public void SaveAsOnnxPostProcess(OnnxContext ctx, string inputName, string[] outputNames)
{
Contracts.Assert(outputNames.Length >= 2);
string opType;
opType = "ArgMax";
var argMaxOutput = ctx.AddIntermediateVariable(NumberDataViewType.Int64, "ArgMaxOutput");
var argMaxNode = ctx.CreateNode(opType, inputName, argMaxOutput, ctx.GetNodeName(opType), "");
argMaxNode.AddAttribute("keepdims", 1);
argMaxNode.AddAttribute("axis", 1);
opType = "Add";
var one = ctx.AddInitializer(1);
var addOutput = ctx.AddIntermediateVariable(NumberDataViewType.Int64, "AddOutput");
var addNode = ctx.CreateNode(opType, new[] { argMaxOutput, one }, new[] { addOutput }, ctx.GetNodeName(opType), "");
opType = "Cast";
var castToUint32Node = ctx.CreateNode(opType, addOutput, outputNames[0], ctx.GetNodeName(opType), "");
var t2 = InternalDataKindExtensions.ToInternalDataKind(DataKind.UInt32).ToType();
castToUint32Node.AddAttribute("to", t2);
opType = "Max";
ctx.CreateNode(opType, inputName, outputNames[1], ctx.GetNodeName(opType), "");
}
private void SaveAsOnnxCore(OnnxContext ctx, string srcVariableName, string dstVariableName)
{
string opType = "Tokenizer";
string tokenizerOutput = ctx.AddIntermediateVariable(null, "TokenizerOutput", true);
var node = ctx.CreateNode(opType, srcVariableName, tokenizerOutput, ctx.GetNodeName(opType), "com.microsoft");
node.AddAttribute("mark", _parent._useMarkerChars);
node.AddAttribute("mincharnum", 1);
node.AddAttribute("pad_value", "");
node.AddAttribute("separators", new string[] { "" });
opType = "Squeeze";
var squeezeOutput = ctx.AddIntermediateVariable(null, "SqueezeOutput", true);
node = ctx.CreateNode(opType, tokenizerOutput, squeezeOutput, ctx.GetNodeName(opType), "");
node.AddAttribute("axes", new long[] { 0 });
opType = "LabelEncoder";
var labelEncoderOutput = ctx.AddIntermediateVariable(null, "LabelEncoderOutput", true);
node = ctx.CreateNode(opType, squeezeOutput, labelEncoderOutput, ctx.GetNodeName(opType));
IEnumerable <string> charStrings = Enumerable.Range(0, 65535).Select(x => ((char)x).ToString());
IEnumerable <long> charValues = Enumerable.Range(0, 65535).Select(x => Convert.ToInt64(x));;
node.AddAttribute("keys_strings", charStrings);
node.AddAttribute("values_int64s", charValues);
opType = "Cast";
var castNode = ctx.CreateNode(opType, labelEncoderOutput, dstVariableName, ctx.GetNodeName(opType), "");
var t = InternalDataKindExtensions.ToInternalDataKind(DataKind.UInt16).ToType();
castNode.AddAttribute("to", t);
}
private void SaveAsOnnxCore(OnnxContext ctx, int iinfo, string srcVariableName, string dstVariableName)
{
const int minimumOpSetVersion = 9;
ctx.CheckOpSetVersion(minimumOpSetVersion, LoaderSignature);
string opType = "Tokenizer";
DataViewType dataViewType;
if (_isSourceVector[iinfo])
{
dataViewType = new VectorDataViewType(TextDataViewType.Instance, _sourceVectorLength[iinfo]);
}
else
{
dataViewType = TextDataViewType.Instance;
}
string tokenizerOutput = ctx.AddIntermediateVariable(dataViewType, "TokenizerOutput", true);
var node = ctx.CreateNode(opType, srcVariableName, tokenizerOutput, ctx.GetNodeName(opType), "com.microsoft");
node.AddAttribute("mark", _parent._useMarkerChars);
node.AddAttribute("mincharnum", 1);
node.AddAttribute("pad_value", "");
node.AddAttribute("separators", new string[] { "" });
opType = "Squeeze";
var squeezeOutput = ctx.AddIntermediateVariable(dataViewType, "SqueezeOutput");
node = ctx.CreateNode(opType, tokenizerOutput, squeezeOutput, ctx.GetNodeName(opType), "");
node.AddAttribute("axes", new long[] { 1 });
opType = "LabelEncoder";
var labelEncoderOutput = ctx.AddIntermediateVariable(NumberDataViewType.Int64, "LabelEncoderOutput");
node = ctx.CreateNode(opType, squeezeOutput, labelEncoderOutput, ctx.GetNodeName(opType));
IEnumerable <string> charStrings = Enumerable.Range(0, 65535).Select(x => ((char)x).ToString());
IEnumerable <long> charValues = Enumerable.Range(0, 65535).Select(x => Convert.ToInt64(x));
node.AddAttribute("keys_strings", charStrings);
node.AddAttribute("values_int64s", charValues);
opType = "Cast";
var castNode = ctx.CreateNode(opType, labelEncoderOutput, dstVariableName, ctx.GetNodeName(opType), "");
var t = InternalDataKindExtensions.ToInternalDataKind(DataKind.UInt16).ToType();
castNode.AddAttribute("to", t);
}
private static ExpressionTransformer Create(IHostEnvironment env, ModelLoadContext ctx)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(ctx, nameof(ctx));
ctx.CheckAtModel(GetVersionInfo());
// *** Binary format ***
// int: number of output columns
// for each output column:
// int: number of inputs
// foreach input
// int: Id of the input column name
// int: Id of the expression
// int: Id of the output column name
// int: the index of the vector input (or -1)
// int[]: The data kinds of the input columns
var columnCount = ctx.Reader.ReadInt32();
env.CheckDecode(columnCount > 0);
var columns = new ColumnInfo[columnCount];
for (int i = 0; i < columnCount; i++)
{
var inputSize = ctx.Reader.ReadInt32();
env.CheckDecode(inputSize >= 0);
var inputColumnNames = new string[inputSize];
for (int j = 0; j < inputSize; j++)
{
inputColumnNames[j] = ctx.LoadNonEmptyString();
}
var expression = ctx.LoadNonEmptyString();
var outputColumnName = ctx.LoadNonEmptyString();
var vectorInputColumn = ctx.Reader.ReadInt32();
env.CheckDecode(vectorInputColumn >= -1);
var inputTypes = new DataViewType[inputSize];
for (int j = 0; j < inputSize; j++)
{
var dataKindIndex = ctx.Reader.ReadInt32();
var kind = InternalDataKindExtensions.FromIndex(dataKindIndex);
inputTypes[j] = ColumnTypeExtensions.PrimitiveTypeFromKind(kind);
}
var node = ExpressionEstimator.ParseAndBindLambda(env, expression, vectorInputColumn, inputTypes, out var perm);
columns[i] = new ColumnInfo(env, inputColumnNames, inputTypes, expression, outputColumnName, vectorInputColumn, node, perm);
}
return(new ExpressionTransformer(env, columns));
}
public static DataKind RawKind(this DataViewType type)
{
InternalDataKind kind;
if (IsVector(type))
{
if (InternalDataKindExtensions.TryGetDataKind(ItemType(type).RawType, out kind))
{
return(Internal2DataKind(kind));
}
}
if (InternalDataKindExtensions.TryGetDataKind(type.RawType, out kind))
{
return(Internal2DataKind(kind));
}
throw Contracts.ExceptNotSupp($"Unable to guess kind for type {type}.");
}
/// <summary>
/// Creates an Onnx inferencing model by vectorizing and following the logic found in <see cref="Map"/>
/// </summary>
bool ISingleCanSaveOnnx.SaveAsOnnx(OnnxContext ctx, string[] outputNames, string featureColumn)
{
float[] featureHistogram = new float[_featureHistogram[0].Length * _labelHistogram.Length];
float[] labelHistogramExpanded = new float[_featureHistogram[0].Length * _labelHistogram.Length];
for (int i = 0; i < _featureHistogram.Length; i++)
{
Array.Copy(_featureHistogram[i], 0, featureHistogram, i * _featureHistogram[i].Length, _featureHistogram[i].Length);
}
for (int i = 0; i < _featureHistogram[0].Length; i++)
{
Array.Copy(_labelHistogram, 0, labelHistogramExpanded, i * _featureHistogram.Length, _featureHistogram.Length);
}
var one = ctx.AddInitializer(1.0f, "one");
var oneInt = ctx.AddInitializer(1, typeof(int), "oneInt");
var zero = ctx.AddInitializer(0.0f, "zero");
var labelCount = ctx.AddInitializer((float)_labelCount, "labelCount");
var trainingCount = ctx.AddInitializer((float)_totalTrainingCount, "totalTrainingCount");
var labelHistogram = ctx.AddInitializer(labelHistogramExpanded.Take(_labelHistogram.Length), new long[] { _labelHistogram.Length, 1 }, "labelHistogram");
var featureHistogramName = ctx.AddInitializer(featureHistogram, new long[] { _featureHistogram.Length, _featureHistogram[0].Length }, "featureHistogram");
var labelHistogramName = ctx.AddInitializer(labelHistogramExpanded, new long[] { _featureHistogram[0].Length, _labelHistogram.Length }, "labelHistogramExpanded");
var learnedAbsentFeatureLogProb = ctx.AddInitializer(_absentFeaturesLogProb, new long[] { _absentFeaturesLogProb.Length, 1 }, "absentFeaturesLogProb");
var typeOne = new VectorDataViewType(NumberDataViewType.Single, 1);
var typeFea = new VectorDataViewType(NumberDataViewType.Single, _featureHistogram[0].Length);
var typeLabelByFea = new VectorDataViewType(NumberDataViewType.Single, _labelHistogram.Length, _featureHistogram[0].Length);
var typeLabelByOne = new VectorDataViewType(NumberDataViewType.Single, _labelHistogram.Length, 1);
var greaterOutput = ctx.AddIntermediateVariable(new VectorDataViewType(BooleanDataViewType.Instance, _featureHistogram[0].Length), "greaterOutput");
var opType = "Greater";
ctx.CreateNode(opType, new[] { featureColumn, zero }, new[] { greaterOutput }, ctx.GetNodeName(opType), "");
opType = "Cast";
var castOutput = ctx.AddIntermediateVariable(typeFea, "CastOutput");
var node = ctx.CreateNode(opType, greaterOutput, castOutput, ctx.GetNodeName(opType), "");
var t = InternalDataKindExtensions.ToInternalDataKind(DataKind.Single).ToType();
node.AddAttribute("to", t);
opType = "ExpandDims";
var isFeaturePresent = ctx.AddIntermediateVariable(new VectorDataViewType(NumberDataViewType.Single, 1, _featureHistogram[0].Length), "isFeaturePresent");
ctx.CreateNode(opType, new[] { castOutput, oneInt }, new[] { isFeaturePresent }, ctx.GetNodeName(opType), "com.microsoft");
//initialize logProb
opType = "Div";
var divOutput = ctx.AddIntermediateVariable(typeOne, "DivOutput");
ctx.CreateNode(opType, new[] { labelHistogram, trainingCount }, new[] { divOutput }, ctx.GetNodeName(opType), "");
opType = "Log";
var logOutput = ctx.AddIntermediateVariable(typeOne, "LogOutput");
ctx.CreateNode(opType, divOutput, logOutput, ctx.GetNodeName(opType), "");
//log1
opType = "Sum";
var sumOutput = ctx.AddIntermediateVariable(_inputType, "SumOutput");
ctx.CreateNode(opType, new[] { featureHistogramName, one }, new[] { sumOutput }, ctx.GetNodeName(opType), "");
var logOutput1 = ctx.AddIntermediateVariable(typeLabelByFea, "LogOutput");
LogMul(ctx, sumOutput, isFeaturePresent, logOutput1);
//log2
opType = "Transpose";
var labelHistogramTrans = ctx.AddIntermediateVariable(typeFea, "Transpose");
ctx.CreateNode(opType, labelHistogramName, labelHistogramTrans, ctx.GetNodeName(opType), "");
opType = "Sub";
var absentFeatureCount = ctx.AddIntermediateVariable(typeFea, "AbsentFeatureCounts");
ctx.CreateNode(opType, new[] { labelHistogramTrans, featureHistogramName }, new[] { absentFeatureCount }, ctx.GetNodeName(opType), "");
opType = "Sum";
sumOutput = ctx.AddIntermediateVariable(typeFea, "SumOutput");
ctx.CreateNode(opType, new[] { labelHistogramTrans, labelCount }, new[] { sumOutput }, ctx.GetNodeName(opType), "");
var logOutput2 = ctx.AddIntermediateVariable(typeLabelByFea, "LogOutput");
LogMul(ctx, sumOutput, isFeaturePresent, logOutput2);
//log3
opType = "Sum";
sumOutput = ctx.AddIntermediateVariable(typeFea, "SumOutput");
ctx.CreateNode(opType, new[] { absentFeatureCount, one }, new[] { sumOutput }, ctx.GetNodeName(opType), "");
var logOutput3 = ctx.AddIntermediateVariable(typeLabelByFea, "LogOutput");
LogMul(ctx, sumOutput, isFeaturePresent, logOutput3);
//result
opType = "Sub";
var logProb = ctx.AddIntermediateVariable(typeLabelByFea, "LogProb");
ctx.CreateNode(opType, new[] { logOutput1, logOutput2 }, new[] { logProb }, ctx.GetNodeName(opType), "");
opType = "Sub";
var absentFeatureLogProb = ctx.AddIntermediateVariable(typeLabelByFea, "AbsentFeatureLogProb");
ctx.CreateNode(opType, new[] { logOutput3, logOutput2 }, new[] { absentFeatureLogProb }, ctx.GetNodeName(opType), "");
opType = "ReduceSum";
var logProbReduceSum = ctx.AddIntermediateVariable(typeLabelByOne, "ReduceSum");
node = ctx.CreateNode(opType, new[] { logProb }, new[] { logProbReduceSum }, ctx.GetNodeName(opType), "");
long[] list = { 2 };
node.AddAttribute("axes", list);
opType = "ReduceSum";
var absentFeatureLogProbReduceSum = ctx.AddIntermediateVariable(typeLabelByOne, "ReduceSum");
node = ctx.CreateNode(opType, new[] { absentFeatureLogProb }, new[] { absentFeatureLogProbReduceSum }, ctx.GetNodeName(opType), "");
node.AddAttribute("axes", list);
opType = "Cast";
castOutput = ctx.AddIntermediateVariable(NumberDataViewType.Single, "CastOutput");
node = ctx.CreateNode(opType, learnedAbsentFeatureLogProb, castOutput, ctx.GetNodeName(opType), "");
t = InternalDataKindExtensions.ToInternalDataKind(DataKind.Single).ToType();
node.AddAttribute("to", t);
opType = "Sub";
var subOutput = ctx.AddIntermediateVariable(typeLabelByOne, "SubOutput");
ctx.CreateNode(opType, new[] { castOutput, absentFeatureLogProbReduceSum }, new[] { subOutput }, ctx.GetNodeName(opType), "");
opType = "Sum";
sumOutput = ctx.AddIntermediateVariable(typeLabelByOne, "SumOutput");
ctx.CreateNode(opType, new[] { subOutput, logProbReduceSum, logOutput }, new[] { sumOutput }, ctx.GetNodeName(opType), "");
opType = "Squeeze";
var squeezeNode = ctx.CreateNode(opType, sumOutput, outputNames[1], ctx.GetNodeName(opType), "");
squeezeNode.AddAttribute("axes", new long[] { 2 });
opType = "ArgMax";
var scoreIndex = ctx.AddIntermediateVariable(new VectorDataViewType(NumberDataViewType.Int64, 1), "ScoreIndex");
node = ctx.CreateNode(opType, new[] { sumOutput }, new[] { scoreIndex }, ctx.GetNodeName(opType), "");
node.AddAttribute("axis", 1);
node.AddAttribute("keepdims", 0);
opType = "Cast";
castOutput = ctx.AddIntermediateVariable(typeOne, "CastOutput");
node = ctx.CreateNode(opType, scoreIndex, castOutput, ctx.GetNodeName(opType), "");
t = InternalDataKindExtensions.ToInternalDataKind(DataKind.Single).ToType();
node.AddAttribute("to", t);
//log3
opType = "Sum";
sumOutput = ctx.AddIntermediateVariable(typeOne, "SumOutput");
ctx.CreateNode(opType, new[] { castOutput, one }, new[] { sumOutput }, ctx.GetNodeName(opType), "");
opType = "Cast";
node = ctx.CreateNode(opType, sumOutput, outputNames[0], ctx.GetNodeName(opType), "");
t = InternalDataKindExtensions.ToInternalDataKind(DataKind.UInt32).ToType();
node.AddAttribute("to", t);
return(true);
}
