private OnnxTransform(IHostEnvironment env, Arguments args, byte[] modelBytes = null)
{
Contracts.CheckValue(env, nameof(env));
_host = env.Register(RegistrationName);
_host.CheckValue(args, nameof(args));
_host.CheckNonWhiteSpace(args.InputColumn, nameof(args.InputColumn));
_host.CheckNonWhiteSpace(args.OutputColumn, nameof(args.OutputColumn));
if (modelBytes == null)
{
_host.CheckNonWhiteSpace(args.ModelFile, nameof(args.ModelFile));
_host.CheckUserArg(File.Exists(args.ModelFile), nameof(args.ModelFile));
Model = new OnnxModel(args.ModelFile);
}
else
{
Model = OnnxModel.CreateFromBytes(modelBytes);
}
Input = args.InputColumn;
Output = args.OutputColumn;
var outputNodeInfo = Model.GetOutputsInfo().Where(x => x.Name == args.OutputColumn).First();
var type = OnnxUtils.OnnxToMlNetType(outputNodeInfo.Type);
var shape = outputNodeInfo.Shape;
var dims = shape.Count > 0 ? shape.Skip(shape[0] < 0 ? 1 : 0).Select(x => (int)x).ToArray() : new[] { 0 };
OutputType = new VectorType(type, dims);
_args = args;
}
protected PerGroupTransformBase(IHostEnvironment env, IDataView input, string labelCol, string scoreCol, string groupCol, string registrationName)
{
Contracts.CheckValue(env, nameof(env));
Host = env.Register(registrationName);
Host.CheckValue(input, nameof(input));
Host.CheckNonWhiteSpace(labelCol, nameof(labelCol));
Host.CheckNonWhiteSpace(scoreCol, nameof(scoreCol));
Host.CheckNonWhiteSpace(groupCol, nameof(groupCol));
Source = input;
LabelCol = labelCol;
ScoreCol = scoreCol;
GroupCol = groupCol;
}
private OnnxTransform(IHostEnvironment env, Arguments args, byte[] modelBytes = null)
{
Contracts.CheckValue(env, nameof(env));
_host = env.Register(RegistrationName);
_host.CheckValue(args, nameof(args));
_host.CheckNonWhiteSpace(args.InputColumn, nameof(args.InputColumn));
_host.CheckNonWhiteSpace(args.OutputColumn, nameof(args.OutputColumn));
if (modelBytes == null)
{
_host.CheckNonWhiteSpace(args.ModelFile, nameof(args.ModelFile));
_host.CheckUserArg(File.Exists(args.ModelFile), nameof(args.ModelFile));
Model = new OnnxModel(args.ModelFile);
}
else
{
Model = OnnxModel.CreateFromBytes(modelBytes);
}
var modelInfo = Model.ModelInfo;
if (modelInfo.InputsInfo.Length != 1)
{
throw env.Except($"OnnxTransform supports Onnx models with one input. The provided model has ${modelInfo.InputsInfo.Length} input(s).");
}
if (modelInfo.OutputsInfo.Length != 1)
{
throw env.Except($"OnnxTransform supports Onnx models with one output. The provided model has ${modelInfo.OutputsInfo.Length} output(s).");
}
Input = args.InputColumn;
Output = args.OutputColumn;
var outputNodeInfo = Model.ModelInfo.OutputsInfo[0];
var type = OnnxUtils.OnnxToMlNetType(outputNodeInfo.Type);
var shape = outputNodeInfo.Shape;
var dims = shape.Count > 0 ? shape.Skip(shape[0] < 0 ? 1 : 0).Select(x => (int)x).ToArray() : new[] { 0 };
OutputType = new VectorType(type, dims);
_args = args;
}
private SchemaBindablePipelineEnsembleBase(IHostEnvironment env, IPredictorModel[] predictors, string registrationName, string scoreColumnKind)
{
Contracts.CheckValue(env, nameof(env));
Host = env.Register(registrationName);
Host.CheckNonEmpty(predictors, nameof(predictors));
Host.CheckNonWhiteSpace(scoreColumnKind, nameof(scoreColumnKind));
PredictorModels = predictors;
_scoreColumnKind = scoreColumnKind;
HashSet <string> inputCols = null;
for (int i = 0; i < predictors.Length; i++)
{
var predModel = predictors[i];
// Get the input column names.
var inputSchema = predModel.TransformModel.InputSchema;
if (inputCols == null)
{
inputCols = new HashSet <string>();
for (int j = 0; j < inputSchema.ColumnCount; j++)
{
if (inputSchema.IsHidden(j))
{
continue;
}
inputCols.Add(inputSchema.GetColumnName(j));
}
_inputCols = inputCols.ToArray();
}
else
{
int nonHiddenCols = 0;
for (int j = 0; j < inputSchema.ColumnCount; j++)
{
if (inputSchema.IsHidden(j))
{
continue;
}
var name = inputSchema.GetColumnName(j);
if (!inputCols.Contains(name))
{
throw Host.Except("Inconsistent schemas: Some schemas do not contain the column '{0}'", name);
}
nonHiddenCols++;
}
Host.Check(nonHiddenCols == _inputCols.Length,
"Inconsistent schemas: not all schemas have the same number of columns");
}
}
}
/// <summary>
/// Returns the feature selection scores for each slot of each column.
/// </summary>
/// <param name="host">The host.</param>
/// <param name="input">The input dataview.</param>
/// <param name="labelColumnName">The label column.</param>
/// <param name="columns">The columns for which to compute the feature selection scores.</param>
/// <param name="numBins">The number of bins to use for numeric features.</param>
/// <returns>A list of scores for each column and each slot.</returns>
public static Single[][] Train(IHost host, IDataView input, string labelColumnName, string[] columns, int numBins)
{
Contracts.CheckValue(host, nameof(host));
host.CheckValue(input, nameof(input));
host.CheckNonWhiteSpace(labelColumnName, nameof(labelColumnName));
host.CheckValue(columns, nameof(columns));
host.Check(columns.Length > 0, "At least one column must be specified.");
host.Check(numBins > 1, "numBins must be greater than 1.");
HashSet <string> colSet = new HashSet <string>();
foreach (string col in columns)
{
if (!colSet.Add(col))
{
throw host.Except("Column '{0}' specified multiple times.", col);
}
}
var colSizes = new int[columns.Length];
return(TrainCore(host, input, labelColumnName, columns, numBins, colSizes));
}
private TensorFlowTransform(IHostEnvironment env, byte[] modelBytes, string[] inputs, string[] outputs)
{
Contracts.CheckValue(env, nameof(env));
_host = env.Register(nameof(RegistrationName));
_host.CheckValue(modelBytes, nameof(modelBytes));
Session = LoadTFSession(modelBytes);
foreach (var input in inputs)
{
_host.CheckNonWhiteSpace(input, nameof(inputs));
if (Session.Graph[input] == null)
{
throw _host.ExceptParam(nameof(inputs), $"Input column '{input}' does not exist in the model");
}
var tfInput = new TFOutput(Session.Graph[input]);
if (!TensorFlowUtils.IsTypeSupported(tfInput.OutputType))
{
throw _host.ExceptParam(nameof(modelBytes), $"Input type '{tfInput.OutputType}' of input column '{input}' is not supported in TensorFlow");
}
}
var newNames = new HashSet <string>();
foreach (var output in outputs)
{
_host.CheckNonEmpty(output, nameof(outputs));
if (!newNames.Add(output))
{
throw _host.ExceptParam(nameof(outputs), $"Output column '{output}' specified multiple times");
}
if (Session.Graph[output] == null)
{
throw _host.ExceptParam(nameof(outputs), $"Output column '{output}' does not exist in the model");
}
}
Inputs = inputs;
TFInputTypes = new TFDataType[Inputs.Length];
TFInputShapes = new TFShape[Inputs.Length];
for (int i = 0; i < Inputs.Length; i++)
{
var tfInput = new TFOutput(Graph[Inputs[i]]);
TFInputTypes[i] = tfInput.OutputType;
TFInputShapes[i] = Graph.GetTensorShape(tfInput);
var newShape = new long[TFInputShapes[i].NumDimensions];
for (int j = 0; j < TFInputShapes[i].NumDimensions; j++)
{
newShape[j] = TFInputShapes[i][j] == -1 ? BatchSize : TFInputShapes[i][j];
}
TFInputShapes[i] = new TFShape(newShape);
}
Outputs = outputs;
OutputTypes = new ColumnType[Outputs.Length];
TFOutputTypes = new TFDataType[Outputs.Length];
for (int i = 0; i < Outputs.Length; i++)
{
var tfOutput = new TFOutput(Graph[Outputs[i]]);
var shape = Graph.GetTensorShape(tfOutput);
int[] dims = shape.ToIntArray().Skip(shape[0] == -1 ? BatchSize : 0).ToArray();
var type = TensorFlowUtils.Tf2MlNetType(tfOutput.OutputType);
OutputTypes[i] = new VectorType(type, dims);
TFOutputTypes[i] = tfOutput.OutputType;
}
}
