[Mobile] iOS - ZipMap output cannot be read

[sanjaymk908]

Describe the issue

I use the objc bindings for onnxruntime in my iOS app. I load my ML classifier (onnx model), supply it inputs & get valid responses

The code looks like this: // Create ORTValue for input data let inputTensor = try createORTValueFromEmbeddings(inputData)

        // Step 2: Prepare input and run session
        let inputs: [String: ORTValue] = [
            "float_input": inputTensor,
        ]
        let outputs = try ortSession.run(
            withInputs: inputs,
            outputNames: ["output_probability", "output_label"],
            runOptions: nil
        )

        let endTime = DispatchTime.now()
        print("ORT session run time: \(Float(endTime.uptimeNanoseconds - startTime.uptimeNanoseconds) / 1.0e6) ms")

        guard let _ = outputs["output_probability"], let labels = outputs["output_label"] else {
            throw CloneInferenceError.Error("Failed to get model output.")
        }
        
        let labelsData = try labels.tensorData() as Data
        let labelValue = labelsData.withUnsafeBytes { (buffer: UnsafeRawBufferPointer) -> Int64 in
            let int64Buffer = buffer.bindMemory(to: Int64.self)
            return int64Buffer[0]
        }

        if labelValue == 0 {
            return "Audio is real"
        } else {
            return "Audio is cloned"
        }

I am able to process/read "output_label" (0 or 1 value) just fine as an Int64. However, "output_probability" which is a float - I just cannot read it using similar steps. Note that it is produced by a ZipMap but "output_label" isn't. Any suggestions?

To reproduce

You cna use this onnx model: https://yella.co.in/cvd-samples/classifier.onnx

And use the objC onnxruntime sample to load it & evaluate it to get the outputs (like above).

Urgency

Its a showstopper for me because w/o output probabilities, the model is kindof useless actually.

Platform

iOS

OS Version

17.6.1

ONNX Runtime Installation

Released Package

Compiler Version (if 'Built from Source')

No response

Package Name (if 'Released Package')

onnxruntime-mobile

ONNX Runtime Version or Commit ID

1.19.0

ONNX Runtime API

Objective-C/Swift

Architecture

ARM64

Execution Provider

Default CPU

Execution Provider Library Version

No response

[vraspar]

I was not able to access the model from provided link. I believe that zipmap op outputs map/dictionary not float

[skottmckay]

Do you need the ZipMap operator in the model? May be simpler to return the output from the LinearClassifier directly.

[sanjaymk908]

I was not able to access the model from provided link. I believe that zipmap op outputs map/dictionary not float

You can try "wget https://yella.co.in/cvd-samples/classifier.onnx" - worked for me.

[edgchen1]

I believe that zipmap op outputs map/dictionary not float

The Objective-C API doesn't support non-tensor ORTValue types at the moment and ZipMap does not output a tensor. A workaround for now is to avoid using non-tensor types in your model or to use the C/C++ API. For the former, directly using the tensor output of LinearClassifier should work.

[sanjaymk908]

I believe that zipmap op outputs map/dictionary not float

The Objective-C API doesn't support non-tensor ORTValue types at the moment and ZipMap does not output a tensor. A workaround for now is to avoid using non-tensor types in your model or to use the C/C++ API. For the former, directly using the tensor output of LinearClassifier should work.

Thanks for clarifying (my tests back these conclusions). wrt avoiding ZipMaps, from my limited understanding, this is whats causing onnx to spit out Zipmaps (?):

during prediction: self.y_pred = self.model.predict(self.X_test)

during mlflow logging: mlflow.sklearn.log_model( model.model, "model_" + run.info.run_name, signature=signature )

ZipMaps appear "natural" to logging scikit classifiers (map "0" to some probability, "1" etc). If you know of some mechanism to, say force log_model to output an array instead, please lmk. I will update this thread with my findings.

[sanjaymk908]

One workaround that worked for me was:

1. Modify the current onnx graph. To output a tensor of floats directly: import onnx from onnx import helper, shape_inference

def convert_output_to_float_tensor(onnx_model_path, new_model_path): # Load the original ONNX model model = onnx.load(onnx_model_path)

# Create a new model graph
graph = model.graph

# Find and remove the ZipMap node if it exists
zipmap_nodes = []
zipmap_input = None
zipmap_output = None

for node in graph.node:
    if node.op_type == "ZipMap":
        zipmap_nodes.append(node)
        zipmap_input = node.input[0]  # Classifier output connected to ZipMap input
        zipmap_output = node.output[0]  # ZipMap output

# Remove all ZipMap nodes from the graph
if zipmap_nodes:
    for zipmap_node in zipmap_nodes:
        graph.node.remove(zipmap_node)
        print(f"Removed ZipMap node: {zipmap_node.name}")

# Now, reconnect the classifier's output to the model's final output
for output in graph.output:
    if output.name == zipmap_output:
        # Create a new output tensor for probabilities
        new_output = helper.make_tensor_value_info("output_probability", onnx.TensorProto.FLOAT, [2])  # Assuming 2 classes
        graph.output.remove(output)  # Remove the old output
        graph.output.append(new_output)  # Add the new output

        # Find the node connected to the original output
        for node in graph.node:
            if zipmap_input in node.output:  # Find the node whose output is connected to ZipMap input
                # Create a new output connection
                node.output.remove(zipmap_input)  # Remove ZipMap input from the node's outputs
                node.output.append("output_probability")  # Connect to new output

        print("Connected output_probability to the linear classifier output.")

# Infer shapes of tensors
inferred_model = shape_inference.infer_shapes(model)

# Save the modified ONNX model
onnx.save(inferred_model, new_model_path)
print(f"Saved modified model with float32[2] output as 'output_probability' to {new_model_path}")

Define the paths for the original and new models

original_model_path = "input.onnx" new_model_path = "input_no_zipmap.onnx"

Run the function to modify the model and convert the output type

convert_output_to_float_tensor(original_model_path, new_model_path)

2. handle the tensor of floats in the swift code appropriately: func evaluate(inputData: [Double]) -> Result<String, Error> { return Result<String, Error> { () -> String in let startTime = DispatchTime.now()

   // Step 1: Create ORTValue for input data
   let inputTensor = try createORTValueFromEmbeddings(inputData)
   
   // Step 2: Prepare input and run session
   let inputs: [String: ORTValue] = [
       "float_input": inputTensor,
   ]
   let outputs = try ortSession.run(
       withInputs: inputs,
       outputNames: ["output_probability", "output_label"],
       runOptions: nil
   )
   
   let endTime = DispatchTime.now()
   print("ORT session run time: \(Float(endTime.uptimeNanoseconds - startTime.uptimeNanoseconds) / 1.0e6) ms")
   
   guard let outputProbabilities = outputs["output_probability"], let labels = outputs["output_label"] else {
       throw CloneInferenceError.Error("Failed to get model output.")
   }
   
   // Step 3: Extract label value
   let labelsData = try labels.tensorData() as Data
   let labelValue = labelsData.withUnsafeBytes { (buffer: UnsafeRawBufferPointer) -> Int64 in
       let int64Buffer = buffer.bindMemory(to: Int64.self)
       return int64Buffer[0]
   }
   
   // Step 4: Extract probabilities
   let probabilitiesData = try outputProbabilities.tensorData() as Data
   let probabilities = probabilitiesData.withUnsafeBytes { (buffer: UnsafeRawBufferPointer) -> [Float] in
       let floatBuffer = buffer.bindMemory(to: Float.self)
       return Array(floatBuffer.prefix(2)) // Assuming two classes
   }
   
   // Print out probabilities
   print("Probabilities: \(probabilities)")
   
   // Step 5: Return the result based on the label value
   if labelValue == 0 {
       return "Success"
   } else {
       return "Failure"
   }
   

   } }

[skottmckay]

There is an option to avoid the ZipMap during sklearn export to ONNX.

https://onnx.ai/sklearn-onnx/auto_examples/plot_convert_zipmap.html

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