109 lines
4 KiB
C#
Executable file
109 lines
4 KiB
C#
Executable file
using Godot;
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using Microsoft.ML.OnnxRuntime;
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using Microsoft.ML.OnnxRuntime.Tensors;
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using System.Collections.Generic;
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using System.Linq;
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namespace GodotONNX
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{
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/// <include file='docs/ONNXInference.xml' path='docs/members[@name="ONNXInference"]/ONNXInference/*'/>
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public partial class ONNXInference : GodotObject
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{
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private InferenceSession session;
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/// <summary>
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/// Path to the ONNX model. Use Initialize to change it.
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/// </summary>
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private string modelPath;
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private int batchSize;
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private SessionOptions SessionOpt;
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/// <summary>
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/// init function
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/// </summary>
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/// <param name="Path"></param>
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/// <param name="BatchSize"></param>
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/// <returns>Returns the output size of the model</returns>
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public int Initialize(string Path, int BatchSize)
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{
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modelPath = Path;
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batchSize = BatchSize;
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SessionOpt = SessionConfigurator.MakeConfiguredSessionOptions();
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session = LoadModel(modelPath);
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return session.OutputMetadata["output"].Dimensions[1];
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}
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/// <include file='docs/ONNXInference.xml' path='docs/members[@name="ONNXInference"]/Run/*'/>
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public Godot.Collections.Dictionary<string, Godot.Collections.Array<float>> RunInference(Godot.Collections.Array<float> obs, int state_ins)
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{
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//Current model: Any (Godot Rl Agents)
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//Expects a tensor of shape [batch_size, input_size] type float named obs and a tensor of shape [batch_size] type float named state_ins
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//Fill the input tensors
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// create span from inputSize
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var span = new float[obs.Count]; //There's probably a better way to do this
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for (int i = 0; i < obs.Count; i++)
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{
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span[i] = obs[i];
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}
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IReadOnlyCollection<NamedOnnxValue> inputs = new List<NamedOnnxValue>
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{
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NamedOnnxValue.CreateFromTensor("obs", new DenseTensor<float>(span, new int[] { batchSize, obs.Count })),
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NamedOnnxValue.CreateFromTensor("state_ins", new DenseTensor<float>(new float[] { state_ins }, new int[] { batchSize }))
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};
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IReadOnlyCollection<string> outputNames = new List<string> { "output", "state_outs" }; //ONNX is sensible to these names, as well as the input names
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IDisposableReadOnlyCollection<DisposableNamedOnnxValue> results;
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//We do not use "using" here so we get a better exception explaination later
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try
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{
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results = session.Run(inputs, outputNames);
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}
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catch (OnnxRuntimeException e)
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{
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//This error usually means that the model is not compatible with the input, beacause of the input shape (size)
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GD.Print("Error at inference: ", e);
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return null;
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}
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//Can't convert IEnumerable<float> to Variant, so we have to convert it to an array or something
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Godot.Collections.Dictionary<string, Godot.Collections.Array<float>> output = new Godot.Collections.Dictionary<string, Godot.Collections.Array<float>>();
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DisposableNamedOnnxValue output1 = results.First();
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DisposableNamedOnnxValue output2 = results.Last();
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Godot.Collections.Array<float> output1Array = new Godot.Collections.Array<float>();
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Godot.Collections.Array<float> output2Array = new Godot.Collections.Array<float>();
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foreach (float f in output1.AsEnumerable<float>())
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{
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output1Array.Add(f);
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}
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foreach (float f in output2.AsEnumerable<float>())
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{
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output2Array.Add(f);
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}
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output.Add(output1.Name, output1Array);
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output.Add(output2.Name, output2Array);
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//Output is a dictionary of arrays, ex: { "output" : [0.1, 0.2, 0.3, 0.4, ...], "state_outs" : [0.5, ...]}
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results.Dispose();
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return output;
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}
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/// <include file='docs/ONNXInference.xml' path='docs/members[@name="ONNXInference"]/Load/*'/>
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public InferenceSession LoadModel(string Path)
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{
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using Godot.FileAccess file = FileAccess.Open(Path, Godot.FileAccess.ModeFlags.Read);
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byte[] model = file.GetBuffer((int)file.GetLength());
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//file.Close(); file.Dispose(); //Close the file, then dispose the reference.
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return new InferenceSession(model, SessionOpt); //Load the model
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}
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public void FreeDisposables()
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{
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session.Dispose();
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SessionOpt.Dispose();
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}
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}
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}
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