private OnnxTransformer(IHostEnvironment env, Options options, byte[] modelBytes = null) :
base(Contracts.CheckRef(env, nameof(env)).Register(nameof(OnnxTransformer)))
{
Host.CheckValue(options, nameof(options));
foreach (var col in options.InputColumns)
{
Host.CheckNonWhiteSpace(col, nameof(options.InputColumns));
}
foreach (var col in options.OutputColumns)
{
Host.CheckNonWhiteSpace(col, nameof(options.OutputColumns));
}
try
{
if (modelBytes == null)
{
Host.CheckNonWhiteSpace(options.ModelFile, nameof(options.ModelFile));
Host.CheckIO(File.Exists(options.ModelFile), "Model file {0} does not exists.", options.ModelFile);
Model = new OnnxModel(options.ModelFile, options.GpuDeviceId, options.FallbackToCpu);
}
else
{
Model = OnnxModel.CreateFromBytes(modelBytes, options.GpuDeviceId, options.FallbackToCpu);
}
}
catch (OnnxRuntimeException e)
{
throw Host.Except(e, $"Error initializing model :{e.ToString()}");
}
var modelInfo = Model.ModelInfo;
Inputs = (options.InputColumns.Count() == 0) ? Model.InputNames.ToArray() : options.InputColumns;
Outputs = (options.OutputColumns.Count() == 0) ? Model.OutputNames.ToArray() : options.OutputColumns;
OutputTypes = new DataViewType[Outputs.Length];
var numModelOutputs = Model.ModelInfo.OutputsInfo.Length;
for (int i = 0; i < Outputs.Length; i++)
{
var idx = Model.OutputNames.IndexOf(Outputs[i]);
if (idx < 0)
{
throw Host.Except($"Column {Outputs[i]} doesn't match output node names of model");
}
var outputNodeInfo = Model.ModelInfo.OutputsInfo[idx];
var shape = outputNodeInfo.Shape;
var dims = AdjustDimensions(shape);
OutputTypes[i] = new VectorType(OnnxUtils.OnnxToMlNetType(outputNodeInfo.Type), dims.ToArray());
}
_options = options;
}
private OnnxTransformer(IHostEnvironment env, Options options, byte[] modelBytes = null) :
base(Contracts.CheckRef(env, nameof(env)).Register(nameof(OnnxTransformer)))
{
Host.CheckValue(options, nameof(options));
foreach (var col in options.InputColumns)
{
Host.CheckNonWhiteSpace(col, nameof(options.InputColumns));
}
foreach (var col in options.OutputColumns)
{
Host.CheckNonWhiteSpace(col, nameof(options.OutputColumns));
}
// Use ONNXRuntime to figure out the right input and output configuration.
// However, ONNXRuntime doesn't provide strongly-typed method to access the produced
// variables, we will inspect the ONNX model file to get information regarding types.
try
{
if (modelBytes == null)
{
// Entering this region means that the model file is passed in by the user.
Host.CheckNonWhiteSpace(options.ModelFile, nameof(options.ModelFile));
Host.CheckIO(File.Exists(options.ModelFile), "Model file {0} does not exists.", options.ModelFile);
// Because we cannot delete the user file, ownModelFile should be false.
Model = new OnnxModel(options.ModelFile, options.GpuDeviceId, options.FallbackToCpu, ownModelFile: false);
}
else
{
// Entering this region means that the byte[] is passed as the model. To feed that byte[] to ONNXRuntime, we need
// to create a temporal file to store it and then call ONNXRuntime's API to load that file.
Model = OnnxModel.CreateFromBytes(modelBytes, options.GpuDeviceId, options.FallbackToCpu);
}
}
catch (OnnxRuntimeException e)
{
throw Host.Except(e, $"Error initializing model :{e.ToString()}");
}
var modelInfo = Model.ModelInfo;
Inputs = (options.InputColumns.Count() == 0) ? Model.ModelInfo.InputNames.ToArray() : options.InputColumns;
Outputs = (options.OutputColumns.Count() == 0) ? Model.ModelInfo.OutputNames.ToArray() : options.OutputColumns;
OutputTypes = new DataViewType[Outputs.Length];
var numModelOutputs = Model.ModelInfo.OutputsInfo.Length;
for (int i = 0; i < Outputs.Length; i++)
{
var outputInfo = Model.ModelInfo.GetOutput(Outputs[i]);
OutputTypes[i] = outputInfo.DataViewType;
}
_options = options;
}
