public void SaveOnnxModelLoadAndScoreFastTree()
{
var mlContext = new MLContext(seed: 1);
// Get the dataset.
var data = mlContext.Data.LoadFromTextFile <HousingRegression>(TestCommon.GetDataPath(DataDir, TestDatasets.housing.trainFilename), hasHeader: true);
// Create a pipeline to train on the housing data.
var pipeline = mlContext.Transforms.Concatenate("Features", HousingRegression.Features)
.Append(mlContext.Transforms.NormalizeMinMax("Features"))
.AppendCacheCheckpoint(mlContext)
.Append(mlContext.Regression.Trainers.FastTree(
new FastTreeRegressionTrainer.Options {
NumberOfThreads = 1, NumberOfTrees = 10
}));
// Fit the pipeline.
var model = pipeline.Fit(data);
// Serialize the pipeline to a file.
var modelFileName = "SaveOnnxLoadAndScoreFastTreeModel.onnx";
var modelPath = TestCommon.DeleteOutputPath(OutDir, modelFileName);
using (var file = File.Create(modelPath))
mlContext.Model.ConvertToOnnx(model, data, file);
// Load the model as a transform.
// Note that when saving an ML.NET model as an ONNX model, the column types and column names will
// change. The name changes as ONNX doesn't not allow the same name for an input and output within the ONNX model.
// Therefore names maintained but have a number appended to the end of the name. In this case, Score0 is the output
// of the ONNX model. We are renaming Score0 to Score using Copy Columns.
// ONNX also uses tensors and will return an output of a tensor with the dimension of [1,1] for a single float.
// Therefore the VectorScoreColumn class (which contains a float [] field called Score) is used for the return
// type on the Prediction engine.
// See #2980 and #2981 for more information.
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(modelPath)
.Append(mlContext.Transforms.CopyColumns("Score", "Score0"));
var onnxModel = onnxEstimator.Fit(data);
// Create prediction engine and test predictions.
var originalPredictionEngine = mlContext.Model.CreatePredictionEngine <HousingRegression, ScoreColumn>(model);
var onnxPredictionEngine = mlContext.Model.CreatePredictionEngine <HousingRegression, VectorScoreColumn>(onnxModel);
// Take a handful of examples out of the dataset and compute predictions.
var dataEnumerator = mlContext.Data.CreateEnumerable <HousingRegression>(mlContext.Data.TakeRows(data, 5), false);
foreach (var row in dataEnumerator)
{
var originalPrediction = originalPredictionEngine.Predict(row);
var onnxPrediction = onnxPredictionEngine.Predict(row);
// Check that the predictions are identical.
Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], precision: 4); // Note the low-precision equality!
}
}
public void SaveOnnxModelLoadAndScoreKMeans()
{
var mlContext = new MLContext(seed: 1);
// Get the dataset.
var data = mlContext.Data.LoadFromTextFile <HousingRegression>(TestCommon.GetDataPath(DataDir, TestDatasets.housing.trainFilename), hasHeader: true);
// Create a pipeline to train on the housing data.
var pipeline = mlContext.Transforms.Concatenate("Features", HousingRegression.Features)
.Append(mlContext.Transforms.NormalizeMinMax("Features"))
.AppendCacheCheckpoint(mlContext)
.Append(mlContext.Clustering.Trainers.KMeans(
new KMeansTrainer.Options {
NumberOfThreads = 1, MaximumNumberOfIterations = 10
}));
// Fit the pipeline.
var model = pipeline.Fit(data);
// Serialize the pipeline to a file.
var modelFileName = "SaveOnnxLoadAndScoreKMeansModel.onnx";
var modelPath = TestCommon.DeleteOutputPath(OutDir, modelFileName);
using (var file = File.Create(modelPath))
mlContext.Model.ConvertToOnnx(model, data, file);
// Load the model as a transform.
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(modelPath);
var onnxModel = onnxEstimator.Fit(data);
// TODO #2980: ONNX outputs don't match the outputs of the model, so we must hand-correct this for now.
// TODO #2981: ONNX models cannot be fit as part of a pipeline, so we must use a workaround like this.
var onnxWorkaroundPipeline = onnxModel.Append(
mlContext.Transforms.CopyColumns("Score", "Score0").Fit(onnxModel.Transform(data)));
// Create prediction engine and test predictions.
var originalPredictionEngine = mlContext.Model.CreatePredictionEngine <HousingRegression, VectorScoreColumn>(model);
// TODO #2982: ONNX produces vector types and not the original output type.
var onnxPredictionEngine = mlContext.Model.CreatePredictionEngine <HousingRegression, VectorScoreColumn>(onnxWorkaroundPipeline);
// Take a handful of examples out of the dataset and compute predictions.
var dataEnumerator = mlContext.Data.CreateEnumerable <HousingRegression>(mlContext.Data.TakeRows(data, 5), false);
foreach (var row in dataEnumerator)
{
var originalPrediction = originalPredictionEngine.Predict(row);
var onnxPrediction = onnxPredictionEngine.Predict(row);
// Check that the predictions are identical.
Common.AssertEqual(originalPrediction.Score, onnxPrediction.Score, precision: 4); // Note the low precision!
}
}
public void SaveOnnxModelLoadAndScoreFastTree()
{
var mlContext = new MLContext(seed: 1);
// Get the dataset.
var data = mlContext.Data.LoadFromTextFile <HousingRegression>(TestCommon.GetDataPath(DataDir, TestDatasets.housing.trainFilename), hasHeader: true);
// Create a pipeline to train on the housing data.
var pipeline = mlContext.Transforms.Concatenate("Features", HousingRegression.Features)
.Append(mlContext.Transforms.NormalizeMinMax("Features"))
.AppendCacheCheckpoint(mlContext)
.Append(mlContext.Regression.Trainers.FastTree(
new FastTreeRegressionTrainer.Options {
NumberOfThreads = 1, NumberOfTrees = 10
}));
// Fit the pipeline.
var model = pipeline.Fit(data);
// Serialize the pipeline to a file.
var modelFileName = "SaveOnnxLoadAndScoreFastTreeModel.onnx";
var modelPath = TestCommon.DeleteOutputPath(OutDir, modelFileName);
using (var file = File.Create(modelPath))
mlContext.Model.ConvertToOnnx(model, data, file);
// Load the model as a transform.
// ONNX uses tensors and will return an output of a tensor with the dimension of [1,1] for a single float.
// Therefore the VectorScoreColumn class (which contains a float [] field called Score) is used for the return
// type on the Prediction engine.
// See #2980 and #2981 for more information.
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(modelPath, gpuDeviceId: _gpuDeviceId, fallbackToCpu: _fallbackToCpu);
var onnxModel = onnxEstimator.Fit(data);
// Create prediction engine and test predictions.
var originalPredictionEngine = mlContext.Model.CreatePredictionEngine <HousingRegression, ScoreColumn>(model);
var onnxPredictionEngine = mlContext.Model.CreatePredictionEngine <HousingRegression, VectorScoreColumn>(onnxModel);
// Take a handful of examples out of the dataset and compute predictions.
var dataEnumerator = mlContext.Data.CreateEnumerable <HousingRegression>(mlContext.Data.TakeRows(data, 5), false);
foreach (var row in dataEnumerator)
{
var originalPrediction = originalPredictionEngine.Predict(row);
var onnxPrediction = onnxPredictionEngine.Predict(row);
// Check that the predictions are identical.
Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], precision: 4);
}
}
public void FitPipelineSaveModelAndPredict()
{
var mlContext = new MLContext(seed: 1);
// Get the dataset.
var data = mlContext.Data.LoadFromTextFile <HousingRegression>(TestCommon.GetDataPath(DataDir, TestDatasets.housing.trainFilename), hasHeader: true);
// Create a pipeline to train on the housing data.
var pipeline = mlContext.Transforms.Concatenate("Features", HousingRegression.Features)
.Append(mlContext.Regression.Trainers.FastTree(
new FastTreeRegressionTrainer.Options {
NumberOfThreads = 1, NumberOfTrees = 10
}));
// Fit the pipeline.
var model = pipeline.Fit(data);
var modelPath = TestCommon.DeleteOutputPath(OutDir, "fitPipelineSaveModelAndPredict.zip");
// Save model to a file.
mlContext.Model.Save(model, data.Schema, modelPath);
// Load model from a file.
ITransformer serializedModel;
using (var file = File.OpenRead(modelPath))
{
serializedModel = mlContext.Model.Load(file, out var serializedSchema);
TestCommon.CheckSameSchemas(data.Schema, serializedSchema);
}
// Create prediction engine and test predictions.
var originalPredictionEngine = mlContext.Model.CreatePredictionEngine <HousingRegression, ScoreColumn>(model);
var serializedPredictionEngine = mlContext.Model.CreatePredictionEngine <HousingRegression, ScoreColumn>(serializedModel);
// Take a handful of examples out of the dataset and compute predictions.
var dataEnumerator = mlContext.Data.CreateEnumerable <HousingRegression>(mlContext.Data.TakeRows(data, 5), false);
foreach (var row in dataEnumerator)
{
var originalPrediction = originalPredictionEngine.Predict(row);
var serializedPrediction = serializedPredictionEngine.Predict(row);
// Check that the predictions are identical.
Assert.Equal(originalPrediction.Score, serializedPrediction.Score);
}
}
public void SaveOnnxModelLoadAndScoreSDCA()
{
var mlContext = new MLContext(seed: 1);
// Get the dataset.
var data = mlContext.Data.LoadFromTextFile <HousingRegression>(TestCommon.GetDataPath(DataDir, TestDatasets.housing.trainFilename), hasHeader: true);
// Create a pipeline to train on the housing data.
var pipeline = mlContext.Transforms.Concatenate("Features", HousingRegression.Features)
.Append(mlContext.Transforms.NormalizeMinMax("Features"))
.AppendCacheCheckpoint(mlContext)
.Append(mlContext.Regression.Trainers.Sdca(
new SdcaRegressionTrainer.Options {
NumberOfThreads = 1, MaximumNumberOfIterations = 10
}));
// Fit the pipeline.
var model = pipeline.Fit(data);
// Serialize the pipeline to a file.
var modelFileName = "SaveOnnxLoadAndScoreSdcaModel.onnx";
var modelPath = TestCommon.DeleteOutputPath(OutDir, modelFileName);
using (var file = File.Create(modelPath))
mlContext.Model.ConvertToOnnx(model, data, file);
// Load the model as a transform.
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(modelPath, gpuDeviceId: _gpuDeviceId, fallbackToCpu: _fallbackToCpu);
var onnxModel = onnxEstimator.Fit(data);
// Create prediction engine and test predictions.
var originalPredictionEngine = mlContext.Model.CreatePredictionEngine <HousingRegression, ScoreColumn>(model);
// TODO #2982: ONNX produces vector types and not the original output type.
var onnxPredictionEngine = mlContext.Model.CreatePredictionEngine <HousingRegression, VectorScoreColumn>(onnxModel);
// Take a handful of examples out of the dataset and compute predictions.
var dataEnumerator = mlContext.Data.CreateEnumerable <HousingRegression>(mlContext.Data.TakeRows(data, 5), false);
foreach (var row in dataEnumerator)
{
var originalPrediction = originalPredictionEngine.Predict(row);
var onnxPrediction = onnxPredictionEngine.Predict(row);
// Check that the predictions are identical.
Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], precision: 4);
}
}
public void DetermineNugetVersionFromModel()
{
var mlContext = new MLContext(seed: 1);
// Get the dataset.
var data = mlContext.Data.LoadFromTextFile <HousingRegression>(TestCommon.GetDataPath(DataDir, TestDatasets.housing.trainFilename), hasHeader: true);
// Create a pipeline to train on the housing data.
var pipeline = mlContext.Transforms.Concatenate("Features", HousingRegression.Features)
.Append(mlContext.Regression.Trainers.FastTree(
new FastTreeRegressionTrainer.Options {
NumberOfThreads = 1, NumberOfTrees = 10
}));
// Fit the pipeline.
var model = pipeline.Fit(data);
// Save model to a file.
var modelPath = TestCommon.DeleteOutputPath(OutDir, "determineNugetVersionFromModel.zip");
mlContext.Model.Save(model, data.Schema, modelPath);
// Check that the version can be extracted from the model.
var versionFileName = @"TrainingInfo" + Path.DirectorySeparatorChar + "Version.txt";
using (ZipArchive archive = ZipFile.OpenRead(modelPath))
{
// The version of the entire model is kept in the version file.
var versionPath = archive.Entries.First(x => x.FullName == versionFileName);
Assert.NotNull(versionPath);
using (var stream = versionPath.Open())
using (var reader = new StreamReader(stream))
{
// The only line in the file is the version of the model.
var line = reader.ReadLine();
Assert.Matches(new Regex(@"(\d+)\.(\d+)\.(\d+)(-[dev|ci|preview\.(\d+)\.(\d+)\.(\d+)]){0,1}"), line);
}
}
}
protected string DeleteOutputPath(string name)
{
return(TestCommon.DeleteOutputPath(OutDir, name));
}