public void TestEstimatorChain()
{
var env = new MLContext();
var dataFile = GetDataPath("images/images.tsv");
var imageFolder = Path.GetDirectoryName(dataFile);
var data = TextLoader.Create(env, new TextLoader.Arguments()
{
Columns = new[]
{
new TextLoader.Column("ImagePath", DataKind.TX, 0),
new TextLoader.Column("Name", DataKind.TX, 1),
}
}, new MultiFileSource(dataFile));
var invalidData = TextLoader.Create(env, new TextLoader.Arguments()
{
Columns = new[]
{
new TextLoader.Column("ImagePath", DataKind.R4, 0),
}
}, new MultiFileSource(dataFile));
var pipe = new ImageLoadingEstimator(env, imageFolder, ("ImageReal", "ImagePath"))
.Append(new ImageResizingEstimator(env, "ImageReal", 100, 100, "ImageReal"))
.Append(new ImagePixelExtractingEstimator(env, "ImagePixels", "ImageReal"))
.Append(new ImageGrayscalingEstimator(env, ("ImageGray", "ImageReal")));
TestEstimatorCore(pipe, data, null, invalidData);
Done();
}
public void TestEstimatorChain()
{
if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
{
return;
}
var env = new MLContext();
var dataFile = GetDataPath("images/images.tsv");
var imageFolder = Path.GetDirectoryName(dataFile);
var data = TextLoader.Create(env, new TextLoader.Options()
{
Columns = new[]
{
new TextLoader.Column("ImagePath", DataKind.String, 0),
new TextLoader.Column("Name", DataKind.String, 1),
}
}, new MultiFileSource(dataFile));
var invalidData = TextLoader.Create(env, new TextLoader.Options()
{
Columns = new[]
{
new TextLoader.Column("ImagePath", DataKind.Single, 0),
}
}, new MultiFileSource(dataFile));
var pipe = new ImageLoadingEstimator(env, imageFolder, ("ImageReal", "ImagePath"))
.Append(new ImageResizingEstimator(env, "ImageReal", 100, 100, "ImageReal"))
.Append(new ImagePixelExtractingEstimator(env, "ImagePixels", "ImageReal"))
.Append(new ImageGrayscalingEstimator(env, ("ImageGray", "ImageReal")));
TestEstimatorCore(pipe, data, null, invalidData);
Done();
}
public void TestEstimatorSaveLoad()
{
IHostEnvironment env = new MLContext(1);
var dataFile = GetDataPath("images/images.tsv");
var imageFolder = Path.GetDirectoryName(dataFile);
var data = TextLoader.Create(env, new TextLoader.Options()
{
Columns = new[]
{
new TextLoader.Column("ImagePath", DataKind.String, 0),
new TextLoader.Column("Name", DataKind.String, 1),
}
}, new MultiFileSource(dataFile));
var pipe = new ImageLoadingEstimator(env, imageFolder, ("ImageReal", "ImagePath"))
.Append(new ImageResizingEstimator(env, "ImageReal", 100, 100, "ImageReal"))
.Append(new ImagePixelExtractingEstimator(env, "ImagePixels", "ImageReal"))
.Append(new ImageGrayscalingEstimator(env, ("ImageGray", "ImageReal")));
pipe.GetOutputSchema(SchemaShape.Create(data.Schema));
var model = pipe.Fit(data);
var tempPath = Path.GetTempFileName();
using (var file = new SimpleFileHandle(env, tempPath, true, true))
{
using (var fs = file.CreateWriteStream())
ML.Model.Save(model, null, fs);
ITransformer model2;
using (var fs = file.OpenReadStream())
model2 = ML.Model.Load(fs, out var schema);
var transformerChain = model2 as TransformerChain <ITransformer>;
Assert.NotNull(transformerChain);
var newCols = ((ImageLoadingTransformer)transformerChain.First()).Columns;
var oldCols = ((ImageLoadingTransformer)model.First()).Columns;
Assert.True(newCols
.Zip(oldCols, (x, y) => x == y)
.All(x => x));
}
Done();
}
public void TestEstimatorSaveLoad()
{
IHostEnvironment env = new MLContext();
var dataFile = GetDataPath("images/images.tsv");
var imageFolder = Path.GetDirectoryName(dataFile);
var data = TextLoader.Create(env, new TextLoader.Arguments()
{
Column = new[]
{
new TextLoader.Column("ImagePath", DataKind.TX, 0),
new TextLoader.Column("Name", DataKind.TX, 1),
}
}, new MultiFileSource(dataFile));
var pipe = new ImageLoadingEstimator(env, imageFolder, ("ImagePath", "ImageReal"))
.Append(new ImageResizingEstimator(env, "ImageReal", "ImageReal", 100, 100))
.Append(new ImagePixelExtractingEstimator(env, "ImageReal", "ImagePixels"))
.Append(new ImageGrayscalingEstimator(env, ("ImageReal", "ImageGray")));
pipe.GetOutputSchema(Core.Data.SchemaShape.Create(data.Schema));
var model = pipe.Fit(data);
var tempPath = Path.GetTempFileName();
using (var file = new SimpleFileHandle(env, tempPath, true, true))
{
using (var fs = file.CreateWriteStream())
model.SaveTo(env, fs);
var model2 = TransformerChain.LoadFrom(env, file.OpenReadStream());
var newCols = ((ImageLoaderTransform)model2.First()).Columns;
var oldCols = ((ImageLoaderTransform)model.First()).Columns;
Assert.True(newCols
.Zip(oldCols, (x, y) => x == y)
.All(x => x));
}
Done();
}
