join(io_dir, "output.json"), "w") as f:
+        json.dump({"output": output_data}, f)
+
+    print("✅ Successfully saved input/output JSONs to:", io_dir)
+
+except Exception as e:
+    print(f"❌ Error: {e}")
+    import traceback
+    traceback.print_exc()

python/testing/core/tests/test_conv2d_model.py

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+import onnxruntime as ort
+import numpy as np
+import json
+import os
+
+def test_conv2d_model_output():
+    # Paths - using relative paths since files are in the same directory as the test
+    onnx_model_path = "python/models/models_onnx/model_conv2d.onnx"
+    input_json_path = "input.json"  # File is in the same directory as this test
+    expected_output_json_path = "expected_output.json"  # File is in the same directory as this test
+
+    # Get the directory where this test file is located
+    test_dir = os.path.dirname(os.path.abspath(__file__))
+
+    # Build full paths to the JSON files
+    input_json_path = os.path.join(test_dir, "input.json")
+    expected_output_json_path = os.path.join(test_dir, "expected_output.json")
+
+    # Load input and expected output
+    with open(input_json_path, "r") as f:
+        input_data = np.array(json.load(f)).astype(np.float32)
+
+    with open(expected_output_json_path, "r") as f:
+        expected_output = np.array(json.load(f)).astype(np.float32)
+
+    # Run ONNX inference
+    session = ort.InferenceSession(onnx_model_path)
+    outputs = session.run(None, {"input": input_data})
+    output = outputs[0]
+
+    # Check that output is close enough
+    np.testing.assert_allclose(output, expected_output, rtol=1e-3, atol=1e-5)
+    print("Test passed!")
+
+if __name__ == "__main__":
+    test_conv2d_model_output()

python/testing/core/tests/test_model_redundant.py

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+print("Testing Redundant Model...")
+
+import torch
+import json
+import os
+import sys
+
+# Add the models directory to Python path - using relative path
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', '..', 'models', 'models_onnx')))
+
+try:
+    print("Importing RedundantModel...")
+    from redundant_layers_model import RedundantModel
+
+    model = RedundantModel()
+    model.eval()
+    dummy_input = torch.randn(1, 1, 28, 28)
+
+    with torch.no_grad():
+        output = model(dummy_input)
+
+    input_data = dummy_input.numpy().tolist()
+    output_data = output.detach().numpy().tolist()
+
+    print("Creating output directory...")
+    # Save input/output
+    io_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'input_output_data', 'model_redundant'))
+    os.makedirs(io_dir, exist_ok=True)
+
+    with open(os.path.join(io_dir, "input.json"), "w") as f:
+        json.dump({"input": input_data}, f)
+
+    with open(os.path.join(io_dir, "output.json"), "w") as f:
+        json.dump({"output": output_data}, f)
+
+    print("✅ Successfully saved input/output JSONs")
+
+except Exception as e:
+    print(f"❌ Error: {e}")
+    import traceback
+    traceback.print_exc()
+
+def test_redundant_model():
+    print("Test passed!")
+    assert True