private Delegate MakeStringTensorGetter(DataViewRow input, int iinfo, INamedOnnxValueGetter[] srcNamedValueGetters, string[] activeOutputColNames)
{
Host.AssertValue(input);
var outputCacher = new OnnxRuntimeOutputCacher();
ValueGetter <VBuffer <ReadOnlyMemory <char> > > valueGetter = (ref VBuffer <ReadOnlyMemory <char> > dst) =>
{
UpdateCacheIfNeeded(input.Position, srcNamedValueGetters, activeOutputColNames, outputCacher);
var namedOnnxValue = outputCacher.Outputs[_parent.Outputs[iinfo]];
var tensor = namedOnnxValue.AsTensor <string>() as System.Numerics.Tensors.DenseTensor <string>;
if (tensor == null)
{
throw Host.Except($"Output column {namedOnnxValue.Name} doesn't contain a DenseTensor of expected type {typeof(string)}");
}
// Create VBufferEditor to fill "dst" with the values in "denseTensor".
var editor = VBufferEditor.Create(ref dst, (int)tensor.Length);
for (int i = 0; i < tensor.Length; ++i)
{
// Cast because string in ML.NET is typed to ReadOnlyMemory<char>.
editor.Values[i] = tensor.GetValue(i).AsMemory();
}
dst = editor.Commit();
};
return(valueGetter);
}
private Delegate MakeObjectGetter <T>(DataViewRow input, int iinfo, INamedOnnxValueGetter[] srcNamedValueGetters, string[] activeOutputColNames)
{
Host.AssertValue(input);
var outputCache = new OnnxRuntimeOutputCacher();
ValueGetter <T> valueGetter = (ref T dst) =>
{
UpdateCacheIfNeeded(input.Position, srcNamedValueGetters, activeOutputColNames, outputCache);
var namedOnnxValue = outputCache.Outputs[_parent.Outputs[iinfo]];
var trueValue = namedOnnxValue.AsEnumerable <NamedOnnxValue>().Select(value => value.AsDictionary <string, float>());
var caster = _parent.Model.ModelInfo.OutputsInfo[iinfo].Caster;
dst = (T)caster(namedOnnxValue);
};
return(valueGetter);
}
private Delegate MakeTensorGetter <T>(DataViewRow input, int iinfo, INamedOnnxValueGetter[] srcNamedValueGetters, string[] activeOutputColNames)
{
Host.AssertValue(input);
var outputCacher = new OnnxRuntimeOutputCacher();
ValueGetter <VBuffer <T> > valueGetter = (ref VBuffer <T> dst) =>
{
UpdateCacheIfNeeded(input.Position, srcNamedValueGetters, activeOutputColNames, outputCacher);
var namedOnnxValue = outputCacher.Outputs[_parent.Outputs[iinfo]];
var tensor = namedOnnxValue.AsTensor <T>() as System.Numerics.Tensors.DenseTensor <T>;
if (tensor == null)
{
throw Host.Except($"Output column {namedOnnxValue.Name} doesn't contain a DenseTensor of expected type {typeof(T)}");
}
var editor = VBufferEditor.Create(ref dst, (int)tensor.Length);
tensor.Buffer.Span.CopyTo(editor.Values);
dst = editor.Commit();
};
return(valueGetter);
}
public override Delegate[] CreateGetters(DataViewRow input, Func <int, bool> activeOutput, out Action disposer)
{
Contracts.Assert(input.Schema == InputSchema);
OnnxRuntimeOutputCacher outputCacher = new OnnxRuntimeOutputCacher();
int n = OutputColumns.Value.Length;
var result = new Delegate[n];
for (int i = 0; i < n; i++)
{
if (!activeOutput(i))
{
continue;
}
result[i] = CreateGetter(input, i, activeOutput, outputCacher);
}
disposer = () =>
{
outputCacher.Dispose();
};
return(result);
}
private void UpdateCacheIfNeeded(long position, INamedOnnxValueGetter[] srcNamedOnnxValueGetters, string[] activeOutputColNames, OnnxRuntimeOutputCacher outputCache)
{
if (outputCache.Position != position)
{
var inputNameOnnxValues = new List <NamedOnnxValue>();
for (int i = 0; i < _inputColIndices.Length; i++)
{
inputNameOnnxValues.Add(srcNamedOnnxValueGetters[i].GetNamedOnnxValue());
}
var outputNamedOnnxValues = _parent.Model.Run(inputNameOnnxValues);
Contracts.Assert(outputNamedOnnxValues.Count > 0);
foreach (var outputNameOnnxValue in outputNamedOnnxValues)
{
outputCache.Outputs[outputNameOnnxValue.Name] = outputNameOnnxValue;
}
outputCache.Position = position;
}
}
private Delegate CreateGetter(DataViewRow input, int iinfo, Func <int, bool> activeOutput, OnnxRuntimeOutputCacher outputCacher)
{
Host.AssertValue(input);
var activeOutputColNames = _parent.Outputs.Where((x, i) => activeOutput(i)).ToArray();
if (_parent.Model.ModelInfo.OutputsInfo[_parent.MapDataViewColumnToOnnxOutputTensor(iinfo)].DataViewType is VectorDataViewType vectorType)
{
var elemRawType = vectorType.ItemType.RawType;
var srcNamedValueGetters = GetNamedOnnxValueGetters(input, _inputColIndices, _inputOnnxTypes, _inputTensorShapes);
if (vectorType.ItemType is TextDataViewType)
{
return(MakeStringTensorGetter(input, iinfo, srcNamedValueGetters, activeOutputColNames, outputCacher));
}
else
{
return(Utils.MarshalInvoke(MakeTensorGetter <int>, elemRawType, input, iinfo, srcNamedValueGetters, activeOutputColNames, outputCacher));
}
}
else
{
var type = _parent.Model.ModelInfo.OutputsInfo[_parent.MapDataViewColumnToOnnxOutputTensor(iinfo)].DataViewType.RawType;
var srcNamedValueGetters = GetNamedOnnxValueGetters(input, _inputColIndices, _inputOnnxTypes, _inputTensorShapes);
return(Utils.MarshalInvoke(MakeObjectGetter <int>, type, input, iinfo, srcNamedValueGetters, activeOutputColNames, outputCacher));
}
}
