private static INamedOnnxValueGetter CreateNamedOnnxValueGetter(DataViewRow input, System.Type onnxType, bool isVector, string colName, int colIndex, OnnxShape onnxShape)
{
var type = OnnxUtils.OnnxToMlNetType(onnxType).RawType;
Contracts.AssertValue(type);
return(Utils.MarshalInvoke(CreateNameOnnxValueGetter <int>, type, input, isVector, colName, colIndex, onnxShape));
}
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;
}
protected override Delegate MakeGetter(DataViewRow input, int iinfo, Func <int, bool> activeOutput, out Action disposer)
{
disposer = null;
Host.AssertValue(input);
//Host.Assert(typeof(T) == _outputItemRawType);
var outputCache = new OutputCache();
var activeOutputColNames = _parent.Outputs.Where((x, i) => activeOutput(i)).ToArray();
var type = OnnxUtils.OnnxToMlNetType(_parent.Model.ModelInfo.OutputsInfo[iinfo].Type).RawType;
Host.Assert(type == _parent.OutputTypes[iinfo].GetItemType().RawType);
var srcNamedValueGetters = GetNamedOnnxValueGetters(input, _parent.Inputs, _inputColIndices, _isInputVector, _inputOnnxTypes, _inputTensorShapes);
return(Utils.MarshalInvoke(MakeGetter <int>, type, input, iinfo, srcNamedValueGetters, activeOutputColNames, outputCache));
}
/// <summary>
/// Returns the <see cref="SchemaShape"/> of the schema which will be produced by the transformer.
/// Used for schema propagation and verification in a pipeline.
/// </summary>
public override SchemaShape GetOutputSchema(SchemaShape inputSchema)
{
Host.CheckValue(inputSchema, nameof(inputSchema));
var result = inputSchema.ToDictionary(x => x.Name);
var resultDic = inputSchema.ToDictionary(x => x.Name);
for (var i = 0; i < Transformer.Inputs.Length; i++)
{
var input = Transformer.Inputs[i];
if (!inputSchema.TryFindColumn(input, out var col))
{
throw Host.ExceptSchemaMismatch(nameof(inputSchema), "input", input);
}
if (!(col.Kind == SchemaShape.Column.VectorKind.VariableVector || col.Kind == SchemaShape.Column.VectorKind.Vector))
{
throw Host.ExceptSchemaMismatch(nameof(inputSchema), "input", input, "vector", col.GetTypeString());
}
var inputsInfo = Transformer.Model.ModelInfo.InputsInfo;
var idx = Transformer.Model.InputNames.IndexOf(input);
if (idx < 0)
{
throw Host.Except($"Column {input} doesn't match input node names of model.");
}
var inputNodeInfo = inputsInfo[idx];
var expectedType = OnnxUtils.OnnxToMlNetType(inputNodeInfo.Type);
if (col.ItemType != expectedType)
{
throw Host.ExceptSchemaMismatch(nameof(inputSchema), "input", input, expectedType.ToString(), col.ItemType.ToString());
}
}
for (var i = 0; i < Transformer.Outputs.Length; i++)
{
resultDic[Transformer.Outputs[i]] = new SchemaShape.Column(Transformer.Outputs[i],
Transformer.OutputTypes[i].IsKnownSizeVector() ? SchemaShape.Column.VectorKind.Vector
: SchemaShape.Column.VectorKind.VariableVector, Transformer.OutputTypes[i].GetItemType(), false);
}
return(new SchemaShape(resultDic.Values));
}
public Mapper(OnnxTransformer parent, DataViewSchema inputSchema) :
base(Contracts.CheckRef(parent, nameof(parent)).Host.Register(nameof(Mapper)), inputSchema, parent)
{
_parent = parent;
_inputColIndices = new int[_parent.Inputs.Length];
_isInputVector = new bool[_parent.Inputs.Length];
_inputTensorShapes = new OnnxShape[_parent.Inputs.Length];
_inputOnnxTypes = new System.Type[_parent.Inputs.Length];
var model = _parent.Model;
for (int i = 0; i < _parent.Inputs.Length; i++)
{
var idx = model.InputNames.IndexOf(_parent.Inputs[i]);
if (idx < 0)
{
throw Host.Except($"Column {_parent.Inputs[i]} doesn't match input node names of model");
}
var inputNodeInfo = model.ModelInfo.InputsInfo[idx];
var shape = inputNodeInfo.Shape;
var inputType = OnnxUtils.OnnxToMlNetType(inputNodeInfo.Type);
var inputShape = AdjustDimensions(inputNodeInfo.Shape);
_inputTensorShapes[i] = inputShape.ToList();
_inputOnnxTypes[i] = inputNodeInfo.Type;
var col = inputSchema.GetColumnOrNull(_parent.Inputs[i]);
if (!col.HasValue)
{
throw Host.ExceptSchemaMismatch(nameof(inputSchema), "input", _parent.Inputs[i]);
}
_inputColIndices[i] = col.Value.Index;
var type = inputSchema[_inputColIndices[i]].Type;
var vectorType = type as VectorType;
_isInputVector[i] = vectorType != null;
if (vectorType != null && vectorType.Size == 0)
{
throw Host.Except($"Variable length input columns not supported");
}
if (type.GetItemType() != inputType)
{
throw Host.ExceptSchemaMismatch(nameof(inputSchema), "input", _parent.Inputs[i], inputType.ToString(), type.ToString());
}
// If the column is one dimension we make sure that the total size of the Onnx shape matches.
// Compute the total size of the known dimensions of the shape.
int valCount = inputShape.Where(x => x > 0).Aggregate((x, y) => x * y);
// The column length should be divisible by this, so that the other dimensions can be integral.
int typeValueCount = type.GetValueCount();
if (typeValueCount % valCount != 0)
{
throw Contracts.Except($"Input shape mismatch: Input '{_parent.Inputs[i]}' has shape {String.Join(",", inputShape)}, but input data is of length {typeValueCount}.");
}
//Host.Assert(_outputItemRawType == _outputColType.ItemType.RawType);
}
}
