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using Godot;
using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using System.Collections.Generic;
using System.Linq;
namespace GodotONNX
{
/// <include file='docs/ONNXInference.xml' path='docs/members[@name="ONNXInference"]/ONNXInference/*'/>
public partial class ONNXInference : GodotObject
{
private InferenceSession session;
/// <summary>
/// Path to the ONNX model. Use Initialize to change it.
/// </summary>
private string modelPath;
private int batchSize;
private SessionOptions SessionOpt;
//init function
/// <include file='docs/ONNXInference.xml' path='docs/members[@name="ONNXInference"]/Initialize/*'/>
public void Initialize(string Path, int BatchSize)
{
modelPath = Path;
batchSize = BatchSize;
SessionOpt = SessionConfigurator.MakeConfiguredSessionOptions();
session = LoadModel(modelPath);
}
/// <include file='docs/ONNXInference.xml' path='docs/members[@name="ONNXInference"]/Run/*'/>
public Godot.Collections.Dictionary<string, Godot.Collections.Array> RunInference(Godot.Collections.Array<float> obs, int state_ins)
{
//Current model: Any (Godot Rl Agents)
//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
//Fill the input tensors
// create span from inputSize
var span = new float[obs.Count]; //There's probably a better way to do this
for (int i = 0; i < obs.Count; i++)
{
span[i] = obs[i];
}
IReadOnlyCollection<NamedOnnxValue> inputs = new List<NamedOnnxValue>
{
NamedOnnxValue.CreateFromTensor("obs", new DenseTensor<float>(span, new int[] { batchSize, obs.Count })),
NamedOnnxValue.CreateFromTensor("state_ins", new DenseTensor<float>(new float[] { state_ins }, new int[] { batchSize }))
};
IReadOnlyCollection<string> outputNames = new List<string> { "output", "state_outs" }; //ONNX is sensible to these names, as well as the input names
IDisposableReadOnlyCollection<DisposableNamedOnnxValue> results;
//We do not use "using" here so we get a better exception explaination later
try
{
results = session.Run(inputs, outputNames);
}
catch (OnnxRuntimeException e)
{
//This error usually means that the model is not compatible with the input, beacause of the input shape (size)
GD.Print("Error at inference: ", e);
return null;
}
//Can't convert IEnumerable<float> to Variant, so we have to convert it to an array or something
Godot.Collections.Dictionary<string, Godot.Collections.Array> output = new();
DisposableNamedOnnxValue output1 = results.First();
DisposableNamedOnnxValue output2 = results.Last();
// Output1 array may contains longs if only discrete actions are used or floats otherwise
Godot.Collections.Array output1Array = new();
Godot.Collections.Array output2Array = new();
// SB3 exported model will send float actions if continuous actions are used in the environment
// (also the case with mixed actions)
if (output1.ElementType == TensorElementType.Float)
{
foreach (float f in output1.AsEnumerable<float>())
{
output1Array.Add(f);
}
}
// SB3 exported model will send int64 actions if only discrete actions are used in the environment
else if (output1.ElementType == TensorElementType.Int64)
{
foreach (long l in output1.AsEnumerable<long>())
{
output1Array.Add(l);
}
}
foreach (float f in output2.AsEnumerable<float>())
{
output2Array.Add(f);
}
output.Add(output1.Name, output1Array);
output.Add(output2.Name, output2Array);
//Output is a dictionary of arrays, ex: { "output" : [0.1, 0.2, 0.3, 0.4, ...], "state_outs" : [0.5, ...]}
results.Dispose();
return output;
}
/// <include file='docs/ONNXInference.xml' path='docs/members[@name="ONNXInference"]/Load/*'/>
public InferenceSession LoadModel(string Path)
{
using FileAccess file = FileAccess.Open(Path, FileAccess.ModeFlags.Read);
byte[] model = file.GetBuffer((int)file.GetLength());
//file.Close(); file.Dispose(); //Close the file, then dispose the reference.
return new InferenceSession(model, SessionOpt); //Load the model
}
public void FreeDisposables()
{
session.Dispose();
SessionOpt.Dispose();
}
}
}
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